Вся Лабораторна - 800грн\\n\\n'\n f'Якщо вас цікавить тільки якась частина, тоді пишить - {owner.user.mention} (для замовлення також)'\n )\n except Exception as e:\n logger.exception(e)\n\n\nasync def web_pract(message: types.Message):\n try:\n owner: types.ChatMember = await bot.get_chat_member(OWNER, OWNER)\n await message.answer(\n 'Хей. А ось і прайс лист для Вашої Практичної роботи, будем раді вашому замовлені ^^\\n\\n'\n ''\n 'Лабораторна №1 - 150грн\\n'\n 'Лабораторна №2 - 150грн\\n'\n 'Лабораторна №3 - 200грн\\n'\n 'Лабораторна №4 - 150грн\\n'\n 'Лабораторна №5 - 200грн\\n'\n 'Лабораторна №6 - 250грн\\n'\n 'Лабораторна №7 - 400грн\\n'\n 'Лабораторна №8 - 350грн\\n'\n 'Лабораторна №9 - 400грн'\n '\\n\\n'\n f'Для замовлення пишить - {owner.user.mention}'\n )\n except Exception as e:\n logger.exception(e)\n\n\ndef register_message_handler_web(dp: Dispatcher):\n dp.register_message_handler(\n web_lab,\n Text(equals=web_buttons[0])\n )\n\n dp.register_message_handler(\n web_pract,\n Text(equals=web_buttons[1])\n )\n","repo_name":"zakharfsk/eazylab_bot","sub_path":"handlers/handler_price_web.py","file_name":"handler_price_web.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"5866888999","text":"import six\n\nfrom keras_tuner import protos\nfrom keras_tuner.api_export import keras_tuner_export\nfrom keras_tuner.engine import conditions as conditions_mod\nfrom keras_tuner.engine.hyperparameters import hp_utils\nfrom keras_tuner.engine.hyperparameters import hyperparameter\n\n\n@keras_tuner_export(\"keras_tuner.engine.hyperparameters.Choice\")\nclass Choice(hyperparameter.HyperParameter):\n \"\"\"Choice of one value among a predefined set of possible values.\n\n Args:\n name: A string. the name of parameter. Must be unique for each\n `HyperParameter` instance in the search space.\n values: A list of possible values. Values must be int, float,\n str, or bool. All values must be of the same type.\n ordered: Optional boolean, whether the values passed should be\n considered to have an ordering. Defaults to `True` for float/int\n values. Must be `False` for any other values.\n default: Optional default value to return for the parameter.\n If unspecified, the default value will be:\n - None if None is one of the choices in `values`\n - The first entry in `values` otherwise.\n \"\"\"\n\n def __init__(self, name, values, ordered=None, default=None, **kwargs):\n super().__init__(name=name, default=default, **kwargs)\n if not values:\n raise ValueError(\"`values` must be provided for `Choice`.\")\n\n # Type checking.\n types = {type(v) for v in values}\n if len(types) > 1:\n raise TypeError(\n \"A `Choice` can contain only one type of value, \"\n f\"found values: {str(values)} with types {types}.\"\n )\n\n # Standardize on str, int, float, bool.\n if isinstance(values[0], six.string_types):\n values = [str(v) for v in values]\n if default is not None:\n default = str(default)\n elif isinstance(values[0], six.integer_types):\n values = [int(v) for v in values]\n if default is not None:\n default = int(default)\n elif not isinstance(values[0], (bool, float)):\n raise TypeError(\n \"A `Choice` can contain only `int`, `float`, `str`, or \"\n \"`bool`, found values: \" + str(values) + \"with \"\n \"types: \" + str(type(values[0]))\n )\n self._values = values\n\n if default is not None and default not in values:\n raise ValueError(\n \"The default value should be one of the choices. \"\n f\"You passed: values={values}, default={default}\"\n )\n self._default = default\n\n # Get or infer ordered.\n self.ordered = ordered\n is_numeric = isinstance(values[0], (six.integer_types, float))\n if self.ordered and not is_numeric:\n raise ValueError(\"`ordered` must be `False` for non-numeric types.\")\n if self.ordered is None:\n self.ordered = is_numeric\n\n def __repr__(self):\n return (\n f\"Choice(name: '{self.name}', \"\n + f\"values: {self._values}, \"\n + f\"ordered: {self.ordered}, default: {self.default})\"\n )\n\n @property\n def values(self):\n return self._values\n\n @property\n def default(self):\n return self._values[0] if self._default is None else self._default\n\n def prob_to_value(self, prob):\n return self._values[hp_utils.prob_to_index(prob, len(self._values))]\n\n def value_to_prob(self, value):\n return hp_utils.index_to_prob(\n self._values.index(value), len(self._values)\n )\n\n def get_config(self):\n config = super().get_config()\n config[\"values\"] = self._values\n config[\"ordered\"] = self.ordered\n return config\n\n @classmethod\n def from_proto(cls, proto):\n values = [getattr(val, val.WhichOneof(\"kind\")) for val in proto.values]\n default = getattr(proto.default, proto.default.WhichOneof(\"kind\"), None)\n conditions = [\n conditions_mod.Condition.from_proto(c) for c in proto.conditions\n ]\n return cls(\n name=proto.name,\n values=values,\n ordered=proto.ordered,\n default=default,\n conditions=conditions,\n )\n\n def to_proto(self):\n if isinstance(self.values[0], six.string_types):\n values = [\n protos.get_proto().Value(string_value=v) for v in self.values\n ]\n default = protos.get_proto().Value(string_value=self.default)\n elif isinstance(self.values[0], six.integer_types):\n values = [\n protos.get_proto().Value(int_value=v) for v in self.values\n ]\n default = protos.get_proto().Value(int_value=self.default)\n else:\n values = [\n protos.get_proto().Value(float_value=v) for v in self.values\n ]\n default = protos.get_proto().Value(float_value=self.default)\n return protos.get_proto().Choice(\n name=self.name,\n ordered=self.ordered,\n values=values,\n default=default,\n conditions=[c.to_proto() for c in self.conditions],\n )\n","repo_name":"keras-team/keras-tuner","sub_path":"keras_tuner/engine/hyperparameters/hp_types/choice_hp.py","file_name":"choice_hp.py","file_ext":"py","file_size_in_byte":5264,"program_lang":"python","lang":"en","doc_type":"code","stars":2783,"dataset":"github-code","pt":"67"}
+{"seq_id":"1819329391","text":"# Import required packages\nimport random\nimport pygame\n\n# Import Parameters\nimport pygame.time\n\nfrom parameters import *\n\n\n# Define collision function\ndef collision(left_paddle, right_paddle, ball):\n '''\n ball (Ball): The ball object of class Ball\n left_paddle (PaddleLeft): The left paddle object of class Paddle\n right_paddle (PaddleRigth): The right paddle object of class Paddle\n\n Returns:\n Boolean value for collision\n '''\n\n # Define object position\n ball_position = ball.get_position()\n ball_radius = ball.get_size()\n pl_top_position = left_paddle.top_right[1]\n pl_down_position = left_paddle.down_right[1]\n pl_right_position = left_paddle.down_right[0]\n pr_top_position = right_paddle.top_left[1]\n pr_down_position = right_paddle.down_left[1]\n pr_left_position = right_paddle.down_left[0]\n\n # Return True if collision has occured\n if ball_position[0] - ball_radius <= pl_right_position and \\\n pl_down_position <= ball_position[1] <= pl_top_position:\n return True\n\n # Return True if collision has occured\n if ball_position[0] + ball_radius >= pr_left_position and \\\n pr_down_position <= ball_position[1] <= pr_top_position:\n return True\n\n # Return False otherwise\n else:\n return False\n\n\n# Define a function to control movement of ball\ndef ball_movement(\n x_direction, y_direction, ball, score, left_paddle, right_paddle, score_time,\n pong_sound, win_sound, loss_sound, goal_sound, edge_sound):\n '''\n x_direction (int): Integer defining the movement direction on x axis\n y_direction (int): Integer defining the movement direction on y axis\n height (int): Defines the height of the playing window\n width (int): Defines the width of the playing window\n ball (Ball): The ball object of class Ball\n score (list): A list with two elements, the score of left and right players\n left_paddle (PaddleLeft): The left paddle object of class Paddle\n right_paddle (PaddleRight): The right paddle object of class Paddle\n score_time (double or None): Time when last goal was scored\n pong_sound (WAV): Sound when ball hits paddle\n win_sound (WAV): Sound when player wins\n loss_sound(WAV): Sound when player loses\n goal_sound(WAV): Sound when goal is scored\n edge_sound(WAV): Sound when upper or lower edge is hit\n\n Returns:\n y_direction (int): Movement direction on y-axis after checking all conditions\n x_direction (int): Movement direction on x-axis after checking all conditions\n score (list): Contains the scores of each player\n score_time (double or None): Time when last goal was scored. \n '''\n\n # New Ball position\n ball.set_position(x_direction, y_direction)\n\n # If ball touches upper edge\n if ball.get_position()[1] + ball.get_size() > HEIGHT:\n # edge sound\n pygame.mixer.Sound.play(edge_sound)\n # invert y movement\n y_direction = y_direction * -1\n return x_direction, y_direction, score, score_time\n\n # If ball touches lower edge\n elif ball.get_position()[1] - ball.get_size() < 0:\n # edge sound\n pygame.mixer.Sound.play(edge_sound)\n # invert y movement\n y_direction = y_direction * -1\n return x_direction, y_direction, score, score_time\n\n # If player scores\n elif ball.get_position()[0] - ball.get_size() > WIDTH:\n # Increase score of right player\n score[0] += 1\n # Play sound for goal\n if score[0] != POINTS_TO_WIN:\n pygame.mixer.Sound.play(goal_sound)\n # Play different sound if goal means loss\n if score[0] == POINTS_TO_WIN:\n pygame.mixer.Sound.play(win_sound)\n # get score time\n score_time = pygame.time.get_ticks()\n x_direction = random.sample([1, -1], 1)[0] * 1.5 # Horizontal movement (left or right)\n y_direction = random.sample([random.uniform(-1, -0.5), random.uniform(0.5, 1)], 1)[0] * 1.5 # Vertical movement (down or up)\n\n return x_direction, y_direction, score, score_time\n\n # If player gets scored on\n elif ball.get_position()[0] + ball.get_size() < 0:\n # Increase score of right player\n score[1] += 1\n # Play sound for goal\n if score[1] != POINTS_TO_WIN:\n pygame.mixer.Sound.play(goal_sound)\n # Play sound if goal means loss\n if score[1] == POINTS_TO_WIN:\n pygame.mixer.Sound.play(loss_sound)\n # get score time\n score_time = pygame.time.get_ticks()\n x_direction = random.sample([1, -1], 1)[0] * 1.5 # Horizontal movement (left or right)\n y_direction = random.sample([random.uniform(-1, -0.5), random.uniform(0.5, 1)], 1)[0] * 1.5 # Vertical movement (down or up)\n\n return x_direction, y_direction, score, score_time\n\n # If ball touches a paddle\n elif collision(left_paddle, right_paddle, ball):\n\n # If ball speed is still below maximal speed\n if abs(x_direction) < 5:\n\n # invert x movement with multiplier\n x_direction = x_direction * -1.25\n y_direction = y_direction * 1.25\n\n\n # If ball speed is above maximal speed\n elif abs(x_direction) > 5:\n\n # invert x movement without multiplier\n x_direction = x_direction * -1\n y_direction = y_direction * 1\n\n ball.set_color(random.sample(range(0, 256, 1), 3))\n left_paddle.set_color(random.sample(range(0, 256, 1), 3))\n right_paddle.set_color(random.sample(range(0, 256, 1), 3))\n pygame.mixer.Sound.play(pong_sound)\n return x_direction, y_direction, score, score_time\n\n # If nothing of the above happens\n else:\n return x_direction, y_direction, score, score_time\n\n\n# Define a function to control movement of paddle\ndef paddle_movement(command, height, paddle):\n '''\n command (int): Integer defining the movement direction on x axis\n height (int): Defines the height of the playing window\n paddle (Paddle): The paddle object of class Paddle\n\n Returns:\n command (int): Integer defining the movement after command was executed\n '''\n\n # New Paddle Position\n paddle.set_position(command)\n\n # get height of top left corner\n top_left_height = paddle.get_polygon()[0][1]\n\n # get height of down left corner\n down_left_height = paddle.get_polygon()[3][1]\n\n # Stop upward movement when upper edge is touched\n if top_left_height == height:\n command = 0\n\n # Stop upward movement when upper edge is touched\n if down_left_height == 0:\n command = 0\n\n return command\n\n\n# Create an AI to decide on how to move the paddle\ndef ai_movement(right_paddle, ball, x_direction, y_direction, difficulty):\n '''\n right_paddle (PaddleRight): Right paddle Object\n ball (Ball): Ball object\n x_direction (int): Integer defining the movement direction of the ball on x axis\n y_direction (int): Integer defining the movement direction of the ball on y axis\n difficulty (int): Integer defining the opponent difficulty\n\n Returns:\n ai_command (int): Command of AI to move paddle up or down\n '''\n\n # If ball is moving to the right\n if x_direction > 0:\n\n # Get current ball position\n position = ball.get_position()\n\n # Calculate final contact point of ball with right edge\n contact_point = position[1] + (y_direction / x_direction) * (WIDTH - position[0])\n\n # Add random movement to AI to prevent it from being to strong\n contact_point += random.normalvariate(0, difficulty)\n\n # If y position of paddle to high\n if right_paddle.position[1] > contact_point:\n ai_command = -3\n\n # If y position of paddle to low\n elif right_paddle.position[1] < contact_point:\n ai_command = 3\n\n # If position correct\n else:\n ai_command = 0\n\n # If ball is moving to the left\n if x_direction < 0:\n ai_command = 0\n\n return ai_command\n\n# Print Messages to screen\ndef write_message(screen, text, size, hposition, color):\n '''\n size (int): Size of message to be written\n hposition (int): Horizontal position of message\n text (string): Text to be depicted in message\n color (tuple): RGB code\n\n Returns:\n image with message on top of screen\n '''\n\n # Print Message\n message_font = pygame.font.SysFont(\"Comic Sans MS\", size)\n message = message_font.render(text, 1, color)\n message_rect = message.get_rect()\n message_rect.center = (WIDTH / 2, HEIGHT / 2 + hposition)\n screen.blit(message, message_rect)\n\n","repo_name":"FStiffler/Pong-Game","sub_path":"pong_game/helperFunctions.py","file_name":"helperFunctions.py","file_ext":"py","file_size_in_byte":8631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"39030266098","text":"import requests\nimport json\nimport random\nfrom currency_converter import CurrencyConverter\nfrom Utils import Screen_cleaner\nfrom time import sleep\n\n# This game will use the free currency api to get the current exchange rate from USD to ILS,\n# will generate a new random number between 1-100\n# and wiil ask the user what he thinks is the value of the generated number from USD to ILS,\n# depending on the user's difficulty his answer will be correct\n# if the guessed value is between the interval surrounding the correct answer\n\n\ndef currency_rate(number):\n url = f\"https://api.apilayer.com/currency_data/convert?to=ils&from=usd&amount={number}\"\n payload = {}\n headers = {\"apikey\": \"GtUN8c7rPHjLTS6jf1iJvY83b918PKPQ\"}\n\n response = requests.request(\"GET\", url, headers=headers, data=payload)\n status_code = response.status_code\n result = response.text\n class Payload(object):\n def __init__(self, result):\n self.__dict__ = json.loads(result)\n\n p = Payload(result)\n return p\n\n\ndef random_dollar():\n dollar = random.randint(1, 100)\n return int(dollar)\n\n\ndef get_money_interval(d, t):\n # Will get the current currency rate from USD to ILS and will generate an interval as follows:\n # a. for given difficulty d, and value of money t the interval will be: (t- (5-d), t+ (5-d))\n c = CurrencyConverter()\n lower_bound = t - (5 - d)\n upper_bound = t + (5 - d)\n # print(\"\\n\", lower_bound, upper_bound)\n lower_bound_con = c.convert(lower_bound, 'USD', 'ILS')\n upper_bound_con = c.convert(upper_bound, 'USD', 'ILS')\n # print(\"\\n\", lower_bound_con, upper_bound_con)\n # interval_after_exchange = (currency_rate(lower_bound).result, currency_rate(upper_bound).result)\n interval_after_exchange = (lower_bound_con, upper_bound_con)\n return interval_after_exchange\n\n\ndef get_guess_from_user(t):\n # A method to prompt a guess from the user to enter a guess of value to a given amount of USD\n con = True\n while con:\n money_guess = input(f\"Hi,Do you Think You Can Guess how much {t} is in ILS rate ? \\n\")\n if money_guess.isnumeric():\n sleep(0.1)\n return money_guess\n else:\n print(\"Its not a number please try again\")\n sleep(0.1)\n continue\n sleep(0.1)\n\n\ndef play(diff):\n # Will call the functions above and play the game. Will return True/False if the user lost or won.\n money = random_dollar()\n interval = get_money_interval(diff, money)\n\n more = True\n while more:\n # get a guess\n guess = get_guess_from_user(money)\n if int(interval[0]) <= int(guess) <= int(interval[1]):\n print(f\"Wow you are A Genius good guess !!! the exect currency is {currency_rate(money).result}\")\n sleep(2)\n Screen_cleaner()\n more = False\n sleep(0.1)\n return True\n\n else:\n print(\"nice try - better luck next time\")\n print(\"You are most welcome to try again\")\n x = input(\"Please Press 'Enter' to try again or any other key to exit \\n\")\n if x == '':\n sleep(0.1)\n continue\n else:\n print(\"Thank you - Hope to see you soon - Bye Bye :) \")\n sleep(2)\n Screen_cleaner()\n more = False\n sleep(0.1)\n sleep(0.1)\n return False\n\n\n\n","repo_name":"iatid23/WorldOfGames","sub_path":"GameServer/CurrencyRouletteGame.py","file_name":"CurrencyRouletteGame.py","file_ext":"py","file_size_in_byte":3436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"41515126838","text":"MENU = {\n \"espresso\": {\n \"ingredients\": {\n \"water\": 50,\n \"coffee\": 18,\n },\n \"cost\": 1.5,\n },\n \"latte\": {\n \"ingredients\": {\n \"water\": 200,\n \"milk\": 150,\n \"coffee\": 24,\n },\n \"cost\": 2.5,\n },\n \"cappuccino\": {\n \"ingredients\": {\n \"water\": 250,\n \"milk\": 100,\n \"coffee\": 24,\n },\n \"cost\": 3.0,\n }\n}\n\nresources = {\n \"water\": 300,\n \"milk\": 200,\n \"coffee\": 100,\n \"money\": 0\n}\n\nexpresso = MENU['espresso']['ingredients']\nlatte = MENU['latte']['ingredients']\ncappuccino = MENU['cappuccino']['ingredients']\n\ndef validations (choice):\n\n if choice == 'expresso':\n if resources['water'] < expresso['water']:\n print('Sorry there is not enough water.')\n return False\n elif resources['coffee'] < expresso['coffee']:\n print('Sorry there is not enough coffee.')\n return False\n \n if choice == 'latte':\n if resources['water'] < latte['water']:\n print('Sorry there is not enough water.')\n return False\n elif resources['coffee'] < latte['coffee']:\n print('Sorry there is not enough coffee.')\n return False\n elif resources['milk'] < latte['milk']:\n print('Sorry there is not enough milk.')\n return False\n \n if choice == 'cappuccino':\n if resources['water'] < cappuccino['water']:\n print('Sorry there is not enough water.')\n return False\n elif resources['coffee'] < cappuccino['coffee']:\n print('Sorry there is not enough coffee.')\n return False\n elif resources['milk'] < cappuccino['milk']:\n print('Sorry there is not enough milk.')\n return False\n \n return True\n\ndef processCoins( choice ):\n\n if not validations(choice=choice):\n return\n \n print('Please insert coins.')\n quartersChoice = int(input('How many quarters?: '))\n dimesChoice = int(input('How many dimes:? '))\n nicklesChoice = int(input('How many nickles?: '))\n penniesChoice = int(input('How many pennies?: '))\n\n quarterTotal = 0.25 * quartersChoice \n dimesTotal = 0.10 * dimesChoice \n nicklesTotal = 0.05 * nicklesChoice \n penniesTotal = 0.01 * penniesChoice \n\n totalList = [ quarterTotal, dimesTotal, nicklesTotal, penniesTotal ]\n total = sum( totalList )\n return total\n\ndef checkTransaction(choice):\n expressoCost = MENU['espresso']['cost']\n latteCost = MENU['latte']['cost']\n cappuccinoCost = MENU['cappuccino']['cost']\n \n if choice == 'expresso':\n\n if expressoCost > processCoins(choice=choice):\n print(\"Sorry that's not enough money. Money refunded.\")\n return\n\n resources['coffee'] = resources['coffee'] - expresso['coffee']\n resources['water'] = resources['water'] - expresso['water']\n resources['money'] = processCoins(choice=choice)\n\n elif choice == 'latte':\n if latteCost > processCoins(choice=choice):\n print(\"Sorry that's not enough money. Money refunded.\")\n return\n\n resources['coffee'] = resources['coffee'] - latte['coffee']\n resources['milk'] = resources['milk'] - latte['milk']\n resources['water'] = resources['water'] - latte['water']\n resources['money'] = processCoins(choice=choice)\n\n elif choice == 'cappuccino':\n if cappuccinoCost > processCoins(choice=choice):\n print(\"Sorry that's not enough money. Money refunded.\")\n return\n\n resources['coffee'] = resources['coffee'] - cappuccino['coffee']\n resources['milk'] = resources['milk'] - cappuccino['milk']\n resources['water'] = resources['water'] - cappuccino['water']\n resources['money'] = processCoins(choice=choice)\n \n\ndef report():\n print(f\"Water: { resources['water']}ml \")\n print(f\"Milk: { resources['milk']}ml \")\n print(f\"Coffee: { resources['coffee']}g \")\n print(f\"Money: $ { resources['money'] }\")\n\ndef coffe_machine():\n should_run = True\n while should_run:\n print(resources)\n choice = input(\"What would you like? (expresso/latte/cappuccino): \").lower()\n if choice == 'expresso':\n checkTransaction(choice=choice)\n elif choice == 'latte':\n checkTransaction(choice=choice)\n elif choice == 'cappuccino':\n checkTransaction(choice=choice)\n elif choice == 'report':\n report()\n elif choice == 'off':\n should_run = False\n else:\n print('Choose a valid option')\n\ncoffe_machine()","repo_name":"kunjolee/bootcamp_100_python","sub_path":"15-coffe-machine/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"16170175346","text":"import numpy as np\nimport cv2\nimport pyzed.sl as sl\nfrom ReadData import read_alignment, read_calibration\nfrom Geometry import World_XY_from_uv_and_Z, inverse_perspective\nfrom utils import getBoxes\n\ndef Init():\n # Create a Camera object\n zed = sl.Camera()\n\n # Create a InitParameters object and set configuration parameters\n init_params = sl.InitParameters()\n init_params.camera_resolution = sl.RESOLUTION.RESOLUTION_HD1080 # Use HD1080 video mode\n init_params.camera_fps = 30 # Set fps at 30\n\n # Open the camera\n err = zed.open(init_params)\n if err != sl.ERROR_CODE.SUCCESS:\n exit(1)\n\n image_left = sl.Mat()\n runtime_parameters = sl.RuntimeParameters()\n\n return zed, image_left, runtime_parameters\n\ndef Record(zed, image_left, runtime_parameters):\n \n # Grab an image, a RuntimeParameters object must be given to grab()\n if zed.grab(runtime_parameters) == sl.ERROR_CODE.SUCCESS:\n # A new image is available if grab() returns SUCCESS\n # Each new frame is added to the SVO file\n \n zed.retrieve_image(image_left, sl.VIEW.VIEW_LEFT)\n # Get the timestamp at the time the image was captured\n timestamp = zed.get_timestamp(sl.TIME_REFERENCE.TIME_REFERENCE_CURRENT) \n print(\"Image resolution: {0} x {1} || Image timestamp: {2}\\n\".format(image_left.get_width(), image_left.get_height(),\n timestamp))\n\n # To recover data from sl.Mat to use it with opencv, we use the get_data() method\n # It returns a numpy array that can be used as a matrix with opencv\n img = image_left.get_data()\n\n # Close the camera\n #zed.close()\n\n return img\n\nzed, image_left, runtime_parameters = Init()\n\nwhile True:\n \n img = Record(zed, image_left, runtime_parameters)\n\n #cv2.imshow(\"Image\", img)\n # cv2.imwrite('Cones_img.png', img)\n #cv2.waitKey(100)\n\n imgpoints = getBoxes(img)\n u = [pixel[0] for pixel in imgpoints]\n v = [pixel[1] for pixel in imgpoints]\n imgpoints = [u, v]\n imgpixels = np.array(imgpoints, dtype=np.int)\n imgpoints = np.array(imgpoints, dtype=np.float64)\n\n # Load the camera parameters: \n # K - Camera matrix, d - Distortion coefficients vector,\n # R - Rotation matrix, t - Translation vector.\n\n K, d = read_calibration()\n Rinv, tinv = read_alignment()\n\n # We invert it to obtain the transformation from camera frame to world, which\n # is what OpenCV gives us by default.\n R, t = inverse_perspective(Rinv, tinv)\n\n N = imgpoints.shape[1]\n # Undistort use (1,N,2) shape so need to reshape the vector.\n imgpoints1 = imgpoints.reshape(1,N,2)\n points_undist = cv2.undistortPoints(imgpoints1, cameraMatrix=K, distCoeffs=d, dst=None, R=None, P=np.eye(3))\n points_undist = points_undist.reshape(N,2) # there is an extra level of array which we no longer need\n\n # Note that we passed in an identity matrix as the new camera matrix (\"P\"). We can pass\n # in any valid intrinsics matrix. The undistort function remaps the points to the new projection.\n # We have Z=0 since we chose points on the floor.\n positions = World_XY_from_uv_and_Z(points_undist, K=np.eye(3), R=R, t=t.reshape(3,1), Z=0.0)\n for i in range(len(positions)):\n cv2.putText(img, \"x:{}, y:{}\".format(positions[i][0], positions[i][1]),tuple(imgpixels[:,i]), fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=0.6, color=(0,255,0))\n\n cv2.imshow('a',img)\n cv2.waitKey(1500)\n\n\n\n\n","repo_name":"TechnionAVFormula/Camera-Calibration","sub_path":"Perception.py","file_name":"Perception.py","file_ext":"py","file_size_in_byte":3477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"23968705931","text":"from __future__ import annotations\n\nfrom pathlib import Path\nfrom sys import stderr\nfrom typing import Callable, NamedTuple\n\nimport pytest\nfrom utility import assert_eq_files\n\nfrom itaxotools.taxi2.distances import (\n Distance, DistanceHandler, DistanceMetric, Distances)\nfrom itaxotools.taxi2.sequences import Sequence\n\nTEST_DATA_DIR = Path(__file__).parent / Path(__file__).stem\n\n\nclass ReadTest(NamedTuple):\n fixture: Callable[[], Distances]\n input: str\n handler: DistanceHandler\n kwargs: dict = {}\n\n @property\n def input_path(self) -> Path:\n return TEST_DATA_DIR / self.input\n\n @property\n def fixed(self) -> Distances:\n return self.fixture()\n\n def validate(self) -> None:\n distances = Distances.fromPath(self.input_path, self.handler, **self.kwargs)\n generated_list = list(distances)\n fixed_list = list(self.fixed)\n assert len(fixed_list) == len(generated_list)\n for distance in fixed_list:\n assert distance in generated_list\n\n\nclass WriteTest(NamedTuple):\n fixture: Callable[[], Distances]\n output: str\n handler: DistanceHandler\n kwargs: dict = {}\n\n @property\n def fixed_path(self) -> Path:\n return TEST_DATA_DIR / self.output\n\n @property\n def fixed(self) -> Sequences:\n return self.fixture()\n\n def get_output_path(self, tmp_path) -> Path:\n return tmp_path / self.output\n\n def validate(self, output_path: Path) -> None:\n with self.handler(output_path, 'w', **self.kwargs) as file:\n for distance in self.fixed:\n file.write(distance)\n assert_eq_files(output_path, self.fixed_path)\n\n\nclass LabelTest(NamedTuple):\n metric: DistanceMetric\n label: str\n\n def check(self):\n assert self.metric == DistanceMetric.fromLabel(self.label)\n assert self.label == str(self.metric)\n\n\nclass MetricTest(NamedTuple):\n metric: DistanceMetric\n seq_x: str\n seq_y: str\n d: float\n precision: float = 0.0\n\n def check(self):\n x = Sequence('idx', self.seq_x)\n y = Sequence('idy', self.seq_y)\n r = self.metric.calculate(x, y)\n assert r.metric == self.metric\n assert r.x.id == 'idx'\n assert r.y.id == 'idy'\n if isinstance(r.d, float):\n assert abs(r.d - self.d) <= self.precision\n else:\n assert r.d == self.d\n assert r.d is None\n\n\nclass MetricFileTest(NamedTuple):\n file: str\n precision: float\n\n def get_metric_tests(self) -> iter[MetricTest]:\n path = TEST_DATA_DIR / self.file\n with DistanceHandler.Linear(path, 'r') as file:\n for d in file:\n yield MetricTest(d.metric, d.x.id, d.y.id, d.d, self.precision)\n\n\ndef distances_simple() -> Distances:\n metric = DistanceMetric.Uncorrected()\n return Distances([\n Distance(metric, Sequence('id1', None), Sequence('id2', None), 0.1),\n Distance(metric, Sequence('id1', None), Sequence('id3', None), 0.2),\n Distance(metric, Sequence('id1', None), Sequence('id4', None), 0.3),\n ])\n\n\ndef distances_multiple() -> Distances:\n return Distances([\n Distance(DistanceMetric.Uncorrected(), Sequence('id1', None), Sequence('id2', None), 0.11),\n Distance(DistanceMetric.UncorrectedWithGaps(), Sequence('id1', None), Sequence('id2', None), 0.12),\n Distance(DistanceMetric.JukesCantor(), Sequence('id1', None), Sequence('id2', None), 0.13),\n Distance(DistanceMetric.Kimura2P(), Sequence('id1', None), Sequence('id2', None), 0.14),\n Distance(DistanceMetric.NCD(), Sequence('id1', None), Sequence('id2', None), 0.15),\n Distance(DistanceMetric.BBC(0), Sequence('id1', None), Sequence('id2', None), 0.16),\n\n Distance(DistanceMetric.Uncorrected(), Sequence('id1', None), Sequence('id3', None), 0.21),\n Distance(DistanceMetric.UncorrectedWithGaps(), Sequence('id1', None), Sequence('id3', None), 0.22),\n Distance(DistanceMetric.JukesCantor(), Sequence('id1', None), Sequence('id3', None), 0.23),\n Distance(DistanceMetric.Kimura2P(), Sequence('id1', None), Sequence('id3', None), 0.24),\n Distance(DistanceMetric.NCD(), Sequence('id1', None), Sequence('id3', None), 0.25),\n Distance(DistanceMetric.BBC(0), Sequence('id1', None), Sequence('id3', None), 0.26),\n\n Distance(DistanceMetric.Uncorrected(), Sequence('id1', None), Sequence('id4', None), 0.31),\n Distance(DistanceMetric.UncorrectedWithGaps(), Sequence('id1', None), Sequence('id4', None), 0.32),\n Distance(DistanceMetric.JukesCantor(), Sequence('id1', None), Sequence('id4', None), 0.33),\n Distance(DistanceMetric.Kimura2P(), Sequence('id1', None), Sequence('id4', None), 0.34),\n Distance(DistanceMetric.NCD(), Sequence('id1', None), Sequence('id4', None), 0.35),\n Distance(DistanceMetric.BBC(0), Sequence('id1', None), Sequence('id4', None), 0.36),\n ])\n\n\ndef distances_square() -> Distances:\n metric = DistanceMetric.Uncorrected()\n return Distances([\n Distance(metric, Sequence('id1', None), Sequence('id1', None), 0.0),\n Distance(metric, Sequence('id1', None), Sequence('id2', None), 0.1),\n Distance(metric, Sequence('id1', None), Sequence('id3', None), 0.2),\n\n Distance(metric, Sequence('id2', None), Sequence('id1', None), 0.1),\n Distance(metric, Sequence('id2', None), Sequence('id2', None), 0.0),\n Distance(metric, Sequence('id2', None), Sequence('id3', None), 0.3),\n\n Distance(metric, Sequence('id3', None), Sequence('id1', None), 0.2),\n Distance(metric, Sequence('id3', None), Sequence('id2', None), 0.3),\n Distance(metric, Sequence('id3', None), Sequence('id3', None), 0.0),\n ])\n\n\ndef distances_square_unknown() -> Distances:\n metric = DistanceMetric.Unknown()\n return Distances([\n Distance(metric, dis.x, dis.y, dis.d) for dis in distances_square()\n ])\n\n\ndef distances_rectangle() -> Distances:\n metric = DistanceMetric.Uncorrected()\n return Distances([\n Distance(metric, Sequence('id1', None), Sequence('id4', None), 0.14),\n Distance(metric, Sequence('id1', None), Sequence('id5', None), 0.15),\n Distance(metric, Sequence('id1', None), Sequence('id6', None), 0.16),\n Distance(metric, Sequence('id1', None), Sequence('id7', None), 0.17),\n Distance(metric, Sequence('id1', None), Sequence('id8', None), 0.18),\n Distance(metric, Sequence('id1', None), Sequence('id9', None), 0.19),\n\n Distance(metric, Sequence('id2', None), Sequence('id4', None), 0.24),\n Distance(metric, Sequence('id2', None), Sequence('id5', None), 0.25),\n Distance(metric, Sequence('id2', None), Sequence('id6', None), 0.26),\n Distance(metric, Sequence('id2', None), Sequence('id7', None), 0.27),\n Distance(metric, Sequence('id2', None), Sequence('id8', None), 0.28),\n Distance(metric, Sequence('id2', None), Sequence('id9', None), 0.29),\n\n Distance(metric, Sequence('id3', None), Sequence('id4', None), 0.34),\n Distance(metric, Sequence('id3', None), Sequence('id5', None), 0.35),\n Distance(metric, Sequence('id3', None), Sequence('id6', None), 0.36),\n Distance(metric, Sequence('id3', None), Sequence('id7', None), 0.37),\n Distance(metric, Sequence('id3', None), Sequence('id8', None), 0.38),\n Distance(metric, Sequence('id3', None), Sequence('id9', None), 0.39),\n ])\n\n\ndef distances_missing() -> Distances:\n metric = DistanceMetric.Uncorrected()\n return Distances([\n Distance(metric, Sequence('id1', None), Sequence('id1', None), 0.0),\n Distance(metric, Sequence('id1', None), Sequence('id2', None), None),\n\n Distance(metric, Sequence('id2', None), Sequence('id1', None), None),\n Distance(metric, Sequence('id2', None), Sequence('id2', None), 0.0),\n ])\n\n\ndef distances_extras() -> Distances:\n return Distances([\n Distance(\n DistanceMetric.Uncorrected(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference1', None, dict(voucher='X', organism='A')),\n 0.11),\n Distance(\n DistanceMetric.UncorrectedWithGaps(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference1', None, dict(voucher='X', organism='A')),\n 0.12),\n Distance(\n DistanceMetric.JukesCantor(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference1', None, dict(voucher='X', organism='A')),\n 0.13),\n Distance(\n DistanceMetric.Kimura2P(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference1', None, dict(voucher='X', organism='A')),\n 0.14),\n\n Distance(\n DistanceMetric.Uncorrected(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference2', None, dict(voucher='Y', organism='B')),\n 0.21),\n Distance(\n DistanceMetric.UncorrectedWithGaps(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference2', None, dict(voucher='Y', organism='B')),\n 0.22),\n Distance(\n DistanceMetric.JukesCantor(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference2', None, dict(voucher='Y', organism='B')),\n 0.23),\n Distance(\n DistanceMetric.Kimura2P(),\n Sequence('query1', None, dict(voucher='K')),\n Sequence('reference2', None, dict(voucher='Y', organism='B')),\n 0.24),\n\n Distance(\n DistanceMetric.Uncorrected(),\n Sequence('query2', None, dict(voucher='L')),\n Sequence('reference3', None, dict(voucher='Z', organism='C')),\n 0.31),\n Distance(\n DistanceMetric.UncorrectedWithGaps(),\n Sequence('query2', None, dict(voucher='L')),\n Sequence('reference3', None, dict(voucher='Z', organism='C')),\n 0.32),\n Distance(\n DistanceMetric.JukesCantor(),\n Sequence('query2', None, dict(voucher='L')),\n Sequence('reference3', None, dict(voucher='Z', organism='C')),\n 0.33),\n Distance(\n DistanceMetric.Kimura2P(),\n Sequence('query2', None, dict(voucher='L')),\n Sequence('reference3', None, dict(voucher='Z', organism='C')),\n None),\n ])\n\n\nread_tests = [\n ReadTest(distances_simple, 'simple.linear', DistanceHandler.Linear),\n ReadTest(distances_multiple, 'multiple.linear', DistanceHandler.Linear),\n ReadTest(distances_missing, 'missing.linear', DistanceHandler.Linear),\n\n ReadTest(distances_square_unknown, 'square.matrix', DistanceHandler.Matrix),\n ReadTest(distances_square, 'square.matrix', DistanceHandler.Matrix,\n dict(metric=DistanceMetric.Uncorrected())),\n ReadTest(distances_rectangle, 'rectangle.matrix', DistanceHandler.Matrix,\n dict(metric=DistanceMetric.Uncorrected())),\n ReadTest(distances_missing, 'missing.matrix', DistanceHandler.Matrix,\n dict(metric=DistanceMetric.Uncorrected())),\n\n ReadTest(distances_extras, 'extras.tsv', DistanceHandler.Linear.WithExtras,\n dict(idxHeader='seqid', idyHeader='id', tagX='_x', tagY='_y')),\n ReadTest(distances_extras, 'extras.tsv', DistanceHandler.Linear.WithExtras,\n dict(idxColumn=0, idyColumn=2, tagX='_x', tagY='_y')),\n]\n\n\nwrite_tests = [\n WriteTest(distances_simple, 'simple.linear', DistanceHandler.Linear,\n dict(formatter='{:.1f}')),\n WriteTest(distances_multiple, 'multiple.linear', DistanceHandler.Linear,\n dict(formatter='{:.2f}')),\n WriteTest(distances_missing, 'missing.linear', DistanceHandler.Linear,\n dict(formatter='{:.1f}')),\n\n WriteTest(distances_square, 'square.matrix', DistanceHandler.Matrix,\n dict(formatter='{:.1f}')),\n WriteTest(distances_rectangle, 'rectangle.matrix', DistanceHandler.Matrix,\n dict(formatter='{:.2f}')),\n WriteTest(distances_missing, 'missing.matrix', DistanceHandler.Matrix,\n dict(formatter='{:.1f}')),\n\n WriteTest(distances_missing, 'missing.formatted.linear', DistanceHandler.Linear,\n dict(formatter='{:.2e}', missing='nan')),\n WriteTest(distances_missing, 'missing.formatted.matrix', DistanceHandler.Matrix,\n dict(formatter='{:.2e}', missing='nan')),\n\n WriteTest(distances_extras, 'extras.tsv', DistanceHandler.Linear.WithExtras,\n dict(idxHeader='seqid', idyHeader='id', tagX='_x', tagY='_y', formatter='{:.2f}')),\n WriteTest(distances_missing, 'missing.formatted.linear', DistanceHandler.Linear.WithExtras,\n dict(idxHeader='idx', idyHeader='idy', tagX='', tagY='', formatter='{:.2e}', missing='nan')),\n\n]\n\n\nlabel_tests = [\n LabelTest(DistanceMetric.Uncorrected(), 'p'),\n LabelTest(DistanceMetric.UncorrectedWithGaps(), 'p-gaps'),\n LabelTest(DistanceMetric.JukesCantor(), 'jc'),\n LabelTest(DistanceMetric.Kimura2P(), 'k2p'),\n LabelTest(DistanceMetric.NCD(), 'ncd'),\n LabelTest(DistanceMetric.NCD(), 'ncd'),\n LabelTest(DistanceMetric.BBC(0), 'bbc(0)'),\n LabelTest(DistanceMetric.BBC(1), 'bbc(1)'),\n]\n\n\nmetric_tests = [\n MetricTest(DistanceMetric.Uncorrected(), 'gg-ccnccta', 'ggaccaccaa', 1.0 / 8.0),\n MetricTest(DistanceMetric.UncorrectedWithGaps(), 'gg-ccnccta', 'ggaccaccaa', 2.0 / 9.0),\n MetricTest(DistanceMetric.Uncorrected(), '---', 'nnn', None),\n]\n\n\nmetric_file_tests = [\n MetricFileTest('metrics.tsv', 0.00051),\n]\n\n\n@pytest.mark.parametrize(\"test\", read_tests)\ndef test_read_distances(test: ReadTest) -> None:\n test.validate()\n\n\n@pytest.mark.parametrize(\"test\", write_tests)\ndef test_write_distances(test: WriteTest, tmp_path: Path) -> None:\n output_path = test.get_output_path(tmp_path)\n test.validate(output_path)\n\n\n@pytest.mark.parametrize(\"test\", label_tests)\ndef test_labels(test: LabelTest) -> None:\n test.check()\n\n\n@pytest.mark.parametrize(\"test\", metric_tests)\ndef test_metrics(test: MetricTest) -> None:\n test.check()\n\n\n@pytest.mark.parametrize(\"test\", metric_file_tests)\ndef test_metrics_from_files(test: MetricFileTest) -> None:\n stack = []\n for metric_test in test.get_metric_tests():\n try:\n metric_test.check()\n except AssertionError as a:\n stack.append(a)\n for a in stack:\n print(a.args[0], '\\n', file=stderr)\n assert len(stack) == 0\n","repo_name":"iTaxoTools/TaxI2","sub_path":"tests/test_distances.py","file_name":"test_distances.py","file_ext":"py","file_size_in_byte":14469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"73249857172","text":"#!/usr/bin/python3\n# Austin Lolli\n# Lab 3 server.py\n#\n# Usage behavior: python3 server.py $port\n\nimport sys\nimport socket\n\ndef usage():\n usg_str = \"Usage: python3 {} $port\\n\"\n sys.stderr.write(usg_str.format(sys.argv[0]))\n exit(1)\n\ntry:\n port = int(sys.argv[1])\nexcept (IndexError, ValueError):\n usage()\n\ndef listen(port):\n # creates a socket called sudp using an internet connection and udp\n sudp = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n # binds the socket to the address, using its own IP and designated port\n sudp.bind(('', port))\n # want to continually listen for messages\n while True:\n # recvfrom returns message and address of sender, store both\n message, addr = sudp.recvfrom(1024)\n message = message.decode()\n # if quit message received from talk program, close socket & stop listening\n if message == \"quit\":\n sudp.close()\n sys.exit(0)\n # any other message is printed as \"IP: message\"\n else:\n print(\"{}: {}\".format(addr[0], message))\n\nif __name__ == \"__main__\":\n listen(port)\n\n\n\n","repo_name":"austin-lolli/networks-labs","sub_path":"Lab4/listen.py","file_name":"listen.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"4687202875","text":"\nimport json\nimport shutil\nfrom collections import namedtuple\nfrom ansible.parsing.dataloader import DataLoader\nfrom ansible.vars.manager import VariableManager\nfrom ansible.inventory.manager import InventoryManager\nfrom ansible.playbook.play import Play\nfrom ansible.executor.task_queue_manager import TaskQueueManager\nfrom ansible.plugins.callback import CallbackBase\nimport ansible.constants as C\nimport uuid\nimport os,sys\nimport tempfile\nimport pdb\n\n\nclass ResultCallback(CallbackBase):\n \"\"\"A sample callback plugin used for performing an action as results come in\n\n If you want to collect all results into a single object for processing at\n the end of the execution, look into utilizing the ``json`` callback plugin\n or writing your own custom callback plugin\n \"\"\"\n\n def v2_runner_on_ok(self, result, **kwargs):\n \"\"\"Print a json representation of the result\n\n This method could store the result in an instance attribute for retrieval later\n \"\"\"\n host = result._host\n sys.stdout.write(json.dumps({host.name: result._result}, indent=4))\n\nclass AnsibleApi:\n def __init__(self,hosts):\n self.filename = \"/tmp/{0}\".format(uuid.uuid4())\n self.tmp = open(self.filename,'a')\n self.tmp.write(hosts)\n self.tmp.close()\n Options = namedtuple('Options',\n ['connection', 'module_path', 'forks', 'become', 'become_method', 'become_user', 'check',\n 'diff'])\n # initialize needed objects\n self.loader = DataLoader()\n self.options = Options(connection='ssh', module_path=None, forks=100, become=None, become_method=None,\n become_user=None,\n check=False,\n diff=False)\n self.passwords = dict(vault_pass='secret')\n self.inventory = InventoryManager(loader=self.loader, sources=self.filename)\n self.variable_manager = VariableManager(loader=self.loader, inventory=self.inventory)\n self.hosts = hosts\n\n def run_cmd(self, cmd):\n # Instantiate our ResultCallback for handling results as they come in\n results_callback = ResultCallback()\n # create inventory and pass to var manager\n # use path to host config file as source or hosts in a comma separated string\n\n\n # create play with tasks\n play_source = dict(\n name=\"Ansible Play\",\n hosts='all',\n gather_facts='no',\n tasks=[\n dict(action=dict(module='shell', args=cmd), register='shell_out')\n ]\n )\n play = Play().load(play_source, variable_manager=self.variable_manager, loader=self.loader)\n\n # actually run it\n tqm = None\n try:\n tqm = TaskQueueManager(\n inventory=self.inventory,\n variable_manager=self.variable_manager,\n loader=self.loader,\n options=self.options,\n passwords=self.passwords,\n stdout_callback=results_callback, # Use our custom callback instead of the ``default`` callback plugin\n )\n result = tqm.run(play)\n return result\n finally:\n if tqm is not None:\n tqm.cleanup()\n os.remove(self.filename)\n # Remove ansible tmpdir\n shutil.rmtree(C.DEFAULT_LOCAL_TMP, True)","repo_name":"hiys/PYTHON","sub_path":"pythonScripts/PyScripts/ansible_api.py","file_name":"ansible_api.py","file_ext":"py","file_size_in_byte":3428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"31908068772","text":"from typing import Protocol\r\n\r\nfrom utils.exceptions import CantGetCoordinates\r\nfrom utils.schemas import Coordinates\r\n\r\n\r\ndef _parse_coordinate(*, coordinate_value: str | float | int) -> float:\r\n try:\r\n return float(coordinate_value)\r\n except ValueError:\r\n raise CantGetCoordinates\r\n\r\n\r\nclass CoordinatesSourceProtocol(Protocol):\r\n @classmethod\r\n def get_coordinates(cls) -> Coordinates:\r\n raise NotImplementedError\r\n\r\n\r\nclass PlugSource(CoordinatesSourceProtocol):\r\n latitude = 55.755864\r\n longitude = 37.617698\r\n\r\n @classmethod\r\n def get_coordinates(cls) -> Coordinates:\r\n return Coordinates(longitude=cls.longitude, latitude=cls.latitude)\r\n\r\n\r\nclass ConsoleSource(CoordinatesSourceProtocol):\r\n\r\n @classmethod\r\n def get_coordinates(cls) -> Coordinates:\r\n lat = input(\"Enter your latitude: \")\r\n lon = input(\"Enter your longitude: \")\r\n latitude = _parse_coordinate(coordinate_value=lat)\r\n longitude = _parse_coordinate(coordinate_value=lon)\r\n return Coordinates(longitude=longitude, latitude=latitude)\r\n\r\n\r\ndef get_coordinates(coordinates_source: CoordinatesSourceProtocol) -> Coordinates:\r\n return coordinates_source.get_coordinates()\r\n","repo_name":"quantum73/weather_cli","sub_path":"services/coordinates.py","file_name":"coordinates.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"11021932443","text":"import model_chen_2019 as mc19\nimport oxfordDataLoader as odl\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\n# Note: Why do I need to re-import torch?\nis_cuda = torch.cuda.is_available()\nif is_cuda:\n device = torch.device(\"cuda\")\nelse:\n device = torch.device(\"cpu\")\n\n# The main area\nif __name__==\"__main__\":\n\n # -------------\n # Get the data:\n # batch_size = 16\n batch_size = 256 # from paper\n z = odl.OxfordDataset(window_samples=200, overlap_samples=190)\n loader = DataLoader(z, batch_size=batch_size, pin_memory=True)\n\n # ----------------\n # Setup the model:\n n_epochs = 10\n #lr = 0.01\n lr = 0.00001 # paper has 1e-5\n criterion = nn.MSELoss()\n\n input_size = 6\n hidden_size = 128 # 256\n num_layers = 3 # 2\n output_size = 3 # 4\n\n model = mc19.Chen_IEEE(input_size, hidden_size, num_layers, output_size)\n model.to(device)\n\n optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n hidden = None\n\n\n # Now train:\n for epoch in range(1, n_epochs+1):\n # Begin your epoch by zeroing your gradients in\n # your optimizer (why?)\n optimizer.zero_grad()\n\n for batch_idx, sample in enumerate(loader):\n\n # Concatenate inputs attitude and acceleration\n # from the sensor along the 2nd dimension,\n # which is basically the index for the data group\n\n # Data in should be {acc, omega} both 3-d vectors\n data_in = torch.cat((sample[4], sample[2]), 2).to(device)\n\n # data_in = torch.cat((sample[1], sample[4]), 2).to(device)\n\n # A note on permuting:\n # if you have a matrix, the zeroth 'axis' is the\n # column, and the first is the row. So,\n # permute(1,0) will put the columns into rows,\n # and the rows into columns. It expands to nth axis\n # with tensors.\n # For this particular operation, we are swapping the\n # zeroth and first axes, and those are:\n # og_zeroth = index\n # og_first = physical values (I think)\n data_in.permute(1,0,2)\n trans = sample[7].to(device) # translation from truth\n # I think this should be:\n trans = torch.cat((sample[7], sample[8]), 2).to(device)\n\n\n # Get the diff. TODO: Understand this better!\n #avg_trans_rate =\n\n delta_trans = torch.cat((torch.zeros(trans.shape[0], 1, 3, dtype=torch.float32).to(device), (trans[:,1:,:] - trans[:,:-1,:])), 1)\n output, hidden = model(data_in, hidden)\n print(\"output size = \" + str(output.size()))\n print(\"delta size = \" + str(delta_trans.size()))\n\n # Note, output and delta_trans must have same shape (?)\n loss = criterion(output, delta_trans)\n if batch_idx != len(loader):\n loss.backward(retain_graph=True)\n else:\n loss.backward()\n\n optimizer.step()\n\n # Small number of epochs, so print all:\n print('Epoch: {}/{}..........'.format(epoch, n_epochs), end='')\n print('Loss: {:0.4f}'.format(loss.item()))\n\n\n\n\n","repo_name":"jbelarge/odom-python","sub_path":"models_jab/goModel.py","file_name":"goModel.py","file_ext":"py","file_size_in_byte":3189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"11955666344","text":"import spsim.simulation_functions\nfrom spsim.simulation_functions import simulate_single_image\nfrom .test_data_model import test_image_parameters_instantiation, \\\n test_simulation_from_input_parameters\n\n\ndef test_simulate_image():\n image_parameters = test_image_parameters_instantiation()\n image = simulate_single_image(image_parameters)\n assert image.shape == (512, 512)\n\n\ndef test_execute_simulation():\n simulation = test_simulation_from_input_parameters()\n spsim.simulation_functions.simulation_as_dask_array()\n","repo_name":"alisterburt/spsim","sub_path":"tests/test_simulation.py","file_name":"test_simulation.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"25982590645","text":"from tkinter import *\n\nwn = Tk()\n\nwn.geometry(\"500x500\")\n\nwn.title(\"To-Do List\")\n\ntitle_lbl = Label(text = \"To-Do List\", font = (\"Georgia\",85,\"bold\"))\ntitle_lbl.pack()\n\nlist_frame = Frame(wn)\nlist_frame.pack(pady = 20)\n\nto_do_list = Listbox(list_frame,\n font = \"Cambria\",\n width = 50,\n height = 5,\n bg = \"SystemButtonFace\",\n bd = 0, \n highlightthickness = 0,\n activestyle = \"none\")\n\nto_do_list.pack()\n\nto_do = [\"Do math homework\", \"Exercise\", \"Read english assignment\"]\n\nfor item in to_do:\n to_do_list.insert(END, item)\n\nitem_entry = Entry(wn, font = (\"Georgia\", 35))\nitem_entry.pack(pady = 20)\n\nbutton_frame = Frame(wn)\nbutton_frame.pack(pady = 20)\n\ndef add_item():\n to_do_list.insert(END, item_entry.get())\n item_entry.delete(0, END)\n\ndef delete_item():\n to_do_list.delete(ANCHOR)\n\ndef clear_item():\n to_do_list.delete(0,END)\n\nadd_button = Button(button_frame, text = \"Add Item\", font = (\"Cambria\", 12), width = 12, command = add_item)\ndelete_button = Button(button_frame, text = \"Delete Item\", font = (\"Cambria\", 12), width = 12, command = delete_item)\nclear_button = Button(button_frame, text = \"Clear\", font = (\"Cambria\", 12), width = 12, command = clear_item)\n\nadd_button.grid(row = 0, column = 0)\ndelete_button.grid(row = 0, column = 1, padx = 10)\nclear_button.grid(row = 0, column = 2)\n\nwn.mainloop()\n","repo_name":"zahrazams/It-List","sub_path":"It List.py","file_name":"It List.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"20566248667","text":"import requests\r\nfrom bcschain.models import Block\r\nfrom django.utils import timezone\r\nfrom datetime import datetime, timedelta\r\nimport pytz\r\nimport time\r\n\r\n\r\ndef update_blocks():\r\n start_date = datetime.strptime('2021-1-21', \"%Y-%m-%d\")\r\n current_date = datetime.today()\r\n first_block = Block.objects.all().first()\r\n msc = pytz.timezone('Etc/GMT-3')\r\n if first_block:\r\n last_timestamp = first_block.timestamp\r\n else:\r\n last_timestamp = datetime.fromtimestamp(1611187200).\\\r\n replace(tzinfo=msc)\r\n\r\n while (current_date - start_date).days >= 0:\r\n datetime_string = f'{current_date.year}-{current_date.month}-{current_date.day}'\r\n url = f'https://bcschain.info/api/blocks/?date={datetime_string}'\r\n response = requests.get(url)\r\n data = response.json()\r\n\r\n for block in data:\r\n timestamp = datetime.fromtimestamp(int(block['timestamp'])).\\\r\n replace(tzinfo=msc)\r\n if timestamp < last_timestamp:\r\n return\r\n height = block['height']\r\n if timestamp == last_timestamp:\r\n try:\r\n Block.objects.get(height=height)\r\n return\r\n except Block.DoesNotExist:\r\n pass\r\n\r\n obj = Block(height=height,\r\n hash=block['hash'],\r\n timestamp=timestamp,\r\n address=block['miner'],\r\n transactions=block['transactionCount'])\r\n obj.save()\r\n\r\n current_date -= timedelta(days=1)\r\n time.sleep(1)\r\n","repo_name":"lepestos/django_bcs","sub_path":"blockUpdater/blockApi.py","file_name":"blockApi.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"9709656291","text":"from __future__ import absolute_import, print_function, division, unicode_literals\nfrom builtins import super, str\n\nimport os\nimport logging\nfrom collections import deque\nimport time\nimport psutil\nimport textwrap\nfrom datetime import datetime\nfrom absl import flags\nfrom contextlib import contextmanager\nfrom typing import List, Deque, Dict, Union, Optional\n\nfrom benchmarks.driver.utils import prompt, remove_prefix\nfrom benchmarks.driver.utils.prompt import pause\nfrom .utils.compatiblity import pathlib, subprocess as sp\nfrom .utils import Popen, execute, ServerError, kill_tree, kill_hard\n\n\nPath = pathlib.Path\nFLAGS = flags.FLAGS\nlogger = logging.getLogger(__name__)\nflags.DEFINE_string('tfserver_endpoint', 'grpc://localhost:2345', 'TF server endpoint to listen on')\n\n\nclass TFDistServer(object):\n\n def __init__(self, env=None, outputdir=None):\n # type: (Dict, Union[Path, str]) -> TFDistServer\n super().__init__()\n\n self.env = os.environ.copy()\n if env is not None:\n self.env.update(env)\n if 'CUDA_VISIBLE_DEVICES' not in self.env:\n self.env['CUDA_VISIBLE_DEVICES'] = '0'\n if 'TF_CPP_MIN_LOG_LEVEL' not in self.env:\n self.env['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n self.endpoint = FLAGS.tfserver_endpoint # type: str\n\n self.output = None\n if outputdir is not None:\n self.output = Path(outputdir)\n\n self._build_cmd()\n\n self.proc = None # type: Optional[Popen]\n\n def _build_cmd(self):\n # type: () -> List[str]\n \"\"\"Build commandline using 'config' information\"\"\"\n self.args = []\n\n self.args += [\n 'python',\n ]\n\n return self.args\n\n @contextmanager\n def run(self):\n # type: () -> None\n \"\"\"Run server\"\"\"\n if self.output:\n captured_stdout_path = self.output/'tfdist.stdout'\n captured_stderr_path = self.output/'tfdist.stderr'\n\n captured_stdout_path.parent.mkdir(exist_ok=True)\n captured_stderr_path.parent.mkdir(exist_ok=True)\n\n stdout, stderr = captured_stdout_path.open('w'), captured_stderr_path.open('w')\n else:\n stdout, stderr = None, None\n\n # noinspection PyBroadException\n try:\n pyscript = textwrap.dedent(f\"\"\"\n import tensorflow as tf\n cluster = tf.train.ClusterSpec({{\"tfworker\": [\"{remove_prefix(self.endpoint, \"grpc://\")}\"]}})\n tf.train.Server(cluster, job_name=\"tfworker\", task_index=0,\n config=tf.ConfigProto(isolate_session_state=True)).join()\n \"\"\")\n # start\n self.proc = execute(self.args, env=self.env, stdin=sp.PIPE, stdout=stdout, stderr=stderr)\n self.proc.stdin.write(pyscript + \"\\n\")\n self.proc.stdin.close() # Ensures the process knows nothing else is coming\n\n time.sleep(2)\n\n logger.info(f'Started tf server with pid: {self.proc.pid}')\n\n # make self the current server\n with self.as_current():\n yield\n except Exception as ex:\n logger.error(f'Got exception while running the tf server: {ex!s}')\n finally:\n self.kill()\n\n if self.output:\n stdout.close()\n stderr.close()\n\n _current = deque() # type: Deque[TFDistServer]\n\n @contextmanager\n def as_current(self):\n TFDistServer._current.append(self)\n yield self\n TFDistServer._current.pop()\n\n @classmethod\n def has_current(cls):\n # type: () -> bool\n return len(cls._current) > 0\n\n @classmethod\n def current_server(cls):\n # type: () -> TFDistServer\n try:\n return cls._current[-1]\n except IndexError:\n raise ServerError('No current running tf server')\n\n def check(self):\n # type: () -> None\n \"\"\"Check that the server is healthy and running\"\"\"\n if self.proc is None:\n raise ServerError('TF Server is not yet started')\n if self.proc.poll() is not None:\n out, err = self.proc.communicate()\n msg = [f'TF Server died unexpectedly with return code: {self.proc.returncode}']\n if out is not None:\n msg.append(f'\\nStandard output:\\n{out}')\n if err is not None:\n msg.append(f'\\nStandard error:\\n{err}')\n raise ServerError('\\n'.join(msg))\n\n def kill(self):\n # type: () -> None\n \"\"\"Kill the server\"\"\"\n if FLAGS.no_server:\n return\n\n if self.proc is None or self.proc.poll() is not None:\n logger.warning('TF Server already died or is not yet started')\n self.proc = None\n return\n\n logger.info(f'Killing TF server with pid: {self.proc.pid}')\n _, alive = kill_tree(self.proc, timeout=2)\n if alive:\n prompt.confirm('TF Server did not respond in time, do you want to kill hard?')\n logger.info(f'Force killing server with pid: {self.proc.pid}')\n kill_hard(alive)\n\n self.proc = None\n\n @classmethod\n def wait_workloads(cls, workloads, timeout=None, callback=None):\n \"\"\"Wait workloads, raise if server died\"\"\"\n if callback is None:\n def done(proc):\n logger.info(f'Workload {proc.workload.canonical_name} exited with {proc.returncode}')\n\n callback = done\n\n gone = []\n alive = [w.proc for w in workloads]\n enter = datetime.now()\n while alive:\n if TFDistServer.has_current():\n TFDistServer.current_server().check()\n\n g, alive = psutil.wait_procs(alive, timeout=.25, callback=callback)\n gone += g\n\n if timeout is not None and (datetime.now() - enter).total_seconds() >= timeout:\n break\n\n return [p.workload for p in gone], [p.workload for p in alive]\n\n","repo_name":"SymbioticLab/Salus","sub_path":"benchmarks/driver/tfserver.py","file_name":"tfserver.py","file_ext":"py","file_size_in_byte":5969,"program_lang":"python","lang":"en","doc_type":"code","stars":135,"dataset":"github-code","pt":"67"}
+{"seq_id":"27314323632","text":"import os\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nimport load_tushare\n\n# _PATH = '/Users/winston/mygitrep/sys23/' # TODO\n_PATH = './'\n\n\nclass CacheCombiner(object):\n\n def __init__(self, conf: dict):\n self.conf = conf\n self.tds = load_tushare.TushareLoader(conf).get_tradedates(\n start_date='20140630', filename=conf['path']['tradedates'])\n\n def combine_index_weight(self):\n \"\"\"合并股指成分股权重并补全交易日\"\"\"\n\n # Cache path\n cache_path = self.conf['path']['cache']\n # Source path\n basic_path = cache_path + 'index_weight/'\n\n # Iterate distinct stock index\n for kind, code in self.conf['csi_pool'].items():\n\n # Current index code (6-digit)\n code1 = code.split('.')[0]\n\n stack = []\n # Iterate all years\n for year in range(int(self.tds.iloc[0][:4]), int(self.tds.iloc[-1][:4]) + 1):\n src_file = basic_path + f'{code1}_{year}.csv'\n if not os.path.exists(src_file):\n continue\n df = pd.read_csv(src_file)\n df = df.pivot(index='trade_date', columns='con_code', values='weight')\n stack.append(df)\n\n # Concat year-by-year weight for current index\n stack = pd.concat(stack, axis=0)\n # Fill with NA for method `ffill` in the next step\n stack.fillna(0, inplace=True)\n # Reindex columns with the whole tradedates (though disk-space-consuming)\n stack = stack.reindex(self.tds.astype(int), method='ffill')\n\n # If row sum less than 98%, we'll set index weight as NA\n bad_weight_sum = (stack.sum(axis=1) < 98)\n stack.loc[bad_weight_sum] = np.nan\n\n # Save\n stack.to_csv(self.conf['path'][kind])\n print(f\"Updated in `{self.conf['path'][kind]}`\")\n\n def combine_st_status(self):\n df = pd.read_csv(self.conf['path']['cache'] + '/namechange.csv')\n # print(df['change_reason'].value_counts())\n # print(df.groupby('change_reason').first())\n mask_begin = (df['change_reason'] == '*ST') | (df['change_reason'] == 'ST')\n mask_end = (df['change_reason'] == '撤销*ST') | (df['change_reason'] == '撤销ST') | (\n df['change_reason'] == '摘星改名') | (df['change_reason'] == '终止上市')\n df['isST'] = (mask_begin * 2 + mask_end * 1).replace(0, np.nan)\n df = df[['ann_date', 'change_reason', 'ts_code', 'isST']].drop_duplicates().dropna()\n print(df[df[['ann_date', 'ts_code']].duplicated(keep=False)])\n df = df.groupby(['ann_date', 'ts_code'])['isST'].sum().astype(int)\n print(df.value_counts().to_dict())\n df = df.replace(3, 0) # TODO: 3 - 当天有ST又取消ST，则视作之后一直是ST\n df = df.replace(2, 0) # 0 - 从公告日开始，标记为ST/*ST; 1 - 从公告日开始，不再是ST/*ST（或退市）\n print(df.value_counts().to_dict())\n df = df.reset_index()\n df = df.rename(columns={'ann_date': 'tradingdate', 'ts_code': 'stockcode', 'isST': 'st_status'})\n df.to_csv(self.conf['path']['st_status'], index=False)\n print(f\"Save {len(df)} rows in `{self.conf['path']['st_status']}`.\")\n\n def infer_tds_todo(self, last_tgt_table: str, is1d=False) -> str:\n \"\"\"根据目标CSV地址，查询末尾日期，确定需要新读取的开始日期；若CSV不存在，选用self.tds开始日期\"\"\"\n if os.path.exists(last_tgt_table):\n if is1d: # a 1d table with columns - `tradingdate`\n # Infer latest combined date from existing combined table\n latest_date = pd.read_csv(last_tgt_table)['tradingdate'].dropna(how='all').iloc[-1]\n # Next date after the latest date\n begin_date = load_tushare.next_calendar_date(latest_date, lfmt='%Y-%m-%d')\n else:\n # Infer latest combined date from existing combined table\n latest_date = pd.read_csv(last_tgt_table, index_col=0).dropna(how='all').index[-1]\n # Next date after the latest date\n begin_date = load_tushare.next_calendar_date(latest_date, lfmt='%Y-%m-%d')\n else:\n # Next date is the beginning of `self.td\n begin_date = pd.to_datetime(self.tds[0]).strftime('%Y-%m-%d')\n return begin_date\n\n def combine_daily(self):\n \"\"\"合并`daily/daily/`中的日度行情指标并进行后复权调整\"\"\"\n cache_path = self.conf['path']['cache']\n daily_path = cache_path + 'daily/' # TODO: download cache - daily\n adj_path = cache_path + 'adj_factor/'\n\n # Next date to update\n begin_date = self.infer_tds_todo(\n last_tgt_table=self.conf['path']['circ_mv']) # TODO\n\n ohlc = ['open', 'high', 'low', 'close']\n volamt = ['vol', 'amount']\n\n tds_todo = self.tds[self.tds >= begin_date]\n if len(tds_todo) > 0:\n cached = {}\n for td in tqdm(tds_todo):\n d = pd.read_csv(f'{daily_path}/{td}.csv', index_col=0).sort_index()\n a = pd.read_csv(f'{adj_path}/{td}.csv', index_col=0)\n try:\n a = a.loc[d.index, 'adj_factor']\n except KeyError as e:\n a = a.loc[a.index.intersection(d.index), 'adj_factor']\n print(f\"{td}: {len(set(d.index) - set(a.index))} index in 'daily' found\" \n f\" but in 'adj_factor' not found:\\n original error message: {e}\\n\")\n\n # TODO: 20230421: 1 index in 'daily' found but in 'adj_factor' not found:\n # original error message: \"['689009.SH'] not in index\"\n # assert not set(d.index) - set(a.index) # 所有日期内所含个股都有对应adjfactor\n\n for c in d.columns:\n if c not in ohlc + volamt:\n continue\n\n sr = d[c]\n sr.name = pd.to_datetime(td, format='%Y%m%d')\n if c not in cached:\n cached[c] = [sr]\n else:\n cached[c].append(sr)\n\n if c in ohlc: # 复权调整\n csr = (d[c] * a).round(9)\n csr.name = pd.to_datetime(td, format='%Y%m%d')\n cc = c + 'Adj'\n if cc not in cached:\n cached[cc] = [csr]\n else:\n cached[cc].append(csr)\n\n for k, v in cached.items(): # 暂时必须延续之前，补充新增交易日\n file = self.conf['path'][k]\n if os.path.exists(file):\n df0 = pd.read_csv(file, index_col=0, parse_dates=True)\n else:\n df0 = pd.DataFrame()\n df1 = pd.concat(v, axis=1).T\n df2 = pd.concat([df0, df1], axis=0)\n df2.to_csv(file)\n print(f\"Updated in `{self.conf['path'][k]}`\")\n print(f\"Updated {len(tds_todo)} rows in `{self.conf['path'][k]}`\")\n\n else:\n print(f\"Skip up-to-time: `{daily_path}`\")\n\n def combine_suspend(self):\n \"\"\"合并停牌信息，最后一天为复牌（R），之前连续的停牌（S），当日又停又复牌（SR or RS），默认NA\"\"\"\n cache_path = self.conf['path']['cache']\n src_path = cache_path + 'suspend_d/' # TODO: download cache path - suspend_d\n tgt_path = self.conf['path']['suspend_d']\n\n begin_date = self.infer_tds_todo(tgt_path, is1d=True)\n\n tds_todo = self.tds[self.tds >= begin_date]\n if len(tds_todo) > 0:\n cached = []\n for td in tqdm(tds_todo):\n s = pd.read_csv(f\"{src_path}{td}.csv\")[['ts_code', 'suspend_type']]\n s = s.groupby('ts_code').sum()\n s = s['suspend_type'].rename(pd.to_datetime(td, format='%Y%m%d'))\n cached.append(s)\n\n if os.path.exists(tgt_path):\n df0 = pd.read_csv(tgt_path, index_col=0, parse_dates=True)\n else:\n df0 = pd.DataFrame()\n df1 = pd.concat(cached, axis=1).T\n df1 = df1.stack()\n df1 = df1.reset_index()\n df1.columns = ['tradingdate', 'stockcode', 'suspend_d']\n df2 = pd.concat([df0, df1], axis=0)\n df2.to_csv(tgt_path, index=False)\n print(f\"Updated {len(tds_todo)} rows in `{tgt_path}`\")\n\n else:\n print(f\"Skip up-to-time: `{tgt_path}`\")\n\n def combine_stk_limit(self):\n \"\"\"合并每日的涨跌停价格\"\"\"\n src_path = self.conf['path']['cache'] + 'stk_limit/'\n begin_date = self.infer_tds_todo(self.conf['path']['down_limit'])\n\n tds_todo = self.tds[self.tds >= begin_date]\n if len(tds_todo) > 0:\n cached = {}\n for td in tqdm(self.tds[self.tds >= begin_date]):\n s = pd.read_csv(f\"{src_path}{td}.csv\", index_col=[1])\n td1 = pd.to_datetime(td, format='%Y%m%d')\n\n for c in ['up_limit', 'down_limit']:\n if c in cached:\n cached[c].append(s[c].rename(td1))\n else:\n cached[c] = [s[c].rename(td1)]\n\n for k, v in cached.items():\n tgt_path = self.conf['path'][k]\n df0 = pd.read_csv(tgt_path, index_col=0, parse_dates=True) \\\n if os.path.exists(tgt_path) else pd.DataFrame()\n df1 = pd.concat(cached[k], axis=1).T\n df2 = pd.concat([df0, df1], axis=0)\n df2.to_csv(tgt_path)\n print(f\"Updated {len(tds_todo)} rows in `{tgt_path}`\")\n\n else:\n print(f\"Skip up-to-time: `{src_path}`\")\n\n def infer_up_down_status(self, force_update_all=False):\n \"\"\"直接从日度价格（原始）和日度涨跌停上下界确定涨跌停状态 OU/OD/CU/CD\"\"\"\n\n tgt_path = self.conf['path']['updown_status']\n\n if force_update_all:\n tds_todo = self.tds\n else:\n tds_todo = self.tds[self.tds >= self.infer_tds_todo(tgt_path)]\n\n if len(tds_todo) > 0:\n cached = []\n for td in tqdm(tds_todo):\n df = pd.read_csv(f\"{self.conf['path']['cache']}daily/{td}.csv\", index_col=0)[['open', 'close']]\n po, pc = df['open'], df['close']\n df = pd.read_csv(f\"{self.conf['path']['cache']}stk_limit/{td}.csv\", index_col=1)[['up_limit', 'down_limit']]\n bu, bd = df['up_limit'], df['down_limit']\n del df\n\n open_up = po[po >= bu.reindex_like(po)] # TODO: =?\n open_down = po[po <= bd.reindex_like(po)]\n close_up = pc[pc >= bu.reindex_like(pc)]\n close_down = pc[pc <= bd.reindex_like(pc)]\n\n open_up[:] = 'OU'\n open_down[:] = 'OD'\n close_up[:] = 'CU'\n close_down[:] = 'CD'\n\n sr = pd.concat([\n open_up,\n open_down,\n close_up[close_up.index.difference(open_up.index)],\n close_down[close_down.index.difference(open_down.index)]\n ]).reset_index()\n sr['tradingdate'] = pd.to_datetime(td, format='%Y%m%d')\n sr.columns = ['stockcode', 'updown_status', 'tradingdate']\n sr = sr[['tradingdate', 'stockcode', 'updown_status']]\n sr = sr.sort_values('stockcode')\n\n cached.append(sr)\n\n if (not force_update_all) and (os.path.exists(tgt_path)):\n df0 = pd.read_csv(tgt_path, parse_dates=['tradingdate'])\n else:\n df0 = pd.DataFrame()\n df1 = pd.concat(cached, axis=0)\n df2 = pd.concat([df0, df1], axis=0)\n\n df2.to_csv(tgt_path, index=False)\n print(f\"Updated {len(tds_todo)} rows in `{tgt_path}`\")\n else:\n print(f\"Skip up-to-time: `{tgt_path}`\")\n\n def combine_daily_basic(self):\n \"\"\"合并`./cache/daily_basic/`中每日的基本面指标\"\"\"\n\n # Cache path\n cache_path = self.conf['path']['cache']\n # Source path\n basic_path = cache_path + 'daily_basic/'\n\n # Next date to update\n begin_date = self.infer_tds_todo(\n last_tgt_table=self.conf['path']['circ_mv'])\n\n tds_todo = self.tds[self.tds >= begin_date]\n if len(tds_todo) > 0:\n\n # Container for new daily data loaded\n cached = {}\n # Iterate for all new dates\n for td in tqdm(tds_todo):\n # Load one-day basic data\n d = pd.read_csv(f'{basic_path}/{td}.csv', index_col=0).loc[:, 'turnover_rate':]\n # Reorder by stock-code\n d.sort_index(inplace=True)\n\n # Cache one-day indices\n for cc in d.columns:\n sr = d[cc]\n sr.name = pd.to_datetime(td, format='%Y%m%d')\n if cc not in cached:\n cached[cc] = [sr]\n else:\n cached[cc].append(sr)\n\n # Concat and save\n for k, v in cached.items():\n file = self.conf['path'][k]\n\n if os.path.exists(file):\n df0 = pd.read_csv(file, index_col=0, parse_dates=True)\n else:\n df0 = pd.DataFrame()\n df1 = pd.concat(v, axis=1).T\n df2 = pd.concat([df0, df1], axis=0)\n df2.to_csv(file)\n\n print(f\"Updated {len(tds_todo)} rows in `{self.conf['path'][k]}`\")\n else:\n print(f\"Skip up-to-time: `{basic_path}`\")\n\n def generate_demo(self, length=30):\n \"\"\"将所有已有表格都生成demo作为示例数据\"\"\"\n for k, file in tqdm(self.conf['path'].items()):\n if file[-4:] != '.csv':\n continue\n pd.read_csv(file, index_col=0).tail(length).to_csv(\n self.conf['path']['cache_demo'] + file.rsplit('/', maxsplit=1)[-1])\n\n\ndef main():\n config_path = f'{_PATH}/config_stk.yaml'\n conf = load_tushare.conf_init(conf_path=config_path)\n if conf['status'] == 1:\n combiner = CacheCombiner(conf)\n combiner.combine_suspend()\n combiner.combine_st_status()\n combiner.combine_daily()\n combiner.combine_stk_limit()\n combiner.infer_up_down_status(force_update_all=False)\n combiner.combine_daily_basic()\n combiner.combine_index_weight()\n if conf['update_cache_demo'] == 1:\n combiner.generate_demo(length=10)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"wins-m/sys23","sub_path":"api/combine_tushare.py","file_name":"combine_tushare.py","file_ext":"py","file_size_in_byte":14943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"69809925334","text":"from RPi import GPIO\r\nfrom time import sleep\r\nimport requests\r\n\r\ndef getserial():\r\n # Extract serial from cpuinfo file\r\n cpuserial = \"0000000000000000\"\r\n try:\r\n f = open('/proc/cpuinfo','r')\r\n for line in f:\r\n if line[0:6]=='Serial':\r\n cpuserial = line[10:26]\r\n f.close()\r\n except:\r\n cpuserial = \"ERROR000000000\"\r\n return cpuserial\r\n\r\nurl = \"http://127.0.0.1:5000/\"\r\nmac = getserial()\r\n\r\ndef sendData(metros):\r\n response = requests.post(url, mac, metros) # mac, metros\r\n if response.ok:\r\n print(\"Upload completed successfully!\")\r\n print(response.text)\r\n else:\r\n print(\"Something went wrong!\")\r\n print(response.text)\r\n\r\n# GPIO pins\r\nclk = 17\r\ndt = 18\r\n\r\n# GPIO setup\r\nGPIO.setmode(GPIO.BCM)\r\nGPIO.setup(clk, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\r\nGPIO.setup(dt, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\r\n\r\ncounter = 0\r\nclkLastState = GPIO.input(clk)\r\n\r\ntry:\r\n while True:\r\n clkState = GPIO.input(clk)\r\n dtState = GPIO.input(dt)\r\n if clkState != clkLastState:\r\n if dtState != clkState:\r\n counter += 1\r\n counter2 = 0\r\n clkLastState = clkState\r\n sleep(0.0001)\r\n # if more than 5sec pass without a rotation, send data\r\n if counter2 > 500:\r\n metros = counter/1200 # 1200 pulses per meter\r\n sendData(metros)\r\n counter = 0 # reset rotation counter\r\n counter2 = 0\r\n else:\r\n counter2 += 1\r\nfinally:\r\n GPIO.cleanup() \r\n\r\n","repo_name":"ElMoha943/PackagingMachine","sub_path":"encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"2882054246","text":"import smtplib\r\nfrom email.mime.text import *\r\nbody='''hi re this is laharika i am sending this mail from my python program'''\r\nmsg=MIMEText(body)\r\nmsg['from']=\"luckyneelam97@gmail.com\"\r\nmsg['to']=\"143sarvani@gmail.com\"\r\nmsg['subject']=\"hi sarru\"\r\nserver=smtplib.SMTP('smtp.gmail.com',587)\r\nserver.starttls()\r\nserver.login(\"luckyneelam97@gmail.com\",\"143momanddad\")\r\nserver.send_message(msg)\r\nprint(\"message sent....\")\r\nserver.quit()","repo_name":"laharikaneelam/Python_exercises","sub_path":"mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"32333419781","text":"class MaquinaTuring:\n def __init__(self, estados, alfabeto, branco, estado_inicial, estado_aceitacao, estado_rejeicao, transicoes):\n self.estados = estados\n self.alfabeto = alfabeto\n self.branco = branco\n self.estado_inicial = estado_inicial\n self.estado_aceitacao = estado_aceitacao\n self.estado_rejeicao = estado_rejeicao\n self.transicoes = transicoes\n self.fitat = [branco]\n self.cabeca = 0\n self.estado_atual = estado_inicial\n\n def executar(self, entrada):\n self.fitat = [self.branco] + list(entrada) + [self.branco]\n self.cabeca = 1\n self.estado_atual = self.estado_inicial\n\n while self.estado_atual != self.estado_aceitacao and self.estado_atual != self.estado_rejeicao:\n simbolo_atual = self.fitat[self.cabeca]\n if (self.estado_atual, simbolo_atual) not in self.transicoes:\n break\n proximo_estado, simbolo_escrito, direcao_movimento = self.transicoes[(self.estado_atual, simbolo_atual)]\n self.fitat[self.cabeca] = simbolo_escrito\n if direcao_movimento == 'L':\n self.cabeca -= 1\n elif direcao_movimento == 'R':\n self.cabeca += 1\n self.estado_atual = proximo_estado\n\n return self.estado_atual == self.estado_aceitacao\n\n# Definindo estados, símbolos e funções de transição\nestados = {'q0', 'q1', 'q2', 'q3', 'q4'}\nalfabeto = {'0', '1', '#', 'X', '_'}\nbranco = '_'\nestado_inicial = 'q0'\nestado_aceitacao = 'q3'\nestado_rejeicao = 'q4'\ntransicoes = {\n ('q0', '0'): ('q1', 'X', 'R'),\n ('q0', '1'): ('q1', 'X', 'R'),\n ('q0', '#'): ('q2', '#', 'L'),\n ('q1', '0'): ('q1', '0', 'R'),\n ('q1', '1'): ('q1', '1', 'R'),\n ('q1', '#'): ('q2', '#', 'L'),\n ('q2', '#'): ('q3', '#', 'L'),\n ('q1', '_'): ('q4', '_', 'L'),\n ('q2', '_'): ('q4', '_', 'L'),\n ('q0', '_'): ('q3', '_', 'L')\n}\n\n# Criar uma instância da Máquina de Turing\nmt = MaquinaTuring(estados, alfabeto, branco, estado_inicial, estado_aceitacao, estado_rejeicao, transicoes)\n\n# Solicitar entrada ao usuário\nentrada = input(\"Digite a string para verificar se está na linguagem: \")\n\n# Executar a Máquina de Turing na entrada fornecida\nresultado = mt.executar(entrada)\n\n# Exibir o resultado\nif resultado:\n print(\"A entrada está na linguagem.\")\nelse:\n print(\"A entrada não está na linguagem.\")\n","repo_name":"Rayan-1/MTteoria","sub_path":"mt.py","file_name":"mt.py","file_ext":"py","file_size_in_byte":2423,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"21110193712","text":"from typing import Union, List, Dict\nfrom src.insights.jobs import read\n\n\ndef get_max_salary(path: str) -> int:\n data_list = read(path)\n all_jobs_max_salaries = []\n all_exceptions = []\n for data in data_list:\n try:\n max_salary = int(data[\"max_salary\"])\n except ValueError:\n all_exceptions.append(data[\"max_salary\"])\n else:\n all_jobs_max_salaries.append(max_salary)\n\n result = max(all_jobs_max_salaries)\n print(set(all_exceptions))\n return result\n\n\ndef get_min_salary(path: str) -> int:\n data_list = read(path)\n all_jobs_min_salaries = []\n all_exceptions = []\n for data in data_list:\n try:\n min_salary = int(data[\"min_salary\"])\n except ValueError:\n all_exceptions.append(data[\"min_salary\"])\n else:\n all_jobs_min_salaries.append(min_salary)\n\n result = min(all_jobs_min_salaries)\n print(set(all_exceptions))\n return result\n\n\ndef matches_salary_range(job: Dict, salary: Union[int, str]) -> bool:\n result = False\n try:\n min_salary = int(job[\"min_salary\"])\n max_salary = int(job[\"max_salary\"])\n salary_search = int(salary)\n max(min_salary, max_salary, salary_search)\n if (min_salary > max_salary):\n raise Exception\n except Exception:\n raise ValueError\n else:\n if (min_salary <= salary_search <= max_salary):\n result = True\n else:\n result = False\n return result\n\n\ndef filter_by_salary_range(\n jobs: List[dict],\n salary: Union[str, int]\n) -> List[Dict]:\n all_jobs_in_salary_range = []\n all_exceptions = []\n for job in jobs:\n try:\n is_in_salary_range = matches_salary_range(job, salary)\n except ValueError:\n all_exceptions.append([job, salary])\n else:\n if (is_in_salary_range):\n all_jobs_in_salary_range.append(job)\n print(all_exceptions)\n return all_jobs_in_salary_range\n","repo_name":"moisesfdasilva/app-job-insights","sub_path":"src/insights/salaries.py","file_name":"salaries.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"71398118613","text":"\nfrom tests.utils.runtest import automakesuite, run\n\nfrom tests.utils.allocators import GetAllocatingTestAllocator\nfrom tests.utils.memory import OffsetPtr, CreateTypes, PtrToStructure\nfrom tests.utils.testcase import TestCase, WithMapper\nfrom tests.utils.typetestcase import TypeTestCase\n\nfrom System import Array, Byte, Char, IntPtr, Type, UInt32\nfrom System.Runtime.InteropServices import Marshal\nfrom Ironclad import CPyMarshal, dgt_int_ptrintptr, dgt_int_ptrptr, dgt_ptr_ptrptr, PythonMapper\nfrom Ironclad.Structs import PyStringObject, PyTypeObject, PyBufferProcs, PySequenceMethods, Py_TPFLAGS\n\n\nclass PyString_TestCase(TestCase):\n\n def byteArrayFromString(self, testString):\n testLength = len(testString)\n chars = testString.ToCharArray()\n return Array.ConvertAll[Char, Byte](chars, lambda c: ord(c))\n\n\n def ptrFromByteArray(self, bytes):\n testData = Marshal.AllocHGlobal(bytes.Length + 1)\n Marshal.Copy(bytes, 0, testData, bytes.Length)\n Marshal.WriteByte(OffsetPtr(testData, bytes.Length), 0)\n return testData\n\n\n def dataPtrFromStrPtr(self, strPtr):\n return OffsetPtr(strPtr, Marshal.OffsetOf(PyStringObject, \"ob_sval\"))\n\n\n def fillStringDataWithBytes(self, strPtr, bytes):\n strDataPtr = self.dataPtrFromStrPtr(strPtr)\n Marshal.Copy(bytes, 0, strDataPtr, len(bytes))\n\n\n def getStringWithValues(self, start, pastEnd):\n return \"\".join(chr(c) for c in range(start, pastEnd))\n\n\n def assertHasStringType(self, ptr, mapper):\n self.assertEquals(CPyMarshal.ReadPtrField(ptr, PyStringObject, \"ob_type\"), mapper.PyString_Type, \"bad type\")\n\n\n def assertStringObjectHasLength(self, strPtr, length):\n stringObject = PtrToStructure(strPtr, PyStringObject)\n self.assertEquals(stringObject.ob_refcnt, 1, \"unexpected refcount\")\n self.assertEquals(stringObject.ob_size, length, \"unexpected ob_size\")\n self.assertEquals(stringObject.ob_shash, -1, \"unexpected currently-useless-field\")\n self.assertEquals(stringObject.ob_sstate, 0, \"unexpected currently-useless-field\")\n \n strDataPtr = self.dataPtrFromStrPtr(strPtr)\n terminatorPtr = OffsetPtr(strDataPtr, length)\n self.assertEquals(Marshal.ReadByte(terminatorPtr), 0, \"string not terminated\")\n\n\n def assertStringObjectHasDataBytes(self, strPtr, expectedBytes):\n strDataPtr = self.dataPtrFromStrPtr(strPtr)\n testLength = len(expectedBytes)\n writtenBytes = Array.CreateInstance(Byte, testLength)\n Marshal.Copy(strDataPtr, writtenBytes, 0, testLength)\n\n self.assertEquals(len(writtenBytes), testLength, \"copied wrong\")\n for (actual, expected) in zip(writtenBytes, expectedBytes):\n self.assertEquals(actual, expected, \"failed to copy string data correctly\")\n\n\nclass PyString_Type_Test(TypeTestCase):\n \n def testString_tp_free(self):\n self.assertUsual_tp_free(\"PyString_Type\")\n \n def testString_tp_dealloc(self):\n self.assertUsual_tp_dealloc(\"PyString_Type\")\n\n\n @WithMapper\n def testFlags(self, mapper, _):\n flags = CPyMarshal.ReadUIntField(mapper.PyString_Type, PyTypeObject, \"tp_flags\")\n self.assertEquals(flags & UInt32(Py_TPFLAGS.HAVE_GETCHARBUFFER), UInt32(Py_TPFLAGS.HAVE_GETCHARBUFFER))\n \n\n @WithMapper\n def testSizes(self, mapper, _):\n tp_basicsize = CPyMarshal.ReadIntField(mapper.PyString_Type, PyTypeObject, 'tp_basicsize')\n self.assertNotEquals(tp_basicsize, 0)\n tp_itemsize = CPyMarshal.ReadIntField(mapper.PyString_Type, PyTypeObject, 'tp_itemsize')\n self.assertNotEquals(tp_itemsize, 0)\n\n\n @WithMapper\n def testStringifiers(self, mapper, _):\n IC_PyString_Str = mapper.GetFuncPtr(\"IC_PyString_Str\")\n tp_str = CPyMarshal.ReadPtrField(mapper.PyString_Type, PyTypeObject, \"tp_str\")\n self.assertEquals(tp_str, IC_PyString_Str)\n \n PyObject_Repr = mapper.GetFuncPtr(\"PyObject_Repr\")\n tp_repr = CPyMarshal.ReadPtrField(mapper.PyString_Type, PyTypeObject, \"tp_repr\")\n self.assertEquals(tp_repr, PyObject_Repr)\n\n\n @WithMapper\n def testSequenceProtocol(self, mapper, _):\n strPtr = mapper.PyString_Type\n \n seqPtr = CPyMarshal.ReadPtrField(strPtr, PyTypeObject, 'tp_as_sequence')\n self.assertNotEquals(seqPtr, IntPtr.Zero)\n concatPtr = CPyMarshal.ReadPtrField(seqPtr, PySequenceMethods, 'sq_concat')\n # concat_core tested further down\n self.assertEquals(concatPtr, mapper.GetFuncPtr('IC_PyString_Concat_Core'))\n \n \n @WithMapper\n def testBufferProtocol(self, mapper, later):\n # should all be implemented in C really, but weaving cpy string type into\n # our code feels too much like hard work for now\n strPtr = mapper.PyString_Type\n \n bufPtr = CPyMarshal.ReadPtrField(strPtr, PyTypeObject, 'tp_as_buffer')\n self.assertNotEquals(bufPtr, IntPtr.Zero)\n getreadbuffer = CPyMarshal.ReadFunctionPtrField(bufPtr, PyBufferProcs, 'bf_getreadbuffer', dgt_int_ptrintptr)\n getwritebuffer = CPyMarshal.ReadFunctionPtrField(bufPtr, PyBufferProcs, 'bf_getwritebuffer', dgt_int_ptrintptr)\n getcharbuffer = CPyMarshal.ReadFunctionPtrField(bufPtr, PyBufferProcs, 'bf_getcharbuffer', dgt_int_ptrintptr)\n getsegcount = CPyMarshal.ReadFunctionPtrField(bufPtr, PyBufferProcs, 'bf_getsegcount', dgt_int_ptrptr)\n \n ptrptr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n later(lambda: Marshal.FreeHGlobal(ptrptr))\n \n strptr = mapper.Store(\"hullo\")\n for getter in (getreadbuffer, getcharbuffer):\n self.assertEquals(getter(strptr, 0, ptrptr), 5)\n self.assertEquals(CPyMarshal.ReadPtr(ptrptr), CPyMarshal.GetField(strptr, PyStringObject, 'ob_sval'))\n self.assertEquals(getter(strptr, 1, ptrptr), -1)\n self.assertMapperHasError(mapper, SystemError)\n \n self.assertEquals(getwritebuffer(strptr, 0, ptrptr), -1)\n self.assertMapperHasError(mapper, SystemError)\n \n self.assertEquals(getsegcount(strptr, ptrptr), 1)\n self.assertEquals(CPyMarshal.ReadInt(ptrptr), 5)\n self.assertEquals(getsegcount(strptr, IntPtr.Zero), 1)\n\n\nclass PyString_FromString_Test(PyString_TestCase):\n\n def testCreatesString(self):\n allocs = []\n mapper = PythonMapper(GetAllocatingTestAllocator(allocs, []))\n deallocTypes = CreateTypes(mapper)\n del allocs[:]\n testString = \"beset on all sides\" + self.getStringWithValues(1, 256)\n bytes = self.byteArrayFromString(testString)\n testData = self.ptrFromByteArray(bytes)\n try:\n strPtr = mapper.PyString_FromString(testData)\n baseSize = Marshal.SizeOf(PyStringObject())\n self.assertEquals(allocs, [(strPtr, len(bytes) + baseSize)], \"allocated wrong\")\n self.assertStringObjectHasLength(strPtr, len(bytes))\n self.assertStringObjectHasDataBytes(strPtr, bytes)\n self.assertEquals(mapper.Retrieve(strPtr), testString, \"failed to map pointer correctly\")\n finally:\n mapper.Dispose()\n Marshal.FreeHGlobal(testData)\n deallocTypes()\n\n\nclass PyString_Concat_Test(PyString_TestCase):\n\n @WithMapper\n def testBasic(self, mapper, addToCleanup):\n part1Ptr = mapper.Store(\"one two\")\n mapper.IncRef(part1Ptr) # avoid garbage collection\n part2Ptr = mapper.Store(\" three\")\n startingRefCnt = mapper.RefCount(part1Ptr)\n \n stringPtrPtr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n addToCleanup(lambda: Marshal.FreeHGlobal(stringPtrPtr))\n \n Marshal.WriteIntPtr(stringPtrPtr, part1Ptr)\n mapper.PyString_Concat(stringPtrPtr, part2Ptr)\n self.assertMapperHasError(mapper, None)\n \n\n newStringPtr = Marshal.ReadIntPtr(stringPtrPtr)\n self.assertEquals(mapper.Retrieve(newStringPtr), \"one two three\")\n\n self.assertEquals(startingRefCnt - mapper.RefCount(part1Ptr), 1)\n\n\n @WithMapper\n def testErrorCaseSecondArg(self, mapper, addToCleanup):\n part1Ptr = mapper.Store(\"one two\")\n mapper.IncRef(part1Ptr) # avoid garbage collection\n startingRefCnt = mapper.RefCount(part1Ptr)\n \n part2Ptr = mapper.Store(3)\n stringPtrPtr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n addToCleanup(lambda: Marshal.FreeHGlobal(stringPtrPtr))\n \n Marshal.WriteIntPtr(stringPtrPtr, part1Ptr)\n mapper.PyString_Concat(stringPtrPtr, part2Ptr)\n self.assertMapperHasError(mapper, TypeError)\n\n self.assertEquals(Marshal.ReadIntPtr(stringPtrPtr), IntPtr(0))\n self.assertEquals(startingRefCnt - mapper.RefCount(part1Ptr), 1)\n\n\n @WithMapper\n def testErrorCaseSecondArg(self, mapper, addToCleanup):\n part1Ptr = mapper.Store(17)\n mapper.IncRef(part1Ptr) # avoid garbage collection\n startingRefCnt = mapper.RefCount(part1Ptr)\n\n part2Ptr = mapper.Store(\"three\")\n stringPtrPtr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n addToCleanup(lambda: Marshal.FreeHGlobal(stringPtrPtr))\n \n Marshal.WriteIntPtr(stringPtrPtr, part1Ptr)\n mapper.PyString_Concat(stringPtrPtr, part2Ptr)\n self.assertMapperHasError(mapper, TypeError)\n\n self.assertEquals(Marshal.ReadIntPtr(stringPtrPtr), IntPtr(0))\n self.assertEquals(startingRefCnt - mapper.RefCount(part1Ptr), 1)\n\n\nclass PyString_ConcatAndDel_Test(PyString_TestCase):\n\n @WithMapper\n def testBasic(self, mapper, addToCleanup):\n part1Ptr = mapper.Store(\"one two\")\n mapper.IncRef(part1Ptr) # avoid garbage collection\n startingPart1RefCnt = mapper.RefCount(part1Ptr)\n \n part2Ptr = mapper.Store(\" three\")\n mapper.IncRef(part2Ptr) # avoid garbage collection\n startingPart2RefCnt = mapper.RefCount(part2Ptr)\n\n stringPtrPtr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n addToCleanup(lambda: Marshal.FreeHGlobal(stringPtrPtr))\n \n Marshal.WriteIntPtr(stringPtrPtr, part1Ptr)\n mapper.PyString_ConcatAndDel(stringPtrPtr, part2Ptr)\n self.assertMapperHasError(mapper, None)\n\n newStringPtr = Marshal.ReadIntPtr(stringPtrPtr)\n self.assertEquals(mapper.Retrieve(newStringPtr), \"one two three\")\n\n self.assertEquals(startingPart1RefCnt - mapper.RefCount(part1Ptr), 1)\n self.assertEquals(startingPart2RefCnt - mapper.RefCount(part2Ptr), 1)\n \n\n\nclass InternTest(PyString_TestCase):\n \n @WithMapper\n def testInternExisting(self, mapper, addToCleanUp):\n testString = \"mars needs women\" + self.getStringWithValues(1, 256)\n bytes = self.byteArrayFromString(testString)\n testData = self.ptrFromByteArray(bytes)\n \n sp1 = mapper.PyString_FromString(testData)\n addToCleanUp(lambda: Marshal.FreeHGlobal(sp1p))\n\n sp2 = mapper.PyString_InternFromString(testData)\n addToCleanUp(lambda: Marshal.FreeHGlobal(testData))\n\n self.assertNotEquals(sp1, sp2)\n self.assertFalse(mapper.Retrieve(sp1) is mapper.Retrieve(sp2))\n self.assertEquals(mapper.RefCount(sp1), 1)\n self.assertEquals(mapper.RefCount(sp2), 2, 'failed to grab extra reference to induce immortality')\n \n mapper.IncRef(sp1)\n sp1p = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n CPyMarshal.WritePtr(sp1p, sp1)\n mapper.PyString_InternInPlace(sp1p)\n sp1i = CPyMarshal.ReadPtr(sp1p)\n self.assertEquals(sp1i, sp2, 'failed to intern')\n self.assertTrue(mapper.Retrieve(sp1i) is mapper.Retrieve(sp2))\n self.assertEquals(mapper.RefCount(sp1), 1, 'failed to decref old string')\n self.assertEquals(mapper.RefCount(sp2), 3, 'failed to incref interned string')\n\n\n\nclass PyString_FromStringAndSize_Test(PyString_TestCase):\n\n def testCreateEmptyString(self):\n allocs = []\n mapper = PythonMapper(GetAllocatingTestAllocator(allocs, []))\n deallocTypes = CreateTypes(mapper)\n del allocs[:]\n\n try:\n testString = \"we run the grease racket in this town\" + self.getStringWithValues(0, 256)\n testLength = len(testString)\n strPtr = mapper.PyString_FromStringAndSize(IntPtr.Zero, testLength)\n baseSize = Marshal.SizeOf(PyStringObject())\n self.assertEquals(allocs, [(strPtr, testLength + baseSize)], \"allocated wrong\")\n self.assertStringObjectHasLength(strPtr, testLength)\n self.assertHasStringType(strPtr, mapper)\n testBytes = self.byteArrayFromString(testString)\n self.fillStringDataWithBytes(strPtr, testBytes)\n\n resultStr = mapper.Retrieve(strPtr)\n self.assertEquals(resultStr, testString, \"failed to read string data\")\n \n strPtr2 = mapper.Store(resultStr)\n self.assertEquals(strPtr2, strPtr, \"did not remember already had this string\")\n self.assertEquals(mapper.RefCount(strPtr), 2, \"did not incref on store\")\n finally:\n mapper.Dispose()\n deallocTypes()\n\n\n def testCreateStringWithData(self):\n allocs = []\n mapper = PythonMapper(GetAllocatingTestAllocator(allocs, []))\n deallocTypes = CreateTypes(mapper)\n del allocs[:]\n\n try:\n testString = \"we also run the shovel racket\" + self.getStringWithValues(0, 256)\n testBytes = self.byteArrayFromString(testString)\n testData = self.ptrFromByteArray(testBytes)\n testLength = len(testString)\n\n strPtr = mapper.PyString_FromStringAndSize(testData, testLength)\n baseSize = Marshal.SizeOf(PyStringObject())\n self.assertEquals(allocs, [(strPtr, testLength + baseSize)], \"allocated wrong\")\n self.assertHasStringType(strPtr, mapper)\n self.assertStringObjectHasLength(strPtr, testLength)\n self.assertStringObjectHasDataBytes(strPtr, testBytes)\n self.assertEquals(mapper.Retrieve(strPtr), testString, \"failed to read string data\")\n finally:\n mapper.Dispose()\n deallocTypes()\n\n\nclass _PyString_Resize_Test(PyString_TestCase):\n\n def testErrorHandling(self):\n allocs = []\n frees = []\n mapper = PythonMapper(GetAllocatingTestAllocator(allocs, frees))\n deallocTypes = CreateTypes(mapper)\n del allocs[:]\n ptrPtr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n\n try:\n data = mapper.PyString_FromStringAndSize(IntPtr.Zero, 365)\n Marshal.WriteIntPtr(ptrPtr, data)\n baseSize = Marshal.SizeOf(PyStringObject())\n self.assertEquals(allocs, [(data, 365 + baseSize)], \"allocated wrong\")\n self.assertEquals(mapper._PyString_Resize(ptrPtr, 2000000000), -1, \"bad return on error\")\n self.assertEquals(type(mapper.LastException), MemoryError, \"wrong exception type\")\n self.assertTrue(data in frees, \"did not deallocate\") \n finally:\n mapper.Dispose()\n Marshal.FreeHGlobal(ptrPtr)\n deallocTypes()\n\n\n def testShrink(self):\n allocs = []\n frees = []\n mapper = PythonMapper(GetAllocatingTestAllocator(allocs, frees))\n deallocTypes = CreateTypes(mapper)\n del allocs[:]\n\n oldLength = 365\n newLength = 20\n ptrPtr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n\n try:\n strPtr = mapper.PyString_FromStringAndSize(IntPtr.Zero, oldLength)\n Marshal.WriteIntPtr(ptrPtr, strPtr)\n \n baseSize = Marshal.SizeOf(PyStringObject())\n self.assertEquals(allocs, [(strPtr, oldLength + baseSize)], \"allocated wrong\")\n self.assertEquals(mapper._PyString_Resize(ptrPtr, newLength), 0, \"bad return on success\")\n \n self.assertHasStringType(strPtr, mapper)\n self.assertStringObjectHasLength(strPtr, newLength)\n\n self.assertEquals(allocs, [(strPtr, oldLength + baseSize)], \"unexpected extra alloc\")\n self.assertEquals(frees, [], \"unexpected frees\")\n finally:\n mapper.Dispose()\n Marshal.FreeHGlobal(ptrPtr)\n deallocTypes()\n\n\n def testGrow(self):\n allocs = []\n frees = []\n mapper = PythonMapper(GetAllocatingTestAllocator(allocs, frees))\n deallocTypes = CreateTypes(mapper)\n del allocs[:]\n\n oldLength = 20\n testString = \"slings and arrows\" + self.getStringWithValues(0, 256)\n newLength = len(testString)\n\n oldStrPtr = mapper.PyString_FromStringAndSize(IntPtr.Zero, oldLength)\n ptrPtr = Marshal.AllocHGlobal(Marshal.SizeOf(IntPtr()))\n \n try:\n Marshal.WriteIntPtr(ptrPtr, oldStrPtr)\n newStrPtr = IntPtr.Zero\n \n baseSize = Marshal.SizeOf(PyStringObject())\n self.assertEquals(allocs, [(oldStrPtr, oldLength + baseSize)], \"allocated wrong\")\n self.assertEquals(mapper._PyString_Resize(ptrPtr, newLength), 0, \"bad return on success\")\n\n newStrPtr = Marshal.ReadIntPtr(ptrPtr)\n expectedAllocs = [(oldStrPtr, oldLength + baseSize), (newStrPtr, newLength + baseSize)]\n self.assertEquals(allocs, expectedAllocs,\n \"allocated wrong\")\n self.assertEquals(frees, [oldStrPtr], \"did not free unused memory\")\n\n self.assertHasStringType(newStrPtr, mapper)\n self.assertStringObjectHasLength(newStrPtr, newLength)\n\n testBytes = self.byteArrayFromString(testString)\n self.fillStringDataWithBytes(newStrPtr, testBytes)\n\n self.assertEquals(mapper.Retrieve(newStrPtr), testString, \"failed to read string data\")\n if oldStrPtr != newStrPtr:\n # this would otherwise fail (very, very rarely)\n self.assertEquals(oldStrPtr in frees, True)\n finally:\n mapper.Dispose()\n Marshal.FreeHGlobal(ptrPtr)\n deallocTypes()\n \n\nclass PyString_Size_Test(PyString_TestCase):\n \n @WithMapper\n def testWorks(self, mapper, _):\n testString = \"Oh, sure, Lisa -- some wonderful, magical animal.\" + self.getStringWithValues(0, 256)\n testLength = len(testString)\n \n strPtr = mapper.Store(testString)\n self.assertEquals(mapper.PyString_Size(strPtr), testLength)\n\n\nclass PyString_OtherMethodsTest(TestCase):\n \n @WithMapper\n def testStringifiers(self, mapper, _):\n src = 'foo \\0 bar \" \\' \" \\' supercalifragilisticexpialidocious'\n srcPtr = mapper.Store(src)\n \n str_ = mapper.Retrieve(mapper.IC_PyString_Str(srcPtr))\n self.assertEquals(str_, src)\n self.assertEquals(mapper.IC_PyString_Str(mapper.Store(object())), IntPtr.Zero)\n self.assertMapperHasError(mapper, TypeError)\n \n for smartquotes in (0, 1):\n # smartquotes is ignored for now\n repr_ = mapper.Retrieve(mapper.PyString_Repr(srcPtr, smartquotes))\n self.assertEquals(repr_, repr(src))\n self.assertEquals(mapper.PyString_Repr(mapper.Store(object()), smartquotes), IntPtr.Zero)\n self.assertMapperHasError(mapper, TypeError)\n \n @WithMapper\n def testConcat(self, mapper, _):\n strs = ('', 'abc', '\\0xo')\n for s1 in strs:\n for s2 in strs:\n s3ptr = mapper.IC_PyString_Concat_Core(mapper.Store(s1), mapper.Store(s2))\n self.assertEquals(mapper.Retrieve(s3ptr), s1 + s2)\n\n\nclass PyString_AsStringTest(PyString_TestCase):\n \n @WithMapper\n def testWorks(self, mapper, _):\n strPtr = mapper.Store(\"You're fighting a business hippy. This is a hippy that understands the law of supply and demand.\")\n strData = CPyMarshal.Offset(strPtr, Marshal.OffsetOf(PyStringObject, 'ob_sval'))\n self.assertEquals(mapper.PyString_AsString(strPtr), self.dataPtrFromStrPtr(strPtr))\n \n notstrPtr = mapper.Store(object())\n self.assertEquals(mapper.PyString_AsString(notstrPtr), IntPtr.Zero)\n self.assertMapperHasError(mapper, TypeError)\n\n\n @WithMapper\n def testDoesNotActualiseString(self, mapper, _):\n testString = \"She's the oldest planet-cracker in existence\"\n strPtr = mapper.PyString_FromStringAndSize(IntPtr.Zero, len(testString))\n \n self.fillStringDataWithBytes(strPtr, self.byteArrayFromString(\"blah blah nonsense blah\"))\n mapper.PyString_AsString(strPtr) # this should NOT bake the string data\n self.fillStringDataWithBytes(strPtr, self.byteArrayFromString(testString))\n \n self.assertEquals(mapper.Retrieve(strPtr), testString)\n\n\nclass PyString_AsStringAndSizeTest(PyString_TestCase):\n \n @WithMapper\n def testWorksWithEmbeddedNulls(self, mapper, addDealloc):\n dataPtrPtr = Marshal.AllocHGlobal(CPyMarshal.PtrSize * 2)\n sizePtr = CPyMarshal.Offset(dataPtrPtr, CPyMarshal.PtrSize)\n addDealloc(lambda: Marshal.FreeHGlobal(dataPtrPtr))\n \n testStr = \"You're fighting a saber-toothed ferret.\" + self.getStringWithValues(0, 256)\n strPtr = mapper.Store(testStr)\n dataPtr = self.dataPtrFromStrPtr(strPtr)\n self.assertEquals(mapper.PyString_AsStringAndSize(strPtr, dataPtrPtr, sizePtr), 0)\n self.assertEquals(CPyMarshal.ReadPtr(dataPtrPtr), dataPtr)\n self.assertEquals(CPyMarshal.ReadInt(sizePtr), len(testStr))\n self.assertMapperHasError(mapper, None)\n \n self.assertEquals(mapper.PyString_AsStringAndSize(strPtr, dataPtrPtr, IntPtr.Zero), -1)\n self.assertMapperHasError(mapper, TypeError)\n \n \n @WithMapper\n def testWorksWithoutEmbeddedNulls(self, mapper, addDealloc):\n dataPtrPtr = Marshal.AllocHGlobal(CPyMarshal.PtrSize * 2)\n sizePtr = CPyMarshal.Offset(dataPtrPtr, CPyMarshal.PtrSize)\n addDealloc(lambda: Marshal.FreeHGlobal(dataPtrPtr))\n \n testStr = \"You're fighting Ed the Undying.\" + self.getStringWithValues(1, 256)\n strPtr = mapper.Store(testStr)\n dataPtr = self.dataPtrFromStrPtr(strPtr)\n self.assertEquals(mapper.PyString_AsStringAndSize(strPtr, dataPtrPtr, sizePtr), 0)\n self.assertEquals(CPyMarshal.ReadPtr(dataPtrPtr), dataPtr)\n self.assertEquals(CPyMarshal.ReadInt(sizePtr), len(testStr))\n self.assertMapperHasError(mapper, None)\n \n CPyMarshal.Zero(dataPtrPtr, CPyMarshal.PtrSize * 2)\n self.assertEquals(mapper.PyString_AsStringAndSize(strPtr, dataPtrPtr, IntPtr.Zero), 0)\n self.assertEquals(CPyMarshal.ReadPtr(dataPtrPtr), dataPtr)\n self.assertMapperHasError(mapper, None)\n\n \n @WithMapper\n def testWorksWithNonString(self, mapper, addDealloc):\n dataPtrPtr = Marshal.AllocHGlobal(CPyMarshal.PtrSize * 2)\n sizePtr = CPyMarshal.Offset(dataPtrPtr, CPyMarshal.PtrSize)\n addDealloc(lambda: Marshal.FreeHGlobal(dataPtrPtr))\n \n self.assertEquals(mapper.PyString_AsStringAndSize(mapper.Store(object()), dataPtrPtr, sizePtr), -1)\n self.assertMapperHasError(mapper, TypeError)\n\n\n @WithMapper\n def testDoesNotActualiseString(self, mapper, addDealloc):\n dataPtrPtr = Marshal.AllocHGlobal(CPyMarshal.PtrSize * 2)\n sizePtr = CPyMarshal.Offset(dataPtrPtr, CPyMarshal.PtrSize)\n addDealloc(lambda: Marshal.FreeHGlobal(dataPtrPtr))\n \n testString = \"You find a frozen Mob Penguin.\"\n strPtr = mapper.PyString_FromStringAndSize(IntPtr.Zero, len(testString))\n \n self.fillStringDataWithBytes(strPtr, self.byteArrayFromString(\"blah blah nonsense\"))\n mapper.PyString_AsStringAndSize(strPtr, dataPtrPtr, sizePtr) # this should NOT bake the string data\n self.fillStringDataWithBytes(strPtr, self.byteArrayFromString(testString))\n \n self.assertEquals(mapper.Retrieve(strPtr), testString)\n \n\nclass PyStringStoreTest(PyString_TestCase):\n \n def testStoreStringCreatesStringType(self):\n allocs = []\n mapper = PythonMapper(GetAllocatingTestAllocator(allocs, []))\n deallocTypes = CreateTypes(mapper)\n del allocs[:]\n\n testString = \"fnord\" + self.getStringWithValues(1, 256)\n testBytes = self.byteArrayFromString(testString)\n testData = self.ptrFromByteArray(testBytes)\n testLength = len(testString)\n\n try:\n strPtr = mapper.Store(testString)\n baseSize = Marshal.SizeOf(PyStringObject())\n \n self.assertEquals(allocs, [(strPtr, testLength + baseSize)], \"allocated wrong\")\n self.assertHasStringType(strPtr, mapper)\n self.assertStringObjectHasLength(strPtr, testLength)\n self.assertStringObjectHasDataBytes(strPtr, testBytes)\n self.assertEquals(mapper.Retrieve(strPtr), testString, \"failed to read string data\")\n \n strPtr2 = mapper.Store(testString)\n self.assertEquals(strPtr2, strPtr, \"did not remember already had this string\")\n self.assertEquals(mapper.RefCount(strPtr), 2, \"did not incref on store\")\n finally:\n mapper.Dispose()\n deallocTypes()\n\nsuite = automakesuite(locals())\n\nif __name__ == '__main__':\n run(suite)\n","repo_name":"IronLanguages/ironclad","sub_path":"tests/stringtest.py","file_name":"stringtest.py","file_ext":"py","file_size_in_byte":25409,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"67"}
+{"seq_id":"71962274133","text":"# 내가 푼 풀이\ndef binary_search(element, some_list, start_index=0, end_index=None):\n # end_index가 따로 주어지지 않은 경우에는 리스트의 마지막 인덱스\n if end_index == None:\n end_index = len(some_list) - 1\n\n # 코드를 작성하세요.\n midpoint = (start_index + end_index) // 2\n if start_index > end_index:\n return None\n if some_list[midpoint] == element:\n return midpoint\n elif some_list[midpoint] > element:\n return binary_search(element, some_list[:midpoint])\n elif some_list[midpoint] < element:\n return midpoint + 1 + binary_search(element, some_list[midpoint+1:])\n\nprint(binary_search(2, [2, 3, 5, 7, 11]))\nprint(binary_search(0, [2, 3, 5, 7, 11]))\nprint(binary_search(5, [2, 3, 5, 7, 11]))\nprint(binary_search(3, [2, 3, 5, 7, 11]))\nprint(binary_search(11, [2, 3, 5, 7, 11]))\n\n# 내가 푼 풀이는 재귀로 들어가는 리스트 자체를 슬라이싱하고 인덱스 값을 더해주면서 반복시키는 것이고\n# 풀이에서의 방법은, 스타트인덱스와 엔드인덱스를 직접 지정해주면서 따로 인덱스값은 저장하지 않는 풀이방법이다.\n\n# 공식 정답\n\ndef binary_search(element, some_list, start_index=0, end_index=None):\n # end_index가 따로 주어지지 않은 경우에는 리스트의 마지막 인덱스\n if end_index == None:\n end_index = len(some_list) - 1\n\n # start_index가 end_index보다 크면 some_list안에 element는 없다\n if start_index > end_index:\n return None\n\n # 범위의 중간 인덱스를 찾는다\n mid = (start_index + end_index) // 2\n\n # 이 인덱스의 값이 찾는 값인지 확인을 해준다\n if some_list[mid] == element:\n return mid\n\n # 찾는 항목이 중간 값보다 작으면 리스트 왼쪽을 탐색해준다\n if element < some_list[mid]:\n return binary_search(element, some_list, start_index, mid - 1)\n\n # 찾는 항목이 중간 값보다 크면 리스트 오른쪽을 탐색해준다\n else:\n return binary_search(element, some_list, mid + 1, end_index)\n\n\nprint(binary_search(2, [2, 3, 5, 7, 11]))\nprint(binary_search(0, [2, 3, 5, 7, 11]))\nprint(binary_search(5, [2, 3, 5, 7, 11]))\nprint(binary_search(3, [2, 3, 5, 7, 11]))\nprint(binary_search(11, [2, 3, 5, 7, 11]))","repo_name":"OhJeongHo/Python_algorithm_study","sub_path":"알고리즘/07. 이진탐색 재귀로 구현하기.py","file_name":"07. 이진탐색 재귀로 구현하기.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"7101972981","text":"import network\nimport rp2\nimport time\nimport socket\n\n\nrp2.country(\"CN\")\nwlan=network.WLAN(network.STA_IF)\nwlan.active(True)\nprint(wlan.scan())\n\n\nssid=\"Schubert\"\npw=\"xinxianshuo\"\nhost=\"10.0.0.1\"\nport=\"55551\"\n\n\ndef light_onboard_led(led_flag):\n led=machine.Pin(\"LED\",machine.Pin.OUT)\n if led_flag==1:\n led.on()\n elif led_flag==0:\n led.off()\n\ndef connect():\n wlan.connect(ssid,pw)\n timeout=10\n while timeout>0:\n if wlan.status()>=3:\n light_onboard_led(1)\n print(\"conntected to WIFI SSID: Schubert\")\n return 0\n timeout -=1\n print(\"waiting for connction to [Schubert]\")\n time.sleep(1)\n light_onboard_led(0) \n print(\"connection failed\")\n\nconnect()\n\n","repo_name":"anduinriver/pico-pilot","sub_path":"wifi.py","file_name":"wifi.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"19043143909","text":"# -*- coding: UTF-8 -*-\nimport math\n\nimport cv2\nimport numpy as np\n\nfrom tools.DecoratorTools import calculate_time\nfrom tools.ImageOperate import clahe_equalize\n\n\ndef compute_overlap(x1, y1, w1, h1, x2, y2, w2, h2):\n\t# 计算矩形框的面积\n\tarea1 = w1 * h1\n\tarea2 = w2 * h2\n\t# 计算相交部分的坐标\n\tx_inter = max(x1, x2)\n\ty_inter = max(y1, y2)\n\tw_inter = min(x1 + w1, x2 + w2) - x_inter\n\th_inter = min(y1 + h1, y2 + h2) - y_inter\n\t# 计算相交部分的面积\n\tif w_inter <= 0 or h_inter <= 0:\n\t\treturn 0.0\n\tarea_inter = w_inter * h_inter\n\t# 计算并集的面积\n\tarea_union = area1 + area2 - area_inter\n\t# 计算重叠度\n\toverlap = area_inter / area_union\n\treturn overlap\n\n\ndef rotate_and_scale(image, angle, scale):\n\t\"\"\"\n\t旋转和比例不变变换\n\t:param image: 输入图像\n\t:param angle: 旋转角度\n\t:param scale: 缩放比例\n\t:return:\n\t\"\"\"\n\timg_resized = cv2.resize(image, (int(scale * image.shape[1]), int(scale * image.shape[0])))\n\trows, cols = img_resized.shape[:2]\n\t# 构造旋转变换矩阵\n\tM = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)\n\tcols_new = int(cols * math.cos(angle) + rows * math.sin(angle))\n\trows_new = int(cols * math.sin(angle) + rows * math.cos(angle))\n\t# 进行旋转变换\n\timg_rotated = cv2.warpAffine(img_resized, M, (cols_new, rows_new))\n\treturn img_rotated\n\n\n@calculate_time\ndef template_match_multi(image, template, angle_step: int = 30, scale_start: float = 0.5, scale_stop: float = 1.0, scale_step: float = 0.2, similarity_threshold: float = 1.0, overlap_threshold=0.3):\n\t\"\"\"\n\t:param image:\n\t:param template:\n\t:param angle_step: 旋转步长\n\t:param scale_start: 放缩起始\n\t:param scale_stop: 放缩中止\n\t:param scale_step: 放缩步长\n\t:param similarity_threshold:相似度阈值\n\t:param overlap_threshold: 重叠阈值\n\t:return:\n\t\"\"\"\n\t# 获取模板图像的大小\n\tth, tw = template.shape[:2]\n\t# 初始化最大匹配度和对应的位置\n\tmatches = []\n\t# 保存保留的检测框索引\n\t# 在一定范围内进行旋转和比例不变变换，并计算相似度\n\tfor angle in range(0, 360, angle_step):\n\t\tfor scale in np.arange(scale_start, scale_stop, scale_step):\n\t\t\t# 进行旋转和比例不变变换\n\t\t\ttemplate_rotated = rotate_and_scale(template, angle, scale)\n\t\t\t# 计算相似度\n\t\t\tresult = cv2.matchTemplate(image, template_rotated, cv2.TM_CCOEFF_NORMED)\n\t\t\t# 找到所有符合阈值的匹配位置\n\t\t\tlocs = np.where(result <= similarity_threshold * result.min())\n\t\t\t# 将匹配结果保存到matches列表中\n\t\t\tfor pt in zip(*locs):\n\t\t\t\tmatches.append([pt, result[pt[0], pt[1]], angle, scale])\n\t\n\t# 对matches按照相似度从大到小排序\n\tmatches = sorted(matches, key=lambda x: x[1], reverse=False)\n\t\n\t# 取出得分最高的检测框\n\tmax_loc = matches[0][0]\n\tcv2.rectangle(image, (max_loc[1], max_loc[0]), (max_loc[1] + tw, max_loc[0] + th), (0, 0, 255), 2)\n\t\n\t# 遍历matches列表，画出符合重叠度要求的检测框\n\tfor match in matches:\n\t\tpt = match[0]\n\t\toverlap = compute_overlap(max_loc[1], max_loc[0], tw, th, pt[1], pt[0], tw, th)\n\t\tif overlap <= overlap_threshold:\n\t\t\tcv2.rectangle(image, (pt[1], pt[0]), (pt[1] + tw, pt[0] + th), (0, 0, 255), 2)\n\t\n\treturn image\n\n\ndef template_match_sift(image, template, threshold=0.75, draw_result=True):\n\t\"\"\"\n\t:param image:\n\t:param template:\n\t:param threshold:\n\t:param draw_result:\n\t:return:\n\t\"\"\"\n\t\n\t# Convert images to grayscale\n\tgray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\tgray_template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)\n\t\n\t# Initialize SIFT detector and FLANN matcher\n\tsift = cv2.xfeatures2d.SIFT_create()\n\tmatcher = cv2.FlannBasedMatcher_create()\n\t\n\t# Extract key_points and descriptors from template and image\n\tkey_points_template, descriptors_template = sift.detectAndCompute(gray_template, None)\n\tkey_points_image, descriptors_image = sift.detectAndCompute(gray_image, None)\n\t\n\t# Match descriptors using FLANN matcher\n\tmatches = matcher.knnMatch(descriptors_template, descriptors_image, k=2)\n\t\n\t# Filter matches by Lowe's ratio test\n\tgood_matches = []\n\tfor m, n in matches:\n\t\tif m.distance < 0.4 * n.distance:\n\t\t\tgood_matches.append(m)\n\t\n\t# Compute homography matrix using RANSAC algorithm\n\tsrc_pts = np.float32([key_points_template[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n\tdst_pts = np.float32([key_points_image[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)\n\tM, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)\n\t\n\t# Apply perspective transform to template\n\th, w = template.shape[:2]\n\ttemplate_warped = cv2.warpPerspective(template, M, (w, h))\n\t\n\t# Match warped template to image using normalized cross-correlation\n\tresult = cv2.matchTemplate(gray_image, cv2.cvtColor(template_warped, cv2.COLOR_BGR2GRAY), cv2.TM_CCOEFF_NORMED)\n\t\n\t# Find locations of matched regions above threshold\n\tlocations = np.where(result >= threshold)\n\tlocations = list(zip(*locations[::-1]))\n\t\n\t# Draw bounding boxes around matched regions\n\tif draw_result:\n\t\tfor pt in locations:\n\t\t\tcv2.rectangle(image, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)\n\t\n\treturn image, locations\n\n\n# 读取目标图像和模板图像\ntarget_img = cv2.imread(r\"D:\\fy.xie\\fenx\\fenx - General\\Ubei\\Test_Label1\\Defect_008.png\")\ntemplate_img = cv2.imread(r\"D:\\fy.xie\\fenx\\fenx - General\\Ubei\\Test_Label1\\template.png\")\ntarget_img = clahe_equalize(target_img)\nimg = template_match_multi(target_img, template_img, 180, 0.5, 1.0, 0.2, 1.0, 0.1)\n# img,_ = template_match_sift(target_img, template_img)\ncv2.namedWindow(\"Barcode Detection\", cv2.WINDOW_NORMAL)\n\ncv2.imshow(\"Barcode Detection\", img)\nif cv2.waitKey(0) == 27:\n\tcv2.destroyAllWindows()\n","repo_name":"VanceXie/codeScan","sub_path":"location/Ineffective/templeMatch_Ineffective.py","file_name":"templeMatch_Ineffective.py","file_ext":"py","file_size_in_byte":5615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"43731891207","text":"import discord\nimport os\nimport random\nimport asyncio\nfrom discord.ext.commands import Bot\nimport keep_alive\nfrom discord.utils import get\n\ntoken = os.environ.get('BOT_TOKEN')\nbot = discord.Client()\n\n@bot.event\nasync def on_message(message):\n if(message.content == \"!verify\"):\n guild = message.guild\n embed = discord.Embed(title=\"Verification\", description=\"A new channel has been created for your verification. Welcome!\", color=0xFF5733)\n await message.channel.send(embed=embed)\n rand = random.randint(1000, 9999)\n global channelname\n channelname = \"user-\" + str(rand)\n global verify_role\n verify_role = await guild.create_role(name = channelname)\n global verified\n verified = get(guild.roles, name=\"Verified\")\n overwrites = {\n guild.default_role: discord.PermissionOverwrite(read_messages = False),\n verified: discord.PermissionOverwrite(read_messages = False),\n verify_role: discord.PermissionOverwrite(read_messages = True)\n }\n channel = await guild.create_text_channel(channelname, overwrites=overwrites)\n global channelid\n channelid = channel.id\n embed = discord.Embed(title=\"Verification\", description=\"Please enter your Minecraft username to verify yourself.\", color=0xFF5733)\n await channel.send(embed=embed)\n await message.author.add_roles(verify_role)\n if message.author.id != 894761300880662528 and \"user\" in message.channel.name:\n guild = message.author.guild\n await message.author.add_roles(get(guild.roles, name=\"Verified\"))\n channel = bot.get_channel(message.channel.id)\n verify_role = get(guild.roles, name=message.channel.name)\n await channel.delete()\n await verify_role.delete()\n name_role = await guild.create_role(name = message.content)\n await message.author.add_roles(name_role)\n\nbot.run(token)\n","repo_name":"IPMElite/VerificationBot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"74648463254","text":"from collections import deque\n\ndef solution(participant, completion):\n participant = deque(sorted(participant))\n completion = deque(sorted(completion))\n \n while participant:\n now = participant.popleft()\n if completion and now == completion.popleft():\n continue\n else:\n return now","repo_name":"unhhyyeexx/ProblemSolving","sub_path":"프로그래머스/lv1/42576. 완주하지 못한 선수/완주하지 못한 선수.py","file_name":"완주하지 못한 선수.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"32802951141","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision as T\nimport numpy as np\nimport pickle\nimport argparse\nimport time\nimport os\nimport datetime\n\nworking_dir = os.path.dirname(os.path.realpath(__file__))\n\nfrom NetworkCreation.common import henaff_init, cayley_init, random_orthogonal_init\nfrom NetworkCreation.Networks import ARUN, RNN, LSTM, GRU\nfrom Training.training_utils import LocalTransform, SinTransform, StepTransform\nfrom torch._utils import _accumulate\nfrom torch.utils.data import Subset\n\nparser = argparse.ArgumentParser(description='(p)sMNIST task')\nparser.add_argument('-g', '--cuda', action='store_true', default=False,\n help='Use CUDA')\nparser.add_argument('-p', '--permute', action='store_true', default=False, \n help='permute the order of sMNIST')\nparser.add_argument('-v', '--verbose', action='store_true', default=False,\n help='Print details')\n\nparser.add_argument('--gain', type=float, default=1.0, \n help='degree of nonlinearity at initialization')\nparser.add_argument('--saturation', type=float, default=1.0, \n help='degree of saturation at initialization')\nparser.add_argument('--random', action='store_true', default=True, \n help='random shape parameters initialization')\nparser.add_argument('--learn_params', action='store_true', default=True,\n help='learn the shape parameters')\nparser.add_argument('--nonlin', type=str, default='gamma2', \n choices=['gamma','gamma2','ReLU'],\n help='Nonlinearity for RNN.')\nparser.add_argument('--net-type', type=str, default='RNN',\n choices=['ANRU', 'RNN', 'LSTM', 'GRU'],\n help='Type of recurrent neural net.')\nparser.add_argument('--nhid', type=int, default=400, \n help='hidden size of recurrent net')\nparser.add_argument('--lr', type=float, default=1e-4,\n help='initial learning rate')\n\nparser.add_argument('--save-freq', type=int, default=25, \n help='frequency (in epochs) to save data')\nparser.add_argument('--seed', type=int, default=400, \n help='random seed for reproducibility')\nparser.add_argument('--rinit', type=str, default=\"henaff\", \n help='recurrent weight matrix initialization')\nparser.add_argument('--iinit', type=str, default=\"kaiming\", \n help='input weight matrix initialization')\nparser.add_argument('--batch', type=int, default=100,\n help='batch size')\nparser.add_argument('--note', type=str, default='',\n help='Any details to be entered manually upon launch')\nparser.add_argument('--test', action='store_true', default=False,\n help='Test model, no training.')\n\nparser.add_argument('--transform', type=str, default='sin',\n help='Transform to be applied on test set')\nparser.add_argument('--transform-ratio', type=float, default=0.0,\n help='Ratio of dataset to apply sin transform on inputs.')\nargs = parser.parse_args()\n\nCUDA = args.cuda\nSAVEFREQ = args.save_freq\nn, s = args.gain, args.saturation\n\n# Set the random seed manually for reproducibility.\ntorch.manual_seed(args.seed)\nnp.random.seed(args.seed)\n\ntorch.cuda.manual_seed(args.seed)\nif torch.cuda.is_available():\n if not CUDA:\n print(\"WARNING: You have a CUDA device, so you should probably run with -g,--cuda. Enabling CUDA.\")\n CUDA = True\ndevice = torch.device(\"cuda\" if CUDA else \"cpu\")\n\n\n###############################################################################\n# Load MNIST data\n###############################################################################\n\nif __name__ == \"__main__\":\n rng = np.random.RandomState(args.seed)\n if args.permute:\n task = 'psMNIST'\n order = rng.permutation(784)\n else:\n task = 'sMNIST'\n order = np.arange(784)\n\n trainset = T.datasets.MNIST(root=working_dir+'/Training/training_data/mnist', train=True, download=True, transform=T.transforms.ToTensor())\n valset = T.datasets.MNIST(root=working_dir+'/Training/training_data/mnist', train=True, download=True, transform=T.transforms.ToTensor())\n offset = 10000\n\n R = rng.permutation(len(trainset))\n lengths = (len(trainset) - offset, offset)\n trainset, valset = [Subset(trainset, R[offset - length:offset]) for offset, length in\n zip(_accumulate(lengths), lengths)]\n testset = T.datasets.MNIST(root=working_dir+'/Training/training_data/mnist', train=False, download=True, transform=T.transforms.ToTensor())\n trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch, shuffle=False, num_workers=2)\n valloader = torch.utils.data.DataLoader(valset, batch_size=args.batch, shuffle=False, num_workers=2)\n testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch, num_workers=2)\n\n\n###############################################################################\n# Saving\n###############################################################################\n\nif __name__ == \"__main__\":\n if args.nonlin is not ('gamma' or 'gamma2'): \n args.learn_params = False\n\n if args.net_type=='RNN':\n udir = f'{args.net_type}_lr{args.lr}_p{args.transform_ratio}_hs{args.nhid}_{args.nonlin}'\n else:\n udir = f'{args.net_type}_lr{args.lr}_p{args.transform_ratio}_hs{args.nhid}'\n exp_time = \"{0:%Y-%m-%d}_{0:%H-%M-%S}\".format(datetime.datetime.now())\n SAVEDIR = os.path.join('./Training/SavedModels',\n task,\n str(args.seed),\n udir,\n str(datetime.date.today()))\n # Savedir is of type: ./Training/SavedModels/psMNIST/400/ARUN_lr0.0001_p0.0_hs400/2020-01-02/\n\n if not args.test:\n # Considering args.verbose runs as just debugging, so allowing overwritting. \n if args.verbose: SAVEDIR+='--X'\n # Else\n if not os.path.exists(SAVEDIR):\n os.makedirs(SAVEDIR)\n elif not args.verbose:\n SAVEDIR += '--0'\n try:\n os.makedirs(SAVEDIR)\n except FileExistsError:\n SAVEDIR = SAVEDIR[:-1]+str(int(SAVEDIR[-1])+1)\n os.makedirs(SAVEDIR)\n\n LOGFILE = os.path.join(SAVEDIR, 'logfile.txt')\n LOGTEST = os.path.join(SAVEDIR, 'test_results.txt')\n with open(LOGFILE, 'w') as fp:\n '''Details and hyperparameters, if desired'''\n fp.write(f'{task} task on {datetime.datetime.now()}\\n')\n fp.write(f'\\nSaving to: {SAVEDIR}')\n if args.note!='':\n fp.write(f'\\nNote : {args.note}')\n \n fp.write('\\n\\nHyperparameters: ')\n for key, val in args.__dict__.items():\n fp.write(('{}: {}, '.format(key, val)))\n\n with open(LOGTEST, 'w') as fp:\n fp.write('Testing data.')\n fp.write('\\n\\nTest => accuracy on original testing set\\nTest T. => accuracy on testing set with SinTransform(freq=1, phase=0, amplitude=0.5)')\n fp.write('\\n\\n'+'='*50+'\\n Epoch Test Test T. \\n'+'='*50)\n\n\n###############################################################################\n# Define the model\n###############################################################################\n\n\nclass Model(nn.Module):\n def __init__(self, hidden_size, rnn):\n super(Model, self).__init__()\n self.hidden_size = hidden_size\n\n # Modules\n self.lin = nn.Linear(hidden_size, 10)\n self.rnn = rnn\n\n self.loss_func = nn.CrossEntropyLoss()\n\n def forward(self, inputs, y, order, \n transform=None, return_ns=False, external_drive=None):\n h_net0, h_net1, a = None, None, None\n hiddens, shape_signals, pre_activations = [], [], []\n\n inputs = inputs[:, order] # permute inputs\n\n for i, input in enumerate(torch.unbind(inputs, dim=1)):\n x = input.unsqueeze(1)\n if transform is not None:\n x = transform(i, x)\n\n if external_drive is None:\n shift = 0.0\n else:\n shift = external_drive.get_factor(i)-1\n \n h_net0 = self.rnn(x, h_net0, external_drive=shift)\n\n for temp in [h_net0, h_net1]:\n if temp is not None and temp.requires_grad: \n temp.retain_grad()\n\n # if return_ns:\n # if args.net_type=='ARUN': \n # shape_signals.append(shape_parameters)\n # pre_activations.append(pre_activs.cpu().detach().numpy())\n # hiddens.append(h_net0.cpu().detach().numpy())\n\n out = self.lin(h_net0) # decode\n loss = self.loss_func(out, y)\n preds = torch.argmax(out, dim=1)\n correct = torch.eq(preds, y).sum().item()\n return loss, correct, hiddens\n\n # if transform is not None: suffix = '_T'\n # elif external_drive is not None: suffix='_D'\n # else: suffix+''\n \n # shape_signals_label = 'shapesignals'+suffix+'.npy'\n # hiddens_label = 'net0_hiddenstates'+suffix+'.npy'\n # preactivs_label = 'net0_preactivations'+suffix+'.npy'\n\n # if return_ns: \n # if args.net_type=='ARUN': \n # np.save(os.path.join(MODELDIR, shape_signals_label), shape_signals)\n # np.save(os.path.join(MODELDIR, preactivs_label), pre_activations)\n # np.save(os.path.join(MODELDIR, hiddens_label), hiddens)\n\n\ndef test_model(net, dataloader, transform=None, return_parameters=False, external_drive=None):\n accuracy = 0\n loss = 0\n net.eval()\n with torch.no_grad():\n for i, data in enumerate(dataloader):\n\n x, y = data\n x = x.view(-1, 784)\n if CUDA:\n x = x.cuda()\n y = y.cuda()\n if args.net_type == 'LSTM' or args.net_type == 'ARUN':\n net.rnn.init_states(x.shape[0])\n loss, c, _ = net.forward(x, y, order, transform=transform, return_ns=False, external_drive=external_drive)\n if args.verbose:\n if i%10==0: print(f'Step {i}, Loss: {loss.item()}')\n accuracy += c\n\n accuracy /= len(testset)\n return loss, accuracy\n\n\ndef save_checkpoint(state, fname):\n filename = os.path.join(SAVEDIR, fname)\n torch.save(state, filename)\n\ndef train_model(net, optimizer, scheduler, num_epochs):\n with open(LOGFILE, 'a') as fp:\n fp.write('\\n\\n'+'-'*70+'\\nBeginning of training.')\n fp.write('\\n\\nTrain => accuracy on training set \\nVal => accuracy on original validation set.\\nVal T. => accuracy on validation set with sinuisoidal transform')\n fp.write('\\n\\n'+'='*45+'\\n Epoch Time Train Val Val T. \\n'+'='*45)\n\n with open(os.path.join(SAVEDIR, 'training_loss_details.txt'), 'w') as fp:\n fp.write('Loss details:\\n')\n\n train_losses, train_accuracies = [], []\n val_losses, val_accuracies = [], []\n val_losses_T, val_accuracies_T = [], []\n save_norms = []\n shape_params = []\n\n best_val_acc = 0\n ta = 0\n for epoch in range(recover, recover+num_epochs):\n s_t = time.time()\n accs = []\n losses = []\n norms = []\n processed = 0\n net.train()\n correct = 0\n\n ts = np.random.binomial(1, args.transform_ratio, size=[500])\n for i, data in enumerate(trainloader, 0):\n inp_x, inp_y = data\n inp_x = inp_x.view(-1, 784)\n if bool(ts[i]):\n trans1 = SinTransform(freq=np.random.uniform(0,2), phase=0, amplitude=0.2)\n else:\n trans1 = None\n\n if CUDA:\n inp_x = inp_x.cuda()\n inp_y = inp_y.cuda()\n if args.net_type == 'LSTM' or args.net_type=='ARUN':\n net.rnn.init_states(inp_x.shape[0])\n\n optimizer.zero_grad()\n\n loss, c, _ = net.forward(inp_x, inp_y, order, transform=trans1)\n correct += c\n processed += inp_x.shape[0]\n\n accs.append(correct / float(processed))\n\n loss.backward()\n if args.verbose:\n if i%10==0: print(f'Step {i}, Loss: {loss.item()}')\n losses.append(loss.item())\n\n if np.isnan(loss.item()):\n raise ValueError\n\n optimizer.step()\n if i%50==0:\n with open(os.path.join(SAVEDIR, 'training_loss_details.txt'), 'a') as fp:\n fp.write('{:3.0f} Loss = {:5.8f}\\n'.format(i, loss.item()))\n\n norm = torch.nn.utils.clip_grad_norm_(net.parameters(), 'inf')\n norms.append(norm)\n\n # Validation\n val_loss, val_acc = test_model(net, valloader, transform=None)\n val_accuracies.append(val_acc)\n val_losses.append(val_loss)\n \n scheduler.step(val_loss) # lr scheduler based on validation loss\n\n # Validation on transformed set\n trans2 = SinTransform(freq=np.random.uniform(0,2), phase=0, amplitude=0.5)\n val_loss_T, val_acc_T = test_model(net, valloader, transform=trans2)\n val_accuracies_T.append(val_acc_T)\n val_losses_T.append(val_loss_T)\n\n with open(LOGFILE, 'a') as fp:\n fp.write('\\n {:3.0f} {} {:2.5f} {:2.5f} {:2.5f}'.\n format(epoch + 1, str(datetime.timedelta(seconds=int(time.time() - s_t))), \n np.mean(accs), val_acc, val_acc_T))\n\n train_losses.append(np.mean(losses))\n train_accuracies.append(np.mean(accs))\n save_norms.append(np.mean(norms))\n\n # Save data\n if epoch % 2 == 0 or epoch == num_epochs - 1:\n with open(os.path.join(SAVEDIR, 'Train_Losses'), 'wb') as fp:\n pickle.dump(train_losses, fp)\n with open(os.path.join(SAVEDIR, 'Val_Losses'), 'wb') as fp:\n pickle.dump(val_losses, fp)\n\n with open(os.path.join(SAVEDIR, 'Train_Accuracy'), 'wb') as fp:\n pickle.dump(train_accuracies, fp)\n with open(os.path.join(SAVEDIR, 'Val_Accuracy'), 'wb') as fp:\n pickle.dump(val_accuracies, fp)\n\n with open(os.path.join(SAVEDIR, 'Grad_Norms'), 'wb') as fp:\n pickle.dump(save_norms, fp)\n\n with open(os.path.join(SAVEDIR, 'val_Accuracy_T'), 'wb') as fp:\n pickle.dump(val_accuracies_T, fp)\n with open(os.path.join(SAVEDIR, 'val_Losses_T'), 'wb') as fp:\n pickle.dump(val_losses_T, fp)\n\n\n if epoch % SAVEFREQ == 0 or epoch == num_epochs - 1:\n save_checkpoint({\n 'state_dict': net.state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'epoch': epoch\n },\n 'e_{}.pth.tar'.format(epoch)\n )\n \n _, test_acc = test_model(net, testloader, transform=None)\n _, test_T_acc = test_model(net, testloader, transform=SinTransform(freq=1, phase=0, amplitude=0.5))\n with open(LOGTEST, 'a') as fp:\n fp.write('\\n {:3.0f} {:2.6f} {:2.6f}'.format(epoch, test_acc, test_T_acc))\n\n return\n\n\n###############################################################################\n# Training\n###############################################################################\n\ninp_size = 1\n\nif __name__ == \"__main__\":\n if args.net_type == 'ARUN':\n rnn = ARUN(inp_size, main_hidden_size=args.nhid, supervisor_hidden_size=50, cuda=CUDA,\n r_initializer=args.rinit, i_initializer=args.iinit, adaptation_type='heterogeneous', verbose=args.verbose)\n elif args.net_type == 'RNN':\n if args.random:\n n0 = 5+2*torch.rand(1); s0 = 0.0\n else:\n n0, s0 = args.gain, args.saturation\n rnn = RNN(inp_size, args.nhid, bias=True, nonlin=args.nonlin, cuda=CUDA, n_init=n0, s_init=s0,\n r_initializer=args.rinit, i_initializer=args.iinit, learn_params=args.learn_params)\n elif args.net_type == 'LSTM':\n rnn = LSTM(inp_size, args.nhid, cuda=CUDA)\n elif args.net_type == 'GRU':\n rnn = GRU(inp_size, args.nhid, cuda=CUDA)\n else:\n print('Net-type unrecognised. Using default: RNN')\n \n # set training modules\n net = Model(args.nhid, rnn)\n optimizer = optim.Adam(net.parameters(), lr=args.lr)\n scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min') \n\n if args.verbose:\n print('\\nNumber of trainable parameters:')\n total_params = 0\n for name, parameter in net.named_parameters():\n if not parameter.requires_grad: continue\n param = parameter.numel()\n total_params+=param\n print(name, param)\n print('-> Total: {:6.0f} ({:3.1f}K)\\n'.format(total_params, total_params/1000))\n\n if CUDA:\n net = net.cuda()\n\n epoch = 0\n recover = 0\n #recover_date = '2021-02-12'\n epoch = recover\n num_epochs = 100 - recover \n\n if recover > 0: \n # Recover a pretrained model + continue training\n MODELDIR = os.path.join('./Training/SavedModels', task, str(args.seed), udir, recover_date)\n last_model = torch.load(os.path.join(MODELDIR, f'e_{recover}.pth.tar'))\n net.load_state_dict(last_model['state_dict'])\n print(f'Recovered: NET={args.net_type}, lr={args.lr}, p={args.transform_ratio}, epoch={recover}')\n\n if not args.test:\n try:\n train_model(net, optimizer, scheduler, num_epochs)\n except ValueError:\n with open(LOGFILE, 'a') as fp:\n fp.write(\"\\n\"+\"*\"*70+\"\\nNan loss encountered, program terminated.\")\n except KeyboardInterrupt:\n with open(LOGFILE, 'a') as fp:\n fp.write(\"\\n\"+\"*\"*70+\"\\nExited from training early\")\n else:\n '''\n Equivalent to: if args.test. Recover a pre-trained model.\n '''\n # set the following yourself\n recover = 50 \n recover_date = '2021-02-07--1'\n\n MODELDIR = os.path.join('./Training/SavedModels', task, str(args.seed), udir, recover_date)\n\n last_model = torch.load(os.path.join(MODELDIR, f'e_{recover}.pth.tar'))\n net.load_state_dict(last_model['state_dict'])\n print(f'Recovered: NET={args.net_type}, lr={args.lr}, p={args.transform_ratio}, epoch={recover}')\n\n start_time = time.time()\n test_loss, test_acc = test_model(net, testloader, transform=None, return_parameters=True)\n print('\\nOriginal:\\n\\tTest loss: {}\\n\\tTest accuracy: {}'.format(test_loss, test_acc))\n\n test_T_loss, test_T_acc = test_model(net, testloader, transform=SinTransform(freq=1, phase=0, amplitude=0.5), return_parameters=True)\n print('\\nTransformed:\\n\\tTest loss: {}\\n\\tTest accuracy: {}'.format(test_T_loss, test_T_acc))\n\n test_D_loss, test_D_acc = test_model(net, testloader, external_drive=StepTransform(step_size=1.0, step_length=200, step_position=200), return_parameters=True)\n print('\\nExternal drive:\\n\\tTest loss: {}\\n\\tTest accuracy: {}'.format(test_D_loss, test_D_acc)) \n print('\\nTime:', time.time() - start_time)\n","repo_name":"vgeadah/NonlinMod","sub_path":"sMNIST_task.py","file_name":"sMNIST_task.py","file_ext":"py","file_size_in_byte":19327,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"67"}
+{"seq_id":"41693328515","text":"import speech_recognition as sr\nimport datetime\nimport requests\nfrom bs4 import BeautifulSoup\nimport googlemaps\n\n# Initialize the recognizer\nrecognizer = sr.Recognizer()\n\n# Initialize the Google Maps API client (you'll need an API key)\ngmaps = googlemaps.Client(key='YOUR_API_KEY')\n\n# Function to capture voice input and convert it to text\ndef get_voice_input():\n with sr.Microphone() as source:\n print(\"Listening...\")\n try:\n audio = recognizer.listen(source, timeout=5)\n text = recognizer.recognize_google(audio)\n return text\n except sr.WaitTimeoutError:\n return \"Timeout: No speech detected.\"\n except sr.UnknownValueError:\n return \"Sorry, I could not understand what you said.\"\n except sr.RequestError as e:\n return f\"Error with the speech recognition service: {e}\"\n\n# Function to generate a text-based response\ndef get_response(user_input):\n user_input = user_input.lower()\n\n if \"hello\" in user_input:\n return \"Hello! How can I assist you?\"\n elif \"how are you\" in user_input:\n return \"I'm just a text-based assistant, but I'm here to help.\"\n elif \"bye\" in user_input:\n return \"Goodbye! Have a great day!\"\n elif \"time\" in user_input:\n current_time = datetime.datetime.now().strftime(\"%H:%M:%S\")\n return f\"The current time is {current_time}.\"\n elif \"search\" in user_input:\n # Extract the search query from the user input\n query = user_input.replace(\"search\", \"\").strip()\n if query:\n search_results = perform_search(query)\n return search_results\n else:\n return \"Please provide a search query.\"\n elif \"guide to\" in user_input:\n place_name = user_input.replace(\"guide to\", \"\").strip()\n if place_name:\n guide = get_guide_to_place(place_name)\n return guide\n else:\n return \"Please specify a place for the guide.\"\n else:\n return \"I'm a simple text-based assistant. How can I assist you?\"\n\n# Function to perform a web search and return search results\ndef perform_search(query):\n search_url = f\"https://www.google.com/search?q={query}\"\n \n try:\n response = requests.get(search_url)\n response.raise_for_status()\n soup = BeautifulSoup(response.text, 'html.parser')\n search_results = soup.find_all(\"div\", class_=\"tF2Cxc\")\n if search_results:\n return search_results[0].get_text()\n else:\n return \"No search results found.\"\n except requests.exceptions.RequestException as e:\n return f\"Error performing the search: {e}\"\n\n# Function to get a guide to a place using Google Maps API\ndef get_guide_to_place(place_name):\n try:\n # Perform a geocode request to get the place details\n geocode_result = gmaps.geocode(place_name)\n\n if geocode_result:\n place_details = geocode_result[0]\n formatted_address = place_details.get(\"formatted_address\", \"Address not found.\")\n place_type = place_details.get(\"types\", [])\n place_type_str = \", \".join(place_type)\n \n return f\"Here's a guide to {place_name}:\\nType: {place_type_str}\\nAddress: {formatted_address}\"\n else:\n return \"Place not found.\"\n except googlemaps.exceptions.ApiError as e:\n return f\"Error getting place details: {e}\"\n\n# Main loop for voice interaction\nwhile True:\n user_input = get_voice_input()\n print(\"You said:\", user_input)\n\n response = get_response(user_input)\n print(\"Assistant:\", response)\n\n if \"exit\" in user_input.lower():\n break\n\n# End the program\nprint(\"Assistant: Goodbye!\")\n","repo_name":"RafikEmad/EmbeddedLinux","sub_path":"01_Python/04_Advanced/Task5_GoogleAssistant/GoogleAssistant.py","file_name":"GoogleAssistant.py","file_ext":"py","file_size_in_byte":3727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"8442679676","text":"import os, sys\nimport csv\nimport json\n# import geojson\n\n\n\n\n\ndef save_csv(data, fileName):\n \"\"\" data: list of dictionaries\n fileName: filePath//fileName.fileType\"\"\"\n with open(fn, 'w', newline='') as fw:\n csvwriter = csv.writer(fw)\n csvwriter.writerow(data[0].keys())\n for rw in save_data:\n csvwriter.writerow(rw.values())\n\ndef save_json(data, fileName):\n \"\"\" data: list of dictionaries (already a json object)\n fileName: filePath//fileName.fileType\"\"\"\n with open(fn, 'w') as fw:\n json.dump(data, fw, indent=4)\n\n\n# def save_geojson(data, fileName):\n# \"\"\" data: list of dictionaries\n# fileName: filePath//fileName.fileType\"\"\"\n\n# # if data is a list of dictionaries, we need to reformat\n# # into geojson data, using any columns in the file that look like Lat/Lon pairs\n\n# # if data is a dictionary, with first term: {'type': \"FeatureCollection\"}\n# # save directly to geojson, no restructuring necessary\n \n# with open(fn, 'w') as fw:\n# geojson.dump(data, fw, indent=4)\n\n\n","repo_name":"blcrosbie/ee_s5p_plugin","sub_path":"ee_s5p_plugin/scripts/file_handler.py","file_name":"file_handler.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"24393475829","text":"import pygame\r\nfrom settings import *\r\nfrom tiles import Tile\r\nfrom level import Level\r\nfrom sys import exit\r\nfrom player import Player\r\nfrom game_data import level_0\r\nclock = pygame.time.Clock()\r\n#init\r\n#kill player==0 if player alive kill player==1 if player is dying\r\n\r\nclass Game():\r\n def __init__(self,joystick):\r\n# super().__init__()\r\n self.joystick=joystick\r\n #screen\r\n infoObject = pygame.display.Info()\r\n screen_width=infoObject.current_w\r\n screen_height=infoObject.current_h\r\n self.screen=pygame.display.set_mode((screen_height,screen_width),pygame.FULLSCREEN)\r\n self.clock=pygame.time.Clock()\r\n #level\r\n self.level=Level(level_0,self.screen)\r\n #backround\r\n pygame.display.set_caption('test game')\r\n self.sky_surface = pygame.image.load('graphics/sky.jpg').convert_alpha()\r\n #fonts\r\n self.def_font=pygame.font.Font(None,50)#default font\r\n\r\n self.running=True\r\n\r\n def run(self,death_count):\r\n quit_game = False\r\n game_over = False\r\n\r\n self.text_name = self.def_font.render(\"Deathcount \" + str(death_count), True, \"red\")\r\n while not quit_game and not game_over:\r\n\r\n for event in pygame.event.get(): #q\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n exit()\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_ESCAPE:\r\n print(\"escape pressed\")\r\n quit_game = True\r\n pygame.quit()\r\n exit()\r\n if event.key == pygame.K_r:\r\n game_over = True\r\n if joystick: #check if joystick package instalized\r\n if pygame.joystick.get_count() > 0: # if theres a pad\r\n button_exit = self.joystick.get_button(7)\r\n if button_exit > 0:\r\n print(\"escape pressed on pad\")\r\n quit_game = True\r\n pygame.quit()\r\n exit()\r\n\r\n\r\n self.screen.blit(self.sky_surface,(0,0))\r\n self.level.run(self.joystick)\r\n self.screen.blit(self.text_name,(940,50))\r\n\r\n\r\n kill_player=self.level.death(0)\r\n #print(kill_player)\r\n if kill_player==1:\r\n\r\n self.level = Level(level_0, self.screen)\r\n self.run(death_count)\r\n pygame.display.update()\r\n #print(death_count)\r\n\r\n\r\n self.clock.tick(60)\r\n #print(self.clock.get_fps())\r\n return quit_game\r\n\r\nif __name__ == \"__main__\":\r\n pygame.init()\r\n # pad initalization\r\n joystick = None\r\n if joystick:\r\n print(\"joystick package is here\")\r\n if pygame.joystick.get_count() > 0:\r\n joystick = pygame.joystick.Joystick(0)\r\n joystick.init()\r\n print(\"joystick initialized\")\r\n else:\r\n joystick = False # look at get_input in player class\r\n print('joystick not initaliazed')\r\n quit_game=False\r\n death_count = 0\r\n while not quit_game:\r\n game = Game(joystick)\r\n game.run(death_count)\r\n death_count += 1\r\n","repo_name":"wolf3213/The-limit","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":3383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"24075235309","text":"'''\r\nSTAGE 3\r\nno max distance\r\nno closest node check\r\nno tree like structure\r\n'''\r\nimport matplotlib.pyplot as plt\r\n# import shapely\r\nfrom shapely.geometry import LineString\r\nfrom shapely.geometry import Polygon\r\n# from shapely.geometry import MultiPolygon as mpg\r\nfrom shapely.geometry import Point\r\nimport random\r\n\r\nclass Node:\r\n # for parent node\r\n parent = None\r\n # for new random node\r\n newNode = None \r\n # for position\r\n position = None\r\n # accepts parent node and new random node of type Point\r\n def __init__(self, parent, newNode, position):\r\n self.parent = parent\r\n self.newNode = newNode\r\n self.position = position\r\n\r\n # parent is a node\r\n # newNode = Node()\r\n # position and parent attribs\r\n # Parent = Node()\r\n # newNode.parent = newNode\r\n # using the object of the next Point\r\n # pass\r\n\r\n# function to return True if new Node isnt Present in any obstacle, \r\n# else False\r\ndef isNodeOkay(newNode):\r\n isOkay = True\r\n for obs in obsList:\r\n if newNode.within(obs):\r\n isOkay = False\r\n # print('Node is Not Okay')\r\n return isOkay\r\n return isOkay\r\n\r\n# function to check if line crosses another Obstacle\r\n# returns true if no cross\r\ndef isLineOkay(newLine):\r\n isOkay = True\r\n for obs in obsList:\r\n if newLine.crosses(obs):\r\n isOkay = False\r\n # print('Line is Not Okay')\r\n return isOkay\r\n return isOkay\r\n\r\ndef plotter(path):\r\n pass\r\n\r\n# sample goal after x interval\r\ndef goalSampler(itr):\r\n pass\r\n\r\n# function to repeat procedure 5 times\r\n'''\r\nPROCEDURE:\r\n choosing random point in given range\r\n\r\n convert point into node using class, pass parent and new node\r\n checking if it lies inside given obstacle list\r\n checking if line crosses any obstacle\r\n adding it to path\r\n traversing to start of \r\n plotting path\r\n'''\r\ndef driver(n, start, goal, d):\r\n plt.scatter(start.x, start.y, marker='x', color='yellow')\r\n plt.scatter(goal.x, goal.y, marker='x', color='green')\r\n # parent = Node()\r\n # newnode = Node(parent);\r\n\r\n path = []\r\n path.append(start)\r\n # node to calculate distance\r\n prev = start\r\n # keep track of number of nodes placed\r\n nodeCtr=0\r\n itr=0\r\n goalSampleCtr = 0\r\n while nodeCtr element\n if a_tag:\n a_links.append(f\"https://blogs.worldbank.org{a_tag['href']}\")\n headers.append(a_tag.text)\n for tag in div:\n p = tag.find('p')\n if p:\n descrip.append(p.text)\n for tag in span:\n date = tag.find('time')\n if date:\n txt = date.text\n frmt_date = datetime.strptime(txt, date_frmt)\n formatted_date = frmt_date.strftime(output_frmt)\n dates.append(formatted_date)\n\n for i in range(len(a_links)):\n dt.append([headers[i], descrip[i], a_links[i], dates[i]])\n\n return dt\n\n\ndef get_quote():\n q = requests.get(quote_link).json()\n return q[\"content\"], q[\"author\"]\n","repo_name":"Mayur-Gowda/WebDev--","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"63523479","text":"\"\"\"\nSimple deploy-v2 mock\n\"\"\"\n\nimport json\nimport urllib.parse\n\nfrom httmock import urlmatch\n\nfrom .utils import handle_http_action, http_return\n\n\ndef deploy_v2(state):\n \"\"\"\n Setup mock with state\n \"\"\"\n record_all_shipments = state['deploy-v2']['record_all_shipments']\n\n @urlmatch(netloc='deploy-v2.test', method='get', path='/v1/minions/.+')\n def get_minion(url, _):\n minion_id = url.path.split('/')[-1]\n ret = handle_http_action(state, f'deploy-v2-minion-get-{minion_id}')\n if ret:\n return ret\n\n if minion_id in state['deploy-v2']['minions']:\n return http_return(body=state['deploy-v2']['minions'][minion_id])\n\n return http_return(code=404, body={'message': 'data not found'})\n\n @urlmatch(netloc='deploy-v2.test', method='put', path='/v1/minions/.+')\n def create_minion(url, request):\n minion_id = url.path.split('/')[-1]\n data = json.loads(request.body)\n ret = handle_http_action(state, f'deploy-v2-minion-create-{minion_id}')\n if ret:\n return ret\n\n data['registered'] = True\n state['deploy-v2']['minions'][minion_id] = data\n return http_return(body=data)\n\n @urlmatch(netloc='deploy-v2.test', method='post', path='/v1/minions/.+/unregister')\n def unregister_minion(url, request):\n minion_id = url.path.split('/')[-2]\n ret = handle_http_action(state, f'deploy-v2-minion-unregister-{minion_id}')\n if ret:\n return ret\n\n if minion_id in state['deploy-v2']['minions']:\n return http_return()\n\n return http_return(code=404, body={'message': 'minion not created'})\n\n @urlmatch(netloc='deploy-v2.test', method='delete', path='/v1/minions/.+')\n def delete_minion(url, _):\n minion_id = url.path.split('/')[-1]\n ret = handle_http_action(state, f'deploy-v2-minion-delete-{minion_id}')\n if ret:\n return ret\n\n if minion_id in state['deploy-v2']['minions']:\n return http_return(body=state['deploy-v2']['minions'].pop(minion_id))\n\n return http_return(code=404, body={'message': 'data not found'})\n\n @urlmatch(netloc='deploy-v2.test', method='get', path='/v1/shipments/.+')\n def get_shipment(url, _):\n shipment_id = url.path.split('/')[-1]\n ret = handle_http_action(state, f'deploy-v2-shipment-get-{shipment_id}')\n if ret:\n return ret\n\n if shipment_id in state['deploy-v2']['shipments']:\n return http_return(body=state['deploy-v2']['shipments'][shipment_id])\n\n return http_return(code=404, body={'message': 'data not found'})\n\n @urlmatch(netloc='deploy-v2.test', method='post', path='/v1/shipments')\n def create_shipment(_, request):\n data = json.loads(request.body)\n commands = '-'.join(x['type'] for x in data['commands'] if x['type'] != 'saltutil.sync_all')\n shipment_id = f'{data[\"fqdns\"][0]}-{commands}'\n\n if record_all_shipments:\n prefix = shipment_id\n suffix_num = 2\n\n while shipment_id in state['deploy-v2']['shipments']:\n suffix_num += 1\n shipment_id = f'{prefix}-{suffix_num}'\n\n ret = handle_http_action(state, f'deploy-v2-shipment-create-{shipment_id}')\n if ret:\n return ret\n\n state['deploy-v2']['shipments'][shipment_id] = data\n state['deploy-v2']['shipments'][shipment_id]['id'] = shipment_id\n state['deploy-v2']['shipments'][shipment_id]['status'] = 'done'\n\n return http_return(body=state['deploy-v2']['shipments'][shipment_id])\n\n @urlmatch(netloc='deploy-v2.test', method='get', path='/v1/jobs')\n def get_jobs(url, request):\n shipment_id = url.query.split('=')[1]\n\n if 'jobs' in state['deploy-v2'] and shipment_id in state['deploy-v2']['jobs']:\n return http_return(body={'jobs': state['deploy-v2']['jobs'][shipment_id]})\n\n return http_return(code=404, body={'message': 'jobs not found for shipment_id=' + shipment_id})\n\n @urlmatch(netloc='deploy-v2.test', method='get', path='/v1/jobresults')\n def get_jobresults(url, request):\n qp = urllib.parse.parse_qs(url.query)\n ext_job_id = qp['extJobId'][0]\n fqdn = qp['fqdn'][0]\n\n if 'jobresults' not in state['deploy-v2']:\n return http_return(code=404, body={'message': 'jobresults not intialized'})\n\n for jr in state['deploy-v2']['jobresults']:\n if jr['fqdn'] == fqdn and jr['extID'] == ext_job_id:\n return http_return(body={'jobResults': [jr]})\n\n return http_return(\n code=404, body={'message': 'jobresults not found for ext_job_id=' + ext_job_id + ', fqdn=' + fqdn}\n )\n\n return (\n get_minion,\n create_minion,\n delete_minion,\n get_shipment,\n create_shipment,\n unregister_minion,\n get_jobs,\n get_jobresults,\n )\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"cloud/test/mocks/deploy_v2.py","file_name":"deploy_v2.py","file_ext":"py","file_size_in_byte":4938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"37603135398","text":"import numpy as np\n\nimport viscojapan as vj\n\ndip_patch_size = 4\nstrike_patch_size = 4\nfault_file = 'fault_bott80km.h5'\n\nslip = vj.inv.test.gen_checkerboard_slip_from_fault_file(\n fault_file,\n dip_patch_size = dip_patch_size,\n strike_patch_size = strike_patch_size)\n\nslip *= 2\n\n\nflt_reader = vj.fm.FaultFileReader(fault_file)\nlats = flt_reader.LLats_mid\nlons = flt_reader.LLons_mid\n\n \nwith open('_checkerboard_slip.txt','wt') as fid:\n for lon, lat, s in zip(np.nditer(lons),\n np.nditer(lats),\n np.nditer(slip),\n ):\n fid.write('%f %f %f\\n'%(lon,lat,s))\n\n","repo_name":"zy31415/viscojapan","sub_path":"agu-paper/checkerboard_test/gmt/gen_checkerboard_slip.py","file_name":"gen_checkerboard_slip.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"72"}
+{"seq_id":"73849064553","text":"def bubble(l):\n rez = []\n r = True\n while r:\n r = False\n for i in range(len(l) - 1):\n if l[i] > l[i + 1]:\n l[i], l[i + 1] = l[i + 1], l[i]\n r = True\n return l\n\n\nprint(bubble([2,0,2,1,1,0]))\n","repo_name":"alex-radchenko-github/codewars-and-leetcode","sub_path":"codewars/7kata/Bubble Sort.py","file_name":"Bubble Sort.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"23950823096","text":"### PAL Grammar rules Parser\n\nimport ply.yacc as yacc\n\n# Get the token map from the lexer. This is required.\nfrom PALLexer import tokens\nfrom random import randint\n#Global key value dictionaries\n#key = Organism ID value = max value of organisms in the model\norgmax = {}\n#key = Organism ID value = initial value of organisms in the model\norginitial = {}\n\n#key = Organism ID value = list of dictionaries key = action name and value = population operator\norgpopactions = {}\n#key = Organism ID value = list of dictionaries key = action name and value = list [action type,operator,different operator,otherorgid]\norgpopactionnames = {}\n\n#key = Organism ID value = list of internal species of that organism\norgspecies = {}\n\n#key = internal species ID value = list of actions of that species\nspeciesactions = {}\n#key = Organism ID value = list of species actions of that organism\norgactions = {}\n\n#key = internal species ID value = list of parameters of that species\norgparameters = {}\n\n#key = Organism ID value value key species id value parameter expression\norginitialresets = {}\n\n\n###Starting rule which specifies the parts of a PAL model\n##def p_palmodel_1(p):\n## 'palmodel : maxpoplist modelcomponent biospeciesbehaviours parameters actionrates organisms populations'\n## p[0] = p[4] + '\\n' + p[5] + '\\n' + p[3] + '\\n' + addonandoffspecies() + '\\n' + p[6]\n\ndef p_palmodel_1(p):\n 'palmodel : maxpoplist modelcomponent populations organisms initialresets biospeciesbehaviours parameters actionrates'\n p[0] = p[7] + '\\n'+ addintialresetparasandactionrates() + '\\n' + p[8] + '\\n' + p[6] + '\\n' + p[3] + '\\n' + p[4]\n\n#initial reset rules\ndef p_intialresets_1(p):\n 'initialresets : initialresets initialreset'\n p[0] = p[1] + p[2]\n\ndef p_intialresets_2(p):\n 'initialresets : initialreset'\n p[0] = p[1]\n\ndef p_initialreset(p):\n 'initialreset : IDENTIFIER IDENTIFIER EQUALS expression SEMICOLON'\n createorginitialresetdictionary(p)\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3] + ' ' + p[4] + ' ; ' + '\\n'\n\n#parameter rules\ndef p_parameters_1(p):\n 'parameters : parameters parameter'\n p[0] = p[1] + p[2]\n\ndef p_parameters_2(p):\n 'parameters : parameter'\n p[0] = p[1]\n\ndef p_parameter(p):\n 'parameter : IDENTIFIER EQUALS expression SEMICOLON'\n p[0] = addorganismidtoparameters(p)\n\n#action rate rules\ndef p_actionrates_1(p):\n 'actionrates : actionrates actionrate'\n p[0] = p[1] + p[2]\n\ndef p_actionrates_2(p):\n 'actionrates : actionrate'\n p[0] = p[1]\n\ndef p_actionrate(p):\n 'actionrate : IDENTIFIER COLON expression SEMICOLON'\n p[0] = addorganismidtoactions(p)\n\n#same expression rule used in parameters and actionrates\ndef p_expression_power(p):\n 'expression : expression POWER term'\n p[0] = p[1] + p[2] + p[3]\n\ndef p_expression(p):\n '''expression : expression PLUS term\n | expression MINUS term '''\n if p[2] == '+':\n p[0] = p[1] + ' + ' + p[3]\n elif p[2] == '-':\n p[0] = p[1] + ' - ' + p[3]\n\ndef p_expression_term(p):\n 'expression : term'\n p[0] = p[1]\n\ndef p_term_times(p):\n 'term : term TIMES factor'\n p[0] = p[1] + ' * ' + p[3]\n\ndef p_term_divides(p):\n 'term : term DIVIDE factor'\n p[0] = p[1] + ' / ' + p[3]\n\ndef p_term_factor(p):\n 'term : factor'\n p[0] = p[1]\n \ndef p_factor(p):\n '''factor : NUMBER\n | IDENTIFIER'''\n p[0] = ' ' + p[1] + ' '\n\ndef p_factor2(p):\n 'factor : MINUS NUMBER'\n p[0] = ' ' + p[1] + p[2] + ' '\n \n\ndef p_factor_expr(p):\n 'factor : LEFTBRACKET expression RIGHTBRACKET'\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3]\n\n#For Heaviside step function,fMA and exp\ndef p_factor_h(p):\n 'factor : IDENTIFIER LEFTBRACKET expression RIGHTBRACKET'\n p[0] = p[1] + p[2] + p[3] + p[4]\n\n##def p_expression_power1(p):\n## 'expression : expression POWER term'\n## p[0] = p[1] + p[2] + p[3]\n\n#biospecies behaviour rules\ndef p_biospeciesbehaviours_1(p):\n 'biospeciesbehaviours : biospeciesbehaviours biospeciesbehaviour'\n p[0] = p[1] + p[2]\n\ndef p_biospeciesbehaviours_2(p):\n 'biospeciesbehaviours : biospeciesbehaviour'\n p[0] = p[1]\n\ndef p_biospeciesbehaviour(p):\n 'biospeciesbehaviour : IDENTIFIER EQUALS bioactions SEMICOLON'\n addtoactiondictionararies(p)\n p[0] = addorganismidtospecies(p)\n\ndef p_bioactions_1(p):\n 'bioactions : bioactions PLUS bioaction'\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3]\n\ndef p_bioactions_2(p):\n 'bioactions : bioaction'\n p[0] = p[1]\n\ndef p_bioaction_1(p):\n 'bioaction : IDENTIFIER baction'\n p[0] = p[1] + ' ' + p[2]\n\ndef p_bioaction_2(p):\n 'bioaction : LEFTBRACKET IDENTIFIER COMMA NUMBER RIGHTBRACKET baction'\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3] + ' ' + p[4] + p[5]+ ' ' + p[6]\n\ndef p_baction(p):\n '''baction : BIOPRODUCT\n | BIOREACTANT\n | BIOMODIFIER\n | BIOACTIVATOR\n | BIOINHIBITOR '''\n p[0] = p[1]\n\n#organism\ndef p_organisms_1(p):\n 'organisms : organisms organism'\n #Add sync between different species organisms\n p[0] = p[1] + '<*> ' + p[2]\n \n \ndef p_organisms_2(p):\n 'organisms : organism'\n p[0] = p[1]\n\ndef p_organism(p):\n 'organism : IDENTIFIER EQUALS biospeciesmodelcomponent'\n addtoorgspeciesdictionary(p)\n p[0] = makemodelcomponent(p)\n\ndef p_biospeciesmodelcomponent_1(p):\n 'biospeciesmodelcomponent : biospeciesmodelcomponent biosync bioinitial'\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3] \ndef p_biospeciesmodelcomponent_2(p):\n 'biospeciesmodelcomponent : bioinitial'\n p[0] = ' ' + p[1] + ' '\n\ndef p_bioinitial(p):\n 'bioinitial : IDENTIFIER LEFTSQUAREBRACKET NUMBER RIGHTSQUAREBRACKET'\n p[0] = p[1] + ' ' + p[2] + p[3] + p[4] \n\ndef p_biosync(p):\n '''biosync : LEFTANGLEBRACKET TIMES RIGHTANGLEBRACKET\n | LEFTANGLEBRACKET listofids RIGHTANGLEBRACKET '''\n p[0] = p[1] + p[2] + p[3]\n\n#population\ndef p_populations_1(p):\n 'populations : populations population'\n# p[0] = p[1] + p[2]\n createpopactionnamesdictionary()\n p[0] = addonandoffspecies()\n \ndef p_populations_2(p):\n 'populations : population'\n p[0] = p[1]\n \ndef p_population(p):\n 'population : populationid EQUALS popactions SEMICOLON'\n addtopopactiondictionary(p)\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3] + ' ' + p[4] + '\\n'\n\n \n#Also used in modelcomponent rules\ndef p_populationid(p):\n 'populationid : IDENTIFIER LEFTDOUBLECURLYBRACKET IDENTIFIER RIGHTDOUBLECURLYBRACKET'\n p[0] = p[1] + ' ' + p[2] + ' '+ p[3] + ' ' + p[4]\n\ndef p_popactions_1(p):\n 'popactions : popactions PLUS popaction'\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3]\n \ndef p_popactions_2(p):\n 'popactions : popaction'\n p[0] = p[1] \n\ndef p_popaction(p):\n 'popaction : IDENTIFIER paction'\n p[0] = p[1] + ' ' + p[2]\n \ndef p_paction(p):\n ''' paction : PALADDITION\n | PALDELETION\n | PALACTIVATOR '''\n p[0] = p[1]\n\n\n#population modelcomponent\ndef p_modelcomponent_1(p):\n ' modelcomponent : modelcomponent palsync popinitial'\n p[0] = p[1] + ' ' + p[2] + ' ' + p[3] + '\\n'\n\ndef p_modelcomponent_2(p):\n ' modelcomponent : popinitial'\n p[0] = p[1]\n\ndef p_popinitial_1(p):\n 'popinitial : populationid LEFTDOUBLESQUAREBRACKET NUMBER RIGHTDOUBLESQUAREBRACKET'\n p[0] = p[1] + p[2] + p[3] + p[4]\n global orginitial\n #get popid and split up string\n popid = p[1].split()\n i=1\n #Get the organism name\n for word in popid:\n if i==3 :\n #Add organism name key and initial value\n orginitial[word] = p[3]\n i = i +1\n \ndef p_palsync(p):\n 'palsync : LEFTCURLYBRACKET listofids RIGHTCURLYBRACKET'\n p[0] = p[1] + p[2] + p[3]\n\ndef p_listofids_1(p):\n 'listofids : listofids COMMA IDENTIFIER'\n p[0] = p[1] + p[2] + p[3]\n\ndef p_listofids_2(p):\n 'listofids : IDENTIFIER'\n p[0] = p[1]\n\n#Max population\ndef p_maxpoplist(p):\n 'maxpoplist : IDENTIFIER COLON listofmaxpops SEMICOLON'\n p[0] = p[1] + p[2] + p[3] + p[4] + '\\n'\n\ndef p_listofmaxpops_1(p):\n 'listofmaxpops : listofmaxpops COMMA popmax'\n p[0] = p[1] + p[2] + p[3]\n\ndef p_listofmaxpops_2(p):\n 'listofmaxpops : popmax'\n p[0] = p[1]\n\ndef p_popmax(p):\n 'popmax : IDENTIFIER LEFTDOUBLECURLYBRACKET IDENTIFIER RIGHTDOUBLECURLYBRACKET NUMBER'\n p[0] = p[1] + p[2] + p[3] + p[4] + ' ' + p[5]\n global orgmax\n orgmax[p[3]] = p[5]\n \n# Error rule for syntax errors\ndef p_error(p):\n if p:\n print(\"Syntax ERROR at token: \",\"Type: \", p.type,\"Value:\" , p.value ,\"LineNo: \", p.lineno)\n else:\n print(\"Syntax error at End Of File\")\n\n#Functions used in parser rules\n\n#Create initialresetdicitionary\ndef createorginitialresetdictionary(p):\n global orgspecies\n global orginitialresets\n\n internalspeciesname = p[2]\n species = []\n speciesreset = {}\n\n for orgid in orgspecies:\n species = orgspecies[orgid]\n if internalspeciesname in species:\n speciesreset[internalspeciesname] = p[4]\n if orgid in orginitialresets:\n orginitialresets[orgid].update(speciesreset)\n else:\n orginitialresets[orgid] = speciesreset\n \n\n#Add on and off species\ndef addonandoffspecies():\n global orgpopactionnames\n global orgmax\n\n noworgid = ''\n popactions = {}\n result = ''\n actionbefore = False\n \n\n for nowid in orgmax:\n \n popactions = orgpopactionnames[nowid]\n\n i=1\n while i < int(orgmax[nowid]) +1:\n onactions,offactions = '',''\n actionbefore=False\n \n for action in popactions:\n infolist=popactions[action]\n if 'async' in infolist:\n if infolist[1] == '<<<':\n #Check if an action of this type has been parsed already\n if onactions == '':\n onactions,offactions = createdeleteactions(i,nowid,action,actionbefore)\n else :\n on,off = createdeleteactions(i,nowid,action,actionbefore)\n onactions += on\n offactions += off\n if infolist[1] == '>>>':\n #Check if an action of this type has been parsed already\n if onactions == '':\n onactions,offactions = createaddactions(i,nowid,action,actionbefore)\n else :\n on,off = createaddactions(i,nowid,action,actionbefore)\n onactions += on\n offactions += off\n actionbefore=True\n if 'switch1' in infolist:\n #Check if an action of this type has been parsed already\n if onactions == '':\n onactions,offactions = createsyncdeleteactions(i,nowid,action,infolist[3],actionbefore)\n else :\n on,off = createsyncdeleteactions(i,nowid,action,infolist[3],actionbefore)\n onactions += on\n offactions += off\n actionbefore= True\n if 'switch2' in infolist:\n #Check if an action of this type has been parsed already\n if onactions == '':\n onactions,offactions = createsyncaddactions(i,nowid,action,infolist[3],actionbefore)\n else :\n on,off = createsyncaddactions(i,nowid,action,infolist[3],actionbefore)\n onactions += on\n offactions += off\n actionbefore= True\n if 'repro1' in infolist:\n #Check if an action of this type has been parsed already\n if onactions == '':\n onactions,offactions = createsyncaddactions(i,nowid,action,infolist[3],actionbefore)\n else :\n on,off = createsyncaddactions(i,nowid,action,infolist[3],actionbefore)\n onactions += on\n offactions += off\n actionbefore= True\n if 'repro2' in infolist:\n #Deal with this type of popaction in internal species actions\n pass\n \n result += 'On_' + nowid + str(i) + ' = ' + onactions+';' + '\\n'\n result += 'Off_' + nowid + str(i) + ' = ' + offactions +';' + '\\n'\n i = i+1\n\n result += '\\n'\n \n return result\n\n#Create delete actions for organism that has a popaction which deletes it\ndef createdeleteactions(number,organismid,action,actionbefore):\n global orgmax\n \n result1 = ''\n result2 = ''\n \n if actionbefore == False:\n result1 = action + '_' + organismid + str(number) + ' <<'\n result2 = action + '_' + organismid + str(number) + ' >>'\n else:\n result1 = ' + ' + action + '_' + organismid + str(number) + ' <<'\n result2 = ' + ' + action + '_' + organismid + str(number) + ' >>'\n \n return result1,result2\n\n#Create add actions for organism that has a popaction which adds it\ndef createaddactions(number,organismid,action,actionbefore):\n global orgmax\n \n result1 = ''\n result2 = ''\n \n if actionbefore == False:\n result1 = action + '_' + organismid + str(number) + ' >>'\n result2 = action + '_' + organismid + str(number) + ' <<'\n else:\n result1 = ' + ' + action + '_' + organismid + str(number) + ' >>'\n result2 = ' + ' + action + '_' + organismid + str(number) + ' <<'\n \n return result1,result2 \n\n#Create sync delete actions for organism that has a popaction which deletes it\ndef createsyncdeleteactions(number,organismid,action,otherorgid,actionbefore):\n global orgmax\n \n result1 = ''\n result2 = ''\n\n #Find max value for other organism\n otherorgmax = orgmax[otherorgid]\n i = 1\n while i < int(otherorgmax) + 1:\n if actionbefore == False:\n result1 = action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' <<'\n result2 = action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' >>'\n actionbefore = True\n else :\n result1 += ' + ' + action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' <<'\n result2 += ' + ' + action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' >>'\n i = i + 1\n \n return result1,result2\n\n#Create sync add actions for organism that has a popaction which adds a new organism\ndef createsyncaddactions(number,organismid,action,otherorgid,actionbefore):\n global orgmax\n \n result1 = ''\n result2 = ''\n\n #Find max value for other organism\n otherorgmax = orgmax[otherorgid]\n i = 1\n while i < int(otherorgmax) + 1:\n if actionbefore == False:\n result1 = action + '_' + otherorgid + str(i) + '_' + organismid + str(number) + ' >>'\n result2 = action + '_' + otherorgid + str(i) + '_' + organismid + str(number) + ' <<'\n actionbefore = True\n else :\n result1 += ' + ' + action + '_' + otherorgid + str(i) + '_' + organismid + str(number) + ' >>'\n result2 += ' + ' + action + '_' + otherorgid + str(i) + '_' + organismid + str(number) + ' <<'\n i = i + 1\n \n return result1,result2\n\n#For internal species pop actions\n#Create sync add actions for organism that has a popaction which adds a new organism\ndef createsyncadd2actions(number,organismid,action,otherorgid,actionbefore):\n global orgmax\n \n result1 = ''\n result2 = ''\n\n #Find max value for other organism\n otherorgmax = orgmax[otherorgid]\n i = 1\n while i < int(otherorgmax) + 1:\n if actionbefore == False:\n result1 = action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' >>'\n result2 = action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' <<'\n actionbefore = True\n else :\n result1 += ' + ' + action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' >>'\n result2 += ' + ' + action + '_' + organismid + str(number) + '_' + otherorgid + str(i) + ' <<'\n i = i + 1\n \n return result1,result2\n\n#Add entry to popactionsdictionary\ndef addtopopactiondictionary(p):\n #Variables\n global orgpopactions\n \n #Split Popid\n popid = p[1].split()\n #Get organism id from popid\n orgid = popid[2]\n\n #split the popactions into separate words\n popacts = p[3].split()\n\n #Add orgid and popactions to dictionary\n orgpopactions[orgid] = makeactionoperatordictionary(popacts)\n\n#Create dictionary of actions and their associated operator\ndef makeactionoperatordictionary(popacts):\n popactions = {}\n actionname=''\n\n for word in popacts:\n if '+' == word:\n pass\n if '(' in word or ')' in word or '>' in word or '<' in word:\n popactions[actionname] = word\n else :\n actionname = word\n \n return popactions\n\n#Create popactionnames dictionary\ndef createpopactionnamesdictionary():\n global orgpopactions\n global orgpopactionnames\n\n \n for nowid in orgmax:\n #Get organism id from popid\n noworgid = nowid\n popactions = orgpopactions[noworgid]\n \n #Make a dictionary of key actionid and values operators for now organism id\n #All the actions that are associated with the now organism population and all the operators associated with that action\n orgsameactions = {}\n for orgid in orgpopactions:\n acts = orgpopactions[orgid]\n for action in acts:\n if action in orgsameactions:\n orgsameactions[action].append(acts[action])\n else:\n orgsameactions[action] = [acts[action]]\n\n for act in popactions:\n if act in orgsameactions:\n #Find if the action is shared with other population\n if len(orgsameactions[act]) < 2:\n #This is pop action that is not synchronised with other population\n makeandaddactiontypedictionary(noworgid,act,'async',orgsameactions[act][0],'none')\n else :\n i = 0\n while i < len(orgsameactions[act]):\n #Find the action and operator associated with now organism population\n if orgsameactions[act][i] == popactions[act]:\n sameaction = act\n sameoperator = orgsameactions[act][i]\n else:#Find the operator associated with other organism population\n differentoperator = orgsameactions[act][i]\n i = i+1\n #Find out what kind of action is synchronised by what type of operators\n #Switch action now organism population decrease other organism population increase\n if sameoperator == '<<<' and differentoperator == '>>>':\n makeandaddactiontypedictionary(noworgid,act,'switch1',sameoperator,differentoperator)\n #Switch action now organism population increase other population decrease\n if sameoperator == '>>>' and differentoperator == '<<<':\n makeandaddactiontypedictionary(noworgid,act,'switch2',sameoperator,differentoperator)\n #Reproduction action now organism population increase other population stays the same\n if sameoperator == '>>>' and differentoperator == '((+))':\n makeandaddactiontypedictionary(noworgid,act,'repro1',sameoperator,differentoperator)\n #Reproduction action now organism population stays the same other population increases\n if sameoperator == '((+))' and differentoperator == '>>>':\n makeandaddactiontypedictionary(noworgid,act,'repro2',sameoperator,differentoperator)\n\n\n#Create dictionary of action and their associated type and add to orgpopactionnames dictionary\ndef makeandaddactiontypedictionary(orgid,actionname,actiontype,sameoperator,differentoperator):\n global orgpopactionnames\n otherorgid=''\n\n #Find other organisms id\n for key in orgpopactions:\n if actionname in orgpopactions[key]:\n if key == orgid:\n pass\n else:\n otherorgid = key\n \n #If action is type async it will not have a different operator or another organism associated with it\n if actiontype == 'async':\n actioninfolist=[actiontype,sameoperator]\n else:\n actioninfolist=[actiontype,sameoperator,differentoperator,otherorgid]\n \n actiontype = {actionname:actioninfolist}\n if orgid in orgpopactionnames:\n orgpopactionnames[orgid].update(actiontype)\n else:\n orgpopactionnames[orgid] = actiontype\n\n\n \n#Add and sometimes multiply parameters based on max value for organism.\n#Add parameter to orgparameter dictionary only if they are associated to an organism.\n#Multiply parameters based on if there are species ids in their expression. \ndef addorganismidtoparameters(p):\n #Variables\n addorgid, orgid = isspeciesinparameter(p)\n global orgmax\n global orgparameters\n right = ''\n\n if addorgid == False:\n right = p[1] + ' ' + p[2] + p[3] + p[4] + '\\n' + '\\n'\n\n if addorgid == True:\n #Add parameter to more than one orgid if needed\n ids = orgid.split()\n\n a = 0\n while a' in word or '<' in word or '+' in word or '-' in word or '.' in word:\n pass\n else :\n #Add to listofactions\n listofactions.append(word)\n \n #Add org id and action list to orgactions dictionary\n if k in orgactions:\n orgactions[k].append(word)\n #Remove any duplicate actions\n orgactions[k] = list(set(orgactions[k]))\n else:\n orgactions[k] = [word]\n \n #Add organism id key and species list value pair dictionary \n speciesactions[speciesid] = listofactions\n \n listofspecies = []\n \n\n#Add multiple species behaviour based on max value for organism\ndef addorganismidtospecies(p):\n #Variables\n global orgmax\n global orgspecies\n listofspecies = []\n speciesid = p[1]\n right = ''\n #split the string of bioactions when + occurs\n acts = p[3].split(' + ')\n acts2 = ''\n \n #Go through each key value pair in orgspecies\n for k, v in orgspecies.items() :\n listofspecies = v\n #Check if speciesid is same as speciesid in list\n if speciesid in listofspecies:\n #Go through each key value pair in orgmax\n for key, value in orgmax.items():\n #Check both keys match\n if key == k:\n j = 1\n #Print out action rate + _i * max value\n while j < int(value)+1:\n acts2 = ''\n acts2 = addorganismidtospeciesactions(acts,j,key)\n initialact = addinitialaction(p[1],key,j)\n #build right hand side\n right += p[1] + '_' + key + str(j) + ' ' + p[2] + ' '+ acts2 + initialact + p[4] + '\\n'\n j=j+1\n listofspecies = []\n right += '\\n'\n return right\n\n#Add initial reset action to reset the species to initial value when it is off\ndef addinitialaction(speciesname,orgid,orgnumber):\n global orginitialresets\n result = ''\n orgresets = orginitialresets[orgid]\n if speciesname in orgresets:\n expression = orgresets[speciesname].split()\n if str(expression[1]) == speciesname:\n result = ' + Initial' + speciesname + '_' + orgid + str(orgnumber) + ' <<'\n if str(expression[3]) == speciesname:\n result = ' + Initial' + speciesname + '_' + orgid + str(orgnumber) + ' >>'\n \n return result\n \n\n#Add organism id to actions and check for popactions\ndef addorganismidtospeciesactions(string,i,k):\n #Reset result string variable\n result = ''\n actionbefore = False\n pop = False\n act = ''\n #Look through bioactions and add _i * max value\n for word in string:\n #split action by spaces\n string2 = word.split()\n #Reset if pop action boolean\n pop = False\n for word2 in string2:\n if '(' in word2 or ',' in word2 or ')' in word2 or '>' in word2 or '<' in word2 or '+' in word2:\n pass\n if checkifpopaction(word2,k) == True:\n pop = True\n #Create popactions\n act = writepopaction(word2,k,i,actionbefore,string2)\n\n if pop == True:\n #Add pop actions to result\n result += ' ' + act\n actionbefore = True\n else:\n #Check if another action was already parsed before this action\n if actionbefore == True:\n #Add choice operator\n result += ' + '\n for word2 in string2:\n if '(' in word2 or ',' in word2 or ')' in word2 or '>' in word2 or '<' in word2 or '+' in word2:\n pass\n else :\n word2 += '_' + k +str(i)\n #Add all words of action to result\n result += ' ' + word2\n actionbefore = True\n \n return result\n\n#Check if an internal species action is a pop action\ndef checkifpopaction(action,orgid):\n global orgpopactionames\n\n popactions = orgpopactionnames[orgid]\n \n if action in popactions:\n return True\n else:\n return False\n\n#Creates specific pop actions for an internal species\ndef writepopaction(action,orgid,orgnumber,actionbefore,actionstring):\n global orgpopactions\n global orgpopactionames\n global orgmax\n result1 = ''\n result2 = ''\n operator = ''\n\n popactions = orgpopactionnames[orgid]\n\n for word in actionstring:\n if word == '>>' or word =='<<':\n operator = word\n \n if action in popactions:\n infolist=popactions[action]\n if operator == '>>':\n result1,result2 = createsyncadd2actions(orgnumber,orgid,action,infolist[3],actionbefore)\n return result1\n if operator == '<<':\n result1,result2 = createsyncadd2actions(orgnumber,orgid,action,infolist[3],actionbefore)\n return result2\n else:\n return action \n \n\n\n#Add organism id key and internal species list value to global variable orgspecies \ndef addtoorgspeciesdictionary(p):\n #variables\n global orgspecies\n listofspecies = []\n biospeciesmodelcomponent = p[3].split()# split the biospeciesmodelcomponent into separate words\n for k, v in orgmax.items() :\n #If IDENTIFIER equals Organism IDENTIFIER key\n if p[1] == k:\n for word in biospeciesmodelcomponent:\n #If the word contains < or [ pass\n if '<' in word or '[' in word:\n pass\n else :#This word is an internal species of organism add to list\n listofspecies.append(word)\n #Add organism id key and species list value pair \n orgspecies[k] = listofspecies\n #Clear local list\n listofspecies = []\n \n \n#Make Bio-PEPA model component out of organism components of PAL model\ndef makemodelcomponent(p):\n #Variables\n global orgmax\n global orginitial\n orgid = p[1]\n biospeciesmodelcomponent = p[3].split()# split the biospeciesmodelcomponent into separate words\n right = ''\n bioid = ''\n \n #Go through each key value pair in orgmax\n for k, v in orgmax.items() :\n #Only print if organism is the same as key organism\n if orgid == k:\n i = 1\n #Multiply organism internal species components to max organism value\n while i < int(v)+1:\n bioid = ''\n #Go through biospeciesmodelcomponent and examine each word\n for word in biospeciesmodelcomponent:\n #Add org id only if word refers to an internal species\n if '<' in word or '[' in word:\n pass\n else :\n word += '_' + k + str(i) \n\n #Add word to bioid even if it has not changed \n bioid += word + ' '\n #Add On and Off components\n if i <= int(orginitial[k]):\n bioid += '<*> On_' + k + str(i) + ' [1] <*> ' + 'Off_' + k + str(i) + ' [0] '\n else :\n bioid += '<*> On_' + k + str(i) + ' [0] <*> ' + 'Off_' + k + str(i) + ' [1] '\n #Add a sync symbol between same organism species\n #but do not add to end of string\n i = i + 1\n if i < int(v) + 1:\n bioid+= '<*> '\n right += bioid\n return right\n\n# Build the parser and name of LALR table\nparser = yacc.yacc(tabmodule=\"parsetab2\")\n\ndef parse(model):\n result = parser.parse(model)\n return result\n\n","repo_name":"MissErinScott/PAL-Parser","sub_path":"PALParser2.py","file_name":"PALParser2.py","file_ext":"py","file_size_in_byte":38134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"24365873962","text":"import threading\r\n\r\ndef SmallCnt(Str):\r\n Cnt = 0\r\n for s in Str:\r\n if(s >= 'a' and s <= 'z'):\r\n Cnt = Cnt + 1\r\n print(\"Small Characters : \",Cnt)\r\n\r\ndef CapitalCnt(Str):\r\n Cnt = 0\r\n for s in Str:\r\n if (s >= 'A' and s <= 'Z'):\r\n Cnt = Cnt + 1\r\n print(\"Capital Characters : \",Cnt)\r\n\r\ndef DigitsCnt(Str):\r\n Cnt = 0\r\n for s in Str:\r\n if (s >= '0' and s <= '9'):\r\n Cnt = Cnt + 1\r\n print(\"Digits : \",Cnt)\r\n\r\ndef main():\r\n print(\"Enter String : \")\r\n Data = input()\r\n\r\n Small = threading.Thread(target = SmallCnt,args = (Data,))\r\n Capital = threading.Thread(target = CapitalCnt,args = (Data,))\r\n Digits = threading.Thread(target = DigitsCnt,args = (Data,))\r\n\r\n Small.start()\r\n Capital.start()\r\n Digits.start()\r\n\r\n Small.join()\r\n Capital.join()\r\n Digits.join()\r\n\r\n print(\"ID of Thread(Small) is : \",id(t1))\r\n print(\"ID of Thread(Capital) is : \",id(t2))\r\n print(\"ID of Thread(Digits) is : \",id(t3))\r\n\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"coldbean03/Python-Assignments","sub_path":"Assignment_7/Assignment7_4.py","file_name":"Assignment7_4.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"24452212289","text":"import pygit2\n\nfrom . import utilities\nfrom .models import Hunk, Line, Modifier, Section\n\n\ndef _get_diff(commit, parent=None):\n if parent is not None:\n return commit.tree.diff_to_tree(parent.tree, swap=True)\n return commit.tree.diff_to_tree(swap=True)\n\n\ndef _get_hunks(commit):\n hunks = dict()\n\n parent = commit.parents[0] if commit.parents else None\n for patch in _get_diff(commit, parent):\n path = patch.delta.new_file.path\n if patch.hunks:\n hunks[path] = list()\n for hunk in patch.hunks:\n hunks[path].append(Hunk(lines=_get_lines(hunk)))\n\n return hunks\n\n\ndef _get_lines(hunk):\n lines = list()\n\n for line in hunk.lines:\n origin = line.origin\n if origin in {'+', '-', ' '}:\n lineno = line.new_lineno if origin == '+' else line.old_lineno\n lines.append(Line(lineno, origin))\n\n return lines\n\n\ndef _get_sections(hunks):\n sections = dict()\n\n for path in hunks:\n sections[path] = list()\n for hunk in hunks[path]:\n sections[path].extend(_get_sectionsforlines(hunk.lines))\n\n return sections\n\n\ndef _get_sectionsforlines(lines):\n sections = list()\n\n index = 0\n header, footer = list(), list()\n while index < len(lines):\n # Section Header\n if not header:\n while lines[index].change == ' ':\n header.append(lines[index].number)\n index += 1\n\n # Section Body\n body = {'+': list(), '-': list()}\n while index < len(lines) and lines[index].change != ' ':\n body[lines[index].change].append(lines[index].number)\n index += 1\n\n # Section Footer\n while index < len(lines) and lines[index].change == ' ':\n footer.append(lines[index].number)\n index += 1\n\n sections.append(Section(header, body, footer))\n\n header = footer\n footer = list()\n\n return sections\n\n\nclass Repository(pygit2.Repository):\n def get_sections(self, commit):\n return _get_sections(_get_hunks(commit))\n\n def blamelines(self, commit, path, lines):\n modifiers = list()\n\n lines = ' '.join(f'-L {l},{l}' for l in lines)\n command = f'git blame {commit.id}^ --show-name -l -w {lines} -- {path}'\n\n stream, thread = utilities.run(command, self.path)\n for line in stream:\n components = line.split()\n modifier = Modifier(sha=components[0], path=components[1])\n modifiers.append(modifier)\n thread.join()\n\n return modifiers\n","repo_name":"samaritan/archeogit","sub_path":"archeogit/repository.py","file_name":"repository.py","file_ext":"py","file_size_in_byte":2577,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"27565467650","text":"#!/usr/bin/env python\n\nimport requests\nimport os\nimport json\nimport re\nfrom lxml import etree, html\nfrom requests.adapters import HTTPAdapter\n\n__author__ = 'Toan Nguyen'\n__version__ = '1.0.0'\n\ntitle = \"\"\"\n ___ ___ __ __ __ __ __ __ \n /\\ | | | | |__ |__) / \\ / \\ |__/ /__` / ` / \\ |\\/| \n/~~\\ |___ |___ | | |___ |__) \\__/ \\__/ | \\ .__/ .\\__, \\__/ | | \n \n ___ __ __ __ __ __ ___ __ \n| | |__ |__) /__` / ` |__) /\\ |__) |__ |__) \n|/\\| |___ |__) .__/ \\__, | \\ /~~\\ | |___ | \\ \n \n By : Toan Nguyen\n me @ nntoan.com\n Version : 1.0.0\nA tool to download all ebook's available on\nwww.allitebooks.com and save them in current\ndirectory.\n\"\"\"\n\naite_sm = 'http://www.allitebooks.com/post-sitemap{0}.xml'\n\nurls = []\n\nprint(title)\n\nwith open('list-of-post-urls-allitbooks.md', 'w') as fp:\n for i in range(1, 8):\n fp.write('\\n\\n## Post Sitemap {0}\\n\\n'.format(i))\n req = requests.get(aite_sm.format(i))\n root = etree.fromstring(req.content)\n for sitemap in root:\n url = sitemap.getchildren()[0].text\n fp.write('[{0}]({1})\\n'.format(url, url))\n urls.append(url)\n print('Post Sitemap ' + str(i) + ' is done')\n\ndown_dict = {}\n\nif not os.path.exists('all-it-ebooks'):\n os.mkdir('all-it-ebooks')\n\nsave_dir = os.path.abspath(os.path.join(os.curdir, 'all-it-ebooks'))\n\nprint('Crawling in Progress ......\\n')\nfor i, url in enumerate(urls[1:]):\n page = requests.Session()\n page.mount(url, HTTPAdapter(max_retries=5))\n page = page.get(url)\n tree = html.fromstring(page.content) \n down_link = tree.xpath(\"//*[@class=\\\"download-links\\\"]/a/@href\")\n file_name = down_link[0].split('/')[-1]\n title = re.sub('[^A-Za-z0-9]+', '-', file_name.split('.')[0]) \n down_dict[title] = down_link[0] \n save_loc = os.path.join(save_dir, file_name)\n\n #Starting to download the files\n data = requests.get(down_link[0], stream=True)\n if not os.path.exists(save_loc):\n print('\\nNow writing {0} - {1}'.format(i + 1, file_name))\n with open(save_loc, 'wb') as f:\n print(url) \n for chunk in data.iter_content(chunk_size=4096):\n if chunk:\n f.write(chunk)\n else:\n print('\\nFile Exists. Skipped {0} - {1}'.format(i + 1, file_name))\n\nprint('\\nAll Urls have been crawled and saved.')\nprint('\\nWriting links to JSON file.\\n')\n\nwith open('all-it-ebooks-download-links.json', 'w') as fp:\n json.dump(down_dict, fp, indent=4)\n print('\\t----- Writing Complete')\n","repo_name":"nntoan/aite-crawler","sub_path":"aite_crawler.py","file_name":"aite_crawler.py","file_ext":"py","file_size_in_byte":2840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"13071763786","text":"#!/usr/bin/python3\n\nif __name__ == '__main__':\n from sys import argv\n import requests\n url = 'https://api.github.com/repos/{}/{}/commits'\\\n .format(argv[2], argv[1])\n r = requests.get(url)\n results = ''\n count = 0\n for commit in r.json():\n count += 1\n if count > 10:\n break\n results += '{}: {}\\n'.format(commit['sha'],\n commit['commit']['author']['name'])\n print(results, end=\"\")\n","repo_name":"SravanthiSinha/holbertonschool-webstack_basics","sub_path":"0x03-python_web_scraping/7-github_commits.py","file_name":"7-github_commits.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"72139764714","text":"def next_bigger(n):\n res = [int(x) for x in str(n)]\n last = res[-1:][0]\n changed = False\n new =[]\n changedPos = 0\n temp= 1\n\n for i in reversed(res[:-1]):\n temp +=1\n if i < last and not changed:\n changed = True\n changedPos = temp\n new.insert(0, last)\n last = i\n\n new.insert(0, last)\n if not changed: return -1\n return(int(''.join(str(e) for e in second(new, changedPos))))\n\ndef second(n, x):\n\n toSort = n[-x:]\n toChange = sorted([i for i in n[-x+1:] if i > int(n[-x:-x+1][0]) ])[0]\n toSort.remove(toChange)\n new = n[:-x]\n new.append(toChange)\n new = new + sorted(toSort)\n return new\n\n\n# below - not in the answer\ndef main():\n\n # assert next_bigger(12234) == 12243\n # assert next_bigger(12341) == 12431\n assert next_bigger(9876543210) == -1\n assert next_bigger(59884848459853) == 59884848483559\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"danncti/my_codewars_solutions","sub_path":"next-number-211018.py","file_name":"next-number-211018.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"72968168553","text":"from controller_db_read import *\nfrom controller_db_write import *\n\n\ndef create_stacks_table(qb_num, dst_num, inc_num_array, exc_num_array):\n players = get_players_from_table()\n dst = \"\"\n included_players = []\n excluded_players = []\n if qb_num == \"\":\n return False\n quarterback = players[qb_num]\n if dst_num != \"\":\n dst = players[dst_num]\n if inc_num_array:\n for element in inc_num_array:\n included_players.append(players[int(element) - 1])\n if exc_num_array:\n for element in exc_num_array:\n excluded_players.append(players[int(element) - 1])\n write_stacks_table(quarterback, dst, included_players, excluded_players)\n return True\n\n","repo_name":"adamnoor/stack_builder","sub_path":"controller_create_stacks.py","file_name":"controller_create_stacks.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"2844663551","text":"# import sys\n\n# A,B,C = map(int, sys.stdin.readline().split())\n# tmp = A\n# for i in range(1,B+1):\n# if tmp < C:\n# tmp **= i\n# elif tmp > C:\n# tmp %= C\n\n# print(tmp)\n\nimport sys\na,b,c = map(int,sys.stdin.readline().split())\n\ndef multi (a,n):\n if n == 1:\n return a%c\n else:\n tmp = multi(a,n//2)\n if n %2 ==0:\n return (tmp * tmp) % c\n else:\n return (tmp * tmp *a) %c\n \nprint(multi(a,b))","repo_name":"move-geun/baekjoon_py","sub_path":"분할정복/1629_곱셈/s1.py","file_name":"s1.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"6194918575","text":"# 크레인 인형뽑기 게임, 터트려서 사라진 인형의 개수 구하기\ndef solution(board, moves):\n answer = 0\n moved = [0] # 인덱스 에러 방지\n for i in moves: # moves의 1,5,3,5,1,2,1,4가 i에 순서대로 대입\n i -= 1\n for j in range(len(board)): # range는 정수로 반복해야 하므로 len으로 board요소의 길이를 잰 다음 range로 반복\n if board[j][i] != 0: # board[j][i]의 값이 0이 아닐때 -> board\n moved.append(board[j][i]) # moved리스트로 해당 값을 옮기고\n board[j][i] = 0 # 0으로 만듬\n if moved[-1] == moved[-2]: # 맨 뒤의 요소 2개가 같다면\n moved.pop(-1) # 같은 요소 제거\n moved.pop(-1)\n answer += 2 # 인형이 2개씩 없어지므로\n break # 크레인으로 인형을 하나 옮기면 다음 moves로 넘어가야함\n return answer","repo_name":"sjoongh/algorithm-study","sub_path":"python/프로그래머스/크레인 인형뽑기.py","file_name":"크레인 인형뽑기.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"5554948835","text":"import numpy as np\n\n\nclass CUSUM:\n def __init__(self, n_nodes, M, eps, h):\n self.M = M\n self.eps = eps\n self.h = h\n self.t = np.zeros((n_nodes, n_nodes))\n self.reference = np.zeros((n_nodes, n_nodes))\n self.g_plus = np.zeros((n_nodes, n_nodes))\n self.g_minus = np.zeros((n_nodes, n_nodes))\n\n def update(self, samples):\n self.t += 1\n still_in_initial_phase = self.t <= self.M\n self.reference += np.where(still_in_initial_phase, samples / self.M, 0)\n\n s_plus = (samples - self.reference) - self.eps\n s_minus = -(samples - self.reference) - self.eps\n self.g_plus = np.maximum(0, self.g_plus + s_plus)\n self.g_minus = np.maximum(0, self.g_minus + s_minus)\n\n changes_detected = (self.g_plus > self.h) | (self.g_minus > self.h)\n return changes_detected\n\n def reset(self, indices):\n self.t[indices] = 0\n self.g_plus[indices] = 0\n self.g_minus[indices] = 0\n self.reference[indices] = 0\n","repo_name":"emaroppo/ola-msi-project","sub_path":"utils/change_detection.py","file_name":"change_detection.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"28748489112","text":"import pytest\nimport sqlalchemy as sa\nfrom fastapi import status as status_codes\nfrom httpx import AsyncClient\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom eda_server.db import models\n\nTEST_RULESET_SIMPLE = \"\"\"\n---\n- name: Test simple\n hosts: all\n sources:\n - name: range\n range:\n limit: 5\n rules:\n - name:\n condition: event.i == 1\n action:\n debug:\n\"\"\"\n\n\n@pytest.mark.asyncio\nasync def test_create_rulebook(client: AsyncClient, db: AsyncSession):\n response = await client.post(\n \"/api/rulebooks\",\n json={\n \"name\": \"test-ruleset-1.yml\",\n \"rulesets\": TEST_RULESET_SIMPLE,\n },\n )\n assert response.status_code == status_codes.HTTP_200_OK\n data = response.json()\n assert \"id\" in data\n assert data[\"name\"] == \"test-ruleset-1.yml\"\n\n rulesets = (await db.execute(sa.select(models.rulesets))).all()\n assert len(rulesets) == 1\n ruleset = rulesets[0]\n assert ruleset[\"rulebook_id\"] == data[\"id\"]\n assert ruleset[\"name\"] == \"Test simple\"\n\n rules = (await db.execute(sa.select(models.rules))).all()\n assert len(rules) == 1\n rule = rules[0]\n assert rule[\"ruleset_id\"] == ruleset[\"id\"]\n assert rule[\"action\"] == {\"debug\": None}\n\n\n@pytest.mark.asyncio\nasync def test_create_inventory(client: AsyncClient):\n response = await client.post(\n \"/api/inventory/\",\n json={\n \"name\": \"test-inventory-01\",\n \"inventory\": \"all: {}\", # noqa: P103\n },\n )\n assert response.status_code == status_codes.HTTP_200_OK\n data = response.json()\n assert \"id\" in data\n assert data[\"name\"] == \"test-inventory-01\"\n assert data[\"inventory\"] == \"all: {}\" # noqa: P103\n\n\n@pytest.mark.asyncio\nasync def test_list_inventories(client: AsyncClient, db: AsyncSession):\n query = sa.insert(models.inventories).values(\n name=\"test-list-inventories-01\", inventory=\"{}\" # noqa: P103\n )\n result = await db.execute(query)\n\n response = await client.get(\"/api/inventories/\")\n\n assert response.status_code == status_codes.HTTP_200_OK\n data = response.json()\n assert data == [\n {\n \"id\": result.inserted_primary_key[0],\n \"name\": \"test-list-inventories-01\",\n \"inventory\": \"{}\", # noqa: P103\n \"project_id\": None,\n }\n ]\n\n\n@pytest.mark.asyncio\nasync def test_read_inventory_not_found(client: AsyncClient):\n\n response = await client.get(\"/api/inventory/42\")\n\n assert response.status_code == status_codes.HTTP_404_NOT_FOUND\n","repo_name":"benthomasson/ansible-events-ui2","sub_path":"tests/integration/api/test_inventory.py","file_name":"test_inventory.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"12779936447","text":"import matplotlib.pyplot as plt\nfrom load_csv import load\n\n\n# Reference: https://tinyurl.com/bddt7c95\n\n\ndef main():\n '''\n Load life expectancy CSV file, and plot it using matplotlib.\n '''\n try:\n dataset = load(\"life_expectancy_years.csv\")\n\n # Extract the rows where the 'country' column is equal to 'Malaysia'\n filter_mask = dataset['country'] == 'Malaysia'\n malaysia_data = dataset[filter_mask]\n\n # Sets the country column as the index of malaysia_data\n malaysia_data.set_index('country', inplace=True)\n\n # print(malaysia_data)\n\n start_index = 0 # Index corresponding to the year 1800\n end_index = 251 # Index corresponding to the year 2050\n\n # return the column names of malaysia_data as panda index\n years = malaysia_data.columns.astype(int).to_numpy()\n years = years[start_index:end_index]\n\n # Select the row corresponding Malaysia\n life = malaysia_data.loc['Malaysia'].to_numpy()\n life = life[start_index:end_index]\n\n # Initialize a plot\n fig, axs = plt.subplots()\n\n # Supertitle\n fig.suptitle(\"Malaysia Life Expectancy Projections\")\n\n # Plot Years as x-axis and Life as y-axis\n axs.plot(years, life)\n axs.set_xlabel(\"Years\")\n axs.set_xticks([1800, 1840, 1880, 1920, 1960, 2000, 2040])\n axs.set_xticklabels(\n ['1800', '1840', '1880', '1920', '1960', '2000', '2040'])\n axs.set_ylabel(\"Life Expectancy\")\n\n # plt.show()\n except Exception as e:\n print(f\"{type(e).__name__}: {e}\")\n except KeyboardInterrupt:\n print(\"Keyboard interuption, bye~\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"alvinyap510/42_python_for_data_science_piscine-2_datatable","sub_path":"ex01/aff_file.py","file_name":"aff_file.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"33402318329","text":"import os\nimport datetime\nfrom collections import namedtuple\nimport click\nfrom tabulate import tabulate\nimport pdftotext\nimport keyboard\n\nfrom .utils import truncate_long_text\nfrom .models.book import Book\n\n\nclass BookAction:\n def __init__(\n self, books_dir=None, session=None, speak_engine=None, start_page=0, **kwargs\n ):\n self.books_dir = books_dir\n self.books = []\n self.session = session\n self.speak_engine = speak_engine\n self.current_page = 0\n self.start_page = start_page\n self.from_beginning = kwargs.get(\"from_beginning\", False)\n\n def bookTup(self):\n return namedtuple(\"Book\", [\"num\", \"book_name\", \"book_path\"])\n\n @staticmethod\n def warning(text):\n \"\"\" Show warning text message using `click.secho` with styling \"\"\"\n fg = \"yellow\"\n bold = True\n click.echo()\n click.secho(text, fg=fg, bold=bold)\n\n def books_to_print(self):\n books = []\n headers = [\"\", \"Book name\", \"Location\"]\n for book in self.books:\n books.append(\n [\n book.num,\n truncate_long_text(book.book_name),\n truncate_long_text(book.book_path),\n ]\n )\n return books, headers\n\n def books_from_db(self):\n books = self.session.query(Book).all()\n headers = [\"ID\", \"Book name\", \"Location\"]\n books_to_print = []\n for book in books:\n books_to_print.append(\n [\n book.id,\n truncate_long_text(book.name),\n truncate_long_text(book.book_path),\n ]\n )\n return books_to_print, headers\n\n def print_books(self, saved=False):\n if saved:\n books, headers = self.books_from_db()\n else:\n books, headers = self.books_to_print()\n click.echo(tabulate(books, headers, tablefmt=\"grid\"))\n\n def books_scanner(self, echo=True, saved=False):\n \"\"\"\n Scan all the books available in the given directory.\n The books are not going to be stored in the database yet.\n \"\"\"\n\n num = 0\n BookTup = self.bookTup()\n\n for bk in os.listdir(self.books_dir):\n book_name = bk.split(\".pdf\")[0]\n num += 1\n book_path = os.path.join(self.books_dir, bk)\n if os.path.isfile(book_path):\n book = BookTup(num, book_name, book_path)\n self.books.append(book)\n if echo:\n self.print_books(saved=saved)\n\n def store_books(self):\n \"\"\"\n Scan and store the books in the given directory to the database\n \"\"\"\n books_to_store = []\n self.books_scanner(echo=False)\n book_query = self.session.query(Book)\n for book in self.books:\n if not book_query.filter_by(name=book.book_name).count():\n books_to_store.append(\n Book(name=book.book_name, book_path=book.book_path)\n )\n if books_to_store:\n self.session.add_all(books_to_store)\n self.session.commit()\n click.echo(\"Stored {} books to database\".format(len(books_to_store)))\n return\n\n click.echo(\"No books to store\")\n\n def read_book(self, book):\n if not self.speak_engine:\n raise Exception(\"pyttsx3 is not initialized\")\n\n with open(book.book_path, \"rb\") as book_file:\n # convert deque to list\n # TODO: use deque but have to make it slicable\n pages = list(pdftotext.PDF(book_file))\n\n current_page = book.stop_at_page\n if not self.from_beginning:\n if self.start_page:\n pages = pages[self.start_page :]\n self.current_page = self.start_page\n elif current_page and not self.start_page:\n pages = pages[current_page:]\n self.current_page = current_page\n\n click.clear()\n click.echo()\n try:\n for ind, page in enumerate(pages):\n if self.from_beginning or (not self.start_page and not current_page):\n self.current_page = ind\n\n click.echo(page)\n click.echo()\n click.secho(\"Press Ctrl + C to stop reading\", bold=True)\n\n self.speak_engine.say(page)\n self.speak_engine.runAndWait()\n click.clear()\n\n # TODO: Make it possible to skip the current page and jump to the next page when press a key.\n # Maybe try asyncio?\n # Already added `keyboard` library for this.\n except KeyboardInterrupt:\n click.echo()\n click.echo(\"Stoped at page {}\".format(self.current_page))\n\n click.echo(\"Do you want to save the current progess? [y/n] \", nl=False)\n answer = click.getchar()\n click.echo()\n if answer in [\"y\", \"Y\", \"yes\"]:\n book.stop_at_page = self.current_page\n book.last_read = datetime.datetime.utcnow()\n self.session.add(book)\n self.session.commit()\n click.echo(\"Your progess is saved.\")\n else:\n click.echo(\"Abort!\")\n","repo_name":"dangquangdon/Python-Book-Reader-CLI","sub_path":"pybookreader/book_actions.py","file_name":"book_actions.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"30886549721","text":"import math\r\n#from osgeo import ogr\r\n#from osgeo import osr\r\nimport csv\r\nimport pyproj\r\nfrom pyproj import Proj, transform\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef euclidean_dist(A,B):\r\n sum = 0\r\n if len(A) == len(B):\r\n points = len(A)\r\n for i in range(points):\r\n sum+=math.sqrt((A[i][0]-B[i][0])**2+(A[i][1]-B[i][1])**2)\r\n return sum/points\r\n else:\r\n return -1\r\n\r\n\r\ndef euclidian_traj (inputFile, outFile):\r\n\r\n counter_user = 0\r\n user_coor_dict = {} # a dictionary that stores the longitudes of all users.\r\n with open(inputFile) as f:\r\n file = f.readline()\r\n\r\n file = f.readlines() # read all the lines in the weibo file\r\n previous_user_id = -1 # track if current user is the first user\r\n for j in file:\r\n data =j.strip('\\n').split('\\t')\r\n current_user_id = data[0] # get user id of the current row\r\n current_user_lon = float(data[2]) # get longitude of the current row\r\n current_user_lat= float(data[1]) # get latitude of the current row\r\n if current_user_id in user_coor_dict: # if some of this user's lat/lon have been added to the dictionary\r\n user_coor_dict[current_user_id].append((current_user_lon,current_user_lat)) # keep adding to the lon/lat list where the key is the user id\r\n else: # if this is a new user\r\n # create a new key\r\n user_coor_dict[current_user_id]= []\r\n # add current lat/lon as the first point of this user\r\n user_coor_dict[current_user_id].append((current_user_lon, current_user_lat))\r\n counter_user += 1\r\n #print('user ' + str(counter_user) + ' extracted')\r\n previous_user_id = current_user_id\r\n\r\n\r\n id1 = []\r\n id2 = []\r\n euc_dist = []\r\n euc_sum = 0\r\n euc_count = 0\r\n items = user_coor_dict.keys() #get all unique id\r\n items_corr = user_coor_dict.keys()\r\n for i in items: # go through each user_id\r\n for j in items_corr:\r\n if i == j: # check for a pair(eliminate zero distance)\r\n continue\r\n else:\r\n x = user_coor_dict.get(i) #get unique values for user in first cycle\r\n y = user_coor_dict.get(j) #get unique values for each user in second cycle\r\n eucl = euclidean_dist(x,y)\r\n a = float(eucl)#calculate euclidean distance\r\n if a != -1:\r\n id1.append(i)\r\n id2.append(j)\r\n euc_dist.append(eucl)\r\n if float(eucl)<10000:\r\n euc_sum += float(eucl)\r\n euc_count +=1\r\n else:\r\n continue\r\n\r\n\r\n\r\n if len(items_corr) == 1: #check if second loop is not equal to 1\r\n break\r\n\r\n print (euc_sum/euc_count)\r\n\r\n\r\n\r\n rows = zip(id1,id2,euc_dist)\r\n\r\n out_file = open(outFile, 'w') # open output file\r\n out_file.write (\"id1 id2 euc_dist\\n\")\r\n writer = csv.writer((out_file), delimiter = '\\t')\r\n for row in rows:\r\n writer.writerow(row)\r\n\r\n\r\n#euclidian_traj('transformed_sorted.txt',\"transformed_euc_dist.txt\")\r\n","repo_name":"lnshbln/twitterTopicAnalysis","sub_path":"euclidianDist.py","file_name":"euclidianDist.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"31177551045","text":"\"\"\"Extension for tox to use on environment creation to install artifacts-keyring to virtualenv automatically.\"\"\"\nimport sys\nfrom typing import Any\n\nfrom azure_devops_artifacts_helpers import __name__, __package__\nfrom azure_devops_artifacts_helpers.seed import EXT_DIR\n\ntry:\n import tox\nexcept ImportError: # pragma: no cover\n tox = False\n\nNAME = __name__ or __package__\nCLI_PARAM = NAME.replace('_', '-')\n\n\ndef _add_option(parser: Any) -> None:\n \"\"\"Common method to add cli option.\"\"\"\n parser.add_argument(f\"--disable-{CLI_PARAM}\", action=\"store_true\",\n default=False, help=\"Disable azure devops artifacts keyring installation\")\n\n\nif tox: # noqa: C901\n\n try:\n tox_v4 = tox.version.version_tuple[0] > 3\n except AttributeError:\n tox_v4 = False # Version 3\n\n if tox_v4:\n from tox.plugin import impl\n from tox.tox_env.api import ToxEnv\n from tox.config.cli.parser import ToxParser\n from tox.config.sets import EnvConfigSet\n from tox.session.state import State\n\n @impl # type: ignore\n def tox_on_install(tox_env: ToxEnv, arguments: Any, section: str, of_type: str) -> None: # noqa: U100\n \"\"\"Called before executing an installation command to install artifacts keyring.\"\"\"\n if tox_env.conf.load(NAME) and not getattr(tox_env.options, f'disable_{NAME}'):\n # To force install from the bundled wheels, we use the following pip flags:\n # -f EXT_DIR --no-index\n # This makes pip look in the bundled wheel dir, and not use the index\n tox_env.installer._execute_installer(['artifacts-keyring', '-f', EXT_DIR, '--no-index'], 'pre_deps')\n\n @impl # type: ignore\n def tox_add_env_config(env_conf: EnvConfigSet, state: State) -> None: # noqa: U100\n \"\"\"Add environment config option for ignoring/skipping azure_devops_artifacts_helpers install.\"\"\"\n env_conf.add_config(keys=NAME,\n of_type=bool,\n default=True,\n desc='Use Azure Devops Artifacts Helpers to seed env')\n\n @impl # type: ignore\n def tox_add_option(parser: ToxParser) -> None:\n \"\"\"Add commandline option.\"\"\"\n _add_option(parser)\n\n def tox_testenv_create(venv: Any, action: str) -> None: # noqa: U100\n \"\"\"Empty method if tox v4 installed.\"\"\"\n\n def tox_addoption(parser: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox v4 installed.\"\"\"\n\n def tox_configure(config: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox v4 installed.\"\"\"\n\n else:\n from tox.config import Parser, TestenvConfig\n from tox.venv import cleanup_for_venv, _SKIP_VENV_CREATION, reporter, VirtualEnv\n\n @tox.hookimpl # type: ignore\n def tox_testenv_create(venv: VirtualEnv, action: str) -> bool:\n \"\"\"Hook for creating tox testenv, monkeypatched from tox.\"\"\"\n if getattr(venv.envconfig, NAME):\n config_interpreter = venv.getsupportedinterpreter()\n args = [sys.executable, \"-m\", \"virtualenv\"]\n if venv.envconfig.sitepackages:\n args.append(\"--system-site-packages\")\n if venv.envconfig.alwayscopy:\n args.append(\"--always-copy\")\n if not venv.envconfig.download:\n args.append(\"--no-download\")\n # add interpreter explicitly, to prevent using default (virtualenv.ini)\n args.extend([\"--python\", str(config_interpreter)])\n # Add seeder explicitly\n args.extend([\"--seeder\", 'azdo-pip'])\n\n cleanup_for_venv(venv)\n\n base_path = venv.path.dirpath()\n base_path.ensure(dir=1)\n args.append(venv.path.basename)\n if not _SKIP_VENV_CREATION:\n try:\n venv._pcall(\n args,\n venv=False,\n action=action,\n cwd=base_path,\n redirect=reporter.verbosity() < reporter.Verbosity.DEBUG,\n )\n except KeyboardInterrupt: # pragma: no cover\n venv.status = \"keyboardinterrupt\"\n raise\n # Return non-None to indicate plugin has completed\n return True\n\n @tox.hookimpl # type: ignore\n def tox_addoption(parser: Parser) -> None:\n \"\"\"Add the cli option.\"\"\"\n _add_option(parser)\n\n @tox.hookimpl # type: ignore\n def tox_configure(config: TestenvConfig) -> None:\n \"\"\"Set the config and process the cli option.\"\"\"\n for env in config.envlist:\n value = not getattr(config.option, f'disable_{NAME}')\n try:\n value &= config.envconfigs[env]._reader.getbool(NAME, True)\n except AttributeError:\n value &= True\n setattr(config.envconfigs[env], NAME, value)\n\n def tox_add_env_config(env_conf: Any, state: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox v3 installed.\"\"\"\n\n def tox_add_option(parser: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox v3 installed.\"\"\"\n\nelse: # pragma: no cover\n def tox_testenv_create(venv: Any, action: str) -> None: # noqa: U100\n \"\"\"Empty method if tox not installed.\"\"\"\n\n def tox_on_install(venv: Any, action: str) -> None: # noqa: U100\n \"\"\"Empty method if tox not installed.\"\"\"\n\n def tox_addoption(parser: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox not installed.\"\"\"\n\n def tox_configure(config: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox not installed.\"\"\"\n\n def tox_add_env_config(env_conf: Any, state: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox not installed.\"\"\"\n\n def tox_add_option(parser: Any) -> None: # noqa: U100\n \"\"\"Empty method if tox not installed.\"\"\"\n","repo_name":"djpugh/azure_devops_artifacts_helpers","sub_path":"src/azure_devops_artifacts_helpers/plugins/tox.py","file_name":"tox.py","file_ext":"py","file_size_in_byte":6184,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"72"}
+{"seq_id":"11962708975","text":"def Computegcd(num1, num2): # initializing a function for gcd computation\r\n while num2 != 0:\r\n num1, num2 = num2, num1 % num2\r\n return num1\r\n\r\ndef is_coprime(x, y): # initializing a function for checking gcd==1 for coprime computation\r\n return Computegcd(x, y) == 1\r\n# getting inputs to compute co-prime\r\nnum1 = int(input(\"Enter first number: \"))\r\nnum2 = int(input(\"Enter second number: \"))\r\n\r\nif is_coprime(num1, num2):\r\n print(num1, \"and\", num2, \"are coprime\")\r\nelse:\r\n print(num1, \"and\", num2, \"are not coprime\")\r\n\r\n\r\n\r\n \r\n","repo_name":"Pooja2420/Python","sub_path":"assign1.2.py","file_name":"assign1.2.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"38762899220","text":"def order_handel(data,date):\n anchor = {'seat':[]}\n for i,item in enumerate(data):\n if i == 0:\n anchor['cinemas_name'] = item[0]\n anchor['img'] = item[2]\n anchor['movie_name'] = item[3]\n anchor['money'] = len(data) * item[4]\n anchor['room'] = item[5]\n anchor['buy_date'] = item[8]\n anchor['batch'] = item[10] #用户订单号\n anchor['tf'] = item[9]\n anchor['movie_date'] = date\n anchor['seat'].append(\n {'id':item[1],'row':item[6],'clumn':item[7]}\n )\n return anchor","repo_name":"potatopeople/movie","sub_path":"movie-h/app/data_handle/user_order.py","file_name":"user_order.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"21555082200","text":"import locale\nfrom collections import Counter\nfrom functools import wraps\nfrom random import sample\n\nfrom flask import flash, render_template, redirect, session\n\nfrom app import app, db\nfrom models import Category, Meal, User, Order\nfrom forms import OrderForm, RegistrationForm, LoginForm\nfrom flask_admin import Admin, AdminIndexView\nfrom flask_admin.contrib.sqla import ModelView\n\n# set language for datetime object\nlocale.setlocale(locale.LC_ALL, 'ru_RU')\n\n\n# Декоратор авторизации\ndef login_required(f):\n @wraps(f)\n def decorated_function(*args, **kwargs):\n print('login_required')\n if not session.get('user'):\n return redirect('/login/')\n return f(*args, **kwargs)\n\n return decorated_function\n\n\ndef cart_info():\n \"\"\"\n Function is used for compute data\n for render in nav_menu and 'cart.html'.\n \"\"\"\n cart = session.get('cart', [])\n meals = [Meal.query.filter_by(id=pos).first() for pos in cart]\n unique_meals = Counter(meals)\n total_price = sum([meal.price for meal in meals])\n return cart, unique_meals, total_price\n\n\n@app.route('/')\ndef main():\n # Get session cart and total_price for nav_menu\n cart, _, total_price = cart_info()\n\n # Get categories from DB\n categories_query = Category.query.all()\n # Create dictionary with 3 random meal from each category\n random_meals = {}\n for cat in categories_query:\n random_meals[cat.title] = sample(cat.meals, 3)\n\n return render_template('main.html', random_meals=random_meals,\n meals_count=len(cart), total_price=total_price)\n\n\n@app.route('/addtocart/')\ndef add_to_cart(meal_id):\n # Save user food choice in session\n cart = session.get('cart', [])\n cart.append(meal_id)\n session['cart'] = cart\n # If user previously delete position from cart, change state for banner\n if session.get('del_banner'):\n session['del_banner'] = False\n return redirect('/cart/')\n\n\n@app.route('/delete/')\ndef delete_meal():\n cart = session.get('cart')\n cart.pop()\n session['cart'] = cart\n # This is used to display 'delete' banner at 'cart.html'\n session['del_banner'] = True\n return redirect('/cart/')\n\n\n@app.route('/cart/', methods=['GET', 'POST'])\ndef cart_page():\n # Get session based user data\n cart, unique_meals, total_price = cart_info()\n\n form = OrderForm()\n # Insert user mail in form if user is logged in\n if session.get('user'):\n form.mail.data = session['user']['mail']\n if form.validate_on_submit():\n # Get user from DB\n user = User.query.filter_by(mail=form.mail.data).first()\n # Create order for user\n order = Order(name=form.name.data, address=form.address.data,\n phone=form.phone.data, total_cost=total_price)\n order.status = 'Done'\n order.user = user\n order.meals = [Meal.query.filter_by(id=pos).first() for pos in cart]\n # If cart is empty - don't create empty order in DB\n if not cart:\n flash('В корзине пусто. Выберите блюдо для заказа.')\n return redirect('/')\n # If cart is not empty - create order for user in DB\n db.session.add(order)\n db.session.commit()\n\n # delete session cart for user after order\n if cart:\n session.pop('cart')\n return redirect('/ordered/')\n\n return render_template('cart.html', form=form, meals=unique_meals,\n meals_count=len(cart), total_price=total_price)\n\n\n@app.route('/account/')\n@login_required\ndef account():\n cart, _, total_price = cart_info()\n mail = session.get('user')['mail']\n user = User.query.filter_by(mail=mail).first()\n client_orders = {}\n orders = Order.query.filter_by(user=user).order_by(Order.date_created.desc()).all()\n for order in orders:\n client_orders[order.id] = []\n client_orders[order.id].append(Counter(order.meals))\n client_orders[order.id].append(order.total_cost)\n client_orders[order.id].append(order.date_created.strftime('%-d %B'))\n\n return render_template('account.html', meals_count=len(cart),\n total_price=total_price, orders=client_orders)\n\n\n@app.route('/login/', methods=['GET', 'POST'])\ndef login():\n form = LoginForm()\n\n if form.validate_on_submit():\n user = User.query.filter_by(mail=form.mail.data).first()\n if user and user.password_valid(form.password.data):\n session['user'] = {'id': user.id,\n 'mail': user.mail\n }\n flash('Рады видеть Вас снова!')\n return redirect('/account/')\n elif not user:\n form.mail.errors.append('Такого пользователя не существует. Проверьте введенную почту.')\n else:\n form.password.errors.append('Неверный пароль.')\n\n return render_template('login.html', form=form)\n\n\n@app.route('/register/', methods=['GET', 'POST'])\ndef register():\n form = RegistrationForm()\n\n if form.validate_on_submit():\n user = User()\n user.mail = form.mail.data\n user.password = form.password.data\n db.session.add(user)\n db.session.commit()\n session['user'] = {'id': user.id,\n 'mail': user.mail\n }\n flash(f'Пользователь: {form.mail.data} с паролем: {form.password.data} зарегистрирован')\n return redirect('/')\n return render_template('register.html', form=form)\n\n\n@app.route('/logout/')\ndef logout():\n session.clear()\n return redirect('/')\n\n\n@app.route('/ordered/')\ndef ordered():\n return render_template('ordered.html')\n\n\n# Hide Admin panel from unauthorized users\nclass MyAdminIndexView(AdminIndexView):\n def is_accessible(self):\n if not session.get('user'):\n return False\n return True\n\n\nclass MyUserView(ModelView):\n column_exclude_list = ['password_hash']\n column_searchable_list = ['mail']\n\n can_create = True\n can_edit = False\n can_delete = True\n\n def is_accessible(self):\n if not session.get('user'):\n return False\n return True\n\n\nclass MyCategoryView(ModelView):\n def is_accessible(self):\n if not session.get('user'):\n return False\n return True\n\n\nclass MyMealView(ModelView):\n def is_accessible(self):\n if not session.get('user'):\n return False\n return True\n\n\nclass MyOrderView(ModelView):\n def is_accessible(self):\n if not session.get('user'):\n return False\n return True\n\n\nadmin = Admin(app, index_view=MyAdminIndexView())\nadmin.add_view(MyUserView(User, db.session))\nadmin.add_view(MyCategoryView(Category, db.session))\nadmin.add_view(MyMealView(Meal, db.session))\nadmin.add_view(MyOrderView(Order, db.session))\n","repo_name":"Lexxkit/Project_Food_Shop","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"9447128488","text":"class Solution:\n\tdef longestPalindrome(self, s):\n\t\tdp = [[0 for _ in range(len(s)+1)] for _ in range(len(s))]\n\t\tindex = 0\n\t\tlongest = 1\n\n\t\tfor i in range(0, len(s)):\n\t\t\tfor j in range(0, len(s)):\n\t\t\t\tif s[i] == s[j]:\n\t\t\t\t\tdp[i][j] = dp[i-1][j+1]+1\n\t\t\t\t\t# in case there could be some words not palindrome\n\t\t\t\t\tif dp[i][j] > longest and abs(i-j) <= longest:\n\t\t\t\t\t\tlongest = dp[i][j]\n\t\t\t\t\t\tindex = j\n\t\treturn s[index:(index+longest)]\n\n\n\ndef main():\n\t# s = \"babad\"\n\t# s = \"cbbd\"\n\t# s = \"abb\"\n\ts = \"ccc\"\n\ts = \"abcdasycsdfghjkldcba\"\n\tfoo = Solution()\n\n\tprint(foo.longestPalindrome(s))\n\nif __name__ == '__main__':\n\tmain()","repo_name":"Ting007/leetcodePractice","sub_path":"5longestP_DP.py","file_name":"5longestP_DP.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"8937279428","text":"from .common import *\nfrom .dataset.MOSEI_dataset import MoseiDataset\nfrom .dataset.MOSEI_dataset_unaligned import MoseiDatasetUnaligned\nfrom .dataset.MOSI_dataset import MosiDataset\nfrom .dataset.MOSI_dataset_unaligned import MosiDatasetUnaligned\nfrom .dataset.IEMOCAP_dataset import IemocapDatasetUnaligned, IemocapDataset\nfrom sklearn.metrics import accuracy_score,f1_score\nfrom .densenet import get_densenet_features\n\nimport sys; sys.path.append('/home/shounak_rtml/11777/CMU-MultimodalSDK')\nfrom mmsdk import mmdatasdk\n\n\n## TODO integrate this into main.py structure to get working on MOSI / MOSEI.\n## NOTE: This code is NOT integrated with the rest of the project yet. This is legacy code from a previous experiment. \n\ndef multiclass_acc(preds, truths):\n return np.sum(np.round(preds) == np.round(truths)) / float(len(truths))\n\ndataset_map = {\n 'mosi': MosiDataset,\n 'mosi_unaligned': MosiDatasetUnaligned,\n 'mosei': MoseiDataset,\n 'mosei_unaligned': MoseiDatasetUnaligned,\n 'iemocap_unaligned': IemocapDatasetUnaligned,\n 'iemocap': IemocapDataset,\n}\n\nclass MosiModel(torch.nn.Module):\n def __init__(self, hidden_channels, out_channels, num_layers):\n super().__init__()\n self.hetero_gnn = HeteroGNN(hidden_channels, out_channels, num_layers)\n\n self.finalW = nn.Sequential(\n Linear(-1, hidden_channels // 4),\n nn.ReLU(),\n nn.Dropout(p=gc['drop_1']),\n Linear(hidden_channels // 4, hidden_channels // 4),\n nn.Dropout(p=gc['drop_2']),\n nn.ReLU(),\n Linear(hidden_channels // 4, out_channels),\n )\n\n def forward(self, batch):\n hetero_out = self.hetero_gnn(batch.x_dict, batch.edge_index_dict, batch.batch_dict)\n return self.finalW(hetero_out).squeeze(axis=-1)\n\n\nclass HeteroGNN(torch.nn.Module):\n def __init__(self, hidden_channels, out_channels, num_layers):\n super().__init__()\n \n self.hidden_channels = hidden_channels\n self.heads = gc['gat_conv_num_heads']\n \n self.lin_dict = torch.nn.ModuleDict()\n for node_type in mods:\n self.lin_dict[node_type] = Linear(-1, hidden_channels)\n\n self.convs = torch.nn.ModuleList()\n\n for i in range(num_layers):\n conv = HeteroConv({\n conn_type: GATv2Conv(gc['graph_conv_in_dim'], hidden_channels//self.heads, heads=self.heads)\n for conn_type in all_connections\n }, aggr='mean')\n \n\n # # UNCOMMENT FOR PARAMETER SHARING\n # mods_seen = {} # mapping from mod to the gatv3conv linear layer for it\n # d = {}\n # for conn_type in all_connections:\n # mod_l, _, mod_r = conn_type\n\n # lin_l = None if mod_l not in mods_seen else mods_seen[mod_l]\n # lin_r = None if mod_r not in mods_seen else mods_seen[mod_r]\n\n # _conv = GATv3Conv(\n # lin_l,\n # lin_r,\n # gc['graph_conv_in_dim'], \n # hidden_channels//self.heads,\n # heads=self.heads\n # )\n # if mod_l not in mods_seen:\n # mods_seen[mod_l] = _conv.lin_l\n # if mod_r not in mods_seen:\n # mods_seen[mod_r] = _conv.lin_r\n # d[conn_type] = _conv\n \n # conv = HeteroConv(d, aggr='mean')\n\n self.convs.append(conv)\n\n self.pes = {k: PositionalEncoding(gc['graph_conv_in_dim']) for k in mods}\n\n def forward(self, x_dict, edge_index_dict, batch_dict):\n x_dict = {key: self.lin_dict[key](x) for key, x in x_dict.items()}\n\n # apply pe\n for m, v in x_dict.items(): # modality, tensor\n idxs = batch_dict[m]\n assert (idxs==(idxs.sort().values)).all()\n _, counts = torch.unique(idxs, return_counts=True)\n x_dict[m] = self.pes[m](v, counts)\n\n for conv in self.convs:\n # x_dict = conv(x_dict, edge_index_dict)\n x_dict, edge_types = conv(x_dict, edge_index_dict, return_attention_weights_dict={elt: True for elt in all_connections})\n\n '''\n x_dict: {\n modality: (\n a -> tensor of shape batch_num_nodes (number of distinct modality nodes concatenated from across whole batch),\n b -> [\n (\n edge_idxs; shape (2, num_edges) where num_edges changes depending on edge_type (and pruning),\n attention weights; shape (num_edges, num_heads)\n )\n ] of length 9 b/c one for each edge type where text modality is dst, in same order as edge_types[modality] list\n )\n }\n '''\n\n attn_dict = {\n k: {\n edge_type: {\n 'edge_index': edge_index,\n 'edge_weight': edge_weight,\n }\n for edge_type, (edge_index, edge_weight) in zip(edge_types[k], v[1])\n } \n for k, v in x_dict.items()\n }\n\n x_dict = {key: x[0].relu() for key, x in x_dict.items()}\n\n # readout: avg nodes (no pruning yet!)\n x = torch.cat([v for v in x_dict.values()], axis=0)\n batch_dicts = torch.cat([v for v in batch_dict.values()], axis=0)\n x = scatter_mean(x,batch_dicts, dim=0)\n return x\n\ndef train_mosi(train_loader, model, optimizer):\n total_loss, total_examples = 0,0\n y_trues = []\n y_preds = []\n model.train()\n criterion = nn.SmoothL1Loss()\n for batch_i, data in enumerate(tqdm(train_loader)): # need index to prune edges\n if 'iemocap' in gc['dataset']:\n data.y = data.y.reshape(-1,4)\n\n if data.num_edges > 1e6:\n print('Data too big to fit in batch')\n continue\n \n cont = False\n for mod in mods:\n if not np.any([mod in elt for elt in data.edge_index_dict.keys()]):\n print(mod, 'dropped from train loader!')\n cont = True\n if cont:\n continue\n \n data = data.to(gc['device'])\n if batch_i == 0:\n with torch.no_grad(): # Initialize lazy modules.\n out = model(data)\n\n optimizer.zero_grad()\n\n out = model(data)\n if 'iemocap' in gc['dataset']:\n loss = criterion(out.view(-1,2), data.y.view(-1))\n \n else:\n loss = criterion(out, data.y)\n \n if gc['use_loss_norm']:\n loss = loss / torch.abs(loss.detach()) # norm\n\n loss.backward()\n optimizer.step()\n total_loss += loss.detach().item()\n total_examples += data.num_graphs\n\n y_true = data.y.detach().cpu().numpy()\n y_pred = out.detach().cpu().numpy()\n \n y_trues.extend(y_true)\n y_preds.extend(y_pred)\n\n del loss\n del out\n del data\n\n torch.cuda.empty_cache()\n return total_loss / total_examples, y_trues, y_preds\n\n@torch.no_grad()\ndef test_mosi(loader, model):\n y_trues = []\n y_preds = []\n ids = []\n model.eval()\n\n l = 0.0\n for batch_i, data in enumerate(loader):\n cont = False\n for mod in mods:\n if not np.any([mod in elt for elt in data.edge_index_dict.keys()]):\n print(mod, 'dropped from test loader!')\n cont = True\n if cont:\n continue\n\n # if 'aiEXnCPZubE_24' in data.id:\n # a=2\n data = data.to(gc['device'])\n if 'iemocap' in gc['dataset']:\n data.y = data.y.reshape(-1,4)\n out = model(data)\n if 'iemocap' in gc['dataset']:\n loss = nn.CrossEntropyLoss()(out, data.y.argmax(-1)).item()\n else:\n loss = F.mse_loss(out, data.y)\n loss = loss / torch.abs(loss.detach()) # norm\n l += F.mse_loss(out, data.y, reduction='mean').item()\n\n y_true = data.y.detach().cpu().numpy()\n y_pred = out.detach().cpu().numpy()\n \n y_trues.extend(y_true)\n y_preds.extend(y_pred)\n # ids.extend(data.id)\n\n del data\n del out\n \n # if valid:\n # scheduler.step(mse)\n return l if l != 0 else loss, y_trues, y_preds\n\n\n\ndef get_loader(ds):\n words = ds[:][0]\n covarep = ds[:][1]\n facet = ds[:][2]\n\n if 'social' in gc['dataset']:\n q,a,inc=[torch.from_numpy(data[:]) for data in ds[0]]\n facet=torch.from_numpy(ds[1][:,:,:].transpose(1,0,2))\n words=torch.from_numpy(ds[2][:,:,:].transpose(1,0,2))\n covarep=torch.from_numpy(ds[3][:,:,:].transpose(1,0,2))\n\n total_data = []\n for i in range(words.shape[0]):\n data = {\n 'text': get_masked(words[i]),\n 'audio': get_masked(covarep[i]),\n 'video': get_masked(facet[i]),\n }\n\n if gc['zero_out_video']:\n data['video'][:]=0\n if gc['zero_out_audio']:\n data['audio'][:]=0\n if gc['zero_out_text']:\n data['text'][:]=0\n \n if sum([len(v) for v in data.values()]) == 0:\n continue\n \n hetero_data = { k: {'x': v} for k,v in data.items()}\n \n data = {\n **data,\n 'text_idx': torch.arange(data['text'].shape[0]),\n 'audio_idx': torch.arange(data['audio'].shape[0]),\n 'video_idx': torch.arange(data['video'].shape[0]),\n }\n \n for mod in mods:\n ret = build_time_aware_dynamic_graph_uni_modal(data[f'{mod}_idx'],[], [], 0, all_to_all=gc['use_all_to_all'], time_aware=True, type_aware=True)\n \n if len(ret) == 0: # no data for this modality\n continue\n elif len(ret) == 1:\n data[mod, 'pres', mod] = ret[0]\n else:\n data[mod, 'pres', mod], data[mod, 'fut', mod], data[mod, 'past', mod] = ret\n\n for mod2 in [modx for modx in mods if modx != mod]: # other modalities\n ret = build_time_aware_dynamic_graph_cross_modal(data[f'{mod}_idx'],data[f'{mod2}_idx'], [], [], 0, time_aware=True, type_aware=True)\n \n if len(ret) == 0:\n continue\n if len(ret) == 2: # one modality only has one element\n if len(data[f'{mod}_idx']) > len(data[f'{mod2}_idx']):\n data[mod2, 'pres', mod], data[mod, 'pres', mod2] = ret\n else:\n data[mod, 'pres', mod2], data[mod2, 'pres', mod] = ret\n \n else:\n if len(data[f'{mod}_idx']) > len(data[f'{mod2}_idx']): # the output we care about is the \"longer\" sequence\n ret = ret[3:]\n else:\n ret = ret[:3]\n\n data[mod, 'pres', mod2], data[mod, 'fut', mod2], data[mod, 'past', mod2] = ret\n \n # quick assertions\n # for mod in mods:\n # assert isinstance(data[mod], torch.Tensor)\n # for mod2 in [modx for modx in mods if modx != mod]:\n # if (mod, 'fut', mod2) in data:\n # assert (data[mod, 'fut', mod2].flip(dims=[0]) == data[mod2, 'past', mod]).all()\n # assert isinstance(data[mod, 'fut', mod2], torch.Tensor) and isinstance(data[mod, 'past', mod2], torch.Tensor) and isinstance(data[mod, 'pres', mod2], torch.Tensor)\n\n hetero_data = {\n **hetero_data,\n **{k: {'edge_index': v} for k,v in data.items() if isinstance(k, tuple) }\n }\n if 'social' in gc['dataset']:\n if gc['graph_qa']:\n hetero_data = {\n **hetero_data,\n 'q': {'x': q[i]},\n 'a': {'x': a[i]},\n 'inc': {'x': inc[i]},\n }\n\n hetero_data = {\n **hetero_data,\n 'q': q[i],\n 'a': a[i],\n 'inc': inc[i],\n 'vis': facet[i],\n 'trs': words[i],\n 'acc': covarep[i],\n }\n \n if gc['qa_strat']==1:\n hi=2\n\n hetero_data = HeteroData(hetero_data)\n \n # hetero_data = T.AddSelfLoops()(hetero_data) # todo: include this as a HP to see if it does anything!\n if 'social' not in gc['dataset']:\n hetero_data.y = ds[i][-1]\n # hetero_data.id = ds.ids[i]\n \n total_data.append(hetero_data)\n\n loader = DataLoader(total_data, batch_size=gc['bs'], shuffle=True)\n return loader\n\ndef avg(intervals: np.array, features: np.array) -> np.array:\n try:\n return np.average(features, axis=0)\n except:\n return features\n\ndef process_utt_id(utt_id):\n '''\n once segmented, the data will be in utterance ids such as this: zhpQh]gh[pa_KU[33]. Sometimes they\n contain brackets in the video id too, so getting the video and utterance id can be tricky. That's what this returns:\n e.g.\n zhpQh]gh[pa_KU, 33\n '''\n return utt_id.rsplit('[',1)[0], int(utt_id.rsplit('[',1)[1].replace(']',''))\n\ndef postprocess_utt_id(utt_id):\n '''\n get to vidid_uttid form\n '''\n return '_'.join(lmap(str, process_utt_id(utt_id)))\n\ndef raw_to_csd():\n # video to csd\n if not exists(join(gc['csd_data'], f'{gc[\"video_feat\"]}.pk')):\n if gc['video_feat'] == 'densenet':\n video_save_path = join(gc[\"csd_data\"], f'{gc[\"video_feat\"]}.pk')\n all_videos = join(gc['raw_data'], 'video')\n get_densenet_features(all_videos, desired_fps=1, temp_save_path=video_save_path)\n \n # if not exists(join(gc['csd_data'], f'{gc[\"audio_feat\"]}.pk')):\n # if gc['audio_feat'] == 'hubert':\n # hubert_save_path = join(gc['csd_data'], f'{gc[\"audio_feat\"]}.pk')\n # from process_audio import get_features_dir\n # get_features_dir(join(gc['raw_data'], 'audio'), save_path=hubert_save_path)\n\n \ndef csd_to_processed():\n orig = pickle.load(open(join(gc['proc_data'],'mosi_data.pkl.bak'), 'rb'))\n\n ds_save_path = join(gc['proc_data'], f'mosi_data_{gc[\"seq_len\"]}.pk')\n \n new_dataset = load_pk(ds_save_path)\n if new_dataset is None:\n dataset = mmdatasdk.mmdataset(recipe={'dummy': join(gc['csd_data'], 'dummy.csd')})\n del dataset.computational_sequences['dummy']\n\n video_save_path = join(gc[\"csd_data\"], f'{gc[\"video_feat\"]}.pk')\n if gc['video_feat'] == 'facet':\n video_save_path = video_save_path.replace('.pk', '.csd')\n add_seq(dataset, video_save_path, 'video')\n\n add_seq(dataset, join(gc[\"csd_data\"], f'{gc[\"text_feat\"]}.csd'), 'text')\n add_seq(dataset, join(gc[\"csd_data\"], f'{gc[\"audio_feat\"]}.csd'), 'audio')\n \n dataset.align('text', collapse_functions=[avg])\n dataset.impute('text')\n add_seq(dataset, join(gc[\"csd_data\"], 'labels.csd'), 'labels')\n dataset.align('labels')\n dataset.hard_unify()\n\n # add ids\n data = {}\n for key in np.sort(arlist(dataset['labels'].keys())):\n new_key = postprocess_utt_id(key)\n data[key] = {\n 'features': np.array([[new_key, *ar(dataset['labels'][key]['intervals']).reshape(-1)]]),\n 'intervals': ar(dataset['labels'][key]['intervals'])\n }\n compseq = mmdatasdk.computational_sequence('id')\n compseq.setData(data, 'id')\n metadata_template['root name'] = 'id'\n compseq.setMetadata(metadata_template, 'id')\n dataset.computational_sequences['id'] = compseq\n\n splits = [\n ('train', mmdatasdk.cmu_mosi.standard_folds.standard_train_fold),\n ('valid', mmdatasdk.cmu_mosi.standard_folds.standard_valid_fold),\n ('test', mmdatasdk.cmu_mosi.standard_folds.standard_test_fold),\n ]\n new_dataset = dataset.get_tensors(\n seq_len=gc['seq_len'],\n non_sequences=['labels', 'id'],\n direction=False,\n folds=lzip(*splits)[1],\n )\n\n # convert to same format as expected\n new_dataset = {\n split: new_dataset[i]\n for i,split in enumerate(lzip(*splits)[0])\n }\n for split in lzip(*splits)[0]:\n new_dataset[split]['id'] = new_dataset[split]['id'].squeeze().astype('bytes')\n \n save_pk(ds_save_path, new_dataset)\n\n return new_dataset\n\n\ndef train_model_mosi():\n # raw_to_csd()\n dataset = csd_to_processed()\n \n # write this to mosi_data.pkl\n save_pk(join(gc['proc_data'],'mosi_data.pkl'), dataset)\n\n checkpoint_dir = 'checkpoints'\n if not os.path.exists(checkpoint_dir):\n os.mkdir(checkpoint_dir)\n ds = dataset_map[gc['dataset']]\n train_dataset = ds(gc['proc_data'],gc, clas=\"train\")\n test_dataset = ds(gc['proc_data'], gc, clas=\"test\")\n valid_dataset = ds(gc['proc_data'],gc, clas=\"valid\")\n\n train_loader, train_labels = get_loader(train_dataset), train_dataset[:][-1]\n valid_loader, valid_labels = get_loader(valid_dataset), valid_dataset[:][-1]\n test_loader, test_labels = get_loader(test_dataset), test_dataset[:][-1]\n\n out_channels = 8 if 'iemocap' in gc['dataset'] else 1\n model = MosiModel(gc['graph_conv_in_dim'], out_channels, gc['num_gat_layers'])\n model = model.to(gc['device'])\n \n optimizer = torch.optim.AdamW(\n model.parameters(), \n lr=gc['global_lr'],\n weight_decay=gc['weight_decay'],\n betas=(gc['beta1'], gc['beta2']),\n eps=gc['eps']\n )\n # scheduler = ReduceLROnPlateau(optimizer, factor=0.5, patience=10, threshold=.002)\n\n eval_fns = {\n 'mosi_unaligned': eval_mosi_mosei,\n 'mosi': eval_mosi_mosei,\n 'mosei_unaligned': eval_mosi_mosei,\n 'iemocap_unaligned': eval_iemocap,\n }\n eval_fn = eval_fns[gc['dataset']]\n\n # best_valid_ie_f1s = {emo: 0 for emo in ie_emos}\n # best_test_ie_f1s = {emo: 0 for emo in ie_emos}\n best = { 'mae': 0, 'corr': 0, 'acc_2': 0, 'acc_7': 0, 'ex_zero_acc': 0, 'f1_raven': 0, 'f1_mult': 0, }\n valid_best = { 'mae': 0, 'corr': 0, 'acc_2': 0, 'acc_7': 0, 'ex_zero_acc': 0, 'f1_raven': 0, 'f1_mult': 0, }\n\n\n for epoch in range(gc['epochs']):\n loss, y_trues_train, y_preds_train = train_mosi(train_loader, model, optimizer)\n train_res = eval_fn('train', y_preds_train, y_trues_train)\n\n valid_loss, y_trues_valid, y_preds_valid = test_mosi(valid_loader, model)\n valid_res = eval_fn('valid', y_preds_valid, y_trues_valid)\n \n if epoch == 10:\n a=2\n test_loss, y_trues_test, y_preds_test = test_mosi(test_loader, model)\n test_res = eval_fn('test', y_preds_test, y_trues_test)\n\n if 'iemocap' in gc['dataset']:\n for emo in ie_emos:\n if valid_res['f1'][emo] > best_valid_ie_f1s[emo]:\n best_valid_ie_f1s[emo] = valid_res['f1'][emo]\n best_test_ie_f1s[emo] = test_res['f1'][emo]\n print(f'Epoch: {epoch:03d}, Loss: {loss:.4f} Valid: '+str([f'{emo}: {valid_res[\"f1\"][emo]:.4f} ' for emo in ie_emos]), 'Test: '+str([f'{emo}: {test_res[\"f1\"][emo]:.4f} ' for emo in ie_emos]))\n\n else: # mosi/mosei\n if valid_res['acc_2'] > valid_best['acc_2']:\n for k in valid_best.keys():\n valid_best[k] = valid_res[k]\n\n for k in best.keys():\n best[k] = test_res[k]\n\n print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_res[\"acc_2\"]:.4f}, ' f'Valid: {valid_res[\"acc_2\"]:.4f}, Test: {test_res[\"acc_2\"]:.4f}')\n \n if 'iemocap' in gc['dataset']:\n print('\\n Test f1s at valid best:', best_test_ie_f1s)\n print('\\n Valid f1s at valid best:', best_valid_ie_f1s)\n return best_test_ie_f1s\n else:\n print(f'\\nBest test acc: {best[\"acc_2\"]:.4f}')\n return {k: float(v) for k,v in best.items()}\n\n print('Model parameters:', count_params(model))\n\n\n\n\n\ndef weighted_acc(preds, truths):\n preds, truths = preds > 0, truths > 0\n tn, fp, fn, tp = confusion_matrix(truths, preds).ravel()\n n, p = len([i for i in preds if i == 0]), len([i for i in preds if i > 0])\n return (tp * n / p + tn) / (2 * n)\n\n\n\ndef eval_iemocap(split, output_all, label_all, epoch=None):\n truths = np.array(label_all)\n results = np.array(output_all)\n test_preds = results.reshape((-1, 4, 2))\n test_truth = truths.reshape((-1, 4))\n emos_f1 = {}\n emos_acc = {}\n for emo_ind, em in enumerate(ie_emos):\n test_preds_i = np.argmax(test_preds[:, emo_ind], axis=1)\n test_truth_i = test_truth[:, emo_ind]\n f1 = f1_score(test_truth_i, test_preds_i, average='weighted')\n emos_f1[em] = f1\n acc = accuracy_score(test_truth_i, test_preds_i)\n emos_acc[em] = acc\n \n return {\n 'f1': emos_f1,\n 'acc': emos_acc\n }\n\ndef eval_mosi_mosei(split, output_all, label_all):\n truth = np.array(label_all)\n preds = np.array(output_all)\n mae = np.mean(np.abs(truth - preds))\n cor = np.corrcoef(preds, truth)[0][1]\n acc = accuracy_score(truth >= 0, preds >= 0)\n non_zeros = np.array([i for i, e in enumerate(truth) if e != 0])\n ex_zero_acc = accuracy_score((truth[non_zeros] > 0), (preds[non_zeros] > 0)) \n\n preds_a7 = np.clip(preds, a_min=-3., a_max=3.)\n truth_a7 = np.clip(truth, a_min=-3., a_max=3.)\n acc_7 = multiclass_acc(preds_a7, truth_a7)\n\n # F1 scores. All of them are recommended by previous work.\n f1_mfn = f1_score(np.round(truth), np.round(preds), average=\"weighted\") # We don't use it, do we?\n f1_raven = f1_score(truth >= 0, preds >= 0, average=\"weighted\") # Non-negative VS. Negative\n f1_mult = f1_score((truth[non_zeros] > 0), (preds[non_zeros] > 0), average='weighted') # Positive VS. Negative\n\n return {\n 'mae': mae,\n 'corr': cor,\n 'acc_2': acc,\n 'acc_7': acc_7,\n 'ex_zero_acc': ex_zero_acc,\n 'f1_raven': f1_raven, # includes zeros, Non-negative VS. Negative\n 'f1_mult': f1_mult, # exclude zeros, Positive VS. Negative\n }\n","repo_name":"sulagna1996/Multimodal-project","sub_path":"MTAG_no_data/models/mosi.py","file_name":"mosi.py","file_ext":"py","file_size_in_byte":22311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"8883893691","text":"from math import sqrt\n\nclass Cell:\n\n #constants\n gamma = 1.4\n\n #initial conditions\n rho = 0\n v = 0\n e = 0\n E = 0\n P = 0\n lambda_plus = 0\n lambda_minus = 0\n\n #variables\n alpha_plus = [0, 0, 0]\n alpha_minus = [0, 0, 0]\n\n U = [0, 0, 0]\n F = [0, 0, 0]\n F_half = [0, 0, 0]\n\n def __init__(self, rho, v, e) -> None:\n self.rho = rho\n self.v = v\n self.e = e\n self.P = (self.gamma-1)*self.rho*self.e\n self.E = rho*e + 1/2*rho*v**2.0\n self.U = [rho, rho*v, self.E]\n self.F = [rho*v,\n rho*(v**2.0+(self.gamma - 1)*e),\n rho*v*(1/2*v**2+self.gamma*e)]\n print(self.F)\n self.lambda_plus = v + sqrt((self.gamma*(self.gamma-1)*self.rho*self.e)/self.rho)\n self.lambda_minus = v - sqrt((self.gamma*(self.gamma-1)*self.rho*self.e)/self.rho)\n print(\"v:\", v)\n print(\"lambda_plus:\", self.lambda_plus)\n print(\"lambda_minus:\", self.lambda_minus)\n\n \n def find_F_half(self, right_cell):\n\n for i in range(3):\n self.alpha_plus[i] = max([0, self.lambda_plus*right_cell.U[i], self.lambda_plus*right_cell.U[i]])\n self.alpha_minus[i] = max([0, -self.lambda_minus*right_cell.U[i], -self.lambda_minus*right_cell.U[i]])\n\n for i in range(3):\n self.F_half[i] = (self.alpha_plus[i]*self.F[i] + self.alpha_minus[i]*right_cell.F[i] \\\n - self.alpha_plus[i]*self.alpha_minus[i]*(right_cell.U[i]-self.U[i])) \\\n / (self.alpha_plus[i] + self.alpha_minus[i])\n\n return self.F_half\n \n def find_F(self):\n self.P = (self.gamma-1)*self.U[0]*(self.U[2]/self.U[0]-1/2*(self.U[1]/self.U[0])**2)\n self.F[0] = self.U[1]\n self.F[1] = self.U[1]**2/self.U[0]+self.P\n self.F[2] = (self.U[2]+self.P)*self.U[1]/self.U[0]\n \n return self.F\n \n def evolve(self, delta_t, delta_x, left_cell):\n print(self.F_half[0])\n print(left_cell.F_half[0])\n print(\"-\")\n for i in range(3):\n self.U[i] = self.U[i] - delta_t*(self.F_half[i] - left_cell.F_half[i])/delta_x\n \n # print(self.F_half[0])\n # print(left_cell.F_half[0])\n # print('-')\n self.rho = self.U[0]\n self.v = self.U[1]\n self.F = self.find_F()\n\n def update():\n return","repo_name":"andredubovski/phys-ua210","sub_path":"project/cells.py","file_name":"cells.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"10367183808","text":"\"\"\"\n Programa10\n Nombre: Danae Barqueras A\n Fecha: 01/02/2023\n Descripcion:comparar y obtener un numero mayor\n\"\"\"\n#Primero definimos cada variable y asignacion\n\ndef mayor(numero1,numero2) :\n if numero1 > numero2 :\n print(numero1)\n elif numero2 > numero1 :\n print(numero2)\n else:\n print(None)\n #Colocamos las formas en las que nuestro programa debe responder\nmayor(10,5)#3\nmayor(5,10)#3\nmayor(5,5)#None\n#Asignamos valores para ejecutar nuestro codigo\n\n\n#Colocamos las variables con las asignaciones\ndef mayor(numero1:int,numero2:int)->int :\n mayor= None\n if numero1 > numero2 :\n mayor = numero1\n mayor=numero1\n elif numero2 > numero1 :\n mayor= numero2\n else:\n mayor=None\n #Damos las decisiones (o posibles) que puede tomar\n return mayor\n\n\nprint(mayor(8,4))#3\nprint(mayor(8,4))#3\nprint(mayor(4,4))#None\n#Asignamos los valores he imprimimos","repo_name":"Danae004/poo_jdba_ti22","sub_path":"semana4/programa10.py","file_name":"programa10.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"3075782879","text":"from datetime import datetime, timedelta\n\nd = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0}\n\nstart_date, end_date = datetime(year=1, month=1, day=1).toordinal(), datetime(year=9999, month=12, day=31).toordinal()\n\nfor year in range(1, 9999 + 1):\n for month in range(1, 12 + 1):\n d[datetime(year=year, month=month, day=13).weekday()] += 1\nprint(*d.values(), sep='\\n')\n","repo_name":"Bl00dWolf/Stepik_Course","sub_path":"Course for profi/3. Работа с датой и временем/3.5 Решение задач/3.5.2 Пятница, 13-е.py","file_name":"3.5.2 Пятница, 13-е.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"15184505749","text":"N, A, B = map(int, input().split())\r\nh = [int(input()) for _ in range(N)]\r\ndef fight(k):\r\n c = k\r\n for i in h:\r\n hp = i - B * k\r\n if hp > 0:\r\n c -= -(-hp // (A - B))\r\n if c < 0:\r\n return False\r\n return True\r\n\r\nlo = 0\r\nhi = 10 ** 9\r\nwhile hi - lo > 1:\r\n mid = (lo + hi) // 2\r\n if fight(mid):\r\n hi = mid\r\n else:\r\n lo = mid\r\nprint(hi)","repo_name":"Kawser-nerd/CLCDSA","sub_path":"Source Codes/AtCoder/arc075/B/3282888.py","file_name":"3282888.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"72"}
+{"seq_id":"73674260394","text":"from django.core.management.base import BaseCommand\nfrom django.conf import settings\nfrom elasticsearch import Elasticsearch\nfrom elasticsearch_dsl import Search, Q\n\n\nclass Command(BaseCommand):\n def handle(self, *args, **options):\n # Инициализация клиента ElasticSearch\n es = Elasticsearch(settings.ELASTIC_HOST, timeout=settings.ELASTIC_TIMEOUT)\n\n t = {\n 4: ['TradeMark', 'TrademarkDetails'],\n 6: ['Design', 'DesignDetails'],\n }\n\n query = Q(\n \"query_string\",\n query=\"Document.idObjType:4 OR Document.idObjType:6\"\n )\n s = Search().using(es).query(query)\n c = s.count()\n i = 0\n\n for h in s.scan():\n body = h.to_dict()\n\n i += 1\n print(f\"{i}/{c} - {body['search_data']['app_number']} - {h.meta.id}\")\n\n representers = []\n\n body_details = body[t[body['Document']['idObjType']][0]][t[body['Document']['idObjType']][1]]\n\n for representer in body_details.get('RepresentativeDetails', {}).get('Representative', []):\n if representer.get('RepresentativeAddressBook'):\n if body['Document']['idObjType'] == 4:\n name = representer['RepresentativeAddressBook']['FormattedNameAddress']['Name']['FreeFormatName'][\n 'FreeFormatNameDetails']['FreeFormatNameDetails']['FreeFormatNameLine']\n else:\n name = representer['RepresentativeAddressBook']['FormattedNameAddress']['Name']['FreeFormatName'][\n 'FreeFormatNameDetails']['FreeFormatNameLine']\n address = representer['RepresentativeAddressBook']['FormattedNameAddress']['Address']['FreeFormatAddress']['FreeFormatAddressLine']\n representers.append(f\"{name}, {address}\")\n\n if representers:\n body['search_data']['agent'] = representers\n es.index(index=settings.ELASTIC_INDEX_NAME,\n doc_type='_doc',\n id=h.meta.id,\n body=body,\n request_timeout=30)\n","repo_name":"alexmon1989/uma","sub_path":"apps/search/management/commands/fill_representer.py","file_name":"fill_representer.py","file_ext":"py","file_size_in_byte":2208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"34742072733","text":"file = open(\"rosalind_dna.txt\",\"r\");\ncount_a = 0;\ncount_c = 0;\ncount_g = 0;\ncount_t = 0;\n\nfor line in file:\n\tcount_a += line.count('A');\n\tcount_c += line.count('C');\n\tcount_g += line.count('G');\n\tcount_t += line.count('T');\n\nfile.close();\nprint(count_a, count_c, count_g, count_t);","repo_name":"raomanus/rosalind","sub_path":"dna.py","file_name":"dna.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"25420856985","text":"import pywt\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom math import sqrt,log10\nfrom sklearn.metrics import mean_squared_error\n\ndef madev(d, axis=None):\n \"\"\" Median absolute deviation of a signal \"\"\"\n# return np.mean(np.absolute(d - np.mean(d, axis)), axis)\n return np.median(np.absolute(d))\n\ndef wavelet_denoising(x):\n c = pywt.wavedec(x,\"sym18\", mode=\"per\",level = 4)\n \n sigma = (1/0.6745) * madev(c[-1])\n \n univ_thresh = sigma * np.sqrt(2 * np.log(len(x)))\n \n c[1:] = (pywt.threshold(i, value=univ_thresh, mode='hard') for i in c[1:])\n \n return pywt.waverec(c, \"sym18\", mode='per')\n\n\nsignal = pd.read_csv('train/subj1_series1_data.csv')\nsignal = signal.drop(\"id\", axis=1)\n\n# from sklearn.preprocessing import StandardScaler,MinMaxScaler\n# scaler= StandardScaler()\n\n# signal = pd.DataFrame(scaler.fit_transform(signal))\n\n\n# for wav in pywt.wavelist(kind='discrete'):\n# try:\nfiltered = pd.DataFrame(wavelet_denoising(signal))\n\n###PLOT###\nplt.figure(figsize=(10, 6))\nplt.plot(signal.iloc[:10000,0], label='Raw')\nplt.plot(filtered.iloc[:10000,0], label='Filtered')\nplt.legend()\nplt.title(\"DWT Denoising with sym18 Wavelet\", size=15)\nplt.show()\n# except:\n# pass \n\n\n\n#########################","repo_name":"janvi2001/HelpingHands","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"4105135682","text":"from albums.models import Collections\n\n\ndef is_collected(user, album):\n if user and album:\n collection = Collections.objects.filter(collecter=user, collected_album=album)\n if collection:\n return True\n else:\n return False\n else:\n return False","repo_name":"bang0127/SYQ_project","sub_path":"SYQ/tools/is_collected.py","file_name":"is_collected.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"34360802988","text":"from yacs.config import CfgNode as CN\r\n\r\n# -----------------------------------------------------------------------------\r\n# Config definition\r\n# -----------------------------------------------------------------------------\r\n_C = CN()\r\n_C.FROM_SCRATCH = True\r\n_C.DATA_TRUNK = None\r\n\r\n_C.OUTPUT_DIR = ''\r\n_C.DATA_DIR = ''\r\n_C.GLOVE_DIR = ''\r\n_C.TENSORBOARD_DIR = ''\r\n\r\n\r\n# -----------------------------------------------------------------------------\r\n# INPUT\r\n# -----------------------------------------------------------------------------\r\n_C.INPUT = CN()\r\n_C.INPUT.MAX_QUERY_LEN = 26\r\n_C.INPUT.MAX_VIDEO_LEN = 200\r\n\r\n# The input frame number (For VidSTG)\r\n_C.INPUT.TRAIN_SAMPLE_NUM = 64\r\n# The input frame rate (For HC_STVG, 20s per input video)\r\n_C.INPUT.SAMPLE_FPS = 3.2\r\n\r\n# The input video resolution\r\n_C.INPUT.RESOLUTION = 224\r\n# Values to be used for image normalization\r\n_C.INPUT.PIXEL_MEAN = [0.485, 0.456, 0.406]\r\n# Values to be used for image normalization\r\n_C.INPUT.PIXEL_STD = [0.229, 0.224, 0.225]\r\n# If perform multiscale training\r\n_C.INPUT.AUG_SCALE = True\r\n# If perform translate augumentation training\r\n_C.INPUT.AUG_TRANSLATE = False\r\n\r\n# Image ColorJitter\r\n_C.INPUT.FLIP_PROB_TRAIN = 0.5\r\n_C.INPUT.TEMP_CROP_PROB = 0.5\r\n\r\n# -----------------------------------------------------------------------------\r\n# Model Config\r\n# -----------------------------------------------------------------------------\r\n_C.MODEL = CN()\r\n_C.MODEL.DEVICE = \"cuda\"\r\n_C.MODEL.WEIGHT = \"\"\r\n_C.MODEL.EMA = True\r\n_C.MODEL.EMA_DECAY = 0.9998\r\n_C.MODEL.QUERY_NUM = 1 # each frame a single query\r\n_C.MODEL.DOWN_RATIO = 4\r\n\r\n# -----------------------------------------------------------------------------\r\n# Vision Encoder options\r\n# -----------------------------------------------------------------------------\r\n\r\n_C.MODEL.VISION_BACKBONE = CN()\r\n_C.MODEL.VISION_BACKBONE.NAME = 'resnet101' # resnet50 or resnet101\r\n_C.MODEL.VISION_BACKBONE.POS_ENC = 'sine' # sine, sineHW or learned\r\n_C.MODEL.VISION_BACKBONE.DILATION = False # If true, we replace stride with dilation in the last convolutional block (DC5)\r\n_C.MODEL.VISION_BACKBONE.FREEZE = False # If true, freeze the vision backbone parameters\r\n\r\n\r\n# -----------------------------------------------------------------------------\r\n# Language Encoder Config\r\n# -----------------------------------------------------------------------------\r\n_C.MODEL.TEXT_MODEL = CN()\r\n_C.MODEL.TEXT_MODEL.NAME = 'roberta-base' # \"bert-base\", \"roberta-large\"\r\n_C.MODEL.TEXT_MODEL.FREEZE = False\r\n\r\n# If true, use LSTM as the text encoder\r\n_C.MODEL.USE_LSTM = False\r\n_C.MODEL.LSTM = CN()\r\n_C.MODEL.LSTM.NAME = 'lstm'\r\n_C.MODEL.LSTM.HIDDEN_SIZE = 512\r\n_C.MODEL.LSTM.BIDIRECTIONAL = True\r\n_C.MODEL.LSTM.DROPOUT = 0\r\n_C.MODEL.LSTM_NUM_LAYERS = 2\r\n\r\n\r\n# -----------------------------------------------------------------------------\r\n# STCAT Pipeline Config\r\n# -----------------------------------------------------------------------------\r\n_C.MODEL.STCAT = CN()\r\n_C.MODEL.STCAT.HIDDEN = 256\r\n_C.MODEL.STCAT.QUERY_DIM = 4 # the anchor dim\r\n_C.MODEL.STCAT.ENC_LAYERS = 6\r\n_C.MODEL.STCAT.DEC_LAYERS = 6\r\n_C.MODEL.STCAT.FFN_DIM = 2048\r\n_C.MODEL.STCAT.DROPOUT = 0.1\r\n_C.MODEL.STCAT.HEADS = 8\r\n_C.MODEL.STCAT.USE_LEARN_TIME_EMBED = False\r\n_C.MODEL.STCAT.USE_ACTION = True # use the actioness head by default\r\n_C.MODEL.STCAT.FROM_SCRATCH = True\r\n\r\n# For 2D-Map prediction\r\n_C.MODEL.STCAT.TEMP_PRED_LAYERS = 6\r\n_C.MODEL.STCAT.CONV_LAYERS = 4\r\n_C.MODEL.STCAT.TEMP_HEAD = 'attn' # attn or conv\r\n_C.MODEL.STCAT.KERNAL_SIZE = 9\r\n_C.MODEL.STCAT.MAX_MAP_SIZE = 128\r\n_C.MODEL.STCAT.POOLING_COUNTS = [15,8,8,8]\r\n\r\n# -----------------------------------------------------------------------------\r\n# DATASET related params\r\n# -----------------------------------------------------------------------------\r\n_C.DATASET = CN()\r\n_C.DATASET.NAME = 'VidSTG'\r\n_C.DATASET.NUM_CLIP_FRAMES = 32\r\n# The minimum gt frames in a sampled clip\r\n_C.DATASET.MIN_GT_FRAME = 4\r\n\r\n\r\n# -----------------------------------------------------------------------------\r\n# DataLoader\r\n# -----------------------------------------------------------------------------\r\n_C.DATALOADER = CN()\r\n# Number of data loading threads\r\n_C.DATALOADER.NUM_WORKERS = 4\r\n_C.DATALOADER.SIZE_DIVISIBILITY = 0\r\n_C.DATALOADER.ASPECT_RATIO_GROUPING = False\r\n\r\n# ---------------------------------------------------------------------------- #\r\n# Solver\r\n# ---------------------------------------------------------------------------- #\r\n_C.SOLVER = CN()\r\n_C.SOLVER.MAX_EPOCH = 30\r\n_C.SOLVER.BATCH_SIZE = 1 # The video number per GPU, should be set 1.\r\n_C.SOLVER.SHUFFLE = True\r\n_C.SOLVER.BASE_LR = 2e-5\r\n_C.SOLVER.VIS_BACKBONE_LR = 1e-5\r\n_C.SOLVER.TEXT_LR = 2e-5\r\n_C.SOLVER.TEMP_LR = 1e-4\r\n_C.SOLVER.OPTIMIZER = 'adamw'\r\n_C.SOLVER.MAX_GRAD_NORM = 0.1\r\n\r\n# loss weight hyper-parameter\r\n_C.SOLVER.BBOX_COEF = 5\r\n_C.SOLVER.GIOU_COEF = 2\r\n_C.SOLVER.TEMP_COEF = 2\r\n_C.SOLVER.ATTN_COEF = 1\r\n_C.SOLVER.ACTIONESS_COEF = 2\r\n\r\n_C.SOLVER.MOMENTUM = 0.9\r\n_C.SOLVER.WEIGHT_DECAY = 0.0001\r\n_C.SOLVER.GAMMA = 0.1\r\n_C.SOLVER.POWER = 0.9 # For Poly LRScheduler\r\n_C.SOLVER.STEPS = (30000,)\r\n\r\n_C.SOLVER.WARMUP_FACTOR = 1.0 / 3\r\n_C.SOLVER.WARMUP_ITERS = 500\r\n\r\n_C.SOLVER.WARMUP_PROP = 0.01\r\n_C.SOLVER.WARMUP_METHOD = \"linear\"\r\n\r\n_C.SOLVER.SCHEDULE = CN()\r\n_C.SOLVER.SCHEDULE.TYPE = \"linear_with_warmup\"\r\n_C.SOLVER.SCHEDULE.DROP_STEP = [8,12]\r\n\r\n# the following paramters are only used for WarmupReduceLROnPlateau\r\n_C.SOLVER.SCHEDULE.PATIENCE = 2\r\n_C.SOLVER.SCHEDULE.THRESHOLD = 1e-4\r\n_C.SOLVER.SCHEDULE.COOLDOWN = 1\r\n_C.SOLVER.SCHEDULE.FACTOR = 0.5\r\n_C.SOLVER.SCHEDULE.MAX_DECAY_STEP = 7\r\n\r\n_C.SOLVER.PRE_VAL = False\r\n_C.SOLVER.TO_VAL = True\r\n_C.SOLVER.VAL_PERIOD = 2500 # every 10% training iterations completed, start a avaluation\r\n_C.SOLVER.CHECKPOINT_PERIOD = 5000\r\n\r\n\r\n_C.SOLVER.USE_ATTN = True # whether to use the guided attention loss, to compare with TubeDETR\r\n_C.SOLVER.SIGMA = 2.0 # standard deviation for the quantized gaussian law used for the kullback leibler divergence loss\r\n_C.SOLVER.USE_AUX_LOSS = True # whether to use auxiliary decoding losses (loss at each layer)\r\n_C.SOLVER.EOS_COEF = 0.1 # The coeff for negative sample","repo_name":"jy0205/STCAT","sub_path":"config/defaults.py","file_name":"defaults.py","file_ext":"py","file_size_in_byte":6186,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"72"}
+{"seq_id":"6186020056","text":"import pdb\nimport numpy as np\nimport math\nfrom zython.logf.printf import printf\n\"\"\"\nIf the memory can not hold the entire set, split it: 2x, 4x, ... does not matter.\n\te.g., if you split four times, making f_in become f_in/4, f_out keeps unchanged.\n\tthen just let CPU merge the 4 set of f_out*N**2 --> it's gonna be fast, 400 accumulation becomes 404 accumulations.\n\"\"\"\n\n\n\"\"\"\n############################################\n# THROUGHPUT UNIT: COMPLEX WORDS PER CYCLE #\n############################################\nN: FFT\nq_1dfft: folding factor for 1d fft pipeline\nq_2dfft: folding factor for 2d fft pipeline\nq_1difft: folding factor for 1d ifft pipeline\nq_2difft: folding factor for 2d ifft pipeline\nP_fft: number of N x N 2D FFT\nP_ifft: number of N x N 2D IFFT\nM_img (words): image buffer size\nM_kern: kernel buffer size\nP_mac: number of N x N tiles the MAC can handle\nq_mac: folding factor for MAC\n\t\ti.e.: the throughput for MAC is P_mac * N*N / q_mac\n\"\"\"\n\n\"\"\"\nOaA first two layers (64 point): latency = 2.719 M cycles\nOaA last three layers (16 point): latency = \nCaP all five layers (64 point): average latency = 2.267 M cycles\n\"\"\"\n\n\n\nbytePerWord = 4\t# 16-bit fixed point COMPLEX word\nclkRate = 200e6\t# fixed for harp: 200M Hz\n\nN = 16\t\t\t# for now, fixed for all \nx = 4\n#config_setting_set = [{\"pick_layers\": },{\"\"}]\nconfig_setting = {\"pick_layers\":-3}\n# meta data for all convolution layers\nif config_setting[\"pick_layers\"] == -3:\n\tlayers = {\t\"f_in\": [256.,384.,384.],\n\t\t\t\t\"f_out\":[384.,384.,256.],\n\t\t\t\t\"l_img\":[27.,13.,13.],\t\t# l_img should be the image after folding\n\t\t\t\t\"l_kern\":[3.,3.,3.]}\nelif config_setting[\"pick_layers\"] == -4:\n\tlayers = {\t\"f_in\": [96,256,384,384],\n\t\t\t\t\"f_out\":[256,384,384,256],\n\t\t\t\t\"l_img\":[55,27,13,13],\n\t\t\t\t\"l_kern\":[5,3,3,3]\n\t\t\t\t}\nelif config_setting[\"pick_layers\"] == 2:\n\tlayers = {\t\"f_in\": [3.,96.],\n\t\t\t\t\"f_out\":[96.,256.],\n\t\t\t\t\"l_img\":[224.,55.],\t\t# l_img should be the image after folding\n\t\t\t\t\"l_kern\":[11.,5.]}\nelif config_setting[\"pick_layers\"] == 5:\n\tlayers = { \t\"f_in\": [3.,96.,256.,384.,384],\n\t\t\t\t\"f_out\":[96.,256.,384.,384.,256.],\n\t\t\t\t\"l_img\":[224.,55.,27.,13.,13.],\n\t\t\t\t\"l_kern\":[11.,5.,3.,3.,3.]}\nelif config_setting['pick_layers'] == 1:\n\tlayers = {\t\"f_in\": [3],\n\t\t\t\t\"f_out\":[96],\n\t\t\t\t\"l_img\":[224],\n\t\t\t\t\"l_kern\":[11]}\n\n\nresources = {\"bw\":\t5e9/bytePerWord/clkRate,\t\t# unit: complex words per cycle\n\t\t\t\"alm\":\t234720,\n\t\t\t\"mem\":\t6.5e6/bytePerWord}\t\t\t\t# unit: complex words\nconsumption = {\"cpx_mac\": 64*3.2}\n\n\ndef target_function_CaP(model_cnn,model_hw,param_algo,M_img,P_mac,q_mac,q_2dfft,q_2difft,q_1dfft,q_1difft,P_fft,P_ifft):\n\tnum_layers = len(model_cnn[\"conv_layers\"])\n\tt_layers = np.zeros(num_layers)\t\t# store the runtime for each layer\n\tN_max = param_algo['fft'].max()\n\tbw = model_hw['bandwidth']/model_hw['clk_rate']/model_hw['byte_per_word']\n\tfor l in range(num_layers):\n\t\tf_in = model_cnn[\"conv_layers\"][l][\"f_in\"]\n\t\tf_out = model_cnn[\"conv_layers\"][l][\"f_out\"]\n\t\tl_img = model_cnn[\"conv_layers\"][l][\"l_img\"]\n\t\tl_kern = model_cnn[\"conv_layers\"][l][\"l_kern\"]\n\t\tN_l = param_algo['fft'][l]\n\t\tD_img = M_img \t\t\t\t\t\t\t# problem here: D_img should be in the unit of f_in*N**2\n\t\t\t\t\t\t\t\t\t\t\t\t# omitting this problem for now\n\t\t#D_img = min(M_img,f_in*N**2)\t\t\t# this is ok. just make sure that you let the M holds the entire image\n\t\t\t\t\t\t\t\t\t\t\t\t# if you change this, you should also change the equation for entire layer.\n\t\tQ = (f_out/f_in)*(2*D_img/(D_img+f_in*f_out*l_kern**2))\t# kernel is complex number\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# factor of 2 in front of D_img, because of loading real number only.\n\t\tbottle1 = P_mac*N_max**2/f_in/q_mac\n\t\tbottle2 = P_ifft*N_max**2/q_2difft/q_1difft\n\t\tbottle3 = P_fft*N_max**2/q_2dfft/q_1dfft*(f_out/f_in)\n\t\tbottle4 = bw*Q/(1+Q)\t\t# this should probably be multiplied by 2, considering that kernel is just real numbers.\n\t\t#printf(\"{:5.3f},{:5.3f},{:5.3f},{:5.3f}\",bottle1,bottle2,bottle3,bottle4)\n\t\t#printf(\"fft throughput: {:5.3f}/{:5.3f}\", M_img/(M_img+f_in*f_out*N**2)*(resources[\"bw\"]-bottle4),resources[\"bw\"])\n\t\tB_ifft = np.min([bottle1,bottle2,bottle3,bottle4])\n\t\t#printf(\"layer {}: bottleneck -- {}\", l, [bottle1,bottle2,bottle3,bottle4])\n\t\tl_img_prime = (l_img+l_kern-1)/(N_l-l_kern+1)*N_l \t# bounded by this, could be lower\n\t\tt_layers[l] = f_out*l_img_prime**2/B_ifft\n\t#printf(t_layers.sum()/1e6)\n\treturn t_layers.sum()\n\ndef target_function_OaA(model_cnn,model_hw,param_algo,M_img,P_mac,q_mac,q_2dfft,q_2difft,q_1dfft,q_1difft,P_fft,P_ifft):\n\tnum_layers = len(model_cnn[\"conv_layers\"])\n\tt_layers = np.zeros(num_layers)\t\t# store the runtime for each layer\n\tN_max = param_algo['fft'].max()\n\tbw = model_hw['bandwidth']/model_hw['clk_rate']/model_hw['byte_per_word']\n\tfor l in range(num_layers):\n\t\tf_in = model_cnn[\"conv_layers\"][l][\"f_in\"]\n\t\tf_out = model_cnn[\"conv_layers\"][l][\"f_out\"]\n\t\tl_img = model_cnn[\"conv_layers\"][l][\"l_img\"]\n\t\tl_kern = model_cnn[\"conv_layers\"][l][\"l_kern\"]\n\t\tN_l = param_algo['fft'][l]\n\t\t#if M_img < f_in*N**2:\n\t\t#\tprintf(\"on-chip memory is not sufficient: you have two choices:\\n* split f_in\\n* reduce N\")\n\t\t#\treturn -1\n\t\tD_img = min(M_img,f_in*math.ceil(l_img/(N_l-l_kern+1))**2*N_l**2)\t\t\t# this is ok. just make sure that you let the M holds the entire image\n\t\t\t\t\t\t\t\t\t\t\t\t# if you change this, you should also change the equation for entire layer.\n\t\tQ = (f_out/f_in)*(2*D_img/(D_img+f_in*f_out*l_kern**2))\t# kernel is complex number\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# factor of two because of loading real number only.\n\t\tbottle1 = P_mac*N_max**2/f_in/q_mac\n\t\tbottle2 = P_ifft*N_max**2/q_2difft/q_1difft\n\t\tbottle3 = P_fft*N_max**2/q_2dfft/q_1dfft*(f_out/f_in)\n\t\tbottle4 = bw*Q/(1+Q)\t\t# this should probably be multiplied by 2, considering that kernel is just real numbers.\n\t\t#printf(\"{:5.3f},{:5.3f},{:5.3f},{:5.3f}\",bottle1,bottle2,bottle3,bottle4)\n\t\t#printf(\"fft throughput: {:5.3f}/{:5.3f}\", M_img/(M_img+f_in*f_out*N**2)*(resources[\"bw\"]-bottle4),resources[\"bw\"])\n\t\tB_ifft = np.min([bottle1,bottle2,bottle3,bottle4])\n\t\t#printf(\"layer {}: bottleneck -- {}\", l, [bottle1,bottle2,bottle3,bottle4])\n\t\tl_img_prime = math.ceil(l_img/(N_l-l_kern+1))*N_l\n\t\tt_layers[l] = f_out*l_img_prime**2/B_ifft\n\t\t#import pdb;pdb.set_trace()\n\t#printf(t_layers)\n\t#printf(t_layers.sum()/1e6)\n\treturn t_layers.sum()\n\n\ndef consumption_mem(param_algo,M_img,P_mac,q_mac,q_2dfft,q_2difft,q_1dfft,q_1difft,P_fft,P_ifft):\n\tN = param_algo['fft'].max()\n\t# FFT\n\tmem1 = P_fft*(2*N/q_2dfft*3*N + N**2)\n\tmem1_= (2*1*3*N + N**2)\t# for kernel. Hacky way\n\t# MAC\n\tmem2 = 0\n\t# IFFT\n\tmem3 = P_ifft*(2*N/q_2difft*3*N + N**2)\n\t# buffer\n\tmem4 = 2*M_img\t# double buffering\n\tmem_sum = mem1 + mem2 + mem3 + mem4 + mem1_\n\t#printf(\"consumption mem: {:4.2f}%, {:4.2f}%, {:4.2f}%, {:4.2f}%, Total {:4.2f}%\", \n\t#\t100*mem1/resources['mem'], 100*mem3/resources['mem'], 100*mem4/resources['mem'], 100*mem1_/resources['mem'],100*mem_sum/resources['mem'])\n\treturn mem1 + mem2 + mem3 + mem4 + mem1_\n\ndef consumption_alm(param_algo,M_img,P_mac,q_mac,q_2dfft,q_2difft,q_1dfft,q_1difft,P_fft,P_ifft):\n\tN = param_algo['fft'].max()\n\t# FFT\n\talm1 = 2*(math.log(N,x)-1)*N/q_1dfft*N/q_2dfft*P_fft*consumption[\"cpx_mac\"]\n\talm1_= 2*(math.log(N,x)-1)*1*4*P_fft*consumption[\"cpx_mac\"]\t\t# for kernel: hacky way\n\t# MAC\n\talm2 = P_mac*N**2/q_mac*consumption[\"cpx_mac\"]\n\t# IFFT\n\talm3 = 2*(math.log(N,x)-1)*N/q_1difft*N/q_2difft*P_ifft*consumption[\"cpx_mac\"]\n\t#if P_mac == 1:\n\talm_sum = alm1 + alm2 + alm3 + alm1_\n\t#printf(\"consumption alm: {:4.2f}%, {:4.2f}%, {:4.2f}%, {:4.2f}%, Total {:4.2f}%\",\n\t#\t100*alm1/resources['alm'], 100*alm2/resources['alm'], 100*alm3/resources['alm'], 100*alm1_/resources['alm'],100*alm_sum/resources['alm'])\n\treturn alm1 + alm2 + alm3 + alm1_\n\n\ndef total_OPS(model_cnn):\n\tnum_ops = 0\n\tfor l in model_cnn['conv_layers']:\n\t\tnum_ops += l['l_img']**2*l['l_kern']**2*l['f_in']*l['f_out']/l['stride']**2\n\treturn num_ops\n\n\n\ndef DSE_baseline(model_cnn, model_hw, param_algo, type='CaP'):\n\tif type=='CaP':\n\t\ttarget_function = target_function_CaP\n\telse:\n\t\ttarget_function = target_function_OaA\n\tbyte_per_word = model_hw['byte_per_word']\n\tclk_rate = model_hw['clk_rate']\n\tlogic_max = model_hw['logic']\n\tmemory_max = model_hw['memory']/byte_per_word*1e6\n\tmemory_max_2 = model_hw[\"memory\"]/byte_per_word/2*1e6\t# double buffer\n\tmemory_min = memory_max_2*0.5\n\tmemory_stride = (memory_max_2-memory_min)/3\n\tM_img_range = np.arange(memory_min,memory_max_2,memory_stride)\t#resources[\"mem\"],800)\t# [e5]\tone cache line is 16 complex words\n\t# problematic for P_mac_range\n\tP_mac_range = np.arange(1,10)#1200/12)\t\t\t\t# [e3]\tbounded by peak bw of the system (5GB)\n\texp_mac_max = np.log(param_algo['fft'].max())/np.log(2)\n\tq_mac_range = 2**np.arange(exp_mac_max)\t\t\t\t\t\t\t# [e1]\tN/2, N/4, N/8, N/16\n\texp_fft2_max = exp_mac_max\n\texp_fft2_min = np.log(param_algo['fft'].max()/param_algo['fft'].min())/np.log(2)\n\texp_fft1_max = exp_fft2_max-2\t\t\t\t\t\t\t\t\t\t# -2 because of radix-4\n\texp_fft1_min = exp_fft2_min\n\tq_2dfft_range = 2**np.arange(exp_fft2_min,exp_fft2_max)\t\t\t\t\t# [e1]\n\tq_2difft_range = 2**np.arange(exp_fft2_min,exp_fft2_max)\t\t\t\t# [e1]\n\tq_1dfft_range = 2**np.arange(exp_fft1_min,exp_fft1_max)\n\tq_1difft_range = 2**np.arange(exp_fft1_min,exp_fft1_max)\n\tP_fft_range = np.array([1,4])\n\tP_ifft_range= np.array([1,4])\n\n\tprev_opt = float('Inf')\n\topt = []\n\tfor i_M_img in M_img_range:\n\t\tprintf(i_M_img,type=None)\n\t\t#if i_M_img%100 == 0:\n\t\t#\tprintf(\"{}\", i_M_img)\n\t\t#\tprintf(\"current opt:\")\n\t\t#\tprintf(\"{}\", opt, type=None)\n\t\t#\tprintf(\"current opt:\")\n\t\t#\tprintf(\"{}\", prev_opt, type=None)\n\t\tfor i_P_mac in P_mac_range:\n\t\t\tfor i_q_mac in q_mac_range:\n\t\t\t\tfor i_q_2dfft in q_2dfft_range:\n\t\t\t\t\tfor i_q_2difft in q_2difft_range:\n\t\t\t\t\t\tfor i_q_1dfft in q_1dfft_range:\n\t\t\t\t\t\t\tfor i_q_1difft in q_1difft_range:\n\t\t\t\t\t\t\t\tfor i_P_fft in P_fft_range:\n\t\t\t\t\t\t\t\t\tfor i_P_ifft in P_ifft_range:\n\t\t\t\t\t\t\t\t\t\tmem = consumption_mem(param_algo,i_M_img,i_P_mac,i_q_mac,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_q_2dfft,i_q_2difft,i_q_1dfft,i_q_1difft,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_P_fft,i_P_ifft)\n\t\t\t\t\t\t\t\t\t\tif mem >= memory_max: continue\n\t\t\t\t\t\t\t\t\t\talm = consumption_alm(param_algo,i_M_img,i_P_mac,i_q_mac,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_q_2dfft,i_q_2difft,i_q_1dfft,i_q_1difft,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_P_fft,i_P_ifft)\n\t\t\t\t\t\t\t\t\t\tif alm >= logic_max: continue\n\t\t\t\t\t\t\t\t\t\tcur_performance = target_function(model_cnn,model_hw,param_algo,i_M_img,i_P_mac,i_q_mac,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_q_2dfft,i_q_2difft,i_q_1dfft,i_q_1difft,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_P_fft,i_P_ifft)\n\t\t\t\t\t\t\t\t\t\tif cur_performance >= prev_opt: continue\n\t\t\t\t\t\t\t\t\t\tprev_opt = cur_performance\n\t\t\t\t\t\t\t\t\t\topt = [\ti_M_img,i_P_mac,i_q_mac,\n\t\t\t\t\t\t\t\t\t\t\t\ti_q_2dfft,i_q_2difft,\n\t\t\t\t\t\t\t\t\t\t\t\ti_q_1dfft,i_q_1difft,\n\t\t\t\t\t\t\t\t\t\t\t\ti_P_fft,i_P_ifft]\n\tprintf(\"optimal configuration: \")\n\tprintf(\"{}\", opt, type=None)\n\tlatency = cur_performance/(clk_rate*1e6)\n\t_latency = latency#((type=='CaP') and [0] or [latency])[0]\n\t#import pdb;pdb.set_trace()\n\treturn {'latency':_latency*1e3,'throughput':total_OPS(model_cnn)/latency/1e9},\\\n\t\t{'Memory':opt[0],'P_mac':opt[1],'q_mac':opt[2],'q_2dfft':opt[3],'q_2difft':opt[4],\n\t\t\t'q_1dfft':opt[5],'q_1difft':opt[6],'P_fft':opt[7],'P_ifft':opt[8]}\n\n\n\n\n\ndef arch_dse(model_cnn, model_hw, param_algo, type='CaP'):\n\tperformance, params = DSE_baseline(model_cnn,model_hw,param_algo,type=type)\n\tconf = [param_algo,params['Memory'],params['P_mac'],params['q_mac'],params['q_2dfft'],params['q_2difft'],params['q_1dfft'],params['q_1difft'],params['P_fft'],params['P_ifft']]\n\tstat_resource = {'logic':consumption_alm(*conf)/model_hw['logic'], 'memory':consumption_mem(*conf)/(model_hw['memory']*1e6/model_hw['byte_per_word'])}\n\treturn performance, params, stat_resource\n\ndef min_bw_time():\n\tdata_traffic = 0\n\tfor i in range(len(layers[\"f_in\"])):\n\t\tif i == 0 or i == 1:\n\t\t\tN = 64\n\t\telse:\n\t\t\tN = 16\n\t\tf_in = layers[\"f_in\"][i]\n\t\tf_out = layers['f_out'][i]\n\t\tl_img = layers['l_img'][i]\n\t\tl_kern = layers['l_kern'][i]\n\t\tdata_traffic += f_in*f_out*l_kern**2/2 + (f_in/2+f_out)*math.ceil((l_img+l_kern-1)/(N-l_kern+1))**2*N**2\n\treturn data_traffic/resources['bw']/clkRate*1000\t\t# unit of ms\n\nif __name__ == \"__main__\":\n\t#printf(min_bw_time())\n\t#exit()\n\t#DSE_baseline()\n\t#M_img,P_mac,q_mac,q_2dfft,q_2difft,q_1dfft,q_1difft,P_fft,P_ifft\n\t#conf = [0.5e6,1,4,64,32,16,16,1,1]\n\tconf = [0.5e6,4,1,16,8,4,4,1,1]\n\t#target_function(*conf)\n\t#consumption_alm(*conf)\n\t#consumption_mem(*conf)\n\t#layers = {\"f_in\":[256],\"f_out\":[384],\"l_img\":[],\"l_kern\":[]}\n\ttarget_function_OaA(*conf)\n\t#target_function_CaP(*conf)\n\tconsumption_alm(*conf)\n\tconsumption_mem(*conf)","repo_name":"ZimpleX/cnn_auto_gen","sub_path":"py_dse/arch_design_space_explore.py","file_name":"arch_design_space_explore.py","file_ext":"py","file_size_in_byte":12309,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"40489475804","text":"# -*- coding: utf-8 -*-\nimport time\nimport cv2\nimport numpy\n\nfrom src.mytools.commons.utils.file_utils import FileUtils\n\n\n\n# 水印去除教程 https://my.oschina.net/u/2400083/blog/732321\n# sudo pip3 install numpy # 安装 numpy，用于在Python中进行科学计算\n# sudo pip3 install opencv-python opencv-contrib-python # 安装 opencv\n\n'''\n水印去除方法一\n1. 基于 inpaint 方法（网上的方法，处理质量较低）\n算法理论：基于Telea在2004年提出的基于快速行进的修复算法（FMM算法），先处理待修复区域边缘上的像素点，然后层层向内推进，直到修复完所有的像素点\n处理方式：由ui人员制作出黑底白色水印且相同位置的水印蒙版图(必须单通道灰度图)，然后使用inpaint方法处理原始图像，具体使用时可把水印区放粗，这样处理效果会好点\n'''\ndef watermark_clear1( src_path ,mask_path ,num=1 ):\n for i in range(num):\n src = cv2.imread(src_path) # 默认的彩色图(IMREAD_COLOR)方式读入原始图像\n mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) # 灰度图(IMREAD_GRAYSCALE)方式读入水印蒙版图像\n\n # 参数：目标修复图像; 蒙版图（定位修复区域）; 选取邻域半径; 修复算法(包括INPAINT_TELEA/INPAINT_NS， 前者算法效果较好)\n dst = cv2.inpaint(src, mask, 3, cv2.INPAINT_TELEA)\n\n cv2.imwrite(src_path, dst)\n\n\n'''\n水印去除方法二\n1. 基于像素的反色中和（处理质量较高）\n参考自ps去水印原理，通过一张白底的反色水印图来中和原图水印\n'''\ndef watermark_clear2( src_path ,mask_path):\n src = cv2.imread(src_path)\n mask = cv2.imread(mask_path)\n save = numpy.zeros(src.shape, numpy.uint8) # 创建一张空图像用于保存\n\n for row in range(src.shape[0]):\n for col in range(src.shape[1]):\n for channel in range(src.shape[2]):\n if mask[row, col, channel] == 0:\n val = 0\n else:\n reverse_val = 255 - src[row, col, channel]\n val = 255 - reverse_val * 256 / mask[row, col, channel]\n if val < 0: val = 0\n\n save[row, col, channel] = val\n\n cv2.imwrite(src_path, save)\n\n\n\n\nstart_time = time.time()\n\n\nfiles = FileUtils.loop_files('D:\\\\data\\\\')\nfor f in files:\n if f.__contains__('src2.jpg'):\n watermark_clear1('D:/data/test/src2.jpg','D:/data/test/mask.png' )\n\n\nprint( \"运行耗时 : \" )\nprint( time.time() - start_time )\n\n\n\n\n","repo_name":"lehman-china/LehmanCodeHelper","sub_path":"src/mytools/images/watermark_clear.py","file_name":"watermark_clear.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"17231945539","text":"import numpy as np\r\nimport pandas\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n\r\ndata = pandas.read_csv(\"vgsales.csv\")\r\n# для построения лог. регрессии были выбраны столбцы 'Genre', 'Year', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales'\r\ndata_sel = data.loc[:, data.columns.isin(['Platform', 'Genre', 'Year', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales'])]\r\ndata_sel = data_sel.dropna()\r\n# строим классификацию по столбцу Platform, PS2 - класс 0, остальные платформы - класс 1\r\ndata_sel['Platform'] = np.where(data_sel['Platform'] == 'PS2', 0, 1)\r\n\r\n# обработаем столбец Genre и присвоим различным значениям столбца цифру от 0 до 6\r\ndata_sel['Genre'] = np.where(data_sel['Genre'] == 'Action', 0, data_sel['Genre'])\r\ndata_sel['Genre'] = np.where(data_sel['Genre'] == 'Adventure', 1, data_sel['Genre'])\r\ndata_sel['Genre'] = np.where(data_sel['Genre'] == 'Sports', 2, data_sel['Genre'])\r\ndata_sel['Genre'] = np.where(data_sel['Genre'] == 'Platform', 3, data_sel['Genre'])\r\ndata_sel['Genre'] = np.where(data_sel['Genre'] == 'Racing', 4, data_sel['Genre'])\r\ndata_sel['Genre'] = np.where(data_sel['Genre'] == 'Fighting', 5, 6)\r\n\r\n# убираем из данных столбец Platform, по которому строим классификацию\r\nplatforms = data_sel.loc[:, data_sel.columns.isin(['Platform'])]\r\nx = data_sel.loc[:, data_sel.columns.isin(['Genre', 'Year', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales'])]\r\n# разделим набор данных на тестовую и обучающую выборку\r\nx_train, x_test, y_train, y_test = train_test_split(x, platforms, test_size=0.3)\r\n\r\n# строим лог. регрессию\r\nclf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')\r\nclf.fit(x_train, y_train)\r\ny_pred = clf.predict(x_test)\r\n\r\n# print(clf.score(x_test, y_test))\r\nprint(\"accuracy: \" + str(np.average(cross_val_score(clf, x_test, y_test, scoring='accuracy'))))\r\n# accuracy: 0.8687488274165641\r\nprint(\"f1: \" + str(np.average(cross_val_score(clf, x_test, y_test, scoring='f1'))))\r\n# f1: 0.9297651473118999\r\nprint(\"precision: \" + str(np.average(cross_val_score(clf, x_test, y_test, scoring='precision'))))\r\n# precision: 0.8687488274165641\r\nprint(\"recall: \" + str(np.average(cross_val_score(clf, x_test, y_test, scoring='recall'))))\r\n# recall : 1.0\r\n\r\ngrid_search_cv = GridSearchCV(cv=3, error_score='raise',\r\n estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\r\n max_features=None, max_leaf_nodes=None,\r\n min_impurity_decrease=0.0,\r\n min_samples_leaf=1, min_samples_split=2,\r\n min_weight_fraction_leaf=0.0, random_state=42,\r\n splitter='best'), n_jobs=None,\r\n param_grid={'max_depth': list(range(2, 20)), 'min_samples_split': [2, 3, 4]},\r\n pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\r\n scoring=None, verbose=1)\r\ngrid_search_cv.fit(x_train, y_train)\r\n\r\nparam_grid = {'n_estimators': [200, 300, 400], 'max_features': ['auto'],\r\n 'max_depth': list(range(1, 20)), 'criterion': ['gini']}\r\n\r\n# построим классификатор типа Случайный Лес\r\nRFC = GridSearchCV(estimator=RandomForestClassifier(), param_grid=param_grid, cv=5, refit=True)\r\nRFC.fit(x_train, y_train)\r\n\r\nprint(\"accuracy: \" + str(np.average(cross_val_score(grid_search_cv.best_estimator_, x_test, y_test, scoring='accuracy'))))\r\n# accuracy: 0.9328435930041067\r\nprint(\"f1: \" + str(np.average(cross_val_score(grid_search_cv.best_estimator_, x_test, y_test, scoring='f1'))))\r\n# f1 : 0.9615368345963002\r\nprint(\"precision: \" + str(np.average(cross_val_score(grid_search_cv.best_estimator_, x_test, y_test, scoring='precision'))))\r\n# precision: 0.9603767058423113\r\nprint(\"recall: \" + str(np.average(cross_val_score(grid_search_cv.best_estimator_, x_test, y_test, scoring='recall'))))\r\n# recall: 0.9627700398537327\r\n\r\n# сравнивая значения метрик для классификаторов типа лог. регрессии и случайного леса,\r\n# делаем вывод, что второй построенный классификатор лучше\r\n","repo_name":"duddoser/srw_2022","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":4548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"10921504033","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 5 23:44:45 2022\n\n@author: PaulJ\n\"\"\"\nfrom os.path import join, abspath\nfrom inspect import getsourcefile\nimport datetime as dt\nfrom typing import Callable\nimport logging\nimport pytest\nfrom _pytest.monkeypatch import MonkeyPatch\n\n# from cashflowsim.roll_die import roll_die\nfrom cashflowsim.assets import Asset, Stock\nfrom cashflowsim.loans import Loan\nfrom cashflowsim.player import Player\nfrom cashflowsim.profession import Profession, get_profession_defs\nfrom cashflowsim.strategy import Strategy, get_strategy_defs\nfrom cashflowsim.player_choice import (\n choose_no_die,\n choose_small_or_big_deal_card,\n choose_to_donate_to_charity,\n choose_to_buy_stock_asset,\n choose_to_buy_asset,\n choose_to_sell_asset,\n choose_to_get_loan_to_buy_asset,\n choose_to_pay_off_loan,\n # choose_to_donate_to_charity,\n)\n\nAPP_DIR: str = \"cashflowsim\"\nGAME_DATA_DIR: str = \"game_data\"\nSTRATEGIES_DIR = \"simulation_strategies\"\nLOG_DIR: str = \"game_logs\"\n\nBOARDSPACES_FN = \"RatRaceBoardSpaces.json\"\nPROFESSIONS_FN = \"ProfessionsList.json\"\nPLAYER1_NAME = \"Player1\"\nPLAYER1_PROF = \"Engineer\"\nPLAYER1_STRATEGY: str = \"Dave Ramsey\"\nPLAYER2_NAME = \"Player2\"\nPLAYER2_PROF: str = \"Doctor\"\nPLAYER2_STRATEGY: str = \"Standard Auto\"\nPLAYER3_NAME: str = \"Player3\"\nPLAYER3_PROF: str = \"Business Manager\"\nPLAYER3_STRATEGY: str = \"LowBDT, LowPRT, HighHighROIT\"\nSTRATEGIES_FN: str = \"Strategies.json\"\n\n\nbase_path: str = \"\\\\\".join(abspath(str(getsourcefile(lambda: 0))).split(\"\\\\\")[:-2])\ngame_data_path: str = join(base_path, APP_DIR, GAME_DATA_DIR)\nboardspace_path_fn: str = join(game_data_path, BOARDSPACES_FN)\nprofession_data_path_fn: str = join(game_data_path, PROFESSIONS_FN)\nstrategies_data_path: str = join(base_path, APP_DIR, STRATEGIES_DIR)\nstrategies_data_path_fn: str = join(strategies_data_path, STRATEGIES_FN)\n\nthis_fn: str = __file__.split(\"\\\\\")[-1].split(\".\")[0]\nlogfile_fn: str = \"\".join([this_fn, \"_log.txt\"])\n\nlogfile_path_fn: str = join(base_path, APP_DIR, LOG_DIR, logfile_fn)\n\nlogging.basicConfig(\n filename=logfile_path_fn,\n # level=logging.DEBUG,\n level=logging.ERROR,\n filemode=\"w\",\n format=\"%(asctime)s - (%(levelname)s) %(message)s\",\n datefmt=\"%d-%b-%Y %H:%M:%S\",\n force=True,\n)\nlogging.info(\n f\"\\n\\n\\nStart of new test run: {dt.datetime.now().strftime('%Y%m%d-%H%M%S')}\"\n)\n\ntry:\n profession_defs: dict[str, Profession] = get_profession_defs(\n profession_data_path_fn\n )\nexcept OSError:\n err_msg = f\"No good Professions json file found, file not found, please fix\"\n logging.error(f\"{err_msg}\\nFile name: {profession_data_path_fn}\")\n raise OSError(err_msg)\nexcept ValueError:\n err_msg = f\"No good Professions Cards json file found, ValueError, please fix\"\n logging.error(f\"{err_msg}\\nFile name: {profession_data_path_fn}\")\n raise ValueError(err_msg)\n\ntry:\n strategy_defs: dict[str, Strategy] = get_strategy_defs(\n strategy_defs_fn=strategies_data_path_fn\n )\nexcept OSError:\n err_msg = f\"No good Strategies json file found, file not found, please fix\"\n logging.error(f\"{err_msg}\\nFile name: {strategies_data_path_fn}\")\n raise OSError(err_msg)\nexcept ValueError:\n err_msg = f\"No good Strategies Cards json file found, ValueError, please fix\"\n logging.error(f\"{err_msg}\\nFile name: {strategies_data_path_fn}\")\n raise ValueError(err_msg)\n\n\n@pytest.fixture\ndef player1() -> Player:\n return Player(\n name=PLAYER1_NAME,\n profession=profession_defs[PLAYER1_PROF],\n strategy=strategy_defs[PLAYER1_STRATEGY],\n )\n\n\n@pytest.fixture\ndef player2() -> Player:\n return Player(\n name=PLAYER2_NAME,\n profession=profession_defs[PLAYER2_PROF],\n strategy=strategy_defs[PLAYER2_STRATEGY],\n )\n\n\n@pytest.fixture\ndef player3() -> Player:\n return Player(\n name=PLAYER3_NAME,\n profession=profession_defs[PLAYER3_PROF],\n strategy=strategy_defs[PLAYER3_STRATEGY],\n )\n\n\ndef test_choose_small_or_big_deal_card_auto(\n player1: Player, player2: Player, player3: Player\n) -> None:\n logging.info(\"Testing Player1 test_choose_small_or_big_deal_card\")\n logging.info(\n f\"Player1 savings: {player1.savings}\"\n f\"\\nPlayer1 big deal threshold: {player1.strategy.big_deal_small_deal_threshold}\"\n )\n assert choose_small_or_big_deal_card(a_player=player1) == \"small\"\n for _ in range(3):\n player1.earn_salary()\n logging.info(f\"Player1 earned salary x 3\")\n logging.info(\n f\"Player1 savings: {player1.savings}\"\n f\"\\nPlayer1 big deal threshold: {player1.strategy.big_deal_small_deal_threshold}\"\n )\n assert choose_small_or_big_deal_card(a_player=player1) == \"big\"\n logging.info(f\"Player1 chose correct big and small deal cards\")\n\n logging.info(\"Testing Player2 test_choose_small_or_big_deal_card\")\n logging.info(\n f\"Player2 savings: {player2.savings}\"\n f\"\\nPlayer2 big deal threshold: {player2.strategy.big_deal_small_deal_threshold}\"\n )\n assert choose_small_or_big_deal_card(a_player=player2) == \"small\"\n for _ in range(2):\n player2.earn_salary()\n logging.info(f\"Player2 earned salary x 2\")\n logging.info(\n f\"Player2 savings: {player2.savings}\"\n f\"\\nPlayer2 big deal threshold: {player2.strategy.big_deal_small_deal_threshold}\"\n )\n assert choose_small_or_big_deal_card(a_player=player2) == \"big\"\n logging.info(f\"Player2 chose correct big and small deal cards\")\n\n logging.info(\"Testing Player3 test_choose_small_or_big_deal_card\")\n logging.info(\n f\"Player3 savings: {player3.savings}\"\n f\"\\nPlayer3 big deal threshold: {player3.strategy.big_deal_small_deal_threshold}\"\n )\n assert choose_small_or_big_deal_card(a_player=player3) == \"small\"\n for _ in range(2):\n player3.earn_salary()\n logging.info(f\"Player3 earned salary x 2\")\n logging.info(\n f\"Player3 savings: {player3.savings}\"\n f\"\\nPlayer3 big deal threshold: {player3.strategy.big_deal_small_deal_threshold}\"\n )\n assert choose_small_or_big_deal_card(a_player=player3) == \"big\"\n logging.info(f\"Player3 chose correct big and small deal cards\")\n\n\ndef test_choose_small_or_big_deal_card_manual(\n player1: Player, monkeypatch: MonkeyPatch\n) -> None:\n logging.info(\"\\n\\nTesting test_choose_small_or_big_deal_card_manual\")\n\n choose_small_deal: list[Callable[[str], str]] = [\n lambda _: \"s\",\n lambda _: \"S\",\n lambda _: \"small\",\n lambda _: \"Small\",\n ]\n\n responses = iter([\"Foo\", \"B\"])\n\n choose_big_deal: list[Callable[[str], str]] = [\n lambda _: \"b\",\n lambda _: \"B\",\n lambda _: \"big\",\n lambda _: \"Big\",\n lambda _: next(responses),\n ]\n\n player1.strategy.manual = True\n for a_small_choice in choose_small_deal:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_small_choice)\n assert choose_small_or_big_deal_card(a_player=player1) == \"small\"\n\n for a_big_choice in choose_big_deal:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_big_choice)\n assert choose_small_or_big_deal_card(a_player=player1) == \"big\"\n\n\ndef test_choose_to_donate_to_charity_manual(\n player1: Player, monkeypatch: MonkeyPatch\n) -> None:\n logging.info(\"\\n\\nTesting test_choose_to_donate_to_charity_manual\")\n\n donate_charity_no: list[Callable[[str], str]] = [\n lambda _: \"n\",\n lambda _: \"N\",\n lambda _: \"no\",\n lambda _: \"No\",\n ]\n\n responses = iter([\"Foo\", \"yes\"])\n\n donate_charity_yes: list[Callable[[str], str]] = [\n lambda _: \"y\",\n lambda _: \"Y\",\n lambda _: \"yes\",\n lambda _: \"Yes\",\n lambda _: next(responses),\n ]\n\n player1.strategy.manual = True\n\n for a_no in donate_charity_no:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_no)\n assert not choose_to_donate_to_charity(a_strategy=player1.strategy)\n\n for a_yes in donate_charity_yes:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_yes)\n assert choose_to_donate_to_charity(a_strategy=player1.strategy)\n\n\ndef test_choose_no_dice_manual(player1: Player, monkeypatch: MonkeyPatch) -> None:\n logging.info(\"\\n\\nTesting test_choose_no_dice_manual\")\n\n responses1 = iter([\"foo\", \"45\", \"1\"])\n responses2 = iter([\"702\", \"2\"])\n\n no_dice_opts: list[Callable[[str], str]] = [\n lambda _: \"1\",\n lambda _: \"2\",\n lambda _: next(responses1),\n lambda _: next(responses2),\n ]\n\n no_dice_answers: list[int] = [1, 2, 1, 2]\n\n player1.strategy.manual = True\n\n for n, a_no_dice in enumerate(no_dice_opts):\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_no_dice)\n # logging.info(f\"a_no_dice: {a_no_dice('')}, answer: {no_dice_answers[n]}\")\n no_dice_chosen = choose_no_die(\n no_die_choice_list=[1, 2], a_strategy=player1.strategy\n )\n assert no_dice_chosen == no_dice_answers[n]\n logging.info(f\"{no_dice_chosen} == {no_dice_answers[n]}\")\n\n\ndef test_choose_no_stock_shares_to_buy_manual(\n player1: Player, monkeypatch: MonkeyPatch\n) -> None:\n logging.info(\"\\n\\nTesting test_choose_no_stock_shares_to_buy_manual\")\n\n a_new_stock = Stock(\n name=\"TBO\",\n asset_type=\"Stock\",\n shares=0,\n dividend_interest=5,\n price_range_low=50,\n price_range_high=500,\n cost_per_share=100,\n )\n\n responses1 = iter([\"foo\", \"-1\", \"0\"])\n responses2 = iter([\"bar\", \"-100\", \"100\"])\n\n no_shares_opts: list[Callable[[str], str]] = [\n lambda _: \"0\",\n lambda _: \"1\",\n lambda _: \"10\",\n lambda _: \"100\",\n lambda _: \"1000000\",\n lambda _: next(responses1),\n lambda _: next(responses2),\n ]\n\n no_shares_answers: list[tuple[bool, int]] = [\n (False, 0),\n (True, 1),\n (True, 10),\n (True, 100),\n (True, 1000000),\n (False, 0),\n (True, 100),\n ]\n\n player1.strategy.manual = True\n\n for n, a_no_shares_response in enumerate(no_shares_opts):\n a_new_stock.shares = 0\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_no_shares_response)\n buy_stock = choose_to_buy_stock_asset(\n a_player=player1, new_stock=a_new_stock\n )\n logging.info(f\"buy_stock: {buy_stock}\")\n assert buy_stock == no_shares_answers[n][0]\n logging.info(f\"{buy_stock} == {no_shares_answers[n][0]}\")\n assert a_new_stock.shares == no_shares_answers[n][1]\n logging.info(f\"{a_new_stock.shares} == {no_shares_answers[n][1]}\")\n\n\ndef test_choose_no_stock_shares_to_buy_auto(player1: Player) -> None:\n logging.info(\"\\n\\nTesting test_choose_no_stock_shares_to_buy_auto\")\n\n a_new_stock = Stock(\n name=\"TBO\",\n asset_type=\"Stock\",\n shares=0,\n dividend_interest=5,\n price_range_low=50,\n price_range_high=500,\n cost_per_share=100,\n )\n\n player1.strategy.manual = False\n player1.strategy.roi_threshold = 0.20\n player1.savings = 10\n player1.strategy.take_any_loans = False\n\n # Try to buy stock with not enough money\n decide_to_buy_stock: bool = choose_to_buy_stock_asset(\n a_player=player1, new_stock=a_new_stock\n )\n try:\n assert not decide_to_buy_stock\n logging.info(f\"Properly decided not to by shares without enough money\")\n except AssertionError:\n logging.info(f\"Improperly decided to shares without enough money\")\n raise\n\n # Try to buy stock with enough money\n player1.savings = 10_000\n no_shares_should_buy: int = player1.savings // a_new_stock.cost_per_share\n decide_to_buy_stock: bool = choose_to_buy_stock_asset(\n a_player=player1, new_stock=a_new_stock\n )\n try:\n assert decide_to_buy_stock\n assert a_new_stock.shares == no_shares_should_buy\n logging.info(\n f\"Properly decided to buy {a_new_stock.shares} shares with enough money\"\n )\n except AssertionError:\n logging.info(f\"Improperly decided to not buy shares with enough money\")\n raise\n\n # Try to not buy stock with enough money but too low roi\n player1.savings = 10_000\n player1.strategy.roi_threshold = 0.9\n player1.strategy.price_ratio_threshold = 0.1\n no_shares_should_buy: int = player1.savings // a_new_stock.cost_per_share\n decide_to_buy_stock: bool = choose_to_buy_stock_asset(\n a_player=player1, new_stock=a_new_stock\n )\n try:\n assert not decide_to_buy_stock\n logging.info(\n f\"Properly decided not to buy stock with enough money but too low roi\"\n )\n except AssertionError:\n logging.info(\n f\"Improperly decided to buy shares with enough money but too low roi\"\n )\n raise\n\n\ndef test_choose_to_buy_asset_manual(player1: Player, monkeypatch: MonkeyPatch) -> None:\n logging.info(\"\\n\\nTesting test_choose_to_buy_asset_manual\")\n\n an_asset = Asset(\n name=\"Rare Gold Coin\",\n asset_type=\"Asset\",\n cost=500,\n cash_flow=0,\n price_range_low=0,\n price_range_high=4000,\n )\n\n no_list: list[Callable[[str], str]] = [\n lambda _: \"n\",\n lambda _: \"N\",\n lambda _: \"no\",\n lambda _: \"No\",\n ]\n\n eventual_yes = iter([\"Foo\", \"yes\"])\n\n yes_list: list[Callable[[str], str]] = [\n lambda _: \"y\",\n lambda _: \"Y\",\n lambda _: \"yes\",\n lambda _: \"Yes\",\n lambda _: next(eventual_yes),\n ]\n\n player1.strategy.manual = True\n\n for a_no in no_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_no)\n assert not choose_to_buy_asset(a_player=player1, asset=an_asset)\n\n for a_yes in yes_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_yes)\n assert choose_to_buy_asset(a_player=player1, asset=an_asset)\n\n\ndef test_choose_to_sell_stock_auto(player1: Player) -> None:\n logging.info(\"\\n\\nTesting test_choose_to_buy_stock_auto\")\n\n an_asset = Stock(\n name=\"MYT4U\",\n asset_type=\"Stock\",\n cost=1,\n shares=100,\n dividend_interest=0,\n price_range_low=0,\n price_range_high=30,\n )\n\n player1.strategy.manual = False\n player1.savings = 100\n\n player1.buy_stock(stock_asset=an_asset, cost_per_share=1)\n\n choose_to_sell_asset(a_player=player1, asset=an_asset, price=2, delta_price=3)\n\n\ndef test_choose_to_sell_stock_manual(player1: Player, monkeypatch: MonkeyPatch) -> None:\n logging.info(\"\\n\\nTesting test_choose_to_buy_stock_manual\")\n\n no_list: list[Callable[[str], str]] = [\n lambda _: \"n\",\n lambda _: \"N\",\n lambda _: \"no\",\n lambda _: \"No\",\n ]\n\n eventual_yes = iter([\"Foo\", \"yes\"])\n\n yes_list: list[Callable[[str], str]] = [\n lambda _: \"y\",\n lambda _: \"Y\",\n lambda _: \"yes\",\n lambda _: \"Yes\",\n lambda _: next(eventual_yes),\n ]\n\n an_asset = Stock(\n name=\"MYT4U\",\n asset_type=\"Stock\",\n cost=1,\n shares=100,\n dividend_interest=0,\n price_range_low=0,\n price_range_high=30,\n )\n\n player1.strategy.manual = False\n player1.savings = 100\n\n player1.buy_stock(stock_asset=an_asset, cost_per_share=1)\n\n player1.strategy.manual = True\n\n for a_no in no_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_no)\n assert not choose_to_sell_asset(a_player=player1, asset=an_asset, price=50)\n\n for a_yes in yes_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_yes)\n assert choose_to_sell_asset(a_player=player1, asset=an_asset, price=50)\n # Reset for next try\n player1.strategy.manual = False\n player1.savings = 100\n player1.buy_stock(stock_asset=an_asset, cost_per_share=1)\n player1.strategy.manual = True\n\n\ndef test_choose_to_get_loan_to_buy_asset_manual(\n player1: Player, monkeypatch: MonkeyPatch\n) -> None:\n logging.info(\"\\n\\nTesting choose_to_get_loan_to_buy_asset_manual\")\n\n an_asset = Asset(\n name=\"Rare Gold Coin\",\n asset_type=\"Asset\",\n cost=500,\n cash_flow=0,\n price_range_low=0,\n price_range_high=4000,\n )\n\n player1.savings = 0\n\n no_list: list[Callable[[str], str]] = [\n lambda _: \"n\",\n lambda _: \"N\",\n lambda _: \"no\",\n lambda _: \"No\",\n ]\n\n eventual_yes = iter([\"Foo\", \"yes\"])\n\n yes_list: list[Callable[[str], str]] = [\n lambda _: \"y\",\n lambda _: \"Y\",\n lambda _: \"yes\",\n lambda _: \"Yes\",\n lambda _: next(eventual_yes),\n ]\n\n player1.strategy.manual = True\n\n for a_no in no_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_no)\n assert not choose_to_get_loan_to_buy_asset(\n a_player=player1, asset=an_asset, loan_amount=500\n )\n\n for a_yes in yes_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_yes)\n assert choose_to_get_loan_to_buy_asset(\n a_player=player1, asset=an_asset, loan_amount=500\n )\n\n\ndef test_choose_to_pay_off_loan_manual(\n player1: Player, monkeypatch: MonkeyPatch\n) -> None:\n logging.info(\"\\n\\nTesting choose_to_pay_off_loan_manual\")\n\n a_loan: Loan = Loan(\n name=\"a_loan\",\n balance=10_000,\n monthly_payment=1000,\n partial_payment_allowed=True,\n )\n b_loan: Loan = Loan(\n name=\"b_loan\",\n balance=10_000,\n monthly_payment=1000,\n partial_payment_allowed=False,\n )\n player1.loan_list = []\n player1.make_loan(loan=a_loan)\n player1.make_loan(loan=b_loan)\n\n eventual_no_1 = iter([\"Foo\", \"-1\", \"3\", \"0\"])\n eventual_no_2 = iter([\"2\", \"5_000\"])\n eventual_no_3 = iter([\"2\", \"10_000\"])\n\n no_list: list[Callable[[str], str]] = [\n lambda _: \"0\",\n lambda _: next(eventual_no_1),\n lambda _: next(eventual_no_2),\n lambda _: next(eventual_no_3),\n ]\n\n eventual_yes_1 = iter([\"1\", \"1000\"])\n eventual_yes_2 = iter([\"Foo\", \"15\", \"1\", \"apple\", \"50\", \"1000\"])\n eventual_yes_3 = iter([\"2\", \"10_000\"])\n\n yes_list: list[Callable[[str], str]] = [\n lambda _: next(eventual_yes_1),\n lambda _: next(eventual_yes_2),\n lambda _: next(eventual_yes_3),\n ]\n\n player1.strategy.loan_payback = \"Manual\"\n\n player1.savings = 5_000\n\n for a_no in no_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_no)\n try:\n assert not choose_to_pay_off_loan(a_player=player1)\n logging.info(f\"Corrrectly chose not to pay off loan\")\n except AssertionError:\n logging.info(f\"Incorrectly chose to pay off loan\")\n raise\n # Re-take loan for next iteration\n if a_loan not in player1.loan_list:\n player1.make_loan(loan=a_loan)\n if b_loan not in player1.loan_list:\n player1.make_loan(loan=b_loan)\n\n player1.savings = 15_000\n\n for a_yes in yes_list:\n with monkeypatch.context() as m:\n m.setattr(\"builtins.input\", a_yes)\n try:\n assert choose_to_pay_off_loan(a_player=player1)\n logging.info(f\"Correctly chose to pay off loan\")\n except AssertionError:\n logging.info(f\"Incorrectly chose not to pay off loan\")\n raise\n # Re-take loan for next iteration\n if a_loan not in player1.loan_list:\n player1.make_loan(loan=a_loan)\n if b_loan not in player1.loan_list:\n player1.make_loan(loan=b_loan)\n\n\ndef test_choose_to_pay_off_loan_auto(player1: Player) -> None:\n logging.info(f\"\\n\\nTesting choose_to_pay_off_loan_auto\")\n logging.info(f\"Player Savings: {player1.savings}\")\n some_loans: list[Loan] = [\n Loan(\n name=\"a_loan\",\n balance=1_000,\n monthly_payment=10,\n partial_payment_allowed=True,\n ),\n Loan(\n name=\"b_loan\",\n balance=10_000,\n monthly_payment=100,\n partial_payment_allowed=True,\n ),\n Loan(\n name=\"c_loan\",\n balance=9_000,\n monthly_payment=500,\n partial_payment_allowed=True,\n ),\n ]\n\n loan_choice_methods: list[tuple[str, int]] = [\n (\"Largest\", 1),\n (\"Smallest\", 0),\n (\"Highest Interest\", 2),\n ]\n\n logging.info(f\"Testing to pay-off loans that should be paid-off\")\n for loan_choice_method in loan_choice_methods:\n logging.info(f\"\\nloan_choice_method: {loan_choice_method}\")\n player1.loan_list = []\n for a_loan in some_loans:\n player1.make_loan(loan=a_loan)\n\n player1.savings = 10_000\n logging.info(f\"player1.savings = {player1.savings}\")\n player1.strategy.loan_payback = loan_choice_method[0]\n\n try:\n assert choose_to_pay_off_loan(a_player=player1)\n assert some_loans[loan_choice_method[1]] not in player1.loan_list\n logging.info(f\"Corrrectly chose loan to pay off\")\n except AssertionError:\n logging.info(f\"Incorrectly chose wrong loan to pay-off\")\n raise\n\n logging.info(f\"Testing to pay-off loans that should not be paid-off\")\n for loan_choice_method in loan_choice_methods:\n logging.info(f\"\\nloan_choice_method: {loan_choice_method}\")\n player1.loan_list = []\n for a_loan in some_loans:\n player1.make_loan(loan=a_loan)\n\n player1.savings = 500\n logging.info(f\"player1.savings = {player1.savings}\")\n player1.strategy.loan_payback = loan_choice_method[0]\n\n try:\n assert not choose_to_pay_off_loan(a_player=player1)\n assert some_loans[loan_choice_method[1]] in player1.loan_list\n logging.info(f\"Corrrectly chose not to pay-off loan\")\n except AssertionError:\n logging.info(f\"Incorrectly chose to pay-off loan\")\n raise\n\n logging.info(f\"Testing to pay-down loan that should be paid-down\")\n loan_choice_method: tuple[str, int] = (\"Largest\", 1)\n logging.info(f\"\\nloan_choice_method: {loan_choice_method}\")\n player1.loan_list = [some_loans[loan_choice_method[1]]]\n\n player1.savings = 1_500\n logging.info(f\"player1.savings = {player1.savings}\")\n player1.strategy.loan_payback = loan_choice_method[0]\n\n try:\n assert choose_to_pay_off_loan(a_player=player1)\n logging.info(f\"Corrrectly chose to pay-down loan\")\n except AssertionError:\n logging.info(f\"Incorrectly chose to not pay-down loan\")\n raise\n\n logging.info(f\"\\nTesting choosing to pay-off loan with no loans\")\n player1.loan_list = []\n loan_payed_off = choose_to_pay_off_loan(a_player=player1)\n logging.info(f\"Result: {loan_payed_off}\")\n assert not loan_payed_off\n logging.info(f\"Correct!\")\n\n logging.info(f\"\\nTesting choosing to pay-off loan with bad strategy name\")\n player1.loan_list = []\n for a_loan in some_loans:\n player1.make_loan(loan=a_loan)\n try:\n player1.strategy.loan_payback = \"Biggest Unit\"\n choose_to_pay_off_loan(a_player=player1)\n except ValueError:\n info_msg = f\"Correctly chose not to pay-off loan.\"\n logging.info(info_msg)\n return\n err_msg = f\"Incorrectly chose to pay-off loan\"\n logging.error(err_msg)\n","repo_name":"pjjefferies/FlowTheCash-Simulation","sub_path":"tests/test_player_choice.py","file_name":"test_player_choice.py","file_ext":"py","file_size_in_byte":23801,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"41055992405","text":"\n# Read the train file onto the data frame, find the class distribution in the train file\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\n\ntrainDataFrame = pd.read_csv(r\"./Dataset/train.csv\", sep='|')\n\nprint(os.getcwd())\n\nprint()\n\n# Find the class distribution\ndef classDistribution(trainDataFrame):\n # Plot to uncover the number of instances associated with each class\n distribution = trainDataFrame['class'].value_counts().plot.bar()\n distribution.plot()\n plt.xlabel(\"Class\")\n plt.ylabel(\"Frequency\")\n plt.suptitle(\"Class distribution\")\n plt.savefig(\"./EDA/Class Distribution.png\")\n # plt.savefig(\"Class Distribution.png\")\n plt.show()\n\nclassDistribution(trainDataFrame)\n\n\n\n","repo_name":"Adarsh-g-s/TextClassification","sub_path":"DataAnalysis.py","file_name":"DataAnalysis.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"30686311315","text":"#요세푸스 문제 0\nimport sys\nn, k = map(int, sys.stdin.readline().rstrip().split())\nqueue = [i for i in range(1, n + 1)]\nanswer = []\nidx = 0\n\nwhile queue:\n idx += k - 1\n if idx > len(queue) - 1:\n idx %= len(queue)\n answer.append(queue.pop(idx))\nprint('<' + str(answer)[1:-1] + '>')","repo_name":"jisuuuu/Algorithm_Study","sub_path":"Baekjoon/Baekjoon_python/boj_11866.py","file_name":"boj_11866.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"18573182228","text":"#!/usr/bin/env python3\n\n\"\"\"\nCreated on 18 Feb 2017\n\n@author: Bruno Beloff (bruno.beloff@southcoastscience.com)\n\nDESCRIPTION\nThe osio_client_auth utility is used to store or read the client ID and client password required by the OpenSensors.io\nCommunity Edition messaging system. This client authentication is required to both subscribe to and publish on any\nmessaging topic.\n\nWhen setting the client authentication, the osio_client_auth utility requests a new device identity from the\nOpenSensors.io system, then stores the generated tokens on the client. The name of the device is taken to be the\nname of the host on which the script is executed. Names (unlike client IDs) are not required to be unique on the\nOpenSensors system.\n\nNote that the scs_mfr/osio_mqtt_client process must be restarted for changes to take effect.\n\nSYNOPSIS\nosio_client_auth.py [-u USER_ID] [-l LAT LNG POSTCODE] [-d DESCRIPTION] [-d DESCRIPTION] [-v]\n\nEXAMPLES\n./osio_client_auth.py -u south-coast-science-test-user -l 50.823204, -0.123005 \"BN2 0DF\" -v\n\nFILES\n~/SCS/osio/osio_client_auth.json\n\nDOCUMENT EXAMPLE\n{\"user_id\": \"south-coast-science-test-user\", \"client-id\": \"5403\", \"client-password\": \"rtxSrK2f\"}\n\nSEE ALSO\nscs_dev/osio_mqtt_client\nscs_mfr/osio_api_auth\nscs_mfr/osio_project\n\"\"\"\n\nimport sys\n\nfrom scs_core.data.json import JSONify\n\nfrom scs_core.gas.afe_calib import AFECalib\n\nfrom scs_core.osio.client.api_auth import APIAuth\nfrom scs_core.osio.client.client_auth import ClientAuth\nfrom scs_core.osio.config.project_source import ProjectSource\nfrom scs_core.osio.manager.device_manager import DeviceManager\nfrom scs_core.osio.manager.user_manager import UserManager\n\nfrom scs_core.sys.system_id import SystemID\n\nfrom scs_dfe.particulate.opc_conf import OPCConf\n\nfrom scs_host.sys.host import Host\n\nfrom scs_mfr.cmd.cmd_osio_client_auth import CmdOSIOClientAuth\n\n\n# --------------------------------------------------------------------------------------------------------------------\n\nif __name__ == '__main__':\n\n # ----------------------------------------------------------------------------------------------------------------\n # cmd...\n\n cmd = CmdOSIOClientAuth()\n\n if cmd.verbose:\n print(\"osio_client_auth: %s\" % cmd, file=sys.stderr)\n\n\n # ----------------------------------------------------------------------------------------------------------------\n # resources...\n\n # OPCConf...\n opc_conf = OPCConf.load(Host)\n\n if cmd.verbose:\n print(\"osio_client_auth: %s\" % opc_conf, file=sys.stderr)\n\n # APIAuth...\n api_auth = APIAuth.load(Host)\n\n if api_auth is None:\n print(\"osio_client_auth: APIAuth not available.\", file=sys.stderr)\n exit(1)\n\n if cmd.verbose:\n print(\"osio_client_auth: %s\" % api_auth, file=sys.stderr)\n\n # SystemID...\n system_id = SystemID.load(Host)\n\n if system_id is None:\n print(\"osio_client_auth: SystemID not available.\", file=sys.stderr)\n exit(1)\n\n if cmd.verbose:\n print(\"osio_client_auth: %s\" % system_id, file=sys.stderr)\n\n # AFECalib...\n afe_calib = AFECalib.load(Host)\n\n if afe_calib is None:\n print(\"osio_client_auth: AFECalib not available.\", file=sys.stderr)\n exit(1)\n\n if cmd.verbose:\n print(\"osio_client_auth: %s\" % afe_calib, file=sys.stderr)\n sys.stderr.flush()\n\n # User manager...\n user_manager = UserManager(api_auth.api_key)\n\n # Device manager...\n device_manager = DeviceManager(api_auth.api_key)\n\n # check for existing registration...\n device = device_manager.find_for_name(api_auth.org_id, system_id.box_label())\n\n\n # ----------------------------------------------------------------------------------------------------------------\n # validate...\n\n # TODO: check whether remote device and local client auth match\n\n if device is None:\n if cmd.set() and not cmd.is_complete():\n print(\"osio_client_auth: No device is registered - you must therefore set a user and location.\",\n file=sys.stderr)\n cmd.print_help(sys.stderr)\n exit(2)\n\n if not cmd.set():\n exit(0)\n\n\n # ----------------------------------------------------------------------------------------------------------------\n # run...\n\n if cmd.set():\n # User...\n if cmd.user_id:\n user = user_manager.find_public(cmd.user_id)\n\n if user is None:\n print(\"osio_client_auth: User not available.\", file=sys.stderr)\n exit(1)\n\n # tags...\n include_particulates = bool(opc_conf is not None)\n tags = ProjectSource.tags(afe_calib, include_particulates)\n\n if device:\n if cmd.user_id:\n print(\"osio_client_auth: Device owner-id cannot be updated.\", file=sys.stderr)\n exit(1)\n\n # find ClientAuth...\n client_auth = ClientAuth.load(Host)\n\n # update Device...\n updated = ProjectSource.update(device, cmd.lat, cmd.lng, cmd.postcode, cmd.description, tags)\n device_manager.update(api_auth.org_id, device.client_id, updated)\n\n # find updated device...\n device = device_manager.find(api_auth.org_id, device.client_id)\n\n else:\n # create Device...\n device = ProjectSource.create(system_id, api_auth, cmd.lat, cmd.lng, cmd.postcode, cmd.description, tags)\n device = device_manager.create(cmd.user_id, device)\n\n # create ClientAuth...\n client_auth = ClientAuth(cmd.user_id, device.client_id, device.password)\n\n client_auth.save(Host)\n\n else:\n # find ClientAuth...\n client_auth = ClientAuth.load(Host)\n\n if cmd.verbose:\n print(client_auth, file=sys.stderr)\n\n print(JSONify.dumps(device))\n","repo_name":"seoss/scs_mfr","sub_path":"src/scs_mfr/osio_client_auth.py","file_name":"osio_client_auth.py","file_ext":"py","file_size_in_byte":5813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"72"}
+{"seq_id":"22034835631","text":"import logging\nimport os\nfrom time import time\n\nfrom fastapi import FastAPI, Request\nfrom opentelemetry import trace\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\nfrom opentelemetry.instrumentation.fastapi import FastAPIInstrumentor\nfrom opentelemetry.sdk.resources import SERVICE_NAME, Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio\nfrom prometheus_fastapi_instrumentator import Instrumentator\nfrom pythonjsonlogger import jsonlogger\nfrom starlette.middleware.base import BaseHTTPMiddleware\n\nfrom .models import Decision, Post, PostList\nfrom .sentiment import get_sentiment\n\n# Set up JSON logging\nformatter = jsonlogger.JsonFormatter()\nhandler = logging.StreamHandler()\n\n# Use OUR `formatter` to format all `logging` entries.\nhandler.setFormatter(formatter)\nroot_logger = logging.getLogger()\nroot_logger.addHandler(handler)\nroot_logger.setLevel(logging.INFO)\n\nfor _log in [\"uvicorn\", \"uvicorn.error\"]:\n # Clear the log handlers for uvicorn loggers, and enable propagation\n # so the messages are caught by our root logger and formatted correctly\n # by structlog\n logging.getLogger(_log).handlers.clear()\n logging.getLogger(_log).propagate = True\n\n# Since we re-create the access logs ourselves, to add all information\n# in the structured log, we clear the handlers and prevent the logs to propagate to\n# a logger higher up in the hierarchy (effectively rendering them silent).\nlogging.getLogger(\"uvicorn.access\").handlers.clear()\nlogging.getLogger(\"uvicorn.access\").propagate = False\n\n\nclass LoggingMiddleware(BaseHTTPMiddleware):\n async def dispatch(self, request: Request, call_next):\n start_time = time()\n response = await call_next(request)\n process_time = time() - start_time\n logging.info(\n {\n \"message\": \"request handled\",\n \"path\": request.url.path,\n \"method\": request.method,\n \"processing_time\": process_time,\n \"status_code\": response.status_code,\n \"query_params\": request.query_params,\n },\n )\n return response\n\n\n# Set up OpenTelemetry\notel_endpoint = os.getenv(\"OTEL_EXPORTER_OTLP_ENDPOINT\")\nif otel_endpoint:\n resource = Resource(attributes={SERVICE_NAME: \"bsky-sentiment\"})\n tp = TracerProvider(resource=resource, sampler=ParentBasedTraceIdRatio(0.2))\n tp.add_span_processor(\n BatchSpanProcessor(\n OTLPSpanExporter(\n endpoint=otel_endpoint + \"v1/traces\",\n )\n )\n )\n trace.set_tracer_provider(tp)\n\napp = FastAPI()\n\n# Instrument FastAPI for OpenTelemetry\nif otel_endpoint:\n FastAPIInstrumentor.instrument_app(app)\n\n# Instrument FastAPI for Prometheus\nInstrumentator().instrument(\n app,\n latency_lowr_buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.5, 5, 10],\n).expose(app, include_in_schema=False)\n\n# Add logging middleware\napp.add_middleware(LoggingMiddleware)\n\n\n@app.post(\"/analyze_sentiment\", response_model=PostList)\nasync def analyze_sentiment(posts: PostList):\n for post in posts.posts:\n sentiment, confidence_score = get_sentiment(post.text)\n post.decision = Decision(\n sentiment=sentiment, confidence_score=float(confidence_score)\n )\n return posts\n","repo_name":"ericvolp12/bsky-experiments","sub_path":"python/sentiment/bsky-sentiment/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3425,"program_lang":"python","lang":"en","doc_type":"code","stars":110,"dataset":"github-code","pt":"72"}
+{"seq_id":"33566994653","text":"from django.conf.urls import patterns, include, url\nfrom django.conf import settings\nfrom django.views.generic import TemplateView\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n # Examples:\n # Uncomment the next line to enable the admin:\n url(r'^admin/', include(admin.site.urls)),\n # url(r'^$', 'django_allauth_template.views.home', name='home'),\n\n url(r'^accounts/', include('allauth.urls')),\n\n url(r'^', TemplateView.as_view(template_name=\"home.html\")),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n \n)\n\n## local static and media settings\nif settings.DEBUG:\n urlpatterns += patterns(\n '',\n (r'^static/(?P.*)$',\n 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n (r'^media/(?P.*)$',\n 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\n )\n","repo_name":"samgclarke/django-allauth-template","sub_path":"django_allauth_template/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"3302980274","text":"import argparse\nimport os\nimport re\nimport shutil\n\nimport numpy as np\nimport pickle\nimport torch\nimport torchvision.models as models\nfrom torch.utils.data import DataLoader\nimport torchvision.transforms as transforms\n\nfrom datasets.dataset import StandardDataset\nfrom datasets.open_dataset import OpenDataset\nfrom models.backbone import encoder32\nfrom models.backbone_resnet import encoder\nfrom models.backbone_wide_resnet import wide_encoder\nfrom evaluate import calc_auroc, collect_rpl_max, seenval_baseline_thresh, summarize, unseenval_baseline_thresh\n\n\n\nif __name__ == '__main__':\n \n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"type\", type=str,\n help=\"e.g. LT or IMAGENET or TINY\")\n parser.add_argument(\"dataset_type\", type=str,\n help=\"e.g. VAL or TEST\")\n parser.add_argument(\"gap\", type=str,\n help=\"TRUE iff use global average pooling layer. Otherwise, use linear layer.\")\n \n parser.add_argument(\"latent_size\", type=int,\n help=\"Dimension of embeddings.\")\n parser.add_argument(\"num_rp_per_cls\", type=int,\n help=\"Number of reciprocal points per class.\")\n parser.add_argument(\"gamma\", type=float,\n help=\"\")\n \n parser.add_argument(\"backbone_type\", type=str,\n help=\"architecture of backbone\")\n parser.add_argument(\"img_size\", type=int,\n help=\"desired square image size.\")\n \n parser.add_argument(\"img_base_path\", type=str,\n help=\"path to folder containing image data, i.e. /data/ or /share/nikola/export/image_datasets/\")\n parser.add_argument(\"dataset_folder_path\", type=str,\n help=\"path to dataset folder.\")\n parser.add_argument(\"dataset_folder\", type=str,\n help=\"name of folder where dataset details live.\")\n \n parser.add_argument(\"model_folder_path\", type=str,\n help=\"full file path to folder where the baseline is located\")\n\n args = parser.parse_args()\n\n with open(args.dataset_folder_path + args.dataset_folder + '/folder_to_name_map.pkl', 'rb') as f:\n folder_to_name = pickle.load(f)\n with open(args.dataset_folder_path + args.dataset_folder + '/name_to_folder_map.pkl', 'rb') as f:\n name_to_folder = pickle.load(f)\n with open(args.dataset_folder_path + args.dataset_folder + '/folder_to_idx.pkl', 'rb') as f:\n folder_to_idx = pickle.load(f)\n with open(args.dataset_folder_path + args.dataset_folder + '/idx_to_folder.pkl', 'rb') as f:\n idx_to_folder = pickle.load(f)\n with open(args.dataset_folder_path + args.dataset_folder + '/closed_classes.pkl', 'rb') as f:\n closed_classes = pickle.load(f)\n \n files = os.listdir(args.model_folder_path)\n model_name = None\n for file in files:\n if file[-3:] == '.pt':\n if model_name is not None:\n raise ValueError('Multiple possible models in ' + args.model_folder_path)\n model_name = file[:-3] \n \n if args.dataset_type == 'VAL':\n with open(args.dataset_folder_path + args.dataset_folder + '/closed_val_img_list.pkl', 'rb') as f:\n seen_data = pickle.load(f)\n with open(args.dataset_folder_path + args.dataset_folder + '/open_val_img_list.pkl', 'rb') as f:\n unseen_data = pickle.load(f)\n elif args.dataset_type == 'TEST':\n with open(args.dataset_folder_path + args.dataset_folder + '/closed_test_img_list.pkl', 'rb') as f:\n seen_data = pickle.load(f)\n with open(args.dataset_folder_path + args.dataset_folder + '/open_test_img_list.pkl', 'rb') as f:\n unseen_data = pickle.load(f)\n if args.type == 'TINY':\n with open(args.dataset_folder_path + args.dataset_folder + '/closedtest_img_to_folder.pkl', 'rb') as f:\n seen_tiny_img2folder = pickle.load(f) \n with open(args.dataset_folder_path + args.dataset_folder + '/opentest_img_to_folder.pkl', 'rb') as f:\n unseen_tiny_img2folder = pickle.load(f) \n \n else:\n raise Exception('Unsupported dataset type')\n\n with open(args.dataset_folder_path + args.dataset_folder + '/closed_train_mean.pkl', 'rb') as f:\n train_mean = pickle.load(f)\n with open(args.dataset_folder_path + args.dataset_folder + '/closed_train_std.pkl', 'rb') as f:\n train_std = pickle.load(f)\n img_normalize = transforms.Normalize(mean=train_mean,\n std=train_std)\n num_classes = len(closed_classes) \n \n \n if args.type == 'TINY' and args.dataset_type == 'TEST':\n seen_dataset = StandardDataset(seen_data, folder_to_idx, folder_to_name, \n transforms.Compose([\n transforms.Resize((args.img_size,args.img_size)),\n transforms.ToTensor(),\n img_normalize, \n ]), args.img_base_path, test_tiny_img2folder=seen_tiny_img2folder)\n unseen_dataset = OpenDataset(unseen_data, transforms.Compose([\n transforms.Resize((args.img_size,args.img_size)),\n transforms.ToTensor(),\n img_normalize,\n ]), args.img_base_path, test_tiny_img2folder=unseen_tiny_img2folder)\n \n else:\n seen_dataset = StandardDataset(seen_data, folder_to_idx, folder_to_name, \n transforms.Compose([\n transforms.Resize((args.img_size,args.img_size)),\n transforms.ToTensor(),\n img_normalize,\n ]), args.img_base_path)\n unseen_dataset = OpenDataset(unseen_data, transforms.Compose([\n transforms.Resize((args.img_size,args.img_size)),\n transforms.ToTensor(),\n img_normalize,\n ]), args.img_base_path)\n \n \n seen_loader = DataLoader(seen_dataset, batch_size=64, shuffle=False, num_workers=3)\n unseen_loader = DataLoader(unseen_dataset, batch_size=64, shuffle=False, num_workers=3)\n\n criterion = torch.nn.CrossEntropyLoss(reduction='none')\n \n if args.backbone_type == 'OSCRI_encoder':\n model = encoder32(latent_size=args.latent_size, num_classes=num_classes, num_rp_per_cls=args.num_rp_per_cls, gap=args.gap == 'TRUE')\n \n elif args.backbone_type == 'wide_resnet':\n model = wide_encoder(args.latent_size, 40, 4, 0, num_classes=num_classes, num_rp_per_cls=args.num_rp_per_cls)\n \n elif args.backbone_type == 'resnet_50':\n backbone = models.resnet50(pretrained=False)\n VISUAL_FEATURES_DIM = 2048\n model = encoder(backbone, VISUAL_FEATURES_DIM, latent_size=args.latent_size, num_classes=num_classes, num_rp_per_cls=args.num_rp_per_cls, gap=args.gap == 'TRUE')\n else:\n raise ValueError(args.backbone_type + ' is not supported.')\n \n model.load_state_dict(torch.load(args.model_folder_path + model_name + '.pt'))\n model.cuda()\n model.eval()\n\n if args.dataset_type == 'VAL':\n seen_confidence_dict = collect_rpl_max(model, 'seen_val', seen_loader, folder_to_name, args.gamma)\n unseen_confidence_dict = collect_rpl_max(model, 'zeroshot_val', unseen_loader, folder_to_name, args.gamma)\n else:\n seen_confidence_dict = collect_rpl_max(model, 'seen_test', seen_loader, folder_to_name, args.gamma)\n unseen_confidence_dict = collect_rpl_max(model, 'zeroshot_test', unseen_loader, folder_to_name, args.gamma)\n \n recordings_folder = args.model_folder_path + args.dataset_type + '_recordings_' + model_name + '/'\n if os.path.isdir(recordings_folder):\n print(recordings_folder + ' already exists. Removing existing and creating new.')\n shutil.rmtree(recordings_folder)\n os.mkdir(recordings_folder)\n with open(recordings_folder + 'seen_confidence_dict.pkl', 'wb') as f:\n pickle.dump(seen_confidence_dict, f)\n with open(recordings_folder + 'unseen_confidence_dict.pkl', 'wb') as f:\n pickle.dump(unseen_confidence_dict, f)\n\n metrics_folder = args.model_folder_path + 'metrics_' + args.dataset_type + '_' + str(model_name) + '/'\n if os.path.isdir(metrics_folder):\n print(metrics_folder + ' already exists. Removing existing and creating new.')\n shutil.rmtree(metrics_folder)\n os.mkdir(metrics_folder)\n \n prob_thresh = set()\n dist_thresh = set()\n for leaf_name in seen_confidence_dict.keys():\n for record in seen_confidence_dict[leaf_name]:\n prob_thresh.add(record['prob'])\n dist_thresh.add(record['dist'])\n for leaf_name in unseen_confidence_dict.keys():\n for record in unseen_confidence_dict[leaf_name]:\n prob_thresh.add(record['prob'])\n dist_thresh.add(record['dist'])\n prob_thresh = list(prob_thresh)\n prob_thresh.sort()\n dist_thresh = list(dist_thresh)\n dist_thresh.sort()\n \n print(\"number of prob thresholds: \" + str(len(prob_thresh)))\n print(\"number of dist thresholds: \" + str(len(dist_thresh)))\n\n prob_seen_info = seenval_baseline_thresh(seen_confidence_dict, prob_thresh, folder_to_idx=folder_to_idx, name_to_folder=name_to_folder, save_path=metrics_folder + 'probseenres.pkl')\n prob_unseen_info = unseenval_baseline_thresh(unseen_confidence_dict, prob_thresh, save_path=metrics_folder + 'probunseenres.pkl')\n \n dist_seen_info = seenval_baseline_thresh(seen_confidence_dict, dist_thresh, folder_to_idx=folder_to_idx, name_to_folder=name_to_folder, save_path=metrics_folder + 'distseenres.pkl', value_key='dist')\n dist_unseen_info = unseenval_baseline_thresh(unseen_confidence_dict, dist_thresh, save_path=metrics_folder + 'distunseenres.pkl', value_key='dist')\n\n dist_auroc_score = calc_auroc(seen_confidence_dict, unseen_confidence_dict, 'dist')\n prob_auroc_score = calc_auroc(seen_confidence_dict, unseen_confidence_dict, 'prob')\n \n print(\"Dist-Auroc score: \" + str(dist_auroc_score))\n print(\"Prob-Auroc score: \" + str(prob_auroc_score))\n \n metrics = summarize(prob_seen_info, prob_unseen_info, prob_thresh, verbose=False)\n dist_metrics = summarize(dist_seen_info, dist_unseen_info, dist_thresh, verbose=False)\n \n metrics['dist_auroc_lwnealstyle'] = dist_auroc_score\n metrics['prob_auroc_lwnealstyle'] = prob_auroc_score\n metrics['dist_OSR_CSR_AUC'] = dist_metrics['OSR_CSR_AUC']\n \n print(\"prob-AUC score: \" + str(metrics['OSR_CSR_AUC']))\n print(\"dist-AUC score: \" + str(metrics['dist_OSR_CSR_AUC']))\n \n with open(metrics_folder + 'metrics.pkl', 'wb') as f:\n pickle.dump(metrics, f)\n \n","repo_name":"KevLuo/OpenSet_ReciprocalPoints","sub_path":"rpl/collect_prediction.py","file_name":"collect_prediction.py","file_ext":"py","file_size_in_byte":11110,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"72"}
+{"seq_id":"41907049230","text":"import requests\nimport json\nimport random\nfrom pokemon import Pokemon\nfrom connection import Client\nimport os\nfrom dotenv import load_dotenv\n\n\nclass Game:\n def __init__(self, client, pokemon_database):\n self.client = client\n self.database = pokemon_database\n\n def get_random_pokemon(self):\n # Getting a random pokemon doc\n doc = self.database.aggregate([\n { \"$sample\": # Randomly samples 1 document from all the documents\n {\"size\": 1}\n },{ \"$project\":\n {'name':1,'stats':1}\n }\n ]\n\n )\n doc = list(doc)\n pokemon = Pokemon(doc)\n return pokemon\n\n def get_pokemon_by_name(self, pokemon_name):\n doc = self.database.find_one({'name':pokemon_name},{'name':1,'stats':1})\n pokemon = Pokemon([doc])\n return pokemon\n\n def fight(self, pokemon1, pokemon2):\n # HP, ATTACK, DEFENCE, SPEC.ATK, SPEC.DEF, SPEED\n\n # Sets the starting health for both pokemon\n health_p1 = pokemon1.health\n health_p2 = pokemon2.health\n\n # Formatting the names to be capitalised\n pokemon1_name = pokemon1.name.title()\n pokemon2_name = pokemon2.name.title()\n print('---------------------------------------')\n print(f'{pokemon1_name} has {health_p1} base health')\n print(f'{pokemon2_name} has {health_p2} base health')\n print('---------------------------------------')\n print(f'{pokemon1_name} has {pokemon1.defence} base defence')\n print(f'{pokemon2_name} has {pokemon2.defence} base defence')\n print('---------------------------------------')\n\n # Checking the speed stats of both pokemon, the faster pokemon goes first\n if pokemon1.speed > pokemon2.speed:\n print(f'{pokemon1_name}, is faster, they attack first')\n order = 0\n elif pokemon1.speed < pokemon2.speed:\n print(f'{pokemon2_name}, is faster, they attack first')\n order = 1\n else:\n # If speed stat is the same randomly decide who goes first\n order = random.choice([0, 1])\n\n # Creating a loop where pokemon will fight until one has no health remaining\n while health_p1 > 0 and health_p2 > 0:\n # Creating a 50% chance for an attack to be a crit\n crit = random.choice([0, 1])\n if order == 0:\n if crit == 0:\n print('Critical Hit!')\n # If attack is a crit then attack can ignore defence\n damage = pokemon1.attack\n else:\n # Damage is calculated using attack stat - opponents defence stat\n damage = max(0, pokemon1.attack-pokemon2.defence)\n # Updates health of opponent\n health_p2 = max(0, health_p2-damage)\n order += 1\n print(f'{pokemon1_name} has done {damage} damage, {pokemon2_name} now has {health_p2} health.')\n else:\n if crit == 0:\n print('Critical Hit!')\n # If attack is a crit then attack can ignore defence\n damage = pokemon2.attack\n else:\n # Damage is calculated using attack stat - opponents defence stat\n damage = max(0, pokemon2.attack-pokemon1.defence)\n # Updates health of opponent\n health_p1 = max(0, health_p1-damage)\n order -= 1\n print(f'{pokemon2_name} has done {damage} damage, {pokemon1_name} now has {health_p1} health.')\n\n ignore = input('')\n\n # Checking who won the fight\n if health_p2 <= 0:\n return False\n elif health_p1 <= 0:\n return True\n\n def one_player(self):\n ai_pokemon = self.get_random_pokemon()\n # Asking if the player would like a random pokemon or not\n ask = input('Do you want random pokemon (y/n): ')\n player_pokemon = self.ask(ask)\n print(f'Your Pokemon is {player_pokemon.name.title()} and you are fighting {ai_pokemon.name.title()}')\n who_won = self.fight(player_pokemon, ai_pokemon)\n if who_won:\n print(f'The Computer won with {ai_pokemon.name.title()}!')\n self.client.update_document_by_name(ai_pokemon.name, 'wins', 1)\n else:\n print(f'You won with {player_pokemon.name.title()}!')\n self.client.update_document_by_name(player_pokemon.name, 'wins', 1)\n\n def two_player(self):\n # Asking both player if they would like random pokemon or not\n ask_p1 = input('Player1, Do you want random pokemon (y/n): ')\n player1_pokemon = self.ask(ask_p1)\n ask_p2 = input('Player2, Do you want random pokemon (y/n): ')\n player2_pokemon = self.ask(ask_p2)\n\n print(f'Player 1, your Pokemon is {player1_pokemon.name.title()} and Player 2 your Pokemon is {player2_pokemon.name.title()}')\n who_won = self.fight(player1_pokemon, player2_pokemon)\n if who_won:\n print(f'Player 1 won with {player1_pokemon.name.title()}!')\n self.client.update_document_by_name(player1_pokemon.name, 'wins', 1)\n else:\n print(f'Player 2 won with{player2_pokemon.name.title()}!')\n self.client.update_document_by_name(player2_pokemon.name, 'wins', 1)\n\n def ask(self, player_response):\n if player_response == 'n':\n name = input('What is the name of your pokemon: ')\n player_pokemon = self.get_pokemon_by_name(name)\n else:\n player_pokemon = self.get_random_pokemon()\n return player_pokemon\n \n def print_leaderboard(self, field_name, limit):\n top = list(self.client.get_top_n(field_name, limit))\n print('-------------------------')\n print('The Pokemon Leader Board:')\n print('-------------------------')\n for item in top:\n print(f\"{item['name']}, wins: {item[field_name]}\")\n\n def start_game(self):\n ask = int(input('Do you want to play 1 or 2 player (1 or 2): '))\n if ask == 1:\n self.one_player()\n elif ask == 2:\n self.two_player()\n else:\n self.print_leaderboard('wins', 10)\n\n\ndef main():\n load_dotenv()\n MONGODB_URI = os.environ[\"MONGODB_URI\"]\n client = Client(MONGODB_URI, 'pokemon_data','pokemon')\n coll = client.get_collection()\n g = Game(client, coll)\n g.start_game()\n\n\nif __name__ == '__main__':\n main()","repo_name":"Ethan-Jolly/SpartaGlobal210","sub_path":"Data_Engineering/mongodb/pokemon_mongodb/Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":6546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"72553963752","text":"import sys\nN = int(input())\nrgb = []\nfor _ in range(N):\n rgb.append(list(map(int, sys.stdin.readline().split())))\n\ndp = [[0] * 3 for _ in range(N + 1)]\nmin_list = []\nfor k in range(3):\n\n dp[0] = [1000 * 1000] * 3\n dp[0][k] = rgb[0][k]\n\n for i in range(1, N):\n dp[i][0] = min(dp[i - 1][1], dp[i - 1][2]) + rgb[i][0]\n dp[i][1] = min(dp[i - 1][0], dp[i - 1][2]) + rgb[i][1]\n dp[i][2] = min(dp[i - 1][0], dp[i - 1][1]) + rgb[i][2]\n\n dp[N - 1][k] = 1000 * 1000\n min_list.extend(dp[N - 1])\n\nprint(min(min_list))","repo_name":"jayyeong/Algorithm","sub_path":"아직 못 푼 문제/17404RGB거리2.py","file_name":"17404RGB거리2.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"16369945802","text":"\"\"\"\nhttps://leetcode.com/problems/two-sum/\n\n\n def twoSum(self, nums: List[int], target: int) -> List[int]:\n decorated_nums = []\n for index, item in enumerate(nums):\n decorated_nums.append((item, index))\n \n for i in decorated_nums:\n for j in decorated_nums:\n if (i[0] + j[0]) == target:\n if i[1] != j[1]:\n return [i[1], j[1]]\n\nToday this algorithm succeeded, however. It remained the same since the first\ntime I tried/submitted more than a week ago, so oh well.\n\"\"\"\n\n#def twoSum2(nums: List[int], target: int) -> List[int]:\ndef twoSum2(nums, target):\n decorated_nums = []\n for index, item in enumerate(nums):\n decorated_nums.append((item, index))\n import itertools\n for i, j in itertools.combinations(nums, 2):\n if (i + j) == target:\n return [i, j]\n \ndef twoSum(nums, target):\n decorated_nums = []\n for index, item in enumerate(nums):\n decorated_nums.append((item, index))\n \n import itertools\n for i, j in itertools.combinations(decorated_nums, 2):\n #for i in decorated_nums:\n if (i[0] + j[0]) == target:\n if i[1] != j[1]:\n return [i[1], j[1]]\n\n\nif __name__ == '__main__':\n assert twoSum([2, 7, 11, 15], 9) == [0, 1]\n assert twoSum([3, 2, 4], 6) == [1, 2]\n assert twoSum([3, 3], 6) == [0, 1]\n","repo_name":"mavas/hackerrank","sub_path":"code/two_sum.py","file_name":"two_sum.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"3240316819","text":"from __future__ import absolute_import\n\nimport mock\n\nfrom changes.backends.jenkins.builder import JenkinsBuilder\nfrom changes.backends.jenkins.buildstep import JenkinsBuildStep\nfrom changes.testutils import TestCase\n\n\nclass JenkinsBuildStepTest(TestCase):\n def get_buildstep(self):\n return JenkinsBuildStep(job_name='foo-bar', jenkins_url=['http://jenkins.example.com'])\n\n def test_get_resource_limits(self):\n buildstep = JenkinsBuildStep(job_name=\"both\", cpus=8, memory=8080)\n assert buildstep.get_resource_limits() == {\n 'cpus': 8,\n 'memory': 8080,\n }\n\n # We specify defaults, so if you don't want a limit, gotta say so.\n only_cpus = JenkinsBuildStep(job_name=\"only_cpus\", cpus=8, memory=None)\n assert only_cpus.get_resource_limits() == {'cpus': 8}\n\n assert JenkinsBuildStep(job_name=\"defaults\").get_resource_limits() == {\n \"memory\": 8192,\n \"cpus\": 4,\n }\n\n def test_get_builder(self):\n buildstep = self.get_buildstep()\n builder = buildstep.get_builder()\n assert builder.job_name == 'foo-bar'\n assert type(builder) == JenkinsBuilder\n\n @mock.patch.object(JenkinsBuildStep, 'get_builder')\n def test_execute(self, get_builder):\n builder = mock.Mock()\n get_builder.return_value = builder\n\n build = self.create_build(self.create_project())\n job = self.create_job(build)\n\n buildstep = self.get_buildstep()\n buildstep.execute(job)\n\n builder.create_job.assert_called_once_with(job)\n\n @mock.patch.object(JenkinsBuildStep, 'get_builder')\n def test_update(self, get_builder):\n builder = mock.Mock()\n get_builder.return_value = builder\n\n build = self.create_build(self.create_project())\n job = self.create_job(build, data={\n 'job_name': 'server',\n 'build_no': '35',\n })\n\n buildstep = self.get_buildstep()\n buildstep.update(job)\n\n builder.sync_job.assert_called_once_with(job)\n\n @mock.patch.object(JenkinsBuildStep, 'get_builder')\n def test_update_step(self, get_builder):\n builder = mock.Mock()\n get_builder.return_value = builder\n\n build = self.create_build(self.create_project())\n job = self.create_job(build, data={\n 'job_name': 'server',\n 'build_no': '35',\n })\n phase = self.create_jobphase(job)\n step = self.create_jobstep(phase, data={\n 'item_id': 13,\n 'job_name': 'server',\n })\n\n buildstep = self.get_buildstep()\n buildstep.update_step(step)\n\n builder.sync_step.assert_called_once_with(step)\n\n @mock.patch.object(JenkinsBuildStep, 'get_builder')\n def test_cancel_step(self, get_builder):\n builder = mock.Mock()\n get_builder.return_value = builder\n\n build = self.create_build(self.create_project())\n job = self.create_job(build, data={\n 'job_name': 'server',\n 'build_no': '35',\n })\n phase = self.create_jobphase(job)\n step = self.create_jobstep(phase, data={\n 'item_id': 13,\n 'job_name': 'server',\n })\n\n buildstep = self.get_buildstep()\n buildstep.cancel_step(step)\n\n builder.cancel_step.assert_called_once_with(step)\n\n @mock.patch.object(JenkinsBuildStep, 'get_builder')\n def test_fetch_artifact(self, get_builder):\n builder = mock.Mock()\n get_builder.return_value = builder\n\n build = self.create_build(self.create_project())\n job = self.create_job(build, data={\n 'job_name': 'server',\n 'build_no': '35',\n })\n phase = self.create_jobphase(job)\n step = self.create_jobstep(phase, data={\n 'item_id': 13,\n 'job_name': 'server',\n })\n artifact = self.create_artifact(step, name='foo.log')\n\n buildstep = self.get_buildstep()\n buildstep.fetch_artifact(artifact)\n\n builder.sync_artifact.assert_called_once_with(artifact)\n","repo_name":"dropbox/changes","sub_path":"tests/changes/backends/jenkins/test_buildstep.py","file_name":"test_buildstep.py","file_ext":"py","file_size_in_byte":4080,"program_lang":"python","lang":"en","doc_type":"code","stars":758,"dataset":"github-code","pt":"72"}
+{"seq_id":"70148360874","text":"import logging as log\nimport sys\nfrom argparse import ArgumentParser\nfrom pathlib import Path\nfrom typing import Tuple\n\nfrom model_analyzer.model_complexity import ModelComputationalComplexity\nfrom model_analyzer.model_metadata import ModelMetaData\n\n\ndef parse_arguments():\n log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)\n parser = ArgumentParser()\n\n parser.add_argument('-m', '--model',\n help='Path to an .xml file of the Intermediate Representation (IR) model'\n 'Or path to .onnx or .prototxt file of ONNX model',\n required=True,\n type=Path)\n\n parser.add_argument('-w', '--weights',\n help='Path to the .bin file of the Intermediate Representation (IR) model. If not specified,'\n 'the weights file name expected to be the same as the .xml file passed'\n 'with --model option',\n type=Path)\n\n parser.add_argument('-o', '--report_dir', '--report-dir',\n help='Output directory',\n type=Path,\n default=Path.cwd())\n\n parser.add_argument('--model_report', '--model-report',\n help='Name for the file where theoretical analysis results are stored',\n type=str,\n default='model_report.csv')\n\n parser.add_argument('--per_layer_mode', '--per-layer-mode',\n help='Enables collecting per-layer complexity metrics',\n action='store_true',\n default=False)\n\n parser.add_argument('--per_layer_report', '--per-layer-report',\n help='File name for the per-layer complexity metrics. '\n 'Should be specified only when --per-layer-mode option' +\n ' is used',\n default='per_layer_report.csv')\n\n parser.add_argument('--sparsity_ignored_layers', '--sparsity-ignored-layers',\n help='Specifies ignored layers names separated by comma',\n default='')\n\n parser.add_argument('--sparsity_ignore_first_conv', '--sparsity-ignore-first-conv',\n help='Ignores first Convolution layer for sparsity computation',\n action='store_true',\n default=False)\n\n parser.add_argument('--sparsity_ignore_fc', '--sparsity-ignore-fc',\n help='Ignores FullyConnected layers for sparsity computation',\n action='store_true',\n default=False)\n\n parser.add_argument('--ignore_unknown_layers', '--ignore-unknown-layers',\n help='Ignores unknown types of layers when counting GFLOPs',\n action='store_true',\n default=False)\n\n arguments = parser.parse_args()\n arguments.model, arguments.weights = process_model_files(arguments)\n return arguments\n\n\ndef process_model_files(cli_args) -> Tuple[Path, Path]:\n if cli_args.model.suffix == '.xml' and not cli_args.weights:\n cli_args.weights = cli_args.model.with_suffix('.bin')\n return cli_args.model, cli_args.weights\n\n\ndef main(cli_args):\n log.info('Loading network files:\\n\\t%s\\n\\t%s', cli_args.model, cli_args.weights)\n\n model_metadata = ModelMetaData(cli_args.model, cli_args.weights)\n\n model_computational_complexity = ModelComputationalComplexity(model_metadata)\n model_computational_complexity.set_ignore_unknown_layers(cli_args.ignore_unknown_layers)\n\n sparsity_ignored_layers = cli_args.sparsity_ignored_layers.split(',')\n model_computational_complexity.set_ignored_layers(sparsity_ignored_layers,\n cli_args.sparsity_ignore_first_conv,\n cli_args.sparsity_ignore_fc)\n\n model_computational_complexity.print_network_info(cli_args.report_dir,\n cli_args.model_report,\n cli_args.per_layer_mode,\n cli_args.per_layer_report)\n\n\nif __name__ == '__main__':\n ARGUMENTS = parse_arguments()\n main(ARGUMENTS)\n","repo_name":"openvinotoolkit/model_analyzer","sub_path":"model_analyzer/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4441,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"72"}
+{"seq_id":"43098687037","text":"# Python module import\nfrom ObjMgr import *\nfrom VisDefinitions import *\n\n# Global class instances\n_cls_info= None\n\ndef init():\n global _cls_info\n # Create an ObjInfo class instance to modify the database and to handle database events\n _cls_info= ObjInfo(ctx.module(\"ObjInfo\"))\n \n # Only receive events which occur in Object OBJ_PATIENT and Layer LAY_PATIENT\n _cls_info.setEventReceiveMode(ERM_SELECTED, OBJ_PATIENT + \",\" + OBJ_COMMUNICATION, LAY_PATIENT + \",\" + LAY_GLOBALEVENTS)\n return\n\n\ndef changed(field):\n _cls_info.setAndNotify(OBJ_PATIENT, LAY_PATIENT, field.name, field.value)\n return\n\n\ndef handleObjectCreatedEvent():\n _cls_info.setAndNotify(OBJ_PATIENT, LAY_GLOBAL, INF_OBJTYPE, \"Patient\")\n return\n\n\ndef handleObjectRemovedEvent():\n ctx.field(\"Name\").value = \"\"\n ctx.field(\"DOB\").value = \"\"\n ctx.field(\"PID\").value = \"\"\n ctx.field(\"Weight\").value = \"\"\n ctx.field(\"Sex\").value = \"\"\n return\n \n\ndef handleAttributeCreatedEvent():\n handleAttributeModifiedEvent()\n return\n\n\ndef handleAttributeRemovedEvent():\n layer = _cls_info.activeLayer()\n if layer == LAY_PATIENT:\n info = _cls_info.activeInfo()\n ctx.field(info).value = \"\"\n return\n\n\ndef handleAttributeModifiedEvent():\n layer = _cls_info.activeLayer()\n if layer == LAY_PATIENT:\n info = _cls_info.activeInfo()\n value = _cls_info.get()\n ctx.field(info).value = value\n else:\n ctx.field(\"Name\").value = _cls_info.get(OBJ_PATIENT, LAY_PATIENT, INF_NAME)\n ctx.field(\"DOB\").value = _cls_info.get(OBJ_PATIENT, LAY_PATIENT, INF_DOB)\n ctx.field(\"PID\").value = _cls_info.get(OBJ_PATIENT, LAY_PATIENT, INF_PID)\n ctx.field(\"Weight\").value = _cls_info.get(OBJ_PATIENT, LAY_PATIENT, INF_WEIGHT)\n ctx.field(\"Sex\").value = _cls_info.get(OBJ_PATIENT, LAY_PATIENT, INF_SEX)\n \n return\n","repo_name":"MeVisLab/communitymodules","sub_path":"UMD/METK/Modules/Macros/MedicalExplorationToolkit/METKPatientObject.py","file_name":"METKPatientObject.py","file_ext":"py","file_size_in_byte":1852,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"72"}
+{"seq_id":"4478502302","text":"\"\"\"Extract recurring tree fragments from constituency treebanks.\n\nNB: there is a known bug in multiprocessing which makes it impossible to detect\nCtrl-C or fatal errors like segmentation faults in child processes which causes\nthe master program to wait forever for output from its children. Therefore if\nyou want to abort, kill the program manually (e.g., press Ctrl-Z and issue\n'kill %1'). If the program seems stuck, re-run without multiprocessing\n(pass --numproc 1) to see if there might be a bug.\"\"\"\n\nimport io\nimport os\nimport re\nimport sys\nimport codecs\nimport logging\nimport tempfile\nif sys.version_info[0] == 2:\n\tfrom itertools import imap as map # pylint: disable=E0611,W0622\nimport multiprocessing\nfrom collections import defaultdict\nfrom getopt import gnu_getopt, GetoptError\nfrom .tree import brackettree, discbrackettree\nfrom .treebank import writetree\nfrom .treetransforms import unbinarize\nfrom . import _fragments\nfrom .util import workerfunc\nfrom .containers import Vocabulary\n\nSHORTUSAGE = '''Usage: discodop fragments [treebank2] [options]\n or: discodop fragments --batch= ... [options]'''\nFLAGS = ('approx', 'indices', 'nofreq', 'complete', 'alt',\n\t\t'relfreq', 'adjacent', 'debin', 'debug', 'quiet', 'help')\nOPTIONS = ('fmt=', 'numproc=', 'numtrees=', 'encoding=', 'batch=', 'cover=',\n\t\t'twoterms=')\nPARAMS = {}\nFRONTIERRE = re.compile(r'\\(([^ ()]+) \\)') # for altrepr()\nTERMRE = re.compile(r'\\(([^ ()]+) ([^ ()]+)\\)') # for altrepr()\n\n\ndef main(argv=None):\n\t\"\"\"Command line interface to fragment extraction.\"\"\"\n\tif argv is None:\n\t\targv = sys.argv[2:]\n\ttry:\n\t\topts, args = gnu_getopt(argv, 'ho:', FLAGS + OPTIONS)\n\texcept GetoptError as err:\n\t\tprint('error:', err, file=sys.stderr)\n\t\tprint(SHORTUSAGE)\n\t\tsys.exit(2)\n\topts = dict(opts)\n\n\tfor flag in FLAGS:\n\t\tPARAMS[flag] = '--' + flag in opts\n\tPARAMS['disc'] = opts.get('--fmt', 'bracket') != 'bracket'\n\tPARAMS['fmt'] = opts.get('--fmt', 'bracket')\n\tnumproc = int(opts.get('--numproc', 1))\n\tif numproc == 0:\n\t\tnumproc = cpu_count()\n\tif not numproc:\n\t\traise ValueError('numproc should be an integer > 0. got: %r' % numproc)\n\tlimit = int(opts.get('--numtrees', 0)) or None\n\tPARAMS['cover'] = None\n\tif '--cover' in opts and ',' in opts['--cover']:\n\t\ta, b = opts['--cover'].split(',')\n\t\tPARAMS['cover'] = int(a), int(b)\n\telif '--cover' in opts:\n\t\tPARAMS['cover'] = int(opts.get('--cover', 0)), 999\n\tPARAMS['twoterms'] = opts.get('--twoterms')\n\tencoding = opts.get('--encoding', 'utf8')\n\tbatchdir = opts.get('--batch')\n\n\tif len(args) < 1:\n\t\tprint('missing treebank argument')\n\tif batchdir is None and len(args) not in (1, 2):\n\t\tprint('incorrect number of arguments:', args, file=sys.stderr)\n\t\tprint(SHORTUSAGE)\n\t\tsys.exit(2)\n\tif batchdir:\n\t\tif numproc != 1:\n\t\t\traise ValueError('Batch mode only supported in single-process '\n\t\t\t\t'mode. Use the xargs command for multi-processing.')\n\treadstdin = None\n\tfor n, fname in enumerate(args):\n\t\tif fname == '-':\n\t\t\tif numproc != 1:\n\t\t\t\t# write to temp file so that contents can be read\n\t\t\t\t# in multiple processes\n\t\t\t\tif readstdin is not None:\n\t\t\t\t\traise ValueError('can only read from stdin once.')\n\t\t\t\twith tempfile.NamedTemporaryFile(delete=False) as tmp:\n\t\t\t\t\ttmp.write(open(sys.stdin.fileno(), 'rb').read())\n\t\t\t\t\targs[n] = tmp.name\n\t\t\t\treadstdin = n\n\t\telif not os.path.exists(fname):\n\t\t\traise ValueError('not found: %r' % fname)\n\tif PARAMS['complete']:\n\t\tif len(args) < 2:\n\t\t\traise ValueError('need at least two treebanks with --complete.')\n\t\tif PARAMS['twoterms'] or PARAMS['adjacent']:\n\t\t\traise ValueError('--twoterms and --adjacent are incompatible '\n\t\t\t\t\t'with --complete.')\n\t\tif PARAMS['approx'] or PARAMS['nofreq']:\n\t\t\traise ValueError('--complete is incompatible with --nofreq '\n\t\t\t\t\t'and --approx')\n\n\tlevel = logging.WARNING if PARAMS['quiet'] else logging.DEBUG\n\tlogging.basicConfig(level=level, format='%(message)s')\n\tif PARAMS['debug'] and numproc > 1:\n\t\tlogger = multiprocessing.log_to_stderr()\n\t\tlogger.setLevel(multiprocessing.SUBDEBUG)\n\n\tlogging.info('Disco-DOP Fragment Extractor')\n\n\tlogging.info('parameters:\\n%s', '\\n'.join(' %s:\\t%r' % kv\n\t\tfor kv in sorted(PARAMS.items())))\n\tlogging.info('\\n'.join('treebank%d: %s' % (n + 1, a)\n\t\tfor n, a in enumerate(args)))\n\n\tif numproc == 1 and batchdir:\n\t\tbatch(batchdir, args, limit, encoding, '--debin' in opts)\n\telse:\n\t\tfragmentkeys, counts = regular(args, numproc, limit, encoding)\n\t\tout = (io.open(opts['-o'], 'w', encoding=encoding)\n\t\t\t\tif '-o' in opts else None)\n\t\tif '--debin' in opts:\n\t\t\tfragmentkeys = debinarize(fragmentkeys)\n\t\tprintfragments(fragmentkeys, counts, out=out)\n\tif readstdin is not None:\n\t\tos.unlink(args[readstdin])\n\n\ndef regular(filenames, numproc, limit, encoding):\n\t\"\"\"non-batch processing. multiprocessing optional.\"\"\"\n\tmult = 1\n\tif PARAMS['approx']:\n\t\tfragments = defaultdict(int)\n\telse:\n\t\tfragments = {}\n\t# detect corpus reading errors in this process (e.g., wrong encoding)\n\tinitworker(\n\t\t\tfilenames[0],\n\t\t\tfilenames[1] if len(filenames) == 2 else None,\n\t\t\tlimit, encoding)\n\tif numproc == 1:\n\t\tmymap, myworker = map, worker\n\telse: # multiprocessing, start worker processes\n\t\tpool = multiprocessing.Pool(\n\t\t\t\tprocesses=numproc, initializer=initworker,\n\t\t\t\tinitargs=(filenames[0], filenames[1] if len(filenames) == 2\n\t\t\t\t\telse None, limit, encoding))\n\t\tmymap, myworker = pool.imap, mpworker\n\tnumtrees = (PARAMS['trees1'].len if limit is None\n\t\t\telse min(PARAMS['trees1'].len, limit))\n\n\tif PARAMS['complete']:\n\t\ttrees1, trees2 = PARAMS['trees1'], PARAMS['trees2']\n\t\tfragmentkeys, bitsets = _fragments.completebitsets(\n\t\t\t\ttrees1, PARAMS['vocab'],\n\t\t\t\tmax(trees1.maxnodes, trees2.maxnodes), PARAMS['disc'])\n\telse:\n\t\tif len(filenames) == 1:\n\t\t\twork = workload(numtrees, mult, numproc)\n\t\telse:\n\t\t\tchunk = numtrees // (mult * numproc) + 1\n\t\t\twork = [(a, a + chunk) for a in range(0, numtrees, chunk)]\n\t\tif numproc != 1:\n\t\t\tlogging.info('work division:\\n%s', '\\n'.join(' %s:\\t%r' % kv\n\t\t\t\tfor kv in sorted(dict(numchunks=len(work), mult=mult).items())))\n\t\tdowork = mymap(myworker, work)\n\t\tfor results in dowork:\n\t\t\tif PARAMS['approx']:\n\t\t\t\tfor frag, x in results.items():\n\t\t\t\t\tfragments[frag] += x\n\t\t\telse:\n\t\t\t\tfragments.update(results)\n\t\tfragmentkeys = list(fragments)\n\t\tbitsets = [fragments[a] for a in fragmentkeys]\n\tif PARAMS['nofreq']:\n\t\tcounts = None\n\telif PARAMS['approx']:\n\t\tcounts = [fragments[a] for a in fragmentkeys]\n\telse:\n\t\ttask = 'indices' if PARAMS['indices'] else 'counts'\n\t\tlogging.info('dividing work for exact %s', task)\n\t\tcountchunk = len(bitsets) // numproc + 1\n\t\twork = list(range(0, len(bitsets), countchunk))\n\t\twork = [(n, len(work), bitsets[a:a + countchunk])\n\t\t\t\tfor n, a in enumerate(work)]\n\t\tcounts = []\n\t\tlogging.info('getting exact %s', task)\n\t\tfor a in mymap(\n\t\t\t\texactcountworker if numproc == 1 else mpexactcountworker, work):\n\t\t\tcounts.extend(a)\n\tif PARAMS['cover']:\n\t\tmaxdepth, maxfrontier = PARAMS['cover']\n\t\tbefore = len(fragmentkeys)\n\t\tcover = _fragments.allfragments(PARAMS['trees1'], PARAMS['vocab'],\n\t\t\t\tmaxdepth, maxfrontier, PARAMS['disc'], PARAMS['indices'])\n\t\tfor a in cover:\n\t\t\tif a not in fragments:\n\t\t\t\tfragmentkeys.append(a)\n\t\t\t\tcounts.append(cover[a])\n\t\tlogging.info('merged %d cover fragments '\n\t\t\t\t'up to depth %d with max %d frontier non-terminals.',\n\t\t\t\tlen(fragmentkeys) - before, maxdepth, maxfrontier)\n\tif numproc != 1:\n\t\tpool.close()\n\t\tpool.join()\n\t\tdel dowork, pool\n\treturn fragmentkeys, counts\n\n\ndef batch(outputdir, filenames, limit, encoding, debin):\n\t\"\"\"batch processing: three or more treebanks specified.\n\n\tCompares the first treebank to all others, and writes the results\n\tto ``outputdir/A_B`` where ``A`` and ``B`` are the respective filenames.\n\tCounts/indices are from the other (B) treebanks.\n\tThere are at least 2 use cases for this:\n\n\t1. Comparing one treebank to a series of others. The first treebank will\n\t\tonly be loaded once.\n\t2. In combination with ``--complete``, the first treebank is a set of\n\t\tfragments used as queries on the other treebanks specified.\"\"\"\n\tinitworker(filenames[0], None, limit, encoding)\n\ttrees1 = PARAMS['trees1']\n\tmaxnodes = trees1.maxnodes\n\tif PARAMS['complete']:\n\t\tfragmentkeys, bitsets = _fragments.completebitsets(\n\t\t\t\ttrees1, PARAMS['vocab'],\n\t\t\t\tmaxnodes, PARAMS['disc'])\n\t\tfragments = True\n\telif PARAMS['approx']:\n\t\tfragments = defaultdict(int)\n\telse:\n\t\tfragments = {}\n\tfor filename in filenames[1:]:\n\t\tPARAMS.update(read2ndtreebank(filename, PARAMS['vocab'],\n\t\t\tPARAMS['fmt'], limit, encoding))\n\t\ttrees2 = PARAMS['trees2']\n\t\tif not PARAMS['complete']:\n\t\t\tfragments = _fragments.extractfragments(trees1, 0, 0,\n\t\t\t\t\tPARAMS['vocab'], trees2, disc=PARAMS['disc'],\n\t\t\t\t\tdebug=PARAMS['debug'], approx=PARAMS['approx'],\n\t\t\t\t\ttwoterms=PARAMS['twoterms'], adjacent=PARAMS['adjacent'])\n\t\t\tfragmentkeys = list(fragments)\n\t\t\tbitsets = [fragments[a] for a in fragmentkeys]\n\t\t\tmaxnodes = max(trees1.maxnodes, trees2.maxnodes)\n\t\tcounts = None\n\t\tif PARAMS['approx'] or not fragments:\n\t\t\tcounts = fragments.values()\n\t\telif not PARAMS['nofreq']:\n\t\t\tlogging.info('getting %s for %d fragments',\n\t\t\t\t\t'indices of occurrence' if PARAMS['indices']\n\t\t\t\t\telse 'exact counts', len(bitsets))\n\t\t\tcounts = _fragments.exactcounts(bitsets, trees1, trees2,\n\t\t\t\t\tindices=PARAMS['indices'],\n\t\t\t\t\tmaxnodes=maxnodes)\n\t\toutputfilename = '%s/%s_%s' % (outputdir,\n\t\t\t\tos.path.basename(filenames[0]), os.path.basename(filename))\n\t\tout = io.open(outputfilename, 'w', encoding=encoding)\n\t\tif debin:\n\t\t\tfragmentkeys = debinarize(fragmentkeys)\n\t\tprintfragments(fragmentkeys, counts, out=out)\n\t\tlogging.info('wrote to %s', outputfilename)\n\n\ndef readtreebanks(filename1, filename2=None, fmt='bracket',\n\t\tlimit=None, encoding='utf8'):\n\t\"\"\"Read one or two treebanks.\"\"\"\n\tvocab = Vocabulary()\n\ttrees1 = _fragments.readtreebank(filename1, vocab,\n\t\t\tfmt, limit, encoding)\n\ttrees2 = _fragments.readtreebank(filename2, vocab,\n\t\t\tfmt, limit, encoding)\n\ttrees1.indextrees(vocab)\n\tif trees2:\n\t\ttrees2.indextrees(vocab)\n\treturn dict(trees1=trees1, trees2=trees2, vocab=vocab)\n\n\ndef read2ndtreebank(filename2, vocab, fmt='bracket',\n\t\tlimit=None, encoding='utf8'):\n\t\"\"\"Read a second treebank.\"\"\"\n\ttrees2 = _fragments.readtreebank(filename2, vocab,\n\t\t\tfmt, limit, encoding)\n\ttrees2.indextrees(vocab)\n\tlogging.info('%r: %d trees; %d nodes (max %d); '\n\t\t\t'word tokens: %d\\n%r',\n\t\t\tfilename2, len(trees2), trees2.numnodes, trees2.maxnodes,\n\t\t\ttrees2.numwords, PARAMS['vocab'])\n\treturn dict(trees2=trees2, vocab=vocab)\n\n\ndef initworker(filename1, filename2, limit, encoding):\n\t\"\"\"Read treebanks for this worker.\n\n\tWe do this separately for each process under the assumption that this is\n\tadvantageous with a NUMA architecture.\"\"\"\n\tPARAMS.update(readtreebanks(filename1, filename2,\n\t\t\tlimit=limit, fmt=PARAMS['fmt'], encoding=encoding))\n\ttrees1 = PARAMS['trees1']\n\tif PARAMS['debug']:\n\t\tprint('\\nproductions:')\n\t\tfor a, b in sorted([(PARAMS['vocab'].prodrepr(n), n)\n\t\t\t\tfor n in range(len(PARAMS['vocab'].prods))],\n\t\t\t\tkey=lambda x: x[1]):\n\t\t\tprint('%d. %s' % (b, a))\n\t\tprint('treebank 1:')\n\t\tfor n in range(trees1.len):\n\t\t\ttrees1.printrepr(n, PARAMS['vocab'])\n\tif not trees1:\n\t\traise ValueError('treebank1 empty.')\n\tm = 'treebank1: %d trees; %d nodes (max: %d); %d word tokens.\\n' % (\n\t\t\ttrees1.len, trees1.numnodes, trees1.maxnodes, trees1.numwords)\n\tif filename2:\n\t\ttrees2 = PARAMS['trees2']\n\t\tif PARAMS['debug']:\n\t\t\tprint('treebank 2:')\n\t\t\tfor n in range(trees2.len):\n\t\t\t\ttrees2.printrepr(n, PARAMS['vocab'])\n\t\tif not trees2:\n\t\t\traise ValueError('treebank2 empty.')\n\t\tm += 'treebank2: %d trees; %d nodes (max %d); %d word tokens.\\n' % (\n\t\t\t\ttrees2.len, trees2.numnodes, trees2.maxnodes, trees2.numwords)\n\tlogging.info('%s%r', m, PARAMS['vocab'])\n\n\ndef initworkersimple(trees, sents, trees2=None, sents2=None):\n\t\"\"\"Initialization for a worker in which a treebank was already loaded.\"\"\"\n\tPARAMS.update(_fragments.getctrees(zip(trees, sents),\n\t\t\tNone if trees2 is None else zip(trees2, sents2)))\n\tassert PARAMS['trees1'], PARAMS['trees1']\n\n\n@workerfunc\ndef mpworker(interval):\n\t\"\"\"Worker function for fragment extraction (multiprocessing wrapper).\"\"\"\n\treturn worker(interval)\n\n\ndef worker(interval):\n\t\"\"\"Worker function for fragment extraction.\"\"\"\n\toffset, end = interval\n\ttrees1 = PARAMS['trees1']\n\ttrees2 = PARAMS['trees2']\n\tassert offset < trees1.len\n\tresult = {}\n\tresult = _fragments.extractfragments(trees1, offset, end,\n\t\t\tPARAMS['vocab'], trees2, approx=PARAMS['approx'],\n\t\t\tdisc=PARAMS['disc'],\n\t\t\tdebug=PARAMS['debug'], twoterms=PARAMS['twoterms'],\n\t\t\tadjacent=PARAMS['adjacent'])\n\tlogging.debug('finished %d--%d', offset, end)\n\treturn result\n\n\n@workerfunc\ndef mpexactcountworker(args):\n\t\"\"\"Worker function for counts (multiprocessing wrapper).\"\"\"\n\treturn exactcountworker(args)\n\n\ndef exactcountworker(args):\n\t\"\"\"Worker function for counting of fragments.\"\"\"\n\tn, m, bitsets = args\n\ttrees1 = PARAMS['trees1']\n\tif PARAMS['complete']:\n\t\tresults = _fragments.exactcounts(bitsets, trees1, PARAMS['trees2'],\n\t\t\t\tindices=PARAMS['indices'])\n\t\tlogging.debug('complete matches chunk %d of %d', n + 1, m)\n\t\treturn results\n\tresults = _fragments.exactcounts(\n\t\t\tbitsets, trees1, trees1, indices=PARAMS['indices'])\n\tif PARAMS['indices']:\n\t\tlogging.debug('exact indices chunk %d of %d', n + 1, m)\n\telse:\n\t\tlogging.debug('exact counts chunk %d of %d', n + 1, m)\n\treturn results\n\n\ndef workload(numtrees, mult, numproc):\n\t\"\"\"Calculate an even workload.\n\n\tWhen *n* trees are compared against themselves, ``n * (n - 1)`` total\n\tcomparisons are made. Each tree ``m`` has to be compared to all trees ``x``\n\tsuch that ``m < x <= n``\n\t(meaning there are more comparisons for lower *n*).\n\n\t:returns: a sequence of ``(start, end)`` intervals such that\n\t\tthe number of comparisons is approximately balanced.\"\"\"\n\t# could base on number of nodes as well.\n\tif numproc == 1:\n\t\treturn [(0, numtrees)]\n\t# here chunk is the number of tree pairs that will be compared\n\tgoal = togo = total = 0.5 * numtrees * (numtrees - 1)\n\tchunk = total // (mult * numproc) + 1\n\tgoal -= chunk\n\tresult = []\n\tlast = 0\n\tfor n in range(1, numtrees):\n\t\ttogo -= numtrees - n\n\t\tif togo <= goal:\n\t\t\tgoal -= chunk\n\t\t\tresult.append((last, n))\n\t\t\tlast = n\n\tif last < numtrees:\n\t\tresult.append((last, numtrees))\n\treturn result\n\n\ndef recurringfragments(trees, sents, numproc=1, disc=True,\n\t\tindices=True, maxdepth=1,\n\t\tmaxfrontier=999):\n\t\"\"\"Get recurring fragments with exact counts in a single treebank.\n\n\t:returns: a dictionary whose keys are fragments as strings, and\n\t\tindices as values. When ``disc`` is ``True``, keys are of the form\n\t\t``(frag, sent)`` where ``frag`` is a unicode string, and ``sent``\n\t\tis a list of words as unicode strings; when ``disc`` is ``False``, keys\n\t\tare of the form ``frag`` where ``frag`` is a unicode string.\n\t:param trees: a sequence of binarized Tree objects, with indices as leaves.\n\t:param sents: the corresponding sentences (lists of strings).\n\t:param numproc: number of processes to use; pass 0 to use detected # CPUs.\n\t:param disc: when disc=True, assume trees with discontinuous constituents;\n\t\tresulting fragments will be of the form (frag, sent);\n\t\totherwise fragments will be strings with words as leaves.\n\t:param indices: when False, return integer counts instead of indices.\n\t:param maxdepth: when > 0, add 'cover' fragments to result, corresponding\n\t\tto all fragments up to given depth; pass 0 to disable.\n\t:param maxfrontier: maximum number of frontier non-terminals (substitution\n\t\tsites) in cover fragments; a limit of 0 only gives fragments that\n\t\tbottom out in terminals; the default 999 is unlimited for practical\n\t\tpurposes.\"\"\"\n\tif numproc == 0:\n\t\tnumproc = cpu_count()\n\tnumtrees = len(trees)\n\tif not numtrees:\n\t\traise ValueError('no trees.')\n\tmult = 1 # 3 if numproc > 1 else 1\n\tfragments = {}\n\ttrees = trees[:]\n\twork = workload(numtrees, mult, numproc)\n\tPARAMS.update(disc=disc, indices=indices, approx=False, complete=False,\n\t\t\tdebug=False, adjacent=False, twoterms=None)\n\tinitworkersimple(trees, list(sents))\n\tif numproc == 1:\n\t\tmymap, myworker = map, worker\n\telse:\n\t\tlogging.info('work division:\\n%s', '\\n'.join(' %s: %r' % kv\n\t\t\t\tfor kv in sorted(dict(numchunks=len(work),\n\t\t\t\t\tnumproc=numproc).items())))\n\t\t# start worker processes\n\t\tpool = multiprocessing.Pool(\n\t\t\t\tprocesses=numproc, initializer=initworkersimple,\n\t\t\t\tinitargs=(trees, list(sents)))\n\t\tmymap, myworker = pool.map, mpworker\n\t# collect recurring fragments\n\tlogging.info('extracting recurring fragments')\n\tfor a in mymap(myworker, work):\n\t\tfragments.update(a)\n\tfragmentkeys = list(fragments)\n\tbitsets = [fragments[a] for a in fragmentkeys]\n\tcountchunk = len(bitsets) // numproc + 1\n\twork = list(range(0, len(bitsets), countchunk))\n\twork = [(n, len(work), bitsets[a:a + countchunk])\n\t\t\tfor n, a in enumerate(work)]\n\tlogging.info('getting exact counts for %d fragments', len(bitsets))\n\tcounts = []\n\tfor a in mymap(\n\t\t\texactcountworker if numproc == 1 else mpexactcountworker, work):\n\t\tcounts.extend(a)\n\t# add all fragments up to a given depth\n\tif maxdepth:\n\t\tcover = _fragments.allfragments(PARAMS['trees1'], PARAMS['vocab'],\n\t\t\t\tmaxdepth, maxfrontier, disc, indices)\n\t\tbefore = len(fragmentkeys)\n\t\tfor a in cover:\n\t\t\tif a not in fragments:\n\t\t\t\tfragmentkeys.append(a)\n\t\t\t\tcounts.append(cover[a])\n\t\tlogging.info('merged %d cover fragments '\n\t\t\t\t'up to depth %d with max %d frontier non-terminals.',\n\t\t\t\tlen(fragmentkeys) - before, maxdepth, maxfrontier)\n\tif numproc != 1:\n\t\tpool.close()\n\t\tpool.join()\n\t\tdel pool\n\tlogging.info('found %d fragments', len(fragmentkeys))\n\treturn dict(zip(fragmentkeys, counts))\n\n\ndef allfragments(trees, sents, maxdepth, maxfrontier=999):\n\t\"\"\"Return all fragments up to a certain depth, # frontiers.\"\"\"\n\tPARAMS.update(disc=True, indices=True, approx=False, complete=False,\n\t\t\tdebug=False, adjacent=False, twoterms=None)\n\tinitworkersimple(trees, list(sents))\n\treturn _fragments.allfragments(PARAMS['trees1'],\n\t\t\tPARAMS['vocab'], maxdepth, maxfrontier,\n\t\t\tdisc=PARAMS['disc'], indices=PARAMS['indices'])\n\n\ndef altrepr(a):\n\t\"\"\"Rewrite bracketed tree to alternative format.\n\n\tReplace double quotes with double single quotes: \" -> ''\n\tQuote terminals with double quotes terminal: -> \"terminal\"\n\tRemove parentheses around frontier nodes: (NN ) -> NN\n\n\t>>> print(altrepr('(NP (DT a) (NN ))'))\n\t(NP (DT \"a\") NN)\n\t\"\"\"\n\treturn FRONTIERRE.sub(r'\\1', TERMRE.sub(r'(\\1 \"\\2\")', a.replace('\"', \"''\")))\n\n\ndef debinarize(fragments):\n\t\"\"\"Debinarize fragments; fragments that fail to debinarize left as-is.\"\"\"\n\tresult = []\n\tfor origfrag in fragments:\n\t\tfrag, sent = (discbrackettree(origfrag) if PARAMS['disc']\n\t\t\t\telse brackettree(origfrag, detectdisc=False))\n\t\ttry:\n\t\t\tfrag = writetree(unbinarize(frag), sent, 0,\n\t\t\t\t\t'discbracket' if PARAMS['disc'] else 'bracket').strip()\n\t\texcept Exception: # pylint: disable=broad-except\n\t\t\tresult.append(origfrag)\n\t\telse:\n\t\t\tresult.append(frag)\n\treturn result\n\n\ndef printfragments(fragments, counts, out=None):\n\t\"\"\"Dump fragments to standard output or some other file object.\"\"\"\n\tif out is None:\n\t\tout = sys.stdout\n\t\tif sys.stdout.encoding is None:\n\t\t\tout = codecs.getwriter('utf8')(out)\n\tif PARAMS['alt']:\n\t\tfor n, a in enumerate(fragments):\n\t\t\tfragments[n] = altrepr(a)\n\tif PARAMS['complete']:\n\t\tlogging.info('total number of matches: %d',\n\t\t\t\tsum(sum(a) for a in counts)\n\t\t\t\tif PARAMS['indices'] else sum(counts))\n\telse:\n\t\tlogging.info('number of fragments: %d', len(fragments))\n\tif PARAMS['nofreq']:\n\t\tfor a in fragments:\n\t\t\tout.write(a + '\\n')\n\t\treturn\n\t# a frequency of 0 is normal when counting occurrences of given fragments\n\t# in a second treebank\n\tif PARAMS['complete']:\n\t\tthreshold = 0\n\t\tzeroinvalid = False\n\t# a frequency of 1 is normal when comparing two treebanks\n\t# or when non-recurring fragments are added\n\telif PARAMS.get('trees2') or PARAMS['cover'] or PARAMS['approx']:\n\t\tthreshold = 0\n\t\tzeroinvalid = True\n\telse: # otherwise, raise alarm.\n\t\tthreshold = 1\n\t\tzeroinvalid = True\n\tif PARAMS['indices']:\n\t\tfor a, theindices in zip(fragments, counts):\n\t\t\tif len(theindices) > threshold:\n\t\t\t\tout.write('%s\\t%s\\n' % (a,\n\t\t\t\t\t[n for n in theindices\n\t\t\t\t\t\tif n - 1 in theindices or n + 1 in theindices]\n\t\t\t\t\tif PARAMS['adjacent'] else\n\t\t\t\t\tstr(theindices)[len(\"array('I', \"):-len(')')]))\n\t\t\telif zeroinvalid:\n\t\t\t\traise ValueError('invalid fragment--frequency=1: %r' % a)\n\telif PARAMS['relfreq']:\n\t\tsums = defaultdict(int)\n\t\tfor a, freq in zip(fragments, counts):\n\t\t\tif freq > threshold:\n\t\t\t\tsums[a[1:a.index(' ')]] += freq\n\t\t\telif zeroinvalid:\n\t\t\t\traise ValueError('invalid fragment--frequency=%d: %r' % (\n\t\t\t\t\tfreq, a))\n\t\tfor a, freq in zip(fragments, counts):\n\t\t\tout.write('%s\\t%d/%d\\n' % (\n\t\t\t\ta, freq, sums[a[1:a.index(' ')]]))\n\telse:\n\t\tfor a, freq in zip(fragments, counts):\n\t\t\tif freq > threshold:\n\t\t\t\tout.write('%s\\t%d\\n' % (a, freq))\n\t\t\telif zeroinvalid:\n\t\t\t\traise ValueError('invalid fragment--frequency=1: %r' % a)\n\n\ndef cpu_count():\n\t\"\"\"Return number of CPUs or 1.\"\"\"\n\ttry:\n\t\treturn multiprocessing.cpu_count()\n\texcept NotImplementedError:\n\t\treturn 1\n\n\ndef test():\n\t\"\"\"Demonstration of fragment extractor.\"\"\"\n\tmain('--fmt=export alpinosample.export'.split())\n\n\n__all__ = ['main', 'regular', 'batch', 'readtreebanks', 'read2ndtreebank',\n\t\t'initworker', 'initworkersimple', 'worker', 'exactcountworker',\n\t\t'workload', 'recurringfragments', 'allfragments', 'debinarize',\n\t\t'printfragments', 'altrepr', 'cpu_count']\n","repo_name":"andreasvc/disco-dop","sub_path":"discodop/fragments.py","file_name":"fragments.py","file_ext":"py","file_size_in_byte":21251,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"72"}
+{"seq_id":"7283058636","text":"import argparse\nimport configparser\nimport os\nfrom typing import Any, Optional, Sequence, Tuple\n\nimport psycopg2\nfrom psycopg2.extras import execute_values\n\nCOMMANDS = [\"init\", \"reset\", \"destroy\"]\n\n\ndef get_db(\n conf: configparser.ConfigParser, superuser: Optional[bool] = False\n) -> psycopg2.extensions.connection:\n cfg_section = \"ingest\" if superuser else \"postgresql\"\n dbname = conf[cfg_section][\"database\"]\n user = conf[cfg_section][\"user\"]\n password = conf[cfg_section].get(\"password\", None)\n host = conf[cfg_section][\"host\"]\n port = conf[cfg_section][\"port\"]\n conn = psycopg2.connect(dbname=dbname, user=user, password=password, host=host, port=port)\n return conn\n\n\ndef clear_db(conf: configparser.ConfigParser) -> None:\n sql = \"\"\"\n DROP SCHEMA IF EXISTS mars CASCADE;\n DROP USER IF EXISTS mars_user;\n \"\"\"\n conn = None\n try:\n conn = get_db(conf, superuser=True)\n cur = conn.cursor()\n cur.execute(sql)\n cur.close()\n conn.commit()\n except psycopg2.DatabaseError as error:\n print(\"error:\", error)\n finally:\n if conn is not None:\n conn.close()\n\n\ndef init_db(conf: configparser.ConfigParser, schema_path: str) -> None:\n conn = None\n try:\n conn = get_db(conf, superuser=True)\n cur = conn.cursor()\n\n with open(schema_path, \"r\") as sql:\n cur.execute(sql.read(), (conf[\"postgresql\"][\"password\"],))\n\n cur.close()\n conn.commit()\n except psycopg2.DatabaseError as error:\n print(\"error:\", error)\n finally:\n if conn is not None:\n conn.close()\n\n\ndef insert_many(\n conf: configparser.ConfigParser, insert_sql: str, data: Sequence[Tuple[Any, ...]]\n) -> None:\n conn = None\n try:\n conn = get_db(conf, superuser=True)\n cur = conn.cursor()\n execute_values(cur, insert_sql, data, page_size=1000)\n cur.close()\n conn.commit()\n except psycopg2.DatabaseError as error:\n print(\"error: \", error)\n finally:\n if conn is not None:\n conn.close()\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"command\", choices=COMMANDS)\n parser.add_argument(\"--config\", help=\"Path to config file.\", default=\"config.ini\", type=str)\n args = parser.parse_args()\n\n config = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation())\n config.read(args.config)\n\n this_file_path = os.path.dirname(os.path.realpath(__file__))\n schema_file = \"schema.sql\"\n schema_full_path = os.path.join(this_file_path, schema_file)\n\n if args.command == \"init\":\n init_db(config, schema_full_path)\n elif args.command == \"reset\":\n clear_db(config)\n init_db(config, schema_full_path)\n elif args.command == \"destroy\":\n clear_db(config)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"appaccess/LAMA-CHI2022","sub_path":"mars/db/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"23648246514","text":"import cv2\n\ngridWidth = 3\ngridHeight = 3\n\nimg = cv2.imread(\"../Resources/cat.jpg\")\nheight, width = img.shape[:2]\n\nframeWidth = width / gridWidth\nframeHeight = height / gridHeight\n\ncv2.imshow(\"Image\", img)\ncv2.waitKey()\ncv2.destroyAllWindows()\n\nfor x in range(0, gridWidth):\n for y in range(0, gridHeight):\n frame = img[int(frameHeight * y) : int(frameHeight * (y + 1)), int(frameWidth * x) : int(frameWidth * (x + 1))]\n cv2.imshow(f\"{x}, {y}\", frame)\n cv2.imwrite(f\"Results/{y} {x}.jpg\", frame)\n\ncv2.waitKey()","repo_name":"AmXLoVE/robototehnika","sub_path":"Homework1/OpencvTest/Task 1/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"28991737836","text":"import time\nimport unittest\n\nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\n\nfrom selenium_config import get_path\n\n\nclass NegativeTest(unittest.TestCase):\n address_good_KC = ['1724 Beacon Way SE Renton, WA 98058', '1925 N 170th St Shoreline, WA 98133']\n address_bad = ['fjweogrgjrgl;jjegjegjgjlgjjgl;jgljgslj', '3000 Royal Hills Dr SE Renton, WA 98058, apt 1d, apt 1d', \\\n '3000 Royal Hills Dr SE Renton WA 98058 apt 1d', \\\n '904 Hiawatha Pl S, Seattle, apt 1C, apt 1C, WA 98144', '', \\\n 'iosfi iosejfdujfi esofjedsj eofe 9898908 kiefkljkj 9898', \\\n '($(*$(#$*#*%(#@*@*!($(*%$*%(*$*$(**!@(*$@(!*$(*(*($*@$*!(@*$', \\\n '()*(* )(_* )* )_()()( )(((( ', \\\n '3000 Royal Hills Dr SE Renton WA 98058 apt 1d apt 1d'\n ]\n address_good = ['8105 SE Henderson St Portland, OR 97206']\n zip_good = ['98058', '98053']\n\n @classmethod\n def setUp(inst):\n options = Options() # set custom paths\n options.binary_location = get_path().crome_path # set custom paths\n inst.driver = webdriver.Chrome(chrome_options=options,\n executable_path=get_path().webdriver_path, ) # set custom paths\n inst.driver.implicitly_wait(30)\n inst.driver.maximize_window()\n # navigate to the application home page\n inst.driver.get(get_path().app_path) # get the homepage\n\n def test_search_by_address_negative(self):\n # self.driver.execute_script(\"alert('This is an alert');\")\n res = True\n for el in self.address_bad:\n self.search_field = self.driver.find_element_by_name('address') # find_element_by_name\n self.search_field.send_keys(el) # input address\n self.search_field.submit() # press search\n if not self.is_element_present(By.CLASS_NAME, \"price\"):\n res = False\n print(\"address=\", el)\n break\n else:\n time.sleep(1)\n self.driver.implicitly_wait(5)\n self.driver.get(get_path().app_path)\n self.assertTrue(res)\n\n @classmethod\n def tearDown(inst):\n # close the browser window\n inst.driver.quit()\n\n def is_element_present(self, how, what):\n \"\"\"\n Helper method to confirm the presence of an element on page\n :params how: By locator type\n :params what: locator value\n \"\"\"\n try:\n self.driver.find_element(by=how, value=what)\n except NoSuchElementException:\n return False\n return True\n\n\nif __name__ == '__main__':\n unittest.main(verbosity=2)\n","repo_name":"huiyuandiknow/Home_Data_App","sub_path":"tests/test_negative.py","file_name":"test_negative.py","file_ext":"py","file_size_in_byte":2928,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"37787663928","text":"import argparse\nimport dataclasses\nimport json\nimport sys\n\nfrom typing import Any, Callable, Dict, List, Tuple, Union # noqa: H301\n\nfrom spahau import defs\nfrom spahau import query\nfrom spahau import response\n\n\nResult = Union[str, response.Response, List[response.Response]]\n\nConfigHandler = Callable[[defs.Config, defs.IPAddress], Result]\n\n\nSELFTEST_DATA_DEFS: Dict[str, List[str]] = {\n \"127.0.0.1\": [],\n \"127.0.0.2\": [\"127.0.0.2\", \"127.0.0.4\", \"127.0.0.10\"],\n}\n\nSELFTEST_DATA = {\n defs.IPAddress.parse(name): [\n response.response_desc(defs.IPAddress.parse(item)) for item in value\n ]\n for name, value in SELFTEST_DATA_DEFS.items()\n}\n\n\ndef cmd_describe(_cfg: defs.Config, address: defs.IPAddress) -> Result:\n \"\"\"Describe the specified RBL return codes.\"\"\"\n return response.response_desc(address)\n\n\ndef cmd_show_hostname(cfg: defs.Config, address: defs.IPAddress) -> Result:\n \"\"\"Build and display the hostname for the query.\"\"\"\n return query.get_hostname(cfg, address)\n\n\ndef cmd_selftest(cfg: defs.Config, address: defs.IPAddress) -> Result:\n \"\"\"Run a self-test.\"\"\"\n expected = SELFTEST_DATA.get(address)\n if expected is None:\n sys.exit(f\"Unknown selftest address '{address}'\")\n\n if not cfg.json:\n print(\n f\"Querying '{address}', expecting {len(expected)} responses: \"\n + \" \".join(f\"'{resp}'\" for resp in expected)\n )\n\n responses = query.query(cfg, address)\n if not cfg.json:\n print(\n f\"...got {len(responses)} responses: \"\n + \" \".join(f\"'{resp}'\" for resp in responses)\n )\n\n if responses != expected:\n sys.exit(f\"Mismatch for '{address}'\")\n\n return responses\n\n\ndef cmd_test(cfg: defs.Config, address: defs.IPAddress) -> Result:\n \"\"\"Describe the specified RBL return codes.\"\"\"\n return query.query(cfg, address)\n\n\ndef parse_arguments() -> Tuple[defs.Config, ConfigHandler]:\n \"\"\"Parse the command-line arguments.\"\"\"\n parser = argparse.ArgumentParser(prog=\"spahau\")\n parser.add_argument(\n \"--describe\",\n \"-D\",\n action=\"store_true\",\n help=\"describe the specified RBL return codes/addresses\",\n )\n parser.add_argument(\n \"--domain\",\n \"-d\",\n type=str,\n default=defs.RBL_DOMAIN,\n help=\"specify the RBL domain to test against\",\n )\n parser.add_argument(\n \"--hostname\",\n \"-H\",\n action=\"store_true\",\n help=\"only output the RBL hostnames, do not send queries\",\n )\n parser.add_argument(\n \"--json\", \"-j\", action=\"store_true\", help=\"display JSON output\"\n )\n parser.add_argument(\n \"--selftest\",\n \"-T\",\n action=\"store_true\",\n help=\"run a self test: try to obtain some expected responses\",\n )\n parser.add_argument(\n \"--verbose\",\n \"-v\",\n action=\"store_true\",\n help=\"verbose operation; display diagnostic output\",\n )\n parser.add_argument(\n \"addresses\",\n type=str,\n nargs=\"+\",\n help=\"the addresses to query or describe\",\n )\n\n args = parser.parse_args()\n\n key = (\n (\"D\" if args.describe else \"\")\n + (\"H\" if args.hostname else \"\")\n + (\"T\" if args.selftest else \"\")\n )\n if len(key) > 1:\n sys.exit(\"At most one of -D, -H, or -T may be specified\")\n handler = {\n \"D\": cmd_describe,\n \"H\": cmd_show_hostname,\n \"T\": cmd_selftest,\n \"\": cmd_test,\n }[key]\n\n return (\n defs.Config(\n addresses=[defs.IPAddress.parse(item) for item in args.addresses],\n domain=str(args.domain),\n json=bool(args.json),\n verbose=bool(args.verbose),\n ),\n handler,\n )\n\n\ndef main() -> None:\n \"\"\"Parse command-line arguments, do cri... err, things.\"\"\"\n cfg, func = parse_arguments()\n\n data: Dict[str, Any] = {}\n for address in cfg.addresses:\n value = func(cfg, address)\n cfg.diag(f\"{address}: got {value}\")\n if cfg.json:\n if not value or isinstance(value, str):\n data[address.text] = value\n elif isinstance(value, response.Response):\n data[address.text] = dataclasses.asdict(value)\n else:\n data[address.text] = [\n dataclasses.asdict(item) for item in value\n ]\n continue\n\n if isinstance(value, (str, response.Response)):\n print(value)\n continue\n\n assert isinstance(value, list)\n if not value:\n print(\n f\"The IP address: {address} is NOT found in \"\n f\"the Spamhaus blacklists.\"\n )\n continue\n\n assert all(isinstance(item, response.Response) for item in value)\n if value and value[0].tag == \"ERROR\":\n print(f\"Could not obtain a response for {address}: {value[0]}\")\n continue\n\n stringified = \" \".join(f\"'{resp}'\" for resp in value)\n print(\n f\"The IP address: {address} is found in the following \"\n f\"Spamhaus public IP zone: {stringified}\"\n )\n\n if cfg.json:\n print(json.dumps(data, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"ppentchev/python-spahau","sub_path":"python/src/spahau/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":5244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"21526673196","text":"\nfrom PyQt5.QtGui import QColor, QFont, QPalette\nfrom PyQt5.QtCore import QEvent, Qt #QString\nfrom PyQt5.QtWidgets import QApplication, QComboBox, QDialog, QDoubleSpinBox, QLabel, QLineEdit, QMessageBox\nfrom PyQt5.QtWidgets import QPushButton, QRadioButton, QSpinBox, QTextEdit, QGridLayout, QHBoxLayout, QVBoxLayout\n\nimport define\nimport logger\nimport center\nimport trader\n\nclass Panel(QDialog):\n def __init__(self, **kwargs):\n super(Panel, self).__init__()\n self.strategy = kwargs.get(\"strategy\", \"\")\n self.version_info = \"V0.1.0-Beta Build 20180422\"\n self.log_text = \"\"\n self.log_cate = \"Panel_Trader_STK_APE\"\n self.logger = logger.Logger()\n \n self.InitUserInterface()\n \n self.symbol = \"\"\n self.exchange = \"\" # SH、SZ 或 SSE、SZE # 目前只订阅个股行情，不考虑沪深个股和指数代码重合问题\n self.trader = None # 策略中赋值\n self.subscribe = False # 行情订阅标志\n self.center = center.Center()\n \n self.quote_data = None\n self.price_round_stock = 2 # 小数位数\n self.price_round_index = 2 # 小数位数\n\n def OnWorking(self): # 供具体策略继承调用，在 运行 前执行一些操作\n if self.subscribe == False:\n self.center.RegQuoteSub(self.strategy, self.OnQuoteStock, \"stock_ltp\") # 目前只订阅个股\n self.subscribe = True\n self.trader = trader.Trader().GetTrader(\"hbzq\")\n if self.trader == None:\n self.logger.SendMessage(\"E\", 4, self.log_cate, \"获取标识为 hbzq 的交易服务失败！\", \"M\")\n\n def OnSuspend(self): # 供具体策略继承调用，在 暂停 前执行一些操作\n pass\n\n def OnContinue(self): # 供具体策略继承调用，在 继续 前执行一些操作\n pass\n\n def OnTerminal(self): # 供具体策略继承调用，在 停止 前执行一些操作\n if self.subscribe == True:\n self.center.DelQuoteSub(self.strategy, \"stock_ltp\")\n self.subscribe = False\n\n def event(self, event):\n if event.type() == define.DEF_EVENT_TRADER_STK_APE_UPDATE_QUOTE:\n if self.quote_data != None:\n self.OnUpdateQuote(self.quote_data, self.price_round_stock)\n return QDialog.event(self, event)\n\n def OnTraderEvent(self, trader, ret_func, task_item): # 交易模块事件通知，供具体策略继承调用\n if ret_func == define.trade_placeorder_s_func:\n self.log_text = \"%s：%s %d：%d\" % (self.strategy, trader, ret_func, task_item.order.order_id)\n self.logger.SendMessage(\"H\", 2, self.log_cate, self.log_text, \"T\")\n\n def OnQuoteStock(self, msg): # 行情触发\n try:\n str_code = msg.data[0].decode()\n if str_code == self.symbol:\n if \"60\" == str_code[0:2] or \"000\" == str_code[0:3] or \"001\" == str_code[0:3] or \"002\" == str_code[0:3] or \"300\" == str_code[0:3]:\n self.quote_data = msg.data\n QApplication.postEvent(self, QEvent(define.DEF_EVENT_TRADER_STK_APE_UPDATE_QUOTE)) # postEvent异步，sendEvent同步\n except Exception as e:\n self.log_text = \"%s：函数 OnQuoteStock 异常！%s\" % (self.strategy, e)\n self.logger.SendMessage(\"E\", 4, self.log_cate, self.log_text, \"M\")\n\n def InitUserInterface(self):\n self.color_red = QPalette()\n self.color_red.setColor(QPalette.WindowText, Qt.red)\n self.color_green = QPalette()\n self.color_green.setColor(QPalette.WindowText, QColor(0, 128, 0))\n self.color_black = QPalette()\n self.color_black.setColor(QPalette.WindowText, Qt.black)\n \n self.list_exchange = [define.DEF_EXCHANGE_STOCK_SH, define.DEF_EXCHANGE_STOCK_SZ]\n self.list_entr_type = [define.DEF_PRICE_TYPE_STOCK_LIMIT, define.DEF_PRICE_TYPE_STOCK_MARKET]\n \n self.setWindowTitle(\"手动交易-股票-APE %s\" % self.version_info)\n self.resize(380, 300)\n self.setFont(QFont(\"SimSun\", 9))\n \n self.label_exchange = QLabel(\"交易市场\")\n self.label_exchange.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_symbol = QLabel(\"证券代码\")\n self.label_symbol.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_name = QLabel(\"证券名称\")\n self.label_name.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_entr_type = QLabel(\"委托方式\")\n self.label_entr_type.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_can_use = QLabel(\"可用金额\")\n self.label_can_use.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_can_sell = QLabel(\"可用数量\")\n self.label_can_sell.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_price = QLabel(\"委托价格\")\n self.label_price.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_volume = QLabel(\"委托数量\")\n self.label_volume.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n \n self.label_can_use_unit = QLabel(\"元\")\n self.label_can_use_unit.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_can_sell_unit = QLabel(\"股\")\n self.label_can_sell_unit.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_price_unit = QLabel(\"元\")\n self.label_price_unit.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.label_volume_unit = QLabel(\"股\")\n self.label_volume_unit.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n \n self.combo_exchange = QComboBox()\n self.combo_exchange.addItems(self.list_exchange)\n self.line_edit_symbol = QLineEdit(\"\")\n self.line_edit_symbol.setStyleSheet(\"color:red\") # 初始红色\n self.line_edit_symbol.setFont(QFont(\"SimSun\", 9))\n self.line_edit_name = QLineEdit(\"\")\n self.line_edit_name.setReadOnly(True)\n self.line_edit_name.setFont(QFont(\"SimSun\", 9))\n self.line_edit_name.setStyleSheet(\"background-color:rgb(240,240,240);color:red\") # 初始红色\n self.line_edit_name.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.combo_entr_type = QComboBox()\n self.combo_entr_type.addItems(self.list_entr_type)\n self.line_edit_can_use = QLineEdit(\"0.00\")\n self.line_edit_can_use.setReadOnly(True)\n self.line_edit_can_use.setStyleSheet(\"background-color:rgb(240,240,240)\")\n self.line_edit_can_use.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.line_edit_can_sell = QLineEdit(\"0\")\n self.line_edit_can_sell.setReadOnly(True)\n self.line_edit_can_sell.setStyleSheet(\"background-color:rgb(240,240,240)\")\n self.line_edit_can_sell.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.spin_price = QDoubleSpinBox()\n self.spin_price.setDecimals(4)\n self.spin_price.setMinimum(0)\n self.spin_price.setMaximum(100000)\n self.spin_price.setStyleSheet(\"color:red\") # 初始红色\n self.spin_price.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.spin_volume = QSpinBox()\n self.spin_volume.setMinimum(0)\n self.spin_volume.setMaximum(1000000)\n self.spin_volume.setSingleStep(100)\n self.spin_volume.setStyleSheet(\"color:red\") # 初始红色\n self.spin_volume.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n \n self.grid_layout_essential = QGridLayout()\n self.grid_layout_essential.setContentsMargins(-1, -1, -1, -1)\n self.grid_layout_essential.addWidget(self.label_exchange, 0, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.label_symbol, 1, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.label_name, 2, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.label_entr_type, 3, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.label_can_use, 4, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.label_can_sell, 5, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.label_price, 6, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.label_volume, 7, 0, 1, 1)\n self.grid_layout_essential.addWidget(self.combo_exchange, 0, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.line_edit_symbol, 1, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.line_edit_name, 2, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.combo_entr_type, 3, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.line_edit_can_use, 4, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.line_edit_can_sell, 5, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.spin_price, 6, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.spin_volume, 7, 1, 1, 1)\n self.grid_layout_essential.addWidget(self.label_can_use_unit, 4, 2, 1, 1)\n self.grid_layout_essential.addWidget(self.label_can_sell_unit, 5, 2, 1, 1)\n self.grid_layout_essential.addWidget(self.label_price_unit, 6, 2, 1, 1)\n self.grid_layout_essential.addWidget(self.label_volume_unit, 7, 2, 1, 1)\n \n self.radio_button_buy = QRadioButton(\"买 入\")\n self.radio_button_buy.setStyleSheet(\"color:red\")\n self.radio_button_buy.setFont(QFont(\"SimSun\", 9))\n self.radio_button_buy.setChecked(True)\n self.radio_button_buy.setFixedWidth(70)\n self.radio_button_sell = QRadioButton(\"卖 出\")\n self.radio_button_sell.setStyleSheet(\"color:green\")\n self.radio_button_sell.setFont(QFont(\"SimSun\", 9))\n self.radio_button_sell.setFixedWidth(70)\n self.button_place_order = QPushButton(\"下 单\")\n self.button_place_order.setFont(QFont(\"SimSun\", 9))\n self.button_place_order.setStyleSheet(\"font:bold;color:red\") # 初始红色\n self.button_place_order.setFixedWidth(70)\n \n self.label_order_id = QLabel(\"撤单委托编号\")\n self.label_order_id.setFixedWidth(70)\n self.label_order_id.setAlignment(Qt.AlignLeft | Qt.AlignVCenter)\n self.line_edit_order_id = QLineEdit(\"\")\n self.line_edit_order_id.setFixedWidth(70)\n self.line_edit_order_id.setStyleSheet(\"color:blue\")\n self.line_edit_order_id.setFont(QFont(\"SimSun\", 9))\n self.button_cancel_order = QPushButton(\"撤 单\")\n self.button_cancel_order.setFont(QFont(\"SimSun\", 9))\n self.button_cancel_order.setStyleSheet(\"font:bold;color:blue\")\n self.button_cancel_order.setFixedWidth(70)\n \n self.h_box_layout_order_buttons = QHBoxLayout()\n self.h_box_layout_order_buttons.setContentsMargins(-1, -1, -1, -1)\n self.h_box_layout_order_buttons.addStretch(1)\n self.h_box_layout_order_buttons.addWidget(self.radio_button_buy)\n self.h_box_layout_order_buttons.addStretch(1)\n self.h_box_layout_order_buttons.addWidget(self.radio_button_sell)\n self.h_box_layout_order_buttons.addStretch(1)\n self.h_box_layout_order_buttons.addWidget(self.button_place_order)\n self.h_box_layout_order_buttons.addStretch(1)\n \n self.h_box_layout_cancel_order = QHBoxLayout()\n self.h_box_layout_cancel_order.setContentsMargins(-1, -1, -1, -1) #\n self.h_box_layout_cancel_order.addStretch(1)\n self.h_box_layout_cancel_order.addWidget(self.label_order_id)\n self.h_box_layout_cancel_order.addStretch(1)\n self.h_box_layout_cancel_order.addWidget(self.line_edit_order_id)\n self.h_box_layout_cancel_order.addStretch(1)\n self.h_box_layout_cancel_order.addWidget(self.button_cancel_order)\n self.h_box_layout_cancel_order.addStretch(1)\n \n self.v_box_layout_order = QVBoxLayout()\n self.v_box_layout_order.setContentsMargins(-1, -1, -1, -1)\n self.v_box_layout_order.addLayout(self.grid_layout_essential)\n self.v_box_layout_order.addLayout(self.h_box_layout_order_buttons)\n self.v_box_layout_order.addLayout(self.h_box_layout_cancel_order)\n \n self.label_high_limit = QLabel(\"涨停\")\n self.label_ask_5 = QLabel(\"卖五\")\n self.label_ask_4 = QLabel(\"卖四\")\n self.label_ask_3 = QLabel(\"卖三\")\n self.label_ask_2 = QLabel(\"卖二\")\n self.label_ask_1 = QLabel(\"卖一\")\n self.label_last = QLabel(\"最新\")\n self.label_last.setMinimumWidth(35)\n self.label_bid_1 = QLabel(\"买一\")\n self.label_bid_2 = QLabel(\"买二\")\n self.label_bid_3 = QLabel(\"买三\")\n self.label_bid_4 = QLabel(\"买四\")\n self.label_bid_5 = QLabel(\"买五\")\n self.label_low_limit = QLabel(\"跌停\")\n \n self.label_high_limit.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_5.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_4.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_3.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_1.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_last.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_1.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_3.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_4.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_5.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_low_limit.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n \n self.label_high_limit_price = QLabel(\"0.00\")\n self.label_high_limit_price.setMinimumWidth(60)\n self.label_ask_price_5 = QLabel(\"0.00\")\n self.label_ask_price_4 = QLabel(\"0.00\")\n self.label_ask_price_3 = QLabel(\"0.00\")\n self.label_ask_price_2 = QLabel(\"0.00\")\n self.label_ask_price_1 = QLabel(\"0.00\")\n self.label_ask_volume_5 = QLabel(\"0\")\n self.label_ask_volume_4 = QLabel(\"0\")\n self.label_ask_volume_3 = QLabel(\"0\")\n self.label_ask_volume_2 = QLabel(\"0\")\n self.label_ask_volume_1 = QLabel(\"0\")\n self.label_last_price = QLabel(\"0.00\")\n self.label_last_price.setMinimumWidth(60)\n self.label_last_up_down = QLabel(\"0.00%\")\n self.label_last_up_down.setMinimumWidth(60)\n self.label_bid_price_1 = QLabel(\"0.00\")\n self.label_bid_price_2 = QLabel(\"0.00\")\n self.label_bid_price_3 = QLabel(\"0.00\")\n self.label_bid_price_4 = QLabel(\"0.00\")\n self.label_bid_price_5 = QLabel(\"0.00\")\n self.label_bid_volume_1 = QLabel(\"0\")\n self.label_bid_volume_2 = QLabel(\"0\")\n self.label_bid_volume_3 = QLabel(\"0\")\n self.label_bid_volume_4 = QLabel(\"0\")\n self.label_bid_volume_5 = QLabel(\"0\")\n self.label_low_limit_price = QLabel(\"0.00\")\n self.label_low_limit_price.setMinimumWidth(60)\n \n self.label_high_limit_price.setPalette(self.color_red)\n self.label_ask_price_5.setPalette(self.color_green)\n self.label_ask_price_4.setPalette(self.color_green)\n self.label_ask_price_3.setPalette(self.color_green)\n self.label_ask_price_2.setPalette(self.color_green)\n self.label_ask_price_1.setPalette(self.color_green)\n self.label_ask_volume_5.setPalette(self.color_green)\n self.label_ask_volume_4.setPalette(self.color_green)\n self.label_ask_volume_3.setPalette(self.color_green)\n self.label_ask_volume_2.setPalette(self.color_green)\n self.label_ask_volume_1.setPalette(self.color_green)\n self.label_last_price.setPalette(self.color_black)\n self.label_last_up_down.setPalette(self.color_black)\n self.label_bid_price_1.setPalette(self.color_red)\n self.label_bid_price_2.setPalette(self.color_red)\n self.label_bid_price_3.setPalette(self.color_red)\n self.label_bid_price_4.setPalette(self.color_red)\n self.label_bid_price_5.setPalette(self.color_red)\n self.label_bid_volume_1.setPalette(self.color_red)\n self.label_bid_volume_2.setPalette(self.color_red)\n self.label_bid_volume_3.setPalette(self.color_red)\n self.label_bid_volume_4.setPalette(self.color_red)\n self.label_bid_volume_5.setPalette(self.color_red)\n self.label_low_limit_price.setPalette(self.color_green)\n \n self.label_high_limit_price.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_price_5.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_price_4.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_price_3.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_price_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_price_1.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_volume_5.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_volume_4.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_volume_3.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_volume_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_ask_volume_1.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_last_price.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_last_up_down.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_price_1.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_price_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_price_3.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_price_4.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_price_5.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_volume_1.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_volume_2.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_volume_3.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_volume_4.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_bid_volume_5.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n self.label_low_limit_price.setAlignment(Qt.AlignRight | Qt.AlignVCenter)\n \n self.grid_layout_quote = QGridLayout()\n self.grid_layout_quote.addWidget(self.label_high_limit, 0, 0) #\n self.grid_layout_quote.addWidget(self.label_ask_5, 1, 0)\n self.grid_layout_quote.addWidget(self.label_ask_4, 2, 0)\n self.grid_layout_quote.addWidget(self.label_ask_3, 3, 0)\n self.grid_layout_quote.addWidget(self.label_ask_2, 4, 0)\n self.grid_layout_quote.addWidget(self.label_ask_1, 5, 0)\n self.grid_layout_quote.addWidget(self.label_last, 6, 0) #\n self.grid_layout_quote.addWidget(self.label_bid_1, 7, 0)\n self.grid_layout_quote.addWidget(self.label_bid_2, 8, 0)\n self.grid_layout_quote.addWidget(self.label_bid_3, 9, 0)\n self.grid_layout_quote.addWidget(self.label_bid_4, 10, 0)\n self.grid_layout_quote.addWidget(self.label_bid_5, 11, 0)\n self.grid_layout_quote.addWidget(self.label_low_limit, 12, 0) #\n self.grid_layout_quote.addWidget(self.label_high_limit_price, 0, 1) #\n self.grid_layout_quote.addWidget(self.label_ask_price_5, 1, 1)\n self.grid_layout_quote.addWidget(self.label_ask_price_4, 2, 1)\n self.grid_layout_quote.addWidget(self.label_ask_price_3, 3, 1)\n self.grid_layout_quote.addWidget(self.label_ask_price_2, 4, 1)\n self.grid_layout_quote.addWidget(self.label_ask_price_1, 5, 1)\n self.grid_layout_quote.addWidget(self.label_last_price, 6, 1) #\n self.grid_layout_quote.addWidget(self.label_bid_price_1, 7, 1)\n self.grid_layout_quote.addWidget(self.label_bid_price_2, 8, 1)\n self.grid_layout_quote.addWidget(self.label_bid_price_3, 9, 1)\n self.grid_layout_quote.addWidget(self.label_bid_price_4, 10, 1)\n self.grid_layout_quote.addWidget(self.label_bid_price_5, 11, 1)\n self.grid_layout_quote.addWidget(self.label_low_limit_price, 12, 1) #\n self.grid_layout_quote.addWidget(self.label_ask_volume_5, 1, 2)\n self.grid_layout_quote.addWidget(self.label_ask_volume_4, 2, 2)\n self.grid_layout_quote.addWidget(self.label_ask_volume_3, 3, 2)\n self.grid_layout_quote.addWidget(self.label_ask_volume_2, 4, 2)\n self.grid_layout_quote.addWidget(self.label_ask_volume_1, 5, 2)\n self.grid_layout_quote.addWidget(self.label_last_up_down, 6, 2) #\n self.grid_layout_quote.addWidget(self.label_bid_volume_1, 7, 2)\n self.grid_layout_quote.addWidget(self.label_bid_volume_2, 8, 2)\n self.grid_layout_quote.addWidget(self.label_bid_volume_3, 9, 2)\n self.grid_layout_quote.addWidget(self.label_bid_volume_4, 10, 2)\n self.grid_layout_quote.addWidget(self.label_bid_volume_5, 11, 2)\n \n self.main_text_edit_bottom = QTextEdit(self)\n self.main_text_edit_bottom.setText(\"\")\n self.main_text_edit_bottom.setFont(QFont(\"SimSun\", 9))\n \n self.h_box_layout_1 = QHBoxLayout()\n self.h_box_layout_1.addLayout(self.v_box_layout_order)\n self.h_box_layout_1.addLayout(self.grid_layout_quote)\n \n self.h_box_layout_2 = QHBoxLayout()\n self.h_box_layout_2.setContentsMargins(-1, -1, -1, -1)\n self.h_box_layout_2.addWidget(self.main_text_edit_bottom)\n \n self.v_box_layout_mian = QVBoxLayout()\n self.v_box_layout_mian.setContentsMargins(-1, -1, -1, -1)\n self.v_box_layout_mian.addLayout(self.h_box_layout_1)\n self.v_box_layout_mian.addLayout(self.h_box_layout_2)\n \n self.setLayout(self.v_box_layout_mian)\n \n self.combo_exchange.activated[str].connect(self.OnChangeExchange)\n self.line_edit_symbol.editingFinished.connect(self.OnChangeSymbol)\n self.radio_button_buy.clicked.connect(self.OnChangeBuySell)\n self.radio_button_sell.clicked.connect(self.OnChangeBuySell)\n self.button_place_order.clicked.connect(self.OnButtonPlaceOrder)\n self.button_cancel_order.clicked.connect(self.OnButtonCancelOrder)\n\n def OnChangeExchange(self, str_exchange):\n self.symbol = \"\"\n self.exchange = \"\"\n self.line_edit_symbol.setText(\"\")\n self.line_edit_name.setText(\"\")\n self.OnChangeSymbol()\n\n def OnChangeSymbol(self):\n self.exchange = \"\"\n self.symbol = self.line_edit_symbol.text() #str(unicode(self.line_edit_symbol.text(), \"gb2312\"))\n \n self.spin_price.setValue(0)\n self.spin_volume.setValue(0)\n \n self.label_high_limit_price.setText(\"0.00\")\n self.label_ask_price_5.setText(\"0.00\")\n self.label_ask_price_4.setText(\"0.00\")\n self.label_ask_price_3.setText(\"0.00\")\n self.label_ask_price_2.setText(\"0.00\")\n self.label_ask_price_1.setText(\"0.00\")\n self.label_ask_volume_5.setText(\"0\")\n self.label_ask_volume_4.setText(\"0\")\n self.label_ask_volume_3.setText(\"0\")\n self.label_ask_volume_2.setText(\"0\")\n self.label_ask_volume_1.setText(\"0\")\n self.label_last_price.setText(\"0.00\")\n self.label_last_up_down.setText(\"0.00%\")\n self.label_bid_price_1.setText(\"0.00\")\n self.label_bid_price_2.setText(\"0.00\")\n self.label_bid_price_3.setText(\"0.00\")\n self.label_bid_price_4.setText(\"0.00\")\n self.label_bid_price_5.setText(\"0.00\")\n self.label_bid_volume_1.setText(\"0\")\n self.label_bid_volume_2.setText(\"0\")\n self.label_bid_volume_3.setText(\"0\")\n self.label_bid_volume_4.setText(\"0\")\n self.label_bid_volume_5.setText(\"0\")\n self.label_low_limit_price.setText(\"0.00\")\n\n def OnChangeBuySell(self):\n if self.radio_button_buy.isChecked():\n self.line_edit_symbol.setStyleSheet(\"color:red\")\n self.line_edit_name.setStyleSheet(\"background-color:rgb(240,240,240);color:red\")\n self.spin_price.setStyleSheet(\"color:red\")\n self.spin_volume.setStyleSheet(\"color:red\")\n self.button_place_order.setStyleSheet(\"font:bold;color:red\")\n if self.radio_button_sell.isChecked():\n self.line_edit_symbol.setStyleSheet(\"color:green\")\n self.line_edit_name.setStyleSheet(\"background-color:rgb(240,240,240);color:green\")\n self.spin_price.setStyleSheet(\"color:green\")\n self.spin_volume.setStyleSheet(\"color:green\")\n self.button_place_order.setStyleSheet(\"font:bold;color:green\")\n\n def OnUpdateQuote(self, data, price_round):\n try:\n self.exchange = data[3].decode() # 证券市场\n self.line_edit_name.setText(str(data[1].decode(\"gbk\"))) # 证券名称 #QString.fromLocal8Bit(data[1].decode(\"gbk\")) # 含中文\n self.label_ask_price_5.setText(str(round(data[13][4], price_round)))\n self.label_ask_price_4.setText(str(round(data[13][3], price_round)))\n self.label_ask_price_3.setText(str(round(data[13][2], price_round)))\n self.label_ask_price_2.setText(str(round(data[13][1], price_round)))\n self.label_ask_price_1.setText(str(round(data[13][0], price_round)))\n self.label_ask_volume_5.setText(str(data[14][4]))\n self.label_ask_volume_4.setText(str(data[14][3]))\n self.label_ask_volume_3.setText(str(data[14][2]))\n self.label_ask_volume_2.setText(str(data[14][1]))\n self.label_ask_volume_1.setText(str(data[14][0]))\n self.label_bid_price_1.setText(str(round(data[15][0], price_round)))\n self.label_bid_price_2.setText(str(round(data[15][1], price_round)))\n self.label_bid_price_3.setText(str(round(data[15][2], price_round)))\n self.label_bid_price_4.setText(str(round(data[15][3], price_round)))\n self.label_bid_price_5.setText(str(round(data[15][4], price_round)))\n self.label_bid_volume_1.setText(str(data[16][0]))\n self.label_bid_volume_2.setText(str(data[16][1]))\n self.label_bid_volume_3.setText(str(data[16][2]))\n self.label_bid_volume_4.setText(str(data[16][3]))\n self.label_bid_volume_5.setText(str(data[16][4]))\n self.label_high_limit_price.setText(str(round(data[17], price_round))) # 涨停价\n self.label_low_limit_price.setText(str(round(data[18], price_round))) # 跌停价\n self.label_last_price.setText(str(round(data[5], price_round))) # 最新价\n if data[10] > 0.0: # 昨收价\n f_last_up_down = (data[5] / data[10]) - 1.0\n self.label_last_up_down.setText((\"%.2f%%\" % (f_last_up_down * 100.0)))\n if f_last_up_down > 0.0:\n self.label_last_up_down.setPalette(self.color_red)\n elif f_last_up_down < 0.0:\n self.label_last_up_down.setPalette(self.color_green)\n else:\n self.label_last_up_down.setPalette(self.color_black)\n else:\n self.label_last_up_down.setText(\"0.00%\")\n except Exception as e:\n self.log_text = \"%s：函数 OnUpdateQuote 异常！%s\" % (self.strategy, e)\n self.logger.SendMessage(\"E\", 4, self.log_cate, self.log_text, \"M\")\n\n def OnButtonPlaceOrder(self):\n if self.trader != None:\n if self.trader.IsTraderReady() == False:\n self.logger.SendMessage(\"E\", 4, self.log_cate, \"交易服务尚未开启！\", \"M\")\n else:\n if self.line_edit_symbol.text() == \"\":\n QMessageBox.warning(self, \"提示\", \"证券代码为空！\", QMessageBox.Ok)\n return\n str_symbol = self.line_edit_symbol.text() #str(unicode(self.line_edit_symbol.text(), \"gb2312\"))\n str_exchange = \"\"\n if self.combo_exchange.currentText() == define.DEF_EXCHANGE_STOCK_SH:\n str_exchange = \"SH\"\n elif self.combo_exchange.currentText() == define.DEF_EXCHANGE_STOCK_SZ:\n str_exchange = \"SZ\"\n f_price = self.spin_price.value()\n n_amount = self.spin_volume.value()\n n_entr_type = 0\n if self.combo_entr_type.currentText() == define.DEF_PRICE_TYPE_STOCK_LIMIT:\n n_entr_type = 1\n elif self.combo_entr_type.currentText() == define.DEF_PRICE_TYPE_STOCK_MARKET:\n n_entr_type = 2\n n_exch_side = 0\n if self.radio_button_buy.isChecked():\n n_exch_side = 1\n elif self.radio_button_sell.isChecked():\n n_exch_side = 2\n order = self.trader.Order(symbol = str_symbol, exchange = str_exchange, price = f_price, amount = n_amount, entr_type = n_entr_type, exch_side = n_exch_side)\n task_place = self.trader.PlaceOrder(order, self.strategy)\n QMessageBox.information(self, \"提示\", \"委托下单提交完成。\", QMessageBox.Ok)\n else:\n self.logger.SendMessage(\"E\", 4, self.log_cate, \"交易服务尚未获取！\", \"M\")\n\n def OnButtonCancelOrder(self):\n if self.trader != None:\n if self.trader.IsTraderReady() == False:\n self.logger.SendMessage(\"E\", 4, self.log_cate, \"交易服务尚未开启！\", \"M\")\n else:\n if self.line_edit_order_id.text() == \"\":\n QMessageBox.warning(self, \"提示\", \"撤单委托编号为空！\", QMessageBox.Ok)\n return\n order = self.trader.Order()\n order.order_id = int(self.line_edit_order_id.text()) # int\n task_cancel = self.trader.CancelOrder(order, self.strategy)\n QMessageBox.information(self, \"提示\", \"委托撤单提交完成。\", QMessageBox.Ok)\n else:\n self.logger.SendMessage(\"E\", 4, self.log_cate, \"交易服务尚未获取！\", \"M\")\n\n####################################################################################################\n\nif __name__ == \"__main__\":\n import sys\n app = QApplication(sys.argv)\n panel = Panel(\"Strategy_Trader_STK_APE\")\n panel.show()\n sys.exit(app.exec_())\n","repo_name":"2bds/quantx","sub_path":"src/main/face/panel_trader_stk_ape.py","file_name":"panel_trader_stk_ape.py","file_ext":"py","file_size_in_byte":30485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73725454954","text":"class Solution:\n def twoSum(self, numbers: List[int], target: int) -> List[int]:\n # resultMap={}\n # for index,i in enumerate(numbers):\n # if i in resultMap.keys():\n # return [resultMap[i],index+1]\n # else:\n # resultMap[target-i]=index+1\n left,right=0,len(numbers)-1\n while lefttarget:\n right-=1\n elif newSum',\n '#include ',\n '#define LIBVMI_EXTRA_GLIB',\n '#include ',\n '#include '\n]\n\n\ndef get_cflags(package):\n includes = pkgconfig.cflags(package)\n if not includes:\n raise RuntimeError('Unable to find pkgconfig cflags'\n ' for {}'.format(package))\n includes = includes.replace('-I', '').split(' ')\n return includes\n\n\ndef get_libs(package):\n libs = pkgconfig.libs(package)\n if not libs:\n raise RuntimeError('Unable to find pkgconfig libs'\n ' for {}'.format(package))\n libs = libs.replace('-l', '').split(' ')\n return libs\n\n\ndef check_header(header):\n inc_path_list = [\n '/usr/include',\n '/usr/local/include'\n ]\n for inc_path in inc_path_list:\n if os.path.exists(inc_path + '/' + header):\n return True\n return False\n\n\n# glib cflags and libs\nglib_includes = get_cflags('glib-2.0')\nglib_libs = get_libs('glib-2.0')\n\n# get libvmi libs\nlibvmi_libs = get_libs('libvmi')\n\nincludes = []\nincludes.extend(glib_includes)\n\nlibs = []\nlibs.extend(libvmi_libs)\nlibs.extend(glib_libs)\n\nffi = FFI()\n\n# checking for events.h\nif check_header('libvmi/events.h'):\n VMI_SOURCES.append('#include ')\n CDEF_HEADERS.append('events_cdef.h')\n\nc_header_source = '\\n'.join(VMI_SOURCES)\n\n# set source\nffi.set_source(\"_libvmi\", c_header_source,\n libraries=libs, include_dirs=includes)\n\n\nscript_dir = os.path.dirname(os.path.realpath(__file__))\n# we read our C definitions from an external file\n# easier to maintain + C syntax highlighting\ncdef_content = \"\"\nfor cdef_path in CDEF_HEADERS:\n with open(os.path.join(script_dir, cdef_path)) as cdef_file:\n cdef_content += cdef_file.read()\n # add newline for next file\n cdef_content += '\\n'\nffi.cdef(cdef_content)\n\n\nif __name__ == \"__main__\":\n ffi.compile(verbose=True)\n","repo_name":"libvmi/python","sub_path":"libvmi/libvmi_build.py","file_name":"libvmi_build.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"72"}
+{"seq_id":"9266696832","text":"# baekjoon 1978 : 소수 찾기\n# solved by JY\n# DATE : 2022.02.09\n# 에라토스테네스의 체\n# 구현\n\nfrom sys import stdin\ninput = stdin.readline\n\nn = int(input())\nans = 0\n\ndef check(num):\n i = 2\n while i*i <= num:\n if num % i == 0: return False\n i += 1\n return True\n\nfor num in list(map(int, input().split())):\n if num != 1 and check(num):\n ans += 1\n\nprint(ans)","repo_name":"Jiyooung/ALGORITHM","sub_path":"baekjoon/2022년/2022-02/JY_B1978_re.py","file_name":"JY_B1978_re.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"29576269455","text":"import getpass\nimport json\nimport networkx as nx\nimport pathlib\n\nfrom datetime import datetime\nfrom typing import Optional, List\nfrom ..common.task import Task\nfrom ..version import __version__\n\nfrom ..wfchef.utils import create_graph\nimport tempfile\n\n\nclass Workflow(nx.DiGraph):\n \"\"\"\n Representation of a workflow. The workflow representation is an extension of the\n `NetworkX DiGraph class `_.\n\n :param name: Workflow name.\n :type name: str\n :param description: Workflow instance description.\n :type description: Optional[str]\n :param wms_name: WMS name.\n :type wms_name: Optional[str]\n :param wms_version: WMS version.\n :type wms_version: Optional[str]\n :param wms_url: URL for the WMS website.\n :type wms_url: Optional[str]\n :param executed_at: Workflow start timestamp in the ISO 8601 format.\n :type executed_at: Optional[str]\n :param makespan: Workflow makespan in seconds.\n :type makespan: Optional[int]\n \"\"\"\n\n def __init__(self,\n name: Optional[str] = \"workflow\",\n description: Optional[str] = None,\n wms_name: Optional[str] = None,\n wms_version: Optional[str] = None,\n wms_url: Optional[str] = None,\n executed_at: Optional[str] = None,\n makespan: Optional[int] = 0.0\n ) -> None:\n \"\"\"Create an object of a workflow representation.\"\"\"\n self.description: Optional[\n str] = description if description else \"Instance generated with WfCommons - https://wfcommons.org\"\n self.created_at: str = str(datetime.now().astimezone().isoformat())\n self.schema_version: str = \"1.4\"\n self.wms_name: Optional[str] = \"WfCommons\" if not wms_name else wms_name\n self.wms_version: Optional[str] = str(__version__) if not wms_version else wms_version\n self.wms_url: Optional[str] = f\"https://docs.wfcommons.org/en/v{__version__}/\" if not wms_url else wms_url\n self.executed_at: Optional[str] = datetime.now().astimezone().isoformat() if not executed_at else executed_at\n self.makespan: Optional[int] = makespan\n self.tasks = {}\n self.tasks_parents = {}\n self.tasks_children = {}\n super().__init__(name=name, makespan=self.makespan, executedat=self.executed_at)\n\n def add_task(self, task: Task) -> None:\n \"\"\"\n Add a Task to the workflow.\n\n :param task: A Task object.\n :type task: Task\n \"\"\"\n self.tasks[task.name] = task\n self.tasks_parents.setdefault(task.name, set())\n self.tasks_children.setdefault(task.name, set())\n self.add_node(task.name, task=task)\n\n def add_dependency(self, parent: str, child: str) -> None:\n \"\"\"\n Add a dependency between tasks.\n\n :param parent: Parent task name.\n :type parent: str\n :param child: Child task name.\n :type child: str\n \"\"\"\n self.tasks_parents[child].add(parent)\n self.tasks_children[parent].add(child)\n self.add_edge(parent, child, weight=0)\n\n def write_json(self, json_file_path: Optional[pathlib.Path] = None) -> None:\n \"\"\"\n Write a JSON file of the workflow instance.\n\n :param json_file_path: JSON output file name.\n :type json_file_path: Optional[pathlib.Path]\n \"\"\"\n workflow_machines = []\n machines_list = []\n workflow_tasks = []\n\n workflow_json = {\n \"name\": self.name,\n \"description\": self.description,\n \"createdAt\": self.created_at,\n \"schemaVersion\": self.schema_version,\n \"author\": {\n \"name\": str(getpass.getuser()),\n \"email\": \"support@wfcommons.org\"\n },\n \"wms\": {\n \"name\": self.wms_name,\n \"version\": self.wms_version,\n \"url\": self.wms_url\n },\n \"workflow\": {\n \"executedAt\": self.executed_at,\n \"makespanInSeconds\": self.makespan,\n \"tasks\": workflow_tasks\n }\n }\n\n # generate tasks parents and children\n tasks_dependencies = {}\n for edge in self.edges:\n for task_name in edge:\n if task_name not in tasks_dependencies:\n tasks_dependencies[task_name] = {\"parents\": [], \"children\": []}\n tasks_dependencies[edge[0]][\"children\"].append(edge[1])\n tasks_dependencies[edge[1]][\"parents\"].append(edge[0])\n\n # add tasks to the workflow json object\n for node in self.nodes:\n task: Task = self.nodes[node][\"task\"]\n task_obj = task.as_dict()\n\n # manage task dependencies\n if task.name in tasks_dependencies:\n task_obj[\"parents\"] = tasks_dependencies[task.name][\"parents\"]\n task_obj[\"children\"] = tasks_dependencies[task.name][\"children\"]\n\n workflow_tasks.append(task_obj)\n\n # add machines to the workflow json object\n if task.machine and task.machine.name not in machines_list:\n machines_list.append(task.machine.name)\n workflow_machines.append(task.machine.as_dict())\n\n if workflow_machines:\n workflow_json[\"workflow\"][\"machines\"] = workflow_machines\n\n # write to file\n if not json_file_path:\n json_file_path = pathlib.Path(f\"{self.name.lower()}.json\")\n with open(json_file_path, \"w\") as outfile:\n outfile.write(json.dumps(workflow_json, indent=4))\n \n self.workflow_json = workflow_json\n\n def write_dot(self, dot_file_path: Optional[pathlib.Path] = None) -> None:\n \"\"\"\n Write a dot file of the workflow instance.\n\n :param dot_file_path: DOT output file name.\n :type dot_file_path: Optional[pathlib.Path]\n \"\"\"\n if not dot_file_path:\n dot_file_path = pathlib.Path(f\"{self.name.lower()}.dot\")\n nx.nx_agraph.write_dot(self, dot_file_path)\n\n def to_nx_digraph(self) -> nx.DiGraph:\n with tempfile.NamedTemporaryFile() as temp:\n self.write_json(pathlib.Path(temp.name))\n return create_graph(pathlib.Path(temp.name))\n\n def roots(self) -> List[Task]:\n return [n for n,d in self.in_degree() if d==0]\n\n def leaves(self) -> List[Task]:\n return [n for n,d in self.out_degree() if d==0]\n","repo_name":"wfcommons/wfcommons","sub_path":"wfcommons/common/workflow.py","file_name":"workflow.py","file_ext":"py","file_size_in_byte":6531,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"72"}
+{"seq_id":"3348508622","text":"class Solution:\n def buildArray(self, target: List[int], n: int) -> List[str]:\n res = []\n build_arr = []\n for i in range(n):\n if build_arr == target:\n break\n if i+1 in target:\n res.append(\"Push\")\n build_arr.append(i+1)\n else:\n res.append(\"Push\")\n res.append(\"Pop\")\n\n return res\n","repo_name":"sasankyadavalli/leetcode","sub_path":"buildArray_1441.py","file_name":"buildArray_1441.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"27005363739","text":"from tkinter import *\nfrom tkinter.ttk import Combobox\nfrom queue import Queue\nfrom tkinter import messagebox as mbox\nimport xlwt \nfrom xlwt import Workbook \nfrom paging_algo import lru,opr,fifo\nimport csv\nimport os\n\nex_qu=[]\nex_size=[]\nex_page_faults=[]\nex_ans=[]\nex_str=[]\nex_algo=[]\nex_que_size=[]\n\ndef on_closing():\n if mbox.askokcancel(\"Quit\", \"Do you want to quit?\"):\n with open('Paging.csv','a+',newline='') as file:\n writer = csv.writer(file,delimiter = ',')\n main_list = []\n for i in range(len(ex_algo)):\n list = []\n list.append(ex_algo[i])\n list.append(ex_size[i])\n list.append(ex_que_size[i])\n list.append(str(ex_qu[i]))\n list.append(ex_page_faults[i])\n list.append(ex_ans[i])\n list.append(ex_str[i])\n main_list.append(list)\n writer.writerows(main_list)\n paging.destroy()\n\n\ndef get_values():\n mem_size = \"\"\n algo_no = \"\"\n str2 = \"\"\n temp_str=\"\"\n process_string = \"\"\n final = \"\"\n mem_size = var1.get()\n algo_no = var2.get()\n process_string = var3.get()\n temp_str=process_string\n if process_string == \"\":\n mbox.showerror('Error', 'Please Enter Required Information!!')\n elif process_string.isdigit():\n if mem_size == \"\" or algo_no == \"\":\n mbox.showerror('Error', 'Please Enter Required Information!!')\n else:\n process_string = list(process_string)\n for i in range(0, len(process_string)):\n process_string[i] = int(process_string[i])\n if algo_no == \"fifo\":\n ans, str2 = fifo(process_string, len(process_string), int(mem_size))\n elif algo_no == \"lru\":\n ans, str2 = lru(process_string, len(process_string), int(mem_size))\n elif algo_no == \"opr\":\n ans, str2 = opr(process_string, len(process_string), int(mem_size))\n ex_final=str2\n temp = min(125, len(str2))\n for i in range(0, temp):\n final += str2[i]\n ans1 = len(process_string) - ans\n lbl1.configure(text=ans)\n lbl2.configure(text=ans1)\n lbl3.configure(text=final)\n if len(ex_size)!=0 and mem_size==ex_size[-1] and temp_str==ex_qu[-1] and algo_no.upper()==ex_algo[-1] :\n \tt=1\n else :\n\t ex_size.append(mem_size)\n\t ex_qu.append(temp_str)\n\t ex_que_size.append(len(temp_str))\n\t ex_algo.append(algo_no.upper())\n\t ex_page_faults.append(ans)\n\t ex_ans.append(ans1)\n\t ex_str.append(ex_final)\n\n else:\n if mem_size == \"\" or algo_no == \"\":\n mbox.showerror('Error', 'Please Enter Required Information!!')\n else:\n mbox.showinfo('Error', 'Process String Should Contain Numbers only!!')\n\n\ndef startAlgo():\n global var1\n global var2\n global var3\n global lbl1\n global lbl2\n global lbl3\n label = Label(\n paging,\n text=\"Page Replacement Algorithms\",\n font=(\"Arial Bold\", 50),\n width=100,\n bg=\"Bisque\",\n fg=\"darkred\",\n pady=30)\n label.pack()\n lb1 = Label(\n paging,\n text=\"Enter Frame Size : \",\n font=(\"Arial Bold\", 20),\n bg=\"linen\",\n fg=\"darkred\")\n lb2 = Label(paging,\n text=\"Enter Algorithm : \",\n font=(\"Arial Bold\", 20),\n bg=\"linen\",\n fg=\"darkred\")\n lb3 = Label(paging,\n text=\"Enter Process String : \",\n font=(\"Arial Bold\", 20),\n bg=\"linen\",\n fg=\"darkred\")\n lb1.place(x=50, y=150)\n lb2.place(x=50, y=200)\n lb3.place(x=50, y=360)\n\n var1 = StringVar()\n data = (\n \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\")\n ms = Combobox(paging, values=data, textvariable=var1, font=(\"Arial Bold\", 20), width=19, state=\"readonly\")\n ms.place(x=370, y=150)\n\n var2 = StringVar()\n var2.set(\"First In First Out\")\n fifo = Radiobutton(paging, text=\"First In First Out\", variable=var2, value=\"fifo\", bg=\"linen\", fg=\"darkred\",\n font=(\"Arial Bold\", 15))\n lru = Radiobutton(paging, text=\"Least Recently Used\", variable=var2, value=\"lru\", bg=\"linen\", fg=\"darkred\",\n font=(\"Arial Bold\", 15))\n opt = Radiobutton(paging, text=\"Optimal Page Replacement\", variable=var2, value=\"opr\", bg=\"linen\", fg=\"darkred\",\n font=(\"Arial Bold\", 15))\n fifo.place(x=370, y=200)\n lru.place(x=370, y=250)\n opt.place(x=370, y=300)\n\n var3 = StringVar()\n al = Entry(paging, textvariable=var3, width=20, font=(\"Arial Bold\", 20)).place(x=370, y=360)\n\n btn = Button(paging,\n text=\"Calculate\",\n font=(\"Arial Bold\", 18),\n bg=\"darkred\",\n fg=\"Bisque\",\n cursor=\"hand2\",\n command=get_values)\n btn.pack(pady=(270, 20))\n\n t1 = Label(paging,\n text=\"Page Fault : \",\n font=(\"Arial Bold\", 22),\n bg=\"Bisque\",\n width=100,\n height=1,\n fg=\"darkred\",\n pady=7\n )\n t1.pack()\n t2 = Label(paging,\n text=\"Page Hit : \",\n font=(\"Arial Bold\", 22),\n bg=\"Bisque\",\n width=100,\n height=1,\n fg=\"darkred\",\n pady=7\n )\n t2.pack()\n lbl1 = Label(paging,\n text=\"0\",\n font=(\"Arial Bold\", 22),\n bg=\"Bisque\",\n fg=\"darkred\")\n lbl1.place(x=620, y=490)\n lbl2 = Label(paging,\n text=\"0\",\n font=(\"Arial Bold\", 22),\n bg=\"Bisque\",\n fg=\"darkred\")\n lbl2.place(x=620, y=540)\n lbl3 = Label(paging,\n text=\"\",\n font=(\"Arial Bold\", 15),\n bg=\"Bisque\",\n fg=\"darkred\",\n width=85,\n height=1,\n pady=10\n )\n lbl3.place(x=0, y=585)\n\n\ndef destroyHome():\n label.destroy()\n cal_instruction.destroy()\n btn_cal.destroy()\n about.destroy()\n startAlgo()\n\n\npaging = Tk()\npaging.geometry(\"1000x650\")\npaging.configure(bg=\"linen\")\npaging.resizable(0, 0)\n# paging.iconbitmap('i1.ico')\npaging.title(\"Page Replacement Algorithms\")\n\n\nif not os.path.exists(\"Paging.csv\"):\n ex_algo.append('Algorithm Used')\n ex_size.append('Frame Size')\n ex_que_size.append('String Length')\n ex_qu.append('Process String')\n ex_page_faults.append('Page Faults')\n ex_ans.append('Page Hit')\n ex_str.append('String')\nlabel = Label(\n paging,\n text=\"Page Replacement Algorithms\",\n font=(\"Arial Bold\", 50),\n width=100,\n bg=\"Bisque\",\n fg=\"darkred\",\n pady=30\n)\nlabel.pack()\n\ncal_instruction = Label(\n paging,\n text=\"Read How To Calculate Page Replacement Algorithms\\n & \\nClick Here To Calculate It.\",\n bg=\"linen\",\n font=(\"Arial Bold\", 20),\n justify=\"center\",\n pady=30\n)\ncal_instruction.pack()\nbtn_cal = Button(\n paging,\n text=\"Calculate\",\n font=(\"Arial Bold\", 35),\n command=destroyHome,\n width=10,\n height=1,\n bg=\"darkred\",\n fg=\"Bisque\",\n cursor=\"hand2\")\nbtn_cal.pack(pady=(10, 20))\n\nabout = Label(\n paging,\n text=\"Enter Paging Replacement Algorithm Which you want Calculate, \\nEnter Process String and Size of Main Memory.\\nAnd then Click on Calculate Button.\",\n width=100,\n height=50,\n font=(\"Arial Bold\", 20),\n bg=\"Bisque\",\n fg=\"darkred\",\n pady=30\n)\nabout.pack()\n\npaging.protocol(\"WM_DELETE_WINDOW\", on_closing)\npaging.mainloop()","repo_name":"YashThakkar27/Paging-Algorithm","sub_path":"Paging.py","file_name":"Paging.py","file_ext":"py","file_size_in_byte":7855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"33712386554","text":"# def season(hemisferio, dia, mes):\r\n# if hemisferio == 0 and mes >= 6 and mes <= 9:\r\n# print(\"VERAO\")\r\n# elif hemisferio == 0 and dia > 20 and mes >= 9 and mes <= 12:\r\n# print(\"OUTONO\")\r\n# elif hemisferio == 0 and dia > 20 and mes >= 12 and mes <= 3:\r\n# print(\"INVERNO\")\r\n# elif hemisferio == 0 and dia > 20 and mes >= 3 and mes <= 6:\r\n# print(\"PRIMAVERA\")\r\n\r\n# elif hemisferio == 1 and dia > 20 and mes >= 12 and mes <= 3:\r\n# print(\"VERAO\")\r\n# elif hemisferio == 1 and dia > 20 and mes >= 3 and mes <= 6:\r\n# print(\"OUTONO\")\r\n# elif hemisferio == 1 and dia > 20 and mes >= 6 and mes <= 9:\r\n# print(\"INVERNO\")\r\n# elif hemisferio == 1 and dia > 20 and mes >= 9 and mes <= 12:\r\n# print(\"PRIMAVERA\")\r\n\r\n# #\r\n# #\r\n# #\r\n\r\n# hemisferio = int(input())\r\n# dia = int(input())\r\n# mes = int(input())\r\n\r\n# season(hemisferio, dia, mes)\r\n\r\n\r\ndef EstacaoAno(dia, mes):\r\n if mes in (1, 2):\r\n return 'VERAO'\r\n elif mes == 3:\r\n if dia < 21:\r\n return 'VERAO'\r\n else:\r\n return 'OUTONO'\r\n elif mes in (4, 5):\r\n return 'OUTONO'\r\n elif mes == 6:\r\n if dia < 21:\r\n return 'OUTONO'\r\n else:\r\n return 'INVERNO'\r\n elif mes in (7, 8):\r\n return 'INVERNO'\r\n elif mes == 9:\r\n if dia < 21:\r\n return 'INVERNO'\r\n else:\r\n return 'PRIMAVERA'\r\n elif mes in (10, 11):\r\n return 'PRIMAVERA'\r\n elif mes == 12:\r\n if dia < 21:\r\n return 'PRIMAVERA'\r\n else:\r\n return 'VERAO'","repo_name":"GuuiBeta/python_programs","sub_path":"Python The Huxley/Questionário 5/Ex5-06.py","file_name":"Ex5-06.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"19386974569","text":"import sys\nfrom math import pi\nr = float(sys.argv[1])\na = 2 * r\nb = 2 * pi * r\nc = pi * (r**2)\ncadena = input(\"eliga una opción 1-4 \")\ncadena = float(cadena)\nif cadena == 1:\n print(\"El diámetro de la circunferencia es\",a)\nif cadena == 2:\n print(\"El perímetro de la cicunferencia es\",b)\nif cadena == 3:\n print(\"El área del circulo es\",c)\nif cadena == 4:\n print(\"saliendo\")\nif cadena != 1 and cadena != 2 and cadena != 3 and cadena != 4:\n print(\"Opción no válida\")\n","repo_name":"nefmen/Programas-python-ejercicios","sub_path":"Clase/ejercicio6.py","file_name":"ejercicio6.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"37609163688","text":"import unittest\nfrom os.path import join\n\nimport viscojapan as vj\n\n__author__ = 'zy'\n\n\nclass Test_slip_overviewer(vj.MyTestCase):\n def setUp(self):\n self.this_script = __file__\n super().setUp()\n\n def test_plot(self):\n res_file = '/home/zy/workspace/viscojapan/tests/share/nrough_05_naslip_11.h5'\n reader = vj.inv.ResultFileReader(res_file)\n slip = reader.get_slip()\n\n plotter = vj.slip.plot.plot_slip_overview(\n slip,\n join(self.outs_dir, 'slip_history.pdf')\n )\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"zy31415/viscojapan","sub_path":"tests/lib/viscojapan/slip/plot/test_plot_slip_overview.py","file_name":"test_plot_slip_overview.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"72"}
+{"seq_id":"33475023555","text":"pattern_mal_cargo = {'Количество паллет (шт)': [4, 6],\n 'Габариты': {'Длинa кузова(м)': [2, 4], 'Ширина кузова(м)':[1.8,2],'Высота кузова(м)':[1.8,2.4]},\n 'Масса(т)': [],\n 'Рабочий объем двигателя(л)': [],\n 'Максимальная скорость(км/ч)': []}\n\npattern_sr_cargo = {'Количество паллет (шт)': [12, 18],\n 'Габариты': {'Длинa кузова(м)': [3.5, 7.2], 'Ширина кузова(м)':[1.9, 2.45],'Высота кузова(м)':[1.9, 2.7]},\n 'Масса(т)': [],\n 'Рабочий объем двигателя(л)': [],\n 'Максимальная скорость(км/ч)': []}\n\npattern_bol_cargo = {'Количество паллет (шт)': [20, 33],\n 'Габариты': {'Длинa кузова(м)': [5.8, 8], 'Ширина кузова(м)':[2.3, 2.45],'Высота кузова(м)':[2.2, 2.7]},\n 'Масса(т)': [],\n 'Рабочий объем двигателя(л)': [],\n 'Максимальная скорость(км/ч)': []}\n\npattern_ochbol_cargo = {'Количество паллет (шт)': [33, 50],\n 'Габариты': {'Длинa кузова(м)': [13.6, 16], 'Ширина кузова(м)':[2.4, 2.5],'Высота кузова(м)':[2.5, 3.1]},\n 'Масса(т)': [],\n 'Рабочий объем двигателя(л)': [],\n 'Максимальная скорость(км/ч)': []}\n\npattern_cargo = {'Количество мест': [2,3],\n 'Грузоподъемность': {'малый (0.5-2т)': pattern_mal_cargo,\n 'средний (2-5т)': pattern_sr_cargo,\n 'большой(5-16т)': pattern_bol_cargo,\n 'сверхтяжелый(от 16т)': pattern_ochbol_cargo}}\n\n\npattern_bus = {'Количество мест': [40, 150],\n 'Габариты': {'Длинa кузова(м)': [4.5,20], 'Ширина кузова(м)':[1.5, 2.55],'Расстояние от подвески до земли(см)':[15, 20]},\n 'Масса(т)': [10, 28],\n 'Рабочий объем двигателя(л)': [2, 4.43],\n 'Максимальная скорость(км/ч)': [60, 120]}\npattern_pas = {'Количество мест': [2, 8],\n 'Габариты': {'Длинa кузова(м)': [3.8,6.1], 'Ширина кузова(м)':[1.5, 2.55],'Расстояние от подвески до земли(см)':[15, 20]},\n 'Масса(т)': [1, 2.5],\n 'Рабочий объем двигателя(л)': [1, 15],\n 'Максимальная скорость(км/ч)': [60, 300]}\npattern_passenger = {'Тип пассажирского транспорта':{'автобус': pattern_bus,'легковой': pattern_pas}}\n\n\npattern_cargo_passenger = {'Количество мест': [5, 12],\n 'Габариты': {'Длинa кузова(м)': [2, 8], 'Ширина кузова(м)':[1.8, 2.5],'Расстояние от подвески до земли(см)':[15, 20]},\n 'Масса(т)': [4, 9],\n 'Рабочий объем двигателя(л)': [2,12],\n 'Максимальная скорость(км/ч)': [60, 95]}\n\n\npattern_special = {'Количество мест': [],\n 'Габариты': {'Длинa кузова(м)': [], 'Ширина кузова(м)':[],'Расстояние от подвески до земли(см)':[]},\n 'Масса(т)': [],\n 'Рабочий объем двигателя(л)': [],\n 'Максимальная скорость(км/ч)': []}\n","repo_name":"lizsimenia/machine-accounting","sub_path":"functional/pattern_cars.py","file_name":"pattern_cars.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"5686390128","text":"# -*- coding: utf-8 -*-\nimport sys\nimport json\nimport argparse\n\nimport nicfit\n\nfrom .. import Identity, IdentityChain\nfrom .utils import prompt\nfrom ..common import thumbprint, newJwk, jwkIsPrivate\n\n\n@nicfit.command.register\nclass identity(nicfit.Command):\n HELP = \"Identity and stuffs\"\n\n def _initArgParser(self, parser):\n parser.add_argument(\"-i\", \"--identity\", default=None,\n type=argparse.FileType('r'),\n help=\"File containing an Identity in JSON format.\")\n parser.add_argument(\"-k\", \"--keyfile\", default=None,\n type=argparse.FileType('r'),\n help=\"File containing a private JWK.\")\n parser.add_argument(\"--iss\", default=None,\n help=\"Identity issuer.\")\n\n def _run(self):\n if self.args.identity:\n ident = Identity.fromJson(json.loads(self.args.identity.read()))\n else:\n if self.args.keyfile:\n try:\n jwk = json.loads(self.args.keyfile.read())\n key = newJwk(**jwk)\n if not jwkIsPrivate(key):\n raise ValueError(\n \"Key file does not contain a private key\")\n except Exception as ex:\n print(\"Error loading key: \" + str(ex), file=sys.stderr)\n return 1\n key._params[\"kid\"] = thumbprint(key)\n else:\n key = Identity.generateKey()\n\n iss = self.args.iss or prompt(\"iss? \")\n ident = Identity(iss, key)\n\n ident.idchain = IdentityChain.fromIdentity(ident,\n ident.acct).serialize()\n print(json.dumps(ident.toJson(private=True), indent=2, sort_keys=True))\n\n idchain = IdentityChain.deserialize(ident.idchain)\n print(\"\\n## IdentityChain ##:\\n\" + str(idchain))\n","repo_name":"nicfit/Clique","sub_path":"clique/app/identity.py","file_name":"identity.py","file_ext":"py","file_size_in_byte":1960,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"5767031042","text":"# Hackaton Challange 2\nfrom random import choice\nimport time\nfilename = \"Dictionary.txt\" #Check that the name is tha same, mine might be different\nfile = open(filename,\"r\")\nraw = file.read()\ndictionaryToCompare={}\nplayer_answers = []\nkeyToAdd = \"\"\npoints = 0\n#load dictionary\nfor k in range(len(raw)):\n\tif raw[k] != raw[2]: #raw[2] is the character that devides my words\n\t\tkeyToAdd += raw[k]\n\telse:\n\t\tdictionaryToCompare[keyToAdd] = 0\n\t\tkeyToAdd = \"\"\n\t\t#print(dictionaryToCompare)\n\n\n#Dices definition\n\ndices = [\n\t\t\t['A','A','E','E','G','N'],\n\t\t\t['E','L','R','T','T','Y'],\n\t\t\t['A','O','O','T','T','W'],\n\t\t\t['A','B','B','J','O','O'],\n\t\t\t['E','H','R','T','V','W'],\n\t\t\t['C','I','M','O','T','U'],\n\t\t\t['D','I','S','T','T','Y'],\n\t\t\t['E','I','O','S','S','T'],\n\t\t\t['D','E','L','R','V','Y'],\n\t\t\t['A','C','H','O','P','S'],\n\t\t\t['H','I','M','N','Q','U'],\n\t\t\t['E','E','I','N','S','U'],\n\t\t\t['E','E','G','H','N','W'],\n\t\t\t['A','F','F','K','P','S'],\n\t\t\t['H','L','N','N','R','Z'],\n\t\t\t['D','E','I','L','R','X']]\n\n# Roll dices\nboard=[\n\t\t[\" \",\" \",\" \",\" \"],\n\t\t[\" \",\" \",\" \",\" \"],\n\t\t[\" \",\" \",\" \",\" \"],\n\t\t[\" \",\" \",\" \",\" \"]\n\t\t]\n\ndef rollDices(board):\n\tchoices_dices=[i for i in range(0,16)] #creates an array of numbers until 15\n\tchoices_face =[j for j in range(0,6)]\n\tfor i in range (0,4):\n\t\tfor j in range(0,4):\n\t\t\tdice_index= choice(choices_dices)\n\t\t\tdice_face = choice(choices_face)\n\t\t\tboard[i][j] = dices[dice_index][dice_face]\n\t\t\tchoices_dices.remove(dice_index) # this dice was already rolled\n\t\t\t\n\ndef print_board(a_board):\n\trow_toprint = \" \"\n\tfor i in range(len(a_board)):\n\t\tfor j in range(len(a_board[1])):\n\t\t\trow_toprint += (a_board[i][j]) + \" \"\n\t\tprint (row_toprint)\n\t\trow_toprint = \" \"\n\n\nrollDices(board)\nprint_board(board)\n\ntime_end = time.time() + (60) #Change the time range\n\n\nwhile time.time() < time_end:\n\tnew_word = input(\"Type a word: \")\n\tplayer_answers.append(new_word)\n\ndef lookForWord(thisWord,this_letter,thisBoard,x,y):\n\t#print(thisBoard)\n\t#print(thisWord,this_letter,x,y)\n\tif (x< len(thisBoard) and y< len(thisBoard)):\n\t\tif(x>= 0 and y >= 0):\n\t\t\tif (thisBoard[x][y] != thisWord[this_letter]):\n\t\t\t\treturn False # no estoy segura de esto\n\t\telse:\n\t\t\treturn False\n\telse:\n\t\treturn False\n\tu = True\n\tif(this_letter= 5):\n\t\t\t\tpoints +=3\n\t\t\t\tprint(player_answers[word] + \" is correct, you get 3 point for this word\")\n\nprint (\"your score is: \" + str(points) + \" points.\")\n\n\n\n\n","repo_name":"maguileracanon/SummerOfCode","sub_path":"Week2/Hackaton.py","file_name":"Hackaton.py","file_ext":"py","file_size_in_byte":4076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"866208898","text":"from pathlib import Path\nimport os\n\n# Build paths inside the project like this: BASE_DIR / 'subdir'.\nBASE_DIR = Path(__file__).resolve().parent.parent\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = 'dq_s@_7zbz4k4ws=_(&_asg#*!_gsyrl%(lr!ky8rsh$&1n6ew'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\nALLOWED_HOSTS = ['0.0.0.0', 'localhost', '127.0.0.1']\n\n# Application definition\nDEFAULT_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django.contrib.sites',\n]\n\nPROJECT_APPS = [\n 'accounts',\n 'blog',\n]\n\nCOMMON_APPS = [\n # allauth\n 'allauth',\n # 'allauth.account',\n 'allauth.socialaccount',\n\n 'sass_processor',\n\n # provider\n 'allauth.socialaccount.providers.kakao',\n]\n\nSASS_PROCESSOR_ENABLED = True\nSASS_PROCESSOR_ROOT = os.path.join(BASE_DIR, 'static')\nSASS_PRECISION = 8\n\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n 'sass_processor.finders.CssFinder',\n)\n\nINSTALLED_APPS = DEFAULT_APPS + PROJECT_APPS + COMMON_APPS\n\nMIDDLEWARE = [\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n]\n\nROOT_URLCONF = 'sns.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [os.path.join(BASE_DIR, 'sns/templates')],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'sns.wsgi.application'\n\n# Database\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': BASE_DIR / 'db.sqlite3',\n }\n}\n\n# Password validation\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\n# Internationalization\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'Asia/Seoul'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\n# Static files (CSS, JavaScript, Images)\nSTATIC_URL = '/static/'\n\nSITE_ID = 1\n\nAUTH_USER_MODEL = 'accounts.User'\n\nAUTHENTICATION_BACKENDS = [\n # Needed to login by username in Django admin, regardless of `allauth`\n 'django.contrib.auth.backends.ModelBackend',\n\n # `allauth` specific authentication methods, such as login by e-mail\n 'allauth.account.auth_backends.AuthenticationBackend',\n]\n\n# static\nSTATIC_URL = '/static/'\nSTATIC_ROOT = os.path.join(BASE_DIR, 'static')\n\n# media\n# MEDIA_ROOT = os.path.join(Path(__file__).resolve().parent.parent, \"uploads\")\nMEDIA_ROOT = os.path.join(BASE_DIR, 'media')\nMEDIA_URL = '/media/'\n\n\nSOCIALACCOUNT_PROVIDERS = {\n 'kakao': {\n 'APP': {\n 'client_id': 'd01f306c90eda69601dda10cdf62631e',\n 'secret': '492230',\n 'key': '',\n }\n }\n}\n","repo_name":"oereo/SNS-project","sub_path":"sns/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"35041305644","text":"# -*- coding: utf-8 -*-\nimport re\nimport time\n\nimport stbt\n\nfrom common.exceptions import NotInScreen\nfrom common.pages.guia import page_guia\nfrom common.pages.home import page_home\nfrom common.pages.pin import page_pin\nfrom common.utils.rcu import RCU\nfrom common.utils.navigation_utils import send_num_rcu_keys\n\n\nclass Img:\n \"\"\"List of reference images locators\"\"\"\n\n MINIGUIDE = \"./images/en_vivo_mini_guide.png\"\n PROGRESS_BAR = \"./images/en_vivo_progress_bar.png\"\n CHANNEL_FOCUS = \"./images/en_vivo_channel_focus.png\"\n ENVENTANAR_FOCUS = \"./images/en_vivo_enventanar_focus.png\"\n SUGERENCIAS_FOCUS = \"./images/en_vivo_sugerencias_focus.png\"\n MINIGUIDE_FOCUS = \"./images/en_vivo_mini_guide_focus.png\"\n PAUSE_ICON = \"./images/en_vivo_pause.png\"\n SUGERENCIAS_ICON = \"./images/en_vivo_sugerencias_icon.png\"\n ENVENTANAR_ICON = \"./images/en_vivo_enventanar_icon.png\"\n GRABAR_ICON = \"./images/en_vivo_grabar.png\"\n HD_ICON = \"./images/en_vivo_hd.png\"\n INICIAR_ICON = \"./images/en_vivo_iniciar.png\"\n OK_ICON = \"./images/en_vivo_ok.png\"\n PARENTAL_TP = \"./images/en_vivo_parental_TP.png\"\n PARENTAL_07 = \"./images/en_vivo_parental_7.png\"\n PARENTAL_12 = \"./images/en_vivo_parental_12.png\"\n PARENTAL_16 = \"./images/en_vivo_parental_16.png\"\n PARENTAL_18 = \"./images/en_vivo_parental_18.png\"\n MOTION_MASK = \"./images/en_vivo_motion_mask.png\"\n\n\nparental_list = [\n Img.PARENTAL_TP,\n Img.PARENTAL_07,\n Img.PARENTAL_12,\n Img.PARENTAL_16,\n Img.PARENTAL_18,\n]\n\n\nclass EnVivo(stbt.FrameObject):\n \"\"\"Page Object for EnVivo\n\n When instantiated, an image capture is done and\n de property is_visible is set to True if we are\n in EnVivo page.\n \"\"\"\n\n def __bool__(self):\n return self.is_visible\n\n def __repr__(self):\n if self.is_visible:\n return \"EnVivo(is_visible=True)\"\n else:\n return \"EnVivo(is_visible=False)\"\n\n @property\n def is_visible(self):\n \"\"\"Returns True if in EnVivo page\"\"\"\n\n region1 = stbt.Region(300, 525, width=45, height=40)\n\n region2 = stbt.Region(308, 640, width=660, height=75)\n\n bar = stbt.match(Img.ROGRESS_BAR, frame=self._frame, region=region1)\n\n ok = stbt.match(Img.OK_ICON, frame=self._frame, region=region2)\n\n return bar and ok\n\n @property\n def parental(self):\n \"\"\"Returns parental if found\n\n Returns:\n string: parental rate\n \"\"\"\n for parental in parental_list:\n if stbt.match(\n parental,\n frame=self._frame,\n region=stbt.Region(244, 470, width=750, height=70),\n ):\n return re.findall(r\"\\d{1,2}|TP\", parental)[0]\n\n return None\n\n @property\n def channel_number(self):\n \"\"\"Returns channel number if found\n\n Returns:\n int: channel\n \"\"\"\n ch = stbt.ocr(region=stbt.Region(77, 505, width=135, height=55))\n\n try:\n return int(ch)\n except ValueError:\n return None\n\n\ndef is_visible():\n \"\"\"Check if in EnVivo Miniguide\n\n Returns:\n [boolean]: [Returns True if in EnVivo]\n \"\"\"\n en_vivo = EnVivo()\n\n if not en_vivo.is_visible:\n stbt.press(RCU.OK)\n time.sleep(1)\n\n en_vivo = en_vivo.refresh()\n\n return en_vivo.is_visible\n\n\ndef assert_screen():\n \"\"\"Raises Exception if not in EnVivo\n\n Returns:\n [obj]: [Returns instance of page]\n \"\"\"\n if is_visible():\n page = EnVivo()\n return page\n else:\n raise NotInScreen(__name__)\n\n\ndef parental():\n \"\"\"Returns the parental rating of the live event.\"\n Checks if is in live (miniguide).\n If not, tries to open miniguide.\n\n Gets parental rating from miniguide:\n - TP\n - 7\n - 12\n - 16\n - 18\n\n Returns:\n string: parental rate\n \"\"\"\n screen = assert_screen()\n\n return screen.parental\n\n\ndef zap_to_ch(ch, unblock=True, pin=None):\n \"\"\"Zaps to desired channel from live\n\n Args:\n ch (list): int channel number\n unblock (bool, optional): True if willing to try to unblock channel.\n Defaults to True.\n pin (list, optional): list of ints with pin (Ex: [1,1,1,1]). Defaults to None.\n\n Returns:\n boolean: True if matches desired channel\n \"\"\"\n\n digit_list = [int(i) for i in str(ch)]\n\n assert len(digit_list) <= 3, \"Invalid number of digits. Must be lowee than 4 digits\"\n\n go_to_live()\n\n assert_screen()\n\n send_num_rcu_keys(digit_list)\n\n time.sleep(5)\n\n _check_live_state(unblock, pin)\n\n if get_channel_number() == ch:\n stbt.draw_text(\n \"SUCCESS: TARGET CH {}, CURRENT CH {},\".format(ch, get_channel_number())\n )\n stbt.press_and_wait(\n RCU.EXIT,\n region=stbt.Region(310, 645, width=60, height=70),\n stable_secs=0.5,\n )\n return True\n else:\n stbt.draw_text(\n \"FAIL: TARGET CH {}, CURRENT CH {}\".format(ch, get_channel_number())\n )\n stbt.press_and_wait(\n RCU.EXIT,\n region=stbt.Region(310, 645, width=60, height=70),\n stable_secs=0.5,\n )\n return False\n\n\ndef go_to_live(unblock=True, pin=None):\n \"\"\"Go to live if accessible from Home Screen\n Allows unblock channel if detects PIN screen is disabled\n If unblock=True and pin is None, default pin is inserted\n\n Args:\n unblock (bool, optional): True if willing to try to unblock channel.\n Defaults to True.\n pin (list, optional): list of ints with pin (Ex: [1,1,1,1]). Defaults to None.\n \"\"\"\n\n if page_home.go_to_home():\n stbt.press_and_wait(\n RCU.MENU,\n region=stbt.Region(310, 645, width=60, height=70),\n stable_secs=0.5,\n )\n _check_live_state(unblock, pin)\n\n\ndef get_channel_number():\n \"\"\"Returns channel number from miniguide if OCR has succeeded\n\n Returns:\n int: channel number\n \"\"\"\n return page_guia.open_guide_get_channel_number()\n\n\ndef assert_motion():\n \"\"\"Check if there is motion in screen to confirm live ch is playing\n\n Returns:\n MotionResult: contains bool variable \"motion\"\n \"\"\"\n return stbt.wait_for_motion(\n timeout_secs=10,\n consecutive_frames=None,\n noise_threshold=None,\n mask=Img.MOTION_MASK,\n frames=None,\n )\n\n\ndef _check_live_state(unblock, pin):\n \"\"\"Handles if channel is blocked before checking live screen\n\n Args:\n unblock (bool): True if we want to unblock channel if it is blocked\n pin (list): 4-digit int PIN\n\n Raises:\n NotInScreen: If not in Live screen\n \"\"\"\n try:\n assert_screen()\n except NotInScreen:\n if _is_channel_blocked() and unblock is True:\n stbt.draw_text(\"Channel is blocked\")\n if not _unblock_channel(pin):\n raise NotInScreen(__name__)\n assert_screen()\n\n elif _is_channel_blocked() and unblock is False:\n stbt.draw_text(\"Channel is blocked\")\n stbt.press_and_wait(RCU.EXIT)\n assert_screen()\n\n else:\n raise NotInScreen(__name__)\n\n\ndef _is_channel_blocked():\n \"\"\"Check if channel is blocked using Page Pin class\n\n Returns:\n True: if blocked. Flase otherwise\n \"\"\"\n return True if page_pin.is_visible() else False\n\n\ndef _unblock_channel(pin=None):\n \"\"\"Access Page Pin to unblock channel\n\n Args:\n pin (list, optional): 4-digit int list. Defaults to None.\n\n Returns:\n [bool]: True if unblocked, False otherwise\n \"\"\"\n page_pin.insert_pin(pin)\n return not page_pin.is_pin_incorrect()\n","repo_name":"bittersoftware/stb-tester-automation","sub_path":"common/pages/en_vivo/page_en_vivo.py","file_name":"page_en_vivo.py","file_ext":"py","file_size_in_byte":7739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"42030982813","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nimport requests\nimport sys\nsys.path.append('..\\Clasificador')\n#print(sys.path)\n\nfrom monkeylearn import MonkeyLearn\n\n# We define the variables need to call the API\napi = 'http://api.meaningcloud.com/class-1.1'\nkeyNoticias = '05ed9a7c754aeee5d5f99470a756a5f8'\nmodelNews = 'news'\nkeyTwitter = 'cd92195fe6c9cc401ab69109099798aa'\nmodelTwitter = 'tweets'\n\nmlZona = MonkeyLearn('aaa6317e0336ea1698db5d01c2aed231abd4c1a1')\n\nclass ClasificadorClass:\n \n def parseoCategoria(self, codigo):\n \tc = \"\"\n \tif codigo == \"1\":\n \t\tc = \"Desastres y accidentes\"\n \tif codigo == \"2\":\n \t\tc = \"Contaminación\" \t\t\n \tif codigo == \"3\":\n \t\tc = \"Eventos\"\n \tif codigo == \"4\":\n \t\tc = \"Criminalidad\"\n \tif codigo == \"5\":\n \t\tc = \"Nada\" \t\t\n \tif codigo == \"6\":\n \t\tc = \"Tráfico\"\n \tif codigo == \"7\":\n \t\tc = \"Transporte público\"\n \tif codigo == \"8\":\n \t\tc = \"Terrorismo\"\n \treturn c\n\n def clasificadorNoticias(self, noticia):\n parameters = {'key': keyNoticias, 'model': modelNews, 'txt': noticia}\n r = requests.request('POST', api, data=parameters)\n response = r.content\n response_json = json.loads(response.decode('utf-8'))\n if(response_json['category_list']):\n cat = self.parseoCategoria(response_json['category_list'][0]['code'])\n else:\n cat = \"Nada\"\n return cat\n \n def clasificarTweets(self, tweet):\n parameters = {'key': keyTwitter, 'model': modelTwitter, 'txt': tweet}\n r = requests.request('POST', api, data=parameters)\n response = r.content\n response_json = json.loads(response.decode('utf-8'))\n if(response_json['category_list']):\n cat = self.parseoCategoria(response_json['category_list'][0]['code'])\n else:\n cat = \"Nada\"\n print(cat) \n print(\"\\n\")\n return cat\n \n\n def clasificadorZona(self, tweet):\n module_id = 'cl_iYYd3Hj2'\n res = mlZona.classifiers.classify(module_id, tweet, sandbox=True)\n resultado = res.result\n zona = resultado[0][0][\"label\"]\n print(zona)\n return zona\n\n\n\n \n","repo_name":"MadAlert/TFG","sub_path":"Python/Clasificador/Clasificador.py","file_name":"Clasificador.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"45022970288","text":"import os\n\nimport networkx as nx\n\nhere, _ = os.path.split(__file__)\nhome = os.path.join(here, os.pardir)\nscript_path = os.path.join(home, 'data', 'sql_updates')\n\ndef get_graph():\n \"\"\"Build a `DiGraph` that captures dependencies between database migration\n tasks. Each node represents a migration script, and each edge represents\n a dependency between tasks. Tasks can be ordered using topological sort.\n \"\"\"\n graph = nx.DiGraph()\n for path in os.listdir(script_path):\n name, ext = os.path.splitext(path)\n if ext == '.sql':\n graph.add_node(name)\n\n graph.add_edge('candidate_history', 'candidate_detail')\n graph.add_edge('candidate_detail', 'candidate_election')\n\n graph.add_edges_from([\n ('candidate_history', 'candidate_history_latest'),\n ('candidate_election', 'candidate_history_latest')\n ])\n\n graph.add_edge('committee_history', 'committee_detail')\n\n graph.add_edges_from([\n ('candidate_history', 'filings'),\n ('committee_history', 'filings'),\n ])\n\n graph.add_edges_from([\n ('filing_amendments_presidential', 'filings'),\n ('filing_amendments_house_senate', 'filings'),\n ('filing_amendments_pac_party','filings'),\n ('filing_amendments_all', 'filings'),\n ])\n\n graph.add_edges_from([\n ('candidate_history', 'candidate_flags'),\n ('candidate_aggregates', 'candidate_flags'),\n ])\n\n graph.add_edges_from([\n ('filing_amendments_presidential', 'reports_presidential'),\n ('filing_amendments_house_senate', 'reports_house_senate'),\n ('filing_amendments_pac_party', 'reports_pac_party'),\n ('filing_amendments_all', 'reports_presidential'),\n ('filing_amendments_all', 'reports_house_senate'),\n ('filing_amendments_all', 'reports_pac_party'),\n ])\n graph.add_edges_from([\n ('totals_house_senate', 'totals_combined'),\n ('totals_presidential', 'totals_combined'),\n ('totals_pac_party', 'totals_combined'),\n ])\n\n graph.add_edges_from([\n ('committee_detail', 'committee_fulltext'),\n ('totals_combined', 'committee_fulltext'),\n ])\n\n graph.add_edge('committee_detail', 'totals_party')\n graph.add_edge('committee_detail', 'totals_pac')\n\n graph.add_edges_from([\n ('candidate_detail', 'candidate_fulltext'),\n ('totals_combined', 'candidate_fulltext'),\n ])\n\n graph.add_edge('totals_combined', 'sched_a_by_size_merged')\n\n graph.add_edges_from([\n ('totals_house_senate', 'candidate_aggregates'),\n ('totals_presidential', 'candidate_aggregates'),\n ('candidate_election', 'candidate_aggregates'),\n ('cand_cmte_linkage', 'candidate_aggregates'),\n ])\n\n graph.add_edges_from([\n ('committee_detail', 'large_aggregates'),\n ('reports_ie', 'large_aggregates'),\n ('communication_cost', 'large_aggregates'),\n ])\n\n graph.add_edge('committee_history', 'communication_cost')\n graph.add_edge('committee_detail', 'sched_a_by_state_recipient_totals')\n\n return graph\n","repo_name":"ArtyEmsee/openFEC","sub_path":"webservices/flow.py","file_name":"flow.py","file_ext":"py","file_size_in_byte":3065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"72"}
+{"seq_id":"31397662985","text":"import os\nimport sys\nimport pickle\nfrom PIL import Image\n\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\nfrom .datasets import register\nimport numpy as np\n\n@register('mini-imagenet')\nclass MiniImageNet(Dataset):\n\n def __init__(self, root_path, split='train', **kwargs):\n self.split_tag = split\n if split not in ['train', 'val', 'test']:\n raise NotImplementedError\n print(\"Okay, \", self.split_tag)\n\n\n data_path = os.getcwd() + \"/materials/mini-imagenet/\" + split\n\n print(\"Data path: \", data_path)\n print(\"Contents: \", os.listdir(data_path))\n data = []\n label = []\n for count, class_id in enumerate(os.listdir(data_path)):\n class_label = count\n class_path = os.path.join(data_path, class_id)\n for class_image in os.listdir(class_path):\n clas_image_path = os.path.join(class_path, class_image)\n class_image_label = class_label\n data.append(clas_image_path)\n label.append(class_image_label)\n print(\"Class: \", class_label, class_id, \" Done\")\n # print(\"Class Image label: \", class_image_content.shape)\n # print(\"class content: \", os.listdir(class_path))\n # sys.exit()\n\n\n\n\n # Need to customize from here\n\n # split_file = 'miniImageNet_category_split_{}.pickle'.format(split_tag)\n # with open(os.path.join(root_path, split_file), 'rb') as f:\n # pack = pickle.load(f, encoding='latin1')\n # data = pack['data']\n # label = pack['labels']\n\n image_size = 80\n\n min_label = min(label)\n label = [x - min_label for x in label]\n\n self.data = data\n self.label = label\n self.n_classes = max(self.label) + 1\n\n norm_params = {'mean': [0.485, 0.456, 0.406],\n 'std': [0.229, 0.224, 0.225]}\n normalize = transforms.Normalize(**norm_params)\n\n self.transform = transforms.Compose([\n transforms.RandomResizedCrop(image_size),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n normalize,\n ])\n #No more need of augment resize\n print(f\"{split} data prep done!\")\n\n def convert_raw(x):\n mean = torch.tensor(norm_params['mean']).view(3, 1, 1).type_as(x)\n std = torch.tensor(norm_params['std']).view(3, 1, 1).type_as(x)\n return x * std + mean\n\n self.convert_raw = convert_raw\n\n def __len__(self):\n return len(self.data)\n\n def get_data(self, i):\n image_path = self.data[i]\n image = np.array(Image.open(image_path))\n return Image.fromarray(image)\n\n def __getitem__(self, i):\n the_data = self.get_data(i)\n # print(\"self.transform(self.data[i]): \", self.transform(the_data).shape)\n return self.transform(the_data), self.label[i]\n\n\nclass MiniImageNet_old_for_reference(Dataset):\n\n def __init__(self, root_path, split='train', **kwargs):\n split_tag = split\n if split == 'train':\n split_tag = 'train_phase_train'\n split_file = 'miniImageNet_category_split_{}.pickle'.format(split_tag)\n with open(os.path.join(root_path, split_file), 'rb') as f:\n pack = pickle.load(f, encoding='latin1')\n data = pack['data']\n label = pack['labels']\n\n image_size = 80\n data = [Image.fromarray(x) for x in data]\n\n min_label = min(label)\n label = [x - min_label for x in label]\n\n self.data = data\n self.label = label\n self.n_classes = max(self.label) + 1\n\n norm_params = {'mean': [0.485, 0.456, 0.406],\n 'std': [0.229, 0.224, 0.225]}\n normalize = transforms.Normalize(**norm_params)\n self.default_transform = transforms.Compose([\n transforms.Resize(image_size),\n transforms.ToTensor(),\n normalize,\n ])\n augment = kwargs.get('augment')\n if augment == 'resize':\n self.transform = transforms.Compose([\n transforms.RandomResizedCrop(image_size),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n normalize,\n ])\n elif augment == 'crop':\n self.transform = transforms.Compose([\n transforms.Resize(image_size),\n transforms.RandomCrop(image_size, padding=8),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n normalize,\n ])\n elif augment is None:\n self.transform = self.default_transform\n\n def convert_raw(x):\n mean = torch.tensor(norm_params['mean']).view(3, 1, 1).type_as(x)\n std = torch.tensor(norm_params['std']).view(3, 1, 1).type_as(x)\n return x * std + mean\n\n self.convert_raw = convert_raw\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, i):\n return self.transform(self.data[i]), self.label[i]\n","repo_name":"pandeydeep9/EvidentialResearch2023","sub_path":"FewShotExperiments/datasets/mini_imagenet.py","file_name":"mini_imagenet.py","file_ext":"py","file_size_in_byte":5126,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"72"}
+{"seq_id":"23774805641","text":"'''\n Add Strings\n\nGiven two non-negative integers, num1 and num2 represented as string, return the sum of num1 and num2 as a string.\n\nYou must solve the problem without using any built-in library for handling large integers (such as BigInteger). You must also not convert the inputs to integers directly.\n\n \n\nExample 1:\n\nInput: num1 = \"11\", num2 = \"123\"\nOutput: \"134\"\n\nExample 2:\n\nInput: num1 = \"456\", num2 = \"77\"\nOutput: \"533\"\n\nExample 3:\n\nInput: num1 = \"0\", num2 = \"0\"\nOutput: \"0\"\n\n \n\nConstraints:\n\n 1 <= num1.length, num2.length <= 104\n num1 and num2 consist of only digits.\n num1 and num2 don't have any leading zeros except for the zero itself.\n\n'''\n\nclass Solution:\n def addStrings(self, num1: str, num2: str) -> str:\n c = 0\n ans = []\n num1_index = len(num1) - 1\n num2_index = len(num2) - 1\n while num1_index >= 0 and num2_index >= 0:\n s = int(num1[num1_index]) + int(num2[num2_index]) + c\n c = s//10\n ans.append(s%10)\n num1_index -= 1\n num2_index -= 1\n if num1_index > num2_index:\n while num1_index >= 0:\n s = int(num1[num1_index]) + c\n c = s//10\n ans.append(s%10)\n num1_index -= 1\n else:\n while num2_index >= 0:\n s = int(num2[num2_index]) + c\n c = s//10\n ans.append(s%10)\n num2_index -= 1\n if c:\n ans.append(c)\n return \"\".join(str(val) for val in ans[::-1])\n\nnum1 = \"11\"\nnum2 = \"123\"\n# Output: \"134\"\n\nnum1 = \"456\"\nnum2 = \"77\"\n# Output: \"533\"\n\nnum1 = \"0\"\nnum2 = \"0\"\n# Output: \"0\"\n\nsol = Solution()\nprint(sol.addStrings(num1, num2))\n","repo_name":"jomesh18/Leetcode","sub_path":"Leetcode_challenge/2021/8.August 2021/9.addStrings.py","file_name":"9.addStrings.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"9550596831","text":"# http://cryto.net/pythonwhois/usage.html\nfrom datetime import datetime\nfrom pytz import utc\nfrom .models import Domain\nfrom .models import Whois\nfrom django.core import serializers\nfrom pythonwhois import get_whois\nfrom pythonwhois.shared import WhoisException\n\nclass WhoisCachier:\n\n def update_whois_model(domain_model):\n try:\n whois = get_whois(domain_model.domain_name, normalized=True)\n except Exception as err:\n print(\"upate_whois() failed: {0}\".format(err))\n return\n datadict = {}\n for k,v in whois.items():\n if not hasattr(Whois, k):\n continue # skip whois fields not in our model\n if k == 'id':\n continue # conflicts with Model field; also is irrelevant data\n if isinstance(v, list):\n if len(v) > 1:\n datadict[k] = ','.join(v)\n else:\n if isinstance(v[0], datetime):\n datadict[k] = v[0].replace(tzinfo=utc)\n else:\n datadict[k] = v[0]\n elif isinstance(v, dict):\n datadict[k] = v\n else:\n datadict[k] = v\n datadict['internal_cache_date'] = datetime.now().replace(tzinfo=utc)\n whois_record, created = Whois.objects.update_or_create(\n domain=domain_model,\n defaults = datadict\n )\n domain_model.whois = whois_record\n domain_model.save()\n","repo_name":"VEuPathDB/docker-hq","sub_path":"hq/domains/whois.py","file_name":"whois.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"1553568455","text":"from models.models import ProblemTypes\n\ndef getAllProblemTypes():\n try:\n problem = ProblemTypes.query.all()\n response = [{'id': i.id, 'description': i.description} for i in problem]\n except:\n reponse = {'message': 'don not exist any status to occurrence'}\n return response\n\ndef getSpecificProblemTypes(id):\n try:\n problem = ProblemTypes.query.filter_by(id=id).first()\n response = {'description': problem.description}\n except:\n response = {'message', 'could not find the status'}\n return response\n\ndef deleteProblemTypes(id):\n try:\n problem = ProblemTypes.query.filter_by(id=id).first()\n problem.delete()\n response = {'message': 'deleted with success'}\n except:\n response = {'message': 'could not find the status'}\n return response\n\ndef constructProblemTypes(data):\n try:\n new_data = ProblemTypes(\n description = data['description']\n )\n new_data.save()\n response = {'message': 'save with success'}\n except:\n response = {'message': 'could not save the status'}\n return response\n","repo_name":"PedroSMarcal/Hackaton2021","sub_path":"backend/utils/ProblemTypeUtil.py","file_name":"ProblemTypeUtil.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"13495748046","text":"# Cidades\n# 1º Exemplo\nprompt = \"\\nPlease enter the name of a city you have visited: \"\nprompt += \"\\n(Enter 'quit' when you are finished.) \"\nwhile True:\n cidade = str(input(prompt))\n if cidade == 'quit':\n break\n else:\n print(f\"I'd love to go to {cidade.title()}!\")\n\n# 2º Exemplo\nwhile True:\n cidade = str(input('Please enter the name of a city you have visited: ')) # noqa\n if cidade == 'quit':\n break\n else:\n print(f\"I'd love to go to {cidade.title()}!\\n\")\n print(\"Enter 'quit' when you are finished.\")\n","repo_name":"JenaCarry/Curso-Intesensivo-Python","sub_path":"capitulo_7/cidades.py","file_name":"cidades.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"9848503569","text":"import os\nimport random\nimport json\nimport torch\nimport torch.nn as nn\n\nfrom collections import namedtuple\n\n# Can be simply regarded as a object, called \"Batch\", having attributes \"input\" and \"target\"\nBatch = namedtuple('Batch', 'input target')\n\nclass Dataset(): \n\n def __init__(self, raw_train_file, train_file, dev_file, test_file, word_to_idx, use_gpu=False):\n # word_to_idx/tag_to_idx: both are functions, whose input is a string and output an int\n self.num_classes = 10\n self.use_gpu = use_gpu\n\n self.train_file = train_file\n self.dev_file = dev_file\n self.test_file = test_file\n\n # Shuffle trainset\n self.shuffle_trainset(raw_train_file, train_file)\n\n self.word_to_idx = word_to_idx\n\n self.num_train_samples = 0\n for batch in self.trainset(batch_size=1000):\n self.num_train_samples += batch[0].shape[0]\n\n self.num_dev_samples = 0\n for batch in self.devset(batch_size=1000):\n self.num_dev_samples += batch[0].shape[0]\n\n self.num_test_samples = 0\n for batch in self.testset(batch_size=1000):\n self.num_test_samples += batch[0].shape[0]\n\n def shuffle_trainset(self, raw_file, out_file):\n\n if os.path.exists(out_file):\n print ('Trainset has been shuffled, do not shuffle.')\n return \n\n lines = []\n with open(raw_file, 'r') as f:\n lines = f.readlines()\n random.shuffle(lines)\n with open(out_file, 'w') as f:\n for line in lines:\n f.write(line)\n \n\n def trainset(self, batch_size=1, drop_last=False):\n for batch in self.sample_batches(self.train_file, batch_size=batch_size, drop_last=drop_last):\n yield batch\n \n def devset(self, batch_size=1, drop_last=False):\n for batch in self.sample_batches(self.dev_file, batch_size=batch_size, drop_last=drop_last):\n yield batch\n\n def testset(self, batch_size=1, drop_last=False):\n for batch in self.sample_batches(self.test_file, batch_size=batch_size, drop_last=drop_last):\n yield batch\n\n def tag_to_idx(self, t):\n t2i = {'体育': 0, '财经': 1, '房产': 2, '家居': 3, '教育': 4, '科技': 5, '时尚': 6, '时政': 7, '游戏': 8, '娱乐': 9}\n return t2i[t]\n\n def sentence_to_tensor(self, s):\n # s: string or list\n # return: 1-d long tensor of shape (seq_len)\n return torch.LongTensor([self.word_to_idx(w) for w in s])\n\n def pad_sequence(self, s):\n # TODO pad to 512?\n return nn.utils.rnn.pad_sequence(s, batch_first=True)\n\n # TODO preprocess string\n def samples(self, file_path):\n\n with open(file_path, 'r') as f:\n for line in f:\n tag, sent = line.strip().split('\\t')\n # print (tag, sent)\n # input ()\n\n sent = self.sentence_to_tensor(sent)\n tag = torch.LongTensor([self.tag_to_idx(tag)])\n\n if self.use_gpu: \n yield sent.cuda(), tag.cuda()\n else: \n yield sent, tag\n \n \n def sample_batches(self, file_path, batch_size=1, drop_last=False):\n # drop_last: drop the last incomplete batch if True\n cnt = 0\n\n # Input and target\n sent_batch, tag_batch = [], [] # (batch_size, seq_len), (batch_size)\n\n for sent, tag in self.samples(file_path):\n # all tensor-like\n\n sent_batch.append(sent)\n tag_batch.append(tag)\n\n cnt += 1\n if cnt >= batch_size:\n\n yield Batch(input=self.pad_sequence(sent_batch), target=torch.cat(tag_batch))\n sent_batch, tag_batch = [], []\n cnt = 0\n\n if cnt > 0 and not drop_last:\n yield Batch(input=self.pad_sequence(sent_batch), target=torch.cat(tag_batch))\n\n\n\n\nif __name__ == '__main__': \n # Usage\n raw_train_file = 'cnews/cnews.train.txt'\n train_file = 'cnews/cnews.train.shuffled.txt'\n dev_file = 'cnews/cnews.val.txt'\n test_file = 'cnews/cnews.test.txt'\n\n def word_to_idx(w):\n w2i = {'当': 1, '希': 2}\n return w2i.get(w, 0)\n\n dataset = Dataset(raw_train_file=raw_train_file, train_file=train_file, dev_file=dev_file, test_file=test_file, word_to_idx=word_to_idx)\n\n print (f'trainset: {dataset.num_train_samples}')\n print (f'devset: {dataset.num_dev_samples}')\n print (f'testset: {dataset.num_test_samples}')\n\n cnt = 0\n for sent_batch, tag_batch in dataset.trainset(batch_size=10, drop_last=False):\n # print (f'sent_batch: {sent_batch.shape}, tag_batch: {tag_batch.shape}')\n # input ()\n cnt += sent_batch.shape[0]\n print (f'trainset: {cnt}')\n \n cnt = 0\n for sent_batch, tag_batch in dataset.devset(batch_size=10, drop_last=False):\n # print (f'sent_batch: {sent_batch.shape}, tag_batch: {tag_batch.shape}')\n # input ()\n cnt += sent_batch.shape[0]\n print (f'devset: {cnt}')\n\n cnt = 0\n for sent_batch, tag_batch in dataset.testset(batch_size=10, drop_last=False):\n # print (f'sent_batch: {sent_batch.shape}, tag_batch: {tag_batch.shape}')\n # input ()\n cnt += sent_batch.shape[0]\n print (f'testset: {cnt}')\n\n","repo_name":"Hongqin-Li/NTFS","sub_path":"data/thucnews/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":5331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"30792405010","text":"import os\nimport socket\nimport string\nimport threading\nimport time\nimport urllib.request\nimport urllib.error\nimport requests\nimport xlrd\nfrom bs4 import BeautifulSoup\nimport pymongo\nfrom common_function import clear_char\n\nTAG_URL_OF_WEAPON = 'url_of_weapon'\nTAG_URL_OF_CACHE = 'url_of_cache'\nBASE_URL = 'http://weapon.huanqiu.com'\nclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\ndb = client[\"huanqiu\"]\n\n\n# db.create_collection('key_url')\n\n\n# col = db[\"key_url\"]\ndef request_url(url):\n data = requests.get(url)\n # col = db[\"cache_url\"]\n # source = col.find_one({'url': url})\n # if source:\n # return source['source']\n # else:\n # print(url)\n # col.insert_one({\n # 'tag': TAG_URL_OF_CACHE,\n # 'url': url,\n # 'source': data.text\n # })\n return data.text\n\n\n# def insert_urlofweapon():\n# col = db[\"key_url\"]\n# col.insert_one({\n# 'tag': TAG_URL_OF_WEAPON,\n# 'url': url\n# 'name': name\n# })\n\n\ndef get_weapon_url(source, keyname):\n # col = db[\"key_url\"]\n soup = BeautifulSoup(source, 'html.parser')\n li = soup.find('li', class_=\"img\")\n url = li.find('a').get('href')\n url = BASE_URL + url\n # if col.find_one({'name': keyname}):\n # col.replace_one(\n # {'name': keyname},\n # {\n # 'tag': TAG_URL_OF_WEAPON,\n # 'url': url,\n # 'name': keyname\n # })\n # else:\n # col.insert_one({\n # 'tag': TAG_URL_OF_WEAPON,\n # 'url': url,\n # 'name': keyname\n # })\n\n\ndef get_weapon_info(keyname,source):\n # data_info=''\n #\n # col = db[\"key_url\"]\n # result = col.find_one({'name': keyname})\n # if col.find_one({'name': keyname}):\n\n soup = BeautifulSoup(source, 'html.parser')\n li = soup.find('li', class_=\"img\")\n url = li.find('a').get('href')\n url = BASE_URL + url\n \n # url = result['url']\n source = request_url(url)\n soup = BeautifulSoup(source, 'html.parser')\n data_info = soup.find('div', class_='dataInfo')\n data_info = data_info.text\n try:\n intron = soup.find('div', class_='intron')\n intron = intron.text\n info = soup.find('div', class_='info')\n info = info.text\n except Exception as e:\n info = ''\n intron = ''\n print(e)\n with open('dirlist', 'r', encoding='utf8') as f_r:\n dirs_lines = f_r.readlines()\n for l in dirs_lines:\n if keyname in l:\n dirname = '../' + l.strip('\\n')\n with open(dirname + '/info.txt', 'a',encoding='utf8') as f:\n f.write(data_info)\n f.write(info)\n f.write(intron)\n try:\n img = soup.find('div', class_=\"maxPic\")\n img = img.find('img').get('src')\n res = requests.get(img)\n res.raw.decode_content = True\n print(img)\n\n if res.status_code == 200:\n # str(random.randint(0, 9999))\n with open(dirname + os.sep + '0001' + '.jpg', 'wb') as image_f:\n image_f.write(res.content)\n except Exception as e:\n print(e)\n\n\nif __name__ == '__main__':\n xls_file = '../../cfg/list.xlsx'\n wb = xlrd.open_workbook(xls_file)\n sheet = wb.sheet_by_index(0)\n for irow in range(5212,sheet.nrows):\n print(irow)\n c_row = sheet.row(irow)\n # A = c_row[0].value\n # B = c_row[1].value\n # C = c_row[2].value\n D = c_row[3].value\n # A = clear_char(A)\n # B = clear_char(B)\n # C = clear_char(C)\n D = clear_char(D)\n print(D)\n # keyname = 'C-295中程预警机'\n keyname = D\n search_url = 'http://weapon.huanqiu.com/search?keyword='\n url = 'http://weapon.huanqiu.com/search?keyword=' + keyname\n keyname = clear_char(keyname)\n\n # data=request_url(url)\n # query = {\"tag\":TAG_URL_OF_WEAPON}\n # col.delete_many(query)\n try:\n source = request_url(url)\n # print(source)\n # get_weapon_url(source, keyname)\n get_weapon_info(keyname,source)\n # print(request_url('http://weapon.huanqiu.com/c_295'))\n except:\n continue\n","repo_name":"superxiaotutu/worm","sub_path":"python_worm/huanqiu/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":4416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"19835435225","text":"import cv2\r\nimport torch\r\nimport numpy as np\r\nfrom matplotlib import cm\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\n\r\nclass MnistModel(nn.Module):\r\n \"\"\"Feedfoward neural network with 1 hidden layer\"\"\"\r\n def __init__(self, in_size, hidden_size, out_size):\r\n super().__init__()\r\n # hidden layer\r\n self.linear1 = nn.Linear(in_size, hidden_size)\r\n # output layer\r\n self.linear2 = nn.Linear(hidden_size, out_size)\r\n \r\n def forward(self, xb):\r\n # Flatten the image tensors\r\n xb = xb.view(xb.size(0), -1)\r\n # Get intermediate outputs using hidden layer\r\n out = self.linear1(xb)\r\n # Apply activation function\r\n out = F.relu(out)\r\n # Get predictions using output layer\r\n out = self.linear2(out)\r\n return out\r\n \r\n def training_step(self, batch):\r\n images, labels = batch \r\n out = self(images) # Generate predictions\r\n loss = F.cross_entropy(out, labels) # Calculate loss\r\n return loss\r\n \r\n def validation_step(self, batch):\r\n images, labels = batch \r\n out = self(images) # Generate predictions\r\n loss = F.cross_entropy(out, labels) # Calculate loss\r\n acc = accuracy(out, labels) # Calculate accuracy\r\n return {'val_loss': loss, 'val_acc': acc}\r\n \r\n def validation_epoch_end(self, outputs):\r\n batch_losses = [x['val_loss'] for x in outputs]\r\n epoch_loss = torch.stack(batch_losses).mean() # Combine losses\r\n batch_accs = [x['val_acc'] for x in outputs]\r\n epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies\r\n return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\r\n \r\n def epoch_end(self, epoch, result):\r\n print(\"Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}\".format(epoch, result['val_loss'], result['val_acc']))\r\n\r\ndef accuracy(outputs, labels):\r\n _, preds = torch.max(outputs, dim=1)\r\n return torch.tensor(torch.sum(preds == labels).item() / len(preds))\r\n\r\ninput_size = 784\r\nhidden_size = 32 # you can change this\r\nnum_classes = 10\r\n\r\nmodel = MnistModel(input_size, hidden_size=32, out_size=num_classes)\r\nmodel.load_state_dict(torch.load('digit/digit.pth'))\r\nmodel.eval()\r\n\r\ndef predict_image(img, model):\r\n xb = img.unsqueeze(0)\r\n yb = model(xb)\r\n _, preds = torch.max(yb, dim=1)\r\n return preds[0].item()\r\n\r\nfrom PIL import Image\r\nimport torchvision.transforms as transforms\r\n\r\nimage = cv2.imread('digit/images/test3.png')\r\n\r\ntransform = transforms.Compose([\r\n transforms.ToTensor(),\r\n transforms.Resize((28, 28))])\r\n \r\nimg_tensor = transform(image)\r\nimg_tensor = img_tensor[0:1, :, :]\r\nprint(img_tensor.shape)\r\nprint('Predicted:', predict_image(img_tensor, model))","repo_name":"Infi-09/Doctor-Prescripton-Handwritten-Recoginition","sub_path":"digit/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2825,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"72"}
+{"seq_id":"71898236712","text":"import random\r\nprint('===== DESAFIO 19 =====')\r\n# Um professor quer sortear um dos seus quatro alunos para apagar o quadro.\r\n# Faça um programa que ajude ele, lendo o nome deles e escrevendo o nome do escolhido.\r\nalunos = input('\\nDigite o nome dos 4 alunos separados por um espaço: ')\r\nalunos_list = alunos.split()\r\n\r\nfor i in range(4):\r\n alunos_list[i] = str(alunos_list[i])\r\n\r\n# print('\\nAlunos incluídos na lista: {}'.format(alunos_list))\r\nprint('Aluno selecionado: {}'.format(random.choice(alunos_list)))\r\n","repo_name":"carolasouza/Curso-em-Video---Python","sub_path":"Mundo 01/exercicios/ex019a.py","file_name":"ex019a.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"1377429952","text":"import importlib.util\nfrom PySide2.QtWidgets import (\n QWidget, QDialog, QDialogButtonBox, QLabel, QLineEdit, QHBoxLayout, QVBoxLayout, QFrame\n)\nimport symba_gui as package\nfrom .editor import Editor\n\n\nclass ChartEditor(QDialog):\n def __init__(self, parent=None, title=None, code=None):\n super().__init__(parent=parent)\n self.setWindowTitle(\"Chart Editor\")\n\n self.wtitle = QLineEdit(title or \"Untitled Chart\")\n self.wcode = Editor()\n self.wcode.setLanguage(\"python\")\n\n if code is None:\n with open(package.dir / \"data/default_chart.py\", \"r\", encoding=\"utf-8\") as f:\n code = f.read()\n \n self.wcode.setText(code)\n\n wcode_frame = QFrame()\n wcode_frame.setFrameShape(wcode_frame.Box)\n wcode_frame.setFrameShadow(wcode_frame.Sunken)\n lycode_frame = QHBoxLayout()\n lycode_frame.setContentsMargins(0, 0, 0, 0)\n wcode_frame.setLayout(lycode_frame)\n lycode_frame.addWidget(self.wcode)\n\n lytitle_container = QHBoxLayout()\n lytitle_container.setContentsMargins(0, 0, 0, 0)\n\n lytitle_container.addWidget(QLabel(\"Chart title:\"))\n lytitle_container.addWidget(self.wtitle)\n\n button_box = QDialogButtonBox(QDialogButtonBox.Save | QDialogButtonBox.Cancel)\n self.wbutton_save = button_box.button(QDialogButtonBox.Save)\n self.wbutton_cancel = button_box.button(QDialogButtonBox.Cancel)\n\n self.wbutton_save.clicked.connect(self.accept)\n self.wbutton_cancel.clicked.connect(self.reject)\n\n line = QFrame()\n line.setFrameShape(QFrame.HLine)\n line.setFrameShadow(QFrame.Sunken)\n\n ly = QVBoxLayout()\n self.setLayout(ly)\n\n ly.addLayout(lytitle_container)\n ly.addWidget(wcode_frame)\n ly.addWidget(line)\n ly.addWidget(button_box)\n \n @property\n def title(self):\n return self.wtitle.text()\n \n @title.setter\n def title(self, text):\n self.wtitle.setText(text)\n \n @property\n def code(self):\n return self.wcode.text()\n \n @code.setter\n def code(self, text):\n self.wcode.setText(text)\n\n\nclass Chart(QWidget):\n \"\"\"A user-made chart that loads from a python file.\"\"\"\n def __init__(self, output_dir, path):\n super().__init__()\n self.output_dir = output_dir\n self.path = path\n self.title = path.stem\n self.wchart = QWidget() # Initialized later in reload()\n\n ly = QVBoxLayout()\n self.setLayout(ly)\n ly.addWidget(self.wchart)\n\n self._editor = None\n\n self.reload()\n\n def reload(self, path=None):\n self.path = path or self.path\n \n self.layout().removeWidget(self.wchart)\n\n # Import the module and call chart() function\n spec = importlib.util.spec_from_file_location(self.title, self.path)\n module = importlib.util.module_from_spec(spec)\n spec.loader.exec_module(module)\n self.wchart = module.chart(str(self.output_dir))\n\n self.layout().addWidget(self.wchart)\n\n def editor(self):\n \"\"\"Return an editor that is linked to this chart.\"\"\"\n with open(self.path, \"r\", encoding=\"utf-8\") as f:\n code = f.read()\n \n if self._editor is None:\n self._editor = ChartEditor(self)\n\n def done(result):\n if not result:\n return # User cancelled\n\n title = self._editor.title\n code = self._editor.code\n\n self.path.unlink()\n self.path = self.path.parent / (title + \".py\")\n\n with open(self.path, \"w\", encoding=\"utf-8\") as f:\n f.write(code)\n\n self.reload()\n \n self._editor.finished.connect(done)\n \n self._editor.title = self.title\n self._editor.code = code\n \n return self._editor\n","repo_name":"johannlussange/symba-gui","sub_path":"symba_gui/chart.py","file_name":"chart.py","file_ext":"py","file_size_in_byte":3953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"15160196219","text":"def main():\r\n n, k = map(int, input().split())\r\n tt = [int(input()) for _ in range(n)]\r\n for i in range(2, n):\r\n if tt[i] + tt[i - 1] + tt[i - 2] < k:\r\n print(i + 1)\r\n return\r\n print(-1)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"Kawser-nerd/CLCDSA","sub_path":"Source Codes/AtCoder/arc036/A/4902666.py","file_name":"4902666.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"72"}
+{"seq_id":"71891980392","text":"from collections import deque\r\nimport sys\r\nN = int(sys.stdin.readline())\r\n# N = int(input())\r\n\r\n# 1번 카드가 제일 위에(마지막 인덱스) 위치\r\nlst = list(range(1, N+1))\r\nlst = deque(lst)\r\n# 두개의 카드가 남을 때까지 반복 \r\nwhile len(lst) > 2: \r\n # 가장 위에 있는 카드 바닥에 버리기 \r\n lst.popleft()\r\n # 그다음 위에 있는 카드를 제일 아래에 있는 카드로 옮기기 \r\n lst.append(lst.popleft())\r\n\r\nprint(lst[-1])","repo_name":"data-sign/algorithm","sub_path":"백준/Unrated/2164. 카드2/카드2.py","file_name":"카드2.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"35437618144","text":"import sys, random\nimport pygame\nfrom pygame.locals import *\nimport pymunk\nimport pymunk.pygame_util\n\nfrom Obstacle import Obstacle\nfrom Creature import Creature\n\n# handles game functionality, including colision and evolution management\nclass Overlord:\n\tdef __init__(self, screen, space, draw_options, display_x, display_y, epsilon, population_size=10, simulation_step=0.1, epochs=1000):\n\n\t\t# screen we are drawing on\n\t\tself.screen = screen\n\n\t\t# simulation space\n\t\tself.space = space\n\n\t\t# used for easy drawing of all simulation objects\n\t\tself.draw_options = draw_options\n\n\t\t# width and height of the screen for the fitness test and side bar\n\t\tself.fitness_display_x, self.fitness_display_y = 0.9*display_x, display_y\n\t\tself.sidebar_display_x, self.sidebar_display_y = 0.1*display_x, display_y\n\t\tself.sidebar_display_start_x = self.fitness_display_x\n\n\t\t#the zone in which creatures can be instantiated\n\t\tself.creation_bounds = self.make_creation_bounds() \n\n\t\t# all living creatures\n\t\tself.population_size = population_size\n\n\t\t# the number of steps in the simulation we advance after each iteration\n\t\tself.simulation_step = simulation_step\n\n\t\t# populate creatures\n\t\tself.creatures = []\n\t\tfor i in range(self.population_size):\n\t\t\tc = Creature(self.creation_bounds)\n\t\t\tself.creatures.append(c)\n\n\t\t# sidebar creatures\n\t\tself.sidebar_creatures = self.populate_sidebar()\n\n\t\t# mutation rate for creatures\n\t\tself.epsilon = epsilon\n\n\t\t# all static obstacles\n\t\tself.obstacles = self.obstacle_set_1()\n\n\t\t# variables used for each generation, this includes fitness scores and current creatures indices\n\t\tself.epoch = 0\n\t\tself.current_creature_ind = 0\n\t\tself.fitness_scores = [None for x in range(self.population_size)]\n\n\t# builds the zone in which creatures can be instantiated\n\tdef make_creation_bounds(self):\n\t\treturn [0.05*self.fitness_display_x, 0.95*self.fitness_display_x, 0.9*self.fitness_display_y, self.fitness_display_y]\n\n\t# displays all the creatures in the population as static objects in the sidebar\n\tdef populate_sidebar(self, refresh=False):\n\n\t\tif refresh:\n\t\t\tself.clear_sidebar()\n\n\t\t# calculate the starting x position and the separation between each sidebar object on the y axis\n\t\tcreature_separation = (0.9*self.sidebar_display_y) / max(1,len(self.creatures))\n\t\tx = self.sidebar_display_start_x + self.sidebar_display_x / 2\n\n\t\t# list of objects in the sidebar\n\t\tsidebar_creatures = []\n\n\t\t# make each creature as a static object in the sidebar\n\t\tcount = 1\n\t\tfor c in self.creatures:\n\t\t\tob = Obstacle(c.vertices, (x,self.sidebar_display_y - creature_separation*count), self.space)\n\t\t\tsidebar_creatures.append(ob)\n\t\t\tcount += 1\n\n\t\treturn sidebar_creatures\n\n\t# removes all objects in the sidebar\n\tdef clear_sidebar(self):\n\t\tfor ob in self.sidebar_creatures:\n\t\t\tself.space.remove(ob.shape,ob.shape.body)\n\n\t# test a creature in the population\n\t#TODO: update the population\n\tdef test_creatures(self):\n\n\t\tcurrent_creature = self.creatures[self.current_creature_ind]\n\n\t\tif current_creature.shape == None:\n\t\t\tcurrent_creature.build_creature(self.space)\n\n\t\tif not current_creature.in_space:\n\t\t\tcurrent_creature.add_to_space(self.space)\n\n\t\t# update the creature's lowest y-value and time of making it there.\n\t\t# if the creature is greater than or equal to the specified species age,\n\t\t# this constitutes a completed life_cycle, which will be a value of either\n\t\t# 1 or 0. When a life cycle is completed, the creature dies and score is recorded\n\t\tlife_cycle = current_creature.update(self.simulation_step)\n\n\t\t# if life cycle is complete (== 1), record best scores for the creature and select the next creature\n\t\tif life_cycle == 1:\n\t\t\tself.fitness_scores[self.current_creature_ind] = (current_creature.lowest_y, current_creature.time_of_low_point)\n\t\t\tself.remove_creature_from_space(current_creature)\n\t\t\tself.current_creature_ind += 1\n\n\t\t\tif self.current_creature_ind >= self.population_size:\n\t\t\t\tself.update_population()\n\t\t\t\tself.epoch += 1\n\n\t\t\t\n\n\n\tdef update_population(self):\n\n\t\tzip_list = zip(self.fitness_scores,self.creatures)\n\n\t\t# calculate 2 best and 1 worst member of the population\n\t\tsorted_scores = []\n\t\tfor i in range(self.population_size):\n\t\t\tfitness_rating = self.calculate_fitness_rating(self.fitness_scores[i])\n\t\t\tsorted_scores.append((fitness_rating,self.creatures[i]))\n\n\t\t# sort scores by fitness rating in descending order\n\t\tsorted_scores.sort(key=lambda t:t[0],reverse=True)\n\n\t\t# delete worst 2 members\n\t\tsorted_scores.pop()\n\t\tsorted_scores.pop()\n\n\t\t# breed 2 best and add child to population\n\t\tp1 = sorted_scores[0][1]\n\t\tp2 = sorted_scores[1][1]\n\t\tnew_creature = Creature(self.creation_bounds,[p1,p2],self.epsilon)\n\t\tself.creatures = [new_creature]\n\t\t\n\t\tfor v in sorted_scores:\n\t\t\tself.creatures.append(v[1])\n\n\t\t# add a completely random member to the population\n\t\tself.creatures.append(Creature(self.creation_bounds))\n\n\t\t# repopulate sidebar with new population\n\t\tself.sidebar_creatures = self.populate_sidebar(refresh=True)\n\n\t\t#reset all creature metadta\n\t\tfor c in self.creatures:\n\t\t\tc.reset()\n\n\t\t#reset current creature index for next iteration\n\t\tself.current_creature_ind = 0\n\n\t# given a tuple of a creature's fitness scores, calculate an arbitrary rating value of the performance\n\tdef calculate_fitness_rating(self,tup):\n\t\treturn 1 / tup[0]\n\t\n\tdef print_population_scores(self):\n\t\tprint(\"########## Epoch: {} ##########\".format(self.epoch))\n\t\tfor i in range(self.population_size):\n\t\t\tprint(\"Creature: {}\".format(i))\n\t\t\tprint(\"\\t Lowest point: {}\".format(self.fitness_scores[i][0]))\n\t\t\tprint(\"\\t Time: {}\\n\".format(self.fitness_scores[i][1]))\n\n\n\t# remove the specified creature from the game space.\n\tdef remove_creature_from_space(self, creature):\n\t\tself.space.remove(creature.shape,creature.shape.body)\n\t\tcreature.in_space = 0\n\n\n\tdef update(self):\n\t\t# wipe the screen\n\t\tself.screen.fill((255,255,255))\n\n\t\t# test a creature. Once a creature has completed testing, the next creature will be tested.\n\t\t# after all creatures have been tested, the epoch ends, the population is updated, and we repeat\n\t\tself.test_creatures()\n\t\t\n\t\t# draw all objects attatched to the space\n\t\tself.space.debug_draw(self.draw_options)\n\n\t\t# increment the simulation by one step\n\t\tfor i in range(1000):\n\t\t\tself.space.step(self.simulation_step/10.0)\n\n\t\t# set the display to the current buffered display\n\t\tpygame.display.flip()\n\n\tdef obstacle_set_1(self):\n\n\t\tlw_p = (0,0)\n\t\tlw_v = [(0, 0), (0.01*self.fitness_display_x,0), (0.01*self.fitness_display_x, self.fitness_display_y), (0, self.fitness_display_y)]\n\t\tlw = Obstacle(lw_v, lw_p, space=self.space)\n\n\t\trw_p = (0.99*self.fitness_display_x,0)\n\t\trw_v = [(0, 0), (0.01*self.fitness_display_x,0), (0.01*self.fitness_display_x, self.fitness_display_y), (0, self.fitness_display_y)]\n\t\trw = Obstacle(rw_v, rw_p, space=self.space)\n\n\t\tbot_p = (0,0)\n\t\tbot_v = [(0,0),(self.fitness_display_x, 0), (self.fitness_display_x,0.01*self.fitness_display_y), (0, 0.01*self.fitness_display_y)]\n\t\tbot = Obstacle(bot_v, bot_p, space = self.space)\n\n\t\tp1 = (0.5*self.fitness_display_x,0)\n\t\tv1 = [(0,0.8*self.fitness_display_y),(0,0.7*self.fitness_display_y), (0.5*self.fitness_display_x,0.7*self.fitness_display_y)]\n\t\tob1 = Obstacle(v1,p1,space=self.space)\n\n\t\tp2 = (0.5*self.fitness_display_x,0)\n\t\tv2 = [(0,0.8*self.fitness_display_y),(0,0.7*self.fitness_display_y), (-0.3*self.fitness_display_x,0.7*self.fitness_display_y)]\n\t\tob2 = Obstacle(v2,p2,space=self.space)\n\n\t\tp3 = (0,0)\n\t\tv3 = [(0,0.7*self.fitness_display_y),(0,0.55*self.fitness_display_y), (0.8*self.fitness_display_x,0.565*self.fitness_display_y), (0.8*self.fitness_display_x,0.57*self.fitness_display_y)]\n\t\tob3 = Obstacle(v3,p3,space=self.space)\n\n\t\tp4 = (0.5*self.fitness_display_x,0)\n\t\tv4 = [(0.5*self.fitness_display_x,0.54*self.fitness_display_y),(0.5*self.fitness_display_x,0.45*self.fitness_display_y), (-0.4*self.fitness_display_x,0.40*self.fitness_display_y)]\n\t\tob4 = Obstacle(v4,p4,space=self.space)\n\n\t\treturn [lw, rw, bot, ob1, ob2, ob3, ob4]\n","repo_name":"Richard-Hansen/EvolutionaryShapes","sub_path":"Overlord.py","file_name":"Overlord.py","file_ext":"py","file_size_in_byte":7944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"14832097007","text":"# Roll Die Dynamic\n\"\"\"Dynamically graphing frequencies of die rolls\"\"\"\n\nfrom matplotlib import animation\nimport matplotlib.pyplot as plt\nimport random\nimport seaborn as sns\nimport sys\n\n\ndef update(frame_number, rolls, faces, frequencies):\n \"\"\"Configures bar plot contents foeach animation frame.\"\"\"\n # roll die and update frequencies\n for _ in range(rolls):\n frequencies[random.randrange(1, 7) - 1] += 1\n \n # reconfigure plot for updated die frequencies\n plt.cla() # clear old contents of current Figure\n axes = sns.barplot(faces, frequencies, palette='bright') # new bars\n axes.set_title(f'Die Frequencies for {sum(frequencies):,} Rolls')\n axes.set(xlabel='Die Value', ylabel='Frequency')\n axes.set_ylim(top=max(frequencies) * 1.10) # scale y-axis by 10%\n\n # display frequency & percentage above each patch (bar)\n for bar, frequency in zip(axes.patches, frequencies):\n text_x = bar.get_x() + bar.get_width() / 2.0\n text_y = bar.get_height()\n text = f'{frequency:,}\\n{frequency / sum(frequencies):.3%}'\n axes.text(text_x, text_y, text, ha='center', va='bottom')\n\n# read command-line arguments for number of frames and rolls per frame\nnumber_of_frames = int(sys.argv[1])\nrolls_per_frame = int(sys.argv[2])\n\nsns.set_style('whitegrid') # white background with gray grid lines\nfigure = plt.figure('Rolling Six-Sided Die') # figure for animation\nvalues = list(range(1, 7)) # die favces for display on x-axis\nfrequencies = [0] * 6 # six - element list of die frequencies\n\n# configure and start animation that calls function update\ndie_animation = animation.FuncAnimation(figure, update, repeat=False, frames=number_of_frames, interval=33,\n fargs=(rolls_per_frame, values, frequencies))\n\nplt.show() # display window","repo_name":"cpoles/data_science","sub_path":"machine_learning/deitel/dicts_sets/roll_die_dynamic.py","file_name":"roll_die_dynamic.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"33941727797","text":"#foarte asemanatoare cu number of islands \n# doar ca in loc sa incrementam un contor in momentul in care gasim o insula, \n# numaram fiecare parcela din insula respectiva si vedem care e cea mai mare\n\ndef dfs(grid, i, j):\n if i<0 or j<0 or i>=len(grid) or j>=len(grid[0]) or grid[i][j]!=1: return 0\n grid[i][j] = 0\n return 1+dfs(grid,i+1,j)+dfs(grid,i,j+1)+dfs(grid,i-1,j)+dfs(grid,i,j-1)\n\nclass Solution:\n def maxAreaOfIsland(self, grid: List[List[int]]) -> int:\n\n if not grid: return 0\n max = 0\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n k = dfs(grid,i,j)\n if k > max: max = k\n return max","repo_name":"piratzii-tm/algorithms_solutions","sub_path":"leetcode/Max_area_of_island.py","file_name":"Max_area_of_island.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"139082889","text":"import pytest\nimport requests\nfrom hamcrest import assert_that, calling, raises, contains_string\nfrom hamcrest.core.base_matcher import BaseMatcher\nfrom library.python.testing.pyremock.lib.pyremock import MatchRequest, MockResponse, assert_expectations\nfrom base_test import BaseTest\n\n\nclass TestAssertExpectations(BaseTest):\n class _RaisingMatcher(BaseMatcher):\n def _matches(self, item):\n raise Exception(\"Match failed\")\n\n def describe_to(self, description):\n return \"\"\n\n def test_exception_in_route_matcher(self):\n request = MatchRequest(method=self._RaisingMatcher())\n mock_response = MockResponse(status=200, body=\"\")\n self.mock_server.expect(request, mock_response)\n\n url = self._make_url(self.mock_port, \"\")\n response = requests.get(url, timeout=1)\n assert response.status_code == requests.codes.not_found\n assert_that(calling(self.mock_server.assert_expectations), raises(AssertionError))\n\n def test_exception_in_request_matcher(self):\n request = MatchRequest(body=self._RaisingMatcher())\n mock_response = MockResponse(status=200, body=\"\")\n self.mock_server.expect(request, mock_response)\n\n url = self._make_url(self.mock_port, \"\")\n response = requests.get(url, timeout=1)\n assert response.status_code == requests.codes.bad_request\n assert_that(calling(self.mock_server.assert_expectations), raises(AssertionError))\n\n @assert_expectations\n def _test_assert_expectations_decorator_with_failed_expectations(self):\n request = MatchRequest()\n mock_response = MockResponse(status=200, body=\"\")\n self.mock_server.expect(request, mock_response)\n\n def test_assert_expectations_decorator_with_failed_expectations(self):\n assert_that(calling(self._test_assert_expectations_decorator_with_failed_expectations),\n raises(AssertionError))\n\n @assert_expectations\n def test_assert_expectations_decorator_with_successed_expectations(self):\n request = MatchRequest()\n mock_response = MockResponse(status=200, body=\"\")\n self.mock_server.expect(request, mock_response)\n\n url = self._make_url(self.mock_port, \"\")\n response = requests.get(url, timeout=1)\n assert response.status_code == requests.codes.ok\n\n @pytest.mark.parametrize(\"arg\", [0, 1])\n @assert_expectations\n def test_assert_expectations_decorator_with_parametrization(self, arg):\n request = MatchRequest(path=contains_string(str(arg)))\n mock_response = MockResponse(status=200, body=\"\")\n self.mock_server.expect(request, mock_response)\n\n url = self._make_url(self.mock_port, \"/%d\" % arg)\n response = requests.get(url, timeout=1)\n assert response.status_code == requests.codes.ok\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"library/testing/pyremock/tests/test_assert_expectations.py","file_name":"test_assert_expectations.py","file_ext":"py","file_size_in_byte":2825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"40399099786","text":"# -*- coding: utf-8 -*-\n\nimport enum\nfrom typing import Optional, Dict\n\nfrom matplotlib import pyplot as plt\nfrom mpl_events.mpl import Figure\n\nfrom .base import InteractorBase\nfrom .zoom import MouseWheelScrollZoomInteractor\nfrom .drag import MouseDragInteractor\nfrom .state import AxesLimitsResetInteractor\n\n\nclass Actions(enum.Flag):\n \"\"\"Interaction action flags\n \"\"\"\n\n ZOOM = enum.auto()\n DRAG = enum.auto()\n RESET = enum.auto()\n\n ALL = ZOOM | DRAG | RESET\n\n\ndef interact(figure: Optional[Figure] = None,\n actions: Actions = Actions.ALL) -> Dict[Actions, InteractorBase]:\n \"\"\"Enables interactors for the figure\n\n Supports interaction actions:\n\n * zoom\n * drag\n * reset axes\n \"\"\"\n if not figure:\n figure = plt.gcf()\n\n if not hasattr(interact, 'interactors'):\n interact.interactors = {}\n\n if figure in interact.interactors:\n for interactor in interact.interactors[figure].values():\n interactor.mpl_disconnect()\n del interact.interactors[figure]\n\n interactors = interact.interactors.setdefault(figure, {})\n\n if Actions.ZOOM in actions:\n interactors[Actions.ZOOM] = MouseWheelScrollZoomInteractor(figure)\n if Actions.DRAG in actions:\n interactors[Actions.DRAG] = MouseDragInteractor(figure)\n if Actions.RESET in actions:\n interactors[Actions.RESET] = AxesLimitsResetInteractor(figure)\n\n return interactors\n","repo_name":"espdev/mpl-interact","sub_path":"mpl_interact/shortcuts.py","file_name":"shortcuts.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"72"}
+{"seq_id":"38936847979","text":"'''\nCreated on 30-Sep-2016\n\n@author: harshit\n'''\n\nimport pandas as pd\n\ntrain_data = pd.read_csv('../data/CAX_Train.csv', dtype={'mpn_qs': object})\n\ntrain_data_title = train_data['title'].astype(str)\ntrain_data_desc = train_data['product_description'].astype(str)\ntrain_data_mpn = train_data['mpn_qs'].astype(str)\n\nnumRows = len(train_data)\n\nf = open('../aux_data/train_mismatch_rows_naive.txt','w')\n\nmisMatchCount = 0\n\nfor i in range(numRows):\n title_split = train_data_title[i].split()\n desc_split = train_data_desc[i].split()\n \n found = False\n \n for tc in title_split:\n if train_data_mpn[i] == tc:\n found = True\n break\n \n if found == False: \n for dc in desc_split:\n if train_data_mpn[i] == dc:\n found = True\n break\n \n if found == False:\n misMatchCount += 1\n f.write( \"\\nrow with id \" + str(i+ 1) + \" couldn't find overlap\")\n\nf.write(\"\\nTotal mismatched: \" + str(misMatchCount) + \" out of \" + str(numRows))\n \n","repo_name":"pandeconscious/crowdanalyticsProductMPN","sub_path":"src/naiveMPNContainmentCheck.py","file_name":"naiveMPNContainmentCheck.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"20875475125","text":"# https://adventofcode.com/2022/day/9\n# AoC 2022 - 9_1\n\nimport numpy as np\n\ndef get_input_data(in_file: str):\n with open(in_file, 'r') as f_in:\n return [_line.strip() for _line in f_in.readlines()]\n \n\ndef update_tail(tail_idx: int, head_idx: int):\n # row: head moves right, col: head moves down\n if tail_idx < head_idx:\n return 1\n # row: head moves left, col: head moves up\n if tail_idx > head_idx:\n return -1\n return 0\n\ndef get_save_matrix_size(_lines: list):\n max_rows, max_cols = 1, 1\n sum_up, sum_right = 0, 0\n for line in _lines: \n direction, steps = line.split()\n steps = int(steps)\n if direction == 'U' or direction == 'D': \n max_rows += steps\n sum_up += steps if direction == 'U' else 0\n if direction == 'R' or direction == 'L':\n max_cols += steps\n sum_right += steps if direction == 'R' else 0\n _grid = np.zeros((max_rows, max_cols))\n _start = {'row': sum_up, 'col': sum_right} \n return _grid, {'row': sum_up, 'col': sum_right}, {'row': sum_up, 'col': sum_right}\n\n\n\ninput_data = \"aoc2022_data/aoc2022_9.txt\"\nlines = get_input_data(input_data)\n\n\n# get maximum matrix size: sum all up and down for vertical (= rows) and \n# all right and left values for horizontal (= cols) direction\n# one additional row and col for start position\n\ngrid, t_pos, h_pos = get_save_matrix_size(lines)\ngrid[h_pos['row'], h_pos['col']] = 1\n\nfor line in lines:\n direction, steps = line.split()\n steps = int(steps)\n for step in range(steps):\n \n # move head \n if direction == 'U':\n h_pos['row'] -= 1\n if direction == 'D':\n h_pos['row'] += 1\n if direction == 'L':\n h_pos['col'] -= 1\n if direction == 'R':\n h_pos['col'] += 1\n \n # update tail \n # critical diagnoal distance \n if abs(t_pos['row'] - h_pos['row']) + abs(t_pos['col'] - h_pos['col']) > 2: \n t_pos['row'] += update_tail(t_pos['row'], h_pos['row'])\n t_pos['col'] += update_tail(t_pos['col'], h_pos['col'])\n # critical horizontal distance \n elif abs(t_pos['row'] - h_pos['row']) > 1 and abs(t_pos['col'] - h_pos['col']) <= 1:\n t_pos['row'] += update_tail(t_pos['row'], h_pos['row'])\n # critical vertical distance\n elif abs(t_pos['row'] - h_pos['row']) <= 1 and abs(t_pos['col'] - h_pos['col']) > 1: \n t_pos['col'] += update_tail(t_pos['col'], h_pos['col'])\n \n # mark as visited\n grid[t_pos['row'], t_pos['col']] = 1\n\nt_visited = int(grid.sum())\nprint(f\"{t_visited} positions are visited by the tail of the rope at least once.\") \n","repo_name":"AntonieV/AoC2022","sub_path":"aoc_2022_day_9-1.py","file_name":"aoc_2022_day_9-1.py","file_ext":"py","file_size_in_byte":2751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"162937809","text":"import pytest\n\n\n@pytest.mark.smtp\nasync def test_auth_invalid_method(env):\n client = await env.nwsmtp.get_client()\n await client._ehlo_or_helo_if_needed()\n\n auth_cmd = b\"AUTH WEIRDMETHOD\"\n code, msg = await client.execute_command(auth_cmd)\n\n assert code == 535\n assert \"5.7.8 Error: Method is not supported.\" in msg\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"mail/tests/infra/test_auth_invalid_method_smtp.py","file_name":"test_auth_invalid_method_smtp.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"11751051708","text":"from typing import Type\nfrom fastapi import UploadFile\nimport magic\nfrom .config import MAX_IMAGE_UPLOAD_SIZE, ALLOWED_MEDIA_TYPES\nfrom .exceptions import BaseAPIException, RequestEntityTooLargeException, \\\n EmptyFileUploadException, UnsupportedMediaTypeException\n\n\ndef get_exception_responses(*exceptions: Type[BaseAPIException]) -> dict:\n responses = dict()\n for exception in exceptions:\n responses.update(exception.response_model())\n return responses\n\n\ndef validate_file_size(file: UploadFile):\n if file.size > MAX_IMAGE_UPLOAD_SIZE:\n raise RequestEntityTooLargeException\n\n\ndef validate_emptiness(files: list[UploadFile]):\n if not len(files):\n raise EmptyFileUploadException\n\n\ndef validate_file_format(file: UploadFile):\n file_type = magic.from_buffer(file.file.read(2048), mime=True)\n if file_type not in ALLOWED_MEDIA_TYPES:\n raise UnsupportedMediaTypeException\n","repo_name":"mrqwer/ml_fastapi_proj","sub_path":"src/dependencies.py","file_name":"dependencies.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"17583872765","text":"import pandas as pd\nfrom statsmodels.tsa.arima_model import ARIMA\nimport numpy as np\nimport csv\n\n\ndef get_distance(predict, expect):\n dist = (predict - expect) ** 2\n return np.sqrt(sum(dist))\n\n\ndef calc_error(pred_list, tar_list):\n diff = [abs(x1 - x2) for (x1, x2) in zip(tar_list, pred_list)]\n val = np.array(np.divide(diff, tar_list, out=np.zeros_like(diff), where=tar_list != 0))\n return 1 - val.mean(0)\n\n\ndef get_train_test_sets(num_history):\n data_set = pd.read_csv('../data/mergedData.csv', index_col=None, header=0)\n data_set['Date'] = pd.to_datetime(data_set.Date, format='%d/%m/%Y')\n cows = data_set.groupby(\"CowID\")\n train = []\n test = []\n\n for cow in cows.groups:\n cow_array = cows.get_group(cow)\n cow_array = cow_array.sort_values(by=['Date'])\n cow_array = cow_array.fillna(cow_array.mean())\n if cow_array.shape[0] < num_history:\n continue\n\n train.append(cow_array.tail(num_history).head(num_history - 1))\n test.append(cow_array.tail(num_history).tail(1))\n\n return train, test, data_set.columns[-15:]\n\n\ndef write_to_file(num_history, mean_error):\n file_name = '../results/arima/' + str(num_history - 1) + '_arima.csv'\n with open(file_name, 'w', newline='') as csv_file:\n res_writer = csv.writer(csv_file)\n for out, error in mean_error:\n res_writer.writerow([str(out), str(error)])\n\n\ndef run_arima(num_history):\n train, test, outputs = get_train_test_sets(num_history)\n mean_error = []\n\n for out in outputs:\n prediction = []\n test_out = []\n j = 0\n for tr in range(len(train)): # len(train)\n try:\n train_out = train[tr][out]\n model = ARIMA(train_out, order=(5, 1, 0))\n model_fit = model.fit(disp=0)\n predict = model_fit.forecast()\n prediction.append(predict[0].min())\n test_out.append(test[tr][out].values)\n\n except:\n j += 1\n continue\n\n error = calc_error(prediction, test_out)\n\n mean_error.append((out, error))\n print(str(out) + \": \", error)\n\n write_to_file(num_history, mean_error)\n\n\nif __name__ == \"__main__\":\n for i in range(10, 31):\n run_arima(i)\n","repo_name":"mirashama/Afimilk---Cow-s-Yield-Prediction","sub_path":"models/Arima.py","file_name":"Arima.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"74391013674","text":"class node:\n def __init__(self, coeff, pwr):\n self.coeff = coeff\n self.next = None\n self.power = pwr\n\n\n# Linked List Class\nclass LinkedList:\n def __init__(self):\n self.head = None\n\n def insert(self, coeff, pwr):\n if self.head is None:\n self.head = node(coeff, pwr)\n else:\n new_node = node(coeff, pwr)\n temp = self.head\n while temp.next:\n temp = temp.next\n temp.next = new_node\n\n\ndef create_list(arr, n):\n lis = LinkedList()\n k = 0\n for i in range(n):\n lis.insert(arr[k], arr[k + 1])\n k += 2\n return lis.head\n\n\ndef add_polynomial(poly1, poly2):\n string = ''\n arr = [0] * 1000\n while poly1 is not None:\n arr[poly1.power - 1] += poly1.coef\n poly1 = poly1.next\n while poly2 is not None:\n arr[poly2.power - 1] += poly2.coef\n poly2 = poly2.next\n for i in range(len(arr) - 1, -1, -1):\n if arr[i] != 0:\n string += \"{0}x^{1} + \".format(arr[i], i + 1)\n print(string.strip()[:-2])\n\n\nif __name__ == '__main__':\n t = int(input())\n for i in range(t):\n n = int(input())\n arr = list(map(int, input().strip().split()))\n poly1 = create_list(arr, n)\n n = int(input())\n arr = list(map(int, input().strip().split()))\n poly2 = create_list(arr, n)\n add_polynomial(poly1, poly2)","repo_name":"Binovizer/GeeksForGeeks-Python","sub_path":"arrays/add_polynomial.py","file_name":"add_polynomial.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"14506423998","text":"from pathlib import Path\nfrom typing import Optional\n\nimport click\nfrom pydantic import BaseSettings, validator\n\n\nALLOWED_FORMAT_VARIABLES = (\n \"media_type\",\n \"media_format\",\n \"file\",\n \"file_extension\",\n \"file_name\",\n \"relative_path\",\n)\n\n\nclass GlobalSettings(BaseSettings):\n format_pattern: Optional[str] = None\n unknown_format_pattern: Optional[str] = None\n storage_location: Path = Path.home().joinpath(\".catalogues/\")\n\n class Config:\n env_prefix = \"CATALOGUER_\"\n\n @validator(\"format_pattern\", \"unknown_format_pattern\", allow_reuse=True)\n def validate_format_pattern(cls, format_pattern: str):\n if format_pattern:\n try:\n format_pattern.format(\n **{field: \"\" for field in ALLOWED_FORMAT_VARIABLES}\n )\n except KeyError as exception:\n raise click.BadParameter(\n f'Unrecognised \"{exception.args[0]}\" format variable. Please check your format pattern.'\n )\n return format_pattern\n\n @validator(\"storage_location\")\n def storage_location_must_exists(cls, storage_location: Path):\n if storage_location:\n storage_location = storage_location.resolve()\n if storage_location.exists() and not storage_location.is_dir():\n raise ValueError(f\"{storage_location} is not a directory\")\n storage_location.mkdir(parents=True, exist_ok=True)\n return storage_location\n","repo_name":"iago1460/cataloguer","sub_path":"cataloguer/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"72"}
+{"seq_id":"8872028095","text":"# 1. 딕셔너리 생성\r\ncities = {\r\n 'Korea' : 'Seoul',\r\n 'Japan' : 'Tokyo',\r\n 'China' : 'Beijing',\r\n 'USA' : 'Washington',\r\n 'France' : 'PARIS'\r\n}\r\n# 2. 딕셔너리의 데이터 접근\r\n\r\nprint(cities)\r\nprint(cities['Korea'])\r\nprint(cities.get('Japan'))\r\ncities['France'] = 'Paris'\r\nprint(cities['France'])\r\ncities['Germany'] = 'Berlin'\r\nprint(cities)\r\nfor country in cities:\r\n print(country)\r\n\r\n#6 반복문으로 딕셔너리의 값 탐색\r\nfor country in cities.values():\r\n print(country)\r\n\r\n#7 반복문으로 딕셔너리의 키 그리고 값 둘다 탐색\r\nfor country, city in cities.item():\r\n print('country: {}, city: {}'.format(country, city))","repo_name":"slihump/study","sub_path":"python/dictionaries_ex.py","file_name":"dictionaries_ex.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73074244392","text":"# -*- coding: utf-8 -*-\n# 依赖包 pip install pycryptodome\n# 如果仍然报没有Module可以尝试将 python安装目录下site-packages的子文件夹crypto改成Crypto\n\nimport Crypto.Cipher as Cipher\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Cipher import PKCS1_v1_5 as PKCS1_v1_5_cipper\nfrom Crypto.Signature import PKCS1_v1_5 as PKCS1_v1_5_sign\nfrom Crypto.Hash import SHA1\nimport base64\n\nclass RsaHelper(object):\n \"\"\"RSA加解密签名类\n \"\"\"\n\n def __init__(self, ciper_lib=PKCS1_v1_5_cipper, sign_lib=PKCS1_v1_5_sign, hash_lib=SHA1,\n pub_file=None, pri_file=None, pub_skey=None, pri_skey=None, pub_key=None, pri_key=None,\n reversed_size=11):\n\n # 加解密库\n self.ciper_lib = ciper_lib\n self.sign_lib = sign_lib\n self.hash_lib = hash_lib\n\n # 公钥密钥\n if pub_key:\n self.pub_key = pub_key\n elif pub_skey:\n self.pub_key = RSA.importKey(pub_skey)\n elif pub_file:\n self.pub_key = RSA.importKey(open(pub_file).read())\n\n if pri_key:\n self.pri_key = pri_key\n elif pri_skey:\n self.pri_key = RSA.importKey(pri_skey)\n elif pri_file:\n self.pri_key = RSA.importKey(open(pri_file).read())\n\n # 分块保留长度\n self.block_reversed_size = reversed_size\n\n # 根据key长度计算分块大小\n def get_block_size(self, rsa_key):\n try:\n # RSA仅支持限定长度内的数据的加解密，需要分块\n # 分块大小\n reserve_size = self.block_reversed_size\n key_size = rsa_key.size_in_bits()\n if (key_size % 8) != 0:\n raise RuntimeError('RSA 密钥长度非法')\n\n # 密钥用来解密，解密不需要预留长度\n if rsa_key.has_private():\n reserve_size = 0\n\n bs = int(key_size / 8) - reserve_size\n except Exception as err:\n print('计算加解密数据块大小出错', rsa_key, err)\n return bs\n\n # 返回块数据\n def block_data(self, data, rsa_key):\n bs = self.get_block_size(rsa_key)\n for i in range(0, len(data), bs):\n yield data[i:i + bs]\n\n # 加密\n def enc_bytes(self, data, key=None):\n text = b''\n try:\n rsa_key = self.pub_key\n if key:\n rsa_key = key\n\n cipher = self.ciper_lib.new(rsa_key)\n for dat in self.block_data(data, rsa_key):\n cur_text = cipher.encrypt(dat)\n text += cur_text\n except Exception as err:\n print('RSA加密失败', data, err)\n return text\n\n # 解密\n def dec_bytes(self, data, key=None):\n text = b''\n try:\n rsa_key = self.pri_key\n if key:\n rsa_key = key\n\n cipher = self.ciper_lib.new(rsa_key)\n for dat in self.block_data(data, rsa_key):\n if type(self.ciper_lib) == Cipher.PKCS1_v1_5:\n cur_text = cipher.decrypt(dat)\n else:\n cur_text = cipher.decrypt(dat, '解密异常')\n text += cur_text\n except Exception as err:\n print('RSA解密失败', data, err)\n return text\n\n # RSA签名\n def sign_bytes(self, data, key=None):\n signature = ''\n try:\n rsa_key = self.pri_key\n if key:\n rsa_key = key\n\n h = self.hash_lib.new(data)\n signature = self.sign_lib.new(rsa_key).sign(h)\n except Exception as err:\n print('RSA签名失败', '', err)\n return signature\n\n # RSA签名验证\n def sign_verify(self, data, sig, key=None):\n try:\n rsa_key = self.pub_key\n if key:\n rsa_key = key\n h = self.hash_lib.new(data)\n self.sign_lib.new(rsa_key).verify(h, sig)\n ret = True\n except (ValueError, TypeError):\n ret = False\n return ret\n\n # 读取标准的rsa公私钥pem文件\n def load_rsa_file(self,fn):\n key = None\n try:\n key = RSA.importKey(open(fn).read())\n except Exception as err:\n print('导入rsa的KEY文件出错', fn, err)\n return key\n\n\n # 标准字符串密钥转rsa格式密钥\n def rsa_key_str2std(self,skey):\n ret = None\n try:\n ret = RSA.importKey(skey)\n except Exception as err:\n print('字符串密钥转rsa格式密钥错误', skey, err)\n return ret\n\nif __name__ == '__main__':\n pub_key = '''-----BEGIN PUBLIC KEY-----\nMIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQC7PyjMEuniN6BPn8oqzIZ6AO1N\njSTO9R3adCCIwKfKIEoWXXM+tHDpktdPKSaAsWJPTNAGvEvtxOfzXib/EMXKqD0e\nUy5MatfpRjRdf1hJVimmfrb09Qx2j7CsKLy7nD23m4xubdYBwvkjMwt/L3JxB5D6\nqryW1wei/j1c+/OCxQIDAQAB\n-----END PUBLIC KEY-----'''\n pri_key = '''-----BEGIN RSA PRIVATE KEY-----\nMIICXQIBAAKBgQC7PyjMEuniN6BPn8oqzIZ6AO1NjSTO9R3adCCIwKfKIEoWXXM+\ntHDpktdPKSaAsWJPTNAGvEvtxOfzXib/EMXKqD0eUy5MatfpRjRdf1hJVimmfrb0\n9Qx2j7CsKLy7nD23m4xubdYBwvkjMwt/L3JxB5D6qryW1wei/j1c+/OCxQIDAQAB\nAoGAT7vGYJgRNf4f6qgNS4pKHTu10RcwPFyOOM7IZ9M5380+HyXuBB6MEjowKwpH\n1fcy+LepwaR+5KG7b5uBGY4H2ticMtdysBd9gLwnY4Eh4j7LCWE54HvELpeWXkWp\nFQdb/NQhcqMAGwYsTnRPdBqkrUmJBTYqEGkIlqCQ5vUJOCECQQDhe0KGmbq1RWp6\nTDvgpA2dUmlt2fdP8oNW8O7MvbDaQRduoZnVRTPYCDKfzFqpNXL1hAYgth1N0vzD\nnv3VoLcpAkEA1JcY+rLv5js1g5Luv8LaI5/3uOg0CW7fmh/LfGuz8k/OxASN+cAO\nUjPHrxtc5xn1zat4/bnV5GEdlOp/DhquPQJBAIV2Fsdi4M+AueiPjPWHRQO0jvDV\njfwFOFZSn5YSRUa6NmtmPY6tumUJXSWWqKb1GwlVTuc3xBqXYsNLLUWwLhkCQQDJ\nUJCiD0LohhdGEqUuSKnj5H9kxddJO4pZXFSI7UEJbJQDwcBkyn+FTm2BH+tZGZdQ\nfVnlA89OJr0poOpSg+eNAkAKY85SR9KASaTiDBoPpJ8N805XEhd0Kq+ghzSThxL3\nfVtKUQLiCh7Yd8oMd/G5S3xWJHUXSioATT8uPRH2bOb/\n-----END RSA PRIVATE KEY-----'''\n\n #r = RsaHelper(pri_file='private_key.pem', pub_file='public_key.pem')\n r = RsaHelper(pub_skey=pub_key,pri_skey=pri_key)\n data = \"hello word 中国\"\n encrydata = r.enc_bytes(data.encode(encoding='utf-8'))\n encrydata =base64.b64encode(encrydata)\n print(encrydata)\n encrydata = 'uDeQATxRbbkjTBVHXa6yi8B0KOIu7HLZuvKpXz5KNYQ8RRUTE5P3MF9d4hOG3qq+zk5z8y/1EngzFvpbsljP5qc71YQZbJEUAXARBAWB9ex7GyVlLzIA/T5bF7OmcoCr4fkWt4OYzC0aaFsKsmGDXS6aWGjD6ObulIjizDZiv8k='\n encrydata = base64.b64decode(encrydata)\n encrydata = r.dec_bytes(encrydata)\n print(encrydata.decode(\"utf-8\"))","repo_name":"lhtzbj12/python_encryption","sub_path":"RsaHelper.py","file_name":"RsaHelper.py","file_ext":"py","file_size_in_byte":6428,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"72"}
+{"seq_id":"246368158","text":"# Represents a Huffman tree for use in encoding/decoding strings.\n# A sample usage is as follows:\n#\n# h = HuffmanTree([('A', 2), ('B', 7), ('C', 1)])\n# assert(h.encode('ABC') == '01100')\n# assert(h.decode(h.encode('ABC')) == 'ABC')\nimport heapq\nfrom collections import defaultdict\nclass TreeNode:\n\n def __init__(self):\n self.left = None\n self.right = None\n self.symbol = None\n self.weight = 0\n self.min_element = None\n\n #in order to make TreeNode object comaparable based on their weigth and min_element\n # min_element is for ties such as (A:2)(B:1)(C:1), we can allocate A to the left.\n def __cmp__(self, other):\n return cmp((self.weight,self.min_element),( other.weight,other.min_element))\n\nclass HuffmanTree:\n # Helper object for building the Huffman tree.\n # You may modify this constructor but the grading script rlies on the left, right, and symbol fields.\n\n\n # The `symbol_list` argument should be a list of tuples `(symbol, weight)`,\n # where `symbol` is a symbol that can be encoded, and `weight` is the\n # the unnormalized probabilitiy of that symbol appearing.\n\n\n\n\n def __init__(self, symbol_list):\n assert(len(symbol_list) >= 2)\n # YOUR CODE HERE\n self.tree=[]\n self.dic=defaultdict()\n self.symbol_list=symbol_list\n self.buildTree(symbol_list)\n self.root = self.tree[0]# (place TreeNode object here)\n\n\n\n def buildTree(self,symbol_list):\n for i in symbol_list:\n node=TreeNode()\n node.weight=i[1]\n node.symbol=i[0]\n node.min_element=node.symbol\n heapq.heappush(self.tree,node)\n\n\n\n\n\n while len(self.tree)>1:\n\n node1=heapq.heappop(self.tree)\n node2=heapq.heappop(self.tree)\n\n new_node=TreeNode()\n new_node.min_element=min(node1.min_element,node2.min_element)\n\n new_node.left=node1\n new_node.right=node2\n new_node.weight=node1.weight+node2.weight\n heapq.heappush(self.tree,new_node)\n\n\n\n\n # Encodes a string of characters into a string of bits using the\n # symbol/weight list provided.\n\n def encodeHelper(self,root,char):\n ####\n\n\n if not root:\n return\n\n elif root.symbol:\n self.dic[root.symbol]=char\n\n\n\n self.encodeHelper(root.left,char+\"0\")\n self.encodeHelper(root.right,char+\"1\")\n\n\n\n def encode(self, s):\n assert(s is not None)\n # YOUR CODE HERE\n res=\"\"\n self.buildTree(self.symbol_list)\n root=self.root\n self.encodeHelper(root,\"\")\n for char in s:\n res+=self.dic[char]\n\n return str(res)\n\n\n\n # Decodes a string of bits into a string of characters using the\n # symbol/weight list provided.\n def decode(self,s):\n assert(s is not None)\n # YOUR CODE HERE\n ans=\"\"\n\n root=self.root\n if not s:\n return \"\"\n\n a = root\n for bit in s:\n\n if bit==\"0\":\n\n root=root.left\n else:\n root=root.right\n\n\n\n if not root.left and not root.right:\n ans+=root.symbol\n root=a\n\n if s!=self.encode(ans):\n return None\n\n return ans\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"acneyouth2/Algo-and-data-strucutre","sub_path":"HW3/huffman.py","file_name":"huffman.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"43139069136","text":"\"\"\"\nSetup template is intended to help new users to quick start the project.\nWe abstract more and more details in the template UP the level, so that, hopefully,\nat the user implementation level (highest level), users don't need to care too much about the details\n\nCONVENTION. We will make use of the following names to name template files from lower to higher levels:\nsetup_template.py (lowest level template) \nsetup_interface1.py\nsetup_interface2.py\nsetup_interface3.py\n...\nsetup.py (highest level, to be defined by users)\n\nRemark: \n1. we shouldn't have too many setup_interfaces. Otherwise, our code design is probably bad.\n2. Ideally, higher level templates use templates from lower levels. \n\"\"\"\n\nfrom .utils import *\nfrom .data import kFoldPrep, kSplitDataset, StandardPrep, StandardDataset\nfrom torch.utils.data import Dataset\n\ndef init_new_template_model(**config):\n model = config['model']\n if model == 'MLP':\n from .model import MLP\n # For NAIVE NUMERICAL DATAFRAME (see data.py, Data Frame Types)\n return MLP(config['layers'])\n \n elif model == 'ResNet1D':\n from .model import eResNet, make_intermediate_layer_settings_eResNet\n # For NAIVE NUMERICAL DATAFRAME (see data.py, Data Frame Types)\n\n cf = config['resnet_conf']\n planes = cf['planes']\n n_blocks = cf['n_blocks']\n s = make_intermediate_layer_settings_eResNet(\n planes=planes, n_blocks=n_blocks, strides=[1,2], \n ) # see projects/resnets.py for more comments \n\n L = config['layers']\n return eResNet(1, L[-1], s, dim=1,input_type='single_flat_channel')\n\n elif model == 'Transformer':\n tfc = config['tf_conf']\n nhead, n_enc, dim_ff = tfc['nhead'], tfc['n_enc'], tfc['dim_ff']\n L = config['layers']\n\n from .model import eTFClassifier\n # For NAIVE NUMERICAL DATAFRAME (see data.py, Data Frame Types)\n # Transformer for classifier \n\n INPUT_DIMENSION, OUT_DIMENSION = L[0], L[-1]\n return eTFClassifier(INPUT_DIMENSION, OUT_DIMENSION, nhead=nhead, n_enc=n_enc, dim_ff=dim_ff)\n\n elif model == 'MLPEmb':\n from .model import MLPEmb\n # For TokenAndFloat DATAFRAME (see data.py, Data Frame Types)\n return MLPEmb(config['layers'], config['TOKEN_FEATURES'], config['NUMERICAL_FEATURES'], config['dict_leng'])\n\n elif model == 'ResNetEmb':\n from .model import eResNetEmb1D\n # For TokenAndFloat DATAFRAME (see data.py, Data Frame Types)\n\n conf = config\n L = config['layers']\n return eResNetEmb1D(conf['TOKEN_FEATURES'], conf['NUMERICAL_FEATURES'], conf['dict_leng'], L['iL_settings'], L['emb_setting'], L['C'])\n\n elif model == 'TransformerEmb':\n from .model import eTFClassifierEmb\n # For TokenAndFloat DATAFRAME (see data.py, Data Frame Types) \n conf = config\n tf_conf = config['tf_conf']\n return eTFClassifierEmb(conf['TOKEN_FEATURES'], conf['NUMERICAL_FEATURES'], conf['dict_leng'], conf['layers']['C'], dim_ff=conf['layers']['dim_ff'], nD=tf_conf['nD'], nhead=tf_conf['nhead'], n_enc=tf_conf['n_enc'],) \n\n else:\n print(model)\n raise NotImplementedError('Model not recognized?') \n\n\n#############################################\n# TypeR1\n#############################################\n\"\"\" \nTypeR1: Raw dataframe. Data is from CSV file.\nCSV file can be loaded as pandas dataframe that has \n1. many columns for features and \n2. one column for target (TARGET_LABEL_NAME). No restriction on data type of target.\n\nData is loaded without any preprocessing.\nUsage: for processed data stored CSV files. For example, in our EEC process, preprocessed data are selected in part through KMeans algorithm and stored in CSV\n\"\"\" \n\nclass DataSetupTypeR1():\n def __init__(self, RAW_DF_DIR, TARGET_LABEL_NAME):\n super(DataSetupTypeR1, self).__init__() # nothing here btw\n\n self.RAW_DF_DIR = RAW_DF_DIR\n self.TARGET_LABEL_NAME = TARGET_LABEL_NAME \n self.load_data()\n\n def load_data(self):\n df = pd.read_csv(self.RAW_DF_DIR, index_col=False) \n features = [feature for feature in df if not feature == self.TARGET_LABEL_NAME]\n df_features = df.loc[:, features] \n\n self.df = df_features.to_numpy()\n self.df_target = df.loc[:, self.TARGET_LABEL_NAME] \n\nclass DatasetTypeR1(Dataset):\n def __init__(self, setupTypeR1):\n # setupTypeR1 is DataSetupTypeR1 (see above)\n super(DatasetTypeR1, self).__init__() # nothing here btw\n\n self.df = setupTypeR1.df\n self.df_target = setupTypeR1.df_target\n\n def __len__(self):\n return len(self.df)\n\n def __getitem__(self, i):\n # overwrite this function on case by case basis\n x = self.df[i]\n y0 = self.df_target[i]\n return x, y0 \n\n\n#############################################\n# TypeK1\n#############################################\n\"\"\" \n!! See data.py \"Data CONVENTION\" to distinguish between kFoldPrep and Dataset object.\n\nData is from CSV file. Data will be indexed for k-fold validation.\nCSV file can be loaded as pandas dataframe that has \n1. many columns, each contains ONLY numbers which can be normalized\n2. no missing or invalid data (data cleaning is done)\n3. one column for target (TARGET_LABEL_NAME). No restriction on data type of target.\n\nData Frame Type: NAIVE NUMERICAL DATAFRAME (refer to data.py for more details)\n\nFeatures are transformed with sklearn pipeline or anything similar that uses .transform()\nThe pipeline has been saved as a cache in DATA_CACHE_DIR\n\nSince this performs kfold validation, it's more suitable for smaller dataset:\n repeated training and validation of large models is computationally costly\n\"\"\"\n\nclass DataSetupTypeK1(kFoldPrep):\n # A kFoldPrep Object \n def __init__(self, DIRS, **kwargs):\n # Parent classes' initiations include\n # self.indices where\n # indices[k] = {'train': train_idx, 'val': val_idx, 'test': test_idx}\n self.TARGET_LABEL_NAME = kwargs['TARGET_LABEL_NAME']\n\n # temporary setup of DIRS because we wanna quickly load the data first \n # We will load the full dir via kFoldPrep init\n self.DIRS = {\n 'DATA_DIR': DIRS['DATA_DIR'],\n 'DATA_CACHE_DIR': DIRS['DATA_CACHE_DIR']} \n\n self.load_data() # load data first\n\n # This is the kFoldPrep init (super class)\n super(DataSetupTypeK1, self).__init__(DIRS, **kwargs)\n\n def load_data(self):\n from edattr.data import replace_invalid_cell_with_blank\n\n # !! Important !! This should mirror dfPP.process_dataframe() in data.py \n df = pd.read_csv(self.DIRS['DATA_DIR'], index_col=False) \n\n features = [feature for feature in df if not feature == self.TARGET_LABEL_NAME]\n df_features = df.loc[:, features] \n\n df_features = df_features.applymap(replace_invalid_cell_with_blank, ftype='numeric') \n \n cache = joblib.load(self.DIRS['DATA_CACHE_DIR'])\n pipe = cache['feature_transform_pipeline']\n\n self.df = pipe.transform(df_features) # = df_ \n self.df_target = df.loc[:, self.TARGET_LABEL_NAME] \n\n def get_data_size(self):\n return len(self.df)\n\nclass DatasetTypeK1(kSplitDataset):\n # Note: kSplitDataset is directly descended from a pytorch Dataset object (from torch.utils.data import Dataset). See data.py\n def __init__(self, setupTypeK1, k, split):\n # setupTypeK1 is DataSetupTypeK1() (see above)\n super(DatasetTypeK1, self).__init__(setupTypeK1, k, split)\n self.df = setupTypeK1.df \n self.df_target = setupTypeK1.df_target\n\n def __len__(self):\n return len(self.indices)\n\n def __getitem__(self, i):\n # overwrite this function on case by case basis\n idx = self.indices[i] # raw index from the CSV file\n x = self.df[idx]\n y0 = self.df_target[idx]\n return idx, x, y0 \n\n\n#############################################\n# TypeK2\n#############################################\n\"\"\" \n!! See data.py \"Data CONVENTION\" to distinguish between Setup Object (like kFoldPrep) and Dataset object.\n\nData is from CSV file. Data will be indexed for k-fold validation.\nCSV file can be loaded as pandas dataframe that has \n1. columns of data that can be converted to either (1) string (2) float.\n2. one column for target (TARGET_LABEL_NAME). No restriction on data type of target.\n\nData Frame Type: TokenAndFloat DATAFRAME (refer to data.py for more details)\n\n# Features are transformed with sklearn pipeline or anything similar that uses .transform()\n# The pipeline has been saved as a cache in DATA_CACHE_DIR\n\nSince this performs kfold validation, it's more suitable for smaller dataset:\n repeated training and validation of large models is computationally costly\n\"\"\"\n\n\nclass DataSetupTypeK2(kFoldPrep):\n def __init__(self, DIRS, **kwargs):\n self.TARGET_LABEL_NAME = kwargs['TARGET_LABEL_NAME']\n self.TOKEN_FEATURES = kwargs['TOKEN_FEATURES']\n self.NUMERICAL_FEATURES = kwargs['NUMERICAL_FEATURES']\n\n # temporary setup of DIRS because we wanna quickly load the data first \n # We will load the full dir via kFoldPrep init\n self.DIRS = {\n 'DATA_DIR': DIRS['DATA_DIR'],\n 'DATA_CACHE_DIR': DIRS['DATA_CACHE_DIR']} \n\n self.load_data() # load data first\n\n super(DataSetupTypeK2, self).__init__(DIRS, **kwargs)\n\n def load_data(self):\n df = pd.read_csv(self.DIRS['DATA_DIR'], index_col=False) \n\n cache = joblib.load(self.DIRS['DATA_CACHE_DIR'])\n NUMERICAL_FEATURES = cache['NUMERICAL_FEATURES']\n TOKEN_FEATURES = cache['TOKEN_FEATURES']\n word_to_ix = cache['word_to_ix']\n\n ##### numerical part #####\n pipe = cache['numerical_feature_transform_pipeline']\n df[NUMERICAL_FEATURES] = pipe.transform(df[NUMERICAL_FEATURES])\n\n ##### tokens part #####\n def word_to_ix_mapping(x):\n return word_to_ix[x] if x in word_to_ix else 0\n df[TOKEN_FEATURES] = df[TOKEN_FEATURES].applymap(word_to_ix_mapping) \n\n self.df = df\n self.df_target = df.loc[:, self.TARGET_LABEL_NAME] \n\n def get_data_size(self):\n return len(self.df)\n\nclass DatasetTypeK2(kSplitDataset):\n # setupTypeK2 is DataSetupTypeK2() (see above)\n def __init__(self, setupTypeK2, k, split):\n super(DatasetTypeK2, self).__init__(setupTypeK2, k, split)\n self.df = setupTypeK2.df \n self.df_target = setupTypeK2.df_target \n\n self.TARGET_LABEL_NAME = setupTypeK2.TARGET_LABEL_NAME\n self.TOKEN_FEATURES = setupTypeK2.TOKEN_FEATURES\n self.NUMERICAL_FEATURES = setupTypeK2.NUMERICAL_FEATURES\n\n def __len__(self):\n return len(self.indices)\n\n def __getitem__(self, i):\n # overwrite this function on case by case basis\n idx = self.indices[i] # raw index from the CSV file\n\n tokens = self.df[self.TOKEN_FEATURES].loc[idx].to_numpy()\n numerics = self.df[self.NUMERICAL_FEATURES].loc[idx].to_numpy()\n x = np.concatenate((tokens, numerics)) # convention: token first then numeric <-- the order matters!\n\n y0 = self.df_target[idx]\n return idx, x, y0 \n\n\n#############################################\n# TypeS2\n#############################################\n\"\"\" \n!! See data.py \"Data CONVENTION\" to distinguish between Setup Object (like kFoldPrep) and Dataset object.\n\nData is from CSV file. Data will be indexed for train/val/test\nCSV file can be loaded as pandas dataframe that has \n1. columns of data that can be converted to either (1) string (2) float.\n2. one column for target (TARGET_LABEL_NAME). No restriction on data type of target.\n\nData Frame Type: TokenAndFloat DATAFRAME (refer to data.py for more details)\n\n# Features are transformed with sklearn pipeline or anything similar that uses .transform()\n# The pipeline has been saved as a cache in DATA_CACHE_DIR\n\nThis is suitable for very large dataset. For example, if you have 100k (one hundred thousand) data points, you can draw 2k for validation and 2k for testing. You can always choose more data points for val/test if you have enough resources\n\"\"\"\n\nclass DataSetupTypeS2(StandardPrep):\n def __init__(self, DIRS, **kwargs):\n self.TARGET_LABEL_NAME = kwargs['TARGET_LABEL_NAME']\n self.TOKEN_FEATURES = kwargs['TOKEN_FEATURES']\n self.NUMERICAL_FEATURES = kwargs['NUMERICAL_FEATURES']\n\n # temporary setup of DIRS because we wanna quickly load the data first \n self.DIRS = {\n 'DATA_DIR': DIRS['DATA_DIR'],\n 'DATA_CACHE_DIR': DIRS['DATA_CACHE_DIR']} \n\n self.load_data() # load data first\n\n super(DataSetupTypeS2, self).__init__(DIRS, **kwargs)\n\n def load_data(self):\n df = pd.read_csv(self.DIRS['DATA_DIR'], index_col=False) \n\n cache = joblib.load(self.DIRS['DATA_CACHE_DIR'])\n NUMERICAL_FEATURES = cache['NUMERICAL_FEATURES']\n TOKEN_FEATURES = cache['TOKEN_FEATURES']\n word_to_ix = cache['word_to_ix']\n\n ##### numerical part #####\n from edattr.data import replace_invalid_cell_with_blank\n pipe = cache['numerical_feature_transform_pipeline']\n df[NUMERICAL_FEATURES] = df[NUMERICAL_FEATURES].applymap(replace_invalid_cell_with_blank, ftype='numeric').convert_dtypes() \n df[NUMERICAL_FEATURES] = pipe.transform(df[NUMERICAL_FEATURES])\n\n ##### tokens part #####\n def word_to_ix_mapping(x):\n return word_to_ix[x] if x in word_to_ix else 0\n df[TOKEN_FEATURES] = df[TOKEN_FEATURES].applymap(word_to_ix_mapping) \n\n self.df = df\n self.df_target = df.loc[:, self.TARGET_LABEL_NAME] \n\n def get_data_size(self):\n return len(self.df) \n\n def save_new_indices(self, n, DATA_STANDARD_INDICES_DIR, \n val_fraction=0.02, test_fraction=0.02, **kwargs):\n # Overwrite the default save_new_indices() function available in StandardPrep \n \n from .data import get_standard_weighted_indices\n self.indices = get_standard_weighted_indices(n, self.df_target, \n shuffle=self.shuffle, \n val_fraction=val_fraction, \n test_fraction=test_fraction,\n NOTE_DIR=DATA_STANDARD_INDICES_DIR + '.txt',\n )\n joblib.dump(self.indices, DATA_STANDARD_INDICES_DIR) \n\n\nclass DatasetTypeS2(StandardDataset):\n # setupTypeS2 is DataSetupTypeS2() (see above)\n def __init__(self, setupTypeS2, split):\n super(DatasetTypeS2, self).__init__(setupTypeS2, split)\n self.df = setupTypeS2.df \n self.df_target = setupTypeS2.df_target \n\n self.TARGET_LABEL_NAME = setupTypeS2.TARGET_LABEL_NAME\n self.TOKEN_FEATURES = setupTypeS2.TOKEN_FEATURES\n self.NUMERICAL_FEATURES = setupTypeS2.NUMERICAL_FEATURES\n\n def __len__(self):\n return len(self.indices)\n\n def __getitem__(self, i):\n # overwrite this function on case by case basis\n idx = self.indices[i] # raw index from the CSV file\n\n tokens = self.df[self.TOKEN_FEATURES].loc[idx].to_numpy()\n numerics = self.df[self.NUMERICAL_FEATURES].loc[idx].to_numpy()\n x = np.concatenate((tokens, numerics)) # convention: token first then numeric <-- the order matters!\n\n y0 = self.df_target[idx]\n return idx, x, y0 \n","repo_name":"ericotjo001/edattr.pypi","sub_path":"src/edattr/setup_template.py","file_name":"setup_template.py","file_ext":"py","file_size_in_byte":15584,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"13912501587","text":"'''\nAPI Flask\n'''\nfrom flask import *\nfrom flask_ngrok import run_with_ngrok\nfrom fastapi import FastAPI\nimport nest_asyncio\nfrom pyngrok import ngrok\nimport uvicorn\n\napp = Flask(__name__)\n\n\n@app.route('/index')\ndef home():\n return 'Hello World'\n\n\nrun_with_ngrok(app)\napp.run()\n\napp = FastAPI()\n\n\n@app.get('/index')\nasync def home():\n return \"Hello World\"\n\n\nngrok_tunnel = ngrok.connect(8000)\nprint(\n 'Public URL:https://docs.google.com/spreadsheets/d/e/2PACX-1vR0dWHCZjcG96JzSkuV3UW5R5K-Fhlr-ZH0P9FSbXIlommHrGTlNek_RRmiCgQnvYbgx-A3Qo9JqGUg/pubhtml',\n ngrok_tunnel.public_url)\nnest_asyncio.apply()\nuvicorn.run(app, port=8000)\n","repo_name":"pauloarturob/Conhecendo_python-DIO","sub_path":"IntegrationWithMongo/API-Flask_dio-PauloArtur.py","file_name":"API-Flask_dio-PauloArtur.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"13953634379","text":"import sys\ninput=sys.stdin.readline\n\nN=int(input())\nINF=(1e5)\ndp=[INF for _ in range(N+1)]\n\ndp[0]=0\n\nfor i in range(1,N+1):\n root=int(i**(1/2))\n for j in range(1,root+1):\n dp[i]=min(dp[i],dp[i-j*j]+1)\n\nprint(dp[N])\n","repo_name":"SongJeKang/goodkang","sub_path":"boj/2023_02_08/1699_squarenum.py","file_name":"1699_squarenum.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"16011202021","text":"import json\nimport requests\n\nclass CurrencyTrading(object):\n \"\"\"Class for trading currency\"\"\"\n def __init__(self):\n data = requests.get(\"https://api.privatbank.ua/p24api/pubinfo?json&exchange&coursid=5\")\n self.json = data.json()\n self.course = [[d[i] for i in d] for d in self.json]\n @property\n def currencys(self):\n return [\"USD\", \"EUR\", \"RUB\", \"UAH\"]\n #return list with result and value\n def trade(self, curr_from, curr_to, ammoun_of_money): \n if curr_from in self.currencys and curr_to in self.currencys and curr_from != curr_to:\n try:\n money = float(ammoun_of_money)\n if money <= 0:\n raise Exception\n #\n if curr_from != \"UAH\":\n money *= float(self.course[self.currencys.index(curr_from)][2])\n if curr_to != \"UAH\":\n money /= float(self.course[self.currencys.index(curr_to)][2])\n else:\n money /= float(self.course[self.currencys.index(curr_to)][3])\n #форматуємо сумму гроше до копійок\n return [\"Success trade\", float(\"\".join(str(money)[0:(str(money).find(\".\")+3)]))]\n except Exception:\n return [\"Wrong ammount of money\", -1]\n else:\n return [\"Wrong currency\" if (curr_from != curr_to) else \"Currencies must be different\" , -1]","repo_name":"Mxkxk/DataProcessing","sub_path":"Labs/Lab8/CurrencyTrading.py","file_name":"CurrencyTrading.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"30686276345","text":"#요세푸스 문제\nimport sys\nn, k = map(int, sys.stdin.readline().rstrip().split())\narr = [i for i in range(1, n + 1)]\nresult = []\nnum = k - 1\n\nfor i in range(n):\n if len(arr) <= num:\n num = num % len(arr)\n result.append(arr.pop(num))\n num += k - 1\nprint('<%s>' %(', '.join(list(map(str, result)))))","repo_name":"jisuuuu/Algorithm_Study","sub_path":"Baekjoon/Baekjoon_python/boj_1158.py","file_name":"boj_1158.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"21906987906","text":"from __future__ import annotations\n\nimport logging\nfrom typing import Iterable, Optional, TypedDict, cast\n\nimport pykka\nimport pylast\n\nfrom mopidy_advanced_scrobbler.models import Play, RecordedPlay\n\nfrom ._service import Service\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass NetworkException(Exception):\n pass\n\n\nclass NowPlayingData(TypedDict):\n artist: str\n title: str\n album: Optional[str]\n mbid: Optional[str]\n duration: Optional[int]\n\n\nclass _PartialNowPlayingData(TypedDict, total=False):\n artist: str\n title: str\n album: Optional[str]\n mbid: Optional[str]\n duration: Optional[int]\n\n\nclass PlayData(NowPlayingData):\n timestamp: int\n\n\ndef format_now_playing_data(play: Play) -> NowPlayingData:\n data: _PartialNowPlayingData = {\n \"artist\": play.artist,\n \"title\": play.title,\n }\n\n if play.album:\n data[\"album\"] = play.album\n else:\n data[\"album\"] = None\n if play.duration:\n data[\"duration\"] = play.duration\n else:\n data[\"duration\"] = None\n if play.musicbrainz_id:\n data[\"mbid\"] = play.musicbrainz_id\n else:\n data[\"mbid\"] = None\n\n return cast(NowPlayingData, data)\n\n\ndef format_play_data(play: Play) -> PlayData:\n base_data = format_now_playing_data(play)\n\n data: PlayData = {\n **base_data,\n \"timestamp\": play.played_at,\n }\n\n return data\n\n\nclass AdvancedScrobblerNetwork(pykka.ThreadingActor):\n def __init__(self, config):\n super().__init__()\n self._config = config\n self._network = None\n\n def on_start(self):\n try:\n logger.info(\"Connecting to Last.fm with username %s\", self._config[\"username\"])\n self._network = pylast.LastFMNetwork(\n api_key=self._config[\"api_key\"],\n api_secret=self._config[\"api_secret\"],\n username=self._config[\"username\"],\n password_hash=pylast.md5(self._config[\"password\"]),\n )\n logger.debug(\"Connected to Last.fm with username %s\", self._config[\"username\"])\n except pylast.PyLastError as exc:\n logger.exception(f\"Error during Advanced-Scrobbler Last.fm setup: {exc}\")\n raise\n\n def send_now_playing_notification(self, play: Play):\n now_playing_data = format_now_playing_data(play)\n\n logger.info(\"Sending 'now playing' notification: %s\", play.track_uri)\n try:\n self._network.update_now_playing(**now_playing_data)\n except pylast.PyLastError as exc:\n logger.exception(f\"Error while sending now playing data to {self._network}: {exc}\")\n raise NetworkException(\n f\"Error while sending now playing data to {self._network}\"\n ) from exc\n\n def submit_scrobble(self, play: RecordedPlay):\n play_data = format_play_data(play)\n\n logger.info(\"Submitting scrobble for play %d: %s\", play.play_id, play.track_uri)\n try:\n self._network.scrobble(**play_data)\n except pylast.PyLastError as exc:\n logger.exception(f\"Error while submitting scrobble to {self._network}: {exc}\")\n raise NetworkException(f\"Error while submitting scrobble to {self._network}\") from exc\n\n def submit_scrobbles(self, plays: Iterable[RecordedPlay]):\n plays_data = []\n play_ids = []\n for play in plays:\n plays_data.append(format_play_data(play))\n play_ids.append(play.play_id)\n\n logger.info(\"Submitting scrobbles for plays: %s\", \", \".join(map(str, play_ids)))\n try:\n self._network.scrobble_many(plays_data)\n except pylast.PyLastError as exc:\n logger.exception(f\"Error while submitting scrobbles to {self._network}: {exc}\")\n raise NetworkException(f\"Error while submitting scrobbles to {self._network}\") from exc\n\n\nnetwork_service = Service(AdvancedScrobblerNetwork)\n","repo_name":"djmattyg007/mopidy-advanced-scrobbler","sub_path":"mopidy_advanced_scrobbler/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":3912,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"1346581029","text":"import numpy as np\n\n\ndef chunk_to_slice(chunk):\n \"\"\"\n Convert an openPMD_api.ChunkInfo to slice\n \"\"\"\n stops = [a + b for a, b in zip(chunk.offset, chunk.extent)]\n indices_per_dim = zip(chunk.offset, stops)\n index_tuple = map(lambda s: slice(s[0], s[1], None), indices_per_dim)\n return tuple(index_tuple)\n\n\ndef get_data(series, record_component, i_slice=None, pos_slice=None,\n output_type=None):\n \"\"\"\n Extract the data from a (possibly constant) dataset\n Slice the data according to the parameters i_slice and pos_slice\n\n Parameters:\n -----------\n series: openpmd_api.Series\n An open, readable openPMD-api series object\n\n record_component: an openPMD.Record_Component\n\n pos_slice: int or list of int, optional\n Slice direction(s).\n When None, no slicing is performed\n\n i_slice: int or list of int, optional\n Indices of slices to be taken.\n\n output_type: a numpy type\n The type to which the returned array should be converted\n\n Returns:\n --------\n An np.ndarray (non-constant dataset) or a single double (constant dataset)\n \"\"\"\n # For back-compatibility: Convert pos_slice and i_slice to\n # single-element lists if they are not lists (e.g. float\n # and int respectively).\n if pos_slice is not None and not isinstance(pos_slice, list):\n pos_slice = [pos_slice]\n if i_slice is not None and not isinstance(i_slice, list):\n i_slice = [i_slice]\n\n # ADIOS2: Actual chunks, all other: one chunk\n chunks = record_component.available_chunks()\n\n # mask invalid regions with NaN: fill value\n # note: NaN is only defined for floating point types\n NaN_value = np.nan if np.issubdtype(record_component.dtype, np.floating) or np.issubdtype(record_component.dtype, np.complexfloating) else 0\n\n # read whole data set\n if pos_slice is None:\n # mask invalid regions with NaN\n # note: full_like triggers a full read, thus we avoid it #340\n data = np.full(record_component.shape, NaN_value, record_component.dtype)\n\n for chunk in chunks:\n chunk_slice = chunk_to_slice(chunk)\n\n # skip empty slices\n # https://github.com/ornladios/ADIOS2\n volume = 1\n for csl in chunk_slice:\n volume *= csl.stop - csl.start\n if volume == 0:\n continue\n\n # read only valid region\n x = record_component[chunk_slice]\n series.flush()\n data[chunk_slice] = x\n # slice: read only part of the data set\n else:\n full_shape = record_component.shape\n\n slice_shape = list(full_shape) # copy\n pos_slice_sorted = pos_slice.copy() # copy for in-place sort\n pos_slice_sorted.sort(reverse=True)\n for dir_index in pos_slice_sorted: # remove indices in list\n del slice_shape[dir_index]\n\n # mask invalid regions with NaN\n data = np.full(slice_shape, NaN_value, dtype=record_component.dtype)\n\n # build requested ND slice with respect to full data\n s = []\n for d in range(len(full_shape)):\n if d in pos_slice:\n s.append(i_slice[pos_slice.index(d)]) # one index in such directions\n else: # all indices in other direction\n s.append(slice(None, None, None))\n s = tuple(s)\n\n # now we check which chunks contribute to the slice\n for chunk in chunks:\n skip_this_chunk = False\n s_valid = list(s) # same as s but reduced to valid regions in chunk\n s_target = [] # starts and stops in sliced array\n chunk_slice = chunk_to_slice(chunk)\n\n # skip empty slices\n # https://github.com/ornladios/ADIOS2\n volume = 1\n for csl in chunk_slice:\n volume *= csl.stop - csl.start\n if volume == 0:\n continue\n\n # read only valid region\n for d, slice_d in enumerate(s):\n start = chunk_slice[d].start\n stop = chunk_slice[d].stop\n if isinstance(slice_d, int):\n # Nothing to do for s_target (dimension sliced out)\n # Nothing to do for s_valid (dimension index is set)\n if slice_d < start or slice_d >= stop:\n # chunk not in slice line/plane\n skip_this_chunk = True\n else:\n if slice_d.start is None or slice_d.start < start:\n s_valid[d] = slice(start, s_valid[d].stop)\n if slice_d.stop is None or slice_d.stop > stop:\n s_valid[d] = slice(s_valid[d].start, stop)\n s_target.append(slice(start, stop))\n\n s_valid = tuple(s_valid)\n s_target = tuple(s_target)\n\n # read\n if not skip_this_chunk:\n x = record_component[s_valid]\n series.flush()\n data[s_target] = x\n\n # Convert to the right type\n if (output_type is not None) and (data.dtype != output_type):\n data = data.astype( output_type )\n # Scale by the conversion factor\n if record_component.unit_SI != 1.0:\n if np.issubdtype(data.dtype, np.floating) or \\\n np.issubdtype(data.dtype, np.complexfloating):\n data *= record_component.unit_SI\n else:\n data = data * record_component.unit_SI\n\n return data\n\n\ndef join_infile_path(*paths):\n \"\"\"\n Join path components using '/' as separator.\n This method is defined as an alternative to os.path.join, which uses '\\\\'\n as separator in Windows environments and is therefore not valid to navigate\n within data files.\n\n Parameters:\n -----------\n *paths: all strings with path components to join\n\n Returns:\n --------\n A string with the complete path using '/' as separator.\n \"\"\"\n # Join path components\n path = '/'.join(paths)\n # Correct double slashes, if any is present\n path = path.replace('//', '/')\n\n return path\n","repo_name":"openPMD/openPMD-viewer","sub_path":"openpmd_viewer/openpmd_timeseries/data_reader/io_reader/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"72"}
+{"seq_id":"20507695821","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport DWM10_Parms\ndef transitiveClosure(pairList):\n logFile = DWM10_Parms.logFile\n print('\\n>>Starting DWM80')\n print('\\n>>Starting DWM80', file=logFile)\n # Bootstap process by add reverse of all pairs to the pairList\n iterationCnt = 0\n clusterList = []\n for pair in pairList:\n clusterList.append(pair)\n pairRev = (pair[1],pair[0])\n clusterList.append(pairRev)\n pairSelf = (pair[0],pair[0])\n clusterList.append(pairSelf)\n pairList = []\n # Change 2.10\n pairList = list(set(clusterList))\n # Sort pairs in order by the first position (the key)\n pairList.sort()\n #print('***sorted pairList size =', len(pairList))\n #print('***sorted pairList =', pairList)\n # All of the pairs with same key are a Key Group\n clusterList = []\n moreWorkToDo = True\n iteration = 0\n while moreWorkToDo:\n moreWorkToDo = False\n iteration +=1\n # Add a caboose record to the end of the pairList\n caboose = ('---','---')\n pairList.append(caboose)\n keyGroup = []\n for j in range(0,len(pairList)-1):\n currentPair = pairList[j]\n keyGroup.append(currentPair)\n # Look ahead to the next key\n nextPair = pairList[j+1]\n currentKey = currentPair[0]\n nextKey = nextPair[0]\n # When next key is different, at end of Key Group and ready to process keyGroup\n if currentKey != nextKey:\n firstGroupPair = keyGroup[0]\n firstGroupPairKey = firstGroupPair[0]\n firstGroupPairValue = firstGroupPair[1]\n # Add new pairs to clusterList from key groups starting with reversed pair and larger than 1 pair\n keyGroupSize = len(keyGroup)\n if firstGroupPairKey > firstGroupPairValue:\n if keyGroupSize>1:\n moreWorkToDo = True\n for k in range(keyGroupSize):\n groupPair = keyGroup[k]\n groupPairValue = groupPair[1]\n newPair = (firstGroupPairValue, groupPairValue)\n clusterList.append(newPair)\n newReversePair = (groupPairValue, firstGroupPairValue)\n clusterList.append(newReversePair)\n # Decide if first pair of keyGroup should move over to clusterList\n lastGroupPair = keyGroup[keyGroupSize-1]\n lastGroupPairValue = lastGroupPair[1]\n if firstGroupPairKey < lastGroupPairValue:\n clusterList.append(firstGroupPair) \n else:\n # pass other key groups forward to cluster list\n clusterList.extend(keyGroup)\n keyGroup = []\n pairList = []\n # Change 2.10\n pairList = list(set(clusterList))\n pairList.sort()\n iterationCnt +=1\n clusterList = []\n print('Total Closure Iterations =',iterationCnt)\n print('Total Closure Iterations =',iterationCnt, file=logFile)\n print('Size of Cluster List =', len(pairList))\n return pairList\n\n","repo_name":"OnaisKhanMohammed/Census-Linking","sub_path":"DWM80_TransitiveClosure.py","file_name":"DWM80_TransitiveClosure.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"30941651499","text":"#!/usr/bin/env python3\n\n\"\"\"Reinforcement-learning based best-response policies.\"\"\"\nimport logging\nfrom numpy import array, float32\nfrom random import Random\nfrom time import time\n\nfrom model import Model\nfrom test import test_model, test_attack_action\n\nfrom utils import ReplayBuffer\nfrom utils import ActorNetwork\nfrom utils import CriticNetwork\n\n\nimport itertools\nimport numpy as np \nimport tensorflow as tf\n\n\nBUFFER_SIZE = 10000\nBATCH_SIZE = 32\nGAMMA = 0.99\nTAU = 0.001 # target Network HyperParameters\nLRA = 0.01#0.0001 # learning rate for Actor\nLRC = 0.01#0.001 # lerning rate for Critic\n\nnp.random.seed(133)\nEXPLORE = 1000\nepisode_count = 2#2000 # number of episodes\nmax_steps = 1000#10000 # self.steps in each episode\nreward = 0\n\n#Tensorflow GPU optimization\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nsess = tf.Session(config=config)\nfrom keras import backend as K\nK.set_session(sess)\n\ndef normalized(vect):\n \"\"\"\n Normalize the given vector.\n :param vect: Vector represented as a list of floats.\n :return: Normalized vector represented as a list of floats.\n \"\"\"\n factor = 1 / sum(vect)\n return [element * factor for element in vect]\n\nclass DDPGAgent:\n \"\"\"\n Learning algorithm inspired by Q-learning. \n States are represented as lists of arbitrary floats, while actions are represented as normalized lists of floats (i.e., floats are greater than or equal to zero and sum up to one).\n Differences compared to Q-learning are described in the documentation of the relevant functions.\n \"\"\"\n\n def __init__(self, state_size, action_size):\n \"\"\"\n Construct a learning algorithm object.\n :param state_size: Length of lists representing states.\n :param action_size: Length of lists representing actions.\n \"\"\"\n self.state_size = state_size\n self.action_size = action_size\n self.actor = ActorNetwork(sess, self.state_size, self.action_size, BATCH_SIZE, TAU, LRA)\n self.critic = CriticNetwork(sess, self.state_size, self.action_size, BATCH_SIZE, TAU, LRC)\n self.buff = ReplayBuffer(BUFFER_SIZE) \n self.step = 0\n self.epsilon = 1\n \n def learn(self, initial_state, state_observe, state_update, rnd=Random(0)):\n \"\"\"\n Q-learning based algorithm for learning the best actions in every state.\n Note that due to performance reasons, the state-value function (i.e., Q) is not updated after every self.step, but only in batches.\n :param initial_state: Initial state, represented as an arbitrary object (note that this can be of a different format than the states used in other functions of QLearning).\n :param state_observe: Observes the state. Function, takes either initial_state or a state output by state_update, returns a list of floats (of length state_size).\n :param state_update: Updates the state based on an action. Function, takes a state (see state_observe) and an action (normalized list of floats), return the next state (may be arbitrary object).\n :param rnd: Random number generator.\n \"\"\"\n logging.info(\"DDPG algorithm starts.\")\n for i in range(episode_count):\n logging.info(\"Episode: %s Replay Buffer: %s\", str(i), str(self.buff.count()))\n state_t = initial_state \n s_t = np.array([state_observe(initial_state)]) # initial observation state\n total_reward = 0.\n\n for j in range(max_steps):\n loss = 0 \n self.epsilon -= 1.0 / EXPLORE \n a_t = np.zeros([1, self.action_size])\n noise_t = np.zeros([1, self.action_size]) # a noise process to select action\n \n # selct action according to the current policy and exploration noise\n a_t_original = self.actor.model.predict(s_t)\n for k in range(self.action_size):\n noise_t[0][k] = max(self.epsilon, 0)*np.random.normal(0, 0.1, 1)[0] # the noise follows Gaussian distribution\n a_t[0][k] = a_t_original[0][k]+noise_t[0][k]\n if a_t[0][k] < 0:\n a_t[0][k] = 0.05 # before normalization, we should make sure a_t[0][k] >= 0\n # normalize the action\n sum_action = sum(a_t[0])\n for k in range(self.action_size):\n a_t[0][k] = a_t[0][k]*1.0/sum_action \n #print(j, a_t[0])\n (state_t1, loss_t) = state_update(state_t, list(a_t[0]))\n r_t = -1*loss_t # reward of the defender is -1*its loss \n s_t1 = np.array([state_observe(state_t1)])\n\n self.buff.add(s_t[0], a_t[0], r_t, s_t1[0]) # add replay buffer\n\n #Do the batch update\n batch = self.buff.getBatch(BATCH_SIZE)\n states = np.asarray([e[0] for e in batch])\n actions = np.asarray([e[1] for e in batch])\n rewards = np.asarray([e[2] for e in batch])\n new_states = np.asarray([e[3] for e in batch])\n y_t = np.asarray([[0] for e in batch])\n\n\n\n target_q_values = self.critic.target_model.predict([new_states, self.actor.target_model.predict(new_states)]) \n\n for k in range(len(batch)):\n y_t[k] = rewards[k] + GAMMA*target_q_values[k]\n\n loss += self.critic.model.train_on_batch([states,actions], y_t) \n a_for_grad = self.actor.model.predict(states)\n grads = self.critic.gradients(states, a_for_grad)\n self.actor.train(states, grads)\n self.actor.target_train()\n self.critic.target_train()\n\n total_reward += r_t\n s_t = s_t1\n state_t = state_t1\n\n self.step += 1\n\n logging.info(\"TOTAL REWARD @ %s-th Episode : Reward %s\", str(i), str(total_reward))\n logging.info(\"Total self.step: %s\", str(self.step))\n logging.info(\"\")\n\n logging.info(\"Finish.\")\n\ndef flatten_lists(lists):\n \"\"\"\n Construct a single list from a list of lists.\n :param lists: List of lists.\n :return: Single list that contains all the elements of all the lists, in the same order.\n \"\"\" \n return [element for inner in lists for element in inner]\n \ndef unflatten_list(lst, dim):\n \"\"\"\n Construct a list of lists from a single list.\n :param lst: List of elements, size must be a multiple of dim.\n :param dim: Number of elements in each inner list.\n :return: List of lists that contain all the elements of the list.\n \"\"\"\n ##print(len(lst))\n ##print(dim)\n assert((len(lst) % dim) == 0)\n lists = []\n for i in range(len(lst) // dim):\n lists.append([lst[j] for j in range(i * dim, (i + 1) * dim)])\n return lists\n \nclass DefenderBestResponse:\n \"\"\"Best-response investigation policy for the defender.\"\"\"\n def __init__(self, model, alpha):\n \"\"\"\n Construct a best-response object using QLearning.\n :param model: Model of the alert prioritization problem (i.e., Model object).\n :param alpha: Attack policy. Function, takes a model and a state, returns the probability of mounting attacks (one-dimensional list) given a model and a state.\n \"\"\"\n agent = DDPGAgent(len(model.alert_types) * model.horizon, len(model.alert_types) * model.horizon)\n def state_update(state, action):\n \"\"\"\n State update function for QLearning.learn.\n :param state: State of the alert prioritization problem (i.e., Model.State object).\n :param action: Action represented as a normalized list of floats.\n :return: Next state (i.e., Model.State object).\n \"\"\"\n delta = model.make_investigation_feasible(state.N, unflatten_list(action, len(model.alert_types))) # make_investigation_feasible ``unnormalizes'' the action\n next_state = model.next_state(state, delta, alpha)\n loss = next_state.U - state.U\n return (next_state, loss)\n agent.learn(Model.State(model),\n lambda state: flatten_lists(state.N),\n state_update)\n \nif __name__ == \"__main__\":\n logging.basicConfig(format='%(asctime)s / %(levelname)s: %(message)s', level=logging.DEBUG)\n model = test_model()\n DefenderBestResponse(model, test_attack_action)\n","repo_name":"aronlaszka/AlertPrioritization","sub_path":"code/v1/src/learning_AC.py","file_name":"learning_AC.py","file_ext":"py","file_size_in_byte":8496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"43286002499","text":"\"\"\"\nTest the stackify.application module\n\"\"\"\n\nimport unittest\nimport os\nfrom mock import patch\nfrom tests.bases import ClearEnvTest\n\nfrom stackify.constants import API_URL\nfrom stackify.constants import DEFAULT_HTTP_ENDPOINT\nfrom stackify.constants import TRANSPORT_TYPE_AGENT_HTTP\nfrom stackify.constants import TRANSPORT_TYPE_AGENT_SOCKET\nfrom stackify.constants import TRANSPORT_TYPE_DEFAULT\nfrom stackify.transport.application import get_configuration\n\n\nclass TestConfig(ClearEnvTest):\n '''\n Test automatic configuration for the ApiConfiguration\n '''\n\n def test_required_kwargs(self):\n '''API configuration requires appname, env and key'''\n env_map = {}\n\n with patch.dict('os.environ', env_map):\n with self.assertRaises(NameError):\n get_configuration()\n with self.assertRaises(NameError):\n get_configuration(application='1')\n with self.assertRaises(NameError):\n get_configuration(application='1', environment='2')\n with self.assertRaises(NameError):\n get_configuration(application='1', environment='2', api_url='3')\n\n get_configuration(application='1', environment='2', api_key='3')\n\n def test_environment_config(self):\n '''API configuration can load from env vars'''\n env_map = {\n 'STACKIFY_APPLICATION': 'test1_appname',\n 'STACKIFY_ENVIRONMENT': 'test1_environment',\n 'STACKIFY_API_KEY': 'test1_apikey',\n 'STACKIFY_API_URL': 'test1_apiurl',\n }\n\n with patch.dict('os.environ', env_map):\n config = get_configuration()\n\n self.assertEqual(config.application, 'test1_appname')\n self.assertEqual(config.environment, 'test1_environment')\n self.assertEqual(config.api_key, 'test1_apikey')\n self.assertEqual(config.api_url, 'test1_apiurl')\n\n def test_kwarg_mix(self):\n '''API configuration can load from a mix of env vars and kwargs'''\n env_map = {\n 'STACKIFY_APPLICATION': 'test2_appname',\n 'STACKIFY_ENVIRONMENT': 'test2_environment',\n }\n\n with patch.dict('os.environ', env_map):\n config = get_configuration(api_key='test2_apikey', api_url='test2_apiurl')\n\n self.assertEqual(config.application, 'test2_appname')\n self.assertEqual(config.environment, 'test2_environment')\n self.assertEqual(config.api_key, 'test2_apikey')\n self.assertEqual(config.api_url, 'test2_apiurl')\n\n def test_kwargs(self):\n '''API configuration can load from kwargs'''\n config = get_configuration(\n application='test3_appname',\n environment='test3_environment',\n api_key='test3_apikey',\n api_url='test3_apiurl',\n )\n\n self.assertEqual(config.application, 'test3_appname')\n self.assertEqual(config.environment, 'test3_environment')\n self.assertEqual(config.api_key, 'test3_apikey')\n self.assertEqual(config.api_url, 'test3_apiurl')\n\n def test_api_url_default(self):\n '''API URL is set automatically'''\n config = get_configuration(\n application='test4_appname',\n environment='test4_environment',\n api_key='test4_apikey',\n )\n\n self.assertEqual(config.application, 'test4_appname')\n self.assertEqual(config.environment, 'test4_environment')\n self.assertEqual(config.api_key, 'test4_apikey')\n self.assertEqual(config.api_url, API_URL)\n\n def test_transport_default(self):\n config = get_configuration(\n application='test4_appname',\n environment='test4_environment',\n api_key='test4_apikey',\n api_url='test3_apiurl',\n )\n\n self.assertEqual(config.application, 'test4_appname')\n self.assertEqual(config.environment, 'test4_environment')\n self.assertEqual(config.api_key, 'test4_apikey')\n self.assertEqual(config.api_url, 'test3_apiurl')\n self.assertEqual(config.transport, 'default')\n\n def test_transport_given(self):\n config = get_configuration(\n application='test5_appname',\n environment='test5_environment',\n api_key='test5_apikey',\n api_url='test5_apiurl',\n transport='test5_transport'\n )\n\n self.assertEqual(config.application, 'test5_appname')\n self.assertEqual(config.environment, 'test5_environment')\n self.assertEqual(config.api_key, 'test5_apikey')\n self.assertEqual(config.api_url, 'test5_apiurl')\n self.assertEqual(config.transport, 'test5_transport')\n\n def test_api_key_is_required_on_default_transport(self):\n with self.assertRaises(NameError):\n get_configuration(\n application='test_appname',\n environment='test_environment',\n api_key='',\n api_url='test_apiurl',\n transport='default'\n )\n\n def test_api_key_is_not_required_on_agent_socket_transport(self):\n config = get_configuration(\n application='test_appname',\n environment='test_environment',\n api_key='',\n api_url='test_apiurl',\n transport='agent_socket'\n )\n\n self.assertEqual(config.application, 'test_appname')\n self.assertEqual(config.environment, 'test_environment')\n self.assertEqual(config.api_key, '')\n self.assertEqual(config.api_url, 'test_apiurl')\n self.assertEqual(config.transport, 'agent_socket')\n\n\nclass ConfigEnvironmentVariableTest(ClearEnvTest):\n def test_transport_environment_variable_default(self):\n os.environ[\"STACKIFY_TRANSPORT\"] = \"default\"\n\n config = get_configuration(\n application='test_appname',\n environment='test_environment',\n api_key='test_apikey',\n api_url='test_apiurl',\n )\n\n assert config.transport == TRANSPORT_TYPE_DEFAULT\n\n del os.environ[\"STACKIFY_TRANSPORT\"]\n\n def test_transport_environment_variable_agent_socket(self):\n os.environ[\"STACKIFY_TRANSPORT\"] = \"agent_socket\"\n\n config = get_configuration(\n application='test_appname',\n environment='test_environment',\n api_key='test_apikey',\n api_url='test_apiurl',\n )\n\n assert config.transport == TRANSPORT_TYPE_AGENT_SOCKET\n\n del os.environ[\"STACKIFY_TRANSPORT\"]\n\n def test_transport_environment_variable_agent_http(self):\n os.environ[\"STACKIFY_TRANSPORT\"] = \"agent_http\"\n\n config = get_configuration(\n application='test_appname',\n environment='test_environment',\n api_key='test_apikey',\n api_url='test_apiurl',\n )\n\n assert config.transport == TRANSPORT_TYPE_AGENT_HTTP\n\n del os.environ[\"STACKIFY_TRANSPORT\"]\n\n def test_http_endpoint_environment_variable_default(self):\n config = get_configuration(\n application='test_appname',\n environment='test_environment',\n api_key='test_apikey',\n api_url='test_apiurl',\n )\n\n assert config.http_endpoint == DEFAULT_HTTP_ENDPOINT\n\n def test_http_endpoint_environment_variable(self):\n os.environ[\"STACKIFY_TRANSPORT_HTTP_ENDPOINT\"] = \"test\"\n\n config = get_configuration(\n application='test_appname',\n environment='test_environment',\n api_key='test_apikey',\n api_url='test_apiurl',\n )\n\n assert config.http_endpoint == \"test\"\n\n del os.environ[\"STACKIFY_TRANSPORT_HTTP_ENDPOINT\"]\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"stackify/stackify-api-python","sub_path":"tests/transport/test_application.py","file_name":"test_application.py","file_ext":"py","file_size_in_byte":7727,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"72"}
+{"seq_id":"34636732516","text":"\"\"\"\nDescriptive exception classes for the AIrsenal API\n\"\"\"\n\n\nclass ApiException(Exception):\n status_code = 500\n\n def __init__(self, message, status_code=None, payload=None):\n Exception.__init__(self)\n self.message = message\n if status_code is not None:\n self.status_code = status_code\n self.payload = payload\n\n def to_dict(self):\n rv = dict(self.payload or ())\n rv[\"status\"] = \"error\"\n rv[\"error\"] = self.message\n return rv\n","repo_name":"alan-turing-institute/AIrsenal","sub_path":"airsenal/api/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":243,"dataset":"github-code","pt":"72"}
+{"seq_id":"39381922109","text":"from fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom ans import Ans_frm\nfrom fastapi.middleware.cors import CORSMiddleware\nimport webbrowser\nimport uvicorn\napp = FastAPI(docs_url='/demo/docs', redoc_url='/demo/redocs',openapi_url='/demo/openapi.json')\norigins = [\"*\"]\napp.add_middleware(\nCORSMiddleware,\n # allow_origins=origins,\n allow_origins=[\"*\"],\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\nclass QuestionInput(BaseModel):\n question: str\n\n\nqna_testing = Ans_frm()\n\n@app.post(\"/demo/predict\")\nasync def predict(question_input: QuestionInput):\n try:\n question = question_input.question\n print(question)\n out=qna_testing.similarity_search_content(question)\n out1=qna_testing.similarity_search_link(question)\n valid1=out[0][-1] \n if (valid1<0.3):\n out=str(out)\n print(out)\n out1=dict(out1[0][0])\n\n link=qna_testing.link_gen(out1[\"page_content\"])\n answer=qna_testing.extract_answer(out)\n result = {\"answer\": answer,\"link\":link}\n else:\n result=None \n print(result)\n return result\n except Exception as e:\n return {\"error\": str(e)}\nif __name__ == \"__main__\":\n url = \"/home/arnav/college_chatbot/src/chartgpt/chat.html\"\n webbrowser.get('google-chrome').open(url)\n\n uvicorn.run(app, port=8000) \n","repo_name":"Arni-tech/college_chatbot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"35903687457","text":"from typing import Tuple, Mapping, FrozenSet, Set, Collection, MutableMapping, Type, Sequence, Optional\n\nfrom frozendict import frozendict # type: ignore\nfrom pydantic import Field\nfrom pydantic.dataclasses import dataclass\n\nfrom scpy.action.action import Action\nfrom scpy.agent.agent import Agent\nfrom scpy.causal_setting.causal_setting import CausalSetting\nfrom scpy.dataclass_config import DataclassConfig\nfrom scpy.preorder import Preorder\nfrom scpy.primitives import Function, Literal, Predicate\nfrom scpy.situation.situation import Situation\nfrom scpy.state.state import State\nfrom scpy.util import from_function_to_predicate\n\n\n@dataclass(frozen=True, order=True, config=DataclassConfig)\nclass DialecticCausalSetting(CausalSetting):\n fact_set: State = Field(default_factory=frozenset)\n awareness_set: FrozenSet[Predicate] = Field(default_factory=frozenset)\n argument_scheme: Mapping[Function, Tuple[FrozenSet[Literal], FrozenSet[Literal]]] = Field(\n default_factory=frozendict)\n conflict_relation: Preorder = Field(default_factory=Preorder)\n strength_preorder: Preorder = Field(default_factory=Preorder)\n fluents: FrozenSet[Predicate] = Field(init=False, repr=False, default_factory=frozenset)\n actions: FrozenSet[Action] = Field(init=False, repr=False, default_factory=frozenset)\n agents: FrozenSet[Agent] = Field(default_factory=frozenset)\n\n def __post_init__(self) -> None:\n actions: Set[Action] = set()\n if isinstance(self.awareness_set, frozenset):\n for pred in self.awareness_set:\n arg = Literal(pred)\n actions.add(Function('position', (arg,)))\n actions.add(Function('position', (-arg,)))\n if isinstance(self.argument_scheme, Mapping):\n for arg, cogn_ctx in self.argument_scheme.items(): # type: ignore[assignment]\n preds, poses = cogn_ctx\n for pos in poses:\n actions.add(Function('supports', (arg, pos)))\n\n object.__setattr__(self, 'actions', frozenset(actions))\n\n def __extract_argument_position(self, action: Action, symbol: str = 'supports') -> \\\n Tuple[Function, Literal]:\n if action.symbol != symbol:\n raise ValueError(\n f\"Unknown Action {action}. Action.symbol is {action.symbol} but should be {symbol}\")\n if len(action.arguments) < 2:\n raise ValueError(\n f\"Unknown Action {action}. Not enough arguments.\")\n arg = action.arguments[0]\n pos = action.arguments[1]\n if not isinstance(arg, Function):\n raise ValueError(\n f\"Unknown Action {action}. {arg} must be of type Function, but is type {type(arg).__name__}.\")\n if not isinstance(pos, Literal):\n raise ValueError(\n f\"Unknown Action {action}. {pos} must be of type Literal, but is type {type(pos).__name__}.\")\n if arg not in self.argument_scheme:\n raise ValueError(f\"Unknown Action {action}. Argument not in argument scheme.\")\n if pos.predicate not in self.awareness_set:\n raise ValueError(f\"Unknown Action {action}. Position not in awareness set.\")\n e = arg.arguments[0]\n if isinstance(e, Literal):\n if e.predicate not in self.awareness_set:\n raise ValueError(f\"Unknown Action {action}. {e.predicate} not in awareness set.\")\n else:\n assert isinstance(e, Function)\n if len(e.arguments) != 2:\n raise ValueError(f\"Unknown Action {action}. {e} should have exactly two arguments.\")\n c = e.arguments[0]\n if not isinstance(c, Literal):\n raise ValueError(\n f\"Unknown Action {action}. {c} must be of type Literal, but is type {type(c).__name__}.\")\n if c.predicate not in self.awareness_set:\n raise ValueError(\n f\"Unknown Action {action}. {c.predicate} not in awareness set.\"\n )\n p = e.arguments[1]\n if isinstance(p, Literal):\n if p.predicate not in self.awareness_set:\n raise ValueError(\n f\"Unknown Action {action}. {p.predicate} not in awareness set.\"\n )\n elif isinstance(p, Function):\n if p.symbol != 'exo':\n raise ValueError(\n f\"Unknown Action {action}. {p} must have the symbol exo.\")\n\n return arg, pos\n\n def __is_well_formed(self, literal: Literal, functor: str, *types: Type) -> bool:\n if literal.predicate.functor != functor:\n return False\n types_: Sequence[Type] = tuple(types)\n if len(types_) < len(literal.predicate.arguments):\n return False\n for i, arg in enumerate(literal.predicate.arguments):\n type_: Type = types_[i]\n # noinspection PyTypeHints\n if not isinstance(arg, type_):\n return False\n if type_ is Literal:\n assert isinstance(arg, Literal)\n if arg.predicate not in self.awareness_set:\n return False\n return True\n\n def __do_initial_state(self, action: Action) -> State:\n if action.symbol != 'position':\n raise ValueError(\n f\"Unknown Action {action}. In initial state Action has to be of form \"\n f\"{Function('position', (Function('Position'),))}.\")\n pos = action.arguments[0]\n if not isinstance(pos, Literal):\n raise ValueError(f\"Unknown Action {action}. Position has to be a Literal\")\n if pos.predicate not in self.awareness_set:\n raise ValueError(f\"Unknown Action {action}. {pos.predicate} is not in the awareness set.\")\n argument_lit = Literal(Predicate('position', (pos,)))\n return frozenset({argument_lit})\n\n def __do_reasoning(self, action: Action, state: State, incomplete: Mapping[Literal, Collection[Literal]],\n functor: str = 'argument') -> State:\n arg, pos = self.__extract_argument_position(action)\n if not (pos in incomplete and not incomplete[pos]) and not any(\n pos in preds for _, preds in incomplete.items()):\n raise ValueError(f\"Unknown Action {action}. Position does not need support.\")\n state_ = set(state)\n argument_pred = Predicate(functor, (arg, pos))\n argument_lit = Literal(argument_pred)\n if -argument_lit in state_:\n state_.remove(-argument_lit)\n state_.add(argument_lit)\n return frozenset(state_)\n\n def __do_attack(self, action: Action, state: State, attack_map: Mapping[Function, Collection[Literal]]) -> State:\n arg, pos = self.__extract_argument_position(action, 'attacks')\n if arg not in attack_map:\n raise ValueError(f\"Unknown action {action}. Does not attack argument.\")\n\n attack_pred = from_function_to_predicate(action)\n attack_lit = Literal(attack_pred)\n state_ = set(state)\n if -attack_lit in state:\n state_.remove(-attack_lit)\n state_.add(attack_lit)\n return frozenset(state_)\n\n def __do_defense(self, action: Action, state: State,\n undefended_attacks: Mapping[Function, Collection[Literal]]) -> State:\n def_argument, def_position = self.__extract_argument_position(action, 'defends')\n if all(self.strength_preorder.is_strictly_preceded(def_argument, att_argument) for att_argument in\n undefended_attacks):\n raise ValueError(f\"Unknown Action {action}. Defense is not stronger than attack.\")\n state_ = set(state)\n def_argument_pred = from_function_to_predicate(action)\n def_argument_lit = Literal(def_argument_pred)\n if -def_argument_lit in state_:\n state_.remove(-def_argument_lit)\n state_.add(def_argument_lit)\n return frozenset(state_)\n\n def __do_consolidate(self, state: State) -> State:\n att: Optional[Literal] = None\n attacking = set()\n defending = set()\n for literal in state:\n if self.__is_well_formed(literal, 'attacks', Function, Literal):\n functor = literal.predicate.functor\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n lit = Literal(Predicate(functor, (arg, -pos)))\n attacking.add(lit)\n att = literal\n elif self.__is_well_formed(literal, 'defends', Function, Literal):\n defending.add(literal)\n changed = True\n while changed:\n changed = False\n for literal in state:\n if self.__is_well_formed(literal, 'supports', Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n for coll in (attacking, defending):\n if literal in coll:\n continue\n for elem in coll:\n arg, _ = elem.predicate.arguments\n assert isinstance(arg, Function)\n preds, _ = self.argument_scheme[arg]\n if pos in preds:\n changed = True\n coll.add(literal)\n break\n\n state_ = set(state)\n for attack in attacking:\n counterargument = Literal(Predicate('counterargument', attack.predicate.arguments))\n if -counterargument in state_:\n state_.remove(-counterargument)\n state_.add(counterargument)\n\n for defence in defending:\n arg = Literal(Predicate('argument', defence.predicate.arguments))\n if -arg in state_:\n state_.remove(-arg)\n state_.add(arg)\n state_ -= (attacking | defending)\n assert not attacking or att is not None\n if att is not None:\n state_.remove(att)\n return frozenset(state_)\n\n def unsupported_literals(self, state: State, argument: Function) -> Collection[Literal]:\n preds, poses = self.argument_scheme[argument]\n supported = set()\n for literal in state:\n if self.__is_well_formed(literal, 'supports', Function, Literal):\n arg, pos = literal.predicate.arguments\n supported.add(pos)\n return {pred for pred in preds if pred not in supported}\n\n def attacks(self, state: State) -> Mapping[Function, Collection[Literal]]:\n arguments = set()\n counterarguments = set()\n for literal in state:\n if self.__is_well_formed(literal, 'argument', Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n arguments.add((arg, pos))\n elif self.__is_well_formed(literal, 'attacks', Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n counterarguments.add((arg, pos))\n att: MutableMapping[Function, Set[Literal]] = {}\n for (arg, pos) in arguments:\n attacks_ = {counterargument for counterargument in self.conflict_relation[arg]\n if (counterargument, -pos) not in counterarguments}\n\n attacks_ |= {counterargument for (counterargument, (preds, poses)) in self.argument_scheme.items()\n if -pos in poses}\n\n for attack in attacks_:\n att.setdefault(attack, set()).add(pos)\n\n return att\n\n def incomplete_reasoning(self, state: State, functor: str = 'attacks') -> Mapping[Literal, Function]:\n reasoning: MutableMapping[Literal, Function] = {}\n supporting: MutableMapping[Literal, Function] = {}\n for literal in state:\n if self.__is_well_formed(literal, functor, Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n reasoning[pos] = arg\n elif self.__is_well_formed(literal, 'supports', Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n supporting[pos] = arg\n\n incomplete_reasoning: MutableMapping[Literal, Function] = {}\n\n for pos, arg in reasoning.items():\n preds, _ = self.argument_scheme[arg]\n if not all(pred in supporting for pred in preds):\n incomplete_reasoning[pos] = arg\n\n return incomplete_reasoning\n\n def undefended_attacks(self, state: State) -> Mapping[Function, Collection[Literal]]:\n attacking: MutableMapping[Function, Set[Literal]] = {}\n defending: MutableMapping[Function, Set[Literal]] = {}\n for literal in state:\n if self.__is_well_formed(literal, 'attacks', Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n attacking.setdefault(arg, set()).add(-pos)\n elif self.__is_well_formed(literal, 'supports', Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n attacking.setdefault(arg, set()).add(pos)\n elif self.__is_well_formed(literal, 'defends', Function, Literal):\n arg, pos = literal.predicate.arguments\n assert isinstance(arg, Function)\n assert isinstance(pos, Literal)\n defending.setdefault(arg, set()).add(pos)\n\n defended = False\n strong_defense_needed = False\n strong_defended = False\n for att_argument, att_positions in attacking.items():\n for def_argument, def_positions in defending.items():\n if self.strength_preorder.is_strictly_preceded(def_argument, att_argument):\n strong_defense_needed = True\n if self.strength_preorder.is_strictly_preceded(att_argument, def_argument):\n strong_defended = True\n if any(-att_position in def_positions for att_position in att_positions):\n defended = True\n\n if not defended or strong_defense_needed and not strong_defended:\n return attacking\n return {}\n\n def incomplete_arguments(self, state: State) -> Mapping[Literal, Collection[Literal]]:\n positions: Set[Literal] = set()\n facts: Set[Function] = set()\n hypotheses: Set[Function] = set()\n arguments: Set[Function] = set()\n\n for literal in state:\n if self.__is_well_formed(literal, 'position', Literal):\n pos = literal.predicate.arguments[0]\n assert isinstance(pos, Literal)\n positions.add(pos)\n elif self.__is_well_formed(literal, 'argument', Function, Literal):\n arg = literal.predicate.arguments[0]\n assert isinstance(arg, Function)\n if arg.symbol == 'fact':\n facts.add(arg)\n elif arg.symbol == 'hyp':\n hypotheses.add(arg)\n else:\n arguments.add(arg)\n\n supported: Set[Literal] = {f.arguments[0] for f in facts if isinstance(f.arguments[0], Literal)} | {\n h.arguments[0] for h in hypotheses if isinstance(h.arguments[0], Literal)}\n incomplete: MutableMapping[Literal, Set[Literal]] = {}\n for pos in positions:\n if pos not in supported:\n incomplete[pos] = set()\n for arg in arguments:\n assert isinstance(arg, Function)\n assert arg in self.argument_scheme\n preds, poses = self.argument_scheme[arg]\n incomplete_ = set()\n for pred in preds:\n if pred not in supported:\n incomplete_.add(pred)\n if not incomplete_:\n supported.update(poses)\n next_incomplete = dict(incomplete)\n for pos, preds_ in incomplete.items():\n if preds_ <= poses:\n del next_incomplete[pos]\n supported.add(pos)\n incomplete = next_incomplete\n else:\n for pos in poses:\n incomplete.setdefault(pos, set())\n incomplete[pos].update(incomplete_)\n return incomplete\n\n def do_state(self, action: Action, state: State) -> State:\n if not state:\n return self.__do_initial_state(action)\n incomplete_arguments = self.incomplete_arguments(state)\n if incomplete_arguments:\n return self.__do_reasoning(action, state, incomplete_arguments, 'argument')\n incomplete_attacks = self.incomplete_reasoning(state, 'attacks')\n if incomplete_attacks:\n incomplete = {}\n for lit, arg in incomplete_attacks.items():\n incomplete[lit] = self.unsupported_literals(state, arg)\n return self.__do_reasoning(action, state, incomplete, 'supports')\n incomplete_defends = self.incomplete_reasoning(state, 'defends')\n if incomplete_defends:\n incomplete = {}\n for lit, arg in incomplete_defends.items():\n incomplete[lit] = self.unsupported_literals(state, arg)\n return self.__do_reasoning(action, state, incomplete, 'supports')\n undefended_attacks = self.undefended_attacks(state)\n if undefended_attacks:\n for attack, att_posses in undefended_attacks.items():\n if attack.symbol == 'exo_e':\n if any(self.__is_well_formed(literal, 'defends', Function, Literal) for literal in state):\n continue\n\n for possible_defence in self.argument_scheme:\n if attack.symbol == 'fact' and possible_defence.symbol != 'fact':\n continue\n if self.strength_preorder.is_strictly_preceded(possible_defence, attack):\n continue\n if not self.conflict_relation.is_similar(attack, possible_defence):\n if possible_defence not in self.argument_scheme:\n continue\n if not any(-att_pos in self.argument_scheme[possible_defence][1] for att_pos in att_posses):\n continue\n if possible_defence.symbol == 'fact':\n f = possible_defence.arguments[0]\n assert isinstance(f, Literal)\n if f not in self.fact_set:\n continue\n if possible_defence.symbol == 'hyp':\n if not self.strength_preorder.is_preceded(attack, possible_defence):\n continue\n return self.__do_defense(action, state, undefended_attacks)\n if is_consolidate(action):\n return self.__do_consolidate(state)\n att = self.attacks(state)\n if att:\n return self.__do_attack(action, state, att)\n return state\n\n def poss(self, action: Action, situation: Situation) -> bool:\n return self.poss_state(action, situation.state)\n\n def poss_state(self, action: Action, state: State) -> bool:\n pass\n\n\ndef is_consolidate(action: Action) -> bool:\n return action.symbol == 'consolidate' and not action.arguments\n\n\nconsolidate_action: Action = Function('consolidate')\n\n\ndef fact(lit: Literal) -> Function:\n return Function('fact', (lit,))\n\n\ndef hyp(lit: Literal) -> Function:\n return Function('hyp', (lit,))\n\n\ndef suff_p(cond: Literal, pos: Literal) -> Function:\n return Function('suff_p', (Function('cond_for', (cond, pos)),))\n\n\ndef necc_p(cond: Literal, pos: Literal) -> Function:\n return Function('necc_p', (Function('cond_for', (cond, pos)),))\n\n\ndef suff_e(cond: Literal, pos: Literal) -> Function:\n return Function('suff_e', (Function('cond_for', (cond, pos)),))\n\n\ndef necc_e(cond: Literal, pos: Literal) -> Function:\n return Function('necc_e', (Function('cond_for', (cond, pos)),))\n\n\ndef sec_suff_p(cond: Literal, pos: Literal) -> Function:\n return Function('sec_suff_p', (Function('cond_for', (cond, pos)),))\n\n\ndef sec_necc_p(cond: Literal, pos: Literal) -> Function:\n return Function('sec_necc_p', (Function('cond_for', (cond, pos)),))\n\n\ndef sec_suff_e(cond: Literal, pos: Literal) -> Function:\n return Function('sec_suff_e', (Function('cond_for', (cond, pos)),))\n\n\ndef exo_e(pos: Literal) -> Function:\n return Function('exo_e', (Function('cond_for', (pos, Function('exo', (pos,)))),))\n\n\ndef position_literal(pos: Literal, sign: bool = True) -> Literal:\n return Literal(Predicate('position', (pos,)), sign)\n\n\ndef argument_literal(arg: Function, pos: Literal, sign: bool = True) -> Literal:\n return Literal(Predicate('argument', (arg, pos)), sign)\n\n\ndef counterargument_literal(arg: Function, pos: Literal, sign: bool = True) -> Literal:\n return Literal(Predicate('counterargument', (arg, pos)), sign)\n\n\ndef supports_literal(arg: Function, pos: Literal, sign: bool = True) -> Literal:\n return Literal(Predicate('supports', (arg, pos)), sign)\n\n\ndef attacks_literal(arg: Function, pos: Literal, sign: bool = True) -> Literal:\n return Literal(Predicate('attacks', (arg, pos)), sign)\n\n\ndef defends_literal(arg: Function, pos: Literal, sign: bool = True) -> Literal:\n return Literal(Predicate('defends', (arg, pos)), sign)\n\n\ndef exo_literal(pos: Literal, sign: bool = True) -> Literal:\n return Literal(Predicate('exo', (pos,)), sign)\n\n\ndef position_action(arg: Function, pos: Literal) -> Action:\n return Function('position', (arg, pos))\n\n\ndef argument_action(arg: Function, pos: Literal) -> Action:\n return Function('argument', (arg, pos))\n\n\ndef counterargument_action(arg: Function, pos: Literal) -> Action:\n return Function('counterargument', (arg, pos))\n\n\ndef supports_action(arg: Function, pos: Literal) -> Action:\n return Function('supports', (arg, pos))\n\n\ndef attacks_action(arg: Function, pos: Literal) -> Action:\n return Function('attacks', (arg, pos))\n\n\ndef defends_action(arg: Function, pos: Literal) -> Action:\n return Function('defends', (arg, pos))\n","repo_name":"Entze/SitCalcPy","sub_path":"scpy/causal_setting/dialectic_causal_setting.py","file_name":"dialectic_causal_setting.py","file_ext":"py","file_size_in_byte":22918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"43079033594","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom random import sample\nimport re\n\nimport string\nimport nltk\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.probability import FreqDist\n\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer \n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nimport gensim\nimport statistics\n\n\nstop_words=set(stopwords.words(\"english\"))\n \nnltk.download('stopwords')\nnltk.download('punkt')\nnltk.download('wordnet')\n\nstop_words=set(stopwords.words(\"english\"))\nlemmatizer = WordNetLemmatizer() \n\n\ndef cleaning_bow_for_training(data):\n \n new_data=data.dropna(how='all', axis=1)\n bow=data.iloc[:,2:].values.tolist()\n \n cleaned_data = [\n [w for w in l if isinstance(w, str)] \n for l in bow\n ]\n return cleaned_data\n\ndef running_model(training_bow):\n model = gensim.models.Word2Vec(training_bow, min_count=1, sg=1)\n model.train(training_bow, total_examples=model.corpus_count, epochs=model.epochs)\n model.wv\n return model\n\n\ndef tags_from_lists(bow, model):\n tags_lists=[]\n for lst in bow:\n while True:\n if len(lst) == 0:\n break\n try:\n tags=model.wv.most_similar(positive=lst, topn=20)\n tags_lists.append(tags)\n except KeyError as e:\n search = re.search(\"'(\\w+)'\", e.args[0])\n if search:\n word = search.group(1)\n else:\n word = ''\n print(word)\n lst.remove(word)\n else:\n break\n return tags_lists\n\n\ndef transforming_in_bow(text, lemmatizer):\n \n sentence = text.translate(str.maketrans('', '',string.punctuation)).split(' ')\n bow = []\n for word in sentence:\n lowcase_text_word=word.lower()\n lemmatized_word=lemmatizer.lemmatize(lowcase_text_word)\n if lemmatized_word not in stop_words:\n bow.append(lemmatized_word)\n return bow\n\ndef tags_from_text(bow, model):\n while True:\n if len(bow) == 0:\n return []\n try:\n tags=model.wv.most_similar(positive=bow, topn=20)\n except KeyError as e:\n search = re.search(\"'(\\w+)'\", e.args[0])\n if search:\n word = search.group(1)\n else:\n word = '' \n print(word)\n bow.remove(word)\n else:\n return tags\n \n\n\n\n\n\n\ndef relevance(tags_text, tags_groups, model):\n distance_lists_max=[]\n for group in tags_groups:\n group_id=[]\n for tag_g in group:\n for tag in tags_text:\n distance=model.wv.distance(tag[0], tag_g[0])\n proportion=1-distance\n relevance=proportion*tag[1]\n group_id.append(relevance)\n distance_lists_max.append(max(group_id))\n return distance_lists_max\n\n\n\ndef relevance_mean(tags_text, tags_groups, model):\n distance_lists_max=[]\n for group in tags_groups:\n group_id=[]\n for tag_g in group:\n for tag in tags_text:\n distance=model.wv.distance(tag[0], tag_g[0])\n proportion=1-distance\n relevance=proportion*tag[1]\n group_id.append(relevance)\n distance_lists_max.append(statistics.mean(group_id))\n return distance_lists_max\n\ndef relevance_count(tags_text, tags_groups, model):\n distance_lists_max=[]\n for group in tags_groups:\n group_id=[]\n for tag_g in group:\n for tag in tags_text:\n distance=model.wv.distance(tag[0], tag_g[0])\n proportion=1-distance\n relevance=proportion*tag[1]\n if relevance>=0.7:\n group_id.append(relevance)\n distance_lists_max.append(len(group_id))\n return distance_lists_max\n\ndef relevance_words(bow, bow_gr, model):\n distance_lists_max=[]\n for group in bow_gr:\n group_id=[]\n for tag_g in group:\n for tag in bow:\n try:\n distance=model.wv.distance(tag, tag_g)\n except:\n if tag in bow:\n bow.remove(tag)\n proportion=1-distance\n group_id.append(proportion)\n distance_lists_max.append(statistics.mean(group_id))\n return distance_lists_max\n\n\n\n\n\n\n\n\n\n\ndef get_the_recommended_titles(relevance, titles_data):\n \n distance_list_titles=pd.DataFrame(relevance, columns=['member'])\n titles_values=pd.merge(left=titles_data[['group_name']], right=distance_list_titles, left_index=True, right_index=True)\n high_values=titles_values.loc[titles_values['member']>=0.10]\n recommended_gr=high_values.loc[high_values['member']>=(high_values['member'].max()-0.03)]\n sorted_rec_gr = recommended_gr.sort_values('member', ascending=False)\n \n return sorted_rec_gr\n\n\n\ndef get_the_recommended_titles_count(relevance, titles_data):\n \n distance_list_titles=pd.DataFrame(relevance, columns=['member'])\n titles_values=pd.merge(left=titles_data[['group_name']], right=distance_list_titles, left_index=True, right_index=True)\n high_values=titles_values.loc[titles_values['member']>=1]\n recommended_gr=high_values.loc[high_values['member']>=(high_values['member'].max()-10)]\n sorted_rec_gr = recommended_gr.sort_values('member', ascending=False)\n \n return sorted_rec_gr\n\n\n\n\ndef input_text_recommendation(text, model, tags_groups, group_titles):\n bow = transforming_in_bow(text,lemmatizer)\n tags_text=tags_from_text(bow, model)\n if len(tags_text) == 0:\n return \"No recommendations.\"\n tags_relevance=relevance(tags_text, tags_groups, model)\n recommended=get_the_recommended_titles(tags_relevance, group_titles).head(10)\n return recommended[['group_name']]\n\n\ndef input_text_recommendation_mean(text, model, tags_groups, group_titles):\n bow = transforming_in_bow(text,lemmatizer)\n tags_text=tags_from_text(bow, model)\n if len(tags_text) == 0:\n return \"No recommendations.\"\n tags_relevance=relevance_mean(tags_text, tags_groups, model)\n recommended=get_the_recommended_titles(tags_relevance, group_titles).head(10)\n return recommended[['group_name']]\n\n\ndef input_text_recommendation_count(text, model, tags_groups, group_titles):\n bow = transforming_in_bow(text,lemmatizer)\n tags_text=tags_from_text(bow, model)\n if len(tags_text) == 0:\n return \"No recommendations.\"\n tags_relevance=relevance_count(tags_text, tags_groups, model)\n recommended=get_the_recommended_titles_count(tags_relevance, group_titles).head(10)\n return recommended[['group_name']]\n\ndef input_text_recommendation_words(text, model, groups_bow, group_titles):\n bow = transforming_in_bow(text,lemmatizer)\n# tags_text=tags_from_text(bow, model)\n# if len(tags_text) == 0:\n# return \"No recommendations.\"\n tags_relevance=relevance_words(bow, groups_bow, model)\n recommended=get_the_recommended_titles(tags_relevance, group_titles).head(10)\n return recommended[['group_name']]\n\n\n\n\ndef transform_concat_lists(df):\n lst=df.values.tolist()\n big_list=[]\n for small_lst in lst:\n big_list+=small_lst\n return big_list\n\ndef perf_measure(y_actual, y_hat):\n TP = 0\n FP = 0\n TN = 0\n FN = 0\n for i in range(len(y_hat)): \n if y_actual[i]==y_hat[i]==1:\n TP += 1\n if y_hat[i]==1 and y_actual[i]!=y_hat[i]:\n FP += 1\n if y_actual[i]==y_hat[i]==0:\n TN += 1\n if y_hat[i]==0 and y_actual[i]!=y_hat[i]:\n FN += 1\n print('TP=',TP,'FP=',FP,'TN=',TN,'FN=',FN)\n return TP, FP, TN, FN\n \n \n \n ","repo_name":"inesa-lisnic/A-recommendation-system-paradigm-based-on-Meetup-database","sub_path":"algorithm_functions.py","file_name":"algorithm_functions.py","file_ext":"py","file_size_in_byte":8072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"16673493255","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.integrate as integrate\nimport scipy.io.wavfile as wavfile\n\nfs, x = wavfile.read(\"Python/Examples/h1.wav\")\n\n\n\n\nT = 1500\nx = x[0:T]\nx_range = np.arange(0,len(x)/fs,1/fs)\n\n\n\n#x = np.abs(x) / np.max(x)\nsch_sum = np.cumsum(x[::-1]**2)[::-1]\nsch_sum_db = 10.0 * np.log10(sch_sum / np.max(sch_sum))\n\nsch_trapz = integrate.cumtrapz(x[::-1]**2,initial=0)[::-1]\nsch_trapz_db = 10.0 * np.log10(sch_trapz / np.max(sch_trapz))\n\nplt.plot(x_range,sch_trapz_db)\nplt.plot(x_range,sch_sum_db)\nplt.xlabel(\"Zeit in [s]\")\nplt.ylabel(\"Amplitude\")\nplt.title(\"Schroeder Rückwärtsintegration\")\nplt.show()","repo_name":"DennisR96/Audio_DSP","sub_path":"Python/Examples/Schroeder.py","file_name":"Schroeder.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"72988256212","text":"import heapq\nclass Solution:\n def networkDelayTime(self, times: List[List[int]], n: int, k: int) -> int:\n adj_list = dict()\n distance = [math.inf for i in range(n)]\n distance[k-1] =0\n time = 0 \n pq = []\n heapq.heapify( pq)\n heapq.heappush(pq, [time, k])\n for u, v, w in times:\n if not adj_list.get(u, False):\n adj_list[u] = []\n if not adj_list.get(v, False):\n adj_list[v] = []\n adj_list[u].append((v,w))\n while(pq):\n Totaltime, node = heappop(pq)\n for neigh, time in adj_list[node]:\n if distance[neigh-1] > Totaltime + time:\n distance[neigh-1] = Totaltime + time\n heapq.heappush(pq, [distance[neigh-1], neigh])\n if max(distance) == math.inf:\n return -1 \n return max(distance)","repo_name":"prbln/Leetcode","sub_path":"744-network-delay-time/network-delay-time.py","file_name":"network-delay-time.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"27867519299","text":"import sys, os, copy, gc, re, gzip, pickle, argparse, logging, warnings\nimport numpy as np\nimport pandas as pd\nimport matplotlib.colors\nfrom jinja2 import Environment, FileSystemLoader\n\nfrom ficture.utils.visualize_factors import visual_hc, image_to_base64\n\ndef factor_report(_args):\n\n parser = argparse.ArgumentParser(prog=\"factor_report\")\n parser.add_argument('--path', type=str, help='')\n parser.add_argument('--pref', type=str, help='')\n parser.add_argument('--model_id', type=str, default='', help='')\n parser.add_argument('--color_table', type=str, default='', help='')\n parser.add_argument('--n_top_gene', type=int, default=20, help='')\n parser.add_argument('--min_top_gene', type=int, default=10, help='')\n parser.add_argument('--max_pval', type=float, default=0.001, help='')\n parser.add_argument('--min_fc', type=float, default=1.5, help='')\n parser.add_argument('--output_pref', type=str, default='', help='')\n\n parser.add_argument('--hc_tree', action='store_true')\n parser.add_argument('--n_top_gene_on_tree', type=int, default=10, help='')\n parser.add_argument('--tree_figure', type=str, default='', help='')\n parser.add_argument('--cprob_cut', type=str, default='.99', help='Only visualize top factors with cumulative probability > cprob_cut')\n parser.add_argument('--model', type=str, default='', help='')\n parser.add_argument('--circle_if', type=int, default=24, help='')\n parser.add_argument('--remake_tree', action='store_true')\n parser.add_argument('--circle', action='store_true')\n parser.add_argument('--vertical', action='store_true')\n args = parser.parse_args(_args)\n\n path=args.path\n pref=args.pref\n ntop = args.n_top_gene\n mtop = args.min_top_gene\n pval_max = args.max_pval\n fc_min = args.min_fc\n ejs = os.path.join(os.path.dirname(__file__), \"factor_report.template.html\")\n if not os.path.isfile(ejs):\n sys.exit(f\"Template file {ejs} not found\")\n if args.remake_tree or args.circle or args.vertical:\n args.hc_tree = True\n\n model_id = args.model_id\n if model_id == '':\n model_id = args.pref\n\n output_pref = args.output_pref\n if output_pref == '':\n output_pref = path+\"/\"+pref\n\n # Color code\n color_f = args.color_table\n if os.path.isfile(args.color_table):\n color_table = pd.read_csv(args.color_table, sep='\\t')\n else:\n color_f = path+\"/figure/\"+model_id+\".rgb.tsv\"\n color_table = pd.read_csv(color_f, sep='\\t')\n K = color_table.shape[0]\n factor_header = np.arange(K).astype(str)\n color_table.Name = color_table.Name.astype(int)\n color_table.sort_values(by = 'Name', inplace=True)\n color_table.index = color_table.Name.values\n color_table['RGB'] = [','.join(x) for x in np.clip((color_table.loc[:, ['R','G','B']].values * 255).astype(int), 0, 255).astype(str) ]\n color_table['HEX'] = [ matplotlib.colors.to_hex(v) for v in np.array(color_table.loc[:, ['R','G','B']]) ]\n node_color = {str(i):v['HEX'] for i,v in color_table.iterrows() }\n\n\n # Posterior count\n f=path+\"/\"+pref+\".posterior.count.tsv.gz\"\n post = pd.read_csv(f, sep='\\t')\n recol = {}\n for u in post.columns:\n v = re.match('^[A-Za-z]*_*(\\d+)$', u.strip())\n if v:\n recol[v.group(0)] = v.group(1)\n post.rename(columns=recol, inplace=True)\n post_umi = post.loc[:, factor_header].sum(axis = 0).astype(int).values\n post_weight = post.loc[:, factor_header].sum(axis = 0).values\n post_weight /= post_weight.sum()\n\n # DE genes\n f=path+\"/DE/\"+pref+\".bulk_chisq.tsv\"\n de = pd.read_csv(f, sep='\\t')\n de.factor = de.factor.astype(int)\n top_gene = []\n top_gene_anno = []\n de['Rank'] = 0\n # Temporary: shorten unspliced gene names\n de.gene = de.gene.str.replace('unspl_', 'u_')\n # Top genes by Chi2\n de.sort_values(by=['factor','Chi2'],ascending=False,inplace=True)\n for k in range(K):\n v = de.loc[de.factor.eq(k), 'gene'].iloc[:args.n_top_gene_on_tree].values\n top_gene_anno.append(', '.join(v))\n de.loc[de.factor.eq(k), 'Rank'] = np.arange(de.factor.eq(k).sum())\n v = de.loc[de.factor.eq(k) & ( (de.Rank < mtop) | \\\n ((de.pval <= pval_max) & (de.FoldChange >= fc_min)) ), \\\n 'gene'].iloc[:ntop].values\n if len(v) == 0:\n top_gene.append([k, '.'])\n else:\n top_gene.append([k, ', '.join(v)])\n # Top genes by fold change\n de.sort_values(by=['factor','FoldChange'],ascending=False,inplace=True)\n for k in range(K):\n de.loc[de.factor.eq(k), 'Rank'] = np.arange(de.factor.eq(k).sum())\n v = de.loc[de.factor.eq(k) & ( (de.Rank < mtop) | \\\n ((de.pval <= pval_max) & (de.FoldChange >= fc_min)) ), \\\n 'gene'].iloc[:ntop].values\n if len(v) == 0:\n top_gene[k].append('.')\n else:\n top_gene[k].append(', '.join(v))\n # Top genes by absolute weight\n for k in range(K):\n v = post.gene.iloc[np.argsort(post.loc[:, str(k)].values)[::-1][:ntop] ].values\n top_gene[k].append(', '.join(v))\n\n # Summary\n table = pd.DataFrame({'Factor':np.arange(K), 'RGB':color_table.RGB.values,\n 'Weight':post_weight, 'PostUMI':post_umi,\n 'TopGene_pval':[x[1] for x in top_gene],\n 'TopGene_fc':[x[2] for x in top_gene],\n 'TopGene_weight':[x[3] for x in top_gene] })\n table.sort_values(by = 'Weight', ascending = False, inplace=True)\n\n f = output_pref+\".factor.info.tsv\"\n table.to_csv(f, sep='\\t', index=False, header=True, float_format=\"%.5f\")\n\n f = output_pref+\".factor.info.tsv\"\n with open(f, 'r') as rf:\n lines = rf.readlines()\n header = lines[0].strip().split('\\t')\n rows = [ list(enumerate(row.strip().split('\\t') )) for row in lines[1:]]\n\n # Load template\n env = Environment(loader=FileSystemLoader(os.path.dirname(ejs)))\n template = env.get_template(os.path.basename(ejs))\n\n image_base64 = None\n tree_alt = None\n tree_caption = None\n\n if args.hc_tree:\n # Hierarchical clustering\n m = re.match(\"^[0\\.]*(\\d+)$\", args.cprob_cut)\n if m is None:\n sys.exit(f\"Invalid --cprob_cut, please use a number between 0 and 1 (e.g. 0.99)\")\n cprob_label = m.group(1)\n cprob_cut = float(args.cprob_cut)\n\n tree_f = args.tree_figure\n if not os.path.exists(args.tree_figure) or args.remake_tree:\n tree_f = os.path.dirname(color_f) + '/' + pref + \".coshc.\"+cprob_label+\".tree.png\"\n if not os.path.exists(tree_f) or args.remake_tree:\n model_f = args.model\n if not os.path.exists(args.model):\n model_f = path + \"/\" + model_id + \".model_matrix.tsv.gz\"\n if not os.path.exists(model_f):\n print(\"Cannot find model file, will cluster based on posterior count\")\n model = post\n else:\n model = pd.read_csv(model_f, sep='\\t')\n model_prob = np.array(model.iloc[:, 1:]).T + .1\n model_prob = model_prob / model_prob.sum(axis = 1).reshape((-1,1))\n\n circle = args.circle\n if not circle and args.circle_if > 0:\n v = np.argsort(post_weight)[::-1]\n w = np.cumsum(post_weight[v] )\n k = np.arange(K)[w > cprob_cut][0]\n if k > args.circle_if:\n circle = True\n tree = visual_hc(model_prob, post_weight, top_gene_anno, \\\n node_color = node_color, circle = circle, \\\n output_f = tree_f, cprob_cut = cprob_cut)\n print(f\"Tree figure path: {tree_f}\")\n image_base64 = image_to_base64(tree_f)\n\n tree_alt = \"Hierarchical clustering of factors based on pairwise coine distance\"\n tree_caption = \"Clustering of factors based on pairwise coine distance. Factors with high abundance jointly accounting for \" + args.cprob_cut + \" of observations are displayed.\"\n\n # Render the HTML file\n html_output = template.render(header=header, rows=rows, image_base64=image_base64, tree_image_alt=tree_alt, tree_image_caption=tree_caption)\n\n f=output_pref+\".factor.info.html\"\n with open(f, \"w\") as html_file:\n html_file.write(html_output)\n\n print(f)\n","repo_name":"seqscope/ficture","sub_path":"ficture/scripts/factor_report.py","file_name":"factor_report.py","file_ext":"py","file_size_in_byte":8446,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"20373555901","text":"#!/usr/bin/python3\n\"\"\"Maria and Ben are playing a game.\n Given a set of consecutive integers\n starting from 1 up to and including n,\n they take turns choosing a prime number\n from the set and removing that number\n and its multiples from the set.\n The player that cannot make a move loses the game.\n\"\"\"\n\n\ndef isWinner(x, nums):\n \"\"\" isWinner \"\"\"\n player = {\"Maria\": 0, \"Ben\": 0}\n n = max(nums)\n primes = [0, 0, 2]\n add_prime(max(nums), primes)\n for round in range(x):\n _sum = sum((i != 0 and i <= nums[round])\n for i in primes[:nums[round] + 1])\n if (_sum % 2):\n winner = \"Maria\"\n else:\n winner = \"Ben\"\n if winner:\n player[winner] += 1\n if player[\"Maria\"] > player[\"Ben\"]:\n return \"Maria\"\n elif player[\"Ben\"] > player[\"Maria\"]:\n return \"Ben\"\n\n return None\n\n\ndef add_prime(n, primes):\n \"\"\" Add prime to list \"\"\"\n last_prime = primes[-1]\n if n > last_prime:\n for i in range(last_prime + 1, n + 1):\n if isPrime(i):\n primes.append(i)\n else:\n primes.append(0)\n\n\ndef isPrime(number):\n \"\"\" check prime number \"\"\"\n for i in range(2, int(number ** 0.5) + 1):\n if not number % i:\n return False\n return True\n","repo_name":"Oluwateezzy/alx-interview","sub_path":"0x0A-primegame/0-prime_game.py","file_name":"0-prime_game.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"25738387060","text":"# color\nred = '\\033[91m'\ngreen = '\\033[92m'\nyellow = '\\033[93m'\nblue = '\\033[94m'\nreset = '\\033[0m'\n\nglobal error_display \nglobal info_display\nglobal ok_display\n\nerror_display = True\ninfo_display = False\nok_display = True\n\ndef ERRORF(msg):\n if error_display:\n print(\"{}[-][ERROR]{} {}\".format(red, reset, msg))\n\ndef INFOF(msg):\n if info_display:\n print(\"{}[+][INFO]{} {}\".format(yellow, reset, msg))\n\ndef OKF(msg):\n if ok_display:\n print(\"{}[+][OK]{} {}\".format(green, reset, msg))\n","repo_name":"valour01/arm_disasssembler_study","sub_path":"adapter/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":56,"dataset":"github-code","pt":"67"}
+{"seq_id":"22740185615","text":"\"\"\"\nThis problem was asked by Microsoft.\n\nA number is considered perfect if its digits sum up to exactly 10.\n\nGiven a positive integer n, return the n-th perfect number.\n\nFor example, given 1, you should return 19. Given 2, you should return 28.\n\"\"\"\n\n# the first perfect num is 19\n# the second perfect num is 28\n# the third +ive num is 37\n\n# closer look reveals:\n# n=1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12\n# -----------------------------------------------\n# 19, 28, 37 46, 64, 46, 73, 82, 91, 100, 109, 118\n\n# so 19+(n*9)\n\n# but there is one problem in this method 10th->100 number if not a perfect num\n# 10th number should be the next one then 10th=>109\n# so we'll also need to push 11th num to be the 12th num and so on\n\n# note that the outliers of this method exist in form of {100, 199, 299,1000, 10000,etc all nums that don't sum to 10}\n# before we return the result we need to adjust for the outliers:\n\n# the result should be\n# n= 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12\n# -----------------------------------------------\n# 19, 28, 37 46, 64, 46, 73, 82, 91, 109, 118, 127\n\n\ndef find_perfect_num(n):\n if n ==0:\n return 'nums start from 1'\n\n n_th_num = 19 # start with 19\n count = 0 # iterator\n\n while(True):\n curr_sum = sum(list(map(int, str(n_th_num))))\n\n if curr_sum ==10:\n count+=1\n\n if count == n:\n return n_th_num\n\n n_th_num += 9\n\n#\n# def outliers(n): # experiment to see if there is a pattern to the outliers -> There isn't :(\n# l=[]\n#\n# for i in range(1, n):\n# num = ((i+1)*9)+1\n#\n# s = sum(list(map(int, str(num))))\n#\n# if s!=10:\n# l.append(num)\n#\n# return l\n\n\nif __name__ == '__main__':\n # print(find_perfect_num(1))\n # print(find_perfect_num(9))\n # print(find_perfect_num(11))\n # print(find_perfect_num(21))\n # print(find_perfect_num(91))\n # print(find_perfect_num(88))\n #\n for i in range(1, 101):\n print(\"{}#: {}\".format(i, find_perfect_num(i)))\n\n # print(outliers(2001))","repo_name":"RafayAK/CodingPrep","sub_path":"DailyCodingProblem/70_Microsoft_Perfect_Numbers_Sum_to_10.py","file_name":"70_Microsoft_Perfect_Numbers_Sum_to_10.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"67"}
+{"seq_id":"2824601222","text":"import os\nimport ctypes\n\n\nclass FileUtils:\n @staticmethod\n def create_hidden_folder(folder_name):\n # 當前工作目錄\n current_directory = os.getcwd()\n\n # 完整路徑\n path = os.path.join(current_directory, folder_name)\n\n # 建立資料夾\n os.makedirs(path, exist_ok=True)\n\n # 在Windows下設置資料夾為隱藏\n if os.name == \"nt\":\n FILE_ATTRIBUTE_HIDDEN = 0x02\n ret = ctypes.windll.kernel32.SetFileAttributesW(path, FILE_ATTRIBUTE_HIDDEN)\n\n if not ret:\n print(\n f\"Could not set {folder_name} as hidden. Error code: {ctypes.GetLastError()}\"\n )\n","repo_name":"MoonShineVFX/smaug-cmd","sub_path":"smaug_cmd/ui/file_util.py","file_name":"file_util.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"40378564035","text":"from micropython import const\nfrom machine import Pin\n\nfrom libs.PID import PID\n\nfrom config import Config\nfrom display import Display\nfrom on_off_switch import OnOffSwitch\nfrom pedal_input import PedalInput\nfrom rotary import Rotary\nfrom speed_measure import Tachometer\nfrom speed_control import SpeedControl\n\n\nclass PotteryWheelManager:\n STOPPED_HOLD_STATE = const(\"STOPPED_HOLD\") # when first stopping, we stop motor and wait for safety conditions\n STOPPED_WAIT_STATE = const(\"STOPPED_WAIT\") # once safety conditions are met we wait for indication to start running\n RUNNING_STATE = const(\"RUNNING\")\n\n def __init__(self, pedal_input: PedalInput, main_switch: OnOffSwitch, tachometer: Tachometer,\n speed_control: SpeedControl, display: Display, rotary: Rotary) -> None:\n super().__init__()\n\n self.pedal_input = pedal_input\n self.main_switch = main_switch\n self.tachometer = tachometer\n self.speed_control = speed_control\n self.display = display\n self.rotary = rotary\n self.rotary.add_handler(self.create_rotary_event_handler())\n\n self._state = self.STOPPED_HOLD_STATE\n self._maxRpm = Config.DEFAULT_MAX_RPM\n\n def create_rotary_event_handler(self):\n \"\"\"create a callback with no \"self\" parameter, so it can be passed as a handler\"\"\"\n def event_handler(event_type):\n if event_type == Rotary.ROT_CW:\n self._maxRpm = min(self._maxRpm + 1, Config.MAX_SUPPORTED_RPM)\n elif event_type == Rotary.ROT_CCW:\n self._maxRpm = min(self._maxRpm - 1, Config.MAX_SUPPORTED_RPM)\n elif event_type == Rotary.SW_PRESS:\n print('PRESS')\n elif event_type == Rotary.SW_RELEASE:\n print('RELEASE')\n\n return event_handler\n\n def loop(self):\n percentage = self.pedal_input.get_pedal_percentage()\n current_speed = self.tachometer.get_current_rpm()\n\n if self._state == self.STOPPED_HOLD_STATE:\n # make sure we are switched off\n self.main_switch.switch_off()\n self.speed_control.set_speed(0)\n if percentage == 0:\n self._state = self.STOPPED_WAIT_STATE\n\n elif self._state == self.STOPPED_WAIT_STATE:\n # if we made it here, we are already giving stop signals\n\n if percentage > 0: # This indicates we need to start running\n self._state = self._state = self.RUNNING_STATE\n\n elif self._state == self.RUNNING_STATE:\n self.main_switch.switch_on()\n","repo_name":"alonkashtan/pottery-wheel-control","sub_path":"pottery-wheel-manager.py","file_name":"pottery-wheel-manager.py","file_ext":"py","file_size_in_byte":2582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"14149509357","text":"from keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping\nfrom model_UNet import get_unet, UNetEvaluator\nfrom config import *\nfrom generators import get_seg_batch\nimport time\n\ndef seg_train():\n print('start seg_train')\n model = get_unet()\n model.summary()\n\n run = '{}-{}'.format(time.localtime().tm_hour, time.localtime().tm_min)\n log_dir = SEG_LOG_DIR.format(run)\n check_point = log_dir + '/checkpoint-{epoch:02d}-{val_loss:.4f}.hdf5'\n\n print(\"seg train round {}\".format(run))\n tensorboard = TensorBoard(log_dir=log_dir, write_graph=False)\n checkpoint = ModelCheckpoint(filepath=check_point, monitor='val_loss', verbose=1, save_best_only=True)\n early_stopping = EarlyStopping(monitor='val_loss', patience=TRAIN_SEG_EARLY_STOPPING_PATIENCE, verbose=1)\n evaluator = UNetEvaluator()\n model.fit_generator(get_seg_batch(TRAIN_SEG_TRAIN_BATCH_SIZE, from_train=True), steps_per_epoch=TRAIN_SEG_STEPS_PER_EPOCH,\n validation_data=get_seg_batch(TRAIN_SEG_VALID_BATCH_SIZE, from_train=False), validation_steps=TRAIN_SEG_VALID_STEPS,\n epochs=TRAIN_SEG_EPOCHS, verbose=2,\n callbacks=[tensorboard, checkpoint, early_stopping, evaluator])\n\nif __name__ == '__main__':\n seg_train()\n","repo_name":"wikke/Tianchi-Medical-LungTumorDetect","sub_path":"train_segmentation.py","file_name":"train_segmentation.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","stars":381,"dataset":"github-code","pt":"67"}
+{"seq_id":"28027270327","text":"\"\"\"\nGiven a string s, return the longest\npalindromic\n\nsubstring\n in s.\n\n\n\nExample 1:\n\nInput: s = \"babad\"\nOutput: \"bab\"\nExplanation: \"aba\" is also a valid answer.\nExample 2:\n\nInput: s = \"cbbd\"\nOutput: \"bb\"\n\"\"\"\nclass Solution:\n def longestPalindrome(self, s: str) -> str:\n n = len(s)\n if n < 2:\n return s\n start, max_len = 0, 1\n for i in range(n):\n odd = s[i - max_len - 1: i + 1]\n even = s[i - max_len: i + 1]\n # print('odd:', odd, ' even:' ,even,'i:', i,'start:', start,'max_lan:', max_len, i - max_len - 1, i - max_len)\n if i - max_len - 1 >= 0 and odd == odd[::-1]:\n start = i - max_len - 1\n max_len += 2\n continue\n if i - max_len >= 0 and even == even[::-1]:\n start = i - max_len\n max_len += 1\n return s[start: start + max_len]\n\nprint(Solution().longestPalindrome('abcdeedcba'))\n","repo_name":"laxman590249/Data-Structures","sub_path":"DataStructures/Strings/longest_palindromic_substring.py","file_name":"longest_palindromic_substring.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"4890755178","text":"import ROOT\nimport CMS_lumi, tdrstyle\nimport array\nfrom subprocess import Popen\nfrom sys import argv, exit, stdout, stderr\nimport math\n\nROOT.gROOT.SetBatch(True)\n\nsaveWhere= '/afs/hep.wisc.edu/home/tost/workingArea/CMSSW_8_0_24_patch1/src/Real_work/oct12plots/'\n\n#filenumber = raw_input(\"How many files to plot? \")\nfilenumber = str(5)\ntitle = \"words\"\n\n#legend_title = []\ninfile = []\nfor n in range(0, int(filenumber)):\n infile.append(argv[n+1])\n #legend_title.append(raw_input(\"What to label \"+str(infile[n])+\": \"))\n\n#some sloppy programming is going to go here\nlegend_title = []\nlegend_title.append(\"Znunu\")\nlegend_title.append(\"smH125\")\nlegend_title.append(\"W+jets\")\nlegend_title.append(\"DiBoson\")\nlegend_title.append(\"Z+jets\")\n\n\n\n########################## All cuts go here ########################\ndef muTauCutTester(event):\n if event.met>150 and event.m_vis<125 and event.pth>120 and event.dR<1.7 and event.pt_1>26 and event.pt_2>20 and event.npv>0 and event.diLeptons==0 and event.charge==0 and event.againstElectronVLooseMVA6_2 > 0 and event.againstMuonTight3_2>0 and event.iso04_1<0.15 and (event.HLT_IsoMu24_v_fired>0 or event.HLT_IsoTkMu24_v_fired>0) and event.byTightIsolationMVArun2v1DBoldDMwLT_2>0 and event.BadMuonFilter==1 and event.Flag_eeBadScFilter_fired == 1 and event.Flag_HBHENoiseFilter_fired == 1 and event.Flag_HBHENoiseIsoFilter_fired == 1 and event.Flag_goodVertices_fired == 1 and event.Flag_EcalDeadCellTriggerPrimitiveFilter_fired == 1:\n return True\ndef eleTauCutTester(event):\n if event.met>150 and event.pth>120 and event.m_vis<125 and event.dR<1.7 and event.pt_1>26 and event.pt_2>20 and event.vertices>0 and event.dilepton_veto==0 and event.iso_1<0.1 and event.tightElectrons<=1 and event.tightMuons==0 and event.charge==0 and event.againstElectronTightMVA6_2 > 0 and event.againstMuonLoose3_2>0 and event.byTightIsolationMVArun2v1DBoldDMwLT_2>0 and event.BadMuonFilter==1 and event.Flag_HBHENoiseFilter_fired==1 and event.Flag_HBHENoiseIsoFilter_fired==1 and event.Flag_globalTightHalo2016Filter_fired==1 and event.Flag_goodVertices_fired==1 and event.Flag_EcalDeadCellTriggerPrimitiveFilter_fired==1:\n return True\ndef diTauCutTester(event):\n if event.met>160 and event.m_vis<125 and event.pth>120 and event.dR<2.1 and event.pt_1>55 and event.pt_2>40 and event.npv>0 and event.tightMuons==0 and event.tightElectrons==0 and event.againstMuonLoose3_1>0 and event.againstElectronVLooseMVA6_1>0 and event.againstElectronVLooseMVA6_2>0 and event.charge==0 and (event.HLT_DoubleMediumIsoPFTau35_Trk1_eta2p1_Reg_v_fired>0 or event.HLT_DoubleMediumCombinedIsoPFTau35_Trk1_eta2p1_Reg_v_fired>0):\n return True\ndef datamuTauCutTester(event):\n if event.BadMuonFilter==1 and event.Flag_eeBadScFilter_fired == 1 and event.Flag_HBHENoiseFilter_fired == 1 and event.Flag_HBHENoiseIsoFilter_fired == 1 and event.Flag_goodVertices_fired == 1 and event.Flag_EcalDeadCellTriggerPrimitiveFilter_fired == 1:\n return True\ndef dataeleTauCutTester(event):\n if event.Flag_eeBadScFilter_fired==1 and event.BadMuonFilter==1 and event.Flag_HBHENoiseFilter_fired==1 and event.Flag_HBHENoiseIsoFilter_fired==1 and event.Flag_globalTightHalo2016Filter_fired==1 and event.Flag_goodVertices_fired==1 and event.Flag_EcalDeadCellTriggerPrimitiveFilter_fired==1:\n return True\n#########################################################################\n\ncolors = [ROOT.kRed, ROOT.kBlue, ROOT.kMagenta, ROOT.kCyan, ROOT.kOrange, ROOT.kSpring, ROOT.kTeal, ROOT.kAzure, ROOT.kPink, ROOT.kYellow]\nchannels = [\"eTau\", \"diTau\"]\nvariables = [\"pt_1\", \"pt_2\", \"dR\", \"pth\", \"m_vis\", \"mt12\", \"npv\", \"mt_1\", \"mt_2\", \"njets\", \"jpt_1\", \"jpt_2\", \"tauDecayMode\", \"nIsoNeutral\"]\n\n\nfor j in range(0, 2):\n for k in range(0, 14):\n channel = channels[j]\n variable = variables[k]\n\n if channel == \"diTau\":\n label = \"#tau_{h}#tau_{h} \"\n if channel == \"muTau\":\n label = \"#mu#tau_{h} \"\n if channel == \"eTau\":\n label = \"e#tau_{h} \"\n\n xaxis = variable\n ratioplot = \"n\"\n if variable == \"pt_1\":\n topbin = 200\n bottombin = 0\n binnumber = 20\n if variable == \"pt_2\":\n topbin = 200\n bottombin = 0\n binnumber = 20\n if variable == \"dR\":\n topbin = 2 \n bottombin = 0\n binnumber = 20\n if variable == \"pth\":\n topbin = 350\n bottombin = 100\n binnumber = 25\n if variable == \"m_vis\":\n topbin = 120\n bottombin = 0\n binnumber = 25\n if variable == \"mt12\":\n topbin = 200\n bottombin = 0\n binnumber = 20\n if variable == \"npv\":\n topbin =80\n bottombin =0\n binnumber =30\n if variable == \"mt_1\":\n topbin = 250\n bottombin = 0\n binnumber = 25\n if variable == \"mt_2\":\n topbin = 250\n bottombin = 0\n binnumber = 25\n if variable == \"njets\":\n topbin = 7\n bottombin = 0\n binnumber = 8\n if variable == \"jpt_1\":\n topbin = 500\n bottombin = 0\n binnumber = 25\n if variable == \"jpt_2\":\n topbin = 500\n bottombin = 0\n binnumber = 25\n if variable == \"tauDecayMode\":\n topbin = 10\n bottombin = 0\n binnumber = 10\n if variable == \"nIsoNeutral\":\n topbin = 5\n bottombin = 0\n binnumber = 20\n\n\n\n\n\n #set the tdr style \n tdrstyle.setTDRStyle()\n ROOT.gStyle.SetOptStat(2210)\n ROOT.gStyle.SetTitleAlign(13)\n\n #change the CMS_lumi variables (see CMS_lumi.py) \n CMS_lumi.lumi_13TeV =str(label)+ \"35.9 fb^{-1}\"\n CMS_lumi.writeExtraText = 1\n CMS_lumi.extraText = \"Preliminary\"\n CMS_lumi.lumi_sqrtS = \"13 TeV\" \n # used with iPeriod = 0, e.g. for simulation-only plots (default is an empty string) \n iPos = 11\n if( iPos==0 ): CMS_lumi.relPosX = 0.12\n H_ref = 800;\n W_ref = 800;\n W = W_ref\n H = H_ref\n iPeriod = 4\n # references for T, B, L, R \n T = 0.08*H_ref\n B = 0.12*H_ref\n L = 0.12*W_ref\n R = 0.04*W_ref\n B_ratio = 0.1*H_ref\n T_ratio = 0.03*H_ref\n B_ratio_label = 0.3*H_ref\n canvas1 = ROOT.TCanvas(\"c2\",\"c2\",50,50,W,H)\n canvas1.SetFillColor(0)\n canvas1.SetBorderMode(0)\n canvas1.SetFrameFillStyle(0)\n canvas1.SetFrameBorderMode(0)\n canvas1.SetTickx(0)\n canvas1.SetTicky(0)\n stacks1 = ROOT.THStack(\"stacks1\",\"\")\n x1_l = 0.93\n y1_l = 0.90\n dx_l = 0.38\n dy_l = 0.20\n x0_l = x1_l-dx_l\n y0_l = y1_l-dy_l\n\n\n if (ratioplot != \"y\"):\n canvas1.SetLeftMargin( L/W )\n canvas1.SetRightMargin( R/W )\n canvas1.SetTopMargin( T/H )\n canvas1.SetBottomMargin( B/H )\n\n\n canvas1.cd()\n\n if (ratioplot == \"y\"):\n plotPad = ROOT.TPad(\"plotpad\",\"\",0.0,0.3,1.0,1.0)\n plotPad.SetLeftMargin(L/W)\n plotPad.SetRightMargin(R/W)\n plotPad.SetTopMargin(T/H)\n plotPad.SetBottomMargin(B_ratio/H)\n plotPad.SetFillColor(0)\n plotPad.SetBottomMargin(0)\n\n ratioPad = ROOT.TPad(\"ratioPad\",\"\",0.0,0.0,1.0,0.31)\n ratioPad.SetLeftMargin(L/W)\n ratioPad.SetRightMargin(R/W)\n ratioPad.SetTopMargin(T_ratio/H)\n ratioPad.SetBottomMargin(B_ratio_label/H)\n ratioPad.SetGridy(1)\n ratioPad.SetFillColor(4000)\n\n if (ratioplot != \"y\"):\n plotPad = ROOT.TPad(\"plotPad\",\"\",0.0,0.0,1.0,1.0)\n plotPad.SetLeftMargin(L*1.4/W)\n plotPad.SetRightMargin(R/W)\n plotPad.SetTopMargin(T/H)\n plotPad.SetBottomMargin(B/H)\n\n plotPad.Draw()\n plotPad.cd()\n\n\n\n####################################### \n legend = ROOT.TLegend(x0_l,y0_l,x1_l, y1_l,\"\",\"brNDC\")\n legend.SetFillColor(ROOT.kWhite)\n legend.SetBorderSize(1)\n\n histo = {str(0):ROOT.TH1F(\"histo0\",title,int(binnumber),float(bottombin),float(topbin)), str(1):ROOT.TH1F(\"histo1\",title,int(binnumber),float(bottombin),float(topbin)), str(2):ROOT.TH1F(\"histo2\",title,int(binnumber),float(bottombin),float(topbin)), str(3):ROOT.TH1F(\"histo3\",title,int(binnumber),float(bottombin),float(topbin)), str(4):ROOT.TH1F(\"histo4\",title,int(binnumber),float(bottombin),float(topbin)), str(5):ROOT.TH1F(\"histo5\",title,int(binnumber),float(bottombin),float(topbin)), str(6):ROOT.TH1F(\"histo6\",title,int(binnumber),float(bottombin),float(topbin)), str(7):ROOT.TH1F(\"histo7\",title,int(binnumber),float(bottombin),float(topbin)), str(8):ROOT.TH1F(\"histo8\",title,int(binnumber),float(bottombin),float(topbin)), str(9):ROOT.TH1F(\"histo9\",title,int(binnumber),float(bottombin),float(topbin))}\n\n\n signal = ROOT.TH1F(\"signal\",title, int(binnumber),float(bottombin), float(topbin))\n signalfile = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/ZpBaryonic_Zp1000_MChi150.root\")\n\n################################################ \n\n\n if channel == \"muTau\":\n signaltree = signalfile.Get(\"muTauEventTree/eventTree\")\n for event in signaltree:\n g = getattr(event, variable)\n zptweight = getattr(event, \"ZPt_reweight\")\n wptweight = getattr(event, \"WPt_reweight\")\n weight = getattr(event, \"__WEIGHT__\")\n genweight = getattr(event, \"GENWEIGHT\")\n puweight = getattr(event, \"puweight\")\n pogid1 = getattr(event, \"POGid1\")\n pogtrigger = getattr(event, \"POGtrigger\")\n tauid1 = getattr(event, \"TAUID1\")\n trackweight = getattr(event, \"trackweight\")\n if muTauCutTester(event):\n signal.Fill(g, 35870*zptweight*wptweight*weight*genweight*puweight*pogid1*pogtrigger*tauid1*trackweight)\n if channel == \"eTau\":\n signaltree = signalfile.Get(\"eleTauEventTree/eventTree\")\n for event in signaltree:\n g = getattr(event, variable)\n zptweight = getattr(event, \"ZPt_reweight\")\n wptweight = getattr(event, \"WPt_reweight\")\n weight = getattr(event, \"__WEIGHT__\")\n genweight = getattr(event, \"GENWEIGHT\")\n puweight = getattr(event, \"puweight\")\n tauid1 = getattr(event, \"TAUID1\")\n idisoweight = getattr(event, \"idisoweight_REDO\")\n trigweight = getattr(event, \"trigweight_REDO\")\n trackweight = getattr(event, \"trackweight\")\n if eleTauCutTester(event):\n signal.Fill(g, 35870*zptweight*wptweight*weight*genweight*puweight*tauid1*idisoweight*trigweight*trackweight)\n if channel == \"diTau\":\n signaltree = signalfile.Get(\"diTauEventTree/eventTree\")\n for event in signaltree:\n g = getattr(event, variable)\n zptweight = getattr(event, \"ZPt_reweight\")\n wptweight = getattr(event, \"WPt_reweight\")\n weight = getattr(event, \"__WEIGHT__\")\n genweight = getattr(event, \"GENWEIGHT\")\n puweight = getattr(event, \"puweight\")\n trigweight = getattr(event, \"trigweight_REDO\")\n tauid1 = getattr(event, \"TAUID1\")\n if diTauCutTester(event):\n signal.Fill(g, 35870*zptweight*wptweight*weight*genweight*puweight*trigweight*tauid1)\n\n\n signal.SetLineColor(ROOT.kRed)\n\n\n\n if channel == \"muTau\":\n datafile1 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAB.root\")\n datafile2 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAC.root\")\n datafile3 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAD.root\")\n datafile4 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAE.root\")\n datafile5 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAF.root\")\n datafile6 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAG.root\")\n datafile7 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAH2.root\")\n datafile8 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/muDATAH3.root\")\n if channel == \"eTau\":\n datafile1 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAB.root\")\n datafile2 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAC.root\")\n datafile3 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAD.root\")\n datafile4 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAE.root\")\n datafile5 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAF.root\")\n datafile6 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAG.root\")\n datafile7 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAH2.root\")\n datafile8 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/eleDATAH3.root\")\n if channel == \"diTau\":\n datafile1 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAB.root\")\n datafile2 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAC.root\")\n datafile3 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAD.root\")\n datafile4 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAE.root\")\n datafile5 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAF.root\")\n datafile6 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAG.root\")\n datafile7 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAH2.root\")\n datafile8 = ROOT.TFile(\"/nfs_scratch/tost/monohiggs_Aug13/tauDATAH3.root\")\n\n\n\n\n\n data = ROOT.TH1F(\"data\",title,int(binnumber),float(bottombin),float(topbin))\n if channel == \"muTau\":\n datatree = datafile1.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile1.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile1.Get(\"diTauEventTree/eventTree\")\n q=0\n for event in datatree:\n g = getattr(event, variable)\n if channel ==\"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"eTau\":\n if eleTauCutTester(event) and dataeleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 1: \"+str(q))\n if channel == \"muTau\":\n datatree = datafile2.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile2.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile2.Get(\"diTauEventTree/eventTree\")\n for event in datatree:\n g = getattr(event, variable)\n if channel == \"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel == \"eTau\":\n if eleTauCutTester(event) and dataeleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 2: \"+str(q))\n if channel == \"muTau\":\n datatree = datafile3.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile3.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile3.Get(\"diTauEventTree/eventTree\")\n for event in datatree:\n g = getattr(event, variable)\n if channel ==\"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"eTau\":\n if eleTauCutTester(event) and dataeleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 3: \"+str(q))\n if channel == \"muTau\":\n datatree = datafile4.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile4.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile4.Get(\"diTauEventTree/eventTree\")\n for event in datatree:\n g = getattr(event, variable)\n if channel ==\"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"eTau\":\n if eleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 4: \"+str(q))\n if channel == \"muTau\":\n datatree = datafile5.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile5.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile5.Get(\"diTauEventTree/eventTree\")\n for event in datatree:\n g = getattr(event, variable)\n if channel ==\"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"eTau\":\n if eleTauCutTester(event) and dataeleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 5: \"+str(q))\n if channel == \"muTau\":\n datatree = datafile6.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile6.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile6.Get(\"diTauEventTree/eventTree\")\n for event in datatree:\n g = getattr(event, variable)\n if channel ==\"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"eTau\":\n if eleTauCutTester(event) and dataeleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 6: \"+str(q))\n if channel == \"muTau\":\n datatree = datafile7.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile7.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile7.Get(\"diTauEventTree/eventTree\")\n for event in datatree:\n g = getattr(event, variable)\n if channel ==\"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"eTau\":\n if eleTauCutTester(event) and dataeleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 7: \"+str(q))\n if channel == \"muTau\":\n datatree = datafile8.Get(\"muTauEventTree/eventTree\")\n if channel ==\"eTau\":\n datatree = datafile8.Get(\"eleTauEventTree/eventTree\")\n if channel ==\"diTau\":\n datatree = datafile8.Get(\"diTauEventTree/eventTree\")\n for event in datatree:\n g = getattr(event, variable)\n if channel ==\"muTau\":\n if muTauCutTester(event) and datamuTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"eTau\":\n if eleTauCutTester(event) and dataeleTauCutTester(event):\n data.Fill(g)\n q=q+1\n if channel ==\"diTau\":\n if diTauCutTester(event):\n data.Fill(g)\n q=q+1\n print (\"Events passed after 8: \"+str(q))\n\n\n\n data.SetMarkerStyle(20)\n data.SetMarkerSize(1.0)\n data.SetLineColor(ROOT.kBlack)\n\n\n\n for i in range(0, int(filenumber)):\n ntuple_file = ROOT.TFile(infile[i])\n print (channel)\n if channel == \"muTau\":\n tree = ntuple_file.Get(\"muTauEventTree/eventTree\")\n if channel == \"eTau\":\n tree = ntuple_file.Get(\"eleTauEventTree/eventTree\")\n if channel == \"diTau\":\n tree = ntuple_file.Get(\"diTauEventTree/eventTree\")\n for event in tree:\n g = getattr(event, variable)\n zptweight = getattr(event, \"ZPt_reweight\")\n wptweight = getattr(event, \"WPt_reweight\")\n if channel == \"muTau\":\n weight = getattr(event, \"__WEIGHT__\")\n genweight = getattr(event, \"GENWEIGHT\")\n puweight = getattr(event, \"puweight\")\n pogid1 = getattr(event, \"POGid1\")\n pogtrigger = getattr(event, \"POGtrigger\")\n tauid1 = getattr(event, \"TAUID1\")\n trackweight = getattr(event, \"trackweight\")\n if muTauCutTester(event):\n histo[str(i)].Fill(g, weight*genweight*puweight*pogid1*pogtrigger*tauid1*trackweight*35.9*1000*zptweight*wptweight)\n if channel == \"eTau\":\n weight = getattr(event, \"__WEIGHT__\")\n genweight = getattr(event, \"GENWEIGHT\")\n puweight = getattr(event, \"puweight\")\n tauid1 = getattr(event, \"TAUID1\")\n idisoweight = getattr(event, \"idisoweight_REDO\")\n trigweight = getattr(event, \"trigweight_REDO\")\n trackweight = getattr(event, \"trackweight\")\n if eleTauCutTester(event):\n histo[str(i)].Fill(g, weight*genweight*puweight*tauid1*idisoweight*trigweight*trackweight*35.9*1000*zptweight*wptweight)\n if channel == \"diTau\":\n weight = getattr(event, \"__WEIGHT__\")\n genweight = getattr(event, \"GENWEIGHT\")\n puweight = getattr(event, \"puweight\")\n trigweight = getattr(event, \"trigweight_REDO\")\n tauid1 = getattr(event, \"TAUID1\")\n if diTauCutTester(event):\n histo[str(i)].Fill(g, weight*genweight*puweight*trigweight*tauid1*35.9*1000*zptweight*wptweight)\n \n\n\n i = 0\n\n for q in range(0,int(filenumber)):\n histo[str(q)].SetFillColor(colors[q])\n histo[str(q)].SetLineColor(ROOT.kBlack)\n legend.AddEntry(histo[str(q)], legend_title[q],\"f\")\n\n legend.AddEntry(signal, \"Signal\", \"l\")\n legend.AddEntry(data, \"Observed\",\"P\")\n \n\n for w in range(0, int(filenumber)):\n stacks1.Add(histo[str(w)])\n\n\n canvas1.SetLogy()\n stacks1.Draw(\"HIST\")\n signal.Draw(\"HIST SAME\")\n\n if (data.GetMaximum() > stacks1.GetMaximum()):\n stacks1.SetMaximum(data.GetMaximum()*1.4)\n if (data.GetMaximum() <= stacks1.GetMaximum()):\n stacks1.SetMaximum(stacks1.GetMaximum()*1.4)\n\n data.Draw(\"e,SAME\")\n CMS_lumi.CMS_lumi(canvas1, iPeriod, iPos)\n stacks1.GetXaxis().SetTitle(xaxis)\n stacks1.GetXaxis().SetLabelSize(0.035)\n \n\n \n canvas1.Modified()\n canvas1.cd()\n canvas1.Update()\n canvas1.RedrawAxis()\n frame = canvas1.GetFrame()\n frame.Draw()\n\n legend.Draw(\"same\")\n saveas = saveWhere+channel+variable+'.png'\n canvas1.Update()\n canvas1.SaveAs(saveas)\n canvas1\n\n signalfile.Close()\n datafile1.Close()\n datafile2.Close()\n datafile3.Close()\n datafile4.Close()\n datafile5.Close()\n datafile6.Close()\n datafile7.Close()\n datafile8.Close()\n ntuple_file.Close()\n","repo_name":"marctost/CMS-files","sub_path":"makeplots.py","file_name":"makeplots.py","file_ext":"py","file_size_in_byte":25391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"34974329970","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom turtle import * # turtle 기능을 읽어 들임\n\ndef tree(length):\n # 나무를 그리는 함수\n if length > 5:\n forward(length)\n right(20)\n tree(length-15)\n left(40)\n tree(length-15)\n right(20)\n backward(length)\n\ncolor(\"green\") # 커서의 색상을 녹색으로 만든다\nleft(90) # 왼쪽으로 90도 회전시켜 위를 향하게 한다\nbackward(150) # 아래쪽으로 내리기\ntree(120) # 나무를 그리는 함수 호출\n\ninput('type to exit') # 그리기 종료 후 입력 대기\n","repo_name":"gstatsr94/python_study","sub_path":"Chapter01/draw_tree.py","file_name":"draw_tree.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"9231180465","text":"from .camera_settings import CameraSettings\nfrom .parameter import Parameter\nfrom .serial_port_settings import SerialPortSettings\n\n\nclass TestSettings:\n\n _cam: list[CameraSettings] = []\n _sp: list[SerialPortSettings] = []\n _cam_display = None\n _cam_leds = None\n _sp_main = None\n _sp_usb = None\n\n @staticmethod\n def get_cam_display():\n return TestSettings._cam_display\n\n @staticmethod\n def set_cam_display(name: str):\n cam_index = TestSettings.get_index_cam(name)\n if cam_index == -1:\n return -1\n else:\n TestSettings._cam_display = TestSettings._cam[cam_index]\n\n @staticmethod\n def get_cam_leds():\n return TestSettings._cam_leds\n\n @staticmethod\n def set_cam_leds(name: str):\n cam_index = TestSettings.get_index_cam(name)\n if cam_index == -1:\n # TODO: Error Code\n return -1\n else:\n TestSettings._cam_leds = TestSettings._cam[cam_index]\n\n @staticmethod\n def get_sp_main():\n return TestSettings._sp_main\n \n @staticmethod\n def set_sp_main(name: str):\n sp_index = TestSettings.get_index_sp(name)\n if sp_index == -1:\n # TODO: Error code\n return -1\n TestSettings._sp_main = TestSettings._sp[sp_index]\n\n @staticmethod\n def add_new_sp_settings(name: str, port: str, baudrate: int):\n TestSettings._sp.append(SerialPortSettings(name, port, baudrate))\n\n @staticmethod\n def get_index_sp(name: str):\n for i in range(len(TestSettings._sp)):\n sp_name = TestSettings._sp[i].get_name()\n if(sp_name == name):\n return i\n return -1\n \n @staticmethod\n def get_sp(name: str):\n index = TestSettings.get_index_sp(name)\n if index == -1:\n return None\n else:\n return TestSettings._sp[index]\n \n @staticmethod\n def delete_sp_settings(name: str):\n sp_index = TestSettings.get_index_sp(name)\n if sp_index != -1:\n TestSettings._sp.pop(sp_index)\n\n @staticmethod\n def add_new_cam_settings(settings: CameraSettings):\n TestSettings._cam.append(settings)\n\n @staticmethod\n def get_index_cam(name: str):\n for i in range(len(TestSettings._cam)):\n cam_name = TestSettings._cam[i].get_name()\n if(cam_name == name):\n return i\n return -1\n \n @staticmethod\n def get_cam(name: str):\n index = TestSettings.get_index_cam(name)\n if index == -1:\n return None\n else:\n return TestSettings._cam[index]\n\n @staticmethod\n def delete_cam_settings(name: str):\n cam_index = TestSettings.get_index_cam(name)\n if cam_index != -1:\n TestSettings._cam.pop(cam_index)\n ","repo_name":"whrovic/HMI_Test_System","sub_path":"src/main/python/com/hmi_test_system/data/hardware_settings/test_settings.py","file_name":"test_settings.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"40161214870","text":"import asyncio\n\nimport discord\nfrom discord.ext import commands\n\nfrom Errors.CreditExhaustedError import CreditExhaustedError\nfrom Errors.EmptyPromptError import EmptyPromptError\nfrom Errors.NotAdminError import NotAdminError\nfrom Helpers import user_parse\nfrom Helpers.Emojis import thumbs_up, complete\nfrom Helpers.Messages import help_message\nfrom Helpers.Wrappers import check_setup\nfrom Implementation.ApiClient import ApiClient\nfrom Implementation.DbHandler import DbHandler\nfrom Implementation.LoggingHandler import LoggingHandler\nfrom Prompts import language_map\n\n\nclass MainCog(commands.Cog):\n def __init__(self,\n discord_bot: commands.Bot,\n db_handler: DbHandler,\n api_client: ApiClient,\n logger: LoggingHandler\n ):\n self.__logger = logger.get_logger(\"main_cog\")\n self.__bot = discord_bot\n self.__db = db_handler\n self.__api = api_client\n\n @commands.Cog.listener()\n async def on_command_error(self, ctx, error):\n \"\"\"\n From: https://gist.github.com/EvieePy/7822af90858ef65012ea500bcecf1612\n \"\"\"\n\n if hasattr(ctx.command, 'on_error'):\n return\n cog = ctx.cog\n if cog:\n if cog._get_overridden_method(cog.cog_command_error) is not None:\n return\n ignored = (commands.CommandNotFound,)\n error = getattr(error, 'original', error)\n if isinstance(error, ignored):\n return\n if isinstance(error, commands.DisabledCommand):\n await ctx.send(f'{ctx.command} has been disabled.')\n elif isinstance(error, commands.NoPrivateMessage):\n try:\n await ctx.author.send(f'{ctx.command} can not be used in Private Messages.')\n except discord.HTTPException:\n pass\n elif isinstance(error, commands.PrivateMessageOnly):\n try:\n await ctx.author.send(f'{ctx.command} can only be used in Private Messages.')\n except discord.HTTPException:\n pass\n\n @commands.Cog.listener()\n async def on_guild_join(self, guild):\n owner_id = guild.owner_id\n guild_id = guild.id\n guild_name = guild.name\n if not self.__db.find_server(guild_id):\n self.__db.add_server({\n \"server_id\": guild_id,\n \"server_name\": guild_name,\n \"owner_id\": owner_id,\n \"api_token\": None,\n \"response_length\": 100,\n \"daily_allowance\": 100,\n \"vips\": [owner_id],\n \"users\": []\n })\n else:\n self.__db.update_server_name_id(guild_id, guild_name, owner_id)\n\n @commands.Cog.listener()\n async def on_guild_remove(self, guild):\n guild_id = guild.id\n self.__db.delete_server(guild_id)\n\n async def cog_before_invoke(self, ctx):\n await ctx.message.add_reaction(thumbs_up)\n\n async def cog_after_invoke(self, ctx):\n await ctx.message.remove_reaction(thumbs_up, self.__bot.user)\n await ctx.message.add_reaction(complete)\n\n async def check_server_token(self, ctx):\n guild_id = ctx.guild.id\n return self.__db.check_server_token(guild_id)\n\n @staticmethod\n async def prompt_setup(ctx):\n await ctx.send(\"Owner of this server hasn't configured the bot yet. If you're the owner, send this bot a DM \"\n \"with command !setup.\")\n\n @staticmethod\n async def empty_warning(ctx):\n await ctx.send(\"Hey, no empty prompts!\")\n\n @staticmethod\n async def openai_down_warning(ctx):\n await ctx.send(\"OpenAI seems to be down. This sometimes happen and is usually resolved within minutes.\")\n\n @staticmethod\n async def credit_warning(ctx):\n user_id, user_name = user_parse(ctx)\n await ctx.send(\"{}, your daily allowance is over. :cry:\".format(user_name))\n\n @staticmethod\n async def not_admin_warning(ctx):\n user_id, user_name = user_parse(ctx)\n await ctx.send(\"{}, this command is only usable by admins.\".format(user_name))\n\n @commands.command()\n async def help(self, ctx):\n await ctx.send(help_message)\n\n @commands.command()\n @commands.dm_only()\n async def setup(self, ctx):\n user_id, user_name = user_parse(ctx)\n owned_servers = self.__db.find_owned_servers(user_id)\n if not len(owned_servers) > 0:\n await ctx.send(\"You don't seem to own any servers that i'm on. If you think this is a mistake, kick the \"\n \"bot and add it again.\")\n return\n\n def check_token(m):\n return m.channel == ctx.channel\n\n owned_server = owned_servers[0]\n\n if len(owned_servers) > 1:\n message = \"\"\n for serv in owned_servers:\n message += \"[\" + str((owned_servers.index(serv) + 1)) + \"]\"\n message += \"server name:\" + serv[\"server_name\"]\n message += \", server id:\" + serv[\"server_id\"]\n message += \"\\n\"\n\n await ctx.send(\"I noticed that you're an admin on multiple servers. Please choose which server \"\n \"you'd like to setup.\"\n \"{}\".format(message)\n )\n try:\n msg = await self.__bot.wait_for(\"message\", check=check_token, timeout=60)\n choice = int(msg.content)\n owned_server = owned_servers[choice]\n except asyncio.TimeoutError:\n await ctx.send(\"Timeout has been reached. You can try again with !setup.\")\n return\n except (TypeError, ValueError):\n await ctx.send(\"Invalid value. Please select a number. (e.g. '1') You can try again with !setup.\")\n return\n\n await ctx.send(\"Important note: For this bot to function and process requests, it has to save your api key in \"\n \"a database. This means that if the bad guys somehow access it, they can use your token and \"\n \"even cause incurring charges. We cannot be held responsible for that and suggest you to host \"\n \"on your own. You can do so by visiting the public repo on github(\"\n \"github.com/e4c6/DiscordGPT-3). If that's not a concern for \"\n \"you, feel free to proceed. If you ever doubt something is going fishy, you should quickly \"\n \"regenerate your api token through the developer portal on beta.openai.com.\")\n\n server_id, server_name = owned_server[\"server_id\"], owned_server[\"server_name\"]\n await ctx.send(\"Hi {}, i see that you're the owner of {}. Now please send me your OpenAI api key within 60 \"\n \"seconds. (and \"\n \"nothing else).\\nIt should be visible to you on https://beta.openai.com/account/api-keys.\".format(\n user_name, server_name))\n\n try:\n msg = await self.__bot.wait_for(\"message\", check=check_token, timeout=60)\n except asyncio.TimeoutError:\n await ctx.send(\"Timeout has been reached. You can try again with !setup.\")\n return\n\n if not msg.content[0:3] == \"sk-\":\n await ctx.send(\"Your api token should start with the characters sk-. You can restart this process when \"\n \"you find it.\")\n return\n\n if len(msg.content) > 50:\n await ctx.send(\"Your key seems abnormally long... You can restart this process when \"\n \"you find the right key.\")\n return\n\n self.__db.set_server_token(server_id, msg.content)\n await ctx.send(\"Successfully set the token, enjoy! If you like this bot, please consider donating via \"\n \" BTC address: 14ozvJYfChmiXwqhfzH4yCqcYR7gzwfVYT\")\n\n @commands.command()\n @commands.guild_only()\n async def config(self, ctx):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n owner_id, vips, _, length, allowance = self.__db.get_server_settings(guild_id)\n if user_id != owner_id:\n raise NotAdminError\n length_str = \"# Length: {}\\n\".format(length)\n allowance_str = \"# Allowance: {}\\n\".format(allowance)\n vips_str = \"\"\"# Vips\\n\"\"\"\n for i in range(len(vips)):\n vips_str += \"{} - {}\\n\".format(i + 1, vips[i])\n vips_str += \"Note: Owners are automatically assigned a vip role.\"\n return await ctx.send(length_str + allowance_str + vips_str)\n\n @commands.command()\n @commands.guild_only()\n async def allowance(self, ctx, allowance: int):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n owner_id, vips, _, length, old_allowance = self.__db.get_server_settings(guild_id)\n if user_id != owner_id:\n raise NotAdminError\n self.__db.update_server_allowance(guild_id, allowance)\n return await ctx.send(\"Successfully updated member allowance to {}\".format(allowance))\n\n @commands.command()\n @commands.guild_only()\n async def length(self, ctx, length: int):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n owner_id, vips, _, old_length, allowance = self.__db.get_server_settings(guild_id)\n if user_id != owner_id:\n raise NotAdminError\n self.__db.update_server_length(guild_id, length)\n return await ctx.send(\"Successfully updated response length to {}\".format(length))\n\n @commands.command()\n @commands.guild_only()\n async def vip(self, ctx, *, member: discord.Member):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n owner_id, vips, _, length, allowance = self.__db.get_server_settings(guild_id)\n if user_id != owner_id:\n raise NotAdminError\n member_id, member_name = member.id, member.display_name\n self.__db.add_vip(guild_id, member_id)\n return await ctx.send(\"Successfully added {} to vips.\".format(member_name))\n\n @commands.command()\n @commands.guild_only()\n async def remove_vip(self, ctx, *, member: discord.Member):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n owner_id, vips, _, length, allowance = self.__db.get_server_settings(guild_id)\n if user_id != owner_id:\n raise NotAdminError\n member_id, member_name = member.id, member.display_name\n self.__db.remove_vip(guild_id, member_id)\n return await ctx.send(\"Successfully removed {} from vips.\".format(member_name))\n\n @commands.command()\n @commands.guild_only()\n async def credit(self, ctx):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, _, _ = self.__db.get_user_settings(guild_id, user_id)\n _, _, _, _, allowance = self.__db.get_server_settings(guild_id)\n return await ctx.send(\"{}, your remaining daily allowance is: {}.\".format(user_name, allowance - today_usage))\n\n @commands.command()\n @commands.guild_only()\n async def settings(self, ctx):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n _, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n return await ctx.send(\n \"{}, your language is set to: {} and temperature is set to: {}\".format(user_name, language, temperature))\n\n @commands.command()\n @commands.guild_only()\n async def language(self, ctx, lang: str):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n possible_languages = list(language_map.keys())\n if lang not in possible_languages:\n return await ctx.send(\"{}, chosen language must be within: {}\".format(user_name, possible_languages))\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n self.__db.update_user_language(guild_id, user_id, lang)\n return await ctx.send(\n \"Sucessfully updated your settings, {}. Old language: {}, New language: {}\".format(user_name, language,\n lang))\n\n @commands.command()\n @commands.guild_only()\n async def temperature(self, ctx, temp: float):\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n if not 0 <= temp <= 1:\n return await ctx.send(\"{}, chosen temperature must equal or be within the range of 0 and 1. (e.g. 0.3) \"\n \"You can read more about temperature on \"\n \"https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277 or \"\n \"https://beta.openai.com/docs/api-reference/create-completion-via-get.\".format(\n user_name))\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n self.__db.update_user_temperature(guild_id, user_id, temp)\n return await ctx.send(\n \"Sucessfully updated your settings, {}. Old temperature: {}, New temperature: {}\".format(user_name,\n temperature,\n temp))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def answer(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.answer(prompt, length=length, api_key=token, language=language,\n temperature=temperature)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def complete(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.complete(prompt, length=length, api_key=token, language=language,\n temperature=temperature)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def song(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.song(song_name=prompt, user_name=user_name, length=length, api_key=token,\n language=language,\n temperature=temperature)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def foulmouth(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.foulmouth_answer(prompt, length=length, api_key=token, language=language,\n temperature=temperature)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def sentiment(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.sentiment(prompt, api_key=token, language=language)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def emojify(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.emojify(prompt, length=length, api_key=token, language=language,\n temperature=temperature)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def sarcasm(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.sarcastic_answer(prompt, length=length, api_key=token, language=language,\n temperature=temperature)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n\n @commands.command()\n @commands.guild_only()\n @check_setup\n async def headline(self, ctx, *prompt: str):\n usage = len(list(\"\".join(prompt)))\n if usage == 0:\n raise EmptyPromptError\n user_id, user_name = user_parse(ctx)\n guild_id = ctx.guild.id\n today_usage, language, temperature = self.__db.get_user_settings(guild_id, user_id)\n owner_id, vips, token, length, allowance = self.__db.get_server_settings(guild_id)\n if usage + today_usage > allowance and user_id not in vips:\n raise CreditExhaustedError\n answer = await self.__api.headline(prompt, length=length, api_key=token, language=language,\n temperature=temperature)\n self.__db.increment_member_usage(guild_id, user_id, usage)\n return await ctx.send(discord.utils.escape_mentions(answer))\n","repo_name":"e4c6/DiscordGPT-3","sub_path":"Cogs/Maincog.py","file_name":"Maincog.py","file_ext":"py","file_size_in_byte":20616,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"67"}
+{"seq_id":"19549670914","text":"import json\nimport boto3\nimport os\nimport sys\nimport logging\nimport logging.config\nimport decimal\nimport kuloko_handler.handler.api_handler as api\n\ndef lambda_handler(event, context):\n try:\n\n session = boto3.session.Session()\n table = boto3.resource('dynamodb', endpoint_url = \"http://dynamodb:8000\").Table('Quote')\n\n sym = 'BTC'\n depth=5\n \n orderbook = api.Orderbook(sym)\n orderbook.depth =depth\n json_orderbook = orderbook.fetch(return_type='json')\n json_orderbook['sym'] = sym\n\n item = json.loads(str(json_orderbook), parse_float=decimal.Decimal)\n table.put_item(\n Item=item\n )\n \n return {\n 'statusCode': 200,\n 'body': json.dumps({\n 'message': 'DONE!'\n }),\n }\n\n except Exception as e:\n logger.exception(e)\n return {\n 'statusCode': 500,\n 'body': json.dumps({\n 'error_message': str(e)\n }),\n }\n\n","repo_name":"tanico-rikudo/kuloko","sub_path":"aws/feed_handler/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"28006907173","text":"'''\n단지번호붙이기\nhttps://www.acmicpc.net/problem/2667\n그래프 이론, 그래프 탐색, 깊이 우선 탐색, 넓이 우선 탐색\n'''\n\n\nimport sys\n\ninput = sys.stdin.readline\n\ndic = {0:[1, 0], 1:[-1, 0], 2:[0, 1], 3:[0, -1]}\n\ndef dfs(x, y):\n result = 1\n for i in range(4):\n nx = x + dic[i][0]\n ny = y + dic[i][1]\n if 0 <= nx < N and 0 <= ny < N:\n if graph[nx][ny] == 1:\n graph[nx][ny] = -1\n result += dfs(nx, ny)\n return result\n\nN = int(input())\ngraph = [[0] * N for _ in range(N)]\n\nfor i in range(N):\n row = input()\n for j in range(N):\n graph[i][j] = int(row[j])\n\nanswer = []\nfor i in range(N):\n for j in range(N):\n if graph[i][j] == 1:\n graph[i][j] = -1\n answer.append(dfs(i, j))\n\nanswer.sort()\nprint(len(answer))\nprint('\\n'.join(map(str, answer)))\n","repo_name":"JooJaeHwan/Baekjoon","sub_path":"Silver/2667.py","file_name":"2667.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"30067732172","text":"# Copyright (c) Quectel Wireless Solution, Co., Ltd.All Rights Reserved.\r\n# \r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n# \r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n# \r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\nimport usocket as socket\r\nimport log\r\nimport net\r\nimport dataCall\r\nimport __wifiLocator\r\n\r\nclass wifilocator:\r\n\r\n def __init__(self, token=None):\r\n self.wifitoken = token\r\n\r\n def getwifilocator(self):\r\n net_sta = net.getState()\r\n if net_sta != -1 and ((net_sta[1][0] == 1) or (net_sta[1][0] == 5)):\r\n call_state = dataCall.getInfo(1, 0)\r\n if (call_state != -1) and (call_state[2][0] == 1):\r\n if len(self.wifitoken) != 16:\r\n return -2\r\n try:\r\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n sockaddr = socket.getaddrinfo('www.queclocator.com', 80)[0][-1]\r\n sock.connect(sockaddr)\r\n cellinfo = __wifiLocator.getWifilocreq(self.wifitoken)\r\n senddata = b\"POST /location/QLOC HTTP/1.0\\r\\nHost: www.queclocator.com\\r\\nContent-Length: {}\\r\\nContent-Type: 05\\r\\nAccept-Charset: utf-8\\r\\n\\r\\n{}\".format(cellinfo[0], cellinfo[1])\r\n sock.write(senddata)\r\n l = sock.readline()\r\n try:\r\n l = l.split(None, 2)\r\n status = int(l[1])\r\n except:\r\n raise ValueError(\"Connect FAIL!\")\r\n reason = \"\"\r\n if status == 200:\r\n while True:\r\n l = sock.readline()\r\n j = l.decode().split(\":\")\r\n if not l or l == b\"\\r\\n\":\r\n break\r\n data = sock.recv(1024)\r\n return __wifiLocator.encodeWifilocreq(data)\r\n else:\r\n if len(l) > 2:\r\n reason = l[2].rstrip()\r\n raise ValueError(\"error info:===='{}'====\".format(reason))\r\n except Exception as e:\r\n print(\"Wifi locator Get the coordinate error:%s \"%str(e))\r\n return -3\r\n else:\r\n return -1\r\n else:\r\n return -1\r\n\r\n\r\n","repo_name":"QuecPython/microPython","sub_path":"ports/quectel/core/wifilocator.py","file_name":"wifilocator.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"71741377814","text":"T = int(input())\n\nfor test_case in range(1, T + 1):\n n, m = map(int, input().split())\n board = [[0 for _ in range(n + 1)]]\n for _ in range(n):\n board.append([0] + list(map(int, input().split())))\n res = 0\n for i in range(1, n + 1):\n for j in range(1, n + 1):\n board[i][j] = board[i-1][j] + board[i][j]\n\n for i in range(1, n - m + 2):\n for j in range(1, n - m + 2):\n res = max(res, sum(board[i + m - 1][j: j + m]) - sum(board[i - 1][j: j + m]))\n\n print(f'#{test_case} {res}')","repo_name":"devyuseon/problem-solving","sub_path":"SWEA/2001.py","file_name":"2001.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"33114033506","text":"import numpy as np\n\n# The simulation will require most of the functions you have already\n# implemented above. If it helps you debug, feel free to copy and\n# paste the code here.\ndef my_distance(drij):\n \"\"\"\n Compute length of displacement vector drij\n assume drij already accounts for PBC\n\n Args:\n drij (np.array) : vector(s) of length 3\n Returns:\n float: length (distance) of vector(s)\n \"\"\"\n return np.linalg.norm(drij, axis=0)\n\n\ndef my_disp_in_box(drij, lbox):\n \"\"\"\n Impose minimum image condition on displacement vector drij=ri-rj\n\n Args:\n drij (np.array): length-3 displacement vector ri-rj\n lbox (float): length of cubic cell\n Returns:\n np.array: drij under MIC\n \"\"\"\n\n return drij - lbox * np.round(drij / lbox)\n\n\ndef my_pos_in_box(pos, lbox):\n \"\"\" wrap positions inside simulation box\n\n Args:\n pos (np.array): positions, shape (natom, ndim)\n lbox (float): box side length\n Returns:\n np.array: pos in box\n \"\"\"\n\n return -lbox / 2 + (pos - lbox / 2) % lbox\n\n\ndef my_kinetic_energy(vel, mass):\n \"\"\" Calculate total kinetic energy.\n\n Args:\n vel (np.array): particle velocities, shape (natom, ndim)\n mass (float): particle mass\n Return:\n float: total kinetic energy\n \"\"\"\n k = 0.0\n for i in vel:\n k += sum(0.5 * mass * i ** 2)\n\n return k\n\n\ndef my_potential_energy(rij, rc):\n \"\"\" Calculate total potential energy.\n\n Args:\n rij (np.array): distance table, shape (natom, natom)\n Return:\n float: total potential energy\n \"\"\"\n vshift = 4 * rc ** (-6) * (rc ** (-6) - 1)\n potential = 0.0\n for i in range(len(rij)):\n for j in range(i + 1, len(rij[0])):\n r = rij[i][j]\n if r <= rc:\n potential += 4 * r ** (-6) * (r ** (-6) - 1) - vshift\n\n return potential\n\n\ndef my_force_on(parameters):\n \"\"\"\n Compute force on atom i\n\n Args:\n i (int): particle index\n pos (np.array) : particle positions, shape (natom, ndim)\n lbox (float): side length of cubic box\n Returns:\n np.array: force on atom i, a length-3 vector\n \"\"\"\n i, pos, lbox, rc = parameters\n Force = np.zeros(3)\n cur = pos[i]\n for atom in pos:\n if (atom == cur).all():\n continue\n r_ij = my_disp_in_box(cur - atom, lbox)\n r = my_distance(r_ij)\n if r <= rc:\n Force += 24 * r ** (-8) * (2 * r ** (-6) - 1) * r_ij\n return Force\n\n\ndef get_distance_table(N, rij):\n drij = np.zeros((N, N))\n for i in range(N):\n for j in range(i + 1, N):\n disp = rij[(i,j)]\n distance = my_distance(disp)\n drij[i][j] = distance\n drij[j][i] = distance\n return drij\n\ndef get_displacement_table(N, R, lbox):\n rij = np.zeros((N, N, 3))\n for i in range(N):\n for j in range(i + 1, N):\n disp = my_disp_in_box(R[i] - R[j], lbox)\n rij[i][j] = disp\n rij[j][i] = disp\n return rij\n\ndef my_temperature(k,N):\n \"\"\" Calculate system temperature.\n\n Args:\n Ek (float): kinetic energy\n atoms (integer): number of atoms\n Return:\n float: temperature\n \"\"\"\n return k/(3*N/2)\n\ndef my_pressure(V,N,T,R,F):\n viral = np.sum([np.dot(R[i],F[i]) for i in range(N)])\n return (N*T + viral/3) / V\n\n","repo_name":"navining/metadynamics","sub_path":"properties.py","file_name":"properties.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"67"}
+{"seq_id":"3302487715","text":"from keras.applications.resnet50 import preprocess_input \nfrom keras.applications import ResNet50\nfrom keras.preprocessing.image import img_to_array\nfrom keras.applications import imagenet_utils\nimport numpy as np\nimport cv2\n\n#daha once yazdigimiz kodlari import ediyoruz\nfrom sliding_window_2 import sliding_window\nfrom image_pyramid import image_pyramid\nfrom non_max_supression_3 import non_max_suppression\n\nWIDTH = 600\nHEIGHT = 600\nPYR_SCALE = 1.5 # image pyramid scale\nWIN_STEP = 16 # sliding step size\nROI_SIZE = (200,150)\nINPUT_SIZE = (224, 224) ## resnete sokacagimiz resmin input boyutu\n# sabit parametreler hicbir zaman degismeyecek parametreler olacakları icin buyuk yazdık\n\nprint(\"Loading ResNet\")\nmodel = ResNet50(weights = \"imagenet\", include_top = True)\n\n\n#nesne takibi yapacagimiz resim\noriginal = cv2.imread(\"husky.jpg\")\noriginal = cv2.resize(original, dsize=(WIDTH, HEIGHT))\n# once bizim fonksiyonlarımıza 600,600 seklinde sokuyoruz en son resnet'e\n# 224,224 seklinde sokacagiz\ncv2.imshow(\"Husky\", original)\n\n(H, W) = original.shape[:2]\n\n# image pyramid\npyramid = image_pyramid(original, PYR_SCALE, ROI_SIZE) # In each iteration, we will run a sliding window.\n\nrois = []\nlocs = []\n\nfor image in pyramid:\n\n #image_pyramid te bir PYR_SCALE uyguluyoruz bunu scaleye de uygulamalıyız cunku bir dengesizlik olusur\n scale = W/float(image.shape[1])\n \n #WIN_STEP piksel kayma\n for (x, y, roiOrig) in sliding_window(image, WIN_STEP, ROI_SIZE):\n x = int(x*scale)\n y = int(y*scale)\n w = int(ROI_SIZE[0]*scale)\n h = int(ROI_SIZE[1]*scale)\n\n #roiOrig alıp sınıflandırmada kullanmam gerekiyor onun için resize ediyoruz\n roi = cv2.resize(roiOrig, INPUT_SIZE)\n roi = img_to_array(roi)\n # preprocess_input kullanarak resnete hazir hale getirmis oluyorum\n roi = preprocess_input(roi)\n\n #rois icerisine roi ekliyorum\n rois.append(roi)\n locs.append((x,y,x+w,y+h))\n\nrois = np.array(rois, dtype=\"float32\")\n\nprint(\"classification\")\n\npreds = model.predict(rois)\n\npreds = imagenet_utils.decode_predictions(preds, top=1)\n\nlabels = {}\n#bu degerden yuksek tahminleri isleme alicam\nmin_conf = 0.9 # 0.95, 0.8\n\nfor (i, p) in enumerate(preds):\n\n # tahminler 3 tane değer dondurulecek 1. imageNet_id, 2. label(sınıf)\n # 3. yüzde tahmin değeri.\n (_, label, prob) = p[0]\n\n #eger prob, min_conf tan buyukse\n if prob >= min_conf:\n #ozzetle min_conf degerinden yuksek tahminleri isleme aliyoruz\n\n box = locs[i]\n #kutu ve olasilik\n L = labels.get(label, [])\n L.append((box, prob))\n labels[label] = L\n\n#ayiklama ve gorsellestirme yapiyoruz\nfor label in labels.keys():\n #orijinal resmi bozmamak icin copy aliyoruz\n clone = original.copy()\n\n #kutucuklari cizdiriyorum\n for (box, prob) in labels[label]:\n #ayikliyorum\n (startX, starY, endX, endY) = box\n #copy uzerinde cizim islemlerini yapiyoruz yesil kalinlik 2\n cv2.rectangle(clone, (startX, starY), (endX, endY), (0,255,0), 2)\n #gosteriyorum\n cv2.imshow(\"ilk\", clone)\n\n\n clone = original.copy()\n\n # non-maxima labelleri boluyoruz (kutular ve olasiliklar olarak)\n boxes = np.array([p[0] for p in labels[label]])\n proba = np.array([p[1] for p in labels[label]])\n\n #kutucukların max olasiliği olani al\n boxes = non_max_suppression(boxes, proba)\n\n #sonra bunu ciz\n for (startX, starY, endX, endY) in boxes:\n cv2.rectangle(clone, (startX, starY), (endX, endY), (0,255,0), 2)\n y = starY - 10 if starY - 10 > 10 else starY + 10\n cv2.putText(clone, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0,255,0), 2)\n \n cv2.imshow(\"max bulunan olasilikli eskimo_dog\",clone)\n\nk = cv2.waitKey(0) & 0xFF # -> esc tusunu al\n\nif k == 27: # esc basinca cik\n cv2.destroyAllWindows()\n \n \n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"umutcnn/derin_ogrenme_keras","sub_path":"7_evrisimsel_sinir_aglari/6_rcnn1.py","file_name":"6_rcnn1.py","file_ext":"py","file_size_in_byte":3891,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"8383475388","text":"class Item:\n \"\"\"\n Node of FP-tree\n \"\"\"\n def __init__(self,id):\n self.id = id\n self.count = 1\n self.children = []\n self.father = None\n\n def get_id(self):\n return self.id\n\n def get_count(self):\n return self.count\n\n def get_children(self):\n return self.children\n\n def get_father(self):\n return self.father\n\n def contain_child(self,item_id):\n for child in self.children:\n if child.id == item_id:\n return True\n return False\n\n def get_child(self,item_id):\n for child in self.children:\n if child.id == item_id:\n return child\n return None\n\n def add_node(self,item_id):\n child = Item(item_id)\n child.father = self\n self.children.append(child)\n\n def __str__(self):\n return (\"id: \"+ self.id +\n \"\\ncount: \" + str(self.count))\n\nclass ItemTree(Item):\n \"\"\"\n Root of FP-tree\n \"\"\"\n def __init__(self):\n self.id = 'root'\n self.count = 0\n self.children = []\n self.father = None\n self.item_trace = {}\n\n def add_path(self,path):\n \"\"\" Add a pattern to fp_tree\n\n :param path: A list, list[0] is the repeat time of this pattern\n :return: None\n \"\"\"\n current_node = self\n for item_index in range(1,len(path)):\n #for item_id in path:\n if current_node.contain_child(path[item_index]):\n current_node = current_node.get_child(path[item_index])\n current_node.count += path[0]\n else:\n current_node.add_node(path[item_index])\n current_node = current_node.get_child(path[item_index])\n current_node.count = path[0]\n if not current_node.id in self.item_trace: #creat the trace list of each item\n self.item_trace[current_node.id] = []\n self.item_trace[current_node.id].append(current_node)\n else:\n self.item_trace[current_node.id].append(current_node)\n\n def has_no_branch(self):\n cur_node = self\n while cur_node.children:\n if len(cur_node.children)>1:\n return False\n cur_node = cur_node.children[0]\n return True\n","repo_name":"FLAYhhh/DataMining","sub_path":"FP-growth/fptree_structure.py","file_name":"fptree_structure.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"38760475381","text":"import json\n\nfrom flask import Blueprint, jsonify, request, render_template, session\nfrom models import User, Game, GameUser, GameCards, user_to_dto, game_to_dto\nfrom app import db\nfrom flask_login import login_user, logout_user, current_user, login_required\n\napi = Blueprint('api', __name__)\n\n\n@api.route('/authenticate', methods=['POST'])\ndef authenticate():\n username = request.json['username']\n if current_user.is_authenticated:\n user = User.query.get(current_user.id)\n if user:\n if username != \"\":\n user.username = username\n else:\n user.username = \"Unknown\"\n db.session.commit()\n return jsonify(user_to_dto(user))\n if username != \"\":\n user = User(username=username)\n else:\n user = User()\n db.session.add(user)\n db.session.commit()\n login_user(user)\n return jsonify(user_to_dto(user))\n\n\n@api.route('/current', methods=['GET'])\ndef get_user():\n if current_user.is_authenticated:\n return jsonify(user_to_dto(current_user))\n return jsonify(\"NotAuthenticated\")\n\n\n@api.route('/logout')\ndef logout():\n # if current_user.is_authenticated:\n # user = User.query.get(current_user.id)\n # if user:\n # db.session.delete(user)\n # db.session.commit()\n logout_user()\n return jsonify(\"LoggedOut\")\n\n\n@api.route('/create')\n@login_required\ndef create():\n game = Game(admin_id=current_user.id)\n db.session.add(game)\n db.session.commit()\n game_user = GameUser(user_id=current_user.id, game_id=game.id)\n db.session.add(game_user)\n db.session.commit()\n session['game_id'] = game.id\n session.permanent = False\n session.modified = True\n return jsonify(game_to_dto(game))\n\n\n@api.route('/game')\ndef get_game():\n if 'game_id' in session.keys():\n if current_user.is_authenticated:\n game = Game.query.get(session['game_id'])\n if game:\n return jsonify(game_to_dto(game))\n session.pop('game_id')\n return jsonify(\"GameNotFound\")\n\n\n@api.route('/start')\n@login_required\ndef start_game():\n if 'game_id' in session.keys():\n game = Game.query.get(session['game_id'])\n if game and game.admin_id == current_user.id:\n game.is_started = True\n db.session.commit()\n return jsonify(game_to_dto(game))\n return jsonify(\"NoPermission\")\n return jsonify(\"GameNotFound\")\n\n\n\n@api.route('/cancel')\ndef cancel_game():\n if 'game_id' in session.keys():\n game_user = GameUser.query.filter_by(user_id=current_user.id, game_id=session['game_id']).first()\n if game_user is not None:\n db.session.delete(game_user)\n db.session.commit()\n session.pop('game_id')\n return jsonify(\"GameCanceled\")\n\n\n@api.route('/join', methods=[\"POST\"])\n@login_required\ndef join_game():\n game_id = request.json['id']\n game = Game.query.get(game_id)\n if game:\n session['game_id'] = game_id\n session.permanent = False\n session.modified = True\n if GameUser.query.filter_by(user_id=current_user.id, game_id=game_id).first() is None:\n game_user = GameUser(user_id=current_user.id, game_id=game_id)\n db.session.add(game_user)\n db.session.commit()\n return jsonify(game_to_dto(game))\n return jsonify(\"GameNotFound\")\n\n\n@api.route('/members')\n@login_required\ndef get_members():\n if 'game_id' in session.keys():\n members = GameUser.query.filter_by(game_id=session['game_id']).all()\n members_names = []\n for member in members:\n user = User.query.get(member.user_id);\n members_names.append(user.username)\n return json.dumps(members_names)\n return jsonify(\"GameNotFound\")\n\n\n@api.route('/cards')\n@login_required\ndef get_cards():\n if 'game_id' in session.keys():\n cards = GameCards.query.filter_by(game_id=session['game_id']).all()\n cards_list = []\n for card in cards:\n cards_list.append({\n \"id\": card.card_id,\n \"sprint\": card.sprint_id,\n \"is_grabbed\": card.is_grabbed,\n \"user_id\": card.user_id\n })\n return json.dumps(cards_list)\n return jsonify(\"GameNotFound\")\n\n\n@api.route('/grabbed', methods=['POST'])\n@login_required\ndef card_grabbed():\n print(request.json)\n if 'game_id' in session.keys():\n card_id = request.json['id']\n card = GameCards.query.filter_by(game_id=session['game_id'], card_id=card_id).first()\n if card:\n card.is_grabbed = True\n card.user_id = current_user.id\n else:\n card = GameCards(game_id=session['game_id'], card_id=card_id, is_grabbed=True, user_id=current_user.id)\n db.session.add(card)\n db.session.commit()\n return jsonify(\"Done\")\n return jsonify('GameNotFound')\n\n\n@api.route('/dropped', methods=['POST'])\n@login_required\ndef card_dropped():\n print(request.json)\n if 'game_id' in session.keys():\n card_id = request.json['id']\n sprint_id = request.json['sprint_id']\n\n card = GameCards.query.filter_by(game_id=session['game_id'], card_id=card_id).first()\n if card:\n card.is_grabbed = False\n card.sprint_id = sprint_id\n card.user_id = None\n else:\n card = GameCards(game_id=session['game_id'], card_id=card_id, sprint_id=sprint_id)\n db.session.add(card)\n db.session.commit()\n return jsonify(\"Done\")\n\n return jsonify('GameNotFound')\n\n\n@api.route(\"/\")\ndef my_index():\n return render_template(\"index.html\")\n","repo_name":"NodirBobiev/safe-summer","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"20611346515","text":"#coding=utf-8\nimport os\nimport tkinter as tk\nimport tkinter.filedialog as fd\nimport tkinter.messagebox as mb\nfrom tkinter import LEFT\n\n\nclass SavePath():\n def __init__(self, root, path_save):\n self.entry_path = tk.Entry(\n root,\n width=100,\n font=\"13\",\n )\n self.path_to_save = path_save\n\n self.entry_path.insert(0, self.path_to_save)\n\n self.btn_paste_1db = tk.Button(\n root,\n text='Выберите путь',\n fg='black',\n padx=\"14\", pady=\"7\", font=\"13\",\n\n highlightbackground='#66a5ad',\n activeforeground='green',\n activebackground='yellow',\n command=self.choose_directory1,\n )\n\n # self.btn_choose.grid(rowspan=1, columnspan=1)\n # self.btn_get.grid(rowspan=1, columnspan=1)\n\n self.List_for_paste_data = [\n self.entry_path,\n self.btn_paste_1db\n ]\n\n def rendre_save_path(self):\n self.btn_paste_1db.pack(expand=1, side=LEFT)\n self.entry_path.pack(expand=2, side=LEFT)\n\n # self.btn_paste_1db.grid(row=1, rowspan=1, columnspan=1)\n # self.btn_paste_2db.grid(rowspan=1, columnspan=1)\n\n def update_save_path(self):\n self.delete_save_path()\n self.rendre_save_path()\n\n def delete_save_path(self):\n for i in self.List_for_paste_data:\n i.pack_forget()\n\n def choose_directory1(self):\n\n directory = fd.askdirectory(title=\"Выбрать папку\", initialdir=self.path_to_save)\n if os.path.exists(directory):\n self.path_to_save = directory\n self.entry_path.delete(0, \"end\")\n self.entry_path.insert(0, self.path_to_save)\n return self.path_to_save\n else:\n mb.showwarning(\"Ошибка выбрана не сущестуюшая папка \", directory)\n","repo_name":"AnyashaTk/Data_anomalies","sub_path":"gui/save_path.py","file_name":"save_path.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"7358410680","text":"import os\nos.chdir('c:/users/rodrigo/desktop/crawlToTheFuture/crawl-to-the-future/crawlers/Way-Back/')\n\nimport waybacktrack\n\nsites = ['thenation.com']\nyears = [2000,2005]\n\nfor site in sites:\n for year in years:\n waybacktrack.archive_domain(domain=site,\n year=year,\n debug=True)\n\n\ntry:\n from cStringIO import StringIO as BytesIO\nexcept ImportError:\n from io import BytesIO\n\nfrom eatiht import etv2\n\nos.chdir('../../www.nytimes.com/content/')\n\nfiles = [f for f in os.listdir('.') if os.path.isfile(f)]\n\nfor f in files:\n try:\n content = etv2.extract(f).get_text()\n\n with open(f + '.txt', 'wb') as fi:\n fi.write(BytesIO(content).read())\n except Exception:\n pass\n","repo_name":"rodricios/crawl-to-the-future","sub_path":"dataset/crawl_extract.py","file_name":"crawl_extract.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"67"}
+{"seq_id":"18570112724","text":"\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nimport sys\nimport res\nimport requests\nimport os\n# from network import Network\n# from ..network.network import Network\ncurrent = os.path.dirname(os.path.realpath(__file__))\nparent = os.path.dirname(current)\nsys.path.append(parent)\n\n\nfrom network.network import Network\n\nimport functools\nfrom PyQt5.QtWidgets import QMainWindow\n\n# from product import icons\n\n# from product.product import Main\n\n# statusOfPostLogin =None\nfrom PyQt5.QtWidgets import QWidget\n# from PyQt5.QtGui import *\n# from PyQt5.QtCore import *\n# class Ui_Form(object):\nclass Ui_Form(QMainWindow):\n# class Ui_Form(QWidget):\n def __init__(self):\n super().__init__()\n # w = QtWidgets.QMainWindow()\n # ex.setupUi(w)\n # w.show()\n self.setupUi(self)\n # # # showing all the widgets\n self.show()\n\n def setupUi(self, Form):\n Form.setObjectName(\"Form\")\n Form.resize(640, 480)\n Form.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n Form.setAttribute(QtCore.Qt.WA_TranslucentBackground)\n Form.setStyleSheet(\"background-color: rgb(255, 255, 255);\")\n self.horizontalLayoutWidget = QtWidgets.QWidget(Form)\n self.horizontalLayoutWidget.setGeometry(QtCore.QRect(0, 0, 641, 481))\n self.horizontalLayoutWidget.setObjectName(\"horizontalLayoutWidget\")\n self.horizontalLayout = QtWidgets.QHBoxLayout(\n self.horizontalLayoutWidget)\n self.horizontalLayout.setContentsMargins(0, 0, 0, 0)\n self.horizontalLayout.setObjectName(\"horizontalLayout\")\n self.label = QtWidgets.QLabel(self.horizontalLayoutWidget)\n self.label.setStyleSheet(\"border-image: url(:/images/4419038.jpg);\")\n self.label.setText(\"\")\n self.label.setObjectName(\"label\")\n self.horizontalLayout.addWidget(self.label)\n self.frame = QtWidgets.QFrame(self.horizontalLayoutWidget)\n self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)\n self.frame.setFrameShadow(QtWidgets.QFrame.Raised)\n self.frame.setObjectName(\"frame\")\n self.verticalLayoutWidget = QtWidgets.QWidget(self.frame)\n self.verticalLayoutWidget.setGeometry(QtCore.QRect(-1, -1, 321, 481))\n self.verticalLayoutWidget.setObjectName(\"verticalLayoutWidget\")\n self.verticalLayout = QtWidgets.QVBoxLayout(self.verticalLayoutWidget)\n self.verticalLayout.setContentsMargins(10, 0, 10, 0)\n self.verticalLayout.setObjectName(\"verticalLayout\")\n self.label_2 = QtWidgets.QLabel(self.verticalLayoutWidget)\n self.label_2.setMaximumSize(QtCore.QSize(16777215, 65))\n font = QtGui.QFont()\n font.setFamily(\"Mikhak Bold\")\n font.setPointSize(14)\n font.setBold(True)\n font.setWeight(75)\n self.label_2.setFont(font)\n self.label_2.setAlignment(QtCore.Qt.AlignCenter)\n self.label_2.setObjectName(\"label_2\")\n self.verticalLayout.addWidget(self.label_2)\n self.lineEdit = QtWidgets.QLineEdit(self.verticalLayoutWidget)\n self.lineEdit.setMinimumSize(QtCore.QSize(0, 65))\n font = QtGui.QFont()\n font.setFamily(\"Mikhak Bold\")\n font.setPointSize(13)\n font.setBold(True)\n font.setWeight(75)\n self.lineEdit.setFont(font)\n self.lineEdit.setStyleSheet(\"border-radius: 25px;\\n\"\n \"border: 1px solid black;\")\n self.lineEdit.setAlignment(QtCore.Qt.AlignCenter)\n self.lineEdit.setObjectName(\"lineEdit\")\n self.verticalLayout.addWidget(self.lineEdit)\n self.lineEdit_2 = QtWidgets.QLineEdit(self.verticalLayoutWidget)\n self.lineEdit_2.setMinimumSize(QtCore.QSize(0, 65))\n font = QtGui.QFont()\n font.setFamily(\"Mikhak Bold\")\n font.setPointSize(13)\n font.setBold(True)\n font.setWeight(75)\n self.lineEdit_2.setFont(font)\n self.lineEdit_2.setStyleSheet(\"border-radius: 25px;\\n\"\n \"border: 1px solid black;\")\n self.lineEdit_2.setAlignment(QtCore.Qt.AlignCenter)\n self.lineEdit_2.setObjectName(\"lineEdit_2\")\n # self.lineEdit_2.setEchoMode(QLieEdit.Password)\n self.lineEdit_2.setEchoMode(QtWidgets.QLineEdit.Password)\n\n self.verticalLayout.addWidget(self.lineEdit_2)\n self.label_3 = QtWidgets.QLabel(self.verticalLayoutWidget)\n self.label_3.setEnabled(True)\n self.label_3.setMinimumSize(QtCore.QSize(0, 20))\n self.label_3.setMaximumSize(QtCore.QSize(16777215, 30))\n self.label_3.hide()\n font = QtGui.QFont()\n font.setFamily(\"Mikhak Bold\")\n font.setBold(True)\n font.setWeight(75)\n self.label_3.setFont(font)\n self.label_3.setStyleSheet(\"color: rgb(255, 0, 0);\")\n self.label_3.setAlignment(QtCore.Qt.AlignCenter)\n self.label_3.setObjectName(\"label_3\")\n self.verticalLayout.addWidget(self.label_3)\n self.frame_2 = QtWidgets.QFrame(self.verticalLayoutWidget)\n self.frame_2.setMaximumSize(QtCore.QSize(16777215, 65))\n self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)\n self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)\n self.frame_2.setObjectName(\"frame_2\")\n self.horizontalLayoutWidget_2 = QtWidgets.QWidget(self.frame_2)\n self.horizontalLayoutWidget_2.setGeometry(QtCore.QRect(0, 0, 301, 61))\n self.horizontalLayoutWidget_2.setObjectName(\"horizontalLayoutWidget_2\")\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout(\n self.horizontalLayoutWidget_2)\n self.horizontalLayout_3.setContentsMargins(0, 0, 0, 0)\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\n self.pushButton_2 = QtWidgets.QPushButton(\n self.horizontalLayoutWidget_2)\n self.pushButton_2.setMaximumSize(QtCore.QSize(16777215, 65))\n #* event\n self.pushButton_2.clicked.connect(self.buttonClicked_exit)\n font = QtGui.QFont()\n font.setFamily(\"Mikhak Bold\")\n font.setPointSize(16)\n font.setBold(True)\n font.setWeight(75)\n self.pushButton_2.setFont(font)\n self.pushButton_2.setStyleSheet(\"\\n\"\n \"QPushButton{\\n\"\n \"border-radius: 25px;\\n\"\n \"background-color: rgb(255, 0, 0);\\n\"\n \"}\\n\"\n \"\\n\"\n \"QPushButton:hover{\\n\"\n \"background-color: rgb(195, 37, 37);\\n\"\n \"}\\n\"\n \"QPushButton:pressed{\\n\"\n \"background-color: #e7e7e7; \\n\"\n \"color: black;\\n\"\n \"}\\n\"\n \"\")\n self.pushButton_2.setObjectName(\"pushButton_2\")\n self.horizontalLayout_3.addWidget(self.pushButton_2)\n self.pushButton = QtWidgets.QPushButton(self.horizontalLayoutWidget_2)\n self.pushButton.setMaximumSize(QtCore.QSize(16777215, 65))\n font = QtGui.QFont()\n font.setFamily(\"Mikhak Bold\")\n font.setPointSize(16)\n font.setBold(True)\n font.setWeight(75)\n self.pushButton.setFont(font)\n self.pushButton.setStyleSheet(\"QPushButton{\\n\"\n \"border-radius: 25px;\\n\"\n \"background-color: rgb(62, 96, 168);\\n\"\n \"}\\n\"\n \"\\n\"\n \"QPushButton:hover{\\n\"\n \"background-color: rgb(51, 80, 138);\\n\"\n \"}\\n\"\n \"QPushButton:pressed{\\n\"\n \"background-color: #e7e7e7; \\n\"\n \"color: black;\\n\"\n \"}\\n\"\n \"\")\n self.pushButton.setObjectName(\"pushButton\")\n self.horizontalLayout_3.addWidget(self.pushButton)\n\n self.pushButton.clicked.connect(self.buttonClicked_login)\n # self.pushButton.clicked.connect(functools.partial(self.buttonClicked_login,statusOfPostLogin))\n\n self.verticalLayout.addWidget(self.frame_2)\n self.horizontalLayout.addWidget(self.frame)\n\n self.retranslateUi(Form)\n QtCore.QMetaObject.connectSlotsByName(Form)\n\n def buttonClicked_exit(self):\n # sender = self.sender()\n print(self.lineEdit.text())\n print(self.lineEdit_2.text())\n sys.exit()\n\n def buttonClicked_login(self):\n #todo\n statusOfPostLogin=Network.post_login(email=self.lineEdit.text(),password=self.lineEdit_2.text())\n print(\"statusOfLogin\")\n print(statusOfPostLogin)\n if statusOfPostLogin == 400:\n self.label_3.show()\n elif statusOfPostLogin == 200:\n self.label_3.hide()\n self.close()\n os.system('python product\\product.py')\n # sys.exit(app.exec_())\n # sys.exit()\n\n def retranslateUi(self, Form):\n _translate = QtCore.QCoreApplication.translate\n Form.setWindowTitle(_translate(\"Form\", \"Form\"))\n self.label_2.setText(_translate(\"Form\", \"به فروشگاه اسپاد خوش آمدید.\"))\n self.lineEdit.setPlaceholderText(_translate(\"Form\", \"ایمیل\"))\n self.lineEdit_2.setPlaceholderText(_translate(\"Form\", \"پسوورد\"))\n self.label_3.setText(_translate(\n \"Form\", \"اطلاعات وارد شده صحیح نمی باشد.\"))\n self.pushButton_2.setText(_translate(\"Form\", \"خروج\"))\n self.pushButton.setText(_translate(\"Form\", \"ورود\"))\n\n\n# if __name__ == \"__main__\":\n# app = QtWidgets.QApplication(sys.argv)\n# Form = QtWidgets.QWidget()\n# ui = Ui_Form()\n# ui.setupUi(Form)\n# Form.show()\n# # Form.close()\n# sys.exit(app.exec_())\n\nif __name__ == \"__main__\":\n app = QtWidgets.QApplication(sys.argv)\n ex = Ui_Form()\n sys.exit(app.exec_())","repo_name":"hoshang1371/desktop-app-fpr-spad-elec","sub_path":"login/logIn_Ui.py","file_name":"logIn_Ui.py","file_ext":"py","file_size_in_byte":10255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"36885472679","text":"def my_fun():\r\n\ti=1\r\n\twhile i<=5:\r\n\t\tprint()\r\n\t\tprint(\"who is the Founder of facebook\")\r\n\t\ta=[\"- mark zuckerberg\",\"- bill gates\",\"- steve jebs\",\"- Larry page\"]\r\n\t\tj=0\r\n\t\twhile j str:\n return self.name.lower().replace(\"_\", \"-\")\n\n @classmethod\n def from_cli_argument(cls, cli_argument: str):\n try:\n return cls[cli_argument.upper().replace(\"-\", \"_\")]\n except KeyError as error:\n raise ArgumentError(\n None, f\"Invalid reason '{cli_argument}'\"\n ) from error\n\n\n@dataclass()\nclass Package:\n name: str\n version: str\n release: str\n reasons: Dict[Reasons, Direction] = field(default_factory=dict)\n\n def __hash__(self) -> int:\n return hash((self.name, self.version, self.release))\n\n def __eq__(self, other: \"Package\") -> bool:\n return (\n self.name == other.name\n and self.version == other.version\n and self.release == other.release\n and self.reasons == other.reasons\n )\n\n def __lt__(self, other: \"Package\") -> bool:\n # Sort by release first, then the other fields\n if self.release != other.release:\n return self.release < other.release\n if self.name != other.name:\n return self.name < other.name\n if self.version != other.version:\n return self.version < other.version\n\n return False\n\n def __str__(self) -> str:\n result = f\"{self.name : <50} {self.version : <40} {self.release : <10}\"\n\n reasons = \", \".join(\n f\"{change}\"\n f\"{' in new package' if direction == Direction.PASSIVE else ''}\"\n for change, direction in self.reasons.items()\n )\n result += f\"{reasons : <10}\"\n\n return result\n","repo_name":"greenbone/troubadix","sub_path":"troubadix/standalone_plugins/changed_packages/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"67"}
+{"seq_id":"70466363735","text":"#!/usr/bin/py\n# -*- coding: UTF-8 -*-\nfrom sympy import *\n\ndef parseProblem(myProblem):\n myProblem = myProblem.split(\"\\n\")\n \n if myProblem[0].upper() == \"MAXIMIZAR\":\n act = 1\n elif myProblem[0].upper() == \"MINIMIZAR\":\n act = 0\n else:\n return None\n del myProblem[0]\n\n funcion = myProblem[0].split('=')\n variables = funcion[0].replace(' ', '').strip('fgh()').split(',')\n funcion = funcion[1].replace('^', '**')\n del myProblem[0]\n\n p = myProblem[0].replace(' ','')\n if p[0].upper()==\"P\":\n p = p.strip('p=()').split(',')\n else:\n p = [0, 0]\n\n return act, variables, funcion, p\n\ndef parseFunction(f, varbls):\n return str(N(f(*flatten(varbls)), 4)).replace('**', '^').replace('*', '')\n\ndef parseVarbls(varbls):\n return str(varbls).replace('[', '').replace(']', '')\n\ndef writeDoc(act, variables, funcion, point):\n document = open(\"../data/data.in\",\"w\")\n document.write(str(act)+\"\\n\")\n for var in variables:\n document.write(var+\" \")\n document.write(\"\\n\"+funcion+\"\\n\")\n for var in point:\n document.write(var+\" \")\n document.close()\n\n\ndef getDataSalida():\n file = open(\"../data/data.out\", \"r\")\n lineas = file.readlines()\n cadena = \"\"\n for i in range(len(lineas)):\n for j in range(len(lineas[i])):\n cadena += lineas[i][j]\n return cadena\n\n\nif __name__ == \"__main__\":\n myProblem = \"Maximizar\\n\\\nf(x1,x2) = -(x1-3)^2-x1*(x2-2)^2\\n\\\np = (0,0)\"\n result = parseProblem(myProblem)\n print(result)\n writeDoc(result[0],result[1],result[2], result[3])","repo_name":"csembriz/Gradiente-Newton","sub_path":"src/Parse.py","file_name":"Parse.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"8167331197","text":"#!/usr/bin/python3\n\n''' Module that reads (n) lines of a text file and prints to stdout'''\n\n\ndef read_lines(filename=\"\", nb_lines=0):\n\n ''' Read (n) lines of file and print to stdout'''\n\n total_lines = 0\n with open(filename, encoding='utf-8') as a_file:\n for a_line in a_file:\n total_lines += 1\n if nb_lines <= 0 or nb_lines >= total_lines:\n print(a_line, end='')\n","repo_name":"cnov20/holbertonschool-higher_level_programming","sub_path":"0x0B-python-input_output/2-read_lines.py","file_name":"2-read_lines.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"26141238699","text":"from math import inf\nimport numpy as np\nfrom utils.lib.least_squares import LeastSquares as LS\nfrom utils.lib.triangulation import Triangulation as TRI\nfrom collections import namedtuple, defaultdict\nfrom utils.helper import from_uppertri_to_full, wrap2pi\n\ndef load_g2o_graph(filename: str, gt: bool, descriptor: bool):\n \n print(f\"Loading file: {filename[15:-20]}\")\n\n Edge = namedtuple(\n 'Edge', ['Type', 'nodeFrom', 'nodeTo', 'poseMeasurement', 'information'] # g2o format of files.\n )\n\n edges = []\n nodes = {}\n nodeTypes = {}\n lm_status = {}\n initial_b_guess = False\n initial_qualified_guess = True\n \n with open(filename, 'r') as file:\n for line in file:\n \n data = line.split() \n \n if data[0] == 'VERTEX_SE2':\n\n nodeType = 'VSE2'\n nodeId = int(data[1])\n pose = np.array(data[2:5],dtype=np.float64)\n nodes[nodeId] = pose\n nodeTypes[nodeId] = nodeType\n\n elif data[0] == 'VERTEX_XY':\n\n nodeType = 'VXY'\n nodeId = int(data[1])\n landmark = np.array(data[2:4],dtype=np.float64) \n nodes[nodeId] = landmark\n nodeTypes[nodeId] = nodeType\n \n\n elif data[0] == 'VERTEX_GPS':\n \n nodeType = 'VGPS'\n nodeId = int(data[1])\n gps_point = np.array(data[2:4],dtype=np.float64)\n nodes[nodeId] = gps_point\n nodeTypes[nodeId] = nodeType\n\n elif data[0] == 'EDGE_SE2':\n \n Type = 'P' #Pose type\n nodeFrom = int(data[1])\n nodeTo = int(data[2])\n poseMeasurement = np.array(data[3:6], dtype=np.float64)\n upperTriangle = np.array(data[6:12], dtype=np.float64)\n information = from_uppertri_to_full(upperTriangle,3)\n edge = Edge(Type, nodeFrom, nodeTo, poseMeasurement, information)\n edges.append(edge)\n \n\n elif data[0] == 'EDGE_SE2_XY':\n \n Type = 'L' #Landmark type\n nodeFrom = int(data[1])\n nodeTo = int(data[2])\n \n poseMeasurement = np.array(data[3:5],dtype=np.float64)\n upperTriangle = np.array(data[5:8],dtype=np.float64)\n information = from_uppertri_to_full(upperTriangle,2)\n \n edge = Edge(Type, nodeFrom, nodeTo, poseMeasurement, information)\n edges.append(edge)\n \n\n elif data[0] == 'EDGE_SE2_GPS':\n \n Type = 'G' #GPS type\n nodeFrom = int(data[1])\n nodeTo = int(data[2]) \n poseMeasurement = np.array(data[3:5],dtype=np.float64)\n upperTriangle = np.array(data[5:8],dtype=np.float64)\n information = from_uppertri_to_full(upperTriangle,2)\n\n edge = Edge(Type, nodeFrom,nodeTo, poseMeasurement, information)\n edges.append(edge)\n\n elif data[0] == 'EDGE_SE2_BEARING':\n\n descriptor = True\n Type = 'B' #Bearing type\n nodeFrom = int(data[1])\n nodeTo = int(data[2])\n poseMeasurement = float(data[3])\n information = float(data[4])\n\n if initial_b_guess:\n initial_bearing_guess(nodes, nodeFrom, nodeTo, nodeTypes, poseMeasurement, lm_status)\n\n edge = Edge(Type, nodeFrom, nodeTo, poseMeasurement, information)\n edges.append(edge)\n\n \n else: \n print(\"Error, edge or vertex not defined\")\n\n lut, x = update_info(nodes)\n \n if gt==False and initial_qualified_guess:\n \n print(\"Noisy data\")\n nodes, nodeTypes, unused_lm = qualified_guess(edges, lut, x, nodes, nodeTypes, least_squares=True, triangulation=False, epsilon=1.0)\n edges, nodes, nodeTypes = remove_unused_landmark(edges, nodes, nodeTypes, unused_lm)\n lut, x = update_info(nodes)\n \n from run_slam import Graph\n graph = Graph(x, nodes, edges, lut, nodeTypes,descriptor)\n\n print('Loaded graph with {} nodes and {} edges'.format(len(graph.nodes), len(graph.edges)))\n print('\\n')\n\n return graph\n\ndef update_info(nodes):\n\n lut = {}\n x = []\n offset = 0\n\n for nodeId in nodes:\n lut.update({nodeId: offset})\n offset = offset + len(nodes[nodeId])\n x.append(nodes[nodeId])\n x = np.concatenate(x, axis=0)\n\n return lut, x\n\ndef remove_unused_landmark(edges, nodes, nodeTypes, unused_lm):\n\n for edge in edges.copy():\n\n if edge.nodeTo in unused_lm:\n print(f\"edgenodes to:{edge.nodeTo}\\n\")\n edges.remove(edge)\n\n\n # Checking nodes\n for ID, _ in nodes.copy().items():\n \n for lm_ID in unused_lm:\n check = True if lm_ID == ID else False\n\n if check:\n del nodes[ID]\n \n # Checking nodetypes\n for ID, _ in nodeTypes.copy().items():\n for lm_ID in unused_lm:\n check = True if lm_ID == ID else False\n\n if check:\n del nodeTypes[ID]\n\n \n return edges, nodes, nodeTypes\n\ndef qualified_guess(edges, lut, x, nodes, nodeTypes, least_squares: bool, triangulation: bool, epsilon: float):\n\n ls = LS()\n tri = TRI()\n mem = defaultdict(list)\n unused_lm = []\n k = 0\n count = 0\n\n for e in edges:\n if e.Type == 'B':\n lost_bearings = []\n _nodePose = e.nodeFrom\n _nodeLm = e.nodeTo\n \n fromIdx = lut[_nodePose]\n x_b = x[fromIdx:fromIdx+3] # Robot pose\n z_ij = e.poseMeasurement # Bearing measurement\n\n if count > 0 and check_parallel_lines(_x_b, _z_ij, x_b, z_ij, epsilon=epsilon):\n k +=1\n \n continue\n else:\n _meas = [x_b[0], x_b[1], x_b[2], z_ij]\n mem[_nodeLm].append(_meas)\n\n _x_b = x_b # Updating old value\n _z_ij = z_ij # Updating old value\n count += 1\n \n\n for ID, meas in mem.items():\n\n m = np.vstack(meas)\n Xr = m[:,0:3]\n z_list = list(m[:,3])\n\n if len(z_list) > 2: # n or more measurements are required for triangulation\n if least_squares:\n Xl = ls.least_squares_klines(Xr, z_list) # Computing least squares best guess\n elif triangulation:\n Xl = tri.triangulation(Xr, z_list) # Computing triangulation best guess\n\n landmark = np.array([Xl[0,0], Xl[1,0]], dtype=np.float64)\n nodeType = 'VXY'\n nodeId = ID\n nodes[nodeId] = landmark\n nodeTypes[nodeId] = nodeType\n\n elif ID not in unused_lm:\n unused_lm.append(ID)\n\n del m\n return nodes, nodeTypes, unused_lm\n\n\ndef initial_bearing_guess(nodes, nodeFrom, nodeTo, nodeTypes, poseMeasurement, lm_status):\n\n x_b = nodes[nodeFrom]\n z_ij = poseMeasurement\n\n lm_status.update(dict([(nodeTo, False)]))\n \n for id, status in lm_status.items():\n if id == nodeTo and status == False:\n \n lambdadistx = 5\n lambdadisty = 5\n xguess = x_b[0]+lambdadistx*np.cos(wrap2pi(x_b[2]+z_ij))\n yguess = x_b[1]+lambdadisty*np.sin(wrap2pi(x_b[2]+z_ij))\n\n nodeType = 'VXY'\n nodeId = nodeTo\n landmark = np.array([xguess,yguess],dtype=np.float64) \n nodes[nodeId] = landmark\n nodeTypes[nodeId] = nodeType\n lm_status[nodeTo] = True\n\ndef check_parallel_lines(p1, z1, p2, z2, epsilon: float) -> bool:\n \"\"\"Checking for parallel lines\n\n Args:\n p1 (vector 3x1): Robot position at time i\n z1 (float): Bearing measurement at time i\n p2 (vector 3x1): Robot position at time j\n z2 (float): Bearing measurement at time j\n epsilon (float, optional): Threshold value. Parallax criterion. Defaults to 1.\n Returns:\n bool: True -> lines are parallel, False -> lines are not parallel\n \"\"\"\n n1 = wrap2pi(z1+p1[2])\n n2 = wrap2pi(z2+p2[2])\n\n diff = np.rad2deg(abs(wrap2pi(n1 - n2)))\n\n parallel = diff < epsilon\n\n return parallel","repo_name":"MJensen1231992/MSc_Proj_2021_SCL_MJ","sub_path":"graphSLAM/utils/g2o_loader.py","file_name":"g2o_loader.py","file_ext":"py","file_size_in_byte":8446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"4661018489","text":"import nltk\nfrom nltk.stem.lancaster import LancasterStemmer\nimport os\nimport json\nimport datetime\nimport numpy as np\nimport ml\nimport time\n\n\nERROR_THRESHOLD = 0.2\n# load our calculated synapse values\nsynapse_file = 'synapses.json' \nwith open(synapse_file) as data_file: \n synapse = json.load(data_file) \n synapse_0 = np.asarray(synapse['synapse0']) \n synapse_1 = np.asarray(synapse['synapse1'])\n\ndef classify(sentence, show_details=False):\n results = ml.think(sentence, show_details)\n\n results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD ] \n results.sort(key=lambda x: x[1], reverse=True) \n return_results =[[classes[r[0]],r[1]] for r in results]\n print (\"%s \\n classification: %s\" % (sentence, return_results))\n return return_results\n\nclassify(\"sudo make me a sandwich\")\nclassify(\"how are you today?\")\nclassify(\"talk to you tomorrow\")\nclassify(\"who are you?\")\nclassify(\"make me some lunch\")\nprint ()\nclassify(\"how was your lunch?\", show_details=True)","repo_name":"vikikkdi/College_Projects","sub_path":"web/f/try.py","file_name":"try.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"10660323668","text":"\"\"\"Escribe un programa que genere los primeros 4 números perfectos. Un número perfecto es un número entero \npositivo que es igual a la suma de sus divisores propios positivos. Dicho de otra forma, un número perfecto \nes aquel que es amigo de sí mismo. Así, 6 es un número perfecto porque sus divisores propios positivos son \n1, 2 y 3; y 6 = 1 + 2 + 3.\"\"\"\n\nnumeros_perfectos = []\nnum = 1\n\nwhile len(numeros_perfectos) < 4:\n suma = 0\n for i in range(1, num):\n if num % i == 0:\n suma += i\n if num == suma:\n numeros_perfectos.append(num)\n num += 1\n\nprint(\"Los primeros 4 números perfectos son:\")\n\nprint(numeros_perfectos)\n\n","repo_name":"RCNicolas/CAMPUS","sub_path":"Python/Ejercicios Review/Review 19 julio/Bucles/Ejercicio4.py","file_name":"Ejercicio4.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"19756781940","text":"import fileinput\nimport math\nfrom copy import deepcopy\n\n\ndef parse():\n pairs = []\n for line in fileinput.input():\n pairs.append(eval(line.strip()))\n fileinput.close()\n return pairs\n\n\ndef set_in_pair_with_directions(root, directions, value):\n node = root\n for dir in directions[:-1]:\n node = node[dir]\n node[directions[-1]] = value\n\n\ndef get_inorder(root):\n regular_by_order = {}\n\n def inorder(pair, order=0, directions=None):\n if not directions:\n directions = []\n if isinstance(pair, int):\n regular_by_order[order] = (pair, directions)\n return order + 1, directions\n\n order, _ = inorder(pair[0], order, directions + [0])\n order, _ = inorder(pair[1], order, directions + [1])\n return order, directions\n\n pair = root\n inorder(pair)\n\n return regular_by_order\n\n\ndef traverse_explode(root):\n def recurse(pair, depth=0, order=0, has_exploded=False):\n if isinstance(pair, int):\n return order + 1, depth, has_exploded\n\n order, left_depth, has_exploded = recurse(\n pair[0], depth + 1, order, has_exploded\n )\n\n if left_depth == 5 and isinstance(pair, list):\n left, right = pair\n if isinstance(left, int) and isinstance(right, int) and not has_exploded:\n explode(root, left, right, order, regular_by_order)\n has_exploded = True\n\n order, right_depth, has_exploded = recurse(\n pair[1], depth + 1, order, has_exploded\n )\n\n return order, max(left_depth, right_depth), has_exploded\n\n regular_by_order = get_inorder(root)\n pair = root\n _, _, exploded = recurse(pair)\n return root, exploded\n\n\ndef traverse_split(root):\n def recurse(pair, has_splitted=False):\n if isinstance(pair, int):\n return has_splitted\n\n left_splitted = recurse(pair[0], has_splitted)\n\n if isinstance(pair[0], int) and pair[0] >= 10 and not has_splitted:\n pair[0] = [math.floor(pair[0] / 2), math.ceil(pair[0] / 2)]\n left_splitted = True\n\n right_splitted = recurse(pair[1], left_splitted)\n\n if isinstance(pair[1], int) and pair[1] >= 10 and not left_splitted:\n pair[1] = [math.floor(pair[1] / 2), math.ceil(pair[1] / 2)]\n right_splitted = True\n\n return left_splitted or right_splitted\n\n pair = root\n splitted = recurse(pair)\n return root, splitted\n\n\ndef explode(root, left, right, order, regular_by_order):\n if order > 1:\n leftmost, leftmost_directions = regular_by_order[order - 2]\n set_in_pair_with_directions(root, leftmost_directions, left + leftmost)\n\n if order < len(regular_by_order) - 1:\n rightmost, rightmost_directions = regular_by_order[order + 1]\n set_in_pair_with_directions(root, rightmost_directions, rightmost + right)\n _, exploded_directions = regular_by_order[order - 1]\n set_in_pair_with_directions(root, exploded_directions[:-1], 0)\n\n\ndef reduce(pair):\n pair, exploded = traverse_explode(pair)\n if exploded:\n return reduce(pair)\n\n pair, splitted = traverse_split(pair)\n if splitted:\n return reduce(pair)\n return pair\n\n\ndef sum_pairs(pairs):\n pair = pairs[0]\n for next_pair in pairs[1:]:\n pair = add_pairs(pair, next_pair)\n pair = reduce(pair)\n return pair\n\n\ndef add_pairs(a, b):\n return [a, b]\n\n\ndef magnitude(pair):\n left, right = pair\n left_magnitude = 3 * left if isinstance(left, int) else 3 * magnitude(left)\n right_magnitude = 2 * right if isinstance(right, int) else 2 * magnitude(right)\n return left_magnitude + right_magnitude\n\n\ndef simulate(pairs):\n magnitudes = []\n for i, x in enumerate(pairs):\n for j, y in enumerate(pairs):\n if i != j:\n magnitudes.append(magnitude(sum_pairs([deepcopy(x), deepcopy(y)])))\n magnitudes.append(magnitude(sum_pairs([deepcopy(y), deepcopy(x)])))\n return max(magnitudes)\n\n\ndef main():\n pairs = parse()\n pair = sum_pairs(pairs)\n print(f\"Part 1: {magnitude(pair)}\")\n\n pairs = parse()\n print(f\"Part 2: {simulate(pairs)}\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"rodrigorahal/advent-of-code-2021","sub_path":"18/snailfish.py","file_name":"snailfish.py","file_ext":"py","file_size_in_byte":4228,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"}
+{"seq_id":"9997424281","text":"from openerp import http\n\n\nclass OutboundSelectPackageController(http.Controller):\n @http.route('/outbound_select_package', type='http', auth='user')\n def select_package(self, **kw):\n env = http.request.env\n current_user = env['res.users'].browse(http.request.uid)\n\n return http.request.render('stock_irm.outbound_select_package', {\n 'status': 'ok',\n 'user_name': current_user.partner_id.name,\n 'worklocation_name': current_user.work_location_id.name,\n 'worklocation_id': current_user.work_location_id.id or 0,\n 'title': 'Outbound Select Package',\n 'user_email': current_user.partner_id.email,\n\n })\n\n @http.route('/outbound_select_package/process_package', auth='user',\n type='json')\n def process_package(self, barcode):\n env = http.request.env\n\n unpack = False\n\n # cart = env['stock.location'].browse(cart_id)\n scanned_package = env['stock.quant.package'].search(\n [('barcode', '=', str(barcode))]\n )\n\n if not scanned_package:\n return {\n 'status': 'error',\n 'error': 'Error',\n 'message': 'The scanned package could not be found.',\n }\n\n bo_cart_to_band_down = env['stock.picking.type'].search(\n [('is_bo_cart_to_band_down', '=', True)], limit=1)\n\n output_to_customer = env['stock.picking.type'].search(\n [('is_output_to_customer', '=', True)], limit=1)\n\n bo_cart_upstairs = bo_cart_to_band_down.default_location_src_id\n\n output_default_loc = output_to_customer.default_location_src_id\n\n current_location = scanned_package.location_id\n\n if current_location not in bo_cart_upstairs.child_ids\\\n and current_location not in output_default_loc.child_ids\\\n and current_location != output_default_loc\\\n and current_location != bo_cart_upstairs:\n return {\n 'status': 'error',\n 'error': 'Error',\n 'message': 'The scanned package should not be on banddown.',\n }\n\n if current_location in output_default_loc.child_ids \\\n or current_location == output_default_loc:\n unpack = True\n\n quant = scanned_package.quant_ids[0]\n picking = quant.reservation_id.picking_id\n procurement_group = picking.group_id\n procurement_group._procurement_order_state()\n is_complete = procurement_group.is_sale_order_complete\n\n if picking.picking_type_id.id == output_to_customer.id:\n env['report'].print_document(\n picking, 'odw_report_delivery.report_delivery_master')\n\n wizard_id = picking.do_enter_transfer_details()['res_id']\n wizard = env['stock.transfer_details'].browse(wizard_id)\n destination = picking.location_dest_id\n\n wizard.write({\n 'item_ids': [(5, False, False)],\n 'packop_ids': [(5, False, False)]\n })\n\n # then create a new wizard item\n wizard_values = {\n 'package_id': scanned_package.id,\n 'destinationloc_id': destination.id,\n 'sourceloc_id': scanned_package.location_id.id,\n }\n\n wizard.write({\n 'packop_ids': [(0, False, wizard_values)]\n })\n\n wizard.sudo().do_detailed_transfer()\n\n if unpack:\n scanned_package.unpack()\n\n if is_complete and not unpack:\n scanned_package.auto_move_pack()\n\n return {'status': 'ok',\n 'is_complete': is_complete}\n\n @http.route('/outbound_select_package/get_package_ids', auth='user', type='json')\n def get_package_ids(self, cart_id):\n env = http.request.env\n cart = env['stock.location'].browse(cart_id)\n package_ids = env['stock.quant.package'].search(\n [('location_id', '=', int(cart.id))]\n )\n\n if not package_ids:\n return {\n 'status': 'error',\n 'error': 'Error',\n 'message': 'The cart does not contain any packages.',\n }\n\n package_list = []\n\n for package in package_ids:\n product = package.quant_ids[0].product_id\n quant = package.quant_ids[0]\n total_qty = 0\n for quant in package.quant_ids:\n total_qty += quant.qty\n\n package_list.append({\n 'package': {\n 'id': package.id,\n 'barcode': package.barcode,\n 'name': package.name,\n },\n 'product': {\n 'id': product.id,\n 'name': product.name,\n 'description': product.description or 'No description',\n 'quantity': total_qty,\n 'image': '/web/binary/image?model=product.product&id=%s&field=image' % product.id,\n }\n })\n\n return {'status': 'ok',\n 'package_ids': package_list,\n }\n","repo_name":"Niboo/legal1","sub_path":"stock_irm/controllers/outbound_select_package.py","file_name":"outbound_select_package.py","file_ext":"py","file_size_in_byte":5112,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"35300019269","text":"# ! change LOCAL to False before submitting !\n# set LOCAL to True for local testing\n\nLOCAL = True\n\nif LOCAL:\n class Node: \n def __init__(self, left=None, right=None, value=0): \n self.right = right\n self.left = left\n self.value = value\n\n\ndef print_range(node, l, r):\n if node == None:\n return\n if node.value >= l:\n print_range(node.left, l, r)\n if l <= node.value and r >= node.value:\n print(node.value)\n if node.value <= r:\n print_range(node.right, l, r)\n\n\ndef test():\n node1 = Node(None, None, 2)\n node2 = Node(None, node1, 1)\n node3 = Node(None, None, 8)\n node4 = Node(None, node3, 8)\n node5 = Node(node4, None, 9)\n node6 = Node(node5, None, 10)\n node7 = Node(node2, node6, 5)\n print_range(node7, 2, 8)\n # expected output: 2 5 8 8\n\n\nif __name__ == '__main__':\n test()","repo_name":"YourKeysAreMine/Basic_Algorithms","sub_path":"trees/K_show_range.py","file_name":"K_show_range.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"40013794860","text":"import os\nimport warnings\nfrom os import path\nfrom typing import Dict\n\nimport torch\nfrom pytorch3d.datasets.shapenet.shapenet_core import ShapeNetCore\n\nfrom conversions.color_interpolation import interpolate_colors\nfrom conversions.colored_voxels import atlas2vertex_color\nfrom conversions.kaolin_mesh_to_voxels import fill, extract_surface, trianglemeshes_to_colored_voxelgrids\n\n\nclass ShapeNetVoxels(ShapeNetCore):\n def __init__(self, data_dir, synsets=None, filled=True) -> None:\n \"\"\"\n Loads pre-calculated voxels objects from ShapeNet, in binvox format\n Store each object's synset id and models id from data_dir. It uses Trimesh library to encode voxels.\n\n Args:\n data_dir: Path to ShapeNetCore data.\n synsets: List of synset categories to load from ShapeNetCore in the form of\n synset offsets or labels. A combination of both is also accepted.\n When no category is specified, all categories in data_dir are loaded.\n filled: internal part are filled\n \"\"\"\n super().__init__(data_dir=data_dir,\n synsets=synsets,\n version=2,\n load_textures=False,\n texture_resolution=1)\n from trimesh.exchange.binvox import load_binvox\n self.load_binvox = load_binvox\n\n self.filled = filled\n self.model_dir = path.join(\"models\", f\"model_normalized.{'solid' if self.filled else 'surface'}.binvox\")\n\n # Re-extract model_id, but for .binvox files\n self.synset_ids = []\n self.model_ids = []\n self.synset_num_models = {}\n for synset in self.synset_start_idxs.keys():\n for model in os.listdir(path.join(data_dir, synset)):\n if not path.exists(path.join(data_dir, synset, model, self.model_dir)):\n msg = (\"Object file not found in the model directory %s \"\n \"under synset directory %s.\"\n ) % (model, synset)\n warnings.warn(msg)\n continue\n self.synset_ids.append(synset)\n self.model_ids.append(model)\n model_count = len(self.synset_ids) - self.synset_start_idxs[synset]\n self.synset_num_models[synset] = model_count\n\n def __getitem__(self, idx: int) -> Dict:\n \"\"\"\n Read a model by the given index.\n\n Args:\n idx: The idx of the model to be retrieved in the dataset.\n\n Returns:\n dictionary with following keys:\n - voxel (str): voxel representation using Trimesh VoxelGrid class for encoding\n - synset_id (str): synset id\n - model_id (str): model id\n - label (str): synset label.\n \"\"\"\n model = self._get_item_ids(idx)\n model_path = path.join(\n self.shapenet_dir, model[\"synset_id\"], model[\"model_id\"], self.model_dir\n )\n with open(model_path, 'rb') as file_obj:\n model[\"voxel\"] = self.load_binvox(file_obj, resolver=None, axis_order='xzy', file_type=None)\n model[\"label\"] = self.synset_dict[model[\"synset_id\"]]\n return model\n\n\nclass RawVoxelsShapeNetDataset(ShapeNetCore):\n \"\"\"\n The voxels are calculated from the ShapeNet mesh in the same way as for the Pkmn dataset,\n to be able to use transfer learning between the 2. This also allows to include the color of the voxels.\n \"\"\"\n\n def __init__(self, root_path, n_cubes, synsets=None):\n super(RawVoxelsShapeNetDataset, self).__init__(root_path, synsets=synsets, version=2, load_textures=True,\n texture_resolution=4)\n self.n_cubes = n_cubes\n\n def __getitem__(self, item):\n x = super().__getitem__(item)\n verts = x[\"verts\"]\n faces = x[\"faces\"]\n atlas_texture = x[\"textures\"]\n\n # Min-max normalization in [-1, 1], centered in 0\n verts = (verts - verts.min(dim=0)[0]) / (verts - verts.min(dim=0)[0]).max()\n verts = 2 * (verts - verts.max(dim=0)[0] / 2)\n\n # We use vertex color on the initial mesh, so faces size must be small enough in comparison of voxel size\n v_colors = atlas2vertex_color(vertices=verts, faces=faces, atlas_texture=atlas_texture, use_mean=True)\n v_colors = (v_colors * 255).clamp(min=0, max=255).to(torch.uint8)\n\n voxels, colored_voxels = trianglemeshes_to_colored_voxelgrids(\n [verts], [faces], self.n_cubes, verts_uvs=None, textures=[v_colors],\n origin=torch.tensor([[-1., -1., -1.]]),\n scale=torch.tensor([2.]),\n return_sparse=False\n )\n voxels = fill(voxels)\n colored_voxels = interpolate_colors(colorgrid=colored_voxels,\n voxelgrid=extract_surface(voxels),\n darken_outside=True)\n\n return {'synset_id': x['synset_id'], 'model_id': x['model_id'], 'label': x['label'],\n 'voxelgrid': voxels[0], 'colorgrid': colored_voxels[0]}\n\n\ndef save_entire_shapenet_voxels_dataset_on_disk(root_path, voxels_folder, n_cubes=64, synsets=None):\n \"\"\"\n Pre-computes colored voxels of the ShapeNet dataset, and store them on disk. They can then be loaded\n by VoxelsDataset class (see pkmn_dataset.py)\n :param root_path: ShapeNet path\n :param voxels_folder: Folder path on which to save voxels\n :param n_cubes: Dimension of the voxel grid (total number of voxels = n_cubes*n_cubes*n_cubes)\n :param synsets: ShapeNet synsets, see Pytorch3d's ShapeNetCore\n \"\"\"\n from tqdm import tqdm\n d = RawVoxelsShapeNetDataset(root_path=root_path, n_cubes=n_cubes, synsets=synsets)\n for i in tqdm(range(len(d))):\n try:\n data = d[i]\n except Exception as e: # Some samples fails, so it just skip them\n print(i, e)\n continue\n new_name = path.join(voxels_folder,\n data['synset_id'] + '_' + data['model_id'] + '_' + data['label'] +\n '_nc' + str(int(n_cubes)) + '.pt')\n indices = extract_surface(data['voxelgrid'].unsqueeze(0)).squeeze(0).nonzero()\n colors = data['colorgrid']\n\n torch.save(f=new_name, obj=(indices, colors))\n","repo_name":"le-Greg/generate-voxel-pkmns-with-deep-learning","sub_path":"datasets/shapenet_voxels.py","file_name":"shapenet_voxels.py","file_ext":"py","file_size_in_byte":6345,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"72"}
+{"seq_id":"17814187971","text":"import contextlib\nimport logging\nimport re\nimport tempfile\nimport typing as tp\nimport zipfile\nfrom pathlib import Path\nfrom unittest.mock import patch\n\nimport pytest\nimport torch\nimport torchaudio\nfrom torchaudio.backend.common import AudioMetaData\n\nimport stopes.modules.speech.postprocess as sprocess\nimport stopes.modules.speech.utils as sputils\nfrom stopes.modules.speech.utils import Audio, AudioBytes, Text\n\n\n@pytest.fixture(scope=\"module\")\ndef sample_audio():\n with contextlib.ExitStack() as stack:\n tmpdir = stack.enter_context(tempfile.TemporaryDirectory())\n lll_data = torchaudio.datasets.LibriLightLimited(tmpdir, download=\"True\")[0]\n\n audio_path = tmpdir + \"/tmp.ogg\"\n torchaudio.save(audio_path, lll_data[0], lll_data[1], format=\"ogg\")\n num_frames = torchaudio.info(tmpdir + \"/tmp.ogg\").num_frames\n\n audio_zip_path = tmpdir + \"/tmp.zip\"\n zip_o = stack.enter_context(zipfile.ZipFile(audio_zip_path, \"w\"))\n with zip_o.open(\"tmp.ogg\", mode=\"w\") as sample_o:\n sample_start = int(sample_o._fileobj.tell()) # type: ignore[attr-defined]\n torchaudio.save(sample_o, lll_data[0], lll_data[1], format=\"ogg\")\n sample_end = int(sample_o._fileobj.tell()) # type: ignore[attr-defined]\n\n yield (audio_path, num_frames), (audio_zip_path, sample_start, sample_end)\n\n\ndef test_speech_utils_parse_audio(caplog: pytest.LogCaptureFixture) -> None:\n \"\"\"\n Expected audio formats (by priority):\n 1. full audio file: \n 2. time slice: |||\n 3. frame slice: \n \"\"\"\n # Invalid samples\n with pytest.raises(ValueError):\n # Too many parts\n sputils.parse_audio(\"path 10 15 20 30 40\")\n with pytest.raises(ValueError):\n # mixed space and pipe\n sputils.parse_audio(\"path 10|15 20\")\n with pytest.raises(ValueError):\n # start_not_int\n sputils.parse_audio(\"path start 10 15\")\n\n # Pipe separated (ms)\n with pytest.raises(RuntimeError):\n # no sample rate specified nor supplied\n sputils.parse_audio(\"path|10|15\")\n with assert_warns(\n caplog, match=\"Sampling factor not present in file, using provided value\"\n ):\n assert sputils.parse_audio(\"path|10|15\", sampling_factor=48) == Audio(\n \"path\", 480, 720, 48, sep=\"|\"\n )\n assert sputils.parse_audio(\"path|10|15|48\") == Audio(\"path\", 480, 720, 48, sep=\"|\")\n\n # Space separated (frames)\n with assert_warns(\n caplog, match=\"Sampling factor is assumed to be 16 for space-split text\"\n ):\n assert sputils.parse_audio(\"path 10 15\") == Audio(\"path\", 10, 15, 16, sep=\" \")\n with assert_warns(\n caplog, match=\"Sampling factor is assumed to be 16 for space-split text\"\n ):\n assert sputils.parse_audio(\"path 10 15\", 48) == Audio(\n \"path\", 10, 15, 16, sep=\" \"\n )\n with assert_warns(\n caplog, match=\"Sampling factor is assumed to be 16 for space-split text\"\n ):\n # with space separated, the 4-th column is a \"batch id\" not a sample rate\n assert sputils.parse_audio(\"path 10 15 32\") == Audio(\n \"path\", 10, 15, 16, sep=\" \"\n )\n with assert_warns(\n caplog, match=\"Sampling factor is assumed to be 16 for space-split text\"\n ):\n assert sputils.parse_audio(\"path 10 15 32\", 48) == Audio(\n \"path\", 10, 15, 16, sep=\" \"\n )\n\n # The warning is not outputted if you try a second time.\n assert caplog.messages == []\n assert sputils.parse_audio(\"path 10 15\") == Audio(\"path\", 10, 15, 16, sep=\" \")\n assert caplog.messages == []\n\n\n@pytest.mark.parametrize(\n \"sampling_factor,expected_output\",\n [\n (None, Audio(\"path\", 0, 43008, 16, sep=\"|\")),\n (8, Audio(\"path\", 0, 43008, 8, sep=\"|\")),\n ],\n)\ndef test_speech_utils_parse_audio_when_audio_path_exists(\n sampling_factor, expected_output\n):\n with patch(\"pathlib.Path.exists\", return_value=True):\n with patch(\n \"torchaudio.info\",\n return_value=AudioMetaData(\n sample_rate=16000,\n num_frames=43008,\n num_channels=1,\n bits_per_sample=32,\n encoding=\"PCM_F\",\n ),\n ):\n assert (\n sputils.parse_audio(\"path\", sampling_factor=sampling_factor)\n == expected_output\n )\n\n\n@pytest.mark.xfail()\ndef test_audio_duration() -> None:\n audio_frames = Audio(\"path\", 16_000, 32_000, 16, sep=\" \")\n audio_ms = Audio(\"path\", 1000, 2000, 16, sep=\"|\")\n # TODO: duration isn't correctly computed for Audio using ms as unit\n assert audio_frames.duration == audio_ms.duration\n\n\ndef test_speech_utils_parse_audio_or_text(caplog) -> None:\n # format 1: -> wav sampling\n # format 2: || -> ms sampling\n start = 10\n end = 15\n\n too_many_spaces = Text(\"path 10 15 20 30 40\")\n too_few_spaces = Text(\"path 10\")\n pipe_space_mixed = Text(\"path 10|15 20\")\n start_not_int = Text(\"path start 10 15\")\n audio_wav = Audio(path=\"path\", start=start, end=end, sampling_factor=16, sep=\" \")\n audio_ms = Audio(path=\"path\", start=start, end=end, sampling_factor=1, sep=\"|\")\n\n wav_string = str(audio_wav)\n ms_string = str(audio_ms)\n texts = [too_many_spaces, too_few_spaces, pipe_space_mixed, start_not_int]\n for text in texts:\n assert sputils.parse_audio_or_text(text.content) == text\n\n with pytest.raises(RuntimeError):\n sputils.parse_audio_or_text(\"path|10|15\")\n\n with assert_warns(\n caplog, match=\"Sampling factor not present in file, using provided value\"\n ):\n sputils.parse_audio_or_text(\"path|10|15\", sampling_factor=1)\n\n assert sputils.parse_audio_or_text(wav_string, sampling_factor=1) == audio_wav\n assert audio_wav.duration == audio_ms.duration / 16\n assert sputils.parse_audio_or_text(ms_string) == audio_ms\n\n\ndef test_speech_utils_convert_to_string() -> None:\n part1 = \"path 5 10 16\"\n part2 = \"path|100|120|16\"\n input_string = f\"1.5\\t{part1}\\t{part2}\"\n mined_result = sputils.split_mining_line(input_string)\n\n rev_part_1 = str(mined_result.src)\n rev_part_2 = str(mined_result.tgt)\n assert f\"{mined_result.score}\\t{rev_part_1}\\t{rev_part_2}\" == input_string\n\n\ndef test_compute_overlap() -> None:\n audio_1 = Audio(\"\", 0, 10)\n audio_2 = Audio(\"\", 15, 25)\n audio_3 = Audio(\"\", 5, 17)\n assert sputils.compute_overlap(audio_1, audio_2) == 0\n assert sputils.compute_overlap(audio_1, audio_2, sputils.IntersectMethods.IOU) == 0\n assert round(sputils.compute_overlap(audio_1, audio_3), 3) == round(5.0 / 12.0, 3)\n assert round(\n sputils.compute_overlap(audio_1, audio_3, sputils.IntersectMethods.IOU), 3\n ) == round(5.0 / 17.0, 3)\n\n\ndef test_postprocess() -> None:\n processor = sprocess.PostProcessAudioModule(\n sprocess.PostProcessAudioConfig(\n output_dir=Path(\"/\"),\n output_filename=\"test.tsv.gz\",\n mining_result_path=Path(\"/\"),\n min_audio_length=2,\n mining_threshold=1,\n max_overlap=0.1,\n )\n )\n # check filters on length and score\n audio_path = \"/path/to/example\"\n # valid:\n assert processor.line_passes_thresholds(\n sputils.MiningLineResult(\n score=1.1,\n src=Audio(audio_path, start=5, end=10, sampling_factor=1, sep=\" \"),\n tgt=Text(\"a\"),\n )\n )\n\n # score too low:\n assert not processor.line_passes_thresholds(\n sputils.MiningLineResult(\n score=0.9,\n src=Audio(path=audio_path, start=-100, end=0, sampling_factor=1, sep=\" \"),\n tgt=Text(\"b\"),\n )\n )\n # duration too small\n assert not processor.line_passes_thresholds(\n sputils.MiningLineResult(\n 1.1,\n Audio(audio_path, -1, 0, sampling_factor=1, sep=\" \"),\n Text(\"c\"),\n )\n )\n\n # check accurate removal of overlapping segments\n sources = {\n audio_path: [\n sputils.MiningLineResult(\n 1.1,\n Audio(audio_path, 5, 10, sampling_factor=1, sep=\" \"),\n Text(\"a\"),\n ),\n sputils.MiningLineResult(\n 1.2,\n Audio(audio_path, 6, 10, sampling_factor=1, sep=\" \"),\n Text(\"b\"),\n ), # score too low\n sputils.MiningLineResult(\n 1.1,\n Audio(audio_path, 6, 11, sampling_factor=1, sep=\" \"),\n Text(\"c\"),\n ), # too small fragment\n ]\n }\n filtered, _ = processor.postprocess(sources=sources)\n assert len(filtered) == 1\n assert filtered[0].tgt.content == \"b\" # type: ignore\n\n\ndef test_parse_audio_deprecated():\n assert sputils.parse_audio_deprecated(\"file.mp3\") == (\"file.mp3\", None, None, None)\n with pytest.raises(ValueError):\n sputils.parse_audio_deprecated(\"file.mp3 21\")\n\n assert sputils.parse_audio_deprecated(\"file.mp3 21 421\") == (\n \"file.mp3\",\n 21,\n 421,\n None,\n )\n assert sputils.parse_audio_deprecated(\"file.mp3 21 421 382\") == (\n \"file.mp3\",\n 21,\n 421,\n 382,\n )\n assert sputils.parse_audio_deprecated(\"file.mp3 21 421 382 dummy\") == (\n \"file.mp3\",\n 21,\n 421,\n 382,\n )\n\n with pytest.raises(ValueError):\n sputils.parse_audio_deprecated(\"file.mp3 21 text\")\n with pytest.raises(ValueError):\n sputils.parse_audio_deprecated(\"file.mp3 21 20\")\n assert sputils.parse_audio_deprecated(\"file.mp3 21 -1\") == (\n \"file.mp3\",\n 21,\n -1,\n None,\n )\n\n\ndef test_parse_audio_can_replace_deprecated(tmp_path: Path):\n assert sputils.parse_audio_deprecated(\"file.mp3\") == (\"file.mp3\", None, None, None)\n\n with pytest.raises(ValueError):\n sputils.parse_audio_deprecated(\"file.mp3 21\")\n\n assert sputils.parse_audio_deprecated(\"file.mp3 21 421\") == (\n \"file.mp3\",\n 21,\n 421,\n None,\n )\n assert sputils.parse_audio_deprecated(\"file.mp3 21 421 382\") == (\n \"file.mp3\",\n 21,\n 421,\n 382,\n )\n assert sputils.parse_audio_deprecated(\"file.mp3 21 421 382 dummy\") == (\n \"file.mp3\",\n 21,\n 421,\n 382,\n )\n\n with pytest.raises(ValueError):\n sputils.parse_audio_deprecated(\"file.mp3 21 text\")\n with pytest.raises(ValueError):\n sputils.parse_audio_deprecated(\"file.mp3 21 20\")\n assert sputils.parse_audio_deprecated(\"file.mp3 21 -1\") == (\n \"file.mp3\",\n 21,\n -1,\n None,\n )\n\n\ndef test_parse_audio_with_resample(sample_audio, caplog):\n audio_path, num_frames = sample_audio[0]\n start_ms, end_ms = 0, int(num_frames / 32)\n audio_text = f\"{audio_path}|{start_ms}|{end_ms}\"\n auto_meta = sputils.parse_audio_or_text(audio_text, sampling_factor=32)\n assert isinstance(auto_meta, Audio)\n\n with assert_warns(caplog, match=\"Audio has sample rate 16000. Resample to 32000\"):\n s = auto_meta.load()\n assert s.dim() == 2\n assert s.shape[1] == 351808\n\n\ndef test_parse_audio_bytes_with_resample(sample_audio, caplog):\n audio_zip_path, start, end = sample_audio[1]\n audio_text = f\"{audio_zip_path}:{start}:{end-start}\"\n auto_meta = sputils.parse_audio_or_text(audio_text, sampling_factor=32)\n assert isinstance(auto_meta, AudioBytes)\n\n with assert_warns(caplog, match=\"Audio has sample rate 16000. Resample to 32000\"):\n s = auto_meta.load()\n assert s.dim() == 2\n assert s.shape[1] == 351840\n\n\n@pytest.mark.parametrize(\"gpu\", [True, False])\n@pytest.mark.parametrize(\"fp16\", [True, False])\n@pytest.mark.parametrize(\"custom_read_func\", [True, False])\ndef test_load_audio_in_devices(sample_audio, gpu, fp16, custom_read_func, caplog):\n # TODO: move load_audio to speech_utils\n from stopes.modules.speech.speech_units import load_audio\n from stopes.modules.speech.utils import read_audio\n\n audio_path, num_frames = sample_audio[0]\n fake_read_func = lambda *a: torch.zeros(1) # noqa\n read_func = fake_read_func if custom_read_func else read_audio # ignore[assignment]\n line = f\"{audio_path}|0|{num_frames}|16\\tcoluimn2\"\n load_res = load_audio(\n 0, gpu, fp16, line, sampling_factor=32, read_audio_func=read_func # type: ignore[arg-type]\n )\n assert load_res[0] == line\n if custom_read_func:\n expected_wav = torch.zeros(1)\n if gpu and torch.cuda.is_available():\n expected_wav = expected_wav.cuda()\n if fp16:\n expected_wav = expected_wav.half()\n assert torch.equal(load_res[1], expected_wav) # type: ignore[arg-type]\n line_no_sample = f\"{audio_path}|0|{num_frames}\\tcoluimn2\"\n with assert_warns(\n caplog, match=\"Sampling factor not present in file, using provided value.\"\n ):\n assert load_audio(0, gpu, fp16, line_no_sample)[0] == line_no_sample\n\n\n@contextlib.contextmanager\ndef assert_warns(caplog, *, match: str) -> tp.Iterator[None]:\n caplog.clear()\n sputils.warn_once.cache_clear()\n\n with caplog.at_level(logging.WARN):\n yield\n assert len(caplog.messages) == 1\n assert re.match(match, caplog.messages[0])\n caplog.clear()\n\n\ndef test_read_audio(tmp_path: Path):\n \"\"\"Testing that read_audio returns a waveform of the correct shape\"\"\"\n wav_path = str(tmp_path / \"wav_old.wav\")\n torch.manual_seed(0)\n\n # Creating a 3-second bi-channel waveform with non-standard sr and saving as .wav\n dur_old = 3\n sr_old = 48000\n wav_old = torch.randn([2, dur_old * sr_old])\n torchaudio.save(wav_path, wav_old, sample_rate=sr_old)\n\n # loading the audio with utils\n sr_new = 16000\n wav_new = sputils.read_audio(wav_path, sampling_rate=sr_new)\n assert (\n len(wav_new.shape) == 1\n ), f\"The loaded wave should be 1D tensor, but it has shape {wav_new.shape}\"\n dur_new = wav_new.shape[-1] / sr_new\n assert (\n dur_new == dur_old\n ), f\"The wav had duration of {dur_old} sec. but was loaded having {dur_new} sec.\"\n","repo_name":"facebookresearch/stopes","sub_path":"stopes/modules/tests/test_speech_utils.py","file_name":"test_speech_utils.py","file_ext":"py","file_size_in_byte":14304,"program_lang":"python","lang":"en","doc_type":"code","stars":209,"dataset":"github-code","pt":"72"}
+{"seq_id":"36342563557","text":"import openai\nopenai.api_key = \"put api key here\"\n\nimport pyttsx3\nimport datetime\nimport speech_recognition as sr\nimport wikipedia\nimport webbrowser\n\nimport os\nimport random\n\nfrom selenium import webdriver \nfrom selenium.webdriver.chrome.options import Options \nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport time\n\nimport requests, json\n\nfrom geopy.geocoders import Nominatim\n\ngeolocator = Nominatim(user_agent=\"geoapiExercises\")\n\n\n\n#register chrome as default web browser\nwebbrowser.register('chrome',None,webbrowser.BackgroundBrowser(\"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe\"))\n\nengine = pyttsx3.init('sapi5')\nvoices = engine.getProperty('voices')\n# for voice in voices:\n# print(f\"Voice: {voice.name}\")\nengine.setProperty('voice',voices[1].id)\n\n\n\n\n#speak function\ndef speak(audio):\n engine.say(audio)\n engine.runAndWait()\n\ndef intro():\n hour = int(datetime.datetime.now().hour)\n if hour>=4 and hour<12:\n speak(\"Good Morning!\")\n elif hour >=12 and hour<18:\n speak(\"Good Afternoon!\")\n else:\n speak(\"Good Evening!\")\n speak(\"I am your assistant Drishti, How may I help you ?\")\n\ndef takeCommand():\n #audio to string\n r=sr.Recognizer()\n with sr.Microphone() as source:\n print(\"Listening...\") \n r.pause_threshold=1\n audio = r.listen(source)\n\n try:\n print(\"Recognizing...\")\n query = r.recognize_google(audio, language='en-IN')\n print(f\"User said: {query}\\n\")\n\n except Exception as e:\n # print(e)\n print(\"Say that again please...\")\n return \"None_354465\" #coded string \n \n return query\n\n#to get current location\ndef getLocation():\n options = Options()\n options.add_argument(\"--use--fake-ui-for-media-stream\")\n driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()),options=options)\n timeout = 20\n driver.get(\"https://mycurrentlocation.net/\")\n wait = WebDriverWait(driver, timeout)\n time.sleep(3)\n \n longitude = driver.find_elements(\"xpath\", '//*[@id=\"longitude\"]') \n longitude = [x.text for x in longitude] \n longitude = str(longitude[0]) \n latitude = driver.find_elements(\"xpath\", '//*[@id=\"latitude\"]') \n latitude = [x.text for x in latitude] \n latitude = str(latitude[0]) \n driver.quit() \n print(latitude,longitude)\n location = geolocator.reverse(latitude+\",\"+longitude)\n print(location)\n return(latitude,longitude,location)\n\ndef getweather():\n\n api_key = 'openweather_api_key_here'\n lat = getLocation()[0]\n lon = getLocation()[1]\n\n complete_url = \"https://api.openweathermap.org/data/2.5/weather?lat={lat}&lon={lon}&appid={api_key}\"\n response = requests.get(complete_url)\n x = response.json()\n\n if x[\"cod\"] != \"404\":\n\n y = x[\"main\"]\n current_temperature = y[\"temp\"]-273.15\n current_pressure = y[\"pressure\"]/1000.0\n current_humidity = y[\"humidity\"]\n\n z = x[\"weather\"]\n weather_description = z[0][\"description\"]\n\n # print following values\n print(\" Temperature (in celsius unit) = \" +\n str(current_temperature) +\n \"\\n atmospheric pressure (in hPa unit) = \" +\n str(current_pressure) +\n \"\\n humidity (in percentage) = \" +\n str(current_humidity) +\n \"\\n description = \" +\n str(weather_description))\n \n speak(f\"According to OpenWeatherMap.org {weather_description} is expected at your location \" +\n f\"\\n with current temperature of {current_temperature} degrees celsius \" +\n f\"\\n Atmospheric pressure is {current_pressure} bar and humidity is {current_humidity} percent\")\n\n else:\n print(\"Sorry, Weather Report not found\")\n speak(\"Sorry, Weather Report not found\")\n\n#chatgpt integration\ndef gpt_response(query):\n model_engine = \"text-davinci-003\"\n\n response = openai.Completion.create(\n engine=model_engine,\n prompt=query,\n max_tokens=1024,\n n=1,\n stop=None,\n temperature=0.5,)\n response = response.choices[0].text\n print(response)\n return response\n\n\n\n\n\n \n \n\nif __name__ ==\"__main__\":\n intro()\n flag = 0 #to check if last response of bot was successful\n while True:\n query = takeCommand().lower()\n \n\n #logic for executing tasks on query\n if 'drishti stop' in query:\n break\n\n elif 'gpt' in query:\n speak(gpt_response(query))\n\n flag =0\n\n elif 'wikipedia' in query:\n speak('Searching Wikipedia...')\n query = query.replace(\"wikipedia\",\"\")\n results = wikipedia.summary(query,sentences = 2)\n speak(\"According to Wikipedia,\")\n print(results)\n speak(results)\n\n flag=0\n\n elif 'play music' in query:\n music_dir = 'pathto\\d_bot\\music'\n songs = os.listdir(music_dir)\n print(songs)\n play = random.choice(songs) \n os.startfile(os.path.join(music_dir, play))\n\n elif 'the time' in query:\n strTime = datetime.datetime.now().strftime(\"%H:%M:%S\")\n speak(f\"The time is {strTime}\")\n\n flag =0\n \n elif 'my location' in query:\n speak(f\"Your current location is {getLocation()[2]}\")\n\n flag =0\n\n elif 'weather' in query:\n getweather()\n\n flag=0\n \n\n elif query != \"None_354465\":\n if flag == 0:\n speak(\"Sorry I can't help with that. Please try again ...\")\n flag = 1\n\n \n \n","repo_name":"nomaan-2k/d_bot","sub_path":"drishti_bot.py","file_name":"drishti_bot.py","file_ext":"py","file_size_in_byte":5804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"19547091257","text":"from pyspark import SparkConf, SparkContext\n\nsc = SparkContext(\"local\", \"test\")\ninvalid = sc.accumulator(0)\n\ndef count_invalid(el):\n invalid.add(el)\n return el\n\ninfo = sc.parallelize([3,4,5])\ninfo2 = info.map(lambda x: count_invalid(x))\n\nfor x in info2.collect():\n print(x)\n\nprint(invalid)\nsc.stop()\n","repo_name":"sency90/allCode","sub_path":"bigdata/finals/practice/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"72"}
+{"seq_id":"70738534632","text":"# Copyright 2023 Abhinav Kumar. All Rights Reserved.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n# ==============================================================================\r\n\r\nimport numpy as np\r\nimport cv2\r\n\r\ncapture = cv2.VideoCapture(0)\r\n\r\n# Get initial frame\r\nresponse, first_frame = capture.read()\r\nfirst_gray = cv2.cvtColor(first_frame, cv2.COLOR_BGR2GRAY)\r\nhsv = np.zeros_like(first_frame)\r\nhsv[..., 1] = 255\r\n\r\nwhile True:\r\n response, frame = capture.read()\r\n frame = cv2.flip(frame, 1)\r\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n\r\n # Calculates dense optical flow\r\n dense_flow = cv2.calcOpticalFlowFarneback(first_gray, frame, None, 0.5, 4, 13, 5, 7, 1.5, 0)\r\n\r\n # Calculate speed and angle(theta) of motion\r\n speed, theta = cv2.cartToPolar(dense_flow[..., 0], dense_flow[..., 1])\r\n hsv[..., 0] = theta * (180 / np.pi) # Adjusted the angle conversion\r\n hsv[..., 2] = cv2.normalize(speed, None, 0, 255, cv2.NORM_MINMAX)\r\n final = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\r\n\r\n cv2.imshow('Dense Optical Flow', final)\r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n\r\n first_gray = frame\r\n\r\ncv2.destroyAllWindows()\r\ncapture.release()\r\n","repo_name":"abhinav16aero/cv","sub_path":"Object_Tracking_Detection/ObjectTracking_DenseOptical.py","file_name":"ObjectTracking_DenseOptical.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"42064088739","text":"from socket import gethostbyname_ex\nfrom queue import Queue\nfrom threading import Thread, Lock\nfrom core.sql_helper import SQL_helper\nfrom rich.console import Console\nimport subprocess\nimport nmap\nimport json\nfrom lib import config\n\nconsole = Console()\nlock = Lock()\nurl_tag = config.url_tag\n\n\n# 数据库域名转ip，检查cdn，插入数据库\nclass Domain2IP(Thread):\n def __init__(self, domain_queue):\n super(Domain2IP, self).__init__()\n self._domain_queue = domain_queue\n\n def run(self):\n while self._domain_queue.empty() is not True:\n domain = self._domain_queue.get() # 拿到domain\n try:\n _res = gethostbyname_ex(domain)\n # print(f'domain:{_res[0]} , aliases:{_res[1]} , ip list:{_res[2]}')\n if len(_res[2]) != 1:\n console.print('[warning] ' + domain + ' cdn checked...', style='bold yellow')\n # self._res_list.append([_res[0], ','.join(_res[1]), ','.join(_res[2])])\n SQL_helper.update_subdomain_sql(\"cdn_checked\", 'True', domain)\n else:\n # self._res_list.append([_res[0], ','.join(_res[1]), ','.join(_res[2])])\n # 更新ip，cdn字段\n SQL_helper.update_subdomain_sql(_res[2][0], 'False', domain)\n except Exception as e:\n console.print(\"[warning]\" + domain + \" 解析失败，不存活!\", style='bold yellow')\n finally:\n self._domain_queue.task_done()\n\n\ndef ip_domain_cdn():\n domain_queue = Queue()\n # 读取数据库数据放入检查队列\n subdomains_list = SQL_helper.read_subdomain_sql()\n # print(subdomains_list)\n for sub_tuple in subdomains_list:\n # print(sub_tuple[1])\n domain_queue.put(sub_tuple[1])\n # 多线程调用解析域名\n for i in range(20): # 线程 20\n a_thread = Domain2IP(domain_queue)\n a_thread.daemon = True\n a_thread.start()\n domain_queue.join()\n\n\ndef shodan_port_check():\n ...\n\n\nclass MulMasscan(Thread):\n def __init__(self, domain_q, t_id):\n super(MulMasscan, self).__init__()\n self._domain_q = domain_q\n self._tid = t_id\n\n def run(self):\n # 启动端口探测，写文件加锁，会变得很慢?\n while self._domain_q.empty() is not True:\n _ip = self._domain_q.get()\n try:\n urls_list_to_db = MulMasscan.masscan_port_check(_ip, self._tid)\n # 插入数据库\n lock.acquire()\n SQL_helper.url_table_insert(urls_list_to_db, url_tag)\n lock.release()\n except:\n console.print('[error] masscan 端口扫描失败!', style='bold red')\n finally:\n self._domain_q.task_done()\n\n @staticmethod\n def service_check(ip, port):\n console.print(\"[info] \" + \"nmap scanning: \" + ip + ':' + port, style='bold blue')\n nm = nmap.PortScanner()\n ret = nm.scan(ip, port, arguments='-Pn,-sS')\n service_name = ret['scan'][ip]['tcp'][int(port)]['name']\n if 'http' in service_name or service_name == 'sun-answerbook':\n if service_name == 'https' or service_name == 'https-alt':\n url = 'https://' + ip + ':' + port\n else:\n url = 'http://' + ip + ':' + port\n return url\n\n @staticmethod\n def masscan_port_check(ip, tid):\n tmp_list = []\n url_list = []\n results_list = []\n console.print('[info] masscan正在进行端口探测...', style=\"bold blue\")\n cmd = 'masscan ' + ip + ' -p 1-65535 -oJ ./tmp/masscanRes/' + tid + '_res --rate 10000' # 每个线程单独去操作自己文件, 服务器卡顿\n\n rsp = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n while True:\n if rsp.poll() == None: # poll() 返回none代表正在运行\n pass\n else:\n break\n\n with open('./tmp/masscanRes/'+tid+'_res', 'r', encoding='utf-8') as wr:\n str0 = wr.read()\n # print('str0',str0)\n if len(str0) == 0:\n return url_list\n str1 = str0[:-4] + str0[-3:] # win 和 lin 切片结果不一样\n # print('str1', str1)\n try:\n json_data = json.loads(str1)\n except:\n console.print('[error] something wrong for json loads!', style='bold red')\n return url_list\n for line in json_data:\n ip = line['ip']\n port = line['ports'][0]['port']\n result_dict = {\n 'ip': ip,\n 'port': port\n }\n tmp_list.append(result_dict)\n if len(tmp_list) > 65535: # 端口过多直接pass\n tmp_list.clear()\n else:\n results_list.extend(tmp_list)\n\n for result in results_list:\n ip = result['ip']\n port = str(result['port'])\n url = MulMasscan.service_check(ip, port) # 没东西\n if url:\n url_list.append(url)\n console.print('[info] get a url: ' + url, style='bold blue')\n # todo 做探活\n return url_list\n\n\ndef port_check():\n ip_list_ = []\n ip_queue = Queue()\n\n subdomain_all_info = SQL_helper.read_subdomain_sql() # subdomain 表信息\n for sub_tuple in subdomain_all_info:\n ip = sub_tuple[2]\n ip_list_.append(ip)\n\n ip_list_ = list(set(ip_list_))\n for ip in ip_list_:\n if ip == \"#\":\n continue\n # 加入队列\n ip_queue.put(ip)\n\n # 多线程启动\n for i in range(20):\n t = MulMasscan(ip_queue, str(i))\n t.daemon = True\n t.start()\n ip_queue.join()\n\n\n# port_check()\n","repo_name":"TimerZz007/misc_scripts","sub_path":"core/auto_collect/port_scan.py","file_name":"port_scan.py","file_ext":"py","file_size_in_byte":5907,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"71888669672","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport gi\nimport os\nimport re\nimport time\nimport subprocess\n\ngi.require_version(\"Gtk\", \"3.0\")\n\nfrom gi.repository import Gtk, Gio, GLib, GObject\n\nclass MainWindow(Gtk.Window):\n\t\"\"\"La ventana principal\"\"\"\n\n\tinstances = 0\n\n\tdef __init__(self, title):\n\t\tsuper(MainWindow, self).__init__(title=title)\n\t\t# revisamos que sólo esté una instancia de esta clase\n\t\tif self.instances > 0:\n\t\t\traise RuntimeError(\"You can not create two instances of MainWindow\")\n\t\telse:\n\t\t\tself.instances += 1\n\t\t# algunos atributos generales\n\t\tself.title = title\n\t\t# preconfiguración\n\t\tself.set_border_width(10)\n\t\tself.set_default_size(400, 150)\n\t\t# gtk stuffs\n\t\tself.header_bar = Gtk.HeaderBar()\n\t\tself.vbox_main = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=6)\n\t\tself.vbox_target = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=6)\n\t\tself.hbox_target_label = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=6)\n\t\tself.hbox_target_filename = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=6)\n\t\tself.vbox_preferences = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=6)\n\t\tself.hbox_preferences_label = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=6)\n\t\tself.hbox_preferences_check = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=6)\n\t\tself.hbox_preferences_frames = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=6)\n\t\t# more\n\t\tself.button_record = Gtk.Button.new_with_label(\"Grabar\")\n\t\tself.button_stop = Gtk.Button()\n\t\tself.button_play = Gtk.Button()\n\t\tself.label_target = Gtk.Label(\"Destino:\")\n\t\tself.entry_target = Gtk.Entry()\n\t\tself.button_explore = Gtk.Button.new_with_mnemonic(\"_Explorar\")\n\t\tself.label_preferences = Gtk.Label(\"Preferencias:\")\n\t\tself.check_mouse = Gtk.CheckButton(\"Grabar el mouse\")\n\t\tself.check_audio = Gtk.CheckButton(\"Grabar audio\")\n\t\tself.button_audio_settings = Gtk.Button.new_with_mnemonic(\"_Configurar Audio\")\n\t\tself.label_frames = Gtk.Label(\"Frames por segundo:\")\n\t\tself.spinbutton_frames = Gtk.SpinButton()\n\n\t\t# configuración gtk\n\t\tself.header_bar.props.title = self.props.title\n\t\tself.header_bar.set_show_close_button(True)\n\t\tself.spinbutton_frames.set_adjustment(Gtk.Adjustment(30, 0, 100, 1, 10, 0))\n\n\t\tself.button_play.add(Gtk.Image.new_from_icon_name(\"gtk-media-play\", Gtk.IconSize.BUTTON))\n\t\tself.button_stop.add(Gtk.Image.new_from_icon_name(\"gtk-media-stop\", Gtk.IconSize.BUTTON))\n\t\tself.header_bar.add(self.button_record)\n\t\tself.header_bar.add(self.button_play)\n\t\tself.header_bar.add(self.button_stop)\n\t\tself.set_titlebar(self.header_bar)\n\t\t# agregamos los elementos a los boxes\n\t\tself.vbox_target.add(self.hbox_target_label)\n\t\tself.vbox_target.add(self.hbox_target_filename)\n\t\tself.vbox_preferences.add(self.hbox_preferences_label)\n\t\tself.vbox_preferences.add(self.hbox_preferences_check)\n\t\tself.vbox_preferences.add(self.hbox_preferences_frames)\n\t\tself.vbox_main.add(self.vbox_target)\n\t\tself.vbox_main.add(self.vbox_preferences)\n\t\tself.add(self.vbox_main)\n\t\t# agregamos otras cosas\n\t\tself.hbox_target_label.add(self.label_target)\n\t\tself.hbox_target_filename.pack_start(self.entry_target, True, True, 0)\n\t\tself.hbox_target_filename.add(self.button_explore)\n\t\tself.hbox_preferences_label.add(self.label_preferences)\n\t\tself.hbox_preferences_check.add(self.check_mouse)\n\t\tself.hbox_preferences_check.add(self.check_audio)\n\t\tself.hbox_preferences_check.add(self.button_audio_settings)\n\t\tself.hbox_preferences_frames.add(self.label_frames)\n\t\tself.hbox_preferences_frames.add(self.spinbutton_frames)\n\t\t# conectando\n\t\tself.button_record.connect(\"clicked\", self.on_button_record_clicked)\n\t\tself.button_stop.connect(\"clicked\", self.on_button_stop_clicked)\n\t\tself.button_play.connect(\"clicked\", self.on_button_play_clicked)\n\t\tself.button_explore.connect(\"clicked\", self.on_button_explore_clicked)\n\t\tself.button_audio_settings.connect(\"clicked\", self.on_button_audio_settings_clicked)\n\t\tself.check_mouse.connect(\"toggled\", self.on_check_mouse_toggled)\n\t\tself.check_audio.connect(\"toggled\", self.on_check_audio_toggled)\n\t\tself.entry_target.connect(\"changed\", self.on_entry_target_changed)\n\n\tdef on_button_record_clicked(self, widget):\n\t\tpass\n\n\tdef on_button_stop_clicked(self, widget):\n\t\tpass\n\n\tdef on_button_play_clicked(self, widget):\n\t\tpass\n\n\tdef on_button_explore_clicked(self, widget):\n\t\tpass\n\n\tdef on_button_audio_settings_clicked(self, widget):\n\t\tpass\n\n\tdef on_check_mouse_toggled(self, widget):\n\t\tpass\n\n\tdef on_check_audio_toggled(self, widget):\n\t\tpass\n\n\tdef on_entry_target_changed(self, widget):\n\t\tpass\n\nclass ExplorerWindow(Gtk.FileChooserDialog):\n\t\"\"\"La ventana del explorador de archivo\"\"\"\n\tdef __init__(self, parent=None):\n\t\tsuper(ExplorerWindow, self).__init__(\n\t\t\tparent=parent,\n\t\t\ttitle=\"Guardar en:\",\n\t\t\taction=Gtk.FileChooserAction.SAVE)\n\t\tself.logger = parent.logger\n\t\tself.path = None\n\t\tself.add_button(Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL)\n\t\tself.add_button(Gtk.STOCK_SAVE_AS, Gtk.ResponseType.OK)\n\t\tself.filter_text = Gtk.FileFilter()\n\t\tself.filter_text.set_name(\"Archivo de vídeo\")\n\t\tself.filter_text.add_mime_type(\"video/mp4\")\n\t\tself.filter_text.add_mime_type(\"video/mkv\")\n\t\tself.filter_text.add_mime_type(\"video/webm\")\n\t\tself.add_filter(self.filter_text)\n\n\tdef explore(self):\n\t\tself.logger.info(\"Se pide explorar\")\n\t\tresponse = self.run()\n\t\tif response == Gtk.ResponseType.OK:\n\t\t\tfilename = self.get_filename()\n\t\t\t# si ya existe, preguntamos\n\t\t\tif os.path.exists(filename):\n\t\t\t\tmessage_dialog = Gtk.MessageDialog(\n\t\t\t\t\tparent=self,\n\t\t\t\t\ttext=\"El archivo %s ya existe, ¿Quiere sobreescribirlo?\" % os.path.basename(filename),\n\t\t\t\t\tuse_markup=False,\n\t\t\t\t\ttype=Gtk.MessageType.WARNING,\n\t\t\t\t\tbuttons=Gtk.ButtonsType.YES_NO,\n\t\t\t\t\tmodal=True)\n\t\t\t\tmessage_dialog.format_secondary_text(\n\t\t\t\t\t\"Si sobreescribes, todo el contenido del archivo será borrado \" +\n\t\t\t\t\t\"para remplazarlo con el nuevo contenido.\")\n\t\t\t\tresponse2 = message_dialog.run()\n\t\t\t\tmessage_dialog.destroy()\n\t\t\t\t# entonces, revisamos qué quiere el usuario\n\t\t\t\tif response2 == Gtk.ResponseType.NO:\n\t\t\t\t\tmessage_dialog.destroy()\n\t\t\t\t\treturn self.explore()\n\t\t\t\telse:\n\t\t\t\t\tself.path = filename\n\t\t\t\t\tmessage_dialog.destroy()\n\t\t\t\t\tself.destroy()\n\t\t\t\t\treturn self.path\n\t\t\telse:\n\t\t\t\tself.path = filename\n\t\t\t\tself.destroy()\n\t\t\t\treturn self.path\n\t\telif response == Gtk.ResponseType.CANCEL:\n\t\t\tself.destroy()\n\t\t\treturn self.path\n\nclass AudioSettinsWindow(Gtk.Dialog):\n\t\"\"\"La ventana que pregunta por más información para configurar la grabación de audio.\"\"\"\n\tdef __init__(self, parent=None):\n\t\tsuper(AudioSettinsWindow, self).__init__(\n\t\t\ttitle=\"Configuración de audio\",\n\t\t\tparent=parent,\n\t\t\tflags=Gtk.DialogFlags.DESTROY_WITH_PARENT)\n\t\tself.logger = parent.logger\n\t\tself._audio_devices = set()\n\t\tself.add_button(Gtk.STOCK_OK, Gtk.ResponseType.OK)\n\t\tself.add_button(Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL)\n\t\t# configuramos un par de cosas\n\t\tself.set_border_width(10)\n\t\tself.set_default_size(350, 300)\n\t\t# personalizamos este diálogo\n\t\tself.vbox_main = self.get_content_area()\n\t\tself.vbox_devices = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=6)\n\t\tself.frame = Gtk.Frame()\n\t\t# las cosas se agregan\n\t\tself.vbox_main.add(Gtk.Label(\"Seleccione el/los dispositivo(s) de audio.\"))\n\t\tself.vbox_main.add(self.frame)\n\t\t# buscamos los dispositivos de audio\n\t\tfor description, name in self._get_audio_devices().items():\n\t\t\ttext = description + \"\\n\"\n\t\t\ttext += \"(\" + name + \")\"\n\t\t\tself.logger.debug(\"Obtenido: \" + text)\n\t\t\tcheck_audio_device = Gtk.CheckButton(text)\n\t\t\tcheck_audio_device.connect(\"toggled\", self.on_check_audio_devices_toggled, name)\n\t\t\tself.vbox_devices.add(check_audio_device)\n\t\t# vamos terminando\n\t\tself.frame.add(self.vbox_devices)\n\t\tself.frame.set_label(\"los dispositivos:\")\n\t\tself.show_all()\n\t\n\tdef _get_audio_devices(self):\n\t\toutput = subprocess.check_output([\"pacmd\", \"list-sources\"])\n\t\tname_devices = re.findall(\"name: <(.*)>\", output.decode())\n\t\tdescription_devices = re.findall(\"device.description = \\\"(.*)\\\"\", output.decode())\n\t\treturn {description: name for description, name in zip(description_devices, name_devices)}\n\n\tdef on_check_audio_devices_toggled(self, widget, name):\n\t\tif widget.get_active():\n\t\t\tself._audio_devices.add(name)\n\t\telse:\n\t\t\tself._audio_devices.remove(name)\n\n\t@property\n\tdef audio_devices(self):\n\t\treturn list(self._audio_devices)\n\nclass ProgressWindow(Gtk.Dialog):\n\t\"\"\"La ventana que muestra qué tal va el procesamiento de los archivos\"\"\"\n\tdef __init__(self, parent):\n\t\tsuper(ProgressWindow, self).__init__(\n\t\t\tself,\n\t\t\tparent=parent,\n\t\t\ttitle=\"Procesando archivos...\",\n\t\t\tflags=Gtk.DialogFlags.DESTROY_WITH_PARENT)\n\t\tself.logger = parent.logger\n\t\tself.process = None\n\t\tself.std = None\n\t\tself.cancelled = False\n\t\tself.re_duration = re.compile(r\"Duration: (\\d\\d):(\\d\\d):(\\d\\d(\\.\\d\\d))\")\n\t\tself.re_time = re.compile(r\"time=(\\d\\d):(\\d\\d):(\\d\\d\\.\\d\\d)\")\n\t\t# agregamos un botón\n\t\tself.add_button(Gtk.STOCK_STOP, Gtk.ResponseType.CANCEL)\n\t\t# configuramos un par de cosas\n\t\tself.set_border_width(10)\n\t\tself.set_default_size(350, 100)\n\t\t# personalizamos este diálogo\n\t\tself.vbox_main = self.get_content_area()\n\t\tself.progress = Gtk.ProgressBar()\n\t\tself.label_info = Gtk.Label()\n\t\t# las cosas se agregan\n\t\tself.vbox_main.add(self.label_info)\n\t\tself.vbox_main.add(self.progress)\n\t\tself.show_all()\n\n\tdef check(self, process, std, info):\n\t\tself.logger.debug(\"Nuevo trabajo para: \" + info)\n\t\tself.process = process\n\t\tself.std = std\n\t\tduration = -1\n\t\tself.label_info.set_text(info)\n\t\tself.progress.set_fraction(0.0)\n\t\tbuffer = str()\n\t\twhile self.process.poll() is None:\n\t\t\tcontent = self.std.read()\n\t\t\tcontent = content.decode()\n\t\t\tcontent = content.replace(\"\\r\", \"\\n\")\n\t\t\tif content:\n\t\t\t\tbuffer += content\n\t\t\t\tif duration < 0:\n\t\t\t\t\tmatched = self.re_duration.search(buffer)\n\t\t\t\t\tif matched:\n\t\t\t\t\t\tnumbers = list(matched.groups())[:3]\n\t\t\t\t\t\tnumbers.reverse()\n\t\t\t\t\t\tduration = sum([float(number)*(60**i) for i,number in enumerate(numbers)])\n\t\t\t\t\t\tself.logger.debug(\"La duration: \" + str(duration))\n\n\t\t\t\t\t\t# revisamos el otro a ver\n\t\t\t\t\t\tmatched = self.re_time.search(buffer)\n\t\t\t\t\t\tif matched:\n\t\t\t\t\t\t\tGLib.idle_add(self.update_progress, fraction=1.0)\n\t\t\t\t\t\t\tself.logger.debug(\"Ya estaba finalizado\")\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tcontinue\n\t\t\t\t#self.logger.debug(\"La duration: \" + str(duration))\n\t\t\t\tif duration > 0:\n\t\t\t\t\tmatched = self.re_time.findall(buffer)\n\t\t\t\t\tmatched.reverse()\n\t\t\t\t\tmatched = matched[:-1]\n\t\t\t\t\tif matched:\n\t\t\t\t\t\tnumbers = list(matched[0])[:3]\n\t\t\t\t\t\tnumbers.reverse()\n\t\t\t\t\t\ttime = sum([float(number)*(60**i) for i,number in enumerate(numbers)])\n\t\t\t\t\t\tfraction = time / duration\n\t\t\t\t\t\tself.logger.debug(\"Valor time: \" + str(time) + \" > \" + str(numbers) + \" = \" + str(fraction))\n\t\t\t\t\t\tGLib.idle_add(self.update_progress, fraction)\n\t\t\t\tbuffer = buffer[:-50]\n\t\treturn self.process.poll()\n\n\tdef cancel(self):\n\t\t# cancela el actual proceso\n\t\tself.logger.info(\"Se cancela el progreso\")\n\t\tself.process.terminate()\n\t\tself.cancelled = True\n\n\tdef update_progress(self, fraction):\n\t\tself.progress.set_fraction(fraction)","repo_name":"David256/BlueLobsterRecorder","sub_path":"classes/gui/window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":10958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"441710668","text":"import pygame\r\nfrom Table import Table\r\nimport settings\r\n\r\n\r\nclass Game:\r\n window = pygame.display.set_mode(settings.WINDOW_SIZE)\r\n clock = pygame.time.Clock()\r\n pygame.display.set_caption(\"Game 2048\")\r\n pygame.init()\r\n\r\n def __init__(self):\r\n self.table = Table((4, 4), 125)\r\n self.game_running = 1\r\n self.FPS = 1\r\n\r\n def run(self):\r\n self.table.update()\r\n self.table.update()\r\n while self.game_running:\r\n self.clock.tick(self.FPS)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n self.game_running = 0\r\n return\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_LEFT:\r\n self.table.update(3)\r\n elif event.key == pygame.K_RIGHT:\r\n self.table.update(1)\r\n elif event.key == pygame.K_DOWN:\r\n self.table.update(2)\r\n elif event.key == pygame.K_UP:\r\n self.table.update(0)\r\n self.draw()\r\n\r\n def draw(self):\r\n self.table.draw(self.window)\r\n pygame.display.flip()\r\n\r\n def update(self):\r\n pass\r\n","repo_name":"MDastan2005/Python-programms","sub_path":"2048/Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"18561399936","text":"#!/usr/bin/python3\n\nplot = '■'\n\nimport sys\n\ndata = ((len(sys.argv) > 1) and open(sys.argv[1]) or sys.stdin).readlines()\ndata = [item.strip().split('\\t') for item in data]\ndata = [(str(label), int(value)) for (label, value) in data ]\n\nlabels, values = zip(*data)\nmax_label_width = max(map(lambda x: len(str(x)), labels))\ndel labels\n\nmax_value_width = max(map(lambda x: len(str(x)), values))\nmax_value = max(values)\ndel values\n\nfrom shutil import get_terminal_size\nterminal_width, terminal_height = get_terminal_size((80,25))\nmax_width = terminal_width - max_label_width - max_value_width - 2\n\nfor label, value in data:\n\tprint(label.rjust(max_label_width), plot * int(max_width * value / max_value), value)\n","repo_name":"patatetom/ttybarchart","sub_path":"ttybarchart.py","file_name":"ttybarchart.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"24259043296","text":"# -*- coding: utf8 -*-\nimport time\n\nimport pandas as pd\nfrom model.data.GoogleDriveAPIAuth import GoogleAPIAuth\nfrom model.data.VariableSet import VariableSet\nvs = VariableSet()\n\n# 비교 지역 선정 (서울 ~ 제주)\ntarget = [vs.region_Gyeonggi, vs.region_Seoul]\n\n## 스프레드시트 사용\n# 스프레스시트 문서 가져오기\ngd = GoogleAPIAuth()\ndoc = gd.gc.open_by_url(vs.spreadsheet_url)\nws = doc.worksheet('응답결과')\n\n# 기존 입력값 이후 셀부터 결과 입력하도록 설정\nvalues = ws.get_all_values()\nheader, rows = values[0], values[1:]\nexcel_source = pd.DataFrame(rows, columns=header)\nprint('기존 입력셀 개수: ' + str(len(excel_source)))\n# 시작하는 셀 설정\nstart = len(excel_source)\n\nresults = []\n# print(target)\n\nfor region in target:\n for city in region:\n for area, result in region.items():\n print(\"지역: \" + area)\n print(\"지역 내 검색 결과: \" + str(len(result)))\n i = 0\n for data in range(len(result) ):\n # 지역 및 코드를 스프레드시트에 입력\n for category, output in result[i].items():\n if category == 'value':\n lcode = output\n if category == 'description':\n location = output\n i += 1\n # 검색 결과를 리스트 자료형으로 저장\n results.append((lcode, location))\n break\n # 전체 목록을 한번에 저장\n ws.update('A' + str(start + 2), results)\n","repo_name":"alvinshin81/alvinshin","sub_path":"usefulScripts/compareLocationData.py","file_name":"compareLocationData.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"43124188056","text":"from aws_cdk.core import (\n Construct,\n Stack,\n Tags,\n)\nfrom aws_cdk.aws_cloudfront import (\n EdgeLambda,\n LambdaEdgeEventType,\n LambdaFunctionAssociation,\n)\nfrom aws_cdk.aws_lambda import (\n Code,\n Runtime,\n)\nfrom aws_cdk.aws_s3 import (\n BlockPublicAccess,\n Bucket,\n BucketEncryption,\n)\n\nfrom openttd.construct.s3_cloud_front import S3CloudFront\nfrom openttd.construct.s3_cloud_front_v2 import S3CloudFrontV2\nfrom openttd.enumeration import Deployment\nfrom openttd.stack.common import lambda_edge\n\n\nclass RedirectStack(Stack):\n application_name = \"Redirect\"\n subdomain_names = [\n \"download\",\n \"farm\",\n \"forum\",\n \"github\",\n \"grfsearch\",\n \"nightly\",\n \"noai\",\n \"nogo\",\n \"proxy.binaries\",\n \"root\",\n \"security\",\n ]\n\n def __init__(self, scope: Construct, id: str, *, deployment: Deployment, **kwargs) -> None:\n super().__init__(scope, id, **kwargs)\n\n Tags.of(self).add(\"Application\", self.application_name)\n Tags.of(self).add(\"Deployment\", deployment.value)\n\n bucket_site = Bucket(\n self,\n \"Site\",\n block_public_access=BlockPublicAccess.BLOCK_ALL,\n )\n\n bucket_access_logs = Bucket(\n self,\n \"AccessLogs\",\n encryption=BucketEncryption.S3_MANAGED,\n block_public_access=BlockPublicAccess.BLOCK_ALL,\n )\n\n for subdomain_name in self.subdomain_names:\n func_version = lambda_edge.create_function(\n self,\n f\"Redirect-{subdomain_name}-{deployment.value}\",\n runtime=Runtime.NODEJS_10_X,\n handler=\"index.handler\",\n code=Code.from_asset(f\"./lambdas/redirect-{subdomain_name}\"),\n )\n\n if subdomain_name == \"grfsearch\":\n S3CloudFrontV2(\n self,\n f\"S3CloudFront-{subdomain_name}\",\n subdomain_name=subdomain_name,\n bucket_site=bucket_site,\n bucket_access_logs=bucket_access_logs,\n edge_lambdas=[\n EdgeLambda(\n event_type=LambdaEdgeEventType.ORIGIN_REQUEST,\n function_version=func_version,\n ),\n ],\n forward_query_string_cache_keys=[\"do\", \"q\"],\n )\n else:\n S3CloudFront(\n self,\n f\"S3CloudFront-{subdomain_name}\",\n subdomain_name=subdomain_name,\n bucket_site=bucket_site,\n bucket_access_logs=bucket_access_logs,\n lambda_function_associations=[\n LambdaFunctionAssociation(\n event_type=LambdaEdgeEventType.ORIGIN_REQUEST,\n lambda_function=func_version,\n ),\n ],\n )\n","repo_name":"OpenTTD/aws-infra","sub_path":"openttd/stack/application/redirect.py","file_name":"redirect.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"15424007386","text":"import requests\nfrom bs4 import BeautifulSoup as bs\nimport os\n\n#website url\n#url = 'https://www.pexels.com/search/model'\nurl = 'https://www.imgmodels.com/new-york/women'\n\npage = requests.get(url)\nsoup = bs(page.text, 'html.parser')\n\nimage_tags = soup.findAll('img')\n#print(image_tags)\n\nif not os.path.exists('models'):\n os.makedirs('models')\n\n#move to new directory\nos.chdir('models')\n\nx = 0\nprint(f\"X is {x}\")\nfor image in image_tags:\n #print(\"image src\")\n print (image.get('data-original'))\n #print (image['alt'])\n \n try:\n url = image.get('data-original')\n source = requests.get(url)\n if source.status_code == 200:\n with open('model-'+str(x)+'.jpg','wb') as f:\n f.write(requests.get(url).content)\n f.close()\n x += 1\n print(f\"x is {x}\")\n except OSError as err:\n print(f\"Something not working {err}\")\n ","repo_name":"klarify-tech/twitter-bot","sub_path":"get-models.py","file_name":"get-models.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"71124647274","text":"#!/usr/bin/python\n#coding=utf-8\n# Application that detects alive hosts using ICMP and ARP\n# by FM\n# Python 3\n# -------------------------------------\nimport os, platform, sys, time, re, ipaddress\nfrom datetime import datetime as dt\nfrom subprocess import Popen, PIPE\n\n\n## Scan an ip address in order to check for host liveness\ndef ip_scan(ip, ping_cmd, arping_cmd):\n\n #print(\"Testing ip \" + ip)\n comm1 = ping_cmd + ip\n resp1 = os.popen(comm1)\n\n status = \"DOWN\"\n for line in resp1.readlines():\n if(line.upper().count(\"TTL\")):\n status = \"UP\"\n #print(ip, \"--> Live\")\n break\n\n if(status == \"DOWN\"):\n comm2 = arping_cmd + ip\n resp2 = os.popen(comm2)\n\n for line in resp2.readlines():\n if(line.upper().count(\"RTT\")):\n status = \"UP, NO ICMP\"\n #print(ip, \"--> Live, NO ICMP\")\n break\n\n ## Get target MAC address\n Popen([\"ping\", \"-c 1\", ip], stdout = PIPE)\n pid = Popen([\"arp\", \"-n\", ip], stdout = PIPE)\n s = pid.communicate()[0]\n hasMac = re.search(r\"(([a-f\\d]{1,2}\\:){5}[a-f\\d]{1,2})\", s.decode())\n if hasMac is not None:\n mac = hasMac.groups()[0]\n else:\n mac = 'None'\n\n hostname = 'None'\n ## Get hostname\n ## TODO\n\n ## Set data\n data = (ip,mac,hostname,status)\n if status == \"UP\":\n print(data)\n\n## MAIN -------------------------------------\nif __name__ == \"__main__\":\n if len(sys.argv) == 2:\n\n try:\n\n ## Validates if the OS is Windows or other\n ## and sets the appropriate commands\n oper = platform.system()\n timeout = 4\n if(oper == \"Windows\"):\n ping_cmd = f\"ping -n 1 -w {timeout} \"\n arping_cmd = f\"arp-ping -n 1 \"\n else:\n ## Ask for the interface for arping\n interface = input(\"Enter the interface: \")\n ping_cmd = f\"ping -c 1 -t {timeout} \"\n arping_cmd = f\"arping -c 1 -i {interface}\"\n \n ## Set the beginning datetime\n t1 = dt.now()\n\n subnets = open(sys.argv[1],\"r\")\n for subnet in subnets:\n subnet = subnet.replace(\"\\n\",\"\").replace(\"\\t\",\"\")\n ## Run the scanner\n print(\"Scanning Subnet: \", subnet)\n\n ## Scans the machines in the defined range\n print(\"Scanning in Progress\")\n\n hosts = [str(ip) for ip in ipaddress.IPv4Network(subnet)]\n for ip in hosts:\n ip_scan(ip, ping_cmd, arping_cmd)\n\n ## Set the ending datetime\n t2 = dt.now()\n \n ## Calculates the scan time and prints it\n total = t2 - t1\n\n print(\"Scanning completed in: \",total)\n\n except KeyboardInterrupt:\n print(\"Execution interrupted: Ctrl+C\")\n sys.exit()\n\n else:\n print('[-] Usage: ' + sys.argv[0] + ' subnet_list.txt')\n","repo_name":"Azkrath/EthicalHacking","sub_path":"Scanners/detect_hosts_v1.py","file_name":"detect_hosts_v1.py","file_ext":"py","file_size_in_byte":3021,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"32554467984","text":"from sqlalchemy import create_engine, text\nimport os\n\ndb_connection_string = os.environ['DB_CONNECTION_STRING']\n\nengine = create_engine(db_connection_string,\n connect_args={\"ssl\": {\n \"ssl_ca\": \"/etc/ssl/cert.pem\"\n }})\n\n\ndef load_product_from_db(product):\n with engine.connect() as conn:\n str = f'select * from products where product = \"{product}\"'\n result = conn.execute(text(str))\n row = result.all()\n if len(row) == 0:\n return None\n else:\n pro = row[0]._mapping\n return dict(pro)\n\n\ndef load_price_from_db():\n with engine.connect() as conn:\n result = conn.execute(text('select * from products'))\n PRICES = []\n for row in result.all():\n PRICES.append(row._mapping)\n return PRICES\n","repo_name":"HardikChhabra/Website_for_Enactus","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"72769477674","text":"import logging\nfrom os import getenv\n\nfrom telethon import TelegramClient\n\nfrom handlers import handle_albums, handle_any_message\n\n\nlog = logging.getLogger(__name__)\n\n\ndef init_bot(targets: list[str]) -> TelegramClient:\n log.info('Initializing client…')\n client = TelegramClient(\n session=getenv('SESSION_NAME') or 'meowgram_parser',\n api_id=getenv('API_ID'),\n api_hash=getenv('API_HASH'),\n )\n log.info('Done')\n log.info('Setting up handlers…')\n handle_albums(client, targets)\n handle_any_message(client, targets)\n log.info('Done')\n return client\n\n\ndef start_bot(client: TelegramClient):\n log.info('Starting client…')\n client.start()\n log.info('Done')\n client.run_until_disconnected()\n","repo_name":"ExposedCat/tg-channels-parser","sub_path":"src/bot_config.py","file_name":"bot_config.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"32390049716","text":"#!/usr/bin/env python\n# Usage: collect_gbk_file.py smiles.tsv target_folder\nimport os.path\nimport shutil\nimport sys\nimport pandas as pd\n\n\ndef get_file_path(tsv_file):\n info = pd.read_csv(tsv_file, sep='\\t')\n for idx in info.index:\n folder_path = info.iloc[idx, 0]\n record_id = info.iloc[idx, 1]\n region_id = info.iloc[idx, 2]\n file_region_id = \"000\" + str(region_id)\n file_region_id = file_region_id[-3:]\n gbk_file_name = f'{record_id}.region{file_region_id}.gbk'\n gbk_file_path = os.path.join(folder_path, gbk_file_name)\n yield gbk_file_path\n\n\ndef mv2target(gbk, target_folder):\n changed_file_name = os.path.basename(os.path.dirname(gbk))[:15]\n target = os.path.join(target_folder, f'{changed_file_name}.{os.path.basename(gbk)}')\n shutil.copyfile(gbk, target)\n\nif __name__ == '__main__':\n tsv_file, target_folder = sys.argv[1], sys.argv[2]\n gbk_files = get_file_path(tsv_file)\n for fp in gbk_files:\n mv2target(fp, target_folder)\n","repo_name":"BioGavin/wlabkit","sub_path":"astool/script/others/collect_gbk_file.py","file_name":"collect_gbk_file.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"72"}
+{"seq_id":"35027514014","text":"#! /usr/bin/python3.9\nimport actions\nimport item_lib\nimport dbmod\n#character=dbmod.get_row('name','bob','saves')\n#character['items'] = character['items'].split(',')\ncharacter = actions.load_being('bob','saves')\nchoice = \"\"\n\nwhile (choice != 'x'):\n print('''What would you like to test?\n 1 - arena\n 2 - print Character stats\n 3 - slay ROUS\n 4 - enter the store\n 5 - print stats for mouse\n 6 - use item\n 0 - Load character''')\n\n choice = str(input ('make your selection: '))\n actions.wiper()\n print ('You chose ' + choice)\n\n if (choice == '1'):\n actions.arena(character)\n elif (choice == '2'):\n creature = dbmod.get_row('name','mouse','creatures')\n actions.print_status(character)\n elif (choice == '3'):\n actions.combat_victory(character['name'],actions.load_being('ROUS','creatures'))\n elif (choice == '4'):\n actions.home_store.enter()\n elif (choice == '5'):\n opponent = dbmod.get_row('name','mouse','creatures')\n print(opponent['name'])\n elif (choice == '6'):\n item_choice = actions.get_item(character['items'])\n actions.use_item(character,item_choice)\n actions.remove_item(character,item_choice)\n elif (choice == '9'):\n pass\n elif (choice == '0'):\n character=dbmod.get_row('name','bob','saves')\n character['items'] = character['items'].split(',')\n elif (choice == 'x'):\n exit()\n","repo_name":"Destom/GameEngine","sub_path":"dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"40605855789","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport sys\nsys.path.insert(0, \"../\")\nimport numpy as np\nimport pandas as pd\nimport time\nfrom collections import Counter\n#Preprocessing\nfrom process_data import parse_and_format\n#Keras\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n\nimport glob\nfrom categorical_focal_loss import SparseCategoricalFocalLoss\nfrom attention_class import MultiHeadSelfAttention #https://apoorvnandan.github.io/2020/05/10/transformer-classifier/\n\n\nimport pdb\n\n#Arguments for argparse module:\nparser = argparse.ArgumentParser(description = '''A program that reads a keras model from a .json and a .h5 file''')\nparser.add_argument('--variable_params', nargs=1, type= str, default=sys.stdin, help = 'Path to csv with variable params.')\nparser.add_argument('--param_combo', nargs=1, type= int, default=sys.stdin, help = 'Parameter combo.')\nparser.add_argument('--checkpointdir', nargs=1, type= str, default=sys.stdin, help = '''path checkpoints with .h5 files containing weights for net.''')\nparser.add_argument('--datadir', nargs=1, type= str, default=sys.stdin, help = 'Path to data directory.')\nparser.add_argument('--test_partition', nargs=1, type= int, default=sys.stdin, help = 'Which CV fold to test on.')\nparser.add_argument('--outdir', nargs=1, type= str, default=sys.stdin, help = '''path to output dir.''')\n\n\n#FUNCTIONS\n#####FUNCTIONS and CLASSES#####\nclass TokenAndPositionEmbedding(layers.Layer):\n def __init__(self, maxlen, vocab_size, embed_dim):\n super(TokenAndPositionEmbedding, self).__init__()\n self.token_emb = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)\n self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)\n\n def call(self, x):\n maxlen = tf.shape(x)[-1]\n positions = tf.range(start=0, limit=maxlen, delta=1)\n positions = self.pos_emb(positions)\n x = self.token_emb(x)\n return x + positions\n\nclass EncoderBlock(layers.Layer):\n def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n super(EncoderBlock, self).__init__()\n self.att = MultiHeadSelfAttention(embed_dim,num_heads)\n self.ffn = keras.Sequential(\n [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n )\n self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n self.dropout1 = layers.Dropout(rate)\n self.dropout2 = layers.Dropout(rate)\n\n def call(self, in_q,in_k,in_v, training): #Inputs is a list with [q,k,v]\n attn_output,attn_weights = self.att(in_q,in_k,in_v) #The weights are needed for downstream analysis\n attn_output = self.dropout1(attn_output, training=training)\n out1 = self.layernorm1(in_q + attn_output)\n ffn_output = self.ffn(out1)\n ffn_output = self.dropout2(ffn_output, training=training)\n return self.layernorm2(out1 + ffn_output), attn_weights\n\nclass DecoderBlock(layers.Layer):\n def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n super(DecoderBlock, self).__init__()\n self.att = MultiHeadSelfAttention(embed_dim,num_heads)\n self.ffn = keras.Sequential(\n [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n )\n self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n self.dropout1 = layers.Dropout(rate)\n self.dropout2 = layers.Dropout(rate)\n\n def call(self, in_q,in_k,in_v, training): #Inputs is a list with [q,k,v]\n #Self-attention\n attn_output1,attn_weights1 = self.att(in_q,in_q,in_q) #The weights are needed for downstream analysis\n attn_output1 = self.dropout1(attn_output1, training=training)\n out1 = self.layernorm1(in_v + attn_output1)\n #Encoder-decoder attention\n attn_output2,attn_weights2 = self.att(out1,in_k,in_v) #The weights are needed for downstream analysis\n attn_output2 = self.dropout1(attn_output2, training=training)\n out2 = self.layernorm1(attn_output2 + attn_output1)\n ffn_output = self.ffn(out2)\n ffn_output = self.dropout2(ffn_output, training=training)\n return self.layernorm2(out2 + ffn_output), attn_weights2\n\ndef create_model(maxlen, vocab_size, embed_dim,num_heads, ff_dim,num_layers,num_iterations):\n '''Create the transformer model\n '''\n\n seq_input = layers.Input(shape=(maxlen,)) #Input aa sequences\n seq_target = layers.Input(shape=(maxlen,)) #Targets - annotations\n kingdom_input = layers.Input(shape=(maxlen,4)) #4 kingdoms, Archaea, Eukarya, Gram +, Gram -\n\n ##Embeddings\n embedding_layer1 = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)\n embedding_layer2 = TokenAndPositionEmbedding(maxlen, 6, embed_dim+4) #Need to add 4 so that x1 and x2 match\n x1 = embedding_layer1(seq_input)\n #Add kingdom input\n x1 = layers.Concatenate()([x1,kingdom_input])\n x2 = embedding_layer2(seq_target)\n\n #Define the transformer\n encoder = EncoderBlock(embed_dim+4, num_heads, ff_dim)\n decoder = DecoderBlock(embed_dim+4, num_heads, ff_dim)\n #Iterate\n for i in range(num_iterations):\n #Encode\n for j in range(num_layers):\n x1, enc_attn_weights = encoder(x1,x1,x1) #q,k,v\n #Decoder\n for k in range(num_layers):\n x2, enc_dec_attn_weights = decoder(x2,x1,x1) #q,k,v - the k and v from the encoder goes into he decoder\n\n x2 = layers.Dense(6, activation=\"softmax\")(x2) #Annotate\n x_rs = layers.Reshape((maxlen,6))(x2)\n x2 = tf.math.argmax(x_rs,axis=-1) #Needed for iterative training\n x2 = embedding_layer2(x2)\n\n x2, enc_dec_attn_weights = decoder(x2,x1,x1) #q,k,v - the k and v from the encoder goes into he decoder\n preds = layers.Dense(6, activation=\"softmax\")(x2) #Annotate\n #preds = layers.Reshape((maxlen,6),name='annotation')(x2)\n #pred_type = layers.Dense(4, activation=\"softmax\",name='type')(x) #Type of protein\n #pred_cs = layers.Dense(1, activation=\"elu\", name='pred_cs')(x)\n\n\n model = keras.Model(inputs=[seq_input,seq_target,kingdom_input], outputs=preds)\n #Optimizer\n initial_learning_rate = 0.001\n lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n initial_learning_rate,\n decay_steps=10000,\n decay_rate=0.96,\n staircase=True)\n\n opt = tf.keras.optimizers.Adam(learning_rate = lr_schedule,amsgrad=True)\n\n #opt = keras.optimizers.Adam(learning_rate=0.001,amsgrad=True)\n #Compile\n model.compile(optimizer = opt, loss= SparseCategoricalFocalLoss(gamma=2), metrics=[\"accuracy\"])\n\n return model\n\ndef load_model(variable_params, param_combo, weights):\n #Params\n net_params = variable_params.loc[param_combo-1]\n #Fixed params\n vocab_size = 21 # Only consider the top 20k words\n maxlen = 70 # Only consider the first 70 amino acids\n #Variable params\n embed_dim = int(net_params['embed_dim']) #32 # Embedding size for each token\n num_heads = int(net_params['num_heads']) #1 # Number of attention heads\n ff_dim = int(net_params['ff_dim']) #32 # Hidden layer size in feed forward network inside transformer\n num_layers = int(net_params['num_layers']) #1 # Number of attention heads\n batch_size = int(net_params['batch_size']) #32\n num_iterations = int(net_params['num_iterations'])\n #Create model\n model = create_model(maxlen, vocab_size, embed_dim,num_heads, ff_dim,num_layers,num_iterations)\n model.load_weights(weights)\n\n #print(model.summary())\n return model\n\ndef get_data(datadir, valid_partition, maxlen):\n '''Get the validation data\n '''\n\n train_meta = pd.read_csv(datadir+'train_meta.csv')\n train_seqs = np.load(datadir+'seqs.npy',allow_pickle=True)\n train_annotations = np.load(datadir+'annotations.npy',allow_pickle=True)\n\n train_CS = train_meta.CS.values\n train_kingdoms = train_meta.Kingdom.values\n train_meta['Type'] = train_meta['Type'].replace({'NO_SP':0,'SP':1,'TAT':2,'LIPO':3})\n train_types = train_meta.Type.values\n #Onehot conversion\n train_kingdoms = np.eye(4)[train_kingdoms]\n\n #Get data\n valid_i = train_meta[train_meta.Partition==valid_partition].index\n #valid\n x_valid_seqs = train_seqs[valid_i]\n x_valid_kingdoms = train_kingdoms[valid_i]\n x_valid_kingdoms = np.repeat(np.expand_dims(x_valid_kingdoms,axis=1),70,axis=1)\n #Random annotations\n x_valid_target_inp = np.random.randint(6,size=(len(valid_i),maxlen))\n x_valid = [x_valid_seqs,x_valid_target_inp,x_valid_kingdoms]\n y_valid = [train_annotations[valid_i],train_types[valid_i]]\n\n return x_valid_seqs,x_valid_target_inp,x_valid_kingdoms, y_valid\n\ndef run_model(model,x_valid_seqs,x_valid_target_inp,x_valid_kingdoms):\n preds = model.predict([x_valid_seqs,x_valid_target_inp,x_valid_kingdoms])\n\n return preds\n\ndef get_pred_types(pred_annotations):\n '''Get the predicted types based on the annotations\n '''\n\n annotation_type_conversion = {0:1,1:2,2:3} #S(0)=SP(1), T(1)=TAT(2),L(2)=LIPO(3) - all other 0 (No SP)\n pred_types = []\n for i in range(len(pred_annotations)):\n if (0 in pred_annotations[i]) or (1 in pred_annotations[i]) or (2 in pred_annotations[i]):\n counts = Counter(pred_annotations[i])\n keys = [*counts.keys()]\n\n key_count=0 #Count the occurance of each annotation - take the max for the type\n key_type = 0\n for key in annotation_type_conversion: #Got through all keys\n if key not in keys:\n continue\n else:\n if counts[key]>key_count:\n key_count=counts[key]\n key_type=annotation_type_conversion[key]\n\n #Save\n pred_types.append(key_type)\n\n else:\n pred_types.append(0)\n\n return np.array(pred_types)\n\n\ndef eval_type_cs(pred_annotations,pred_annotation_probs,pred_types,true_annotations,true_types,kingdom):\n '''Evaluate the capacity to predict the clevage site\n annotation_conversion = {'S':0,'T':1,'L':2,'I':3,'M':4,'O':5}\n annotation [S: Sec/SPI signal peptide | T: Tat/SPI signal peptide | L: Sec/SPII signal peptide | I: cytoplasm | M: transmembrane | O: extracellular]\n S: Sec/SPI signal peptide | T: Tat/SPI signal peptide | L: Sec/SPII signal peptide |\n 'NO_SP':0,'SP':1,'TAT':2,'LIPO':3\n SP = Sec/SPI\n TAT = Tat/SPI\n LIPO = Sec/SPII\n\n Reported for CS:\n Recall, TPR = TP/P\n Precision, PPV = TP/(TP+FP)\n\n Reported for detection\n MCC = (TP*TN-FP*FN)/np.sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))\n '''\n\n Types = {'SP':1,'LIPO':3,'TAT':2}\n Signal_type_annotations = {'SP':0,'TAT':1,'LIPO':2} #S,T,L\n #Save\n fetched_types = []\n MCCs = []\n Recalls = []\n Precisions = []\n\n if kingdom == 'EUKARYA':\n Types = {'SP':1} #Only one type in Eukarya\n #Go through all types\n for type_name in Types:\n type_enc = Types[type_name]\n P = np.argwhere(true_types==type_enc)[:,0]\n if len(P)<1:\n continue\n N = np.argwhere(true_types!=type_enc)[:,0]\n #Calc TP and FP\n #Get the pred pos and neg\n pred_P = np.argwhere(pred_types==type_enc)[:,0]\n pred_N = np.argwhere(pred_types!=type_enc)[:,0]\n #TP and TN\n TP = np.intersect1d(P,pred_P).shape[0]\n if TP<1:\n continue\n FP = len(pred_P)-TP\n TN = np.intersect1d(N,pred_N).shape[0]\n FN= len(pred_N)-TN\n #MCC\n MCC = (TP*TN-FP*FN)/np.sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))\n\n #Get the CS\n type_annotation = Signal_type_annotations[type_name]\n #Get all true positive CSs\n P_annotations = true_annotations[np.intersect1d(P,pred_P)]\n P_CS = []\n for i in range(len(P_annotations)):\n P_CS.append(np.argwhere(P_annotations[i]==type_annotation)[-1,0])\n\n #Get all pred positive CSs from the true positives (all the other will be wrong)\n P_CS_pred = []\n P_annotations_pred = pred_annotations[np.intersect1d(P,pred_P)]\n P_annotation_probs_pred = pred_annotation_probs[np.intersect1d(P,pred_P)]\n for i in range(len(P_annotations_pred)):\n try:\n pdb.set_trace()\n P_CS_pred.append(np.argwhere(P_annotations_pred[i]==type_annotation)[-1,0])\n except:\n P_CS_pred.append(0)\n\n\n #Get the TP and FP CS\n TP_CS = {0:0,1:0,2:0,3:0} #exact CS, +/-1 error, +/-2 error, +/-3 error\n FP_CS = {0:0,1:0,2:0,3:0}\n for i in range(len(P_CS)):\n CS_diff = P_CS[i]-P_CS_pred[i]\n for d in range(0,4):\n if CS_diff<=d and CS_diff>=-d:\n TP_CS[d]+=1\n else:\n FP_CS[d]+=1\n\n #Add the FPs from the wrong detection\n for d in range(0,4):\n FP_CS[d] += FP\n\n #Calculate CS precision and recall\n CS_precision = {}\n CS_recall = {}\n\n for d in range(0,4):\n try:\n CS_precision[d]=TP_CS[d]/(TP_CS[d]+FP_CS[d])\n CS_recall[d] = TP_CS[d]/P.shape[0]\n except:\n pdb.set_trace()\n\n\n #Save\n fetched_types.append(type_name)\n MCCs.append(MCC)\n Precisions.append([*CS_precision.values()])\n Recalls.append([*CS_recall.values()])\n\n\n return fetched_types, MCCs, Precisions, Recalls\n\n\n######################MAIN######################\nargs = parser.parse_args()\nvariable_params=pd.read_csv(args.variable_params[0])\nparam_combo=args.param_combo[0]\ncheckpointdir = args.checkpointdir[0]\ndatadir = args.datadir[0]\ntest_partition = args.test_partition[0]\noutdir = args.outdir[0]\n\nkingdom_conversion = {'ARCHAEA':0,'NEGATIVE':2,'POSITIVE':3,'EUKARYA':1}\n#Load and run model\nall_pred_annotations = []\nall_pred_annotation_probs = []\nall_true_annotations = []\nall_true_types = []\nall_kingdoms = []\n\n#Get data for each valid partition\nfor valid_partition in np.setdiff1d(np.arange(5),test_partition):\n #weights\n weights=glob.glob(checkpointdir+'vp'+str(valid_partition)+'/*.hdf5')\n if len(weights)<1:\n continue\n #model\n model = load_model(variable_params, param_combo, weights[0])\n #Get data\n x_valid_seqs,x_valid_target_inp,x_valid_kingdoms, y_valid = get_data(datadir, valid_partition,70)\n #Predict\n preds = run_model(model,x_valid_seqs,x_valid_target_inp,x_valid_kingdoms)\n\n #Fetch\n pred_annotations = np.argmax(preds,axis=2)\n true_annotations = y_valid[0]\n true_types = y_valid[1]\n kingdoms = np.argmax(x_valid_kingdoms[:,0,:],axis=1)\n #Save\n all_pred_annotations.extend([*pred_annotations])\n all_pred_annotation_probs.extend([*preds])\n all_true_types.extend([*true_types])\n all_true_annotations.extend([*true_annotations])\n all_kingdoms.extend([*kingdoms])\n\n\n\n#Array conversions\nall_pred_annotations = np.array(all_pred_annotations) #The type will be fetched from the annotations\nall_pred_annotation_probs = np.array(all_pred_annotation_probs)\nall_true_annotations = np.array(all_true_annotations)\nall_true_types = np.array(all_true_types)\nall_kingdoms = np.array(all_kingdoms)\n#Get pred types based on pred annotations\nall_pred_types = get_pred_types(all_pred_annotations)\n#Evaluate per kingdom\nevaluated_kingdoms = []\nall_types = []\nall_MCCs = []\nall_precisions = []\nall_recalls = []\n\nfor key in kingdom_conversion:\n kingdom_indices = np.argwhere(all_kingdoms==kingdom_conversion[key])[:,0]\n #Get pred\n kingdom_pred_annotations = all_pred_annotations[kingdom_indices]\n kingdom_pred_annotation_probs = all_pred_annotation_probs[kingdom_indices]\n kingdom_pred_types = all_pred_types[kingdom_indices]\n #Get true\n kingdom_true_annotations = all_true_annotations[kingdom_indices]\n kingdom_true_types = all_true_types[kingdom_indices]\n #Eval\n fetched_types, MCCs, Precisions, Recalls = eval_type_cs(kingdom_pred_annotations,kingdom_pred_annotation_probs,kingdom_pred_types,kingdom_true_annotations,kingdom_true_types,key)\n\n #Save\n evaluated_kingdoms.extend([key]*len(fetched_types))\n all_types.extend(fetched_types)\n all_MCCs.extend(MCCs)\n all_precisions.extend(Precisions)\n all_recalls.extend(Recalls)\n\n#Create df\neval_df = pd.DataFrame()\neval_df['Kingdom']=evaluated_kingdoms\neval_df['Type']=all_types\neval_df['MCC']=all_MCCs\neval_df['Recall']=all_recalls\neval_df['Precision']=all_precisions\n\neval_df.to_csv(outdir+'eval_df'+str(test_partition)+'.csv')\nprint(eval_df)\n","repo_name":"patrickbryant1/TransPep","sub_path":"model/validation/run_trained.py","file_name":"run_trained.py","file_ext":"py","file_size_in_byte":16650,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"6110611292","text":"#!~/anaconda3/bin/python\n\nimport datetime\nimport pandas as pd\nimport numpy as np\nfrom ast import literal_eval\nimport math as m\n\n\n# for extracting time from db date\ndef extract_time(series):\n temp = pd.DatetimeIndex(series)\n return temp.time\n\n\n# for extracting date from db date\ndef extract_date(series):\n temp = pd.DatetimeIndex(series)\n return temp.date\n\n\n# for converting a string date to datetime\ndef convert_date(string_date):\n d = datetime.datetime.strptime(string_date, '%Y-%m-%d').date()\n return d\n\n\n# for extracting yesterday's date\ndef yesterday_date(n):\n ydate = datetime.date.fromordinal(datetime.date.today().toordinal() - n)\n return ydate\n\n\n# for extracting biweekly date\ndef biweekly_date(start_date, n):\n bdate = datetime.date.fromordinal(start_date.toordinal() - n)\n return bdate\n\n\n# for getting previous date\ndef diff_date(pdate, n):\n previous_date = datetime.date.fromordinal(pdate.toordinal() - n)\n return previous_date\n\n\n# for getting future date\ndef add_date(fdate, n):\n future_date = datetime.date.fromordinal(fdate.toordinal() + n)\n return future_date\n\n\n# for getting unique values from a column\ndef unique_values(series):\n new_series = series.unique()\n return new_series\n\n\n# for getting values from a column\ndef getting_values(series):\n new_series = list(series.get_values())\n if len(new_series) == 1:\n new_series = new_series[len(new_series)-1]\n return new_series\n else:\n return new_series\n\n\n# for extracting information between specific dates\ndef extract_range_information(df, column, start_value, end_value):\n df = df[(df[column] >= start_value) & (df[column] <= end_value)]\n return df\n\n\n# Extract information at a specific value\ndef extract_single_information(df, column, value):\n df = df[df[column] == value]\n return df\n\n\n# Calculating stats: total count\ndef total_stats(df, column_name):\n total_df = len(df[column_name].unique())\n return total_df\n\n\n# yesterday's stats\ndef yesterday_stats(df, column_name):\n ydate = yesterday_date(n=1)\n yesterday_count = len(df[df[column_name] == ydate])\n return yesterday_count\n\n\n# average over some number\ndef average_stats(df, avg_n):\n count = len(df)\n average = round(count/avg_n,2)\n return average\n\n\n# getting the last value of a dataframe\ndef last_value(df, column_name, n):\n value = df[column_name].tail(n).get_values()\n if len(value) == 1:\n value = value[len(value)-1]\n return value\n\n\n# extracting calender dates\ndef cal_dates(start_date, end_date):\n dates = pd.DataFrame({'Calender Dates': pd.date_range(start=start_date, end=end_date)})\n temp = pd.DatetimeIndex(dates['Calender Dates'])\n dates['Dates'] = temp.date\n return dates\n\n\ndef cal_dates_count(start_date, end_date):\n dates = pd.DataFrame({'Calender Dates': pd.date_range(start=start_date, end=end_date)})\n temp = pd.DatetimeIndex(dates['Calender Dates'])\n dates['Dates'] = temp.date\n return len(dates)\n\n\n# per day count\ndef per_day_count(date_series, df, column_date, count_column):\n count = []\n Date = []\n ids = []\n\n for d in date_series:\n df_d = df[df[column_date] == d]\n consumer_id = df_d['Consumer ID'].unique()\n df_one = np.sum(df_d[count_column])\n ids.append(consumer_id)\n if df_one != 0:\n count_per_day = df_one\n count.append(count_per_day)\n else:\n count_per_day = 0\n count.append(count_per_day)\n Date.append(d)\n\n df_new = pd.DataFrame({'Dates': Date, 'Count/day': count, 'Consumer IDs': ids})\n return df_new\n\n\n# convert string series into numeric\ndef convert_literal(series):\n ids = (series).apply(literal_eval).sum()\n return ids\n\n\n# Convert timestamp to date\ndef convert_timestamp(temp):\n temp = pd.to_datetime(temp)\n temp = temp.to_pydatetime(temp)\n temp = temp.date()\n return temp\n\n\n# Returns a dataframe calculating frequencies\ndef dataframe_frequencies(df, column1, column2):\n df_new = pd.DataFrame({column2 + '_frequency':\n df.groupby([column1, column2]).size()}).reset_index()\n return df_new\n\n\n# Returns merged dataframe\ndef dataframe_merging(df1, df2, column1, column2):\n df_new = pd.merge(df1, df2, how='left', left_on=[column1, column2], right_on=[column1, column2])\n return df_new\n\n\n# Scaled Ratings\ndef scaled_ratings(implicit_rating, a, b):\n\n x = []\n for i in range(0, len(implicit_rating)):\n x_new = a + ((implicit_rating[i] - np.min(implicit_rating)) * (b - a)) / (\n np.max(implicit_rating) - np.min(implicit_rating))\n x_new = round(x_new, 2)\n if m.isnan(x_new) and np.min(implicit_rating) == np.max(implicit_rating):\n x.append(b)\n else:\n x.append(x_new)\n return x\n\n\n# This function takes in dataframe with coupon views and outputs the raw implicit ratings\n# based on frequencies of selected criterias\ndef implicit_ratings(df_coupon, df_store):\n\n # Calculating frequencies for each criteria from same table\n df_user_coupon = dataframe_frequencies(df_coupon, 'user_id', 'coupon_id')\n df_coupon_category = dataframe_frequencies(df_coupon, 'user_id', 'category_id')\n df_coupon_discount = dataframe_frequencies(df_coupon, 'user_id', 'discount')\n df_coupon_retailer = dataframe_frequencies(df_coupon, 'user_id', 'retailer_id')\n df_coupon_store = dataframe_frequencies(df_store, 'user_id', 'retailer_id')\n\n # extract suitable ids from implicit ratings\n df_implicit_ratings = df_coupon[['user_id', 'coupon_id', 'category_id', 'retailer_id', 'discount']]\n df_implicit_ratings = df_implicit_ratings.drop_duplicates()\n\n # Merge frequencies and corresponding ids\n df_implicit_ratings = dataframe_merging(df_implicit_ratings, df_user_coupon, 'user_id', 'coupon_id')\n df_implicit_ratings = dataframe_merging(df_implicit_ratings, df_coupon_category, 'user_id', 'category_id')\n df_implicit_ratings = dataframe_merging(df_implicit_ratings, df_coupon_discount, 'user_id', 'discount')\n df_implicit_ratings = dataframe_merging(df_implicit_ratings, df_coupon_store, 'user_id', 'retailer_id')\n df_implicit_ratings = dataframe_merging(df_implicit_ratings, df_coupon_retailer, 'user_id', 'retailer_id')\n\n # Filling zeros where frequencies are not available\n df_implicit_ratings.fillna(0, inplace=True)\n\n # Adding retailer frequencies because it is available both from coupon views and coupon ratings\n df_implicit_ratings['retailer_frequency'] = df_implicit_ratings['retailer_id_frequency_x'] + df_implicit_ratings[\n 'retailer_id_frequency_y']\n\n # Getting coupon ratings\n df_implicit_ratings = df_implicit_ratings[['user_id', 'coupon_id', 'coupon_id_frequency',\n 'category_id_frequency', 'discount_frequency',\n 'retailer_frequency']]\n\n # Taking average of retailer frequency\n df_implicit_ratings = df_implicit_ratings.groupby(['user_id', 'coupon_id', 'coupon_id_frequency',\n 'category_id_frequency',\n 'discount_frequency']).mean().reset_index()\n\n # Taking average of frequencies\n df_implicit_ratings['implicit_rating'] = df_implicit_ratings[['coupon_id_frequency', 'category_id_frequency',\n 'discount_frequency', 'retailer_frequency']].mean(axis=1)\n return df_implicit_ratings\n\n","repo_name":"sarwatfatimam/recommender-system","sub_path":"dealsmash.py","file_name":"dealsmash.py","file_ext":"py","file_size_in_byte":7554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"15195798369","text":"import sys\r\n\r\nn, m = map(int, input().split())\r\ned = [[] for _ in range(n)]\r\nv = [None] * n\r\nfor _ in range(m):\r\n\tl, r, d = map(int, input().split())\r\n\ted[l-1].append([r-1, d])\r\n\ted[r-1].append([l-1, -d])\r\n\r\nvisited = [False] * n\r\n\r\ndef dfs(start):\r\n\tglobal ed, v, visited\r\n\ts = [start]\r\n\tv[start] = 0\r\n\twhile s:\r\n\t\tlabel = s.pop()\r\n\t\tif visited[label] == False:\r\n\t\t\tvisited[label] = True\r\n\t\t\tfor e in ed[label]:\r\n\t\t\t\tif v[e[0]] is None:\r\n\t\t\t\t\tv[e[0]] = v[label] + e[1]\r\n\t\t\t\t\ts.append(e[0])\r\n\t\t\t\telse:\r\n\t\t\t\t\tif v[e[0]] != v[label] + e[1]:\r\n\t\t\t\t\t\tprint(\"No\")\r\n\t\t\t\t\t\tsys.exit()\r\n\treturn\r\n\r\nfor i in range(n):\r\n\tif v[i] is None:\r\n\t\tdfs(i)\r\n\r\nprint(\"Yes\")","repo_name":"Kawser-nerd/CLCDSA","sub_path":"Source Codes/AtCoder/arc090/B/3897504.py","file_name":"3897504.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"72"}
+{"seq_id":"41109604195","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n # user login and authentication endpoints\n path('',views.UserView.as_view(),name=\"user\"),\n path('register',views.RegisterView.as_view(),name=\"register\"),\n path('login',views.LoginView.as_view(),name=\"login\"),\n path('logout',views.LogoutView.as_view(),name=\"logout\"),\n\n # product endpoint\n path('product',views.ProductView.as_view(),name=\"product\"),\n\n # category endpoint\n path('category',views.CategoryView.as_view(),name=\"category\"),\n\n # order endpoint\n path('order',views.OrderView.as_view(),name=\"order\"),\n]","repo_name":"code002-ZeroTwo/c2cecom","sub_path":"c2c/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"38079302879","text":"#탐 : 당장의 결정이 향후의 수를 줄여줌\n#엘 : LIS(bisect)\n#이 : 이진트리로 구조화 시켜야하는경우(이진탐색트리)\n#디 : 전체루트를 한바퀴 검색하는경우\n#비 : 한루트를 끝까지 검새하는경우\n#다 : 캐싱이 반복적으로 사용되는경우\n#그 : 기타 그래프 이론(사이클관련)\n # 다익스트라\n # 플로이드워셜\n # 크루스칼\n\n#Idea\n#len(LIS) 와 그 LIS를 역추적한것의 경우의수 + 시작점\n#Code\nN = int(input());\nXY = [];\nfor i in range(N):\n XY.append(tuple(map(int,input().split(\" \"))))\n\nXY.sort(key=lambda x:(x[0],x[1]));\nsumForXY = [];\nfor i in XY :\n sumForXY.append(i[0]+i[1]);\nprint(XY);\nprint(sumForXY)\n#Case\n'''\n11\n8 6\n7 4\n5 4\n5 1\n5 6\n6 2\n3 2\n4 3\n4 5\n3 5\n2 4\n>>>\n4\n3\n'''","repo_name":"ysk0951/algorithm","sub_path":"2022/LIS/3133.py","file_name":"3133.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"9507502845","text":"# Pins\nMOTOR1PIN1 = 26 # BOARD 37\nMOTOR1PIN2 = 19 # BOARD 35\nMOTOR1PIN3 = 13 # BOARD 33\nMOTOR1PIN4 = 6 # BOARD 31\n\nMOTOR2PIN1 = 21 # BOARD 40\nMOTOR2PIN2 = 20 # BOARD 38\nMOTOR2PIN3 = 16 # BOARD 36\nMOTOR2PIN4 = 12 # BOARD 32\n\nLEDPIN = 25 # BOARD 22\n\nSTART_CLEANING_PIN = 1 # BOARD 27\nDONE_CLEANING_PIN = 0 # BOARD 28\nSTOP_PIN = 5 # BOARD 29\n# BOARD 30 COMMON GND\n\nMOTOR1 = [MOTOR1PIN1, MOTOR1PIN2, MOTOR1PIN3, MOTOR1PIN4] # LONG \nMOTOR2 = [MOTOR2PIN1, MOTOR2PIN2, MOTOR2PIN3, MOTOR2PIN4] # SHORT\n\n# Colors\nPOSITIVE_GREEN = \"#6eff56\"\nNEGATIVE_RED = \"#ff3333\"\nCONFUSED_YELLOW = \"#fcff56\"\n\n# Distance\nHALF_STEPS_TO_1CM = 48.75\nGRID_SIDE_LENGTH = 3.2\nMAX_MOVES = 5\n","repo_name":"wilburlua910/CSC3003_OIP","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"12412175603","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 28 09:17:50 2022\n\n@author: storm\n\"\"\"\n\nimport json\nimport csv \nimport pandas as pd\n\ndata_business = []\n\n#10k lines - D:/Documents - HDD/GitHub/F22-DV4-03/Test for yelp datasets/yelp_academic_dataset_business_10000lines.json\n#full data - D:/ONEDRIVE_FREE_FOLDER/Uni-Datavidenskab/YELP/yelp_academic_dataset_business.json\n\nwith open('D:/ONEDRIVE_FREE_FOLDER/Uni-Datavidenskab/YELP/yelp_academic_dataset_business.json', encoding=\"utf8\") as f:\n for line in f:\n data_business.append(json.loads(line))\n \n\n\n\n\n\ndef string_seperate(string):\n n = 0\n cate_list = []\n string = string.replace('\"','')\n string = string.replace('}','')\n string = string.replace('{','')\n a_list = string.split(\", \")\n for element in a_list:\n cate_list.append(element)\n n = n +1\n return cate_list\n\n\n\n#Counts how many of each attribute that exists\n# mydict = {}\n# n = 0\n# attribute_list = []\n# value_list = [] \n# for i in data_business:\n# if data_business[n]['attributes'] != None:\n# keys, values = zip(*data_business[n]['attributes'].items())\n# for element in keys:\n# mydict[element] = mydict.get(element, 0) + 1\n# n = n + 1\n# if n % 1000 == 0:\n# print(n) \n\n# print(mydict)\n# df = pd.DataFrame(list(mydict.items()),columns = ['Attribute','Amount'])\n\n# df_sorted = df.sort_values(by='Amount', ascending=False)\n\n# df_sorted.to_csv('amount_of_attributes.csv', index=False) \n\n\n \n# n = 0\n# big_string = \"\"\n# for i in data_business:\n# if data_business[n]['attributes'] != None:\n# big_string = big_string + str(data_business[n]['attributes']) \n# n = n + 1\n# if n % 1000 == 0:\n# print(n) \n\n# print(big_string)\n\n# values = data_business[11]['attributes']['Ambience']['hipster']\n\n# print(values)\n# Counts how many of each attribute that exists\nmydict = {}\nn = 0\nattribute_list = []\nvalue_list = [] \nfor i in data_business:\n try:\n catagory_list = string_seperate(data_business[n]['attributes']['DietaryRestrictions'])\n for element in catagory_list:\n mydict[element] = mydict.get(element, 0) + 1\n n = n + 1\n if n % 1000 == 0:\n print(str(round(n/1500,2))+\"% done\")\n except:\n n = n + 1\n if n % 1000 == 0:\n print(str(round(n/1500,2))+\"% done\")\n \ndf = pd.DataFrame(list(mydict.items()),columns = ['Atmosphere','Amount'])\n\ndf_sorted = df.sort_values(by='Amount', ascending=False)\n\ndf_sorted.to_csv('DietaryRestrictions.csv', index=False) ","repo_name":"AAU-WebDataScience/F22-DV4-03","sub_path":"misc/count_attributes.py","file_name":"count_attributes.py","file_ext":"py","file_size_in_byte":2561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"70143552553","text":"#!/usr/bin/env python3\nfrom soupsieve import select\nimport math\nimport rclpy\nfrom functools import partial\nfrom rclpy.node import Node\nfrom turtlesim.msg import Pose\nfrom geometry_msgs.msg import Twist\nfrom my_robot_interfaces.msg import Turtle\nfrom my_robot_interfaces.msg import TurtleArray\nfrom my_robot_interfaces.srv import CatchTurtle\n\nclass TurtleControllerNode(Node): # MODIFY NAME\n def __init__(self):\n super().__init__(\"turtle_controller\") # MODIFY NAME\n self.turtle_to_catch = None\n #self.x_target = 4.0\n #self.y_target = 8.0\n \n self.pose_ = None\n self.pose_subscriber_ = self.create_subscription(\n Pose, \"turtle1/pose\", self.callback_turtle_pose, 10)\n \n self.alive_turtle_subscriber_ = self.create_subscription(\n TurtleArray, \"alive_turtles\", self.callback_alive_turtles, 10)\n self.cmd_vel_publisher_ = self.create_publisher(Twist, \"/turtle1/cmd_vel\", 10)\n\n self.control_loo_timer_ = self.create_timer(0.01, self.control_loop)\n\n def callback_turtle_pose(self, msg):\n self.pose_ = msg\n \n def callback_alive_turtles(self, msg):\n if len(msg.turtles)>0:\n self.turtle_to_catch = msg.turtles[0]\n \n\n def control_loop(self):\n if self.pose_ == None or self.turtle_to_catch == None:\n return\n \n x_dist = self.turtle_to_catch.x - self.pose_.x\n y_dist = self.turtle_to_catch.y - self.pose_.y\n target_dist = math.sqrt(x_dist * x_dist + y_dist * y_dist)\n\n msg = Twist()\n if target_dist > 0.5:\n #pose\n msg.linear.x = 2 * target_dist # tuning the distance by multiplying with 2 for better path \n\n #orientation\n target_theta = math.atan2(y_dist, x_dist)\n diff = target_theta - self.pose_.theta\n\n #normalize the angle\n if diff > math.pi:\n diff -= 2 * math.pi\n elif diff < -math.pi:\n diff += 2 * math.pi\n\n msg.angular.z = 6 * diff # tuning the angle by multiplying with 6 for better path \n \n\n else:\n #target reached \n msg.linear.x = 0.0\n msg.angular.z = 0.0\n self.call_catch_turtle_server(self.turtle_to_catch.name)\n self.turtle_to_catch = None\n\n self.cmd_vel_publisher_.publish(msg)\n\n def call_catch_turtle_server(self, turtle_name):\n client = self.create_client(CatchTurtle, \"catch_turtle\")\n while not client.wait_for_service(1.0):\n self.get_logger().warn(\"Waiting for Server...\")\n\n request = CatchTurtle.Request()\n request.name = turtle_name\n\n future = client.call_async(request)\n future.add_done_callback(\n partial(self.callback_call_catch_turtle, turtle_name=turtle_name))\n\n def callback_call_catch_turtle(self, future, turtle_name):\n try:\n response = future.result()\n if not response.success:\n self.get_logger().error(\"Turtle\" + str(turtle_name) + \"could not be caught\")\n\n except Exception as e:\n self.get_logger().error(\"Service call failed %r\" % (e,))\n\n\n\ndef main(args=None):\n rclpy.init(args=args)\n node = TurtleControllerNode() # MODIFY NAME\n rclpy.spin(node)\n rclpy.shutdown()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"chch1019/turtlesimsim_catch_them_all_py","sub_path":"turtlesimsim_catch_them_all_py/turtle_controller.py","file_name":"turtle_controller.py","file_ext":"py","file_size_in_byte":3385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"11491612059","text":"class Solution:\n # @param A : list of integers\n # @param B : integer\n # @return an integer\n def BinarySearch(self, A, l, r, b, order=1):\n while l <= r:\n mid = l + (r-l+1)//2\n if A[mid] == b:\n return mid\n elif A[mid] > b:\n if order == 1:\n r = mid - 1\n else:\n l = mid + 1\n else:\n if order == 1:\n l = mid + 1\n else:\n r = mid - 1\n return -1\n\n def BitonicSearch(self, arr, l, r):\n while l <= r:\n mid = l + (r-l+1)//2\n if arr[mid] > arr[mid-1]:\n ans = mid\n l = mid + 1\n else:\n r = mid - 1\n return ans\n\n def solve(self, A, B):\n\n if A[0] == B:\n return 0\n if A[-1] == B:\n return len(A) - 1\n\n start = 0\n end = len(A)-1\n\n bitonic_index = self.BitonicSearch(A, 0, len(A)-1)\n sa1 = sa2 = -1\n sa1 = self.BinarySearch(A, 0, bitonic_index, B)\n sa2 = self.BinarySearch(A, bitonic_index+1, len(A)-1, B, -1)\n return max(sa1, sa2)\n\n\nif __name__ == '__main__':\n s = Solution()\n A = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11]\n B = 12\n print(s.solve(A, B))\n","repo_name":"akashdeep3194/Scaler","sub_path":"d48/Search in Bitonic Array!.py","file_name":"Search in Bitonic Array!.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"83943269","text":"import logging\n\nimport yatest.common\n\nfrom crypta.lib.python.yt import schema_utils\nfrom crypta.lib.python.yt.test_helpers import (\n tables,\n tests,\n)\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_app_info_schema():\n return schema_utils.yt_schema_from_dict({\n \"BundleId\": \"string\",\n \"PlatformID\": \"uint64\",\n \"RegionName\": \"string\",\n \"Title\": \"string\",\n \"VendorName__raw\": \"string\",\n })\n\n\ndef get_apps_clustering_schema():\n return schema_utils.yt_schema_from_dict({\n \"app_id\": \"uint64\",\n \"bundle_id\": \"string\",\n \"cluster_id\": \"uint64\",\n \"devids_count\": \"uint64\",\n \"id_type\": \"string\",\n })\n\n\ndef test_build_apps_for_suggester(clean_local_yt, config, config_file, local_yt_and_yql_env):\n return tests.yt_test(\n yt_client=clean_local_yt.get_yt_client(),\n binary=yatest.common.binary_path(\"crypta/siberia/bin/custom_audience/build_apps_for_suggester/bin/crypta-siberia-custom-audience-build-apps-for-suggester\"),\n args=[\n \"--config\", config_file,\n ],\n data_path=yatest.common.test_source_path(\"data\"),\n input_tables=[\n (tables.get_yson_table_with_schema(\n 'app_info.yson',\n config.RmpAppsPath,\n schema=get_app_info_schema(),\n ), (tests.TableIsNotChanged())),\n (tables.get_yson_table_with_schema(\n 'apps_clustering.yson',\n config.AppsClusteringPath,\n schema=get_apps_clustering_schema(),\n ), (tests.TableIsNotChanged())),\n ],\n output_tables=[\n (tables.YsonTable('apps.yson', config.OutputPath, yson_format=\"pretty\"), [tests.Diff()]),\n ],\n env=local_yt_and_yql_env,\n )\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"crypto/test/main (79).py","file_name":"main (79).py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"15757833702","text":"from mesa.visualization.ModularVisualization import ModularServer\nfrom mesa.visualization.UserParam import UserSettableParameter\nfrom mesa.visualization.modules import CanvasGrid, ChartModule\n\nfrom src.money_model.model import MoneyModel\n\n\ndef agent_portrayal(agent):\n \"\"\"\n Rendering portrayal of an agent\n :param agent: agent to render\n :return: portrayal dict\n \"\"\"\n portrayal = {\"Shape\": \"circle\",\n \"Filled\": \"true\"\n }\n\n if agent.wealth > 0:\n portrayal[\"Color\"] = \"red\"\n portrayal[\"Layer\"] = 0\n portrayal[\"r\"] = 0.5\n else:\n portrayal[\"Color\"] = \"grey\"\n portrayal[\"Layer\"] = 1\n portrayal[\"r\"] = 0.2\n\n return portrayal\n\n\n# canvas grid with dimensions 10 x 10, drawn in 500 x 500 pixels\ngrid = CanvasGrid(agent_portrayal, 10, 10, 500, 500)\n\n# plot gini\nchart = ChartModule([{\"Label\": \"Gini\",\n \"Color\": \"black\"\n }],\n data_collector_name=\"datacollector\"\n )\n\n# user-definable number of agents\n# parameter changes don't take place until the model is reset\nn_slider = UserSettableParameter(\"slider\", \"Number of Agents\", 100, 2, 200, 1)\n\n# create server\nserver = ModularServer(MoneyModel,\n [grid, chart],\n \"Money Model\",\n {\"N\": n_slider, \"width\": 10, \"height\": 10})\nserver.port = 8521 # default\nserver.launch()\n","repo_name":"SZanlongo/mesa-tutorial","sub_path":"src/money_model/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"24571050929","text":"import os\nimport logging.config\nfrom tools.config_loader import conf\nfrom datetime import datetime\nfrom config.settings import Settings\n\n\nstandard_format = '[%(asctime)s] [%(levelname)s] [%(threadName)s] [%(name)s] | %(message)s'\nsimple_format = '[%(asctime)s] [%(levelname)s] [%(filename)s:%(lineno)d] | %(message)s'\n\nlogfile_dir = os.path.join(Settings.base_dir, 'log')\nlogfile_name = 'log{0}.log'.format(datetime.now().strftime('%Y-%m-%d'))\nos.makedirs(logfile_dir) if not os.path.isdir(logfile_dir) else ...\nlogfile_path = os.path.join(logfile_dir, logfile_name)\n\n\nLOGGING_DIC = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'standard': {\n 'format': standard_format\n },\n 'simple': {\n 'format': simple_format\n },\n },\n 'filters': {}, # filter可以不定义\n 'handlers': {\n # 打印到终端的日志\n 'console': {\n 'level': conf.get(\"terminal.level\"),\n 'class': 'logging.StreamHandler', # 打印到屏幕\n 'formatter': 'simple'\n },\n # 打印到文件的日志,收集info及以上的日志\n 'default': {\n 'level': conf.get(\"logger.level\"),\n 'class': 'logging.handlers.RotatingFileHandler',\n 'formatter': 'standard',\n 'filename': logfile_path,\n 'maxBytes': 1024 * 1024 * 100, # 日志大小100M (*****)\n 'backupCount': 5,\n 'encoding': 'utf-8',\n },\n },\n 'loggers': {\n '': {\n 'handlers': ['default', 'console'],\n 'level': conf.get(\"logger.level\").upper(),\n 'propagate': False, # 向上（更高level的logger）传递\n },\n },\n}\n\n\nlogging.config.dictConfig(LOGGING_DIC) # 导入上面定义的logging配置\nlogger = logging.getLogger(__name__) # 生成一个log实例\n\n\n","repo_name":"Zhangwenke-git/execution-engine","sub_path":"tools/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"22596222396","text":"# -*- coding: utf-8 -*-\n\n# opt_utils.py\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sklearn\nimport sklearn.datasets\nimport operator\nfrom functools import reduce\n\nimport numpy as np\n\ndef sigmoid(Z):\n \"\"\"\n Implements the sigmoid activation in numpy\n\n Arguments:\n Z -- numpy array of any shape\n\n Returns:\n A -- output of sigmoid(z), same shape as Z\n cache -- returns Z as well, useful during backpropagation\n \"\"\"\n\n A = 1/(1+np.exp(-Z))\n cache = Z\n\n return A, cache\n\ndef sigmoid_backward(dA, cache):\n \"\"\"\n Implement the backward propagation for a single SIGMOID unit.\n\n Arguments:\n dA -- post-activation gradient, of any shape\n cache -- 'Z' where we store for computing backward propagation efficiently\n\n Returns:\n dZ -- Gradient of the cost with respect to Z\n \"\"\"\n\n Z = cache\n\n s = 1/(1+np.exp(-Z))\n dZ = dA * s * (1-s)\n\n assert (dZ.shape == Z.shape)\n\n return dZ\n\ndef relu(Z):\n \"\"\"\n Implement the RELU function.\n\n Arguments:\n Z -- Output of the linear layer, of any shape\n\n Returns:\n A -- Post-activation parameter, of the same shape as Z\n cache -- a python dictionary containing \"A\" ; stored for computing the backward pass efficiently\n \"\"\"\n\n A = np.maximum(0,Z)\n\n assert(A.shape == Z.shape)\n\n cache = Z\n return A, cache\n\ndef relu_backward(dA, cache):\n \"\"\"\n Implement the backward propagation for a single RELU unit.\n\n Arguments:\n dA -- post-activation gradient, of any shape\n cache -- 'Z' where we store for computing backward propagation efficiently\n\n Returns:\n dZ -- Gradient of the cost with respect to Z\n \"\"\"\n\n Z = cache\n dZ = np.array(dA, copy=True) # just converting dz to a correct object.\n\n # When z <= 0, you should set dz to 0 as well.\n dZ[Z <= 0] = 0\n\n assert (dZ.shape == Z.shape)\n\n return dZ\n\n\n\n\n\ndef initialize_parameters(layer_dims):\n\n\n np.random.seed(3)\n parameters = {}\n L = len(layer_dims) # number of layers in the network\n\n for l in range(1, L):\n parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l - 1]) * np.sqrt(2 / layer_dims[l - 1])\n parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))\n\n # assert (parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l - 1])\n # assert (parameters['W' + str(l)].shape == layer_dims[l], 1)\n\n return parameters\n\n\ndef linear_activation_forward(A_prev, W, b, activation):\n \"\"\"\n 实现LINEAR-> ACTIVATION 这一层的前向传播\n\n 参数：\n A_prev - 来自上一层（或输入层）的激活，维度为(上一层的节点数量，示例数）\n W - 权重矩阵，numpy数组，维度为（当前层的节点数量，前一层的大小）\n b - 偏向量，numpy阵列，维度为（当前层的节点数量，1）\n activation - 选择在此层中使用的激活函数名，字符串类型，【\"sigmoid\" | \"relu\"】\n\n 返回：\n A - 激活函数的输出，也称为激活后的值\n cache - 一个包含“linear_cache”和“activation_cache”的字典，我们需要存储它以有效地计算后向传递\n \"\"\"\n\n if activation == \"sigmoid\":\n Z, linear_cache = linear_forward(A_prev, W, b)\n A, activation_cache = sigmoid(Z)\n elif activation == \"relu\":\n Z, linear_cache = linear_forward(A_prev, W, b)\n A, activation_cache = relu(Z)\n\n assert (A.shape == (W.shape[0], A_prev.shape[1]))\n cache = (linear_cache, activation_cache)\n\n return A, cache\n\n\ndef linear_forward(A, W, b):\n \"\"\"\n 实现前向传播的线性部分。\n\n 参数：\n A - 来自上一层（或输入数据）的激活，维度为(上一层的节点数量，示例的数量）\n W - 权重矩阵，numpy数组，维度为（当前图层的节点数量，前一图层的节点数量）\n b - 偏向量，numpy向量，维度为（当前图层节点数量，1）\n\n 返回：\n Z - 激活功能的输入，也称为预激活参数\n cache - 一个包含“A”，“W”和“b”的字典，存储这些变量以有效地计算后向传递\n \"\"\"\n Z = np.dot(W, A) + b\n assert (Z.shape == (W.shape[0], A.shape[1]))\n cache = (A, W, b)\n\n return Z, cache\n\n\ndef forward_propagation(X, parameters):\n caches = []\n A = X\n L = len(parameters) // 2\n for l in range(1, L):\n A_prev = A\n A, cache = linear_activation_forward(A_prev, parameters['W' + str(l)], parameters['b' + str(l)], \"relu\")\n caches.append(cache)\n\n AL, cache = linear_activation_forward(A, parameters['W' + str(L)], parameters['b' + str(L)], \"sigmoid\")\n caches.append(cache)\n\n assert (AL.shape == (1, X.shape[1]))\n\n return AL, caches\n\n\ndef linear_backward(dZ, lambd, cache):\n \"\"\"\n 为单层实现反向传播的线性部分（第L层）\n\n 参数：\n dZ - 相对于（当前第l层的）线性输出的成本梯度\n cache - 来自当前层前向传播的值的元组（A_prev，W，b）\n\n 返回：\n dA_prev - 相对于激活（前一层l-1）的成本梯度，与A_prev维度相同\n dW - 相对于W（当前层l）的成本梯度，与W的维度相同\n db - 相对于b（当前层l）的成本梯度，与b维度相同\n \"\"\"\n\n A_prev, W, b = cache\n m = A_prev.shape[1]\n dW = np.dot(dZ, A_prev.T) / m + ((lambd * W) / m)\n db = np.sum(dZ, axis=1, keepdims=True) / m\n dA_prev = np.dot(W.T, dZ)\n\n assert (dA_prev.shape == A_prev.shape)\n assert (dW.shape == W.shape)\n assert (db.shape == b.shape)\n\n return dA_prev, dW, db\n\n\ndef linear_activation_backward(dA, cache, lambd,activation=\"relu\"):\n linear_cache, activation_cache = cache\n if activation == \"relu\":\n dZ = relu_backward(dA, activation_cache)\n dA_prev, dW, db = linear_backward(dZ, lambd, linear_cache)\n elif activation == \"sigmoid\":\n dZ = sigmoid_backward(dA, activation_cache)\n dA_prev, dW, db = linear_backward(dZ, lambd, linear_cache)\n\n return dA_prev, dW, db\n\n\ndef backward_propagation(AL, Y, caches,lambd):\n grads = {}\n L = len(caches)\n m = AL.shape[1]\n Y = Y.reshape(AL.shape)\n dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n\n current_cache = caches[L-1]\n grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_activation_backward(dAL, current_cache, lambd,\"sigmoid\")\n\n for l in reversed(range(L-1)):\n current_cache = caches[l]\n dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads[\"dA\" + str(l + 2)], current_cache,lambd, \"relu\")\n grads[\"dA\" + str(l + 1)] = dA_prev_temp\n grads[\"dW\" + str(l + 1)] = dW_temp\n grads[\"db\" + str(l + 1)] = db_temp\n\n return grads\n\n\ndef compute_cost(AL, Y,caches,lambd):\n m = Y.shape[1]\n cost = -np.sum(np.multiply(np.log(AL), Y) + np.multiply(np.log(1 - AL), 1 - Y)) / m\n\n L = len(caches)\n current_cache = caches[L - 1]\n linear_cache, activation_cache = current_cache\n A_prev, W, b = linear_cache\n L2_regularization_cost = lambd *(np.sum(np.square(W)))/ (2 * m)\n\n cost_all = cost + L2_regularization_cost\n\n return cost_all\n\n\ndef predict(X, y, parameters):\n \"\"\"\n This function is used to predict the results of a n-layer neural network.\n\n Arguments:\n X -- data set of examples you would like to label\n parameters -- parameters of the trained model\n\n Returns:\n p -- predictions for the given dataset X\n \"\"\"\n\n m = X.shape[1]\n p = np.zeros((1, m), dtype=np.int)\n\n # Forward propagation\n a3, caches = forward_propagation(X, parameters)\n\n # convert probas to 0/1 predictions\n for i in range(0, a3.shape[1]):\n if a3[0, i] > 0.5:\n p[0, i] = 1\n else:\n p[0, i] = 0\n\n # print results\n\n # print (\"predictions: \" + str(p[0,:]))\n # print (\"true labels: \" + str(y[0,:]))\n print(\"Accuracy: \" + str(np.mean((p[0, :] == y[0, :]))))\n\n return p\n\n\ndef predict_dec(parameters, X):\n \"\"\"\n Used for plotting decision boundary.\n\n Arguments:\n parameters -- python dictionary containing your parameters\n X -- input data of size (m, K)\n\n Returns\n predictions -- vector of predictions of our model (red: 0 / blue: 1)\n \"\"\"\n\n # Predict using forward propagation and a classification threshold of 0.5\n a3, cache = forward_propagation(X, parameters)\n predictions = (a3 > 0.5)\n return predictions\n\n\ndef plot_decision_boundary(model, X, y):\n # Set min and max values and give it some padding\n x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n h = 0.01\n # Generate a grid of points with distance h between them\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n # Predict the function value for the whole grid\n Z = model(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n # Plot the contour and training examples\n plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n plt.ylabel('x2')\n plt.xlabel('x1')\n #plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)\n plt.scatter(X[0, :], X[1, :], c=reduce(operator.add, y), cmap=plt.cm.Spectral)\n plt.show()\n\n\ndef load_dataset(is_plot=True):\n np.random.seed(3)\n train_X, train_Y = sklearn.datasets.make_moons(n_samples=300, noise=.2) # 300 #0.2\n # Visualize the data\n if is_plot:\n plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y, s=40, cmap=plt.cm.Spectral);\n train_X = train_X.T\n train_Y = train_Y.reshape((1, train_Y.shape[0]))\n\n return train_X, train_Y","repo_name":"WZJ2333/CNN_Andrew","sub_path":"opt_utils.py","file_name":"opt_utils.py","file_ext":"py","file_size_in_byte":9610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"23216835167","text":"import torch\nimport sys\nimport os\nfrom copy import deepcopy\nimport numpy as np\nimport time\n\nfrom NetworkAtari import Atari_2600\nfrom utils import select_action, save, eval_model\nfrom init_atari import init\nfrom optim import optimize_model\n\nfrom ResNet_18_Atari import resnet\n\nuse_cuda = torch.cuda.is_available()\nFloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor\nTensor = FloatTensor\n\ndef training_Atari(game_name, model, GAMMA=0.99, EPS_START = 1, EPS_END = 0.01,\n iter_param = 1e4, max_frame = 50000000,\n mem_size = int(1e4), momentum = 0.95):\n \"\"\"\n ** Arguments\n Model Deep Learning architecture Resnet18 (resnet)\n or classic DeepMind model (Atari_2600)\n game_name Name of the game\n BATCH_SIZE Size of the batch\n GAMMA Discount factor\n EPS_START Initial value in epsilon greedy exploration\n EPS_END Final value in epsilon greedy exploration\n iter_END nb of frames over wich epsilon is linearly\n annealed to its final value\n iter_param frequency with which we update target network\n max_frame Maximum number of training frames\n mem_size Size of the Replay memory\n lr Learning rate\n momentum Momentum\n \n This algorithm is used for two different architectures : Resnet and more \n classical CNN like DeepMind models for Atari\n \"\"\"\n\n start = time.time()\n \n if model == resnet:\n mod = 'resnet'\n lr = 2.5e-4\n BATCH_SIZE=64\n save_test = 50\n iter_END = 100000\n \n elif model==Atari_2600:\n mod='dqn'\n lr = 1e-4\n BATCH_SIZE=256\n save_test=200\n iter_END = 1000000\n \n recover = input(\"Do you want to resume a previous training ? (y/n) \").lower()\n controller = False\n while not controller:\n if recover==\"n\":\n #If you don't load a previous training it loads a model with\n #random paramaters\n env, env_no_clipping, nb_actions, optimizer, memory, model_atari = \\\n init(game_name,momentum,lr,model,mem_size,mod)\n controller=True\n counter_iter_param=1\n session=0\n \n elif recover==\"y\":\n env, env_no_clipping, nb_actions, optimizer, memory, _ = \\\n init(game_name,momentum,lr,model,mem_size,mod)\n name = input(\"Please enter the name of the file you want to load: \")\n if os.path.exists(name):\n if use_cuda:\n model_atari = torch.load(name)\n else:\n model_atari=torch.load(name, map_location=lambda storage, \n loc: storage)\n controller=True\n counter_iter_param = int(input(\"\"\"Please enter the number of \n frames viewed in the last session: \"\"\"))\n session = int(input(\"Please enter number session: \"))\n else:\n print(\"This file doesn't exist\")\n \n reward_list = []\n eval_reward_list = []\n model_atari_0 = deepcopy(model_atari)\n eps_threshold = 1\n for i_episode in range(1,int(1e6)):\n\n print((i_episode,game_name,model_atari.name,eps_threshold))\n \n #We reset environment and make appropriate transformation on raw pixel\n state = env.reset()\n state = torch.from_numpy(np.array(state)).float()\n state = state.transpose(0,2).transpose(1,2).unsqueeze(0)\n if use_cuda:\n state = state.cuda()\n\n tot_reward = 0\n step = 0\n ended = done =False\n while (not done) and (not ended):\n #Update of the epsilon\n if counter_iter_param = max_frame)\n step += 1\n done = done or (step >= 18000)\n reward_list.append((counter_iter_param,i_episode,tot_reward))\n \n \n if i_episode%save_test==0 or ended:\n evaluation = eval_model(env_no_clipping,model_atari,nb_actions)\n eval_reward_list.append((counter_iter_param,i_episode,evaluation))\n print(\"Saved\")\n #We save data in different files that can be load after training\n save(eval_reward_list,\"evaluation_model_{}_{}.txt\".format(game_name,model_atari.name))\n save(reward_list,\"current_results_{}_{}.txt\".format(game_name,model_atari.name))\n torch.save(model_atari,\"model_atari_repr{}_{}_{}.pkl\".format(session,game_name,model_atari.name))\n if ended:\n break\n\n \n end = time.time()\n t = end-start\n save(t,\"run_time_{}_{}.txt\".format(game_name,model_atari.name))\n return 'Fin'\n\n\n\n\nif __name__ == '__main__':\n if use_cuda:\n print(\"Using CUDA\")\n else:\n print(\"WARNING: Cuda not available\")\n\n assert len(sys.argv) > 2, \"Please give the name of the game as argument\"\n model = sys.argv[2]\n if model == \"resnet\":\n training_Atari(sys.argv[1], model = resnet)\n elif model == \"dqn\":\n training_Atari(sys.argv[1], model = Atari_2600)\n else:\n assert False, \"Unknown model\"\n \n","repo_name":"deepadawan/Res_DQN","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"6626034023","text":"#Lightning Export dataset cleaning\n\nimport numpy as np\n\n#import the data, and make sure the formatting is correct\n# PLEASE NOTE: the dtype of this array is float64\n# that is to say, each entry is of type float64\ncsv = np.genfromtxt('LightningExport2.csv', delimiter = \",\", usecols=np.arange(0,21), skip_header=1)\n\n#extract the 9 useful columns\ncsv = csv[:, [0, 2, 3, 12, 14, 15, 16, 17, 18]]\nindices = np.argsort = [0,2,1,4,5,6,7,8,3]\ncsv = csv[:, indices]\n#the columns of the csv now represent\n# 0 - MLS number\n# 1 - soldprice (our y)\n# 2 - listprice\n# 3 - bedroom count\n# 4 - bathroom count\n# 5 - sqft of property\n# 6 - age\n# 7 - lot size\n\n#elimnating rows with nan\n#originally, we have 54760 listings\n#after we remove the rows with NaNs, we have 54696 listings\n#this preserves over 99.9% of listings, so it seems like a reasonably sane action to take\ncsv = csv[~np.isnan(csv).any(axis=1)]\n\n#remove duplicates\n#now we have 53,167 listings!\nunique_keys, indices = np.unique(csv[:,0], return_index=True)\ncsv = csv[indices]\n\n#change zip code to categorical - 1-hot\n#we appear to have 621 unique zips!\nzips = csv[:,8]\nunique_zips = np.sort(np.unique(zips))\nnum_zips = np.unique(zips).shape[0]\n\nonehot = np.zeros((zips.shape[0], num_zips))\nfor i in range(zips.shape[0]):\n\tindex = np.where(unique_zips == zips[i])\n\tonehot[i,index] = 1\n\nnp.save(\"nothot\", csv)\n\n#replace last column in the array with 621 columns of one-hot encoding\ncsv = np.delete(csv, 8, axis = 1)\ncsv = np.concatenate((csv, onehot), axis=1)\n\n\n\n\n#save the clean data\n#np.save(\"clean\", csv)\n\n# counter = 1\n# for row in csv:\n# \tprint(counter)\n# \tprint(row)\n# \tcounter +=1","repo_name":"catabia/real_estate_price_prediction","sub_path":"experiments_without_list_price/LEcleaning.py","file_name":"LEcleaning.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73715559592","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom datetime import datetime\nfrom fagaiwei.settings import session, NewsItemInfo\nfrom fagaiwei.items import FagaiweiItem\nclass ZjzxSpider(scrapy.Spider):\n #新浪财经\n name = 'sinacaijing'\n allowed_domains = ['sina.com.cn']\n start_urls = [\n 'http://finance.sina.com.cn/roll/index.d.html?cid=56995' , #期市要闻\n 'http://roll.finance.sina.com.cn/finance/jj4/index_1.shtml' , #基金新闻\n 'http://finance.sina.com.cn/forex/' , #外汇\n 'http://finance.sina.com.cn/nmetal/' , #黄金\n ]\n\n def parse(self, response):\n url_list = response.xpath('//ul[@data-client=\"scroll\"]/li/a/@href | //ul[@class=\"list_009\"]/li/a/@href ').extract()\n for url in url_list:\n result = session.query(NewsItemInfo).filter_by(url=url, web_id=42).count()\n if result:\n # print(\"{} 存在\".format(url))\n pass\n else:\n yield scrapy.Request(url,callback=self.process_detail,meta={'web':response.url})\n\n def process_detail(self,response):\n if response.xpath('//*[@id=\"artibody\"]'):\n item = FagaiweiItem()\n item['web_id'] = 42\n item['url'] = response.url\n item['title'] = response.xpath('//div[contains(@class,\"main-content\")]/h1/text()').extract_first(default='')\n item['web'] = response.meta.get('web')\n item['keyword'] = ''\n item['webname'] = response.xpath('//a[contains(@class,\"source\")]/text()|//span[contains(@class,\"source\")]/text()').extract_first(default='新浪财经')\n item['pub_time'] = response.xpath('//span[@class=\"date\"]/text()').extract_first(default=datetime.now().strftime(\"%Y-%m-%d %H:%M\")).replace('年','-').replace('月','-').replace('日','')\n content = '\\n'.join(response.xpath('//*[@id=\"artibody\"]/p/text() | \\\n //*[@id=\"artibody\"]//p/span/a/text() | \\\n //*[@id=\"artibody\"]//p/span/text() | \\\n //*[@id=\"artibody\"]/div//p/text()').extract())\n if not content:\n content = '这可能是图片或者文件，打开查看！'\n item['content'] = content.replace('\\u3000','').replace('\\xa0','').replace('\\t\\t\\t\\n','').replace('\\n\\n','')\n yield item","repo_name":"KKtoNN/python_spider","sub_path":"fagaiwei/fagaiwei/spiders/42sinacaijing.py","file_name":"42sinacaijing.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"24483814230","text":"print(\"starting\")\r\n\r\nsumOfPals = 0\r\n\r\ndef inBinary(x):\r\n largestPower = 0\r\n binaryText = \"\"\r\n currentNum=x\r\n while x>=2**(largestPower+1):\r\n largestPower +=1\r\n for i in range(largestPower,-1,-1):\r\n if 2**i<=currentNum:\r\n binaryText += \"1\"\r\n currentNum -=2**i\r\n else:\r\n binaryText += \"0\"\r\n return binaryText\r\n\r\ndef isPal(x):\r\n l = len(x)-1\r\n for i in range(0,len(x)):\r\n if i >= l-i:\r\n break\r\n if x[i] != x[l-i]:\r\n return False\r\n return True\r\n\r\nfor i in range(1,1000001,2):\r\n print(i)\r\n if isPal(str(i)) and isPal(inBinary(i)):\r\n sumOfPals += i\r\n\r\nprint(sumOfPals)\r\n\r\ninput(\"press Something\")\r\n","repo_name":"alexandrepoulin/ProjectEulerInPython","sub_path":"problems/problem 36.py","file_name":"problem 36.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"18596004281","text":"import logging\n\nfrom lib.termcolor import colored\n\n\n# git://gist.github.com/1238935.git\nclass Logger(object):\n def __init__(self, name):\n self.color_map = {'debug': {'color': 'grey', 'attrs': ['bold']},\n 'info': {'color': 'white'},\n 'warn': {'color': 'yellow', 'attrs': ['bold']},\n 'error': {'color': 'red'},\n 'fatal': {'color': 'red', 'attrs': ['bold']},\n }\n self.logger = logging.getLogger(name)\n self.logger.setLevel(logging.INFO)\n self.stdout = logging.StreamHandler()\n self.stdout.setLevel(logging.INFO)\n self.stdout.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(message)s', '%Y-%m-%d %H:%M:%S'))\n self.logger.addHandler(self.stdout)\n\n def __getattr__(self, status, attrs=[]):\n if status in ('debug', 'info', 'warn', 'error', 'fatal'):\n return lambda msg, *args: getattr(self.logger, status)(\n colored(msg, **self.color_map[status]), *args)\n","repo_name":"knmkr/dbsnp-pg","sub_path":"script/python/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"72"}
+{"seq_id":"41458749969","text":"#\n# @lc app=leetcode id=728 lang=python3\n#\n# [728] Self Dividing Numbers\n#\n\n# @lc code=start\nclass Solution:\n def selfDividingNumbers(self, left: int, right: int) -> List[int]:\n result = []\n for i in range(left, right+1):\n flag = True\n str_i = str(i)\n for j in str_i:\n num_j = int(j)\n if num_j == 0 or i % num_j != 0:\n flag = False\n break\n if flag:\n result.append(i)\n\n return result\n\n\n# @lc code=end\n","repo_name":"HOZH/leetCode","sub_path":"leetCodePython2023/728.self-dividing-numbers.py","file_name":"728.self-dividing-numbers.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"75025161191","text":"#Still working with files \r\n\r\nwith open(\"life-expectancy.csv\") as world_data:\r\n world_data.readline()\r\n the_life_exp_range = []\r\n country_name = []\r\n year_value = []\r\n \r\n for i in world_data:\r\n i = i.strip()\r\n world_h = i.split(\",\")\r\n \r\n the_life_exp_range.append(world_h)\r\n \r\n max_value = -1\r\n max_vvalue_2 = -1\r\n max_country = ''\r\n max_year = ''\r\n min_value = float(world_h[3])\r\n min_value_2 = float(world_h[3])\r\n min_country = ''\r\n min_year = ''\r\n year_to_check = int(input('Enter the year of interest? '))\r\n \r\n average_list = []\r\n maximum_year = ''\r\n maximum_country = ''\r\n maximum_value = -1\r\n minimum_value = float(world_h[3])\r\n minimum_country = ''\r\n\r\n for exp_values in the_life_exp_range:\r\n country_name = exp_values[0]\r\n country_code = exp_values[1]\r\n country_year = int(exp_values[2])\r\n country_value = float(exp_values[3])\r\n \r\n if country_value > max_value:\r\n max_value = country_value\r\n max_country = country_name\r\n max_year = country_year\r\n \r\n elif country_value < min_value:\r\n min_value = country_value\r\n min_country = country_name\r\n min_year = country_year\r\n \r\n elif year_to_check == country_year:\r\n average_list.append(country_value)\r\n for numbers in average_list:\r\n \r\n length = len(country_value)\r\n total = sum(country_value)\r\n average = total / length\r\n \r\n if country_value > maximum_value:\r\n maximum_value = country_value\r\n maximum_country = country_name\r\n else:\r\n if country_value < minimum_value:\r\n minimum_value = country_value\r\n minimum_country = country_name\r\n \r\n \r\n \r\nprint(\"The Overall max life expectany is: {} from {}, in {}. \".format(max_value, max_country, max_year))\r\nprint()\r\nprint(\"The Overall min life expectany is: {} from {}, in {}. \\n\".format(min_value, min_country, min_year))\r\nprint(\"For the year {}\".format(year_to_check))\r\nprint(\"The average life expentency accross all countries was {:,.2f}\".format(average))\r\nprint(\"The max life expetency was in {} with {:,.2f}\".format(maximum_country, maximum_value))\r\nprint(\"The min life expetency was in {} with {}\".format(minimum_country, minimum_value))\r\nprint()\r\n\r\n\r\n","repo_name":"code-lova/BYU-Python-programming","sub_path":"programming_building_blocks/week11/stretch.py","file_name":"stretch.py","file_ext":"py","file_size_in_byte":2534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"19505181150","text":"# coding: utf-8\n\nimport io\nimport re\nimport sys\n\nimport template\n\n\n# noinspection PyShadowingBuiltins\ndef compile(source, out_file, newline_in=(u'\\r\\n', u'\\n', u'\\r'),\n naive_border=False, nop_character=u'ㅇ',\n newline_out=u'\\n', nop_pattern=(\n ('nop_ignored', 'nop_ignored_v', 'nop_ignored'),\n ('nop_ignored_h', 'nop', 'nop_ignored_h'),\n ('nop_ignored', 'nop_ignored_v', 'nop_ignored'),\n ), remove_trailing_newline=True,\n remove_leading_newline=False):\n re_splitlines = re.compile(u'|'.join(\n re.escape(s)\n for s in reversed(sorted(newline_in, key=len))\n ))\n lines = re_splitlines.split(source)\n\n if remove_trailing_newline == -1:\n remove_trailing_newline = len(lines)\n for __ in range(remove_trailing_newline):\n if not lines or lines[-1]:\n break\n lines.pop()\n\n if remove_leading_newline == -1:\n remove_leading_newline = len(lines)\n offset = 0\n for __ in range(remove_leading_newline):\n if len(lines) <= offset or lines[offset]:\n break\n offset += 1\n if offset:\n lines = lines[offset:]\n\n height = len(lines)\n width = max(len(line) for line in lines) if lines else 0\n\n left_end = [0] * height\n right_end = [len(line) for line in lines]\n top_end = [height] * width\n bottom_end = [0] * width\n\n for r in range(height):\n for c in range(len(lines[r])):\n top_end[c] = min(top_end[c], r)\n bottom_end[c] = r\n\n for c in range(width):\n if top_end[c] > bottom_end[c]:\n bottom_end[c] = top_end[c]\n\n # templates\n border_intersection = template.template_border_intersection\n if naive_border:\n border_top = template.template_border_top_naive\n border_left = template.template_border_left_naive\n else:\n border_top = template.template_border_top\n border_left = template.template_border_left\n\n # TODO: XXX\n nop = [\n [getattr(template, 'template_' + s) for s in r]\n for r in nop_pattern\n ]\n\n hangul_templates = template.hangul_templates\n\n # top border\n for r_t in range(len(border_intersection)):\n translate_table = {\n 0x3147: ord(nop_character), # ord(u'ㅇ') == 0x3147\n }\n out_file.write(border_intersection[r_t].translate(translate_table))\n for c in range(width):\n out_file.write(border_top[r_t].translate(translate_table))\n out_file.write(newline_out)\n\n for r, line in enumerate(lines):\n for r_t in range(len(border_left)):\n translate_table = {\n 0x3147: ord(nop_character), # ord(u'ㅇ') == 0x3147\n }\n out_file.write(border_left[r_t].translate(translate_table))\n for c in range(width):\n translate_table = {\n 0x3147: ord(nop_character), # ord(u'ㅇ') == 0x3147\n }\n if c >= len(line) or not (u'\\uAC00' <= line[c] <= u'\\uD7A3'):\n cell_char = None\n\n left_over = c < left_end[r]\n right_over = c >= right_end[r]\n top_over = r < top_end[c]\n bottom_over = r >= bottom_end[c]\n\n # XXX\n cell_template = nop[1 - top_over + bottom_over][1 - left_over + right_over]\n else:\n cell_char = line[c]\n code_point = ord(cell_char)\n vowel = (code_point - 0xAC00) // 28 % 21\n consonants = code_point - vowel * 28\n\n translate_table.update({\n 0xCC28: consonants, # ord(u'차') == 0xCC28\n 0xCC98: consonants + 112, # ord(u'처') == 0xCC98\n 0xCD08: consonants + 224, # ord(u'초') == 0xCD08\n 0xCD94: consonants + 364, # ord(u'추') == 0xCD94\n })\n\n cell_template = hangul_templates[vowel]\n\n out_file.write(cell_template[r_t].translate(translate_table))\n out_file.write(newline_out)\n\n\ndef main(filename_in, filename_out):\n with io.open(filename_in, 'r', encoding='utf-8', newline=u'') as file_in:\n source = file_in.read()\n\n with io.open(filename_out, 'w', encoding='utf-8', newline=u'') as file_out:\n compile(source, file_out)\n\n\nif __name__ == '__main__':\n main(*sys.argv[1:])\n","repo_name":"Sait2000/jeoheui","sub_path":"compiler.py","file_name":"compiler.py","file_ext":"py","file_size_in_byte":4500,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"131803749","text":"import uuid\nfrom ffmpy import FFRuntimeError\nfrom unittest import mock\nfrom django.urls import reverse\n\nimport cars.settings\nfrom cars.core.constants import AppPlatform\nfrom cars.core.mds.wrapper import MDSDocumentsWrapper\nfrom cars.django.tests import CarsharingAPITestCase\nfrom cars.users.factories.user import UserFactory\nfrom cars.users.factories.user_documents import UserDocumentPhotoFactory\nfrom cars.users.models.user_documents import UserDocumentBackgroundVideo\nfrom cars.users.models.app_install import AppInstall\n\n\nclass FFmpegMock(object):\n\n def __init__(self, inputs, outputs):\n self.inputs = inputs\n self.outputs = outputs\n\n def run(self, stdout, stderr):\n '''Modifies first input's content and writes it to outputs.'''\n with open(next(iter(self.inputs.keys())), 'rb') as infile:\n modified_data = b'converted_' + infile.read()\n for outfile_name in self.outputs:\n with open(outfile_name, 'wb') as outfile:\n outfile.write(modified_data)\n\n\ndef make_ffprobe_mock(return_format):\n\n class FFProbeMock(object):\n\n def __init__(self, name):\n video_mock = mock.MagicMock(codec_name=return_format)\n self.video = [video_mock]\n\n return FFProbeMock\n\n\nclass UserDocumentBackgroundVideoUploadTestCase(CarsharingAPITestCase):\n\n def setUp(self):\n user_uid = cars.settings.YAUTH_TEST_USER['login']\n self.user = UserFactory.create(uid=user_uid, username=user_uid)\n self.app_install = AppInstall.objects.get(user=self.user)\n self.photo = UserDocumentPhotoFactory.create(document__user=self.user)\n self.mds_client = MDSDocumentsWrapper.from_settings()\n # no convertions for common tests\n self.set_platform(AppPlatform.IOS.value)\n\n def set_platform(self, platform):\n self.app_install.platform = platform\n self.app_install.save()\n\n def get_url(self, photo):\n return reverse(\n 'drive:user-document-photo-background-video',\n kwargs={\n 'document_id': photo.document.id,\n 'photo_id': photo.id,\n },\n )\n\n def upload(self, data, content_type='video/mp4'):\n url = self.get_url(self.photo)\n response = self.client.put(url, data=data, content_type=content_type)\n return response\n\n def test_ok(self):\n response = self.upload(b'test')\n self.assert_response_ok(response)\n video = UserDocumentBackgroundVideo.objects.get(photo=self.photo)\n mds_response = self.mds_client.get_user_document_background_video(video)\n self.assertEqual(mds_response['Body'].read(), b'test')\n\n def test_different_content_type(self):\n self.upload(b'test', content_type='video/webm')\n video = UserDocumentBackgroundVideo.objects.get(photo=self.photo)\n self.assertEqual(video.mime_type, 'video/webm')\n\n def test_no_data(self):\n response = self.upload(None)\n self.assert_response_bad_request(response)\n\n def test_other_user(self):\n user = UserFactory.create()\n photo = UserDocumentPhotoFactory.create(document__user=user)\n url = self.get_url(photo)\n response = self.client.put(url, data=b'test', content_type='video/mp4')\n self.assert_response_not_found(response)\n\n @mock.patch('ffprobe3.FFProbe')\n @mock.patch('ffmpy.FFmpeg')\n def test_conversion_not_needed_platform(self, ffmpy_mock, ffprobe_mock):\n '''Check that if platform is iOS, no ffmpeg-conversion is run.'''\n self.set_platform(AppPlatform.IOS.value)\n response = self.upload(b'video_content_0')\n self.assert_response_ok(response)\n self.assertFalse(ffmpy_mock.called)\n self.assertFalse(ffprobe_mock.called)\n\n video = UserDocumentBackgroundVideo.objects.get(photo=self.photo)\n mds_response = self.mds_client.get_user_document_background_video(video)\n self.assertEqual(mds_response['Body'].read(), b'video_content_0')\n\n @mock.patch('ffprobe3.FFProbe')\n @mock.patch('ffmpy.FFmpeg')\n def test_conversion_not_needed_format(self, ffmpy_mock, ffprobe_mock):\n '''Check that if ffprobe returns h264, no ffmpeg-conversion is run.'''\n ffprobe_mock.side_effect = make_ffprobe_mock('h264')\n\n self.set_platform(AppPlatform.ANDROID.value)\n response = self.upload(b'video_content_0')\n self.assert_response_ok(response)\n self.assertTrue(ffprobe_mock.called)\n self.assertFalse(ffmpy_mock.called)\n\n video = UserDocumentBackgroundVideo.objects.get(photo=self.photo)\n mds_response = self.mds_client.get_user_document_background_video(video)\n self.assertEqual(mds_response['Body'].read(), b'video_content_0')\n\n @mock.patch('ffprobe3.FFProbe')\n @mock.patch('ffmpy.FFmpeg')\n def test_conversion_needed(self, ffmpy_mock, ffprobe_mock):\n '''Check that if platform is Android, video is being converted with ffmpeg.'''\n ffprobe_mock.side_effect = make_ffprobe_mock('mpeg4')\n ffmpy_mock.side_effect = FFmpegMock\n self.set_platform(AppPlatform.ANDROID.value)\n response = self.upload(b'video_content_1')\n self.assert_response_ok(response)\n\n video = UserDocumentBackgroundVideo.objects.get(photo=self.photo)\n mds_response = self.mds_client.get_user_document_background_video(video)\n self.assertEqual(mds_response['Body'].read(), b'converted_video_content_1')\n\n @mock.patch('ffprobe3.FFProbe')\n @mock.patch('ffmpy.FFmpeg')\n def test_conversion_fails(self, ffmpy_mock, ffprobe_mock):\n '''Check that if ffmpeg run fails, unconverted video is uploaded to mds.'''\n ffprobe_mock.side_effect = make_ffprobe_mock('mpeg4')\n ffmpy_mock.side_effect = FFRuntimeError('', 42,\n b'', b'')\n self.set_platform(AppPlatform.ANDROID.value)\n response = self.upload(b'video_content_2')\n self.assert_response_ok(response)\n\n video = UserDocumentBackgroundVideo.objects.get(photo=self.photo)\n mds_response = self.mds_client.get_user_document_background_video(video)\n self.assertEqual(mds_response['Body'].read(), b'video_content_2')\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"drive/tests/test_video_upload.py","file_name":"test_video_upload.py","file_ext":"py","file_size_in_byte":6284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"15191478229","text":"import heapq\r\n\r\nK = int(input())\r\nadj = [dict() for i in range(K)]\r\nfor i in range(1, K):\r\n adj[i] = dict()\r\n adj[i][(i+1)%K] = 1\r\n adj[i][(i*10)%K] = 0\r\n\r\ndef dijkstra(adj):\r\n q = []# p q\r\n d = dict()# min cost from start\r\n p = dict()# parent node\r\n n = len(adj)# num of node\r\n\r\n for i in range(n):\r\n d[i] = float('inf')\r\n\r\n start_id = 1\r\n d[start_id] = 0\r\n heapq.heappush(q, (0, start_id))\r\n S = set()\r\n\r\n while len(S) != n:\r\n dist, node_id = heapq.heappop(q)\r\n S.add(node_id)\r\n for u, c in adj[node_id].items():\r\n if c + d[node_id] < d[u]:\r\n d[u] = c + d[node_id]\r\n heapq.heappush(q, (d[u], u))\r\n\r\n return d\r\n\r\nd = dijkstra(adj)\r\nprint(d[0]+1)","repo_name":"Kawser-nerd/CLCDSA","sub_path":"Source Codes/AtCoder/arc084/B/1995966.py","file_name":"1995966.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"72"}
+{"seq_id":"36969446536","text":"\"\"\"\nMake a project directory with associated setup\n\"\"\"\n\nimport argparse\nfrom copy import deepcopy\nimport errno\nimport frontmatter\nfrom functools import wraps\nimport inspect\nfrom licenses import LICENSES\nimport logging\nimport os\nfrom pprint import pformat\nimport re\nimport requests\nimport shutil\nfrom string import Formatter\nimport subprocess\nimport sys\nimport traceback\n\n\nDEFAULT_LOG_LEVEL = logging.INFO\nPOSITIONAL_ARGUMENTS = sorted([\n ['-l', '--loglevel', logging.getLevelName(DEFAULT_LOG_LEVEL),\n 'desired logging level (' +\n 'case-insensitive string: DEBUG, INFO, WARNING, or ERROR'],\n ['-v', '--verbose', False, 'verbose output (logging level == INFO)'],\n ['-w', '--veryverbose', False,\n 'very verbose output (logging level == DEBUG)'],\n ['-c', '--create', False, 'create directory at indicated path'],\n ['-p', '--pyvenv', False, 'create a python virtual environment'],\n ['-n', '--pyversion', '3', 'version of python to use in virtual '\n 'environment'],\n ['-g', '--git', False, 'create a new git repository'],\n ['-s', '--script', False, 'set up with a python script'],\n ['-k', '--package', False, 'set up as a python package'],\n ['-r', '--readme', False, 'add a readme file template'],\n ['-q', '--quiet', False, 'suppress output (logging level == CRITICAL)'],\n ['-x', '--license', 'agpl-3.0', 'license to use (\"none\" is an option)'],\n ['-kv', '--pkgversion', '0.1', 'PEP440 version number to use in setup.py'],\n ['-kd', '--pkgdescription', 'change me', 'description to use in setup.py'],\n ['-kh', '--pkghomepage', 'http://change.me', 'home page to use in '\n 'setup.py'],\n ['-ka', '--pkgauthor', 'Change Me', 'user name to use in setup.py'],\n ['-ke', '--pkgemail', 'change@me.org', 'email address to use in setup.py'],\n ['-cs', '--classdevstatus', '1 - Planning', 'development status '\n 'classifier to use in '\n 'setup.py'],\n ['-ca', '--classaudience', 'Developers', 'intended audience classifier '\n 'to use in setup.py'],\n ['-ct', '--classtopic', 'Change Me', 'topic classifier to use in '\n 'setup.py'],\n ['-kk', '--pkgkeywords', '\"change me\", \"please change me', 'keywords to '\n 'use in '\n 'setup.py']\n])\ntemplate_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),\n 'templates')\nGITIGNORE_FILE = os.path.join(template_dir, 'gitignore.txt')\nTEMPLATES = {\n 'script-2': os.path.join(template_dir, 'script_template_2.py'),\n 'script-3': os.path.join(template_dir, 'script_template_3.py'),\n 'package-3': os.path.join(template_dir, 'package_template_3.py'),\n 'readme': os.path.join(template_dir, 'README.md'),\n 'requirements': os.path.join(template_dir, 'requirements_dev.txt'),\n 'setup': os.path.join(template_dir, 'setup_template.py'),\n 'setup_config': os.path.join(template_dir, 'setup.cfg'),\n 'manifest': os.path.join(template_dir, 'MANIFEST.in'),\n 'test_template3': os.path.join(template_dir, 'test_template3.py')\n}\nTEMPLATE_RENAMES = {\n 'setup_template.py': 'setup.py'\n}\nPACKAGE_SUBDIRECTORIES = ['scripts', 'tests', 'data']\nLICENSE_FIXES = {\n 'cal': {\n 'prefix': ('https://raw.githubusercontent.com/github/'\n 'choosealicense.com/gh-pages/_licenses/'),\n 'suffix': '.txt'\n }\n}\nDEFAULT_DEPENDENCIES = [\n 'airtight',\n 'better_exceptions',\n 'coverage',\n 'nose'\n]\n\n\ndef arglogger(func):\n \"\"\"\n decorator to log argument calls to functions\n \"\"\"\n @wraps(func)\n def inner(*args, **kwargs):\n logger = logging.getLogger(func.__name__)\n logger.debug(\"called with arguments: %s, %s\" % (args, kwargs))\n return func(*args, **kwargs)\n return inner\n\n\n@arglogger\ndef main(args):\n \"\"\"\n main function\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n\n where = os.path.abspath(args.where)\n # global variables\n if args.script and args.package:\n raise ValueError('cannot create both a script and a package')\n if args.create:\n create_directory(where)\n if args.pyvenv:\n create_venv(where, args.pyversion)\n if args.git:\n create_git(where)\n if args.readme:\n create_readme(where, args.git)\n if args.script:\n init_script(where, args.pyversion, args.git)\n if args.license.lower() != 'none':\n create_license(where, args.license, args.git)\n if args.package:\n init_package(where, args)\n\n\n@arglogger\ndef create_directory(where):\n \"\"\"\n create the project directory at the indicated path\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n try:\n os.makedirs(where)\n except OSError as e:\n if e.errno == errno.EEXIST and os.path.isdir(where):\n logger.critical(\n 'script run with directory creation, but {0} already exists'\n ''.format(where))\n sys.exit(1)\n logger.info('created new project directory at {0}'.format(where))\n\n\n@arglogger\ndef create_license(where, license, git=False):\n \"\"\"\n add preferred LICENSE file\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n fn = 'LICENSE.txt'\n ld = LICENSES[license]\n try:\n src = ld['src']\n except KeyError:\n logger.warning('License data not found for \"{0}\". License creation '\n 'skipped.'.format(license))\n else:\n logger.debug('src: \"{0}\"'.format(src))\n logger.debug('src[0:2]: \"{0}\"'.format(src[0:2]))\n if src[0:2] == '::':\n logger.debug('src[2:]: \"{0}\"'.format(src[2:]))\n src = src[2:]\n url = (LICENSE_FIXES[src]['prefix'] + license +\n LICENSE_FIXES[src]['suffix'])\n else:\n url = src\n targets = [(url, os.path.join(where, fn))]\n fetch(targets, strip_yaml=True)\n if git:\n title = ld['title']\n git_it(where, fn, 'assigned the {0} using text from: {1}'\n ''.format(title, url))\n logger.info('instantiated and committed {0} using {1} from '\n '{2}'.format(fn, title, url))\n else:\n logger.info('instantiated {0} using {0} from {1}'.format(fn, title,\n url))\n\n\n@arglogger\ndef create_readme(where, git=False):\n \"\"\"\n create an initial readme file\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n src = TEMPLATES['readme']\n src = os.path.expanduser(src)\n src = os.path.abspath(src)\n dest_fn = os.path.basename(src)\n dest = os.path.join(where, dest_fn)\n shutil.copy2(src, dest)\n logger.debug('copied {0} to {1}'.format(src, dest))\n if git:\n git_it(os.path.dirname(dest), dest_fn,\n 'include default readme template')\n logger.info('instantiated {0} and committed it'.format(dest_fn))\n else:\n logger.info('instantiated {0}'.format(dest_fn))\n\n\n@arglogger\ndef create_venv(where, python_version):\n \"\"\"\n set up python virtual environment\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n v = '/usr/local/bin/python{0}'.format(python_version)\n venv_name = os.path.basename(where)\n env_dir = '~/Envs/{0}'.format(venv_name)\n if os.path.exists(env_dir):\n logger.critical(\n 'script run with venv creation, but {0} already exists'\n ''.format(env_dir))\n sys.exit(1)\n # somewhy following returns failure code 1 even when successful,\n # so can't try\n cmd = 'mkvirtualenv -v -p {0} {1} && deactivate'.format(v, env_dir)\n run(cmd, check=False) # mkvirtualenv returns non-zero code despite success\n logger.info('instantiated python {0} virtual environment at {1}'\n ''.format(python_version, env_dir))\n run('workon {} && pip install -U pip && deactivate'.format(venv_name))\n logger.info('upgraded pip to latest version')\n for dependency in DEFAULT_DEPENDENCIES:\n cmd = 'workon {} && pip install -U {} && deactivate'.format(\n venv_name, dependency)\n run(cmd)\n logger.info('installed dependency \"{}\"'.format(dependency))\n\n\n@arglogger\ndef create_git(where):\n \"\"\"\n create git repository\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n cmd = 'git init {0}'.format(where)\n run(cmd)\n logger.info('initialized git repository at {0}'.format(where))\n logger.debug('trying to set up .gitignore')\n fp = os.path.join(where, '.gitignore')\n shutil.copy(GITIGNORE_FILE, fp)\n git_it(where, '.gitignore', 'intial values for .gitignore')\n logger.info('instantiated .gitignore and committed it')\n\n\n@arglogger\ndef init_script(where, py_ver, git=False):\n \"\"\"\n include a python script template\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n src = TEMPLATES['script-{0}'.format(py_ver)]\n logger.debug('src: {0}'.format(src))\n src = os.path.expanduser(src)\n logger.debug('src: {0}'.format(src))\n src = os.path.abspath(src)\n logger.debug('src: {0}'.format(src))\n dest_fn = '{0}.py'.format(os.path.basename(where))\n dest = os.path.join(where, dest_fn)\n shutil.copy2(src, dest)\n logger.debug('copied {0} to {1}'.format(src, dest))\n if git:\n git_it(os.path.dirname(dest), dest_fn,\n 'include default script template')\n logger.info('added script template as {0}'.format(dest_fn))\n\n\n@arglogger\ndef init_package(where, args):\n \"\"\"\n set up as a python package\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n\n # create subordinate package folders\n for sub_dir in PACKAGE_SUBDIRECTORIES:\n os.makedirs(os.path.join(where, sub_dir))\n pkg_name = os.path.basename(where)\n pkg_name_parts = pkg_name.split('.')\n pkg_pile = os.path.join(where, *pkg_name_parts)\n os.makedirs(pkg_pile)\n\n # stub out additional files using internal templates\n templates = [\n (TEMPLATES['requirements'], []),\n (TEMPLATES['setup'], []),\n (TEMPLATES['setup_config'], []),\n (TEMPLATES['manifest'], []),\n (\n TEMPLATES['script-{}'.format(args.pyversion)],\n ['scripts']\n ),\n (\n TEMPLATES['package-{}'.format(args.pyversion)],\n pkg_name_parts\n ),\n (\n TEMPLATES['test_template{}'.format(args.pyversion)],\n ['tests']\n )\n ]\n for template in templates:\n logger.debug('template: {0}'.format(template[0]))\n src = os.path.expanduser(template[0])\n src = os.path.abspath(src)\n dest_fn = os.path.basename(src)\n dest = os.path.join(where, *template[1], dest_fn)\n shutil.copy2(src, dest)\n logger.debug('copied {0} to {1}'.format(src, dest))\n dest_fn = fixup_template(\n os.path.join(where, *template[1]), template[0], args)\n if args.git:\n git_it(os.path.dirname(dest), dest_fn,\n 'include default {0} template'.format(dest_fn))\n logger.info('instantiated {0} and committed it'.format(dest_fn))\n else:\n logger.info('instantiated {0}'.format(dest_fn))\n\n@arglogger\ndef fixup_template(where, template, args):\n \"\"\"\n rename template and substitute variables if necessary\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n fn = os.path.basename(template)\n logger.debug('running fixup_template on {0}'.format(fn))\n with open(os.path.join(where, fn), 'r') as f:\n t = f.read()\n logger.debug('setting up replacements for {0}'.format(fn))\n fkeys = [v[1] for v in Formatter().parse(t) if v[1] is not None]\n if len(fkeys) > 0:\n logger.debug('fkeys: {0}'.format(', '.join(fkeys)))\n replacements = {}\n missed = []\n for fk in fkeys:\n try:\n val = vars(args)[fk]\n except KeyError:\n missed.append(fk)\n else:\n replacements[fk] = val\n logger.debug('replacements: {0}'.format(', '.join(['[{0}]: \"{1}\"'.format(k, v)\n for k, v in replacements.items()])))\n if 'pkgreadme' in missed:\n replacements['pkgreadme'] = os.path.basename(TEMPLATES['readme'])\n if 'project_name' in missed:\n replacements['project_name'] = os.path.basename(where)\n if 'classlicense' in missed:\n replacements['classlicense'] = LICENSES[args.license]['classifier']\n logger.debug(\"missed: {0}\".format(', '.join(missed)))\n logger.debug('read replacements from args')\n logger.debug(replacements)\n logger.debug('attemping replacements in {0}'.format(fn))\n logger.debug(t)\n logger.debug(replacements)\n t = t.format(**replacements)\n logger.debug(t)\n shutil.copy2(os.path.join(where, fn),\n os.path.join(where, '{0}.bak'.format(fn)))\n with open(os.path.join(where, fn), 'w') as f:\n f.write(t)\n try:\n new_fn = TEMPLATE_RENAMES[fn]\n except KeyError:\n pass\n else:\n os.rename(os.path.join(where, fn), os.path.join(where, new_fn))\n return(new_fn)\n return(fn)\n\n\n@arglogger\ndef make_subdir(where, git, dname, init, children):\n \"\"\"\n create a subdirectory\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n target = os.path.join(where, dname)\n logger.debug('trying to make \"{0}\"'.format(target))\n try:\n os.makedirs(target)\n except OSError as e:\n if e.errno == errno.EEXIST and os.path.isdir(where):\n logger.critical(\n 'script run with directory creation, but {0} already exists'\n ''.format(target))\n sys.exit(1)\n if init:\n fn = '__init__.py'\n fp = os.path.join(target, fn)\n with open(fp, 'w'):\n pass\n if git:\n git_it(target, fn, 'make {0} part of the package by adding '\n '__init__.py'.format(target))\n logger.info('instantiated {0} and committed it'.format(fp))\n else:\n logger.info('instantiated {0}'.format(fp))\n for child in children:\n make_subdir(target, git, *child)\n\n\n@arglogger\ndef git_it(where, what, msg):\n \"\"\"\n add and commit something to the git repository\n \"\"\"\n cmd = 'git add {0} && git commit -m \"{1}\"'.format(what, msg)\n run(cmd, where)\n\n\n@arglogger\ndef run(cmd, where=None, check=True):\n \"\"\"\n use subprocess to execute a desired command in the shell\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n run_params = [\n 'bash',\n '-c',\n '. ~/.bash_profile'\n ]\n if where is not None:\n run_params[-1] += ' && cd {0}'.format(where)\n run_params[-1] += ' && {0}'.format(cmd)\n logger.debug('run_params: \\n {0}'.format('\\n '.join(run_params)))\n try:\n result = subprocess.run(\n run_params,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT,\n check=check).stdout\n except subprocess.CalledProcessError as e:\n logger.critical('subprocess execution failed with status code '\n '{0}:\\n '.format(e.returncode) +\n 'command was: \"{0}\\n \"'.format(run_params))\n\n\ndef fetch(targets, strip_yaml=False):\n \"\"\"\n fetch file(s) from url(s), concatenate, and save locally\n \"\"\"\n logger = logging.getLogger(sys._getframe().f_code.co_name)\n for target in targets:\n logger.debug('requesting {0}'.format(target[0]))\n r = requests.get(target[0], stream=True)\n if r.status_code == 200:\n # appending ensures we can aggregate, e.g., .gitignore content\n with open('{0}'.format(target[1]), 'ab') as f:\n r.raw.decode_content = True\n shutil.copyfileobj(r.raw, f)\n else:\n raise Exception('fetch of {0} failed with status code {1}'\n ''.format([0], r.status_code))\n sys.exit(1)\n logger.debug('successfully saved {0} as {1}'.format(*target))\n if strip_yaml:\n for target in targets:\n fp = target[1]\n post = frontmatter.load(fp)\n shutil.copy(fp, os.path.splitext(fp)[0] + '.bak')\n with open(fp, 'w') as f:\n f.write(post.content)\n logger.debug('removed yaml front matter from {0}'.format(fp))\n\n\nif __name__ == \"__main__\":\n log_level = DEFAULT_LOG_LEVEL\n log_level_name = logging.getLevelName(log_level)\n logging.basicConfig(level=log_level)\n\n try:\n parser = argparse.ArgumentParser(\n description=__doc__,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n for p in POSITIONAL_ARGUMENTS:\n d = {\n 'help': p[3]\n }\n if type(p[2]) == bool:\n if p[2] is False:\n d['action'] = 'store_true'\n d['default'] = False\n else:\n d['action'] = 'store_false'\n d['default'] = True\n else:\n d['default'] = p[2]\n parser.add_argument(\n p[0],\n p[1],\n **d)\n parser.add_argument(\n 'where',\n type=str,\n help='path to desired project directory')\n args = parser.parse_args()\n if args.loglevel is not None:\n args_log_level = re.sub('\\s+', '', args.loglevel.strip().upper())\n try:\n log_level = getattr(logging, args_log_level)\n except AttributeError:\n logging.error(\n \"command line option to set log_level failed \"\n \"because '%s' is not a valid level name; using %s\"\n % (args_log_level, log_level_name))\n if args.veryverbose:\n log_level = logging.DEBUG\n elif args.verbose:\n log_level = logging.INFO\n elif args.quiet:\n log_level = logging.CRITICAL\n log_level_name = logging.getLevelName(log_level)\n logging.getLogger().setLevel(log_level)\n fn_this = inspect.stack()[0][1].strip()\n title_this = __doc__.strip()\n logging.info(': '.join((fn_this, title_this)))\n if log_level != DEFAULT_LOG_LEVEL:\n logging.warning(\n \"logging level changed to %s via command line option\"\n % log_level_name)\n else:\n logging.info(\"using default logging level: %s\" % log_level_name)\n logging.debug(\"command line: '%s'\" % ' '.join(sys.argv))\n try:\n main(args)\n except ValueError as e:\n logging.critical(e)\n sys.exit(1)\n except NotImplementedError as e:\n logging.critical(e)\n sys.exit(1)\n sys.exit(0)\n except KeyboardInterrupt as e: # Ctrl-C\n raise e\n except SystemExit as e: # sys.exit()\n raise e\n except Exception as e:\n print(\"ERROR, UNEXPECTED EXCEPTION\")\n print(str(e))\n traceback.print_exc()\n os._exit(1)\n","repo_name":"paregorios/make-project","sub_path":"make.py","file_name":"make.py","file_ext":"py","file_size_in_byte":19534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"25137667145","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 7 03:35:13 2018\n\nHeat map creator\n\n@Author: PouyaRZ\n\"\"\"\n\nimport numpy as np\nimport geopandas as gp\n#import geoplot\nimport pandas as pd\nfrom shapely.geometry import Point\nimport matplotlib.pyplot as plt\n\ngeojs = gp.read_file('../Data/neighbourhoods.geojson')\n\ndf = pd.read_csv('../Data/data_cleaned.csv')\ndf = df[['latitude','longitude','price']]\n\ndf['coords'] = list(zip(df.longitude, df.latitude))\ndf['coords'] = df['coords'].apply(Point)\ndf['price'] = np.exp(df['price'])\ndf = df[df['price']<=200]\n\n#df.drop('longitude','latitude')\n\n\ngdf = gp.GeoDataFrame(df, geometry='coords')\n\nbase = geojs.plot(color='white', edgecolor='black', linewidth = 1, figsize=(10,10))\n\ngdf.plot(ax=base, marker='o', column='price', markersize=1, legend=True)\n\nplt.xlabel('Longitude')\nplt.ylabel('Latitude')\nplt.title('NYC Airbnb Data Price Range')\n\nplt.savefig('Price_map.svg', bbox_inches='tight')","repo_name":"PouyaREZ/AirBnbPricePrediction","sub_path":"Main/Price_map_creator.py","file_name":"Price_map_creator.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"72"}
+{"seq_id":"7198129777","text":"import torch\nimport torchvision\nimport pytorch_lightning as pl \nfrom pytorch_lightning.callbacks import ModelCheckpoint\n\nimport os \nimport argparse\nimport yaml \nimport pickle \n\n#.datasets import MNISTDataModule, EndToEndDataModule, EndToEndNoTestDataModule, ReasoningDataModule, fetch_perception_data\nfrom training import models, basic_models#BasicLSTM, MNISTModel, Neuroplytorch, ReasoningModel, MNISTWindow\nfrom data import data, datasets\n\ndef get_complex_parameters(complex_events_dict) -> tuple: \n ce_fsm_list, ce_time_list = [], [] \n for k in complex_events_dict.keys():\n complex_event = complex_events_dict[k]\n ce_fsm_list.append(torch.tensor(complex_event['PATTERN'])) \n max_time = [float('inf') if a=='INF' else a for a in complex_event['MAX_TIME']]\n ce_time_list.append(torch.tensor([max_time, complex_event['EVENTS_BETWEEN']]))\n \n return ce_fsm_list, ce_time_list\n \nif __name__==\"__main__\":\n parser = argparse.ArgumentParser() \n\n # This distinguishes between problems, i.e. the different scenarios, pattern parameters etc.\n parser.add_argument('--name', dest='config_name', type=str, default='basic_neuro_experiment')\n parser.add_argument('--logic', dest='check_logic', type=int, default=0) # if False, run end-to-end, if True run logic_check on reasoning layer\n\n args = vars(parser.parse_args())\n\n # TODO: on run, save the config file as hyperparameters for the logger\n with open(f'./configs/{args[\"config_name\"]}.yaml') as file:\n x = yaml.load(file, Loader=yaml.FullLoader)\n training = x['TRAINING'] \n complex_events = x['COMPLEX EVENTS']\n\n ce_fsm_list, ce_time_list = get_complex_parameters(complex_events)\n assert(data.check_complex_parameters(ce_fsm_list, ce_time_list), \"Pattern and temporal metadata don't match, check the config file\")\n\n MODULE_NAME = args['config_name']\n\n # TODO: redo and double check these, make sure **kwargs are going in the right places etc. \n perception_model_args = training['PERCEPTION']['PARAMETERS'].get('MODEL', {})\n reasoning_model_args = training['REASONING']['PARAMETERS'].get('MODEL', {})\n end_to_end_model_args = training['NEUROPLYTORCH']['PARAMETERS'].get('MODEL', {})\n\n perception_dataset_args = training['PERCEPTION']['PARAMETERS'].get('DATASET', {})\n reasoning_dataset_args = training['REASONING']['PARAMETERS'].get('DATASET', {})\n end_to_end_dataset_args = training['NEUROPLYTORCH']['PARAMETERS'].get('DATASET', {})\n\n perception_loss_str = training['PERCEPTION'].get('PRETRAIN', {}).get('LOSS_FUNCTION', 'MSELoss')\n reasoning_loss_str = training['REASONING'].get('LOSS_FUNCTION', 'MSELoss')\n\n pretrain_perception = training['PERCEPTION'].get('PRETRAIN', {}).get('PRETRAIN_PERCEPTION', False)\n pretrain_num_epochs = training['PERCEPTION'].get('PRETRAIN', {}).get('PRETRAIN_EPOCHS', 10)\n\n pretrain_lr = training['PERCEPTION'].get('PRETRAIN', {}).get('LEARNING_RATE', 0.001)\n reasoning_lr = training['REASONING'].get('LEARNING_RATE', 0.001)\n\n reasoning_epochs = training['REASONING']['EPOCHS']\n reasoning_num_data = training['REASONING']['EPOCHS']\n\n end_to_end_lr = training['NEUROPLYTORCH'].get('LEARNING_RATE', 0.001)\n end_to_end_loss_str = training['NEUROPLYTORCH'].get('LOSS_FUNCTION', 'MSELoss')\n end_to_end_epochs = training['NEUROPLYTORCH']['EPOCHS']\n \n no_test = end_to_end_dataset_args.get('no_test', True)\n\n window_size = training.get('WINDOW_SIZE', 10)\n num_primitive_events = training.get('NUM_PRIMITIVE_EVENTS', 10)\n input_size = perception_model_args.pop('input_size', None)\n\n use_gpu = int(torch.cuda.is_available())\n\n \n\n # fetch raw input data \n x = [] \n if training['DATASET']['TYPE']=='Pytorch Dataset':\n x = datasets.fetch_perception_data(dataset_str=training['DATASET']['NAME'], dataset_loc=training['DATASET']['LOCATION'])\n else:\n x = datasets.fetch_perception_data_local(dataset_loc=training['DATASET']['LOCATION'], dataset_type=training['DATASET']['TYPE'], **perception_dataset_args)\n\n\n\n\n # if pretrain_perception then train the perception model before attaching to Neuroplytorch, else leave untrained\n perception_model = basic_models.get_model(training['PERCEPTION']['MODEL'])(input_size=input_size, output_size=num_primitive_events, **perception_model_args)\n if input_size==None: perception_model = basic_models.get_model(training['PERCEPTION']['MODEL'])(output_size=num_primitive_events, **perception_model_args)\n if pretrain_perception:\n perception_data = datasets.get_datamodule(training['PERCEPTION']['PRETRAIN']['DATA_MODULE'])(data_dir=training['DATASET']['NAME'], **perception_dataset_args)\n model = models.get_model(training['PERCEPTION']['PRETRAIN']['MODEL_MODULE'])(loss_str=perception_loss_str, lr=pretrain_lr, **perception_model_args)\n trainer = pl.Trainer(max_epochs=pretrain_num_epochs, gpus=use_gpu, precision=16) \n trainer.fit(model, perception_data)\n\n perception_model = model.model\n\n perception_model = models.PerceptionWindow(perception_model=perception_model, window_size=window_size, num_primitive_events=num_primitive_events)\n\n\n\n\n # if a reasoning model already exists\n if os.path.exists(f'./models/reasoning/reasoning_model_{reasoning_loss_str}_{MODULE_NAME}.pt'):\n reasoning_model = models.ReasoningModel(input_size=num_primitive_events, output_size=len(ce_fsm_list), loss_str=reasoning_loss_str, lr=reasoning_lr)\n reasoning_model = reasoning_model.model\n reasoning_model.load_state_dict(torch.load(f'./models/reasoning/reasoning_model_{reasoning_loss_str}_{MODULE_NAME}.pt'))\n\n # otherwise synthesise data and train reasoning model separate from Neuroplytorch model\n else:\n reasoning_data = datasets.ReasoningDataModule(ce_fsm_list=ce_fsm_list, ce_time_list=ce_time_list, num_primitive_events=num_primitive_events, \n window_size=window_size, **reasoning_dataset_args)\n\n model = models.ReasoningModel(input_size=num_primitive_events, output_size=len(ce_fsm_list), loss_str=reasoning_loss_str, lr=reasoning_lr)\n trainer = pl.Trainer(max_epochs=reasoning_epochs, gpus=use_gpu, precision=16)\n\n trainer.fit(model, reasoning_data)\n trainer.test(model, reasoning_data)\n\n model.save_weights(f'./models/reasoning/reasoning_model_{reasoning_loss_str}_{MODULE_NAME}.pt')\n reasoning_model = model.model \n try: os.remove('curr_tmp_reasoning_model.pt')\n except Exception: pass \n\n\n\n\n if args['check_logic']:\n models.check_reasoning_logic(reasoning_model, ce_fsm_list, ce_time_list, num_primitive_events, window_size)\n\n else:\n\n # Push raw data with pattern parameters into an end-to-end dataset (NoTest implies the test set is used as validation)\n no_test_args = end_to_end_dataset_args.pop('no_test', True)\n end_data = datasets.EndToEndNoTestDataModule if no_test_args else datasets.EndToEndDataModule\n end_data = end_data(dataset=x, ce_fsm_list=ce_fsm_list, ce_time_list=ce_time_list, num_primitive_events=num_primitive_events, \n window_size=window_size, **end_to_end_dataset_args)\n\n # create a Neuroplytorch model from the reasoning model and perception model from previous and train\n end_model = models.Neuroplytorch(reasoning_model=reasoning_model, window_size=window_size, num_primitive_events=num_primitive_events,loss_str=end_to_end_loss_str, \n perception_model=perception_model, lr=end_to_end_lr)\n\n checkpoint_callback = ModelCheckpoint(\n monitor=\"val_loss\",\n dirpath=\"checkpoints\",\n filename=f\"{MODULE_NAME}\"+\"-{epoch:02d}-{val_loss:.2f}\",\n save_top_k=3,\n mode=\"min\",\n )\n\n trainer = pl.Trainer(max_epochs=end_to_end_epochs, gpus=use_gpu, precision=16)\n trainer.fit(end_model, end_data)\n\n end_model.save_model(f'models/neuroplytorch/{reasoning_loss_str}_{MODULE_NAME}')\n\n","repo_name":"dais-ita/Neuroplytorch","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8311,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"15457957791","text":"import collections\nimport datetime\nimport os\nimport re\n\nfrom twisted.internet import defer\nfrom twisted.web import html, resource, server\n\nfrom buildbot.status.web.base import HtmlResource\nfrom buildbot.util import json\n\n\n_IS_INT = re.compile('^[-+]?\\d+$')\n\n\nFLAGS = \"\"\"\\\n - as_text\n - By default, application/json is used. Setting as_text=1 change the type\n to text/plain and implicitly sets compact=0 and filter=1. Mainly useful to\n look at the result in a web browser.\n - compact\n - By default, the json data is compact and defaults to 1. For easier to read\n indented output, set compact=0.\n - select\n - By default, most children data is listed. You can do a random selection\n of data by using select= multiple times to coagulate data.\n \"select=\" includes the actual url otherwise it is skipped.\n - filter\n - Filters out null, false, and empty string, list and dict. This reduce the\n amount of useless data sent.\n - callback\n - Enable uses of JSONP as described in\n http://en.wikipedia.org/wiki/JSONP. Note that\n Access-Control-Allow-Origin:* is set in the HTTP response header so you\n can use this in compatible browsers.\n\"\"\"\n\nEXAMPLES = \"\"\"\\\n - /json\n - Root node, that *doesn't* mean all the data. Many things (like logs) must\n be explicitly queried for performance reasons.\n - /json/builders/\n - All builders.\n - /json/builders/\n - A specific builder as compact text.\n - /json/builders//builds\n - All *cached* builds.\n - /json/builders//builds/_all\n - All builds. Warning, reads all previous build data.\n - /json/builders//builds/\n - Where is either positive, a build number, or negative, a past\n build.\n - /json/builders//builds/-1/source_stamp/changes\n - Build changes\n - /json/builders//builds?select=-1&select=-2\n - Two last builds on '' builder.\n - /json/builders//builds?select=-1/source_stamp/changes&select=-2/source_stamp/changes\n - Changes of the two last builds on '' builder.\n - /json/builders//slaves\n - Slaves associated to this builder.\n - /json/builders/?select=&select=slaves\n - Builder information plus details information about its slaves. Neat eh?\n - /json/slaves/\n - A specific slave.\n - /json?select=slaves//&select=project&select=builders//builds/\n - A selection of random unrelated stuff as an random example. :)\n\"\"\"\n\n\ndef RequestArg(request, arg, default):\n return request.args.get(arg, [default])[0]\n\n\ndef RequestArgToBool(request, arg, default):\n value = RequestArg(request, arg, default)\n if value in (False, True):\n return value\n value = value.lower()\n if value in ('1', 'true'):\n return True\n if value in ('0', 'false'):\n return False\n # Ignore value.\n return default\n\n\ndef FilterOut(data):\n \"\"\"Returns a copy with None, False, \"\", [], () and {} removed.\n Warning: converts tuple to list.\"\"\"\n if isinstance(data, (list, tuple)):\n # Recurse in every items and filter them out.\n items = map(FilterOut, data)\n if not filter(lambda x: not x in ('', False, None, [], {}, ()), items):\n return None\n return items\n elif isinstance(data, dict):\n return dict(filter(lambda x: not x[1] in ('', False, None, [], {}, ()),\n [(k, FilterOut(v)) for (k, v) in data.iteritems()]))\n else:\n return data\n\n\nclass JsonResource(resource.Resource):\n \"\"\"Base class for json data.\"\"\"\n\n contentType = \"application/json\"\n cache_seconds = 60\n help = None\n pageTitle = None\n level = 0\n\n def __init__(self, status):\n \"\"\"Adds transparent lazy-child initialization.\"\"\"\n resource.Resource.__init__(self)\n # buildbot.status.builder.Status\n self.status = status\n\n def getChildWithDefault(self, path, request):\n \"\"\"Adds transparent support for url ending with /\"\"\"\n if path == \"\" and len(request.postpath) == 0:\n return self\n if (path == \"help\" or path == \"help/\") and self.help:\n pageTitle = ''\n if self.pageTitle:\n pageTitle = self.pageTitle + ' help'\n res = HelpResource(self.help, pageTitle=pageTitle, parent_node=self)\n res.level = self.level + 1\n return res\n # Equivalent to resource.Resource.getChildWithDefault()\n if self.children.has_key(path):\n return self.children[path]\n return self.getChild(path, request)\n\n def putChild(self, name, res):\n \"\"\"Adds the resource's level for help links generation.\"\"\"\n\n def RecurseFix(res, level):\n res.level = level + 1\n for c in res.children.itervalues():\n RecurseFix(c, res.level)\n\n RecurseFix(res, self.level)\n resource.Resource.putChild(self, name, res)\n\n def render_GET(self, request):\n \"\"\"Renders a HTTP GET at the http request level.\"\"\"\n d = defer.maybeDeferred(lambda : self.content(request))\n def handle(data):\n if isinstance(data, unicode):\n data = data.encode(\"utf-8\")\n request.setHeader(\"Access-Control-Allow-Origin\", \"*\")\n if RequestArgToBool(request, 'as_text', False):\n request.setHeader(\"content-type\", 'text/plain')\n else:\n request.setHeader(\"content-type\", self.contentType)\n request.setHeader(\"content-disposition\",\n \"attachment; filename=\\\"%s.json\\\"\" % request.path)\n # Make sure we get fresh pages.\n if self.cache_seconds:\n now = datetime.datetime.utcnow()\n expires = now + datetime.timedelta(seconds=self.cache_seconds)\n request.setHeader(\"Expires\",\n expires.strftime(\"%a, %d %b %Y %H:%M:%S GMT\"))\n request.setHeader(\"Pragma\", \"no-cache\")\n return data\n d.addCallback(handle)\n def ok(data):\n request.write(data)\n request.finish()\n def fail(f):\n request.processingFailed(f)\n return None # processingFailed will log this for us\n d.addCallbacks(ok, fail)\n return server.NOT_DONE_YET\n\n @defer.deferredGenerator\n def content(self, request):\n \"\"\"Renders the json dictionaries.\"\"\"\n # Supported flags.\n select = request.args.get('select')\n as_text = RequestArgToBool(request, 'as_text', False)\n filter_out = RequestArgToBool(request, 'filter', as_text)\n compact = RequestArgToBool(request, 'compact', not as_text)\n callback = request.args.get('callback')\n\n # Implement filtering at global level and every child.\n if select is not None:\n del request.args['select']\n # Do not render self.asDict()!\n data = {}\n # Remove superfluous /\n select = [s.strip('/') for s in select]\n select.sort(cmp=lambda x,y: cmp(x.count('/'), y.count('/')),\n reverse=True)\n for item in select:\n # Start back at root.\n node = data\n # Implementation similar to twisted.web.resource.getChildForRequest\n # but with a hacked up request.\n child = self\n prepath = request.prepath[:]\n postpath = request.postpath[:]\n request.postpath = filter(None, item.split('/'))\n while request.postpath and not child.isLeaf:\n pathElement = request.postpath.pop(0)\n node[pathElement] = {}\n node = node[pathElement]\n request.prepath.append(pathElement)\n child = child.getChildWithDefault(pathElement, request)\n\n # some asDict methods return a Deferred, so handle that\n # properly\n if hasattr(child, 'asDict'):\n wfd = defer.waitForDeferred(\n defer.maybeDeferred(lambda :\n child.asDict(request)))\n yield wfd\n child_dict = wfd.getResult()\n else:\n child_dict = {\n 'error' : 'Not available',\n }\n node.update(child_dict)\n\n request.prepath = prepath\n request.postpath = postpath\n else:\n wfd = defer.waitForDeferred(\n defer.maybeDeferred(lambda :\n self.asDict(request)))\n yield wfd\n data = wfd.getResult()\n\n if filter_out:\n data = FilterOut(data)\n if compact:\n data = json.dumps(data, sort_keys=True, separators=(',',':'))\n else:\n data = json.dumps(data, sort_keys=True, indent=2)\n if callback:\n # Only accept things that look like identifiers for now\n callback = callback[0]\n if re.match(r'^[a-zA-Z$][a-zA-Z$0-9.]*$', callback):\n data = '%s(%s);' % (callback, data)\n yield data\n\n @defer.deferredGenerator\n def asDict(self, request):\n \"\"\"Generates the json dictionary.\n\n By default, renders every childs.\"\"\"\n if self.children:\n data = {}\n for name in self.children:\n child = self.getChildWithDefault(name, request)\n if isinstance(child, JsonResource):\n wfd = defer.waitForDeferred(\n defer.maybeDeferred(lambda :\n child.asDict(request)))\n yield wfd\n data[name] = wfd.getResult()\n # else silently pass over non-json resources.\n yield data\n else:\n raise NotImplementedError()\n\n\ndef ToHtml(text):\n \"\"\"Convert a string in a wiki-style format into HTML.\"\"\"\n indent = 0\n in_item = False\n output = []\n for line in text.splitlines(False):\n match = re.match(r'^( +)\\- (.*)$', line)\n if match:\n if indent < len(match.group(1)):\n output.append('')\n indent = len(match.group(1))\n elif indent > len(match.group(1)):\n while indent > len(match.group(1)):\n output.append('
')\n indent -= 2\n if in_item:\n # Close previous item\n output.append('')\n output.append('')\n in_item = True\n line = match.group(2)\n elif indent:\n if line.startswith((' ' * indent) + ' '):\n # List continuation\n line = line.strip()\n else:\n # List is done\n if in_item:\n output.append(' ')\n in_item = False\n while indent > 0:\n output.append('')\n indent -= 2\n\n if line.startswith('/'):\n if not '?' in line:\n line_full = line + '?as_text=1'\n else:\n line_full = line + '&as_text=1'\n output.append('' +\n html.escape(line) + '')\n else:\n output.append(html.escape(line).replace(' ', ' '))\n if not in_item:\n output.append('
')\n\n if in_item:\n output.append('')\n while indent > 0:\n output.append('')\n indent -= 2\n return '\\n'.join(output)\n\n\nclass HelpResource(HtmlResource):\n def __init__(self, text, pageTitle, parent_node):\n HtmlResource.__init__(self)\n self.text = text\n self.pageTitle = pageTitle\n self.parent_level = parent_node.level\n self.parent_children = parent_node.children.keys()\n\n def content(self, request, cxt):\n cxt['level'] = self.parent_level\n cxt['text'] = ToHtml(self.text)\n cxt['children'] = [ n for n in self.parent_children if n != 'help' ]\n cxt['flags'] = ToHtml(FLAGS)\n cxt['examples'] = ToHtml(EXAMPLES).replace(\n 'href=\"/json',\n 'href=\"%sjson' % (self.level * '../'))\n\n template = request.site.buildbot_service.templates.get_template(\"jsonhelp.html\")\n return template.render(**cxt)\n\nclass BuilderPendingBuildsJsonResource(JsonResource):\n help = \"\"\"Describe pending builds for a builder.\n\"\"\"\n pageTitle = 'Builder'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n\n def asDict(self, request):\n # buildbot.status.builder.BuilderStatus\n d = self.builder_status.getPendingBuildRequestStatuses()\n def to_dict(statuses):\n return defer.gatherResults(\n [ b.asDict_async() for b in statuses ])\n d.addCallback(to_dict)\n return d\n\n\nclass BuilderJsonResource(JsonResource):\n help = \"\"\"Describe a single builder.\n\"\"\"\n pageTitle = 'Builder'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n self.putChild('builds', BuildsJsonResource(status, builder_status))\n self.putChild('slaves', BuilderSlavesJsonResources(status,\n builder_status))\n self.putChild(\n 'pendingBuilds',\n BuilderPendingBuildsJsonResource(status, builder_status))\n\n def asDict(self, request):\n # buildbot.status.builder.BuilderStatus\n return self.builder_status.asDict_async()\n\n\nclass BuildersJsonResource(JsonResource):\n help = \"\"\"List of all the builders defined on a master.\n\"\"\"\n pageTitle = 'Builders'\n\n def __init__(self, status):\n JsonResource.__init__(self, status)\n for builder_name in self.status.getBuilderNames():\n self.putChild(builder_name,\n BuilderJsonResource(status,\n status.getBuilder(builder_name)))\n\n\nclass BuilderSlavesJsonResources(JsonResource):\n help = \"\"\"Describe the slaves attached to a single builder.\n\"\"\"\n pageTitle = 'BuilderSlaves'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n for slave_name in self.builder_status.slavenames:\n self.putChild(slave_name,\n SlaveJsonResource(status,\n self.status.getSlave(slave_name)))\n\n\nclass BuildJsonResource(JsonResource):\n help = \"\"\"Describe a single build.\n\"\"\"\n pageTitle = 'Build'\n\n def __init__(self, status, build_status):\n JsonResource.__init__(self, status)\n self.build_status = build_status\n self.putChild('source_stamp',\n SourceStampJsonResource(status,\n build_status.getSourceStamp()))\n self.putChild('steps', BuildStepsJsonResource(status, build_status))\n\n def asDict(self, request):\n return self.build_status.asDict()\n\n\nclass AllBuildsJsonResource(JsonResource):\n help = \"\"\"All the builds that were run on a builder.\n\"\"\"\n pageTitle = 'AllBuilds'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n\n def getChild(self, path, request):\n # Dynamic childs.\n if isinstance(path, int) or _IS_INT.match(path):\n build_status = self.builder_status.getBuild(int(path))\n if build_status:\n # Don't cache BuildJsonResource; that would defeat the cache-ing\n # mechanism in place for BuildStatus objects (in BuilderStatus).\n return BuildJsonResource(self.status, build_status)\n return JsonResource.getChild(self, path, request)\n\n def asDict(self, request):\n results = {}\n # If max is too big, it'll trash the cache...\n max = int(RequestArg(request, 'max',\n self.builder_status.buildCacheSize/2))\n for i in range(0, max):\n child = self.getChildWithDefault(-i, request)\n if not isinstance(child, BuildJsonResource):\n continue\n results[child.build_status.getNumber()] = child.asDict(request)\n return results\n\n\nclass BuildsJsonResource(AllBuildsJsonResource):\n help = \"\"\"Builds that were run on a builder.\n\"\"\"\n pageTitle = 'Builds'\n\n def __init__(self, status, builder_status):\n AllBuildsJsonResource.__init__(self, status, builder_status)\n self.putChild('_all', AllBuildsJsonResource(status, builder_status))\n\n def getChild(self, path, request):\n # Transparently redirects to _all if path is not ''.\n return self.children['_all'].getChildWithDefault(path, request)\n\n def asDict(self, request):\n # This would load all the pickles and is way too heavy, especially that\n # it would trash the cache:\n # self.children['builds'].asDict(request)\n # TODO(maruel) This list should also need to be cached but how?\n builds = dict([\n (int(file), None)\n for file in os.listdir(self.builder_status.basedir)\n if _IS_INT.match(file)\n ])\n return builds\n\n\nclass BuildStepJsonResource(JsonResource):\n help = \"\"\"A single build step.\n\"\"\"\n pageTitle = 'BuildStep'\n\n def __init__(self, status, build_step_status):\n # buildbot.status.buildstep.BuildStepStatus\n JsonResource.__init__(self, status)\n self.build_step_status = build_step_status\n # TODO self.putChild('logs', LogsJsonResource())\n\n def asDict(self, request):\n return self.build_step_status.asDict()\n\n\nclass BuildStepsJsonResource(JsonResource):\n help = \"\"\"A list of build steps that occurred during a build.\n\"\"\"\n pageTitle = 'BuildSteps'\n\n def __init__(self, status, build_status):\n JsonResource.__init__(self, status)\n self.build_status = build_status\n # The build steps are constantly changing until the build is done so\n # keep a reference to build_status instead\n\n def getChild(self, path, request):\n # Dynamic childs.\n build_step_status = None\n if isinstance(path, int) or _IS_INT.match(path):\n build_step_status = self.build_status.getSteps()[int(path)]\n else:\n steps_dict = dict([(step.getName(), step)\n for step in self.build_status.getSteps()])\n build_step_status = steps_dict.get(path)\n if build_step_status:\n # Create it on-demand.\n child = BuildStepJsonResource(self.status, build_step_status)\n # Cache it.\n index = self.build_status.getSteps().index(build_step_status)\n self.putChild(str(index), child)\n self.putChild(build_step_status.getName(), child)\n return child\n return JsonResource.getChild(self, path, request)\n\n def asDict(self, request):\n # Only use the number and not the names!\n results = {}\n index = 0\n for step in self.build_status.getSteps():\n results[index] = step.asDict()\n index += 1\n return results\n\n\nclass ChangeJsonResource(JsonResource):\n help = \"\"\"Describe a single change that originates from a change source.\n\"\"\"\n pageTitle = 'Change'\n\n def __init__(self, status, change):\n # buildbot.changes.changes.Change\n JsonResource.__init__(self, status)\n self.change = change\n\n def asDict(self, request):\n return self.change.asDict()\n\n\nclass ChangesJsonResource(JsonResource):\n help = \"\"\"List of changes.\n\"\"\"\n pageTitle = 'Changes'\n\n def __init__(self, status, changes):\n JsonResource.__init__(self, status)\n for c in changes:\n # c.number can be None or clash another change if the change was\n # generated inside buildbot or if using multiple pollers.\n if c.number is not None and str(c.number) not in self.children:\n self.putChild(str(c.number), ChangeJsonResource(status, c))\n else:\n # Temporary hack since it creates information exposure.\n self.putChild(str(id(c)), ChangeJsonResource(status, c))\n\n def asDict(self, request):\n \"\"\"Don't throw an exception when there is no child.\"\"\"\n if not self.children:\n return {}\n return JsonResource.asDict(self, request)\n\n\nclass ChangeSourcesJsonResource(JsonResource):\n help = \"\"\"Describe a change source.\n\"\"\"\n pageTitle = 'ChangeSources'\n\n def asDict(self, request):\n result = {}\n n = 0\n for c in self.status.getChangeSources():\n # buildbot.changes.changes.ChangeMaster\n change = {}\n change['description'] = c.describe()\n result[n] = change\n n += 1\n return result\n\n\nclass ProjectJsonResource(JsonResource):\n help = \"\"\"Project-wide settings.\n\"\"\"\n pageTitle = 'Project'\n\n def asDict(self, request):\n return self.status.asDict()\n\n\nclass SlaveJsonResource(JsonResource):\n help = \"\"\"Describe a slave.\n\"\"\"\n pageTitle = 'Slave'\n\n def __init__(self, status, slave_status):\n JsonResource.__init__(self, status)\n self.slave_status = slave_status\n self.name = self.slave_status.getName()\n self.builders = None\n\n def getBuilders(self):\n if self.builders is None:\n # Figure out all the builders to which it's attached\n self.builders = []\n for builderName in self.status.getBuilderNames():\n if self.name in self.status.getBuilder(builderName).slavenames:\n self.builders.append(builderName)\n return self.builders\n\n def getSlaveBuildMap(self, buildcache, buildercache):\n for builderName in self.getBuilders():\n if builderName not in buildercache:\n buildercache.add(builderName)\n builder_status = self.status.getBuilder(builderName)\n\n buildnums = range(-1, -(builder_status.buildCacheSize - 1), -1)\n builds = builder_status.getBuilds(buildnums)\n\n for build_status in builds:\n if not build_status or not build_status.isFinished():\n # If not finished, it will appear in runningBuilds.\n break\n slave = buildcache[build_status.getSlavename()]\n slave.setdefault(builderName, []).append(\n build_status.getNumber())\n return buildcache[self.name]\n\n def asDict(self, request):\n if not hasattr(request, 'custom_data'):\n request.custom_data = {}\n if 'buildcache' not in request.custom_data:\n # buildcache is used to cache build information across multiple\n # invocations of SlaveJsonResource. It should be set to an empty\n # collections.defaultdict(dict).\n request.custom_data['buildcache'] = collections.defaultdict(dict)\n\n # Tracks which builders have been stored in the buildcache.\n request.custom_data['buildercache'] = set()\n\n results = self.slave_status.asDict()\n # Enhance it by adding more information.\n results['builders'] = self.getSlaveBuildMap(\n request.custom_data['buildcache'],\n request.custom_data['buildercache'])\n return results\n\n\nclass SlavesJsonResource(JsonResource):\n help = \"\"\"List the registered slaves.\n\"\"\"\n pageTitle = 'Slaves'\n\n def __init__(self, status):\n JsonResource.__init__(self, status)\n for slave_name in status.getSlaveNames():\n self.putChild(slave_name,\n SlaveJsonResource(status,\n status.getSlave(slave_name)))\n\n\nclass SourceStampJsonResource(JsonResource):\n help = \"\"\"Describe the sources for a SourceStamp.\n\"\"\"\n pageTitle = 'SourceStamp'\n\n def __init__(self, status, source_stamp):\n # buildbot.sourcestamp.SourceStamp\n JsonResource.__init__(self, status)\n self.source_stamp = source_stamp\n self.putChild('changes',\n ChangesJsonResource(status, source_stamp.changes))\n # TODO(maruel): Should redirect to the patch's url instead.\n #if source_stamp.patch:\n # self.putChild('patch', StaticHTML(source_stamp.path))\n\n def asDict(self, request):\n return self.source_stamp.asDict()\n\nclass MetricsJsonResource(JsonResource):\n help = \"\"\"Master metrics.\n\"\"\"\n title = \"Metrics\"\n\n def asDict(self, request):\n metrics = self.status.getMetrics()\n if metrics:\n return metrics.asDict()\n else:\n # Metrics are disabled\n return None\n\n\n\nclass JsonStatusResource(JsonResource):\n \"\"\"Retrieves all json data.\"\"\"\n help = \"\"\"JSON status\n\nRoot page to give a fair amount of information in the current buildbot master\nstatus. You may want to use a child instead to reduce the load on the server.\n\nFor help on any sub directory, use url /child/help\n\"\"\"\n pageTitle = 'Buildbot JSON'\n\n def __init__(self, status):\n JsonResource.__init__(self, status)\n self.level = 1\n self.putChild('builders', BuildersJsonResource(status))\n self.putChild('change_sources', ChangeSourcesJsonResource(status))\n self.putChild('project', ProjectJsonResource(status))\n self.putChild('slaves', SlavesJsonResource(status))\n self.putChild('metrics', MetricsJsonResource(status))\n # This needs to be called before the first HelpResource().body call.\n self.hackExamples()\n\n def content(self, request):\n result = JsonResource.content(self, request)\n # This is done to hook the downloaded filename.\n request.path = 'buildbot'\n return result\n\n def hackExamples(self):\n global EXAMPLES\n # Find the first builder with a previous build or select the last one.\n builder = None\n for b in self.status.getBuilderNames():\n builder = self.status.getBuilder(b)\n if builder.getBuild(-1):\n break\n if not builder:\n return\n EXAMPLES = EXAMPLES.replace('', builder.getName())\n build = builder.getBuild(-1)\n if build:\n EXAMPLES = EXAMPLES.replace('', str(build.getNumber()))\n if builder.slavenames:\n EXAMPLES = EXAMPLES.replace('', builder.slavenames[0])\n\n# vim: set ts=4 sts=4 sw=4 et:\n","repo_name":"houseoflifeproperty/bitpop","sub_path":"build/third_party/buildbot_8_4p1/buildbot/status/web/status_json.py","file_name":"status_json.py","file_ext":"py","file_size_in_byte":27016,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"72"}
+{"seq_id":"29585326781","text":"import multiprocessing\nimport math\nimport logging\nimport datetime\nfrom multiprocessing.pool import Pool\n\nlogging.basicConfig(\n format=\"%(asctime)s - %(name)s - [%(filename)s:%(lineno)d] - %(levelname)s - %(message)s\",\n datefmt=\"%Y-%m-%d %I:%M:%S %p\",\n level=logging.INFO,\n)\nlogger = logging.getLogger(__name__)\n\n\ndef sqrt_even_numbers(limit: int):\n for n in range(1, limit + 1):\n if n % 2 == 0:\n # math.sqrt(n)\n # print(f\"number: {n}, square root: {math.sqrt(n)}\")\n logger.info(\"number: %s, square root: %s\", n, math.sqrt(n))\n\n\ndef main():\n # sqrt_even_numbers(10)\n print(\"Starting multiprocessing now...\")\n\n t0 = datetime.datetime.now()\n # create pool of processors to use\n pool = Pool(processes=multiprocessing.cpu_count())\n\n # send computation task to different processors in the pool\n pool.apply_async(func=sqrt_even_numbers, args=(100000,))\n pool.apply_async(func=sqrt_even_numbers, args=(200000,))\n pool.apply_async(func=sqrt_even_numbers, args=(300000,))\n pool.apply_async(func=sqrt_even_numbers, args=(400000,))\n\n # close the pool\n pool.close()\n\n # wait for the tasks to complete\n pool.join()\n\n dt = datetime.datetime.now() - t0\n\n print(f\"Total time taken in seconds: {dt.total_seconds():,.2f}\")\n\n\nif __name__ == \"__main__\":\n # sqrt_even_numbers()\n main()\n","repo_name":"andy-ifeanyi/concurrency_pattern_python","sub_path":"multiprocessing/basic_multiprocessing.py","file_name":"basic_multiprocessing.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"8735012946","text":"#problem 34 / find first and last position of element in sorted array\nclass Solution(object):\n def searchRange(self, nums, target):\n \"\"\"\n :type nums: List[int]\n :type target: int\n :rtype: List[int]\n \"\"\"\n leftidx = self.binarysearch(nums,target,True)\n if leftidx == len(nums) or nums[leftidx] != target:\n return [-1,-1]\n return [leftidx,self.binarysearch(nums,target,False)-1]\n \n def binarysearch(self,nums,target,ifleft):\n lo = 0\n hi = len(nums)\n while lo < hi:\n mid = (lo+hi)/2\n if nums[mid] > target or (ifleft and target == nums[mid]):\n hi = mid\n else:\n lo = mid+1\n return lo","repo_name":"digitalladder/leetcode","sub_path":"problem34.py","file_name":"problem34.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"32566416879","text":"import threading\nfrom multiprocessing.pool import ThreadPool\n\nfrom pyhtmlgui import Observable\n\nfrom app.devices.devices import DevicesInstance\nfrom app.files.shots import ShotsInstance\n\n\nclass Task_SyncShots(Observable):\n def __init__(self):\n super().__init__()\n self.name = \"Sync Shots\"\n self.status = \"idle\"\n self.worker = None\n\n def set_status(self, value):\n self.status = value\n self.notify_observers()\n\n def run(self):\n if self.worker is None:\n self.worker = threading.Thread(target=self._run, daemon=True)\n self.worker.start()\n\n def _run(self):\n if self.status != \"idle\":\n return\n self.set_status(\"list\")\n cameras = DevicesInstance().cameras.list()\n\n cameras = [c for c in cameras if c.status == \"online\"]\n with ThreadPool(20) as p:\n p.map(lambda device: device.camera.shots._refresh_list(), cameras)\n\n self.set_status(\"shots\")\n with ThreadPool(5) as p:\n p.map(lambda shot: shot._sync_remote(), ShotsInstance().shots)\n\n self.set_status(\"idle\")\n self.worker = None\n\n \n_taskSyncShotsInstance = None\n\n\ndef TaskSyncShotsInstance():\n global _taskSyncShotsInstance\n if _taskSyncShotsInstance is None:\n _taskSyncShotsInstance = Task_SyncShots()\n return _taskSyncShotsInstance\n","repo_name":"dirk-makerhafen/openpi3dscan","sub_path":"server/app/tasks/task_SyncShots.py","file_name":"task_SyncShots.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"34435636101","text":"# uninhm\n# https://atcoder.jp/contests/abc089/tasks/abc089_c\n# combinatorics, semi-brute force\n\nfrom collections import Counter\n\nn = int(input())\ns = [input() for _ in range(n)]\n\nc = Counter()\n\nfor name in s:\n c[name[0]] += 1\n\nmarch = 'MARCH'\n\nans = 0\nfor i in range(len(march)):\n for j in range(i+1, len(march)):\n for k in range(j+1, len(march)):\n ans += c[march[i]] * c[march[j]] * c[march[k]]\n\nprint(ans)\n","repo_name":"Vicfred/kyopro","sub_path":"atcoder/abc089C_march.py","file_name":"abc089C_march.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"72"}
+{"seq_id":"3716121238","text":"'''\r\n\tArima model\r\n\thttps://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/\r\n'''\r\nfrom pandas import read_csv\r\nfrom pandas import datetime\r\nfrom pandas import DataFrame\r\nfrom statsmodels.tsa.arima_model import ARIMA\r\nfrom matplotlib import pyplot\r\nimport numpy as np \r\n\r\ndef parser(x):\r\n#\treturn datetime.strptime(str(x), '%Y%m.0')\r\n\treturn datetime.strptime(str(x), '%m')\r\n\r\nseries = read_csv(\r\n#\t'/home/evanb/output/data-randomforest2.txt', \r\n\t'/home/evanb/datasets/onedist.csv',\r\n\theader=0, \r\n\tparse_dates=[11], \r\n\t#index_col=0, \r\n\tsqueeze=True, \r\n\tdate_parser=parser,\r\n\tlow_memory=False\r\n)\r\nprint(series)\r\n# fit model\r\nmodel = ARIMA(np.asarray(series), order=(3,1,0))\r\nmodel_fit = model.fit(disp=0)\r\nprint(model_fit.summary())\r\n# plot residual errors\r\nresiduals = DataFrame(model_fit.resid)\r\nresiduals.plot()\r\npyplot.show()\r\nresiduals.plot(kind='kde')\r\npyplot.show()\r\nprint(residuals.describe())\r\n\r\n","repo_name":"ebradham/mlcode","sub_path":"arima.py","file_name":"arima.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"26334362070","text":"import sys\n\ndef isPrime(number): \n counter = 0\n for i in range(number + 1):\n if i == 0:\n continue\n if number % i == 0:\n counter += 1\n \n if counter == 2:\n print(True)\n else: \n print(False)\n\nif __name__ == \"__main__\":\n isPrime(int(sys.argv[1]))\n","repo_name":"tam2628/linear_regression","sub_path":"checkprime.py","file_name":"checkprime.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"1997020156","text":"from setuptools import setup, find_packages\nfrom MineRL.globals import VERSION\n\n\nextras = {}\ntest_deps = ['pytest']\n\nall_deps = []\nfor group_name in extras:\n all_deps += extras[group_name]\nall_deps = all_deps + test_deps\nextras['all'] = all_deps\n\n\nsetup(\n name='MineRL',\n version=VERSION,\n author='heron',\n author_email='wyatt.lansford@heronsystems.com',\n description='Minecraft Offline Learning Env',\n long_description='',\n long_description_content_type=\"text/markdown\",\n url='https://github.com/wyattlansford/MineRL',\n license='Closed',\n python_requires='>=3.6.0',\n packages=find_packages(),\n install_requires=[\n ],\n test_requires=test_deps,\n extras_require=extras,\n include_package_data=True\n)","repo_name":"wyattlansford/MineRL","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"12668710329","text":"import csv\nimport json\nimport os\nimport re\nfrom glob import glob\nfrom pathlib import Path\nfrom typing import List, Dict\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom cmcrameri import cm\nfrom zipfile import BadZipFile\n\nsns.set_style(\"dark\")\nsns.set_context(\"paper\")\n\nX_LABEL = [\n f\"{int(x)}:00 AM\" if x < 12 else f\"{int(x - [12 if x != 12 else 0])}:00 PM\"\n for x in np.arange(8, 22, 2)\n]\n\n\ndef add_bool_arg(parser, name, default=False):\n \"\"\"\n Adds boolean arguments to parser by registering both the positive argument and the \"no\"-argument.\n :param parser:\n :param name: Name of argument.\n :param default:\n :return:\n \"\"\"\n group = parser.add_mutually_exclusive_group(required=False)\n group.add_argument(\"--\" + name, dest=name, action=\"store_true\")\n group.add_argument(\"--no-\" + name, dest=name, action=\"store_false\")\n parser.set_defaults(**{name: default})\n\n\ndef occupancy_reward_function(\n colours: List[str], current_state: Dict[str, float], global_mode=False\n):\n \"\"\"\n Rewards occupancy rates between 75% and 90%. Punishes deviations exponentially.\n :param current_state: State dictionary.\n :param colours: Colours of different CPZs.\n :param global_mode: Whether or not to use the global occupancies or the one of the individual CPZs.\n :return: reward\n \"\"\"\n reward = 0\n if global_mode:\n cpz_occupancies = [current_state[\"overall_occupancy\"]]\n else:\n cpz_occupancies = [current_state[f\"{c}-lot occupancy\"] for c in colours]\n\n for val in cpz_occupancies:\n if 0.75 < val < 0.9:\n reward += 1\n elif val <= 0.75:\n value = 1 - (abs(val - 0.825) / 0.825) ** -1.2\n min_value = 1 - (abs(0 - 0.825) / 0.825) ** -1.2\n max_value = 1 - (abs(0.75 - 0.825) / 0.825) ** -1.2\n max_distance = max_value - min_value\n actual_distance = value - min_value\n reward += actual_distance / max_distance\n elif val >= 0.9:\n value = 1 - (abs(val - 0.825) / 0.825) ** -1.2\n min_value = 1 - (abs(1 - 0.825) / 0.825) ** -1.2\n max_value = 1 - (abs(0.9 - 0.825) / 0.825) ** -1.2\n max_distance = max_value - min_value\n actual_distance = value - min_value\n reward += actual_distance / max_distance\n return reward / len(cpz_occupancies)\n\n\ndef n_cars_reward_function(colours: List[str], current_state: Dict[str, float]):\n \"\"\"\n Minimizes the number of cars in the simulation.\n :param colours: Colours of different CPZs (only present to be able to use one call in custom_environment.py).\n :param current_state:State dictionary.\n :return: reward\n \"\"\"\n return optimize_attr(current_state, \"n_cars\", mode=\"min\")\n\n\ndef social_reward_function(colours: List[str], current_state: Dict[str, float]):\n \"\"\"\n Maximizes the normalized share of poor cars in the model.\n :param colours: Colours of different CPZs (only present to be able to use one call in custom_environment.py).\n :param current_state:State dictionary.\n :return: reward\n \"\"\"\n return optimize_attr(current_state, \"normalized_share_low\")\n\n\ndef speed_reward_function(colours: List[str], current_state: Dict[str, float]):\n \"\"\"\n Maximizes the average speed of the turtles in the model.\n :param colours: Colours of different CPZs (only present to be able to use one call in custom_environment.py).\n :param current_state:State dictionary.\n :return: reward\n \"\"\"\n return optimize_attr(current_state, \"mean_speed\")\n\n\ndef composite_reward_function(colours: List[str], current_state: Dict[str, float]):\n \"\"\"\n Maximizes 1/2 occupancy_reward_function + 1/4 n_cars_reward_function + 1/4 social_reward_function\n :param colours: Colours of different CPZs (only present to be able to use one call in custom_environment.py).\n :param current_state:State dictionary.\n :return: reward\n \"\"\"\n return (\n 0.5 * occupancy_reward_function(colours, current_state, global_mode=True)\n + 0.25 * n_cars_reward_function(colours, current_state)\n + 0.25 * social_reward_function(colours, current_state)\n )\n\n\ndef optimize_attr(current_state: Dict[str, float], attr: str, mode=\"max\"):\n \"\"\"\n Abstract function to optimize attributes.\n :param mode: either \"min\" or \"max\" (default).\n :param current_state: State dictionary.\n :param attr: Attribute in state dictionary to optimize.\n :return: reward-value\n \"\"\"\n if mode == \"min\":\n return abs(current_state[attr] - 1) ** 2\n else:\n return current_state[attr] ** 2\n\n\ndef document_episode(nl, path: Path, reward_sum):\n \"\"\"\n Create directory for current episode and command NetLogo to save model as csv.\n :param nl: NetLogo-Session of environment.\n :param path: Path of current episode.\n :param reward_sum: Sum of accumulated rewards for episode.\n :return:\n \"\"\"\n path.mkdir(parents=True, exist_ok=True)\n # Get all directories to check which episode this is\n dirs = glob(str(path) + \"/E*.pkl\")\n current_episode = 1\n if dirs:\n last_episode = max(\n [int(re.findall(\"E(\\d+)\", dirs[i])[0]) for i in range(len(dirs))]\n )\n current_episode = last_episode + 1\n episode_path = str(path / f\"E{current_episode}_{np.around(reward_sum, 8)}\").replace(\n \"\\\\\", \"/\"\n )\n\n nl.command(f'export-world \"{episode_path}.csv\"')\n nl.command(f'export-view \"{episode_path}.png\"')\n\n # Save relevant data as pickle to save storage\n df = get_data_from_run(f\"{episode_path}.csv\")\n df.to_pickle(f\"{episode_path}.pkl\", compression=\"zip\")\n\n # Delete csv\n os.remove(f\"{episode_path}.csv\")\n\n\ndef label_episodes(path: Path, df: pd.DataFrame, mode: str):\n \"\"\"\n Identifies worst, median and best episode of run. Renames them and saves plots.\n :param path: Path of current Experiment.\n :param df: DataFrame containing the results.\n :param mode: Usually either \"training\" or \"evaluation\".\n :return:\n \"\"\"\n episode_files = glob(str(path) + \"/E*.pkl\")\n performances = dict()\n performances[\"max\"] = np.around(df.rewards.max(), 8)\n performances[\"min\"] = np.around(df.rewards.min(), 8)\n performances[\"median\"] = np.around(\n df.rewards.sort_values(ignore_index=True)[np.ceil(len(df) / 2) - 1], 8\n )\n\n print(f\"Performances for {mode}:\")\n print(performances)\n\n for metric in performances.keys():\n if performances[metric] == 0.0:\n performances[metric] = 0\n found = False\n for episode in episode_files:\n # Baseline\n if mode not in [\"training\", \"eval\"]:\n if str(performances[metric]) == episode.split(\"_\")[-1].split(\".pkl\")[0]:\n found = True\n elif str(performances[metric]) in episode:\n found = True\n if found:\n new_path = path / mode / metric\n new_path.mkdir(parents=True, exist_ok=True)\n save_plots(new_path, episode)\n os.rename(\n episode,\n str(new_path / f\"{mode}_{metric}_{performances[metric]}.pkl\"),\n )\n os.rename(\n episode.replace(\"pkl\", \"png\"),\n str(new_path / f\"view_{mode}_{metric}_{performances[metric]}.png\"),\n )\n episode_files.remove(episode)\n break\n\n\ndef delete_unused_episodes(path: Path):\n \"\"\"\n Deletes episodes that did not produce either min, median or max performances to save storage.\n :param path: Path of current Experiment\n :return:\n \"\"\"\n # Get all episodes not moved due to being min, median or max\n episode_files = glob(str(path) + \"/E*\")\n\n # Remove files of episodes\n for file in episode_files:\n if os.path.isfile(file) and \"eval\" not in file:\n os.remove(file)\n\n print(\"Unused Files deleted!\")\n\n\ndef save_plots(outpath: Path, episode_path: str):\n \"\"\"\n Calls all plot functions for given episode.\n :param outpath: Path to save plots.\n :param episode_path: Path of current episode.\n :return:\n \"\"\"\n try:\n data_df = pd.read_pickle(episode_path, compression=\"zip\")\n except FileNotFoundError:\n data_df = get_data_from_run(episode_path)\n\n for func in [\n plot_fees,\n plot_occup,\n plot_social,\n plot_n_cars,\n plot_speed,\n plot_income_stats,\n plot_share_yellow,\n plot_share_parked,\n plot_share_vanished,\n ]:\n func(data_df, outpath)\n\n\ndef get_data_from_run(episode_path):\n \"\"\"\n Extracts data for plots from episode.csv saved by NetLogo.\n :param episode_path: Path of current episode.\n :return: DataFrame with data of current episode.\n \"\"\"\n # Open JSON file containing the indexing information required to extract the information needed for plotting\n with open(\"df_index.json\", \"r\") as fp:\n INDEX_DICT = json.load(fp=fp)\n\n with open(episode_path, newline=\"\") as csvfile:\n file_reader = csv.reader(csvfile, delimiter=\",\", quotechar='\"')\n for i, row in enumerate(file_reader):\n for key in INDEX_DICT.keys():\n if INDEX_DICT[key][\"title\"] in row:\n INDEX_DICT[key][\"i\"] = i\n\n data_df = pd.read_csv(\n episode_path, skiprows=INDEX_DICT[\"fee\"][\"i\"] + 11, nrows=21601\n )\n data_df = data_df.rename(\n columns={\n \"y\": \"yellow_lot_fee\",\n \"y.1\": \"teal_lot_fee\",\n \"y.2\": \"green_lot_fee\",\n \"y.3\": \"blue_lot_fee\",\n }\n )\n data_df = data_df[\n [\"x\", \"yellow_lot_fee\", \"green_lot_fee\", \"teal_lot_fee\", \"blue_lot_fee\"]\n ]\n data_df.x = data_df.x / 1800\n del INDEX_DICT[\"fee\"]\n\n i = 0\n # Catch exceptions for different versions of NetLogo model run\n while i < len(INDEX_DICT.keys()):\n key = sorted(INDEX_DICT)[i]\n try:\n temp_df = pd.read_csv(\n episode_path,\n skiprows=INDEX_DICT[key][\"i\"] + INDEX_DICT[key][\"offset\"],\n nrows=21601,\n )\n for j, col in enumerate(INDEX_DICT[key][\"cols\"]):\n temp_df = temp_df.rename(columns={f\"y.{j}\" if j > 0 else \"y\": col})\n temp_df = temp_df[INDEX_DICT[key][\"cols\"]]\n data_df = data_df.join(temp_df)\n i += 1\n except KeyError:\n INDEX_DICT[key][\"offset\"] += 1\n\n return data_df\n\n\ndef plot_fees(data_df, outpath):\n \"\"\"\n Plot fees for CPZs over run of episode.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n color_list = [\n cm.imola_r(0),\n cm.imola_r(1.0 * 1 / 3),\n cm.imola_r(1.0 * 2 / 3),\n cm.imola_r(1.0),\n ]\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n ax.plot(\n data_df.x,\n data_df.yellow_lot_fee,\n linewidth=4,\n color=color_list[0],\n linestyle=\"solid\",\n )\n ax.plot(\n data_df.x,\n data_df.green_lot_fee,\n linewidth=4,\n color=color_list[1],\n linestyle=\"dashed\",\n )\n ax.plot(\n data_df.x,\n data_df.teal_lot_fee,\n linewidth=4,\n color=color_list[2],\n linestyle=\"dashed\",\n )\n ax.plot(\n data_df.x,\n data_df.blue_lot_fee,\n linewidth=4,\n color=color_list[3],\n linestyle=\"dashed\",\n )\n\n ax.set_ylim(bottom=0, top=10.1)\n\n ax.set_ylabel(\"Hourly Fee in €\", fontsize=30)\n ax.set_xlabel(\"\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n\n create_colourbar(fig)\n fig.savefig(str(outpath / \"fees.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_occup(data_df, outpath):\n \"\"\"\n Plot occupation levels of different CPZs over run of episode.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n # Save plot with three variants of legend location\n for loc in [\"lower right\", \"right\", \"upper right\"]:\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n\n color_list = [\n cm.imola_r(0),\n cm.imola_r(1.0 * 1 / 3),\n cm.imola_r(1.0 * 2 / 3),\n cm.imola_r(1.0),\n ]\n ax.plot(\n data_df.x, data_df.yellow_lot_occup / 100, linewidth=2, color=color_list[0]\n )\n ax.plot(\n data_df.x, data_df.green_lot_occup / 100, linewidth=2, color=color_list[1]\n )\n ax.plot(\n data_df.x, data_df.teal_lot_occup / 100, linewidth=2, color=color_list[2]\n )\n ax.plot(\n data_df.x, data_df.blue_lot_occup / 100, linewidth=2, color=color_list[3]\n )\n ax.plot(\n data_df.x,\n data_df.garages_occup / 100,\n label=\"Garage(s)\",\n linewidth=2,\n color=\"black\",\n )\n ax.plot(\n data_df.x,\n data_df.overall_occup / 100,\n label=\"Kerbside Parking Overall\",\n linewidth=4,\n color=cm.berlin(1.0),\n linestyle=(0, (1, 5)),\n ) if \"composite\" in str(outpath).lower() else None\n ax.plot(\n data_df.x,\n [0.75] * len(data_df.x),\n linewidth=2,\n color=\"red\",\n linestyle=\"dashed\",\n )\n ax.plot(\n data_df.x,\n [0.90] * len(data_df.x),\n linewidth=2,\n color=\"red\",\n linestyle=\"dashed\",\n )\n ax.set_ylim(bottom=0, top=1.01)\n\n ax.set_ylabel(\"Utilised Capacity\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n create_colourbar(fig)\n ax.legend(fontsize=25, loc=loc)\n\n fig.savefig(str(outpath / f\"occupancy_{loc}.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_social(data_df, outpath):\n \"\"\"\n PLot shares of different income classes over run of episode.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n # Save plot with three variants of legend location\n for loc in [\"lower right\", \"right\", \"upper right\"]:\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n color_list = [cm.bamako(0), cm.bamako(1.0 * 1 / 2), cm.bamako(1.0)]\n ax.plot(\n data_df.x,\n data_df.low_income / 100,\n label=\"Low Income\",\n linewidth=3,\n color=color_list[0],\n )\n ax.plot(\n data_df.x,\n data_df.middle_income / 100,\n label=\"Middle Income\",\n linewidth=3,\n color=color_list[1],\n )\n ax.plot(\n data_df.x,\n data_df.high_income / 100,\n label=\"High Income\",\n linewidth=3,\n color=color_list[2],\n )\n ax.set_ylim(bottom=0, top=1.01)\n\n ax.set_ylabel(\"Share of Cars per Income Class\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n ax.legend(fontsize=25, loc=loc)\n\n fig.savefig(str(outpath / f\"social_{loc}.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_speed(data_df, outpath):\n \"\"\"\n Plot average speed over run of episode.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n ax.plot(data_df.x, data_df.average_speed, linewidth=3, color=cm.bamako(0))\n ax.plot(\n data_df.x,\n data_df.average_speed.rolling(50).mean(),\n linewidth=3,\n color=cm.bamako(1.0),\n )\n\n ax.set_ylim(bottom=0, top=1.01)\n\n ax.set_ylabel(\"Average Normalised Speed\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n\n fig.savefig(str(outpath / \"speed.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_n_cars(data_df, outpath):\n \"\"\"\n Plot number of cars over run of episode.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n ax.plot(data_df.x, data_df.cars_overall / 100, linewidth=3, color=cm.bamako(0))\n ax.set_ylim(bottom=0, top=1.01)\n\n ax.set_ylabel(\"Share of Initially Spawned Cars\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n\n fig.savefig(str(outpath / \"n_cars.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_income_stats(data_df, outpath):\n \"\"\"\n Plot mean, median and std. of income distribution of run of episode.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n # Save plot with three variants of legend location\n for loc in [\"lower right\", \"right\", \"upper right\"]:\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n color_list = [cm.berlin(0), cm.berlin(1.0 * 1 / 2), cm.berlin(1.0)]\n ax.plot(\n data_df.x, data_df[\"mean\"], label=\"Mean\", linewidth=3, color=color_list[0]\n )\n ax.plot(\n data_df.x,\n data_df[\"median\"],\n label=\"Median\",\n linewidth=3,\n color=color_list[1],\n )\n ax.plot(\n data_df.x,\n data_df[\"std\"],\n label=\"Standard Deviation\",\n linewidth=3,\n color=color_list[2],\n )\n ax.set_ylim(bottom=0, top=max(data_df[[\"mean\", \"median\", \"std\"]].max()) + 1)\n\n ax.set_ylabel(\"Income in €\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n ax.legend(fontsize=25, loc=loc)\n\n fig.savefig(str(outpath / f\"income_stats_{loc}.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_share_yellow(data_df, outpath):\n \"\"\"\n Plot share of different income classes on yellow CPZ.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n # Save plot with three variants of legend location\n for loc in [\"lower right\", \"right\", \"upper right\"]:\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n color_list = [cm.bamako(0), cm.bamako(1.0 * 1 / 2), cm.bamako(1.0)]\n ax.plot(\n data_df.x,\n data_df.share_y_low / 100,\n label=\"Low Income\",\n linewidth=3,\n color=color_list[0],\n )\n ax.plot(\n data_df.x,\n data_df.share_y_middle / 100,\n label=\"Middle Income\",\n linewidth=3,\n color=color_list[1],\n )\n ax.plot(\n data_df.x,\n data_df.share_y_high / 100,\n label=\"High Income\",\n linewidth=3,\n color=color_list[2],\n )\n ax.set_ylim(bottom=0, top=1.01)\n\n ax.set_ylabel(\"Share of Cars in Yellow CPZ\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n ax.legend(fontsize=25, loc=loc)\n\n fig.savefig(str(outpath / f\"share_yellow_{loc}.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_share_parked(data_df, outpath):\n \"\"\"\n Plot share of parked cars per income class.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n # Save plot with three variants of legend location\n for loc in [\"lower right\", \"right\", \"upper right\"]:\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n color_list = [cm.bamako(0), cm.bamako(1.0 * 1 / 2), cm.bamako(1.0)]\n ax.plot(\n data_df.x,\n data_df.share_p_low / 100,\n label=\"Low Income\",\n linewidth=3,\n color=color_list[0],\n )\n ax.plot(\n data_df.x,\n data_df.share_p_middle / 100,\n label=\"Middle Income\",\n linewidth=3,\n color=color_list[1],\n )\n ax.plot(\n data_df.x,\n data_df.share_p_high / 100,\n label=\"High Income\",\n linewidth=3,\n color=color_list[2],\n )\n ax.set_ylim(bottom=0, top=1.01)\n\n ax.set_ylabel(\"Share of Cars Finding Parking\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n ax.legend(fontsize=25, loc=loc)\n\n fig.savefig(str(outpath / f\"share_parked_{loc}.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef plot_share_vanished(data_df, outpath):\n \"\"\"\n Plot share of vanished cars per income class.\n :param data_df: DataFrame with data from current episode.\n :param outpath: Path to save plot.\n :return:\n \"\"\"\n # Save plot with three variants of legend location\n for loc in [\"lower right\", \"right\", \"upper right\"]:\n fig, ax = plt.subplots(1, 1, figsize=(20, 8), dpi=300)\n color_list = [cm.bamako(0), cm.bamako(1.0 * 1 / 2), cm.bamako(1.0)]\n ax.plot(\n data_df.x,\n data_df.share_v_low / (data_df.low_income[0] / 100 * 525),\n label=\"Low Income\",\n linewidth=3,\n color=color_list[0],\n )\n ax.plot(\n data_df.x,\n data_df.share_v_middle / (data_df.middle_income[0] / 100 * 525),\n label=\"Middle Income\",\n linewidth=3,\n color=color_list[1],\n )\n ax.plot(\n data_df.x,\n data_df.share_v_high / (data_df.high_income[0] / 100 * 525),\n label=\"High Income\",\n linewidth=3,\n color=color_list[2],\n )\n ax.set_ylim(bottom=0, top=1.01)\n\n ax.set_ylabel(\"Normalised Share of Cars Vanished\", fontsize=30)\n ax.grid(True)\n ax.tick_params(axis=\"both\", labelsize=25)\n ax.set_xlabel(\"Time of Day\", fontsize=30)\n ax.set_xticks(ticks=np.arange(0, max(data_df[\"x\"]) + 1, 2))\n ax.set_xticklabels(labels=X_LABEL)\n ax.legend(fontsize=25, loc=loc)\n\n fig.savefig(str(outpath / f\"share_vanished_{loc}.pdf\"), bbox_inches=\"tight\")\n plt.close(fig)\n\n\ndef create_colourbar(fig):\n \"\"\"\n Draws colourbar with colour of different CPZs on given figure.\n :param fig: Figure to draw colourbar on.\n :return:\n \"\"\"\n cmap = cm.imola\n\n fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8, wspace=0.01)\n cb_ax = fig.add_axes([0.8, 0.1, 0.015, 0.8])\n\n bounds = [0, 1, 2, 3, 4]\n norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n cbar = fig.colorbar(\n matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap),\n cax=cb_ax,\n orientation=\"vertical\",\n )\n\n cbar.set_ticks([])\n cbar.ax.set_ylabel(\n r\"$\\Leftarrow$ Distance of CPZ to City Centre\", fontsize=25, loc=\"top\"\n )\n\n\ndef draw_radar_plot(input_dir):\n \"\"\"\n\n :param input_dir:\n :return:\n \"\"\"\n if glob(input_dir + \"/*.pkl\"):\n median_runs = glob(input_dir + \"/*.pkl\")\n median_labels = [re.findall(\"([a-zA-Z]+).pkl\", run)[0] for run in median_runs]\n else:\n median_runs = glob(input_dir + \"/*.csv\")\n median_labels = [re.findall(\"([a-zA-Z]+).csv\", run)[0] for run in median_runs]\n\n categories = [\n \"Optimize Occupancy\",\n \"Preserve Social Composition\",\n \" Maximize Speed\",\n \"Minimize Cars\",\n ]\n categories = [*categories, categories[0]]\n color_list = sns.color_palette(\"colorblind\")\n occup_scores = []\n social_scores = []\n speed_scores = []\n n_cars_scores = []\n performance_dict = dict()\n for i, run in enumerate(median_runs):\n try:\n df = pd.read_pickle(run, compression=\"zip\")\n except (FileNotFoundError, BadZipFile):\n df = get_data_from_run(run)\n label = median_labels[i]\n performance_dict[label] = dict()\n # Overall time\n occup_score = 0\n for c in [\"yellow\", \"green\", \"teal\", \"blue\"]:\n occup_score += (\n len(df[(df[f\"{c}_lot_occup\"] > 75) & (df[f\"{c}_lot_occup\"] < 90)])\n / len(df)\n ) * 0.25\n occup_scores.append(occup_score)\n n_cars_scores.append(1 - df[\"cars_overall\"].iloc[-1] / 100)\n speed_scores.append(df.average_speed.mean())\n social_scores.append(df.low_income.iloc[-1])\n\n for i, label in enumerate(median_labels):\n scores = []\n for score_list in [occup_scores, social_scores, speed_scores, n_cars_scores]:\n scores.append(score_list[i] / max(score_list))\n scores.append(scores[0])\n performance_dict[label][\"scores\"] = scores\n\n plt.rc(\"xtick\", labelsize=30)\n plt.rc(\"ytick\", labelsize=30)\n\n label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(scores))\n\n fig = plt.figure(figsize=(20, 20))\n ax = fig.add_subplot(111, polar=True)\n for run_label, colour_i in zip(median_labels, [7, 0, 2, 1, 4, 8, 9]):\n if \"static\" in run_label or \"dynamic\" in run_label:\n label = r\"$\\mathrm{Baseline_{\" + run_label + r\"}}$\"\n else:\n label = r\"$\\mathrm{r_{\" + run_label + r\"}}$\"\n ax.plot(\n label_loc,\n performance_dict[run_label][\"scores\"],\n label=label,\n linewidth=4,\n color=color_list[colour_i],\n )\n ax.fill(\n label_loc,\n performance_dict[run_label][\"scores\"],\n alpha=0.25,\n color=color_list[colour_i],\n )\n\n ax.set_ylim(0, 1)\n\n ax.set_thetagrids(np.degrees(label_loc), labels=categories)\n for label, category in zip(ax.get_xticklabels(), categories):\n if \"Speed\" in category:\n label.set_horizontalalignment(\"left\")\n elif \"Occup\" in category:\n label.set_horizontalalignment(\"right\")\n ax.set_theta_offset(np.pi)\n ax.legend(fontsize=28, loc=\"upper right\", bbox_to_anchor=(1.35, 1.15))\n # ax.xaxis.set_tick_params(pad=15)\n ax.axes.yaxis.set_ticklabels([])\n #\n plt.tight_layout()\n ax.spines[\"polar\"].set_color(\"#222222\")\n\n fig.savefig(\"radar_plot.pdf\", bbox_inches=\"tight\")\n\n plt.show()\n","repo_name":"JakobKappenberger/ai-priced-parking","sub_path":"src/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":27542,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"40127159523","text":"import os\nimport typing\nimport inspect\nimport pickle\nimport warnings\nimport portalocker\nimport pathlib\n\nExportable = type(typing.Any)(\n \"Exportable\",\n doc=\"\"\"Special type indicating an exportable type for the offshore package.\n This type behaves like Any for all practical purposes.\n \"\"\",\n)\n\n\nclass Offshore:\n def __init__(self, filename=\".offshore\", autosave=False, autoload=False):\n self._stack = inspect.stack()\n self._path = pathlib.Path(os.getcwd()) / filename\n self._autosave = bool(autosave)\n self._autoload = bool(autoload)\n self._store = {}\n\n try:\n self._load()\n except FileNotFoundError:\n self.dump()\n\n def __getattr__(self, item):\n if self._autoload:\n self.load()\n\n try:\n return self._store[item]\n except KeyError as e:\n raise AttributeError(str(e))\n\n def __setattr__(self, key, value):\n if key.startswith(\"_\"):\n self.__dict__[key] = value\n return\n\n self._store[key] = value\n\n if self._autosave:\n self.dump()\n\n def __getitem__(self, item):\n if self._autoload:\n self.load()\n\n return self._store[item]\n\n def __setitem__(self, key, value):\n self._store[key] = value\n\n if self._autosave:\n self.dump()\n\n def __delitem__(self, key):\n del self._store[key]\n\n if self._autosave:\n self.dump()\n\n def __contains__(self, item):\n if self._autoload:\n self.load()\n\n return item in self._store\n\n def __len__(self):\n if self._autoload:\n self.load()\n\n return len(self._store)\n\n def _load(self):\n with portalocker.Lock(str(self._path), \"rb\", timeout=60) as file:\n self._store = pickle.load(file)\n\n @staticmethod\n def _parse_stack(stack):\n frame = stack[1][0]\n global_vars = frame.f_globals\n module = inspect.getmodule(frame)\n\n if module is None:\n return [], global_vars\n\n annotations = [key for key, value in typing.get_type_hints(module).items() if value is Exportable]\n return annotations, global_vars\n\n def clear(self):\n self._store = {}\n self.dump()\n\n def snapshot(self):\n annotations, global_vars = self._parse_stack(self._stack)\n\n if not annotations:\n warnings.warn(f\"No exportable variables found\")\n\n for key in annotations:\n self._store[key] = global_vars[key]\n\n self.dump()\n\n def restore(self):\n annotations, global_vars = self._parse_stack(self._stack)\n\n if not annotations:\n warnings.warn(f\"No exportable variables found\")\n\n self.load()\n\n for key in annotations:\n if key not in self._store:\n warnings.warn(f\"Key '{key}' was not found in the state store and has not been restored\")\n continue\n\n global_vars[key] = self._store[key]\n\n def dump(self):\n with portalocker.Lock(str(self._path), \"wb\", timeout=60) as file:\n pickle.dump(self._store, file)\n\n file.flush()\n os.fsync(file.fileno())\n\n def load(self):\n try:\n self._load()\n except FileNotFoundError:\n warnings.warn(f\"State store not found in '{str(self._path)}'\")\n","repo_name":"kpdemetriou/offshore","sub_path":"offshore/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3383,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"4273916733","text":"import codecs\n\nline_seen=set()#初始化空的无序集合\n\nin_file=codecs.open('I:/codes/track4new.txt','r',encoding='utf-8')\n\nout_file=codecs.open('I:/codes/track4.1.txt','w',encoding='utf-8')\n\nlines=in_file.readlines()\n\nfor line in lines:\n if line not in line_seen:\n out_file.write(line)\n line_seen.add(line)\n\nin_file.close()\nout_file.close()\n","repo_name":"LuicelZhou/MultiView-Tracking-ReID","sub_path":"Detecting_and_Tracking/cleaning.py","file_name":"cleaning.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"20876661221","text":"'''\n딱지놀이\nhttps://www.acmicpc.net/problem/14696\n백준 브론즈1 14696\n\n두 어린이 A, B가 딱지놀이를 한다. 딱지놀이 규칙은 다음과 같다. 두 어린이는 처음에 여러 장의 딱지를 가지고 있고, 매 라운드마다 각자 자신이 가진 딱지 중 하나를 낸다. 딱지에는 별(★), 동그라미(●), 네모(■), 세모(▲), 네 가지 모양 중 하나 이상의 모양이 표시되어 있다. 두 어린이가 낸 딱지 중 어느 쪽이 더 강력한 것인지는 다음 규칙을 따른다.\n\n만약 두 딱지의 별의 개수가 다르다면, 별이 많은 쪽의 딱지가 이긴다.\n별의 개수가 같고 동그라미의 개수가 다르다면, 동그라미가 많은 쪽의 딱지가 이긴다.\n별, 동그라미의 개수가 각각 같고 네모의 개수가 다르다면, 네모가 많은 쪽의 딱지가 이긴다.\n별, 동그라미, 네모의 개수가 각각 같고 세모의 개수가 다르다면, 세모가 많은 쪽의 딱지가 이긴다.\n별, 동그라미, 네모, 세모의 개수가 각각 모두 같다면 무승부이다.\n\n라운드의 수 N과 두 어린이가 순서대로 내는 딱지의 정보가 주어졌을 때, 각 라운드별로 딱지놀이의 결과를 구하는 프로그램을 작성하시오.\n'''\n\n\nN = int(input()) # 총 라운드\n\nfor _ in range(N):\n A = {4:0, 3:0, 2:0, 1:0} # 보유한 딱지 개수\n B = {4:0, 3:0, 2:0, 1:0}\n _, *arg = map(int, input().split())\n for a in arg:\n A[a] += 1\n _, *arg = map(int, input().split())\n for b in arg:\n B[b] += 1\n\n\n for i in range(4, 0, -1): # 4부터 점검\n if A[i] > B[i]:\n print('A')\n break\n elif B[i] > A[i]:\n print('B')\n break\n else:\n print('D') # 무승부\n\n\n","repo_name":"seoda0000/TIL","sub_path":"AlgorithmProblemSolving/04_백준/Bronze/14696_딱지놀이.py","file_name":"14696_딱지놀이.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"ko","doc_type":"code","stars":4,"dataset":"github-code","pt":"67"}
+{"seq_id":"42074071111","text":"from random import randint\nfrom termcolor import colored\nimport colorama\n\ncolorama.init()\n\n\ndef alg(a, i, j):\n if j - i == 1:\n if a[i] < 0 and a[j] > 0:\n return i\n else:\n return \"non c'è\"\n\n m = (i + j) // 2\n if a[m] > 0:\n return alg(a, i, m)\n else:\n return alg(a, m, j)\n\n\nLIM = 15\n\nfor _ in range(LIM // 2):\n array = [randint(LIM * -1, LIM) for _ in (range(randint(2, LIM)))]\n array.sort()\n # to remove duplicates\n array = list(dict.fromkeys(array))\n if array[- 1] < 0:\n array.append(array[- 1] * -1 + 1)\n if array[0] > 0:\n array[0] = array[0] * -1\n _n = len(array)\n\n alg_res = alg(array, 0, _n - 1)\n\n expected_value = \"non c'è\"\n for ind in range(_n - 1):\n if array[ind] < 0 and array[ind + 1] > 0:\n expected_value = ind\n break\n\n print(f'array: {array}\\nn : {_n}\\nexpected result: {expected_value}\\nalg result: {alg_res}\\nsame results: {colored(\"True\", \"green\") if alg_res == expected_value else colored(\"False\", \"red\")}\\n')\n","repo_name":"OB-UNISA/Algorithm-Design","sub_path":"exercises/exercises2_10.py","file_name":"exercises2_10.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"30450677949","text":"from django.db import models\nfrom user_accounts.models import CustomUser\n\n# Create your models here.\nLOG = (\n ('Report','Report'),\n ('Refer','Refer'),\n ('Tip Upload','Tip Uplod'),\n ('Physical','Physical'),\n ('Relationship','Relationship'),\n ('Book Reading','Book Reading'),\n ('Meditation','Meditation'),\n\n)\nclass PointLogs(models.Model):\n log_description = models.CharField(max_length=500)\n user = models.ForeignKey(CustomUser,on_delete=models.CASCADE)\n datetime = models.DateTimeField(auto_now_add = True)\n log_type = models.CharField(max_length=100,choices=LOG)","repo_name":"Prakash617/bookwish","sub_path":"logs/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"23077780623","text":"#-*- encoding:UTF-8 -*-\n'''\nCreated on 2019/6/30\n\n@author: xcKev\n'''\n\nimport difflib\nimport alvintools.common_filer as filer\nimport alvintools.common_logger as log\nimport sys\ncurrent_log=log.get_log('comparer', log.LOG_DIR, 'comparer')\n\ndef compare_file(file_path1,file_path2,diff_path):\n if file_path1 == \"\" or file_path2 ==\"\":\n current_log.info(F\"path can't be blank: first path:{file_path1},second path:{file_path2}\")\n sys.exit()\n else:\n current_log.info(F\"comparing file between {file_path1} and {file_path2}\")\n text1_lines = filer.get_file_details(file_path1)\n text2_lines = filer.get_file_details(file_path2)\n diff = difflib.HtmlDiff()\n result = diff.make_file(text1_lines, text2_lines)\n try:\n result_h = open(diff_path,'w')\n result_h.write(result)\n current_log.info(\"Compared successfully finished\\n\")\n except IOError as error:\n current_log.error(F\"Failed to write html diff file: {error}\")","repo_name":"Kevin-san/ToolLesson","sub_path":"core/alvintools/common_comparer.py","file_name":"common_comparer.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"7885179866","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nfrom Text_Traffic_Analysis.Packet_Segment import pkt_seg_by_delimiters\nfrom Text_Traffic_Analysis.Select_Words import top_words_set, select_key_words\nfrom Text_Traffic_Analysis.Format_Extract import infer_protocol_format\nfrom Text_Traffic_Analysis.Protocol_Feature import get_traffic_feature\n\ndef Text_Re(DATA_PATH, MODE, NAME):\n\n print(os.path.join(DATA_PATH, \"text_tcp/0\"))\n if os.path.exists(os.path.join(DATA_PATH, \"text_tcp/0\")):\n DATA_PATH += \"/text_tcp/0/\"\n else:\n print(\"[info] No text data set.\")\n return\n if len(os.listdir(DATA_PATH)) < 50:\n print(\"[info] No text data.\")\n return \n\n SEG_OUT_PATH = './run_file/seg_' + NAME \n WORDS_PATH = './run_file/words_' + NAME \n P_OUT_PATH = './run_file/pattern_' + NAME \n\n print(\"Data directory path: \", DATA_PATH)\n\n # 协议逆向开始\n first_words = pkt_seg_by_delimiters(DATA_PATH, SEG_OUT_PATH)\n\n tagged_weighted_word = top_words_set(SEG_OUT_PATH, WORDS_PATH)\n\n f_words, b_words, f_data, b_data = select_key_words(tagged_weighted_word, DATA_PATH, P_OUT_PATH, MODE)\n\n f_formats, b_formats = infer_protocol_format(P_OUT_PATH)\n\n # 基于逆向的报文格式推断协议特征字符串\n get_traffic_feature(NAME, f_formats, b_formats, f_data, b_data, DATA_PATH, MODE)\n","repo_name":"737898487/2018","sub_path":"Text_Traffic_Analysis/Text_Entrance.py","file_name":"Text_Entrance.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"43088349066","text":"import numpy as np\n\n\nALL_DIRECTIONS = np.array(\n [[-1, -1], [-1, 0], [-1, 1],\n [0, -1], [0, 0], [0, 1],\n [1, -1], [1, 0], [1, 1]]\n)\nNW, N, NE, W, NOOP, E, SW, S, SE = ALL_DIRECTIONS\n\n\ndef get_movement_vectors(num_directions: int):\n mapping = {9: ALL_DIRECTIONS,\n 8: np.stack([NW, N, NE, W, E, SW, S, SE]),\n 5: np.stack([W, N, E, S, NOOP]),\n 4: np.stack([W, N, E, S])}\n if num_directions not in mapping:\n raise ValueError(\"Can only handle 9, 8, 5 or 4 directions!\")\n return mapping[num_directions]\n\n\nclass MovementTranslator:\n\n @staticmethod\n def translate(opencv_keypress):\n return {\n -1: NOOP, 83: E, 81: W, 82: N, 84: S, 27: None,\n 119: N, 115: S, 97: W, 100: E\n }[opencv_keypress]\n","repo_name":"csxeba/grund","sub_path":"grund/util/movement.py","file_name":"movement.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"35608957389","text":"'''\nCreated on 12 Nov 2013\n\n@author: fafey\n'''\n\nfrom z3 import *\n\n\nx = Int('x')\ny = Int('x')\nfun = Function('fun', IntSort(), IntSort())\n\n\n\nsolve(x > 5, x<7, ForAll(x, fun(x) == 10), fun(y) == 1)\n","repo_name":"songhui/cspadapt","sub_path":"vmplacement/z4/src/DatatypeTest.py","file_name":"DatatypeTest.py","file_ext":"py","file_size_in_byte":196,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"}
+{"seq_id":"71345239253","text":"# Charles Buyas cjb8qf\n\n\nimport random\nprint(\"Input a -1 to play with a random number\")\nstart = int(input(\"What should the answer be?: \"))\ncount = 0\nif start == -1:\n start = random.randint(1, 100)\nelse:\n num = start\n\nwhile count < 4:\n guess = int(input(\"Guess a number: \"))\n if int(guess) == int(start):\n print(\"You win!\")\n exit(0)\n elif int(guess) > int(start):\n count += 1\n print(\"The number is lower than that.\")\n else:\n count += 1\n print(\"The number is higher than that.\")\n\nif count == 4:\n guess = int(input(\"Guess a number: \"))\n if int(guess) == int(start):\n print(\"You win!\")\n exit(0)\n else:\n print(\"You lose; the number was \" + str(start) + \".\")\n","repo_name":"AliveSphere/Introductory_PyCharm_Files","sub_path":"POTD/higher_lower.py","file_name":"higher_lower.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"39676764635","text":"# 브루트포스 풀이\n'''\n주어진 테트로미노를 회전, 대칭하여 만들어지는 가지수는 총 19종류이다.\n문제에서 주어지는 종이를 2중 for문으로 탐색하면서, 주어진 테트로미노 형태의 숫자 합계를 비교한다.\n각 테트로미노당 정사각형 4개로 이루어져 있으므로 i, j가 이동하는 delta를 3개씩 19종류, 총 57개의 delta를 만들어둔다.\n본 풀이에서 delta의 순서는 문제에 주어진 도형을 회전/대칭 후 회전 하는 순서대로 설정하였다.\n'''\n# [A] 테트로미노의 경우의 수에 따른 delta 설정\ndi = [0, 0, 0, 1, 2, 3, 0, 1, 1, 1, 2, 2, 0, 0, 1, 0, 1, 2, 0, 0, -1, 0, -1, -2, 1, 1, 1, 0, 1, 2, 0, 0, 1, 1, 1, 2, 0, -1, -1, 1, 1, 2, 0, 1, 1, 0, 0, 1, -1, 0, 1, 1, 1, 1, 1, 2, 1]\ndj = [1, 2, 3, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 2, 0, 1, 1, 1, 1, 2, 2, 1, 1, 1, 0, 1, 2, 1, 0, 0, 1, 2, 2, 0, 1, 1, 1, 1, 2, 0, -1, -1, 1, 1, 2, 1, 2, 1, 1, 1, 1, -1, 0, 1, 0, 0, 1]\ndirection = [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48, 51, 54]\n\n# [1] 입력값 설정\nfrom sys import stdin\nN, M = map(int, stdin.readline().split())\npaper = [[] for _ in '_'*N]\nfor i in range(N):\n paper[i] = list(map(int, stdin.readline().split()))\n\n# [2] 종이에 적혀진 수 탐색 시작\nresult = 0\nfor i in range(N):\n for j in range(M):\n for dr in direction:\n sumV = paper[i][j] # dr이 갱신될 때마다 sumV도 초기화된다.\n\n ni, nj = i+di[dr], j+dj[dr] # 첫 번째 ni, nj\n if 0 <= ni < N and 0 <= nj < M:\n sumV += paper[ni][nj]\n\n ni, nj = i+di[dr+1], j+dj[dr+1] # 두 번째 ni, nj\n if 0 <= ni < N and 0 <= nj < M:\n sumV += paper[ni][nj]\n\n ni, nj = i+di[dr+2], j+dj[dr+2] # 마지막 ni, nj\n if 0 <= ni < N and 0 <= nj < M:\n sumV += paper[ni][nj]\n\n # 총 4개의 수를 더했다면 result값과 비교\n if result < sumV:\n result = sumV\n\nprint(result)\n\nㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡ\n# DFS 풀이\n# [A] delta 방향 설정\ndi = [-1, 1, 0, 0]\ndj = [0, 0, -1, 1] # 상하좌우\n\n# [B] dfs 함수 설정\ndef dfs(i, j, n, sumV):\n global result\n # [B-1] 가지치기 조건. 아무리 max를 더해도 result보다 작을 때\n if result > sumV + maxV * (4 - n):\n return\n # [B-2] 종료 조건. 4개의 정사각형을 더했다면 result값과 비교\n if n == 4:\n if result < sumV:\n result = sumV\n return\n\n # [B-3] 상하좌우 방향대로 dfs 탐색\n for dr in range(4):\n ni, nj = i+di[dr], j+dj[dr]\n if 0 <= ni < N and 0 <= nj < M and not visited[ni][nj]:\n if n == 2: # [B-4] 이 조건을 넣지 않으면 'ㅏ' 블럭이 만들어지지 않는다.\n visited[ni][nj] = 1\n dfs(i, j, n+1, sumV + paper[ni][nj])\n visited[ni][nj] = 0\n\n visited[ni][nj] = 1\n dfs(ni, nj, n+1, sumV + paper[ni][nj])\n visited[ni][nj] = 0\n\n# [1] 입력값 설정\nfrom sys import stdin\nN, M = map(int, stdin.readline().split())\npaper = [[] for _ in '_'*N]\nfor i in range(N):\n paper[i] = list(map(int, stdin.readline().split()))\n\n# [2] dfs 탐색\nresult = 0\nmaxV = max(map(max, paper))\nvisited = [[0]*M for _ in '_'*N]\nfor i in range(N):\n for j in range(M):\n visited[i][j] = 1\n dfs(i, j, 1, paper[i][j])\n visited[i][j] = 0\n\nprint(result)","repo_name":"Seori15/algorithm","sub_path":"python/BOJ/BOJ_14500_테트로미노.py","file_name":"BOJ_14500_테트로미노.py","file_ext":"py","file_size_in_byte":3631,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"}
+{"seq_id":"39533115944","text":"\"\"\"StudentiUniMi URL Configuration\n\nThe `urlpatterns` list routes URLs to views.\n\"\"\"\nfrom django.conf import settings\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom django.http import HttpResponse, HttpRequest\nfrom django.urls import path, include\nfrom django.views.generic import RedirectView\nfrom sentry_sdk import configure_scope\n\nimport telegrambot.urls\nimport university.urls\n\nadmin.site.site_header = \"Network StudentiUniMi - administration\"\n\n\ndef healthcheck(_: HttpRequest) -> HttpResponse:\n with configure_scope() as scope:\n if scope.transaction:\n scope.transaction.sampled = False\n return HttpResponse(\"hello, you hacker!\\n\", content_type=\"text/plain\")\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('api/', include(university.urls)),\n path('telegrambot/', include(telegrambot.urls)),\n url(r'^robots.txt$', lambda r: HttpResponse(\n \"User-Agent: *\\nDisallow: /\",\n content_type=\"text/plain\",\n ), name=\"robots_txt\"),\n url(r\"^healthcheck$\", healthcheck, name=\"healthcheck\"),\n]\n\nif not settings.DEBUG:\n urlpatterns.append(\n path('', RedirectView.as_view(url=\"https://api.studentiunimi.it/admin/\"),\n name='root-redirect'),\n )\n","repo_name":"StudentiUniMi/backend","sub_path":"StudentiUniMi/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"67"}
+{"seq_id":"25164658409","text":"import numpy as np\nfrom gurobipy import quicksum\n\nfrom CounterfactualAnalysis.TreeEnsembleSolver import CESolver_TreeEnsemble\n\nclass CESolver_RandomForest(CESolver_TreeEnsemble):\n def __init__(self, estimator, lambda0, lambda1, lambda2, eps, timelimit):\n super().__init__(estimator, lambda0, lambda1, lambda2, eps, timelimit)\n self.T = self.estimator.n_estimators\n self.M1 = 1\n self.M2 = 1\n\n def build(self, x0, yCE):\n super().build(x0, yCE)\n self.class_assignment = self.model.addConstrs((quicksum(self.getWeight(t,l,yCE)*self.z[t,l] for t in range(self.T) for l in self.getLeaves(t)) >= 1.e-4 + quicksum(self.getWeight(t,l,k)*self.z[t,l] for t in range(self.T) for l in self.getLeaves(t)) for k in self.K if k!=yCE))\n self.reset.append(self.class_assignment)\n\n def getWeight(self, t, l, k):\n value = self.getTree(t).value[l,0,:]\n return (1/self.T*(value[np.where(self.K==k)[0]]/np.sum(value)))[0]\n","repo_name":"ceciliasalvatore/sFCCA","sub_path":"CounterfactualAnalysis/RandomForestSolver.py","file_name":"RandomForestSolver.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"15341155148","text":"import base64\r\nimport mysql.connector\r\nfrom datetime import date\r\nimport time\r\nimport numpy as np\r\nfrom keras.models import load_model\r\nfrom keras.utils import load_img, img_to_array\r\nfrom io import BytesIO\r\n\r\ndef predict_value(bytes_data):\r\n model = load_model(\"E:/APASD/predimg.h5\")\r\n # Create a binary buffer from the bytes object\r\n buffer = BytesIO(bytes_data)\r\n\r\n image = load_img(buffer, target_size=(150,150))\r\n image = img_to_array(image)\r\n image = np.expand_dims(image,axis=0)\r\n val = model.predict(image)\r\n if val>0.5:\r\n return \"Signature\"\r\n else:\r\n return \"Photograph\"\r\n\r\ndef insertIMAGEDATA(image):\r\n try:\r\n connection = mysql.connector.connect(\r\n host=\"127.0.0.1\",\r\n user=\"root\",\r\n database=\"mydatabase\",\r\n password=\"P@ssword1\"\r\n )\r\n \r\n cursor = connection.cursor()\r\n query = \"\"\" INSERT INTO IMAGEDATA\r\n (UPLOADED_DATE, DATE, UPLOADED_TIME, TIME, IMAGE, PREDICTED_VALUE) \r\n VALUES (%s,%s,%s,%s,%s,%s)\"\"\"\r\n \r\n uploaded_date = date.today()\r\n r_date = uploaded_date.strftime(\"%d %B, %Y\")\r\n uploaded_time = time.strftime(\"%I:%M:%S\",time.localtime())\r\n r_time = time.strftime(\"%I:%M %p\",time.localtime())\r\n predicted_value = predict_value(image)\r\n insert_tuple = (uploaded_date, r_date, uploaded_time, r_time, image, predicted_value)\r\n cursor.execute(query, insert_tuple)\r\n connection.commit()\r\n\r\n except mysql.connector.Error as error:\r\n print(\"Failed inserting data into MySQL table {}\".format(error))\r\n\r\n except Exception as e:\r\n print(e)\r\n\r\n finally:\r\n if connection.is_connected():\r\n cursor.close()\r\n connection.close()\r\n\r\ndef retrieveIMAGEDATA():\r\n final_array = []\r\n try:\r\n connection = mysql.connector.connect(\r\n host=\"127.0.0.1\",\r\n user=\"root\",\r\n database=\"mydatabase\",\r\n password=\"Deepak@973\"\r\n )\r\n\r\n cursor = connection.cursor()\r\n query = \"\"\"SELECT DATE, TIME, IMAGE, PREDICTED_VALUE FROM IMAGEDATA ORDER BY UPLOADED_DATE DESC,UPLOADED_TIME DESC LIMIT 5\"\"\"\r\n cursor.execute(query)\r\n record = cursor.fetchall()\r\n for row in record:\r\n sub_dict = dict()\r\n sub_dict[\"uploaded_date\"] = str(row[0])\r\n sub_dict[\"uploaded_time\"] = str(row[1])\r\n sub_dict[\"image\"] = base64.b64encode(row[2]).decode('utf-8')\r\n sub_dict[\"predicted_value\"] = str(row[3])\r\n final_array.append(sub_dict)\r\n connection.commit()\r\n\r\n except mysql.connector.Error as error:\r\n print(\"Failed inserting data into MySQL table {}\".format(error))\r\n\r\n except Exception as e:\r\n print(e)\r\n\r\n finally:\r\n if connection.is_connected():\r\n cursor.close()\r\n connection.close()\r\n return final_array\r\n","repo_name":"Deepak973-create/Photo-Sign-Detection","sub_path":"my_project/my_library.py","file_name":"my_library.py","file_ext":"py","file_size_in_byte":2970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"70023404375","text":"\"\"\" Compute average energy in input bins.\n\"\"\"\nimport sys\nfrom copy import copy\n\nfrom pyrate.core.Algorithm import Algorithm\n\n\nclass AverageBinEnergy(Algorithm):\n __slots__ = ()\n\n def __init__(self, name, store, logger):\n super().__init__(name, store, logger)\n\n def execute(self, config):\n\n e = self.store.get(\"EVENT:nT:edepScint\")\n\n self.store.put(config[\"name\"], e)\n\n\n# EOF\n","repo_name":"fscutti/pyrate","sub_path":"pyrate/algorithms/muondet/AverageBinEnergy.py","file_name":"AverageBinEnergy.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"29439243256","text":"# rosalind_lgis.txt\nimport sys\nimport numpy as np \n\nf = sys.argv[1]\n\nnum_list = []\nwith open(f,'r') as handle:\n for line in handle:\n line = line.replace('\\n','')\n num_list.append(line.split(' ')) \nx = []\nfor i in num_list:\n for j in i:\n x.append(int(j))\n\nx1 = x[:30]\n'''\ni_count = {}\nd_count = {}\nfor i in range(len(x1)):\n if i == 0:\n i_count[x1[i]] = 1\n d_count[x1[i]] = 1\n elif max(list(i_count.keys())) < x1[i]:\n i_count[x1[i]] = max(i_count.keys())+1\n elif max(i_count.keys()) > x1[i]:\n i_count[x1[i]] = max(i_count.keys()< x1[i]) +1\n\nprint(i_count)\n'''\nd = {1:2,2:3,4:5,6:8,3:2}\nprint(max(list(d.keys())) < 5)\n","repo_name":"kjh918/rosalind","sub_path":"Bioinformatics_Stronghold/rosalind_LIS2.py","file_name":"rosalind_LIS2.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"40511390855","text":"from typing import Literal, Tuple\n\nimport torch\nimport torch.nn as nn\n\nfrom src.layers.initialization import weights_init\nfrom src.training.loss import ReconstructionLoss\nfrom .base import BaseModel, TrainingInit\n\n\nclass VariationalAutoEncoder(BaseModel):\n def __init__(self,\n encoder: nn.Sequential,\n latent: nn.Module,\n decoder: nn.Sequential,\n recons_loss: ReconstructionLoss,\n latent_loss: nn.Module,\n training: TrainingInit,\n ):\n super().__init__(training)\n self.encoder = encoder\n self.decoder = decoder\n self.latent = latent\n self.recons_loss = recons_loss\n self.latent_loss = latent_loss\n\n self.reset_parameters()\n\n def reset_parameters(self):\n weights_init(self.encoder)\n weights_init(self.decoder)\n\n def forward(self, inputs):\n h = self.encoder(inputs)\n z, params = self.latent(h)\n recons = self.decoder(z)\n return recons, z, params\n\n def embed(self, inputs):\n return self.latent(self.encoder(inputs))[0]\n\n def decode(self, z):\n return self.decoder(z)\n\n def posterior(self, inputs):\n return self.latent(self.encoder(inputs))[1]\n\n def _step(\n self,\n batch: Tuple[torch.Tensor, torch.Tensor],\n batch_idx: int,\n phase: Literal[\"train\", \"val\", \"test\"]\n ):\n is_train = phase == \"train\"\n if is_train and hasattr(self.latent_loss, \"update_parameters\"):\n self.latent_loss.update_parameters(self.global_step)\n\n inputs, targets = batch\n recons, z, params = self.forward(inputs)\n\n recons_loss = self.recons_loss(recons, targets)\n latent_loss = self.latent_loss(z, params)\n\n loss = recons_loss + latent_loss\n\n self.log_dict(\n {\n f\"{phase}/loss\": loss,\n f\"{phase}/latent_term\": latent_loss,\n f\"{phase}/reconstruction_loss\": recons_loss\n },\n on_epoch=not is_train,\n on_step=is_train,\n prog_bar=is_train,\n sync_dist=not is_train,\n rank_zero_only=True\n )\n\n return loss\n","repo_name":"miltonllera/ocdm","sub_path":"src/model/autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"11665353399","text":"num = input()\n\ndial = ['ABC', 'DEF', 'GHI', 'JKL', 'MNO', 'PQRS', 'TUV', 'WXYZ']\ntime = 0\n\nfor n in num:\n for d in dial:\n if n in d:\n time += dial.index(d)+3\n\nprint(time)","repo_name":"SsoYeon-kim/CodingTest-Python","sub_path":"BJ/step_06/5622.py","file_name":"5622.py","file_ext":"py","file_size_in_byte":191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"33727350735","text":"from git import Repo\nimport argparse\n\nimport chain_runner\n\n\ndef parse_arguments():\n parser = argparse.ArgumentParser(description='index_generator_chain_runner.py --project-dir '\n '--project-id --branch ')\n parser.add_argument('-pd', '--project-dir', help='Project directory for indexing ', required=True)\n parser.add_argument('-pi', '--project-id', help='Project Id', required=True)\n parser.add_argument('-b', '--branch', help='Branch', required=False)\n args = parser.parse_args()\n\n return args.project_dir, args.project_id, args.branch\n\n\nrepo_directory, project_id, branch = parse_arguments()\n\nprint('project_dir = ' + repo_directory)\nprint('project_id = ' + project_id)\n\nrepo = Repo(repo_directory)\n\nrepo.git.checkout(\"develop\")\nrepo.remotes.origin.fetch()\ncommits_behind = repo.iter_commits('develop..develop@{u}')\ncommits = list(commits_behind)\n\nif len(commits) == 0:\n print(\"Current branch is {} behind. Pulling new code\".format(len(commits)))\n repo_is_dirty = repo.is_dirty()\n if repo_is_dirty:\n print(\"Dirty\")\n print(\"Stashing...\")\n repo.git.stash('save')\n\n repo.remotes.origin.pull()\n chain_runner.generate_index_and_send(repo_directory, project_id)\n\n if repo_is_dirty:\n print(\"unstashing\")\n repo.git.stash('pop')\nelse:\n print(\"No updates nothing to do here\")\n","repo_name":"damintsew/idea-shared-index-standalone-runner","sub_path":"remote_git_checker.py","file_name":"remote_git_checker.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"67"}
+{"seq_id":"13733780478","text":"import discord\nimport random\nimport asyncio\nfrom discord.ext import commands\nfrom discord.ext.commands import has_permissions, CheckFailure\n\n\nclass Interaction(commands.Cog):\n\n def __init__(self, client):\n self.client = client\n\n @commands.command(aliases=[\"dankr8\"])\n async def dankrater(self, ctx):\n v = [\"100%\",\n \"99%\",\n \"98%\",\n \"95%\",\n \"87%\",\n \"85%\",\n \"82%\",\n \"80%\",\n \"76%\",\n \"74%\",\n \"70%\",\n \"69%\",\n \"65%\",\n \"54%\",\n \"57%\",\n \"50%\",\n \"49%\"\n \"45%\",\n \"47%\",\n \"42%\",\n \"40%\",\n \"36%\",\n \"35%\",\n \"25%\",\n \"23%\",\n \"18%\",\n \"15%\",\n \"13%\",\n \"10%\",\n \"7%\",\n \"5%\",\n \"3%\",\n \"2%\",\n \"1%\"]\n embed=discord.Embed(color=0x0338b5)\n embed.add_field(name=\"Dank r8 machine\", value=f\"Il tuo potenziale memetico è: **{random.choice(v)}**\", inline=False)\n await ctx.send(embed=embed)\n\n @commands.command()\n async def say(self, ctx, *, arg):\n if (arg) == \"Sono stupido\":\n await ctx.send(\"Lo sappiamo.\")\n elif (arg) == \"sono stupido\":\n await ctx.send(\"Lo sappiamo.\")\n elif (arg) == \"Sono uno stupido\":\n await ctx.send(\"Lo sappiamo.\")\n elif (arg) == \"sono uno stupido\":\n await ctx.send(\"Lo sappiamo.\")\n else:\n await ctx.send(f\"{arg} \\n\\n\\n- **{ctx.author}**\")\n\n # Command of questions. The bot send a random response for any type of question\n @commands.command(aliases=[\"8ball\", \"oracolo\", \"predizione\", \"domanda\"])\n async def erdubbio(self, ctx, *, question):\n responses = [\"E' certo.\",\n \"E' stato deciso così.\",\n \"Senza dubbio.\",\n \"Già....indubbiamente.\",\n \"Contaci.\",\n \"Per come la vedo io, si.\",\n \"Preferibilmente.\",\n \"Vedila così.\",\n \"Sì.\",\n \"Un punto in più per il 'Sì'.\",\n \"uhm... Sono un po' confuso, potresti ripetere la domanda?\",\n \"Chiedimelo più tardi..\",\n \"E' meglio non dirtelo ora.\",\n \"Mi risulta difficile predirlo ora.\",\n \"Concentrati e chiedimelo di nuovo.\",\n \"Non ci contare.\",\n \"La mia risposta è no.\",\n \"Le mie fonti dicono di no.\",\n \"Pessima prospettiva.\",\n \"Dubito.\"]\n embed = discord.Embed(color=0xa7c7fb)\n embed.add_field(name=\"(?) ErDubbio (?)\", value=\"*Poni le tue più strambe domande a questo magnifico oracolo dotato di tanta saggezza e righe di codice\", inline=False)\n embed.add_field(name=\":question: Domandona:\", value=f\"{question}\", inline=True)\n embed.add_field(name=\":speech_left: Risposta epica:\", value=f\"{random.choice(responses)}\", inline=True)\n embed.set_footer(text=\"Leonardus Project\")\n await ctx.send(embed=embed)\n\n @commands.command(aliases=[\"hack\"])\n async def akeraggio(self, ctx, member: commands.MemberConverter):\n message = await ctx.send(f\"Sto hackerando con paint {member}...\")\n await asyncio.sleep(2)\n await message.edit(content='Sono penetrato nel sistema!')\n await asyncio.sleep(3)\n await message.delete()\n await asyncio.sleep(0.2)\n message = await ctx.send(\"[▙] Eseguo un leak dell'email discord...(2fa Bypass)\")\n await asyncio.sleep(3)\n await message.edit(content=\"[▛] **Gotcha!**\")\n email = await ctx.send(f\"**EMAIL:** `{member}@email.net` \\n**PASSWORD:** `PASSW0RD`\")\n await asyncio.sleep(4)\n await message.delete()\n await email.delete()\n await asyncio.sleep(0.1)\n message = await ctx.send(\"[▟] Spio i messaggi recenti...\")\n await asyncio.sleep(2)\n dms = [\"send nudes\",\n \"Ammetto che adoro i canditi\",\n \"Napoli merda\",\n \"Tifo Juve\",\n \"Ieri ho rubato 2 orologi\",\n \"Cyca mala criminale\",\n \"mlmlml, che belli i bimbi neri\"]\n await message.edit(content=f\"**Leak degli ultimi dms**: '`{random.choice(dms)}`'\")\n await asyncio.sleep(3)\n await message.edit(content=f\"[▙] Rubo le credenziali di steam...\")\n await asyncio.sleep(3)\n await message.edit(content=f\"[▛] Credenziali di steam rubate :)\")\n await asyncio.sleep(2)\n await message.edit(content=f\"[▜] Traccio l'IP...\")\n await asyncio.sleep(3)\n await message.edit(content=f\"[▟] **IP TROVATO:** `127.0.0.1`\")\n await asyncio.sleep(2)\n await message.edit(content=f\"[▙] Scopro la cronologia...\")\n await asyncio.sleep(3)\n await message.edit(content=f\"**CRONOLOGIA TROVATA** \\n*Lista:* \\n`How to buil a bomb`\\n`How to kidnapp`\\n`Come dichiarare le variabili in HTML`\")\n await asyncio.sleep(5)\n await message.edit(content=f\"*Rivendo i dati al governo...*\")\n await asyncio.sleep(3)\n await message.edit(content=f\"*Rendo {member} ricercato in 5 paesi differenti...*\")\n await asyncio.sleep(4)\n await message.edit(content=f\"*Infetto il computer di {member} con diversi virus...*\")\n await asyncio.sleep(2)\n await message.edit(content=f\"Fine. *{member}* è stato hackerato!\")\n await ctx.send(\"Processo di hackeraggio **100%** *reale* e *pericoloso* terminato.\")\n\n\n\n\n\n\n\ndef setup(client):\n client.add_cog(Interaction(client))","repo_name":"sl04zy/Leonardus-Project","sub_path":"src/cogs/Interaction.py","file_name":"Interaction.py","file_ext":"py","file_size_in_byte":5847,"program_lang":"python","lang":"it","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"}
+{"seq_id":"72758450134","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on: Friday December 17th of January 2020\n\nAuthor: Daniel Cortild (https://github.com/DanielCortild)\n\nTicTacToe Judger Module\nThe Judger Monitors the Game\n\"\"\"\n\nfrom modules.state import State\n\nclass Judger:\n\n def __init__ ( self, p1, p2, learning = True ):\n\n self.p1 = p1\n self.p1Symbol = 1\n self.p1.setSymbol( self.p1Symbol )\n\n self.p2 = p2\n self.p2Symbol = -1\n self.p2.setSymbol( self.p2Symbol )\n\n self.currentPlayer = None\n\n self.learning = learning\n\n self.currentState = State()\n\n def giveReward ( self ):\n\n if self.currentState.winner == self.p1Symbol:\n\n self.p1.feedReward(1)\n self.p2.feedReward(0)\n\n elif self.currentState.winner == self.p2Symbol:\n\n self.p1.feedReward(0)\n self.p2.feedReward(1)\n\n else:\n\n self.p1.feedReward(0.5)\n self.p2.feedReward(0.5)\n\n def feedCurrentState ( self ):\n\n self.p1.feedState( self.currentState )\n self.p2.feedState( self.currentState )\n\n def reset ( self ):\n\n self.p1.reset()\n self.p2.reset()\n\n self.currentState = State()\n self.currentPlayer = None\n\n def play ( self, show = False ):\n\n self.reset()\n self.feedCurrentState()\n\n if show:\n self.currentState.show()\n\n while True:\n\n if self.currentPlayer == self.p1:\n self.currentPlayer = self.p2\n else:\n self.currentPlayer = self.p1\n\n [i, j, symbol] = self.currentPlayer.takeAction()\n\n self.currentState = self.currentState.nextState( i, j, symbol )\n hashValue = self.currentState.getHash()\n\n self.feedCurrentState()\n\n if show:\n self.currentState.show()\n\n if self.currentState.isEnd():\n\n if self.learning:\n self.giveReward()\n\n return self.currentState.winner\n","repo_name":"DanielCortild/TicTacToe","sub_path":"Python/modules/judger.py","file_name":"judger.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"11780679149","text":"def encrypt(s,key):\n n=len(s)\n matrix=[[0 for _ in range(n)] for _ in range(key)]\n bool=False\n i=0\n j=0\n for k in range(n):\n matrix[i][j]=s[k]\n k+=1\n if i==0 or i==key-1:\n bool=not bool\n \n if bool:\n i+=1\n else:\n i-=1\n j+=1\n\n ans=\"\"\n for i in range(key):\n for j in range(n):\n if matrix[i][j]!=0:\n ans+=matrix[i][j]\n return ans\n\n\ns=input(\"Enter the string to be encrypted : \")\nk=int(input(\"Enter the key value of rail fence : \"))\nnew=encrypt(s,k)\nprint(\"The encrypted String is : \" , new)","repo_name":"Iamayushgupta/Blockchain","sub_path":"cryptography/rail_fence.py","file_name":"rail_fence.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"31142745079","text":"# Ask the user for a number and determine whether the number is prime or not.\n# (For those who have forgotten, a prime number is a number that has no divisors.).\n# You can (and should!) use your answer in Exercise 4 to help you.\n# Take this opportunity to practice using functions, described below\n\n\ndef is_divisor(num, divisor):\n return num % divisor == 0\n\n\ndef is_prime(num):\n count = 0\n for i in range(2, num):\n if is_divisor(num, i):\n count += 1\n if count == 0:\n return True\n else:\n return False\n\n\nprime_number = int(input(\"Please enter a number: \"))\nprint(is_prime(prime_number))\n\n","repo_name":"nitzanpap/practiceAndExercises","sub_path":"pythonExercises/exercise11.py","file_name":"exercise11.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"33057914787","text":"\nimport tensorflow as tf\nfrom model.common import conv2d_bn_mish\n\ndef spp(x):\n pool_sizes = [5, 9, 13]\n pooling_results = [tf.keras.layers.MaxPooling2D((pool_size,pool_size), strides=(1, 1),padding='same')(x) for pool_size in pool_sizes]\n spp_result = tf.keras.layers.Concatenate()(pooling_results+[x])\n spp_result = conv2d_bn_mish(spp_result, x.shape[3], (1, 1))\n return spp_result\n\n","repo_name":"wangermeng2021/Scaled-YOLOv4-tensorflow2","sub_path":"model/spp.py","file_name":"spp.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"67"}
+{"seq_id":"71577354132","text":"import sys\nfrom obj.BibliotekaObj import Biblioteka\nimport utils.constants as c\nimport utils.db as db\nimport utils.utils as u\n\nTEST_MODE = False\n\ndef sprawdzCzyTrybTestowy():\n if len(sys.argv) > 1:\n if sys.argv[1] == 'test':\n TEST_MODE = True\n print(\"Uruchomiono w trybie testowym...\")\n\nif __name__ == \"__main__\":\n sprawdzCzyTrybTestowy()\n db.inicjujDane()\n biblioteka = Biblioteka(TEST_MODE)\n biblioteka.ladujBiblioteke()\n while True:\n menuWybor = u.czyscWejscie(input(c.ASCII_MENU), trybTestowania=TEST_MODE)\n if (menuWybor == '1'):\n biblioteka.dodajKsiazke()\n elif (menuWybor == '2'):\n biblioteka.wypozyczKsiazke()\n elif (menuWybor == '3'):\n biblioteka.oddajKsiazke()\n elif (menuWybor == '4'):\n biblioteka.podejrzyjHistorieKsiazki()\n elif (menuWybor == '5'):\n biblioteka.dodajCzytacza()\n elif (menuWybor == '6'):\n print(f\"Zamykanie programu...\")\n break\n else:\n print(\"Wybrano nie istniejącą opcję w menu...\")\n SystemExit(0)\n","repo_name":"gkk-dev-ops/py-beginner-exercises","sub_path":"Projekt/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"10208812690","text":"# -*- encoding: utf-8 -*-\n# pylint: disable=E0203,E1101,C0111\n\"\"\"\n@file\n@brief Runtime operator.\n\"\"\"\nimport numpy\nfrom ._op import OpRun\n\n\nclass ZipMapDictionary(dict):\n \"\"\"\n Custom dictionary class much faster for this runtime,\n it implements a subset of the same methods.\n \"\"\"\n __slots__ = ['_rev_keys', '_values', '_mat']\n\n @staticmethod\n def build_rev_keys(keys):\n res = {}\n for i, k in enumerate(keys):\n res[k] = i\n return res\n\n def __init__(self, rev_keys, values, mat=None):\n \"\"\"\n @param rev_keys returns by @see me build_rev_keys,\n *{keys: column index}*\n @param values values\n @param mat matrix if values is a row index,\n one or two dimensions\n \"\"\"\n if mat is not None:\n if not isinstance(mat, numpy.ndarray):\n raise TypeError( # pragma: no cover\n f'matrix is expected, got {type(mat)}.')\n if len(mat.shape) not in (2, 3):\n raise ValueError( # pragma: no cover\n f\"matrix must have two or three dimensions but got {mat.shape}.\")\n dict.__init__(self)\n self._rev_keys = rev_keys\n self._values = values\n self._mat = mat\n\n def __getstate__(self):\n \"\"\"\n For pickle.\n \"\"\"\n return dict(_rev_keys=self._rev_keys,\n _values=self._values,\n _mat=self._mat)\n\n def __setstate__(self, state):\n \"\"\"\n For pickle.\n \"\"\"\n if isinstance(state, tuple):\n state = state[1]\n self._rev_keys = state['_rev_keys']\n self._values = state['_values']\n self._mat = state['_mat']\n\n def __getitem__(self, key):\n \"\"\"\n Returns the item mapped to keys.\n \"\"\"\n if self._mat is None:\n return self._values[self._rev_keys[key]]\n return self._mat[self._values, self._rev_keys[key]]\n\n def __setitem__(self, pos, value):\n \"unused but used by pickle\"\n pass\n\n def __len__(self):\n \"\"\"\n Returns the number of items.\n \"\"\"\n return len(self._values) if self._mat is None else self._mat.shape[1]\n\n def __iter__(self):\n for k in self._rev_keys:\n yield k\n\n def __contains__(self, key):\n return key in self._rev_keys\n\n def items(self):\n if self._mat is None:\n for k, v in self._rev_keys.items():\n yield k, self._values[v]\n else:\n for k, v in self._rev_keys.items():\n yield k, self._mat[self._values, v]\n\n def keys(self):\n for k in self._rev_keys.keys():\n yield k\n\n def values(self):\n if self._mat is None:\n for v in self._values:\n yield v\n else:\n for v in self._mat[self._values]:\n yield v\n\n def asdict(self):\n res = {}\n for k, v in self.items():\n res[k] = v\n return res\n\n def __str__(self):\n return f\"ZipMap({str(self.asdict())!r})\"\n\n\nclass ArrayZipMapDictionary(list):\n \"\"\"\n Mocks an array without changing the data it receives.\n Notebooks :ref:`onnxnodetimerst` illustrates the weaknesses\n and the strengths of this class compare to a list\n of dictionaries.\n\n .. index:: ZipMap\n \"\"\"\n\n def __init__(self, rev_keys, mat):\n \"\"\"\n @param rev_keys dictionary *{keys: column index}*\n @param mat matrix if values is a row index,\n one or two dimensions\n \"\"\"\n if mat is not None:\n if not isinstance(mat, numpy.ndarray):\n raise TypeError( # pragma: no cover\n f'matrix is expected, got {type(mat)}.')\n if len(mat.shape) not in (2, 3):\n raise ValueError( # pragma: no cover\n f\"matrix must have two or three dimensions but got {mat.shape}.\")\n list.__init__(self)\n self._rev_keys = rev_keys\n self._mat = mat\n\n @property\n def dtype(self):\n return self._mat.dtype\n\n def __len__(self):\n return self._mat.shape[0]\n\n def __iter__(self):\n for i in range(len(self)):\n yield self[i]\n\n def __getitem__(self, i):\n return ZipMapDictionary(self._rev_keys, i, self._mat)\n\n def __setitem__(self, pos, value):\n raise RuntimeError(\n f\"Changing an element is not supported (pos=[{pos}]).\")\n\n @property\n def values(self):\n \"\"\"\n Equivalent to ``DataFrame(self).values``.\n \"\"\"\n if len(self._mat.shape) == 3:\n return self._mat.reshape((self._mat.shape[1], -1))\n return self._mat\n\n @property\n def columns(self):\n \"\"\"\n Equivalent to ``DataFrame(self).columns``.\n \"\"\"\n res = [(v, k) for k, v in self._rev_keys.items()]\n if len(res) == 0:\n if len(self._mat.shape) == 2:\n res = [(i, 'c%d' % i) for i in range(self._mat.shape[1])]\n elif len(self._mat.shape) == 3:\n # multiclass\n res = [(i, 'c%d' % i)\n for i in range(self._mat.shape[0] * self._mat.shape[2])]\n else:\n raise RuntimeError( # pragma: no cover\n \"Unable to guess the right number of columns for \"\n \"shapes: {}\".format(self._mat.shape))\n else:\n res.sort()\n return [_[1] for _ in res]\n\n @property\n def is_zip_map(self):\n return True\n\n def __str__(self):\n return f\"ZipMaps[{', '.join(map(str, self))}]\"\n\n\nclass ZipMap(OpRun):\n \"\"\"\n The class does not output a dictionary as\n specified in :epkg:`ONNX` specifications\n but a @see cl ArrayZipMapDictionary which\n is wrapper on the input so that it does not\n get copied.\n \"\"\"\n\n atts = {'classlabels_int64s': [], 'classlabels_strings': []}\n\n def __init__(self, onnx_node, desc=None, **options):\n OpRun.__init__(self, onnx_node, desc=desc,\n expected_attributes=ZipMap.atts,\n **options)\n if hasattr(self, 'classlabels_int64s') and len(self.classlabels_int64s) > 0:\n self.rev_keys_ = ZipMapDictionary.build_rev_keys(\n self.classlabels_int64s)\n elif hasattr(self, 'classlabels_strings') and len(self.classlabels_strings) > 0:\n self.rev_keys_ = ZipMapDictionary.build_rev_keys(\n self.classlabels_strings)\n else:\n self.rev_keys_ = {}\n\n def _run(self, x, attributes=None, verbose=0, fLOG=None): # pylint: disable=W0221\n res = ArrayZipMapDictionary(self.rev_keys_, x)\n return (res, )\n","repo_name":"sdpython/mlprodict","sub_path":"mlprodict/onnxrt/ops_cpu/op_zipmap.py","file_name":"op_zipmap.py","file_ext":"py","file_size_in_byte":6872,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"67"}
+{"seq_id":"1119918561","text":"from cartoframes.auth import set_default_credentials\nfrom cartoframes import read_carto, to_carto\nimport geopandas as gpd\nimport pandas as pd\nimport os\nfrom shapely.validation import make_valid\nimport requests\nimport re\nfrom bs4 import BeautifulSoup\nimport glob\nimport json\n\nfrom dotenv import load_dotenv\nload_dotenv('/home/chemmerly/cred/.env')\n\ndata_dir = \"data\"\n\n#download list of EBSA data urls from https://chm.cbd.int/database\nraw_data_file = 'Aichi-Targets-data.csv'\n\n# read in the csv with the urls for the EBSA jsons\nurl_df = pd.read_csv(raw_data_file,encoding='latin-1')\nurl_list = url_df['CHM Url']\n\n# regex pattern for the finding a geojson \nmatch_st = re.compile(r'geojson') \nfor url in url_list:\n # scrape the page for the geojson\n r = requests.get(url) \n c = r.content \n soup = BeautifulSoup(c)\n for link in soup.findAll('a', attrs={'href': re.compile(\"geojson$\")}):\n href = link.get('href')\n url = 'https://chm.cbd.int' + href\n # download raw data\n r = requests.get(url)\n j = json.loads(r.content)\n #store data as geojson files\n raw_data_file = os.path.join(data_dir, os.path.basename(url))\n with open(raw_data_file, \"w\") as file:\n json.dump(j, file)\n\n#create list of geojson data for each ebsa from stored geojson file\nebsa_files = glob.glob(os.path.join(data_dir, '*geojson'))\ngdf_list = []\nfor file in ebsa_files:\n try:\n gdf = gpd.read_file(file)\n gdf_list.append(gdf)\n except Exception:\n print(\"Could not read \" + file)\n\n#create geopandas dataframe of EBSA data from list\ngdf_ebsa = gpd.GeoDataFrame(pd.concat(gdf_list))\n\n#store EBSA data locally as shapefiles\ngdf_ebsa.to_file('merged_ebsa.shp',driver='ESRI Shapefile')\n\n#upload EBSA data to Carto\ngdf_ebsa.columns = [x.lower().replace(' ', '_') for x in gdf_ebsa.columns]\ndataset_name = \"Ecologically and Biologically Significant Areas\"\nCARTO_USER = os.getenv('CARTO_WRI_RW_USER')\nCARTO_KEY = os.getenv('CARTO_WRI_RW_KEY')\nset_default_credentials(username=CARTO_USER, base_url=\"https://{user}.carto.com/\".format(user=CARTO_USER),api_key=CARTO_KEY)\nto_carto(gdf_ebsa, dataset_name + '_edit', if_exists='replace')","repo_name":"clairehemmerly/ocean_watch","sub_path":"EBSA_files_fetch.py","file_name":"EBSA_files_fetch.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"26321872271","text":"import pygame\npygame.init()\nwidth, height = 1280, 720\nwindow = pygame.display.set_mode((width, height))\npygame.display.set_caption(\"My Awesome Game\")\nfps = 30\nclock = pygame.time.Clock()\nstart = True\nwhile start:\n # Get Events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n start = False\n pygame.quit()\n window.fill((240, 143, 250))\n yellow,pink,green = (253,235,8),(253,8,200),(58,223,117)\n pygame.draw.polygon(window,yellow,((491, 100), (788, 100), (937, 357),(788, 614), (491, 614), (342, 357)))\n pygame.draw.circle(window, green, (640, 360), 200)\n pygame.draw.line(window, pink, (468, 392), (812, 392), 10)\n pygame.draw.rect(window, pink, (468, 307, 345, 70), border_radius=5)\n\n\n # Update Display\n pygame.display.update()\n # Set FPS\n clock.tick(fps)","repo_name":"kirankuyate2157/python_programs","sub_path":"download_proggrams/Game_development_opencv-main/game_dev/draw_shapes.py","file_name":"draw_shapes.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"2787667447","text":"import numpy as np\nimport tensorflow as tf\n\nfrom surreal.spaces.primitive_spaces import PrimitiveSpace, Bool, Int, Float, Text\nfrom surreal.spaces.container_spaces import ContainerSpace, Dict, Tuple\nfrom surreal.utils.errors import SurrealError, SurrealSpaceError\nfrom surreal.utils.util import convert_dtype, LARGE_INTEGER, force_tuple\n\n\n# TODO: replace completely by `Component.get_variable` (python-backend)\ndef get_list_registry(from_space, capacity=None, initializer=0, flatten=True, add_batch_rank=False):\n \"\"\"\n Creates a list storage for a space by providing an ordered dict mapping space names\n to empty lists.\n\n Args:\n from_space: Space to create registry from.\n capacity (Optional[int]): Optional capacity to initialize list.\n initializer (Optional(any)): Optional initializer for list if capacity is not None.\n flatten (bool): Whether to produce a FlattenedDataOp with auto-keys.\n\n add_batch_rank (Optional[bool,int]): If from_space is given and is True, will add a 0th rank (None) to\n the created variable. If it is an int, will add that int instead of None.\n Default: False.\n\n Returns:\n dict: Container dict mapping core to empty lists.\n \"\"\"\n if flatten:\n if capacity is not None:\n var = from_space.flatten(\n custom_scope_separator=\"-\", scope_separator_at_start=False,\n mapping=lambda k, primitive: [initializer for _ in range(capacity)]\n )\n else:\n var = from_space.flatten(\n custom_scope_separator=\"-\", scope_separator_at_start=False,\n mapping=lambda k, primitive: []\n )\n else:\n if capacity is not None:\n var = [initializer for _ in range(capacity)]\n else:\n var = []\n return var\n\n\ndef get_space_from_data(data, num_categories=None, main_axes=None):\n \"\"\"\n Tries to re-create a Space object given some DataOp (e.g. a tf op).\n This is useful for shape inference on returned ops after having run through a graph_fn.\n\n Args:\n data (any): The data to create a corresponding Space for.\n\n num_categories (Optional[int]): An optional indicator, what the `num_categories` property for\n an Int should be.\n\n Returns:\n Space: The inferred Space object.\n \"\"\"\n # Dict.\n if isinstance(data, dict):\n spec = {}\n for key, value in data.items():\n\n # OBSOLETE THIS! Special case for Ints:\n # If another key exists, with the name: `_num_[key]` -> take num_categories from that key's value.\n #if key[:5] == \"_num_\":\n # continue\n #num_categories = data.get(\"_num_{}\".format(key))\n\n num_categories = num_categories.get(key, None) if isinstance(num_categories, dict) else num_categories\n spec[key] = get_space_from_data(value, num_categories=num_categories, main_axes=main_axes)\n # Return\n if spec[key] == 0:\n return 0\n return Dict(spec, main_axes=main_axes)\n # Tuple.\n elif isinstance(data, tuple):\n spec = []\n for i in data:\n space = get_space_from_data(i, main_axes=main_axes)\n if space == 0:\n return 0\n spec.append(space)\n return Tuple(spec, main_axes=main_axes)\n # Primitive Space -> Infer from data dtype and shape.\n else:\n # `data` itself is a single value, simple python type.\n if isinstance(data, int):\n int_high = {\"high\": num_categories} if num_categories is not None else {}\n return PrimitiveSpace.make(spec=type(data), shape=(), **int_high)\n elif isinstance(data, (bool, float)):\n return PrimitiveSpace.make(spec=type(data), shape=())\n elif isinstance(data, str):\n raise SurrealError(\"Cannot derive Space from str data ({})!\".format(data))\n # A single numpy array.\n elif isinstance(data, (np.ndarray, tf.Tensor)):\n dtype = convert_dtype(data.dtype, \"np\")\n int_high = {\"high\": num_categories} if num_categories is not None and \\\n dtype in [np.uint8, np.int16, np.int32, np.int64] else {}\n # Must subtract main_axes from beginning of data.shape.\n shape = tuple(data.shape[len(main_axes or []):])\n return PrimitiveSpace.make(\n spec=dtype, shape=shape, main_axes=main_axes, **int_high\n )\n # Try inferring the Space from a python list.\n elif isinstance(data, list):\n return try_space_inference_from_list(data)\n # No Space: e.g. the tf.no_op, a distribution (anything that's not a tensor).\n # PyTorch Tensors do not have get_shape so must check backend.\n elif hasattr(data, \"dtype\") is False or not hasattr(data, \"get_shape\"):\n return 0\n\n raise SurrealError(\"ERROR: Cannot derive Space from data '{}' (unknown type?)!\".format(data))\n\n\ndef sanity_check_space(\n space, allowed_types=None, allowed_sub_types=None, non_allowed_types=None, non_allowed_sub_types=None,\n must_have_batch_rank=None, must_have_time_rank=None, must_have_batch_or_time_rank=False,\n must_have_categories=None, num_categories=None,\n must_have_lower_limit=None, must_have_upper_limit=None,\n rank=None, shape=None\n):\n \"\"\"\n Sanity checks a given Space for certain criteria and raises exceptions if they are not met.\n\n Args:\n space (Space): The Space object to check.\n allowed_types (Optional[List[type]]): A list of types that this Space must be an instance of.\n\n allowed_sub_types (Optional[List[type]]): For container core, a list of sub-types that all\n flattened sub-Spaces must be an instance of.\n\n non_allowed_types (Optional[List[type]]): A list of type that this Space must not be an instance of.\n\n non_allowed_sub_types (Optional[List[type]]): For container core, a list of sub-types that all\n flattened sub-Spaces must not be an instance of.\n\n must_have_batch_rank (Optional[bool]): Whether the Space must (True) or must not (False) have the\n `has_batch_rank` property set to True. None, if it doesn't matter.\n\n must_have_time_rank (Optional[bool]): Whether the Space must (True) or must not (False) have the\n `has_time_rank` property set to True. None, if it doesn't matter.\n\n must_have_batch_or_time_rank (Optional[bool]): Whether the Space must (True) or must not (False) have either\n the `has_batch_rank` or the `has_time_rank` property set to True.\n\n must_have_categories (Optional[bool]): For IntBoxes, whether the Space must (True) or must not (False) have\n global bounds with `num_categories` > 0. None, if it doesn't matter.\n\n num_categories (Optional[int,tuple]): An int or a tuple (min,max) range within which the Space's\n `num_categories` rank must lie. Only valid for IntBoxes.\n None if it doesn't matter.\n\n must_have_lower_limit (Optional[bool]): If not None, whether this Space must have a lower limit.\n must_have_upper_limit (Optional[bool]): If not None, whether this Space must have an upper limit.\n\n rank (Optional[int,tuple]): An int or a tuple (min,max) range within which the Space's rank must lie.\n None if it doesn't matter.\n\n shape (Optional[tuple[int]]): A tuple of ints specifying the required shape. None if it doesn't matter.\n\n Raises:\n RLGraphSpaceError: If any of the conditions is not met.\n \"\"\"\n flattened_space = space.flatten()\n\n # Check the types.\n if allowed_types is not None:\n if not isinstance(space, force_tuple(allowed_types)):\n raise SurrealSpaceError(\n space, \"ERROR: Space ({}) is not an instance of {}!\".format(space, allowed_types)\n )\n\n if allowed_sub_types is not None:\n for flat_key, sub_space in flattened_space.items():\n if not isinstance(sub_space, force_tuple(allowed_sub_types)):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: sub-Space '{}' ({}) is not an instance of {}!\".\n format(flat_key, sub_space, allowed_sub_types)\n )\n\n if non_allowed_types is not None:\n if isinstance(space, force_tuple(non_allowed_types)):\n raise SurrealSpaceError(\n space,\n \"ERROR: Space ({}) must not be an instance of {}!\".format(space, non_allowed_types)\n )\n\n if non_allowed_sub_types is not None:\n for flat_key, sub_space in flattened_space.items():\n if isinstance(sub_space, force_tuple(non_allowed_sub_types)):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: sub-Space '{}' ({}) must not be an instance of {}!\".\n format(flat_key, sub_space, non_allowed_sub_types)\n )\n\n if must_have_batch_or_time_rank is True:\n if space.has_batch_rank is False and space.has_time_rank is False:\n raise SurrealSpaceError(\n space,\n \"ERROR: Space ({}) does not have a batch- or a time-rank, but must have either one of \"\n \"these!\".format(space)\n )\n\n if must_have_batch_rank is not None:\n if (space.has_batch_rank is False and must_have_batch_rank is True) or \\\n (space.has_batch_rank is not False and must_have_batch_rank is False):\n # Last chance: Check for rank >= 2, that would be ok as well.\n if must_have_batch_rank is True and len(space.get_shape(main_axes=\"B\")) >= 2:\n pass\n # Something is wrong.\n elif space.has_batch_rank is not False:\n raise SurrealSpaceError(\n space,\n \"ERROR: Space ({}) has a batch rank, but is not allowed to!\".format(space)\n )\n else:\n raise SurrealSpaceError(\n space,\n \"ERROR: Space ({}) does not have a batch rank, but must have one!\".format(space)\n )\n\n if must_have_time_rank is not None:\n if (space.has_time_rank is False and must_have_time_rank is True) or \\\n (space.has_time_rank is not False and must_have_time_rank is False):\n # Last chance: Check for rank >= 3, that would be ok as well.\n if must_have_time_rank is True and len(space.get_shape(main_axes=[\"B\", \"T\"])) >= 2:\n pass\n # Something is wrong.\n elif space.has_time_rank is not False:\n raise SurrealSpaceError(\n space,\n \"ERROR: Space ({}) has a time rank, but is not allowed to!\".format(space)\n )\n else:\n raise SurrealSpaceError(\n space,\n \"ERROR: Space ({}) does not have a time rank, but must have one!\".format(space)\n )\n\n if must_have_categories is not None:\n for flat_key, sub_space in flattened_space.items():\n if not isinstance(sub_space, Int):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: Space {}({}) is not an Int. Only Int Spaces can have categories!\".\n format(\"\" if flat_key == \"\" else \"'{}' \".format(flat_key), space)\n )\n elif sub_space.global_bounds is False:\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: Space {}({}) must have categories (globally valid value bounds)!\".\n format(\"\" if flat_key == \"\" else \"'{}' \".format(flat_key), space)\n )\n\n if must_have_lower_limit is not None:\n for flat_key, sub_space in flattened_space.items():\n low = sub_space.low\n if must_have_lower_limit is True and (low == -LARGE_INTEGER or low == float(\"-inf\")):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: Space {}({}) must have a lower limit, but has none!\".\n format(\"\" if flat_key == \"\" else \"'{}' \".format(flat_key), space)\n )\n elif must_have_lower_limit is False and (low != -LARGE_INTEGER and low != float(\"-inf\")):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: Space {}({}) must not have a lower limit, but has one ({})!\".\n format(\"\" if flat_key == \"\" else \"'{}' \".format(flat_key), space, low)\n )\n\n if must_have_upper_limit is not None:\n for flat_key, sub_space in flattened_space.items():\n high = sub_space.high\n if must_have_upper_limit is True and (high != LARGE_INTEGER and high != float(\"inf\")):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: Space {}({}) must have an upper limit, but has none!\".\n format(\"\" if flat_key == \"\" else \"'{}' \".format(flat_key), space)\n )\n elif must_have_upper_limit is False and (high == LARGE_INTEGER or high == float(\"inf\")):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: Space {}({}) must not have a upper limit, but has one ({})!\".\n format(\"\" if flat_key == \"\" else \"'{}' \".format(flat_key), space, high)\n )\n\n if rank is not None:\n if isinstance(rank, int):\n for flat_key, sub_space in flattened_space.items():\n if sub_space.rank != rank:\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: A Space (flat-key={}) of '{}' has rank {}, but must have rank \"\n \"{}!\".format(flat_key, space, sub_space.rank, rank)\n )\n else:\n for flat_key, sub_space in flattened_space.items():\n if not ((rank[0] or 0) <= sub_space.rank <= (rank[1] or float(\"inf\"))):\n raise SurrealSpaceError(\n\n sub_space,\n \"ERROR: A Space (flat-key={}) of '{}' has rank {}, but its rank must be between {} and \"\n \"{}!\".format(flat_key, space, sub_space.rank, rank[0], rank[1])\n )\n\n if shape is not None:\n for flat_key, sub_space in flattened_space.items():\n if sub_space.shape != shape:\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: A Space (flat-key={}) of '{}' has shape {}, but its shape must be \"\n \"{}!\".format(flat_key, space, sub_space.get_shape(), shape)\n )\n\n if num_categories is not None:\n for flat_key, sub_space in flattened_space.items():\n if not isinstance(sub_space, Int):\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: A Space (flat-key={}) of '{}' is not an Int. Only Int Spaces can have \"\n \"categories!\".format(flat_key, space)\n )\n elif isinstance(num_categories, int):\n if sub_space.num_categories != num_categories:\n raise SurrealSpaceError(\n sub_space,\n \"ERROR: A Space (flat-key={}) of '{}' has `num_categories` {}, but must have {}!\".\n format(flat_key, space, sub_space.num_categories, num_categories)\n )\n elif not ((num_categories[0] or 0) <= sub_space.num_categories <= (num_categories[1] or float(\"inf\"))):\n raise SurrealSpaceError(sub_space,\n \"ERROR: A Space (flat-key={}) of '{}' has `num_categories` {}, but this value must be between \"\n \"{} and {}!\".format(flat_key, space, sub_space.num_categories, num_categories[0], num_categories[1])\n )\n\n\ndef check_space_equivalence(space1, space2):\n \"\"\"\n Compares the two input Spaces for equivalence and returns the more generic Space of the two.\n The more generic Space is the one that has the properties has_batch_rank and/or has _time_rank set (instead of\n hard values in these ranks).\n E.g.: Float((64,)) is equivalent with Float((), +batch-rank). The latter will be returned.\n\n NOTE: Float((2,)) and Float((3,)) are NOT equivalent.\n\n Args:\n space1 (Space): The 1st Space to compare.\n space2 (Space): The 2nd Space to compare.\n\n Returns:\n Union[Space,False]: False is the two core are not equivalent. The more generic Space of the two if they are\n equivalent.\n \"\"\"\n # Spaces are the same: Return one of them.\n if space1 == space2:\n return space1\n # One has batch-rank, the other doesn't, but has one more rank.\n elif space1.has_batch_rank and not space2.has_batch_rank and \\\n (np.asarray(space1.rank) == np.asarray(space2.rank) - 1).all():\n return space1\n elif space2.has_batch_rank and not space1.has_batch_rank and \\\n (np.asarray(space2.rank) == np.asarray(space1.rank) - 1).all():\n return space2\n # TODO: time rank?\n\n return False\n\n\ndef try_space_inference_from_list(list_op):\n \"\"\"\n Attempts to infer shape space from a list op. A list op may be the result of fetching state from a Python\n memory.\n\n Args:\n list_op (list): List with arbitrary sub-structure.\n\n Returns:\n Space: Inferred Space object represented by list.\n \"\"\"\n shape = len(list_op)\n if shape > 0:\n # Try to infer more things by looking inside list.\n elem = list_op[0]\n if isinstance(elem, tf.Tensor):\n list_type = elem.dtype\n inner_shape = elem.shape\n return PrimitiveSpace.make(spec=convert_dtype(list_type, \"np\"), shape=(shape,) + inner_shape,\n add_batch_rank=True)\n elif isinstance(elem, list):\n inner_shape = len(elem)\n return PrimitiveSpace.make(spec=convert_dtype(float, \"np\"), shape=(shape, inner_shape),\n add_batch_rank=True)\n elif isinstance(elem, int):\n # In case of missing comma values, check all other items in list for float.\n # If one float in there -> Float, otherwise -> Int.\n has_floats = any(isinstance(el, float) for el in list_op)\n if has_floats is False:\n return Int.make(shape=(shape,), add_batch_rank=True)\n else:\n return Float.make(shape=(shape,), add_batch_rank=True)\n elif isinstance(elem, float):\n return Float.make(shape=(shape,), add_batch_rank=True)\n else:\n # Most general guess is a Float box.\n return Float(shape=(shape,))\n\n\ndef get_default_distribution_from_space(\n space, *, num_mixture_experts=0, bounded_distribution_type=\"beta\",\n discrete_distribution_type=\"categorical\", gumbel_softmax_temperature=1.0\n):\n \"\"\"\n Args:\n space (Space): The primitive Space for which to derive a default distribution spec.\n\n num_mixture_experts (int): If > 0, use a mixture distribution over the determined \"base\"-distribution using n\n experts. TODO: So far, this only works for continuous distributions.\n\n bounded_distribution_type (str): The lookup class string for a bounded Float distribution.\n Default: \"beta\".\n\n discrete_distribution_type(str): The class of distributions to use for discrete action core. For options\n check the components.distributions package. Default: categorical. Agents requiring reparameterization\n may require a GumbelSoftmax distribution instead.\n\n gumbel_softmax_temperature (float): Temperature parameter for the Gumbel-Softmax distribution used\n for discrete actions.\n\n Returns:\n Dict: A Spec dict, from which a valid default distribution object can be created.\n \"\"\"\n # Int: Categorical.\n if isinstance(space, Int):\n assert discrete_distribution_type in [\"gumbel-softmax\", \"categorical\"]\n if discrete_distribution_type == \"gumbel-softmax\":\n return dict(type=\"gumbel-softmax\", temperature=gumbel_softmax_temperature)\n else:\n return dict(type=discrete_distribution_type)\n\n # Bool: Bernoulli.\n elif isinstance(space, Bool):\n return dict(type=\"bernoulli\")\n\n # Continuous action space: Normal/Beta/etc. distribution.\n elif isinstance(space, Float):\n # Unbounded -> Normal distribution.\n if not is_bounded_space(space):\n single = dict(type=\"normal\")\n # Bounded -> according to the bounded_distribution parameter.\n else:\n assert bounded_distribution_type in [\"beta\", \"squashed-normal\"]\n single = dict(type=bounded_distribution_type, low=space.low, high=space.high)\n\n # Use a mixture distribution?\n if num_mixture_experts > 0:\n return dict(type=\"mixture\", _args=single, num_experts=num_mixture_experts)\n else:\n return single\n\n # Container Space.\n elif isinstance(space, ContainerSpace):\n return dict(\n type=\"joint-cumulative\",\n distributions=tf.nest.pack_sequence_as(space.structure, tf.nest.map_structure(lambda s: get_default_distribution_from_space(s), tf.nest.flatten(space)))\n )\n else:\n raise SurrealError(\"No distribution defined for space {}!\".format(space))\n\n\ndef is_bounded_space(box_space):\n if not isinstance(box_space, Float):\n return False\n # Unbounded.\n if box_space.low == float(\"-inf\") and box_space.high == float(\"inf\"):\n return False\n # Bounded.\n elif box_space.low != float(\"-inf\") and box_space.high != float(\"inf\"):\n return True\n # TODO: Semi-bounded -> Exponential distribution.\n else:\n raise SurrealError(\n \"Semi-bounded core for distribution-generation are not supported yet! You passed in low={} high={}.\".\n format(box_space.low, box_space.high)\n )\n","repo_name":"ducandu/surreal","sub_path":"surreal/spaces/space_utils.py","file_name":"space_utils.py","file_ext":"py","file_size_in_byte":22267,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"67"}
+{"seq_id":"15140857611","text":"import re\n\nwhile True:\n input_text = input()\n if len(input_text) != 0:\n match_pattern = re.compile(r'\\d+')\n matches = match_pattern.finditer(input_text)\n for match in matches:\n print(match.group(0), end=' ')\n else:\n break","repo_name":"radoslav-petkov/SoftUni---Fundamentals---Python---2022","sub_path":"25.Regular Expressions - Exercise/01. Capture the Numbers.py","file_name":"01. Capture the Numbers.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"44363882427","text":"import numpy as np\n\nfrom CNN.layers import convolutional_layer, maxpool_layer, loss_function, softmax, backpropagation_maxpool, backpropagation_convolutionalLayer\n\n\ndef build_net(image, label, parameters, stride, pooling_filter, pooling_stride):\n [weight1, weight2, weight3, weight4, bias1, bias2, bias3, bias4] = parameters\n\n #forward propagation\n first_convolution = convolutional_layer(image, weight1, bias1, stride)\n first_convolution[first_convolution <= 0] = 0\n\n second_convolution = convolutional_layer(first_convolution, weight2, bias2, stride)\n second_convolution[second_convolution <= 0] = 0\n\n pooling_layer = maxpool_layer(second_convolution, pooling_filter, pooling_stride)\n\n (num_filters, height_width, same) = pooling_layer.shape\n\n flatten = pooling_layer.reshape((num_filters * height_width * height_width, 1))\n\n fully_connected1 = weight3.dot(flatten) + bias3\n fully_connected1[fully_connected1 <= 0] = 0\n\n fully_connected2 = weight4.dot(fully_connected1) + bias4\n\n prediction = softmax(fully_connected2)\n\n loss = loss_function(prediction, label)\n\n #backpropagation\n derivative_second_fully_conn = prediction - label\n gradient_weight4 = derivative_second_fully_conn.dot(fully_connected1.T)\n gradient_bias4 = np.sum(derivative_second_fully_conn, axis=1).reshape(bias4.shape)\n\n derivative_first_fully_conn = weight4.T.dot(derivative_second_fully_conn)\n derivative_first_fully_conn[fully_connected1 <= 0] = 0\n gradient_weight3 = derivative_first_fully_conn.dot(flatten.T)\n gradient_bias3 = np.sum(derivative_first_fully_conn, axis=1).reshape(bias3.shape)\n\n der_second_fc = weight3.T.dot(derivative_first_fully_conn)\n der_maxpool = der_second_fc.reshape(pooling_layer.shape)\n\n der_conv2 = backpropagation_maxpool(der_maxpool, second_convolution, pooling_filter, pooling_stride)\n der_conv2[second_convolution <= 0] = 0\n\n der_conv1, gradient_weight2, der_bias2 = backpropagation_convolutionalLayer(der_conv2, first_convolution, weight2, stride)\n der_conv1[first_convolution <= 0] = 0\n\n image_der, gradient_weight1, der_bias1 = backpropagation_convolutionalLayer(der_conv1, image, weight1, stride)\n\n gradients = [gradient_weight1, gradient_weight2, gradient_weight3, gradient_weight4, bias1, bias2, bias3, bias4]\n return gradients, loss\n\n\n# Adams optimizer\n\ndef adam_optimizer(batch, num_classes, alpha, dim, n_c, beta1, beta2, parameters, cost_array, E=1e-7):\n\n [weight1, weight2, weight3, weight4, bias1, bias2, bias3, bias4] = parameters\n\n batch_size = len(batch)\n\n images = batch[:, 0:-1]\n images = images.reshape((batch_size, n_c, dim, dim))\n\n labels = batch[:, -1]\n\n cost = 0\n\n # initialize gradients with zeros\n grad_w1 = np.zeros(weight1.shape)\n grad_w2 = np.zeros(weight2.shape)\n grad_w3 = np.zeros(weight3.shape)\n grad_w4 = np.zeros(weight4.shape)\n grad_b1 = np.zeros(bias1.shape)\n grad_b2 = np.zeros(bias2.shape)\n grad_b3 = np.zeros(bias3.shape)\n grad_b4 = np.zeros(bias4.shape)\n\n # initialize momentum parameters with zeros\n moment_param_w1 = np.zeros(weight1.shape)\n moment_param_w2 = np.zeros(weight2.shape)\n moment_param_w3 = np.zeros(weight3.shape)\n moment_param_w4 = np.zeros(weight4.shape)\n moment_param_b1 = np.zeros(bias1.shape)\n moment_param_b2 = np.zeros(bias2.shape)\n moment_param_b3 = np.zeros(bias3.shape)\n moment_param_b4 = np.zeros(bias4.shape)\n\n # initialize RMS-prop parameters with zeros\n rmsprop_w1 = np.zeros(weight1.shape)\n rmsprop_w2 = np.zeros(weight2.shape)\n rmsprop_w3 = np.zeros(weight3.shape)\n rmsprop_w4 = np.zeros(weight4.shape)\n rmsprop_b1 = np.zeros(bias1.shape)\n rmsprop_b2 = np.zeros(bias2.shape)\n rmsprop_b3 = np.zeros(bias3.shape)\n rmsprop_b4 = np.zeros(bias4.shape)\n\n\n for i in range(batch_size):\n image = images[i]\n label = np.eye(num_classes)[int(labels[i])].reshape((num_classes, 1))\n\n gradients, loss = build_net(image, label, parameters, 1, 2, 2)\n\n [gradient_weight1, gradient_weight2, gradient_weight3, gradient_weight4, bias1, bias2, bias3, bias4] = gradients\n\n grad_w1 += gradient_weight1\n grad_w2 += gradient_weight2\n grad_w3 += gradient_weight3\n grad_w4 += gradient_weight4\n grad_b1 += bias1\n grad_b2 += bias2\n grad_b3 += bias3\n grad_b4 += bias4\n\n cost += loss\n\n # update momentum and RMS-prop parameters\n moment_param_w1 = beta1 * moment_param_w1 + (1 - beta1) * grad_w1 / batch_size\n rmsprop_w1 = beta2 * rmsprop_w1 + (1 - beta2) * (grad_w1 / batch_size) ** 2\n weight1 -= alpha * moment_param_w1 / np.sqrt(rmsprop_w1 + E)\n\n moment_param_w2 = beta1 * moment_param_w2 + (1 - beta1) * grad_w2 / batch_size\n rmsprop_w2 = beta2 * rmsprop_w2 + (1 - beta2) * (grad_w2 / batch_size) ** 2\n weight2 -= alpha * moment_param_w2 / np.sqrt(rmsprop_w2 + E)\n\n moment_param_w3 = beta1 * moment_param_w3 + (1 - beta1) * grad_w3 / batch_size\n rmsprop_w3 = beta2 * rmsprop_w3 + (1 - beta2) * (grad_w3 / batch_size) ** 2\n weight3 -= alpha * moment_param_w3 / np.sqrt(rmsprop_w3 + E)\n\n moment_param_w4 = beta1 * moment_param_w4 + (1 - beta1) * grad_w4 / batch_size\n rmsprop_w4 = beta2 * rmsprop_w4 + (1 - beta2) * (grad_w4 / batch_size) ** 2\n weight4 -= alpha * moment_param_w4 / np.sqrt(rmsprop_w4 + E)\n\n moment_param_b1 = beta1 * moment_param_b1 + (1 - beta1) * grad_b1 / batch_size\n rmsprop_b1 = beta2 * rmsprop_b1 + (1 - beta2) * (grad_b1 / batch_size) ** 2\n bias1 -= alpha * moment_param_b1 / np.sqrt(rmsprop_b1 + E)\n\n moment_param_b2 = beta1 * moment_param_b2 + (1 - beta1) * grad_b2 / batch_size\n rmsprop_b2 = beta2 * rmsprop_b2 + (1 - beta2) * (grad_b2 / batch_size) ** 2\n bias2 -= alpha * moment_param_b2 / np.sqrt(rmsprop_b2 + E)\n\n moment_param_b3 = beta1 * moment_param_b3 + (1 - beta1) * grad_b3 / batch_size\n rmsprop_b3 = beta2 * rmsprop_b3 + (1 - beta2) * (grad_b3 / batch_size) ** 2\n bias3 -= alpha * moment_param_b3 / np.sqrt(rmsprop_b3 + E)\n\n moment_param_b4 = beta1 * moment_param_b4 + (1 - beta1) * grad_b4 / batch_size\n rmsprop_b4 = beta2 * rmsprop_b4 + (1 - beta2) * (grad_b4 / batch_size) ** 2\n bias4 -= alpha * moment_param_b4 / np.sqrt(rmsprop_b4 + E)\n\n cost = cost / batch_size\n cost_array.append(cost)\n\n parameters = [weight1, weight2, weight3, weight4, bias1, bias2, bias3, bias4]\n\n return parameters, cost_array","repo_name":"DayanaPankova/Shut-Down-My-PC-When-I-Fall-Asleep-","sub_path":"CNN/build_net.py","file_name":"build_net.py","file_ext":"py","file_size_in_byte":6459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"22256773793","text":"import glob\nfrom prody import *\nimport numpy as np\nimport os\nimport time\nimport subprocess\nimport argparse\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy.cluster.hierarchy import dendrogram, linkage, cophenet, fcluster \nfrom scipy.spatial.distance import pdist\nimport pickle\n\nschrodinger_path = '/data/general_software/schrodinger2019-1'\n\ndef parserfunc():\n parser = argparse.ArgumentParser(\n description ='Given a directory with mae files, redactar blablabla')\n\n parser.add_argument('-d', dest=\"maedir\", help = \"MAEs directory\", required=True)\n parser.add_argument('-o', dest=\"outdir\", help = \"Output directory\", required=True)\n \n args = parser.parse_args()\n return args\n\ndef siteMap(maes,asl,delimiter=None,outfmt='mae',max_processes=4):\n \"\"\"\n Run a SiteMap calculation for a list of MAEs (can't be pdbs).\n maes: 'list'. MAE list of elements\n asl: 'str'. ASL (atom specification Language)\n delimiter: 'str'. Delimiter to obtain an identifier from each MAE name\n outfmt: 'str'. Outfile format. Either .mae or .pdb\n max_processes: Number of processors used to paralalize the different executions\n \"\"\"\n MAEnames = [os.path.basename(mae) for mae in maes]\n if delimiter != None:\n IDs = [ maename.replace(\".mae\",\"\").split(delimiter)[0] for maename in MAEnames]\n else:\n IDs = [ maename.replace(\".mae\",\"\") for maename in MAEnames]\n\n cmd_SiteMap = ['%s/sitemap -j %s -prot %s -sitebox 12 -resolution standard -reportsize 20 -writestructs no -writevis yes -maxsites 1 -siteasl \"%s\" -WAIT'%(schrodinger_path,IDs[i],mae,asl) for i,mae in enumerate(maes)]\n cmd_SiteMap = [cmd.replace(\"//\",\"/\") for cmd in cmd_SiteMap]\n\n processes = set()\n\n for cmd in cmd_SiteMap:\n print(cmd)\n processes.add(subprocess.Popen(cmd,shell=True))\n for p in processes:\n p.wait()\n if p.wait() != 0:\n print(\"There was an error\")\n\ndef _clean_siteMap(outdir,outfmt='maegz'):\n \"\"\"\n Move the SiteMap output to an specified directory\n outdir: 'str'. Output directory\n outfmt: 'str'. Outfile format of the PrepWizard. Either .mae or .pdb\n \"\"\"\n\n if outfmt != 'maegz':\n raise ValueError('outfmt must be maegz')\n os.system('mv *.%s %s'%(outfmt,outdir))\n logdir = '%s/logs'%outdir\n logdir = logdir.replace('//','/')\n if not os.path.isdir(logdir):\n os.system('mkdir %s'%logdir)\n os.system('mv *.vis %s'%logdir)\n os.system('mv *.smap %s'%logdir)\n os.system('mv *.log %s'%logdir)\n\ndef _group_siteMap(sites,out,outdir):\n \"\"\"\n Group all volume sites from SiteMap into a single mae file\n sites:\n out:\n outdir:\n \"\"\"\n conc_sites = ''\n for site in sites:\n conc_sites = conc_sites + ' ' + site\n\n cmd = '%s/utilities/structcat -imae %s -omae %s'%(schrodinger_path,conc_sites,out)\n cmd = cmd.replace('//','/')\n print(cmd)\n os.system(cmd)\n try:\n aux = 'mv %s %s'%(out,outdir)\n os.system(aux)\n except:\n print('wrong outdir')\n\ndef _uncompress_maegz(inp):\n \"\"\"\n inp:\n \"\"\"\n out = inp.replace('.maegz','.mae')\n cmd = '%s/utilities/structcat -imae %s -omae %s'%(schrodinger_path,inp,out)\n cmd = cmd.replace('//','/')\n print(cmd)\n os.system(cmd)\n\ndef get_volumeOverlapMatrix(sites,out,max_processes=4):\n \"\"\"\n Generate pairwise volume overlap matrix\n sites: 'str'. single file containing multiple SitMap files\n \"\"\"\n cmd = '%s/run volume_cluster.py -j %s -HOST localhost:%d -sc -r 2 %s'%(schrodinger_path,out,max_processes,sites)\n cmd = cmd.replace('//','/')\n print(cmd)\n p = subprocess.Popen(cmd, shell=True)\n p.wait()\n if p.wait() != 0:\n print(\"There was an error\")\n \n\ndef _clean_volumeMatrix(out,coutdir):\n \"\"\"\n Move the VolumeMatrix output to an specified directory\n out: 'str'\n outdir: 'str'. Output directory\n \"\"\"\n if not os.path.isdir(coutdir):\n os.system('mkdir %s'%coutdir)\n\n os.system('mv *.csv %s'%(coutdir))\n logdir = '%s/logs/'%coutdir\n logdir = logdir.replace('//','/')\n if not os.path.isdir(logdir):\n os.system('mkdir %s'%logdir)\n os.system('mv *.mae %s'%logdir)\n os.system('mv *.log %s'%logdir)\n\n\n\nif __name__ == '__main__':\n arg = parserfunc()\n inpdir = arg.maedir\n outdir = arg.outdir\n\n # create output directory\n if not os.path.isdir('%s/siteMap'%outdir):\n os.system('mkdir %s/siteMap'%outdir)\n\n # Compute the volume of each target specific binding site\n print(\"\\n-------------RUNNING SITEMAP----------------\\n\")\n TARGs = glob.glob('%s/*_prep.mae'%(inpdir))\n siteMap(maes=TARGs,asl = \"(res.num 145) AND ((atom.ptype \\' HB2 \\'))\",delimiter='_prep',outfmt='mae',max_processes=30)\n _clean_siteMap(outdir='%s/siteMap'%(outdir))\n sites = glob.glob('%s/siteMap/*_out.maegz'%outdir)\n _group_siteMap(sites=sites,out='Mpro_sites.maegz',outdir='%s/siteMap/'%outdir)\n _uncompress_maegz(inp='%s/siteMap/Mpro_sites.maegz'%outdir)\n\n # Find targets without binding site arround the specified atom\n print(\"\\n--------------CHECK STEP--------------------\\n\")\n print(\"These targets do not have the binding site around the specified atom. Please, remove them for further analysis:\")\n TARGs_IDs = [os.path.basename(TARG).split('_')[1] for TARG in TARGs]\n sites_IDs = [os.path.basename(site).split('_')[1] for site in sites]\n print(set(TARGs_IDs)-set(sites_IDs))\n\n # Get the volume overlapping matrix of the target sites\n print(\"\\n-----------VOLUME OVERLAPPING MATRIX----------\\n\")\n get_volumeOverlapMatrix(sites='%s/siteMap/Mpro_sites.maegz'%(outdir),out='Mpro_volumeMatrix',max_processes=4)\n _clean_volumeMatrix(out='Mpro_volumeMatrix',coutdir='%s/volumeMatrix/'%(outdir))\n\n\n","repo_name":"juliavilmor/Mpro","sub_path":"scripts/targetanalysis/volume_sitemap.py","file_name":"volume_sitemap.py","file_ext":"py","file_size_in_byte":5787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"5300891552","text":"\"\"\"\nGiven an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct.\n\n\n\nExample 1:\n\nInput: nums = [1,2,3,1]\nOutput: true\nExample 2:\n\nInput: nums = [1,2,3,4]\nOutput: false\nExample 3:\n\nInput: nums = [1,1,1,3,3,4,3,2,4,2]\nOutput: true\n\n\nConstraints:\n\n1 <= nums.length <= 105\n-109 <= nums[i] <= 109\n\"\"\"\nfrom typing import List\n\n\"\"\"\nSolution:\n\nfrom the concept of set in python\n\nset have unique elements\n\nso convert list to set and check both length\n\nif the length is same then no duplicate elements\n\n\n\"\"\"\n\n\nclass Solution:\n def containsDuplicate(self, nums: List[int]) -> bool:\n return len(nums) != len(set(nums))\n\n\nif __name__ == '__main__':\n n = [1, 2, 3, 1]\n n2 = [1, 2, 3, 4]\n n3 = [1, 1, 1, 3, 3, 4, 3, 2, 4, 2]\n s = Solution()\n print(s.containsDuplicate(n))\n print(s.containsDuplicate(n2))\n print(s.containsDuplicate(n3))\n","repo_name":"mihirh19/Python","sub_path":"LeetCodeSolution/0217.Contains_Duplicate.py","file_name":"0217.Contains_Duplicate.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"29368810985","text":"import praw\nimport os\n\nfrom enum import Enum\nfrom constants import *\n\nclass Reply(Enum):\n BASE_REPLY = 1\n\nclass AnkiHelpBot:\n responses: dict[Reply, str]\n reddit: praw.Reddit\n\n def __init__(self) -> None:\n self.responses = self.loadReplies()\n self.reddit = self.connect()\n\n\n def main(self):\n subreddit = self.reddit.subreddit(CONST_SUBREDDIT)\n for submission in subreddit.stream.submissions(skip_existing=True):\n self.process_submission(submission)\n\n def process_submission(self, submission):\n print(self.responses[Reply.BASE_REPLY])\n if submission.link_flair_text == \"Question\":\n submission.reply(body=self.responses[Reply.BASE_REPLY])\n\n\n def loadReplies(self) -> dict[Reply, str]:\n response: dict[Reply, str] = {}\n\n with open('replies/baseReply.md', 'r') as baseReply:\n baseReplyText = baseReply.read()\n response[Reply.BASE_REPLY] = baseReplyText\n \n return response\n\n def connect(self) -> praw.Reddit:\n return praw.Reddit(\n client_id=os.getenv(CONST_CLIENT_ID_ENV),\n client_secret=os.getenv(CONST_CLIENT_SECRET_ENV),\n redirect_uri=\"https://github.com/LanguageLatte/public\",\n password=os.getenv(CONST_PASSWORD_ENV),\n user_agent=CONST_USER_AGENT,\n username=CONST_USERNAME,\n )\n\nif __name__ == \"__main__\":\n ankiHelpBot = AnkiHelpBot()\n ankiHelpBot.main()","repo_name":"LanguageLatte/public","sub_path":"AnkiHelpBot/AnkiHelpBot.py","file_name":"AnkiHelpBot.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"67"}
+{"seq_id":"70639444375","text":"'''\r\n1로 만들기\r\nhttps://www.acmicpc.net/problem/1463\r\n'''\r\nN = int(input())\r\n\r\ndp = [0] * (N+4) # 숫자 N일때의 답을 해당 인덱스에 삽입\r\ndp[2] = 1\r\ndp[3] = 1\r\ndp[4] = 2\r\nfor n in range(5, N+1): # 5부터 bottom-up으로 dp 계산\r\n op_a = 0 # 연산 1 플래그\r\n if n % 3 == 0:\r\n op_a = 1\r\n\r\n op_b = 0 # 연산 2 플래그\r\n if n % 2 == 0:\r\n op_b = 1\r\n\r\n op_flag = (op_a, op_b, 1) # 연산 1, 2, 3 플래그\r\n if op_flag == (1, 1, 1):\r\n dp[n] = min(dp[n//3] + 1, dp[n//2] + 1, dp[n-1] + 1)\r\n elif op_flag == (1, 0, 1):\r\n dp[n] = min(dp[n//3] + 1, dp[n-1] + 1)\r\n elif op_flag == (0, 1, 1):\r\n dp[n] = min(dp[n//2] + 1, dp[n-1] + 1)\r\n elif op_flag == (0, 0, 1):\r\n dp[n] = dp[n-1] + 1\r\n\r\nprint(dp[N])\r\n\r\n\r\n\r\n###\r\n# 테스트케이스 참고(https://www.acmicpc.net/board/view/49959)\r\n\r\n''' DFS 연습\r\ndef dfs(n):\r\n global answer, cnt\r\n\r\n if n < 1:\r\n return\r\n elif n == 1:\r\n answer = min(cnt, answer)\r\n\r\n return\r\n else:\r\n for o in op:\r\n if o == 'a' and n % 3 == 0:\r\n cnt += 1\r\n dfs(n // 3)\r\n cnt -= 1\r\n elif o == 'b' and n % 2 == 0:\r\n cnt += 1\r\n dfs(n // 2)\r\n cnt -= 1\r\n else:\r\n cnt += 1\r\n dfs(n - 1)\r\n cnt -= 1\r\n\r\n\r\nN = int(input())\r\n\r\ncnt = 0\r\nanswer = sys.maxsize\r\nop = ['a', 'b', 'c']\r\ndfs(N)\r\n\r\nprint(answer)\r\n'''\r\n","repo_name":"buyeolim/ps_prac","sub_path":"BOJ/python3/1463.py","file_name":"1463.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"20271338661","text":"# See 'Leetcode 206. Reverse Linked List' for practice\n\nfrom manim import *\n\nfrom src.list_utilities.LinkedList import LinkedList\nfrom src.list_utilities.Node import Node, NoneNode\n\n\nclass ReverseList(Scene):\n def construct(self):\n title = MarkupText(\"Reverse linked list\", font_size=45).shift(UP * 3)\n self.add(title)\n\n self.show_task(title)\n self.show_reverse_list_code(title)\n\n orig_nodes = [Node(str(j)) for j in range(1, 4)]\n orig_list = LinkedList(nodes=orig_nodes)\n self.play(FadeIn(orig_list.visual_list))\n self.wait(1)\n\n none_start = NoneNode()\n none_start.set_next(orig_list.head)\n none_end = NoneNode()\n orig_list.tail.set_next(none_end)\n self.show_none_start_end(orig_list, none_start, none_end)\n\n self.reverse_list(none_start, orig_list.head)\n self.play(FadeOut(none_start.vn_arrows.vnode.group),\n FadeOut(orig_list.head.vn_arrows.left_arrow))\n self.wait(2)\n\n def show_task(self, title: MarkupText) -> None:\n orig_nodes = [Node(str(j)) for j in range(1, 4)]\n orig_list = LinkedList(nodes=orig_nodes)\n\n rev_nodes = [Node(str(j)) for j in range(3, 0, -1)]\n rev_list = LinkedList(nodes=rev_nodes)\n\n down_arrow = MathTex(r\"\\Downarrow\", color=WHITE)\n result = VGroup(orig_list.visual_list, down_arrow, rev_list.visual_list)\\\n .arrange(DOWN, buff=0.5).next_to(title, DOWN * 5)\n self.play(FadeIn(result, run_time=3))\n self.wait(2)\n self.play(FadeOut(result))\n\n def show_reverse_list_code(self, title: MarkupText) -> None:\n code = '''\n prev, cur = None, head\n while cur:\n next_node = cur.next\n cur.next = prev\n prev, cur = cur, next_node\n return prev\n '''\n rendered_code = Code(code=code, tab_width=4, insert_line_no=False,\n language=\"Python\", font=\"Monospace\", font_size=14)\\\n .next_to(title, RIGHT * 2)\n self.play(Create(rendered_code))\n\n def show_none_start_end(self, list_: LinkedList, start: Node, end: Node) -> None:\n end.vn_arrows.group.next_to(list_.visual_list, RIGHT)\n start.vn_arrows.group.next_to(list_.visual_list, LEFT)\n self.play(FadeIn(end.vn_arrows.group, run_time=2))\n\n start.vn_arrows.remove_arrow()\n self.play(FadeIn(start.vn_arrows.group, run_time=2))\n self.wait(1)\n\n def reverse_list(self, none_node: Node, head: Node) -> Node:\n prev, cur = none_node, head\n tp = TextPointers(prev, cur, self)\n self.wait(0.8)\n\n while not cur.is_none:\n next_node = cur.next\n move_arrow(prev, cur, self)\n cur.next = prev\n tp.move_prev_cur()\n prev, cur = cur, next_node\n tp.update_next(cur)\n self.wait(0.8)\n\n self.play(FadeOut(tp.cur, cur.vn_arrows.group), tp.get_prev_to_cur_transform())\n self.wait(0.5)\n return prev\n\n\ndef move_arrow(prev: Node, cur: Node, scene: Scene) -> None:\n scene.wait(0.8)\n prev.vn_arrows.flip_arrow()\n scene.play(CounterclockwiseTransform(cur.vn_arrows.right_arrow, prev.vn_arrows.right_arrow))\n cur.vn_arrows.set_right_to_left_arrow()\n cur.vn_arrows.set_right_arrow()\n\n\nclass TextPointers:\n def __init__(self, prev: Node, cur: Node, scene: Scene) -> None:\n self.__font_size = 18\n self.__prev = Text(\"prev\", font_size=self.__font_size)\n self.__cur = Text(\"cur\", font_size=self.__font_size)\n self.__next = Text(\"next\", font_size=self.__font_size)\n self.scene = scene\n\n self.__prev.next_to(prev.vn_arrows.vnode.group, DOWN)\n self.__cur.next_to(cur.vn_arrows.vnode.group, DOWN)\n if cur.next:\n self.__next.next_to(cur.next.vn_arrows.vnode.group, DOWN)\n self.scene.play(FadeIn(self.__prev, self.__cur))\n\n @property\n def cur(self) -> Text:\n return self.__cur\n\n def move_prev_cur(self) -> None:\n self.__prev.generate_target()\n self.__prev.target.move_to(self.__cur)\n\n self.__cur.generate_target()\n self.__cur.target.move_to(self.__next)\n\n self.scene.play(MoveToTarget(self.__prev), MoveToTarget(self.__cur))\n self.scene.wait(1)\n\n def update_next(self, cur: Node) -> None:\n if cur.next:\n self.__next.next_to(cur.next.vn_arrows.vnode.group, DOWN)\n\n def get_prev_to_cur_transform(self) -> Transform:\n head_text = Text(\"head\", font_size=self.__font_size).shift(self.__prev.get_center())\n return Transform(self.__prev, head_text)\n","repo_name":"andreysmykov/algorithms_animation","sub_path":"src/reverse_linked_list/reverse_list.py","file_name":"reverse_list.py","file_ext":"py","file_size_in_byte":4646,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"25144537282","text":"import app\nimport logging\n\nfrom flask import jsonify\nfrom flask_restplus import Resource\nfrom .models import Search\n\nLOG = logging.getLogger(__name__)\napi = Search.api \n\n@api.route('//')\nclass Search(Resource):\n @api.doc(\"search\")\n def get(self,c_type, tag):\n result = []\n if c_type == \"company\":\n search_resp = app.App.mongodb.db.company.find( { '$text': { '$search': tag } } ).sort('_id')\n \n elif c_type == \"customer\":\n search_resp = app.App.mongodb.db.customer.find( { '$text': { '$search': tag } } ).sort('_id')\n \n for doc in search_resp:\n result.append(doc)\n\n return jsonify(result)","repo_name":"venkaiaha/Sree-app-flask","sub_path":"app/api/search/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"3329038158","text":"#Input de dados\nd = float(input(\"Digite a distância percorrida pelo usuário(km):\"))\nt = int(input(\"Digite o número de dias do aluguel:\"))\n#Dados armazenados\nP = 60\np = 0.15\n#Cálculo do pagamento\nV = (P*t) + (p*d)\n#Output\nprint(\"Valor a pagar: R$ %5.2f\" % V)","repo_name":"axelife2021/Python","sub_path":"Capítulo 3/ex3.14.py","file_name":"ex3.14.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"13264873269","text":"import pandas as pd\nfrom sklearn.feature_selection import RFE\nimport copy\nimport numpy as np\nimport warnings\nimport numpy as np\nfrom sklearn.impute import SimpleImputer\nimport matplotlib.pyplot as plt\nimport yaml\n\n\ndef add_binary_where_the_nan_was(table, column):\n \"\"\"takes the table and next to each columnputs information whether there was a nan\"\"\"\n table[f'bool_nan_{column}'] = table[column].isna()\n return table\n\n\ndef get_rid_of_outliers(table, column):\n up, low = np.percentile(table[column], [1, 99])\n y = np.clip(table[column], up, low)\n # pd.Series(y).hist(bins=30)\n table = table.drop(columns=[column])\n table[f'no_outliers_{column}'] = pd.Series(y)\n return table\n\n\ndef WoE_for_categorical_values(table, column, ret_woe=False):\n \"\"\"\n takes a table, with a categorical value in columns and returns WOE and IV for that variable\n \"\"\"\n\n different_values = table[column].unique().shape[0]\n list_of_bads = [0] * different_values # in each element of a list it contains woe for a corresponding interval\n list_of_goods = [0] * different_values\n for i in range(len(table[str(column)])): # iterate over every sample\n # print(i)\n for j in range(different_values): # how many separate values are there to deal with\n if table[column][i] == table[column].unique()[j] and table['target'][i] == 1: # default is an event here\n list_of_bads[j] += 1\n elif table[column][i] == table[column].unique()[j] and table['target'][i] == 0:\n list_of_goods[j] += 1\n\n total_bads = table.target.sum()\n total_goods = len(table.target) - total_bads\n distr_goods = []\n distr_bads = []\n WoE = []\n\n for i in range(len(list_of_goods)):\n distr_goods.append(list_of_goods[i] / total_goods)\n distr_bads.append(list_of_bads[i] / total_bads)\n\n # check whether there are no groups with 0 counts for good or bad - if there are drop the columns with that variable\n # all together\n flag = False\n if 0 in distr_goods or 0 in distr_bads:\n print(\"In at least one of the bins there is either no goods or bads distribution. Dropping that variable\")\n flag = True\n\n for i in range(len(list_of_goods)):\n WoE.append(np.log(distr_goods[i] / distr_bads[i]) * 100)\n\n # Information Value of the whole characteristic\n distr_bads_nans = table['target'][table[column].isna()].sum()/total_bads\n # how many is nan and is not default\n distr_goods_nans = (table['target'][table[column].isna()].shape[0] - \\\n table['target'][table[column].isna()].sum())/total_goods\n WoE_nan = np.log(distr_goods_nans / distr_bads_nans) * 100\n WoE = WoE.insert(0, WoE_nan) # inserting the value correspinding to NaNs in the first place\n\n # Information Value of the whole characteristic\n differences = [distr_goods[i] - distr_bads[i] for i in range(len(distr_goods))]\n differences.insert(0, distr_goods_nans-distr_bads_nans)\n IV = np.dot(differences, np.transpose(WoE))\n\n if ret_woe and not flag:\n return WoE, IV\n elif not ret_woe and not flag:\n return IV\n elif flag:\n return table.drop(columns=[column])\n# consider correlation for all continuous data\n\n\ndef drop_columns_with_many_nans(table, threshold=0.2):\n \"\"\"drops columns that contain over 20% of nan values\"\"\"\n for col in table.columns:\n if table[col].isna().sum() >= threshold * table[col].shape[0]:\n table = table.drop(columns=[col])\n return table\n\n\ndef woe_and_iv_continuous_data(table, column, number_of_bins, ret_woe=False):\n \"\"\"assumes that target is provided in column 'target'\n 1 - event, ie default\n 0 - no default\n returns bins, woe - tuples, iv - scalar\n \"\"\"\n\n bins = pd.qcut(table[str(column)], number_of_bins, retbins=True)[1]\n # bins = pd.cut(table[str(column)], number_of_bins, retbins=True)[1]\n bins[-1] += 1 # to include all points\n list_of_bads = [0] * number_of_bins # in each element of a list it contains woe for a corresponding interval\n list_of_goods = [0] * number_of_bins\n for i in range(len(table[str(column)])):\n for j in range(number_of_bins):\n if bins[j] <= table[column][i] < bins[j+1] and table['target'][i] == 1: # default is an event here\n list_of_bads[j] += 1\n elif bins[j] <= table[column][i] < bins[j+1] and table['target'][i] == 0:\n list_of_goods[j] += 1\n\n\n # WoE = ln(distr_goods / distr_bads) * 100\n\n total_bads = table.target.sum() # bad = default\n total_goods = len(table.target) - total_bads\n distr_goods = []\n distr_bads = []\n WoE = []\n\n for i in range(len(list_of_goods)):\n distr_goods.append(list_of_goods[i] / total_goods)\n distr_bads.append(list_of_bads[i] / total_bads)\n\n # check whether there are no groups with 0 counts for good or bad\n\n if 0 in distr_goods or 0 in distr_bads:\n warnings.warn(\"In at least one of the bins there is either no goods or bads distribution. Check the binning\")\n exit()\n\n for i in range(len(list_of_goods)):\n WoE.append(np.log(distr_goods[i] / distr_bads[i]) * 100)\n\n # group also nans\n # how many is nan and is default\n distr_bads_nans = table['target'][table[column].isna()].sum()/total_bads\n # how many is nan and is not default\n distr_goods_nans = (table['target'][table[column].isna()].shape[0] - \\\n table['target'][table[column].isna()].sum())/total_goods\n # WoE_nan = np.log(distr_goods_nans / distr_bads_nans) * 100\n # WoE = WoE.insert(0, WoE_nan) # inserting the value correspinding to NaNs in the first place\n\n # Information Value of the whole characteristic\n differences = [distr_goods[i] - distr_bads[i] for i in range(len(distr_goods))]\n # differences.insert(0, distr_goods_nans-distr_bads_nans)\n IV = np.dot(differences, np.transpose(WoE))\n\n if ret_woe:\n return bins, WoE, IV\n else:\n return IV/100\n\n\ndef input_missing_values(table, column, median=True, mode=False):\n if median:\n imp_mean = SimpleImputer(missing_values=np.nan, strategy='median')\n elif mode:\n imp_mean = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n\n imp_mean.fit(table[column].values.reshape(-1, 1))\n table[column] = imp_mean.transform(table[column].values.reshape(-1, 1))\n return table\n\n\ndef correlation(dataset, threshold=0.6):\n # deals only with numeric data, float64 and int64, so here the values\n\n col_corr = set() # Set of all the names of deleted columns\n corr_matrix = dataset.corr()\n for i in range(len(corr_matrix.columns)):\n for j in range(i):\n if (corr_matrix.iloc[i, j] >= threshold) and (corr_matrix.columns[j] not in col_corr):\n colname = corr_matrix.columns[i] # getting the name of column\n col_corr.add(colname)\n if colname in dataset.columns:\n del dataset[colname] # deleting the column from the dataset\n print(col_corr)\n return dataset\n\n\ndef exclude_data_few_unique_values(dataset):\n \"\"\"drops columns that have either 0 distinct values (NANS) or only 1 distinct value\"\"\"\n col_to_drop = []\n\n for column in dataset.columns:\n if dataset[column].nunique() == 1 or dataset[column].nunique() == 0:\n col_to_drop.append(column)\n\n for column in col_to_drop:\n dataset = dataset.drop(columns=column)\n\n return dataset\n\n\ndef split_dataset(table):\n \"\"\"categorical features object,\n numerical - float64\n ordinal - int64, the distinction between categorical and ordinal is to belooked into\"\"\"\n table_categorical = table.select_dtypes('object')\n table_numerical = table.select_dtypes('float64')\n table_ordinal = table.select_dtypes('int64')\n return table_numerical, table_categorical, table_ordinal\n\n\ndef bin_dataset(table, column):\n \"\"\" bins numerical value\"\"\"\n if column is not 'target':\n return pd.qcut(table[column], 4)\n\n\ndef drop_duplicated_ones_and_values_leaking_data_from_the_future(table):\n list_to_drop = ['id', 'member_id', 'url', 'emp_title', 'issue_d', 'funded_amnt', 'funded_amnt_inv',\n 'sub_grade', 'int_rate', 'addr_state', 'out_prncp', 'out_prncp_inv', 'total_pymnt',\n 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries',\n 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt', 'zip_code',\n 'earliest_cr_line', 'next_pymnt_d', 'last_credit_pull_d', 'disbursement_method', 'delinq_amnt',\n 'open_rv_24m']\n\n for column in list_to_drop:\n try: # as some of these columns might already have been dropped\n table = table.drop(columns=column)\n except:\n KeyError\n return table\n\n\ndef look_at_value_distribution(table, columns):\n \"\"\"for later stage of analysis, to manually exclude data that has very few examples of some values\"\"\"\n print(table[columns].value_counts())\n\n\ndef fill_nans(table, column): # only after values with too many nans were excluded\n return table[column].fillna(\"MISSING\")\n\n\ndef calculate_woe_iv(dataset, feature):\n lst = []\n for i in range(dataset[feature].nunique()):\n val = list(dataset[feature].unique())[i]\n lst.append({\n 'Value': val,\n 'All': dataset[dataset[feature] == val].count()[feature],\n 'Good': dataset[(dataset[feature] == val) & (dataset['target'] == 0)].count()[feature],\n 'Bad': dataset[(dataset[feature] == val) & (dataset['target'] == 1)].count()[feature]\n })\n\n dset = pd.DataFrame(lst)\n dset['Distr_Good'] = dset['Good'] / dset['Good'].sum()\n dset['Distr_Bad'] = dset['Bad'] / dset['Bad'].sum()\n dset['WoE'] = np.log(dset['Distr_Good'] / dset['Distr_Bad'])\n dset = dset.replace({'WoE': {np.inf: 0, -np.inf: 0}})\n dset['IV'] = (dset['Distr_Good'] - dset['Distr_Bad']) * dset['WoE']\n iv = dset['IV'].sum()\n\n dset = dset.sort_values(by='WoE')\n\n return dset, iv\n\n\ndef conc_tables(t1, t2, t3): # concatenates tables\n return pd.concat([t1, t2, t3])\n\n\n\ndef std_test_as_train(X_train, X_test):\n from sklearn.preprocessing import StandardScaler\n\n for col in X_train.columns:\n scaler = StandardScaler()\n scaler.fit(X_train[col].to_numpy().reshape(-1, 1))\n X_train[col] = scaler.transform(X_train[col].to_numpy().reshape(-1, 1))\n X_test[col] = scaler.transform(X_test[col].to_numpy().reshape(-1, 1))\n return X_train, X_test\n\n\ndef stack(X_train, y_train, X_test, y_test):\n from sklearn.linear_model import LogisticRegression\n from vecstack import stacking\n from sklearn import svm\n import xgboost as xgb\n from sklearn.metrics import roc_auc_score, classification_report, f1_score\n\n models = [LogisticRegression(), svm.SVC(), xgb.XGBClassifier(n_jobs=-1)]\n\n S_train, S_test = stacking(models, X_train, y_train, X_test, regression=False, verbose=2)\n\n model = xgb.XGBClassifier(seed=0, n_jobs=-1, learning_rate=0.1,n_estimators=100, max_depth=3)\n\n # Fit 2-nd level model\n model = model.fit(S_train, y_train)\n\n # Predict\n y_pred = model.predict(S_test)\n\n # Final prediction score\n print('Final prediction score: [%.8f]' % f1_score(y_test, y_pred))\n print(classification_report(y_test, y_pred))\n print(roc_auc_score(y_test, y_pred))\n\n\ndef stack_regression(X_train, y_train, X_test, y_test):\n from sklearn.linear_model import LinearRegression\n from vecstack import stacking\n from sklearn import linear_model\n import xgboost as xgb\n from sklearn.metrics import mean_squared_error, mean_absolute_error\n\n models = [linear_model.SGDRegressor(max_iter=1000, tol=1e-3), LinearRegression(), xgb.XGBRegressor(n_jobs=-1)]\n\n S_train, S_test = stacking(models, X_train, y_train, X_test, regression=False, verbose=2)\n\n model = xgb.XGBRegressor(seed=0, n_jobs=-1, learning_rate=0.1,n_estimators=100, max_depth=3)\n\n # Fit 2-nd level model\n model = model.fit(S_train, y_train)\n\n # Predict\n y_pred = model.predict(S_test)\n\n\n print(mean_squared_error(y_test, y_pred))\n\n\ndef upsample_data(X_train, y_train):\n\n from imblearn.over_sampling import SMOTE\n sm = SMOTE(random_state=42)\n X_res, y_res = sm.fit_resample(X_train, y_train)\n return X_res, y_res\n\n\n\"\"\"for later\"\"\"\ndef bayes_optim(X_train, X_test, y_train, y_test):\n\n from bayes_opt import BayesianOptimization\n from sklearn.ensemble import RandomForestClassifier\n from sklearn.model_selection import cross_val_score\n\n def bayesian_optimization(X_train, X_test, y_train, y_test, function, parameters):\n n_iterations = 10\n gp_params = {\"alpha\": 1e-4}\n BO = BayesianOptimization(function, parameters)\n BO.maximize(n_iter=n_iterations, **gp_params)\n\n return BO.ma\n\n\n def rfc_optimization(cv_splits):\n def function(n_estimators, max_depth, min_samples_split):\n return cross_val_score(\n RandomForestClassifier(\n n_estimators=int(max(n_estimators, 0)),\n max_depth=int(max(max_depth, 1)),\n min_samples_split=int(max(min_samples_split, 2)),\n n_jobs=-1,\n random_state=42,\n class_weight=\"balanced\"),\n X=X_train,\n y=y_train,\n cv=cv_splits,\n scoring=\"f1_macro\",\n n_jobs=-1).mean()\n\n parameters = {\"n_estimators\": (10, 1000),\n \"max_depth\": (1, 150),\n \"min_samples_split\": (2, 10)}\n\n return function, parameters\n\n # Train model\n def train(X_train, y_train, X_test, y_test, function, parameters):\n cv_splits = 4\n\n best_solution = bayesian_optimization(X_train, y_train, X_test, y_test, function, parameters)\n params = best_solution[\"params\"]\n\n model = RandomForestClassifier(\n n_estimators=int(max(params[\"n_estimators\"], 0)),\n max_depth=int(max(params[\"max_depth\"], 1)),\n min_samples_split=int(max(params[\"min_samples_split\"], 2)),\n n_jobs=-1,\n random_state=42,\n class_weight=\"balanced\")\n\n model.fit(X_train, y_train)\n\n return model\n\n function, parameters = rfc_optimization(10)\n print(train(X_train, y_train, X_test, y_test, function, parameters))\n\n\ndef encode_ordinal_category_encoders(table):\n import category_encoders\n for col in table.columns:\n if str(col)[:7] != 'encoded':\n encode = category_encoders.ordinal.OrdinalEncoder()\n encode.fit(table[col])\n table[f'encoded_ordinal{col}'] = encode.transform(table[col])\n return table\n\n\ndef encode_ordinal_sklearn(table, col):\n from sklearn.preprocessing import OrdinalEncoder\n enc = OrdinalEncoder()\n enc.fit(table[col].values.reshape(-1, 1))\n table[f'ordinal_encoded_{col}'] = enc.transform(table[col].values.reshape(-1, 1))\n return table\n\n\ndef encode_categorical_TargetEncoder(table, y):\n import category_encoders\n for col in table.columns:\n if str(col)[:7] != 'encoded':\n encode = category_encoders.target_encoder.TargetEncoder()\n encode.fit(table[col], y)\n table[f'encoded_target{col}'] = encode.transform(table[col])\n return table\n\n\ndef drop_cat_not_encoded(table):\n \"\"\"drops non encoded categorical columns\"\"\"\n for col in table.columns:\n if str(col)[:7] != 'encoded':\n table = table.drop(columns=[col])\n return table\n\n\ndef fit_xgboost(X, y, upsample=True):\n from sklearn.metrics import roc_auc_score, classification_report, f1_score\n import xgboost as xgb\n\n model = xgb.XGBClassifier(seed=0, n_jobs=-1, learning_rate=0.1,\n n_estimators=10, max_depth=5)\n\n # Fit 2-nd level model\n if upsample:\n X_res, y_res = upsample_data(X, y)\n X_train, X_test, y_train, y_test = train_test_split(X_res, y_res)\n model = model.fit(X_train, y_train)\n else:\n X_train, X_test, y_train, y_test = train_test_split(X, y)\n model = model.fit(X_train, y_train)\n\n\n # Predict\n y_pred = model.predict(X_test)\n print('f1:\\t\\n', f1_score(y_test, y_pred))\n print('roc_auc_score:\\t\\n', roc_auc_score(y_test, y_pred))\n print(classification_report(y_test, y_pred))\n return model\n\n\ndef date_feature(date):\n \"\"\"number of month, day of week, vacation, assuming US vacations\"\"\"\n from dateutil import parser\n import holidays\n us_holidays = holidays.UnitedStates()\n datetime_obj = parser.parse(date)\n weekday = datetime_obj.weekday() # 0 is monday\n month = datetime_obj.month\n year = datetime_obj.year\n is_holiday = datetime_obj in us_holidays or weekday==6 or weekday==5 # includes weekends as holidays\n return weekday, month, year, int(is_holiday)\n\n\ndef mean_encoding(table, column, target, drop=False):\n \"\"\"assumes no nans\"\"\"\n \"\"\"assumes target is a column in table\"\"\"\n table[f'mean_encoded_{column}'] = table[column].map(table.iloc[table.index].groupby(column)[target].mean())\n if drop:\n table = table.drop(columns=[column])\n return table\n\n\ndef frequency_encode(table, column, drop=False):\n \"\"\"assumes no nans\"\"\"\n encoding = table.groupby(column).size()\n encoding = encoding / table.shape[0]\n table[f'freq_enc_{column}'] = table[column].map(encoding)\n if drop:\n table = table.drop(columns=[column])\n return table\n\n\ndef kfold_mean_encoding(table, column, target, nfolds=5):\n\n from sklearn.model_selection import KFold\n\n skf = KFold(nfolds, shuffle=False)\n\n for tr_ind, val_ind in skf.split(table[column]):\n\n X_tr, X_val = table[[column, target]].iloc[tr_ind], table[[column, target]].iloc[val_ind]\n\n table[column].iloc[val_ind] = table[column].iloc[val_ind].map(X_tr.groupby(column)[target].mean())\n\n table[column] = table[column].fillna(table[target].mean())\n\n return table\n\n\ndef train_test_split(X, y, test_size=0.2):\n from sklearn.model_selection import train_test_split\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)\n return X_train, X_test, y_train, y_test\n\n\ndef log_transform(table, column, drop=True):\n table[f'log_{column}'] = pd.Series(np.log(1 + table[column]))\n if drop:\n table = table.drop(columns=[column])\n return table\n\n\ndef remove_duplicates(table):\n return table.drop_duplicates()\n\n\ndef compare_data_distributions(table1, table2):\n \"\"\"This is a two-sided test for the null hypothesis that 2 independent samples are drawn from the same\n continuous distribution. \"\"\"\n from scipy import stats\n pvalue = stats.ks_2samp(table1, table2)[1]\n if pvalue > .10:\n return True\n else:\n return False\n\n\n","repo_name":"MateuszLewandowski1/mateusz.h.lewandowski-gmail.com","sub_path":"utils_ds.py","file_name":"utils_ds.py","file_ext":"py","file_size_in_byte":18925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"1110640887","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\n\nclass Solution:\n def mergeKLists(self, lists: List[Optional[ListNode]]) -> Optional[ListNode]:\n if len(lists) == 0:\n return None\n\n if len(lists) > 1:\n temp = None\n for i in range(1,len(lists)):\n list1 = lists[0]\n list2 = lists[i]\n\n lists[0] = self.mergeList(list1, list2)\n \n return lists[0] \n\n def mergeList(self, list1: [ListNode], list2: [ListNode]) -> Optional[ListNode]:\n node = ListNode()\n tail = node\n\n while list1 and list2:\n if list1 and list2:\n if list1.val < list2.val:\n tail.next = list1\n list1 = list1.next\n\n else:\n tail.next = list2\n list2 = list2.next\n\n tail = tail.next\n \n if list1 or list2:\n tail.next = list1 if list1 else list2\n \n return node.next\n\n\n# class Solution:\n# def mergeKLists(self, lists: List[Optional[ListNode]]) -> Optional[ListNode]:\n# if not lists:\n# return None\n# if len(lists) == 1:\n# return lists[0]\n# mid = len(lists) // 2\n# l, r = self.mergeKLists(lists[:mid]), self.mergeKLists(lists[mid:])\n# return self.merge(l, r)\n \n# def merge(self, l, r):\n# dummy = p = ListNode()\n# while l and r:\n# if l.val < r.val:\n# p.next = l\n# l = l.next\n# else:\n# p.next = r\n# r = r.next\n# p = p.next\n# p.next = l or r\n# return dummy.next\n \n","repo_name":"tayyab-tariq/Leetcode","sub_path":"Merge k Sorted Lists.py","file_name":"Merge k Sorted Lists.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"34352783806","text":"\"\"\"\n Predicting on predictions (NN's can be stacked)\n\"\"\"\n\n\ndef w_sum(a, b):\n assert len(a) == len(b)\n output = 0\n for i in range(len(a)):\n output += (a[i] * b[i])\n return output\n\n# VECTOR MATRIX MULTIPLICATION\ndef vect_mat_mul(vect, matrix):\n # For each output we are performing a weighted sum of inputs\n # this function iterates through each row of weigths and makes\n # a prediction using w_sum\n assert len(vect) == len(matrix)\n\n output = [0, 0, 0]\n\n for i in range(len(vect)):\n output[i] = w_sum(vect, matrix[i])\n\n return output\n\n # toes wins fans\nih_wgt = [\n [0.1, 0.2, -0.1], # hid[0]\n [-0.1, 0.1, 0.9], # hid[1]\n [0.1, 1.4, 0.1] # hid[2]\n]\n\n # hid[0] hid[1] hid[2]\nhp_wgt = [\n [0.3, 1.1, -0.3], # hurt?\n [0.1, 0.2, 0.0], # win?\n [0.0, 1.3, 0.1] # sad?\n]\n\n\ndef neural_network(input, weights):\n hid = vect_mat_mul(input, weights[0])\n pred = vect_mat_mul(hid, weights[1])\n return pred\n\n\nweights = [ih_wgt, hp_wgt]\n\ntoes = [8.5, 9.5, 9.9, 9.0]\nwlrec = [0.64, 0.8, 0.8, 0.9]\nnfans = [1.2, 1.3, 0.5, 1.0]\n\ninput = [toes[0], wlrec[0], nfans[0]]\npredictions = neural_network(input, weights)\nprint(predictions)\n","repo_name":"charliecharlieO-o/grokking-nn-study","sub_path":"chapter3/ex5.py","file_name":"ex5.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"45336172237","text":"# -*- coding: utf-8 -*-\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\n\nimport pandas as pd\nimport plotly.graph_objs as go\n\nfrom dash.dependencies import Input, Output\n\n\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n\napp.layout = html.Div(children=[\n html.H1(children='Hello Dash'),\n\n html.Div(children='''\n Dash: A web application framework for Python.\n '''),\n\n dcc.Graph(\n id='example-graph' \n )\n])\n\ndef prepare_chart_bar_data():\n # In real world, below prob done something like...\n # df2018= pd.read_sql('mySQL',db)\n data2018 = [['January', 6], ['February', 7], ['March', 2]] \n data2019 = [['January', 7], ['February', 12], ['March', 5]] \n df2018 = pd.DataFrame(data2018, columns = ['Month', 'SnowDays']) \n df2019 = pd.DataFrame(data2019, columns = ['Month', 'SnowDays']) \n\n # Assemble our list data for graphic, we'll use the plotly graph object \"go\"\n \n bars_data_set_a = {'data':\n [\n {'x': df2018['Month'].tolist(),\n 'y': df2018['SnowDays'].tolist(),\n 'type':'bar',\n 'name':'2018'}\n ]\n }\n\n bars_data_set_b = {'data':\n [\n {'x': df2019['Month'].tolist(),\n 'y': df2019['SnowDays'].tolist(),\n 'type':'bar',\n 'name':'2019'}\n ],\n 'layout': {'title':'visualization'}}\n\n #my_figure_dict = dict(bars_data_set_a, **bars_data_set_b)\n bars_data_set_a.update(bars_data_set_b)\n\n my_figure_dict = bars_data_set_a\n\n return my_figure_dict\n\n\n@app.callback(\n Output('example-graph', 'figure'),\n [Input('example-graph', '')])\ndef update_figure(figure):\n return prepare_chart_bar_data()\n\nif __name__ == '__main__':\n app.run_server(debug=True)\n\n \n # data': [\n # {'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar', 'name': 'SF'},\n # {'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar', 'name': u'Montréal'},\n # ],\n # 'layout': {\n # 'title': 'Dash Data Visualization'\n\n \n \n # return {\n # 'data': [\n # {'x': df2018['Month'], 'y': df2018['SnowDays'], 'type': 'bar', 'name': '2018'},\n # {'x': df2019['Month'], 'y': df2019['SnowDays'], 'type': 'bar', 'name': '2019'},\n # ],\n # 'layout': {\n # 'title': 'Dash Data Visualization'\n # }\n # }\n\n \n\n\n \n\n","repo_name":"maxrottersman/MaxDashProject","sub_path":"Archived_Experiments/alt_dash_example_01_function_first_3_addmoredataabsraction.py","file_name":"alt_dash_example_01_function_first_3_addmoredataabsraction.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"70635768533","text":"import os\nimport numpy as np\nfrom rich.progress import track\nimport pandas as pd\nfrom visualizers.DataVisualizers import visualize_line\n\ndef run(name, points, runners, generator, tests_per_point=10, ret_data=lambda x, y, z: x,\n data_names=[], x_name=\"X\", output=None, **kwargs):\n res = []\n\n for point in track(np.arange(*points)):\n for i in range(tests_per_point):\n test = generator(point)\n for name, runner in runners.items():\n result = runner(test, **kwargs)\n fix_res = point, *ret_data(result, test, name), name\n\n res.append(fix_res)\n\n\n\n np_arr = np.array(res)\n dataset = pd.DataFrame(np_arr, columns=[x_name, *data_names, \"Runner\"])\n\n for i in data_names: \n visualize_line(f\"{name}: {i}\", x_name, i, dataset, output=output, hue=\"Runner\")\n\n","repo_name":"atrin-hojjat/Uni-AI-Course-Reports","sub_path":"Report 03/code/tests/TestRunner.py","file_name":"TestRunner.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"11611158114","text":"# 1. opencv >>>> PIL\n#\n# IO cost\n# 1. hdf5, tfrecord (Tensorflow) several small image (text) to a larger file\n# 2. preloader\n# 3. cache (lmdb or redis) key, value cahe\n#\n# pin_memory = True\n\n# tfrecord\nimport tensorflow as tf\nimport os\nfrom PIL import Image\nimport numpy as np\n\ndef _bytes_feature(value):\n \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[tf.io.encode_jpeg(value).numpy()]))\n\ndef _int64_feature(value):\n \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\ndef image_example(image_path, label):\n image_raw = Image.open(image_path)\n image_raw = np.array(image_raw)\n\n feature = {\n 'image': _bytes_feature(image_raw.tobytes()),\n 'label': _int64_feature(label),\n }\n\n return tf.train.Example(features=tf.train.Features(feature=feature))\n\ndef create_tfrecord(output_filename, image_folder):\n writer = tf.io.TFRecordWriter(output_filename)\n\n # Your dataset: list of tuples (image_path, label)\n dataset = [\n (\"path/to/image1.jpg\", 0),\n (\"path/to/image2.jpg\", 1),\n # Add more images and labels as needed\n ]\n\n for image_path, label in dataset:\n tf_example = image_example(image_path, label)\n writer.write(tf_example.SerializeToString())\n\n writer.close()\n\n# Specify the output TFRecord file and the folder containing images\ntfrecord_filename = 'output.tfrecord'\nimage_folder_path = 'path/to/your/image/folder'\n\n# Create TFRecord file\ncreate_tfrecord(tfrecord_filename, image_folder_path)\n\n# several small images to hdf5 file\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nimport h5py\nfrom PIL import Image\nimport os\nfrom multiprocessing import Pool\n\nclass CustomDataset(Dataset):\n def __init__(self, root_dir):\n self.root_dir = root_dir\n self.image_paths = [os.path.join(root_dir, file) for file in os.listdir(root_dir) if file.endswith(\".jpg\")]\n self.transform = transforms.Compose([transforms.ToTensor()])\n\n def __len__(self):\n return len(self.image_paths)\n\n def __getitem__(self, idx):\n image_path = self.image_paths[idx]\n image = Image.open(image_path).convert('RGB')\n image = self.transform(image)\n return image\n\ndef process_images(args):\n idx, image_path = args\n image = Image.open(image_path).convert('RGB')\n image = transforms.ToTensor()(image)\n return idx, image.numpy()\n\ndef convert_to_hdf5_multiprocess(dataset, output_filename='output.h5', num_processes=4):\n with h5py.File(output_filename, 'w') as hdf5_file:\n images_group = hdf5_file.create_group('images')\n\n # Use multiprocessing to parallelize image processing\n with Pool(num_processes) as pool:\n results = pool.map(process_images, enumerate(dataset.image_paths))\n\n for idx, image_data in results:\n images_group.create_dataset(f'image_{idx}', data=image_data)\n\n# Specify the folder containing images\nimage_folder_path = 'path/to/your/image/folder'\n\n# Create a dataset\ndataset = CustomDataset(image_folder_path)\n\n# Convert to HDF5 using multiple processes\nhdf5_filename = 'output.h5'\nconvert_to_hdf5_multiprocess(dataset, hdf5_filename, num_processes=4)\n\n# redis \nimport redis\nfrom PIL import Image\nfrom io import BytesIO\nimport base64\n\n# Connect to the Redis server\nredis_client = redis.StrictRedis(host='localhost', port=6379, decode_responses=True)\n\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n encoded_image = base64.b64encode(image_file.read()).decode('utf-8')\n return encoded_image\n\ndef decode_image(encoded_image):\n decoded_image = base64.b64decode(encoded_image)\n return Image.open(BytesIO(decoded_image))\n\ndef store_images_in_redis(image_folder):\n # Assuming each image has a unique filename\n image_files = [f for f in os.listdir(image_folder) if f.endswith('.jpg')]\n\n for image_file in image_files:\n image_path = os.path.join(image_folder, image_file)\n encoded_image = encode_image(image_path)\n key = f\"image:{image_file}\"\n redis_client.set(key, encoded_image)\n\ndef retrieve_image_from_redis(image_key, output_path):\n encoded_image = redis_client.get(image_key)\n if encoded_image:\n decoded_image = decode_image(encoded_image)\n decoded_image.save(output_path)\n\n# Specify the folder containing images\nimage_folder_path = 'path/to/your/image/folder'\n\n# Store images in Redis\nstore_images_in_redis(image_folder_path)\n\n# Retrieve an image from Redis (replace 'your_image_key' with an actual key)\nimage_key = 'image:your_image.jpg'\noutput_image_path = 'output_image.jpg'\nretrieve_image_from_redis(image_key, output_image_path)\n\n\n","repo_name":"ChenXie-sci/model_optimization","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":4826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"17229798699","text":"import pathlib\nfrom .gen_utils import *\nfrom dk2cxx import *\n\n\ndef format_functions_cpp(globals: list[dk2map.Global], blocks: UserBlocks = None):\n def format_cpp_head():\n yield format_middle(f\"warning: file is managed by {pathlib.Path(__file__).name}\")\n yield format_middle(f\"you can edit code only in *_user_code blocks\")\n yield f\"#include \"\n yield empty_line\n yield f\"using namespace dk2;\"\n yield empty_line\n yield f\"#define relink_stub(name) printf(\\\"[fatal]: stub \\\"#name\\\" call\\\\n\\\"); ::abort();\"\n yield empty_line\n yield from map(format_autogen_line, format_cpp_head())\n yield format_block_line(\"head_user_code\")\n if blocks is not None:\n yield from blocks.head\n else:\n yield f\"\"\n yield f\"// user code\"\n yield f\"\"\n yield format_end_of_block_line()\n\n def format_cpp_body():\n yield f\"#pragma optimize( \\\"\\\", off )\"\n for glob in filter(filter_function_var, globals):\n fun_t = glob.type # type: dk2map.FunctionType\n suffix = \" // assembly\" if fun_t.declspec is dk2map.Declspec.Assembly else ''\n # ret = ''\n # if fun_t.ret.kind is dk2map.TypeKind.Ptr:\n # ret = ' return NULL; '\n # elif fun_t.ret.kind is dk2map.TypeKind.Bool:\n # ret = ' return false; '\n # elif fun_t.ret.kind in [\n # dk2map.TypeKind.Int, dk2map.TypeKind.Float,\n # dk2map.TypeKind.Char, dk2map.TypeKind.Winapi\n # ]:\n # ret = ' return 0; '\n name = f\"dk2::{format_function_name(glob.name)}\"\n ret = f\" relink_stub({name}); \"\n yield f\"/*{glob.va:08X}*/ {format_function(fun_t, name)} {{{ret}}}{suffix}\"\n yield empty_line\n yield f\"#pragma optimize( \\\"\\\", on )\"\n yield from map(format_autogen_line, format_cpp_body())\n yield format_block_line(\"tail_user_code\")\n if blocks is not None:\n yield from blocks.tail\n else:\n yield f\"\"\n yield f\"// user code\"\n yield f\"\"\n yield format_end_of_block_line()\n\n","repo_name":"DiaLight/Ember","sub_path":"dk2/gen/gen_functions_cpp.py","file_name":"gen_functions_cpp.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"67"}
+{"seq_id":"25164537359","text":"from flask import Flask, request, abort\nfrom flask_restful import Resource, Api\nfrom marshmallow import Schema, fields\n\n\nclass BarQuerySchema(Schema):\n key1 = fields.Str(required=True)\n key2 = fields.Str(required=True)\n kucundayu = fields.Int()\n\n\napp = Flask(__name__)\napi = Api(app)\nschema = BarQuerySchema()\n\n\nclass BarAPI(Resource):\n def get(self):\n print(request.args)\n errors = schema.validate(request.args)\n if errors:\n abort(400, str(errors))\n msg = \"OK\"\n #print(request.args['key1'])\n msg += request.args['key1'] + \", \"\n #print(request.args['key1'])\n msg += request.args['key2'] + \", \"\n\n if 'kucundayu' in request.args:\n print(request.args['kucundayu'])\n msg += request.args['kucundayu'] + \", \"\n return msg\n\napi.add_resource(BarAPI, '/bar', endpoint='bar')\n\n# omit of you intend to use `flask run` command\nif __name__ == '__main__':\n app.run(debug=True)","repo_name":"CeciliaRuiSun/Asset-Management-Web-App","sub_path":"guide/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"2179910509","text":"'''Written by Yinshi Liu'''\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n#define constants\nL = 1*10**(-8)\nm = 9.109*10**(-31)\nsigma = L/25\nkappa = 500/L\nx_0 = L/5\nP = 1024\ndt = 1*10**(-18)\nN = 3000\nh_bar = 1.05*10**(-34)\n\np = np.arange(1, P)\na = L/P\nx = p*a - L/2\npsi = np.zeros(P-1, dtype = \"complex_\")\n\n#normalization constant\npsi_0 = 1/((2*np.pi*sigma**2)**(1/4))\n#initial psi \nfor i in range(1, P-1):\n psi[i] = psi_0 * np.exp(-(x[i]-x_0)**2/(4*sigma**2) + 1j*kappa*x[i])\nprint(psi)\nplt.figure(figsize = (6,4))\nplt.plot(x, psi)\nplt.xlabel('x(m)')\nplt.ylabel('real ψ')\nplt.title('wavefunction ψ at T = 0')\nplt.tight_layout()\nplt.savefig('Fig 1.1.png')\n#define the Hamiltonian\nA = -h_bar**2/(2*m*a**2)\n#V(x) = 0 in potential well\nB = -2*A\nvec_diag = B*np.ones(P-1)\nD = np.diag(vec_diag, k=0)\nsup = A*np.eye(P-1, k = 1)\nsub = A*np.eye(P-1, k = -1)\nH = D + sup + sub\n\n#define time independent matrix L, R\nLeft = np.eye(P-1) + (dt/(2*h_bar))*1j*H\nRight = np.eye(P-1) - (dt/(2*h_bar))*1j*H\n\n#define position and time arrays\ntime = []\nx_exp = []\nprob = []\npsi_total = []\nprob = []\npos = []\nenergy = []\n#begin time steps\ni = 0\nwhile i < 3000:\n #calculate psi(n+1)\n v = np.matmul(Right, psi)\n psi_new = np.linalg.solve(Left, v)\n psi = np.copy(psi_new)\n psi_total.append(psi_new)\n if i == 750:\n plt.figure(figsize = (6,4))\n plt.plot(x, psi)\n plt.xlabel('x(m)')\n plt.ylabel('real ψ')\n plt.title('wavefunction ψ at T = T/4')\n plt.tight_layout()\n plt.savefig('Fig 1.2.png')\n if i == 1500:\n plt.figure(figsize = (6,4))\n plt.plot(x, psi)\n plt.xlabel('x(m)')\n plt.ylabel('real ψ')\n plt.title('wavefunction ψ at T = T/2')\n plt.tight_layout()\n plt.savefig('Fig 1.3.png')\n if i == 2250:\n plt.figure(figsize = (6,4))\n plt.plot(x, psi)\n plt.xlabel('x(m)')\n plt.ylabel('real ψ')\n plt.title('wavefunction ψ at T = 3T/4')\n plt.tight_layout()\n plt.savefig('Fig 1.4.png')\n time.append(i*dt)\n i += 1\nplt.figure(figsize = (6,4))\nplt.plot(x, psi)\nplt.xlabel('x(m)')\nplt.ylabel('real ψ')\nplt.title('wavefunction ψ at T = T')\nplt.tight_layout()\nplt.savefig('Fig 1.5.png')\n\n#verify normalization\n#integrate psi*conj(psi) using trap. rule\nfor i in range(len(psi_total)):\n psi_t = psi_total[i]\n psi_magnitude = psi_t*np.conj(psi_t)\n prob.append(np.sum(psi_magnitude)*a)\n#calculate expected value of x\nfor i in range(len(psi_total)):\n psi_t = psi_total[i]\n position = x*psi_t*np.conj(psi_t)\n pos.append(np.sum(position)*a)\n#calculate total energy\nfor i in range(len(psi_total)):\n psi_t = psi_total[i]\n psi_magnitude = psi_t*np.conj(psi_t)\n E = np.matmul(H, psi_magnitude)\n energy.append(np.sum(E*a))\nplt.figure()\nplt.plot(time, prob)\nplt.ylim(0.9, 1.1)\nplt.xlabel('time(s)')\nplt.ylabel('Total Probability')\nplt.title('Normalization of ψ over time')\nplt.savefig('Fig 1.7.png')\n\nplt.figure()\nplt.plot(time, pos)\nplt.xlabel('time(s)')\nplt.ylabel('Expected position')\nplt.title('Expected position over time')\nplt.savefig('Fig 1.6.png')\n\nplt.figure()\nplt.plot(time, energy)\nplt.xlabel('time(s)')\nplt.ylabel('Energy (J)')\nplt.title('Energy conservation over time')\nplt.savefig('Fig 1.8.png')\n","repo_name":"Lenventor/Computation-physics-PHY407","sub_path":"Lab09_TIme-dependent Schrodinger equation (Crank-Nicolson Method).py","file_name":"Lab09_TIme-dependent Schrodinger equation (Crank-Nicolson Method).py","file_ext":"py","file_size_in_byte":3300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"13000620983","text":"import click\nimport botocore, botocore.session\nfrom botocore.exceptions import ClientError\nimport time\nimport cv2\nimport sys\nimport errno\nfrom os import listdir\nfrom os.path import isfile, join\n\nFILE_NAME = 'selfie.png'\n\ndef capture_frame():\n ### Capture image from cam\n camera = cv2.VideoCapture(0)\n time.sleep(0.2) # If you don't wait, the image will be dark\n\n if camera.isOpened(): # try to get the first frame\n rval, frame = camera.read()\n else:\n rval = False\n exit -1\n\n cv2.imwrite(FILE_NAME, frame)\n del(camera) # so that others can use the camera as soon as possible\n\ndef add_face(ctx, file, collection, name):\n session = botocore.session.Session(profile=ctx.obj['PROFILE'])\n\n ### Send to Rekognition to add it to faces collection\n rekognition = session.create_client('rekognition')\n dynamodb = session.create_client('dynamodb')\n\n ### Read image from file system\n with open(file, 'rb') as image:\n response = rekognition.index_faces(\n CollectionId=collection,\n Image={\n 'Bytes': image.read()\n }\n )\n\n ### If successful, add info in DDB\n if len(response['FaceRecords']) > 0:\n ddb_response = dynamodb.put_item(\n TableName=collection,\n Item={\n 'face-id': {\n 'S': response['FaceRecords'][0]['Face']['FaceId'],\n },\n 'name': {\n 'S': name,\n }\n }\n )\n click.secho(\"All done. {} has been successfully added.\".format(name), fg='blue')\n else:\n click.secho(\"Sorry, something went wrong while adding {}. Try again or see an admin for help.\".format(name), fg='yellow')\n\n@click.group()\n@click.option('--profile', metavar='AWS_PROFILE', default='default', envvar='AWS_DEFAULT_PROFILE',\n help='The name of the AWS profile to use. You can configure a profile with the AWS CLI command: aws configure --profile .')\n@click.pass_context\ndef cli(ctx, profile):\n \"\"\"TBD\"\"\"\n ctx.obj = {}\n ctx.obj['PROFILE'] = profile\n\n@cli.command()\n@click.option('--collection', prompt='Please enter the collection name', help='Name of the collection to add the faces to')\n@click.option('--path', prompt='Please enter the path to the images', help='Path to a directory containing the faces images')\n@click.pass_context\ndef setup(ctx, collection, path):\n \"\"\"Sets up a collection with faces (pictures) from the local filesystem.\"\"\"\n session = botocore.session.Session(profile=ctx.obj['PROFILE'])\n\n rekognition = session.create_client('rekognition')\n dynamodb = session.create_client('dynamodb')\n\n ### Creates DDB table\n try:\n response = dynamodb.create_table(\n AttributeDefinitions=[\n {\n 'AttributeName': 'face-id',\n 'AttributeType': 'S',\n }\n ],\n KeySchema=[\n {\n 'AttributeName': 'face-id',\n 'KeyType': 'HASH',\n }\n ],\n ProvisionedThroughput={\n 'ReadCapacityUnits': 5,\n 'WriteCapacityUnits': 5,\n },\n TableName=collection\n )\n\n click.secho(\"DynamoDB table {} created.\".format(collection), fg='blue')\n except ClientError as e:\n click.secho(\"Sorry, something went wrong: {}. Try again or see an admin for help.\".format(e), fg='yellow')\n\n ### Creates Rekognition collection\n response = rekognition.create_collection(\n CollectionId=collection\n )\n\n if response['StatusCode'] == 200:\n click.secho(\"Collection {} created.\".format(collection), fg='blue')\n else:\n click.secho(\"Sorry, something went wrong. Try again or see an admin for help.\", fg='yellow')\n\n ### Adds faces to collection and info to DDB\n faces = [f for f in listdir(path) if isfile(join(path, f))]\n for face in faces:\n click.secho(\"Working on {}\".format(face), fg='blue')\n try:\n add_face(ctx, join(path, face), collection, face.split('.')[0])\n except IOError as exc:\n if exc.errno != errno.EISDIR: # Do not fail if a directory is found, just ignore it.\n raise # Propagate other kinds of IOError.\n\n@cli.command()\n@click.option('--name', nargs=2, prompt='Please enter the full name', help='Full name of the person being added')\n@click.option('--collection', prompt='Please enter the collection name', help='Name of the collection to add the face to')\n@click.pass_context\ndef add(ctx, name, collection):\n \"\"\"Captures an image from the camera and adds it to the collection.\"\"\"\n ### Capture image from cam\n capture_frame()\n\n ### Send to Rekognition to add it to faces collection\n add_face(ctx, FILE_NAME, collection, name)\n\n\n@cli.command()\n@click.option('--collection', prompt='Please enter the collection name', help='Name of the collection to compare the face to')\n@click.pass_context\ndef capture(ctx, collection):\n \"\"\"Captures an image from the camera and compares it to the faces in the collection.\"\"\"\n session = botocore.session.Session(profile=ctx.obj['PROFILE'])\n ### Capture image from cam\n capture_frame()\n\n ### Send to Rekognition to compare it to faces in collection\n rekognition = session.create_client('rekognition')\n dynamodb = session.create_client('dynamodb')\n\n with open(\"selfie.png\", 'rb') as image:\n response = rekognition.search_faces_by_image(\n CollectionId=collection,\n Image={\n 'Bytes': image.read()\n },\n MaxFaces=1,\n FaceMatchThreshold=80\n )\n\n ### If a match is found, get info from DDB\n if len(response['FaceMatches']) == 1:\n ddb_response = dynamodb.get_item(\n TableName=collection,\n Key={\n 'face-id': {\n 'S': response['FaceMatches'][0]['Face']['FaceId'],\n }\n }\n )\n click.secho(\"Welcome {}! You can now proceed.\".format(ddb_response['Item']['name']['S']), fg='green')\n else:\n click.secho(\"Sorry, we couldn't recognize you. Try again or see an admin for help.\", fg='yellow')\n","repo_name":"dstroppa/facer","sub_path":"facer.py","file_name":"facer.py","file_ext":"py","file_size_in_byte":6276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"35803075785","text":"# standard library imports\nfrom typing import TYPE_CHECKING, Optional\nimport time\nimport random\nimport contextlib\n\n# third party imports\nimport numpy as np\nimport emcee\nimport arviz as az\nfrom loguru import logger\nfrom tabulate import tabulate\n\n# local imports\nfrom probeye.subroutines import pretty_time_delta\nfrom probeye.subroutines import check_for_uninformative_priors\nfrom probeye.inference.scipy.solver import ScipySolver\nfrom probeye.subroutines import stream_to_logger\nfrom probeye.subroutines import print_dict_in_rows\nfrom probeye.subroutines import extract_true_values\n\n# imports only needed for type hints\nif TYPE_CHECKING: # pragma: no cover\n from probeye.definition.inverse_problem import InverseProblem\n\n\nclass EmceeSolver(ScipySolver):\n \"\"\"\n Provides emcee-sampler which is a pure-Python implementation of Goodman & Weare’s\n Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. For more\n information, check out https://emcee.readthedocs.io/en/stable/.\n\n Parameters\n ----------\n problem\n Describes the inverse problem including e.g. parameters and data.\n seed\n Random state used for random number generation.\n show_progress\n When True, the progress of a solver routine will be shown (for example as a\n progress-bar) if such a feature is available. Otherwise, the progress will\n not shown.\n \"\"\"\n\n def __init__(\n self,\n problem: \"InverseProblem\",\n seed: Optional[int] = None,\n show_progress: bool = True,\n ):\n logger.debug(f\"Initializing {self.__class__.__name__}\")\n # check that the problem does not contain a uninformative prior\n check_for_uninformative_priors(problem)\n # initialize the scipy-based solver (ScipySolver)\n super().__init__(problem, seed=seed, show_progress=show_progress)\n\n def emcee_summary(\n self, posterior_samples: np.ndarray, true_values: Optional[dict] = None\n ) -> dict:\n \"\"\"\n Computes and prints a summary of the posterior samples containing mean, median,\n standard deviation, 5th percentile and 95th percentile. Note, that this method\n was based on code from the taralli package: https://gitlab.com/tno-bim/taralli.\n\n Parameters\n ----------\n posterior_samples\n The generated samples in an array with as many columns as there are latent\n parameters, and n rows, where n = n_chains * n_steps.\n true_values\n True parameter values, if known.\n\n Returns\n -------\n Keys are the different statistics 'mean', 'median', 'sd' (standard\n deviation), 'q05' and 'q95' (0.05- and 0.95-quantile). The values are\n dictionaries with the parameter names as keys and the respective statistics\n as values.\n \"\"\"\n\n # used for the names in the first column\n var_names = self.problem.get_theta_names(tex=False, components=True)\n\n # compute some stats for each column (i.e., each parameter)\n mean = np.mean(posterior_samples, axis=0)\n quantiles = np.quantile(posterior_samples, [0.50, 0.05, 0.95], axis=0)\n median = quantiles[0, :]\n quantile_05 = quantiles[1, :]\n quantile_95 = quantiles[2, :]\n\n # compute the sample standard deviations for each parameter\n cov_matrix = np.atleast_2d(np.cov(posterior_samples.T))\n sd = np.sqrt(np.diag(cov_matrix))\n\n # assemble the summary array\n if true_values:\n col_names = [\"\", \"true\", \"mean\", \"median\", \"sd\", \"5%\", \"95%\"]\n true = extract_true_values(true_values, var_names)\n row_names = np.array(var_names)\n tab = np.hstack(\n (\n row_names.reshape(-1, 1),\n true.reshape(-1, 1),\n mean.reshape(-1, 1),\n median.reshape(-1, 1),\n sd.reshape(-1, 1),\n quantile_05.reshape(-1, 1),\n quantile_95.reshape(-1, 1),\n )\n )\n\n # print the generated table, and return a summary dict for later use\n print(tabulate(tab, headers=col_names, floatfmt=\".2f\"))\n return {\n \"true\": {name: val for name, val in zip(row_names, true)},\n \"mean\": {name: val for name, val in zip(row_names, mean)},\n \"median\": {name: val for name, val in zip(row_names, median)},\n \"sd\": {name: val for name, val in zip(row_names, sd)},\n \"q05\": {name: val for name, val in zip(row_names, quantile_05)},\n \"q95\": {name: val for name, val in zip(row_names, quantile_95)},\n }\n else:\n col_names = [\"\", \"mean\", \"median\", \"sd\", \"5%\", \"95%\"]\n row_names = np.array(var_names)\n tab = np.hstack(\n (\n row_names.reshape(-1, 1),\n mean.reshape(-1, 1),\n median.reshape(-1, 1),\n sd.reshape(-1, 1),\n quantile_05.reshape(-1, 1),\n quantile_95.reshape(-1, 1),\n )\n )\n\n # print the generated table, and return a summary dict for later use\n print(tabulate(tab, headers=col_names, floatfmt=\".2f\"))\n return {\n \"mean\": {name: val for name, val in zip(row_names, mean)},\n \"median\": {name: val for name, val in zip(row_names, median)},\n \"sd\": {name: val for name, val in zip(row_names, sd)},\n \"q05\": {name: val for name, val in zip(row_names, quantile_05)},\n \"q95\": {name: val for name, val in zip(row_names, quantile_95)},\n }\n\n def run(\n self,\n n_walkers: int = 20,\n n_steps: int = 1000,\n n_initial_steps: int = 100,\n true_values: Optional[dict] = None,\n **kwargs,\n ) -> az.data.inference_data.InferenceData:\n \"\"\"\n Runs the emcee-sampler for the InverseProblem the EmceeSolver was initialized\n with and returns the results as an arviz InferenceData obj.\n\n Parameters\n ----------\n n_walkers\n Number of walkers used by the estimator.\n n_steps\n Number of steps to run.\n n_initial_steps\n Number of steps for initial (burn-in) sampling.\n true_values\n True parameter values, if known.\n kwargs\n Additional key-word arguments channeled to emcee.EnsembleSampler.\n\n Returns\n -------\n inference_data\n Contains the results of the sampling procedure.\n \"\"\"\n\n # log which solver is used\n logger.info(\n f\"Solving problem using emcee sampler with {n_initial_steps} + {n_steps} \"\n f\"samples and {n_walkers} walkers\"\n )\n if kwargs:\n logger.info(\"Additional options:\")\n print_dict_in_rows(kwargs, printer=logger.info)\n else:\n logger.info(\"No additional options specified\")\n\n # draw initial samples from the parameter's priors\n logger.debug(\"Drawing initial samples\")\n if self.seed is not None:\n np.random.seed(self.seed)\n sampling_initial_positions = np.zeros(\n (n_walkers, self.problem.n_latent_prms_dim)\n )\n theta_names = self.problem.get_theta_names(tex=False, components=False)\n for parameter_name in theta_names:\n idx = self.problem.parameters[parameter_name].index\n idx_end = self.problem.parameters[parameter_name].index_end\n samples = self.sample_from_prior(parameter_name, n_walkers)\n if (idx_end - idx) == 1:\n sampling_initial_positions[:, idx] = samples\n else:\n sampling_initial_positions[:, idx:idx_end] = samples\n\n # The following code is based on taralli and merely adjusted to the variables\n # in the probeye setup; see https://gitlab.com/tno-bim/taralli\n\n # ............................................................................ #\n # Pre-process #\n # ............................................................................ #\n\n def logprob(x):\n # Skip loglikelihood evaluation if logprior is equal\n # to negative infinity\n logprior = self.logprior(x)\n if logprior == -np.inf:\n return logprior\n\n # Otherwise return logprior + loglikelihood\n return logprior + self.loglike(x)\n\n logger.debug(\"Setting up EnsembleSampler\")\n sampler = emcee.EnsembleSampler(\n nwalkers=n_walkers,\n ndim=self.problem.n_latent_prms_dim,\n log_prob_fn=logprob,\n **kwargs,\n )\n\n if self.seed is not None:\n random.seed(self.seed)\n sampler.random_state = np.random.mtrand.RandomState(self.seed)\n\n # ............................................................................ #\n # Initial sampling, burn-in: used to avoid a poor starting point #\n # ............................................................................ #\n\n logger.debug(\"Starting sampling (initial + main)\")\n start = time.time()\n state = sampler.run_mcmc(\n initial_state=sampling_initial_positions,\n nsteps=n_initial_steps,\n progress=self.show_progress,\n )\n sampler.reset()\n\n # ............................................................................ #\n # Sampling of the posterior #\n # ............................................................................ #\n sampler.run_mcmc(\n initial_state=state, nsteps=n_steps, progress=self.show_progress\n )\n end = time.time()\n runtime_str = pretty_time_delta(end - start)\n logger.info(\n f\"Sampling of the posterior distribution completed: {n_steps} steps and \"\n f\"{n_walkers} walkers.\"\n )\n logger.info(f\"Total run-time (including initial sampling): {runtime_str}.\")\n logger.info(\"\")\n logger.info(\"Summary of sampling results (emcee)\")\n posterior_samples = sampler.get_chain(flat=True)\n with contextlib.redirect_stdout(stream_to_logger(\"INFO\")): # type: ignore\n self.summary = self.emcee_summary(\n posterior_samples, true_values=true_values\n )\n logger.info(\"\") # empty line for visual buffer\n self.raw_results = sampler\n\n # translate the results to a common data structure and return it\n var_names = self.problem.get_theta_names(tex=True, components=True)\n inference_data = az.from_emcee(sampler, var_names=var_names)\n return inference_data\n","repo_name":"BAMresearch/probeye","sub_path":"probeye/inference/emcee/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":10995,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"67"}
+{"seq_id":"20750269076","text":"n = int(input())\narr = [list(map(int, input().split())) for _ in range(n)]\narr.sort(key = lambda x : x[0])\n\ndp = [0] * len(arr)\ndp[0] = 1\nfor i in range(1, len(dp)):\n max_num = 0\n for j in range(0, i):\n if arr[j][1] < arr[i][1]:\n max_num = max(max_num, dp[j])\n dp[i] = max_num + 1\n\nprint(len(dp)-max(dp))","repo_name":"jongbin26/coding_test","sub_path":"python/2565.py","file_name":"2565.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"10325728306","text":"from django.contrib.auth.backends import ModelBackend\nfrom django.contrib.auth.models import Permission\nfrom django.db.models import Q, Exists, OuterRef\nfrom ..models.archive import Archive\n\nclass ArchiveBackend(ModelBackend):\n def get_group_permissions(self, user_obj, obj=None):\n perm_cache = super().get_group_permissions(user_obj, obj)\n group_perms = Permission.objects.filter(\n archivegrouppermission__group__user__id=user_obj.id,\n content_type__app_label='archive',\n )\n group_perms = group_perms.values_list(\n 'content_type__app_label',\n 'archivegrouppermission__archive__slug',\n 'codename',\n ).order_by()\n perm_cache = perm_cache.union({\n \"{label}.any.{codename}\".format(label=label, codename=codename)\n for label, slug, codename in group_perms\n })\n perm_cache = perm_cache.union({\n \"{label}.archive.{slug}.{codename}\".format(label=label, slug=slug, codename=codename)\n for label, slug, codename in group_perms\n })\n perms = Permission.objects.filter(\n group__user=user_obj,\n content_type__app_label='archive',\n )\n perms = perms.values_list('content_type__app_label', 'codename').order_by()\n perm_cache = perm_cache.union(self.implied_per_archive_permissions(perms))\n user_obj._group_perm_cache = perm_cache\n return perm_cache\n\n def implied_per_archive_permissions(self, values):\n return {\n \"{label}.archive.{slug}.{codename}\".format(label=label, codename=codename, slug=archive.slug)\n for label, codename in values\n for archive in Archive.objects.all()\n } | {\n \"{label}.any.{codename}\".format(label=label, codename=codename)\n for label, codename in values\n }\n\n def get_user_permissions(self, user_obj, obj=None):\n perm_cache = super().get_user_permissions(user_obj, obj)\n perms = Permission.objects.filter(\n archiveuserpermission__user=user_obj,\n content_type__app_label='archive',\n )\n perms = perms.values_list('content_type__app_label', 'archiveuserpermission__archive__slug', 'codename').order_by()\n perm_cache = perm_cache.union({\n \"{label}.any.{codename}\".format(label=label, codename=codename)\n for label, slug, codename in perms\n })\n perm_cache = perm_cache.union({\n \"{label}.archive.{slug}.{codename}\".format(label=label, slug=slug, codename=codename)\n for label, slug, codename in perms\n })\n if user_obj.is_superuser:\n perms = Permission.objects.filter(\n content_type__app_label='archive',\n )\n else:\n perms = Permission.objects.filter(\n user=user_obj,\n content_type__app_label='archive',\n )\n perms = perms.values_list('content_type__app_label', 'codename').order_by()\n perm_cache = perm_cache.union(self.implied_per_archive_permissions(perms))\n\n user_obj._user_perm_cache = perm_cache\n return perm_cache\n\n def has_perm(self, user_obj, perm, obj=None):\n return super().has_perm(user_obj, perm, obj)\n","repo_name":"workjonathan/kronofoto","sub_path":"kronofoto/archive/auth/backends.py","file_name":"backends.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"33381489748","text":"from __future__ import print_function\n\nimport argparse\nfrom collections import Counter\nimport os\nimport re\nimport subprocess\n\nfile_re = re.compile(r'diff --git a/(\\S+)')\ndiff_region_re = re.compile(r'@@\\s[-]\\S+\\s[+](\\S+)\\s@@')\nsource_line_re = re.compile(r'Coverage for [^:]+:\\s+(\\d+%)<\\/title>')\nsummary_end_re = re.compile(r'\\s+
')\n\n__version__ = \"0.7\"\n\n\nclass DiffCollectionFailed(Exception):\n pass\n\n\nclass SourceLine(object):\n\n def __init__(self, line_number, is_context=True, code=''):\n self.line_number = line_number\n self.is_context = is_context\n self.code = code\n self.status = '???'\n\n def __eq__(self, other):\n return (self.line_number == other.line_number and\n self.is_context == other.is_context)\n\n def __repr__(self):\n return \"SourceLine(line_number=%d, is_context=%s)\" % (self.line_number,\n self.is_context)\n\n\nclass SourceModule(object):\n\n def __init__(self, filename, lines):\n self.filename = filename\n self.lines = lines\n self.line_num_map = {l.line_number: l for l in lines}\n self.cover_file = (filename.replace('/', '_').replace('.', '_') +\n \".html\")\n self.have_report = False\n self.coverage = '??%'\n\n def update_line_status(self, line_number, status):\n if line_number in self.line_num_map:\n line = self.line_num_map[line_number]\n if status.startswith('pln'):\n line.status = ' '\n else:\n line.status = status[4:7]\n\n def report(self):\n output = self.filename\n if not self.have_report:\n return \"%s (No coverage data)\\n\" % output\n if not self.lines or all(l.is_context for l in self.lines):\n return \"%s (No added/changed lines)\\n\" % output\n stats = Counter([l.status for l in self.lines if not l.is_context])\n output += \" (run={}, mis={}, par={}, ign={}) {}\\n\".format(\n stats['run'], stats['mis'], stats['par'], stats[' '],\n self.coverage)\n last_line = None\n for line in self.lines:\n if last_line and line.line_number != (last_line + 1):\n output += \"\\n\"\n output += \"{:5d} {} {} {}\\n\".format(\n line.line_number, line.status,\n ' ' if line.is_context else '+', line.code)\n last_line = line.line_number\n return output\n\n\ndef check_coverage_status(coverage_info, module):\n for coverage_line in coverage_info:\n m = title_re.match(coverage_line)\n if m:\n module.coverage = m.group(1)\n continue\n if summary_end_re.match(coverage_line):\n return\n m = source_line_re.match(coverage_line)\n if m:\n line_num = int(m.group(1))\n status = m.group(2)\n module.update_line_status(line_num, status)\n\n\ndef check_coverage_file(root, module):\n \"\"\"Check the lines in coverage file and report coverage status.\"\"\"\n report_file = os.path.join(root, 'cover', module.cover_file)\n if not os.path.isfile(report_file):\n return # No coverage data for file\n with open(report_file) as coverage_info:\n coverage_lines = coverage_info.readlines()\n check_coverage_status(coverage_lines, module)\n module.have_report = True\n\n\ndef collect_diff_lines(diff_region, start, last):\n \"\"\"Find added and context lines in a diff region.\n\n Note: If the diff region is at the start or end of the file, there\n may not be context lines.\n \"\"\"\n lines = []\n line_num = start\n while line_num <= last:\n line = next(diff_region)\n if line.startswith('-'):\n continue\n lines.append(SourceLine(line_num, is_context=line.startswith(' '),\n code=line[1:]))\n line_num += 1\n return lines\n\n\ndef parse_diffs(diff_output):\n \"\"\"Collect the file and ranges of diffs added, if any.\"\"\"\n added_lines = []\n source_file = ''\n diff_lines = iter(diff_output.splitlines())\n for line in diff_lines:\n m = file_re.match(line)\n if m:\n source_file = m.group(1)\n continue\n m = diff_region_re.match(line)\n if m:\n start, comma, num = m.group(1).partition(',')\n start = int(start)\n if num:\n last = start + int(num) - 1\n else:\n last = start\n added_lines += collect_diff_lines(diff_lines, start, last)\n return (source_file, added_lines)\n\n\ndef collect_diffs_for_files(root, versions, source_files, context_lines):\n \"\"\"Generator to obtain the diffs for files.\"\"\"\n os.chdir(root)\n for filename in source_files:\n command = ['git', 'diff', '-U%d' % context_lines,\n '-w', versions, '--', filename]\n p = subprocess.Popen(command,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n diff_lines, err = p.communicate()\n if err:\n raise DiffCollectionFailed(\n \"Unable to collect diffs for file %s/%s: %s\" %\n (root, filename, err))\n yield diff_lines\n\n\ndef collect_diff_files(root, versions):\n \"\"\"Generator to obtain all the diff files.\"\"\"\n command = ['git', 'diff', '--name-only', versions]\n os.chdir(root)\n p = subprocess.Popen(command,\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n out, err = p.communicate()\n if err:\n raise DiffCollectionFailed(\"Unable to find diff files to examine \"\n \"in %s: %s\" % (root, err))\n for filename in out.splitlines():\n if not os.path.basename(filename).startswith('.'):\n yield filename\n\n\ndef validate(parser, provided_args=None):\n args = parser.parse_args(provided_args)\n args.root = os.path.abspath(args.root)\n if not os.path.isdir(args.root):\n parser.error(\"The repo-dir must be a directory pointing to the top \"\n \"of the Git repo\")\n if not os.path.isdir(os.path.join(args.root, 'cover')):\n parser.error(\"Missing cover directory for project\")\n if args.commits == 'working':\n args.commits = 'HEAD'\n elif args.commits == 'committed':\n args.commits = 'HEAD^..HEAD'\n return args\n\n\ndef main():\n args = validate(setup_parser())\n files = collect_diff_files(args.root, args.commits)\n diff_files = collect_diffs_for_files(args.root, args.commits, files,\n args.context)\n for diffs in diff_files:\n source_file, lines = parse_diffs(diffs)\n module = SourceModule(source_file, lines)\n check_coverage_file(args.root, module)\n print(module.report())\n\n\ndef setup_parser():\n parser = argparse.ArgumentParser(\n description='Reports coverage for a commit in repo.')\n parser.add_argument(\n '-c', '--context', action='store', type=int, default=3,\n help='Number of context lines around diff regions. Default=3.')\n parser.add_argument(\n '-w', '--which', action='store', default=\"working\", dest='commits',\n help=\"Which commit(s) to compare. Use 'working', 'commit', or \"\n \"custom commit specification. Latest should be same as cover run. \"\n \"Default='working'.\")\n parser.add_argument(dest='root', metavar='repo-dir',\n help=\"Root of Git repo\")\n return parser\n","repo_name":"pmichali/commit-coverage","sub_path":"commit_coverage/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7578,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"21931316268","text":"\"\"\" get rates module \"\"\"\n\nfrom concurrent.futures import ThreadPoolExecutor\nfrom datetime import date\nimport threading\n\nimport requests\n\nfrom rates_demo.business_days import business_days\nimport rates_demo.rates_orchestrator as ro\n\ndef get_rates(base_url: str) -> list[str]:\n \"\"\" get rates \"\"\"\n\n start_date = date(2021, 1, 1)\n end_date = date(2021, 1, 31)\n rates: list[str] = []\n\n for business_day in business_days(start_date, end_date):\n rates_url = \"\".join([base_url, \"/api/\",\n business_day.strftime(\"%Y-%m-%d\"),\n \"?base=USD&symbols=EUR\"])\n\n response = requests.request(\"GET\", rates_url)\n rates.append(response.text)\n\n return rates\n\n\ndef get_rate_task(base_url: str, business_day: date) -> None:\n \"\"\" get rate for a single day from the rest api \"\"\"\n\n rates_url = \"\".join([base_url, \"/api/\",\n business_day.strftime(\"%Y-%m-%d\"),\n \"?base=USD&symbols=EUR\"])\n\n ro.process_rates_queue.put(requests.request(\"GET\", rates_url).text)\n\n\ndef get_rates_threaded(base_url: str) -> None:\n \"\"\" get rates using multiple threads \"\"\"\n\n start_date = date(2021, 1, 1)\n end_date = date(2021, 1, 31)\n threads: list[threading.Thread] = []\n\n for business_day in business_days(start_date, end_date):\n a_thread = threading.Thread(\n target=get_rate_task, args=(base_url, business_day))\n a_thread.start()\n threads.append(a_thread)\n\n for a_thread in threads:\n a_thread.join()\n\n ro.get_rates_done.set()\n\n\n# def get_rates_threadpool(base_url: str) -> list[str]:\n# \"\"\" get rates using multiple threads \"\"\"\n\n# start_date = date(2021, 1, 1)\n# end_date = date(2021, 1, 31)\n\n# with ThreadPoolExecutor() as executor:\n\n# return list(executor.map(\n# lambda params: get_rate_task(*params),\n# [ (base_url, business_day) for business_day\n# in business_days(start_date, end_date)]\n# ))\n\n\n","repo_name":"t4d-classes/advanced-python_05102021","sub_path":"python_demos/rates_demo/get_rates.py","file_name":"get_rates.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73826630951","text":"import ipaddress\n\nclass Validator:\n\n ALLOWED_STATES = [\n 'NY',\n 'CA',\n 'AZ',\n 'TX',\n 'OH'\n ]\n\n ALLOWED_ESTIMATION_TYPES = [\n 'normal',\n 'premium'\n ]\n\n MANDATORY_PARAMETERS = [\n 'km',\n 'type',\n 'base_amount',\n 'state',\n ]\n\n NUMBER_PARAMETERS = [\n 'km',\n 'base_amount'\n ]\n\n def validate(self, params, ip):\n self.validate_ip(ip)\n self.validate_params_integrity(params)\n self.validate_state(params['state'])\n self.validate_type(params['type'])\n self.validate_numbers(params)\n\n def validate_params_integrity(self, params):\n params_sent = list(params.keys())\n for param in self.MANDATORY_PARAMETERS:\n if param not in params_sent:\n raise Exception(\"param \"+param+\" is a mandatory\")\n if params[param] == '':\n raise Exception(\"param \"+param+\" shouldn't be empty\")\n\n def validate_state(self, state:str):\n if state.upper() not in self.ALLOWED_STATES:\n raise Exception('unsupported state')\n\n def validate_type(self, type:str):\n if type.lower() not in self.ALLOWED_ESTIMATION_TYPES:\n raise Exception('unsupported type')\n\n def validate_ip(self, ip):\n try:\n ipaddress.ip_address(ip)\n except ValueError:\n raise Exception(\"ip is not valid\")\n\n def validate_numbers(self, params):\n for param in self.NUMBER_PARAMETERS:\n if not params[param].isnumeric():\n try:\n float(params[param])\n except ValueError:\n raise Exception(\"param \"+param+\" must be numeric\")\n\n","repo_name":"dsmunoz/total-calculator","sub_path":"services/validator.py","file_name":"validator.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73830448874","text":"from django.urls import path, include\n\nfrom wind_simulation_model.views import (\n wind_station_simulation_main, wind_station_simulation_now, wind_station_simulation_forecast,\n calculate_solar_radiation, get_forecast_data, turbines, turbine_in_detail, add_turbine,\n edit_turbine, edit_turbine_field, delete_turbine_table, add_turbine_field, get_all_forecast_data\n)\n\n\nurlpatterns = [\n path('solar_radiation', calculate_solar_radiation, name='calculate_solar_radiation'),\n\n path('wind_simulation', wind_station_simulation_main, name='wind_station_simulation_main'),\n path('wind_simulation/now', wind_station_simulation_now, name='wind_station_simulation_now'),\n path('wind_simulation/forecast', wind_station_simulation_forecast, name='wind_station_simulation_forecast'),\n\n path('wind_simulation/list_of_turbines', turbines, name='list_of_turbines'),\n path('wind_simulation/list_of_turbines/', turbine_in_detail, name='turbine_ib_detail'),\n\n path('wind_simulation/add_turbine', add_turbine, name='add_turbine'),\n path('wind_simulation/list_of_turbines//add', add_turbine_field, name='add_turbine_field'),\n\n path('wind_simulation/list_of_turbines//edit', edit_turbine, name='edit_turbine'),\n path('wind_simulation/list_of_turbines//edit/', edit_turbine_field, name='edit_turbine_field'),\n\n path('wind_simulation/list_of_turbines//delete', delete_turbine_table, name='delete_turbine_table'),\n\n path('get_forecast_data', get_forecast_data, name='get_forecast_data'),\n path('get_all_forecast_data', get_all_forecast_data, name='get_forecast_data'),\n]\n","repo_name":"Oleg-tech/solar-system-monitoring-service","sub_path":"house_monitoring_system/wind_simulation_model/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"12732656918","text":"import re\n\n\nclass Keyword:\n def __init__(self, value: str):\n self.value = value\n\n def __eq__(self, other):\n if not isinstance(other, Keyword):\n return False\n\n return self.value == other.value\n\n def __repr__(self):\n return f'Keyword(value=\"{self.value}\")'\n\n\nclass Literal:\n def __init__(self, value: str):\n self.value = value\n\n def __eq__(self, other):\n if not isinstance(other, Literal):\n return False\n\n return self.value == other.value\n\n def __repr__(self):\n return f'Literal(value=\"{self.value}\")'\n\n\nclass Select:\n def __init__(\n self,\n select_list: list[str],\n select_from: list[str],\n join: list[tuple[str, str, tuple]],\n where: tuple\n ):\n self.select_list = select_list\n self.select_from = select_from\n self.join = join\n self.where = where\n\n def __eq__(self, other):\n if not isinstance(other, Select):\n return False\n\n return (\n self.select_list == other.select_list\n and self.select_from == other.select_from\n and self.join == other.join\n and self.where == other.where\n )\n\n def __repr__(self):\n return f'Select(select_list={self.select_list}, select_from={self.select_from}, join={self.join}, where={self.where})'\n\n\nclass InvalidTokenError(Exception):\n pass\n\n\nTokens = list[Keyword | Literal | Select]\n\n\ndef parse_sql(s: str) -> Tokens:\n position = skip_white_spaces(s, 0)\n select, position = read_query(s, position)\n return [select]\n\n\ndef skip_white_spaces(s: str, position: int) -> int:\n while position < len(s) and s[position] in (' ', '\\t', '\\n', '\\r'):\n position += 1\n return position\n\n\ndef read_query(s: str, position: int) -> tuple[Select, int]:\n _, position = read_keyword('select', s, position)\n select_list, position = read_list(s, position)\n _, position = read_keyword('from', s, position)\n select_from, position = read_list(s, position)\n join, position = read_joins(s, position)\n where, position = read_where(s, position)\n return Select(select_list=select_list, select_from=select_from, join=join, where=where), position\n\n\ndef read_list(s: str, position: int) -> tuple[list, int]:\n items = []\n while position < len(s):\n value, position = read_until_separator(s, position)\n items.append(value)\n position = skip_white_spaces(s, position)\n if position >= len(s) or s[position] != ',':\n break\n position += 1\n position = skip_white_spaces(s, position)\n\n position = skip_white_spaces(s, position)\n return items, position\n\n\ndef read_until(values: list[str], s: str, position: int) -> tuple[str, int]:\n start = position\n while position < len(s):\n found_value = any(s[position:].lower().startswith(value) for value in values)\n if found_value:\n break\n\n position += 1\n\n return s[start:position], position\n\n\ndef read_until_white_space(s: str, position: int) -> tuple[str, int]:\n value, position = read_until([' ', '\\t', '\\n', '\\r'], s, position)\n position = skip_white_spaces(s, position)\n return value, position\n\n\ndef read_until_white_space_or_bracket(s: str, position: int) -> tuple[str, int]:\n value, position = read_until([' ', '\\t', '\\n', '\\r', ')', '('], s, position)\n position = skip_white_spaces(s, position)\n return value, position\n\n\ndef read_until_separator(s: str, position: int) -> tuple[str, int]:\n return read_until([',', ' ', '\\t', '\\n', '\\r'], s, position)\n\n\ndef read_keyword(keyword: str, s: str, position: int) -> tuple[Keyword, int]:\n t = s[position:].lower()\n if t.startswith(keyword):\n position = skip_white_spaces(s, position + len(keyword))\n return Keyword(keyword), position\n\n raise InvalidTokenError(f'Invalid token at position {position}: expected keyword \"{keyword}\"')\n\n\ndef read_keywords(keywords: list[str], s: str, position: int) -> tuple[Keyword, int]:\n t = s[position:].lower()\n\n for keyword in keywords:\n if t.startswith(keyword):\n position = skip_white_spaces(s, position + len(keyword))\n return Keyword(keyword), position\n\n raise InvalidTokenError(f'Invalid token at position {position}: expected keywords \"{keywords}\"')\n\n\ndef read_literal(s: str, position: int) -> tuple[Literal, int]:\n start = position\n while position < len(s) and s[position] not in (' ', '\\t', '\\n', '\\r'):\n position += 1\n\n return Literal(s[start:position]), position\n\n\ndef read_joins(s: str, position: int) -> tuple[list, int]:\n joins = []\n while position < len(s):\n position = skip_white_spaces(s, position)\n if re.match(r'^left\\s+outer\\s+join\\s+', s[position:], re.IGNORECASE) is not None:\n join, position = read_left_outer_join(s, position)\n elif re.match(r'^inner\\s+join\\s+', s[position:], re.IGNORECASE) is not None:\n join, position = read_inner_join(s, position)\n elif re.match(r'^join\\s+', s[position:], re.IGNORECASE) is not None:\n join, position = read_join(s, position)\n else:\n break\n\n joins.extend(join)\n\n position = skip_white_spaces(s, position)\n return joins, position\n\n\ndef read_left_outer_join(s: str, position: int) -> tuple[list, int]:\n _, position = read_keyword('left', s, position)\n _, position = read_keyword('outer', s, position)\n _, position = read_keyword('join', s, position)\n table, position = read_until_white_space(s, position)\n _, position = read_keyword('on', s, position)\n conditions, position = read_conditions(s, position)\n return [('left outer join', table, conditions)], position\n\n\ndef read_inner_join(s: str, position: int) -> tuple[list, int]:\n _, position = read_keyword('inner', s, position)\n _, position = read_keyword('join', s, position)\n value, position = read_until_white_space(s, position)\n _, position = read_keyword('on', s, position)\n conditions, position = read_conditions(s, position)\n return [('inner join', value, conditions)], position\n\n\ndef read_join(s: str, position: int) -> tuple[list, int]:\n _, position = read_keyword('join', s, position)\n value, position = read_until_white_space(s, position)\n _, position = read_keyword('on', s, position)\n conditions, position = read_conditions(s, position)\n return [('join', value, conditions)], position\n\n\ndef read_where(s: str, position: int) -> tuple[tuple | None, int]:\n if not s[position:].lower().startswith('where'):\n return None, position\n\n _, position = read_keyword('where', s, position)\n conditions, position = read_conditions(s, position)\n\n return conditions, position\n\n\ndef read_conditions(s: str, position: int) -> tuple[tuple, int]:\n operands = []\n operators = []\n while position < len(s):\n if s[position] == '(':\n operators.append('(')\n position = skip_white_spaces(s, position + 1)\n\n left, position = read_until_white_space(s, position)\n operator, position = read_until_white_space(s, position)\n right, position = read_until_white_space_or_bracket(s, position)\n\n condition = (left, operator, right)\n operands.append(condition)\n\n if position < len(s) and s[position] == ')':\n position = skip_white_spaces(s, position + 1)\n while len(operators) > 0 and operators[-1] != '(':\n boolean_operator = operators.pop()\n right_condition = operands.pop()\n left_condition = operands.pop()\n operands.append((boolean_operator, left_condition, right_condition))\n operators.pop()\n\n if re.match(r'^(and|or)', s[position:], re.IGNORECASE) is None:\n break\n\n keyword, position = read_keywords(['and', 'or'], s, position)\n boolean_operator = keyword.value\n\n while len(operators) > 0 and operators[-1] == 'and' and boolean_operator == 'or':\n last_boolean_operator = operators.pop()\n\n if last_boolean_operator == '(':\n break\n\n right_condition = operands.pop()\n left_condition = operands.pop()\n operands.append((last_boolean_operator, left_condition, right_condition))\n\n operators.append(boolean_operator)\n\n while len(operators) > 0:\n boolean_operator = operators.pop()\n right_condition = operands.pop()\n left_condition = operands.pop()\n operands.append((boolean_operator, left_condition, right_condition))\n\n conditions = operands.pop()\n\n position = skip_white_spaces(s, position)\n return conditions, position\n","repo_name":"inikolaev/toy-database-engine","sub_path":"sql_parser.py","file_name":"sql_parser.py","file_ext":"py","file_size_in_byte":8715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"35607249368","text":"from saida_gym.starcraft.vultureVsZealot import VultureVsZealot\r\n## gym 환경 import VultureVsZealot\r\n\r\nfrom collections import deque\r\nimport numpy as np\r\nimport random\r\nimport os\r\nimport math\r\nimport pickle\r\nimport time\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nfrom torch.distributions import Categorical ## 분포 관련\r\nfrom tensorboardX import SummaryWriter\r\n\r\nclass Actor(nn.Module):\r\n def __init__(self, state_size, action_size):\r\n super(Actor, self).__init__()\r\n self.fc1 = nn.Linear(state_size,128) ## input state\r\n self.fc2 = nn.Linear(128,512)\r\n self.fc3 = nn.Linear(512,128)\r\n self.fc4 = nn.Linear(128,action_size) ## output each action\r\n\r\n def forward(self, x, soft_dim):\r\n x = torch.tanh(self.fc1(x))\r\n x = torch.tanh(self.fc2(x))\r\n x = torch.tanh(self.fc3(x))\r\n \r\n prob_each_actions = F.softmax(self.fc4(x),dim=soft_dim) ## NN에서 각 action에 대한 확률을 추정한다.\r\n\r\n return prob_each_actions\r\n\r\ndef scale_velocity(v):\r\n return v / 6.4\r\n\r\ndef scale_coordinate(pos):\r\n if pos > 0:\r\n return 1 if pos > 320 else int(pos / 16) / 20\r\n else:\r\n return -1 if pos < -320 else int(pos / 16) / 20\r\n\r\ndef scale_angle(angle):\r\n return (angle - math.pi) / math.pi\r\n\r\ndef scale_cooldown(cooldown):\r\n return (cooldown + 1) / 15\r\n\r\ndef scale_vul_hp(hp):\r\n return hp / 80\r\n\r\ndef scale_zeal_hp(hp):\r\n return hp / 160\r\n\r\ndef scale_bool(boolean):\r\n return 1 if boolean else 0\r\n\r\ndef rearrange_State(observation, state_size, env):\r\n state_arr = deque(maxlen=state_size)\r\n\r\n my_x = 0\r\n my_y = 0\r\n if observation.my_unit:\r\n for idx, me in enumerate(observation.my_unit): ## 9\r\n my_x = me.pos_x\r\n my_y = me.pos_y\r\n state_arr.append(math.atan2(me.velocity_y, me.velocity_x) / math.pi)\r\n state_arr.append(scale_velocity(math.sqrt((me.velocity_x) ** 2 + (me.velocity_y) ** 2)))\r\n state_arr.append(scale_cooldown(me.cooldown))\r\n state_arr.append(scale_vul_hp(me.hp))\r\n state_arr.append(scale_angle(me.angle))\r\n state_arr.append(scale_bool(me.accelerating))\r\n state_arr.append(scale_bool(me.braking))\r\n state_arr.append(scale_bool(me.attacking))\r\n state_arr.append(scale_bool(me.is_attack_frame))\r\n for i, terrain in enumerate(me.pos_info): ##12\r\n state_arr.append(terrain.nearest_obstacle_dist / 320)\r\n else:\r\n for _ in range(state_size - 11):\r\n state_arr.append(0)\r\n\r\n if observation.en_unit:\r\n for idx, enemy in enumerate(observation.en_unit): ## 11\r\n state_arr.append(math.atan2(enemy.pos_y - my_y, enemy.pos_x - my_x) / math.pi)\r\n state_arr.append(scale_coordinate(math.sqrt((enemy.pos_x - my_x) ** 2 + (enemy.pos_y - my_y) ** 2)))\r\n state_arr.append(math.atan2(enemy.velocity_y, enemy.velocity_x) / math.pi)\r\n state_arr.append(scale_velocity(math.sqrt((enemy.velocity_x) ** 2 + (enemy.velocity_y) ** 2)))\r\n state_arr.append(scale_cooldown(enemy.cooldown))\r\n state_arr.append(scale_zeal_hp(enemy.hp + enemy.shield))\r\n state_arr.append(scale_angle(enemy.angle))\r\n state_arr.append(scale_bool(enemy.accelerating))\r\n state_arr.append(scale_bool(enemy.braking))\r\n state_arr.append(scale_bool(enemy.attacking))\r\n state_arr.append(scale_bool(enemy.is_attack_frame))\r\n else:\r\n for _ in range(11):\r\n state_arr.append(0)\r\n \r\n\r\n return state_arr\r\n\r\ndef reward_reshape(state, next_state, reward, done):\r\n\r\n KILL_REWARD = 10\r\n DEAD_REWARD = -10\r\n DAMAGED_REWARD = -4\r\n HIT_REWARD = 2\r\n\r\n if done:\r\n if reward > 0: ## env에서 반환된 reward가 1 이면, 질럿을 잡음.\r\n reward = KILL_REWARD\r\n if next_state[3] == 1.0 and next_state[-6] == 0: ## perfect clear했다면 추가 bonus reward\r\n reward+=5\r\n \r\n return reward\r\n # 잡은 경우\r\n else: ## 게임이 종료되고 -1 값을 받게 된다면, \r\n reward = DEAD_REWARD\r\n return reward\r\n else: ## 게임이 종료되지 않았다면,\r\n my_pre_hp = state[3]\r\n my_cur_hp = next_state[3]\r\n \r\n en_pre_hp = state[-6]\r\n en_cur_hp = next_state[-6]\r\n \r\n if my_pre_hp - my_cur_hp > 0: ## 벌쳐가 맞아 버렸네 ㅠㅠ\r\n reward += DAMAGED_REWARD\r\n if en_pre_hp - en_cur_hp > 0: ## 질럿을 때려 버렸네 ㅠㅠ\r\n reward += HIT_REWARD\r\n \r\n ## 벌쳐가 맞고, 질럿도 때리는 2가지 동시 case가 있을 거 같아. reward를 +=을 했고 각각 if문으로 처리했습니다.\r\n \r\n return reward\r\n\r\ndef main():\r\n \r\n load = True\r\n episode = 0 ## 21710:91.1% 21610: 91% 21600: 90% 여러 모델 테스트 결과 중 3 모델이 90% 이상을 보였음.\r\n ## 환경의 초기, 진행되는 episode의 조건에 따라 86% ~ 91% 까지 perfect score를 보임. \r\n \r\n env = VultureVsZealot(version=0, frames_per_step=12, action_type=0, move_angle=20, move_dist=3, verbose=0, no_gui=False\r\n ,auto_kill=False)\r\n print_interval = 10\r\n \r\n learning_rate=0.00003\r\n torch.manual_seed(500)\r\n \r\n state_size = 38\r\n \r\n action_size= 19\r\n \r\n\r\n actor = Actor(state_size, action_size)\r\n \r\n \r\n if load: ## 경로를 model 파일 경로+ model 파일 이름으로 변경해주세요. 저는 원래 episode 번호로 구분했습니다.\r\n actor.load_state_dict(torch.load(os.path.join('C:/SAIDA_RL/python/saida_agent_example/vultureZealot/save_ppo3_clear/','clear_ppo_actor_'+str(episode)+'.pkl')))\r\n \r\n actor_optimizer = optim.Adam(actor.parameters(), lr=learning_rate)\r\n \r\n episode = 0\r\n clear_cnt=0\r\n for n_iter in range(1000):\r\n step = 0\r\n \r\n state = env.reset()\r\n \r\n state = rearrange_State(state, state_size, env)\r\n episode+=1\r\n temp_score = 0.0\r\n while True:\r\n \r\n prob_each_actions = actor(torch.Tensor(state), soft_dim=0)\r\n \r\n distribution = Categorical(prob_each_actions)\r\n \r\n action = distribution.sample().item() \r\n \r\n next_state, reward, done, info = env.step([action])\r\n next_state = rearrange_State(next_state, state_size, env)\r\n \r\n reward = reward_reshape(state, next_state, reward, done) \r\n \r\n mask = 0 if done else 1 \r\n \r\n state = next_state\r\n \r\n temp_score += reward \r\n \r\n if next_state[3] == 1.0 and next_state[-6] == 0:\r\n clear_cnt+=1\r\n print(\"clear: \",next_state[3],next_state[-6],\"clear_score: \",temp_score, \"clear_cnt: \", clear_cnt,\" / \", n_iter+1)\r\n \r\n if done: \r\n print(\"step: \", step, \"per_episode_score: \",temp_score)\r\n \r\n break\r\n\r\n print(\"clear count: \",clear_cnt,\" percent: \",(clear_cnt/n_iter)) \r\n env.close()\r\n \r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"betastarcraft/Problem_1","sub_path":"01_Vulture_vs_Zealot/Perfect_Performance_test/vz_PPO_test.py","file_name":"vz_PPO_test.py","file_ext":"py","file_size_in_byte":7387,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"26276723435","text":"total = 0\n\ndef get_mul(num):\n sum = 1\n while num > 1:\n sum *= num\n num -= 1\n return sum\ndef get_num(s, num, d, sum):\n if d <= 0:\n return\n if sum <= 0:\n return\n if num * num > sum:\n get_num(s, num - 1, d, sum)\n if num * num == sum:\n global total\n total += 2**(27-d+1) * get_mul(len(set(s+str(num))))\n # print(24-d+1, len(set(s+str(num))) - 1)\n print(s + str(num) + '0' * (d - 1) + ' ' + str(2**(24-d+1) * get_mul(len(set(s+str(num))) - 1)))\n while num > 0:\n if num * num < sum:\n # print(s + str(num), num - 1, d - 1, sum - num * num)\n if (num)**2 <= sum - num * num:\n get_num(s + str(num), num, d - 1, sum - num * num)\n else:\n get_num(s + str(num), num - 1, d - 1, sum - num * num)\n num -= 1\nget_num(' ', 8, 24, 79)\nprint(total)","repo_name":"xiaoxiongfeng/lattice","sub_path":"zn.py","file_name":"zn.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"14774213674","text":"from collections import OrderedDict\n\nd = OrderedDict()\nd['foo'] = 1\nd['bar'] = 2\nd['span'] = 4\nprint(d)\nfor key in d:\n print(key, d[key])\n\nimport json\n\nj = json.dumps(d)\nprint(j)\n\nj = json.loads(j)\nprint(j)\n","repo_name":"ayumi64/Python_Cookbook_Learn","sub_path":"Section01 数据结构和算法/s1.7_字典排序.py","file_name":"s1.7_字典排序.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"17497666515","text":"from __future__ import nested_scopes\n\nfrom twisted.web import resource as resourcelib\nfrom twisted.web import client, microdom, domhelpers, server\n\nurlTemplate = 'http://www.livejournal.com/users/%s/rss'\n\nclass LJSyndicatingResource(resourcelib.Resource):\n\n def render_GET(self, request):\n url = urlTemplate % request.args['user'][0]\n client.getPage(url).addCallback(\n microdom.parseString).addCallback(\n lambda t: domhelpers.findNodesNamed(t, 'item')).addCallback(\n lambda itms: zip([domhelpers.findNodesNamed(x, 'title')[0]\n for x in itms],\n [domhelpers.findNodesNamed(x, 'link')[0]\n for x in itms]\n )).addCallback(\n lambda itms: '%s
' %\n '\\n'.join(\n ['%s ' % (\n domhelpers.getNodeText(link), domhelpers.getNodeText(title))\n for (title, link) in itms])\n ).addCallback(lambda s: (request.write(s),request.finish())).addErrback(\n lambda e: (request.write('Error: %s' % e),request.finish()))\n return server.NOT_DONE_YET\n\nresource = LJSyndicatingResource()\n","repo_name":"ActiveState/OpenKomodoIDE","sub_path":"contrib/twisted/TwistedWeb-0.6.0/doc/examples/lj.rpy.py","file_name":"lj.rpy.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":460,"dataset":"github-code","pt":"72"}
+{"seq_id":"40045448266","text":"from pycat.window import Window\nfrom pycat.sprite import Sprite, RotationMode\nfrom pycat.keyboard import KeyCode\nfrom pycat.scheduler import Scheduler\nfrom pycat.collision import is_aabb_collision\nfrom pycat.label import Label\nimport random\n\nw = Window(background_image=\"forest_04.png\", draw_sprite_rects=True)\n\ncL_sprite = [] \n\nclass Card (Sprite):\n\n def on_create(self):\n self.is_visible = False\n \n\n def on_left_click(self):\n if self in cL_sprite:\n pass\n else:\n if len(cL_sprite) < 2 :\n self.is_visible = True\n cL_sprite.append(self)\n\nscore = 0\n\nclass Button(Sprite):\n\n def on_create(self):\n self.image = \"button.png\"\n self.scale = float(0.66)\n self.x = 1100\n self.y = 300\n def on_left_click(self):\n if len(cL_sprite) == 2:\n sprite1:Sprite = cL_sprite[0]\n sprite2:Sprite = cL_sprite[1]\n if sprite1.image == sprite2.image: \n sprite1.delete()\n sprite2.delete()\n button.score += 1\n score_label.text = 'BEST MATCH: '+str(button.score)\n else:\n sprite1.is_visible = False \n sprite2.is_visible = False\n cL_sprite.clear()\n\nbutton = w.create_sprite(Button)\nbutton.score = 0\n\nscore_label = Label(\"BEST MATCH: 0\",x=1000,y=400)\nw.add_label(score_label)\n\np1 = w.create_sprite(Card, x = 200,y = 100, image = \"avatar_01.png\", scale = float(1.6))\np2 = w.create_sprite(Card, x = 200,y = 220, image = \"avatar_02.png\", scale = float(1.6))\np3 = w.create_sprite(Card, x = 200,y = 340, image = \"avatar_03.png\", scale = float(1.6))\np4 = w.create_sprite(Card, x = 200,y = 460, image = \"avatar_04.png\", scale = float(1.6))\np5 = w.create_sprite(Card, x = 400,y = 100, image = \"avatar_01.png\", scale = float(1.6))\np6 = w.create_sprite(Card, x = 400,y = 220, image = \"avatar_02.png\", scale = float(1.6))\np7 = w.create_sprite(Card, x = 400,y = 340, image = \"avatar_03.png\", scale = float(1.6))\np8 = w.create_sprite(Card, x = 400,y = 460, image = \"avatar_04.png\", scale = float(1.6))\np08 = w.create_sprite(Card, x = 600,y = 100, image = \"avatar_01.png\", scale = float(1.6))\np07 = w.create_sprite(Card, x = 600,y = 220, image = \"avatar_02.png\", scale = float(1.6))\np06 = w.create_sprite(Card, x = 600,y = 340, image = \"avatar_03.png\", scale = float(1.6))\np05 = w.create_sprite(Card, x = 600,y = 460, image = \"avatar_04.png\", scale = float(1.6))\np04 = w.create_sprite(Card, x = 800,y = 100, image = \"avatar_01.png\", scale = float(1.6))\np03 = w.create_sprite(Card, x = 800,y = 220, image = \"avatar_02.png\", scale = float(1.6))\np02 = w.create_sprite(Card, x = 800,y = 340, image = \"avatar_03.png\", scale = float(1.6))\np01 = w.create_sprite(Card, x = 800,y = 460, image = \"avatar_04.png\", scale = float(1.6))\n\nw.run()","repo_name":"duck003/pypeanuts.github.io","sub_path":"chapter/chapter04/4-1ft.py","file_name":"4-1ft.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"23020548374","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport copy\nimport random\nimport ReverseCommon\nimport ReverseBoard\nimport Game\nimport sys, os\nimport pickle\nimport numpy as np\nfrom functions import sigmoid, softmax, dtanh, init_KeysCount, myMax, myMin, myRandom, dMean_squared_error\n\n\"\"\" プレーヤの基盤クラス(AIも含む) \"\"\"\nclass Player:\n def __init__(self, color):\n \"\"\" コンストラクタ \"\"\"\n self._color = color\n self.network = {}\n self._history = []\n\n def next_move(self, board):\n \"\"\" 次の手を返す \"\"\"\n pass\n\n def save_network(self, file_name):\n pass\n\n def update_network(self, ritu=1, weight_init_std = 0.01):\n pass\n\n @property\n def color(self):\n \"\"\" 自分の色を返す \"\"\"\n return self._color\n\n\"\"\" ランダムで石を置くAI \"\"\"\nclass RandomAi(Player):\n def next_move(self, board):\n # 石を置ける全候補地\n all_candidates = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n # ランダムで次の手を選ぶ\n index = random.randint(0, len(all_candidates) - 1)\n return all_candidates[index]\n\n\"\"\"今回の１手で最も石が取れる場所に置くAI\"\"\"\nclass NextStoneMaxAi(Player):\n def next_move(self, board):\n # 石を置ける全候補地\n all_candidatess = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n # 今回の一手で最も石が取れる場所一覧\n filtered_candidates = []\n max_score = -1\n for candidates in all_candidatess:\n next_board = ReverseCommon.put_stone(board.stone_status, self._color, candidates[0], candidates[1])\n score = ReverseCommon.get_score(board.stone_status, self._color)\n if score >= max_score:\n filtered_candidates.append(candidates)\n max_score = score\n\n return filtered_candidates[random.randint(0, len(filtered_candidates) - 1)]\n\n\"\"\"人間です。\"\"\"\nclass Human(Player):\n def next_move(self, board):\n all_candidates = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n while True:\n try:\n # x,yの形式で入力する\n next_move_str = raw_input(\"next_move > \")\n next_move_str_split = next_move_str.split(\",\")\n if len(next_move_str_split) == 2:\n next_move = [int(next_move_str_split[0]), int(next_move_str_split[1])]\n if next_move in all_candidates:\n return next_move\n else:\n print (\"can't put there.\")\n except ValueError:\n print (\"format error.\")\n\n\"\"\" 最低限の手の良し悪しを知っているAI \"\"\"\nclass RandomAiKnowGoodMove(Player):\n def next_move(self, board):\n known_good_moves = [[0, 0], [0, 7], [7, 0], [7, 7]]\n known_bad_moves = [[0, 1], [1, 0], [1, 1], [0, 6], [1, 6], [1, 7], [6, 0], [6, 1], [7, 1], [7, 6], [6, 7], [6, 6]]\n all_candidates = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n # 4隅が取れるなら取る\n good_moves = list(filter(lambda good_move: good_move in known_good_moves, all_candidates))\n\n if len(good_moves) > 0:\n return good_moves[random.randint(0, len(good_moves) - 1)]\n\n # 4隅に隣接する場所は避ける\n #not_bad_moves = filter(lambda not_bad_move: not_bad_move not in (known_good_moves + known_bad_moves), all_candidates)\n not_bad_moves = list(filter(lambda not_bad_move: not_bad_move not in (known_good_moves + known_bad_moves), all_candidates))\n if len(not_bad_moves) > 0:\n return not_bad_moves[random.randint(0, len(not_bad_moves) - 1)]\n\n return all_candidates[random.randint(0, len(all_candidates) - 1)]\n\n\n\"\"\" DQNで石を置くAI (Deep Q-Network)\"\"\"\nclass DeepQNetWork(Player):\n def __init__(self, color, filename):\n super(DeepQNetWork, self).__init__(color)\n self.KeysCount = {}\n self.load_network(filename)\n\n def load_network(self, file_name=\"DQN_BLACK.pkl\"):\n if os.path.isfile(file_name) :\n with open(file_name, 'rb') as f:\n obj = pickle.load(f)\n self.KeysCount = obj[0]\n self.network = obj[1]\n else:\n self.KeysCount = init_KeysCount(8, 0.5, 5)\n\n count = self.network['SC']\n print(\"累積学習=%s\" %(count))\n\n #print(sorted(self.KeysCount.items(), key=lambda x: x[0]))\n for obj in sorted(self.KeysCount.items(), key=lambda x: int(x[0])):\n print(str(obj[0]) + \"=\" + str(obj))\n\n def save_network(self, file_name=\"DQN_BLACK.pkl\"):\n with open(file_name, 'wb') as f:\n pickle.dump([self.KeysCount, self.network], f)\n\n def update_network(self, ritu=1, weight_init_std = 0.001):\n #print('*'*10 + '(update_network)' + '*'*10)\n #print(str(select) + \"::\" + str(ReverseCommon.IndexToPoint(select)))\n #self._history.append([Key,select])\n for i in range(0,len(self._history)):\n key = self._history[i][0]\n act = self._history[i][1]\n #print(str(i+1) + \" key=\" + key + \" act=\" + str(act) + \" qWait=\" + str(self.network[key][act]))\n if(key in self.network):\n acc = self.network[key][act] + ritu * weight_init_std\n if acc > 10:\n acc = 10\n elif acc < -10:\n acc = -10\n self.network[key][act] = acc\n #self.network[key][act] += ((i % 22) + 1) * ritu * weight_init_std\n #print(self.network[key])\n self._history = []\n\n def next_move(self, board):\n #Key = ReverseCommon.boardToKey(board.stone_status)\n Key = ReverseCommon.boardToKey3(board.stone_status)\n #print(Key)\n actions = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n #print(actions)\n #print(Key in self.network)\n if(not (Key in self.network)):\n self.wait_set(Key, actions, board)\n\n #print(self.network)\n #print(self.network[Key])\n #print(len(self._history))\n if (Key in self.network):\n if self._color == ReverseCommon.BLACK:\n select = min(self.network[Key],key=(lambda x:self.network[Key][x]))\n else:\n select = max(self.network[Key],key=(lambda x:self.network[Key][x]))\n else:\n # 石を置ける全候補地からランダムで次の手を選ぶ\n #all_candidates = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n #index = random.randint(0, len(all_candidates) - 1)\n #select = ReverseCommon.PointToIndex(all_candidates[index])\n\n # 石を置ける全候補地\n all_candidatess = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n # 今回の一手で最も石が取れる場所一覧\n filtered_candidates = []\n max_score = -1\n for candidates in all_candidatess:\n score = ReverseCommon.get_score(board.stone_status, self._color)\n if score >= max_score:\n filtered_candidates.append(candidates)\n max_score = score\n\n index = random.randint(0, len(filtered_candidates) - 1)\n select = ReverseCommon.PointToIndex(filtered_candidates[index])\n #filtered_candidates[random.randint(0, len(filtered_candidates) - 1)]\n\n #print(str(select) + \"::\" + str(ReverseCommon.IndexToPoint(select)))\n self._history.append([Key,select])\n return ReverseCommon.IndexToPoint(select)\n\n \"\"\"\n Dropoutを実装する\n この学習モデルは(白)後手番学習\n Keyは手数によって(2手目6石 6**2、4手目8石 8**2、・・・・、60手目64石 64**2)\n 通の組み合わせがある\n dropout_rait=0.5仮\n 各手番の組み合わせ数 * dropout_rait で調整を試みる\n \"\"\"\n def wait_set(self, Key, actions, board):\n\n aa = ReverseCommon.get_remain(board.stone_status)\n bb = self.KeysCount[str(64-aa)]\n #print(\"%2d::%s::%2d::%2d::%2d::%2d\" % (len(self._history),Key,aa,64-aa,bb[0],bb[1]))\n\n #if (len(self._history) < 2): return\n if (bb[1] >= bb[0]): return\n #print(Key)\n self.KeysCount[str(64-aa)][1] += 1\n wait = {}\n Waits = np.random.random(len(actions))\n i = 0\n for wact in actions:\n wPoint = ReverseCommon.PointToIndex(wact)\n wait[wPoint] = Waits[i]\n i += 1\n #print(str(wact) + \":::\" + str(wPoint) + \":::\" + str(wait[wPoint]))\n\n self.network[Key] = wait\n #print(self.network[Key] )\n\nclass QNetWork(Player):\n def __init__(self, color, filename):\n super(QNetWork, self).__init__(color)\n self.load_network(filename)\n\n def load_network(self,file_name):\n if os.path.isfile(file_name) :\n with open(file_name, 'rb') as f:\n self.network = pickle.load(f)\n\n def save_network(self, file_name):\n with open(file_name, 'wb') as f:\n pickle.dump(self.network, f)\n\n def update_network(self, ritu=1, weight_init_std = 0.001):\n ALPHA = 0.1\n GAMMA = 0.99\n for i in range(0,len(self._history)-1):\n Key, select = self._history[i][0], self._history[i][1]\n #self.network[Key][select] += ALPHA * (reward + GAMMA*self.network[next_s].max() - self.network[Key][select])\n next_key =self._history[i+1][0]\n if next_key in self.network:\n a = min(self.network[next_key],key=(lambda x:self.network[next_key][x]))\n aaa = self.network[next_key][a]\n #print(self.network[next_key][a])\n else:\n aaa = 0.5\n #print(\"Key=%s::select=%s\" %(Key,select))\n bbb = 0.0\n if Key in self.network:\n bbb = self.network[Key][select]\n self.network[Key][select] += ALPHA * (ritu + GAMMA*aaa - bbb)\n #print(\"bef=%f: aft=%f\" %(mmm,self.network[Key][select]))\n #print(\"bbb=%s::aaa=%s\" %(bbb,aaa))\n self._history = []\n\n def wait_set(self, Key, actions):\n #print(Key)\n if len(actions) < 2:\n return\n wait = {}\n Waits = np.random.random(len(actions))\n i = 0\n for wact in actions:\n wPoint = ReverseCommon.PointToIndex(wact)\n wait[wPoint] = Waits[i]\n\n self.network[Key] = wait\n\n def next_move(self, board):\n Key = ReverseCommon.boardToKey3(board.stone_status)\n #print(Key)\n # 石を置ける全候補地\n actions = ReverseCommon.get_puttable_points(board.stone_status, self._color)\n\n if(not (Key in self.network)):\n self.wait_set(Key, actions)\n\n if (Key in self.network):\n if self._color == ReverseCommon.BLACK:\n select = min(self.network[Key],key=(lambda x:self.network[Key][x]))\n else:\n select = max(self.network[Key],key=(lambda x:self.network[Key][x]))\n #print(\"select=%s\" % (select))\n result = ReverseCommon.IndexToPoint(select)\n else:\n # ランダムで次の手を選ぶ\n index = random.randint(0, len(actions) - 1)\n result = actions[index]\n select = ReverseCommon.PointToIndex(result)\n\n self._history.append([Key,select])\n return result\n","repo_name":"mit4351/Reversi","sub_path":"wx_dqn_bkp/Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":11649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"31213715495","text":"import datetime\nimport re\n\nfrom app import Config, app, db, parsers\nfrom app.models import mixins\n\n\nclass HeatResult(db.Model):\n heat_id = db.Column(db.Integer, db.ForeignKey(\"heat.id\"), primary_key=True)\n athlete_id = db.Column(\n db.Integer,\n db.ForeignKey(\"athlete.id\"),\n # primary_key=True\n )\n\n index = db.Column(db.Integer, primary_key=True)\n score = db.Column(db.Numeric(4, 2), default=None)\n\n heat = db.relationship(\"Heat\", back_populates=\"athletes\")\n athlete = db.relationship(\"Athlete\", back_populates=\"heats\")\n\n\ndef _is_placeholder_athlete_name(athlete_name):\n # probably overkill on precision but whatever\n regexs = [\n \"Round of [0-9]{1,2}, Heat [0-9]{1,2} winner\",\n \"finals, Heat [0-9]{1,2} winner$\",\n \"Event seed #[0-9]{1,2}\",\n \"Round seed #[0-9]{1,2}\",\n ]\n if any([re.search(r, athlete_name) is not None for r in regexs]):\n return True\n return False\n\n\nclass Athlete(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(128))\n heats = db.relationship(\"HeatResult\", back_populates=\"athlete\")\n\n\nclass Heat(mixins.Updatable, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n completed = db.Column(db.Boolean, default=False)\n status = db.Column(db.Integer, default=0)\n round_id = db.Column(db.Integer, db.ForeignKey(\"round.id\"))\n athletes = db.relationship(\"HeatResult\", back_populates=\"heat\")\n\n @classmethod\n def create(cls, **kwargs):\n obj = cls(completed=False, **kwargs)\n obj.update()\n return obj\n\n def _do_update(self):\n try:\n status, scores = parsers.get_heat_data(self.round.url, self.id)\n except Exception as e:\n app.logger.error(e)\n return\n\n app.logger.debug(\n f\"Read scores {scores} for heat {self.id} with status {status}\"\n )\n self.update_with_status_and_scores(status, scores)\n\n def update_with_status_and_scores(self, status, scores):\n self.status = status\n\n for index, (athlete_name, score) in enumerate(scores):\n # if we're being reported a placeholder athlete name,\n # then make sure that the heat hasn't started yet. If it has,\n # we need to investigate\n if _is_placeholder_athlete_name(athlete_name) and status != 0:\n app.logger.warn(\n f\"Still using placeholder name {athlete_name} \"\n f\"in ongoing heat {self.id}\"\n )\n heat_result = HeatResult.query.filter_by(\n heat=self, index=index\n )\n\n try:\n athlete = heat_result.first().athlete\n except AttributeError:\n # case 1: there's no existing entry, so just move on and\n # pretend this never happend\n continue\n\n # case 2: that heat result has a placeholder\n # name attached to it: delete it and move on\n if _is_placeholder_athlete_name(athlete.name):\n app.logger.warn(\n f\"Removing placeholder heat result from heat {self.id}\"\n )\n heat_result.delete()\n continue\n # case 3: that heat used to have a real athete associated\n # with it. I guess keep it for now?\n else:\n athlete_name = athlete.name\n\n # create athlete if they don't exist. Note that his means\n # we will create some placeholder athletes, but\n # they won't have any heat results after things get updated\n athlete = Athlete.query.filter_by(name=athlete_name).first()\n if athlete is None:\n athlete = Athlete(name=athlete_name)\n app.logger.debug(f\"Adding athlete {athlete_name} to database\")\n db.session.add(athlete)\n db.session.commit()\n\n # index heat result by the heat and their order in the heat\n # this way when rounds update from some TBD placeholder to\n # a real athlete name we can update the athlete name accordingly\n # the cost of this is having some placeholder athlete names in\n # our athlete db but that seems like a small price to pay\n heat_result = HeatResult.query.filter_by(\n heat=self, index=index\n ).first()\n\n # first case: this heat result hasn't been created yet\n # instantiate it and add it to our session\n if heat_result is None:\n heat_result = HeatResult(\n heat=self, index=index, athlete=athlete\n )\n self.athletes.append(heat_result)\n else:\n # second case: the heat has been created but the athlete\n # being used to instantiate is being updated\n if heat_result.athlete.name != athlete_name:\n # if this is not because the instantiated athlete was\n # a placeholder, we won't do this automatically, but\n # will leave it to you to do manually if you think this\n # is correct\n if not _is_placeholder_athlete_name(\n heat_result.athlete.name\n ):\n app.logger.warn(\n \"Athlete {} is being replaced by \"\n \"athlete {} in heat {}.\".format(\n heat_result.athlete.name, athlete_name, self.id\n )\n )\n\n # updat the heat result athlete\n heat_result.athlete = athlete\n\n # update the heat result score\n heat_result.score = score\n self.completed = status == 2\n\n\nclass Round(mixins.Updatable, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n completed = db.Column(db.Boolean, default=False)\n number = db.Column(db.Integer)\n event_id = db.Column(db.Integer, db.ForeignKey(\"event.id\"))\n heats = db.relationship(\"Heat\", backref=\"round\", lazy=\"dynamic\")\n\n @property\n def url(self):\n return parsers.get_round_url(self)\n\n @property\n def sorted_heats(self):\n return sorted(self.heats, key=lambda heat: heat.id)\n\n @classmethod\n def create(cls, **kwargs):\n obj = cls(**kwargs)\n heat_ids = parsers.get_heat_ids(obj.url)\n for id in heat_ids:\n app.logger.debug(f\"Creating heat {id}\")\n Heat.create(id=id, round=obj)\n obj.completed = all([heat.completed for heat in obj.heats])\n return obj\n\n def _do_update(self):\n no_more_updates = False\n heats = sorted(self.heats, key=lambda h: h.id)\n for heat in heats:\n if no_more_updates:\n # only update upcoming heats if they're currently\n # populated by placeholder athlete names\n athletes = [r.athlete.name for r in heat.athletes]\n if any(list(map(_is_placeholder_athlete_name, athletes))):\n app.logger.info(f\"Updating athletes for heat {heat.id}\")\n heat.update()\n continue\n else:\n app.logger.info(f\"Updating heat {heat.id}\")\n completed = heat.update()\n\n # if this heat is upcoming, then all proceeding\n # heats in this round are by definition upcoming\n # too, so we can stop trying to update heats\n if heat.status == 0:\n no_more_updates = True\n\n # if the last heat returned completd = True,\n # then we're done\n self.completed = completed\n\n\nclass Event(mixins.Updatable, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n stat_id = db.Column(db.Integer)\n name = db.Column(db.String(128))\n completed = db.Column(db.Boolean, default=False)\n year = db.Column(db.Integer, db.ForeignKey(\"season.year\"))\n rounds = db.relationship(\"Round\", backref=\"event\", lazy=\"dynamic\")\n\n @property\n def url(self):\n return parsers.get_event_url(self)\n\n @property\n def sorted_rounds(self):\n return sorted(self.rounds, key=lambda r: r.number)\n\n @classmethod\n def create(cls, id: int, stat_id: int, name: str, year: int, **kwargs):\n obj = cls(id=id, stat_id=stat_id, name=name, year=year)\n\n # first verify that status is ok and that we're close enough to the\n # event to warrant building it\n status, start_date = parsers.get_event_data_from_event_homepage(obj)\n if status in (\"canceled\", \"postponed\"):\n raise parsers.EventNotReady(\n \"Status for event {} is currently {}\".format(obj.name, status)\n )\n elif (\n datetime.datetime.now()\n + datetime.timedelta(days=Config.LEAD_DAYS_FOR_EVENT_CREATION)\n ) < start_date:\n raise parsers.EventNotReady(\n \"Start date for event {} is {} days away, \"\n \"stopping creation\".format(\n obj.name, (start_date - datetime.datetime.now()).days\n )\n )\n\n # double check that event is ready to be scraped by\n # making sure that all the rounds have valid links\n try:\n round_ids = parsers.get_round_ids(obj)\n except parsers.EventNotReady:\n raise parsers.EventNotReady(\n \"No valid round links for event {}\".format(obj.name)\n )\n\n # initialize all the internal rounds and heats\n kwargs = {\"event\": obj, \"completed\": False}\n for n, round_id in enumerate(round_ids):\n app.logger.debug(f\"Creating round {round_id}\")\n if n < 2:\n round_ = Round.create(id=round_id, number=n, **kwargs)\n continue\n else:\n round_ = Round(id=round_id, number=n, **kwargs)\n break\n\n rounds = parsers.parse_bracket(round_.url)\n for i in range(1, len(rounds) + 1):\n heats = rounds[round_.id]\n for id in sorted(heats.keys()):\n heat = Heat(id=id, completed=False, round=round_)\n status, scores = heats[id]\n heat.update_with_status_and_scores(status, scores)\n\n round_.completed = all([i.completed for i in round_.heats])\n\n if i < len(rounds):\n app.logger.debug(f\"Creating round {round_id + i}\")\n round_ = Round(id=round_id + i, number=2 + i, **kwargs)\n\n # if this is an event from the past, we can set it completed up front\n obj.completed = all([round_.completed for round_ in obj.rounds])\n return obj\n\n @property\n def results(self):\n csv_string = \"RoundNum,HeatNum,AthleteName,Score\"\n for i, round_ in enumerate(self.rounds):\n for j, heat in enumerate(round_.heats):\n for athlete in heat.athletes:\n score = athlete.score or 0.0\n csv_string += \"\\n{},{},{},{:0.2f}\".format(\n i, j, athlete.athlete.name, score\n )\n return csv_string\n\n def _do_update(self):\n sorted_rounds = sorted(self.rounds, key=lambda round: round.id)\n\n # do first two rounds normally\n for round_ in sorted_rounds[:2]:\n if not round_.update():\n break\n else:\n # if both the first two rounds are completed,\n # start iterating through the bracket rounds\n rounds = parsers.parse_bracket(sorted_rounds[2].url)\n all_rounds_complete = True\n\n for round_ in sorted_rounds[2:]:\n if round_.completed:\n continue\n\n all_heats_complete = True\n for heat in round_.sorted_heats:\n if heat.completed:\n continue\n\n status, scores = rounds[round_.id][heat.id]\n heat.update_with_status_and_scores(status, scores)\n\n if not heat.status:\n # if this heat is still upcoming, there's\n # no point in updating anything after this\n break\n all_heats_complete &= heat.completed\n else:\n # if we never broke, then all of the heats\n # have at least started, so mark whether the\n # round has completed and move on to the next\n # one to account for potentially overlapping\n # heats between rounds\n round_.completed = all_heats_complete\n all_rounds_complete &= all_heats_complete\n continue\n\n # otherwise if we broke, one of this round's\n # heats hasn't started which means there's no\n # need to look at later rounds, so\n break\n else:\n # if all the rounds have completed, mark\n # the whole event as done\n self.completed = all_rounds_complete\n\n\nclass Season(db.Model):\n year = db.Column(db.Integer, primary_key=True)\n events = db.relationship(\"Event\", backref=\"season\", lazy=\"dynamic\")\n\n @property\n def url(self):\n return parsers.get_season_url(self)\n\n @classmethod\n def create(cls, year, **kwargs):\n # allow for possibility that season starts in late\n # of the year before\n if year > (datetime.datetime.now().year + 1):\n raise ValueError(f\"Cannot create season for future year {year}\")\n\n # instantiate the season then add all the events we can to it\n obj = cls(year=year, **kwargs)\n db.session.add(obj)\n for name, id in parsers.get_event_ids(obj.url).items():\n # skipping freshwater pro by default since\n # its format is so different\n if name == \"freshwater-pro\":\n continue\n\n # don't need to create events that have already been created\n # not quite sure why or when this might occur but hey why not\n event = Event.query.filter_by(name=name, id=id, season=obj).first()\n if event is not None:\n continue\n\n try:\n stat_id = parsers.get_event_stat_id(id, year, name)\n except ValueError:\n app.logger.info(\n f\"Skipping creation of event {name} {year} \"\n \"because no stat ID was available\"\n )\n continue\n\n # ignore this event if it's not ready yet\n try:\n event = Event.create(\n name=name, id=id, stat_id=stat_id, year=year\n )\n except parsers.EventNotReady:\n continue\n else:\n obj.events.append(event)\n db.session.add(event)\n db.session.commit()\n return obj\n\n\ndef delete_season(year):\n season = Season.query.filter_by(year=year)\n events = Event.query.filter_by(season=season.first())\n for event in events:\n rounds = Round.query.filter_by(event=event)\n for round in rounds:\n heats = Heat.query.filter_by(round=round)\n for heat in heats:\n HeatResult.query.filter_by(heat=heat).delete()\n heats.delete()\n rounds.delete()\n events.delete()\n season.delete()\n","repo_name":"alecgunny/kook-tracker","sub_path":"kook-tracker/app/models/wsl.py","file_name":"wsl.py","file_ext":"py","file_size_in_byte":15695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"74988259","text":"from unittest.mock import AsyncMock\n\nimport pytest\n\nfrom crm.agency_cabinet.common.server.common.structs import TaskStatuses\nfrom crm.agency_cabinet.ord.common import structs\nfrom crm.agency_cabinet.ord.proto import reports_pb2, request_pb2, common_pb2\nfrom crm.agency_cabinet.ord.common.exceptions import UnsuitableAgencyException, NoSuchReportException\n\n\n@pytest.fixture(autouse=True)\ndef procedure(mocker):\n mock = AsyncMock()\n mock.return_value = structs.ReportExportResponse(report_export_id=1, status=TaskStatuses.in_progress)\n\n mocker.patch(\n \"crm.agency_cabinet.ord.server.src.procedures.ReportExport\",\n return_value=mock,\n )\n\n return mock\n\n\nasync def test_export_report(handler, procedure):\n expected = reports_pb2.ReportExportOutput(\n result=reports_pb2.ReportExport(\n report_export_id=1,\n status=1\n )\n )\n\n request_pb = request_pb2.RpcRequest(\n report_export=reports_pb2.ReportExportInput(\n agency_id=1,\n report_id=1\n )\n )\n\n data = await handler(request_pb.SerializeToString())\n res = reports_pb2.ReportExportOutput.FromString(data)\n\n assert res == expected\n\n\n@pytest.mark.parametrize(\n ('side_effect', 'expected_message'),\n [\n (\n UnsuitableAgencyException,\n reports_pb2.ReportExportOutput(unsuitable_agency=common_pb2.ErrorMessageResponse(message=''))\n ),\n (\n NoSuchReportException,\n reports_pb2.ReportExportOutput(no_such_report=common_pb2.ErrorMessageResponse(message=''))\n ),\n ]\n)\nasync def test_returns_error(handler, mocker, side_effect, expected_message):\n mock = AsyncMock()\n mock.return_value = None\n mock.side_effect = [side_effect]\n\n with mocker.patch(\n \"crm.agency_cabinet.ord.server.src.procedures.ReportExport\",\n return_value=mock,\n ):\n input_pb = request_pb2.RpcRequest(\n report_export=reports_pb2.ReportExportInput(\n agency_id=1,\n report_id=1\n )\n )\n\n result = await handler(input_pb.SerializeToString())\n\n assert reports_pb2.ReportExportOutput.FromString(result) == expected_message\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"crm/tests/handlers/test_export_report.py","file_name":"test_export_report.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"26268451687","text":"#!/usr/bin/python\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport sys\nfrom pprint import pprint\n\ndirroot = os.path.dirname(\n os.path.dirname(\n os.path.realpath(__file__)))\n\nsys.path.extend([os.path.join(dirroot, 'retrieval'),\n os.path.join(dirroot, 'preprocess')])\n\nfrom acser import Acser\nfrom fipser import Fipser\n\nclass Acorn(object):\n def __init__(self):\n self.fips = Fipser()\n self.acs = Acser()\n \n def resolve(self, loc, schema=None):\n f = self.resolve_fips(loc)\n g = self.resolve_geo(f, schema)\n \n return {'fips': f,\n 'geoid': g}\n\n def resolve_fips(self, loc):\n f = self.fips.resolve(loc)\n return f\n\n def resolve_geo(self, fips, schema=None):\n s, c, p = fips['state_fips'], fips['county_fips'], fips['place_fips']\n g = self.acs.geo_lookup(s, c, p, schema)\n \n return g\n\ndef _get_args():\n parser = argparse.ArgumentParser(description='Resolve a place name into FIPS \\\n Codes and GeoID')\n parser.add_argument('place', metavar='loc', type=str,\n help='A string of the location to resolve')\n \n parser.add_argument('--schema', nargs='?', type=str, \n default=None, help='The schema to use to resolve geoheaders.')\n\n args = parser.parse_args()\n return args\n\ndef main():\n ac = Acorn()\n args = _get_args()\n \n result = ac.resolve(args.place, args.schema)\n\n print(args.place, args.schema)\n print('\\t', end='')\n pprint(result, indent=2)\n\nif __name__ == '__main__':\n main()\n","repo_name":"giantoak/acorn","sub_path":"acorn/retrieval/acorn.py","file_name":"acorn.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"5512914809","text":"import globals\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport do_mpc\nfrom casadi import *\nfrom casadi.tools import *\n\nimport sys\n\nfrom model import simple_bicycle_model\nfrom map import Map, Obstacle\nfrom reference_path import ReferencePath\nfrom simulator import Simulator\nfrom MPC import MPC\n\nsys.path.append('../../')\n\n\ndef environment_setup(map):\n\n # Load map file\n map = Map(file_path=map, origin=[-1, -2], resolution=0.005)\n\n # Specify waypoints\n waypoints_x = [-0.75, -0.25, -0.25, 0.25, 0.25, 1.25, 1.25, 0.75, 0.75, 1.25, 1.25, -0.75, -0.75, -0.25]\n waypoints_y = [-1.5, -1.5, -0.5, -0.5, -1.5, -1.5, -1, -1, -0.5, -0.5, 0, 0, -1.5, -1.5]\n\n # Specify path resolution\n path_resolution = 0.05 # m / wp\n\n # Create smoothed reference path\n reference_path = ReferencePath(\n map,\n waypoints_x,\n waypoints_y,\n path_resolution,\n smoothing_distance=5,\n max_width=0.23,\n circular=True,\n )\n\n # add obstacles to the map - more can be added if necessary\n obs1 = Obstacle(cx=0.0, cy=0.05, radius=0.05)\n obs2 = Obstacle(cx=-0.85, cy=-0.5, radius=0.07)\n obs3 = Obstacle(cx=-0.75, cy=-1.5, radius=0.06)\n obs4 = Obstacle(cx=-0.35, cy=-1.0, radius=0.08)\n obs5 = Obstacle(cx=0.35, cy=-1.0, radius=0.02)\n obs6 = Obstacle(cx=0.78, cy=-1.47, radius=0.06)\n obs7 = Obstacle(cx=0.73, cy=-0.9, radius=0.03)\n obs8 = Obstacle(cx=1.2, cy=0.0, radius=0.04)\n obs9 = Obstacle(cx=0.67, cy=-0.05, radius=0.07)\n map.add_obstacles([obs1, obs2, obs3, obs4, obs5, obs6, obs7, obs8, obs9])\n\n return reference_path\n\n\ndef MPC_Problem_setup(reference_path, ay_max=4.0, a_min=-1, a_max=1):\n # model setup\n vehicle = simple_bicycle_model(\n length=0.12, width=0.06, reference_path=reference_path, Ts=0.05\n )\n vehicle.model_setup()\n\n controller = MPC(vehicle)\n\n sim = Simulator(vehicle)\n\n # compute the vehicle speed profile based on speed and acceleration constraints\n speed_constraints = {\n 'a_min': a_min,\n 'a_max': a_max,\n 'v_min': 0.0,\n 'v_max': 1.0,\n 'ay_max': ay_max,\n }\n vehicle.reference_path.compute_speed_profile(speed_constraints)\n\n return vehicle, controller, sim\n\n\nif __name__ == '__main__':\n\n ''' User settings: '''\n show_animation = True\n\n map = 'maps/sim_map.png'\n\n # set up the map/ assign the waypoints/ add obstacles\n reference_path = environment_setup(map)\n\n # initiate the vehicle, controller and simulator objects\n vehicle, controller, sim = MPC_Problem_setup(reference_path)\n\n # set initial state\n x0 = np.array([vehicle.reference_path.waypoints[0].x, vehicle.reference_path.waypoints[0].y,\n vehicle.reference_path.waypoints[0].psi, 0.3, 0])\n controller.mpc.x0 = x0\n sim.simulator.x0 = x0\n controller.mpc.set_initial_guess()\n\n '''\n Main MPC Loop\n '''\n t = 0\n while globals.s < reference_path.length:\n # get control inputs and update the distance covered by the vehicle\n u = controller.get_control(x0)\n x0 = sim.simulator.make_step(u)\n controller.update_distance(x0)\n\n # plot the path and update the driveable area of the environment\n x_predicted = controller.mpc.data.prediction(('_x', 'pos_x'), t_ind=t)[0]\n y_predicted = controller.mpc.data.prediction(('_x', 'pos_y'), t_ind=t)[0]\n reference_path.show(x_predicted, y_predicted)\n sim.show(x0)\n\n # update boundary constraints for the next time step\n controller.constraints()\n t += 1\n\n plt.axis('off')\n plt.pause(0.001)\n\n input('Press any key to exit.')\n","repo_name":"omprabhu31/mpc_self_driving_cars","sub_path":"mpc-path-planning/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"8670496081","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom neutron_lib import policy as neutron_policy\nfrom oslo_policy import policy\n\nfrom neutron.conf.policies import base\n\nDEPRECATED_REASON = (\n \"The default security group rules API supports \"\n \"system scope and default roles.\")\n\n\nCOLLECTION_PATH = '/default-security-group-rules'\nRESOURCE_PATH = '/default-security-group-rules/{id}'\n\n\nrules = [\n policy.DocumentedRuleDefault(\n name='create_default_security_group_rule',\n check_str=base.ADMIN,\n scope_types=['project'],\n description='Create a templated of the security group rule',\n operations=[\n {\n 'method': 'POST',\n 'path': COLLECTION_PATH,\n },\n ],\n deprecated_rule=policy.DeprecatedRule(\n name='create_default_security_group_rule',\n check_str=neutron_policy.RULE_ADMIN_ONLY,\n deprecated_reason=DEPRECATED_REASON,\n deprecated_since='2023.2')\n ),\n policy.DocumentedRuleDefault(\n name='get_default_security_group_rule',\n # NOTE(slaweq): it can't be ADMIN_OR_PROJECT_READER constant from the\n # base module because that is using \"project_id\" in the check string\n # and this resource don't belongs to any project thus such\n # check string would fail enforcement.\n check_str='role:reader',\n scope_types=['project'],\n description='Get a templated of the security group rule',\n operations=[\n {\n 'method': 'GET',\n 'path': COLLECTION_PATH,\n },\n {\n 'method': 'GET',\n 'path': RESOURCE_PATH,\n },\n ],\n deprecated_rule=policy.DeprecatedRule(\n name='get_default_security_group_rule',\n check_str=neutron_policy.RULE_ANY,\n deprecated_reason=DEPRECATED_REASON,\n deprecated_since='2023.2')\n ),\n policy.DocumentedRuleDefault(\n name='delete_default_security_group_rule',\n check_str=base.ADMIN,\n scope_types=['project'],\n description='Delete a templated of the security group rule',\n operations=[\n {\n 'method': 'DELETE',\n 'path': RESOURCE_PATH,\n },\n ],\n deprecated_rule=policy.DeprecatedRule(\n name='delete_default_security_group_rule',\n check_str=neutron_policy.RULE_ADMIN_ONLY,\n deprecated_reason=DEPRECATED_REASON,\n deprecated_since='2023.2')\n ),\n]\n\n\ndef list_rules():\n return rules\n","repo_name":"openstack/neutron","sub_path":"neutron/conf/policies/default_security_group_rules.py","file_name":"default_security_group_rules.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","stars":1353,"dataset":"github-code","pt":"72"}
+{"seq_id":"5245940471","text":"import os\nimport torch.utils.data as data\nimport numpy as np\nimport torch\nimport cv2\nimport pickle\nfrom utils.image import flip, shuffle_lr, get_affine_transform, affine_transform\nfrom utils.image import draw_gaussian, adjust_aspect_ratio\nfrom utils.image import transform_preds\n\nclass ThreeDPW(data.Dataset):\n def __init__(self, opt, split):\n print('==> initializing 3D {} data.'.format(split))\n self.data_path = os.path.join(opt.data_dir, '3dpw', 'sequenceFiles', split)\n self.num_joints = 16\n self.acc_idxs = [0, 1, 2, 3, 4, 5, 10, 11, 14, 15]\n self.shuffle_ref = [[0, 5], [1, 4], [2, 3], \n [10, 15], [11, 14], [12, 13]]\n self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)\n self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)\n self.edges = [[0, 1], [1, 2], [2, 6], [6, 3], [3, 4], [4, 5], \n [10, 11], [11, 12], [12, 8], [8, 13], [13, 14], [14, 15], \n [6, 8], [8, 9]]\n self.edges_3d = [[3, 2], [2, 1], [1, 0], [0, 4], [4, 5], [5, 6], \\\n [0, 7], [7, 8], [8, 10],\\\n [16, 15], [15, 14], [14, 8], [8, 11], [11, 12], [12, 13]]\n annot = {}\n tags = ['image','joints','center','scale']\n self.files = os.listdir(self.data_path)\n self.split = split\n self.opt = opt\n\n def _load_image(self, index):\n with open(os.path.join(self.data_path, self.files[index]), 'rb') as f:\n data = pickle.load(f, encoding='latin1')\n sequence = data['sequence']\n img_id = data['img_frame_ids']\n # assuming image filename is in format sequence_imgid.jpg\n image_filename = \"{}_{}.jpg\".format(sequence, img_id)\n image_path = os.path.join(self.opt.data_dir, '3dpw', 'imageFiles', sequence, image_filename)\n img = cv2.imread(image_path)\n return img, data\n\n def _get_part_info(self, data):\n pts = np.array(data['poses2d']).copy().astype(np.float32)\n c = np.mean(pts, axis=0)\n s = max(pts[:, 0].max() - pts[:, 0].min(), pts[:, 1].max() - pts[:, 1].min())\n return pts, c, s\n \n def __getitem__(self, index):\n img, data = self._load_image(index)\n pts, c, s = self._get_part_info(data)\n r = 0\n\n # Transformations as in MPII\n if self.split == 'train':\n sf = self.opt.scale\n rf = self.opt.rotate\n s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)\n r = np.clip(np.random.randn()*rf, -rf*2, rf*2) if np.random.random() <= 0.6 else 0\n\n trans_input = get_affine_transform(c, s, r, [self.opt.input_w, self.opt.input_h])\n inp = cv2.warpAffine(img, trans_input, (self.opt.input_w, self.opt.input_h),\n flags=cv2.INTER_LINEAR)\n\n trans_output = get_affine_transform(c, s, r, [self.opt.output_w, self.opt.output_h])\n out = np.zeros((self.num_joints, self.opt.output_h, self.opt.output_w), dtype=np.float32)\n\n for i in range(self.num_joints):\n if pts[i, 0] > 0 or pts[i, 1] > 0:\n pt = affine_transform(pts[i], trans_output)\n out[i] = draw_gaussian(out[i], pt, self.opt.hm_gauss)\n\n inp = (inp.astype(np.float32) / 255.)\n inp = (inp - self.mean) / self.std\n inp = inp.transpose(2, 0, 1)\n \n return inp, out\n\n def __len__(self):\n return len(self.files)\n \n def convert_eval_format(self, pred, conf, meta):\n ret = np.zeros((pred.shape[0], pred.shape[1], 2))\n for i in range(pred.shape[0]):\n ret[i] = transform_preds(\n pred[i], meta['center'][i].numpy(), meta['scale'][i].numpy(), \n [self.opt.output_h, self.opt.output_w])\n return ret\n","repo_name":"MeghanaChillara0203/pytorch-pose-hg-3d_CLASSPROJECT","sub_path":"src/lib/datasets/threedpw.py","file_name":"threedpw.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"72"}
+{"seq_id":"17496587825","text":"import weakref\n\nfrom twisted.enterprise.util import DBError\n\nclass Reflector:\n \"\"\"Base class for enterprise reflectors. This implements row caching.\n \"\"\"\n populated = 0\n\n def __init__(self, rowClasses):\n \"\"\"\n Initialize me against a database.\n\n @param rowClasses: a list of row class objects that describe the\n database schema.\n \"\"\"\n\n self.rowCache = weakref.WeakValueDictionary() # does not hold references to cached rows.\n self.rowClasses = rowClasses\n self.schema = {}\n self._populate()\n\n def __getstate__(self):\n d = self.__dict__.copy()\n del d['rowCache']\n return d\n\n def __setstate__(self, state):\n self.__dict__ = state\n self.rowCache = weakref.WeakValueDictionary()\n self._populate()\n\n def _populate(self):\n \"\"\"Implement me to populate schema information for the reflector.\n \"\"\"\n raise DBError(\"not implemented\")\n\n def populateSchemaFor(self, tableInfo):\n \"\"\"This is called once for each registered rowClass to add it\n and its foreign key relationships for that rowClass to the\n schema.\"\"\"\n\n self.schema[ tableInfo.rowTableName ] = tableInfo\n\n # add the foreign key to the foreign table.\n for foreignTableName, childColumns, parentColumns, containerMethod, autoLoad in tableInfo.rowForeignKeys:\n self.schema[foreignTableName].addForeignKey(childColumns,\n parentColumns, tableInfo.rowClass,\n containerMethod, autoLoad)\n\n def getTableInfo(self, rowObject):\n \"\"\"Get a TableInfo record about a particular instance.\n\n This record contains various information about the instance's\n class as registered with this reflector.\n\n @param rowObject: a L{RowObject} instance of a class previously\n registered with me.\n @raises twisted.enterprise.row.DBError: raised if this class was not\n previously registered.\n \"\"\"\n try:\n return self.schema[rowObject.rowTableName]\n except KeyError:\n raise DBError(\"class %s was not registered with %s\" % (\n rowObject.__class__, self))\n\n def buildWhereClause(self, relationship, row):\n \"\"\"util method used by reflectors. builds a where clause to link a row to another table.\n \"\"\"\n whereClause = []\n for i in range(0,len(relationship.childColumns)):\n value = getattr(row, relationship.parentColumns[i][0])\n whereClause.append( [relationship.childColumns[i][0], EQUAL, value] )\n return whereClause\n\n def addToParent(self, parentRow, rows, tableName):\n \"\"\"util method used by reflectors. adds these rows to the parent row object.\n If a rowClass does not have a containerMethod, then a list attribute \"childRows\"\n will be used.\n \"\"\"\n parentInfo = self.getTableInfo(parentRow)\n relationship = parentInfo.getRelationshipFor(tableName)\n if not relationship:\n raise DBError(\"no relationship from %s to %s\" % ( parentRow.rowTableName, tableName) )\n\n if not relationship.containerMethod:\n if hasattr(parentRow, \"childRows\"):\n for row in rows:\n if row not in parentRow.childRows:\n parentRow.childRows.append(row)\n else:\n parentRow.childRows = rows\n return\n\n if not hasattr(parentRow, relationship.containerMethod):\n raise DBError(\"parent row (%s) doesnt have container method <%s>!\" % (parentRow, relationship.containerMethod))\n\n meth = getattr(parentRow, relationship.containerMethod)\n for row in rows:\n meth(row)\n\n ####### Row Cache ########\n\n def addToCache(self, rowObject):\n \"\"\"NOTE: Should this be recursive?! requires better container knowledge...\"\"\"\n self.rowCache[ rowObject.getKeyTuple() ] = rowObject\n\n def findInCache(self, rowClass, kw):\n keys = []\n keys.append(rowClass.rowTableName)\n for keyName, keyType in rowClass.rowKeyColumns:\n keys.append( kw[keyName] )\n keyTuple = tuple(keys)\n return self.rowCache.get(keyTuple)\n\n def removeFromCache(self, rowObject):\n \"\"\"NOTE: should this be recursive!??\"\"\"\n key = rowObject.getKeyTuple()\n if self.rowCache.has_key(key):\n del self.rowCache[key]\n\n ####### Row Operations ########\n\n def loadObjectsFrom(self, tableName, parent=None, data=None,\n whereClause=[], loadChildren=1):\n \"\"\"Implement me to load objects from the database.\n\n @param whereClause: a list of tuples of (columnName, conditional, value)\n so it can be parsed by all types of reflectors. eg.::\n whereClause = [(\"name\", EQUALS, \"fred\"), (\"age\", GREATERTHAN, 18)]\n \"\"\"\n raise DBError(\"not implemented\")\n\n def updateRow(self, rowObject):\n \"\"\"update this rowObject to the database.\n \"\"\"\n raise DBError(\"not implemented\")\n\n def insertRow(self, rowObject):\n \"\"\"insert a new row for this object instance.\n \"\"\"\n raise DBError(\"not implemented\")\n\n def deleteRow(self, rowObject):\n \"\"\"delete the row for this object from the database.\n \"\"\"\n raise DBError(\"not implemented\")\n\n# conditionals\nEQUAL = 0\nLESSTHAN = 1\nGREATERTHAN = 2\nLIKE = 3\n\n\n__all__ = ['Reflector', 'EQUAL', 'LESSTHAN', 'GREATERTHAN', 'LIKE']\n","repo_name":"ActiveState/OpenKomodoIDE","sub_path":"contrib/twisted/TwistedCore-2.4.0/twisted/enterprise/reflector.py","file_name":"reflector.py","file_ext":"py","file_size_in_byte":5624,"program_lang":"python","lang":"en","doc_type":"code","stars":460,"dataset":"github-code","pt":"72"}
+{"seq_id":"466910499","text":"# coding: utf-8\n\nfrom __future__ import unicode_literals, absolute_import, division, print_function\n\nimport pytest\n\nfrom travel.rasp.train_api.train_partners.base.train_details.utils import set_pets, OWNER_DOSS, BRAND_LASTOCHKA\n\nOWNER_FPK = 'ФПК'\nBRAND_BAIKAL = 'Байкал'\n\n\nclass Coach(object):\n def __init__(self, pet_in_coach, pet_places_only):\n self.pet_in_coach = pet_in_coach\n self.pet_places_only = pet_places_only\n self.pets_allowed = False\n self.pets_segregated = False\n\n\nclass ExpectedCoach(object):\n def __init__(self, pets_allowed, pets_segregated):\n self.pets_allowed = pets_allowed\n self.pets_segregated = pets_segregated\n\n\n@pytest.mark.parametrize('coach_owner, train_brand, coaches, expected_coaches', (\n (OWNER_FPK, BRAND_BAIKAL, [Coach(False, False), Coach(False, False)],\n [ExpectedCoach(False, False), ExpectedCoach(False, False)]),\n\n (OWNER_FPK, BRAND_BAIKAL, [Coach(False, False), Coach(True, False)],\n [ExpectedCoach(False, False), ExpectedCoach(True, False)]),\n\n (OWNER_DOSS, BRAND_LASTOCHKA, [Coach(False, False), Coach(False, False)],\n [ExpectedCoach(False, False), ExpectedCoach(False, False)]),\n\n (OWNER_DOSS, BRAND_LASTOCHKA, [Coach(False, False), Coach(True, False)],\n [ExpectedCoach(False, False), ExpectedCoach(False, True)]),\n\n (OWNER_DOSS, BRAND_LASTOCHKA, [Coach(False, False), Coach(False, True)],\n [ExpectedCoach(False, False), ExpectedCoach(True, True)]),\n\n (OWNER_DOSS, BRAND_LASTOCHKA, [Coach(False, False), Coach(True, False), Coach(False, True)],\n [ExpectedCoach(False, False), ExpectedCoach(False, True), ExpectedCoach(True, True)])\n))\ndef test_set_pets(coach_owner, train_brand, coaches, expected_coaches):\n coaches_with_pets = set_pets(coach_owner, train_brand, coaches)\n\n for index, coach in enumerate(coaches_with_pets):\n check_coach(coach, expected_coaches[index])\n\n\ndef check_coach(actual_coach, expected_coach):\n assert actual_coach.pets_allowed == expected_coach.pets_allowed\n assert actual_coach.pets_segregated == expected_coach.pets_segregated\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"travel/tests/train_partners/base/train_details/utils/test_set_pets.py","file_name":"test_set_pets.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"15804123217","text":"from random import choice\nimport pygame\n\nfrom settings import settings\nfrom score import score\nfrom player import player\nfrom sprite_group import groups\nfrom blood import blood \n\n\ndef check_player_collision():\n \"\"\"Check if player collides with a mob.\"\"\"\n if not player.is_invulnerable:\n mobs_hitbox_hitten = pygame.sprite.groupcollide(\n groups[\"mobs_hitbox\"], \n groups[\"player_hitbox\"], \n False, False)\n for mob_hitbox in mobs_hitbox_hitten.keys():\n for mob in groups[\"mobs\"].sprites():\n if mob.hitbox == mob_hitbox:\n if not mob.is_invincible:\n player.hp -= 1\n if player.hp == 0:\n player_dead()\n else:\n choice(settings.player_sounds[\"hit\"]).play()\n player.go_invulnerable()\n\ndef player_dead():\n \"\"\"\n Empty mob_hitbox, mobs and heart groups, reset settings and for the player:\n - Play death sound\n - Set status to Death\n - Set is_dying to True.\n - Slow down animation speed\n \"\"\"\n choice(settings.player_sounds[\"death\"]).play().set_volume(0.5)\n player.frame_index = 0\n player.status = \"Death\"\n player.is_dying = True\n player.animation_speed = 0.1\n groups[\"mobs_hitbox\"].empty()\n groups[\"mobs\"].empty()\n groups[\"heart\"].remove(blood)\n settings.reset()\n\ndef set_and_draw_intro_background():\n \"\"\"Set and draw the background of the intro screen.\"\"\"\n intro_background_surf = pygame.Surface(settings.screen_size)\n pygame.Surface.fill(intro_background_surf, \"#207a6b\")\n intro_background_rect = intro_background_surf.get_rect(topleft = (0,0))\n settings.screen.blit(intro_background_surf,intro_background_rect)\n\ndef set_and_draw_intro_avatar():\n \"\"\"Set and draw the avatar of the intro screen.\"\"\"\n avatar_center = (600,150)\n avatar_surf = pygame.image.load(\n \"graphics/Knight/Idle/idle1.png\").convert_alpha()\n avatar_surf = pygame.transform.scale2x(avatar_surf)\n avatar_rect = avatar_surf.get_rect(center=avatar_center)\n settings.screen.blit(avatar_surf, avatar_rect)\n\ndef set_and_draw_intro_scrore():\n \"\"\"Set and draw current score on the intro screen.\"\"\"\n score_str = f\"Your score: {score.user_score}\"\n score_surf = pygame.font.Font.render(\n settings.intro_font,\n score_str,\n False,\n settings.intro_color\n )\n score_rect = score_surf.get_rect(topleft=(20, 120))\n settings.screen.blit(score_surf, score_rect)\n\ndef set_and_draw_intro_best_scrore():\n \"\"\"Set and draw best score on the intro screen.\"\"\"\n best_score_str = f\"Best score: {score.best_score}\" \n best_score_surf = pygame.font.Font.render(\n settings.intro_font,\n best_score_str,\n False,\n settings.intro_color\n )\n best_score_rect = best_score_surf.get_rect(topleft=(20, 60))\n settings.screen.blit(best_score_surf, best_score_rect)\n\ndef set_and_draw_intro_title():\n \"\"\"Set and draw intro screen's title.\"\"\"\n title_str = \"You are ambushed!!!\"\n title_surf = pygame.font.Font.render(\n settings.intro_font,\n title_str,\n False,\n settings.intro_color\n )\n title_rect = title_surf.get_rect(midtop=(settings.screen_width/2, 20))\n settings.screen.blit(title_surf, title_rect)\n\ndef set_and_draw_intro_controls():\n \"\"\"Set and draw the controls on the intro screen.\"\"\"\n controls = [\"Left / Right: Arrows\", \"Jump: up arrow\",\"Attack: Space bar\", \"Super Attack: left shift\"]\n for index, control in enumerate(controls):\n surf = pygame.font.Font.render(\n settings.intro_font,\n control,\n False,\n settings.intro_color\n )\n y_pos = 80 + 60 * index \n rect = surf.get_rect(topleft=(20, y_pos))\n settings.screen.blit(surf, rect)\n\ndef set_and_draw_intro_start():\n start_str = \"Hit Return to start\"\n start_surf = pygame.font.Font.render(\n settings.intro_font, \n start_str,\n False,\n settings.intro_color\n )\n start_rect = start_surf.get_rect(midtop=(settings.screen_width/2, 320))\n settings.screen.blit(start_surf, start_rect)\n\ndef draw_intro_screen():\n \"\"\"Draw the intro screen.\"\"\"\n set_and_draw_intro_background()\n set_and_draw_intro_avatar()\n if settings.game_started:\n set_and_draw_intro_scrore()\n set_and_draw_intro_best_scrore()\n else:\n set_and_draw_intro_title()\n set_and_draw_intro_controls()\n set_and_draw_intro_start()\n","repo_name":"OGR-67/ambush","sub_path":"game_over.py","file_name":"game_over.py","file_ext":"py","file_size_in_byte":4619,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"30680802193","text":"def face():\n\timport jetson.inference\n\timport jetson.utils\n\n\tnet = jetson.inference.detectNet(\"ssd-mobilenet-v2\", threshold=0.5)\n\tcamera = jetson.utils.videoSource(\"csi://0\") # '/dev/video0' for V4L2\n\tdisplay = jetson.utils.videoOutput(\"display://0\") # 'my_video.mp4' for file\n\n\twhile display.IsStreaming():\n\t\timg = camera.Capture()\n\t\tdetections = net.Detect(img)\n\t\tfor detection in detections:\n\t\t\tif (net.GetClassDesc(detection.ClassID) == \"person\"):\n\t\t\t\treturn\n\t\tdisplay.Render(img)\n\t\tdisplay.SetStatus(\"Object Detection | Network {:.0f} FPS\".format(net.GetNetworkFPS()))\nface()\n\t\n","repo_name":"leowong1120/ceng3410_project","sub_path":"my_detection.py","file_name":"my_detection.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"25809220446","text":"i = int(input('Digite os termos: '))\nn = int(1)\nna = int(0)\nnb = int(0)\nc = int(0)\nif i == 1:\n print('0')\nwhile c < i-1:\n nb = n\n n = na + n\n if c == 0:\n print('0', end=' -> ')\n print(f'{nb}',end=' -> ')\n na = nb\n c += 1\nprint('FIM')","repo_name":"eduardoschmitt/Python-3-Curso-em-V-deo","sub_path":"ex063.py","file_name":"ex063.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"41221532411","text":"class Solution:\n def addToArrayForm(self, A: List[int], K: int) -> List[int]:\n s=\"\"\n for i in range(len(A)):\n s+=str(A[i])\n l=str(int(s)+K)\n res=[]\n for i in l:\n res.append(i)\n return res\n \n","repo_name":"Lazy-coder-Hemlock/Leet_Code-Solutions","sub_path":"989. Add to Array-Form of Integer.py","file_name":"989. Add to Array-Form of Integer.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"34140606","text":"from __future__ import absolute_import, division, print_function\n\nfrom langkit.dsl import (\n ASTNode, abstract, Field, T, Bool, LexicalEnv, synthetic, Struct,\n UserField, NullField, Symbol, LogicVar, Annotations\n)\nfrom langkit.envs import EnvSpec, add_env, add_to_env_kv, handle_children\nfrom langkit.expressions import (\n Self, Entity, langkit_property, Property, AbstractProperty, Not, No, If,\n ArrayLiteral, String, Var, AbstractKind, Let, Bind, LogicTrue, LogicFalse,\n Or, And, PropertyError, ignore, Try, Cond, Predicate, DynamicVariable,\n bind, CharacterLiteral\n)\n\n\nclass Renaming(Struct):\n from_symbol = UserField(type=T.Symbol)\n to_symbol = UserField(type=T.Symbol)\n\n\nclass Substitution(Struct):\n from_symbol = UserField(type=T.Symbol)\n to_term = UserField(type=T.Term)\n\n\nclass Template(Struct):\n origin = UserField(type=T.Term)\n instance = UserField(type=T.Term)\n new_symbols = UserField(type=T.Symbol.array)\n\n\nclass Binding(Struct):\n target = UserField(type=T.Term)\n domain_val = UserField(type=T.Term)\n\n\nclass UnifyEquation(Struct):\n eq = UserField(type=T.Equation)\n renamings = UserField(type=Renaming.array)\n\n\nclass DomainEquation(Struct):\n eq = UserField(type=T.Equation)\n templates = UserField(type=T.Identifier.array)\n\n\nclass UnifyQuery(Struct):\n first = UserField(type=T.Term)\n second = UserField(type=T.Term)\n\n\nclass TypingsDescription(Struct):\n bindings = UserField(type=T.Binding.array)\n equations = UserField(type=T.UnifyQuery.array)\n new_symbols = UserField(type=T.Symbol.array)\n\n\nclass UnificationContext(Struct):\n symbols = UserField(type=T.Symbol.array)\n vars = UserField(type=T.LogicVarArray)\n self_parent = UserField(type=T.Term)\n other_parent = UserField(type=T.Term)\n\n\nclass HigherOrderUnificationContext(Struct):\n arg = UserField(type=T.Term)\n res = UserField(type=T.Term)\n\n\nclass ExtractionContext(Struct):\n target = UserField(type=T.Symbol)\n first_context = UserField(type=T.Term)\n second_context = UserField(type=T.Term)\n\n\nclass ConstrainedTerm(Struct):\n term = UserField(type=T.Term)\n constraints = UserField(type=Substitution.array)\n\n\nclass Constructor(Struct):\n template = UserField(type=Template)\n substs = UserField(type=Substitution.array)\n\n\nclass SynthesisContext(Struct):\n intros = UserField(type=T.Introduction.array)\n bound_generics = UserField(type=T.Symbol.array)\n\n\nclass SynthesizationHole(Struct):\n sym = UserField(type=T.Symbol)\n domain_val = UserField(type=T.Term)\n ctx = UserField(type=SynthesisContext)\n\n\nclass SynthesizationAttempt(Struct):\n term = UserField(type=T.Term)\n holes = UserField(type=SynthesizationHole.array)\n free_symbols = UserField(type=T.Symbol.array)\n\n\nunification_context = DynamicVariable(\n \"unification_context\", type=UnificationContext\n)\n\nho_unification_context = DynamicVariable(\n \"ho_unification_context\", type=HigherOrderUnificationContext\n)\n\nextraction_context = DynamicVariable(\n \"extraction_context\", type=ExtractionContext\n)\n\nsynthesis_context = DynamicVariable(\n \"synthesis_context\", type=SynthesisContext\n)\n\n\n@abstract\nclass DependzNode(ASTNode):\n \"\"\"\n Root node class for Dependz AST nodes.\n \"\"\"\n @langkit_property(public=True)\n def make_ident(name=T.Symbol):\n return Self.unit.root.make_ident_from_self(name)\n\n @langkit_property(public=True)\n def make_apply(t1=T.Term, t2=T.Term):\n return Self.unit.root.make_apply_from_self(t1, t2)\n\n @langkit_property(public=True)\n def make_abstraction(id=T.Identifier, rhs=T.Term):\n return Self.unit.root.make_abstraction_from_self(id, rhs)\n\n @langkit_property(public=True)\n def make_arrow(t1=T.Term, t2=T.Term,\n t3=(T.Term, No(T.Term))):\n return Self.unit.root.make_arrow_from_self(t1, t2, t3)\n\n @langkit_property()\n def make_nested_intro(name=T.Identifier, dom=T.Term,\n bound_generics=T.Symbol.array):\n return Self.unit.root.make_nested_intro_from_self(name, dom,\n bound_generics)\n\n @langkit_property(return_type=T.LogicVarArray, memoized=True)\n def make_logic_var_array():\n return LogicVarArray.new()\n\n @langkit_property(memoized=True, return_type=T.SyntheticId)\n def make_ident_from_self(name=T.Symbol):\n return SyntheticId.new(name=name)\n\n @langkit_property(memoized=True, return_type=T.SyntheticApply)\n def make_apply_from_self(t1=T.Term, t2=T.Term):\n return SyntheticApply.new(lhs=t1, rhs=t2)\n\n @langkit_property(memoized=True, return_type=T.SyntheticAbstraction)\n def make_abstraction_from_self(id=T.Identifier, rhs=T.Term):\n return SyntheticAbstraction.new(ident=id, term=rhs)\n\n @langkit_property(memoized=True, return_type=T.SyntheticArrow)\n def make_arrow_from_self(t1=T.Term, t2=T.Term,\n t3=(T.Term, No(T.Term))):\n return SyntheticArrow.new(lhs=t1, rhs=t2, binder=t3)\n\n @langkit_property(memoized=True, return_type=T.NestedIntroduction)\n def make_nested_intro_from_self(name=T.Identifier, dom=T.Term,\n bound_generics=T.Symbol.array):\n return NestedIntroduction.new(ident=name, term=dom,\n bound_generics=bound_generics)\n\n @langkit_property(return_type=T.Symbol)\n def unique_fresh_symbol(prefix=T.Symbol):\n return Self.concat_symbol_and_integer(\n prefix,\n Self.unit.root.next_global_integer\n )\n\n @langkit_property(external=True, return_type=T.Int,\n uses_entity_info=False, uses_envs=False)\n def next_global_integer():\n pass\n\n @langkit_property(external=True, return_type=T.Symbol,\n uses_entity_info=False, uses_envs=False)\n def concat_symbol_and_integer(s=T.Symbol, i=T.Int):\n pass\n\n @langkit_property(external=True, return_type=T.LogicVar,\n uses_entity_info=False, uses_envs=False)\n def create_logic_var():\n pass\n\n @langkit_property(external=True, return_type=T.LogicVar,\n uses_entity_info=False, uses_envs=False)\n def create_named_logic_var(name=T.Symbol):\n pass\n\n @langkit_property(external=True, return_type=T.Bool,\n uses_entity_info=False, uses_envs=False)\n def set_allow_orphan_relations(do_allow=T.Bool):\n pass\n\n @langkit_property(return_type=T.Bool)\n def solve_allowing_orphans(equation=T.Equation):\n ignore(Var(Self.set_allow_orphan_relations(True)))\n return equation.solve\n\n @langkit_property(return_type=T.DependzNode, activate_tracing=True)\n def here():\n return Self\n\n @langkit_property(public=True, external=True, return_type=T.Bool,\n uses_entity_info=False, uses_envs=False)\n def dump_mmz_map():\n pass\n\n @langkit_property(return_type=Substitution.array,\n activate_tracing=True)\n def unify_all_with_constraints(queries=UnifyQuery.array,\n domain_constraints=UnifyQuery.array,\n symbols=T.Symbol.array,\n allow_incomplete=(T.Bool, False)):\n vars = Var(Self.make_logic_var_array)\n ctx = Var(UnificationContext.new(\n symbols=symbols,\n vars=vars,\n self_parent=No(Term),\n other_parent=No(Term)\n ))\n\n query_results = Var(queries.map(\n lambda q: unification_context.bind(\n ctx,\n q.first.unify_equation(q.second)\n )\n ))\n\n equations = Var(And(\n query_results.logic_all(lambda r: r.eq),\n\n unification_context.bind(\n ctx,\n domain_constraints.logic_all(\n lambda q: q.first.has_domain_equation(q.second)\n )\n )\n\n ))\n renamings = Var(query_results.mapcat(lambda r: r.renamings))\n\n return If(\n Try(equations.solve, allow_incomplete),\n\n symbols.map(\n lambda s: Substitution.new(\n from_symbol=s,\n to_term=vars.elem(s).get_value._.cast(Term).rename_all(\n renamings\n )\n )\n ).filter(lambda s: Not(s.to_term.is_null)),\n\n PropertyError(Substitution.array, \"Unification failed\")\n )\n\n @langkit_property(public=True, return_type=Substitution.array,\n activate_tracing=False)\n def unify_all(queries=UnifyQuery.array, symbols=T.Symbol.array,\n allow_incomplete=(T.Bool, False)):\n return Self.unify_all_with_constraints(\n queries, No(UnifyQuery.array), symbols, allow_incomplete\n )\n\n\n@synthetic\nclass LogicVarArray(DependzNode):\n @langkit_property(return_type=T.LogicVar, memoized=True)\n def elem(s=T.Symbol):\n return Self.create_named_logic_var(s)\n\n\n@abstract\nclass Term(DependzNode):\n annotations = Annotations(custom_trace_image=True)\n\n to_string = AbstractProperty(public=True, type=T.String)\n\n @langkit_property(return_type=T.Term,\n dynamic_vars=[unification_context])\n def solve_time_substitution():\n symbols = Var(unification_context.symbols)\n vars = Var(unification_context.vars)\n\n substs = Var(symbols.map(\n lambda s: Substitution.new(\n from_symbol=s,\n to_term=vars.elem(s).get_value._.cast_or_raise(Term).node\n )\n ).filter(\n lambda s: Not(s.to_term.is_null)\n ))\n\n return Self.substitute_all(substs).normalize\n\n @langkit_property(return_type=T.Term.entity,\n dynamic_vars=[unification_context])\n def solve_time_substituted_entity():\n return Entity.solve_time_substitution.as_bare_entity\n\n @langkit_property(return_type=T.Bool,\n dynamic_vars=[unification_context],\n activate_tracing=False)\n def unifies_with(other=T.Term):\n current_self = Var(Self.solve_time_substitution.normalize)\n current_other = Var(other.solve_time_substitution.normalize)\n return Try(\n Let(\n lambda substs=current_self.unify(\n current_other,\n unification_context.symbols,\n allow_incomplete=True\n ): True\n ),\n False\n )\n\n @langkit_property(return_type=T.Term.entity,\n dynamic_vars=[unification_context, extraction_context],\n activate_tracing=False)\n def extract_value():\n first_term = Var(\n extraction_context.first_context.solve_time_substitution.normalize\n )\n second_term = Var(extraction_context.second_context.normalize)\n\n original = Var(\n unification_context.vars.elem(\n extraction_context.target\n ).get_value._.cast_or_raise(Term)\n )\n\n return If(\n Not(original.is_null),\n original,\n Try(\n Let(\n lambda substs=first_term.unify(\n second_term,\n unification_context.symbols,\n allow_incomplete=True\n ): substs.find(\n lambda s: s.from_symbol == extraction_context.target\n ).then(\n lambda s: s.to_term.normalize.as_entity,\n default_val=No(Term.entity)\n )\n ),\n No(Term.entity)\n )\n )\n\n @langkit_property(return_type=T.Equation,\n dynamic_vars=[unification_context])\n def extract_equation(other=T.Term, source=T.Symbol, target=T.Symbol):\n return extraction_context.bind(\n ExtractionContext.new(\n target=target,\n first_context=Self,\n second_context=other\n ),\n Bind(\n unification_context.vars.elem(source),\n unification_context.vars.elem(target),\n conv_prop=Term.extract_value,\n eq_prop=Term.equivalent_entities\n )\n )\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context])\n def first_order_match_match_equation(other=T.Term):\n return Self.first_order_rigid_rigid_equation(other)\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context])\n def first_order_match_equation(other=T.Term):\n tmp = Var(unification_context.vars.elem(\n Self.unique_fresh_symbol(\"tmp\")\n ))\n return UnifyEquation.new(\n eq=And(\n Bind(tmp, Self.as_bare_entity,\n conv_prop=Term.solve_time_substituted_entity,\n eq_prop=Term.equivalent_entities),\n Bind(tmp, other.as_bare_entity,\n conv_prop=Term.solve_time_substituted_entity,\n eq_prop=Term.equivalent_entities)\n ),\n renamings=No(Renaming.array)\n )\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context])\n def first_order_flexible_flexible_equation(other=T.Term):\n vars = Var(unification_context.vars)\n self_var = Var(vars.elem(Self.cast(Identifier).sym))\n other_var = Var(vars.elem(other.cast(Identifier).sym))\n\n return UnifyEquation.new(\n eq=If(\n Self.cast(Identifier).sym == other.cast(Identifier).sym,\n LogicTrue(),\n Bind(self_var, other_var)\n ),\n renamings=No(Renaming.array)\n )\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context])\n def first_order_flexible_semirigid_equation(other=T.Term):\n self_sym = Var(Self.cast(Identifier).sym)\n self_var = Var(unification_context.vars.elem(self_sym))\n tmp = Var(unification_context.vars.elem(\n Self.unique_fresh_symbol(\"tmp\")\n ))\n\n return UnifyEquation.new(\n eq=Or(\n And(\n Bind(tmp, other.as_bare_entity),\n Bind(tmp, self_var,\n conv_prop=Term.solve_time_substituted_entity,\n eq_prop=Term.equivalent_entities)\n ),\n And(\n Predicate(Term.unifies_with, self_var, other),\n other.free_symbols.filter(\n lambda s: unification_context.symbols.contains(s)\n ).logic_all(\n lambda s: Self.extract_equation(other, self_sym, s)\n )\n )\n ),\n renamings=No(Renaming.array)\n )\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context])\n def first_order_flexible_rigid_equation(other=T.Term):\n self_var = Var(\n unification_context.vars.elem(Self.cast(Identifier).sym)\n )\n\n return UnifyEquation.new(\n eq=Bind(\n self_var,\n other.as_bare_entity,\n conv_prop=Term.solve_time_substituted_entity,\n eq_prop=Term.equivalent_entities\n ),\n renamings=No(Renaming.array)\n )\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context],\n activate_tracing=False)\n def first_order_rigid_rigid_equation(other=T.Term):\n\n def to_logic(bool):\n return If(bool, LogicTrue(), LogicFalse())\n\n def combine(x, y, *others):\n assert (len(others) % 2 == 0)\n\n if len(others) == 0:\n return x.unify_equation(y)\n else:\n return Let(\n lambda\n e1=x.unify_equation(y),\n e2=combine(*others):\n\n UnifyEquation.new(\n eq=And(e1.eq, e2.eq),\n renamings=e1.renamings.concat(e2.renamings)\n )\n )\n\n def unify_case(expected_type, then):\n return other.cast(expected_type).then(\n then,\n default_val=UnifyEquation.new(\n eq=LogicFalse(),\n renamings=No(Renaming.array)\n )\n )\n\n def is_metavar(term):\n return term.cast(Identifier).then(\n lambda id: unification_context.symbols.contains(id.sym)\n )\n\n updated_ctx = Var(UnificationContext.new(\n vars=unification_context.vars,\n symbols=unification_context.symbols,\n self_parent=Self,\n other_parent=other\n ))\n return unification_context.bind(updated_ctx, Self.match(\n lambda id=Identifier: unify_case(\n Identifier,\n lambda oid: UnifyEquation.new(\n eq=to_logic(id.sym == oid.sym),\n renamings=No(Renaming.array)\n )\n ),\n\n lambda ab=Abstraction: unify_case(\n Abstraction,\n lambda o: Let(\n lambda sym=ab.term.free_fresh_symbol(\"eq\", o.term): Let(\n lambda\n rab=ab.term.rename(ab.ident.sym, sym),\n rob=o.term.rename(o.ident.sym, sym):\n\n Let(\n lambda r=rab.unify_equation(rob):\n UnifyEquation.new(\n eq=r.eq,\n renamings=r.renamings.concat(Renaming.new(\n from_symbol=sym,\n to_symbol=ab.ident.sym\n ).singleton)\n )\n )\n )\n )\n ),\n\n lambda ap=Apply: unify_case(\n Apply,\n lambda oap: combine(\n ap.lhs, oap.lhs,\n ap.rhs, oap.rhs\n )\n ),\n\n lambda ar=Arrow: unify_case(\n Arrow,\n lambda oar: Cond(\n And(ar.binder.is_null, oar.binder.is_null),\n combine(ar.lhs, oar.lhs, ar.rhs, oar.rhs),\n\n Or(ar.binder.is_null & Not(oar.has_constraining_binder),\n oar.binder.is_null & Not(ar.has_constraining_binder)),\n Let(\n lambda\n res=combine(ar.lhs, oar.lhs, ar.rhs, oar.rhs),\n unused=Self.make_ident(\n Self.free_fresh_symbol(\"unused\")\n ):\n\n UnifyEquation.new(\n eq=And(\n res.eq,\n Cond(\n ar.binder.is_null & is_metavar(oar.binder),\n Bind(\n unification_context.vars.elem(\n oar.binder.cast(Identifier).sym\n ),\n unused.as_bare_entity\n ),\n\n oar.binder.is_null & is_metavar(ar.binder),\n Bind(\n unification_context.vars.elem(\n ar.binder.cast(Identifier).sym\n ),\n unused.as_bare_entity\n ),\n\n LogicTrue()\n )\n ),\n renamings=res.renamings\n )\n ),\n\n Or(ar.binder.is_null, oar.binder.is_null),\n UnifyEquation.new(\n eq=LogicTrue(),\n renamings=No(Renaming.array)\n ),\n\n combine(\n ar.binder, oar.binder,\n ar.lhs, oar.lhs,\n ar.rhs, oar.rhs\n )\n )\n )\n ))\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context])\n def first_order_unify_equation(other=T.Term):\n symbols = Var(unification_context.symbols)\n\n def is_match(term):\n return And(\n term.is_match_application,\n term.cast(Apply).rhs.cast(Identifier).then(\n lambda id: symbols.contains(id.sym)\n )\n )\n\n self_is_match = Var(is_match(Self))\n other_is_match = Var(is_match(other))\n\n self_is_metavar = Var(Self.cast(Identifier).then(\n lambda id: symbols.contains(id.sym)\n ))\n other_is_metavar = Var(other.cast(Identifier).then(\n lambda id: symbols.contains(id.sym)\n ))\n\n self_has_metavar = Var(symbols.any(lambda s: Self.is_free(s)))\n other_has_metavar = Var(symbols.any(lambda s: other.is_free(s)))\n\n return Cond(\n self_is_match & other_is_match,\n Self.first_order_match_match_equation(other),\n\n self_is_match,\n Self.first_order_match_equation(other),\n\n other_is_match,\n other.first_order_match_equation(Self),\n\n self_is_metavar & other_is_metavar,\n Self.first_order_flexible_flexible_equation(other),\n\n self_is_metavar & other_has_metavar,\n Self.first_order_flexible_semirigid_equation(other),\n\n other_is_metavar & self_has_metavar,\n other.first_order_flexible_semirigid_equation(Self),\n\n self_is_metavar,\n Self.first_order_flexible_rigid_equation(other),\n\n other_is_metavar,\n other.first_order_flexible_rigid_equation(Self),\n\n Self.first_order_rigid_rigid_equation(other)\n )\n\n @langkit_property(return_type=T.Bool,\n dynamic_vars=[unification_context,\n ho_unification_context],\n activate_tracing=False)\n def higher_order_check_current_solution():\n return Entity.make_apply(\n Self,\n ho_unification_context.arg\n ).unifies_with(\n ho_unification_context.res\n )\n\n @langkit_property(return_type=T.Term.entity,\n dynamic_vars=[unification_context,\n ho_unification_context])\n def higher_order_construct_imitation():\n res = Var(ho_unification_context.res)\n body = Var(res.solve_time_substitution)\n fresh_sym = Var(body.free_fresh_symbol(\"ho\"))\n\n return Entity.make_abstraction(\n Self.make_ident(fresh_sym),\n body\n ).as_bare_entity\n\n @langkit_property(return_type=T.Term.entity,\n dynamic_vars=[unification_context,\n ho_unification_context])\n def higher_order_construct_projection():\n arg = Var(ho_unification_context.arg)\n res = Var(ho_unification_context.res)\n fresh_sym = Var(res.free_fresh_symbol(\"ho\"))\n\n return Entity.make_abstraction(\n Self.make_ident(fresh_sym),\n res.solve_time_substitution.anti_substitute(\n arg.solve_time_substitution,\n fresh_sym\n )\n ).as_bare_entity\n\n @langkit_property(return_type=T.UnifyEquation,\n activate_tracing=False,\n dynamic_vars=[unification_context])\n def higher_order_single_arg_equation(arg=T.Term, res=T.Term,\n ho_sym=T.Symbol):\n metavar = Var(unification_context.vars.elem(ho_sym))\n\n ho_ctx = Var(HigherOrderUnificationContext.new(\n arg=arg,\n res=res\n ))\n\n imitate = Var(ho_unification_context.bind(\n ho_ctx,\n Bind(\n metavar,\n Self.as_bare_entity,\n eq_prop=Term.equivalent_entities,\n conv_prop=Term.higher_order_construct_imitation\n )\n ))\n\n project = Var(ho_unification_context.bind(\n ho_ctx,\n Bind(\n metavar,\n Self.as_bare_entity,\n eq_prop=Term.equivalent_entities,\n conv_prop=Term.higher_order_construct_projection\n )\n ))\n\n ignore = Var(ho_unification_context.bind(\n ho_ctx,\n And(\n Predicate(\n Term.higher_order_check_current_solution,\n metavar\n ),\n res.free_symbols.filter(\n lambda s: unification_context.symbols.contains(s)\n ).logic_all(\n lambda s: Self.extract_equation(res, ho_sym, s)\n )\n )\n ))\n\n return UnifyEquation.new(\n eq=Or(\n project,\n ignore,\n imitate,\n ),\n renamings=No(Renaming.array)\n )\n\n @langkit_property(return_type=T.UnifyEquation,\n dynamic_vars=[unification_context])\n def higher_order_unify_equation(other=T.Term, ho_term=T.Identifier):\n is_single_arg_equation = Var(And(\n Self.cast(Apply).lhs == ho_term,\n other.is_a(Term)\n ))\n return Cond(\n is_single_arg_equation,\n Self.higher_order_single_arg_equation(\n Self.cast(Apply).rhs,\n other,\n ho_term.sym\n ),\n\n UnifyEquation.new(\n eq=LogicTrue(),\n renamings=No(Renaming.array)\n )\n )\n\n @langkit_property(return_type=UnifyEquation, uses_entity_info=False,\n dynamic_vars=[unification_context])\n def unify_equation(other=T.Term):\n symbols = Var(unification_context.symbols)\n\n def outermost_metavar_application_of(term, parent_term):\n return term.cast(Apply)._.left_most_term.cast(Identifier).then(\n lambda id: If(\n # Check the id is indeed a metavariable, and that it's the\n # first time we discover this higher order application.\n # (if it's not, it means term.parent must not be an Apply).\n And(symbols.contains(id.sym),\n Not(parent_term.cast(Apply)._.lhs == term)),\n id,\n No(Identifier)\n )\n )\n\n self_hoa = Var(outermost_metavar_application_of(\n Self, unification_context.self_parent\n ))\n other_hoa = Var(outermost_metavar_application_of(\n other, unification_context.other_parent\n ))\n\n unify_eqs = Var(\n Self.first_order_unify_equation(other).singleton\n .concat(self_hoa.then(\n lambda id:\n Self.higher_order_unify_equation(other, id).singleton\n )).concat(other_hoa.then(\n lambda id:\n other.higher_order_unify_equation(Self, id).singleton\n ))\n )\n\n return UnifyEquation.new(\n eq=unify_eqs.logic_any(lambda eq: eq.eq),\n renamings=unify_eqs.mapcat(lambda eq: eq.renamings)\n )\n\n @langkit_property(return_type=Substitution.array)\n def unify_with_constraints(other=T.Term,\n domain_constraints=UnifyQuery.array,\n symbols=T.Symbol.array,\n allow_incomplete=(T.Bool, False)):\n return Self.unify_all_with_constraints(\n UnifyQuery.new(first=Self, second=other).singleton,\n domain_constraints,\n symbols,\n allow_incomplete\n )\n\n @langkit_property(public=True, return_type=Substitution.array)\n def unify(other=T.Term, symbols=T.Symbol.array,\n allow_incomplete=(T.Bool, False)):\n return Self.unify_all(\n UnifyQuery.new(first=Self, second=other).singleton,\n symbols,\n allow_incomplete\n )\n\n @langkit_property(return_type=T.Term, memoized=True)\n def final_result_domain():\n return Self.normalize.match(\n lambda ar=Arrow: ar.rhs.final_result_domain,\n lambda x: x\n )\n\n @langkit_property(return_type=T.Int, memoized=True)\n def param_count():\n return Self.normalize.match(\n lambda ar=Arrow: ar.rhs.param_count + 1,\n lambda _: 0\n )\n\n @langkit_property(return_type=T.Term.array)\n def call_args():\n return Self.match(\n lambda ap=Apply: ap.lhs.call_args.concat(ap.rhs.singleton),\n lambda _: No(Term.array)\n )\n\n @langkit_property(return_type=T.Bool, public=True)\n def is_call_to(sym=T.Symbol, left_args=T.Int):\n return Self.match(\n lambda i=Identifier: (i.sym == sym) & (left_args == 0),\n lambda ap=Apply: ap.lhs.is_call_to(sym, left_args - 1),\n lambda _: False\n )\n\n @langkit_property(return_type=T.Term.array)\n def find_calls_to(sym=T.Symbol, args=T.Int):\n return Self.match(\n lambda ap=Apply: ap.lhs.find_calls_to(sym, args).concat(\n ap.rhs.find_calls_to(sym, args)\n ),\n lambda ar=Arrow: ar.lhs.find_calls_to(sym, args).concat(\n ar.rhs.find_calls_to(sym, args)\n ).concat(ar.binder._.find_calls_to(sym, args)),\n lambda ab=Abstraction: ab.term.find_calls_to(sym, args),\n lambda _: No(Term.array)\n ).concat(Self.is_call_to(sym, args).then(\n lambda _: Self.singleton\n ))\n\n @langkit_property(return_type=T.Constructor.array)\n def constructors_impl(constructors=T.Introduction.array,\n generics=T.Symbol.array,\n domain_constraints=(UnifyQuery.array,\n No(UnifyQuery.array))):\n return constructors.map(\n lambda c: c.as_template(c.ident)\n ).mapcat(\n lambda c: Let(\n lambda inst=c.instance.final_result_domain:\n\n Try(\n Let(\n lambda substs=Self.unify_with_constraints(\n inst,\n domain_constraints,\n c.new_symbols.concat(generics),\n allow_incomplete=True\n ):\n\n Constructor.new(\n template=Template.new(\n origin=c.origin,\n instance=c.instance.substitute_all(substs),\n new_symbols=c.new_symbols.filter(\n lambda sym: Not(substs.any(\n lambda subst: subst.from_symbol == sym\n ))\n )\n ),\n substs=substs\n ).singleton\n ),\n No(Constructor.array)\n )\n )\n )\n\n @langkit_property(return_type=T.Constructor.array.array)\n def grouped_constructors_impl(constrs=T.Constructor.array, i=T.Int):\n filtered = Var(constrs.filter(\n lambda c:\n (c.template.instance.param_count +\n c.template.new_symbols.length) == i\n ))\n not_filtered = Var(constrs.filter(\n lambda c:\n (c.template.instance.param_count +\n c.template.new_symbols.length) != i\n ))\n return filtered.singleton.concat(\n not_filtered.then(\n lambda rest: Self.grouped_constructors_impl(rest, i + 1)\n )\n )\n\n @langkit_property(return_type=T.Constructor.array.array)\n def grouped_constructors(constructors=T.Introduction.array,\n generics=T.Symbol.array,\n domain_constraints=UnifyQuery.array):\n return Self.grouped_constructors_impl(\n Self.constructors_impl(constructors, generics, domain_constraints),\n 0\n )\n\n @langkit_property(public=True, return_type=T.Term.entity.array)\n def constructors():\n normed = Var(Self.normalize)\n generics = Var(normed.free_symbols)\n\n ignore(Var(Cond(\n normed.is_a(Arrow),\n PropertyError(T.Bool, \"Cannot list constructors of arrow types\"),\n\n normed.is_a(Abstraction),\n PropertyError(T.Bool, \"Abstractions are not valid domains\"),\n\n True\n )))\n\n intros = Var(Self.unit.root.cast(Program).all_constructors)\n\n return normed.constructors_impl(intros, generics).map(\n lambda c: c.template.origin.as_bare_entity\n )\n\n @langkit_property(return_type=SynthesizationAttempt,\n dynamic_vars=[synthesis_context])\n def synthesize_abstraction():\n ar = Var(Self.cast_or_raise(Arrow))\n\n sym = Var(Self.unique_fresh_symbol(ar.binder._.cast(Identifier).then(\n lambda id: id.sym,\n default_val=\"x\"\n )))\n id = Var(Self.make_ident(sym))\n\n body_attempt = Var(ar.binder.then(\n lambda b: b.cast(Identifier).then(\n lambda i: i.intro.then(\n lambda _: ar.rhs,\n default_val=ar.rhs.substitute(i.sym, id)\n ),\n default_val=PropertyError(\n Term, \"Non-identifier binders are not handled yet.\"\n )\n ),\n default_val=ar.rhs\n ).then(\n lambda rhs: synthesis_context.bind(\n SynthesisContext.new(\n intros=synthesis_context.intros.concat(\n Self.make_nested_intro(\n name=id, dom=ar.lhs,\n bound_generics=synthesis_context.bound_generics\n ).cast(Introduction).singleton\n ),\n bound_generics=synthesis_context.bound_generics\n ),\n rhs.synthesize_impl\n )\n ))\n\n return SynthesizationAttempt.new(\n term=Self.make_abstraction(id, body_attempt.term),\n holes=body_attempt.holes,\n free_symbols=body_attempt.free_symbols\n )\n\n @langkit_property(return_type=SynthesizationAttempt,\n dynamic_vars=[synthesis_context])\n def synthesize_apply_arrow(built=T.Term, ar=T.Arrow):\n binder = Var(ar.binder)\n\n is_introduced = Var(binder.then(\n lambda b: b.free_symbols.all(\n lambda s: synthesis_context.intros.any(\n lambda i: i.ident.sym == s\n )\n )\n ))\n\n arg = Var(If(\n is_introduced,\n SynthesizationAttempt.new(\n term=binder,\n holes=No(SynthesizationHole.array),\n free_symbols=No(T.Symbol.array)\n ),\n synthesis_context.bind(\n SynthesisContext.new(\n intros=synthesis_context.intros,\n bound_generics=synthesis_context.bound_generics.concat(\n ar.lhs.free_symbols.filter(\n lambda s:\n Not(synthesis_context.bound_generics.contains(s))\n )\n )\n ),\n ar.lhs.synthesize_impl\n )\n ))\n\n new_built = Var(Self.make_apply(built, arg.term))\n\n rhs_type = Var(If(\n binder.is_null | is_introduced,\n ar.rhs,\n ar.rhs.substitute(\n binder.cast(Identifier).then(\n lambda b: b.sym,\n default_val=PropertyError(\n Symbol, \"Non-identifier binders are not handled yet.\"\n )\n ),\n arg.term\n )\n ))\n\n rec = Var(Self.synthesize_apply(new_built, rhs_type))\n\n return SynthesizationAttempt.new(\n term=rec.term,\n holes=rec.holes.concat(arg.holes),\n free_symbols=rec.free_symbols\n )\n\n @langkit_property(return_type=SynthesizationAttempt,\n dynamic_vars=[synthesis_context])\n def synthesize_apply(built=T.Term, callee_type=T.Term):\n return callee_type.match(\n lambda ar=Arrow:\n Self.synthesize_apply_arrow(built, ar),\n\n lambda _: SynthesizationAttempt.new(\n term=built,\n holes=No(SynthesizationHole.array),\n free_symbols=No(T.Symbol.array)\n )\n )\n\n @langkit_property(return_type=Constructor.array,\n dynamic_vars=[synthesis_context],\n activate_tracing=True)\n def synthesizable_constructors(generics=T.Symbol.array,\n domain_constraints=UnifyQuery.array):\n intros = Var(\n Self.unit.root.cast(Program).all_constructors\n .concat(synthesis_context.intros)\n )\n\n return Self.grouped_constructors(\n intros, generics, domain_constraints\n ).mapcat(\n lambda constrs: constrs\n )\n\n @langkit_property(return_type=SynthesizationAttempt,\n dynamic_vars=[synthesis_context])\n def synthesize_impl():\n return Self.normalize.match(\n lambda ar=Arrow: ar.synthesize_abstraction,\n lambda ab=Abstraction: PropertyError(\n SynthesizationAttempt, \"Abstractions are not valid domains\"\n ),\n lambda other: Let(\n lambda hole=Self.make_ident(Self.unique_fresh_symbol(\"hole\")):\n\n SynthesizationAttempt.new(\n term=hole,\n holes=SynthesizationHole.new(\n sym=hole.sym,\n domain_val=other,\n ctx=synthesis_context\n ).singleton,\n free_symbols=No(T.Symbol.array)\n )\n )\n )\n\n @langkit_property(return_type=SynthesizationHole.array)\n def updated_hole_set(original_holes=SynthesizationHole.array,\n substs=T.Substitution.array,\n potential_emerging_holes=T.Identifier.array,\n index=(T.Int, 0)):\n h = Var(original_holes.at(index))\n\n subst = Var(substs.find(lambda s: s.from_symbol == h.sym))\n is_replaced = Var(Not(subst.is_null))\n\n emerging_holes = Var(If(\n is_replaced,\n potential_emerging_holes.filter(\n lambda id: subst.to_term.is_free(id.sym)\n ),\n No(T.Identifier.array),\n ))\n\n rest_potential_emerging_holes = Var(potential_emerging_holes.filter(\n lambda h: Not(emerging_holes.contains(h))\n ))\n\n updated_ctx = Var(SynthesisContext.new(\n intros=h.ctx.intros.map(\n lambda i: Self.make_nested_intro(\n name=i.ident,\n dom=i.term.substitute_all(substs),\n bound_generics=h.ctx.bound_generics\n ).cast(Introduction)\n ),\n bound_generics=h.ctx.bound_generics\n ))\n\n return Cond(\n Not(is_replaced),\n\n SynthesizationHole.new(\n sym=h.sym,\n domain_val=h.domain_val.substitute_all(substs),\n ctx=updated_ctx\n ).singleton,\n\n emerging_holes.length > 0,\n emerging_holes.map(\n lambda id: SynthesizationHole.new(\n sym=id.sym,\n domain_val=id.domain_val,\n ctx=updated_ctx\n )\n ),\n\n No(SynthesizationHole.array)\n ).concat(If(\n index == original_holes.length - 1,\n No(SynthesizationHole.array),\n Self.updated_hole_set(\n original_holes,\n substs,\n rest_potential_emerging_holes,\n index + 1\n )\n ))\n\n @langkit_property(return_type=SynthesizationAttempt,\n dynamic_vars=[synthesis_context],\n activate_tracing=True)\n def construct_attempt(from_attempt=SynthesizationAttempt,\n from_hole=SynthesizationHole, constr=Constructor):\n\n dom = Var(from_hole.domain_val)\n\n atp = Var(dom.synthesize_apply(\n constr.template.origin,\n constr.template.instance\n ))\n\n substs = Var(Substitution.new(\n from_symbol=from_hole.sym,\n to_term=atp.term\n ).singleton.concat(constr.substs))\n\n updated_holes = Var(Self.updated_hole_set(\n atp.holes.concat(from_attempt.holes),\n substs,\n constr.template.new_symbols.mapcat(\n lambda s: Self.make_ident(s).then(\n lambda id: If(\n Not(id.domain_val.is_null),\n id.cast(Identifier).singleton,\n No(Identifier.array)\n )\n )\n )\n ))\n\n return SynthesizationAttempt.new(\n term=from_attempt.term.substitute_all(substs, unsafe=True),\n holes=updated_holes,\n free_symbols=from_attempt.free_symbols.filter(\n lambda sym: Not(substs.any(lambda s: s.from_symbol == sym))\n ).concat(\n constr.template.new_symbols.filter(\n lambda s: Not(updated_holes.any(lambda h: h.sym == s))\n )\n )\n )\n\n @langkit_property(return_type=SynthesizationAttempt.array,\n activate_tracing=True)\n def synthesize_attempt(attempt=SynthesizationAttempt,\n origin=T.Introduction):\n first_hole = Var(attempt.holes.at(0))\n free_syms = Var(\n attempt.holes.map(lambda h: h.sym).concat(attempt.free_symbols)\n )\n\n hole_constraints = Var(\n attempt.holes.filter(\n lambda h: first_hole.domain_val.is_free(h.sym)\n ).map(\n lambda h: UnifyQuery.new(\n first=Self.make_ident(h.sym),\n second=h.domain_val\n )\n )\n )\n\n constrs = Var(synthesis_context.bind(\n first_hole.ctx,\n first_hole.domain_val.synthesizable_constructors(\n free_syms,\n hole_constraints\n )\n ))\n\n return synthesis_context.bind(first_hole.ctx, constrs.mapcat(\n lambda constr: Try(\n Self.construct_attempt(attempt, first_hole, constr).singleton,\n No(SynthesizationAttempt.array)\n )\n ))\n\n @langkit_property(return_type=T.Term)\n def synthesize_breadth_first_search(attempts=SynthesizationAttempt.array,\n origin=T.Introduction, depth=T.Int):\n result = Var(attempts.find(lambda atp: atp.holes.length == 0))\n return Cond(\n Not(result.is_null),\n result.term,\n\n depth == 0,\n attempts.at(0).term,\n\n Self.synthesize_breadth_first_search(\n attempts.mapcat(\n lambda atp: Self.synthesize_attempt(atp, origin)\n ),\n origin,\n depth - 1\n )\n )\n\n @langkit_property(return_type=T.Term)\n def sanitize_synthesization(from_term=T.Term):\n arrow_type = Var(from_term.cast(Arrow))\n binder = Var(arrow_type._.binder.cast(Identifier))\n abs = Var(Self.cast(Abstraction))\n return If(\n Not(abs.is_null) & Not(binder.is_null),\n Self.make_abstraction(\n binder,\n abs.term.substitute(\n abs.ident.sym, binder\n ).sanitize_synthesization(arrow_type.rhs)\n ),\n Self\n )\n\n @langkit_property(public=True, return_type=T.Term)\n def synthesize(origin=(T.Introduction, No(T.Introduction))):\n return synthesis_context.bind(\n SynthesisContext.new(\n intros=No(Introduction.array),\n bound_generics=Self.free_symbols\n ),\n Self.synthesize_breadth_first_search(\n Self.synthesize_impl.singleton,\n origin,\n 10\n )\n )._.sanitize_synthesization(Self)\n\n @langkit_property(public=True, return_type=T.Symbol.array, memoized=True)\n def free_symbols(deep=(T.Bool, False)):\n return Self.free_symbols_impl(deep, 0)\n\n @langkit_property(public=True, return_type=T.Symbol.array)\n def free_symbols_impl(deep=T.Bool, cur_depth=T.Int):\n def combine(l, r):\n return l.concat(r.filter(\n lambda s: Not(l.contains(s))\n ))\n\n def rec(node, inc_depth, then):\n return Let(\n lambda l=node.free_symbols_impl(\n deep, (cur_depth + 1) if inc_depth else cur_depth\n ): then(l)\n )\n\n return Self.match(\n lambda id=Identifier: id.intro.then(\n lambda _: No(T.Symbol.array),\n default_val=id.sym.singleton\n ),\n\n lambda ab=Abstraction:\n ab.term.free_symbols_impl(deep, cur_depth).filter(\n lambda s: s != ab.ident.sym\n ),\n\n lambda ap=Apply: rec(\n ap.lhs, False,\n lambda l: rec(\n ap.rhs, False,\n lambda r: combine(l, r)\n )\n ),\n\n lambda ar=Arrow: If(\n And(Not(deep), cur_depth > 0),\n No(T.Symbol.array),\n rec(\n ar.lhs, True,\n lambda l: rec(\n ar.rhs, False,\n lambda r: ar.binder.then(\n lambda b: rec(\n b, False,\n lambda x: combine(x, combine(l, r))\n ),\n default_val=combine(l, r)\n )\n )\n )\n )\n )\n\n @langkit_property(return_type=T.Bool, public=True)\n def is_free(sym=T.Symbol):\n return Self.match(\n lambda id=Identifier:\n id.sym == sym,\n\n lambda ap=Apply:\n ap.lhs.is_free(sym) | ap.rhs.is_free(sym),\n\n lambda ab=Abstraction:\n (ab.ident.sym != sym) & ab.term.is_free(sym),\n\n lambda ar=Arrow:\n ar.lhs.is_free(sym)\n | ar.rhs.is_free(sym)\n | ar.binder._.is_free(sym)\n )\n\n @langkit_property(return_type=T.Symbol)\n def free_fresh_symbol(prefix=T.Symbol, other=(T.Term, No(T.Term)),\n i=(T.Int, 0)):\n sym = Var(Self.concat_symbol_and_integer(prefix, i))\n return If(\n And(Self.is_free(sym), Or(other.is_null, other.is_free(sym))),\n Self.free_fresh_symbol(prefix, other, i + 1),\n sym\n )\n\n @langkit_property(return_type=T.Term)\n def eval_case(matches=T.Term, then_case=T.Term, else_case=T.Term):\n constr = Var(matches.cast_or_raise(Identifier))\n return Cond(\n Self.cast(Identifier)._.sym == constr.sym,\n then_case.eval,\n\n Self.cast(Apply)._.left_most_term.cast(Identifier)\n ._.sym == constr.sym,\n Self.cast(Apply).replace_left_most_term_with(then_case).eval,\n\n else_case._.extract_case_and_eval(Self)\n\n # todo: handle arrows here?\n )\n\n @langkit_property(return_type=T.Term)\n def extract_case_and_eval(arg=T.Term):\n case_expr = Var(Self.cast_or_raise(Apply))\n case_id = Var(case_expr.left_most_term.cast(Identifier))\n case_lhs = Var(case_expr.lhs.cast_or_raise(Apply))\n has_else = Var(case_lhs.lhs != case_id)\n return If(\n case_id._.sym == \"case\",\n If(\n has_else,\n arg.eval_case(\n case_lhs.lhs.cast_or_raise(Apply).rhs,\n case_lhs.rhs,\n case_expr.rhs\n ),\n arg.eval_case(\n case_lhs.rhs,\n case_expr.rhs,\n No(T.Term)\n )\n ),\n PropertyError(T.Term, \"expected `case`\")\n )\n\n @langkit_property(return_type=T.Bool, memoized=True)\n def is_match_application():\n elim_call = Var(Self.cast(Apply))\n elim_evaled = Var(elim_call._.lhs.eval.cast(Apply))\n elim_id = Var(elim_evaled._.lhs)\n return elim_id.cast(Identifier)._.sym == \"match\"\n\n @langkit_property(return_type=T.Term, memoized=True)\n def eval_match():\n elim_call = Var(Self.cast_or_raise(Apply))\n elim_evaled = Var(elim_call.lhs.eval.cast(Apply))\n return If(\n Self.is_match_application,\n elim_evaled.rhs.extract_case_and_eval(\n elim_call.rhs.eval\n )._or(Self),\n Self\n )\n\n @langkit_property(public=True, return_type=T.Term, memoized=True)\n def eval():\n return Self.match(\n lambda id=Identifier: id.intro._.definition.then(\n lambda d: d.term.node.eval,\n default_val=id\n ),\n lambda ap=Apply: ap.lhs.eval.cast(Abstraction).then(\n lambda ab: ab.term.substitute(ab.ident.sym, ap.rhs).eval,\n default_val=ap.eval_match\n ),\n lambda ab=Abstraction: ab.term.cast(Apply).then(\n lambda ap: If(\n And(\n ap.rhs.cast(Identifier)._.sym == ab.ident.sym,\n Not(ap.lhs.is_free(ab.ident.sym))\n ),\n ap.lhs.eval,\n ab\n ),\n )._or(ab),\n lambda ar=Arrow: ar\n )\n\n @langkit_property(public=True, return_type=T.Bool)\n def equivalent_entities(other=T.Term.entity):\n return Entity.node.equivalent(other.node)\n\n @langkit_property(return_type=T.Bool, memoized=True)\n def equivalent(other=T.Term):\n return Self.match(\n lambda id=Identifier: other.cast(Identifier).then(\n lambda o: o.sym == id.sym\n ),\n lambda ap=Apply: other.cast(Apply).then(\n lambda o: And(\n ap.lhs.equivalent(o.lhs),\n ap.rhs.equivalent(o.rhs)\n )\n ),\n lambda ab=Abstraction: other.cast(Abstraction).then(\n lambda o: ab.term.free_fresh_symbol(\"eq\", o.term).then(\n lambda sym:\n ab.term.rename(ab.ident.sym, sym).equivalent(\n o.term.rename(o.ident.sym, sym)\n )\n )\n ),\n lambda ar=Arrow: other.cast(Arrow).then(\n lambda o: And(\n ar.lhs.equivalent(o.lhs),\n ar.rhs.equivalent(o.rhs),\n ar.binder.then(\n lambda b: o.binder.then(\n lambda ob: Or(\n b.equivalent(ob),\n And(\n Not(ar.has_constraining_binder),\n Not(o.has_constraining_binder)\n )\n ),\n default_val=Not(ar.has_constraining_binder)\n ),\n default_val=Not(o.has_constraining_binder)\n )\n )\n )\n )\n\n @langkit_property(return_type=T.Term)\n def substitute_all(substs=Substitution.array, idx=(T.Int, 0),\n unsafe=(T.Bool, False)):\n return substs.at(idx).then(\n lambda r: Self.substitute(\n r.from_symbol,\n r.to_term,\n unsafe\n ).substitute_all(\n substs, idx + 1, unsafe\n ),\n default_val=Self\n )\n\n @langkit_property(public=True, return_type=T.Term, memoized=True)\n def substitute(sym=T.Symbol, val=T.Term, unsafe=(T.Bool, False)):\n return Self.match(\n lambda id=Identifier: If(\n id.sym == sym,\n val,\n id\n ),\n lambda ap=Apply: ap.make_apply(\n ap.lhs.substitute(sym, val, unsafe),\n ap.rhs.substitute(sym, val, unsafe)\n ),\n lambda ab=Abstraction: If(\n ab.ident.sym == sym,\n ab,\n If(\n val.is_free(ab.ident.sym) & Not(unsafe),\n ab.term.free_fresh_symbol(ab.ident.sym).then(\n lambda symp: ab.make_abstraction(\n ab.make_ident(symp),\n ab.term\n .rename(ab.ident.sym, symp)\n .substitute(sym, val, False)\n )\n ),\n ab.make_abstraction(\n ab.ident,\n ab.term.substitute(sym, val, unsafe)\n )\n )\n ),\n lambda ar=Arrow: Self.make_arrow(\n ar.lhs.substitute(sym, val, unsafe),\n ar.rhs.substitute(sym, val, unsafe),\n ar.binder._.substitute(sym, val, unsafe)\n )\n )\n\n @langkit_property(return_type=T.Term, memoized=True)\n def anti_substitute(val=T.Term, sym=T.Symbol):\n return If(\n Self.equivalent(val),\n Self.make_ident(sym),\n Self.match(\n lambda id=Identifier: id,\n lambda ap=Apply: ap.make_apply(\n ap.lhs.anti_substitute(val, sym),\n ap.rhs.anti_substitute(val, sym)\n ),\n lambda ab=Abstraction: ab.make_abstraction(\n ab.ident.anti_substitute(val, sym)\n .cast_or_raise(Identifier),\n ab.term.anti_substitute(val, sym)\n ),\n lambda ar=Arrow: ar.make_arrow(\n ar.lhs.anti_substitute(val, sym),\n ar.rhs.anti_substitute(val, sym),\n ar.binder._.anti_substitute(val, sym)\n )\n )\n )\n\n @langkit_property(return_type=T.Bool, memoized=True)\n def contains_term(t=T.Term, include_self=(T.Bool, False)):\n return If(\n include_self & Self.equivalent(t),\n True,\n Self.match(\n lambda id=Identifier: False,\n lambda ap=Apply: Or(\n ap.lhs.contains_term(t, True),\n ap.rhs.contains_term(t, True)\n ),\n lambda ab=Abstraction: Or(\n ab.ident.contains_term(t, True),\n ab.term.contains_term(t, True)\n ),\n lambda ar=Arrow: Or(\n ar.lhs.contains_term(t, True),\n ar.rhs.contains_term(t, True),\n ar.binder._.contains_term(t, True)\n )\n )\n )\n\n @langkit_property(return_type=T.Term.entity)\n def normalized_entities():\n return Entity.node.normalize.as_entity\n\n @langkit_property(public=True, return_type=T.Term, memoized=True)\n def normalize():\n evaled = Var(Self.eval)\n to_norm = Var(Cond(\n # prevent infinite evaluation\n evaled.contains_term(Self),\n Self,\n\n Not(Self.is_free(\"match\")) & evaled.is_free(\"match\"),\n Self,\n\n evaled\n ))\n\n return to_norm.match(\n lambda id=Identifier: id,\n lambda ap=Apply: ap.make_apply(\n ap.lhs.normalize,\n ap.rhs.normalize\n ),\n lambda ab=Abstraction: ab.make_abstraction(\n ab.ident,\n ab.term.normalize\n ),\n lambda ar=Arrow: ar.make_arrow(\n ar.lhs.normalize,\n ar.rhs.normalize,\n ar.binder._.normalize\n )\n )\n\n @langkit_property(return_type=T.Term)\n def rename_all(renamings=Renaming.array, idx=(T.Int, 0)):\n return renamings.at(idx).then(\n lambda r:\n Self.rename(r.from_symbol, r.to_symbol).rename_all(\n renamings, idx + 1\n ),\n default_val=Self\n )\n\n @langkit_property(return_type=T.Term, public=True, memoized=True)\n def rename(old=T.Symbol, by=T.Symbol):\n return Self.match(\n lambda id=Identifier: If(\n id.sym == old,\n id.make_ident(by),\n id\n ),\n lambda ap=Apply: ap.make_apply(\n ap.lhs.rename(old, by),\n ap.rhs.rename(old, by)\n ),\n lambda ab=Abstraction: If(\n old == ab.ident.sym,\n ab,\n ab.make_abstraction(\n ab.ident,\n ab.term.rename(old, by)\n )\n ),\n lambda ar=Arrow: ar.make_arrow(\n ar.lhs.rename(old, by),\n ar.rhs.rename(old, by),\n ar.binder._.rename(old, by)\n )\n )\n\n @langkit_property(return_type=T.LogicVar, memoized=True)\n def domain_var():\n return Self.create_logic_var\n\n @langkit_property(return_type=T.Identifier.array)\n def find_occurrences(sym=T.Symbol):\n return Self.match(\n lambda id=Identifier: If(\n id.sym == sym,\n id.singleton,\n No(Identifier.array)\n ),\n\n lambda ap=Apply:\n ap.lhs.find_occurrences(sym)\n .concat(ap.rhs.find_occurrences(sym)),\n\n lambda ab=Abstraction: If(\n ab.ident.sym == sym,\n No(Identifier.array),\n ab.term.find_occurrences(sym)\n ),\n\n lambda ar=Arrow:\n ar.lhs.find_occurrences(sym)\n .concat(ar.rhs.find_occurrences(sym))\n .concat(ar.binder._.find_occurrences(sym))\n )\n\n @langkit_property(return_type=T.Term.array)\n def flat_terms():\n return Self.singleton.concat(Self.match(\n lambda ap=Apply:\n ap.lhs.flat_terms.concat(ap.rhs.flat_terms),\n\n lambda ab=Abstraction:\n ab.ident.flat_terms.concat(ab.term.flat_terms),\n\n lambda ar=Arrow:\n ar.lhs.flat_terms\n .concat(ar.rhs.flat_terms)\n .concat(ar.binder._.flat_terms),\n\n lambda _: No(Term.array)\n ))\n\n @langkit_property(return_type=T.DomainEquation,\n uses_entity_info=False,\n activate_tracing=True)\n def domain_equation(bindings=Binding.array):\n relevant_binding = Var(bindings.find(\n lambda b: b.target == Self\n ))\n\n result = Var(Self.match(\n lambda id=Identifier: id.intro.then(\n lambda intro: Cond(\n Not(relevant_binding.is_null),\n DomainEquation.new(\n eq=LogicTrue(),\n templates=No(Identifier.array)\n ),\n\n intro.generic_formals.length > 0,\n DomainEquation.new(\n eq=LogicTrue(),\n templates=id.singleton\n ),\n\n DomainEquation.new(\n eq=Bind(\n Self.domain_var, intro.term.normalized_entities,\n conv_prop=Term.normalized_entities,\n eq_prop=Term.equivalent_entities\n ),\n templates=No(Identifier.array)\n )\n ),\n default_val=DomainEquation.new(\n eq=LogicTrue(),\n templates=No(Identifier.array)\n )\n ),\n lambda ap=Apply: Let(\n lambda\n lhs_eq=ap.lhs.domain_equation(bindings),\n rhs_eq=ap.rhs.domain_equation(bindings):\n\n DomainEquation.new(\n eq=And(\n lhs_eq.eq,\n rhs_eq.eq,\n Bind(ap.lhs.domain_var, ap.rhs.domain_var,\n conv_prop=Arrow.param,\n eq_prop=Term.equivalent_entities),\n Bind(ap.lhs.domain_var, ap.domain_var,\n conv_prop=Arrow.result,\n eq_prop=Term.equivalent_entities)\n ),\n templates=lhs_eq.templates.concat(rhs_eq.templates)\n )\n ),\n lambda ab=Abstraction: Let(\n lambda term_eq=ab.term.domain_equation(bindings):\n\n DomainEquation.new(\n eq=And(\n ab.term.find_occurrences(ab.ident.sym).logic_all(\n lambda id: Bind(\n ab.ident.domain_var, id.domain_var,\n conv_prop=Term.normalized_entities,\n eq_prop=Term.equivalent_entities\n ),\n ),\n Bind(ab.domain_var, ab.ident.domain_var,\n conv_prop=Arrow.param,\n eq_prop=Term.equivalent_entities),\n Bind(ab.domain_var, ab.term.domain_var,\n conv_prop=Arrow.result,\n eq_prop=Term.equivalent_entities),\n term_eq.eq\n ),\n templates=term_eq.templates\n )\n ),\n\n lambda ar=Arrow: Let(\n lambda\n lhs_eq=ar.lhs.domain_equation(bindings),\n rhs_eq=ar.rhs.domain_equation(bindings),\n binder_eq=ar.binder.then(\n lambda b: b.domain_equation(bindings),\n default_val=DomainEquation.new(\n eq=LogicTrue(),\n templates=No(Identifier.array)\n )\n ):\n\n DomainEquation.new(\n eq=And(\n lhs_eq.eq,\n rhs_eq.eq,\n binder_eq.eq,\n\n ar.binder.then(\n lambda b: Bind(\n b.domain_var, ar.lhs.normalize.as_bare_entity,\n eq_prop=Term.equivalent_entities\n ),\n default_val=LogicTrue()\n ),\n\n Or(\n And(\n Predicate(Term.is_highest_ranked_term,\n ar.lhs.domain_var, ar.rhs.domain_var),\n Bind(ar.domain_var, ar.lhs.domain_var,\n eq_prop=Term.equivalent_entities),\n ),\n And(\n Predicate(Term.is_highest_ranked_term,\n ar.rhs.domain_var, ar.lhs.domain_var),\n Bind(ar.domain_var, ar.rhs.domain_var,\n eq_prop=Term.equivalent_entities)\n )\n )\n ),\n templates=lhs_eq.templates.concat(\n rhs_eq.templates\n ).concat(\n binder_eq.templates\n )\n )\n )\n ))\n\n return relevant_binding.then(\n lambda b: DomainEquation.new(\n eq=Bind(\n Self.domain_var, b.domain_val.as_bare_entity,\n eq_prop=Term.equivalent_entities\n ) & result.eq,\n templates=result.templates\n ),\n default_val=result\n )\n\n @langkit_property(public=False, return_type=TypingsDescription,\n activate_tracing=True)\n def instantiate_templates(result_domain=T.Term,\n templates=Template.array,\n reps=T.Substitution.array):\n must_synthesize_arrow = Var(\n Self.cast(Abstraction).then(\n lambda ab: result_domain.cast(Arrow).then(\n lambda ar: False,\n default_val=True\n ),\n default_val=False\n )\n )\n actual_result_domain = Var(If(\n must_synthesize_arrow,\n Let(\n lambda\n lhs_sym=Self.unique_fresh_symbol(\"lhs\"),\n rhs_sym=Self.unique_fresh_symbol(\"rhs\"),\n binder_sym=Self.unique_fresh_symbol(\"binder\"):\n\n Self.make_arrow(\n Self.make_ident(lhs_sym),\n Self.make_ident(rhs_sym),\n Self.make_ident(binder_sym)\n )\n ),\n result_domain\n ))\n instantiation = Var(Self.instantiate_templates_impl(\n actual_result_domain,\n templates,\n reps\n ))\n return If(\n must_synthesize_arrow,\n Let(\n lambda ar=actual_result_domain.cast_or_raise(Arrow):\n\n TypingsDescription.new(\n bindings=instantiation.bindings,\n equations=instantiation.equations.concat(\n UnifyQuery.new(\n first=ar,\n second=result_domain\n ).singleton\n ),\n new_symbols=instantiation.new_symbols.concat(\n ArrayLiteral([\n ar.lhs.cast(Identifier).sym,\n ar.rhs.cast(Identifier).sym,\n ar.binder.cast(Identifier).sym\n ])\n )\n )\n ),\n instantiation\n )\n\n @langkit_property(public=False, return_type=TypingsDescription)\n def instantiate_templates_impl(result_domain=T.Term,\n templates=Template.array,\n reps=T.Substitution.array):\n def make_binding(domain):\n return Binding.new(\n target=Self,\n domain_val=domain\n )\n\n def rec_apply(ap, f):\n return Let(\n lambda\n lhs_res=ap.lhs.instantiate_templates(\n result_domain.then(\n lambda r_dom: Self.make_arrow(No(T.Term), r_dom)\n ),\n templates,\n reps\n ): Let(\n lambda\n rhs_res=ap.rhs.instantiate_templates(\n lhs_res.bindings.at(0).domain_val.cast(Arrow)._.lhs,\n templates, reps\n ):\n\n f(lhs_res, rhs_res)\n )\n )\n\n templated_result = Var(Self.match(\n lambda id=Identifier: TypingsDescription.new(\n bindings=templates.find(lambda t: t.origin == id).then(\n lambda t: t.instance,\n default_val=id.domain_val._or(result_domain)\n ).then(\n lambda dom: make_binding(dom)\n ).singleton,\n\n equations=No(UnifyQuery.array),\n\n new_symbols=No(Symbol.array)\n ),\n\n lambda ap=Apply: rec_apply(\n ap,\n lambda lhs_res, rhs_res: Let(\n lambda qs=rhs_res.bindings.at(0).domain_val.then(\n lambda rhs_dom: UnifyQuery.new(\n first=lhs_res.bindings.at(0)\n .domain_val.cast(Arrow).lhs,\n second=rhs_dom\n ).singleton\n ).concat(\n lhs_res.bindings.at(0)\n .domain_val.cast(Arrow)._.binder.then(\n lambda b: UnifyQuery.new(\n first=b,\n second=rhs_res.bindings.at(0)\n .target.substitute_all(reps).normalize\n ).singleton\n )\n ):\n\n TypingsDescription.new(\n bindings=make_binding(\n lhs_res.bindings.at(0)\n .domain_val.cast_or_raise(Arrow).rhs\n ).singleton.concat(\n lhs_res.bindings\n ).concat(\n rhs_res.bindings\n ),\n\n equations=lhs_res.equations.concat(\n rhs_res.equations\n ).concat(\n qs\n ),\n\n new_symbols=lhs_res.new_symbols.concat(\n rhs_res.new_symbols\n )\n )\n )\n ),\n\n lambda ab=Abstraction: Let(\n lambda\n term_res=ab.term.instantiate_templates(\n result_domain.cast(Arrow)._.rhs,\n templates,\n reps.filter(\n lambda s: s.from_symbol != ab.ident.sym\n ).concat(result_domain.cast(Arrow)._.binder.then(\n lambda b: Substitution.new(\n from_symbol=ab.ident.sym,\n to_term=b\n ).singleton\n ))\n ),\n id_dom=ab.ident.domain_val._or(\n result_domain.cast(Arrow)._.lhs\n ):\n\n TypingsDescription.new(\n bindings=id_dom.then(\n lambda lhs_dom:\n\n term_res.bindings.at(0).domain_val.then(\n lambda rhs_dom: make_binding(\n Self.make_arrow(\n lhs_dom,\n rhs_dom,\n result_domain.cast(Arrow)._.binder\n )\n )\n )\n ).singleton.concat(term_res.bindings),\n\n equations=term_res.equations.concat(id_dom.then(\n lambda lhs_dom:\n ab.term.find_occurrences(ab.ident.sym).map(\n lambda id: term_res.bindings.find(\n lambda b: b.target == id\n ).then(\n lambda b: UnifyQuery.new(\n first=lhs_dom,\n second=b.domain_val\n )\n )\n )\n )),\n\n new_symbols=term_res.new_symbols\n )\n ),\n\n lambda ar=Arrow: Let(\n lambda\n lhs_res=ar.lhs.instantiate_templates(\n No(Term), templates, reps\n ),\n rhs_res=ar.rhs.instantiate_templates(\n No(Term), templates, reps\n ),\n binder_res=ar.binder.then(\n lambda b: b.instantiate_templates(\n ar.lhs, templates, reps\n )\n ),\n new_sym=Self.unique_fresh_symbol(\"arrow\"):\n\n TypingsDescription.new(\n bindings=make_binding(\n Self.make_ident(new_sym)\n ).singleton.concat(\n lhs_res.bindings\n ).concat(\n rhs_res.bindings\n ).concat(\n binder_res.bindings\n ),\n\n equations=lhs_res.equations.concat(\n rhs_res.equations\n ).concat(\n binder_res.equations\n ),\n\n new_symbols=new_sym.singleton.concat(\n lhs_res.new_symbols\n ).concat(\n rhs_res.new_symbols\n )\n )\n )\n ))\n\n return Self.domain_val.then(\n lambda expected_dom:\n\n templated_result.bindings.at(0).domain_val.then(\n lambda found_dom: Let(\n lambda q=UnifyQuery.new(\n first=found_dom,\n second=expected_dom\n ):\n\n TypingsDescription.new(\n bindings=templated_result.bindings,\n equations=q.singleton.concat(\n templated_result.equations\n ),\n new_symbols=templated_result.new_symbols\n )\n )\n ),\n default_val=templated_result\n )\n\n @langkit_property(return_type=Bool)\n def reset_domain_vars():\n return Self.flat_terms.logic_all(\n lambda x: Bind(x.domain_var, No(Term.entity))\n ).solve\n\n @langkit_property(public=False, return_type=T.Bool)\n def check_domains_internal(expected_domain=T.Term,\n bindings=Binding.array, tries=T.Int):\n term_eq = Var(Self.domain_equation(bindings))\n domain_eq = Var(And(\n Bind(Self.domain_var, expected_domain.as_bare_entity,\n eq_prop=Term.equivalent_entities),\n term_eq.eq\n ))\n return term_eq.templates.then(\n lambda templates: (tries != 0) & Try(\n Self.solve_allowing_orphans(domain_eq),\n True\n ).then(lambda _: Let(\n lambda instances=templates.map(\n lambda t: t.intro.as_template(t)\n ):\n\n Self.instantiate_templates(\n expected_domain,\n instances,\n No(Substitution.array)\n ).then(lambda result: Self.check_domains_internal(\n expected_domain,\n Let(\n lambda substs=Self.unify_all(\n result.equations,\n instances.mapcat(\n lambda i: i.new_symbols\n ).concat(\n result.new_symbols\n )\n ): result.bindings.map(\n lambda b: Binding.new(\n target=b.target,\n domain_val=\n b.domain_val.substitute_all(substs).normalize\n )\n ).filter(\n lambda b: b.domain_val.free_symbols.all(\n lambda sym: Not(instances.any(\n lambda i: i.new_symbols.contains(sym)\n ))\n )\n )\n ),\n tries - 1\n ))\n )),\n default_val=domain_eq.solve\n )\n\n @langkit_property(return_type=T.Bool, activate_tracing=True,\n dynamic_vars=[unification_context])\n def has_domain(domain_val=T.Term.entity):\n constrained_term = Var(Self.solve_time_substitution)\n domain_term = Var(domain_val.node.solve_time_substitution)\n\n ignore(Var(constrained_term.reset_domain_vars))\n\n return Try(\n constrained_term.check_domains_internal(\n domain_term, No(Binding.array), -1\n ),\n False\n )\n\n @langkit_property(return_type=T.Equation,\n dynamic_vars=[unification_context])\n def has_domain_equation(other=T.Term):\n # Use the fact that predicates with more than 1 var are evaluated last\n vars = Var(unification_context.vars)\n term_var = Var(vars.elem(Self.unique_fresh_symbol(\"$tmp_trm\")))\n domain_var = Var(vars.elem(Self.unique_fresh_symbol(\"$tmp_dom\")))\n return And(\n Bind(term_var, Self.as_bare_entity),\n Bind(domain_var, other.as_bare_entity),\n Predicate(Term.has_domain, term_var, domain_var)\n )\n\n @langkit_property(return_type=T.Term, public=True)\n def domain_val():\n return Self.domain_var.get_value._.node.cast_or_raise(Term)\n\n @langkit_property(return_type=T.Bool)\n def is_highest_ranked_term(other=DependzNode.entity):\n other_term = Var(other.cast_or_raise(Term).node)\n return Self.self_or_higher_ranked_term(other_term) == Self\n\n @langkit_property(return_type=T.Term, memoized=True)\n def self_or_higher_ranked_term(other=T.Term):\n self_chain = Var(Self.domain_chain)\n other_chain = Var(other.domain_chain)\n first_common = Var(self_chain.find(\n lambda d1: other_chain.any(\n lambda d2: d1.equivalent(d2)\n )\n ))\n return first_common._or(PropertyError(Term, \"Terms are incompatible\"))\n\n @langkit_property(return_type=T.Term.array, memoized=True)\n def domain_chain():\n id = Var(Self.cast_or_raise(Identifier))\n domain = Var(id.intro._.term)\n return Self.singleton.concat(If(\n domain.cast(Identifier).then(lambda d: d.intro._.term == domain),\n domain.node.singleton,\n domain._.domain_chain\n ))\n\n\n@abstract\nclass Identifier(Term):\n sym = AbstractProperty(type=Symbol)\n\n to_string = Property(Self.sym.image)\n\n @langkit_property(public=True, return_type=T.Bool)\n def is_introducing():\n return Self.parent.cast(Introduction).then(\n lambda i: i.ident == Self\n )\n\n @langkit_property(public=True, return_type=T.Introduction.entity,\n memoized=True)\n def intro():\n return Self.node_env.get_first(Self.sym).cast(Introduction)\n\n\nclass SourceId(Identifier):\n token_node = True\n sym = Property(Self.symbol)\n\n\n@synthetic\nclass SyntheticId(Identifier):\n name = UserField(type=T.Symbol)\n sym = Property(Self.name)\n\n\nclass Apply(Term):\n lhs = Field(type=Term)\n rhs = Field(type=Term)\n\n to_string = Property(String(\"(\").concat(\n Self.lhs.to_string.concat(String(' ')).concat(Self.rhs.to_string)\n ).concat(String(\")\")))\n\n @langkit_property(return_type=Term)\n def left_most_term():\n return Self.lhs.cast(Apply).then(\n lambda ap: ap.left_most_term,\n default_val=Self.lhs\n )\n\n @langkit_property(return_type=Term)\n def replace_left_most_term_with(other=Term):\n return Self.make_apply(\n Self.lhs.cast(Apply).then(\n lambda ap: ap.replace_left_most_term_with(other),\n default_val=other\n ),\n Self.rhs\n )\n\n @langkit_property(return_type=Term.entity.array, public=True)\n def as_term_array():\n return Self.left_most_term.as_bare_entity.singleton.concat(\n Self.call_args.map(lambda x: x.as_bare_entity)\n )\n\n\n@synthetic\nclass SyntheticApply(Apply):\n pass\n\n\nclass Abstraction(Term):\n ident = Field(type=Identifier)\n term = Field(type=Term)\n\n @langkit_property()\n def to_string():\n actual_self = Var(If(\n Self.is_synthesized,\n Self.first_available_standard_symbol.then(\n lambda s: Self.make_abstraction(\n Self.make_ident(s),\n Self.term.rename(Self.ident.sym, s)\n ),\n default_val=Self\n ),\n Self,\n ))\n return String(\"(\\\\\").concat(\n actual_self.ident.to_string.concat(String('. ')).concat(\n actual_self.term.to_string\n )\n ).concat(String(\")\"))\n\n @langkit_property(return_type=Bool)\n def is_synthesized():\n return Self.ident.to_string.contains(CharacterLiteral(\"$\"))\n\n @langkit_property(return_type=Symbol)\n def first_available_standard_symbol():\n return ArrayLiteral([\"x\", \"y\", \"z\", \"e\"], element_type=T.Symbol).find(\n lambda s: Not(Self.term.is_free(s))\n )\n\n\n@synthetic\nclass SyntheticAbstraction(Abstraction):\n pass\n\n\nclass Arrow(Term):\n binder = Field(type=Term)\n lhs = Field(type=Term)\n rhs = Field(type=Term)\n\n @langkit_property()\n def to_string():\n lhs_str = Var(Self.lhs._.to_string)\n rhs_str = Var(Self.rhs.to_string)\n return String('(').concat(Self.binder.then(\n lambda b: If(\n Self.has_constraining_binder,\n b.to_string.concat(String(':')).concat(lhs_str),\n lhs_str\n ),\n default_val=lhs_str\n ).concat(String(' -> ')).concat(rhs_str).concat(String(')')))\n\n @langkit_property(return_type=Term.entity)\n def param():\n return Entity.lhs.normalized_entities\n\n @langkit_property(return_type=Term.entity)\n def result():\n return Entity.rhs.normalized_entities\n\n @langkit_property(return_type=T.Bool)\n def has_constraining_binder():\n return Self.binder.then(\n lambda b: b.cast(Identifier).then(\n lambda id: If(\n id.intro.is_null,\n Self.lhs.is_free(id.sym) | Self.rhs.is_free(id.sym),\n True\n ),\n default_val=True\n )\n )\n\n\n@synthetic\nclass SyntheticArrow(Arrow):\n pass\n\n\nclass Introduction(DependzNode):\n \"\"\"\n Identifer : Term\n \"\"\"\n ident = Field(type=Identifier)\n term = Field(type=Term)\n\n @langkit_property(public=True, return_type=T.Definition.entity,\n memoized=True)\n def definition():\n return Self.children_env.get_first('__definition').cast(T.Definition)\n\n @langkit_property(public=True, return_type=T.Symbol.array,\n memoized=True)\n def generic_formals():\n return Self.term.free_symbols\n\n @langkit_property(public=True, return_type=Template)\n def as_template(origin_term=T.Term):\n renamings = Var(Self.generic_formals.map(lambda s: Renaming.new(\n from_symbol=s,\n to_symbol=Self.unique_fresh_symbol(s)\n )))\n return Template.new(\n origin=origin_term,\n instance=Self.term.rename_all(renamings).normalize,\n new_symbols=renamings.map(lambda r: r.to_symbol)\n )\n\n env_spec = EnvSpec(\n add_to_env_kv(Self.ident.sym, Self),\n add_env()\n )\n\n\n@synthetic\nclass NestedIntroduction(Introduction):\n bound_generics = UserField(type=T.Symbol.array)\n\n @langkit_property()\n def generic_formals():\n return Self.term.free_symbols.filter(\n lambda s: Not(Self.bound_generics.contains(s))\n )\n\n\nclass Definition(DependzNode):\n \"\"\"\n Identifier = Term\n \"\"\"\n ident = Field(type=SourceId)\n term = Field(type=Term)\n\n @langkit_property(public=True, return_type=T.String)\n def eval_and_print():\n return Self.term.normalize.to_string\n\n @langkit_property(public=True, return_type=T.Bool)\n def check_domains(tries=(T.Int, -1)):\n return Self.term.check_domains_internal(\n Self.ident.intro.term.normalize,\n No(Binding.array), tries\n )\n\n env_spec = EnvSpec(\n handle_children(),\n add_to_env_kv(\n '__definition', Self,\n dest_env=Self.ident.intro.children_env\n )\n )\n\n\nclass Program(DependzNode.list):\n @langkit_property(return_type=Introduction.array,\n memoized=True)\n def all_introductions():\n return Self.children_env.get(No(T.Symbol)).filtermap(\n lambda n: n.cast(Introduction).node,\n lambda n: n.is_a(Introduction)\n )\n\n @langkit_property(return_type=Introduction.array,\n memoized=True)\n def all_constructors():\n return Self.all_introductions.filter(\n lambda n: n.definition.is_null\n )\n\n env_spec = EnvSpec(\n add_env()\n )\n","repo_name":"Roldak/Dependz","sub_path":"dependz/language/ast.py","file_name":"ast.py","file_ext":"py","file_size_in_byte":86617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"25390508318","text":"import cv2\r\nimport numpy as np\r\nimg = cv2.imread(\"meterP.PNG\")\r\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\ncanny = cv2.Canny(gray, 50, 200)\r\nB = 0\r\nG = 0\r\nR = 0\r\nn = 0\r\nr = 8\r\nh = w = r * 2 + 1\r\nkernel = np.zeros((h, w), dtype=np.uint8)\r\ncv2.circle(kernel, (r, r), r, 1, -1) \r\nopeningimg = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)\r\nret,markerImag = cv2.threshold(openingimg,50,255,cv2.THRESH_BINARY_INV)\r\n\r\ncontours, hierarchy = cv2.findContours(markerImag,cv2.cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\r\nprint(len(contours))\r\nclone = img.copy()\r\nclone2 = img.copy()\r\narea=[]\r\nfor c in contours:\r\n area.append(cv2.contourArea(c))\r\nmax_area = np.argmax(np.array(area))\r\ncv2.drawContours(clone2, contours, max_area, (0, 255, 0), cv2.FILLED)\r\n#cv2.fillPoly(clone2, max_area, (0,0,255))\r\n\"\"\"\r\nfor c in contours:\r\n (x, y, w, h) = cv2.boundingRect(c)\r\n cv2.rectangle(clone, (x, y), (x + w, y + h), (0, 255, 0), 2)\r\n\"\"\" \r\n\"\"\"\r\nfor c in contours:\r\n if len(c)>100:\r\n cv2.drawContours(clone, c, -1, (0, 255, 0), 2)\r\n\"\"\"\r\ncv2.imshow('openingimg', openingimg)\r\ncv2.imshow('markerImag', markerImag)\r\ncv2.imshow('img', img)\r\ncv2.imshow('clone2', clone2)\r\n\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()","repo_name":"chuyc511/Embedded_image","sub_path":"AnalogGauge/Gauge/Contour.py","file_name":"Contour.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"28517264789","text":"import torch\nimport torch.nn as nn\n#import torch.nn.functional as F\nfrom opts import options\n\nopt = options()\n\ndef conv3x3(iChannels, oChannels, stride):\n return nn.Conv2d(iChannels, oChannels, (3, 3), (stride, stride), (1, 1))\n\ndef conv1x1(iChannels, oChannels, stride):\n return nn.Conv2d(iChannels, oChannels, (1, 1), (stride, stride), (0, 0))\n\nclass resBlock(nn.Module):\n def __init__(self, iChannels, oChannels, stride):\n super(resBlock, self).__init__()\n\n n = oChannels\n self.residual = nn.Sequential(\n conv3x3(int(iChannels), int(n/4), 1), nn.BatchNorm2d(int(n/4), 1e-3), nn.PReLU(),\n conv3x3(int(n/4), int(n/2), stride), nn.BatchNorm2d(int(n/2), 1e-3), nn.PReLU(),\n conv3x3(int(n/2), int(n), 1), nn.BatchNorm2d(int(n), 1e-3)\n )\n if stride > 1 or iChannels != n:\n self.identity = nn.Sequential(conv1x1(iChannels, oChannels, stride), nn.BatchNorm2d(int(n), 1e-3))\n else:\n self.identity = 'Identity'\n self.prelu = nn.PReLU()\n\n def forward(self, x):\n out = self.residual(x)\n if self.identity == 'Identity':\n out += x\n else:\n out += self.identity(x)\n out = self.prelu(out)\n return out\n\nclass Net(nn.Module):\n def __init__(self, iChannels, output):\n super(Net, self).__init__()\n if opt.mode == 'xy' or 'z' or 'alpha' or 'beta':\n self.features = nn.Sequential(\n nn.Conv2d(int(iChannels), 64, 7, 1, 3), nn.BatchNorm2d(64, 1e-3), nn.PReLU(),\n nn.Conv2d(64, 128, 5, 1, 2), nn.BatchNorm2d(128, 1e-3), nn.PReLU(),\n resBlock(128,128,1), resBlock(128,128,1), resBlock(128,128,1),\n resBlock(128,256,1),\n resBlock(256,256,1), resBlock(256,256,1), resBlock(256,256,1),\n conv1x1(256, 128, 1), nn.BatchNorm2d(128, 1e-3), nn.PReLU(),\n conv1x1(128, 64, 1), nn.BatchNorm2d(64, 1e-3), nn.PReLU(),\n conv1x1(64, 1, 1), nn.BatchNorm2d(1, 1e-3), nn.PReLU()\n )\n self.FCs = nn.Sequential(\n nn.Linear(opt.imHeight * opt.imWidth, 10), nn.PReLU(),\n nn.Linear(10,int(output))\n )\n self.Hardtanh = nn.Hardtanh(-opt.crange, opt.crange)\n\n if opt.mode == 'aberration':\n if opt.labelsize <= 12:\n self.features = nn.Sequential(\n nn.Conv2d(int(iChannels), 64, 7, 1, 3), nn.BatchNorm2d(64, 1e-3), nn.PReLU(),\n nn.Conv2d(64, 128, 5, 1, 2), nn.BatchNorm2d(128, 1e-3), nn.PReLU(),\n resBlock(128, 128, 1), resBlock(128, 128, 1), resBlock(128, 128, 1),\n resBlock(128, 256, 4),\n resBlock(256, 256, 1), resBlock(256, 256, 1), resBlock(256, 256, 1),\n conv1x1(256, 128, 1), nn.BatchNorm2d(128, 1e-3), nn.PReLU(),\n conv1x1(128, 64,1), nn.BatchNorm2d(64, 1e-3), nn.PReLU(),\n conv1x1(64, 1, 1), nn.BatchNorm2d(1, 1e-3), nn.PReLU()\n )\n self.FCs = nn.Sequential(\n nn.Linear(int((opt.imHeight/4) * (opt.imWidth/4)), int(output))\n )\n else:\n self.features = nn.Sequential(\n nn.Conv2d(int(iChannels), 64, 7, 1, 3), nn.BatchNorm2d(64, 1e-3), nn.PReLU(),\n nn.Conv2d(64, 128, 5, 1, 2), nn.BatchNorm2d(128, 1e-3), nn.PReLU(),\n resBlock(128, 128, 1), resBlock(128, 128, 1), resBlock(128, 128, 1),\n resBlock(128, 256, 4),\n resBlock(256, 256, 1), resBlock(256, 256, 1), resBlock(256, 256, 1),\n resBlock(256, 1024, 4),\n resBlock(1024, 1024, 1), resBlock(1024, 1024, 1), resBlock(1024, 1024, 1),\n conv1x1(1024, int(output), 1)\n )\n\n def forward(self, x):\n x = self.features(x)\n if opt.labelsize > 12:\n x = x.view(-1, x.size(1))\n else:\n x = x.view(-1, x.size(2)*x.size(3))\n x = self.FCs(x)\n if opt.mode == 'xy' or 'z':\n x = self.Hardtanh(x)\n return x\n","repo_name":"HuanglabPurdue/smNet","sub_path":"smNet_Pytorch/smNet/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4382,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"72"}
+{"seq_id":"72624412072","text":"from building_energy_standards_data.database_engine.database import DBOperation\nfrom building_energy_standards_data.database_engine.database_util import (\n is_float,\n getattr_either,\n)\n\nRECORD_HELP = \"\"\"\nMust provide a tuple that contains:\ntemplate: TEXT\nnumber_of_poles: NUMERIC\ntype: TEXT\nsynchronous_speed: TEXT\nminimum_capacity: NUMERIC\nmaximum_capacity: NUMERIC\nnominal_full_load_efficiency: NUMERIC\nannotation: TEXT (optional)\n\"\"\"\n\nCREATE_HVAC_REQUIREMENT_MOTORS_TABLE = \"\"\"\nCREATE TABLE IF NOT EXISTS %s\n(id INTEGER PRIMARY KEY, \ntemplate TEXT NOT NULL, \nnumber_of_poles NUMERIC,\ntype TEXT NOT NULL,\nsynchronous_speed NUMERIC NOT NULL,\nminimum_capacity NUMERIC,\nmaximum_capacity NUMERIC NOT NULL,\nnominal_full_load_efficiency NUMERIC NOT NULL,\nannotation TEXT);\n\"\"\"\n\nINSERT_A_MOTOR_RECORD = \"\"\"\n INSERT INTO %s (\ntemplate, \nnumber_of_poles,\ntype,\nsynchronous_speed,\nminimum_capacity,\nmaximum_capacity,\nnominal_full_load_efficiency,\nannotation\n) \nVALUES (?, ?, ?, ? , ?, ?, ?, ?);\n\"\"\"\n\nRECORD_TEMPLATE = {\n \"template\": \"\",\n \"number_of_poles\": 0.0,\n \"type\": \"\",\n \"synchronous_speed\": 0.0,\n \"minimum_capacity\": 0.0,\n \"maximum_capacity\": 0.0,\n \"nominal_full_load_efficiency\": 0.0,\n \"annotation\": \"\",\n}\n\n\nclass HVACMinimumRequirementMotors(DBOperation):\n def __init__(self, table_name, initial_data_directory):\n super(HVACMinimumRequirementMotors, self).__init__(\n table_name=table_name,\n record_template=RECORD_TEMPLATE,\n initial_data_directory=initial_data_directory,\n create_table_query=CREATE_HVAC_REQUIREMENT_MOTORS_TABLE % table_name,\n insert_record_query=INSERT_A_MOTOR_RECORD % table_name,\n )\n\n def get_record_info(self):\n \"\"\"\n A function to return the record info of the table\n :return:\n \"\"\"\n return RECORD_HELP\n\n def validate_record_datatype(self, record):\n str_expected = [\n \"template\",\n \"type\",\n ]\n\n for f in str_expected:\n if record.get(f):\n assert isinstance(\n record[f], str\n ), f\"{f} requires to be a string, instead got {record[f]}\"\n\n float_expected = [\n \"number_of_poles\",\n \"synchronous_speed\",\n \"minimum_capacity\",\n \"maximum_capacity\",\n \"nominal_full_load_efficiency\",\n ]\n\n for f in float_expected:\n if record.get(f):\n assert is_float(\n record.get(f)\n ), f\"{f} requires to be numeric data type, instead got {record[f]}\"\n return True\n\n def _preprocess_record(self, record):\n \"\"\"\n\n :param record: dict\n :return:\n \"\"\"\n\n return (\n getattr_either(\"template\", record),\n getattr_either(\"number_of_poles\", record),\n getattr_either(\"type\", record),\n getattr_either(\"synchronous_speed\", record),\n getattr_either(\"minimum_capacity\", record),\n getattr_either(\"maximum_capacity\", record),\n getattr_either(\"nominal_full_load_efficiency\", record),\n getattr_either(\"annotation\", record),\n )\n","repo_name":"pnnl/building-energy-standards-data","sub_path":"building_energy_standards_data/database_tables/hvac_minimum_requirement_motors.py","file_name":"hvac_minimum_requirement_motors.py","file_ext":"py","file_size_in_byte":3221,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"13328082944","text":"import streamlit as st\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import (accuracy_score, log_loss, classification_report)\nfrom imblearn.over_sampling import SMOTE\n\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedShuffleSplit\n\nimport SessionState\n\n'''\n# Retenção de Funcionários\n\nEste conjunto de dados foi disponibilizado pela IBM e pode ser acessado neste [link](https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) do Kaggle.\n\nO desenvolvimento do modelo e análise de features tem como referência este [notebook](https://www.kaggle.com/arthurtok/employee-attrition-via-ensemble-tree-based-methods) disponível também no Kaggle.\n\nOs dados são fictícios.\n\nOs campos de avaliação seguem estas definições:\n'''\n\ncol1, col2 = st.beta_columns(2)\n\nwith col1:\n '''\n Education\n\n * 1 Below College\n * 2 College\n * 3 Bachelor\n * 4 Master\n * 5 Doctor\n\n EnvironmentSatisfaction\n\n * 1 Low\n * 2 Medium\n * 3 High\n * 4 Very High\n\n JobInvolvement\n\n * 1 Low\n * 2 Medium\n * 3 High\n * 4 Very High\n\n JobSatisfaction\n\n * 1 Low\n * 2 Medium\n * 3 High\n * 4 Very High\n '''\n\nwith col2:\n '''\n PerformanceRating\n\n * 1 Low\n * 2 Good\n * 3 Excellent\n * 4 Outstanding\n\n RelationshipSatisfaction\n\n * 1 Low\n * 2 Medium\n * 3 High\n * 4 Very High\n\n WorkLifeBalance\n\n * 1 Bad\n * 2 Good\n * 3 Better\n * 4 Best\n '''\n\n'''\n## Conjunto de Dados\n\n'''\n\nattrition = pd.read_csv('data/WA_Fn-UseC_-HR-Employee-Attrition.csv')\n\nattrition = attrition.drop(['EmployeeCount'], axis=1)\nattrition = attrition.drop(['EmployeeNumber'], axis=1)\nattrition = attrition.drop(['StandardHours'], axis=1)\nattrition = attrition.drop(['Over18'], axis=1)\n\nattrition\n\n\n'''\n## Análise Exploratória Rápida\n\n\n'''\n\nst.vega_lite_chart(attrition, {\n \"mark\": \"rect\",\n \"width\": 700,\n \"height\": 300,\n \"encoding\": {\n \"x\": {\n \"bin\": {\"maxbins\": 60},\n \"field\": \"Age\",\n \"type\": \"quantitative\"\n },\n \"y\": {\n \"bin\": {\"maxbins\": 40},\n \"field\": \"TotalWorkingYears\",\n \"type\": \"quantitative\"\n },\n \"color\": {\n \"aggregate\": \"count\",\n \"type\": \"quantitative\"\n }\n },\n \"config\": {\n \"view\": {\n \"stroke\": \"transparent\"\n }\n }\n}\n)\n\nst.vega_lite_chart(attrition, {\n \"mark\": \"rect\",\n \"width\": 700,\n \"height\": 300,\n \"encoding\": {\n \"x\": {\n \"bin\": {\"maxbins\": 60},\n \"field\": \"Age\",\n \"type\": \"quantitative\"\n },\n \"y\": {\n \"bin\": {\"maxbins\": 40},\n \"field\": \"YearsInCurrentRole\",\n \"type\": \"quantitative\"\n },\n \"color\": {\n \"aggregate\": \"count\",\n \"type\": \"quantitative\"\n }\n },\n \"config\": {\n \"view\": {\n \"stroke\": \"transparent\"\n }\n }\n}\n)\n\nst.vega_lite_chart(attrition, {\n \"mark\": \"rect\",\n \"width\": 700,\n \"height\": 300,\n \"encoding\": {\n \"x\": {\n \"bin\": {\"maxbins\": 60},\n \"field\": \"YearsAtCompany\",\n \"type\": \"quantitative\"\n },\n \"y\": {\n \"bin\": {\"maxbins\": 40},\n \"field\": \"JobSatisfaction\",\n \"type\": \"quantitative\"\n },\n \"color\": {\n \"aggregate\": \"count\",\n \"type\": \"quantitative\"\n }\n },\n \"config\": {\n \"view\": {\n \"stroke\": \"transparent\"\n }\n }\n}\n)\n\nst.vega_lite_chart(attrition, {\n \"mark\": \"rect\",\n \"width\": 700,\n \"height\": 300,\n \"encoding\": {\n \"x\": {\n \"bin\": {\"maxbins\": 60},\n \"field\": \"WorkLifeBalance\",\n \"type\": \"quantitative\"\n },\n \"y\": {\n \"bin\": {\"maxbins\": 40},\n \"field\": \"JobSatisfaction\",\n \"type\": \"quantitative\"\n },\n \"color\": {\n \"aggregate\": \"count\",\n \"type\": \"quantitative\"\n }\n },\n \"config\": {\n \"view\": {\n \"stroke\": \"transparent\"\n }\n }\n}\n)\n\n# Define a dictionary for the target mapping\ntarget_map = {'Yes': 1, 'No': 0}\n# Use the pandas apply method to numerically encode our attrition target variable\nattrition[\"Attrition_numerical\"] = attrition[\"Attrition\"].apply(\n lambda x: target_map[x])\n\n# creating a list of only numerical values\nnumerical = [u'Age', u'DailyRate', u'DistanceFromHome',\n u'Education', u'EnvironmentSatisfaction',\n u'HourlyRate', u'JobInvolvement', u'JobLevel', u'JobSatisfaction',\n u'MonthlyIncome', u'MonthlyRate', u'NumCompaniesWorked',\n u'PercentSalaryHike', u'PerformanceRating', u'RelationshipSatisfaction',\n u'StockOptionLevel', u'TotalWorkingYears',\n u'TrainingTimesLastYear', u'WorkLifeBalance', u'YearsAtCompany',\n u'YearsInCurrentRole', u'YearsSinceLastPromotion', u'YearsWithCurrManager']\n\nplt.figure(figsize=(19, 15))\n\ncorrMatrix = attrition[numerical].corr()\n\n'''\n## Matriz de Correlação\n\n\nSuporta a decisão de escolhas de atributos para serem utilizados no treinamento. Quanto mais correlação houver, melhor será para o modelo, o inverso também ocorre, pois ao abrir mão de atributos que não contribuem para o aprendizado para o modelo, ele ficará mais preciso e mais leve.\n\n'''\n\nst.set_option('deprecation.showPyplotGlobalUse', False)\nsns.heatmap(corrMatrix)\n# Use Matplotlib to render seaborn\nst.pyplot()\n\n'## Atributos Utilizados no Treinamento '\n\n# Drop the Attrition_numerical column from attrition dataset first - Don't want to include that\nattrition = attrition.drop(['Attrition_numerical'], axis=1)\n\n# Empty list to store columns with categorical data\ncategorical = []\nfor col, value in attrition.iteritems():\n if value.dtype == 'object':\n categorical.append(col)\n\n# Store the numerical columns in a list numerical\nnumerical = attrition.columns.difference(categorical)\ncategorical.remove('Attrition')\n\natributes_numerical = st.multiselect(\n 'Atributos Numéricos Selecionados', numerical.values.tolist(), numerical.values.tolist())\natributes_categorical = st.multiselect(\n 'Atributos Categóricos Selecionados', categorical, categorical)\n\n# Store the categorical data in a dataframe called attrition_cat\nattrition_cat = attrition[atributes_categorical]\nattrition_cat = pd.get_dummies(attrition_cat)\n\n# Store the numerical features to a dataframe attrition_num\nattrition_num = attrition[atributes_numerical]\n\n# Concat the two dataframes together columnwise\nattrition_final = pd.concat([attrition_num, attrition_cat], axis=1)\n\n# Define a dictionary for the target mapping\ntarget_map = {'Yes': 1, 'No': 0}\n# Use the pandas apply method to numerically encode our attrition target variable\ntarget = attrition[\"Attrition\"].apply(lambda x: target_map[x])\n\nsession_state = SessionState.get(trained=False, train=None)\n\nseed = 1 # We set our random seed to zero for reproducibility\n# Random Forest parameters\nrf_params = {\n 'n_jobs': -1,\n 'n_estimators': 1000,\n 'max_features': 0.3,\n 'max_depth': 4,\n 'min_samples_leaf': 2,\n 'max_features': 'sqrt',\n 'random_state': seed,\n 'verbose': 0\n}\n\nrf = RandomForestClassifier(**rf_params)\n\n# Split data into train and test sets as well as for validation and testing\ntrain, test, target_train, target_val = train_test_split(attrition_final,\n target,\n train_size=0.80,\n random_state=1)\noversampler = SMOTE(random_state=0)\nsmote_train, smote_target = oversampler.fit_sample(train, target_train)\n\nif st.button('Treinar Modelo') or session_state.trained:\n 'Iniciando o treinamento...'\n\n rf.fit(smote_train, smote_target)\n\n session_state.model = rf\n session_state.trained = True\n\n 'Treinamento terminado.'\n 'Verificando predições...'\n\n rf_predictions = rf.predict(test)\n\n st.success('Modelo treinado e valido com sucesso!')\n score = \"Pontuação de Precisão (Accurácia): {}\".format(\n accuracy_score(target_val, rf_predictions))\n session_state.score = score\n\n st.info(score)\n\n plt.figure(figsize=(19, 15))\n (pd.Series(rf.feature_importances_, index=attrition_final.columns.values)\n .nlargest(20)\n .plot(kind='barh'))\n st.pyplot()\n\n test_value = [20, 2000, 10, 5, 2, 94, 3, 4, 4, 5993, 19479, 8, 11, 3, 1, 0, 8, 0, 1, 6, 4,\n 0, 5, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1]\n\n '## Teste de Inferência Unitária'\n 'Supondo um determinado profissional, varie os valores que foram utilizados no treinamento e compare com os resultados ao final, incluindo sua probabilidade.'\n\n col2_1, col2_2, col2_3 = st.beta_columns(3)\n\n with col2_1:\n s_business_travel = st.selectbox(\n \"Business Travel\", attrition[\"BusinessTravel\"].unique())\n\n if s_business_travel == 'Non_Travel':\n test_value[23] = 1\n test_value[24] = 0\n test_value[25] = 0\n elif s_business_travel == 'Travel_Frequently':\n test_value[23] = 0\n test_value[24] = 1\n test_value[25] = 0\n else:\n test_value[23] = 0\n test_value[24] = 0\n test_value[25] = 1\n\n s_department = st.selectbox(\n \"Department\", attrition[\"Department\"].unique())\n\n if s_department == 'Human Resources':\n test_value[26] = 1\n test_value[27] = 0\n test_value[28] = 0\n elif s_business_travel == 'Research & Development':\n test_value[26] = 0\n test_value[27] = 1\n test_value[28] = 0\n else:\n test_value[26] = 0\n test_value[27] = 0\n test_value[28] = 1\n\n s_education_field = st.selectbox(\n \"Education Field\", attrition[\"EducationField\"].unique())\n\n if s_education_field == 'Human Resources':\n test_value[29] = 1\n test_value[30] = 0\n test_value[31] = 0\n test_value[32] = 0\n test_value[33] = 0\n test_value[34] = 0\n elif s_business_travel == 'Life Sciences':\n test_value[29] = 0\n test_value[30] = 1\n test_value[31] = 0\n test_value[32] = 0\n test_value[33] = 0\n test_value[34] = 0\n elif s_business_travel == 'Marketing':\n test_value[29] = 0\n test_value[30] = 0\n test_value[31] = 1\n test_value[32] = 0\n test_value[33] = 0\n test_value[34] = 0\n elif s_business_travel == 'Medical':\n test_value[29] = 0\n test_value[30] = 0\n test_value[31] = 0\n test_value[32] = 1\n test_value[33] = 0\n test_value[34] = 0\n elif s_business_travel == 'Other':\n test_value[29] = 1\n test_value[30] = 0\n test_value[31] = 0\n test_value[32] = 0\n test_value[33] = 1\n test_value[34] = 0\n else:\n test_value[29] = 0\n test_value[30] = 0\n test_value[31] = 0\n test_value[32] = 0\n test_value[33] = 0\n test_value[34] = 1\n\n with col2_2:\n s_gender = st.selectbox(\"Gender\", attrition[\"Gender\"].unique())\n\n if s_gender == 'Human Resources':\n test_value[35] = 1\n test_value[36] = 0\n else:\n test_value[35] = 0\n test_value[36] = 1\n\n s_job_role = st.selectbox(\"Job Role\", attrition[\"JobRole\"].unique())\n\n if s_job_role == 'Healthcare Representative':\n test_value[37] = 1\n test_value[38] = 0\n test_value[39] = 0\n test_value[40] = 0\n test_value[41] = 0\n test_value[42] = 0\n test_value[43] = 0\n test_value[44] = 0\n test_value[45] = 0\n elif s_job_role == 'Human Resources':\n test_value[37] = 0\n test_value[38] = 1\n test_value[39] = 0\n test_value[40] = 0\n test_value[41] = 0\n test_value[42] = 0\n test_value[43] = 0\n test_value[44] = 0\n test_value[45] = 0\n elif s_job_role == 'Laboratory Technician':\n test_value[37] = 0\n test_value[38] = 0\n test_value[39] = 1\n test_value[40] = 0\n test_value[41] = 0\n test_value[42] = 0\n test_value[43] = 0\n test_value[44] = 0\n test_value[45] = 0\n elif s_job_role == 'Manager':\n test_value[37] = 0\n test_value[38] = 0\n test_value[39] = 0\n test_value[40] = 1\n test_value[41] = 0\n test_value[42] = 0\n test_value[43] = 0\n test_value[44] = 0\n test_value[45] = 0\n elif s_job_role == 'Manufacturing Director':\n test_value[37] = 0\n test_value[38] = 0\n test_value[39] = 0\n test_value[40] = 0\n test_value[41] = 1\n test_value[42] = 0\n test_value[43] = 0\n test_value[44] = 0\n test_value[45] = 0\n elif s_job_role == 'Research Director':\n test_value[37] = 0\n test_value[38] = 0\n test_value[39] = 0\n test_value[40] = 0\n test_value[41] = 0\n test_value[42] = 1\n test_value[43] = 0\n test_value[44] = 0\n test_value[45] = 0\n elif s_job_role == 'Research Scientist':\n test_value[37] = 0\n test_value[38] = 0\n test_value[39] = 0\n test_value[40] = 0\n test_value[41] = 0\n test_value[42] = 0\n test_value[43] = 1\n test_value[44] = 0\n test_value[45] = 0\n elif s_job_role == 'Sales Executive':\n test_value[37] = 0\n test_value[38] = 0\n test_value[39] = 0\n test_value[40] = 0\n test_value[41] = 0\n test_value[42] = 0\n test_value[43] = 0\n test_value[44] = 1\n test_value[45] = 0\n else:\n test_value[37] = 0\n test_value[38] = 0\n test_value[39] = 0\n test_value[40] = 0\n test_value[41] = 0\n test_value[42] = 0\n test_value[43] = 0\n test_value[44] = 0\n test_value[45] = 1\n\n s_marital_status = st.selectbox(\n \"Marital Status\", attrition[\"MaritalStatus\"].unique())\n\n if s_marital_status == 'Divorced':\n test_value[46] = 1\n test_value[47] = 0\n test_value[48] = 0\n elif s_business_travel == 'Married ':\n test_value[46] = 0\n test_value[47] = 1\n test_value[48] = 0\n else:\n test_value[46] = 0\n test_value[47] = 0\n test_value[48] = 1\n\n with col2_3:\n\n s_over_time = st.selectbox(\"Over Time\", attrition[\"OverTime\"].unique())\n\n if s_over_time == 'No':\n test_value[49] = 1\n test_value[50] = 0\n else:\n test_value[49] = 0\n test_value[50] = 1\n\n col3_1, col3_2 = st.beta_columns(2)\n\n with col3_1:\n n_age = st.slider(\"Age\", int(attrition[\"Age\"].min()), int(\n 2*attrition[\"Age\"].max()), test_value[0])\n test_value[0] = n_age\n\n n_daily_rate = st.slider(\"Daily Rate\", int(attrition[\"DailyRate\"].min()), int(\n 2*attrition[\"DailyRate\"].max()), test_value[1])\n test_value[1] = n_daily_rate\n\n n_dist_home = st.slider(\"Distance from Home\", int(attrition[\"DistanceFromHome\"].min(\n )), int(2*attrition[\"DistanceFromHome\"].max()), test_value[2])\n test_value[2] = n_dist_home\n\n n_education = st.slider(\"Education\", int(attrition[\"Education\"].min()), int(\n 2*attrition[\"Education\"].max()), test_value[3])\n test_value[3] = n_education\n\n n_env_satisf = st.slider(\"Environment Satisfaction\", int(attrition[\"EnvironmentSatisfaction\"].min(\n )), int(attrition[\"EnvironmentSatisfaction\"].max()), test_value[4])\n test_value[4] = n_env_satisf\n\n n_hour_rate = st.slider(\"Hourly Rate\", int(attrition[\"HourlyRate\"].min()), int(\n 2*attrition[\"HourlyRate\"].max()), test_value[5])\n test_value[5] = n_hour_rate\n\n n_job_involv = st.slider(\"Job Involvement\", int(attrition[\"JobInvolvement\"].min(\n )), int(attrition[\"JobInvolvement\"].max()), test_value[6])\n test_value[6] = n_job_involv\n\n n_job_level = st.slider(\"Job Level\", int(attrition[\"JobLevel\"].min()), int(\n attrition[\"JobLevel\"].max()), test_value[7])\n test_value[7] = n_job_level\n\n n_job_satisf = st.slider(\"Job Satisfaction\", int(attrition[\"JobSatisfaction\"].min(\n )), int(attrition[\"JobSatisfaction\"].max()), test_value[8])\n test_value[8] = n_job_satisf\n\n n_month_income = st.slider(\"Monthly Income\", int(attrition[\"MonthlyIncome\"].min(\n )), int(2*attrition[\"MonthlyIncome\"].max()), test_value[9])\n test_value[9] = n_month_income\n\n n_monthy_rate = st.slider(\"Monthly Rate\", int(attrition[\"MonthlyRate\"].min()), int(\n 2*attrition[\"MonthlyRate\"].max()), test_value[10])\n test_value[10] = n_monthy_rate\n\n num_comp_work = st.slider(\"Num. Companies Worked\", int(\n 2*attrition[\"NumCompaniesWorked\"].min()), int(attrition[\"NumCompaniesWorked\"].max()), test_value[11])\n test_value[11] = num_comp_work\n\n with col3_2:\n\n sal_hike = st.slider(\"% Salary Hike\", int(\n attrition[\"PercentSalaryHike\"].min()), int(2*attrition[\"PercentSalaryHike\"].max()), test_value[12])\n test_value[12] = sal_hike\n\n n_perf_rating = st.slider(\"Performance Rating\", int(attrition[\"PerformanceRating\"].min(\n )), int(attrition[\"PerformanceRating\"].max()), test_value[13])\n test_value[13] = n_perf_rating\n\n n_relat_satisf = st.slider(\"Relationship Satisfaction\", int(attrition[\"RelationshipSatisfaction\"].min(\n )), int(attrition[\"RelationshipSatisfaction\"].max()), test_value[15])\n test_value[14] = n_relat_satisf\n\n n_stock_op = st.slider(\"Stock Option Level\", int(\n attrition[\"StockOptionLevel\"].min()), int(attrition[\"StockOptionLevel\"].max()), test_value[15])\n test_value[15] = n_stock_op\n\n n_total_work_year = st.slider(\"Total Working Years\", int(\n 2*attrition[\"TotalWorkingYears\"].min()), int(attrition[\"TotalWorkingYears\"].max()), test_value[16])\n test_value[16] = n_total_work_year\n\n n_train_last_y = st.slider(\"Training Times Last Year\", int(\n 2*attrition[\"TrainingTimesLastYear\"].min()), int(attrition[\"TrainingTimesLastYear\"].max()), test_value[17])\n test_value[17] = n_train_last_y\n\n n_worklife_bal = st.slider(\"Work Life Balance\", int(\n attrition[\"WorkLifeBalance\"].min()), int(attrition[\"WorkLifeBalance\"].max()), test_value[18])\n test_value[18] = n_worklife_bal\n\n n_years_comp = st.slider(\"Years at Company\", int(attrition[\"YearsAtCompany\"].min(\n )), int(2*attrition[\"YearsAtCompany\"].max()), test_value[19])\n test_value[19] = n_years_comp\n\n n_years_role = st.slider(\"Years in Current Role\", int(attrition[\"YearsInCurrentRole\"].min(\n )), int(2*attrition[\"YearsInCurrentRole\"].max()), test_value[20])\n test_value[20] = n_years_role\n\n n_years_promo = st.slider(\"Years since Last Promotion\", int(attrition[\"YearsSinceLastPromotion\"].min(\n )), int(2*attrition[\"YearsSinceLastPromotion\"].max()), test_value[21])\n test_value[21] = n_years_promo\n\n n_years_manager = st.slider(\"Years with Current Manager\", int(\n attrition[\"YearsWithCurrManager\"].min()), int(2*attrition[\"YearsWithCurrManager\"].max()), test_value[22])\n test_value[22] = n_years_manager\n\n test_value = [test_value]\n\n try:\n\n prediction_id = rf.predict(test_value)[0]\n proba = int(100*rf.predict_proba(test_value)[0][prediction_id])\n\n if rf.predict(test_value)[0] == 1:\n st.warning(\"Attrition (\" + str(proba) + \"%)\")\n else:\n st.success(\"No Attrition (\" + str(proba) + \"%)\")\n st.balloons()\n\n st.progress(proba)\n\n except Exception as e:\n st.warning(e)\n","repo_name":"EYLatamSouth/ta-employee-attrition","sub_path":"streamlit_app.py","file_name":"streamlit_app.py","file_ext":"py","file_size_in_byte":20845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"12222576515","text":"import requests, json\nfrom config import *\nimport alpaca_trade_api as tradeapi\nimport time\nimport pandas as pd\nimport datetime\n# for more documentation you can check https://algotrading101.com/learn/alpaca-trading-api-guide/\n\nclass OrderEngine(object):\n\n def __init__(self, price, symbol, side):\n self.price = price\n self.symbol = symbol\n self.side = side\n BASE_URL = \"https://paper-api.alpaca.markets\"\n self.ACCOUNT_URL = \"{}/v2/account\".format(BASE_URL)\n self.ORDERS_URL = \"{}/v2/orders\".format(BASE_URL)\n self.HEADERS = {'APCA-API-KEY-ID': API_KEY, 'APCA-API-SECRET-KEY': SECRET_KEY}\n self.api = tradeapi.REST(API_KEY, SECRET_KEY, BASE_URL, api_version='v2')\n self.account = self.api.get_account()\n self.balance = self.account.cash \n self.conn = tradeapi.stream2.StreamConn(API_KEY, SECRET_KEY, BASE_URL)\n self.positions_list = self.api.list_positions()\n self.portfolio_history = self.initinalise_portfolio_history()\n self.qty = self.KELLY_CRITERION()\n \n\n def initinalise_portfolio_history(self):\n todays_date = datetime.date.today()\n \n day = int(todays_date.strftime('%d'))\n if day < 10:\n day = int(todays_date.strftime('%d')[1]) \n day = '0' + str(day)\n\n month = int(todays_date.strftime('%m'))\n if month < 10:\n month = str(todays_date.strftime('%m'))\n\n try:\n portfolio_history = self.api.get_portfolio_history(\n date_end='2021-{}-{}'.format(month,day),\n period='1D',\n timeframe='5Min'\n )\n print('2021-{}-{}'.format(month,day))\n except requests.exceptions.ConnectionError as err: \n print(err)\n portfolio_history = ''\n return portfolio_history\n\n def initialise_universe(self):\n #cancel all orders before end of the day\n self.api.close_all_positions()\n # a bunch of other api calls that can be useful\n self.api.cancel_all_orders()\n #create a random watchlist\n universe = self.api.create_watchlist()\n print(self.account)\n print(self.balance) \n print(universe)\n return universe\n # if you want to know the leverage\n #print(api.get_account_configurations())\n\n def initialise_alpaca_universe(self):\n assets = self.api.list_assets()\n assets_list = []\n for asset in assets:\n assets_list.append(asset.symbol)\n df = pd.DataFrame(assets_list)\n df.to_csv('Alpace tradable assets.csv')\n\n def update_probability_w(self):\n trade_won = 0\n day_trade_count = self.account.daytrade_count\n # you can access portfolio history from the database\n\n for p_and_l in self.portfolio_history.profit_loss:\n try:\n if p_and_l > 0:\n trade_won += 1\n else:\n pass\n except TypeError:\n pass\n p_w = trade_won/day_trade_count\n return p_w\n\n # kelly criterion assumes that returns are stationary\n def KELLY_CRITERION(self):\n try:\n p_w = self.update_probability_w()\n except ZeroDivisionError:\n p_w = 0.5\n p_l = 1 - p_w\n lot_size = (2*p_w - p_l)/2\n try:\n qty = (lot_size*float(self.balance))/self.price\n qty = round(qty)\n except ZeroDivisionError:\n qty = 0\n except ValueError:\n qty = 1\n except OverflowError:\n qty = 1\n #UHEWRBVIUWBVQIUVBQPOIUVQOIUBVQIUBQIUBVOIUEQRBNQIUBVNOIUQBVLQI\n qty = 1\n return qty\n\n def get_account(self):\n r = requests.get(self.ACCOUNT_URL, headers=self.HEADERS)\n\n return json.loads(r.content)\n\n def create_order(self,qty=0, time_in_force='gtc'):\n if qty == 0:\n pass\n else:\n self.qty = qty\n types = 'market'\n if self.side == 'buy':\n order = self.api.submit_order(\n self.symbol,\n self.qty,\n self.side, \n types,\n time_in_force, \n order_class='bracket',\n stop_loss={'stop_price': 0.995*self.price}, \n take_profit={'limit_price': 1.01*self.price}\n )\n elif self.side == 'sell':\n order = self.api.submit_order(\n self.symbol,\n self.qty,\n self.side, \n types,\n time_in_force, \n order_class='bracket',\n stop_loss={'stop_price': 1.005*self.price}, \n take_profit={'limit_price': 0.99*self.price}\n )\n else:\n print('what is the side')\n print(order)\n time.sleep(1)\n\n def get_orders(self):\n r = requests.get(self.ORDERS_URL, headers=self.HEADERS)\n\n return json.loads(r.content)\n\n # Submit a trailing stop order to sell 1 share of Apple at a\n # trailing stop of\n def create_trailing_sl_order(self, get_order=False):\n order = self.api.submit_order(\n symbol=self.symbol,\n qty=self.qty,\n side=self.side,\n type='trailing_stop',\n trail_percent=0.4, # stop price will be hwm*0.996\n time_in_force='gtc',\n )\n if get_order:\n return order\n else:\n pass\n\n def get_order_by_id(self):\n # Submit a market order and assign it a Client Order ID.\n self.api.submit_order(\n symbol='AAPL',\n qty=1,\n side='buy',\n type='market',\n time_in_force='gtc',\n client_order_id='my_first_order'\n )\n\n # Get our order using its Client Order ID.\n my_order = self.api.get_order_by_client_order_id('my_first_order')\n print('Got order #{}'.format(my_order.id))\n \n def portfolio_history_error(self, func):\n def wrapper(*a,**kw):\n try:\n container = func(*a,**kw)\n except tradeapi.rest.APIError as err:\n print(err)\n container = None\n return container\n return wrapper\n\n def close_orders(self,order_id):\n self.api.cancel_order(order_id=order_id)","repo_name":"kesler20/trading_bot","sub_path":"orders.py","file_name":"orders.py","file_ext":"py","file_size_in_byte":6370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"30436130741","text":"import os\r\nimport sys\r\nimport random\r\nimport argparse\r\n\r\nimport tensorflow.compat.v1 as tf\r\n\r\nfrom src.configuration import ChatConfig\r\n\r\ntf.disable_v2_behavior()\r\nimport numpy as np\r\n\r\nfrom src.utils import matrix_initializer, truncated_normal_initializer_variable, zero_initializer_variable\r\nfrom data_utils.prepare_dialogue_data import get_word_count, construct_word_dict, read_emotion_words, construct_vocab, \\\r\n get_word_list, read_word_embeddings\r\n\r\n__author__ = \"Jocelyn\"\r\n\r\n# emotion_dict = {\"anger\": 0, \"disgust\": 1, \"happiness\": 2, \"like\": 3, \"sadness\": 4, \"neutral\": 5}\r\nemotion_dict = {\"anger\": 0, \"disgust\": 1, \"fear\": 2, \"joy\": 3, \"neutral\": 4, \"sadness\": 5, \"surprise\": 6}\r\nid2emotion = {idx: emotion for emotion, idx in emotion_dict.items()}\r\nFLAGS = None\r\n\r\n\r\nclass LstmClassifier(object):\r\n def __init__(self, word_embeddings, words2idx, embedding_size, hidden_size, emotion_class, batch_size,\r\n max_len, use_lstm, session, keep_prob=2.0, learning_rate=0.1, lr_decay=0.5, name=\"lstm_classifier\"):\r\n self.embedding_size = embedding_size\r\n self.hidden_size = hidden_size\r\n self.emotion_class = emotion_class\r\n self.batch_size = batch_size\r\n self.max_len = max_len\r\n self.use_lstm = use_lstm\r\n self.sess = session\r\n self.keep_prob = keep_prob\r\n self.learning_rate_decay_factor = lr_decay\r\n self.name = name\r\n\r\n # word embeddings\r\n self.words2idx = words2idx\r\n self.idx2words = {idx: word for word, idx in self.words2idx.items()}\r\n print(\"word embeddings size here\")\r\n # print(np.shape(word_embeddings))\r\n self.embeddings = matrix_initializer(w=word_embeddings, name=self.name + \"_word_embeddings\")\r\n self.vocab_size = len(words2idx)\r\n\r\n # softmax\r\n self.sfx_w = truncated_normal_initializer_variable(width=hidden_size,\r\n shape=[2 * self.hidden_size, self.emotion_class],\r\n name=self.name+\"_softmax_w\")\r\n self.sfx_b = zero_initializer_variable(shape=[self.emotion_class], name=self.name+\"_softmax_b\")\r\n\r\n # placeholder\r\n self.input_x = tf.placeholder(shape=[self.batch_size, self.max_len], dtype=tf.int32, name=self.name+\"_input_x\")\r\n self.input_y = tf.placeholder(shape=[self.batch_size], dtype=tf.int32, name=self.name+\"_input_y\")\r\n self.input_len = tf.placeholder(shape=[self.batch_size], dtype=tf.int32, name=self.name+\"_input_len\")\r\n\r\n self.input_x_beam = tf.placeholder(shape=[self.batch_size,20, self.max_len], dtype=tf.int32,\r\n name=self.name + \"_input_x_beam\")\r\n self.input_len_beam = tf.placeholder(shape=[self.batch_size,20], dtype=tf.int32, name=self.name + \"_input_len_beam\")\r\n self.input_emo= tf.placeholder(shape=[self.batch_size,20],dtype=tf.int32, name=self.name+\"_input_emo\")\r\n\r\n\r\n self.forward_cell = self.rnn_cell()\r\n self.backward_cell = self.rnn_cell()\r\n\r\n # loss\r\n self.loss = self.compute_loss()\r\n self.pred_scores = self.predict_scores()\r\n self.pred_labels = tf.argmax(self.pred_scores, axis=1)\r\n self.beam_pred_labels= self.beam_fetch()\r\n\r\n tf.summary.scalar(\"loss\", self.loss)\r\n\r\n self.global_step = tf.Variable(0, name=self.name + \"_global_step\", trainable=False)\r\n self.lr = tf.Variable(learning_rate, dtype=tf.float32,name=self.name + \"_lr\",trainable=False)\r\n self.train = self.optimize()\r\n\r\n self.sess.run(tf.global_variables_initializer())\r\n\r\n # self.variables_dict={'lstm_classifier_global_step':self.global_step,'lstm_classifier_softmax_b':self.sfx_b,'lstm_classifier_softmax_w':self.sfx_w,'lstm_classifier_word_embeddings':self.embeddings}\r\n self.saver = tf.train.Saver()\r\n\r\n def basic_rnn_cell(self):\r\n if self.use_lstm:\r\n return tf.nn.rnn_cell.BasicLSTMCell(self.hidden_size, forget_bias=0.0, state_is_tuple=True,\r\n reuse=tf.get_variable_scope().reuse)\r\n else:\r\n return tf.nn.rnn_cell.GRUCell(self.hidden_size, reuse=tf.get_variable_scope().reuse)\r\n\r\n def rnn_cell(self):\r\n single_cell = self.basic_rnn_cell\r\n if self.keep_prob < 1.0:\r\n def single_cell():\r\n return tf.nn.rnn_cell.DropoutWrapper(single_cell, output_keep_prob=self.keep_prob)\r\n cell = single_cell()\r\n # cell = tf.nn.rnn_cell.MultiRNNCell([single_cell() for _ in range(self.num_layers)], state_is_tuple=True)\r\n return cell\r\n\r\n def lstm_process(self):\r\n input_embeddings = tf.nn.embedding_lookup(self.embeddings, self.input_x) # [batch, max_time, embedding size]\r\n\r\n initiate_state_forward = self.forward_cell.zero_state(self.batch_size, dtype=tf.float32)\r\n initiate_state_backward = self.backward_cell.zero_state(self.batch_size, dtype=tf.float32)\r\n outputs, states = tf.nn.bidirectional_dynamic_rnn(self.forward_cell, self.backward_cell, input_embeddings,\r\n sequence_length=self.input_len,\r\n initial_state_fw=initiate_state_forward,\r\n initial_state_bw=initiate_state_backward,\r\n dtype=tf.float32)\r\n output_fw_state, output_bw_state = states\r\n final_states = tf.concat([output_fw_state, output_bw_state], axis=-1) # [2, batch, 2 * hidden]\r\n split_states_outputs = tf.split(final_states, num_or_size_splits=2, axis=0)\r\n final_states = tf.reshape(split_states_outputs[1], [self.batch_size, 2 * self.hidden_size])\r\n \"\"\"\r\n outputs, states = tf.nn.dynamic_rnn(self.forward_cell, input_embeddings, sequence_length=self.input_len,\r\n initial_state=initiate_state_forward, dtype=tf.float32)\r\n split_states_outputs = tf.split(states, num_or_size_splits=2, axis=0)\r\n final_states = tf.reshape(split_states_outputs[1], [self.batch_size, self.hidden_size])\r\n \"\"\"\r\n return final_states\r\n\r\n def lstm_process_beam(self):\r\n input_x_re=[]\r\n final_states_re=[]\r\n # 每个beam单独处理\r\n for i in range(20):\r\n input_x_inn = []\r\n input_len_inn=[]\r\n for j in range(self.batch_size):\r\n # input_x_inn形状是[batch_size,max_len]\r\n # input_len_beam形状就是[batch_size]\r\n input_x_inn.append(self.input_x_beam[j][i])\r\n input_len_inn.append(self.input_len_beam[j][i])\r\n # input_x_re形状就成了[beam,batch_size,max_len]\r\n print(\"input_len_beam here:\")\r\n print(len(input_len_inn))\r\n input_x_re.append(input_x_inn)\r\n\r\n input_embeddings = tf.nn.embedding_lookup(self.embeddings,input_x_inn) # [batch, max_time, embedding size]\r\n\r\n initiate_state_forward = self.forward_cell.zero_state(self.batch_size, dtype=tf.float32)\r\n initiate_state_backward = self.backward_cell.zero_state(self.batch_size, dtype=tf.float32)\r\n outputs, states = tf.nn.bidirectional_dynamic_rnn(self.forward_cell, self.backward_cell, input_embeddings,\r\n sequence_length=input_len_inn,\r\n initial_state_fw=initiate_state_forward,\r\n initial_state_bw=initiate_state_backward,\r\n dtype=tf.float32)\r\n output_fw_state, output_bw_state = states\r\n final_states = tf.concat([output_fw_state, output_bw_state], axis=-1) # [2, batch, 2 * hidden]\r\n split_states_outputs = tf.split(final_states, num_or_size_splits=2, axis=0)\r\n final_states = tf.reshape(split_states_outputs[1], [self.batch_size, 2 * self.hidden_size])\r\n final_states_re.append(final_states)\r\n return final_states_re\r\n\r\n\r\n def compute_loss(self):\r\n final_states = self.lstm_process()\r\n logits = tf.matmul(final_states, self.sfx_w) + self.sfx_b\r\n entropy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.input_y, logits=logits)\r\n loss = tf.reduce_sum(entropy_loss) / self.batch_size\r\n return loss\r\n\r\n def predict_scores(self):\r\n final_states = self.lstm_process()\r\n logits = tf.matmul(final_states, self.sfx_w) + self.sfx_b\r\n scores = tf.nn.softmax(logits, dim=-1)\r\n return scores\r\n\r\n def beam_fetch(self):\r\n final_states=self.lstm_process_beam()\r\n scores_re=[]\r\n score_res_gro=[]\r\n for i in range(20):\r\n logits=tf.matmul(final_states[i],self.sfx_w)+self.sfx_b\r\n scores=tf.nn.softmax(logits,dim=-1)\r\n scores_re.append(scores)\r\n # score_re的形状：[beam,batch_size,total-7]\r\n for j in range(self.batch_size):\r\n score_res_col=[]\r\n for i in range(20):\r\n emo_index=self.input_emo[j][i]\r\n emo_tar=scores_re[i][j][emo_index]\r\n score_res_col.append(emo_tar)\r\n print('length of score_res_col')\r\n print(len(score_res_col))\r\n score_res_gro.append(score_res_col)\r\n\r\n\r\n return score_res_gro\r\n\r\n\r\n\r\n def optimize(self):\r\n optimizer = tf.train.GradientDescentOptimizer(self.lr)\r\n trainer = optimizer.minimize(self.loss)\r\n return trainer\r\n\r\n def train_step(self, this_input_x, this_input_y, this_input_len):\r\n output_feed = [self.train, self.loss]\r\n input_feed = {self.input_x: this_input_x,\r\n self.input_y: this_input_y,\r\n self.input_len: this_input_len}\r\n _, loss = self.sess.run(output_feed, input_feed)\r\n return loss\r\n\r\n def predict_step(self, this_input_x, this_input_len):\r\n output_feed = [self.pred_labels]\r\n input_feed = {self.input_x: this_input_x,\r\n self.input_len: this_input_len}\r\n results = self.sess.run(output_feed, input_feed)\r\n return results[0]\r\n\r\n def beam_predict_step(self, this_input_x, this_input_len,this_input_emo):\r\n output_feed = [self.beam_pred_labels]\r\n input_feed = {self.input_x_beam: this_input_x,\r\n self.input_len_beam: this_input_len,\r\n self.input_emo:this_input_emo\r\n }\r\n results = self.sess.run(output_feed, input_feed)\r\n return results[0]\r\n\r\n def get_train_batch(self, input_responses, input_labels, input_lengths, index):\r\n this_input_x = input_responses[index * self.batch_size: (index + 1) * self.batch_size]\r\n this_input_y = input_labels[index * self.batch_size: (index + 1) * self.batch_size]\r\n this_input_len = input_lengths[index * self.batch_size: (index + 1) * self.batch_size]\r\n return this_input_x, this_input_y, this_input_len\r\n\r\n def get_pred_batch(self, input_responses, input_lengths, index):\r\n this_input_x = input_responses[index * self.batch_size: (index + 1) * self.batch_size]\r\n this_input_len = input_lengths[index * self.batch_size: (index + 1) * self.batch_size]\r\n return this_input_x, this_input_len\r\n\r\n# 是一个将回答的句子分成单词的函数\r\ndef read_emotional_response_label_file(train_res_file, train_label_file, max_len=30):\r\n f1 = open(train_res_file, \"r\", encoding=\"utf-8\")\r\n f2 = open(train_label_file, \"r\", encoding=\"utf-8\")\r\n res_lines = f1.readlines()\r\n label_lines = f2.readlines()\r\n\r\n\r\n train_responses = []\r\n train_labels = []\r\n train_lens = []\r\n for res_line, label_line in zip(res_lines, label_lines):\r\n # 去掉label首尾的空格\r\n label = label_line.strip()\r\n\r\n if label not in emotion_dict.keys():\r\n continue\r\n words = res_line.strip().split()\r\n print(words)\r\n # split()函数是用来分割句子的，用空格和/n来切分,其实就是把句子分成一个个单词\r\n if len(words) > max_len:\r\n # 如果超过了，就直接砍掉后面多余的\r\n words = words[: max_len]\r\n train_responses.append(words)\r\n # 在response后面把words拼上去，其实也就是把response分成单词放进train_responses里面\r\n train_labels.append(emotion_dict[label])\r\n # 把情绪的也放进去\r\n train_lens.append(len(words))\r\n # 把回答的长度也放进去\r\n return train_responses, train_labels, train_lens\r\n\r\n# 是一个将回答的句子分成单词的函数\r\ndef read_test_emotional_response_label_file(train_res_file, max_len=30):\r\n f1 = open(train_res_file, \"r\", encoding=\"utf-8\")\r\n res_lines = f1.readlines()\r\n print(\"lines from file\")\r\n print(len(res_lines))\r\n train_responses = []\r\n train_lens = []\r\n for res_line in res_lines:\r\n words = res_line.strip().split()\r\n # print(words)\r\n # split()函数是用来分割句子的，用空格和/n来切分,其实就是把句子分成一个个单词\r\n if len(words) > max_len:\r\n # 如果超过了，就直接砍掉后面多余的\r\n words = words[: max_len]\r\n train_responses.append(words)\r\n # 把情绪的也放进去\r\n train_lens.append(len(words))\r\n # 把回答的长度也放进去\r\n return train_responses, train_lens\r\n\r\ndef read_test_data(filename, max_len):\r\n f = open(filename, \"r\", encoding=\"utf-8\")\r\n lines = f.readlines()\r\n\r\n responses = []\r\n train_lens = []\r\n for line in lines:\r\n words = line.strip().split()\r\n if len(words) > max_len:\r\n words = words[: max_len]\r\n responses.append(words)\r\n train_lens.append(len(words))\r\n return responses, train_lens\r\n\r\n# 补齐responses的函数（补齐成max_len）\r\ndef response_to_indexs(train_responses, word_dict, word_unk_id, max_len):\r\n new_responses = []\r\n for response in train_responses:\r\n # 在通用词词典里找到句子中的单词，如果没有就用unk\r\n new_response = [word_dict[word] if word in word_dict else word_unk_id for word in response]\r\n if len(new_response) < max_len:\r\n # 如果长度不够就用unk补齐\r\n remain = max_len - len(new_response)\r\n for i in range(remain):\r\n new_response.append(word_unk_id)\r\n new_responses.append(new_response)\r\n return new_responses\r\n\r\n# 处理不能被batchsize整除的样本集\r\ndef align_train_batch_size(train_responses, emotion_labels, response_lens, batch_size):\r\n length = len(train_responses)\r\n if length % batch_size != 0: # 如果不能被完整分批次\r\n remain = batch_size - length % batch_size # 是去掉多余的后剩下有多少\r\n total_data = [[res, label, length] for res, label, length in\r\n zip(train_responses, emotion_labels, response_lens)]\r\n sequence = range(length)\r\n for _ in range(remain):\r\n index = random.choice(sequence) # 会随机选中一个response的index\r\n total_data.append(total_data[index])\r\n train_responses = [data[0] for data in total_data]\r\n emotion_labels = [data[1] for data in total_data]\r\n response_lens = [data[2] for data in total_data]\r\n return train_responses, emotion_labels, response_lens\r\n\r\ndef align_test_batch_size(test_response_without,response_lens, batch_size):\r\n length = len(test_response_without)\r\n if length % batch_size != 0: # 如果不能被完整分批次\r\n remain = batch_size - length % batch_size # 是去掉多余的后剩下有多少\r\n total_data = [[res, length] for res, length in\r\n zip(test_response_without, response_lens)]\r\n sequence = range(length)\r\n for _ in range(remain):\r\n index = random.choice(sequence) # 会随机选中一个response的index\r\n total_data.append(total_data[index])\r\n test_response_without = [data[0] for data in total_data]\r\n response_lens = [data[1] for data in total_data]\r\n return test_response_without, response_lens\r\n\r\n\r\n\r\ndef shuffle_train_data(train_responses, emotion_labels, response_lens):\r\n total_data = [[res, label, length] for res, label, length in\r\n zip(train_responses, emotion_labels, response_lens)]\r\n # 随机排序\r\n random.shuffle(total_data)\r\n train_responses = [data[0] for data in total_data]\r\n emotion_labels = [data[1] for data in total_data]\r\n response_lens = [data[2] for data in total_data]\r\n return train_responses, emotion_labels, response_lens\r\n\r\n\r\ndef write_labels(file_name, labels):\r\n f = open(file_name, \"w\", encoding=\"utf-8\")\r\n for label in labels:\r\n f.write(id2emotion[label])\r\n f.write(\"\\n\")\r\n f.close()\r\n\r\n\r\ndef split_train_valid_data(train_responses, train_labels, train_lens):\r\n # 回答的总长度，即有多少个回答\r\n total_length = len(train_responses)\r\n train_len = int(total_length * 0.9)\r\n # 取总长度的0.1\r\n valid_len = total_length - train_len\r\n # 随意取total_length里的一部分作为valid_len范围（验证集）里的有效长度\r\n sequence = random.sample(range(total_length), valid_len)\r\n training_res, training_labels, training_lens, valid_res, valid_labels, valid_lens = [], [], [], [], [], []\r\n for i in range(total_length):\r\n # 如果i在valid_len里面\r\n if i in sequence:\r\n valid_res.append(train_responses[i]) # 把对应的回答放进去\r\n valid_labels.append(train_labels[i]) # 把标签放进去\r\n valid_lens.append(train_lens[i]) # 把这个回答的长度放进去（选中的这个回答的长度）\r\n else: # 如果不是，就不要放进valid数组\r\n training_res.append(train_responses[i])\r\n training_labels.append(train_labels[i])\r\n training_lens.append(train_lens[i])\r\n return training_res, training_labels, training_lens, valid_res, valid_labels, valid_lens\r\n\r\n\r\ndef compute_accuracy(pred_labels, true_labels):\r\n total_len = len(pred_labels)\r\n num = 0\r\n for pred, true_label in zip(pred_labels, true_labels):\r\n if pred == true_label:\r\n num += 1\r\n acc = float(num) / total_len\r\n return acc\r\n\r\n# 读embeddings文件并且转成embedding的函数\r\ndef read_total_embeddings(embedding_file, vocab_size):\r\n embeddings = list()\r\n # 构造一个词典\r\n word2id = dict()\r\n id2word = dict()\r\n word_list=[]\r\n f = open(embedding_file, \"r\", encoding=\"utf-8\")\r\n for line in f.readlines()[: vocab_size]:\r\n # strip() 方法用于移除字符串头尾指定的字符（默认为空格或换行符）或字符序列。\r\n # split()函数是用来分割句子的，用空格和/n来切分,其实就是把句子分成一个个单词\r\n lemmas = line.strip().split()\r\n # 说明这个文件每一行的第一个单词都是这个embedding代表的单词\r\n word = lemmas[0].strip()\r\n embedding = list()\r\n for lemma in lemmas[1:]:\r\n # 把str模式的float转成真正的float\r\n embedding.append(float(lemma.strip()))\r\n # print(\"embedding:\" + str(len(embedding)))\r\n index = len(word2id)\r\n # 制作单词到index的词典\r\n word2id[word] = index\r\n # 制作index到单词的词典\r\n id2word[index] = word\r\n embeddings.append(embedding)\r\n word_list.append(word)\r\n\r\n return embeddings, word2id, id2word,word_list\r\n\r\ndef index_test_data(test_data, id2words):\r\n \"\"\"\r\n write post and most correct response data\r\n :param test_data:\r\n :param write_file:\r\n :param id2words:\r\n :param test_emotion_labels:\r\n :return:\r\n \"\"\"\r\n # f = open(write_file, \"w\", encoding=\"utf-8\")\r\n generate_data=[]\r\n for data in test_data:\r\n generate_data_b=[]\r\n for data_b in data:\r\n words = [id2words[index] if index in range(len(id2words)) else 0 for index in data_b]\r\n\r\n sentence = \" \".join(str(word) for word in words)\r\n final_sentence = sentence\r\n generate_data_b.append(final_sentence)\r\n generate_data.append(generate_data_b)\r\n return generate_data\r\n\r\ndef change_file_format(origin):\r\n # [origin]的形状是：[batch_size,beam_size,max_len]\r\n start_symbol = \"\"\r\n end_symbol = \"\"\r\n new=[]\r\n\r\n # f1 = open(file1, \"r\", encoding=\"utf-8\")\r\n # f2 = open(file2, \"w\", encoding=\"utf-8\")\r\n for line in origin:\r\n new_b=[]\r\n for line_b in line:\r\n line_str=' '.join(line_b)\r\n words = line_str.strip().split()\r\n words = words[1:]\r\n if start_symbol in words:\r\n start_index = words.index(start_symbol) + 1\r\n else:\r\n start_index = 0\r\n if end_symbol in words:\r\n end_index = words.index(end_symbol)\r\n else:\r\n end_index = len(words)\r\n selected_words = words[start_index: end_index]\r\n sentence = \" \".join(selected_words)\r\n new_b.append(sentence)\r\n new.append(new_b)\r\n return new\r\n\r\ndef add_emo_beam(beam_size,emos_data):\r\n # emos_data的形状是：[batch_size,1]\r\n new_emos=[]\r\n for emo in emos_data:\r\n new_emos_in = []\r\n for i in range(beam_size):\r\n new_emos_in.append(emo)\r\n new_emos.append(new_emos_in)\r\n # new_emos的形状就变成了[batch_size,beam_size]\r\n # 并且同一个beam里，情感都是一样的\r\n return new_emos\r\n # for i in range(beam_size):\r\n # new_emos_in = []\r\n # for emo in emos_data:\r\n # new_emos_in.append(emo)\r\n # new_emos.append(new_emos_in)\r\n # # new_emos的形状就变成了[batch_size,beam_size]\r\n # # 并且同一个beam里，情感都是一样的\r\n # return new_emos\r\n\r\n\r\ndef read_beam_test(test_res,test_emo,max_len=30):\r\n # test_res:[batch_size,beam,max_len]\r\n # test_emo:[batch_size,beam]\r\n test_responses=[]\r\n test_lens=[]\r\n test_emos=[]\r\n for res_b,emo_b in zip(test_res,test_emo):\r\n test_responses_b = []\r\n test_lens_b = []\r\n test_emos_b = []\r\n for res,emo in zip(res_b,emo_b):\r\n words= res.strip().split()\r\n emo_s=emo\r\n if emo_s not in range(7):\r\n continue\r\n if len(words) > max_len:\r\n # 如果超过了，就直接砍掉后面多余的\r\n words = words[: max_len]\r\n test_responses_b.append(words)\r\n test_lens_b.append(len(words))\r\n test_emos_b.append(emo_s)\r\n test_responses.append(test_responses_b)\r\n test_lens.append(test_lens_b)\r\n test_emos.append(test_emos_b)\r\n return test_responses,test_lens,test_emos\r\n\r\ndef response_to_indexs_b(train_responses, word_dict, word_unk_id, max_len):\r\n new_responses = []\r\n for response_o in train_responses:\r\n new_responses_b=[]\r\n for response in response_o:\r\n # 在通用词词典里找到句子中的单词，如果没有就用unk\r\n new_response = [word_dict[word] if word in word_dict else word_unk_id for word in response]\r\n if len(new_response) < max_len:\r\n # 如果长度不够就用unk补齐\r\n remain = max_len - len(new_response)\r\n for i in range(remain):\r\n new_response.append(word_unk_id)\r\n new_responses_b.append(new_response)\r\n new_responses.append(new_responses_b)\r\n return new_responses\r\n\r\ndef train_b(train_response_file, train_label_file, test_res_file, test_label_file, max_len, word_count_file, vocab_size,\r\n embedding_file, embedding_size, batch_size, num_epoch, hidden_size, emotion_class, pred_label_file, session,generate_words, scores,this_emotion_labels,lstm_emotion_machine):\r\n print(\"read word and embeddings\\n\")\r\n model_path = os.path.dirname(os.path.dirname(os.path.abspath(\"lstm_classifier.py\")))\r\n data_dir = os.path.join(model_path, \"data\")\r\n embeddings, total_word_dict, id2word,word_l=read_total_embeddings(embedding_file, vocab_size)\r\n word_unk_id = total_word_dict['']\r\n\r\n print(\"reading training data\\n\")\r\n # 将回答的句子分成单词\r\n train_responses, train_labels, train_lens = read_emotional_response_label_file(train_response_file,\r\n train_label_file,\r\n max_len)\r\n # 补齐responses（补齐成max_len）\r\n train_responses = response_to_indexs(train_responses, total_word_dict, word_unk_id, max_len)\r\n # train_responses, train_labels什么的都会更新，会把valid的放出去，也就是随机分出验证集\r\n train_responses, train_labels, train_lens, valid_res, valid_labels, valid_lens = \\\r\n split_train_valid_data(train_responses, train_labels, train_lens)\r\n # 处理不能被batch_size整除的train样本集，随机挑一些直接砍掉\r\n train_responses, train_labels, train_lens = align_train_batch_size(train_responses, train_labels, train_lens,\r\n batch_size)\r\n # 处理不能被batch_size整除的valid样本集，随机挑一些直接砍掉\r\n valid_res, valid_labels, valid_lens = align_train_batch_size(valid_res, valid_labels, valid_lens, batch_size)\r\n\r\n # 读测试数据，和训练集一样的操作\r\n print(\"Read prediction data!\\n\")\r\n test_responses,test_lens = read_test_emotional_response_label_file(test_res_file,max_len)\r\n print(len(test_responses))\r\n\r\n test_responses = response_to_indexs(test_responses, total_word_dict, word_unk_id, max_len)\r\n\r\n test_length = len(test_responses)\r\n test_responses, test_lens = align_test_batch_size(test_responses,test_lens, batch_size)\r\n\r\n # 定义bi-lstm的模型\r\n print(\"Define model!\\n\")\r\n lstm_emotion_machine = LstmClassifier(embeddings, word_dict, embedding_size, hidden_size, emotion_class, batch_size,max_len, True, session, learning_rate=0.1)\r\n # 开始训练\r\n # 开始训练\r\n print(\"training\\n\")\r\n # 一共有多少批训练集\r\n\r\n train_batch = int(len(train_responses) / batch_size)\r\n # 一共有多少批验证集\r\n valid_batch = int(len(valid_res) / batch_size)\r\n valid_accs = []\r\n best_valid_acc = -1.0\r\n ckpt_path = os.path.join(os.path.join(model_path, \"data_utils/check_path_lstm\"), \"check_path_lstm\")\r\n # 开始训练\r\n for i in range(num_epoch):\r\n print(\"Now train epoch %d!\\n\" % (i + 1))\r\n # 首先随机排序，即打乱顺序\r\n train_responses, train_labels, train_lens = shuffle_train_data(train_responses, train_labels, train_lens)\r\n\r\n for j in range(train_batch):\r\n # 处理输入，处理成能直接喂进去的形式\r\n this_res, this_label, this_len = lstm_emotion_machine.get_train_batch(train_responses, train_labels,\r\n train_lens, j)\r\n # 计算损失\r\n loss = lstm_emotion_machine.train_step(this_res, this_label, this_len)\r\n print(\"epoch=%d, batch=%d, loss=%f\\n\" % ((i + 1), (j + 1), loss))\r\n # 验证集验证开始\r\n labels = []\r\n for k in range(valid_batch):\r\n this_res, this_label, this_len = lstm_emotion_machine.get_train_batch(valid_res, valid_labels,\r\n valid_lens, k)\r\n # 输出预测出来的labels,可能有多个？\r\n this_labels = lstm_emotion_machine.predict_step(this_res, this_len)\r\n labels.extend(this_labels)\r\n # 计算准确度\r\n accuracy = compute_accuracy(labels, valid_labels)\r\n print(\"epoch=%d, accuracy=%f\\n\" % ((i + 1), accuracy))\r\n valid_accs.append(accuracy)\r\n # 保存这次的结果\r\n if best_valid_acc < accuracy:\r\n best_valid_acc = accuracy\r\n lstm_emotion_machine.saver.save(lstm_emotion_machine.sess, ckpt_path, global_step=(i + 1) * train_batch)\r\n #lstm_emotion_machine.save_weights(FLAGS.checkpoint_path)\r\n # 取最好的和平均的准确率\r\n best_acc = np.max(valid_accs)\r\n ave_acc = np.average(valid_accs)\r\n print(\"best acc=%f, average acc=%f\\n\" % (best_acc, ave_acc))\r\n\r\n\r\n # last_ckpt = lstm_emotion_machine.saver.last_checkpoints\r\n # print(\"test here\")\r\n # print(last_ckpt)\r\n # restore_path = lstm_emotion_machine.saver.last_checkpoints[0]\r\n\r\n\r\n checkpoint_path = os.path.join(os.path.join(model_path, \"data_utils/check_path_lstm\"), \"check_path_lstm-11728\")\r\n\r\n\r\n # lstm_emotion_machine.saver.restore(lstm_emotion_machine.sess, checkpoint_path)\r\n\r\n # 移动过来的\r\n generate_words=index_test_data(generate_words,id2word)\r\n # 去掉start和end_id\r\n generate_words=change_file_format(generate_words)\r\n # 把emo也变成beam个重复的数据形状\r\n this_emotion_labels=add_emo_beam(20,this_emotion_labels)\r\n\r\n test_responses,test_lens,this_emotion_labels= read_beam_test(generate_words,this_emotion_labels,FLAGS.max_len)\r\n test_responses = response_to_indexs_b(test_responses, total_word_dict, word_unk_id, FLAGS.max_len)\r\n\r\n total_labels = []\r\n # for k in range(pred_batches):\r\n # 处理输入\r\n # this_res, this_len = lstm_emotion_machine.get_pred_batch(test_responses, chat_config.max_len, k)\r\n # 输出预测的labels，形状是[batch_size,beam,1]\r\n # tf.reset_default_graph()\r\n this_labels_scores = lstm_emotion_machine.beam_predict_step(test_responses, test_lens, this_emotion_labels)\r\n scores_mul = []\r\n max_col = []\r\n for ge_score, emo_score in zip(scores, this_labels_scores):\r\n scores_mul_b = []\r\n for ge_score_b, emo_score_b in zip(ge_score, emo_score):\r\n scores_mul_b.append(ge_score_b * emo_score_b)\r\n scores_mul.append(scores_mul_b)\r\n # max_col的形状是[batch_size,1],类型是tensor\r\n for scores in scores_mul:\r\n max_index = scores.index(max(scores))\r\n max_col.append(max_index)\r\n for i in range(batch_size):\r\n print(max_col[i])\r\n\r\n total_labels.append(generate_words[i][max_col[i]])\r\n return total_labels\r\n\r\n '''\r\n pred_batches = int(len(test_responses) / batch_size)\r\n total_labels = []\r\n for k in range(pred_batches):\r\n # 处理输入\r\n this_res, this_len = lstm_emotion_machine.get_pred_batch(test_responses, test_lens, k)\r\n # 输出预测的labels\r\n this_labels = lstm_emotion_machine.predict_step(this_res, this_len)\r\n total_labels.extend(this_labels)\r\n\r\n print(\"check here length\")\r\n print(len(test_responses))\r\n print(len(total_labels))\r\n write_labels(pred_label_file, total_labels)\r\n\r\n return lstm_emotion_machine\r\n '''\r\n # print(len(total_labels))\r\n\r\n\r\ndef train(config_file,train_response_file, train_label_file, test_res_file, test_label_file, max_len, word_count_file, vocab_size,\r\n embedding_file, embedding_size, batch_size, num_epoch, hidden_size, emotion_class, pred_label_file,pre_train_word_count_file,emotion_words_dir, session):\r\n chat_config = ChatConfig(config_file)\r\n print(\"read word and embeddings\\n\")\r\n total_embeddings, total_word2id, total_id2word,total_word_list,=read_total_embeddings(embedding_file, vocab_size)\r\n pre_word_count = get_word_count(pre_train_word_count_file, chat_config.word_count)\r\n emotion_words_dict = read_emotion_words(emotion_words_dir, pre_word_count)\r\n word_list = construct_vocab(total_word_list, emotion_words_dict, chat_config.generic_word_size,chat_config.emotion_vocab_size, FLAGS.unk)\r\n word_dict = construct_word_dict(word_list, FLAGS.unk, FLAGS.start_symbol, FLAGS.end_symbol)\r\n id2words = {idx: word for word, idx in word_dict.items()}\r\n word_unk_id = word_dict[FLAGS.unk]\r\n word_start_id = word_dict[FLAGS.start_symbol]\r\n word_end_id = word_dict[FLAGS.end_symbol]\r\n final_word_list = get_word_list(id2words)\r\n print(\"Read word embeddings!\\n\")\r\n # 读所有的词向量\r\n embeddings = read_word_embeddings(total_embeddings, total_word2id, final_word_list, chat_config.embedding_size)\r\n\r\n print(\"reading training data\\n\")\r\n # 将回答的句子分成单词\r\n train_responses, train_labels, train_lens = read_emotional_response_label_file(train_response_file,\r\n train_label_file,\r\n max_len)\r\n # 补齐responses（补齐成max_len）\r\n train_responses = response_to_indexs(train_responses, word_dict, word_unk_id, max_len)\r\n # train_responses, train_labels什么的都会更新，会把valid的放出去，也就是随机分出验证集\r\n train_responses, train_labels, train_lens, valid_res, valid_labels, valid_lens = \\\r\n split_train_valid_data(train_responses, train_labels, train_lens)\r\n # 处理不能被batch_size整除的train样本集，随机挑一些直接砍掉\r\n train_responses, train_labels, train_lens = align_train_batch_size(train_responses, train_labels, train_lens,\r\n batch_size)\r\n\r\n # 处理不能被batch_size整除的valid样本集，随机挑一些直接砍掉\r\n valid_res, valid_labels, valid_lens = align_train_batch_size(valid_res, valid_labels, valid_lens, batch_size)\r\n\r\n # 读测试数据，和训练集一样的操作\r\n print(\"Read prediction data!\\n\")\r\n test_responses,test_lens = read_test_emotional_response_label_file(test_res_file,max_len)\r\n print(len(test_responses))\r\n\r\n test_responses = response_to_indexs(test_responses, word_dict, word_unk_id, max_len)\r\n\r\n test_length = len(test_responses)\r\n test_responses, test_lens = align_test_batch_size(test_responses,test_lens, batch_size)\r\n\r\n # 定义bi-lstm的模型\r\n print(\"Define model!\\n\")\r\n lstm_emotion_machine = LstmClassifier(embeddings, word_dict, embedding_size, hidden_size, emotion_class, batch_size,max_len, True, session, learning_rate=0.1)\r\n # 开始训练\r\n print(\"training\\n\")\r\n # 一共有多少批训练集\r\n train_batch = int(len(train_responses) / batch_size)\r\n # 一共有多少批验证集\r\n valid_batch = int(len(valid_res) / batch_size)\r\n valid_accs = []\r\n best_valid_acc = -1.0\r\n ckpt_path = os.path.join(FLAGS.checkpoint_path, \"check_path_lstm\")\r\n # 开始训练\r\n for i in range(num_epoch):\r\n print(\"Now train epoch %d!\\n\" % (i + 1))\r\n # 首先随机排序，即打乱顺序\r\n train_responses, train_labels, train_lens = shuffle_train_data(train_responses, train_labels, train_lens)\r\n\r\n for j in range(train_batch):\r\n # 处理输入，处理成能直接喂进去的形式\r\n this_res, this_label, this_len = lstm_emotion_machine.get_train_batch(train_responses, train_labels,\r\n train_lens, j)\r\n # 计算损失\r\n loss = lstm_emotion_machine.train_step(this_res, this_label, this_len)\r\n print(\"epoch=%d, batch=%d, loss=%f\\n\" % ((i + 1), (j + 1), loss))\r\n # 验证集验证开始\r\n labels = []\r\n for k in range(valid_batch):\r\n this_res, this_label, this_len = lstm_emotion_machine.get_train_batch(valid_res, valid_labels,\r\n valid_lens, k)\r\n # 输出预测出来的labels,可能有多个？\r\n this_labels = lstm_emotion_machine.predict_step(this_res, this_len)\r\n labels.extend(this_labels)\r\n # 计算准确度\r\n accuracy = compute_accuracy(labels, valid_labels)\r\n print(\"epoch=%d, accuracy=%f\\n\" % ((i + 1), accuracy))\r\n valid_accs.append(accuracy)\r\n # 保存这次的结果\r\n if best_valid_acc < accuracy:\r\n best_valid_acc = accuracy\r\n lstm_emotion_machine.saver.save(lstm_emotion_machine.sess, ckpt_path, global_step=(i + 1) * train_batch)\r\n #lstm_emotion_machine.save_weights(FLAGS.checkpoint_path)\r\n # 取最好的和平均的准确率\r\n best_acc = np.max(valid_accs)\r\n ave_acc = np.average(valid_accs)\r\n print(\"best acc=%f, average acc=%f\\n\" % (best_acc, ave_acc))\r\n\r\n #开始测试\r\n # restore_path = lstm_emotion_machine.saver.last_checkpoints[-1]\r\n # lstm_emotion_machine.saver.restore(lstm_emotion_machine.sess, restore_path)\r\n #\r\n #\r\n # restore_path = lstm_emotion_machine.saver.last_checkpoints[0]\r\n # checkpoint_path = os.path.join(os.path.join(model_path, \"data_utils/check_path_lstm\"), \"check_path_lstm-11728\")\r\n #\r\n # lstm_emotion_machine.saver.restore(lstm_emotion_machine.sess, checkpoint_path)\r\n pred_batches = int(len(test_responses) / batch_size)\r\n total_labels = []\r\n for k in range(pred_batches):\r\n # 处理输入\r\n this_res, this_len = lstm_emotion_machine.get_pred_batch(test_responses, test_lens, k)\r\n # 输出预测的labels\r\n this_labels = lstm_emotion_machine.predict_step(this_res, this_len)\r\n total_labels.extend(this_labels)\r\n\r\n print(\"check here length\")\r\n print(len(test_responses))\r\n print(len(total_labels))\r\n last_ckpt = lstm_emotion_machine.saver.last_checkpoints\r\n print(\"test here\")\r\n print(last_ckpt)\r\n write_labels(pred_label_file, total_labels)\r\n\r\n return lstm_emotion_machine\r\n # print(len(total_labels))\r\n\r\n\r\n\r\n\r\ndef main(_):\r\n with tf.device(\"/gpu:1\"):\r\n sess = tf.Session(config=tf.ConfigProto(\r\n allow_soft_placement=True, log_device_placement=True))\r\n train(FLAGS.config_file,FLAGS.train_response_file, FLAGS.train_label_file, FLAGS.test_response_file, FLAGS.test_label_file,\r\n FLAGS.max_len, FLAGS.word_count_file, FLAGS.vocab_size, FLAGS.embedding_file, FLAGS.embedding_size,\r\n FLAGS.batch_size, FLAGS.num_epoch, FLAGS.hidden_size, FLAGS.emotion_class, FLAGS.pred_label_file,FLAGS.pre_train_word_count_file,FLAGS.emotion_words_dir, sess)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n model_path = os.path.dirname(os.path.dirname(os.path.abspath(\"lstm_classifier.py\")))\r\n data_dir = os.path.join(model_path, \"data\")\r\n\r\n parse = argparse.ArgumentParser()\r\n parse.add_argument(\"--train_response_file\", type=str,\r\n default=os.path.join(data_dir, \"stc_data/test/utt123_trans_CN_jieba.txt\"))\r\n parse.add_argument(\"--train_label_file\", type=str,\r\n default=os.path.join(data_dir, \"stc_data/test/utt123_emo_CN.txt\"))\r\n # parse.add_argument(\"--test_response_file\", type=str,\r\n # default=\"/Users/zhouziyi/Desktop/Lab/Graduate Design/corpus/douban/post.without.txt\")\r\n parse.add_argument(\"--test_response_file\", type=str,\r\n default=os.path.join(data_dir, \"stc_data/test/trans/gen_ex_pre.final.txt\"))\r\n # test label是用于计算测试准确度的，不涉及别的\r\n parse.add_argument(\"--test_label_file\", type=str,\r\n default=os.path.join(data_dir, \"stc_data/train_test/test.label.lstm.filter.txt\"))\r\n parse.add_argument(\"--max_len\", type=int, default=15)\r\n # word_count_file并没有用处\r\n parse.add_argument(\"--word_count_file\", type=str,\r\n default=os.path.join(data_dir, \"emotion_words_human/word.count.7.120.CN.txt\"))\r\n parse.add_argument(\"--vocab_size\", type=int, default=40000)\r\n parse.add_argument(\"--embedding_file\", type=str,\r\n default=os.path.join(data_dir, \"embedding/7_classes_trans_metric.txt\"),\r\n help=\"word embedding file path\")\r\n parse.add_argument(\"--embedding_size\", type=int, default=50)\r\n parse.add_argument(\"--batch_size\", type=int, default=8)\r\n parse.add_argument(\"--num_epoch\", type=int, default=500)\r\n parse.add_argument(\"--hidden_size\", type=int, default=256)\r\n parse.add_argument(\"--emotion_class\", type=int, default=7)\r\n # pred_label_file并没有用处\r\n parse.add_argument(\"--pred_label_file\", type=str, default=os.path.join(data_dir, \"com_data/5-19-check/generated_sentences_2.final_lstm_classifier.txt\"))\r\n parse.add_argument(\"--unk\", type=str, default=\"\", help=\"symbol for unk words\")\r\n parse.add_argument(\"--start_symbol\", type=str, default=\"\", help=\"symbol for response sentence start\")\r\n parse.add_argument(\"--end_symbol\", type=str, default=\"\", help=\"symbol for response sentence end\")\r\n parse.add_argument(\"--learning_rate\", type=float, default=0.1)\r\n parse.add_argument(\"--checkpoint_path\", type=str, default=os.path.join(model_path, \"data_utils/check_path_lstm_ori_ex\"))\r\n # parse.add_argument(\"--checkpoint_path\", type=str, default=os.path.join(model_path, \"data_utils\"))\r\n parse.add_argument(\"--pre_train_word_count_file\", type=str,\r\n default=os.path.join(data_dir, \"emotion_words_human/word.count.7.120.CN.txt\"),\r\n help=\"nlp cc word count file\")\r\n parse.add_argument(\"--emotion_words_dir\", type=str,\r\n default=os.path.join(data_dir, \"emotion_words_human/7_class_CN_120\"),\r\n help=\"emotion words directory\")\r\n parse.add_argument(\"--config_file\", type=str, default=os.path.join(model_path, \"conf/dialogue1.conf\"),\r\n help=\"configuration file path\")\r\n\r\n FLAGS, unparsed = parse.parse_known_args()\r\n tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"slptongji/GERP","sub_path":"data_utils/lstm_classifier.py","file_name":"lstm_classifier.py","file_ext":"py","file_size_in_byte":42240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73291037034","text":"class Solution:\n # Simpler way: find new index for each element based on len and k\n # Since we don't want to lose data, store values in new auxillary array\n # Then, since we are modifying the input array, copy the values over\n # Complexity: Time O(n) Space O(n)\n def rotatee(self, nums: List[int], k: int) -> None:\n L = len(nums)\n aux = [0 for i in range(L)]\n for i in range(L):\n aux[(i + k) % L] = nums[i]\n for i in range(L):\n nums[i] = aux[i]\n\n # Math trick: Reverse array, then reverse array from [0, k) and [k, len)\n # Complexity: Time O(n) Space O(1)\n def reverse(self, nums: List[int], i: int, j: int):\n while (i < j):\n nums[i], nums[j] = nums[j], nums[i]\n i += 1\n j -= 1\n \n def rotate(self, nums: List[int], k: int) -> None:\n L = len(nums)\n if L < 2:\n return\n k %= L\n self.reverse(nums, 0, L - 1)\n self.reverse(nums, 0, k - 1)\n self.reverse(nums, k, L - 1)","repo_name":"jwestfromtheeast/CodingChallenges","sub_path":"python/easy/189RotateArray.py","file_name":"189RotateArray.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"40961554485","text":"import msprime\nimport numpy as np\nimport sys\nimport timeit\nimport time\nimport struct\n\nprint(msprime.__file__,msprime.__version__)\n\nnodes = msprime.NodeTable()\nedges = msprime.EdgeTable()\n\ng = []\nwith open(sys.argv[1],\"rb\") as f:\n print(sys.argv[1])\n while True:\n a=struct.unpack('i',f.read(4))\n if a[0]==-1:\n break\n t=struct.unpack('d',f.read(8))\n g.append(t[0])\n # for line in f:\n # l = line.rstrip().split(\" \")\n # g.append(float(l[1]))\ntimes = np.array(g)\ntimes -= times.max()\ntimes *= -1.0\n\nnodes.append_columns(time=times,flags=[-1]*len(times))\n\np = []\nc = []\nl = []\nr = []\n\nwith open(sys.argv[2],\"rb\") as f:\n while True:\n pi=struct.unpack('i',f.read(4))\n if pi[0] == -1:\n break\n ci=struct.unpack('i',f.read(4))\n li=struct.unpack('d',f.read(8))\n ri=struct.unpack('d',f.read(8))\n p.append(pi[0])\n c.append(ci[0])\n l.append(li[0])\n r.append(ri[0])\n\nedges.set_columns(parent=p,child=c,left=l,right=r)\n\n\nN=int(sys.argv[3])\n#samples=[i for i in range(len(times)-2*N,len(times))] \nsamples=[i for i in range(0,len(times),132)]\nts=None\n\nA=time.time()\nmsprime.sort_tables(nodes=nodes,edges=edges)\nB=time.time()\nts=msprime.simplify_tables(nodes=nodes,edges=edges,samples=samples)\nC=time.time()\n\nprint(\"Sorting: \",B-A,\"seconds\")\nprint(\"Simplifying: \",C-B,\"seconds\")\n\n\nwith open(sys.argv[4],'w') as f:\n for i in edges:\n f.write(\"{} {} {:.6f} {:.6f}\\n\".format(i.parent,i.child,i.left,i.right,nodes[i.parent].time))\nwith open(sys.argv[5],'w') as f:\n for i in nodes:\n f.write(\"{}\\n\".format(i.time))\n\n\n","repo_name":"molpopgen/fwdpp_experimental","sub_path":"check_fwd_sim_data_with_msprime.py","file_name":"check_fwd_sim_data_with_msprime.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"41678057686","text":"import unittest\n\nfrom naps import SimPlatform, SocMemory, axil_read, axil_write, do_nothing, SimSocPlatform\nfrom naps.soc.platform.zynq import ZynqSocPlatform\n\n\nclass SocMemoryTest(unittest.TestCase):\n def test_smoke(self):\n platform = ZynqSocPlatform(SimPlatform())\n\n dut = SocMemory(width=32, depth=128)\n\n def testbench():\n axi = platform.axi_lite_master\n memorymap = platform.memorymap\n for addr in range(128):\n yield from axil_write(axi, 4*addr + 0x40000000, addr)\n for addr in range(128):\n self.assertEqual(addr, (yield from axil_read(axi, 4*addr + 0x40000000)))\n\n platform.sim(dut, (testbench, \"axi_lite\"))\n\n def test_with_driver(self):\n platform = SimSocPlatform(SimPlatform())\n\n dut = SocMemory(width=64, depth=128)\n\n def driver(design):\n for i in range(128):\n design[i] = i * i << 30\n yield from do_nothing(10)\n for i in reversed(range(128)):\n self.assertEqual(design[i], i * i << 30)\n yield from do_nothing(10)\n platform.add_driver(driver)\n\n platform.sim(dut)\n","repo_name":"apertus-open-source-cinema/naps","sub_path":"naps/cores/peripherals/soc_memory_test.py","file_name":"soc_memory_test.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"72"}
+{"seq_id":"31489421760","text":"import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import make_scorer\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.ensemble import RandomForestRegressor\n\n#Импорт данных обучающей выборки\ndf_train=pd.read_csv('gold_recovery_train_new.csv')\ndf_train_clear=df_train.fillna(value=None, method=\"ffill\")\nprint(df_train_clear.isna().sum()*100/len(df_train))\n#Импорт данных тестовой выборки\ndf_test=pd.read_csv('gold_recovery_test.csv')\ndf_test=df_test.fillna(value=None, method=\"ffill\")\nprint(df_test.isna().sum()*100/len(df_test))\n#Импорт данных полной выборки выборки\ndf_full=pd.read_csv('/datasets/gold_recovery_full_new.csv')\ndf_full=df_full.fillna(value=None, method=\"ffill\")\nprint(df_full.isnull().sum()*100/len(df_full))\n\n#анализ параметров, не используемых в тестовой выборке\ndf_traincs=df_train.columns\ndf_testcs=df_test.columns\ndf_diff=[]\nfor train in df_traincs:\n if train not in df_testcs:\n df_diff.append(train)\nprint(df_diff)\n\n#Очистка данных\ndf_train_clear1=df_train\ndf_train_clear1=df_train.dropna()\n\n#Оценка эффективности обогащения\nF=df_train_clear1['rougher.input.feed_au']\nprint(F.isna().value_counts())\nC=df_train_clear1['rougher.output.concentrate_au']\nprint(C.isna().value_counts())\nT=df_train_clear1['rougher.output.tail_au']\nprint(T.isna().value_counts())\nA=C*(F-T)\nprint(A.isna().value_counts())\nB=F*(C-T)\nprint(B.isna().value_counts())\ndf_train_clear1['rougher_recovery']=(A/B)*100\nprint(df_train_clear1['rougher_recovery'].isna().value_counts())\nprint(mean_absolute_error(df_train_clear1['rougher.output.recovery'], df_train_clear1['rougher_recovery']))\n\n#Сравнение концентрации материалов на каждом этапе очистки\nprint(\"------------------------------------------------------\")\nprint(\"Средняя концентрация свинца после флотации\", df_full[\"rougher.output.concentrate_pb\"].mean())\nprint(\"Средняя концентрация свинца после первичного этапа очистки\", df_full['primary_cleaner.output.concentrate_pb'].mean())\nprint(\"Средняя концентрация свинца на выходе\", df_full[\"final.output.concentrate_pb\"].mean())\nprint(\"------------------------------------------------------\")\nprint(\"Средняя концентрация серебра после флотации\", df_full[\"rougher.output.concentrate_ag\"].mean())\nprint(\"Средняя концентрация серебра после первичного этапа очистки\", df_full['primary_cleaner.output.concentrate_ag'].mean())\nprint(\"Средняя концентрация серебра на выходе\", df_full[\"final.output.concentrate_ag\"].mean())\nprint(\"------------------------------------------------------\")\nprint(\"Средняя концентрация золота после флотации\", df_full[\"rougher.output.concentrate_au\"].mean())\nprint(\"Средняя концентрация золота после первичного этапа очистки\", df_full['primary_cleaner.output.concentrate_au'].mean())\nprint(\"Средняя концентрация золота на выходе\", df_full[\"final.output.concentrate_au\"].mean())\n\n#Сравнение распределений размеров гранул сырья\nplt.title(\"Сравнение распределений размеров гранул сырья золота\")\nsns.set(style=\"darkgrid\")\nfig = sns.kdeplot(df_train[\"rougher.output.concentrate_au\"], shade=True, color=\"r\")\nfig = sns.kdeplot(df_train[\"primary_cleaner.output.concentrate_au\"], shade=True, color=\"b\")\nfig = sns.kdeplot(df_train[\"final.output.concentrate_au\"], shade=True, color=\"g\")\nplt.legend(['Флотация', 'Первичная очистка', \"На выходе\" ], loc=1)\nplt.show()\n\nplt.title(\"Сравнение распределений размеров гранул сырья серебра\")\nsns.set(style=\"darkgrid\")\nfig = sns.kdeplot(df_train[\"rougher.output.concentrate_ag\"], shade=True, color=\"r\")\nfig = sns.kdeplot(df_train[\"primary_cleaner.output.concentrate_ag\"], shade=True, color=\"b\")\nfig = sns.kdeplot(df_train[\"final.output.concentrate_ag\"], shade=True, color=\"g\")\nplt.legend(['Флотация', 'Первичная очистка', \"На выходе\" ], loc=1)\nplt.show()\n\nplt.title(\"Сравнение распределений размеров гранул сырья свинца\")\nsns.set(style=\"darkgrid\")\nfig = sns.kdeplot(df_train[\"rougher.output.concentrate_pb\"], shade=True, color=\"r\")\nfig = sns.kdeplot(df_train[\"primary_cleaner.output.concentrate_pb\"], shade=True, color=\"b\")\nfig = sns.kdeplot(df_train[\"final.output.concentrate_pb\"], shade=True, color=\"g\")\nplt.legend(['Флотация', 'Первичная очистка', \"На выходе\" ], loc=1)\nplt.show()\n\nplt.title(\"Сравнение распределений размеров гранул сырья на выборках\")\nsns.set(style=\"darkgrid\")\nfig = sns.kdeplot(df_train['rougher.input.feed_size'], shade=True, color=\"r\")\nfig = sns.kdeplot(df_test['rougher.input.feed_size'], shade=True, color=\"b\")\nplt.legend(['Обучающая выборка', 'Тестовая выборка'], loc=1)\nplt.show()\n\n#Сравнение концентраций металлов на разных этапах обработки\ndf_train_clear1['input']=df_train_clear1['rougher.input.feed_ag']+df_train_clear1['rougher.input.feed_pb']+df_train_clear1['rougher.input.feed_au']\\\n +df_train_clear1['rougher.input.feed_sol']\ndf_train_clear1['rougher']=df_train_clear1['rougher.output.concentrate_ag']+df_train_clear1['rougher.output.concentrate_pb']+df_train_clear1['rougher.output.concentrate_au']\\\n +df_train_clear1['rougher.output.concentrate_sol']\ndf_train_clear1['black']=df_train_clear1['primary_cleaner.output.concentrate_ag']+df_train_clear1['primary_cleaner.output.concentrate_pb']\\\n +df_train_clear1['primary_cleaner.output.concentrate_au']+df_train_clear1['primary_cleaner.output.concentrate_sol']\ndf_train_clear1['finale']=df_train_clear1['final.output.concentrate_ag']+df_train_clear1['final.output.concentrate_pb']+df_train_clear1['final.output.concentrate_au']\\\n +df_train_clear1['final.output.concentrate_sol']\n#Графическое представление полученных результатов\nfig = plt.subplots(figsize=(20, 7))\nfig = sns.distplot(a=df_train_clear1['rougher'], hist=True, kde=True, rug=False , color=\"r\")\nfig = sns.distplot(a=df_train_clear1['black'], hist=True, kde=True, rug=False , color=\"g\")\nfig = sns.distplot(a=df_train_clear1['finale'], hist=True, kde=True, rug=False , color=\"b\")\nfig = sns.distplot(a=df_train_clear1['input'], hist=True, kde=True, rug=False , color=\"y\")\nplt.legend(['rougher', 'primary', 'final', 'input'], loc=1)\nplt.show()\n#Отброс выброса\ndf_train_clear1=df_train_clear1.query('rougher > 20 & black>20 & finale>20 & input >20')\ndf_train_clear1['final_recovery']=df_train_clear1['final.output.recovery']\ndf_train_clear1=df_train_clear1.query('50 < final_recovery < 95')\nprint(df_train_clear1.shape)\n\n#Статистические показатели обучающей выборки\ndf_train_clear1.boxplot('final.output.recovery')\ndf_train_clear1['final.output.recovery'].describe()\n\n#ML\n#Задание кастомной метрики\ndef smape(A, F):\n return 100/len(A) * np.sum(2 * np.abs(F - A) / (np.abs(A) + np.abs(F)))\n\ntotal_smape=0.25*smape_rougher+0.75*smape_final1\nprint('Итоговое симметричное среднее абсолютное процентное отклонение', total_smape)\n\n#Подготовка фичей и таргетов выборок\ntrain_target1=df_train_clear1['rougher.output.recovery']\ntrain_target2=df_train_clear1['final_recovery']\ntrain_features=df_train_clear1[df_test.columns].drop('date', axis=1)\nprint(train_features.shape)\n\ndf_full_target=df_full[['date','rougher.output.recovery','final.output.recovery']]\ndf_test_ready = df_test.merge(df_full_target, on='date', how='left')\ndf_test_ready=df_test_ready.dropna()\n\ntest_features=df_test_ready.drop(['date','rougher.output.recovery', 'final.output.recovery'], axis=1)\ntest_target1=df_test_ready['rougher.output.recovery']\ntest_target2=df_test_ready['final.output.recovery']\nprint(test_features.shape)\n\n#Обучения моделей\n#Линейная регрессия\nmodel_lr =LinearRegression()\nmodel_lr.fit(train_features, train_target1)\nresult=model_lr.predict(test_features)\nsmape1=smape(test_target1, result)\nprint(smape1)\n\nmodel_lr1 =LinearRegression()\nmodel_lr1.fit(train_features, train_target2)\nresult1=model_lr.predict(test_features)\nsmape2=smape(test_target2, result1)\nprint(smape2)\n\n#Случайный лес (подбор параметров вставлять не стал) с кросс-валидацией\nmodel_rougher=RandomForestRegressor(random_state=12345, n_estimators=40, max_depth=1)\nmodel_rougher.fit(train_features, train_target1)\nresult=model_rougher.predict(test_features)\nsmape_rougher=smape(test_target1, result)\nscores_rougher = cross_val_score(model_rougher, test_features, result, cv=5)\nfinal_score = scores_rougher.mean()\nprint(smape_rougher)\nprint(scores_rougher)\n\nmodel_final1=RandomForestRegressor(random_state=12345, n_estimators=40, max_depth=7)\nmodel_final1.fit(train_features, train_target2)\nresult=model_final1.predict(test_features)\nsmape_final1=smape(test_target2, result)\nscores_final = cross_val_score(model_final1, test_features, result, cv=5)\nfinal_score1 = scores_final.mean()\nprint(smape_final1)\nprint(final_score1)\n\n#Инициализация подбора гиперпараметров линейной регрессии\nscores = make_scorer(smape, greater_is_better = False)\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import make_scorer\n\n\ndef lr_gridsearchcv(features, target, scores):\n model = LinearRegression()\n param_grid = {\n 'copy_X': [True, False],\n 'fit_intercept': [True, False],\n 'normalize': [True, False]\n }\n my_scorer = make_scorer(scores, greater_is_better=False)\n CV = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring=my_scorer)\n CV.fit(features, target)\n print('Лучшее значение метрики: {:.2f}'.format(-CV.best_score_))\n print('Лучшие гиперпараметры: ', CV.best_params_)\n return CV.best_params_\nlr_gridsearchcv(train_features, train_target1, smape)\nlr_gridsearchcv(train_features, train_target2, smape)\n\n#Сравнение результатов линейной регрессии с подобранными гиперпараметрами (которыепо сути дефолтные, не зря учил)\nmodel_final=LinearRegression(copy_X=True, normalize = True)\nmodel_final.fit(train_features, train_target2)\nresult1=model_final.predict(test_features)\nsmape_final=smape(test_target2, result1)\nscores_final = cross_val_score(model_final, test_features, result1, cv=5)\nfinal_score1 = scores_final.mean()\nprint(smape_final)\n\n\n#Сравнение полученных результатов с дамми\nfrom sklearn.dummy import DummyRegressor\ndummy_regressor_rougher = DummyRegressor(strategy=\"median\")\ndummy_regressor_rougher.fit(train_features, train_target2)\ndummy_rougher_pred = dummy_regressor_rougher.predict(test_features)\nsmape_dummy_rougher = smape(test_target2, dummy_rougher_pred)\n\nprint(smape_dummy_rougher)\n\ndummy_regressor_final = DummyRegressor(strategy=\"median\")\ndummy_regressor_final.fit(train_features, train_target1)\ndummy_final_pred = dummy_regressor_final.predict(test_features)\nsmape_dummy_final = smape(test_target1, dummy_final_pred)\n\nprint(smape_dummy_final)\n\ntotal_smape=0.25*smape_dummy_rougher+0.75*smape_dummy_final\nprint(total_smape)","repo_name":"PTyneu/Gold","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":12279,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"41763357407","text":"# ********************************************* #\n# Advent of Code Day 9 #\n# Trevor Loula #\n# Cedarville University Leaderboard #\n# ********************************************* #\n\n# Sliding Window Two-Sum\n\nimport sys\n\nclass Day9:\n\n def __init__(self, window_size):\n self.lines = list(int(line.strip()) for line in open('input/day-9.txt'))\n self.window_size = window_size\n self.target = 0\n\n @staticmethod\n def two_sum(nums, target):\n for num in nums:\n complement = int(target) - int(num)\n if complement in nums: return [num, complement]\n return False\n\n def part_1(self):\n for i in range(self.window_size, len(self.lines)):\n if not Day9.two_sum(self.lines[i-self.window_size:i], self.lines[i]):\n self.target = self.lines[i]\n return self.target\n print(\"No number without property\")\n\n def part_2(self):\n for lo in range(len(self.lines)):\n cont_sum = 0\n for hi in range(lo, len(self.lines)):\n cont_sum += self.lines[hi]\n if cont_sum == self.target:\n return min(self.lines[lo:hi]) + max(self.lines[lo:hi])\n elif cont_sum > self.target:\n break\n print(\"Contiguous sum not found\")\n\ndef main(args):\n if len(args) != 2:\n print(\"Usage: python day-9.py window_size\")\n\n day_9 = Day9(int(args[1]))\n num_1 = day_9.part_1()\n num_2 = day_9.part_2()\n\n print(\"First number without property:\", num_1)\n print(\"Sum of min and max numbers in contiguous set adding to target:\", num_2)\n\nif __name__ == \"__main__\":\n main(sys.argv)\n","repo_name":"tloula/advent-of-code","sub_path":"day-9.py","file_name":"day-9.py","file_ext":"py","file_size_in_byte":1749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"34737494353","text":"print('GERADOR DE PA')\nprint('-'*7)\nprimeiro = int(input('Primeiro termo: '))\nrazao = int(input('Razão: '))\ntermo = primeiro\ncont = 1\nlimite = 11\nwhile cont <= limite:\n print('{} -> '.format(termo),end='')\n termo += + razao\n cont += 1\n if limite == cont:\n l2 = int(input('PAUSE\\nQuantos mais?'))\n limite += + l2\nprint('CONSEGUI PORRA')\n","repo_name":"RaoniSilvestre/Exercicios-Python","sub_path":"mundo-2/ex061b.py","file_name":"ex061b.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"6137605349","text":"from pytorch_lightning.utilities import rank_zero_only\nfrom pytorch_lightning.loggers import LightningLoggerBase\nimport pandas as pd\nfrom IPython.display import DisplayHandle\n\nclass TableLogger(LightningLoggerBase):\n def __init__(self, name='TableLogger', version=None):\n super().__init__()\n self.table = pd.DataFrame()\n self.metrics = []\n self.val_metrics = {}\n self.display_handle = DisplayHandle()\n self._version = version\n self._experiment = self.table\n self._name = name\n\n def average_metrics(self):\n avg_metrics = {key: np.mean([m[key] for m in self.metrics])\n for key in self.metrics[0].keys()}\n avg_metrics.update(self.val_metrics)\n self.table = self.table.append(avg_metrics, ignore_index=True)\n self.metrics = []\n\n def display(self):\n if len(self.table) == 1:\n self.display_handle.display(self.table)\n else:\n self.display_handle.update(self.table)\n\n @rank_zero_only\n def log_hyperparams(self, params):\n # Save hparams into the logger dir\n # Calls logger.save afterwards\n pass\n\n @rank_zero_only\n def log_metrics(self, metrics, step):\n if 'val/loss' in metrics:\n self.val_metrics = metrics\n self.average_metrics()\n self.display()\n else:\n self.metrics.append(metrics)\n\n @rank_zero_only\n def finalize(self, status):\n self.agg_and_log_metrics(None)\n if len(self.metrics) > 0:\n self.average_metrics()\n self.display_handle.update(self.table)\n self.save()\n\n def save(self):\n self.table.to_csv(f'{self.name}/version_{self.version}/logs.csv', index=False)\n\n @property\n def experiment(self):\n return self._experiment\n\n @property\n def name(self):\n return self._name\n\n @property\n def version(self):\n if self._version is None:\n self._version = max(int(re.search(r'\\d+$', str(v)).group()) for v in Path(self.name).iterdir())+1\n return self._version\n","repo_name":"ttumiel/components","sub_path":"components/lightning/table_logger.py","file_name":"table_logger.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"29085062098","text":"# coding: utf-8\n\n\"\"\"\nInfluxDB OSS API Service.\n\nThe InfluxDB v2 API provides a programmatic interface for all interactions with InfluxDB. Access the InfluxDB API using the `/api/v2/` endpoint. # noqa: E501\n\nOpenAPI spec version: 2.0.0\nGenerated by: https://openapi-generator.tech\n\"\"\"\n\n\nimport pprint\nimport re # noqa: F401\n\n\nclass VariableLinks(object):\n \"\"\"NOTE: This class is auto generated by OpenAPI Generator.\n\n Ref: https://openapi-generator.tech\n\n Do not edit the class manually.\n \"\"\"\n\n \"\"\"\n Attributes:\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n openapi_types = {\n '_self': 'str',\n 'org': 'str',\n 'labels': 'str'\n }\n\n attribute_map = {\n '_self': 'self',\n 'org': 'org',\n 'labels': 'labels'\n }\n\n def __init__(self, _self=None, org=None, labels=None): # noqa: E501,D401,D403\n \"\"\"VariableLinks - a model defined in OpenAPI.\"\"\" # noqa: E501\n self.__self = None\n self._org = None\n self._labels = None\n self.discriminator = None\n\n if _self is not None:\n self._self = _self\n if org is not None:\n self.org = org\n if labels is not None:\n self.labels = labels\n\n @property\n def _self(self):\n \"\"\"Get the _self of this VariableLinks.\n\n :return: The _self of this VariableLinks.\n :rtype: str\n \"\"\" # noqa: E501\n return self.__self\n\n @_self.setter\n def _self(self, _self):\n \"\"\"Set the _self of this VariableLinks.\n\n :param _self: The _self of this VariableLinks.\n :type: str\n \"\"\" # noqa: E501\n self.__self = _self\n\n @property\n def org(self):\n \"\"\"Get the org of this VariableLinks.\n\n :return: The org of this VariableLinks.\n :rtype: str\n \"\"\" # noqa: E501\n return self._org\n\n @org.setter\n def org(self, org):\n \"\"\"Set the org of this VariableLinks.\n\n :param org: The org of this VariableLinks.\n :type: str\n \"\"\" # noqa: E501\n self._org = org\n\n @property\n def labels(self):\n \"\"\"Get the labels of this VariableLinks.\n\n :return: The labels of this VariableLinks.\n :rtype: str\n \"\"\" # noqa: E501\n return self._labels\n\n @labels.setter\n def labels(self, labels):\n \"\"\"Set the labels of this VariableLinks.\n\n :param labels: The labels of this VariableLinks.\n :type: str\n \"\"\" # noqa: E501\n self._labels = labels\n\n def to_dict(self):\n \"\"\"Return the model properties as a dict.\"\"\"\n result = {}\n\n for attr, _ in self.openapi_types.items():\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Return the string representation of the model.\"\"\"\n return pprint.pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"For `print` and `pprint`.\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Return true if both objects are equal.\"\"\"\n if not isinstance(other, VariableLinks):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Return true if both objects are not equal.\"\"\"\n return not self == other\n","repo_name":"influxdata/influxdb-client-python","sub_path":"influxdb_client/domain/variable_links.py","file_name":"variable_links.py","file_ext":"py","file_size_in_byte":4082,"program_lang":"python","lang":"en","doc_type":"code","stars":629,"dataset":"github-code","pt":"72"}
+{"seq_id":"41058469894","text":"import pandas as pd\nimport numpy as np\nfrom multiprocessing import Pool, cpu_count\nimport os.path\nimport gc\nimport pickle\n\n# Thanks to https://www.kaggle.com/onodera/multilabel-fscore\ndef multilabel_fscore(y_true, y_pred):\n if not isinstance(y_pred, np.ndarray):\n y_pred = np.array([y_pred])\n\n precision = sum([1 for i in y_pred if i in y_true]) / len(y_pred)\n recall = sum([1 for i in y_true if i in y_pred]) / len(y_true)\n denom = (precision + recall)\n if denom == 0:\n denom = 1\n return (2 * precision * recall) / denom\n\n\ndef applyParallel(dfGrouped, func):\n with Pool(cpu_count()) as p:\n ret_list = p.map(func, [group for name, group in dfGrouped])\n return pd.concat(ret_list)\n\n\n\n#####################\n# https://www.kaggle.com/mmueller/f1-score-expectation-maximization-in-o-n/code\n\nimport matplotlib.pylab as plt\nfrom datetime import datetime\nfrom numba import jit\n\nclass F1Optimizer():\n def __init__(self):\n pass\n\n @staticmethod\n @jit\n def get_expectations(P, pNone=None):\n expectations = []\n P = np.sort(P)[::-1]\n\n n = np.array(P).shape[0]\n DP_C = np.zeros((n + 2, n + 1))\n if pNone is None:\n pNone = (1.0 - P).prod()\n\n DP_C[0][0] = 1.0\n for j in range(1, n):\n DP_C[0][j] = (1.0 - P[j - 1]) * DP_C[0, j - 1]\n\n for i in range(1, n + 1):\n DP_C[i, i] = DP_C[i - 1, i - 1] * P[i - 1]\n for j in range(i + 1, n + 1):\n DP_C[i, j] = P[j - 1] * DP_C[i - 1, j - 1] + (1.0 - P[j - 1]) * DP_C[i, j - 1]\n\n DP_S = np.zeros((2 * n + 1,))\n DP_SNone = np.zeros((2 * n + 1,))\n for i in range(1, 2 * n + 1):\n DP_S[i] = 1. / (1. * i)\n DP_SNone[i] = 1. / (1. * i + 1)\n for k in range(n + 1)[::-1]:\n f1 = 0\n f1None = 0\n for k1 in range(n + 1):\n f1 += 2 * k1 * DP_C[k1][k] * DP_S[k + k1]\n f1None += 2 * k1 * DP_C[k1][k] * DP_SNone[k + k1]\n for i in range(1, 2 * k - 1):\n DP_S[i] = (1 - P[k - 1]) * DP_S[i] + P[k - 1] * DP_S[i + 1]\n DP_SNone[i] = (1 - P[k - 1]) * DP_SNone[i] + P[k - 1] * DP_SNone[i + 1]\n expectations.append([f1None + 2 * pNone / (2 + k), f1])\n\n return np.array(expectations[::-1]).T\n\n @staticmethod\n @jit\n def maximize_expectation(P, pNone=None):\n expectations = F1Optimizer.get_expectations(P, pNone)\n\n ix_max = np.unravel_index(expectations.argmax(), expectations.shape)\n max_f1 = expectations[ix_max]\n\n predNone = True if ix_max[0] == 0 else False\n best_k = ix_max[1]\n\n return best_k, predNone, max_f1\n\n @staticmethod\n def _F1(tp, fp, fn):\n return 2 * tp / (2 * tp + fp + fn)\n\n @staticmethod\n def _Fbeta(tp, fp, fn, beta=1.0):\n beta_squared = beta ** 2\n return (1.0 + beta_squared) * tp / ((1.0 + beta_squared) * tp + fp + beta_squared * fn)\n\n\ndef print_best_prediction(P, pNone=None):\n print(\"Maximize F1-Expectation\")\n print(\"=\" * 23)\n P = np.sort(P)[::-1]\n n = P.shape[0]\n L = ['L{}'.format(i + 1) for i in range(n)]\n\n if pNone is None:\n print(\"Estimate p(None|x) as (1-p_1)*(1-p_2)*...*(1-p_n)\")\n pNone = (1.0 - P).prod()\n\n PL = ['p({}|x)={}'.format(l, p) for l, p in zip(L, P)]\n print(\"Posteriors: {} (n={})\".format(PL, n))\n print(\"p(None|x)={}\".format(pNone))\n\n opt = F1Optimizer.maximize_expectation(P, pNone)\n best_prediction = ['None'] if opt[1] else []\n best_prediction += (L[:opt[0]])\n f1_max = opt[2]\n\n print(\"Prediction {} yields best E[F1] of {}\\n\".format(best_prediction, f1_max))\n\n\ndef save_plot(P, filename='expected_f1.png'):\n E_F1 = pd.DataFrame(F1Optimizer.get_expectations(P).T, columns=[\"/w None\", \"/wo None\"])\n best_k, _, max_f1 = F1Optimizer.maximize_expectation(P)\n\n plt.style.use('ggplot')\n plt.figure()\n E_F1.plot()\n plt.title('Expected F1-Score for \\n {}'.format(\"P = [{}]\".format(\",\".join(map(str, P)))), fontsize=12)\n plt.xlabel('k')\n plt.xticks(np.arange(0, len(P) + 1, 1.0))\n plt.ylabel('E[F1(P,k)]')\n plt.plot([best_k], [max_f1], 'o', color='#000000', markersize=4)\n plt.annotate('max E[F1(P,k)] = E[F1(P,{})] = {:.5f}'.format(best_k, max_f1), xy=(best_k, max_f1),\n xytext=(best_k, max_f1 * 0.8), arrowprops=dict(facecolor='black', shrink=0.05, width=1, headwidth=7),\n horizontalalignment='center', verticalalignment='top')\n plt.gcf().savefig(filename)\n\n\n\ndef timeit(P):\n s = datetime.now()\n F1Optimizer.maximize_expectation(P)\n e = datetime.now()\n return (e-s).microseconds / 1E6\n\n\ndef benchmark(n=100, filename='runtimes.png'):\n results = pd.DataFrame(index=np.arange(1,n+1))\n results['runtimes'] = 0\n\n for i in range(1,n+1):\n runtimes = []\n for j in range(5):\n runtimes.append(timeit(np.sort(np.random.rand(i))[::-1]))\n results.iloc[i-1] = np.mean(runtimes)\n\n x = results.index\n y = results.runtimes\n results['quadratic fit'] = np.poly1d(np.polyfit(x, y, deg=2))(x)\n\n plt.style.use('ggplot')\n plt.figure()\n results.plot()\n plt.title('Expectation Maximization Runtimes', fontsize=12)\n plt.xlabel('n = |P|')\n plt.ylabel('time in seconds')\n plt.gcf().savefig(filename)\n\n\n#####################\nfrom sklearn.model_selection import ParameterGrid\n#from ggplot import *\nimport time\nimport lightgbm as lgb\nimport pandas as pd\n\n\ndef grid_search(lgb_train, lgb_valid, param_grid, early_stopping_rounds=150):\n \"\"\"\"\n param_grid = {'eta': [0.1], 'max_depth': [4, 6], 'subsample': [0.8, 1]}\n grid_search(lgb_train, lgb_valid, param_grid)\n \"\"\"\n grid = ParameterGrid(param_grid)\n data = pd.DataFrame(list(grid))\n data['time'] = 0\n data['best_score'] = 0\n data['best_iter'] = 0\n\n for i, params in enumerate(grid):\n print(\"Etape \" + str(i + 1))\n start = time.time()\n #ipdb.set_trace()\n model = lgb.train(params, lgb_train, 100000, valid_sets=[lgb_train, lgb_valid],\n early_stopping_rounds=early_stopping_rounds, verbose_eval=50)\n\n end = time.time()\n data.loc[i,'time'] = end - start\n data.loc[i,'best_score'] = model.best_score['valid_1'][params['metric']]\n data.loc[i,'best_iter'] = model.best_iteration\n return data\n\n#########\ndef read_data():\n aisles = pd.read_csv(\"./data/aisles.csv\")\n departments = pd.read_csv(\"./data/departments.csv\")\n order_prior = pd.read_csv(\"./data/order_products__prior.csv\")\n order_train = pd.read_csv(\"./data/order_products__train.csv\")\n orders = pd.read_csv(\"./data/orders.csv\")\n products = pd.read_csv(\"./data/products.csv\")\n\n # orders = orders.groupby(\"user_id\").\\\n # apply(utils.add_fe_to_orders)\n\n # pickle.dump(orders, open(\"orders.p\", \"wb\"))\n orders = pickle.load(open(\"orders.p\", \"rb\"))\n product2vec = pickle.load(open(\"product2vec.p\", \"rb\"))\n #product2vec = pd.read_csv(\"./data/product_embeddings.csv\").drop([\"product_name\", \"aisle_id\", \"department_id\"], axis=1)\n return aisles, departments, order_prior, order_train, orders, products, product2vec\n\n# Fucking slow :/ - So i pickled it\n# Way faster & easier with dplyr...\ndef add_fe_to_orders(group):\n group[\"date\"] = group.iloc[::-1]['days_since_prior_order'].cumsum()[::-1].shift(-1).fillna(0)\n max_group = group[\"order_number\"].max()\n group[\"order_number_reverse\"] = max_group - group[\"order_number\"]\n return group\n\ndef get_products_fe(order_prior):\n products_fe = order_prior. \\\n groupby([\"product_id\"]). \\\n agg({'reordered': {'p_reorder_rt': \"mean\", 'p_count': \"size\"}, \\\n 'add_to_cart_order': {\"p_add_to_cart_order\": \"mean\"}, \\\n 'order_number': {\"P_recency_order\": \"mean\"}, \\\n 'order_number_reverse': {\"P_recency_order_r\": \"mean\"}, \\\n 'date': {\"P_recency_date\": \"mean\"}})\n\n products_fe.columns = products_fe.columns.droplevel(0)\n products_fe = products_fe.reset_index()\n\n # bool_reordered = if a product is bought once, prob to be reordered at least once\n up_fe2 = order_prior.groupby([\"user_id\", \"product_id\"]).agg(\"size\").rename(\"up_nb_ordered\").reset_index()\n up_fe2[\"bool_reordered\"] = (up_fe2[\"up_nb_ordered\"] > 1).astype(\"int\")\n\n products_fe2 = up_fe2.groupby('product_id')[\"bool_reordered\"]. \\\n agg([\"mean\", \"size\"]).reset_index(). \\\n rename(index=str, columns={\"mean\": \"p_reorder_rt_bool\", \"size\": \"p_active_user\"})\n\n products_fe = pd.merge(products_fe, products_fe2, how=\"left\", on=\"product_id\")\n del products_fe2\n\n # Product trend in a way\n products_trend = order_prior.query(\"order_number_reverse < 3\"). \\\n groupby([\"product_id\", \"order_number_reverse\"]).size(). \\\n rename(\"p_size\").reset_index()\n\n products_trend[\"p_trend_rt\"] = products_trend[\"p_size\"] / products_trend[\"p_size\"].shift(-1)\n products_trend[\"p_trend_diff\"] = products_trend[\"p_size\"] - products_trend[\"p_size\"].shift(-1)\n\n cond = products_trend[\"product_id\"] != products_trend[\"product_id\"].shift(-1)\n products_trend.loc[cond, \"p_trend_rt\"] = np.nan\n products_trend.loc[cond, \"p_trend_diff\"] = np.nan\n products_trend = products_trend.query(\"order_number_reverse == 1\").drop(\"order_number_reverse\", 1)\n\n products_fe = pd.merge(products_fe, products_trend, how=\"left\", on=\"product_id\")\n\n del cond, products_trend\n\n product_freq = order_prior.copy()\n product_freq = product_freq.sort_values([\"user_id\", \"product_id\", \"order_number\"])\n\n product_freq[\"p_freq_days\"] = product_freq[\"date\"].shift() - product_freq[\"date\"]\n product_freq[\"p_freq_order\"] = product_freq[\"order_number\"] - product_freq[\"order_number\"].shift()\n product_freq = product_freq.query(\"reordered == 1\")\n\n product_freq = product_freq.groupby(\"product_id\"). \\\n agg({'p_freq_days': {'p_freq_days': \"mean\"}, \\\n 'p_freq_order': {'p_freq_order': \"mean\"}})\n\n product_freq.columns = product_freq.columns.droplevel(0)\n product_freq = product_freq.reset_index()\n\n products_fe = pd.merge(products_fe, product_freq, how=\"left\", on=\"product_id\")\n\n del product_freq\n\n return products_fe\n\n\ndef get_products_fe_mod(order_prior, order_train, nfold=5):\n order_train = order_train.copy()\n order_train[\"user_fold\"] = order_train[\"user_id\"].mod(nfold)\n\n products_fe_mod=[]\n for fold in range(nfold):\n #print(\"Folder: \" + str(fold))\n order_train_tmp = order_train.query(\"user_fold != @fold\").drop(\"user_fold\", axis=1)\n order_train_tmp = pd.concat([order_prior, order_train_tmp])\n products_fe_tmp = get_products_fe(order_train_tmp)\n products_fe_tmp[\"user_fold\"] = fold\n products_fe_mod.append(products_fe_tmp)\n\n products_fe_mod = pd.concat(products_fe_mod)\n\n return products_fe_mod\n\n\ndef get_users_fe(orders, order_prior):\n users_fe = order_prior. \\\n groupby(\"user_id\"). \\\n agg({'reordered': {'U_rt_reordered': 'mean'}})\n\n users_fe.columns = users_fe.columns.droplevel(0)\n users_fe = users_fe.reset_index()\n\n # User basket sum, mean, std\n # TODO same but only on reordered products?\n users_fe2 = order_prior. \\\n groupby([\"user_id\", \"order_id\"]).size(). \\\n reset_index(). \\\n drop(\"order_id\", axis=1). \\\n groupby(\"user_id\"). \\\n agg([np.sum, np.mean, np.std])\n\n users_fe2.columns = [\"U_basket_sum\", \"U_basket_mean\", \"U_basket_std\"]\n users_fe2 = users_fe2.reset_index()\n\n users_fe = pd.merge(users_fe, users_fe2, on=\"user_id\")\n del users_fe2\n\n # u_active_p == user distinct products\n # bool_reordered = if a product is bought once, prob to be reordered at least once\n up_fe2 = order_prior.groupby([\"user_id\", \"product_id\"]).agg(\"size\").rename(\"up_nb_ordered\").reset_index()\n up_fe2[\"bool_reordered\"] = (up_fe2[\"up_nb_ordered\"] > 1).astype(\"int\")\n\n users_fe4 = up_fe2.groupby('user_id')[\"bool_reordered\"]. \\\n agg([\"mean\", \"size\"]).reset_index(). \\\n rename(index=str, columns={\"mean\": \"u_reorder_rt_bool\", \"size\": \"u_active_p\"})\n\n users_fe = pd.merge(users_fe, users_fe4, on=\"user_id\")\n del users_fe4\n\n users_fe5 = orders.query(\"order_number_reverse != 0\"). \\\n groupby(\"user_id\"). \\\n agg({'date': {'U_date_inscription': 'max'}, \\\n 'days_since_prior_order': {'U_days_since_mean': 'mean', \\\n 'U_days_since_std': 'std'}})\n\n users_fe5.columns = users_fe5.columns.droplevel(0)\n users_fe5 = users_fe5.reset_index()\n\n users_fe = pd.merge(users_fe, users_fe5, on=\"user_id\")\n del users_fe5\n\n # TODO U_none_reordered_strike\n # New way\n # TODO test if it's help or not, keeping it might be used as a skrinking term\n # .query(\"order_number !=1\")\n user_fe_none = order_prior. \\\n groupby([\"order_id\", \"user_id\"]). \\\n agg({'reordered': {'reordered': \"sum\"}, \\\n 'order_number_reverse': {'order_number_reverse': 'first'}})\n\n user_fe_none.columns = user_fe_none.columns.droplevel(0)\n user_fe_none = user_fe_none.reset_index()\n\n user_fe_none[\"reordered\"] = (user_fe_none[\"reordered\"] < 1).astype(int)\n user_fe_none[\"U_none_reordered_strike\"] = user_fe_none[\"reordered\"] * 1 / 2 ** (\n user_fe_none[\"order_number_reverse\"])\n\n user_fe_none = user_fe_none. \\\n groupby(\"user_id\"). \\\n agg({'reordered': {'U_none_reordered_mean': \"mean\"}, \\\n 'U_none_reordered_strike': {'U_none_reordered_strike': \"sum\"}})\n\n user_fe_none.columns = user_fe_none.columns.droplevel(0)\n user_fe_none = user_fe_none.reset_index()\n\n users_fe = pd.merge(users_fe, user_fe_none, on=\"user_id\")\n del user_fe_none\n return users_fe\n\n\ndef get_users_products(order_prior):\n # Could be something else than 1/2\n order_prior[\"UP_date_strike\"] = 1 / 2 ** (order_prior[\"date\"] / 7)\n # order_prior[\"UP_order_strike\"] = 100000 * 1/2 ** (order_prior[\"order_number_reverse\"])\n order_prior[\"UP_order_strike\"] = 1 / 2 ** (order_prior[\"order_number_reverse\"])\n\n users_products = order_prior. \\\n groupby([\"user_id\", \"product_id\"]). \\\n agg({'reordered': {'up_nb_reordered': \"size\"}, \\\n 'add_to_cart_order': {'up_add_to_cart_order_mean': \"mean\"}, \\\n 'add_to_cart_order_relative': {'up_add_to_cart_order_relative_mean': \"mean\"}, \\\n 'add_to_cart_order_inverted': {'up_add_to_cart_order_inverted_mean': \"mean\"}, \\\n 'order_number_reverse': {'up_last_order_number': \"min\", 'up_first_order_number': \"max\"}, \\\n 'date': {'up_last_order_date': \"min\", 'up_first_date_number': \"max\"}, \\\n 'UP_date_strike': {\"UP_date_strike\": \"sum\"}, \\\n 'UP_order_strike': {\"UP_order_strike\": \"sum\"}})\n\n users_products.columns = users_products.columns.droplevel(0)\n users_products = users_products.reset_index()\n return users_products\n\n\ndef get_users_products_none(users_products):\n users_products_none = users_products.groupby(\"user_id\"). \\\n agg({'UP_date_strike': {'O_date_strike_max': \"max\", 'O_date_strike_sum': \"sum\", 'O_date_strike_mean': \"mean\"}, \\\n 'UP_order_strike': {'O_order_strike_max': \"max\", 'O_order_strike_sum': \"sum\",\n 'O_date_order_mean': \"mean\"}})\n\n users_products_none.columns = users_products_none.columns.droplevel(0)\n users_products_none = users_products_none.reset_index()\n return users_products_none\n\n\ndef get_aisles_fe(order_prior, products, aisles):\n aisles_order = pd.merge(order_prior, products, on=\"product_id\")\n aisles_order = pd.merge(aisles_order, aisles, on=\"aisle_id\")\n\n aisles_fe = aisles_order. \\\n groupby([\"aisle_id\"]). \\\n agg({'reordered': {'a_reorder_rt': \"mean\", 'a_count': \"size\"}, \\\n 'add_to_cart_order': {\"a_add_to_cart_order\": \"mean\"}})\n\n aisles_fe.columns = aisles_fe.columns.droplevel(0)\n aisles_fe = aisles_fe.reset_index()\n\n # bool_reordered = if a product is bought once, prob to be reordered at least once\n aisles_fe2 = aisles_order.groupby([\"user_id\", \"aisle_id\"]).agg(\"size\").rename(\"UA_nb_ordered\").reset_index()\n aisles_fe2[\"UA_bool_reordered\"] = (aisles_fe2[\"UA_nb_ordered\"] > 1).astype(\"int\")\n\n aisles_fe2 = aisles_fe2.groupby('aisle_id')[\"UA_bool_reordered\"]. \\\n agg([\"mean\", \"size\"]).reset_index(). \\\n rename(index=str, columns={\"mean\": \"a_reorder_rt_bool\", \"size\": \"a_active_user\"})\n\n aisles_fe = pd.merge(aisles_fe, aisles_fe2, how=\"left\", on=\"aisle_id\")\n del aisles_fe2\n return aisles_fe\n\n\ndef get_user_aisle_fe(order_prior, products, aisles, users_fe):\n aisles_order = pd.merge(order_prior, products, on=\"product_id\")\n aisles_order = pd.merge(aisles_order, aisles, on=\"aisle_id\")\n user_aisle_fe = aisles_order. \\\n groupby([\"user_id\", \"aisle_id\"]). \\\n agg({'product_id': {\"UA_product_rt\": \"nunique\"}})\n\n user_aisle_fe.columns = user_aisle_fe.columns.droplevel(0)\n user_aisle_fe = user_aisle_fe.reset_index()\n\n user_aisle_fe = pd.merge(user_aisle_fe, users_fe[[\"user_id\", \"u_active_p\"]], how=\"left\", on=\"user_id\")\n user_aisle_fe[\"UA_product_rt\"] = user_aisle_fe[\"UA_product_rt\"] / user_aisle_fe[\"u_active_p\"]\n user_aisle_fe = user_aisle_fe.drop(\"u_active_p\", axis=1)\n return user_aisle_fe\n\n\ndef get_departments_fe(order_prior, products, aisles, departments):\n departments_order = pd.merge(order_prior, products, on=\"product_id\")\n departments_order = pd.merge(departments_order, aisles, on=\"aisle_id\")\n departments_order = pd.merge(departments_order, departments, on=\"department_id\")\n\n departments_fe = departments_order. \\\n groupby([\"department_id\"]). \\\n agg({'reordered': {'d_reorder_rt': \"mean\", 'd_count': \"size\"}, \\\n 'add_to_cart_order': {\"d_add_to_cart_order\": \"mean\"}})\n\n departments_fe.columns = departments_fe.columns.droplevel(0)\n departments_fe = departments_fe.reset_index()\n\n # bool_reordered = if a product is bought once, prob to be reordered at least once\n departments_fe2 = departments_order.groupby([\"user_id\", \"department_id\"]).agg(\"size\").rename(\n \"UD_nb_ordered\").reset_index()\n departments_fe2[\"UD_bool_reordered\"] = (departments_fe2[\"UD_nb_ordered\"] > 1).astype(\"int\")\n\n departments_fe2 = departments_fe2.groupby('department_id')[\"UD_bool_reordered\"]. \\\n agg([\"mean\", \"size\"]).reset_index(). \\\n rename(index=str, columns={\"mean\": \"d_reorder_rt_bool\", \"size\": \"d_active_user\"})\n\n departments_fe = pd.merge(departments_fe, departments_fe2, how=\"left\", on=\"department_id\")\n del departments_fe2\n return departments_fe\n\n\ndef get_user_department_fe(order_prior, products, aisles, departments, users_fe):\n departments_order = pd.merge(order_prior, products, on=\"product_id\")\n departments_order = pd.merge(departments_order, aisles, on=\"aisle_id\")\n departments_order = pd.merge(departments_order, departments, on=\"department_id\")\n\n user_department_fe = departments_order. \\\n groupby([\"user_id\", \"department_id\"]). \\\n agg({'product_id': {\"UD_product_rt\": \"nunique\"}})\n\n user_department_fe.columns = user_department_fe.columns.droplevel(0)\n user_department_fe = user_department_fe.reset_index()\n\n user_department_fe = pd.merge(user_department_fe, users_fe[[\"user_id\", \"u_active_p\"]], how=\"left\", on=\"user_id\")\n user_department_fe[\"UD_product_rt\"] = user_department_fe[\"UD_product_rt\"] / user_department_fe[\"u_active_p\"]\n user_department_fe = user_department_fe.drop(\"u_active_p\", axis=1)\n return user_department_fe\n\n\ndef get_user_past_product(order_prior, orders, order_train):\n user_past_product = order_prior[[\"user_id\", \"product_id\"]].drop_duplicates()\n\n reordered_train = pd.merge(orders, order_train, on=[\"order_id\", \"user_id\"])\n reordered_train = reordered_train.query(\"reordered == 1\")\n\n user_past_product = pd.merge(user_past_product, reordered_train[[\"user_id\", \"product_id\", \"reordered\"]],\n on=[\"user_id\", \"product_id\"], how=\"left\")\n\n user_past_product[\"reordered\"] = user_past_product[\"reordered\"].fillna(0)\n return user_past_product\n\n\ndef get_order_none(order_train):\n order_none = order_train. \\\n groupby([\"order_id\", \"user_id\"])[\"reordered\"].sum(). \\\n reset_index()\n\n order_none[\"reordered\"] = (order_none[\"reordered\"] < 1).astype(int)\n return order_none\n\n\ndef get_df(df, user_past_product, users_fe, products_fe_mod, products, aisles,\n aisles_fe, departments, departments_fe, users_products, product2vec, user_aisle_fe,\n user_department_fe, nfold=5):\n df_set = pd.merge(df, user_past_product, on=[\"user_id\"])\n df_set = pd.merge(df_set, users_fe, on=\"user_id\")\n\n df_set[\"user_fold\"] = df_set[\"user_id\"].mod(nfold)\n df_set = pd.merge(df_set, products_fe_mod, how='left', on=[\"product_id\",\"user_fold\"])\n #df_set = pd.merge(df_set, products_fe, how='left', on=\"product_id\")\n\n df_set = pd.merge(df_set, products, how='left', on=\"product_id\")\n df_set = pd.merge(df_set, aisles, how='left', on=\"aisle_id\")\n df_set = pd.merge(df_set, aisles_fe, how='left', on=\"aisle_id\")\n df_set = pd.merge(df_set, departments, how='left', on=\"department_id\")\n df_set = pd.merge(df_set, departments_fe, how='left', on=\"department_id\")\n df_set = pd.merge(df_set, users_products, how='left', on=[\"user_id\", \"product_id\"])\n df_set = pd.merge(df_set, product2vec, how='left', on=\"product_id\")\n df_set = pd.merge(df_set, user_aisle_fe, how='left', on=[\"user_id\", \"aisle_id\"])\n df_set = pd.merge(df_set, user_department_fe, how='left', on=[\"user_id\", \"department_id\"])\n\n # Should be done in appropriate place\n df_set[\"UP_rt_reordered\"] = df_set[\"up_nb_reordered\"] / df_set[\"order_number\"] # Maybe delete because it might overfit?\n df_set[\"UP_rt_reordered_since_first\"] = df_set[\"up_nb_reordered\"] / df_set[\"up_first_order_number\"]\n df_set[\"UP_days_no-reordered\"] = df_set[\"up_last_order_date\"] - df_set[\"date\"]\n df_set[\"UP_freq_nb_no-reordered\"] = df_set[\"up_last_order_number\"] / df_set[\"p_freq_order\"]\n df_set[\"UP_freq_days_no-reordered\"] = df_set[\"UP_days_no-reordered\"] / df_set[\"p_freq_days\"]\n df_set[\"UP_sum_basket_rt\"] = df_set[\"up_nb_reordered\"] / df_set[\"U_basket_sum\"]\n df_set[\"O_days_since_prior_order_diff\"] = df_set[\"days_since_prior_order\"] - df_set[\"U_days_since_mean\"]\n df_set[\"O_days_since_prior_order_rt\"] = df_set[\"days_since_prior_order\"] / df_set[\"U_days_since_mean\"]\n\n df_set = df_set.drop(\"user_fold\", 1)\n return df_set\n\n\ndef get_mult_none(df, X_df, model_gbm):\n df_pred = df[[\"order_id\", \"user_id\"]].copy()\n df_pred[\"pred\"] = model_gbm.predict(X_df, num_iteration=model_gbm.best_iteration)\n df_pred[\"pred_minus\"] = 1 - df_pred[\"pred\"]\n\n df_pred = df_pred.groupby([\"order_id\", \"user_id\"]). \\\n agg({'pred_minus': {'pred_none_prod': \"prod\"}, \\\n 'pred': {'pred_basket_sum': \"sum\", 'pred_basket_std':'std'}})\n\n df_pred.columns = df_pred.columns.droplevel(0)\n df_pred = df_pred.reset_index()\n\n return df_pred\n\n\ndef get_mult_none_cv(df_full, df_test, param, early_stopping_rounds=150, nfold=5):\n to_drop = [\"order_id\", \"user_id\", \"eval_set\", \"product_id\", \"product_name\", \"department\", \"aisle\", \\\n \"order_number_reverse\", \"date\", \"UP_days_no-reordered\"]\n\n res=[]\n res_test = []\n\n X_test = df_test.drop(to_drop + [\"reordered\"], axis=1)\n\n for fold in range(nfold):\n print(\"Folder: \" + str(fold))\n df_valid = df_full.query(\"(user_id % @nfold) == @fold\")\n df_train = df_full.query(\"(user_id % @nfold) != @fold\")\n X_train = df_train.drop(to_drop + [\"reordered\"], axis=1)\n X_valid = df_valid.drop(to_drop + [\"reordered\"], axis=1)\n y_train = df_train[\"reordered\"]\n y_valid = df_valid[\"reordered\"]\n\n lgb_train = lgb.Dataset(X_train, label=y_train)\n lgb_valid = lgb.Dataset(X_valid, label=y_valid)\n model_gbm = lgb.train(param, lgb_train, 100000, valid_sets=[lgb_train, lgb_valid], early_stopping_rounds=early_stopping_rounds,\n verbose_eval=100)\n res.append(get_mult_none(df_valid, X_valid, model_gbm))\n res_test.append(get_mult_none(df_test, X_test, model_gbm))\n del lgb_train, lgb_valid, model_gbm\n gc.collect()\n\n res = pd.concat(res)\n res_test = pd.concat(res_test)\n res_test = res_test.groupby([\"order_id\", \"user_id\"]).mean().reset_index()\n res = pd.concat([res, res_test])\n return res\n\n\ndef add_old_orders(df_full, df_full_none, df_full_old, df_full_none_old):\n df_col = df_full.columns.tolist()\n df_col_none = df_full_none.columns.tolist()\n df_full = pd.concat([df_full, df_full_old])\n df_full_none = pd.concat([df_full_none, df_full_none_old])\n df_full = df_full[df_col]\n df_full_none = df_full_none[df_col_none]\n return df_full, df_full_none\n\n\ndef get_df_none(df, order_none, users_fe):\n df_set = pd.merge(df, order_none, on=[\"order_id\", \"user_id\"], how=\"left\")\n df_set = pd.merge(df_set, users_fe, on=\"user_id\", how=\"left\")\n #df_set = pd.merge(df_set, mult_none_cv, on=[\"order_id\", \"user_id\"], how=\"left\")\n df_set[\"O_days_since_prior_order_diff\"] = df_set[\"days_since_prior_order\"] - df_set[\"U_days_since_mean\"]\n df_set[\"O_days_since_prior_order_rt\"] = df_set[\"days_since_prior_order\"] / df_set[\"U_days_since_mean\"]\n return df_set\n\n\ndef get_df_none_add(df_set, mult_none_cv):\n df_set = pd.merge(df_set, mult_none_cv, on=[\"order_id\", \"user_id\"], how=\"left\")\n return df_set\n\n\ndef get_df_pred(df, X_df, df_none, X_df_none, model_gbm, model_gbm_none):\n df = df.copy()\n df_none = df_none.copy()\n df[\"pred\"] = model_gbm.predict(X_df, num_iteration=model_gbm.best_iteration)\n df_none[\"pred\"] = np.clip(model_gbm_none.predict(X_df_none, num_iteration=model_gbm_none.best_iteration), 0,1)\n df_none[\"product_id\"] = \"None\"\n df_pred_none = df_none[[\"order_id\", \"user_id\", \"product_id\", \"pred\"]].copy()\n\n # Copy avoid warning\n df_pred = df[[\"order_id\", \"user_id\", \"product_id\", \"pred\"]].copy()\n\n df_test_pred = pd.concat([df_pred, df_pred_none])\n return df_test_pred\n\n\ndef calibrate_none(df_pred, none_penalisation=0):\n df_pred = df_pred.copy()\n cond = df_pred[\"product_id\"] == 'None'\n df_pred.loc[cond, \"pred\"] = df_pred.loc[cond, \"pred\"] - none_penalisation\n return df_pred\n\n\ndef groupby_optimised_pred(group):\n group_none = group.iloc[0:1]\n none_gain = group_none[\"pred\"].values[0]\n group_no_none = group.iloc[1:]\n group_no_none[\"precision\"] = group_no_none[\"pred\"].expanding().mean()\n group_no_none[\"precision_none\"] = (group_no_none[\"pred\"].expanding().sum()) / (group_no_none.expanding()[\"pred\"].count() + 1)\n group_no_none[\"recall\"] = group_no_none[\"pred\"].expanding().sum() / (group_no_none[\"pred\"].sum() - 0)\n group_no_none[\"f_score\"] = (2 * group_no_none[\"precision\"] * group_no_none[\"recall\"]) / \\\n (group_no_none[\"precision\"] + group_no_none[\"recall\"])\n group_no_none[\"f_score_none\"] = (2 * group_no_none[\"precision_none\"] * group_no_none[\"recall\"]) / \\\n (group_no_none[\"precision_none\"] + group_no_none[\"recall\"]) + none_gain\n\n if group_no_none[\"f_score\"].max() > group_no_none[\"f_score_none\"].max():\n max_score = group_no_none[\"f_score\"].max()\n max_index = np.where(group_no_none[\"f_score\"] == group_no_none[\"f_score\"].max())[0][0]\n else:\n max_score = group_no_none[\"f_score_none\"].max()\n max_index = np.where(group_no_none[\"f_score_none\"] == group_no_none[\"f_score_none\"].max())[0][0]\n\n group_no_none = group_no_none[0:(max_index + 1)]\n\n res = group_no_none # .drop([\"precision\", \"recall\", \"f_score\"], axis=1)\n\n if none_gain > max_score + 0.0:\n res = group_none\n\n # if (none_gain > group_no_none[\"pred\"].values[0] + 0.11): #Worsen score, need to understand why\n # res = group_none\n\n return res\n\n\ndef groupby_optimised_pred(group):\n group_none = group.iloc[0:1]\n none_gain = group_none[\"pred\"].values[0]\n group_no_none = group.iloc[1:]\n group_no_none[\"precision\"] = group_no_none[\"pred\"].expanding().mean()\n basket_size = group_no_none[\"pred\"].sum() #0.75 #- 0.10 # Empirically found, could be finest, f-score is asymetric\n group_no_none[\"recall\"] = group_no_none[\"pred\"].expanding().sum() / basket_size\n group_no_none[\"f_score\"] = (2 * group_no_none[\"precision\"] * group_no_none[\"recall\"]) / (group_no_none[\"precision\"] + group_no_none[\"recall\"])\n f_score = group_no_none[\"f_score\"].max()\n\n max_index = np.where(group_no_none[\"f_score\"] == f_score)[0][0]\n group_no_none = group_no_none[0:(max_index+1)] # Could be (max_index+k) with k>1 if the limit is risky maybe?\n\n # f_score_none is the expected f_score if we add none\n precision_none = (group_no_none[\"pred\"].sum()) / (group_no_none.shape[0] + 1)\n recall_none = group_no_none.iloc[-1][\"recall\"]\n f_score_none = (2 * precision_none * recall_none) / (precision_none + recall_none)\n\n res = group_no_none #.drop([\"precision\", \"recall\", \"f_score\"], axis=1)\n # Add none if it's worth it\n # 0.07 and not 0 because f_score is under-estimated, could be finest\n if none_gain - (f_score - f_score_none) > 0.07:\n res = pd.concat([res, group_none])\n\n if (none_gain > f_score + 0.0):\n res = group_none\n\n #if (none_gain > group_no_none[\"pred\"].values[0] + 0.11): #Worsen score, need to understand why\n # res = group_none\n\n return res\n\n\ndef filter_optimised_pred(df):\n df[\"is_none\"] = (df[\"product_id\"] == 'None').astype(int)\n df = df.sort_values([\"is_none\", \"pred\"], ascending=False).drop(\"is_none\", axis=1)\n df = df. \\\n groupby(\"user_id\"). \\\n apply(groupby_optimised_pred).reset_index(drop=True)\n return df\n\n\ndef filter_maximize_expectation(df):\n df[\"is_none\"] = (df[\"product_id\"] == 'None').astype(int)\n df = df.sort_values([\"is_none\", \"pred\"], ascending=False).drop(\"is_none\", axis=1)\n df = df. \\\n groupby(\"user_id\"). \\\n apply(groupby_maximize_expectation).reset_index(drop=True)\n return df\n\n\ndef groupby_maximize_expectation(group):\n group_none = group.iloc[0:1]\n none_gain = group_none[\"pred\"].values[0]\n group_no_none = group.iloc[1:]\n pred = group_no_none[\"pred\"].values\n # Avoid weird llvm bug with numba\n if pred.shape[0] == 1:\n pred = np.append(pred, 0)\n\n best_k, predNone, max_f1 = F1Optimizer.maximize_expectation(pred, none_gain)\n\n res = group_no_none.iloc[:best_k]\n if predNone:\n res = pd.concat([res, group_none])\n\n return res\n\n\ndef compute_fscore(df, df_pred):\n df_none_true = df. \\\n groupby([\"order_id\", \"user_id\"])[\"reordered\"].sum().\\\n reset_index()\n\n # Warning bellow but don't know where :/\n # If atleast one reorderd then None is 0, otherwise 1\n df_none_true[\"reordered\"] = (df_none_true[\"reordered\"] < 1).astype(int)\n df_none_true.loc[:,\"product_id\"] = \"None\"\n df = pd.concat([df, df_none_true])\n df_y_true = df.query(\"reordered==1\").groupby(\"user_id\")[\"product_id\"].unique().reset_index()\n df_y_true.columns = [\"user_id\", \"y_true\"]\n df_y_pred = df_pred.groupby(\"user_id\")[\"product_id\"].unique().reset_index()\n df_y_pred.columns = [\"user_id\", \"y_pred\"]\n\n res = pd.merge(df_y_true, df_y_pred, on=\"user_id\", how=\"left\")\n cond = res[\"y_pred\"].isnull()\n res.loc[cond, \"y_pred\"] = np.array(['None'])\n\n res = res.apply(lambda x: multilabel_fscore(x[\"y_true\"], x[\"y_pred\"]), axis=1)\n return res.mean()\n\n\n","repo_name":"jacquespeeters/instacart-market-basket-analysis","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":31617,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"72"}
+{"seq_id":"23683326812","text":"import platform\nfrom flask import Flask, json, request\n\napp = Flask(__name__)\nALL_METHODS = \"GET POST PUT DELETE HEAD PATCH\".split(' ')\n\n\n@app.route('/', defaults={'path': ''}, methods=ALL_METHODS)\n@app.route('/', methods=ALL_METHODS)\ndef catch_all(path):\n return json.jsonify(dict(request=dict(path=path,\n headers=dict(request.headers),\n method=request.method,\n form=request.form,\n url=request.url),\n server=dict(hostname=platform.node(),\n remote=request.remote_addr,\n )))\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000)\n","repo_name":"factorysh/docker-http-mirror","sub_path":"mirror.py","file_name":"mirror.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"28482206014","text":"# https://leetcode.com/problems/furthest-building-you-can-reach/\n\nclass Solution:\n def furthestBuilding(self, heights: List[int], bricks: int, ladders: int) -> int:\n heap = []\n\n for i in range(len(heights) - 1):\n if bricks < 0:\n return i - 1\n\n if heights[i] >= heights[i+1]:\n continue\n\n climb = heights[i+1] - heights[i]\n if len(heap) < ladders:\n heappush(heap, climb)\n\n elif ladders == 0 or climb <= heap[0]:\n bricks -= climb\n\n elif climb > heap[0]:\n bricks -= heappop(heap)\n heappush(heap, climb)\n\n return i if bricks < 0 else i + 1\n","repo_name":"nawrazi/competitive-programming","sub_path":"week_14/furthest-building-you-can-reach.py","file_name":"furthest-building-you-can-reach.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"5793756191","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy, h5py\nimport helper as help\n\n\n\n\ndef load_dataset():\n with h5py.File('E:\\pycharmprojects\\CNN\\week2-programming-project\\datasets/train_catvnoncat.h5',\n \"r\") as train_dataset:\n train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:])\n train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:])\n\n with h5py.File('E:\\pycharmprojects\\CNN\\week2-programming-project\\datasets/test_catvnoncat.h5', \"r\") as test_dataset:\n test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:])\n test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:])\n classes = np.array(test_dataset[\"list_classes\"][:])\n\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n\n\n\ntrain_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()\n\nprint(classes[0].decode(\"utf-8\"))\nprint(classes[1].decode(\"utf-8\"))\nindex = 25\n\nplt.subplot(141)\nplt.imshow(train_set_x_orig[index])\n\nprint(\"y = \" + str(train_set_y[:, index]) + \", it's a '\" + classes[np.squeeze(train_set_y[:, index])].decode(\n \"utf-8\") + \"' picture.\")\n\nprint(train_set_x_orig.shape)\n\n# splitting the multicolor image at index into r g and b streams\n\ntest_r = train_set_x_orig[index].copy()\ntest_g = train_set_x_orig[index].copy()\ntest_b = train_set_x_orig[index].copy()\n\n# idea behind this slicing : completely removing the r and b component from an image will give me the green component only\ntest_r[:, :, 1] = 0\ntest_r[:, :, 2] = 0\ntest_g[:, :, 0] = 0\ntest_g[:, :, 2] = 0\ntest_b[:, :, 0] = 0\ntest_b[:, :, 1] = 0\n\n# playing around with image coloring\n# test_random=train_set_x_orig[index].copy()\n# test_random[:,:,0]=(test_random[:,:,0]+255*np.random.rand())%255\n# test_random[:,:,1]=(test_random[:,:,1]+255*np.random.rand())%255\n# test_random[:,:,2]=(test_random[:,:,2]+255*np.random.rand())%255\n\nplt.subplot(142)\nplt.imshow(test_r)\nplt.subplot(143)\nplt.imshow(test_g)\nplt.subplot(144)\nplt.imshow(test_b)\nplt.show()\n\nm_train = train_set_x_orig.shape[0]\nm_test = test_set_y.shape[1]\nimg_height = train_set_x_orig.shape[1]\nimg_width = train_set_x_orig.shape[2]\nprint(f\"{m_train} {m_test} {img_height} {img_width}\")\n\n# flattening the vector\n# x=(test_r+test_g+test_b).reshape(3*img_height*img_width,1)\n# print(x)\nflatten_train = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T\nflatten_test = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T\n\nprint(test_set_y.shape)\nprint(train_set_y.shape)\nprint(flatten_train.shape)\nprint(flatten_test.shape)\n\n# standardizing the data as a part of preprocessing (improves the quality of data)\ntrain_set_x = flatten_train / 255\ntest_set_x = flatten_test / 255\n\n# preprocessing complete\n\n# implementing the neural network for image classification\n\nhelp.model(train_set_x_orig,train_set_y,train_set_x_orig,test_set_y)\n","repo_name":"anushka567/CNN","sub_path":"catOrNotCat/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"20750067326","text":"from collections import deque\nn = int(input())\ngraph = []\nfor _ in range(n):\n graph.append(list(map(int, input().split())))\n\ndx = [0, -1, 1, 0]\ndy = [-1, 0, 0, 1]\n\nsize, count, ans = 2, 0, 0\ndef bfs(x, y):\n short = []\n flag = True\n min_dist = 100000000000\n global size, count, ans\n visited = [[0] * n for _ in range(n)]\n queue = deque()\n queue.append((x, y))\n while(queue):\n x, y = queue.popleft()\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if 0 <= nx < n and 0 <= ny < n:\n #최단거리 먹을 수 있는 물고기 칸 -> 같은 거리에 있으면 왼쪽\n if 0 < graph[nx][ny] < size and visited[nx][ny] == 0:\n visited[nx][ny] = visited[x][y] + 1\n if min_dist >= visited[nx][ny]:\n min_dist = visited[nx][ny]\n short.append([nx, ny])\n flag = False\n #비어있는 칸(0)이거나 size 같은 물고기 칸 + 다른 칸에서 물고기를 먹었다면 flag=False가 되어 더이상 queue에 추가 x\n elif (graph[nx][ny] == 0 or graph[nx][ny] == size) and visited[nx][ny] == 0 and flag:\n visited[nx][ny] = visited[x][y] + 1\n queue.append((nx, ny))\n if short:\n short.sort()\n count += 1\n if count >= size:\n size += 1\n count = 0\n ans += visited[short[0][0]][short[0][1]]\n graph[short[0][0]][short[0][1]] = 0\n return [short[0][0], short[0][1]]\n else:\n return 0\n \nfor i in range(n):\n for j in range(n):\n if graph[i][j] == 9:\n graph[i][j] = 0\n res = bfs(i, j)\n while(res):\n res = bfs(res[0], res[1])\nprint(ans)\n ","repo_name":"jongbin26/coding_test","sub_path":"python/16236.py","file_name":"16236.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"13788446861","text":"import os\nimport struct\nimport numpy as np\nimport cv2\n\n\"\"\"\nLoosely inspired by http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py\nwhich is GPL licensed.\n\"\"\"\n\ndef read(dataset = \"training\", path = \".\"):\n \"\"\"\n Python function for importing the MNIST data set. It returns an iterator\n of 2-tuples with the first element being the label and the second element\n being a numpy.uint8 2D array of pixel data for the given image.\n \"\"\"\n\n if dataset is \"training\":\n fname_img = os.path.join(path, 'train-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\n elif dataset is \"testing\":\n fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\n fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\n else:\n raise ValueError(\"dataset must be 'testing' or 'training'\")\n\n # Load everything in some numpy arrays\n with open(fname_lbl, 'rb') as flbl:\n magic, num = struct.unpack(\">II\", flbl.read(8))\n lbl = np.fromfile(flbl, dtype=np.int8)\n r_lbl = np.zeros((num, 10))\n for i in range(len(lbl)):\n r_lbl[i][lbl[i]] = 1\n\n with open(fname_img, 'rb') as fimg:\n magic, num, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n img = np.fromfile(fimg, dtype=np.uint8).reshape(len(lbl), rows, cols)\n img = np.divide(img, 255.0)\n get_img = lambda idx: (r_lbl[idx].astype(np.float64), img[idx].reshape(-1).astype(np.float64))\n # Create an iterator which returns each image in turn\n yield num\n while True:\n for i in range(len(lbl)):\n yield get_img(i)\n\ndef package_data(f,size):\n r_img = None\n r_label = None\n for i in range(size):\n if i == 0:\n r_label, r_img = next(f)\n else:\n tmp_label, tmp_img = next(f)\n r_label = np.concatenate((r_label, tmp_label))\n r_img = np.concatenate((r_img, tmp_img))\n return reshape(r_img, size), reshape(r_label, size)\n\ndef reshape(n_a, size):\n l_size = n_a.shape[0]\n row_size = l_size // size\n col_size = size\n return n_a.reshape(col_size, row_size)\n\nif __name__ == '__main__':\n f = read(path='MNIST_data')\n print(next(f))\n # label, img = next(f)\n # print(img.shape)\n # cv2.imshow('img',img)\n # cv2.waitKey(0)\n # print(\"label: {}\".format(label))\n # print(\"img : {}\".format(img))\n # print(img.shape)\n img, label = package_data(f, 500)\n index = 450\n print(img[index])\n # cv2.imshow('img', img[index].reshape(28,28))\n # cv2.waitKey(0)\n # print(img[99])\n # ascii_show(img[0])","repo_name":"neineihao/Deep_Learning","sub_path":"mnist_read.py","file_name":"mnist_read.py","file_ext":"py","file_size_in_byte":2579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"21895154600","text":"\"\"\"Panel dashboard.\"\"\"\nimport os\nfrom fkiss.termcolor import cprint\n\ntry:\n import param\n import panel as pn\nexcept ImportError as exc:\n cprint(\"Use `conda install panel` or `pip install panel` to install the python package.\", \"red\")\n raise exc\n\ndef _df(df):\n return pn.widgets.DataFrame(df, disabled=True)\n\n\n# Possible approach to display big SVG files: https://github.com/ariutta/svg-pan-zoom\n#\n\n\nclass ProjectViewer(param.Parameterized):\n \"\"\"\n A Dashboard to browse the source code, visualize connections among directories,\n files and procedures inside the Abinit project.\n Panel can can be executed either inside a jupyter notebook or as a standalone bokeh app.\n \"\"\"\n\n engine = pn.widgets.Select(value=\"dot\",\n options=['dot', 'neato', 'twopi', 'circo', 'fdp', 'sfdp', 'patchwork', 'osage'])\n\n def __init__(self, proj, **params):\n super().__init__(**params)\n self.proj = proj\n self._layout()\n\n def _layout(self):\n self.dir2files = self.proj.groupby_dirname()\n self.dirname2path = {os.path.basename(p): p for p in self.dir2files}\n self.dir_select = pn.widgets.Select(name=\"Directory\", options=list(self.dirname2path.keys()))\n self.all_pubs = self.proj.get_all_public_procedures()\n\n width = 200\n self.file_select = pn.widgets.Select(name=\"Fortran File\", width=width)\n self.pubproc_select = pn.widgets.Select(name=\"Public Procedure\", width=width)\n self.datatype_select = pn.widgets.Select(name=\"Datatype\", width=width)\n\n self.find_proc = pn.widgets.AutocompleteInput(name='Find Procedure', options=list(self.all_pubs.keys()),\n placeholder='Enter procedure name', width=width)\n self.find_proc_btn = pn.widgets.Button(name=\"Find Procedure\", button_type='primary', width=width)\n self.find_proc_btn.on_click(self.on_find_proc_btn)\n\n self.all_datatypes_and_fortfiles = self.proj.get_all_datatypes_and_fortfile()\n self.find_dtype = pn.widgets.AutocompleteInput(name='Find Datatype',\n options=list(self.all_datatypes_and_fortfiles.keys()),\n placeholder='Enter datatype name', width=width)\n self.find_dtype_btn = pn.widgets.Button(name=\"Find DataType\", button_type='primary', width=width)\n self.find_dtype_btn.on_click(self.on_find_dtype_btn)\n\n self.tabs = pn.Tabs(\n (\"Directory\", self.view_dirname),\n (\"File\", self.view_fort_file),\n (\"Procedure\", self.view_pubproc),\n (\"Datatype\", self.view_datatype),\n )\n\n controllers = pn.Row(\n pn.Column(self.dir_select, self.file_select),\n pn.Column(self.pubproc_select, self.datatype_select),\n pn.Column(self.find_proc, self.find_proc_btn),\n pn.Column(self.find_dtype, self.find_dtype_btn),\n #pn.Column(self.engine),\n #, self.rerun_btn),\n #sizing_mode='scale_width'\n )\n\n self.panel = pn.Column(controllers, self.tabs, sizing_mode=\"scale_width\")\n\n def _find_fort_file(self, dirpath):\n for fort_file in self.dir2files[dirpath]:\n if fort_file.name == self.file_select.value: return fort_file\n else:\n raise ValueError(\"Cannot find fortran file with name: `%s` in `%s`\" % (\n self.file_select.value, dirpath))\n\n @param.depends('dir_select.value')\n def view_dirname(self):\n dirpath = self.dirname2path[self.dir_select.value]\n # Update widgets.\n self.file_select.options = [f.name for f in self.dir2files[dirpath]]\n if hasattr(self, \"tabs\"): self.tabs.active = 0\n\n return pn.Row(_df(self.proj.get_stats_dir(dirpath)),\n self.proj.get_graphviz_dir(dirpath, engine=self.engine.value),\n sizing_mode=\"scale_width\")\n\n @param.depends('file_select.value')\n def view_fort_file(self):\n dirpath = self.dirname2path[self.dir_select.value]\n fort_file = self._find_fort_file(dirpath)\n\n # Update widgets.\n self.pubproc_select.options = list(fort_file.all_public_procedures.keys())\n self.datatype_select.options = list(fort_file.all_datatypes.keys())\n if hasattr(self, \"tabs\"): self.tabs.active = 1\n\n return pn.Row(_df(fort_file.get_stats()),\n fort_file.get_graphviz(engine=self.engine.value),\n sizing_mode=\"scale_width\")\n\n @param.depends('pubproc_select.value')\n def view_pubproc(self):\n pubname = self.pubproc_select.value\n if pubname is None: return\n obj = self.proj.find_public_entity(pubname)\n graph = self.proj.get_graphviz_pubname(pubname, engine=self.engine.value)\n if hasattr(self, \"tabs\"): self.tabs.active = 2\n return pn.Row(obj, graph, sizing_mode=\"scale_width\")\n\n @param.depends('datatype_select.value')\n def view_datatype(self):\n typename = self.datatype_select.value\n if typename is None: return\n dirpath = self.dirname2path[self.dir_select.value]\n fort_file = self._find_fort_file(dirpath)\n #print(\"fort_file.path\", fort_file.path)\n dtype = fort_file.all_datatypes[typename]\n if hasattr(self, \"tabs\"): self.tabs.active = 3\n return pn.Row(dtype)\n\n def on_find_proc_btn(self, event):\n pubname = self.find_proc.value\n if pubname is None: return # or pubname not in self.all_pubs: return\n proc = self.all_pubs[pubname]\n dirpath = proc.dirpath\n file_basename = os.path.basename(proc.path)\n fort_file = self.proj.fort_files[file_basename]\n\n # Update widgets.\n self.dir_select.value = os.path.basename(dirpath)\n self.file_select.options = [f.name for f in self.dir2files[dirpath]]\n self.pubproc_select.options = list(fort_file.all_public_procedures.keys())\n self.datatype_select.options = list(fort_file.all_datatypes.keys())\n\n self.pubproc_select.value = pubname\n if hasattr(self, \"tabs\"): self.tabs.active = 2\n\n def on_find_dtype_btn(self, event):\n dname = self.find_dtype.value\n dtype, fort_file = self.all_datatypes_and_fortfiles[dname]\n\n # Update widgets.\n self.dir_select.value = os.path.basename(fort_file.dirname)\n self.file_select.value = fort_file.basename\n self.datatype_select.value = dname\n if hasattr(self, \"tabs\"): self.tabs.active = 3\n","repo_name":"abinit/abinit","sub_path":"fkiss/viewer.py","file_name":"viewer.py","file_ext":"py","file_size_in_byte":6788,"program_lang":"python","lang":"en","doc_type":"code","stars":193,"dataset":"github-code","pt":"72"}
+{"seq_id":"35852833096","text":"import os\nfrom typing import Dict, List, Tuple\n\nimport numpy as np\nimport onnxruntime as ort\nfrom PIL import Image\nfrom PIL.Image import Image as PILImage\n\n\nclass BaseSession:\n def __init__(\n self,\n model_name: str,\n sess_opts: ort.SessionOptions,\n providers=None,\n *args,\n **kwargs\n ):\n self.model_name = model_name\n\n self.providers = []\n\n _providers = ort.get_available_providers()\n if providers:\n for provider in providers:\n if provider in _providers:\n self.providers.append(provider)\n else:\n self.providers.extend(_providers)\n\n self.inner_session = ort.InferenceSession(\n str(self.__class__.download_models(*args, **kwargs)),\n providers=self.providers,\n sess_options=sess_opts,\n )\n\n def normalize(\n self,\n img: PILImage,\n mean: Tuple[float, float, float],\n std: Tuple[float, float, float],\n size: Tuple[int, int],\n *args,\n **kwargs\n ) -> Dict[str, np.ndarray]:\n im = img.convert(\"RGB\").resize(size, Image.LANCZOS)\n\n im_ary = np.array(im)\n im_ary = im_ary / np.max(im_ary)\n\n tmpImg = np.zeros((im_ary.shape[0], im_ary.shape[1], 3))\n tmpImg[:, :, 0] = (im_ary[:, :, 0] - mean[0]) / std[0]\n tmpImg[:, :, 1] = (im_ary[:, :, 1] - mean[1]) / std[1]\n tmpImg[:, :, 2] = (im_ary[:, :, 2] - mean[2]) / std[2]\n\n tmpImg = tmpImg.transpose((2, 0, 1))\n\n return {\n self.inner_session.get_inputs()[0]\n .name: np.expand_dims(tmpImg, 0)\n .astype(np.float32)\n }\n\n def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]:\n raise NotImplementedError\n\n @classmethod\n def checksum_disabled(cls, *args, **kwargs):\n return os.getenv(\"MODEL_CHECKSUM_DISABLED\", None) is not None\n\n @classmethod\n def u2net_home(cls, *args, **kwargs):\n return os.path.expanduser(\n os.getenv(\n \"U2NET_HOME\", os.path.join(os.getenv(\"XDG_DATA_HOME\", \"~\"), \".u2net\")\n )\n )\n\n @classmethod\n def download_models(cls, *args, **kwargs):\n raise NotImplementedError\n\n @classmethod\n def name(cls, *args, **kwargs):\n raise NotImplementedError\n","repo_name":"danielgatis/rembg","sub_path":"rembg/sessions/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2351,"program_lang":"python","lang":"en","doc_type":"code","stars":12170,"dataset":"github-code","pt":"72"}
+{"seq_id":"33783927020","text":"# Code is modified from version provided at https://web3py.readthedocs.io/en/latest/examples.html#example-code\n\n\"\"\"A stateful event scanner for Ethereum-based blockchains using Web3.py.\n\nWith the stateful mechanism, you can do one batch scan or incremental scans,\nwhere events are added wherever the scanner left off.\n\"\"\"\n\nimport os\nimport sched\nfrom eth_typing import BlockNumber\nfrom web3.providers.rpc import HTTPProvider\nimport json\nimport datetime\nimport time\nimport logging\nfrom abc import ABC, abstractmethod\nfrom typing import Tuple, Optional, Callable, List, Iterable\nfrom sqlitedict import SqliteDict\n\nfrom web3 import Web3\nfrom web3.contract import Contract\nfrom web3.datastructures import AttributeDict\nfrom web3.exceptions import BlockNotFound\nfrom eth_abi.codec import ABICodec\n\n# Currently this method is not exposed over official web3 API,\n# but we need it to construct eth_getLogs parameters\nfrom web3._utils.filters import construct_event_filter_params\nfrom web3._utils.events import get_event_data\nfrom dotenv import load_dotenv, find_dotenv\n\nfrom discord_webhook import DiscordWebhook\n\nload_dotenv(find_dotenv())\n\nlogger = logging.getLogger(__name__)\nFIRST_BLOCK_TO_SCAN = 21800000\nRUN_EVERY_X_SECONDS = 4\n\nIS_DEV = \"PRODUCTION\" not in os.environ or os.environ[\"PRODUCTION\"] != \"true\"\n\nSCRIPT_DIR = os.path.dirname(__file__) # <-- absolute dir the script is in\nDEPLOYMENT_REL_PATH = (\n \"../../../ui/deployment.json\" if IS_DEV else \"../../../ui/deployment-prod.json\"\n)\nPAWN_SHOP_REL_PATH = (\n \"../../../build/contracts/PawnShop.json\"\n if IS_DEV\n else \"../../../prod-deployment/contracts/PawnShop.json\"\n)\n\nDISCORD_WEBHOOK_URL = os.environ.get(\"DISCORD_WEBHOOK_URL\")\n\nADDRESSES = json.load(open(os.path.join(SCRIPT_DIR, DEPLOYMENT_REL_PATH)))\n\nCONTRACT_ADDRESS = ADDRESSES[\"PawnShop\"]\nCONTRACT_FILE = json.load(\n open(os.path.join(SCRIPT_DIR, PAWN_SHOP_REL_PATH), \"r\"))\nCONTRACT_ABI = CONTRACT_FILE[\"abi\"]\nFIRST_BLOCK_TO_SCAN = ADDRESSES[\"deploymentBlock\"]\n\nDB_FOLDER_PREFIX = os.path.join(SCRIPT_DIR, \"../../db/jewel\")\n\n\nclass IEventScannerState(ABC):\n \"\"\"Application state that remembers what blocks we have scanned in the case of crash.\"\"\"\n\n @abstractmethod\n def get_last_scanned_block(self) -> int:\n \"\"\"Number of the last block we have scanned on the previous cycle.\n\n :return: 0 if no blocks scanned yet\n \"\"\"\n\n @abstractmethod\n def start_chunk(self, block_number: int):\n \"\"\"Scanner is about to ask data of multiple blocks over JSON-RPC.\n\n Start a database session if needed.\n \"\"\"\n\n @abstractmethod\n def end_chunk(self, block_number: int):\n \"\"\"Scanner finished a number of blocks.\n\n Persistent any data in your state now.\n \"\"\"\n\n @abstractmethod\n def process_event(\n self, block_when: datetime.datetime, event: AttributeDict, latest_block_number: BlockNumber\n ) -> object:\n \"\"\"Process incoming events.\n\n This function takes raw events from Web3, transforms them to your application internal\n format, then saves them in a database or some other state.\n\n :param block_when: When this block was mined\n\n :param event: Symbolic dictionary of the event data\n\n :return: Internal state structure that is the result of event tranformation.\n \"\"\"\n\n @abstractmethod\n def delete_data(self, since_block: int) -> int:\n \"\"\"Delete any data since this block was scanned.\n\n Purges any potential minor reorg data.\n \"\"\"\n\n\nclass EventScanner:\n \"\"\"Scan blockchain for events and try not to abuse JSON-RPC API too much.\n\n Can be used for real-time scans, as it detects minor chain reorganisation and rescans.\n Unlike the easy web3.contract.Contract, this scanner can scan events from multiple contracts at once.\n For example, you can get all transfers from all tokens in the same scan.\n\n You *should* disable the default `http_retry_request_middleware` on your provider for Web3,\n because it cannot correctly throttle and decrease the `eth_getLogs` block number range.\n \"\"\"\n\n def __init__(\n self,\n web3: Web3,\n contract: Contract,\n state: IEventScannerState,\n events: List,\n filters: {},\n max_chunk_scan_size: int = 10000,\n max_request_retries: int = 30,\n request_retry_seconds: float = 3.0,\n ):\n \"\"\"\n :param contract: Contract\n :param events: List of web3 Event we scan\n :param filters: Filters passed to getLogs\n :param max_chunk_scan_size: JSON-RPC API limit in the number of blocks we query. (Recommendation: 10,000 for mainnet, 500,000 for testnets)\n :param max_request_retries: How many times we try to reattempt a failed JSON-RPC call\n :param request_retry_seconds: Delay between failed requests to let JSON-RPC server to recover\n \"\"\"\n\n self.logger = logger\n self.contract = contract\n self.web3 = web3\n self.state = state\n self.events = events\n self.filters = filters\n\n # Our JSON-RPC throttling parameters\n self.min_scan_chunk_size = 10 # 12 s/block = 120 seconds period\n self.max_scan_chunk_size = max_chunk_scan_size\n self.max_request_retries = max_request_retries\n self.request_retry_seconds = request_retry_seconds\n\n # Factor how fast we increase the chunk size if results are found\n # # (slow down scan after starting to get hits)\n self.chunk_size_decrease = 0.5\n\n # Factor how was we increase chunk size if no results found\n self.chunk_size_increase = 2.0\n\n @property\n def address(self):\n return self.token_address\n\n def get_block_timestamp(self, block_num) -> datetime.datetime:\n \"\"\"Get Ethereum block timestamp\"\"\"\n try:\n block_info = self.web3.eth.getBlock(block_num)\n except BlockNotFound:\n # Block was not mined yet,\n # minor chain reorganisation?\n return None\n last_time = block_info[\"timestamp\"]\n return datetime.datetime.fromtimestamp(last_time, datetime.timezone.utc)\n\n def get_suggested_scan_start_block(self):\n \"\"\"Get where we should start to scan for new token events.\n\n If there are no prior scans, start from block 1.\n Otherwise, start from the last end block minus ten blocks.\n We rescan the last ten scanned blocks in the case there were forks to avoid\n misaccounting due to minor single block works (happens once in a hour in Ethereum).\n These heurestics could be made more robust, but this is for the sake of simple reference implementation.\n \"\"\"\n\n end_block = self.get_last_scanned_block()\n if end_block:\n return max(1, end_block - self.NUM_BLOCKS_RESCAN_FOR_FORKS)\n return 1\n\n def get_suggested_scan_end_block(self):\n \"\"\"Get the last mined block on Ethereum chain we are following.\"\"\"\n\n # Do not scan all the way to the final block, as this\n # block might not be mined yet\n return self.web3.eth.blockNumber\n\n def get_last_scanned_block(self) -> int:\n return self.state.get_last_scanned_block()\n\n def delete_potentially_forked_block_data(self, after_block: int):\n \"\"\"Purge old data in the case of blockchain reorganisation.\"\"\"\n self.state.delete_data(after_block)\n\n def scan_chunk(self, start_block, end_block) -> Tuple[int, datetime.datetime, list]:\n \"\"\"Read and process events between to block numbers.\n\n Dynamically decrease the size of the chunk if the case JSON-RPC server pukes out.\n\n :return: tuple(actual end block number, when this block was mined, processed events)\n \"\"\"\n\n block_timestamps = {}\n get_block_timestamp = self.get_block_timestamp\n\n # Cache block timestamps to reduce some RPC overhead\n # Real solution might include smarter models around block\n def get_block_when(block_num):\n if block_num not in block_timestamps:\n block_timestamps[block_num] = get_block_timestamp(block_num)\n return block_timestamps[block_num]\n\n all_processed = []\n\n for event_type in self.events:\n # Callable that takes care of the underlying web3 call\n def _fetch_events(_start_block, _end_block):\n return _fetch_events_for_all_contracts(\n self.web3,\n event_type,\n self.filters,\n from_block=_start_block,\n to_block=_end_block,\n )\n\n # Do `n` retries on `eth_getLogs`,\n # throttle down block range if needed\n end_block, events = _retry_web3_call(\n _fetch_events,\n start_block=start_block,\n end_block=end_block,\n retries=self.max_request_retries,\n delay=self.request_retry_seconds,\n )\n\n for evt in events:\n idx = evt[\n \"logIndex\"\n ] # Integer of the log index position in the block, null when its pending\n\n # We cannot avoid minor chain reorganisations, but\n # at least we must avoid blocks that are not mined yet\n assert idx is not None, \"Somehow tried to scan a pending block\"\n\n block_number = evt[\"blockNumber\"]\n\n tx = {}\n if evt[\"event\"] == \"UTXORedeemed\":\n tx = self.web3.eth.get_transaction(evt[\"transactionHash\"])\n\n # Get UTC time when this event happened (block mined timestamp)\n # from our in-memory cache\n block_when = get_block_when(block_number)\n\n logger.debug(\n \"Processing event %s, block: %d\",\n evt[\"event\"],\n evt[\"blockNumber\"],\n )\n processed = self.state.process_event(\n block_when, evt, tx=tx, latest_block_number=self.web3.eth.blockNumber)\n all_processed.append(processed)\n\n end_block_timestamp = get_block_when(end_block)\n return end_block, end_block_timestamp, all_processed\n\n def estimate_next_chunk_size(self, current_chuck_size: int, event_found_count: int):\n \"\"\"Try to figure out optimal chunk size\n\n Our scanner might need to scan the whole blockchain for all events\n\n * We want to minimize API calls over empty blocks\n\n * We want to make sure that one scan chunk does not try to process too many entries once, as we try to control commit buffer size and potentially asynchronous busy loop\n\n * Do not overload node serving JSON-RPC API by asking data for too many events at a time\n\n Currently Ethereum JSON-API does not have an API to tell when a first event occured in a blockchain\n and our heuristics try to accelerate block fetching (chunk size) until we see the first event.\n\n These heurestics exponentially increase the scan chunk size depending on if we are seeing events or not.\n When any transfers are encountered, we are back to scanning only a few blocks at a time.\n It does not make sense to do a full chain scan starting from block 1, doing one JSON-RPC call per 20 blocks.\n \"\"\"\n\n if event_found_count > 0:\n # When we encounter first events, reset the chunk size window\n current_chuck_size = self.min_scan_chunk_size\n else:\n current_chuck_size *= self.chunk_size_increase\n\n current_chuck_size = max(self.min_scan_chunk_size, current_chuck_size)\n current_chuck_size = min(self.max_scan_chunk_size, current_chuck_size)\n return int(current_chuck_size)\n\n def scan(\n self,\n start_block,\n end_block,\n start_chunk_size=20,\n progress_callback=Optional[Callable],\n ) -> Tuple[list, int]:\n \"\"\"Perform a token balances scan.\n\n Assumes all balances in the database are valid before start_block (no forks sneaked in).\n\n :param start_block: The first block included in the scan\n\n :param end_block: The last block included in the scan\n\n :param start_chunk_size: How many blocks we try to fetch over JSON-RPC on the first attempt\n\n :param progress_callback: If this is an UI application, update the progress of the scan\n\n :return: [All processed events, number of chunks used]\n \"\"\"\n\n assert start_block <= end_block\n\n current_block = start_block\n\n # Scan in chunks, commit between\n chunk_size = start_chunk_size\n last_scan_duration = last_logs_found = 0\n total_chunks_scanned = 0\n\n # All processed entries we got on this scan cycle\n all_processed = []\n\n while current_block <= end_block:\n\n self.state.start_chunk(current_block, chunk_size)\n\n # Print some diagnostics to logs to try to fiddle with real world JSON-RPC API performance\n estimated_end_block = current_block + chunk_size\n logger.debug(\n \"Scanning token transfers for blocks: %d - %d, chunk size %d, last chunk scan took %f, last logs found %d\",\n current_block,\n estimated_end_block,\n chunk_size,\n last_scan_duration,\n last_logs_found,\n )\n\n start = time.time()\n actual_end_block, end_block_timestamp, new_entries = self.scan_chunk(\n current_block, estimated_end_block\n )\n\n # Where does our current chunk scan ends - are we out of chain yet?\n current_end = actual_end_block\n\n last_scan_duration = time.time() - start\n all_processed += new_entries\n\n if progress_callback:\n progress_callback(\n start_block,\n end_block,\n current_block,\n end_block_timestamp,\n chunk_size,\n len(new_entries),\n )\n\n # Try to guess how many blocks to fetch over `eth_getLogs` API next time\n chunk_size = self.estimate_next_chunk_size(\n chunk_size, len(new_entries))\n\n # Set where the next chunk starts\n current_block = current_end + 1\n total_chunks_scanned += 1\n self.state.end_chunk(min(current_end, end_block))\n\n return all_processed, total_chunks_scanned\n\n\ndef _retry_web3_call(func, start_block, end_block, retries, delay) -> Tuple[int, list]:\n \"\"\"A custom retry loop to throttle down block range.\n\n If our JSON-RPC server cannot serve all incoming `eth_getLogs` in a single request,\n we retry and throttle down block range for every retry.\n\n For example, Go Ethereum does not indicate what is an acceptable response size.\n It just fails on the server-side with a \"context was cancelled\" warning.\n\n :param func: A callable that triggers Ethereum JSON-RPC, as func(start_block, end_block)\n :param start_block: The initial start block of the block range\n :param end_block: The initial start block of the block range\n :param retries: How many times we retry\n :param delay: Time to sleep between retries\n \"\"\"\n for i in range(retries):\n try:\n return end_block, func(start_block, end_block)\n except Exception as e:\n # Assume this is HTTPConnectionPool(host=\"localhost\", port=8545): Read timed out. (read timeout=10)\n # from Go Ethereum. This translates to the error \"context was cancelled\" on the server side:\n # https://github.com/ethereum/go-ethereum/issues/20426\n if i < retries - 1:\n # Give some more verbose info than the default middleware\n logger.warning(\n \"Retrying events for block range %d - %d (%d) failed with %s, retrying in %s seconds\",\n start_block,\n end_block,\n end_block - start_block,\n e,\n delay,\n )\n # Decrease the `eth_getBlocks` range\n end_block = start_block + ((end_block - start_block) // 2)\n # Let the JSON-RPC to recover e.g. from restart\n time.sleep(delay)\n continue\n else:\n logger.warning(\"Out of retries\")\n raise\n\n\ndef _fetch_events_for_all_contracts(\n web3, event, argument_filters: dict, from_block: int, to_block: int\n) -> Iterable:\n \"\"\"Get events using eth_getLogs API.\n\n This method is detached from any contract instance.\n\n This is a stateless method, as opposed to createFilter.\n It can be safely called against nodes which do not provide `eth_newFilter` API, like Infura.\n \"\"\"\n\n if from_block is None:\n raise TypeError(\n \"Missing mandatory keyword argument to getLogs: fromBlock\")\n\n # Currently no way to poke this using a public Web3.py API.\n # This will return raw underlying ABI JSON object for the event\n abi = event._get_event_abi()\n\n # Depending on the Solidity version used to compile\n # the contract that uses the ABI,\n # it might have Solidity ABI encoding v1 or v2.\n # We just assume the default that you set on Web3 object here.\n # More information here https://eth-abi.readthedocs.io/en/latest/index.html\n codec: ABICodec = web3.codec\n\n # Here we need to poke a bit into Web3 internals, as this\n # functionality is not exposed by default.\n # Construct JSON-RPC raw filter presentation based on human readable Python descriptions\n # Namely, convert event names to their keccak signatures\n # More information here:\n # https://github.com/ethereum/web3.py/blob/e176ce0793dafdd0573acc8d4b76425b6eb604ca/web3/_utils/filters.py#L71\n data_filter_set, event_filter_params = construct_event_filter_params(\n abi,\n codec,\n address=argument_filters.get(\"address\"),\n argument_filters=argument_filters,\n fromBlock=from_block,\n toBlock=to_block,\n )\n\n logger.debug(\n \"Querying eth_getLogs with the following parameters: %s\", event_filter_params\n )\n\n # Call JSON-RPC API on your Ethereum node.\n # get_logs() returns raw AttributedDict entries\n logs = web3.eth.get_logs(event_filter_params)\n\n # Convert raw binary data to Python proxy objects as described by ABI\n all_events = []\n for log in logs:\n # Convert raw JSON-RPC log result to human readable event by using ABI data\n # More information how processLog works here\n # https://github.com/ethereum/web3.py/blob/fbaf1ad11b0c7fac09ba34baff2c256cffe0a148/web3/_utils/events.py#L200\n evt = get_event_data(codec, abi, log)\n # Note: This was originally yield,\n # but deferring the timeout exception caused the throttle logic not to work\n all_events.append(evt)\n return all_events\n\n\nclass SqliteDictState(IEventScannerState):\n \"\"\"Store the state of scanned blocks and all events.\n\n All state is a dict backed by sqlite, by using sqlitedict.\n \"\"\"\n\n def __init__(self):\n pass\n\n def reset(self):\n \"\"\"Create initial state of nothing scanned.\"\"\"\n self.state_meta.clear()\n self.state_utxo.clear()\n self.state_utxos_by_user.clear()\n self.state_utxo_redemptions.clear()\n self.state_meta[\"last_scanned_block\"] = FIRST_BLOCK_TO_SCAN\n self.state_meta[\"last_utxo_redemption_index\"] = 0\n self.commit()\n\n def restore(self):\n self.state_meta = SqliteDict(\n filename=os.path.join(DB_FOLDER_PREFIX, \"./state_meta.sqlite\"),\n autocommit=False,\n flag=\"c\",\n journal_mode=\"WAL\",\n )\n self.state_utxo = SqliteDict(\n filename=os.path.join(DB_FOLDER_PREFIX, \"./state_utxo.sqlite\"),\n autocommit=False,\n flag=\"c\",\n journal_mode=\"WAL\",\n )\n self.state_utxo_seen_events = SqliteDict(\n filename=os.path.join(\n DB_FOLDER_PREFIX, \"./state_utxo_seen_events.sqlite\"),\n autocommit=False,\n flag=\"c\",\n journal_mode=\"WAL\",\n )\n self.state_utxos_by_user = SqliteDict(\n filename=os.path.join(\n DB_FOLDER_PREFIX, \"./state_utxos_by_user.sqlite\"),\n autocommit=False,\n flag=\"c\",\n journal_mode=\"WAL\",\n )\n self.state_utxo_redemptions = SqliteDict(\n filename=os.path.join(\n DB_FOLDER_PREFIX, \"./state_utxo_redemptions.sqlite\"),\n autocommit=False,\n flag=\"c\",\n journal_mode=\"WAL\",\n )\n self.state_aggregates = SqliteDict(\n filename=os.path.join(\n DB_FOLDER_PREFIX, \"./state_aggregates.sqlite\"),\n autocommit=False,\n flag=\"c\",\n journal_mode=\"WAL\",\n )\n\n def save(self):\n logger.debug(\"...CLOSING STATE...\")\n self.state_meta.close()\n self.state_utxo.close()\n self.state_utxo_seen_events.close()\n self.state_utxos_by_user.close()\n self.state_utxo_redemptions.close()\n self.state_aggregates.close()\n logger.debug(\"...STATE CLOSED...\")\n\n def commit(self):\n logger.debug(\"...COMITTING STATE...\")\n self.state_meta.commit(blocking=True)\n self.state_utxo.commit(blocking=True)\n self.state_utxo_seen_events.commit(blocking=True)\n self.state_utxos_by_user.commit(blocking=True)\n self.state_utxo_redemptions.commit(blocking=True)\n self.state_aggregates.commit(blocking=True)\n logger.debug(\"...STATE COMMITED...\")\n\n #\n # EventScannerState methods implemented below\n #\n\n def get_last_scanned_block(self):\n \"\"\"The number of the last block we have stored.\"\"\"\n return (\n self.state_meta[\"last_scanned_block\"]\n if \"last_scanned_block\" in self.state_meta\n else FIRST_BLOCK_TO_SCAN\n )\n\n def delete_data(self, since_block):\n \"\"\"Remove potentially reorganised blocks from the scan data.\"\"\"\n pass\n\n def start_chunk(self, block_number, chunk_size):\n \"\"\"Save at the end of each chunk, so we can resume in the case of a crash or CTRL+C\"\"\"\n self.commit()\n\n def end_chunk(self, block_number):\n \"\"\"Save at the end of each chunk, so we can resume in the case of a crash or CTRL+C\"\"\"\n # Next time the scanner is started we will resume from this block\n self.state_meta[\"last_scanned_block\"] = block_number\n self.commit()\n\n def process_event(\n self, block_when: datetime.datetime, event: AttributeDict, tx: AttributeDict, latest_block_number: BlockNumber\n ) -> str:\n \"\"\"Record a ERC-20 transfer in our database.\"\"\"\n # Events are keyed by their transaction hash and log index\n # One transaction may contain multiple events\n # and each one of those gets their own log index\n\n # event_name = event.event # \"Transfer\"\n log_index = event.logIndex # Log index within the block\n # transaction_index = event.transactionIndex # Transaction index within the block\n txhash = event.transactionHash.hex() # Transaction hash\n block_number = event.blockNumber\n event_type = event[\"event\"]\n\n utxoObject = None\n\n # Convert ERC-20 Transfer event to our internal format\n args = event[\"args\"]\n\n def is_event_seen(db):\n key = f\"{block_number}-{txhash}-{log_index}-{event_type}\"\n return key in db and db[key] is True\n\n def mark_event_as_seen(db):\n key = f\"{block_number}-{txhash}-{log_index}-{event_type}\"\n db[key] = True\n\n # TODO: refactor all this so it\"s not just one big function\n if event_type == \"UTXOCreated\":\n if not is_event_seen(self.state_utxo_seen_events):\n utxoObject = {\n \"utxoAddress\": args[\"utxoAddress\"].lower(),\n \"minter\": args[\"minter\"].lower(),\n }\n\n if (\n utxoObject[\"minter\"] in self.state_utxos_by_user\n and self.state_utxos_by_user[utxoObject[\"minter\"]] is not None\n ):\n list_for_minter = self.state_utxos_by_user[utxoObject[\"minter\"]]\n if (\n utxoObject[\"utxoAddress\"]\n not in self.state_utxos_by_user[utxoObject[\"minter\"]]\n ):\n list_for_minter.append(utxoObject[\"utxoAddress\"])\n self.state_utxos_by_user[utxoObject[\"minter\"]\n ] = list_for_minter\n else:\n self.state_utxos_by_user[utxoObject[\"minter\"]] = [\n utxoObject[\"utxoAddress\"]\n ]\n mark_event_as_seen(self.state_utxo_seen_events)\n else:\n logger.warn(\n f\"Already seen UTXOCreated event {block_number}-{txhash}-{log_index}\"\n )\n elif event_type == \"UTXOValue\":\n if not is_event_seen(self.state_utxo_seen_events):\n utxoObject = {\n \"utxoAddress\": args[\"UTXOAddress\"].lower(),\n \"newVal\": str(args[\"newVal\"]),\n \"blockNumber\": block_number,\n \"timestamp\": block_when.timestamp() * 1000, # milliseconds\n }\n if int(args[\"newVal\"]) > 0 and block_number > (latest_block_number - 100):\n notify_new_stash(args[\"newVal\"])\n mark_event_as_seen(self.state_utxo_seen_events)\n else:\n logger.warn(\n f\"Already seen UTXOValue event {block_number}-{txhash}-{log_index}\"\n )\n elif event_type == \"UTXORedeemed\":\n if not is_event_seen(self.state_utxo_redemptions):\n last_utxo_redemption_index = (\n self.state_meta[\"last_utxo_redemption_index\"]\n if \"last_utxo_redemption_index\" in self.state_meta\n else \"0\"\n ) or \"0\"\n next_utxo_redemption_index = int(\n last_utxo_redemption_index) + 1\n _redeem = args[\"redeemooor\"].lower()\n if args[\"redeemooor\"].lower() == ADDRESSES[\"PawnShopRouter\"].lower():\n _redeem = tx[\"from\"].lower()\n self.state_utxo_redemptions[next_utxo_redemption_index] = {\n \"tx\": txhash,\n \"utxoAddress\": args[\"UTXOAddress\"].lower(),\n \"redeemedBy\": str(_redeem),\n \"amount\": str(args[\"redeemedAmount\"]),\n \"fee\": str(args[\"feePaid\"]),\n \"amountInJewel\": str(args[\"feeRatio\"]),\n \"totalCost\": str(args[\"totalCost\"]),\n \"blockNumber\": block_number,\n \"timestamp\": block_when.timestamp() * 1000, # milliseconds\n }\n if block_number > (latest_block_number - 100):\n notify_redeemed_stash(int(args[\"redeemedAmount\"]))\n mark_event_as_seen(self.state_utxo_redemptions)\n self.state_meta[\n \"last_utxo_redemption_index\"\n ] = next_utxo_redemption_index\n else:\n logger.warn(\n f\"Already seen UTXORedeemed event {block_number}-{txhash}-{log_index}\"\n )\n\n if utxoObject is None:\n # no update made to object\n pass\n elif (\n utxoObject[\"utxoAddress\"] in self.state_utxo\n and self.state_utxo[utxoObject[\"utxoAddress\"]] is not None\n ):\n current_object = self.state_utxo[utxoObject[\"utxoAddress\"]]\n current_object.update(utxoObject)\n self.state_utxo[utxoObject[\"utxoAddress\"]] = current_object\n else:\n self.state_utxo[utxoObject[\"utxoAddress\"]] = utxoObject\n\n # AGGREGATES\n if not is_event_seen(self.state_aggregates):\n # large numbers are always stored as string because JSON cannot handle massive uint256\n def get_agg(key):\n return int(\n (\n self.state_aggregates[key]\n if key in self.state_aggregates\n else \"0\"\n )\n or \"0\"\n )\n\n def get_agg_in_cur_bucket(key):\n bucket_key = block_when.replace(\n minute=0, second=0, microsecond=0\n ).isoformat(timespec=\"seconds\")\n key_ = f\"{key}-{bucket_key}\"\n return int(\n (\n self.state_aggregates[key_]\n if key_ in self.state_aggregates\n else \"0\"\n )\n or \"0\"\n )\n\n def update_agg(key, value, bucketValue=None):\n self.state_aggregates[key] = str(value)\n bucket_time = block_when.replace(\n minute=0, second=0, microsecond=0\n ).isoformat(timespec=\"seconds\")\n self.state_aggregates[f\"{key}-{bucket_time}\"] = str(\n bucketValue if bucketValue is not None else value\n )\n\n current_total_stashes = get_agg(\"totalStashes\")\n current_locked_jewel_in_protocol = get_agg(\"lockedJewelTotal\")\n current_total_fees_paid = get_agg(\"totalFeesPaid\")\n current_total_fees_paid_in_jewel = get_agg(\"totalFeesPaidInJewel\")\n current_total_redemptions_volume = get_agg(\n \"totalRedemptionsVolume\")\n\n if event_type == \"UTXOCreated\":\n update_agg(\"totalStashes\", current_total_stashes + 1)\n elif event_type == \"MintedFromUTXO\":\n update_agg(\n \"lockedJewelTotal\",\n current_locked_jewel_in_protocol +\n int(args[\"mintedAmount\"]),\n )\n elif event_type == \"UTXORedeemed\":\n update_agg(\n \"lockedJewelTotal\",\n current_locked_jewel_in_protocol -\n int(args[\"redeemedAmount\"]),\n )\n update_agg(\n \"totalFeesPaid\", current_total_fees_paid +\n int(args[\"feePaid\"])\n )\n update_agg(\n \"totalFeesPaidInJewel\",\n current_total_fees_paid_in_jewel + int(args[\"feeRatio\"]),\n )\n update_agg(\n \"totalRedemptionsVolume\",\n current_total_redemptions_volume +\n int(args[\"redeemedAmount\"]),\n )\n\n mark_event_as_seen(self.state_aggregates)\n\n self.commit()\n\n # Return a pointer that allows us to look up this event later if needed\n return f\"{block_number}-{txhash}-{log_index}\"\n\n\nscheduler = sched.scheduler(time.time, time.sleep)\n\n\ndef run(sc):\n api_url = (\n os.environ[\"HARMONY_RPC_URL\"]\n if \"HARMONY_RPC_URL\" in os.environ\n else \"http://127.0.0.1:8545\"\n )\n\n # Enable logs to the stdout.\n # DEBUG is very verbose level\n logging.basicConfig(level=logging.INFO)\n\n provider = HTTPProvider(api_url)\n\n # Remove the default JSON-RPC retry middleware\n # as it correctly cannot handle eth_getLogs block range\n # throttle down.\n provider.middlewares.clear()\n\n web3 = Web3(provider)\n\n # Prepare stub ERC-20 contract object\n abi = CONTRACT_ABI\n CONTRACT = web3.eth.contract(abi=abi)\n\n # Restore/create our persistent state\n state = SqliteDictState()\n state.restore()\n\n # chain_id: int, web3: Web3, abi: dict, state: EventScannerState, events: List, filters: {}, max_chunk_scan_size: int=10000\n scanner = EventScanner(\n web3=web3,\n contract=CONTRACT,\n state=state,\n events=[\n CONTRACT.events.UTXOValue,\n CONTRACT.events.UTXOCreated,\n CONTRACT.events.UTXORedeemed,\n CONTRACT.events.MintedFromUTXO,\n ],\n filters={\"address\": CONTRACT_ADDRESS},\n # How many maximum blocks at the time we request from JSON-RPC\n # and we are unlikely to exceed the response size limit of the JSON-RPC server\n max_chunk_scan_size=1000,\n )\n\n # Assume we might have scanned the blocks all the way to the last Ethereum block\n # that mined a few seconds before the previous scan run ended.\n # Because there might have been a minor Etherueum chain reorganisations\n # since the last scan ended, we need to discard\n # the last few blocks from the previous scan results.\n chain_reorg_safety_blocks = 20\n scanner.delete_potentially_forked_block_data(\n state.get_last_scanned_block() - chain_reorg_safety_blocks\n )\n\n # Scan from [last block scanned] - [latest ethereum block]\n # Note that our chain reorg safety blocks cannot go negative\n start_block = max(state.get_last_scanned_block() -\n chain_reorg_safety_blocks, 0)\n end_block = scanner.get_suggested_scan_end_block()\n blocks_to_scan = end_block - start_block\n\n logger.info(f\"Scanning events from blocks {start_block} - {end_block}\")\n\n # Render a progress bar in the console\n start = time.time()\n\n def _update_progress(\n start, end, current, current_block_timestamp, chunk_size, events_count\n ):\n if current_block_timestamp:\n formatted_time = current_block_timestamp.strftime(\"%d-%m-%Y\")\n else:\n formatted_time = \"no block time available\"\n logger.info(\n f\"Current block: {current} ({formatted_time}), blocks in a scan batch: {chunk_size}, events processed in a batch {events_count}\"\n )\n\n # Run the scan\n result, total_chunks_scanned = scanner.scan(\n start_block, end_block, progress_callback=_update_progress\n )\n\n state.save()\n duration = time.time() - start\n logger.info(\n f\"Scanned total {len(result)} events, in {duration} seconds, total {total_chunks_scanned} chunk scans performed\"\n )\n scheduler.enter(RUN_EVERY_X_SECONDS, 1, run, (sc,))\n\n\nscheduler.enter(RUN_EVERY_X_SECONDS, 1, run, (scheduler,))\n\n\ndef notify_new_stash(value):\n webhook = DiscordWebhook(\n url=DISCORD_WEBHOOK_URL,\n content=f\"New JEWEL stash has been minted! {value/1e18:.3f} locked JEWEL can be claimed.\",\n )\n response = webhook.execute()\n\n\ndef notify_redeemed_stash(redeemed_value):\n webhook = DiscordWebhook(\n url=DISCORD_WEBHOOK_URL,\n content=f\"A JEWEL stash has just been redeemed for {redeemed_value/1e18:.3f}. Need to be quicker next time!\",\n )\n response = webhook.execute()\n","repo_name":"gmguild/gmJEWEL","sub_path":"backend/scanners/jewel/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":35136,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"72"}
+{"seq_id":"4050878382","text":"# -*- coding: utf-8 -*-\n\"\"\"\nDO MIXTURE PROPORTION ESTIMATION \nUsing gradient thresholding of the $\\C_S$-distance\n\"\"\"\nfrom cvxopt import matrix, solvers, spmatrix\nfrom math import sqrt\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.close('all')\nimport scipy.linalg as scilin\n\n\ndef find_nearest_valid_distribution(u_alpha, kernel, initial=None, reg=0):\n \"\"\" (solution,distance_sqd)=find_nearest_valid_distribution(u_alpha,kernel):\n Given a n-vector u_alpha summing to 1, with negative terms, \n finds the distance (squared) to the nearest n-vector summing to 1, \n with non-neg terms. Distance calculated using nxn matrix kernel. \n Regularization parameter reg -- \n\n min_v (u_alpha - v)^\\top K (u_alpha - v) + reg* v^\\top v\"\"\"\n\n P = matrix(2 * kernel)\n n = kernel.shape[0]\n q = matrix(np.dot(-2 * kernel, u_alpha))\n A = matrix(np.ones((1, n)))\n b = matrix(1.)\n G = spmatrix(-1., range(n), range(n))\n h = matrix(np.zeros(n))\n dims = {'l': n, 'q': [], 's': []}\n solvers.options['show_progress'] = False\n solution = solvers.coneqp(\n P,\n q,\n G,\n h,\n dims,\n A,\n b,\n initvals=initial\n )\n distance_sqd = solution['primal objective'] + np.dot(u_alpha.T,\n np.dot(kernel, u_alpha))[0, 0]\n return (solution, distance_sqd)\n\n\ndef get_distance_curve(\n kernel,\n lambda_values,\n N,\n M=None,\n ):\n \"\"\" Given number of elements per class, full kernel (with first N rows corr.\n to mixture and the last M rows corr. to component, and set of lambda values\n compute $\\hat d(\\lambda)$ for those values of lambda\"\"\"\n\n d_lambda = []\n if M == None:\n M = kernel.shape[0] - N\n prev_soln=None \n for lambda_value in lambda_values:\n u_lambda = lambda_value / N * np.concatenate((np.ones((N, 1)),\n np.zeros((M, 1)))) + (1 - lambda_value) / M \\\n * np.concatenate((np.zeros((N, 1)), np.ones((M, 1))))\n #Can I save distance here?\n (solution, distance_sqd) = \\\n find_nearest_valid_distribution(u_lambda, kernel, initial=prev_soln)\n prev_soln = solution\n d_lambda.append(sqrt(distance_sqd))\n d_lambda = np.array(d_lambda)\n return d_lambda\n\n\ndef compute_best_rbf_kernel_width(X_mixture,X_component): \n N=X_mixture.shape[0]\n M=X_component.shape[0]\n # compute median of pairwise distances\n X=np.concatenate((X_mixture,X_component))\n dot_prod_matrix=np.dot(X,X.T)\n norms_squared=sum(np.multiply(X,X).T) \n distance_sqd_matrix=np.tile(norms_squared,(N+M,1)) + \\\n np.tile(norms_squared,(N+M,1)).T - 2*dot_prod_matrix \n kernel_width_median = sqrt(np.median(distance_sqd_matrix))\n kernel_width_vals= np.logspace(-1,1,5) * kernel_width_median\n \n # Find best kernel width\n \n max_dist_RKHS=0\n for kernel_width in kernel_width_vals: \n kernel=np.exp(-distance_sqd_matrix/(2.*kernel_width**2.)) \n dist_diff = np.concatenate((np.ones((N, 1)) / N, \n -1 * np.ones((M,1)) / M))\n distribution_RKHS_distance = sqrt(np.dot(dist_diff.T, \n np.dot(kernel, dist_diff))[0,0])\n if distribution_RKHS_distance > max_dist_RKHS:\n max_dist_RKHS=distribution_RKHS_distance\n best_kernel_width=kernel_width \n kernel=np.exp(-distance_sqd_matrix/(2.*best_kernel_width**2.))\n return best_kernel_width,kernel\n\n \ndef mpe(kernel,N,M,nu,epsilon=0.04,lambda_upper_bound=8.):\n \"\"\" Do mixture proportion estimation (as in paper)for N points from \n mixture F and M points from component H, given kernel of size (N+M)x(N+M), \n with first N points from the mixture and last M points from \n the component, and return estimate of lambda_star where\n G =lambda_star*F + (1-lambda_star)*H\"\"\"\n\n dist_diff = np.concatenate((np.ones((N, 1)) / N, -1 * np.ones((M,1)) / M))\n distribution_RKHS_distance = sqrt(np.dot(dist_diff.T, \n np.dot(kernel, dist_diff))[0,0])\n lambda_left=1.\n lambda_right=lambda_upper_bound \n while lambda_right-lambda_left>epsilon:\n lambda_value=(lambda_left+lambda_right)/2. \n u_lambda = lambda_value / N * np.concatenate((np.ones((N, 1)),\n np.zeros((M, 1)))) + (1 - lambda_value) / M \\\n * np.concatenate((np.zeros((N, 1)), np.ones((M, 1))))\n (solution, distance_sqd) = \\\n find_nearest_valid_distribution(u_lambda, kernel)\n d_lambda_1=sqrt(distance_sqd)\n \n lambda_value=(lambda_left+lambda_right)/2. + epsilon/2. \n u_lambda = lambda_value / N * np.concatenate((np.ones((N, 1)),\n np.zeros((M, 1)))) + (1 - lambda_value) / M \\\n * np.concatenate((np.zeros((N, 1)), np.ones((M, 1))))\n (solution, distance_sqd) = \\\n find_nearest_valid_distribution(u_lambda, kernel)\n d_lambda_2=sqrt(distance_sqd)\n \n slope_lambda=(d_lambda_2 - d_lambda_1)*2./epsilon \n \n if slope_lambda > nu*distribution_RKHS_distance:\n lambda_right=(lambda_left+lambda_right)/2.\n else:\n lambda_left=(lambda_left+lambda_right)/2.\n \n return (solution, (lambda_left+lambda_right)/2.)\n \n\ndef wrapper(X_mixture,X_component): \n \"\"\" Takes in 2 arrays containing the mixture and component data as \n numpy arrays, and prints the estimate of kappastars using the two gradient \n thresholds as detailed in the paper as KM1 and KM2\"\"\"\n \n N=X_mixture.shape[0] \n M=X_component.shape[0]\n best_width,kernel=compute_best_rbf_kernel_width(X_mixture,X_component) \n lambda_values=np.array([1.00,1.05]) \n dists=get_distance_curve(kernel,lambda_values,N=N,M=M)\n begin_slope=(dists[1]-dists[0])/(lambda_values[1]-lambda_values[0])\n dist_diff = np.concatenate((np.ones((N, 1)) / N, -1 * np.ones((M,1)) / M))\n distribution_RKHS_dist = sqrt(np.dot(dist_diff.T, np.dot(kernel, dist_diff))[0,0])\n thres_par=0.2 \n nu1=(1-thres_par)*begin_slope + thres_par*distribution_RKHS_dist\n nu1=nu1/distribution_RKHS_dist \n (solution, lambda_star_est_1)=mpe(kernel,N,M,nu=nu1)\n kappa_star_est_1=(lambda_star_est_1-1)/lambda_star_est_1 \n nu2=1/sqrt(np.min([M,N]))\n nu2=nu2/distribution_RKHS_dist\n if nu2>0.9:\n nu2=nu1\n (solution, lambda_star_est_2)=mpe(kernel,N,M,nu=nu2) \n kappa_star_est_2=(lambda_star_est_2-1)/lambda_star_est_2\n return (kappa_star_est_2,kappa_star_est_1, solution)\t\n \ndef id_example(total_observations = 1000, kappa_star = 0.4, size = 100, mean_comp = 10.):\n \"\"\"Attempts to show how we might adapt the algorithm to give\n an identification of the most similar observations\"\"\"\n total_observations = total_observations\n kappa_star = kappa_star\n size = size\n mean_comp = np.repeat(mean_comp, size)\n X_mixture = np.concatenate((np.random.randn(int((1-kappa_star)*total_observations/2), size), \n np.random.randn(int(kappa_star*total_observations/2), size)+\n mean_comp))\n X_component = np.random.randn(total_observations/2, size)+ mean_comp\n (KM1,KM2,solution)=wrapper(X_mixture,X_component)\n\n #Take the part of the solution that comes from the mixture\n mixture_solution = solution['x'][0:(X_mixture.shape[0]-1)]\n print(\"Estimate of Kappa^star = \", KM2)\n print(\"\\n Probabilities: \")\n print(mixture_solution)\n\n #Now, using the solution, find the component in the mixture\n #First, define container for the results\n comp_in_mixture = np.ones((2,int(round(KM2*X_mixture.shape[0]))))\n\n #Next, find solutions that have the smallest probability -- these are the ones most likely from the component\n t = int(0)\n for i in mixture_solution:\n #Find the minimum probability record so far\n max_prob = np.argmax(comp_in_mixture[1])\n\n #If the next record has a higher probability, replace the minimum\n if i < comp_in_mixture[1][max_prob]:\n comp_in_mixture[0][max_prob] = t\n comp_in_mixture[1][max_prob] = i\n\n #Increment the index\n t+=1\n return (KM2, comp_in_mixture)\n\n\nif __name__=='__main__':\n \"\"\" Calls wrapper with a GMM as the mixture distribution and one of the \n components as the component distribution. Replace the X_mixture and X_component \n variables according to your data\"\"\"\n total_observations = np.random.randint(2,1000)\n kappa_star = np.random.uniform(0.1,0.9)\n size = np.random.randint(2,100)\n mean_comp=np.random.uniform(0,15,size)\n X_mixture = np.concatenate((np.random.randn(int((1-kappa_star)*total_observations/2), size), \n np.random.randn(int(kappa_star*total_observations/2), size)+\n mean_comp))\n X_component = np.random.randn(total_observations/2, size)+ mean_comp\n print(\"Kappa* = \",kappa_star)\n (KM1,KM2,solution)=wrapper(X_mixture,X_component)\n print(\"Observations = \",total_observations)\n print(\"Covariates = \",size)\n print(\"KM1_estimate = \",KM1)\n print(\"KM2_estimate = \",KM2)\n print(\"Solution = \", solution)\n","repo_name":"stewarthkerr/STAT771","sub_path":"project/code/Kernel_MPE_grad_threshold.py","file_name":"Kernel_MPE_grad_threshold.py","file_ext":"py","file_size_in_byte":9508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"42177189790","text":"#!/usr/bin/env python\nimport os\nimport signal\nimport sys\n\n# Signal handler for ctrl+c\ndef signalHandler(sig, frame):\n os.system(\"pkill flask\")\n os.system(\"pkill node\")\n os.system(\"pkill 'ng serve'\")\n os.system(\"pkill 'Python'\")\n print(\"App closed\")\n sys.exit(0)\n\n# Bind signal SIGINT\nsignal.signal(signal.SIGINT, signalHandler)\n\n# Start server\nos.system(\"python3 server.py &\")\n\n# Start client\nos.system(\"python3 client.py &\")\n\n# Wait for ctrl+c\nsignal.pause()\n\n","repo_name":"jferdelyi/Generator","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"185297089","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport runner # noqa\n\nfrom core.types import MarketSku, Model, Offer, Region, RegionalModel, Shop\nfrom core.testcase import TestCase, main\n\n\nclass _C:\n rid_unknown = 3\n\n\nclass T(TestCase):\n @classmethod\n def prepare(cls):\n cls.disable_check_empty_output()\n\n cls.index.regional_models += [\n RegionalModel(hyperid=175941311, rids=[54, 194], has_good_cpa=False, has_cpa=True),\n ]\n\n cls.index.models += [\n Model(hyperid=175941311, hid=44),\n Model(hyperid=10, hid=44),\n Model(hyperid=200, hid=55),\n ]\n\n for i in range(20):\n cls.index.models.append(Model(hyperid=201 + i, hid=55))\n\n cls.index.regiontree = [\n Region(rid=194, name='Саратов'),\n Region(\n rid=54,\n name='Екб',\n children=[\n Region(rid=100500, name='улица в ЕКб'),\n ],\n ),\n ]\n\n cls.index.shops += [\n Shop(fesh=4, priority_region=54, regions=[54, 194], cpa=Shop.CPA_REAL, name='СPA Екб'),\n Shop(fesh=5, priority_region=54, regions=[54, 194], cpa=Shop.CPA_NO, cpc=Shop.CPC_REAL, name='CPC Екб'),\n ]\n\n cls.index.mskus += [\n MarketSku(hyperid=175941311, hid=44, sku=1010),\n MarketSku(hyperid=665306170, hid=44, sku=1020),\n MarketSku(hyperid=10, hid=44, sku=1030),\n MarketSku(hyperid=200, hid=55, sku=1050),\n ]\n\n cls.index.offers += [\n Offer(fesh=4, title=\"CPA офер #2\", cpa=Offer.CPA_REAL, hyperid=175941311, price=200, sku=1010, hid=44),\n Offer(\n fesh=5,\n title=\"CPA_NO офер, модель из конфига\",\n cpa=Offer.CPA_NO,\n price=100,\n hyperid=175941311,\n sku=1010,\n hid=44,\n ),\n Offer(fesh=5, title=\"CPA_NO 10\", cpa=Offer.CPA_NO, hyperid=10, sku=1030, hid=44),\n Offer(fesh=4, title=\"CPA_REAL 55\", cpa=Offer.CPA_REAL, hyperid=200, price=555, sku=1050, hid=55),\n ]\n\n # Несколько моделей в магазинах в Екатеринбурге\n # У модели 665306170 нет офферов\n # У модели 10 есть только cpc\n # В выдаче должны быть только эти две модели, у остальных есть cpa\n # Параметр use-default-offers не должен повлиять\n def test_out_of_stock_cpa_model_filter(self):\n \"\"\"Проверяем выдачу моделей с фильтрацией по отсутствию cpa-офферов\"\"\"\n for do in ('0', '1'):\n request = (\n 'place=prime&pp=18&entities=product&local-offers-first=0&rids=54&hid=44'\n + '&cpa-out-of-stock-models=1&use-default-offers=%s' % do\n )\n response = self.report.request_json(request)\n self.assertFragmentIn(\n response,\n {\n \"search\": {\n \"total\": 2,\n \"totalModels\": 2,\n \"results\": [\n {\"id\": 10, \"type\": \"model\", \"offers\": {\"count\": 0}},\n {\"id\": 665306170, \"type\": \"model\", \"offers\": {\"count\": 0}},\n ],\n }\n },\n allow_different_len=False,\n )\n\n # https://st.yandex-team.ru/MARKETOUT-44507 - не отдаем модели \"не в продаже\" для экспресса\n def test_out_of_stock_cpa_model_filter_with_express(self):\n \"\"\"Проверяем выдачу моделей с фильтрацией по отсутствию cpa-офферов + с фильтром по экспресс-доставке.\n Условия аналогичны test_out_of_stock_cpa_model_filter, но не должны найти ничего, хотя у моделей нет cpa-офферов.\"\"\"\n\n for do in ('0', '1'):\n request = (\n 'place=prime&pp=18&entities=product&local-offers-first=0&&rids=54&hid=44'\n + '&cpa-out-of-stock-models=1&filter-express-delivery=1&use-default-offers=%s' % do\n )\n response = self.report.request_json(request)\n self.assertFragmentIn(\n response,\n {\"search\": {\"total\": 0, \"totalModels\": 0, \"results\": []}},\n allow_different_len=False,\n )\n\n def test_out_of_stock_cpa_model_filter_shutdown(self):\n \"\"\"Проверяем отключение выдачи через rearr\"\"\"\n\n for do in ('0', '1'):\n request = (\n 'place=prime&pp=18&entities=product&local-offers-first=0&&rids=54&hid=44&cpa-out-of-stock-models=1'\n + '&rearr-factors=market_disable_out_of_stock_models_search=1&use-default-offers=%s' % do\n )\n response = self.report.request_json(request)\n self.assertFragmentIn(\n response,\n {\"search\": {\"total\": 0, \"totalModels\": 0, \"results\": []}},\n allow_different_len=False,\n )\n\n # Проверяем выдачу моделей не в продаже внутри основной выдачи на прайме\n # Условия теста - см. test_out_of_stock_cpa_model_filter + нужен cpa=real\n # В выдаче должны быть две модели не в продаже + обычные результаты\n def test_out_of_stock_models_on_prime(self):\n \"\"\"Проверяем подмешивание моделей с фильтрацией по отсутствию cpa-офферов в основную выдачу; требуется cpa=only\"\"\"\n request = (\n 'place=prime&pp=18&local-offers-first=0&rids=54&hid=44'\n + '&use-default-offers=1&cpa=real'\n + '&rearr-factors=market_oos_enable=1'\n )\n response = self.report.request_json(request)\n self.assertFragmentIn(\n response,\n {\n \"search\": {\n \"results\": [\n {\"id\": 10, \"entity\": \"product\", \"offers\": {\"count\": 0}},\n {\"id\": 665306170, \"entity\": \"product\", \"offers\": {\"count\": 0}},\n ],\n }\n },\n allow_different_len=True,\n )\n self.assertFragmentIn(\n response,\n {\n \"search\": {\n \"results\": [\n {\"entity\": \"offer\"},\n ],\n }\n },\n allow_different_len=True,\n )\n\n # Проверяем ограничение на количество моделей (не в продаже) в основной выдаче\n # Условия теста - см. test_out_of_stock_cpa_model_filter + нужен cpa=real + нужны схлопывание с разгруппировкой\n # В выдаче должны быть 5 моделей не в продаже (hid=55, hyperid>200) + тестовый схлопнутый cpa-оффер (hid=55, hyperid=200)\n def test_out_of_stock_models_limit_on_prime(self):\n # с how=random используется plain-итератор, без - rearrangeable\n for extra_args in ('', '&how=random'):\n request = (\n 'place=prime&pp=18&local-offers-first=0&rids=54&hid=55'\n + '&use-default-offers=1&cpa=real'\n + '&allow-collapsing=1&allow-ungrouping=1'\n + '&rearr-factors=market_oos_enable=1;market_oos_limit=5;market_oos_limit_per_shard=10'\n + extra_args\n )\n\n response = self.report.request_json(request)\n\n self.assertFragmentIn(\n response,\n {\n \"search\": {\n \"results\": [\n {\"id\": 200, \"entity\": \"product\", \"offers\": {\"count\": 1}},\n ],\n }\n },\n allow_different_len=True,\n )\n\n self.assertFragmentIn(\n response,\n {\n \"search\": {\n \"results\": [\n {\"entity\": \"product\", \"offers\": {\"count\": 0}},\n ]\n * 5,\n }\n },\n allow_different_len=True,\n )\n\n assert len(response.root['search']['results']) == 6\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Alexander-Berg/2022-test-examples-3","sub_path":"market/GENERAL/test_out_of_stock_models.py","file_name":"test_out_of_stock_models.py","file_ext":"py","file_size_in_byte":8845,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"817713489","text":"from typing import List\n\n\nclass Solution:\n def findWords(self, words: List[str]) -> List[str]:\n row1 = set('qwertyuiopQWERTYUIOP')\n row2 = set('asdfghjklASDFGHJKL')\n row3 = set('zxcvbnmZXCVBNM')\n result = []\n for word in words:\n w = set(list(word))\n if w.issubset(row1) or w.issubset(row2) or w.issubset(row3):\n result.append(word)\n return result","repo_name":"ahmaddroobi99/ProblemSolving","sub_path":"keyboradRow.py","file_name":"keyboradRow.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"18410157555","text":"#必要なライブラリをインポート\nimport pandas as pd\nimport streamlit as st\nimport numpy as np\n\n\nst.title('お得な物件を簡単検索!!')\n\nst.text('下記の要素から条件を絞って、物件を抽出しましょう')\nst.text('\"予測値との差\"の値が小さいほど、家賃の相場よりも安いということになります。')\n\nst.header('操作説明')\nst.text('1. \"最低築年数\"の値を決める。\"最高築年数\"の値を決める。')\nst.text('2. 希望の\"間取り\"を選択')\nst.text('3. \"区\"\"、\"市町村\"を選択する')\n\n\n#st.subheader('This is a subheader')\n#st.text('Hello World!,this is text')\n\n# st.write()はMarkdown表記対応\n#st.write('# headline1')\n# 以下のように明示的に書くことも可能\n#st.markdown('## headline2')\n\n#st.write('# 全体の流れ')\nst.text('↓ 東京新宿区の全ての物件')\n\n#DF読み込み\ndf = pd.read_csv('new_geo_df_search.csv')\n\ndf = df.drop(['間取り'],axis=1)\n\n#Streamlitのマジックコマンドst.write()を使用して物件情報を表示\nst.write(df)\n\n#物件の絞り込み機能の追加:\n#Streamlitのウィジェットを使用して、家賃、間取り、専有面積などの絞り込み条件を指定できるようにする。\n\n# 家賃の範囲を指定するスライダー\nage_range = st.slider('築年数の範囲', min_value=0, max_value=int(df['築年数'].max()), value=(0, int(df['築年数'].max())), step=1)\nmin_age = age_range[0]\nmax_age = age_range[1]\n\n\n# 間取りの選択肢\nroom_types = ['DK', 'K', 'L', 'S']\nselected_room_type = st.multiselect('間取り', room_types)\n\n# 区の選択肢\nwards = df['区'].unique()\nselected_wards = st.selectbox('区', wards)\n\n# 市町村の選択肢\ncities = ['すべて選択'] + df['市町村'].unique().tolist()\nselected_cities = st.multiselect('市町村', cities)\n\n# 選択された区,市町村をリストとして扱う\n# isin()を使う際にリストである必要がある。\nselected_wards = [selected_wards] if isinstance(selected_wards, str) else selected_wards\nselected_cities = [selected_cities] if isinstance(selected_cities, str) else selected_cities\n\n\n# 絞り込み条件に基づいて物件をフィルタリング\n#、市町村の選択肢に「すべて選択」が追加され、選択された市町村に基づいて物件を絞り込む処理が行われる。\n# また、選択された市町村が「すべて選択」の場合は、市町村の絞り込み条件を無視して区の条件のみを適用\n\n\nif 'すべて選択' in selected_cities:\n filtered_df = df[\n (df['築年数'] >= min_age) & (df['築年数'] <= max_age) &\n (df['間取りDK'] == (selected_room_type == 'DK')) &\n (df['間取りK'] == (selected_room_type == 'K')) &\n (df['間取りL'] == (selected_room_type == 'L')) &\n (df['間取りS'] == (selected_room_type == 'S')) &\n (df['区'].isin(selected_wards))\n ]\nelse:\n filtered_df = df[\n (df['築年数'] >= min_age) & (df['築年数'] <= max_age) &\n (df['間取りDK'] == (selected_room_type == 'DK')) &\n (df['間取りK'] == (selected_room_type == 'K')) &\n (df['間取りL'] == (selected_room_type == 'L')) &\n (df['間取りS'] == (selected_room_type == 'S')) &\n (df['区'].isin(selected_wards)) &\n (df['市町村'].isin(selected_cities))\n ]\n\n\n \n#st.write('## 物件を出力!!')\n\nst.text('並び順は予測値との差になってます。')\n# 絞り込んだ物件情報の表示\nst.write(filtered_df.sort_values(by='予測値との差',ascending=True))\n\nst.text('マッピング')\n# マッピングのための緯度経度データを作成\n # 緯度経度データを抽出し、欠損値を除外\ndf_map = filtered_df.rename(columns={'緯度': 'latitude', '経度': 'longitude'})[['latitude', 'longitude']].dropna()\n# マップをプロット\nst.map(df_map)\n\n\n\n\nst.header('今後の改良点')\nst.text('1. 全体的なデザイン')\nst.text('2. 列の並び替え')\nst.text('4. 出力された物件情報がもっと直感的にわかりやすいものにしたい')\nst.text('5. 列がonehot-encodingのままなので、整理したい')\nst.text('7. 機械学習をして分析アプリとしての機能も追加したい。')\nst.text('8. 間取りの部分をなんとかする。')\nst.text('9. googlemapにも表示させてやりたい。これに関しては住所がざっくりしてるからgoogleマップの良さが出ない。')\nst.text('10. 物件を選んだら、マップ上で光るようにしたい')\n\n\n","repo_name":"miyazaki-tsubasa/suumo","sub_path":"suumo_code.py","file_name":"suumo_code.py","file_ext":"py","file_size_in_byte":4567,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"33644232718","text":"import argparse\nimport os\nfrom pathlib import Path\nimport cv2\nimport numpy as np\nfrom tqdm import tqdm\n\n\ndef parse_bisenet_hair_ratio(path):\n hair_ratio_list = []\n for subdir, dirs, files in os.walk(path):\n print(subdir)\n for filename in tqdm(files):\n #For BiSeNet segmentation there is a color mask version and mask version. Just using the mask!\n if filename.split(\"_\")[-2] != \"color\":\n img = cv2.imread(subdir + os.sep + filename, cv2.IMREAD_GRAYSCALE)\n\n # 17 is the hair region\n img[img!=17] = 0\n img[img== 17] = 1\n\n hair_area = len(img[img == 1])\n total_area = img.shape[0] * img.shape[1]\n\n per_skin = round(hair_area/total_area *100,2)\n\n filename = filename.replace(\"_mask.png\", \".JPG\")\n\n hair_ratio_list.append([filename,per_skin])\n\n return hair_ratio_list\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Get the hair ratio for all images given the path to BiSeNet segmentation mask\")\n parser.add_argument(\"--source\", \"-s\", required = True, help=\"Path to source directory of BiseNet. Make sure this is for each group - each separate for C_M,C_F and so on\")\n parser.add_argument(\"--destination\", \"-d\", required = True, help=\"path to save txt file\")\n parser.add_argument(\"--name\", \"-n\", required = True, help=\"Name to save the file.\")\n args = parser.parse_args()\n\n hair_ratio_list = parse_bisenet_hair_ratio(args.source)\n\n if not os.path.exists(args.destination):\n os.makedirs(args.destination)\n \n save_name = os.path.join(args.destination,args.name)\n\n np.savetxt(\"{}.txt\".format(save_name), hair_ratio_list, fmt='%s', delimiter=' ', newline='\\n')","repo_name":"abhatta1234/codesForPapers_PhD_NotreDame","sub_path":"Hair-Dimension-Balancing_ArXiv_2021/prelims/bisenet_parsing.py","file_name":"bisenet_parsing.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"35367468236","text":"#!/usr/bin/env python\nimport rospy\nfrom geometry_msgs.msg import Twist # import Twist message\n\ndef multiplier_msg(data,self):\n self.distance_multiplier = data.linear.y # set linear multiplier\n self.angle_multiplier = data.angular.y # set angular multiplier\n\ndef turtle_msg(data, self):\n self.x = data.linear.x # set linear component\n self.z = data.angular.z # set angular component\n\n dist = 75*self.distance_multiplier # set a distance offset\n ang = 25*self.angle_multiplier # set the angle offest\n\n # retrieve the target parameters\n self.distance = rospy.get_param(\"distance/target\")\n self.angle = rospy.get_param(\"angle/target\")\n\n if self.x > 0:\n # move forward\n self.distance = self.distance + dist\n elif self.x < 0:\n # move backward\n self.distance = self.distance - dist\n elif self.z > 0:\n # turn left\n self.angle = self.angle + ang\n elif self.z < 0:\n # turn right\n self.angle = self.angle - ang\n else:\n rospy.loginfo(\"Parse error\")\n\n # set the target parameters\n rospy.set_param(\"distance/target\",self.distance)\n rospy.set_param(\"angle/target\",self.angle)\n\nclass TheNode(object):\n # This class holds the rospy logic for updating the PID targets\n # from a published teleop_turtle_key message or keyboard input\n\n def __init__(self):\n\n rospy.init_node('pid_remote_drive') # intialize node\n\n self.x = 0 # linear component variable\n self.z = 0 # angular component variable\n\n self.distance = 0 # distance variable\n self.angle = 0 # angle variable\n\n self.angle_multiplier = 1 # angle multiplier variable\n self.distance_multiplier = 1 # distance multiplier variable\n\n def main_loop(self):\n # initialize subscriber node for messages from teleop_turtle_key\n rospy.Subscriber('/turtle1/cmd_vel', Twist, turtle_msg, self)\n rospy.Subscriber('/key_input', Twist, multiplier_msg, self)\n\n rospy.spin() # wait for messages\n\nif __name__ == '__main__':\n try:\n a = TheNode()\n a.main_loop()\n except rospy.ROSInterruptException:\n pass\n","repo_name":"dacools/lab2","sub_path":"scripts/pid_remote_drive.py","file_name":"pid_remote_drive.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"38073255754","text":"#NN test file\n#####################################################################\n#Importing Packages\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.callbacks import ModelCheckpoint\nimport keras as keras\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\nfrom sklearn.utils import shuffle\n\n\n\n######################################################################\n#Reading in the features and making a Train/test split\npq_X_train = pq.read_table('Feature_train.parquet')\npq_y_train = pq.read_table('Stress_train.parquet')\n\npq_X_test = pq.read_table('Feature_test.parquet')\npq_y_test = pq.read_table('Stress_test.parquet')\n\nX_train= pq_X_train.to_pandas()\ny_train= pq_y_train.to_pandas()\nX_test= pq_X_test.to_pandas()\ny_test= pq_y_test.to_pandas()\n\nprint(X_train.shape, y_train.shape)\nprint(X_test.shape, y_test.shape)\ny_train=y_train.to_numpy()\ny_test=y_test.to_numpy()\ny_train=y_train.ravel()\ny_test=y_test.ravel()\n\n# ########################################################################\nLDA\nlda=LDA(n_components=1)\ntrain_lda=lda.fit(X_train, y_train)\ntest_lda=lda.predict(X_test)\n\n# print(test_lda.shape)\n# print(y_test.shape)\n\n#######################################################################\n# Neural Network\n\n# Best Batchsize= 16\n# Best Hidden layer nodes= approx 16,32,32\n# Best droppout= 0.19\n# Best optimizer = Nadam\n# Best loss = \"binary_crossentropy\"\n\nfor j in range (10):\n print(j)\n dropoutje=0.1*j\n\n input_nodes = X_train.shape[1]\n hidden_layer_1_nodes = 16\n hidden_layer_2_nodes = 32\n hidden_layer_3_nodes = 32\n output_layer = 1\n\n # initializing a sequential model\n full_model = Sequential()\n\n # adding layers\n full_model.add(Dense(hidden_layer_1_nodes,input_dim=input_nodes , activation='relu'))\n #full_model.add(Dropout(dropoutje))\n full_model.add(Dense(hidden_layer_2_nodes, activation='relu'))\n #full_model.add(Dropout(dropoutje))\n full_model.add(Dense(hidden_layer_3_nodes, activation='relu'))\n #full_model.add(Dropout(dropoutje))\n full_model.add(Dense(output_layer, activation='sigmoid'))\n\n #full_model.summary()\n\n # Compiling the DNN\n full_model.compile(optimizer=\"Nadam\", loss=\"binary_crossentropy\", metrics=['accuracy'])\n\n history = full_model.fit(X_train,y_train,validation_data=(X_test,y_test), epochs=512, batch_size=(16), verbose=0)\t\n\n req1=np.mean(history.history['val_accuracy'])>=np.mean(history.history['accuracy'])\n req2=np.max(history.history['val_accuracy'])>=np.max(history.history['accuracy'])\n print(\"Val:\",np.max(history.history['val_accuracy']), \"Acc\", np.max(history.history['accuracy']))\n #req3=np.max(history.history['val_accuracy'])>0.96\n if (req1==True and req2==True): # (req1==True and req2==True): and \n print(\"This is the accuracy:\", np.max(history.history['accuracy']))\n full_model.save(f\"New best NN {j}\")\n plt.figure()\n plt.plot(history.history['accuracy'], label = f\"training{j}\")\n plt.plot(history.history['val_accuracy'], label = f\"test{j}\")\n print(\"This is the validation, not overfitted:\", np.max(history.history['val_accuracy']))\n plt.legend()\n\n\n# new_model = keras.models.load_model('Best NN 8')\n# new_model.summary()\n# # Evaluate the restored model\n# loss, acc = new_model.evaluate(X_test, y_test, verbose=2)\n# print('Restored model, accuracy: {:5.2f}%'.format(100 * acc))\n","repo_name":"thomasritmeester/EPO-4_BioBombs","sub_path":"Machine learning/NN test file.py","file_name":"NN test file.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"5129199847","text":"import random\nimport math\n\nC = 1.4\n\ndef ucb_score(node, parent_visits, exploration_constant):\n if node.visits == 0:\n return float('inf')\n exploitation_term = node.wins / node.visits\n exploration_term = math.sqrt(math.log(parent_visits) / node.visits)\n return exploitation_term + exploration_constant * exploration_term\n\nclass Node: \n def __init__(self, state, parent=None, position=None):\n self.state = state\n self.parent = parent\n self.children = []\n self.position = position\n self.visits = 0\n self.wins = 0\n\n def select(self):\n ucb_scores = [ucb_score(child, self.visits, C) for child in self.children]\n max_ucb = max(ucb_scores)\n max_indices = [i for i in range(len(ucb_scores)) if ucb_scores[i] == max_ucb]\n return self.children[random.choice(max_indices)]\n\n def expand(self):\n for move in self.state.get_possible_moves():\n copy = self.state.copy()\n copy.make_move(move)\n child = Node(copy, self, move)\n self.children.append(child)\n\n def update(self, result):\n self.visits += 1\n self.wins += result\n if self.parent:\n self.parent.update(result)\n \n\nclass TicTacToe:\n def __init__(self):\n self.board = [['-' for _ in range(3)] for _ in range(3)]\n self.current_player = 'X'\n\n def copy(self):\n copy_game = TicTacToe()\n copy_game.board = [row[:] for row in self.board]\n copy_game.current_player = self.current_player\n return copy_game\n\n def get_possible_moves(self):\n moves = []\n for row in range(3):\n for col in range(3):\n if self.board[row][col] == '-':\n moves.append((row, col))\n return moves\n\n def make_move(self, move):\n row, col = move\n self.board[row][col] = self.current_player\n self.current_player = 'O' if self.current_player == 'X' else 'X'\n\n def is_win(self, player):\n if any(all(self.board[row][col] == player for col in range(3)) for row in range(3)):\n return True\n if any(all(self.board[row][col] == player for row in range(3)) for col in range(3)):\n return True\n if all(self.board[i][i] == player for i in range(3)):\n return True\n if all(self.board[i][2-i] == player for i in range(3)):\n return True\n return False\n\n def is_draw(self):\n for row in range(3):\n for col in range(3):\n if self.board[row][col] == '-':\n return False\n return True\n\n def is_terminal(self):\n return self.is_win('X') or self.is_win('O') or self.is_draw()\n\n def get_winner(self):\n if self.is_win('X'):\n return 'X'\n elif self.is_win('O'):\n return 'O'\n else:\n return None\n\n def print_board(self):\n print(\" 0 1 2\")\n for i in range(3):\n row_str = f\"{i} \"\n for j in range(3):\n row_str += f\"{self.board[i][j]} \"\n print(row_str) \ndef simulate_game(game, debug=False):\n while not game.is_terminal():\n game.make_move(random.choice(game.get_possible_moves()))\n winner = game.get_winner()\n if winner == 'X':\n return -1\n elif winner == 'O':\n return 1\n return 0\n\ndef print_tree(node, depth=0):\n if node is None:\n return\n indent = ' ' * depth\n print(f\"{indent} {node.position}\")\n print(f\"{indent} v:{node.visits} w:{node.wins}\")\n for child in node.children:\n print_tree(child, depth + 2)\n\ndef monte_carlo_tree_search(game, num_iterations, debug=False):\n \"\"\"\n Performs Monte Carlo Tree Search from the given node for a certain number of iterations.\n\n :param node: The root node of the tree to search.\n :param num_simulations: The number of simulations to run.\n :return: The best move found by the search.\n \"\"\"\n root = Node(game)\n for _ in range(num_iterations):\n node = root\n while node.children:\n # Selection: Choose a child node to explore using UCB scores\n node = node.select()\n if not node.visits:\n # Expansion: Expand the selected node by adding a new child node\n node.expand()\n\n # Simulation: Simulate a game from the new child node\n result = simulate_game(node.state.copy(), debug=debug)\n\n # Backpropagation: Update the tree statistics from the simulation result\n node.update(result)\n \n if debug:\n print_tree(root)\n bestNode = max(root.children, key=lambda node: node.visits)\n return bestNode.position\n\ngame = TicTacToe()\n\n\nprint(\"--------------------\")\nprint(\"TIK TAK TOE\")\nprint(\"--------------------\")\ngame.print_board()\n\nwhile not game.is_terminal():\n if game.current_player == 'X':\n xy = input(\"Enter row and column as x,y: \").split(',')\n if len(xy) == 2 and xy[0].isdigit() and xy[1].isdigit():\n row, col = int(xy[0]), int(xy[1])\n game.make_move((row, col))\n else:\n raise RuntimeError(\"Invalid input\")\n else:\n move = monte_carlo_tree_search(game, 100, False)\n game.make_move(move)\n\n game.print_board()\n print(\"\\n\")\n\nwinner = game.get_winner()\nif winner:\n print(f\"{winner} wins!\")\nelse:\n print(\"It's a draw!\")","repo_name":"stevenandersonz/ai-stuff","sub_path":"mcts/mcts.py","file_name":"mcts.py","file_ext":"py","file_size_in_byte":5397,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"36406738978","text":"#\n# @lc app=leetcode.cn id=1805 lang=python3\n#\n# [1805] 字符串中不同整数的数目\n#\n\n# @lc code=start\nclass Solution:\n def numDifferentIntegers(self, word: str) -> int:\n x = \"\"\n res = set()\n for o in word:\n if '0' <= o <= '9':\n x += o\n else:\n if x:\n res.add(int(x))\n x = \"\"\n if x:\n res.add(int(x))\n return len(res)\n# @lc code=end\n\n","repo_name":"yarzzz/leetcode_python3","sub_path":"1805.字符串中不同整数的数目.py","file_name":"1805.字符串中不同整数的数目.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"37676340969","text":"from influxdb import InfluxDBClient\nclient = InfluxDBClient(host='localhost', port=8086)\nclient.create_database('pyexample')\nclient.get_list_database()\nclient.switch_database('pyexample')\n\njson_body = [\n {\n \"measurement\": \"brushEvents\",\n \"tags\": {\n \"user\": \"Carol\",\n \"brushId\": \"6c89f539-71c6-490d-a28d-6c5d84c0ee2f\"\n },\n \"time\": \"2018-03-28T8:01:00Z\",\n \"fields\": {\n \"duration\": 127\n }\n },\n {\n \"measurement\": \"brushEvents\",\n \"tags\": {\n \"user\": \"Carol\",\n \"brushId\": \"6c89f539-71c6-490d-a28d-6c5d84c0ee2f\"\n },\n \"time\": \"2018-03-29T8:04:00Z\",\n \"fields\": {\n \"duration\": 132\n }\n },\n {\n \"measurement\": \"brushEvents\",\n \"tags\": {\n \"user\": \"Carol\",\n \"brushId\": \"6c89f539-71c6-490d-a28d-6c5d84c0ee2f\"\n },\n \"time\": \"2018-03-30T8:02:00Z\",\n \"fields\": {\n \"duration\": 129\n }\n }\n]\n\n\nclient.write_points(json_body)\n\nclient.query('SELECT \"duration\" FROM \"pyexample\".\"autogen\".\"brushEvents\" WHERE time > now() - 4d GROUP BY \"user\"')","repo_name":"Microshak/MicroNotes","sub_path":"Databases/Influx/influx.py","file_name":"influx.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"72"}
+{"seq_id":"73350607273","text":"import time\n\ndef fib(n):\n starting_time = time.time() \n fi = [0] * (n+1) \n fi[1] = 1\n for i in range (2,n+1): \n fi[i] = fi[i-1] + fi[i-2] \n end_time = time.time() \n executiontime = end_time - starting_time\n return fi[n], executiontime\n\nn = int(input(\"Input an integer from 15 to 35: \"))\nfibvalue,_ = fib(n)\n_,executiontime = fib(n)\nprint(f\"fib({n})=\",fibvalue, end='\\n')\nprint(f\"fib({n}) took\",executiontime, \"seconds\")\n\n","repo_name":"ITSC-3155-Spring23-MMejias/intermediate-python-exercises-2-mitchelln02","sub_path":"exercise_02/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"17897483820","text":"import os\nimport time\n\nimport pytest\nfrom gi.repository import Gtk\n\n\n@pytest.fixture\ndef session(mocker):\n from tomate.session import Session\n\n return mocker.Mock(Session)\n\n\n@pytest.fixture\ndef graph():\n from tomate.graph import graph\n\n graph.providers.clear()\n\n return graph\n\n\n@pytest.fixture\ndef config(mocker):\n from tomate.config import Config\n\n parent_directory = os.path.dirname(os.path.dirname(__file__))\n icon_path = os.path.join(\n parent_directory, \"data/icons/hicolor/16x16/apps/tomate-plugin.png\"\n )\n instance = mocker.Mock(\n Config,\n SECTION_SHORTCUTS=Config.SECTION_SHORTCUTS,\n SECTION_TIMER=Config.SECTION_TIMER,\n **{\"get_int.return_value\": 25, \"get_icon_path.return_value\": icon_path}\n )\n\n return instance\n\n\n@pytest.fixture\ndef plugin_manager(mocker):\n return mocker.Mock()\n\n\n@pytest.fixture\ndef lazy_proxy(mocker):\n from tomate.proxy import lazy_proxy\n\n return mocker.Mock(lazy_proxy)\n\n\ndef refresh_gui(delay=0):\n while Gtk.events_pending():\n Gtk.main_iteration_do(False)\n time.sleep(delay)\n","repo_name":"proganalysis/python3_types","sub_path":"Result/4079files/source/1370.py","file_name":"1370.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"42243925997","text":"from datetime import datetime\nimport time\n\n\nclass TuringMachine:\n\n def checkInstruction(self, currentState, currentChar):\n for it in self.data.instructions[currentState]:\n if it.currentChar == currentChar:\n return it\n\n def printPointer(self, number, state, instruction):\n result = \"\"\n for i in range(number):\n result += \" \"\n result += \"|\"\n tmp = \"Stan: \" + str(state)\n result += tmp.center(10)\n result += str(instruction)\n self.lines.append(result)\n print(result)\n\n def currentWord(self, word):\n tmp = []\n for it in word:\n tmp.append(it)\n return \"\".join(tmp)\n\n def changeChar(self, word, index, newChar):\n tmp = []\n for i in range(len(word)):\n if i == index:\n tmp.append(newChar)\n else:\n tmp.append(word[i])\n return \"\".join(tmp)\n\n def printWord(self, word):\n print(\"___\" + word + \"___\")\n self.lines.append(\"___\" + word + \"___\" + \"\\n\")\n\n def saveToFile(self):\n file = open(\"resultOfSimulationMT.txt\", \"a\")\n now = datetime.now()\n file.write(\"-------------------------------------------------\".center(50))\n file.write(\"\\nData uruchomienia symulatora: \" + now.strftime(\"%d/%m/%Y %H:%M:%S\" + \"\\n\\n\"))\n file.write(\"Słowo początkowe: \" + self.data.word + \"\\n\")\n for it in self.lines:\n file.write(it + \"\\n\")\n file.write(\"-------------------------------------------------\".center(50))\n file.close()\n print(\"Wynik działania symulatora został zapisany do pliku resultOfSimulationMT.txt\")\n\n def run(self, data):\n start = time.time()\n self.lines = []\n self.data = data\n self.lines.append(self.data.description + \"\\n\")\n print(self.data.description)\n word = self.currentWord(self.data.word)\n\n currentState = int(self.data.initialState)\n i = 0\n while True:\n if self.data.word[i] == '_':\n i += 1\n else:\n break\n counter = 0\n while True:\n counter += 1\n if counter > (len(self.data.word) * 1000):\n print(\"Maszyna prawdopodobnie została zapętlona w instrukcji '\" + str(instruction) + \"' i niespodziewanie zakończyła swoją pracę.\")\n self.lines.append(\"Maszyna prawdopodobnie została zapętlona i niespodziewanie zakończyła swoją pracę.\")\n return\n\n if i >= len(word) or i < 0:\n instruction = self.checkInstruction(currentState, '_')\n else:\n if word[i] not in self.data.alphabet:\n print(\"Maszyna napotkała znak spoza podanego alfabetu i nieoczekiwanie zakończyła swoją pracę.\")\n self.lines.append(\"Maszyna napotkała znak spoza podanego alfabetu i nieoczekiwanie zakończyła swoją pracę.\")\n return\n instruction = self.checkInstruction(currentState, word[i])\n self.printPointer(i + 3, currentState, instruction)\n\n self.printWord(word)\n\n word = self.changeChar(word, i, instruction.newChar)\n if instruction.move == 'l':\n currentState = int(instruction.nextState)\n i -= 1\n elif instruction.move == \"s\":\n self.printPointer(i + 3, instruction.nextState, instruction)\n self.printWord(word)\n print(\"Symulator kończy pracę w stanie: \" + instruction.nextState)\n self.lines.append(\"Symulator kończy pracę w stanie: \" + instruction.nextState)\n break\n elif instruction.move == \"r\":\n i += 1\n currentState = int(instruction.nextState)\n\n diff = time.time() - start\n tmp = \"_\"\n for it in word:\n if it != \"_\":\n tmp += it\n tmp += \"_\"\n print(\"Koniec działania symulatora.\")\n print(\"Słowo po zakończeniu pracy symulatora: \" + tmp)\n print(\"\\nPodsumowanie:\")\n print(\"\\tLiczba kroków wykonanych przez symulator: \" + str(counter))\n print(\"\\tCzas wykonania programu: \" + \"{:.4f}\".format(diff))\n\n self.lines.append(\"Słowo po zakończeniu pracy symulatora: \" + tmp)\n self.lines.append(\"Koniec działania symulatora.\")\n self.lines.append(\"\\nPodsumowanie:\")\n self.lines.append(\"\\tLiczba kroków wykonanych przez symulator: \" + str(counter))\n self.lines.append(\"\\tCzas wykonania programu: \" + \"{:.4f}\".format(diff))\n self.saveToFile()\n","repo_name":"MateuszKopczan/TuringMachineSimulator","sub_path":"TuringMachine.py","file_name":"TuringMachine.py","file_ext":"py","file_size_in_byte":4679,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"37821630647","text":"import argparse\nimport getpass\nimport random\nimport time\nimport warnings\nfrom argparse import ArgumentParser\nfrom typing import Type\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom pytorch_lightning.loggers import (\n LightningLoggerBase,\n MLFlowLogger,\n TensorBoardLogger,\n)\n\nfrom deep_memory_update.data import BaseDataModule\nfrom deep_memory_update.models import BaseModel\nfrom deep_memory_update.models.utils import ThresholdedEarlyStopping\n\nwarnings.filterwarnings(\n \"ignore\",\n message=\"Your `IterableDataset` has `__len__` defined\",\n category=UserWarning,\n)\nwarnings.filterwarnings(\n \"ignore\",\n message=r\"The dataloader, (.*?) does not have many workers\",\n category=UserWarning,\n)\n\n\nclass Experiment:\n def __init__(\n self,\n model: Type[BaseModel],\n data_module: Type[BaseDataModule],\n parser_default: dict = None,\n ):\n self.model = model\n self.data_module = data_module\n self.early_stopping = ThresholdedEarlyStopping\n self.parser_default = parser_default if parser_default is not None else {}\n\n def run(self):\n parser = self.create_parser()\n args = parser.parse_args()\n\n if args.seed is not None:\n pl.seed_everything(args.seed)\n\n if args.fast_dev_run:\n args.batch_size_val = args.batch_size\n args.batch_size_test = args.batch_size\n\n if args.distributed_backend == \"ddp\":\n # When using a single GPU per process and per\n # DistributedDataParallel, we need to divide the batch size\n # ourselves based on the total number of GPUs we have\n args.batch_size = int(args.batch_size / max(1, args.gpus))\n args.workers = int(args.workers / max(1, args.gpus))\n\n data_module = self.data_module(**vars(args))\n model = self.model(\n **vars(args),\n input_size=data_module.input_size(),\n output_size=data_module.output_size(),\n loss_weight=data_module.loss_weight(),\n pad_sequence=data_module.pad_sequence,\n )\n early_stopping = self.early_stopping(**vars(args))\n\n logger = self.create_logger(logger_name=args.logger_name)\n trainer = pl.Trainer.from_argparse_args(args, logger=logger)\n if args.checkpoint_monitor:\n checkpoint_callback = ModelCheckpoint(\n monitor=args.checkpoint_monitor,\n save_top_k=args.checkpoint_top_k,\n mode=args.checkpoint_mode,\n )\n trainer.callbacks.append(checkpoint_callback)\n\n if args.thresholded_early_stopping:\n trainer.callbacks.append(early_stopping)\n trainer.logger.log_hyperparams(args)\n\n start = time.time()\n trainer.fit(model, datamodule=data_module)\n end = time.time()\n\n if not args.no_evaluate:\n if args.checkpoint_monitor:\n trainer.test(ckpt_path=checkpoint_callback.best_model_path)\n else:\n trainer.test(ckpt_path=\"best\")\n\n print(\"Elapsed time:\", \"%.2f\" % (end - start))\n\n def create_logger(self, logger_name: str = \"tb\") -> LightningLoggerBase:\n if logger_name == \"tb\":\n return TensorBoardLogger(\n save_dir=\"tb_logs\",\n name=self.data_module.data_name,\n )\n elif logger_name == \"mlf\":\n return MLFlowLogger(\n experiment_name=self.data_module.data_name,\n tags={\n \"mlflow.runName\": self.model.model_name,\n \"mlflow.user\": getpass.getuser(),\n },\n )\n else:\n raise RuntimeError(f\"Wrong logger name: {logger_name}\")\n\n def create_parser(self):\n parser = ArgumentParser(add_help=True)\n parser = self.add_trainer_parser(parser)\n parser = self.add_experiment_parser(parser)\n parser = self.data_module.add_model_specific_args(parser)\n parser = self.model.add_model_specific_args(parser)\n parser = self.early_stopping.add_callback_specific_args(parser)\n parser.set_defaults(\n progress_bar_refresh_rate=2,\n **self.parser_default,\n )\n parser.formatter_class = argparse.ArgumentDefaultsHelpFormatter\n return parser\n\n def add_trainer_parser(self, parser: ArgumentParser):\n parser = pl.Trainer.add_argparse_args(parser)\n parser.set_defaults(\n deterministic=True,\n max_epochs=100,\n )\n return parser\n\n def add_experiment_parser(self, parser: ArgumentParser):\n parser.add_argument(\n \"--no-evaluate\",\n dest=\"no_evaluate\",\n action=\"store_true\",\n help=\"do not evaluate model on validation set\",\n )\n parser.add_argument(\n \"--seed\",\n dest=\"seed\",\n type=int,\n default=random.randrange(1 << 32 - 1),\n help=\"seed for model training.\",\n )\n parser.add_argument(\n \"--logger-name\",\n dest=\"logger_name\",\n type=str,\n choices=[\"tb\", \"mlf\"],\n default=\"tb\",\n help=\"Logger name.\",\n )\n parser.add_argument(\n \"--checkpoint-monitor\",\n dest=\"checkpoint_monitor\",\n type=str,\n default=\"\",\n help=\"Metric used for checkpointing\",\n )\n parser.add_argument(\n \"--checkpoint-top-k\",\n dest=\"checkpoint_top_k\",\n type=int,\n default=1,\n help=\"Save top k models\",\n )\n parser.add_argument(\n \"--checkpoint-mode\",\n dest=\"checkpoint_mode\",\n type=str,\n choices=[\"min\", \"max\"],\n default=\"min\",\n help=\"Mode for the checkpoint monitoring\",\n )\n return parser\n","repo_name":"fuine/dmu","sub_path":"deep_memory_update/experiments/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":5945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"19947691529","text":"from flask_restful import Resource\nfrom bson import ObjectId\nfrom flask import request\nfrom dao.session import get_session\nfrom dao.user import get_user_by_id, get_user_by_mail\nfrom dao.game_instance import create_game_instance, get_pending_request, accepted_game_instance, get_game_invitations_to_user, get_inactive_game_instances,get_completed_game_instances\nfrom views.game_instance import single as single_game_instance ,multiple as multiple_game_instance\n\ndef add_buttonValue_to_game_instance(gi,user):\n for obj in gi:\n opposition_user = obj[\"user1\"]\n if ObjectId(obj[\"user2\"]) != user[\"_id\"]:\n opposition_user = obj[\"user2\"]\n user_alias = get_user_by_id(opposition_user)\n obj[\"buttonValue\"] = user_alias[\"alias\"]\n return gi\n\nclass GameInstance(Resource):\n\n def get(self):\n params = request.args.to_dict()\n\n session = get_session(params['token'])\n user_id = session['user']\n\n if user_id:\n user = get_user_by_id(user_id)\n if not user:\n return {\"response\" : \"User not found\"}, 404\n\n gi_pending = get_game_invitations_to_user(user_id)\n gi_pending = multiple_game_instance(gi_pending)\n\n gi_inaction = get_inactive_game_instances(user_id)\n gi_inaction = multiple_game_instance(gi_inaction)\n\n gi_completed = get_completed_game_instances(user_id)\n gi_completed = multiple_game_instance(gi_completed)\n return {\"response\" : {\"pending\" : add_buttonValue_to_game_instance(gi_pending,user),\n \"inaction\" : add_buttonValue_to_game_instance(gi_inaction,user),\n \"completed\" : add_buttonValue_to_game_instance(gi_completed, user)}}\n\n def post(self):\n params = request.args.to_dict()\n\n session = get_session(params['token'])\n user_id = session['user']\n\n if user_id:\n requesting_user = get_user_by_id(user_id)\n if not requesting_user:\n return {\"response\" : \"User not found\"}, 404\n\n payload = request.json\n\n try:\n email = payload['email']\n except:\n return {\"response\" : \"Bad request\"} , 400\n\n requested_user = get_user_by_mail(email)\n if not requested_user:\n return {\"response\" : \"Requested User not found\"}, 404\n\n user1, user2 = requesting_user[\"_id\"], requested_user[\"_id\"]\n\n if get_pending_request(user1 ,user2):\n return {\"response\" : \"Already requested for game instance\"}\n\n gi = create_game_instance(user1, user2)\n return {\"response\" : str(gi)}\n\n def put(self):\n params = request.args.to_dict()\n\n session = get_session(params['token'])\n user_id = session['user']\n\n if user_id:\n requested_user = get_user_by_id(user_id)\n if not requested_user:\n return {\"response\" : \"User not found\"}, 404\n\n payload = request.json\n\n try:\n userid = payload[\"userid\"]\n except:\n return {\"response\" : \"Bad request\"} , 400\n\n requesting_user = get_user_by_id(userid)\n if not requesting_user:\n return {\"response\" : \"Requested User not found\"}, 404\n\n user1, user2 = requesting_user[\"_id\"], requested_user[\"_id\"]\n gi = get_pending_request(user1, user2)\n\n accepted_game_instance(gi[\"_id\"])\n\n return {\"response\" : single_game_instance(gi)}\n","repo_name":"sumitkukade/TicTacToe","sub_path":"gameservice/business_logic/serviceapis/gameinstance.py","file_name":"gameinstance.py","file_ext":"py","file_size_in_byte":3458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"28482292094","text":"# https://leetcode.com/problems/parallel-courses-iii/\n\nclass Solution:\n def minimumTime(self, n: int, relations: List[List[int]], time: List[int]) -> int:\n @lru_cache(None)\n def getTime(course):\n if not graph[course]:\n return time[course - 1]\n\n max_time = float(-inf)\n for crs in graph[course]:\n max_time = max(getTime(crs), max_time)\n\n return time[course - 1] + max_time\n\n graph = defaultdict(list)\n\n for i, (pre, course) in enumerate(relations):\n graph[course].append(pre)\n\n graph[n + 1] = [i + 1 for i in range(n)]\n time.append(0)\n\n return getTime(n + 1)\n","repo_name":"nawrazi/competitive-programming","sub_path":"week_17/parallel-courses-iii.py","file_name":"parallel-courses-iii.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73248859434","text":"s = input().split()[0]\n\ncycles = []\nfor i in range(len(s)):\n s = s[1::] + s[0]\n cycles.append(s)\ncycles.sort()\n\nres = ''\nfor i in cycles:\n res += i[-1]\n\nprint(res)","repo_name":"priamoryki/ITMO","sub_path":"semester-1/discrete-math/Lab-3/TaskB.py","file_name":"TaskB.py","file_ext":"py","file_size_in_byte":172,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"1228624345","text":"import json\r\nimport math\r\nimport shutil\r\nimport sys\r\nimport time\r\nimport traceback\r\nfrom json import JSONEncoder, JSONDecoder\r\nfrom time import sleep\r\nfrom typing import Any, Callable, List, Union\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom torch import nn\r\nimport os\r\nimport multiprocessing as mp\r\n\r\nimport torch\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom ids.generate_default_config import PARAM_CONFIG_FILE_NAME\r\nfrom ids.traffic_classifier import BusClassifier\r\nfrom ids.dataprocess import Dataset, set_log_output\r\nfrom ids.models.gru_1553B_bus_classifier import BusClassifierGruNet\r\nfrom ids.models.lstm_1553B_bus_classifier import BusClassifierLSTMNet\r\nfrom ids.utils import create_tensor, time_since, read_param_config, get_parameters, LogFilePath\r\n\r\n\r\ndef load_parameter_from_file(file_name):\r\n file_name = os.path.abspath(file_name)\r\n if not file_name:\r\n return\r\n elif not os.path.exists(file_name):\r\n print(\"Config file not exists.\")\r\n else:\r\n return read_param_config(file_name)\r\n\r\n\r\ndef auto_train(model_parameter: dict, output=sys.stdout):\r\n is_trained = False\r\n model_common_prefix = str(model_parameter['Index'])\r\n modal_file_name = model_common_prefix + '.pth'\r\n model_path = os.path.join(model_parameter['TrainedModelPath'], modal_file_name)\r\n if os.path.exists(model_path):\r\n print(\"Model already trained. Only test.\", file=output, flush=True)\r\n is_trained = True\r\n train_log_path = os.path.join(model_parameter['TrainLogPath'], model_common_prefix + '.txt')\r\n train_result_path = os.path.join(model_parameter['TrainResultPath'], model_common_prefix + '.result')\r\n test_log_path = os.path.join(model_parameter['TestLogPath'], model_common_prefix + '.txt')\r\n test_result_path = os.path.join(model_parameter['TestResultPath'], model_common_prefix + '.result')\r\n if is_trained:\r\n f_train_log = sys.stdout\r\n f_train_result = sys.stdout\r\n else:\r\n f_train_log = open(train_log_path, 'w')\r\n f_train_result = open(train_result_path, 'w')\r\n f_test_log = open(test_log_path, 'w')\r\n f_test_result = open(test_result_path, 'w')\r\n try:\r\n # 数据读取\r\n # Use Class Dataset to preprocess raw data\r\n print(\"Reading Dataset......\", file=output, end='', flush=True)\r\n dataset = Dataset(model_parameter['TrainDatasetPath'], look_back=model_parameter['SeqLen'])\r\n # dataset = Dataset(model_parameter['TrainDatasetPath'], look_back=model_parameter['SeqLen'], is_binary=True)\r\n class_labels = dataset.get_label_classes()\r\n train_set, test_set = dataset.get_train_test_data(proportion=0.8, generator=527)\r\n print(\"Success\", file=output, flush=True)\r\n\r\n # Create DataLoader For Pytorch\r\n print(\"Generating DataLoader......\", file=output, end='', flush=True)\r\n train_loader = DataLoader(train_set, batch_size=model_parameter['BatchSize'], num_workers=0, drop_last=True)\r\n test_loader = DataLoader(test_set, batch_size=model_parameter['BatchSize'], shuffle=True, num_workers=0,\r\n pin_memory=False)\r\n print(\"Success\", file=output, flush=True)\r\n\r\n N_MESS = dataset.get_feature_num()\r\n N_TYPE = dataset.get_label_num()\r\n if N_TYPE == 2:\r\n N_TYPE = 1\r\n # Create Bus Classifier\r\n print(\"Defining Model......\", file=output, end='', flush=True)\r\n model_type = model_parameter['TypeName']\r\n if \"GRU\" in model_type:\r\n classifier = BusClassifierGruNet(input_size=N_MESS, hidden_size=model_parameter['HiddenSize'],\r\n output_size=N_TYPE, batch_first=True, num_layers=model_parameter['Layer'],\r\n bidirectional=model_parameter['Bidirectional'],\r\n dropout=model_parameter['DropRate']) # 定义模型\r\n elif model_type == \"LSTM\":\r\n classifier = BusClassifierLSTMNet(input_size=N_MESS, hidden_size=model_parameter['HiddenSize'],\r\n output_size=N_TYPE, batch_first=True, num_layers=model_parameter['Layer'],\r\n bidirectional=model_parameter['Bidirectional'],\r\n dropout=model_parameter['DropRate']) # 定义模型\r\n else:\r\n print(\"Unsupported Model Type.\", file=output, flush=True)\r\n return\r\n model_active_func = model_parameter['ActiveFunction']\r\n if model_active_func == 'ReLU':\r\n classifier.af = nn.ReLU()\r\n elif model_active_func == 'Tanh':\r\n classifier.af = nn.Tanh()\r\n elif model_active_func == 'Sigmoid':\r\n classifier.af = nn.Sigmoid()\r\n print(\"Success\", file=output, flush=True)\r\n\r\n classifier = create_tensor(classifier)\r\n # 定义损失函数criterion，使用交叉熵损失函数\r\n print(\"Defining Loss Function......\", file=output, end='', flush=True)\r\n if N_TYPE > 2:\r\n criterion = torch.nn.CrossEntropyLoss()\r\n else:\r\n criterion = torch.nn.BCEWithLogitsLoss()\r\n print(\"Success\", file=output, flush=True)\r\n # 梯度下降使用的Adam算法\r\n print(\"Defining optimizer......\", file=output, end='', flush=True)\r\n opt_choice = model_parameter['Optimizer']\r\n lr = model_parameter['LearningRate']\r\n if opt_choice == 'Adam':\r\n optimizer = torch.optim.Adam(classifier.parameters(), lr=lr)\r\n elif opt_choice == 'NAdam':\r\n optimizer = torch.optim.NAdam(classifier.parameters(), lr=lr)\r\n elif opt_choice == 'RMSprop':\r\n optimizer = torch.optim.RMSprop(classifier.parameters(), lr=lr)\r\n elif opt_choice == 'Adagrad':\r\n optimizer = torch.optim.Adagrad(classifier.parameters(), lr=lr)\r\n elif opt_choice == 'Adadelta':\r\n optimizer = torch.optim.Adadelta(classifier.parameters(), lr=lr)\r\n elif opt_choice == 'Adamax':\r\n optimizer = torch.optim.Adamax(classifier.parameters(), lr=lr)\r\n else:\r\n print(f'Wrong optimizer:{opt_choice}', file=output, flush=True)\r\n return\r\n print(\"Success\", file=output, flush=True)\r\n bc = BusClassifier(classifier, num_epoch=model_parameter['EpochNum'], criterion=criterion, optimizer=optimizer,\r\n model_path=model_path, train_log_output=f_train_log, train_result_output=f_train_result,\r\n test_log_output=f_test_log, test_result_output=f_test_result)\r\n bc.set_class_labels(class_labels)\r\n bc.set_test_path(model_parameter['TestLogPath'])\r\n bc.set_model_name(model_common_prefix)\r\n\r\n train_result = None\r\n if not is_trained:\r\n print(\"Start training......\", file=output, flush=True)\r\n train_result = bc.trainModel(train_loader)\r\n print(\"Success\", file=output, flush=True)\r\n print(\"Start evaluating......\", file=output, flush=True)\r\n test_result = bc.eval_model(test_loader)\r\n print(\"Success\", file=output, flush=True)\r\n except Exception as e:\r\n print(e)\r\n traceback.print_exc()\r\n return\r\n finally:\r\n if not is_trained:\r\n f_train_log.close()\r\n f_train_result.close()\r\n f_test_log.close()\r\n f_test_result.close()\r\n return train_result, test_result\r\n\r\n\r\nSAVE_FILE_NAME = 'Result.xlsx'\r\n\r\n\r\ndef save_results_to_file(results, is_binary=False):\r\n \"\"\"\r\n :param results: param,train result,test result\r\n :return:\r\n \"\"\"\r\n sz = len(results)\r\n\r\n r = []\r\n for param, train_result, test_result in results:\r\n res = test_result.copy()\r\n for k, v in param.items():\r\n if 'Train' in k: continue\r\n if 'Test' in k: continue\r\n if 'Log' in k: continue\r\n if 'Path' in k: continue\r\n if 'Result' in k: continue\r\n res[k] = v\r\n tc = res['TimeCost'].strip().split(' ')\r\n mnt = int(tc[0][:-1])\r\n sec = float(tc[1][:-1])\r\n tc = mnt * 60 + sec\r\n res['TimeCost'] = tc\r\n r.append(res)\r\n\r\n output = pd.DataFrame(r)\r\n output.to_excel(SAVE_FILE_NAME, index=False)\r\n\r\n\r\nif __name__ == '__main__':\r\n config_file_name = os.path.basename(PARAM_CONFIG_FILE_NAME)\r\n config = load_parameter_from_file(config_file_name)\r\n parsed_param = get_parameters(config)\r\n # copy config file to train and test log\r\n shutil.copyfile(config_file_name, os.path.join(parsed_param['TrainLogPath'], config_file_name))\r\n shutil.copyfile(config_file_name, os.path.join(parsed_param['TestLogPath'], config_file_name))\r\n common_param = parsed_param.copy()\r\n del common_param['Parameters']\r\n print(parsed_param)\r\n with open(LogFilePath, 'w') as main_log:\r\n set_log_output(main_log)\r\n start = time.time()\r\n count = 0\r\n results = []\r\n for p in parsed_param['Parameters']:\r\n p.update(common_param)\r\n count += 1\r\n print(f'Now is {time_since(start)}', file=main_log)\r\n print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())), file=main_log)\r\n print(f\"No {count} Model is training.\", file=main_log, flush=True)\r\n print(p, file=main_log, flush=True)\r\n ret = auto_train(p, output=main_log)\r\n if ret is not None:\r\n results.append((p, *ret))\r\n results.sort(key=lambda x: x[2]['Acc'], reverse=True)\r\n print('\\n\\n\\n\\n\\n', file=main_log)\r\n save_results_to_file(results)\r\n","repo_name":"MstKenway/DeepAnomalyDetection","sub_path":"DeepAnomalyDetectionWithPreciseDifferentiation/auto_train.py","file_name":"auto_train.py","file_ext":"py","file_size_in_byte":9707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"14169792199","text":"\"\"\"\nSearch backend. Most likely to be deprecated soon.\n\"\"\"\n\nimport operator\nimport warnings\n\nfrom django_elasticsearch_dsl import fields\nfrom elasticsearch_dsl.query import Q\nfrom rest_framework.filters import BaseFilterBackend\nfrom rest_framework.settings import api_settings\nimport six\n\nfrom ..mixins import FilterBackendMixin\nfrom ...compat import coreapi, coreschema\n\n__title__ = 'django_elasticsearch_dsl_drf.filter_backends.search.historical'\n__author__ = 'Artur Barseghyan '\n__copyright__ = '2017-2020 Artur Barseghyan'\n__license__ = 'GPL 2.0/LGPL 2.1'\n__all__ = ('SearchFilterBackend',)\n\n\nclass SearchFilterBackend(BaseFilterBackend, FilterBackendMixin):\n \"\"\"Search filter backend for Elasticsearch.\n\n Example:\n\n >>> from django_elasticsearch_dsl_drf.filter_backends import (\n >>> SearchFilterBackend\n >>> )\n >>> from django_elasticsearch_dsl_drf.viewsets import (\n >>> BaseDocumentViewSet,\n >>> )\n >>>\n >>> # Local article document definition\n >>> from .documents import ArticleDocument\n >>>\n >>> # Local article document serializer\n >>> from .serializers import ArticleDocumentSerializer\n >>>\n >>> class ArticleDocumentView(BaseDocumentViewSet):\n >>>\n >>> document = ArticleDocument\n >>> serializer_class = ArticleDocumentSerializer\n >>> filter_backends = [SearchFilterBackend,]\n >>> search_fields = (\n >>> 'title',\n >>> 'content',\n >>> )\n >>> search_nested_fields = {\n >>> 'state': ['name'],\n >>> 'documents.author': ['title', 'description'],\n >>> }\n \"\"\"\n\n search_param = api_settings.SEARCH_PARAM\n\n def get_search_query_params(self, request):\n \"\"\"Get search query params.\n\n :param request: Django REST framework request.\n :type request: rest_framework.request.Request\n :return: List of search query params.\n :rtype: list\n \"\"\"\n query_params = request.query_params.copy()\n return query_params.getlist(self.search_param, [])\n\n def construct_nested_search(self, request, view):\n \"\"\"Construct nested search.\n\n We have to deal with two types of structures:\n\n Type 1:\n\n >>> search_nested_fields = {\n >>> 'country': {\n >>> 'path': 'country',\n >>> 'fields': ['name'],\n >>> },\n >>> 'city': {\n >>> 'path': 'country.city',\n >>> 'fields': ['name'],\n >>> },\n >>> }\n\n Type 2:\n\n >>> search_nested_fields = {\n >>> 'country': {\n >>> 'path': 'country',\n >>> 'fields': [{'name': {'boost': 2}}]\n >>> },\n >>> 'city': {\n >>> 'path': 'country.city',\n >>> 'fields': [{'name': {'boost': 2}}]\n >>> },\n >>> }\n\n :param request: Django REST framework request.\n :param queryset: Base queryset.\n :param view: View.\n :type request: rest_framework.request.Request\n :type queryset: elasticsearch_dsl.search.Search\n :type view: rest_framework.viewsets.ReadOnlyModelViewSet\n :return: Updated queryset.\n :rtype: elasticsearch_dsl.search.Search\n \"\"\"\n if not hasattr(view, 'search_nested_fields'):\n return []\n\n # TODO: Support query boosting\n\n query_params = self.get_search_query_params(request)\n __queries = []\n for search_term in query_params:\n for label, options in view.search_nested_fields.items():\n queries = []\n path = options.get('path')\n\n for _field in options.get('fields', []):\n\n # In case if we deal with structure 2\n if isinstance(_field, dict):\n # TODO: take options (such as boost) into consideration\n field = \"{}.{}\".format(path, _field['name'])\n # In case if we deal with structure 1\n else:\n field = \"{}.{}\".format(path, _field)\n\n field_kwargs = {\n field: search_term\n }\n\n queries.append(\n Q(\"match\", **field_kwargs)\n )\n\n __queries.append(\n Q(\n \"nested\",\n path=path,\n query=six.moves.reduce(operator.or_, queries)\n )\n )\n\n return __queries\n\n def construct_search(self, request, view):\n \"\"\"Construct search.\n\n We have to deal with two types of structures:\n\n Type 1:\n\n >>> search_fields = (\n >>> 'title',\n >>> 'description',\n >>> 'summary',\n >>> )\n\n Type 2:\n\n >>> search_fields = {\n >>> 'title': {'boost': 2},\n >>> 'description': None,\n >>> 'summary': None,\n >>> }\n\n :param request: Django REST framework request.\n :param queryset: Base queryset.\n :param view: View.\n :type request: rest_framework.request.Request\n :type queryset: elasticsearch_dsl.search.Search\n :type view: rest_framework.viewsets.ReadOnlyModelViewSet\n :return: Updated queryset.\n :rtype: elasticsearch_dsl.search.Search\n \"\"\"\n query_params = self.get_search_query_params(request)\n __queries = []\n for search_term in query_params:\n __values = self.split_lookup_name(search_term, 1)\n __len_values = len(__values)\n if __len_values > 1:\n field, value = __values\n if field in view.search_fields:\n # Initial kwargs for the match query\n field_kwargs = {field: {'query': value}}\n # In case if we deal with structure 2\n if isinstance(view.search_fields, dict):\n extra_field_kwargs = view.search_fields[field]\n if extra_field_kwargs:\n field_kwargs[field].update(extra_field_kwargs)\n # The match query\n __queries.append(\n Q(\"match\", **field_kwargs)\n )\n else:\n for field in view.search_fields:\n # Initial kwargs for the match query\n field_kwargs = {field: {'query': search_term}}\n\n # In case if we deal with structure 2\n if isinstance(view.search_fields, dict):\n extra_field_kwargs = view.search_fields[field]\n if extra_field_kwargs:\n field_kwargs[field].update(extra_field_kwargs)\n\n # The match query\n __queries.append(\n Q(\"match\", **field_kwargs)\n )\n return __queries\n\n def filter_queryset(self, request, queryset, view):\n \"\"\"Filter the queryset.\n\n :param request: Django REST framework request.\n :param queryset: Base queryset.\n :param view: View.\n :type request: rest_framework.request.Request\n :type queryset: elasticsearch_dsl.search.Search\n :type view: rest_framework.viewsets.ReadOnlyModelViewSet\n :return: Updated queryset.\n :rtype: elasticsearch_dsl.search.Search\n \"\"\"\n warnings.warn(\n \"{} is deprecated. Switch to `CompoundSearchFilterBackend`.\"\n \"\".format(\n self.__class__.__name__\n )\n )\n __queries = self.construct_search(request, view) + \\\n self.construct_nested_search(request, view)\n\n if __queries:\n queryset = queryset.query('bool', should=__queries)\n return queryset\n\n def get_coreschema_field(self, field):\n if isinstance(field, fields.IntegerField):\n field_cls = coreschema.Number\n else:\n field_cls = coreschema.String\n return field_cls()\n\n def get_schema_fields(self, view):\n assert coreapi is not None, 'coreapi must be installed to ' \\\n 'use `get_schema_fields()`'\n assert coreschema is not None, 'coreschema must be installed to ' \\\n 'use `get_schema_fields()`'\n\n _search_fields = getattr(view, 'search_fields', None)\n if isinstance(_search_fields, dict):\n search_fields = list(_search_fields.keys())\n else:\n search_fields = _search_fields\n\n return [] if not search_fields else [\n coreapi.Field(\n name=self.search_param,\n required=False,\n location='query',\n schema=coreschema.String(\n description='Search in '\n '{}.'.format(', '.join(search_fields))\n )\n )\n ]\n","repo_name":"barseghyanartur/django-elasticsearch-dsl-drf","sub_path":"src/django_elasticsearch_dsl_drf/filter_backends/search/historical.py","file_name":"historical.py","file_ext":"py","file_size_in_byte":9200,"program_lang":"python","lang":"en","doc_type":"code","stars":360,"dataset":"github-code","pt":"72"}
+{"seq_id":"18131946904","text":"\"\"\"\npart 1: anyone answered yes\npart 2: everyone answered yes\n\n\"\"\"\n\n\nif __name__ == \"__main__\":\n part_1_count, part_2_count = 0, 0\n groups=open(\"input.txt\").read().split(\"\\n\\n\")\n for entry in groups:\n part_1_count+=len(set(entry.replace(\"\\n\", \"\")))\n part_2_count+=len(set.intersection(*map(set, entry.split())))\n print(part_1_count, part_2_count)\n\n\n\n","repo_name":"bethlamacchia/aoc2020","sub_path":"day6/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"32414361939","text":"\"\"\"This module makes a dynamic array class without the use of a built-in list class\"\"\"\n\nimport ctypes\nimport sys\n#We're using the ctypes library to make a raw array\n\nclass DynamicArray(object):\n \"\"\"A dynamic list class\"\"\"\n\n def __init__(self):\n \"\"\"initiates the list\"\"\"\n self.element_count = 0 # the initial list is empty\n self.capacity = 1 # by default the list can only accept 1 element\n self.a_list = self.make_array(self.capacity)\n\n def __len__(self):\n \"\"\"returns the number of elements stored in the list\"\"\"\n return self.element_count\n\n def __getitem__(self,k):\n \"\"\"return the item at the given index, k\"\"\"\n if not 0 <= k < self.element_count: # if the index passed isn't between 0 and the length of the array\n return IndexError(\"K is out of bounds!\") # we need to return an error\n\n return self.a_list[k]\n\n def append(self,element):\n \"\"\"add an element to the end of the list\"\"\"\n # if our list is at capacity, we need to make it larger before we add another element\n\n if self.element_count == self.capacity:\n self._resize(2*self.capacity) # double the capacity\n\n self.a_list[self.element_count] = element\n self.element_count += 1\n\n def _resize(self, new_capacity):\n \"\"\"resize the initial array to the new capacity\"\"\"\n b_list = self.make_array(new_capacity) # <~ new, bigger array\n\n for k in range(self.element_count): # reference all existing values from the initial array into the new one\n b_list[k] = self.a_list[k]\n\n self.a_list = b_list # make the initial array into the bigger array\n self.capacity = new_capacity # set our new bigger capacity\n\n def make_array(self, new_capacity):\n \"\"\"return our array object\"\"\"\n return (new_capacity * ctypes.py_object)()\n\narr = DynamicArray()\n\nfor i in range(10):\n print(len(arr), sys.getsizeof(arr))\n arr.append(i)\n","repo_name":"Laurens-GitHub/Data-Structures-Algorithms","sub_path":"Array Sequences/Other/Dynamic_Array.py","file_name":"Dynamic_Array.py","file_ext":"py","file_size_in_byte":1967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"8850306439","text":"import os\nimport re\nimport sys\nimport numpy as np\nfrom typing import TypeVar, NamedTuple, Callable, Mapping, Tuple\n\n\nsys.path.append('../')\nsys.path.append('../MDP')\nfrom MDP.mdp import MDP, MDP_RL, Policy\nimport MDP.mdpUtils as mu\nfrom MDP.rlAlgorithms import MDPAlgorithmRL, MonteCarloEG\nfrom MDP.mdpAlgorithms import MDPAlgorithm, ValueIteration \n\n\nP = TypeVar('P')\n\n'''\nFINANCIAL APPLICATION #1 - Merton's Portfolio Optimization Problem\nSee e.g. https://en.wikipedia.org/wiki/Merton%27s_portfolio_problem\n\n\nFROM CLASS NOTES:\nThink of this as a continuous-time Stochastic Control problem\nThe State is (t, Wt)\nThe Action is [πt, ct]\nThe Reward per unit time is U(ct)\nThe Return is the usual accumulated discounted Reward\nFind Policy : (t, Wt) → [πt, ct] that maximizes the Expected Return\nNote: ct ≥ 0, but πt is unconstrained\n'''\n \n\nclass MertonProblem():\n\n def __init__(self, **kwargs) -> None:\n '''\n Constructor. Declare the parameters that we will be using\n to solve this portfolio optimization model \n ''' \n\n self.rho: float = kwargs[\"rho\"] # Discount rate\n self.T: float = kwargs[\"T\"] # Expiry value\n self.r: float = kwargs[\"r\"] # Riskless rate (return for the riskless asset)\n\n self.W0: float = kwargs[\"W0\"] # Initial wealth\n self.mu: np.ndarray = kwargs[\"mu\"] # Means of the risky rate (1-D array of length = # of Risky Assets)\n self.cov: np.ndarray = kwargs[\"cov\"] # Risky rate covariants (2-D square array of length = # of Risky Assets)\n self.numRiskyAssets = len(self.mu) # The number of risky assets that we have defined for the problem\n\n self.epsilon: float = kwargs[\"epsilon\"] # Bequest parameter for B(T)\n self.gamma: float = kwargs[\"gamma\"] # Parameter for CRRA utility function\n\n self.SIM_TIME = kwargs[\"SIM_TIME\"] # Total simulation time\n\n def utilityFunc(self, x: float) -> float:\n '''\n Utility function for the problem. For Merton's problem we use the \n Constant Relative-Risk Aversion model represented by the gamma param\n '''\n p = 1. - self.gamma\n if p == 0:\n result = np.log(x)\n else:\n result = x**p/p\n return result\n\n def getCFOptAllocation(self) -> np.ndarray:\n '''\n Calculates the closed-form solution of the optimal\n stock allocation among the different assets, π(W,t).\n Note however that the function takes no arguments since in the closed-form\n the optimal allocation depends on neither Wealth nor time\n '''\n # Handle the 1D case without matrix operations\n if self.cov.shape == (1,1):\n result = (1/self.cov[0][0])*(self.mu[0]-self.r)/self.gamma\n else:\n result = np.linalg.inv(self.cov).dot(self.mu-self.r)/self.gamma\n return result\n\n\n def getNu(self) -> float:\n '''\n Helper method to calculate the nu-parameter used in \n the defintion of the closed-form solution\n '''\n t1 = (self.mu - self.r).dot(self.getCFOptAllocation())/2*self.gamma\n t2 = self.r/self.gamma\n return (self.rho/self.gamma) - self.gamma*(t1+t2)\n\n\n def getCFOptConsumption(self, t: float) -> float: \n '''\n Calculates the closed-form solution of the optimal fraction\n of wealth to consume at a given time step\n '''\n nu = self.getNu()\n\n if nu == 0:\n optCons = 1./(self.T - t + self.epsilon)\n else:\n optCons = nu / (1. + (nu * self.epsilon - 1) *\n np.exp(-nu * (self.T - t))) \n return optCons\n\n\n def makeDiscretizedMDP(self) -> MDP:\n '''\n Takes the information prescribed for this Merton object\n and builds a discretized MDP out of it so that it can be solved\n using dynamic programming.\n\n To do this we will make several simplifying assumptions:\n - Discretize the state- and action-space so we can solve using a tabular method\n - Since we can't model stochastic dynamics we will make the risky asset only have two possible\n outcomes (+mu and -mu), with 50/50 probability. This assumption lets us directly prescribe\n transition probabilities into the data structure \n \n '''\n mdpData = dict()\n states = set()\n actions = set()\n terminalStates = set()\n\n # Discretize the state- and action-spaces\n # State-space discretization. We will discretize only up to\n # the max limit of growth of wealth by 5 stdevs of the highest value\n # of risky asset return over the total simulation time\n wStep = 0.01\n Wmax = round(self.W0 + (5*(np.max(self.cov)+np.max(self.mu))*self.SIM_TIME),2)\n N = (Wmax // wStep) + 2\n w = 0\n\n for w in np.linspace(0.,Wmax,N):\n for t in range(self.SIM_TIME+1):\n if t == self.SIM_TIME:\n terminalStates.add((round(w,2), t))\n else:\n states.add((round(w,2), t))\n\n # Action-space discretization\n actionStep = 0.01\n aN = (1//actionStep) + 1\n for pi in np.linspace(0, 1, aN):\n for c in np.linspace(0, 1, aN):\n actions.add((pi,c))\n\n # Fill out the dict with our discretized state- and action-spaces. \n for state in states:\n mdpData[state] = dict()\n for action in actions:\n W, t = state\n pi, c = action\n mdpData[state][action] = dict()\n\n # Determine the two possible successor states, for each of the possible\n # two return values of the risky asset.\n # the net of our returns from all assets, less the amount we invested (c_t*W_t)\n # (Note that is NOT the same as reward, which is computed using the utility function) \n wPos = round(W + W*c*((1-pi)*self.r + pi*self.mu[0]), 2) \n wNeg = round(W + W*c*((1-pi)*self.r - pi*self.mu[0]), 2) \n # Assign reward of the final wealth if it's a terminal state. Otherwise\n # the reward is zero\n if t == self.SIM_TIME:\n tNew = t + 1\n rPos = self.utilityFunc(wPos)\n rNeg = self.utilityFunc(wNeg)\n else:\n tNew = t\n rPos = 0\n rNeg = 0\n\n succPos = (wPos, tNew)\n succNeg = (wNeg, tNew)\n # Populate dict\n if state not in terminalStates:\n mdpData[state][action][succPos] = (0.5, rPos) \n mdpData[state][action][succNeg] = (0.5, rNeg) \n else:\n mdpData[state][action][state] = (1.0, 0)\n\n return MDP(mdpData, (1-self.rho))\n\n\n def makeRLMDP(self) -> MDP_RL:\n '''\n Helper method used to transform the parameter definitions for this Merton problem\n into an MDP representation that can be solved using RL. Here we will prescribe a\n state-action dict, a dynamics model, and a set of terminal states.\n '''\n states = set()\n terminalStates = set()\n actions = set()\n stateActionDict = dict()\n\n # Discretize the state- and action-spaces\n # State-space discretization. We will discretize only up to\n # the max limit of growth of wealth by 5 stdevs of the highest value\n # of the risky asset return realized for each step of the total simulation time\n wStep = 0.01\n Wmax = round(self.W0 + (5*(np.max(self.cov)+np.max(self.mu))*self.SIM_TIME),2)\n N = (Wmax // wStep) + 2\n w = 0\n print(Wmax)\n for w in np.linspace(0.,Wmax,N):\n for t in range(self.SIM_TIME+1):\n if t == self.SIM_TIME:\n terminalStates.add((round(w,2), t))\n else:\n states.add((round(w,2), t))\n print(states)\n\n # Action-space discretization\n actionStep = 0.01\n aN = (1//actionStep) + 1\n for pi in np.linspace(0, 1, aN):\n for c in np.linspace(0, 1, aN):\n actions.add((pi,c))\n\n # Form the state-action dictionary\n for state in states:\n stateActionDict[state] = set()\n for action in actions:\n stateActionDict[state].add(action)\n\n\n # Declare the dynamics model to increment the state\n def dynamics(state, action):\n W, t = state\n pi, c = action\n\n # Increment time forward\n tNew = t + 1\n\n # Increment wealth forward based on action\n wNew = W + W*c*((1-pi)*self.r + pi*np.sum(np.random.multivariate_normal(self.mu, self.cov, 1))) \n wNew = round(wNew, 2)\n\n # Form the new successor state\n succState = (wNew, tNew)\n\n # If we move into a terminal state, calculate the terminal utility\n # as reward. Otherwise, reward is zero\n if tNew == self.SIM_TIME:\n reward = self.utilityFunc(wNew)\n else:\n reward = 0\n\n return succState, reward\n\n return MDP_RL(stateActionDict, terminalStates, dynamics)\n\n\nif __name__ == \"__main__\":\n # Declare params for a simple case with one risky asset\n # and one riskless asset\n params = dict()\n params[\"T\"] = 0.4\n params[\"rho\"] = 0.04\n params[\"r\"] = 0.04\n\n params[\"W0\"] = 1.00\n params[\"mu\"] = np.array([0.08])\n params[\"cov\"] = np.array([[0.0009]])\n\n params[\"epsilon\"] = 1e-8\n params[\"gamma\"] = 0.2\n\n params[\"SIM_TIME\"] = 5\n\n # Instantiate the object\n mp = MertonProblem(**params)\n\n # First, calculate the closed-form solution over our time range\n # for both allocation (constant) and consumption (return the optimal\n # consumption fraction for each timestep)\n optAlloc = mp.getCFOptAllocation()\n optCons = [mp.getCFOptConsumption(t*mp.T/mp.SIM_TIME) for t in range(mp.SIM_TIME)]\n # print(optAlloc) \n # print(optCons) \n\n # RL Solution\n ############################################\n # # Create the RL MDP representation\n # rl_mdp = mp.makeRLMDP()\n\n # # Make a random policy out of the info\n # polData = dict()\n # for state in rl_mdp.stateActionDict.keys():\n # polData[state] = dict()\n # numActions = len(rl_mdp.stateActionDict[state])\n # for action in rl_mdp.stateActionDict[state]:\n # polData[state][action] = 1/numActions\n # startPol = Policy(polData)\n \n # # Instantiate the MC solver\n # mc_eg = MonteCarloEG(rl_mdp, 1-params[\"rho\"])\n \n # startState = (params[\"W0\"], 0)\n\n # # Run simulation and perform model-free policy iteration\n # # using EG policy improvement\n # policy, optValue = mc_eg.simulate_eg(startState, startPol, 100)\n\n # # Report the results\n # for state in policy.polData.keys():\n # maxAct, _ = mu.maximizeOverDict(policy.polData[state])\n # print(maxAct)\n\n ############################################\n\n\n # Discrete MDP solution\n ############################################\n # Create the MDP representation\n mdp = mp.makeDiscretizedMDP()\n\n # Solve using value iteration\n vi = ValueIteration(1e-8)\n vi.solve(mdp)\n\n print(vi.V)\n print(vi.pi)\n\n\n","repo_name":"anatu/Stanford-CME241-W2020","sub_path":"financial_applications/merton_v2.py","file_name":"merton_v2.py","file_ext":"py","file_size_in_byte":11514,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"8670256671","text":"import sys\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\n\nfrom neutron._i18n import _\nfrom neutron.agent import dhcp_agent\nfrom neutron.cmd.sanity import checks\nfrom neutron.common import config\nfrom neutron.conf.agent import common as agent_config\nfrom neutron.conf.agent import securitygroups_rpc\nfrom neutron.conf import common as common_config\nfrom neutron.conf.db import l3_hamode_db\nfrom neutron.conf.plugins.ml2 import config as ml2_conf\nfrom neutron.conf.plugins.ml2.drivers import linuxbridge as lb_conf\nfrom neutron.conf.plugins.ml2.drivers.mech_sriov import agent_common as \\\n sriov_conf\nfrom neutron.conf.plugins.ml2.drivers import ovs_conf\n\n\nLOG = logging.getLogger(__name__)\n\n\ndef setup_conf():\n config.register_common_config_options()\n ovs_conf.register_ovs_agent_opts(cfg.CONF)\n lb_conf.register_linuxbridge_opts(cfg.CONF)\n sriov_conf.register_agent_sriov_nic_opts(cfg.CONF)\n ml2_conf.register_ml2_plugin_opts(cfg.CONF)\n securitygroups_rpc.register_securitygroups_opts(cfg.CONF)\n dhcp_agent.register_options(cfg.CONF)\n l3_hamode_db.register_db_l3_hamode_opts(cfg.CONF)\n common_config.register_core_common_config_opts(cfg.CONF)\n\n\nclass BoolOptCallback(cfg.BoolOpt):\n def __init__(self, name, callback, **kwargs):\n if 'default' not in kwargs:\n kwargs['default'] = False\n self.callback = callback\n super(BoolOptCallback, self).__init__(name, **kwargs)\n\n\ndef check_ovs_vxlan():\n result = checks.ovs_vxlan_supported()\n if not result:\n LOG.error('Check for Open vSwitch VXLAN support failed. '\n 'Please ensure that the version of openvswitch '\n 'being used has VXLAN support.')\n return result\n\n\ndef check_ovs_geneve():\n result = checks.ovs_geneve_supported()\n if not result:\n LOG.error('Check for Open vSwitch Geneve support failed. '\n 'Please ensure that the version of openvswitch '\n 'and kernel being used has Geneve support.')\n return result\n\n\ndef check_iproute2_vxlan():\n result = checks.iproute2_vxlan_supported()\n if not result:\n LOG.error('Check for iproute2 VXLAN support failed. Please ensure '\n 'that the iproute2 has VXLAN support.')\n return result\n\n\ndef check_ovs_patch():\n result = checks.patch_supported()\n if not result:\n LOG.error('Check for Open vSwitch patch port support failed. '\n 'Please ensure that the version of openvswitch '\n 'being used has patch port support or disable features '\n 'requiring patch ports (gre/vxlan, etc.).')\n return result\n\n\ndef check_read_netns():\n required = checks.netns_read_requires_helper()\n if not required and cfg.CONF.AGENT.use_helper_for_ns_read:\n LOG.warning(\"The user that is executing neutron can read the \"\n \"namespaces without using the root_helper. Disable \"\n \"the use_helper_for_ns_read option to avoid a \"\n \"performance impact.\")\n # Don't fail because nothing is actually broken. Just not optimal.\n result = True\n elif required and not cfg.CONF.AGENT.use_helper_for_ns_read:\n LOG.error(\"The user that is executing neutron does not have \"\n \"permissions to read the namespaces. Enable the \"\n \"use_helper_for_ns_read configuration option.\")\n result = False\n else:\n # everything is configured appropriately\n result = True\n return result\n\n\n# NOTE(ihrachyshka): since the minimal version is currently capped due to\n# missing hwaddr matching in dnsmasq < 2.67, a better version of the check\n# would actually start dnsmasq server and issue a DHCP request using a IPv6\n# DHCP client.\ndef check_dnsmasq_version():\n result = checks.dnsmasq_version_supported()\n if not result:\n LOG.error('The installed version of dnsmasq is too old. '\n 'Please update to at least version %s.',\n checks.get_minimal_dnsmasq_version_supported())\n return result\n\n\ndef check_ovs_qos_direct_ports_supported():\n result = checks.ovs_qos_direct_port_supported()\n if not result:\n LOG.error('The installed version of OVS does not support '\n 'QoS rules for direct ports. '\n 'Please update to version %s or newer.',\n checks.get_ovs_version_for_qos_direct_port_support())\n return result\n\n\ndef check_dnsmasq_local_service_supported():\n result = checks.dnsmasq_local_service_supported()\n if not result:\n LOG.error('The installed version of dnsmasq is too old. '\n 'Please update to a version supporting the '\n '--local-service option.')\n return result\n\n\ndef check_keepalived_ipv6_support():\n result = checks.keepalived_ipv6_supported()\n if not result:\n LOG.error('The installed version of keepalived does not support '\n 'IPv6. Please update to at least version 1.2.10 for '\n 'IPv6 support.')\n return result\n\n\ndef check_keepalived_garp_on_sighup_support():\n result = checks.keepalived_garp_on_sighup_supported()\n if not result:\n LOG.error('The installed version of keepalived may not support '\n 'sending gratious ARP on SIGHUP, which may delay '\n 'dataplane downtime during HA router failover. '\n 'Please use at least version 1.2.20 which support '\n 'sending garp on SIGHUP.')\n return result\n\n\ndef check_dibbler_version():\n result = checks.dibbler_version_supported()\n if not result:\n LOG.error('The installed version of dibbler-client is too old. '\n 'Please update to at least version %s.',\n checks.get_minimal_dibbler_version_supported())\n return result\n\n\ndef check_nova_notify():\n result = checks.nova_notify_supported()\n if not result:\n LOG.error('Nova notifications are enabled, but novaclient is not '\n 'installed. Either disable nova notifications or '\n 'install python-novaclient.')\n return result\n\n\ndef check_arp_responder():\n result = checks.arp_responder_supported()\n if not result:\n LOG.error('Check for Open vSwitch ARP responder support failed. '\n 'Please ensure that the version of openvswitch '\n 'being used has ARP flows support.')\n return result\n\n\ndef check_arp_header_match():\n result = checks.arp_header_match_supported()\n if not result:\n LOG.error('Check for Open vSwitch support of ARP header matching '\n 'failed. ARP spoofing suppression will not work. A '\n 'newer version of OVS is required.')\n return result\n\n\ndef check_icmpv6_header_match():\n result = checks.icmpv6_header_match_supported()\n if not result:\n LOG.error('Check for Open vSwitch support of ICMPv6 header '\n 'matching failed. ICMPv6 Neighbor Advt spoofing (part '\n 'of arp spoofing) suppression will not work. A newer '\n 'version of OVS is required.')\n return result\n\n\ndef check_ovsdb_native():\n result = checks.ovsdb_native_supported()\n if not result:\n LOG.error('Check for native OVSDB support failed.')\n return result\n\n\ndef check_ovs_conntrack():\n result = checks.ovs_conntrack_supported()\n if not result:\n LOG.error('Check for Open vSwitch support of conntrack support '\n 'failed. OVS/CT firewall will not work. A newer '\n 'version of OVS (2.5+) and linux kernel (4.3+) are '\n 'required. See '\n 'https://github.com/openvswitch/ovs/blob/master/FAQ.md '\n 'for more information.')\n return result\n\n\ndef check_gre_conntrack():\n result = checks.gre_conntrack_supported()\n if not result:\n LOG.warning('Kernel module %s is not loaded. GRE tunnels from '\n 'VM to VM will not work with OVS firewall driver.',\n checks.CONNTRACK_GRE_MODULE)\n return result\n\n\ndef check_ebtables():\n result = checks.ebtables_supported()\n if not result:\n LOG.error('Cannot run ebtables. Please ensure that it '\n 'is installed.')\n return result\n\n\ndef check_ipset():\n result = checks.ipset_supported()\n if not result:\n LOG.error('Cannot run ipset. Please ensure that it '\n 'is installed.')\n return result\n\n\ndef check_ip6tables():\n result = checks.ip6tables_supported()\n if not result:\n LOG.error('Cannot run ip6tables. Please ensure that it '\n 'is installed.')\n return result\n\n\ndef check_conntrack():\n result = checks.conntrack_supported()\n if not result:\n LOG.error('Cannot run conntrack. Please ensure that it '\n 'is installed.')\n return result\n\n\ndef check_dhcp_release6():\n result = checks.dhcp_release6_supported()\n if not result:\n LOG.error('No dhcp_release6 tool detected. The installed version '\n 'of dnsmasq does not support releasing IPv6 leases. '\n 'Please update to at least version %s if you need this '\n 'feature. If you do not use IPv6 stateful subnets you '\n 'can continue to use this version of dnsmasq, as '\n 'other IPv6 address assignment mechanisms besides '\n 'stateful DHCPv6 should continue to work without '\n 'the dhcp_release6 utility. '\n 'Current version of dnsmasq is ok if other checks '\n 'pass.',\n checks.get_dnsmasq_version_with_dhcp_release6())\n return result\n\n\ndef check_bridge_firewalling_enabled():\n result = checks.bridge_firewalling_enabled()\n if not result:\n LOG.error('Bridge firewalling is not enabled. It may be the case '\n 'that bridge and/or br_netfilter kernel modules are not '\n 'loaded. Alternatively, corresponding sysctl settings '\n 'may be overridden to disable it by default.')\n return result\n\n\ndef check_ip_nonlocal_bind():\n result = checks.ip_nonlocal_bind()\n if not result:\n LOG.error('This kernel does not isolate ip_nonlocal_bind kernel '\n 'option in namespaces. Please update to kernel '\n 'version > 3.19.')\n return result\n\n\ndef check_min_tx_rate_support():\n result = checks.min_tx_rate_support()\n if not result:\n LOG.warning('There are SR-IOV network interfaces that do not support '\n 'setting the minimum TX rate (dataplane enforced minimum '\n 'guaranteed bandwidth) \"ip-link vf min_tx_rate\".')\n return result\n\n\ndef check_ovn_nb_db_schema_port_group_support():\n result = checks.ovn_nb_db_schema_port_group_supported()\n if not result:\n LOG.warning('OVN NB DB schema does not support Port_Group. This '\n 'support was added in DB schema version 5.11.')\n return result\n\n\ndef check_ovn_nb_db_schema_stateless_nat():\n result = checks.ovn_nb_db_schema_stateless_nat_supported()\n if not result:\n LOG.warning('OVN NB DB schema does not support stateless NAT. This '\n 'support was added in DB schema version 5.17.')\n return result\n\n\ndef check_ovn_sb_db_schema_virtual_port():\n result = checks.ovn_sb_db_schema_virtual_port_supported()\n if not result:\n LOG.warning('OVN SB DB schema does not support virtual ports. This '\n 'support was added in DB schema version 2.5.')\n return result\n\n\ndef check_ovn_nb_db_schema_gateway_chassis():\n result = checks.ovn_nb_db_schema_gateway_chassis_supported()\n if not result:\n LOG.warning('OVN NB DB schema does not support \"Chassis_Gateway\" '\n 'table. This support was added in DB schema version 5.7.')\n return result\n\n\ndef check_ovn_localnet_learn_fdb_support():\n result = checks.ovn_localnet_learn_fdb_support()\n if not result:\n LOG.warning('OVN does not support localnet_learn_fdb option. '\n 'This support was added in OVN 22.09.')\n return result\n\n\n# Define CLI opts to test specific features, with a callback for the test\nOPTS = [\n BoolOptCallback('ovs_vxlan', check_ovs_vxlan, default=False,\n help=_('Check for OVS vxlan support')),\n BoolOptCallback('ovs_geneve', check_ovs_geneve, default=False,\n help=_('Check for OVS Geneve support')),\n BoolOptCallback('iproute2_vxlan', check_iproute2_vxlan, default=False,\n help=_('Check for iproute2 vxlan support')),\n BoolOptCallback('ovs_patch', check_ovs_patch, default=True,\n help=_('Check for patch port support')),\n BoolOptCallback('nova_notify', check_nova_notify,\n help=_('Check for nova notification support')),\n BoolOptCallback('arp_responder', check_arp_responder,\n help=_('Check for ARP responder support')),\n BoolOptCallback('arp_header_match', check_arp_header_match,\n help=_('Check for ARP header match support')),\n BoolOptCallback('icmpv6_header_match', check_icmpv6_header_match,\n help=_('Check for ICMPv6 header match support')),\n BoolOptCallback('read_netns', check_read_netns,\n help=_('Check netns permission settings')),\n BoolOptCallback('dnsmasq_local_service_supported',\n check_dnsmasq_local_service_supported,\n help=_('Check for local-service support in dnsmasq')),\n BoolOptCallback('ovs_qos_direct_port_supported',\n check_ovs_qos_direct_ports_supported,\n help=_('Check if the ovs supports QoS for direct ports')),\n BoolOptCallback('dnsmasq_version', check_dnsmasq_version,\n help=_('Check minimal dnsmasq version'),\n deprecated_for_removal=True,\n deprecated_since='Pike'),\n BoolOptCallback('ovsdb_native', check_ovsdb_native,\n help=_('Check ovsdb native interface support')),\n BoolOptCallback('ovs_conntrack', check_ovs_conntrack,\n help=_('Check ovs conntrack support')),\n BoolOptCallback('gre_conntrack', check_gre_conntrack,\n help=_('Check if conntrack for gre tunnels traffic is '\n 'supported')),\n BoolOptCallback('ebtables_installed', check_ebtables,\n help=_('Check ebtables installation')),\n BoolOptCallback('keepalived_ipv6_support', check_keepalived_ipv6_support,\n help=_('Check keepalived IPv6 support')),\n BoolOptCallback('keepalived_garp_on_sighup_support',\n check_keepalived_garp_on_sighup_support,\n help=_('Check keepalived support sending garp on '\n 'SIGHUP.')),\n BoolOptCallback('dibbler_version', check_dibbler_version,\n help=_('Check minimal dibbler version'),\n deprecated_for_removal=True,\n deprecated_since='Pike'),\n BoolOptCallback('ipset_installed', check_ipset,\n help=_('Check ipset installation')),\n BoolOptCallback('ip6tables_installed', check_ip6tables,\n help=_('Check ip6tables installation')),\n BoolOptCallback('conntrack_installed', check_conntrack,\n help=_('Check conntrack installation')),\n BoolOptCallback('dhcp_release6', check_dhcp_release6,\n help=_('Check dhcp_release6 installation')),\n BoolOptCallback('bridge_firewalling', check_bridge_firewalling_enabled,\n help=_('Check bridge firewalling'),\n default=False),\n BoolOptCallback('ip_nonlocal_bind', check_ip_nonlocal_bind,\n help=_('Check ip_nonlocal_bind kernel option works with '\n 'network namespaces.'),\n default=False),\n BoolOptCallback('check_min_tx_rate_support', check_min_tx_rate_support,\n help=_('Check if the configured SR-IOV NICs support '\n 'the \"ip-link vf min_tx_rate\" parameter.'),\n default=False),\n BoolOptCallback('ovn_nb_db_schema_port_group_support',\n check_ovn_nb_db_schema_port_group_support,\n help=_('Check OVN NB DB schema support Port_Group'),\n default=False),\n BoolOptCallback('ovn_nb_db_schema_stateless_nat_support',\n check_ovn_nb_db_schema_stateless_nat,\n help=_('Check OVN NB DB schema support stateless NAT'),\n default=False),\n BoolOptCallback('ovn_sb_db_schema_virtual_port_support',\n check_ovn_sb_db_schema_virtual_port,\n help=_('Check OVN SB DB schema support virtual ports'),\n default=False),\n BoolOptCallback('ovn_nb_db_schema_gateway_chassis_support',\n check_ovn_nb_db_schema_gateway_chassis,\n help=_('Check OVN NB DB schema support Gateway_Chassis'),\n default=False),\n BoolOptCallback('ovn_localnet_learn_fdb_support',\n check_ovn_localnet_learn_fdb_support,\n help=_('Check OVN supports localnet_learn_fdb option'),\n default=False),\n]\n\n\ndef enable_tests_from_config():\n \"\"\"If a test can depend on configuration, use this function to set the\n appropriate CLI option to enable that test. It will then be possible to\n run all necessary tests, just by passing in the appropriate configs.\n \"\"\"\n\n cfg.CONF.set_default('arp_header_match', True)\n cfg.CONF.set_default('icmpv6_header_match', True)\n if 'vxlan' in cfg.CONF.AGENT.tunnel_types:\n cfg.CONF.set_default('ovs_vxlan', True)\n if 'geneve' in cfg.CONF.AGENT.tunnel_types:\n cfg.CONF.set_default('ovs_geneve', True)\n if ('vxlan' in cfg.CONF.ml2.type_drivers or\n cfg.CONF.VXLAN.enable_vxlan):\n cfg.CONF.set_default('iproute2_vxlan', True)\n if (cfg.CONF.notify_nova_on_port_status_changes or\n cfg.CONF.notify_nova_on_port_data_changes):\n cfg.CONF.set_default('nova_notify', True)\n if cfg.CONF.AGENT.arp_responder:\n cfg.CONF.set_default('arp_responder', True)\n if not cfg.CONF.AGENT.use_helper_for_ns_read:\n cfg.CONF.set_default('read_netns', True)\n if cfg.CONF.dhcp_driver == 'neutron.agent.linux.dhcp.Dnsmasq':\n cfg.CONF.set_default('dnsmasq_local_service_supported', True)\n cfg.CONF.set_default('dnsmasq_version', True)\n if cfg.CONF.l3_ha:\n cfg.CONF.set_default('keepalived_ipv6_support', True)\n cfg.CONF.set_default('ip_nonlocal_bind', True)\n if cfg.CONF.SECURITYGROUP.enable_ipset:\n cfg.CONF.set_default('ipset_installed', True)\n if cfg.CONF.SECURITYGROUP.enable_security_group:\n cfg.CONF.set_default('ip6tables_installed', True)\n if cfg.CONF.SECURITYGROUP.firewall_driver in (\n 'iptables',\n 'iptables_hybrid',\n ('neutron.agent.linux.iptables_firewall.'\n 'IptablesFirewallDriver'),\n ('neutron.agent.linux.iptables_firewall.'\n 'OVSHybridIptablesFirewallDriver'),\n ):\n cfg.CONF.set_default('bridge_firewalling', True)\n if cfg.CONF.SRIOV_NIC.physical_device_mappings:\n cfg.CONF.set_default('check_min_tx_rate_support', True)\n if 'ovn' in cfg.CONF.ml2.mechanism_drivers:\n cfg.CONF.set_default('ovn_nb_db_schema_port_group_support', True)\n cfg.CONF.set_default('ovn_nb_db_schema_stateless_nat_support', True)\n cfg.CONF.set_default('ovn_sb_db_schema_virtual_port_support', True)\n cfg.CONF.set_default('ovn_nb_db_schema_gateway_chassis_support', True)\n\n\ndef all_tests_passed():\n return all(opt.callback() for opt in OPTS if cfg.CONF.get(opt.name))\n\n\ndef main():\n setup_conf()\n cfg.CONF.register_cli_opts(OPTS)\n cfg.CONF.set_override('use_stderr', True)\n config.setup_logging()\n config.init(sys.argv[1:], default_config_files=[])\n agent_config.setup_privsep()\n\n if cfg.CONF.config_file:\n enable_tests_from_config()\n\n return 0 if all_tests_passed() else 1\n","repo_name":"openstack/neutron","sub_path":"neutron/cmd/sanity_check.py","file_name":"sanity_check.py","file_ext":"py","file_size_in_byte":20367,"program_lang":"python","lang":"en","doc_type":"code","stars":1353,"dataset":"github-code","pt":"72"}
+{"seq_id":"27682455567","text":"\nfrom django.urls import path,include\nfrom .views import department_details, list_departments, create_employee, edit_employee\n\nurlpatterns = [\n path('',department_details,name='details'),\n path('/',list_departments,name='list'),\n path('create/',create_employee,name='create_employee'),\n path('edit//',edit_employee,name='edit_employee')\n]","repo_name":"darimachine/SoftUniWebBasicsProjects","sub_path":"employees_app/employees_app/employees/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"4541596068","text":"import os\nfrom setuptools import setup, find_packages\nimport md_urlunshorten_users as app\n\n\ndef read(fname):\n try:\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n except IOError:\n return ''\n\n\nsetup(\n name=\"md-urlunshorten-users\",\n version=app.__version__,\n description=read('DESCRIPTION'),\n long_description=read('README.rst'),\n license='The MIT License',\n platforms=['OS Independent'],\n keywords='django, metrics, dashboard, widget, urlunshorten',\n author='Martin Brochhaus',\n author_email='mbrochh@gmail.com',\n url=\"https://github.com/bitmazk/md-urlunshorten-users\",\n packages=find_packages(),\n include_package_data=True,\n)\n","repo_name":"bitlabstudio/md-urlunshorten-users","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"27793012059","text":"from datetime import date, timezone\nfrom random import choice\nfrom typing import List\n\nfrom fastapi import APIRouter, Depends, UploadFile, status\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom db.db import get_session\nfrom exceptions import base as exceptions\nfrom models import base as models\nfrom schemas import base as schemas\nfrom services.db import (account_crud, transaction_crud, user_cashback_crud,\n user_crud)\n\nrouter = APIRouter()\n\n\n@router.post(\n '/get_user/',\n status_code=status.HTTP_200_OK,\n response_model=schemas.User\n)\nasync def get_user(\n user_credentials: schemas.UserCredentials,\n db: AsyncSession = Depends(get_session)\n) -> schemas.User:\n users: List[models.User] = await user_crud.all(db)\n user: models.User = choice(users)\n return schemas.User.from_orm(user)\n\n\n@router.post(\n '/get_user_by_photo/',\n status_code=status.HTTP_200_OK,\n response_model=schemas.User\n)\nasync def get_user_by_photo(\n file_in: UploadFile,\n db: AsyncSession = Depends(get_session)\n) -> schemas.User:\n users: List[models.User] = await user_crud.all(db)\n user: models.User = choice(users)\n return schemas.User.from_orm(user)\n\n\n@router.get( \n '/get_user_accounts/',\n status_code=status.HTTP_200_OK,\n response_model=List[schemas.Account]\n)\nasync def get_user_accounts(\n user_id: int,\n db: AsyncSession = Depends(get_session)\n) -> List[schemas.Account]:\n user: models.User | None = await user_crud.get(db, id=user_id)\n if not user:\n raise exceptions.UserNotFoundException\n accounts: List[models.Account] = await account_crud.filter_by(\n db=db, with_cards=True,\n with_bank=True, user_id=user_id\n )\n\n return [\n schemas.Account(\n bank=account.bank.name,\n number=account.number,\n cards=[\n schemas.Card(\n card_number=card.card_number\n )\n for card in account.cards\n ]\n )\n for account in accounts\n ]\n\n\n@router.post(\n '/get_account_cashbacks/',\n status_code=status.HTTP_200_OK,\n response_model=schemas.AccountWithCashbacks\n)\nasync def get_account_cashback(\n account: schemas.AccountRequest,\n db: AsyncSession = Depends(get_session)\n) -> schemas.AccountWithCashbacks:\n cashbacks = await user_cashback_crud.account_cashbacks(\n db=db,\n account_number=account.account_number,\n month=account.month\n )\n \n return schemas.AccountWithCashbacks(\n month=account.month,\n cashbacks=[\n schemas.AccountCashback(\n product_type=cashback.cashback.product_type,\n value=cashback.value\n )\n for cashback in cashbacks\n ]\n )\n\n\n@router.post(\n '/get_account_transactions/',\n status_code=status.HTTP_200_OK,\n response_model=schemas.AccountWithTransactions\n)\nasync def get_account_transactions(\n data: schemas.TransactionsRequest,\n db: AsyncSession = Depends(get_session)\n) -> schemas.AccountWithTransactions:\n transactions = await transaction_crud.get_account_transactions(\n db=db, account_number=data.account_number,\n start_datetime=data.start_datetime\n )\n\n return schemas.AccountWithTransactions(\n number=data.account_number,\n transactions=[\n schemas.Transaction.from_orm(transaction)\n for transaction in transactions\n ]\n )\n","repo_name":"AlexStr94/finodays","sub_path":"mock_server/api/v1/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"36776019578","text":"from django.contrib.auth import get_user_model\n\nUser = get_user_model()\n\nWIND_ALPHA_TABLE = (\n (0, 7),\n (.6, 10.5),\n (1.8, 16.7),\n (3.5, 24.2),\n (5.3, 32.4),\n (7.5, 40.6),\n (9.9, 49.5),\n (12.5, 58.2),\n (15.3, 68.7),\n (18.3, 79),\n (21.6, 91),\n (25.2, 105),\n (29, 116.3)\n)\n\n\nWEAR_CLO = (\n (0.3, 'Трусы, футболка, шорты, легкие носки, сандалии'),\n (0.6, 'Мужская одежда: рубашка с короткими рукавами, легкие брюки, легкие носки, обувь.'\n 'Женская одежда: Трусы, женское белье, чулки, платье, обувь'),\n (1.0, 'Мужская одежда: Трусы, рубашка, брюки, пиджак, носки, обувью'\n 'Женская одежда: Трусы, чулки, блузка, длинная юбка, пиджак, обувь'),\n (1.3, 'Нижняя одежда с длинными рукавами и штанинами, рубашка, '\n 'брюки, свитер, пиджак, носки, обувь'),\n (1.5, 'Нижняя одежда с короткими рукавами и штанинами, рубашка, '\n 'брюки, жилет, пиджак, пальто, носки, обувь'),\n (2.0, 'Нижняя одежда с короткими рукавами и штанинами, '\n 'рубашка, брюки, пиджак, '\n ' стеганая куртка и штаны, носки, обувь, шапка, перчатки'),\n (2.55, 'Нижняя одежда с длинными рукавами и штанинами, термозащитная куртка и брюки, '\n 'парка (аляска) с тяжелой подбивкой, штаны с тяжелой подбивкой, носки, обувь, шапка, перчатки')\n)\n\n\ndef get_alpha(wind_speed):\n l, r = -1, 12\n while r - l > 1:\n m = (r + l) // 2\n if WIND_ALPHA_TABLE[m][0] < wind_speed:\n l = m\n else:\n r = m\n return WIND_ALPHA_TABLE[r][1]\n\n\ndef get_general_wear_recomendation(CLO):\n l, r = -1, 6\n while r - l > 1:\n m = (r + l) // 2\n if CLO > WEAR_CLO[m][0]:\n l = m\n else:\n r = m\n return WEAR_CLO[r]\n","repo_name":"IgnatInyutsin/weather-app","sub_path":"backend/api/restapi/app/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"70004502312","text":"from bs4 import BeautifulSoup\nimport requests\nfrom requests.exceptions import ConnectionError, ChunkedEncodingError\nfrom langdetect import detect\nimport re\nfrom tqdm import trange\nimport os\n\nsongs_folder = '../songs/'\nfiles_skipped = 0\ntry:\n with open('songs_done.txt', 'r') as done_file:\n count = done_file.readline()\n count = int(count)\nexcept FileNotFoundError:\n count = -1\nwith open('links.txt', 'r') as f:\n links = f.readlines()\n for i in trange(len(links)):\n if i <= count:\n continue\n link = links[i]\n try:\n html = requests.get(link).text\n except ConnectionError or ChunkedEncodingError:\n continue\n soup = BeautifulSoup(html, 'html.parser')\n song = soup.find_all('p', {'class': 'songtext'})[0]\n song_filtered = song.get_text()\n try:\n lang = detect(song_filtered[:50])\n except Exception:\n continue\n if lang != 'en':\n with open('songs_done.txt', 'w') as done_file:\n done_file.write(str(i))\n continue\n song_title = re.findall(r'/s.*?.html', link)[0][1:-5]\n try:\n with open(songs_folder + song_title + '.txt', 'w') as sf:\n sf.write(song_filtered)\n except UnicodeEncodeError as err:\n print(song_title + lang)\n print('%d files skipped' % (files_skipped + 1))\n files_skipped += 1\n try:\n os.remove(songs_folder + song_title + '.txt')\n except FileNotFoundError:\n print('Skipped file is not deleted because it is not there')\n with open('songs_done.txt', 'w') as done_file:\n done_file.write(str(i))\n","repo_name":"bharadwajpro/Tf-Idf-","sub_path":"Assignment-1/scraping/getlyrics.py","file_name":"getlyrics.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"31538118825","text":"from .utils import grouper\n\n\ndef get_day_groups(trains):\n \"\"\"\n Get a list of all distinctive sets of days\n\n get_day_groups([\n Train(days={1, 2, 3}),\n Train(days={0, 1}),\n Train(days={4}),\n ])\n => [{0}, {1}, {2, 3}, {4}]\n \"\"\"\n day_sets = {frozenset(train.days) for train in trains}\n all_days = frozenset.union(*day_sets)\n groups = {all_days}\n\n for day_set in day_sets:\n couples = ((group & day_set, group - day_set) for group in groups)\n groups = {group for couple in couples for group in couple if group}\n\n return sorted(groups, key=tuple)\n\n\n# Fix: find trains of the previous day?\ndef get_next_train(train, trains):\n \"Get direct/earliest connection after a train\"\n trains = [t for t in trains if t.departure_time > train.arrival_time]\n if trains:\n return min(trains, key=lambda t: t.departure_time)\n\n\ndef get_prev_train(train, trains):\n \"Get direct/latest connection before a train\"\n trains = [t for t in trains if t.arrival_time < train.departure_time]\n if trains:\n return max(trains, key=lambda t: t.arrival_time)\n\n\ndef group_trains(trains, sorted_stops):\n \"\"\"\n Group trains by connections\n All trains that are connected belong to a same group\n Return sorted groups as lists of trains\n\n group_trains([\n Train('T', '#1', viridian=time(12, 30), pewter=time(13, 0)),\n Train('T', '#2', viridian=time(16, 0), pewter=time(16, 30)),\n Train('T', '#3', pewter=time(13, 30), cerulean=time(14, 30)),\n Train('T', '#4', pewter=time(17, 0), cerulean=time(18, 0)),\n ], ['viridian', 'pewter', 'cerulean'])\n => [\n [Train('T', '#1', ...), Train('T', '#3', ...)],\n [Train('T', '#2', ...), Train('T', '#4', ...)],\n ]\n \"\"\"\n trains_from_stop = {stop: [] for stop in sorted_stops}\n trains_to_stop = {stop: [] for stop in sorted_stops}\n\n with grouper() as g:\n for train in trains:\n # Groups are associated to a departure time\n # when merged, groups get associated to earlier departure times\n g.add(train, train.departure_time)\n trains_from_stop[train.departure].append(train)\n trains_to_stop[train.arrival].append(train)\n\n for stop in sorted_stops:\n # Connect each train with next one\n for train in trains_from_stop[stop]:\n next_train = get_next_train(train, trains_from_stop[train.arrival])\n if next_train:\n g.merge(train, next_train, merge_key=min)\n\n # Connect each train with previous one\n for train in trains_to_stop[stop]:\n prev_train = get_prev_train(train, trains_to_stop[train.departure])\n if prev_train:\n g.merge(train, prev_train, merge_key=min)\n\n # Sort trains in groups by time of arrival & departure\n grouped_trains = {\n time: sorted(trains, key=lambda t: (t.departure_time, t.arrival_time))\n for time, trains in g.groups()\n }\n # Sort groups by departure time of the first train\n return [trains for _, trains in sorted(grouped_trains.items())]\n","repo_name":"entwanne/horaires_trains","sub_path":"train_schedule/group.py","file_name":"group.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"26257675637","text":"# This files contains your custom actions which can be used to run\n# custom Python code.\n#\n# See this guide on how to implement these action:\n# https://rasa.com/docs/rasa/core/actions/#custom-actions/\n\n\n# This is a simple example for a custom action which utters \"Hello World!\"\n\nfrom typing import Any, Text, Dict, List\n\nfrom rasa_sdk import Action, Tracker\nfrom rasa_sdk.executor import CollectingDispatcher\n\nimport mysql.connector\nimport traceback\n\nclass ActionHelloWorld(Action):\n\n def name(self) -> Text:\n return \"action_hello_world\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n query = \"select text from test_table where id = 1\"\n\n try:\n mydb = mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n passwd=\"123456789\",\n database=\"rasa\"\n )\n\n cursor = mydb.cursor()\n cursor.execute(query)\n\n results = cursor.fetchall()\n print(results)\n except:\n print(\"Error occured while connecting to database or fetching data from database. Error Trace: {}\".format(traceback.format_exc()))\n\n dispatcher.utter_message(text=\"Hello World!\",json_message=results)\n\n return []\n","repo_name":"NgocNam1512/Rasa_japanese","sub_path":"actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"14708564048","text":"import pandas as pd \nimport os\nimport numpy as np\nfrom pandas import DataFrame\n\n\n\nfile_path = 'C:\\\\Users\\\\andy4\\\\Downloads\\\\'\nbase_path = os.path.join(file_path, 'long_form.xlsx')\nfile_path = 'C:\\\\Users\\\\andy4\\\\Downloads\\\\test.xlsx'\ndf = pd.read_excel(base_path)\nall_data = np.array(df)\n\n\n\nPA_follow_count = [0] * 1389\nstore_list = []\n\ncounter = 0\ncounter1 = 0\n\ndata_store = [[\"1\" for _ in range(64)] for _ in range(1389)]\n\nfinal_data_store = [[\"1\" for _ in range(64)] for _ in range(15904)]\n\nfor i in range (0,15903):\n PA_follow_count[(int(all_data[i][0]) - 1)] = PA_follow_count[(int(all_data[i][0]) - 1)] + 1\n \n\nfor i in range (0,15903):\n if all_data[i][2] == 0 or all_data[i][2] == 1 :\n \n for j in range (2,64):\n data_store[counter][j] = all_data[i][j]\n \n counter = counter + 1\n\n \nfor i in range (0,1389):\n for j in range (0,PA_follow_count[i]):\n for k in range (0,64):\n final_data_store[counter1][k] = data_store[i][k]\n \n counter1 = counter1 + 1\n \n \nDataFrame(final_data_store).to_excel(file_path, sheet_name='test', index=False, header=True)\nprint('done.')\n","repo_name":"AndyFreeeeman/Excel_Data_Process","sub_path":"Excel_Duplicate.py","file_name":"Excel_Duplicate.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"19461030091","text":"import torch\nimport torch.nn as nn\nfrom fastai.vision.all import (\n L, Path, TensorCategory, Category,\n Transform, Pipeline, ToTensor, Normalize, Resize, IntToFloatTensor,\n PILImage, PILImageBW, Image, ResizeMethod, PadMode,\n Categorize,\n RandomSplitter,\n Datasets,\n Learner,\n get_image_files,\n show_image,\n Metric,\n TensorImage,\n load_learner,\n)\nimport random\nimport numpy as np\nimport re\n\n\ndef seed_all(seed_value):\n random.seed(seed_value)\n np.random.seed(seed_value)\n torch.manual_seed(seed_value)\n\n if torch.cuda.is_available():\n torch.cuda.manual_seed(seed_value)\n torch.cuda.manual_seed_all(seed_value)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n\ndef label_func(path: Path):\n \"\"\"\n Read label from file name\n :param path: path of file\n :return: label\n \"\"\"\n return list(path.name.split('.')[0])\n\n\nclass CategorizeList(Categorize):\n def __init__(self, vocab=None, add_na=False, blank='-'):\n super(CategorizeList, self).__init__(vocab=vocab, add_na=add_na, sort=False, )\n self.blank = blank\n\n @property\n def blank_idx(self):\n return self.vocab.o2i[self.blank]\n\n @property\n def n_classes(self):\n return len(self.vocab.items)\n\n def setups(self, dsets):\n dsets = sum(dsets, L(self.blank))\n super(CategorizeList, self).setups(dsets=dsets)\n\n def encodes(self, os):\n return TensorCategory([self.vocab.o2i[o] for o in os])\n\n def decodes(self, os, raw=False):\n s = ''.join([self.vocab[o] for o in os])\n if not raw:\n s = re.sub(self.blank, '', re.sub(r'(\\w)\\1+', r'\\1', s))\n\n return Category(s)\n\n\nclass BeforeBatchTransform(Transform):\n \"\"\"\n Resize image before create batch\n \"\"\"\n\n def __init__(self, height=32, width=32 * 5, keep_ratio=False, min_ratio=5.):\n super(BeforeBatchTransform, self).__init__()\n self.height, self.width = height, width\n self.keep_ratio, self.min_ratio = keep_ratio, min_ratio\n\n def encodes(self, items):\n images, *labels = zip(*items)\n\n height, width = self.height, self.width\n\n if self.keep_ratio:\n max_ratio = self.min_ratio\n for image in images:\n w, h = image.size\n max_ratio = max(max_ratio, w / h)\n width = int(np.floor(height * max_ratio))\n\n rs_tfm = Resize(size=(height, width), method=ResizeMethod.Pad, pad_mode=PadMode.Border)\n images = [rs_tfm(image) for image in images]\n return zip(images, *labels)\n\n\nclass CreateBatchTransform(Transform):\n \"\"\"\n Create batch\n \"\"\"\n\n def __init__(self):\n super(CreateBatchTransform, self).__init__()\n self.pipeline = Pipeline(funcs=[ToTensor, ])\n\n def encodes(self, items):\n images, *labels = zip(*items)\n\n # process images\n images = self.pipeline(images)\n xs = TensorImage(torch.stack(images, dim=0))\n\n # process labels\n if len(labels) > 0:\n ys = labels[0]\n y_lengths = torch.LongTensor([y.size(0) for y in ys])\n ys = torch.cat(ys, dim=0)\n return xs, (ys, y_lengths)\n return xs,\n\n\ndef conv_block(in_c, out_c, ks, stride, p, bn=False, leaky_relu=False):\n layers = list()\n layers.append(nn.Conv2d(\n in_channels=in_c,\n out_channels=out_c,\n kernel_size=ks,\n stride=stride,\n padding=p,\n ))\n\n if bn:\n layers.append(nn.BatchNorm2d(num_features=out_c))\n\n if leaky_relu:\n layers.append(nn.LeakyReLU(0.2, True))\n else:\n layers.append(nn.ReLU())\n return layers\n\n\nclass CNN(nn.Module):\n def __init__(self, in_channels=3, leaky_relu=False, ):\n super(CNN, self).__init__()\n ks = [3, 3, 3, 3, 3, 3, 2]\n s = [1, 1, 1, 1, 1, 1, 1]\n p = [1, 1, 1, 1, 1, 1, 0]\n c = [64, 128, 256, 256, 512, 512, 512]\n mp = [(2, 2), (2, 2), None, ((1, 2), 2), None, ((1, 2), 2), None]\n bn = [False, False, False, False, True, True, False]\n\n layers = []\n for i in range(len(ks)):\n in_c = in_channels if i == 0 else c[i - 1]\n layers.extend(\n conv_block(in_c=in_c, out_c=c[i], ks=ks[i], stride=s[i], p=p[i], bn=bn[i], leaky_relu=leaky_relu))\n if mp[i] is not None:\n kernel_size, stride = mp[i]\n layers.append(nn.MaxPool2d(kernel_size=kernel_size, stride=stride))\n\n self.cnn = nn.Sequential(*layers)\n\n def forward(self, x):\n out = self.cnn(x)\n return out\n\n\nclass RNN(nn.Module):\n def __init__(self, input_size, hidden_size, output_size, batch_first=False, bidirectional=True):\n super(RNN, self).__init__()\n self.input_size, self.hidden_size, self.output_size = input_size, hidden_size, output_size\n self.batch_first, self.bidirectional = batch_first, bidirectional\n\n self.lstm = nn.LSTM(\n input_size=input_size,\n hidden_size=hidden_size,\n bidirectional=bidirectional,\n batch_first=batch_first,\n )\n\n self.h2o = nn.Linear(in_features=hidden_size * 2 if bidirectional else hidden_size, out_features=output_size)\n\n def forward(self, x):\n out, _ = self.lstm(x)\n return self.h2o(out)\n\n\nclass CRNN(nn.Module):\n def __init__(self, in_channels, rnn_hidden_size, n_classes, leaky_relu=False):\n super(CRNN, self).__init__()\n self.cnn = CNN(in_channels=in_channels, leaky_relu=leaky_relu)\n self.rnn = nn.Sequential(\n RNN(\n input_size=512,\n hidden_size=rnn_hidden_size,\n output_size=rnn_hidden_size,\n batch_first=False,\n bidirectional=True\n ),\n RNN(\n input_size=rnn_hidden_size,\n hidden_size=rnn_hidden_size,\n output_size=n_classes,\n batch_first=False,\n bidirectional=True\n ),\n )\n\n def forward(self, x):\n cnn_out = self.cnn(x)\n cnn_out = cnn_out.squeeze(2).permute(2, 0, 1)\n rnn_out = self.rnn(cnn_out)\n return rnn_out\n\n\nclass CTCLoss(nn.Module):\n def __init__(self, blank=0):\n super(CTCLoss, self).__init__()\n self.ctc_loss = nn.CTCLoss(blank=blank, zero_infinity=True)\n\n def forward(self, output, target):\n T, N, C = output.size()\n target, target_lengths = target\n output_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long, device=output.device)\n output = output.log_softmax(2)\n return self.ctc_loss(output, target, output_lengths, target_lengths)\n\n\nclass AccMetric(Metric):\n def __init__(self):\n self.y_true, self.y_pred = [], []\n\n def reset(self):\n self.y_true, self.y_pred = [], []\n\n def accumulate(self, learn):\n label_categorize = learn.dls.tfms[1][-1]\n yb_pred = learn.pred.permute(1, 0, 2).argmax(dim=2)\n (yb, y_lengths), = learn.yb\n\n yb = torch.split(yb, y_lengths.cpu().tolist())\n self.y_true.extend([label_categorize.decode(y, raw=True) for y in yb])\n\n self.y_pred.extend([label_categorize.decode(y) for y in yb_pred])\n\n @property\n def value(self):\n # print(self.y_pred[:4], self.y_true[:4])\n n_correct = (np.array(self.y_pred) == np.array(self.y_true)).sum()\n return n_correct / float(len(self.y_true))\n\n @property\n def name(self):\n return 'accuracy'\n\n\n","repo_name":"thanhlt998/ocr_captcha_crnn","sub_path":"crnn.py","file_name":"crnn.py","file_ext":"py","file_size_in_byte":7568,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"72"}
+{"seq_id":"2947878758","text":"import pandas as pd\nimport time\n\nfrom scrapy import Selector\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import Select\n\nclass AspenWebcrawlerCPS(object):\n\n def __init__(self):\n # Set Website to Crawl\n url = \"https://aspen.cps.edu/aspen/logon.do\"\n\n # ---------REGULAR BROWSER---------------------------------\n # self.browser = webdriver.Chrome('/usr/local/bin/chromedriver')\n #\n # ---------HEADLESS BROWSER---------------------------------\n chrome_options = webdriver.ChromeOptions()\n chrome_options.add_argument('headless')\n self.browser = webdriver.Chrome('/usr/local/bin/chromedriver',\n chrome_options=chrome_options\n )\n # ---------NAVIGATE TO WEBSITE---------------------------------\n self.browser.get(url)\n\n def login_aspen_website(self, aspen_username, aspen_password):\n \"\"\"\n\n :param aspen_username: This is the same CPS username used to log into cps email address\n :param aspen_password: This is the same CPS password used to log into cps email address\n :return: will log you into aspen website\n \"\"\"\n time.sleep(1)\n\n # fill in username and Password and click submit\n username = self.browser.find_element_by_name(\"username\")\n username.clear()\n username.send_keys(aspen_username)\n\n password = self.browser.find_element_by_name(\"password\")\n password.clear()\n password.send_keys(aspen_password)\n\n self.browser.find_element_by_id(\"logonButton\").click()\n\n time.sleep(2)\n\n def select_view(self, view):\n \"\"\"\n\n :param view: (str) this parameter takes one of three strings - \"school\", \"staff\" or \"health\".\n :return: will navigate to chosen view in aspen.\n \"\"\"\n # Selects view button\n self.browser.find_element_by_id(\"contextMenu\").click()\n\n if view.lower() == \"school\":\n self.browser.find_element_by_id(\"contextSchool\").click()\n\n elif view.lower() == \"health\":\n self.browser.find_element_by_id(\"contextHealth\").click()\n\n elif view.lower() == \"staff\":\n self.browser.find_element_by_id(\"contextStaff\").click()\n else:\n raise Exception(\"Incorrect Selection: must choose 'school', 'health', or 'staff', view\")\n\n def select_school(self, school):\n \"\"\"\n\n :param school: this parameter takes one of four strings - \"ACADEMY\", \"ASCEND\", \"BLOOM\" or \"ONE\"\n :return: wll navigate to that school page\n \"\"\"\n window_before = self.browser.window_handles[0]\n\n self.browser.find_element_by_xpath(\n '//*[@id=\"header\"]/table[1]/tbody/tr/td[3]/div/table/tbody/tr/td[2]/div/a').click()\n\n window_after = self.browser.window_handles[1]\n self.browser.switch_to.window(window_after)\n\n time.sleep(2)\n\n if school.lower() == \"academy\":\n self.browser.find_element_by_id(\"skl01000000769\").click()\n\n elif school.lower() == \"ascend\":\n self.browser.find_element_by_id(\"skl01000000088\").click()\n\n elif school.lower() == \"bloom\":\n self.browser.find_element_by_id(\"skl01000000817\").click()\n elif school.lower() == \"one\":\n self.browser.find_element_by_id(\"skl01000000853\").click()\n else:\n raise Exception(\"Incorrect Selection: must choose academy, ascend, bloom, or one\")\n\n self.browser.find_element_by_id(\"okButton\").click()\n\n self.browser.switch_to.window(window_before)\n\n def select_tab(self, top_tab_selection):\n \"\"\"\n\n :param top_page_tab: (str) can choose one of the top tabs in the school view.\n :return: Navigates to a tab within school view\n \"\"\"\n time.sleep(2)\n\n self.browser.find_element_by_id(\"topTabs\")\n self.browser.find_element_by_link_text(top_tab_selection.capitalize()).click()\n\n time.sleep(2)\n\n def select_filter(self, student_group):\n \"\"\"\n :student_group: (str) filter selection (either \"active\" which is \"All Active Students\" or \"former\" which is \"Former Students\"\n :return:\n \"\"\"\n\n # choose either active or former students\n self.browser.find_element_by_id(\"filterMenu\").click()\n\n time.sleep(1)\n\n if student_group.lower() == \"active\":\n self.browser.find_element_by_xpath('//*[@id=\"filterMenu_Option1\"]/td[2]').click()\n elif student_group.lower() == \"former\":\n self.browser.find_element_by_xpath('//*[@id=\"filterMenu_Option7\"]/td[2]').click()\n else:\n raise Exception(\"Not a valid selection\")\n\n\nclass StudentIdentifyingInfo(AspenWebcrawlerCPS):\n\n def __init__(self):\n AspenWebcrawlerCPS.__init__(self)\n\n def build_quick_report_students_tab(self, selection_list):\n \"\"\"\n\n :selection_list: (list) list of report parameters you would like to choose\n :return:\n \"\"\"\n\n # click report menu\n self.browser.find_element_by_xpath('//*[@id=\"reportsMenu\"]').click()\n\n # switch windows (quick report opens up new window)\n window_before = self.browser.window_handles[0]\n\n # select \"quick report\"\n self.browser.find_element_by_xpath(\n '/html/body/form/table/tbody/tr[2]/td/div/table[2]/tbody/tr[1]/td[2]/table[1]/tbody/tr/td[2]/div[2]/table[1]/tbody/tr[24]/td[2]').click()\n window_after = self.browser.window_handles[1]\n self.browser.switch_to.window(window_after)\n\n # click next button twice\n self.browser.find_element_by_id(\"wizNextButton\").click()\n self.browser.find_element_by_id(\"wizNextButton\").click()\n\n # Remove all fields that are already in selected field.\n select = Selector(text=self.browser.page_source)\n selected_fields = select.xpath('//*[@id=\"selectedFieldIds\"]/option//text()').extract()\n\n # collect list of all fields in \"Selected Fields\"\n selected_fields_converted = []\n for element in selected_fields:\n selected_fields_converted.append(element.strip())\n\n select = Select(self.browser.find_element_by_xpath('//*[@id=\"selectedFieldIds\"]'))\n\n # Remove element from list that you would like to leave in selected field\n # NOTE: This is done because 'School > Name' does not appear in available field if removed\n selected_fields_converted.remove('School > Name')\n\n for element in selected_fields_converted:\n select.select_by_visible_text(element)\n\n self.browser.find_element_by_id(\"removeButton\").click()\n\n # Choose elements and add to selected field\n select = Select(self.browser.find_element_by_xpath('//*[@id=\"availableFieldIds\"]'))\n\n for selection in selection_list:\n select.select_by_visible_text(selection)\n\n self.browser.find_element_by_id(\"addButton\").click()\n\n # Hit next button three times\n self.browser.find_element_by_id(\"wizNextButton\").click()\n self.browser.find_element_by_id(\"wizNextButton\").click()\n self.browser.find_element_by_id(\"wizNextButton\").click()\n\n # Click the finish button and switch to new popup window\n self.browser.find_element_by_id(\"finishButton\").click()\n window_final = self.browser.window_handles[1]\n self.browser.switch_to.window(window_final)\n\n # find table and save to html\n self.browser.find_element_by_xpath('/html/body/table')\n content = self.browser.page_source\n\n # read in html table to pd.dataframe\n custom_report = pd.read_html(content, flavor='html5lib', header=0)[0]\n\n return custom_report\n\n\nclass StudentAttendance(AspenWebcrawlerCPS):\n\n def __init__(self):\n AspenWebcrawlerCPS.__init__(self)\n\n def _cleanAttendance(self, attendance_file):\n \"\"\"\n\n :param attendance_file:\n :return:\n \"\"\"\n\n # Drop empty rows\n attendance_file.dropna(subset=['Student.1'], inplace=True)\n\n # Chose subset of columns to keep\n attendance_file = attendance_file[\n ['Student', 'Grade', 'Homeroom', 'Member', 'Present', 'Absent', 'Tardy', 'Dismiss']]\n\n # filter out renments of excel pagination\n attendance_file = attendance_file.loc[attendance_file['Student'] != \"CHARTER\"]\n attendance_file = attendance_file.loc[attendance_file['Student'] != \"Student\"]\n attendance_file = attendance_file.loc[~attendance_file['Student'].str.contains(\"Page [0-9]\", regex=True)]\n\n return (attendance_file)\n\n def pull_attendance_report(self):\n \"\"\"\n\n :return:\n \"\"\"\n\n # click report menu\n self.browser.find_element_by_xpath('//*[@id=\"reportsMenu\"]').click()\n\n # switch windows (quick report opens up new window)\n window_before = self.browser.window_handles[0]\n\n # click Student Membership under report menu\n self.browser.find_element_by_xpath('//*[@id=\"reportsMenu_Option11\"]/td[2]').click()\n window_after = self.browser.window_handles[1]\n self.browser.switch_to.window(window_after)\n\n # Format: Select CSV\n self.browser.find_element_by_id('format').click()\n self.browser.find_element_by_xpath('//*[@id=\"format\"]/option[2]').click()\n\n # Students to include: Select all students\n # self.browser.find_element_by_xpath('// *[ @ id = \"tab_0\"] / td[2] / select').click()\n # self.browser.find_element_by_xpath('//*[@id=\"tab_0\"]/td[2]/select/option[2]').click()\n\n\n # click run\n self.browser.find_element_by_xpath(\n '/html/body/table/tbody/tr[2]/td/form/table/tbody/tr[5]/td/button[1]').click()\n\n window_final = self.browser.window_handles[1]\n self.browser.switch_to.window(window_final)\n\n time.sleep(3)\n\n content = self.browser.page_source\n\n # read in html table to pd.dataframe\n custom_report = pd.read_html(content,\n flavor='html5lib',\n header=13)[0]\n\n custom_report_clean = self._cleanAttendance(custom_report)\n\n return (custom_report_clean)\n","repo_name":"kippchicago/aspen_webcrawler","sub_path":"aspen_webcrawler/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"35587711892","text":"import random\nimport time\nINVERSION = 0\n\n\ndef random_int_list(start, stop, length):\n start, stop = (int(start), int(stop)) if start <= stop else (\n int(stop), int(start))\n length = int(abs(length)) if length else 0\n random_list = []\n for i in range(length):\n random_list.append(random.randint(start, stop))\n return random_list\n\n\ndef INVERSION_NUM(list):\n n = 0\n for i in range(0, len(list)):\n for j in range(i, len(list)):\n if i < j and list[i] > list[j]:\n n += 1\n return n\n\n\ndef MERGE(list, p, q, r):\n global INVERSION\n L = list[p:q]\n R = list[q:r]\n i = j = 0\n while i < len(L) and j < len(R):\n if L[i] <= R[j]:\n list[p] = L[i]\n i += 1\n else:\n list[p] = R[j]\n INVERSION += q + j - p\n j += 1\n p += 1\n if i > j:\n for j in range(j, len(R)):\n list[p] = R[j]\n p += 1\n else:\n for i in range(i, len(L)):\n list[p] = L[i]\n p += 1\n\n\ndef MERGE_SORT(list, p, r):\n if p < r - 1:\n q = int((r - p) / 2 + p)\n MERGE_SORT(list, p, q)\n MERGE_SORT(list, q, r)\n MERGE(list, p, q, r)\n return INVERSION\n\nA = random_int_list(1, 10000, 100)\nB = A[:]\nstart = time.clock()\nprint(MERGE_SORT(B, 0, len(B)))\nend = time.clock()\nprint(\"MERGE: %f s\" % (end - start))\n\nB = A[:]\nstart = time.clock()\nprint(INVERSION_NUM(B))\nend = time.clock()\nprint(\"INVERSION: %f s\" % (end - start))\n","repo_name":"mixterjim/Learn","sub_path":"Python/CLRS/2-4_Inversion.py","file_name":"2-4_Inversion.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"26768210741","text":"import sqlite3\n\ncon = sqlite3.connect('pcfb.sqlite')\n\n## Creating the tables\n##con.execute(\"create table people (id integer primary key, name varchar, position varchar, phone varchar, office varchar);\")\n##con.execute(\"create table experiment ( id integer primary key, name varchar, researcher integer, description text, foreign key(researcher) references people(id))\")\n\ncon.execute('insert into people values (0, \\'Alice\\', \\'Research Director\\', 555-123-0001, \\'4b\\')')\ncon.execute('insert into people values (1, \\'Bob\\', \\'Research assistant\\', 555-123-0002, \\'17\\')')\ncon.execute('insert into people values (2, \\'Charles\\', \\'Research assistant\\', 555-123-0001, \\'24\\')')\ncon.execute('insert into people values (3, \\'David\\', \\'Research assistant\\', 555-123-0001, \\'8\\')')\ncon.execute('insert into people values (4, \\'Edwards\\', \\'Toadie\\', \\'None\\', \\'Basement\\')')\n\ncon.execute('insert into experiment values (0, \\'EBV Vaccine trial\\', 0, \\'A vaccine trial\\')')\ncon.execute('insert into experiment values (1, \\'Flu antibody study\\', 2, \\'Study of the morphology of flu antibodies\\')')\n\nr = con.execute('select * from people')\n\nfor i in r:\n print(i)\n\nr = con.execute('select p.name, e.name from people as p join experiment as e where e.researcher == p.id')\n\nfor i in r:\n print('Name: %s\\n\\tExperiment: %s' % (i[0],i[1]))\n\n## Write a script to add a new user and experiment to the database;\n## To add a new user:\ncon.execute('insert into people values (5, \\'New User\\', \\'New User\\', \\'New User\\', \\'New User\\')')\n\n## To add a new experiment:\ncon.execute('insert into experiment values (2, \\'New Experiment\\', 2, \\'New Experiment\\')')\n\n## Reassign her experiments to the new user\n## Take the experiments that were Alices and set it to a new user\ncon.execute('UPDATE experiment SET researcher = 5 WHERE id = 2;')\n\n## Remove Alice\ncon.execute('delete from people where name=\\'Alice\\'');\nprint('After deleting Alice')\n\nr = con.execute('select * from people')\n\nfor i in r:\n print(i)\n\n## Print out all of the experiment names with who owns each experiment.\nr = con.execute('select p.name, e.name from people as p join experiment as e where e.researcher == p.id')\n\nprint('Printing out experiments after deleting Alice: ')\nfor i in r:\n print('Name: %s\\n\\tExperiment: %s' % (i[0],i[1]))","repo_name":"sfidahussain/aliceIsGone","sub_path":"aliceIsGone.py","file_name":"aliceIsGone.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"34427044240","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Apr 15 17:44:20 2023\r\n\r\n@author: Jenyi\r\n\r\nFunctions for plotting UMAP and TSNEs\r\n\"\"\"\r\n\r\nimport os\r\nimport umap.umap_ as umap\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport matplotlib.colors as mcolors\r\n\r\ndef plot_umap(data, \r\n\t\t\t labels, \r\n\t\t\t title_list,\r\n\t\t\t save_path:str='./'):\r\n\t\r\n \"\"\"\r\n\tFor plotting UMAP.\r\n\tParameters:\r\n\t\tdata: data matrix of (# samples, # genes)\r\n\t\tlabels: cell label matrix of (# samples) containing cluster labels of each cell\r\n\t\ttitle_list: list with title for each plot\r\n\t\tsave_path: str path to save the UMAP to\r\n\t\t\r\n\tReturns:\r\n\t\tNone\r\n\t\r\n \"\"\"\r\n\r\n trans = umap.UMAP(n_neighbors=5, random_state=42).fit(data)\r\n color_list = list(mcolors.CSS4_COLORS.keys())\r\n\t\r\n fig, ax = plt.subplots(1, len(labels), figsize=(10,5))\r\n for i, l_arr in enumerate(labels):\r\n num_clusters = np.unique(l_arr)\r\n temp = zip(num_clusters, num_clusters)\r\n annotation_map = {k:v for k, v in temp} \t\t\r\n for n in num_clusters:\r\n temp_trans = trans.embedding_[np.where(l_arr == n)[0], :]\r\n ax[i].scatter(temp_trans[:, 0], temp_trans[:, 1], s= 5, color=color_list[int(n)], label=annotation_map[n])\r\n ax[i].axis('off')\r\n ax[i].set_title(title_list[i])\r\n\t\t\t\r\n plt.legend(bbox_to_anchor=(1.2, 1.00))\r\n\r\n plt.suptitle('UMAP of cell type clustering', fontsize=16) \r\n plt.savefig(os.path.join(save_path, 'umap.jpg'), dpi=60)\r\n\t\r\n return\r\n\r\n","repo_name":"jenyiw/MTG-SC-Clustering","sub_path":"scripts/plotumapFunctions.py","file_name":"plotumapFunctions.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"24620616954","text":"## Burst Image Restoration and Enhancement\n## Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Hsuan Yang\n## https://arxiv.org/abs/2110.03680\n\nimport os\n\n######################################## Pytorch lightning ########################################################\n\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom pytorch_lightning import Trainer, seed_everything\nseed_everything(13)\n\n######################################## Model and Dataset ########################################################\n\nfrom Network import BIPNet\nfrom datasets.zurich_raw2rgb_dataset import ZurichRAW2RGB\nfrom datasets.synthetic_burst_train_set import SyntheticBurst\nfrom torch.utils.data.dataloader import DataLoader\n\n##################################################################################################################\n\nlog_dir = './logs/Track_1/'\n\nclass Args:\n def __init__(self):\n self.image_dir = \"./Zurich-RAW-to-DSLR-Dataset\"\n self.model_dir = log_dir + \"saved_model\"\n self.result_dir = log_dir + \"results\"\n self.batch_size = 1\n self.num_epochs = 100\n self.lr = 1e-4\n self.burst_size = 14\n self.NUM_WORKERS = 6\n \nargs = Args()\n\n######################################### Data loader ######################################################\n\ndef load_data(image_dir, burst_size):\n\n train_zurich_raw2rgb = ZurichRAW2RGB(root=image_dir, split='train')\n train_dataset = SyntheticBurst(train_zurich_raw2rgb, burst_size=burst_size, crop_sz=384) \n train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True, drop_last=True, num_workers=args.NUM_WORKERS, pin_memory=True)\n\n test_zurich_raw2rgb = ZurichRAW2RGB(root=image_dir, split='test')\n test_dataset = SyntheticBurst(test_zurich_raw2rgb, burst_size=burst_size, crop_sz=384) \n test_loader = DataLoader(test_dataset, batch_size=1, num_workers=args.NUM_WORKERS, pin_memory=True)\n\n return train_loader, test_loader\n\n######################################### Load BIPNet ####################################################\n\nmodel = BIPNet()\nmodel.cuda()\nmodel.summarize()\n\nif not os.path.exists(args.model_dir):\n os.makedirs(args.model_dir, exist_ok=True) \n\n######################################### Training #######################################################\n\ntrain_loader, test_loader = load_data(args.image_dir, args.burst_size)\n\ncheckpoint_callback = ModelCheckpoint(\n monitor='val_psnr',\n dirpath=args.model_dir,\n filename='{epoch:02d}-{val_psnr:.2f}',\n save_top_k=3,\n save_last=True,\n mode='max',\n)\n\ntrainer = Trainer(gpus=-1,\n auto_select_gpus=True,\n accelerator='ddp',\n max_epochs=300,\n precision=16,\n gradient_clip_val=0.01,\n callbacks=[checkpoint_callback],\n benchmark=True,\n deterministic=True,\n val_check_interval=0.25,\n progress_bar_refresh_rate=100,\n profiler=\"advanced\")#,\n #resume_from_checkpoint = args.model_dir + './BIPNet.ckpt')\n\ntrainer.fit(model, train_loader, test_loader)\n","repo_name":"akshaydudhane16/BIPNet","sub_path":"Burst_Super_Resolution/BIPNet_Track_1_training.py","file_name":"BIPNet_Track_1_training.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","stars":116,"dataset":"github-code","pt":"72"}
+{"seq_id":"27224405054","text":"################################################################################\n\"\"\"\n@Time : 2020/12/19\n@File : db_client.py\n@Author : cenquanyu@baidu.com\n\"\"\"\nimport logging\nimport pymysql\nimport log\n\n\nclass DbClient(object):\n \"\"\"DB操作类\n \"\"\"\n def __init__(self, user, passwd):\n self.dbname = 'dbname'\n self.dbhost = 'xx.xx.xxx.xx'\n self.db = pymysql.connect(self.dbhost, user, passwd, self.dbname, charset='utf8')\n self.cursor = self.db.cursor()\n\n def execute_sql(self, sql):\n \"\"\"在db连接上执行sql语句\"\"\"\n self.cursor.execute(sql)\n\n def execute_sql_fetchall(self, sql):\n \"\"\"在db连接上执行sql语句, 并返回结果\"\"\"\n self.cursor.execute(sql)\n return self.cursor.fetchall()\n\n def execute_sql_fetchall_dictlist(self, sql):\n \"\"\"在db连接上执行sql语句, 并返回dictlist\"\"\"\n with self.db.cursor(cursor=pymysql.cursors.DictCursor) as cursor:\n cursor.execute(sql)\n return cursor.fetchall()\n\n def insert_all(self, table, *args, **kwargs):\n \"\"\"插入数据\"\"\"\n data_dict = kwargs\n length = 0\n ksql, vsql = '', ''\n for k, v in data_dict:\n if length == 0:\n ksql = \"INSERT INTO \" + table + \"(\" + k\n vsql = \"VALUES (\" + v\n else:\n ksql += ',' + k\n vsql += ',' + v\n length += 1\n ksql += \")\"\n vsql += \")\"\n sql = ksql + vsql\n try:\n self.db.ping(reconnect=True)\n self.execute_sql(sql)\n self.db.commit()\n logging.info('Insert data \\n')\n logging.info(data_dict)\n logging.info('---- Data Insert success ----')\n except Exception as e:\n logging.error(e)\n self.db.rollback()\n self.db.close()\n\n def insert(self, sql):\n \"\"\"插入数据(使用sql)\"\"\"\n try:\n self.db.ping(reconnect=True)\n self.execute_sql(sql)\n self.db.commit()\n except Exception as e:\n logging.error(e)\n self.db.rollback()\n self.db.close()\n\n def update(self, sql):\n \"\"\"更新数据(使用sql)\"\"\"\n try:\n self.db.ping(reconnect=True)\n self.execute_sql(sql)\n self.db.commit()\n except Exception as e:\n logging.error(e)\n self.db.rollback()\n self.db.close()\n\n def query_all(self, sql):\n \"\"\"查询数据(返回元组)\"\"\"\n squery = ''\n try:\n self.db.ping(reconnect=True)\n squery = self.execute_sql_fetchall(sql)\n except Exception as e:\n logging.error(e)\n self.db.close()\n\n return squery\n\n def query_dictlist(self, sql):\n \"\"\"查询数据(返回字典列表)\"\"\"\n squery = ''\n try:\n self.db.ping(reconnect=True)\n squery = self.execute_sql_fetchall_dictlist(sql)\n except Exception as e:\n logging.error(e)\n self.db.close()\n\n return squery","repo_name":"7Bcoding/unreachable_host_monitor","sub_path":"db_client.py","file_name":"db_client.py","file_ext":"py","file_size_in_byte":3127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"70444781993","text":"# encoding:UTF-8\nimport json\nimport os\nimport platform\nimport sys\nimport time\nfrom Queue import Queue\nfrom threading import Thread\nfrom time import sleep\n\nimport requests\n# 首先获取漫画的基本信息\nfrom pyquery import PyQuery as pq\nfrom requests.exceptions import ConnectionError, ReadTimeout, TooManyRedirects, RequestException\n\nimport init_proxy\nimport read_comic_to_db\n\nproxy_list = []\nurl = \"http://lofi.e-hentai.org\"\nheaders = {\n 'cache-control': \"no-cache\",\n 'postman-token': \"5bd3dab9-6df8-e0b8-a2e7-a714c104c74b\",\n 'cookie': 'xres=3'\n}\nindex = 0\n\n\ndef now():\n return time.strftime(\"[%Y-%m-%d %H:%M:%S]\", time.localtime(int(time.time())))\n\n\ndef init(proxy, page=1, max_page=20, use_proxy=True, dir_name=None):\n global index, proxy_list\n if dir_name is None:\n # 首先创建一个基于当前时间的文件夹\n dir_name = time.strftime(\"%Y-%m-%d %H%M%S\", time.localtime(int(time.time())))\n # 在Windows环境下 文件夹不允许带冒号\n if platform.system() == \"Windows\":\n dir_name = time.strftime(\"%Y-%m-%d %H%M%S\", time.localtime(int(time.time())))\n dir_name = \"data-\" + dir_name\n os.mkdir(dir_name)\n proxies = {\n \"http\": proxy,\n }\n while page < max_page:\n querystring = {\n \"page\": page,\n \"f_apply\": \"Search\",\n \"f_search\": \"Chinese\"\n }\n response = None\n while response is None:\n try:\n if use_proxy:\n response = requests.request(\"GET\", url, headers=headers, params=querystring, proxies=proxies,\n timeout=10)\n else:\n response = requests.request(\"GET\", url, headers=headers, params=querystring, timeout=10)\n if response.text.count('squid') > 0 or response.text.count(\"http://warning.or.kr\") > 0:\n print(now() + \"当前代理出现异常,切换代理\")\n raise ConnectionError(\"has been blocked\")\n if response.status_code != 200:\n print(now() + \"非正常返回结果,代码[\" + str(response.status_code) + \"],切换代理\")\n raise ConnectionError(\"has been blocked\")\n if response.text.count(\n 'Your IP address has been temporarily banned for excessive pageloads which indicates that you are using automated mirroring/harvesting software.') > 0:\n print(now() + \"当前代理已被Ban,切换代理\")\n raise ConnectionError(\"has been blocked\")\n print(now() + \"入侵成功!当前为第\" + str(page) + \"页\")\n except ConnectionError:\n # 如果代理连不上,换一个\n index += 1\n if index == len(proxy_list):\n index = 0\n init_proxy.create_proxy()\n print(now() + \"重新获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n print(\n now() + \"代理[\" + proxies.get('http') + \"]不可用,切换至[\" + proxy_list[index] + \"],当前为第\" + str(page) + \"页\")\n proxies = {\n \"http\": proxy_list[index]\n }\n continue\n except ReadTimeout:\n # 如果代理连不上,换一个\n index += 1\n if index == len(proxy_list):\n index = 0\n init_proxy.create_proxy()\n print(now() + \"重新获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n print(now() + \"代理[\" + proxies.get('http') + \"]超时,切换至[\" + proxy_list[index] + \"],当前为第\" + str(page) + \"页\")\n proxies = {\n \"http\": proxy_list[index]\n }\n continue\n # 获得漫画栏,对漫画栏的每个元素进行迭代 div id = ig\n comic_list = pq(response.text).find('.ig')\n comic_json_list = []\n list_len = 0\n for elements in comic_list:\n comic = read_basic_comic_info(elements)\n # 获取完成基本信息之后,就要遍历漫画的每张图片了\n # imgList = read_basic_comic_info()\n # comic[\"imgList\"] = json.dumps(imgList)\n comic_json_list.append(comic)\n list_len += 1\n print(now() + \"读取漫画信息中[\" + json.dumps(comic) + \"]\")\n\n comic_json_list = json.dumps(comic_json_list)\n print(now() + \"读取了 \" + str(list_len) + \" 个漫画 , 准备写入\")\n if list_len == 0:\n print(now() + \"读取漫画失败,重新读取\")\n continue\n # 最后将当前页的数据写回json\n with open(dir_name + \"/\" + str(page) + \".json\", 'w') as f:\n f.write(comic_json_list)\n page += 1\n print(now() + \"数据写入成功,等待睡眠2秒后进行下一页查询\")\n time.sleep(2)\n return dir_name\n\n\n# 生成一个Comic对象\ndef read_basic_comic_info(element):\n # 链接\n comic_link = pq(element).find(\"a\").eq(0).attr(\"href\")\n # 漫画ID\n comic_id = comic_link.split('/')[5]\n # 漫画标题\n comic_title = pq(element).find(\"a\").eq(1).text()\n # 漫画时间\n comic_date = pq(element).find('td .ik').eq(0).next().text().split(\"by\")[0]\n # 漫画分类\n comic_category = pq(element).find('td .ik').eq(1).next().text()\n # 漫画标签\n comic_tag = pq(element).find('td .ik').eq(2).next().text()\n # 漫画评分\n comic_rank = str(pq(element).find('td .ik').eq(3).next().text().count(\"*\"))\n # 漫画封面\n comic_cover = pq(element).find(\"a\").eq(0).children().attr(\"src\")\n\n dict = {\"comicLink\": comic_link, \"comicId\": comic_id, \"comicTitle\": comic_title, \"comicDate\": comic_date,\n \"comicCategory\": comic_category, \"comicTag\": comic_tag, \"comicRank\": comic_rank, \"comicCover\": comic_cover,\n \"imgList\": []}\n\n return dict\n\n\ndef read_comic_img_info(comic_link, headers, proxy, use_proxy=True):\n if not os.path.exists('ComicData') or not os.path.isdir('ComicData'):\n os.mkdir('ComicData')\n global index, proxy_list\n if len(proxy_list) == 0:\n print(now() + '代理列表为空，重新获取')\n init_proxy.create_proxy()\n print(now() + \"获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n comic_id = comic_link.split('/')[5]\n if os.path.exists('ComicData/' + comic_id + \".json\"):\n print(now() + \"漫画[\" + comic_id + \"]的数据已存在\")\n return\n print(now() + \"正在读取漫画[\" + comic_id + \"]的数据\")\n this_page_link = comic_link\n prev_page_link = ''\n proxy = {\n \"http\": proxy\n }\n page = 0\n img_list = []\n total_page = 999\n # 当上一页和当前页相同时,说明完结了,退出\n while this_page_link != prev_page_link:\n if this_page_link is None:\n break\n try:\n if use_proxy:\n response = requests.request(\"GET\", this_page_link, headers=headers, proxies=proxy, timeout=10)\n else:\n response = requests.request(\"GET\", this_page_link, headers=headers, timeout=10)\n # 代理出了问题\n if response.text.count('squid') or response.text.count(\"http://warning.or.kr\") > 0:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"当前代理出现异常,切换代理\")\n raise ConnectionError(\"has been blocked\")\n if response.status_code != 200:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"非正常返回结果,代码[\" + str(response.status_code) + \"],切换代理\")\n raise ConnectionError(\"has been blocked\")\n # print(now()+response.text)\n if page == 0:\n # 目录页 提取第一页的链接\n tmp = pq(response.text).find('.gi').eq(0).find('a').attr('href')\n if tmp is None:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"读取数据错误,重新读取,切换代理\")\n raise ConnectionError(\"has been blocked\")\n if tmp.count(\"509.gif\") > 0:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"当前代理已被Ban,重新读取,切换代理\")\n raise ConnectionError(\"has been blocked\")\n prev_page_link = this_page_link\n this_page_link = tmp\n page += 1\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"目录页读取完成,开始进入第一页\")\n else:\n # 图片页 提取下一页的链接\n img_link = pq(response.text)(\"#sm\").attr('src')\n # 图片页链接非法\n if img_link is None:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"读取数据错误,重新读取,切换代理\")\n raise ConnectionError(\"has been blocked\")\n if img_link.count(\"509.gif\") > 0:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"当前代理已被Ban,重新读取,切换代理\")\n raise ConnectionError(\"has been blocked\")\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"第\" + str(page) + \"页数据为[\" + img_link + \"]\")\n # 下载中\n download_count = 0\n result = download_img(img_link, comic_id)\n while download_count < 5 and not result:\n download_count += 1\n print(\n now() + \"读取漫画[\" + comic_id + \"]:\" + \"图片下载失败[\" + img_link + \"],重试第\" + str(download_count) + \"次中\")\n result = download_img(img_link, comic_id)\n if download_count >= 5 and not result:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"下载图片失败,重新读取,切换代理\")\n raise ConnectionError(\"has been blocked\")\n # 下载完成\n img_list.append(img_link)\n td = pq(response.text)('#ia').children().eq(0).children().children()\n total_page = int(td.eq(1).text().split('/')[1])\n tmp = td.eq(2).children().attr('href')\n prev_page_link = this_page_link\n this_page_link = tmp\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"第\" + str(page) + \"页读取完成,总共有\" + str(total_page) + \"页\")\n if total_page == page:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"当前漫画已读取完毕\")\n break\n if this_page_link is None:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"读取下一页出现错误\")\n raise ConnectionError(\"has been blocked\")\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"下一页为[\" + this_page_link + \"]\")\n page += 1\n except ConnectionError:\n # 如果代理连不上,换一个\n index += 1\n if index == len(proxy_list):\n index = 0\n init_proxy.create_proxy()\n print(now() + \"重新获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n if index >= len(proxy_list):\n index = 0\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"代理[\" + proxy.get('http') + \"]不可用,切换至[\" + proxy_list[index] +\n \"],当前为\" + (\"目录\" if page == 0 else \"第\" + str(page)) + \"页\")\n proxy = {\n \"http\": proxy_list[index]\n }\n continue\n except TooManyRedirects:\n # 如果代理连不上,换一个\n index += 1\n if index == len(proxy_list):\n index = 0\n init_proxy.create_proxy()\n print(now() + \"重新获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n if index >= len(proxy_list):\n index = len(proxy_list) - 1\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"代理[\" + proxy.get('http') + \"]链接不可用,切换至[\" + proxy_list[index] +\n \"],当前为\" + (\"目录\" if page == 0 else \"第\" + str(page)) + \"页\")\n proxy = {\n \"http\": proxy_list[index]\n }\n continue\n except ReadTimeout:\n # 如果代理连不上,换一个\n index += 1\n if index == len(proxy_list):\n index = 0\n init_proxy.create_proxy()\n print(now() + \"重新获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n if index >= len(proxy_list):\n index = len(proxy_list) - 1\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"代理[\" + proxy.get('http') + \"]超时,切换至[\" + proxy_list[index] +\n \"],当前为\" + (\"目录\" if page == 0 else \"第\" + str(page)) + \"页\")\n proxy = {\n \"http\": proxy_list[index]\n }\n continue\n except RequestException as e:\n # 如果代理连不上,换一个\n index += 1\n if index == len(proxy_list):\n index = 0\n init_proxy.create_proxy()\n print(now() + \"重新获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n if index >= len(proxy_list):\n index = len(proxy_list) - 1\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"出现未知异常 [{0:s}]\".format(e))\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"代理[\" + proxy.get('http') + \"]出现异常,切换至[\" + proxy_list[index] +\n \"],当前为\" + (\"目录\" if page == 0 else \"第\" + str(page)) + \"页\")\n proxy = {\n \"http\": proxy_list[index]\n }\n continue\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"数据查询成功,等待睡眠2秒后进行下一页查询\")\n time.sleep(2)\n if this_page_link == prev_page_link and total_page != page:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"漫画[\" + comic_id + \"]写入出现问题,页数不符\")\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"当前漫画所有页读取完成,进行写入\")\n json_list = json.dumps(img_list)\n with open(\"ComicData/\" + comic_id + \".json\", 'w') as f:\n f.write(json_list)\n print(now() + \"漫画[\" + comic_id + \"]写入完成\")\n\n\n# 下载图片\ndef download_img(img_link, comic_id):\n try:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"下载图片中[\" + img_link + \"]\")\n if not os.path.exists(\"ComicData/\" + comic_id):\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"创建目录[\" + comic_id + \"]\")\n os.mkdir(\"ComicData/\" + comic_id)\n file_name = img_link.split(\"/\")[-1]\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"文件名称为[\" + file_name + \"]\")\n r = requests.get(img_link, timeout=10)\n with open(\"ComicData/\" + comic_id + \"/\" + file_name, \"wb\") as code:\n code.write(r.content)\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"写入完成\")\n return True\n except ReadTimeout as e:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"下载超时 : [{0:s}]\".format(e))\n return False\n except Exception as e:\n print(now() + \"读取漫画[\" + comic_id + \"]:\" + \"下载出现异常 : [{0:s}]\".format(e))\n return False\n\n\n# 从头开始\ndef start(page=1, max_page=20):\n global proxy_list\n init_proxy.create_proxy()\n print(now() + \"获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n\n dir_name = init(proxy_list[index], page, max_page)\n print(now() + \"梳理获取的数据\")\n new_list = []\n for i in range(1, max_page - 1):\n with open(dir_name + '/' + str(i) + \".json\", 'r') as f:\n new_list += json.loads(f.read(-1))\n\n with open(dir_name + '/total.json', 'w') as f:\n f.write(json.dumps(new_list))\n print(now() + \"梳理完成\")\n\n # 将梳理结果写入数据库\n try:\n read_comic_to_db.driver(dir_name + '/total.json')\n except Exception:\n print(now() + \"写入数据库失败,跳过写入操作\")\n # 梳理完成后开始读取图片\n after(dir_name + '/total.json')\n\n\n# 从头开始\ndef multi_start(page=1, max_page=20):\n global proxy_list\n init_proxy.create_proxy()\n print(now() + \"获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n\n dir_name = init(proxy_list[index], page, max_page)\n print(now() + \"梳理获取的数据\")\n new_list = []\n for i in range(1, max_page - 1):\n with open(dir_name + '/' + str(i) + \".json\", 'r') as f:\n new_list += json.loads(f.read(-1))\n\n with open(dir_name + '/total.json', 'w') as f:\n f.write(json.dumps(new_list))\n print(now() + \"梳理完成\")\n\n # 将梳理结果写入数据库\n try:\n read_comic_to_db.driver(dir_name + '/total.json')\n except Exception:\n print(now() + \"写入数据库失败,跳过写入操作\")\n # 梳理完成后开始读取图片\n multi_thread_after(dir_name + '/total.json')\n\n\n# 读取每个漫画的图片\ndef after(total_json_file):\n global proxy_list\n init_proxy.create_proxy()\n print(now() + \"获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n data = ''\n # 将total数据全部读取\n with open(total_json_file, 'r') as f:\n for line in f.readlines():\n data += line.strip()\n data = json.loads(data)\n for comic in data:\n comic_json_after(comic)\n\n\n# 传入一个json对象，进行图片的读取\ndef comic_json_after(comic_json, get_proxy_again=False):\n global proxy_list, headers\n if get_proxy_again:\n init_proxy.create_proxy()\n print(now() + \"获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n read_comic_img_info(comic_json['comicLink'], headers, proxy_list[index])\n\n\n# 读取每个漫画的图片 多线程版本\ndef multi_thread_after(total_json_file):\n global proxy_list\n init_proxy.create_proxy()\n print(now() + \"获取代理成功\")\n with open('proxy.json', 'r') as f:\n proxy_list = json.loads(f.read(-1))\n data = ''\n # 将total数据全部读取\n with open(total_json_file, 'r') as f:\n for line in f.readlines():\n data += line.strip()\n json_list_data = json.loads(data)\n\n # q是任务队列\n q = Queue()\n # data是comic对象队列\n comic_list = Queue()\n # NUM是并发线程总数\n NUM = 15\n # JOBS是有多少任务,也就是comic_list的大小\n JOBS = 0\n\n # 在工作之前，将json_list_data的数据放入comic_list中\n for comic in json_list_data:\n JOBS += 1\n comic_list.put(comic)\n\n print(now() + \"要执行\" + str(JOBS) + \"个任务\")\n\n # 这个是工作进程，负责不断从队列取数据并处理\n def working():\n while True:\n comic_json = comic_list.get()\n print(now() + \"执行新任务:下载[\" + comic_json['comicId'] + \"]漫画\")\n comic_json_after(comic_json)\n sleep(1)\n q.task_done()\n\n # fork NUM个线程等待队列\n for i in range(NUM):\n print(now() + \"创建线程[\" + str(i) + \"]中\")\n t = Thread(target=working)\n t.setDaemon(True)\n t.start()\n # 把JOBS排入队列\n for i in range(JOBS):\n # print(now() + \"执行第[\" + str(i) + \"]个任务\")\n q.put(i)\n # 等待所有JOBS完成\n q.join()\n\n\nreload(sys)\nsys.setdefaultencoding('utf8')\n","repo_name":"Hope6537/hope-tactical-equipment","sub_path":"hope-python-script/comic_hentai/search_and_download_comic.py","file_name":"search_and_download_comic.py","file_ext":"py","file_size_in_byte":20539,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"72"}
+{"seq_id":"34065144999","text":"import cv2, sys\nimport numpy as np\nfrom openvino.inference_engine import IENetwork, IECore\n\nmodel_name = 'age-gender-recognition-retail-0013'\npath = \"./intel/age-gender-recognition-retail-0013/FP16/\"\nclasses = {\n 0: \"Girl\",\n 1: \"Boy\"\n}\n\nie = IECore() # Load CPU extenstion\nnet = ie.read_network(model=path+model_name+'.xml', \n weights=path+model_name+'.bin') # Read IR model\n\ninput_blob = next(iter(net.input_info)) # Get input name\n\nout_blob = []\nfor output in iter(net.outputs):\n out_blob.append(output)\n\nbatch, channel, height, width = net.input_info[input_blob].input_data.shape # Get input shape\n\nprint(\"Load IR to device\")\nexec_net = ie.load_network(network=net, device_name='CPU') # Load IR model to device\n\nprint(\"Start\")\ncap = cv2.VideoCapture(0)\nwhile True:\n ret, ori_image = cap.read()\n # cv2.imshow(\"input\", image) # Read image and manipulate\n ori_shape = ori_image.shape # Record original size\n image = cv2.resize(ori_image, (width, height)) # Resize to network image size\n t_image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW\n\n \"\"\"Infer!!!\"\"\"\n res = exec_net.infer(inputs={input_blob: t_image}) # Inference\n \n for blob in out_blob:\n\n if blob == \"age_conv3\":\n age_info = \"Age: {}\".format(res[blob][0][0][0][0]*100)\n print(age_info)\n else:\n prob = res[blob][0]\n gender_info = \"Gender: {}\".format(classes[np.argmax(prob)])\n print(gender_info)\n \n cv2.putText(ori_image, age_info+\"\\t\"+gender_info, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)\n cv2.imshow(\"results\", ori_image) \n \n if 0xFF & cv2.waitKey(1) == 27:\n break\n\ncv2.destroyAllWindows()\n","repo_name":"p513817/openvino-age-gender-py-sample","sub_path":"legacy/age_gender_recognition.py","file_name":"age_gender_recognition.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"21014147500","text":"from flask import Flask, request\nfrom flask_cors import CORS\nfrom pymongo import MongoClient\nimport json\n\nclient = MongoClient('localhost:27017')\ndb = client.cameras\n\napp = Flask(__name__)\nCORS(app)\n\n@app.route('/add_camera', methods=['POST'])\ndef add_camera():\n if request.method == 'POST':\n data = request.get_json(force=True)\n rtsp = data['rtsp']\n lip = data['lip']\n lport = data['lport']\n tport = data['tport']\n sip = data['sip']\n sport = data['sport']\n\n if sip == \"\" or tport == \"\":\n p = rtsp.replace(\"[ip]\", lip, 1)\n q = p.replace(\"[port]\", lport, 1)\n rtsp = q\n else:\n p = rtsp.replace(\"[ip]\", sip, 1)\n q = p.replace(\"[port]\", tport, 1)\n rtsp = q\n \n db.camera.insert_one(\n {\n \"rtsp\": rtsp,\n \"lip\": lip,\n \"lport\": lport,\n \"tport\": tport,\n \"sip\": sip,\n \"sport\": sport\n }\n )\n\n return \"success\"\n\n@app.route('/show_camera', methods=['GET'])\ndef show_camera():\n l = []\n data = db.camera.find()\n for d in data: \n l.append(d[\"rtsp\"])\n return json.dumps(l)\n\n@app.route('/delete_camera', methods=['POST'])\ndef delete_camera():\n if request.method == 'POST':\n data = request.get_json(force=True)\n rtsp_link = data['rtsp']\n \n db.camera.delete_many({\"rtsp\":rtsp_link})\n\n return \"success\"\n\nif __name__ == '__main__':\n app.run(host=\"10.138.0.32\", debug=True)\n","repo_name":"bipni/tunnel","sub_path":"gcp-instance/addcamera.py","file_name":"addcamera.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"73249393194","text":"def polynomial_mul(a, b):\n result = [0 for _ in range(len(a) + len(b) - 1)]\n for i in range(len(a)):\n for j in range(len(b)):\n result[i + j] += a[i] * b[j]\n return [i % MOD for i in result]\n\n\ndef get_result(a, q):\n global n, k\n while n >= k:\n for i in range(k, 2 * k):\n a[i] = sum((-q[j] * a[i - j]) % MOD for j in range(1, k + 1)) % MOD\n r = polynomial_mul(q, [(q[i] if i % 2 == 0 else -q[i]) % MOD for i in range(len(q))])\n a = [a[i] % MOD for i in range(len(a)) if i % 2 == n % 2]\n a.extend(0 for _ in range(2 * k - len(a)))\n q = [r[i] for i in range(len(r)) if i % 2 == 0]\n n //= 2\n return a[n]\n\n\nMOD = 104_857_601\nk, n = map(int, input().split())\nn -= 1\na = list(map(int, input().split()))\na.extend(0 for _ in range(2 * k - len(a)))\nc = [1, *map(lambda x: MOD - int(x), input().split())]\nprint(get_result(a, c))\n","repo_name":"priamoryki/ITMO","sub_path":"semester-4/discrete-math/Lab-1/TaskI.py","file_name":"TaskI.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"10859045019","text":"#求学生成绩平均数\n#\n# def main():\n# number = int(input('请输入学生人数: '))\n# names = [None] * number\n# scores = [None] * number\n# for index in range(len(names)):\n# names[index] = input('请输入第%d个学生的名字: ' % (index + 1))\n# scores[index] = float(input('请输入第%d个学生的成绩: ' % (index + 1)))\n# total = 0\n# for index in range(len(names)):\n# print('%s: %.1f分' % (names[index], scores[index]))\n# total += scores[index]\n# print('平均成绩是: %.1f分' % (total / number))\n#\n#\n# if __name__ == '__main__':\n# main()\n\ndef main():\n name = []\n sorce = []\n a = 0\n need = 'y'\n while need == 'y':\n a += 1\n # nam = input('请输入第%d个学生姓名' % (a))\n # name.append(nam)\n sorc = input('请输入第%d个学生成绩' % (a))\n sorce.append(sorc)\n # print(sorce)\n need = input('是否继续添加？y/n')\n\n total = 0\n for i in range(0, len(sorce)):\n total += int(sorce[i])\n print(\"平均成绩为: \", total // len(sorce))\n\n\nif __name__ == '__main__':\n main()\n\n","repo_name":"Lonong/Python_first","sub_path":"python/studentsys/sorce_mean.py","file_name":"sorce_mean.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"20723428256","text":"import os\nfrom urllib.parse import urlparse\nimport requests\n\n\ndef download_images(all_image_links, safe_folder):\n os.makedirs(safe_folder, exist_ok=True)\n for index, space_image_link in enumerate(all_image_links):\n path = urlparse(space_image_link).path\n extension = os.path.splitext(path)[1]\n image_name = f'space_image_{index}{extension}'\n filename = os.path.join(safe_folder, image_name)\n response = requests.get(space_image_link)\n response.raise_for_status()\n with open(filename, 'wb') as file:\n file.write(response.content)","repo_name":"remboinc/download-photos-from-NASA","sub_path":"download_images.py","file_name":"download_images.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"24673817084","text":"#Clock Assignment\nimport turtle\nimport datetime\nscreen=turtle.Screen()\nClock=turtle.Turtle()\n#screen setup\nscreen.bgcolor(\"light green\")\nscreen.title(\"Clock Assignment\")\nscreen.setup(width=1000, height=700)\npen=turtle.Turtle()\npen.speed(0)\npen.penup()\npen.goto(0,0)\n#Draw Layout of Clock\ndef draw_layout_of_clock(clock,x,y,radius, angle,color): #In this function the first arg is a positional argument\n clock.speed(0)\n clock.penup()\n clock.color(color)\n clock.begin_fill() #call the turtle to fill circle before drawn\n clock.goto(x+radius,y) # Move turtle along x axis by distance of radius and \n clock.setheading(angle) #set the turtle head to the given angle\n clock.pendown()\n clock.circle(radius)# draw circle\n clock.end_fill() #Fill circle after last call of draw_layout_of_clock to begin fill.\n clock.penup()\n clock.goto(0,0)\n\ndraw_layout_of_clock(Clock,0,0,300,90,\"dimgray\") #Outercircle to decorate outerside of clock \ndraw_layout_of_clock(Clock,0, 0,280,90 ,\"darkgray\")# \ndraw_layout_of_clock(Clock,0,0,8,90,\"black\")#inner most circle\n#Writing clock points on the circle by using hash \nHash=turtle.Turtle() \nHash.hideturtle()\npen.penup()\npen.goto(0,0)\nHash.setheading(90)\nHash.speed(0)\nfor i in range (60):\n if i%5==0:\n Hash.penup()\n Hash.pencolor(\"orange\")\n Hash.pensize(4)\n Hash.forward(260)\n Hash.pendown()\n Hash.forward(19)\n Hash.penup()\n Hash.goto(0,0)\n Hash.right(6)\n else:\n Hash.pensize(2)\n Hash.pencolor(\"black\")\n Hash.speed(0)\n Hash.goto(0,0)\n Hash.forward(274)\n Hash.pendown()\n Hash.forward(5)\n Hash.penup()\n Hash.goto(0,0)\n Hash.right(6) \n\npen.color(\"black\")\npen.penup()\nClock_Number=turtle.Turtle()\nClock_Number.setheading(60)\nClock_Number.speed(0)\ni=1\n#The following while loop writes the number of clock at the directed position 1-12\nwhile i<=12:\n Clock_Number.penup()\n Clock_Number.goto(0,-15)\n Clock_Number.forward(240)\n Clock_Number.pendown()\n Clock_Number.write(i,align=\"center\",font=(\"Stencil\",20,\"normal\"))\n Clock_Number.penup()\n Clock_Number.goto(0,0)\n Clock_Number.right(30)\n i=i+1\n#drawing hour hand shape,size and color\nHour_hand=turtle.Turtle()\nHour_hand.color (\"black\")\nHour_hand.shape(\"arrow\")\nHour_hand.speed(10)\nHour_hand.shapesize(stretch_wid=0.3, stretch_len=15)\n#Drawing minute hand shape,size and color\nMinute_hand=turtle.Turtle()\nMinute_hand.color(\"green\")\nMinute_hand.shape(\"arrow\")\nMinute_hand.speed(10)\nMinute_hand.shapesize(stretch_wid=0.3, stretch_len=20)\n\n#draw second hand shape,size and color\nSecond_Hand=turtle.Turtle()\nSecond_Hand.color(\"red\")\nSecond_Hand.shape(\"arrow\")\nSecond_Hand.speed(10)\nSecond_Hand.shapesize(stretch_wid=0.2, stretch_len=26)\n\n#The following function is to Move Hour hand clockwise by converting current hour and minute to angle in degree\n#because the point where angle is (0) degree time is 3 O'clock \n#multiplied by -30 means to move hour hand clockwise and angle goes from 0 to -360 in 24 hours.\n\ndef Move_Hour_Hand():\n Current_hr=datetime.datetime.now().hour # get Current hour from computer\n Current_Min=datetime.datetime.now().minute \n angle = (Current_hr - 3) * -30 +(-0.5*Current_Min) #move hour hand smoothly that is for every 2 minute move by -1 degree\n Hour_hand.setheading(angle)\nscreen.ontimer(Move_Hour_Hand, 60000)\n\n#The following function is to move Minute hand by converting current minute and second to angle in degree\n#Here at point where turtle angle is 0 degree minute is 15.The measure of angle between min and min is 6 degree \n#when second hand move by angle of -10 degree, minute hand move by -1 degree\ndef Move_Minute_Hand():\n Current_Min=datetime.datetime.now().minute\n Current_Sec=datetime.datetime.now().second\n angle= ((Current_Min - 15) *-6)+(-Current_Sec * 0.1) \n Minute_hand.setheading(angle)\n screen.ontimer(Move_Minute_Hand,1000) # Here ontimer checks the function called move_minute_hand after 1 sec\n#moving second hand\ndef Move_Second_Hand():\n Current_Sec=datetime.datetime.now().second\n angle= (Current_Sec - 15) * -6\n Second_Hand.setheading(angle)\n screen.ontimer(Move_Second_Hand,1) #Here ontimer checks the move_second_hand function after 1 milisecond to make second precise.\n#The following ontimer is an infinite loop.\nscreen.ontimer(Move_Hour_Hand, 1)\nscreen.ontimer(Move_Minute_Hand, 1)\nscreen.ontimer(Move_Second_Hand, 1)\nturtle.hideturtle()\nscreen.mainloop()\n\n","repo_name":"Usmaelabdureman/Analog-clock","sub_path":"analog-clock.py","file_name":"analog-clock.py","file_ext":"py","file_size_in_byte":4509,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"29172726322","text":"'''\nCalculate how many more points the first person, a, should win in order to satisfy the condition that they have a score higher or equal to 11 \nand higher than the second person, b, by at least 2 points. \n(find the min. If they always same scors, then infinity)\n'''\n\ndef points_to_win(a, b):\n if a >= 11 and a >= b + 2:\n return 0 \n elif a < 11 and b < 11:\n return 11 - a # First person needs to reach 11 points first.\n else:\n return max(11 - a, 2 - (a - b))\n\n# test\n# a_score = 8\n# b_score = 6\n# a_score = 30\n# b_score = 31\n# a_score = 8\n# b_score = 8\na_score = 18\nb_score = 18\nadditional_points = points_to_win(a_score, b_score)\nprint(f\"Person a should win {additional_points} more points.\")\n\n\n","repo_name":"ChopinNo3Op9/funny_coding","sub_path":"extra scores to win.py","file_name":"extra scores to win.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"36066644880","text":"\"\"\"show language field\n\nRevision ID: 22f62dd63bd4\nRevises: 1852cafc4891\nCreate Date: 2019-08-25 14:39:43.373197\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '22f62dd63bd4'\ndown_revision = '1852cafc4891'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n op.add_column('shows',\n sa.Column(\n 'language', \n sa.String(100),\n )\n )\n\n\ndef downgrade():\n raise NotImplemented()\n","repo_name":"thomaserlang/seplis","sub_path":"seplis/api/migration/versions/22f62dd63bd4_show_language_field.py","file_name":"22f62dd63bd4_show_language_field.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"39069301065","text":"\nfrom collections import OrderedDict\nfrom train import train\nimport numpy as np\nimport torch.nn as nn\nfrom models.style_verbose import G_mapping,G_synthesis\nfrom models.styleGan import Discriminator,StyleBased_Generator\nfrom dataset import return_cifar10\nimport os\n\ndef check_paths():\n base_dir = os.getcwd()\n paths = ['generated_images','model_weights']\n for path in paths:\n dir_path = os.path.join(base_dir,path)\n if not os.path.isdir(path): \n os.mkdir(dir_path) \n\ndef main():\n check_paths()\n # Load data\n cifar10_path = '/home/shuza/Documents/Data/cifar-10-batches-py'\n train_data,test_data = return_cifar10(cifar10_path)\n train_x,train_labels = train_data\n test_x,test_labels = test_data\n\n params = {\n 'base_dir': os.getcwd(),\n 'image_dir' : os.path.join(os.getcwd(),'generated_images'),\n 'weight_dir' : os.path.join(os.getcwd(),'model_weights'),\n 'iterations':1000,\n 'batch_size':1,\n 'latent_dim':512,\n 'alpha': 0,\n 'resolution':32,\n 'step' : 3\n }\n datasets = {\n 'x_train':train_x.transpose(0,3,2,1),\n 'x_test':test_x.transpose(0,3,2,1),\n 'train_labels':train_labels,\n 'test_labels':test_labels\n }\n # generator = nn.Sequential(OrderedDict([\n # ('g_mapping', G_mapping()),\n # #('truncation', Truncation(avg_latent)),\n # ('g_synthesis', G_synthesis()) \n # ]))\n # Model params\n n_fc = 8\n dim_latent = 512\n dim_input = 4\n\n generator = StyleBased_Generator(n_fc,dim_latent,dim_input)\n discriminator = Discriminator()\n train(params,datasets,generator,discriminator)\n\nif __name__ == \"__main__\":\n main()","repo_name":"Morgan-Griffiths/GAN","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"72825716393","text":"import csv\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas\nimport pytest\nfrom astropy import units as u\nfrom astropy import wcs\nfrom astropy.coordinates import ICRS\nfrom astropy.io import fits\nfrom astropy.io.fits import Header\nfrom astropy_healpix import HEALPix\nfrom logzero import logger\nfrom . import sample_headers\n\nfrom src.fitstools.catalog import Catalogs\nfrom src.fitstools.util import read_headers\n\nour_path = os.path.dirname(__file__)\ndeep_sky_file = os.path.join(our_path, \"../fitstools/data/deep_sky.csv\")\nhyperleda_file = os.path.join(our_path, \"../fitstools/data/hyperleda.csv\")\ntest_file = os.path.join(Path(__file__).parent, \"test-data\\\\M57_2020-05-30T022249_30sec_HaOIII_COLD_-17C_frame19.fit\")\n\ndef test_csv_reader():\n logger.info(\"start\")\n count = 0\n catalog = []\n with open(hyperleda_file, newline='', encoding=\"utf-8\") as fh:\n fh.readline() # skip first line\n fh.readline() # skip 2nd line\n reader = csv.reader(fh)\n for line in reader:\n if len(line) < 3:\n continue\n ra_deg = (int(line[0]) / float(864000 / 360)) # * u.deg\n dec_deg = (int(line[1]) / float(324000 / 90)) # * u.deg\n # catalog.append([ra_deg, dec_deg])\n count += 1\n\n logger.info(\"stop - \" + str(count))\n\n\ndef test_pandas_reader():\n header = read_headers(test_file)\n w = wcs.WCS(header).celestial\n hp = HEALPix(nside=16, frame='icrs')\n\n logger.info(\"start\")\n df = pandas.read_csv(hyperleda_file, skiprows=1, engine='c', usecols=[0, 1, 2], header=0,\n names=['ra', 'dec', 'name'], dtype={'ra': np.int, 'dec': np.int, 'name': np.object})\n df['ra'] = df['ra'] / np.float(864000 / 360)\n df['dec'] = df['dec'] / np.float(324000 / 90)\n ra = df.ra.to_numpy() * u.deg\n dec = df.dec.to_numpy() * u.deg\n df['hp'] = hp.lonlat_to_healpix(ra, dec)\n\n logger.info(\"stop - \" + str(len(df)))\n cat = df.groupby('hp')\n logger.info(\"groupby - \" + str(len(df)))\n\n@pytest.fixture()\ndef catalog():\n catalog = Catalogs()\n return catalog\n\n\ndef test_find_objects(catalog):\n header = Header.fromstring(sample_headers.header_sgp_fixed_wcs, \"\\n\")\n objects = catalog.find_objects(header)\n logger.info(objects)\n names = map(lambda t: t[2], objects)\n assert \"M57/NGC6720/Ring_Nebula\" in names\n assert \"IC1296\" in names\n\n\ndef test_wcs_astap():\n with fits.open(test_file, mode='readonly') as hdul:\n validation = wcs.validate(hdul)\n print(validation)\n print(\"------------------------------------------\")\n\n header = read_headers(test_file)\n w = wcs.WCS(header).celestial\n print(w.wcs.name)\n w.wcs.print_contents()\n print(\"------------------------------------------\")\n\n w.printwcs()\n print(\"------------------------------------------\")\n\n fp = w.calc_footprint()\n for point in fp:\n c = ICRS(point[0] * u.degree, point[1] * u.degree)\n rahmsstr = c.ra.to_string(u.hour)\n decdmsstr = c.dec.to_string(u.degree, alwayssign=True)\n print(rahmsstr + ' ' + decdmsstr)\n\n\ndef test_wcs_sgp():\n with fits.open(test_file, mode='readonly') as hdul:\n validation = wcs.validate(hdul)\n print(validation)\n print(\"------------------------------------------\")\n\n header = read_headers(test_file)\n w = wcs.WCS(header).celestial\n print(w.wcs.name)\n w.wcs.print_contents()\n print(\"------------------------------------------\")\n\n w.printwcs()\n print(\"------------------------------------------\")\n\n fp = w.calc_footprint()\n for point in fp:\n c = ICRS(point[0] * u.degree, point[1] * u.degree)\n rahmsstr = c.ra.to_string(u.hour)\n decdmsstr = c.dec.to_string(u.degree, alwayssign=True)\n print(rahmsstr + ' ' + decdmsstr)\n","repo_name":"bcolyn/fitstools","sub_path":"src/tests/test_catalog.py","file_name":"test_catalog.py","file_ext":"py","file_size_in_byte":3811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"33867249762","text":"import logging\nimport time\nimport pyautogui\nfrom copy import copy\n\n\ndef go_back():\n pyautogui.hotkey('ctrl', 'backspace')\n\n\ndef scroll(self, amount=1):\n for i in range(amount):\n move_mouse(self, self.width // 2, int(self.height * self.scroll_up_mouse_pos))\n pyautogui.dragTo(0, int(self.height * self.scroll_down_mouse_pos), self.scroll_time)\n move_mouse(self, self.width // 2, int(self.height * self.scroll_up_mouse_pos))\n pyautogui.dragTo(0, int(self.height * self.scroll_down_mouse_pos), self.scroll_time)\n time.sleep(self.scroll_time)\n\n\ndef move_mouse(self, rel_x1, rel_y1, rel_x2=None, rel_y2=None, start_x=None, start_y=None, sleep_time=None,\n click=False, sleep=False):\n if start_x is None:\n start_x = copy(self.start_x)\n if start_y is None:\n start_y = copy(self.start_y)\n if sleep_time is None:\n sleep_time = copy(self.loading_time)\n if rel_x2 is None and rel_y2 is None:\n pyautogui.moveTo(start_x + rel_x1, start_y + rel_y1)\n elif rel_x2 is None:\n pyautogui.moveTo(start_x + rel_x1, start_y + rel_y1 + (rel_y2 - rel_y1) // 2)\n elif rel_y2 is None:\n pyautogui.moveTo(start_x + rel_x1 + (rel_x2 - rel_x1) // 2, start_y + rel_y1)\n else:\n pyautogui.moveTo(start_x + rel_x1 + (rel_x2 - rel_x1) // 2, start_y + rel_y1 + (rel_y2 - rel_y1) // 2)\n if click:\n pyautogui.click()\n if sleep:\n time.sleep(sleep_time)\n","repo_name":"TrueBalkar/TrusteePlusAnalyzer","sub_path":"scanner/navigation/essentials.py","file_name":"essentials.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"70654824873","text":"from django.shortcuts import render\n\nimport requests\nfrom datetime import datetime\n\n# Create your views here.\n\ndef index(request):\n url = 'https://apiv3.apifootball.com/?action=get_events&from=2023-02-01&to=2023-02-07&league_id=164&APIkey=ba3ed274d9d1bc601524bf1422864036f2bfcacd977264d887fa1954bd9772e9'\n r = requests.get(url)\n jsonResp = r.json()\n# print(jsonResp)\n\n return render(request, 'soccerapp/index.html', {'jr': jsonResp})\n\ndef today(request):\n today = datetime.today().strftime('%Y-%m-%d')\n url = f'https://apiv3.apifootball.com/?action=get_events&from={today}&to={today}&league_id=164&APIkey=ba3ed274d9d1bc601524bf1422864036f2bfcacd977264d887fa1954bd9772e9'\n print(url)\n r = requests.get(url)\n jsonResp = r.json()\n\n return render(request, 'soccerapp/index.html', {'jr': jsonResp})","repo_name":"yernarsha/django_soccer_results","sub_path":"soccerapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"24216218495","text":"\"\"\"\nPart 1\n------\nWe assume the data is a directed acyclic graph (DAG) of tasks.\n\nRepresented by a set of nodes, and a dictionary mapping Target nodes to a list\nof their pre-requisites.\n\nWe need to build a basic task scheduler:\n Starting with leaf nodes (initial available nodes)\n Loop until no available nodes:\n Given all available nodes, schedule first (in alphabetical order)\n Update list of available nodes\n Create a new tree: where scheduled node is removed from all\n dependency lists\n\nScratch that, new idea\n----------------------\n\nBuild a list of Node objects\n Nodes know their pre-requisite nodes (prev)\n and which nodes they are pre-requisites of (next)\nIdentify leaf nodes as those nodes with no prev\nSet leaf nodes to available\nLoop:\n Given available nodes, schedule / resolve first\n For each of the scheduled_node's next_nodes\n Remove scheduled_node from their prev\n Update list of available - all nodes with no prev\n\nPart 2\n------\nA worker is an object that either points to a Node or points to nothing (idle)\nand chips away at the Node's cost at each timestep in a loop\n\nMain Loop:\n For each idle worker:\n assign to any available\n\n complete 1 second of work\n\n if no seconds of work left, make idle\n\"\"\"\nimport re\nfrom copy import deepcopy\nfrom string import ascii_uppercase\nfrom typing import Dict, List, Set\n\n\nLETTER_COST = {letter: (i + 1) for i, letter in enumerate(ascii_uppercase)}\n\n\nclass Node:\n def __init__(self,\n name: str,\n prev: Set[str] = None,\n next: Set[str] = None):\n self.name = name\n self.cost = LETTER_COST[name]\n self.prev = prev if prev is not None else set()\n self.next = next if next is not None else set()\n\n def __repr__(self):\n return f'Node({self.name},' \\\n f'cost={self.cost},' \\\n f'prev={self.prev},' \\\n f'next={self.next})'\n\n def update(self):\n self.cost -= 1\n if self.cost < 0:\n raise RuntimeError('cost must not be less than zero')\n\n\ndef parse_nodes(lines: List[str]) -> Dict[str, Node]:\n pattern = re.compile(\n r'^Step ([A-Z]) must be finished before step ([A-Z]) can begin.$')\n nodes: Dict[str, Node] = {}\n for line in lines:\n req, target = re.match(pattern, line.strip()).groups()\n\n if req not in nodes:\n nodes[req] = Node(req)\n nodes[req].next.add(target)\n\n if target not in nodes:\n nodes[target] = Node(target)\n nodes[target].prev.add(req)\n\n return nodes\n\n\ndef leaf_nodes(nodes: Dict[str, Node]) -> List[str]:\n return [name for name, node in nodes.items() if not node.prev]\n\n\ndef schedule_nodes(nodes: Dict[str, Node]) -> List[str]:\n nodes = deepcopy(nodes)\n schedule = []\n available = sorted(leaf_nodes(nodes))\n while available:\n scheduled = available[0]\n scheduled_node = nodes.pop(scheduled)\n\n for name in scheduled_node.next:\n node = nodes[name]\n node.prev.discard(scheduled)\n\n available = sorted(leaf_nodes(nodes))\n\n schedule.append(scheduled)\n\n return schedule\n\n\ndef multi_schedule_nodes(nodes: Dict[str, Node],\n num_workers: int,\n base_cost: int) -> int:\n\n for _, node in nodes.items():\n node.cost += base_cost\n\n workers = [id for id in range(num_workers)]\n jobs = {id: None for id in workers}\n\n available = sorted(leaf_nodes(nodes))\n done: List[str] = []\n time = 0\n\n msg = 'Second '\n for id in workers:\n msg += f'Worker {id} '\n msg += 'Done'\n print(msg)\n\n while True:\n # Schedule jobs to idle workers\n idle_workers = [id for id, node in jobs.items() if node is None]\n for worker in idle_workers:\n if not available:\n break\n jobs[worker] = available.pop(0)\n\n # Logging\n msg = f'{time:4d} '\n sorted_job_nodes = [jobs[id] for id in workers]\n for node in sorted_job_nodes:\n if node is None:\n node = '.'\n msg += f' {node:^8} '\n done_str = ''.join(done)\n msg += done_str\n print(msg)\n\n if not nodes:\n break\n\n # Apply work: reduce each job node's cost by 1\n active_workers = [id for id, node in jobs.items() if node is not None]\n for worker in active_workers:\n name = jobs[worker]\n node = nodes[name]\n node.update()\n\n # If node is completed, update graph\n if node.cost == 0:\n # Set worker to idle\n jobs[worker] = None\n # Remove this node from the list\n nodes.pop(name)\n done.append(name)\n # Remove this node from next node's state\n for next_name in node.next:\n next_node = nodes[next_name]\n next_node.prev.discard(name)\n\n scheduled = jobs.values()\n available = [name for name in sorted(leaf_nodes(nodes))\n if name not in scheduled]\n # print(nodes)\n\n time += 1\n\n return time\n\n\ntest_lines = \"\"\"Step C must be finished before step A can begin.\n Step C must be finished before step F can begin.\n Step A must be finished before step B can begin.\n Step A must be finished before step D can begin.\n Step B must be finished before step E can begin.\n Step D must be finished before step E can begin.\n Step F must be finished before step E can begin.\"\"\".split('\\n')\ntest_nodes = parse_nodes(test_lines)\nassert leaf_nodes(test_nodes) == ['C']\nassert ''.join(schedule_nodes(test_nodes)) == 'CABDFE'\nassert multi_schedule_nodes(test_nodes, 2, 0) == 15\n\nif __name__ == '__main__':\n with open('data/day_07.txt') as f:\n nodes = parse_nodes(f.readlines())\n print(''.join(schedule_nodes(nodes)))\n print(multi_schedule_nodes(nodes, 5, 60))\n","repo_name":"seeM/advent-of-code-2018","sub_path":"day_07.py","file_name":"day_07.py","file_ext":"py","file_size_in_byte":6112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"15174835199","text":"s = input()\r\n\r\nans = 0\r\nfor i, x in enumerate(s):\r\n if i % 2 == 0 and x == 'p':\r\n ans -= 1\r\n elif i % 2 == 1 and x == 'g':\r\n ans += 1\r\n\r\nprint(ans)","repo_name":"Kawser-nerd/CLCDSA","sub_path":"Source Codes/AtCoder/arc062/B/4408233.py","file_name":"4408233.py","file_ext":"py","file_size_in_byte":167,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"72"}
+{"seq_id":"39993157361","text":"\"\"\"\nТак как ограничения в задаче малы - можно решить перебором\n\"\"\"\n\n\ndef walk_right(lst, house):\n \"\"\"\n Двигаемся от текущего дома вправо\n\n :param lst: список с домами\n :param house: текущий индекс здания\n :return: расстояние от дома до магазина\n \"\"\"\n num = house + 1\n while num < 10:\n if lst[num] == 2:\n return num - house\n num += 1\n return 11\n\n\ndef walk_left(lst, house):\n \"\"\"\n Двигаемся от текущего дома влево\n\n :param lst: список с домами\n :param house: текущий индекс здания\n :return: расстояние от дома до магазина\n \"\"\"\n num = house - 1\n while num >= 0:\n if lst[num] == 2:\n return house - num\n num -= 1\n return 11\n\n\nif __name__ == '__main__':\n a = list(map(int, input().split())) # считываем весь список домов\n\n max_path = 0 # максимальное расстояние\n\n for index, elem in enumerate(a):\n if elem == 1:\n # если элемент - дом, то ищем минимальное расстояние до магазина\n # и сравниваем с текущим максимумом\n max_path = max(min(walk_right(a, index),\n walk_left(a, index)),\n max_path)\n print(max_path) # выодим ответ\n","repo_name":"danfimov/yandex-algorithm-trainings","sub_path":"Division_B/Homework_2/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"31367099295","text":"from django.shortcuts import render, redirect\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom .models import Products\r\nfrom .forms import ProductsForm\r\nfrom .filters import filterProducts\r\n\r\n\r\n\r\n\r\n# Create your views here.\r\n\r\n\r\n@login_required(login_url='login')\r\ndef index(request):\r\n return render(request, 'index.html')\r\n\r\n@login_required(login_url='login')\r\ndef addProduct(request):\r\n if request.method =='POST':\r\n name = request.POST['productname']\r\n category = request.POST['productcategory']\r\n quantity = request.POST['productquantity']\r\n price = request.POST['productprice']\r\n\r\n new_product = Products(name = name, category = category, quantity = quantity, price = price)\r\n new_product.save()\r\n\r\n return render(request, 'addProduct.html')\r\n\r\n@login_required(login_url='login')\r\ndef displayProduct(request):\r\n items = Products.objects.all() # using ORM\r\n # items = Products.objects.raw('SELECT * FROM displayProduct') -> for using SQL\r\n context = {\r\n 'items': items,\r\n \r\n }\r\n return render(request, 'displayProduct.html', context)\r\n\r\ndef delete(request, pk):\r\n remove = Products.objects.get(id=pk)\r\n if request.method == 'POST':\r\n remove.delete()\r\n return redirect ('displayProduct')\r\n return render(request, 'delete.html')\r\n\r\ndef update(request, pk):\r\n product = Products.objects.get(id=pk)\r\n form = ProductsForm(instance=product)\r\n if request.method == 'POST':\r\n form = ProductsForm(request.POST, instance=product)\r\n if form.is_valid():\r\n form.save()\r\n return redirect('displayProduct')\r\n\r\n context = {\r\n 'form':form,\r\n }\r\n return render(request, 'update.html', context)\r\n\r\n\r\n\r\n\r\n\r\n\r\n@login_required(login_url='login')\r\ndef searchProduct(request):\r\n product = Products.objects.all()\r\n filters = filterProducts(request.GET, queryset=product)\r\n\r\n context = {\r\n 'filters' : filters\r\n }\r\n return render(request, 'searchProduct.html', context)\r\n\r\n\r\n\r\n\r\n\r\n\r\n@login_required(login_url='login')\r\ndef totalpriceofProduct(request,self):\r\n price = self.product.price\r\n quantity = self.quantity\r\n total = price*quantity\r\n \r\n print(total)\r\n \r\n return render(request, 'totalpriceofProduct.html')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n@login_required(login_url='login')\r\ndef contacts(request):\r\n if request.method == \"POST\":\r\n contacts=contacts()\r\n name=request.POST.get('name')\r\n email=request.POST.get('emal')\r\n message=request.POST,get('message')\r\n\r\n contacts.name = name\r\n contacts.email = email\r\n contacts.message = message\r\n contacts.save()\r\n return redirect ('displayProduct')\r\n\r\n return render(request, 'contacts.html')\r\n\r\n\r\n\r\n\r\n\r\n@login_required(login_url='login')\r\ndef contactsfinal(request):\r\n return render(request, 'contactsfinal.html')\r\n","repo_name":"amanuel-lab/Warehouse-Managment-System-Final-Project","sub_path":"warehouseproject/warehouseapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"18975694905","text":"import logging\nimport sys\nfrom functools import reduce\nimport operator\nimport os\nimport shutil\nimport tempfile\nimport unittest\n\nlogging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n\nlogger = logging.getLogger()\n\n\ndef get_from_dict(data_dict, map_list):\n return reduce(operator.getitem, map_list, data_dict)\n\n\ndef set_in_dict(data_dict, map_list, value):\n get_from_dict(data_dict, map_list[:-1])[map_list[-1]] = value\n\n\ndef list_files(start_path):\n for root, dirs, files in os.walk(start_path, followlinks=True):\n level = root.replace(start_path, \"\").count(os.sep)\n indent = \" \" * 4 * level\n print(\"{}{}/\".format(indent, os.path.basename(root)))\n sub_indent = \" \" * 4 * (level + 1)\n for f in files:\n file_name = f\n if os.path.islink(os.path.join(root, f)):\n f += \"*\"\n print(\"{}{}\".format(sub_indent, file_name))\n\n\ndef dir_dict(start_path):\n structure = {}\n for root, dirs, files in os.walk(start_path, followlinks=True):\n current_level = os.path.relpath(root, start_path).split(os.sep)\n depth = len(current_level)\n if depth >= 1 and current_level[0] != \".\":\n current_dict = get_from_dict(structure, current_level)\n else:\n current_dict = structure\n\n for d in dirs:\n current_dict[d] = {}\n for f in files:\n current_dict[f] = \"\"\n return structure\n\n\ndef handle_links(function, path, execinfo):\n if function is os.path.islink:\n os.unlink(path)\n\n\ndef create_files_from_tree(tree, parent=\"\"):\n for name, content in tree.items():\n name = os.path.join(parent, name)\n if type(content) is dict:\n os.makedirs(name)\n create_files_from_tree(content, parent=name)\n else:\n open(name, \"a\").close()\n\n\nclass FileLinkTestCase(unittest.TestCase):\n def setUp(self):\n self.source_dir = tempfile.mkdtemp()\n self.source_tree = {}\n\n self.dest_dir = tempfile.mkdtemp()\n self.dest_tree = {}\n\n self.expected_tree = {}\n\n self.config_file_path = os.path.join(self.source_dir, \".cfgcaddy.yml\")\n\n open(self.config_file_path, \"a\").close()\n\n def tearDown(self):\n shutil.rmtree(self.source_dir, onerror=handle_links)\n shutil.rmtree(self.dest_dir, onerror=handle_links)\n\n def recursive_dircmp(self, dircmp_obj):\n self.assertListEqual(dircmp_obj.left_only, [])\n self.assertListEqual(dircmp_obj.right_only, [])\n for directory, sub_directory in dircmp_obj.subdirs.items():\n self.recursive_dircmp(sub_directory)\n\n def assertDestMatchesExpected(self):\n dest_tree = dir_dict(self.dest_dir)\n\n self.assertEqual(self.expected_tree, dest_tree)\n","repo_name":"tstapler/cfgcaddy","sub_path":"tests/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"74317359272","text":"\nfrom PyQt5.QtWidgets import (\n QSlider,\n QLabel,\n QHBoxLayout,\n QVBoxLayout,\n QGroupBox,\n QCheckBox,\n QPushButton,\n QLineEdit\n)\n\nfrom PyQt5.QtGui import QDoubleValidator, QValidator\n\nimport numpy as np\nfrom pyqtgraph.Qt import QtCore\nfrom PyQt5.QtCore import Qt, pyqtSignal\nfrom functools import partial\n\n# class NotEmptyValidator(QValidator):\n\n \n # def validate(self, text: str, pos):\n # if text.strip().isdigit():\n # state = QValidator.Acceptable\n # else:\n # state = QValidator.Intermediate # so that field can be made empty temporarily\n # return state, text, pos\n \n'''This code makes the GUI interface for each component. \nThere is a lot of copypaste in this section'''\n\nclass LensGui():\n\n def __init__(self, name, f):\n '''GUI for the Lens component\n ----------\n name : str\n Name of component\n f : float\n Focal length\n '''\n self.box = QGroupBox(name)\n self.fslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.fslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.fslider.setMinimum(-10)\n self.fslider.setMaximum(10)\n self.fslider.setValue(1)\n self.fslider.setTickPosition(QSlider.TicksBelow)\n \n self.flineedit = QLineEdit(\"{:.4f}\".format(f))\n self.flineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n \n self.fwobblefreqlineedit = QLineEdit(\"{:.4f}\".format(1))\n self.fwobbleamplineedit = QLineEdit(\"{:.4f}\".format(0.5))\n \n qdoublevalidator = QDoubleValidator()\n self.flineedit.setValidator(qdoublevalidator)\n self.flineeditstep.setValidator(qdoublevalidator)\n self.fwobblefreqlineedit.setValidator(qdoublevalidator)\n self.fwobbleamplineedit.setValidator(qdoublevalidator)\n \n self.fwobble = QCheckBox('Wobble Lens Current')\n\n hbox = QHBoxLayout()\n hbox_lineedit = QHBoxLayout()\n hbox_lineedit.addWidget(QLabel('Focal Length = '))\n hbox_lineedit.addWidget(self.flineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step = '))\n hbox_lineedit.addWidget(self.flineeditstep)\n hbox_slider = QHBoxLayout()\n hbox_slider.addWidget(self.fslider)\n hbox_wobble = QHBoxLayout()\n hbox_wobble.addWidget(self.fwobble)\n hbox_wobble.addWidget(QLabel('Wobble Frequency'))\n hbox_wobble.addWidget(self.fwobblefreqlineedit)\n hbox_wobble.addWidget(QLabel('Wobble Amplitude'))\n hbox_wobble.addWidget(self.fwobbleamplineedit)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox_lineedit)\n vbox.addLayout(hbox_slider)\n vbox.addLayout(hbox_wobble)\n vbox.addStretch()\n\n self.box.setLayout(vbox)\n \n self.table = QGroupBox(name)\n self.flabel_table = QLabel('Focal Length = ' + \"{:.2f}\".format(f))\n self.flabel_table.setMinimumWidth(80)\n hbox = QHBoxLayout()\n hbox = QHBoxLayout()\n hbox.addWidget(self.flabel_table)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox)\n self.table.setLayout(vbox)\n \nclass AstigmaticLensGui():\n '''Gui for the Astigmatic Lens component\n ''' \n def __init__(self, name, gui_label, comp_type, fx, fy):\n '''\n\n Parameters\n ----------\n name : str\n Name of component\n gui_label : str\n Label of focal length slider in GUI\n fx : float\n Focal length in x axis\n fy : float\n Focal length in y axis\n '''\n self.box = QGroupBox(name)\n self.fxslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.fxslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.fxslider.setMinimum(-10)\n self.fxslider.setMaximum(10)\n self.fxslider.setValue(1)\n self.fxslider.setTickPosition(QSlider.TicksBelow)\n self.fxlineedit = QLineEdit(\"{:.4f}\".format(fx))\n self.fxlineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n \n qdoublevalidator = QDoubleValidator()\n self.fxlineedit.setValidator(qdoublevalidator)\n self.fxlineeditstep.setValidator(qdoublevalidator)\n \n hbox = QHBoxLayout()\n hbox_lineedit = QHBoxLayout()\n hbox_lineedit.addWidget(QLabel('Focal Length X = '))\n hbox_lineedit.addWidget(self.fxlineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step X = '))\n hbox_lineedit.addWidget(self.fxlineeditstep)\n hbox_slider = QHBoxLayout()\n hbox_slider.addWidget(self.fxslider)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox_lineedit)\n vbox.addLayout(hbox_slider)\n vbox.addStretch()\n \n self.fyslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.fyslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.fyslider.setMinimum(-10)\n self.fyslider.setMaximum(10)\n self.fyslider.setValue(1)\n self.fyslider.setTickPosition(QSlider.TicksBelow)\n \n self.fylineedit = QLineEdit(\"{:.4f}\".format(fy))\n self.fylineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n self.fywobblineedit = QLineEdit(\"{:.4f}\".format(fy))\n self.fwobblefreqlineedit = QLineEdit(\"{:.4f}\".format(1))\n self.fwobbleamplineedit = QLineEdit(\"{:.4f}\".format(0.5))\n \n self.fylineedit.setValidator(qdoublevalidator)\n self.fylineeditstep.setValidator(qdoublevalidator)\n self.fwobbleamplineedit.setValidator(qdoublevalidator)\n self.fwobblefreqlineedit.setValidator(qdoublevalidator)\n \n self.fwobble = QCheckBox('Wobble Lens Current')\n\n hbox = QHBoxLayout()\n hbox_lineedit = QHBoxLayout()\n hbox_lineedit.addWidget(QLabel('Focal Length Y = '))\n hbox_lineedit.addWidget(self.fylineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step Y = '))\n hbox_lineedit.addWidget(self.fylineeditstep)\n hbox_slider = QHBoxLayout()\n hbox_slider.addWidget(self.fyslider)\n \n vbox.addLayout(hbox_lineedit)\n vbox.addLayout(hbox_slider)\n \n if comp_type == 'Quadrupole':\n pass\n else:\n hbox_wobble = QHBoxLayout()\n hbox_wobble.addWidget(self.fwobble)\n hbox_wobble.addWidget(QLabel('Wobble Frequency'))\n hbox_wobble.addWidget(self.fwobblefreqlineedit)\n hbox_wobble.addWidget(QLabel('Wobble Amplitude'))\n hbox_wobble.addWidget(self.fwobbleamplineedit)\n vbox.addLayout(hbox_wobble)\n \n vbox.addStretch()\n\n self.box.setLayout(vbox)\n \n self.table = QGroupBox(name)\n \n self.flabelx_table = QLabel('Focal Length X = ' + \"{:.2f}\".format(fx))\n self.flabelx_table.setMinimumWidth(80)\n hbox = QHBoxLayout()\n hbox.addWidget(self.flabelx_table)\n \n self.flabely_table = QLabel('Focal Length Y = ' + \"{:.2f}\".format(fy))\n self.flabely_table.setMinimumWidth(80)\n hbox = QHBoxLayout()\n hbox.addWidget(self.flabely_table)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox)\n self.table.setLayout(vbox)\n\nclass SampleGui():\n '''Gui to allow a user to move the sample in the 3D model\n ''' \n def __init__(self, name, x, y):\n '''\n\n Parameters\n ----------\n name : str\n Name of the component\n x : float\n X position of component\n y : float\n Y position of component\n '''\n self.box = QGroupBox(name)\n self.xslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.xslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.xslider.setMinimum(-100)\n self.xslider.setMaximum(100)\n self.xslider.setValue(int(round(x)))\n self.xslider.setTickPosition(QSlider.TicksBelow)\n\n self.xlabel = QLabel('X Position = ' + \"{:.2f}\".format(x))\n self.xlabel.setMinimumWidth(80)\n\n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.xlabel)\n hbox.addSpacing(10)\n hbox.addWidget(self.xslider)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n self.yslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.yslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.yslider.setMinimum(-100)\n self.yslider.setMaximum(100)\n self.yslider.setValue(int(round(y)))\n self.yslider.setTickPosition(QSlider.TicksBelow)\n\n self.ylabel = QLabel('Y Position = ' + \"{:.2f}\".format(y))\n self.ylabel.setMinimumWidth(80)\n\n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.ylabel)\n hbox.addSpacing(10)\n hbox.addWidget(self.yslider)\n\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n vbox.addStretch()\n\n self.box.setLayout(vbox)\n \n self.table = QGroupBox(name)\n self.xlabel_table = QLabel('X Position = ' + \"{:.2f}\".format(x))\n self.xlabel_table.setMinimumWidth(80)\n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.xlabel_table)\n \n self.ylabel_table = QLabel('Y Position = ' + \"{:.2f}\".format(y))\n self.ylabel_table.setMinimumWidth(80)\n hbox_labels.addWidget(self.ylabel_table)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n self.table.setLayout(vbox)\n\nclass DeflectorGui():\n '''GUI for the deflector component\n ''' \n def __init__(self, name, defx, defy):\n '''\n\n Parameters\n ----------\n name : str\n Name of component\n defx : float\n Initial X deflection of deflector\n defy : float\n Initial Y deflection of deflector\n '''\n self.box = QGroupBox(name)\n self.defxslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.defxslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.defxslider.setMinimum(-10)\n self.defxslider.setMaximum(10)\n self.defxslider.setValue(1)\n self.defxslider.setTickPosition(QSlider.TicksBelow)\n \n qdoublevalidator = QDoubleValidator()\n self.defxlineedit = QLineEdit(\"{:.4f}\".format(defx))\n self.defxlineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n self.defxlineedit.setValidator(qdoublevalidator)\n self.defxlineeditstep.setValidator(qdoublevalidator)\n \n self.defyslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.defyslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.defyslider.setMinimum(-10)\n self.defyslider.setMaximum(10)\n self.defyslider.setValue(1)\n self.defyslider.setTickPosition(QSlider.TicksBelow)\n self.defylineedit = QLineEdit(\"{:.4f}\".format(defy))\n self.defylineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n self.defylineedit.setValidator(qdoublevalidator)\n self.defylineeditstep.setValidator(qdoublevalidator)\n\n hbox = QHBoxLayout()\n hbox_lineedit = QHBoxLayout()\n hbox_lineedit.addWidget(QLabel('X Deflection = '))\n hbox_lineedit.addWidget(self.defxlineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step X = '))\n hbox_lineedit.addWidget(self.defxlineeditstep)\n hbox_lineedit.addWidget(QLabel('Y Deflection = '))\n hbox_lineedit.addWidget(self.defylineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step Y = '))\n hbox_lineedit.addWidget(self.defylineeditstep)\n hbox_slider = QHBoxLayout()\n hbox_slider.addWidget(self.defxslider)\n hbox_slider.addWidget(self.defyslider)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox_lineedit)\n vbox.addLayout(hbox_slider)\n vbox.addStretch()\n\n self.box.setLayout(vbox)\n \n self.table = QGroupBox(name)\n self.defxlabel_table = QLabel('X Deflection = ' + \"{:.2f}\".format(defx))\n self.defxlabel_table.setMinimumWidth(80)\n self.defylabel_table = QLabel('Y Deflection = ' + \"{:.2f}\".format(defy))\n self.defylabel_table.setMinimumWidth(80)\n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.defxlabel_table)\n hbox_labels.addWidget(self.defylabel_table)\n \n vbox = QVBoxLayout()\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n self.table.setLayout(vbox)\n\n\nclass DoubleDeflectorGui():\n '''GUI for the double deflector component\n ''' \n def __init__(self, name, updefx, updefy, lowdefx, lowdefy):\n '''\n\n Parameters\n ----------\n name : str\n Name of component\n updefx : float\n Initial X deflection of upper deflector\n updefy : float\n Initial Y deflection of upper deflector\n lowdefx : float\n Initial X deflection of lower deflector\n lowdefy : float\n Initial Y deflection of lower deflector\n ''' \n self.box = QGroupBox(name)\n \n self.updefxslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.updefxslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.updefxslider.setMinimum(-10)\n self.updefxslider.setMaximum(10)\n self.updefxslider.setValue(1)\n self.updefxslider.setTickPosition(QSlider.TicksBelow)\n self.updefxlineedit = QLineEdit(\"{:.4f}\".format(updefx))\n self.updefxlineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n \n qdoublevalidator = QDoubleValidator()\n self.updefxlineedit.setValidator(qdoublevalidator)\n self.updefxlineeditstep.setValidator(qdoublevalidator)\n \n self.updefyslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.updefyslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.updefyslider.setMinimum(-10)\n self.updefyslider.setMaximum(10)\n self.updefyslider.setValue(1)\n self.updefyslider.setTickPosition(QSlider.TicksBelow)\n self.updefylineedit = QLineEdit(\"{:.4f}\".format(updefy))\n self.updefylineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n self.updefylineedit.setValidator(qdoublevalidator)\n self.updefylineeditstep.setValidator(qdoublevalidator)\n \n hbox = QHBoxLayout()\n hbox_lineedit = QHBoxLayout()\n hbox_lineedit.addWidget(QLabel('Upper X Deflection = '))\n hbox_lineedit.addWidget(self.updefxlineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step Upper X = '))\n hbox_lineedit.addWidget(self.updefxlineeditstep)\n hbox_lineedit.addWidget(QLabel('Upper Y Deflection = '))\n hbox_lineedit.addWidget(self.updefylineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step Upper Y = '))\n hbox_lineedit.addWidget(self.updefylineeditstep)\n \n hbox_slider = QHBoxLayout()\n hbox_slider.addWidget(self.updefxslider)\n hbox_slider.addWidget(self.updefyslider)\n \n vbox = QVBoxLayout()\n vbox.addLayout(hbox_lineedit)\n vbox.addLayout(hbox_slider)\n vbox.addStretch()\n \n self.lowdefxslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.lowdefxslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.lowdefxslider.setMinimum(-10)\n self.lowdefxslider.setMaximum(10)\n self.lowdefxslider.setValue(1)\n self.lowdefxslider.setTickPosition(QSlider.TicksBelow)\n self.lowdefxlineedit = QLineEdit(\"{:.4f}\".format(lowdefx))\n self.lowdefxlineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n self.lowdefxlineedit.setValidator(qdoublevalidator)\n self.lowdefxlineeditstep.setValidator(qdoublevalidator)\n \n self.lowdefyslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.lowdefyslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.lowdefyslider.setMinimum(-10)\n self.lowdefyslider.setMaximum(10)\n self.lowdefyslider.setValue(1)\n self.lowdefyslider.setTickPosition(QSlider.TicksBelow)\n self.lowdefylineedit = QLineEdit(\"{:.4f}\".format(lowdefy))\n self.lowdefylineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n self.lowdefylineedit.setValidator(qdoublevalidator)\n self.lowdefylineeditstep.setValidator(qdoublevalidator)\n \n hbox = QHBoxLayout()\n hbox_lineedit = QHBoxLayout()\n hbox_lineedit.addWidget(QLabel('Lower X Deflection = '))\n hbox_lineedit.addWidget(self.lowdefxlineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step Lower X = '))\n hbox_lineedit.addWidget(self.lowdefxlineeditstep)\n hbox_lineedit.addWidget(QLabel('Lower Y Deflection = '))\n hbox_lineedit.addWidget(self.lowdefylineedit)\n hbox_lineedit.addWidget(QLabel('Slider Step Lower Y = '))\n hbox_lineedit.addWidget(self.lowdefylineeditstep)\n \n hbox_slider = QHBoxLayout()\n hbox_slider.addWidget(self.lowdefxslider)\n hbox_slider.addWidget(self.lowdefyslider)\n \n vbox.addLayout(hbox_lineedit)\n vbox.addLayout(hbox_slider)\n vbox.addStretch()\n\n self.xbuttonwobble = QCheckBox(\"Wobble Upper Deflector X\")\n self.defxwobblefreqlineedit = QLineEdit(\"{:.4f}\".format(1))\n self.defxwobbleamplineedit = QLineEdit(\"{:.4f}\".format(0.5))\n self.defxratiolabel = QLabel('Deflector X Response Ratio = ')\n self.defxratiolineedit = QLineEdit(\"{:.4f}\".format(0.0))\n self.defxratiolineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n \n self.defxratiolineedit.setValidator(qdoublevalidator)\n self.defxratiolineeditstep.setValidator(qdoublevalidator)\n \n self.defxratioslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.defxratioslider.setMinimum(-10)\n self.defxratioslider.setMaximum(10)\n self.defxratioslider.setValue(1)\n self.defxratioslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.defxratioslider.setTickPosition(QSlider.TicksBelow)\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.xbuttonwobble)\n hbox.addWidget(QLabel('Wobble X Frequency'))\n hbox.addWidget(self.defxwobblefreqlineedit)\n hbox.addWidget(QLabel('Wobble X Amplitude'))\n hbox.addWidget(self.defxwobbleamplineedit)\n vbox.addLayout(hbox)\n \n hbox = QHBoxLayout()\n hbox.addWidget(self.defxratiolabel)\n hbox.addWidget(self.defxratiolineedit)\n hbox.addWidget(QLabel('Def Ratio X Response Slider Step = '))\n hbox.addWidget(self.defxratiolineeditstep)\n vbox.addLayout(hbox)\n \n hbox = QHBoxLayout()\n hbox.addWidget(self.defxratioslider)\n vbox.addLayout(hbox)\n\n self.ybuttonwobble = QCheckBox(\"Wobble Upper Deflector Y\")\n self.defywobblefreqlineedit = QLineEdit(\"{:.4f}\".format(1))\n self.defywobbleamplineedit = QLineEdit(\"{:.4f}\".format(0.5))\n self.defyratiolabel = QLabel('Deflector Y Response Ratio = ')\n self.defyratiolineedit = QLineEdit(\"{:.4f}\".format(0.0))\n self.defyratiolineeditstep = QLineEdit(\"{:.4f}\".format(0.1))\n self.defyratiolineedit.setValidator(qdoublevalidator)\n self.defyratiolineeditstep.setValidator(qdoublevalidator)\n \n self.defyratioslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.defyratioslider.setMinimum(-10)\n self.defyratioslider.setMaximum(10)\n self.defyratioslider.setValue(1)\n self.defyratioslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.defyratioslider.setTickPosition(QSlider.TicksBelow)\n \n self.usedefratio = QCheckBox(\"Use Def Ratio\")\n \n hbox = QHBoxLayout()\n hbox.addWidget(self.ybuttonwobble)\n hbox.addWidget(QLabel('Wobble Y Frequency'))\n hbox.addWidget(self.defywobblefreqlineedit)\n hbox.addWidget(QLabel('Wobble Y Amplitude'))\n hbox.addWidget(self.defywobbleamplineedit)\n vbox.addLayout(hbox)\n \n hbox = QHBoxLayout()\n hbox.addWidget(self.defyratiolabel)\n hbox.addWidget(self.defyratiolineedit)\n hbox.addWidget(QLabel('Def Ratio Y Response Slider Step = '))\n hbox.addWidget(self.defyratiolineeditstep)\n vbox.addLayout(hbox)\n \n hbox = QHBoxLayout()\n hbox.addWidget(self.defyratioslider)\n vbox.addLayout(hbox)\n hbox = QHBoxLayout()\n hbox.addWidget(self.usedefratio)\n vbox.addLayout(hbox)\n \n self.box.setLayout(vbox)\n\n hbox = QHBoxLayout()\n self.table = QGroupBox(name)\n self.updefxlabel_table = QLabel('Upper X Deflection = ' + \"{:.2f}\".format(updefx))\n self.updefxlabel_table.setMinimumWidth(80)\n self.updefylabel_table = QLabel('Upper Y Deflection = ' + \"{:.2f}\".format(updefy))\n self.updefylabel_table.setMinimumWidth(80)\n self.lowdefxlabel_table = QLabel('Lower X Deflection = ' + \"{:.2f}\".format(updefx))\n self.lowdefxlabel_table.setMinimumWidth(80)\n self.lowdefylabel_table = QLabel('Lower Y Deflection = ' + \"{:.2f}\".format(updefy))\n self.lowdefylabel_table.setMinimumWidth(80)\n self.defyratiolabel_table = QLabel('Y Deflector Ratio = ' + \"{:.2f}\".format(1))\n self.defxratiolabel_table = QLabel('X Deflector Ratio = ' + \"{:.2f}\".format(1))\n \n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.updefxlabel_table)\n hbox_labels.addWidget(self.updefylabel_table)\n hbox_labels.addWidget(self.lowdefxlabel_table)\n hbox_labels.addWidget(self.lowdefylabel_table)\n hbox_labels.addWidget(self.defxratiolabel_table)\n hbox_labels.addWidget(self.defyratiolabel_table)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox_labels)\n self.table.setLayout(vbox)\n\n\nclass BiprismGui():\n '''GUI for the biprism component\n ''' \n def __init__(self, name, deflection, theta):\n '''\n\n Parameters\n ----------\n name : str\n Name of component\n deflection : float\n Deflection angle in Slope units\n theta : int\n Angle of biprism. Determines if the biprism creates deflects in the x or y direction.\n Two options: 0 or 1. 0 for 0 degrees, 1 for 90 degree rotation. \n\n '''\n self.box = QGroupBox(name)\n self.defslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.defslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.defslider.setMinimum(-2000)\n self.defslider.setMaximum(2000)\n self.defslider.setValue(int(round(deflection*1000)))\n self.defslider.setTickPosition(QSlider.TicksBelow)\n\n self.deflabel = QLabel('Biprism Deflection = ' + \"{:.2f}\".format(deflection))\n self.deflabel.setMinimumWidth(80)\n\n vbox = QVBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.deflabel)\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.defslider)\n\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n self.rotslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.rotslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.rotslider.setMinimum(0)\n self.rotslider.setMaximum(1)\n self.rotslider.setValue(theta)\n self.rotslider.setTickPosition(QSlider.TicksBelow)\n\n self.rotlabel = QLabel('Rotation (Radians) = ' + \"{:.2f}\".format(theta))\n self.rotlabel.setMinimumWidth(80)\n\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.rotlabel)\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.rotslider)\n\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n vbox.addStretch()\n\n self.box.setLayout(vbox)\n \n self.table = QGroupBox(name)\n self.deflabel_table = QLabel('Biprism Deflection = ' + \"{:.2f}\".format(deflection))\n self.rotlabel_table = QLabel('Rotation (Radians) = ' + \"{:.2f}\".format(theta))\n \n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.deflabel_table)\n hbox_labels.addWidget(self.rotlabel_table)\n \n vbox = QVBoxLayout()\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n self.table.setLayout(vbox)\n\n# \nclass ModelGui():\n '''Overall GUI of the model\n ''' \n def __init__(self, num_rays, beam_type, gun_beam_semi_angle, beam_tilt_x, beam_tilt_y, beam_radius):\n '''\n\n Parameters\n ----------\n num_rays : int\n Number of rays in the model\n beam_type : str\n Type of initial beam: Axial, paralell of point. \n gun_beam_semi_angle : float\n Semi angle of the beam \n beam_tilt_x : float\n Initial x tilt of the beam in radians\n beam_tilt_y : float\n Initial y tilt of the beam in radians\n '''\n self.box = QGroupBox('Model Settings')\n self.rayslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.rayslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.rayslider.setMinimum(4)\n self.rayslider.setMaximum(15)\n self.rayslider.setValue(int(np.log2(num_rays)))\n self.rayslider.setTickPosition(QSlider.TicksBelow)\n\n self.raylabel = QLabel(str(num_rays))\n self.raylabel.setMinimumWidth(80)\n self.modelraylabel = QLabel('Number of Rays')\n\n vbox = QVBoxLayout()\n vbox.addStretch()\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.rayslider)\n hbox.addSpacing(15)\n hbox.addWidget(self.raylabel)\n\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.modelraylabel)\n hbox_labels.addStretch()\n\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n self.beamangleslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.beamangleslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.beamangleslider.setMinimum(0)\n self.beamangleslider.setMaximum(157)\n self.beamangleslider.setValue(int(round(gun_beam_semi_angle, 2)*100))\n self.beamangleslider.setTickPosition(QSlider.TicksBelow)\n\n self.beamanglelabel = QLabel(str(round(gun_beam_semi_angle, 2)))\n self.beamanglelabel.setMinimumWidth(80)\n self.modelbeamanglelabel = QLabel('Axial/Paralell Beam Semi Angle')\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.beamangleslider)\n hbox.addSpacing(15)\n hbox.addWidget(self.beamanglelabel)\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.modelbeamanglelabel)\n hbox_labels.addStretch()\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n self.beamwidthslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.beamwidthslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.beamwidthslider.setMinimum(-100)\n self.beamwidthslider.setMaximum(99)\n self.beamwidthslider.setValue(1)\n self.beamwidthslider.setTickPosition(QSlider.TicksBelow)\n\n self.beamwidthlabel = QLabel('0')\n self.beamwidthlabel.setMinimumWidth(80)\n self.modelbeamwidthlabel = QLabel('Paralell Beam Width')\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.beamwidthslider)\n hbox.addSpacing(15)\n hbox.addWidget(self.beamwidthlabel)\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.modelbeamwidthlabel)\n hbox_labels.addStretch()\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n self.checkBoxAxial = QCheckBox(\"Axial Beam\")\n\n self.checkBoxPoint = QCheckBox(\"Point Beam\")\n\n self.checkBoxParalell = QCheckBox(\"Paralell Beam\")\n\n self.checkBoxParalell.stateChanged.connect(\n partial(self.uncheck, self.checkBoxParalell))\n self.checkBoxPoint.stateChanged.connect(\n partial(self.uncheck, self.checkBoxPoint))\n self.checkBoxAxial.stateChanged.connect(\n partial(self.uncheck, self.checkBoxAxial))\n\n hbox.addWidget(self.checkBoxAxial)\n hbox.addWidget(self.checkBoxPoint)\n hbox.addWidget(self.checkBoxParalell)\n\n if beam_type == 'axial':\n self.checkBoxAxial.setChecked(True)\n elif beam_type == 'paralell':\n self.checkBoxParalell.setChecked(True)\n elif beam_type == 'point':\n self.checkBoxPoint.setChecked(True)\n\n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n self.anglelabel = QLabel('Beam Tilt Offset')\n hbox_labels.addWidget(self.anglelabel)\n\n self.xanglelabel = QLabel(\n 'Beam Tilt X (Radians) = ' + \"{:.3f}\".format(beam_tilt_x))\n self.xangleslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.xangleslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.xangleslider.setMinimum(-200)\n self.xangleslider.setMaximum(200)\n self.xangleslider.setValue(0)\n self.xangleslider.setTickPosition(QSlider.TicksBelow)\n\n self.yanglelabel = QLabel(\n 'Beam Tilt Y (Radians) = ' + \"{:.3f}\".format(beam_tilt_y))\n self.yangleslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.yangleslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.yangleslider.setMinimum(-200)\n self.yangleslider.setMaximum(200)\n self.yangleslider.setValue(0)\n self.yangleslider.setTickPosition(QSlider.TicksBelow)\n\n hbox.addWidget(self.xangleslider)\n hbox.addWidget(self.xanglelabel)\n\n hbox.addWidget(self.yangleslider)\n hbox.addWidget(self.yanglelabel)\n\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n self.view_label = QLabel('Set Camera View')\n self.init_button = QPushButton('Initial View')\n self.x_button = QPushButton('X View')\n self.y_button = QPushButton('Y View')\n\n hbox_label = QHBoxLayout()\n hbox_label.addWidget(self.view_label)\n hbox_push_buttons = QHBoxLayout()\n hbox_push_buttons.addWidget(self.init_button)\n hbox_push_buttons.addSpacing(15)\n hbox_push_buttons.addWidget(self.x_button)\n hbox_push_buttons.addSpacing(15)\n hbox_push_buttons.addWidget(self.y_button)\n\n vbox.addLayout(hbox_label)\n vbox.addLayout(hbox_push_buttons)\n\n self.box.setLayout(vbox)\n\n def uncheck(self, btn):\n '''Determines which button is checked, and unchecks others\n\n Parameters\n ----------\n btn : Pyqt5 Button\n '''\n # checking if state is checked\n if btn.isChecked() == True:\n\n # if first check box is selected\n if btn == self.checkBoxAxial:\n\n # making other check box to uncheck\n self.checkBoxParalell.setChecked(False)\n self.checkBoxPoint.setChecked(False)\n\n # if second check box is selected\n elif btn == self.checkBoxParalell:\n\n # making other check box to uncheck\n self.checkBoxAxial.setChecked(False)\n self.checkBoxPoint.setChecked(False)\n\n # if third check box is selected\n elif btn == self.checkBoxPoint:\n\n # making other check box to uncheck\n self.checkBoxAxial.setChecked(False)\n self.checkBoxParalell.setChecked(False)\n\n\nclass ApertureGui():\n '''GUI for the aperture component\n ''' \n def __init__(self, name, min_radius, max_radius, inner_radius, x, y):\n '''\n\n Parameters\n ----------\n name : str\n Name of component\n min_radius : float\n Minimum radius of the aperture\n max_radius : float\n Max radius of the aperture\n inner_radius : float\n Initial inner radius of the aperture\n x : float\n X position of component\n y : float\n y position of component\n '''\n self.box = QGroupBox(name)\n self.radiusslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.radiusslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.radiusslider.setMinimum(int(round(1)))\n self.radiusslider.setMaximum(int(round(max_radius*1000)))\n self.radiusslider.setValue(int(round(inner_radius*1000)))\n self.radiusslider.setTickPosition(QSlider.TicksBelow)\n\n self.radiuslabel = QLabel('Aperture Radius = ' +\n \"{:.2f}\".format(inner_radius))\n self.radiuslabel.setMinimumWidth(80)\n\n vbox = QVBoxLayout()\n vbox.addStretch()\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.radiusslider)\n\n hbox_label = QHBoxLayout()\n hbox_label.addWidget(self.radiuslabel)\n\n vbox.addLayout(hbox_label)\n vbox.addLayout(hbox)\n\n self.xslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.xslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.xslider.setMinimum(-100)\n self.xslider.setMaximum(100)\n self.xslider.setValue(int(round(x*1e2)))\n self.xslider.setTickPosition(QSlider.TicksBelow)\n\n self.xlabel = QLabel('X Position = ' + \"{:.2f}\".format(x))\n self.xlabel.setMinimumWidth(80)\n\n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.xlabel)\n hbox.addSpacing(10)\n hbox.addWidget(self.xslider)\n\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n self.yslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.yslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.yslider.setMinimum(-100)\n self.yslider.setMaximum(100)\n self.yslider.setValue(int(round(y*1e2)))\n self.yslider.setTickPosition(QSlider.TicksBelow)\n\n self.ylabel = QLabel('Y Position = ' + \"{:.2f}\".format(y))\n self.ylabel.setMinimumWidth(80)\n\n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.ylabel)\n hbox.addSpacing(10)\n hbox.addWidget(self.yslider)\n\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n\n vbox.addStretch()\n\n self.box.setLayout(vbox)\n \n self.table = QGroupBox(name)\n self.radiuslabel_table = QLabel('Aperture Radius = ' + \"{:.2f}\".format(inner_radius))\n self.xlabel_table = QLabel('X Position = ' + \"{:.2f}\".format(x))\n self.ylabel_table = QLabel('Y Position = ' + \"{:.2f}\".format(y))\n \n hbox = QHBoxLayout()\n hbox_labels = QHBoxLayout()\n hbox_labels.addWidget(self.xlabel_table)\n hbox_labels.addWidget(self.ylabel_table)\n hbox_labels.addWidget(self.radiuslabel_table)\n\n vbox = QVBoxLayout()\n vbox.addLayout(hbox_labels)\n vbox.addLayout(hbox)\n self.table.setLayout(vbox)\n \nclass ExperimentGui():\n '''GUI for the aperture component\n ''' \n def __init__(self):\n '''\n\n Parameters\n ----------\n name : str\n Name of component\n min_radius : float\n Minimum radius of the aperture\n max_radius : float\n Max radius of the aperture\n inner_radius : float\n Initial inner radius of the aperture\n x : float\n X position of component\n y : float\n y position of component\n '''\n self.box = QGroupBox('Experiment')\n self.scanpixelsslider = QSlider(QtCore.Qt.Orientation.Horizontal)\n self.scanpixelsslider.setTickPosition(QSlider.TickPosition.TicksBelow)\n self.scanpixelsslider.setMinimum(2)\n self.scanpixelsslider.setMaximum(8)\n self.scanpixelsslider.setValue(256)\n \n self.scanpixelslabel = QLabel('Scan pixels = ' + str(int(self.scanpixelsslider.value())))\n self.scanpixelslabel.setMinimumWidth(80)\n \n self.overfocuslabel = QLabel('Overfocus = Not Set')\n self.overfocuslabel.setMinimumWidth(80)\n \n self.cameralengthlabel = QLabel('Camera length = Not Set')\n self.cameralengthlabel.setMinimumWidth(80)\n \n self.semiconvlabel = QLabel('Semi conv = Not Set')\n self.semiconvlabel.setMinimumWidth(80)\n \n self.scanpixelsizelabel = QLabel('Scan pixel size = Not Set')\n self.scanpixelsizelabel.setMinimumWidth(80)\n\n vbox = QVBoxLayout()\n vbox.addStretch()\n\n hbox = QHBoxLayout()\n hbox.addWidget(self.scanpixelsslider)\n hbox.addSpacing(15)\n hbox.addWidget(self.scanpixelslabel)\n hbox.addSpacing(15)\n hbox.addWidget(self.overfocuslabel)\n hbox.addSpacing(15)\n hbox.addWidget(self.semiconvlabel)\n hbox.addSpacing(15)\n hbox.addWidget(self.scanpixelsizelabel)\n hbox.addSpacing(15)\n hbox.addWidget(self.cameralengthlabel)\n \n \n vbox.addLayout(hbox)\n \n self.FOURDSTEM_experiment_button = QPushButton('Run 4D STEM Experiment')\n \n hbox_push_buttons = QHBoxLayout()\n hbox_push_buttons.addWidget(self.FOURDSTEM_experiment_button)\n vbox.addLayout(hbox_push_buttons)\n\n self.box.setLayout(vbox)\n\n","repo_name":"AMCLab/TemGymBasic","sub_path":"src/temgymbasic/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":37672,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"72"}
+{"seq_id":"31416794359","text":"import numpy as np\nfrom numpy.linalg import inv, norm\nimport matplotlib.pyplot as plt\n\n\n# Define the polynomial model\ndef polynomial_model(M, x):\n polynomial_terms = []\n for i in range(M+1):\n polynomial_terms.append(np.array(x)**i)\n return polynomial_terms\n\n\n# Define Sine function\ndef sinusoidal(x):\n return np.sin(2 * np.pi * x)\n\n\n# Function for generating training data , where y_train = y_true + gaussian noise\ndef generate_toy_data(sample_size, std):\n x = np.linspace(0, 1, sample_size)\n y = sinusoidal(x) + np.random.normal(scale=std, size=x.shape)\n return x, y\n\n# Function that predicts m and s^2\ndef bayesian_prediction(a, b, Phi_test, Phi_train, y_train):\n Sigma = np.linalg.inv(a * np.eye(M + 1) + b * Phi_train.T @ Phi_train)\n m = b * Phi_test @ Sigma @ Phi_train.T @ y_train\n sigma = 1 / b + Phi_test @ Sigma @ Phi_test.T\n var = np.diag(sigma)\n\n return m, var\n\n\n# Number of training data points\nN = 10\n\n# Degree of polynomial for φ(x)\nM = 9\n\n# Gaussian noise\nm_noise = 0\nbeta = 11.1\ns_noise = 1/beta\n\n# Prior parameter\nalpha = 0.0005\n\n\n# Generate train data\nx_train, y_train = generate_toy_data(sample_size=N, std=s_noise)\n\n# Generate Φ(x)\nPhi_train = []\nfor i in range(N):\n Phi_train.append(polynomial_model(M, x_train[i]))\nPhi_train = np.array(Phi_train)\n\n# Generate test data\nx_test = np.linspace(0, 1, 1000)\nPhi_test = np.array([polynomial_model(M=M, x=x) for x in x_test])\n\n# Calculate the true and the predicted value\nt = sinusoidal(x_test)\nm, var = bayesian_prediction(a=alpha, b=beta, Phi_test=Phi_test, Phi_train=Phi_train, y_train=y_train)\n\n\nplt.figure()\nplt.scatter(x_train, y_train, facecolor=\"none\", edgecolor=\"mediumseagreen\", s=50, label=\"Training data\")\nplt.plot(x_test, t, color='steelblue', label='True Model')\nplt.plot(x_test, m, color='firebrick', label='Bayesian Linear Regression')\nplt.fill_between(x_test, m - var, m + var, alpha=0.2, color='firebrick', label='Variance')\nplt.xlabel('$x$')\nplt.xlim(-0.02, 1.01)\nplt.ylabel('$y$')\nplt.title('Bayesian Linear Regression vs True Model')\nplt.legend()\nplt.savefig('./output/ex5/bayesian_approach.pdf')\n\n\n\n","repo_name":"aspav/DSIT","sub_path":"MLCB/Supervised Learning/ex_5.py","file_name":"ex_5.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"72"}
+{"seq_id":"8711144583","text":"import subprocess\nimport sys\nimport os\nimport time\nimport re\nimport chardet\n\ndef check_cron_job_exists():\n try:\n result = subprocess.run([\"sudo\", \"crontab\", \"-u\", \"gde\", \"-l\"], stdout=subprocess.PIPE, text=True)\n match = re.search(r'\\*/2 \\* \\* \\* \\*', result.stdout)\n return match is not None\n except subprocess.CalledProcessError:\n return False\n\ndef detect_encoding(file_path):\n with open(file_path, 'rb') as f:\n result = chardet.detect(f.read())\n return result['encoding']\n\ndef check_file_content(file_path):\n if not os.path.exists(file_path):\n return False\n\n encoding = detect_encoding(file_path)\n\n with open(file_path, 'r', encoding=encoding) as f:\n lines = f.readlines()\n\n if not lines:\n return False\n\n last_line = lines[-1].strip()\n\n # Check if the last line in the file contains a timestamp\n match = re.search(r'\\d{4}\\. \\w{3}\\. \\d{1,2}\\., \\w+, \\d{2}:\\d{2}:\\d{2} \\w+', last_line)\n return match is not None\n\nif __name__ == \"__main__\":\n file_path = \"/tmp/timestamps.txt\"\n\n if check_cron_job_exists():\n print(\"The cron job is configured correctly.\")\n time.sleep(5)\n\n if check_file_content(file_path):\n print(\"The cron job is running and appending to the file.\")\n else:\n print(\"The cron job is running but not appending to the file in the correct format.\")\n sys.exit(1)\n else:\n print(\"The cron job is NOT configured correctly.\")\n sys.exit(1)\n\n","repo_name":"p0m3lO/labs","sub_path":"Basic/task7.py","file_name":"task7.py","file_ext":"py","file_size_in_byte":1521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"483166261","text":"class Solution:\n def reorderLogFiles(self, logs: list[str]) -> list[str]:\n letters = [log for log in logs if log.split()[1].isalpha()]\n digits = [log for log in logs if log.split()[1].isdigit()]\n \n letters.sort(key = lambda x : (x.split()[1:], x.split()[0]))\n return letters + digits\n\nsolution = Solution()\ninput1 = logs = [\"dig1 8 1 5 1\",\"let1 art can\",\"dig2 3 6\",\"let2 own kit dig\",\"let3 art zero\"]\nresult = solution.reorderLogFiles(input1)\nprint(f\"{result}\")","repo_name":"bkim1999/Python-Algorithm-Interview","sub_path":"String Manipulation/3. Reorder Data in Log Files.py","file_name":"3. Reorder Data in Log Files.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"14793331512","text":"'''\nGiven an unsorted integer array, \nfind the smallest missing positive integer.\n'''\n\n\nclass Solution:\n def firstMissingPositive(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n nums.append(0)\n n = len(nums)\n for i in range(n):\n if nums[i] < 0 or nums[i] >= n:\n nums[i] = 0\n for i in range(n):\n nums[nums[i]%n] += n\n for i in range(1, n):\n if nums[i] // n == 0:\n return i\n return n","repo_name":"smileshy777/practice","sub_path":"array/hard/first_missing_positive.py","file_name":"first_missing_positive.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"10253065941","text":"\"\"\"\nTic Tac Toe Player\n\"\"\"\n\nimport copy\nimport math\nimport sys\n\nX = \"X\"\nO = \"O\"\nEMPTY = None\nN = 3\n\n\ndef initial_state():\n \"\"\"\n Returns starting state of the board.\n \"\"\"\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],]\ndef player(board):\n \"\"\"\n Returns player who has the next turn on a board.\n \"\"\"\n countX = 0\n countO = 0\n for i in range(N):\n countX = countX + board[i].count(X)\n countO = countO + board[i].count(O)\n \n if ((countX == 0 and countO == 0) or countX == countO):\n return X\n elif (countX > countO):\n return O\n else:\n return X\n\n\ndef actions(board):\n \"\"\"\n Returns set of all possible actions (i, j) available on the board.\n \"\"\"\n actions = set()\n for i in range(N):\n for j in range(N):\n if (board[i][j] is EMPTY):\n actions.add((i, j))\n return actions\n\n\ndef result(board, action):\n \"\"\"\n Returns the board that results from making move (i, j) on the board.\n \"\"\"\n (i, j) = action\n\n \n # invalid action\n if (i < 0 or j < 0 or i > N or j > N or board[i][j] is not EMPTY):\n raise ValueError((i, j))\n \n # resulting board\n resulting = copy.deepcopy(board)\n resulting[i][j] = player(board)\n return resulting\n \n\n\ndef winner(board):\n \"\"\"\n Returns the winner of the game, if there is one.\n \"\"\"\n # horizontal and vertical \n for i in range(N):\n if board[i].count(X) == N or [board[j][i] for j in range(N)].count(X) == N:\n return X\n elif board[i].count(O) == N or [board[j][i] for j in range(N)].count(O) == N:\n return O\n\n # diagonal\n if [board[i][i] for i in range(N)].count(X) == N:\n return X\n\n if [board[i][i] for i in range(N)].count(O) == N:\n return O\n \n if [board[i][N-i-1] for i in range(N)].count(X) == N:\n return X\n \n if [board[i][N-i-1] for i in range(N)].count(O) == N:\n return O\n\n return None\n \n\ndef terminal(board):\n \"\"\"\n Returns True if game is over, False otherwise.\n \"\"\"\n # there is winner\n if winner(board) is not None:\n return True\n\n # game is running\n for i in range(N):\n for j in range(N):\n if (board[i][j] is EMPTY):\n return False\n\n return True\n\np\ndef utility(board):\n \"\"\"\n Returns 1 if X has won the game, -1 if O has won, 0 otherwise.\n \"\"\"\n if winner(board) == X:\n return 1\n elif winner(board) == O:\n return -1\n return 0\n\n\ndef minimax(board):\n \"\"\"\n Returns the optimal action for the current player on the board.\n \"\"\"\n if terminal(board): \n return None\n\n elif player(board) == X:\n val = -sys.maxsize - 1\n alpha = -sys.maxsize - 1\n beta = sys.maxsize\n\n for a in actions(board):\n alpha = minvalue(result(board, a), alpha, beta)\n if (alpha > val):\n val = alpha\n action = a\n return action\n \n elif player(board) == O:\n val = sys.maxsize\n alpha = -sys.maxsize - 1\n beta = sys.maxsize\n\n for a in actions(board):\n beta = maxvalue(result(board, a), alpha, beta)\n if (beta < val):\n val = beta\n action = a\n return action\n \n \ndef maxvalue(board, alpha, beta):\n if terminal(board):\n return utility(board)\n \n for action in actions(board):\n alpha = max(alpha, minvalue(result(board, action), alpha, beta))\n if (alpha >= beta):\n return beta\n \n return alpha\n\n\ndef minvalue(board, alpha, beta):\n if terminal(board):\n return utility(board)\n\n for a in actions(board):\n beta = min(beta, maxvalue(result(board, a), alpha, beta))\n if (beta <= alpha):\n return alpha\n \n return beta","repo_name":"quocthanhp/Tic-Tac-Toe","sub_path":"tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":3915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"8671114781","text":"from alembic import op\nimport sqlalchemy as sa\n\n\"\"\"increase tag elements from 60 to 255 chars\n\nRevision ID: 8df53b0d2c0e\nRevises: 6135a7bd4425\nCreate Date: 2021-03-29 14:44:35.607053\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '8df53b0d2c0e'\ndown_revision = '6135a7bd4425'\n\nTABLE = 'tags'\n\n\ndef upgrade():\n op.alter_column(TABLE, 'tag', existing_type=sa.String(60),\n type_=sa.String(255), existing_nullable=False)\n","repo_name":"openstack/neutron","sub_path":"neutron/db/migration/alembic_migrations/versions/xena/expand/8df53b0d2c0e_increase_tag_elements_from_60_to_255_.py","file_name":"8df53b0d2c0e_increase_tag_elements_from_60_to_255_.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":1353,"dataset":"github-code","pt":"72"}
+{"seq_id":"44046954895","text":"\nimport os\nimport os.path as osp\nimport shutil\nimport torch\nimport torch.nn as nn\nfrom torch.nn.parallel import DistributedDataParallel\n\n\nclass BaseModel():\n\n @property\n def name(self):\n return 'BaseModel'\n\n def initialize(self, opt):\n self.opt = opt\n self.isTrain = opt.isTrain\n self.Tensor = torch.cuda.FloatTensor\n self.save_dir = osp.join(opt.checkpoints_dir)\n\n # helper saving function that can be used by subclasses\n def save_network(self, network, model_name, epoch, stage_id=None):\n if stage_id is None:\n save_filename = f\"{epoch}_net_{model_name}\"\n else:\n save_filename = f\"{epoch}_net_{model_name}_stage_{stage_id:02d}\"\n save_path = osp.join(self.save_dir, save_filename)\n if isinstance(network, nn.DataParallel) or isinstance(network, DistributedDataParallel):\n network = network.module\n state_dict = network.state_dict()\n for key, param in state_dict.items():\n state_dict[key] = param.cpu()\n torch.save(state_dict, save_path)\n\n def save_info(self, save_info, epoch, stage_id=None):\n if stage_id is None:\n save_filename = f'{epoch}_info.pth'\n else:\n save_filename = f'{epoch}_info_stage_{stage_id:02d}.pth'\n save_path = osp.join(self.save_dir, save_filename)\n torch.save(save_info, save_path)\n\n # helper loading function that can be used by subclasses\n def load_network(self, network, model_name, epoch, stage_id=None):\n if stage_id is None:\n save_filename = f\"{epoch}_net_{model_name}.pth\"\n else:\n save_filename = f\"{epoch}_net_{model_name}_stage_{stage_id:02d}.pth\"\n save_path = osp.join(self.save_dir, save_filename)\n if not osp.exists(save_path):\n print(f\"{save_path} does not exist !!!\")\n return False\n else:\n if self.opt.dist:\n network.module.load_state_dict(torch.load(\n save_path, map_location=lambda storage, loc: storage.cuda(torch.cuda.current_device())))\n else:\n saved_weights = torch.load(save_path)\n network.load_state_dict(saved_weights)\n return True\n\n def load_info(self, epoch):\n save_filename = '{}_info.pth'.format(epoch)\n save_path = osp.join(self.save_dir, save_filename)\n # saved_info = torch.load(save_path)\n if self.opt.dist:\n saved_info = torch.load(save_path, map_location=lambda storage, loc: storage.cuda(\n torch.cuda.current_device()))\n else:\n saved_info = torch.load(save_path)\n return saved_info","repo_name":"penincillin/IHMR","sub_path":"src/models/base_model.py","file_name":"base_model.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"72"}
+{"seq_id":"10108653468","text":"import random\ndef get_random_string(length: int) -> str:\n chars = list(range(48, 58)) + list(range(65, 91)) + list(range(97, 123))\n\n result = \"\"\n for i in range(length):\n char_code = random.choice(chars)\n result += chr(char_code)\n\n return result\n","repo_name":"Videnin/Hillel_Videnin","sub_path":"HW_32.py","file_name":"HW_32.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"70128472874","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/1/2 20:37\n# @Author : LeonHardt\n# @File : jieba_cut.py\n\n\nimport os\nimport glob\nimport codecs\n\n\nimport jieba\nimport jieba.analyse\n\n# -------------------------------------------\n# file direction set\n# load user_dict\n# -------------------------------------------\njieba.load_userdict('all_dict.txt')\n\n\n# ------------------------------------------\n# article cut\n\n\ndef file_cut(path, data_name):\n \"\"\"\n Parameters\n ----------\n path: str\n file path\n data_name:\n text name\n Returns\n -------\n None\n cut the passage and write\n \"\"\"\n source_file = path + '/' + data_name # data name\n source = codecs.open(source_file, 'r', encoding='utf-8')\n\n out_path = path + '/out_file/'\n if os.path.exists(out_path) is not True:\n os.makedirs(out_path)\n\n out_file = out_path + data_name\n if os.path.exists(out_file):\n os.remove(out_file)\n\n stop_dict = [] # stop dict\n with open('stop_words_dict.txt', 'r', encoding='utf-8') as dic:\n line = dic.readline()\n while line != \"\":\n line = line.rstrip('\\n')\n stop_dict.append(line)\n line = dic.readline()\n\n out = codecs.open(out_file, 'w', 'utf-8')\n line = source.readline()\n while line!=\"\":\n line = line.rstrip('\\n')\n line.replace('\\t', '').replace('\\n', '').replace(' ', '')\n seg_list = list(jieba.cut(line, cut_all=False)) # cut the passage\n for word in seg_list:\n if word in stop_dict:\n seg_list.remove(word)\n output = ' '.join(seg_list)\n print(output)\n out.write(output)\n line = source.readline()\n print('END ALL')\n\n# ----------------------------------------------------\n# main\n\n\nif __name__ == '__main__':\n file_path = os.getcwd() + '/lib/text_data/my_use/'\n file_list = glob.iglob(file_path + '*.txt')\n for file in file_list:\n file_name = file.split('\\\\')[-1]\n file_cut(file_path, file_name)\n\n # file_cut(file_path, '我杀了他.txt')","repo_name":"LeonHardt427/mining_Higashino","sub_path":"jieba_cut.py","file_name":"jieba_cut.py","file_ext":"py","file_size_in_byte":2082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"17634261850","text":"import os\nfrom typing import Optional, Any\n\nfrom algo_ops.ops.op import Op\nfrom algo_ops.pipeline.pipeline import Pipeline\nfrom ocr_ops.framework.op.abstract_ocr_op import EasyOCROp\nfrom ocr_ops.framework.op.ffmpeg_op import FFMPEGOp\nfrom ocr_ops.framework.pipeline.ocr_pipeline import OCRMethod\nfrom ocr_ops.instances import ocr\n\nfrom card_recognizer.api.operating_mode import OperatingMode\nfrom card_recognizer.classifier.core.word_classifier import WordClassifier\nfrom card_recognizer.pulls_estimator.pulls_estimator import PullsEstimator\nfrom card_recognizer.pulls_estimator.pulls_summary import PullsSummary\nfrom card_recognizer.reference.core.build import ReferenceBuild\n\n\nclass CardRecognizer(Pipeline):\n def __init__(\n self,\n set_name: Optional[str] = \"master\",\n classification_method: str = \"shared_words\",\n mode: OperatingMode = OperatingMode.SINGLE_IMAGE,\n min_run_length: Optional[int] = 5,\n min_run_conf: Optional[float] = 0.1,\n run_tol: Optional[int] = 10,\n ):\n # load classifier\n ref_pkl_path = ReferenceBuild.get_set_pkl_path(set_name=set_name)\n self.classifier = WordClassifier(\n ref_pkl_path=ref_pkl_path,\n vect_method=\"encapsulation_match\",\n classification_method=classification_method,\n )\n\n # load OCR pipeline\n if mode in [\n OperatingMode.IMAGE_DIR,\n OperatingMode.VIDEO,\n OperatingMode.PULLS_IMAGE_DIR,\n OperatingMode.PULLS_VIDEO,\n OperatingMode.BOOSTER_PULLS_IMAGE_DIR,\n OperatingMode.BOOSTER_PULLS_VIDEO,\n ]:\n store_intermediate_images = False\n else:\n store_intermediate_images = True\n self.ocr_pipeline = ocr.basic_ocr_with_text_cleaning_pipeline(\n vocab_words=self.classifier.reference.vocab(),\n ocr_method=OCRMethod.EASYOCR,\n store_intermediate_images=store_intermediate_images,\n )\n\n # make pipeline\n if mode == OperatingMode.VIDEO:\n ops = [FFMPEGOp(), self.ocr_pipeline, self.classifier]\n elif mode == OperatingMode.SINGLE_IMAGE:\n ops = [\n self.ocr_pipeline,\n self.classifier,\n ]\n elif mode == OperatingMode.IMAGE_DIR:\n ops = [\n self.ocr_pipeline,\n self.classifier,\n ]\n elif mode == OperatingMode.PULLS_IMAGE_DIR:\n ops = [\n self.ocr_pipeline,\n self.classifier,\n PullsEstimator(\n min_run_length=min_run_length,\n min_run_conf=min_run_conf,\n run_tol=run_tol,\n num_cards_to_select=None,\n ),\n PullsSummary(operating_mode=mode),\n ]\n elif mode == OperatingMode.PULLS_VIDEO:\n ops = [\n FFMPEGOp(),\n self.ocr_pipeline,\n self.classifier,\n PullsEstimator(\n min_run_length=min_run_length,\n min_run_conf=min_run_conf,\n run_tol=run_tol,\n num_cards_to_select=None,\n figs_paging=True,\n ),\n PullsSummary(operating_mode=mode),\n ]\n elif mode == OperatingMode.BOOSTER_PULLS_IMAGE_DIR:\n ops = [\n self.ocr_pipeline,\n self.classifier,\n PullsEstimator(\n min_run_length=min_run_length,\n min_run_conf=min_run_conf,\n run_tol=run_tol,\n ),\n PullsSummary(operating_mode=mode),\n ]\n elif mode == OperatingMode.BOOSTER_PULLS_VIDEO:\n ops = [\n FFMPEGOp(),\n self.ocr_pipeline,\n self.classifier,\n PullsEstimator(\n min_run_length=min_run_length,\n min_run_conf=min_run_conf,\n run_tol=run_tol,\n ),\n PullsSummary(operating_mode=mode),\n ]\n else:\n raise ValueError(\"Unsupported mode: \" + str(mode))\n super().__init__(ops=ops)\n\n def find_op_by_class(self, op_class: Any) -> Optional[Op]:\n for op in self.ops.values():\n if isinstance(op, op_class):\n return op\n return None\n\n def set_output_path(self, output_path: Optional[str] = None):\n \"\"\"\n Set output path for results.\n \"\"\"\n ffmpeg_op = self.find_op_by_class(op_class=FFMPEGOp)\n if ffmpeg_op is not None:\n ffmpeg_op.image_out_path = os.path.join(\n output_path, \"uncompressed_video_frames\"\n )\n pulls_estimator_op = self.find_op_by_class(op_class=PullsEstimator)\n if pulls_estimator_op is not None:\n pulls_estimator_op.output_fig_path = output_path\n autosave_path = os.path.join(output_path, \"ocr_bounding_boxes\")\n self.ocr_pipeline.ocr_op.autosave_output_img_path = autosave_path\n\n def set_summary_file(self, summary_file: str):\n \"\"\"\n Set summary file.\n \"\"\"\n pulls_summary_op = self.find_op_by_class(op_class=PullsSummary)\n if pulls_summary_op is None:\n raise ValueError(\"There is no pulls summary op found in this pipeline.\")\n if pulls_summary_op is not None:\n pulls_summary_op.summary_file = summary_file\n\n def to_pickle(self, out_pkl_path: str) -> None:\n # temporarily remove un-pickleable elements\n easy_ocr_instance = None\n if isinstance(self.ocr_pipeline.ocr_op, EasyOCROp):\n easy_ocr_instance = self.ocr_pipeline.ocr_op.easy_ocr_reader\n self.ocr_pipeline.ocr_op.easy_ocr_reader = None\n\n # super call to pickle\n super().to_pickle(out_pkl_path=out_pkl_path)\n\n # restore state\n if isinstance(self.ocr_pipeline.ocr_op, EasyOCROp):\n self.ocr_pipeline.ocr_op.easy_ocr_reader = easy_ocr_instance\n","repo_name":"prateekt/pokemon-card-recognizer","sub_path":"card_recognizer/api/card_recognizer.py","file_name":"card_recognizer.py","file_ext":"py","file_size_in_byte":6117,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"40797303088","text":"from bot.app.controllers.logger import *\nimport bot.app.config as config\nfrom datetime import datetime, timedelta, timezone\nfrom google.cloud import datastore\nimport json\n\n# Start logger\nlogger = logging.getLogger(__name__)\n\n\n# Define a model class to store data in Google Cloud Firestore (using Datastore compatibility)\nclass TwitterCursor(object):\n \"\"\"\n This class creates an object that stores ID number of the last tweet retrieved from Twitter servers, as well as the\n date and time it was last modified. Its methods not only allow you to get, set, and update its data in the database,\n but also to import/export as a dictionary, export as JSON, as well as checking if the data is recent enough or you\n should reload it from the database.\n \"\"\"\n\n def __init__(self, last_id=1):\n \"\"\"\n This method initialises an instance by storing a tweet ID number and recording the date and time of its\n creation. The ID number can be either provided at creation time or it will default to 1 (int) as it would be\n the case if your bot has just been activated in a new account. In case that you enable this bot in an already\n existent account you should visit the URL that only checks mentions to make sure of overwrite the default value\n with the actual last tweet ID number!\n\n :param last_id: The tweet ID number that should be set as a cursor for future request to the Twitter API\n :type last_id: int\n \"\"\"\n\n # Log informational message\n logger.info(msg='Started a new TwitterCursor instance')\n\n # Start the datastore client instance\n self.db_client = datastore.Client()\n\n # Log informational message\n logger.info(msg='Started a new datastore client instance')\n\n # Set a single default entity key so that the entity is always assigned the same record (number 1) in datastore\n # because there's no need to store this datum more than once\n self.entity_key = self.db_client.key('tw_cursor_last_id', 1)\n\n # Check first if there's any cursor data recorded in the datastore, in case that the bot is restarting\n db_data = self.get_from_db()\n\n # If data is absent from datastore, then set default values because this is the first time the bot has ran\n if db_data is None:\n self.last_id = last_id\n self.creation_time = datetime.now(tz=timezone.utc)\n else:\n # Otherwise, set the cursor using the data from the datastore\n self.last_id = db_data['last_id']\n self.creation_time = db_data['creation_time']\n\n def get(self, obj_property):\n \"\"\"\n This method gets the current instance data and returns the requested property.\n\n :param obj_property: The specific property that should be returned\n :type obj_property: str or int\n :return: The current data for that cursor property\n :rtype: str or int\n \"\"\"\n\n # Map the cursor properties to possible parameter names to ensure returning the correct one\n obj_map = {'last_id': self.last_id, 'creation_time': self.creation_time}\n\n # Return requested datum\n return obj_map[obj_property]\n\n def set(self, obj_property, value):\n \"\"\"\n This method sets the provided value for the requested property in the current instance.\n\n :param obj_property: The cursor property whose value should be overwritten\n :type obj_property: str\n :param value: The new value that should overwrite the current data for a given property\n :type value: int or datetime\n :return: An informational message stating success or False\n :rtype: str or bool\n \"\"\"\n\n # Log debugging information\n logger.debug(msg='Cursor set method received this for value: {0}'.format(value))\n logger.debug(msg='Cursor set method received this for type(value): {0}'.format(type(value)))\n\n # Set new value into property and exit\n if setattr(self, obj_property, value):\n return 'OK'\n else:\n return False\n\n def to_dict(self):\n \"\"\"\n Get the current cursor data and return it as a dictionary.\n\n :return: Cursor data as a dictionary\n :rtype: dict\n \"\"\"\n\n # Return data\n return {'creation_time': self.creation_time, 'last_id': self.last_id}\n\n def from_dict(self, dict_data):\n \"\"\"\n Import data from a dictionary and overwrite current values for any property.\n\n :param dict_data: Dictionary containing cursor data\n :type dict_data: dict[str or int]\n :return: True\n :rtype: bool\n \"\"\"\n\n # Iterate over the dictionary data and set new values\n for item in dict_data:\n setattr(self, item, dict_data[item])\n\n # Return success signal\n return True\n\n def to_json(self, indented=True):\n \"\"\"\n This method gets the current cursor data and return it in JSON format.\n\n :param indented: Whether the JSON data returned should be indented for pretty-printing\n :type indented: bool\n :return: Current cursor data in JSON format\n :rtype: str\n \"\"\"\n\n # Convert the cursor properties to a dictionary and set values as data types that can be exported in JSON format\n structure = {'creation_time': self.creation_time.isoformat(), 'last_id': self.last_id}\n\n # Check whether data should be indented or not and return it\n if indented:\n return json.dumps(structure, indent=4, sort_keys=True)\n else:\n return json.dumps(structure, sort_keys=True)\n\n def from_json(self, json_data):\n \"\"\"\n This method imports data provided in JSON format and overwrites current values for any cursor property.\n\n :param json_data: Cursor data provided in JSON format\n :type json_data: str\n :return: An informational message\n :rtype: str\n \"\"\"\n\n # Load JSON data ina temporary variable\n cursor_data = json.loads(json_data)\n\n # Overwrite current property values with new ones, converting them to an appropriate format if necessary\n self.last_id = cursor_data.last_id\n self.creation_time = datetime.fromisoformat(date_string=cursor_data.creation_time)\n\n # Return informational message\n return 'json data imported'\n\n def send_to_db(self):\n \"\"\"\n Store current cursor data into the database and return a copy of it.\n\n :return: A copy of the current cursor data that was sent to database\n \"\"\"\n\n # Create a new datastore entity\n data_item = datastore.Entity(key=self.entity_key)\n\n # Get current cursor data\n cursor_data = self.to_dict()\n\n # Log debugging messages\n logger.debug(msg='data_item entity is: {0}'.format(data_item))\n logger.debug(msg='cursor_data is: {0}'.format(cursor_data))\n logger.debug(msg='type(cursor_data) is: {0}'.format(type(cursor_data)))\n\n # Populate datastore entity with current cursor data\n data_item.update(cursor_data)\n\n # Store datastore entity in the database\n self.db_client.put(entity=data_item)\n\n # Return a copy of current cursor data\n return {'item_id': str(data_item.id), 'cursor_data': cursor_data}\n\n def get_from_db(self):\n \"\"\"\n Retrieve cursor data directly from the datastore and return it.\n\n :return: Cursor data as it is stored in the database\n :rtype: dict\n \"\"\"\n\n # Get cursor data from the database\n db_data = self.db_client.get(key=self.entity_key)\n\n # Log debugging information\n logger.debug(msg='Retrieved cursor data from db: {0}'.format(repr(db_data)))\n\n # Return cursor data\n return db_data\n\n def update_from_db(self):\n \"\"\"\n This method retrieves the cursor data stored in the database and update current cursor values with those in\n the database.\n\n :return: If database is not empty, return cursor data. Otherwise return None\n :rtype: dict or None\n \"\"\"\n db_data = self.db_client.get(key=self.entity_key)\n logger.info(msg='Retrieved cursor data from db: {0}'.format(repr(db_data)))\n\n if db_data is not None:\n self.from_dict(dict_data=db_data)\n\n return {'last_id': self.last_id, 'creation_time': self.creation_time}\n else:\n return None\n\n def is_it_recent(self):\n \"\"\"\n This method checks whether the current values of the cursor instance are reasonably recent (i.e. less than\n twice the twitter_interval setting in the config.py module). If it's not, you should get the cursor data from\n the database in case another instance from your app in GAE has been active as well. Otherwise, you may end up\n replying twice to the same tweet mention.\n\n :return: Confirmation of whether this cursor instance holds recent data or not\n :rtype: bool\n \"\"\"\n\n # Get current date and time from the operating system\n now = datetime.now(tz=timezone.utc)\n\n # Calculate the date and time difference between now and the data stored in the cursor\n delta = now - self.creation_time\n\n # Calculate if the magnitude of the difference exceeds the threshold\n interval = timedelta(minutes=2 * config.twitter_interval)\n\n # Make a decision and return it\n if delta > interval:\n return False\n else:\n return True\n","repo_name":"tiktaalik-dev/Event-Info-Bot","sub_path":"bot/app/models/twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":9573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"45173142407","text":"import os\nfrom flask import Flask, request, abort, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom models import setup_db, Location, Event\nfrom flask_cors import CORS\nfrom auth import AuthError, requires_auth\n\ndef create_app(test_config=None):\n # create and configure the app\n app = Flask(__name__)\n setup_db(app)\n CORS(app)\n\n @app.after_request\n def after_request(response):\n response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization,true')\n response.headers.add('Access-Control-Allow-Methods', 'GET,PATCH,POST,DELETE,OPTIONS')\n return response\n \n @app.route('/')\n def get_greeting():\n # excited = os.environ['EXCITED']\n greeting = \"Hello !!!\" \n return greeting\n\n @app.route('/locations', methods=['GET'])\n @requires_auth('get:locations')\n def locations(jwt):\n locations = Location.query.order_by(Location.id).all()\n data = []\n for location in locations:\n data.append({\n \"id\": location.id,\n \"name\": location.name,\n \"location\": location.location\n })\n\n if len(locations) == 0:\n abort(404)\n else:\n format_locations = [location.format() for location in locations]\n\n return jsonify({\n 'success': True,\n 'locations': format_locations, \n 'total_locations': len(format_locations),\n })\n\n @app.route('/locations', methods=['POST'])\n @requires_auth('post:locations')\n def add_location(jwt):\n body = request.get_json()\n new_name = body.get('name', None)\n new_location = body.get('location', None)\n\n try:\n location = Location(name=new_name, location=new_location)\n print(location)\n location.insert()\n\n return jsonify({\n 'success': True,\n })\n except Exception as e: \n print(e)\n\n @app.route('/events', methods=['GET'])\n @requires_auth('get:events')\n def events(jwt):\n events = Event.query.order_by(Event.id).all()\n data = []\n for event in events:\n data.append({\n \"id\": event.id,\n \"name\": event.name,\n \"location\": event.location\n })\n\n if len(events) == 0:\n abort(404)\n else:\n formatted_events = [event.format() for event in events]\n return jsonify({\n 'success': True,\n 'events': formatted_events,\n 'data': data,\n 'total_events': len(formatted_events),\n })\n \n @app.route('/events', methods=['POST'])\n @requires_auth('post:events')\n def add_event(jwt):\n body = request.get_json()\n new_name = body.get('name', None)\n new_location = body.get('location', None)\n\n try:\n event = Event(name=new_name, location=new_location)\n print(event)\n event.insert()\n\n return jsonify({\n 'success': True,\n })\n except Exception as e: \n print(e)\n\n\n @app.route('/events/', methods=['PATCH'])\n @requires_auth('patch:events')\n def update_event(jwt, id):\n try:\n event = Event.query.filter(Event.id == id).one_or_none()\n\n if event is None:\n abort(404)\n \n new_name = request.json.get(\"name\")\n new_location = request.json.get(\"location\")\n event.name = new_name\n event.location = new_location\n event.update()\n events = Event.query.order_by(Event.id).all()\n events_formatted = [event.format() for event in events]\n\n return jsonify({\n \"success\": True,\n \"events\": events_formatted\n }), 200\n except Exception as e: \n print(e)\n\n @app.route('/events/', methods=['DELETE'])\n @requires_auth('delete:events')\n def delete_event(jwt, id):\n try:\n event = Event.query.filter(Event.id == id).one_or_none()\n\n if event is None:\n abort(404)\n\n event.delete()\n\n return jsonify({\n 'success': True,\n 'delete': id\n }), 200\n except:\n abort(422)\n \n @app.errorhandler(404)\n def not_found(error):\n return jsonify({\n \"success\": False, \n \"error\": 404,\n \"message\": \"Resource not found!\"\n }), 404\n\n @app.errorhandler(422)\n def not_found(error):\n return jsonify({\n \"success\": False, \n \"error\": 422,\n \"message\": \"Unprocessable!\"\n }), 422\n\n @app.errorhandler(400)\n def not_found(error):\n return jsonify({\n \"success\": False, \n \"error\": 400,\n \"message\": \"Bad request!\"\n }), 400\n\n @app.errorhandler(405)\n def not_found(error):\n return jsonify({\n \"success\": False, \n \"error\": 405,\n \"message\": \"Method not allowed!\"\n }), 405\n\n @app.errorhandler(AuthError)\n def auth_error(e):\n print(e)\n return jsonify({\n \"success\": False,\n \"error\": e.status_code,\n \"message\": e.error\n }), e.status_code\n\n return app\n\napp = create_app()\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=8080, debug=True)\n ","repo_name":"Leny73/fsnd","sub_path":"projects/capstone/starter/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"3291265236","text":"f = open(\"/home/student/PycharmProjects/Vex1cK's/4353.txt\")\na = []\nmk = 0\nk = 0\nfor s in f:\n a.append(int(s))\nfor i in range(len(a)-1):\n if str(a[i])[-1] == \"5\":\n if str(a[i+1])[-1] == \"5\":\n k+=1\n mk = max(mk,a[i]+a[i+1])\nprint(k,mk)\n\n\n","repo_name":"olgaObnosova/EGE","sub_path":"№17/4353.py","file_name":"4353.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"35545731934","text":"__author__ = \"Kyle Vitautas Lopin\"\n\n# standard libraries\nimport filecmp\nimport os\nimport unittest\nfrom unittest import mock\n\n# local files\nimport amp_frame\nimport graph_properties\nimport properties\n\nFILE_DIR = os.path.dirname(os.path.realpath(__file__))\nSOLN_FILE = os.path.join(FILE_DIR, 'test1_soln.csv')\nSAVED_FILE = os.path.join(FILE_DIR, 'test.csv')\n\n\nclass TestAmpFrame(unittest.TestCase):\n @classmethod\n @mock.patch(\"amp_frame.AmpFrame.make_graph_area\")\n def setUpClass(cls, mocked_graph) -> None:\n cls.mocked_root = mock.Mock()\n cls.mocked_root.frames = [mock.Mock(), mock.Mock()]\n cls.mocked_root.device_params = properties.DeviceParameters()\n print(dir(cls.mocked_root))\n graph_props = graph_properties.GraphProps()\n cls.amp_frame = amp_frame.AmpFrame(cls.mocked_root, None, graph_props)\n\n @classmethod\n def tearDownClass(cls) -> None:\n if os.path.exists(SAVED_FILE):\n os.remove(SAVED_FILE)\n\n def test_save_data(self):\n device = mock.Mock()\n device.time = [1, 2, 3]\n device.data = [11, 12, 13]\n with mock.patch('amp_frame.open_file') as mock_open:\n mock_open.return_value = SAVED_FILE\n self.amp_frame.save_data(device)\n self.assertTrue(filecmp.cmp(SAVED_FILE, SOLN_FILE))\n","repo_name":"KyleLopin/Potentiostat_GUI","sub_path":"tests/unit_tests/test_amp_frame.py","file_name":"test_amp_frame.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"72"}
+{"seq_id":"15217321236","text":"# encoding: utf-8\nfrom django.db import models\nfrom google.cloud import bigquery\nimport os\nimport json\nimport csv\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nMEDIA_ROOT = \"django-jquery-file-upload/media\"\n\nclass Picture(models.Model):\n \"\"\"This is a small demo using just two fields. The slug field is really not\n necessary, but makes the code simpler. ImageField depends on PIL or\n pillow (where Pillow is easily installable in a virtualenv. If you have\n problems installing pillow, use a more generic FileField instead.\n\n \"\"\"\n file = models.FileField(upload_to=\"pictures\")\n slug = models.SlugField(max_length=50, blank=True)\n\n def __str__(self):\n return self.file.name\n\n @models.permalink\n def get_absolute_url(self):\n return ('upload-new', )\n\n def save(self, *args, **kwargs):\n self.slug = self.file.name\n super(Picture, self).save(*args, **kwargs)\n print(\"Uploading data to BIG Query\")\n self.upload_to_big_query(os.path.join( BASE_DIR, MEDIA_ROOT, self.file.name))\n\n def delete(self, *args, **kwargs):\n \"\"\"delete -- Remove to leave file.\"\"\"\n self.file.delete(False)\n super(Picture, self).delete(*args, **kwargs)\n\n def parse_file(self, file_path):\n data_rows = []\n with open(file_path, 'rb') as csvfile:\n r = csv.reader(csvfile, delimiter=',', quotechar='|')\n for row in r:\n if len(row) == 7 and \"Electric usage\" in row[0]:\n row_item = {}\n row_item[\"zip\"] = zipcode\n row_item[\"type\"] = row[0]\n row_item[\"date\"] = row[1]\n row_item[\"start\"] = row[2]\n row_item[\"end\"] = row[3]\n row_item[\"usage\"] = row[4]\n row_item[\"units\"] = row[5]\n data_rows.append(json.loads(json.dumps(row_item)))\n elif \"Address\" in row:\n zipcode = self.get_zipcode(row)\n return data_rows\n\n def get_zipcode(self, address):\n return address[-1].split()[1].strip('\\\"')\n\n def upload_to_big_query(self, file_path):\n # DO CSV Stuff for content\n rows = self.parse_file(file_path)\n bigquery_client = bigquery.Client()\n dataset_ref = bigquery_client.dataset(\"gbd_store\")\n table_ref = dataset_ref.table(\"pge_electric\")\n table = bigquery_client.get_table(table_ref)\n errors = bigquery_client.create_rows(table, rows)\n if not errors:\n print(\"Successfully uploaded data to big query\")\n else:\n print(\"ERRORS:\")\n print(errors)\n","repo_name":"supriya-premkumar/GreenButtonData","sub_path":"fileupload/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"70020025514","text":"from theano import tensor\r\n\r\nfrom blocks.bricks import Initializable, Random, Bias, NDimensionalSoftmax\r\nfrom blocks.bricks.base import application, Brick, lazy\r\nfrom blocks.bricks.parallel import Fork, Merge\r\nfrom blocks.bricks.lookup import LookupTable\r\nfrom blocks.bricks.recurrent import recurrent\r\nfrom blocks.roles import add_role, COST\r\nfrom blocks.utils import dict_union, dict_subset\r\nfrom blocks.bricks.sequence_generators import (\r\n BaseSequenceGenerator, FakeAttentionRecurrent)\r\nfrom attention_with_topicalq import AttentionRecurrent\r\n\r\nfrom blocks.bricks.wrappers import WithExtraDims\r\n\r\nclass PickTargetProb(Brick):\r\n \"\"\"A softmax brick.\r\n\r\n Works with 2-dimensional inputs only. If you need more,\r\n see :class:`NDimensionalSoftmax`.\r\n \"\"\"\r\n\r\n @application(inputs=['y', 'x'], outputs=['cost'])\r\n def apply(self, application_call, y, x):\r\n if y.ndim == x.ndim - 1:\r\n print(\"y.ndim == x.ndim - 1\");\r\n indices = tensor.arange(y.shape[0]) * x.shape[1] + y\r\n cost = x.flatten()[indices]\r\n else:\r\n raise TypeError('rank mismatch between x and y')\r\n return cost\r\n\r\n\r\nclass NDPickTargetProb(PickTargetProb):\r\n decorators = [WithExtraDims()]\r\n\r\nclass SelectTarget(Random):\r\n\r\n @application\r\n def emit(self, probs):\r\n batch_size = probs.shape[0]\r\n pvals_flat = probs.reshape((batch_size, -1))\r\n generated = self.theano_rng.multinomial(pvals=pvals_flat)\r\n return generated.reshape(probs.shape).argmax(axis=-1)\r\n\r\n @application\r\n def cost(self, y,x):\r\n indices = tensor.arange(y.shape[0]) * x.shape[1] + y\r\n cost = x.flatten()[indices]\r\n\r\n return cost\r\n\r\n\r\nclass SequenceGenerator(BaseSequenceGenerator):\r\n r\"\"\"A more user-friendly interface for :class:`BaseSequenceGenerator`.\r\n\r\n Parameters\r\n ----------\r\n readout : instance of :class:`AbstractReadout`\r\n The readout component for the sequence generator.\r\n transition : instance of :class:`.BaseRecurrent`\r\n The recurrent transition to be used in the sequence generator.\r\n Will be combined with `attention`, if that one is given.\r\n attention : object, optional\r\n The attention mechanism to be added to ``transition``,\r\n an instance of\r\n :class:`~blocks.bricks.attention.AbstractAttention`.\r\n add_contexts : bool\r\n If ``True``, the\r\n :class:`.AttentionRecurrent` wrapping the\r\n `transition` will add additional contexts for the attended and its\r\n mask.\r\n \\*\\*kwargs : dict\r\n All keywords arguments are passed to the base class. If `fork`\r\n keyword argument is not provided, :class:`.Fork` is created\r\n that forks all transition sequential inputs without a \"mask\"\r\n substring in them.\r\n\r\n \"\"\"\r\n def __init__(self, readout,topicWordReadout,topic_vector_names, transition,topical_name,content_name,q_dim,q_name, attention=None,topical_attention=None,\r\n use_step_decay_cost=False,\r\n use_doubly_stochastic=False, lambda_ds=0.001,\r\n use_concentration_cost=False, lambda_ct=10,\r\n use_stablilizer=False, lambda_st=50,\r\n add_contexts=True, **kwargs):\r\n self.use_doubly_stochastic = use_doubly_stochastic\r\n self.use_step_decay_cost = use_step_decay_cost\r\n self.use_concentration_cost = use_concentration_cost\r\n self.use_stablilizer = use_stablilizer\r\n self.lambda_ds = lambda_ds\r\n self.lambda_ct = lambda_ct\r\n self.lambda_st = lambda_st\r\n normal_inputs = [name for name in transition.apply.sequences\r\n if 'mask' not in name]\r\n kwargs.setdefault('fork', Fork(normal_inputs))\r\n if attention:\r\n transition = AttentionRecurrent(\r\n transition, attention,topical_attention,topical_attended_name='topical_attended',topical_attended_mask_name='topical_attended_mask',content_name=content_name,topical_name=topical_name,\r\n add_contexts=add_contexts, name=\"att_trans\")\r\n else:\r\n transition = FakeAttentionRecurrent(transition,\r\n name=\"with_fake_attention\")\r\n\r\n self.topicWordReadout=topicWordReadout;\r\n self._topic_vector_names=topic_vector_names;\r\n self.probPick=NDPickTargetProb();\r\n self.sampleTarget=SelectTarget();\r\n #self._q_names=[q_name];\r\n self.topical_name=topical_name;\r\n self.content_name=content_name;\r\n self._topical_context_names=['topical_attended','topical_attended_mask'];\r\n super(SequenceGenerator, self).__init__(\r\n readout, transition, **kwargs)\r\n self.children+=[self.topicWordReadout,self.probPick,self.sampleTarget];\r\n\r\n def _push_allocation_config(self):\r\n\r\n super(SequenceGenerator, self)._push_allocation_config();\r\n transition_sources = (self._state_names + self._context_names +\r\n self._glimpse_names)\r\n self.topicWordReadout.source_dims = [self.transition.get_dim(name)\r\n if name in transition_sources\r\n else self.readout.get_dim(name)\r\n for name in self.readout.source_names]\r\n self.topicWordReadout.push_allocation_config()\r\n\r\n @application\r\n def cost_matrix(self, application_call, outputs,tw_outputs, tw_binary,mask=None, **kwargs):\r\n \"\"\"Returns generation costs for output sequences.\r\n\r\n See Also\r\n --------\r\n :meth:`cost` : Scalar cost.\r\n\r\n \"\"\"\r\n # We assume the data has axes (time, batch, features, ...)\r\n batch_size = outputs.shape[1]\r\n\r\n # Prepare input for the iterative part\r\n states = dict_subset(kwargs, self._state_names, must_have=False)\r\n # masks in context are optional (e.g. `attended_mask`)\r\n contexts = dict_subset(kwargs, self._context_names, must_have=False)\r\n topical_word_contexts=dict_subset(kwargs, self._topical_context_names)\r\n topical_embeddings=dict_subset(kwargs,[self.topical_name]);\r\n content_embeddings=dict_subset(kwargs,[self.content_name]);\r\n #q=dict_subset(kwargs, self._q_names, must_have=True,pop=True);\r\n feedback = self.readout.feedback(outputs)\r\n inputs = self.fork.apply(feedback, as_dict=True)\r\n\r\n # Run the recurrent network\r\n results = self.transition.apply(\r\n mask=mask, return_initial_states=True, as_dict=True,\r\n **dict_union(inputs, states, contexts,topical_word_contexts,topical_embeddings,content_embeddings))\r\n\r\n # Separate the deliverables. The last states are discarded: they\r\n # are not used to predict any output symbol. The initial glimpses\r\n # are discarded because they are not used for prediction.\r\n # Remember, glimpses are computed _before_ output stage, states are\r\n # computed after.\r\n states = {name: results[name][:-1] for name in self._state_names}\r\n glimpses = {name: results[name][1:] for name in self._glimpse_names}\r\n glimpses_modified={'weighted_averages':glimpses['weighted_averages'],'weigths':glimpses['weights']}\r\n\r\n # Compute the cost\r\n feedback = tensor.roll(feedback, 1, 0)\r\n feedback = tensor.set_subtensor(\r\n feedback[0],\r\n self.readout.feedback(self.readout.initial_outputs(batch_size)))\r\n readouts = self.readout.readout(\r\n feedback=feedback, **dict_union(states, glimpses_modified, contexts))\r\n #costs = self.readout.cost(readouts, outputs)\r\n\r\n #topicSumVec = dict_subset(kwargs, self._topic_vector_names, must_have=True);\r\n twReadouts=self.topicWordReadout.readout(feedback=feedback,**dict_union(states,glimpses_modified, contexts));\r\n twExp=tensor.exp(twReadouts);\r\n rwExp=tensor.exp(readouts);\r\n Z=twExp.sum(keepdims=True,axis=2)+rwExp.sum(keepdims=True,axis=2);#remains uncertain,keepdims, and the # of axis\r\n twExp/=Z;\r\n rwExp/=Z;\r\n twCost=self.probPick.apply(tw_outputs,twExp,extra_ndim=twExp.ndim - 2);\r\n rwCost=self.probPick.apply(outputs,rwExp,extra_ndim=rwExp.ndim - 2);\r\n totalCost=twCost*tw_binary+rwCost;\r\n costs=-tensor.log(totalCost);\r\n\r\n if self.use_doubly_stochastic:\r\n # Doubly stochastic cost\r\n # \\lambda\\sum_{i}(1-\\sum_{t}w_{t, i})^2\r\n # the first dimensions of weights returned by transition\r\n # is batch, time\r\n weights = glimpses['weights']\r\n weights_sum_time = tensor.sum(weights, 0)\r\n penalties = tensor.ones_like(weights_sum_time) - weights_sum_time\r\n penalties_squared = tensor.pow(penalties, 2)\r\n ds_costs = tensor.sum(penalties_squared, 1)\r\n costs += (self.lambda_ds * ds_costs)[None, :]\r\n\r\n def step_decay_cost(states):\r\n # shape is time, batch, features\r\n eta = 0.0001\r\n xi = 100\r\n states_norm = states.norm(2, axis=2)\r\n zz = tensor.zeros([1, states.shape[1]])\r\n padded_norm = tensor.join(0, zz, states_norm)[:-1, :]\r\n diffs = states_norm - padded_norm\r\n costs = eta * (xi ** diffs)\r\n return costs\r\n\r\n if self.use_step_decay_cost:\r\n costs += step_decay_cost(states['states'])\r\n\r\n def stablilizer_cost(states):\r\n states_norm = states.norm(2, axis=2)\r\n zz = tensor.zeros([1, states.shape[1]])\r\n padded_norm = tensor.join(0, zz, states_norm)[:-1, :]\r\n diffs = states_norm - padded_norm\r\n costs = tensor.pow(diffs, 2)\r\n return costs\r\n\r\n if self.use_stablilizer:\r\n costs += self.lambda_st * stablilizer_cost(states['states'])\r\n\r\n if self.use_concentration_cost:\r\n # weights has shape [batch, time, source sentence len]\r\n weights = glimpses['weights']\r\n maxis = tensor.max(weights, axis=2)\r\n lacks = tensor.ones_like(maxis) - maxis\r\n costs += self.lambda_ct * lacks\r\n\r\n if mask is not None:\r\n costs *= mask\r\n\r\n for name, variable in list(glimpses.items()) + list(states.items()):\r\n application_call.add_auxiliary_variable(\r\n variable.copy(), name=name)\r\n\r\n # This variables can be used to initialize the initial states of the\r\n # next batch using the last states of the current batch.\r\n for name in self._state_names:\r\n application_call.add_auxiliary_variable(\r\n results[name][-1].copy(), name=name+\"_final_value\")\r\n\r\n return costs\r\n\r\n\r\n @recurrent\r\n def generate(self, outputs,tw_vocab_overlap, **kwargs):\r\n \"\"\"A sequence generation step.\r\n\r\n Parameters\r\n ----------\r\n outputs : :class:`~tensor.TensorVariable`\r\n The outputs from the previous step.\r\n\r\n Notes\r\n -----\r\n The contexts, previous states and glimpses are expected as keyword\r\n arguments.\r\n\r\n \"\"\"\r\n states = dict_subset(kwargs, self._state_names)\r\n # masks in context are optional (e.g. `attended_mask`)\r\n contexts = dict_subset(kwargs, self._context_names, must_have=False)\r\n topical_word_contexts=dict_subset(kwargs, self._topical_context_names)\r\n topical_embeddings=dict_subset(kwargs,[self.topical_name]);\r\n content_embeddings=dict_subset(kwargs,[self.content_name]);\r\n glimpses = dict_subset(kwargs, self._glimpse_names)\r\n next_glimpses = self.transition.take_glimpses(\r\n as_dict=True,\r\n **dict_union(\r\n states, glimpses,topical_embeddings,content_embeddings,contexts,topical_word_contexts));\r\n glimpses_modified={'weighted_averages':next_glimpses['weighted_averages'],'weigths':next_glimpses['weights']}\r\n next_readouts = self.readout.readout(\r\n feedback=self.readout.feedback(outputs),\r\n **dict_union(states, glimpses_modified, contexts))\r\n next_tw_readouts=self.topicWordReadout.readout(\r\n feedback=self.readout.feedback(outputs),\r\n **dict_union(states, glimpses_modified, contexts))\r\n twExp=tensor.exp(next_tw_readouts);\r\n rwExp=tensor.exp(next_readouts);\r\n Z=twExp.sum(keepdims=True,axis=1)+rwExp.sum(keepdims=True,axis=1);#remains uncertain,keepdims, and the # of axis\r\n twExp/=Z;\r\n rwExp/=Z;\r\n probs=tensor.dot(twExp,tw_vocab_overlap)+rwExp;\r\n next_outputs = self.sampleTarget.emit(probs);\r\n next_costs = self.sampleTarget.cost(next_outputs, probs)\r\n next_costs=-tensor.log(next_costs);\r\n next_feedback = self.readout.feedback(next_outputs)\r\n next_inputs = (self.fork.apply(next_feedback, as_dict=True)\r\n if self.fork else {'feedback': next_feedback})\r\n next_states = self.transition.compute_states(\r\n as_list=True,\r\n **dict_union(next_inputs, states, next_glimpses, contexts,topical_word_contexts))\r\n return (next_states + [next_outputs] +\r\n list(next_glimpses.values()) + [next_costs])\r\n\r\n @generate.delegate\r\n def generate_delegate(self):\r\n return self.transition.apply\r\n\r\n @generate.property('states')\r\n def generate_states(self):\r\n return self._state_names + ['outputs'] + self._glimpse_names\r\n\r\n @generate.property('outputs')\r\n def generate_outputs(self):\r\n return (self._state_names + ['outputs'] +\r\n self._glimpse_names + ['costs'])\r\n\r\n @generate.property('contexts')\r\n def generate_contexts(self):\r\n return (self.transition.apply.contexts++self._topical_context_names+[self.content_name]+[self.topical_name]\r\n +['tw_vocab_overlap'])\r\n\r\n def get_dim(self, name):\r\n if name in (self._state_names + self._context_names +\r\n self._glimpse_names):\r\n return self.transition.get_dim(name)\r\n elif name == 'outputs':\r\n return self.readout.get_dim(name)\r\n return super(BaseSequenceGenerator, self).get_dim(name)\r\n\r\n @application\r\n def initial_states(self, batch_size, *args, **kwargs):\r\n # TODO: support dict of outputs for application methods\r\n # to simplify this code.\r\n state_dict = dict(\r\n self.transition.initial_states(\r\n batch_size, as_dict=True, *args, **kwargs),\r\n outputs=self.readout.initial_outputs(batch_size))\r\n return [state_dict[state_name]\r\n for state_name in self.generate.states]\r\n\r\n @initial_states.property('outputs')\r\n def initial_states_outputs(self):\r\n return self.generate.states\r\n","repo_name":"LynetteXing1991/TA-Seq2Seq","sub_path":"SequenceGenerator_forPickTopicWord.py","file_name":"SequenceGenerator_forPickTopicWord.py","file_ext":"py","file_size_in_byte":14844,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"72"}
+{"seq_id":"33068761021","text":"from __future__ import annotations\n\nimport inspect\nimport logging\nimport os\nimport warnings\nfrom abc import ABCMeta, abstractmethod\nfrom collections.abc import Sequence\nfrom uuid import uuid4\n\nimport numpy as np\nimport rich\nimport torch\nfrom anndata import AnnData\nfrom mudata import MuData\n\nfrom scvi import REGISTRY_KEYS, settings\nfrom scvi._types import AnnOrMuData, MinifiedDataType\nfrom scvi.autotune._types import TunableMixin\nfrom scvi.data import AnnDataManager\nfrom scvi.data._compat import registry_from_setup_dict\nfrom scvi.data._constants import (\n _MODEL_NAME_KEY,\n _SCVI_UUID_KEY,\n _SETUP_ARGS_KEY,\n _SETUP_METHOD_NAME,\n)\nfrom scvi.data._utils import _assign_adata_uuid, _check_if_view, _get_adata_minify_type\nfrom scvi.dataloaders import AnnDataLoader\nfrom scvi.model._utils import parse_device_args\nfrom scvi.model.base._utils import _load_legacy_saved_files\nfrom scvi.utils import attrdict, setup_anndata_dsp\nfrom scvi.utils._docstrings import devices_dsp\n\nfrom ._utils import _initialize_model, _load_saved_files, _validate_var_names\n\nlogger = logging.getLogger(__name__)\n\n\n_UNTRAINED_WARNING_MESSAGE = \"Trying to query inferred values from an untrained model. Please train the model first.\"\n\n_SETUP_INPUTS_EXCLUDED_PARAMS = {\"adata\", \"mdata\", \"kwargs\"}\n\n\nclass BaseModelMetaClass(ABCMeta):\n \"\"\"Metaclass for :class:`~scvi.model.base.BaseModelClass`.\n\n Constructs model class-specific mappings for :class:`~scvi.data.AnnDataManager` instances.\n ``cls._setup_adata_manager_store`` maps from AnnData object UUIDs to :class:`~scvi.data.AnnDataManager` instances.\n This mapping is populated everytime ``cls.setup_anndata()`` is called.\n ``cls._per_isntance_manager_store`` maps from model instance UUIDs to AnnData UUID::class:`~scvi.data.AnnDataManager` mappings.\n These :class:`~scvi.data.AnnDataManager` instances are tied to a single model instance and populated either\n during model initialization or after running ``self._validate_anndata()``.\n \"\"\"\n\n @abstractmethod\n def __init__(cls, name, bases, dct):\n cls._setup_adata_manager_store: dict[\n str, type[AnnDataManager]\n ] = {} # Maps adata id to AnnDataManager instances.\n cls._per_instance_manager_store: dict[\n str, dict[str, type[AnnDataManager]]\n ] = {} # Maps model instance id to AnnDataManager mappings.\n super().__init__(name, bases, dct)\n\n\nclass BaseModelClass(TunableMixin, metaclass=BaseModelMetaClass):\n \"\"\"Abstract class for scvi-tools models.\n\n Notes\n -----\n See further usage examples in the following tutorials:\n\n 1. :doc:`/tutorials/notebooks/dev/model_user_guide`\n \"\"\"\n\n _data_loader_cls = AnnDataLoader\n\n def __init__(self, adata: AnnOrMuData | None = None):\n # check if the given adata is minified and check if the model being created\n # supports minified-data mode (i.e. inherits from the abstract BaseMinifiedModeModelClass).\n # If not, raise an error to inform the user of the lack of minified-data functionality\n # for this model\n data_is_minified = (\n adata is not None and _get_adata_minify_type(adata) is not None\n )\n if data_is_minified and not issubclass(type(self), BaseMinifiedModeModelClass):\n raise NotImplementedError(\n f\"The {type(self).__name__} model currently does not support minified data.\"\n )\n self.id = str(uuid4()) # Used for cls._manager_store keys.\n if adata is not None:\n self._adata = adata\n self._adata_manager = self._get_most_recent_anndata_manager(\n adata, required=True\n )\n self._register_manager_for_instance(self.adata_manager)\n # Suffix registry instance variable with _ to include it when saving the model.\n self.registry_ = self._adata_manager.registry\n self.summary_stats = self._adata_manager.summary_stats\n\n self.is_trained_ = False\n self._model_summary_string = \"\"\n self.train_indices_ = None\n self.test_indices_ = None\n self.validation_indices_ = None\n self.history_ = None\n\n @property\n def adata(self) -> AnnOrMuData:\n \"\"\"Data attached to model instance.\"\"\"\n return self._adata\n\n @adata.setter\n def adata(self, adata: AnnOrMuData):\n if adata is None:\n raise ValueError(\"adata cannot be None.\")\n self._validate_anndata(adata)\n self._adata = adata\n self._adata_manager = self.get_anndata_manager(adata, required=True)\n self.registry_ = self._adata_manager.registry\n self.summary_stats = self._adata_manager.summary_stats\n\n @property\n def adata_manager(self) -> AnnDataManager:\n \"\"\"Manager instance associated with self.adata.\"\"\"\n return self._adata_manager\n\n def to_device(self, device: str | int):\n \"\"\"Move model to device.\n\n Parameters\n ----------\n device\n Device to move model to. Options: 'cpu' for CPU, integer GPU index (eg. 0),\n or 'cuda:X' where X is the GPU index (eg. 'cuda:0'). See torch.device for more info.\n\n Examples\n --------\n >>> adata = scvi.data.synthetic_iid()\n >>> model = scvi.model.SCVI(adata)\n >>> model.to_device('cpu') # moves model to CPU\n >>> model.to_device('cuda:0') # moves model to GPU 0\n >>> model.to_device(0) # also moves model to GPU 0\n \"\"\"\n my_device = torch.device(device)\n self.module.to(my_device)\n\n @property\n def device(self) -> str:\n \"\"\"The current device that the module's params are on.\"\"\"\n return self.module.device\n\n @staticmethod\n def _get_setup_method_args(**setup_locals) -> dict:\n \"\"\"Returns a dictionary organizing the arguments used to call ``setup_anndata``.\n\n Must be called with ``**locals()`` at the start of the ``setup_anndata`` method\n to avoid the inclusion of any extraneous variables.\n \"\"\"\n cls = setup_locals.pop(\"cls\")\n method_name = None\n if \"adata\" in setup_locals:\n method_name = \"setup_anndata\"\n elif \"mdata\" in setup_locals:\n method_name = \"setup_mudata\"\n\n model_name = cls.__name__\n setup_args = {}\n for k, v in setup_locals.items():\n if k not in _SETUP_INPUTS_EXCLUDED_PARAMS:\n setup_args[k] = v\n return {\n _MODEL_NAME_KEY: model_name,\n _SETUP_METHOD_NAME: method_name,\n _SETUP_ARGS_KEY: setup_args,\n }\n\n @staticmethod\n def _create_modalities_attr_dict(\n modalities: dict[str, str], setup_method_args: dict\n ) -> attrdict:\n \"\"\"Preprocesses a ``modalities`` dictionary used in ``setup_mudata()`` to map modality names.\n\n Ensures each field key has a respective modality key, defaulting to ``None``.\n Raises a ``UserWarning`` if extraneous modality mappings are detected.\n\n Parameters\n ----------\n modalities\n Dictionary mapping ``setup_mudata()`` argument name to modality name.\n setup_method_args\n Output of ``_get_setup_method_args()``.\n \"\"\"\n setup_args = setup_method_args[_SETUP_ARGS_KEY]\n filtered_modalities = {\n arg_name: modalities.get(arg_name, None) for arg_name in setup_args.keys()\n }\n extra_modalities = set(modalities) - set(filtered_modalities)\n if len(extra_modalities) > 0:\n raise ValueError(\n f\"Extraneous modality mapping(s) detected: {extra_modalities}\"\n )\n return attrdict(filtered_modalities)\n\n @classmethod\n def register_manager(cls, adata_manager: AnnDataManager):\n \"\"\"Registers an :class:`~scvi.data.AnnDataManager` instance with this model class.\n\n Stores the :class:`~scvi.data.AnnDataManager` reference in a class-specific manager store.\n Intended for use in the ``setup_anndata()`` class method followed up by retrieval of the\n :class:`~scvi.data.AnnDataManager` via the ``_get_most_recent_anndata_manager()`` method in\n the model init method.\n\n Notes\n -----\n Subsequent calls to this method with an :class:`~scvi.data.AnnDataManager` instance referring to the same\n underlying AnnData object will overwrite the reference to previous :class:`~scvi.data.AnnDataManager`.\n \"\"\"\n adata_id = adata_manager.adata_uuid\n cls._setup_adata_manager_store[adata_id] = adata_manager\n\n def _register_manager_for_instance(self, adata_manager: AnnDataManager):\n \"\"\"Registers an :class:`~scvi.data.AnnDataManager` instance with this model instance.\n\n Creates a model-instance specific mapping in ``cls._per_instance_manager_store`` for this\n :class:`~scvi.data.AnnDataManager` instance.\n \"\"\"\n if self.id not in self._per_instance_manager_store:\n self._per_instance_manager_store[self.id] = {}\n\n adata_id = adata_manager.adata_uuid\n instance_manager_store = self._per_instance_manager_store[self.id]\n instance_manager_store[adata_id] = adata_manager\n\n def deregister_manager(self, adata: AnnData | None = None):\n \"\"\"Deregisters the :class:`~scvi.data.AnnDataManager` instance associated with `adata`.\n\n If `adata` is `None`, deregisters all :class:`~scvi.data.AnnDataManager` instances\n in both the class and instance-specific manager stores, except for the one associated\n with this model instance.\n \"\"\"\n cls_manager_store = self._setup_adata_manager_store\n instance_manager_store = self._per_instance_manager_store[self.id]\n\n if adata is None:\n instance_managers_to_clear = list(instance_manager_store.keys())\n cls_managers_to_clear = list(cls_manager_store.keys())\n else:\n adata_manager = self._get_most_recent_anndata_manager(adata, required=True)\n cls_managers_to_clear = [adata_manager.adata_uuid]\n instance_managers_to_clear = [adata_manager.adata_uuid]\n\n for adata_id in cls_managers_to_clear:\n # don't clear the current manager by default\n is_current_adata = (\n adata is None and adata_id == self.adata_manager.adata_uuid\n )\n if is_current_adata or adata_id not in cls_manager_store:\n continue\n del cls_manager_store[adata_id]\n\n for adata_id in instance_managers_to_clear:\n # don't clear the current manager by default\n is_current_adata = (\n adata is None and adata_id == self.adata_manager.adata_uuid\n )\n if is_current_adata or adata_id not in instance_manager_store:\n continue\n del instance_manager_store[adata_id]\n\n @classmethod\n def _get_most_recent_anndata_manager(\n cls, adata: AnnOrMuData, required: bool = False\n ) -> AnnDataManager | None:\n \"\"\"Retrieves the :class:`~scvi.data.AnnDataManager` for a given AnnData object specific to this model class.\n\n Checks for the most recent :class:`~scvi.data.AnnDataManager` created for the given AnnData object via\n ``setup_anndata()`` on model initialization. Unlike :meth:`scvi.model.base.BaseModelClass.get_anndata_manager`,\n this method is not model instance specific and can be called before a model is fully initialized.\n\n Parameters\n ----------\n adata\n AnnData object to find manager instance for.\n required\n If True, errors on missing manager. Otherwise, returns None when manager is missing.\n \"\"\"\n if _SCVI_UUID_KEY not in adata.uns:\n if required:\n raise ValueError(\n f\"Please set up your AnnData with {cls.__name__}.setup_anndata first.\"\n )\n return None\n\n adata_id = adata.uns[_SCVI_UUID_KEY]\n\n if adata_id not in cls._setup_adata_manager_store:\n if required:\n raise ValueError(\n f\"Please set up your AnnData with {cls.__name__}.setup_anndata first. \"\n \"It appears the AnnData object has been setup with a different model.\"\n )\n return None\n\n adata_manager = cls._setup_adata_manager_store[adata_id]\n if adata_manager.adata is not adata:\n raise ValueError(\n \"The provided AnnData object does not match the AnnData object \"\n \"previously provided for setup. Did you make a copy?\"\n )\n\n return adata_manager\n\n def get_anndata_manager(\n self, adata: AnnOrMuData, required: bool = False\n ) -> AnnDataManager | None:\n \"\"\"Retrieves the :class:`~scvi.data.AnnDataManager` for a given AnnData object specific to this model instance.\n\n Requires ``self.id`` has been set. Checks for an :class:`~scvi.data.AnnDataManager`\n specific to this model instance.\n\n Parameters\n ----------\n adata\n AnnData object to find manager instance for.\n required\n If True, errors on missing manager. Otherwise, returns None when manager is missing.\n \"\"\"\n cls = self.__class__\n if _SCVI_UUID_KEY not in adata.uns:\n if required:\n raise ValueError(\n f\"Please set up your AnnData with {cls.__name__}.setup_anndata first.\"\n )\n return None\n\n adata_id = adata.uns[_SCVI_UUID_KEY]\n if self.id not in cls._per_instance_manager_store:\n if required:\n raise AssertionError(\n \"Unable to find instance specific manager store. \"\n \"The model has likely not been initialized with an AnnData object.\"\n )\n return None\n elif adata_id not in cls._per_instance_manager_store[self.id]:\n if required:\n raise AssertionError(\n \"Please call ``self._validate_anndata`` on this AnnData object.\"\n )\n return None\n\n adata_manager = cls._per_instance_manager_store[self.id][adata_id]\n if adata_manager.adata is not adata:\n logger.info(\n \"AnnData object appears to be a copy. Attempting to transfer setup.\"\n )\n _assign_adata_uuid(adata, overwrite=True)\n adata_manager = self.adata_manager.transfer_fields(adata)\n self._register_manager_for_instance(adata_manager)\n\n return adata_manager\n\n def get_from_registry(\n self,\n adata: AnnOrMuData,\n registry_key: str,\n ) -> np.ndarray:\n \"\"\"Returns the object in AnnData associated with the key in the data registry.\n\n AnnData object should be registered with the model prior to calling this function\n via the ``self._validate_anndata`` method.\n\n Parameters\n ----------\n registry_key\n key of object to get from data registry.\n adata\n AnnData to pull data from.\n\n Returns\n -------\n The requested data as a NumPy array.\n \"\"\"\n adata_manager = self.get_anndata_manager(adata)\n if adata_manager is None:\n raise AssertionError(\n \"AnnData not registered with model. Call `self._validate_anndata` \"\n \"prior to calling this function.\"\n )\n return adata_manager.get_from_registry(registry_key)\n\n def _make_data_loader(\n self,\n adata: AnnOrMuData,\n indices: Sequence[int] | None = None,\n batch_size: int | None = None,\n shuffle: bool = False,\n data_loader_class=None,\n **data_loader_kwargs,\n ):\n \"\"\"Create a AnnDataLoader object for data iteration.\n\n Parameters\n ----------\n adata\n AnnData object with equivalent structure to initial AnnData.\n indices\n Indices of cells in adata to use. If `None`, all cells are used.\n batch_size\n Minibatch size for data loading into model. Defaults to `scvi.settings.batch_size`.\n shuffle\n Whether observations are shuffled each iteration though\n data_loader_class\n Class to use for data loader\n data_loader_kwargs\n Kwargs to the class-specific data loader class\n \"\"\"\n adata_manager = self.get_anndata_manager(adata)\n if adata_manager is None:\n raise AssertionError(\n \"AnnDataManager not found. Call `self._validate_anndata` prior to calling this function.\"\n )\n\n adata = adata_manager.adata\n\n if batch_size is None:\n batch_size = settings.batch_size\n if indices is None:\n indices = np.arange(adata.n_obs)\n if data_loader_class is None:\n data_loader_class = self._data_loader_cls\n\n if \"num_workers\" not in data_loader_kwargs:\n data_loader_kwargs.update({\"num_workers\": settings.dl_num_workers})\n\n dl = data_loader_class(\n adata_manager,\n shuffle=shuffle,\n indices=indices,\n batch_size=batch_size,\n **data_loader_kwargs,\n )\n return dl\n\n def _validate_anndata(\n self, adata: AnnOrMuData | None = None, copy_if_view: bool = True\n ) -> AnnData:\n \"\"\"Validate anndata has been properly registered, transfer if necessary.\"\"\"\n if adata is None:\n adata = self.adata\n\n _check_if_view(adata, copy_if_view=copy_if_view)\n\n adata_manager = self.get_anndata_manager(adata)\n if adata_manager is None:\n logger.info(\n \"Input AnnData not setup with scvi-tools. \"\n + \"attempting to transfer AnnData setup\"\n )\n self._register_manager_for_instance(\n self.adata_manager.transfer_fields(adata)\n )\n else:\n # Case where correct AnnDataManager is found, replay registration as necessary.\n adata_manager.validate()\n\n return adata\n\n def _check_if_trained(\n self, warn: bool = True, message: str = _UNTRAINED_WARNING_MESSAGE\n ):\n \"\"\"Check if the model is trained.\n\n If not trained and `warn` is True, raise a warning, else raise a RuntimeError.\n \"\"\"\n if not self.is_trained_:\n if warn:\n warnings.warn(\n message, UserWarning, stacklevel=settings.warnings_stacklevel\n )\n else:\n raise RuntimeError(message)\n\n @property\n def is_trained(self) -> bool:\n \"\"\"Whether the model has been trained.\"\"\"\n return self.is_trained_\n\n @property\n def test_indices(self) -> np.ndarray:\n \"\"\"Observations that are in test set.\"\"\"\n return self.test_indices_\n\n @property\n def train_indices(self) -> np.ndarray:\n \"\"\"Observations that are in train set.\"\"\"\n return self.train_indices_\n\n @property\n def validation_indices(self) -> np.ndarray:\n \"\"\"Observations that are in validation set.\"\"\"\n return self.validation_indices_\n\n @train_indices.setter\n def train_indices(self, value):\n self.train_indices_ = value\n\n @test_indices.setter\n def test_indices(self, value):\n self.test_indices_ = value\n\n @validation_indices.setter\n def validation_indices(self, value):\n self.validation_indices_ = value\n\n @is_trained.setter\n def is_trained(self, value):\n self.is_trained_ = value\n\n @property\n def history(self):\n \"\"\"Returns computed metrics during training.\"\"\"\n return self.history_\n\n def _get_user_attributes(self):\n \"\"\"Returns all the self attributes defined in a model class, e.g., `self.is_trained_`.\"\"\"\n attributes = inspect.getmembers(self, lambda a: not (inspect.isroutine(a)))\n attributes = [\n a for a in attributes if not (a[0].startswith(\"__\") and a[0].endswith(\"__\"))\n ]\n attributes = [a for a in attributes if not a[0].startswith(\"_abc_\")]\n return attributes\n\n def _get_init_params(self, locals):\n \"\"\"Returns the model init signature with associated passed in values.\n\n Ignores the initial AnnData.\n \"\"\"\n init = self.__init__\n sig = inspect.signature(init)\n parameters = sig.parameters.values()\n\n init_params = [p.name for p in parameters]\n all_params = {p: locals[p] for p in locals if p in init_params}\n all_params = {\n k: v\n for (k, v) in all_params.items()\n if not isinstance(v, AnnData) and not isinstance(v, MuData)\n }\n # not very efficient but is explicit\n # separates variable params (**kwargs) from non variable params into two dicts\n non_var_params = [p.name for p in parameters if p.kind != p.VAR_KEYWORD]\n non_var_params = {k: v for (k, v) in all_params.items() if k in non_var_params}\n var_params = [p.name for p in parameters if p.kind == p.VAR_KEYWORD]\n var_params = {k: v for (k, v) in all_params.items() if k in var_params}\n\n user_params = {\"kwargs\": var_params, \"non_kwargs\": non_var_params}\n\n return user_params\n\n @abstractmethod\n def train(self):\n \"\"\"Trains the model.\"\"\"\n\n def save(\n self,\n dir_path: str,\n prefix: str | None = None,\n overwrite: bool = False,\n save_anndata: bool = False,\n save_kwargs: dict | None = None,\n **anndata_write_kwargs,\n ):\n \"\"\"Save the state of the model.\n\n Neither the trainer optimizer state nor the trainer history are saved.\n Model files are not expected to be reproducibly saved and loaded across versions\n until we reach version 1.0.\n\n Parameters\n ----------\n dir_path\n Path to a directory.\n prefix\n Prefix to prepend to saved file names.\n overwrite\n Overwrite existing data or not. If `False` and directory\n already exists at `dir_path`, error will be raised.\n save_anndata\n If True, also saves the anndata\n save_kwargs\n Keyword arguments passed into :func:`~torch.save`.\n anndata_write_kwargs\n Kwargs for :meth:`~anndata.AnnData.write`\n \"\"\"\n if not os.path.exists(dir_path) or overwrite:\n os.makedirs(dir_path, exist_ok=overwrite)\n else:\n raise ValueError(\n f\"{dir_path} already exists. Please provide another directory for saving.\"\n )\n\n file_name_prefix = prefix or \"\"\n save_kwargs = save_kwargs or {}\n\n if save_anndata:\n file_suffix = \"\"\n if isinstance(self.adata, AnnData):\n file_suffix = \"adata.h5ad\"\n elif isinstance(self.adata, MuData):\n file_suffix = \"mdata.h5mu\"\n self.adata.write(\n os.path.join(dir_path, f\"{file_name_prefix}{file_suffix}\"),\n **anndata_write_kwargs,\n )\n\n model_save_path = os.path.join(dir_path, f\"{file_name_prefix}model.pt\")\n\n # save the model state dict and the trainer state dict only\n model_state_dict = self.module.state_dict()\n\n var_names = self.adata.var_names.astype(str)\n var_names = var_names.to_numpy()\n\n # get all the user attributes\n user_attributes = self._get_user_attributes()\n # only save the public attributes with _ at the very end\n user_attributes = {a[0]: a[1] for a in user_attributes if a[0][-1] == \"_\"}\n\n torch.save(\n {\n \"model_state_dict\": model_state_dict,\n \"var_names\": var_names,\n \"attr_dict\": user_attributes,\n },\n model_save_path,\n **save_kwargs,\n )\n\n @classmethod\n @devices_dsp.dedent\n def load(\n cls,\n dir_path: str,\n adata: AnnOrMuData | None = None,\n accelerator: str = \"auto\",\n device: int | str = \"auto\",\n prefix: str | None = None,\n backup_url: str | None = None,\n ):\n \"\"\"Instantiate a model from the saved output.\n\n Parameters\n ----------\n dir_path\n Path to saved outputs.\n adata\n AnnData organized in the same way as data used to train model.\n It is not necessary to run setup_anndata,\n as AnnData is validated against the saved `scvi` setup dictionary.\n If None, will check for and load anndata saved with the model.\n %(param_accelerator)s\n %(param_device)s\n prefix\n Prefix of saved file names.\n backup_url\n URL to retrieve saved outputs from if not present on disk.\n\n Returns\n -------\n Model with loaded state dictionaries.\n\n Examples\n --------\n >>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save\n >>> model.get_....\n \"\"\"\n load_adata = adata is None\n _, _, device = parse_device_args(\n accelerator=accelerator,\n devices=device,\n return_device=\"torch\",\n validate_single_device=True,\n )\n\n (\n attr_dict,\n var_names,\n model_state_dict,\n new_adata,\n ) = _load_saved_files(\n dir_path,\n load_adata,\n map_location=device,\n prefix=prefix,\n backup_url=backup_url,\n )\n adata = new_adata if new_adata is not None else adata\n\n _validate_var_names(adata, var_names)\n\n registry = attr_dict.pop(\"registry_\")\n if _MODEL_NAME_KEY in registry and registry[_MODEL_NAME_KEY] != cls.__name__:\n raise ValueError(\n \"It appears you are loading a model from a different class.\"\n )\n\n if _SETUP_ARGS_KEY not in registry:\n raise ValueError(\n \"Saved model does not contain original setup inputs. \"\n \"Cannot load the original setup.\"\n )\n\n # Calling ``setup_anndata`` method with the original arguments passed into\n # the saved model. This enables simple backwards compatibility in the case of\n # newly introduced fields or parameters.\n method_name = registry.get(_SETUP_METHOD_NAME, \"setup_anndata\")\n getattr(cls, method_name)(\n adata, source_registry=registry, **registry[_SETUP_ARGS_KEY]\n )\n\n model = _initialize_model(cls, adata, attr_dict)\n model.module.on_load(model)\n model.module.load_state_dict(model_state_dict)\n\n model.to_device(device)\n model.module.eval()\n model._validate_anndata(adata)\n return model\n\n @classmethod\n def convert_legacy_save(\n cls,\n dir_path: str,\n output_dir_path: str,\n overwrite: bool = False,\n prefix: str | None = None,\n **save_kwargs,\n ) -> None:\n \"\"\"Converts a legacy saved model ( None:\n \"\"\"Print args used to setup a saved model.\n\n Parameters\n ----------\n dir_path\n Path to saved outputs.\n prefix\n Prefix of saved file names.\n \"\"\"\n registry = BaseModelClass.load_registry(dir_path, prefix)\n AnnDataManager.view_setup_method_args(registry)\n\n @staticmethod\n def load_registry(dir_path: str, prefix: str | None = None) -> dict:\n \"\"\"Return the full registry saved with the model.\n\n Parameters\n ----------\n dir_path\n Path to saved outputs.\n prefix\n Prefix of saved file names.\n\n Returns\n -------\n The full registry saved with the model\n \"\"\"\n attr_dict = _load_saved_files(dir_path, False, prefix=prefix)[0]\n\n # Legacy support for old setup dict format.\n if \"scvi_setup_dict_\" in attr_dict:\n raise NotImplementedError(\n \"Viewing setup args for pre v0.15.0 models is unsupported. \"\n \"Update your save files with ``convert_legacy_save`` first.\"\n )\n\n return attr_dict.pop(\"registry_\")\n\n def view_anndata_setup(\n self, adata: AnnOrMuData | None = None, hide_state_registries: bool = False\n ) -> None:\n \"\"\"Print summary of the setup for the initial AnnData or a given AnnData object.\n\n Parameters\n ----------\n adata\n AnnData object setup with ``setup_anndata`` or\n :meth:`~scvi.data.AnnDataManager.transfer_fields`.\n hide_state_registries\n If True, prints a shortened summary without details of each state registry.\n \"\"\"\n if adata is None:\n adata = self.adata\n try:\n adata_manager = self.get_anndata_manager(adata, required=True)\n except ValueError as err:\n raise ValueError(\n f\"Given AnnData not setup with {self.__class__.__name__}. \"\n \"Cannot view setup summary.\"\n ) from err\n adata_manager.view_registry(hide_state_registries=hide_state_registries)\n\n\nclass BaseMinifiedModeModelClass(BaseModelClass):\n \"\"\"Abstract base class for scvi-tools models that can handle minified data.\"\"\"\n\n @property\n def minified_data_type(self) -> MinifiedDataType | None:\n \"\"\"The type of minified data associated with this model, if applicable.\"\"\"\n return (\n self.adata_manager.get_from_registry(REGISTRY_KEYS.MINIFY_TYPE_KEY)\n if REGISTRY_KEYS.MINIFY_TYPE_KEY in self.adata_manager.data_registry\n else None\n )\n\n @abstractmethod\n def minify_adata(\n self,\n *args,\n **kwargs,\n ):\n \"\"\"Minifies the model's adata.\n\n Minifies the adata, and registers new anndata fields as required (can be model-specific).\n This also sets the appropriate property on the module to indicate that the adata is minified.\n\n Notes\n -----\n The modification is not done inplace -- instead the model is assigned a new (minified)\n version of the adata.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def _get_fields_for_adata_minification(minified_data_type: MinifiedDataType):\n \"\"\"Return the anndata fields required for adata minification of the given type.\"\"\"\n\n def _update_adata_and_manager_post_minification(\n self, minified_adata: AnnOrMuData, minified_data_type: MinifiedDataType\n ):\n \"\"\"Update the anndata and manager inplace after creating a minified adata.\"\"\"\n # Register this new adata with the model, creating a new manager in the cache\n self._validate_anndata(minified_adata)\n new_adata_manager = self.get_anndata_manager(minified_adata, required=True)\n # This inplace edits the manager\n new_adata_manager.register_new_fields(\n self._get_fields_for_adata_minification(minified_data_type)\n )\n # We set the adata attribute of the model as this will update self.registry_\n # and self.adata_manager with the new adata manager\n self.adata = minified_adata\n\n @property\n def summary_string(self):\n \"\"\"Summary string of the model.\"\"\"\n summary_string = super().summary_string\n summary_string += \"\\nModel's adata is minified?: {}\".format(\n hasattr(self, \"minified_data_type\") and self.minified_data_type is not None\n )\n return summary_string\n","repo_name":"scverse/scvi-tools","sub_path":"scvi/model/base/_base_model.py","file_name":"_base_model.py","file_ext":"py","file_size_in_byte":34481,"program_lang":"python","lang":"en","doc_type":"code","stars":1037,"dataset":"github-code","pt":"72"}
+{"seq_id":"20358782705","text":"from os import listdir, system\nfrom os.path import isfile, join\nimport sys\n\nRAW_PDF_DIR = 'rawPDFs'\nCHARACTER_BOX_DIR = 'CharacterBoxes'\n\nbatch, name, numPages = sys.argv[1:]\nnumPages = int(numPages)\n\n\nfor page in range(1,numPages+1):\n\t# print batch, name, page\n\tcmd = \"python FindCharactersOnPage.py {} {} {} > {}/{}-{}-page-{}-{}.csv\".format(batch, name, page, CHARACTER_BOX_DIR, batch, name, page, CHARACTER_BOX_DIR)\n\tsystem(cmd)\n","repo_name":"todd9527/cs229-project","sub_path":"allPagesFindCharactersOnPage.py","file_name":"allPagesFindCharactersOnPage.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"27436581592","text":"import re\nimport os \n\n# Read in Data\nf = open(os.path.dirname(os.path.abspath(__file__)) + '/day05.txt', 'r')\ncontent = f.read()\nf.close()\n\n## Split into instructions and create stack\ncontent = [x for x in content.split('\\n\\n')]\n\n## Format Instructions \ninstructions = content[1]\n# instructions example\n# instructions = \"\"\"move 1 from 2 to 1\n# move 3 from 1 to 3\n# move 2 from 2 to 1\n# move 1 from 1 to 2\n# \"\"\"\ninstructions = [x for x in instructions.split('\\n')]\ninstructions = [[int(y) for y in re.findall(r'\\d+', x)] for x in instructions]\n\n## Format Stack\nstack=content[0]\nstack = [\n ['H','R','B','D','Z','F','L','S'],\n ['T','B','M','Z','R'],\n ['Z','L','C','H','N','S'],\n ['S','C','F','J'],\n ['P','G','H','W','R','Z','B'],\n ['V','J','Z','G','D','N','M','T'],\n ['G','L','N','W','F','S','P','Q'],\n ['M','Z','R'],\n ['M','C','L','G','V','R','T']\n]\n\n# stack example\n# stack = [\n# ['z','n'],\n# ['m','c','d'],\n# ['p']\n# ]\n\nfor x in range(len(instructions)):\n if len(instructions[x]) == 0: break\n \n move = instructions[x][0]\n origin = instructions[x][1]-1\n destination = instructions[x][2]-1\n\n # Move from origin to destination reversing order - delete end of list where origin is\n moving = stack[origin][-move:]\n\n # Reverse list if part 1 \n # moving.reverse() \n\n stack[destination] = stack[destination] + moving\n stack[origin] = stack[origin][:-move]\n\n\nprint(''.join([x[-1] for x in stack]))\n","repo_name":"shaunksk/advent-of-code","sub_path":"2022/day05.py","file_name":"day05.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"27487129376","text":"import sqlite3\nfrom jNlp.jTokenize import jTokenize\n\nconn = sqlite3.connect('streaming_data.sqlite')\ncur = conn.cursor() \n\nresult = cur.execute(\"SELECT message FROM tweets WHERE date BETWEEN 'Fri Sep 08 00:00:00 +0000 2017' AND 'Fri Sep 08 00:05:00 +0000 2017'\")\nfhand = open('ja_stopword.txt', encoding =\"utf-8\")\nstop_words=[]\n\nfor messages in result:\n\tfor message in messages:\n\t\tt_message = jTokenize(message)\n\t\tprint('-'.join(t_message))\n\n\n#for words in fhand:\n#\twords = words.strip('\\n')\n#\tif words not in stop_words:\n#\t\tstop_words.append(words)\n\t\n\n#filtered_sentence = [w for w in msg_tokenized if not w in stop_words]\n#print(\"CLEANED:\")\n#print(filtered_sentence)\n","repo_name":"jesseulundo/Twitter_Anlytics","sub_path":"stop_words_tweet_part.py","file_name":"stop_words_tweet_part.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"16533088827","text":"import sys\n\nsys.stdin = open('c:\\\\Users\\\\multicampus\\\\Desktop\\\\vscode\\\\algorithm\\\\SWEA_D4\\\\1210\\\\input.txt')\n\n\ndef find(boardi, b):\n a = 100\n while a>0:\n if boardi[a][b+1]:\n while boardi[a][b+1]:\n b += 1\n a -= 1\n elif boardi[a][b-1]:\n while boardi[a][b-1]:\n b -= 1\n a -= 1\n else:\n a -= 1\n return b-1\n\n\nfor tc in range(10):\n tc = int(input())\n board = [ [0 for _ in range(102) ] ]\n for _ in range(100):\n board.append([0]+list(map(int, input().split()))+[0])\n finish = board[-1].index(2)\n result = find(board, int(finish))\n print(f'#{tc} {result}')\n","repo_name":"yuueuni/algorithm","sub_path":"swea/SWEA_D4/1210/1210.py","file_name":"1210.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"18191694586","text":"import csv\nimport time\nimport torch\nimport random\nimport numpy as np\n\nfrom tqdm import tqdm\nfrom torch.utils import data\nfrom datetime import datetime\nfrom torch.nn.utils.rnn import pad_sequence\nfrom dateutil.relativedelta import relativedelta\n\n\nrandom.seed(2022)\n\ndef toymd(time):\n return datetime.utcfromtimestamp(time)#.strftime('%Y-%m-%d')\n\ndef get_timebin_from_timestamp(times, timebins): # times: timestamps\n binfeature = np.zeros(len(timebins))\n for eachtime in times: \n binidxs = np.where(timebins <= toymd(eachtime))[0] # select an element\n \n if len(binidxs) != 0: \n target_bin_idx = binidxs[-1] \n binfeature[target_bin_idx] += 1 \n return binfeature\n\ndef get_timebin_from_timestamp4eachuser(times, timebins): # times: timestamps of consumed items \n time_bins = [] # frequency bins that will be used as features\n for eachtime in times: \n binfeature = np.zeros(len(timebins))\n\n binidxs = np.where(timebins <= toymd(eachtime))[0] # select an element\n \n if len(binidxs) != 0: \n target_bin_idx = binidxs[-1] \n binfeature[target_bin_idx] += 1 \n time_bins.append(binfeature)\n return time_bins\n\nclass SEQRS_Dataset(data.Dataset):\n \n def build_consumption_history(self, uirt):\n # Build a dictionary for user: items consumed by the user\n uidict = {}\n allitems = set()\n for u, i, _, _ in uirt.astype(int):\n if u not in uidict: uidict[u] = set()\n uidict[u].add(i)\n allitems.add(i)\n \n ui_cand_dict = {} \n for u in tqdm(uidict):\n allnegitems = list(allitems - uidict[u])\n truncated_negitems = allnegitems[:1000]\n ui_cand_dict[u] = np.array(truncated_negitems)\n# ui_cand_dict[u] = np.array(list(allitems - uidict[u]))\n \n # TODO: Its size increases proportionally \n \n return uidict, allitems, ui_cand_dict\n \n def __init__(self, path, trn_numneg, opt): \n dpath = '/'.join(path.split('/')[:-1])\n if dpath[-1] != '/': dpath += '/'\n dtype = path.split('/')[-1] # dtype {trn, vld, tst}\n \n self.opt = opt\n \n st = time.time() \n \n if dtype == 'trn': \n self.numneg = trn_numneg \n \n self.uirt = np.load(dpath+dtype)\n \n # userhist contains both items and times\n if dtype != 'tst': # trainig or validation\n userhist = np.load(dpath+'userhist', allow_pickle=True).item() \n itemhist = np.load(dpath+'itemhist', allow_pickle=True).item()\n neighborhist = np.load(dpath+'neighborhist', allow_pickle=True).item()\n\n itlist = [[iid, itemhist[iid]] for iid in itemhist]\n \n elif dtype == 'tst':\n userhist = np.load(dpath+'userhist_wvld', allow_pickle=True).item() \n itemhist = np.load(dpath+'itemhist_wvld', allow_pickle=True).item() \n neighborhist = np.load(dpath+'neighborhist_wvld', allow_pickle=True).item() \n \n itlist = [[iid, itemhist[iid]] for iid in itemhist]\n \n # recent item first\n for u in userhist: # + truncation wiht MAX # of users' hist\n itemseq, timeseq = userhist[u]\n reverse_idx = (-timeseq).argsort() \n userhist[u] = [itemseq[reverse_idx][:opt.maxhist], timeseq[reverse_idx][:opt.maxhist]] # NOTE: trauncation from the front is right because the descending sorting \n for i in neighborhist: # + truncation wiht MAX # of users' hist\n userseq, timeseq = neighborhist[i] \n reverse_idx = (-timeseq).argsort()\n neighborhist[i] = [userseq[reverse_idx][:opt.maxhist], timeseq[reverse_idx][:opt.maxhist]] \n for i in itemhist:\n timeseq = itemhist[i]\n reverse_idx = (-timeseq).argsort() \n itemhist[i] = timeseq[reverse_idx][:opt.maxhist] \n # itemhist[i] = timeseq[reverse_idx] # NOTE This version works well..\n\n # 3.1 Building bin basis\n trntimes = np.load(opt.dataset_path + '/trn')[:,-1].astype(int) # ascending sorted\n mintime = toymd(min(trntimes))\n \n timedelta = relativedelta(weeks=opt.binsize)\n\n if dtype == 'trn':\n trnfront_time = trntimes.max() - 60 * 60 * 24 * 7 * opt.period \n trnfront_idx = np.where(trntimes < trnfront_time)[0][-1] \n self.label_period_start_time = trntimes[trnfront_idx]\n trnlabeltime = toymd(self.label_period_start_time)\n \n bins = np.array([mintime + timedelta*i for i in range(1000) # quick implementation\n if mintime + timedelta*i < trnlabeltime] + [trnlabeltime]) \n \n self.bins = bins[-int(opt.bin_ratio * len(bins)):] # Time bin truncation\n elif dtype == 'vld':\n trnmaxtime = toymd(max(trntimes))\n \n bins = np.array([mintime + timedelta*i for i in range(1000) # quick implementation\n if mintime + timedelta*i < trnmaxtime] + [trnmaxtime]) \n \n self.bins = bins[-int(opt.bin_ratio * len(bins)):] \n elif dtype == 'tst':\n vld = [l for l in csv.reader(\n open('/'.join(opt.dataset_path.split('/')[:-1])+'/split/vld.csv'))]\n vldtimes = np.array(vld)[:,-1].astype(int) \n\n\n vldmaxtime = toymd(max(vldtimes)) \n\n bins = np.array([mintime + timedelta*i for i in range(1000) # quick implementation\n if mintime + timedelta*i < vldmaxtime] + [vldmaxtime]) \n self.bins = bins[-int(opt.bin_ratio * len(bins)):]\n\n\n # 3.2 Build time bins for each user's all consumed items \n self.freqbins = {}\n for u in userhist: \n consumed_iids = userhist[u][1]\n fb = get_timebin_from_timestamp4eachuser(consumed_iids, self.bins) \n self.freqbins[u] = np.array(fb) \n\n self.neighbor_user_freqbins = {}\n for i in neighborhist: \n self.neighbor_user_freqbins[i] = {} \n consumed_users, consumed_times = neighborhist[i]\n fb = get_timebin_from_timestamp4eachuser(consumed_times, self.bins) \n \n self.neighbor_user_freqbins[i]['user'] = consumed_users\n self.neighbor_user_freqbins[i]['time'] = consumed_times\n self.neighbor_user_freqbins[i]['freqbin'] = np.array(fb)\n \n\n # Data building for training/validation/test \n if dtype == 'trn': \n trndata = [[u, userhist[u]] for u in userhist] \n \n \n # #.Time bin processing for items\n trntimes = np.concatenate([i[1] for i in itlist]).astype(int)\n \n mintime = toymd(min(trntimes))\n trnmaxtime = toymd(max(trntimes))\n\n trnfront_time = trntimes.max() - 60 * 60 * 24 * 7 * opt.period \n trnfront_idx = np.where(trntimes < trnfront_time)[0][-1] \n label_period_st = trntimes[trnfront_idx] # st: start time\n \n\n self.item_fldict = {} # dictionary of (Item ID - feature, label)\n # item_feature, item_label = [], [] \n for iid in itemhist:\n item_time = itemhist[iid]\n\n feature = item_time[item_time= label_period_st)))\n\n binfeature = get_timebin_from_timestamp(feature, self.bins)\n\n self.item_fldict[iid] = [binfeature, label]\n\n\n self.first, self.second = zip(*trndata) # user and their sequential items with times\n self.third = np.zeros(len(self.first)) # Negative items. 'train_collate' does the job. \n \n # # Global maximum sacrifies some computation\n maxnum_neighbor = max([len(self.neighbor_user_freqbins[i]['user']) for i in self.neighbor_user_freqbins])\n maxnum_neighbor_time = max([len(self.neighbor_user_freqbins[i]['time']) for i in self.neighbor_user_freqbins])\n maxnum_neighbor_freqbin = max([len(self.neighbor_user_freqbins[i]['freqbin']) for i in self.neighbor_user_freqbins])\n\n self.fourth = []\n for i in range(len(self.first)):\n u = self.first[i]\n all_neighbors = []\n all_neighbors_time = []\n all_neighbors_freqbin =[]\n\n for j in self.second[i][0]:\n neighbors = torch.LongTensor(self.neighbor_user_freqbins[j]['user'])\n neighbors_time = torch.LongTensor(self.neighbor_user_freqbins[j]['time'])\n neighbors_freqbin = torch.LongTensor(self.neighbor_user_freqbins[j]['freqbin'])\n\n all_neighbors.append(neighbors)\n all_neighbors_time.append(neighbors_time)\n all_neighbors_freqbin.append(neighbors_freqbin)\n \n\n # First make sure padding to the maximum length\n all_neighbors[0] = torch.cat([all_neighbors[0], torch.zeros(maxnum_neighbor-len(all_neighbors[0]))])\n all_neighbors_time[0] = torch.cat([all_neighbors_time[0], torch.zeros(maxnum_neighbor_time-len(all_neighbors_time[0]))])\n all_neighbors_freqbin[0] = torch.cat([all_neighbors_freqbin[0], torch.zeros(maxnum_neighbor_freqbin-all_neighbors_freqbin[0].shape[0],all_neighbors_freqbin[0].shape[1])])\n\n all_neighbors = pad_sequence(all_neighbors, batch_first=True)\n all_neighbors_time = pad_sequence(all_neighbors_time, batch_first=True)\n all_neighbors_freqbin = pad_sequence(all_neighbors_freqbin, batch_first=True)\n\n self.fourth.append({'user':all_neighbors, 'time':all_neighbors_time, 'freqbin':all_neighbors_freqbin})\n \n self.numuser = len(set(self.uirt[:,0]))\n self.numitem = len(set(self.uirt[:,1]))\n \n\n elif dtype in ['vld', 'tst']: # Evaluation\n # Find user histories \n \n # Build validation data for ranking evaluation\n newuir = []\n for row in self.uirt: \n user = row[0]\n uhist = userhist[user] \n items = row[1:] # 1 positive item + 100 negative items\n newuir.append([[user, uhist], items])\n \n self.first, self.second = zip(*newuir)\n self.third = np.zeros(len(self.first)) # this is dummy data\n self.fourth = np.zeros(len(self.first)) # this is dummy data\n\n # # Global maximum sacrifies some computation\n maxnum_neighbor = max([len(self.neighbor_user_freqbins[i]['user']) for i in self.neighbor_user_freqbins])\n maxnum_neighbor_time = max([len(self.neighbor_user_freqbins[i]['time']) for i in self.neighbor_user_freqbins])\n maxnum_neighbor_freqbin = max([len(self.neighbor_user_freqbins[i]['freqbin']) for i in self.neighbor_user_freqbins])\n\n users = [i[0] for i in self.first]\n items = [row[1:] for row in self.uirt]\n self.fourth = []\n for i in range(len(users)):\n u = users[i]\n all_neighbors = []\n all_neighbors_time = []\n all_neighbors_freqbin =[]\n\n for j in items[i]:\n neighbors = torch.LongTensor(self.neighbor_user_freqbins[j]['user'])\n neighbors_time = torch.LongTensor(self.neighbor_user_freqbins[j]['time'])\n neighbors_freqbin = torch.LongTensor(self.neighbor_user_freqbins[j]['freqbin'])\n\n all_neighbors.append(neighbors)\n all_neighbors_time.append(neighbors_time)\n all_neighbors_freqbin.append(neighbors_freqbin)\n\n # Pdding here \n\n # First make sure padding to the maximum length\n all_neighbors[0] = torch.cat([all_neighbors[0], torch.zeros(maxnum_neighbor-len(all_neighbors[0]))])\n all_neighbors_time[0] = torch.cat([all_neighbors_time[0], torch.zeros(maxnum_neighbor_time-len(all_neighbors_time[0]))])\n all_neighbors_freqbin[0] = torch.cat([all_neighbors_freqbin[0], torch.zeros(maxnum_neighbor_freqbin-all_neighbors_freqbin[0].shape[0],all_neighbors_freqbin[0].shape[1])])\n\n all_neighbors = pad_sequence(all_neighbors, batch_first=True)\n all_neighbors_time = pad_sequence(all_neighbors_time, batch_first=True)\n all_neighbors_freqbin = pad_sequence(all_neighbors_freqbin, batch_first=True)\n\n self.fourth.append({'user':all_neighbors, 'time':all_neighbors_time, 'freqbin':all_neighbors_freqbin})\n\n\n self.item_fdict = {} # dictionary of (Item ID - feature) \n for iid in itemhist: \n item_time = itemhist[iid] \n feature = item_time\n \n # make time stamps to timebins\n binfeature = get_timebin_from_timestamp(feature, self.bins)\n \n self.item_fdict[iid] = binfeature\n\n \n \n print('{} data : {:.2}s'.format(dtype.upper(), time.time()-st))\n\n def __getitem__(self, index):\n # Training: [user, positive, negative]\n # Testing: [user, canidate item, label] \n return self.first[index], self.second[index], self.third[index], self.fourth[index]\n \n def __len__(self):\n \"\"\"Returns the total number of user-item pairs.\"\"\"\n return len(self.first)\n \n \n def train_collate(self, batch):\n # Input: [user, postive item, dummy]\n # Output: [user, positive item, negative item]\n batch = [i for i in filter(lambda x:x is not None, batch)]\n \n uids, items_times, _, neighbor_info = zip(*batch) # users and their history \n iids, raw_times = zip(*items_times) \n\n flatten_user = np.concatenate([[uids[i]]*len(iids[i]) for i in range(len(uids))])\n flatten_item = np.concatenate(iids) \n flatten_time = np.concatenate(raw_times)\n\n flatten_history = [[iids]*len(iids[i]) for i in range(len(iids))]\n\n # User's consumption history (items) to compute virtual features\n num_items = torch.LongTensor([len(i) for i in iids]) \n pad_items = pad_sequence([torch.LongTensor(it) for it in iids], batch_first=True)\n pad_items = torch.repeat_interleave(pad_items, num_items, dim=0)\n\n # Making data into tensor\n users = torch.LongTensor(flatten_user)\n items = torch.LongTensor(flatten_item)\n times = torch.LongTensor(flatten_time)\n\n \n pack_times = pad_sequence([torch.FloatTensor(ti) for ti in raw_times], batch_first=True)\n\n # NOTE: randomly-selecting negative samples shows almost similar performance to using items not consumed by users, while the random-sampling drastically reduces the computation time\n all_neg_items = np.random.randint(self.opt.numitem, size=(self.opt.numneg, len(users)))\n all_neg_items = torch.LongTensor(all_neg_items)\n\n # Flatten item (extrinsic data) \n flatten_items = items # Not matrix format but redundant data\n\n # Find feature & label for extrinsic prediction\n item_freqbins, item_labels = [], []\n for iid in flatten_items.tolist():\n ft, lb = self.item_fldict[iid]\n item_freqbins.append(ft)\n item_labels.append(lb)\n \n item_freqbins = pad_sequence([torch.LongTensor(itf) for itf in item_freqbins], batch_first=True)\n item_labels = torch.LongTensor(item_labels) \n \n # Intrinsic frequncy bins\n freqbins = [self.freqbins[u] for u in uids] \n pad_freqbins = pad_sequence([torch.FloatTensor(fb) for fb in freqbins], batch_first=True) \n pad_freqbins = torch.repeat_interleave(pad_freqbins, num_items, dim=0)\n\n pad_pack_times = torch.repeat_interleave(pack_times, num_items, dim=0)\n\n # data for neighborhodd (Extrinsic prediction)\n\n pad_neighbors = torch.cat([neighbor_info[i]['user'] for i in range(len(neighbor_info))], dim=0).long()\n pad_neighbors_time = torch.cat([neighbor_info[i]['time'] for i in range(len(neighbor_info))], dim=0)\n pad_neighbors_freqbin = torch.cat([neighbor_info[i]['freqbin'] for i in range(len(neighbor_info))], dim=0)\n\n return users, items, times, pad_freqbins, pad_items, pad_pack_times, all_neg_items, item_freqbins, item_labels, pad_neighbors, pad_neighbors_time, pad_neighbors_freqbin\n\n\n def test_collate(self, batch):\n batch = [i for i in filter(lambda x:x is not None, batch)]\n\n uinfo, cand_items, labels, neighbor_info = zip(*batch) \n \n uid, items_times = zip(*uinfo)\n items, times = zip(*items_times)\n\n users = torch.LongTensor(uid)\n labels = torch.LongTensor(labels)\n cand_items = torch.LongTensor(np.array(cand_items)) \n items = pad_sequence([torch.LongTensor(it) for it in items], batch_first=True)\n times = pad_sequence([torch.LongTensor(ti.astype(int)) for ti in times], batch_first=True)\n \n flat_canditems = cand_items.flatten().tolist()\n\n item_freqbins = torch.FloatTensor(np.array([self.item_fdict[i] for i in flat_canditems])) \n\n # Frequncy bins \n freqbins = [self.freqbins[u] for u in uid]\n maxfb = max([fb.shape[0] for fb in freqbins]) \n \n # Padding frequency bins\n numbins = len(self.bins)\n pad_freqbins = []\n for fb in freqbins:\n zeropad = np.zeros((maxfb - fb.shape[0], numbins))\n fb = np.concatenate([fb, zeropad])\n pad_freqbins.append(fb)\n pad_freqbins = np.array(pad_freqbins)\n pad_freqbins = torch.FloatTensor(pad_freqbins)\n \n all_user_info = [users, items, times, pad_freqbins] \n \n pad_neighbors = torch.cat([neighbor_info[i]['user'] for i in range(len(neighbor_info))], dim=0).long()\n pad_neighbors_time = torch.cat([neighbor_info[i]['time'] for i in range(len(neighbor_info))], dim=0)\n pad_neighbors_freqbin = torch.cat([neighbor_info[i]['freqbin'] for i in range(len(neighbor_info))], dim=0)\n\n return all_user_info, cand_items, labels, item_freqbins, pad_neighbors, pad_neighbors_time, pad_neighbors_freqbin\n\n \n\nclass DataLoader: \n def __init__(self, opt):\n self.opt = opt\n self.dpath = opt.dataset_path + '/'\n self.bs = opt.batch_size\n self.trn_numneg = opt.numneg\n \n self.trn_loader, self.vld_loader, self.tst_loader = self.get_loaders_for_seqrs() \n \n print((\"train/val/test/ divided by batch size {:d}/{:d}/{:d}\".format(\n len(self.trn_loader), len(self.vld_loader),len(self.tst_loader))))\n print(\"=\" * 80)\n \n def get_loaders_for_seqrs(self):\n print(\"\\n📋 Loading data...\\n\")\n trn_loader = self.get_each_loader(self.dpath+'trn', self.bs, self.trn_numneg, shuffle=True)\n vld_loader = self.get_each_loader(self.dpath+'vld', int(self.bs/2), self.trn_numneg, shuffle=False)\n tst_loader = self.get_each_loader(self.dpath+'tst', int(self.bs/2), self.trn_numneg, shuffle=False) \n \n return trn_loader, vld_loader, tst_loader\n \n def get_each_loader(self, data_path, batch_size, trn_negnum, shuffle=True, num_workers=0):\n \"\"\"Builds and returns Dataloader.\"\"\"\n\n dataset = SEQRS_Dataset(data_path, trn_negnum, self.opt)\n\n if data_path.endswith('trn'):\n collate = dataset.train_collate\n else:\n collate = dataset.test_collate\n\n data_loader = data.DataLoader(dataset=dataset,\n batch_size=batch_size,\n shuffle=shuffle,\n num_workers=num_workers,\n collate_fn=collate)\n\n return data_loader\n \n def get_loaders(self):\n return self.trn_loader, self.vld_loader, self.tst_loader\n \n def get_embedding(self):\n return self.input_embedding\n \n\n ","repo_name":"anon-subm/PERIS","sub_path":"dataloaders/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":20657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"72228909034","text":"import argparse\nimport torch\n\nparser = argparse.ArgumentParser(\"Average model parameters\")\nparser.add_argument(\"--model_list\", required=True, help=\"list of model to be averaged\")\nparser.add_argument(\"--output_file\", required=True, help=\"output model file\")\nargs = parser.parse_args()\n\nmodel_list = args.model_list\noutput_file = args.output_file\n\nwith open(model_list, 'r') as f:\n model_files = [line.strip() for line in f.readlines()]\ncount = len(model_files)\n\nstate_dict = {}\nfor i, model_file in enumerate(model_files):\n model_dict = torch.load(model_file, map_location='cpu')\n for key, param in model_dict.items():\n if i == 0:\n state_dict[key] = param\n else:\n assert key in state_dict\n state_dict[key] += param\nfor k in state_dict.keys():\n if state_dict[k] is not None:\n state_dict[k] = torch.true_divide(state_dict[k], count)\ntorch.save(state_dict, output_file)\n","repo_name":"tencent-ailab/3m-asr","sub_path":"tools/average_model.py","file_name":"average_model.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":110,"dataset":"github-code","pt":"72"}
+{"seq_id":"20663244975","text":"def run() -> int:\n sum_of_the_squares = sum(i ** 2 for i in range(1, 101))\n square_of_the_sum = sum(j for j in range(1, 101)) ** 2\n return square_of_the_sum - sum_of_the_squares\n\n\nif __name__ == \"__main__\":\n from timeit import default_timer as timer\n\n start = timer()\n print(\"Solution:\", run())\n print(\"Duration:\", timer() - start)\n","repo_name":"gregorgabrovsek/ProjectEuler","sub_path":"Problem006.py","file_name":"Problem006.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"5154735929","text":"from qtstrap import *\nfrom .constants import *\nimport pyqtgraph as pg\nimport numpy as np\n\n\nclass DataSelector(QWidget):\n selectionChanged = Signal()\n\n def __init__(self, name='', parent=None):\n super().__init__(parent=parent)\n self.name = name\n\n changed = self.selectionChanged\n \n self.on = PersistentCheckBox(f'{name}_on', changed=changed)\n self.points = PersistentCheckBox(f'{name}_points', changed=changed)\n self.line = PersistentCheckBox(f'{name}_line', changed=changed)\n self.freqs = PersistentListWidget(f'{name}_freqs', items=['none']+freqs, default=['none'], selectionMode=QAbstractItemView.ExtendedSelection, changed=changed)\n field_names = [data_field_names[f] for f in data_fields]\n self.x = PersistentComboBox(f'{name}_x', items=field_names, changed=changed)\n self.y = PersistentComboBox(f'{name}_y', items=field_names, changed=changed)\n\n with CVBoxLayout(self) as layout:\n with layout.hbox():\n layout.add(QLabel(name))\n layout.add(QLabel(), 1)\n layout.add(QLabel('Pts:'))\n layout.add(self.points)\n layout.add(QLabel('Line:'))\n layout.add(self.line)\n layout.add(QLabel('On:'))\n layout.add(self.on)\n\n with layout.hbox():\n layout.add(QLabel('X:'))\n layout.add(self.x, 1)\n\n with layout.hbox():\n layout.add(QLabel('Y:'))\n layout.add(self.y, 1)\n\n def get_params(self):\n if self.on.checkState() and self.x.currentText() != self.y.currentText():\n return {\n 'x': data_field_names.inverse[self.x.currentText()][0], \n 'y': data_field_names.inverse[self.y.currentText()][0], \n 'on': self.on.checkState(), \n 'points': self.points.checkState(), \n 'line': self.line.checkState(), \n 'freqs': self.freqs.selected_items(),\n }\n return\n\n\nclass GraphTab(QWidget):\n def __init__(self, parent=None):\n super().__init__(parent=parent)\n \n self.plot_layout = pg.GraphicsLayoutWidget()\n self.custom_plots = [DataSelector(f'Custom Plot {i}', self) for i in range(1, 5)]\n\n def normalize(x):\n x = np.asarray(x)\n return (x - x.min()) / (np.ptp(x))\n\n self.freq_colors = dict(zip(freqs, normalize([float(f) for f in freqs])))\n\n self.freqs = PersistentListWidget(\n 'graph_freqs', items=freqs, selectionMode=QAbstractItemView.ExtendedSelection, changed=self.draw_plots)\n self.freq_tabs = PersistentTabWidget('graph_tabs')\n self.freq_tabs.addTab(self.freqs, 'all')\n for plot in self.custom_plots:\n self.freq_tabs.addTab(plot.freqs, plot.name[11:])\n self.freq_tabs.restore_state()\n\n self.fwd_v = PersistentCheckBox('graph_fwd_v', changed=self.draw_plots)\n self.fwd_w = PersistentCheckBox('graph_fwd_w', changed=self.draw_plots)\n\n with CHBoxLayout(self) as layout:\n with layout.vbox():\n layout.add(QLabel('Reference Plots:'))\n with layout.hbox():\n layout.add(QLabel('Forward Volts:'))\n layout.add(self.fwd_v)\n with layout.hbox():\n layout.add(QLabel('Forward Watts:'))\n layout.add(self.fwd_w)\n layout.add(HLine())\n layout.add(QLabel('Custom Plot Freqs:'))\n layout.add(self.freq_tabs, 1)\n layout.add(self.custom_plots)\n layout.add(self.plot_layout, 1)\n\n self.data = {}\n\n for plot in self.custom_plots:\n plot.selectionChanged.connect(self.draw_plots)\n\n def get_color_map(self, freqs):\n x = np.asarray([float(f) for f in freqs])\n lower = x.min()\n upper = x.max()\n normals = (x - lower) / (upper - lower)\n\n return dict(zip(freqs, normals))\n\n def poly_to_points(self, x1, poly):\n x2 = range(int(max(x1)))\n y2 = [poly(p) for p in x2]\n return x2, y2\n\n def add_forward_volts_plot(self):\n title = 'Forward Volts'\n labels = {'bottom':'Meter: Forward Watts', 'left':'Target: Forward Volts'}\n plot = self.plot_layout.addPlot(title=title, labels=labels)\n self.plot_added()\n plot.showGrid(x=True, y=True)\n plot.showButtons()\n plot.addLegend()\n\n colors = self.get_color_map(freqs)\n for freq in freqs:\n points = [p for p in self.data if p['freq'] == freq]\n x = [p['m_fwd'] for p in points]\n y = [p['t_fwd_volts'] for p in points]\n\n poly = np.poly1d(np.polyfit(x, y, 2))\n x2, y2 = self.poly_to_points(x, poly)\n\n plot.plot(x2, y2, pen=pg.hsvColor(colors[freq]), name=freq)\n plot.plot(x, y, pen=None, symbol='o', symbolSize=3, symbolPen=pg.hsvColor(colors[freq]))\n\n def add_forward_watts_plot(self):\n title = 'Forward Watts with Reference Line'\n labels = {'bottom':'Meter: Forward Watts', 'left':'Target: Forward Watts'}\n plot = self.plot_layout.addPlot(title=title, labels=labels)\n self.plot_added()\n plot.showGrid(x=True, y=True)\n plot.showButtons()\n plot.addLegend()\n\n reference_points = list(range(0, 210, 10))\n plot.plot(reference_points, reference_points, pen='w')\n \n colors = self.get_color_map(freqs)\n for freq in freqs:\n points = [p for p in self.data if p['freq'] == freq]\n x = [p['m_fwd'] for p in points]\n y = [p['t_fwd_watts'] for p in points]\n\n poly = np.poly1d(np.polyfit(x, y, 2))\n x2, y2 = self.poly_to_points(x, poly)\n\n plot.plot(x2, y2, pen=pg.hsvColor(colors[freq]), name=freq)\n plot.plot(x, y, pen=None, symbol='o', symbolSize=3, symbolPen=pg.hsvColor(colors[freq]))\n\n def add_custom_plot(self, params):\n title = 'Custom Plot'\n labels = {'bottom':data_field_names[params['x']], 'left':data_field_names[params['y']]}\n plot = self.plot_layout.addPlot(title=title, labels=labels)\n plot.showGrid(x=True, y=True)\n plot.showButtons()\n plot.addLegend()\n\n self.plot_added()\n\n plot_freqs = params['freqs']\n if 'none' in params['freqs']:\n plot_freqs = freqs\n colors = self.get_color_map(plot_freqs)\n\n for freq in plot_freqs:\n points = [p for p in self.data if p['freq'] == freq]\n x = [p[params['x']] for p in points]\n y = [p[params['y']] for p in points]\n\n name = freq\n if params['line']:\n poly = np.poly1d(np.polyfit(x, y, 2))\n x2, y2 = self.poly_to_points(x, poly)\n\n plot.plot(x2, y2, pen=pg.hsvColor(colors[freq]), name=name)\n name = None\n if params['points']:\n plot.plot(x, y, pen=None, symbol='o', symbolSize=3, symbolPen=pg.hsvColor(colors[freq]), name=name)\n name = None\n\n def reset_plot(self):\n self.plot_layout.clear()\n self.number_of_plots = 0\n\n def plot_added(self):\n self.number_of_plots += 1\n if self.number_of_plots % 2 == 0:\n self.plot_layout.nextRow()\n\n def draw_plots(self):\n freqs = self.freqs.selected_items()\n plot_params = [plot.get_params() for plot in self.custom_plots]\n self.reset_plot()\n \n if self.fwd_v.isChecked():\n self.add_forward_volts_plot()\n if self.fwd_w.isChecked():\n self.add_forward_watts_plot()\n\n for params in plot_params:\n if params is None:\n continue\n\n self.add_custom_plot(params)\n\n def set_data(self, data):\n self.data = data\n self.draw_plots()","repo_name":"DaelonSuzuka/DeviceManager","sub_path":"src/plugins/apps/calibration/graph_tab.py","file_name":"graph_tab.py","file_ext":"py","file_size_in_byte":7946,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"18165878657","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 10 18:39:51 2017\r\n\r\n@author: Sanju Menon\r\n\r\n*** hug -f sometests.py --manual_reload ***\r\n*** \r\nTF_IDF_Intent\r\nAmbiguousTable\r\nTable\r\npayroll_entity_extraction\r\nLocationExtraction\r\n\r\nexample-mesg.docx\r\npayroll_intent_example\r\n\r\n\r\n\r\n\"\"\"\r\n\r\nfrom IntentWithPayload import get_intent_payload\r\n\r\n\r\n\r\nimport hug\r\n\r\n \r\n@hug.cli()\r\n@hug.get()\r\n@hug.local()\r\ndef test(text: str):\r\n \r\n response = get_intent_payload(text)\r\n print(response)\r\n return {'response': response} \r\n\r\n\r\nif __name__ == '__main__':\r\n text = 'Meal Allowance \\n\\n Emp id: 052 – BTBPM1234X \\n\\t060 - GLXPS8254F'\r\n test(text)\r\n\r\n\r\n\r\n\r\n\r\n\r\n ","repo_name":"rosnet/payrollNLU","sub_path":"sometests.py","file_name":"sometests.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"22787825807","text":"from collections import defaultdict\r\n\r\nclass Solution:\r\n def findRedundantConnection(self, edges: List[List[int]]) -> List[int]:\r\n # Time: O(?)\r\n # Space: O(?)\r\n \r\n def arrive(current, end):\r\n # Prevent loop\r\n if current in seen:\r\n return\r\n seen.add(current)\r\n \r\n # Check if we've reached the goal\r\n if current == end:\r\n return True\r\n \r\n # Continue DFS\r\n for nextNode in adj[current]:\r\n if arrive(nextNode, end):\r\n return True\r\n \r\n # Relax edge\r\n seen.remove(current)\r\n \r\n # Initialize our adjacency list\r\n adj = defaultdict(list)\r\n \r\n # Keep building our graph up edge by edge, return the one that causes redundancy\r\n seen = set()\r\n \r\n for n1, n2 in edges:\r\n # If we arrive at n2, that means there's already a path connected - rendering this current edge redundant\r\n if arrive(n1, n2):\r\n return [n1, n2]\r\n \r\n # Safe to connect edge\r\n adj[n1].append(n2)\r\n adj[n2].append(n1)\r\n \r\n return","repo_name":"NaralC/Algorithms-Interview-Questions","sub_path":"Leetcode/Medium/0684-Redundant-Connection.py","file_name":"0684-Redundant-Connection.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"33311824131","text":"import os\nimport random\n\nfrom wrappers import MLMModelWrapper, MODEL_MAPPING\n\nimport sys\n\nimport torch\nfrom absl import app\nimport numpy as np\n\nfrom config import FLAGS\nfrom pathlib import Path\n\nfrom text_input_pipeline import GutenbergReader\nfrom my_utils.util import get_gpus_with_enough_memory\n\nFIXED_DEVICE_IDXS = None #[0]\n# CHOSEN_RUN_DIR = Path('output','constant','same')\nCHOSEN_RUN_DIR = Path('output','huggingface_baseline_encoder','no_pretrain')\n\n#TODO bring this up-to-date if gonna use\ndef main(_):\n FLAGS.device_idxs = get_gpus_with_enough_memory(\n 8000) if not FIXED_DEVICE_IDXS else FIXED_DEVICE_IDXS # Hack to not use flagvalue of og model when pertaining to GPU usage\n reader = GutenbergReader()\n data_dict = reader.get_data_dict()\n train_dataset, test_dataset, val_dataset, vocab = (data_dict[key] for key in\n ('train', 'test', 'val', 'vocab'))\n model = MLMModelWrapper(MODEL_MAPPING[FLAGS.model], vocab)\n model_device = f'cuda:{FLAGS.device_idxs[0]}' if len(FLAGS.device_idxs) != 0 else 'cpu'\n model = model.cuda(model_device)\n\n best_val_run_path = Path(latest_run_dir, 'best.th')\n model_states = [Path(latest_run_dir, m) for m in os.listdir(latest_run_dir) if 'model_state_epoch_' in m]\n latest_run_path = max(model_states, key=os.path.getmtime)\n\n for trained_model_path in (best_val_run_path, latest_run_path):\n print(f'Testing {trained_model_path}')\n model.load_state_dict(torch.load(trained_model_path,map_location=torch.device(model_device)))\n model = model.cuda(model_device)\n model.eval() # Set to eval mode\n for name, dataset in zip(('Train', 'Test', 'Val'), (train_dataset, test_dataset, val_dataset)):\n print(f'Testing {name}')\n instances = random.sample(dataset, 1)\n predictions = model.forward_on_instances(\n instances)\n input_texts = [tokens_to_mask_aware_text(model.token_indexer,\n model.token_indexer.convert_ids_to_tokens(\n instances[batch_sample].fields['input_ids'].array),\n predictions[batch_sample]['mask'])\n for batch_sample in range(len(predictions))]\n input_ids = torch.cat([instance.as_tensor_dict()['input_ids'][None,:] for instance in instances]).numpy()\n prediction_ids = np.vstack([pred['vocab_scores'].argmax(1)[None,:] for pred in predictions])\n mask = np.vstack([predictions[batch_sample]['mask'] for batch_sample in range(len(predictions))])\n filled_in_predictions = np.where(mask,\n prediction_ids,\n input_ids)\n predicted_texts = [tokens_to_mask_aware_text(model.token_indexer,\n model.token_indexer.convert_ids_to_tokens(\n predictions[batch_sample]['vocab_scores'].argmax(1)),\n predictions[batch_sample]['mask'])\n for batch_sample in range(len(predictions))]\n\n filled_in_predicted_texts = [tokens_to_mask_aware_text(model.token_indexer,\n model.token_indexer.convert_ids_to_tokens(\n filled_in_predictions[batch_sample]),\n predictions[batch_sample]['mask'])\n for batch_sample in range(len(predictions))]\n for input_text, predicted_text, filled_in_predicted_text in zip(input_texts, predicted_texts, filled_in_predicted_texts):\n print(f'Input: {input_text}')\n print(f'Filled in Prediction: {filled_in_predicted_text}')\n print(f'All output: {predicted_text}')\n print(' ')\n\n\n# Copied from HF transformers\ndef tokens_to_mask_aware_text(tokenizer, filtered_tokens, mask,\n skip_special_tokens=False, clean_up_tokenization_spaces=True):\n # To avoid mixing byte-level and unicode for byte-level BPT\n # we need to build string separately for added tokens and byte-level tokens\n # cf. https://github.com/huggingface/transformers/issues/1133\n sub_texts = []\n current_sub_text = []\n for token, isMasked in zip(filtered_tokens, mask):\n if skip_special_tokens and token in tokenizer.all_special_ids:\n continue\n if token in tokenizer.added_tokens_encoder:\n if current_sub_text:\n sub_texts.append(tokenizer.convert_tokens_to_string(current_sub_text))\n current_sub_text = []\n sub_texts.append(token)\n else:\n if isMasked:\n current_sub_text.append(tokenizer.mask_token)\n current_sub_text.append(token)\n current_sub_text.append(tokenizer.mask_token)\n else:\n current_sub_text.append(token)\n if current_sub_text:\n sub_texts.append(tokenizer.convert_tokens_to_string(\n current_sub_text))\n text = \" \".join(sub_texts)\n\n if clean_up_tokenization_spaces:\n clean_text = tokenizer.clean_up_tokenization(text)\n return clean_text\n else:\n return text\n\n\nif __name__ == '__main__':\n all_runs = [Path('.', 'output', model_dir, run)\n for model_dir in os.listdir(Path('.', 'output')) for run in os.listdir(Path('.', 'output', model_dir))\n if os.path.isdir(Path('.', 'output', model_dir)) and os.path.isdir(Path('.', 'output', model_dir, run))]\n latest_run_dir = max(all_runs, key=os.path.getmtime) if not CHOSEN_RUN_DIR else CHOSEN_RUN_DIR\n flagfile = Path(latest_run_dir, 'flagfile.txt')\n app.run(main, sys.argv + [f'--flagfile={flagfile}'])\n","repo_name":"Natithan/DIRT","sub_path":"src/check_predictions_qualitative.py","file_name":"check_predictions_qualitative.py","file_ext":"py","file_size_in_byte":6071,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"22636704202","text":"import cv2\nimport numpy as np\n\n######### ex-01 ############\n# 검은색 사각형 안에 흰색 사각형\n# image_rectangle = np.ones((400,400), dtype='uint8') # 검은색 정사각형 생성\n# cv2.rectangle(image_rectangle, (50,50),(300,300),(255,255,255), -1) # 안에 들어갈 흰색 사각형\n# cv2.imshow(\"image show\", image_rectangle)\n# cv2.waitKey(0)\n\n\n######### ex-02 ############\n# 검은색 사각형 안에 흰색 원\n# img_circle = np.ones((400,400), dtype='uint8')\n# cv2.circle(img_circle, (300,300), 70, (255,255,255), -1)\n# cv2.imshow(\"image show\", img_circle)\n# cv2.waitKey(0)\n\n######### ex-03 ############\n#### 비트 연산에 대해 알아보자.(합,교집합으로 쉽게 이해)\n# 위에 2줄씩 가져옴.\nimg_rectangle = np.ones((400,400), dtype='uint8')\ncv2.rectangle(img_rectangle, (50,50),(300,300),(255,255,255), -1) \nimg_circle = np.ones((400,400), dtype='uint8')\ncv2.circle(img_circle, (300,300), 70, (255,255,255), -1)\n\n\n### AND\n# bitwiseAnd = cv2.bitwise_and(img_rectangle, img_circle)\n# cv2.imshow(\"image bitwiseAnd\", bitwiseAnd)\n# cv2.waitKey(0)\n\n### OR\n# bitwiseOr = cv2.bitwise_or(img_rectangle, img_circle)\n# cv2.imshow(\"image bitwiseOr\", bitwiseOr)\n# cv2.waitKey(0)\n\n### XOR (교집합)\n# bitwiseXor = cv2.bitwise_xor(img_rectangle, img_circle)\n# cv2.imshow(\"image bitwiseXor\", bitwiseXor)\n# cv2.waitKey(0)\n\n### NOT\n# rectangle기준\n# bitwiseNot = cv2.bitwise_not(img_rectangle)\n# cv2.imshow(\"rectangle Not\", bitwiseNot)\n# cv2.waitKey(0)\n\n\n# circle기준\n# bitwiseNot = cv2.bitwise_not(img_circle)\n# cv2.imshow(\"circle Not\", bitwiseNot)\n# cv2.waitKey(0)\n\n\n######### ex-04 마스킹 ############\nmask = np.zeros((683,1024,3), dtype='uint8') # 683, 1024뒤에 3 안붙이면 흑백만 나옴(컬러가 안된다고 함.)\ncv2.rectangle(mask, (60,50), (280,280), (255,255,255), -1)\ncv2.rectangle(mask, (420,50), (550,230), (255,255,255), -1)\ncv2.rectangle(mask, (750,50), (920,280), (255,255,255), -1) \ncv2.imshow('....', mask)\ncv2.waitKey(0)\n\n#################### 과제 ##########################\n# img = cv2.imread('./face.png')\n# print(img.shape)\n# mask = np.zeros_like(img)\n# # mask = np.zeros((300,332), dtype='uint8')\n# # cv2.rectangle(mask, (60,50), (280,280), (255,255,255), -1)\n# # cv2.rectangle(mask, (420,50), (550,230), (255,255,255), -1)\n# # cv2.rectangle(mask, (750,50), (920,280), (255,255,255), -1)\n# masked = cv2.bitwise_and(img, mask)\n# cv2.imshow('....', masked)\n# cv2.waitKey(0)","repo_name":"Byunggu-Son/MS_AI_School","sub_path":"이미지 다루기 및 데이터셋 구축/DAY47-22_12_08_image preprocessing & OpenCV/ex_02.py","file_name":"ex_02.py","file_ext":"py","file_size_in_byte":2435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"16781265632","text":"#!/usr/bin/env python3\n\nimport cryptolib\n\n## SECRET DATA\nPREF = b'comment1=cooking%20MCs;userdata='\nSUFF = b';comment2=%20like%20a%20pound%20of%20bacon'\nBS = 16\nIV = cryptolib.randbin(BS)\nKey = cryptolib.randbin(BS)\n##\n\ndef encrypt(userdata: str) -> bytes:\n # NB1: The statement is unclear about \"quoting out\" ';' and '=' from the whole\n # data or only from the userdata part. We consider here that it makes more\n # sense to only apply this to the userdata. This does not change anything\n # to the attack anyway...\n # NB2: We encode the data here. Otherwise, it would simply be possible to\n # inject arbitrary data and to build an arbitrary CBC encrypted message\n # block-by-block past the prefix (no need for the bitflipping attack).\n userdata = userdata.replace(';','\";\"').replace('=','\"=\"').encode()\n data = PREF + userdata + SUFF\n return cryptolib.AES_CBC_encrypt(Key, IV, cryptolib.pad(data, BS))\n\nTARGET = b';admin=true;'\ndef decrypt(ciph: bytes) -> bool:\n data = cryptolib.unpad(cryptolib.AES_CBC_decrypt(Key, IV, ciph), BS)\n # NB: The attack would not work if we had decoded the data here (as the\n # modified encrypted block will be decrypted to seemingly random data).\n print(data)\n return TARGET in data\n\nif __name__=='__main__':\n assert not decrypt(encrypt(TARGET.decode()))\n # We have seen in previous challenges how to detect:\n # - ECB/CBC mode\n # - the block size BS\n # - the size of the prefix and suffix data\n # hence, to focus on the attack, we assume here that\n # we already know it is CBC, BS and the prefix size.\n PREF_SIZE = len(PREF)\n target = bytearray(TARGET)\n # we flip the parity bit of the filtered bytes of the target\n flips = [i for i,c in enumerate(target) if c in b';=']\n for b in flips:\n target[b] ^= 1\n target = target.decode()\n # we inject the userdata\n r = (-PREF_SIZE)%BS\n ciph = encrypt('0'*r + target)\n # and flip the corresponding bits in the previous block\n off = ((PREF_SIZE-1)//BS)*BS\n ciph = bytearray(ciph)\n for b in flips:\n ciph[off+b] ^= 1\n # this block will be decrypted to seemingly random data\n # the the next one will be corrected to the target\n assert decrypt(ciph)\n","repo_name":"blegloannec/cryptopals","sub_path":"2/2.16.py","file_name":"2.16.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"15384211147","text":"import openai\nimport r2pipe\nimport sys\nimport os\n\nopenai.api_key = os.environ['OPENAI_API_KEY']\n\ndef createR2Pipe():\n try:\n pipe = r2pipe.open()\n pipe.cmd('a')\n return pipe\n except:\n print('Unexpected error')\n return None\n\n\ndef askGPT(func: str) -> str:\n resp = openai.ChatCompletion.create(\n model=\"gpt-3.5-turbo\",\n messages=[\n {\"role\": \"user\", \"content\": \"How does this function work\"},\n {\"role\": \"user\", \"content\": func}\n ]\n )\n\n return resp['choices'][0]['message']['content']\n\n\ndef decompile(func: str, pipe) -> str:\n return pipe.cmd(f'pdg @{func}')\n\n\ndef main(func):\n pipe = createR2Pipe()\n dec = decompile(func, pipe)\n\n resp = askGPT(dec)\n print(resp)\n\nif __name__ == '__main__':\n argv = sys.argv\n if len(argv) != 2:\n print(f'Usage: {argv[0]} function (addr|name)')\n exit(1)\n func = sys.argv[1]\n main(func)\n","repo_name":"n01e0/r2gpt","sub_path":"r2gpt.py","file_name":"r2gpt.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"73231415573","text":"\"\"\"\n* Assignment: Loop For Text\n* Required: no\n* Complexity: medium\n* Lines of code: 14 lines\n* Time: 13 min\n\nEnglish:\n 1. Given is text of the \"Moon Speech\" by John F. Kennedy's [1]\n 2. Sentences are separated by period (`.`)\n 3. Clean each sentence from whitespaces at the beginning and at the end\n 4. Words are separated by spaces\n 5. Print the total number in whole text:\n a. adverbs (words ending with \"ly\")\n b. sentences\n c. words\n d. letters\n e. characters (including spaces inside sentences, but not comas `,`)\n f. comas (`,`)\n 6. Run doctests - all must succeed\n\nPolish:\n 1. Dany jest tekst przemówienia \"Moon Speech\" wygłoszonej\n przez John F. Kennedy'ego [1]\n 2. Zdania oddzielone są kropkami (`.`)\n 3. Każde zdanie oczyść z białych znaków na początku i końcu\n 4. Słowa oddzielone są spacjami\n 5. Wypisz także ile jest łącznie w całym tekście:\n a. przysłówków (słów zakończonych na \"ly\")\n b. zdań\n c. słów\n d. liter\n e. znaków (łącznie ze spacjami wewnątrz zdań, ale bez przecinków `,`)\n f. przecinków (`,`)\n 6. Uruchom doctesty - wszystkie muszą się powieść\n\nReferences:\n [1] Kennedy, J.F. Moon Speech - Rice Stadium.\n Year: 1962.\n Retrieved: 2021-03-06.\n URL: http://er.jsc.nasa.gov/seh/ricetalk.htm\n\nTests:\n >>> import sys; sys.tracebacklimit = 0\n\n >>> assert result is not Ellipsis, \\\n 'Assign your result to variable `result`'\n >>> assert type(result) is dict, \\\n 'Variable `result` has invalid type, should be dict'\n\n >>> print(result) # doctest: +NORMALIZE_WHITESPACE\n {'sentences': 7,\n 'words': 71,\n 'characters': 347,\n 'letters': 283,\n 'commas': 1,\n 'adverbs': 0}\n\"\"\"\n\nTEXT = \"\"\"\n We choose to go to the Moon.\n We choose to go to the Moon in this decade and do the other things.\n Not because they are easy, but because they are hard.\n Because that goal will serve to organize and measure the best of our energies and skills.\n Because that challenge is one that we are willing to accept.\n One we are unwilling to postpone.\n And one we intend to win\n\"\"\"\n\n# Number of occurrences of each grammar object\n# type: dict[str,int]\nresult = {\n 'sentences': 0,\n 'words': 0,\n 'characters': 0,\n 'letters': 0,\n 'commas': 0,\n 'adverbs': 0,\n}\n\n# Solution\nfor sentence in TEXT.split('.'):\n sentence = sentence.strip()\n words = sentence.split()\n letters = sentence.replace(',', '').replace(' ', '')\n characters = sentence.replace(',', '')\n comas = sentence.count(',')\n\n result['sentences'] += 1\n result['words'] += len(words)\n result['letters'] += len(letters)\n result['characters'] += len(characters)\n result['commas'] += comas\n\n for word in words:\n if word.endswith('ly'):\n result['adverbs'] += 1\n","repo_name":"astromatt/python3.info","sub_path":"basics/loop/assignments/loop_nested_c.py","file_name":"loop_nested_c.py","file_ext":"py","file_size_in_byte":2920,"program_lang":"python","lang":"en","doc_type":"code","stars":158,"dataset":"github-code","pt":"67"}
+{"seq_id":"26144924001","text":"import requests\n\n\nclass TestCourses:\n # Just for TokenAuthentication - Change in settings project\n # https://www.django-rest-framework.org/api-guide/authentication/#tokenauthentication\n headers = {'Authorization': 'Token 51caba258784f54c952f193d8df916b71d3b911c'}\n baseURL = 'http://localhost:8000/api/courses/'\n\n def test_get_courses(self):\n response = requests.get(url=self.baseURL, headers=self.headers)\n\n assert response.status_code == 200\n\n def test_get_course(self):\n response = requests.get(url=f'{self.baseURL}4')\n\n assert response.status_code == 200\n\n def test_post_course(self):\n data = {\n \"title\": \"Curso teste22\",\n \"url\": \"http://cursoteste22.com\"\n }\n response = requests.post(url=self.baseURL, headers=self.headers, data=data)\n\n assert response.status_code == 201\n assert response.json()['title'] == data['title']\n\n def test_put_course(self):\n data = {\n \"title\": \"test updated course\",\n \"url\": \"testupdated.com.br\"\n }\n response = requests.put(url=self.baseURL, headers=self.headers, data=data)\n\n assert response.status_code == 200\n assert response.json()['title'] == data['title']\n\n def test_delete_course(self):\n response = requests.delete(url=self.baseURL, headers=self.headers)\n\n assert response.status_code == 204 and len(response.text) == 0\n","repo_name":"isaias0rt0n/school-api","sub_path":"test_pytest.py","file_name":"test_pytest.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"24057828321","text":"from yandex_music import Client\nfrom yandex_music.exceptions import UnauthorizedError\nimport time\nimport vlc\nimport os\nimport sys\nimport random\n\n\nclear = lambda: os.system('cls')\n\ndef getClient():\n try:\n with open ('token.txt') as file:\n token = file.readline()\n\n client = Client(token).init()\n print('Success Authorization.')\n except UnauthorizedError:\n print('Your OAuth token is likely expired')\n sys.exit()\n except:\n print(\"Can't read \\\"token.txt\\\"\")\n sys.exit()\n\n return client\n\n\nclient = getClient()\nuserPlaylist = client.users_likes_tracks()\ntracks = len(userPlaylist.tracks)\ntracks = list(range(tracks))\nrandom.shuffle(tracks)\n\ntrackIndex = 0\n\nsongs = [{'name': '', 'track': userPlaylist[tracks[trackIndex]].fetch_track()},\n {'name': '', 'track': None}]\n\nwhile not(songs[0]['track'].available):\n trackIndex += 1\n songs[0]['track'] = userPlaylist[tracks[trackIndex]].fetch_track()\n\n if trackIndex >= len(tracks):\n print(\"All track unavailable.\")\n input('Press to exit')\n sys.exit()\n\n\nclear()\nprint('Downloading...')\nsongs[0]['track'].download('song0.mp3')\nname = ''\nfor artist in songs[0]['track'].artists:\n name += (artist.name + ', ')\nname = name[0:-2]\n \nsongs[0]['name'] = f\"{name} - {songs[0]['track'].title}\"\n\nqueue = 0\nwhile True:\n clear()\n print(songs[queue]['name'])\n player = vlc.MediaPlayer('song'+ str(queue) +'.mp3')\n player.play()\n \n queue = (queue + 1) % 2\n trackIndex += 1\n\n songs[queue]['track'] = userPlaylist[tracks[trackIndex]].fetch_track()\n while not(songs[queue]['track'].available):\n trackIndex += 1\n songs[queue]['track'] = userPlaylist[tracks[trackIndex]].fetch_track()\n\n if trackIndex >= len(tracks):\n trackIndex = 0\n songs[queue]['track'] = userPlaylist[tracks[trackIndex]].fetch_track()\n\n songs[queue]['track'].download('song'+ str(queue) +'.mp3')\n \n name = ''\n for artist in songs[queue]['track'].artists:\n name += (artist.name + ', ')\n name = name[0:-2]\n \n songs[queue]['name'] = f\"{name} - {songs[queue]['track'].title}\"\n\n length = player.get_length()\n\n while not(length):\n time.sleep(1)\n length = player.get_length()\n\n position = player.get_position()\n time.sleep((1 - position) * length / 1000)","repo_name":"John-Nerevarine/training-projects","sub_path":"yndx-msc/yndxMsc.py","file_name":"yndxMsc.py","file_ext":"py","file_size_in_byte":2377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"13942610556","text":"from mrjob.job import MRJob\n\nclass MRUsuariosPeliculasPro(MRJob):\n\n def mapper (self, _, line):\n fields = line.split(',') \n\n pelicula = fields[1]\n rating = float(fields[2])\n\n yield pelicula, (1, rating)\n\n def reducer (self, pelicula, valores):\n usuarios_totales = 0\n rating_total = 0\n\n for value in valores:\n usuarios_totales += value[0]\n rating_total += value[1]\n \n average_rating = rating_total / usuarios_totales\n yield pelicula, (usuarios_totales, average_rating)\n \nif __name__ == '__main__':\n MRUsuariosPeliculasPro.run()","repo_name":"mpocampod/Reto","sub_path":"retoCodigos/peliculas/usuarios_peliculas.py","file_name":"usuarios_peliculas.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"12255246259","text":"from logging import getLogger\nfrom sdmx2jsonld.common.commonclass import CommonClass\nfrom json import load\nfrom random import getrandbits\nfrom os.path import dirname, join\n\nlogger = getLogger()\n\n\nclass Distribution(CommonClass):\n def __init__(self):\n super().__init__(entity=\"Distribution\")\n self.data = {\n \"id\": \"urn:ngsi-ld:Distribution:\",\n \"type\": \"Distribution\",\n \"accessUrl\": {\n \"type\": \"Property\",\n \"value\": [\"/ngsi-ld/v1/entities?type=https://smartdatamodels.org/dataModel.SDMX/Observation\"],\n },\n \"description\": {\n \"type\": \"Property\",\n \"value\": \"Distribution of statistical data observations.\",\n },\n \"format\": {\"type\": \"Property\", \"value\": \"JSON_LD\"},\n \"language\": {\"type\": \"Property\", \"value\": list()},\n \"status\": {\"type\": \"Property\", \"value\": \"Completed\"},\n \"title\": {\"type\": \"Property\", \"value\": list()},\n \"@context\": [\n \"https://raw.githubusercontent.com/smart-data-models/dataModel.STAT-DCAT-AP/master/context.jsonld\"\n ],\n }\n\n def generate_data(self, catalogue):\n # Generate random id for the distribution\n random_bits = getrandbits(128)\n hash1 = \"%032x\" % random_bits\n self.data[\"id\"] += hash1\n\n # Title is extracted from the dcterms:title from the Catalogue\n self.data[\"title\"][\"value\"] = catalogue.data[\"title\"][\"value\"]\n\n # language es obtained from language from the Catalogue\n self.data[\"language\"][\"value\"] = catalogue.data[\"language\"][\"value\"]\n\n # accessURL is generated from the configuration file.\n config_path = dirname(dirname(dirname(__file__)))\n config_path = join(join(config_path, \"common\"), \"config.json\")\n with open(config_path) as config_file:\n config = load(config_file)\n\n self.data[\"accessUrl\"][\"value\"][0] = config[\"broker\"] + self.data[\"accessUrl\"][\"value\"][0]\n","repo_name":"flopezag/IoTAgent-Turtle","sub_path":"sdmx2jsonld/transform/distribution.py","file_name":"distribution.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"14370726825","text":"import discord\nfrom embeds import get_default_embed\nfrom resources import get_emoji, get_icon\nfrom utils import unix_timestamp, room_url, img_url\nfrom utils.formatters import format_warnings\nfrom recnetpy.dataclasses.room import Room\nfrom database import RoomStats\n\ndef room_embed(room: Room, cached_stats: RoomStats = \"None\", hide_details: bool = False) -> discord.Embed:\n \"\"\"\n Makes an embed for a single room\n \"\"\"\n \n em = get_default_embed()\n em.title = f\"^{room.name}\"\n em.url = room_url(room.name)\n \n warnings = None\n if not hide_details: \n em.set_image(url=img_url(room.image_name))\n em.description = f\"```{room.description}```\"\n thumbnail_url = get_icon(\"rro\") if room.is_rro else get_icon(\"ugc\")\n em.set_thumbnail(url=thumbnail_url)\n \n # Warnings\n warnings = format_warnings(room.warnings)\n custom_warning = f\"```{room.custom_warning}```\"\n \n if warnings: em.add_field(name=\"Warnings\", value=\" \".join(warnings), inline=False)\n if room.custom_warning: em.add_field(name=\"Custom Warning\", value=custom_warning, inline=False)\n else:\n em.set_thumbnail(url=img_url(room.image_name))\n \n # Player retention \n retention = round(room.visit_count / room.visitor_count, 2) if room.visitor_count > 0 else 0\n \n # Cheer ratio\n cheer_ratio = round((room.cheer_count / room.visitor_count) * 100, 2) if room.visitor_count > 0 else 0\n \n cheer_dif, favorite_dif, visitor_dif, visit_dif, last_check = 0, 0, 0, 0, 0\n if cached_stats not in (\"None\", None):\n cheer_dif = room.cheer_count - cached_stats.cheers if room.cheer_count != cached_stats.cheers else 0\n favorite_dif = room.favorite_count - cached_stats.favorites if room.favorite_count != cached_stats.favorites else 0\n visitor_dif = room.visitor_count - cached_stats.visitors if room.visitor_count - cached_stats.visitors else 0\n visit_dif = room.visit_count - cached_stats.visits if room.visit_count != cached_stats.visits else 0\n last_check = cached_stats.cached_timestamp\n \n statistics = [\n f\"{get_emoji('cheer')} `{room.cheer_count:,}`{f' *(+{cheer_dif:,})*' if cheer_dif else ''} — Cheers\",\n f\"{get_emoji('favorite')} `{room.favorite_count:,}`{f' *(+{favorite_dif:,})*' if favorite_dif else ''} — Favorites\",\n f\"{get_emoji('visitors')} `{room.visitor_count:,}`{f' *(+{visitor_dif:,})*' if visitor_dif else ''} — Visitors\",\n f\"{get_emoji('visitor')} `{room.visit_count:,}`{f' *(+{visit_dif:,})*' if visit_dif else ''} — Visits\",\n f\"{get_emoji('visitors')} `{retention}` — Average Revisits\",\n f\"{get_emoji('cheer')} `{cheer_ratio}%` — Cheer to Visitor Ratio\"\n ]\n \n if last_check:\n statistics.append(f\"\\nYou last checked this room out {unix_timestamp(last_check, 'R')}!\")\n elif cached_stats != \"None\":\n statistics.append(f\"\\nYou can see the statistical difference the next time you view this room!\")\n \n if statistics:\n em.add_field(name=\"Statistics\", value=\"\\n\".join(statistics), inline=False)\n \n em.set_footer(text=\"Information is cut due to API limitations. We are working with Rec Room to bring back data.\", icon_url=get_icon(\"rectnet\"))\n\n return em\n\n\nasync def fetch_room_embed(room: Room, *args, **kwargs) -> discord.Embed:\n \"\"\"\n Fetches the necessary data and returns the embed\n \"\"\"\n room = await room.client.rooms.fetch(room.id, 78)\n return room_embed(room, *args, **kwargs)","repo_name":"RecNetBot-Development/RecNetBot","sub_path":"embeds/room_embed.py","file_name":"room_embed.py","file_ext":"py","file_size_in_byte":3535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"31549352034","text":"# First we import the \"random\" module\nimport random\n\n# Now we create a function name head_or_tails (I couldn't think of any professional name)\n\ndef head_or_tails():\n\n# Now i created a variable coin which i assigned to a list which containes heads or tails\n# and then gave to the random.choice function which i later assigned to computer, the reason\n# why i used the .lower() function is because some people might write it in uppercase or lowercase\n# you can make it whatever case suits you\n\n coin = ['Heads', 'Tails']\n computer = random.choice(coin).lower()\n\n# After that i took the input from the user and lowercased it, Why you may ask its written ↑ \n# then i printed computer so it can be sure that we know what came\n\n user = input(\"Heads or tails ? \").lower()\n print(computer)\n\n# Now, you might be able to tell what this code means, right.\n# No, well heres a brief explanation\n# We used an if statement to see if the the value the user chosed si equal to the value computer chose\n# If it is true we print you win if fase you lose\n\n if user == computer:\n print(\"You win!\")\n else:\n print(\"You lose.\")\n\n# Now the real code begins here\n# We use a while loop and give it a condition that do you want to play and took he users input and lowercased it\n# Now, if the you chose No you whould break the while loop and will not be able to play and brek out of the loop\n# But if you chose yes you will later on continue the loop and later be able to play\n\nwhile True:\n print(\"Do you want to play?:\")\n human = input(\"Yes or No : \").lower()\n\n if human == \"no\":\n print(\"bye bye\")\n break\n elif human != \"yes\":\n print(\"Invalid Input\")\n continue\n\n head_or_tails()\n\n# Hope you like it\n","repo_name":"alikhan819/My-Portfolio","sub_path":"Coin.py","file_name":"Coin.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"39411519799","text":"from functools import wraps\n\nfrom rate_limit.core import RedisRateLimiter\n\nAPI = 'API'\n\n\ndef rate_limit(system=None, redis_url=None, limit=1, window=1, blocking_timeout=None, request_method=None):\n '''\n :param system: Имя вашего приложения либо константа 'Api'\n :param redis_url: Адрес redis\n :param limit: Разрешенное количество запросов\n :param window: Временное окно в секундах в котором выполняется разрешенное количество запросов\n :param blocking_timeout: Время ожидания блокировки исходящих запросов\n :param request_method: Базовый адрес запроса (шаблон урла)\n :return: response\n '''\n def decorator(func):\n if system == API or system.upper() == API:\n @wraps(func)\n def wrapper(request, *args, **kwargs):\n request_method = request.method\n user = request.user\n url_pattern = str(request.resolver_match.route)\n method_name = f\"{request_method} {url_pattern}\"\n redis_ratelimiter = RedisRateLimiter(\n system=system,\n key=method_name,\n redis_url=redis_url,\n limit=limit,\n window=window,\n blocking_timeout=blocking_timeout,\n user=user\n )\n is_limited = redis_ratelimiter.is_limited()\n if is_limited:\n return func(request, *args, **kwargs)\n return redis_ratelimiter.limited()\n else:\n @wraps(func)\n def wrapper(*args, **kwargs):\n method_name = kwargs.get(\"method_name\")\n if method_name:\n kwargs.pop(\"method_name\")\n if not method_name:\n method_name = args[0].base_url\n if method_name:\n if request_method:\n method_name = f\"{request_method} {method_name}\"\n\n return RedisRateLimiter(\n system=system,\n key=method_name,\n redis_url=redis_url,\n limit=limit,\n window=window,\n blocking_timeout=blocking_timeout,\n ).redis_rate_limit(func, *args, **kwargs)\n else:\n return func(*args, **kwargs)\n return wrapper\n\n return decorator\n","repo_name":"allkotraz/django-rate-limit","sub_path":"decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":2653,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"27293559518","text":"import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom wordcloud import WordCloud\nimport spacy\nimport spacy.cli\n# spacy.cli.download(\"en_core_web_lg\")\n# import en_core_web_lg\n# nlp = en_core_web_lg.load()\nimport requests\nimport re\nimport csv\nimport math\nimport sys\nfrom collections import Counter\nimport nltk\n# nltk.download('punkt')\n# nltk.download('wordnet')\nfrom nltk.corpus import wordnet as wn\n# nltk.download('brown')brown\nfrom nltk.corpus import brown\nimport random\n\n\n\"\"\" load \"\"\"\nplt.ion()\ndf = pd.read_csv('/Users/xs/PycharmProjects/TREATRCOMM/data/patients_data.csv')\nprint(df.info(), df.head())\n\n\"\"\" preprocessing \"\"\"\nprint(df.isna().sum())\nnull_num = df.isna()['Treatment'].sum()\nprint (f\"Percentage of missing value in \\' Treatment \\' is {null_num * 100 /df.shape[0]} % \")\nplt.figure() # figsize=(15,7)\nplt.bar(df.columns, df.isna().sum())\nplt.title('Missing value for each columns')\nplt.xlabel('Columns')\nplt.ylabel('Number of Missing Values')\n\ndf.dropna(subset=['Treatment'], inplace=True)\nprint(df.isna().sum())\n\n\"\"\" data cleaning \"\"\"\n# remove words not belong to the treatments\nl = ['Fatigue', 'Anxious mood','Pain', 'Insomnia', 'Skin pain', 'Psoriatic plaques (scaly patches)', 'swelling)']\nrecord = set()\nfor index, row in df.iterrows():\n new = re.split(',|\"', row['Treatment'])\n for word in l:\n if word in new:\n record.add(index)\ndf.drop(list(record), inplace=True)\ndf.reset_index(drop=True, inplace=True)\n\ndf.sort_values('Condition', ascending=True, inplace=True)\ndf.reset_index(drop=True, inplace=True)\nprint(f\"Final size of the dataset is: {df.shape[0]}\")\n\n\"\"\" EDA \"\"\"\nclass EDA:\n def __init__(self):\n pass\n\n\nclass Mapping(EDA):\n\n # create mapping\n def one_to_n_mapping(self, df, mapping_from, mapping_to):\n feature_name = df[mapping_from].unique()\n mapping = dict()\n visited = set()\n for feature in feature_name:\n if feature not in visited:\n visited.add(feature)\n mapping[feature] = set()\n for feature in feature_name:\n new = df.loc[df[mapping_from] == feature]\n for index, row in new.iterrows():\n results = row[mapping_to].split(',')\n for result in results:\n mapping[feature].add(result)\n return mapping\n\n def n_to_one_mapping(self, df, mapping_from, mapping_to):\n visited = set()\n mapping = dict()\n for index, row in df.iterrows():\n new = re.split(',|\"', row[mapping_from])\n for feature in new:\n if feature not in visited and feature:\n visited.add(feature)\n mapping[feature] = set()\n\n m = self.one_to_n_mapping(df, mapping_to, mapping_from)\n for index, row in df.iterrows():\n new = re.split(',|\"', row[mapping_from])\n for feature in new:\n for m_key, m_value in m.items():\n if feature not in m_value:\n continue\n mapping[feature].add(m_key)\n return mapping\n\n\nclass Visualization(EDA):\n\n # word cloud visualization\n def word_cloud(self, df, mapping_from, mapping=None):\n if mapping:\n res = ''\n for feature in df[mapping_from].unique():\n a = mapping[feature]\n k = ' '.join(a)\n res += k\n else:\n df_feature = df[mapping_from]\n res = ' '.join(df_feature)\n\n wordcloud = WordCloud(width=1000, height=500, background_color='white').generate(res)\n plt.figure()\n plt.imshow(wordcloud, interpolation=\"bilinear\")\n plt.axis('off')\n\n def top_n_visualization(self, dictionary, first_n=10, xlabel=None, ylabel=None):\n plt.figure()\n sns_2 = sns.barplot(x=list(dictionary.keys())[:first_n], y=list(dictionary.values())[:first_n])\n sns_2.set_title(\"Most {} frequent {}\".format(first_n, xlabel))\n sns_2.set_xlabel(xlabel)\n sns_2.set_ylabel(\"Number of {}\".format(ylabel))\n plt.xticks(rotation=45)\n\n\nmap = Mapping()\n\n# each condition will have n treatments\nmapping_condition_treatment = map.one_to_n_mapping(df, mapping_from='Condition', mapping_to='Treatment')\nprint('condition-treatments: ', mapping_condition_treatment)\n\n# each condition will have n symptoms\nmapping_condition_symptom = map.one_to_n_mapping(df, mapping_from='Condition', mapping_to='Symptom')\n\n# each symptom occurs in n conditions\nmapping_symptom_condition = map.n_to_one_mapping(df, mapping_from='Symptom', mapping_to='Condition')\n\n# each treatment occurs in n conditions\nmapping_treatment_condition = map.n_to_one_mapping(df, mapping_from='Treatment', mapping_to='Condition')\n\nprint(len(mapping_condition_treatment['ADD (Attention Deficit Disorder)']))\nprint(\"number of unique conditions:\", len(df['Condition'].unique()))\nprint('\\n')\ncondition_dict = df['Condition'].value_counts()\nprint(condition_dict)\n\n# Most n frequent Conditions\nvs = Visualization()\ncondition_dict = dict(condition_dict)\nvs.top_n_visualization(condition_dict, first_n=10, xlabel=\"Conditions\", ylabel=\"Patients\")\nprint('condition-observations: ', condition_dict)\nprint(len(condition_dict))\n\ncondition_length_dict = {key: len(value) for key, value in mapping_condition_treatment.items()}\ncondition_length_dict = dict(sorted(condition_length_dict.items(), key=lambda x: x[1], reverse=True))\nvs.top_n_visualization(condition_length_dict, first_n=10, xlabel=\"Conditions\", ylabel=\"Treatments\")\nprint('condition-treatments: ', condition_length_dict)\nprint(len(condition_length_dict))\n\n# Most n frequent Treatments\ntreatment_length_dict = {key: len(value) for key, value in mapping_treatment_condition.items()}\ntreatment_length_dict = dict(sorted(treatment_length_dict.items(), key=lambda x: x[1], reverse=True))\nvs.top_n_visualization(treatment_length_dict, first_n=10, xlabel=\"Treatments\", ylabel=\"Conditions\")\nprint('treatment-conditions: ', treatment_length_dict)\nprint(len(treatment_length_dict))\n\n# Most n frequent Symptoms\nsymptom_length_dict = {key: len(value) for key, value in mapping_symptom_condition.items()}\nsymptom_length_dict = dict(sorted(symptom_length_dict.items(), key=lambda x: x[1], reverse=True))\nvs.top_n_visualization(symptom_length_dict, first_n=10, xlabel=\"Symptoms\", ylabel=\"Conditions\")\nprint('symptom-conditions: ', symptom_length_dict)\nprint(len(symptom_length_dict))\n\n# # Word cloud of condition frequency\n# vs.word_cloud(df, mapping_from='Condition')\n#\n# # Word cloud of treatment for different conditions\n# vs.word_cloud(df, 'Condition', mapping_condition_treatment)\n#\n# # Word cloud of symptoms for different conditions\n# vs.word_cloud(df, 'Condition', mapping_condition_symptom)\n\n\n\"\"\" train test split \"\"\"\n\n\"\"\" Recommendation \"\"\"\nnew_df = df.drop(columns=['Name', 'City', 'State'])\n# data = new_df.to_numpy()\ndata_train, data_test, y_train, y_test = train_test_split(new_df.iloc[:, :-1], new_df.iloc[:, -1], test_size=0.2, random_state=0)\ndata_train = pd.concat([data_train, y_train], axis=1).to_numpy()\ndata_test = data_test.to_numpy()\ny_test = y_test.to_numpy()\n\nALPHA = 0.2\nBETA = 0.45\nETA = 0.4\nPHI = 0.2\nDELTA = 0.85\n\nbrown_freqs = dict()\nN = 0\n\nWORD = re.compile(r'\\w+')\nsymp_list = []\nsymptoms_list = []\nsymp_similar = []\nfinal_list = []\ni = 0\nthreshold_val = 0.4\n\n\nclass WordSimilarity:\n def get_best_synset_pair(self, word_1, word_2):\n max_sim = -1.0\n synsets_1 = wn.synsets(word_1)\n synsets_2 = wn.synsets(word_2)\n if len(synsets_1) == 0 or len(synsets_2) == 0:\n return None, None\n else:\n max_sim = -1.0\n best_pair = None, None\n for synset_1 in synsets_1:\n for synset_2 in synsets_2:\n sim = wn.path_similarity(synset_2, synset_1)\n if sim > max_sim:\n max_sim = sim\n best_pair = synset_1, synset_2\n return best_pair\n\n def length_dist(self, synset_1, synset_2):\n l_dist = sys.maxsize\n if synset_1 is None or synset_2 is None:\n return 0.0\n if synset_1 == synset_2:\n l_dist = 0.0\n else:\n wset_1 = set([str(x.name()) for x in synset_1.lemmas()])\n wset_2 = set([str(x.name()) for x in synset_2.lemmas()])\n if len(wset_1.intersection(wset_2)) > 0:\n l_dist = 1.0\n else:\n l_dist = synset_1.shortest_path_distance(synset_2)\n if l_dist is None:\n l_dist = 0.0\n return math.exp(-ALPHA * l_dist)\n\n def hierarchy_dist(self, synset_1, synset_2):\n\n h_dist = sys.maxsize\n if synset_1 is None or synset_2 is None:\n return h_dist\n if synset_1 == synset_2:\n # return the depth of one of synset_1 or synset_2\n h_dist = max([x[1] for x in synset_1.hypernym_distances()])\n else:\n # find the max depth of least common ancestor\n hypernyms_1 = {x[0]: x[1] for x in synset_1.hypernym_distances()}\n hypernyms_2 = {x[0]: x[1] for x in synset_2.hypernym_distances()}\n lcs_candidates = set(hypernyms_1.keys()).intersection(\n set(hypernyms_2.keys()))\n if len(lcs_candidates) > 0:\n lcs_dists = []\n for lcs_candidate in lcs_candidates:\n lcs_d1 = 0\n if lcs_candidate in hypernyms_1:\n lcs_d1 = hypernyms_1[lcs_candidate]\n lcs_d2 = 0\n if lcs_candidate in hypernyms_2:\n lcs_d2 = hypernyms_2[lcs_candidate]\n lcs_dists.append(max([lcs_d1, lcs_d2]))\n h_dist = max(lcs_dists)\n else:\n h_dist = 0\n return ((math.exp(BETA * h_dist) - math.exp(-BETA * h_dist)) /\n (math.exp(BETA * h_dist) + math.exp(-BETA * h_dist)))\n\n def word_similarity(self, word_1, word_2):\n synset_pair = self.get_best_synset_pair(word_1, word_2)\n return (self.length_dist(synset_pair[0], synset_pair[1]) *\n self.hierarchy_dist(synset_pair[0], synset_pair[1]))\n\n # def word_similarity(self, word_1, word_2):\n # synset_pair = self.get_best_synset_pair(word_1, word_2)\n # if not synset_pair[0] or not synset_pair[1]:\n # return 0\n # return synset_pair[0].wup_similarity(synset_pair[1])\n\n\nclass TextualSimilarity(WordSimilarity):\n\n def cosine_similarity(self, vec1, vec2):\n\n intersection = set(vec1.keys()) & set(vec2.keys())\n numerator = sum([vec1[x] * vec2[x] for x in intersection])\n\n sum1 = sum([vec1[x] ** 2 for x in vec1.keys()])\n sum2 = sum([vec2[x] ** 2 for x in vec2.keys()])\n denominator = math.sqrt(sum1) * math.sqrt(sum2)\n\n if not denominator:\n return 0.0\n else:\n return float(numerator) / denominator\n\n def tokenize(self, text):\n\n words = WORD.findall(text)\n return Counter(words)\n\n def textual_similarity(self, text1, text2):\n\n vector1 = self.tokenize(text1.lower())\n vector2 = self.tokenize(text2.lower())\n cosine = self.cosine_similarity(vector1, vector2)\n return cosine\n\n\nclass SentenceSimilarity(WordSimilarity):\n\n def most_similar_word(self, word, word_set):\n\n max_sim = -1.0\n sim_word = \"\"\n for ref_word in word_set:\n sim = self.word_similarity(word, ref_word)\n if sim > max_sim:\n max_sim = sim\n sim_word = ref_word\n return sim_word, max_sim\n\n def info_content(self, lookup_word):\n\n global N\n if N == 0:\n for sent in brown.sents():\n for word in sent:\n word = word.lower()\n if word not in brown_freqs:\n brown_freqs[word] = 0\n brown_freqs[word] = brown_freqs[word] + 1\n N = N + 1\n lookup_word = lookup_word.lower()\n n = 0 if lookup_word not in brown_freqs else brown_freqs[lookup_word]\n return 1.0 - (math.log(n + 1) / math.log(N + 1))\n\n def semantic_vector(self, words, joint_words, info_content_norm):\n\n sent_set = set(words)\n semvec = np.zeros(len(joint_words))\n i = 0\n for joint_word in joint_words:\n if joint_word in sent_set:\n semvec[i] = 1.0\n if info_content_norm:\n semvec[i] = semvec[i] * math.pow(self.info_content(joint_word), 2)\n else:\n sim_word, max_sim = self.most_similar_word(joint_word, sent_set)\n semvec[i] = PHI if max_sim > PHI else 0.0\n if info_content_norm:\n semvec[i] = semvec[i] * self.info_content(joint_word) * self.info_content(sim_word)\n i = i + 1\n return semvec\n\n def semantic_similarity(self, sentence_1, sentence_2, info_content_norm):\n\n words_1 = nltk.word_tokenize(sentence_1)\n words_2 = nltk.word_tokenize(sentence_2)\n temp_words = words_1 + words_2\n joint_words = sorted(list(set(words_1).union(set(words_2))), key=temp_words.index)\n vec_1 = self.semantic_vector(words_1, joint_words, info_content_norm)\n vec_2 = self.semantic_vector(words_2, joint_words, info_content_norm)\n return np.dot(vec_1, vec_2.T) / (np.linalg.norm(vec_1) * np.linalg.norm(vec_2))\n\n def word_order_vector(self, words, joint_words, windex):\n\n wovec = np.zeros(len(joint_words))\n i = 0\n wordset = set(words)\n for joint_word in joint_words:\n if joint_word in wordset:\n for w_i, word in enumerate(words):\n if word == joint_word:\n wovec[i] = w_i + 1\n else:\n sim_word, max_sim = self.most_similar_word(joint_word, wordset)\n if max_sim > ETA:\n wovec[windex[joint_word]] = windex[sim_word] + 1\n else:\n wovec[windex[joint_word]] = 0\n i = i + 1\n return wovec\n\n def isfloat(self, value):\n try:\n float(value)\n return True\n except ValueError:\n return False\n\n def word_order_similarity(self, sentence_1, sentence_2):\n\n words_1 = nltk.word_tokenize(sentence_1)\n words_2 = nltk.word_tokenize(sentence_2)\n temp_word = words_1 + words_2\n joint_words = sorted(list(set(words_1).union(set(words_2))), key=temp_word.index)\n windex = {x[1]: x[0] for x in enumerate(joint_words)}\n r1 = self.word_order_vector(words_1, joint_words, windex)\n r2 = self.word_order_vector(words_2, joint_words, windex)\n return 1.0 - (np.linalg.norm(r1 - r2) / np.linalg.norm(r1 + r2))\n\n def sentence_similarity(self, sentence_1, sentence_2, info_content_norm):\n\n return DELTA * self.semantic_similarity(sentence_1, sentence_2, info_content_norm) + \\\n (1.0 - DELTA) * self.word_order_similarity(sentence_1, sentence_2)\n\n\nclass RecommendationSystem:\n\n def __init__(self, data, top_k, top_n):\n self.txtsim = TextualSimilarity()\n self.sentsim = SentenceSimilarity()\n self.data = data\n self.top_k_users = top_k\n self.top_n_recommendations = top_n\n\n def find_similar_symptoms(self, user_symptom):\n similar_list = []\n file = open('/Users/xs/PycharmProjects/TREATRCOMM/data/symptoms_similar.txt', 'r')\n\n for line in file:\n line = line.rstrip('\\n')\n symptoms_file = line.split('\\t')\n\n for symp in user_symptom:\n if symp == symptoms_file[0]:\n for item in symptoms_file:\n similar_list.append(item)\n break\n\n # textual similarity: cosine similarity\n text_sim = self.txtsim.textual_similarity(symp, symptoms_file[0])\n # semantic similarity + word order similarity\n semantic_val = self.sentsim.sentence_similarity(symp, symptoms_file[0], True)\n if self.sentsim.isfloat(semantic_val):\n semantic_sim = float(semantic_val)\n else:\n semantic_sim = 0.0\n if text_sim > 0.75 or semantic_sim > threshold_val:\n for item in symptoms_file:\n similar_list.append(item)\n break\n\n # use conceptnet to find similar symptoms (query)\n for symp in user_symptom:\n symptom = symp.lower().replace(' ', '_')\n obj = requests.get('http://api.conceptnet.io/query?node=/c/en/' + symptom + '&start=/c/en&end=/c/en&rel=/r/Synonym').json()\n for link in obj['edges']:\n word1 = link['end']['label']\n word2 = link['start']['label']\n similar_list.append(word1)\n similar_list.append(word2)\n\n return list(set(similar_list))\n\n def find_similar_patients(self, user_symptom, user_disease, age, gender):\n\n # use ConceptNet to find user_symptom and user_disease synonyms\n fuzzy_user_disease = []\n user_disease_slash = re.sub(r\"\\(.*?\\)|\\{.*?}|\\[.*?]\", \"\", user_disease.lower()).strip().replace(' ', '_')\n p1 = re.compile(r'[(](.*?)[)]', re.S)\n user_disease_parenthesis = re.findall(p1, user_disease.lower())\n if user_disease_parenthesis:\n user_disease_parenthesis = user_disease_parenthesis[0].strip().replace(' ', '_')\n obj2 = requests.get(\n 'http://api.conceptnet.io/query?node=/c/en/' + user_disease_parenthesis + '&start=/c/en&end=/c/en&rel=/r/Synonym').json()\n for link2 in obj2['edges']:\n word3 = link2['end']['label']\n word4 = link2['start']['label']\n fuzzy_user_disease.append(word3.lower())\n fuzzy_user_disease.append(word4.lower())\n # print(user_disease_slash)\n # print(user_disease_parenthesis)\n\n obj = requests.get(\n 'http://api.conceptnet.io/query?node=/c/en/' + user_disease_slash + '&start=/c/en&end=/c/en&rel=/r/Synonym').json()\n for link in obj['edges']:\n word1 = link['end']['label']\n word2 = link['start']['label']\n fuzzy_user_disease.append(word1.lower())\n fuzzy_user_disease.append(word2.lower())\n\n fuzzy_user_disease.append(user_disease)\n fuzzy_user_disease = list(set(fuzzy_user_disease))\n print(fuzzy_user_disease)\n\n # 找相似的symptom，和input的symptom合并\n similar_symptom = self.find_similar_symptoms(user_symptom)\n # print('similar symptom', similar_symptom)\n\n for i, item in enumerate(similar_symptom):\n similar_symptom[i] = item.lower()\n user_symp_len = len(user_symptom)\n user_symptom = user_symptom + similar_symptom\n symptom_list = list(set(user_symptom))\n for i, item in enumerate(symptom_list):\n symptom_list[i] = item.strip('\\r')\n # print('symptom_list', symptom_list)\n\n similarity = []\n top_treatments = []\n\n for row in self.data:\n patient_gender, patient_age, conditions, symptoms_to_check, patient_treatments = row[0], row[1], row[2],\\\n row[3].split(','), row[4]\n\n # user input disease not same to the condition in current check row, next row\n for fuzzy in fuzzy_user_disease:\n if fuzzy.lower() not in conditions.lower():\n continue\n\n # if same\n for i, symptom in enumerate(symptoms_to_check):\n symptoms_to_check[i] = symptom.lower()\n common_symptom = [symptom for symptom in symptom_list if symptom.lower() in symptoms_to_check]\n similarity_val = float(len(common_symptom) / (len(symptoms_to_check) + user_symp_len))\n if (similarity_val, patient_gender, patient_age, patient_treatments) in similarity:\n continue\n\n similarity.append((similarity_val, patient_gender, patient_age, patient_treatments))\n\n similarity.sort(reverse=True)\n print('similarity', similarity)\n\n # matching patients\n users_matched = 0\n for value, p_gender, p_age, p_treatments in similarity:\n # return top k similar patients\n if users_matched == self.top_k_users:\n break\n\n # filter: gender, difference of age > 5, similarity = 0\n if p_gender.lower() != gender.lower() or abs(int(p_age) - int(age)) > 20 or value == 0:\n continue\n\n # find treatments from similar patients\n tms = p_treatments.split(',')\n for tm in tms:\n top_treatments.append(tm)\n users_matched += 1\n\n # recommend treatments\n treatments = dict()\n for treatment in top_treatments:\n treatments[treatment] = treatments.get(treatment, 0) + 1\n treatments = dict(sorted(treatments.items(), key=lambda x: x[1], reverse=True))\n # print('similar conditions', fuzzy_user_disease, '\\nsimilar symptoms', symptom_list)\n print('treatment dict:', treatments)\n top_n_treatments = list(treatments)[:self.top_n_recommendations]\n print('final top N treatment', top_n_treatments)\n return top_n_treatments\n\n def collaborative_filter(self, symptom, disease, age, gender):\n\n user_symptom = symptom.split(',')\n for i, item in enumerate(user_symptom):\n user_symptom[i] = item.lower()\n\n return self.find_similar_patients(user_symptom, disease, age, gender)\n\n\nclass ReferenceSystem:\n \"\"\" Reference System: Trivial and Baseline \"\"\"\n def __init__(self, data, k):\n self.data = data\n self.top_k = k\n\n def trivial_system(self, run_time=10):\n pass\n # random recommendation\n treatment_index = random.randint(0, len(treatment_length_dict) - 1)\n treatment_number = random.randint(0, 10)\n\n # run more than 10 times to take avg:\n # trivial 1. random recommendation base on index without any prior knowledge (658 * length choose randint?)\n # trivial 2. random choose k patients (similar to recommend top k in RS) and\n # combine their treatments as the y_pred (658 * (k1 + k2 + ...))\n\n def baseline_system(self):\n pass\n\n # baseline 1. POP (popular products): this model recommends the most popular products in the training set.\n # (658 * length choose randint?)\n # baseline 2. random recommendation within condition-treatments mapping (658 * length choose randint?)\n # baseline 3. recommend top k frequent treatments within condition-treatments mapping (658 * k) 教授：根据这个symptom的最常用的treatment来推荐，topk就推荐k个最常见的\n\n # Q: length of the prediction? 5? random int?\n\n\ndef evaluation(model, reference):\n \"\"\" Evaluation \"\"\"\n # Micro precision (joint1 + joint2 + ... / yhat1 + yhat2 + ...), recall, f1\n\n count = 0\n record = dict()\n xgrid_min, xgrid_max = 0, -sys.maxsize\n y_len, y_hat_len, total_intercept = 0, 0, 0\n for i in range(len(data_test)):\n count += 1\n print('==='*20)\n print('Patient No.' + str(count), data_test[i])\n symptom, condition, age, gender = data_test[i][3], data_test[i][2], data_test[i][1], data_test[i][0]\n if reference == 'trivial':\n y_pred = model.trivial_system(symptom, condition, age, gender)\n elif reference == 'baseline':\n y_pred = model.baseline_system(symptom, condition, age, gender)\n else:\n y_pred = model.collaborative_filter(symptom, condition, age, gender)\n xgrid_min, xgrid_max = 0, max(xgrid_max, len(y_pred))\n record[len(y_pred)] = record.get(len(y_pred), 0) + 1\n print('recommendation:', y_pred)\n y_true = y_test[i].split(',')\n print('y_true', y_true)\n total_intercept += len(set(y_true).intersection(set(y_pred)))\n\n y_len += len(y_true)\n y_hat_len += len(y_pred)\n print(total_intercept, y_len, y_hat_len)\n precision_k = total_intercept / y_hat_len\n recall_k = total_intercept / y_len\n f1_k = (2 * precision_k * recall_k) / (precision_k + recall_k)\n print('top{} similar patients, with top{} treatments precision = {}, recall = {}, f1 = {}'.format(k, n, precision_k, recall_k, f1_k))\n plt.figure()\n plt.title('Distribution of the length of prediction')\n plt.bar(*zip(*record.items()))\n plt.xlabel('Length of the prediction')\n plt.ylabel('Number of Datapoints')\n\n\nif __name__ == '__main__':\n \"\"\" no sentence semantic similarity; fuzzy search; w+h distance measure; visited set \"\"\"\n k = 3\n n = 3\n\n # trivial system\n # rfs_trivial = ReferenceSystem(data=data_train, top_k=k)\n # evaluation(rfs_trivial, 'trivial')\n #\n # # baseline system\n # # Pop : POP (popular products): this model recommends the most popular products in the training set.\n # # [python]https://github.com/ss87021456/Recommendation-System-Baseline\n # rfs_baseline = ReferenceSystem(data=data_train, top_k=k)\n # evaluation(rfs_baseline, 'baseline')\n\n # CF\n trs = RecommendationSystem(data=data_train, top_k=k, top_n=n)\n evaluation(trs, 'rs')\n\n\n\n\n# line = ['Female', 41, 'Rheumatoid Arthritis (RA)', 'Depressed mood']\n\n# print(trs.collaborative_filter('Pain,Excessive yawning,Depressed mood,Constipation,Excess saliva,Emotional lability', 'PLS (Primary Lateral Sclerosis)', 56, 'Male'))\n# print('Recommend Treatments: ', trs.collaborative_filter('Depressed mood', 'high blood pressure', 35, 'male'))\n #'Excessive daytime sleepiness (somnolence),Bowel problems, Emotional lability,Anxious mood,Brain fog,Stiffness/Spasticity,Bladder problems,Pain', 'Multiple Sclerosis', 54,'Female'))\n# 'Fingolimod,Interferon beta-1a IM Injection,Interferon beta-1a SubQ injection,Dimethyl fumarate,Teriflunomide,Baclofen']\n\n\nplt.ioff()\nplt.show()\n","repo_name":"sxu75374/TreatME-A-semantic-patient-similarity-based-treatment-recommendation-system","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":26377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"16607499551","text":"from collections import defaultdict\nfrom typing import DefaultDict\n\nINPUT_FILE = \"./sample.txt\" if False else \"./input.txt\"\n\npolymer_template: str or None = None\npair_insertion_rules: DefaultDict = defaultdict(str)\nwith open(INPUT_FILE, \"r\") as input_file:\n for i, line in enumerate(map(str.strip, input_file.readlines())):\n if i == 0:\n polymer_template = line\n continue\n if i == 1 or not line:\n continue\n combination, insertion = list(map(str.strip, line.split(\"->\")))\n pair_insertion_rules[combination] = insertion\n\n\ndef part_1(steps=10):\n def get_new_polymer_template(template: str):\n new_template = list(template)\n inserted = []\n for x in range(len(template) - 1):\n insert = pair_insertion_rules[f\"{template[x]}{template[x + 1]}\"]\n if insert:\n new_template.insert(x + len(inserted) + 1, insert)\n inserted.append(insert)\n return \"\".join(new_template)\n\n new_template = str(polymer_template)\n for _ in range(steps):\n new_template = get_new_polymer_template(new_template)\n\n counter = defaultdict(int)\n most_common = new_template[0]\n least_common = new_template[0]\n for x in list(new_template):\n counter[x] += 1\n if counter[x] > counter[most_common]:\n most_common = x\n elif counter[x] < counter[least_common]:\n least_common = x\n print(f\"Part 1: {counter[most_common] - counter[least_common]}\")\n\n\ndef part_2():\n single_count = defaultdict(int)\n for x in list(polymer_template):\n single_count[x] += 1\n\n pairs = defaultdict(int)\n for x in range(len(polymer_template) - 1):\n pairs[polymer_template[x] + polymer_template[x + 1]] += 1\n\n for _ in range(40):\n inserted = []\n for pair in pair_insertion_rules.keys():\n if pair in pairs:\n inserted.append((pair, pair_insertion_rules[pair], pairs[pair]))\n del pairs[pair]\n\n for pair, insert, cnt in inserted:\n single_count[insert] += cnt\n pairs[pair[0] + insert] += cnt\n pairs[insert + pair[1]] += cnt\n\n print(f\"Part 2: {max(single_count.values()) - min(single_count.values())}\")\n\n\npart_1()\npart_2()\n","repo_name":"JJStoker/AOC","sub_path":"2021/14/extended_polymerization.py","file_name":"extended_polymerization.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"67"}
+{"seq_id":"14065250349","text":"from slappsim.Petri import *\nfrom slappsim.States import Start\nfrom slappsim.Function import Function, FunctionStart\nfrom slappsim.Structures import Structure\nfrom slappsim.States import End\nfrom typing import List, Dict, Tuple\nfrom functools import partial\nimport multiprocessing\n\n\ndef zero_delay():\n return 0\n\n\nclass PetriApp(Petri):\n \"\"\"Serverless application CSPN model\n\n Conditional stochastic Petri net model of the serverless application\n\n Attributes:\n functions: A list of serverless functions in the CSPN model (F).\n transitions: A list of transitions in the CSPN model.\n structures: A list of structures in the serverless application (optional).\n delays: A dictionary that contains the partial functions to generate random delay for different delay types.\n pgs: Price per GB-second USD.\n ppi: Price per invocation in USD.\n\n \"\"\"\n\n def __init__(self, functions: List[Function], transitions: List[Transition], structures: List[Structure] = None,\n delays: Dict[str, partial] = None,\n pgs: float = 0.0000166667, ppi: float = 0.0000002) -> None:\n \"\"\"Inits the application CSPN model.\"\"\"\n self.functions = functions\n self.functions_map = {function.name: function for function in functions if function.name is not None}\n self.transitions = set(transitions)\n self.structures = [transition.parent_structure for transition in self.transitions if\n transition.parent_structure is not None]\n self.structures = list(set(self.structures))\n Petri.__init__(self, transitions=list(self.transitions))\n self.arcs = [item for transition in self.transitions for item in transition.in_arcs + transition.out_arcs]\n self.places = set([arc.place for arc in self.arcs])\n self.terminals = set([place for place in self.places if issubclass(place.__class__, End)])\n self.cost = None\n self.cost_records = []\n self.pgs = pgs\n self.pmms = self.pgs / 1024 / 1000\n self.ppi = ppi\n self.delays = delays if delays is not None else {}\n self.scheduling_overhead = None\n self.update_delays()\n\n def update_delays(self) -> None:\n \"\"\"Updates the delay\n\n Generates new delays for different types of transitions.\n Initializes the partial function to generate the scheduling overhead of the application.\n\n \"\"\"\n zero_delay_fun = partial(zero_delay)\n for transition in self.transitions:\n if transition.label is not None and self.delays.get(transition.label, None) is not None:\n transition.delay_fun = self.delays[transition.label]\n transition.sample()\n self.scheduling_overhead = self.delays.get(\"SchedulingOverhead\", zero_delay_fun)\n\n def reset(self) -> None:\n \"\"\"Resets the CSPN model\n\n Removes all tokens.\n Generates new firing delay (response time) and communication delay for serverless functions.\n\n \"\"\"\n for place in self.places:\n if type(place) is Start:\n place.holding = Token(elapsed_time=0)\n else:\n place.holding = None\n for function in self.functions:\n function.sample()\n\n def execute(self) -> Tuple[float, float, str, List]:\n \"\"\"Runs the CSPNs.\n\n Checks the enabled transitions, fires the enabled transitions, and solves the conflicts.\n\n Returns:\n A 4-tuple that contains the end-to-end response time, total cost, exit status, and firing logs.\n \"\"\"\n cost_list = []\n terminals_with_holding = []\n firing_logs = []\n while True:\n fired_transitions = self.run()\n for transition in fired_transitions:\n if transition.label == \"FunctionExecution\" or transition.label == \"Task\":\n cost_list.append((transition.last_fired_time, transition.get_cost(self.pmms, self.ppi)))\n firing_logs.append((transition.uid, transition.last_fired_time, transition.required_time,\n transition.get_cost(self.pmms, self.ppi)))\n else:\n firing_logs.append((transition.uid, transition.last_fired_time, transition.required_time, 0))\n is_terminated = False\n for terminal in self.terminals:\n if terminal.holding is not None:\n terminals_with_holding.append(terminal)\n if len(fired_transitions) == 0:\n is_terminated = True\n if is_terminated:\n break\n terminal_holdings = [terminal.holding for terminal in terminals_with_holding]\n elapsed_time = [token.elapsed_time for token in terminal_holdings]\n terminal_time = min(elapsed_time)\n cost = sum([cost for time, cost in cost_list if time <= terminal_time])\n exit_point = terminals_with_holding[elapsed_time.index(terminal_time)]\n firing_logs = [log for log in firing_logs if log[1] <= terminal_time]\n terminal_time = terminal_time + self.scheduling_overhead()\n return terminal_time, cost, exit_point.label, firing_logs\n\n def _execute(self, k: int) -> Tuple[List, List, List, List]:\n \"\"\"Runs the CSPNs k times\n\n Returns:\n A 4-tuple that contains the list of the end-to-end response time, list of the total cost, list of the exit\n status, and list of the firing logs. Each list has k elements.\n \"\"\"\n terminal_time = []\n cost = []\n exit_status = []\n firing_logs = []\n for i in range(k):\n self.reset()\n ert, c, status, log = self.execute()\n terminal_time.append(ert)\n cost.append(c)\n exit_status.append(status)\n firing_logs.append(log)\n return terminal_time, cost, exit_status, firing_logs\n\n def profile(self, k: int, cpu_count: int = None) -> Tuple[List, List, List, List]:\n \"\"\"Runs the CSPNs k times (parallel computing)\"\"\"\n cpu_count = multiprocessing.cpu_count() if cpu_count is None else cpu_count\n pool = multiprocessing.Pool(cpu_count)\n results = pool.map(self._execute, [int(k / cpu_count) for i in range(cpu_count)])\n pool.close()\n pool.join()\n terminal_time = []\n cost = []\n exit_status = []\n firing_logs = []\n for res in results:\n ert, c, status, log = res\n terminal_time += ert\n cost += c\n exit_status += status\n firing_logs += log\n return terminal_time, cost, exit_status, firing_logs\n","repo_name":"pacslab/SLAppMdlOpt","sub_path":"slappsim/PetriApp.py","file_name":"PetriApp.py","file_ext":"py","file_size_in_byte":6713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"26708941676","text":"import struct\nfrom PIL import Image\nimport fir_filter\nimport dll\nfrom math import sin, cos, pi\n\n\nrate = 315/88/108\n\nsync_start = 0\nsync_start_prev = 0\nsync_cnt = 0\nbackport_cnt = 0\ncolorburst_cnt = 0\nbackport_post_cnt = 0\nline_cnt = 0\n\nin_sync = False\nin_backporch = False\nin_colorburst = False\nin_backport_post = False\nin_line = False\nin_field = False\n\nline_has_color = False\n\nfields = []\nlines = []\nline = []\n\ncolorburst = []\ncos_filter = fir_filter.FirFilterLowPassRect(64, 0.044/8)\nsin_filter = fir_filter.FirFilterLowPassRect(64, 0.044/8)\ny_filter = fir_filter.FirFilterLowPassRect(64, 0.143/8)\n\n# Line sync is 63, so +- 10%\n#LINE_SYNC_MIN = 57\nLINE_SYNC_MIN = 50 * 8\nLINE_SYNC_MAX = 69 * 8\n\n#FIELD_SYNC_LEN = 362\nFIELD_SYNC_MIN = 350 * 8\nFIELD_SYNC_MAX = 370 * 8\n\nFIELD_CNT_MIN = 253\nFIELD_CNT_MAX = 254\n\nline_value = []\nprev_line_value = []\n\nPRE_START_ACTIVE_VIDEO_LINES = 11\n\nFRAME_HEIGHT = (FIELD_CNT_MAX - PRE_START_ACTIVE_VIDEO_LINES) * 2\n\nline_max_val = 0\nline_min_val = 1000000\n\nprint('Processing input.')\nwith open('upsampled.raw', 'rb') as f:\n b = f.read()\nj = 0\nfor i in range(len(b)):\n pair = b[i*2:i*2+2]\n value = struct.unpack('= LINE_SYNC_MIN and sync_cnt <= LINE_SYNC_MAX:\n #print('Line sync. Lines so far: %d' % (len(lines)))\n in_backporch = True\n in_field = False\n\n elif sync_cnt >= FIELD_SYNC_MIN and sync_cnt <= FIELD_SYNC_MAX:\n #print('Field sync')\n if not in_field:\n #print('New field. Existing field len %d' % (len(lines)))\n if len(lines) < FIELD_CNT_MIN or len(lines) > FIELD_CNT_MAX:\n pass\n #print('Invalid previous field. Skipping')\n else:\n fields.append(lines[PRE_START_ACTIVE_VIDEO_LINES:])\n if len(fields) == 2:\n break\n lines = []\n in_field = True\n\n sync_cnt = 0\n\n\n if in_backporch:\n backport_cnt += 1\n\n if backport_cnt > 65:\n backport_cnt = 0\n in_backporch = False\n in_colorburst = True\n colorburst = []\n\n if in_colorburst:\n colorburst.append(value)\n colorburst_cnt += 1\n\n if colorburst_cnt > 270:\n colorburst_cnt = 0\n in_colorburst = False\n in_backport_post = True\n # Does it look like this was a colorburst? Ideally would run through a\n # filter or detector or something but for now just check if there was\n # significant variation in the min and max excursion of the signal.\n def figure_out_things():\n global line_has_color\n mi, ma = 1000000, 0\n a = 0\n for v in colorburst:\n a += v\n if v < mi: mi = v\n if v > ma: ma = v\n a /= len(colorburst)\n s = ma - mi\n if s < 100:\n # Probably not a colorburst.\n return\n else:\n line_has_color = True\n # Normalize and offset so the delay locked loop can sync with it.\n burst_offset = []\n for v in colorburst:\n burst_offset.append((v-a)/s*2)\n # Calibrate the delay locked loop.\n #print(burst_offset)\n dll.lock(burst_offset)\n #print('Colorburst over at i %d' % (i))\n figure_out_things()\n\n # It's important from this point on that the DLL osscilator tick one for\n # each sample so that it remains in lock with the colorburst signal, even if\n # we're not decoding color at the moment.\n if in_backport_post:\n dll.tick()\n backport_post_cnt += 1\n\n if backport_post_cnt > 151:\n backport_post_cnt = 0\n in_backport_post = False\n in_line = True\n line_max_val = 0\n line_min_val = 1000000\n print(len(lines))\n\n if in_line:\n cos_osc, sin_osc = dll.tick()\n \n if value > line_min_val: line_min_val = value\n if value < line_max_val: line_max_val = value\n \n offset_px = max(value - 530, 0)\n scaled_px = min(int(offset_px / 6.046875), 255)\n scaled_px /= 255\n line_value.append(scaled_px)\n\n deg33 = 11*pi/60\n\n if len(prev_line_value) > line_cnt:\n averaged_px = ( -1 * prev_line_value[line_cnt] + scaled_px ) / 2\n else:\n averaged_px = scaled_px\n\n u_mult = cos_osc * averaged_px\n v_mult = sin_osc * averaged_px\n\n # Filter the u and v to 600 kHz since that's what the limit of vision\n # bandwidth is and to remove the demodulation sidebands.\n # Filter the y to 4.2 since that's the NTSC vision bandwidth limit.\n # (Note that we're assuming we're sampling at 13.5 MHz which means that\n # the original picture has a bandwidth of 6.75 Mhz)\n uu = cos_filter.filter(u_mult) * 2\n vv = sin_filter.filter(v_mult) * 2\n yy = y_filter.filter(scaled_px)\n\n # Given y and scaled u and v, turn back into RGB.\n rr = vv/0.877 + yy\n bb = uu/0.493 + yy\n gg = -0.509*(rr-yy) - 0.194*(bb-yy) + yy\n line.append((int(rr*255),int(gg*255),int(bb*255)))\n line_cnt += 1\n\n #if line_cnt > 714 * 8:\n if line_cnt > 714 * 8:\n line_cnt = 0\n in_line = False\n lines.append(line)\n prev_line_value = line_value\n line_value = []\n print('Line min %d max %d' % (line_min_val, line_max_val))\n #print('Line %d min %d max %d' % (len(lines), min(line), max(line)))\n line = []\n #print('Reading line %d' % (len(lines)))\n\nprint('Done')\nprint('Creating image.')\nim = Image.new('RGB', (715, FRAME_HEIGHT))\nfor f in range(2):\n for y, line in enumerate(fields[f]):\n #for x, px in enumerate(line):\n for x in range(len(line)//8):\n px = line[x*8]\n im.putpixel((x,y*2+f), px)\nim.save('image.png')\nprint('Done')","repo_name":"gabesk/tv_python","sub_path":"convert_fields_inter_3.py","file_name":"convert_fields_inter_3.py","file_ext":"py","file_size_in_byte":6637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"28510846393","text":"import socket\nimport sys\nimport threading\nimport time\nimport functools\nfrom PyQt5 import QtCore, QtGui\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QPushButton\nfrom PyQt5.QtWidgets import QVBoxLayout, QHBoxLayout, QMessageBox, QTabWidget\nfrom PyQt5.QtWidgets import QGridLayout, QScrollArea, QLabel, QListView\nfrom PyQt5.QtWidgets import QLineEdit, QComboBox, QGroupBox, QAction\nfrom PyQt5.QtGui import QStandardItemModel, QStandardItem, QFont\n\nclass MyTableWidget(QWidget):\n def __init__(self, parent):\n super(QWidget, self).__init__(parent)\n \n self.conn = socket.socket()\n self.connected = False\n \n self.layout = QVBoxLayout(self)\n self.tabs = QTabWidget()\n self.tabs.resize(300,200) \n self.tab1 = QWidget()\n self.tab2 = QWidget()\n self.tabs.addTab(self.tab1, \"Home\")\n self.tabs.addTab(self.tab2, \"Chat Room\")\n self.tabs.setTabEnabled(1,False)\n \n gridHome = QGridLayout()\n self.tab1.setLayout(gridHome)\n self.IPBox = QGroupBox(\"IP\")\n self.IPLineEdit = QLineEdit()\n self.IPLineEdit.setText(\"127.0.0.1\")\n IPBoxLayout = QVBoxLayout()\n IPBoxLayout.addWidget(self.IPLineEdit)\n self.IPBox.setLayout(IPBoxLayout)\n self.portBox = QGroupBox(\"port\")\n self.portLineEdit = QLineEdit()\n self.portLineEdit.setText(\"33002\")\n portBoxLayout = QVBoxLayout()\n portBoxLayout.addWidget(self.portLineEdit)\n self.portBox.setLayout(portBoxLayout)\n self.nameBox = QGroupBox(\"Név\")\n self.nameLineEdit = QtWidgets.QLineEdit()\n nameBoxLayout = QVBoxLayout()\n nameBoxLayout.addWidget(self.nameLineEdit)\n self.nameBox.setLayout(nameBoxLayout)\n self.connStatus = QLabel(\"Státusz\", self)\n font = QFont()\n font.setPointSize(16)\n self.connStatus.setFont(font)\n self.connBtn = QPushButton(\"Csatlakozás\")\n self.connBtn.clicked.connect(self.connect_server)\n self.disconnBtn = QPushButton(\"Kilépés\")\n self.disconnBtn.clicked.connect(self.disconnect_server)\n gridHome.addWidget(self.IPBox,0,0,1,1)\n gridHome.addWidget(self.portBox,0,1,1,1)\n gridHome.addWidget(self.nameBox,1,0,1,1)\n gridHome.addWidget(self.connStatus,1,1,1,1)\n gridHome.addWidget(self.connBtn,2,0,1,1)\n gridHome.addWidget(self.disconnBtn,2,1,1,1)\n gridHome.setColumnStretch(0, 1)\n gridHome.setColumnStretch(1, 1)\n gridHome.setRowStretch(0, 0)\n gridHome.setRowStretch(1, 0)\n gridHome.setRowStretch(2, 9)\n \n \n gridChatRoom = QGridLayout()\n self.tab2.setLayout(gridChatRoom)\n self.messageRecords = QLabel(\"Welcome to chat room\", self)\n self.messageRecords.setStyleSheet(\"background-color: white;\");\n self.messageRecords.setAlignment(QtCore.Qt.AlignTop)\n self.messageRecords.setAutoFillBackground(True);\n self.scrollRecords = QScrollArea()\n self.scrollRecords.setWidget(self.messageRecords)\n self.scrollRecords.setWidgetResizable(True)\n self.sendTo = \"ALL\"\n self.sendChoice = QLabel(\"Send to :ALL\", self)\n self.sendComboBox = QComboBox(self)\n self.sendComboBox.addItem(\"ALL\")\n self.sendComboBox.activated[str].connect(self.send_choice)\n self.lineEdit = QLineEdit()\n self.lineEnterBtn = QPushButton(\"Enter\")\n self.lineEnterBtn.clicked.connect(self.enter_line)\n self.lineEdit.returnPressed.connect(self.enter_line)\n self.friendList = QListView()\n self.friendList.setWindowTitle('Room List')\n self.model = QStandardItemModel(self.friendList)\n self.friendList.setModel(self.model)\n self.emojiBox = QGroupBox(\"Emoji\")\n self.emojiBtn1 = QPushButton(\"ก็ʕ•͡ᴥ•ʔ ก้\")\n self.emojiBtn1.clicked.connect(functools.partial(self.send_emoji, \"ก็ʕ•͡ᴥ•ʔ ก้\"))\n self.emojiBtn2 = QPushButton(\"(｡◕∀◕｡)\")\n self.emojiBtn2.clicked.connect(functools.partial(self.send_emoji, \"(｡◕∀◕｡)\"))\n self.emojiBtn3 = QPushButton(\"( ˘･з･)\")\n self.emojiBtn3.clicked.connect(functools.partial(self.send_emoji, \"( ˘･з･)\"))\n self.emojiBtn4 = QPushButton(\"ᕦ(ò_óˇ)ᕤ\")\n self.emojiBtn4.clicked.connect(functools.partial(self.send_emoji, \"ᕦ(ò_óˇ)ᕤ\"))\n emojiLayout = QHBoxLayout()\n emojiLayout.addWidget(self.emojiBtn1)\n emojiLayout.addWidget(self.emojiBtn2)\n emojiLayout.addWidget(self.emojiBtn3)\n emojiLayout.addWidget(self.emojiBtn4)\n self.emojiBox.setLayout(emojiLayout)\n gridChatRoom.addWidget(self.scrollRecords,0,0,1,3)\n gridChatRoom.addWidget(self.friendList,0,3,1,1)\n gridChatRoom.addWidget(self.sendComboBox,1,0,1,1)\n gridChatRoom.addWidget(self.sendChoice,1,2,1,1)\n gridChatRoom.addWidget(self.lineEdit,2,0,1,3)\n gridChatRoom.addWidget(self.lineEnterBtn,2,3,1,1)\n gridChatRoom.addWidget(self.emojiBox,3,0,1,4)\n gridChatRoom.setColumnStretch(0, 9)\n gridChatRoom.setColumnStretch(1, 9)\n gridChatRoom.setColumnStretch(2, 9)\n gridChatRoom.setColumnStretch(3, 1)\n gridChatRoom.setRowStretch(0, 9)\n \n \n self.layout.addWidget(self.tabs)\n self.setLayout(self.layout) \n \n def enter_line(self):\n \n if self.sendTo != self.sendComboBox.currentText():\n self.message_display_append(\"Sikertelen küldés!\")\n self.lineEdit.clear()\n return\n line = self.lineEdit.text()\n if line == \"\":\n return\n if self.sendTo != \"ALL\":\n send_msg = bytes(\"{\"+self.userName+\"}\"+line, \"utf-8\")\n self.conn.send(send_msg)\n time.sleep(0.1)\n send_msg = bytes(\"{\"+self.sendTo+\"}\"+line, \"utf-8\")\n self.conn.send(send_msg)\n self.lineEdit.clear()\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n \n def send_emoji(self, emoji):\n if self.sendTo != self.sendComboBox.currentText():\n self.message_display_append(\"The person left. Private message not delivered\")\n return\n if self.sendTo != \"ALL\":\n send_msg = bytes(\"{\"+self.userName+\"}\"+emoji, \"utf-8\")\n self.conn.send(send_msg)\n time.sleep(0.1)\n send_msg = bytes(\"{\"+self.sendTo+\"}\"+emoji, \"utf-8\")\n self.conn.send(send_msg)\n \n def message_display_append(self, newMessage, textColor = \"#000000\"):\n oldText = self.messageRecords.text()\n appendText = oldText+\"
\"+newMessage+\"\"\n self.messageRecords.setText(appendText)\n time.sleep(0.2) #this helps the bar set to bottom, after all message already appended\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n \n def updateRoom(self):\n while self.connected:\n data = self.conn.recv(1024)\n data = data.decode(\"utf-8\")\n print(data)\n if data != \"\":\n if \"{CLIENTS}\" in data:\n welcome = data.split(\"{CLIENTS}\")\n self.update_send_to_list(welcome[1])\n self.update_room_list(welcome[1])\n if not welcome[0][5:] == \"\":\n self.message_display_append(welcome[0][5:])\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n elif data[:5] == \"{MSG}\":\n self.message_display_append(data[5:], \"#006600\")\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n else:\n self.message_display_append(\"{private}\"+data, \"#cc33cc\")\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n time.sleep(0.1)\n \n def connect_server(self):\n if self.connected == True:\n return\n name = self.nameLineEdit.text()\n if name == \"\":\n self.connStatus.setText(\"Státusz :\"+\"Adj meg egy nevet\")\n return\n self.userName = name\n IP = self.IPLineEdit.text()\n if IP == \"\":\n IP = \"127.0.0.1\"\n port = self.portLineEdit.text()\n if port == \"\" or not port.isnumeric():\n self.portLineEdit.setText(\"33002\")\n self.connStatus.setText(\"Státusz :\"+\"Sikertelen kapcsolódás\")\n return \n else:\n port = int(port)\n try:\n self.conn.connect((IP, port))\n except:\n self.connStatus.setText(\"Státusz :\"+\"Sikertelen kapcsolódás\")\n self.conn = socket.socket()\n return\n send_msg = bytes(\"{REGISTER}\"+name, \"utf-8\")\n self.conn.send(send_msg)\n self.connected = True \n self.connStatus.setText(\"Status :\"+\"Sikeres kapcsolódás\")\n self.nameLineEdit.setReadOnly(True)\n self.tabs.setTabEnabled(1,True)\n self.rT = threading.Thread(target= self.updateRoom)\n self.rT.start()\n\n def disconnect_server(self):\n if self.connected == False:\n return\n send_msg = bytes(\"{QUIT}\", \"utf-8\")\n self.conn.send(send_msg)\n self.connStatus.setText(\"Státusz :\"+\" Lecsatlakozva\")\n self.nameLineEdit.setReadOnly(False)\n self.nameLineEdit.clear()\n self.tabs.setTabEnabled(1,False)\n self.connected = False\n self.rT.join()\n self.conn.close()\n self.conn = socket.socket()\n \n def update_room_list(self, strList):\n L = strList.split(\"|\")\n self.model.clear()\n for person in L:\n item = QStandardItem(person)\n item.setCheckable(False)\n self.model.appendRow(item)\n \n def update_send_to_list(self, strList):\n L = strList.split(\"|\")\n self.sendComboBox.clear()\n self.sendComboBox.addItem(\"ALL\")\n for person in L:\n if person != self.userName:\n self.sendComboBox.addItem(person)\n previous = self.sendTo\n index = self.sendComboBox.findText(previous)\n print(\"previous choice:\",index)\n if index != -1:\n self.sendComboBox.setCurrentIndex(index)\n else:\n self.sendComboBox.setCurrentIndex(0)\n \n def send_choice(self,text):\n self.sendTo = text\n print(self.sendTo)\n self.sendChoice.setText(\"Küldés: \"+text)\n\nclass Window(QMainWindow):\n def __init__(self):\n super(Window, self).__init__()\n self.setGeometry(50, 50, 500, 300)\n self.setWindowTitle(\"ChatApp\") \n self.table_widget = MyTableWidget(self)\n self.setCentralWidget(self.table_widget)\n self.show()\n \n def closeEvent(self, event):\n close = QMessageBox()\n close.setText(\"Biztos vagy benne?\")\n close.setStandardButtons(QMessageBox.Yes | QMessageBox.Cancel)\n close = close.exec()\n if close == QMessageBox.Yes:\n self.table_widget.disconnect_server()\n event.accept()\n else:\n event.ignore()\n\ndef run():\n app = QApplication(sys.argv)\n GUI = Window()\n sys.exit(app.exec_())\n\nif __name__ == \"__main__\":\n run() ","repo_name":"milanmarko/chatAppIkt","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11639,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"}
+{"seq_id":"13180372297","text":"\n# coding: utf-8\n\n# # Demo model applied to CIFAR-10\n# Dataset https://www.cs.toronto.edu/~kriz/cifar.html\n\n# We use only the 1st batch of CIFAR-10 dataset: 10000 images 32x32x3 and 10 labels.\n\n# ### Load dependencies\n\n# In[1]:\n\n\nimport os\nimport tarfile\nimport pickle\nimport urllib.request\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport operator\nimport mxnet as mx\nimport logging\n\n\n# ### Prepare training and test datasets\n# `download_data` downloads data from a given `url`, if no file with the same name has been downloaded so far, \n# `prepare_img` transforms 3 channels (rgb) to one channel (their mean value), reshapes data, adding one axis of length 1 and normalizes the entries to [0,1], \n# `read_CIFAR10` creates labels and datasets from the corresponding \"batch\" files (see [CIFAR dataset description](https://www.cs.toronto.edu/~kriz/cifar.html))\n# \n# With these functions we download data from CIFAR web page, create training/test datasets/labels and then split them into batches of size 150 (shuffle for training). \n# Dataset gets the name `'data'`, labels - `'label'`\n\n# In[2]:\n\n\ndef download_data(url, force_download=True): \n fname = url.split(\"/\")[-1]\n \n if force_download or not os.path.exists(fname):\n urllib.request.urlretrieve(url, fname)\n \n return fname\n\n\ndef unpickle(file):\n with open(file, 'rb') as fo:\n dict = pickle.load(fo, encoding='bytes')\n \n return dict\n\n\ndef read_CIFAR10():\n url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n tar = tarfile.open(download_data(url, False))\n tar.extractall()\n tar.close()\n \n dict_1 = unpickle(\"cifar-10-batches-py/data_batch_1\")\n \n train_img = dict_1[b'data'].reshape(10000, 3, 1024)\n train_img = train_img.transpose(0,2,1)\n train_lbl = np.array(dict_1[b'labels'])\n \n dict_test_1 = unpickle(\"cifar-10-batches-py/test_batch\")\n \n test_img = dict_test_1[b'data'].reshape(10000, 3, 1024)\n test_img = test_img.transpose(0,2,1)\n test_lbl = np.array(dict_test_1[b'labels'])\n\n return train_img, train_lbl, test_img, test_lbl\n\n\ndef prepare_img(img):\n # mean across rgb\n img = np.mean(img, -1)\n \n # normalize\n img = img.astype(np.float32) / 255\n \n # reshape to 4-D\n img = img.reshape(img.shape[0], 1, 32, 32)\n \n return img\n\n\n# In[6]:\n\n\ntrain_img, train_lbl, test_img, test_lbl = read_CIFAR10()\n\ntrain_img = prepare_img(train_img)\ntest_img = prepare_img(test_img)\n\nbatch_size = 150\n\ntrain_data_iter = mx.io.NDArrayIter(data={'data': train_img}, label= {'label': train_lbl}, batch_size=batch_size, shuffle=True)\ntest_data_iter = mx.io.NDArrayIter(data={'data': test_img}, label= {'label': test_lbl}, batch_size=batch_size)\n\n\n# Load and print label names:\n\n# In[4]:\n\n\ndef load_classes():\n path = 'cifar-10-batches-py/'\n file = 'batches.meta'\n \n f = open(path+file, 'rb')\n dict = pickle.load(f)\n return dict['label_names']\n\ndef print_classes(label_names):\n for i in range(0, 10):\n print(str(i) + \" : \" + label_names[i] + \" \")\n\nlabel_names = load_classes()\nprint_classes(label_names)\n\n\n# ### Display example training data\n# \n# The original Fashion-MNIST consists of 28x28 grayscale images.\n\n# In[7]:\n\n\npict_number = 7\n\nfor i in range(pict_number):\n plt.subplot(1,pict_number,i+1)\n #dsp_img = train_img[i].reshape(32,32,3)\n dsp_img = train_img[i].reshape(32,32)\n #plt.imshow(dsp_img)\n plt.imshow(dsp_img, cmap = \"Greys_r\")\n plt.axis('off')\n\nplt.show()\n\nprint('labels: %s' % (train_lbl[0:pict_number],))\nprint(operator.itemgetter(*train_lbl[0:pict_number])(label_names))\n\n\n# ### Build MXNet model\n# \n# For building our NN we will use Symbol API, an interface for symbolic programming.\n# \n# First, we create two placeholders for labels and data inputs with `mx.sym.Variable` and give them names`'fashion_item_label'` and `'fashion_data'` correspondingly. On the next step the data will be flatten to 2-D.\n\n# In[8]:\n\n\nlabel = mx.sym.Variable('label')\n\n# input\ndata = mx.symbol.Variable('data')\n\n# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)\ndata = mx.sym.flatten(data=data, name='flatten')\n\n\n# `Symbol` also supports a rich set of neural network layers. The folowing code constructs a 3-layer fully connected neural network with the 1st hidden layer having 128 neurons, the 2nd hidden layer having 64 neurons, both activated by ReLU activation function and softmax output layer `mlp`.\n# \n# Then we visualize the structure of that network with `mx.viz.plot_network`.\n\n# In[9]:\n\n\n# 1st fully-connected layer + activation function\nfc1 = mx.sym.FullyConnected(data=data, num_hidden=128)\nact1 = mx.sym.Activation(data=fc1, act_type=\"relu\")\n\n# 2nd fully-connected layer + activation function\nfc2 = mx.sym.FullyConnected(data=act1, num_hidden=64)\nact2 = mx.sym.Activation(data=fc2, act_type=\"relu\")\n\n# 3rd fully connected layer (MNIST uses 10 classes)\nfc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)\n\n# softmax with cross entropy loss\nmlp = mx.sym.SoftmaxOutput(data=fc3, label=label, name='softmax')\n\nmx.viz.plot_network(mlp)\n\n\n# ### Train the model and commit checkpoints\n# \n# First, set up logging to INFO level and the context (now CPU is used).\n\n# In[10]:\n\n\nlogging.basicConfig(level=logging.INFO)\nlogging.getLogger().setLevel(logging.INFO)\n\nctx = mx.cpu() # for GPU change to mx.gpu()\n\n\n# MXNet modularizes commonly used code for training and inference in the `module` (`mod` for short) package.\n# \n# We can construct a module by specifying the following parameters:\n# \n# - `symbol`: the network definition (`mlp` in our case)\n# - `context`: the device (or a list of devices) to use for execution (`ctx` in our case)\n# - `data_names` : the list of input data variable names (`'data'` in our case)\n# - `label_names` : the list of input label variable names (`'label'` in our case)\n\n# In[11]:\n\n\nmod = mx.mod.Module(symbol=mlp, data_names=['data'], label_names=['label'], context=ctx, logger=logging)\n\n\n# `Module` provides both high-level and intermediate-level interfaces for executing predefined networks. We will use a high-level-interface function [`mod.fit`](http://mxnet.io/api/python/module.html#mxnet.module.BaseModule.fit) to train the model, which internally executes the following steps:\n# \n# - `bind` : Prepares environment for the computation by allocating memory.\n# - `init_params` : Assigns and initializes parameters.\n# - `init_optimizer` : Initializes optimizers. Defaults to `sgd`.\n# - `metric.create` : Creates evaluation metric from input metric name.\n# - `forward` : Forward computation.\n# - `update_metric` : Evaluates and accumulates evaluation metric on outputs of the last forward computation.\n# - `backward` : Backward computation.\n# - `update` : Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch.\n# \n# We will save checkpoints for each epoch in \"cifar-epoch№.params\".\n# \n# **NOTE**: If you want to initialize parameters in a certain way, use the commented intermediate-level-interface functions: `mod.bind` and `mod.init_params` and set `force_init = False` in `mod.fit`. In this case `mod.fit` will raise a warning, that since parameters are already initialized, init_params call is ignored.\n\n# In[12]:\n\n\nmod.bind(data_shapes=train_data_iter.provide_data, label_shapes=train_data_iter.provide_label)\nmod.init_params(initializer=mx.init.Xavier(magnitude=2.))\n \nmod.fit(train_data_iter, # train data\n eval_data = test_data_iter, # validation data\n optimizer ='sgd', # use SGD to train\n optimizer_params = {'learning_rate' : 0.2}, # use fixed learning rate\n eval_metric = mx.metric.Accuracy(), # report accuracy during training\n num_epoch = 300, # train for at most 10 dataset passes\n epoch_end_callback = mx.callback.do_checkpoint('cifar'), \n force_rebind = True,\n force_init = False) \n\n\n# ### Run predictions for 10 example elements\n# \n# To predict with `module`, we, first, prepare a dataset for prediction and then call `mod.predict`. It will collect and\n# return all the prediction results.\n\n# In[13]:\n\n\npred_data_iter = mx.io.NDArrayIter(data={'data': test_img[0:batch_size]}, batch_size=batch_size)\npred_digits = mod.predict(eval_data=pred_data_iter).asnumpy()\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n# starting index of items we want to predict\npred_start = 10\n# number of items we want to predict\npred_number = 5\n\nfor i in range(pred_number):\n plt.subplot(1,pred_number,i+1)\n plt.imshow(test_img[i + pred_start].reshape(32,32), cmap='Greys')\n plt.axis('off')\n \nplt.show()\n\nfor x in range(pred_start, pred_start + pred_number):\n label = np.where(pred_digits[x,0:pred_number] == pred_digits[x,0:pred_number].max())[0]\n print(\"Predicted label for image %s is %s (%s)\" % (x, label, label_names[label[0]]))\n print(\"Correct label:\", label_names[test_lbl[x]])\n\n\n# # END OF THE FILE\n# Code below needs adjustments\n\n# ### Downloading images for prediction from amazon.com\n\n# In[ ]:\n\n\nurllib.request.urlretrieve('https://images-na.ssl-images-amazon.com/images/I/81OaXwn1x4L._UX679_.jpg', 'predict1.jpg')\nurllib.request.urlretrieve('https://images-eu.ssl-images-amazon.com/images/I/31TcgNHsbIL._AC_UL260_SR200,260_.jpg', 'predict2.jpg')\nurllib.request.urlretrieve('https://images-eu.ssl-images-amazon.com/images/I/41hWhZBIc3L._AC_UL260_SR200,260_.jpg', 'predict3.jpg')\n\n\n# ### Load model from checkpoint for prediction\n# \n# To load the saved module parameters, call the `mx.mod.load_checkpoint` function. It loads the symbolic definition of NN and all the associated parameters. We can then set the loaded parameters into the module.\n\n# In[ ]:\n\n\nprediction_model_check_point = 10\nprediction_model_prefix = 'fashion_mnist'\n\nprediction_sym, arg_params, aux_params = mx.model.load_checkpoint(prediction_model_prefix, prediction_model_check_point)\nprediction_model = mx.mod.Module(symbol=prediction_sym, data_names=['fashion_data'], label_names=['fashion_item_label'])\n\n\n# Now we need to bind the model with new data- and label shapes and restore the parameter values. Since we want to make predictions for one image at a time, the data shape now must be (1,1,28,28), label shape - (1,)\n\n# In[ ]:\n\n\nprediction_model.bind(for_training=False, data_shapes=[('fashion_data', (1,1,28,28))], label_shapes = [('fashion_item_label', (1,))])\nprediction_model.set_params(arg_params=arg_params, aux_params=aux_params, allow_missing=True)\n\n\n# Now we define the **prediction function** `predict_fashion`, which:\n# - takes image as an input\n# - formats it to the needed shape (1, 28, 28), since now it must be a one-element dataset\n# - creates a trivial data iterator of batch size 1\n# - makes and prints predictions\n\n# In[ ]:\n\n\ndef predict_fashion(img):\n # format data to run prediction\n array = np.full((1, 28, 28), img, dtype=np.float32)\n pred_data_iter = mx.io.NDArrayIter(data={'fashion_data': to4d(array)}, batch_size=1)\n pred_digits = prediction_model.predict(eval_data=pred_data_iter).asnumpy()\n label = (np.where(pred_digits[0] == pred_digits[0].max())[0])\n print(\"Predicted fashion label for image is %s (%s) \" % (label,fashion_labels[label[0]]))\n\n\n# ### Predict labels for downloaded images\n# \n# Here we first load the image and then adjust it to the colours (with help of bit-wise inversion `cv2.bitwise_not` ) and to the size (with `skimage.transform.resize`) of the training dataset\n\n# In[ ]:\n\n\nfor i in range(3):\n img = mpimg.imread('predict'+str(i+1)+'.jpg')\n plt.imshow(img)\n plt.axis('off')\n plt.show() \n # get colours in line with train data\n img = cv2.bitwise_not(img)\n img = np.array (np.mean(img, -1))\n\n # resize image\n img = skimage.transform.resize(img, (28, 28), mode ='reflect')\n\n predict_fashion(img)\n\n","repo_name":"b2net/AI_DL_AWS","sub_path":"MXNet/CIFAR10/Demo_CIFAR10.py","file_name":"Demo_CIFAR10.py","file_ext":"py","file_size_in_byte":12034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"70137706774","text":"from numpy import load\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.preprocessing import Normalizer\r\nfrom sklearn.metrics import confusion_matrix, accuracy_score\r\nfrom sklearn.metrics import plot_confusion_matrix\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn import svm\r\nfrom random import choice\r\nfrom numpy import load\r\nfrom numpy import expand_dims\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\nimport datetime\r\nfrom info_logging import log\r\nimport pickle\r\nimport pandas as pd\r\n\r\ndef test(model):\r\n labels = []\r\n y_true = []\r\n y_pred = []\r\n\r\n data = load('Image_DataSet_100_processed-embeddings.npz')\r\n trainy, testX, testy = data['arr_1'], data['arr_2'], data['arr_3']\r\n # normalize input vectors\r\n in_encoder = Normalizer(norm='l2')\r\n testX = in_encoder.transform(testX)\r\n # label encode targets\r\n out_encoder = LabelEncoder()\r\n out_encoder.fit(trainy)\r\n testy = out_encoder.transform(testy)\r\n\r\n # load faces test\r\n data = load('Image_DataSet_100_processed.npz')\r\n testX_faces = data['arr_2']\r\n\r\n fig = plt.figure(figsize=(25, 10))\r\n idx = 0\r\n for itr in range(10):\r\n selection = choice([i for i in range(testX.shape[0])])\r\n random_face_pixels = testX_faces[selection]\r\n random_face_emb = testX[selection]\r\n random_face_class = testy[selection]\r\n random_face_name = out_encoder.inverse_transform([random_face_class])\r\n # prediction for the face\r\n samples = expand_dims(random_face_emb, axis=0)\r\n yhat_class = model.predict(samples)\r\n yhat_prob = model.predict_proba(samples)\r\n \r\n # get name\r\n class_index = yhat_class[0]\r\n class_probability = yhat_prob[0,class_index] * 100\r\n predict_names = out_encoder.inverse_transform(yhat_class)\r\n \r\n ax = fig.add_subplot(2, 5, idx + 1, xticks=[], yticks=[])\r\n idx += 1\r\n plt.imshow(random_face_pixels)\r\n ax.set_title(\"\\n\\nPredicted Name: {} \\n Actual Name: {} \\n probability: {}\".format(predict_names[0], random_face_name[0], class_probability),\r\n color=(\"green\" if predict_names[0] == random_face_name[0] else \"red\"))\r\n fig.savefig('test_visualization.jpg')\r\n log(\"Visualized results can be found in test_visualization.jpg file\")\r\n\r\n for selection in range(testy.shape[0]):\r\n random_face_pixels = testX_faces[selection]\r\n random_face_emb = testX[selection]\r\n random_face_class = testy[selection]\r\n random_face_name = out_encoder.inverse_transform([random_face_class])\r\n # prediction for the face\r\n samples = expand_dims(random_face_emb, axis=0)\r\n yhat_class = model.predict(samples)\r\n yhat_prob = model.predict_proba(samples)\r\n\r\n if random_face_name[0] not in labels:\r\n labels.append(random_face_name[0])\r\n y_true.append(random_face_name[0])\r\n \r\n # get name\r\n class_index = yhat_class[0]\r\n class_probability = yhat_prob[0,class_index] * 100\r\n predict_names = out_encoder.inverse_transform(yhat_class)\r\n y_pred.append(predict_names)\r\n\r\n return y_true, y_pred, labels\r\n\r\nif __name__ == '__main__':\r\n #load model\r\n filename = 'finalized_model.sav'\r\n model = pickle.load(open(filename, 'rb'))\r\n y_true, y_pred, label = test(model)\r\n score = accuracy_score(y_true, y_pred)\r\n log(\"Accuracy Score on test data: \" + str(score))\r\n cm = confusion_matrix(y_true, y_pred, labels=label)\r\n np.savetxt(\"confusion_mat.csv\", cm, delimiter=\",\")","repo_name":"gaurav82692/Face_detection_stack_svm","sub_path":"Other Codes/visualization - Copy.py","file_name":"visualization - Copy.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"5467754802","text":"import matplotlib.pyplot as plt\nimport networkx as nx\nfrom dfs import undirected_cc\n\nsize = [40,30,30]\nprobs = [[0.1,0,0],[0,0.1,0],[0,0,0.1]]\n\n\n\nG= nx.stochastic_block_model(size,probs)\ncomponent = undirected_cc(G)\nprint (component)\npos = nx.spring_layout(G)\nnc = [component[v] for v in G.nodes()]\nnx.draw(G,pos,node_size=100,node_color=nc)\nplt.show()\n","repo_name":"lucatrevisan/lucatrevisan.github.io","sub_path":"code-samples/cc-test.py","file_name":"cc-test.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"}
+{"seq_id":"6180185229","text":"import urllib\nfrom urllib.error import HTTPError, URLError\nfrom bs4 import BeautifulSoup , SoupStrainer\nimport re\nimport datetime\nfrom dateutil.relativedelta import relativedelta\nfrom sunpy.time import parse_time\nimport numpy as np\n\nsavedir_a = \"/Users/laurahayes/spaceweather_stuff/fun_stuff/final_ahead/\"\nsavedir_b = \"/Users/laurahayes/spaceweather_stuff/fun_stuff/final_behind/\"\n\nt1 = parse_time(\"2011-01-01\").datetime\nt2 = parse_time(\"2014-10-01\").datetime\ndate_list = [t1]\n\nfrom parfive import Downloader\n\nwhile t2>=t1:\n t1 = t1 + relativedelta(days=1)\n date_list.append(t1)\n\nbase_url = \"https://www.solarmonitor.org/data/\"\n\nurl_tests_b = [d.strftime(base_url+\"%Y/%m/%d/fits/strb/\") for d in date_list]\nurl_tests_a = [d.strftime(base_url+\"%Y/%m/%d/fits/stra/\") for d in date_list]\n\nbeacon_url_a = \"https://stereo-ssc.nascom.nasa.gov/data/beacon//ahead/secchi/img/euvi/\"\nbeacon_url_b = \"https://stereo-ssc.nascom.nasa.gov/data/beacon//behind/secchi/img/euvi/\"\n\n\nbeacon_test_a = [d.strftime(beacon_url_a+\"%Y%m%d/\") for d in date_list]\nbeacon_test_b = [d.strftime(beacon_url_b+\"%Y%m%d/\") for d in date_list]\n\n\ndef list_path_files(url_path, file_name):\n test = urllib.request.urlopen(url_path)\n soup = BeautifulSoup(test, features=\"lxml\")\n\n fits_links = []\n for link in soup.findAll('a'):\n if link.get('href') is not None and link.get('href').find(file_name) != -1:\n fits_links.append(url_path + link.get('href').split('/')[-1]) \n\n return fits_links\n\ndef find_file_times(file_name):\n if isinstance(file_name, (list, np.array)):\n t_list = []\n for i in range(len(file_name)):\n t = re.search('\\d{8}_\\d{6}', file_name[i]).group()\n tt = datetime.datetime.strptime(t, '%Y%m%d_%H%M%S')\n t_list.append(tt)\n return t_list\n else:\n\n t = re.search('\\d{8}_\\d{6}', file_name).group()\n tt = datetime.datetime.strptime(t, '%Y%m%d_%H%M%S')\n return tt\n\ndef get_urls_hours(beacon_tests):\n files_beacon = []\n for u in beacon_tests:\n print(u)\n try:\n urls = list_path_files(u, \"fts\")\n files_beacon.append(urls)\n except HTTPError:\n print(\"error\")\n files_beacon.append([])\n\n datess = [find_file_times(x) for x in files_beacon]\n hours = []\n for d in datess:\n try:\n hours.append(parse_time(d).strftime(\"%H\").astype(int))\n except:\n hours.append([])\n\n date_list_12 = [d + relativedelta(hours=12) for d in date_list]\n\n testy = []\n final_urls = []\n for i in range(len(datess)):\n if len(datess[i])>0:\n ind = np.argmin(np.abs(np.array(datess[i]) - date_list_12[i]))\n testy.append(datess[i][ind])\n final_urls.append(files_beacon[i][ind])\n else:\n testy.append([])\n final_urls.append([])\n return files_beacon, final_urls, testy\n\n# all_ahead, final_ahead, time_ahead = get_urls_hours(beacon_test_a)\n# all_behind, final_behind, time_behind = get_urls_hours(beacon_test_b)\n\ndef save_files():\n lala = {\"file_list\":files_beacon_ahead, \"final_urls\":final_urls, \"dates_final\":testy}\n lala2 = {\"file_list\":all_behind, \"final_urls\":final_behind, \"dates_final\":time_behind}\n with open(\"ahead_files.pkl\", \"wb\") as handle:\n pickle.dump(lala, handle)\n\n with open(\"behind_files.pkl\", \"wb\") as handle:\n pickle.dump(lala2, handle)\n\n\ndef download_files(file_list, savedir):\n dl = Downloader()\n for f in file_list:\n dl.enqueue_file(f, path=savedir)\n files = dl.download()\n\ndef download_files2(file_list, savedir):\n for f in file_list:\n if isinstance(f, str):\n fname = savedir + f.split(\"/\")[-1]\n urllib.request.urlretrieve(f, fname)\n else:\n print(f)\n\n\n# filess = []\n# for u in url_tests_a:\n# print(u)\n# try:\n# urls = list_path_files(u, \"fts.gz\")\n# filess.append(urls)\n# except HTTPError:\n# print(\"error\")\n# filess.append([])","repo_name":"hayesla/STEREO-AIA-stereoscopic-analysis","sub_path":"getting_beacon_data_tests.py","file_name":"getting_beacon_data_tests.py","file_ext":"py","file_size_in_byte":4014,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"67"}
+{"seq_id":"16413009861","text":"import uuid\nfrom flask import request\nfrom flask.views import MethodView\nfrom flask_smorest import abort, Blueprint\nfrom db import stores\nfrom schemas import StoreSchema\n\nblp = Blueprint(\"stores\", __name__, description=\"Operations on Stores\")\n\n\n@blp.route(\"/store\")\nclass Store(MethodView):\n @blp.response(200, StoreSchema(many=True))\n def get(self):\n # return {\"stores\": list(stores.values())}\n return stores.values()\n\n @blp.arguments(StoreSchema)\n @blp.response(201, StoreSchema)\n def post(self, data):\n # data = request.get_json()\n\n # if \"name\" not in data:\n # abort(400, message=\"Bad Request, 'name' must be included\")\n\n for store in stores.values():\n if data[\"name\"] == store[\"name\"]:\n abort(400, message=\"This store name is already used.\")\n storeId = uuid.uuid4().hex\n # newStore = {\"name\": data[\"name\"], \"items\": []}\n # kwargs bcoz data is a str else data.copy() or just data\n store = {**data, \"store_id\": storeId}\n stores[storeId] = store\n # return store, 201\n return store\n\n\n@blp.route(\"/store/\")\nclass Store(MethodView):\n @blp.response(200, StoreSchema)\n def get(self, storeId):\n try:\n return stores[storeId]\n except KeyError:\n # return {\"error\": \"Store not found\"}, 404\n abort(400, message=\"Bad Request, 'name' must be included\")\n\n def delete(self, storeId):\n try:\n del stores[storeId]\n return {\"message\": \"Store deleted\"}\n except KeyError:\n abort(404, message=\"Store not found\")\n","repo_name":"1998tapan/StoreRepoFlask","sub_path":"resources/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"71368163415","text":"def problem1(n):\n if n == 0:\n return 0 \n return n + problem1(n-1)\n# print(problem1(4))\n\n# 584321\n# 1\n# 2\n# 3\n# 4\n\ndef problem2(n,total=0):\n if n//10 == 0:\n total += n\n return total \n total+= n%10\n n=n//10\n return problem2(n,total)\nprint(problem2(584321))\n","repo_name":"KhuziamaR/Data-Structures-Algorithms-","sub_path":"DSA_Python/recursion/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"28303033134","text":"from django.db import models\r\nfrom django.utils.translation import gettext_lazy as _\r\n\r\n\r\nclass Assistentes(models.Model):\r\n \"\"\"Os Assistentes de uma instancia.\r\n\r\n \"\"\"\r\n\r\n\r\n militante = models.ForeignKey(\r\n to='core.Militantes',\r\n verbose_name=_('militante'),\r\n on_delete=models.CASCADE,\r\n related_name='assistencias',\r\n )\r\n\r\n instancia = models.ForeignKey(\r\n to='core.Instancias',\r\n verbose_name=_('instancia'),\r\n on_delete=models.CASCADE,\r\n related_name='assistentes',\r\n )\r\n\r\n class Meta:\r\n verbose_name = _('assistente')\r\n verbose_name_plural = _('assistentes')\r\n\r\n def __str__(self):\r\n\r\n return f'{self.militante.apelido}'\r\n","repo_name":"lsd1953/mar-len","sub_path":"backend/core/models/assistentes.py","file_name":"assistentes.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"42826437215","text":"import logging\n\nlog = logging.getLogger(__name__)\n\n\ndef slicing(source, call, dfg, sequence, discarded):\n # call是call的指令\n # source是当前的指令\n slices = []\n deps = set()\n overs = set()\n trv_stack = [source]\n visited = {node: False for node in dfg.nodes()}\n nodes = dfg.nodes()\n # 意思是sstore肯定要丢弃\n discarded.add(source)\n\n while len(trv_stack) > 0:\n instr = trv_stack[0]\n if visited[instr]:\n visited[instr] = False\n # 后序遍历后，又先序输出\n slices.append(instr)\n trv_stack.pop(0)\n else:\n visited[instr] = True\n if instr == call:\n raise RuntimeError('Violating data flow dependencies. CALL: {:#x}, lift: {:#x}'.format(call, instr))\n if not nodes[instr]['instr'].reserved:\n # 枚举数据流的相关指令\n # suc是后继啦\n for suc in dfg[instr]:\n if suc not in discarded:\n break\n # 循环正常进行则将该指令加入丢弃序列，即后继所有指令都需丢弃\n # 如果它后继的所有指令都需要丢弃，则它也需要丢弃\n else:\n discarded.add(instr)\n\n # dependence则记录的是和地址相关的东西\n # overwrite也是记录的修改地址的东西\n for dep in nodes[instr]['instr'].dependence:\n if dep in sequence:\n # 如果dependence里面还包括该序列则说明\n deps.add(dep)\n overs.update(nodes[instr]['instr'].overwrite)\n for pre in dfg.predecessors(instr):\n # 后序遍历，重复说明有环，报错\n if visited[pre]:\n raise RuntimeError('Error slicing, loop detected. lift: {:#x}, pre: {:#x}'.format(instr, pre))\n trv_stack.insert(0, pre)\n return slices, deps, overs\n\n\ndef lifting(sstore, call, dfg, trace, sliced, lifted, discarded):\n # 判断call,sstore是否在记录序列中\n if (call, sstore) not in trace:\n raise KeyError('Error tracing executed instructions from CALL to SSTORE. CALL: {:#x}, SSTORE: {:#x}'\n .format(call, sstore))\n # 初始栈只放sstore的指令位置\n lift_stack = [sstore]\n # 获取记录的call,sstore trace集合\n sequence = trace[(call, sstore)]\n # 获取数据流中的节点\n nodes = dfg.nodes()\n #\n lifts = set()\n overwrites = set()\n dependencies = set()\n num = 0\n while len(lift_stack) > 0:\n num += 1\n if num >= 10000:\n raise RuntimeError('loop')\n instr = lift_stack.pop(0)\n # if nodes[call]['instr'].layer != nodes[instr]['instr'].layer:\n # raise RuntimeError('Violating control flow dependencies. CALL: {:#x}, lift: {:#x}'.format(call, instr))\n name = nodes[instr]['instr'].name\n num += 1\n\n # 如果中间的指令遇到这几个调用，则报错\n if name == 'CALL' or name == 'CALLCODE' or name == 'DELEGATECALL' or name == 'STATICCALL':\n raise RuntimeError('Cannot lift CALL, CALLCODE, DELEGATECALL or STATICCALL. CALL: {:#x}, lift: {:#x}'\n .format(call, instr))\n\n # 开始分片\n slices, deps, overs = slicing(instr, call, dfg, sequence, discarded)\n # slices指从当前指令开始前序找所有与之关联的指令\n lifts.update(slices)\n overwrites.update(overs)\n if instr not in sliced:\n sliced[instr] = slices\n if instr not in lifted:\n lifted[instr] = {call: sequence}\n else:\n if call not in lifted[instr]:\n for pos in dict(lifted[instr]):\n if call in lifted[instr][pos]:\n break\n elif pos in sequence:\n lifted[instr][call] = sequence\n del lifted[instr][pos]\n break\n else:\n lifted[instr][call] = sequence\n\n for dep in deps:\n lift_stack.insert(0, dep)\n for instr in sequence.difference(lifts):\n dependencies.update(nodes[instr]['instr'].dependence)\n # 重写的指令里面不能有dependencies相关的指令\n if len(overwrites.intersection(dependencies)) > 0:\n raise RuntimeError('Violating memory/storage dependencies. CALL: {:#x}, SSTORE: {:#x}'.format(call, sstore))\n\n\ndef set_report(report, call, sstore, msg):\n report['Reentrancy'].append(\n {\n 'callOffset': call,\n 'sStoreOffset': sstore,\n 'result': msg\n }\n )\n\n\ndef execute(dfg, trace, reentrancy, report):\n sliced = {}\n lifted = {}\n discarded = set()\n for vul in reentrancy:\n call = vul[0]\n sstore = vul[1]\n old_sliced = sliced.copy()\n old_lifted = lifted.copy()\n old_discarded = discarded.copy()\n try:\n lifting(sstore, call, dfg, trace, sliced, lifted, discarded)\n except Exception as e:\n if str(e).strip('\\'') == 'Timeout.':\n raise e\n else:\n sliced = old_sliced\n lifted = old_lifted\n discarded = old_discarded\n set_report(report, call, sstore, str(e).strip('\\''))\n else:\n set_report(report, call, sstore, 'Done.')\n nodes = dfg.nodes()\n for instr in discarded:\n nodes[instr]['instr'].discarded = True\n patches = {}\n for instr in lifted:\n for pos in lifted[instr]:\n if pos not in patches:\n patches[pos] = {}\n # 但其实所有lifted里面都是 call:sequence啊\n # 也就是存储的patches[call][instr]\n patches[pos][instr] = sliced[instr]\n return call, patches\n","repo_name":"eliQAQ/detect-vulnerable-contract","sub_path":"patch/patch.py","file_name":"patch.py","file_ext":"py","file_size_in_byte":5948,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"4083999966","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nThis is a module for the data in the interactive Jupyternotebook.\n\"\"\"\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndata = pd.read_csv(\"./data/data_combined_final.csv\")\n\n\ndef get_data():\n \"\"\"Function to get the data.\"\"\"\n return data\n\ndef get_user_feedback(user_value, data, variable):\n \"\"\"This functions return the user feedback when comparing the user input data to the data in the dataset\"\"\"\n if user_value < data[variable].mean():\n feedback = f\"You have a lower value than the average person. \\n The difference is {round(data[variable].mean() - user_value, 2)}.\"\n elif user_value == data[variable].mean():\n feedback = f\"You have the same value as the average person.\"\n else:\n feedback = f\"You have a higher value than the average person. \\n The difference is {round(user_value - data[variable].mean(), 2)}.\"\n return feedback\n\ndef plot(dataset, userdata, plotted_variables):\n \"\"\"Function to plot a scatterplot of the data with the input of the user\n if the input plotted_variables is length is one the x-axis does not have any value \n and the y-axis is the value of the variable in plotted_variables\"\"\"\n\n if len(plotted_variables) == 1:\n variable = plotted_variables[0]\n y_data = dataset[variable]\n x_data = [0.5]*len(y_data)\n y_user = userdata[variable]\n x_user = 0.5\n \n else:\n x_data = dataset[plotted_variables[0]]\n y_data = dataset[plotted_variables[1]]\n x_user = userdata[plotted_variables[0]]\n y_user = userdata[plotted_variables[1]]\n \n\n # if the plotted is just one variable the x-axis does not have any value name and any values\n if len(plotted_variables) == 1:\n plt.figure(figsize=(10, 6))\n plt.xlabel(' ')\n plt.xticks([])\n plt.ylabel(plotted_variables[0])\n\n plt.scatter(x_data, y_data, color='pink', marker='o', s=100, alpha=0.5, label='Data')\n plt.scatter([x_user], [y_user], color='purple', marker='o', s=100, label=userdata['Name'])\n plt.axhline(y=y_data.mean(), color='black', linestyle='--', label=f\"Average {plotted_variables[0]}\")\n y_min, y_max = plt.ylim()\n x_min, x_max = plt.xlim()\n feedback = get_user_feedback(userdata[plotted_variables[0]], dataset, plotted_variables[0])\n plt.text(x_min + 0.56*(x_max-x_min), y_min + 0.4*(y_max-y_min), feedback, fontsize = 10)\n plt.legend()\n # if the plotted is two variables the x-axis and y-axis have values and value names\n else:\n plt.figure(figsize=(10, 6))\n\n plt.xlabel(plotted_variables[0])\n plt.ylabel(plotted_variables[1])\n\n plt.scatter(x_data, y_data, color='pink', marker='o', s=100, alpha=0.5, label='Data')\n plt.scatter([x_user], [y_user], color='purple', marker='o', s=100, label=userdata['Name'])\n plt.axhline(y=y_data.mean(), color='black', linestyle='--', label= f\"Average {plotted_variables[1]} \" )\n #plt.axvline(x=x_data.mean(), color='black', linestyle='--', label= f\"Average {plotted_variables[0]}\")\n y_min, y_max = plt.ylim()\n x_min, x_max = plt.xlim()\n feedback_x = get_user_feedback(userdata[plotted_variables[0]], dataset, plotted_variables[0])\n feedback_y = get_user_feedback(userdata[plotted_variables[1]], dataset, plotted_variables[1])\n #plt.text(x_min + 0.56*(x_max-x_min), y_min + 0.4*(y_max-y_min), feedback_x, fontsize = 10)\n plt.text(x_min + 0.56*(x_max-x_min), y_min + 0.3*(y_max-y_min), feedback_y, fontsize = 10)\n plt.legend()\n\n","repo_name":"heksaani/DataScienceProject","sub_path":"modules/data_module.py","file_name":"data_module.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"30634528618","text":"import pygame\nimport random\nimport math\nimport time\nfrom node import Node\n\nscreen_width = 1000\nscreen_height = 1000\nscreen = pygame.display.set_mode((screen_width, screen_height))\nblack = (0, 0, 0)\nwhite = (255, 255, 255)\nred = (255, 0, 0)\nblue = (0, 0, 255)\ngreen = (55, 155, 222)\npurple = (128, 0, 128)\n\ndef euclidean_distance(x1, y1, x2, y2):\n return math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)\n\nclass RRT:\n def __init__(self, start, goal, obstacles):\n self.start = Node(start[0], start[1])\n self.goal = Node(goal[0], goal[1])\n self.obstacles = obstacles\n self.nodes = [self.start]\n self.edges = []\n\n def generate_rrt(self, k, delta_t, max_paths):\n paths = []\n found_paths = 0\n shortest_path = None\n shortest_distance = math.inf\n\n while found_paths < max_paths:\n path = None\n for i in range(k):\n rand_node = self.random_state()\n nearest_node = self.nearest_neighbor(rand_node)\n u = self.select_input(rand_node, nearest_node)\n new_node = self.new_state(nearest_node, u, delta_t)\n if not self.collision(new_node, nearest_node):\n self.nodes.append(new_node)\n new_node.parent = nearest_node\n self.edges.append((nearest_node, new_node, u))\n pygame.draw.line(screen, blue, (nearest_node.x, nearest_node.y), (new_node.x, new_node.y), 2)\n pygame.display.update()\n if self.goal_reached(new_node):\n path = self.get_path(new_node)\n break\n if path is not None:\n paths.append(path)\n found_paths += 1\n self.exclude_nodes(path)\n\n # Draw the path in red\n for i in range(len(path) - 1):\n pygame.draw.line(screen, red, (path[i].x, path[i].y), (path[i + 1].x, path[i + 1].y), 3)\n pygame.display.update()\n\n # Calculate the path distance\n path_distance = sum(euclidean_distance(path[i].x, path[i].y, path[i+1].x, path[i+1].y) for i in range(len(path) - 1))\n if path_distance < shortest_distance:\n shortest_distance = path_distance\n shortest_path = path\n\n # Draw the shortest path in purple\n if shortest_path:\n for i in range(len(shortest_path) - 1):\n pygame.draw.line(screen, purple, (shortest_path[i].x, shortest_path[i].y),\n (shortest_path[i + 1].x, shortest_path[i + 1].y), 4)\n pygame.display.update()\n\n return paths\n\n def random_state(self):\n x = random.randint(0, screen_width)\n y = random.randint(0, screen_height)\n return Node(x, y)\n\n def nearest_neighbor(self, node):\n min_distance = math.inf\n nearest_node = None\n for n in self.nodes:\n distance = euclidean_distance(n.x, n.y, node.x, node.y)\n if distance < min_distance:\n min_distance = distance\n nearest_node = n\n return nearest_node\n\n def select_input(self, rand_node, nearest_node):\n return math.atan2(rand_node.y - nearest_node.y, rand_node.x - nearest_node.x)\n\n def new_state(self, nearest_node, u, delta_t):\n x = nearest_node.x + delta_t * math.cos(u)\n y = nearest_node.y + delta_t * math.sin(u)\n return Node(x, y)\n\n def line_intersection(self, p1, p2, q1, q2):\n def clockwiseLineSegment(A, B, C):\n return (C.y - A.y) * (B.x - A.x) > (B.y - A.y) * (C.x - A.x)\n if (p1 == q1) or (p1 == q2) or (p2 == q1) or (p2 == q2):\n return False\n return (clockwiseLineSegment(p1, q1, q2) != clockwiseLineSegment(p2, q1, q2)) and (clockwiseLineSegment(p1, p2, q1) != clockwiseLineSegment(p1, p2, q2))\n\n def collision(self, node, parent_node):\n for obstacle in self.obstacles:\n p1 = parent_node\n p2 = node\n rect_points = [\n (obstacle.left, obstacle.top),\n (obstacle.left, obstacle.bottom),\n (obstacle.right, obstacle.bottom),\n (obstacle.right, obstacle.top),\n ]\n for i in range(len(rect_points)):\n q1 = Node(rect_points[i][0], rect_points[i][1])\n q2 = Node(rect_points[(i + 1) % len(rect_points)][0], rect_points[(i + 1) % len(rect_points)][1])\n\n if self.line_intersection(p1, p2, q1, q2):\n return True\n return False\n\n def goal_reached(self, node):\n goal_radius = 50\n goalFlag = euclidean_distance(self.goal.x, self.goal.y, node.x, node.y) < goal_radius\n return goalFlag\n\n def get_path(self, node):\n path = []\n while node.parent is not None:\n path.append(node)\n node = node.parent\n path.append(self.start)\n path.reverse()\n return path\n\n def exclude_nodes(self, nodes_to_exclude):\n self.nodes = [node for node in self.nodes if node not in nodes_to_exclude]","repo_name":"dravendoom/RRTvisualize","sub_path":"rrt.py","file_name":"rrt.py","file_ext":"py","file_size_in_byte":5199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"36856972132","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport autoslug.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('popolo', '__first__'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Category',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=512)),\n ('slug', autoslug.fields.AutoSlugField(editable=False)),\n ],\n options={\n 'verbose_name': 'Category',\n 'verbose_name_plural': 'Categories',\n },\n ),\n migrations.CreateModel(\n name='Fulfillment',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('percentage', models.PositiveIntegerField(default=0)),\n ('status', models.TextField(default=b'', blank=True)),\n ('description', models.TextField(default=b'', blank=True)),\n ],\n options={\n 'verbose_name': 'Fulfilment',\n 'verbose_name_plural': 'Fulfilments',\n },\n ),\n migrations.CreateModel(\n name='InformationSource',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('url', models.URLField()),\n ('display_name', models.CharField(max_length=512)),\n ('date', models.DateField()),\n ],\n options={\n 'verbose_name': 'Information Source',\n 'verbose_name_plural': 'Information Sources',\n },\n ),\n migrations.CreateModel(\n name='Milestone',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('date', models.DateField()),\n ('description', models.TextField()),\n ],\n options={\n 'ordering': ('date',),\n 'verbose_name': 'Milestone',\n 'verbose_name_plural': 'Milestones',\n },\n ),\n migrations.CreateModel(\n name='Promise',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=2048)),\n ('description', models.TextField(blank=True)),\n ('date', models.DateField(null=True, blank=True)),\n ('order', models.PositiveIntegerField(default=0)),\n ('category', models.ForeignKey(related_name='promises', to='promises.Category', null=True)),\n ('person', models.ForeignKey(to='popolo.Person')),\n ],\n options={\n 'ordering': ('order',),\n 'verbose_name': 'Promise',\n 'verbose_name_plural': 'Promises',\n },\n ),\n migrations.CreateModel(\n name='VerificationDocument',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('url', models.URLField()),\n ('display_name', models.CharField(max_length=512)),\n ('date', models.DateField()),\n ('promise', models.ForeignKey(related_name='verification_documents', to='promises.Promise', null=True)),\n ],\n options={\n 'verbose_name': 'Verification Document',\n 'verbose_name_plural': 'Verification Documents',\n },\n ),\n migrations.AddField(\n model_name='milestone',\n name='promise',\n field=models.ForeignKey(related_name='milestones', to='promises.Promise'),\n ),\n migrations.AddField(\n model_name='informationsource',\n name='promise',\n field=models.ForeignKey(related_name='information_sources', to='promises.Promise'),\n ),\n migrations.AddField(\n model_name='fulfillment',\n name='promise',\n field=models.OneToOneField(to='promises.Promise'),\n ),\n ]\n","repo_name":"ciudadanointeligente/ddah-promises","sub_path":"promises/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":4437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"15867375642","text":"from sqlalchemy import Column, Integer, ForeignKey, String, Table\nfrom .Base import DeclarativeBase\n\n\n# association table between User and Event\nuser_has_event = Table(\n\t'User_has_Event',\n\tDeclarativeBase.metadata,\n\tColumn('username', String(200), ForeignKey('User.username')),\n\tColumn('event_id', Integer, ForeignKey('Event.event_id'))\n)","repo_name":"s-collins/seminars","sub_path":"services/etl/orm/User_has_Event.py","file_name":"User_has_Event.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"7866425113","text":"import yaml\nimport os\nimport sys\n\ndata_path = '/eos/cms/store/user/cmsbuild/profiling/data/'\ncmssw = os.listdir(data_path)\ncmssw.sort(key = lambda x: (x.split('_')[1:4],10-len(x.split('_')),len(x)))\n\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--operator\", type=str, help=\"operator\", default=None)\nparser.add_argument(\"--workflow\", type=str, help=\"workflow\", default=None)\nparser.add_argument(\"--release\", type=str, help=\"CMSSW release\", default=None)\nargs = parser.parse_args()\n\nnew = args.release\nold = cmssw[cmssw.index(new)-1]\nnew_architecture = os.listdir(os.path.join(data_path,new))[0]\nold_architecture = os.listdir(os.path.join(data_path,old))[0]\nworkflow = args.workflow\noperator = args.operator\n\nresult_path = \"{0}/{1}/{2}/\".format(new,new_architecture,workflow)\n\nfor step in ['step3','step4','step5']:\n\t\n\tnew_data = os.path.join(data_path,new,new_architecture,workflow,'{}_TimeMemoryInfo.log'.format(step))\n\told_data = os.path.join(data_path,old,old_architecture,workflow,'{}_TimeMemoryInfo.log'.format(step))\n\n\tos.makedirs(result_path,exist_ok = True)\n\tif os.path.isfile(new_data) and os.path.isfile(old_data):\n\t\tos.system(\"source ./{8} {0}{2}/{3}/{6}/{7} {0}{4}/{5}/{6}/{7} > {1}{7}.txt\".format(\n\t\tdata_path,result_path,\n\t\told,old_architecture,\n\t\tnew,new_architecture,\n\t\tworkflow,step,operator))\n\n","repo_name":"xoqhdgh1002/cms-reco-profiling-web","sub_path":"jenkins/scripts/comparison/make_compare_data.py","file_name":"make_compare_data.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"70729415895","text":"# Import appropriate libraries.\nimport time\nimport random\nfrom rpi_ws281x import PixelStrip, Color\nfrom Adafruit_LED_Backpack import SevenSegment\n\nLED_COUNT = 64 # Number of LED pixels.\nLED_PIN = 12 # GPIO pin connected to the pixels\nLED_FREQ_HZ = 800000 # LED signal frequency in hertz\nLED_DMA = 10 # DMA channel to use for generating signal\nLED_BRIGHTNESS = 10 # Set to 0 for darkest and 255 for brightest\nLED_INVERT = False # True to invert the signal (when using NPN\nLED_CHANNEL = 0 # set to '1' for GPIOs 13, 19, 41, 45 or 53\n\n# Prompt the user for their name and say hello.\nname = input(\"Hello! What is your name?\\n\")\nprint(\"Hello there \" + name + \"! I am thinking of a number between 1 and 100.\")\n\n# Prepare the game's variables.\nmy_number = random.randint(1, 100)\nguess_history = []\n\nstrip = PixelStrip(LED_COUNT, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS, LED_CHANNEL)\nstrip.begin()\n\ndef colorAll(strip, color):\n \"\"\"Wipe color across display a pixel at a time.\"\"\"\n for i in range(strip.numPixels()):\n strip.setPixelColor(i, color)\n strip.show()\n\n\nsegment = SevenSegment.SevenSegment(address=0x70)\n# Initialize the display. Must be called once before using the display.\nsegment.begin()\nsegment.set_digit(3, 0)\nsegment.set_digit(2, 1)\nsegment.write_display()\n# Begin the guessing loop with 10 tries.\nfor guess_count in range(1, 11):\n # Prompt the user for a number.\n valid_guess = False\n while not valid_guess:\n # Validate user input.\n try:\n number_guessed = int(input(\"Take a guess...\\n\"))\n valid_guess = True\n except ValueError:\n # The user didn't give us a number!\n print(\"Please provide a valid number.\")\n\n # Find out how close the user was to the real number.\n guess_difference = abs(my_number - number_guessed)\n\n # Add this guess to the guess history list.\n guess_history.append(number_guessed)\n\n # Check how close the guess is.\n if number_guessed < my_number and guess_difference > 10:\n colorAll(strip, Color(255, 0, 0))\n print(\"Your guess is very low. Try again.\")\n elif number_guessed > my_number and guess_difference > 10:\n colorAll(strip, Color(255, 0, 0))\n print(\"Your guess is very high. Try again.\")\n elif number_guessed < my_number and guess_difference <= 10:\n colorAll(strip, Color(255, 0, 0))\n print(\"You're close, but your guess is too low. Try again.\")\n elif number_guessed > my_number and guess_difference <= 10:\n colorAll(strip, Color(255, 0, 0))\n print(\"You're close, but your guess is too high. Try again.\")\n else:\n # They guessed the number correctly!\n break\n segment.clear()\n segment.set_digit(3, 10 - guess_count)\n segment.write_display()\n\n# Check if a correct guess was made.\nif number_guessed == my_number:\n colorAll(strip, Color(0, 255, 0)) # Green wipe\n print(\"Great job \" + name + \"! You guessed my number in \" + str(guess_count) + \" guesses.\")\n print(\"Your guesses were: \" + \" \".join(str(i) for i in guess_history))\n time.sleep(3)\n colorAll(strip, Color(0, 0, 0))\n segment.clear()\n segment.write_display()\nelse:\n print(\"Sorry! You didn't guess my number. The number I am thinking of is \" + str(my_number) + \".\")\n time.sleep(3)\n colorAll(strip, Color(0, 0, 0))\n segment.clear()\n segment.write_display()\n","repo_name":"farinaanthony96/Sales_Associate_Projects","sub_path":"src/guessing_game.py","file_name":"guessing_game.py","file_ext":"py","file_size_in_byte":3434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"6987158261","text":"#!/usr/bin/env python3\nimport os\nfrom flask import Flask, render_template, request, redirect, jsonify, \\\n url_for, flash, send_from_directory\nfrom werkzeug.utils import secure_filename\n\nfrom sqlalchemy import create_engine, asc\nfrom sqlalchemy.orm import sessionmaker\nfrom database_setup import Base, Brand, Model, User\n\nfrom flask import session as login_session\nimport random\nimport string\n\n# from oauth2client import client\nfrom oauth2client.client import flow_from_clientsecrets\nfrom oauth2client.client import FlowExchangeError\nimport httplib2\nimport json\nfrom flask import make_response\nimport requests\nimport logging\n\nUPLOAD_FOLDER = 'static'\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\nCLIENT_ID = json.loads(\n open('client_secrets.json', 'r').read())['web']['client_id']\n\n# Connect to Database and create database session\nengine = create_engine('postgresql://computershop:12345@localhost/catalog')\nBase.metadata.bind = engine\n\nDBSession = sessionmaker(bind=engine)\n\n\n# Create a state token to prevent request\n# Store it in the session for later\n@app.route('/login')\ndef show_login():\n \"\"\" Create state token and store it in session.\"\"\"\n\n state = ''.join(random.choice(string.ascii_uppercase + string.\n digits) for x in range(32))\n login_session['state'] = state\n # Render the login template\n return render_template('login.html', STATE=state)\n\n\n@app.route('/gconnect', methods=['POST'])\ndef gconnect():\n \"\"\"Connect with google.\"\"\"\n # Validate state token\n if request.args.get('state') != login_session['state']:\n response = make_response(json.dumps('Invalid state parameter.'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n # Obtain authorization code\n code = request.data\n\n try:\n # Upgrade the authorization code into a credentials object\n oauth_flow = flow_from_clientsecrets('client_secrets.json', scope='')\n oauth_flow.redirect_uri = 'postmessage'\n credentials = oauth_flow.step2_exchange(code)\n except FlowExchangeError:\n response = make_response(\n json.dumps('Failed to upgrade the authorization code.'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Check that the access token is valid.\n access_token = credentials.access_token\n url = ('https://www.googleapis.com/oauth2/v1/tokeninfo?access_token=%s'\n % access_token)\n h = httplib2.Http()\n result = json.loads(h.request(url, 'GET')[1])\n # If there was an error in the access token info, abort.\n if result.get('error') is not None:\n response = make_response(json.dumps(\n result.get('error')), 500)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Verify that the access token is used for the intended user.\n gplus_id = credentials.id_token['sub']\n if result['user_id'] != gplus_id:\n response = make_response(\n json.dumps(\n \"Token's user ID doesn't match given user ID.\"), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Verify that the access token is valid for this app.\n if result['issued_to'] != CLIENT_ID:\n response = make_response(\n json.dumps(\"Token's client ID does not match app's.\"), 401)\n logging.info(\"Token's client ID does not match app's.\")\n response.headers['Content-Type'] = 'application/json'\n return response\n\n stored_access_token = login_session.get('access_token')\n stored_gplus_id = login_session.get('gplus_id')\n if stored_access_token is not None and gplus_id == stored_gplus_id:\n response = make_response(json.dumps(\n 'Current user is already connected.'),\n 200)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n # Store the access token in the session for later use.\n login_session['access_token'] = credentials.access_token\n login_session['gplus_id'] = gplus_id\n\n # Get user info\n userinfo_url = \"https://www.googleapis.com/oauth2/v1/userinfo\"\n params = {'access_token': credentials.access_token, 'alt': 'json'}\n answer = requests.get(userinfo_url, params=params)\n\n data = answer.json()\n\n login_session['username'] = data['name']\n login_session['picture'] = data['picture']\n login_session['email'] = data['email']\n # ADD PROVIDER TO LOGIN SESSION\n # login_session['provider'] = 'google'\n\n # see if user exists, if it doesn't make a new one\n user_id = get_user_id(data[\"email\"])\n if user_id is None:\n user_id = create_user(login_session)\n login_session['userid'] = user_id\n\n output = ''\n output += 'Welcome, '\n output += login_session['username']\n output += '!
'\n output += '![](\"'\n)
'\n flash(\"you are now logged in as %s\" % login_session['username'])\n logging.info(\"done!\")\n return output\n\n\n# User Helper Functions\n\n# DISCONNECT - Revoke a current user's token and reset their login_session\n\n\n@app.route('/gdisconnect')\ndef gdisconnect():\n \"\"\"Disconnect from google.\"\"\"\n # Only disconnect a connected user.\n access_token = login_session.get('access_token')\n if access_token is None:\n response = make_response(\n json.dumps('Current user not connected.'), 401)\n response.headers['Content-Type'] = 'application/json'\n return response\n url = 'https://accounts.google.com/o/oauth2/revoke?token=%s' % access_token\n h = httplib2.Http()\n result = h.request(url, 'GET')[0]\n if result['status'] == '200':\n response = make_response(json.dumps(\n 'Successfully disconnected.'), 200)\n response.headers['Content-Type'] = 'application/json'\n return response\n else:\n response = make_response(json.dumps(\n 'Failed to revoke token for given user.'), 400)\n response.headers['Content-Type'] = 'application/json'\n return response\n\n\n# Disconnect based on provider\n@app.route('/disconnect')\ndef disconnect():\n \"\"\"Delete stored session.\"\"\"\n del login_session['access_token']\n del login_session['username']\n del login_session['userid']\n del login_session['picture']\n del login_session['email']\n del login_session['gplus_id']\n del login_session['state']\n\n flash(\"You have successfully been logged out.\")\n return redirect(url_for('show_brands'))\n\n\n# User Helper Functions\ndef create_user(login_session):\n \"\"\"create new user and return user id.\"\"\"\n session = DBSession()\n new_user = User(name=login_session['username'], email=login_session[\n 'email'], picture=login_session['picture'])\n session.add(new_user)\n session.commit()\n user = session.query(User).filter_by(\n email=login_session['email']).one_or_none()\n return user.id\n\n\ndef get_user_info(user_id):\n \"\"\"Return user information\"\"\"\n\n session = DBSession()\n user = session.query(User).filter_by(id=user_id).one_or_none()\n return user\n\n\ndef get_user_id(email):\n \"\"\"Return user id.\"\"\"\n\n session = DBSession()\n user = session.query(User).filter_by(email=email).one_or_none()\n if user is None:\n return None\n return user.id\n\n\n# JSON APIs to view Brand Information\n@app.route('/brand//model/JSON')\ndef brand_json(brand_id):\n \"\"\"Returns Laptop Models in a JSON Format\"\"\"\n session = DBSession()\n # brand = session.query(Brand).filter_by(id = brand_id).one()\n models = session.query(Model).filter_by(brand_id=brand_id).all()\n return jsonify(Models=[i.serialize for i in models])\n\n\n@app.route('/brand//model//JSON')\ndef model_json(brand_id, model_id):\n \"\"\"Returns Laptop Model in a JSON Format\"\"\"\n\n session = DBSession()\n model = session.query(Model).filter_by(id=model_id).one()\n return jsonify(Model=model.serialize)\n\n\n@app.route('/brand/JSON')\ndef brands_json():\n session = DBSession()\n brands = session.query(Brand).all()\n return jsonify(brands=[r.serialize for r in brands])\n\n\n# Show all brands\n@app.route('/')\n@app.route('/brand/')\ndef show_brands():\n \"\"\"Render brands.Html with permission (edit, delete).\"\"\"\n session = DBSession()\n brands = session.query(Brand).order_by(asc(Brand.name))\n\n if 'username' not in login_session:\n return render_template('brands.html', brands=brands,\n is_admin=False)\n else:\n return render_template('brands.html', brands=brands,\n is_admin=True)\n\n\n@app.route('/static/')\ndef send_file(filename):\n \"\"\"Return photo path.\"\"\"\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\n\n\n# Create a new brand\n@app.route('/brand/new/', methods=['GET', 'POST'])\ndef new_brand():\n \"\"\"Add new Laptop brand.\"\"\"\n try:\n session = DBSession()\n if request.method == 'POST':\n name = request.form['name']\n if name != '':\n file = request.files['photo']\n # if user does not select file, browser also\n # submit a empty part without filename\n # if file.filename == '':\n # flash('No selected file')\n # return redirect(request.url)\n if file and allowed_file(file.filename):\n filename = secure_filename(name +\n file.filename).replace(\" \", \"\")\n photo = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n file.save(photo)\n else:\n flash('File Extention is not allowed')\n newbrand = Brand(name=request.form['name'], photo=filename,\n user_id=login_session['userid'])\n session.add(newbrand)\n session.commit()\n flash('New Brand %s Successfully Created' % newbrand.name)\n return redirect(url_for('show_brands'))\n else:\n return render_template('brandNew.html')\n except Exception as ex:\n return logging.error(str(ex))\n\n\n# Edit brand\n@app.route('/brand//edit/', methods=['GET', 'POST'])\ndef edit_brand(brand_id):\n \"\"\"Edit brand if the created user is the same as the editing user.\"\"\"\n session = DBSession()\n edited_brand = session.query(Brand).filter_by(id=brand_id).one()\n if request.method == 'POST':\n created_user = edited_brand.user_id\n logged_user = login_session['userid']\n if created_user != logged_user:\n flash('You are not authorized to edit')\n return redirect(url_for('show_brands'))\n\n edit_btn = request.form.get('edit')\n if edit_btn is not None:\n if request.form['name']:\n edited_brand.name = request.form['name']\n edited_brand.user_id = login_session['userid']\n session.commit()\n flash('Brand Successfully Edited %s' % edited_brand.name)\n return redirect(url_for('show_brands'))\n else:\n return render_template('brandEdit.html', brand=edited_brand)\n\n\n# Delete brand and its models\n@app.route('/brand//delete/', methods=['GET', 'POST'])\ndef delete_brand(brand_id):\n \"\"\"Delete brand if the created user is the same as the deleting user.\"\"\"\n session = DBSession()\n deleted_brand = session.query(Brand).filter_by(id=brand_id).one()\n if request.method == 'POST':\n delete_btn = request.form.get('delete')\n creadted_user = deleted_brand.user_id\n logged_user = login_session['userid']\n if creadted_user != logged_user:\n flash('You are not authorized to delete')\n return redirect(url_for('show_brands', brand_id=brand_id))\n if delete_btn is not None:\n delete_btn = request.form.get('delete')\n if delete_btn is not None:\n session.delete(deleted_brand)\n flash('%s Successfully Deleted' % deleted_brand.name)\n session.commit()\n return redirect(url_for('show_brands', brand_id=brand_id))\n\n else:\n return render_template('brandDelete.html', brand=deleted_brand)\n\n\n# Show a brand model\n@app.route('/brand//')\n@app.route('/brand//model/')\ndef show_model(brand_id):\n \"\"\"Render model.Html with user permission (edit, delete).\"\"\"\n session = DBSession()\n brand = session.query(Brand).filter_by(id=brand_id).one()\n models = session.query(Model).filter_by(brand_id=brand_id).all()\n\n user = get_user_info(brand.user_id)\n\n if 'userid' in login_session:\n userid_session = login_session['userid']\n if not user or 'username' not in login_session or \\\n brand.user_id != userid_session:\n return render_template('model.html', models=models, brand=brand,\n creator=user, is_admin=False)\n else:\n return render_template('model.html', models=models, brand=brand,\n creator=user, is_admin=True)\n else:\n return render_template('model.html', models=models, brand=brand,\n creator=user, is_admin=False)\n\n\n# Create a new brand model\n@app.route('/brand//model/new/', methods=['GET', 'POST'])\ndef new_model(brand_id):\n \"\"\"Add new laptop model.\"\"\"\n session = DBSession()\n # brand = session.query(Brand).filter_by(id = brand_id).one()\n if request.method == 'POST':\n file = request.files['photo']\n # if user does not select file, browser also\n # submit a empty part without filename\n # if file.filename == '':\n # flash('No selected file')\n # return redirect(request.url)\n if file and allowed_file(file.filename):\n filename = secure_filename(request.form['name'] +\n file.filename).replace(\" \", \"\")\n photo = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n file.save(photo)\n\n newmodel = Model(name=request.form['name'],\n description=request.form['description'],\n price=request.form['price'],\n photo=filename, brand_id=brand_id)\n session.add(newmodel)\n session.commit()\n flash('New Labtop Model %s Item Successfully Created' %\n newmodel.name)\n return redirect(url_for('show_model', brand_id=brand_id))\n else:\n flash('Brand must have a logo')\n return render_template('modelNew.html', brand_id=brand_id)\n else:\n return render_template('modelNew.html', brand_id=brand_id)\n\n\n# helper for allowed photos ext.\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\n# Edit a brand model\n@app.route('/brand//model//edit',\n methods=['GET', 'POST'])\ndef edit_model(brand_id, model_id):\n \"\"\"Modify laptop model only\n\n if the created user is the same as the editing user.\n \"\"\"\n session = DBSession()\n edited_model = session.query(Model).filter_by(id=model_id).one()\n if request.method == 'POST':\n edit_btn = request.form.get('edit')\n creadted_user = edited_model.user_id\n logged_user = login_session['userid']\n if creadted_user != logged_user:\n flash('You are not authorized to edit')\n return redirect(url_for('show_model', brand_id=brand_id))\n if edit_btn is not None:\n if request.form['name']:\n edited_model.name = request.form['name']\n if request.form['description']:\n edited_model.description = request.form['description']\n if request.form['price']:\n edited_model.price = request.form['price']\n edit_model.user_id = login_session['userid']\n # if 'photo' not in request.files:\n # flash('No file part')\n # return redirect(request.url)\n file = request.files['photo']\n # if user does not select file, browser also\n # submit a empty part without filename\n # if file.filename == '':\n # flash('No selected file')\n # return redirect(request.url)\n if file and allowed_file(file.filename):\n filename = secure_filename(request.form['name'] +\n file.filename).replace(\" \", \"\")\n photo = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n file.save(photo)\n edited_model.photo = filename\n session.add(edited_model)\n session.commit()\n flash('Menu Item Successfully Edited')\n return redirect(url_for('show_model', brand_id=brand_id))\n else:\n return render_template('modelEdit.html', brand_id=brand_id,\n model_id=model_id, model=edited_model)\n\n\n# Delete brand Model\n@app.route('/brand//model//delete',\n methods=['GET', 'POST'])\ndef delete_model(brand_id, model_id):\n \"\"\"Remove model\"\"\"\n session = DBSession()\n deleted_model = session.query(Model).filter_by(id=model_id).one()\n\n if request.method == 'POST':\n delete_btn = request.form.get('delete')\n edit_btn = request.form.get('edit')\n creadted_user = deleted_model.user_id\n logged_user = login_session['userid']\n if creadted_user != logged_user:\n flash('You are not authorized to delete')\n return redirect(url_for('show_model', brand_id=brand_id))\n if delete_btn is not None:\n session.delete(deleted_model)\n session.commit()\n flash('Menu Item Successfully Deleted')\n return redirect(url_for('show_model', brand_id=brand_id))\n else:\n return redirect(url_for('show_model', brand_id=brand_id))\n else:\n return render_template('modelDelete.html', model=deleted_model)\n\n\nif __name__ == '__main__':\n app.secret_key = 'super_secret_key'\n app.debug = True\n app.run(host='0.0.0.0', port=5000)\n","repo_name":"SamehPierre/item_catalog","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":18548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"1108695881","text":"import matplotlib.pyplot as plt\nfrom scipy.signal import correlate\nimport numpy as np\ndef PlotAutoCorr(samples,n,L):\n\t# normalise samples\n\tnormed_samples = (samples - np.mean(samples,axis=0))/np.std(samples,axis=0)\n\t# calculate autocorrelation\n\tcorr = correlate(normed_samples,normed_samples,mode='same',method='fft')/(n*L*3)\n\tprint(corr.shape)\n\treturn plt.plot(range(-n//2,n//2),corr)\n\n\nenergy = np.genfromtxt('data/fake/sk_energies.txt')\n#seqs = np.genfromtxt('data/fake/sk_encode.txt',dtype=int)\n\nPlotAutoCorr(energy,100000,1)\n#PlotAutoCorr(seqs[:,0],100000,1)\nplt.savefig('data/fake/sk_autocorr.png')","repo_name":"andrewcboardman/apta_ml","sub_path":"data_stats/check_gen_data.py","file_name":"check_gen_data.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"26884571777","text":"import pexpect\nimport unittest\nimport subprocess\nimport sys\nimport os\nfrom . import PexpectTestCase\n\nunicode_type = str if pexpect.PY3 else unicode\n\n\ndef timeout_callback(values):\n if values[\"event_count\"] > 3:\n return 1\n return 0\n\n\ndef function_events_callback(values):\n try:\n previous_echoed = (values[\"child_result_list\"][-1]\n .decode().split(\"\\n\")[-2].strip())\n if previous_echoed.endswith(\"stage-1\"):\n return \"echo stage-2\\n\"\n elif previous_echoed.endswith(\"stage-2\"):\n return \"echo stage-3\\n\"\n elif previous_echoed.endswith(\"stage-3\"):\n return \"exit\\n\"\n else:\n raise Exception(\"Unexpected output {0}\".format(previous_echoed))\n except IndexError:\n return \"echo stage-1\\n\"\n\n\nclass RunFuncTestCase(PexpectTestCase.PexpectTestCase):\n if sys.platform != 'win32':\n runfunc = staticmethod(pexpect.run)\n cr = b'\\r'\n empty = b''\n prep_subprocess_out = staticmethod(lambda x: x)\n\n def setUp(self):\n self.runenv = os.environ.copy()\n self.runenv['PS1'] = 'GO:'\n super(RunFuncTestCase, self).setUp()\n\n def test_run_exit(self):\n (data, exitstatus) = self.runfunc(sys.executable + ' exit1.py', withexitstatus=1)\n assert exitstatus == 1, \"Exit status of 'python exit1.py' should be 1.\"\n\n def test_run(self):\n the_old_way = subprocess.Popen(\n args=['uname', '-m', '-n'],\n stdout=subprocess.PIPE\n ).communicate()[0].rstrip()\n\n (the_new_way, exitstatus) = self.runfunc(\n 'uname -m -n', withexitstatus=1)\n the_new_way = the_new_way.replace(self.cr, self.empty).rstrip()\n\n self.assertEqual(self.prep_subprocess_out(the_old_way), the_new_way)\n self.assertEqual(exitstatus, 0)\n\n def test_run_callback(self):\n # TODO it seems like this test could block forever if run fails...\n events = {pexpect.TIMEOUT: timeout_callback}\n self.runfunc(\"cat\", timeout=1, events=events)\n\n def test_run_bad_exitstatus(self):\n (the_new_way, exitstatus) = self.runfunc(\n 'ls -l /najoeufhdnzkxjd', withexitstatus=1)\n assert exitstatus != 0\n\n def test_run_event_as_string(self):\n events = [\n # second match on 'abc', echo 'def'\n ('abc\\r\\n.*GO:', 'echo \"def\"\\n'),\n # final match on 'def': exit\n ('def\\r\\n.*GO:', 'exit\\n'),\n # first match on 'GO:' prompt, echo 'abc'\n ('GO:', 'echo \"abc\"\\n')\n ]\n\n (data, exitstatus) = pexpect.run(\n 'bash --norc',\n withexitstatus=True,\n events=events,\n env=self.runenv,\n timeout=10)\n assert exitstatus == 0\n\n def test_run_event_as_function(self):\n events = [\n ('GO:', function_events_callback)\n ]\n\n (data, exitstatus) = pexpect.run(\n 'bash --norc',\n withexitstatus=True,\n events=events,\n env=self.runenv,\n timeout=10)\n assert exitstatus == 0\n\n def test_run_event_as_method(self):\n events = [\n ('GO:', self._method_events_callback)\n ]\n\n (data, exitstatus) = pexpect.run(\n 'bash --norc',\n withexitstatus=True,\n events=events,\n env=self.runenv,\n timeout=10)\n assert exitstatus == 0\n\n def test_run_event_typeerror(self):\n events = [('GO:', -1)]\n with self.assertRaises(TypeError):\n pexpect.run('bash --norc',\n withexitstatus=True,\n events=events,\n env=self.runenv,\n timeout=10)\n\n def _method_events_callback(self, values):\n try:\n previous_echoed = (values[\"child_result_list\"][-1].decode()\n .split(\"\\n\")[-2].strip())\n if previous_echoed.endswith(\"foo1\"):\n return \"echo foo2\\n\"\n elif previous_echoed.endswith(\"foo2\"):\n return \"echo foo3\\n\"\n elif previous_echoed.endswith(\"foo3\"):\n return \"exit\\n\"\n else:\n raise Exception(\"Unexpected output {0!r}\"\n .format(previous_echoed))\n except IndexError:\n return \"echo foo1\\n\"\n\n\nclass RunUnicodeFuncTestCase(RunFuncTestCase):\n if sys.platform != 'win32':\n runfunc = staticmethod(pexpect.runu)\n cr = b'\\r'.decode('ascii')\n empty = b''.decode('ascii')\n prep_subprocess_out = staticmethod(lambda x: x.decode('utf-8', 'replace'))\n\n def test_run_unicode(self):\n if pexpect.PY3:\n char = chr(254) # þ\n pattern = ''\n else:\n char = unichr(254) # analysis:ignore\n pattern = ''.decode('ascii')\n\n def callback(values):\n if values['event_count'] == 0:\n return char + '\\n'\n else:\n return True # Stop the child process\n\n output = pexpect.runu(self.PYTHONBIN + ' echo_w_prompt.py',\n env={'PYTHONIOENCODING': 'utf-8'},\n events={pattern: callback})\n assert isinstance(output, unicode_type), type(output)\n assert ('' + char) in output, output\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"pexpect/pexpect","sub_path":"tests/test_run.py","file_name":"test_run.py","file_ext":"py","file_size_in_byte":5426,"program_lang":"python","lang":"en","doc_type":"code","stars":2458,"dataset":"github-code","pt":"67"}
+{"seq_id":"70740480853","text":"#!/usr/bin/python3\n\nimport os\nimport dynein.run as run\nimport glob\nimport numpy as np\nimport argparse\nimport subprocess\n\ndef latex_format(x):\n if isinstance(x, float) or isinstance(x, int):\n x = '{:e}'.format(x)\n if 'e+0' in x:\n m,e = x.split('e+0')\n if m == '1':\n return r'10^{'+e+'}'\n return m + r'\\times 10^{' + e+ '}'\n if 'e+' in x:\n m,e = x.split('e+')\n if m == '1':\n return r'10^{'+e+'}'\n return m + r'\\times 10^{' + e+ '}'\n if 'e-0' in x:\n m,e = x.split('e-0')\n if m == '1':\n return r'10^{-'+e+'}'\n return m + r'\\times 10^{-' + e+ '}'\n if 'e' in x:\n m,e = x.split('e')\n if m == '1':\n return r'10^{'+e+'}'\n return m + r'\\times 10^{' + e+ '}'\n # if isinstance(x, str):\n # x = x.replace('-', '_')\n return x\n\nparser = argparse.ArgumentParser(description=\"script to generate unbinding probabilities of bb dynein\")\n\nparser.add_argument('-k_b', '--binding', dest='k_b', action='store', type=float,\n default=1e8, help=\"pre-exponential binding constant\", metavar='')\nparser.add_argument('-k_ub', '--unbinding', dest='k_ub', action='store', type=float,\n default=100, help=\"pre-exponential unbinding constant\", metavar='')\nparser.add_argument('-t', '--runtime', dest='runtime', action='store', type=float,\n default=1.0, help='total runtime for simulation in seconds', metavar='')\nparser.add_argument('-exp', '--exp-unbinding-constant', dest='exp_unbinding_constant',\n action='store', type=float, default=0.0, help=\"exponential unbinding constant\", metavar='')\n\nparser.add_argument('-s', '--seed', dest ='seed', action='store', type=float, default=1.0, help =\"random number seed\", metavar='')\nparser.add_argument('-cb', '--cb', dest ='cb', action='store', type=float, default=0.1, help =\"cb\", metavar='')\nparser.add_argument('-cm', '--cm', dest ='cm', action='store', type=float, default=0.4, help =\"cm\", metavar='')\nparser.add_argument('-ct', '--ct', dest ='ct', action='store', type=float, default=0.2, help =\"ct\", metavar='')\n\nparser.add_argument('-l', '--label', dest='label', action='store', type=str, default='default', help=\"label for run\", metavar='')\n\nparser.add_argument('-w', '--writerate', dest='write_rate', action='store', type=str, default=1e6, help=\"writes per second\", metavar='')\n\nargs = parser.parse_args()\n\nif os.path.exists('run-unbinding-rate-simulations.py'):\n os.chdir('../')\nos.system(\"make simulate_unbinding_rates\")\n\nif not os.path.exists('data/unbinding_probability/'):\n os.makedirs('data/unbinding_probability/')\n\nfor L in [1, 5, 10, 15, 20, 25, 30, 35, 40]:\n basename = \"%s__L-%s,s-%s\" % (args.label, str(L), args.seed)\n\n cmd = [\"./simulate_unbinding_rates\",\n \"--label\", \"%s\" % str(args.label),\n \"--k_b\", \"%g\" % float(args.k_b),\n \"--k_ub\", \"%g\" % float(args.k_ub),\n \"--c\", \"%g\" % float(args.exp_unbinding_constant),\n \"--cb\", \"%g\" % float(args.cb),\n \"--cm\", \"%g\" % float(args.cm),\n \"--ct\", \"%g\" % float(args.ct),\n \"--ls\", \"10.49\",\n \"--lt\", \"23.8\",\n \"--eqb\", \"120\",\n \"--eqmpre\", \"200\",\n \"--eqmpost\", \"224\",\n \"--eqt\", \"0\",\n \"--write_rate\", \"%g\" % float(args.write_rate),\n \"--runtime\", \"%g\" % float(args.runtime),\n \"--seed\", \"%g\" % float(args.seed),\n \"--dt\", \"1e-10\",\n \"--L\", \"%g\" % float(L)]\n\n if not os.path.exists('runlogs'):\n os.makedirs('runlogs')\n out = open('runlogs/' + basename + '.out', 'w')\n\n print(\"Running: \", \" \".join(cmd), out)\n out.flush()\n process_object = subprocess.Popen(cmd, stdout=out, stderr=subprocess.PIPE)\n err = process_object.communicate()[1]\n if (err != b''):\n print(\"\\n##################################\",\n \"\\nSimulation exited in error: \\n\\n\",\n err.decode(\"utf-8\"),\n \"\\n##################################\\n\\n\")\n\nwith open(\"data/unbinding_probability/%s.tex\" % args.label, \"w\") as f:\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"runlabel\").replace(\"_\",\"\"), latex_format(args.label)) + '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"kb\").replace(\"_\",\"\"), latex_format(args.k_b)) + '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"kub\").replace(\"_\",\"\"), latex_format(args.k_ub)) + '\\n')\n f.write(r'\\newcommand\\%s{%s}' %(latex_format(\"cexp\").replace(\"_\",\"\"), latex_format(args.exp_unbinding_constant)) + '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"cb\").replace(\"_\",\"\"), latex_format(args.cb))+ '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"cm\").replace(\"_\",\"\"), latex_format(args.cm))+ '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"ct\").replace(\"_\",\"\"), latex_format(args.ct))+ '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"ls\").replace(\"_\",\"\"), latex_format(10.49))+ '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"lt\").replace(\"_\",\"\"), latex_format(23.8))+ '\\n')\n f.write(r'\\newcommand\\%s{%s}' % (latex_format(\"w_rate\").replace(\"_\",\"\"), latex_format(args.write_rate))+ '\\n')\n","repo_name":"elliotc12/dynein_walk","sub_path":"scripts/run-unbinding-rate-simulations.py","file_name":"run-unbinding-rate-simulations.py","file_ext":"py","file_size_in_byte":5299,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"67"}
+{"seq_id":"9344001837","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sklearn\nimport sklearn.datasets\nimport sklearn.linear_model\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import StandardScaler\n\nmin_max_scaler = MinMaxScaler()\nss = StandardScaler()\nnp.random.seed(1) # 使每次随机产生的数都相同\n\n\n# 导入数据\n\nInputIndex = [\n 'Rn', 'PAR', 'fdif', 'PARdif', 'PARdir',\n 'Ta', 'Ts', 'Vpd', 'RH', 'Ustar', 'O3',\n 'VWC5', 'VWC25', 'VWC50', 'VWC100', 'VWC150', 'VWC200']\nOutputIndex = ['NEE']\ndata = pd.read_csv('data/example.csv')\n\n# 分割训练集合验证集，test_size=0.4代表从总的数据集合train中随机选取40%作为验证集，随机种子为0\ntrain = data[InputIndex]\ntarget = data[OutputIndex]\n\ntrX, teX, trY, teY = train_test_split(train, target, test_size=0.2, random_state=0)\nX = min_max_scaler.fit_transform(trX)\nY = min_max_scaler.fit_transform(trY)\ntest_X = min_max_scaler.transform(teX)\ntest_y = min_max_scaler.transform(teY)\n\n\nshape_X = X.shape # X, 行17列\nshape_Y = Y.shape # Y， 1列\nm = X.shape[1] # 样本数\n\ndef sigmoid(x):\n \"\"\"\n Compute the sigmoid of x\n\n Arguments:\n x -- A scalar or numpy array of any size.\n\n Return:\n s -- sigmoid(x)\n \"\"\"\n s = 1 / (1 + np.exp(-x))\n return s\n\n# 定义神经网络结构\ndef layer_sizes(X, Y):\n \"\"\"\n Arguments:\n X -- input dataset of shape (input size, number of examples)\n Y -- labels of shape (output size, number of examples)\n Returns:\n n_x -- the size of the input layer\n n_h -- the size of the hidden layer\n n_y -- the size of the output layer\n \"\"\"\n n_x = X.shape[0] # 输入层神经元个数\n n_h = 20 # 隐藏层神经元个数\n n_y = Y.shape[0] # 输出神经元个数\n\n return (n_x, n_h, n_y)\n\n\n# 初始化模型参数\ndef initialize_parameters(n_x, n_h, n_y):\n \"\"\"\n Argument:\n n_x -- size of the input layer\n n_h -- size of the hidden layer\n n_y -- size of the output layer\n Returns:\n params -- python dictionary containing your parameters:\n W1 -- weight matrix of shape (n_h, n_x)\n b1 -- bias vector of shape (n_h, 1)\n W2 -- weight matrix of shape (n_y, n_h)\n b2 -- bias vector of shape (n_y, 1)\n \"\"\"\n np.random.seed(2)\n W1 = np.random.randn(n_h, n_x) * 0.01\n b1 = np.zeros((n_h, 1))\n W2 = np.random.randn(n_y, n_h) * 0.01\n b2 = np.zeros((n_y, 1))\n assert (W1.shape == (n_h, n_x))\n assert (b1.shape == (n_h, 1))\n assert (W2.shape == (n_y, n_h))\n assert (b2.shape == (n_y, 1))\n\n parameters = {\"W1\": W1,\n \"b1\": b1,\n \"W2\": W2,\n \"b2\": b2}\n\n return parameters\n\n\n# 前向传播\ndef forward_propagation(X, parameters):\n \"\"\"\n Argument:\n X -- input data of size (n_x, m)\n parameters -- python dictionary containing your parameters (output of initialization function)\n Returns:\n A2 -- The sigmoid output of the second activation\n cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n \"\"\"\n # Retrieve each parameter from the dictionary \"parameters\"\n W1 = parameters[\"W1\"]\n b1 = parameters[\"b1\"]\n W2 = parameters[\"W2\"]\n b2 = parameters[\"b2\"]\n # Implement Forward Propagation to calculate A2 (probabilities)\n # np.dot 代表矩阵相乘\n Z1 = np.dot(W1, X) + b1\n A1 = sigmoid(Z1)\n\n Z2 = np.dot(W2, A1) + b2\n A2 = Z2\n\n assert (A2.shape == (1, X.shape[1]))\n\n cache = {\"Z1\": Z1,\n \"A1\": A1,\n \"Z2\": Z2,\n \"A2\": A2}\n\n return A2, cache\n\n\n# 计算cost\ndef compute_cost(A2, Y, parameters):\n \"\"\"\n Computes the cross-entropy cost given in equation (13)\n Arguments:\n A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n Y -- \"true\" labels vector of shape (1, number of examples)\n parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n Returns:\n \"\"\"\n\n cost = np.sqrt(((A2 - Y) ** 2).mean())\n assert (isinstance(cost, float))\n\n return cost\n\n\n# 反向传播\ndef backward_propagation(parameters, cache, X, Y):\n \"\"\"\n Implement the backward propagation using the instructions above.\n Arguments:\n parameters -- python dictionary containing our parameters\n cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n X -- input data of shape (2, number of examples)\n Y -- \"true\" labels vector of shape (1, number of examples)\n Returns:\n grads -- python dictionary containing your gradients with respect to different parameters\n \"\"\"\n m = X.shape[1] # 样本数目\n\n # First, retrieve W1 and W2 from the dictionary \"parameters\".\n W1 = parameters[\"W1\"]\n W2 = parameters[\"W2\"]\n\n # Retrieve also A1 and A2 from dictionary \"cache\".\n # A1,A2是每一层的输出结果\n A1 = cache[\"A1\"]\n A2 = cache[\"A2\"]\n\n # Backward propagation: calculate dW1, db1, dW2, db2.\n # 输出层误差\n dZ2 = A2 - Y\n # 隐藏层到输出层权重求导数，最后一层是线性值\n dW2 = np.dot(dZ2, A1.T) / m\n db2 = np.sum(dZ2, axis=1, keepdims=True) / m\n dZ1 = np.multiply(np.dot(W2.T, dZ2), (1 - np.power(A1, 2)))\n # dZ1 = np.multiply(np.dot(W2.T, dZ2), (A1*(1-A1)))\n dW1 = np.dot(dZ1, X.T) / m\n db1 = np.sum(dZ1, axis=1, keepdims=True) / m\n\n grads = {\"dW1\": dW1,\n \"db1\": db1,\n \"dW2\": dW2,\n \"db2\": db2,\n \"dZ1\": dZ1}\n\n return grads\n\n\n# 更新参数\ndef update_parameters(parameters, grads, learning_rate=1.2):\n \"\"\"\n Updates parameters using the gradient descent update rule given above\n Arguments:\n parameters -- python dictionary containing your parameters\n grads -- python dictionary containing your gradients\n Returns:\n parameters -- python dictionary containing your updated parameters\n \"\"\"\n # Retrieve each parameter from the dictionary \"parameters\"\n W1 = parameters[\"W1\"]\n b1 = parameters[\"b1\"]\n W2 = parameters[\"W2\"]\n b2 = parameters[\"b2\"]\n\n # Retrieve each gradient from the dictionary \"grads\"\n dW1 = grads[\"dW1\"]\n db1 = grads[\"db1\"]\n dW2 = grads[\"dW2\"]\n db2 = grads[\"db2\"]\n\n # Update rule for each parameter\n W1 = W1 - learning_rate * dW1\n b1 = b1 - learning_rate * db1\n W2 = W2 - learning_rate * dW2\n b2 = b2 - learning_rate * db2\n\n parameters = {\"W1\": W1,\n \"b1\": b1,\n \"W2\": W2,\n \"b2\": b2}\n\n return parameters\n\n\n# 打包模型\n# num_iterations: 训练次数，如果nn_model中未指定，默认为10000\ndef nn_model(X, Y, n_h, num_iterations=10000, print_cost=False):\n \"\"\"\n Arguments:\n X -- dataset of shape (2, number of examples)\n Y -- labels of shape (1, number of examples)\n n_h -- size of the hidden layer\n num_iterations -- Number of iterations in gradient descent loop\n print_cost -- if True, print the cost every 1000 iterations\n Returns:\n parameters -- parameters learnt by the model. They can then be used to predict.\n \"\"\"\n np.random.seed(3)\n\n n_x = layer_sizes(X, Y)[0]\n n_y = layer_sizes(X, Y)[2]\n\n # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n parameters = initialize_parameters(n_x, n_h, n_y)\n W1 = parameters[\"W1\"]\n b1 = parameters[\"b1\"]\n W2 = parameters[\"W2\"]\n b2 = parameters[\"b2\"]\n\n # Loop (gradient descent)\n for i in range(0, num_iterations):\n # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n A2, cache = forward_propagation(X, parameters)\n # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n cost = compute_cost(A2, Y, parameters)\n # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n grads = backward_propagation(parameters, cache, X, Y)\n # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n parameters = update_parameters(parameters, grads)\n\n # Print the cost every 1000 iterations\n if print_cost and i % 1000 == 0:\n print(\"Cost after iteration %i: %f\" % (i, cost))\n\n return parameters\n\n\n# 预测函数\ndef predict(parameters, X):\n \"\"\"\n Using the learned parameters, predicts a class for each example in X\n Arguments:\n parameters -- python dictionary containing your parameters\n X -- input data of size (n_x, m)\n Returns\n predictions -- vector of predictions of our model (red: 0 / blue: 1)\n \"\"\"\n\n # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n A2, cache = forward_propagation(X, parameters)\n # predictions = (A2 > 0.5)\n\n return A2, cache\n\n\n# 训练\nparameters = nn_model(X.T, Y.T, n_h=20, num_iterations=3000, print_cost=True)\n# 预测\npredictions, cache = predict(parameters, test_X.T)\nprint('RMSE: ', (np.sqrt(((predictions - test_y.T) ** 2).mean())))\n\n# print(parameters)\n\ngrads = backward_propagation(parameters, cache, test_X.T, test_y.T)\n\n# 开始计算偏导数\nw2 = np.sum(parameters['W2'], axis=1)/parameters['W2'].shape[0]\na = np.dot(parameters['W1'], test_X.T)\ndI = sigmoid(a) * (1-sigmoid(a))\n\n\nd = np.dot(parameters['W1'].T, dI) * w2\n# print(d)\n# 对计算结果进行归一化操作\n\ndraw_y = ss.fit_transform(d.T)\nres = min_max_scaler.inverse_transform(test_X)\n\n# 绘图\ncloumns = train.columns.tolist()\nfor index in range(len(cloumns)):\n plt.figure(index) # 创建图表1\n y = draw_y[:, index]\n x = res[:, index]\n plt.xlabel(cloumns[index])\n plt.ylabel(\"NEE-\"+cloumns[index])\n plt.scatter(x, y)\n plt.savefig(\"res/\"+cloumns[index]+\".png\")","repo_name":"CuriousLei/DerivationOfANN","sub_path":"tf.py","file_name":"tf.py","file_ext":"py","file_size_in_byte":9772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"31423508311","text":"# input은 숫자와 * / - +\n# input()\n# Calculator 클래스 생성\n# 계산하는 함수\n# 결과값을 출력하는 함수\n\nclass calc:\n\n\n def __init__(self, input):\n self.input = input\n self.string = []\n\n def parse(self):\n self.string = self.input.split(' ')\n \n def calculate(self):\n i = 0\n a = self.string\n# 1 + 2 - 4 * 3\n while i < len(a):\n if a[i] == \"*\":\n result = int(a[i-1]) * int(a[i+1])\n a[i] = result\n a.pop(i+1)\n a.pop(i-1)\n elif a[i] == \"/\":\n result = int(a[i-1]) / int(a[i+1])\n a[i] = result\n a.pop(i+1)\n a.pop(i-1)\n i+=1\n i=0\n while i < len(a):\n if a[i] == \"-\":\n result = int(a[i-1]) - int(a[i+1])\n a[i] = result\n a.pop(i+1)\n a.pop(i-1)\n i = i-1\n elif a[i] == \"+\":\n result = int(a[i-1]) + int(a[i+1])\n a[i] = result\n a.pop(i+1)\n a.pop(i-1)\n i = i-1\n i+=1\n print(a[0])\n\n\n\ndef get_input():\n str = input()\n return str\n\n# str = get_input()\n\ncal = calc(\"1 + 2 - 4 * 3\")\ncal.parse()\ncal.calculate()","repo_name":"RoseBLINK/2022Python-study","sub_path":"pythonGrammar/practice1.py","file_name":"practice1.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"3414609750","text":"import os\nimport json\n\nfrom appwrite.client import Client\nfrom appwrite.services.database import Database\n\ndef init_client():\n # Initialize the Appwrite client\n client = Client()\n client.set_endpoint(os.getenv(\"APPWRITE_ENDPOINT\"))\n client.set_project(os.getenv(\"APPWRITE_PROJECT_ID\"))\n client.set_key(os.getenv(\"APPWRITE_API_KEY\"))\n\n return client\n\ndef main():\n payload = json.loads(os.getenv(\"APPWRITE_FUNCTION_EVENT_DATA\"))\n user_collection_id = os.getenv(\"APPWRITE_USER_COLLECTION_ID\")\n\n userId = payload[\"$id\"]\n userName = payload[\"name\"]\n email = payload[\"email\"]\n\n\n client = init_client()\n database = Database(client)\n\n database.create_document(user_collection_id, {'user_id': userId, 'user_name': userName, 'email': email}, read=['*'])\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"appwrite/demos-for-functions","sub_path":"python/create_user_profile/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":119,"dataset":"github-code","pt":"67"}
+{"seq_id":"23675751548","text":"def factorial(val):\n if val == 0:\n return 1\n return factorial(val-1) * val \nval = factorial(int(input()))\ncnt = 0\nwhile True:\n if val % 10**(cnt+1) == 0:\n cnt +=1 \n else:\n break # for consecutive search \nprint(cnt)","repo_name":"amo33/study_projects","sub_path":"Study/python_start/1676.py","file_name":"1676.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"16328293552","text":"from flask import request, session, abort\nfrom flask_restx import Namespace, Resource\n\nfrom delivery.calc import calculate_path\nfrom delivery.models import db, Package, Token, Node, User\nfrom delivery.schemas import PackageSchema\nfrom delivery.utils import authed, notify_user, verify_keys\n\npackages = Namespace('packages')\n\n\n@packages.route('')\nclass Packages(Resource):\n @authed\n def get(self):\n if 'filter' not in request.args.keys():\n abort(403, 'insufficient arguments')\n f = request.args['filter']\n ret = {}\n for k, v in {'sending': 'sender_id',\n 'receiving': 'receiver_id',\n 'delivering': 'courier_id',\n 'manage': 'manager_id'}.items():\n pkgs = Package.query.filter_by(**{v: session['user_id']}).all() \\\n if f == 'all' or f == k else []\n ret[k] = [PackageSchema(view=k).dump(item) for item in pkgs]\n return ret\n\n @authed\n @verify_keys({'token': str, 'user_id': int})\n def head(self):\n req = request.json\n package = Package.query.filter_by(token=req['token']).first()\n if package is None:\n abort(404, '快件不存在')\n node = package.current_node\n if node.manager_id != session['user_id']:\n abort(403, '只有快件所在站点的管理员能够调用')\n user = User.query.filter_by(id=req['id']).first()\n if user is None:\n abort(404, '用户不存在')\n package.courier_id = req['id']\n db.session.commit()\n return {'msg': '成功指派送货员'}\n\n @authed\n @verify_keys({'token': str, 'node_uuid': str})\n def post(self):\n token = Token.query.filter_by(token=request.json['token']).first()\n first_node = Node.query.filter_by(\n uuid=request.json['node_uuid']).first()\n if not token:\n abort(404, '收货节点不存在')\n if not first_node:\n abort(404, '发货节点不存在')\n try:\n path = calculate_path(first_node.id, token.address.id)\n except ValueError:\n abort(404, '收发货节点无法联通')\n\n package = Package(\n sender_id=session['user_id'],\n receiver_id=token.user_id,\n next_node_id=first_node.id,\n path=path,\n )\n db.session.add(package)\n db.session.commit()\n return {'uuid': package.token}\n\n @authed\n @verify_keys({'uuid': str})\n def put(self):\n package = Package.query.filter_by(token=request.json['uuid']).first()\n\n if not package:\n abort(404, '快件不存在')\n if package.receiver_id == session['user_id']:\n package.progress = len(package.path) - 1\n db.session.commit()\n return {'msg': '快件成功送达'}\n if package.next_node.manager_id != session['user_id']:\n abort(403, '只有节点管理员能够调用')\n\n package.progress = package.progress + 1\n package.manager_id = package.current_node.manager_id\n if package.progress == len(package.path) - 1:\n notify_user(\n package.receiver.open_id,\n 'Inifv86VCZGFaBhIh2ECIini4tJPgBxpC9Gni68zsSM', {\n 'node': package.current_node.token,\n 'phone': package.current_node.manager.phone,\n 'username': package.current_node.manager.username, \n 'code': package.token[:4]\n })\n db.session.commit()\n return {'msg': f'快件成功抵达{package.current_node.id}号节点'}\n","repo_name":"FrankArchive/Delivery","sub_path":"backend/delivery/api/packages.py","file_name":"packages.py","file_ext":"py","file_size_in_byte":3640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"13081807872","text":"\"\"\"\nThis module runs a series of test cases on modules a and b of the Ormuco\nquestions.\n\nUsage: python2 test.py or python3 test.py\ntests for a.py output if lines are overlaping\ntests for b.py output -1, 0 or 1 according to if the first of two version\nnumbers is smaller than, equal to or larger than the second one.\n\nFor details of modules a and b refer to their respective files.\n\"\"\"\n\nimport time\nimport a\nimport b\nimport c\n\n\ndef _test_module(f, test_cases):\n \"\"\"Runs test_cases with function f on origin module\n\n arguments\n f: module with function to run test_cases on\n test_cases: list with test values to run into module function f\n\n \"\"\"\n print('## Testing %s' % f.__name__)\n for t in test_cases:\n print('Testing %s with values: %s' % (f.__name__, str(t)))\n print(f(t[0], t[1]))\n print()\n\n\ntest_cases_a = [\n ([0, 10],[10, 20]), \n ([-10, -9],[-1, -2]), \n ([-10, -5],[-7, 2]), \n ([-5, -10],[2, -7]), \n ([-10, 10],[1, 2]),\n ]\n\ntest_cases_b = [\n ('1.1.1', '1.1.1'),\n ('1.1.1', '1.1.1', '.'),\n ('1_1_1', '1_1_2', '_'),\n ('1_1_2', '1_1_1', '_'),\n ('1_2_1', '1_1_2', '_'),\n ('1_10_1', '1_5_20', '_'),\n ('0.1.1', '1.1.1'),\n ('1.1az.1', '1.1b.1'),\n ('1a.1.1', '1.2d.1'),\n ('1abc.1def.1abc', '1abc.2def.1abc')\n ]\n\ntest_cases_c = [\n ('t1', 1),\n ('t2', 2),\n ('t3', 3),\n ('t4', 4),\n ('t5', 5),\n ('t6', 6),\n ('t7', 7)\n ]\n\n_test_module(a.are_lines_overlaping, test_cases_a)\n_test_module(b.compare_versions, test_cases_b)\n\n# test c.py\nmax_cached_items = 5\nstale_delay = 2 # in seconds\n\nprint('## Testing lru (c.py)')\nprint('Setting up lru with max 5 items and expiry time of 2 seconds')\nlru = c.Lru(max_cached_items, stale_delay)\nfor k, v in test_cases_c:\n print('Adding element in lru')\n lru.set_value(k, v)\n print('Current values in lru %s' % lru.get_values())\nprint('wainting for 1 second')\ntime.sleep(1)\nlru.set_value('t6', 6)\nprint('waiting for initial values to expire')\ntime.sleep(1)\nprint('Current values in lru %s' % lru.get_values())\n\n","repo_name":"classmathieuloyer/ormuco","sub_path":"mathieu_loyer_test.py","file_name":"mathieu_loyer_test.py","file_ext":"py","file_size_in_byte":2136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"25723341895","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport sys\nsys.path.append(\"../../\")\nimport os\nimport logging\nimport argparse\nimport ConfigParser\nimport common.datetime_wrapper as datetime\nimport common.hadoop_shell_wrapper as hadoop_shell\nimport common.spark_submit_wrapper as spark\n\n\nFILE_DIR = os.path.dirname(os.path.abspath(__file__))\n\nlogging.basicConfig(\n level=logging.INFO,\n format='[%(asctime)s - %(filename)s - %(levelname)s] %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S')\n\n\ndef process(args):\n config = ConfigParser.SafeConfigParser()\n config.read(args.conf)\n\n embed_module_option = \"{}_embedding\".format(args.module)\n\n input_path_bert_embedding = config.get(\"common\", \"hdfs_output\", 0, {\n \"date\": args.date,\n \"dir\": config.get(\"embedding_history\", \"dir\")\n })\n input_path_video_ctr_stat = config.get(\"inputs\", \"video_ctr_stat\", 0, {\"date\": args.date})\n output_path = config.get(\"common\", \"hdfs_output\", 0, {\n \"date\": args.date,\n \"dir\": config.get(embed_module_option, \"dir\")\n })\n\n if hadoop_shell.exists_all(output_path, flag=True, print_missing=True):\n logging.info(\"output already exists, skip\")\n return 0\n\n if not hadoop_shell.exists_all_with_retry(\n input_path_video_ctr_stat,\n flag=True,\n print_info=True,\n retry=config.getint(\"common\", \"upstream_retry\"),\n interval=config.getint(\"common\", \"upstream_interval\")):\n logging.error(\"finally, input not ready, exit!\")\n return 1\n\n if not hadoop_shell.rmr(output_path):\n logging.error(\"fail to clear output folder\")\n return 1\n\n ss = spark.SparkSubmitWrapper()\n\n ss.set_master(\"yarn\")\\\n .set_deploy_mode(\"cluster\")\\\n .set_driver_memory(\"1G\")\\\n .set_executor_memory(\"1G\") \\\n .add_conf(\"spark.executor.memoryOverhead\", 2048) \\\n .set_executor_cores(2)\\\n .set_num_executors(100)\\\n .add_conf(\"spark.network.timeout\", 600)\\\n .set_name(config.get(\"common\", \"job_name\", 0, {'date': args.date, 'module': \"FilterEmbedding-{}\".format(args.module)}))\\\n .set_queue(config.get(\"common\", \"job_queue\"))\\\n .set_class(\"com.td.ml.x2vec.bert.FilterEmbedding\")\\\n .set_app_jar(FILE_DIR + \"/../../lib/\" + config.get(\"common\", \"jar\"))\\\n .add_app_argument(\"normalize\", config.getboolean(\"bert\", \"normalize\")) \\\n .add_app_argument(\"ctr_bound\", config.getfloat(embed_module_option, \"ctr_bound\")) \\\n .add_app_argument(\"display_bound\", config.getint(embed_module_option, \"display_bound\")) \\\n .add_app_argument(\"click_bound\", config.getint(embed_module_option, \"click_bound\")) \\\n .add_app_argument(\"input_path_bert_embedding\", input_path_bert_embedding)\\\n .add_app_argument(\"input_path_video_ctr_stat\", input_path_video_ctr_stat)\\\n .add_app_argument(\"output_path\", output_path)\n\n return 0 if ss.run(print_cmd=True, print_info=True) else 1\n\n\ndef del_expire(args):\n config = ConfigParser.SafeConfigParser()\n config.read(args.conf)\n\n embed_module_option = \"{}_embedding\".format(args.module)\n\n if not args.expire or config.getint(\"common\", \"expire\") <= 0:\n return 0\n\n lifetime = config.getint(embed_module_option, \"lifetime\")\n\n if lifetime > 0:\n dt_expire = datetime.DateTime(args.date).apply_offset_by_day(-lifetime)\n expire_path = config.get(\"common\", \"hdfs_output\", 0, {\n \"date\": dt_expire,\n \"dir\": config.get(embed_module_option, \"dir\")\n })\n if not hadoop_shell.rmr(expire_path):\n logging.error(\"fail to del expired path: %s\", expire_path)\n return 1\n return 0\n\n\ndef run(args):\n if process(args) == 0 and del_expire(args) == 0:\n return 0\n return 1\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='build user inference samples')\n parser.add_argument('--date', dest='date', required=True, help=\"which date(%%Y-%%m-%%d)\")\n parser.add_argument('--conf', dest='conf', required=True, help=\"conf file\")\n parser.add_argument('--module', dest='module', required=True, choices=[\"recommendable\", \"queryable\"])\n parser.add_argument('--expire', dest='expire', action=\"store_true\", help=\"whether to del expired path\")\n\n arguments = parser.parse_args()\n\n try:\n if not datetime.DateTime(arguments.date).is_perfect_date():\n raise RuntimeError(\"passed arg [date={}] format error\".format(arguments.date))\n sys.exit(run(arguments))\n except Exception as ex:\n logging.exception(\"exception occur in %s, %s\", __file__, ex)\n sys.exit(1)\n","repo_name":"Jayyyyyyyyyyyy/x2vec","sub_path":"src/scheduling/bert/modules/run_filter_embedding.py","file_name":"run_filter_embedding.py","file_ext":"py","file_size_in_byte":4642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"74937177173","text":"\"\"\"\nbased on Python Syntax highlighting from:\nhttp://diotavelli.net/PyQtWiki/Python%20syntax%20highlighting\n\"\"\"\nimport sys\n\nfrom PyQt4.QtCore import QRegExp\nfrom PyQt4.QtGui import QColor, QTextCharFormat, QFont, QSyntaxHighlighter, QPen\n\nfrom tools import loader\n\ndef format(color, style=''):\n \"\"\"Return a QTextCharFormat with the given attributes.\n \"\"\"\n _color = QColor()\n _color.setNamedColor(color)\n\n _format = QTextCharFormat()\n _format.setFontFamily('monospace')\n _format.setForeground(_color)\n if 'bold' in style:\n _format.setFontWeight(QFont.Bold)\n if 'italic' in style:\n _format.setFontItalic(True)\n\n return _format\n\n\n# Syntax styles that can be shared by all languages\nSTYLES = {\n 'keyword': format('darkMagenta', 'bold'),\n 'operator': format('darkRed'),\n 'brace': format('#858585'),\n 'definition': format('black', 'bold'),\n 'string': format('green'),\n 'string2': format('darkGreen'),\n 'comment': format('gray', 'italic'),\n 'properObject': format('darkBlue', 'italic'),\n 'numbers': format('brown'),\n 'spaces': format('#BFBFBF'),\n}\n\n\nclass Highlighter (QSyntaxHighlighter):\n keywords = []\n\n # operators\n operators = []\n\n # braces\n braces = []\n def __init__(self, document, lang):\n QSyntaxHighlighter.__init__(self, document)\n langSyntax = loader.syntax[lang]\n Highlighter.keywords = langSyntax.get('keywords', [])\n Highlighter.braces = langSyntax.get('brace', [])\n Highlighter.operators = langSyntax.get('operators', [])\n\n rules = []\n\n # Keyword, operator, and brace rules\n rules += [(r'\\b%s\\b' % w, 0, STYLES['keyword'])\n for w in Highlighter.keywords]\n rules += [(r'%s' % o, 0, STYLES['operator'])\n for o in Highlighter.operators]\n rules += [(r'%s' % b, 0, STYLES['brace'])\n for b in Highlighter.braces]\n\n # All other rules\n proper = langSyntax.get('properObject', None)\n if proper is not None:\n proper = '\\\\b' + str(proper[0]) + '\\\\b'\n rules += [\n # 'self'\n (proper, 0, STYLES['properObject'])]\n\n rules.append((r'__\\w+__', 0, STYLES['properObject']))\n \n definition = langSyntax.get('definition', [])\n for de in definition:\n expr = '\\\\b' + de + '\\\\b\\\\s*(\\\\w+)'\n rules.append((expr, 1, STYLES['definition']))\n\n rules += [\n # Numeric literals\n (r'\\b[+-]?[0-9]+[lL]?\\b', 0, STYLES['numbers']),\n (r'\\b[+-]?0[xX][0-9A-Fa-f]+[lL]?\\b', 0, STYLES['numbers']),\n (r'\\b[+-]?[0-9]+(?:\\.[0-9]+)?(?:[eE][+-]?[0-9]+)?\\b', 0, STYLES['numbers']),\n ]\n\n stringChar = langSyntax.get('string', [])\n for sc in stringChar:\n expr = r'\"[^\"\\\\]*(\\\\.[^\"\\\\]*)*\"' if sc == '\"' else r\"'[^'\\\\]*(\\\\.[^'\\\\]*)*'\"\n rules.append((expr, 0, STYLES['string']))\n\n # Multi-line strings (expression, flag, style)\n # FIXME: The triple-quotes in these two lines will mess up the\n # syntax highlighting from this point onward\n self.tri_single = (QRegExp(\"'''\"), 1, STYLES['string2']) #'''\n self.tri_double = (QRegExp('\"\"\"'), 2, STYLES['string2']) #\"\"\"\n\n comments = langSyntax.get('comment', [])\n for co in comments:\n expr = co + '[^\\\\n]*'\n rules.append((expr, 0, STYLES['comment']))\n\n rules.append(('\\s+', 0, STYLES['spaces']))\n\n # Build a QRegExp for each pattern\n self.rules = [(QRegExp(pat), index, fmt)\n for (pat, index, fmt) in rules]\n\n\n def highlightBlock(self, text):\n \"\"\"Apply syntax highlighting to the given block of text.\n \"\"\"\n # Do other syntax formatting\n for expression, nth, format in self.rules:\n index = expression.indexIn(text, 0)\n\n while index >= 0:\n # We actually want the index of the nth match\n index = expression.pos(nth)\n length = expression.cap(nth).length()\n self.setFormat(index, length, format)\n index = expression.indexIn(text, index + length)\n\n self.setCurrentBlockState(0)\n\n # Do multi-line strings\n in_multiline = self.match_multiline(text, *self.tri_single)\n if not in_multiline:\n in_multiline = self.match_multiline(text, *self.tri_double)\n\n\n def match_multiline(self, text, delimiter, in_state, style):\n \"\"\"Do highlighting of multi-line strings. ``delimiter`` should be a\n ``QRegExp`` for triple-single-quotes or triple-double-quotes, and\n ``in_state`` should be a unique integer to represent the corresponding\n state changes when inside those strings. Returns True if we're still\n inside a multi-line string when this function is finished.\n \"\"\"\n # If inside triple-single quotes, start at 0\n if self.previousBlockState() == in_state:\n start = 0\n add = 0\n # Otherwise, look for the delimiter on this line\n else:\n start = delimiter.indexIn(text)\n # Move past this match\n add = delimiter.matchedLength()\n\n # As long as there's a delimiter match on this line...\n while start >= 0:\n # Look for the ending delimiter\n end = delimiter.indexIn(text, start + add)\n # Ending delimiter on this line?\n if end >= add:\n length = end - start + add + delimiter.matchedLength()\n self.setCurrentBlockState(0)\n # No; multi-line string\n else:\n self.setCurrentBlockState(in_state)\n length = text.length() - start + add\n # Apply formatting\n self.setFormat(start, length, style)\n # Look for the next match\n start = delimiter.indexIn(text, start + length)\n\n # Return True if still inside a multi-line string, False otherwise\n if self.currentBlockState() == in_state:\n return True\n else:\n return False\n","repo_name":"calpe20/PYTHONIZANDO","sub_path":"TKINTER/ninja-ide/gui/qt/main_panel/editor/highlighter.py","file_name":"highlighter.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"42694070377","text":"'''\nCreated on May 21, 2021\nTranslated May 23, 2021\n\n@author: bleem\n'''\nfrom src.common import Constants\nfrom src.common import Converter\nfrom src.common.BehaviorsInfo import BehaviorsInfo\nfrom src.common.RescorlaWagnerParameters import RescorlaWagnerParameters\nfrom src.etbd.MutationInfo import MutationInfo\nfrom src.etbd.RecombinationInfo import RecombinationInfo\nfrom src.etbd.SelectionInfo import SelectionInfo\n\n\n# This class is an abstract container for the specific organisms\nclass AnOrganism:\n\t''' \n\tclassdocs\n\t'''\n\n\tdef __init__(self, json_data):\n\t\tself.m_currentSDColor = Constants.SD_COLOR_NULL\n\t\tself.stuBehaviorsInfo = self.load_behaviors_info(json_data)\n\n\tdef get_behaviors_info(self):\n\t\treturn self.stuBehaviorsInfo\n\n\tdef load_behaviors_info(self, json_data):\n\t\tstuBehaviorsInfo = BehaviorsInfo()\n\t\t# Dim objPopulation As Behaviors\n\t\t# Dim objStringBuilder As New System.Text.StringBuilder\n\t\t#Read form--------------------------------------------------\n\t\t# Get discriminative stimulus\n\t\tstuBehaviorsInfo.set_sdid(json_data.get_sdid())\n\t\tif stuBehaviorsInfo.get_sdid() == -1:\n\t\t\treturn # This SD as already been used.\n\n\t\t# Gray codes\n\t\tif json_data.use_gray_codes():\n\t\t\tstuBehaviorsInfo.set_use_gray_codes(True)\n\t\telse:\n\t\t\tstuBehaviorsInfo.set_use_gray_codes(False)\n\n\t\t# Properties\n\t\tstuBehaviorsInfo.set_decay_of_transfer(json_data.get_decay_of_transfer())\n\t\tstuBehaviorsInfo.set_fomo_a(json_data.get_fomo_a())\n\n\t\t#-----Viscosity\n\t\tif json_data.add_viscosity():\n\t\t\tstuBehaviorsInfo.set_viscosity_ticks(json_data.get_viscosity_ticks())\n\t\t\tif json_data.get_viscosity_selected_index() == 0:\n\t\t\t\t# \"original\"\n\t\t\t\tstuBehaviorsInfo.set_create_from_synthetic(False)\n\t\t\telse:\n\t\t\t\t# \"amalgamated\"\n\t\t\t\tstuBehaviorsInfo.set_create_from_synthetic(True)\n\n\t\telse:\n\t\t\t# if populations are to have no viscosity, then ViscosityTicks = 0\n\t\t\t# Note that when ViscosityTicks = 1 there is also no viscosity.\n\t\t\t# When ViscosityTicks = 0, the standard method of emitting a behavior\n\t\t\t# (random selection among phenotypes) is used; when ViscosityTicks = 1\n\t\t\t# the method based on relative frequencies is used.\n\t\t\t# Both methods should give the same results.\n\t\t\tstuBehaviorsInfo.set_viscosity_ticks(0)\n\n\t\tstuBehaviorsInfo.set_num_behaviors(json_data.get_num_behaviors())\n\t\tstuBehaviorsInfo.set_low_phenotype(json_data.get_low_phenotype())\n\t\tstuBehaviorsInfo.set_high_phenotype(json_data.get_high_phenotype())\n\t\tstuBehaviorsInfo.set_percent_to_replace(json_data.get_percent_to_replace())\n\t\tstuBehaviorsInfo.set_percent_to_replace_2(json_data.get_percent_to_replace_2())\n\t\tstuBehaviorsInfo.set_fitness_method(json_data.get_fitness_method())\n\t\tstuBehaviorsInfo.set_fitness_landscape(json_data.get_fitness_landscape())\n\t\tstuBehaviorsInfo.set_punishment_method(json_data.get_punishment_method())\n\t\t# Data structures\n\t\tstuBehaviorsInfo.set_RW_info(self.load_RW_info(json_data))\n\t\tstuBehaviorsInfo.set_selection_info(self.load_selection_info(json_data))\n\t\tstuBehaviorsInfo.set_recombination_info(self.load_recombination_info(json_data))\n\t\tstuBehaviorsInfo.set_mutation_info(self.load_mutation_info(json_data))\n\t\t# Non-ETBD parameters\n\t\tstuBehaviorsInfo.set_num_hidden_nodes(json_data.get_num_hidden_nodes())\n\t\tstuBehaviorsInfo.set_num_output_nodes(json_data.get_num_output_nodes())\n\t\tstuBehaviorsInfo.set_num_firing_hidden_nodes(json_data.get_num_firing_hidden_nodes())\n\n\t\tstuBehaviorsInfo.set_net_one_magnitude_slope(json_data.get_net_one_magnitude_slope())\n\t\tstuBehaviorsInfo.set_net_one_magnitude_intercept(json_data.get_net_one_magnitude_intercept())\n\t\tstuBehaviorsInfo.set_net_one_neutral_magnitude(json_data.get_net_one_neutral_magnitude())\n\n\t\tstuBehaviorsInfo.set_net_two_neutral_magnitude(json_data.get_net_two_neutral_magnitude())\n\t\tstuBehaviorsInfo.set_net_two_selection_strength_exponent(json_data.get_net_two_selection_strength_exponent())\n\t\tstuBehaviorsInfo.set_net_two_selection_strength_multiplier(json_data.get_net_two_selection_strength_multiplier())\n\t\tstuBehaviorsInfo.set_net_two_num_hidden_nodes(json_data.get_net_two_num_hidden_nodes())\n\n\t\tstuBehaviorsInfo.set_ml_learning_rate(json_data.get_ml_learning_rate())\n\t\tstuBehaviorsInfo.set_ml_num_slots(json_data.get_ml_num_slots())\n\t\tstuBehaviorsInfo.set_ml_reward_multiplier(json_data.get_ml_reward_multiplier())\n\t\tstuBehaviorsInfo.set_ml_reward_exponent(json_data.get_ml_reward_exponent())\n\t\tstuBehaviorsInfo.set_ml_pessimism(json_data.get_ml_pessimism())\n\t\tstuBehaviorsInfo.set_ml_extinction(json_data.get_ml_extinction())\n\t\tstuBehaviorsInfo.set_ml_epsilon(json_data.get_ml_epsilon())\n\t\tstuBehaviorsInfo.set_ml_discount_rate(json_data.get_ml_discount_rate())\n\n\t\treturn stuBehaviorsInfo\n\n\tdef reset_state(self):\n\t\traise NotImplementedError\n\t\t# TODO - implement this for the other organism types\n\n\tdef is_ready_to_emit(self):\n\t\traise NotImplementedError\n\t\t# TODO - implement this for the other organism types\n\n\tdef emit_behavior(self):\n\t\traise NotImplementedError\n\t\t# TODO - implement this for the other organism types\n\n\tdef set_selection(self, selectionParameter, value):\n\t\traise NotImplementedError\n\t\t# TODO - implement this for the other organism types\n\n\tdef load_selection_info(self, json_data):\n\n\t\tstuSelectionInfo = SelectionInfo()\n\t\tstuSelectionInfo.set_selection_method(json_data.get_selection_method())\n\t\tstuSelectionInfo.set_continuous_function_form(json_data.get_continuous_function_form())\n\n\t\t# High Phenotype------------------------------------------------------(added to implement punishment)\n\t\tstuSelectionInfo.set_high_phenotype(json_data.get_high_phenotype())\n\n\t\t# Fitness Landscare---------------------------------------------------(added to implement punishment)\n\t\tstuSelectionInfo.set_fitness_landscape(json_data.get_fitness_landscape())\n\t\tstuSelectionInfo.set_matchmaking_method(json_data.get_matchmaking_method())\n\n\t\treturn stuSelectionInfo\n\n\tdef load_recombination_info(self, json_data):\n\n\t\tstuRecombinationInfo = RecombinationInfo()\n\t\tstuRecombinationInfo.set_method(json_data.get_recombination_method())\n\n\t\tif json_data.get_recombination_method() == Constants.RECOMBINATION_METHOD_CROSSOVER:\n\t\t\tstuRecombinationInfo.set_points(json_data.get_crossover_points())\n\n\t\treturn stuRecombinationInfo\n\n\tdef load_mutation_info(self, json_data):\n\n\t\tstuMutationInfo = MutationInfo()\n\t\tstuMutationInfo.set_method(json_data.get_mutation_method())\n\n\t\tif stuMutationInfo.get_method() == Constants.MUTATION_METHOD_GAUSSIAN:\n\t\t\tstuMutationInfo.set_sd(json_data.get_gaussian_mutation_sd())\n\t\t\tstuMutationInfo.set_boundary(json_data.get_mutation_boundary())\n\n\t\tstuMutationInfo.set_rate(json_data.get_mutation_rate())\n\n\t\t# Redundant info needed by the Mutator object\n\t\tif json_data.use_gray_codes():\n\t\t\tstuMutationInfo.set_use_gray_codes(True)\n\t\telse:\n\t\t\tstuMutationInfo.set_use_gray_codes(False)\n\t\tstuMutationInfo.set_high_phenotype(json_data.get_high_phenotype())\n\t\tstuMutationInfo.set_low_phenotype(json_data.get_low_phenotype())\n\n\t\treturn stuMutationInfo\n\n\tdef get_sdcolor(self):\n\t\treturn self.m_currentSDColor\n\n\tdef get_sdcolor_str(self):\n\t\treturn Converter.convert_sd_color_to_string(self.m_currentSDColor)\n\n\tdef set_sdcolor(self, value):\n\t\traise NotImplementedError\n\n\tdef load_RW_info(self, json_data):\n\n\t\tstuRWInfo = RescorlaWagnerParameters()\n\n\t\tstuRWInfo.set_alpha(json_data.get_alpha())\n\t\tstuRWInfo.set_beta_0(json_data.get_beta_0())\n\t\tstuRWInfo.set_beta_1(json_data.get_beta_1())\n\t\tstuRWInfo.set_berg_a(1) # Hard coded to 1 for now.\n\t\tstuRWInfo.set_lambda(1) # Hard coded to 1 for now.\n\n\t\treturn stuRWInfo\n","repo_name":"misterriley/PyETBD","sub_path":"src/orgs/AnOrganism.py","file_name":"AnOrganism.py","file_ext":"py","file_size_in_byte":7479,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"25751003092","text":"from django.db import models\n\n# Create your models here.\nDUES_TYPE = [\n\t('NEWQ1', 'New member - Jan to Dec'),\n\t('NEWQ2', 'New member - Apr to Dec'),\n\t('NEWQ3', 'New member - Jul to Dec'),\n\t('NEWQ4', 'New member - Oct to Dec'),\n\t('RENEW', 'Renewal - Jan to Dec'),\n]","repo_name":"pineapplejuice/earc2-members","sub_path":"helpers/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"28026990567","text":"data = \"\"\"Given a string containing just the characters '(' and ')', return the length of the longest valid (well-formed) parentheses substring\nExample 1:\n\nInput: s = \"(()\"\nOutput: 2\nExplanation: The longest valid parentheses substring is \"()\".\nExample 2:\n\nInput: s = \")()())\"\nOutput: 4\nExplanation: The longest valid parentheses substring is \"()()\".\n\n\"\"\"\n\n\nclass Solution:\n\n def longestValidParentheses(self, s: str) -> int:\n stack = []\n max_1 = 0\n count = 0\n for c in s:\n if c == '(':\n stack.append('(')\n elif c == ')' and stack:\n if stack[-1] == '(':\n count += 2\n stack.pop()\n else:\n count = 0\n if max_1 < count:\n max_1 = count\n max_2 = 0\n count = 0\n if max_1 != len(s):\n stack = []\n for c in s[::-1]:\n if c == ')':\n stack.append(')')\n elif c == '(' and stack:\n if stack[-1] == ')':\n count += 2\n stack.pop()\n else:\n count = 0\n if max_2 < count:\n max_2 = count\n print(max_1, max_2)\n return max_1 if max_1 <= max_2 and max_2 != 0 else max_2\n\n\nprint(Solution().longestValidParentheses(\")()())\"))\n","repo_name":"laxman590249/Data-Structures","sub_path":"DataStructures/DP/maximum_valid_para.py","file_name":"maximum_valid_para.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"21445685063","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[4]:\n\n\nimport numpy as np\nimport math\nimport molecule_predictionv2_0 as mp\n\n\n# In[5]:\n\n\nimport tensorflow as tf\n\n\n# In[6]:\n\n\ndat = mp.molecule_prediction_data_wrapper()\n\n\n# In[21]:\n\n\natom_vector_size = 1024\nnum_timesteps = 5\nhidden_unit_size = 1024\nbatchSize = 32\nnum_epochs = 200\nbatch_gen = dat.batch_gen\ndataset = tf.data.Dataset. from_generator(batch_gen.generate, (tf.float32,tf.float32),\n output_shapes= (tf.TensorShape([30,1054]), \n tf.TensorShape([3]))) \ndataset = dataset.shuffle(buffer_size = batchSize*10) \ndataset = dataset.repeat(num_epochs).batch(batchSize)\ndataset = dataset.prefetch(buffer_size = 2)\ndata_source = dataset.make_one_shot_iterator()\nbatch_in, batch_y = data_source.get_next()\n\n\n# In[8]:\n\n\nwith tf.variable_scope('message_transform_network'):\n hidden1 = tf.keras.layers.Dense(2048,activation='relu')\n hidden1.build((None,atom_vector_size))\n hidden2 = tf.keras.layers.Dense(2048,activation='tanh')\n hidden2.build((None,2048))\n hidden3 = tf.keras.layers.Dense(1024,activation='relu')\n hidden3.build((None,2048))\n out_message = tf.keras.layers.Dense(atom_vector_size)\n out_message.build((None, 1024))\ndef apply_edge_neural_network_transform(input):\n return out_message.apply(hidden3.apply(hidden2.apply(hidden1.apply(input))))\n\n\n# In[9]:\n\n\ndef get_messages(ordered_atoms_vector, adjacency_matrix):\n transformed = apply_edge_neural_network_transform(ordered_atoms_vector)\n return tf.matmul(adjacency_matrix,transformed)\n\n\n# In[10]:\n\n\nwith tf.variable_scope(\"RecusiveUnitLTSM\"):\n shared_lstm_cell = tf.keras.layers.LSTMCell(hidden_unit_size);\ndef iterate_time_step(states_vectors_t, messages):\n (outputs_t, states_vectors_t_1) = shared_lstm_cell(messages, states_vectors_t)\n return outputs_t, states_vectors_t_1\n\n\n# In[11]:\n\n\ninitial_state1 = tf.Variable(np.random.normal(size=(1,hidden_unit_size)),\n trainable=True,dtype=tf.float32);\ninitial_state2 = tf.Variable(np.random.normal(size=(1,hidden_unit_size)),\n trainable=True,dtype=tf.float32);\ninitial_states1 = tf.reshape(tf.tile(initial_state1, (1,tf.shape(batch_in)[1])),\n [tf.shape(batch_in)[1], hidden_unit_size])\ninitial_states2 = tf.reshape(tf.tile(initial_state2, (1,tf.shape(batch_in)[1])),\n [tf.shape(batch_in)[1], hidden_unit_size])\ninitial_states = (initial_states1,initial_states2)\n\n\n# In[12]:\n\n\ndef extract_atom_vectors_ad_matrix(input_mat):\n num_atoms = tf.shape(input_mat)[0]\n return tf.slice(input_mat,[0,0],[num_atoms,atom_vector_size]), tf.slice(input_mat,[0,atom_vector_size],[num_atoms,num_atoms])\n\n\n# In[13]:\n\n\ndef graph_neural_network(concatenated_input_mat):\n global initial_states\n initial_output,ad_matrix = extract_atom_vectors_ad_matrix(concatenated_input_mat)\n outputs_x = initial_output\n states_vectors_x = initial_states\n for i in range(num_timesteps):\n messages = get_messages(outputs_x, ad_matrix)\n outputs_x, states_vectors_x = iterate_time_step(states_vectors_x,messages) \n final_outputs = outputs_x\n out = tf.reduce_sum(final_outputs, axis=0)\n return out \n\n\n# In[25]:\n\n\nfinal_outputs=tf.map_fn(graph_neural_network,batch_in)\nprediction = tf.keras.layers.Dense(3)(final_outputs)\nwith tf.name_scope(\"loss\"):\n loss = tf.losses.mean_squared_error(batch_y, prediction)\n\n\n# In[15]:\n\n\nlearning_rate = 0.001\nwith tf.name_scope(\"train\"):\n global training_op\n optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n training_op = optimizer.minimize(loss)\n\n\n# In[27]:\n\n\ninit = tf.global_variables_initializer()\nsaver = tf.train.Saver()\nwith tf.Session() as sess:\n init.run()\n for epoch in range(num_epochs):\n for iteration in range(batch_gen.samples// batchSize):\n _,loss_value = sess.run([training_op,loss])\n if iteration % 500 == 0:\n print(\"Epoch \" + str(epoch) + \" Step \" + str(iteration) + \" loss \" + str(loss_value))\n if epoch % 20 == 0:\n save_path = saver.save(sess, \"../models/graph_model_\" + str(epoch) + \".ckpt\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"neilhazra/KaggleMoleculePrediction","sub_path":"MoleculePrediction/Preliminary/GraphNeuralNetworkTest.py","file_name":"GraphNeuralNetworkTest.py","file_ext":"py","file_size_in_byte":4300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"37521617327","text":"from __future__ import print_function, division\nfrom functools import partial\nimport os\nimport sys\nimport re\nimport copy\nimport time\nimport shutil\nimport codecs\nimport tarfile\nimport tempfile\nif sys.version_info[0] < 3:\n\tfrom cStringIO import StringIO\n\tstring_types = basestring\nelse:\n\tfrom io import StringIO\n\tstring_types = str\nfrom textwrap import TextWrapper\nfrom unicodedata import normalize\nfrom collections import defaultdict\nfrom xml.sax.saxutils import escape\nif (sys.version_info[0] < 3):\n\timport urlparse, urllib\nelse:\n\timport urllib, urllib.parse as urlparse\nimport zipfile\n\nfrom operator import itemgetter\nfrom decimal import Decimal, getcontext\ngetcontext().prec = 8\n\n\n#------------------------------------------------\n# Set up logging\n#------------------------------------------------\nimport logging\nlog = logging.getLogger(\".DynamicWeb\")\n# The log initialization shall be performed after Gramps start-up (i.e. not here)\n\n#------------------------------------------------\n# Gramps module\n#------------------------------------------------\n\n\nfrom gramps.gen.const import IMAGE_DIR, GRAMPS_LOCALE as glocale\ntry:\n\t_trans = glocale.get_addon_translator(__file__)\nexcept ValueError:\n\t_trans = glocale.translation\n_ = _trans.sgettext\n\nfrom gramps.version import VERSION, VERSION_TUPLE\nDWR_VERSION_410 = (VERSION_TUPLE[0] >= 4) and (VERSION_TUPLE[1] >= 1)\nDWR_VERSION_412 = (VERSION_TUPLE[0] >= 4) and (VERSION_TUPLE[1] >= 1) and (VERSION_TUPLE[2] >= 2)\nDWR_VERSION_420 = (VERSION_TUPLE[0] >= 4) and (VERSION_TUPLE[1] >= 2)\nfrom gramps.gen.lib import (ChildRefType, Date, EventType, FamilyRelType, Name,\n\t\t\t\t\t\t\tNameType, Person, UrlType, NoteType,\n\t\t\t\t\t\t\tEventRoleType, Family, Event, Place, Source,\n\t\t\t\t\t\t\tCitation, MediaObject, Repository, Note, Tag,\n\t\t\t\t\t\t\tMediaRef, Location)\nif (DWR_VERSION_410):\n\tfrom gramps.gen.lib import PlaceType\nfrom gramps.gen.lib.date import Today\nfrom gramps.gen.const import PROGRAM_NAME, URL_HOMEPAGE\nfrom gramps.gen.plug.menu import (PersonOption, NumberOption, StringOption,\n\tBooleanOption, EnumeratedListOption, FilterOption,\n\tNoteOption, MediaOption, DestinationOption, ColorOption)\nfrom gramps.gen.plug.report import (Report, Bibliography)\nfrom gramps.gen.plug.report import utils as report_utils\nfrom gramps.gen.plug.report import MenuReportOptions\n\nfrom gramps.gen.utils.config import get_researcher\nfrom gramps.gen.utils.string import conf_strings\nfrom gramps.gen.utils.file import media_path_full\nfrom gramps.gen.utils.alive import probably_alive\nfrom gramps.gen.utils.db import get_source_and_citation_referents, get_birth_or_fallback, get_death_or_fallback, get_marriage_or_fallback\nfrom gramps.gen.constfunc import win, conv_to_unicode, get_curr_dir\nif (sys.version_info[0] < 3):\n\tfrom gramps.gen.constfunc import UNITYPE\nelse:\n\tUNITYPE = str\nfrom gramps.gen.config import config\nfrom gramps.gui.thumbnails import get_thumbnail_path, run_thumbnailer\nfrom gramps.gen.utils.image import image_size, resize_to_jpeg_buffer\nfrom gramps.gen.mime import get_description\nfrom gramps.gen.display.name import displayer as _nd\nif (DWR_VERSION_412):\n\tfrom gramps.gen.display.place import displayer as _pd\nfrom gramps.gen.datehandler import get_date_formats, displayer as _dd\nfrom gramps.gen.proxy import PrivateProxyDb, LivingProxyDb\nfrom gramps.plugins.lib.libhtmlconst import _CHARACTER_SETS, _CC, _COPY_OPTIONS\n\n# import HTML Class from src/plugins/lib/libhtml.py\nfrom gramps.plugins.lib.libhtml import Html, xml_lang\n\n# import styled notes from src/plugins/lib/libhtmlbackend.py\nfrom gramps.plugins.lib.libhtmlbackend import HtmlBackend, process_spaces\n\nfrom gramps.plugins.lib.libgedcom import make_gedcom_date, DATE_QUALITY\n\nfrom gramps.plugins.webreport.narrativeweb import first_letter\n\nfrom gramps.gen.utils.place import conv_lat_lon\nfrom gramps.gui.pluginmanager import GuiPluginManager\n\nfrom gramps.gen.relationship import get_relationship_calculator\nif (DWR_VERSION_410):\n\tfrom gramps.gen.utils.location import get_main_location\n\nfrom gramps.gui.widgets.fanchart import (\n\tGENCOLOR,\n\tGRADIENTSCALE,\n\tBACKGROUND_SCHEME1,\n\tBACKGROUND_SCHEME2,\n\tBACKGROUND_GENDER,\n\tBACKGROUND_WHITE,\n\tBACKGROUND_GRAD_GEN,\n\tBACKGROUND_GRAD_AGE,\n\tBACKGROUND_SINGLE_COLOR,\n\tBACKGROUND_GRAD_PERIOD,\n)\nfrom gramps.gui.utils import hex_to_rgb\n\nSORT_KEY = glocale.sort_key\n\n#------------------------------------------------\n# constants\n#------------------------------------------------\n\n#: Maximum number of pages containing custom text\nNB_CUSTOM_PAGES = 5\n#: Maximum number of pages =\nNB_TOTAL_PAGES_MAX = 15\n#: Liste of the pages (description, title, file name)\nPAGES_NAMES = [\n\t(_(\"Person page\"), _(\"Person\"), \"person.html\"),\n\t(_(\"Surnames index page\"), _(\"Surnames\"), \"surnames.html\"),\n\t(_(\"Individuals index page\"), _(\"Individuals\"), \"persons.html\"),\n\t(_(\"Families index page\"), _(\"Families\"), \"families.html\"),\n\t(_(\"Sources index page\"), _(\"Sources\"), \"sources.html\"),\n\t(_(\"Media index page\"), _(\"Media\"), \"medias.html\"),\n\t(_(\"Places index page\"), _(\"Places\"), \"places.html\"),\n\t(_(\"Addresses page\"), _(\"Addresses\"), \"address.html\"),\n\t(_(\"Repositories index page\"), _(\"Repositories\"), \"repositories.html\"),\n\t(_(\"SVG graphical tree\"), _(\"Tree\"), \"tree_svg.html\"),\n] + [\n\t(_(\"Custom page %(index)i\") % {\"index\": i + 1}, _(\"Custom\"), \"custom_%i.html\" % (i + 1))\n\tfor i in range(NB_CUSTOM_PAGES)\n]\n\n# Constants used as indexes in L{PAGES_NAMES}\n(PAGE_PERSON,\nPAGE_SURNAMES,\nPAGE_PERSON_INDEX,\nPAGE_FAMILY_INDEX,\nPAGE_SOURCE_INDEX,\nPAGE_MEDIA_INDEX,\nPAGE_PLACE_INDEX,\nPAGE_ADDRESS_INDEX,\nPAGE_REPOSITORY_INDEX,\nPAGE_SVG_TREE,\nPAGE_CUSTOM) = range(11)\n\n# List of the descriptions of the tree graphs types\nSVG_TREE_TYPES = [\n\t_(\"Ascending tree\"),\n\t_(\"Descending tree\"),\n\t_(\"Descending tree with spouses\"),\n\t_(\"Ascending and descending tree\"),\n\t_(\"Ascending and descending tree with spouses\"),\n]\n(SVG_TREE_TYPE_ASCENDING,\nSVG_TREE_TYPE_DESCENDING,\nSVG_TREE_TYPE_DESCENDING_SPOUSES,\nSVG_TREE_TYPE_ASCDESC,\nSVG_TREE_TYPE_ASCDESC_SPOUSES) = range(len(SVG_TREE_TYPES))\nDEFAULT_SVG_TREE_TYPE = SVG_TREE_TYPE_ASCDESC\n\nSVG_TREE_SHAPES = [\n\t_(\"Vertical (↓)\"),\n\t_(\"Vertical (↑)\"),\n\t_(\"Horizontal (→)\"),\n\t_(\"Horizontal (←)\"),\n\t_(\"Full Circle\"),\n\t_(\"Half Circle\"),\n\t_(\"Quadrant\"),\n]\n(SVG_TREE_SHAPE_VERTICAL_TOP_BOTTOM,\nSVG_TREE_SHAPE_VERTICAL_BOTTOM_TOP,\nSVG_TREE_SHAPE_HORIZONTAL_LEFT_RIGHT,\nSVG_TREE_SHAPE_HORIZONTAL_RIGHT_LEFT,\nSVG_TREE_SHAPE_CIRCLE,\nSVG_TREE_SHAPE_HALF_CIRCLE,\nSVG_TREE_SHAPE_QUADRANT) = range(len(SVG_TREE_SHAPES))\nDEFAULT_SVG_TREE_SHAPE = SVG_TREE_SHAPE_HORIZONTAL_LEFT_RIGHT\n\nSVG_TREE_DISTRIB_ASC = [\n\t_('Size proportional to number of ancestors'),\n\t_('Homogeneous parents distribution'),\n]\nSVG_TREE_DISTRIB_DSC = [\n\t_('Size proportional to number of descendants'),\n\t_('Homogeneous children distribution'),\n]\n(SVG_TREE_DISTRIB_PROPORTIONAL,\nSVG_TREE_DISTRIB_HOMOGENEOUS) = range(len(SVG_TREE_DISTRIB_ASC))\nDEFAULT_SVG_TREE_DISTRIB = SVG_TREE_DISTRIB_PROPORTIONAL\n\nSVG_TREE_BACKGROUNDS = [\n\t_('Gender colors'),\n\t_('Generation based gradient'),\n\t_('Age based gradient'),\n\t_('Single main (filter) color'),\n\t_('Time period based gradient'),\n\t_('White'),\n\t_('Color scheme classic report'),\n\t_('Color scheme classic view'),\n]\n(SVG_TREE_BACKGROUND_GENDER,\nSVG_TREE_BACKGROUND_GENERATION,\nSVG_TREE_BACKGROUND_AGE,\nSVG_TREE_BACKGROUND_SINGLE,\nSVG_TREE_BACKGROUND_PERIOD,\nSVG_TREE_BACKGROUND_WHITE,\nSVG_TREE_BACKGROUND_SCHEME1,\nSVG_TREE_BACKGROUND_SCHEME2) = range(len(SVG_TREE_BACKGROUNDS))\nDEFAULT_SVG_TREE_BACKGROUND = SVG_TREE_BACKGROUND_GENERATION\n\n#: Templates for the website, in the form: [directory, name]\n# First template is the default one:\n# The files in the default template are used when they are not present in another template\n# Only the files that are different from the default template are present in the other templates directories\nWEB_TEMPLATE_LIST = (\n\t(\"dwr_default\", _(\"Default\")),\n\t(\"dwr_mainz\", _(\"Mainz\")),\n)\n\n\nINCLUDE_LIVING_VALUE = 99 #: Arbitrary number\n\n# Indexes in the L{DynamicWebReport.obj_dict} and L{DynamicWebReport.bkref_dict} elements\nOBJDICT_NAME = 0\nOBJDICT_GID = 1\nOBJDICT_INDEX = 2\nBKREF_CLASS = 0\nBKREF_HANDLE = 1\nBKREF_REFOBJ = 2\n\n\n_html_dbl_quotes = re.compile(r'([^\"]*) \" ([^\"]*) \" (.*)', re.VERBOSE)\n_html_sng_quotes = re.compile(r\"([^']*) ' ([^']*) ' (.*)\", re.VERBOSE)\n\ndef html_escape(text):\n\t\"\"\"Convert the text and replace some characters with a &# variant.\"\"\"\n\t# First single characters, no quotes\n\ttext = escape(text)\n\t# Deal with double quotes.\n\tm = _html_dbl_quotes.match(text)\n\twhile m:\n\t\ttext = \"%s\" \"“\" \"%s\" \"”\" \"%s\" % m.groups()\n\t\tm = _html_dbl_quotes.match(text)\n\t# Replace remaining double quotes.\n\ttext = text.replace('\"', '"')\n\t# Deal with single quotes.\n\ttext = text.replace(\"'s \", '’s ')\n\tm = _html_sng_quotes.match(text)\n\twhile m:\n\t\ttext = \"%s\" \"‘\" \"%s\" \"’\" \"%s\" % m.groups()\n\t\tm = _html_sng_quotes.match(text)\n\t# Replace remaining single quotes.\n\ttext = text.replace(\"'\", ''')\n\n\treturn text\n\n\ndef script_escape(text):\n\t\"\"\"Convert the text and escape quotes, backslashes and end-of-lines\n\t\"\"\"\n\treturn(text.\n\t\treplace(\"\\\\\", \"\\\\\\\\\").\n\t\treplace(\"'\", \"\\\\'\").\n\t\treplace(\"\\\"\", \"\\\\\\\"\").\n\t\treplace(\"\\n\", \"\\\\n\")\n\t)\n\n\ndef html_text(html):\n\t\"\"\"Get the string corresponding to an L{Html} object\"\"\"\n\tif (isinstance(html, string_types)): return(html.strip())\n\tsw = StringIO()\n\thtml.write(partial(print, file = sw), indent = \"\", tabs = \"\")\n\treturn(sw.getvalue().strip())\n\n\ndef format_date(date, gedcom = False, iso = False):\n\t\"\"\"Give the date as a string\n\t@param iso: If True, the date should be given in ISO format: YYYY-MM-DD\n\t@type iso: Boolean\n\t\"\"\"\n\tif (not date): return(\"\")\n\t\n\tval = \"\"\n\t\n\tif (iso):\n\t\t# TODO: export ISO dates\n\t\t# if (iso): val = DateDisplay.display(date) or \"\"\n\t\t# else: val = _dd.display(date) or \"\"\n\t\tpass\n\t\t\n\telif (gedcom):\n\t\tstart = date.get_start_date()\n\t\tif start != Date.EMPTY:\n\t\t\tcal = date.get_calendar()\n\t\t\tmod = date.get_modifier()\n\t\t\tquality = date.get_quality()\n\t\t\tif quality in DATE_QUALITY:\n\t\t\t\tqual_text = DATE_QUALITY[quality] + \" \"\n\t\t\telse:\n\t\t\t\tqual_text = \"\"\n\t\t\tif mod == Date.MOD_SPAN:\n\t\t\t\tval = \"%sFROM %s TO %s\" % (\n\t\t\t\t\tqual_text,\n\t\t\t\t\tmake_gedcom_date(start, cal, mod, None), \n\t\t\t\t\tmake_gedcom_date(date.get_stop_date(), cal, mod, None))\n\t\t\telif mod == Date.MOD_RANGE:\n\t\t\t\tval = \"%sBET %s AND %s\" % (\n\t\t\t\t\tqual_text,\n\t\t\t\t\tmake_gedcom_date(start, cal, mod, None), \n\t\t\t\t\tmake_gedcom_date(date.get_stop_date(), cal, mod, None))\n\t\t\telse:\n\t\t\t\tval = make_gedcom_date(start, cal, mod, quality)\n\t\t\t\t\n\telse:\n\t\t# Regular Gramps place displayer\n\t\tval = _dd.display(date) or \"\"\n\t\n\treturn(val)\n\n\ndef rmtree_fix(dirname):\n\t\"\"\"Windows fix: Python shutil.rmtree does not work properly on Windows.\n\tUnfortunately this fix is not completely working. Don't know why.\n\tThe strategy is to rename the directory first, in order to let Windows delete it in differed time.\n\t\"\"\"\n\t#TODO: Fix shutil.rmtree on Windows\n\ttmp = dirname + \"_removetree_tmp\"\n\tos.rename(dirname, tmp)\n\tshutil.rmtree(tmp)\n\t# Wait for rmtree to complete\n\tfor i in range(100):\n\t\tif (not os.path.exists(tmp)): break\n\t\ttime.sleep(0.1)\n\n\n\nclass DynamicWebReport(Report):\n\t\"\"\"\n\tClass DynamicWebReport\n\t\n\tExtracts information from the database and exports the data into Javascript and HTML files\n\t\n\tThe database extraction is performed by the method L{_build_obj_dict}. It recursively calls the methods \"_add_***\".\n\t\n\tThe database extraction builds:\n\t - indexes of the objects selected for the report as dictionaries,\n\t - for each object (of the report), references to the objects calling this object.\n\t \n\tThe indexes of the objects selected are stored as dictionaries \"obj_dict[class][handle]\",\n\tindexed by the object class,\n\tindexed by the database handle,\n\tcontaining for each report object the following information:\n\t - object file name, if any,\n\t - object name,\n\t - gramps id,\n\t - object index, starting from 0,\n\t only counting the objects selected,\n\t each object type is counted separately.\n\t\n\tThe references to objects are stored as dictionaries \"bkref_dict[class][handle]\",\n\tindexed by the object class,\n\tindexed by the database handle,\n\tcontaining for each report object the following information:\n\t - class of the object referencing it,\n\t - handle of the object referencing it,\n\t - reference object (MediaRef, EventRef) if any.\n\t \n\tThe report is generated by L{write_report}\n\t\"\"\"\n\n\tdef __init__(self, database, options, user):\n\t\t\"\"\"\n\t\tCreate WebReport object that produces the report.\n\n\t\tThe arguments are:\n\n\t\tdatabase - the Gramps database instance\n\t\toptions - instance of the Options class for this report\n\t\tuser - instance of a gen.user.User()\n\t\t\"\"\"\n\n\t\tReport.__init__(self, database, options, user)\n\t\tself.user = user\n\t\tmenu = options.menu\n\t\tself.link_prefix_up = True\n\t\tself.options = {}\n\n\t\tfor optname in menu.get_all_option_names():\n\t\t\tmenuopt = menu.get_option_by_name(optname)\n\t\t\tself.options[optname] = menuopt.get_value()\n\n\t\tif not self.options['incpriv']:\n\t\t\tself.database = PrivateProxyDb(database)\n\t\telse:\n\t\t\tself.database = database\n\n\t\tlivinginfo = self.options['living']\n\t\tyearsafterdeath = self.options['yearsafterdeath']\n\n\t\tif livinginfo != INCLUDE_LIVING_VALUE:\n\t\t\tself.database = LivingProxyDb(self.database, livinginfo, None, yearsafterdeath)\n\n\t\tfilters_option = menu.get_option_by_name('filter')\n\t\tself.filter = filters_option.get_filter()\n\n\t\tself.target_path = self.options['target'] #: Destination directory\n\t\tself.ext = \".html\" #: HTML fiules extension\n\t\tself.title = self.options['title'] #: Web site title. Web pages title are in the form \"title of the page - title of the site\"\n\n\t\tself.author = get_researcher().get_name() #: Database author name. Used in copyright text.\n\t\tif self.author:\n\t\t\tself.author = self.author.replace(',,,', '')\n\n\t\t# The following data are local copies of the options. Refer to the L{DynamicWebOptions} class for more details.\n\t\tself.inc_events = self.options['inc_events']\n\t\tself.inc_places = self.options['inc_places']\n\t\tself.inc_families = self.options['inc_families']\n\t\tself.inc_gallery = self.options['inc_gallery']\n\t\tself.copy_media = self.options['copy_media']\n\t\tself.inc_notes = self.options['inc_notes']\n\t\tself.print_notes_type = self.options['print_notes_type']\n\t\tself.inc_sources = self.options['inc_sources']\n\t\tself.inc_repositories = self.options['inc_repositories']\n\t\t# Repositories are not exported unless sources are exported\n\t\tself.inc_repositories = self.inc_repositories and self.inc_sources\n\t\tself.inc_addresses = self.options['inc_addresses']\n\t\tself.name_format = self.options['name_format']\n\t\tself.short_name_format = self.options['short_name_format']\n\t\tself.encoding = self.options['encoding']\n\t\tself.copyright = self.options['copyright']\n\t\tself.inc_gendex = self.options['inc_gendex']\n\t\tself.template = self.options['template']\n\t\tself.pages_number = self.options['pages_number']\n\t\tself.page_content = [\n\t\t\tself.options['page_content_%i' %i]\n\t\t\tfor i in range(self.pages_number)\n\t\t]\n\t\tself.page_name = [\n\t\t\tself.options['page_name_%i' %i]\n\t\t\tfor i in range(len(PAGES_NAMES))\n\t\t]\n\t\tself.custom_menu = [\n\t\t\tself.options['custom_menu_%i' %i]\n\t\t\tfor i in range(NB_CUSTOM_PAGES)\n\t\t]\n\t\tself.custom_note = [\n\t\t\tself.options['custom_note_%i' %i]\n\t\t\tfor i in range(NB_CUSTOM_PAGES)\n\t\t]\n\t\t# Filter pages that cannot be exported due to other options\n\t\tself.page_content = [pc for pc in self.page_content if (not(\n\t\t\t(pc == PAGE_FAMILY_INDEX and not self.inc_families) or\n\t\t\t(pc == PAGE_MEDIA_INDEX and not self.inc_gallery) or\n\t\t\t(pc == PAGE_SOURCE_INDEX and not self.inc_sources) or\n\t\t\t(pc == PAGE_REPOSITORY_INDEX and not self.inc_repositories) or\n\t\t\t(pc == PAGE_PLACE_INDEX and not self.inc_places)\n\t\t))]\n\t\tself.pages_number = len(self.page_content)\n\n\t\tself._backend = HtmlBackend()\n\t\tself._backend.build_link = self.build_link\n\n\n\tdef write_report(self):\n\t\t\"\"\"\n\t\tReport generation\n\t\t\"\"\"\n\t\t\n\t\t# Initialize the logger\n\t\t# This initialization shall be performed after Gramps has start-up\n\t\t# import importlib\n\t\t# logging = importlib.reload(logging)\n\t\t# global log\n\t\t# log = logging.getLogger(\".DynamicWeb\")\n\t\t\n\t\t# Create directory\n\t\tdir_name = self.target_path\n\t\tif dir_name is None:\n\t\t\tdir_name = get_curr_dir()\n\t\telif not os.path.isdir(dir_name):\n\t\t\tparent_dir = os.path.dirname(dir_name)\n\t\t\tif not os.path.isdir(parent_dir):\n\t\t\t\tmsg = _(\"Neither %(current)s nor %(parent)s are directories\") % \\\n\t\t\t\t\t {'current': dir_name, 'parent': parent_dir}\n\t\t\t\tself.user.notify_error(msg)\n\t\t\t\treturn\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tos.mkdir(dir_name)\n\t\t\t\texcept IOError as value:\n\t\t\t\t\tmsg = _(\"Could not create the directory: %(path)s\") % {\n\t\t\t\t\t\t \"path\": dir_name + \"\\n\" + value[1]}\n\t\t\t\t\tself.user.notify_error(msg)\n\t\t\t\t\treturn\n\t\t\t\texcept:\n\t\t\t\t\tmsg = _(\"Could not create the directory: %(path)s\") % {\"path\": dir_name}\n\t\t\t\t\tself.user.notify_error(msg)\n\t\t\t\t\treturn\n\t\tconfig.set('paths.website-directory', os.path.dirname(self.target_path) + os.sep)\n\n\n\t\t# for use with discovering biological, half, and step siblings for use\n\t\t# in display_ind_parents()...\n\t\t# self.rel_class = get_relationship_calculator()\n\n\t\t#: List of images already copied\n\t\tself.images_copied = set()\n\n\t\t#: List of thumbnails already created\n\t\tself.thumbnail_created = set()\n\n\t\t#################################################\n\t\t# Pass 1 Build the lists of objects to be output\n\n\t\tself._build_obj_dict()\n\t\tself._sort_obj_dict()\n\n\t\t#################################################\n\t\t# Pass 2 Generate the web pages\n\t\t\n\t\twith self.user.progress(_(\"Dynamic Web Site Report\"), _(\"Exporting family tree data ...\"), 10) as step:\n\t\t\tself.created_files = []\n\t\t\t# Create directories\n\t\t\tfor dirname in [\"thumb\"] + ([\"image\"] if (self.copy_media) else []):\n\t\t\t\tdirpath = os.path.join(self.target_path, dirname)\n\t\t\t\tif (not os.path.isdir(dirpath)): os.mkdir(dirpath)\n\t\t\t# Copy web site files\n\t\t\tself.copy_template_files()\n\t\t\tstep()\n\t\t\t# Export database as Javascript files\n\t\t\tself._export_individuals()\n\t\t\tstep()\n\t\t\tself._export_families()\n\t\t\tstep()\n\t\t\tself._export_sources()\n\t\t\tself._export_citations()\n\t\t\tself._export_repositories()\n\t\t\tstep()\n\t\t\tself._export_places()\n\t\t\tstep()\n\t\t\tself._export_media()\n\t\t\tstep()\n\t\t\tself._export_surnames()\n\t\t\tstep()\n\t\t\t# Generate HTML files\n\t\t\tself._export_pages()\n\t\t\tstep()\n\t\t\t# Create GENDEX file\n\t\t\tself.build_gendex(self.obj_dict[Person])\n\t\t\tstep()\n\t\t\t# Create an archive file of the web site\n\t\t\tself.create_archive()\n\t\t\tstep()\n\n\n\tdef _export_individuals(self):\n\t\t\"\"\"\n\t\tExport individuals data in Javascript file\n\t\tThe individuals data is stored in the Javascript Array \"I\"\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'I' is sorted by person name\\n\"\n\t\t\t\"// 'I' gives for individual:\\n\"\n\t\t\t\"// - Gramps ID\\n\"\n\t\t\t\"// - The complete name\\n\"\n\t\t\t\"// - The short name\\n\"\n\t\t\t\"// - The names as a list of:\\n\"\n\t\t\t\"// [full name, type, title, nick, call, given, suffix, list of surnames, family nickname,\\n\"\n\t\t\t\"// notes, list of the name source citations index (in table 'C')]\\n\"\n\t\t\t\"// - The gender\\n\"\n\t\t\t\"// - The birth year in the form '1700', '?' (date unknown)\\n\"\n\t\t\t\"// - The birth place\\n\"\n\t\t\t\"// - The death year in the form '1700', '?' (date unknown), '' (not dead)\\n\"\n\t\t\t\"// - The death place\\n\"\n\t\t\t\"// - The death age\\n\"\n\t\t\t\"// - A list of events, with for each event:\\n\"\n\t\t\t\"// - The event name\\n\"\n\t\t\t\"// - The event date\\n\"\n\t\t\t\"// - The event date in ISO format (sortable)\\n\"\n\t\t\t\"// - The event place index (in table 'P'), -1 if none\\n\"\n\t\t\t\"// - The event description\\n\"\n\t\t\t\"// - The event text and notes (including event reference notes)\\n\"\n\t\t\t\"// - A list of the event media index, in the form:\\n\"\n\t\t\t\"// - media index (in table 'M')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the event source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of addresses, with for each address:\\n\"\n\t\t\t\"// - The address date\\n\"\n\t\t\t\"// - The address date in ISO format (sortable)\\n\"\n\t\t\t\"// - The address place in the form:\\n\"\n\t\t\t\"// [street, locality, parish, city, state, county, zip, country]\\n\"\n\t\t\t\"// - The address notes\\n\"\n\t\t\t\"// - A list of the address source citations index (in table 'C')\\n\"\n\t\t\t\"// - The person notes\\n\"\n\t\t\t\"// - A list of the person media references, in the form:\\n\"\n\t\t\t\"// - media index (in table 'M')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the person source citations index (in table 'C')\\n\"\n\t\t\t\"// - The list of the person attributes in the form:\\n\"\n\t\t\t\"// [attribute, value, note, list of citations]\\n\"\n\t\t\t\"// - The list of the person URL in the form:\\n\"\n\t\t\t\"// [type, url, description]\\n\"\n\t\t\t\"// - A list of partners families index (in table 'F')\\n\"\n\t\t\t\"// - A list of parents families in the form:\\n\"\n\t\t\t\"// [index (in table 'F'), relation to father, relation to mother, notes, list of citations]\\n\"\n\t\t\t\"// - A list of associations in the form:\\n\"\n\t\t\t\"// [person index (in table 'I'), relationship, notes, list of citations (in table 'C')]\\n\"\n\t\t\t\"I = [\")\n\t\tsep = \"\\n\"\n\t\tperson_list = list(self.obj_dict[Person].keys())\n\t\tperson_list.sort(key = lambda x: self.obj_dict[Person][x][OBJDICT_INDEX])\n\t\tfor person_handle in person_list:\n\t\t\tperson = self.database.get_person_from_handle(person_handle)\n\t\t\tsw.write(sep)\n\t\t\tsw.write(\"[\\\"\" + self.obj_dict[Person][person_handle][OBJDICT_GID] + \"\\\",\")\n\t\t\t# Names\n\t\t\tname = self.get_name(person) or \"\"\n\t\t\tsw.write(\"\\\"\" + script_escape(name) + \"\\\",\")\n\t\t\tname = self.get_short_name(person) or \"\"\n\t\t\tsw.write(\"\\\"\" + script_escape(name) + \"\\\",\\n\")\n\t\t\tsw.write(self.get_name_data(person) + \",\\n\")\n\t\t\t# Gender\n\t\t\tgender = \"\"\n\t\t\tif (person.get_gender() == Person.MALE): gender = \"M\"\n\t\t\tif (person.get_gender() == Person.FEMALE): gender = \"F\"\n\t\t\tif (person.get_gender() == Person.UNKNOWN): gender = \"U\"\n\t\t\tsw.write(\"\\\"\" + gender + \"\\\",\")\n\t\t\t# Years\n\t\t\tsw.write(\"\\\"\" + self.get_birth_year(person) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\" + self.get_birth_place(person) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\" + self.get_death_year(person) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\" + self.get_death_place(person) + \"\\\",\\n\")\n\t\t\t# Age at death\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_death_age(person)) + \"\\\",\\n\")\n\t\t\t# Events\n\t\t\tsw.write(\"[\\n\" + self._data_events(person) + \"\\n],\\n\")\n\t\t\t# Addresses\n\t\t\tsw.write(\"[\\n\" + self._data_addresses(person) + \"\\n],\\n\")\n\t\t\t# Get individual notes\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_notes_text(person)) + \"\\\",\\n\")\n\t\t\t# Get individual media\n\t\t\tsw.write(self._data_media_reference_index(person))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get individual sources\n\t\t\tsw.write(self._data_source_citation_index(person))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get individual attributes\n\t\t\tsw.write(self._data_attributes(person))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get individual URL\n\t\t\tsw.write(self._data_url_list(person))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Families (partners)\n\t\t\tsw.write(self._data_families_index(person))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Families (parents)\n\t\t\tsw.write(self._data_parents_families_index(person))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Associations\n\t\t\tsw.write(self._data_associations(person))\n\t\t\tsw.write(\"\\n]\")\n\t\t\t#\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_indi.js\", sw.getvalue())\n\n\n\tdef get_name_data(self, person):\n\t\tprimary_name = person.get_primary_name()\n\t\tall_names = [primary_name] + person.get_alternate_names()\n\t\tfirst_name = primary_name.get_first_name()\n\t\ttext = \"[\"\n\t\tsep = \"\"\n\t\tfor name in all_names:\n\t\t\ttext += sep + \"[\"\n\t\t\tname.set_display_as(self.name_format)\n\t\t\tpname = _nd.display_name(name)\n\t\t\ttext += \"\\\"\" + script_escape(pname) + \"\\\",\"\n\t\t\t# Type\n\t\t\ttext += \"\\\"\" + script_escape(str(name.get_type())) + \"\\\",\"\n\t\t\t# Title\n\t\t\ttitle = name.get_title() or \"\"\n\t\t\ttext += \"\\\"\" + script_escape(str(title)) + \"\\\",\"\n\t\t\t# Nickname\n\t\t\tnick_name = name.get_nick_name()\n\t\t\tif (nick_name == first_name or not nick_name): nick_name = \"\"\n\t\t\ttext += \"\\\"\" + script_escape(str(nick_name)) + \"\\\",\"\n\t\t\t# Callname\n\t\t\tcall_name = name.get_call_name()\n\t\t\tif (call_name == first_name or not call_name): call_name = \"\"\n\t\t\ttext += \"\\\"\" + script_escape(str(call_name)) + \"\\\",\"\n\t\t\t# Given\n\t\t\tgiven = name.get_first_name() or \"\"\n\t\t\ttext += \"\\\"\" + script_escape(str(given)) + \"\\\",\"\n\t\t\t# Suffix\n\t\t\tsuffix = name.get_suffix() or \"\"\n\t\t\ttext += \"\\\"\" + script_escape(str(suffix)) + \"\\\",\"\n\t\t\t# Surnames\n\t\t\tsurnames = name.get_surname_list()\n\t\t\ttext += \"[\" + \",\".join([\n\t\t\t\t\"\\\"\" + script_escape(str(surname.get_surname() or \"\")) + \"\\\"\"\n\t\t\t\tfor surname in surnames]) + \"],\"\n\t\t\t# Family nickname\n\t\t\tfnick = name.get_family_nick_name() or \"\"\n\t\t\ttext += \"\\\"\" + script_escape(str(fnick)) + \"\\\",\"\n\t\t\t# Get name date\n\t\t\tdatetext = format_date(name.date) or \"\"\n\t\t\ttext += \"\\\"\" + script_escape(datetext) + \"\\\",\"\n\t\t\t# Get name notes\n\t\t\ttext += \"\\\"\" + script_escape(self.get_notes_text(name)) + \"\\\",\"\n\t\t\t# Get name sources\n\t\t\ttext += self._data_source_citation_index(name) + \"]\"\n\t\t\tsep = \",\"\n\t\ttext += \"]\"\n\t\treturn(text)\n\n\n\tdef get_name_object(self, person, maiden_name = None):\n\t\t\"\"\"\n\t\tReturn person's name, unless maiden_name given, unless married_name\n\t\tlisted.\n\t\t@param: person -- person object from database\n\t\t@param: maiden_name -- Female's family surname\n\t\t\"\"\"\n\t\t# Get all of a person's names\n\t\tprimary_name = person.get_primary_name()\n\t\tmarried_name = None\n\t\tnames = [primary_name] + person.get_alternate_names()\n\t\tfor name in names:\n\t\t\tif int(name.get_type()) == NameType.MARRIED:\n\t\t\t\tmarried_name = name\n\t\t\t\tbreak # use first\n\t\t# Now, decide which to use:\n\t\tif maiden_name is not None:\n\t\t\tif married_name is not None:\n\t\t\t\tname = Name(married_name)\n\t\t\telse:\n\t\t\t\tname = Name(primary_name)\n\t\t\t\tsurname_obj = name.get_primary_surname()\n\t\t\t\tsurname_obj.set_surname(maiden_name)\n\t\telse:\n\t\t\tname = Name(primary_name)\n\t\treturn(name)\n\n\n\tdef get_name(self, person, maiden_name = None):\n\t\tname = self.get_name_object(person, maiden_name)\n\t\tname.set_display_as(self.name_format)\n\t\treturn _nd.display_name(name)\n\n\n\tdef get_short_name(self, person, maiden_name = None):\n\t\tname = self.get_name_object(person, maiden_name)\n\t\tname.set_display_as(self.short_name_format)\n\t\treturn _nd.display_name(name)\n\n\n\tdef _export_families(self):\n\t\t\"\"\"\n\t\tExport families data in Javascript file\n\t\tThe families data is stored in the Javascript Array \"F\"\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'F' is sorted by family full name\\n\"\n\t\t\t\"// 'F' gives for each family:\\n\"\n\t\t\t\"// - Gramps ID\\n\"\n\t\t\t\"// - The family full name\\n\"\n\t\t\t\"// - The family union type\\n\"\n\t\t\t\"// - The marriage year in the form '1700', '?' (unknown), or '' (not married)\\n\"\n\t\t\t\"// - The marriage place\"\n\t\t\t\"// - A list of events, with for each event:\\n\"\n\t\t\t\"// - The event name\\n\"\n\t\t\t\"// - The event date\\n\"\n\t\t\t\"// - The event date in ISO format (sortable)\\n\"\n\t\t\t\"// - The event place index (in table 'P'), -1 if none\\n\"\n\t\t\t\"// - The event description\\n\"\n\t\t\t\"// - The event text and notes (including event reference notes)\\n\"\n\t\t\t\"// - A list of the event media references, in the form:\\n\"\n\t\t\t\"// - media index (in table 'M')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the event source citations index (in table 'C')\\n\"\n\t\t\t\"// - The family notes\\n\"\n\t\t\t\"// - A list of the family media references, in the form:\\n\"\n\t\t\t\"// - media index (in table 'M')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the family source citations index (in table 'C')\\n\"\n\t\t\t\"// - The list of the family attributes in the form:\\n\"\n\t\t\t\"// [attribute, value, note, list of citations]\\n\"\n\t\t\t\"// - A list of spouses index (in table 'I')\\n\"\n\t\t\t\"// - A list of child in the form:\\n\"\n\t\t\t\"// [index (in table 'I'), relation to father, relation to mother, notes, list of citations]\\n\"\n\t\t\t\"F = [\")\n\t\tsep = \"\\n\"\n\t\tfamily_list = list(self.obj_dict[Family].keys())\n\t\tfamily_list.sort(key = lambda x: self.obj_dict[Family][x][OBJDICT_INDEX])\n\t\tfor family_handle in family_list:\n\t\t\tfamily = self.database.get_family_from_handle(family_handle)\n\t\t\tsw.write(sep)\n\t\t\tsw.write(\"[\\\"\" + self.obj_dict[Family][family_handle][OBJDICT_GID] + \"\\\",\")\n\t\t\t# Names\n\t\t\tname = self.get_family_name(family) or \"\"\n\t\t\tsw.write(\"\\\"\" + script_escape(name) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\" + script_escape(str(family.get_relationship())) + \"\\\",\\n\")\n\t\t\t# Years\n\t\t\tsw.write(\"\\\"\" + self.get_marriage_year(family) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\" + self.get_marriage_place(family) + \"\\\",\\n\")\n\t\t\t# Events\n\t\t\tsw.write(\"[\\n\" + self._data_events(family) + \"\\n],\\n\")\n\t\t\t# Get family notes\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_notes_text(family)) + \"\\\",\\n\")\n\t\t\t# Get family media\n\t\t\tsw.write(self._data_media_reference_index(family))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get family sources\n\t\t\tsw.write(self._data_source_citation_index(family))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get family attributes\n\t\t\tsw.write(self._data_attributes(family))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Partners\n\t\t\tsw.write(self._data_partners_index(family))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Children\n\t\t\tsw.write(self._data_children_index(family))\n\t\t\tsw.write(\"\\n]\")\n\t\t\t#\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_fam.js\", sw.getvalue())\n\n\n\tdef _data_events(self, object):\n\t\t\"\"\"\n\t\tBuild events data related to L{object} in a string representing a Javascript Array\n\t\tL{object} could be: a person or a family\n\t\t@return: events as a string representing a Javascript Array\n\t\t\"\"\"\n\t\t# Builds an event list that gives for each event:\n\t\t# - Gramps ID\\n\"\n\t\t# - The event name\n\t\t# - The event date\n\t\t# - The event date in ISO format (sortable)\n\t\t# - The event place index (in table 'P'), -1 if none\n\t\t# - The event description\n\t\t# - The event text and notes (including event reference notes)\n\t\t# - A list of the event media index, in the form:\n\t\t# - media index (in table 'M')\n\t\t# - media thumbnail path\n\t\t# - [x1, y1, x2, y2] of the media reference\n\t\t# - notes of the media reference\n\t\t# - list of the media reference source citations index (in table 'C')\\n\"\n\t\t# - A list of the event source citations index (in table 'C')\n\t\tevent_ref_list = object.get_event_ref_list()\n\t\tif not event_ref_list: return(\"\")\n\t\trows = []\n\t\tfor event_ref in event_ref_list:\n\t\t\tif (event_ref.ref not in self.obj_dict[Event]): continue\n\t\t\tevent = self.database.get_event_from_handle(event_ref.ref)\n\t\t\tif (not event): continue\n\t\t\ttrow = \"\\t[\"\n\t\t\tevt_type = str(event.get_type())\n\t\t\tevent_role = event_ref.get_role()\n\t\t\tif (event_role != EventRoleType.PRIMARY and event_role != EventRoleType.FAMILY):\n\t\t\t\tevt_type += \" (%s)\" % event_role\n\t\t\tplace_index = -1\n\t\t\tplace_handle = event.get_place_handle()\n\t\t\tif (place_handle and (place_handle in self.obj_dict[Place])):\n\t\t\t\tplace_index = self.obj_dict[Place][place_handle][OBJDICT_INDEX]\n\t\t\tevt_desc = event.get_description()\n\t\t\ttrow += \"\\\"\" + self.obj_dict[Event][event_ref.ref][OBJDICT_GID] + \"\\\",\"\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_type)) + \"\\\",\"\n\t\t\tevt_date = format_date(event.get_date_object())\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_date)) + \"\\\",\"\n\t\t\tevt_date = format_date(event.get_date_object(), True)\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_date)) + \"\\\",\"\n\t\t\ttrow += str(place_index) + \",\"\n\t\t\tif (evt_desc is None): evt_desc = \"\"\n\t\t\ttrow += \"\\\"\" + script_escape(html_escape(evt_desc)) + \"\\\",\"\n\t\t\t# Get event notes\n\t\t\tnotelist = event.get_note_list()\n\t\t\tnotelist.extend(event_ref.get_note_list())\n\t\t\tattrlist = event.get_attribute_list()\n\t\t\tattrlist.extend(event_ref.get_attribute_list())\n\t\t\ttrow += \"\\\"\" + script_escape(self.get_notes_attributes_text(notelist, attrlist)) + \"\\\",\"\n\t\t\t# Get event media\n\t\t\ttrow += self._data_media_reference_index(event)\n\t\t\ttrow += \",\"\n\t\t\t# Get event sources\n\t\t\tcitationlist = event.get_citation_list()\n\t\t\tcitationlist.extend(event_ref.get_citation_list())\n\t\t\tfor attr in attrlist: citationlist.extend(attr.get_citation_list())\n\t\t\ttrow += self._data_source_citation_index_from_list(citationlist)\n\t\t\t#\n\t\t\ttrow += \"]\"\n\t\t\trows.append(trow)\n\t\treturn(\",\\n\".join(rows))\n\n\n\tdef _data_addresses(self, object):\n\t\t\"\"\"\n\t\tExport addresses data related to L{object} in a string representing a Javascript Array\n\t\tL{object} could be: a person or a repository\n\t\t@return: events as a string representing a Javascript Array\n\t\t\"\"\"\n\t\t# Builds an address list that gives for each address:\n\t\t# - The address date\\n\"\n\t\t# - The address date in ISO format (sortable)\\n\"\n\t\t# - The address place in the form:\\n\"\n\t\t# [street, locality, parish, city, state, county, zip, country]\\n\"\\n\"\n\t\t# - The address notes\\n\"\n\t\t# - A list of the address source citations index (in table 'C')\\n\"\n\t\tif (not self.inc_addresses): return(\"\")\n\t\taddrlist = object.get_address_list()\n\t\tif not addrlist: return(\"\")\n\t\trows = []\n\t\tfor addr in addrlist:\n\t\t\ttext = \"\\t[\"\n\t\t\taddr_date = format_date(addr.get_date_object())\n\t\t\ttext += \"\\\"\" + script_escape(html_escape(addr_date)) + \"\\\",\"\n\t\t\taddr_date = format_date(addr.get_date_object(), True)\n\t\t\ttext += \"\\\"\" + script_escape(html_escape(addr_date)) + \"\\\",\"\n\t\t\taddr_data = [\n\t\t\t\taddr.get_street(),\n\t\t\t\taddr.get_locality(),\n\t\t\t\t\"\",\n\t\t\t\taddr.get_city(),\n\t\t\t\taddr.get_state(),\n\t\t\t\taddr.get_county(),\n\t\t\t\taddr.get_postal_code(),\n\t\t\t\taddr.get_country(),\n\t\t\t\taddr.get_phone(),\n\t\t\t]\n\t\t\ttext += \"[\\\"\" + \"\\\",\\\"\".join([script_escape(data) for data in addr_data]) + \"\\\"],\"\n\t\t\t# Get address notes\n\t\t\ttext += \"\\\"\" + script_escape(self.get_notes_text(addr)) + \"\\\",\"\n\t\t\t# Get address sources\n\t\t\ttext += self._data_source_citation_index(addr) + \"]\"\n\t\t\trows.append(text)\n\t\treturn(\",\\n\".join(rows))\n\n\n\tdef _export_sources(self):\n\t\t\"\"\"\n\t\tExport sources data in Javascript file\n\t\tThe sources data is stored in the Javascript Array \"S\"\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'S' is sorted by source title\\n\"\n\t\t\t\"// 'S' gives for each source:\\n\"\n\t\t\t\"// - Gramps ID\\n\"\n\t\t\t\"// - The source title\\n\"\n\t\t\t\"// - The source text (author, etc.)\\n\"\n\t\t\t\"// - The source notes\\n\"\n\t\t\t\"// - A list of the source media references, in the form:\\n\"\n\t\t\t\"// - media index (in table 'M')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the citations index (in table 'C') referencing this source\\n\"\n\t\t\t\"// - A list of the repositories for this source, in the form:\\n\"\n\t\t\t\"// - repository index (in table 'R')\\n\"\n\t\t\t\"// - media type\\n\"\n\t\t\t\"// - call number\\n\"\n\t\t\t\"// - notes of the repository reference\\n\"\n\t\t\t\"// - The list of the sources attributes in the form:\\n\"\n\t\t\t\"// [attribute, value, note, list of citations]\\n\"\n\t\t\t\"S = [\")\n\t\tsep = \"\\n\"\n\t\tsource_list = list(self.obj_dict[Source])\n\t\tif (not self.inc_sources): source_list = []\n\t\tsource_list.sort(key = lambda x: self.obj_dict[Source][x][OBJDICT_INDEX])\n\t\tfor source_handle in source_list:\n\t\t\tsource = self.database.get_source_from_handle(source_handle)\n\t\t\tsw.write(sep)\n\t\t\tsw.write(\"[\\\"\" + self.obj_dict[Source][source_handle][OBJDICT_GID] + \"\\\",\")\n\t\t\ttitle = source.get_title() or \"\"\n\t\t\tsw.write(\"\\\"\" + script_escape(html_escape(title)) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\")\n\t\t\tfor (label, value) in [\n\t\t\t\t(_(\"Author\"), source.get_author()),\n\t\t\t\t(_(\"Abbreviation\"), source.get_abbreviation()),\n\t\t\t\t(_(\"Publication information\"), source.get_publication_info())]:\n\t\t\t\tif value:\n\t\t\t\t\thtml = Html(\"p\") + Html(\"b\", label + \": \") + value\n\t\t\t\t\tsw.write(script_escape(html_text(html)))\n\t\t\tsw.write(\"\\\",\\n\")\n\t\t\t# Get source notes\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_notes_text(source)) + \"\\\",\\n\")\n\t\t\t# Get source media\n\t\t\tsw.write(self._data_media_reference_index(source))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get source citations\n\t\t\tsw.write(self._data_bkref_index(Source, source_handle, Citation))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get repositories references\n\t\t\tsw.write(self._data_repo_reference_index(source))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get source attributes\n\t\t\tif (DWR_VERSION_410):\n\t\t\t\tsw.write(self._data_attributes_src(source))\n\t\t\telse:\n\t\t\t\tsw.write(\"[]\")\n\t\t\tsw.write(\"\\n]\")\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_sour.js\", sw.getvalue())\n\n\n\tdef _export_citations(self):\n\t\t\"\"\"\n\t\tExport citations data in Javascript file\n\t\tThe citations data is stored in the Javascript Array \"C\"\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'C' gives for each source citation:\\n\"\n\t\t\t\"// - Gramps ID\\n\"\n\t\t\t\"// - The source index (in table 'S')\\n\"\n\t\t\t\"// - The citation text (page, etc.)\\n\"\n\t\t\t\"// - The citation notes\\n\"\n\t\t\t\"// - A list of the citation media references, in the form:\\n\"\n\t\t\t\"// - media index (in table 'M')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - A list of the person index (in table 'I') referencing this citation\\n\"\n\t\t\t\"// (including the person events referencing this citation)\\n\"\n\t\t\t\"// - A list of the family index (in table 'F') referencing this citation\\n\"\n\t\t\t\"// (including the family events referencing this citation)\\n\"\n\t\t\t\"// - A list of the media index (in table 'M') referencing this citation\\n\"\n\t\t\t\"// (including the media references referencing this citation)\\n\"\n\t\t\t\"// - A list of the place index (in table 'P') referencing this citation\\n\"\n\t\t\t\"// (including the media references referencing this citation)\\n\"\n\t\t\t\"// - A list of the repository index (in table 'R') referencing this citation\\n\"\n\t\t\t\"C = [\")\n\t\tsep = \"\\n\"\n\t\tcitation_list = list(self.obj_dict[Citation])\n\t\tif (not self.inc_sources): citation_list = []\n\t\tcitation_list.sort(key = lambda x: self.obj_dict[Citation][x][OBJDICT_INDEX])\n\t\tfor citation_handle in citation_list:\n\t\t\tcitation = self.database.get_citation_from_handle(citation_handle)\n\t\t\tsource_handle = citation.get_reference_handle()\n\t\t\tsw.write(sep)\n\t\t\tsw.write(\"[\\\"\" + self.obj_dict[Citation][citation_handle][OBJDICT_GID] + \"\\\",\")\n\t\t\tsw.write(str(self.obj_dict[Source][source_handle][OBJDICT_INDEX])+ \",\\n\")\n\t\t\tsw.write(\"\\\"\")\n\t\t\tconfidence = citation.get_confidence_level()\n\t\t\tif ((confidence in conf_strings) and confidence != Citation.CONF_NORMAL):\n\t\t\t\tconfidence = _(conf_strings[confidence])\n\t\t\telse:\n\t\t\t\tconfidence = None\n\t\t\tfor (label, value) in [\n\t\t\t\t(_(\"Date\"), format_date(citation.get_date_object())),\n\t\t\t\t(_(\"Page\"), citation.get_page()),\n\t\t\t\t(_(\"Confidence\"), confidence),\n\t\t\t]:\n\t\t\t\tif value:\n\t\t\t\t\thtml = Html(\"p\") + Html(\"b\", label + \": \") + value\n\t\t\t\t\tsw.write(script_escape(html_text(html)))\n\t\t\tsw.write(\"\\\",\\n\")\n\t\t\t# Get citation notes\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_notes_text(citation)) + \"\\\",\\n\")\n\t\t\t# Get citation media\n\t\t\tsw.write(self._data_media_reference_index(citation))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get references\n\t\t\tsw.write(self._data_bkref_index(Citation, citation_handle, Person))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_bkref_index(Citation, citation_handle, Family))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_bkref_index(Citation, citation_handle, MediaObject))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_bkref_index(Citation, citation_handle, Place))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_bkref_index(Citation, citation_handle, Repository))\n\t\t\tsw.write(\"\\n]\")\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_cita.js\", sw.getvalue())\n\n\n\tdef _export_repositories(self):\n\t\t\"\"\"\n\t\tExport repositories data in Javascript file\n\t\tThe repositories data is stored in the Javascript Array \"R\"\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'R' is sorted by repository name\\n\"\n\t\t\t\"// 'R' gives for each repository:\\n\"\n\t\t\t\"// - Gramps ID\\n\"\n\t\t\t\"// - The repository name\\n\"\n\t\t\t\"// - The repository type\\n\"\n\t\t\t\"// - A list of addresses, with for each address:\\n\"\n\t\t\t\"// - The address date\\n\"\n\t\t\t\"// - The address date in ISO format (sortable)\\n\"\n\t\t\t\"// - The address place in the form:\\n\"\n\t\t\t\"// [street, locality, parish, city, state, county, zip, country]\\n\"\n\t\t\t\"// - The address notes\\n\"\n\t\t\t\"// - A list of the address source citations index (in table 'C')\\n\"\n\t\t\t\"// - The repository notes\\n\"\n\t\t\t\"// - The list of the repository URL in the form:\\n\"\n\t\t\t\"// [type, url, description]\\n\"\n\t\t\t\"// - A list of the sources referencing this repository, in the form:\\n\"\n\t\t\t\"// - source index (in table 'S')\\n\"\n\t\t\t\"// - media type\\n\"\n\t\t\t\"// - call number\\n\"\n\t\t\t\"// - notes of the repository reference\\n\"\n\t\t\t\"R = [\")\n\t\tsep = \"\\n\"\n\t\trepo_list = list(self.obj_dict[Repository])\n\t\tif (not self.inc_repositories): repo_list = []\n\t\trepo_list.sort(key = lambda x: self.obj_dict[Repository][x][OBJDICT_INDEX])\n\t\tfor repo_handle in repo_list:\n\t\t\trepo = self.database.get_repository_from_handle(repo_handle)\n\t\t\tsw.write(sep)\n\t\t\tsw.write(\"[\\\"\" + self.obj_dict[Repository][repo_handle][OBJDICT_GID] + \"\\\",\")\n\t\t\tname = repo.get_name() or \"\"\n\t\t\tsw.write(\"\\\"\" + script_escape(name) + \"\\\",\\n\")\n\t\t\ttype = repo.get_type() or \"\"\n\t\t\tsw.write(\"\\\"\" + script_escape(str(type)) + \"\\\",\\n\")\n\t\t\t# Addresses\n\t\t\tsw.write(\"[\\n\" + self._data_addresses(repo) + \"\\n],\\n\")\n\t\t\t# Get repository notes\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_notes_text(repo)) + \"\\\",\\n\")\n\t\t\t# Get repository URL\n\t\t\tsw.write(self._data_url_list(repo))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get source references\n\t\t\tsw.write(self._data_repo_backref_index(repo, Source))\n\t\t\tsw.write(\"\\n]\")\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_repo.js\", sw.getvalue())\n\n\n\tdef _export_media(self):\n\t\t\"\"\"\n\t\tExport media data in Javascript file\n\t\tThe media data is stored in the Javascript Array \"M\"\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'M' is sorted by media title\\n\"\n\t\t\t\"// 'M' gives for each media object:\\n\"\n\t\t\t\"// - Gramps ID\\n\"\n\t\t\t\"// - The media title\\n\"\n\t\t\t\"// - The media path in Gramps\\n\"\n\t\t\t\"// - The media path were the media is really located\\n\"\n\t\t\t\"// - The media MIME type\\n\"\n\t\t\t\"// - The media date\\n\"\n\t\t\t\"// - The media date in ISO format (sortable)\\n\"\n\t\t\t\"// - The media notes\\n\"\n\t\t\t\"// - A list of the media source citations index (in table 'C')\\n\"\n\t\t\t\"// - The list of the media attributes in the form:\\n\"\n\t\t\t\"// [attribute, value, note, list of citations]\\n\"\n\t\t\t\"// - Media thumbnail path\\n\"\n\t\t\t\"// - A list of the person referencing this media (including the person events referencing this media), in the form:\\n\"\n\t\t\t\"// - person index (in table 'I')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the family referencing this media (including the family events referencing this media), in the form:\\n\"\n\t\t\t\"// - family index (in table 'F')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the source referencing this media (including the source citations referencing this media), in the form:\\n\"\n\t\t\t\"// - source index (in table 'S')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"// - A list of the places referencing this media, in the form:\\n\"\n\t\t\t\"// - place index (in table 'P')\\n\"\n\t\t\t\"// - media thumbnail path\\n\"\n\t\t\t\"// - [x1, y1, x2, y2] of the media reference\\n\"\n\t\t\t\"// - notes of the media reference\\n\"\n\t\t\t\"// - list of the media reference source citations index (in table 'C')\\n\"\n\t\t\t\"M = [\")\n\t\tsep = \"\\n\"\n\t\tmedia_list = list(self.obj_dict[MediaObject])\n\t\tif (not self.inc_gallery): media_list = []\n\t\tmedia_list.sort(key = lambda x: self.obj_dict[MediaObject][x][OBJDICT_INDEX])\n\t\tfor media_handle in media_list:\n\t\t\tmedia = self.database.get_object_from_handle(media_handle)\n\t\t\tsw.write(sep)\n\t\t\tsw.write(\"[\\\"\" + self.obj_dict[MediaObject][media_handle][OBJDICT_GID] + \"\\\",\")\n\t\t\ttitle = media.get_description() or \"\"\n\t\t\tsw.write(\"\\\"\" + script_escape(html_escape(title)) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\" + script_escape(media.get_path()) + \"\\\",\\n\")\n\t\t\tpath = self.get_media_web_path(media)\n\t\t\tsw.write(\"\\\"\" + script_escape(path) + \"\\\",\\n\")\n\t\t\tsw.write(\"\\\"\" + script_escape(media.get_mime_type()) + \"\\\",\\n\")\n\t\t\t# Get media date\n\t\t\tdate = format_date(media.get_date_object()) or \"\"\n\t\t\tsw.write(\"\\\"\" + date + \"\\\",\\n\")\n\t\t\tdate = format_date(media.get_date_object(), True) or \"\"\n\t\t\tsw.write(\"\\\"\" + date + \"\\\",\\n\")\n\t\t\t# Get media notes\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_notes_text(media)) + \"\\\",\\n\")\n\t\t\t# Get media sources\n\t\t\tsw.write(self._data_source_citation_index(media))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get media attributes\n\t\t\tsw.write(self._data_attributes(media))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get media thumbnail\n\t\t\tsw.write(\"\\\"\" + self.copy_thumbnail(media, (0,0,100,100)) + \"\\\",\\n\")\n\t\t\t# Get media references\n\t\t\tsw.write(self._data_media_backref_index(media, Person))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_media_backref_index(media, Family))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_media_backref_index(media, Source))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_media_backref_index(media, Place))\n\t\t\tsw.write(\"\\n\")\n\t\t\tsw.write(\"]\")\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_media.js\", sw.getvalue())\n\n\n\tdef _export_places(self):\n\t\t\"\"\"\n\t\tExport places data in Javascript file\n\t\tThe places data is stored in the Javascript Array \"P\"\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'P' is sorted by place name\\n\"\n\t\t\t\"// 'P' gives for each media object:\\n\"\n\t\t\t\"// - Gramps ID\\n\"\n\t\t\t\"// - The place name\\n\"\n\t\t\t\"// - The place locations parts for the main and alternate names, in the form:\\n\"\n\t\t\t\"// (index 0 is main name, others are for alternate names)\\n\"\n\t\t\t\"// [street, locality, parish, city, state, county, zip, country]\\n\"\n\t\t\t\"// - The coordinates [latitude, longitude]\\n\\n\"\n\t\t\t\"// - The place notes\\n\"\n\t\t\t\"// - A list of the place source citations index (in table 'C')\\n\"\n\t\t\t\"// - The list of the place URL in the form:\\n\"\n\t\t\t\"// [type, url, description]\\n\"\n\t\t\t\"// - A list of the person index (in table 'I') for events referencing this place\\n\"\n\t\t\t\"// (including the persons directly referencing this place)\\n\"\n\t\t\t\"// - A list of the family index (in table 'F') for events referencing this place\\n\"\n\t\t\t\"P=[\")\n\t\tsep = \"\\n\"\n\t\tplace_list = list(self.obj_dict[Place])\n\t\tplace_list.sort(key = lambda x: self.obj_dict[Place][x][OBJDICT_INDEX])\n\t\tfor place_handle in place_list:\n\t\t\tplace = self.database.get_place_from_handle(place_handle)\n\t\t\tsw.write(sep)\n\t\t\tsw.write(\"[\\\"\" + self.obj_dict[Place][place_handle][OBJDICT_GID] + \"\\\",\")\n\t\t\tplace_name = report_utils.place_name(self.database, place_handle)\n\t\t\tsw.write(\"\\\"\" + script_escape(place_name) + \"\\\"\")\n\t\t\tif (not self.inc_places):\n\t\t\t\tsw.write(\"]\")\n\t\t\t\tsep = \",\\n\"\n\t\t\t\tcontinue\n\t\t\tsw.write(\",\\n\")\n\t\t\tlocations = []\n\t\t\tif (DWR_VERSION_410):\n\t\t\t\tml = get_main_location(self.database, place)\n\t\t\t\tloc = Location()\n\t\t\t\tloc.street = ml.get(PlaceType.STREET, '')\n\t\t\t\tloc.locality = ml.get(PlaceType.LOCALITY, '')\n\t\t\t\tloc.city = ml.get(PlaceType.CITY, '')\n\t\t\t\tloc.parish = ml.get(PlaceType.PARISH, '')\n\t\t\t\tloc.county = ml.get(PlaceType.COUNTY, '')\n\t\t\t\tloc.state = ml.get(PlaceType.STATE, '')\n\t\t\t\tloc.postal = place.get_code()\n\t\t\t\tloc.country = ml.get(PlaceType.COUNTRY, '')\n\t\t\t\tlocations.append(loc)\n\t\t\telse:\n\t\t\t\tif (place.main_loc):\n\t\t\t\t\tml = place.get_main_location()\n\t\t\t\t\tif (ml and not ml.is_empty()): locations.append(ml)\n\t\t\taltloc = place.get_alternate_locations()\n\t\t\tif (altloc):\n\t\t\t\taltloc = [nonempt for nonempt in altloc if (not nonempt.is_empty())]\n\t\t\t\tlocations += altloc\n\t\t\tloctabs = []\n\t\t\tfor loc in locations:\n\t\t\t\tloctab = [\n\t\t\t\t\tloc.street,\n\t\t\t\t\tloc.locality,\n\t\t\t\t\tloc.city,\n\t\t\t\t\tloc.parish,\n\t\t\t\t\tloc.county,\n\t\t\t\t\tloc.state,\n\t\t\t\t\tloc.postal,\n\t\t\t\t\tloc.country,\n\t\t\t\t]\n\t\t\t\tloctab = [(data or \"\") for data in loctab]\n\t\t\t\tloctab = [\"\\\"\" + script_escape(data) + \"\\\"\" for data in loctab]\n\t\t\t\tloctabs.append(\"[\" + \",\".join(loctab) + \"]\")\n\t\t\tsw.write(\"[\" + \",\".join(loctabs) + \"],\\n\")\n\t\t\tlatitude = place.get_latitude()\n\t\t\tlongitude = place.get_longitude()\n\t\t\tif (latitude and longitude):\n\t\t\t\tcoords = conv_lat_lon(latitude, longitude, \"D.D8\")\n\t\t\telse:\n\t\t\t\tcoords = (\"\", \"\")\n\t\t\tsw.write(\"[\\\"\" + \"\\\",\\\"\".join(coords) + \"\\\"]\\n,\")\n\t\t\t# Get place notes\n\t\t\tsw.write(\"\\\"\" + script_escape(self.get_notes_text(place)) + \"\\\",\\n\")\n\t\t\t# Get place media\n\t\t\tsw.write(self._data_media_reference_index(place))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get place sources\n\t\t\tsw.write(self._data_source_citation_index(place))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get place URL\n\t\t\tsw.write(self._data_url_list(place))\n\t\t\tsw.write(\",\\n\")\n\t\t\t# Get back references\n\t\t\tsw.write(self._data_bkref_index(Place, place_handle, Person))\n\t\t\tsw.write(\",\\n\")\n\t\t\tsw.write(self._data_bkref_index(Place, place_handle, Family))\n\t\t\tsw.write(\"\\n]\")\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_place.js\", sw.getvalue())\n\n\n\tdef get_notes_text(self, object):\n\t\tif (not self.inc_notes): return(\"\")\n\t\tnotelist = object.get_note_list()\n\t\thtmllist = self.dump_notes(notelist)\n\t\tif (not htmllist): return(\"\")\n\t\treturn(html_text(htmllist))\n\n\n\tdef get_notes_attributes_text(self, notelist, attrlist):\n\t\tif (not self.inc_notes): return(\"\")\n\t\t# Get notes\n\t\thtmllist = self.dump_notes(notelist)\n\t\t# Get attributes\n\t\tfor attr in attrlist:\n\t\t\tif (not htmllist): htmllist = Html(\"div\")\n\t\t\thtmllist.extend(Html(\n\t\t\t\t\"p\", _(\"%(type)s: %(value)s\") % {\n\t\t\t\t'type': Html(\"b\", attr.get_type()),\n\t\t\t\t'value': attr.get_value()\n\t\t\t\t}\n\t\t\t))\n\t\t\t# Also output notes attached to the attributes\n\t\t\tnotelist2 = attr.get_note_list()\n\t\t\thtmlnotelist = self.dump_notes(notelist2)\n\t\t\tif (htmlnotelist): htmllist.extend(htmlnotelist)\n\t\tif (not htmllist): return(\"\")\n\t\treturn(html_text(htmllist))\n\n\n\tdef dump_notes(self, notelist):\n\t\t\"\"\"\n\t\tdump out of list of notes with very little elements of its own\n\n\t\t@param: notelist -- list of notes\n\t\t\"\"\"\n\t\tnotesection = None\n\t\tif (not notelist): return(notesection)\n\t\tif (not self.inc_notes): return(notesection)\n\t\tfor note_handle in notelist:\n\t\t\tif (not notesection): notesection = Html(\"div\")\n\t\t\tthis_note = self.database.get_note_from_handle(note_handle)\n\t\t\tif this_note is not None:\n\t\t\t\tif (self.print_notes_type):\n\t\t\t\t\tnotesection.extend(Html(\"i\", str(this_note.type), class_=\"NoteType\"))\n\t\t\t\tnotesection.extend(self.get_note_format(this_note))\n\t\treturn(notesection)\n\n\tdef get_note_format(self, note):\n\t\t\"\"\"\n\t\twill get the note from the database, and will return either the\n\t\tstyled text or plain note\n\t\t\"\"\"\n\t\ttext = \"\"\n\t\tif note is not None:\n\t\t\t# retrieve the body of the note\n\t\t\tnote_text = note.get()\n\t\t\t# styled notes\n\t\t\thtmlnotetext = self.styled_note(note.get_styledtext(),\n\t\t\t\t\t\t\t\t\t\t\tnote.get_format(), contains_html =\n\t\t\t\t\t\t\t\t\t\t\tnote.get_type() == NoteType.HTML_CODE)\n\t\t\ttext = htmlnotetext or Html(\"p\", note_text)\n\t\t# return text of the note to its callers\n\t\treturn(text)\n\n\tdef styled_note(self, styledtext, format, contains_html=False):\n\t\t\"\"\"\n\t\tstyledtext : assumed a StyledText object to write\n\t\tformat : = 0 : Flowed, = 1 : Preformatted\n\t\tstyle_name : name of the style to use for default presentation\n\t\t\"\"\"\n\t\ttext = str(styledtext)\n\n\t\tif (not text): return('')\n\n\t\ts_tags = styledtext.get_tags()\n\t\thtmllist = Html(\"div\", class_=\"grampsstylednote\")\n\t\tif contains_html:\n\t\t\tmarkuptext = self._backend.add_markup_from_styled(text,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t s_tags,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t split='\\n',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t escape=False)\n\t\t\thtmllist += markuptext\n\t\telse:\n\t\t\tmarkuptext = self._backend.add_markup_from_styled(text,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t s_tags,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t split='\\n')\n\t\t\tlinelist = []\n\t\t\tlinenb = 1\n\t\t\tsigcount = 0\n\t\t\tfor line in markuptext.split('\\n'):\n\t\t\t\t[line, sigcount] = process_spaces(line, format)\n\t\t\t\tif sigcount == 0:\n\t\t\t\t\t# The rendering of an empty paragraph ''\n\t\t\t\t\t# is undefined so we use a non-breaking space\n\t\t\t\t\tif linenb == 1:\n\t\t\t\t\t\tlinelist.append(' ')\n\t\t\t\t\thtmllist.extend(Html('p') + linelist)\n\t\t\t\t\tlinelist = []\n\t\t\t\t\tlinenb = 1\n\t\t\t\telse:\n\t\t\t\t\tif linenb > 1:\n\t\t\t\t\t\tlinelist[-1] += '
'\n\t\t\t\t\tlinelist.append(line)\n\t\t\t\t\tlinenb += 1\n\t\t\tif linenb > 1:\n\t\t\t\thtmllist.extend(Html('p') + linelist)\n\t\t\t# if the last line was blank, then as well as outputting the previous para,\n\t\t\t# which we have just done,\n\t\t\t# we also output a new blank para\n\t\t\tif sigcount == 0:\n\t\t\t\tlinelist = [\" \"]\n\t\t\t\thtmllist.extend(Html('p') + linelist)\n\t\treturn(htmllist)\n\n\n\tdef _data_source_citation_index(self, object):\n\t\t\"\"\"\n\t\tExport sources citations indexes related to L{object}\n\t\tSee L{_data_source_citation_index_from_list}\n\t\t\"\"\"\n\t\tcitationlist = object.get_citation_list()\n\t\treturn(self._data_source_citation_index_from_list(citationlist))\n\n\tdef _data_source_citation_index_from_list(self, citationlist):\n\t\t\"\"\"\n\t\tList sources citations indexes of the L{citationlist} in a string representing a Javascript Array\n\t\t@return: citations indexes as a string representing a Javascript Array\n\t\t\"\"\"\n\t\tif (not self.inc_sources): return(\"[]\")\n\t\tif not citationlist: return(\"[]\")\n\t\tsep = \"\"\n\t\ttxt = \"[\"\n\t\tfor citation_handle in citationlist:\n\t\t\tif (not txt): txt = Html(\"div\")\n\t\t\tcitation = self.database.get_citation_from_handle(citation_handle)\n\t\t\tif (citation is not None and (citation_handle in self.obj_dict[Citation])):\n\t\t\t\tsource_handle = citation.get_reference_handle()\n\t\t\t\tsource = self.database.get_source_from_handle(source_handle)\n\t\t\t\tif (source is not None and (source_handle in self.obj_dict[Source])):\n\t\t\t\t\ttitle = source.get_title()\n\t\t\t\t\tif (not title): title = source.get_gramps_id()\n\t\t\t\t\ttxt += sep + str(self.obj_dict[Citation][citation_handle][OBJDICT_INDEX])\n\t\t\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _data_repo_reference_index(self, object):\n\t\t\"\"\"\n\t\tBuild a list of the repositories references index, in the form given by L{_data_repo_ref}\n\t\t\"\"\"\n\t\tif (not self.inc_repositories): return(\"[]\")\n\t\trefs = object.get_reporef_list()\n\t\tif (not refs): return(\"[]\")\n\t\tsep = \"\\n\"\n\t\ttxt = \"[\"\n\t\tfor ref in refs:\n\t\t\trepo_handle = ref.get_reference_handle()\n\t\t\tif (repo_handle in self.obj_dict[Repository]):\n\t\t\t\ttxt += sep + \"\\t\" + self._data_repo_ref(ref, self.obj_dict[Repository][repo_handle][OBJDICT_INDEX])\n\t\t\t\tsep = \",\\n\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_repo_ref(self, ref, index):\n\t\t\"\"\"\n\t\tBuild a repository reference, in the form:\n\t\t - repository index (in table 'R')\n\t\t - media type\n\t\t - call number\n\t\t - notes of the repository reference\n\t\t\"\"\"\n\t\trepo_handle = ref.get_reference_handle()\n\t\trepo = self.database.get_repository_from_handle(repo_handle)\n\t\ttxt = \"[\"\n\t\ttxt += str(index) + \",\"\n\t\ttxt += \"\\\"\" + script_escape(str(ref.get_media_type())) + \"\\\",\"\n\t\ttxt += \"\\\"\" + script_escape(ref.get_call_number()) + \"\\\",\"\n\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(ref)) + \"\\\"\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _data_media_reference_index(self, object):\n\t\t\"\"\"\n\t\tBuild a list of the media references index, in the form given by L{_data_media_ref}\n\t\t\"\"\"\n\t\tif (not self.inc_gallery): return(\"[]\")\n\t\trefs = object.get_media_list()\n\t\tif (not refs): return(\"[]\")\n\t\tsep = \"\\n\"\n\t\ttxt = \"[\"\n\t\tfor ref in refs:\n\t\t\tmedia_handle = ref.get_reference_handle()\n\t\t\tif (media_handle in self.obj_dict[MediaObject]):\n\t\t\t\ttxt += sep + \"\\t\" + self._data_media_ref(ref, self.obj_dict[MediaObject][media_handle][OBJDICT_INDEX])\n\t\t\t\tsep = \",\\n\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_media_ref(self, ref, index):\n\t\t\"\"\"\n\t\tBuild a media reference, in the form:\n\t\t - media index (in table 'M')\n\t\t - media thumbnail path\n\t\t - [x1, y1, x2, y2] of the media reference\n\t\t - notes of the media reference\n\t\t - list of the media reference source citations index (in table 'C')\n\t\t\"\"\"\n\t\tmedia_handle = ref.get_reference_handle()\n\t\tmedia = self.database.get_object_from_handle(media_handle)\n\t\ttxt = \"[\"\n\t\ttxt += str(index)\n\t\ttxt += \",\\\"\"\n\t\ttxt += self.copy_thumbnail(media, ref.get_rectangle())\n\t\ttxt += \"\\\",[\"\n\t\trect = ref.get_rectangle() or (0,0,100,100)\n\t\ttxt += \",\".join(str(x) for x in rect)\n\t\ttxt += \"],\"\n\t\tattrlist = ref.get_attribute_list()\n\t\ttxt += \"\\\"\" + script_escape(self.get_notes_attributes_text(ref.get_note_list(), attrlist)) + \"\\\",\"\n\t\tcitationlist = ref.get_citation_list()\n\t\tfor attr in attrlist: citationlist.extend(attr.get_citation_list())\n\t\t# BUG: it seems that attribute references are given by both ref.get_citation_list and attr.get_citation_list\n\t\ttxt += self._data_source_citation_index_from_list(citationlist)\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef get_media_web_path(self, media):\n\t\t\"\"\"\n\t\tReturn the path of the media from the web pages\n\t\tThis function could be called several times for the same media\n\t\tThis function copies the media to the web pages directories if necessary\n\t\t\"\"\"\n\t\tmedia_path = media.get_path()\n\t\tif (media_path):\n\t\t\tnorm_path = media_path_full(self.database, media_path)\n\t\t\tif (os.path.isfile(norm_path)):\n\t\t\t\tif (self.copy_media):\n\t\t\t\t\text = os.path.splitext(norm_path)[1]\n\t\t\t\t\tiname = str(media.get_handle()) + ext\n\t\t\t\t\tiname = iname.lower()\n\t\t\t\t\tif (iname not in self.images_copied):\n\t\t\t\t\t\tself.copy_file(norm_path, iname, \"image\")\n\t\t\t\t\t\tself.images_copied.add(iname)\n\t\t\t\t\tweb_path = \"image/\" + iname\n\t\t\t\telse:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tweb_path = os.path.relpath(norm_path, self.target_path)\n\t\t\t\t\t\tweb_path = web_path.replace(\"\\\\\", \"/\")\n\t\t\t\t\texcept:\n\t\t\t\t\t\tweb_path = urlparse.urljoin('file:', urllib.pathname2url(norm_path))\n\t\t\t\t\t\tlog.warning(_(\"Impossible to convert \\\"%(path)s\\\" to a relative path.\") % {\"path\": norm_path})\n\t\t\t\treturn(web_path)\n\t\tlog.warning(\"Warning: File not found \\\"%(path)s\\\"\" % {\"path\": str(media_path)})\n\t\treturn(media_path)\n\n\n\tdef copy_thumbnail(self, media, region = None):\n\t\t\"\"\"\n\t\tGiven a handle (and optional region) make (if needed) an\n\t\tup-to-date cache of a thumbnail, and call copy_file\n\t\tto copy the cached thumbnail to the website.\n\t\tReturn the new path to the image.\n\t\t\"\"\"\n\t\tif (region and region[0] == 0 and region[1] == 0 and region[2] == 100 and region[3] == 100):\n\t\t\tregion = None\n\t\thandle = media.get_handle()\n\t\ttname = handle + ((\"-%d,%d-%d,%d.png\" % region) if region else \".png\")\n\t\tif (media.get_mime_type()):\n\t\t\tfrom_path = get_thumbnail_path(\n\t\t\t\tmedia_path_full(self.database, media.get_path()),\n\t\t\t\tmedia.get_mime_type(),\n\t\t\t\tregion)\n\t\t\tif not os.path.isfile(from_path):\n\t\t\t\tfrom_path = os.path.join(IMAGE_DIR, \"document.png\")\n\t\telse:\n\t\t\tfrom_path = os.path.join(IMAGE_DIR, \"document.png\")\n\t\tif (tname not in self.thumbnail_created):\n\t\t\tself.copy_file(from_path, tname, \"thumb\")\n\t\t\tself.thumbnail_created.add(tname)\n\t\tweb_path = \"thumb/\" + tname\n\t\treturn(web_path)\n\n\n\tdef _data_attributes(self, object):\n\t\t\"\"\"\n\t\tBuild the list of the L{object} attributes as a Javascript string, in the form:\n\t\t [attribute, value, note, list of citations]\n\t\t\"\"\"\n\t\tattrlist = object.get_attribute_list()\n\t\ttxt = \"[\"\n\t\tsep = \"\"\n\t\tfor attr in attrlist:\n\t\t\ttxt += sep + \"[\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_type())) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_value())) + \"\\\",\"\n\t\t\t# Get attribute notes\n\t\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(attr)) + \"\\\",\"\n\t\t\t# Get attribute sources\n\t\t\ttxt += self._data_source_citation_index(attr)\n\t\t\ttxt += \"]\"\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_attributes_src(self, source):\n\t\t\"\"\"\n\t\tBuild the list of the L{source} sources attributes as a Javascript string, in the form:\n\t\t [attribute, value, \"\", []]\n\t\t\"\"\"\n\t\tattrlist = source.get_attribute_list()\n\t\ttxt = \"[\"\n\t\tsep = \"\"\n\t\tfor attr in attrlist:\n\t\t\ttxt += sep + \"[\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_type())) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_value())) + \"\\\",\"\n\t\t\t# There aren't any attribute notes\n\t\t\ttxt += \"\\\"\\\",\"\n\t\t\t# There aren't any attribute sources\n\t\t\ttxt += \"[]\"\n\t\t\ttxt += \"]\"\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_url_list(self, object):\n\t\t\"\"\"\n\t\tBuild the list of the L{object} URL as a Javascript string, in the form:\n\t\t [type, url, description]\n\t\t\"\"\"\n\t\turllist = object.get_url_list()\n\t\ttxt = \"[\"\n\t\tsep = \"\"\n\t\tfor url in urllist:\n\t\t\t_type = url.get_type()\n\t\t\turi = url.get_path()\n\t\t\tdescr = url.get_description()\n\t\t\t# Email address\n\t\t\tif _type == UrlType.EMAIL:\n\t\t\t\tif not uri.startswith(\"mailto:\"):\n\t\t\t\t\turi = \"mailto:%(email)s\" % { 'email' : uri }\n\t\t\t# Web Site address\n\t\t\telif _type == UrlType.WEB_HOME:\n\t\t\t\tif not (uri.startswith(\"http://\") or uri.startswith(\"https://\")):\n\t\t\t\t\turi = \"http://%(website)s\" % { \"website\" : uri }\n\t\t\t# FTP server address\n\t\t\telif _type == UrlType.WEB_FTP:\n\t\t\t\tif not (uri.startswith(\"ftp://\") or uri.startswith(\"ftps://\")):\n\t\t\t\t\turi = \"ftp://%(ftpsite)s\" % { \"ftpsite\" : uri }\n\t\t\ttxt += sep + \"[\"\n\t\t\ttxt += \"\\\"\" + str(_type) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(uri) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(descr) + \"\\\"\"\n\t\t\ttxt += \"]\"\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _export_surnames(self):\n\t\t\"\"\"\n\t\tExport surnames data in Javascript file\n\t\tThe surnames data is stored in the Javascript Array \"SN\"\n\t\t\"\"\"\n\t\t# Extract the surnames data\n\t\tsurnames = defaultdict(list) #: Dictionary giving for each surname: the list of person handles with this surname\n\t\tsortnames = {} #: Dictionary giving for each person handle: a sortable string for the person\n\t\tperson_list = list(self.obj_dict[Person].keys())\n\t\tfor person_handle in person_list:\n\t\t\tperson = self.database.get_person_from_handle(person_handle)\n\t\t\tprimary_name = person.get_primary_name()\n\t\t\tif (primary_name.group_as):\n\t\t\t\tsurname = primary_name.group_as\n\t\t\telse:\n\t\t\t\tsurname = self.database.get_name_group_mapping(_nd.primary_surname(primary_name))\n\t\t\t# Treat people who have no name with those whose name is just 'whitespace'\n\t\t\tif (surname is None or surname.isspace()):\n\t\t\t\tsurname = \"\"\n\t\t\tsortnames[person_handle] = _nd.sort_string(primary_name)\n\t\t\tsurnames[surname].append(person_handle)\n\t\t# Sort surnames\n\t\tsurns_keys = list(surnames.keys())\n\t\tsurns_keys.sort(key = SORT_KEY)\n\t\t# Generate the file\n\t\tsw1 = StringIO()\n\t\tsw1.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'SN' is sorted by surname\\n\"\n\t\t\t\"// 'SN' gives for each surname:\\n\"\n\t\t\t\"// - the surname\\n\"\n\t\t\t\"// - the surname first letter\\n\"\n\t\t\t\"// - the list of persion index (in table 'I') with this surname\\n\"\n\t\t\t\"\\nSN = [\")\n\t\tsep = \"\\n\"\n\t\tfor s in surns_keys:\n\t\t\t# Sort persons\n\t\t\tsurnames[s].sort(key = lambda x: sortnames[x])\n\t\t\ttab = \",\".join([str(self.obj_dict[Person][x][OBJDICT_INDEX]) for x in surnames[s]])\n\t\t\tsw1.write(sep + \"[\\\"\" + script_escape(s) + \"\\\", \\\"\" + first_letter(s).strip() + \"\\\", [\" + tab + \"]]\")\n\t\t\tsep = \",\\n\"\n\t\tsw1.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_surns.js\", sw1.getvalue())\n\n\n\tdef _data_families_index(self, person):\n\t\tfams = []\n\t\tfamily_list = person.get_family_handle_list()\n\t\tif (family_list):\n\t\t\tfams = [self.obj_dict[Family][family_handle][OBJDICT_INDEX] for family_handle in family_list if (family_handle in self.obj_dict[Family])]\n\t\treturn(\n\t\t\t\"[\" +\n\t\t\t\",\".join([str(i) for i in fams]) +\n\t\t\t\"]\")\n\n\tdef _data_partners_index(self, family):\n\t\tindis = []\n\t\tperson_handle = family.get_father_handle()\n\t\tif (person_handle and (person_handle in self.obj_dict[Person])):\n\t\t\tindis.append(self.obj_dict[Person][person_handle][OBJDICT_INDEX])\n\t\tperson_handle = family.get_mother_handle()\n\t\tif (person_handle and (person_handle in self.obj_dict[Person])):\n\t\t\tindis.append(self.obj_dict[Person][person_handle][OBJDICT_INDEX])\n\t\treturn(\n\t\t\t\"[\" +\n\t\t\t\",\".join([str(i) for i in indis]) +\n\t\t\t\"]\")\n\n\tdef _data_parents_families_index(self, person):\n\t\tlinks = []\n\t\tfamily_list = person.get_parent_family_handle_list()\n\t\tif (family_list):\n\t\t\tfor family_handle in family_list:\n\t\t\t\tif (family_handle not in self.obj_dict[Family]): continue\n\t\t\t\tfamily = self.database.get_family_from_handle(family_handle)\n\t\t\t\tchild_refs = [\n\t\t\t\t\tchild_ref\n\t\t\t\t\tfor child_ref in family.get_child_ref_list()\n\t\t\t\t\tif (child_ref.ref == person.get_handle())\n\t\t\t\t]\n\t\t\t\tif (len(child_refs) >= 1):\n\t\t\t\t\tindex = self.obj_dict[Family][family_handle][OBJDICT_INDEX]\n\t\t\t\t\tlinks.append(self._data_child_ref(index, child_refs[0]))\n\t\treturn(\"[\" + \",\".join(links) + \"]\")\n\n\tdef _data_children_index(self, family):\n\t\tlinks = [\n\t\t\tself._data_child_ref(self.obj_dict[Person][child_ref.ref][OBJDICT_INDEX], child_ref)\n\t\t\tfor child_ref in family.get_child_ref_list()\n\t\t\tif (child_ref.ref in self.obj_dict[Person])\n\t\t]\n\t\treturn(\"[\" + \",\".join(links) + \"]\")\n\n\tdef _data_child_ref(self, index, child_ref):\n\t\t# Child reference in the form:\n\t\t# [index, relation to father, relation to mother, notes, list of citations]\n\t\ttxt = \"[\"\n\t\ttxt += str(index) + \",\"\n\t\ttxt += \"\\\"\" + script_escape(str(child_ref.get_father_relation())) + \"\\\",\"\n\t\ttxt += \"\\\"\" + script_escape(str(child_ref.get_mother_relation())) + \"\\\",\"\n\t\t# Get child reference notes\n\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(child_ref)) + \"\\\",\"\n\t\t# Get child reference sources\n\t\ttxt += self._data_source_citation_index(child_ref)\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _data_associations(self, person):\n\t\tassoclist = person.get_person_ref_list()\n\t\trels = []\n\t\tfor person_ref in assoclist:\n\t\t\ttxt = \"[\"\n\t\t\tif (person_ref.ref not in self.obj_dict[Person]): continue\n\t\t\ttxt += \"%i,\" % self.obj_dict[Person][person_ref.ref][OBJDICT_INDEX]\n\t\t\ttxt += \"\\\"\" + script_escape(str(person_ref.get_relation())) + \"\\\",\"\n\t\t\t# Get association notes\n\t\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(person_ref)) + \"\\\",\"\n\t\t\t# Get association sources\n\t\t\ttxt += self._data_source_citation_index(person_ref)\n\t\t\ttxt += \"]\"\n\t\t\trels.append(txt)\n\t\treturn(\"[\" + \",\".join(rels) + \"]\")\n\n\n\tdef get_birth_year(self, person):\n\t\tev = get_birth_or_fallback(self.database, person)\n\t\treturn(self._get_year_text(ev) or \"?\")\n\tdef get_death_year(self, person):\n\t\tev = get_death_or_fallback(self.database, person)\n\t\treturn(self._get_year_text(ev))\n\tdef get_marriage_year(self, family):\n\t\tev = get_marriage_or_fallback(self.database, family)\n\t\treturn(self._get_year_text(ev))\n\tdef _get_year_text(self, event):\n\t\ty = \"\"\n\t\tif (event):\n\t\t\ty = \"?\"\n\t\t\tdate = event.get_date_object()\n\t\t\tmod = date.get_modifier()\n\t\t\tstart = date.get_start_date()\n\t\t\tif (mod == Date.MOD_NONE and start != Date.EMPTY):\n\t\t\t\ty = str(start[2])\n\t\treturn(y)\n\n\tdef get_birth_place(self, person):\n\t\tev = get_birth_or_fallback(self.database, person)\n\t\treturn(self._get_place_text(ev))\n\tdef get_death_place(self, person):\n\t\tev = get_death_or_fallback(self.database, person)\n\t\treturn(self._get_place_text(ev))\n\tdef get_marriage_place(self, family):\n\t\tev = get_marriage_or_fallback(self.database, family)\n\t\treturn(self._get_place_text(ev))\n\tdef _get_place_text(self, event):\n\t\tplace_name = \"\"\n\t\tif (event):\n\t\t\tplace_handle = event.get_place_handle()\n\t\t\tif (place_handle and (place_handle in self.obj_dict[Place])):\n\t\t\t\tplace_name = report_utils.place_name(self.database, place_handle)\n\t\treturn(place_name)\n\n\tdef get_death_age(self, person):\n\t\tev_birth = get_birth_or_fallback(self.database, person)\n\t\tbirth_date = None\n\t\tif (ev_birth): birth_date = ev_birth.get_date_object()\n\t\tev_death = get_death_or_fallback(self.database, person)\n\t\tdeath_date = None\n\t\tif (ev_death): death_date = ev_death.get_date_object()\n\t\tif (birth_date):\n\t\t\talive = probably_alive(person, self.database, Today())\n\t\t\tif (not alive and death_date):\n\t\t\t\tnyears = death_date - birth_date\n\t\t\t\tnyears.format(precision = 3)\n\t\t\t\treturn(str(nyears))\n\t\treturn(\"\");\n\n\n\tdef _export_pages(self):\n\t\t\"\"\"\n\t\tGenerate the HTML pages\n\t\t\"\"\"\n\t\t\n\t\t# Check pages configuration (in the options)\n\t\tpcset = set(self.page_content)\n\t\tif (len(pcset) != len(self.page_content)):\n\t\t\tlog.error(_(\"The pages configuration is not valid: several pages have the same content\"))\n\t\t\treturn\n\t\t\t\n\t\t# Export the script containing the web pages configuration\n\t\tself._export_script_configuration()\n\t\t\n\t\t# List of the scripts and CSS stylesheets used in the HTML pages\n\t\t# Note: other scripts and stylesheets are dynamically loaded in \"dwr_start.js\"\n\t\t# \"dwr_start.js\" is loaded in all pages uncontitionally (see L{write_header})\n\t\tdbscripts = [\"dwr_db_indi.js\", \"dwr_db_fam.js\", \"dwr_db_sour.js\", \"dwr_db_cita.js\", \"dwr_db_media.js\", \"dwr_db_place.js\", \"dwr_db_repo.js\", \"dwr_db_surns.js\"] #: list of the scripts to embed in the HTML\n\t\tmapscripts = [] #: list of the scripts to embed in the HTML pages that show a map\n\t\tmapstyles = [] #: list of the CSS stylesheets to embed in the HTML pages that show a map\n\t\tif (self.options['placemappages'] or self.options['familymappages']):\n\t\t\tif (self.options['mapservice'] == \"Google\"):\n\t\t\t\tmapscripts = [\"http://maps.googleapis.com/maps/api/js?sensor=false\"]\n\t\t\telse:\n\t\t\t\tmapscripts = [\"http://openlayers.org/en/v3.0.0/build/ol.js\"]\n\t\t\t\tmapstyles = [\"http://openlayers.org/en/v3.0.0/css/ol.css\"]\n\t\t\t\t# mapscripts = [\"ol.js\"]\n\t\t\t\t# mapstyles = [\"ol.css\"]\n\t\t\t\t# mapscripts = [\"http://cdn.leafletjs.com/leaflet-0.7.3/leaflet.js\"]\n\t\t\t\t# mapstyles = [\"http://cdn.leafletjs.com/leaflet-0.7.3/leaflet.css\"]\n\t\t\t\t\n\t\t#: List of page to generate: index in L{PAGES_NAMES}, Javascript code for generating the page\n\t\tparts = {\n\t\t\tPAGE_PERSON: (dbscripts, \"DwrMain(PAGE_INDI);\"),\n\t\t\tPAGE_SURNAMES: (dbscripts, \"printSurnamesIndex();\"),\n\t\t\tPAGE_PERSON_INDEX: (dbscripts, \"printPersonsIndex();\"),\n\t\t\tPAGE_FAMILY_INDEX: (dbscripts, \"printFamiliesIndex();\"),\n\t\t\tPAGE_SOURCE_INDEX: (dbscripts, \"printSourcesIndex();\"),\n\t\t\tPAGE_MEDIA_INDEX: (dbscripts, \"printMediaIndex();\"),\n\t\t\tPAGE_PLACE_INDEX: (dbscripts, \"printPlacesIndex();\"),\n\t\t\tPAGE_ADDRESS_INDEX: (dbscripts, \"printAddressesIndex();\"),\n\t\t\tPAGE_REPOSITORY_INDEX: (dbscripts, \"printReposIndex();\"),\n\t\t\tPAGE_SVG_TREE: (dbscripts, \"DwrMain(PAGE_SVG_TREE);\"),\n\t\t}\n\t\t\n\t\t# Export the HTML pages listed in L{PAGES_NAMES}\n\t\tfor i in range(self.pages_number):\n\t\t\tpc = self.page_content[i] # Get the page i contents defined in the options\n\t\t\tfilename = PAGES_NAMES[pc][2]\n\t\t\ttitle = self.page_name[pc]\n\t\t\tif (pc in parts):\n\t\t\t\t# The page is not a custom page\n\t\t\t\t(scripts, cmd) = parts[pc]\n\t\t\t\tself._export_html_page(filename, title, cmd, True, scripts)\n\t\t\telse:\n\t\t\t\t# The page is a custom page\n\t\t\t\ti_cst = pc - PAGE_CUSTOM\n\t\t\t\tself._export_custom_page(filename, title, self.custom_menu[i_cst], self.custom_note[i_cst])\n\n\t\t# The person page is required\n\t\tif (PAGE_PERSON not in self.page_content):\n\t\t\tself._export_html_page(\"person.html\", self.page_name[PAGE_PERSON], \"DwrMain(PAGE_INDI);\", True, dbscripts)\n\n\t\t# The search results page is required\n\t\tself._export_html_page(\"search.html\", _(\"Search results\"), \"DwrMain(PAGE_SEARCH);\", True, dbscripts)\n\n\t\t# Page for printing a family (if needed)\n\t\tif (self.inc_families):\n\t\t\tself._export_html_page(\"family.html\", self.page_name[PAGE_FAMILY_INDEX], \"DwrMain(PAGE_FAM);\", True, dbscripts + mapscripts , mapstyles)\n\t\t\n\t\t# Generate page surnames pages (if surnames page is used)\n\t\tif (PAGE_SURNAMES in self.page_content):\n\t\t\t# Page for persons with a given surname\n\t\t\tself._export_html_page(\"surname.html\", self.page_name[PAGE_SURNAMES], \"printSurnameIndex();\", True, dbscripts)\n\t\t\t# Page for surnames sorted by quantity\n\t\t\tself._export_html_page(\"surnames2.html\", self.page_name[PAGE_SURNAMES], \"printSurnamesIndex2();\", True, dbscripts)\n\n\t\t# Page for a single family (if needed)\n\t\tif (self.inc_sources):\n\t\t\tself._export_html_page(\"source.html\", self.page_name[PAGE_SOURCE_INDEX], \"DwrMain(PAGE_SOURCE);\", True, dbscripts)\n\n\t\t# Page for a single media (if needed)\n\t\tif (self.inc_gallery):\n\t\t\tself._export_html_page(\"media.html\", self.page_name[PAGE_MEDIA_INDEX], \"DwrMain(PAGE_MEDIA);\", True, dbscripts)\n\n\t\t# Page for a single place (if needed)\n\t\tif (self.inc_places):\n\t\t\tself._export_html_page(\"place.html\", self.page_name[PAGE_PLACE_INDEX], \"DwrMain(PAGE_PLACE);\", True, dbscripts + mapscripts , mapstyles)\n\n\t\t# Page for a single repository (if needed)\n\t\tif (self.inc_repositories):\n\t\t\tself._export_html_page(\"repository.html\", self.page_name[PAGE_REPOSITORY_INDEX], \"DwrMain(PAGE_REPO);\", True, dbscripts)\n\n\t\t# Page for full-screen SVG graph (if SVG graph is used)\n\t\tif (PAGE_SVG_TREE in self.page_content):\n\t\t\tself._export_html_page(\"tree_svg_full.html\", self.page_name[PAGE_SVG_TREE], \"DwrMain(PAGE_SVG_TREE_FULL);\", False, dbscripts)\n\t\t\tself._export_html_page(\"tree_svg_conf.html\", self.page_name[PAGE_SVG_TREE], \"DwrMain(PAGE_SVG_TREE_CONF);\", True, dbscripts)\n\t\t\tself._export_html_page(\"tree_svg_save.html\", self.page_name[PAGE_SVG_TREE], \"DwrMain(PAGE_SVG_TREE_SAVE);\", True, dbscripts)\n\n\n\tdef _export_script_configuration(self):\n\t\t\"\"\"\n\t\tGenerate \"dwr_conf.js\", which contains:\n\t\t - The pages configuration (mostly extract of the report options),\n\t\t - The localization (translated strings)\n\t\t - Gramps constants that could be used in the Javascript\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\"// This file is generated\\n\\n\")\n\t\tsw.write(\"NB_GENERATIONS_MAX = %i;\\n\" % int(self.options[\"graphgens\"]))\n\t\tsw.write(\"PAGES_TITLE = [\")\n\t\tsw.write(\", \".join([\n\t\t\t(\"\\\"\" + script_escape(self.page_name[self.page_content[i]]).replace(\" \", \" \") + \"\\\"\")\n\t\t\tfor i in range(self.pages_number)]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"PAGES_FILE = [\")\n\t\tsw.write(\", \".join([\n\t\t\t(\"\\\"\" + script_escape(PAGES_NAMES[self.page_content[i]][2]) + \"\\\"\")\n\t\t\tfor i in range(self.pages_number)]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_TYPES_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_TYPES]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_SHAPES_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_SHAPES]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_DISTRIB_ASC_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_DISTRIB_ASC]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_DISTRIB_DSC_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_DISTRIB_DSC]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_BACKGROUND_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_BACKGROUNDS]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_TYPE = %s;\\n\" % self.options['svg_tree_type'])\n\t\tsw.write(\"SVG_TREE_SHAPE = %s;\\n\" % self.options['svg_tree_shape'])\n\t\tsw.write(\"SVG_TREE_DISTRIB_ASC = %s;\\n\" % self.options['svg_tree_distrib_asc'])\n\t\tsw.write(\"SVG_TREE_DISTRIB_DSC = %s;\\n\" % self.options['svg_tree_distrib_dsc'])\n\t\tsw.write(\"SVG_TREE_BACKGROUND = %s;\\n\" % self.options['svg_tree_background'])\n\t\tsw.write(\"SVG_TREE_COLOR1 = \\\"%s\\\";\\n\" % self.options['svg_tree_color1'])\n\t\tsw.write(\"SVG_TREE_COLOR2 = \\\"%s\\\";\\n\" % self.options['svg_tree_color2'])\n\t\tsw.write(\"SVG_TREE_SHOW_DUP = \" + (\"true\" if (self.options['svg_tree_dup']) else \"false\") + \";\\n\")\n\t\tsw.write(\"SVG_TREE_COLOR_DUP = \\\"%s\\\";\\n\" % self.options['svg_tree_color_dup'])\n\t\tsw.write(\"GRAMPS_PREFERENCES = [];\\n\")\n\t\tfor pref in [\n\t\t\t'bordercolor-gender-female-alive',\n\t\t\t'bordercolor-gender-female-death',\n\t\t\t'bordercolor-gender-male-alive',\n\t\t\t'bordercolor-gender-male-death',\n\t\t\t'bordercolor-gender-unknown-alive',\n\t\t\t'bordercolor-gender-unknown-death',\n\t\t\t'color-gender-female-alive',\n\t\t\t'color-gender-female-death',\n\t\t\t'color-gender-male-alive',\n\t\t\t'color-gender-male-death',\n\t\t\t'color-gender-unknown-alive',\n\t\t\t'color-gender-unknown-death',\n\t\t\t]:\n\t\t\tsw.write(\"GRAMPS_PREFERENCES['%s'] = \\\"%s\\\";\\n\" % (pref, config.get('preferences.%s' % pref)))\n\t\tsw.write(\"SVG_TREE_COLOR_SCHEME0 = [\" + \", \".join(\n\t\t\t[(\"\\\"#%02x%02x%02x\\\"\" % (r, g, b)) for (r, g, b) in GENCOLOR[BACKGROUND_WHITE]])\n\t\t\t+ \"];\\n\")\n\t\tsw.write(\"SVG_TREE_COLOR_SCHEME1 = [\" + \", \".join(\n\t\t\t[(\"\\\"#%02x%02x%02x\\\"\" % (r, g, b)) for (r, g, b) in GENCOLOR[BACKGROUND_SCHEME1]])\n\t\t\t+ \"];\\n\")\n\t\tsw.write(\"SVG_TREE_COLOR_SCHEME2 = [\" + \", \".join(\n\t\t\t[(\"\\\"#%02x%02x%02x\\\"\" % (r, g, b)) for (r, g, b) in GENCOLOR[BACKGROUND_SCHEME2]])\n\t\t\t+ \"];\\n\")\n\t\tsw.write(\"FOOTER=\\\"\" + script_escape(self.get_header_footer_notes(\"footernote\")) + \"\\\";\\n\")\n\t\tsw.write(\"HEADER=\\\"\" + script_escape(self.get_header_footer_notes(\"headernote\")) + \"\\\";\\n\")\n\t\tsw.write(\"COPYRIGHT=\\\"\" + script_escape(self.get_copyright_license()) + \"\\\";\\n\")\n\t\tsw.write(\"INDEX_SHOW_BIRTH=\" + (\"true\" if (self.options['showbirth']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_DEATH=\" + (\"true\" if (self.options['showdeath']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_MARRIAGE=\" + (\"true\" if (self.options['showmarriage']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_PARTNER=\" + (\"true\" if (self.options['showpartner']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_PARENTS=\" + (\"true\" if (self.options['showparents']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_ALL_SIBLINGS=\" + (\"true\" if (self.options['birthorder']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_BKREF_TYPE=\" + (\"true\" if (self.options['bkref_type']) else \"false\") + \";\\n\")\n\t\tsw.write(\"SORT_CHILDREN=\" + (\"true\" if (self.options['showallsiblings']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_EVENTS=\" + (\"true\" if (self.inc_events) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_FAMILIES=\" + (\"true\" if (self.inc_families) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_SOURCES=\" + (\"true\" if (self.inc_sources) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_MEDIA=\" + (\"true\" if (self.inc_gallery) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_PLACES=\" + (\"true\" if (self.inc_places) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_REPOSITORIES=\" + (\"true\" if (self.inc_repositories) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_NOTES=\" + (\"true\" if (self.inc_notes) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_ADDRESSES=\" + (\"true\" if (self.inc_addresses) else \"false\") + \";\\n\")\n\t\tsw.write(\"MAP_PLACE=\" + (\"true\" if (self.options['placemappages']) else \"false\") + \";\\n\")\n\t\tsw.write(\"MAP_FAMILY=\" + (\"true\" if (self.options['familymappages']) else \"false\") + \";\\n\")\n\t\tsw.write(\"MAP_SERVICE=\\\"\" + script_escape(self.options['mapservice']) + \"\\\";\\n\")\n\t\tsw.write(\"__ = {\")\n\t\tsep = \"\\n\"\n\t\tfor (s, translated) in (\n\t\t\t(\"(filtered from _MAX_ total entries)\", _(\"(filtered from _MAX_ total entries)\")),\n\t\t\t(\"(sort by name)\", _(\"(sort by name)\")),\n\t\t\t(\"(sort by quantity)\", _(\"(sort by quantity)\")),\n\t\t\t(\": activate to sort column ascending\", _(\": activate to sort column ascending\")),\n\t\t\t(\": activate to sort column descending\", _(\": activate to sort column descending\")),\n\t\t\t(\"Click on a person to center the graph on this person.
When clicking on the center person, the person page is shown.
The type of graph could be selected in the list (on the top left side of the graph)
The number of ascending end descending generations could also be adjusted.
Use the mouse wheel or the buttons to zoom in and out.
The graph could also be shown full-screen.\", _(\"
Click on a person to center the graph on this person.
When clicking on the center person, the person page is shown.
The type of graph could be selected in the list (on the top left side of the graph)
The number of ascending end descending generations could also be adjusted.
Use the mouse wheel or the buttons to zoom in and out.
The graph could also be shown full-screen.\")),\n\t\t\t(\"
This page provides the SVG raw code.
Copy the contents into a text editor and save as an SVG file.
Make sure that the text editor encoding is UTF-8.
\", _(\"This page provides the SVG raw code.
Copy the contents into a text editor and save as an SVG file.
Make sure that the text editor encoding is UTF-8.
\")),\n\t\t\t(\"Address\", _(\"Address\")),\n\t\t\t(\"Addresses\", _(\"Addresses\")),\n\t\t\t(\"Age at Death\", _(\"Age at Death\")),\n\t\t\t(\"Alternate Name\", _(\"Alternate Name\")),\n\t\t\t(\"Ancestors\", _(\"Ancestors\")),\n\t\t\t(\"Associations\", _(\"Associations\")),\n\t\t\t(\"Attribute\", _(\"Attribute\")),\n\t\t\t(\"Attributes\", _(\"Attributes\")),\n\t\t\t(\"Background\", _(\"Background\")),\n\t\t\t(\"Call Name\", _(\"Call Name\")),\n\t\t\t(\"Call Number\", _(\"Call Number\")),\n\t\t\t(\"Children\", _(\"Children\")),\n\t\t\t(\"Church Parish\", _(\"Church Parish\")),\n\t\t\t(\"Citation\", _(\"Citation\")),\n\t\t\t(\"Citations\", _(\"Citations\")),\n\t\t\t(\"City\", _(\"City\")),\n\t\t\t(\"Click on the map to show it full-screen\", _(\"Click on the map to show it full-screen\")),\n\t\t\t(\"Configuration\", _(\"Configuration\")),\n\t\t\t(\"Country\", _(\"Country\")),\n\t\t\t(\"County\", _(\"County\")),\n\t\t\t(\"Date\", _(\"Date\")),\n\t\t\t(\"Descendants\", _(\"Descendants\")),\n\t\t\t(\"Description\", _(\"Description\")),\n\t\t\t(\"Event\", _(\"Event\")),\n\t\t\t(\"Events\", _(\"Events\")),\n\t\t\t(\"Expand\", _(\"Expand\")),\n\t\t\t(\"Families Index\", _(\"Families Index\")),\n\t\t\t(\"Family Nick Name\", _(\"Family Nick Name\")),\n\t\t\t(\"Father\", _(\"Father\")),\n\t\t\t(\"Female\", _(\"Female\")),\n\t\t\t(\"File ready\", _(\"File ready\")),\n\t\t\t(\"Gender\", _(\"Gender\")),\n\t\t\t(\"Graph help\", _(\"Graph help\")),\n\t\t\t(\"Latitude\", _(\"Latitude\")),\n\t\t\t(\"Link\", _(\"Link\")),\n\t\t\t(\"Loading...\", _(\"Loading...\")),\n\t\t\t(\"Locality\", _(\"Locality\")),\n\t\t\t(\"Location\", _(\"Location\")),\n\t\t\t(\"Longitude\", _(\"Longitude\")),\n\t\t\t(\"Male\", _(\"Male\")),\n\t\t\t(\"Map\", _(\"Map\")),\n\t\t\t(\"Maximize\", _(\"Maximize\")),\n\t\t\t(\"Media found:\", _(\"Media found:\")),\n\t\t\t(\"Media Index\", _(\"Media Index\")),\n\t\t\t(\"Media Type\", _(\"Media Type\")),\n\t\t\t(\"Media\", _(\"Media\")),\n\t\t\t(\"Mother\", _(\"Mother\")),\n\t\t\t(\"Name\", _(\"Name\")),\n\t\t\t(\"Nick Name\", _(\"Nick Name\")),\n\t\t\t(\"No data available in table\", _(\"No data available in table\")),\n\t\t\t(\"No matching records found\", _(\"No matching records found\")),\n\t\t\t(\"No matching surname.\", _(\"No matching surname.\")),\n\t\t\t(\"None.\", _(\"None.\")),\n\t\t\t(\"Notes\", _(\"Notes\")),\n\t\t\t(\"OK\", _(\"OK\")),\n\t\t\t(\"Parents\", _(\"Parents\")),\n\t\t\t(\"Path\", _(\"Path\")),\n\t\t\t(\"Person page\", _(\"Person page\")),\n\t\t\t(\"Person to search for\", _(\"Person to search for\")),\n\t\t\t(\"Person\", _(\"Person\")),\n\t\t\t(\"Persons found:\", _(\"Persons found:\")),\n\t\t\t(\"Persons Index\", _(\"Persons Index\")),\n\t\t\t(\"Phone\", _(\"Phone\")),\n\t\t\t(\"Place\", _(\"Place\")),\n\t\t\t(\"Places found:\", _(\"Places found:\")),\n\t\t\t(\"Places Index\", _(\"Places Index\")),\n\t\t\t(\"Postal Code\", _(\"Postal Code\")),\n\t\t\t(\"Preparing file ...\", _(\"Preparing file ...\")),\n\t\t\t(\"Processing...\", _(\"Processing...\")),\n\t\t\t(\"References\", _(\"References\")),\n\t\t\t(\"Relationship to Father\", _(\"Relationship to Father\")),\n\t\t\t(\"Relationship to Mother\", _(\"Relationship to Mother\")),\n\t\t\t(\"Relationship\", _(\"Relationship\")),\n\t\t\t(\"Repositories\", _(\"Repositories\")),\n\t\t\t(\"Repository\", _(\"Repository\")),\n\t\t\t(\"Restore\", _(\"Restore\")),\n\t\t\t(\"Save tree as file\", _(\"Save tree as file\")),\n\t\t\t(\"Search:\", _(\"Search:\")),\n\t\t\t(\"Select the background color scheme\", _(\"Select the background color scheme\")),\n\t\t\t(\"Select the children distribution (fan charts only)\", _(\"Select the children distribution (fan charts only)\")),\n\t\t\t(\"Select the number of ascending generations\", _(\"Select the number of ascending generations\")),\n\t\t\t(\"Select the number of descending generations\", _(\"Select the number of descending generations\")),\n\t\t\t(\"Select the parents distribution (fan charts only)\", _(\"Select the parents distribution (fan charts only)\")),\n\t\t\t(\"Select the shape of graph\", _(\"Select the shape of graph\")),\n\t\t\t(\"Select the type of graph\", _(\"Select the type of graph\")),\n\t\t\t(\"Several matches.
Precise your search or choose in the lists below.\", _(\"Several matches.
Precise your search or choose in the lists below.\")),\n\t\t\t(\"Show _MENU_ entries\", _(\"Show _MENU_ entries\")),\n\t\t\t(\"Showing 0 to 0 of 0 entries\", _(\"Showing 0 to 0 of 0 entries\")),\n\t\t\t(\"Showing _START_ to _END_ of _TOTAL_ entries\", _(\"Showing _START_ to _END_ of _TOTAL_ entries\")),\n\t\t\t(\"Siblings\", _(\"Siblings\")),\n\t\t\t(\"Source\", _(\"Source\")),\n\t\t\t(\"Sources found:\", _(\"Sources found:\")),\n\t\t\t(\"Sources Index\", _(\"Sources Index\")),\n\t\t\t(\"Sources\", _(\"Sources\")),\n\t\t\t(\"Spouses\", _(\"Spouses\")),\n\t\t\t(\"State/ Province\", _(\"State/ Province\")),\n\t\t\t(\"Street\", _(\"Street\")),\n\t\t\t(\"Surnames Index\", _(\"Surnames Index\")),\n\t\t\t(\"SVG tree children distribution\", _(\"SVG tree children distribution\")),\n\t\t\t(\"SVG tree graph shape\", _(\"SVG tree graph shape\")),\n\t\t\t(\"SVG tree graph type\", _(\"SVG tree graph type\")),\n\t\t\t(\"SVG tree parents distribution\", _(\"SVG tree parents distribution\")),\n\t\t\t(\"There is no matching name.\", _(\"There is no matching name.\")),\n\t\t\t(\"Title\", _(\"Title\")),\n\t\t\t(\"Type\", _(\"Type\")),\n\t\t\t(\"Unknown\", _(\"Unknown\")),\n\t\t\t(\"Use the search box above in order to find a person.
Women are listed with their maiden name.\", _(\"Use the search box above in order to find a person.
Women are listed with their maiden name.\")),\n\t\t\t(\"Used for family\", _(\"Used for family\")),\n\t\t\t(\"Used for media\", _(\"Used for media\")),\n\t\t\t(\"Used for person\", _(\"Used for person\")),\n\t\t\t(\"Used for place\", _(\"Used for place\")),\n\t\t\t(\"Used for source\", _(\"Used for source\")),\n\t\t\t(\"Value\", _(\"Value\")),\n\t\t\t(\"Web Link\", _(\"Web Link\")),\n\t\t\t(\"Web Links\", _(\"Web Links\")),\n\t\t\t(\"Without surname\", _(\"Without surname\")),\n\t\t\t(\"Zoom in\", _(\"Zoom in\")),\n\t\t\t(\"Zoom out\", _(\"Zoom out\")),\n\t\t\t):\n\t\t\tsw.write(sep + \"\\\"\" + script_escape(s) + \"\\\": \\\"\" + script_escape(translated) + \"\\\"\")\n\t\t\tsep = \",\\n\"\n\t\tfor (code, translated, s) in EventType._DATAMAP:\n\t\t\tsw.write(sep + \"\\\"\" + script_escape(s) + \"\\\": \\\"\" + script_escape(translated) + \"\\\"\")\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n};\\n\")\n\t\tsw.write(\n\t\t\t(\"URLTYPE_UNKNOWN = %i;\\n\" % UrlType.UNKNOWN) +\n\t\t\t(\"URLTYPE_CUSTOM = %i;\\n\" % UrlType.CUSTOM) +\n\t\t\t(\"URLTYPE_EMAIL = %i;\\n\" % UrlType.EMAIL) +\n\t\t\t(\"URLTYPE_WEB_HOME = %i;\\n\" % UrlType.WEB_HOME) +\n\t\t\t(\"URLTYPE_WEB_SEARCH = %i;\\n\" % UrlType.WEB_SEARCH) +\n\t\t\t(\"URLTYPE_WEB_FTP = %i;\\n\" % UrlType.WEB_FTP))\n\t\tself.update_file(\"dwr_conf.js\", sw.getvalue(), \"UTF-8\")\n\n\n\tdef _export_html_page(self, filename, title, cmd, menu, scripts = [], styles = []):\n\t\t\"\"\"\n\t\tGenerate an HTML page\n\t\t@param filename: output HTML file name\n\t\t@param title: Title of the page (prepended to L{self.title}\n\t\t@param cmd: Javascript code that generates the page\n\t\t@param menu: Whether to put a menu on the page\n\t\t@param scripts: Scripts embedded in the page\n\t\t@param styles: CSS stylesheets embedded in the page\n\t\t\"\"\"\n\t\t(page, head, body) = self.write_header(title, menu)\n\t\tfor style in styles:\n\t\t\thead += Html(\"link\", rel = \"stylesheet\", href = style, type = \"text/css\")\n\t\tfor script in scripts:\n\t\t\thead += Html(\"script\", language = \"javascript\", src = script, charset = self.encoding)\n\t\tbody += Html(\"script\", cmd, language = \"javascript\")\n\t\tself.update_file(filename, html_text(page))\n\n\n\tdef _export_custom_page(self, filename, title, menu, note):\n\t\t\"\"\"\n\t\tGenerate an HTML custom page\n\t\t@param filename: output HTML file name\n\t\t@param title: Title of the page (prepended to L{self.title}\n\t\t@param menu: Whether to put a menu on the page\n\t\t@param note: note that contains the page contents\n\t\t\"\"\"\n\t\t(page, head, body) = self.write_header(title, menu)\n\t\tif (note):\n\t\t\thtml = self.get_note_format(self.database.get_note_from_gramps_id(note))\n\t\t\tbody += self.replace_note_fields(html)\n\t\tself.update_file(filename, html_text(page))\n\n\n\tdef write_header(self, title, menu):\n\t\t\"\"\"\n\t\tGenerate an HTML page header\n\t\t@param title: Title of the page (prepended to L{self.title}\n\t\t@param menu: Whether to put a menu on the page\n\t\t@return: List of L{Html} objects as follows: (page, head, body)\n\t\t\"\"\"\n\t\t\"\"\"\n\t\tNote. 'title' is used as currentsection in the navigation links and\n\t\tas part of the header title.\n\t\t\"\"\"\n\t\t# Begin each html page...\n\t\txmllang = xml_lang()\n\t\t(page, head, body) = Html.page('%s - %s' % (\n\t\t\t\thtml_escape(title),\n\t\t\t\thtml_escape(self.title.strip()),\n\t\t\t),\n\t\t\tself.encoding, xmllang)\n\t\t# Header constants\n\t\thead += Html(\"meta\", attr = 'name=\"viewport\" content=\"width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=1\"')\n\t\thead += Html(\"meta\", attr = 'name=\"apple-mobile-web-app-capable\" content=\"yes\"')\n\t\thead += Html(\"meta\", attr = 'name=\"generator\" content=\"%s %s %s\"' % (PROGRAM_NAME, VERSION, URL_HOMEPAGE))\n\t\thead += Html(\"meta\", attr = 'name=\"author\" content=\"%s\"' % self.author)\n\t\t# Create script and favicon links\n\t\thead += Html(\"link\", type = \"image/x-icon\", href = \"data/favicon.ico\", rel = \"shortcut icon\")\n\t\thead += Html(\"script\", language = 'javascript', src = 'data/dwr_start.js')\n\t\t# Disable menu\n\t\tif (not menu):\n\t\t\tbody.attr = \"class='dwr-menuless'\"\n\t\treturn(page, head, body)\n\n\n\tdef get_header_footer_notes(self, item):\n\t\t\"\"\"\n\t\tGive the header/footer note converted to an HTML string\n\t\t@param item: Option giving the note. See options \"footernote\" and \"headernote\"\n\t\t@return: text of the note\n\t\t@rtype: L{String}\n\t\t\"\"\"\n\t\tnote = self.options[item]\n\t\tif (note):\n\t\t\thtml = self.get_note_format(self.database.get_note_from_gramps_id(note))\n\t\t\treturn(self.replace_note_fields(html))\n\t\treturn(\"\")\n\n\n\tdef replace_note_fields(self, html):\n\t\t\"\"\"\n\t\tModify the notes for HTML pages generation\n\t\tThis allow to add special features or computed data in the pages\n\t\t@param html: Note converted to HTML string\n\t\t@return: Modified string\n\t\t\"\"\"\n\t\ttext = html_text(html)\n\t\t# __SEARCH_FORM__ is replaced by a search form\n\t\ttext = text.replace(\"__SEARCH_FORM__\",\n\t\t\t\"\\n\")\n\t\t# __NB_INDIVIDUALS__ is replaced by the number of persons\n\t\t# __NB_FAMILIES__ is replaced by the number of families\n\t\t# __NB_MEDIA__ is replaced by the number of media objects\n\t\t# __NB_SOURCES__ is replaced by the number of sources\n\t\t# __NB_REPOSITORIES__ is replaced by the number of repositories\n\t\t# __NB_PLACES__ is replaced by the number of places\n\t\ttext = text.replace(\"__NB_INDIVIDUALS__\", str(len(self.obj_dict[Person])))\n\t\ttext = text.replace(\"__NB_FAMILIES__\", str(len(self.obj_dict[Family])))\n\t\ttext = text.replace(\"__NB_MEDIA__\", str(len(self.obj_dict[MediaObject])))\n\t\ttext = text.replace(\"__NB_SOURCES__\", str(len(self.obj_dict[Source])))\n\t\ttext = text.replace(\"__NB_REPOSITORIES__\", str(len(self.obj_dict[Repository])))\n\t\ttext = text.replace(\"__NB_PLACES__\", str(len(self.obj_dict[Place])))\n\t\t# __MEDIA___ is replaced by the media with gramps ID \n\t\t# __THUMB___ is replaced by the thumbnail of the media with gramps ID \n\t\ttext2 = text\n\t\tfor mo in re.finditer(r\"__(MEDIA|THUMB)_(.*?)__\", text):\n\t\t\tgid = mo.group(2)\n\t\t\tmedia = self.database.get_object_from_gramps_id(gid)\n\t\t\tif (not media): continue\n\t\t\ttm = mo.group(1)\n\t\t\tif (tm == \"THUMB\"):\n\t\t\t\tpath = self.copy_thumbnail(media)\n\t\t\t\ttext2 = (\n\t\t\t\t\ttext2[ : -(len(text) - mo.start(0))] +\n\t\t\t\t\t\"![](\" + path + \")
\" +\n\t\t\t\t\ttext2[-(len(text) - mo.end(0)) : ])\n\t\t\telse:\n\t\t\t\tpath = self.get_media_web_path(media)\n\t\t\t\ttext2 = (\n\t\t\t\t\ttext2[ : -(len(text) - mo.start(0))] +\n\t\t\t\t\t\"\" +\n\t\t\t\t\ttext2[-(len(text) - mo.end(0)) : ])\n\t\ttext = text2\n\t\t# __EXPORT_DATE__ is replaced by the current date\n\t\t# __GRAMPS_VERSION__ is replaced by the Gramps version\n\t\t# __GRAMPS_HOMEPAGE__ is replaced by the Gramps homepage\n\t\ttext = text.replace(\"__EXPORT_DATE__\", format_date(Today()))\n\t\ttext = text.replace(\"__GRAMPS_VERSION__\", VERSION)\n\t\ttext = text.replace(\"__GRAMPS_HOMEPAGE__\", \"Gramps\")\n\t\t# Relative URL are managed\n\t\ttext = text.replace(\"relative://relative.\", \"\")\n\t\t# __HOME_PERSON_NAME__ is replaced by the home person name\n\t\t# __HOME_PERSON_URL__ is replaced by the home person page URL\n\t\t# center_person = self.database.get_person_from_gramps_id(self.options['pid'])\n\t\t# if (center_person and (center_person.handle in self.obj_dict[Person])):\n\t\t\t# person_name = self.get_name(center_person)\n\t\t\t# person_url = \"person.html?idx=%i\" % self.obj_dict[Person][center_person.handle][OBJDICT_INDEX]\n\t\t\t# text = text.replace(\"__HOME_PERSON_NAME__\", person_name)\n\t\t\t# text = text.replace(\"__HOME_PERSON_URL__\", person_url)\n\t\treturn(text)\n\n\t\t\n\tdef get_copyright_license(self):\n\t\t\"\"\"\n\t\twill return either the text or image of the copyright license\n\t\t\"\"\"\n\t\ttext = \"\"\n\t\tif (self.copyright == 0):\n\t\t\tif self.author:\n\t\t\t\tyear = Today().get_year()\n\t\t\t\ttext = \"© %(year)d %(person)s
\" % {\n\t\t\t\t\t'person' : self.author,\n\t\t\t\t\t'year' : year}\n\t\telif (0 < self.copyright < len(_CC)):\n\t\t\turl = \"data/somerights20.gif\"\n\t\t\ttext = \"\" + _CC[self.copyright] % {'gif_fname' : url} + \"
\"\n\t\t# return text or image to its callers\n\t\treturn(text)\n\n\n\tdef update_file(self, fout, txt, encoding = None):\n\t\t\"\"\"\n\t\tWrite a string in a file.\n\t\tThe file is not overwritten if the file exists and already contains the string \n\t\t@param fout: output file name\n\t\t@param txt: file contents\n\t\t@param encoding: encoding as passed to Python function codecs.open \n\t\t\"\"\"\n\t\tif (encoding is None): encoding = self.encoding\n\t\tf = os.path.join(self.target_path, fout)\n\t\tself.created_files.append(f)\n\t\tif (os.path.exists(f)):\n\t\t\ttry:\n\t\t\t\tfr = codecs.open(f, \"r\", encoding = encoding, errors=\"xmlcharrefreplace\")\n\t\t\t\ttxtr = fr.read()\n\t\t\t\tfr.close()\n\t\t\t\tif (txtr == txt):\n\t\t\t\t\tlog.info(\"File \\\"%s\\\" not overwritten (identical)\" % fout)\n\t\t\t\t\treturn\n\t\t\texcept:\n\t\t\t\tpass\n\t\tfw = codecs.open(f, \"w\", encoding = encoding, errors=\"xmlcharrefreplace\")\n\t\tfw.write(txt)\n\t\tfw.close()\n\t\tlog.info(\"File \\\"%s\\\" generated\" % fout)\n\n\tdef copy_file(self, from_fname, to_fname, to_dir=\"\"):\n\t\t\"\"\"\n\t\tCopy a file from a source to a (report) destination.\n\t\tIf to_dir is not present and if the target is not an archive,\n\t\tthen the destination directory will be created.\n\n\t\tNormally 'to_fname' will be just a filename, without directory path.\n\n\t\t'to_dir' is the relative path name in the destination root. It will\n\t\tbe prepended before 'to_fname'.\n\t\t\n\t\tThe file is not copied if the contents of 'from_fname' 'to_fname' are identical\n\t\t\"\"\"\n\t\t# log.debug(\"copying '%s' to '%s/%s'\" % (from_fname, to_dir, to_fname))\n\t\tdest = os.path.join(self.target_path, to_dir, to_fname)\n\t\tdestdir = os.path.dirname(dest)\n\t\tif not os.path.isdir(destdir):\n\t\t\tos.makedirs(destdir)\n\n\t\tif from_fname != dest:\n\t\t\ttry:\n\t\t\t\tdest_temp = dest + \".temp\"\n\t\t\t\tshutil.copyfile(from_fname, dest_temp)\n\t\t\t\tself.created_files.append(dest)\n\t\t\t\tif (os.path.exists(dest)):\n\t\t\t\t\tfr = codecs.open(dest, \"rb\")\n\t\t\t\t\told_bytes = fr.read()\n\t\t\t\t\tfr.close()\n\t\t\t\t\tfr = codecs.open(dest_temp, \"rb\")\n\t\t\t\t\tnew_bytes = fr.read()\n\t\t\t\t\tfr.close()\n\t\t\t\t\tif (old_bytes == new_bytes):\n\t\t\t\t\t\tos.remove(dest_temp)\n\t\t\t\t\t\tlog.info(\"File \\\"%s\\\" not overwritten (identical)\" % dest)\n\t\t\t\t\t\treturn\n\t\t\t\t\tos.remove(dest)\n\t\t\t\tos.rename(dest_temp, dest)\n\t\t\t\tlog.info(\"File \\\"%s\\\" generated\" % dest)\n\t\t\texcept:\n\t\t\t\tlog.warning(_(\"Copying error: %(error)s\") % {\"error\": sys.exc_info()[1]})\n\t\t\t\tlog.error(_(\"Impossible to copy \\\"%(src)s\\\" to \\\"%(dst)s\\\"\") % {\"src\": from_fname, \"dst\": to_fname})\n\t\telif self.warn_dir:\n\t\t\tself.user.warn(\n\t\t\t\t_(\"Possible destination error\") + \"\\n\" +\n\t\t\t\t_(\"You appear to have set your target directory \"\n\t\t\t\t \"to a directory used for data storage. This \"\n\t\t\t\t \"could create problems with file management. \"\n\t\t\t\t \"It is recommended that you consider using \"\n\t\t\t\t \"a different directory to store your generated \"\n\t\t\t\t \"web pages.\"))\n\t\t\tself.warn_dir = False\n\n\n\n\tdef copy_template_files(self):\n\t\t\"\"\"\n\t\tCopy the template files to the target directory\n\t\t\n\t\tThe template files are:\n\t\t - The files contained in the chosen template directory,\n\t\t - The files contained in the default template directory, unless they are also present in the chosen template directory\n\t\t\"\"\"\n\t\t# Get template path\n\t\ttmpl_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"templates\", WEB_TEMPLATE_LIST[self.template][0])\n\t\tdefault_tmpl_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"templates\", WEB_TEMPLATE_LIST[0][0])\n\t\ttry:\n\t\t\t# Copy template files\n\t\t\tself.copy_template_files_sub(tmpl_path)\n\t\t\t# Copy default template files if not already copied\n\t\t\tself.copy_template_files_sub(default_tmpl_path)\n\t\texcept:\n\t\t\tlog.error(_(\"Unable to copy web site template files from \\\"%(path)s\\\"\") % {\"path\": tmpl_path})\n\t\t\traise\n\t\t\t\n\tdef copy_template_files_sub(self, tmpl_path):\n\t\t\"\"\"\n\t\tCopy the template files from L{tmpl_path} to the target directory\n\t\tThe files already present in the target directory are not overwritten\n\t\t@param tmpl_path: template directory, as listed in L{WEB_TEMPLATE_LIST}\n\t\t\"\"\"\n\t\tfor (root, dirnames, files) in os.walk(tmpl_path):\n\t\t\tdst_path = root.replace(tmpl_path, self.target_path, 1)\n\t\t\t# Create sub-directories\n\t\t\tfor dirname in dirnames:\n\t\t\t\t# Remove files that have the same name as directories\n\t\t\t\tdstdirname = os.path.join(dst_path, dirname)\n\t\t\t\tif (os.path.isfile(dstdirname) or os.path.islink(dstdirname)):\n\t\t\t\t\tos.remove(dstdirname)\n\t\t\t\t# Create directory if needed\n\t\t\t\tif (not os.path.isdir(dstdirname)):\n\t\t\t\t\tos.mkdir(dstdirname)\n\t\t\t# Copy files\n\t\t\tfor file in files:\n\t\t\t\tsrc = os.path.join(root, file)\n\t\t\t\tdst = os.path.join(dst_path, file)\n\t\t\t\tif (dst in self.created_files):\n\t\t\t\t\t# File was already copied\n\t\t\t\t\tcontinue\n\t\t\t\tif (os.path.isfile(dst)):\n\t\t\t\t\t# If file already exists, check dates\n\t\t\t\t\tstat_src = os.stat(src)\n\t\t\t\t\tstat_dst = os.stat(dst)\n\t\t\t\t\t# If target file is newer, do not overwrite => If target file is older, delete it\n\t\t\t\t\tif (stat_src.st_mtime >= stat_dst.st_mtime):\n\t\t\t\t\t\tos.remove(dst)\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog.info(_(\"Keeping \\\"%(dst)s\\\" (newer than \\\"%(src)s\\\")\") % {'src': src, 'dst': dst})\n\t\t\t\tif (not os.path.exists(dst)):\n\t\t\t\t\tshutil.copyfile(src, dst)\n\t\t\t\t\tlog.info(_(\"Copying \\\"%(src)s\\\" to \\\"%(dst)s\\\"\") % {'src': src, 'dst': dst})\n\t\t\t\tself.created_files.append(dst)\n\n\n\tdef create_archive(self):\n\t\t\"\"\"\n\t\tCreate an archive of the whole web site\n\t\t\"\"\"\n\t\tif (not self.options['archive']): return\n\t\t\n\t\t# Get archive path and type\n\t\tarch_path = self.options['archive_file']\n\t\text = os.path.splitext(arch_path)[1].lower()\n\t\tif (ext not in [\".zip\", \".tgz\"]):\n\t\t\tarch_path += \".zip\"\n\t\t\text = \".zip\"\n\t\t\n\t\tif (os.path.isdir(arch_path)):\n\t\t\tlog.error(_('Invalid file name'))\n\t\t\tlog.error(_('The archive file must be a file, not a directory'))\n\t\t\treturn\n\t\t\t\n\t\t# Get base path for the files inside the archive\n\t\tbasepath = os.path.splitext(os.path.basename(arch_path))[0]\n\t\t\n\t\tif (ext == \".zip\"):\n\t\t\ttry:\n\t\t\t\tfzip = zipfile.ZipFile(arch_path, \"w\", zipfile.ZIP_DEFLATED, True)\n\t\t\texcept:\n\t\t\t\tlog.error(_(\"Unable to overwrite archive file \\\"%(path)s\\\"\") % {\"path\": arch_path})\n\t\t\t\traise\n\t\t\tfor file in self.created_files:\n\t\t\t\tarc_rel_path = file.replace(self.target_path, basepath, 1)\n\t\t\t\tif (sys.version_info[0] < 3):\n\t\t\t\t\tfile = file.encode(\"cp437\")\n\t\t\t\t\tarc_rel_path = arc_rel_path.encode(\"cp437\")\n\t\t\t\ttry:\n\t\t\t\t\tfzip.write(file, arc_rel_path)\n\t\t\t\texcept:\n\t\t\t\t\tlog.error(_(\"Unable to add file \\\"%(file)s\\\" to archive \\\"%(archive)s\\\"\") % {\"file\": file, \"archive\": arch_path})\n\t\t\t\t\traise\n\t\t\tfzip.close()\n\n\t\tif (ext == \".tgz\"):\n\t\t\ttry:\n\t\t\t\ttgz = tarfile.open(arch_path, \"w:gz\")\n\t\t\texcept:\n\t\t\t\tlog.error(_(\"Unable to overwrite archive file \\\"%(path)s\\\"\") % {\"path\": arch_path})\n\t\t\t\traise\n\t\t\tfor file in self.created_files:\n\t\t\t\tarc_rel_path = file.replace(self.target_path, basepath, 1)\n\t\t\t\ttry:\n\t\t\t\t\ttgz.add(file, arc_rel_path)\n\t\t\t\texcept:\n\t\t\t\t\tlog.error(_(\"Unable to add file \\\"%(file)s\\\" to archive \\\"%(archive)s\\\"\") % {\"file\": path, \"archive\": arch_path})\n\t\t\t\t\traise\n\t\t\ttgz.close()\n\n\n\tdef build_link(self, prop, handle, obj_class):\n\t\t\"\"\"\n\t\tBuild a link to an item.\n\t\t\n\t\tThis function is used when converting a Gramps note with hyperlinks into an HTML string\n\t\t\"\"\"\n\t\tif prop == \"gramps_id\":\n\t\t\tif obj_class in self.database.get_table_names():\n\t\t\t\tobj = self.database.get_table_metadata(obj_class)[\"gramps_id_func\"](handle)\n\t\t\t\tif obj:\n\t\t\t\t\thandle = obj.get_handle()\n\t\t\t\telse:\n\t\t\t\t\traise AttributeError(\"gramps_id '%s' not found in '%s'\" % handle, obj_class)\n\t\t\telse:\n\t\t\t\traise AttributeError(\"invalid gramps_id lookup in table name '%s'\" % obj_class)\n\t\thref = \"search.html\"\n\t\ti = -1\n\t\tif (obj_class == \"Person\"):\n\t\t\thref = \"person.html\"\n\t\t\tif (handle in self.obj_dict[Person]):\n\t\t\t\thref = \"%s?idx=%i\" % (href, self.obj_dict[Person][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Family\"):\n\t\t\thref = \"family.html\"\n\t\t\tif (handle in self.obj_dict[Family]):\n\t\t\t\thref = \"%s?fdx=%i\" % (href, self.obj_dict[Family][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Source\"):\n\t\t\thref = \"source.html\"\n\t\t\tif (handle in self.obj_dict[Source]):\n\t\t\t\thref = \"%s?sdx=%i\" % (href, self.obj_dict[Source][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Citation\"):\n\t\t\thref = \"source.html\"\n\t\t\tsource_handle = self.database.get_citation_from_handle(handle).get_reference_handle()\n\t\t\tif (source_handle in self.obj_dict[Source]):\n\t\t\t\thref = \"%s?sdx=%i\" % (href, self.obj_dict[Source][source_handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Repository\"):\n\t\t\thref = \"repository.html\"\n\t\t\tif (handle in self.obj_dict[Repository]):\n\t\t\t\thref = \"%s?rdx=%i\" % (href, self.obj_dict[Repository][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Media\"):\n\t\t\thref = \"media.html\"\n\t\t\tif (handle in self.obj_dict[MediaObject]):\n\t\t\t\thref = \"%s?mdx=%i\" % (href, self.obj_dict[MediaObject][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Place\"):\n\t\t\thref = \"place.html\"\n\t\t\tif (handle in self.obj_dict[Place]):\n\t\t\t\thref = \"%s?pdx=%i\" % (href, self.obj_dict[Place][handle][OBJDICT_INDEX])\n\t\telse:\n\t\t\tprint(_(\"DynamicWebReport ignoring link type '%(class)s'\") % {\"class\": obj_class})\n\t\treturn(href)\n\n\n\tdef _data_bkref_index(self, obj_class, obj_handle, ref_class):\n\t\t\"\"\"\n\t\tBuild a list of object indexes referencing a given object\n\t\t@param obj_class: Referenced object class\n\t\t@param obj_handle: Referenced object handle\n\t\t@param ref_class: Class of the refencing objects\n\t\t@return: String representing the Javascript Array of the object indexes (of class L{ref_class}) referencing a given object (L{obj_class}, L{obj_handle})\n\t\t\"\"\"\n\t\tbkref_list = self.bkref_dict[obj_class][obj_handle]\n\t\tif (not bkref_list): return (\"[]\")\n\t\t# Sort by referenced object\n\t\tbkref_list = sorted(bkref_list, key = lambda bkref: self.obj_dict[bkref[BKREF_CLASS]][bkref[BKREF_HANDLE]][OBJDICT_NAME])\n\t\t# Filter bkref_list (keep only ref_class) and remove duplicates\n\t\tseen = set()\n\t\tbkref_list = [bkref_handle\n\t\t\tfor (bkref_class, bkref_handle, media_ref) in bkref_list\n\t\t\tif (bkref_class == ref_class and not (bkref_handle in seen or seen.add(bkref_handle)))]\n\t\treturn(\"[\" +\n\t\t\t\",\".join([str(self.obj_dict[ref_class][bkref_handle][OBJDICT_INDEX]) for bkref_handle in bkref_list]) +\n\t\t\t\"]\")\n\n\n\tdef _data_repo_backref_index(self, repo, ref_class):\n\t\t\"\"\"\n\t\tBuild a list of object referencing a given repository, in the form:\n\t\t - object index (in table 'I', 'F', 'S')\n\t\t - media type\n\t\t - call number\n\t\t - notes of the repository reference\n\t\t@param repo: Referenced repository\n\t\t@param ref_class: Class of the refencing objects\n\t\t@return: String representing the Javascript Array of the references to L{repo}\n\t\t\"\"\"\n\t\trepo_handle = repo.get_handle()\n\t\tif (repo_handle not in self.obj_dict[Repository]): return(\"[]\")\n\t\tbkref_list = self.bkref_dict[Repository][repo_handle]\n\t\tif (not bkref_list): return (\"[]\")\n\t\tsep = \"\"\n\t\ttxt = \"[\"\n\t\tfor (bkref_class, bkref_handle, repo_ref) in bkref_list:\n\t\t\tif (ref_class != bkref_class): continue\n\t\t\ti = self.obj_dict[ref_class][bkref_handle][OBJDICT_INDEX]\n\t\t\tobject = self.get_object_from_handle(bkref_class, bkref_handle)\n\t\t\ttxt += sep + self._data_repo_ref(repo_ref, i)\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_media_backref_index(self, media, ref_class):\n\t\t\"\"\"\n\t\tBuild a list of object referencing a given media, in the form:\n\t\t - object index (in table 'I', 'F', 'S')\n\t\t - media thumbnail path\n\t\t - [x1, y1, x2, y2] of the media reference\n\t\t - notes of the media reference\n\t\t - list of the media reference source citations index (in table 'C')\n\t\t@param media: Referenced repository\n\t\t@param ref_class: Class of the refencing objects\n\t\t@return: String representing the Javascript Array of the references to L{media}\n\t\t\"\"\"\n\t\tmedia_handle = media.get_handle()\n\t\tif (media_handle not in self.obj_dict[MediaObject]): return(\"[]\")\n\t\tbkref_list = self.bkref_dict[MediaObject][media_handle]\n\t\tif (not bkref_list): return (\"[]\")\n\t\tsep = \"\"\n\t\ttxt = \"[\"\n\t\tfor (bkref_class, bkref_handle, media_ref) in bkref_list:\n\t\t\tif (ref_class != bkref_class): continue\n\t\t\ti = self.obj_dict[ref_class][bkref_handle][OBJDICT_INDEX]\n\t\t\tobject = self.get_object_from_handle(bkref_class, bkref_handle)\n\t\t\ttxt += sep + self._data_media_ref(media_ref, i)\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef get_object_from_handle(self, class_, handle):\n\t\t\"\"\"\n\t\tGet an object from its handle and class\n\t\t\"\"\"\n\t\tobject = None\n\t\tif (class_ == Person):\n\t\t\tobject = self.database.get_person_from_handle(handle)\n\t\telif (class_ == Family):\n\t\t\tobject = self.database.get_family_from_handle(handle)\n\t\telif (class_ == Event):\n\t\t\tobject = self.database.get_event_from_handle(handle)\n\t\telif (class_ == Source):\n\t\t\tobject = self.database.get_source_from_handle(handle)\n\t\telif (class_ == Citation):\n\t\t\tobject = self.database.get_citation_from_handle(handle)\n\t\telif (class_ == Place):\n\t\t\tobject = self.database.get_place_from_handle(handle)\n\t\telif (class_ == Repository):\n\t\t\tobject = self.database.get_repository_from_handle(handle)\n\t\treturn(object)\n\n\n\t##############################################################################################\n\t################################################################################## GENDEX data\n\t##############################################################################################\n\n\tdef build_gendex(self, ind_list):\n\t\tif (not self.inc_gendex): return\n\t\tfp_gendex = StringIO()\n\t\tfor person_handle in ind_list:\n\t\t\tself.write_gendex(fp_gendex, person_handle)\n\t\tself.update_file(\"gendex.txt\", fp_gendex.getvalue())\n\n\tdef write_gendex(self, fp, person_handle):\n\t\t\"\"\"\n\t\tReference|SURNAME|given name /SURNAME/|date of birth|place of birth|date of death|place of death|\n\t\t* field 1: file name of web page referring to the individual\n\t\t* field 2: surname of the individual\n\t\t* field 3: full name of the individual\n\t\t* field 4: date of birth or christening (optional)\n\t\t* field 5: place of birth or christening (optional)\n\t\t* field 6: date of death or burial (optional)\n\t\t* field 7: place of death or burial (optional) \n\t\t\"\"\"\n\t\tif (not(person_handle and (person_handle in self.obj_dict[Person]))): return\n\t\tperson = self.database.get_person_from_handle(person_handle)\n\t\turl = \"person.html?idx=%i\" % self.obj_dict[Person][person_handle][OBJDICT_INDEX]\n\t\tsurname = person.get_primary_name().get_surname()\n\t\tfullname = person.get_primary_name().get_gedcom_name()\n\t\t\n\t\t# get birth info:\n\t\t(dob, pob) = self.get_gendex_data(person.get_birth_ref())\n\t\t\n\t\t# get death info:\n\t\t(dod, pod) = self.get_gendex_data(person.get_death_ref())\n\t\tfp.write(\n\t\t\t'|'.join((url, surname, fullname, dob, pob, dod, pod)) + '|\\n')\n\n\tdef get_gendex_data(self, event_ref):\n\t\t\"\"\"\n\t\tGiven an event, return the date and place a strings\n\t\t\"\"\"\n\t\tdoe = \"\" # date of event\n\t\tpoe = \"\" # place of event\n\t\tif (event_ref):\n\t\t\tevent = self.database.get_event_from_handle(event_ref.ref)\n\t\t\tif (event):\n\t\t\t\tdate = event.get_date_object()\n\t\t\t\tdoe = format_date(date, gedcom = True)\n\t\t\t\tif (event.get_place_handle()):\n\t\t\t\t\tplace_handle = event.get_place_handle()\n\t\t\t\t\tif (place_handle):\n\t\t\t\t\t\tplace = self.database.get_place_from_handle(place_handle)\n\t\t\t\t\t\tif (place):\n\t\t\t\t\t\t\tif (DWR_VERSION_412):\n\t\t\t\t\t\t\t\tpoe = _pd.display(self.database, place)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tpoe = place.get_title()\n\t\treturn(doe, poe)\n\t\t\n\n\t##############################################################################################\n\t##############################################################################################\n\t#\n\t# Objects dictionaries construction\n\t#\n\t##############################################################################################\n\t##############################################################################################\n\n\tdef _build_obj_dict(self):\n\t\t\"\"\"\n\t\tConstruct the dictionaries of objects to be included in the reports. There\n\t\tare two dictionaries, which have the same structure: they are two level\n\t\tdictionaries,the first key is the class of object (e.g. gen.lib.Person).\n\t\tThe second key is the handle of the object.\n\n\t\tFor the obj_dict, the value is a tuple containing:\n\t\t - the gramps_id\n\t\t - the text name for the object\n\t\t - the index (number starting at 0)\n\n\t\tFor the bkref_dict, the value is a tuple containing:\n\t\t - the class of object that refers to the 'key' object\n\t\t - the handle for the object that refers to the 'key' object\n\t\t - the reference object:\n\t\t\t- None in most cases\n\t\t\t- for media it is a MediaRef object\n\t\t\n\t\tThis method recursively calls the methods \"_add_***\"\n\t\t\"\"\"\n\t\t_obj_class_list = (Person, Family, Event, Place, Source, Citation,\n\t\t\t\t\t\t MediaObject, Repository, Note, Tag)\n\n\t\t# setup a dictionary of the required structure\n\t\tself.obj_dict = defaultdict(lambda: defaultdict(set))\n\t\tself.bkref_dict = defaultdict(lambda: defaultdict(set))\n\n\n\t\t# initialise the dictionary to empty in case no objects of any\n\t\t# particular class are included in the web report\n\t\tfor obj_class in _obj_class_list:\n\t\t\tself.obj_dict[obj_class] = defaultdict(set)\n\n\t\tind_list = self.database.iter_person_handles()\n\t\twith self.user.progress(_(\"Dynamic Web Site Report\"),\n\t\t\t\t\t\t\t\t _(\"Applying Person Filter...\"),\n\t\t\t\t\t\t\t\t self.database.get_number_of_people()) as step:\n\t\t\tind_list = self.filter.apply(self.database, ind_list,\n\t\t\t\t\t\t\t\t\t\t step)\n\n\t\twith self.user.progress(_(\"Dynamic Web Site Report\"),\n\t\t\t\t\t\t\t\t _(\"Constructing list of other objects...\"),\n\t\t\t\t\t\t\t\t sum(1 for _ in ind_list)) as step:\n\t\t\tfor handle in ind_list:\n\t\t\t\t# FIXME work around bug that self.database.iter under python 3\n\t\t\t\t# returns (binary) data rather than text\n\t\t\t\tif (not isinstance(handle, UNITYPE)):\n\t\t\t\t\thandle = handle.decode(\"UTF-8\")\n\t\t\t\tstep()\n\t\t\t\tself._add_person(handle, \"\", \"\")\n\n\t\tlog.debug(\"final object dictionary \\n\" +\n\t\t\t\t \"\".join((\"%s: %s\\n\" % item) for item in self.obj_dict.items()))\n\n\t\tlog.debug(\"final backref dictionary \\n\" +\n\t\t\t\t \"\".join((\"%s: %s\\n\" % item) for item in self.bkref_dict.items()))\n\n\n\tdef _add_person(self, person_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd person_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Person][person_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the person is already added\n\t\tif (person_handle in self.obj_dict[Person]): return\n\t\t# Add person in the dictionaries of objects\n\t\tperson = self.database.get_person_from_handle(person_handle)\n\t\tif (not person): return\n\t\tperson_name = self.get_person_name(person)\n\t\tself.obj_dict[Person][person_handle] = [person_name, person.gramps_id, len(self.obj_dict[Person])]\n\t\t# Person events\n\t\tevt_ref_list = person.get_event_ref_list()\n\t\tif evt_ref_list:\n\t\t\tfor evt_ref in evt_ref_list:\n\t\t\t\tself._add_event(evt_ref.ref, Person, person_handle, evt_ref)\n\t\t# Person citations\n\t\tfor citation_handle in person.get_citation_list():\n\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Person name citations\n\t\tfor name in [person.get_primary_name()] + \\\n\t\t\t\t\t\tperson.get_alternate_names():\n\t\t\tfor citation_handle in name.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# LDS Ordinance citations\n\t\tfor lds_ord in person.get_lds_ord_list():\n\t\t\tfor citation_handle in lds_ord.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Attribute citations\n\t\tfor attr in person.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Person families\n\t\tfamily_handle_list = person.get_family_handle_list()\n\t\tif family_handle_list:\n\t\t\tfor family_handle in person.get_family_handle_list():\n\t\t\t\tself._add_family(family_handle, Person, person_handle)\n\t\t# Person media\n\t\tfor media_ref in person.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Person, person_handle, media_ref)\n\t\t# Association citations\n\t\tfor assoc in person.get_person_ref_list():\n\t\t\tfor citation_handle in assoc.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Addresses citations\n\t\tfor addr in person.get_address_list():\n\t\t\tfor citation_handle in addr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\n\n\tdef get_person_name(self, person):\n\t\t\"\"\"\n\t\tReturn a string containing the person's primary name in the name format chosen in the web report options\n\t\t@param: person -- person object from database\n\t\t\"\"\"\n\t\tname_format = self.options['name_format']\n\t\tprimary_name = person.get_primary_name()\n\t\tname = Name(primary_name)\n\t\tname.set_display_as(name_format)\n\t\treturn _nd.display_name(name)\n\n\n\tdef _add_family(self, family_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd family_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Family][family_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the family is already added\n\t\tif (family_handle in self.obj_dict[Family]): return\n\t\t# Add family in the dictionaries of objects\n\t\tfamily = self.database.get_family_from_handle(family_handle)\n\t\tfamily_name = self.get_family_name(family)\n\t\tself.obj_dict[Family][family_handle] = [family_name, family.gramps_id, len(self.obj_dict[Family])]\n\t\t# Family events\n\t\tevt_ref_list = family.get_event_ref_list()\n\t\tif evt_ref_list:\n\t\t\tfor evt_ref in evt_ref_list:\n\t\t\t\tself._add_event(evt_ref.ref, Family, family_handle, evt_ref)\n\t\t# Family child references\n\t\tfor child_ref in family.get_child_ref_list():\n\t\t\tfor citation_handle in child_ref.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# LDS Ordinance citations\n\t\tfor lds_ord in family.get_lds_ord_list():\n\t\t\tfor citation_handle in lds_ord.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# Attributes citations\n\t\tfor attr in family.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# Family citations\n\t\tfor citation_handle in family.get_citation_list():\n\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# Family media\n\t\tfor media_ref in family.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Family, family_handle, media_ref)\n\n\n\tdef get_family_name(self, family):\n\t\t\"\"\"\n\t\tReturn a string containing the name of the family (e.g. 'Family of John Doe and Jane Doe')\n\t\t@param: family -- family object from database\n\t\t\"\"\"\n\t\thusband_handle = family.get_father_handle()\n\t\tspouse_handle = family.get_mother_handle()\n\n\t\thusband = self.database.get_person_from_handle(husband_handle)\n\t\tspouse = self.database.get_person_from_handle(spouse_handle)\n\n\t\tif husband and spouse:\n\t\t\thusband_name = self.get_person_name(husband)\n\t\t\tspouse_name = self.get_person_name(spouse)\n\t\t\ttitle_str = _(\"Family of %(husband)s and %(spouse)s\") % {\n\t\t\t\t\"husband\": husband_name,\n\t\t\t\t\"spouse\": spouse_name}\n\t\telif husband:\n\t\t\thusband_name = self.get_person_name(husband)\n\t\t\t# Only the name of the husband is known\n\t\t\ttitle_str = _(\"Family of %(father)s\") % {\"father\": husband_name}\n\t\telif spouse:\n\t\t\tspouse_name = self.get_person_name(spouse)\n\t\t\t# Only the name of the wife is known\n\t\t\ttitle_str = _(\"Family of %(mother)s\") % {\"mother\": spouse_name}\n\t\telse:\n\t\t\ttitle_str = \"\"\n\n\t\treturn title_str\n\n\n\tdef _add_event(self, event_handle, bkref_class, bkref_handle, event_ref):\n\t\t\"\"\"\n\t\tAdd event_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Check if event reference already added\n\t\trefs = []\n\t\tif (event_handle in self.bkref_dict[Event]):\n\t\t\trefs = [bkref[BKREF_REFOBJ] for bkref in self.bkref_dict[Event][event_handle]]\n\t\t\t# The event reference is already recorded\n\t\t\tif (event_ref in refs): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Event][event_handle].add((bkref_class, bkref_handle, event_ref))\n\t\t# Event reference attributes citations\n\t\tfor attr in event_ref.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, bkref_class, bkref_handle)\n\t\t# Check if the event is already added\n\t\tif (event_handle in self.obj_dict[Event]): return\n\t\t# Add event in the dictionaries of objects\n\t\tevent = self.database.get_event_from_handle(event_handle)\n\t\tif (not event): return\n\t\tevent_name = str(event.get_type())\n\t\tevent_desc = event.get_description()\n\t\t# The event description can be Y on import from GEDCOM. See the\n\t\t# following quote from the GEDCOM spec: \"The occurrence of an event is\n\t\t# asserted by the presence of either a DATE tag and value or a PLACe tag\n\t\t# and value in the event structure. When neither the date value nor the\n\t\t# place value are known then a Y(es) value on the parent event tag line\n\t\t# is required to assert that the event happened.\"\"\n\t\tif not (event_desc == \"\" or event_desc is None or event_desc ==\"Y\"):\n\t\t\tevent_name = event_name + \": \" + event_desc\n\t\tself.obj_dict[Event][event_handle] = [event_name, event.gramps_id, len(self.obj_dict[Event])]\n\t\t# Event place\n\t\tplace_handle = event.get_place_handle()\n\t\tif (place_handle):\n\t\t\tself._add_place(place_handle, bkref_class, bkref_handle)\n\t\t# Event citations\n\t\tfor citation_handle in event.get_citation_list():\n\t\t\tself._add_citation(citation_handle, bkref_class, bkref_handle)\n\t\t# Event attributes citations\n\t\tfor attr in event.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, bkref_class, bkref_handle)\n\t\t# Event media\n\t\tfor media_ref in event.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, bkref_class, bkref_handle, media_ref)\n\n\n\tdef _add_place(self, place_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd place_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Place][place_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the place is already added\n\t\tif (place_handle in self.obj_dict[Place]): return\n\t\t# Add place in the dictionaries of objects\n\t\tplace = self.database.get_place_from_handle(place_handle)\n\t\tif (DWR_VERSION_412):\n\t\t\tplace_name = _pd.display(self.database, place)\n\t\telse:\n\t\t\tplace_name = place.get_title()\n\t\tself.obj_dict[Place][place_handle] = [place_name, place.gramps_id, len(self.obj_dict[Place])]\n\n\t\tif (self.inc_places):\n\t\t\t# Place citations\n\t\t\tfor citation_handle in place.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Place, place_handle)\n\t\t\t# Place media\n\t\t\tfor media_ref in place.get_media_list():\n\t\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\t\tself._add_media(media_handle, Place, place_handle, media_ref)\n\n\n\tdef _add_source(self, source_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd source_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_sources): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Source][source_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the source is already added\n\t\tif (source_handle in self.obj_dict[Source]): return\n\t\t# Add source in the dictionaries of objects\n\t\tsource = self.database.get_source_from_handle(source_handle)\n\t\tsource_name = source.get_title()\n\t\tself.obj_dict[Source][source_handle] = [source_name, source.gramps_id, len(self.obj_dict[Source])]\n\t\t# Source repository\n\t\tif self.inc_repositories:\n\t\t\tfor repo_ref in source.get_reporef_list():\n\t\t\t\trepo_handle = repo_ref.get_reference_handle()\n\t\t\t\tself._add_repository(repo_handle, Source, source_handle, repo_ref)\n\t\t# Source media\n\t\tfor media_ref in source.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Source, source_handle, media_ref)\n\n\n\tdef _add_citation(self, citation_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd citation_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_sources): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Citation][citation_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the citation is already added\n\t\tif (citation_handle in self.obj_dict[Citation]): return\n\t\t# Add citation in the dictionaries of objects\n\t\tcitation = self.database.get_citation_from_handle(citation_handle)\n\t\tcitation_name = citation.get_page() or \"\"\n\t\tsource_handle = citation.get_reference_handle()\n\t\tself.obj_dict[Citation][citation_handle] = [citation_name, citation.gramps_id, len(self.obj_dict[Citation])]\n\t\t# Citation source\n\t\tself._add_source(source_handle, Citation, citation_handle)\n\t\t# Citation media\n\t\tfor media_ref in citation.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Source, source_handle, media_ref)\n\n\n\tdef _add_media(self, media_handle, bkref_class, bkref_handle, media_ref):\n\t\t\"\"\"\n\t\tAdd media_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_gallery): return\n\t\t# Check if media reference already added\n\t\trefs = []\n\t\tif (media_handle in self.bkref_dict[MediaObject]):\n\t\t\trefs = [bkref[BKREF_REFOBJ] for bkref in self.bkref_dict[MediaObject][media_handle]]\n\t\t\t# The media reference is already recorded\n\t\t\tif (media_ref in refs): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[MediaObject][media_handle].add((bkref_class, bkref_handle, media_ref))\n\t\t# Citations for media reference, media reference attributes\n\t\tcitation_list = media_ref.get_citation_list()\n\t\tfor attr in media_ref.get_attribute_list():\n\t\t\tcitation_list.extend(attr.get_citation_list())\n\t\tfor citation_handle in citation_list:\n\t\t\tself._add_citation(citation_handle, MediaObject, media_handle)\n\t\t# Check if the media is already added\n\t\tif (media_handle in self.obj_dict[MediaObject]): return\n\t\t# Add media in the dictionaries of objects\n\t\tmedia = self.database.get_object_from_handle(media_handle)\n\t\tmedia_name = \"Media\"\n\t\tself.obj_dict[MediaObject][media_handle] = [media_name, media.gramps_id, len(self.obj_dict[MediaObject])]\n\t\t# Citations for media, media attributes\n\t\tcitation_list = media.get_citation_list()\n\t\tfor attr in media.get_attribute_list():\n\t\t\tcitation_list.extend(attr.get_citation_list())\n\t\tfor citation_handle in citation_list:\n\t\t\tself._add_citation(citation_handle, MediaObject, media_handle)\n\n\n\tdef _add_repository(self, repo_handle, bkref_class, bkref_handle, repo_ref):\n\t\t\"\"\"\n\t\tAdd repo_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_repositories): return\n\t\t# Check if repository reference already added\n\t\trefs = []\n\t\tif (repo_handle in self.bkref_dict[Repository]):\n\t\t\trefs = [bkref[BKREF_REFOBJ] for bkref in self.bkref_dict[Repository][repo_handle]]\n\t\t\t# The repository reference is already recorded\n\t\t\tif (repo_ref in refs): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Repository][repo_handle].add((bkref_class, bkref_handle, repo_ref))\n\t\t# Check if the repository is already added\n\t\tif (repo_handle in self.obj_dict[Repository]): return\n\t\t# Add repository in the dictionaries of objects\n\t\trepo = self.database.get_repository_from_handle(repo_handle)\n\t\trepo_name = repo.name\n\t\tself.obj_dict[Repository][repo_handle] = [repo_name, repo.gramps_id, len(self.obj_dict[Repository])]\n\t\t# Addresses citations\n\t\tfor addr in repo.get_address_list():\n\t\t\tfor citation_handle in addr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Repository, repo_handle)\n\n\t\t\t\t\n\t##############################################################################################\n\t##############################################################################################\n\t#\n\t# Objects dictionaries sorting\n\t#\n\t##############################################################################################\n\t##############################################################################################\n\n\n\tdef _sort_obj_dict(self):\n\t\t\"\"\"\n\t\tSort the dictionaries of objects to be included in the reports.\n\t\tThe dictionaries are sorted by name.\n\t\tThe sorting is performed by modifying the index of the objects.\n\t\t\"\"\"\n\t\t\n\t\t# Sort persons\n\t\tsortkeys = {}\n\t\tobjs = list(self.obj_dict[Person].keys())\n\t\tfor handle in objs:\n\t\t\tsortkeys[handle] = self.get_person_name_sort_key(handle)\n\t\tobjs.sort(key = lambda x: sortkeys[x])\n\t\tfor (i, x) in enumerate(objs):\n\t\t\tself.obj_dict[Person][x][OBJDICT_INDEX] = i\n\t\t\t\n\t\t# Sort families\n\t\tsortkeys = {}\n\t\tobjs = list(self.obj_dict[Family].keys())\n\t\tfor handle in objs:\n\t\t\tsortkeys[handle] = self.get_family_name_sort_key(handle)\n\t\tobjs.sort(key = lambda x: sortkeys[x])\n\t\tfor (i, x) in enumerate(objs):\n\t\t\tself.obj_dict[Family][x][OBJDICT_INDEX] = i\n\n\t\t# Sort others\n\t\tfor cls in (Source, Repository, MediaObject, Place):\n\t\t\tobjs = list(self.obj_dict[cls].keys())\n\t\t\tsortkeys = {}\n\t\t\tfor handle in objs:\n\t\t\t\tsortkeys[handle] = SORT_KEY(self.obj_dict[cls][handle][OBJDICT_NAME])\n\t\t\tobjs.sort(key = lambda x: sortkeys[x])\n\t\t\tfor (i, x) in enumerate(objs):\n\t\t\t\tself.obj_dict[cls][x][OBJDICT_INDEX] = i\n\t\t\n\n\tdef get_person_name_sort_key(self, handle):\n\t\t\"\"\"\n\t\tReturn a sort key for a person\n\t\t\"\"\"\n\t\tperson = self.database.get_person_from_handle(handle)\n\t\tprimary_name = person.get_primary_name()\n\t\tsort_str = _nd.sort_string(primary_name)\n\t\treturn(SORT_KEY(sort_str))\n\t\t\n\t\t\n\tdef get_family_name_sort_key(self, handle):\n\t\t\"\"\"\n\t\tReturn a sort key for a family\n\t\t\"\"\"\n\t\tfamily = self.database.get_family_from_handle(handle)\n\t\thusband_handle = family.get_father_handle()\n\t\tspouse_handle = family.get_mother_handle()\n\n\t\thusband = self.database.get_person_from_handle(husband_handle)\n\t\tspouse = self.database.get_person_from_handle(spouse_handle)\n\n\t\tif husband and spouse:\n\t\t\tsort_key = self.get_person_name_sort_key(husband_handle) + SORT_KEY(\" \") + self.get_person_name_sort_key(spouse_handle)\n\t\telif husband:\n\t\t\tsort_key = self.get_person_name_sort_key(husband_handle)\n\t\telif spouse:\n\t\t\tsort_key = self.get_person_name_sort_key(spouse_handle)\n\t\telse:\n\t\t\tsort_key = SORT_KEY(\"\")\n\n\t\treturn(sort_key)\n\t\t\n\t\t\n\t\t\n\t\t\n##################################################################################################\n##################################################################################################\n#\n# DynamicWebReport Menu Options\n#\n##################################################################################################\n##################################################################################################\n\nclass DynamicWebOptions(MenuReportOptions):\n\t\"\"\"\n\tCreates the DynamicWebReport Menu Options\n\tDefines options and provides handling interface.\n\t\n\tMethods:\n\t- add_menu_options: called by Gramps to generate the options menu. It calls all the other methods \"__add_***_options\"\n\t- __add_***_options: One method for each tab of the options menu.\n\t- __***_changed: methods called when an option impacts other options\n\t\"\"\"\n\tdef __init__(self, name, dbase):\n\t\n\t\tself.__db = dbase #: Gramps database\n\t\t\n\t\t# The data below are used when some options change the behavior of other options. For example: a boolean option enables/disables another option. These data are used in the methods \"__***_changed\".\n\t\tself.__pid = None\n\t\tself.__filter = None\n\t\tself.__living = None\n\t\tself.__yearsafterdeath = None\n\t\t\n\t\t#: This help explains how Gramps note are modified in order to generate custom pages\n\t\tself.note_help = _(\n\t\t\t\"In this note, the following special words are processed:\\n\"\n\t\t\t\"__SEARCH_FORM__ is replaced by a search form.\\n\"\n\t\t\t\"__NB_INDIVIDUALS__ is replaced by the number of persons.\\n\"\n\t\t\t\"__NB_FAMILIES__ is replaced by the number of families.\\n\"\n\t\t\t\"__NB_MEDIA__ is replaced by the number of media objects.\\n\"\n\t\t\t\"__NB_SOURCES__ is replaced by the number of sources.\\n\"\n\t\t\t\"__NB_REPOSITORIES__ is replaced by the number of repositories.\\n\"\n\t\t\t\"__NB_PLACES__ is replaced by the number of places.\\n\"\n\t\t\t\"__MEDIA___ is replaced by the media with gramps ID .\\n\"\n\t\t\t\"__THUMB___ is replaced by the thumbnail of the media with gramps ID .\\n\"\n\t\t\t\"__EXPORT_DATE__ is replaced by the current date.\\n\"\n\t\t\t\"__GRAMPS_VERSION__ is replaced by the GRAMPS version.\\n\"\n\t\t\t\"__GRAMPS_HOMEPAGE__ is replaced by the GRAMPS homepage link.\\n\"\n\t\t\t\"URL starting with \\\"relative://relative.\\\" are replaced by the relative URL \\\"\\\".\\n\")\n\t\t\t\n\t\tMenuReportOptions.__init__(self, name, dbase)\n\n\t\t\n\tdef add_menu_options(self, menu):\n\t\t\"\"\"\n\t\tAdd options to the menu for the web site.\n\t\t\n\t\tIt calls all the other methods \"__add_***_options\" (one method for each tab of the options menu).\n\t\t\"\"\"\n\t\tself.__add_report_options(menu)\n\t\tself.__add_privacy_options(menu)\n\t\tself.__add_options_options(menu)\n\t\tself.__add_pages_advanced_options(menu)\n\t\tself.__add_pages_options(menu)\n\t\tself.__add_trees_options(menu)\n\t\tself.__add_custom_pages_options(menu)\n\t\tself.__add_select_pages_options(menu)\n\n\t\t\n\tdef __add_report_options(self, menu):\n\t\t\"\"\"\n\t\tOptions on the \"Report\" tab.\n\t\t\"\"\"\n\t\tcategory_name = _(\"Report\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\tdbname = self.__db.get_dbname()\n\t\tdefault_dir = dbname + \"_\" + \"dynamicweb\"\n\t\ttarget = DestinationOption(_(\"Destination\"),\n\t\t\tos.path.join(config.get(\"paths.website-directory\"), default_dir))\n\t\ttarget.set_help(_(\"The destination directory for the web files\"))\n\t\ttarget.set_directory_entry(True)\n\t\taddopt(\"target\", target)\n\n\t\tself.__archive = BooleanOption(_('Store web pages in archive'), False)\n\t\tself.__archive.set_help(_(\"Whether to create an archive file (in ZIP or TGZ format) containing the web site\"))\n\t\taddopt(\"archive\", self.__archive)\n\t\tself.__archive.connect(\"value-changed\", self.__archive_changed)\n\n\t\tself.__archive_file = DestinationOption(_(\"Archive file\"),\n\t\t\tos.path.join(config.get(\"paths.website-directory\"), default_dir, \"archive.zip\"))\n\t\tself.__archive_file.set_help(_(\"The archive file name (with \\\".zip\\\" or \\\".tgz\\\" extension)\"))\n\t\tself.__archive_file.set_directory_entry(False)\n\t\taddopt(\"archive_file\", self.__archive_file)\n\n\t\tself.__archive_changed()\n\n\t\ttitle = StringOption(_(\"Web site title\"), _(\"My Family Tree\"))\n\t\ttitle.set_help(_(\"The title of the web site\"))\n\t\taddopt(\"title\", title)\n\n\t\tself.__filter = FilterOption(_(\"Filter\"), 0)\n\t\tself.__filter.set_help(\n\t\t\t _(\"Select filter to restrict people that appear on web site\"))\n\t\taddopt(\"filter\", self.__filter)\n\t\tself.__filter.connect(\"value-changed\", self.__filter_changed)\n\n\t\tself.__pid = PersonOption(_(\"Filter Person\"))\n\t\tself.__pid.set_help(_(\"The center person for the filter\"))\n\t\taddopt(\"pid\", self.__pid)\n\t\tself.__pid.connect(\"value-changed\", self.__pid_changed)\n\n\t\tself.__pid_changed()\n\n\t\t# We must figure out the value of the first option before we can create the EnumeratedListOption\n\t\tfmt_list = _nd.get_name_format()\n\t\tdefaultnum = _nd.get_default_format()\n\t\tdefault = 0\n\t\tfor ind, val in enumerate(fmt_list):\n\t\t\tif val[0] == defaultnum:\n\t\t\t\tdefault = ind\n\t\t\t\tbreak\n\t\tname_format = EnumeratedListOption(_(\"Name format\"), fmt_list[default][0])\n\t\tfor num, name, fmt_str, act in fmt_list:\n\t\t\tname_format.add_item(num, name)\n\t\tname_format.set_help(_(\"Select the format to display the complete names\"))\n\t\taddopt(\"name_format\", name_format)\n\t\tshort_name_format = EnumeratedListOption(_(\"Name format (short)\"), fmt_list[default][0])\n\t\tfor num, name, fmt_str, act in fmt_list:\n\t\t\tshort_name_format.add_item(num, name)\n\t\tshort_name_format.set_help(_(\"Select the format to display a shorter version of the names\"))\n\t\taddopt(\"short_name_format\", short_name_format)\n\t\t\n\t\ttemplate = EnumeratedListOption(_(\"Web site template\"), 0)\n\t\tfor (i, (directory, name)) in enumerate(WEB_TEMPLATE_LIST):\n\t\t\ttemplate.add_item(i, name)\n\t\ttemplate.set_help(_(\"Select the template of the web site\"))\n\t\taddopt(\"template\", template)\n\n\t\tcpright = EnumeratedListOption(_(\"Copyright\"), 0)\n\t\tfor index, copt in enumerate(_COPY_OPTIONS):\n\t\t\tcpright.add_item(index, copt)\n\t\tcpright.set_help( _(\"The copyright to be used for the web files\"))\n\t\taddopt(\"copyright\", cpright)\n\n\n\tdef __add_privacy_options(self, menu):\n\t\t\"\"\"\n\t\tOptions on the \"Privacy\" tab.\n\t\t\"\"\"\n\t\tcategory_name = _(\"Privacy\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\tincpriv = BooleanOption(_(\"Include records marked private\"), False)\n\t\tincpriv.set_help(_(\"Whether to include private objects\"))\n\t\taddopt(\"incpriv\", incpriv)\n\n\t\tinc_notes = BooleanOption(_(\"Export notes\"), True)\n\t\tinc_notes.set_help(_(\"Whether to export notes in the web pages\"))\n\t\taddopt(\"inc_notes\", inc_notes)\n\n\t\tinc_sources = BooleanOption(_(\"Export sources\"), True)\n\t\tinc_sources.set_help(_(\"Whether to export sources and citations in the web pages\"))\n\t\taddopt(\"inc_sources\", inc_sources)\n\n\t\tinc_addresses = BooleanOption(_(\"Export addresses\"), True)\n\t\tinc_addresses.set_help(_(\"Whether to export addresses in the web pages\"))\n\t\taddopt(\"inc_addresses\", inc_addresses)\n\n\t\tself.__living = EnumeratedListOption(_(\"Living People\"),\n\t\t\t\t\t\t\t\t\t\t\t LivingProxyDb.MODE_EXCLUDE_ALL)\n\t\tself.__living.add_item(LivingProxyDb.MODE_EXCLUDE_ALL,\n\t\t\t\t\t\t\t _(\"Exclude\"))\n\t\tself.__living.add_item(LivingProxyDb.MODE_INCLUDE_LAST_NAME_ONLY,\n\t\t\t\t\t\t\t _(\"Include Last Name Only\"))\n\t\tself.__living.add_item(LivingProxyDb.MODE_INCLUDE_FULL_NAME_ONLY,\n\t\t\t\t\t\t\t _(\"Include Full Name Only\"))\n\t\tself.__living.add_item(INCLUDE_LIVING_VALUE,\n\t\t\t\t\t\t\t _(\"Include\"))\n\t\tself.__living.set_help(_(\"How to handle living people\"))\n\t\taddopt(\"living\", self.__living)\n\t\tself.__living.connect(\"value-changed\", self.__living_changed)\n\n\t\tself.__yearsafterdeath = NumberOption(_(\"Years from death to consider \"\n\t\t\t\t\t\t\t\t\t\t\t\t \"living\"), 30, 0, 100)\n\t\tself.__yearsafterdeath.set_help(_(\"This allows you to restrict \"\n\t\t\t\t\t\t\t\t\t\t \"information on people who have not \"\n\t\t\t\t\t\t\t\t\t\t \"been dead for very long\"))\n\n\t\taddopt(\"yearsafterdeath\", self.__yearsafterdeath)\n\n\t\tself.__living_changed()\n\n\n\tdef __add_options_options(self, menu):\n\t\tcategory_name = _(\"Options\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\tinc_repositories = BooleanOption(_('Include repository pages'), False)\n\t\tinc_repositories.set_help(_('Whether or not to include the Repository Pages.'))\n\t\taddopt(\"inc_repositories\", inc_repositories)\n\n\t\tinc_gallery = BooleanOption(_(\"Include images and media objects\"), True)\n\t\tinc_gallery.set_help(_(\"Whether to include a media objects in the web pages\"))\n\t\taddopt(\"inc_gallery\", inc_gallery)\n\n\t\tcopy_media = BooleanOption(_(\"Copy images and media objects\"), True)\n\t\tcopy_media.set_help(_(\"Whether to make a copy of the media objects.\"\n\t\t\t\" When the objects are not copied, they are referenced by their relative path name\"))\n\t\taddopt(\"copy_media\", copy_media)\n\n\t\tprint_notes_type = BooleanOption(_(\"Print the notes type\"), True)\n\t\tprint_notes_type.set_help(_(\"Whether to print the notes type in the notes text\"))\n\t\taddopt(\"print_notes_type\", print_notes_type)\n\n\t\tself.__inc_places = BooleanOption(_(\"Print place pages\"), True)\n\t\tself.__inc_places.set_help(_(\"Whether to show pages for the places\"))\n\t\taddopt(\"inc_places\", self.__inc_places)\n\t\tself.__inc_places.connect(\"value-changed\", self.__placemap_options_changed)\n\n\t\tself.__placemappages = BooleanOption(_(\"Include Place map on Place Pages\"), False)\n\t\tself.__placemappages.set_help(_(\n\t\t\t\"Whether to include a place map on the Place Pages, \"\n\t\t\t\"where Latitude/ Longitude are available.\"))\n\t\tself.__placemappages.connect(\"value-changed\", self.__placemap_options_changed)\n\t\taddopt(\"placemappages\", self.__placemappages)\n\n\t\tself.__familymappages = BooleanOption(_(\n\t\t\t\"Include Family Map Pages with \"\n\t\t\t\"all places shown on the map\"), False)\n\t\tself.__familymappages.set_help(_(\n\t\t\t\"Whether or not to add an individual page map \"\n\t\t\t\"showing all the places on this page. \"\n\t\t\t\"This will allow you to see how your family \"\n\t\t\t\"traveled around the country.\"))\n\t\tself.__familymappages.connect(\"value-changed\", self.__placemap_options_changed)\n\t\taddopt(\"familymappages\", self.__familymappages)\n\n\t\tmapopts = [\n\t\t\t[_(\"Google\"), \"Google\"],\n\t\t\t[_(\"OpenStreetMap\"), \"OpenStreetMap\"]\n\t\t]\n\t\tself.__mapservice = EnumeratedListOption(_(\"Map Service\"), mapopts[0][1])\n\t\tfor trans, opt in mapopts:\n\t\t\tself.__mapservice.add_item(opt, trans)\n\t\tself.__mapservice.set_help(_(\"Choose your choice of map service for creating the Place Map Pages\"))\n\t\tself.__mapservice.connect(\"value-changed\", self.__placemap_options_changed)\n\t\taddopt(\"mapservice\", self.__mapservice)\n\n\t\tself.__placemap_options_changed()\n\n\n\tdef __add_pages_advanced_options(self, menu):\n\t\tcategory_name = _(\"Advanced\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\tencoding = EnumeratedListOption(_('Character set encoding'), _CHARACTER_SETS[0][1])\n\t\tfor eopt in _CHARACTER_SETS:\n\t\t\tencoding.add_item(eopt[1], eopt[0])\n\t\tencoding.set_help(_(\"The encoding to be used for the web files\"))\n\t\taddopt(\"encoding\", encoding)\n\n\t\tinc_families = BooleanOption(_(\"Include family pages\"), False)\n\t\tinc_families.set_help(_(\"Whether or not to include family pages\"))\n\t\taddopt(\"inc_families\", inc_families)\n\n\t\tinc_events = BooleanOption(_('Include event pages'), False)\n\t\tinc_events.set_help(_('Add a complete events list and relevant pages or not'))\n\t\taddopt(\"inc_events\", inc_events)\n\t\tinc_events.set_available(False)\n\n\t\tshowbirth = BooleanOption(_(\"Include a column for birth dates on the index pages\"), True)\n\t\tshowbirth.set_help(_('Whether to include a birth column'))\n\t\taddopt(\"showbirth\", showbirth)\n\n\t\tshowdeath = BooleanOption(_(\"Include a column for death dates on the index pages\"), False)\n\t\tshowdeath.set_help(_('Whether to include a death column'))\n\t\taddopt(\"showdeath\", showdeath)\n\n\t\tshowmarriage = BooleanOption(_(\"Include a column for marriage dates on the index pages\"), False)\n\t\tshowmarriage.set_help(_('Whether to include a marriage column'))\n\t\taddopt(\"showmarriage\", showmarriage)\n\n\t\tshowpartner = BooleanOption(_(\"Include a column for partners on the index pages\"), False)\n\t\tshowpartner.set_help(_('Whether to include a partners column'))\n\t\taddopt(\"showpartner\", showpartner)\n\n\t\tshowparents = BooleanOption(_(\"Include a column for parents on the index pages\"), False)\n\t\tshowparents.set_help(_('Whether to include a parents column'))\n\t\taddopt(\"showparents\", showparents)\n\n\t\tshowallsiblings = BooleanOption(_(\"Include half and/ or step-siblings on the individual pages\"), False)\n\t\tshowallsiblings.set_help(_( \"Whether to include half and/ or step-siblings with the parents and siblings\"))\n\t\taddopt('showallsiblings', showallsiblings)\n\n\t\tbirthorder = BooleanOption(_('Sort all children in birth order'), False)\n\t\tbirthorder.set_help(_('Whether to display children in birth order or in entry order?'))\n\t\taddopt(\"birthorder\", birthorder)\n\n\t\tbkref_type = BooleanOption(_('Include references in indexes'), False)\n\t\tbkref_type.set_help(_('Whether to include the references to the items in the index pages. For example, in the media index page, the names of the individuals, families, places, sources that reference the media.'))\n\t\taddopt(\"bkref_type\", bkref_type)\n\n\t\tinc_gendex = BooleanOption(_('Include GENDEX file (/gendex.txt)'), False)\n\t\tinc_gendex.set_help(_('Whether to include a GENDEX file or not'))\n\t\taddopt(\"inc_gendex\", inc_gendex)\n\n\n\tdef __add_trees_options(self, menu):\n\t\tcategory_name = _(\"Trees\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\tpage_defs = [\n\t\t\tPAGE_SVG_TREE,\n\t\t]\n\t\tfor page_def in page_defs:\n\t\t\tname = PAGES_NAMES[page_def][0]\n\t\t\ttitle = PAGES_NAMES[page_def][1]\n\t\t\tpage_name = StringOption(_(\"Title for the tree \\\"%(name)s\\\"\") % {\"name\": name}, title)\n\t\t\tpage_name.set_help(_(\"Name for the page that shows the tree \\\"%(name)s\\\"\") % {\"name\": name})\n\t\t\taddopt(\"page_name_%i\" % page_def, page_name)\n\n\t\tgraphgens = NumberOption(_(\"Maximum number of generations\"), 10, 3, 30)\n\t\tgraphgens.set_help(_(\"The maximum number of generations to include in the ancestor and descendant trees and graphs\"))\n\t\taddopt(\"graphgens\", graphgens)\n\n\t\tsvg_tree_type = EnumeratedListOption(_(\"SVG tree graph type\"), str(DEFAULT_SVG_TREE_TYPE))\n\t\tfor (i, opt) in enumerate(SVG_TREE_TYPES):\n\t\t\tsvg_tree_type.add_item(str(i), opt)\n\t\tsvg_tree_type.set_help(_(\"Choose the default SVG tree graph type\"))\n\t\taddopt(\"svg_tree_type\", svg_tree_type)\n\t\t\n\t\tsvg_tree_shape = EnumeratedListOption(_(\"SVG tree graph shape\"), str(DEFAULT_SVG_TREE_SHAPE))\n\t\tfor (i, opt) in enumerate(SVG_TREE_SHAPES):\n\t\t\tsvg_tree_shape.add_item(str(i), opt)\n\t\tsvg_tree_shape.set_help(_(\"Choose the default SVG tree graph shape\"))\n\t\taddopt(\"svg_tree_shape\", svg_tree_shape)\n\t\t\n\t\tsvg_tree_distrib_asc = EnumeratedListOption(_(\"SVG tree parents distribution\"), str(DEFAULT_SVG_TREE_DISTRIB))\n\t\tfor (i, opt) in enumerate(SVG_TREE_DISTRIB_ASC):\n\t\t\tsvg_tree_distrib_asc.add_item(str(i), opt)\n\t\tsvg_tree_distrib_asc.set_help(_(\"Choose the default SVG tree parents distribution (for fan charts only)\"))\n\t\taddopt(\"svg_tree_distrib_asc\", svg_tree_distrib_asc)\n\t\t\n\t\tsvg_tree_distrib_dsc = EnumeratedListOption(_(\"SVG tree children distribution\"), str(DEFAULT_SVG_TREE_DISTRIB))\n\t\tfor (i, opt) in enumerate(SVG_TREE_DISTRIB_DSC):\n\t\t\tsvg_tree_distrib_dsc.add_item(str(i), opt)\n\t\tsvg_tree_distrib_dsc.set_help(_(\"Choose the default SVG tree children distribution (for fan charts only)\"))\n\t\taddopt(\"svg_tree_distrib_dsc\", svg_tree_distrib_dsc)\n\t\t\n\t\tsvg_tree_background = EnumeratedListOption(_(\"Background\"), str(DEFAULT_SVG_TREE_BACKGROUND))\n\t\tfor (i, opt) in enumerate(SVG_TREE_BACKGROUNDS):\n\t\t\tsvg_tree_background.add_item(str(i), opt)\n\t\tsvg_tree_background.set_help(_(\"Choose the background color scheme for the persons in the SVG tree graph\"))\n\t\taddopt(\"svg_tree_background\", svg_tree_background)\n\n\t\tsvg_tree_color1 = ColorOption(_(\"Start gradient/Main color\"), \"#EF2929\")\n\t\taddopt(\"svg_tree_color1\", svg_tree_color1)\n\n\t\tsvg_tree_color2 = ColorOption(_(\"End gradient/2nd color\"), \"#3D37E9\")\n\t\taddopt(\"svg_tree_color2\", svg_tree_color2)\n\n\t\tself.__svg_tree_dup = BooleanOption(_(\"Show duplicates\"), True)\n\t\tself.__svg_tree_dup.set_help(_(\"Whether to use a special color for the persons that appear several times in the SVG tree\"))\n\t\tself.__svg_tree_dup.connect(\"value-changed\", self.__svg_tree_dup_changed)\n\t\taddopt(\"svg_tree_dup\", self.__svg_tree_dup)\n\t\t\n\t\tself.__svg_tree_color_dup = ColorOption(_(\"Color for duplicates\"), \"#888A85\")\n\t\taddopt(\"svg_tree_color_dup\", self.__svg_tree_color_dup)\n\n\n\tdef __add_pages_options(self, menu):\n\t\tcategory_name = _(\"Pages\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\theadernote = NoteOption(_('HTML user header'))\n\t\theadernote.set_help( _(\"A note to be used as the page header\"))\n\t\taddopt(\"headernote\", headernote)\n\n\t\tfooternote = NoteOption(_('HTML user footer'))\n\t\tfooternote.set_help( _(\"A note to be used as the page footer\"))\n\t\taddopt(\"footernote\", footernote)\n\n\t\tpage_defs = [\n\t\t\tPAGE_PERSON,\n\t\t\tPAGE_SURNAMES,\n\t\t\tPAGE_PERSON_INDEX,\n\t\t\tPAGE_FAMILY_INDEX,\n\t\t\tPAGE_SOURCE_INDEX,\n\t\t\tPAGE_MEDIA_INDEX,\n\t\t\tPAGE_PLACE_INDEX,\n\t\t\tPAGE_ADDRESS_INDEX,\n\t\t\tPAGE_REPOSITORY_INDEX,\n\t\t]\n\t\tfor page_def in page_defs:\n\t\t\tname = PAGES_NAMES[page_def][0]\n\t\t\ttitle = PAGES_NAMES[page_def][1]\n\t\t\tpage_name = StringOption(_(\"Title for the page \\\"%(name)s\\\"\") % {\"name\": name}, title)\n\t\t\tpage_name.set_help(_(\"Name for the page \\\"%(name)s\\\"\") % {\"name\": name})\n\t\t\taddopt(\"page_name_%i\" % page_def, page_name)\n\n\n\tdef __add_custom_pages_options(self, menu):\n\t\tcategory_name = _(\"Custom pages\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\tfor i in range(NB_CUSTOM_PAGES):\n\t\t\tpage_def = PAGE_CUSTOM + i\n\t\t\tpage_name = StringOption(_(\"Title for the custom page %(index)i\") % {\"index\": i + 1}, _(\"Custom page %(index)i\") % {\"index\": i + 1})\n\t\t\tpage_name.set_help(_(\"Name for the custom page %(index)i\") % {\"index\": i + 1})\n\t\t\taddopt(\"page_name_%i\" % page_def, page_name)\n\n\t\t\tcustom_note = NoteOption(_(\"Note for custom page %(index)i\") % {\"index\": i + 1})\n\t\t\tcustom_note.set_help(_(\"A note to be used for the custom page content.\\n\") + self.note_help)\n\t\t\taddopt(\"custom_note_%i\" % i, custom_note)\n\n\t\t\tcustom_menu = BooleanOption(_(\"Menu for the custom page %(index)i\") % {\"index\": i + 1}, True)\n\t\t\tcustom_menu.set_help(_(\"Whether to print a menu for the custom page\"))\n\t\t\taddopt(\"custom_menu_%i\" % i, custom_menu)\n\n\n\tdef __add_select_pages_options(self, menu):\n\t\tcategory_name = _(\"Pages selection\")\n\t\taddopt = partial(menu.add_option, category_name)\n\n\t\tself.__pages_number = NumberOption(_(\"Number of pages\"), 11, 1, NB_TOTAL_PAGES_MAX)\n\t\tself.__pages_number.set_help(_(\"Number pages in the web site.\"))\n\t\taddopt(\"pages_number\", self.__pages_number)\n\t\tself.__pages_number.connect(\"value-changed\", self.__pages_contents_changed)\n\n\t\tpage_defs = [\n\t\t\tPAGE_CUSTOM,\n\t\t\tPAGE_SURNAMES,\n\t\t\tPAGE_PERSON,\n\t\t\tPAGE_PERSON_INDEX,\n\t\t\tPAGE_FAMILY_INDEX,\n\t\t\tPAGE_SOURCE_INDEX,\n\t\t\tPAGE_MEDIA_INDEX,\n\t\t\tPAGE_PLACE_INDEX,\n\t\t\tPAGE_ADDRESS_INDEX,\n\t\t\tPAGE_REPOSITORY_INDEX,\n\t\t\tPAGE_SVG_TREE,\n\t\t] + [PAGE_CUSTOM + i for i in range (1, NB_CUSTOM_PAGES)\n\t\t] + [PAGE_CUSTOM] * NB_TOTAL_PAGES_MAX\n\n\t\tself.__page_content = []\n\t\tfor i in range(NB_TOTAL_PAGES_MAX):\n\t\t\tpage_def = page_defs[i]\n\t\t\tpage_content = EnumeratedListOption(_(\"Contents of page %(index)i\") % {\"index\": i + 1}, page_def)\n\t\t\tfor (j, pname) in enumerate(PAGES_NAMES):\n\t\t\t\tpage_content.add_item(j, pname[0])\n\t\t\tpage_content.set_help(_(\"Contents of the page\"))\n\t\t\taddopt(\"page_content_%i\" % i, page_content)\n\t\t\tself.__page_content.append(page_content)\n\t\t\tself.__page_content[i].connect(\"value-changed\", self.__pages_contents_changed)\n\n\t\tself.__pages_contents_changed()\n\n\n\tdef __archive_changed(self):\n\t\t\"\"\"\n\t\tDisable the archive file when archive is disabled \n\t\t\"\"\"\n\t\tenable = self.__archive.get_value()\n\t\tself.__archive_file.set_available(enable)\n\n\tdef __pid_changed(self):\n\t\t\"\"\"\n\t\tUpdate the filter list based on the selected person\n\t\t\"\"\"\n\t\tgid = self.__pid.get_value()\n\t\tperson = self.__db.get_person_from_gramps_id(gid)\n\t\tfilter_list = report_utils.get_person_filters(person, False)\n\t\tself.__filter.set_filters(filter_list)\n\n\tdef __filter_changed(self):\n\t\t\"\"\"\n\t\tHandle filter change. If the filter is not specific to a person,\n\t\tdisable the person option\n\t\t\"\"\"\n\t\tfilter_value = self.__filter.get_value()\n\t\tif filter_value in [1, 2, 3, 4]:\n\t\t\t# Filters 1, 2, 3 and 4 rely on the center person\n\t\t\tself.__pid.set_available(True)\n\t\telse:\n\t\t\t# The rest don't\n\t\t\tself.__pid.set_available(False)\n\n\tdef __living_changed(self):\n\t\t\"\"\"\n\t\tHandle a change in the living option\n\t\t\"\"\"\n\t\tif self.__living.get_value() == INCLUDE_LIVING_VALUE:\n\t\t\tself.__yearsafterdeath.set_available(False)\n\t\telse:\n\t\t\tself.__yearsafterdeath.set_available(True)\n\n\tdef __pages_contents_changed(self):\n\t\tnb = self.__pages_number.get_value()\n\t\tfor i in range(NB_TOTAL_PAGES_MAX):\n\t\t\tif (i < nb):\n\t\t\t\tself.__page_content[i].set_available(True)\n\t\t\telse:\n\t\t\t\tself.__page_content[i].set_available(False)\n\n\tdef __placemap_options_changed(self):\n\t\t\"\"\"\n\t\tHandles the changing nature of the place map Options\n\t\t\"\"\"\n\t\t# get values for all Place Map Options tab...\n\t\tplace_active = self.__inc_places.get_value()\n\t\tplace_map_active = self.__placemappages.get_value()\n\t\tfamily_active = self.__familymappages.get_value()\n\t\tmapservice_opts = self.__mapservice.get_value()\n\t\t# google_opts = self.__googleopts.get_value()\n\n\t\tif (place_active):\n\t\t\tself.__placemappages.set_available(True)\n\t\t\tself.__familymappages.set_available(True)\n\t\t\tself.__mapservice.set_available(True)\n\t\t\t# self.__googleopts.set_available(True)\n\n\t\tif (place_map_active or family_active):\n\t\t\tself.__mapservice.set_available(True)\n\t\telse:\n\t\t\tself.__mapservice.set_available(False)\n\n\t\t# if (family_active and mapservice_opts == \"Google\"):\n\t\t\t# self.__googleopts.set_available(True)\n\t\t# else:\n\t\t\t# self.__googleopts.set_available(False)\n\n\t\tif (not place_active):\n\t\t\tself.__placemappages.set_available(False)\n\t\t\tself.__familymappages.set_available(False)\n\t\t\tself.__mapservice.set_available(False)\n\t\t\t# self.__googleopts.set_available(False)\n\n\tdef __svg_tree_dup_changed(self):\n\t\t\"\"\"\n\t\tHandles the duplicate color enable\n\t\t\"\"\"\n\t\tenable = self.__svg_tree_dup.get_value()\n\t\tself.__svg_tree_color_dup.set_available(enable)\n","repo_name":"daleathan/gramps-addons-code-svn","sub_path":"contrib/DynamicWeb/dynamicweb.py","file_name":"dynamicweb.py","file_ext":"py","file_size_in_byte":148390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"2127931495","text":"from person import Person\nfrom fight import Fight\n\nclass Fighter(Person):\n\n def __init__(self, name: str, age: int = 0, wealth: int = 0, skills: dict = None) -> None:\n \"\"\"\n Constructor for the Fighter class.\n\n :type skills: object\n :param name: str\n :param age: int\n :param wealth: int\n \"\"\"\n\n Person.__init__(self, name, age, wealth)\n self.__skills = None\n self.challenged = []\n\n if age < 18:\n print(name + \" cannot be a fighter.\")\n return\n\n self.__skills = {\"spear\": 0,\n \"unarmed_combat\": 0,\n \"mace\": 0,\n \"broadsword\": 0}\n\n if skills:\n self.__skills = skills\n if self.__skills[\"spear\"] > 10 or self.__skills[\"spear\"] < 0:\n print(\"The skill level for spear is invalid.\")\n\n if self.__skills[\"unarmed_combat\"] > 10 or self.__skills[\"unarmed_combat\"] < 0:\n print(\"The skill level for unarmed_combat is invalid.\")\n\n if self.__skills[\"mace\"] > 10 or self.__skills[\"mace\"] < 0:\n print(\"The skill level for mace is invalid.\")\n\n if self.__skills[\"broadsword\"] > 10 or self.__skills[\"broadsword\"] < 0:\n print(\"The skill level for broadsword is invalid.\")\n\n self.level = 2\n\n\n @property\n def getSkills(self) -> dict:\n \"\"\"\n Returns the skills of the fighter.\n\n :return: __skills: dict\n \"\"\"\n if self.age > 18:\n return self.__skills\n\n else:\n return {}\n\n def setSkills(self, newSkills: dict) -> None:\n \"\"\"\n Sets the skills to these new skills\n\n :param newSkills: dict\n :return: None\n \"\"\"\n\n self.__skills = newSkills\n\n def challenge(self, fighter2: object, skill: str) -> None:\n \"\"\"\n Challenges another fighter.\n\n :param fighter2: object\n :param skill: str\n :return: None\n \"\"\"\n # A fighter cannot fight themselves\n\n if not self.isEqual(fighter2):\n\n if not self.getSkills and not isinstance(fighter2, Fighter):\n print(self.getName + \" and \" + fighter2.getName + \" both are not fighters and hence cannot fight.\")\n\n elif not self.getSkills:\n print(self.getName + \" is not a fighter.\")\n return\n\n elif not isinstance(fighter2, Fighter):\n print(fighter2.getName + \" is not a fighter.\")\n return\n\n elif not fighter2.getSkills:\n print(fighter2.getName + \" is not a fighter.\")\n return\n\n # If they are both fighters:\n\n elif self.getSkills and fighter2.getSkills:\n\n # If the wealth of both the fighters is more than 0\n\n if self.getWealth <= 0:\n print(self.getName + \" has no wealth to fight.\")\n return None\n\n if fighter2.getWealth <= 0:\n print(fighter2.getName + \" has no wealth to fight.\")\n return None\n\n # If the skill they are fighting with does not exist\n\n if skill not in self.getSkills or skill not in fighter2.getSkills:\n print(skill + \" is not a valid skill for the fighter\")\n return None\n\n # If the skill they are using is over 0 then they fight.\n\n duel = Fight(self, fighter2, skill)\n winner = duel.winner()\n\n else:\n print(\"A fighter cannot fight themselves!\")\n return None\n\n def withdraw(self, withdrawFighter: str) -> None:\n \"\"\"\n Withdraws the fighter's name from the list\n\n :param withdrawFighter: str\n :return: None\n \"\"\"\n if self.getSkills and withdrawFighter.getSkills:\n\n pos = -1\n\n for x in range(0, len(self.challenged)):\n if withdrawFighter == self.challenged[x].getFighter1 or withdrawFighter == self.challenged[x].getFighter1:\n pos = x\n else:\n pass\n\n if x > -1:\n self.challenged.remove(x)\n\n else:\n print(self.getName + \" has not challenged \" + withdrawFighter.getName)\n\n else:\n print(self.getName + \" does not have any challenges since it is not a fighter.\")\n\n def __str__(self) -> str:\n \"\"\"\n Returns the state of the Fighter.\n\n :return: str\n \"\"\"\n if self.getSkills:\n return Person.__str__(self) + \" \" + self.getName + \"'s skills are: \\n\\tSpear: \" + str(\n self.getSkills[\"spear\"]) \\\n + \"\\n\\tUnarmed Combat: \" + str(self.getSkills[\"unarmed_combat\"]) + \"\\n\\tMace: \" + str(\n self.getSkills[\"mace\"]) \\\n + \"\\n\\tBroadsword: \" + str(self.getSkills[\"broadsword\"])\n\n else:\n return Person.__str__(self)\n","repo_name":"Avik9/Battle-Time","sub_path":"fighter.py","file_name":"fighter.py","file_ext":"py","file_size_in_byte":5041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"20581016909","text":"import asyncio\r\n\r\nimport discord\r\nfrom discord import client\r\nfrom discord.ext import commands\r\n\r\n\r\n\r\n\r\nclass admin(commands.Cog):\r\n\r\n def __init__(self, client):\r\n self.client = client\r\n\r\n\r\n @commands.command()\r\n @commands.has_permissions(ban_members=True)\r\n async def kick(self, ctx, member: discord.Member, *, reason=None):\r\n await ctx.message.delete(delay=0)\r\n await member.send(f\"You was kicked from server\")\r\n await ctx.send(f\"Member {member.mention} was kicked from this server!\")\r\n await member.kick(reason=reason)\r\n\r\n @commands.command()\r\n @commands.has_permissions(ban_members=True)\r\n async def ban(self, ctx, member: discord.Member, *, reason=None):\r\n await ctx.send(f\"Member {member.mention} was banned on this server\")\r\n await member.ban(reason=reason)\r\n await ctx.message.delete(delay=0)\r\n\r\n @commands.command()\r\n @commands.has_permissions(ban_members=True)\r\n async def unban(self, ctx, user_id: int):\r\n user = await client.fetch_user(user_id)\r\n await ctx.guild.unban(user)\r\n await ctx.message.delete(delay=0)\r\n\r\n @commands.command()\r\n @commands.has_permissions(ban_members=True)\r\n async def ping(self, ctx):\r\n await ctx.send(\"pong!\")\r\n\r\n\r\n\r\n\r\nasync def setup(client):\r\n await client.add_cog(admin(client))","repo_name":"nkplka/discord","sub_path":"cogs/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"12073523384","text":"#!/usr/bin/env python\n#######\n# To allow this module to be imported by other triggers\n# execute the commands below:\n# $ mkdir -p /usr/share/foreman-community/hooks\n# $ touch /usr/share/foreman-community/hooks/__init__.py\n# $ cp functions.py /usr/share/foreman-community/hooks/\n########\nimport json\nimport sys\nimport tempfile\n\nHOOK_TEMP_DIR = \"/usr/share/foreman/tmp\"\n\n# HOOK_EVENT = update, create, before_destroy etc.\n# HOOK_OBJECT = to_s representation of the object, e.g. host's fqdn\nHOOK_EVENT, HOOK_OBJECT = (sys.argv[1], sys.argv[2])\n\n\ndef get_json_hook():\n '''\n Create JSON object to be imported by hook/trigger\n Saves the data received via stdin to file.\n It does not require to save to a file, but it may be useful\n to troubleshooting.\n '''\n\n with tempfile.NamedTemporaryFile(\n dir=HOOK_TEMP_DIR,\n # set to False for troubleshooting\n delete=True,\n prefix=\"foreman_hooks.\") as hook:\n\n json_hook = sys.stdin.read()\n hook.file.write(json_hook)\n hook.file.flush()\n return json.loads(json_hook)\n","repo_name":"theforeman/foreman_hooks","sub_path":"examples/python/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"67"}
+{"seq_id":"40283674361","text":"from itertools import islice\nfrom types import SimpleNamespace\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport numpy as np\nimport pickle\n\nclass Measurement:\n def __init__(self, params):\n self.params = params\n self.x = []\n self.w = []\n self.v = []\n self.noise = []\n self.LQG = []\n\n if params.scheme in ['noisy_lloyd_max', 'separate']:\n # Quantization index translated into bits\n self.bits = []\n # Entire history believed by the decoder (at each step)\n self.decoded_bits_history = []\n self.correctly_decoded = []\n\n def record(self, sim):\n self.x.append(sim.plant.x)\n self.w.append(sim.plant.w)\n self.v.append(sim.plant.v)\n self.noise.append(sim.channel.last_noise)\n self.LQG.append(sim.LQG.evaluate(sim.t))\n self.channel_average_power = sim.channel.average_power()\n\n if hasattr(self, 'bits'):\n self.bits = sim.encoder.get_bits_history()\n self.decoded_bits_history.append(list(\n sim.decoder.stack_decoder.first_nodes[-1].input_history()))\n self.correctly_decoded.append(\n all((word == history_word).all()\n for word, history_word in \\\n zip(self.bits, self.decoded_bits_history[-1])))\n print(\"Correctly decoded: {}\".format(self.correctly_decoded[-1]))\n\n def save(self, filename):\n with open(filename, 'wb') as f:\n pickle.dump(self, f)\n\n @staticmethod\n def load(filename):\n with open(filename, 'rb') as f:\n measurement = pickle.load(f)\n assert isinstance(measurement, Measurement)\n return measurement\n\n @staticmethod\n def average(measurements):\n new = Measurement(measurements[0].params)\n\n def average_sequence(sequences):\n sequences = [np.array(sequence).flatten() for sequence in sequences]\n slices = list(zip(*sequences))\n return np.array(list(map(np.mean, slices)))\n\n new.x = average_sequence(m.x for m in measurements)\n new.w = average_sequence(m.w for m in measurements)\n new.v = average_sequence(m.v for m in measurements)\n new.noise = average_sequence(m.noise for m in measurements)\n new.LQG = average_sequence(m.LQG for m in measurements)\n\n return new\n\n def get_noise_record(self):\n noise = SimpleNamespace()\n\n noise.x1 = self.x[0]\n noise.w_sequence = self.w[:]\n noise.v_sequence = self.v[:]\n noise.n_sequence = list(np.array(self.noise).flatten())\n\n return noise\n\n\n def plot(self, label=None):\n self.plot_setup()\n self.plot_LQG(label=label)\n self.plot_bounds()\n if hasattr(self, 'correctly_decoded'):\n self.plot_correctly_decoded()\n plt.legend()\n\n def plot_setup(self, label=\"Time [steps]\"):\n plt.xlabel(label)\n plt.grid()\n\n def plot_x(self):\n plt.plot(list(range(len(self.x))), self.x)\n plt.ylabel(\"Plant state\")\n\n def plot_LQG(self, label=None, *args, **kwargs):\n plt.plot(list(range(len(self.LQG))), 10 * np.log10(self.LQG),\n label=label, *args, **kwargs)\n plt.ylabel(r\"$\\bar{J}_t$ [dB]\")\n\n def plot_bounds(self, lower_label=\"Theoretical average lower bound\",\n upper_label=\"Theoretical prediction\",\n lower_args=['--'], lower_kwargs={},\n upper_args=['--'], upper_kwargs={}):\n params = self.params\n\n # Upper bound\n if params.analog and hasattr(params, 'SDR0'):\n plt.plot((1, len(self.LQG)),\n 10 * np.log10(params.LQR_inf_upper_bound()) * np.ones(2),\n *upper_args, label=upper_label, **upper_kwargs)\n\n # Lower bound\n if params.analog:\n plt.plot((1, len(self.LQG)),\n 10 * np.log10(params.LQR_inf_lower_bound()) * np.ones(2),\n *lower_args, label=lower_label, **lower_kwargs)\n\n def plot_correctly_decoded(self, y=0):\n RECTANGLE_HEIGHT = 0.8\n\n # Find intervals of consecutive Trues\n intervals = []\n start = None\n for (t, good) in enumerate(self.correctly_decoded, 1):\n if not start and not good:\n start = t\n elif start and (good or t == len(self.correctly_decoded)):\n intervals.append((start, t))\n start = None\n\n for i, (start, stop) in enumerate(intervals):\n print(\"({}, {})\".format(start, stop))\n plt.gca().add_patch(\n patches.Rectangle(\n (start, y - RECTANGLE_HEIGHT/2),\n stop - start,\n RECTANGLE_HEIGHT,\n label=\"Decoding errors\" if i == 0 else None,\n color='purple'\n )\n )\n","repo_name":"eliasrg/SURF2017","sub_path":"code/measurements.py","file_name":"measurements.py","file_ext":"py","file_size_in_byte":4960,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"67"}
+{"seq_id":"72229322132","text":"from slack_ttt.base_logic_source import BaseLogicSource\nfrom slack_ttt.response_message import ResponseMessageBuilder as RMB\nfrom simple_salesforce import Salesforce\nfrom settings import (\n SFDC_USERNAME,\n SFDC_PASSWORD,\n SFDC_TOKEN\n )\n\nclass SalesforceLogicSource(BaseLogicSource):\n\n def initialize(self, params):\n self.sfdc = Salesforce(username=SFDC_USERNAME,\n password=SFDC_PASSWORD,\n security_token=SFDC_TOKEN)\n\n self.channel_id = params['channel_id'][0]\n self.requester = '@' + params['user_name'][0]\n self.command = None\n if 'text' in params:\n self.command = params['text'][0]\n\n def __sfdc_rest_call(self, resource):\n return self.sfdc.apexecute('handleTTTCommand',\n method='POST',\n data={'resource': resource,\n 'channelId': self.channel_id,\n 'requestor':self.requester,\n 'command': self.command\n })\n\n def __generate_response(self, response_map):\n print('*** RESPONSE: ',response_map);\n game_response_type = response_map['game_response_type']\n values = response_map['values'].split(',')\n slack_response_type = None\n if \"slack_response_type\" in response_map:\n slack_response_type = response_map[\"slack_response_type\"]\n else:\n slack_response_type = 'Ephemeral'\n\n response = None\n if 'response' in response_map:\n response = response_map['response']\n\n return RMB.respond(None,\n game_response_type=game_response_type,\n values=values,\n slack_response_type=slack_response_type,\n response=response)\n\n\n\n def new_game(self):\n response = self.__sfdc_rest_call('/ttt')\n return self.__generate_response(response['body'])\n\n\n def game_help(self):\n super().game_help()\n \"\"\"Help handler which provides information about the game and how to play it.\n\n :return:\n \"\"\"\n return RMB.respond(None,\n game_response_type='help_text',\n values=[])\n\n def accept_game(self):\n response = self.__sfdc_rest_call('/ttt-accept')\n return self.__generate_response(response['body'])\n\n def decline_game(self):\n response = self.__sfdc_rest_call('/ttt-decline')\n return self.__generate_response(response['body'])\n\n def display_board(self):\n response = self.__sfdc_rest_call('/ttt-board')\n return self.__generate_response(response['body'])\n\n def play_move(self):\n response = self.__sfdc_rest_call('/ttt-move')\n return self.__generate_response(response['body'])\n\n def end_game(self):\n response = self.__sfdc_rest_call('/ttt-end')\n return self.__generate_response(response['body'])","repo_name":"dineshrajpurohit/slack_ttt","sub_path":"slack_ttt/salesforce/salesforce_logic.py","file_name":"salesforce_logic.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"8812218353","text":"import json\nimport os\n\nimport pytest\nfrom jinja2 import DictLoader\nfrom schemachange.cli import JinjaTemplateProcessor\n\n\ndef test_from_environ_not_set():\n processor = JinjaTemplateProcessor(\"\", None)\n\n # overide the default loader\n templates = {\"test.sql\": \"some text {{ env_var('MYVAR') }}\"}\n processor.override_loader(DictLoader(templates))\n\n with pytest.raises(ValueError) as e:\n context = processor.render(\"test.sql\", None, True)\n\n assert str(e.value) == \"Could not find environmental variable MYVAR and no default value was provided\"\n\n\ndef test_from_environ_set():\n processor = JinjaTemplateProcessor(\"\", None)\n\n # set MYVAR env variable\n os.environ[\"MYVAR\"] = \"myvar_from_environment\"\n\n # overide the default loader\n templates = {\"test.sql\": \"some text {{ env_var('MYVAR') }}\"}\n processor.override_loader(DictLoader(templates))\n\n context = processor.render(\"test.sql\", None, True)\n\n # unset MYVAR env variable\n del os.environ[\"MYVAR\"]\n\n assert context == \"some text myvar_from_environment\"\n\n\ndef test_from_environ_not_set_default():\n processor = JinjaTemplateProcessor(\"\", None)\n\n # overide the default loader\n templates = {\"test.sql\": \"some text {{ env_var('MYVAR', 'myvar_default') }}\"}\n processor.override_loader(DictLoader(templates))\n\n context = processor.render(\"test.sql\", None, True)\n\n assert context == \"some text myvar_default\"\n","repo_name":"Snowflake-Labs/schemachange","sub_path":"tests/test_jinja_env_var_template.py","file_name":"test_jinja_env_var_template.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","stars":407,"dataset":"github-code","pt":"67"}
+{"seq_id":"13679918612","text":"#!/usr/bin/python3\n'''Modules to be imported'''\nimport MySQLdb\nimport sys\n\n\ndef main(args):\n '''main function'''\n\n # check if passedargs are as needed\n if (len(args) != 5):\n print(\"checknumber of args\")\n sys.exit(1)\n\n # create a database connection\n db_connection = MySQLdb.connect(host=\"localhost\",\n port=3306,\n user=args[1],\n passwd=args[2],\n db=args[3])\n\n # create a cursor\n cur = db_connection.cursor()\n\n # sql query\n query = \"SELECT cities.name FROM cities JOIN states ON\\\n cities.state_id = states.id WHERE states.name = '{}' ORDER BY cities.id ASC\"\n\n formatted = query.format(args[4])\n\n # execute the query\n cur.execute(formatted)\n\n # loop todisplay results\n print(\", \".join(row[0] for row in cur.fetchall()))\n\n # close connections\n cur.close()\n db_connection.close()\n\n\n# Hey, I won't run when I'mimported\nif __name__ == \"__main__\":\n main(sys.argv)\n","repo_name":"LionMara/alx-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/5-filter_cities.py","file_name":"5-filter_cities.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"}
+{"seq_id":"15280369221","text":"import textwrap\nfrom django.db.models import Max\n\nfrom src.models import *\n\n\ndef parse_history():\n hist_list = []\n hist = HistoryItem.objects.all()\n for h in hist:\n lines = h.content\n lines = [line for line in lines.split('\\n') if line.strip()]\n ls_1_flag = 0\n ls_2_flag = 0\n for i in xrange(len(lines)):\n lines[i] = lines[i].rstrip()\n if lines[i][0] == \"#\":\n lines[i] = \"\" + lines[i][1:] + \"
\"\n elif lines[i][0] != '-':\n if lines[i][0] == \"!\":\n lines[i] = \"by \" + lines[i][1:] + \"
\"\n else:\n lines[i] = \"\" + lines[i] + \"
\"\n\n else:\n if lines[i][:2] != '-\\\\':\n lines[i] = \"- \" + lines[i][1:] + \"
\"\n if ls_1_flag:\n lines[i] = \"
\" + lines[i]\n ls_1_flag = i\n\n else:\n lines[i] = \"\" + lines[i][2:] + \" \"\n if ls_2_flag < ls_1_flag:\n lines[i] = \"\" + lines[i]\n ls_2_flag = i\n lines.append(\"
\")\n date_string = h.date.strftime(\"%b %d, %Y (%a)\")\n lines.insert(0, \"%s
\" % date_string)\n hist_list.insert(0, ''.join(lines))\n\n return hist_list\n\n\ndef get_rmdb_stats():\n rmdb_ids = [d.values()[0] for d in RMDBEntry.objects.values('rmdb_id').distinct()]\n N_all = 0\n N_RNA = 0\n # N_RNA = ConstructSection.objects.values('name').distinct().count()\n N_puzzle = 0\n N_eterna = 0\n N_constructs = 0\n N_datapoints = 0\n for rmdb_id in rmdb_ids:\n entries = RMDBEntry.objects.filter(rmdb_id=rmdb_id).order_by('-version')\n if len(entries) >= 0:\n N_all += 1\n N_RNA += len(entries)\n\n if 'RNAPZ' in rmdb_id:\n N_puzzle += 1\n if 'ETERNA' in rmdb_id:\n N_eterna += 1\n e = entries[0]\n N_datapoints += e.data_count\n N_constructs += e.construct_count\n return {'N_all': N_all, 'N_RNA': N_RNA, 'N_puzzle': N_puzzle, 'N_eterna': N_eterna, 'N_constructs': N_constructs, 'N_datapoints': N_datapoints}\n\n\ndef browse_json_list(names_d):\n constructs = []\n for c in names_d:\n entries = RMDBEntry.objects.filter(constructsection__name=c['name']).filter(status='PUB').order_by('rmdb_id', '-version')\n entry_ids = []\n (SS_entries, TT_entries, MA_entries, MM_entries, MR_entries) = ([], [], [], [], [])\n\n for e in entries:\n if e.rmdb_id not in entry_ids:\n entry_ids.append(e.rmdb_id)\n comment = e.comments.split()\n for i, m in enumerate(comment):\n if len(m) > 40:\n comment[i] = ' '.join(textwrap.wrap(m, 40))\n entry = {'rmdb_id': e.rmdb_id, 'version': e.version, 'construct_count': e.construct_count, 'data_count': e.data_count, 'authors': e.authors, 'comments': ' '.join(comment), 'title': e.publication.title, 'latest': e.supercede_by}\n if e.type == \"SS\" or e.type == \"DC\":\n SS_entries.append(entry)\n elif e.type == \"TT\":\n TT_entries.append(entry)\n elif e.type == \"MM\":\n MM_entries.append(entry)\n elif e.type == \"MR\":\n MR_entries.append(entry)\n elif e.type == \"MA\":\n MA_entries.append(entry)\n\n constructs.append({'name': c['name'], 'SS_entry': SS_entries, 'TT_entry': TT_entries, 'MM_entry': MM_entries, 'MA_entry': MA_entries, 'MR_entry': MR_entries})\n return constructs\n\n\ndef get_rmdb_category(flag):\n latest_versions = RMDBEntry.objects.values('rmdb_id').annotate(latest_version=Max('version'))\n q_statement = Q()\n for pair in latest_versions:\n q_statement |= (Q(entry__rmdb_id=pair['rmdb_id']) & Q(entry__version=pair['latest_version']))\n\n LatestConstructSec = ConstructSection.objects.filter(q_statement)\n\n if flag == \"puzzle\":\n names_d = LatestConstructSec.filter(name__icontains='Puzzle').values('name').distinct()\n elif flag == \"eterna\":\n names_d = LatestConstructSec.filter(name__icontains='EteRNA').values('name').distinct()\n else:\n names_d = LatestConstructSec.exclude(name__icontains='EteRNA').exclude(name__icontains='Puzzle').values('name').distinct()\n names_d = names_d.order_by('name')\n return browse_json_list(names_d)\n\n","repo_name":"DasLab/Server_RMDB","sub_path":"src/util/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":4646,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"67"}
+{"seq_id":"42010684078","text":"def check_cycle(cycle, x):\n if (cycle-20) % 40 == 0:\n return cycle * x\n\n return 0\n\ndef check_x(cycle, x):\n c = (cycle - 1) % 40\n\n if x-1 <= c <= x+1:\n return True \n \n return False\n\n\ndef count_signal_strength(lines):\n x = 1\n cycle = 0\n signal_strength_sum = 0\n letters = \"\"\n\n for line in lines:\n\n if line[0:4] == \"noop\":\n cycle += 1\n signal_strength_sum += check_cycle(cycle, x)\n if check_x(cycle, x):\n letters += \"#\"\n else:\n letters += \" \"\n else:\n val = line.split(\" \")\n\n for i in range(0, 2):\n cycle += 1\n signal_strength_sum += check_cycle(cycle, x)\n if check_x(cycle, x):\n letters += \"#\"\n else:\n letters += \" \"\n\n if i == 1:\n x += int(val[1])\n \n return signal_strength_sum, letters\n\nwith open(\"input.txt\", \"r\") as f:\n lines = [l.strip() for l in f.readlines()]\n\nday10 = count_signal_strength(lines)\nprint(\"Part 1: The sum of the signal strengths is\", day10[0])\n\nletters = \"\"\nfor i in range(0,len(day10[1])):\n if i % 40 == 0:\n letters += \"\\n\"\n letters += day10[1][i]\n\nprint(\"Part 2: The solution is:\", \"\\n\", letters)","repo_name":"sagakortesaari/adventofcode","sub_path":"2022/10/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"2314841057","text":"from PyQt5.QtWidgets import QMessageBox\n\n\ndef alert(self, str, call_code):\n dlg = QMessageBox(self)\n dlg.setWindowTitle(\"Предупреждение\")\n if call_code == 1:\n dlg.setText(f\"Вы взяли:\\n{str}\")\n elif call_code == 2:\n dlg.setText(f\"Вы вернули:\\n{str}\")\n button = dlg.exec()\n if button == QMessageBox.Ok:\n print(\"OK!\")","repo_name":"Viteeeq/KMU-Project-test","sub_path":"app/alerts.py","file_name":"alerts.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"17915681670","text":"#!/usr/bin/env python3\n\n#For use with normally closed (NC) Reed Switch connected to ground & GPIO input\n#If using normally open (NO), simply reverse the booleans.\n\nimport RPi.GPIO as GPIO\nimport time\nimport smtplib\nimport _thread\nimport cred\n\ntry:\n need_clean = False\n\n #Message Template\n #Leading '\\n' is required for sending an email with ':' (SMS/MMS Gateway)\n MSG = '\\nDoor was '\n DOOR_MSG = {True:'opened', False:'closed'}\n\n #Setting up connection to SMTP Server for sending email/sms.\n print('Setting up SMS...')\n #Function to call on new thread\n #Because of race conditions, this needs to be done quickly or on diff thread\n def send_msg(opened:bool):\n #Replace args with your email provider's SMTP details\n server = smtplib.SMTP( \"smtp.gmail.com\", 587 )\n server.starttls()\n server.login( cred.FROM, cred.PASS )\n #Compile message string to print and send.\n #Ex: '\\nDoor was closed at 5:50:20 PM'\n #This way is used because it is quickest and we have race conditions!\n str_print =''.join([MSG, DOOR_MSG[opened], ' at ',\n time.strftime('%I:%M:%S %p')])\n print(str_print)\n server.sendmail(cred.FROM, cred.TO, str_print)\n server.quit()\n\n\n\n #Initializing GPIO\n print('Setting up hardware...')\n PIN = 12\n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)\n\n #next_state to check for to send message\n next_state = True\n\n need_clean = True\n\n #Running actual program\n print('Ready!')\n #Run infinitely\n while True:\n #Check for next state\n if GPIO.input(PIN) == next_state:\n #Send message on different thread\n _thread.start_new_thread(send_msg, (next_state,))\n #Negate next_state\n next_state = not next_state\n time.sleep(0.3)\n \nexcept KeyboardInterrupt:\n GPIO.cleanup() #For Keyboard Interrupt exit\n need_clean = False\n\nif need_clean: \n GPIO.cleanup() #For normal exit\nprint('\\nEnd!')\n","repo_name":"proganalysis/python3_types","sub_path":"Result/4079files/source_2/3789.py","file_name":"3789.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"449183580","text":"import socket\nimport sys\nfrom datetime import timedelta\nfrom typing import AnyStr, IO, List, Optional, TYPE_CHECKING, Tuple, Union\n\nimport requests\nimport requests.adapters\nfrom requests import Response\nfrom urllib3.connection import HTTPConnection\n\nfrom azure.core.tracing.decorator import distributed_trace\nfrom azure.core.tracing import SpanKind\n\nfrom azure.kusto.data._telemetry import Span, MonitoredActivity\n\nfrom .client_base import ExecuteRequestParams, _KustoClientBase\nfrom .client_request_properties import ClientRequestProperties\nfrom .data_format import DataFormat\nfrom .exceptions import KustoClosedError, KustoNetworkError\n\nfrom .kcsb import KustoConnectionStringBuilder\nfrom .response import KustoResponseDataSet, KustoStreamingResponseDataSet\nfrom .streaming_response import JsonTokenReader, StreamingDataSetEnumerator\n\nif TYPE_CHECKING:\n pass\n\n\nclass HTTPAdapterWithSocketOptions(requests.adapters.HTTPAdapter):\n def __init__(self, *args, **kwargs):\n self.socket_options = kwargs.pop(\"socket_options\", None)\n super(HTTPAdapterWithSocketOptions, self).__init__(*args, **kwargs)\n\n def __getstate__(self):\n state = super(HTTPAdapterWithSocketOptions, self).__getstate__()\n state[\"socket_options\"] = self.socket_options\n return state\n\n def init_poolmanager(self, *args, **kwargs):\n if self.socket_options is not None:\n kwargs[\"socket_options\"] = self.socket_options\n super(HTTPAdapterWithSocketOptions, self).init_poolmanager(*args, **kwargs)\n\n\nclass KustoClient(_KustoClientBase):\n \"\"\"\n Kusto client for Python.\n The client is a wrapper around the Kusto REST API.\n To read more about it, go to https://docs.microsoft.com/en-us/azure/kusto/api/rest/\n\n The primary methods are:\n `execute_query`: executes a KQL query against the Kusto service.\n `execute_mgmt`: executes a KQL control command against the Kusto service.\n \"\"\"\n\n _mgmt_default_timeout = timedelta(hours=1)\n _query_default_timeout = timedelta(minutes=4)\n _streaming_ingest_default_timeout = timedelta(minutes=10)\n _client_server_delta = timedelta(seconds=30)\n\n # The maximum amount of connections to be able to operate in parallel\n _max_pool_size = 100\n\n def __init__(self, kcsb: Union[KustoConnectionStringBuilder, str]):\n \"\"\"\n Kusto Client constructor.\n :param kcsb: The connection string to initialize KustoClient.\n :type kcsb: azure.kusto.data.KustoConnectionStringBuilder or str\n \"\"\"\n super().__init__(kcsb, False)\n\n # Create a session object for connection pooling\n self._session = requests.Session()\n\n adapter = HTTPAdapterWithSocketOptions(\n socket_options=(HTTPConnection.default_socket_options or []) + self.compose_socket_options(), pool_maxsize=self._max_pool_size\n )\n self._session.mount(\"http://\", adapter)\n self._session.mount(\"https://\", adapter)\n\n def close(self):\n if not self._is_closed:\n self._session.close()\n if self._aad_helper:\n self._aad_helper.close()\n super().close()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()\n\n def set_proxy(self, proxy_url: str):\n super().set_proxy(proxy_url)\n self._session.proxies = {\"http\": proxy_url, \"https\": proxy_url}\n\n def set_http_retries(self, max_retries: int):\n \"\"\"\n Set the number of HTTP retries to attempt\n \"\"\"\n adapter = HTTPAdapterWithSocketOptions(\n socket_options=(HTTPConnection.default_socket_options or []) + self.compose_socket_options(),\n pool_maxsize=self._max_pool_size,\n max_retries=max_retries,\n )\n self._session.mount(\"http://\", adapter)\n self._session.mount(\"https://\", adapter)\n\n @staticmethod\n def compose_socket_options() -> List[Tuple[int, int, int]]:\n # Sends TCP Keep-Alive after MAX_IDLE_SECONDS seconds of idleness, once every INTERVAL_SECONDS seconds, and closes the connection after MAX_FAILED_KEEPALIVES failed pings (e.g. 20 => 1:00:30)\n MAX_IDLE_SECONDS = 30\n INTERVAL_SECONDS = 180 # Corresponds to Azure Load Balancer Service 4 minute timeout, with 1 minute of slack\n MAX_FAILED_KEEPALIVES = 20\n\n if (\n sys.platform == \"linux\"\n and hasattr(socket, \"SOL_SOCKET\")\n and hasattr(socket, \"SO_KEEPALIVE\")\n and hasattr(socket, \"TCP_KEEPIDLE\")\n and hasattr(socket, \"TCP_KEEPINTVL\")\n and hasattr(socket, \"TCP_KEEPCNT\")\n ):\n return [\n (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),\n (socket.IPPROTO_TCP, socket.TCP_KEEPIDLE, MAX_IDLE_SECONDS),\n (socket.IPPROTO_TCP, socket.TCP_KEEPINTVL, INTERVAL_SECONDS),\n (socket.IPPROTO_TCP, socket.TCP_KEEPCNT, MAX_FAILED_KEEPALIVES),\n ]\n elif (\n sys.platform == \"win32\"\n and hasattr(socket, \"SOL_SOCKET\")\n and hasattr(socket, \"SO_KEEPALIVE\")\n and hasattr(socket, \"TCP_KEEPIDLE\")\n and hasattr(socket, \"TCP_KEEPCNT\")\n ):\n return [\n (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),\n (socket.IPPROTO_TCP, socket.TCP_KEEPIDLE, MAX_IDLE_SECONDS),\n (socket.IPPROTO_TCP, socket.TCP_KEEPCNT, MAX_FAILED_KEEPALIVES),\n ]\n elif sys.platform == \"darwin\" and hasattr(socket, \"SOL_SOCKET\") and hasattr(socket, \"SO_KEEPALIVE\") and hasattr(socket, \"IPPROTO_TCP\"):\n TCP_KEEPALIVE = 0x10\n return [(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1), (socket.IPPROTO_TCP, TCP_KEEPALIVE, INTERVAL_SECONDS)]\n else:\n return []\n\n def execute(self, database: Optional[str], query: str, properties: Optional[ClientRequestProperties] = None) -> KustoResponseDataSet:\n \"\"\"\n Executes a query or management command.\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return: Kusto response data set.\n :rtype: azure.kusto.data.response.KustoResponseDataSet\n \"\"\"\n query = query.strip()\n if query.startswith(\".\"):\n return self.execute_mgmt(database, query, properties)\n return self.execute_query(database, query, properties)\n\n @distributed_trace(name_of_span=\"KustoClient.query_cmd\", kind=SpanKind.CLIENT)\n def execute_query(self, database: Optional[str], query: str, properties: Optional[ClientRequestProperties] = None) -> KustoResponseDataSet:\n \"\"\"\n Execute a KQL query.\n To learn more about KQL go to https://docs.microsoft.com/en-us/azure/kusto/query/\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return: Kusto response data set.\n :rtype: azure.kusto.data.response.KustoResponseDataSet\n \"\"\"\n database = self._get_database_or_default(database)\n Span.set_query_attributes(self._kusto_cluster, database, properties)\n\n return self._execute(self._query_endpoint, database, query, None, self._query_default_timeout, properties)\n\n @distributed_trace(name_of_span=\"KustoClient.control_cmd\", kind=SpanKind.CLIENT)\n def execute_mgmt(self, database: Optional[str], query: str, properties: Optional[ClientRequestProperties] = None) -> KustoResponseDataSet:\n \"\"\"\n Execute a KQL control command.\n To learn more about KQL control commands go to https://docs.microsoft.com/en-us/azure/kusto/management/\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return: Kusto response data set.\n :rtype: azure.kusto.data.response.KustoResponseDataSet\n \"\"\"\n database = self._get_database_or_default(database)\n Span.set_query_attributes(self._kusto_cluster, database, properties)\n\n return self._execute(self._mgmt_endpoint, database, query, None, self._mgmt_default_timeout, properties)\n\n @distributed_trace(name_of_span=\"KustoClient.streaming_ingest\", kind=SpanKind.CLIENT)\n def execute_streaming_ingest(\n self,\n database: Optional[str],\n table: str,\n stream: IO[AnyStr],\n stream_format: Union[DataFormat, str],\n properties: Optional[ClientRequestProperties] = None,\n mapping_name: str = None,\n ):\n \"\"\"\n Execute streaming ingest against this client\n If the Kusto service is not configured to allow streaming ingestion, this may raise an error\n To learn more about streaming ingestion go to:\n https://docs.microsoft.com/en-us/azure/data-explorer/ingest-data-streaming\n :param Optional[str] database: Target database. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str table: Target table.\n :param io.BaseIO stream: stream object which contains the data to ingest.\n :param DataFormat stream_format: Format of the data in the stream.\n :param ClientRequestProperties properties: additional request properties.\n :param str mapping_name: Pre-defined mapping of the table. Required when stream_format is json/avro.\n \"\"\"\n database = self._get_database_or_default(database)\n Span.set_streaming_ingest_attributes(self._kusto_cluster, database, table, properties)\n\n stream_format = stream_format.kusto_value if isinstance(stream_format, DataFormat) else DataFormat[stream_format.upper()].kusto_value\n endpoint = self._streaming_ingest_endpoint + database + \"/\" + table + \"?streamFormat=\" + stream_format\n if mapping_name is not None:\n endpoint = endpoint + \"&mappingName=\" + mapping_name\n\n self._execute(endpoint, database, None, stream, self._streaming_ingest_default_timeout, properties)\n\n def _execute_streaming_query_parsed(\n self,\n database: Optional[str],\n query: str,\n timeout: timedelta = _KustoClientBase._query_default_timeout,\n properties: Optional[ClientRequestProperties] = None,\n ) -> StreamingDataSetEnumerator:\n response = self._execute(self._query_endpoint, database, query, None, timeout, properties, stream_response=True)\n response.raw.decode_content = True\n return StreamingDataSetEnumerator(JsonTokenReader(response.raw))\n\n @distributed_trace(name_of_span=\"KustoClient.streaming_query\", kind=SpanKind.CLIENT)\n def execute_streaming_query(\n self,\n database: Optional[str],\n query: str,\n timeout: timedelta = _KustoClientBase._query_default_timeout,\n properties: Optional[ClientRequestProperties] = None,\n ) -> KustoStreamingResponseDataSet:\n \"\"\"\n Execute a KQL query without reading it all to memory.\n The resulting KustoStreamingResponseDataSet will stream one table at a time, and the rows can be retrieved sequentially.\n\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param timedelta timeout: timeout for the query to be executed\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return KustoStreamingResponseDataSet:\n \"\"\"\n Span.set_query_attributes(self._kusto_cluster, database, properties)\n\n return KustoStreamingResponseDataSet(self._execute_streaming_query_parsed(database, query, timeout, properties))\n\n def _execute(\n self,\n endpoint: str,\n database: Optional[str],\n query: Optional[str],\n payload: Optional[IO[AnyStr]],\n timeout: timedelta,\n properties: Optional[ClientRequestProperties] = None,\n stream_response: bool = False,\n ) -> Union[KustoResponseDataSet, Response]:\n \"\"\"Executes given query against this client\"\"\"\n if self._is_closed:\n raise KustoClosedError()\n self.validate_endpoint()\n request_params = ExecuteRequestParams(\n database,\n payload,\n properties,\n query,\n timeout,\n self._request_headers,\n self._mgmt_default_timeout,\n self._client_server_delta,\n self.client_details,\n )\n json_payload = request_params.json_payload\n request_headers = request_params.request_headers\n timeout = request_params.timeout\n if self._aad_helper:\n request_headers[\"Authorization\"] = self._aad_helper.acquire_authorization_header()\n\n # trace http post call for response\n invoker = lambda: self._session.post(\n endpoint,\n headers=request_headers,\n json=json_payload,\n data=payload,\n timeout=timeout.seconds,\n stream=stream_response,\n allow_redirects=False,\n )\n\n try:\n response = MonitoredActivity.invoke(\n invoker, name_of_span=\"KustoClient.http_post\", tracing_attributes=Span.create_http_attributes(\"POST\", endpoint, request_headers)\n )\n except Exception as e:\n raise KustoNetworkError(endpoint, None if properties is None else properties.client_request_id) from e\n\n if stream_response:\n try:\n response.raise_for_status()\n if 300 <= response.status_code < 400:\n raise Exception(\"Unexpected redirection, got status code: \" + str(response.status))\n return response\n except Exception as e:\n raise self._handle_http_error(e, self._query_endpoint, None, response, response.status_code, response.json(), response.text)\n\n response_json = None\n try:\n if 300 <= response.status_code < 400:\n raise Exception(\"Unexpected redirection, got status code: \" + str(response.status))\n response_json = response.json()\n response.raise_for_status()\n except Exception as e:\n raise self._handle_http_error(e, endpoint, payload, response, response.status_code, response_json, response.text)\n # trace response processing\n return MonitoredActivity.invoke(lambda: self._kusto_parse_by_endpoint(endpoint, response_json), name_of_span=\"KustoClient.processing_response\")\n","repo_name":"Azure/azure-kusto-python","sub_path":"azure-kusto-data/azure/kusto/data/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":15152,"program_lang":"python","lang":"en","doc_type":"code","stars":174,"dataset":"github-code","pt":"72"}
+{"seq_id":"15926714498","text":"import jax.numpy as np\nimport pytest\nfrom jax import grad, vjp\n\nimport phylojax.treelikelihood as treelikelihood\nfrom phylojax.io import read_tree_and_alignment\nfrom phylojax.sitepattern import get_dna_leaves_partials_compressed\nfrom phylojax.tree import postorder_indices, preorder_indices\n\n\ndef extract_branch_lengths(tree):\n nodes = [None] * (len(tree.taxon_namespace) * 2 - 1)\n for node in tree.postorder_node_iter():\n nodes[node.index] = node.edge_length\n nodes.pop()\n return np.array([[float(x) for x in nodes]])\n\n\ndef test_calculate_unrooted_efficient(hello_tree_file, hello_fasta_file, jc69_model):\n tree, dna = read_tree_and_alignment(hello_tree_file, hello_fasta_file, False, False)\n indices = postorder_indices(tree)\n pre_indices = preorder_indices(tree)\n branch_lengths = extract_branch_lengths(tree)\n bls = np.expand_dims(branch_lengths, axis=-1)\n partials, weights = get_dna_leaves_partials_compressed(dna)\n tip_partials = np.array(partials[: len(tree.taxon_namespace)])\n frequencies = np.broadcast_to(\n jc69_model.frequencies, branch_lengths.shape[:-1] + (4,)\n )\n frequencies = np.expand_dims(frequencies, axis=-2)\n mats = jc69_model.p_t(bls)\n mats = np.expand_dims(mats, -3)\n props = np.array([[[1.0]]])\n partials = treelikelihood.calculate_partials(\n tip_partials,\n indices,\n mats,\n props,\n )\n\n # calculate log likelihood at every node using upper partials\n uppers = treelikelihood.calculate_upper_partials(partials, pre_indices, mats)\n for i in range(uppers.shape[0]):\n log_p2 = treelikelihood.calculate_treelikelihood_upper(\n partials[i],\n uppers[i],\n weights,\n mats[:, i, ...],\n frequencies,\n props,\n )\n assert -84.852358 == pytest.approx(float(log_p2), 0.0001)\n\n # analytical derivatives\n expected_gradient = (21.0223, -5.34462, -17.7298, -17.7298)\n dmats = jc69_model.dp_dt(bls)\n dmats = np.expand_dims(dmats, -3)\n likelihoods = frequencies @ np.sum(partials[-1], -3)\n gradient = treelikelihood.calculate_treelikelihood_gradient(\n likelihoods, partials, uppers, weights, dmats, frequencies, props\n )\n assert np.allclose(gradient.squeeze(), expected_gradient, rtol=0.0001)\n\n # analytical derivatives using custom_jvp\n log_p3 = treelikelihood.calculate_treelikelihood_custom(\n branch_lengths, tip_partials, weights, (indices, pre_indices), jc69_model, props\n )\n assert -84.852358 == pytest.approx(float(log_p3), 0.0001)\n g = grad(treelikelihood.calculate_treelikelihood_custom)\n gradient2 = g(\n branch_lengths,\n tip_partials,\n weights,\n (np.array(indices, dtype=np.int32), np.array(pre_indices, dtype=np.int32)),\n jc69_model,\n props,\n )\n assert np.allclose(gradient2.squeeze(), expected_gradient, rtol=0.0001)\n\n\ndef test_calculate_unrooted(hello_tree_file, hello_fasta_file, jc69_model):\n tree, dna = read_tree_and_alignment(hello_tree_file, hello_fasta_file, False, False)\n indices = indices = postorder_indices(tree)\n branch_lengths = extract_branch_lengths(tree)\n bls = np.expand_dims(branch_lengths, axis=-1)\n partials, weights = get_dna_leaves_partials_compressed(dna)\n tip_partials = np.array(partials[: len(tree.taxon_namespace)])\n frequencies = np.broadcast_to(\n jc69_model.frequencies, branch_lengths.shape[:-1] + (4,)\n )\n\n def fn(bls):\n mats = jc69_model.p_t(bls)\n return treelikelihood.calculate_treelikelihood(\n tip_partials,\n weights,\n indices,\n np.expand_dims(mats, -3),\n np.expand_dims(frequencies, axis=-2),\n np.array([[[1.0]]]),\n )\n\n y, vjp_fn = vjp(fn, bls)\n gradient = vjp_fn(np.ones(y.shape))[0]\n expected_gradient = (21.0223, -5.34462, -17.7298, -17.7298)\n assert all(\n [\n a == pytest.approx(b, 0.0001)\n for a, b in zip(expected_gradient, np.squeeze(gradient))\n ]\n )\n\n mats = jc69_model.p_t(bls)\n log_p = treelikelihood.calculate_treelikelihood(\n tip_partials,\n weights,\n indices,\n np.expand_dims(mats, -3),\n np.expand_dims(frequencies, axis=-2),\n np.array([[[1.0]]]),\n )\n assert -84.852358 == pytest.approx(float(log_p), 0.0001)\n\n\ndef test_calculate_likelihood_rooted(flu_a_tree_file, flu_a_fasta_file, jc69_model):\n tree, dna = read_tree_and_alignment(flu_a_tree_file, flu_a_fasta_file, True, True)\n indices = postorder_indices(tree)\n partials, weights = get_dna_leaves_partials_compressed(dna)\n tip_partials = np.array(partials[: len(tree.taxon_namespace)])\n branch_lengths = extract_branch_lengths(tree) * 0.001\n mats = jc69_model.p_t(np.expand_dims(branch_lengths, axis=-1))\n frequencies = np.broadcast_to(\n jc69_model.frequencies, branch_lengths.shape[:-1] + (4,)\n )\n\n log_p = treelikelihood.calculate_treelikelihood(\n tip_partials,\n weights,\n indices,\n np.expand_dims(mats, -3),\n np.expand_dims(frequencies, axis=-2),\n np.array([[[1.0]]]),\n )\n assert -4777.616349 == pytest.approx(float(log_p), 0.0001)\n","repo_name":"4ment/phylojax","sub_path":"test/test_likelihood.py","file_name":"test_likelihood.py","file_ext":"py","file_size_in_byte":5258,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"34703218471","text":"# @TIME : 2019/7/29 下午10:42\n# @File : tire_tree.py\n\nclass TrieNode:\n\n def __init__(self):\n self.children = [None] * 26\n self.isEndOfWord = False\n\nclass Tire:\n\n def __init__(self):\n self.root = self.getNode()\n\n def getNode(self):\n return TrieNode()\n\n def _charToIndex(self, ch):\n \"\"\"Converts key current character into index\n # use only 'a' through 'z' and lower case \"\"\"\n rel = ord(ch) - ord('a')\n return rel\n\n def insert(self, key):\n pCrawl = self.root # 头指针还保留着, 这个设计不错\n length = len(key)\n for level in range(length):\n a = key[level]\n index = self._charToIndex(key[level])\n aa = pCrawl.children[index]\n if not pCrawl.children[index]:\n pCrawl.children[index] = self.getNode()\n\n pCrawl = pCrawl.children[index] # 像LinkList的节点移动\n\n pCrawl.isEndOfWord = True\n\n def search(self, key):\n pCrawl = self.root\n length = len(key)\n for level in range(length):\n a = key[level]\n index = self._charToIndex(key[level])\n if not pCrawl.children[index]:\n return False\n pCrawl = pCrawl.children[index]\n\n return pCrawl != None and pCrawl.isEndOfWord\n\n\nif __name__ == \"__main__\":\n\n keys = ['the', 'a', 'there', 'anaswe', 'any', 'by', 'their']\n\n t = Tire()\n\n for key in keys:\n t.insert(key)\n\n a = 1\n print(t.search('there'))\n\n\n","repo_name":"yjfiejd/2019xuexi","sub_path":"Leetcode-practice/tire_tree.py","file_name":"tire_tree.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"40644446530","text":"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom confspirator import groups as conf_group\nfrom confspirator import fields as conf_field\nimport paramiko\n\nfrom adjutant.config import CONF\n\nfrom adjutant_moc.actions import serializers\nfrom adjutant_moc.actions import base\n\n\nclass Mailman(object):\n\n def __init__(self, hostname, port, username, key):\n self.hostname = hostname\n self.port = port\n self.username = username\n self.key = key\n self.client = paramiko.client.SSHClient()\n\n\n def __enter__(self):\n self.client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n self.client.connect(\n hostname=self.hostname,\n port=self.port,\n username=self.username,\n pkey=paramiko.RSAKey.from_private_key_file(self.key))\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.client.close()\n\n def _execute(self, command):\n stdin, stdout, stderr = self.client.exec_command(command)\n\n errors = stderr.read()\n if errors:\n raise ConnectionError(errors)\n\n # Note(knikolla): Not entirely sure if closing before reading is fine.\n r = stdout.read().decode('utf-8').split('\\n')\n return r\n\n def is_already_subscribed(self, email, list):\n command = ('/usr/lib/mailman/bin/list_members %s' % list)\n return email in self._execute(command)\n\n def subscribe(self, email, list):\n command = (\n 'echo %s | /usr/lib/mailman/bin/add_members -r - %s'\n % (email, list)\n )\n self._execute(command)\n\n\n# TODO knikolla: there are going to be issues here when we\n# add invited users, since get email returns the one who created the task.\nclass MailingListSubscribeAction(base.MocBaseAction):\n\n required = [\n 'email'\n ]\n\n serializer = serializers.MailingListSubscribeSerializer\n\n config_group = conf_group.DynamicNameConfigGroup(\n children=[\n conf_field.StrConfig(\n \"private_key\",\n help_text=\"Location of private key for mailing list server.\",\n default=\"/.ssh/id_rsa\",\n sample_default=\"/.ssh/id_rsa\",\n ),\n conf_field.HostNameConfig(\n \"host\",\n help_text=\"Mailing list server host.\",\n default=\"mail.massopen.cloud\",\n sample_default=\"mail.massopen.cloud\",\n ),\n conf_field.IntConfig(\n \"port\",\n help_text=\"Mailing list server SSH port\",\n default=22,\n sample_default=22,\n ),\n conf_field.StrConfig(\n \"user\",\n help_text=\"Mailing list server user.\",\n default=\"moc-tools\",\n sample_default=\"moc-tools\",\n ),\n conf_field.StrConfig(\n \"list\",\n help_text=\"Mailing list to add users to.\",\n default=\"kaizen-users\",\n sample_default=\"kaizen-users\",\n ),\n ],\n )\n\n def _get_email(self):\n if CONF.identity.username_is_email:\n return self.action.task.keystone_user['username']\n\n def _approve(self):\n try:\n with Mailman(self.config.host, self.config.port,\n self.config.user, self.config.private_key) as mailman:\n if mailman.is_already_subscribed(self._get_email(),\n self.config.list):\n self.add_note('%s already subscribed to mailing list.'\n % self._get_email())\n else:\n mailman.subscribe(self._get_email(), self.config.list)\n self.add_note('%s successfully subscribed to mailing list.'\n % self._get_email())\n except paramiko.ssh_exception.SSHException as e:\n self.add_note('Unable to connect to Mailing List server. '\n 'Proceeding regardless. %s' % str(e))\n\n self.action.state = 'complete'\n self.action.save()\n\n def _submit(self, token_data):\n pass\n","repo_name":"CCI-MOC/adjutant-moc","sub_path":"adjutant_moc/actions/mailing_list.py","file_name":"mailing_list.py","file_ext":"py","file_size_in_byte":4721,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"40345380575","text":"import logging\n\nlogger = logging.getLogger('scheduler')\n_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s - %(module)s - %(lineno)d')\n\n\nfh = logging.FileHandler('scheduler_log.log', encoding='utf-8')\nfh.setLevel(logging.DEBUG)\nfh.setFormatter(_formatter)\n\ncr = logging.FileHandler('error.log', encoding='utf-8')\ncr.setLevel(logging.ERROR)\ncr.setFormatter(_formatter)\n\nlogger.addHandler(fh)\nlogger.addHandler(cr)\nlogger.setLevel(logging.DEBUG)\n\n\nif __name__ == '__main__':\n logger.info('Info')\n logger.warning('Warning')\n logger.debug('debug')\n logger.error('error')\n logger.critical('critical')\n","repo_name":"ZakirovRail/scheduler","sub_path":"log_config.py","file_name":"log_config.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"17236192959","text":"'''\n Fast projections onto the hypercube with a sum constraint (simplex).\n\n Two methods implemented: one that does direct search of kkt conditions,\n one that does iterative search (interpolation search).\n\n Uses numba to speed up computation.\n\n Benjamin Recht, March 6, 2017\n edited by Sarah Dean, March 7, 2017\n'''\nimport numpy as np\n#from numba import jit\n\n#@jit('void(double[:], intp, double[:], intp[:])', nopython=True, nogil=True)\ndef project_kkt(z, r, zProj, sortOrder):\n '''\n Jitted code to compute the ppr projection. Requires sort by numpy, but\n then goes to town.\n\n Input: z: a vector assumed to be a numpy array\n sortOrder: the arguments that sort z in descending order\n r: the targe sum\n\n Returns: zProj projected onto the set\n '''\n n = len(z)\n\n # check if there is a trivial solution\n if z[sortOrder[r-1]]>=z[sortOrder[r]]+1:\n for i in range(n):\n if i= -theta:\n A+=1\n if z[i] >= 1-theta:\n C+=1\n\n # compute sum of all entries in active set\n active_sum = 0.0\n for i in range(C+1,A+1):\n active_sum+=z[sortOrder[i]]\n\n while 1:\n # compute distance to increasing clipped set\n DeltaC = 1-theta-z[sortOrder[C+1]]\n # compute distance to increasing active set\n DeltaA = -theta-z[sortOrder[A+1]]\n Delta = min(DeltaA,DeltaC)\n\n # if we can take a step without changing active and clipped sets\n # and achive sum r, then we are done\n if C+1 + active_sum + (A-C)*(theta+Delta) >= r:\n beta = (r-C-1-active_sum)/(A-C)\n for i in range(n):\n zProj[i] = max(min(z[i]+theta,1.0),0.0)\n break\n else:\n # otherwise, update the active and clipped sets, update\n # the sum of the active set, and update theta.\n if DeltaA==Delta:\n while 1:\n A+=1\n active_sum = active_sum+z[sortOrder[A]]\n if z[sortOrder[A+1]] < (-theta-Delta):\n break\n if DeltaC==Delta:\n while 1:\n C+=1\n active_sum = active_sum-z[sortOrder[C]]\n if z[sortOrder[C+1]] < (1-theta-Delta):\n break\n theta += Delta\n\n\n#@jit('void(double[:], double, double[:], double)', nopython=True, nogil=True)\ndef project_is(z, r, zProj, tol=1.0e-6):\n '''\n Jitted code to compute the ppr projection via binary search.\n\n Input: z: a vector assumed to be a numpy array\n r: the targe sum\n tol: tolerance for the binary search\n\n Returns: zProj projected onto the set\n '''\n n = len(z)\n\n # compute minimum and maximum values of the input\n maxval = z[0]\n minval = z[0]\n for i in range(n):\n if z[i]>maxval:\n maxval = z[i]\n elif z[i] 1e+9:\n # for i in range(n):\n # z[i] = 100*z[i]/extreme\n # maxval = 100*maxval/extreme\n # minval = 100*minval/extreme\n\n # set incredibly conservative upper and lower bounds for theta\n L = -maxval\n U = 1-minval\n\n valU = 1.0*n\n valL = 0.0\n j = 0\n while 1:\n # interpolation search:\n theta = L + ((r - valL)/ (valU - valL))*(U - L)\n\n cur_sum = 0.0\n for i in range(n):\n zProj[i] = max(min(z[i]+theta,1.0),0.0)\n cur_sum += zProj[i]\n\n if abs(cur_sum-r)r:\n U = theta\n valU = cur_sum\n else:\n L = theta\n valL = cur_sum\n j = j + 1\n if j>10000:\n raise ArithmeticError('no projection convergence')\n\ndef project(z, r, alg='is'):\n '''\n Computes a projection onto the subset of the hypercube that sums to r\n using the algorithms described in Barman, Liu, Draper, and Recht, 2011\n or by using interpolation search\n\n Input: z: a vector assumed to be a numpy array\n r: the target sum\n alg: desired algorithm to run (must be 'kkt' or 'is')\n\n Returns: zProj projected onto the set\n '''\n\n n = len(z)\n assert r SceneNode:\n response = await self._connection.command(\n \"addFile\", self._prepared_params([str(path), folder_id])\n )\n raise scenenode_factory(self._connection, response)\n\n async def add_source(\n self, source_id: str, options: Optional[ISceneNodeAddOptions] = None\n ) -> SceneNode:\n options_dict = {}\n if options:\n if options.id is not None:\n option_dict[\"id\"] = options.id\n if options.source_add_options is not None:\n source_add_options = {}\n if options.source_add_options.channel is not None:\n source_add_options[\"channel\"] = options.source_add_options.channel\n if options.source_add_options.is_temporary is not None:\n source_add_options[\n \"isTemporary\"\n ] = options.source_add_options.is_temporary\n options_dict[\"sourceAddOptions\"] = source_add_options\n response = await self._connection.command(\n \"addSource\", self._prepared_params([source_id, options_dict])\n )\n return sceneitem_factory(self._connection, response)\n\n async def can_add_source(self, source_id: str) -> bool:\n response = await self._connection.command(\n \"canAddSource\", self._prepared_params([source_id])\n )\n return response\n\n async def clear(self):\n response = await self._connection.command(\"clear\", self._prepared_params())\n self._check_empty(response)\n\n async def create_and_add_source(\n self, name: str, type_: TSourceType, settings: Optional[dict[Any]] = None\n ): # -> SceneItem:\n response = await self._connection.command(\n \"createAndAddSource\", self._prepared_params([name, type_, settings])\n )\n return sceneitem_factory(self._connection, response)\n\n async def create_folder(self, name: str): # -> SceneItem:\n response = await self._connection.command(\n \"createFolder\", self._prepared_params([name])\n )\n return sceneitemfolder_factory(self._connection, response)\n\n async def get_folder(self, scene_folder_id: str): # -> SceneItemFolder:\n response = await self._connection.command(\n \"getFolder\", self._prepared_params([scene_folder_id])\n )\n return sceneitemfolder_factory(self._connection, response)\n\n async def get_folders(self): # -> list[SceneItemFolder]:\n response = await self._connection.command(\"getFolders\", self._prepared_params())\n return [\n sceneitemfolder_factory(self._connection, subitem) for subitem in response\n ]\n\n async def get_item(self, scene_folder_id: str): # -> Optional[SceneItem]:\n response = await self._connection.command(\n \"getItem\", self._prepared_params([scene_folder_id])\n )\n return sceneitem_factory(self._connection, response) if response else None\n\n async def get_items(self): # -> list[SceneItem]:\n response = await self._connection.command(\"getItems\", self._prepared_params())\n return [sceneitem_factory(self._connection, subitem) for subitem in response]\n\n async def get_model(self):\n response = await self._connection.command(\"getModel\", self._prepared_params())\n return scene_factory(self._connection, response)\n\n async def get_nested_items(self): # -> Source\n response = await self._connection.command(\n \"getNestedItems\", self._prepared_params()\n )\n return [sceneitem_factory(self._connection, source) for source in response]\n\n async def get_nested_scenes(self): # -> Source\n response = await self._connection.command(\n \"getNestedScenes\", self._prepared_params()\n )\n return [scene_factory(self._connection, source) for source in response]\n\n async def get_nested_sources(self): # -> Source\n response = await self._connection.command(\n \"getNestedSources\", self._prepared_params()\n )\n return [source_factory(self._connection, source) for source in response]\n\n async def get_node(self, scene_node_id): # -> Source\n response = await self._connection.command(\n \"getNode\", self._prepared_params([scene_node_id])\n )\n return scenenode_factory(self._connection, source)\n\n async def get_node_by_name(self, name: str) -> Optional[Source]:\n response = await self._connection.command(\n \"getNodeByName\", self._prepared_params([name])\n )\n if not response:\n return None\n else:\n return scenenode_factory(self._connection, response)\n\n async def get_nodes(self): # -> Source\n response = await self._connection.command(\"getNodes\", self._prepared_params())\n result = [scenenode_factory(self._connection, source) for source in response]\n # Update attributes as a side-effect\n self._nodes = result\n return result\n\n async def get_root_nodes(self):\n response = await self._connection.command(\n \"getRootNodes\", self._prepared_params()\n )\n return [scenenode_factory(self._connection, node) for node in response]\n\n async def get_selection(self, ids: Optional[list[str]] = None):\n response = await self._connection.command(\n \"getSelection\", self._prepared_params(ids)\n )\n return selection_factory(self._connection, response)\n\n async def get_source(self):\n response = await self._connection.command(\"getSource\", self._prepared_params())\n return source_factory(self._connection, response)\n\n async def make_active(self):\n response = await self._connection.command(\"makeActive\", self._prepared_params())\n self._check_empty(response)\n\n async def remove(self):\n response = await self._connection.command(\"remove\", self._prepared_params())\n self._check_empty(response)\n\n async def remove_folder(self, folder_id):\n response = await self._connection.command(\n \"removeFolder\", self._prepared_params([folder_id])\n )\n self._check_empty(response)\n\n async def remove_item(self, scene_item_id):\n response = await self._connection.command(\n \"removeItem\", self._prepared_params([scene_item_id])\n )\n self._check_empty(response)\n\n async def set_name(self, new_name):\n response = await self._connection.command(\n \"setName\", self._prepared_params([new_name])\n )\n self._check_empty(response)\n\n # Update local cache.\n self._name = new_name\n\n\nregister(Scene)\n","repo_name":"Julian-O/PySLOBS","sub_path":"pyslobs/slobs/scene.py","file_name":"scene.py","file_ext":"py","file_size_in_byte":7838,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"72"}
+{"seq_id":"7582251019","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport statsmodels.formula.api as smf\n\ndf = pd.read_csv('r_data.csv')\n\nbase = -1\nmeans = {}\nstart = -0.27 #Set start\nstop = -0.15 #Set stop\nps = []\nrs = []\nbases = []\nroofs = []\nks = []\n\nps_2 = []\nrs_2 = []\nbases_2 = []\nroofs_2 = []\nks_2 = []\n\nps_3 = []\nrs_3 = []\nbases_3 = []\nroofs_3 = []\nks_3 = []\nfor i in range(0, 100):\n base = start + i * 0.0006\n for j in range(0, 100):\n roof = stop - (j * 0.0006)\n df_j = df[df['Autocorrelation'] >= base]\n df_j_2 = df_j[df_j['Autocorrelation'] <= roof] \n\n for k in range(0, 5):\n df_j_2_shifted = df_j_2.copy()\n try:\n df_j_2_shifted['MKT'] = df_j_2_shifted.MKT.shift(k)\n df_j_2_shifted['SMB'] = df_j_2_shifted.SMB.shift(k)\n df_j_2_shifted['HML'] = df_j_2_shifted.HML.shift(k)\n df_j_2_shifted = df_j_2_shifted.dropna(how='any', axis=0)\n except ValueError:\n break\n\n reg = 'HML~Autocorrelation'\n reg2 = 'SMB~Autocorrelation'\n reg3 = 'MKT~Autocorrelation'\n\n try:\n reg_res = smf.ols(reg, df_j_2_shifted).fit()\n reg_res2 = smf.ols(reg2, df_j_2_shifted).fit()\n reg_res3 = smf.ols(reg3, df_j_2_shifted).fit()\n p_value = reg_res.pvalues[1]\n p_value2 = reg_res2.pvalues[1]\n p_value3 = reg_res3.pvalues[1]\n r_value = reg_res.rsquared\n r_value_2 = reg_res2.rsquared\n r_value_3 = reg_res3.rsquared\n\n if p_value <= 0.05:\n ps.append(p_value)\n bases.append(base)\n roofs.append(roof)\n ks.append(k)\n rs.append(r_value)\n if p_value2 <= 0.05:\n ps_2.append(p_value2)\n bases_2.append(base)\n roofs_2.append(roof)\n ks_2.append(k)\n rs_2.append(r_value_2)\n if p_value3 <= 0.05:\n ps_3.append(p_value3)\n bases_3.append(base)\n roofs_3.append(roof)\n ks_3.append(k)\n rs_3.append(r_value_3)\n\n except ValueError:\n break\n\nres_hml = pd.DataFrame(list(zip(bases, roofs, ks, rs)), columns=['Base', 'Roof', 'Lag', 'Rsq'])\nres_smb = pd.DataFrame(list(zip(bases_2, roofs_2, ks_2, rs_2)), columns=['Base', 'Roof', 'Lag', 'Rsq'])\nres_mkt = pd.DataFrame(list(zip(bases_3, roofs_3, ks_3, rs_3)), columns=['Base', 'Roof', 'Lag', 'Rsq'])\n\nres_hml.to_csv(r'res_hml_treshold_neg_1.csv')\nres_smb.to_csv(r'res_smb_treshold_neg_1.csv')\nres_mkt.to_csv(r'res_mkt_treshold_neg_1.csv')","repo_name":"fl0wZyy/honors_thesis_code","sub_path":"main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":2832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"39667683409","text":"import pytesseract\nfrom PIL import Image\n\nimg = Image.open('') # relative path to image\npytesseract.pytesseract.tesseract_cmd = r''\n\ncustom_config = r'--oem 3 --psm 6'\n\ntext = pytesseract.image_to_string(img, config=custom_config)\n\nwith open('', 'w') as text_file: # text file for text from picture\n text_file.write(text)\n","repo_name":"vvv2006v/image-text-recognition","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"16584658812","text":"from scipy.fftpack import fft\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pylab import*\nfrom scipy.io import wavfile\n\ny = [683, 766, 812, 912, 1024, 1085, 1230]\n\nx = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"]\nplt.plot(y)\n\nylabel('Frequency')\nxlabel(x[0]+\" \"+\n x[1]+\" \"+\n x[2]+\" \"+\n x[3]+\" \"+\n x[4]+\" \"+\n x[5]+\" \"+\n x[6]+\" \")\n\nplt.grid()\nplt.show()\n","repo_name":"GBortoto/Unknown","sub_path":"Plot - Notas.py","file_name":"Plot - Notas.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"7336261372","text":"hints = [1,5,1]\na = [0,0,0,0,0,0,0,0,0,0]\nb = [0,1,0,0,0,0,0,0,0,0]\nc = [0,1,0,1,1,1,1,1,0,0]\nd = [0,1,0,1,1,1,1,1,0,1]\ne = [1,0,1,1,1,1,1,0,1,0]\n\ndef space():\n return [0 for i in range(20)]\n \nhints_2 = [6,6,6]\n\ndef len_hints(arr):\n return sum(arr) + len(arr) - 1\n \ndef how_many_counted(arr):\n seen = False\n blanks = 0\n for el in arr:\n if el == 0:\n if not seen:\n blanks += 1\n else:\n if not seen:\n blanks += 1\n seen = True\n # print('returning blanks:',blanks)\n return blanks\n \ndef usable_space(space):\n res = []\n space_sum = sum(space)\n if space_sum == 0:\n return space, space_sum\n i = len(space) - 1\n while i in range(len(space)):\n if space[i] == 1:\n i += 1\n break\n i -= 1\n if i >= 0:\n i += 1\n space_sum += how_many_counted(space[:i + 1])\n # print('returning i:',i)\n # print(space[i:])\n return space[i:], space_sum\n \n \ndef calc_diff(arr, space, index):\n length = len(arr)\n if length <= index:\n # print('already finished!')\n return -1\n print('fitting ' + str(arr) + ' into ' + str(space) + ' starting at ' + str(arr[index]))\n new_space, space_to_remove = usable_space(space)\n spaces_len = len(new_space)\n v = (sum(arr[:index]))\n # print('v',v)\n covered_space = sum(space)\n to_be_covered = sum(arr)\n space_left_to_cover = len_hints(arr[index:])\n arr_len = space_left_to_cover\n if covered_space == to_be_covered:\n # print('base')\n return -1\n # print('new_space',new_space)\n # print('space_to_remove',space_to_remove)\n # print('spaces_len',spaces_len)\n # print('arr_len',arr_len)\n return spaces_len - arr_len\n \ndef calc_buffer_size(arr, space, index):\n pass\n\nhints_length = len_hints(hints)\n# print(hints_length)\n\nprint('a_diff:', calc_diff(hints, a, 0) == 1)\n# print('\\n')\nprint('b_diff:', calc_diff(hints, b, 1) == 0)\n# print('\\n')\nprint('c_diff:', calc_diff(hints, c, 2) == 0)\n# print('\\n')\nprint('d_diff:', calc_diff(hints, d, 2) == -1)\n# print('\\n')\nprint('e_diff:', calc_diff(hints, e, 3) == -1)\n# print('\\n')\nprint('large_diff:', calc_diff(hints_2, space(), 0) == 0)\n# print('\\n')\nprint('large_diff:', calc_diff([18], space(), 0) == 2)\n# print('\\n')\nprint('large_diff:', calc_diff([5,5,5], space(), 0) == 3)\n# print('\\n')\n\nten_ones = [1 for i in range(10)]\nprint('large_diff:', calc_diff([10,4,4], (ten_ones + space()[:-10]), 1) == 0)\n\ndef fill_row(row, hints, index, buff):\n hint = hints[index]\n new_row = row#.copy()\n left_hints = hints[:index]\n right_hints = hints[index:]\n left_space = len_hints(left_hints) + 1\n right_space = len_hints(right_hints)\n left_puzzle_sum = sum(row[:left_space])\n if left_puzzle_sum == 0:\n buff -= left_space\n # right_puzzle_sum = sum(row[right_space:])\n # print('left_hints:',left_hints,' right_hints:',right_hints,' left_space:',left_space,' right_space:', right_space,' buff:',buff)\n i = 0\n while i in range(len(new_row)):\n #print(new_row[i])\n left = left_space + buff\n right = (len(new_row) - (right_space + buff)) + hint\n # print('left:',left,' right',right)\n if hint > 0 and hint > buff:\n if i >= left and i < right: # or ((i) == left == right == buff):\n new_row[i] = 1\n i += 1\n # print('new_row:',new_row)\n return new_row\n\ndef horizontal_row_fill(puzzle_row, hints):\n i = 0\n temp_row = puzzle_row.copy()\n while i in range(len(hints)):\n buffer_space = calc_diff(hints, puzzle_row, i)\n # print('buffer_space:',buffer_space)\n temp_row = fill_row(puzzle_row, hints, i, buffer_space)\n \n i += 1\n # if i == len(hints):\n # return temp_row\n return temp_row\n \ndef ten_space():\n return [0 for i in range(10)]\nprint()\nprint(horizontal_row_fill(space(), hints_2) == [1,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1])\nprint(horizontal_row_fill(space(), [5,5,5]) == [0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0])\nprint(horizontal_row_fill(space(), [18]) == [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0])\nprint(horizontal_row_fill(ten_space(), hints) == [0,0,0,1,1,1,1,0,0,0])\nprint(horizontal_row_fill(ten_space(), [1]) == ten_space())\nprint(horizontal_row_fill(ten_space(), [6]) == [0,0,0,0,1,1,0,0,0,0])\nprint(horizontal_row_fill(ten_space(), [4,5]) == [1,1,1,1,0,1,1,1,1,1])\nprint(horizontal_row_fill(ten_space(), [7]) == [0,0,0,1,1,1,1,0,0,0])\n\nsample_smiley = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n [0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0],\n [0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0],\n [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]]\nsample_smiley_h_hints = [[0],[11],[1,1],[1,3,3,1],[1,3,3,1],[1,1],[1,9,1],[1,7,1],[2,2],[11]]\n \ncount_index = 0\nnew_puzzle = [[0 for i in range(17)] for j in range(len(sample_smiley_h_hints))]\nwhile count_index in range(len(sample_smiley_h_hints)):\n new_puzzle[count_index] = horizontal_row_fill(new_puzzle[count_index], sample_smiley_h_hints[count_index])\n count_index += 1\nprint(new_puzzle)\n\ndef printA(arr):\n for row in arr:\n print(row)\nprintA(new_puzzle)\n \n ","repo_name":"abriggs914/Pygames","sub_path":"Pixel Puzzle Solver/Mark1/horizontal_hint_solver.py","file_name":"horizontal_hint_solver.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"14833931679","text":"\"\"\"Microbenchmarks for the torch.fft module\"\"\"\nfrom argparse import ArgumentParser\nfrom collections import namedtuple\nfrom collections.abc import Iterable\n\nimport torch\nimport torch.fft\nfrom torch.utils import benchmark\nfrom torch.utils.benchmark.op_fuzzers.spectral import SpectralOpFuzzer\n\n\ndef _dim_options(ndim):\n if ndim == 1:\n return [None]\n elif ndim == 2:\n return [0, 1, None]\n elif ndim == 3:\n return [0, 1, 2, (0, 1), (0, 2), None]\n raise ValueError(f\"Expected ndim in range 1-3, got {ndim}\")\n\n\ndef run_benchmark(name: str, function: object, dtype: torch.dtype, seed: int, device: str, samples: int,\n probability_regular: float):\n cuda = device == 'cuda'\n spectral_fuzzer = SpectralOpFuzzer(seed=seed, dtype=dtype, cuda=cuda,\n probability_regular=probability_regular)\n results = []\n for tensors, tensor_params, params in spectral_fuzzer.take(samples):\n shape = [params['k0'], params['k1'], params['k2']][:params['ndim']]\n str_shape = ' x '.join([f\"{s:<4}\" for s in shape])\n sub_label = f\"{str_shape} {'' if tensor_params['x']['is_contiguous'] else '(discontiguous)'}\"\n for dim in _dim_options(params['ndim']):\n for nthreads in (1, 4, 16) if not cuda else (1,):\n measurement = benchmark.Timer(\n stmt='func(x, dim=dim)',\n globals={'func': function, 'x': tensors['x'], 'dim': dim},\n label=f\"{name}_{device}\",\n sub_label=sub_label,\n description=f\"dim={dim}\",\n num_threads=nthreads,\n ).blocked_autorange(min_run_time=1)\n measurement.metadata = {\n 'name': name,\n 'device': device,\n 'dim': dim,\n 'shape': shape,\n }\n measurement.metadata.update(tensor_params['x'])\n results.append(measurement)\n return results\n\n\nBenchmark = namedtuple('Benchmark', ['name', 'function', 'dtype'])\nBENCHMARKS = [\n Benchmark('fft_real', torch.fft.fftn, torch.float32),\n Benchmark('fft_complex', torch.fft.fftn, torch.complex64),\n Benchmark('ifft', torch.fft.ifftn, torch.complex64),\n Benchmark('rfft', torch.fft.rfftn, torch.float32),\n Benchmark('irfft', torch.fft.irfftn, torch.complex64),\n]\nBENCHMARK_MAP = {b.name: b for b in BENCHMARKS}\nBENCHMARK_NAMES = [b.name for b in BENCHMARKS]\nDEVICE_NAMES = ['cpu', 'cuda']\n\ndef _output_csv(file, results):\n file.write('benchmark,device,num_threads,numel,shape,contiguous,dim,mean (us),median (us),iqr (us)\\n')\n for measurement in results:\n metadata = measurement.metadata\n device, dim, shape, name, numel, contiguous = (\n metadata['device'], metadata['dim'], metadata['shape'],\n metadata['name'], metadata['numel'], metadata['is_contiguous'])\n\n if isinstance(dim, Iterable):\n dim_str = '-'.join(str(d) for d in dim)\n else:\n dim_str = str(dim)\n shape_str = 'x'.join(str(s) for s in shape)\n\n print(name, device, measurement.task_spec.num_threads, numel, shape_str, contiguous, dim_str,\n measurement.mean * 1e6, measurement.median * 1e6, measurement.iqr * 1e6,\n sep=',', file=file)\n\n\nif __name__ == '__main__':\n parser = ArgumentParser(description=__doc__)\n parser.add_argument('--device', type=str, choices=DEVICE_NAMES, nargs='+', default=DEVICE_NAMES)\n parser.add_argument('--bench', type=str, choices=BENCHMARK_NAMES, nargs='+', default=BENCHMARK_NAMES)\n parser.add_argument('--seed', type=int, default=0)\n parser.add_argument('--samples', type=int, default=10)\n parser.add_argument('--probability-regular', '--probability_regular', type=float, default=1.0)\n parser.add_argument('-o', '--output', type=str)\n args = parser.parse_args()\n\n num_benchmarks = len(args.device) * len(args.bench)\n i = 0\n results = []\n for device in args.device:\n for bench in (BENCHMARK_MAP[b] for b in args.bench):\n results += run_benchmark(\n name=bench.name, function=bench.function, dtype=bench.dtype,\n seed=args.seed, device=device, samples=args.samples,\n probability_regular=args.probability_regular)\n i += 1\n print(f'Completed {bench.name} benchmark on {device} ({i} of {num_benchmarks})')\n\n if args.output is not None:\n with open(args.output, 'w') as f:\n _output_csv(f, results)\n\n compare = benchmark.Compare(results)\n compare.trim_significant_figures()\n compare.colorize()\n compare.print()\n","repo_name":"pytorch/pytorch","sub_path":"torch/utils/benchmark/examples/spectral_ops_fuzz_test.py","file_name":"spectral_ops_fuzz_test.py","file_ext":"py","file_size_in_byte":4716,"program_lang":"python","lang":"en","doc_type":"code","stars":72779,"dataset":"github-code","pt":"72"}
+{"seq_id":"10423813779","text":"from DbConnection import DbConnection\nimport pandas as pd\nimport matplotlib.pyplot as plot\n\n\nclass Trader:\n def __init__(self):\n self.__str__ = \"Trader\"\n self.email = \"\"\n self.company = \"\"\n self.db = DbConnection('market', 'stocks') # create database connection object\n self.menu_options = [x for x in dir(self) if x.startswith(\"m_\")] # create options menu for user using class attributes\n\n while True:\n self._menu()\n # get input from user prompt and call the appropriate function name\n try:\n option_num = int(input(\"Enter option number and press ENTER, or 0 to quit: \"))\n if option_num == 0:\n print(\"Good bye!\")\n return\n try:\n func = getattr(self, self.menu[option_num])\n print(func())\n except AttributeError:\n print(\"Unable to retrieve function name %s from menu option\" % self.menu[option_num])\n except:\n print(\"Invalid option\")\n\n def _menu(self):\n print(\"\\nMenu\\n====\")\n self.menu = {}\n menu_item_number = 1\n for item in self.menu_options:\n self.menu[menu_item_number] = item\n menu_item_number += 1\n for option_num, option_name in self.menu.items():\n print(\"%s. %s\" % (option_num, option_name))\n\n def m_get_industry_tickers(self):\n \"\"\"Get Industry tickers\"\"\"\n industry = input(\"Enter name of industry and press ENTER: \")\n return self.db.get_industry_tickers(industry)\n\n def m_get_countries(self):\n \"\"\"Get list of countries\"\"\"\n countries = self.db.get_countries()\n print(\"\\nCountries\\n=======\")\n for country in countries:\n print(country)\n\n def m_get_sectors(self):\n \"\"\"Get list of sectors\"\"\"\n sectors = self.db.get_sectors()\n print(\"\\nSectors\\n=======\")\n for sector in sectors:\n print(sector)\n\n def m_get_industries(self):\n \"\"\"Get list of industries\"\"\"\n industries = self.db.get_industries()\n print(\"\\nIndustries\\n=======\")\n for industry in industries:\n print(industry)\n\n def m_get_50_day_simple_moving_avg_count(self):\n \"\"\"Get 50 day simple moving average count\"\"\"\n low = input(\"Enter lower threshold value for 50 day average count and press ENTER: \")\n high = input(\"Enter high threshold value for 50 day average count and press ENTER: \")\n return self.db.get_50_day_simple_moving_avg_count(low, high)\n\n def m_get_total_outstanding_shares_by_sector(self):\n \"\"\"Get total outstanding shares by sector\"\"\"\n sector = input(\"Enter name of sector and press ENTER: \")\n return self.db.get_total_outstanding_shares_by_sector(sector)\n\n def m_get_analyst_recommendation_score(self): # new function\n \"\"\"Get tickers recommended by analysts where score greater than low\"\"\"\n ticker = input(\"Enter name of ticker and press ENTER: \")\n return self.db.get_analyst_recommendation_score(ticker)\n\n def m_plot_stock_price_by_sector_by_country(self):\n \"\"\"Generate box plot for stock prices by sector by country\"\"\"\n sector = input(\"Enter name of sector and press ENTER: \")\n country = input(\"Enter name of country and press ENTER: \")\n dataset = self.db.get_stock_price_by_sector_by_country(sector, country)\n data_frame = pd.DataFrame(dataset, columns=['Price', 'Ticker'])\n try:\n data_frame.plot.bar(x='Ticker', y='Price', rot=90, title=\"Stock prices for %s sector in %s\" % (sector, country))\n plot.show(block=True)\n except TypeError:\n print(\"No data to plot\")\n\n def m_plot_stock_price_by_industry_by_country(self):\n \"\"\"Generate box plot for stock prices by industry by country\"\"\"\n industry = input(\"Enter name of industry and press ENTER: \")\n country = input(\"Enter name of country and press ENTER: \")\n dataset = self.db.get_stock_price_by_industry_by_country(industry, country)\n data_frame = pd.DataFrame(dataset, columns=['Price', 'Ticker'])\n try:\n data_frame.plot.bar(x='Ticker', y='Price', rot=90,\n title=\"Stock prices for %s industry in %s\" % (industry, country))\n plot.show(block=True)\n except TypeError:\n print(\"No data to plot\")\n\n def m_plot_stock_recommendation_score_by_sector_by_country(self):\n \"\"\"Generate box plot for stock recommendation scores by sector by country\"\"\"\n sector = input(\"Enter name of sector and press ENTER: \")\n country = input(\"Enter name of country and press ENTER: \")\n dataset = self.db.get_stock_recommendation_score_by_sector_by_country(sector, country)\n data_frame = pd.DataFrame(dataset, columns=['Analyst Recom', 'Ticker'])\n try:\n data_frame.plot.bar(x='Ticker', y='Analyst Recom', rot=90, title=\"Analyst recommendation scores for %s sector in %s\" % (sector, country))\n plot.show(block=True)\n except TypeError:\n print(\"No Data to plot\")\n\n def m_plot_stock_recommendation_score_by_industry_by_country(self):\n \"\"\"Generate box plot for stock recommendation scores by sector by country\"\"\"\n industry = input(\"Enter name of industry and press ENTER: \")\n country = input(\"Enter name of country and press ENTER: \")\n dataset = self.db.get_stock_recommendation_score_by_industry_by_country(industry, country)\n data_frame = pd.DataFrame(dataset, columns=['Analyst Recom', 'Ticker'])\n try:\n data_frame.plot.bar(x='Ticker', y='Analyst Recom', rot=90,\n title=\"Analyst recommendation scores for %s industry in %s\" % (industry, country))\n plot.show(block=True)\n except TypeError:\n print(\"No Data to plot\")\n\n\nclass Manager(Trader):\n def __init__(self):\n super().__init__()\n self.__str__ = \"Hedge Fund Manager\"\n \n\n def m_create_document(self):\n \"\"\"Create document\"\"\"\n document = input(\"Enter Document in key, value pair format and press ENTER: \")\n return self.db.create_document(document)\n\n def m_update_volume(self):\n \"\"\"Update ticker volume\"\"\"\n ticker = input(\"Enter ticker symbol and press ENTER: \")\n volume = input(\"Enter volume for ticker and press ENTER: \")\n return self.db.update_volume(ticker, volume)\n\n \n \n \n\n def m_delete_document(self):\n \"\"\"Delete document\"\"\"\n ticker = input(\"Enter ticker symbol and press ENTER: \")\n return self.db.delete_document(ticker)\n\n def m_update_analyst_recommendation_score(self):\n \"\"\"Update analyst recommendation score\"\"\"\n ticker = input(\"Enter ticker symbol and press ENTER: \")\n score = input(\"Enter new score: \")\n return self.db.update_recommendation(ticker, score)\n\n\n\n","repo_name":"felixt-snhu/felixt-snhu.github.io","sub_path":"capstone_project/management.py","file_name":"management.py","file_ext":"py","file_size_in_byte":7162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"42383216883","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport h5py\nimport matplotlib.pyplot as plt\nimport ThermalImagingAnalysis as tai\nimport ActivityPatterns as ap\nimport numpy as np\nfrom sklearn.metrics import f1_score\n\npPenalty = \"Penalty_Gaussian_1024fr_2.5Hz_TruncatedWaveletBasis.mat\"\n#pData = \"sep_1072240.mat\"\npData = \"../../SEP/626510_sep.mat\"\n\nf = h5py.File(pData, \"r\")\nS = f[\"S1024\"].value\n#S = f[\"img\"].value\n#T = f[\"T\"].value\nT = f[\"T1024\"].value\n\nf_P = h5py.File(pPenalty, \"r\")\nP = f_P[\"BPdir2\"].value # learned penalty matrix\nprint('[INFO] P is being transposed\\n')\nP = P.transpose() # P appears to be stored as transposed version of itself\nB = f_P[\"B\"].value # basis matrix\n\nS2 = S[0:1024,]\nT2 = T[0:1024,]\ndel S;\ndel T;\n\nX = ap.computeGaussianActivityPattern(np.squeeze(T2)).transpose();\nZ = tai.semiparamRegressionRaw(S2,X,B,P);\n\nZ_true = groundtruthImg.flatten()\nfor i in range(len(Z_true)):\n if Z_true[i] != 0:\n Z_true[i] = 1\n else:\n Z_true[i] = 0\n\nZ_pred = numpy.zeros(len(Z_true))\nfor i in range(len(Z_pred)):\n if Z[i] >= 5.2:\n Z_pred[i] = 1\n else:\n Z_pred[i] = 0\n \nF1 = f1_score(Z_true, Z_pred, average='binary')\n\n\n\nwith h5py.File(\"result.h5\",\"w\") as f:\n d1 = f.create_dataset('Z',data=Z)\n","repo_name":"nish03/MRF-in-Neural-Imaging","sub_path":"semiparamRegression/applyModel_noMRF.py","file_name":"applyModel_noMRF.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"}
+{"seq_id":"69889692072","text":"# Author: Christian Brodbeck \nimport fnmatch\nimport re\n\nimport numpy as np\n\nfrom .._data_obj import cellname\nfrom . import stats\nfrom .contrast import parse\n\n\n# array functions: work on array, take axis argument\nnp_afuncs = {'min': np.min,\n 'max': np.max,\n 'sum': np.sum}\n# binary functions: work on two arrays\nnp_bfuncs = {'subtract': np.subtract,\n 'add': np.add}\n# unary functions: work on a single array\nnp_ufuncs = {'abs': np.abs,\n 'negative': np.negative}\n\n\nclass TContrastSpec:\n \"Parse a contrast expression and expose methods to apply it\"\n\n def __init__(self, contrast, cells, indexes):\n \"\"\"Parse a contrast expression and expose methods to apply it\n\n Parameters\n ----------\n contrast : str\n Contrast specification.\n cells : tuple of cells\n Cells that occur in the contrast (each cell is represented by a str\n or a tuple of str).\n indexes : dict {cell: index}\n Indexes for the data of every cell.\n \"\"\"\n ast = parse(contrast)\n n_buffers, cells_in_contrast = _t_contrast_rel_properties(ast)\n pcells, mcells = _t_contrast_rel_expand_cells(cells_in_contrast, cells)\n\n self.contrast = contrast\n self.indexes = indexes\n self._ast = ast\n self._pcells = pcells\n self._mcells = mcells\n self._n_buffers = n_buffers\n\n # data buffers\n self._buffer_shape = None\n self._buffer = None\n self._y_perm = None\n\n def map(self, y):\n \"Apply contrast without retainig data buffers\"\n buff = np.empty((self._n_buffers,) + y.shape[1:])\n data = _t_contrast_rel_data(y, self.indexes, self._pcells, self._mcells)\n tmap = _t_contrast_rel(self._ast, data, buff)\n return tmap\n\n def __call__(self, y, out, perm):\n \"Apply contrast to permutation of the data, storing and recycling data buffers\"\n buffer_shape = (self._n_buffers,) + y.shape[1:]\n if self._buffer_shape != buffer_shape:\n self._buffer = np.empty(buffer_shape)\n self._y_perm = np.empty_like(y)\n self._buffer_shape = buffer_shape\n self._y_perm[perm] = y\n data = _t_contrast_rel_data(self._y_perm, self.indexes, self._pcells, self._mcells)\n tmap = _t_contrast_rel(self._ast, data, self._buffer, out)\n return tmap\n\n\ndef _t_contrast_rel_properties(item):\n \"\"\"Find properties of a compiled t-contrast\n\n Parameters\n ----------\n item : tuple\n Contrast specification.\n\n Returns\n -------\n n_buffers : int\n Number of buffer maps needed.\n cells : set\n names of all cells that occur in the contrast.\n \"\"\"\n if item[0] == 'ufunc':\n needed_buffers, cells = _t_contrast_rel_properties(item[2])\n return needed_buffers + 1, cells\n elif item[0] in ('bfunc', 'afunc'):\n _, _, items_ = item\n local_buffers = len(items_)\n cells = set()\n for i, item_ in enumerate(items_):\n available_buffers = local_buffers - i - 1\n needed_buffers, cells_ = _t_contrast_rel_properties(item_)\n additional_buffers = needed_buffers - available_buffers\n if additional_buffers > 0:\n local_buffers += additional_buffers\n cells.update(cells_)\n return local_buffers, cells\n else:\n return 0, set(item[1:])\n\n\ndef _t_contrast_rel_expand_cells(cells, all_cells):\n \"\"\"Find cells that are an average of other cells\n\n Parameters\n ----------\n cells : set\n Cells occurring in the contrast.\n all_cells : tuple\n All cells in the data.\n\n Returns\n -------\n primary_cells : set\n All cells that occur directly in the data.\n mean_cells : dict\n ``{name: components}`` dictionary (components being a tuple with all\n cells to be averaged).\n \"\"\"\n # check all cells have same number of components\n ns = set(1 if isinstance(cell, str) else len(cell) for cell in all_cells)\n ns.update(1 if isinstance(cell, str) else len(cell) for cell in cells)\n if len(ns) > 1:\n msg = (\"Not all cells have the same number of components: %s\" %\n str(tuple(cells) + tuple(all_cells)))\n raise ValueError(msg)\n\n # convert cells to str for fnmatch\n all_cellnames = tuple(cellname(cell, '|') for cell in all_cells)\n\n primary_cells = set()\n mean_cells = {}\n for cell in cells:\n if cell in all_cells:\n primary_cells.add(cell)\n else:\n r = re.compile(fnmatch.translate(cellname(cell, '|')))\n base = tuple(c for c, cn in zip(all_cells, all_cellnames) if r.match(cn))\n if len(base) == 0:\n raise ValueError(\"%r does not match any cells in data %r\" %\n (cellname(cell, '|'), ', '.join(all_cellnames)))\n mean_cells[cell] = base\n primary_cells.update(base)\n\n return primary_cells, mean_cells\n\n\ndef _t_contrast_rel_data(y, indexes, cells, mean_cells):\n \"Create {cell: data} dictionary\"\n data = {cell: y[indexes[cell]] for cell in cells}\n for name, cells_ in mean_cells.items():\n cell = cells_[0]\n x = data[cell].copy()\n for cell in cells_[1:]:\n x += data[cell]\n x /= len(cells_)\n data[name] = x\n\n return data\n\n\ndef _t_contrast_rel(item, data, buff, out=None):\n \"Execute a t_contrast (recursive)\"\n if item[0] == 'ufunc':\n _, func, item_ = item\n tmap = _t_contrast_rel(item_, data, buff[1:], buff[0])\n tmap = func(tmap, out)\n elif item[0] == 'bfunc':\n _, func, items = item\n tmap1 = _t_contrast_rel(items[0], data, buff[2:], buff[1])\n tmap2 = _t_contrast_rel(items[1], data, buff[2:], buff[0])\n tmap = func(tmap1, tmap2, out)\n elif item[0] == 'afunc':\n _, func, items_ = item\n tmaps = buff[:len(items_)]\n for i, item_ in enumerate(items_):\n _t_contrast_rel(item_, data, buff[i + 1:], tmaps[i])\n tmap = func(tmaps, axis=0, out=out)\n else:\n _, c1, c0 = item\n tmap = stats.t_1samp(data[c1] - data[c0], out)\n\n return tmap\n","repo_name":"christianbrodbeck/Eelbrain","sub_path":"eelbrain/_stats/t_contrast.py","file_name":"t_contrast.py","file_ext":"py","file_size_in_byte":6249,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"72"}
+{"seq_id":"2326628638","text":"import random\n\nt = 100\nprint(t)\nfor _ in range(t):\n d = []\n n = random.randint(100, 1000)\n for i in range(n):\n d.append(random.randint(4, 1000000))\n print(n)\n print(\" \".join([str(i) for i in d]))","repo_name":"lebahoang/cp","sub_path":"gcj/2022/qualification/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"41302595372","text":"\n\n\n### 예전에 푼 풀이\n\nN = int(input())\nli_a = list(map(int, input().split(\" \")))\nli_b = list(map(int, input().split(\" \")))\n\nsum = 0\nfor k in range(N) :\n max, min = 0, 100\n\n for i in range(len(li_a)) :\n if li_a[i] > max :\n max = li_a[i]\n A = max\n li_a.remove(max)\n\n for i in range(len(li_b)) :\n if li_b[i] < min :\n min = li_b[i]\n B = min\n li_b.remove(min)\n sum += A*B\n \nprint(sum)\n","repo_name":"h-dragon93/Algorithm_with_Python","sub_path":"Baekjoon_1026.py","file_name":"Baekjoon_1026.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"10227841899","text":"import datetime\nimport json\nfrom django.shortcuts import render\nfrom django.conf import settings\nfrom google.oauth2 import id_token\nfrom google.auth.transport import requests\nfrom rest_framework import views, viewsets, mixins\nfrom rest_framework.parsers import FileUploadParser\nfrom core.serializers import *\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.exceptions import ValidationError\nfrom django.core import validators\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.db.models import Q\nfrom rest_framework.decorators import detail_route\n\nimport face_recognition\n\n\n# Create your views here.\nclass GoogleLoginView(views.APIView):\n\n def post(self, request, format=None):\n try:\n idinfo = id_token.verify_oauth2_token(token, requests.Request(), settings.CLIENT_ID)\n if idinfo['iss'] not in ['accounts.google.com', 'https://accounts.google.com']:\n raise ValueError('Wrong issuer.')\n userid = idinfo['sub']\n except ValueError:\n pass\n\n\nclass UserViewSet(mixins.RetrieveModelMixin,\n mixins.UpdateModelMixin,\n mixins.CreateModelMixin,\n mixins.ListModelMixin,\n viewsets.GenericViewSet,):\n \"\"\"\n Доступ к текущему пользователю: `/users/me/` \\n\n Получить токен: `/auth-token/`, username = `email`, password = `password` \\n\n Авторизация по токену: `curl -X GET http://127.0.0.1:8000/api/example/ -H 'Authorization: Token 9944b09199c62bcf9418ad846dd0e4bbdfc6ee4b'`\n \"\"\"\n\n serializer_class = UserSerializer\n queryset = User.objects.none()\n\n def dispatch(self, request, *args, **kwargs):\n self.perform_authentication(self.initialize_request(request, *args, **kwargs))\n if kwargs.get('pk') == 'me' and request.user:\n kwargs['pk'] = request.user.pk\n return super(UserViewSet, self).dispatch(request, *args, **kwargs)\n\n def create(self, request, *args, **kwargs):\n try:\n validators.validate_email(request.data['email'])\n except:\n raise ValidationError({\"email\": _('Enter a valid email address.')})\n if User.objects.filter(email=request.data['email']).count() > 0:\n raise ValidationError({\"email\": _('Email is already registered')})\n if not request.data.get('password', None):\n raise ValidationError({\"password\": _('Password must be present')})\n if len(request.data['password']) < 6:\n raise ValidationError({\"password\": _('Password is too short')})\n if not request.data.get('first_name', None):\n raise ValidationError({\"first_name\": _('Name must be present')})\n user = User.objects.create_user(\n request.data['username'],\n request.data['email'],\n request.data['password'],\n first_name=request.data.get('first_name',\"\"),\n last_name=request.data.get('first_name',\"\"),\n status=request.data.get(\"status\"),\n photo=request.data.get(\"photo\")\n )\n serializer = self.get_serializer(user)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n def update(self, request, *args, **kwargs):\n if kwargs.get('pk') == request.user.pk:\n user = User.objects.get(pk=request.user.pk)\n if request.data['status'] not in ('work in office', 'not working'):\n user.status = request.data['status']\n user.comment = request.data['comment']\n user.save()\n Entry.objects.create(user=user,type=request.data['status']).save()\n serializer = self.get_serializer(user)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n else:\n return Response([_('Wrong credentials')], status=status.HTTP_400_BAD_REQUEST)\n\n def get_queryset(self):\n q = self.request.GET.get('q')\n if q:\n return User.objects.filter(Q(first_name__icontains=q) | Q(last_name__icontains=q))\n else:\n return User.objects.all()\n\n @detail_route(methods=['get'])\n def entries(self, request, pk=None):\n user = self.get_object()\n entries = user.entry_set\n dt_start = request.GET.get('start')\n if dt_start:\n entries = entries.filter(datetimestamp__gte=dt_start)\n dt_end = request.GET.get('end')\n if dt_end:\n entries = entries.filter(datetimestamp__lte=dt_end)\n return Response(EntrySerializer(entries, many=True).data, status=status.HTTP_200_OK)\n \n\nclass EntryViewSet(viewsets.ModelViewSet):\n \"\"\"\n Example:\n\n `curl -H \"Authorization: Token 512e3026a7c11dc6cc52027a6c1677b012f0dfae\" -H \"Content-Disposition: attachment; filename=андрюха.png\" --data-binary @\"../Downloads/андрюха.png\" https://absolute.cloud.technokratos.com/api/entries/`\n \"\"\"\n queryset = Entry.objects.all()\n serializer_class = EntrySerializer\n parser_classes = (FileUploadParser,)\n\n def create(self, request, *args, **kwargs):\n # request.data._mutable = True\n request.data['photo'] = request.data['file']\n today_min = datetime.datetime.combine(datetime.date.today(), datetime.time.min)\n\n entry = Entry.objects.create(user=None, type=None, photo=request.data['photo'])\n\n unknown_image = face_recognition.load_image_file(request.data['file'])\n unknown_encoding = face_recognition.face_encodings(unknown_image)[0]\n\n for user in User.objects.all():\n if user.photo:\n known_image = face_recognition.load_image_file(user.photo)\n known_encoding = face_recognition.face_encodings(known_image)[0]\n results = face_recognition.compare_faces([known_encoding], unknown_encoding)\n print(results)\n if results[0]:\n entry.user = user\n print(entry.user)\n if not Entry.objects.filter(user=user):\n entry.type = 'enter'\n user.status = 'work in office'\n elif Entry.objects.filter(user=user, type='enter', datetimestamp__gte=today_min):\n entry.type = 'exit'\n user.status = 'not working'\n else:\n entry.type = 'enter'\n user.status = 'work in office'\n entry.save()\n user.save()\n break\n return Response(EntrySerializer(entry).data)\n\n\nclass ProjectViewSet(viewsets.ModelViewSet):\n queryset = Project.objects.all()\n serializer_class = ProjectSerializer\n","repo_name":"blinovandrey/hackaton_face_recognition","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"22540262328","text":"from math import factorial\n\nsum = 0\nfor cnt in range(1, 100000):\n a = list(str(cnt))\n sum_ = 0\n for n in a:\n sum_ += factorial(int(n))\n if sum_ == cnt:\n sum += cnt\n\nprint(sum - factorial(2) - factorial(1))\n","repo_name":"melank/ProjectEuler","sub_path":"py/problem34.py","file_name":"problem34.py","file_ext":"py","file_size_in_byte":232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"40979558655","text":"import datetime\n\ndef encrypt(text, height):\n result = \"\"\n for i in range(height):\n for j in range(i, len(text), height):\n result += text[j]\n return result\n\ndef decrypt(text, height):\n result = \"\"\n width = int(len(text) / height)\n for i in range(width):\n for j in range(height):\n index = i + j * width\n if index < len(text):\n result += text[index]\n return result\n\ntext = input(\"Введіть текст: \")\nheight = int(input(\"Введіть висоту частоколу (секретний ключ): \"))\nencrypted_text = encrypt(text, height)\nprint(\"Зашифрований текст: \", encrypted_text)\ndecrypted_text = decrypt(encrypted_text, height)\nprint(\"Розшифрований текст: \", decrypted_text)\n\n\ndef printTimeStamp(name):\n print('Автор програми: ' + name)\n print('Час компіляції: ' + str(datetime.datetime.now()))\n\n\nprintTimeStamp(\"Yan Savinov\")\n","repo_name":"YanSav10/CSBC","sub_path":"practice-7/task-2.py","file_name":"task-2.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"19156180635","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import pairwise_distances_argmin\nfrom sklearn.datasets import load_sample_image\nfrom sklearn.utils import shuffle\n\nfrom PIL import Image\nimg = Image.open(\"C:\\\\Users\\\\Sci_Dell_Cpu\\\\Desktop\\\\aaa.webp\")\nchina = np.asarray(img)\n\nn_clusters = 64\nchina = np.array(china,dtype=np.float64) / china.max()\nw,h,d = original_shape = tuple(china.shape)\nassert d == 3\nimage_array = np.reshape(china,(w*h,d))\n\nkmeans = KMeans(n_clusters=n_clusters,random_state=0).fit(image_array)\n\nlabels = kmeans.predict(image_array)\n\nimage_kmeans = image_array.copy()\nfor i in range(w*h):\n image_kmeans[i] = kmeans.cluster_centers_[labels[i]]\n\nimport pandas as pd\npd.DataFrame(image_kmeans).drop_duplicates().shape\n\nimage_kmeans = image_kmeans.reshape(w,h,d)\n\ncentroid_random = shuffle(image_array,random_state=0)[:n_clusters]\nlabels_random = pairwise_distances_argmin(centroid_random,image_array,axis=0)\nimage_random = image_array.copy()\nfor i in range(w*h):\n image_random[i] = centroid_random[labels_random[i]]\nimage_random = image_random.reshape(w,h,d)\n\npic=plt.subplot(2,3,1)\npic.imshow(china)\npic=plt.subplot(2,2,2)\npic.imshow(image_kmeans)\npic=plt.subplot(2,2,3)\npic.imshow(image_random)","repo_name":"GuoJia563/sklearn0","sub_path":"kmeans_VQ_mypic.py","file_name":"kmeans_VQ_mypic.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"7540499708","text":"import tkinter as tk\nfrom tkinter import messagebox as mgb\nfrom tkinter.messagebox import *\nfrom tkinter import colorchooser\nfrom tkinter import filedialog\n\n\ndef test_messagebox(master=None):\n mgb.showinfo(title=\"InfoWindow\", message='What the fuck')\n mgb.showwarning(\"Spam\", \"Egg Warning\")\n mgb.showerror(\"Spam\", \"Egg Alert\")\n # print(\"info\", showinfo(\"Spam\", \"Egg Information\"))\n # print(\"warning\", showwarning(\"Spam\", \"Egg Warning\"))\n # print(\"error\", showerror(\"Spam\", \"Egg Alert\"))\n # print(\"question\", askquestion(\"Spam\", \"Question?\"))\n # print(\"proceed\", askokcancel(\"Spam\", \"Proceed?\"))\n # print(\"yes/no\", askyesno(\"Spam\", \"Got it?\"))\n print(\"yes/no/cancel\", askyesnocancel(\"Spam\", \"Want it?\"))\n print(\"try again\", askretrycancel(\"Spam\", \"Try again?\"))\n\n\ndef test_filedialog(master=None):\n def callback():\n print(\"fileName: \", filedialog.askopenfilename())\n\n tk.Button(master, text=\"打开文件\", command=callback).pack()\n\n\ndef test_colorchooser(master=None):\n def callback():\n print(\"color:\", colorchooser.askcolor())\n\n tk.Button(master, text=\"选择颜色\", command=callback).pack()\n\n\nif __name__ == \"__main__\":\n root = tk.Tk()\n root.title(\"3 Standard Dialog\")\n height, width = 300, 300\n root.geometry(\"%dx%d+%d+%d\" % (width, height,\n (root.winfo_screenwidth() - width) // 2,\n (root.winfo_screenheight()- height) // 2))\n # test_messagebox(root)\n # test_colorchooser(root)\n test_filedialog(root)\n\n root.mainloop()\n\n","repo_name":"ZhenZHAO/GUI-Tkinter","sub_path":"20_std_dialog.py","file_name":"20_std_dialog.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"2391352730","text":"# Create a function that accepts two parameters, namely;\n# 1. A list of type string\n# 2. A name\n# The function should then check if a word is in that list. (Let the word exist e.g THIKA [Busia, Thika, Nakuru, Nyeri])\n# Return that word in reverse order if found.\n#If the word is not found, append the word and return the list.\n\ncounties = [\"Nairobi\",\"Mombasa\",\"Thika\",\"Nakuru\",\"Turkana\",\"Garissa\",\"Lamu\",\"Nandi\",\"Bungoma\"]\ncounty = \"Bungoma\"\n\ndef checker(a,b):\n if a not in b:\n b.append(a)\n printlist(b)\n return b\n else:\n y = a[::-1]\n print(y)\n return y\n\n\n\ndef printlist(thelist):\n for i in thelist:\n print (i)\nk=0\nwhile k !=1:\n checker(county,counties)\n k = int(input(\"To continue, press 0, To exit press 1:\\t\"))\n#print (checker(county,counties))\n","repo_name":"KOdunga/Python_Introduction","sub_path":"PycharmProjects/untitled1/assign2.py","file_name":"assign2.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"14942139437","text":"from sqlalchemy import create_engine, MetaData, Table\r\nimport json\r\nimport pandas as pd\r\n\r\ndb_url = \"postgres://nfhbnzfykqrsgq:481860bcda89b87b1c373f024d84c81b41e5ec7c06e8410866f7605984b5789e@ec2-35-175-155-248.compute-1.amazonaws.com:5432/d4mi4q3a82eu6k\"\r\ntable_name = 'realdata'\r\ndata_column_name = 'datastring'\r\n# boilerplace sqlalchemy setup\r\nengine = create_engine(db_url)\r\nmetadata = MetaData()\r\nmetadata.bind = engine\r\ntable = Table(table_name, metadata, autoload=True)\r\n# make a query and loop through\r\ns = table.select()\r\nrows = s.execute()\r\n\r\ndata = []\r\n#status codes of subjects who completed experiment\r\n#statuses = [3,4,5,7]\r\n# if you have workers you wish to exclude, add them here\r\nexclude = []\r\nfor row in rows:\r\n # only use subjects who completed experiment and aren't excluded\r\n \r\n data.append(row[data_column_name])\r\n\r\n# Now we have all participant datastrings in a list.\r\n# Let's make it a bit easier to work with:\r\n\r\n# parse each participant's datastring as json object\r\n# and take the 'data' sub-object\r\ndata = [json.loads(part)['data'] for part in data]\r\n\r\n# insert uniqueid field into trialdata in case it wasn't added\r\n# in experiment:\r\nfor part in data:\r\n for record in part:\r\n record['trialdata']['uniqueid'] = record['uniqueid']\r\n\r\n# flatten nested list so we just have a list of the trialdata recorded\r\n# each time psiturk.recordTrialData(trialdata) was called.\r\ndata = [record['trialdata'] for part in data for record in part]\r\n\r\n# Put all subjects' trial data into a dataframe object from the\r\n# 'pandas' python library: one option among many for analysis\r\ndata_frame = pd.DataFrame(data)\r\ndata_frame.to_csv('alldataindatabase.csv',index=False)","repo_name":"ShanJG/active_removal_serial_dependence","sub_path":"registered report/task/retrieve.py","file_name":"retrieve.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"}
+{"seq_id":"33048171663","text":"import functools\n\n\nclass Participant:\n \n def __init__(self, name, email, exclude):\n self.name = name\n self.email = email\n \n if exclude:\n self.exclude = exclude\n else:\n self.exclude = []\n \n self.giftee = None\n\n # The possible giftees. Will be populated in main.py\n self.domain = []\n \n\n @property\n def nameWithMatch(self):\n stringOut = self.name\n if self.giftee:\n stringOut += \" buys for {}\".format(self.giftee.name)\n else:\n stringOut += \" (no match)\"\n return stringOut\n\n '''\n Removes the current object from its own domain\n '''\n def removeSelf(self):\n for i, part in enumerate(self.domain):\n if part == self:\n self.domain.pop(i)\n return\n\n '''\n Updates the domain to exclude objects in the exclude list.\n '''\n def cullDomain(self):\n for i, part in enumerate(self.domain):\n if part in self.exclude:\n self.domain.pop(i)\n\n '''\n Converts the list of names in self.exclude to a list of Participant objects\n '''\n def convertExcludeToObjects(self, participants):\n if not self.exclude:\n pass\n else:\n for i in range(len(self.exclude)):\n nameString = self.exclude[i].strip().lower()\n \n for part in participants:\n objectName = part.name.strip().lower()\n if objectName == nameString:\n self.exclude[i] = part\n break\n\n \n\n\n def __str__(self):\n returnString = \"Name: {}\\nemail: {}\\nExclude:\\n\".format(self.name, self.email)\n if len(self.exclude) == 0:\n returnString += \"\\tNone\"\n elif self.exclude:\n for item in self.exclude:\n if isinstance(item, str):\n returnString += \"\\t\" + item + \"\\n\"\n else:\n returnString += \"\\t\" + item.name + \"\\n\"\n else:\n returnString += \"None\\n\"\n return returnString\n\n\n '''\n The following methods allow objects to be sorted by the number of exceptions.\n '''\n def __lt__(self, other):\n return len(self.exclude) < len(other.exclude)\n\n def __gt__(self, other):\n return len(self.exclude) > len(other.exclude)\n \n def __eq__(self, other):\n return len(self.exclude) == len(other.exclude)\n\n def __ne__(self, other):\n return not self.__eq__(other)\n \n def __le__(self, other):\n return self.__eq__(other) or self.__lt__(other)\n \n def __ge__(self, other):\n return self.__eq__(other) or self.__gt__(other)\n","repo_name":"enanram/SecretSanta","sub_path":"participant.py","file_name":"participant.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"18226220650","text":"# Print welcome message.\nprint(\"Welcome to the tip calculator!\")\n# Request bill data.\ntotal_bill = float(input(\"What was the total bill? $\"))\npercent_tip = int(input(\"How much tip would you like to give? 10, 12, or 15?\"))\npeople = int(input(\"How many people to split the bill? \"))\n# Calculate tip and total bill\ntip = (percent_tip + 100) / 100\nnew_bill = total_bill * tip\n# Split bill per person\nunit_bill = new_bill / people\n# Round the result to 2 decimal places.\nnew_unit_bill = round(unit_bill, 2)\n# Print result\nprint(f\"Each person should pay: ${new_unit_bill}\")\n","repo_name":"Manuel-7tin/Python-course-projects","sub_path":"2_tip-calculator/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"16221180814","text":"import json\nimport tweepy\nfrom tweepy import OAuthHandler\n\n\n\nCONSUMER_KEY = secrets[\"CONSUMER_KEY\"]\nCONSUMER_SECRET = secrets[\"CONSUMER_SECRET\"]\nOAUTH_TOKEN = secrets[\"OAUTH_TOKEN\"]\nOAUTH_TOKEN_SECRET = secrets[\"OAUTH_TOKEN_SECRET\"]\n\nauth = OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\nauth.set_access_token(OAUTH_TOKEN, OAUTH_TOKEN_SECRET)\n\napi = tweepy.API(auth)\n\nDUB_WOE_ID = 560743\nLON_WOE_ID = 44418\nCHICAGO_ID = 2379574\n\n# http://docs.tweepy.org/en/latest/api.html?highlight=trends_place#API.trends_place\ndub_trends = api.trends_place(DUB_WOE_ID)\nlon_trends = api.trends_place(LON_WOE_ID)\nchi_trends = api.trends_place(CHICAGO_ID)\n\n# for trend in json.dumps(dub_trends)[0]['trends']:\n# print(trend)\n\n# print json.dumps(dub_trends, indent=1)\n# print json.dumps(lon_trends, indent=1)\n# print json.dumps(chi_trends, indent=1)\n\ndub_trends_set = set([trend['name']\n for trend in dub_trends[0]['trends']])\n\nlon_trends_set = set([trend['name']\n for trend in lon_trends[0]['trends']])\n\nchi_trends_set = set([trend['name']\n for trend in chi_trends[0]['trends']])\n\ncommon_trends = set.intersection(dub_trends_set, chi_trends_set)\n\nprint(common_trends)\n","repo_name":"QsBBQ/mytweet","sub_path":"twitter_intro.py","file_name":"twitter_intro.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"27698617180","text":"#!/usr/bin/python3\n\nimport os\nimport time\nimport subprocess\nfrom decimal import Decimal\nfrom multiprocessing import Process\n\ntop_dir = os.path.dirname(os.path.abspath(__file__))\n\n# 0xFFA50800\tPS System Monitor\n# 0xFFA50C00\tPL System Monitor\n# subprocess.run(\"ls -al\", shell=True)\n\ndef get_temperature(address):\n datafile = os.popen(\"devmem %s\" % address)\n rawdata = int(datafile.readline().strip(),16)\n result = \"%.2f\" %(Decimal(rawdata) * Decimal(509.3140064) / Decimal(65535) - Decimal(280.23087870))\n return result\n\ndef get_cpustatus():\n datafile = os.popen(\"top -b -n 1 | grep 'Cpu(s):'\")\n result = datafile.readline().strip()\n return result\n\ndef get_time():\n datafile = os.popen(\"date +%F' '%T' '%z\")\n result = datafile.readline().strip()\n return result\n\ndef get_log():\n result = \"%s %s PS temperature: %s C. PL temperature: %s C.\" %(get_time(),get_cpustatus(),get_temperature(address = \"0xFFA50800\"),get_temperature(address = \"0xFFA50C00\"))\n return result\n\ndef function_log(logfile, during):\n while True:\n print(\"==== Process log <%s> is running ====\" %os.getpid())\n writefile = open(\"%s/%s\"%(top_dir,logfile),\"a\")\n logdata = \"%s\" %get_log()\n print(logdata)\n writefile.write(\"%s\\n\" %logdata)\n writefile.close()\n time.sleep(during)\n\ndef function_build(builddir):\n while True:\n print(\"==== Process build <%s> is running ====\" %os.getpid())\n build_dir = \"%s/%s\" %(top_dir,builddir)\n subprocess.run(\"mkdir -p %s\" %build_dir, shell=True )\n os.chdir(\"%s\" %build_dir)\n subprocess.run(\"rm -rf *\" , shell=True)\n subprocess.run(\"cmake /home/root/workspace/PaddleLiteSample/classification\" , shell=True)\n subprocess.run(\"make\", shell=True )\n os.chdir(\"%s\" %top_dir)\n\ndef function_ai(builddir):\n print(\"==== Process AI <%s> is running ====\" %os.getpid())\n build_dir = \"/home/root/workspace/PaddleLiteSample/classification/%s\" %builddir\n subprocess.run(\"mkdir -p %s\" %build_dir, shell=True )\n os.chdir(\"%s\" %build_dir)\n subprocess.run(\"rm -rf *\" , shell=True)\n subprocess.run(\"cmake /home/root/workspace/PaddleLiteSample/classification\" , shell=True)\n subprocess.run(\"make\", shell=True )\n while True:\n print(\"==== Process AI <%s> is running ====\" %os.getpid())\n subprocess.run(\"./image_classify ../configs/resnet50/drink.json\" , shell=True)\n os.chdir(\"%s\" %top_dir)\n\ndef program_stress_test(idle_time, stress_time, idle2_time, logfile, during, processes, builddir):\n file_log = \"%s/%s\"%(top_dir,logfile)\n writefile = open(\"%s\"%file_log,\"a\")\n writefile.write(\"==============================================================================================================================================================\\n\")\n writefile.write(\"idle: %s S. stree: %s S. idle2: %s S. logfile: %s/%s. processes: %s.\\n\" %(idle_time, stress_time, idle2_time, top_dir, logfile, processes))\n writefile.write(\"==============================================================================================================================================================\\n\")\n writefile.close()\n process_log = Process(target = function_log, args =(logfile, during, ), name = \"worker_log\" )\n process_log.start()\n time.sleep(idle_time)\n record = []\n for i in range(processes):\n if ( i == 0 ):\n process_work = Process(target = function_ai, args =(\"build\",), name = \"worker_ai\" )\n else:\n process_work = Process(target = function_build, args =(\"%s_%s\"%(builddir,i),), name = \"worker_%s\"%i )\n process_work.start()\n record.append(process_work)\n time.sleep(stress_time)\n for process_work in record:\n process_work.terminate()\n for process_work in record:\n process_work.join()\n time.sleep(idle2_time)\n process_log.terminate()\n process_log.join()\n for i in range(processes):\n subprocess.run(\"rm -rf %s/%s_%s \" %(top_dir,builddir,i ) , shell=True)\n record.clear()\n \nif __name__ == '__main__':\n program_stress_test(idle_time = 1800, stress_time= 3600, idle2_time = 1800, logfile = \"stress.log\", during = 5, processes = 6, builddir = \"worker\")\n\n\n\n\n","repo_name":"robe-zhang/edgeboard_fz5c_stress_test","sub_path":"stress.py","file_name":"stress.py","file_ext":"py","file_size_in_byte":4254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"35472827554","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n!+ 去他奶奶的dbghelp.SymXXX()，既然系统是固定的，咱自己解析sym!\n文件格式:\nkernel32.dll.txt:\n 0000AA5C 0000AA8D lstrcmpW\n 00061B7D 00061BAD GetProcessId\n ...\nntdll.dll.txt\n 0006FBD2 0006FBDF _tolower\n ...\n\"\"\"\n\nimport os\nimport inspect\n\n\nfile_path = os.path.abspath(inspect.getsourcefile(lambda: 0))\nfile_dir = os.path.dirname(inspect.getsourcefile(lambda: 0))\n\n\n# ---------------------------------------------------------------------------\n\ndef _pt_log(line):\n \"\"\"\n proxy to log.pt_log()\n \"\"\"\n log.pt_log(line)\n\n\n# ---------------------------------------------------------------------------\n# self-parsed symbol\n\n#\n# structure of v_tmp_symbols:\n# {\"kernel32.dll\": [(0x0000AA5C, 0x0000AA8D, lstrcmpW),\n# (0x00061B7D, 0x00061BAD, GetProcessId),\n# ...],\n# \"ntdll.dll\": [(0x0006FBD2, 0x0006FBDF, _tolower),\n# ...]}\n#\nglobal v_tmp_symbols\nv_tmp_symbols = {}\n\n\ndef _syms_load(sym_name, file_path):\n \"\"\"\n load self-parsed symbol from specified file path\n\n @param: sym_name : string : module name of symbols\n @param: file_path : string : symbol file path\n \"\"\"\n assert os.path.exists(file_path)\n\n global v_tmp_symbols\n assert sym_name not in v_tmp_symbols\n v_tmp_symbols[sym_name] = []\n\n try:\n f = open(file_path)\n except:\n _pt_log(\">>> open sym file error: %s\" % sym_name)\n else:\n for line in f:\n\n assert line.count(\" \") == 2\n splits = line.split(\" \")\n assert len(splits) == 3\n # offset format: HEX\n func_item = (int(splits[0], 16), int(splits[1], 16), splits[2].strip(\"\\n\"))\n\n v_tmp_symbols[sym_name].append(func_item)\n f.close()\n\n\ndef load_sysdll_syms():\n \"\"\"\n load self-parsed symbols of system dlls\n\n this should only be called once during each debug session\n\n !+ to export such xxx.txt symbol file, use my python scripts.\n \"\"\"\n # xp sp3\n sym_path = os.path.join(file_dir, \"symbols_xpsp3\")\n for parent, dirnames, filenames in os.walk(sym_path):\n\n for dirname in dirnames:\n _pt_log(\">>> load sysdll symbol, ignore dir: %s\" % dirname)\n\n for filename in filenames:\n\n # only parse txt files\n if not filename.endswith(\".txt\"):\n # _pt_log(\">>> load sysdll symbol, ignore file: %s\" % filename)\n continue\n\n sym_name = filename.replace(\".txt\", \"\")\n # _pt_log(\"loading symbols for module %-10s from file: %s\" % (sym_name, filename))\n _syms_load(sym_name.lower(), os.path.join(parent, filename))\n\n\ndef load_debugee_syms():\n \"\"\"\n load self-parsed symbolf of debugee.\n this shall be called after debugee file set\n \"\"\"\n target_file = util.gen_path_tail_debugee(\"_ida_names.txt\", has_ext=False)\n if os.path.exists(target_file):\n\n _pt_log(\">>> loading debugee symbol from file: %s\" % target_file)\n _syms_load(util.debugee_name(), target_file)\n\n else:\n _pt_log(\">>> debugee symbol file not found: %s\" % target_file)\n\n\nglobal v_tmp_pted_no_sym_md_names\nv_tmp_pted_no_sym_md_names = []\n\n\ndef sym_resolve(md_name, offset):\n \"\"\"\n resolve symbol from pre-loaded symbols\n\n @param: md_name : string : module name, as key in v_tmp_symbols\n @param: offset : int : offset that address relative to module base\n\n @return: tuple : (func_name, func_offset) or (None, None)\n \"\"\"\n assert md_name and len(md_name) != 0\n\n md_name = md_name.lower()\n\n global v_tmp_symbols\n assert v_tmp_symbols and len(v_tmp_symbols) != 0\n\n # if debugee symbol not loaded, return None, but no \"ASSERT\", because it's quite common.\n if md_name == util.debugee_name() and md_name not in v_tmp_symbols:\n return (None, None)\n\n # if not, parse module, put txt file under folder, run this again\n if md_name not in v_tmp_symbols:\n\n global v_tmp_pted_no_sym_md_names\n if md_name not in v_tmp_pted_no_sym_md_names:\n _pt_log(\">>> no sym for this md: %s\" % md_name)\n v_tmp_pted_no_sym_md_names.append(md_name)\n\n # assert False\n # for sample loading dll of its own...\n return (None, None)\n\n for func_item in v_tmp_symbols[md_name]:\n if func_item[0] <= offset and offset <= func_item[1]:\n return (func_item[2], offset - func_item[0])\n\n return (None, None)\n\n\ndef get_sym_sys_dll_names():\n \"\"\"\n get sys dll names that have symbols\n\n @return: list : a list of dict\n \"\"\"\n global v_tmp_symbols\n if len(v_tmp_symbols) == 0:\n load_sysdll_syms()\n # all md names is lower()ed\n return v_tmp_symbols.keys()\n\n\ndef check_has_sys_dll_sym(sys_dll_name):\n \"\"\"\n @param: sys_dll_name : string : system dll name\n\n @return: bool :\n \"\"\"\n global v_tmp_symbols\n return sys_dll_name in v_tmp_symbols\n\n\ndef check_is_sys_dll(sys_dll_name):\n \"\"\"\n @param: sys_dll_name : string : system dll name\n\n @return: bool :\n \"\"\"\n global v_tmp_symbols\n return sys_dll_name in v_tmp_symbols\n\n\n# ---------------------------------------------------------------------------\n# END OF FILE\n# ---------------------------------------------------------------------------\n","repo_name":"xrkk/DbgBasedApiMon","sub_path":"_util/sym/sym.py","file_name":"sym.py","file_ext":"py","file_size_in_byte":5377,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"72"}
+{"seq_id":"41792162018","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport json\nimport sys\nimport logging\n\nimport six\nfrom docker.errors import APIError\n\nfrom ..exceptions import DockerStatusError\nfrom ..docker_api import APIClient\nfrom . import use_get_archive\nfrom .docker_util import DockerUtilityMixin\n\nlog = logging.getLogger(__name__)\n\nLOG_PROGRESS_FORMAT = \"{0} {1} {2}\"\nLOG_CONTAINER_FORMAT = \"[%s] %s\"\n\n\ndef parse_response(response):\n \"\"\"\n Decodes the JSON response, simply ignoring syntax errors. Therefore it should be used for filtering visible output\n only.\n\n :param response: Server response as a JSON string.\n :type response: unicode | str\n :return: Decoded object from the JSON string. Returns an empty dictionary if input was invalid.\n :rtype: dict\n \"\"\"\n if isinstance(response, six.binary_type):\n response = response.decode('utf-8')\n try:\n obj = json.loads(response)\n except ValueError:\n return {}\n return obj\n\n\nclass DockerClientWrapper(DockerUtilityMixin, APIClient):\n \"\"\"\n Adds a few utility functions to the Docker API client.\n \"\"\"\n def _docker_log_stream(self, response, raise_on_error):\n log_str = None\n image_str = None\n for e in response:\n output = parse_response(e)\n if 'stream' in output:\n log_str = output['stream']\n if log_str.startswith('Successfully built '):\n image_str = log_str\n self.push_log(log_str, logging.INFO)\n elif 'error' in output:\n log_str = output['error']\n self.push_log(log_str, logging.ERROR)\n if raise_on_error:\n raise DockerStatusError(log_str, output.get('errorDetail'))\n return image_str or log_str # Line with image id or last line written to stdout\n\n def _docker_status_stream(self, response, raise_on_error):\n result = {}\n for e in response:\n output = parse_response(e)\n if output:\n result.update(output)\n if 'status' in output:\n oid = output.get('id')\n progress = output.get('progress', '')\n if oid:\n self.push_progress(output['status'], oid, progress)\n else:\n self.push_log(output['status'], logging.INFO)\n elif 'error' in output:\n error_message = output['error']\n self.push_log(error_message, logging.ERROR)\n if raise_on_error:\n raise DockerStatusError(error_message, output.get('errorDetail'))\n return result\n\n def push_progress(self, status, object_id, progress):\n \"\"\"\n Handles streamed progress information.\n\n :param status: Status text.\n :type status: unicode | str\n :param object_id: Object that the progress is reported on.\n :type object_id: unicode | str\n :param progress: Progress bar.\n :type progress: unicode | str\n \"\"\"\n pass\n\n def build(self, tag, add_latest_tag=False, add_tags=None, raise_on_error=True, **kwargs):\n \"\"\"\n Overrides the superclass `build()` and filters the output. Messages are deferred to `push_log`, whereas the\n final message is checked for a success message. If the latter is found, only the new image id is returned.\n\n :param tag: Tag of the new image to be built. Unlike in the superclass, this is obligatory.\n :type tag: unicode | str\n :param add_latest_tag: In addition to the image ``tag``, tag the image with ``latest``.\n :type add_latest_tag: bool\n :param add_tags: Additional tags. Can also be used as an alternative to ``add_latest_tag``.\n :type add_tags: list[unicode | str]\n :param raise_on_error: Raises errors in the status output as a DockerStatusException. Otherwise only logs\n errors.\n :type raise_on_error: bool\n :param kwargs: See :meth:`docker.client.Client.build`.\n :return: New, generated image id or `None`.\n :rtype: unicode | str\n \"\"\"\n response = super(DockerClientWrapper, self).build(tag=tag, **kwargs)\n # It is not the kwargs alone that decide if we get a stream, so we have to check.\n if isinstance(response, tuple):\n image_id = response[0]\n else:\n last_log = self._docker_log_stream(response, raise_on_error)\n if last_log and last_log.startswith('Successfully built '):\n # Remove prefix\n if last_log[-1] == '\\n':\n image_id = last_log[19:-1]\n else:\n image_id = last_log[19:]\n else:\n image_id = None\n\n if not image_id:\n return None\n\n self.add_extra_tags(image_id, tag, add_tags, add_latest_tag)\n return image_id\n\n def login(self, username, password=None, email=None, registry=None, reauth=False, **kwargs):\n \"\"\"\n Login to a Docker registry server.\n\n :param username: User name for login.\n :type username: unicode | str\n :param password: Login password; may be ``None`` if blank.\n :type password: unicode | str\n :param email: Optional; email address for login.\n :type email: unicode | str\n :param registry: Optional registry URL to log in to. Uses the Docker index by default.\n :type registry: unicode | str\n :param reauth: Re-authenticate, even if the login has been successful before.\n :type reauth: bool\n :param kwargs: Additional kwargs to :meth:`docker.client.Client.login`.\n :return: ``True`` if the login has succeeded, or if it has not been necessary as it succeeded before. ``False``\n otherwise.\n :rtype: bool\n \"\"\"\n response = super(DockerClientWrapper, self).login(username, password, email, registry, reauth=reauth, **kwargs)\n return response.get('Status') == 'Login Succeeded' or response.get('username') == username\n\n def pull(self, repository, tag=None, stream=False, raise_on_error=True, **kwargs):\n \"\"\"\n Pulls an image repository from the registry.\n\n :param repository: Name of the repository.\n :type repository: unicode | str\n :param tag: Optional tag to pull; by default pulls all tags of the given repository.\n :type tag: unicode | str\n :param stream: Use the stream output format with additional status information.\n :type stream: bool\n :param raise_on_error: Raises errors in the status output as a DockerStatusException. Otherwise only logs\n errors.\n :type raise_on_error: bool\n :param kwargs: Additional kwargs for :meth:`docker.client.Client.pull`.\n :return: ``True`` if the image has been pulled successfully.\n :rtype: bool\n \"\"\"\n response = super(DockerClientWrapper, self).pull(repository, tag=tag, stream=stream, **kwargs)\n if stream:\n result = self._docker_status_stream(response, raise_on_error)\n else:\n result = self._docker_status_stream(response.split('\\r\\n') if response else (), raise_on_error)\n return result and not result.get('error')\n\n def push(self, repository, stream=False, raise_on_error=True, **kwargs):\n \"\"\"\n Pushes an image repository to the registry.\n\n :param repository: Name of the repository (can include a tag).\n :type repository: unicode | str\n :param stream: Use the stream output format with additional status information.\n :type stream: bool\n :param raise_on_error: Raises errors in the status output as a DockerStatusException. Otherwise only logs\n errors.\n :type raise_on_error: bool\n :param kwargs: Additional kwargs for :meth:`docker.client.Client.push`.\n :return: ``True`` if the image has been pushed successfully.\n :rtype: bool\n \"\"\"\n response = super(DockerClientWrapper, self).push(repository, stream=stream, **kwargs)\n if stream:\n result = self._docker_status_stream(response, raise_on_error)\n else:\n result = self._docker_status_stream(response.split('\\r\\n') if response else (), raise_on_error)\n return result and not result.get('error')\n\n def push_container_logs(self, container):\n \"\"\"\n Reads the current container logs and passes them to :meth:`~push_log`. Removes a trailing empty line and\n prefixes each log line with the container name.\n\n :param container: Container name or id.\n :type container: unicode | str\n \"\"\"\n logs = self.logs(container).decode('utf-8')\n log_lines = logs.split('\\n')\n if log_lines and not log_lines[-1]:\n log_lines.pop()\n for line in log_lines:\n self.push_log(LOG_CONTAINER_FORMAT, logging.INFO, container, line)\n\n def remove_container(self, container, raise_on_error=True, raise_not_found=False, **kwargs):\n \"\"\"\n Removes a container. For convenience optionally ignores API errors.\n\n :param container: Container name or id.\n :type container: unicode | str\n :param raise_on_error: Errors on stop and removal may result from Docker volume problems, that may not\n affect further actions. Such errors are always logged, but do not raise an exception if this is set to\n ``True``.\n :type raise_on_error: bool\n :param raise_not_found: Whether to raise 404 errors, i.e. that the container to be removed was not\n found. Default is ``False``.\n :type raise_not_found: bool\n :param kwargs: Additional keyword args for :meth:`docker.client.Client.remove_container`.\n \"\"\"\n try:\n super(DockerClientWrapper, self).remove_container(container, **kwargs)\n except APIError as e:\n exc_info = sys.exc_info()\n if e.response.status_code == 404:\n if raise_not_found:\n six.reraise(*exc_info)\n else:\n self.push_log(\"Failed to remove container '%s': %s\", logging.ERROR, container, e.explanation)\n if raise_on_error:\n six.reraise(*exc_info)\n\n def remove_image(self, image, raise_on_error=True, raise_not_found=False, **kwargs):\n \"\"\"\n Removes a container. For convenience optionally ignores API errors.\n\n :param image: Image name or id.\n :type image: unicode | str\n :param raise_on_error: Errors on image removal may not affect further actions. Such errors are always\n logged, but do not raise an exception if this is set to ``True``.\n :param raise_not_found: Whether to raise 404 errors, i.e. that the image to be removed was not\n found. Default is ``False``.\n :type raise_not_found: bool\n :param kwargs: Additional keyword args for :meth:`docker.client.Client.remove_image`.\n \"\"\"\n try:\n super(DockerClientWrapper, self).remove_image(image, **kwargs)\n except APIError as e:\n exc_info = sys.exc_info()\n if e.response.status_code == 404:\n if raise_not_found:\n six.reraise(*exc_info)\n else:\n self.push_log(\"Failed to remove image '%s': %s\", logging.ERROR, image, e.explanation)\n if raise_on_error:\n six.reraise(*exc_info)\n\n def stop(self, container, raise_on_error=True, **kwargs):\n \"\"\"\n Stops a container. For convenience optionally ignores API errors.\n\n :param container: Container name.\n :type container: unicode | str\n :param raise_on_error: Errors on stop and removal may result from Docker volume problems, that may not\n affect further actions. Such errors are always logged, but do not raise an exception if this is set to\n ``True``.\n :type raise_on_error: bool\n :param kwargs: Additional keyword args for :meth:`docker.client.Client.stop`.\n \"\"\"\n try:\n super(DockerClientWrapper, self).stop(container, **kwargs)\n except APIError as e:\n exc_info = sys.exc_info()\n self.push_log(\"Failed to stop container '%s': %s\", logging.ERROR, container, e.explanation)\n if raise_on_error:\n six.reraise(*exc_info)\n\n def copy_resource(self, container, resource, local_filename):\n \"\"\"\n *Experimental:* Copies a resource from a Docker container to a local tar file. For details, see\n :meth:`docker.client.Client.copy`.\n\n :param container: Container name or id.\n :type container: unicode | str\n :param resource: Resource inside the container.\n :type resource: unicode | str\n :param local_filename: Local file to store resource into. Will be overwritten if present.\n :type local_filename: unicode | str\n \"\"\"\n if use_get_archive(self.api_version):\n raw = self.get_archive(container, resource)[0]\n else:\n raw = self.copy(container, resource)\n try:\n with open(local_filename, 'wb+') as f:\n for buf in raw:\n f.write(buf)\n finally:\n raw.close()\n\n def save_image(self, image, local_filename):\n \"\"\"\n *Experimental:* Copies an image from Docker to a local tar file. For details, see\n :meth:`docker.client.Client.get_image`.\n\n :param image: Image name or id.\n :type image: unicode | str\n :param local_filename: Local file to store image into. Will be overwritten if present.\n :type local_filename: unicode | str\n \"\"\"\n raw = self.get_image(image)\n try:\n with open(local_filename, 'wb+') as f:\n for buf in raw:\n f.write(buf)\n finally:\n raw.close()\n","repo_name":"merll/docker-map","sub_path":"dockermap/client/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":14010,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"72"}
+{"seq_id":"11926348335","text":"\ninp = open(\"day13.input\").readlines()\nts = int(inp[0])\nbusses = [int(x) for x in inp[1].strip().split(\",\") if x != \"x\"]\nprint(ts, busses)\n\nminutes = 0\nwhile not any(map(lambda n: (ts+minutes) % n == 0, busses)):\n minutes += 1\nbus = list(filter(lambda n: (ts+minutes) % n == 0, busses))\n\nprint(ts+minutes, bus, minutes*bus[0])\nschedule = [int(x) for x in inp[1].strip().replace(\"x\", \"-1\").split(\",\")]\n\nnb = []\nfor i in range(len(schedule)):\n if schedule[i] == -1:\n continue\n nb += [(i, schedule[i])]\n\n\nn, a = zip(*nb)\nprint(n, a)\n\nstepSize = 1\nwaiting = True\nts = stepSize\n#i = 1\nbroken = False\nfor i in range(len(a)):\n while (ts+n[i]) % a[i] != 0:\n ts += stepSize\n print(f\"Found ts {ts} satisfying bus {i}, {a[i]}\")\n stepSize *= a[i]\n\nprint(ts)\nprint(\"#\"*80)\n","repo_name":"cpebble/advent_of_code","sub_path":"2020/day13.py","file_name":"day13.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"}
+{"seq_id":"6770680889","text":"#!/usr/bin/env python3\n\nimport random\n\nBLACK, WHITE = True, False\nHARD, SOFT = True, False\n\n\nclass Bench:\n def __init__(self, width, height):\n self.grid = []\n self.source_location = None\n\n for i in range(height):\n self.grid.append([Space()] * width)\n\n def display(self):\n for row in self.grid:\n print(\" \".join(cell.display for cell in row))\n\n def place(self, x, y, component):\n if not isinstance(self.grid[y][x], Space):\n raise ValueError\n if isinstance(component, Source):\n if self.source_location:\n raise ValueError # only one source allowed\n self.source_location = (x, y)\n self.grid[y][x] = component\n\n def run(self, iterations=1):\n for i in range(iterations):\n # classical for now\n p = dict(\n color=random.choice([BLACK, WHITE]),\n hardness=random.choice([HARD, SOFT]),\n )\n x, y = self.source_location\n dx, dy = 1, 0\n while True:\n x += dx\n y += dy\n try:\n p, dx, dy = self.grid[y][x].hit(p, dx, dy)\n if (dx, dy) == (0, 0):\n break\n except IndexError:\n break\n\n\nclass Space:\n display = \".\"\n\n def hit(self, p, dx, dy):\n return p, dx, dy\n\n\nclass Source:\n display = \"S\"\n\n\nclass Box:\n def hit(self, p, dx, dy):\n if (dx, dy) != (1, 0): # particle must come from left\n return p, 0, 0\n if p[self.property_name]:\n return p, 0, -1\n else:\n return p, 1, 0\n\n\nclass ColorBox(Box):\n display = \"C\"\n property_name = \"color\"\n\n\nclass HardnessBox(Box):\n display = \"H\"\n property_name = \"hardness\"\n\n\nclass Mirror:\n display = \"/\"\n\n def hit(self, p, dx, dy):\n return p, -dy, -dx\n\n\nclass Joiner:\n display = \"M\"\n\n def hit(self, p, dx, dy):\n if (dx, dy) == (1, 0) or (dx, dy) == (0, -1):\n return p, 1, 0\n else:\n return p, 0, 0\n\n\nclass Wall:\n display = \"W\"\n\n def hit(self, p, dx, dy):\n return p, 0, 0\n\n\nclass Detector:\n display = \"D\"\n\n def __init__(self):\n self.count = 0\n\n def hit(self, p, dx, dy):\n self.count += 1\n return p, 0, 0\n\n\nif __name__ == \"__main__\":\n bench = Bench(10, 10)\n bench.place(1, 8, Source())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(8, 8, detector_1)\n bench.place(6, 6, detector_2)\n bench.display()\n bench.run()\n print(detector_1.count)\n print(detector_2.count)\n\n bench.place(6, 8, Mirror())\n bench.display()\n bench.run(2)\n print(detector_1.count)\n print(detector_2.count)\n\n bench = Bench(10, 10)\n bench.place(1, 8, Source())\n bench.place(5, 8, ColorBox())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(7, 8, detector_1)\n bench.place(5, 6, detector_2)\n bench.display()\n bench.run(100)\n print(detector_1.count)\n print(detector_2.count)\n\n bench = Bench(10, 10)\n bench.place(1, 8, Source())\n bench.place(3, 8, ColorBox())\n bench.place(5, 8, HardnessBox())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(7, 8, detector_1)\n bench.place(5, 6, detector_2)\n bench.display()\n bench.run(100)\n print(detector_1.count)\n print(detector_2.count)\n\n bench = Bench(10, 10)\n bench.place(1, 8, Source())\n bench.place(3, 8, ColorBox())\n bench.place(5, 8, HardnessBox())\n bench.place(7, 8, ColorBox())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(9, 8, detector_1)\n bench.place(7, 6, detector_2)\n bench.display()\n bench.run(100)\n # the result here will be very different classical vs quantum\n print(detector_1.count)\n print(detector_2.count)\n\n bench = Bench(13, 10)\n bench.place(1, 8, Source())\n bench.place(3, 8, ColorBox())\n bench.place(5, 8, HardnessBox())\n bench.place(5, 6, Mirror())\n bench.place(7, 8, Mirror())\n bench.place(7, 6, Joiner())\n bench.place(9, 6, HardnessBox())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(11, 6, detector_1)\n bench.place(9, 4, detector_2)\n bench.display()\n bench.run(100)\n print(detector_1.count)\n print(detector_2.count)\n\n bench = Bench(13, 10)\n bench.place(1, 8, Source())\n bench.place(3, 8, HardnessBox())\n bench.place(5, 8, HardnessBox())\n bench.place(5, 6, Mirror())\n bench.place(7, 8, Mirror())\n bench.place(7, 6, Joiner())\n bench.place(9, 6, ColorBox())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(11, 6, detector_1)\n bench.place(9, 4, detector_2)\n bench.display()\n bench.run(100)\n print(detector_1.count)\n print(detector_2.count)\n\n bench = Bench(13, 10)\n bench.place(1, 8, Source())\n bench.place(3, 8, ColorBox())\n bench.place(5, 8, HardnessBox())\n bench.place(5, 6, Mirror())\n bench.place(7, 8, Mirror())\n bench.place(7, 6, Joiner())\n bench.place(9, 6, ColorBox())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(11, 6, detector_1)\n bench.place(9, 4, detector_2)\n bench.display()\n bench.run(100)\n # the result here will be very different classical vs quantum\n print(detector_1.count)\n print(detector_2.count)\n\n bench = Bench(13, 10)\n bench.place(1, 8, Source())\n bench.place(3, 8, ColorBox())\n bench.place(5, 8, HardnessBox())\n bench.place(5, 6, Mirror())\n bench.place(7, 8, Mirror())\n bench.place(6, 8, Wall())\n bench.place(7, 6, Joiner())\n bench.place(9, 6, ColorBox())\n detector_1 = Detector()\n detector_2 = Detector()\n bench.place(11, 6, detector_1)\n bench.place(9, 4, detector_2)\n bench.display()\n bench.run(100)\n # the result here will be very different classical vs quantum\n print(detector_1.count)\n print(detector_2.count)\n","repo_name":"jtauber/quantum-workbench","sub_path":"qwb.py","file_name":"qwb.py","file_ext":"py","file_size_in_byte":5991,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"}
+{"seq_id":"33721984566","text":"from rest_framework import generics\nfrom rest_framework.views import APIView\nfrom adminn.models import Product\nfrom client.models import Coupon\nfrom client.serializer import CouponSerializer, ProductWithOptionSerializer\nfrom .serializer import *\nfrom adminn.serializers import ProductSerializer\nfrom .models import *\nfrom rest_framework.permissions import AllowAny, IsAuthenticated\nfrom .models import User\nfrom rest_framework.response import Response\nfrom django.http import HttpResponse, JsonResponse\nfrom adminn.models import Option\nfrom rest_framework.decorators import api_view, permission_classes\nfrom django.core.mail import send_mail\nfrom rest_framework.parsers import FormParser, MultiPartParser\nimport vonage, os\nfrom django.conf import settings\nprint(f\"EMAIL_HOST = {settings.EMAIL_HOST}\")\n\nclient = vonage.Client(key=os.environ.get(\"SMS_KEY\"), secret=os.environ.get(\"SMS_SECRET\"))\nsms = vonage.Sms(client)\n\n\ndef generate__otp():\n otp = random.randint(100000, 999999)\n return otp\n\n\ndef send_otp(ph_number, otp):\n response = sms.send_message({\n 'from': 'VIMANI',\n 'to': ph_number,\n 'text': f\"Your OTP is {otp}\",\n })\n return response\n\n\ndef send_email(email, subject, otp):\n message = f\"This is verification code for your vimani account.\\n\\n{otp}\"\n send_mail(subject, message, 'Vimani Pvt.
- ')\n indent = len(match.group(1))\n elif indent > len(match.group(1)):\n while indent > len(match.group(1)):\n output.append('
')\n\n if in_item:\n output.append('')\n while indent > 0:\n output.append('')\n indent -= 2\n return '\\n'.join(output)\n\n\nclass HelpResource(HtmlResource):\n def __init__(self, text, pageTitle, parent_node):\n HtmlResource.__init__(self)\n self.text = text\n self.pageTitle = pageTitle\n self.parent_level = parent_node.level\n self.parent_children = parent_node.children.keys()\n\n def content(self, request, cxt):\n cxt['level'] = self.parent_level\n cxt['text'] = ToHtml(self.text)\n cxt['children'] = [ n for n in self.parent_children if n != 'help' ]\n cxt['flags'] = ToHtml(FLAGS)\n cxt['examples'] = ToHtml(EXAMPLES).replace(\n 'href=\"/json',\n 'href=\"%sjson' % (self.level * '../'))\n\n template = request.site.buildbot_service.templates.get_template(\"jsonhelp.html\")\n return template.render(**cxt)\n\nclass BuilderPendingBuildsJsonResource(JsonResource):\n help = \"\"\"Describe pending builds for a builder.\n\"\"\"\n pageTitle = 'Builder'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n\n def asDict(self, request):\n # buildbot.status.builder.BuilderStatus\n d = self.builder_status.getPendingBuildRequestStatuses()\n def to_dict(statuses):\n return defer.gatherResults(\n [ b.asDict_async() for b in statuses ])\n d.addCallback(to_dict)\n return d\n\n\nclass BuilderJsonResource(JsonResource):\n help = \"\"\"Describe a single builder.\n\"\"\"\n pageTitle = 'Builder'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n self.putChild('builds', BuildsJsonResource(status, builder_status))\n self.putChild('slaves', BuilderSlavesJsonResources(status,\n builder_status))\n self.putChild(\n 'pendingBuilds',\n BuilderPendingBuildsJsonResource(status, builder_status))\n\n def asDict(self, request):\n # buildbot.status.builder.BuilderStatus\n return self.builder_status.asDict_async()\n\n\nclass BuildersJsonResource(JsonResource):\n help = \"\"\"List of all the builders defined on a master.\n\"\"\"\n pageTitle = 'Builders'\n\n def __init__(self, status):\n JsonResource.__init__(self, status)\n for builder_name in self.status.getBuilderNames():\n self.putChild(builder_name,\n BuilderJsonResource(status,\n status.getBuilder(builder_name)))\n\n\nclass BuilderSlavesJsonResources(JsonResource):\n help = \"\"\"Describe the slaves attached to a single builder.\n\"\"\"\n pageTitle = 'BuilderSlaves'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n for slave_name in self.builder_status.slavenames:\n self.putChild(slave_name,\n SlaveJsonResource(status,\n self.status.getSlave(slave_name)))\n\n\nclass BuildJsonResource(JsonResource):\n help = \"\"\"Describe a single build.\n\"\"\"\n pageTitle = 'Build'\n\n def __init__(self, status, build_status):\n JsonResource.__init__(self, status)\n self.build_status = build_status\n self.putChild('source_stamp',\n SourceStampJsonResource(status,\n build_status.getSourceStamp()))\n self.putChild('steps', BuildStepsJsonResource(status, build_status))\n\n def asDict(self, request):\n return self.build_status.asDict()\n\n\nclass AllBuildsJsonResource(JsonResource):\n help = \"\"\"All the builds that were run on a builder.\n\"\"\"\n pageTitle = 'AllBuilds'\n\n def __init__(self, status, builder_status):\n JsonResource.__init__(self, status)\n self.builder_status = builder_status\n\n def getChild(self, path, request):\n # Dynamic childs.\n if isinstance(path, int) or _IS_INT.match(path):\n build_status = self.builder_status.getBuild(int(path))\n if build_status:\n # Don't cache BuildJsonResource; that would defeat the cache-ing\n # mechanism in place for BuildStatus objects (in BuilderStatus).\n return BuildJsonResource(self.status, build_status)\n return JsonResource.getChild(self, path, request)\n\n def asDict(self, request):\n results = {}\n # If max is too big, it'll trash the cache...\n max = int(RequestArg(request, 'max',\n self.builder_status.buildCacheSize/2))\n for i in range(0, max):\n child = self.getChildWithDefault(-i, request)\n if not isinstance(child, BuildJsonResource):\n continue\n results[child.build_status.getNumber()] = child.asDict(request)\n return results\n\n\nclass BuildsJsonResource(AllBuildsJsonResource):\n help = \"\"\"Builds that were run on a builder.\n\"\"\"\n pageTitle = 'Builds'\n\n def __init__(self, status, builder_status):\n AllBuildsJsonResource.__init__(self, status, builder_status)\n self.putChild('_all', AllBuildsJsonResource(status, builder_status))\n\n def getChild(self, path, request):\n # Transparently redirects to _all if path is not ''.\n return self.children['_all'].getChildWithDefault(path, request)\n\n def asDict(self, request):\n # This would load all the pickles and is way too heavy, especially that\n # it would trash the cache:\n # self.children['builds'].asDict(request)\n # TODO(maruel) This list should also need to be cached but how?\n builds = dict([\n (int(file), None)\n for file in os.listdir(self.builder_status.basedir)\n if _IS_INT.match(file)\n ])\n return builds\n\n\nclass BuildStepJsonResource(JsonResource):\n help = \"\"\"A single build step.\n\"\"\"\n pageTitle = 'BuildStep'\n\n def __init__(self, status, build_step_status):\n # buildbot.status.buildstep.BuildStepStatus\n JsonResource.__init__(self, status)\n self.build_step_status = build_step_status\n # TODO self.putChild('logs', LogsJsonResource())\n\n def asDict(self, request):\n return self.build_step_status.asDict()\n\n\nclass BuildStepsJsonResource(JsonResource):\n help = \"\"\"A list of build steps that occurred during a build.\n\"\"\"\n pageTitle = 'BuildSteps'\n\n def __init__(self, status, build_status):\n JsonResource.__init__(self, status)\n self.build_status = build_status\n # The build steps are constantly changing until the build is done so\n # keep a reference to build_status instead\n\n def getChild(self, path, request):\n # Dynamic childs.\n build_step_status = None\n if isinstance(path, int) or _IS_INT.match(path):\n build_step_status = self.build_status.getSteps()[int(path)]\n else:\n steps_dict = dict([(step.getName(), step)\n for step in self.build_status.getSteps()])\n build_step_status = steps_dict.get(path)\n if build_step_status:\n # Create it on-demand.\n child = BuildStepJsonResource(self.status, build_step_status)\n # Cache it.\n index = self.build_status.getSteps().index(build_step_status)\n self.putChild(str(index), child)\n self.putChild(build_step_status.getName(), child)\n return child\n return JsonResource.getChild(self, path, request)\n\n def asDict(self, request):\n # Only use the number and not the names!\n results = {}\n index = 0\n for step in self.build_status.getSteps():\n results[index] = step.asDict()\n index += 1\n return results\n\n\nclass ChangeJsonResource(JsonResource):\n help = \"\"\"Describe a single change that originates from a change source.\n\"\"\"\n pageTitle = 'Change'\n\n def __init__(self, status, change):\n # buildbot.changes.changes.Change\n JsonResource.__init__(self, status)\n self.change = change\n\n def asDict(self, request):\n return self.change.asDict()\n\n\nclass ChangesJsonResource(JsonResource):\n help = \"\"\"List of changes.\n\"\"\"\n pageTitle = 'Changes'\n\n def __init__(self, status, changes):\n JsonResource.__init__(self, status)\n for c in changes:\n # c.number can be None or clash another change if the change was\n # generated inside buildbot or if using multiple pollers.\n if c.number is not None and str(c.number) not in self.children:\n self.putChild(str(c.number), ChangeJsonResource(status, c))\n else:\n # Temporary hack since it creates information exposure.\n self.putChild(str(id(c)), ChangeJsonResource(status, c))\n\n def asDict(self, request):\n \"\"\"Don't throw an exception when there is no child.\"\"\"\n if not self.children:\n return {}\n return JsonResource.asDict(self, request)\n\n\nclass ChangeSourcesJsonResource(JsonResource):\n help = \"\"\"Describe a change source.\n\"\"\"\n pageTitle = 'ChangeSources'\n\n def asDict(self, request):\n result = {}\n n = 0\n for c in self.status.getChangeSources():\n # buildbot.changes.changes.ChangeMaster\n change = {}\n change['description'] = c.describe()\n result[n] = change\n n += 1\n return result\n\n\nclass ProjectJsonResource(JsonResource):\n help = \"\"\"Project-wide settings.\n\"\"\"\n pageTitle = 'Project'\n\n def asDict(self, request):\n return self.status.asDict()\n\n\nclass SlaveJsonResource(JsonResource):\n help = \"\"\"Describe a slave.\n\"\"\"\n pageTitle = 'Slave'\n\n def __init__(self, status, slave_status):\n JsonResource.__init__(self, status)\n self.slave_status = slave_status\n self.name = self.slave_status.getName()\n self.builders = None\n\n def getBuilders(self):\n if self.builders is None:\n # Figure out all the builders to which it's attached\n self.builders = []\n for builderName in self.status.getBuilderNames():\n if self.name in self.status.getBuilder(builderName).slavenames:\n self.builders.append(builderName)\n return self.builders\n\n def getSlaveBuildMap(self, buildcache, buildercache):\n for builderName in self.getBuilders():\n if builderName not in buildercache:\n buildercache.add(builderName)\n builder_status = self.status.getBuilder(builderName)\n\n buildnums = range(-1, -(builder_status.buildCacheSize - 1), -1)\n builds = builder_status.getBuilds(buildnums)\n\n for build_status in builds:\n if not build_status or not build_status.isFinished():\n # If not finished, it will appear in runningBuilds.\n break\n slave = buildcache[build_status.getSlavename()]\n slave.setdefault(builderName, []).append(\n build_status.getNumber())\n return buildcache[self.name]\n\n def asDict(self, request):\n if not hasattr(request, 'custom_data'):\n request.custom_data = {}\n if 'buildcache' not in request.custom_data:\n # buildcache is used to cache build information across multiple\n # invocations of SlaveJsonResource. It should be set to an empty\n # collections.defaultdict(dict).\n request.custom_data['buildcache'] = collections.defaultdict(dict)\n\n # Tracks which builders have been stored in the buildcache.\n request.custom_data['buildercache'] = set()\n\n results = self.slave_status.asDict()\n # Enhance it by adding more information.\n results['builders'] = self.getSlaveBuildMap(\n request.custom_data['buildcache'],\n request.custom_data['buildercache'])\n return results\n\n\nclass SlavesJsonResource(JsonResource):\n help = \"\"\"List the registered slaves.\n\"\"\"\n pageTitle = 'Slaves'\n\n def __init__(self, status):\n JsonResource.__init__(self, status)\n for slave_name in status.getSlaveNames():\n self.putChild(slave_name,\n SlaveJsonResource(status,\n status.getSlave(slave_name)))\n\n\nclass SourceStampJsonResource(JsonResource):\n help = \"\"\"Describe the sources for a SourceStamp.\n\"\"\"\n pageTitle = 'SourceStamp'\n\n def __init__(self, status, source_stamp):\n # buildbot.sourcestamp.SourceStamp\n JsonResource.__init__(self, status)\n self.source_stamp = source_stamp\n self.putChild('changes',\n ChangesJsonResource(status, source_stamp.changes))\n # TODO(maruel): Should redirect to the patch's url instead.\n #if source_stamp.patch:\n # self.putChild('patch', StaticHTML(source_stamp.path))\n\n def asDict(self, request):\n return self.source_stamp.asDict()\n\nclass MetricsJsonResource(JsonResource):\n help = \"\"\"Master metrics.\n\"\"\"\n title = \"Metrics\"\n\n def asDict(self, request):\n metrics = self.status.getMetrics()\n if metrics:\n return metrics.asDict()\n else:\n # Metrics are disabled\n return None\n\n\n\nclass JsonStatusResource(JsonResource):\n \"\"\"Retrieves all json data.\"\"\"\n help = \"\"\"JSON status\n\nRoot page to give a fair amount of information in the current buildbot master\nstatus. You may want to use a child instead to reduce the load on the server.\n\nFor help on any sub directory, use url /child/help\n\"\"\"\n pageTitle = 'Buildbot JSON'\n\n def __init__(self, status):\n JsonResource.__init__(self, status)\n self.level = 1\n self.putChild('builders', BuildersJsonResource(status))\n self.putChild('change_sources', ChangeSourcesJsonResource(status))\n self.putChild('project', ProjectJsonResource(status))\n self.putChild('slaves', SlavesJsonResource(status))\n self.putChild('metrics', MetricsJsonResource(status))\n # This needs to be called before the first HelpResource().body call.\n self.hackExamples()\n\n def content(self, request):\n result = JsonResource.content(self, request)\n # This is done to hook the downloaded filename.\n request.path = 'buildbot'\n return result\n\n def hackExamples(self):\n global EXAMPLES\n # Find the first builder with a previous build or select the last one.\n builder = None\n for b in self.status.getBuilderNames():\n builder = self.status.getBuilder(b)\n if builder.getBuild(-1):\n break\n if not builder:\n return\n EXAMPLES = EXAMPLES.replace('
Coverage for [^:]+:\\s+(\\d+%)<\\/title>')\nsummary_end_re = re.compile(r'\\s+
- %s
\"+newMessage+\"\"\n self.messageRecords.setText(appendText)\n time.sleep(0.2) #this helps the bar set to bottom, after all message already appended\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n \n def updateRoom(self):\n while self.connected:\n data = self.conn.recv(1024)\n data = data.decode(\"utf-8\")\n print(data)\n if data != \"\":\n if \"{CLIENTS}\" in data:\n welcome = data.split(\"{CLIENTS}\")\n self.update_send_to_list(welcome[1])\n self.update_room_list(welcome[1])\n if not welcome[0][5:] == \"\":\n self.message_display_append(welcome[0][5:])\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n elif data[:5] == \"{MSG}\":\n self.message_display_append(data[5:], \"#006600\")\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n else:\n self.message_display_append(\"{private}\"+data, \"#cc33cc\")\n self.scrollRecords.verticalScrollBar().setValue(self.scrollRecords.verticalScrollBar().maximum())\n time.sleep(0.1)\n \n def connect_server(self):\n if self.connected == True:\n return\n name = self.nameLineEdit.text()\n if name == \"\":\n self.connStatus.setText(\"Státusz :\"+\"Adj meg egy nevet\")\n return\n self.userName = name\n IP = self.IPLineEdit.text()\n if IP == \"\":\n IP = \"127.0.0.1\"\n port = self.portLineEdit.text()\n if port == \"\" or not port.isnumeric():\n self.portLineEdit.setText(\"33002\")\n self.connStatus.setText(\"Státusz :\"+\"Sikertelen kapcsolódás\")\n return \n else:\n port = int(port)\n try:\n self.conn.connect((IP, port))\n except:\n self.connStatus.setText(\"Státusz :\"+\"Sikertelen kapcsolódás\")\n self.conn = socket.socket()\n return\n send_msg = bytes(\"{REGISTER}\"+name, \"utf-8\")\n self.conn.send(send_msg)\n self.connected = True \n self.connStatus.setText(\"Status :\"+\"Sikeres kapcsolódás\")\n self.nameLineEdit.setReadOnly(True)\n self.tabs.setTabEnabled(1,True)\n self.rT = threading.Thread(target= self.updateRoom)\n self.rT.start()\n\n def disconnect_server(self):\n if self.connected == False:\n return\n send_msg = bytes(\"{QUIT}\", \"utf-8\")\n self.conn.send(send_msg)\n self.connStatus.setText(\"Státusz :\"+\" Lecsatlakozva\")\n self.nameLineEdit.setReadOnly(False)\n self.nameLineEdit.clear()\n self.tabs.setTabEnabled(1,False)\n self.connected = False\n self.rT.join()\n self.conn.close()\n self.conn = socket.socket()\n \n def update_room_list(self, strList):\n L = strList.split(\"|\")\n self.model.clear()\n for person in L:\n item = QStandardItem(person)\n item.setCheckable(False)\n self.model.appendRow(item)\n \n def update_send_to_list(self, strList):\n L = strList.split(\"|\")\n self.sendComboBox.clear()\n self.sendComboBox.addItem(\"ALL\")\n for person in L:\n if person != self.userName:\n self.sendComboBox.addItem(person)\n previous = self.sendTo\n index = self.sendComboBox.findText(previous)\n print(\"previous choice:\",index)\n if index != -1:\n self.sendComboBox.setCurrentIndex(index)\n else:\n self.sendComboBox.setCurrentIndex(0)\n \n def send_choice(self,text):\n self.sendTo = text\n print(self.sendTo)\n self.sendChoice.setText(\"Küldés: \"+text)\n\nclass Window(QMainWindow):\n def __init__(self):\n super(Window, self).__init__()\n self.setGeometry(50, 50, 500, 300)\n self.setWindowTitle(\"ChatApp\") \n self.table_widget = MyTableWidget(self)\n self.setCentralWidget(self.table_widget)\n self.show()\n \n def closeEvent(self, event):\n close = QMessageBox()\n close.setText(\"Biztos vagy benne?\")\n close.setStandardButtons(QMessageBox.Yes | QMessageBox.Cancel)\n close = close.exec()\n if close == QMessageBox.Yes:\n self.table_widget.disconnect_server()\n event.accept()\n else:\n event.ignore()\n\ndef run():\n app = QApplication(sys.argv)\n GUI = Window()\n sys.exit(app.exec_())\n\nif __name__ == \"__main__\":\n run() ","repo_name":"milanmarko/chatAppIkt","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11639,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"} +{"seq_id":"13180372297","text":"\n# coding: utf-8\n\n# # Demo model applied to CIFAR-10\n# Dataset https://www.cs.toronto.edu/~kriz/cifar.html\n\n# We use only the 1st batch of CIFAR-10 dataset: 10000 images 32x32x3 and 10 labels.\n\n# ### Load dependencies\n\n# In[1]:\n\n\nimport os\nimport tarfile\nimport pickle\nimport urllib.request\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport operator\nimport mxnet as mx\nimport logging\n\n\n# ### Prepare training and test datasets\n# `download_data` downloads data from a given `url`, if no file with the same name has been downloaded so far, \n# `prepare_img` transforms 3 channels (rgb) to one channel (their mean value), reshapes data, adding one axis of length 1 and normalizes the entries to [0,1], \n# `read_CIFAR10` creates labels and datasets from the corresponding \"batch\" files (see [CIFAR dataset description](https://www.cs.toronto.edu/~kriz/cifar.html))\n# \n# With these functions we download data from CIFAR web page, create training/test datasets/labels and then split them into batches of size 150 (shuffle for training). \n# Dataset gets the name `'data'`, labels - `'label'`\n\n# In[2]:\n\n\ndef download_data(url, force_download=True): \n fname = url.split(\"/\")[-1]\n \n if force_download or not os.path.exists(fname):\n urllib.request.urlretrieve(url, fname)\n \n return fname\n\n\ndef unpickle(file):\n with open(file, 'rb') as fo:\n dict = pickle.load(fo, encoding='bytes')\n \n return dict\n\n\ndef read_CIFAR10():\n url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n tar = tarfile.open(download_data(url, False))\n tar.extractall()\n tar.close()\n \n dict_1 = unpickle(\"cifar-10-batches-py/data_batch_1\")\n \n train_img = dict_1[b'data'].reshape(10000, 3, 1024)\n train_img = train_img.transpose(0,2,1)\n train_lbl = np.array(dict_1[b'labels'])\n \n dict_test_1 = unpickle(\"cifar-10-batches-py/test_batch\")\n \n test_img = dict_test_1[b'data'].reshape(10000, 3, 1024)\n test_img = test_img.transpose(0,2,1)\n test_lbl = np.array(dict_test_1[b'labels'])\n\n return train_img, train_lbl, test_img, test_lbl\n\n\ndef prepare_img(img):\n # mean across rgb\n img = np.mean(img, -1)\n \n # normalize\n img = img.astype(np.float32) / 255\n \n # reshape to 4-D\n img = img.reshape(img.shape[0], 1, 32, 32)\n \n return img\n\n\n# In[6]:\n\n\ntrain_img, train_lbl, test_img, test_lbl = read_CIFAR10()\n\ntrain_img = prepare_img(train_img)\ntest_img = prepare_img(test_img)\n\nbatch_size = 150\n\ntrain_data_iter = mx.io.NDArrayIter(data={'data': train_img}, label= {'label': train_lbl}, batch_size=batch_size, shuffle=True)\ntest_data_iter = mx.io.NDArrayIter(data={'data': test_img}, label= {'label': test_lbl}, batch_size=batch_size)\n\n\n# Load and print label names:\n\n# In[4]:\n\n\ndef load_classes():\n path = 'cifar-10-batches-py/'\n file = 'batches.meta'\n \n f = open(path+file, 'rb')\n dict = pickle.load(f)\n return dict['label_names']\n\ndef print_classes(label_names):\n for i in range(0, 10):\n print(str(i) + \" : \" + label_names[i] + \" \")\n\nlabel_names = load_classes()\nprint_classes(label_names)\n\n\n# ### Display example training data\n# \n# The original Fashion-MNIST consists of 28x28 grayscale images.\n\n# In[7]:\n\n\npict_number = 7\n\nfor i in range(pict_number):\n plt.subplot(1,pict_number,i+1)\n #dsp_img = train_img[i].reshape(32,32,3)\n dsp_img = train_img[i].reshape(32,32)\n #plt.imshow(dsp_img)\n plt.imshow(dsp_img, cmap = \"Greys_r\")\n plt.axis('off')\n\nplt.show()\n\nprint('labels: %s' % (train_lbl[0:pict_number],))\nprint(operator.itemgetter(*train_lbl[0:pict_number])(label_names))\n\n\n# ### Build MXNet model\n# \n# For building our NN we will use Symbol API, an interface for symbolic programming.\n# \n# First, we create two placeholders for labels and data inputs with `mx.sym.Variable` and give them names`'fashion_item_label'` and `'fashion_data'` correspondingly. On the next step the data will be flatten to 2-D.\n\n# In[8]:\n\n\nlabel = mx.sym.Variable('label')\n\n# input\ndata = mx.symbol.Variable('data')\n\n# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)\ndata = mx.sym.flatten(data=data, name='flatten')\n\n\n# `Symbol` also supports a rich set of neural network layers. The folowing code constructs a 3-layer fully connected neural network with the 1st hidden layer having 128 neurons, the 2nd hidden layer having 64 neurons, both activated by ReLU activation function and softmax output layer `mlp`.\n# \n# Then we visualize the structure of that network with `mx.viz.plot_network`.\n\n# In[9]:\n\n\n# 1st fully-connected layer + activation function\nfc1 = mx.sym.FullyConnected(data=data, num_hidden=128)\nact1 = mx.sym.Activation(data=fc1, act_type=\"relu\")\n\n# 2nd fully-connected layer + activation function\nfc2 = mx.sym.FullyConnected(data=act1, num_hidden=64)\nact2 = mx.sym.Activation(data=fc2, act_type=\"relu\")\n\n# 3rd fully connected layer (MNIST uses 10 classes)\nfc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)\n\n# softmax with cross entropy loss\nmlp = mx.sym.SoftmaxOutput(data=fc3, label=label, name='softmax')\n\nmx.viz.plot_network(mlp)\n\n\n# ### Train the model and commit checkpoints\n# \n# First, set up logging to INFO level and the context (now CPU is used).\n\n# In[10]:\n\n\nlogging.basicConfig(level=logging.INFO)\nlogging.getLogger().setLevel(logging.INFO)\n\nctx = mx.cpu() # for GPU change to mx.gpu()\n\n\n# MXNet modularizes commonly used code for training and inference in the `module` (`mod` for short) package.\n# \n# We can construct a module by specifying the following parameters:\n# \n# - `symbol`: the network definition (`mlp` in our case)\n# - `context`: the device (or a list of devices) to use for execution (`ctx` in our case)\n# - `data_names` : the list of input data variable names (`'data'` in our case)\n# - `label_names` : the list of input label variable names (`'label'` in our case)\n\n# In[11]:\n\n\nmod = mx.mod.Module(symbol=mlp, data_names=['data'], label_names=['label'], context=ctx, logger=logging)\n\n\n# `Module` provides both high-level and intermediate-level interfaces for executing predefined networks. We will use a high-level-interface function [`mod.fit`](http://mxnet.io/api/python/module.html#mxnet.module.BaseModule.fit) to train the model, which internally executes the following steps:\n# \n# - `bind` : Prepares environment for the computation by allocating memory.\n# - `init_params` : Assigns and initializes parameters.\n# - `init_optimizer` : Initializes optimizers. Defaults to `sgd`.\n# - `metric.create` : Creates evaluation metric from input metric name.\n# - `forward` : Forward computation.\n# - `update_metric` : Evaluates and accumulates evaluation metric on outputs of the last forward computation.\n# - `backward` : Backward computation.\n# - `update` : Updates parameters according to the installed optimizer and the gradients computed in the previous forward-backward batch.\n# \n# We will save checkpoints for each epoch in \"cifar-epoch№.params\".\n# \n# **NOTE**: If you want to initialize parameters in a certain way, use the commented intermediate-level-interface functions: `mod.bind` and `mod.init_params` and set `force_init = False` in `mod.fit`. In this case `mod.fit` will raise a warning, that since parameters are already initialized, init_params call is ignored.\n\n# In[12]:\n\n\nmod.bind(data_shapes=train_data_iter.provide_data, label_shapes=train_data_iter.provide_label)\nmod.init_params(initializer=mx.init.Xavier(magnitude=2.))\n \nmod.fit(train_data_iter, # train data\n eval_data = test_data_iter, # validation data\n optimizer ='sgd', # use SGD to train\n optimizer_params = {'learning_rate' : 0.2}, # use fixed learning rate\n eval_metric = mx.metric.Accuracy(), # report accuracy during training\n num_epoch = 300, # train for at most 10 dataset passes\n epoch_end_callback = mx.callback.do_checkpoint('cifar'), \n force_rebind = True,\n force_init = False) \n\n\n# ### Run predictions for 10 example elements\n# \n# To predict with `module`, we, first, prepare a dataset for prediction and then call `mod.predict`. It will collect and\n# return all the prediction results.\n\n# In[13]:\n\n\npred_data_iter = mx.io.NDArrayIter(data={'data': test_img[0:batch_size]}, batch_size=batch_size)\npred_digits = mod.predict(eval_data=pred_data_iter).asnumpy()\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n# starting index of items we want to predict\npred_start = 10\n# number of items we want to predict\npred_number = 5\n\nfor i in range(pred_number):\n plt.subplot(1,pred_number,i+1)\n plt.imshow(test_img[i + pred_start].reshape(32,32), cmap='Greys')\n plt.axis('off')\n \nplt.show()\n\nfor x in range(pred_start, pred_start + pred_number):\n label = np.where(pred_digits[x,0:pred_number] == pred_digits[x,0:pred_number].max())[0]\n print(\"Predicted label for image %s is %s (%s)\" % (x, label, label_names[label[0]]))\n print(\"Correct label:\", label_names[test_lbl[x]])\n\n\n# # END OF THE FILE\n# Code below needs adjustments\n\n# ### Downloading images for prediction from amazon.com\n\n# In[ ]:\n\n\nurllib.request.urlretrieve('https://images-na.ssl-images-amazon.com/images/I/81OaXwn1x4L._UX679_.jpg', 'predict1.jpg')\nurllib.request.urlretrieve('https://images-eu.ssl-images-amazon.com/images/I/31TcgNHsbIL._AC_UL260_SR200,260_.jpg', 'predict2.jpg')\nurllib.request.urlretrieve('https://images-eu.ssl-images-amazon.com/images/I/41hWhZBIc3L._AC_UL260_SR200,260_.jpg', 'predict3.jpg')\n\n\n# ### Load model from checkpoint for prediction\n# \n# To load the saved module parameters, call the `mx.mod.load_checkpoint` function. It loads the symbolic definition of NN and all the associated parameters. We can then set the loaded parameters into the module.\n\n# In[ ]:\n\n\nprediction_model_check_point = 10\nprediction_model_prefix = 'fashion_mnist'\n\nprediction_sym, arg_params, aux_params = mx.model.load_checkpoint(prediction_model_prefix, prediction_model_check_point)\nprediction_model = mx.mod.Module(symbol=prediction_sym, data_names=['fashion_data'], label_names=['fashion_item_label'])\n\n\n# Now we need to bind the model with new data- and label shapes and restore the parameter values. Since we want to make predictions for one image at a time, the data shape now must be (1,1,28,28), label shape - (1,)\n\n# In[ ]:\n\n\nprediction_model.bind(for_training=False, data_shapes=[('fashion_data', (1,1,28,28))], label_shapes = [('fashion_item_label', (1,))])\nprediction_model.set_params(arg_params=arg_params, aux_params=aux_params, allow_missing=True)\n\n\n# Now we define the **prediction function** `predict_fashion`, which:\n# - takes image as an input\n# - formats it to the needed shape (1, 28, 28), since now it must be a one-element dataset\n# - creates a trivial data iterator of batch size 1\n# - makes and prints predictions\n\n# In[ ]:\n\n\ndef predict_fashion(img):\n # format data to run prediction\n array = np.full((1, 28, 28), img, dtype=np.float32)\n pred_data_iter = mx.io.NDArrayIter(data={'fashion_data': to4d(array)}, batch_size=1)\n pred_digits = prediction_model.predict(eval_data=pred_data_iter).asnumpy()\n label = (np.where(pred_digits[0] == pred_digits[0].max())[0])\n print(\"Predicted fashion label for image is %s (%s) \" % (label,fashion_labels[label[0]]))\n\n\n# ### Predict labels for downloaded images\n# \n# Here we first load the image and then adjust it to the colours (with help of bit-wise inversion `cv2.bitwise_not` ) and to the size (with `skimage.transform.resize`) of the training dataset\n\n# In[ ]:\n\n\nfor i in range(3):\n img = mpimg.imread('predict'+str(i+1)+'.jpg')\n plt.imshow(img)\n plt.axis('off')\n plt.show() \n # get colours in line with train data\n img = cv2.bitwise_not(img)\n img = np.array (np.mean(img, -1))\n\n # resize image\n img = skimage.transform.resize(img, (28, 28), mode ='reflect')\n\n predict_fashion(img)\n\n","repo_name":"b2net/AI_DL_AWS","sub_path":"MXNet/CIFAR10/Demo_CIFAR10.py","file_name":"Demo_CIFAR10.py","file_ext":"py","file_size_in_byte":12034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"} +{"seq_id":"70137706774","text":"from numpy import load\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.preprocessing import Normalizer\r\nfrom sklearn.metrics import confusion_matrix, accuracy_score\r\nfrom sklearn.metrics import plot_confusion_matrix\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn import svm\r\nfrom random import choice\r\nfrom numpy import load\r\nfrom numpy import expand_dims\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\nimport datetime\r\nfrom info_logging import log\r\nimport pickle\r\nimport pandas as pd\r\n\r\ndef test(model):\r\n labels = []\r\n y_true = []\r\n y_pred = []\r\n\r\n data = load('Image_DataSet_100_processed-embeddings.npz')\r\n trainy, testX, testy = data['arr_1'], data['arr_2'], data['arr_3']\r\n # normalize input vectors\r\n in_encoder = Normalizer(norm='l2')\r\n testX = in_encoder.transform(testX)\r\n # label encode targets\r\n out_encoder = LabelEncoder()\r\n out_encoder.fit(trainy)\r\n testy = out_encoder.transform(testy)\r\n\r\n # load faces test\r\n data = load('Image_DataSet_100_processed.npz')\r\n testX_faces = data['arr_2']\r\n\r\n fig = plt.figure(figsize=(25, 10))\r\n idx = 0\r\n for itr in range(10):\r\n selection = choice([i for i in range(testX.shape[0])])\r\n random_face_pixels = testX_faces[selection]\r\n random_face_emb = testX[selection]\r\n random_face_class = testy[selection]\r\n random_face_name = out_encoder.inverse_transform([random_face_class])\r\n # prediction for the face\r\n samples = expand_dims(random_face_emb, axis=0)\r\n yhat_class = model.predict(samples)\r\n yhat_prob = model.predict_proba(samples)\r\n \r\n # get name\r\n class_index = yhat_class[0]\r\n class_probability = yhat_prob[0,class_index] * 100\r\n predict_names = out_encoder.inverse_transform(yhat_class)\r\n \r\n ax = fig.add_subplot(2, 5, idx + 1, xticks=[], yticks=[])\r\n idx += 1\r\n plt.imshow(random_face_pixels)\r\n ax.set_title(\"\\n\\nPredicted Name: {} \\n Actual Name: {} \\n probability: {}\".format(predict_names[0], random_face_name[0], class_probability),\r\n color=(\"green\" if predict_names[0] == random_face_name[0] else \"red\"))\r\n fig.savefig('test_visualization.jpg')\r\n log(\"Visualized results can be found in test_visualization.jpg file\")\r\n\r\n for selection in range(testy.shape[0]):\r\n random_face_pixels = testX_faces[selection]\r\n random_face_emb = testX[selection]\r\n random_face_class = testy[selection]\r\n random_face_name = out_encoder.inverse_transform([random_face_class])\r\n # prediction for the face\r\n samples = expand_dims(random_face_emb, axis=0)\r\n yhat_class = model.predict(samples)\r\n yhat_prob = model.predict_proba(samples)\r\n\r\n if random_face_name[0] not in labels:\r\n labels.append(random_face_name[0])\r\n y_true.append(random_face_name[0])\r\n \r\n # get name\r\n class_index = yhat_class[0]\r\n class_probability = yhat_prob[0,class_index] * 100\r\n predict_names = out_encoder.inverse_transform(yhat_class)\r\n y_pred.append(predict_names)\r\n\r\n return y_true, y_pred, labels\r\n\r\nif __name__ == '__main__':\r\n #load model\r\n filename = 'finalized_model.sav'\r\n model = pickle.load(open(filename, 'rb'))\r\n y_true, y_pred, label = test(model)\r\n score = accuracy_score(y_true, y_pred)\r\n log(\"Accuracy Score on test data: \" + str(score))\r\n cm = confusion_matrix(y_true, y_pred, labels=label)\r\n np.savetxt(\"confusion_mat.csv\", cm, delimiter=\",\")","repo_name":"gaurav82692/Face_detection_stack_svm","sub_path":"Other Codes/visualization - Copy.py","file_name":"visualization - Copy.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"67"} +{"seq_id":"5467754802","text":"import matplotlib.pyplot as plt\nimport networkx as nx\nfrom dfs import undirected_cc\n\nsize = [40,30,30]\nprobs = [[0.1,0,0],[0,0.1,0],[0,0,0.1]]\n\n\n\nG= nx.stochastic_block_model(size,probs)\ncomponent = undirected_cc(G)\nprint (component)\npos = nx.spring_layout(G)\nnc = [component[v] for v in G.nodes()]\nnx.draw(G,pos,node_size=100,node_color=nc)\nplt.show()\n","repo_name":"lucatrevisan/lucatrevisan.github.io","sub_path":"code-samples/cc-test.py","file_name":"cc-test.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"67"} +{"seq_id":"6180185229","text":"import urllib\nfrom urllib.error import HTTPError, URLError\nfrom bs4 import BeautifulSoup , SoupStrainer\nimport re\nimport datetime\nfrom dateutil.relativedelta import relativedelta\nfrom sunpy.time import parse_time\nimport numpy as np\n\nsavedir_a = \"/Users/laurahayes/spaceweather_stuff/fun_stuff/final_ahead/\"\nsavedir_b = \"/Users/laurahayes/spaceweather_stuff/fun_stuff/final_behind/\"\n\nt1 = parse_time(\"2011-01-01\").datetime\nt2 = parse_time(\"2014-10-01\").datetime\ndate_list = [t1]\n\nfrom parfive import Downloader\n\nwhile t2>=t1:\n t1 = t1 + relativedelta(days=1)\n date_list.append(t1)\n\nbase_url = \"https://www.solarmonitor.org/data/\"\n\nurl_tests_b = [d.strftime(base_url+\"%Y/%m/%d/fits/strb/\") for d in date_list]\nurl_tests_a = [d.strftime(base_url+\"%Y/%m/%d/fits/stra/\") for d in date_list]\n\nbeacon_url_a = \"https://stereo-ssc.nascom.nasa.gov/data/beacon//ahead/secchi/img/euvi/\"\nbeacon_url_b = \"https://stereo-ssc.nascom.nasa.gov/data/beacon//behind/secchi/img/euvi/\"\n\n\nbeacon_test_a = [d.strftime(beacon_url_a+\"%Y%m%d/\") for d in date_list]\nbeacon_test_b = [d.strftime(beacon_url_b+\"%Y%m%d/\") for d in date_list]\n\n\ndef list_path_files(url_path, file_name):\n test = urllib.request.urlopen(url_path)\n soup = BeautifulSoup(test, features=\"lxml\")\n\n fits_links = []\n for link in soup.findAll('a'):\n if link.get('href') is not None and link.get('href').find(file_name) != -1:\n fits_links.append(url_path + link.get('href').split('/')[-1]) \n\n return fits_links\n\ndef find_file_times(file_name):\n if isinstance(file_name, (list, np.array)):\n t_list = []\n for i in range(len(file_name)):\n t = re.search('\\d{8}_\\d{6}', file_name[i]).group()\n tt = datetime.datetime.strptime(t, '%Y%m%d_%H%M%S')\n t_list.append(tt)\n return t_list\n else:\n\n t = re.search('\\d{8}_\\d{6}', file_name).group()\n tt = datetime.datetime.strptime(t, '%Y%m%d_%H%M%S')\n return tt\n\ndef get_urls_hours(beacon_tests):\n files_beacon = []\n for u in beacon_tests:\n print(u)\n try:\n urls = list_path_files(u, \"fts\")\n files_beacon.append(urls)\n except HTTPError:\n print(\"error\")\n files_beacon.append([])\n\n datess = [find_file_times(x) for x in files_beacon]\n hours = []\n for d in datess:\n try:\n hours.append(parse_time(d).strftime(\"%H\").astype(int))\n except:\n hours.append([])\n\n date_list_12 = [d + relativedelta(hours=12) for d in date_list]\n\n testy = []\n final_urls = []\n for i in range(len(datess)):\n if len(datess[i])>0:\n ind = np.argmin(np.abs(np.array(datess[i]) - date_list_12[i]))\n testy.append(datess[i][ind])\n final_urls.append(files_beacon[i][ind])\n else:\n testy.append([])\n final_urls.append([])\n return files_beacon, final_urls, testy\n\n# all_ahead, final_ahead, time_ahead = get_urls_hours(beacon_test_a)\n# all_behind, final_behind, time_behind = get_urls_hours(beacon_test_b)\n\ndef save_files():\n lala = {\"file_list\":files_beacon_ahead, \"final_urls\":final_urls, \"dates_final\":testy}\n lala2 = {\"file_list\":all_behind, \"final_urls\":final_behind, \"dates_final\":time_behind}\n with open(\"ahead_files.pkl\", \"wb\") as handle:\n pickle.dump(lala, handle)\n\n with open(\"behind_files.pkl\", \"wb\") as handle:\n pickle.dump(lala2, handle)\n\n\ndef download_files(file_list, savedir):\n dl = Downloader()\n for f in file_list:\n dl.enqueue_file(f, path=savedir)\n files = dl.download()\n\ndef download_files2(file_list, savedir):\n for f in file_list:\n if isinstance(f, str):\n fname = savedir + f.split(\"/\")[-1]\n urllib.request.urlretrieve(f, fname)\n else:\n print(f)\n\n\n# filess = []\n# for u in url_tests_a:\n# print(u)\n# try:\n# urls = list_path_files(u, \"fts.gz\")\n# filess.append(urls)\n# except HTTPError:\n# print(\"error\")\n# filess.append([])","repo_name":"hayesla/STEREO-AIA-stereoscopic-analysis","sub_path":"getting_beacon_data_tests.py","file_name":"getting_beacon_data_tests.py","file_ext":"py","file_size_in_byte":4014,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"67"} +{"seq_id":"16413009861","text":"import uuid\nfrom flask import request\nfrom flask.views import MethodView\nfrom flask_smorest import abort, Blueprint\nfrom db import stores\nfrom schemas import StoreSchema\n\nblp = Blueprint(\"stores\", __name__, description=\"Operations on Stores\")\n\n\n@blp.route(\"/store\")\nclass Store(MethodView):\n @blp.response(200, StoreSchema(many=True))\n def get(self):\n # return {\"stores\": list(stores.values())}\n return stores.values()\n\n @blp.arguments(StoreSchema)\n @blp.response(201, StoreSchema)\n def post(self, data):\n # data = request.get_json()\n\n # if \"name\" not in data:\n # abort(400, message=\"Bad Request, 'name' must be included\")\n\n for store in stores.values():\n if data[\"name\"] == store[\"name\"]:\n abort(400, message=\"This store name is already used.\")\n storeId = uuid.uuid4().hex\n # newStore = {\"name\": data[\"name\"], \"items\": []}\n # kwargs bcoz data is a str else data.copy() or just data\n store = {**data, \"store_id\": storeId}\n stores[storeId] = store\n # return store, 201\n return store\n\n\n@blp.route(\"/store/
Welcome, '\n output += login_session['username']\n output += '!
'\n output += ''\n\t\t\t\t\tlinelist.append(line)\n\t\t\t\t\tlinenb += 1\n\t\t\tif linenb > 1:\n\t\t\t\thtmllist.extend(Html('p') + linelist)\n\t\t\t# if the last line was blank, then as well as outputting the previous para,\n\t\t\t# which we have just done,\n\t\t\t# we also output a new blank para\n\t\t\tif sigcount == 0:\n\t\t\t\tlinelist = [\" \"]\n\t\t\t\thtmllist.extend(Html('p') + linelist)\n\t\treturn(htmllist)\n\n\n\tdef _data_source_citation_index(self, object):\n\t\t\"\"\"\n\t\tExport sources citations indexes related to L{object}\n\t\tSee L{_data_source_citation_index_from_list}\n\t\t\"\"\"\n\t\tcitationlist = object.get_citation_list()\n\t\treturn(self._data_source_citation_index_from_list(citationlist))\n\n\tdef _data_source_citation_index_from_list(self, citationlist):\n\t\t\"\"\"\n\t\tList sources citations indexes of the L{citationlist} in a string representing a Javascript Array\n\t\t@return: citations indexes as a string representing a Javascript Array\n\t\t\"\"\"\n\t\tif (not self.inc_sources): return(\"[]\")\n\t\tif not citationlist: return(\"[]\")\n\t\tsep = \"\"\n\t\ttxt = \"[\"\n\t\tfor citation_handle in citationlist:\n\t\t\tif (not txt): txt = Html(\"div\")\n\t\t\tcitation = self.database.get_citation_from_handle(citation_handle)\n\t\t\tif (citation is not None and (citation_handle in self.obj_dict[Citation])):\n\t\t\t\tsource_handle = citation.get_reference_handle()\n\t\t\t\tsource = self.database.get_source_from_handle(source_handle)\n\t\t\t\tif (source is not None and (source_handle in self.obj_dict[Source])):\n\t\t\t\t\ttitle = source.get_title()\n\t\t\t\t\tif (not title): title = source.get_gramps_id()\n\t\t\t\t\ttxt += sep + str(self.obj_dict[Citation][citation_handle][OBJDICT_INDEX])\n\t\t\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _data_repo_reference_index(self, object):\n\t\t\"\"\"\n\t\tBuild a list of the repositories references index, in the form given by L{_data_repo_ref}\n\t\t\"\"\"\n\t\tif (not self.inc_repositories): return(\"[]\")\n\t\trefs = object.get_reporef_list()\n\t\tif (not refs): return(\"[]\")\n\t\tsep = \"\\n\"\n\t\ttxt = \"[\"\n\t\tfor ref in refs:\n\t\t\trepo_handle = ref.get_reference_handle()\n\t\t\tif (repo_handle in self.obj_dict[Repository]):\n\t\t\t\ttxt += sep + \"\\t\" + self._data_repo_ref(ref, self.obj_dict[Repository][repo_handle][OBJDICT_INDEX])\n\t\t\t\tsep = \",\\n\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_repo_ref(self, ref, index):\n\t\t\"\"\"\n\t\tBuild a repository reference, in the form:\n\t\t - repository index (in table 'R')\n\t\t - media type\n\t\t - call number\n\t\t - notes of the repository reference\n\t\t\"\"\"\n\t\trepo_handle = ref.get_reference_handle()\n\t\trepo = self.database.get_repository_from_handle(repo_handle)\n\t\ttxt = \"[\"\n\t\ttxt += str(index) + \",\"\n\t\ttxt += \"\\\"\" + script_escape(str(ref.get_media_type())) + \"\\\",\"\n\t\ttxt += \"\\\"\" + script_escape(ref.get_call_number()) + \"\\\",\"\n\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(ref)) + \"\\\"\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _data_media_reference_index(self, object):\n\t\t\"\"\"\n\t\tBuild a list of the media references index, in the form given by L{_data_media_ref}\n\t\t\"\"\"\n\t\tif (not self.inc_gallery): return(\"[]\")\n\t\trefs = object.get_media_list()\n\t\tif (not refs): return(\"[]\")\n\t\tsep = \"\\n\"\n\t\ttxt = \"[\"\n\t\tfor ref in refs:\n\t\t\tmedia_handle = ref.get_reference_handle()\n\t\t\tif (media_handle in self.obj_dict[MediaObject]):\n\t\t\t\ttxt += sep + \"\\t\" + self._data_media_ref(ref, self.obj_dict[MediaObject][media_handle][OBJDICT_INDEX])\n\t\t\t\tsep = \",\\n\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_media_ref(self, ref, index):\n\t\t\"\"\"\n\t\tBuild a media reference, in the form:\n\t\t - media index (in table 'M')\n\t\t - media thumbnail path\n\t\t - [x1, y1, x2, y2] of the media reference\n\t\t - notes of the media reference\n\t\t - list of the media reference source citations index (in table 'C')\n\t\t\"\"\"\n\t\tmedia_handle = ref.get_reference_handle()\n\t\tmedia = self.database.get_object_from_handle(media_handle)\n\t\ttxt = \"[\"\n\t\ttxt += str(index)\n\t\ttxt += \",\\\"\"\n\t\ttxt += self.copy_thumbnail(media, ref.get_rectangle())\n\t\ttxt += \"\\\",[\"\n\t\trect = ref.get_rectangle() or (0,0,100,100)\n\t\ttxt += \",\".join(str(x) for x in rect)\n\t\ttxt += \"],\"\n\t\tattrlist = ref.get_attribute_list()\n\t\ttxt += \"\\\"\" + script_escape(self.get_notes_attributes_text(ref.get_note_list(), attrlist)) + \"\\\",\"\n\t\tcitationlist = ref.get_citation_list()\n\t\tfor attr in attrlist: citationlist.extend(attr.get_citation_list())\n\t\t# BUG: it seems that attribute references are given by both ref.get_citation_list and attr.get_citation_list\n\t\ttxt += self._data_source_citation_index_from_list(citationlist)\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef get_media_web_path(self, media):\n\t\t\"\"\"\n\t\tReturn the path of the media from the web pages\n\t\tThis function could be called several times for the same media\n\t\tThis function copies the media to the web pages directories if necessary\n\t\t\"\"\"\n\t\tmedia_path = media.get_path()\n\t\tif (media_path):\n\t\t\tnorm_path = media_path_full(self.database, media_path)\n\t\t\tif (os.path.isfile(norm_path)):\n\t\t\t\tif (self.copy_media):\n\t\t\t\t\text = os.path.splitext(norm_path)[1]\n\t\t\t\t\tiname = str(media.get_handle()) + ext\n\t\t\t\t\tiname = iname.lower()\n\t\t\t\t\tif (iname not in self.images_copied):\n\t\t\t\t\t\tself.copy_file(norm_path, iname, \"image\")\n\t\t\t\t\t\tself.images_copied.add(iname)\n\t\t\t\t\tweb_path = \"image/\" + iname\n\t\t\t\telse:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tweb_path = os.path.relpath(norm_path, self.target_path)\n\t\t\t\t\t\tweb_path = web_path.replace(\"\\\\\", \"/\")\n\t\t\t\t\texcept:\n\t\t\t\t\t\tweb_path = urlparse.urljoin('file:', urllib.pathname2url(norm_path))\n\t\t\t\t\t\tlog.warning(_(\"Impossible to convert \\\"%(path)s\\\" to a relative path.\") % {\"path\": norm_path})\n\t\t\t\treturn(web_path)\n\t\tlog.warning(\"Warning: File not found \\\"%(path)s\\\"\" % {\"path\": str(media_path)})\n\t\treturn(media_path)\n\n\n\tdef copy_thumbnail(self, media, region = None):\n\t\t\"\"\"\n\t\tGiven a handle (and optional region) make (if needed) an\n\t\tup-to-date cache of a thumbnail, and call copy_file\n\t\tto copy the cached thumbnail to the website.\n\t\tReturn the new path to the image.\n\t\t\"\"\"\n\t\tif (region and region[0] == 0 and region[1] == 0 and region[2] == 100 and region[3] == 100):\n\t\t\tregion = None\n\t\thandle = media.get_handle()\n\t\ttname = handle + ((\"-%d,%d-%d,%d.png\" % region) if region else \".png\")\n\t\tif (media.get_mime_type()):\n\t\t\tfrom_path = get_thumbnail_path(\n\t\t\t\tmedia_path_full(self.database, media.get_path()),\n\t\t\t\tmedia.get_mime_type(),\n\t\t\t\tregion)\n\t\t\tif not os.path.isfile(from_path):\n\t\t\t\tfrom_path = os.path.join(IMAGE_DIR, \"document.png\")\n\t\telse:\n\t\t\tfrom_path = os.path.join(IMAGE_DIR, \"document.png\")\n\t\tif (tname not in self.thumbnail_created):\n\t\t\tself.copy_file(from_path, tname, \"thumb\")\n\t\t\tself.thumbnail_created.add(tname)\n\t\tweb_path = \"thumb/\" + tname\n\t\treturn(web_path)\n\n\n\tdef _data_attributes(self, object):\n\t\t\"\"\"\n\t\tBuild the list of the L{object} attributes as a Javascript string, in the form:\n\t\t [attribute, value, note, list of citations]\n\t\t\"\"\"\n\t\tattrlist = object.get_attribute_list()\n\t\ttxt = \"[\"\n\t\tsep = \"\"\n\t\tfor attr in attrlist:\n\t\t\ttxt += sep + \"[\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_type())) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_value())) + \"\\\",\"\n\t\t\t# Get attribute notes\n\t\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(attr)) + \"\\\",\"\n\t\t\t# Get attribute sources\n\t\t\ttxt += self._data_source_citation_index(attr)\n\t\t\ttxt += \"]\"\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_attributes_src(self, source):\n\t\t\"\"\"\n\t\tBuild the list of the L{source} sources attributes as a Javascript string, in the form:\n\t\t [attribute, value, \"\", []]\n\t\t\"\"\"\n\t\tattrlist = source.get_attribute_list()\n\t\ttxt = \"[\"\n\t\tsep = \"\"\n\t\tfor attr in attrlist:\n\t\t\ttxt += sep + \"[\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_type())) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(str(attr.get_value())) + \"\\\",\"\n\t\t\t# There aren't any attribute notes\n\t\t\ttxt += \"\\\"\\\",\"\n\t\t\t# There aren't any attribute sources\n\t\t\ttxt += \"[]\"\n\t\t\ttxt += \"]\"\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_url_list(self, object):\n\t\t\"\"\"\n\t\tBuild the list of the L{object} URL as a Javascript string, in the form:\n\t\t [type, url, description]\n\t\t\"\"\"\n\t\turllist = object.get_url_list()\n\t\ttxt = \"[\"\n\t\tsep = \"\"\n\t\tfor url in urllist:\n\t\t\t_type = url.get_type()\n\t\t\turi = url.get_path()\n\t\t\tdescr = url.get_description()\n\t\t\t# Email address\n\t\t\tif _type == UrlType.EMAIL:\n\t\t\t\tif not uri.startswith(\"mailto:\"):\n\t\t\t\t\turi = \"mailto:%(email)s\" % { 'email' : uri }\n\t\t\t# Web Site address\n\t\t\telif _type == UrlType.WEB_HOME:\n\t\t\t\tif not (uri.startswith(\"http://\") or uri.startswith(\"https://\")):\n\t\t\t\t\turi = \"http://%(website)s\" % { \"website\" : uri }\n\t\t\t# FTP server address\n\t\t\telif _type == UrlType.WEB_FTP:\n\t\t\t\tif not (uri.startswith(\"ftp://\") or uri.startswith(\"ftps://\")):\n\t\t\t\t\turi = \"ftp://%(ftpsite)s\" % { \"ftpsite\" : uri }\n\t\t\ttxt += sep + \"[\"\n\t\t\ttxt += \"\\\"\" + str(_type) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(uri) + \"\\\",\"\n\t\t\ttxt += \"\\\"\" + script_escape(descr) + \"\\\"\"\n\t\t\ttxt += \"]\"\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _export_surnames(self):\n\t\t\"\"\"\n\t\tExport surnames data in Javascript file\n\t\tThe surnames data is stored in the Javascript Array \"SN\"\n\t\t\"\"\"\n\t\t# Extract the surnames data\n\t\tsurnames = defaultdict(list) #: Dictionary giving for each surname: the list of person handles with this surname\n\t\tsortnames = {} #: Dictionary giving for each person handle: a sortable string for the person\n\t\tperson_list = list(self.obj_dict[Person].keys())\n\t\tfor person_handle in person_list:\n\t\t\tperson = self.database.get_person_from_handle(person_handle)\n\t\t\tprimary_name = person.get_primary_name()\n\t\t\tif (primary_name.group_as):\n\t\t\t\tsurname = primary_name.group_as\n\t\t\telse:\n\t\t\t\tsurname = self.database.get_name_group_mapping(_nd.primary_surname(primary_name))\n\t\t\t# Treat people who have no name with those whose name is just 'whitespace'\n\t\t\tif (surname is None or surname.isspace()):\n\t\t\t\tsurname = \"\"\n\t\t\tsortnames[person_handle] = _nd.sort_string(primary_name)\n\t\t\tsurnames[surname].append(person_handle)\n\t\t# Sort surnames\n\t\tsurns_keys = list(surnames.keys())\n\t\tsurns_keys.sort(key = SORT_KEY)\n\t\t# Generate the file\n\t\tsw1 = StringIO()\n\t\tsw1.write(\n\t\t\t\"// This file is generated\\n\\n\"\n\t\t\t\"// 'SN' is sorted by surname\\n\"\n\t\t\t\"// 'SN' gives for each surname:\\n\"\n\t\t\t\"// - the surname\\n\"\n\t\t\t\"// - the surname first letter\\n\"\n\t\t\t\"// - the list of persion index (in table 'I') with this surname\\n\"\n\t\t\t\"\\nSN = [\")\n\t\tsep = \"\\n\"\n\t\tfor s in surns_keys:\n\t\t\t# Sort persons\n\t\t\tsurnames[s].sort(key = lambda x: sortnames[x])\n\t\t\ttab = \",\".join([str(self.obj_dict[Person][x][OBJDICT_INDEX]) for x in surnames[s]])\n\t\t\tsw1.write(sep + \"[\\\"\" + script_escape(s) + \"\\\", \\\"\" + first_letter(s).strip() + \"\\\", [\" + tab + \"]]\")\n\t\t\tsep = \",\\n\"\n\t\tsw1.write(\"\\n];\\n\")\n\t\tself.update_file(\"dwr_db_surns.js\", sw1.getvalue())\n\n\n\tdef _data_families_index(self, person):\n\t\tfams = []\n\t\tfamily_list = person.get_family_handle_list()\n\t\tif (family_list):\n\t\t\tfams = [self.obj_dict[Family][family_handle][OBJDICT_INDEX] for family_handle in family_list if (family_handle in self.obj_dict[Family])]\n\t\treturn(\n\t\t\t\"[\" +\n\t\t\t\",\".join([str(i) for i in fams]) +\n\t\t\t\"]\")\n\n\tdef _data_partners_index(self, family):\n\t\tindis = []\n\t\tperson_handle = family.get_father_handle()\n\t\tif (person_handle and (person_handle in self.obj_dict[Person])):\n\t\t\tindis.append(self.obj_dict[Person][person_handle][OBJDICT_INDEX])\n\t\tperson_handle = family.get_mother_handle()\n\t\tif (person_handle and (person_handle in self.obj_dict[Person])):\n\t\t\tindis.append(self.obj_dict[Person][person_handle][OBJDICT_INDEX])\n\t\treturn(\n\t\t\t\"[\" +\n\t\t\t\",\".join([str(i) for i in indis]) +\n\t\t\t\"]\")\n\n\tdef _data_parents_families_index(self, person):\n\t\tlinks = []\n\t\tfamily_list = person.get_parent_family_handle_list()\n\t\tif (family_list):\n\t\t\tfor family_handle in family_list:\n\t\t\t\tif (family_handle not in self.obj_dict[Family]): continue\n\t\t\t\tfamily = self.database.get_family_from_handle(family_handle)\n\t\t\t\tchild_refs = [\n\t\t\t\t\tchild_ref\n\t\t\t\t\tfor child_ref in family.get_child_ref_list()\n\t\t\t\t\tif (child_ref.ref == person.get_handle())\n\t\t\t\t]\n\t\t\t\tif (len(child_refs) >= 1):\n\t\t\t\t\tindex = self.obj_dict[Family][family_handle][OBJDICT_INDEX]\n\t\t\t\t\tlinks.append(self._data_child_ref(index, child_refs[0]))\n\t\treturn(\"[\" + \",\".join(links) + \"]\")\n\n\tdef _data_children_index(self, family):\n\t\tlinks = [\n\t\t\tself._data_child_ref(self.obj_dict[Person][child_ref.ref][OBJDICT_INDEX], child_ref)\n\t\t\tfor child_ref in family.get_child_ref_list()\n\t\t\tif (child_ref.ref in self.obj_dict[Person])\n\t\t]\n\t\treturn(\"[\" + \",\".join(links) + \"]\")\n\n\tdef _data_child_ref(self, index, child_ref):\n\t\t# Child reference in the form:\n\t\t# [index, relation to father, relation to mother, notes, list of citations]\n\t\ttxt = \"[\"\n\t\ttxt += str(index) + \",\"\n\t\ttxt += \"\\\"\" + script_escape(str(child_ref.get_father_relation())) + \"\\\",\"\n\t\ttxt += \"\\\"\" + script_escape(str(child_ref.get_mother_relation())) + \"\\\",\"\n\t\t# Get child reference notes\n\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(child_ref)) + \"\\\",\"\n\t\t# Get child reference sources\n\t\ttxt += self._data_source_citation_index(child_ref)\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef _data_associations(self, person):\n\t\tassoclist = person.get_person_ref_list()\n\t\trels = []\n\t\tfor person_ref in assoclist:\n\t\t\ttxt = \"[\"\n\t\t\tif (person_ref.ref not in self.obj_dict[Person]): continue\n\t\t\ttxt += \"%i,\" % self.obj_dict[Person][person_ref.ref][OBJDICT_INDEX]\n\t\t\ttxt += \"\\\"\" + script_escape(str(person_ref.get_relation())) + \"\\\",\"\n\t\t\t# Get association notes\n\t\t\ttxt += \"\\\"\" + script_escape(self.get_notes_text(person_ref)) + \"\\\",\"\n\t\t\t# Get association sources\n\t\t\ttxt += self._data_source_citation_index(person_ref)\n\t\t\ttxt += \"]\"\n\t\t\trels.append(txt)\n\t\treturn(\"[\" + \",\".join(rels) + \"]\")\n\n\n\tdef get_birth_year(self, person):\n\t\tev = get_birth_or_fallback(self.database, person)\n\t\treturn(self._get_year_text(ev) or \"?\")\n\tdef get_death_year(self, person):\n\t\tev = get_death_or_fallback(self.database, person)\n\t\treturn(self._get_year_text(ev))\n\tdef get_marriage_year(self, family):\n\t\tev = get_marriage_or_fallback(self.database, family)\n\t\treturn(self._get_year_text(ev))\n\tdef _get_year_text(self, event):\n\t\ty = \"\"\n\t\tif (event):\n\t\t\ty = \"?\"\n\t\t\tdate = event.get_date_object()\n\t\t\tmod = date.get_modifier()\n\t\t\tstart = date.get_start_date()\n\t\t\tif (mod == Date.MOD_NONE and start != Date.EMPTY):\n\t\t\t\ty = str(start[2])\n\t\treturn(y)\n\n\tdef get_birth_place(self, person):\n\t\tev = get_birth_or_fallback(self.database, person)\n\t\treturn(self._get_place_text(ev))\n\tdef get_death_place(self, person):\n\t\tev = get_death_or_fallback(self.database, person)\n\t\treturn(self._get_place_text(ev))\n\tdef get_marriage_place(self, family):\n\t\tev = get_marriage_or_fallback(self.database, family)\n\t\treturn(self._get_place_text(ev))\n\tdef _get_place_text(self, event):\n\t\tplace_name = \"\"\n\t\tif (event):\n\t\t\tplace_handle = event.get_place_handle()\n\t\t\tif (place_handle and (place_handle in self.obj_dict[Place])):\n\t\t\t\tplace_name = report_utils.place_name(self.database, place_handle)\n\t\treturn(place_name)\n\n\tdef get_death_age(self, person):\n\t\tev_birth = get_birth_or_fallback(self.database, person)\n\t\tbirth_date = None\n\t\tif (ev_birth): birth_date = ev_birth.get_date_object()\n\t\tev_death = get_death_or_fallback(self.database, person)\n\t\tdeath_date = None\n\t\tif (ev_death): death_date = ev_death.get_date_object()\n\t\tif (birth_date):\n\t\t\talive = probably_alive(person, self.database, Today())\n\t\t\tif (not alive and death_date):\n\t\t\t\tnyears = death_date - birth_date\n\t\t\t\tnyears.format(precision = 3)\n\t\t\t\treturn(str(nyears))\n\t\treturn(\"\");\n\n\n\tdef _export_pages(self):\n\t\t\"\"\"\n\t\tGenerate the HTML pages\n\t\t\"\"\"\n\t\t\n\t\t# Check pages configuration (in the options)\n\t\tpcset = set(self.page_content)\n\t\tif (len(pcset) != len(self.page_content)):\n\t\t\tlog.error(_(\"The pages configuration is not valid: several pages have the same content\"))\n\t\t\treturn\n\t\t\t\n\t\t# Export the script containing the web pages configuration\n\t\tself._export_script_configuration()\n\t\t\n\t\t# List of the scripts and CSS stylesheets used in the HTML pages\n\t\t# Note: other scripts and stylesheets are dynamically loaded in \"dwr_start.js\"\n\t\t# \"dwr_start.js\" is loaded in all pages uncontitionally (see L{write_header})\n\t\tdbscripts = [\"dwr_db_indi.js\", \"dwr_db_fam.js\", \"dwr_db_sour.js\", \"dwr_db_cita.js\", \"dwr_db_media.js\", \"dwr_db_place.js\", \"dwr_db_repo.js\", \"dwr_db_surns.js\"] #: list of the scripts to embed in the HTML\n\t\tmapscripts = [] #: list of the scripts to embed in the HTML pages that show a map\n\t\tmapstyles = [] #: list of the CSS stylesheets to embed in the HTML pages that show a map\n\t\tif (self.options['placemappages'] or self.options['familymappages']):\n\t\t\tif (self.options['mapservice'] == \"Google\"):\n\t\t\t\tmapscripts = [\"http://maps.googleapis.com/maps/api/js?sensor=false\"]\n\t\t\telse:\n\t\t\t\tmapscripts = [\"http://openlayers.org/en/v3.0.0/build/ol.js\"]\n\t\t\t\tmapstyles = [\"http://openlayers.org/en/v3.0.0/css/ol.css\"]\n\t\t\t\t# mapscripts = [\"ol.js\"]\n\t\t\t\t# mapstyles = [\"ol.css\"]\n\t\t\t\t# mapscripts = [\"http://cdn.leafletjs.com/leaflet-0.7.3/leaflet.js\"]\n\t\t\t\t# mapstyles = [\"http://cdn.leafletjs.com/leaflet-0.7.3/leaflet.css\"]\n\t\t\t\t\n\t\t#: List of page to generate: index in L{PAGES_NAMES}, Javascript code for generating the page\n\t\tparts = {\n\t\t\tPAGE_PERSON: (dbscripts, \"DwrMain(PAGE_INDI);\"),\n\t\t\tPAGE_SURNAMES: (dbscripts, \"printSurnamesIndex();\"),\n\t\t\tPAGE_PERSON_INDEX: (dbscripts, \"printPersonsIndex();\"),\n\t\t\tPAGE_FAMILY_INDEX: (dbscripts, \"printFamiliesIndex();\"),\n\t\t\tPAGE_SOURCE_INDEX: (dbscripts, \"printSourcesIndex();\"),\n\t\t\tPAGE_MEDIA_INDEX: (dbscripts, \"printMediaIndex();\"),\n\t\t\tPAGE_PLACE_INDEX: (dbscripts, \"printPlacesIndex();\"),\n\t\t\tPAGE_ADDRESS_INDEX: (dbscripts, \"printAddressesIndex();\"),\n\t\t\tPAGE_REPOSITORY_INDEX: (dbscripts, \"printReposIndex();\"),\n\t\t\tPAGE_SVG_TREE: (dbscripts, \"DwrMain(PAGE_SVG_TREE);\"),\n\t\t}\n\t\t\n\t\t# Export the HTML pages listed in L{PAGES_NAMES}\n\t\tfor i in range(self.pages_number):\n\t\t\tpc = self.page_content[i] # Get the page i contents defined in the options\n\t\t\tfilename = PAGES_NAMES[pc][2]\n\t\t\ttitle = self.page_name[pc]\n\t\t\tif (pc in parts):\n\t\t\t\t# The page is not a custom page\n\t\t\t\t(scripts, cmd) = parts[pc]\n\t\t\t\tself._export_html_page(filename, title, cmd, True, scripts)\n\t\t\telse:\n\t\t\t\t# The page is a custom page\n\t\t\t\ti_cst = pc - PAGE_CUSTOM\n\t\t\t\tself._export_custom_page(filename, title, self.custom_menu[i_cst], self.custom_note[i_cst])\n\n\t\t# The person page is required\n\t\tif (PAGE_PERSON not in self.page_content):\n\t\t\tself._export_html_page(\"person.html\", self.page_name[PAGE_PERSON], \"DwrMain(PAGE_INDI);\", True, dbscripts)\n\n\t\t# The search results page is required\n\t\tself._export_html_page(\"search.html\", _(\"Search results\"), \"DwrMain(PAGE_SEARCH);\", True, dbscripts)\n\n\t\t# Page for printing a family (if needed)\n\t\tif (self.inc_families):\n\t\t\tself._export_html_page(\"family.html\", self.page_name[PAGE_FAMILY_INDEX], \"DwrMain(PAGE_FAM);\", True, dbscripts + mapscripts , mapstyles)\n\t\t\n\t\t# Generate page surnames pages (if surnames page is used)\n\t\tif (PAGE_SURNAMES in self.page_content):\n\t\t\t# Page for persons with a given surname\n\t\t\tself._export_html_page(\"surname.html\", self.page_name[PAGE_SURNAMES], \"printSurnameIndex();\", True, dbscripts)\n\t\t\t# Page for surnames sorted by quantity\n\t\t\tself._export_html_page(\"surnames2.html\", self.page_name[PAGE_SURNAMES], \"printSurnamesIndex2();\", True, dbscripts)\n\n\t\t# Page for a single family (if needed)\n\t\tif (self.inc_sources):\n\t\t\tself._export_html_page(\"source.html\", self.page_name[PAGE_SOURCE_INDEX], \"DwrMain(PAGE_SOURCE);\", True, dbscripts)\n\n\t\t# Page for a single media (if needed)\n\t\tif (self.inc_gallery):\n\t\t\tself._export_html_page(\"media.html\", self.page_name[PAGE_MEDIA_INDEX], \"DwrMain(PAGE_MEDIA);\", True, dbscripts)\n\n\t\t# Page for a single place (if needed)\n\t\tif (self.inc_places):\n\t\t\tself._export_html_page(\"place.html\", self.page_name[PAGE_PLACE_INDEX], \"DwrMain(PAGE_PLACE);\", True, dbscripts + mapscripts , mapstyles)\n\n\t\t# Page for a single repository (if needed)\n\t\tif (self.inc_repositories):\n\t\t\tself._export_html_page(\"repository.html\", self.page_name[PAGE_REPOSITORY_INDEX], \"DwrMain(PAGE_REPO);\", True, dbscripts)\n\n\t\t# Page for full-screen SVG graph (if SVG graph is used)\n\t\tif (PAGE_SVG_TREE in self.page_content):\n\t\t\tself._export_html_page(\"tree_svg_full.html\", self.page_name[PAGE_SVG_TREE], \"DwrMain(PAGE_SVG_TREE_FULL);\", False, dbscripts)\n\t\t\tself._export_html_page(\"tree_svg_conf.html\", self.page_name[PAGE_SVG_TREE], \"DwrMain(PAGE_SVG_TREE_CONF);\", True, dbscripts)\n\t\t\tself._export_html_page(\"tree_svg_save.html\", self.page_name[PAGE_SVG_TREE], \"DwrMain(PAGE_SVG_TREE_SAVE);\", True, dbscripts)\n\n\n\tdef _export_script_configuration(self):\n\t\t\"\"\"\n\t\tGenerate \"dwr_conf.js\", which contains:\n\t\t - The pages configuration (mostly extract of the report options),\n\t\t - The localization (translated strings)\n\t\t - Gramps constants that could be used in the Javascript\n\t\t\"\"\"\n\t\tsw = StringIO()\n\t\tsw.write(\"// This file is generated\\n\\n\")\n\t\tsw.write(\"NB_GENERATIONS_MAX = %i;\\n\" % int(self.options[\"graphgens\"]))\n\t\tsw.write(\"PAGES_TITLE = [\")\n\t\tsw.write(\", \".join([\n\t\t\t(\"\\\"\" + script_escape(self.page_name[self.page_content[i]]).replace(\" \", \" \") + \"\\\"\")\n\t\t\tfor i in range(self.pages_number)]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"PAGES_FILE = [\")\n\t\tsw.write(\", \".join([\n\t\t\t(\"\\\"\" + script_escape(PAGES_NAMES[self.page_content[i]][2]) + \"\\\"\")\n\t\t\tfor i in range(self.pages_number)]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_TYPES_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_TYPES]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_SHAPES_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_SHAPES]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_DISTRIB_ASC_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_DISTRIB_ASC]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_DISTRIB_DSC_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_DISTRIB_DSC]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_BACKGROUND_NAMES = [\")\n\t\tsw.write(\", \".join([(\"\\\"\" + script_escape(n) + \"\\\"\") for n in SVG_TREE_BACKGROUNDS]))\n\t\tsw.write(\"];\\n\")\n\t\tsw.write(\"SVG_TREE_TYPE = %s;\\n\" % self.options['svg_tree_type'])\n\t\tsw.write(\"SVG_TREE_SHAPE = %s;\\n\" % self.options['svg_tree_shape'])\n\t\tsw.write(\"SVG_TREE_DISTRIB_ASC = %s;\\n\" % self.options['svg_tree_distrib_asc'])\n\t\tsw.write(\"SVG_TREE_DISTRIB_DSC = %s;\\n\" % self.options['svg_tree_distrib_dsc'])\n\t\tsw.write(\"SVG_TREE_BACKGROUND = %s;\\n\" % self.options['svg_tree_background'])\n\t\tsw.write(\"SVG_TREE_COLOR1 = \\\"%s\\\";\\n\" % self.options['svg_tree_color1'])\n\t\tsw.write(\"SVG_TREE_COLOR2 = \\\"%s\\\";\\n\" % self.options['svg_tree_color2'])\n\t\tsw.write(\"SVG_TREE_SHOW_DUP = \" + (\"true\" if (self.options['svg_tree_dup']) else \"false\") + \";\\n\")\n\t\tsw.write(\"SVG_TREE_COLOR_DUP = \\\"%s\\\";\\n\" % self.options['svg_tree_color_dup'])\n\t\tsw.write(\"GRAMPS_PREFERENCES = [];\\n\")\n\t\tfor pref in [\n\t\t\t'bordercolor-gender-female-alive',\n\t\t\t'bordercolor-gender-female-death',\n\t\t\t'bordercolor-gender-male-alive',\n\t\t\t'bordercolor-gender-male-death',\n\t\t\t'bordercolor-gender-unknown-alive',\n\t\t\t'bordercolor-gender-unknown-death',\n\t\t\t'color-gender-female-alive',\n\t\t\t'color-gender-female-death',\n\t\t\t'color-gender-male-alive',\n\t\t\t'color-gender-male-death',\n\t\t\t'color-gender-unknown-alive',\n\t\t\t'color-gender-unknown-death',\n\t\t\t]:\n\t\t\tsw.write(\"GRAMPS_PREFERENCES['%s'] = \\\"%s\\\";\\n\" % (pref, config.get('preferences.%s' % pref)))\n\t\tsw.write(\"SVG_TREE_COLOR_SCHEME0 = [\" + \", \".join(\n\t\t\t[(\"\\\"#%02x%02x%02x\\\"\" % (r, g, b)) for (r, g, b) in GENCOLOR[BACKGROUND_WHITE]])\n\t\t\t+ \"];\\n\")\n\t\tsw.write(\"SVG_TREE_COLOR_SCHEME1 = [\" + \", \".join(\n\t\t\t[(\"\\\"#%02x%02x%02x\\\"\" % (r, g, b)) for (r, g, b) in GENCOLOR[BACKGROUND_SCHEME1]])\n\t\t\t+ \"];\\n\")\n\t\tsw.write(\"SVG_TREE_COLOR_SCHEME2 = [\" + \", \".join(\n\t\t\t[(\"\\\"#%02x%02x%02x\\\"\" % (r, g, b)) for (r, g, b) in GENCOLOR[BACKGROUND_SCHEME2]])\n\t\t\t+ \"];\\n\")\n\t\tsw.write(\"FOOTER=\\\"\" + script_escape(self.get_header_footer_notes(\"footernote\")) + \"\\\";\\n\")\n\t\tsw.write(\"HEADER=\\\"\" + script_escape(self.get_header_footer_notes(\"headernote\")) + \"\\\";\\n\")\n\t\tsw.write(\"COPYRIGHT=\\\"\" + script_escape(self.get_copyright_license()) + \"\\\";\\n\")\n\t\tsw.write(\"INDEX_SHOW_BIRTH=\" + (\"true\" if (self.options['showbirth']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_DEATH=\" + (\"true\" if (self.options['showdeath']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_MARRIAGE=\" + (\"true\" if (self.options['showmarriage']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_PARTNER=\" + (\"true\" if (self.options['showpartner']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_PARENTS=\" + (\"true\" if (self.options['showparents']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_ALL_SIBLINGS=\" + (\"true\" if (self.options['birthorder']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INDEX_SHOW_BKREF_TYPE=\" + (\"true\" if (self.options['bkref_type']) else \"false\") + \";\\n\")\n\t\tsw.write(\"SORT_CHILDREN=\" + (\"true\" if (self.options['showallsiblings']) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_EVENTS=\" + (\"true\" if (self.inc_events) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_FAMILIES=\" + (\"true\" if (self.inc_families) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_SOURCES=\" + (\"true\" if (self.inc_sources) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_MEDIA=\" + (\"true\" if (self.inc_gallery) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_PLACES=\" + (\"true\" if (self.inc_places) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_REPOSITORIES=\" + (\"true\" if (self.inc_repositories) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_NOTES=\" + (\"true\" if (self.inc_notes) else \"false\") + \";\\n\")\n\t\tsw.write(\"INC_ADDRESSES=\" + (\"true\" if (self.inc_addresses) else \"false\") + \";\\n\")\n\t\tsw.write(\"MAP_PLACE=\" + (\"true\" if (self.options['placemappages']) else \"false\") + \";\\n\")\n\t\tsw.write(\"MAP_FAMILY=\" + (\"true\" if (self.options['familymappages']) else \"false\") + \";\\n\")\n\t\tsw.write(\"MAP_SERVICE=\\\"\" + script_escape(self.options['mapservice']) + \"\\\";\\n\")\n\t\tsw.write(\"__ = {\")\n\t\tsep = \"\\n\"\n\t\tfor (s, translated) in (\n\t\t\t(\"(filtered from _MAX_ total entries)\", _(\"(filtered from _MAX_ total entries)\")),\n\t\t\t(\"(sort by name)\", _(\"(sort by name)\")),\n\t\t\t(\"(sort by quantity)\", _(\"(sort by quantity)\")),\n\t\t\t(\": activate to sort column ascending\", _(\": activate to sort column ascending\")),\n\t\t\t(\": activate to sort column descending\", _(\": activate to sort column descending\")),\n\t\t\t(\"
Click on a person to center the graph on this person.
When clicking on the center person, the person page is shown.
The type of graph could be selected in the list (on the top left side of the graph)
The number of ascending end descending generations could also be adjusted.
Use the mouse wheel or the buttons to zoom in and out.
The graph could also be shown full-screen.\", _(\"
Click on a person to center the graph on this person.
When clicking on the center person, the person page is shown.
The type of graph could be selected in the list (on the top left side of the graph)
The number of ascending end descending generations could also be adjusted.
Use the mouse wheel or the buttons to zoom in and out.
The graph could also be shown full-screen.\")),\n\t\t\t(\"
This page provides the SVG raw code.
Copy the contents into a text editor and save as an SVG file.
Make sure that the text editor encoding is UTF-8.
This page provides the SVG raw code.
Copy the contents into a text editor and save as an SVG file.
Make sure that the text editor encoding is UTF-8.
Precise your search or choose in the lists below.\", _(\"Several matches.
Precise your search or choose in the lists below.\")),\n\t\t\t(\"Show _MENU_ entries\", _(\"Show _MENU_ entries\")),\n\t\t\t(\"Showing 0 to 0 of 0 entries\", _(\"Showing 0 to 0 of 0 entries\")),\n\t\t\t(\"Showing _START_ to _END_ of _TOTAL_ entries\", _(\"Showing _START_ to _END_ of _TOTAL_ entries\")),\n\t\t\t(\"Siblings\", _(\"Siblings\")),\n\t\t\t(\"Source\", _(\"Source\")),\n\t\t\t(\"Sources found:\", _(\"Sources found:\")),\n\t\t\t(\"Sources Index\", _(\"Sources Index\")),\n\t\t\t(\"Sources\", _(\"Sources\")),\n\t\t\t(\"Spouses\", _(\"Spouses\")),\n\t\t\t(\"State/ Province\", _(\"State/ Province\")),\n\t\t\t(\"Street\", _(\"Street\")),\n\t\t\t(\"Surnames Index\", _(\"Surnames Index\")),\n\t\t\t(\"SVG tree children distribution\", _(\"SVG tree children distribution\")),\n\t\t\t(\"SVG tree graph shape\", _(\"SVG tree graph shape\")),\n\t\t\t(\"SVG tree graph type\", _(\"SVG tree graph type\")),\n\t\t\t(\"SVG tree parents distribution\", _(\"SVG tree parents distribution\")),\n\t\t\t(\"There is no matching name.\", _(\"There is no matching name.\")),\n\t\t\t(\"Title\", _(\"Title\")),\n\t\t\t(\"Type\", _(\"Type\")),\n\t\t\t(\"Unknown\", _(\"Unknown\")),\n\t\t\t(\"Use the search box above in order to find a person.
Women are listed with their maiden name.\", _(\"Use the search box above in order to find a person.
Women are listed with their maiden name.\")),\n\t\t\t(\"Used for family\", _(\"Used for family\")),\n\t\t\t(\"Used for media\", _(\"Used for media\")),\n\t\t\t(\"Used for person\", _(\"Used for person\")),\n\t\t\t(\"Used for place\", _(\"Used for place\")),\n\t\t\t(\"Used for source\", _(\"Used for source\")),\n\t\t\t(\"Value\", _(\"Value\")),\n\t\t\t(\"Web Link\", _(\"Web Link\")),\n\t\t\t(\"Web Links\", _(\"Web Links\")),\n\t\t\t(\"Without surname\", _(\"Without surname\")),\n\t\t\t(\"Zoom in\", _(\"Zoom in\")),\n\t\t\t(\"Zoom out\", _(\"Zoom out\")),\n\t\t\t):\n\t\t\tsw.write(sep + \"\\\"\" + script_escape(s) + \"\\\": \\\"\" + script_escape(translated) + \"\\\"\")\n\t\t\tsep = \",\\n\"\n\t\tfor (code, translated, s) in EventType._DATAMAP:\n\t\t\tsw.write(sep + \"\\\"\" + script_escape(s) + \"\\\": \\\"\" + script_escape(translated) + \"\\\"\")\n\t\t\tsep = \",\\n\"\n\t\tsw.write(\"\\n};\\n\")\n\t\tsw.write(\n\t\t\t(\"URLTYPE_UNKNOWN = %i;\\n\" % UrlType.UNKNOWN) +\n\t\t\t(\"URLTYPE_CUSTOM = %i;\\n\" % UrlType.CUSTOM) +\n\t\t\t(\"URLTYPE_EMAIL = %i;\\n\" % UrlType.EMAIL) +\n\t\t\t(\"URLTYPE_WEB_HOME = %i;\\n\" % UrlType.WEB_HOME) +\n\t\t\t(\"URLTYPE_WEB_SEARCH = %i;\\n\" % UrlType.WEB_SEARCH) +\n\t\t\t(\"URLTYPE_WEB_FTP = %i;\\n\" % UrlType.WEB_FTP))\n\t\tself.update_file(\"dwr_conf.js\", sw.getvalue(), \"UTF-8\")\n\n\n\tdef _export_html_page(self, filename, title, cmd, menu, scripts = [], styles = []):\n\t\t\"\"\"\n\t\tGenerate an HTML page\n\t\t@param filename: output HTML file name\n\t\t@param title: Title of the page (prepended to L{self.title}\n\t\t@param cmd: Javascript code that generates the page\n\t\t@param menu: Whether to put a menu on the page\n\t\t@param scripts: Scripts embedded in the page\n\t\t@param styles: CSS stylesheets embedded in the page\n\t\t\"\"\"\n\t\t(page, head, body) = self.write_header(title, menu)\n\t\tfor style in styles:\n\t\t\thead += Html(\"link\", rel = \"stylesheet\", href = style, type = \"text/css\")\n\t\tfor script in scripts:\n\t\t\thead += Html(\"script\", language = \"javascript\", src = script, charset = self.encoding)\n\t\tbody += Html(\"script\", cmd, language = \"javascript\")\n\t\tself.update_file(filename, html_text(page))\n\n\n\tdef _export_custom_page(self, filename, title, menu, note):\n\t\t\"\"\"\n\t\tGenerate an HTML custom page\n\t\t@param filename: output HTML file name\n\t\t@param title: Title of the page (prepended to L{self.title}\n\t\t@param menu: Whether to put a menu on the page\n\t\t@param note: note that contains the page contents\n\t\t\"\"\"\n\t\t(page, head, body) = self.write_header(title, menu)\n\t\tif (note):\n\t\t\thtml = self.get_note_format(self.database.get_note_from_gramps_id(note))\n\t\t\tbody += self.replace_note_fields(html)\n\t\tself.update_file(filename, html_text(page))\n\n\n\tdef write_header(self, title, menu):\n\t\t\"\"\"\n\t\tGenerate an HTML page header\n\t\t@param title: Title of the page (prepended to L{self.title}\n\t\t@param menu: Whether to put a menu on the page\n\t\t@return: List of L{Html} objects as follows: (page, head, body)\n\t\t\"\"\"\n\t\t\"\"\"\n\t\tNote. 'title' is used as currentsection in the navigation links and\n\t\tas part of the header title.\n\t\t\"\"\"\n\t\t# Begin each html page...\n\t\txmllang = xml_lang()\n\t\t(page, head, body) = Html.page('%s - %s' % (\n\t\t\t\thtml_escape(title),\n\t\t\t\thtml_escape(self.title.strip()),\n\t\t\t),\n\t\t\tself.encoding, xmllang)\n\t\t# Header constants\n\t\thead += Html(\"meta\", attr = 'name=\"viewport\" content=\"width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=1\"')\n\t\thead += Html(\"meta\", attr = 'name=\"apple-mobile-web-app-capable\" content=\"yes\"')\n\t\thead += Html(\"meta\", attr = 'name=\"generator\" content=\"%s %s %s\"' % (PROGRAM_NAME, VERSION, URL_HOMEPAGE))\n\t\thead += Html(\"meta\", attr = 'name=\"author\" content=\"%s\"' % self.author)\n\t\t# Create script and favicon links\n\t\thead += Html(\"link\", type = \"image/x-icon\", href = \"data/favicon.ico\", rel = \"shortcut icon\")\n\t\thead += Html(\"script\", language = 'javascript', src = 'data/dwr_start.js')\n\t\t# Disable menu\n\t\tif (not menu):\n\t\t\tbody.attr = \"class='dwr-menuless'\"\n\t\treturn(page, head, body)\n\n\n\tdef get_header_footer_notes(self, item):\n\t\t\"\"\"\n\t\tGive the header/footer note converted to an HTML string\n\t\t@param item: Option giving the note. See options \"footernote\" and \"headernote\"\n\t\t@return: text of the note\n\t\t@rtype: L{String}\n\t\t\"\"\"\n\t\tnote = self.options[item]\n\t\tif (note):\n\t\t\thtml = self.get_note_format(self.database.get_note_from_gramps_id(note))\n\t\t\treturn(self.replace_note_fields(html))\n\t\treturn(\"\")\n\n\n\tdef replace_note_fields(self, html):\n\t\t\"\"\"\n\t\tModify the notes for HTML pages generation\n\t\tThis allow to add special features or computed data in the pages\n\t\t@param html: Note converted to HTML string\n\t\t@return: Modified string\n\t\t\"\"\"\n\t\ttext = html_text(html)\n\t\t# __SEARCH_FORM__ is replaced by a search form\n\t\ttext = text.replace(\"__SEARCH_FORM__\",\n\t\t\t\"\\n\")\n\t\t# __NB_INDIVIDUALS__ is replaced by the number of persons\n\t\t# __NB_FAMILIES__ is replaced by the number of families\n\t\t# __NB_MEDIA__ is replaced by the number of media objects\n\t\t# __NB_SOURCES__ is replaced by the number of sources\n\t\t# __NB_REPOSITORIES__ is replaced by the number of repositories\n\t\t# __NB_PLACES__ is replaced by the number of places\n\t\ttext = text.replace(\"__NB_INDIVIDUALS__\", str(len(self.obj_dict[Person])))\n\t\ttext = text.replace(\"__NB_FAMILIES__\", str(len(self.obj_dict[Family])))\n\t\ttext = text.replace(\"__NB_MEDIA__\", str(len(self.obj_dict[MediaObject])))\n\t\ttext = text.replace(\"__NB_SOURCES__\", str(len(self.obj_dict[Source])))\n\t\ttext = text.replace(\"__NB_REPOSITORIES__\", str(len(self.obj_dict[Repository])))\n\t\ttext = text.replace(\"__NB_PLACES__\", str(len(self.obj_dict[Place])))\n\t\t# __MEDIA_
© %(year)d %(person)s
\" % {\n\t\t\t\t\t'person' : self.author,\n\t\t\t\t\t'year' : year}\n\t\telif (0 < self.copyright < len(_CC)):\n\t\t\turl = \"data/somerights20.gif\"\n\t\t\ttext = \"\" + _CC[self.copyright] % {'gif_fname' : url} + \"
\"\n\t\t# return text or image to its callers\n\t\treturn(text)\n\n\n\tdef update_file(self, fout, txt, encoding = None):\n\t\t\"\"\"\n\t\tWrite a string in a file.\n\t\tThe file is not overwritten if the file exists and already contains the string \n\t\t@param fout: output file name\n\t\t@param txt: file contents\n\t\t@param encoding: encoding as passed to Python function codecs.open \n\t\t\"\"\"\n\t\tif (encoding is None): encoding = self.encoding\n\t\tf = os.path.join(self.target_path, fout)\n\t\tself.created_files.append(f)\n\t\tif (os.path.exists(f)):\n\t\t\ttry:\n\t\t\t\tfr = codecs.open(f, \"r\", encoding = encoding, errors=\"xmlcharrefreplace\")\n\t\t\t\ttxtr = fr.read()\n\t\t\t\tfr.close()\n\t\t\t\tif (txtr == txt):\n\t\t\t\t\tlog.info(\"File \\\"%s\\\" not overwritten (identical)\" % fout)\n\t\t\t\t\treturn\n\t\t\texcept:\n\t\t\t\tpass\n\t\tfw = codecs.open(f, \"w\", encoding = encoding, errors=\"xmlcharrefreplace\")\n\t\tfw.write(txt)\n\t\tfw.close()\n\t\tlog.info(\"File \\\"%s\\\" generated\" % fout)\n\n\tdef copy_file(self, from_fname, to_fname, to_dir=\"\"):\n\t\t\"\"\"\n\t\tCopy a file from a source to a (report) destination.\n\t\tIf to_dir is not present and if the target is not an archive,\n\t\tthen the destination directory will be created.\n\n\t\tNormally 'to_fname' will be just a filename, without directory path.\n\n\t\t'to_dir' is the relative path name in the destination root. It will\n\t\tbe prepended before 'to_fname'.\n\t\t\n\t\tThe file is not copied if the contents of 'from_fname' 'to_fname' are identical\n\t\t\"\"\"\n\t\t# log.debug(\"copying '%s' to '%s/%s'\" % (from_fname, to_dir, to_fname))\n\t\tdest = os.path.join(self.target_path, to_dir, to_fname)\n\t\tdestdir = os.path.dirname(dest)\n\t\tif not os.path.isdir(destdir):\n\t\t\tos.makedirs(destdir)\n\n\t\tif from_fname != dest:\n\t\t\ttry:\n\t\t\t\tdest_temp = dest + \".temp\"\n\t\t\t\tshutil.copyfile(from_fname, dest_temp)\n\t\t\t\tself.created_files.append(dest)\n\t\t\t\tif (os.path.exists(dest)):\n\t\t\t\t\tfr = codecs.open(dest, \"rb\")\n\t\t\t\t\told_bytes = fr.read()\n\t\t\t\t\tfr.close()\n\t\t\t\t\tfr = codecs.open(dest_temp, \"rb\")\n\t\t\t\t\tnew_bytes = fr.read()\n\t\t\t\t\tfr.close()\n\t\t\t\t\tif (old_bytes == new_bytes):\n\t\t\t\t\t\tos.remove(dest_temp)\n\t\t\t\t\t\tlog.info(\"File \\\"%s\\\" not overwritten (identical)\" % dest)\n\t\t\t\t\t\treturn\n\t\t\t\t\tos.remove(dest)\n\t\t\t\tos.rename(dest_temp, dest)\n\t\t\t\tlog.info(\"File \\\"%s\\\" generated\" % dest)\n\t\t\texcept:\n\t\t\t\tlog.warning(_(\"Copying error: %(error)s\") % {\"error\": sys.exc_info()[1]})\n\t\t\t\tlog.error(_(\"Impossible to copy \\\"%(src)s\\\" to \\\"%(dst)s\\\"\") % {\"src\": from_fname, \"dst\": to_fname})\n\t\telif self.warn_dir:\n\t\t\tself.user.warn(\n\t\t\t\t_(\"Possible destination error\") + \"\\n\" +\n\t\t\t\t_(\"You appear to have set your target directory \"\n\t\t\t\t \"to a directory used for data storage. This \"\n\t\t\t\t \"could create problems with file management. \"\n\t\t\t\t \"It is recommended that you consider using \"\n\t\t\t\t \"a different directory to store your generated \"\n\t\t\t\t \"web pages.\"))\n\t\t\tself.warn_dir = False\n\n\n\n\tdef copy_template_files(self):\n\t\t\"\"\"\n\t\tCopy the template files to the target directory\n\t\t\n\t\tThe template files are:\n\t\t - The files contained in the chosen template directory,\n\t\t - The files contained in the default template directory, unless they are also present in the chosen template directory\n\t\t\"\"\"\n\t\t# Get template path\n\t\ttmpl_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"templates\", WEB_TEMPLATE_LIST[self.template][0])\n\t\tdefault_tmpl_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"templates\", WEB_TEMPLATE_LIST[0][0])\n\t\ttry:\n\t\t\t# Copy template files\n\t\t\tself.copy_template_files_sub(tmpl_path)\n\t\t\t# Copy default template files if not already copied\n\t\t\tself.copy_template_files_sub(default_tmpl_path)\n\t\texcept:\n\t\t\tlog.error(_(\"Unable to copy web site template files from \\\"%(path)s\\\"\") % {\"path\": tmpl_path})\n\t\t\traise\n\t\t\t\n\tdef copy_template_files_sub(self, tmpl_path):\n\t\t\"\"\"\n\t\tCopy the template files from L{tmpl_path} to the target directory\n\t\tThe files already present in the target directory are not overwritten\n\t\t@param tmpl_path: template directory, as listed in L{WEB_TEMPLATE_LIST}\n\t\t\"\"\"\n\t\tfor (root, dirnames, files) in os.walk(tmpl_path):\n\t\t\tdst_path = root.replace(tmpl_path, self.target_path, 1)\n\t\t\t# Create sub-directories\n\t\t\tfor dirname in dirnames:\n\t\t\t\t# Remove files that have the same name as directories\n\t\t\t\tdstdirname = os.path.join(dst_path, dirname)\n\t\t\t\tif (os.path.isfile(dstdirname) or os.path.islink(dstdirname)):\n\t\t\t\t\tos.remove(dstdirname)\n\t\t\t\t# Create directory if needed\n\t\t\t\tif (not os.path.isdir(dstdirname)):\n\t\t\t\t\tos.mkdir(dstdirname)\n\t\t\t# Copy files\n\t\t\tfor file in files:\n\t\t\t\tsrc = os.path.join(root, file)\n\t\t\t\tdst = os.path.join(dst_path, file)\n\t\t\t\tif (dst in self.created_files):\n\t\t\t\t\t# File was already copied\n\t\t\t\t\tcontinue\n\t\t\t\tif (os.path.isfile(dst)):\n\t\t\t\t\t# If file already exists, check dates\n\t\t\t\t\tstat_src = os.stat(src)\n\t\t\t\t\tstat_dst = os.stat(dst)\n\t\t\t\t\t# If target file is newer, do not overwrite => If target file is older, delete it\n\t\t\t\t\tif (stat_src.st_mtime >= stat_dst.st_mtime):\n\t\t\t\t\t\tos.remove(dst)\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog.info(_(\"Keeping \\\"%(dst)s\\\" (newer than \\\"%(src)s\\\")\") % {'src': src, 'dst': dst})\n\t\t\t\tif (not os.path.exists(dst)):\n\t\t\t\t\tshutil.copyfile(src, dst)\n\t\t\t\t\tlog.info(_(\"Copying \\\"%(src)s\\\" to \\\"%(dst)s\\\"\") % {'src': src, 'dst': dst})\n\t\t\t\tself.created_files.append(dst)\n\n\n\tdef create_archive(self):\n\t\t\"\"\"\n\t\tCreate an archive of the whole web site\n\t\t\"\"\"\n\t\tif (not self.options['archive']): return\n\t\t\n\t\t# Get archive path and type\n\t\tarch_path = self.options['archive_file']\n\t\text = os.path.splitext(arch_path)[1].lower()\n\t\tif (ext not in [\".zip\", \".tgz\"]):\n\t\t\tarch_path += \".zip\"\n\t\t\text = \".zip\"\n\t\t\n\t\tif (os.path.isdir(arch_path)):\n\t\t\tlog.error(_('Invalid file name'))\n\t\t\tlog.error(_('The archive file must be a file, not a directory'))\n\t\t\treturn\n\t\t\t\n\t\t# Get base path for the files inside the archive\n\t\tbasepath = os.path.splitext(os.path.basename(arch_path))[0]\n\t\t\n\t\tif (ext == \".zip\"):\n\t\t\ttry:\n\t\t\t\tfzip = zipfile.ZipFile(arch_path, \"w\", zipfile.ZIP_DEFLATED, True)\n\t\t\texcept:\n\t\t\t\tlog.error(_(\"Unable to overwrite archive file \\\"%(path)s\\\"\") % {\"path\": arch_path})\n\t\t\t\traise\n\t\t\tfor file in self.created_files:\n\t\t\t\tarc_rel_path = file.replace(self.target_path, basepath, 1)\n\t\t\t\tif (sys.version_info[0] < 3):\n\t\t\t\t\tfile = file.encode(\"cp437\")\n\t\t\t\t\tarc_rel_path = arc_rel_path.encode(\"cp437\")\n\t\t\t\ttry:\n\t\t\t\t\tfzip.write(file, arc_rel_path)\n\t\t\t\texcept:\n\t\t\t\t\tlog.error(_(\"Unable to add file \\\"%(file)s\\\" to archive \\\"%(archive)s\\\"\") % {\"file\": file, \"archive\": arch_path})\n\t\t\t\t\traise\n\t\t\tfzip.close()\n\n\t\tif (ext == \".tgz\"):\n\t\t\ttry:\n\t\t\t\ttgz = tarfile.open(arch_path, \"w:gz\")\n\t\t\texcept:\n\t\t\t\tlog.error(_(\"Unable to overwrite archive file \\\"%(path)s\\\"\") % {\"path\": arch_path})\n\t\t\t\traise\n\t\t\tfor file in self.created_files:\n\t\t\t\tarc_rel_path = file.replace(self.target_path, basepath, 1)\n\t\t\t\ttry:\n\t\t\t\t\ttgz.add(file, arc_rel_path)\n\t\t\t\texcept:\n\t\t\t\t\tlog.error(_(\"Unable to add file \\\"%(file)s\\\" to archive \\\"%(archive)s\\\"\") % {\"file\": path, \"archive\": arch_path})\n\t\t\t\t\traise\n\t\t\ttgz.close()\n\n\n\tdef build_link(self, prop, handle, obj_class):\n\t\t\"\"\"\n\t\tBuild a link to an item.\n\t\t\n\t\tThis function is used when converting a Gramps note with hyperlinks into an HTML string\n\t\t\"\"\"\n\t\tif prop == \"gramps_id\":\n\t\t\tif obj_class in self.database.get_table_names():\n\t\t\t\tobj = self.database.get_table_metadata(obj_class)[\"gramps_id_func\"](handle)\n\t\t\t\tif obj:\n\t\t\t\t\thandle = obj.get_handle()\n\t\t\t\telse:\n\t\t\t\t\traise AttributeError(\"gramps_id '%s' not found in '%s'\" % handle, obj_class)\n\t\t\telse:\n\t\t\t\traise AttributeError(\"invalid gramps_id lookup in table name '%s'\" % obj_class)\n\t\thref = \"search.html\"\n\t\ti = -1\n\t\tif (obj_class == \"Person\"):\n\t\t\thref = \"person.html\"\n\t\t\tif (handle in self.obj_dict[Person]):\n\t\t\t\thref = \"%s?idx=%i\" % (href, self.obj_dict[Person][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Family\"):\n\t\t\thref = \"family.html\"\n\t\t\tif (handle in self.obj_dict[Family]):\n\t\t\t\thref = \"%s?fdx=%i\" % (href, self.obj_dict[Family][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Source\"):\n\t\t\thref = \"source.html\"\n\t\t\tif (handle in self.obj_dict[Source]):\n\t\t\t\thref = \"%s?sdx=%i\" % (href, self.obj_dict[Source][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Citation\"):\n\t\t\thref = \"source.html\"\n\t\t\tsource_handle = self.database.get_citation_from_handle(handle).get_reference_handle()\n\t\t\tif (source_handle in self.obj_dict[Source]):\n\t\t\t\thref = \"%s?sdx=%i\" % (href, self.obj_dict[Source][source_handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Repository\"):\n\t\t\thref = \"repository.html\"\n\t\t\tif (handle in self.obj_dict[Repository]):\n\t\t\t\thref = \"%s?rdx=%i\" % (href, self.obj_dict[Repository][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Media\"):\n\t\t\thref = \"media.html\"\n\t\t\tif (handle in self.obj_dict[MediaObject]):\n\t\t\t\thref = \"%s?mdx=%i\" % (href, self.obj_dict[MediaObject][handle][OBJDICT_INDEX])\n\t\telif (obj_class == \"Place\"):\n\t\t\thref = \"place.html\"\n\t\t\tif (handle in self.obj_dict[Place]):\n\t\t\t\thref = \"%s?pdx=%i\" % (href, self.obj_dict[Place][handle][OBJDICT_INDEX])\n\t\telse:\n\t\t\tprint(_(\"DynamicWebReport ignoring link type '%(class)s'\") % {\"class\": obj_class})\n\t\treturn(href)\n\n\n\tdef _data_bkref_index(self, obj_class, obj_handle, ref_class):\n\t\t\"\"\"\n\t\tBuild a list of object indexes referencing a given object\n\t\t@param obj_class: Referenced object class\n\t\t@param obj_handle: Referenced object handle\n\t\t@param ref_class: Class of the refencing objects\n\t\t@return: String representing the Javascript Array of the object indexes (of class L{ref_class}) referencing a given object (L{obj_class}, L{obj_handle})\n\t\t\"\"\"\n\t\tbkref_list = self.bkref_dict[obj_class][obj_handle]\n\t\tif (not bkref_list): return (\"[]\")\n\t\t# Sort by referenced object\n\t\tbkref_list = sorted(bkref_list, key = lambda bkref: self.obj_dict[bkref[BKREF_CLASS]][bkref[BKREF_HANDLE]][OBJDICT_NAME])\n\t\t# Filter bkref_list (keep only ref_class) and remove duplicates\n\t\tseen = set()\n\t\tbkref_list = [bkref_handle\n\t\t\tfor (bkref_class, bkref_handle, media_ref) in bkref_list\n\t\t\tif (bkref_class == ref_class and not (bkref_handle in seen or seen.add(bkref_handle)))]\n\t\treturn(\"[\" +\n\t\t\t\",\".join([str(self.obj_dict[ref_class][bkref_handle][OBJDICT_INDEX]) for bkref_handle in bkref_list]) +\n\t\t\t\"]\")\n\n\n\tdef _data_repo_backref_index(self, repo, ref_class):\n\t\t\"\"\"\n\t\tBuild a list of object referencing a given repository, in the form:\n\t\t - object index (in table 'I', 'F', 'S')\n\t\t - media type\n\t\t - call number\n\t\t - notes of the repository reference\n\t\t@param repo: Referenced repository\n\t\t@param ref_class: Class of the refencing objects\n\t\t@return: String representing the Javascript Array of the references to L{repo}\n\t\t\"\"\"\n\t\trepo_handle = repo.get_handle()\n\t\tif (repo_handle not in self.obj_dict[Repository]): return(\"[]\")\n\t\tbkref_list = self.bkref_dict[Repository][repo_handle]\n\t\tif (not bkref_list): return (\"[]\")\n\t\tsep = \"\"\n\t\ttxt = \"[\"\n\t\tfor (bkref_class, bkref_handle, repo_ref) in bkref_list:\n\t\t\tif (ref_class != bkref_class): continue\n\t\t\ti = self.obj_dict[ref_class][bkref_handle][OBJDICT_INDEX]\n\t\t\tobject = self.get_object_from_handle(bkref_class, bkref_handle)\n\t\t\ttxt += sep + self._data_repo_ref(repo_ref, i)\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\tdef _data_media_backref_index(self, media, ref_class):\n\t\t\"\"\"\n\t\tBuild a list of object referencing a given media, in the form:\n\t\t - object index (in table 'I', 'F', 'S')\n\t\t - media thumbnail path\n\t\t - [x1, y1, x2, y2] of the media reference\n\t\t - notes of the media reference\n\t\t - list of the media reference source citations index (in table 'C')\n\t\t@param media: Referenced repository\n\t\t@param ref_class: Class of the refencing objects\n\t\t@return: String representing the Javascript Array of the references to L{media}\n\t\t\"\"\"\n\t\tmedia_handle = media.get_handle()\n\t\tif (media_handle not in self.obj_dict[MediaObject]): return(\"[]\")\n\t\tbkref_list = self.bkref_dict[MediaObject][media_handle]\n\t\tif (not bkref_list): return (\"[]\")\n\t\tsep = \"\"\n\t\ttxt = \"[\"\n\t\tfor (bkref_class, bkref_handle, media_ref) in bkref_list:\n\t\t\tif (ref_class != bkref_class): continue\n\t\t\ti = self.obj_dict[ref_class][bkref_handle][OBJDICT_INDEX]\n\t\t\tobject = self.get_object_from_handle(bkref_class, bkref_handle)\n\t\t\ttxt += sep + self._data_media_ref(media_ref, i)\n\t\t\tsep = \",\"\n\t\ttxt += \"]\"\n\t\treturn(txt)\n\n\n\tdef get_object_from_handle(self, class_, handle):\n\t\t\"\"\"\n\t\tGet an object from its handle and class\n\t\t\"\"\"\n\t\tobject = None\n\t\tif (class_ == Person):\n\t\t\tobject = self.database.get_person_from_handle(handle)\n\t\telif (class_ == Family):\n\t\t\tobject = self.database.get_family_from_handle(handle)\n\t\telif (class_ == Event):\n\t\t\tobject = self.database.get_event_from_handle(handle)\n\t\telif (class_ == Source):\n\t\t\tobject = self.database.get_source_from_handle(handle)\n\t\telif (class_ == Citation):\n\t\t\tobject = self.database.get_citation_from_handle(handle)\n\t\telif (class_ == Place):\n\t\t\tobject = self.database.get_place_from_handle(handle)\n\t\telif (class_ == Repository):\n\t\t\tobject = self.database.get_repository_from_handle(handle)\n\t\treturn(object)\n\n\n\t##############################################################################################\n\t################################################################################## GENDEX data\n\t##############################################################################################\n\n\tdef build_gendex(self, ind_list):\n\t\tif (not self.inc_gendex): return\n\t\tfp_gendex = StringIO()\n\t\tfor person_handle in ind_list:\n\t\t\tself.write_gendex(fp_gendex, person_handle)\n\t\tself.update_file(\"gendex.txt\", fp_gendex.getvalue())\n\n\tdef write_gendex(self, fp, person_handle):\n\t\t\"\"\"\n\t\tReference|SURNAME|given name /SURNAME/|date of birth|place of birth|date of death|place of death|\n\t\t* field 1: file name of web page referring to the individual\n\t\t* field 2: surname of the individual\n\t\t* field 3: full name of the individual\n\t\t* field 4: date of birth or christening (optional)\n\t\t* field 5: place of birth or christening (optional)\n\t\t* field 6: date of death or burial (optional)\n\t\t* field 7: place of death or burial (optional) \n\t\t\"\"\"\n\t\tif (not(person_handle and (person_handle in self.obj_dict[Person]))): return\n\t\tperson = self.database.get_person_from_handle(person_handle)\n\t\turl = \"person.html?idx=%i\" % self.obj_dict[Person][person_handle][OBJDICT_INDEX]\n\t\tsurname = person.get_primary_name().get_surname()\n\t\tfullname = person.get_primary_name().get_gedcom_name()\n\t\t\n\t\t# get birth info:\n\t\t(dob, pob) = self.get_gendex_data(person.get_birth_ref())\n\t\t\n\t\t# get death info:\n\t\t(dod, pod) = self.get_gendex_data(person.get_death_ref())\n\t\tfp.write(\n\t\t\t'|'.join((url, surname, fullname, dob, pob, dod, pod)) + '|\\n')\n\n\tdef get_gendex_data(self, event_ref):\n\t\t\"\"\"\n\t\tGiven an event, return the date and place a strings\n\t\t\"\"\"\n\t\tdoe = \"\" # date of event\n\t\tpoe = \"\" # place of event\n\t\tif (event_ref):\n\t\t\tevent = self.database.get_event_from_handle(event_ref.ref)\n\t\t\tif (event):\n\t\t\t\tdate = event.get_date_object()\n\t\t\t\tdoe = format_date(date, gedcom = True)\n\t\t\t\tif (event.get_place_handle()):\n\t\t\t\t\tplace_handle = event.get_place_handle()\n\t\t\t\t\tif (place_handle):\n\t\t\t\t\t\tplace = self.database.get_place_from_handle(place_handle)\n\t\t\t\t\t\tif (place):\n\t\t\t\t\t\t\tif (DWR_VERSION_412):\n\t\t\t\t\t\t\t\tpoe = _pd.display(self.database, place)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tpoe = place.get_title()\n\t\treturn(doe, poe)\n\t\t\n\n\t##############################################################################################\n\t##############################################################################################\n\t#\n\t# Objects dictionaries construction\n\t#\n\t##############################################################################################\n\t##############################################################################################\n\n\tdef _build_obj_dict(self):\n\t\t\"\"\"\n\t\tConstruct the dictionaries of objects to be included in the reports. There\n\t\tare two dictionaries, which have the same structure: they are two level\n\t\tdictionaries,the first key is the class of object (e.g. gen.lib.Person).\n\t\tThe second key is the handle of the object.\n\n\t\tFor the obj_dict, the value is a tuple containing:\n\t\t - the gramps_id\n\t\t - the text name for the object\n\t\t - the index (number starting at 0)\n\n\t\tFor the bkref_dict, the value is a tuple containing:\n\t\t - the class of object that refers to the 'key' object\n\t\t - the handle for the object that refers to the 'key' object\n\t\t - the reference object:\n\t\t\t- None in most cases\n\t\t\t- for media it is a MediaRef object\n\t\t\n\t\tThis method recursively calls the methods \"_add_***\"\n\t\t\"\"\"\n\t\t_obj_class_list = (Person, Family, Event, Place, Source, Citation,\n\t\t\t\t\t\t MediaObject, Repository, Note, Tag)\n\n\t\t# setup a dictionary of the required structure\n\t\tself.obj_dict = defaultdict(lambda: defaultdict(set))\n\t\tself.bkref_dict = defaultdict(lambda: defaultdict(set))\n\n\n\t\t# initialise the dictionary to empty in case no objects of any\n\t\t# particular class are included in the web report\n\t\tfor obj_class in _obj_class_list:\n\t\t\tself.obj_dict[obj_class] = defaultdict(set)\n\n\t\tind_list = self.database.iter_person_handles()\n\t\twith self.user.progress(_(\"Dynamic Web Site Report\"),\n\t\t\t\t\t\t\t\t _(\"Applying Person Filter...\"),\n\t\t\t\t\t\t\t\t self.database.get_number_of_people()) as step:\n\t\t\tind_list = self.filter.apply(self.database, ind_list,\n\t\t\t\t\t\t\t\t\t\t step)\n\n\t\twith self.user.progress(_(\"Dynamic Web Site Report\"),\n\t\t\t\t\t\t\t\t _(\"Constructing list of other objects...\"),\n\t\t\t\t\t\t\t\t sum(1 for _ in ind_list)) as step:\n\t\t\tfor handle in ind_list:\n\t\t\t\t# FIXME work around bug that self.database.iter under python 3\n\t\t\t\t# returns (binary) data rather than text\n\t\t\t\tif (not isinstance(handle, UNITYPE)):\n\t\t\t\t\thandle = handle.decode(\"UTF-8\")\n\t\t\t\tstep()\n\t\t\t\tself._add_person(handle, \"\", \"\")\n\n\t\tlog.debug(\"final object dictionary \\n\" +\n\t\t\t\t \"\".join((\"%s: %s\\n\" % item) for item in self.obj_dict.items()))\n\n\t\tlog.debug(\"final backref dictionary \\n\" +\n\t\t\t\t \"\".join((\"%s: %s\\n\" % item) for item in self.bkref_dict.items()))\n\n\n\tdef _add_person(self, person_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd person_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Person][person_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the person is already added\n\t\tif (person_handle in self.obj_dict[Person]): return\n\t\t# Add person in the dictionaries of objects\n\t\tperson = self.database.get_person_from_handle(person_handle)\n\t\tif (not person): return\n\t\tperson_name = self.get_person_name(person)\n\t\tself.obj_dict[Person][person_handle] = [person_name, person.gramps_id, len(self.obj_dict[Person])]\n\t\t# Person events\n\t\tevt_ref_list = person.get_event_ref_list()\n\t\tif evt_ref_list:\n\t\t\tfor evt_ref in evt_ref_list:\n\t\t\t\tself._add_event(evt_ref.ref, Person, person_handle, evt_ref)\n\t\t# Person citations\n\t\tfor citation_handle in person.get_citation_list():\n\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Person name citations\n\t\tfor name in [person.get_primary_name()] + \\\n\t\t\t\t\t\tperson.get_alternate_names():\n\t\t\tfor citation_handle in name.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# LDS Ordinance citations\n\t\tfor lds_ord in person.get_lds_ord_list():\n\t\t\tfor citation_handle in lds_ord.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Attribute citations\n\t\tfor attr in person.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Person families\n\t\tfamily_handle_list = person.get_family_handle_list()\n\t\tif family_handle_list:\n\t\t\tfor family_handle in person.get_family_handle_list():\n\t\t\t\tself._add_family(family_handle, Person, person_handle)\n\t\t# Person media\n\t\tfor media_ref in person.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Person, person_handle, media_ref)\n\t\t# Association citations\n\t\tfor assoc in person.get_person_ref_list():\n\t\t\tfor citation_handle in assoc.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\t\t# Addresses citations\n\t\tfor addr in person.get_address_list():\n\t\t\tfor citation_handle in addr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Person, person_handle)\n\n\n\tdef get_person_name(self, person):\n\t\t\"\"\"\n\t\tReturn a string containing the person's primary name in the name format chosen in the web report options\n\t\t@param: person -- person object from database\n\t\t\"\"\"\n\t\tname_format = self.options['name_format']\n\t\tprimary_name = person.get_primary_name()\n\t\tname = Name(primary_name)\n\t\tname.set_display_as(name_format)\n\t\treturn _nd.display_name(name)\n\n\n\tdef _add_family(self, family_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd family_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Family][family_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the family is already added\n\t\tif (family_handle in self.obj_dict[Family]): return\n\t\t# Add family in the dictionaries of objects\n\t\tfamily = self.database.get_family_from_handle(family_handle)\n\t\tfamily_name = self.get_family_name(family)\n\t\tself.obj_dict[Family][family_handle] = [family_name, family.gramps_id, len(self.obj_dict[Family])]\n\t\t# Family events\n\t\tevt_ref_list = family.get_event_ref_list()\n\t\tif evt_ref_list:\n\t\t\tfor evt_ref in evt_ref_list:\n\t\t\t\tself._add_event(evt_ref.ref, Family, family_handle, evt_ref)\n\t\t# Family child references\n\t\tfor child_ref in family.get_child_ref_list():\n\t\t\tfor citation_handle in child_ref.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# LDS Ordinance citations\n\t\tfor lds_ord in family.get_lds_ord_list():\n\t\t\tfor citation_handle in lds_ord.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# Attributes citations\n\t\tfor attr in family.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# Family citations\n\t\tfor citation_handle in family.get_citation_list():\n\t\t\tself._add_citation(citation_handle, Family, family_handle)\n\t\t# Family media\n\t\tfor media_ref in family.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Family, family_handle, media_ref)\n\n\n\tdef get_family_name(self, family):\n\t\t\"\"\"\n\t\tReturn a string containing the name of the family (e.g. 'Family of John Doe and Jane Doe')\n\t\t@param: family -- family object from database\n\t\t\"\"\"\n\t\thusband_handle = family.get_father_handle()\n\t\tspouse_handle = family.get_mother_handle()\n\n\t\thusband = self.database.get_person_from_handle(husband_handle)\n\t\tspouse = self.database.get_person_from_handle(spouse_handle)\n\n\t\tif husband and spouse:\n\t\t\thusband_name = self.get_person_name(husband)\n\t\t\tspouse_name = self.get_person_name(spouse)\n\t\t\ttitle_str = _(\"Family of %(husband)s and %(spouse)s\") % {\n\t\t\t\t\"husband\": husband_name,\n\t\t\t\t\"spouse\": spouse_name}\n\t\telif husband:\n\t\t\thusband_name = self.get_person_name(husband)\n\t\t\t# Only the name of the husband is known\n\t\t\ttitle_str = _(\"Family of %(father)s\") % {\"father\": husband_name}\n\t\telif spouse:\n\t\t\tspouse_name = self.get_person_name(spouse)\n\t\t\t# Only the name of the wife is known\n\t\t\ttitle_str = _(\"Family of %(mother)s\") % {\"mother\": spouse_name}\n\t\telse:\n\t\t\ttitle_str = \"\"\n\n\t\treturn title_str\n\n\n\tdef _add_event(self, event_handle, bkref_class, bkref_handle, event_ref):\n\t\t\"\"\"\n\t\tAdd event_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Check if event reference already added\n\t\trefs = []\n\t\tif (event_handle in self.bkref_dict[Event]):\n\t\t\trefs = [bkref[BKREF_REFOBJ] for bkref in self.bkref_dict[Event][event_handle]]\n\t\t\t# The event reference is already recorded\n\t\t\tif (event_ref in refs): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Event][event_handle].add((bkref_class, bkref_handle, event_ref))\n\t\t# Event reference attributes citations\n\t\tfor attr in event_ref.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, bkref_class, bkref_handle)\n\t\t# Check if the event is already added\n\t\tif (event_handle in self.obj_dict[Event]): return\n\t\t# Add event in the dictionaries of objects\n\t\tevent = self.database.get_event_from_handle(event_handle)\n\t\tif (not event): return\n\t\tevent_name = str(event.get_type())\n\t\tevent_desc = event.get_description()\n\t\t# The event description can be Y on import from GEDCOM. See the\n\t\t# following quote from the GEDCOM spec: \"The occurrence of an event is\n\t\t# asserted by the presence of either a DATE tag and value or a PLACe tag\n\t\t# and value in the event structure. When neither the date value nor the\n\t\t# place value are known then a Y(es) value on the parent event tag line\n\t\t# is required to assert that the event happened.\"\"\n\t\tif not (event_desc == \"\" or event_desc is None or event_desc ==\"Y\"):\n\t\t\tevent_name = event_name + \": \" + event_desc\n\t\tself.obj_dict[Event][event_handle] = [event_name, event.gramps_id, len(self.obj_dict[Event])]\n\t\t# Event place\n\t\tplace_handle = event.get_place_handle()\n\t\tif (place_handle):\n\t\t\tself._add_place(place_handle, bkref_class, bkref_handle)\n\t\t# Event citations\n\t\tfor citation_handle in event.get_citation_list():\n\t\t\tself._add_citation(citation_handle, bkref_class, bkref_handle)\n\t\t# Event attributes citations\n\t\tfor attr in event.get_attribute_list():\n\t\t\tfor citation_handle in attr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, bkref_class, bkref_handle)\n\t\t# Event media\n\t\tfor media_ref in event.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, bkref_class, bkref_handle, media_ref)\n\n\n\tdef _add_place(self, place_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd place_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Place][place_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the place is already added\n\t\tif (place_handle in self.obj_dict[Place]): return\n\t\t# Add place in the dictionaries of objects\n\t\tplace = self.database.get_place_from_handle(place_handle)\n\t\tif (DWR_VERSION_412):\n\t\t\tplace_name = _pd.display(self.database, place)\n\t\telse:\n\t\t\tplace_name = place.get_title()\n\t\tself.obj_dict[Place][place_handle] = [place_name, place.gramps_id, len(self.obj_dict[Place])]\n\n\t\tif (self.inc_places):\n\t\t\t# Place citations\n\t\t\tfor citation_handle in place.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Place, place_handle)\n\t\t\t# Place media\n\t\t\tfor media_ref in place.get_media_list():\n\t\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\t\tself._add_media(media_handle, Place, place_handle, media_ref)\n\n\n\tdef _add_source(self, source_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd source_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_sources): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Source][source_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the source is already added\n\t\tif (source_handle in self.obj_dict[Source]): return\n\t\t# Add source in the dictionaries of objects\n\t\tsource = self.database.get_source_from_handle(source_handle)\n\t\tsource_name = source.get_title()\n\t\tself.obj_dict[Source][source_handle] = [source_name, source.gramps_id, len(self.obj_dict[Source])]\n\t\t# Source repository\n\t\tif self.inc_repositories:\n\t\t\tfor repo_ref in source.get_reporef_list():\n\t\t\t\trepo_handle = repo_ref.get_reference_handle()\n\t\t\t\tself._add_repository(repo_handle, Source, source_handle, repo_ref)\n\t\t# Source media\n\t\tfor media_ref in source.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Source, source_handle, media_ref)\n\n\n\tdef _add_citation(self, citation_handle, bkref_class, bkref_handle):\n\t\t\"\"\"\n\t\tAdd citation_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_sources): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Citation][citation_handle].add((bkref_class, bkref_handle, None))\n\t\t# Check if the citation is already added\n\t\tif (citation_handle in self.obj_dict[Citation]): return\n\t\t# Add citation in the dictionaries of objects\n\t\tcitation = self.database.get_citation_from_handle(citation_handle)\n\t\tcitation_name = citation.get_page() or \"\"\n\t\tsource_handle = citation.get_reference_handle()\n\t\tself.obj_dict[Citation][citation_handle] = [citation_name, citation.gramps_id, len(self.obj_dict[Citation])]\n\t\t# Citation source\n\t\tself._add_source(source_handle, Citation, citation_handle)\n\t\t# Citation media\n\t\tfor media_ref in citation.get_media_list():\n\t\t\tmedia_handle = media_ref.get_reference_handle()\n\t\t\tself._add_media(media_handle, Source, source_handle, media_ref)\n\n\n\tdef _add_media(self, media_handle, bkref_class, bkref_handle, media_ref):\n\t\t\"\"\"\n\t\tAdd media_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_gallery): return\n\t\t# Check if media reference already added\n\t\trefs = []\n\t\tif (media_handle in self.bkref_dict[MediaObject]):\n\t\t\trefs = [bkref[BKREF_REFOBJ] for bkref in self.bkref_dict[MediaObject][media_handle]]\n\t\t\t# The media reference is already recorded\n\t\t\tif (media_ref in refs): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[MediaObject][media_handle].add((bkref_class, bkref_handle, media_ref))\n\t\t# Citations for media reference, media reference attributes\n\t\tcitation_list = media_ref.get_citation_list()\n\t\tfor attr in media_ref.get_attribute_list():\n\t\t\tcitation_list.extend(attr.get_citation_list())\n\t\tfor citation_handle in citation_list:\n\t\t\tself._add_citation(citation_handle, MediaObject, media_handle)\n\t\t# Check if the media is already added\n\t\tif (media_handle in self.obj_dict[MediaObject]): return\n\t\t# Add media in the dictionaries of objects\n\t\tmedia = self.database.get_object_from_handle(media_handle)\n\t\tmedia_name = \"Media\"\n\t\tself.obj_dict[MediaObject][media_handle] = [media_name, media.gramps_id, len(self.obj_dict[MediaObject])]\n\t\t# Citations for media, media attributes\n\t\tcitation_list = media.get_citation_list()\n\t\tfor attr in media.get_attribute_list():\n\t\t\tcitation_list.extend(attr.get_citation_list())\n\t\tfor citation_handle in citation_list:\n\t\t\tself._add_citation(citation_handle, MediaObject, media_handle)\n\n\n\tdef _add_repository(self, repo_handle, bkref_class, bkref_handle, repo_ref):\n\t\t\"\"\"\n\t\tAdd repo_handle to the L{self.obj_dict}, and recursively all referenced objects\n\t\t\"\"\"\n\t\tif (not self.inc_repositories): return\n\t\t# Check if repository reference already added\n\t\trefs = []\n\t\tif (repo_handle in self.bkref_dict[Repository]):\n\t\t\trefs = [bkref[BKREF_REFOBJ] for bkref in self.bkref_dict[Repository][repo_handle]]\n\t\t\t# The repository reference is already recorded\n\t\t\tif (repo_ref in refs): return\n\t\t# Update the dictionaries of objects back references\n\t\tself.bkref_dict[Repository][repo_handle].add((bkref_class, bkref_handle, repo_ref))\n\t\t# Check if the repository is already added\n\t\tif (repo_handle in self.obj_dict[Repository]): return\n\t\t# Add repository in the dictionaries of objects\n\t\trepo = self.database.get_repository_from_handle(repo_handle)\n\t\trepo_name = repo.name\n\t\tself.obj_dict[Repository][repo_handle] = [repo_name, repo.gramps_id, len(self.obj_dict[Repository])]\n\t\t# Addresses citations\n\t\tfor addr in repo.get_address_list():\n\t\t\tfor citation_handle in addr.get_citation_list():\n\t\t\t\tself._add_citation(citation_handle, Repository, repo_handle)\n\n\t\t\t\t\n\t##############################################################################################\n\t##############################################################################################\n\t#\n\t# Objects dictionaries sorting\n\t#\n\t##############################################################################################\n\t##############################################################################################\n\n\n\tdef _sort_obj_dict(self):\n\t\t\"\"\"\n\t\tSort the dictionaries of objects to be included in the reports.\n\t\tThe dictionaries are sorted by name.\n\t\tThe sorting is performed by modifying the index of the objects.\n\t\t\"\"\"\n\t\t\n\t\t# Sort persons\n\t\tsortkeys = {}\n\t\tobjs = list(self.obj_dict[Person].keys())\n\t\tfor handle in objs:\n\t\t\tsortkeys[handle] = self.get_person_name_sort_key(handle)\n\t\tobjs.sort(key = lambda x: sortkeys[x])\n\t\tfor (i, x) in enumerate(objs):\n\t\t\tself.obj_dict[Person][x][OBJDICT_INDEX] = i\n\t\t\t\n\t\t# Sort families\n\t\tsortkeys = {}\n\t\tobjs = list(self.obj_dict[Family].keys())\n\t\tfor handle in objs:\n\t\t\tsortkeys[handle] = self.get_family_name_sort_key(handle)\n\t\tobjs.sort(key = lambda x: sortkeys[x])\n\t\tfor (i, x) in enumerate(objs):\n\t\t\tself.obj_dict[Family][x][OBJDICT_INDEX] = i\n\n\t\t# Sort others\n\t\tfor cls in (Source, Repository, MediaObject, Place):\n\t\t\tobjs = list(self.obj_dict[cls].keys())\n\t\t\tsortkeys = {}\n\t\t\tfor handle in objs:\n\t\t\t\tsortkeys[handle] = SORT_KEY(self.obj_dict[cls][handle][OBJDICT_NAME])\n\t\t\tobjs.sort(key = lambda x: sortkeys[x])\n\t\t\tfor (i, x) in enumerate(objs):\n\t\t\t\tself.obj_dict[cls][x][OBJDICT_INDEX] = i\n\t\t\n\n\tdef get_person_name_sort_key(self, handle):\n\t\t\"\"\"\n\t\tReturn a sort key for a person\n\t\t\"\"\"\n\t\tperson = self.database.get_person_from_handle(handle)\n\t\tprimary_name = person.get_primary_name()\n\t\tsort_str = _nd.sort_string(primary_name)\n\t\treturn(SORT_KEY(sort_str))\n\t\t\n\t\t\n\tdef get_family_name_sort_key(self, handle):\n\t\t\"\"\"\n\t\tReturn a sort key for a family\n\t\t\"\"\"\n\t\tfamily = self.database.get_family_from_handle(handle)\n\t\thusband_handle = family.get_father_handle()\n\t\tspouse_handle = family.get_mother_handle()\n\n\t\thusband = self.database.get_person_from_handle(husband_handle)\n\t\tspouse = self.database.get_person_from_handle(spouse_handle)\n\n\t\tif husband and spouse:\n\t\t\tsort_key = self.get_person_name_sort_key(husband_handle) + SORT_KEY(\" \") + self.get_person_name_sort_key(spouse_handle)\n\t\telif husband:\n\t\t\tsort_key = self.get_person_name_sort_key(husband_handle)\n\t\telif spouse:\n\t\t\tsort_key = self.get_person_name_sort_key(spouse_handle)\n\t\telse:\n\t\t\tsort_key = SORT_KEY(\"\")\n\n\t\treturn(sort_key)\n\t\t\n\t\t\n\t\t\n\t\t\n##################################################################################################\n##################################################################################################\n#\n# DynamicWebReport Menu Options\n#\n##################################################################################################\n##################################################################################################\n\nclass DynamicWebOptions(MenuReportOptions):\n\t\"\"\"\n\tCreates the DynamicWebReport Menu Options\n\tDefines options and provides handling interface.\n\t\n\tMethods:\n\t- add_menu_options: called by Gramps to generate the options menu. It calls all the other methods \"__add_***_options\"\n\t- __add_***_options: One method for each tab of the options menu.\n\t- __***_changed: methods called when an option impacts other options\n\t\"\"\"\n\tdef __init__(self, name, dbase):\n\t\n\t\tself.__db = dbase #: Gramps database\n\t\t\n\t\t# The data below are used when some options change the behavior of other options. For example: a boolean option enables/disables another option. These data are used in the methods \"__***_changed\".\n\t\tself.__pid = None\n\t\tself.__filter = None\n\t\tself.__living = None\n\t\tself.__yearsafterdeath = None\n\t\t\n\t\t#: This help explains how Gramps note are modified in order to generate custom pages\n\t\tself.note_help = _(\n\t\t\t\"In this note, the following special words are processed:\\n\"\n\t\t\t\"__SEARCH_FORM__ is replaced by a search form.\\n\"\n\t\t\t\"__NB_INDIVIDUALS__ is replaced by the number of persons.\\n\"\n\t\t\t\"__NB_FAMILIES__ is replaced by the number of families.\\n\"\n\t\t\t\"__NB_MEDIA__ is replaced by the number of media objects.\\n\"\n\t\t\t\"__NB_SOURCES__ is replaced by the number of sources.\\n\"\n\t\t\t\"__NB_REPOSITORIES__ is replaced by the number of repositories.\\n\"\n\t\t\t\"__NB_PLACES__ is replaced by the number of places.\\n\"\n\t\t\t\"__MEDIA_\"\n elif lines[i][0] != '-':\n if lines[i][0] == \"!\":\n lines[i] = \"by \" + lines[i][1:] + \"
\"\n else:\n lines[i] = \"
\" + lines[i] + \"
- \"\n\n else:\n if lines[i][:2] != '-\\\\':\n lines[i] = \"
- \" + lines[i][1:] + \" \"\n if ls_1_flag:\n lines[i] = \"
\" + lines[i]\n ls_2_flag = i\n lines.append(\"
\")\n date_string = h.date.strftime(\"%b %d, %Y (%a)\")\n lines.insert(0, \"%s
\" % date_string)\n hist_list.insert(0, ''.join(lines))\n\n return hist_list\n\n\ndef get_rmdb_stats():\n rmdb_ids = [d.values()[0] for d in RMDBEntry.objects.values('rmdb_id').distinct()]\n N_all = 0\n N_RNA = 0\n # N_RNA = ConstructSection.objects.values('name').distinct().count()\n N_puzzle = 0\n N_eterna = 0\n N_constructs = 0\n N_datapoints = 0\n for rmdb_id in rmdb_ids:\n entries = RMDBEntry.objects.filter(rmdb_id=rmdb_id).order_by('-version')\n if len(entries) >= 0:\n N_all += 1\n N_RNA += len(entries)\n\n if 'RNAPZ' in rmdb_id:\n N_puzzle += 1\n if 'ETERNA' in rmdb_id:\n N_eterna += 1\n e = entries[0]\n N_datapoints += e.data_count\n N_constructs += e.construct_count\n return {'N_all': N_all, 'N_RNA': N_RNA, 'N_puzzle': N_puzzle, 'N_eterna': N_eterna, 'N_constructs': N_constructs, 'N_datapoints': N_datapoints}\n\n\ndef browse_json_list(names_d):\n constructs = []\n for c in names_d:\n entries = RMDBEntry.objects.filter(constructsection__name=c['name']).filter(status='PUB').order_by('rmdb_id', '-version')\n entry_ids = []\n (SS_entries, TT_entries, MA_entries, MM_entries, MR_entries) = ([], [], [], [], [])\n\n for e in entries:\n if e.rmdb_id not in entry_ids:\n entry_ids.append(e.rmdb_id)\n comment = e.comments.split()\n for i, m in enumerate(comment):\n if len(m) > 40:\n comment[i] = ' '.join(textwrap.wrap(m, 40))\n entry = {'rmdb_id': e.rmdb_id, 'version': e.version, 'construct_count': e.construct_count, 'data_count': e.data_count, 'authors': e.authors, 'comments': ' '.join(comment), 'title': e.publication.title, 'latest': e.supercede_by}\n if e.type == \"SS\" or e.type == \"DC\":\n SS_entries.append(entry)\n elif e.type == \"TT\":\n TT_entries.append(entry)\n elif e.type == \"MM\":\n MM_entries.append(entry)\n elif e.type == \"MR\":\n MR_entries.append(entry)\n elif e.type == \"MA\":\n MA_entries.append(entry)\n\n constructs.append({'name': c['name'], 'SS_entry': SS_entries, 'TT_entry': TT_entries, 'MM_entry': MM_entries, 'MA_entry': MA_entries, 'MR_entry': MR_entries})\n return constructs\n\n\ndef get_rmdb_category(flag):\n latest_versions = RMDBEntry.objects.values('rmdb_id').annotate(latest_version=Max('version'))\n q_statement = Q()\n for pair in latest_versions:\n q_statement |= (Q(entry__rmdb_id=pair['rmdb_id']) & Q(entry__version=pair['latest_version']))\n\n LatestConstructSec = ConstructSection.objects.filter(q_statement)\n\n if flag == \"puzzle\":\n names_d = LatestConstructSec.filter(name__icontains='Puzzle').values('name').distinct()\n elif flag == \"eterna\":\n names_d = LatestConstructSec.filter(name__icontains='EteRNA').values('name').distinct()\n else:\n names_d = LatestConstructSec.exclude(name__icontains='EteRNA').exclude(name__icontains='Puzzle').values('name').distinct()\n names_d = names_d.order_by('name')\n return browse_json_list(names_d)\n\n","repo_name":"DasLab/Server_RMDB","sub_path":"src/util/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":4646,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"67"} +{"seq_id":"42010684078","text":"def check_cycle(cycle, x):\n if (cycle-20) % 40 == 0:\n return cycle * x\n\n return 0\n\ndef check_x(cycle, x):\n c = (cycle - 1) % 40\n\n if x-1 <= c <= x+1:\n return True \n \n return False\n\n\ndef count_signal_strength(lines):\n x = 1\n cycle = 0\n signal_strength_sum = 0\n letters = \"\"\n\n for line in lines:\n\n if line[0:4] == \"noop\":\n cycle += 1\n signal_strength_sum += check_cycle(cycle, x)\n if check_x(cycle, x):\n letters += \"#\"\n else:\n letters += \" \"\n else:\n val = line.split(\" \")\n\n for i in range(0, 2):\n cycle += 1\n signal_strength_sum += check_cycle(cycle, x)\n if check_x(cycle, x):\n letters += \"#\"\n else:\n letters += \" \"\n\n if i == 1:\n x += int(val[1])\n \n return signal_strength_sum, letters\n\nwith open(\"input.txt\", \"r\") as f:\n lines = [l.strip() for l in f.readlines()]\n\nday10 = count_signal_strength(lines)\nprint(\"Part 1: The sum of the signal strengths is\", day10[0])\n\nletters = \"\"\nfor i in range(0,len(day10[1])):\n if i % 40 == 0:\n letters += \"\\n\"\n letters += day10[1][i]\n\nprint(\"Part 2: The solution is:\", \"\\n\", letters)","repo_name":"sagakortesaari/adventofcode","sub_path":"2022/10/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"} +{"seq_id":"2314841057","text":"from PyQt5.QtWidgets import QMessageBox\n\n\ndef alert(self, str, call_code):\n dlg = QMessageBox(self)\n dlg.setWindowTitle(\"Предупреждение\")\n if call_code == 1:\n dlg.setText(f\"Вы взяли:\\n{str}\")\n elif call_code == 2:\n dlg.setText(f\"Вы вернули:\\n{str}\")\n button = dlg.exec()\n if button == QMessageBox.Ok:\n print(\"OK!\")","repo_name":"Viteeeq/KMU-Project-test","sub_path":"app/alerts.py","file_name":"alerts.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"} +{"seq_id":"17915681670","text":"#!/usr/bin/env python3\n\n#For use with normally closed (NC) Reed Switch connected to ground & GPIO input\n#If using normally open (NO), simply reverse the booleans.\n\nimport RPi.GPIO as GPIO\nimport time\nimport smtplib\nimport _thread\nimport cred\n\ntry:\n need_clean = False\n\n #Message Template\n #Leading '\\n' is required for sending an email with ':' (SMS/MMS Gateway)\n MSG = '\\nDoor was '\n DOOR_MSG = {True:'opened', False:'closed'}\n\n #Setting up connection to SMTP Server for sending email/sms.\n print('Setting up SMS...')\n #Function to call on new thread\n #Because of race conditions, this needs to be done quickly or on diff thread\n def send_msg(opened:bool):\n #Replace args with your email provider's SMTP details\n server = smtplib.SMTP( \"smtp.gmail.com\", 587 )\n server.starttls()\n server.login( cred.FROM, cred.PASS )\n #Compile message string to print and send.\n #Ex: '\\nDoor was closed at 5:50:20 PM'\n #This way is used because it is quickest and we have race conditions!\n str_print =''.join([MSG, DOOR_MSG[opened], ' at ',\n time.strftime('%I:%M:%S %p')])\n print(str_print)\n server.sendmail(cred.FROM, cred.TO, str_print)\n server.quit()\n\n\n\n #Initializing GPIO\n print('Setting up hardware...')\n PIN = 12\n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(PIN, GPIO.IN, pull_up_down=GPIO.PUD_UP)\n\n #next_state to check for to send message\n next_state = True\n\n need_clean = True\n\n #Running actual program\n print('Ready!')\n #Run infinitely\n while True:\n #Check for next state\n if GPIO.input(PIN) == next_state:\n #Send message on different thread\n _thread.start_new_thread(send_msg, (next_state,))\n #Negate next_state\n next_state = not next_state\n time.sleep(0.3)\n \nexcept KeyboardInterrupt:\n GPIO.cleanup() #For Keyboard Interrupt exit\n need_clean = False\n\nif need_clean: \n GPIO.cleanup() #For normal exit\nprint('\\nEnd!')\n","repo_name":"proganalysis/python3_types","sub_path":"Result/4079files/source_2/3789.py","file_name":"3789.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"72"} +{"seq_id":"449183580","text":"import socket\nimport sys\nfrom datetime import timedelta\nfrom typing import AnyStr, IO, List, Optional, TYPE_CHECKING, Tuple, Union\n\nimport requests\nimport requests.adapters\nfrom requests import Response\nfrom urllib3.connection import HTTPConnection\n\nfrom azure.core.tracing.decorator import distributed_trace\nfrom azure.core.tracing import SpanKind\n\nfrom azure.kusto.data._telemetry import Span, MonitoredActivity\n\nfrom .client_base import ExecuteRequestParams, _KustoClientBase\nfrom .client_request_properties import ClientRequestProperties\nfrom .data_format import DataFormat\nfrom .exceptions import KustoClosedError, KustoNetworkError\n\nfrom .kcsb import KustoConnectionStringBuilder\nfrom .response import KustoResponseDataSet, KustoStreamingResponseDataSet\nfrom .streaming_response import JsonTokenReader, StreamingDataSetEnumerator\n\nif TYPE_CHECKING:\n pass\n\n\nclass HTTPAdapterWithSocketOptions(requests.adapters.HTTPAdapter):\n def __init__(self, *args, **kwargs):\n self.socket_options = kwargs.pop(\"socket_options\", None)\n super(HTTPAdapterWithSocketOptions, self).__init__(*args, **kwargs)\n\n def __getstate__(self):\n state = super(HTTPAdapterWithSocketOptions, self).__getstate__()\n state[\"socket_options\"] = self.socket_options\n return state\n\n def init_poolmanager(self, *args, **kwargs):\n if self.socket_options is not None:\n kwargs[\"socket_options\"] = self.socket_options\n super(HTTPAdapterWithSocketOptions, self).init_poolmanager(*args, **kwargs)\n\n\nclass KustoClient(_KustoClientBase):\n \"\"\"\n Kusto client for Python.\n The client is a wrapper around the Kusto REST API.\n To read more about it, go to https://docs.microsoft.com/en-us/azure/kusto/api/rest/\n\n The primary methods are:\n `execute_query`: executes a KQL query against the Kusto service.\n `execute_mgmt`: executes a KQL control command against the Kusto service.\n \"\"\"\n\n _mgmt_default_timeout = timedelta(hours=1)\n _query_default_timeout = timedelta(minutes=4)\n _streaming_ingest_default_timeout = timedelta(minutes=10)\n _client_server_delta = timedelta(seconds=30)\n\n # The maximum amount of connections to be able to operate in parallel\n _max_pool_size = 100\n\n def __init__(self, kcsb: Union[KustoConnectionStringBuilder, str]):\n \"\"\"\n Kusto Client constructor.\n :param kcsb: The connection string to initialize KustoClient.\n :type kcsb: azure.kusto.data.KustoConnectionStringBuilder or str\n \"\"\"\n super().__init__(kcsb, False)\n\n # Create a session object for connection pooling\n self._session = requests.Session()\n\n adapter = HTTPAdapterWithSocketOptions(\n socket_options=(HTTPConnection.default_socket_options or []) + self.compose_socket_options(), pool_maxsize=self._max_pool_size\n )\n self._session.mount(\"http://\", adapter)\n self._session.mount(\"https://\", adapter)\n\n def close(self):\n if not self._is_closed:\n self._session.close()\n if self._aad_helper:\n self._aad_helper.close()\n super().close()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()\n\n def set_proxy(self, proxy_url: str):\n super().set_proxy(proxy_url)\n self._session.proxies = {\"http\": proxy_url, \"https\": proxy_url}\n\n def set_http_retries(self, max_retries: int):\n \"\"\"\n Set the number of HTTP retries to attempt\n \"\"\"\n adapter = HTTPAdapterWithSocketOptions(\n socket_options=(HTTPConnection.default_socket_options or []) + self.compose_socket_options(),\n pool_maxsize=self._max_pool_size,\n max_retries=max_retries,\n )\n self._session.mount(\"http://\", adapter)\n self._session.mount(\"https://\", adapter)\n\n @staticmethod\n def compose_socket_options() -> List[Tuple[int, int, int]]:\n # Sends TCP Keep-Alive after MAX_IDLE_SECONDS seconds of idleness, once every INTERVAL_SECONDS seconds, and closes the connection after MAX_FAILED_KEEPALIVES failed pings (e.g. 20 => 1:00:30)\n MAX_IDLE_SECONDS = 30\n INTERVAL_SECONDS = 180 # Corresponds to Azure Load Balancer Service 4 minute timeout, with 1 minute of slack\n MAX_FAILED_KEEPALIVES = 20\n\n if (\n sys.platform == \"linux\"\n and hasattr(socket, \"SOL_SOCKET\")\n and hasattr(socket, \"SO_KEEPALIVE\")\n and hasattr(socket, \"TCP_KEEPIDLE\")\n and hasattr(socket, \"TCP_KEEPINTVL\")\n and hasattr(socket, \"TCP_KEEPCNT\")\n ):\n return [\n (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),\n (socket.IPPROTO_TCP, socket.TCP_KEEPIDLE, MAX_IDLE_SECONDS),\n (socket.IPPROTO_TCP, socket.TCP_KEEPINTVL, INTERVAL_SECONDS),\n (socket.IPPROTO_TCP, socket.TCP_KEEPCNT, MAX_FAILED_KEEPALIVES),\n ]\n elif (\n sys.platform == \"win32\"\n and hasattr(socket, \"SOL_SOCKET\")\n and hasattr(socket, \"SO_KEEPALIVE\")\n and hasattr(socket, \"TCP_KEEPIDLE\")\n and hasattr(socket, \"TCP_KEEPCNT\")\n ):\n return [\n (socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1),\n (socket.IPPROTO_TCP, socket.TCP_KEEPIDLE, MAX_IDLE_SECONDS),\n (socket.IPPROTO_TCP, socket.TCP_KEEPCNT, MAX_FAILED_KEEPALIVES),\n ]\n elif sys.platform == \"darwin\" and hasattr(socket, \"SOL_SOCKET\") and hasattr(socket, \"SO_KEEPALIVE\") and hasattr(socket, \"IPPROTO_TCP\"):\n TCP_KEEPALIVE = 0x10\n return [(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1), (socket.IPPROTO_TCP, TCP_KEEPALIVE, INTERVAL_SECONDS)]\n else:\n return []\n\n def execute(self, database: Optional[str], query: str, properties: Optional[ClientRequestProperties] = None) -> KustoResponseDataSet:\n \"\"\"\n Executes a query or management command.\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return: Kusto response data set.\n :rtype: azure.kusto.data.response.KustoResponseDataSet\n \"\"\"\n query = query.strip()\n if query.startswith(\".\"):\n return self.execute_mgmt(database, query, properties)\n return self.execute_query(database, query, properties)\n\n @distributed_trace(name_of_span=\"KustoClient.query_cmd\", kind=SpanKind.CLIENT)\n def execute_query(self, database: Optional[str], query: str, properties: Optional[ClientRequestProperties] = None) -> KustoResponseDataSet:\n \"\"\"\n Execute a KQL query.\n To learn more about KQL go to https://docs.microsoft.com/en-us/azure/kusto/query/\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return: Kusto response data set.\n :rtype: azure.kusto.data.response.KustoResponseDataSet\n \"\"\"\n database = self._get_database_or_default(database)\n Span.set_query_attributes(self._kusto_cluster, database, properties)\n\n return self._execute(self._query_endpoint, database, query, None, self._query_default_timeout, properties)\n\n @distributed_trace(name_of_span=\"KustoClient.control_cmd\", kind=SpanKind.CLIENT)\n def execute_mgmt(self, database: Optional[str], query: str, properties: Optional[ClientRequestProperties] = None) -> KustoResponseDataSet:\n \"\"\"\n Execute a KQL control command.\n To learn more about KQL control commands go to https://docs.microsoft.com/en-us/azure/kusto/management/\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return: Kusto response data set.\n :rtype: azure.kusto.data.response.KustoResponseDataSet\n \"\"\"\n database = self._get_database_or_default(database)\n Span.set_query_attributes(self._kusto_cluster, database, properties)\n\n return self._execute(self._mgmt_endpoint, database, query, None, self._mgmt_default_timeout, properties)\n\n @distributed_trace(name_of_span=\"KustoClient.streaming_ingest\", kind=SpanKind.CLIENT)\n def execute_streaming_ingest(\n self,\n database: Optional[str],\n table: str,\n stream: IO[AnyStr],\n stream_format: Union[DataFormat, str],\n properties: Optional[ClientRequestProperties] = None,\n mapping_name: str = None,\n ):\n \"\"\"\n Execute streaming ingest against this client\n If the Kusto service is not configured to allow streaming ingestion, this may raise an error\n To learn more about streaming ingestion go to:\n https://docs.microsoft.com/en-us/azure/data-explorer/ingest-data-streaming\n :param Optional[str] database: Target database. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str table: Target table.\n :param io.BaseIO stream: stream object which contains the data to ingest.\n :param DataFormat stream_format: Format of the data in the stream.\n :param ClientRequestProperties properties: additional request properties.\n :param str mapping_name: Pre-defined mapping of the table. Required when stream_format is json/avro.\n \"\"\"\n database = self._get_database_or_default(database)\n Span.set_streaming_ingest_attributes(self._kusto_cluster, database, table, properties)\n\n stream_format = stream_format.kusto_value if isinstance(stream_format, DataFormat) else DataFormat[stream_format.upper()].kusto_value\n endpoint = self._streaming_ingest_endpoint + database + \"/\" + table + \"?streamFormat=\" + stream_format\n if mapping_name is not None:\n endpoint = endpoint + \"&mappingName=\" + mapping_name\n\n self._execute(endpoint, database, None, stream, self._streaming_ingest_default_timeout, properties)\n\n def _execute_streaming_query_parsed(\n self,\n database: Optional[str],\n query: str,\n timeout: timedelta = _KustoClientBase._query_default_timeout,\n properties: Optional[ClientRequestProperties] = None,\n ) -> StreamingDataSetEnumerator:\n response = self._execute(self._query_endpoint, database, query, None, timeout, properties, stream_response=True)\n response.raw.decode_content = True\n return StreamingDataSetEnumerator(JsonTokenReader(response.raw))\n\n @distributed_trace(name_of_span=\"KustoClient.streaming_query\", kind=SpanKind.CLIENT)\n def execute_streaming_query(\n self,\n database: Optional[str],\n query: str,\n timeout: timedelta = _KustoClientBase._query_default_timeout,\n properties: Optional[ClientRequestProperties] = None,\n ) -> KustoStreamingResponseDataSet:\n \"\"\"\n Execute a KQL query without reading it all to memory.\n The resulting KustoStreamingResponseDataSet will stream one table at a time, and the rows can be retrieved sequentially.\n\n :param Optional[str] database: Database against query will be executed. If not provided, will default to the \"Initial Catalog\" value in the connection string\n :param str query: Query to be executed.\n :param timedelta timeout: timeout for the query to be executed\n :param azure.kusto.data.ClientRequestProperties properties: Optional additional properties.\n :return KustoStreamingResponseDataSet:\n \"\"\"\n Span.set_query_attributes(self._kusto_cluster, database, properties)\n\n return KustoStreamingResponseDataSet(self._execute_streaming_query_parsed(database, query, timeout, properties))\n\n def _execute(\n self,\n endpoint: str,\n database: Optional[str],\n query: Optional[str],\n payload: Optional[IO[AnyStr]],\n timeout: timedelta,\n properties: Optional[ClientRequestProperties] = None,\n stream_response: bool = False,\n ) -> Union[KustoResponseDataSet, Response]:\n \"\"\"Executes given query against this client\"\"\"\n if self._is_closed:\n raise KustoClosedError()\n self.validate_endpoint()\n request_params = ExecuteRequestParams(\n database,\n payload,\n properties,\n query,\n timeout,\n self._request_headers,\n self._mgmt_default_timeout,\n self._client_server_delta,\n self.client_details,\n )\n json_payload = request_params.json_payload\n request_headers = request_params.request_headers\n timeout = request_params.timeout\n if self._aad_helper:\n request_headers[\"Authorization\"] = self._aad_helper.acquire_authorization_header()\n\n # trace http post call for response\n invoker = lambda: self._session.post(\n endpoint,\n headers=request_headers,\n json=json_payload,\n data=payload,\n timeout=timeout.seconds,\n stream=stream_response,\n allow_redirects=False,\n )\n\n try:\n response = MonitoredActivity.invoke(\n invoker, name_of_span=\"KustoClient.http_post\", tracing_attributes=Span.create_http_attributes(\"POST\", endpoint, request_headers)\n )\n except Exception as e:\n raise KustoNetworkError(endpoint, None if properties is None else properties.client_request_id) from e\n\n if stream_response:\n try:\n response.raise_for_status()\n if 300 <= response.status_code < 400:\n raise Exception(\"Unexpected redirection, got status code: \" + str(response.status))\n return response\n except Exception as e:\n raise self._handle_http_error(e, self._query_endpoint, None, response, response.status_code, response.json(), response.text)\n\n response_json = None\n try:\n if 300 <= response.status_code < 400:\n raise Exception(\"Unexpected redirection, got status code: \" + str(response.status))\n response_json = response.json()\n response.raise_for_status()\n except Exception as e:\n raise self._handle_http_error(e, endpoint, payload, response, response.status_code, response_json, response.text)\n # trace response processing\n return MonitoredActivity.invoke(lambda: self._kusto_parse_by_endpoint(endpoint, response_json), name_of_span=\"KustoClient.processing_response\")\n","repo_name":"Azure/azure-kusto-python","sub_path":"azure-kusto-data/azure/kusto/data/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":15152,"program_lang":"python","lang":"en","doc_type":"code","stars":174,"dataset":"github-code","pt":"72"} +{"seq_id":"15926714498","text":"import jax.numpy as np\nimport pytest\nfrom jax import grad, vjp\n\nimport phylojax.treelikelihood as treelikelihood\nfrom phylojax.io import read_tree_and_alignment\nfrom phylojax.sitepattern import get_dna_leaves_partials_compressed\nfrom phylojax.tree import postorder_indices, preorder_indices\n\n\ndef extract_branch_lengths(tree):\n nodes = [None] * (len(tree.taxon_namespace) * 2 - 1)\n for node in tree.postorder_node_iter():\n nodes[node.index] = node.edge_length\n nodes.pop()\n return np.array([[float(x) for x in nodes]])\n\n\ndef test_calculate_unrooted_efficient(hello_tree_file, hello_fasta_file, jc69_model):\n tree, dna = read_tree_and_alignment(hello_tree_file, hello_fasta_file, False, False)\n indices = postorder_indices(tree)\n pre_indices = preorder_indices(tree)\n branch_lengths = extract_branch_lengths(tree)\n bls = np.expand_dims(branch_lengths, axis=-1)\n partials, weights = get_dna_leaves_partials_compressed(dna)\n tip_partials = np.array(partials[: len(tree.taxon_namespace)])\n frequencies = np.broadcast_to(\n jc69_model.frequencies, branch_lengths.shape[:-1] + (4,)\n )\n frequencies = np.expand_dims(frequencies, axis=-2)\n mats = jc69_model.p_t(bls)\n mats = np.expand_dims(mats, -3)\n props = np.array([[[1.0]]])\n partials = treelikelihood.calculate_partials(\n tip_partials,\n indices,\n mats,\n props,\n )\n\n # calculate log likelihood at every node using upper partials\n uppers = treelikelihood.calculate_upper_partials(partials, pre_indices, mats)\n for i in range(uppers.shape[0]):\n log_p2 = treelikelihood.calculate_treelikelihood_upper(\n partials[i],\n uppers[i],\n weights,\n mats[:, i, ...],\n frequencies,\n props,\n )\n assert -84.852358 == pytest.approx(float(log_p2), 0.0001)\n\n # analytical derivatives\n expected_gradient = (21.0223, -5.34462, -17.7298, -17.7298)\n dmats = jc69_model.dp_dt(bls)\n dmats = np.expand_dims(dmats, -3)\n likelihoods = frequencies @ np.sum(partials[-1], -3)\n gradient = treelikelihood.calculate_treelikelihood_gradient(\n likelihoods, partials, uppers, weights, dmats, frequencies, props\n )\n assert np.allclose(gradient.squeeze(), expected_gradient, rtol=0.0001)\n\n # analytical derivatives using custom_jvp\n log_p3 = treelikelihood.calculate_treelikelihood_custom(\n branch_lengths, tip_partials, weights, (indices, pre_indices), jc69_model, props\n )\n assert -84.852358 == pytest.approx(float(log_p3), 0.0001)\n g = grad(treelikelihood.calculate_treelikelihood_custom)\n gradient2 = g(\n branch_lengths,\n tip_partials,\n weights,\n (np.array(indices, dtype=np.int32), np.array(pre_indices, dtype=np.int32)),\n jc69_model,\n props,\n )\n assert np.allclose(gradient2.squeeze(), expected_gradient, rtol=0.0001)\n\n\ndef test_calculate_unrooted(hello_tree_file, hello_fasta_file, jc69_model):\n tree, dna = read_tree_and_alignment(hello_tree_file, hello_fasta_file, False, False)\n indices = indices = postorder_indices(tree)\n branch_lengths = extract_branch_lengths(tree)\n bls = np.expand_dims(branch_lengths, axis=-1)\n partials, weights = get_dna_leaves_partials_compressed(dna)\n tip_partials = np.array(partials[: len(tree.taxon_namespace)])\n frequencies = np.broadcast_to(\n jc69_model.frequencies, branch_lengths.shape[:-1] + (4,)\n )\n\n def fn(bls):\n mats = jc69_model.p_t(bls)\n return treelikelihood.calculate_treelikelihood(\n tip_partials,\n weights,\n indices,\n np.expand_dims(mats, -3),\n np.expand_dims(frequencies, axis=-2),\n np.array([[[1.0]]]),\n )\n\n y, vjp_fn = vjp(fn, bls)\n gradient = vjp_fn(np.ones(y.shape))[0]\n expected_gradient = (21.0223, -5.34462, -17.7298, -17.7298)\n assert all(\n [\n a == pytest.approx(b, 0.0001)\n for a, b in zip(expected_gradient, np.squeeze(gradient))\n ]\n )\n\n mats = jc69_model.p_t(bls)\n log_p = treelikelihood.calculate_treelikelihood(\n tip_partials,\n weights,\n indices,\n np.expand_dims(mats, -3),\n np.expand_dims(frequencies, axis=-2),\n np.array([[[1.0]]]),\n )\n assert -84.852358 == pytest.approx(float(log_p), 0.0001)\n\n\ndef test_calculate_likelihood_rooted(flu_a_tree_file, flu_a_fasta_file, jc69_model):\n tree, dna = read_tree_and_alignment(flu_a_tree_file, flu_a_fasta_file, True, True)\n indices = postorder_indices(tree)\n partials, weights = get_dna_leaves_partials_compressed(dna)\n tip_partials = np.array(partials[: len(tree.taxon_namespace)])\n branch_lengths = extract_branch_lengths(tree) * 0.001\n mats = jc69_model.p_t(np.expand_dims(branch_lengths, axis=-1))\n frequencies = np.broadcast_to(\n jc69_model.frequencies, branch_lengths.shape[:-1] + (4,)\n )\n\n log_p = treelikelihood.calculate_treelikelihood(\n tip_partials,\n weights,\n indices,\n np.expand_dims(mats, -3),\n np.expand_dims(frequencies, axis=-2),\n np.array([[[1.0]]]),\n )\n assert -4777.616349 == pytest.approx(float(log_p), 0.0001)\n","repo_name":"4ment/phylojax","sub_path":"test/test_likelihood.py","file_name":"test_likelihood.py","file_ext":"py","file_size_in_byte":5258,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"72"} +{"seq_id":"34703218471","text":"# @TIME : 2019/7/29 下午10:42\n# @File : tire_tree.py\n\nclass TrieNode:\n\n def __init__(self):\n self.children = [None] * 26\n self.isEndOfWord = False\n\nclass Tire:\n\n def __init__(self):\n self.root = self.getNode()\n\n def getNode(self):\n return TrieNode()\n\n def _charToIndex(self, ch):\n \"\"\"Converts key current character into index\n # use only 'a' through 'z' and lower case \"\"\"\n rel = ord(ch) - ord('a')\n return rel\n\n def insert(self, key):\n pCrawl = self.root # 头指针还保留着, 这个设计不错\n length = len(key)\n for level in range(length):\n a = key[level]\n index = self._charToIndex(key[level])\n aa = pCrawl.children[index]\n if not pCrawl.children[index]:\n pCrawl.children[index] = self.getNode()\n\n pCrawl = pCrawl.children[index] # 像LinkList的节点移动\n\n pCrawl.isEndOfWord = True\n\n def search(self, key):\n pCrawl = self.root\n length = len(key)\n for level in range(length):\n a = key[level]\n index = self._charToIndex(key[level])\n if not pCrawl.children[index]:\n return False\n pCrawl = pCrawl.children[index]\n\n return pCrawl != None and pCrawl.isEndOfWord\n\n\nif __name__ == \"__main__\":\n\n keys = ['the', 'a', 'there', 'anaswe', 'any', 'by', 'their']\n\n t = Tire()\n\n for key in keys:\n t.insert(key)\n\n a = 1\n print(t.search('there'))\n\n\n","repo_name":"yjfiejd/2019xuexi","sub_path":"Leetcode-practice/tire_tree.py","file_name":"tire_tree.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"} +{"seq_id":"40644446530","text":"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom confspirator import groups as conf_group\nfrom confspirator import fields as conf_field\nimport paramiko\n\nfrom adjutant.config import CONF\n\nfrom adjutant_moc.actions import serializers\nfrom adjutant_moc.actions import base\n\n\nclass Mailman(object):\n\n def __init__(self, hostname, port, username, key):\n self.hostname = hostname\n self.port = port\n self.username = username\n self.key = key\n self.client = paramiko.client.SSHClient()\n\n\n def __enter__(self):\n self.client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n self.client.connect(\n hostname=self.hostname,\n port=self.port,\n username=self.username,\n pkey=paramiko.RSAKey.from_private_key_file(self.key))\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.client.close()\n\n def _execute(self, command):\n stdin, stdout, stderr = self.client.exec_command(command)\n\n errors = stderr.read()\n if errors:\n raise ConnectionError(errors)\n\n # Note(knikolla): Not entirely sure if closing before reading is fine.\n r = stdout.read().decode('utf-8').split('\\n')\n return r\n\n def is_already_subscribed(self, email, list):\n command = ('/usr/lib/mailman/bin/list_members %s' % list)\n return email in self._execute(command)\n\n def subscribe(self, email, list):\n command = (\n 'echo %s | /usr/lib/mailman/bin/add_members -r - %s'\n % (email, list)\n )\n self._execute(command)\n\n\n# TODO knikolla: there are going to be issues here when we\n# add invited users, since get email returns the one who created the task.\nclass MailingListSubscribeAction(base.MocBaseAction):\n\n required = [\n 'email'\n ]\n\n serializer = serializers.MailingListSubscribeSerializer\n\n config_group = conf_group.DynamicNameConfigGroup(\n children=[\n conf_field.StrConfig(\n \"private_key\",\n help_text=\"Location of private key for mailing list server.\",\n default=\"/.ssh/id_rsa\",\n sample_default=\"/.ssh/id_rsa\",\n ),\n conf_field.HostNameConfig(\n \"host\",\n help_text=\"Mailing list server host.\",\n default=\"mail.massopen.cloud\",\n sample_default=\"mail.massopen.cloud\",\n ),\n conf_field.IntConfig(\n \"port\",\n help_text=\"Mailing list server SSH port\",\n default=22,\n sample_default=22,\n ),\n conf_field.StrConfig(\n \"user\",\n help_text=\"Mailing list server user.\",\n default=\"moc-tools\",\n sample_default=\"moc-tools\",\n ),\n conf_field.StrConfig(\n \"list\",\n help_text=\"Mailing list to add users to.\",\n default=\"kaizen-users\",\n sample_default=\"kaizen-users\",\n ),\n ],\n )\n\n def _get_email(self):\n if CONF.identity.username_is_email:\n return self.action.task.keystone_user['username']\n\n def _approve(self):\n try:\n with Mailman(self.config.host, self.config.port,\n self.config.user, self.config.private_key) as mailman:\n if mailman.is_already_subscribed(self._get_email(),\n self.config.list):\n self.add_note('%s already subscribed to mailing list.'\n % self._get_email())\n else:\n mailman.subscribe(self._get_email(), self.config.list)\n self.add_note('%s successfully subscribed to mailing list.'\n % self._get_email())\n except paramiko.ssh_exception.SSHException as e:\n self.add_note('Unable to connect to Mailing List server. '\n 'Proceeding regardless. %s' % str(e))\n\n self.action.state = 'complete'\n self.action.save()\n\n def _submit(self, token_data):\n pass\n","repo_name":"CCI-MOC/adjutant-moc","sub_path":"adjutant_moc/actions/mailing_list.py","file_name":"mailing_list.py","file_ext":"py","file_size_in_byte":4721,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"72"} +{"seq_id":"40345380575","text":"import logging\n\nlogger = logging.getLogger('scheduler')\n_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s - %(module)s - %(lineno)d')\n\n\nfh = logging.FileHandler('scheduler_log.log', encoding='utf-8')\nfh.setLevel(logging.DEBUG)\nfh.setFormatter(_formatter)\n\ncr = logging.FileHandler('error.log', encoding='utf-8')\ncr.setLevel(logging.ERROR)\ncr.setFormatter(_formatter)\n\nlogger.addHandler(fh)\nlogger.addHandler(cr)\nlogger.setLevel(logging.DEBUG)\n\n\nif __name__ == '__main__':\n logger.info('Info')\n logger.warning('Warning')\n logger.debug('debug')\n logger.error('error')\n logger.critical('critical')\n","repo_name":"ZakirovRail/scheduler","sub_path":"log_config.py","file_name":"log_config.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"72"} +{"seq_id":"17236192959","text":"'''\n Fast projections onto the hypercube with a sum constraint (simplex).\n\n Two methods implemented: one that does direct search of kkt conditions,\n one that does iterative search (interpolation search).\n\n Uses numba to speed up computation.\n\n Benjamin Recht, March 6, 2017\n edited by Sarah Dean, March 7, 2017\n'''\nimport numpy as np\n#from numba import jit\n\n#@jit('void(double[:], intp, double[:], intp[:])', nopython=True, nogil=True)\ndef project_kkt(z, r, zProj, sortOrder):\n '''\n Jitted code to compute the ppr projection. Requires sort by numpy, but\n then goes to town.\n\n Input: z: a vector assumed to be a numpy array\n sortOrder: the arguments that sort z in descending order\n r: the targe sum\n\n Returns: zProj projected onto the set\n '''\n n = len(z)\n\n # check if there is a trivial solution\n if z[sortOrder[r-1]]>=z[sortOrder[r]]+1:\n for i in range(n):\n if i